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  • Review Article
  • Published: 02 July 2024

Towards nutrition with precision: unlocking biomarkers as dietary assessment tools

  • Cătălina Cuparencu   ORCID: orcid.org/0000-0003-3889-5498 1 ,
  • Tuğçe Bulmuş-Tüccar 1 , 2 ,
  • Jan Stanstrup 1 ,
  • Giorgia La Barbera   ORCID: orcid.org/0000-0003-0583-906X 1 ,
  • Henrik M. Roager   ORCID: orcid.org/0000-0002-2504-8313 1 &
  • Lars O. Dragsted   ORCID: orcid.org/0000-0003-0609-6317 1  

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Precision nutrition requires precise tools to monitor dietary habits. Yet current dietary assessment instruments are subjective, limiting our understanding of the causal relationships between diet and health. Biomarkers of food intake (BFIs) hold promise to increase the objectivity and accuracy of dietary assessment, enabling adjustment for compliance and misreporting. Here, we update current concepts and provide a comprehensive overview of BFIs measured in urine and blood. We rank BFIs based on a four-level utility scale to guide selection and identify combinations of BFIs that specifically reflect complex food intakes, making them applicable as dietary instruments. We discuss the main challenges in biomarker development and illustrate key solutions for the application of BFIs in human studies, highlighting different strategies for selecting and combining BFIs to support specific study designs. Finally, we present a roadmap for BFI development and implementation to leverage current knowledge and enable precision in nutrition research.

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Diet is essential for human health; however, measuring what humans eat in their daily lives is extremely challenging 1 , 2 . Even when the most accurate dietary instruments are used, that is, weighed dietary records 3 , researchers have to rely on the correctness and completeness of the records. Other dietary instruments, such as 24-h recalls and food frequency questionnaires (FFQs), have even stronger subjective elements contributing to vague relationships with health outcomes.

Omics technologies have promised to increase the reliability of dietary assessment by substituting or complementing traditional dietary instruments with objective BFIs 4 , 5 , 6 , 7 , 8 . Many metabolomics-based studies have been conducted to discover blood or urine metabolites as potential BFIs associated with the intake of certain foods or dietary patterns 4 . The validity of these BFIs in dietary research requires that they fulfill several aspects of biology and analytical performance 9 . Current BFIs are mostly qualitative (see Box 1 for key terminology used in this Review) and reflect recent intakes. Improved usefulness of BFIs would depend on their ability to reflect the quantity of the food consumed, and on kinetic aspects of BFIs that, alongside the frequency of consumption, determine a potential time window for sampling of blood, urine or other biospecimens. Although the routine application of BFIs in nutrition science is yet to meet its full potential, BFIs are increasingly used to (1) assess dietary adherence in meal studies and longer-term interventions 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , (2) evaluate misreporting in dietary records 18 , 19 , and (3) reevaluate associations between diet and disease risk in epidemiology 16 , 20 , 21 . For example, a biomarker panel was used to calibrate FFQs and correct for random and systematic errors, resulting in a reduced hazard from 37% to 8% when evaluating the hazard ratios for type 2 diabetes after a 40% increment over the median meat intake 20 , 21 . This approach shows great promise to clarify the causal relationship between diet and cardiometabolic diseases using BFIs.

Despite the large interest in the applications of BFIs, most publications are still quite technical, targeting a narrow audience of experts, which in turn leads to a limited implementation of BFIs in nutrition studies. Here, we present an overview and the status of candidate BFIs for the most common foods in a diet, we discuss current methodologies and challenges in BFI development, and we propose solutions to further advance the field and leverage a broader application of BFIs in the field of nutrition and health research.

Box 1 Key terminology in the field of BFIs

Biomarker: an objective tool measured in a biological sample to assess the exposure (that is, food intake), effect (functional response) or susceptibility (for example, nutrient status) of an organism 7 .

Biomarker of food intake (BFI) : a measure of the consumption of specific food groups, foods or food components (such as ingredients, or additives) used to estimate recent or average intake. BFIs are circulating or excreted metabolites of food constituents, usually non-nutrients. In contrast, susceptibility markers are measures of health status or risk, while effect biomarkers are measures of functional response to an exposure or challenge.

Validated BFI : a BFI that is validated by all criteria listed in Fig. 1 relevant for the intended use of the BFI (that is, qualitative or quantitative assessment); the term ‘putative BFIs’ is used for markers only shown to be plausible, while candidate biomarkers have been validated in humans by additional criteria (for example, showing dose or time response and robustness), yet still not fully validated.

Combined BFI : a panel of BFIs based on a combination of several metabolites to cover intake of a specific food, a food group or a diet.

Qualitative BFI : one that indicates whether the food was consumed—binary yes or no, usually used to detect compliance.

Quantitative BFI : one that indicates the amount of food consumed, in grams or as low or high intake, usually used to quantify or classify consumption of a given food.

Dietary instrument : a tool for dietary assessment performed through weighed or unweighed registration of food intakes, by interviews, or by questionnaires.

Kinetic profile of a BFI: the concentration curve of the BFI in a biological sample after food intake.

Half-life ( t ½ ): the time interval to reduce the concentration of a BFI to one half.

Time window for sampling : the time period after food intake where a BFI can be measured by a state-of-the-art analytical method.

Profiling (or metabolite profiling): a qualitative or quantitative technique, used to measure multiple metabolites, including BFIs, in biological samples.

Discovery study (controlled) : a controlled meal or dietary intervention study designed to find putative BFIs and assess elimination kinetics, dose–response or other parameters to ensure relevance of the BFI.

Discovery study (free-living individuals): a study in free-living individuals (usually cross-sectional), ideally conducted with time-matched dietary registration and biological sampling, used to associate BFIs with reported food intake.

Confirmation study (similar design) : an independent study used to confirm putative or candidate BFIs and assess additional validation criteria, under comparable but not necessarily identical conditions (that is, the level of dietary control can differ, cross-over can be parallel, but not cross-sectional)

Confirmation study (contrasting design): independent studies used to confirm the robustness (Fig. 1 ) of candidate BFIs in different study settings. BFIs discovered in a controlled study should be confirmed in free-living individuals to assure minimal interference from the diet; BFI discovered in free-living individuals should be confirmed in controlled studies to clarify confounding and assess elimination kinetics, dose–response or other parameters to assure relevance of the BFI.

Qualitative prediction study : a study using statistical models based on single or combined BFIs to classify participants into groups of consumers and non-consumers of a given food or food group.

Quantitative prediction study: a study providing quantitative information about food intake using a calibration curve, that is, a curve showing the relationship between the concentration of a BFI and its signal measured by an analytical instrument.

Status for BFIs

The last decade of BFI work has resulted in promising single and combined BFIs able to classify or measure intakes of several specific foods or food groups. Recently, a series of systematic reviews assessed the utility of the proposed BFIs in nutrition research using a set of eight predefined criteria (Table 1 ) 9 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 . Subsequent reviews have summarized some of the best candidate BFIs, either by evaluating their robustness 36 , that is, the BFI stands out in both dietary interventions and observational studies documenting minimal interference from a varied dietary background, or by focusing on how well they correlate with other biomarkers or self-reported dietary intake, thereby testing their reliability 37 . Here, we shortlist and rank candidate BFIs using a four-level utility scale (Table 1 ). We base this scale on three of the validation criteria that are critical for the practical usefulness of BFIs in nutrition studies, namely BFI plausibility, robustness and reliability 9 . Additional validation criteria (for example, time kinetics, analytical performance and reproducibility) inherently follow as soon as the three validation criteria included in the proposed ranking are fulfilled, as illustrated in Table 1 . The validation terms have been defined and discussed in detail elsewhere 9 and are briefly described in Table 1 ; the same semantics are used in this paper.

A BFI is plausible when it is chemically related and specific to a single food or food group, thereby providing a very low risk of misclassification due to other exposures. Some BFIs (labelled ‘c’ in Tables 1 – 3 ) are not plausible markers for a specific food due to their presence in several other foods but may still improve the estimation of intake as part of a combination with more specific BFIs. BFI robustness ensures that BFIs reported in dietary interventions stand out also when the intakes are measured as part of a varied diet and that BFIs reported in observational studies have a true relationship to the food investigated. The reliability of a BFI implies that fair agreement with other biomarkers or dietary instruments has been observed; this agreement can be qualitative or quantitative, depending on the quality of the comparator.

To investigate the status of BFI validation according to these three criteria, we conducted a thorough evaluation of all BFI reviews 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 that have used the Guidelines for Biomarker of Food Intake Reviews search criteria and methodology 38 covering biomarkers for the most frequently consumed foods and food groups in a diet. We then performed two systematic searches, one targeting experimental studies published after 2017, the year when the BFI reviews were conducted, and another one targeting observational studies published after 2010, the approximate time when BFIs started to receive considerable attention. The search terms and results of the literature search are detailed in the Supplementary Fig. 1 .

According to the utility ranking proposed in Table 1 , validated urinary BFIs at utility level 1 exist for total meat and fish, chicken, some processed meats, fatty fish, total fruit, citrus fruit (orange), banana, whole-grain wheat and rye, alcohol, beer, wine and coffee (Table 2 ). Other food groups such as total plant foods, vegetables, legumes, dairy and several specific fruits, tubers and vegetables have promising candidate BFIs (utility level 2), still requiring additional validation. A comprehensive table providing the justification for the ranking of BFIs measured in urine or blood is provided in Supplementary Table 1 .

The validated BFIs measured in blood after intake of fatty fish, whole-grain wheat and rye, citrus or alcohol are presented in Table 3 together with candidate BFIs for plant foods, dairy, some meats including salami, and certain non-alcoholic beverages. Fewer foods are covered by blood BFIs and their level of validation is lower than for urine, likely due to the lower number of studies investigating BFIs in blood. However, good candidates exist for several foods pending extensive validation. In a recent metabolomics study of individuals on a controlled version of their habitual diet, the observed correlates in urine and plasma were remarkably similar 39 .

Only a few dose–response studies have been carried out so far. BFIs indicating intake as a continuous variable (g/day) are known for intake of orange and citrus, apple, total fruit, whole-grain wheat and rye 40 and chicken 12 , 41 , 42 , 43 . For other foods such as coffee, meat or bananas, only a preliminary classification into low/high intake levels has been possible thus far 18 , 19 , 44 .

Biomarker discovery and validation

Plausibility, robustness and reliability are key validation criteria that are intertwined and essential for the implementation of BFIs in nutrition studies, as they require BFIs to be specific, unaffected by a varied dietary matrix, and comparable with other intake measurements. Validating BFIs according to these three criteria requires the use of several study types (Box 1 ) to describe the conditions under which the BFIs can be used and to estimate their variability. Such studies include: (1) discovery studies, preferably conducted in controlled settings but alternatively from observations in free-living individuals; (2) confirmation in independent studies of similar and of contrasting designs; and (3) prediction studies, including trials covering different populations. Below, we outline the methodology and challenges associated with the development of BFIs according to the validation criteria relevant for our proposed ranking.

BFI plausibility

The most common approach to discover BFIs is to conduct small meal studies, designed to report metabolites associated with a recent, controlled intake of one specific food. This approach unravels BFIs that are chemically plausible, but not necessarily specific, given the large overlap in composition of many foods, particularly in the plant kingdom, but also across proteinaceous, fatty or processed foods. Many food items do not contain specific components, and finding a single, unique BFI for every food is thus not a feasible strategy. BFIs can be considered sufficiently specific if levels coming from other consumed foods are low and/or their frequency of consumption is too low to interfere. Proline betaine is such an example, as it is found at low levels in many plant foods, yet its high content in oranges makes it a well validated and useful BFI for orange and citrus intake 42 .

Observational studies may also be used to associate metabolites from blood or urine with habitual diet estimated either from FFQs, or better, from 24-h recalls coinciding with biological sampling. However, these dietary instruments add a considerable risk of confounding, given the strong association between dietary patterns and specific lifestyle 45 , 46 . For instance, the association of ethyl glucuronide, an alcohol BFI, with red meat intake 47 , 48 , or of trimethylamine oxide (TMAO), a BFI of fish consumption, with green tea in a Japanese population 48 , are good examples of implausible BFIs reported in cohort studies due to confounding. Observational studies may still be used to positively confirm associations between BFIs and foods, but are not always reliable for BFI discovery, particularly when dietary registration and biological sampling do not coincide.

BFI robustness

Investigating BFIs in both experimental and observational studies is crucial to ensure their robustness. Highly controlled discovery studies may suffer from bias due to their small size and narrow recruitment base, while discovery in observational studies may be confounded. Confirmation of BFIs in larger cross-sectional studies or in dietary interventions, respectively, provides robustness by covering a varied dietary background and a representative population, or by confirming the true relationship between the BFI and the food consumed. Only 30% of putative BFIs reported in a discovery profiling study were replicated in a confirmation study 49 , even for experimental studies conducted under similar conditions.

Endogenous metabolites are usually not robust BFIs, despite being relevant biomarkers to investigate health outcomes 50 . Carnitine and acyl-carnitines are good examples of endogenously formed compounds often reported as meat BFIs in observational and intervention studies; yet they originate both from ingested meats and from our own muscle and fat metabolism 21 , 51 . Endogenous metabolites may be subject to large interindividual differences in response due to genetic and microbial variability (as described below). Sometimes this will cause a lack of robustness, as in the case of TMAO, a BFI originating from certain kinds of fish 23 that may also be formed endogenously from microbial conversion of dietary phospholipids or carnitine. Endogenous or microbial–host co-metabolites 34 , 52 may be useful BFIs as part of a pattern but usually not by themselves.

BFI reliability

BFI reliability requires that the BFI is comparable with other objective biomarkers; this is currently not often possible but should become a method of choice when more BFIs are validated. Reliability may alternatively mean that the BFI agrees with subjective measures of food intake, evaluated by correlating BFI levels with information from dietary instruments 42 , 47 , 53 , 54 , often referred to in the literature as ‘relative validity’ 55 . Correlations between individual candidate BFIs and 24-h dietary recalls or 3-day food records, tend to be higher than correlations with FFQs 37 . This confirms the better quality of the former but also highlights that reliability validated against dietary instruments must be interpreted with caution. All dietary instruments, including BFIs, bring a certain risk of inaccuracy, that is, from incomplete registration, incomplete sample collection or incomplete databases associated with the dietary registration tool, and such flaws can add further variation in the assessment of BFI reliability. The use of several independent studies comparing biomarkers and dietary instruments is therefore needed to assure BFI reliability. If this is done in independent laboratories, reproducibility will also be ascertained.

Another approach that was not included in the definition of reliability as described in Dragsted et al. 9 but has been tested later 18 , 19 , 44 , is the data-driven approach where prediction models, developed from randomized controlled trials, are used to indicate whether a certain food has been consumed (yes/no) and/or to what extent (high/low/none). This approach documents a stronger reliability compared to correlations, as it flags BFIs that can eventually predict intake without relying on other dietary registrations. Evaluating reliability of a candidate BFI is challenged not only by factors related to the comparator, but also by factors related to the biomarker per se and to sampling. Biomarker half-lives vary and for biomarkers with short elimination half-lives the comparison can be performed accurately by dietary records only if the record is time-matched with biological sampling. Only a handful of studies provide this type of matched data 42 , 53 , 56 , 57 ; whereas in a majority of studies, reliability is tested based on randomly collected samples, resulting at best in low-to-moderate correlation with average food intake 37 , 39 .

