literature review on blood group

The ABO blood group system revisited: a review and update

• Articles in this Issue

Article Category : Review

Published online : mar 17, 2020, page range: 48 - 59, doi: https://doi.org/10.21307/immunohematology-2019-231, © 2009 j.r. storry, et al., published by sciendo.

1. Camp FR, Ellis FR. Selected contributions to the literature of blood groups and immunology. Fort Knox, KY: US Army Medical Research Laboratory, 1966. Search in Google Scholar

2. Watkins WM. Biochemistry and genetics of the ABO, Lewis and P blood group systems. In: Harris H, Hirschhorn K, eds. Advances in human genetics. New York: Plenum Press, 1980:1–136. Search in Google Scholar

3. Clausen H, Hakomori S. ABH and related histo-blood group antigens; immunochemical differences in carrier isotypes and their distribution. Vox Sang 1989;6:1–20. 10.1111/j.1423-0410.1989.tb03040.x Search in Google Scholar

4. Yazer MH, Palcic MM. The importance of disordered loops in ABO glycosyltransferases. Transfus Med Rev 2005;19:210–16. 10.1016/j.tmrv.2005.02.008 Search in Google Scholar

5. Yamamoto F. Molecular genetics of ABO. Vox Sang 2000;78:91–103. Search in Google Scholar

6. Chester MA, Olsson ML. The ABO blood group gene: a locus of considerable genetic diversity. Transfus Med Rev 2001;15:177–200. 10.1053/tmrv.2001.24591 Search in Google Scholar

7. Landsteiner K. Zur Kenntnis der antifermentativen, lytischen und agglutinierenden Wirkungen des Blutserums und der Lymphe. Zbl Bakt 1900;27:357–62. Search in Google Scholar

8. Landsteiner K. Über Agglutinationserscheinungen normalen Menschlichen Blutes. Wien Klin Wochenschr 1901;14:1132–4. Search in Google Scholar

9. Decastello A, Sturli A. Ueber die isoagglutinine im gesunder und kranker menschen. Munch Med Wochenschrift 1902;49:1090–5. Search in Google Scholar

10. Race RR, Sanger R. Blood groups in man. 6th ed. Oxford, UK: Blackwell Scientific Publications, 1975. Search in Google Scholar

11. von Dungern E, Hirzsfeld L. Uber die gruppenspezifische strukturen des blutes III. Immun Forsch 2191;8:526–62. Search in Google Scholar

12. Mourant AE, Kope AC, Domaniewska K. The distribution of human blood groups and other polymorphisms. New York: Oxford University Press, 1976. Search in Google Scholar

13. Roychoudhuri AK, Nei M. Human polymorphic genes world distribution. Oxford: Oxford University Press, 1988. Search in Google Scholar

14. Lalueza-Fox C, Gigli E, de la Rasilla M, Fortea J, Rosas A, Bertranpetit J, Krause J. Genetic characterization of the ABO blood group in Neandertals. BMC Evol Biol 2008;8:342. 10.1186/1471-2148-8-342 Search in Google Scholar

15. Crainic K, Durigon M, Oriol R. ABO tissue antigens of Egyptian mummies. Forensic Sci Int 1989;43:113–24. 10.1016/0379-0738(89)90130-8 Search in Google Scholar

16. Korchagina EY, Pochechueva TV, Obukhova PS, Formanovsky AA, Imberty A, Rieben R, Bovin NV. Design of the blood group AB glycotope. Glycoconj J 2005;22:127–33. 10.1007/s10719-005-0508-x 16133833 Search in Google Scholar

17. Clausen H, Levery SB, Dabelsteen E, Hakomori S. Blood group ABH antigens: a new series of blood group A-associated structures (genetic regulation and tissue distribution). Transplant Proc 1987;19:4408–12. Search in Google Scholar

18. Svensson L, Rydberg L, de Mattos LC, Henry SM. Blood group A and A revisited: an immunochemical analysis. Vox Sang 2009;96:56–61. 10.1111/j.1423-0410.2008.01112.x 19121199 Search in Google Scholar

19. Daniels GL, Fletcher A, Garratty G, et al. Blood group terminology 2004: from the International Society of Blood Transfusion committee on terminology for red cell surface antigens. Vox Sang 2004;87:304–16. 10.1111/j.1423-0410.2004.00564.x 15585029 Search in Google Scholar

20. Blumenfeld OO, Patnaik SK. Allelic genes of blood group antigens: a source of human mutations and cSNPs documented in the Blood Group Antigen Gene Mutation Database. Hum Mutat 2004;23:8–16. 10.1002/humu.10296 14695527 Search in Google Scholar

21. Epstein AA, Ottenberg R. Simple method of performing serum reactions. Proc N Y Pathol Soc 1908;8:117–23. Search in Google Scholar

22. von Dungern E, Hirszfeld L. Concerning heredity of group specific structures of blood. Transfusion 1962;2:70–2. 10.1111/j.1537-2995.1962.tb00195.x 13888468 Search in Google Scholar

23. Bernstein F. Zusammenfassande betrachtungen über die erblichen Blutstrukturen des Menschen. Z Indukt Abstamm u VererbLehre 1925;37:237–70. Search in Google Scholar

24. Crow JF. Felix Bernstein and the first human marker locus. Genetics 1993;133:4–7. 10.1093/genetics/133.1.4 Search in Google Scholar

25. Ferguson-Smith MA, Aitken DA, Turleau C, de Grouchy J. Localisation of the human ABO: Np-1: AK-1 linkage group by regional assignment of AK-1 to 9q34. Hum Genet 1976;34:35–43. 10.1007/BF00284432 Search in Google Scholar

26. Yamamoto F, Clausen H, White T, Marken J, Hakomori S. Molecular genetic basis of the histo-blood group ABO system. Nature 1990;345:229–33. 10.1038/345229a0 Search in Google Scholar

27. Yamamoto F, Marken J, Tsuji T, White T, Clausen H, Hakomori S. Cloning and characterization of DNA complementary to human UDP-GalNAc:Fuc-alpha1-2Gal alpha1-3GalNAc transferase (histo-blood group A transferase) mRNA. J Biol Chem 1990;265:1146–51. 10.1016/S0021-9258(19)40170-1 Search in Google Scholar

28. Clausen H, White T, Takio K, et al. Isolation to homogeneity and partial characterization of a histo-blood group A defined Fuc-alpha1-2Gal alpha1-3- N -acetylgalactosaminyltransferase from human lung tissue. J Biol Chem 1990;265:1139–45. 10.1016/S0021-9258(19)40169-5 Search in Google Scholar

29. Yamamoto F, McNeill PD, Hakomori S. Human histo-blood group A2 transferase coded by A2 allele, one of the A subtypes, is characterized by a single base deletion in the coding sequence, which results in an additional domain at the carboxyl terminal. Biochem Biophys Res Commun 1992;187:366–74. 10.1016/S0006-291X(05)81502-5 Search in Google Scholar

30. Yamamoto F, McNeill PD, Hakomori S. Genomic organization of human histo-blood group ABO genes. Glycobiology 1995;5:51–8. 10.1093/glycob/5.1.51 7772867 Search in Google Scholar

31. Bennett EP, Steffensen R, Clausen H, Weghuis DO, van Kessel AG. Genomic cloning of the human histo-blood group ABO locus. Biochem Biophys Res Commun 1995;206:318–25. 10.1006/bbrc.1995.1044 7598760 Search in Google Scholar

32. Kominato Y, Hata Y, Takizawa H, et al. Alternative promoter identified between a hypermethylated upstream region of repetitive elements and a CpG island in human ABO histo-blood group genes. J Biol Chem 2002;277:37936–48. 10.1074/jbc.M204238200 12151392 Search in Google Scholar

33. Hosseini-Maaf B, Smart E, Chester MA, Olsson ML. The Abantu phenotype in the ABO blood group system is due to a splice-site mutation in a hybrid between a new O1 -like allelic lineage and the A2 allele. Vox Sang 2005;88:256–64. 10.1111/j.1423-0410.2005.00626.x 15877647 Search in Google Scholar

34. Calafell F, Roubinet F, Ramirez-Soriano A, Saitou N, Bertranpetit J, Blancher A. Evolutionary dynamics of the human ABO gene. Hum Genet 2008;124:123–35. 10.1007/s00439-008-0530-8 18629539 Search in Google Scholar

35. Roubinet F, Kermarrec N, Despiau S, Apoil PA, Dugoujon JM, Blancher A. Molecular polymorphism of O alleles in five populations of different ethnic origins. Immunogenetics 2001;53:95–104. 10.1007/s002510100305 11345596 Search in Google Scholar

36. Olsson ML, Guerreiro JF, Zago MA, Chester MA. Molecular analysis of the O alleles at the blood group ABO locus in populations of different ethnic origin reveals novel crossing-over events and point mutations. Biochem Biophys Res Commun 1997;234:779–82. 10.1006/bbrc.1997.6713 9175793 Search in Google Scholar

37. Yazer MH, Olsson ML. The O2 allele: questioning the phenotypic definition of an ABO allele. Immunohematology 2008;24:138–47. 10.21307/immunohematology-2019-288 Search in Google Scholar

38. Hosseini-Maaf B, Irshaid NM, Hellberg A, et al. New and unusual O alleles at the ABO locus are implicated in unexpected blood group phenotypes. Transfusion 2005;45:70–81. 10.1111/j.1537-2995.2005.04195.x 15647021 Search in Google Scholar

39. Seltsam A, Das GC, Wagner FF, Blasczyk R. Nondeletional ABO*O alleles express weak blood group A phenotypes. Transfusion 2005;45:359–65. 10.1111/j.1537-2995.2005.04228.x 15752153 Search in Google Scholar

40. Lee HJ, Barry CH, Borisova SN, et al. Structural basis for the inactivity of human blood group O2 glycosyltransferase. J Biol Chem 2005;280:525–9. 10.1074/jbc.M410245200 15475562 Search in Google Scholar

41. Wagner FF, Blasczyk R, Seltsam A. Nondeletional ABO*O alleles frequently cause blood donor typing problems. Transfusion 2005;45:1331–4. 10.1111/j.1537-2995.2005.00206.x 16078922 Search in Google Scholar

42. Yazer MH, Hult AK, Hellberg A, Hosseini-Maaf B, Palcic MM, Olsson ML. Investigation into A antigen expression on O2 heterozygous group O-labeled red blood cell units. Transfusion 2008;48:1650–7. 10.1111/j.1537-2995.2008.01732.x 18482182 Search in Google Scholar

43. Olsson ML, Chester MA. Heterogeneity of the blood group A x allele: genetic recombination of common alleles can result in the A x phenotype. Transfus Med 1998;8:231–8. 10.1046/j.1365-3148.1998.00161.x 9800297 Search in Google Scholar

44. Olsson ML, Irshaid NM, Hosseini-Maaf B, et al. Genomic analysis of clinical samples with serologic ABO blood grouping discrepancies: identification of 15 novel A and B subgroup alleles. Blood 2001;98:1585–93. 10.1182/blood.V98.5.1585 11520811 Search in Google Scholar

45. Yu LC, Twu YC, Chou ML, Chang CY, Wu CY, Lin M. Molecular genetic analysis for the B(3) allele. Blood 2002;100:1490–2. 10.1182/blood-2002-01-0188 12149236 Search in Google Scholar

46. Seltsam A, Gruger D, Just B, et al. Aberrant intracellular trafficking of a variant B glycosyltransferase. Transfusion 2008;48:1898–905. 10.1111/j.1537-2995.2008.01782.x 18513251 Search in Google Scholar

47. Hosseini-Maaf B, Hellberg A, Chester MA, Olsson ML. An extensive PCR-ASP strategy for clinical ABO blood group genotyping that avoids potential errors caused by null, subgroup and hybrid alleles. Transfusion 2007;47:2110–25. 10.1111/j.1537-2995.2007.01436.x 17958541 Search in Google Scholar

48. Olsson ML, Chester MA. Polymorphism and recombination events at the ABO locus: a major challenge for genomic ABO blood grouping strategies. Transfus Med 2001;11:295–313. 10.1046/j.1365-3148.2001.00320.x 11532186 Search in Google Scholar

49. Procter J, Crawford J, Bunce M, Welsh KI. A rapid molecular method (polymerase chain reaction with sequence-specific primers) to genotype for ABO blood group and secretor status and its potential for organ transplants. Tissue Antigens 1997;50:475–83 [published erratum appears in Tissue Antigens 1998;51:319]. 10.1111/j.1399-0039.1997.tb02902.x 9389321 Search in Google Scholar

50. Kominato Y, Tsuchiya T, Hata N, Takizawa H, Yamamoto F. Transcription of human ABO histo-blood group genes is dependent upon binding of transcription factor CBF/NF-Y to minisatellite sequence. J Biol Chem 1997;272:25890–8. 10.1074/jbc.272.41.25890 9325321 Search in Google Scholar

51. Irshaid NM, Chester MA, Olsson ML. Allele-related variation in minisatellite repeats involved in the transcription of the blood group ABO gene. Transfus Med 1999;9:219–26. 10.1046/j.1365-3148.1999.00202.x 10555816 Search in Google Scholar

52. Yu LC, Chang CY, Twu YC, Lin M. Human histo-blood group ABO glycosyltransferase genes: different enhancer structures with different transcriptional activities. Biochem Biophys Res Commun 2000;273:459–66. 10.1006/bbrc.2000.2962 10873628 Search in Google Scholar

53. Seltsam A, Wagner FF, Grüger D, Gupta CD, Bade-Doeding C, Blasczyk R. Weak blood group B phenotypes may be caused by variations in the CCAAT-binding factor/NF-Y enhancer region of the ABO gene. Transfusion 2007;47:2330–5. 10.1111/j.1537-2995.2007.01475.x 17764507 Search in Google Scholar

54. Twu YC, Hsieh CY, Yu LC. Expression of the histo-blood group B gene predominates in AB-genotype cells. Transfusion 2006;46:1988–96. 10.1111/j.1537-2995.2006.01007.x Search in Google Scholar

55. Thuresson B, Chester MA, Storry JR, Olsson ML. ABO transcript levels in peripheral blood and erythropoietic culture show different allele-related patterns independent of the CBF/NF-Y enhancer motif and multiple novel allele-specific variations in the 5’- and 3’-noncoding regions. Transfusion 2008;48:493–504. 10.1111/j.1537-2995.2007.01554.x Search in Google Scholar

56. Hata Y, Kominato Y, Yamamoto FI, Takizawa H. Characterization of the human ABO gene promoter in erythroid cell lineage. Vox Sang 2002;82:39–46. 10.1046/j.0042-9007.2001.00134.x Search in Google Scholar

57. Bianco T, Farmer BJ, Sage RE, Dobrovic A. Loss of red cell A, B, and H antigens is frequent in myeloid malignancies. Blood 2001;97:3633–9. 10.1182/blood.V97.11.3633 Search in Google Scholar

58. Hosoi E, Hirose M, Hamano S, Kuroda Y. Detection of histo-blood group ABO mRNA in human chronic myeloid leukemia cell lines using reverse transcription-polymerase chain reaction (RT-PCR). Cancer Lett 1998;133:191–6. 10.1016/S0304-3835(98)00225-0 Search in Google Scholar

59. Bianco-Miotto T, Hussey DJ, Day TK, O’Keefe DS, Dobrovic A. DNA methylation of the ABO promoter underlies loss of ABO allelic expression in a significant proportion of leukemic patients. PLoS ONE 2009;4:e4788. 10.1371/journal.pone.0004788 2650780 19274076 Search in Google Scholar

60. Chihara Y, Sugano K, Kobayashi A, et al. Loss of blood group A antigen expression in bladder cancer caused by allelic loss and/or methylation of the ABO gene. Lab Invest 2005;85:895–907. 10.1038/labinvest.3700268 15880137 Search in Google Scholar

61. Dabelsteen E, Gao S. ABO blood-group antigens in oral cancer. J Dent Res 2005;84:21–8. 10.1177/154405910508400103 15615870 Search in Google Scholar

62. Orntoft TF, Meldgaard P, Pedersen B, Wolf H. The blood group ABO gene transcript is down-regulated in human bladder tumors and growth-stimulated urothelial cell lines. Cancer Res 1996;56:1031–6. Search in Google Scholar

63. Kabat EA. Blood group substances. Their chemistry and immunohistochemistry. New York: Academic Press, 1956. 10.1016/B978-1-4832-2933-1.50006-0 Search in Google Scholar

64. Morgan WT, Watkins WM. The detection of a product of the blood group O gene and the relationship of the so-called O-substance to the agglutinates A and B. Br J Exp Pathol 1948;29:159–73. Search in Google Scholar

65. Morgan WT, Watkins WM. The inhibition of the haemagglutinins in plant seeds by human blood group substances and simple sugars. Br J Exp Pathol 1953;34:94–103. Search in Google Scholar

66. Watkins WM, Morgan WT. Inhibition by simple sugars of enzymes which decompose the blood-group substances. Nature 1955;175:676–7. 10.1038/175676a0 14370191 Search in Google Scholar

67. Watkins WM, Morgan WT. Possible genetical pathways for the biosynthesis of blood group mucopolysaccharides. Vox Sang 1959;4:97–119. 10.1111/j.1423-0410.1959.tb04023.x 13669421 Search in Google Scholar

68. Ceppellini R. Physiological genetics of human blood group factors. In: Wolstenholme GEW, O’Connor CM, eds. Ciba Foundation symposium on biochemistry of human genetics. London: Churchill, 1959:242–61. Search in Google Scholar

69. Leloir LF. Nucleoside diphosphate sugars and saccharide synthesis. Biochem J 1964;91:1–8. 10.1042/bj0910001 Search in Google Scholar

70. Watkins WM. Gene-enzyme relationships of the A, B, H and Le blood group genes (abstract). Transfusion 1967;7:367. Search in Google Scholar

71. Hearn VM, Smith ZG, Watkins WM. An a-N-acetyl-D-galactosaminyltransferase associated with the human blood-group A character. Biochem J 1968;109:315–17. 10.1042/bj1090315 Search in Google Scholar

72. Race C, Ziderman D, Watkins WM. An alpha-d-galactosyltransferase associated with the blood-group B character. Biochem J 1968;107:733–5. 10.1042/bj1070733 Search in Google Scholar

73. Schenkel-Brunner H, Tuppy H. Enzymes from human gastric mucosa conferring blood-group A and B specificities upon erythrocytes. Eur J Biochem 1970;17:218–22. 10.1111/j.1432-1033.1970.tb01156.x Search in Google Scholar

74. Schachter H, Michaels MA, Tilley CA, Crookston MC, Crookston JH. Qualitative differences in the N-acetyl-D-galactosaminyltransferases produced by human A1 and A2 genes. Proc Natl Acad Sci U S A 1973;70:220–4. 10.1073/pnas.70.1.220 Search in Google Scholar

75. Topping MD, Watkins WM. Isolectric points of the human blood group A-1, A-2 and B gene- associated glycosyltransferases in ovarian cyst fluids and serum. Biochem Biophys Res Commun 1975;64:89–96. 10.1016/0006-291X(75)90223-5 Search in Google Scholar

76. Clausen H, Levery SB, Nudelman E, Tsuchiya S, Hakomori S. Repetitive A epitope (type 3 chain A) defined by blood group A1-specific monoclonal antibody TH1: chemical basis of qualitative A1 and A2 distinction. Proc Natl Acad Sci U S A 1985;82:1199–203. 10.1073/pnas.82.4.1199 397222 2579390 Search in Google Scholar

77. Varki A, Cummings R, Esko J, Freeze H, Hart G, Marth J. Essentials of glycobiology. Cold Spring Harbor, NY: Cold Spring Harbor Laboratory Press, 1999. Search in Google Scholar

78. Oriol R, Candelier JJ, Mollicone R. Molecular genetics of H. Vox Sang 2000;78(Suppl 2):105–8. Search in Google Scholar

79. Paulson JC, Colley KJ. Glycosyltransferases. Structure, localization, and control of cell type-specific glycosylation. J Biol Chem 1989;264:17615–18. Search in Google Scholar

80. Clausen H, Bennett EP, Dabelsteen E. Carbohydrates of the cell surface: molecular aspects of glycosyltransferases and their genes. APMIS Suppl 1992;27:9–17. Search in Google Scholar

81. Patenaude SI, Seto NO, Borisova SN, et al. The structural basis for specificity in human ABO(H) blood group biosynthesis. Nat Struct Biol 2002;9:685–90. 10.1038/nsb832 12198488 Search in Google Scholar

82. Hosseini-Maaf B, Letts JA, Persson M, et al. Structural basis for red cell phenotypic changes in newly identified, naturally occurring subgroup mutants of the human blood group B glycosyltransferase. Transfusion 2007;47:864–75. 10.1111/j.1537-2995.2007.01203.x 17465952 Search in Google Scholar

83. Springer GF, Williamson P, Readler BL. Blood group active gram-negative bacteria and higher plants. Ann N Y Acad Sci 1962;97:104–10. 10.1111/j.1749-6632.1962.tb34626.x 13915943 Search in Google Scholar

84. Cooling LW, Sitwala K, Dake LR, Judd WJ, Davenport R. ABO typing discrepancies in children requiring long-term nutritional support: it is the gut after all! Transfusion 2007;47:10A. Search in Google Scholar

85. Kunkel HG, Fudenberg H, Ovary Z. High molecular weight antibodies. Ann NY Acad Sci 1960;86:966–73. 10.1111/j.1749-6632.1960.tb42853.x Search in Google Scholar

86. Kunkel HG, Rockey JH. ß 2 A and other immunoglobulins in isolated anti-A antibodies. Proc Soc Exp Biol Med 1963;113:278. 10.3181/00379727-113-28341 Search in Google Scholar

87. Garratty G. Blood group antigens as tumor markers, parasitic/bacterial/viral receptors, and their association with immunologically important proteins. Immunol Invest 1995;24:213–32. 10.3109/08820139509062774 Search in Google Scholar

88. Daniel-Johnson J, Lee-Stroka A, Schechterly C, et al. Probiotic-associated high-titer anti-B in a group A platelet donor as a cause of severe hemolytic transfusion reactions. Transfusion 2008;48S(Suppl 2):260A. Search in Google Scholar

89. Arendrup M, Hansen JE, Clausen H, Nielsen C, Mathiesen LR, Nielsen JO. Antibody to histo-blood group A antigen neutralizes HIV produced by lymphocytes from blood group A donors but not from blood group B or O donors. AIDS 1991;5:441–4. 10.1097/00002030-199104000-00014 Search in Google Scholar

90. Preece AF, Strahan KM, Devitt J, Yamamoto F, Gustafsson K. Expression of ABO or related antigenic carbohydrates on viral envelopes leads to neutralization in the presence of serum containing specific natural antibodies and complement. Blood 2002;99:2477–82. 10.1182/blood.V99.7.2477 Search in Google Scholar

91. Cserti CM, Dzik WH. The ABO blood group system and Plasmodium falciparum malaria. Blood 2007;110:2250–8. 10.1182/blood-2007-03-077602 Search in Google Scholar

92. Uneke CJ. Plasmodium falciparum malaria and ABO blood group: is there any relationship? Parasitol Res 2007;100:759–65. 10.1007/s00436-006-0342-5 Search in Google Scholar

93. Chen Q, Heddini A, Barragan A, Fernandez V, Pearce SF, Wahlgren M. The semiconserved head structure of Plasmodium falciparum erythrocyte membrane protein 1 mediates binding to multiple independent host receptors. J Exp Med 2000;192:1–10. 10.1084/jem.192.1.1 Search in Google Scholar

94. Loscertales MP, Owens S, O’Donnell J, Bunn J, Bosch-Capblanch X, Brabin BJ. ABO blood group phenotypes and Plasmodium falciparum malaria: unlocking a pivotal mechanism. Adv Parasitol 2007;65:1–50. 10.1016/S0065-308X(07)65001-5 Search in Google Scholar

95. Rowe JA, Handel IG, Thera MA, et al. Blood group O protects against severe Plasmodium falciparum malaria through the mechanism of reduced rosetting. Proc Natl Acad Sci U S A 2007;104:17471–6. 10.1073/pnas.0705390104 2077280 17959777 Search in Google Scholar

96. Gagneux P, Varki A. Evolutionary considerations in relating oligosaccharide diversity to biological function. Glycobiology 1999;9:747–55. 10.1093/glycob/9.8.747 10406840 Search in Google Scholar

97. Mollison PL, Engelfriet CP, Contreras M. Blood transfusion in clinical medicine. 10th ed. London: Blackwell Scientific, 1997. Search in Google Scholar

98. Issitt PD, Anstee DJ. Applied blood group serology. 4th ed. Miami: Montgomery Scientific Publications, 1998. Search in Google Scholar

99. Sazama K. Reports of 355 transfusion-associated deaths: 1976 through 1985. Transfusion 1990;30:583–90. 10.1046/j.1537-2995.1990.30790385515.x 2402771 Search in Google Scholar

100. Stainsby D, Jones H, Asher D, et al. Serious hazards of transfusion: a decade of hemovigilance in the UK. Transfus Med Rev 2006;20:273–82. 10.1016/j.tmrv.2006.05.002 17008165 Search in Google Scholar

101. Linden JV, Wagner K, Voytovich AE, Sheehan J. Transfusion errors in New York State: an analysis of 10 years’ experience. Transfusion 2000;40:1207–13. 10.1046/j.1537-2995.2000.40101207.x 11061857 Search in Google Scholar

102. Shanwell A, Andersson TM, Rostgaard K, et al. Post-transfusion mortality among recipients of ABO-compatible but non-identical plasma. Vox Sang 2009;96:316–23. 10.1111/j.1423-0410.2009.01167.x 19254234 Search in Google Scholar

103. Blumberg N, Heal JM, Hicks GL Jr, Risher WH. Association of ABO-mismatched platelet transfusions with morbidity and mortality in cardiac surgery. Transfusion 2001;41:790–3. 10.1046/j.1537-2995.2001.41060790.x 11399821 Search in Google Scholar

104. Romano EL, Soyano A, Montano RF, et al. Treatment of ABO hemolytic disease with synthetic blood group trisaccharides. Vox Sang 1994;66:194–9. 10.1111/j.1423-0410.1994.tb00309.x 8036789 Search in Google Scholar

105. Rydberg L. ABO-incompatibility in solid organ transplantation. Transfus Med 2001;11:325–42. 10.1046/j.1365-3148.2001.00313.x 11532188 Search in Google Scholar

106. West LJ, Pollock-Barziv SM, Dipchand AI, et al. ABO-incompatible heart transplantation in infants. N Engl J Med 2001;344:793–800. 10.1056/NEJM200103153441102 11248154 Search in Google Scholar

107. West LJ. B-cell tolerance following ABO-incompatible infant heart transplantation. Transplantation 2006;81:301–7. 10.1097/01.tp.0000203829.78700.f3 16477211 Search in Google Scholar

108. Tobian AA, Shirey RS, Montgomery RA, Tisch DJ, Ness PM, King KE. Therapeutic plasma exchange reduces ABO titers to permit ABO-incompatible renal transplantation. Transfusion 2009 Feb 6 [Epub ahead of print]. 10.1111/j.1537-2995.2008.02085.x 19210321 Search in Google Scholar

109. Yazer MH, Triulzi DJ. Immune hemolysis following ABO-mismatched stem cell or solid organ transplantation. Curr Opin Hematol 2007;14:664–70. 10.1097/MOH.0b013e3282e9a576 17898572 Search in Google Scholar

110. Avent ND, Martinez A, Flegel WA, et al. The BloodGen project: toward mass-scale comprehensive genotyping of blood donors in the European Union and beyond. Transfusion 2007;47(Suppl):40S–6S. 10.1111/j.1537-2995.2007.01309.x 17593285 Search in Google Scholar

111. Kline TR, Runyon MK, Pothiawala M, Ismagilov RF. ABO, D blood typing and subtyping using plug-based microfluidics. Anal Chem 2008;80:6190–7. 10.1021/ac800485q 2592685 18646778 Search in Google Scholar

112. Goldstein J, Siviglia G, Hurst R, Lenny L, Reich L. Group B erythrocytes enzymatically converted to group O survive normally in A, B, and O individuals. Science 1982;215:168–70. 10.1126/science.6274021 6274021 Search in Google Scholar

113. Liu QP, Sulzenbacher G, Yuan H, et al. Bacterial glycosidases for the production of universal red blood cells. Nat Biotechnol 2007;25:454–64. 10.1038/nbt1298 17401360 Search in Google Scholar

114. Olsson ML, Clausen H. Modifying the red cell surface: towards an ABO-universal blood supply. Br J Haematol 2008;140:3–12. Search in Google Scholar

115. Liu QP, Yuan H, Bennett EP, et al. Identification of a GH110 subfamily of alpha 1,3-galactosidases: novel enzymes for removal of the alpha 3Gal xenotransplantation antigen. J Biol Chem 2008;283:8545–54. 10.1074/jbc.M709020200 2417185 18227066 Search in Google Scholar

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An association between fingerprint patterns with blood group and lifestyle based diseases: a review

• Published: 18 August 2020
• Volume 54 , pages 1803–1839, ( 2021 )

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• Vijaykumar Patil   ORCID: orcid.org/0000-0003-3567-2440 1 &
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In the current era of the digital world, the hash of any digital means considered as a footprint or fingerprint of any digital term but from the ancient era, human fingerprint considered as the most trustworthy criteria for identification and it also cannot be changed with time even up to the death of an individual. In the court of law, fingerprint-proof is undeniably the most dependable and acceptable evidence to date. Fingerprint designs are exclusive in each human and the chance of two individuals having identical fingerprints is an exceptional case about one in sixty-four thousand million also the fingerprint minutiae patterns of the undistinguishable twins are different, and the ridge pattern of each fingertip remain unchanged from birth to till death. Fingerprints can be divided into basic four categories i.e. Loop, whorl, arch, and composites, nevertheless, there are more than 100 interleaved ridge and valleys physiognomies, called Galton’s details, in a single rolled fingerprint. Due to the immense potential of fingerprints as an effective method of identification, the present research paper tries to investigate the problem of blood group identification and analysis of diseases those arises with aging like hypertension, type 2-diabetes and arthritis from a fingerprint by analyzing their patterns correlation with blood group and age of an individual. The work has been driven by studies of anthropometry, biometric trademark, and pattern recognition proposing that it is possible to predict blood group using fingerprint map reading. Dermatoglyphics as a diagnostic aid used from ancient eras and now it is well established in number of diseases which have strong hereditary basis and is employed as a method for screening for abnormal anomalies. Apart from its use in predicting the diagnosis of disease; dermatoglyphics is also used in forensic medicine in individual identification, physical anthropology, human genetics and medicine. However, the Machine and Deep Learning techniques, if used for fingerprint minutiae patterns to be trained by Neural Network for blood group prediction and classification of common clinical diseases arises with aging based on lifestyle would be an unusual research work.

