Sarcopenic obesity and myosteatosis are associated with higher mortality in patients with cirrhosis
Bibliographic record
Abstract
BACKGROUND AND AIMS: Obesity is frequently associated with cirrhosis, and cirrhotic patients may develop simultaneous loss of skeletal muscle and gain of adipose tissue, culminating in the condition of sarcopenic obesity. Additionally, muscle depletion is characterized by both a reduction in muscle size and increased proportion of muscular fat, termed myosteatosis. In this study, we aimed to establish the frequency and clinical significance of sarcopenia, sarcopenic obesity and myosteatosis in cirrhotic patients. METHODS: We analysed 678 patients with cirrhosis. Sarcopenia, sarcopenic obesity and myosteatosis were analysed by CT scan using the third lumbar vertebrae skeletal muscle and attenuation indexes, using previously validated gender-and body mass index-specific cutoffs. RESULTS: Patients were predominately men (n = 457, 67%), and cirrhosis aetiology was hepatitis C virus in 269 patients (40%), alcohol in 153 (23%), non-alcoholic steatohepatitis/cryptogenic in 96 (14%), autoimmune liver disease in 55 (8%), hepatitis B virus in 43 (6%), and others in 5 patients (1%). Sarcopenia was present in 292 (43%), 135 had sarcopenic obesity (20%) and 353 had myosteatosis (52%). Patients with sarcopenia (22 ± 3 vs. 95 ± 22 months, P < 0.001), sarcopenic obesity (22 ± 3 vs. 95 ± 22 months, P < 0.001), and myosteatosis (28 ± 5 vs. 95 ± 22 months, P < 0.001) had worse median survival than patients without muscular abnormalities. By multivariate Cox regression analysis, both sarcopenia [hazard ratio (HR) 2.00, 95% confidence interval (CI) 1.44-2.77, P < 0.001], and myosteatosis (HR 1.42, 95% CI 1.02-1.07, P = 0.04) were associated with mortality. CONCLUSIONS: Sarcopenia, sarcopenic obesity and myosteatosis are often present in patients with cirrhosis, and sarcopenia and myosteatosis are independently associated with a higher long-term mortality in cirrhosis.
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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 | 0.001 |
| 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".