Interactions between Polygenic Risk of Obesity and Dietary Factors on Anthropometric Outcomes: A Systematic Review and Meta-Analysis of Observational Studies
Bibliographic record
Abstract
BACKGROUND: Diet is an important determinant of health and may moderate genetic susceptibility to obesity, but meta-analyses of available evidence are lacking. OBJECTIVES: This study aimed to systematically review and meta-analyze evidence on the moderating effect of diet on genetic susceptibility to obesity, assessed with polygenic risk scores (PRS). METHODS: A systematic search was conducted using MEDLINE, EMBASE, Web of Science, and the Cochrane Library to retrieve observational studies that examined PRS-diet interactions on obesity-related outcomes. Dietary exposures of interest included diet quality/dietary patterns and consumption of specific food and beverage groups. Random-effects meta-analyses were performed for pooled PRS- healthy eating index (HEI) interaction coefficients on body mass index (BMI) (on the basis of data from 4 cohort studies) and waist circumference (WC) (on the basis of data from 3 cohort studies). RESULTS: Out of 36 retrieved studies, 78% were conducted among European samples. Twelve out of 21 articles examining dietary indices/patterns, and 16 out of 21 articles examining food/beverage groups observed some significant PRS-diet interactions. However, within many articles, findings are inconsistent when testing different combinations of obesity PRS-dietary factors and outcomes. Nevertheless, higher HEI scores and adherence to plant-based dietary patterns emerged as the more prominent diet quality/patterns that moderated genetic susceptibility to obesity, whereas higher consumption of fruits and vegetables, and lower consumption of fried foods and sugar-sweetened beverages emerged as individual food/beverage moderators. Results from the meta-analysis suggest that a higher HEI attenuates genetic susceptibility on BMI (pooled PRS∗HEI coefficient: -0.08; 95% confidence interval (CI): -0.15, 0.00; P = 0.0392) and WC (-0.37; 95% CI: -0.60, -0.15; P = 0.0013). CONCLUSIONS: Current observational evidence suggests a moderating role of overall diet quality in polygenic risk of obesity. Future research should aim to identify genetic loci that interact with dietary exposures on anthropometric outcomes and conduct analyses among diverse ethnic groups. TRIAL REGISTRATION NUMBER: This study was registered at the International Prospective Register of Systematic Reviews as CRD42022312289.
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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 | 0.022 | 0.053 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.040 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".