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1 Introduction

The study of fingerprint patterns was introduced by Dr. Harold Cummins in 1926 but it is already in use before several hundred years ago. Fingerprint patterns have been normally used for identification of an individual. Now a days every organization or even may Government institutes in India, use fingerprint verification to identify everyone uniquely and it also have been used as a biometric modality for gender and age identification. An individual is their own key; behind this catchy principle biometrics have become an attractive alternative to traditional identification methods such as tokens or passwords (Fernandes et al. 2013 ). Current fingerprint matching methods were started in the sixteenth century. It was Henry Fauld in 1880 who first experimentally proposed the singularity and uniqueness of fingerprint. Herschel (Ravindran et al. 2017 ) added to the establishment of current fingerprinting identification. In the nineteenth century Sir Francis Galton (McBean et al. 2014 ) directed broad investigations and ordered the sorts of fingerprints relying on essential example as loops, whorls and arches. It was Cummins (Ferraz et al. 2010 ) who authored the expression “Dermatoglyphics (derma ¼ skin, glyphic ¼ bends), to dermal edge arrangements on the digits, of palms and sole and furthermore demonstrated that edge design are resolved incompletely by heredity or natural impact which produce pressure and strain in their development during fetal life. The fingerprint design whorl might be winding, oval, roundabout or any assortment of a loop and record for around 30%. Arches are the basic type up till now uncommon (about 5%). The fingerprint design has edges running from one side to the opposite side of the print without having any re-bend. The term composite is utilized for mix of type example that doesn’t fit into any of the above characterization (Azhagiri et al. 2018 ). Till date, analysts or researches have generally used fingerprint details as perspectives to build up any individuals uniqueness. The ridge patterns have been comprehensively classified into five different kinds called as arch, tented arch, whorl, ulnar and radial loop. An individual can have any of the above type in any of the its fingers. All things considered, dominant part of fingerprints found in populace review shows that 70 percent of the prints are loops, 20–25 percent being whorls though just 5 to 10 percent consider arch or tented arch patterns or designs (Singh and Majumdar 2015 ). Some examinations done on twins have presumed that monozygotic or indistinguishable twins have comparable however not indistinguishable examples found.

Dermatoglyphics as a diagnostic aid used from ancient eras and now it is well established in number of diseases which have strong hereditary basis and is employed as a method for screening for abnormal anomalies. Fingerprint minutiae patterns of ridges are determined as unique through the combination of genetic and environment factors. Person identification using fingerprint algorithms are well sophisticated and are being established all over the world for security and authentication. The fingerprint also used to classify gender and age group but very few manual attempts have been made to explore relationship between fingerprint patterns with blood group and common clinical diseases like hypertension, type-2 diabetes and arthritis. It will be helpful for anthropologists to predict blood group and classify common clinical diseases than conventional pathological techniques from the fingerprints those are obtained from mined articles using Deep Learning Techniques for early reminder to prevent such common clinical diseases those arises with aging and also crime investigators to minimize the range of the suspects it would be predicted using Deep Neural Network. The dermatoglyphics and its important role in the diagnosis of different diseases like hypertension, type-2 diabetes and arthritis with genetic bases. Apart from its use in predicting the diagnosis of disease; dermatoglyphics is also used in forensic medicine in individual identification, physical anthropology, human genetics and medicine.

2 Objectives of research

The research work is aimed in developing the Deep Neural Network algorithms for accurate classification of the fingerprints obtained which include:

To Enhancement Fingerprint Image During sampling or in data set preparation step fingers of an individual recorded using fingerprint scanner. To enhance the fingerprint images precisely, the research focuses to develop various pre-processing algorithms like—Segmentation, Normalization, Orientation estimation, Ridge frequency estimation, Gabor filter and Binarisation and Thinning etc.

To Extraction of Features from Fingerprints and Finding Similarity Vector To build similarity vector using features of captured sample images of fingerprint required a feature extraction algorithm. The implementation of the biometric features extraction algorithms needs to extract features like—the ridge count, ridge thickness to valley thickness ratio (RTVTR), white lines count, ridge count asymmetry, minutiae map(MM) orientation collinearity maps(OCM), Gabor Feature maps(GFM), orientation map (OM) for pattern type, 2D wavelet transform (DWT)

To Predict Blood Group by using Extracted Feature of Fingerprints The unsupervised machine learning technique will apply for classification of blood group which helps to identify relationship patterns of different features of fingerprints with ABO blood type and then prediction will perform with the application of Machine Learning and Convolutional Neural Network (CNN) technology with the help of rigid frequency count and distance formula to conclude blood group from feature vector.

To Classify and Analyse lifestyle diseases like hypertension, arthritis and diabetes Normally common clinical diseases arise with the age but, now in current era these are no more only relevant to the age; due to busy schedule or lifestyle of an individual they arise at any stage of life. With the fingerprint images and blood group of an individual, the dataset include the external attributes like age, weight, height, skin color, eyes color, work nature, eating habits (vegetarian or non-vegetarian), region (rural or urban), addiction (if any like drink, smoke), etc.

The rest of the paper is organized as follows. The conceptual background discussed in Sect.  2 . The literature review specificity discusses all the methods used in Sect.  3 and the evaluation and discussion included in Sect.  4 which illustrates the summary of different dataset/samples and methodologies used. Finally, in Sect.  5 , we conclude the paper.

3 Conceptual background

The common types of fingerprint are as arch, tented arch, whorl, ulnar and radial loop, the Fig.  2 shows the different types of whorl patterns from fingerprint design. A whorl is portrayed by two deltas and one focal roundabout center. The center may have various examples. It might be winding, concentric circles, vertically compacted circles or even of the state of eye of a peacock quill. The edges start from one end, rise and hover towards the middle and go down towards the opposite end. Recent Advanced studies in genetics and developmental biology have guaranteed that the various projections of the human mind are physiologically associated with various fingers of both the hands. The practical coordination represented by the left half of the cerebral side of the equator is identified with the fingers of the right hand and the other way around. Consequently, the focalized left half of mind is associated with the fingers of right hand, the thumb is facilitated by the unrivaled frontal projection, index is associated with the mediocre frontal flap, middle finger with parietal projection, ring finger with the fleeting projection and little finger with the rear piece of cerebrum, which is the occipital flap. Correspondingly, the left half of the mind is associated with similar flaps of the cerebrum. Each projection zone is liable for a portion of the other impression of the encompassing (Singh and Majumdar 2015 ). Tented arch is a pattern that is portrayed by a straight upstanding edge at the center of a straightforward arch pattern. Loops are the most ordinarily up-to-the-minute highlights on a person’s fingerprints just as in a subjective example space of a few fingerprints shown in Figs.  1 and 2 . They are described by edges that start trickling out of a crosswise of the fingertip, circle from place to place the focal point of finger cushion, and back to a similar course where they began from. These loops can either flee from the thumb. Because of an individual area of arm bones—Radia and Ulna, any loop opening endlessly from thumb is an ulnar loop, and the one which opening near the thumb is a radial loop. A spiral whorl is described round patterns that are fit as a fiddle at the core or center. This pattern has two deltas at the two corners. The concentric whorl pattern is showed by having concentric rings of edge patterns. Lengthened whorl pattern is described by a long oval whorl flanked by two triradial on either side. Every single other component of a whorl is likewise present right now a pattern. It is one of the uncommon fingerprint patterns. It appears to be a Tai-Chi pattern at the inside or the center, encompassed by different roundabout layers of edges. Since the image has two symmetric yet oppositely situated arrangements, the subjects having imploding whorls grandstand doublemindedness. Composite whorl or twofold circle is one of the uncommon fingerprint patterns. It is either present on thumb or at most, the index finger. It is once more, one of the uncommon whorl patterns that contain a peacock’s eye-molded circle contained inside a whorl. The center comprises of more than one spiral which are lined by a straight line at one of the corners. It to some degree seems as though the pattern on a peacock’s tail quills. This pattern ought to have one triradius on either the left or right side. At the point when a pattern can’t unmistakably have the option to sort into any of the above pattern types, comprises a blend of at least two above talked about patterns like a combination of concentric whorl and transformed loop and so on., it is professed to be a variation pattern. They don’t contain any plain arch or loop, be outspread or ulnar. They do exclude any regular pattern. Although people have been utilizing fingerprints as a methods for recognizable proof for quite a while however right now, have put forth an attempt to make stride further to “study a connection between pattern of fingerprint and ABO Rh blood group”, with the goal that one can get a thought regarding the normal blood group from the investigation of finger impression pattern and the other way around. natural attributes like fingerprints and blood groups can’t be overlooked and imitated like keys, passwords, and so on subsequently are viewed as increasingly dependable, true and solid in scientific sciences.

Arch and Arch with Loop

Ulnar Loop, Radial Loop, and Composite Whorl

Other than an investigation of “blood group” commonness in itself isn’t just significant for transfusion medication yet additionally for organ transplantation and hereditary research, forecast of specific malignancies/infections for certain blood groups just as in advancement contemplates that help researchers to comprehend the spot person involve in development’s stretching tree (Fayrouz et al. 2011 ).

Different types of fingerprint patterns are as follows:

There are four unlike whorl patterns as: the plain whorl, the central pocket loop, the double loop, and the accidental whorl also it has different kinds of form shown in Figs.  3 , 4 and 5 . Their normal highlights are that they have at any rate two deltas and at least one of the ridgelines bends around the center to shape a circle or winding or other adjusted, continually bending structure. The accidental whorl can be any pattern or blend of patterns that don’t fit into any of the above characterizations. The expression “Composite” is utilized to portray such patterns. Positive distinguishing proof utilizing fingerprints can be set up just if 16 to 20 purposes of closeness exist in the minutiae (Kanchan and Chattopadhyay 2006 ; Vij 2005 ; Subrahmanyam 1999 ). Arches are the most straightforward patterns and furthermore the rarest. There are two sorts: plain arches and tented arches. In these two types, the ridgelines stream into the print from one side, an ascent in the pattern, and stream out to the opposite side of the print. Loops are shaped by ridgelines that stream in from one side of the print, clear up in the middle like a tented arch, and afterward bend back around and stream out or will in general stream out as an afterthought from where they entered. Loops are assigned as being either spiral or ulnar, contingent upon which side of the finger the lines enter. The loop is the most well-known of the considerable number of patterns. Target/Concentric Whorl Spiral Whorl Concentric Whorl Elongated Whorl Imploding Whorl Imploding Whorl Accidental Whorl or Variant Accidental Whorl or Variant Most programmed frameworks for unique fingerprint examination depend on minutiae coordinating Minutiae are neighborhood discontinuities in the finger impression pattern. An aggregate of 150 distinctive minutiae types have been recognized some of the minutiae forms shown in Fig.  6 . The ridge closure and ridge bifurcation minutiae types are employed as unique mark of acknowledgment.

Target/Concentric Whorl, Spiral Whorl and Concentric Whorl

Elongated Whorl, Imploding Whorl and Imploding Whorl

Accidental Whorl or Variant and Accidental Whorl or Variant

Different minutiae types and Ridge ending & Bifurcation (Fingerprint Identification – Project 2)

Now a day’s biometric applications are designed for

Authentication or Identification of an individual applications like e-records security, cellular phones access, medical records management, library access and virtual learning etc.

E-Governance: like, Digital signature, Aadhar cards, Driver’s licenses, border travel control, passport control, and welfare-disbursement etc.

Digital Forensic: such as, corpse identification, criminal investigation, terrorist identification, parenthood determination, and missing children (Ramasubramanian and Alexander 2009 ).

Blood group structures were discovered way back in 1900 by Karl Landsteiner. Total 19 foremost groups have been identified which vary in their occurrence of spreading various races of mankind. Clinically, only ‘ABO’ and ‘Rhesus’ groups are of major importance. ‘ABO’ system is additional discriminated as A, B, AB, O blood group types according to presence of corresponding antigen in plasma (Ferraz 2013 ). Yet another biological record that remains unchanged throughout the lifetime of an individual is the blood group. Determining the blood group of a person from the samples obtained at the site of crime, helps identify a person. Landsteiner classified blood groups under the ABO blood group system (IMAQ 2004 ). Dermatoglyphics as a diagnostic aid used from ancient eras and now it is well established in number of diseases which have strong hereditary basis and is employed as a method for screening for abnormal anomalies. The study of possible predilection of certain disease and malignancies from blood groups are some of the factors which encourages one to carry the study further. Fingerprint minutiae patterns of ridges are determined as unique through the combination of genetic and environment factors. The identification of minutiae shown in Fig.  6 , it shows minutiae like Ridge ending & Bifurcation. Dermatoglyphics and its important role in the diagnosis of different diseases like hypertension, type-2 diabetes and arthritis with genetic bases.

Machine learning is getting popular in all industries with the main purpose of improving revenue and decreasing costs; by using Machine learning technique they automate and optimize their process to solve challenging tasks very efficiently. The proposed research work aims in creating a system that finds the relationship between blood group and minutes patterns of fingerprints which will be helpful to predict blood group and common clinical diseases of an individual by analyzing its fingerprints. The fingerprint having basic four categories which are loop, whorl, arch and composites but also there are more than 100 interleaved ridge and valleys which explore unique characteristics of an individual which will help to design Deep Neural Network or Convolutional Neural Network (CNN) which predict blood group and common clinical diseases like hypertension, type 2-diabetes and arthritis. All ten fingerprints will be acquired in real time of both male and female from different age group and from various locations of country by using optical fingerprint scanner with external characteristics to from the large dataset as process. The fingerprint data will be acquiring using fingerprint scanner. All fingers of an individual are scanned to build dataset for training model with other necessary data collected by simple registration form where external attributes like age, weight, height, skin color, eyes color, work nature, eating habits (vegetarian or non-vegetarian), region (rural or urban), addiction (if any like drink, smoke), etc. will be recorded. The obtained fingerprint from database goes through various preprocessing stages for enhancement and removing the noise before feature extraction process which include Segmentation, Normalization, Orientation Estimation, Ridge Frequency Estimation, Gabor filter, Binarisation and Thinning from which the Orientation Estimation and Ridge Frequency Estimation. After the preprocessing of fingerprints, it goes through four steps of feature extraction, one is frequency domain feature vector obtaining by undergoing image through different levels of processing to build feature vector from acquired finger dataset. The all combined vectors within the dataset will then be allowed to pass for unsupervised training model. It uses clustering technique which helps to find similarity measure or relationship between features of fingerprint and external attributes. The clusters formed by unsupervised learning attempts to build neural network model which is deep neural network technique including features extracted from fingers and external attributes of an individual, it will be used to generate predictive model to know the blood group of an individual as well as it used to analyses disease arises with aging. The deep neural network model uses similarity measures or minimum distance for the entire combined feature vectors database. The proposed study divided into four different cases as Prediction of Blood Group, Analysis and Classification of Hypertension Disease, Analysis of Arthritis Disease and Analysis of Diabetics Disease.

4 Literature review

To work out the blood group of an individual, red cells of that individual are blended in with various neutralizer arrangements. If, for instance, the arrangement contains hostile to B antibodies and the individual has B antigens on cells, it will cluster together. On the off chance that the blood doesn’t respond to any of the counter An or hostile to B antibodies, it is blood group O. A progression of tests with various kinds of antibodies can be utilized to distinguish blood group. On the off chance that the individual has a blood transfusion, the blood of the individual will be tried against an example of contributor cells that contains ABO and RhD antigens. If there is no response, contributor blood with a similar ABO and RhD type can be utilized. It shows that the blood has responded with certain antibody and is hence not perfect with blood containing that sort of counteracting agent If the blood doesn’t agglutinate, it demonstrates that blood doesn’t have antigens restricting the extraordinary immune response in the reagent. In the current framework, the blood group is resolved physically. Right now, arrangements, for solutions such as anti-a, anti-b, anti-d to the three samples of blood occurred. After some time, agglutination might happen.

Contingent on the agglutination, the blood group can be controlled by the individual physically. The weaknesses of this framework are more odds of human blunders are conceivable. Only specialists can tell the blood type by observing at the agglutination procedure. The traditional method of distinguishing the blood group is usually the plate test and the tube test (Pimenta et al. 2012 ). Both of which are performed by under comprehensive analog procedures with human observation. In the current era of digitization, it is not an efficient way to handle such a basic yet indispensable medical technique in a full analog atmosphere. There are also a few techniques such as micro plate testing and gel centrifugation (Pimenta et al. 2012 ; Ramasubramanian and Alexander 2009 ).

Fernandes et al. ( 2015 ) presented result in his research paper allow concluding that ABO, Rh phenotype, reverse, and Crossmatching individuals blood group is possible with the developed device and procedure. They proposed device that allows blood type identification near the patient, outdoor a conventional laboratory, without the need of to be a specialized assistant to interpret the test result of blood, and in a very short time (5 min).The fast response time by device enables us it will be used in emergency situations, which is an advantage compared with the automatic commercial systems used in clinical laboratories (in average, response time of 30 min). In addition, the methodology and test protocol applied to the sample’s preparation is simple, without the need of sample dilutions or incubations. The prototype was implemented with noncomplex electronic components for a low-cost device. The implemented device distinguishes agglutinated from non-agglutinated samples using a classification algorithm (developed by the authors), based on the variation of OD discrete values of samples, for each blood test. The device operation was validated for ABO, Rh phenotype, reverse, and Crossmatching human blood typing based on donor’s blood samples provided by the IPST and test results agreed with their typing using their gold standard commercial and automatic systems.

Pimenta et al. ( 2012 ) The examination group is working in the advancement of programmed and scaled down gadgets for clinical applications. A case of this work is the advancement of a scaled down, minimal effort, versatile and programmed framework to blood typing in crisis circumstances, considering a spectrophotometric approach and within the sight of agglutination (cooperation between red blood cells’ surface and explicit reagents). The use of a basic and quick exploratory convention permits deciding blood typing and empowers the structure of an electronic programmed framework. This framework will be helpful to decrease a few impediments of the current frameworks and techniques to blood typing. The outcomes can be influenced by a few variations that makes more enthusiastically the structure of a programmed framework, for example, the trial framework utilized for spectrophotometric estimations; the agglutination quality, which influence the contrasts among control and test samples; the time spent in test readiness since it is important blood and reagents weakening; and perform spectra estimation as quickly as time permits in light of the fact that the agglutinated cells continuously will in general settle in the base of the cuvette.

Proposed method by Fernandes et al. ( 2013 ) depends on the examination of the Rh phenotypes in human blood type dependent on the plate test and utilizing a spectrophotometric approach. This examination will be remembered for the versatile gadget recently created by the exploration group for deciding ABO human blood type. In this way, this paper presents the Rh phenotype assurance, including the D, C, c, E and e antigens, utilizing optical retention estimations, in the obvious range, to recognize an agglutinated test (cooperation among antigens and antibodies) from a non-agglutinated test (no association). To decide the nearness or nonappearance of every antigen five samples were set up by setting 50 f.lL of the particular reagent and 12.5 f.lL of entire blood in the plate, as depicted in the reagents manual. Every arrangement was blended for around one moment in a region of 2.5 cm2. At that point, the plate was situated in the estimating set-up of the spectrophotometer. An O.D. range estimation of the 50 f.lL reagents was likewise important to set the pattern and to additionally adjust the necessary gadgets.

The authors Pimenta et al. ( 2012 ) presents the standards for the improvement of a scaled down, ease, compact and programmed framework, in view of a spectrophotometric approach, are introduced. The framework will have the option to decide ABO and Rh blood types in a brief timeframe and in situ, which is reasonable to crisis circumstances and permit the blood typing outside an ordinary clinical research facility. For that, the essential components of the framework ought to be: a light source, a light receptor and a microcontroller. 1. Approval of the general test convention the convention applied in the framework use blood samples (from the Portuguese Blood Institute) and business antibodies as reagents (monoclonal Anti-An, Anti-B, Anti-AB and polyclonal Anti-D from Hos Lab Diagnostic). Four test samples should be set up for each blood test. Each test is acquired by blending blood in with a immune response. Blending blood in with the reagents it tends to be acquired two kinds of samples: agglutinated, if there is antigen-counter acting agent connection; or non-agglutinated if there is no association. For instance, blending A positive blood type with Anti-A, Anti-AB or Anti-D it is gotten an agglutinated test since this blood type has the A and D antigens. With the Anti-B, it is gotten from a non-agglutinated test. 2. Scaling down of the test framework After the approval of the spectrophotometry estimations to human blood typing, with the use of a quick and straightforward convention, the following stage was the execution of a particular light source framework by utilizing Light Emitting Diodes (LEDs) and a photodiode estimating gadget (S1336-5BQ photodiode from Hamamatsu), e.g., keeping away from the massive and costly framework dependent on a light source and monochromator.

Ramasubramanian and Alexander ( 2009 ) a coordinated fiberoptic a microfluidic gadget for the location of agglutination for blood type crossmatching has been portrayed. The gadget comprises of a straight microfluidic channel through with a responded RBC suspension is siphoned with the assistance of a syringe siphon. The stream meets an optical way made by a producer got fiber optic pair incorporated into the microfluidic gadget. A 650 nm laser diode is utilized as the light source and a silicon photodiode is utilized to recognize the light power. The separating between the tips of the two optic filaments can be balanced. At the point when fiber separating is enormous and the centralization of the suspension is high, the dispersing wonder turns into the prevailing system for agglutination identification while at low focuses and little dividing, opto-interruption turns into the predominant component. An agglutination quality factor (ASF) is determined from the information. Studies with an assortment of blood types demonstrate that the detecting technique effectively distinguishes the agglutination response in all cases. A dispensable coordinated gadget can be intended for future usage of the strategy for the close bedside pre-transfusion check. Stomach muscle positive blood type will respond with against A, hostile to B, and against D antibodies and cause agglutination. Henceforth, we just present outcomes for AB positive sort in detail right now. The information is illustrative of the outcomes acquired Mouad.

Ali et al. ( 2016 ) proposed fingerprint Recognition framework is separated into four phases. First is the Acquisition stage to catch the fingerprint picture, the second is the Pre-processing stage to enhancement, binarization, thinning fingerprint picture. The third stage is the Feature Extraction Stage to remove the element from the thinning picture by use minutiae extractor strategies to separate ridge ending and ridge bifurcation from thinning. The fourth stage is coordinating (Identification, Verification) to coordinate two minutiae focuses by utilizing the minutiae matcher technique in which closeness and distance measurements is utilized. The calculation is tried precisely and dependably by utilizing fingerprint pictures from various databases. The fingerprint acknowledgment framework is separated into three phases that are fingerprint picture pre-processing, include extraction and coordinating. The coordinating stage is partitioning into two procedure ID and confirmation. At the hour of catch the fingerprint picture, the pre-processing stage is applied to it. The yield of this stage will be passed to the component extraction organize which separates the minutiae point (ridge ending, Bifurcation) from thinning fingerprint picture, at that point the bogus minutiae evacuation is applied to remove genuine minutiae. At long last, the genuine minutiae are put away in tangle lab record. At that point if the fingerprint is as of now selecting? at that point send it to the coordinating stage in any case do the enrolment stage and store it in the database as a format. In ID case (one-to-many coordinating), the info include set, which is coordinating with N format from the database, N coordinating will be finished. The outcome will be considered as a coordinating Score. If coordinating Score more like 1, at that point the two fingers from a similar client. If coordinating score close to Zero, at that point the two fingers from deferent clients. In confirmation case (coordinated coordinating), the info includes set, which is coordinating with one layout from the database, one coordinating will be done and chosen either the information fingerprint checked or unsubstantiated.

Siva Sundhara Raja and Abinaya ( 2019 ) proposed work comprises of the accompanying stages as pre-processing, feature extraction and classification, as portrayed in Fig.  7 . The input pre-processing is the method to play out certain tasks for improving pictures preceding computational processing. It is a system that is utilized to conceal the data that isn’t appropriate to the picture for additional processing. The pre-processing steps incorporate the accompanying: picture enhancement, picture resizing procedure, and thinning process. The picture enhancement process amends the lucidity of ridges and valley structure in the fingerprint picture. Right now, the histogram evening out technique is utilized. They have taken two types of blood gatherings. mage resizing is utilized to extend or compress the all outnumber of pixels. With the goal that it has the predetermined number of lines and sections. The focal point bending is done when we zoom the focal point, it will transform into a bent shape rather than the keener shape. Thinning is a morphological activity that is utilized to dispose of picking front picture components from double pictures. The feature extraction is a technique for catching the visual substance of pictures for ordering and recuperating. The methodologies depend on GLCM, Wavelet Features, Laws of surface features, Minutiae Extraction. The Major features of the fingerprints like ridge endings, ridge bifurcations are called Minutiae shown in Fig.  8 . Minutiae states the distinction between one fingerprint from another fingerprint. A ridge ending is the place the ridge suddenly ends while ridge bifurcation is the place the ridge isolates into at least two branches. The extraction of minutiae turns out to be even more testing on account of the commotion present and lack of difference in the picture.

Minutiae feature for different blood group fingerprint (Siva Sundhara Raja and Abinaya 2019 )

Ridge, Terminations, valley, Bifurcations (Jain and Singh 2015 )

Ravindran et al. ( 2017 ) proposed work taken blood sample images and pre-processed it by using various techniques such as color plane extraction, color to gray image conversion. These blood image pre-processing terms can dramatically increase the uniformity of a visual investigation of collected samples. Also, they are applied several filter processes which strengthen or reduce certain image details enable an easier or faster assessment. Operators can augment a camera image with just a few clicks. Filtering encompasses several image filters for image optimization mixed filter for edge detection improvement, noise suppression, character alteration, etc. Image processing includes It includes several functions for image processing. Contrast increase by static or dynamic binarization, lookup tables or image plane separation. Resolution reduction via binning.

The methodology proposed by Rhiannon S. McBean et al. ( 2014 ) having two different genetic technologies: single nucleotide variant (SNV) mapping by DNA microarray and second method was massively parallel sequencing (MPS), concerning blood bunch genotyping. The steadiest transmissible change related with blood group bunch antigens are SNVs. To perform prediction of the blood type antigen phenotypes, SNV mapping which includes profoundly multiplexed genotyping can be performed on business microarray stages. Microarrays recognize just known SNVs, along these lines, to type uncommon or novel alleles not represented in the cluster, further Sanger sequencing of the district is frequently required to determine genotype. A model talked about right now the recognizable proof of uncommon and novel RHD alleles in the Australian populace. Enormously equal sequencing, otherwise called cutting edge sequencing, has a high throughput limit and maps all purposes of variety from a reference grouping, taking into consideration recognizable proof of novel SNVs. Instances of the use of this innovation to determine the hereditary premise of vagrant blood bunch antigens are presented here. In general, the assurance of a full profile of blood bunch SNVs, notwithstanding serological phenotyping, gives a premise to the arrangement of perfect blood in this manner offering improved transfusion security.

Ferraz et al. ( 2010 ) has developed technique that allows to analyses an image captured by a CCD camera detecting the occurrence of agglutination, through image processing techniques developed for determine the occurrence of agglutination. Secondly allows determine the blood type of the patient through the classification algorithm developed. Finally, allows store the information in a database built. The built database can store images captured and used in image processing techniques (each image contain four samples of blood and reagent), the standard deviation calculated in each four samples of the image, the result based by the value of standard deviation obtained for each of the samples (if agglutinated or not agglutinated in the sample of blood and reagent) and the result obtained by the classification algorithm (corresponding of blood type).The image will be processed by image processing techniques developed with the IMAQ Vision software from National Instruments (IMAQ 2004 ). The descriptions of all the functions presented are presented in the references mentioned (IMAQ 2004 ; Relf 2003 ).

The strategy proposed in Tejaswini and Mallikarjuna Swamy ( 2014 ) caught pictures of slide tests were a camera comprises of a shading picture made from three examples of blood and reagent. The picture preparing technique is probed the few pictures gained. These pictures are prepared utilizing MATLAB programming. The picture preparing strategies, for example, shading plane extraction, thresholding, and morphological activities were performed on the pictures. The picture got in the wake of applying auto thresholding grouping capacity it very well may be seen that the item and foundation are isolated. In the following stage, neighborhood limit activity utilizing Niblack work is applied it ascertains a pixel-wise edge and it very well may be seen just the outskirt portioned picture. The Narkis Banu and Kalpana ( 2018 ) and Relf ( 2003 ) present the blood group identification using blood cell images which takes from slide tests. Picture acquired by the utilization of cutting-edge morphology; it very well may be seen that the portioned picture is filled utilizing shutting activity. Progressed morphological activity Opening is performed it tends to be seen that it smoothens the shapes of cells by evacuating little articles. At that point the pictures acquired by applying the shading plane extraction HSL luminance plane and measure work. At long last, the blood gathering can be resolved. The utilization of picture preparing procedures empowers programmed discovery of agglutination and decides the blood sort of the patient in a short interim of time. The strategy is appropriate and accommodating in crisis circumstances.

Keerthana and Ranganathan ( 2017 ) build up an inserted framework that utilizes an Image preparing calculation to perform blood tests dependent on blood composing frameworks. Along these lines, the framework permits us to decide the blood sort of an individual killing customary transfusions dependent on the rule of the all-inclusive contributor, decreasing transfusion response dangers and capacity of result without human mistakes. This paper helps in lessening human intercession and perform total test independently from adding antigens to definite age of the outcome and gives the outcomes in most limited conceivable term with exactness and precision alongside capacity of result for additional references. Actualizing a quality framework in the lab limits mistakes and guarantees that the correct test is performed on the correct example, the correct outcomes got, and the correct blood item gave to the correct patient at the ideal time. The proposed framework presents the plan and usage of a smart compact gadget that gives the best possible data that we require for the investigation with the decreased expense and the profoundly prepared administrators are not required. This framework utilizes an AI calculation like a neural system that underpins MATLAB programming for blood bunch recognizable proof and identification of blood check examination. This framework additionally discovers an answer utilizing various calculations and strategies which gives us the most extreme precision in blood bunch distinguishing proof and tallying.

