Inflammatory Markers as Predictors of Diabetic Nephropathy in Type 2 Diabetes Mellitus: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background and Objectives: Diabetic nephropathy (DN) is a major complication of diabetes mellitus and a leading cause of end-stage renal disease. Inflammatory markers such as neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), systemic immune-inflammation index (SII), and red cell distribution width (RDW) have been proposed as potential predictors of DN progression. This study systematically reviews and meta-analyzes the role of these markers in DN. Materials and Methods: A comprehensive literature search was conducted to identify studies evaluating NLR, PLR, SII, and RDW in type 2 diabetes patients with normoalbuminuria, microalbuminuria, and macroalbuminuria. Five databases were searched: PubMed, Scopus, Embase, Web of Science, and LILACS. The Newcastle Ottawa Scale was used to assess the risk of bias in selected articles. Results: Out of 1556 records that were identified through searches, 40 were selected for the review. Finally, 35 were included for meta-analyses, including 13,519 patients. Higher levels of NLR, PLR, SII, and RDW were observed in macro- and microalbuminuria compared to normoalbuminuria, with significantly elevated NLR in microalbuminuria. Meta-analyses showed that NLR and RDW were significantly associated with higher odds of DN (NLR: OR 1.84, p < 0.001; RDW: OR 1.9, p = 0.023). However, PLR and SII were not significantly associated with DN. A longitudinal study confirmed SII as a significant predictor of DN progression (hazard ratio: 3.24, p = 0.023). Conclusions: This study highlights the potential of NLR and RDW as predictive markers for diabetic nephropathy.
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.032 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".