Exploring leukocyte differential count ratio profiles as inflammatory biomarkers in diabetic retinopathy: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Diabetic retinopathy (DR) is increasingly prevalent and a major cause of irreversible blindness, particularly in developing countries. Limited access to ophthalmologists often leads to delayed diagnosis, emphasizing the need for more affordable and widely accessible screening methods to facilitate early identification. Recently, several studies have demonstrated variability in findings regarding the relationship between leukocyte differential count ratio biomarkers and DR. This study aims to investigate the association between leukocyte differential count ratios-NLR (Neutrophil-to-Lymphocyte Ratio), PLR (Platelet-to-Lymphocyte Ratio), MLR (Monocyte-to-Lymphocyte Ratio), and SII (Systemic Immune-Inflammation Index)-and the stages of diabetic retinopathy (DR). METHODS: A comprehensive literature search was conducted across several databases up to September 2024, with a focus on identifying studies examining the relationship between the leukocyte differential count ratio profiles and diabetic retinopathy. Review Manager was used to conduct the meta-analyses. The Newcastle Ottawa Scale (NOS) were used to assess the included studies. RESULTS: A total of 38 studies were included in the systematic review and 27 studies were included in the meta-analysis. The mean differences in the NLR and PLR values were significantly different among the groups and were higher in the PDR group (0.68 (95%CI 0.42-0.95, p < 0.05) and 19.57 (95%CI 10.68-28.46, p < 0.05; respectively). These findings were followed by significant differences in SII value 202.53 (95% CI 196.19-208.86, p < 0.05). Moreover, the MLR values were not significantly different among the groups (p > 0.05). CONCLUSION: NLR, PLR, and SII are associated with both the presence and progression of DR, with increasing levels of NLR and PLR reflecting a higher risk and severity of the disease. However, it is still necessary to justify the need to combine them with other clinical parameters to confirm the diagnosis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.035 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".