The work proposed by Berlitz et al. ( 2012 ) utilized protein A covering of the gold surface of QCM biosensors for the immobilization of antibodies against blood bunch antigens An and B, which allows the recognizable proof of the four principle blood bunches A, B, AB and 0 with two estimations. The Rhesus framework was inspected with various examinations on and the fruitful recognition of Rh-D, Rh-C, Rh-c, Rh-E and Rh-e antigens on human erythrocytes. The brisk, simple and dependable identification of blood bunch antigens An and B offers the chance of deciding the patient’s association to the AB0 blood bunch framework by two estimations on hostile to An and against B sharpened quartz sensors. In Satoh and Itoh ( 2004 ) author also proposed a method which uses genetic analyzer for blood group prediction is recognized by the presence of three common representative alleles such as A, B, and O, in Satoh et al. ( 2001 ) the author proposed the analysis of four SNPs at nucleotide positions 261, 796, 802 and 803 to reflect serologic specificity. Sensor cells organized in equal and working on weakened entire blood without earlier planning of the blood tests will lessen the necessary time to approx. 3 min. Swapping the quartz sensors, encouraged by the card mounted sensors and the licensed module holder, broadens the blood bunch investigation into the Rhesus, Kell and further blood bunch frameworks. In any event, for blood bunch antigens with a low antigen number for every cell, techniques are accessible to get ready biosensor coatings with satisfactorily upgraded affectability.

The proposed method utilized by Dalvi and Kumar Pulipaka ( 2018 ) three samples of blood are blended in with three distinct reagents namely anti-An, anti-B and anti-D is taken on a slide. After some time, agglutination may or may not occur. After the occurrence of agglutination, the slide containing three samples of blood blended in with three distinct reagents is captured as an image and allowed to process in MATLAB image processing toolbox. This framework lessens the chances of false detection of a blood group. Image processing techniques utilized for blood group detection are 1. Pre-processing techniques, 2. Thresholding, 3. Morphological operations, 4. HSL plane 5. Quantification. The color plane contains color information in image s. ‘Comparing’ sections in an image is the concept utilized in image processing. Comparison in Grayscale involves straightforward scalar algebraic operators. In color plane extraction, they first convert the RGB image into a gray image and then channel the obtained outcome utilizing median separating. Thresholding operation in image processing is utilized to create binary images. The grayscale samples are grouped into two parts as background and object. Right now, thresholding is performed utilizing Otsu’s method. More than one threshold is resolved for a given image and segmentation is done creating certain regions. One background with many objects is the consequence of this staggered thresholding. It is a bunching based image thresholding. Morphological is a tool for extraction image components that are valuable in the representation. In morphological operation, there are two fundamental operations, for example, dilation and erosion as far as the union of an image with a translated shape called an organizing component. Here, closing operation is performed where dilation is followed by erosion. Also, edge detection utilizing the Canny edge detection strategy is performed. Morphological operations are utilized to eliminate noise spikes and ragged edges. HSL plane stands for Hue, Saturation, and Luminance. It is the representation of the RGB color model. Shade is expressed in a degree around a color wheel, while saturation and brightness are set as a percentage. Quantification is expressed as a number or measure of quantity. It measures power only in the region of the intrigued area. Area (percentage of surface examined for full image), mean (average value of the pixel), standard deviation, least and maximum values of pixel power are resolved. Also, region properties are extracted. Utilizing the value of standard deviation, the occurrence of agglutination is recognized and accordingly the blood group is resolved.

Fayrouz et al. ( 2011 ) study reveals an association between the pattern of the unique mark and ABO blood group. With ongoing advances in unique mark detecting technology and improvement in the accuracy and matching velocity of the finger impression matching algorithms, automatic personal identification is becoming an attractive/complement to the traditional methods of identification. As biometric technology matures, there will be an increased interaction among the biometric market and its identification application, since fingerprints will remain an integral part of the preferred biometric-based identification solutions in the years to come, a relationship of unique finger impression pattern to blood group presents scope for additional identification data which can be utilized for personal identification purpose, also investigation of possible predilection of certain disease and malignancies from blood groups are some of the factors which encourages one to carry the examination further. Chosen randomly having distinctive ABO blood groups, with the objective to a) Study the distribution of unique finger impression pattern among the subjects having diverse ABO and Rh blood group b) Correlate any relation between their characters and blood group. The data from the investigation showed that the male: female ratio was 1.2:1. Most subjects (48.9%) right now of blood group O followed by blood group A (33.1%), B (12.8%) and AB (5.2%). Rh-positive cases constitute about 87.2% of all considered cases. The general distribution of the pattern of finger showed a high recurrence of Loops enlisting 50.5%; followed by whorls (35.1%) and arches (14.4%). In Rhþve cases of blood group An and O loops occurrences were the most elevated (52% and 4.3% individually) at that point whorls (33.4% and 30.6% separately), while in blood group B whorls were predominance in both Rhþve and Rh_ve cases. In all blood groups, there was the high recurrence of loops in thumb, record and little fingers.

Ferraz ( 2013 ) utilizes the slide test and image processing techniques utilizing the IMAQ Vision from National Instruments. The image captured after the slide test is processed and recognizes the occurrence of agglutination. Next, the classification algorithm decides the blood type in the analysis. Finally, all the information is stored in a database. In this way, the framework allows deciding the blood type in a crisis, eliminating transfusions based on the rule of universal donor and diminishing transfusion reaction dangers. This framework is based on a slide test for deciding blood types and the software developed utilizing image processing techniques. The slide test consists of the blend of one drop of blood and one drop of each reagent, anti-An, anti-B, anti-AB, and anti-D, being the outcome interpreted according to the occurrence or not of agglutination. The agglutination reaction means that occurred reaction between the antibody and the antigen, indicating the presence of the antigen appropriate. The combination of the occurrence of agglutination, or nonoccurrence, decides the blood kind of the patient (Datasheet of DiamedDiaclon Anti-A 2008 ; Fingerprint Identification – Project 2). Accordingly, the software developed based in image processing techniques allows, through an image captured after the procedure of the slide test distinguish the occurrence of agglutination and consequently the blood sort of the patient.

Thakar and Sharma ( 2016 ) process remarkable highlights which they found inside the fingerprint designs which make us equipped for offering input. In any case, different examinations have demonstrated that even the prepared and experienced specialists submit different sorts the fingerprint, these might be a result of the utilization of discretionary/nonstandard phrasing like clockwise/anticlockwise or bearings and so forth recorded as a hard copy a report. The traditional technique for fingerprint correlation with the focal point to find details in bearings, which is a tedious system should be changed. Right now, a framework, optical sensor-based per users are utilized to peruse and gained fingerprint pictures in the accompanying three phases: Firstly, picture handling calculations are utilized to get dark tone impressions of the fingerprint picture. Also, the prepared picture is accordingly used to extricate the details (just bifurcations and ridge finishing). The third step is examining the position of various details (bifurcations and ridge finishing) on fingerprints, with the assistance of situation coordinated calculations. The separated pictures are contrasted, and the databases present in the framework and results are gotten. In any case, as opposed to the abovementioned, in the present examination, a semi-self-enough method has been utilized. In this way, in the present examination, a far-reaching endeavor has been made to extricate physically all the particulars present in the unique mark with the assistance of Adobe Photoshop (CS5) Software and to build up an adjusted lattice which can be utilized to methodically discover the position of the details alongside estimating certain extra element like Angle.

Azhagiri et al. ( 2018 ) study uncovers a huge relationship of blood groups O, A, B, AB to Hypertension, Peptic ulcer, Anemia, Rheumatoid Arthritis, Gastritis, Diabetes, and Bronchial asthma. The transcendence of the circle was most noteworthy among all blood groups. As indicated by this investigation, following outcomes were watched, Loops were the most well-known unique finger impression example and Arches were the least normal, Whorls and blended were moderate, Highest quantities of Loops were found in blood groups O, B contrasted with An and AB, Blood bunch O positive is the most well-known, O negative and AB negative is the rarest, Loops, whorls, blended and arches were most elevated in females, Group A was the most widely recognized blood bunch among guys, Blood bunch O, B, were the most regularly observed blood groups in females, Some basic clinical grumblings were found in all the blood groups.

Kanchan and Chattopadhyay ( 2006 ) the result shows that each fingerprint is exceptional; loops are the most normally happening fingerprint design while arches are the least normal. Guys have a higher occurrence of whorls and females have a higher frequency of loops. Loops are transcendent in blood groups A, B, AB and O in both Rh-positive and Rh-negative people except for in O pessimistic where whorls are progressively normal. They can reason that there is a relationship between the conveyance of fingerprint designs, blood gathering, and sexual orientation and along these lines expectation of sex and blood gathering of an individual is conceivable dependent on his fingerprint design.

Rastogi and Pillai ( 2010 ) present investigation shows that there is an association between the appropriation of fingerprint designs, blood gathering and sexual orientation. The discoveries of the examination can be finished up as follows: Each fingerprint is one of a kind consequently it tends to be successfully utilized as a proof for distinguishing proof in the courtroom. Loops are the most generally happening unique mark example and Arches are the least normal. Blood bunch O positive is the most widely recognized and A negative is the rarest. Loops are overwhelming in blood bunch A, B, AB and O in both Rh positive and Rh-negative people except for in O adverse where whorls are progressively normal. Whorls are progressively normal in blood bunch O negative. Loops and arches are most extreme found in blood bunch some time whorls are increasingly regular in blood bunch O. Blood groups An and B were the most widely recognized (similarly dominating) among guys, blood bunch O was the most generally observed blood bunch in females. Guys have a higher frequency of whorls and females have a higher rate of loops. Hence prediction of sexual orientation and blood gathering of a for every child is conceivable dependent on his fingerprint design. Comparative examinations ought to be led to a bigger example in order to build the precision of prediction.

Joshi et al. ( 2016 ) present examination uncovers that there was an association between dissemination of fingerprint (dermatoglyphic) design, Gender and blood groups. The general circulation example of the essential fingerprint was of a similar request in people with ABO; Rh blood groups for example high recurrence of loops, moderate of whorls and low of arches. The discoveries of the examination can be finished up as the Loops are the most normally happening unique mark example and Arches are the least normal, Blood bunch O positive is the most well-known and A negative is the rares, Loops are prevalent in blood bunch A, B, AB and O in both Rh positive and Rh negative people aside from in O antagonistic where whorls are increasingly normal and Males have a higher occurrence of whorls and females have a higher frequency of loops.

Jha et al. ( 2015 ) study uncovered that blood bunch AB and O had a most noteworthy rate of loops (76.92% and 71.05% separately) trailed by whorls (23.08% and 28.95% individually), comparatively in blood bunch An and B the design of the loop was normal with 57.14% in the two cases. The study inferred that there is a solid connection between blood groups and fingerprint design. From the examination, it was presumed that the recurrence appropriation loops design were most noteworthy in blood bunch AB (76.92), O (71.05%) and B (57.14%) individually. So also, the examination likewise inferred that the dispersion of whorls was most elevated in blood bunch A with 42.86% circulation, then in blood bunch B with 39.28% dissemination and for blood bunch AB it was discovered 23.08% and arches were least in blood bunch B with 3.70% appropriation. Further investigation ought to be completed by expanding the example size to get the progressively exact portrayal of the populace and need increasingly comparative examinations in different districts as well with the goal that near examination should be possible.

The study proposed by Sudikshya et al. ( 2018 ) corresponds to the connection between different patterns of fingerprints and “ABO” blood groups and “Rh” blood types in Nepalese guys and females. In spite of the fact that they realize that fingerprints are rarely indistinguishable and they never show signs of change from birth till death, this examination is an endeavor made to connect fingerprints with sex, diverse blood groups, and Rh blood types which may thus improve the legitimacy of fingerprints in distinguishing proof and legal medication and can be utilized for conceivable prediction of specific illnesses. From the present investigation, the accompanying ends are drawn: (1) Loops are the most regularly discovered fingerprint pattern and arches are minimal normal in the two guys and females and furthermore in “ABO” blood groups. (2) The recurrence of loops is most noteworthy followed by whorls and arches in Rh +ve blood types, while the frequency of whorls is most elevated followed by loops and arches in Rh -ve blood types. (3) Our outcomes uncover the most elevated rate of loops in the center and little finger in all blood groups, while the whorls are usually found in ring fingers in all blood groups. The frequencies of whorls are likewise most elevated in forefinger and thumb in all blood groups aside from in blood bunch “O” where loops are as often as possible present. (4) From this investigation, they can infer that circulation of essential pattern of the fingerprint isn’t identified with sexual orientation and ABO and Rh blood gathering, yet its conveyance is identified with singular digits of two hands.

Narayana et al. ( 2016 ) they present the examination is an endeavor to relate fingerprint patterns with sexual orientation and blood gathering of a person. Fingerprint patterns can be of help in anticipating the sexual orientation and blood gathering of a person. It might help in expanding the legitimacy of fingerprints in recognizable proof of people and tackling of wrongdoings. They got results areas: Loops were the most generally discovered pattern and composite the least. In the loop pattern, the commonest pattern was an ulnar loop, which was measurably huge right now. Blood bunch O positive was the most well-known and AB negative was the rarest. Rh-positive blood groups were more contrasted with Rh-negative blood groups, which is demonstrated right now and critically dependent on the information too. Blood bunch B was the most widely recognized among Rh-positive blood groups followed by O, An and AB blood groups. Among Rh-negative B and A blood groups were similarly predominant followed by O and AB. Loops were most elevated in guys, whorls and arches were most noteworthy in females. Loops were prevalent in all the blood groups with the exception of A positive where whorls were prevailing. The most elevated number of the considerable number of patterns was found in blood bunch O and least in AB among Rh-positive blood groups and factually demonstrated critical right now. Composites were least generally found in all the blood groups.

The Study by Sangam et al. ( 2011 ) they found the loops were connected more with O gathering, whorls with AB gathering and arches with B gathering. Thumbs introduced a high recurrence of whorls in A + ves. Record and ring fingers were related to the high recurrence of whorls in A-ves and AB + ves. So the prediction of blood gathering somewhat might be conceivable with the investigation of unique finger impression patterns which might be of extraordinary incentive in criminological medication, however, impact provincial varieties, sex and hereditary variables ought not to be disregarded.

Deopa et al. ( 2014 ) has an endeavor has been made in the present work to examine their connection with sexual orientation and blood gathering of a person. Loops were the most widely recognized (58.29%) fingerprint pattern while whorls were moderate (37.00%) and arches were the least normal (4.71%). Guys had a higher rate of whorls and females had a higher frequency of loops. Loops are overwhelming in blood bunch A, B, AB and O in both Rh-positive and Rh-negative people aside from in ‘A’ constructive blood bunch where whorls prevail marginally. Whorls were most noteworthy in An and AB positive blood gathering, and loops were most elevated in O and B blood gathering. Arches were least in all blood groups. There is an association between conveyance of fingerprint patterns, blood gathering, and sexual orientation and in this way prediction of sex and blood gathering of an individual is conceivable dependent on his fingerprint pattern.

Shivhare et al. ( 2017 ) does the investigation which uncovered the association between dermatoglyphic, blood gathering and sex: Most subjects have a place with Rh-positive and O blood gathering. Loops are the regular and arches are extraordinary fingerprints. Loops were most noteworthy in B blood gathering and least in AB blood gathering. Whorls most elevated in An and least in B blood gathering. Arches were most elevated in AB and least in B. Loops higher in female and most reduced in male, whorls most noteworthy in male and least in female and arches most elevated in male and most reduced in the female. Loops were most noteworthy in Rh-positive and least in Rh-negative. Whorls most elevated in Rh-negative and least in Rh-positive. Arches were most noteworthy in Rh-positive and least in Rh-negative.

The investigation does by Radhika ( 2016 ) uncovered that there is an association between the dissemination of fingerprint patterns, blood gathering, and sexual orientation. Loop was most as often as possible seen fingerprint followed by whorl curve and composite. O positive is the most regular blood gathering and AB negative is missing. Loops are dominating in blood bunch O followed by B and An in Rh-positive subjects, trailed by whorls. Curve and composite were basic among O and A positive subjects.

Morris et al. ( 2016 ) proposed method having outcomes that fingerprint asymmetry could be formed into a significant instrument for anticipating the danger of Type 2 diabetes mellitus and Type 1 diabetes mellitus and that wavelet examination is a technique that can be utilized to evaluate asymmetry in fingerprints. The benefit of fingerprints scored utilizing wavelet-based strategies over hereditary testing, is that it can demonstrate gestational condition and would be considerably less costly. The expense is significant, given late reports that both hazard mindful and chance unconscious people were keen on hereditary testing, however distinguished the requirement for minimal effort tests. They propose an increasingly far-reaching examination of fingerprint asymmetry as a predictor of both T2DM and T1DM chance, scoring asymmetry with wavelet investigation and contrasting with the prescient capacity of hereditary qualities alone, is justified.

The propose work by used information digging Veena Vijayan and Anjali ( 2015 ) which can be used for evaluating distinctive disease patterns, remedial data extraction, quiet support and organization and finding of clinical parameters. Here a choice emotionally supportive network is recommended that predicts diabetes which utilizes choice stump as a base classifier in AdaBoost calculation. The framework utilizes a worldwide dataset taken from UCI vault of AI for preparing which contains 768 cases and 9 characteristics and utilizations nearby dataset for approval which had been gathered from better places in Kerala. The PC data framework with the AdaBoo-choice stump classifier gives a precision of 80.729% for anticipating diabetes with an extremely low estimation of blunder rate.

Tafa and Pervetica ( 2015 ) study the dataset which comprises of 402 examples taken from three unique areas in Kosovo. The characteristics of the database are BMI (weight file), glucose level before dinner and after the feast, the systolic and diastolic blood pressure, the genetic factor, the customary eating regimen, and day by day physical exercises. The last two qualities are assessed as follows. With respect to the issue of customary eating routine, while depending on contributions from the clinical clinicians, patients were inquired as to whether they took their dinners in roughly the same equidistant everyday interims, in any event, three times each day and furthermore if their suppers were not voluminous. The introduced approach depends on the joint usage of two calculations in Matlab that have been executed on the recently gained dataset with the various credits when contrasted with the past work right now. The calculations are executed and assessed freely however the dynamic depends on the joint results from the two calculations. The point of this methodology is to settle on the choice progressively dependable.

Mehta and Mehta ( 2015 ) study the one hundred type II diabetes mellitus patients (50male and 50 female) were chosen for study and contrasted and equivalent number of controls. Fingerprints were acquired by a printing technique. Parameters contemplated were arches, whorls, loops. Circulation of fingertip patterns indicated a noteworthy contrast between diabetics and controls. The appropriation of fingertip patterns on both ways submit male diabetics and controls. The whorls were altogether expanded though loops and arches were essentially diminished in male diabetics when contrasted with controls. The whorls were fundamentally expanded while loops were essentially diminished in female diabetics when contrasted with female controls. Nonetheless, arches were fundamentally diminished in the left hand of female diabetics.

Ameer et al. ( 2014 ) investigation right now spellbinding. An aggregate of one hundred patients partook right now was a completely known instance of diabetes mellitus. Out of these one hundred patients, most of the patients were having a place with a whorl pattern of fingerprints I-e. fifty half while the number of patients having a place with the Loop pattern was forty-five 45%, the composite was just 2,2%, and no specific patient having a place with Arch pattern. There is having to build up a definite and tremendous examination to investigate the association of fingerprint pattern with Diabetic disease. This investigation offered reasonable weighting on the circulation of fingerprint pattern among diabetic disease patients. Confinements of study where it was just restricted to clinical OPD patients and Limited uniquely to diabetes mellitus patients.

The investigation by Roshani et al. ( 2016 ) concluded WHORLS are the most regular pattern in both right and left hands of both male and female diabetic subjects and LOOPS are the most normal pattern in both right and left hands, in the two guys and females in non-diabetic subjects. The Arches were fundamentally diminished in both the right and left hand of males and females in diabetics and non-diabetic subjects. This examination shows a huge association between fingerprint patterns and diabetes in both genders. Study might be helpful to distinguish the high-chance people in the populace, for type-2 diabetes mellitus; the most punctual prediction and finding of patients with type-2 diabetes mellitus will improve the aftereffect of treatment and further entanglements. Notwithstanding; there are a few investigations that appeared inverse outcomes to our examination; subsequently, there is having to do additionally contemplates and bigger examples ought to be analyzed in detail to additionally approve the discoveries of this examination and arrive at a complete resolution.

Smail et al. ( 2019 ) examination demonstrated that the type of loop in typical guys is 37.50%, even with the diabetes female gathering the type of loop is 18.75%. This investigation demonstrated that the control male arches bunch is no worth which is zero, while in the patient female the arches bunch esteem is high to 25%. Confinements of the examination gave us that the control male whorls run is 6.25%, however for the female patients is 12%. 50 which expanded worth. Their investigation demonstrated that the type of loop in typical females is 31.25%, yet in the diabetes male gathering, the type of loop is 0%. This investigation indicated that the control female arches bunch is 6.25%, yet in the patient’s male, the arches bunch esteem is high to 25%. Another impediment of the examination gave us that the control female whorls run and the diabetes male or equivalent worth which is about 18.75%. For the all-out typical and diabetes guys, the loop bunch in the ordinary male is higher than diabetes (68.75% > 18.75%). For the all-out ordinary and diabetes male the arches bunch in the typical male is lower than diabetes (6.25% < 50%). For the all-out typical and diabetes male, the entire gathering in the ordinary male is underestimating than diabetes (25% < 31.25%). Arches are found in five percent of fingerprint patterns. The ridges run starting with one side then onto the next of patterns, making no retrogressive turns. Usually, there is no information in a curve pattern.

Igbigbi et al. ( 2001 ) have inspected the plantar and advanced prints of the sole of 99 indigenous Malawian patients matured 25–66 years going to the clinical outpatient center for diabetes mellitus, fundamental hypertension and a mix of the two conditions at Lilongwe and Queen Elizabeth focal emergency clinics. The gathering comprised of 27 diabetics (15 guys, 12 females), 21 hypertensives (9 guys, 12 females) and 51 diabetics with hypertension (21 guys, 30 females). All patients were analyzed as Type 2 diabetics after the age of 20 years. Their outcomes indicated that soles of all patients had a greater number of loops than arches and a greater number of arches than whorls, which were limited to the distal zones. In hypertension, whorls were found in zones I, II and III though, in patients with diabetes and hypertension, the whorls were found in zones I, III and IV. In digits, the most prevalent ridge pattern was arches in all patients, trailed by loops and whorls were missing. In the principal digit, diabetic patients had no arches, yet ladies’ hypertensives demonstrated arches. In patients with diabetes and hypertension, arches were available in both genders yet in men it was limited to the correct foot. Loops were discovered distinctly in the first digit in quite a while. The recurrence of loops was most noteworthy in diabetic patients, high in diabetics with hypertension and least in patients with hypertension alone.

Tarca and Tuluc ( 2005 ) have considered a complete number of 133 patients with Type 1Diabetes Mellitus, out of which 58(33 guys and 25 females) were youngsters and adolescents of ages somewhere in the range of 4 and 18 years. The disease showed in these cases between the age of 2 and 17 years. Among the female patients, the loop was found in around equivalent extents in both the hands (58.95% on the left hand and 60.60% on the correct hand) while if there should arise an occurrence of typical subject the loops were found on the left side as it were. The loop dissemination on the five fingers indicated an expanded recurrence on the fingers from V and III. Whorls were increasingly visited in the male arrangement and on the correct hands. Arches were increasingly visiting in the female arrangement. The presence of these markers, before the clinical sign of the disease, makes workable for their utilization in anticipation of insulin subordinate diabetes mellitus.

Udoaka and Lawyer-Egbe ( 2009 ) have considered an absolute number of 90(50 guys and 40 females) grown-up diabetic patients and the contrasted and the same number of ordinary subjects as controls. There was no huge contrast in the computerized patterns in the two groups. The atd point, dat edge, the absolute ridge include were fundamentally more noteworthy in the diabetic patients contrasted with the typical subjects. The pattern force record was higher in the diabetic guys however it was lower in the female diabetics. Their perceptions can be utilized for distinguishing proof of diabetics.

Nezhad and Shah ( 2010 ) have considered 30 patients of diabetes type 1 and 30 typical subjects as control gathering. The mean period of patients and control bunch was 22 ± 11 and 38 ± 8 individually. Among these 42% were guys and 58% were females. They have discovered that the state of loop and whorl are heterogeneous, and their number varies altogether contrast with a control gathering ( p  = 0.001, p  = 0.004.). The a-b 32 ridge includes demonstrated an expansion in the ridge considers as a real part of the diabetic men than control gathering. The atd point size in both the experimental group and control gathering of females was more than guys. These creators are of the sentiment that dermatoglyphics can be an appropriate strategy for hereditary examinations and diabetes type 1.

Sumathi and Desai ( 2007 ) have considered a sum of 100 patients of diabetes mellitus Type 2 and hypertension of either sex or age gathering of 35–55 years. They were coordinated with hundred controls. They discovered diminished a-b ridge include in female diabetics. The accompanying huge parameters have been found in their investigation in the palmar dermatoglyphics in type 2 diabetes with hypertension. In both male and female patients, there is the nearness of diminished I1 pattern and nearness of expanded I3 pattern in the left hand. The nearness of diminished whorls saw in two hands of male patients. The nearness of expanded ulnar loops and whorls in two hands of female patients.

Padmini et al. ( 2011 ) their exploration concentrate on dermatoglyphics in diabetes mellitus underlined that however dermatoglyphics by and large don’t assume any significant job in clinical analysis yet, it can fill in as a pointer to pick out subjects from a huge gathering of individuals for additional examinations to affirm or preclude diabetes mellitus have studied fingerprints and palmar prints from 200 subjects, 100 guys and 100 females in the age gathering of 25 years to 80 years, of which 95% of cases were non-insulin subordinate diabetics and 5% of cases were insulin subordinate diabetics contrasted and 200 controls. Higher occurrence of variety in methods for ulnar loops (83.2), composite whorl (1.8), all-out finger ridge tally 33 (108.6), total finger ridge check (138.55), dat points of the right hand (59.77) and left hand (62.3) in diabetics than in controls was seen by them. The rest of the parameters were low in diabetics than in controls. In male diabetics increment in methods for ulnar loops (41.6), outspread loops (1.7), complete finger ridge check (106.25), supreme finger ridge tally (137.58),atd edge of right hand (41.68) and left hand (41.67), dat edges of left hand (62.42) and adt of right hand (80.45) was seen than in controls. In female diabetics, huge increment in basic arches (5.7), all-out finger ridge tally (110.94), total finger ridge tally (139.52), dat points of two hands right (61.6) and left hand (62.17) was seen than in controls.

Sharma and Sharma ( 2012 ) their investigation of 50 diabetic cases and 50 controls chose from the SMS Hospital, Jaipur, India, found that the complete finger ridge check, total finger ridge tally, and the a-b ridge include were higher in all the patients. The atd points in the hands of the two sides in the patients were expanded in all the groups aside from left side in guys. In any case, they varied fundamentally on the correct side and on the left side in females, p  < .001. In the general groups right tda edge was huge. The smidgen and the tda edges on the two sides of the hands in all the groups were lower in the patients aside from left tda edge in 34 guys. Be that as it may, they varied just all together in the left bit, right tda in females. The aftereffects of their examination work demonstrated that dermatoglyphic variations from the norm might be utilized as an indicative instrument for anticipating the chance of the advancement of diabetes sometime in the future.

Taiwo and Adebanjo ( 2012 ) have completed an examination to explain whether fingerprint pattern of dermatoglyphics is related to withtype2 diabetes or not. Dermatoglyphic information was acquired from controls and type 2 diabetic subjects going to the Diabetic Clinic of Lagos University Teaching Hospital. They saw all-out finger ridge tally was fundamentally higher ( p  < 0.05) in diabetic subjects than in nondiabetics. Considering the association between fingerprint pattern and type2 diabetes, dermatoglyphics might be utilized for the early ID of hazard bunch people for reconnaissance purposes so as to forestall disease beginning.

Rakate and Zambare ( 2014 ) have looked at the distinctions in the complete finger ridge tally, a–b ridge includes and atd point in patients with type 2 diabetes mellitus with a control gathering. Their examination was completed on 75 type 2 diabetic patients (51 male and 24 female) of 30 to 60 years and 75 non-diabetic persons (47 guys and 28 females) of the equivalent 35 age bunch as a benchmark group. In their examination, they found an expansion in the number of whorls, absolute finger ridge tally, a-b ridge tally alongside more extensive atd point in type 2 diabetes mellitus patients.

Desai and Hadimani ( 2013 ) have opined that dermatoglyphics is a developing order and its simple and prepared pertinence renders it as a valuable device to the clinician. The dermatoglyphics isn’t to analyze and not for characterizing a current disease yet to forestall by anticipating a disease and to distinguish individuals with hereditary inclination to build up specific diseases. They have attempted to decide noteworthy palmar dermatoglyphic parameters if there should be an occurrence of sputum positive tuberculosis, diabetes mellitus type 2 with basic hypertension, dermatitis, innate coronary illness, and Down disorder and contrasted and the benchmark group. Their investigation demonstrated that there were some hereditary components that were engaged with the causation of different diseases referenced previously. Itis conceivable to anticipate from dermatoglyphics people’s possibility of gaining disease. Noteworthy discoveries they watched were:1. The nearness of diminished whorls, 2. The nearness of expanded ulnar loops,3. The nearness of expanded simian line in the left hand of considered patients.

Shivaleela et al. ( 2013 ) have done an investigation to discover the recurrence of different fingerprint patterns in type 2 diabetes mellitus with and without ischemic coronary illness. Their examination likewise planned to discover the recurrence of fingerprint patterns in type 2diabetes Mellitus patients having the family ancestry of cardiovascular disease. Twenty-five type 2diabetes Mellitus male patients in the age gathering of 38-65 years were chosen, of which 18 had an ischemic coronary illness and 16 patients had the family ancestry of cardiovascular occasions. There was a higher recurrence of whorls in type 2diabetes Mellitus patients than different patterns. Less recurrence of arches, high recurrence of whorls and ulnar loops were seen in type 2diabetes Mellitus patients contrasted and type 2diabetes Mellitus patients without ischemic coronary illness. The thing that matters was not factually huge. Dermatoglyphics in type 2diabetes Mellitus and in patients with family ancestry of cardiovascular disease didn’t show dominance of any of the fingerprints in ischemic coronary illness. Therefore, they opined dermatoglyphics might be symptomatic apparatus in type 2diabetes Mellitus however not in recognizing the hazard class for ischemic coronary illness.

