The Key to Precision Nutrition

The important role of nutrition in health requires the development of dietary assessment tools that can accurately assess causal relationships with various health-related outcomes.

According to a recently published study, Natural Metabolism To examine the potential utility of food intake biomarkers (BFIs) in objective and accurate dietary assessment.

study: Towards accurate nutritional management: Utilizing biomarkers as dietary assessment toolsImage credit: Gorodenkoff / Shutterstock.com

What is the BFI?

The BFI is frequently used to assess dietary adherence in nutrition intervention and dietary studies, to evaluate the extent of misreporting, and to validate epidemiologically derived food-disease risk associations. Food frequency questionnaires (FFQs) and dietary recalls are also useful assessment tools, but their subjective nature can lead to biased reporting and poor adherence.

BFIs are metabolites of ingested food and are defined as a measure of the intake of specific food groups, foods, or dietary components. BFIs can be ranked based on their robustness where minimal interference from diverse dietary backgrounds affects the use of BFIs in research.

The reliability of the BFI means that this marker is in qualitative and/or quantitative agreement with other biomarkers or dietary measurement instruments. Validity depends on the specificity and chemical relationship of the metabolites and the nutrient in question, which limits the risk of misclassification due to other factors.

The biological variability of BFIs depends on dietary absorption, distribution, metabolism, and excretion (ADME), as well as enzyme/transporter concentrations, genetic variation, gut microbial metabolism, etc. Importantly, this characterization has not been reported for most BFIs.

Intraclass correlations (ICCs) also reflect variation within populations or groups for different factors. If the ICC is low, the BFI may be related to incorrect sampling times, infrequent consumption, or large variations in responses over time within and between individuals and populations.

About the Research

Following a valid BFI review that adhered to appropriate guidelines and methodology, the researchers conducted two systematic searches of experimental and observational studies, after which they ranked the reported BFIs in a four-tier classification system based on their robustness, reliability, and validity.

If all criteria were met, the BFI was classified as belonging to practical level 1. At level 2, the candidate BFI is valid and robust but it is unclear whether it is reliable, level 3 BFIs are valid but lack robustness and reliability, and level 4 BFIs have not been reported for foods.

If these criteria are met, additional characteristics such as time kinetics, which refers to the sampling window or period during which the BFI is sampled after nutrient intake, analytical performance, and reproducibility are also evaluated.

Level 1 and Level 2 BFI

Actionable level 1 or verified urine BFIs were found for total meat, total fish, poultry, fatty fish, total fruit, citrus, bananas, whole wheat or rye, alcohol, beer, wine, and coffee. Level 1 blood BFIs exist for fatty fish, whole wheat and rye, citrus, and alcohol.

Candidate Level 2 BFIs in urine include a variety of plant foods such as total plant foods, legumes and vegetables, dairy products, and some specific fruits and vegetables. Level 2 blood BFIs are present in plant foods, dairy products, some meats, and some non-alcoholic beverages, but these BFIs have been less validated and cover a smaller number of foods.

BFI Identification and Verification

Discovery and validation of BFIs require discovery studies followed by confirmatory and predictive studies. Dietary studies will identify plausible BFIs, but these may not be specific unless the levels of the marker in other foods are very low or are rarely consumed.

For example, betaine is used to detect orange and citrus consumption, even though it is found in high concentrations in oranges and in lower concentrations in many other foods, but discovery studies may be very small or unrepresentative.

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

The BFI is less robust for endogenous metabolites, which are produced both internally and from exogenous dietary sources, and are also associated with large variability due to genetic and microbial differences between individuals.

Predictive studies use models based on randomized controlled trials to identify the consumption of specific foods. This approach is superior to correlational studies in identifying BFIs that can predict intake, but the accuracy depends on the sampling window.

Several databases are available for searching metabolites, 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 to interconnect these databases and compare unknown compounds to known spectra, including using the Global Natural Products Social Mass Spectrometry Search Tool (MASST).

BFI Applications

The choice of BFI depends on the study objectives. Qualitative BFIs may be suitable for identifying non-adherence or for analyzing protocol compliance. Conversely, combining signature BFIs may increase specificity and identify whole diets or dietary patterns.

A phased approach helps to identify actual consumers of the target foods before assessing consumption in a second phase, and even less powerful BFIs can have a role in this type of study.

Habitual dietary patterns can be captured by multiple samplings. The frequency and number of samplings depends on the sampling period and intake frequency. The optimal sampling methods identified in the current study include spot urine samples such as first morning void or overnight cumulative samples, dried urine sport, vacuum tube storage samples, dried spot samples, and microsampling.

Remote sampling increases the number of participants and improves 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.

Improved sampling and analytical methods could also improve the accuracy of nutrition studies and establish more reliable associations between dietary intake and health outcomes.

Future developments

Future studies are needed to validate the development of single-marker and multimarker BFIs using different samples, food groups, diets, prepared foods, and processed foods. Quantitative BFIs need to be characterized by dose-response studies, and combinations of BFIs need to be established to predict and classify intake and dietary patterns.

Precision nutrition is particularly important to curb obesity and cardiometabolic disease. Individual responses to diet vary widely, so a one-size-fits-all diet is unlikely to work. Personalized dietary interventions have been shown to be excellent drivers of behavior change and improve dietary quality..”