Understanding BFIs
Biomarkers of Food Intake (BFIs) are critical tools in nutritional research, providing a more objective measure of dietary adherence than traditional methods like food frequency questionnaires (FFQs) and dietary recalls. These biomarkers help evaluate dietary compliance, identify misreporting, and validate associations between diet and disease risk.
A BFI is a metabolite of ingested food, representing the consumption of specific food groups, foods, or food components. The robustness of a BFI is determined by its minimal interference from a varied dietary background, ensuring its reliability in research.
Reliability in BFIs is achieved when the marker qualitatively and quantitatively aligns with other biomarkers or dietary assessment tools. Plausibility depends on the specific chemical relationship between the metabolite and the nutrient, minimizing the risk of misclassification due to external factors.
Biologic variability in BFIs is influenced by absorption, distribution, metabolism, and elimination (ADME) processes, as well as genetic variations and gut microbial metabolism. However, this variability is often underreported.
Intra-class correlation (ICC) measures variability within a population in response to different factors. A low ICC indicates issues such as incorrect sampling times, low consumption frequency, or significant variation in individual responses.
About the Study
Researchers conducted systematic reviews of experimental and observational studies, adhering to validated BFI guidelines and methodologies. They ranked reported BFIs using a four-level classification system based on robustness, reliability, and plausibility.
BFIs meeting all criteria were classified as level one. Level two BFIs were plausible and robust but lacked reliability. Level three BFIs were plausible but neither robust nor reliable, while level four BFIs were insufficiently reported.
Additional characteristics like time kinetics, analytical performance, and reproducibility were also assessed for BFIs meeting these criteria.
Level One and Two BFIs
Level one urine BFIs were identified for total meat, fish, chicken, fatty fish, total fruit, citrus fruit, banana, whole-grain wheat or rye, alcohol, beer, wine, and coffee. Blood BFIs at level one included fatty fish, whole grain wheat and rye, citrus, and alcohol.
Level two urine BFIs were found for total plant foods, including legumes and vegetables, dairy, and some specific fruits and vegetables. Blood BFIs at level two included plant foods, dairy products, some meats, and non-alcoholic drinks, though these were less validated.
Identification and Validation of BFIs
The discovery and validation of BFIs involve multiple stages. Meal studies identify plausible BFIs, though specificity can be an issue unless other foods contain low levels of the marker or are rarely consumed.
For instance, betaine, found in high levels in oranges, can indicate citrus consumption but is also present in many other foods. Small or poorly representative discovery studies may lead to inaccuracies.
Observational studies can link blood or urine metabolites to diet but may be confounded by lifestyle factors. For example, in Japan, the frequent consumption of fish and green tea can confound BFIs, as trimethylamine oxide (TMAO) is associated with both, complicating BFI discovery.
Endogenous metabolites are less reliable BFIs due to significant inter-individual genetic and microbial variations.
Prediction studies, which use models based on randomized controlled trials, offer a more accurate identification of food consumption, depending on the sampling window.
Several databases, including Massbank, METLIN Gen2, mzCloud (Thermo Scientific), and HMDB, assist in metabolite searches. The Global Natural Products Social Molecular Networking initiative connects these databases, allowing comparison of unknown compounds against known spectra using tools like the Global Natural Products Social Mass Spectrometry Search Tool (MASST).
Applications of BFIs
BFI selection depends on study objectives. Qualitative BFIs are useful for identifying non-compliance or conducting per-protocol analyses. Combining signature BFIs offers greater specificity, potentially identifying entire meals or dietary patterns.
A stepwise approach can help identify actual consumers of a specific food before assessing the amount consumed, allowing less robust BFIs to be useful in studies.
Multiple sampling captures habitual dietary patterns, with frequency and number depending on the sampling window and consumption frequency. Optimal methods identified include spot urine samples, dried urine spots, vacuum tube stored samples, dried spot samples, and microsampling.
Remote sampling increases participant numbers and enhances monitoring of dietary patterns and changes over time. This method can also improve epidemiological studies aiming to link diet and disease risk.
Future Development
Future research should focus on validating single and multimarker BFIs using various samples, food groups, and diets, including cooked and processed foods. Quantitative BFIs need dose-response studies, while combinations of BFIs should be established to predict and classify intake and dietary patterns.
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