Umana et al. ( 2013 ) completed an examination to decide the association between fingerprints patterns and diabetes mellitus utilizing subjects in Zaria, Nigeria. Their consequences of 101 type 2 diabetic patients were contrasted and 126 typical subjects. From their outcomes, there was an association between fingerprint patterns of guys with diabetes mellitus. From the above investigation, they reasoned that the male with a Arch pattern of fingerprint in their correct hand is inclined to create diabetes mellitus at a later stage.

Mittal and Lala ( 2013 ) have endeavored to discover an association of the dermatoglyphics patterns of the sound people and diabetes mellitus patients. An aggregate of 200 subjects took part in their investigation of which 100 were diabetic patients (50 guys and 50 females) and 100 were solid people utilized as controls (50 guys and 50 females). They signify ‘atd’ point in both the hands of both the genders of diabetic patients was fundamentally more extensive when contrasted with that of the controls. Contrasted with that of control, the mean tda edges in both the hands of both the genders of diabetics were restricted. They signify ‘dat’ edges were essentially thin just in the left hand of diabetic females when contrasted with left hands of female controls.

Burute et al. ( 2013 ) have meant to contemplate the different dermatoglyphic patterns in the patients of the development beginning diabetes mellitus (Type 2 diabetes mellitus) and contrasted and the dermatoglyphic patterns of controls. They did their investigation on 101 (51 male and 50 female) clinically analyzed patients of development beginning diabetes mellitus. For correlation, solid controls (Total = 100, 50 guys and 50 females) were considered. In female diabetics, essentially higher recurrence of arches and lower recurrence of whorls were seen than in controls. In diabetic female’s outright finger ridge tally and complete finger ridge include were essentially lower than in controls. Discoveries of their examination feature on the potential markers to foresee type 2 diabetes mellitus on a bigger example size after a fastidious investigation of various fingertip dermatoglyphic factors.

Rakate and Zambare ( 2014 ) have looked at the distinctions on the fingertip patterns to be specific, curve, loop, and whorl in patients with type 2 diabetes mellitus with a control gathering. Test for their investigation included palmar prints of 350 type 2diabetic patients old enough gathering between 30 and 60 years out of them 240 were guys and 110 were female contrasted and same age gathering of 350 non-diabetic people as control the gathering, out of the 240 were guys and 110 were females. In the example of 350 type 2 diabetes mellitus patients, they watched an expansion in the number of whorls in two hands of guys and females. The P-esteem was 0.001. The Ulnar loop and curve patterns were available in less incentive in diabetic patients which were measurably inconsequential. The plain whorl was available essentially higher in esteem in diabetic patients of guys and females. In diabetic guys in right hand 482 whorls where present. Yet, in charge just 326 whorls were available; this distinction was critical at 0.000 levels. In the left hand of diabetic, whorl was altogether more 418 than control 267, P-esteem was 0.000. Diabetic females additionally indicated higher recurrence of whorl pattern in two hands; on right hand 158 contrasted and control 121 and 184 on left hand contrasted and 130. The p values were 0.010 on the right hand and 0.000 on the left hand. The Central pocket loop whorl pattern watched more in diabetic patients. In the diabetic male they 40 discovered 96 on the right hand and on the left hand 99 Central pocket loop whorl pattern which was more contrasted and control right hand 82 and left hand 82. The P-values were 0.138 and 0.094 separately. In diabetic females additionally Central pocket loop whorl was watched all the righter hand 36, left hand 40 than control bunch right hand 29 and left hand 36; P-values were 0.237 and 0.320. The twofold loop whorl was watched more in diabetic patients. In diabetic guys on the right hand, 54 twofold loop whorls were available which were more than control 38. Likewise, on left hand 69 in diabetic though 67 in charge. The P-values were 0.044 and 0.430 separately. In diabetic females, likewise, more recurrence of twofold loop whorl was seen on right hand 28 contrasted and control 15 and left hand 25 contrasted and 20 in charge. The P-values were 0.021 and 0.223. At the point when they thought about a wide range of whorls together among diabetic and control gathering, noteworthy contrasts were seen in the two guys and females. In diabetic guys on the right hand, 632 whorls were available which were more than 446 in ordinary. Comparative discoveries were seen on left hand 586 in diabetic while 416 in charge. The P-values were 0.000 and 0.000 separately. In diabetic females, likewise, more recurrence of whorl was seen on right hand 222 contrasted and control 165. The p values were 0.000.

Karim et al. ( 2014 ) have looked at the distinctions in the fingerprint patterns and finger ridge include in patients with type 2 diabetic Mellitus with a control bunch in Erbil city, Kurdistan area, Iraq. In their examination, 50 non-insulin subordinate diabetes mellitus patients, 25 guys, and 25 females were contrasted and 50 (25 guys and 25 females) sound controls. The appropriation of fingertip patterns of male patients indicated no noteworthy contrast in ulnar loops, spiral loops and rose arches while plane arches expanded altogether ( p  < 0.05) in diabetic type 2 patients contrasted and controls, whorls diminished fundamentally ( p  < 0.05). Higher frequency of ulnar loops, spiral loops and plane arches in female diabetics contrasted and control females. They saw that essentially expanded ( p  < 0.05) center finger ridge include in the left hand of male diabetic patients. Fundamentally expanded ( p  < 0.05) file and little finger ridge check of the right hand was seen in female diabetic patients contrasted and control female groups.

Bala et al. ( 2015 ) have considered an all-out 210 subjects out of which 70 subjects having diabetes (32 guys and 38 females), 70 subjects having diabetes with hypertension (32 guys and 38 females) and 70 typical sound people (32 guys and 38 females) as control having a place with Gangtok area of Sikkim. All were clinically analyzed and affirmed by examinations as diabetic and diabetic with hypertensive patients. In their investigation, an examination of diabetic with control bunch indicated the mean estimations of atd and dat edges in two hands of diabetic patients lower than control, though mean estimations of adt edges were higher than control bunch on both right and left sides. The huge distinction was found in the correct hands of diabetes mellitus gathering. In both right and left hands of males and females, the mean estimations of atd point and dat edge of the diabetic gathering were lower than in charge. The mean estimations of adt edge were higher than control. No noteworthy contrast was found. The mean estimations of a-b ridge include in two hands were higher in diabetic male and female aside from in the left hands of male and profoundly noteworthy distinction was found in both hands of the female.

Mehta and Mehta ( 2015 ) have analyzed fingertip patterns of type 2 diabetic patients with controls. One hundred type 2 diabetes mellitus patients (50 male and 50 female) were chosen for study and contrasted and equivalent number of controls. In two hands of guys and females’ diabetic patients’ frequency of whorls was essentially expanded. Frequency of loops was fundamentally diminished in two hands of male and female diabetics contrasted with controls. Arches were essentially diminished in both ways’ hands of male diabetes mellitus patients. Arches were fundamentally decreased in left hand of female diabetics. In their investigation, they expressed that dermatoglyphics can be utilized as a screening device for the conclusion of people who are progressively inclined to create diabetes mellitus and, in this way, forestalling the future diabetic intricacies.

Bala et al. ( 2016 ) have examined a sum of 100 type 2 diabetic patients (50 guys and 50 females) were contrasted and 100 diabetics with hypertension patients of Hilly district. The mean estimations of all-out finger ridge check and total finger ridge include were higher in male and lower in female diabetic gathering than diabetic with hypertension gathering. The mean estimations of a-b ridge include were lower in males and higher in females in diabetic gathering and a critical contrast was found. The mean estimations of atd edge were higher in diabetic gathering than diabetic with hypertension gathering. The mean estimations of dat edge were lower in the right hands and higher in the left hands of diabetic gathering. The mean estimations of adt point were higher in guys and lower in female diabetic gathering than diabetic with hypertension gathering. In the right hands, the mean estimations of fingertip ridge include were lower in all digits aside from in 2nd, fourth, and fifth digits in the male diabetic gathering. In left hands, the mean estimations of fingertip ridge include were lower in all digits of diabetic gathering apart from in 2nd, fourth and fifth digits and no critical contrast were found. In their examination, they watched an expansion in ulnar loops in the correct hand of male diabetic and diminished frequency in the left hand of male and in two hands of female diabetics.

Tafazoli et al. ( 2013 ) collected samples for this exploration was gotten from 40 patients with acquired Essential hypertension disease and 20 ordinary subjects with no indications of Essential hypertension disease until 2 ages prior. Print of their palm and fingers was acquired in two groups by printing ink. Patterns of fingertips, ATD edges, A-B ridge and the various types of details were found, recognized and measured outwardly within four divisions of fingerprints. The consequence of this investigation indicated two patterns of fingertips; Whorl and curve in diseases are more than ordinary individuals. The most widely recognized tips in ordinary individuals is loop. In normal the quantity of the A-B ridge in female diseases and male-typical are individually more on normal than female ordinary and male sicknesses. The ATD point is lower in patients than in typical. Consequences of the overview on details demonstrated that frequency of the ridge Ending (E) is between with the most elevated frequency. The frequency of Bifurcation (B) is lower than the ridge finishing which it is between  % in the left hands. Additionally, in some part of the fingers noteworthy distinction was found. 32-52.52 17.39-35 but in the right hand the second most patterns intermingling (C) which is between 14.13 and 38.66%. The third most patterns in the left hand was seen in pattern unions (C) with a rate between 8.62 and 32.6, however, in the right hand, the third most patterns are bifurcations (B), which is between 3.57 and 24.81%.

The method proposed by Chakravathy et al. ( 2018 ) does observational examination included a correlation of palmar dermatoglyphic parameters among cases and controls. Parameters were dissected quantitatively-atd edge; subjectively outspread loop, ulnar loop, curve, whorl and composite. As appeared in (Graph I) out of all-out of 250 cases, 120 (48%) were guys and 130 (52%) were females. Of the absolute 250 controls, 137 (54.8%) were guys and 113 (45.2) were females. As indicated by (Graph II), They found that signify “atd” edge was higher in cases than controls and there was the factually critical association of signifying “atd” point in cases contrasted and controls. They likewise discovered both ways signify “atd” point was higher in cases than controls with solid factually huge association of right signify “atd” edge in cases than controls. By dissecting subjective parameters in study gathering, conveyance of dermatoglyphic patterns was measurably noteworthy in situations when contrasted and controls. The outspread loop was increasingly visiting in cases while ulnar loop was more ordinarily found in normotensive controls. The examination of subjective parameters in each hand of cases and controls demonstrated an appropriation of dermatoglyphic parameters that were factually huge. As appeared, looking at subjective parameters in each finger of the two cases and controls for the conveyance of dermatoglyphic patterns indicated factually noteworthy association.

Lahiri et al. ( 2013 ) used digital and palmar dermatoglyphic investigation of 145 normotensive subjects and 131hypertensive subjects was performed. The parameters utilized were advanced ridge pattern, complete ridge tally, and atd edge. The outcome demonstrated that the twofold loop whorl patterns are available with higher frequency in hypertensives. If there should be an occurrence of the hypertensive people, frequency of twofold loop whorl pattern and curve are 4.57% and 5.79% individually yet those are simply 0.44% and 1.33% in normotensives. In spite of the fact that the distinctions of occurrences of whorl just as ulnar loop between two groups are not all that obvious, yet factually huge ( p  < 0.05). The occurrences of explicit patterns in the hypertensive and the normotensive populace have appeared. The all-out ridge tally unmistakably expressed that it is horribly raised if there should be an occurrence of the hypertensive populace contrasted with the normotensive populace. The various estimations of Average Total Ridge Count of hypertensive and normotensive people have appeared with their comparing frequency. Subsequent to processing the remedied atd edge, a normal of the atd points of the two hands is made. The mean worth, most extreme and least qualities and model estimation of rectified atd edge (normal of both hands considered) in hypertensive gathering and normotensive gathering. The general pattern of this parameter is best comprehended by measurable examination (t trial) of all out arrangement of information which shows contrasts in estimations of atd edges of the hypertensive and normotensive gathering are of factual hugeness ( p  < 0.05).

Tafazoli et al. ( 2013 ) does the examination and conclude an observational, systematic and handy examination utilizing a case–control observational methodology with straightforward irregular testing and without substitution did on two groups of solid subjects and patients experiencing hypertension; individuals with no other explicit hereditary diseases which thus influence dermatoglyphic readings. that loops were the most widely recognized patterns on left digits in all patients (guys and females). From a factual perspective, there is an important distinction in frequency of various patterns on the left digit 4 ( p  = 0.05) between genders yet other left digits indicated no measurably noteworthy contrast. On the correct digits, loops were higher in rate in the two sexual orientations however no huge factual distinction was seen between sexes for frequency of various patterns on right digits ( p  < 0.05). The higher occurrence of whorls on digit II and of loops on different digits. Their is no huge distinction in the frequency of the patterns between two hands ( p  < 0.05). As illustrated, frequency in the dissemination of loops were 70 and 58.8% for the left hand and 63.3 and 66.7%, for the correct submit guys and females, separately. The distinction between frequencies of patterns was not measurably huge on any of the two h ands in both genders neither the right nor the left nor both frequency of loops on the left and the correct digits were 70–63.3% in guys and 57.8-66.7% in females, separately. The contrast between frequencies of the patterns was not factually critical on two hands of both genders. The information shows that the frequency appropriations of whorls in the control and test (hypertensive) bunch were 75 and 63% on the correct hand and 79 and 70% on the left hand of guys, individually. No factually critical contrast was seen between the frequency appropriation of patterns on two hands of guys in the two groups ( p  = 0.04, p  = 0.02) level of Whorl and Arch is more in charge bunch than intolerant gathering while the level of Loop is more in-understanding gathering than solid gathering in the two sexual orientations. The percent dispersion of whorls on digits of guys was as high as 77 and 66.5% in the control and the hypertensive gathering, individually. There has been no factually noteworthy contrast between two groups for frequency of patterns on digits in guys. level of whorl digit patterns in females was 53.7 and 62.5% in the benchmark group and in the hypertensive gathering, individually. There has been no factually huge distinction between the two female groups in the frequency of patterns on digits.

The dataset utilized and examination by Wanga et al. ( 2015 ) was gathered from the Behavior Risk Factor Surveillance System (BRFSS) of Centers for Disease Control and Prevention (CDC) and is openly accessible and downloadable from the BRFSS site. BRFSS is the world’s biggest and persistently led phone-based wellbeing study in regard to social hazard factors, incessant wellbeing conditions and utilization of preventive administrations. Built-up in 1984 with 15 states taking an interest in the study, it has a long history in conduct and incessant disease observation. The essential point of BRFSS is to track and measure singular wellbeing conditions and hazard practices that add to the main source of high grimness and death rates in the grown-up populace who are matured 18 years and the older in United States. The review covers a wide scope of wellbeing hazard factors, preventive wellbeing practices and wellbeing conditions, including hypertension, diabetes and carcinoma related things. By gathering an assortment of data and sharing them with general society, BRFSS empowers scientists to examine the connections between interminable diseases and their hazard factors. We propose to anticipate hypertension just utilizing the surveys other than clinical test information, anthropometric information or hereditary information. Its adequacy exhibits the practicability of building up a hypertension observation framework for an enormous size of the populace in a non-obtrusive and prudent way. What’s more, the outcomes from this examination might be utilized to manage the advancement of projects outfitted towards forestalling and relieving explicit hypertension chance elements. (2) They propose to coordinate calculated relapse examination and fake neural systems for synchronous hazard factor determination and hypertension prediction. In spite of the fact that theyt now, the proposed approach is basically a general system that can encourage specialists to break down other ceaseless diseases and different types of information. (3) They detail the choice of fake neural system engineering and the setting of applicable parameters, which is a troublesome and testing task in model learning. This can conceivably assuage analysts of the mind-boggling model determination issue and empower them to concentrate on the issues under scrutiny. (4) To manage the class irregularity issues, they propose a viable under-examining method. Based on a bunch calculation and choosing the delegate tests from each group in the extent of the group size, the proposed strategy can choose the most discriminative examples from the greater part class while making us lose the minimal measure of data.

Ravindranath et al. ( 2003 ) proposed qualitative dermatoglyphics involving identical fingerprint pattern, interdigital pattern, hypothenar pattern and palmar wrinkle was concentrated on 26 female and 11 male rheumatoid joint pain patients. Examination between understanding male and control male; and patient female and control female has been finished. ‘Chi’ square test was performed. In male patients, with hands together, arches were expanded, loops/whorls were diminished. Incomplete Simian wrinkle was fundamentally expanded. In the correct hand, patterns were expanded in the third interdigital zone. Then again, in female patients there was a noteworthy increment in whorls and diminishing in loops on the principal finger on both the hands, increment in arches on the third finger; the two arches and whorls on the fourth finger of left hand. The present examination has accentuated that dermatoglyphics could be applied as a symptomatic device to patients with rheumatoid joint pain. Mazumdar et al. found there is an association between rheumatoid joint pain and dermatoglyphics. The conceivable relationship amongst RA and dermatoglyphics may empower dermatoglyphics as a marker device in the analysis of rheumatoid joint pain. The present examination has been attempted to discover the likelihood that the fingerprints and palmprints assume a significant job in the analysis of rheumatoid joint pain. The frequency of twofold loop whorl in the rheumatoid joint pain bunch was seen in pointing finger and center finger of right hand and left hand separately (Mazumdar 2015 ). The ulnar loops were altogether present in right and little finger of the left hand of rheumatoid joint pain patients contrasted and control. The complete fingerprint ridges were progressively various in right and left hand of rheumatoid joint inflammation gathering.

Narayanan et al. ( 2017 ) investigation was a case–control concentrate with 60 cases with rheumatoid joint pain and 60 controls. The result appears out of the all-out 60 cases, 12(20%) were male and 48(80%) were females. Of the complete 60 controls, 12(20%) were guys and 48(80%) were females. The subjective parameters in female was a measurably critical increment in the number of whorls morally justified and left hands of female patients contrasted with the controls. There was a factually noteworthy reduction in the outspread loops in both the hands of female patients contrasted with the controls and the abatement is increasingly huge in the correct hand. There was no measurably huge distinction in the ulnar loop pattern circulation in either hand of female There was a factually critical increment in the finger ridge includes of right to deliver male patients contrasted with controls. There was a critical increment in the ridge check of patients. There was a factually noteworthy reduction in arches in the left hand of females with Rheumatoid Arthritis contrasted with the benchmark group. The left hand in male patients contrasted with the controls. Absolute finger ridge check (included ridge tally of both ways hand) was fundamentally expanded in male Rheumatoid joint pain patients contrasted with the controls. The ridge includes of the correct submit female patients was altogether higher than that of controls. The ridge includes left-hand fingers in female patients was fundamentally higher than that of controls. The complete finger ridge tally (right-hand ridge tally + left-hand ridge tally) was essentially expanded in female patients contrasted with controls. The expansion in all-out finger ridge include was increasingly huge in female patients contrasted with the male patients. There was no critical contrast in the pattern force in male patients contrasted with the controls. There was a measurably critical increment in the pattern power in the correct hand of females in examination with controls.

Rajangam et al. ( 2008 ) perform examination of male patients indicated a pattern towards criticalness for ‘all-out finger ridge tally’, centrality in left hand for ‘total finger ridge check’, and morally justified for ‘a-b ridge tally’. Then again, in the female patients, ‘supreme finger ridge check’ was seen as critical for the right hand and ‘a-b ridge means’ left hand. The watched contrasts between the male and female patients just as with that of the control might be a direct result of the expanded whorl pattern adding to two tallies and the width of the palm and fingers, along these lines a more noteworthy number of ridges might be available. Obviously, the detail of spreading the fingers and the palm, likewise should be remembered.

Hwang et al. ( 2004 ) concluded that the outspread loop and whorl were progressively visit while the curve and ulnar loop were less continuous; these attributes of the spiral loop and whorl were unmistakable in the correct hand and fifth finger. The frequency of the spiral loop was turned around in their left hands and the third fingers. The complete fingerprint ridges were increasingly various in the RA gathering. Contrasts of both palmprint ridges and palmprint point atd between the RA and the benchmark groups were not conspicuous, then again, palmprint ridges c-d was progressively various in the RA gathering. The shut wrinkle was progressively visit though open and meeting wrinkles were less incessant in the RA gathering. The typical wrinkle was less continuous though Simian and Sydney wrinkles were progressively visit in the RA gathering; the general attributes were unmistakable in their correct hands. The general attributes of Sydney’s wrinkles were turned around in the left hands of female RA gatherings. The all-out level of palm wrinkle transversely was lower in the RA gathering; the attributes of the Sydney wrinkle were progressively conspicuous.

Swati and Sujata ( 2016 ) used conventional radiograph which has been a standard path for distinguishing the JSW in RA since bones are obviously noticeable in X-beam. Toward the beginning of the disease, 90% of the side effects of Arthritis are found in hands. Manual reviewing strategies are not ready to separate a little contrast in JSW figuring. Electronic appraisal of joint space has an effect in dynamic and observing the treatment of joint pain patients. The info hand X-beam picture of the typical hand is of 1000 × 2000 pixels, this picture is resized in the preprocessing step, resized picture goals is 500 × 500 pixels. The division utilizes ASM strategy, the quantity of emphases required is 1000. The state of hand I followed flawlessly after the 1000 emphasis. The consequence of the ASM division after 900 emphasizes. To separate bone and non-bone districts binarization activity is performed. Figure  6 shows the aftereffect of Binarization. Binarization is finished by Otsu’s calculation. At that point, the skeletonization of the paired picture is completed. Skeletonization brings about midline discovery it utilizes a diminishing procedure. At that point, the pinnacle and valley focuses are recognized along with the skeleton. The key focuses that is the specific joint area is followed by LLM. RA tolerant joint area estimation process is appeared. At that point the different factual highlights are determined like mean, middle, change. Joint area precision is determined by considering the number of exact joints recognized partitioned by the absolute number of joints that are 14.

Gobikrishnan et al. ( 2016 ) collected patient’s data with rheumatoid joint pain in knee locale with disease span short of what one year and ordinary people with no knee disease utilized for this investigation. What’s more, this examination was endorsed by the institutional moral council. Absolutely 5 patients and 5 control subjects warm picture information was gathered. The mean period of patients utilized for the investigation was 40 ± 10. What’s more, the disease length was 5 ± 2. The benchmark group mean age was 40 ± 10. The patients with no clinical proof of knee inclusion was dismissed for this examination. The information was gathered by directing a camp at SRM organization of clinical science. Before the picture obtaining method composed endorsement from the patient to take part in the investigation was taken. The patients who had knee torment other than rheumatoid joint inflammation were dismissed for this investigation. The highlights like Standard deviation, Mean, Skewness and kurtosis were extricated for patients and control subjects fragmented picture. The acquired outcome indicated essentialness. The standard deviation was beneath 10 for control bunch Above 10 for patients experiencing rheumatoid joint pain. Mean worth was underneath 5 for control gathering or more 5 for patients experiencing rheumatoid joint inflammation. Kurtosis was beneath 10 for control gathering or more 10 for patients experiencing rheumatoid joint inflammation and Skewness was underneath 10 for patients experiencing rheumatoid joint pain or more 10 for control gathering. The worth discovered higher for patients because of higher temperature variety.

4.1 Practices used for blood group prediction

The Table  1 shows the different methods of blood group identification, which includes some of the traditional as well as unusual those are build using electrical or electronics components such as diode, sensors. The few researchers tried a software-based approach by processing image of blood sample, but only few used method called fingerprint pattern analysis to predict blood group with limited accuracy because thy apply this method with traditional paper and ink as sample collection mechanism, so it not provide high accuracy. In current era of digitization there are several image (fingerprint) computation techniques which explore a greater number of features from fingerprint image which extends the accuracy of prediction process. As per literature, there are many methods are available for determination of blood group from those some having pros and cons, but the popular and most traditional one is to take blood sample of an individual and test it against various antibodies to determine blood type 3 to 5 min but it not convenient to the small children’s and individual having blood phobia. The fingerprint having lots of potential which explore different unique patterns those may leads to identify blood group very quickly and accurately.

4.2 Analysis and prediction of lifestyle-based diseases

The Table  2 show various methods and dataset or samples which are used for analysis and prediction of lifestyle-based diseases such as Diabetes, Blood pressure/Hypertension, Rheumatoid arthritis. The diseases arise with age but not all humankind suffers from such age-based disease. The methods and dataset used to study age-based disease are limited only daily activity, X-ray samples and some are the techniques used after arrival of such diseases. The age, blood group, daily activity, lifestyle of individual and fingerprint patterns analysis helps researchers to generate indication or risk prediction in early age of an individual.

5 Evaluation and discussion

5.1 blood group.

As per literature, all authors attempt traditional method for sample collection as ink and paper, so they were only analyses the fingerprint patterns visible to human eyes those are like Loops, Arches and Whorls. Above literature shows relation between blood group and finger-print pattern summaries as follows:

Loops were the determined common finger-print design and Arches were the least common.

Whorls and mixed were moderate.

More no of loops was originating in blood groups O, B related to A and AB.

Blood group O +ve is the maximum found in samples, O −ve and AB −ve is the fewest.

Loops, whorls, mixed and arches were uppermost in females.

Group A was the utmost common group among sampled males.

Blood group O, B, were the record usually seen in females.

5.2 Diabetes

In type 1 DM there is increased frequency in whorls, and decreased ulnar loop, increased frequency of Sydney line, and increased incidences of arches in females (Ravendranath and Thomas 1995 ). In Maturity onset diabetes mellitus, there is decrease in mean value of TFRC, AFRC, increase in arches and decrease in whorls (Ravindranath et al. 2003 ). The fingerprint of an individuals with T2DM would be more irregular than an individual without T2DM, regulatory for gender and age. Diabetes regulate if wavelet analysis, a technique already used in forensics for fingerprint archival and matching, but not in previous studies of fingerprints as disease markers, would give results like the traditional ridge count or pattern analysis. The fingertips with whorls or double loops, applied a which RC formula that comprised half-unit values for those ridges situated between the core and delta point or between multiple cores. All ridge counting was as blinded to the diabetic and anthropometric status of the participants. The type 1 diabetes, there is increased frequency in whorls, and decreased ulnar loop, increased frequency of Sydney line, and increased incidences of arches in females (Roshani et al. 2016 ).

5.3 Above literature shows relation between diabetes patients and finger-print pattern summaries as follows

Surge in arches in diabetes in both sexes

Growth in rate of recurrence of loops and arches and a lessened frequency of whorls especially in mid finger

Reduced number of arches in the right hand of male and left hand of female having diabetics, it was more in diabetic males and females than in the controls

Growth in radial loop, ulnar loop in both male and female diabetics.

Increase in frequency of whorls in both types of gender in diabetics

5.4 Blood pressure/hypertension

There is increase in TFRC, decreased frequency of axial triradius ‘t’ in right palm of females and ‘t and t’ in right palm of male, decreased atd angle and absence of axial of triradial in 10% cases (Mandasescu et al. 1999 ). Above literature shows relation between patients having Hypertension and not a Hypertension finger-print pattern summary as follows:

Higher prevalence of whorls and loops are associated with higher level of blood pressure

Whorls and loops are prime ridge patterns in hypertensive patients

ATD angle showed the mean of angle in patient surge rather than in control group

Larger frequency of ridge endings in the thumbs and index fingers

Amplified frequency in bifurcations and convergences in the middle, ring and little fingers

5.5 Rheumatoid arthritis

There is increase in arches and decrease in loops and whorls in males, whereas in females there is increase in whorls and decrease in loops on the 1st finger of both hands (Sengupta and Boruah 1996 ), with increase in arches on 3rd digit and whorls on 4th digit of left hand (Bala et al. 2015 ). Above literature study shows change in fingerprint patterns of patients having summarized as follows:

Ulnar loop was the most prominent digital pattern in both genders,

Decrease in the radial loop in both male and female patients

Loops were significantly decreased in the third finger of males and a first and fourth finger of females

Decrease in the ulnar loops in both the hands of male and female patients.

Increase in the whorl pattern in the right hand of male patients and in both the hands of female patients

Decrease in the arches of the left hand of female patients.

5.6 Strengths and weaknesses of present research study

The strength of the present research work is that fingerprint itself having lots of unique and hidden patents and it also currently used as a traditional, effective, and unique identification method of an individual. The dermatoglyphics as a diagnostic aid used from ancient eras and now it is well established in number of diseases which have strong hereditary basis and is employed as a method for screening for abnormal anomalies. There are more than 100 fingerprint minutiae patterns of ridges are determined as unique through the combination of genetic and environment factors.

The weaknesses are in acquisition of fingerprint and finding different unique patterns from people of different age group due to the human digital fingerprint varies in texture as person ages, so it is very difficult to classify fingerprints because there are the fingerprints having the characteristics of two or more patterns changes with age of an individual. Normally common clinical diseases like hypertension, arthritis and diabetes arises with aging, but due to busy schedule or lifestyle of an individual, it arises at any stage of life. So, it may lead to increase sample size and distribution of features required. The large datasets of fingerprint images acquired in real operational conditions are, rightly so, secured under data protection regulations that severely restrict the access to these data, even for research purposes.

6 Conclusion

The fingerprints are having immense potential to have an effective method of identification. In this research, it investigates the problem of blood group identification and analysis of disease those arises with aging or disease called as lifestyle-based like hypertension, type 2-diabetes and arthritis from fingerprint by analyzing their patterns correlation with blood group and age of an individual. With the literature review study, it is observed that fingers of an individual are having multiple unique patterns those are need to be extracted with computerized method with fingerprints image captured using digital device which allow to find known association of fingerprints patterns which may enhance the authenticity of the fingerprints in blood group identification and early indication of lifestyle-based diseases of an individual.

The fingerprint used as a traditional, effective, and unique identification method of an individual, in future it allows researchers to investigate with various diseases other than those are arised with age but also helps to explore different antibodies or reactive process of human body in several diseases. Also, similar study helps to predict the risk of any kind of diseases in early age of an individual. The analysis and classification of community based on age, blood group, fingerprint patterns and lifestyle diseases help to tackle any pandemic in future like COVID-19 in which mankind may suffer a lot having lifestyle-based diseases like hypertension, type 2-diabetes.

