The key to sensitive nutrition

The critical role of nutrition in health necessitates the development of dietary assessment tools that can accurately assess causal relationships with various health outcomes.

A recently published study Nature Metabolism Examines the potential utility of food intake biomarkers (BFIs) in objective and accurate dietary assessments.

To work: Towards precision nutrition: Unlocking biomarkers as dietary assessment tools.Image Credit: Gorodenkoff / Shutterstock.com

What are BFIs?

BFIs are commonly used to assess dietary adherence in nutrition intervention and food studies, to assess the extent of misreporting, and to validate epidemiologically derived associations between foods 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.

The BFI is a metabolite of the food consumed and is defined as a measure of the consumption of specific food groups, foods, or food components. BFIs can be ranked according to their robustness, where minimal interference from a diverse dietary background affects the use of the BFI in research.

Reliability in BFIs means that this marker is in qualitative and/or quantitative agreement with other biomarkers or dietary tools. Relevance depends on the specificity and chemical relationship of the metabolite to the nutrient in question, limiting the risk of misclassification due to other factors.

For BFIs, biological variability depends on food absorption, distribution, metabolism, and excretion (ADME), enzyme/transporter concentrations, genetic variation, and gut microbial metabolism. Importantly, this feature has not been reported for most BFIs.

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

About the work

Following a review of validated BFIs that met 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 the reported BFIs according to their robustness, reliability, and plausibility.

If all criteria were met, the BFI was classified as belonging to the first level of utility. At the second level, the candidate BFI is plausible and robust but is not known to be reliable. Third-level BFIs are plausible but lack robustness and reliability, while fourth-level BFIs have not been reported for foods.

If these criteria are met, additional properties such as time kinetics, analytical performance and reproducibility, which refers to the sampling window or time period for sampling BFI after food intake, are also evaluated.

Level one and two BFIs

Level one or confirmed urine BFIs were found for total meat, total fish, chicken, oily fish, total fruit, citrus fruit, bananas, whole grain wheat or rye, alcohol, beer, wine, and coffee. Level one blood BFIs are available for oily fish, whole grain wheat and rye, citrus fruit, and alcohol.

Level 2 candidate BFIs in urine include total plant foods and a variety of plant foods, including legumes and vegetables, dairy products, and some specific fruits and vegetables. Level 2 blood BFIs are available for plant foods, dairy products, some meats, and some soft drinks, but these BFIs include fewer foods and have less validation.

Identification and verification of BFIs

Discovery and validation of BFIs requires exploratory studies, followed by validation and predictive studies. Food studies identify plausible BFIs, but these may be nonspecific unless other foods contain very low levels of the marker or are consumed infrequently.

For example, betaine is found at high levels in oranges and is used to detect orange or citrus consumption, despite being found at low levels in many other foods. However, exploratory studies may be too small or underrepresentative.

Observational studies can be used to determine associations between blood or urine metabolites and diet, but are subject to confounding by lifestyle factors. When two types of foods, such as fish and green tea, are frequently consumed together in Japan, confounding of the BFI of fish occurs because trimethylamine oxide (TMAO) may also be associated with green tea, and therefore these foods are not suitable for BFI discovery.

Endogenous metabolites are poorly stable BFIs as they are produced from both endogenous and exogenous foods. These metabolites are also associated with significant variation in interindividual genetic and microbial differences.

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

Various databases can be used for metabolite searching, including Massbank, METLIN Gen2, mzCloud (Thermo Scientific), mzCloud Advanced, Mass Spectral Database, and HMDB. The Global Natural Products Social Molecular Networking initiative is leading efforts such as the Global Natural Products Social Mass Spectrometry Search Tool (MASST) to connect these databases and compare unknown compounds with known spectra.

BFI applications

The choice of BFI depends on the purpose of the study. Qualitative BFIs are sufficient to identify non-adherence or to perform per-protocol analyses. Conversely, a combination of signature BFIs provides greater specificity and may even identify a complete meal or dietary pattern.

A stepwise approach may help identify actual consumers of a food of interest before assessing the amount consumed in a second step, allowing even less reliable BFIs to play a role in such studies.

Habitual dietary patterns can be captured by multiple sampling, where the frequency and number depend on the sampling window and frequency of consumption. Optimal sampling methods identified in the present study include spot urine samples such as first morning urine or overnight collected samples, dried urine spores, samples stored in vacuum tubes, dried spot samples, and microsampling.

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

Improving sampling and analytical methods can increase the precision of nutrition research and establish reliable relationships between dietary intakes and health outcomes.

Future developments

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

Precision nutrition is of particular importance for preventing obesity and cardiometabolic diseases, for which a one-size-fits-all approach does not appear to work because of the wide range of individual responses to diet. Personalized dietary interventions are good drivers of behavior change and have been shown to improve diet quality..”