Evidence map›Paper›PMID 36352412›Full record

ArticleBMC medicine2022

Association of lipid, inflammatory, and metabolic biomarkers with age at onset for incident cardiovascular disease.

Xue Tian, Shuohua Chen, Yingting Zuo, Yijun Zhang, Xiaoli Zhang, Qin Xu, Yanxia Luo, Shouling Wu, Anxin Wang

Open access · goldAbstract read
In one paragraph

Article in BMC medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed
3.8field-weighted citation impact, top 5% of its field
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

21 citing papers in PubMed, 33 citations in OpenAlex.

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  14. The Association between Serum Lipid Profile Levels and Hypertension Grades: A Cross-Sectional Study at a Health Examination Center.High blood pressure & cardiovascular prevention : the official journal of the Italian Society of Hypertension · 2025
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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

9 authors at 3 institutions in 1 country.

Xue Tian *Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, No.119 South 4th Ring West Road, Fengtai District, Beijing, 100070, China.
Shuohua Chen *Department of Cardiology, Kailuan Hospital, North China University of Science and Technology, 57 Xinhua East Rd, Tangshan, 063000, China.
Yingting ZuoDepartment of Neurology, Beijing Tiantan Hospital, Capital Medical University, No.119 South 4th Ring West Road, Fengtai District, Beijing, 100070, China.
Yijun ZhangDepartment of Neurology, Beijing Tiantan Hospital, Capital Medical University, No.119 South 4th Ring West Road, Fengtai District, Beijing, 100070, China.
Xiaoli ZhangDepartment of Neurology, Beijing Tiantan Hospital, Capital Medical University, No.119 South 4th Ring West Road, Fengtai District, Beijing, 100070, China.
Qin XuDepartment of Neurology, Beijing Tiantan Hospital, Capital Medical University, No.119 South 4th Ring West Road, Fengtai District, Beijing, 100070, China.
Yanxia LuoDepartment of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, No.10 Xitoutiao, You'anmen Wai, Fengtai District, Beijing, 100069, China. lyx100@ccmu.edu.cn.
Shouling WuDepartment of Cardiology, Kailuan Hospital, North China University of Science and Technology, 57 Xinhua East Rd, Tangshan, 063000, China. drwusl@163.com.
Anxin WangDepartment of Neurology, Beijing Tiantan Hospital, Capital Medical University, No.119 South 4th Ring West Road, Fengtai District, Beijing, 100070, China. wanganxin@bjtth.org.
Capital Medical University · CNNorth China University of Science and Technology · CNBeijing Tian Tan Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundRisk profiles for premature cardiovascular disease (CVD) are unclear. This study aimed to examine baseline risk profiles for incident CVD by age at onset in Chinese population.

methodsA total of 97,841 participants without CVD were enrolled from the Kailuan cohort study. Four age groups were examined (< 55, 55 to < 65, 65 to < 75, and ≥ 75 years) for CVD onset. Risk profiles included clinical, lipid, metabolic, and inflammatory risk factors and biomarkers.

resultsOf the clinical factors, diabetes was associated with the highest relative risk for incident CVD in participants younger than 55 years (sub-distributional hazard ratio [sHR], 4.08; 95% confidence interval [CI], 3.47-4.80). Risk factors that were also noted for CVD onset in participants younger than 55 years included hypertension, metabolism syndrome, overweight or obese, dyslipidemia, and smoking. Among the biomarkers, insulin resistance measured by triglyceride-glucose index had the highest sHR (1.42; 95% CI, 1.35-1.49) for CVD in participants younger than 55 years. In comparison, weaker but significant associations with CVD in participants younger than 55 years were noted for most lipids, metabolic biomarkers, and inflammatory biomarkers. Most risk factors and biomarkers had associations that attenuated with increasing age at onset. Some biomarkers had similar CVD age association, while a few had no association with CVD onset at any age.

conclusionsThese findings showed that diabetes and insulin resistance, in addition to hypertension, metabolism syndrome, overweight or obese, dyslipidemia, and smoking, appeared to be the strongest risk factors for premature onset of CVD, and most risk factors had attenuated relative rates at older ages.

Indexed as

Cardiovascular DiseasesDiabetes MellitusDyslipidemiasHypertensionInsulin ResistanceAgedAge of OnsetBiomarkersCohort StudiesHumansIncidenceObesityOverweightRisk FactorsTriglyceridesBiomarkersTriglyceridesAge-related riskCardiovascular diseaseRisk factorsYoung adults

Identifiers

PMID36352412
PMCPMC9647925
OpenAlexW4308730648

What OpenQuestion holds

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LicenceCC BY
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.