Time response

Although not included in the proposed ranking, time response is crucial for the implementation of BFIs, as it serves as guidance to determine the appropriate time window for biological sampling, that is, (a) when to best sample to be able to measure a BFI and (b) which BFIs will be measurable in a random sample.

Time response refers to the kinetic profile of a BFI after food intake and is determined in acute meal studies by repeated measurements, resulting in time-resolved profiles of elimination. These profiles may be used to roughly estimate a BFI elimination half-life ( t ½ ), which is rarely known for BFIs. Elimination t ½ may be estimated as 15–20% of the width of the time window over which the marker could still be measured in time–response studies. This depends on the sensitivity of the analytical instrument and assumes that the analytical performance allows measuring the BFI for at least 5–6 t ½ after consumption of a standard portion of the food, as well as assuming first-order elimination kinetics. When more than three time points exist on the BFI elimination curve, the elimination t ½ can be measured more accurately, but this is rarely done in time–response (BFI discovery) studies, where typically 3–6 samples are collected over a period of 24 h, some of them representing the absorption phase. In Supplementary Table 1 , we propose useful time windows for sampling following food intake for most of the BFIs included in this survey. The term immediately implies that a certain food must be consumed within the time window for sampling or otherwise the BFI will not be measurable. With ever-increasing analytical sensitivity of the mass spectrometers measuring the BFIs, time windows for sampling will expand.

Biological variability

Factors affecting intraindividual and interindividual variability of a BFI are shown in Fig. 1a . Unlike recovery biomarkers such as Na + or K + that are excreted in quantitative proportion to intake 5 , 58 , most BFIs are highly dependent on absorption, distribution, metabolism and elimination (ADME) and rely on diffusion rates, availability of transporters and enzymes, or gene polymorphisms 59 , among others. The gut microbiota also plays a role in generating a myriad of microbial metabolites 60 , 61 , 62 . The individual microbiome is shaped by several factors such as diet, physical activity, and abiotic factors such as pH or transit time 63 , 64 , 65 , adding additional complexity. Documenting a low variability of a BFI within and between individuals is therefore important, given the differences in ADME and gut microbial metabolism. Repeated measurements of BFIs in controlled dietary settings could be used to quantify the intraindividual and interindividual variability of a BFI and this measure should become an additional validation criterion to consider in the future (Table 1 ). Intra-class correlation coefficients (ICCs) have been used to assess the variation in biomarker levels measured in repeated samples from the same individuals in relation to the overall variation of the biomarker in the population studied 66 , 67 , 68 , 69 . However, ICCs measured in observational studies not only reflect variability, but also are affected by BFI t ½ , the dietary habits and frequency of food consumption in the study population, and the time of sampling. A low ICC does not imply that a BFI is not useful; it may indicate that (1) the BFI has a short time window of sampling, (2) the corresponding food is consumed infrequently, or (3) the response within and between individuals varies considerably over time. A high ICC would indicate a low biological variability of the BFI along with recent and/or frequent intake of its corresponding food, or that the BFI has a long time window of sampling (due to a long t ½ ). ICCs reported recently were shown to reproduce well over time for BFIs with long t ½ (that is, 0.83 for α-carotene), but to be quite variable for other candidate BFIs (that is, 0.1–0.9 for legume BFIs or 0.07–0.66 for chicken BFIs, where values close to 1 indicate stable BFI levels within an individual) 37 , highlighting that the utility of these biomarkers is population dependent. Biological variability calculated in a similar way to ICCs but measured in controlled experiments, where the same individuals are repeatedly provided the same amounts of specific foods, will provide measurements of biological (intraindividual and interindividual) variability independent of other sources of variability.

figure 1

a , Factors with considerable interindividual variation affecting blood levels and elimination into urine after a meal. b , Blood concentrations as a function of time, half-life and frequency of consumption, assuming food intake is evenly distributed over time and the analytical method sufficiently sensitive. c , Conceptual figure showing the likelihood of detecting a BFI in urine as a function of its half-life and the frequency of weekly ingestion of a specific food, when one or three spot urine samples are collected. The data were simulated under the assumption of fast absorption, first-order kinetics, a one-compartment model and intervals for intake and sampling during daytime.

Analytical performance

The biological validation of BFIs requires as a minimum that their stability and analytical performance are sufficient for studies using metabolite profiling 9 , 70 , 71 . The stability under normal sampling and storage conditions must allow repeated measurements of a sample while showing no degradation. This is assumed in most observational studies but rarely verified. Analytical performance includes many criteria, for example, selectivity/specificity/identity, limits of detection and quantification, accuracy and precision 66 , 67 ; however, their detailed discussion is beyond the scope of this Review.

The interplay between BFI elimination and frequency of food intake

The plasma or urine elimination t ½ together with the absorption rate of BFIs and their variability within individuals determine the time window for detection or quantification along the principles of pharmacokinetics or toxicokinetics. Fast uptake and elimination of a BFI means that there is only a narrow time window for detection and sampling. While this is ideal for compliance assessment in intervention trials, it can also have a broader application in dietary assessment if the corresponding food is consumed very frequently, or if an extended sampling time or frequent sampling is used. For coffee, some reported BFIs are excreted with a time window of 1–12 h ( t ½  ~ 2 h), but daily coffee drinkers can be classified with high sensitivity and specificity using urine samples collected over 24 h (ref. 44 ), and for frequent coffee consumption over the day, any spot sample may suffice. ICCs for coffee BFIs are also less varied and quite high (quinic acid ICC 0.81), indicating that either a constant daily consumption level or no consumption at all is prevalent in the studied population 37 . With additional coffee markers having longer t ½ (that is, time window for sampling 6–24 h, t ½ ~3.5 h), less frequent intake of coffee every 1–2 days would likely be caught from a 24-h urine sample.

In Fig. 1 , we highlight the interaction between BFIs with different elimination t ½ , the frequency of consuming the corresponding food, and the time of sampling. Hypothetical kinetic curves were simulated to illustrate the concept. The figure demonstrates the importance of selecting the right BFIs for the purpose and underlines an important aspect of interpreting biomarker data in epidemiology.

Average intakes are not well estimated from random, single samples by BFIs having short t ½ , since they easily fluctuate with very recent intake (Fig. 1b ). Proline betaine with an elimination t ½ of around 4 h in urine (time window for sampling, 0–24 h), has been validated in cross-sectional studies with good results 42 , 43 , reflecting that urine was collected within the same period as dietary records were administered in a study that differs from the typical observational study with one spot sample and a FFQ. BFIs with longer t ½ (12–24 h, time window for sampling up to ~6 days), reflecting consumption in the previous week, could theoretically be captured even from a single urine sample; such candidate BFIs are suggested only for certain lentils, like chickpea (hypaphorine) 72 , for coffee (dihydroxy-ent-kaurenoic acid) 73 , walnuts and pomegranate (urolithins) 74 , and based on a preliminary estimation for onion (isoalliin mercapturate) 75 . Lipid-soluble serum BFIs with even longer elimination t ½ are few, for example, n-3 or n-6 fatty acids 23 and carotenoids 76 , already having long histories of use to determine average dietary intakes in observational studies; notably, these markers still only have low-to-moderate correlations with intake of seafood, oils or fruit and vegetables, respectively 37 . Variable levels in the foods (for example, both supplements and fatty fish have high n-3 fatty acids, while carotenoid levels vary widely across fruits and vegetables), as well as misreporting and measurement errors, are likely contributing to the moderate correlations; calibration with other BFIs might help resolve these issues and provide more accurate validation of the reliability of these markers.

In theory, using several BFIs with different t ½ for the same food could be used to assess the frequency of food intake by estimating the time since last ingestion; however, this potential has still to be realized.

Key concepts for the implementation of biomarkers as dietary assessment tools

Additional research efforts are required to find and validate enough BFIs to cover qualitative and quantitative food intakes. In the meantime, the field will benefit from efficient strategies to utilize current BFIs and to apply multiple BFIs in nutrition studies, showcasing their value. New identification tools, sampling strategies and data sharing platforms will greatly facilitate this development. Here we discuss key concepts to implement biomarkers as dietary assessment tools and allow for a future with better precision in nutrition research.

Application-based selection of BFIs

The decision on which BFIs to use in which nutrition study depends largely on their intended use, whether it is to assess compliance in a dietary intervention or to evaluate dietary intake in observational studies. In Box 2 , we outline the difference in the characteristics of the BFIs required for these two purposes.

In many cases, the purpose of using BFIs in dietary intervention studies is to identify non-compliant participants, or to perform per-protocol analyses 11 by adjusting for misreporting and non-adherence. For this, BFIs that perform well in classifying participants into consumers or non-consumers of specific foods, that is, qualitative markers, may well fulfill the requirements. The adherence to specific dietary patterns can also be monitored very efficiently by combining qualitative BFIs covering key foods for the pattern 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 77 , 78 or for an index of a specific diet 13 . Qualitative BFIs can further provide a more refined representation of a human diet compared to the information obtained from dietary instruments, for example, by providing a clearer indication of food processing; fermented dairy or fried potatoes were shown to have candidate BFIs that differ from unfermented dairy or boiled potato 79 , 80 . The ultimate purpose of using BFIs is to quantitatively measure specific food intakes, and strategies to overcome the current lack of quantitative BFIs are discussed below.

Box 2 BFI characteristics in dietary and nutrition research according to their intended use

For compliance assessment in dietary interventions

Purpose: to evaluate participant compliance

Requirements:

Validated BFI(s) of the targeted food(s) or food group(s)

Qualitative BFIs: to indicate whether the food was consumed or not

BFIs indicative of recent intake (short to medium time windows for sampling) to ascertain frequent consumption of the targeted food(s)

Sampling schemes should match with intake frequencies in the study

For evaluating diets in epidemiology

Purpose: to assess longer-term adherence to a dietary pattern and/or a specific food intake

Validated BFIs of key foods and food groups

Quantitative BFIs: to indicate intake levels or at least high versus low intakes

BFIs indicative of habitual intake , preferably BFIs with longer time windows for sampling in studies with one or few samples per participant; (shorter-term markers may also be used if more samples are available to indicate that key foods are consumed frequently).

Combination of BFIs

Combination of two or more biomarkers to indicate consumption of a food or food group is well known as a solution to improve the specificity observed for single BFIs 4 . The concept is simple: furaneol, for example, is an aroma compound found in strawberry 81 and pineapple 82 , but the compound is also excreted after intake of heated or roasted foods 83 ; when measured along with the unspecific red/blue berry BFI pelargonidin, the combined BFIs would differentiate strawberry from pineapple or other exposures 62 . In this way, combined BFIs defined as a panel of two or more biomarkers have been developed to classify consumers or non-consumers of foods such as nuts, banana, meat, beer or coffee in a qualitative fashion; the combined BFIs show higher performance in predicting intake compared to single BFIs 18 , 44 , 49 , 84 , 85 , 86 . BFI ratios can also be used to evaluate intake of a specific food group 19 , 40 . In the case of red meat having no specific BFI, the ratio between anserine and carnosine was shown to perform well in evaluating whether red or white terrestrial meat was consumed 19 . This is a promising approach for foods lacking specific BFIs and may be applied further to differentiate aquatic from terrestrial meats (that is, by 3-carboxy-4-methyl-5-propyl-2-furanpropionate (CMPF)/carnosine ratio) or to discriminate between groups of plant foods. Similarly, BFI ratios can discriminate wheat and rye intakes 58 , just as the ratio of long-chain n-3 to n-6 fatty acids has been used to detect marine fish intake 87 . Complex foods such as beer may likewise be identified in a diet by unique combinations of BFIs related to constituents and processing 85 , 88 . By analogy, a specific meal would require BFIs for the key foods characterizing the meal; the entire continuum from complex meals to dietary patterns can be investigated by combining BFIs of signature foods, characteristic of each pattern 89 , for example, olive oil, nuts, fish, fresh fruit and vegetables, and wine, characterizing the Mediterranean diet 90 . Targeting BFIs for specific foods in a diet has been applied in a Nordic diet study 77 , and more recently for the Mediterranean diet 16 , 78 and could likely be further extended to other dietary patterns, including flexitarian, vegan or vegetarian diets, or to evaluate adherence to the ongoing green dietary transition and its consequent health effects. Taking the new EAT-Lancet diet as an example, BFIs would cover key food groups (and their contrasts, for example, to differentiate between consumption of animal versus plant foods, white (poultry and fish) versus red (and processed) meats, starchy versus non-starchy vegetables, and saturated versus unsaturated fats) 91 . BFIs for many of the recommended foods already exist, pending limited additional validation. Figure 2 shows examples of BFI combinations with five different combinatorial approaches. Importantly, while all five approaches for combining markers have been documented for at least one food/food group/combined meal or dietary pattern, as exemplified above, not all the combinations suggested in Fig. 2 have been tested and validated. Some suggestions are meant to illustrate the principle, awaiting validation by all criteria suggested here (Table 1 ).

figure 2

(1) Selected BFIs for measuring the intake of a specific food, that is, a whole-grain product, a fruit or vegetable, meat or fish, a glass of milk, and so on (only one or a few BFIs need to be detected for each); (2a) ratio of two BFIs for differentiating between two specific foods, that is, red versus white meats, whole-grain wheat or rye, two fruits, fermented versus non-fermented dairy; the orange rings are suggested ratios pending validation; (2b) selected BFIs for measuring intake of a food group as a whole, that is, whole-grain products, total fruit, meat and fish (require combination of BFIs for different foods within the same food group, where all selected BFIs should be measured); (3) selected BFIs characterizing a mixed meal, targeting BFIs specific to all important meal constituents; (4) monitoring all available BFIs for assessing the habitual diet or targeting specific key food groups to discriminate between dietary patterns; the red dot represents other potential BFIs for fruits not listed.

Differentiating BFIs from effect biomarkers

When constructing combined markers, it is important to differentiate true BFIs from early effect markers 7 . A thorough review covering potential biomarkers observed in healthy dietary patterns has recently been published 89 . Notably, early effect biomarkers of host or microbiome metabolism were also typically reported as diet-specific metabolite patterns in many dietary studies 21 , 89 , 92 ; while this might be an asset in relation to prediction of longer-term effects, it does not adequately differentiate the adherence to a certain diet from its early health effects. Ideally, only (candidate) BFIs should be used for constructing combined markers of dietary patterns to make them independent of their metabolic effects.

Making the best out of the few quantitative biomarkers

A combination of BFIs can also be used as a stepwise approach from qualitative to quantitative assessment. Since combined qualitative BFIs can classify consumers and non-consumers of a given food or food group, the application of quantitative BFIs could be limited to only those individuals having actually consumed the food of interest, as verified by qualitative BFIs; this helps to reduce noise and evaluate the amount consumed with better accuracy. Consequently, the less robust BFIs having a fair dose–response relationship to a food may still be useful to estimate the amount of food consumed, as indicated in a preliminary assessment of meat intake levels 19 .

