Early Neuropathy as a Predictor of Subclinical Diabetic Nephropathy in Well‐Controlled Type 2 Diabetic Patients: A Cross‐Sectional Study
Bibliographic record
Abstract
Background Diabetic neuropathy (DN) and nephropathy (DKD) are prevalent microvascular complications in Type 2 diabetes mellitus (T2DM), often evolving silently. Detecting early nephropathy remains a clinical challenge, especially in patients with preserved renal function. Objective The objective was to determine whether the Toronto Clinical Scoring System (TCS) for diabetic neuropathy can predict early nephropathy (albuminuria) in people with well‐controlled T2DM who have a normal eGFR. Methods We conducted a cross‐sectional study with 122 T2DM patients (HbA1c < 7 % , eGFR > 90) to look for peripheral neuropathy using TCS and nephropathy using the urinary albumin‐to‐creatinine ratio (UACR). Patients were classified based on the presence of albuminuria (UACR ≥ 30 mg/g). Statistical analyses included t ‐tests, chi‐square tests, Spearman correlation, and logistic regression. Results Patients with diabetic nephropathy or neuropathy were significantly older and exhibited higher systolic blood pressure and albuminuria. A clear stepwise increase in albuminuria was observed with rising neuropathy severity, with nephropathy prevalence ranging from 42% in patients without neuropathy to 72% in those with severe neuropathy. A significant positive correlation between TCS and UACR ( ρ = 0.29, p = 0.0012) supports a progressive link between nerve and kidney involvement. Conclusion Clinical diabetic neuropathy is significantly associated with early nephropathy in well‐controlled T2DM patients. Routine neuropathy assessment may serve as a simple, cost‐effective predictor of subclinical renal damage. Future prospective studies should investigate whether early intervention in patients with neuropathy can attenuate or delay renal injury and whether this predictive link holds true across diverse ethnic and age groups.
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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 | 0.001 | 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".