Association of Chronic Kidney Disease with Muscle Deficits in Children
Bibliographic record
Abstract
The effect of chronic kidney disease (CKD) on muscle mass in children, independent of poor growth and delayed maturation, is not well understood. We sought to characterize whole body and regional lean mass (LM) and fat mass (FM) in children and adolescents with CKD and to identify correlates of LM deficits in CKD. We estimated LM and FM from dual energy x-ray absorptiometry scans in 143 children with CKD and 958 controls at two pediatric centers. We expressed whole body, trunk, and leg values of LM and FM as Z-scores relative to height, sitting height, and leg length, respectively, using the controls as the reference. We used multivariable regression models to compare Z-scores in CKD and controls, adjusted for age and maturation, and to identify correlates of LM Z-scores in CKD. Greater CKD severity associated with greater leg LM deficits. Compared with controls, leg LM Z-scores were similar in CKD stages 2 to 3 (difference: 0.02 [95% CI: -0.20, 0.24]; P = 0.8), but were lower in CKD stages 4 to 5 (-0.41 [-0.66, -0.15]; P = 0.002) and dialysis (-1.03 [-1.33, -0.74]; P < 0.0001). Among CKD participants, growth hormone therapy associated with greater leg LM Z-score (0.58 [0.03, 1.13]; P = 0.04), adjusted for CKD severity. Serum albumin, bicarbonate, and markers of inflammation did not associate with LM Z-scores. CKD associated with greater trunk LM and FM, variable whole body LM, and normal leg FM, compared with controls. In conclusion, advanced CKD associates with significant deficits in leg lean mass, indicating skeletal muscle wasting. These data call for prospective studies of interventions to improve muscle mass among children with CKD.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".