Cystatin C and Chronic Complications of Diabetes Mellitus in a Subsaharian Population
Bibliographic record
Abstract
Background: Type 2 diabetes (T2D) is a chronic and progressive condition whose early management is crucial in preventing the occurrence of its chronic degenerative complications. Cystatin C (CysC) is a biomarker that may have a beneficial interest in the early detection of microvascular and macrovascular complications of T2D. Methods: We carried out a cross-sectional analytic study at the National Obesity Center of the Yaounde Central Hospital. We recruited 135 patients with T2D and performed a neurologic physical exam using the Toronto clinical score, a fundoscopy, ECG, ABI, and the following paraclinicals: dosage of serum levels of Cystatin C, lipid profile (with determination of Atherogenic indexes), Creatinine (with calculation of glomerular filtration rate using CKD-EPI formula) and hs-CRP. Results: Prevalences of diabetic retinopathy, nephropathy, neuropathy and PAD were 3.0%, 8.9%, 38.5%, 28.1% respectively. Electrocardiographic signs of myocardial ischemia were present in 5.9 % of the participants. We found 0.3[0.5-0.3] mg/l as median levels of the HDL-cholesterol and 0.9[1.2-0.7] mg/l for LDL-cholesterol. The median value of GFR was 105.7[119,0-85.7] ml/min/1.73m². The median serum CysC level was 0.8[0.9-0.6]mg/l and varied with age (p=0.01), A1C (p=0.016) and high blood pressure (HBP) (p=0.006). There was a relationship between serum CysC and diabetic nephropathy (p=0.01) and neuropathy (p=0.025). There was no significant relationship with diabetic retinopathy (p=0.225), PAD (p=0.169) and ECG signs of myocardial ischemia (p=0.669). Conclusion: Chronic microvascular and macrovascular complications of type 2 diabetes are common and in our study are predominantly represented by diabetic neuropathy and PAD. Serum CysC can be useful in the diagnosis of chronic complications of T2D.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".