Quadriceps muscle fat infiltration is associated with cardiometabolic risk in <scp>COPD</scp>
Bibliographic record
Abstract
PURPOSE: Losses of peripheral muscle mass and ectopic fat accumulation have been associated with cardiometabolic morbidity in COPD. We aimed at identifying the relationship between quadriceps muscle fat infiltration and cardiovascular risk. MATERIALS AND METHODS: From 2009 to 2014, 78 COPD patients (64 ± 8 years; 80% male) were prospectively included after having given written consent (ethical committee approval number: 2006-A00491-50). The cohort was divided into three groups (tertiles) according to body mass index (BMI), low [15 < BMI≤23·3], middle [23·3 < BMI≤27·6] and high [27·6 < BMI≤36] kg/m²). Measurements were respiratory function, plasmatic biomarkers and surrogate markers of cardiovascular risk (arterial stiffness and endothelial function). Mid-thigh quadriceps muscle volume and per cent of muscle fat infiltration, as assessed by 64-slice CT scanning, were compared between the tertiles. ANOVA or Kruskal-Wallis tests were used for statistical analyses with Bonferroni's correction for the 'post hoc' tests. RESULTS: Intramuscular fat volume was 52% [95% CI, 43 to 60%] of total quadriceps volume in high BMI vs. 47% [38 to 55%] and 34% [29 to 38%] in the middle and low-BMI groups, respectively (P<0·0001), without differences between groups in fat-free muscle volumes. Elevated muscle fat infiltration correlated with lower thiol to protein ratios in the whole population reflecting impaired antioxidant capacity (r = 0·50; P = 0·009). Furthermore, muscle fat infiltration was linked to endothelial dysfunction (r = -0·49, P = 0·01) in the low-BMI group. CONCLUSION: Skeletal muscle fat infiltration may be an indicator of increased cardiometabolic risk in both obese and lean COPD patients.
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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 | 0.000 |
| 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".