Ali MMH, Mahale VH, Yannawar P, Gaikwad AT (2016) Fingerprint recognition for person identification and verification based on minutiae matching. In: 2016 IEEE 6th international conference on advanced computing, pp 332–339

Ameer Y, Zafar R, Abbasi AMH, Rasheed MA, Habib H, Salahuddin T, Warriach SA, Tariq A, Ahmed T (2014) Finger prints pattern variation in diabetic patients. PJMHS 8(1):162

Google Scholar  

Azhagiri R, Anitha M, Hemapriya J (2018) Analysis of left thumb print pattern among different human blood groups. Int J Anat Var 11:103–106

Bala A, Deswal A, Sarmah PC, Khandalwal B, Tamang BK (2015) Palmar dermatoglyphics patterns in diabetes mellitus and diabetic with hypertension patients in Gangtok region. Int J Adv Res 3(4):1117–1125

Bala A, Deswal A, Sarmah PC, Khandelwal B, Tamang BK (2016) Comparative study of dermatoglyphic patterns of diabetes mellitus and diabetic with hypertension patients of hilly region. Int J Sci Study 3(11):108–112

Berlitz PA, Claussen J, Doring JS, Drechsel H, North Off H, Gehring FK (2012) Rapid automated blood group analysis with QCM biosensors. In: 5th European conference of international federation for medical and biological engineering IFMBE proceedings, vol 37, pp 1039–1042

Burute P, Kazi SN, Vasanti V (2013) A role of dermatoglyphic fingertip patterns in the prediction of maturity onset diabetes mellitus (type II). IOSR J Dental Med Sci 8(1):01–05

Chakravathy PG, Shirali A, Chowta KN, Ramapuram JT, Madi D, Raj Singh Chouhan R (2018) A handy tool for hypertension prediction: dermatoglyphics. Indian Heart J 70:116. https://doi.org/10.1016/j.ihj.2018.07.007

Article   Google Scholar  

Dalvi A, Kumar Pulipaka H (2018) Determination of blood group using Image processing. Int J Sci Eng Res 9:24–27

Datasheet of DiamedDiaclon Anti-A (2008) Diaclon Anti-B. Diaclon Anti-AB, Cressiers/Morat

Deopa D, Prakash C, Tayal I (2014) A study of fingerprint in relation to gender and blood group among medical students in Uttarakhand Region. J Indian Acad Forensic Med 32:24–27

Desai SD, Hadimani GA (2013) Dermatoglyphics and health. Genet Anatom Karnataka 7(1):01–09

Fayrouz NE, Farida N, Irshad AH (2011) Relation between fingerprints and different blood groups. J Forens Legal Med 1:18–21

Fernandes JM, Soares FO, Minas G (2013) RH phenotypes analysis by spectrophotometry in human blood typing. In: Proceedings of the IEEE 3rd Portuguese meeting, pp 133–136

Fernandes J, Pimenta S, Soares FO, Minas G (2015) A complete blood typing device for automatic agglutination detection based on absorption spectrophotometry. IEEE Trans Instr Measur 64(1):112–119. https://doi.org/10.1109/TIM.2014.2331428

Ferraz A (2013) Automatic system for determining of blood type using image processing technique. IEEE 3rd Portuguese meeting in bioengineering (ENBENG), pp 20–23

Ferraz A(2013) Automatic system for determination of blood types using image processing techniques. In: 2013 IEEE 3rd Portuguese meeting in bioengineering (ENBENG)

Ferraz A, Carvalho V, Soares F (2010) Development of a human blood type detection automatic system. Proc Eng 5:496–499

Fingerprint Identification – Project 2, American University of Beirut Faculty of Engineering and Architecture Department of Electrical and Computer Engineering-Adaptive Filtering and Neural Networks

Gobikrishnan M, Rajalakshmi T, Snekhalatha U (2016) Diagnosis of rheumatoid arthritis in knee using fuzzy C means segmentation technique. International conference on communication and signal processing IEEE, April 6–8, 2016, IndiaSant SM, Vare AM, Fakhruddin S (1980) Dermatoglyphic traits in Diabetes mellitus. J Anat Soc India. 29: 43

Hwang S-B, Chung M-S, Park J-S, Suh C-H (2004) Dermatoglyphic characteristics of patients with rheumatoid arthritis. Korean J Phys Anthropol 18:313

Igbigbi PS, Msamati BC, Ng’ambi TM (2001) Plantar and digital dermatoglyphic patterns in Malawian patients with diabetes, hypertension and diabetes with hypertension. Int J Diabet Metabol 9:24–31

IMAQ (2004) IMAQ vision concepts manual. National Instruments, Austin

Jain V, Singh AK (2015) An approach for minutia extraction in latent fingerprint matching. Int J 6(1):51–58

Jha L, Das KD, Ahamad Z (2015) Fingerprint pattern examination of right hand thumb in relation to Blood Group. Janaki Med College J Med Sci 3:58–61

Joshi S, Garg D, Bajaj P, Jindal V (2016) Efficacy of fingerprint to determine gender and blood group. J Dent Oral Care Med 2(1):103. https://doi.org/10.15744/2454-3276.2.103

Kanchan T, Chattopadhyay S (2006) Distribution of fingerprint patterns among medical students. J Indian Acad Forens Med 28:65–68

Karim JK, Mohammed AL, Saleem A (2014) Dermatoglyphics study of fingerprints pattern’s variations of a group of type II diabetic mellitus patients in erbil City. Zanco J Pure Appl Sci 26(4):11–16

Keerthana D, Ranganathan L (2017) Design and development of blood sample analyzer using intelligent machine vision techniques. Int J Eng Sci Comput 7:12017–12020

Lahiri A, Bandyopadhyay S, Adhya S, Ghosh S, Goswami S, Bhattacharya P (2013) A study on relationship between dermatoglyphics and hypertension. IOSR J Dental Med Sci 7(6):62–65

Mandasescu S, Richards B, Cadman J (1999) Detection of pre- diabetics by palmar prints: a computer study leading to a low-cost tool. In: XIV international congress of the federation for medical informatics. Dec 31, Germany, Manchester, GMDS: 2000

Mazumdar S (2015) A case study on dermatoglyphics in rheumatoid arthritis. Int J Sci Res 4:448–449

McBean RS, Hyland CA, Flower RL (2014) Approaches to determination of a full profile of blood group genotypes: single nucleotide variant mapping and massively parallel sequencing. Comput Struct Biotechnol J 11:147–151

Mehta AA, Mehta AA (2015a) Study of fingerprint patterns in type II diabetes mellitus. Int J Anat Res 2015:1046–1048

Mehta AA, Mehta AA (2015b) Study of fingerprint patterns in type II diabetes mellitus. Int J Anatomy Res 3(2):1046–1048

Mittal M, Lala BS (2013) Dermatoglyphics: an economical tool for prediction of diabetes mellitus. Int J Med Health Sci 2(3):292–297

Morris MR, Bjoern Ch, Ludwar ES, Mamo MN, Shubrook JH (2016) A new method to assess asymmetry in fingerprints could be used as an early indicator of type 2 diabetes mellitus. J Diab Sci Technol 10(4):864–871

Narayana BL, Rangaiah YKC, Khalid MA (2016) Study of fingerprint patterns in relation to gender and blood group. J Evol Med Dent Sci 5(14):630–633. https://doi.org/10.14260/jemds/2016/144

Narayanan SK, Pais CC, Shenoy PK (2017) Use of palmar dermatoglyphics in rheumatoid arthritis: a case-control study. IAIM 4(12):70–76

Narkis Banu A, Kalpana V (2018) An automatic system to detect human blood group of many individuals in a parellel manner using image processing. Int J Pure Appl Math 118:3119–3127

Nezhad HR, Shah NM (2010) Application of dermatoglyphic traits for diagnosis of diabetic type 1 patients. Int J Environ Sci Dev 1(1):36–39

Pimenta S, Minas G, Soares FO (2012) Spectrophotometric approach for automatic human blood typing. In: Proceedings of the IEEE 2nd Portuguese meeting, pp 101–104

Pimenta S, Soares FO, Minas G (2012) Development of an automatic electronic system to human blood typing. In: Annual international conference of the IEEE engineering in medicine and biology society (EMBC), pp 2712–2715

Pramila Padmini M, Narasinga Rao B, Malleswari B (2011) The study of dermatoglyphics in diabetics of North Coastal Andhra Pradesh population. Indian J Fund Appl Life Sci 1(2):75–80

Radhika RH (2016) Pattern of fingerprints and their relation with blood groups. Indian J Basic Appl Med Res 5:744–749

Rajangam S, Ravindranth R, Shubha R, Nagesh HV, Johnson J (2008) Dermatoglyphics-quantitative analysis in rheumatoid arthritis. Anthropologist 10(3):233–235. https://doi.org/10.1080/09720073.2008.11891054

Rakate N, Zambare B (2014) Fingertip patterns: a diagnostic tool to predict diabetes mellitus. Nat J Med Dental Res 2(3):49–53

Ramasubramanian MK, Alexander SP (2009) An integrated fiberoptic–microfluidic device for agglutination detection and blood typing. Biomed Microdev 11:217–229

Rastogi P, Pillai KR (2010) A study of fingerprints in relation to gender and blood group. J Indian Acad Forensic Med 32(1):11

Ravendranath R, Thomas IM (1995) Finger ridge count and fingerprint pattern in maturity onset diabetes mellitus. Ind J Med Sci 49:153–156

Ravindran G, Joby T, Pravin M, Pandiyan P (2017) Determination and classification of blood types using image processing techniques. Int J Comput Appl 157(1):12–16

Ravindranath R, Shubha R, Nagesh HV, Johnson J, Rajangam S (2003a) Dermatoglyphics in rheumatoid arthritis. Indian J Med Sci 57(10):437–441

Ravindranath R, Shubha R, Nagesh HV (2003b) Dermatoglyphics in rheumatoid arthritis. Ind J Med Sci 57:437–441

Relf CG (2003) Image acquisition and processing with LabVIEW. CRC, Boca Raton

Roshani S, Pallavi I, Prabhakar S, Amita S (2016a) Comparative study on the dermatoglyphic pattern among diabetic (type-2) and non-diabetic adults in North Indian population. Eur J Pharm Med Res 2016:430–433

Roshani S, Amita S, Prabhakar S, Bezbaruah NK, Anshu M (2016b) Dermatoglyphic patterns among type 2 diabetic adults in North Indian population. Int J Curr Med Pharma 2(8):609–611

Sangam MR, Ramesh Babu A, Krupadanam K, Anasuya K (2011) Finger print pattern in different blood groups. J Indian Acad Forensic Med 67:343–345

Satoh K, Itoh Y (2004) Forensic ABO blood grouping by 4 SNPs analyses using an ABI PRISMR 3100 genetic analyser. Int Congr Ser 1288:49–51

Satoh K et al (2001) Evaluation of ABO subtyping by DNA sequencing. Jpn J Leg Med 55:111

Sengupta S, Boruah J (1996) Finger dermatoglyphic patterns in diabetes mellitus. J Hum Ecol 7(3):203–206. https://doi.org/10.1080/09709274.1996.11907220

Sharma MK, Sharma H (2012) Dermatoglyphics: a diagnostic tool to predict diabetes. J Clin Diagn Res 6(3):327–332

Shivaleela C, Hanji CV, Kumar GV (2013) Utility of dermatoglyphics in Type II diabetes mellitus (T2DM) to assess the risk for IHD: Apilot study. Biomed Res 24(2):242–244

Shivhare PR, Sharma SK, Ray SK, Minj A, Saha K (2017) A study of dermatoglyphic pattern in relation to ABO, Rh blood group and gender among the population of Chhattisgarh. Int J Sci Study 4:61–65

Singh M, Majumdar O (2015) Dermatoglyphics: blueprints of human cognition on fingerprints. Comput Sci Electron J 6:124–146

Siva Sundhara Raja D, Abinaya J (2019) A cost-effective method for blood group detection using fingerprints. Int J Adv Study Res Work 2:2581

Smail HO, Kareem SS, Abdulkareem NM (2019) Comparative study of the fingerprint pattern among diabetic (type 1) & non-diabetic children in Koya City. J Adv Laborat Res Biol 10(2):41–47

Subrahmanyam BV (1999) Modi‟s medical jurisprudence and toxicology, 22nd edn. Butterworths, New Delhi, pp 71–77

Sudikshya KC, Maharjan N, Adhikari N, Shrestha P (2018) Qualitative analysis of primary fingerprint pattern indifferent blood group and gender in Nepalese. Hindawi Anat Res Int 2018:7

Sumathi S, Desai SD (2007) Study of dermatoglyphics in patients with type II diabetes mellitus essential hypertension in the age group between 35–55 Years. Anal Med 10(2):22–28

Swati AB, Dr. Sujata NK (2016) Automatic Analysis of Rheumatoid arthritis based on statistical features. In: 2016 International conference on automatic control and dynamic optimization techniques (ICACDOT). International Institute of Information Technology (I 2 IT), Pune, 2016 IEEE

Tafa Z, Pervetica N (2015) An intelligent system for diabetes prediction. In: 4th Mediterranean conference on embedded computing Budva, Montenegro pp 378–382

Tafazoli M, Dezfooli SR, Shahri NM, Shahri HM (2013a) The study of dermatoglyphic patterns and distribution of the minutiae in inherited essential hypertension disease. Curr Res J Biol Sci 5(6):252–261

Tafazoli M, Dezfooli SR, Kazemi T, Shahri HM, Shahri NM (2013b) Comparison of dermatoglyphic patterns between healthy and hypertensive people. Asian J Med Sci 5(1):19–25

Taiwo IA, Adebanjo OO (2012) Evaluation of association between digital dermatoglyphic traits and type-2 diabetes in Lagos, Nigeria. Niger Quart J Hospital Med 22(3):191–199

Tarca A, Tuluc E (2005) Dermatoglyphics in insulin: dependent diabetes or diabetes mellitus type 1 (T1 DM). J Prevent Med 13(1-2):43–53

Tejaswini HV, Mallikarjuna Swamy MS (2014) Determination and classification of blood types using image processing techniques. ITSI Trans Electr Electron Eng 2:2320–8945

Thakar MK, Sharma T (2016) Digital grid method for fingerprint identification and objective report writing. Egypt J Forens Sci 6:194–201

Udoaka AL, Lawyer-Egbe K (2009) Dermatoglyphic patterns of diabetic mellitus patients of Ijaw Origin in Port Harcourt, Nigeria. Niger J Health Biomed Sci 8(2):72–82

Umana UE, Bello R, Timbuak J, Ibegbu A, Musa SA, Ikyembe D, Hamman WO (2013) Dermatoglyphic and cheiloscopic patterns among diabetic patients: a study in Ahmadu Bello University Teaching Hospital Zaria, Nigeria. J Biol Life Sci 4(2):206–214

Veena Vijayan V, Anjali C (2015) Prediction and diagnosis of diabetes mellitus -a machine learning approach. In: 2015 IEEE recent advances in intelligent computational systems (RAICS), pp 10–12

Vij K (2005) Textbook of forensic medicine and toxicology, 3rd edn. Elsevier, New Delhi, pp 89–91

Wanga A, An N, Chen G, Li L, Alterovitz G (2015) Predicting hypertension without measurement: a non-invasive, questionnaire-based approach. Expert Syst Appl 42:7601–7609

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Patil, V., Ingle, D.R. An association between fingerprint patterns with blood group and lifestyle based diseases: a review. Artif Intell Rev 54 , 1803–1839 (2021). https://doi.org/10.1007/s10462-020-09891-w

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The Association between ABO Blood Group and the Risk of Colorectal Cancer: A Systematic Literature Review and Meta-Analysis

Affiliations.

• 1 Firoozabadi Clinical Research Development Unit (F A CRD U), Iran University of Medical Sciences (IUMS), Tehran, Iran.
• 2 Department of Epidemiology, School of Public Health, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
• 3 MD, Tehran Azad University of Medical Sciences, Tehran, Iran.
• 4 Student Research Committee, School of Medicine, Guilan University of Medical Sciences, Rasht, Iran.
• 5 MD, Iran University of Medical Sciences, Tehran, Iran.
• 6 Resident of Radiology, Rasoul-e-Akram Hospital, Iran University of Medical Sciences, Tehran, Iran.
• 7 Department of General surgery, School of Medicine, Iran University of Medical Sciences, Tehran, Iran.
• PMID: 37642040
• PMCID: PMC10685215
• DOI: 10.31557/APJCP.2023.24.8.2555

Introduction: Recently, studies have investigated the relationship between blood types and cancers. Contradictory results regarding the relationship between blood group type and colorectal cancer(CC) have been reported. The purpose of this study was to systematically investigate the distribution of ABO blood group frequency and evaluate its relationship with CC.

Material and methods: To conduct this systematic meta-analysis, we searched PubMed, Scopus, Web of Science, and Google Scholar databases using appropriate MESH terms until July 2022. All observational studies which assessed the ABO blood group frequency distribution and the association between ABO and CC were included. The Risk of Bias Assessment tool was used to assess the quality of studies. A random model was used to estimate the odds ratio (OR). The Egger test was used to assess the publication bias.

Results: Overall,14 studies (413,132 patients) were included. According to the pooled estimation, blood groups A, B, AB, and O frequency in patients with CC were 37%,18%,9%, and 31%, respectively. The OR of CC in people with the A blood group was higher than in the other groups (OR: 1.11, 95% CI:1.03,1.19, P:0.001). In contrast, the OR of CC in people with the O blood group was significantly lower than in other blood groups (OR: 0.93, 95% CI:0.83,0.97, P:0.001). No significant relationship was observed for B and AB blood groups with CC.

Conclusions: This Meta-analysis showed that blood group type A has a greater risk of developing CC, while blood group type O was associated with lower chances of CC.

Keywords: ABO blood group system; Meta-analysis; Prevalence; Systematic review; colorectal cancer.

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Conflict of interest statement

PRISMA Flow Chart of the…

PRISMA Flow Chart of the Included Studies

Forest Plot of Frequency Distribution…

Forest Plot of Frequency Distribution of ABO Blood Groups Type in Colorectal Cancer…

Forest Plot of Association between…

Forest Plot of Association between Blood Type A and CC

Forest Plot of Association between Blood Type B and CC

Forest Plot of Association between Blood Type AB and CC

Forest Plot of association between…

Forest Plot of association between Blood Type O and CC

Publication Bias Assessment of ABO…

Publication Bias Assessment of ABO Blood Group

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ABO and Rhesus blood groups and multiple health outcomes: an umbrella review of systematic reviews with meta-analyses of observational studies

• Fang-Hua Liu 1 , 2   na1 ,
• Jia-Kai Guo 1 , 3   na1 ,
• Wei-Yi Xing 1 , 2   na1 ,
• Xue-Li Bai 1 , 4   na1 ,
• Yu-Jiao Chang 1 , 2   na1 ,
• Zhao Lu 1 , 5 ,
• Miao Yang 1 , 6 ,
• Ying Yang 1 , 7 ,
• Wen-Jing Li 1 , 8 ,
• Xian-Xian Jia 1 , 6 ,
• Tao Zhang 1 , 5 ,
• Jing Yang 1 , 9 ,
• Jun-Tong Chen 10 ,
• Song Gao 4 ,
• Lang Wu 11 ,
• De-Yu Zhang 4 ,
• Chuan Liu 4 ,
• Ting-Ting Gong 4 &
• Qi-Jun Wu   ORCID: orcid.org/0000-0001-9421-5114 1 , 2 , 4 , 12  

BMC Medicine volume  22 , Article number:  206 ( 2024 ) Cite this article

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A Correction to this article was published on 11 July 2024

This article has been updated

Numerous studies have been conducted to investigate the relationship between ABO and Rhesus (Rh) blood groups and various health outcomes. However, a comprehensive evaluation of the robustness of these associations is still lacking.

We searched PubMed, Web of Science, Embase, Scopus, Cochrane, and several regional databases from their inception until Feb 16, 2024, with the aim of identifying systematic reviews with meta-analyses of observational studies exploring associations between ABO and Rh blood groups and diverse health outcomes. For each association, we calculated the summary effect sizes, corresponding 95% confidence intervals, 95% prediction interval, heterogeneity, small-study effect, and evaluation of excess significance bias. The evidence was evaluated on a grading scale that ranged from convincing (Class I) to weak (Class IV). We assessed the certainty of evidence according to the Grading of Recommendations Assessment, Development, and Evaluation criteria (GRADE). We also evaluated the methodological quality of included studies using the A Measurement Tool to Assess Systematic Reviews (AMSTAR). AMSTAR contains 11 items, which were scored as high (8–11), moderate (4–7), and low (0–3) quality. We have gotten the registration for protocol on the PROSPERO database (CRD42023409547).

The current umbrella review included 51 systematic reviews with meta-analysis articles with 270 associations. We re-calculated each association and found only one convincing evidence (Class I) for an association between blood group B and type 2 diabetes mellitus risk compared with the non-B blood group. It had a summary odds ratio of 1.28 (95% confidence interval: 1.17, 1.40), was supported by 6870 cases with small heterogeneity ( I 2  = 13%) and 95% prediction intervals excluding the null value, and without hints of small-study effects ( P for Egger’s test > 0.10, but the largest study effect was not more conservative than the summary effect size) or excess of significance ( P  < 0.10, but the value of observed less than expected). And the article was demonstrated with high methodological quality using AMSTAR (score = 9). According to AMSTAR, 18, 32, and 11 studies were categorized as high, moderate, and low quality, respectively. Nine statistically significant associations reached moderate quality based on GRADE.

Conclusions

Our findings suggest a potential relationship between ABO and Rh blood groups and adverse health outcomes. Particularly the association between blood group B and type 2 diabetes mellitus risk.

Peer Review reports

Blood groups can be categorized based on different systems, such as the ABO blood group system, the Rhesus (Rh) blood group system, and the MN blood group system [ 1 ]. ABO blood group system is the most frequently applied [ 2 ]. Each of the two alleles possesses antigen A, B, or neither. These alleles come together to be a combination, determining an individual’s blood type phenotype, thus perform as the type of O, A, B, or AB. The Rh blood group system is more polymorphic than others among human blood groups, which is composed of numerous antigens and next to ABO. The ABO and Rh blood group system are extensively utilized in clinical practice, affecting host susceptibility [ 3 , 4 ].

The previous study suggested that blood groups are involved in disease mechanisms at the molecular level mediated either through the blood group antigens or by the blood group reactive antibodies [ 5 ]. In addition, J. Höglund et al. found 39 plasma proteins were associated with variation at the ABO locus. For example, proteins with functions related to tumorigenesis (CA9, Gal-9, and KLK6) and pro-inflammatory or anti-inflammatory functions (IFN-gamma-R1, IL-18BP, and MARCO) [ 6 ]. Generally, the overexpression of these proteins leads to an abnormal cell proliferation or cell growth. Thus, blood group may influence disease development through protein expression levels.

Numerous systematic reviews with meta-analyses have been published, which explored correlations between ABO and Rh blood groups with various health outcomes [ 7 , 8 , 9 ]. However, to date, the association between these blood groups and human health outcomes remains controversial [ 10 , 11 , 12 ]. Most of them have primarily concentrated on one single disease end-point, lacking a comprehensive evaluation of the aforementioned relationships. In addition, the strength and reliability of the evidence remains unclear. To overcome the inherent limitations of systematic reviews with meta-analyses and provide a comprehensive overview of the claimed associations of ABO and Rh blood groups with health outcomes, in the form of an umbrella review (UR), is necessary.

UR synthesizes evidence from various systematic reviews with meta-analyses on a subject, appraising the certainty, precision, and potential bias of the correlations, thus facilitating evidence grading based on well-defined criteria [ 13 ]. We set out to conduct an UR to comprehensively evaluate systematic reviews with meta-analyses of observational studies, which examined associations of ABO and Rh blood groups with a range of health outcomes. This endeavor was aimed at presenting an overview of the breadth and validity for aforementioned associations. We thus hoped to provide both clinicians and policy makers with robust data to identify high-risk groups and inform clinical practice and guidelines.

Protocol registration

We have gotten the registration for the protocol of this UR with the International Prospective Register of Systematic Reviews (PROSPERO; registration number CRD42023409547). The study followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses reporting guideline [ 14 ] (Additional file 1 : Table S1) and the Meta-analysis of Observational Studies in Epidemiology reporting guideline [ 15 ] (Additional file 1 : Table S2).

Search strategy

We systematically searched PubMed, Web of Science, Embase, Scopus, Cochrane Library, and several regional databases (Latin American and Caribbean Health Sciences Literature, Western Pacific Region Index Medicus, Index Medicus for South-East Asia Region, Index Medicus for the Eastern Mediterranean Region, and African Index Medicus) on the date from inception until Feb 16, 2024, to identify systematic reviews with meta-analyses of observational studies evaluating associations between ABO as well as Rh blood groups and diverse health outcomes. We used the keywords (“ABO” OR “blood group” OR “blood type” OR “Rh”) AND (“meta-analysis” OR “systematic review” OR “systematic overview”) (Additional file 1 : Table S3) to search. Besides, the literature search was reviewed by hand-checking the reference lists of all systematic reviews with meta-analyses.

Eligibility criteria

Articles were selected based on the following PECOS (Population, Exposure, Comparison, Outcome, Study design) strategy:

Population: population with ABO or Rh blood groups;

Exposure: ABO (blood types A, B, O, and AB) and Rh (Rh positive [Rh +] and Rh negative [Rh −]) blood groups (any method used to assess blood type, including genetic tests and forward/reverse agglutination tests, was accepted);

Comparison: different blood groups;

Outcome: any health outcome (e.g., cancer, coronavirus disease 2019 [COVID-19], coronary artery disease, etc.). Ascertained health outcomes using self-report, observed (e.g., clinical diagnoses) or objective [e.g., biomarkers, certified mortality] criteria); and

Study design: systematic reviews with meta-analyses of observational studies (cohort, case–control, or cross-sectional studies).

The exclusion criteria were established as follows: (1) systematic reviews without quantitative analysis, (2) systematic reviews with meta-analyses without study-level data (e.g., effect sizes, 95% confidence intervals [CIs], the number of cases, and participants/control), (3) studies on genetic polymorphisms, animal studies, laboratory studies, conference abstracts and randomized controlled trials, or (4) systematic reviews with meta-analyses conducted in languages other than English.

Given the requirement for a minimum of three original studies to calculate 95% prediction intervals (PIs), we incorporated meta-analyses comprising at least 3 original studies [ 16 ]. Associations were considered to overlap if they assessed the same research topic and were examined in more than one systematic review with meta-analysis [ 17 ]. The inclusion of primary studies once or more may be led by incorporating results of reviews with overlapping associations, and biased findings and estimates could be caused by incorporating results as well [ 18 , 19 ]. Therefore, the systematic review with meta-analysis which contained the largest number of primary studies was picked up if two or more systematic reviews with meta-analyses overlapped, while the one with the largest sample size of participants if more than one systematic review with meta-analysis kept the same numbered primary studies.

To ascertain the eligible articles, four experienced investigators (Y-JC, J-KG, J-TC, and YY) matched in pairs and screened titles, abstracts, and full texts independently. We also checked the references of relevant studies to confirm any other eligible articles by hand. If there were any discrepancies, they would be made out by a third reviewer (Q-JW).

Data extraction

Ten trained investigators (Y-JC, J-KG, YY, X-XJ, W-JL, T-Z, YY, MY, ZL, and X-LB) were paired to extract data independently, discrepancies were settled by a third reviewer (Q-JW) when it was needed. From every meta-analysis we identified, it was abstracted of the contents on the name of the first author, journal, publication year, exposures of interest, outcomes of interest, comparison, meta-analysis metrics (RR [risk ratio], OR [odds ratio], or HR [hazard ratio]), and the number of studies considered. From the individual studies included in every meta-analysis, it was extracted of the name of the first author, publication year, epidemiological study design, number of cases and controls in the observational case–control studies or total population in the observational cohort studies, maximally adjusted risk estimates, and 95% CIs.

Data analysis

Estimation of summary effect —We utilized a random-effects model for each meta-analysis to do a calculation for the summary effect size and corresponding 95% CI [ 20 ].

Estimation of prediction interval —We got the 95% prediction intervals (PIs) for the summary random effect sizes, because it can explain heterogeneity between varied studies and the uncertainty for the effect, with an expectation in another study concerning on the same relationship [ 21 ].

Assessment of heterogeneity —We evaluated heterogeneity with the I 2 metric. And I 2 value exceeding 50% is judged large heterogeneity, and 75% is judged very large heterogeneity similarly [ 22 ]. We also produced τ 2 statistic to assess the heterogeneity.

Assessment of small study effects —Through Egger’s regression asymmetry test [ 23 ], we evaluated small-study effects (i.e., whether larger studies are more likely to give indirectly smaller estimates of effect size when compared with smaller ones) [ 24 ]. Reasons for distinctions between small and large studies such as publication and other reporting biases, genuine heterogeneity, chance, or other conditions are revealed through small study effects [ 24 ]. They were considered to exist when the largest study effect was more conservative than the summary effect size in the meta-analysis and it was found that P value < 0.10 in the regression asymmetry test.

Evaluation of excess significance —We assessed excess significance bias by analyzing whether the number of observed studies ( O ) with nominally statistically significant results (“positive” studies, P  < 0.05) was larger than the expected number of studies ( E ) with statistically significant results using the chi-square test [ 25 ]. The effect size of the largest study (that is, the smallest standard error) in a meta-analysis assessed the strength of the study which needed to use a noncentral t distribution [ 26 , 27 ]. The excess significance test was judged positive when it comes to both O  >  E and P  < 0.10 [ 22 ].

Strength of evidence —According to the established criteria applied in previously published URs [ 13 , 28 , 29 , 30 ] and based on our calculation, significant associations ( P  < 0.05) between ABO and Rh blood groups and health outcomes were divided into 4 levels of evidence strength (convincing [Class I], highly suggestive [Class II], suggestive [Class III], or weak [Class IV] evidence) to draw conclusions. This criterion was evaluated based on statistical significance, number of cases, heterogeneity, largest study, 95% PI, small-study effect, and excess significance bias. P value ≥ 0.05 demonstrated a statistically non-significant association (Additional file 1 : Table S4).

Certainty of the evidence —The credibility of the evidence was qualitatively assessed by two reviewers (W-YX and X-LB) using the GRADE (Grading of Recommendations, Assessment, Development, and Evaluations) method. As recommended by GRADE, the level of evidence was graded the high, moderate, low, and very low determined by risk of bias, inconsistency, indirectness, imprecision, and publication bias.

Sensitivity analyses —To verify the robustness of our findings, we conducted sensitivity analyses to assess the concordance of the summary associations, which were initially graded as convincing (Class I) or highly suggestive (Class II) evidence. The sensitivity analyses were realized by excluding small-sample studies (< 25th percentile) from meta-analyses with evidence of small-study effects and primary studies with low-quality evidence (Newcastle–Ottawa Scale < 6 [ 31 ], Agency for Healthcare Research and Quality < 8 [ 32 ], or effective public health practice project guideline rating moderate and low rather than strong quality [ 33 ]. Further sensitivity analysis was performed with the meta-analyses due to overlap in the main analysis. All statistical analyses were conducted in STATA version 17 and RStudio version 3.6.2.

