Neutrophil-to-lymphocyte ratio predicts cardiovascular events in patients with type 2 diabetes: post hoc analysis of SUSTAIN 6 and PIONEER 6
Bibliographic record
Abstract
Abstract Background Inflammation plays an important role in atherosclerosis. The neutrophil-to-lymphocyte ratio (NLR) may serve as a clinically useful biomarker of inflammation and cardiovascular (CV) disease, although this relationship has not been studied in people with type 2 diabetes (T2D). Purpose This post hoc analysis investigated the relationship between NLRs and CV outcomes in T2D CV outcomes trials for two formulations of semaglutide, a glucagon-like peptide-1 receptor agonist. Methods In pooled analyses of the SUSTAIN 6 and PIONEER 6 trials, 6,480 patients with T2D at high CV risk received placebo or semaglutide (once-weekly subcutaneously up to 1.0 mg, or once-daily orally up to 14 mg). NLRs were calculated from complete blood counts at randomisation. Adjudicated outcomes included 3-point major adverse CV events (MACE: composite of CV death, non-fatal myocardial infarction [MI] or non-fatal stroke; primary outcome), expanded MACE, CV death and all-cause death (secondary outcomes). Patient characteristics and CV outcomes were analysed according to baseline NLR tertiles using pooled trial data. Estimation of hazard ratios (HRs) for all outcomes across NLR tertiles used a Cox proportional hazards model. A Cox spline regression with continuous NLR as covariate adjusted for treatment was used to predict the event rate of first MACE at 2 years. Results Overall, baseline NLR was recorded in 6,364 patients. Mean baseline NLRs were 1.5, 2.2 and 3.6 in the low, middle and high tertiles, respectively. Patients in the high NLR tertile were older (66.6 years), more likely to be male (70.0%), had longer duration of diabetes (15.3 years), higher body weight (93.3 kg), lower diastolic blood pressure (75.5 mmHg) and estimated glomerular filtration rate (70.4 mL/min/1.73m2) vs those in the lower NLR tertiles (all p<0.0001). Higher NLR was associated with an increased risk of MACE (HR [95% confidence interval (CI)]: 1.37 [1.05; 1.80; p=0.02] and 1.86 [1.45; 2.41; p<0.0001] for the middle and high tertiles, respectively, vs the low tertile). The high NLR tertile was also associated with a 74% increased risk of expanded MACE and twofold risk for CV death and all-cause death vs the low NLR tertile (Figure 1). Spline regression indicated that NLR values >5 increased the risk of first MACE substantially (Figure 2). Further analysis of NLR and MACE by tertiles showed a more pronounced association in patients without prior MI and/or stroke (HR [95% CI]: 1.64 [1.07; 2.56]; p=0.03 and 2.09 [1.38; 3.21]; p=0.0006 in the middle and high tertiles, respectively, vs the low tertile). Conclusion Baseline NLR predicts MACE, CV death and all-cause death in patients with T2D and high CV risk. NLR is readily accessible from routinely obtained and inexpensive blood counts; it could offer a convenient, clinically useful inflammatory biomarker for CV risk prediction in this population. Funding Acknowledgement Type of funding sources: Private company. Main funding source(s): Novo Nordisk A/S Figure 1Figure 2
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".