Ability of cystatin C to detect acute changes in glomerular filtration rate provoked by hyperglycaemia in uncomplicated Type 1 diabetes
Bibliographic record
Abstract
AIMS: Systematic study of hyperfiltration in diabetic nephropathy has been hindered by the lack of a simple glomerular filtration rate (GFR) measure that is accurate in this range of renal function. Serum cystatin C (GFR(CYSTATIN C) ) reflects long-term trends in GFR in normal or elevated ranges. To test whether it can reflect acute changes, we examined the impact of clamped hyperglycaemia on GFR(CYSTATIN C) and GFR(INULIN) in subjects with Type 1 diabetes. METHODS: GFR(INULIN) and GFR(CYSTATIN C) were measured in 32 normotensive, normoalbuminuric subjects during clamped euglycaemia and hyperglycaemia. For comparison, GFR(MDRD) was estimated according to the four-variable equation. RESULTS: During clamped euglycaemia, agreement between GFR(CYSTATIN C) and GFR(INULIN) was excellent, with mean bias +1.9 (90% distribution -29 to +31) ml min(-1) 1.73 m(-2), while GFR(MDRD) had mean bias +11.4 (-45 to +51) ml min(-1) 1.73 m(-2). With exposure to clamped hyperglycaemia, the mean increase in GFR(CYSTATIN C) (+17.5 ± 13.5 ml min(-1) 1.73 m(-2) ) reflected that observed with GFR(INULIN) (+15.3 ± 28.1 ml min(-1) 1.73 m(-2), P = 0.74), while GFR(MDRD) demonstrated a mean decline of -4.4 ± 33.6 ml min(-1) 1.73 m(-2) (P = 0.01). In all 24 subjects in whom GFR(INULIN) increased in response to hyperglycaemia, GFR(CYSTATIN C) reflected a concordant change (sensitivity, 100%) while GFR(MDRD) increased in 10/24 (sensitivity, 42%). In the eight remaining subjects, specificity was 25 and 75% for GFR(CYSTATIN C) and GFR(MDRD), respectively. CONCLUSION: GFR(CYSTATIN C) reflects normal and elevated renal function better than GFR(MDRD) even under the acute influences of hyperglycaemia, suggesting a role for cystatin C in clinical practice and research for the study of early renal function changes in Type 1 diabetes.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.001 | 0.001 |
| 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 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".