Associations Between Diet Quality and Ovarian Dysmorphology in Premenopausal Women Are Mediated by Obesity and Metabolic Aberrations (OR36-03-19)
Bibliographic record
Abstract
The link between diet quality and reproductive health in women is unclear. Ultrasonographic markers of ovarian morphology including ovarian volume (OV) and follicle number per ovary (FNPO) reflect disturbances in folliculogenesis and provide an objective and non-invasive means of gauging the severity of reproductive dysfunction. To test the hypothesis that lower diet quality is associated with adverse ovarian morphology, we evaluated the associations between dietary patterns with OV and FNPO in premenopausal women. One hundred and eleven women (18–45y) were evaluated for OV and FNPO 2–5, 6–9, and 2–9 mm by ultrasonography. Dietary patterns were scored by the Healthy Eating Index (HEI) 2015, alternative HEI-2010, alternate Mediterranean Diet (aMED), and Dietary Approaches to Stop Hypertension (DASH) indices using a food frequency questionnaire. Linear regressions were used to test the direct effect of dietary patterns on OV and FNPO, and simple and serial mediation analyses tested the indirect effects of dietary patterns on OV and FNPO through obesity, insulin resistance (IR), hyperandrogenism, and oligo-anovulation (OA). There were no direct associations between dietary patterns and OV or FNPO (P ≥ 0.89). However, inverse indirect associations were observed between aMED and DASH scores with OV and FNPO (P < 0.05). The associations were individually mediated by waist circumference (WC) and free androgen index (FAI). Namely, a 1-unit increase in aMED score was associated with a 1% decrease in OV by reducing WC, whereas a 1-unit increase in DASH score was associated with a 0.4–0.5% decrease in FNPO (2–9 and 2–5 mm) by reducing FAI. Serial mediation analyses showed negative indirect associations between aMED and DASH scores with OV (0.2%) and/or FNPO (0.3–0.5%) sequentially through decreased WC, IR, FAI, and OA (P < 0.05). These findings provide novel mechanistic insights by which obesity and metabolic aberrations adversely impact reproductive health, as evidenced by ovarian dysmorphology. Strategies that promote healthy eating patterns (e.g., aMED and DASH) may also improve ovarian function and reproductive outcomes across the lifespan. Funded by Cornell University, National Institutes of Health, and Canadian Institutes of Health Research.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".