Exploring Cystatin C as an Early Indicator of End-Stage Diabetic Nephropathy in Patients With Type 2 Diabetes
Bibliographic record
Abstract
Background: Diabetes, a prevalent metabolic disorder worldwide, significantly impacts various bodily organs. Type 2 diabetes, in particular, leads to chronic complications affecting the brain, eyes, cardiovascular system, and kidneys. Diabetic nephropathy, characterized by altered glomerular filtration rate (GFR) and increased urinary albumin excretion, represents a progressive kidney dysfunction. This study aimed to explore the association between serum cystatin C levels and diabetic nephropathy as a predictive marker. Methods: In this investigation, a total of 179 patients were categorized into four groups: a diabetic nephropathy group, a nephropathy group, a diabetes group, and a healthy control group. After obtaining informed consent and recording demographic indicators and clinical symptoms, a 5 mL blood sample was collected from each participant to assess the cystatin C levels using the enzyme-linked immunosorbent assay method. Results: The study findings revealed no significant differences in demographic indicators among the groups (P > 0.05). Additionally, the average serum cystatin C level in the diabetes group did not significantly differ from that in the control group (P > 0.05). However, a notable increase was observed in the mean serum cystatin C levels between the healthy group and both the diabetic nephropathy and nephropathy groups (P = 0.001). Conclusion: The study findings indicate that serum cystatin C levels serve as a potential warning and predictive factor for nephropathy in diabetic patients. J Endocrinol Metab. 2024;14(5):240-249 doi: https://doi.org/10.14740/jem991
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".