Diagnostic Power of Serum Creatinine/Cystatin C Ratio for Identifying Low MRI-Muscle Volume and Low Grip Strength: Data From 9 731 to 149 707 UK Biobank Older Adults
Bibliographic record
Abstract
BACKGROUND: Biomarkers for sarcopenia are lacking. We examined the diagnostic power of serum creatinine to cystatin C ratio for identifying low magnetic resonance imaging-muscle volume and low grip strength in a large observational study of UK Biobank older adults. METHODS: Serum creatinine and cystatin C were measured via immunoassays (Beckman Coulter AU5800 and Siemens Advia 1800, respectively) and grip strength by hydraulic hand dynamometer at baseline visit (2008-2010). magnetic resonance imaging-thigh fat-free muscle volume and DXA-derived appendicular lean mass were measured at imaging visit (2014-2018). Extreme outliers were removed, and covariates (demographic, lifestyle, and clinical factors, as well as time elapsed between baseline-imaging visit) were adjusted for in statistical models. RESULTS: 12 873 older adults (mean age: 63.5 ± 2.7 years, 44.2% women) were included for fat-free muscle volume and appendicular lean mass/body mass index; 149 707 older adults (mean age: 64.0 ± 2.9 years, 50.5% women) for grip strength. Despite significant associations (p < .05), in fully adjusted models, creatinine to cystatin C showed poor to acceptable diagnostic power for identifying low fat-free muscle volume when using cutpoints of 20th percentile (area under the curve: 0.577 men; 0.622 women) and T scores of -2 (area under the curve: 0.596 men; 0.659 women) and -2.5 (area under the curve: 0.609 men; 0.722 women). In fully adjusted model, creatinine to cystatin C showed poor diagnostic power (area under the curves: <0.70) for identifying low appendicular lean mass/body mass index or low grip strength, irrespective of the cutpoint used. CONCLUSIONS: Creatinine to cystatin C may not be a suitable biomarker for identifying low muscle volume or low strength in older adults. This finding, drawn from a large sample size and the use of advanced medical imaging, marks an important contribution to the sarcopenia field.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.002 | 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".