The key to accurate nutrition

The critical role of nutrition for health requires the development of dietary assessment tools that can accurately identify causal relationships with various health-related outcomes.

A recent study published in Nature Metabolism investigates the potential utility of biomarkers of food consumption (BFIs) for objective and accurate assessments of diet.

Study: Towards precision nutrition: unlocking biomarkers as tools for diet assessment. Image credit: Gorodenkoff /

What are BFIs?

BFIs are commonly used to assess dietary adherence in nutrition intervention and meal studies, to assess the extent of misreporting, and to validate epidemiologically derived associations between food and disease risk. Although food frequency questionnaires (FFQs) and dietary recalls are also useful assessment tools, their subjective nature can lead to biased reporting and poor adherence.

A BFI is a metabolite of ingested food and is defined as a measure of the consumption of specific food groups, foods or food components. BFIs can be ranked based on their robustness, with minimal interference from a varied dietary background affecting the use of the BFI in research.

Reliability in BFIs implies that this marker is qualitatively and/or quantitatively consistent with other biomarkers or dietary instruments. Plausibility depends on the specificity and chemical relationship of the metabolite to the nutrient of interest, which limits the risk of misclassification due to other factors.

Biological variability for BFIs depends on absorption, distribution, metabolism and elimination (ADME) of the food, as well as enzyme/transporter concentrations, genetic variation and gut microbial metabolism. Importantly, this property has not been reported for most BFIs.

Intra-class correlation (ICC) also reflects variability within a population or group in response to various factors. When ICC is low, BFI may be associated with improper sampling time, low frequency of consumption, or gross variation in response over time within and between individuals and populations.

About the study

After validating BFI reviews that adhered to appropriate guidelines and methodologies, the researchers conducted two systematic searches for experimental and observational studies. A four-level classification system was then used to rank reported BFIs based on their robustness, reliability, and plausibility.

If all criteria were met, the BFI was classified as belonging to utility level one. At level two, the candidate BFI is plausible and robust, but not reliable. Level three BFIs are plausible, but lack robustness and reliability, while level four BFIs were not reported for the food products.

If these criteria are met, additional characteristics are also assessed, including time kinetics (this refers to the sampling window or time period in which the BFI is sampled after nutrient intake), analytical performance, and reproducibility.

BFI’s level one and two

Utility level one or validated urine BFIs were found for total meat, total fish, chicken, fatty fish, total fruit, citrus fruit, banana, whole wheat or rye, alcohol, beer, wine, and coffee. Level one blood BFIs exist for fatty fish, whole wheat or rye, citrus, and alcohol.

Level two candidate BFIs in urine include total plant foods and various plant foods, including legumes and legumes, dairy products, and some specific fruits and vegetables. Level two blood BFIs exist for plant foods, dairy products, some meats, and some nonalcoholic beverages; however, these BFIs include fewer foods with less validation.

Identification and validation of BFIs

Discovery and validation of BFIs requires discovery studies, followed by confirmation and prediction studies. Meal studies identify plausible BFIs; however, they may not be specific unless other foods contain very low levels of the marker or are rarely consumed.

For example, betaine is present in high concentrations in oranges and is used to detect orange or citrus consumption, despite being found in low concentrations in many other foods. However, detection studies can be very small or unrepresentative.

Observational studies can be used to identify associations between blood or urine metabolites and diet, but are subject to confounding by lifestyle factors. When two foods are commonly consumed together, such as fish and green tea in Japan, confounding occurs with the BFI of fish, as trimethylamine oxide (TMAO) can also be associated with green tea, making these foods unsuitable for BFI discovery.

Endogenous metabolites are poorly robust BFIs, as they are produced both endogenously and from exogenous foods. These metabolites are also associated with significant variations with interindividual genetic and microbial differences.

Prediction studies use models based on randomized controlled trials to identify the consumption of a particular food. This approach outperforms correlation studies by identifying BFIs that can predict intake but depend on the sampling window for accuracy.

Several databases, such as Massbank, METLIN Gen2, mzCloud (Thermo Scientific), mzCloud Advanced, Mass Spectral Database, and HMDB, are available for metabolite research. The Global Natural Products Social Molecular Networking initiative is leading efforts to connect these databases and compare unknown compounds to known spectra, such as through the Global Natural Products Social Mass Spectrometry Search Tool (MASST).

BFI applications

The selection of BFIs depends on the purpose of the study. Qualitative BFIs are sufficient for identifying non-compliance or performing per-protocol analyses. Conversely, a combination of signature BFIs offers more specificity and can even identify an entire meal or dietary pattern.

A stepwise approach could help identify actual consumers of a given food, before determining the amount consumed in a second step. This would allow even less robust BFIs to play a role in these types of studies.

Feeding habit patterns can be captured by multiple sampling, the frequency and number of which depend on the sampling window and frequency of consumption. Optimal sampling methods identified in the current study include spot urine samples such as first-morning or overnight cumulative samples, dried urine sports, vacuum tube-stored samples, dried spot samples, and microsampling.

Remote sampling increases the number of potential participants and the ability to monitor dietary patterns and changes over time. These methods can also improve epidemiological studies aimed at identifying correlations between diet and disease risk.

Refining sampling and analysis methods can also improve the precision of dietary research and establish reliable links between food intake and health outcomes.

Future development

Future studies are needed to validate the development of single and multimarker BFI using different samples, food groups and diets, as well as cooked and processed foods. Quantitative BFIs should also be characterized by dose-response studies, while BFI combinations should be established to predict and classify intake and dietary patterns.

Precision nutrition is of particular importance to control obesity and cardiometabolic diseases, where a one-diet-fits-all approach does not seem to work due to the highly variable individual responses to diet. Personalized dietary interventions are good drivers of behavior change, which have been shown to improve diet quality.”

Journal reference:

  • Caparencu, C., Bulmus-Tuccar, T., Stanstrup, J., and all. (2024). Towards precision nutrition: unlocking biomarkers as tools in dietary assessment. Nature Metabolism. doi:10.1038/s42255-024-01067-y.

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