The Prevalence Of Nonalcoholic Steatohepatitis (NASH) Across Levels Of Cardiorespiratory Fitness In Men
Bibliographic record
Abstract
Nonalcoholic steatohepatitis (NASH) is characterized by ectopic fat deposition in the liver and is associated with hepatic in.ammation, hepatocyte dysfunction, and with Nonalcoholic steatohepatitis (NASH) is characterized by ectopic fat deposition in the liver and is associated with hepatic inflammation, hepatocyte dysfunction, and with cirrhosis in 20% of afficted individuals. The underlying causes of NASH are largely unknown, however, increases in population levels of obesity and physical inactivity may be links in the etiological chain. PURPOSE To determine the prevalence of NASH across levels of cardiorespiratory fitness and body mass index (BMI) in men. METHODS Participants were 154 nonsmoking men from the Aerobics Center Longitudinal Study who were free of known CHD, cancer, and metabolic disease; and were not on statin therapy. We measured liver and spleen fat by computed tomography and defined NASH as having plasma alanine aminotransferase (ALT)>35 U/L with a liver/spleen (L/S) attenuation ratio ≤1.0. Fitness was defined as thirds of maximal METs achieved during a graded treadmill exercise test. BMI was categorized as normal weight (NW;18.5–24.9 kg/m2), overweight (OW;25 −29.9 kg/m2) & obese (OB:=30 kg/m2). RESULTS The prevalence of NASH was higher across categories of BMI:NW (0%), OW (4.8%), & OB (15.8%, Trend χ2df=2 =8.045, p < 0.018); and declined across the lowest (12%), middle (8%) and highest (0%) third of fitness (Trend χ2df=2 =6.47, P=0.039). Using multivariable logistic regression, age (p=0.03), alcohol use (p=0.06) and BMI (p=0.02) were directly associated with higher risk of having NASH while higher levels of fitness (p=0.05) were inversely associated with lower risk of having NASH. CONCLUSION Higher levels of fitness and lower levels of BMI are associated with a lower prevalence of NASH.TableSupported by NIH grant HL62508- 04 & AG06945
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".