Multiple samples to capture long-term dietary intakes through BFIs

In nutritional epidemiology, the central focus is the average dietary intake over months or years for determining its relationship with disease (Box 2 ). Capturing the habitual diet through 3-day dietary records has worked well so far at the nutrient level 93 ; yet at a food group level, it was shown that certain food groups (for example, fish, fruit and vegetables) are overestimated, other food groups (for example, meat and sweets) are underestimated, and only some food groups (for example, dairy and cereals) are similar when comparing FFQs to 24-h recalls 94 . With a biomarker approach, three 24-h urine samples collected more than 1 month apart were shown to reflect long-term intakes sufficiently well in observational studies as indicated for some polyphenol-containing foods 95 and for sweeteners 55 . Collection of several blood or urine samples over a longer period may thus allow for an improved estimate of the average dietary intake by BFIs. The number of repeated samples necessary for a good evaluation of the habitual diet may be estimated; as a rule of thumb, the time interval between intakes divided by the time window for sampling of the BFI, both measured in days, will provide the numbers of samples necessary for a high likelihood of measuring the BFI. For example, if a food is consumed rarely, such as every 30 days, at least ten randomly spread out spot samples should be collected, pooled and analysed together for a high probability of measuring the monthly consumption rate through a BFI with a time window for sampling of at least 3 days.

Most BFIs discovered to date reflect recent dietary intake, meaning they are able to measure if a certain food was consumed within a short time interval before sampling; it is therefore crucial for sampling schemes to match dietary registrations, but that is rarely the case in current nutritional epidemiology. In a recent paper 39 , habitual diets were provided in experimental settings with the aim of identifying BFIs reflecting the overall diet. However, with only one sample taken at the end of the intervention, the measured metabolites including known BFIs would only reflect their corresponding time window for sampling, and not the average intake over time, as illustrated by the moderate correlations 39 .

Considering the interplay between food intake frequency, sample type and sampling frequency, single BFIs with shorter t ½ are less likely to detect food intake (Fig. 1c ), even with good sensitivity of the analytical method. With a single 24-h urine sample collection, only a BFI with t ½ above 12 h would have a high probability of being detected, providing that the corresponding food is consumed at least every 3 days (that is, its corresponding time window for sampling). Selecting BFIs with longer time windows for sampling, collecting more samples or assessing foods with higher intake frequency will increase the likelihood of detection. Conversely, narrow time windows for sampling and low frequency of intake and/or sampling will lead to a low probability of BFI detection in epidemiology.

The likelihood of detecting food intake by measuring BFIs is changing in proportion to the frequency of sampling; with three samples, many more BFIs with shorter t ½ will reach a reasonable likelihood of detection; thus, foods that are consumed less frequently can be covered (Fig. 1c ). Even though a plasma sample represents a very short sampling time, the probabilities of detection in blood and spot urine samples are not very different as simulated in Fig. 1 and illustrated recently 39 . We propose that the best coverage of BFIs in epidemiology will be reached with more samples per participant (for example, 5–10), while putting emphasis on BFIs with longer time windows for sampling, preferably reaching 3 days or more.

Effective sampling methodologies

Collection of multiple samples requires efficient and simple ways for sampling and storage. Spot urine samples, in particular the first morning void, the post-dinner and the overnight cumulative samples 96 , as well as dried urine spots 97 , were shown to reflect the metabolome of food intake almost as well as a 24-h urine sample. For storage, the use of vacuum tubes without preservatives, and without the need to immediately freeze the samples after collection, showed good coverage for most metabolites 98 and good replication in an independent study 13 . Some metabolites in dried blood spot samples have been shown to be more stable over 12 months than in the frozen blood samples 99 . However, dried spots also have some caveats, for example, the amount of sample that was dried must be known to calculate the correct concentration of BFIs. More recent microsampling technologies that use exact finger prick blood collection showed a better reproducibility and high correlation to venous blood samples 100 . These strategies should be tested and validated further since a more general use of remote sampling of dried specimens or microsamples may facilitate the collection and storage of repetitive samples, thereby reducing sampling costs and improving the chance of detecting average intake of foods in epidemiology. In addition, remote sampling makes the collection of multiple samples easier for study participants, providing a great advantage in larger and/or longer-term studies, and would further help monitor longitudinal trajectories of individual dietary habits, improving precision nutrition and the evaluation of dietary effects on disease risk in epidemiological studies 101 .

Finally, the exploration of BFIs in blood lipid fractions, nails and hair has already shown to be useful for some specific BFIs with long t ½ , thereby representing longer periods of food intake 4 , 102 , 103 . These sampling strategies need better exploration in nutrition science.

Biomarker identification and data sharing

A considerable effort is needed to further support identification and measurement of novel candidate biomarkers and to share spectral information on BFIs. Several new tools for improving identification have emerged 104 , and spectral databases such as Massbank 105 , METLIN Gen2 (refs. 106 , 107 , 108 ), mzCloud (Thermo Scientific, mzCloud Advanced Mass Spectral Database, available at https://www.mzcloud.org/ ) and HMDB 109 cover hundreds of thousands of chemical compounds. Yet a limited number of these are BFIs, further highlighting the need for new approaches to map the uncovered (dark) metabolome of foods and their digestion products. The Global Natural Products Social Molecular Networking 110 has established itself at the forefront of making connections between different datasets and building large molecular networks to compare MS/MS spectra of any unknown compound, for example, an unidentified putative BFI. The Global Natural Products Social Mass Spectrometry Search Tool (MASST) 111 is able to search across openly available studies and find datasets where a given spectrum was previously reported, while its new extension, foodMASST 112 , may enable faster identification of compounds that are unique to specific foods and thereby support biomarker validation.

Nevertheless, collaborative efforts to share experimental data and to allow cross-study search for known and unknown metabolites are essential to advance the field by facilitating BFI validation. The ideal database would have highly structured data and metadata that would allow precise questions to be answered by queries across studies, including querying different data types, for example, metabolomics and clinical data from relevant studies. This can be implemented by adopting the use of open and structured databases and making data truly FAIR (findable, accessible, interoperable and reusable) 113 . Data repositories such as Metabolights 114 and Metabolomics Workbench 115 aim to fulfill several of these objectives, but a better coverage of human studies and better options for metabolite search across different datasets would be a great step forward.

Future research

The BFI field still presents many challenges, but much progress has already been achieved. Some BFIs are well validated and are, therefore, already applicable in some areas of nutrition research, while the candidate BFIs require better validation. To further unlock biomarkers as dietary assessment tools, we suggest the following roadmap (Fig. 3 ):

Continue to identify new BFIs and combined BFIs in different biological samples (blood, urine and hair) for an extensive coverage of foods and food groups. BFIs should also differentiate within and between food groups and diets, for example, contrasting plant versus animal protein, whole versus refined grain, or ‘healthy versus unhealthy’ diet types, as defined by dietary guidelines. Importantly, BFIs of cooking and processing (for example, refining) must also be included 80 , 116 and further developed. The effort should be accompanied by more common sharing of human nutritional metabolomics data and associated spectra in FAIR data repositories and libraries to support identification of new and currently unidentified BFIs as well as to indicate BFI specificity to the food in question.

Fully validate current and future single and combined candidate BFIs in experimental and observational studies. To extend the current panel of validated qualitative BFIs (utility level 1), candidate BFIs ranked at utility level 2 in Tables 2 and 3 should be validated for reliability in cohorts where sampling coincides with dietary registration. Combinations of BFIs should be optimized to predict intake, for example, classify consumers and non-consumers of specific foods 18 , 19 , 44 , tiered approaches 19 and diet pattern classifications 16 , 78 , and quantitative BFIs should be characterized by performing dose–response studies. The biological variability should be documented for most BFIs.

Apply BFIs in multiple nutrition studies to gain experience, for example, use BFI panels to cover intake of complex foods and/or to assess compliance and recalculate the relationship with health outcomes based on objectively measured intake. Develop algorithms to address patterns and frequencies of food consumption using BFIs with short, medium and longer time windows for sampling, in studies with repetitive sampling and longer follow-up times 13 . Explore new and less burdensome sampling methodologies, for example, dry or micro sample spots collected remotely.

Develop analytical methods targeting multiple BFIs 117 , 118 . Bring BFI technology into routine use by standardizing methodology across laboratories and by developing best practice protocols to quantify the human diet through biomarkers.

Harvest the benefits of improved dietary assessment to make nutrition research more precise and improve the trust in associations between food intake and health outcomes. This is a crucial step for advancing precision nutrition, as it allows to objectively assess the adherence to tailored dietary interventions, thereby improving power when integrating dietary data with metabolic phenotypes, for example, metabotypes 119 . Precision nutrition is of particular importance to curb obesity and cardiometabolic diseases that are preventable through lifestyle interventions but for which a one-diet-fits-all approach doesn’t seem to work due to the highly varied individual response to diets 118 , 120 , 121 . Personalized dietary interventions are good drivers of behaviour change, shown to improve diet quality, thereby benefiting patients and the society 122 , 123 .

figure 3

Five-step strategy to discover and validate single and combined BFIs, test them in proof-of-concept studies, and develop routine methods to implement BFIs at a larger scale in nutrition research.

The last decade of BFI work has shown great progress for the future application of BFIs in nutrition research. Key concepts including BFI validation, time windows for sampling, frequency of food intake and improved sampling schemes are central to unlocking biomarkers as objective dietary assessment tools. In the present Review, we suggest concrete ways to apply these concepts into new studies with precision nutrition, as well as several strategies to combine BFIs to reflect complex food intakes and support specific study designs. We illustrate that validated BFIs exist for some of the most consumed foods and can be used to evaluate adherence in dietary interventions or to qualitatively assess food intake. While quantitative BFIs are few, developing more efficient strategies for quantitative food intake assessment, that is, by differentiating qualitative and quantitative BFIs, will be an important step forward. Finally, collaborative efforts are highly encouraged to speed up BFI identification, that is, by sharing FAIR datasets in nutritional metabolomics among research groups. Altogether, these directions will pave the road for the routine application of BFIs in nutrition research and improve precision in nutrition.

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Acknowledgements

This work was supported by a Semper Ardens grant from the Carlsberg Foundation (CF15-0574) to L.O.D., a grant from Novo Nordisk Foundation (NNF19OC0056246: PRIMA - towards Personalized dietary Recommendations based on the Interaction between diet, Microbiome and Abiotic conditions in the gut) to L.O.D. and H.M.R. and an International Postdoctoral Research Fellowship Programme 2219 from The Scientific and Technological Research Council of Türkiye - TÜBİTAK - to T.B.-T.

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C.C., conceptualization; data curation, writing original draft, overall editing, primary responsibility for the final content; figures; Supplementary Information ; supervision; project management; T.B.-T., literature search; data curation; references; Supplementary Information ; figures; J.S., writing original draft; kinetics simulations figures; G.L.B., writing original draft, overall editing; figures; H.M.R., conceptualization; writing original draft; figures; L.O.D., conceptualization, data curation, writing original draft, overall editing, primary responsibility for the final content; figures, Supplementary Information ; supervision. All authors contributed substantially to discussions around article content, and finally reviewed, revised and accepted the final version of the manuscript.

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Evidence for ranking BFIs measured in urine and blood according to the utility levels defined in Fig. 1, together with estimated time windows for sampling for each reported biomarker.

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Cuparencu, C., Bulmuş-Tüccar, T., Stanstrup, J. et al. Towards nutrition with precision: unlocking biomarkers as dietary assessment tools. Nat Metab (2024). https://doi.org/10.1038/s42255-024-01067-y

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Dr. Gao won the Irwin H. Rosenberg Pre-doctoral Award from the Jean Mayor USDA Human Nutrition Research Center on Aging at Tufts(2006), the Wayne A. Hening Sleep Medicine Investigator Award from the American Academy of Neurology (2011), the Leadership/Expertise Alumni Award from the Tufts Nutrition School (2012), and the Samuel Fomon Young Physician Investigator Award from American Society for Nutrition(2015). He was selected into the Tufts Honorable Alumni Registry in 2015.   

Dr. Gao received his M.S. in Epidemiology from Peking Union Medical College and his M.D. from Shanghai Second Medical University. He received his Ph.D. in nutritional epidemiology from Tufts University. 

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Dr. Qi Sun is Associate Professor of Medicine in Channing Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School. He is also Associate Professor in the Departments of Nutrition and Epidemiology, Harvard T.H. Chan School of Public Health. Dr. Sun’s primary research interests include identifying and examining biomedical risk factors, particularly dietary biomarkers, in relation to type 2 diabetes, obesity, and cardiovascular disease through epidemiological investigations. His research is primarily based on several large-scale cohort studies including the Nurses’ Health Studies and the Health Professionals Follow-up Study. Dr. Sun is also interested in understanding the role of environmental pollutants, such as perfluoroalkyl substances and legacy persistent organic pollutants, in the etiology of weight change and type 2 diabetes. In the era of precision nutrition, Dr. Sun develops a new research interest of understanding the role of microbiome in mediating and modulating diet-health associations. Dr. Sun is currently leading a few NIH-funded projects that focus on food biomarker discovery and validation, diet-microbiome-health inter-relationships, as well as associations between obesogens and weight change in human populations.

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The proportional hazards assumption was violated ( P  < .001). Therefore, follow-up time was stratified by the midpoint, and hazard ratios (HRs) were calculated using an interaction term between follow-up period and the exposure variable. Follow-up period 1 was the first 12 years of follow-up, and follow-up period 2 was the last 15 years of follow-up. P value represents the significance of the likelihood ratio test of each effect modifier. Models were stratified by study and adjusted for sex (male or female), age at enrollment (years), race and ethnicity, education (≤11 years; 12 years, completed high school or General Educational Development; post–high school training; some college; college and postgraduate; or other), body mass index (BMI; calculated as weight in kilograms divided by height in meters squared) category, marital status (married or living as married, divorced or separated, widowed, or never married), smoking status (never smoker, former smoker, current smoker ≤20 cigarettes/d, current smoker 21-40 cigarettes/d, or current smoker >40 cigarettes/d), alcohol consumption (0 drinks per day, <1 drink/d, 1 to <2 drinks/d, 2 to <3 drinks/d, ≥3 drinks/d), physical activity level (never, low, moderate, high), coffee intake (0 cups/d, <1 cup/d, 1 cup/d, 2-3 cups/d, 4-5 cups/d, or ≥6 cups/d), family history of cancer (yes or no), Healthy Eating Index 2015 (HEI-2015) quartile (quartile 1, 21.55 to <60.90; quartile 2, 60.90 to <68.00; quartile 3, 68:00 to <74.20; quartile 4, 74.20 to <96.10), and use of individual supplements (yes or no). NA indicates not applicable.

