Evidence map›Paper›PMID 42854013›Full record

Observational studyMedicine2026

Association between neutrophil percentage-to-albumin ratio and all-cause mortality in stroke survivors stratified by triglyceride-glucose index: A prospective observational study based on the NHANES database from 1999 to 2018.

Ning Liu, Zhongzhou Su, Sheng Zhang

Abstract readObservational Study
In one paragraph

Observational study in Medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

3 authors.

Ning LiuDepartment of Neurosurgery, Huzhou Central Hospital, Fifth School of Clinical Medicine of Zhejiang Chinese Medical University, Huzhou, Zhejiang, China.
Zhongzhou Su

Funding

This research was funded by an individual start-up fund from Huzhou Central Hospital
6 · The paper itself

Abstract

The neutrophil percentage-to-albumin ratio (NPAR) is a novel inflammatory biomarker with prognostic value in critical illnesses, but its association with outcomes in stroke survivors remains unclear. No prior study has explored whether this association differs across triglyceride-glucose index (TyG) strata, a surrogate marker of insulin resistance (IR) and metabolic disturbance in cardiovascular disease. Data from the National Health and Nutrition Examination Survey 1999 to 2018 were analyzed for 1187 stroke survivors aged 40 to 80 years. Kaplan-Meier curves, multivariate Cox models, restricted cubic spline (RCS) analysis, and subgroup analyses were used to examine the relationship between baseline NPAR and all-cause mortality across TyG levels. Over a median follow-up of 86 months, 496 (41.79%) of the 1187 participants died; median survival was 133 months. Kaplan-Meier analysis showed significant mortality differences across NPAR tertiles in all participants and within intermediate- and high-IR groups (all P < .001). As a continuous variable, NPAR was significantly associated with mortality in the intermediate-risk (hazard ratio = 1.18) and high-risk (hazard ratio = 1.10) groups, but not in the low-risk group. As a categorical variable, the highest NPAR tertile (T3) was associated with higher mortality across all adjustment models in IR intermediate- and high-risk groups, whereas in the IR low-risk group, significance was observed only in the minimally adjusted model. Restricted cubic spline analysis revealed a linear association between NPAR and mortality in all participants (P for nonlinear = .291), as well as in IR intermediate- and high-risk groups, but no association in the IR low-risk group (P for overall = .187). Subgroup analyses showed significant interactions across TyG subgroups (P for interaction = .047). Two sensitivity analyses confirmed the stability of the main findings. Monitoring NPAR levels alongside TyG-based stratification may facilitate comprehensive risk assessment and inform targeted management strategies for stroke survivors.

Indexed as

Blood GlucoseNeutrophilsStrokeTriglyceridesAdultAgedAged, 80 and overBiomarkersFemaleHumansInsulin ResistanceKaplan-Meier EstimateMaleMiddle AgedNutrition SurveysPrognosisBiomarkersBlood GlucoseTriglyceridesall-cause mortalityNPARstrokesurvivorTyG

Identifiers

PMID42854013
PMCPMC13657450

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.