Identification of blood biomarkers of a healthy dietary pattern as facilitated by cluster analysis in patients from the MEDDINI study: a pilot randomised trial
Abstract
Dietary biomarkers may help objectively assessing dietary pattern adherence. This study performed K-means clustering analysis on quantitative food diary data from a dietary intervention study. Standardised dietary data (134 food diaries) from 57 participants were K-means clustered stepwise until fully optimised and cross-validated. The primary endpoint was to develop distinct dietary clusters and to evaluate the performanceof 90 plasma metabolites. The secondary endpoint was to analyse the biomarker-food groups relationships from those distinct dietary patterns. The final two cluster models comprised of 6 specific food types. Cluster 1 included participants with higher intake of fruit and vegetables, legumes, fish and whole grain cereals, and lower intake of meat and sweet foods than Cluster 2. Ten plasma metabolites significantly differed between the clusters (p p < 0.02; q < 0.11) and were included: palmitic acid, tyrosine, β-carotene, α-carotene and betaine. The DHA-to-Osbond acid ratio was an optimal indicator distinguishing healthy from unhealthy dietary patterns (ROC: 0.78). Combining clustering and metabolite profiling methods effectively identifies biomarkers of particular dietary patterns and highlights several robust food-metabolite correlations.
How strong is the evidence?
Evidence Hub reads this as a randomized trial, done in people. Its evidence level is 2 of 5 (High · randomized trial). Randomized trials of a reasonable size are the strongest single-study design. Reported size: 57 participants or records.
Read the paper
Related studies
- eggNOG 5.0: a hierarchical, functionally and phylogenetically annotated orthology resource based on 5090 organisms and 2502 viruses
- Online Survival Analysis Software to Assess the Prognostic Value of Biomarkers Using Transcriptomic Data in Non-Small-Cell Lung Cancer
- OptiType: precision HLA typing from next-generation sequencing data
- Nicotinic Cholinergic System and COVID-19: In Silico Identification of an Interaction between SARS-CoV-2 and Nicotinic Receptors with Potential Therapeutic Targeting Implications
- Saccharomyces cerevisiae cells have three Omega class glutathione S-transferases acting as 1-Cys thiol transferases
- Hemolysis at low blood flow rates: in-vitro and in-silico evaluation of a centrifugal blood pump
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.