The proportional hazards assumption was violated ( P  < .001). Therefore, follow-up time was stratified by the midpoint, and hazard ratios (HRs) were calculated using an interaction term between follow-up period and the exposure variable. Maximum follow-up time was 24 years for the AARP cohort (172 496 participants; 78 523 deaths), 27 years for the Prostate, Lung, Colorectal and Ovarian (PLCO) cancer cohort (42 732 participants; 15 898 deaths), and 26 years for the Agricultural Health Study (AHS) cohort (19 365 participants; 3149 deaths); follow-up period 1 was the first 12 years of follow-up and follow-up period 2 was the last 15 years of follow-up. Models were adjusted sex (male or female), age at enrollment (years), race and ethnicity (American Indian or Alaska Native, Asian or Pacific Islander, Hispanic, non-Hispanic Black, or non-Hispanic White), education (≤11 years; 12 years, completed high school or General Educational Development; post–high school training; some college; college and postgraduate; or other), body mass index (calculated as weight in kilograms divided by height in meters squared) category (<18.5, 18.5 to <25, 25 to <30, ≥30), marital status (married or living as married, divorced or separated, widowed, or never married), smoking status (never smoker, former smoker, current smoker ≤20 cigarettes/d, current smoker 21-40 cigarettes/d, or current smoker >40 cigarettes/d), alcohol consumption (0 drinks per day, <1 drink/d, 1 to <2 drinks/d, 2 to <3 drinks/d, ≥3 drinks/d), physical activity level (never, low, moderate, high), coffee intake (0 cups/d, <1 cup/d, 1 cup/d, 2-3 cups/d, 4-5 cups/d, or ≥6 cups/d), family history of cancer (yes or no), Healthy Eating Index 2015 quartile (NIH-AARP: quartile 1, 21.5 to <61.5; quartile 2, 61.5 to <68.6; quartile 3, 68.6 to <74.7; quartile 4, 74.7 to 98; PLCO: quartile 1, 28.5 to <60.8; quartile 2, 60.8 to <67.3; quartile 3, 67.3 to <73.1; quartile 4, 73.1 to 95; AHS: quartile 1, 21.9 to <55.3; quartile 2, 55.3 to <61.8; quartile 3, 61.8 to <68.2; quartile 4, 68.2 to 95), and use of individual supplements (yes or no).

eFigure 1. Flowchart for Final Analytic Sample

eTable 1. Baseline Characteristics of Study Participants by Cohort, According to Multivitamin Use

eTable 2. Study-Specific Hazard Ratios Between Multivitamin Use and Mortality

eFigure 2. Forest Plot of Stratified Baseline Estimates for the Association of Nondaily Multivitamin Use and All-Cause Mortality

eTable 3. Baseline Characteristics of Time-Varying Analysis Participants by Cohort, According to Multivitamin Use

Data Sharing Statement

  • The Limited Value of Multivitamin Supplements JAMA Network Open Invited Commentary June 26, 2024 Neal D. Barnard, MD; Hana Kahleova, MD, PhD; Roxanne Becker, MBChB

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Loftfield E , O’Connell CP , Abnet CC, et al. Multivitamin Use and Mortality Risk in 3 Prospective US Cohorts. JAMA Netw Open. 2024;7(6):e2418729. doi:10.1001/jamanetworkopen.2024.18729

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Multivitamin Use and Mortality Risk in 3 Prospective US Cohorts

  • 1 Metabolic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, Maryland
  • 2 Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, Maryland
  • 3 Occupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, Maryland
  • Invited Commentary The Limited Value of Multivitamin Supplements Neal D. Barnard, MD; Hana Kahleova, MD, PhD; Roxanne Becker, MBChB JAMA Network Open

Question   What is the association between long-term, daily multivitamin use and mortality in generally healthy adults?

Findings   In this cohort study of 390 124 generally healthy adults with more than 20 years of follow-up, daily multivitamin use was not associated with a mortality benefit.

Meaning   These findings suggest that multivitamin use to improve longevity is not supported.

Importance   One in 3 US adults uses multivitamins (MV), with a primary motivation being disease prevention. In 2022, the US Preventive Services Task Force reviewed data on MV supplementation and mortality from randomized clinical trials and found insufficient evidence for determining benefits or harms owing, in part, to limited follow-up time and external validity.

Objective   To estimate the association of MV use with mortality risk, accounting for confounding by healthy lifestyle and reverse causation whereby individuals in poor health initiate MV use.

Design, Setting, and Participants   This cohort study used data from 3 prospective cohort studies in the US, each with baseline MV use (assessed from 1993 to 2001), and follow-up MV use (assessed from 1998 to 2004), extended duration of follow-up up to 27 years, and extensive characterization of potential confounders. Participants were adults, without a history of cancer or other chronic diseases, who participated in National Institutes of Health–AARP Diet and Health Study (327 732 participants); Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (42 732 participants); or Agricultural Health Study (19 660 participants). Data were analyzed from June 2022 to April 2024.

Exposure   Self-reported MV use.

Main Outcomes and Measures   The main outcome was mortality. Cox proportional hazard models were used to estimate hazard ratios (HRs) and 95% CIs.

Results   Among 390 124 participants (median [IQR] age, 61.5 [56.7-66.0] years; 216 202 [55.4%] male), 164 762 deaths occurred during follow-up; 159 692 participants (40.9%) were never smokers, and 157 319 participants (40.3%) were college educated. Among daily MV users, 49.3% and 42.0% were female and college educated, compared with 39.3% and 37.9% among nonusers, respectively. In contrast, 11.0% of daily users, compared with 13.0% of nonusers, were current smokers. MV use was not associated with lower all-cause mortality risk in the first (multivariable-adjusted HR, 1.04; 95% CI, 1.02-1.07) or second (multivariable-adjusted HR, 1.04; 95% CI, 0.99-1.08) halves of follow-up. HRs were similar for major causes of death and time-varying analyses.

Conclusions and Relevance   In this cohort study of US adults, MV use was not associated with a mortality benefit. Still, many US adults report using MV to maintain or improve health.

In the United States, nearly 1 in 3 adults reports recent multivitamin (MV) use. 1 Prevalence of use is higher among older adults, women, non-Hispanic White individuals, and those with a college education. 1 Motivations for using MV include to maintain or improve health and prevent chronic disease 2 ; consequently, understanding the relationship between MV use and mortality is critically important to public health guidance. In 2022, the US Preventive Services Task Force (USPSTF) reviewed data on MV supplementation and all-cause mortality from randomized clinical trials and found insufficient evidence for determining benefits or harms, owing, in part, to limited follow-up time and lack of external validity. 3 With decades of follow-up and large populations, prospective cohorts can address these limitations; however, findings from observational studies on MV use and mortality have been mixed, 4 - 20 and important issues, which may explain study heterogeneity, need to be systematically addressed. First, confounding by healthy lifestyle is a major concern, as MV users often report eating healthier diets, exercising more, and smoking cigarettes less; this phenomenon has been referred to as the healthy user effect . 21 Second, it is unclear how MV use changes over time and how such changes may affect health. One issue of concern is that patients with diagnosed disease may increase their MV intake because of perceived health benefits; this has been termed the sick user effect . 6

In this study, we investigated the hypothesis that daily MV use is associated with lower mortality risk among generally healthy US adults by leveraging data from 3 large and geographically diverse US cohorts with repeat assessments of MV use and extended follow-up for mortality outcomes. With a combined sample size exceeding 390 000 adults and 164 000 deaths, we aimed to evaluate the association of MV use with the leading causes of chronic disease–related death (ie, cardiovascular disease and cancer) and to systematically explore sources of bias that contribute to uncertainty surrounding the association between MV use and mortality.

For this cohort study, the protocol for each included cohort study was approved by the Special Studies Institutional Review Board of the National Cancer Institute; the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial was also approved by the institutional review board at each of the 10 screening centers. Informed consent was obtained from participants or implied from completion and return of study questionnaires. All data used in this analysis was deidentified. Our study follows the Strengthening the Reporting of Observational Studies in Epidemiology ( STROBE ) reporting guideline for cohort studies.

Participants were adults in the National Institutes of Health–AARP Diet and Health Study (NIH-AARP) cohort; the PLCO Cancer Screening Trial cohort; and the Agricultural Health Study (AHS) cohort (eFigure 1 in Supplement 1 ). The NIH-AARP Diet and Health Study began in 1995 to 1996, when questionnaires were mailed to current AARP members who were aged 50 to 71 years and resided in 1 of 2 US metropolitan areas or 6 states. 22 Overall, 566 398 adults completed the baseline questionnaire. We excluded participants who responded via proxy (15 760 participants); died before questionnaire was received (42 participants); had self-reported or registry-confirmed cancer at baseline (50 727 participants); had self-reported diabetes, myocardial infarction, stroke, or end-stage kidney disease at baseline (105 871 participants); reported extreme caloric intake (4863 participants); or were missing covariates of interest (61 403 participants). The time-varying analysis, which incorporated follow-up questionnaire data collected in 2004, included participants who were censored before the follow-up questionnaire was administered and those who completed a follow-up questionnaire.

The PLCO study was a randomized cancer-screening trial that enrolled 154 887 participants at 10 US centers between 1993 and 2001. 23 Participants were aged 55 to 74 years at baseline. A dietary questionnaire, which assessed MV use, was administered to the screening group and was considered invalid if the participant died before its completion, had more than 8 missing or multiple-frequency responses, had a missing completion date, or had extreme calorie consumption. We excluded participants who were missing a baseline questionnaire (4918 participants); had a missing or invalid dietary questionnaire (84 392 participants); had cancer (6992 participants) or self-reported a history of diabetes, myocardial infarction, or stroke (9265 participants) at baseline; or were missing covariate data (6588 participants). For the time-varying analysis, we included participants who completed a follow-up dietary history questionnaire approximately 3 years after study entry, beginning in 1998, or who were censored before the dietary history questionnaire.

The AHS enrolled 52 394 licensed pesticide applicators, 4916 commercial pesticide applicators, and 32 345 spouses of private applicators who were aged 18 years or older from Iowa and North Carolina from 1993 to 1997. 24 Using AHS Data Release Version 202210.00, we excluded participants who did not return a take-home questionnaire (32 019 participants; applicators only); died before receipt of baseline questionnaire (14 participants); were younger than 18 years at enrollment (32 participants); were out-of-state enrollees (257 participants); had self-reported or registry-confirmed cancer at baseline (3404 participants); had self-reported diabetes, myocardial infarction, stroke, or kidney failure at baseline (2772 participants); did not complete a dietary history questionnaire (20 234 participants); had extreme caloric intake (546 participants); or were missing covariate data (10 717 participants). For the time-varying analysis, we included participants who completed a follow-up dietary history questionnaire, from 1999-2003, approximately 5 years after study entry.

Our final analytic sample for the complete-case, pooled analysis included 390 124 participants. The time-varying complete-case analysis included 234 593 of these participants.

In NIH-AARP, PLCO, and AHS, participants were asked on baseline and follow-up questionnaires about past supplement use. Participants who responded “yes, once per month or more” (NIH-AARP), “yes” (PLCO), or “yes, fairly regularly” (AHS) were asked specifically about frequency of MV use using predefined categories, ranging from never to every day; in subsequent questions, these participants were asked about use of other vitamins and minerals not including MV. Categories of MV use were harmonized across the 3 cohorts such that participants were classified as nonusers, nondaily users, or daily users of MV.

Potential confounders were harmonized across studies and included sex, age, self-reported race and ethnicity, education level, smoking status and intensity, body mass index (BMI; calculated as weight in kilograms divided by height in meters squared), marital status, physical activity level, alcohol intake, coffee intake, Healthy Eating Index 2015 (HEI-2015), 25 other individual supplement use, and family history of cancer. In NIH-AARP, race and ethnicity were queried in the same question with the following 6 predefined categories: American Indian or Alaskan Native; Asian; Black, not Hispanic; Hispanic; Pacific Islander; and White, not Hispanic. In PLCO, race was queried about using 5 predefined categories (ie, American Indian or Alaskan Native, Asian, Black, Pacific Islander, and White), and Hispanic ethnicity was asked in a separate question. Black and White participants who reported Hispanic ethnicity were classified as Hispanic. In AHS, race was queried about using 5 predefined categories (ie, American Indian or Alaskan Native, Asian or Pacific Islander, Black, White, or other), and Hispanic ethnicity was asked in a separate question. Participants who reported Hispanic ethnicity were classified as Hispanic. To harmonize the race and ethnicity variable across the 3 cohorts, Asian and Pacific Islander categories in NIH-AARP and PLCO were combined to match AHS. Race and ethnicity data were included because these data were assessed in each of the 3 cohorts as part of the collection of demographic data.

Participants were followed from baseline MV assessment until date of death, loss to follow-up, or the end of the study period (NIH-AARP and AHS: December 31, 2019; PLCO: December 31, 2020). Death was ascertained through the National Death Index. Cause-specific mortality was ascertained from the International Classification of Diseases, Ninth Revision (ICD-9) or International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) code for underlying cause of death from death certificates. Causes of death were defined as cancer ( ICD-9 codes: 140-208, 238.6; ICD-10 codes: C00-C97), heart disease ( ICD-9 codes: 390-398, 402, 404, 410-429; ICD-10 codes: I00-I09, I11, I13, I20-I51), and cerebrovascular diseases ( ICD-9 codes: 430-438; ICD-10 codes: I60-I69). 26 For cause-specific mortality analyses, proportional hazards models were fit separately for each type of cause-specific mortality, and persons who died from other causes were censored at their date of death. 27

We conducted individual study and pooled analyses. Baseline results from the 3 studies were similar and meta-analysis indicated low between-study heterogeneity. Therefore, pooled analyses are presented as the main results. Owing to study differences in covariate assessment during follow-up, time-varying analyses were run in each cohort, and risk estimates were meta-analyzed.

First, we tabulated demographic and lifestyle factors by MV use and assessed differences for categorical and continuous variables using χ 2 and Kruskal-Wallis tests, respectively. We used Cox proportional hazard regression models to estimate hazard ratios (HRs) and 95% CIs for the associations of nondaily and daily MV use, with nonusers as the reference group, with mortality risk. Time since baseline was used as the underlying time metric. We tested the proportional hazard assumption using the cox.zph function in the survival package in R statistical software version 4.2.2 (R Project for Statistical Computing). Due to violation of this assumption ( P  < .001), we estimated relative risks in the first and second halves of follow-up (ie, follow-up period 1 [FP1] and follow-up period 2 [FP2], respectively) by including a binary variable for follow-up period, defined based on the midpoint of study follow-up, and the interaction term between this variable and MV use in all models.

We ran age- and sex-adjusted models and further adjusted for race and ethnicity, education, marital status, BMI, cigarette smoking, daily alcohol intake, daily coffee intake, HEI-2015, family history of cancer, and individual supplement use. All covariates, except for family history of cancer, changed at least 1 of the pooled risk coefficients by more than 10%. Multivariable-adjusted models were stratified by potential modifiers; modification was evaluated using the likelihood ratio test comparing models with and without an interaction term between the variable of interest and MV use. To assess potential bias due to unmeasured, underlying health conditions, we estimated HRs for MV use and all-cause mortality in the first 5 years, 5 to 10 years, and 10 or more years after baseline MV assessment.

