Evaluation of Preconception Dietary Patterns in Women Enrolled in a Multisite Study
Bibliographic record
Abstract
Background: Diet indices are widely used in nutritional research across communities but do not "capture" the full extent of diet variability across multiple countries. Empirically derived dietary patterns can provide additional information because they reflect combinations of foods potentially associated with health outcomes. Limited studies have evaluated preconception dietary patterns in heterogeneous populations. Objectives: ) derive pooled and site-specific preconception dietary patterns, and 2) evaluate these patterns using anthropometric measures and metabolic biomarkers. Methods: = 1720) in the United Kingdom, Singapore, and New Zealand completed interviewer-administered harmonized FFQs and lifestyle questionnaires at recruitment. Across-cohort ("pooled") and site-specific dietary patterns were derived, and associations between dietary pattern scores and BMI, waist-to-hip ratio, plasma lipids, and glycemia assessed using multivariable linear regression, expressing results as SD change in outcome per SD change in dietary pattern score. Results: The pooled analysis identified 3 dietary patterns: "Vegetables/Fruits/Nuts" ("Healthy"), "Fried potatoes/Processed meat/Sweetened beverages" ("Less Healthy"), and "Fish/Poultry/Noodles/Rice" ("Mixed"). The "Healthy" and "Less Healthy" pooled pattern scores were highly correlated with their corresponding site-specific dietary pattern scores ("Healthy": ρ = 0.87-0.93; "Less Healthy": ρ = 0.65-0.88). Women with higher scores for the "Healthy" pooled pattern had a lower waist-to-hip ratio (standardized β: -0.10; 95% CI: -0.18, -0.01); those with higher scores for the "Less Healthy" pooled pattern had a higher BMI (standardized β: 0.17; 95% CI: 0.09, 0.24), higher LDL cholesterol (standardized β: 0.10; 95% CI: 0.01, 0.19), and less optimal glucose profiles. However, we noted higher adherence to the "Healthy" pooled pattern with higher BMI. Conclusions: The "Healthy" and "Less Healthy" pooled patterns were comparable to the corresponding site-specific patterns. Although the associations between these patterns and objective anthropometric/metabolic measures were largely in the expected directions, future studies are required to confirm these findings.This trial is registered at clinicaltrials.gov (NCT02509988).
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".