Adverse metabolic phenotype of adolescent girls with non‐alcoholic fatty liver disease plus polycystic ovary syndrome compared with other girls and boys
Bibliographic record
Abstract
BACKGROUND AND AIMS: Non-alcoholic fatty liver disease (NAFLD) and polycystic ovary syndrome (PCOS) share risk associations of adiposity and insulin resistance. We examined the impact of a PCOS diagnosis on the metabolic phenotype of adolescent girls with NAFLD and compared this to girls without PCOS or NAFLD and to age-matched boys. METHODS: Community-based adolescents from the Raine Cohort participated in assessments for NAFLD (572 girls and 592 boys) and PCOS (244 girls). One hundred and ninety-nine girls attended both assessments. RESULTS: Amongst the 199 girls, PCOS was diagnosed in 16.1% and NAFLD in 18.6%. NAFLD was diagnosed in 10.1% of the boys. NAFLD was more prevalent in girls with PCOS than girls without PCOS (37.5% vs 15.1%, P = 0.003). Girls with NAFLD plus PCOS had greater adiposity (waist circumference, body mass index, suprailiac skinfold thickness [SST], serum androgens, high-sensitivity C-reactive protein, ferritin, homeostasis model assessment for insulin resistance (HOMA-IR), and lower serum sex hormone binding globulin levels than girls with NAFLD without a PCOS diagnosis (all P < 0.05). Girls with NAFLD plus PCOS had similar adiposity, HOMA-IR, and adiponectin levels to boys with NAFLD, but more adiposity, serum leptin and HOMA-IR than both girls and boys without NAFLD. PCOS (odds ratios 2.99, 95% confidence intervals 1.01-8.82, P = 0.048) and SST (odds ratios 1.14, 95% confidence intervals 1.08-1.20, P < 0.001) independently predicted NAFLD in adolescent girls, however, serum androgens and HOMA-IR levels did not. CONCLUSIONS: Adolescent girls with NAFLD plus PCOS have a similar metabolic phenotype to boys with NAFLD. Increasing SST and pre-existing PCOS independently predict NAFLD in adolescent girls.
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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 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".