Assessment of the methodological quality of meta-analyses

We used A Measurement Tool to Assess Systematic Reviews (AMSTAR) to evaluate the quality of systematic reviews and meta-analyses, which was considered as a valid and dependable measurement tool [ 34 ]. This instrument contains a total of 11 items. A “yes” scores one point, and the other answers score 0 points. The AMSTAR was graded as low (0–3 points), moderate (4–7 points), or high quality (8–11 points) [ 34 ]. Ten trained investigators (Y-CS, Z-PN, W-YX, YY, W-JL, ZL, JY, X-LB, MY, and J-NS) matched in pairs, and AMSTAR was used independently to assess the eligible systematic reviews with meta-analyses on methodological quality. Disagreements were made the final decision by the third author (Q-JW).

Literature identification and selection

We retrieved 6474 records from PubMed, Web of Science, Embase, Scopus, Cochrane Library, and several regional databases. According to the criterion, 159 full-text articles were retrieved and checked for inclusion after duplicate removal, title, and abstract screening. There were no additional eligible articles found by hand-checking the reference lists of all systematic reviews. Overall, 51 systematic reviews with meta-analyses corresponded to 270 unique associations were included [ 7 , 9 , 11 , 12 , 31 , 32 , 33 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 ] (Fig. 1 ). The two pairs of four investigators showed high consistency in terms of study selection, with kappa values of 0.893 and 0.926, respectively. The excluded articles and the reasons behind their removal are provided in Additional file 1 : Table S5. For meta-analyses excluded due to a lack of data relating to quantitative synthesis, we further summarized their findings in Additional file 1 : Table S6.

PRISMA flow chart. Flow chart of included and excluded systematic reviews and meta-analyses

Characteristics of included meta-analyses

The 51 systematic reviews with meta-analyses corresponded to 270 unique associations: 105 on cancer outcomes (39%), 91 on infectious outcomes (34%), 25 on cardiovascular outcomes (9%), 22 on oral-related outcomes (8%), 12 on pregnancy-related outcomes (4%), 5 on metabolic disease (2%), and 10 on other outcomes (4%) (Fig. 2 ). The systematic reviews with meta-analyses included in this UR were published from 2007 until 2023. The number of studies per association ranged from 3 to 49. One hundred and ninety-five meta-analyses included ≥  1000 cases (Additional file 1 : Table S7).

Map of 270 blood group related outcomes: percentage of outcomes per outcome category for all studies

Summary findings

Among 270 associations included in our UR. Eighty-nine associations (33%) presented a nominally statistically significant effect ( P  < 0.05). Of these, 41 (46%) were in conformity with the principle of statistical significance at P  < 10  −3 , and 24 (27%) reached P  < 10  −6 . When calculating the effect size of the largest data study of the associations, 61 (69%) of the 89 associations showed statistical significance. After estimating the 95% PI, 66 (74%) contained null values. Twenty-three (26%) and 19 (21%) associations had significant (50% <  I 2  ≤ 75%) and considerable ( I 2  > 75%) heterogeneity estimates, respectively. Twenty-one (24%) of the 89 associations presented evidence for small-study effects, and 29 (33%) associations presented evidence for excess significance bias.

Cancer outcomes

We summarized 105 associations between blood group and cancer outcomes. The magnitude of the observed summary random effects estimates ranged from 0.65 to 1.54 (Additional file 2 : Fig. S1). Thirty-one meta-analyses (30%) presented a nominally statistically significant effect ( P  < 0.05). Of these, 14 associations were graded as suggestive or above evidence (Fig. 3 , Additional file 1 : Tables S7–8).

Forest plot showing studies investigating the association between blood group and health outcomes. CI, confidence interval; CagA, cytotoxin-associated gene A

Esophageal cancer

We found blood group B was associated with a higher risk of esophageal cancer (OR =  1.20; 95% CI: 1.10, 1.31), compared with blood group non-B. And the association was graded as suggestive evidence.

Gastric cancer

Blood group A was associated with a higher risk of gastric cancer, both compared with blood group non-A (OR =  1.11; 95% CI: 1.07, 1.15) and blood group O (OR =  1.19; 95% CI: 1.14, 1.24). However, blood group O was associated with a lower risk of gastric cancer (OR =  0.91; 95% CI: 0.89, 0.94), compared with blood group non-O. These above associations were graded as highly suggestive evidence.

Pancreatic cancer

Compared with blood group O, blood group A was associated with higher risk of pancreatic cancer (OR =  1.33; 95% CI: 1.27, 1.40), cytotoxin-associated gene A (CagA) endemic pancreatic cancer (OR =  1.46; 95% CI: 1.24, 1.50) and CagA-nonendemic pancreatic cancer (OR =  1.43; 95% CI: 1.24, 1.64); blood group B was associated with higher risk of pancreatic cancer (OR =  1.20; 95% CI: 1.10, 1.31) and CagA-nonendemic pancreatic cancer (OR =  1.42; 95% CI: 1.19, 1.69); blood group AB was associated with higher risk of CagA-nonendemic pancreatic cancer (OR =  1.54; 95% CI: 1.26, 1.88); Blood group non-O was also associated with higher risk of pancreatic cancer (OR =  1.31; 95% CI: 1.22, 1.42). Compared with blood group non-O, blood group O was associated with a higher risk of pancreatic cancer (OR =  1.32; 95% CI: 1.22, 1.42), CagA endemic pancreatic cancer (OR =  1.20; 95% CI: 1.11, 1.30), and CagA-nonendemic pancreatic cancer (OR =  1.42; 95% CI: 1.28, 1.59). According to the UR criteria, the associations between blood group A and pancreatic cancer, CagA endemic, and CagA-nonendemic pancreatic cancer risk, blood group O and pancreatic cancer and CagA-nonendemic pancreatic cancer, blood group non-O and pancreatic cancer were graded as highly suggestive evidence. The remaining associations were graded as suggestive evidence.

Infectious disease outcomes

Ninety-one associations between blood group and infectious disease outcomes were investigated. The magnitude of the observed summary random effects estimates ranged from 0.50 to 47.85 (Additional file 2 : Fig. S2). Overall, 27 (32%) of 85 associations reached a statistically significant value at P  < 0.05. Ten associations were supported by suggestive or above evidence (Fig. 3 , Additional file 1 : Tables S7–8).

Coronavirus disease 2019 (COVID-19)

We found blood group A (OR =  1.25; 95% CI: 1.14, 1.37) and blood group B (OR =  1.15; 95% CI: 1.07, 1.22) were associated with an increased risk of COVID-19 infection, compared with blood group O. But blood group O was associated with a decreased risk of COVID-19 infection (OR =  0.88; 95% CI: 0.82, 0.94), compared with blood group O. The association between blood group O and COVID-19 infection was supported by highly suggestive evidence. The other two associations were supported by suggestive evidence.

Human immunodeficiency virus (HIV)

Four blood groups of ABO blood group system were associated with an increased risk of HIV infection (RR =  24.25; 95% CI: 21.60, 27.23; blood group A versus blood group non-A, RR =  21.29; 95% CI: 18.62, 24.36; blood group B versus blood group non-B, RR =  5.44; 95% CI: 4.10, 7.22; blood group AB versus blood group non-AB, and RR =  47.85; 95% CI: 44.01, 52.03; blood group O versus blood group non-O). And these four associations were supported by highly suggestive evidence.

P. falciparum

Blood group A (OR =  1.68; 95% CI:1.32, 2.14), blood group B (OR =  1.97; 95% CI:1.49, 2.59), and blood group non-O (OR =  1.86; 95% CI:1.49, 2.33) were associated with an increased risk of P. falciparum infection, compared with blood group O. All of these associations were supported by suggestive evidence.

Cardiovascular and cerebrovascular outcomes

Twenty-five associations between blood group and cardiovascular and cerebrovascular outcomes were summarized. The magnitude of the observed summary random effects estimates ranged from 0.58 to 2.55 (Additional file 2 : Fig. S3). Of which, 21 (84%) associations gave a show on statistically significant effect nominally ( P  < 0.05), and 7 associations reached suggestive or above evidence (Fig. 3 , Additional file 1 : Tables S7–8).

Myocardial infarction (MI)

Blood group A (OR =  1.29; 95% CI: 1.16, 1.45) and blood group non-O (OR =  1.25; 95% CI:1.14, 1.37) had an increased risk of MI compared with blood group O. Another association showed blood group O had an increased risk of MI (OR =  1.28; 95% CI:1.17, 1.40) compared with blood group non-O. All three associations reached suggestive evidence.

Peripheral vascular disease (PVD)

Compared with blood group O, blood group A (OR =  1.44; 95% CI: 1.19, 1.74) and blood group non-O (OR =  1.45; 95% CI:1.35, 1.56) had an increased risk of PVD. The associations between blood group A and blood group non-O and PVD risk were reached suggestive and highly suggestive evidence, respectively.

Venous thromboembolism (VTE)

Blood group A (OR =  1.63; 95% CI: 1.40, 1.89) and blood group non-O (OR =  2.10; 95% CI:1.83, 2.40) had an increased risk of VTE compared with blood group O. And the two associations reached highly suggestive evidence.

Oral-related outcome

Twenty-two associations between blood group and oral-related outcomes were summarized. The magnitude of the observed summary random effects estimates ranged from 0.70 to 1.36 (Additional file 2 : Fig. S4). Only one association gave a show on statistically significant effect nominally ( P  < 0.05). No association reached suggestive or above evidence (Additional file 1 : Tables S7–8).

Pregnancy-related outcomes

We summarized twelve associations between blood group and pregnancy-related outcomes. The summary random effects estimate magnitude ranged from 0.90 to 1.49 (Additional file 2 : Fig. S5). Only two associations were statistically significant at P  < 0.05. And no association reached suggestive or above evidence (Additional file 1 : Tables S7–8).

Metabolic disease outcomes

We summarized 5 associations between blood group and metabolic disease outcomes. The magnitude of the observed summary random effects estimates ranged from 0.91 to 1.28 (Additional file 2 : Fig. S6). Only 2 (40%) of 5 associations were nominally statistically significant at a P  < 0.05 level. One association was supported by suggestive or above evidence (Fig. 3 , Additional file 1 : Tables S7–8).

Type 2 diabetes mellitus incidence (T2DM)

Blood group B, compared with blood group non-B, had a greater risk of T2DM (OR =  1.28; 95% CI: 1.17, 1.40), and the association was supported by convincing evidence.

Other outcomes

Ten associations between blood group and other outcomes (such as bleeding complication, decreased ovarian reserve) were summarized. The summary random effects estimate magnitude ranged from 0.84 to 1.33 (Additional file 2 : Fig. S7). Only one association was statistically significant at P  < 0.05. And which was supported by highly suggestive evidence (Fig. 3 , Additional file 1 : Tables S7–8).

Bleeding complication

Blood group O was associated with a higher risk of bleeding complication (OR =  1.33; 95% CI: 1.25, 1.42), compared with blood group non-O, which was supported by highly suggestive evidence.

In summary, we found an association between blood group B and an increased risk of T2DM incidence (OR =  1.28; 95% CI: 1.17, 1.40) was rated as convincing evidence when it was taken as comparison for blood group non-B, by owing over 1000 cases, random P value < 10 –6 , not large heterogeneity ( I 2  < 50%), 95% PI excluding the null value, no hints for small-study effects and excess significance bias (Fig. 3 ). Eighteen associations were rated as highly suggestive evidence, they reached a statistically significant value at P  < 10  −6 , had more 1000 cases, and the P value of the largest study was less than 0.05, such as comparison with blood group O, both blood group A (OR =  1.63; 95% CI: 1.40, 1.89) and non-O blood group (OR =  2.10; 95% CI: 1.83, 2.40) increased the risk of VTE incidence. In addition, we found 14 associations were rated as suggestive evidence. Fifty-six associations were rated as weak evidence and the remaining 181 associations were not significant (Additional file 1 : Tables S7–8).

Methodological quality of the meta-analyses

With the measurement tool AMSTAR, 18 (35%) articles were categorized as high quality. Of the 51 articles, 32 (63%) articles and only 1 (2%) article were categorized as moderate and low quality, respectively (Additional file 1 : Table S9).

Certainty of the evidence

Based on the GRADE approach, no health outcomes reached high credibility criteria. Nine of 89 health outcomes met the moderate certainty criteria. Thirty-three and 47 of 89 health outcomes met the low and very low certainty criteria, respectively (Additional file 1 : Table S10).

Sensitivity analyses

Findings from sensitivity analyses are reported in Additional file 1 : Tables S11–13. Removal of small-sized studies from the meta-analyses with evidence of small-study effects, these evidence ratings were not modified. When excluding low-quality studies, the associations between blood group A and gastric cancer, pancreatic cancer, and VTE and blood group O and pancreatic cancer retained their highly suggestive evidence ratings. When we focused on the associations excluded due to overlap, twelve associations were downgraded because of random P value.

Main findings

This is the first UR to provide a comprehensive overview of the observational data assessing associations between the ABO and Rh blood groups and multiple health outcomes. And we found 89 statistically significant associations. Convincing (Class I) evidence was only presented for the association between blood group B and T2DM risk. Highly suggestive (Class II) evidence was presented for 18 associations, such as HIV and VTE.

Comparison with previous studies

The positive association between blood group B and the risk of T2DM detected in this UR was supported by a prospective cohort study. This study included 82,104 women and followed for 18 years in France, throughout which 3553 women had a validated diagnosis of T2DM. After adjustment for potential confounders, blood group B increased the risk of T2DM compared with blood group O (HR =  1.21; 95% CI: 1.07, 1.36) [ 79 ]. A comparative cross-sectional study, including 326 participants (163 T2DM patients and 163 age and sex-matched healthy individuals), confirmed the harmful association of blood group B with T2DM risks (OR =  1.96; 95% CI: 1.05, 3.65), compared with the non-B blood group [ 80 ]. A meta-analysis revealed blood group B was significantly associated with an increased risk of T2DM (RR =  1.05; 95% CI: 0.93, 1.18), compared with the non-B blood group [ 81 ]. Nevertheless, caution is warranted in interpreting the observed association between blood group B and T2DM risk. Despite our result being consistent with findings from a prospective cohort study conducted in France, it is important to note that they exclusively included women. Subgroup analysis stratified by gender is needed in the future. In addition, the above studies have different control groups, sample sizes, and study designs. Further well-designed, large-scale prospective studies are needed to clarify the association between blood group B and T2DM.

The association between blood groups and HIV infection wase debated. In our UR, we found all ABO blood group was associated with an increased risk of HIV infection, and all of them were supported by highly suggestive evidence. A cross-sectional study conducted in Nairobi, Kenya among 280 female sex workers showed blood group A (OR =  1.56; 95% CI: 1.06, 2.28) was associated with HIV infection, compared with blood group O. However, blood group B (OR =  1.63; 95% CI: 0.94, 2.80) and blood group AB (OR =  1.50; 95% CI: 0.57, 3.93) were not associated with HIV infection [ 82 ]. A previous cross-sectional study conducted in South Africa investigated the associations between ABO blood groups and HIV infection among blood donors. The results suggested that the ABO blood group was not related to HIV infection. However, the point estimate for OR assesses blood group AB and HIV infection is 1.03 [ 83 ]. Cross-sectional studies cannot be used to infer causality and potential biases should be considered in the observational studies. Further well-designed longitudinal studies and controlling for different sources of bias are warranted to assess causality.

The harmful association between blood group A and non-O blood group and VTE incidence observed in our UR was supported by previous studies. For example, a previous study that included 7830 patients found blood group A was associated with VTE incidence (OR =  2.16; 95% CI: 1.10, 4.24) [ 84 ] in comparison with the blood group.

Biological plausibility

Multifactorial mechanisms might explain the increased risk of T2DM associated with blood groups. The previous study showed ABO blood group is in association with the level of plasma soluble intercellular adhesion molecule-1 and tumor necrosis factor receptor-2 [ 85 ]. And the above markers are identified to contribute a higher risk of T2DM. Moreover, a study suggested that the ABO blood group, being a gene-determined host factor, modulated the composition of the intestinal microbiota [ 86 ], which played an important role in influencing metabolism including glucose metabolism, energy balance, and low-grade inflammation [ 87 ].

For potential mechanisms between blood group and HIV infection, some studies indicated that expression of glycosyltransferase could be induced due to HIV and further synthetization of antigens of blood type on lymphocyte surfaces [ 88 , 89 ]. Therefore, apart from releasing new virion particles from lymphocytes, HIV could also integrate antigens of the blood group into its envelope surface [ 89 ]. The presence of these antigens sensitizes the virus against neutralizing antibodies and complements specific blood groups, potentially influencing the virus’s transmission between individuals and different blood groups [ 88 ].

It has not been thoroughly clear of the exact mechanism revealing the ABO blood group and VTE. The most likely hypothesis is that ABO plays a role in dominating the glycosylation degree of von Willebrand factor via modifying GT expressions [ 90 ]. von Willebrand factor multimeric composition is regulated in plasma by ADAMTS13. Proteolysis is enhanced by von Willebrand factor deglycosylation by ADAMTS13 [ 91 ]. In addition, individuals with blood group A1 and blood group B are at the level of 20% higher circulating Willebrand factor on average, which factor VIII levels than for O or A2 [ 92 , 93 ], high plasma levels of Willebrand factor, and factor VIII having association with increased VTE risk [ 94 , 95 , 96 , 97 ].

Strengths and limitations

To our knowledge, this is the first UR that systematically and comprehensively appraises the hierarchy of evidence relating blood groups to various health outcomes. Beyond summarizing the findings for a series of health end-point, we further an inquiry into bias and heterogeneity in the observational blood group literature. Compared with an individual systematic review or meta-analysis. This UR helped to summarize the complicated and vast amounts of research by comparing and contrasting the results of individual reviews, which provided an efficient overview of the findings for a particular problem [ 98 ]. Moreover, we adhered to a systematic methodology involving a search strategy in electronic databases and study selection and extraction conducted by two separate researchers. We also used standard approaches to evaluate the methodological quality and epidemiological evidence strength of the included studies.

UR provides top-tier evidence and important insights, but several limitations should be considered. First, some systematic reviews and meta-analyses did not acquire the level of evidence because they did not provide the number of cases. Second, we used I 2 (an estimate of the proportion of variance reflecting true differences in effect size) and τ 2 (an estimate of true variation in the summary estimate) to evaluate statistical heterogeneity. According to UR criteria, I 2  < 50% was applied as one of the criteria for convincing evidence in our UR, assigning the best evidence grade to robust associations. Several systematic reviews with meta-analyses examined the clinical and methodological heterogeneity by performing subgroup analyses stratified by these characteristics. Of note, we also extracted this information and analyzed it in the present UR. For example, within the subgroup comprising pancreatic cancer patients classified as either CagA-nonendemic or CagA endemic and COVID-19 patients with hospitalization, we found the results from subgroup analyses were consistent with the main findings. Future studies should better explore clinical and methodological heterogeneity to verify the association between blood groups and various health outcomes. Third, for the same health outcome (e.g., COVID-19 infection), the comparison group is different (e.g., A vs B, A vs AB, A vs O, and A vs non-A). Therefore, the findings between the blood group and health outcome in our study should be interpreted with caution. Fourth, we identified studies from published systematic reviews with meta-analyses, which may have omitted some individual studies for not in the systematic reviews with meta-analyses above. However, the systematic reviews with meta-analyses included in the current study were those of included the largest number of primary studies, which was unlikely to affect our results. Fifth, the reliability of the UR relies directly on the incorporated systematic reviews with meta-analyses. However, some included systematic reviews with meta-analyses existed risk of bias, which might decrease the robustness of statistical analyses. The study did not adjust for confounding factors that could have mediated associations between blood group and outcomes, because adjustment for potential confounders was unavailable in published systematic reviews with meta-analyses. Sixth, as this UR only included observational data, limitations common to this approach may influence the results of this review, such as information bias and residual confounding. There was a limited number of systematic reviews with meta-analyses that exclusively included prospective study designs, where information bias was reduced. However, case–control and cross-sectional study designs were more common than prospective study designs and were associated with a higher potential for information bias and reverse causation.

This comprehensive UR will help investigators to judge the relative priority of health outcomes related to the ABO blood group and RH blood group for future research and clinical management of the disease. In summary, compared with the non-B blood group, we found the association between blood group B and increased risk of T2DM incidence (OR =  1.28; 95% CI: 1.17, 1.40) was supported by convincing evidence. We also found 18 associations, such as blood group A and the risk of VTE incidence (OR =  1.63; 95% CI: 1.40, 1.89) and non-O blood group and the risk of VTE incidence (OR =  2.10; 95% CI: 1.83, 2.40), were supported by highly suggestive evidence. To enhance the quality of evidence regarding these associations and be able to give strong recommendations, future studies should consider several aspects. For example, set the same control group to increase the comparability of results, use standard definition of exposure or outcome to reduce clinical heterogeneity, and match the characteristics between cases and controls to reduce the impact of potential confounding. In addition, future studies understanding mechanisms between blood groups and various health outcomes are needed.

Availability of data and materials

Not applicable.

Change history

11 july 2024.

A Correction to this paper has been published: https://doi.org/10.1186/s12916-024-03520-x

Abbreviations

A Measurement Tool to Assess Systematic Reviews

Confidence intervals

Human immunodeficiency virus infection

Hazard ratio

Prediction intervals

Peripheral vascular disease

Type 2 diabetes mellitus

• Umbrella review

Venous thromboembolism

Daniels G. Human blood group systems. In: Practical transfusion medicine. 2017. p. 20–8.

Chapter   Google Scholar  

Landsteiner K, Wiener AS. An agglutinable factor in human blood recognized by immune sera for rhesus blood. Proc Soc Exp Biol Med. 1940;43(1):223.

Article   CAS   Google Scholar  

Cooling L. Blood groups in infection and host susceptibility. Clin Microbiol Rev. 2015;28(3):801–70.

Article   CAS   PubMed   PubMed Central   Google Scholar  

Avent ND, Reid ME. The Rh blood group system: a review. Blood. 2000;95(2):375–87.

Article   CAS   PubMed   Google Scholar  

Bruun-Rasmussen P, Hanefeld Dziegiel M, Banasik K, Johansson PI, Brunak S. Associations of ABO and Rhesus D blood groups with phenome-wide disease incidence: a 41-year retrospective cohort study of 482,914 patients. Elife. 2023;12:e83116.

Hoglund J, Karlsson T, Johansson T, Ek WE, Johansson A. Characterization of the human ABO genotypes and their association to common inflammatory and cardiovascular diseases in the UK Biobank. Am J Hematol. 2021;96(11):1350–62.

Clark P, Wu O. ABO(H) blood groups and pre-eclampsia. A systematic review and meta-analysis. Thromb Haemost. 2008;100(3):469–74.

CAS   PubMed   Google Scholar  

Iodice S, Maisonneuve P, Botteri E, Sandri MT, Lowenfels AB. ABO blood group and cancer. Eur J Cancer. 2010;46(18):3345–50.

Getawa S, Bayleyegn B, Aynalem M, Worku YB, Adane T. Relationships of ABO and Rhesus blood groups with type 2 diabetes mellitus: a systematic review and meta-analysis. J Int Med Res. 2022;50(10):3000605221129547.

Bawazir WM. Systematic review and meta-analysis of the susceptibility of ABO blood groups to venous thromboembolism in individuals with Factor V Leiden. Diagnostics (Basel). 2022;12(8):1936.

Dentali F, Sironi AP, Ageno W, Turato S, Bonfanti C, Frattini F, et al. Non-O blood type is the commonest genetic risk factor for VTE: results from a meta-analysis of the literature. Semin Thromb Hemost. 2012;38(5):535–48.

Article   PubMed   Google Scholar  

Zhang Q, Peng H, Hu L, Ren R, Peng X, Song J. Association between ABO blood group and venous thromboembolism risk in patients with peripherally inserted central catheters: a meta-analysis and systematic review. Front Oncol. 2022;12:906427.

Article   PubMed   PubMed Central   Google Scholar  

Ioannidis JP. Integration of evidence from multiple meta-analyses: a primer on umbrella reviews, treatment networks and multiple treatments meta-analyses. CMAJ. 2009;181(8):488–93.

Moher D, Liberati A, Tetzlaff J, Altman DG, Group P. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med. 2009;6(7):e1000097.

Stroup DF, Berlin JA, Morton SC, Olkin I, Williamson GD, Rennie D, et al. Meta-analysis of observational studies in epidemiology: a proposal for reporting. Meta-analysis Of Observational Studies in Epidemiology (MOOSE) group. JAMA. 2000;283(15):2008–12.

Bellou V, Belbasis L, Tzoulaki I, Evangelou E, Ioannidis JP. Environmental risk factors and Parkinson’s disease: an umbrella review of meta-analyses. Parkinsonism Relat Disord. 2016;23:1–9.

Pieper D, Antoine SL, Mathes T, Neugebauer EA, Eikermann M. Systematic review finds overlapping reviews were not mentioned in every other overview. J Clin Epidemiol. 2014;67(4):368–75.

Senn SJ. Overstating the evidence: double counting in meta-analysis and related problems. BMC Med Res Methodol. 2009;9:10.

Smith V, Devane D, Begley CM, Clarke M. Methodology in conducting a systematic review of systematic reviews of healthcare interventions. BMC Med Res Methodol. 2011;11(1):15.

Deeks JJ, Higgins JP, Altman DG. Chapter 10: Analysing data and undertaking meta-analyses. In: Cochrane handbook for systematic reviews of interventions. 2nd ed. 2019.

Google Scholar  

Riley RD, Higgins JP, Deeks JJ. Interpretation of random effects meta-analyses. BMJ. 2011;342:d549.

Higgins JP, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses. BMJ. 2003;327(7414):557–60.

Egger M, Davey Smith G, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. BMJ. 1997;315(7109):629–34.

Sterne JA, Sutton AJ, Ioannidis JP, Terrin N, Jones DR, Lau J, et al. Recommendations for examining and interpreting funnel plot asymmetry in meta-analyses of randomised controlled trials. BMJ. 2011;343:d4002.

Ioannidis JP, Trikalinos TA. An exploratory test for an excess of significant findings. Clin Trials. 2007;4(3):245–53.

Hayhoe B, Kim D, Aylin PP, Majeed FA, Cowie MR, Bottle A. Adherence to guidelines in management of symptoms suggestive of heart failure in primary care. Heart (British Cardiac Society). 2019;105(9):678–85.

PubMed   Google Scholar  

Lubin JH, Gail MH. On power and sample size for studying features of the relative odds of disease. Am J Epidemiol. 1990;131(3):552–66.

Qin X, Chen J, Jia G, Yang Z. Dietary factors and pancreatic cancer risk: an umbrella review of meta-analyses of prospective observational studies. Adv Nutr. 2023;14(3):451–64.

Liu D, Meng X, Tian Q, Cao W, Fan X, Wu L, et al. Vitamin D and multiple health outcomes: an umbrella review of observational studies, randomized controlled trials, and Mendelian randomization studies. Adv Nutr. 2022;13(4):1044–62.

Aromataris E, Fernandez R, Godfrey CM, Holly C, Khalil H, Tungpunkom P. Summarizing systematic reviews: methodological development, conduct and reporting of an umbrella review approach. Int J Evid Based Healthc. 2015;13(3):132–40.

Cui H, Qu Y, Zhang L, Zhang W, Yan P, Yang C, et al. Epidemiological and genetic evidence for the relationship between ABO blood group and human cancer. Int J Cancer. 2023;153(2):320–30.

Noori M, Shokri P, Nejadghaderi SA, Golmohammadi S, Carson-Chahhoud K, Bragazzi NL, et al. ABO blood groups and risk of human immunodeficiency virus infection: a systematic review and meta-analysis. Rev Med Virol. 2022;32(3):e2298.

Degarege A, Gebrezgi MT, Beck-Sague CM, Wahlgren M, de Mattos LC, Madhivanan P. Effect of ABO blood group on asymptomatic, uncomplicated and placental Plasmodium falciparum infection: systematic review and meta-analysis. BMC Infect Dis. 2019;19(1):86.

Shea BJ, Hamel C, Wells GA, Bouter LM, Kristjansson E, Grimshaw J, et al. AMSTAR is a reliable and valid measurement tool to assess the methodological quality of systematic reviews. J Clin Epidemiol. 2009;62(10):1013–20.

Singh A, Purohit BM. ABO blood groups and its association with oral cancer, oral potentially malignant disorders and oral submucous fibrosis- a systematic review and meta-analysis. Asian Pac J Cancer Prev. 2021;22(6):1703–12.

Degarege A, Gebrezgi MT, Ibanez G, Wahlgren M, Madhivanan P. Effect of the ABO blood group on susceptibility to severe malaria: a systematic review and meta-analysis. Blood Rev. 2019;33:53–62.

Panda AK, Panda SK, Sahu AN, Tripathy R, Ravindran B, Das BK. Association of ABO blood group with severe falciparum malaria in adults: case control study and meta-analysis. Malar J. 2011;10:309.

Adegnika AA, Luty AJ, Grobusch MP, Ramharter M, Yazdanbakhsh M, Kremsner PG, et al. ABO blood group and the risk of placental malaria in sub-Saharan Africa. Malar J. 2011;10:101.

Bhattacharjee S, Banerjee M, Pal R. ABO blood groups and severe outcomes in COVID-19: a meta-analysis. Postgrad Med J. 2022;98(e2):e136–7.

Wu BB, Gu DZ, Yu JN, Yang J, Shen WQ. Association between ABO blood groups and COVID-19 infection, severity and demise: a systematic review and meta-analysis. Infect Genet Evol. 2020;84:104485.

Lubkin DT, Van Gent JM, Cotton BA, Brill JB. Mortality and outcomes by blood group in trauma patients: a systematic review and meta-analysis. Vox Sang. 2023;118(6):421–9.

Banchelli F, Negro P, Guido M, D’Amico R, Fittipaldo VA, Grima P, et al. The role of ABO blood type in patients with SARS-CoV-2 infection: a systematic review. J Clin Med. 2022;11(11):3029.

Dentali F, Sironi AP, Ageno W, Bonfanti C, Crestani S, Frattini F, et al. Relationship between ABO blood group and hemorrhage: a systematic literature review and meta-analysis. Semin Thromb Hemost. 2013;39(1):72–82.

Dentali F, Sironi AP, Ageno W, Crestani S, Franchini M. ABO blood group and vascular disease: an update. Semin Thromb Hemost. 2014;40(1):49–59.

