Polygenic Risk and Nutrient Intake Interactions on Obesity Outcomes: A Systematic Review and Meta‐Analysis of Observational Studies
Bibliographic record
Abstract
BACKGROUND: Diet is an important determinant of body weight and may modulate genetic susceptibility to obesity. OBJECTIVE: This systematic review and meta-analysis aimed to synthesize evidence related to interactions between polygenic risk and nutrient intakes on obesity outcomes. METHODS: MEDLINE, EMBASE, Web of Science, and Cochrane Library were systematically searched to identify observational studies that assessed interactions between polygenic risk and nutrient intakes on obesity-related outcomes. Random effects meta-analyses were performed for pooled polygenic risk score (PRS)-total fat intake and PRS-protein intake interaction coefficients on body mass index (BMI). RESULTS: Twenty-six publications were retrieved with studies conducted among European, Asian, and African samples. Dietary fats (saturated fat, omega-3, and trans fat) and energy intake were most frequently reported to interact with PRS on obesity outcomes, but the total number of studies available was low. No significant interactions were identified in meta-analyses of PRS interactions with total fat intake and protein intake on BMI. Several studies were rated as low quality, heterogeneity was high, and although study samples were racially diverse, PRSs tended to be based on samples of European ancestry. CONCLUSION: Evidence of interactions between polygenic risk and nutrient intakes on obesity outcomes is limited and inconsistent. Further research addressing limitations related to study quality and polygenic risk characterization is needed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.019 | 0.004 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".