Obesity, Insulin Resistance, and Hyperandrogenism Mediate the Link between Poor Diet Quality and Ovarian Dysmorphology in Reproductive-Aged Women
Bibliographic record
Abstract
The relationship between diet quality and ovarian morphology has biological plausibility yet remains unclear and was therefore evaluated. In a multicenter cross-sectional analysis, four dietary patterns were scored for 111 consecutive reproductive-aged women (18–45 years) using (1) Healthy Eating Index (HEI-2015); (2) alternative HEI-2010; (3) alternate Mediterranean Diet (aMED); (4) and Dietary Approaches to Stop Hypertension (DASH) indices. Ovarian volume (OV) and follicle number per ovary (FNPO) were evaluated on transvaginal ultrasonography. Relationships between dietary and ovarian morphology indices were evaluated by linear regression and mediation analyses. Associations between aMED and DASH scores and OV/FNPO were completely mediated by obesity, insulin resistance, and hyperandrogenism (All: p < 0.05), unlike direct associations (All: p ≥ 0.89). Namely, a 1-standard deviation [SD] increase in aMED score was associated with decreases in OV (0.09 SD; 0.4 mL) through reducing waist circumference. Likewise, a 1 SD increase in aMED and DASH score was associated with decreases in OV (0.07 SD; 0.3 mL) by reducing glucose response to a 75 g glucose tolerance test. A 1 SD increase in DASH score was associated with decreased FNPO (0.07 SD; 2 follicles) by reducing free androgen index (All: p < 0.05). Adherence to aMED and DASH eating plans was indirectly associated with significant improvements in ovarian form, providing novel mechanistic insights for future interventions about contributions of diet quality on ovarian function.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".