Liu F, Li C, Zhu J, Ren L, Qi X. ABO blood type and risk of hepatocellular carcinoma: a meta-analysis. Expert Rev Gastroenterol Hepatol. 2018;12(9):927–33.

Urabe F, Kimura S, Iwatani K, Yasue K, Koike Y, Tashiro K, et al. The impact of ABO blood type on developing venous thromboembolism in cancer patients: systematic review and meta-analysis. J Clin Med. 2021;10(16):3692.

Balaouras G, Eusebi P, Kostoulas P. Systematic review and meta-analysis of the effect of ABO blood group on the risk of SARS-CoV-2 infection. PLoS ONE. 2022;17(7):e0271451.

Yang H, Tan Z, Zhang Y, Sun J, Huang P. ABO blood classification and the risk of lung cancer: a meta-analysis and trial sequential analysis. Oncol Lett. 2022;24(4):340.

Risch HA, Lu L, Wang J, Zhang W, Ni Q, Gao YT, et al. ABO blood group and risk of pancreatic cancer: a study in Shanghai and meta-analysis. Am J Epidemiol. 2012;177(12):1326–37.

Article   Google Scholar  

Risch HA, Lu L, Wang J, Zhang W, Ni Q, Gao YT, et al. ABO blood group and risk of pancreatic cancer: a study in Shanghai and meta-analysis. Am J Epidemiol. 2013;177(12):1326–37.

Takagi H, Umemoto T, All-Literature Investigation of Cardiovascular Evidence G. Meta-analysis of non-O blood group as an independent risk factor for coronary artery disease. Am J Cardiol. 2015;116(5):699–704.

Cheung JLS, Cheung VLS, Athale U. Impact of ABO Blood Group on the Development of Venous Thromboembolism in Children With Cancer: A Systematic Review and Meta-Analysis. J Pediatr Hematol Oncol. 2021;43(6):216–23.

Deng J, Jia M, Cheng X, Yan Z, Fan D, Tian X. ABO blood group and ovarian reserve: a meta-analysis and systematic review. Oncotarget. 2017;8(15):25628–36.

Zhao J, Yao Z, Hao J, Xu B, Wang Y, Li Y. Association of ABO blood groups with ovarian reserve, and outcomes after assisted reproductive technology: systematic review and meta-analyses. Reprod Biol Endocrinol. 2021;19(1):20.

Ai L, Li J, Wang W, Li Y. ABO blood group and risk of malaria during pregnancy: a systematic review and meta-analysis. Epidemiol Infect. 2022;150:e25.

Al-Askar M. Is there an association between periodontal diseases and ABO blood group? Systematic review and meta-analysis. Quintessence Int. 2022;53(5):404–12.

Bahardoust M, Barahman G, Baghaei A, Ghadimi P, Asadi Shahir MH, NajafiKandovan M, et al. The association between ABO blood group and the risk of colorectal cancer: a systematic literature review and meta-analysis. Asian Pac J Cancer Prev. 2023;24(8):2555–63.

Loscertales MP, Owens S, O’Donnell J, Bunn J, Bosch-Capblanch X, Brabin BJ. ABO blood group phenotypes and Plasmodium falciparum malaria: unlocking a pivotal mechanism. Adv Parasitol. 2007;65:1–50.

Gutierrez-Valencia M, Leache L, Librero J, Jerico C, Enguita German M, Garcia-Erce JA. ABO blood group and risk of COVID-19 infection and complications: a systematic review and meta-analysis. Transfusion. 2022;62(2):493–505.

Franchini M, Cruciani M, Mengoli C, Marano G, Candura F, Lopez N, et al. ABO blood group and COVID-19: an updated systematic literature review and meta-analysis. Blood Transfus. 2021;19(4):317–26.

PubMed   PubMed Central   Google Scholar  

He M, Wolpin B, Rexrode K, Manson JE, Rimm E, Hu FB, et al. ABO blood group and risk of coronary heart disease in two prospective cohort studies. Arterioscler Thromb Vasc Biol. 2012;32(9):2314–20.

Liu N, Zhang T, Ma L, Zhang H, Wang H, Wei W, et al. The impact of ABO blood group on COVID-19 infection risk and mortality: a systematic review and meta-analysis. Blood Rev. 2021;48:100785.

Razzaghi N, Seraj H, Heydari K, Azadeh H, Salehi A, Behnamfar M, et al. ABO blood groups associations with ovarian cancer: a systematic review and meta-analysis. Ind J Gynecol Oncol. 2020;18(4). https://doi.org/10.1007/s40944-020-00463-y .

Wu O, Bayoumi N, Vickers MA, Clark P. ABO(H) blood groups and vascular disease: a systematic review and meta-analysis. J Thromb Haemost. 2008;6(1):62–9.

Ding P. ABO blood groups and prognosis of gastric cancer: a meta-analysis. 2019.

Tiongco RE, Paragas NA, Dominguez MJ, Lasta SL, Pandac JK, Pineda-Cortel MR. ABO blood group antigens may be associated with increased susceptibility to schistosomiasis: a systematic review and meta-analysis. J Helminthol. 2018;94:e21.

Jing SW, Xu Q, Zhang XY, Jing ZH, Zhao ZJ, Zhang RH, et al. Are people with blood group O more susceptible to nasopharyngeal carcinoma and have worse survival rates? A systematic review and meta-analysis. Front Oncol. 2021;11:698113.

Miao SY, Zhou W, Chen L, Wang S, Liu XA. Influence of ABO blood group and Rhesus factor on breast cancer risk: a meta-analysis of 9665 breast cancer patients and 244,768 controls. Asia Pac J Clin Oncol. 2014;10(2):101–8.

Itenov TS, Sessler DI, Khanna AK, Ostrowski SR, Johansson PI, Erikstrup C, et al. ABO blood types and sepsis mortality. Ann Intensive Care. 2021;11(1):61.

Li T, Wang Y, Wu L, Ling Z, Li C, Long W, et al. The association between ABO blood group and preeclampsia: a systematic review and meta-analysis. Front Cardiovasc Med. 2021;8:665069.

Nayeri T, Moosazadeh M, Dalimi Asl A, Ghaffarifar F, Sarvi S, Daryani A. Toxoplasma gondii infection and ABO blood groups: a systematic review and meta-analysis. Trans R Soc Trop Med Hyg. 2024;118(4):234–46.

Wang W, Liu L, Wang Z, Lu X, Wei M, Lin T, et al. ABO blood group and esophageal carcinoma risk: from a case-control study in Chinese population to meta-analysis. Cancer Causes Control. 2014;25(10):1369–77.

Jing W, Zhao S, Liu J, Liu M. ABO blood groups and hepatitis B virus infection: a systematic review and meta-analysis. BMJ Open. 2020;10(1):e034114.

Rattanapan Y, Duangchan T, Wangdi K, Mahittikorn A, Kotepui M. Association between Rhesus blood groups and malaria infection: a systematic review and meta-analysis. Trop Med Infect Dis. 2023;8(4):190.

Liao Y, Xue L, Gao J, Wu A, Kou X. ABO blood group-associated susceptibility to norovirus infection: a systematic review and meta-analysis. Infect Genet Evol. 2020;81:104245.

Chakrani Z, Robinson K, Taye B. Association between ABO blood groups and helicobacter pylori infection: a meta-analysis. Sci Rep. 2018;8(1):17604.

Wang Z, Liu L, Ji J, Zhang J, Yan M, Zhang J, et al. ABO blood group system and gastric cancer: a case-control study and meta-analysis. Int J Mol Sci. 2012;13(10):13308–21.

Chen Z, Yang SH, Xu H, Li JJ. ABO blood group system and the coronary artery disease: an updated systematic review and meta-analysis. Sci Rep. 2016;6:23250.

Fagherazzi G, Gusto G, Clavel-Chapelon F, Balkau B, Bonnet F. ABO and Rhesus blood groups and risk of type 2 diabetes: evidence from the large E3N cohort study. Diabetologia. 2015;58(3):519–22.

Walle M, Tesfaye A, Getu F. The association of ABO and Rhesus blood groups with the occurrence of type 2 diabetes mellitus: a comparative cross-sectional study. Medicine (Baltimore). 2023;102(35):e34803.

Cano EA, Esguerra MA, Batausa AM, Baluyut JR, Cadiz R, Docto HF, et al. Association between ABO blood groups and type 2 diabetes mellitus: a meta-analysis. Curr Diabetes Rev. 2023;19(6):e270422204139.

Chanzu NM, Mwanda W, Oyugi J, Anzala O. Mucosal blood group antigen expression profiles and HIV infections: a study among female sex workers in Kenya. PLoS ONE. 2015;10(7):e0133049.

Jacobs G, Van den Berg K, Vermeulen M, Swanevelder R, Custer B, Murphy EL. Association of ABO and RhD blood groups with the risk of HIV infection. PLoS ONE. 2023;18(4):e0284975.

Englisch C, Moik F, Nopp S, Raderer M, Pabinger I, Ay C. ABO blood group type and risk of venous thromboembolism in patients with cancer. Blood Adv. 2022;6(24):6274–81.

Barbalic M, Dupuis J, Dehghan A, Bis JC, Hoogeveen RC, Schnabel RB, et al. Large-scale genomic studies reveal central role of ABO in sP-selectin and sICAM-1 levels. Hum Mol Genet. 2010;19(9):1863–72.

Makivuokko H, Lahtinen SJ, Wacklin P, Tuovinen E, Tenkanen H, Nikkila J, et al. Association between the ABO blood group and the human intestinal microbiota composition. BMC Microbiol. 2012;12:94.

Cani PD, Osto M, Geurts L, Everard A. Involvement of gut microbiota in the development of low-grade inflammation and type 2 diabetes associated with obesity. Gut Microbes. 2012;3(4):279–88.

Arendrup M, Hansen JE, Clausen H, Nielsen C, Mathiesen LR, Nielsen JO. Antibody to histo-blood group A antigen neutralizes HIV produced by lymphocytes from blood group A donors but not from blood group B or O donors. AIDS. 1991;5(4):441–4.

Neil SJ, McKnight A, Gustafsson K, Weiss RA. HIV-1 incorporates ABO histo-blood group antigens that sensitize virions to complement-mediated inactivation. Blood. 2005;105(12):4693–9.

Jenkins PV, O’Donnell JS. ABO blood group determines plasma von Willebrand factor levels: a biologic function after all? Transfusion. 2006;46(10):1836–44.

Schulz KF, Altman DG, Moher D, Group C. CONSORT 2010 statement: updated guidelines for reporting parallel group randomised trials. BMC Med. 2010;8:18.

Morange PE, Tregouet DA, Frere C, Saut N, Pellegrina L, Alessi MC, et al. Biological and genetic factors influencing plasma factor VIII levels in a healthy family population: results from the Stanislas cohort. Br J Haematol. 2005;128(1):91–9.

O’Donnell J, Laffan MA. The relationship between ABO histo-blood group, factor VIII and von Willebrand factor. Transfus Med. 2001;11(4):343–51.

Koster T, Blann AD, Briet E, Vandenbroucke JP, Rosendaal FR. Role of clotting factor VIII in effect of von Willebrand factor on occurrence of deep-vein thrombosis. Lancet. 1995;345(8943):152–5.

Kraaijenhagen RA, in’t Anker PS, Koopman MM, Reitsma PH, Prins MH, van den Ende A, et al. High plasma concentration of factor VIIIc is a major risk factor for venous thromboembolism. Thromb Haemost. 2000;83(1):5–9.

Rietveld IM, Lijfering WM, le Cessie S, Bos MHA, Rosendaal FR, Reitsma PH, et al. High levels of coagulation factors and venous thrombosis risk: strongest association for factor VIII and von Willebrand factor. J Thromb Haemost. 2019;17(1):99–109.

Souto JC, Almasy L, Muniz-Diaz E, Soria JM, Borrell M, Bayen L, et al. Functional effects of the ABO locus polymorphism on plasma levels of von Willebrand factor, factor VIII, and activated partial thromboplastin time. Arterioscler Thromb Vasc Biol. 2000;20(8):2024–8.

Papatheodorou S. Umbrella reviews: what they are and why we need them. Eur J Epidemiol. 2019;34(6):543–6.

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Acknowledgements

This work was supported by the National Key Research and Development Program of China (No. 2022YFC2704205 to Wu QJ), the Natural Science Foundation of China (No. 82073647 and No. 82373674 to Wu QJ and No.82103914 to Gong TT), Outstanding Scientific Fund of Shengjing Hospital (Wu QJ), and 345 Talent Project of Shengjing Hospital of China Medical University (Gong TT).

Author information

Fang-Hua Liu, Jia-Kai Guo, Wei-Yi Xing, Xue-Li Bai, and Yu-Jiao Chang contributed equally to this work.

Authors and Affiliations

Department of Clinical Epidemiology, Shengjing Hospital of China Medical University, Shenyang, China

Fang-Hua Liu, Jia-Kai Guo, Wei-Yi Xing, Xue-Li Bai, Yu-Jiao Chang, Zhao Lu, Miao Yang, Ying Yang, Wen-Jing Li, Xian-Xian Jia, Tao Zhang, Jing Yang & Qi-Jun Wu

Clinical Research Center, Shengjing Hospital of China Medical University, Shenyang, China

Fang-Hua Liu, Wei-Yi Xing, Yu-Jiao Chang & Qi-Jun Wu

Hospital Management Office, Shengjing Hospital of China Medical University, Shenyang, China

Jia-Kai Guo

Department of Obstetrics and Gynecology, The Fourth Affiliated Hospital of China Medical University, Shenyang, China

Xue-Li Bai, Song Gao, De-Yu Zhang, Chuan Liu, Ting-Ting Gong & Qi-Jun Wu

Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China

Zhao Lu & Tao Zhang

Department of Pediatrics, Shengjing Hospital of China Medical University, Shenyang, China

Miao Yang & Xian-Xian Jia

Department of Hematology, Shengjing Hospital of China Medical University, Shenyang, China

Department of Otolaryngology Head and Neck Surgery, Shengjing Hospital of China Medical University, Shenyang, China

Wen-Jing Li

Department of Endocrinology, Shengjing Hospital of China Medical University, Shenyang, China

School of Medicine, Zhejiang University, Hangzhou, China

Jun-Tong Chen

Cancer Epidemiology Division, Population Sciences in the Pacific Program, University of Hawaii Cancer Center, University of Hawaii at Manoa, Honolulu, HI, USA

NHC Key Laboratory of Advanced Reproductive Medicine and Fertility (China Medical University), National Health Commission, Shenyang, China

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Contributions

F-HL, J-KG, T-TG, and Q-JW contributed to the study design. J-KG, ZL, MY, X-LB, YY, W-JL, X-XJ, and TZ collection of data. F-HL and W-YX analysis of data. F-HL, J-KG, Y-JC, YJ, J-TC, SG, LW, D-YZ, CL, T-TG, and Q-JW wrote the first draft of the manuscript and edited the manuscript. All authors read and approved the final manuscript. F-HL, J-KG, W-YX, X-LB, and Y-JC contributed equally to this work.

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Correspondence to De-Yu Zhang , Chuan Liu , Ting-Ting Gong or Qi-Jun Wu .

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Supplementary Information

12916_2024_3423_moesm1_esm.xlsx.

Additional file 1: Tables S1-13. Table S1-PRISMA checklist of items to include when reporting a systematic review or meta-analysis; Table S2-MOOSE checklist for meta-analyses of observational studies; Table S3-Search strategy; Table S4-Criteria for categorizing the credibility of evidence in the umbrella review; Table S5-The list of the excluded records during the process of full-text review; Table S6-The summary results of meta-analyses excluded due to lack of data for quantitative synthesis; Table S7-Description of 270 associations investigating the associations between ABO and Rhesus blood groups and multiple health outcomes; Table S8-Strength assessment of evidence from 270 associations examining associations between ABO and Rhesus blood groups and multiple health outcomes; Table S9-Methodological quality assessment of the included articles with AMSTAR; Table S10-The results of GRADE assessment of the evidence certainty on the associations between ABO and Rhesus blood groups and multiple health outcomes; Table S11-Sensitivity analysis results of omission of small-sized studies (< 25th percentile) from those meta-analyses with evidence of small-study effects; Table S12-Sensitivity analysis results of omission of primary studies with low-quality evidence; Table S13-Sensitivity analysis results of excluded meta-analyses due to overlap.

12916_2024_3423_MOESM2_ESM.docx

Additional file 2: Fig. S1-7. Fig. S1-Summary effects sizes with inverse of the variance of association between blood group and cancer outcomes; Fig. S2-Summary effects sizes with inverse of the variance of association between blood group and infectious disease outcomes; Fig. S3-Summary effects sizes with inverse of the variance of association between blood group and cardiovascular and cerebrovascular outcomes; Fig. S4-Summary effects sizes with inverse of the variance of association between blood group and oral related outcomes; Fig. S5-Summary effects sizes with inverse of the variance of association between blood group and pregnancy related outcomes; Fig. S6-Summary effects sizes with inverse of the variance of association between blood group and metabolic disease outcomes; Fig. S7-Summary effects sizes with inverse of the variance of association between blood group and other outcomes.

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Liu, FH., Guo, JK., Xing, WY. et al. ABO and Rhesus blood groups and multiple health outcomes: an umbrella review of systematic reviews with meta-analyses of observational studies. BMC Med 22 , 206 (2024). https://doi.org/10.1186/s12916-024-03423-x

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Stevens-Johnson Syndrome/Toxic epidermal necrolysis complicated with fulminant type 1 diabetes mellitus: a case report and literature review

• Xiaofang Zhang 1 ,
• Dihua Huang 1 ,
• Dajun Lou 1 ,
• Xuwei Si 1 &
• Jiangfeng Mao 2  

BMC Endocrine Disorders volume  24 , Article number:  172 ( 2024 ) Cite this article

Metrics details

Stevens-Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN) are rare but life-threatening skin lesion triggered by hypersensitive drug reaction. They are characterized by extensive epidermal necrosis and skin exfoliation. Fulminant type 1 diabetes mellitus (FT1DM) is featured by a rapid-onset of hyperglycemia with ketoacidosis due to severely destroyed β-cell function. Fulminant type 1 diabetes mellitus as a sequela of SJS/TEN has rarely been reported.

Case presentation

We present a 73-year-old female patient who developed SJS/TEN skin allergic reaction after taking carbamazepine and phenytoin for 35 days. Then, hyperglycemia and diabetic ketoacidosis occurred 20 days after discontinuation of antiepileptic drugs. A very low serum C-peptide level (8.79 pmol/l) and a near-normal glycosylated hemoglobin level met the diagnostic criteria for fulminant T1DM. Intravenous immunoglobulin (IVIG) and insulin were promptly administered, and the patient recovered finally.

Conclusions

This rare case indicates that monitoring blood glucose is necessary in SJS/TEN drug reaction, and comprehensive therapy with rehydration, insulin, antibiotics, and IVIG may improve the prognosis.

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Severe cutaneous adverse reactions (SCARs) are clinically classified into several subtypes, including drug reaction with eosinophilia and systemic symptoms (DRESS), Stevens-Johnson syndrome (SJS), toxic epidermal necrolysis (TEN), and acute generalized exanthematous pustulosis (AGEP). SJS and TEN are rare and life-threatening, which are characterized by extensive bullae formation, epidermal necrolysis, and exfoliation. SJS and TEN share the same pathophysiology and are classified into three categories according to severity of cutaneous lesion: lesion area less than 10% of the total body surface area (BSA) is defined as SJS; lesion area covering 10–30% of BSA is defined as SJS/TEN; lesion area over 30% of the total BSA is defined as TEN [ 1 ]. Most cases of SJS/TEN are triggered by infections and medications, the later including aromatic antiepileptics, sulfonamides, and allopurinol [ 2 , 3 ].

Fulminant type 1 diabetes mellitus (FT1DM) was first described as a new subtype of type 1 diabetes mellitus (T1DM) by Imagawa in 2000 [ 4 ]. It is characterized by rapid-onset hyperglycemia with ketoacidosis due to destroyed β-cell function and near-normal glycosylated hemoglobin levels. The islet-related autoantibodies are negative, and increasing levels of serum pancreatic enzyme are often observed [ 4 ]. It mainly occurs in Asian populations, including Japan, Korea, China, and the Philippines. FT1DM accounts for around 20% of T1DM in Japan and 7% of T1DM in South Korea [ 5 , 6 ]. A variety of pathological factors, including genetics, viral infection, pregnancy, autoimmunity, and drugs, are considered to contribute to the pathogenesis of FT1DM [ 7 ], but the specific mechanism of the disease remains largely unknown.

In recent years, clinical case reports have demonstrated that drug reaction with eosinophilia and systemic symptoms (DRESS) can trigger the storm of FT1DM [ 8 ]. DRESS typically presents with fevers, facial edema, erupting skin rash, eosinophilia, atypical lymphocytosis, and lymphadenopathy [ 9 ]. However, SJS/TEN induced FT1DM has rarely been reported. Herein, a case of FT1DM and SJS/TEN triggered by antiepileptic drugs was reported.

A 73-year-old female was admitted to the hospital with complaints of seizures for two months and pruritus for 25 days. She had been in a good, healthy state until seizures attacked her two months ago. Carbamazepine and phenytoin sodium were administered (carbamazepine 0.1 g three times a day orally and phenytoin sodium 0.1 g twice a day orally), and symptoms were well controlled. She discontinued these two medications 35 days later due to the advent of severe skin lesions. Red and purpuric rashes appeared on the face, upper torso, and proximal upper extremities. Erosion and exudation occurred in mucosa of the eyes and mouth, which provoked a mixed sensation of burning pain and itching. She presented with swollen eyelids, dry mouth, and ulcerated lips. Then the epidermis became loose and detached, and the dermis was exposed to the outside (Fig.  1 .a.b.c). She had a fever simultaneously. There was no evidence of weight loss. Medications were administered for symptom relieving, including eye drops with levofloxacin, tobramycin, and dexamethasone. Four days before her admission, a blood glucose of 23.8 mmol/L and highly positive urine ketones were detected.

Skin lesion in a patient with SJS/TEN and FT1DM. Figure a, b, and c showed generalized skin rashes and desquamation on the surface of the face, abdomen, and arms on admission. She had swollen face and eyelids and inflammation of conjunctiva. Ulcerations were detected on the eyelids and lips. Figure d, e, and f showed that rashes receded noticeably during therapy. The black arrow mark in Figure e indicates a positive Nikolsky sign, which manifests blisters and skin erosion upon a gentle rubbing on the lesion and leaves a glistening surface beneath

The medical history included hypertension for 15 years, which was well controlled with irbesartan tablets. There were no reported allergies to other medications or food, and no family history of diabetes was indicated. Her mother had a history of similar seizures. Physical examination showed that the patient was lethargic with generalized red and desquamative skin lesions. The face and eyelids were swollen, with inflamed conjunctiva in both eyes. The eyelids and lips were ulcerated. The ratio of body surface area involved was approximately 23%, using the patient’s own hand area (palm plus digits) as a tool to estimate. Lymphnodes behind the ears and under the jaw were enlarged and palpable. Her vital signs were 37.2 °C for body temperature and 141/62 mmHg for blood pressure. The pulse rate was 92 beats per minute and the respiratory rate was 19 times per minute.

Laboratory tests revealed increased white blood cell and neutrophil count, but no eosinophil count. The white blood cell count was 33.62 × 10 9 /L, while the neutrophil cell count was 18.5 × 10 9 /L. The high-sensitive C-reactive protein was 11.0 mg/L. Liver transaminase and myocardial enzymes were increased with abnormal coagulation function. Thyroid function revealed a state of nonthyroid illness syndrome with normal TSH levels (1.38 µIU/ml) but slightly lower FT3 (2.09 pmol/L) and FT4 (8.87 pmol/L) values. The results of the arterial blood gas analysis demonstrated that the patient had metabolic acidosis, with PH 7.16, pCO2 11.1 mmHg, pO2 155.3 mmHg (under a state of oxygen inhalation), actual bicarbonate 3.8 mmol/L, standard bicarbonate 6.9 mmol/L, base excess − 25.1 mmol/l, lactic acid 5.87 mmol/L, and SpO2 98.2%. Her HbA1c was 7%, and her fasting serum C-peptide level was extremely low at 8.79 pmol/L. Diabetes associated autoantibodies (GAD-Ab, IC-Ab, IA-Ab, IAA-Ab, ZnT8-Ab) were all negative. Pathogens including respiratory syncytial virus, adenovirus, influenza virus, parainfluenza virus, mycoplasma pneumoniae, chlamydia pneumoniae, echovirus, and coxsackievirus B were negative. Tests for viral hepatitis, syphilis, and the human immunodeficiency virus were negative. Autoimmune antibodies, including anti-SSA, anti-SSB, anti-SM, anti-dsDNA, anti-mitochondrial, rheumatoid factor, were negative. The main laboratory test results are listed in Table  1 .

SJS/TEN and fulminant T1DM were clinically diagnosed based on the morphology and extent of skin lesions and blood glucose profile. The patient was transferred to the ICU, where pumped intravenous insulin, fluid infusion, and other symptomatic relieving medications were administered. Exudate from the buccal mucosa was taken for pathogen culture before cefodizime sodium was used for anti-infection. High-dose steroids were not given considering the possible side effects on hyperglycemia and bacterial infections. Intravenous immunoglobulin (IVIG) at 400 mg/kg/day (25 g/d) was given for 3 days to alleviate infection and the immune response storm. Nutritional support, i.e., TPF-D, was given through the enteral feeding tube before the patient was able to tolerate normal diet. Daily oral examinations and cleanings were performed by ICU nurses. The wound care was guided by a dermatologist. The skin was inspected daily for the extent of detachment and infection. Regular cleansing was carried out with gently warmed saline or chlorhexidine. Topical antimicrobial agent mupirocin was applied to sloughy areas. Non-adherent dressings were utilized to protect the denuded dermis. The general condition was stabilized, and glycemia and urine ketone were well controlled. Five days after admission, the skin lesions receded dramatically (Fig.  1 .d.e.f.). Subcutaneous injections of insulin Glulisine were given before each meal, along with insulin Detemir before sleep time. The blood glucose fluctuated greatly, as in the case of classic T1DM patients. After 10 days of treatment, the patient recovered and was discharged from the hospital. The timeline is shown in Fig.  2 .

Timeline of the case progression. IVIG: intravenous immunoglobulin

Discussion and conclusions

Stevens-Johnson syndrome (SJS) and toxic epidermal necrolysis (TEN) are rare types of severe drug hypersensitive reactions with high mortalities (4.8% for SJS and 14.8% for TEN, respectively) [ 10 ]. SJS has an incidence of 8–9 cases per million people annually, while TEN has a low incidence of 1–2 cases per million people annually [ 10 ]. They frequently manifest with extensive erythema, erosions, blisters, fever, and mucocutaneous symptoms and complicate with varying degrees of hepatic, renal, and respiratory impairment. Malaise, fever, and upper respiratory tract symptoms often precede the skin rash by a few days. Mucosa of the eyes, mouth, and genitalia are mostly involved in SJS/TEN [ 11 ]. They have a positive test for the Nikolsky sign, which manifests blisters and skin erosion upon a gentle rubbing on the lesion and leaves a glistening surface beneath [ 12 ].

Antiepileptic drugs (carbamazepine, phenytoin, phenobarbital and lamotrigine), antibiotics, antipyretic analgesics, and sulfonamides are the main causes for SJS/TEN [ 13 ]. In rare situations, HIV [ 14 ], mycoplasma [ 15 ], immune checkpoint inhibitors (ICIs) [ 16 ], COVID-19 infection and vaccination [ 17 ] may precipitate SJS/TEN. The histopathological features of SJS/TEN are extensive necrosis of epidermal keratinocytes, mainly mediated by drug-specific cytotoxic T cells [ 18 ].

In this case, abrupt hyperglycemia was another prominent clinical manifestation, meeting the diagnostic criteria for fulminant T1DM [ 19 ]. Diagnosis of FT1DM could be made when all three criteria could be fulfilled. (1) Occurrence of diabetic ketosis or ketoacidosis after the onset of hyperglycemic symptoms (approximately within 7 days); (2) Plasma glucose level ≥ 16.0 mmol/L and glycated hemoglobin level < 8.7% at the first visit; (3) Urinary C-peptide excretion < 10 µg/day or fasting and 2-hour postprandial serum C-peptide level < 0.3 ng/mL (< 0.10 nmol/L) and < 0.5 ng/mL (< 0.17 nmol/L), respectively. Our patient presented with remarkable hyperglycemia, ketoacidosis, HbA1c 7%, low fasting serum C-peptide, and negative islet-associated antibodies. All these features point to a diagnosis of FT1DM.

Several studies found that FT1DM was induced by severe cutaneous adverse reactions (SCARs), especially by drug reaction with eosinophilia and systemic symptoms (DRESS). Onuma et al. concluded that the incidence of DRESS-related FT1DM was 0.54%, higher than that of idiopathic FT1DM (0.01%) in Japan papulation [ 20 ]. FT1DM usually occurs after the summit of medication reactions and during the glucocorticoids tapering period. A survey of 145 patients with DRESS, conducted by the Asian Research Committee on Severe Cutaneous Adverse Reactions, showed that the incidence of DRESS induced FT1DM was 3.45% [ 21 ]. However, SJS/TEN syndrome induced FT1DM has rarely been reported. Herein, we describe an elderly female patient who developed SJS/TEN after taking antiepileptic medicine for 35 days. FT1DM occurred 20 days after the discontinuation of medication.

The pathogenesis of FT1DM associated with SJS/TEN remains unclear. Genetic susceptibility and drug induced overactive immune responses may be involved. Drug-induced SJS/TEN has been demonstrated to be an HLA class I-restricted CD8 + T cell-mediated disease [ 22 ]. The systemic immune response may destroy pancreatic β cells and pancreatic exocrine function via activated cytotoxic T cells [ 23 ].

Therapeutic regime for SJS/TEN includes discontinuation of allergenic drugs, glucocorticoids, nutrition support, insulin, and intravenous immunoglobulin (IVIG) [ 24 ]. Extensive epidermolysis and necrosis lead to thermoregulation disorder, fluid loss, and blood volume shortage. Skin infection induced sepsis is the major cause of death [ 25 ]. Thus, rehydration and antibiotics are dominant forces in improving outcomes. Corticosteroids, IVIG, cyclosporine, TNF-α inhibitors, and plasma exchange have some beneficial effects on mitigating skin lesions [ 26 ], while the evidence for lowering mortality remains controversial. It should be noted that long-term and high-dose glucocorticoids may increase the risk of sepsis and have a detrimental impact on blood glucose control in FT1DM. Therefore, for those who cannot tolerate glucocorticoids, intravenous immunoglobulin (IVIG) or immunosuppressive agents are an alternative.

