Evidence map›Paper›PMID 40322532›Full record

ArticleJournal of inflammation research2025

Association Between NLR, MLR and Stroke Incidence, All-Cause Mortality Among Low-Income Aging Populations: A Prospective Cohort Study.

Dongjing Liu, Xiaonan Fan, Junwei Wang, Ruihui Weng, Jun Tu, Jinghua Wang, Xianjia Ning, Yu Zhao

Abstract read
In one paragraph

Article in Journal of inflammation research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Dongjing Liu *Department of Science and Education, Shenzhen Third People's Hospital and The Second Hospital Affiliated with The Southern University of Science and Technology, Shenzhen, Guangdong, People's Republic of China.
Xiaonan Fan *National Clinical Research Center for Infectious Disease, Shenzhen Third People's Hospital and The Second Hospital Affiliated with The Southern University of Science and Technology, Shenzhen, Guangdong, People's Republic of China.
Junwei WangDepartment of Cardiology, Shenzhen Third People's Hospital and The Second Hospital Affiliated with The Southern University of Science and Technology, Shenzhen, Guangdong, People's Republic of China.
Ruihui WengDepartment of Neurology, Shenzhen Third People's Hospital and The Second Hospital Affiliated with The Southern University of Science and Technology, Shenzhen, Guangdong, People's Republic of China.
Jun TuDepartment of Neurology, Tianjin Medical University General Hospital, Tianjin, 300052, People's Republic of China.
Jinghua WangDepartment of Neurology, Tianjin Medical University General Hospital, Tianjin, 300052, People's Republic of China.ORCID 0000-0003-2619-0570
Xianjia NingDepartment of Neurology, Tianjin Medical University General Hospital, Tianjin, 300052, People's Republic of China.
Yu ZhaoDepartment of Neurology, Shenzhen Third People's Hospital and The Second Hospital Affiliated with The Southern University of Science and Technology, Shenzhen, Guangdong, People's Republic of China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: This study aimed to assess the association of the Neutrophil-to-Lymphocyte Ratio (NLR) and Monocyte-to-Lymphocyte Ratio (MLR) in predicting stroke incidence and all-cause mortality in low-income elderly populations. Methods: This prospective cohort study included participants who were middle-aged or elderly individuals from a low-income population in China. Participants were selected into the cohort and complete baseline assessments, which included questionnaire surveys, physical examinations, blood tests, and carotid artery ultrasound evaluations. Cox proportional hazards regression analysis was used to assess the associations of the NLR and MLR with the incidence of stroke and all-cause mortality. The predictive performance of the model was evaluated using the area under the receiver operating characteristic curve (AUC-ROC). Results: A total of 3948 participants were enrolled in the study. Over a median follow-up period of 7 years, 262 participants experienced stroke events and 227 participants died. After adjusting for potential confounding variables, the final model revealed that a higher NLR was significantly associated with an increased risk of stroke (HR: 1.776, 95% CI: 1.250-2.254, P = 0.001) and all-cause mortality (HR: 1.558, 95% CI: 1.148-2.116, P = 0.004). Furthermore, a higher MLR was found to be associated with an increased risk of all-cause mortality (HR: 1.397, 95% CI: 1.054-1.852, P = 0.020), but no significant association was observed between MLR and stroke incidence. ROC analysis revealed that the AUC for NLR in predicting stroke was 0.55 (95% CI: 0.52-0.59, P=0.005), while the AUC for MLR was 0.58 (95% CI: 0.54-0.62, P<0.001). Similarly, the AUC for NLR in predicting all-cause mortality was 0.57 (95% CI: 0.53-0.61, P<0.001), and the AUC for MLR was 0.61 (95% CI: 0.57-0.65, P<0.001). Conclusion: These findings indicate that NLR is associated with an increased risk of stroke and all-cause mortality, while higher MLR is associated with all-cause mortality but not with stroke incidence. However, the modest predictive performance of both markers suggests that their clinical utility remains limited. Further research is needed to validate these associations and explore their potential role in comprehensive risk assessment models.

Indexed as

all-cause deathelderly populationslow-income populationMLRNLRstroke

Identifiers

PMID40322532
PMCPMC12048293

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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.