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Neutrophil-to-lymphocyte ratio predicts cardiovascular events in patients with type 2 diabetes: post hoc analysis of SUSTAIN 6 and PIONEER 6

2021· article· en· W3209538168 on OpenAlexaff
Subodh Verma, Mansoor Husain, Claus Madsen, Lawrence A. Leiter, Sabitha Rajan, Tina Vilsbøll, Søren Rasmussen, Peter Libby

Bibliographic record

VenueEuropean Heart Journal · 2021
Typearticle
Languageen
FieldMedicine
TopicInflammatory Biomarkers in Disease Prognosis
Canadian institutionsTed Rogers Centre for Heart ResearchUniversity of TorontoSt. Michael's Hospital
FundersNovo Nordisk
KeywordsMedicineMaceHazard ratioInternal medicinePost-hoc analysisType 2 diabetesNeutrophil to lymphocyte ratioProportional hazards modelMyocardial infarctionSemaglutideDiabetes mellitusLymphocytePercutaneous coronary interventionConfidence intervalEndocrinologyLiraglutide

Abstract

fetched live from OpenAlex

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 imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.004
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.009
GPT teacher head0.224
Teacher spread0.215 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations4
Published2021
Admission routes1
Has abstractyes

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