In conclusion, SJS/TEN is a rare but severe systemic drug adverse reaction, mostly involving generalized skin and mucosa. It may occasionally precipitate pancreatic β-cell destruction and lead to FT1DM. Therefore, monitoring blood glucose is necessary in those patients, and comprehensive therapy with rehydration, insulin, antibiotics, and IVIG may improve the prognosis of the disease.

Data availability

No datasets were generated or analysed during the current study.

Abbreviations

Stevens-Johnson Syndrome

Toxic Epidermal Necrolysis

Fulminant Type 1 Diabetes Mellitus

Intravenous immunoglobulin

Severe cutaneous adverse reactions

Drug reaction with eosinophilia and systemic symptoms

Acute generalized exanthematous pustulosis

Bastuji-Garin S, Rzany B, Stern RS, et al. Clinical classification of cases of toxic epidermal necrolysis, Stevens-Johnson syndrome, and Erythema Multiforme. Arch Dermatol. 1993;129:92–6.

Article   PubMed   Google Scholar  

Chang WC, Abe R, Anderson P, et al. SJS/TEN 2019: from science to translation. J Dermatol Sci. 2020;98:2–12.

Article   PubMed   PubMed Central   Google Scholar  

Mockenhaupt M, Viboud C, Dunant A, et al. Toxic epidermal necrolysis: Assessment of Medication risks with emphasis on recently marketed drugs. The EuroSCAR-Study. J Invest Dermatol. 2008;128:35–44.

Imagawa A, Hanafusa T, Miyagawa J, et al. A novel subtype of type 1 diabetes mellitus characterized by a rapid onset and an absence of diabetes-related antibodies. N Engl J Med. 2000;342:301–7.

Hanafusa T, Imagawa A. Fulminant type 1 diabetes: a novel clinical entity requiring special attention by all medical practitioners. Nat Clin Pract Endocrinol Metab. 2007;3:36–45.

Song SO, Yun JS, Ko SH, et al. Prevalence and clinical characteristics of fulminant type 1 diabetes mellitus in Korean adults: a multi-institutional joint research. J Diabetes Investig. 2022;13:47–53.

Hosokawa Y, Hanafusa T, Imagawa A. Pathogenesis of fulminant type 1 diabetes: genes, viruses and the immune mechanism, and usefulness of patient-derived induced pluripotent stem cells for future research. J Diabetes Investig. 2019;10:1158–64.

Zhu B, Wu J, Chen G, et al. Fulminant type 1 diabetes mellitus caused by drug reaction with eosinophilia and systemic symptoms (DRESS): a case report and review of the literature. Front Endocrinol. 2019;10:474.

Article   Google Scholar  

Ardern-Jones MR, Mockenhaupt M. Making a diagnosis in severe cutaneous drug hypersensitivity reactions. Curr Opin Allergy Clin Immunol. 2019;19:283–93.

Hsu DY, Brieva J, Silverberg NB, et al. Morbidity and mortality of Stevens-Johnson syndrome and toxic epidermal necrolysis in United States adults. J Invest Dermatol. 2016;136:1387–97.

Guvenir H, Arikoglu T, Vezir E, et al. Clinical phenotypes of severe cutaneous drug hypersensitivity reactions. Curr Pharm Des. 2019;25:3840–54.

Arndt KA, Feingold DS. The sign of Pyotr Vasilyewich Nikolsky. N Engl J Med. 1970;282:1154–5.

Lerch M, Mainetti C, Terziroli Beretta-Piccoli B, et al. Current perspectives on Stevens-Johnson syndrome and toxic epidermal necrolysis. Clin Rev Allergy Immunol. 2018;54:147–76.

Mittmann N, Knowles SR, Koo M, et al. Incidence of toxic epidermal necrolysis and Stevens-Johnson syndrome in an HIV cohort: an observational, retrospective case series study. Am J Clin Dermatol. 2012;13:49–54.

Mulvey JM, Padowitz A, Lindley-Jones M, et al. Mycoplasma pneumoniae associated with Stevens Johnson syndrome. Anaesth Intensive Care. 2007;35:414–7.

Maloney NJ, Ravi V, Cheng K, et al. Stevens-Johnson syndrome and toxic epidermal necrolysis-like reactions to checkpoint inhibitors: a systematic review. Int J Dermatol. 2020;59:e183–8.

Gu Y, Sebaratnam DF. Contextualising associations of SJS/TEN with COVID-19 and the vaccine. Burns. 2023;49:1776–7.

Le Cleach L, Delaire S, Boumsell L, et al. Blister fluid T lymphocytes during toxic epidermal necrolysis are functional cytotoxic cells which express human natural killer (NK) inhibitory receptors. Clin Exp Immunol. 2000;119:225–30.

Imagawa A, Hanafusa T, Awata T, et al. Report of the Committee of the Japan Diabetes Society on the research of fulminant and acute-onset type 1 diabetes mellitus: new diagnostic criteria of fulminant type 1 diabetes mellitus. J Diabetes Investig. 2012;3:536–9.

Onuma H, Tohyama M, Imagawa A, et al. High frequency of HLA B62 in fulminant type 1 diabetes with the drug-induced hypersensitivity syndrome. J Clin Endocrinol Metab. 2012;97:E2277–81.

Kano Y, Tohyama M, Aihara M, et al. Sequelae in 145 patients with drug-induced hypersensitivity syndrome/drug reaction with eosinophilia and systemic symptoms: Survey conducted by the Asian Research Committee on severe cutaneous adverse reactions (ASCAR). J Dermatol. 2015;4:276–82.

Elmore S, Apoptosis. A review of programmed cell death. Toxicol Pathol. 2007;35:495–516.

Chiou CC, Chung WH, Hung SI, et al. Fulminant type 1 diabetes mellitus caused by drug hypersensitivity syndrome with human herpesvirus 6 infection. J Am Acad Dermatol. 2006;54(2 Suppl):S14–7.

Schneider JA, Cohen PR. Stevens-Johnson syndrome and toxic epidermal necrolysis: a concise review with a Comprehensive Summary of therapeutic interventions emphasizing supportive measures. Adv Ther. 2017;34:1235–44.

de Prost N, Ingen-Housz-Oro S, Duong T, et al. Bacteremia in Stevens-Johnson syndrome and toxic epidermal necrolysis: Epidemiology, risk factors, and predictive value of skin cultures. Med (Baltim). 2010;89:28–36.

Zimmermann S, Sekula P, Venhoff M, et al. Systemic immunomodulating therapies for Stevens-Johnson syndrome and toxic epidermal necrolysis: a systematic review and Meta-analysis. JAMA Dermatol. 2017;153:514–22.

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Department of Endocrinology and Metabolism, Shaoxing People’s Hospital, Zhejiang Province, China, 312000

Xiaofang Zhang, Dihua Huang, Dajun Lou & Xuwei Si

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Patient evaluation: D.L. and X.S.; Patient treatment: X.Z. and D.H.; Visualization and figure: D.H.; Conceptualization: D.L. and X.S.; Data analysis and literature review: X.Z. and J.M.; Original draft preparation: X.Z.; draft revision and editing: J.M. All authors have read and agreed to the published version of the manuscript.

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Zhang, X., Huang, D., Lou, D. et al. Stevens-Johnson Syndrome/Toxic epidermal necrolysis complicated with fulminant type 1 diabetes mellitus: a case report and literature review. BMC Endocr Disord 24 , 172 (2024). https://doi.org/10.1186/s12902-024-01683-5

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ABO blood group and COVID-19: an updated systematic literature review and meta-analysis

Massimo franchini.

1 Department of Haematology and Transfusion Medicine, “Carlo Poma” Hospital, Mantua, Italy

Mario Cruciani

2 Infection Control Committee and Antibiotic Stewardship Programme, AULSS9 Scaligera, Verona, Italy

3 Italian National Blood Centre, Rome, Italy

Carlo Mengoli

Giuseppe marano, fabio candura, nadia lopez, ilaria pati, simonetta pupella, vincenzo de angelis, associated data.

Following the first reports in the literature, the association between the ABO blood group and SARS-CoV-2 infection has been investigated by a number of studies, although with varying results. The main object of this systematic review was to assess the relationship between the ABO blood group and the occurrence and severity of COVID-19.

Materials and methods

A systematic literature search using appropriate MeSH terms was performed through Medline and PubMed. The outcomes considered were the prevalence of the blood group O vs non-O types in SARS-CoV-2 infected and non-infected subjects, and the severity of SARS-CoV-2 infection according to ABO group. The methodological quality of the studies included in the analysis was assessed with the Newcastle-Ottawa Scale, and the overall quality of the available evidence using the GRADE system. Benchmarks used to evaluate the effect size were odd ratios (ORs) for case control studies and risk ratios (RRs) for cohort studies.

Twenty-one studies were included in the analysis. Overall, individuals with group O had a lower infection rate compared to individuals of non-O group (OR: 0.81; 95% CI: 0.75, 0.86). However, the difference in the effect size was significantly lower in cohort studies compared to case control studies. No evidence was found indicating an effect of the O type on the disease severity in the infected patients.

We have found low/very low evidence that group O individuals are less susceptible to SARS-CoV-2 infection compared to those in the non-O group. No evidence was found indicating an effect of the O type on disease severity in SARS-CoV-2 infection.

INTRODUCTION

The ABO blood group is the most important among human blood group systems and consists of complex carbohydrate moieties at the extracellular surface of red blood cell (RBC) membrane 1 , 2 . While the A and B alleles of the ABO locus encode A and B glycosyltransferase activities, which convert precursor H antigen into either A or B determinants by adding an extra saccharide unit, group O individuals lack such transferase enzymes and express basic, unchanged H-antigen 3 . Along with their expression on RBCs, ABO antigens (namely A, B, AB and O) are also highly expressed on the surface of a variety of human cells and tissues 4 . Although the physiological role of ABO antigens and their related anti-A and anti-B natural isoagglutinins is still largely unknown, they play a prominent role in blood transfusion and cell, tissue, and organ transplantation 4 . In addition, several studies over the last 50 years have documented a close link between ABO blood groups and a wide array of diseases, including cancers and cardiovascular disorders 5 . The latter association is particularly relevant, considering the profound influence of ABO antigens on haemostasis, particularly in modulating von Willebrand factor (VWF) and factor VIII (FVIII) circulating levels 6 – 10 . In addition, the ABO blood group-related susceptibility to various types of viral infections, including HIV, hepatitis B, dengue and influenza viruses, has been consistently reported by several investigators over the last 20 years 11 – 14 . This has recently gained renewed interest thanks to the observation on the association between ABO blood type and the pandemic Coronavirus Disease 2019 (COVID-19) 15 . In particular, it has been hypothesised that individuals belonging to O blood type are less susceptible to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection than those belonging to non-O blood groups, or that they have a milder disease 16 . The hypothesis for this phenomenon lies in the presence in O blood group subjects of IgG anti-A isoagglutinins which would prevent the binding of SARS-CoV-2 to its receptor thereby stopping the virus entering the targeted human cells 17 . In this review, we will show and critically discuss the results of a systematic literature review and meta-analysis on the correlation between ABO blood groups and SARS-CoV-2 infection and severity, along with its possible implications for future health policies.

MATERIALS AND METHODS

Search methods.

For this systematic review, we analysed the medical literature for published articles on the association between ABO blood type and SARS-CoV-2 infection. The Medline and PubMed electronic databases were searched for English language articles published from 1 st January 2020 to 30 th December 2020. Only those articles that had been subjected to peer review were included in the final analysis. The Medical Subject Heading and key words used were: “novel coronavirus disease”, “COVID-19”, “SARS-CoV-2”, “acute respiratory distress syndrome”, “ABO blood groups”, and “ABO blood type”. We also screened the reference lists of the most relevant review articles for additional studies that had not been captured in our initial literature search. Studies were selected independently by two reviewers (M.F. and M.C.), with disagreements resolved through discussion and on the basis of the opinion of a third reviewer (C.M.).

Criteria for study selection

Inclusion criteria were: 1) studies that reported ABO blood group prevalence among SARS-CoV-2 infected subjects and in non-infected subjects; 2) studies that reported severity of SARS-CoV-2 according to ABO group. Both cohort studies and case control studies were included; case reports were excluded.

The outcomes were: i) prevalence of the blood group O vs non-O types in SARS-CoV-2 infected subjects and in non-infected subjects; and ii) the severity of SARS-CoV-2 infection according to ABO group. The severity of SARS-CoV-2 infection we have considered were the endpoints used to define the severity reported in the selected studies.

Quality assessment

We evaluated both the quality of reporting and the methodological quality of the studies included in the analysis. For this purpose, we used the Newcastle-Ottawa Scale (NOS) checklist. The NOS is a 9-point scale that assigns points on the basis of the process of selection of the cohorts or of the case and of the controls (0–4 points), of the comparability of the cohorts or of the case and of the controls (0–2 points), and of the identification of the exposure and of the outcomes of study participants (0–3 points). The NOS was developed to assess the quality of non-randomised studies for the purpose of incorporating quality assessments in the interpretation of meta-analytic results. This scale is recommended by the Cochrane non-randomised studies methods 18 , 19 . This quality assessment was performed independently in duplicate (M.C., M.F.) and any disagreement was resolved by consensus. Using the NOS, a study can be awarded a maximum of 4 stars for selection, a maximum of 2 stars for comparability, and a maximum of 3 stars for outcome. Since some of the studies reporting prevalence of ABO group among SARS-CoV-2 infected and non-infected subjects also reported severity of SARS-CoV-2 infection, and as such, in this context, can be regarded as cohort studies, the quality assessment was performed separately for the two pre-specified outcomes. We considered a study which scored ≥7 a high-quality study, and the remaining as non-high quality studies. The publication bias was investigated by the funnel plot and the Egger test for funnel plot asymmetry in meta-analysis.

Summary of findings

We used the principles of the GRADE system to assess the quality of the body of evidence associated with specific outcomes, and constructed “Summary of findings” tables ( Tables I and ​ andII). II ). These tables present key information concerning the certainty of the evidence, the magnitude of the effects in the groups of subjects examined, and the sum of available data for the main outcomes. The “Summary of findings” tables also include an overall grading of the evidence related to each of the main outcomes using the GRADE approach, which defines the certainty of a body of evidence as the extent to which one can be confident that an estimate of effect or association is close to the true quantity of specific interest. The certainty of a body of evidence involves consideration of within-trial risk of bias (methodological quality), directness of evidence, heterogeneity, precision of effect estimates, and risk of publication bias. Outcomes in terms of occurrence of infection and severity of infection are presented in Table II .

Main characteristics and results of the studies on the association between ABO blood groups and COVID-19

Summary of findings table. Relationship between ABO blood group and occurrence and severity of COVID-19

GRADE Working Group grades of evidence

High quality: Further research is very unlikely to change our confidence in the estimate of effect. Moderate quality: Further research is likely to have an important impact on our confidence in the estimate of effect and may change the estimate. Low quality: Further research is very likely to have an important impact on our confidence in the estimate of effect and is likely to change the estimate. Very low quality: We are very uncertain about the estimate. CI: confidence interval; RR: risk ratio.

Statistical analysis

The role of the O-type blood group in SARS-CoV-2 infection was evaluated comparing the prevalence of O type in infected patients (cases) and in non-infected subjects (controls). The meta-analysis was performed using the inverse variance (IV) method for study weighting, pooling odd ratios (ORs) and/or risk ratios (RRs) at study level. A random-effects approach was followed, with DerSimonian-Laird estimator for tau. The I -squared index for inconsistence was calculated to address the heterogeneity between studies.

The effect size calculation for case-control studies is based on the prevalence of the exposure in the diseased and in the not diseased, and should be calculated with an OR ratio 20 . In any case, often the OR is a good approximation of RR, especially if the incidence in both exposed and not exposed is low (<10%) and the true RR remains close to 1. For cohort studies, we evaluated the mean relative risk (RR) for the infection as this represents a more understandable quantification of effect size and preventable fraction in the exposed population (PFE).

Subgroup analyses

Subgroup analysis was carried out according to the study design (case control or cohort). Differences in effect size between case control and cohort studies were evaluated with a test for subgroup difference. p<0.1 was considered to be a statistically significant subgroup effect 21 . Stata 16.1 and package “meta” with R version 4.0.3 software were used to perform calculations.

Overall, we identified 746 references through electronic and manual searches ( Figure 1 ). Seven hundred and eight studies were excluded as duplicates or as not relevant to this review according to the title and/or abstract. After reading the full text of the remaining 38 potentially eligible studies, 17 were excluded (reviews, commentaries, non-peer reviewed publication, insufficient data). Only those studies fulfilling the selection criteria were included in the final analysis. Thus, for this systematic review, we considered 21 studies fulfilling our pre-specified criteria 22 – 42 . Table I summarises the main characteristics and results of these studies. Some studies included different cohorts of patients and, where 2×2 data were available, we calculated separate ORs for each cohort. Ellinghaus et al . provided data from Italian and Spanish hospitals 22 . Leaf et al . 31 stratified the analysis according to race/ethnicity (White non-Hispanic, Black non-Hispanic, Asian non-Hispanic, and Hispanic). Zhao et al . 36 collected patients and controls at the Jinyintan Hospital in Wuhan, and at Shenzhen Hospital, Guangdong Province, in the People’s Republic of China. In a Spanish study, Muñiz-Diaz et al . 41 included a cohort of blood donors recruited for convalescent plasma donation after recovering from a mild SARS-CoV-2 infection, and a cohort of patients with severe SARS-CoV-2 infection who were transfused during hospitalisation (donors and transfused).

Flow chart of the selection of studies

Bias assessment

The NOS checklists for individual studies are presented in the Online Supplementary Content (Tables SI–SIII) . In cohort studies, the quality was judged high for both the outcomes analysed since all studies achieved from 7 to 9 stars. For the outcome prevalence of COVID-19 in case control studies, the NOS score was <7 in 8 of the 11 case control studies ( Online Supplementary Table SII ).

Publication bias was evaluated for the outcome prevalence of infection. On the whole, the asymmetric aspect of the funnel plot seems to be at least partly due to the different distributions of the study effect sizes according to the study designs ( Online Supplementary Figures S1 and S2 ). When the two designs were examined jointly, the Egger test was significant (p=0.025); however, when the two designs were examined separately the significance was lost.

Quantitative analysis

Outcome: prevalence of o type vs non-o blood types in sars-cov-2 infected patients.

The outcome prevalence of ABO groups in COVID-19 infected or non-infected individuals was reported in 17 studies, including 7 cohort studies 24 , 28 , 30 , 37 – 39 , 42 and 10 case control studies 22 , 23 , 25 – 27 , 29 , 32 , 33 , 36 , 41 . Figure 2 reports the forest plot of the prevalence of the blood group O vs non-O types in cases (infected SARS-CoV-2 patients) and control (non-infected) subjects. The role of the O-type blood group in SARS-CoV-2 infection was evaluated in 17 studies, accounting for 19 2×2 tables. Overall mean OR was 0.81 (95% confidence interval [CI]: 0.75, 0.86). The effect was significantly different from the null hypothesis of absence of effect by O type on the probability of SARS-CoV-2 infection (z=6.19, p<0.0001). The effect was protective, suggesting a lower risk in subjects of O type. The quality of the evidence was graded as very low for inconsistency due to heterogeneity, and for risk of biases in case control studies (confounding, selection, ascertainment) ( Table II ). The mean OR of case control studies was 0.73 (95% CI: 0.64, 0.83), whereas the mean OR of cohort studies was 0.89 (95% CI: 0.85, 0.94). Thus, the null hypothesis of OR=1 was rejected in both cases, but the difference in the effect size was significantly lower in cohort studies compared to case control studies (test for subgroup difference: Q=8.31, degree of freedom: 1, p=0.0039).

Forest plot of the prevalence of the blood group 0 vs non-0 types in cases (infected SARS-CoV-2 patients) and control (uninfected) subjects

Mean RR, evaluated in cohort studies, was 0.92 (95% CI: 0.87, 0.97), with z-score=3.21, p=0.001, and substantial heterogeneity ( I -squared=72.7%). PFE was 8.1% (95% CI: 3.2%, 12.7%). The quality of the evidence in cohort studies was graded as low due to inconsistency, and because not all the studies performed matching or adjustment of plausible prognostic variables.

Outcome: severity of SARS-CoV-2 infection

The differential exposure to O blood group in cases (severe infection) and controls (non-severe infection) was expressed as RR, and the RRs were then pooled using a random-effect model. The outcome severity of SARS-CoV-2 infection was reported in 14 studies 23 , 25 , 27 , 29 , 30 – 32 , 34 – 36 , 38 – 40 , 42 . The severity of the disease was not uniformly defined in the cohorts of infected patients, and 8 endpoints of severity were assessed, each vs the opposite condition. The endpoints included in the selected studies were: i) severe vs non-severe clinical disease (7 studies) 23 , 27 , 29 , 30 , 34 , 38 , 40 ; ii) death (11 studies) 25 , 29 – 32 , 34 – 36 , 39 , 40 ; iii) hospitalisation vs non-hospitalisation (1 study) 42 ; iv) need for intubation (4 studies) 29 – 31 , 39 ; v) requirement of proning in the treatment of respiratory insufficiency (1 study) 30 ; vi) acute kidney injury at admission (1 study) 31 ; vii) shock (1 study) 31 ; viii) thrombosis (1 study) 40 .

Endpoints indicating clinical severity and related O type distribution are summarised in Figure 3 ; none were significantly associated to the O type blood group. In other words, no evidence was found indicating an effect of the O type on the disease severity in the infected patients. The RR was 1.0 (95% CI: 0.92–1.09) for the endpoint severe infection, and 0.95 (95% CI: 0.89–1.02) for death. The quality of the evidence was graded as low due to imprecision (95% CI includes line of no effect), and because not all the studies adjusted for prognostic factors

Forest plot of the severity of SARS-CoV-2 infection according to blood group (O vs non-O blood group)

Due to the severity of the disease, mostly unpredictable, the identification of risk factors associated with SARS-CoV-2 infection and outcomes has become a research priority. Thus, following the first reports in literature on the association between ABO blood group and COVID-19, this has been the focus of attention in a number of investigative studies and, in particular, whether ABO blood type was associated not only with COVID-19 onset but also with its severity and disease-related mortality 43 . This growing interest is not surprising considering that the study of the interaction between ABO blood system and infections has a long history and extensive literature 12 . Individuals with blood group O were reported to be more susceptible to Norovirus, and also had a significantly higher prevalence of Helicobacter pylori , but were less susceptible to SARS and hepatitis B virus 11 , 44 – 47 . In another study, blood group A was associated with an increased risk of acute respiratory distress syndrome (ARDS) in trauma and sepsis patients 48 . A study by Lebiush et al . on influenza A (H1N1) observed a higher seroconversion rate in blood groups A and B 49 . Elnady et al . found that individuals with blood type A were more susceptible to rotavirus gastroenteritis than those with blood type B 50 . Among patients infected with dengue virus, Murugananthan et al . found that individuals with AB blood type had a 2.5 times higher risk of developing dengue haemorrhagic fever than those with other blood types 51 . Finally, there is strong evidence that ABO phenotype modulates severity of Plasmodium falciparum- associated malaria, with group A associated with severe disease and blood group O with milder disease 52 .

Regarding the issue of this systematic review, the first reports on the evidence of a relationship between SARS-CoV-2 infection and ABO blood groups were published early in China and so far 21 articles have been published worldwide, covering a large number of cases. The pathogenic mechanism underlying this association is quite complex and encompasses several molecular pathways. Further experimental studies are needed to better characterise the role of anti-SARS-CoV-2 neutralising anti-A IgG antibodies in COVID-19 onset, and the importance of plasma VWF/FVIII levels and endothelial cell activation in COVID-19-induced coagulopathy and pulmonary microvascular occlusion 17 , 53 . Whatever the underlying mechanism, this correlation is quite intriguing as it allows us to make some considerations that could have potentially important implications. First, the ABO-driven COVID-19 susceptibility could account for the inter-ethnic epidemiological difference in SARS-CoV-2 infection. Indeed, Africa is the continent with the lowest number of cases and deaths (1,044,513 confirmed cases with 21,722 deaths at 30 th August 2020; data available at: https://covid19.who.int/ ). Curiously, among the different ethnicities, Africans have the highest percentage of O blood type (up to 60%). Another interesting observation regards the distribution of COVID-19 across different ages and genders. In fact, it is equally well known that men and the elderly are more affected by SARS-CoV-2 infection than women and young people. Previous studies had demonstrated that women with O blood type have higher anti-A IgG antibody levels than males, and that the titer of anti-A and anti-B isoagglutinins declines with age 54 , 55 . Although these findings do not constitute the definitive proof of the causal association between ABO blood type and gender-, age- and ethnic-related differences in the epidemiological distribution of COVID-19, these considerations are quite curious and deserve further investigation.

The results of this systematic review, which are in agreement with those published in another recent meta-analysis 15 , indicate the lower susceptibility of O blood type individuals to being infected by SARS-CoV-2 than non-O subjects. The investigational hypothesis of a protective effect exerted by the O type on the risk of the SARS-CoV-2 infection was confirmed by both study designs, case control and cohort. However, the effect size was lower in cohort studies compared to case control studies. This is not surprising since, compared to cohort design, case control studies are more susceptible to bias due to confounding co-variates with different distributions in cases (infected patients) and the control population. Sample size for case control studies is based on prevalence of exposure, not on incidence of outcome. Because the prevalence of the exposure is usually larger than the incidence of outcome, in most practical situations a case control study is more powerful than a cohort study for the same problem. On the other hand, case control studies are very likely to suffer from bias. Controls should be drawn from the same general population that gave rise to the cases. However, in practice, the comparability of cases and controls is difficult to achieve, making this aspect the Achilles heel of case control design. In the present systematic review, no evidence was found indicating an effect of the O blood type on the disease severity in the infected patients. Larger, well-designed epidemiological trials are, however, needed to clarify the relationship between the ABO blood group system and the risk of developing COVID-19 or a more severe disease.

Supplementary Information

The Authors declare no conflicts of interest.

A Complete Review Article on ABC of Blood Groups

• Asian Journal of Health Sciences 2(1):45-9

• Tomo Riba Institute of Health and Medical Sciences

• Saint James School Of Medicine

• Dr.Ulhas Patil Medical College and General Hospital
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The ABO blood group system revisited: a review and update

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Culture & Society

The Remains Of The Body Review: The Body Fantasises Touch And Glory

The Remains of the Body is not a manual on queer literature and behaviour. It simply is a juicy story and like blood, the story moves through our veins and arteries.

In the course of our lifetime, all we do is crave for companionship. It drives our emotions, perspectives, and the need to make our life worthy of living. Saikat Majumdar’s recent novel, The Remains of the Body is not just a story about people exploring sexuality. It also cultivates the different ideas of relationship they build by relying on pleasure, freedom and where or how all of them get restricted. Cis/het men often fail to understand the fundamentals of queer literature. It comes with the conditioning which is based on conclusions and not on understanding. Yet, if read by breaking the conditioning we are pushed to adopt, queer literature holds the ability to wash away the linings of gender. This particular work of Saikat Majumdar made the man in me hard and melted the woman that likes to come out of me by coating my body with warmth, shivers and excitement.

The story revolves around three friends, Avik, Kaustav and Sunetra. While Avik and Kaustav happen to be childhood buddies, Sunetra is the person who works as the trigger factor. She allows both the men to understand their choices. The story has been told by an observer who manages to know the bits and pieces of these three interconnected individuals. It is easy to write a steamy novel with repetitive graphic sex making the book a source of quick orgasm. What perfumes readers is when they get to devour the sexual tension between characters by keeping love and lust in homeostasis. It does not exhaust the readers and elevates the orgasm of a thought more than what is so usual. The writer succeeds in putting a seed of the intention of the book. The titration of bisexuality complements the same sex tension and vice-versa. The steam of the story/characters condenses and rains. Subsequently, drenching the reader irrespective of gender, class, caste or race.

The somatic growth of the story allows the readers to be with the body of the characters. The words, on the other hand, magnify the mind for those who want to know the psyche behind the culture of sensuality and sensitivity. Most of us are not fond of going beyond the body. Thus, the author establishes a common origin wherein we get to understand the consequence of touch to a very molecular level. So, the author makes lust appear with absolute reality. The body hair, erect nipples, radiant thighs etc., are often used to define the content of the mind. At the same time, the constricted hands of marriage, constituents of friendship, emotional vulnerability of the characters help in creating a definition of body. The torment inside Kaustav is often found in people who struggle to commit to their own choices. So, when he feels the touch of Sunetra, he is afraid to accept his own emotion. Her touch mostly overpowers his feeling towards Avik, his growing bulge and also the visuals of his naked body. It makes the reader look beyond a single choice of an individual. The tension possesses the power to haunt and seduce the reader.

In the context of most notorious books, fantasies take an upper hand over their source. So, in the book, Emmanuelle by Emmanuelle Arsan, the author graphically develops the image of breasts, orgasm and the saliva dripping of their mouths. The same happens in the books of Marquis de Sade or Henry Miller. In this book, the author does not allow his own visuals about sex take the upper hand. So, even the Avik and Sunetra’s romance outside and around marriage reveals the discomfort in their marital life more than the lustrous thought of multiple partners. As human beings, we are fond of provoking our own thoughts, but a late orgasm is the result of good literature. The fantasies of these three individuals describe how people discover their own sexual choices in real life. Despite the moral constructs around our fantasies, the author subtly screams how if we do not break these heavy walls, everything else is going to stop us from telling and imagining them.

BY Anjana Basu

The author’s idea of marriage is that of an ideal individual. The society does not work to embrace a kind of marriage that ideal individuals keep before us as thinkers or readers. We are libertines when it comes to relationship. To keep a single relationship fertile, we work on it by exhausting our very self. And if we let our thoughts pass through monogamy, the entire act is referred to as perversion. The book does not offer a conclusion, but every reader wants to have one. So, in this book, Avik, Kaustav and Sunetra behave as libertines mostly do. It should be the only way to explore our deepest secrets.

The Remains of the Body is not a manual on queer literature and behaviour. It simply is a juicy story and like blood, the story moves through our veins and arteries. We have to feel what the story does to us. That would be the answer to all our questions. It is a kind of love that wears a leather thong.

• Book Review
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