Personalized Nutrition Recommendations Improve Plasma Metabolite Concentrations Related to Dietary Intake
Bibliographic record
Abstract
The objectives of our proof-of-concept study was to assess the efficacy of personalized nutrition interventions on diet and chronic disease risk. Fasting plasma samples were collected at day 1 and day 100 of a cohort of 148 adults (aged 23–65y) volunteers with a median (range) BMI of 25.8 (17.2–48.3). At both time points 119 metabolites were quantitated using LC-MS/MS. Based on their metabolite concentrations and dietary preferences, each participant received their own personalized nutrition recommendations through an AI-assisted online platform and were advised to follow the recommendations for 100 days. Plasma metabolite concentrations from Day 1 and Day 100 were compared using a paired t-test with Holm-Bonferroni correction (P < 0.05). After 100 days, statistically significant changes in acylcarnitine, phosphatidylcholine and amino acid concentrations indicated participants had increased their intakes of omega-3 fats and whole grains and decreased their intakes of saturated fat. For example, Betaine, a biomarker of whole grain intake, increased significantly in concentration from Day 1 to Day 100 [mean (SD): 34.3 (13.2) to 45.3 (15.6) umol/l]. Overall, 55 of the analyzed 119 metabolites’ (46%) concentrations had previously been linked to dietary intake according to a systematic literature search that was used to generate evidence-based personalized nutrition recommendations. Concentrations of 33 (60%) of these 55 metabolites changed significantly. The majority [26 (79%)] of the metabolite concentrations changed in a predicted manner consistent with the literature, particularly those metabolites associated to chronic disease risk. For example, a significant decrease in asymmetric dimethylarginine concentration [mean (SD): 0.54 (0.15) to 0.48 (0.12) umol/L] which is understood to result in a decreased cardiovascular disease risk. Adherence with an evidence-based personalized nutrition plan based on a panel of serum metabolomic data can significantly modify serum metabolite concentrations in a direction that can reduce the risk of chronic disease. Further analysis in how these changes relate to chronic disease risk is warranted. This study was supported by Mitacs and Molecular You.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".