Evidence map›Paper›PMID 39511639›Full record

ArticleCardiovascular diabetology2024

Changes in the estimated glucose disposal rate and incident cardiovascular disease: two large prospective cohorts in Europe and Asia.

Xiaowei Zheng, Wenyang Han, Yiqun Li, Minglan Jiang, Xiao Ren, Pinni Yang, Yiming Jia, Lulu Sun, Ruirui Wang, Mengyao Shi and 2 more

Abstract read
In one paragraph

Article in Cardiovascular diabetology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.

0numbers the graph read from it
0cells of the map it votes in
36citing 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

36 citing papers in PubMed.

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  12. Accelerometer-measured weekly and diurnal moderate-to-vigorous physical activity patterns and incident osteoporosis.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2026
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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

12 authors.

Xiaowei ZhengPublic Health Research Center and Department of Public Health and Preventive Medicine, Wuxi School of Medicine, Jiangnan University, Wuxi, 214122, Jiangsu, China.
Wenyang HanPublic Health Research Center and Department of Public Health and Preventive Medicine, Wuxi School of Medicine, Jiangnan University, Wuxi, 214122, Jiangsu, China.
Yiqun LiPublic Health Research Center and Department of Public Health and Preventive Medicine, Wuxi School of Medicine, Jiangnan University, Wuxi, 214122, Jiangsu, China.
Minglan JiangPublic Health Research Center and Department of Public Health and Preventive Medicine, Wuxi School of Medicine, Jiangnan University, Wuxi, 214122, Jiangsu, China.
Xiao RenPublic Health Research Center and Department of Public Health and Preventive Medicine, Wuxi School of Medicine, Jiangnan University, Wuxi, 214122, Jiangsu, China.
Pinni YangDepartment of Epidemiology, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Major Chronic Non-Communicable Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, Suzhou, 215123, Jiangsu, China.
Yiming JiaDepartment of Epidemiology, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Major Chronic Non-Communicable Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, Suzhou, 215123, Jiangsu, China.
Lulu SunDepartment of Epidemiology, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Major Chronic Non-Communicable Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, Suzhou, 215123, Jiangsu, China.
Ruirui WangDepartment of Epidemiology, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Major Chronic Non-Communicable Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, Suzhou, 215123, Jiangsu, China.
Mengyao ShiDepartment of Epidemiology, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Major Chronic Non-Communicable Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, Suzhou, 215123, Jiangsu, China.
Zhengbao ZhuDepartment of Epidemiology, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Major Chronic Non-Communicable Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, Suzhou, 215123, Jiangsu, China. zbzhu@suda.edu.cn.
Yonghong ZhangDepartment of Epidemiology, School of Public Health, Jiangsu Key Laboratory of Preventive and Translational Medicine for Major Chronic Non-Communicable Diseases, MOE Key Laboratory of Geriatric Diseases and Immunology, Suzhou Medical College of Soochow University, Suzhou, 215123, Jiangsu, China. yhzhang@suda.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND AND

aimsPrevious study found that estimated glucose disposal rate (eGDR) was significantly associated with cardiovascular disease (CVD). However, little is known about the change in eGDR over time and its association with the development of CVD. The aim of this study was to investigate the association of change in eGDR with CVD risk.

methodsThis study used data of two prospective cohorts: UK Biobank and China Health and Retirement Longitudinal Study (CHARLS) with two measurements of eGDR. Changes in the eGDR were classified using K‑means clustering analysis, and the cumulative eGDR was also calculated. Cox proportional hazard models were used to calculate the hazard ratio (HR) and 95% confidence interval (95% CI) after adjusting for potential confounders.

resultsA total of 11,682 individuals from the UK Biobank, and 4,974 individuals from the CHARLS were included. The median follow-up periods were 9.7 years in the UK Biobank and 3.0 years in the CHARLS. Compared with persistently high level of eGDR (class 1), individuals with low level increasing (class 3) and persistently low level of eGDR (class 4) showed elevated risks of incident CVD in both UK Biobank (HR = 2.79, 95% 2.15-3.62 for class 3; HR = 3.19, 95% 2.50-4.08 for class 4) and CHARLS (HR = 1.66, 95% 1.29-2.13 for class 3; HR = 1.69, 95% 1.34-2.14 for class 4). In addition, lower level of cumulative eGDR were associated with elevated risks of incident CVD. The dose-response curve between cumulative eGDR and CVD risk showed a negative linear relationship.

conclusionDifferent changes in eGDR level are associated with different risks of incident CVD. Dynamic monitoring of eGDR level is of significant importance for the CVD prevention and treatment.

Indexed as

Blood GlucoseCardiovascular DiseasesAdultAgedBiomarkersChinaFemaleHumansIncidenceLongitudinal StudiesMaleMiddle AgedPrognosisProspective StudiesRisk AssessmentRisk FactorsBiomarkersBlood GlucoseCHARLSCVDeGDREstimated glucose disposal rateUK Biobank

Identifiers

PMID39511639
PMCPMC11545867

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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.