Monocyte-to-lymphocyte ratio as an inflammatory biomarker for predicting diabetic peripheral neuropathy in type 2 diabetes mellitus patients
Bibliographic record
Abstract
BACKGROUND: Type 2 diabetes mellitus (T2DM) is an intricate metabolic disorder often accompanied by low-grade inflammation. This study aimed to investigate the relationship between the monocyte-to-lymphocyte ratio (MLR) and diabetic peripheral neuropathy (DPN) in patients with T2DM. METHOD: A total of 236 individuals diagnosed with T2DM participated in the research. Clinical parameters were assessed, including the Toronto Clinical Neuropathy Score (TCNS), Compound Muscle Action Potential (CMAP), Sensory Nerve Action Potential (SNAP), nerve conduction velocity (NCV), complete blood count, biochemical markers, and inflammatory indicators. These parameters were analyzed and compared, followed by logistic regression and receiver operating characteristic (ROC) curve analyses. RESULTS: The findings demonstrated that patients with DPN were generally older and had higher MLR levels and glycated hemoglobin levels, lower high-density lipoprotein cholesterol(HDL-C), longer disease duration, and higher FPG compared to patients without DPN. Additionally, CMAP and SNAP of median nerve (MN), ulnar nerve(UN), peroneal nerve (PN), and tibial nerve (TN) were significantly lower in the higher MLR group than in the higher MLR group( P < 0.05). ROC analysis indicated that MLR had an area under the curve (AUC) of 0.625, suggesting a limited discriminative ability to identify DPN. CONCLUSION: This study underscores the potential ability of MLR as a predictive biomarker for DPN in patients with T2DM, emphasizing the important role of inflammation in the development of this condition. But it has a low performance for DPN diagnosis. CLINICAL TRIAL NUMBER: Not applicable.
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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".