In time-varying analyses, we incorporated MV use assessed approximately 9, 3, and 5 years after baseline in NIH-AARP, PLCO, and AHS, respectively. In NIH-AARP, BMI, smoking, physical activity, individual supplement use, and family history of cancer were reassessed. In PLCO, daily alcohol intake, coffee intake, HEI-2015 quartile, and individual supplement use were reassessed. In AHS, smoking, BMI, daily alcohol intake, and individual supplement use were reassessed. The survival package in R was used to run a Cox proportional hazard regression model with a time-dependent variable for MV use. Other covariates were updated where possible; otherwise, baseline data were kept constant. The R function tmerge was used to create the time-dependent dataset for these analyses. The results from the individual study time-varying analyses were meta-analyzed using a fixed-effects model. The metafor package in R was used for the meta-analysis. A 2-sided P  < .05 was considered statistically significant. Data were analyzed from June 2022 to April 2024.

In our analytic sample of 390 124 participants (median [IQR] age, 61.5 [56.7-66.0] years; 216 202 [55.4%] male), including 327 732 from NIH-AARP, 42 732 from PLCO, and 19 660 from AHS, there were 7 861 485 person-years of follow-up (NIH-AARP: 6 576 546 person-years; PLCO: 827 313 person-years; AHS: 457 626 person-years). A total of 164 762 participants died over the study period, with 49 836 deaths attributed to cancer, 35 060 deaths attributed to heart diseases, and 9275 attributed to cerebrovascular diseases ( Table 1 ). Participants in the AHS were younger (median [IQR] age, 47.0 [38.0-56.0] years) than participants in NIH-AARP (median [IQR] age, 61.9 [57.2-66.1] years) and PLCO (median [IQR] age, 62.0 [58.0-66.0] years), but, in each of the 3 cohorts, median age was similar for daily MV users and nonusers ( Table 2 ). Among daily MV users, 49.3% and 42.0% were female and college educated, compared with 39.3% and 37.9% among nonusers, respectively. In contrast, 11.0% of daily users, compared with 13.0% of nonusers, were current smokers. Participants who used MV, compared with those who did not, were also more likely to use individual supplements and have lower BMI and better diet quality ( Table 2 ). Daily MV users were less likely than nonusers to be currently married in NIH-AARP and PLCO but more likely to be married in AHS, likely owing to different age distributions. MV use did not vary meaningfully by race or ethnicity or family history of cancer ( Table 2 ; eTable 1 in Supplement 1 ).

In the pooled analysis, daily MV users had a higher mortality risk than nonusers (FP1: HR, 1.04; 95% CI, 1.02-1.07; FP2: HR, 1.04; 95% CI, 0.99-1.08) ( Table 3 ). However, HR estimates were close to 1.0 for risk of all-cause, heart disease, cancer, and cerebrovascular disease mortality ( Table 3 ; eTable 2 in Supplement 1 ).

We observed potential qualitative effect modification by age, smoking status, and BMI, but not by sex, race and ethnicity, or HEI-2015. In the FP1, HR estimates for daily MV use and all-cause mortality were higher for the youngest (<55 years) age group (HR, 1.15; 95% CI, 1.05-1.26); HR estimates for daily MV vs nonusers were similar by smoking and BMI status ( Figure 1 ) but varied for nondaily MV vs nonusers (eFigure 2 in Supplement 1 ), such that in FP1, HR estimates for nondaily MV use and all-cause mortality were higher for former (HR, 1.10; 95% CI, 1.05-1.16) and current (HR, 1.09; 95% CI, 1.02-1.16) smokers as well as for individuals with a BMI in the normal range (HR, 1.10; 95% CI, 1.09-1.22).

Time-varying analyses included 234 593 participants (eTable 3 in Supplement 1 ). HR estimates were similar in the NIH-AARP and PLCO cohorts in FP1 (NIH-AARP: HR, 1.04; 95% CI, 1.01-1.07; PLCO: HR, 1.06; 95% CI, 1.00-1.12) with a higher mortality risk for daily MV users compared with nonusers ( Figure 2 ). In FP2, the HR for NIH-AARP, the largest of the 3 studies, was attenuated and the 95% CI included 1.00. In the meta-analysis incorporating the time-varying estimates from all 3 cohorts, daily MV use, compared with nonuse, was associated with a 4% higher risk of all-cause mortality in FP1 (HR, 1.04; 95% CI, 1.02-1.07) but not in FP2 (HR, 0.98; 95% CI, 0.93-1.04) ( Figure 2 ).

In this cohort study of 390 124 generally healthy US adults with more than 20 years of follow-up, daily MV use was not associated with a mortality benefit. In contrast, we found that daily MV use vs nonuse was associated with 4% higher mortality risk. The results of the time-varying analysis, incorporating a second MV use assessment, were consistent with the pooled baseline estimates and support our conclusion of no mortality benefit. Finally, by pooling data from 3 large cohorts, we could explore heterogeneity across key population subgroups, including understudied sociodemographic subgroups, which was identified as a research gap in the 2022 USPSTF review. 3 In stratified analyses, we found no evidence of effect modification by race and ethnicity, education, or diet quality.

In the US, MV use declined by 6% from 1999 to 2011 but remains popular, with nearly 1 in 3 adults reporting recent use. 1 , 2 This downward trend may, in part, reflect growing uncertainty about the effectiveness of MV supplementation for preventing disease, following the publication of several studies that reported no benefit of MV use for reducing risk of cardiovascular disease, cancer, or mortality. 6 , 13 , 20 In 2014, the USPSTF concluded that “current evidence is insufficient to assess the balance of benefits and harms of the use of multivitamins for the prevention of cardiovascular disease or cancer,” 28 and in 2022, after conducting a pooled analysis of 9 randomized clinical trials, the USPSTF conclusion remained largely the same, stating that “the evidence is insufficient to determine the balance of benefits and harms of supplementation with multivitamins for the prevention of cardiovascular disease or cancer.” 3 However, in line with our findings, 1 of the included studies, the Physicians’ Health Study II, which was a large randomized double-blind, placebo-controlled trial of a daily MV use, observed no benefit for reducing cardiovascular disease or mortality in male physicians despite more than a decade of treatment and follow-up. 29

Prospective cohort studies have also been inconsistent. A few studies have found no benefit of MV use for reducing cardiovascular disease, cancer, or mortality. 5 - 8 , 13 , 20 , 30 Others have found potential benefit for daily MV use and cardiovascular disease mortality, 9 , 12 and 1 study in a nationally representative sample of US adults with approximately 20 years of follow-up found that women who reported use of multivitamin or multimineral products for more than 3 years, compared with those who did not, had a lower risk of cardiovascular disease mortality. 19 Yet, other studies have reported adverse associations for MV use and mortality among older women 14 and for prostate cancer mortality among men. 15 , 31

Varying results across observational studies may be explained, in part, by differences in MV composition or by confounding. 32 For example, MV users may be more health conscious than nonusers; this could translate into healthier diets, more frequent engagement in physical activity and preventative care, or lower rates of obesity and smoking. Confounding by healthy lifestyle would likely result in spurious inverse associations. Conversely, it could be argued that those who are sick or older than 65 years are more likely to initiate MV use. This phenomenon could result in a noncausal positive association, since these individuals have a higher risk of mortality than their healthier or younger counterparts. Furthermore, baseline measures do not account for the possibility of change in MV use over time.

Strengths of our study are highlighted by how we addressed these concerns. First, we harmonized and pooled complete data from individuals who participated in 1 of 3 large cohorts that collected detailed information on demographics and lifestyle factors and were therefore able to evaluate potential differences in relative risk by demographic and lifestyle factors. Additionally, with the extended follow-up periods of 24 years in NIH-AARP, 27 years in PLCO, and 26 years in AHS and repeated assessments of MV use, we were better able evaluate the long-term association of daily MV use with mortality risk.

Our study has some limitations. First, it is an observational study and residual confounding by poorly measured or unmeasured confounders (eg, health care utilization) may bias risk estimates. However, we excluded individuals with a history of cancer and other chronic disease at baseline and those with missing data; additionally, we carefully adjusted for major mortality risk factors and, where possible, updated variables, like smoking and BMI, in time-varying analyses. Second, there is the possibility for nondifferential exposure misclassification owing to faulty memory of sporadic MV usage. For this reason, we focused our interpretation on daily use vs nonuse. Additionally, the prospective nature of the study mitigates the potential for differential exposure misclassification. Third, selection bias is possible as the participants with missing data could be systematically different than those with complete data. However, age- and sex-adjusted HR estimates from the complete case analytic sample and the larger sample with missing covariate data were similar. Nevertheless, because of these exclusions, generalizability to the total US population may be limited. Fourth, the 3 studies include mostly White individuals, but pooling across the 3 studies improved statistical power for subgroup analyses. Lastly, we cannot assess latency of the association of MV use and the cumulative association over the life span.

In this cohort study of 390 124 US adults without a history of major chronic diseases, we did not find evidence to support improved longevity among healthy adults who regularly take multivitamins. However, we cannot preclude the possibility that daily MV use may be associated with other health outcomes related to aging. 33

Accepted for Publication: April 24, 2024.

Published: June 26, 2024. doi:10.1001/jamanetworkopen.2024.18729

Open Access: This is an open access article distributed under the terms of the CC-BY License . © 2024 Loftfield E et al. JAMA Network Open .

Corresponding Author: Erikka Loftfield, PhD, MPH, NCI Shady Grove, Division of Cancer Epidemiology and Genetics, Metabolic Epidemiology Branch, 9609 Medical Center Dr, Rockville, MD 20850-9768 ( [email protected] ).

Author Contributions: Dr Loftfield and Ms O’Connell had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Loftfield, Abnet.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Loftfield, O'Connell, Graubard.

Critical review of the manuscript for important intellectual content: Loftfield, Abnet, Liao, Beane Freeman, Hofmann, Freedman, Sinha.

Statistical analysis: Loftfield, O'Connell, Graubard.

Obtained funding: Loftfield.

Administrative, technical, or material support: Abnet, Liao, Beane Freeman, Hofmann, Freedman, Sinha.

Supervision: Loftfield, Abnet, Beane Freeman.

Conflict of Interest Disclosures: None reported.

Funding/Support: This work was supported by the intramural research program of the National Institutes of Health, the National Institute of Environmental Health Sciences (grant No. Z01-ES049030), and National Cancer Institute (grant No. Z01-CP010119) and by the Office of Dietary Supplements Research Scholars Award, National Institutes of Health.

Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Disclaimers: The views expressed herein are solely those of the authors and do not necessarily reflect those of the Florida Cancer Data System or Florida Department of Health. The Pennsylvania Department of Health specifically disclaims responsibility for any analyses, interpretations or conclusions.

Data Sharing Statement: See Supplement 2 .

Additional Contributions: Sigurd Hermansen and Kerry Grace Morrissey (Westat), aided in study outcomes ascertainment and management for the National Institutes of Health–AARP Diet and Health Study, and Leslie Carroll (Information Management Services) provided data support and analysis. They were compensated for this work.

Additional Information: Cancer incidence data from the Atlanta metropolitan area were collected by the Georgia Center for Cancer Statistics, Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta. Cancer incidence data from California were collected by the California Cancer Registry, California Department of Public Health’s Cancer Surveillance and Research Branch, Sacramento. Cancer incidence data from the Detroit metropolitan area were collected by the Michigan Cancer Surveillance Program, Community Health Administration, Lansing, Michigan. The Florida cancer incidence data used in this report were collected by the Florida Cancer Data System (Miami) under contract with the Florida Department of Health, Tallahassee. Cancer incidence data from Louisiana were collected by the Louisiana Tumor Registry, Louisiana State University Health Sciences Center School of Public Health, New Orleans. Cancer incidence data from New Jersey were collected by the New Jersey State Cancer Registry, The Rutgers Cancer Institute of New Jersey, New Brunswick. Cancer incidence data from North Carolina were collected by the North Carolina Central Cancer Registry, Raleigh. Cancer incidence data from Pennsylvania were supplied by the Division of Health Statistics and Research, Pennsylvania Department of Health, Harrisburg. Cancer incidence data from Arizona were collected by the Arizona Cancer Registry, Division of Public Health Services, Arizona Department of Health Services, Phoenix. Cancer incidence data from Texas were collected by the Texas Cancer Registry, Cancer Epidemiology and Surveillance Branch, Texas Department of State Health Services, Austin. Cancer incidence data from Nevada were collected by the Nevada Central Cancer Registry, Division of Public and Behavioral Health, State of Nevada Department of Health and Human Services, Carson City.

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  • v.4(5); 2013 Sep

Nutrition research to affect food and a healthy lifespan 1, 2

Sarah d. ohlhorst.

3 American Society for Nutrition, Bethesda, MD

Robert Russell

4 NIH Office of Dietary Supplements, Bethesda, MD, and Jean Mayer USDA Human Nutrition Research Center on Aging at Tufts University, Boston, MA

Dennis Bier

5 USDA/Agricultural Research Service Children’s Nutrition Research Center, Department of Pediatrics, Baylor College of Medicine, Houston, TX

David M. Klurfeld

6 Human Nutrition Program, USDA/Agricultural Research Service, Beltsville, MD

Zhaoping Li

7 Center for Human Nutrition, University of California Los Angeles, and David Geffen School of Medicine at UCLA, Los Angeles, CA

Jonathan R. Mein

8 Monsanto Center for Food and Nutrition Research, Monsanto Vegetable Seed, Kannapolis, NC

John Milner

9 NIH National Cancer Institute, Bethesda, MD

A. Catharine Ross

10 Department of Nutritional Sciences, Pennsylvania State University, University Park, PA; and

Patrick Stover

11 Division of Nutritional Sciences, Cornell University, Ithaca, NY.

Emily Konopka

Proper nutrition offers one of the most effective and least costly ways to decrease the burden of many diseases and their associated risk factors, including obesity. Nutrition research holds the key to increasing our understanding of the causes of obesity and its related comorbidities and thus holds promise to markedly influence global health and economies. After outreach to 75 thought leaders, the American Society for Nutrition (ASN) convened a Working Group to identify the nutrition research needs whose advancement will have the greatest projected impact on the future health and well-being of global populations. ASN’s Nutrition Research Needs focus on the following high priority areas: 1 ) variability in individual responses to diet and foods; 2 ) healthy growth, development, and reproduction; 3 ) health maintenance; 4 ) medical management; 5 ) nutrition-related behaviors; and 6 ) food supply/environment. ASN hopes the Nutrition Research Needs will prompt collaboration among scientists across all disciplines to advance this challenging research agenda given the high potential for translation and impact on public health. Furthermore, ASN hopes the findings from the Nutrition Research Needs will stimulate the development and adoption of new and innovative strategies that can be applied toward the prevention and treatment of nutrition-related diseases. The multidisciplinary nature of nutrition research requires stakeholders with differing areas of expertise to collaborate on multifaceted approaches to establish the evidence-based nutrition guidance and policies that will lead to better health for the global population. In addition to the identified research needs, ASN also identified 5 tools that are critical to the advancement of the Nutrition Research Needs: 1 ) omics, 2 ) bioinformatics, 3 ) databases, 4 ) biomarkers, and 5 ) cost-effectiveness analysis.

