PMON220 Women With Polycystic Ovary Syndrome Have Worse Lifestyle Behaviors Compared to Controls: A Systematic Review and Meta-Analysis of 36,360 Women
Bibliographic record
Abstract
Abstract Women with polycystic ovary syndrome (PCOS) exhibit a propensity for obesity and weight gain during their reproductive years. Accordingly, lifestyle (dietary and/or physical activity [PA]) modification is recommended as first-line therapy to manage PCOS. However, current recommendations are based on healthy lifestyle practices for the general public since evidence for unique lifestyle approaches in PCOS is limited and low-quality. We synthesized evidence on dietary and PA behaviors between women with PCOS and those without PCOS to identify any unique lifestyle behaviors that could underlie the propensity of weight gain and obesity and be targeted for precision nutrition and PA interventions in PCOS. Primary outcomes were diet quality, energy intake, and PA. Secondary outcomes included macronutrients, micronutrients, food groups, foods, glycemic indices, sedentary time, and sitting levels. Databases of MEDLINE, Web of Science, Scopus, and CINAHL were searched until June 1, 2021, to identify observational studies documenting diet and PA between women with and without PCOS (18-50yrs). Data were pooled by random-effects models and expressed as (standardized) mean differences (S[MD]) and 95%CIs. Risk of bias was assessed by the Newcastle-Ottawa scale (NOS). Forty-five studies (N=36,360 participants; [n=7,348 PCOS; 29,012 Controls]) were eligible (NOS scores≥7). Women with PCOS had higher energy (MD: 132.91; 95%CI: 4.34 to 261.47 kcal/d; I2=96%), lower folic acid (MD: -25.11; 95%CI: -49.60 to -0.62 µg/d; I2=72%) and lower zinc (MD: -1.41; 95%CI: -2.76 to -0.07 mg/d; I2=96%) intakes (All: P≤0.04), and a tendency for lower total PA (SMD: -0.40; 95%CI: -0.80 to 0.00; I2=98%; P=0.05) vs. Controls. On subgroup analyses, lower intakes of magnesium (MD: -38.98; 95%CI: -69.44 to -8.53 mg/d; I2=37%) were noted in younger PCOS populations (<30yrs.), and higher total fat (SMD: 0.20; 95%CI: 0.02 to 0.38; I2=45%) and polyunsaturated fatty acids (PUFA; SMD: 0.29; 95%CI: 0.10 to 0.48; I2=0%) was evident in PCOS when food records were used (All: P≤0.03). Conversely, macronutrients (carbohydrate, protein, fiber), alcohol, micronutrients (vitamin D, iron, calcium, sodium), glycemic index, and glycemic load were similar. Most eligible studies reported lower total adherence to healthy eating patterns or poorer consumption of major food groups (grains, fruits, vegetables, proteins, seeds, nuts, dairy), as described narratively since variable study methodology did not permit meta-analyses. Collective evidence supports that women with PCOS have lower overall diet quality, poorer dietary intakes (higher energy, total fat, and PUFA, and lower folic acid, zinc, and magnesium), and a tendency for lower total PA vs. those without PCOS. Considerable heterogeneity among studies reinforces the need for research to address any relative contributions of other factors (e.g., genetic, metabolic, or sociodemographic) to observed differences. These clarifications may improve lifestyle behaviors and associated weight management and obesity-related complications (infertility, diabetes, cardiovascular disease) in this high-risk population and may contribute to future evidence-based guideline recommendations on managing PCOS in the emerging era of precision lifestyle medicine. Presentation: Monday, June 13, 2022 12:30 p.m. - 2:30 p.m.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.026 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".