Sex Hormone-Binding Globulin Reduction in Metabolic Disorders May Play a Role in NAFLD Development
Bibliographic record
Abstract
Low plasma sex hormone-binding globulin (SHBG) levels are a hallmark in chronic metabolic diseases, including nonalcoholic fatty liver disease (NAFLD), which represents a spectrum of disease ranging from hepatocellular steatosis through steatohepatitis to fibrosis and irreversible cirrhosis. The functional link between altered SHBG production and NAFLD development and progression remains unclear. We investigated the effects of overexpressing human SHBG in 2 mouse models of NAFLD: a genetically induced double transgenic mouse and a diet-induced model. Remarkably, SHBG overexpression in both NAFLD models significantly reduced liver fat accumulation by reducing key lipogenic enzymes. These findings were corroborated by modulating SHBG expression and by adding exogenous SHBG in HepG2 cells, suggesting the cell autonomous nature of the mechanism. Mechanistically, exogenous SHBG treatment downregulated key lipogenic enzymes by reducing PPARγ messenger RNA and protein levels through activation of extracellular signal-regulated kinase-1/2 mitogen-activated protein kinase pathway. Taking together, we found that SHBG modulates hepatic lipogenesis. This is of importance because reduction of SHBG plasma levels in obese and type 2 diabetic subjects could be directly associated with NAFLD development through an increase in hepatic lipogenesis. Our results point to SHBG as a therapeutic target for preventing or arresting NAFLD development.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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".