The key to precision nutrition

The crucial role of nutrition in health requires the development of dietary assessment tools capable of accurately assessing cause-effect relationships with various health-related outcomes.

A recent study published in Metabolism of nature examines the potential utility of biomarkers of dietary intake (BFI) on objective and accurate dietary assessments.

Study: Towards precision nutrition: using biomarkers as dietary assessment tools. Photo credit: Gorodenkoff / Shutterstock.com

What are BFIs?

Food frequency questionnaires (FFQs) and dietary recalls are also useful assessment tools, but their subjective nature can lead to biased reporting and poor compliance.

An IBF is a metabolite of ingested food and is defined as a measure of the consumption of specific food groups, foods, or food components. IBFs can be classified according to their robustness, in which minimal interference from a varied dietary context affects the use of the IBF in research.

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

The biological variability of high-fat foods depends on the absorption, distribution, metabolism, and elimination (ADME) of the food, as well as enzyme/transporter concentrations, genetic variation, and gut microbial metabolism. Importantly, this characteristic has not been reported for most high-fat foods.

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

About the study

After validating the BFI analyses and following appropriate guidelines and methodologies, the researchers conducted two systematic searches of experimental and observational studies. Subsequently, a four-tier classification system was used to classify the reported BFIs based on their robustness, reliability, and plausibility.

If all criteria were met, the IFB was classified as belonging to utility level 1. At level 2, the candidate IFB is plausible and robust, but is not considered reliable. Level 3 IFBs are plausible, but lack robustness and reliability, while Level 4 IFBs were not reported for foods.

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

BFI Level 1 and 2

Level 1 or validated urinary IAAs were found for total meat, total fish, chicken, oily fish, total fruit, citrus, banana, whole wheat or rye, alcohol, beer, wine, and coffee. Level 1 blood IAAs exist for oily fish, whole wheat and rye, citrus, and alcohol.

Level 2 urine IBFs include total plant foods and a variety of plant foods, including legumes and vegetables, dairy, and some specific fruits and vegetables. Level 2 blood IBFs exist for plant foods, dairy, some meats, and some soft drinks; however, these IBFs include fewer foods and are less validated.

Identification and validation of IBFs

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

For example, betaine is present at high levels in oranges and is used to detect orange or citrus consumption, although it is present at low levels in many other foods. However, discovery studies may be very limited or unrepresentative.

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

Endogenous metabolites are not very robust IFBs, as they are produced both endogenously and from exogenous foods. These metabolites are also associated with significant variations due to inter-individual genetic and microbial differences.

Prediction studies use models based on randomized controlled trials to identify consumption of a given food. This approach outperforms correlation studies in identifying IABs that can predict consumption, but depends 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 searching. The Global Natural Products Social Molecular Networking initiative is leading efforts to interconnect these databases and compare unknown compounds to known spectra, such as with the Global Natural Products Social Mass Spectrometry Search Tool (MASST).

BFI Requests

The selection of IBFs depends on the objective of the study. Qualitative IBFs are sufficient to identify cases of non-compliance or to perform analyses according to the protocol. Conversely, a combination of characteristic IBFs offers greater specificity and can even identify an entire meal or diet.

A stepwise approach could help identify actual consumers of a food of interest before assessing the amount consumed in a second step, allowing even less robust IBFs to play a role in these types of studies.

Dietary habits can be captured by multiple sampling, with the frequency and number depending on the sampling window and frequency of consumption. Optimal sampling methods identified in the current study include spot urine samples such as first morning voids or overnight cumulative samples, dried urine samples, 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 their 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 accuracy of nutrition research and establish reliable associations between dietary intakes and health outcomes.

Future development

Further studies are needed to validate the development of single and multiple biomarker indices using different samples, food groups, and diets, as well as cooked and processed foods. Quantitative biomarker indices also need to be characterized by dose-response studies, while combinations of biomarker indices need to be established to predict and classify dietary and intake patterns.

Precision nutrition is particularly important for addressing obesity and cardiometabolic diseases, where a one-size-fits-all approach does not appear to work due to 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..”