Sarcopenia in chronic kidney disease: prevalence by different definitions and relationship with adiposity
Bibliographic record
Abstract
This was a cross-sectional study with chronic kidney disease (CKD) patients under non-dialysis-dependent (NDD), hemodialysis (HD), and kidney transplant (KTx) treatment aimed to evaluate the prevalence of sarcopenia using the European Working Group on Sarcopenia in Older People (EWGSOP2) and the Foundation for the National Institutes of Health (FNIH) guidelines, and to analyze the relationship between sarcopenia and its components and body adiposity. Body composition was assessed by dual-energy X-ray absorptiometry and anthropometry. Bioelectrical impedance provided data on the phase angle and body water. The prevalence of sarcopenia in the total sample ( n = 243; 53% men, 48 ± 10 years) was 7% according to the FNIH and 5% according to the EWGSOP2 criteria, and was low in each CKD group independently of the criteria applied (maximum 11% prevalence). Low muscle mass was present in 39% (FNIH) and 36% (EWGSOP2) and dynapenia in 10% of the patients. Patients who were sarcopenic according to the EWGSOP2 criteria presented low body adiposity. Conversely, patients who were sarcopenic according to the FNIH criteria presented high adiposity. This study suggests that in CKD (i) sarcopenia and low muscle mass prevalence varies according to the diagnostic criteria; (ii) sarcopenia and low muscle mass are common conditions; (iii) the association with body adiposity depends on the criteria used to define low muscle mass; and (iv) the FNIH criteria detected higher adiposity in individuals with sarcopenia. Novelty: Prevalence of sarcopenia and low muscle mass in CKD varied according to the diagnostic criteria. Association of excess adiposity with sarcopenia and low muscle mass depends on muscle mass index applied. FNIH criteria detected higher adiposity in individuals with sarcopenia and low muscle mass.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".