INTRODUCTION

The attainment of good nutrition depends on and encompasses the entire food supply. Plant and animal foods and their various components are the primary vehicles that provide nourishment to human beings. Nutrition is vital, not only in the growth and development of humans and animals but also in the prevention and treatment of disease. Nutrition is also fundamental to the maintenance of good health and functionality. Basic and applied research on the interrelations between nutrition and noncommunicable diseases, nutrient composition, and nutrition monitoring represents the underpinnings for healthy populations and robust economies. Thus, innovative nutrition research and education provide the basis for solutions to larger health-related issues, allowing individuals to live healthier, more productive lives.

The importance of nutrition, as an integral part of the solution to many societal, environmental, and economic challenges facing the world, has just started to be fully appreciated. The American Society for Nutrition (ASN) has identified the “grand” challenges facing nutrition research and science in the 21st century, termed “Nutrition Research Needs.” Findings from these Nutrition Research Needs will elucidate strategies that can be applied toward the prevention and treatment of both infectious and noncommunicable diseases, including cardiovascular disease, diabetes, and cancer. Nutrition research holds the key to increasing our understanding of the underlying causes of obesity and its related comorbidities and thus holds promise to markedly influence global economies. Knowledge about adequate nutrition also has an important role in reducing or ending global and domestic food insecurity through direct and purposeful agricultural practices. Population growth will undeniably lead to increased global demand for a safe, available, sustainable, and affordable food supply, while continuing to demand nutritional adequacy.

The ASN Nutrition Research Needs project was originally conceptualized by ASN’s Public Policy Committee to identify worldwide nutrition research needs. This effort will be used to educate and communicate to policy makers and other stakeholders the need and value of increased nutrition research funding to meet societal needs. ASN’s Public Policy Committee reached out to nearly 75 thought leaders in September 2011 to develop a draft list of nutrition research needs.

In February 2012, ASN convened a Working Group of nutrition scientists and researchers representing a cross-section of the Society’s membership to determine the nutrition research needs that will have the greatest impact on the health and well-being of global populations. The names of the Working Group members are listed in the Acknowledgments. Starting with the draft list, the Working Group narrowed down and pulled together 6 nutrition research needs for which advancement would have the greatest projected impact on future health and well-being.

The ASN then informed its membership of the 6 priority research needs and sought further member input. A workshop was held during ASN’s 2012 Scientific Sessions and Annual Meeting in San Diego, CA, with nearly 250 attendees. The research needs were also shared via ASN’s member newsletter, which reaches the entire membership base of nearly 5000 individuals, to inform and seek input from members who did not attend the annual meeting or the workshop. Member feedback on the Nutrition Research Needs was incorporated during development of the final document.

THE TOP NUTRITION RESEARCH NEEDS

The top 6 nutrition research needs cut across the entire research spectrum from basic science to health policy, from discovery to application. Specific research areas are listed under each research need. These 6 nutrition research needs are highlighted in the hope that they will prompt scientists from all disciplines to collaborate to advance these challenging research needs that have high potential for translation and public health impact. Although the topics presented focus principally on human nutrition research, the Working Group recognized that nutrition research using animal models is an essential foundation for making new discoveries that can be translated to advances in human nutrition. Further, the importance of animal nutrition research is emphasized within these research needs in particular: “Understanding the role of nutrition in health maintenance” and “Understanding the food supply/environment.” The research community will benefit from clearly articulated nutrition research priorities that will lead to science-based information, help to shape policy and enhance future funding for nutrition research, and thereby further promote the field of nutrition science.

1) Understanding variability in individual responses to diet and foods

A top priority for future nutrition research is the need to better understand variability in metabolic responses to diet and food. Enormous variability exists in individual responses to diet and food components that affect overall health. Discoveries underpinning this variability will lead to advances in personalized nutrition interventions and will better inform health and food policies, including Dietary Reference Intakes (DRIs) for nutrient needs and, ideally, future recommendations for known bioactive food components. Research in the following areas is necessary to determine the origins and architecture of variability and to explain similar or dissimilar responses to diet and food components by subpopulations, as influenced by genetic, epigenetic, and ethnic and/or racial differences.

Omics research, such as nutrigenetics and nutrigenomics (e.g., epigenetics, transcriptomics, proteomics, and metabolomics), will help to determine how specific nutrients interact with genes, proteins, and metabolites to predict an individual’s health. Omics provide information on individualized nutrient requirements, including how nutrients are digested, absorbed, and metabolized, and their functions in the body. Omics will help to determine and reflect an individual’s nutritional status and will aid in the creation of new nutritional and disease biomarkers.

Microbiome.

Diverse microbes, such as bacteria and viruses, live in and on the body and contribute to the microbiome, which is estimated to have 10 times as many cells as the body itself ( 1 ). Microbes can vary in type and quantity, making each organism’s microbiome unique—although subpopulations may have similar microbiome characteristics. The microbiota needs to be better defined, and changes due to diet, age, physiologic state, and disease need to be determined. Research is needed to determine the microbiome’s role in varying biological responses to diet and food components and its importance in disease prevention and progression. Conversely, research is also needed to determine how the microbiome is influenced by diet and other environmental factors.

Biological networks.

Basic research is needed to provide a better understanding of biological networks, such as an individuals’ genome (DNA/RNA protein profiles), and how these networks affect metabolic responses to diet and food. Environmental interactions, including nutrients and other dietary components, bacteria, viruses, and chemical contaminants, all may affect the responsiveness of biological networks to specific foods and the entire diet.

Tissue specificity and temporality.

Research is needed to describe the mechanisms by which dietary factors affect variability in development and functioning, including which tissues are most influenced by dietary factors and when during the most critical stages in life this influence occurs.

2) Understanding the impact of nutrition on healthy growth, development, and reproduction

Epigenetics/imprinting..

Epigenetics and imprinting research examines how exposures to dietary components during critical periods of development may “program” long-term health and well-being. Research is needed to determine how early nutritional events contribute to disease later in life and alter normal developmental progression.

Early nutrition.

Research is necessary to better understand the role of diet and individual food components on normal growth and development. This includes the role of parent’s preconception diets, the maternal diet during pregnancy, and early nutritional events. Studies indicate that the timing of an infant’s introduction to solid foods may increase the likelihood of becoming obese later in life ( 2 ). These findings are important given that the number of overweight children in the United States has increased dramatically in recent years ( 3 ). Research is now needed to determine the best approaches to influence these factors during early life. The important role of nutrition throughout early life on growth and development, as well as on health and well-being, needs to be continually assessed.

Nutrition and reproductive health.

The impact of nutrition on reproductive health, including before and after conception, requires further research. Nutrition has a direct impact on both maternal and paternal fertility and the ability to conceive and also plays a key role in preventing diseases related to reproductive organs, including prostate and ovarian cancers. Although numerous studies have investigated how fruit and vegetable consumption may affect risk of breast, prostate, and other cancers, there is no clear consensus in the scientific literature. Thus, well-designed controlled intervention studies are needed to determine whether effects are limited to subpopulations, what factors influence a response and what mechanisms may account for changes in health.

3) Understanding the role of nutrition in health maintenance

Health maintenance includes noncommunicable disease prevention and treatment as well as weight management. The role that food components, particularly novel ingredients, contribute to health maintenance requires continuing research. Researchers and the public rely on dietary guidance, including the DRIs, to guide nutrition recommendations and health policy. Research is needed to better define the nutrient needs that best support health maintenance in all populations and their subgroups, from infancy throughout life. Nutrition across life is a fundamental issue that requires investigation so that recommendations will “match” with true biological needs.

Optimal bodily function.

Research is needed to determine the roles that nutrition and fitness, both singularly and together, have in maintaining bodily functions, including cognitive, immune, skeletal, muscular, and other functions. Evolving research areas include prevention of disease-related processes, such as inflammation, and definition of mechanisms that have an important role in health maintenance, such as immunocompetence. Animal models are used to understand the requirements for optimal health in humans and production animals.

Energy balance.

Research is also needed to examine the use of a systems approach to achieve energy balance including and integrating environmental, biological, psychosocial, and food system factors. A systems approach is preferable because the standard experimental approach of varying one factor at a time has accomplished little to address the populationwide problem of energy imbalance. A solution-oriented approach that is comprehensive in nature and takes into account the complexities of achieving energy balance must be created. Although far more research is needed to identify systemwide changes that maximize energy balance, intriguing examples exist. “Shape Up Somerville, MA,” effectively reduced weight gain in high-risk children through a multifaceted community-based environmental change campaign ( 4 ). Shape Up Somerville increased the community’s physical activity and healthful eating through physical infrastructure improvements and citywide policy and programming changes.

4) Understanding the role of nutrition in medical management

The rapid translation of nutrition research advances into evidence-based practice and policy is a priority for ensuring optimal patient care and effective disease management. Nutrition researchers have a key role in bridging the gap between disease prevention and disease treatment by fostering clinical research, providing innovative education for caregivers and patients, and delineating best practices for medical nutrition in primary care settings.

Disease progression.

To improve the medical management of disease, research is needed to determine how nutritional factors influence both disease initiation and progression, as well as how nutrition affects a patient’s response to therapy. Genetic and epigenetic variations among individuals can result in both positive and negative responses to diets, to specific foods, and to novel food components. The issue of individual variability is of considerable importance in refining medical management, including nutrition support, and requires continuing research.

Expanded research will allow us to better understand and minimize unfavorable impacts of both reduced and elevated nutrient intakes on disease progression and overall health. Disease/mortality response curves are U-shaped for many nutrients (that is, there is an increased risk of adverse outcomes if the nutrient is ingested in either too low or too high amounts). The importance of achieving a proper nutrient balance is seen in the example of chronic inflammation. Chronic inflammation contributes to many noncommunicable diseases and can result from high intakes of proinflammatory omega-6 fatty acids in the face of low intakes of anti-inflammatory omega-3 fatty acids ( 5 ). Research will help to determine the desired intake for essential and nonessential nutrients alone and when combined with other nutrients in the diet.

Nutrition support for special subgroups.

Nutrition research is needed to establish the required nutritional needs that best support survival, growth, and development in subpopulations, such as in chronically diseased patients, in children, and in aging adults. With the success of medical advances, as have been seen with in vitro fertilization and neonatal care, caring for preterm infants presents a new challenge in early nutritional management. Preterm infants have special nutrition needs that will greatly affect their future growth and development, as well as their eventual health status as adults.

5) Understanding nutrition-related behaviors

Drivers of food choice..

Understanding the link between behavior and food choices can help tackle obesity and other nutrition-related issues that are a public health priority. Individual food choices can be influenced by a number of different drivers including the following:

  • Government policy
  • Environmental cues
  • Cultural differences
  • Communication tools, such as social networking and food marketing

Research is needed to identify the impact of these various drivers and understand how they work alone or together to influence nutrition-related behavior. Research will show how these drivers should be altered to have the highest positive influence on individual behavior and therefore public health. For example, the state of Mississippi recorded a 13% decline in obesity among elementary school students from 2005 to 2011 ( 6 ). Multiple changes in the environment occurred, such as the setting of standards for foods sold in school vending machines, setting a requirement for more school exercise time, mandating healthier environments in childcare settings, and establishing programs that encouraged fruit and vegetable consumption. The challenge now is to determine what effect these combined actions will have on obesity-related behaviors in the long run.

Nutrition and brain functioning.

Further explorations of the biochemical and behavioral bases for food choices and intake over time are essential. Brain function as it relates to food desire and choice needs to be clarified through research, and the multiple hormones that affect eating require further study as well. Factors such as meal frequency and size, speed of meal consumption, and how these factors are influenced by social cues require objective data, which can only be provided by research. Understanding how the marketing of healthy behaviors could help consumers achieve dietary guidance goals should be a priority. As part of this approach, innovative and practical methods for accurately measuring and evaluating food purchases and eating occasions must be developed.

Imprinting.

Because of the high propensity of obese children remaining obese as adults ( 7 ), additional research is needed to determine how eating and satiety behaviors are imprinted during critical periods of development and to show how food components affect neural biochemistry and brain functioning—and therefore shape behavior. This research will provide us with a better understanding of how and why an individual makes particular food choices. Although scientists recently validated the concept that food availability during pregnancy has permanent effects on gene expression in children ( 8 ), human studies are needed to confirm or refute the hypothesis that fetal programming, resulting from maternal obesity, leads to excess weight in children and into adulthood.

6) Understanding the food supply/environment

Food environment and food choice..

Simply knowing or understanding what constitutes a healthy diet is not enough to change an individual’s diet or lifestyle. Understanding how the food environment affects dietary and lifestyle choices is necessary before effective policies can be instituted that will change a population’s diet in a meaningful way. Examples of key questions that should be addressed include the following:

  • Is current dietary guidance an effective way of communicating dietary change?
  • Do food assistance programs promote positive dietary patterns or have negative dietary and health consequences?
  • What role does food advertising play in food decision-making among different age groups and educational levels?
  • How do farm-to-fork food systems, with an increased emphasis on local agricultural production and consumption, influence dietary patterns and behaviors?
  • How can farm-to-fork food systems ultimately be used to promote healthy behaviors and improve public health?
  • How can we most effectively measure, monitor, and evaluate dietary change?

Food composition and novel foods and food ingredients.

Having an affordable, available, sustainable, safe, and nutritious food supply is also an important underpinning for making significant changes to a population’s diet and lifestyle. Examples of key research areas to address include the following:

  • Enhancing our knowledge of the nutrient and phytonutrient content and bioavailability of foods produced, processed, and consumed
  • Studying how to better align and foster collaboration between nutrition and agricultural production
  • Can shifting agricultural focus from principally agronomic to include quality factors (such as taste, flavor, and nutritional value) have positive effects on fruit and vegetable consumption?
  • Can we leverage technologies, such as biotechnology and nanotechnology, to develop novel foods and food ingredients that will improve health, both domestically and abroad, and provide credible, tangible functional health benefits?

Public/private partnerships.

To tackle these enormous challenges requires the coordinated efforts of public and private partners. The development of public/private partnerships between food and agricultural industries, government, academia, and nongovernmental organizations has the potential to advance nutrition research, enabling meaningful changes to be made to American and global diets (e.g., increased fruit and vegetable consumption to match government recommendations). We need to examine successful examples of public/private partnerships that have resulted in improved nutritional status and food security in specific populations ( 9 ).

CROSS-CUTTING TOOLS TO ADVANCE NUTRITION RESEARCH

Nutrition research is truly a cross-cutting discipline, and the Working Group identified several tools that are also necessary to advance the priority needs in nutrition research. Adequately powered intervention trials continue to be essential for validating research theories arising from experimental and epidemiologic studies. However, the development of new, impactful tools will help us to more effectively quantify dietary intake and food waste and to determine the effectiveness of nutrition standards, such as DRI values and the Dietary Guidelines for Americans . Although not a traditional tool, multidisciplinary partnerships among scientific societies, government, industry, academia, and others are fundamental to advance the nutrition research agenda. ASN and its membership must be proactive not only in efforts to advance nutrition research (including initiating and leading partnerships) but also in developing the tools needed to enhance the field. ASN recognizes the need to facilitate effective communication among academia, industry, government agencies, consumers, and other stakeholders to advance nutrition.

