Platelet–Lymphocyte Ratio, Neutrophil–Lymphocyte Ratio and Their Dynamic Changes with Type 2 Diabetes Mellitus: A Cohort Study in China
Bibliographic record
Abstract
This study aimed at investigating the relationships between Platelet-Lymphocyte ratio (PLR) and Neutrophil–Lymphocyte ratio (NLR) and their dynamic changes (∆PLR, ∆NLR) with type 2 diabetes mellitus (T2DM) in a Chinese cohort study. This study recruited 41,439 individuals who were diagnosed without T2DM at first health examination and completed at least one follow-up. The relationships between NLR, PLR, ∆PLR, ∆NLR and T2DM risk were analyzed using the Cox regression model with corresponding Hazard Ratios (HRs) and 95% Confidence Intervals (CIs). PLR exhibited significant correlation with T2DM risk in a linear reverse dose–response pattern, the corresponding HRs and 95% CIs were 0.81 (0.72, 0.90), 0.71 (0.63, 0.80) and 0.56 (0.49, 0.64) respectively (Ptrend < 0.001) for Q2, Q3 and Q4 vs Q1 after adjusting for age, gender, BMI, TG, TC, HDL-C, FPG, ALT, AST, heart rate, smoking, family history of diabetes, and alcohol consumption at baseline in Model 3. The significance remained in subgroups of women, <45 years, ≥45 years, BMI ≥ 24, with fatty liver disease, without fatty liver disease and normotension. Comparing with the largest decrease group of NLR (∆NLR < −0.32), the risk of T2DM increased for −0.003 ≤ ∆NLR < 0.31 (HR 1.17, 95% CI 1.01–1.36) and ∆NLR ≥ 0.31 (HR 1.23, 95% CI 1.06–1.43). Higher PLR could reduce the risk of T2DM. Larger increase of NLR could increase T2DM risk.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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".