Alanine Aminotransferase Is a Marker of Lipotoxicity Consequences and Hyperandrogenemia in Women with Polycystic Ovary Syndrome
Bibliographic record
Abstract
Background: Several studies have reported higher levels of Alanine aminotransferase (ALT) in women with polycystic ovary syndrome (PCOS) compared with control subjects. Plasma ALT levels are considered a marker of hepatic lipotoxicity because of their significant associations with different hepatic metabolic dysfunctions, such as hepatic steatosis and hepatic insulin resistance. Methods: Retrospective chart review aiming to assess, in PCOS women, the relationship between ALT levels and measures of lipotoxicity consequences that are available clinically, both during fasting and using the oral glucose tolerance test. Results: Women (n = 132) with PCOS, were in average 27.9 years of age, with a mean body mass index of 34.1 kg/m2 and 49% had a metabolic syndrome (MetS). ALT levels were significantly correlated with homeostatic model assessment for insulin resistance (r = 0.42, P < 0.001), HDL-C (r = −0.31, P < 0.001), Matsuda index (−0.45, P < 0.001), insulin secretion-sensitivity index-2 (−0.26, P = 0.043), and free testosterone (0.38, P < 0.001), but not with fasting glucose and triglyceride levels. ALT cutoff ≥24 IU/L was associated with all these parameters, including fasting glucose (P = 0.021) and triglyceride levels (P = 0.041), and detected more women with the MetS (59.2% vs. 36.1%, P = 0.008) and whole-body insulin resistance (Matsuda index <12.3 L2·10/mmol2, 85.3% vs. 51.9%, P = 0.004). Conclusions. Plasma ALT levels seem to be a strong predictor not only of liver lipotoxicity but also of systemic lipotoxic consequences and hyperandrogenemia in women with PCOS. Although it requires validation in another study, an ALT cutoff of ≥24 IU/L may help clinicians identify women with increased metabolic risks.
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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 | 0.001 |
| 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) | 0.001 | 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".