The key to maintaining accuracy

Due to the critical role of nutrition in health, it is necessary to develop nutrition assessment tools that can accurately assess causal relationships with various health-related outcomes.

A recent study published in Nature Metabolism examines the potential use of biomarkers of food intake (BFIs) for objective and accurate assessments of nutrition.

Study: Towards precision nutrition: unlocking biomarkers as nutritional assessment tools. Image Credit: Gorodenkoff /

What are BFIs?

BFIs are often used to assess dietary adherence in nutritional intervention and meal studies, assess the extent of misreporting, as well as 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 compliance.

BFI is a metabolite of ingested food and is defined as a measure of the consumption of specific food groups, foods or specific food ingredients. BFIs can be classified based on their robustness, where minimal interference from different dietary backgrounds affects the use of the BFI in research.

Reliability in BFIs implies that this marker is in qualitative and/or quantitative agreement with other nutritional biomarkers or instruments. Plausibility depends on the specificity and chemical affinity of the metabolite to the nutrient in question, limiting the risk of misclassification due to other factors.

Biological diversity for BFIs depends on absorption, distribution, metabolism and elimination (ADME) of the food, as well as enzyme concentration/transport, genetic diversity, and gut microbial metabolism. Importantly, this characteristic was not reported for most BFIs.

Intraclass correlation (ICC) also shows the variation within a population or group in response to different factors. When ICC is low, the BFI may be related to incorrect sampling time, low frequency of consumption, or large variability in response over time within and between individuals and populations.

About the study

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

If all criteria were met, the BFI was classified as a utility level one member. At level two, the BFI candidate is plausible and strong but not known to be reliable. Level tjree BFIs are plausible but lack robustness and reliability, but level four BFIs have not been reported for the foods.

If these criteria are met, additional characteristics are also evaluated including temporal kinetics, which refers to the sampling window or time period for sampling the BFI after nutrient ingestion, analytical performance and reproducibility.

Level one and two BFIs

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

Level two candidate BFIs in urine include whole plant foods and a variety of plant foods including legumes and vegetables, dairy, and some specific fruits and vegetables. Blood BFIs at level two exist for plant foods, dairy products, some meats, and some non-alcoholic beverages; however, these BFIs include fewer foods with less validation.

Identification and validation of BFIs

Finding and validating BFIs requires discovery studies, followed by confirmatory and predictive studies. Meal studies identify plausible BFIs; however, these may not be specific unless other foods contain very low levels of the marker or are eaten infrequently.

For example, betaine is present at high levels in oranges and is used to detect orange or citrus consumption, despite the fact that it is found in many other foods at low levels. Finding studies may, however, be very small or poorly representative.

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

Endogenous metabolites are weak BFIs, as they are produced both endogenously and from exogenous foods. These metabolites are also associated with significant inter-individual genetic and microbial differences.

Forecasting studies use models based on randomized controlled trials to identify consumption of certain foods. This approach prefers correlational studies by identifying BFIs that may predict intake, but relies 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 searches. A Global Natural Products Social Molecular Networking initiative is leading efforts to interconnect these databases and compare unknown compounds with known spectra, such as the Global Natural Products Social Mass Spectrometry Search Tool (MASST).

BFI applications

The choice of BFI depends on the aim of the study. Qualitative BFIs are sufficient to identify non-compliance or perform per-protocol analyses. Conversely, a combination of signature BFIs provides greater specificity and may even identify an entire meal or diet pattern.

A step-by-step approach could help identify actual consumers of food of interest before assessing consumption in the second step, which would allow less powerful BFIs to play a role in those studies.

Typical dietary patterns can be captured through multiple sampling, with the frequency and number depending on the sampling window and frequency of consumption. Among the best sampling methods identified in the present study are urine spot samples such as first morning or overnight cumulative vacuum samples, dried urine sports, samples stored in vacuum tubes, 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 that aim to identify a correlation between diet and disease risk.

Precision sampling and analysis methods may also improve the precision of nutrition research and establish reliable links between dietary intakes and health outcomes.

Future development

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

Precise nutrition is of particular importance in the prevention of obesity and cardio-metabolic diseases where a one-diet approach does not work for everyone due to the highly heterogeneous individual response to diet. Personalized food interventions are good drivers of behavior change, shown to improve diet quality.”

Journal reference:

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

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