Association of CKD with Sarcopenia: A Population-Wide Study
Bibliographic record
Abstract
Background: Sarcopenia, defined as the loss of muscle mass, is a growing public health concern and is an underrecognized problem in adults with Chronic Kidney Disease (CKD). The diagnosis of sarcopenia can be made via measurement of appendicular lean mass index (ALMi, indexed to height by m2), traditionally obtained through whole-body dual-energy X-ray absorptiometry (DXA) scans, however these are not frequently performed. As a result, large population-based studies examining the relationship between CKD and sarcopenia are lacking. Methods: Using Manitoba longitudinal administrative health data, we identified adults who had at least one DXA scan linkable to serum creatinine values within 365 days, between 2007 and 2022. Serum creatinine was used to calculate estimated glomerular filtration rate, and estimated ALMi (eALMi) was calculated through central DXA scans via a previously developed algorithm. Linear, logistic, and Cox proportional hazards models were executed to examine the relationship between CKD, sarcopenia, and adverse clinical outcomes. Results: Our cohort contained 24,660 individuals (64.4 ± 12.5 years, 84.4% female), with 3,204 individuals (13.0%) having eALMi indicating sarcopenia. 22,648 individuals (91.8%) had eGFR > 60, and 2,012 (8.2%) had eGFR < 60. After adjustment for age, sex, estimated central mass index, and comorbid conditions, the presence of eGFR < 60 was associated with higher odds of sarcopenia (OR: 1.39; 95% CI: 1.16–1.67). In individuals with two DXA scans (n=2,985), eGFR < 60 at baseline was associated with a larger decline in eALMi compared to individuals with preserved eGFR (OR: 1.61; 95% CI: 1.05–2.45). Both sarcopenia and declining eALMi were also associated with adverse clinical outcomes including hospitalization and emergency room visits, home care use, long-term care use, and all-cause mortality. Conclusion: Our results show that CKD is associated with sarcopenia and leads to more rapid declines in appendicular lean mass over time. These findings further validate our central DXA based measurement of eALMi and sarcopenia and highlight the importance preservation of muscle mass in individuals with CKD, especially in those with reduced eGFR. Funding: Government Support – Non-U.S.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".