Evidence map›Paper›PMID 40001222›Full record

ArticleDiabetology & metabolic syndrome2025

Metabolic syndrome including both elevated blood pressure and elevated fasting plasma glucose is associated with higher mortality risk: a prospective study.

Shu Li, Chi Pang Wen, Huakang Tu, Sicong Wang, Xue Li, Andi Xu, Wenyuan Li, Xifeng Wu

Abstract read
In one paragraph

Article in Diabetology & metabolic syndrome, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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.

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3 · Its place in the literature

Who cites it

10 citing papers in PubMed.

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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

8 authors.

Shu Li *Center of Clinical Big Data and Analytics of the Second Affiliated Hospital and School of Public Health, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Chi Pang Wen *National Institute for Data Science in Health and Medicine, Zhejiang University, Hangzhou, Zhejiang, China.
Huakang TuCenter of Clinical Big Data and Analytics of the Second Affiliated Hospital and School of Public Health, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Sicong WangCenter of Clinical Big Data and Analytics of the Second Affiliated Hospital and School of Public Health, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Xue LiCenter of Clinical Big Data and Analytics of the Second Affiliated Hospital and School of Public Health, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Andi XuCenter of Clinical Big Data and Analytics of the Second Affiliated Hospital and School of Public Health, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Wenyuan LiCenter of Clinical Big Data and Analytics of the Second Affiliated Hospital and School of Public Health, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China. wenyuanli@zju.edu.cn.
Xifeng WuCenter of Clinical Big Data and Analytics of the Second Affiliated Hospital and School of Public Health, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China. xifengw@zju.edu.cn.ORCID http://orcid.org/0000-0001-5174-3769

Funding

Cancer Center, Zhejiang University and Key Research and Development Program of Zhejiang Province 2020C03002Healthy Zhejiang One Million People Cohort K20230085the Leading Innovative and Entrepreneur Team Introduction Program of Zhejiang 2019R01007
6 · The paper itself

Abstract

backgroundMetabolic syndrome (MetS) encompasses a collection of metabolic abnormalities. This study aims to determine which combination of MetS components has the highest mortality risk, and to investigate the causal relationships between MetS components and longevity.

methodsProspective analyses were conducted on 340,196 participants from the MJ cohort at baseline, and 121,936 participants had follow-up MetS information. We defined MetS according to the NCEP ATP III criteria. The study's outcomes included mortality from cardiovascular disease (CVD), cancer, and all causes combined. We employed Cox proportional hazard models to calculate hazard ratios (HRs) and 95% confidence intervals. Multivariable Mendelian randomization (MVMR) was employed to infer causality using the genetic data of MetS components and longevity.

resultsElevated blood pressure (BP) was the initial split for all-cause mortality, cancer mortality, and CVD mortality. Participants with MetS, especially those with elevated BP and elevated fasting plasma glucose (FPG), had higher mortality risks than those with other types of MetS. In the MJ cohort, participants with elevated BP and FPG (BG-type MetS) had a 44% (HR = 1.44, 95% CI = 1.37-1.51), 73% (HR = 1.73, 95% CI = 1.62-1.84), and 34% (HR = 1.34, 95% CI = 1.27-1.42) increased risk of all-cause mortality, cancer mortality, and CVD mortality, respectively, compared with non-BG-type MetS (12%, 24%, 5%). The highest mortality rate and mortality risk were observed in participants with BG-type MetS at baseline and follow-up (mortality rate/1000 person years = 9.73, 95% CI = 8.81-10.74; HR = 1.52, 95% CI = 1.35-1.72). SBP and FPG increases that were genetically proxied to a 1-standard deviation higher level decreased the probabilities of living to the 90th percentile age by 41% (OR = 0.59, 95% CI = 0.40-0.86) and 32% (OR = 0.68, 95% CI = 0.48-0.98) in MVMR, respectively.

conclusionsIndividuals with BG-type MetS are at a higher risk of death than those with other types of MetS. Therefore, these individuals should be targeted to improve MetS outcomes.

Indexed as

Mendelian randomizationMetabolic syndromeMortality

Identifiers

PMID40001222
PMCPMC11863470

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LicenceCC BY-NC-ND
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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.