Estimating GFR by Serum Creatinine, Cystatin C, and β2-Microglobulin in Older Adults: Results From the Canadian Study of Longevity in Type 1 Diabetes
Bibliographic record
Abstract
IntroductionGlomerular filtration rate (GFR) is routinely used for clinical assessment of kidney function. However, the accuracy of estimating equations in older adults is uncertain.MethodsIn 66 adults with ≥50 years type 1 diabetes (T1D) duration and 73 nondiabetic controls from age/sex-matched subgroups (65 ± 8 years old and 77[55%] were women) we evaluated the performance of estimated GFR (eGFR) by creatinine (Modification of Diet and Renal Disease [MDRD], Chronic Kidney Disease–Epidemiology [CKD-EPI]cr), cystatin C (CKD-EPIcys, CKD-EPIcr-cys), and β2-microglobulin (β2M) compared with measured GFR by inulin clearance (mGFR). Performance was evaluated using metrics of bias (mean difference), precision (SD), and accuracy (proportion of eGFR that differed by >20% of mGFR).ResultsMean mGFR was 104 ± 18 ml/min per 1.73 m2 (range: 70–154 ml/min per 1.73 m2) and was not different between T1D and controls (103 ± 17 vs. 105 ± 19 ml/min per 1.73 m2, P = 0.39). All equations significantly underestimated mGFR (bias: −15 to −30 ml/min per 1.73 m2, P < 0.001 for all comparisons) except for β2M, which had bias of 1.9 ml/min per 1.73 m2 (P = 0.61). Bias was greatest in cystatin C–based equations. Precision was lowest for β2M (SD: 43.5 ml/min per 1.73 m2, P < 0.001 for each comparison). Accuracy was lowest for CKD-EPIcysC (69.1%, P < 0.001 for each comparison). Cystatin C–based equations demonstrated greater bias and lower accuracy in older age subgroups (<60, 60–69, ≥70 years). All equations demonstrated greater bias across higher ranges of mGFR (60–89, 90–119, ≥120 ml/min per 1.73 m2). Results were similar between T1D and controls except that β2M had lower performance in T1D.ConclusionBetter estimates of GFR in older adults are needed for research and clinical practice, as this subgroup of the population has an amplified risk for the development of chronic kidney disease (CKD) that requires accurate GFR estimation methods.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".