Omics (especially genomics, proteomics, and metabolomics) will enable us to determine how specific nutrients interact with genes, proteins, and metabolites to predict the future health of an individual. A field of study that encompasses technological advances as well as omics-based research, it is sometimes referred to as personalized nutrition. Omics hold the keys to major nutrition breakthroughs in noncommunicable disease and obesity prevention. Omics provide information on how well nutrients are digested, absorbed, metabolized, and used by an individual. Moreover, omics will lead to new biomarkers that reveal a person’s nutritional status and health status all at one time.

2) Bioinformatics

Bioinformatics is an interdisciplinary field that uses computer science and information technology to develop and enhance techniques to make it easier to acquire, store, organize, retrieve, and use biological data. Bioinformatics will enable nutrition researchers to manage, analyze, and understand nutrition data and to make connections between diet and health that were not previously possible. Databases are necessary to gain the full benefits of bioinformatics, because they make nutrition data easily accessible in a machine-readable format.

3) Databases

Accurate, up-to-date food and nutrient databases are essential to track and observe trends related to the nutrition and health of individuals. Databases link food and supplement composition and intake data to health outcomes. Nutrient databases should be expanded to cover more foods and their bioactive components, including nonessential nutrients. Nutrition data must be incorporated into databases related to novel research areas, such as nutrigenomics and the microbiome, to adequately link these areas with nutrition. Data collection must also be improved with enhancements such as photographic food intake documentation, direct upload of food composition and sensory characteristics (if not proprietary) from food manufacturers, and biological sample collection.

4) Biomarkers

Intake, effect, and exposure biomarkers allow us to determine and monitor the health and nutritional status of individuals and subpopulations, including ethnic and racial minorities. Biomarkers that are responsive to diet and nutrition will help assess disease progression and variability in response to treatment, while improving early diagnosis and prevention. Biomarkers must continue to be developed and validated to accurately track food and nutrient intake given our rapidly changing food supply.

5) Cost-effectiveness analysis

Cost-effectiveness analysis is a tool used to calculate and compare the relative costs and benefits of nutrition research interventions. Cost effectiveness analysis helps to determine the most cost-effective option that will have the greatest benefit to public health.

CONCLUSIONS

The multidisciplinary nature of nutrition research requires collaboration among research scientists with differing areas of expertise, many different stakeholders, and multifaceted approaches to develop the knowledge base required for establishing the evidence-based nutrition guidance and policies that will lead to better health and well-being of world populations. Proper nutrition offers one of the most effective and least costly ways to decrease the burden of chronic and noncommunicable diseases and their risk factors, including obesity. Although there is skepticism about the ability to complete large, well-controlled dietary interventions at a reasonable cost in the United States, the success of the Lyon Diet Heart study in France ( 10 , 11 ) and the PREvención con DIeta MEDiterránea (PREDIMED) study in Spain ( 12 ), both of which used variations of the Mediterranean diet, show this approach can be successful, even in the presence of drug treatment of cardiovascular risks in the latter study. Both of these studies showed significant reductions in cardiovascular disease (and cancer in the Lyon study) after relatively modest dietary changes.

Perhaps the greatest barrier to advancing the connections between food and health is the variability in individual responses to diet; it is also the origin of public skepticism to acceptance of dietary advice and the opportunity for entrepreneurship in the private sector. Imagine being able to identify, with certainty, those most likely to benefit from prescriptive nutrition advice through the various omic technologies and then providing these groups of people with customized nutrition advice based on their metabolic risk profiles. This is the new frontier of the nutritional sciences that offers the opportunity to predictably engineer our physiologic networks for health through diet. The confidence this approach would bring to the skeptical consumer would improve adherence to weight management and disease treatment techniques and improve the chances of success for disease prevention. To realize the full positive impact of achieving good nutrition on disease prevention and the health of populations, we must have the will to invest in and support the 6 key areas of nutrition research that have been outlined above.

Acknowledgments

The Nutrition Research Needs Working Group consisted of Dennis Bier, David M Klurfeld, Zhaoping Li, Jonathan R Mein, John Milner, A Catharine Ross, Robert Russell (Chair), and Patrick Stover. They were supported by ASN staff members Sarah D. Ohlhorst and Emily Konopka.

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Faculty Scholarship

International society of sports nutrition position stand: ketogenic diets.

Alex Leaf , Alex Leaf LLC, Scientific Affairs Jeffrey A. Rothschild , Auckland University of Technology Tim M. Sharpe , University of Western States Stacy T. Sims , Auckland University of Technology Chad J. Macias , University of Western States Geoff G. Futch , Springfield College - Springfield, MA Michael D. Roberts , Auburn University Jeffrey R. Stout , University of Central Florida Michael J. Ormsbee , Florida State University Alan A. Aragon , Fit Advancement, LLC Bill I. Campbell , University of South Florida Shawn M. Arent , University of South Carolina Dominic P. D'Agostino , University of South Florida Michelle T. Barrack , California State University, Long Beach Chad Kerksick , Lindenwood University Follow Richard B. Kreider , Texas A & M University - College Station Douglas S. Kalman , Nova Southeastern University Jose Antonio , Nova Southeastern University

Document Type

Publication title.

Journal of the International Society of Sports Nutrition

Position statement

The International Society of Sports Nutrition (ISSN) provides an objective and critical review of the use of a ketogenic diet in healthy exercising adults, with a focus on exercise performance and body composition. However, this review does not address the use of exogenous ketone supplements. The following points summarize the position of the ISSN:

1. A ketogenic diet induces a state of nutritional ketosis, which is generally defined as serum ketone levels above 0.5 mM. While many factors can impact what amount of daily carbohydrate intake will result in these levels, a broad guideline is a daily dietary carbohydrate intake of less than 50 grams per day.

2. Nutritional ketosis achieved through carbohydrate restriction and a high dietary fat intake is not intrinsically harmful and should not be confused with ketoacidosis, a life-threatening condition most commonly seen in clinical populations and metabolic dysregulation.

3. A ketogenic diet has largely neutral or detrimental effects on athletic performance compared to a diet higher in carbohydrates and lower in fat, despite achieving significantly elevated levels of fat oxidation during exercise (~1.5 g/min).

4. The endurance effects of a ketogenic diet may be influenced by both training status and duration of the dietary intervention, but further research is necessary to elucidate these possibilities. All studies involving elite athletes showed a performance decrement from a ketogenic diet, all lasting six weeks or less. Of the two studies lasting more than six weeks, only one reported a statistically significant benefit of a ketogenic diet.

5. A ketogenic diet tends to have similar effects on maximal strength or strength gains from a resistance training program compared to a diet higher in carbohydrates. However, a minority of studies show superior effects of non-ketogenic comparators.

6. When compared to a diet higher in carbohydrates and lower in fat, a ketogenic diet may cause greater losses in body weight, fat mass, and fat-free mass, but may also heighten losses of lean tissue. However, this is likely due to differences in calorie and protein intake, as well as shifts in fluid balance.

7. There is insufficient evidence to determine if a ketogenic diet affects males and females differently. However, there is a strong mechanistic basis for sex differences to exist in response to a ketogenic diet.

https://doi.org/10.1080/15502783.2024.2368167

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Leaf, Alex; Rothschild, Jeffrey A.; Sharpe, Tim M.; Sims, Stacy T.; Macias, Chad J.; Futch, Geoff G.; Roberts, Michael D.; Stout, Jeffrey R.; Ormsbee, Michael J.; Aragon, Alan A.; Campbell, Bill I.; Arent, Shawn M.; D'Agostino, Dominic P.; Barrack, Michelle T.; Kerksick, Chad; Kreider, Richard B.; Kalman, Douglas S.; and Antonio, Jose, "International society of sports nutrition position stand: ketogenic diets" (2024). Faculty Scholarship . 651. https://digitalcommons.lindenwood.edu/faculty-research-papers/651

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Experts Introduce the First Nutrition Guidelines for People Taking Anti-Obesity Drugs

Imagesbybarbara / Getty Images

Key Takeaways

  • The first comprehensive evidence-based review on nutrition recommendations for people taking anti-obesity medications has been published. It offers guidance for calorie and nutrient intake. 
  • Nearly 40% of American adults are living with obesity. Some of them will need to take these new medications to achieve and maintain a weight that better supports their health.
  • Not eating enough contributes to nutrient deficiencies and loss of muscle mass for people who are trying to lose weight.

Roughly 42% of all adults in the United States live with obesity . Eating a nutritious diet and exercising are fundamental for weight management, but some people need more help getting to and staying at a weight that supports their health.

As new anti-obesity medications enter the market, there hasn't been much guidance about nutrition for patients who are on these drugs.

A recent research paper offers a comprehensive review of nutrition recommendations for patients on Wegovy or Zepbound, which can reduce appetite and increase satiety. This new guidance can help clinicians identify and manage patients who are at risk of nutritional deficiencies because of reduce food intake, the researchers wrote.

The authors of the paper acknowledged that target nutrient intakes vary from person to person, and there’s no dietary pattern that’s considered the best or most effective for weight loss.

They recommend a balance of nutrient-dense foods and beverages that provide plenty of vitamins and minerals. Additionally, they recommend choosing foods that are low in added sugars , saturated fats, and sodium.

Here’s a general breakdown of nutrient intake guidelines for people on Wegovy or Zepbound.

Calories provide your body with the energy it needs to perform basic functions like breathing, moving, and thinking. The general energy intake during weight loss is between 1,200 and 1,500 calories per day for women and between 1,500 and 1,800 for men.

That said, energy requirements vary depending on your age, sex, body weight, physical activity levels, and other factors. Your energy intake should be personalized according to your needs and goals and by a nutritionist.

You might find it useful to track calories, but Isabella Ferrari, MCN, RD, CSO, LD , senior clinical manager at Doherty Nutrition, told Verywell that calorie counting can be detrimental for some people.

“It’s super important to have a dietitian on your side when you’re trying to lose weight because we don’t want the calorie counting or calorie tracking to become an obsessive behavior where people can’t live their life without knowing how many calories they’re going to track,” said Ferrari.

People living with obesity need a protein intake of at least 60 to 75 grams per day, and up to 1.5 grams per kilogram of body weight per day is recommended, especially if you’re having bariatric surgery or other weight reduction treatments.

The recommended protein allowance for most adults with no health concerns is 0.8 grams per kilogram of body weight per day.

Carbohydrates 

A common weight loss misconception is that you need to cut carbohydrates to lose weight. However, research has shown that severe carb restriction does not produce long-term weight reduction and may even restrict the nutrition that you’d normally get from eating plenty of fruits, vegetables, and whole grains.

If you’re taking newer anti-obesity medications, Almandoz recommends focusing on balanced nutrition. The recommended amount of carbohydrates for healthy adults can work for people trying to lose weight: 135 to 245 grams per day for a 1200- to 1500-calorie diet, or 170 to 290 grams per day for a 1500- to 1800-calorie diet.

For patients who are recommended or prefer a low-carbohydrate diet, Almandoz suggests making sure that you’re drinking 2 to 3 liters of fluid per day.

Dietary fats help your body absorb fat-soluble vitamins, like vitamins A, D, E, and K. While there’s less evidence for recommended fat intake ranges, the Acceptable Macronutrient Distribution Range (AMDR) for fat for most adults is 20% to 35% of energy intake for a 1,200- to 1,500- calorie diet. 

Around 90% of Americans don’t get enough fiber , but this nutrient is key for preventing constipation and helping you feel fuller longer. The adequate intake level of fiber is 21 to 25 grams per day for women and 30 to 38 grams for men. To meet your fiber requirements, focus on fiber-dense foods like:

  • Whole grains 

“Unfortunately, many people in the U.S. consume a lower-quality diet that is high in ultra-processed foods,” said Almandoz. “Without appropriate nutrition assessment and guidance, we run the risk that people who take these new anti-obesity medications will just eat less of a low-quality diet.”

If you don’t eat a lot of fiber, you’ll want to ramp up slowly to avoid issues like constipation.

Since you don’t want to run the risk of nutritional deficiencies and loss of muscle mass, talk with your healthcare provider and nutritionist about your diet if you’re considering anti-obesity medications.

What This Means For You

If you’re considering an anti-obesity drug, be sure to talk to your healthcare provider and a nutritionist about how to make sure you’re getting adequate nutrition while you’re taking the medications.

Hales CM, Carroll MD, Fryar CD, Ogden CL. Prevalence of obesity and severe obesity among adults: United States, 2017–2018 . NCHS Data Brief , no 360. Hyattsville, MD: National Center for Health Statistics. 2020.

Almandoz JP, Wadden TA, Tewksbury C, et al. Nutritional considerations with antiobesity medications . Obesity (Silver Spring) . Published online June 10, 2024. doi:10.1002/oby.24067

Koliaki C, Spinos T, Spinou Μ, Brinia ΜE, Mitsopoulou D, Katsilambros N. Defining the optimal dietary approach for safe, effective and sustainable weight loss in overweight and obese adults . Healthcare (Basel) . 2018;6(3):73. doi:10.3390/healthcare6030073

Salleh SN, Fairus AAH, Zahary MN, Bhaskar Raj N, Mhd Jalil AM. Unravelling the effects of soluble dietary fibre supplementation on energy intake and perceived satiety in healthy adults: evidence from systematic review and meta-analysis of randomised-controlled trials . Foods . 2019;8(1):15. doi:10.3390/foods8010015

By Kayla Hui, MPH Hui is a health writer with a master's degree in public health. In 2020, she won a Pulitzer Center Fellowship to report on the mental health of Chinese immigrant truck drivers.

Top Nutrition Research Paper Topics for Students

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Table of contents

  • 1 Nutrition Research Topics for College Students
  • 2 Interesting Nutrition Topics for Research Paper
  • 3 Research Topics in Nutrition and Dietetics
  • 4 Sports Nutrition Topics for Research
  • 5 Nutritional Responses to the COVID-19 Pandemic
  • 6 Advances in Food Technology and Biotechnology
  • 7 Emerging Topics in Nutrition and Health
  • 8 Innovations in Food and Dietary Patterns
  • 9 Climate Change and Nutrition Research Topics
  • 10 Other Popular Nutrition Paper Topics
  • 11 Conclusion

Sometimes, coming up with an interesting topic is much more challenging than even writing a 10-page essay. After all, there are so many unique themes you could divulge, and choosing the only one that would suit your needs best can be overwhelming.

To narrow down your list of potential nutrition topics for research, it’s in your best interest to start with broader themes that spark your interest. For example, do you want to know more about how food impacts health and disease? Are you more interested in the psychological and emotional connection to food? Perhaps you’ve always been curious about nutrition and muscle development or weight loss?

Once you have a general direction, you’d like to go in, and finding suitable topics becomes much easier.

But if you’re still struggling with finding inspiration for your next essay, you should check out PapersOwl’s nutrition research paper topic suggestions. We’ve compiled a list of dozens of unique topics that’ll help you finish your assignment.

