Myosteatosis is associated with adiposity, metabolic derangements and mortality in patients with chronic kidney disease
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Myosteatosis has been associated with sarcopenia, and increased mortality risk in patients on hemodialysis. We aimed to explore the associations between myosteatosis, as assessed by computed tomography (CT), with demographic parameters, body composition metrics, muscle strength, metabolic parameters and mortality in patients with chronic kidney disease (CKD). SUBJECTS/METHODS: We enrolled 216 patients (age 60.3 ± 10.6 years, 63% men) with CKD stages 3-5. Abdominal CT scans at the third lumbar vertebra (L3) were used to assess body composition. Abdominal obesity was determined by abdominal adipose tissue (AT), sarcopenia by low skeletal muscle area (SMA) and low handgrip strength. Myosteatosis was evaluated by two parameters using CT scans at L3: mean muscle attenuation and percentage of intermuscular adipose tissue (%IMAT) within SMA. We evaluated the correlation between parameters of myosteatosis with demographic, clinical and metabolic variables. To determine independent predictors of myosteatosis, a multiple linear regression model was fitted. Mortality risk was evaluated with Cox-regression analysis. RESULTS: for multiple linear regression: muscle attenuation model 0.535, P < 0.001; %IMAT model 0.462, P < 0.001). Moreover, higher %IMAT and lower attenuation were associated with a higher mortality risk. CONCLUSION: In patients with CKD, increased myosteatosis, as assessed by abdominal CT, was associated with old age, adiposity, metabolic dysfunction, and higher mortality risk.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".