Sarcopenia and sarcopenic obesity in chronic kidney disease: update on prevalence, outcomes, risk factors and nutrition treatment
Bibliographic record
Abstract
PURPOSE OF REVIEW: This review summarizes literature from the last 18 months reporting on sarcopenia (or its components) in chronic kidney disease (CKD). RECENT FINDINGS: The prevalence of sarcopenia in CKD is reported to be 5-62.5%, with higher rates observed later in the disease. Sarcopenic obesity rates are reported to be 2-23%. Sarcopenia in CKD is associated with increased risk of mortality, cardiovascular disease and vascular calcification. Risk factors include kidney disease itself and the impacts of CKD on lifestyle (reduced physical activity, diet changes). In earlier stages of CKD, if the risks from sarcopenia outweigh the risk of reaching end-stage renal disease, ensuring adequate energy intake combined with modest protein liberalization and physical activity may be indicated. Protein intakes above 1.3 g/kg of body weight per day should be avoided. For dialysis patients, interventions that provide a combination of carbohydrate, protein and fat appear more effective than those that provide protein alone, though it may take as long as 48 weeks for detectable changes in muscle mass. SUMMARY: Sarcopenia is prevalent in CKD as kidney disease significantly impacts muscle mass and function. Nutrition interventions can improve components of sarcopenia, with an emphasis on adequate energy and protein.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".