And if you need more than just suggestions, you can always find nutrition and nursing papers for sale on our platform.

Without further ado, let’s get into some of the best topic ideas!

Nutrition Research Topics for College Students

The following are some of the best nutrition research paper topics for college students who want to learn more about the themes that directly affect them. In case you need assistance with writing any of the following topics, you can order custom research papers and receive authentic, plagiarism-free content written by nutrition experts.

  • Stress eating a growing problem among college students
  • The cause and effects of Freshman 15
  • How healthy foods can help deal with mental health issues
  • Sleep and nutrition –how they relate to each other
  • How healthy eating impacts a college athlete’s performance
  • Is breakfast the most important meal of the day?
  • Why women are more likely to suffer from anemia
  • Preventing/curing hangovers with smarter food choices
  • The impact of social media on students’ dietary choices
  • What are superfoods, and can they be beneficial?
  • The rising popularity of the paleo diet
  • What makes fast food so addictive?
  • Most common eating disorders among college students
  • Diet and mood – how they’re intertwined
  • Can healthy foods improve cognition and brain power?

Interesting Nutrition Topics for Research Paper

If you’d prefer a bit more exciting topic that encourages debate and gets your readers immersed, take a look at the following nutrition research paper topic suggestions.

  • Overcoming unhealthy emotional relationship with food
  • The intricate relationship between smoking and weight
  • How sleep moderates ghrelin and leptin levels
  • Cannabinoids as nutritional supplements
  • Prevalence of diabetes among college students
  • How helpful are gummy vitamins?
  • Genetic predispositions for becoming obese
  • How parents’ eating habits impact children’s dietary choices
  • Preventing eating disorders in teens and young adults
  • How the body positivity movement can be harmful to young adults
  • In-depth review of US school lunches – what needs to change?
  • Preventing chronic diseases with better food choices
  • Is overhydration more dangerous than dehydration?
  • The impact of social media on women’s body image
  • Hormones and nutrition – how are they connected?
  • Community health initiatives and their impact on nutrition

Research Topics in Nutrition and Dietetics

Analyzing diets and their impact on our health and fitness is always intriguing. Learn more about nutrition and dietetics with some of the following nutrition research topics:

  • Keto diet and risk considerations
  • Dietary changes during the COVID-19 pandemic
  • Nutrition vs. physical activity for healthy weight management
  • Methods for improving physical fitness while limiting calorie intake
  • In-depth analysis of yo-yo dieting
  • How going vegan impacts health
  • Dietary fats – the good and the bad
  • The dark side of the juice cleanse
  • The role of proteins in weight loss
  • How popular diet trends affect your health
  • Is intermittent fasting a good way to lose weight?
  • In-depth analysis of compulsive eating disorder Pica
  • Harmful trends that promote eating disorders
  • How to ensure proper nutrient intake on a plant-based diet
  • Are vegan foods always healthier?
  • Staple food: its role in global nutrition and dietary guidelines

Sports Nutrition Topics for Research

Student-athletes always want to know more about how food and nutrition impact their performance. The following topics can be just as useful to them as they are to med students.

  • Prevalence of eating disorders among female athletes
  • How plant-based diets impact athletes’ performance
  • How much protein you need for optimal muscle development
  • The role of BCAA’s in weightlifting performance among seniors
  • Is when you eat just as important as what you eat?
  • The impact of food choices on muscle recovery
  • Maintaining electrolyte balance during endurance training
  • The role of creatine in improving athletic performance
  • How to safely cut weight ahead of a competition
  • Effects of dietary fibers on carbohydrate uptake and absorption
  • What athletes need to know about BMI
  • Most effective supplements for bone and tendon health in combat athletes
  • How caffeine impacts athletic performance
  • In-depth analysis of Peri-Workout nutrition for strength athletes
  • Examining the effects of low-carb diet trends on athletic performance
  • Nutritional contributions to bone health and prevention of osteoporosis

Nutritional Responses to the COVID-19 Pandemic

  • The role of diet in modulating immune response during COVID-19
  • Impact of nutritional status on COVID-19 outcomes
  • Dietary interventions to reduce COVID-19-related inflammation
  • Changes in physical activity levels due to pandemic restrictions
  • Nutritional strategies to support at-home workouts
  • Psychological impact of reduced sports activities and nutritional adjustments
  • Disruptions in food supply chains during the pandemic
  • Adaptations in dietary habits due to food shortages and lockdowns
  • Long-term implications of pandemic-induced dietary changes on health
  • Vitamin D deficiency: causes, effects, and solutions

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Advances in Food Technology and Biotechnology

  • Cellular Agriculture: Biotechnology for Sustainable Food
  • Innovations in lab-grown meat production
  • Environmental benefits of cellular agriculture
  • Consumer perceptions and acceptance of cultured meat products
  • Application of Nanotechnology in Food Science
  • Enhancements in food safety and quality through nanotechnology
  • Nanoparticles in food packaging for extended shelf life
  • Potential health risks and regulatory challenges of nanotechnology in food
  • Sustainable Food Production Through Biotechnology
  • Genetic modifications for improved crop yield and nutrition
  • Biotechnological approaches to reduce food waste
  • Role of biotechnology in addressing global food security
  • Healthier alternatives to common high-calorie foods

Emerging Topics in Nutrition and Health

  • Potential benefits of cannabinoids in managing chronic diseases
  • Use of cannabinoids as nutritional supplements
  • Regulatory and safety considerations in cannabinoid use
  • Benefits of human milk for preterm infants
  • Challenges in breastfeeding preterm babies
  • Strategies to enhance the nutritional quality of human milk
  • Dietary approaches to promote muscle health across the lifespan
  • Role of proteins and supplements in muscle maintenance
  • Impact of nutrition on muscle recovery and performance
  • Food addiction: understanding and addressing the issue
  • The Mediterranean diet: benefits and implementation

Innovations in Food and Dietary Patterns

  • Technological advancements in cultured meat production
  • Economic and ethical considerations
  • Consumer acceptance and market potential
  • Health benefits of a predominantly plant-based diet with occasional meat
  • Environmental impacts of flexitarian dietary patterns
  • Strategies to promote flexitarianism among different populations
  • Interactions between diet, gut microbiota, and health
  • Personalized nutrition plans based on gut microbiome analysis
  • Future prospects for microbiome-targeted dietary interventions
  • The ketogenic diet: benefits, risks, and long-term effects

Climate Change and Nutrition Research Topics

  • Impact of climate change on global food security
  • Sustainable agricultural practices to combat climate change
  • Policy frameworks for climate-resilient food systems
  • Promoting plant-based diets for environmental sustainability
  • Reducing food waste through dietary changes
  • Integrating nutrition and sustainability goals in public health policies
  • Effects of thermal processing on nutrient retention
  • Innovations in food processing to enhance nutritional value
  • Consumer education on processed food and health
  • The rise of organic food: benefits and challenges

Other Popular Nutrition Paper Topics

Miscellaneous topics can often be some of the most interesting ones, especially since few students ever opt for them. Browse through these ten unique topics and choose the one that suits you best.

Once you’ve found a great topic, writing becomes a much easier task. But if you can’t find the time for your paper, a quick search for services that can “ write my research paper for me ” could be a God-send.

  • Infant brain development and nutrition
  • How a mother’s dietary choices impact the quality of breastmilk
  • Symptoms of malnutrition among children
  • Immune system and diet – how they’re connected
  • The real science behind GMO food
  • The effects of thermal processing on nutrients
  • Factors contributing to obesity among young Americans
  • Different nutritional needs among different age groups
  • Dietary differences between low-income and high-income households
  • Foods that boost serotonin levels
  • Strategies to prevent eating disorders in adolescents and adults
  • Examining the impact of different dietary practices on health
  • The importance of dietary fiber in maintaining digestive health
  • Effective dietary strategies for managing chronic diseases
  • Addressing childhood obesity through better nutrition
  • The impact of fast food consumption on public health
  • Analyzing health claims on food labels and their accuracy

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Food Safety and Packaging Innovations:

  • Biodegradable packaging materials composed of natural polysaccharides
  • Ensuring safe infant formula and baby food
  • Analytical strategies for the determination of microplastics and emerging migrants from packaging in food

Nutritional Strategies for Disease Prevention and Management:

  • Ketogenic metabolic therapies in prevention & treatment of non-communicable diseases
  • Nutritional strategies and diet-microbiota interaction to improve skeletal muscle function
  • Exploring nutrition to mitigate the negative effects of air pollution
  • Functional foods for metabolic health
  • Nutritional management of patients with inborn errors of metabolism

Gut Health and Microbiota:

  • Dietary modulation of gut microbiota-x axis
  • Efficacy of probiotic-enriched foods on digestive health and overall well-being
  • Effects of probiotics, prebiotics, and postbiotics on microbiota-gut-brain axis

Bioactive Compounds and Nutraceuticals:

  • Food derived bioactive metabolites: Unlocking their potential health benefits and medical potential
  • Phenolic compounds in a circular economy: Extraction from industrial by-products and wastes, potential activity and applications
  • Advances in sulfated polysaccharides and precision nutrition
  • Immune cell metabolism beyond energy supply – An emerging era to showcase novel roles in immune effector functions

Marine and Aquatic Nutrition:

  • Processing and utilization of marine food resources
  • Quality and flavor changes in aquatic products
  • Seaweeds as a promising alternative protein source for the sustainable world

Pediatric and Maternal Nutrition:

  • Peptide in promoting lactation and infant development
  • Innovative approaches to nutrition counseling in pediatric dietetics – Guidelines, practices, and future directions
  • The first 1000 days: Window of opportunity for child health and development

Advances in Dietary Supplements:

  • Advancements in dietary supplements: Enhancing sport performance and recovery
  • Marine peptides in regulation of bone immunomodulation, bone joint and other bone-related disease

Environmental and Sustainable Nutrition:

  • Food system transformation and the realization of the UN sustainable development goals
  • Advanced nutritional research driven by artificial intelligence

Processing and Preservation Technologies:

  • New developments in low-temperature food preservation technologies: Safety, sustainability, modeling and emerging issues
  • Storage and deep-processing of fruit and vegetable products
  • Recent advances in quality control technology for fresh fruits and vegetables

Exploring the ways how human bodies work and react has interested people for thousands of years. No wonder there are a lot of engaging dietary and nursing research topics for modern students to choose from. You’ve already got acquainted with 70 of our top nutrition topics for research papers, so gather inspiration from our list and get started with your essay!

If you need further assistance with your writing, PapersOwl’s experts are available 24/7. Contact us, and place your order for custom nutrition papers.

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Agriculture and fisheries

OECD work on agriculture, food and fisheries helps governments assess the performance of their sectors, anticipate market trends, and evaluate and design policies to address the challenges they face in their transition towards sustainable and resilient food systems. The OECD facilitates dialogue through expert networks, funds international research cooperation efforts, and maintains international standards facilitating trade in seeds, produce and tractors.

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Policy issues.

  • Agricultural policy monitoring Well-designed agricultural policies can help farmers meet increasing global demand for safe and nutritious food in a sustainable way. However, some current policies can have negative consequences for food security, markets, the environment, at both the domestic and global levels. The OECD’s regular monitoring of agricultural policies across 54 countries representing three-quarters of global agricultural value-added provides a comprehensive understanding of their nature, implementation and impact, with a view to helping guide governments towards more effective and efficient policy making. Learn more
  • Agricultural productivity and innovation Achieving resilient, sustainable and productive agriculture and food systems will require innovation. Innovation in agriculture means learning to do things differently, to do different things, and to do more and better with less. It is an opportunity for food systems to deliver on challenging new demands, while ensuring the sustainable use of scarce natural resources. The OECD is helping support countries in developing better policies for productive, sustainable and resilient agriculture through work to benchmark the performance of agriculture and food systems, assess countries' policies and provide tailored policy advice. A focus of this work is how governments and the private sector can work together to strengthen agricultural innovation systems and foster innovative practices that increase productivity and sustainability. Learn more
  • Agricultural trade and markets Agricultural trade plays a crucial role in providing livelihoods for farmers and people employed along the food supply chain and contributes to reducing global food insecurity. A growing share of agro-food trade involves global value chains (GVCs), where the different stages of agricultural and food production processes are spread over several countries. Learn more
  • Agriculture and sustainability The agriculture sector faces the triple challenge of providing sufficient and nutritious food for an increasing global population, while at the same time preserving the environment and natural resources for future generations and maintaining sustainable livelihoods in rural areas. Policies have a key role to play in tackling these challenges, while addressing mounting pressures from climate change and other risk factors. Learn more
  • Fisheries and aquaculture Fisheries and aquaculture provide food for billions of people and play an important role in the local economy and cultural life of coastal communities around the world. Fish products are among the most traded foods and their exports are essential for food security. But fish stocks and ecosystems are under stress from climate change, illegal fishing, excessive fishing pressure and pollution. Learn more
  • Food systems Food systems worldwide face a “triple challenge”: to provide food security and nutrition for a growing population, to contribute to the livelihoods of millions of people working along the food supply chain, and to do so in an environmentally sustainable way. Moreover, food systems need to become more resilient across those dimensions. Learn more
  • OECD standards for agriculture The OECD Codes and Schemes aim to facilitate and streamline international trade by simplifying procedures, enhancing transparency, reducing non-tariff barriers to trade, promoting harmonisation of standards, and enhancing environmental protection. They help to strengthen market confidence by assuring quality control, providing for inspections, and improving product traceability. Learn more
  • Co-operative Research Programme: Sustainable Agriculture and Food Systems The OECD's Co-operative Research Programme: Sustainable Agricultural and Food Systems (CRP) exists to strengthen scientific knowledge and provide relevant scientific information and advice that will inform future policy decisions related to the sustainability of agriculture, food, fisheries and forests. It does this through facilitating international co-operation among research scientists and institutions, by sponsoring international events (conferences, workshops) and individual research fellowships, placing a policy emphasis on all the activities it funds. It focuses on global issues such as food security, climate change, and the inter-connectedness of economies through trade and scientific co-operation. Learn more

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  1. Home Page: The Journal of Nutrition

    The American Journal of Clinical Nutrition. July 2024Volume 120Issue 1. A highly rated peer-reviewed, primary research journal in nutrition and dietetics, publishes the latest research on topics in nutrition, such as obesity, vitamins and minerals, nutrition and disease, and energy metabolism.

  2. Nutrition Research

    About the journal. Original research articles presenting hypothesis-driven studies performed in humans, or in animal models or cellular systems with physiological relevance to humans. Narrative and systematic reviews and meta-analyses focusing on fundamental and applied nutrition. Research methodology and study design of human clinical trials.

  3. Articles

    Nutrition Journal publishes cutting-edge research on human nutrition, covering epidemiology, interventions, and global issues. Read the latest articles here.

  4. Home Page: The American Journal of Clinical Nutrition

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  27. Top Nutrition Research Paper Topics for Students

    The following are some of the best nutrition research paper topics for college students who want to learn more about the themes that directly affect them. In case you need assistance with writing any of the following topics, you can order custom research papers and receive authentic, plagiarism-free content written by nutrition experts.

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