Imaging-Based Body Fat Distribution in Polycystic Ovary Syndrome: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Women with polycystic ovary syndrome (PCOS) are generally considered to be central obese and at higher risks of metabolic disturbances. Imaging methods are the golden standards for detecting body fat distribution. However, evidence based on magnetic resonance imaging (MRI) and computed tomography (CT) is conflicting. This study systematically reviewed the imaging-based body fat distribution in PCOS patients and quantitatively evaluated the difference in body fat distribution between PCOS and BMI-matched controls. Methods: PUBMED, EMBASE, and Web of Science were searched up to December 2019, and studies quantitatively compared body fat distribution by MRI, CT, ultrasound, or X-ray absorptiometry (DXA) between women with PCOS and their BMI-matched controls were included. Two researchers independently reviewed the articles, extract data and evaluated the study quality based on Newcastle-Ottawa Scale (NOS). Results: 47 studies were included in systematic review and 39 were eligible for meta-analysis. Compared to BMI-matched controls, higher accumulations of visceral fat (SMD 0.41; 95%CI: 0.23-0.59), abdominal subcutaneous fat (SMD 0.31; 95%CI: 0.20-0.41), total body fat (SMD 0.19; 95% CI: 0.06-0.32), trunk fat (SMD 0.47; 95% CI: 0.17-0.77), and android fat (SMD 0. 36; 95% CI: 0.06-0.66) were identified in PCOS group. However, no significant difference was identified in all the above outcomes in subgroups only including studies using golden standards MRI or CT to evaluate body fat distribution (SMD 0.19; 95%CI: -0.04-0.41 for visceral fat; SMD 0.15; 95%CI: -0.01-0.31 for abdominal subcutaneous fat). Moreover, meta-regression and subgroup analyses showed that young and non-obese patients were more likely to accumulate android fat. Conclusions: PCOS women seem to have abdominal fat accumulation when compared with BMI-matched controls. However, MRI- and CT- assessed fat distribution was similar between PCOS and controls, suggesting central obesity may be independent of PCOS. These findings will help us reappraise the relationship between PCOS and abnormal fat deposition and develop specialized lifestyle interventions for PCOS patients. Systematic Review Registration: PROSPERO, identifier CRD42018102983.
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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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.027 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".