Evidence map›Paper›PMID 36988954›Full record

ArticleJAMA network open2023

Age and Genetic Risk Score and Rates of Blood Lipid Changes in China.

Jianxin Li, Mengyao Liu, Fangchao Liu, Shufeng Chen, Keyong Huang, Jie Cao, Chong Shen, Xiaoqing Liu, Ling Yu, Yingxin Zhao and 8 more

Open access · goldAbstract read
In one paragraph

Article in JAMA network open, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed
10.8field-weighted citation impact, top 1% 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

24 citing papers in PubMed, 34 citations in OpenAlex.

  1. Article
  2. Article
  3. Genetic factors contributing to atherosclerosis.Current opinion in cardiology · 2026
    Review
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  14. [Spousal correlations of blood lipid based on a family design].Beijing da xue xue bao. Yi xue ban = Journal of Peking University. Health sciences · 2025
    Article
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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

18 authors at 7 institutions in 1 country.

Jianxin LiDepartment of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Mengyao LiuDepartment of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Fangchao LiuDepartment of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Shufeng ChenDepartment of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Keyong HuangDepartment of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Jie CaoDepartment of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Chong ShenDepartment of Epidemiology, School of Public Health, Nanjing Medical University, Nanjing, China.
Xiaoqing LiuDivision of Epidemiology, Guangdong Provincial People's Hospital and Cardiovascular Institute, Guangzhou, China.
Ling YuDepartment of Cardiology, Fujian Provincial Hospital, Fuzhou, China.
Yingxin ZhaoCardio-Cerebrovascular Control and Research Center, Institute of Basic Medicine, Shandong Academy of Medical Sciences, Jinan, China.
Huan ZhangDepartment of Epidemiology, School of public health, Medical College of Soochow University, Suzhou, China.
Shujun GuDepartment of Chronic Disease Control and Prevention, Changshu Center for Disease Control and Prevention, Changshu, China.
Liancheng ZhaoDepartment of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Ying LiDepartment of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Dongsheng HuDepartment of Biostatistics and Epidemiology, School of Public Health, Shenzhen University Health Science Center, Shenzhen, China.
Jianfeng HuangDepartment of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Dongfeng GuDepartment of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Xiangfeng LuDepartment of Epidemiology, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Chinese Academy of Medical Sciences & Peking Union Medical College · CNFujian Provincial Hospital · CNGuangdong Provincial People's Hospital · CNNanjing Medical University · CNShandong First Medical University · CNSoochow University · CNZhengzhou University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Importance: Blood lipids are the primary cause of atherosclerosis. However, little is known about relationships between rates of blood lipid changes and age and genetic risk. Objective: To evaluate associations of blood lipid change rates with age and polygenic risk. Design, Setting, and Participants: This cohort is from the Prediction for Atherosclerotic Cardiovascular Disease Risk in China, which was established from 1998 to 2008. Participants were followed up until 2020 (mean [SD] follow-up, 13.8 [4.3] years) and received 4 repeated lipid measurements. Data analysis was performed from June to August 2022. A total of 47 691 participants with available genotype data were recruited, and 37 317 participants aged 18 years or older were included in the final analysis after excluding participants who were lost to follow-up or with major chronic diseases, and those without blood lipid measurements at baseline and any follow-up survey. Exposures: Age and polygenic risk scores based on 126 lipid-related genetic variants. Main Outcomes and Measures: The estimated annual changes (EAC) of blood lipids in milligrams per deciliter. Results: This study evaluated 37 317 participants (mean [SD] age of 51.37 [10.82] years; 15 664 [41.98%] were male). The associations of EACs of blood lipids with age differed substantially between male and female participants. Male participants experienced declining change as they got older for total cholesterol (EAC, 0.34 [95% CI, 0.14 to 0.54] mg/dL for age <40 years vs 0.01 [95% CI, -0.11 to 0.13] mg/dL for age ≥60 years), triglyceride (EAC, 3.28 [95% CI, 2.50 to 4.07] mg/dL for age <40 years vs -1.70 [95% CI, -2.02 to -1.38] mg/dL for age ≥60 years), and low-density lipoprotein cholesterol (LDL-C) (EAC, 0.15 [95% CI, -0.02 to 0.32] mg/dL for age <40 years vs 0.01 [95% CI, -0.10 to 0.11] mg/dL for age ≥60 years). Female participants had inverse V-shaped associations and the greatest rate of change appeared in the age group of 40 to 49 years (EAC for total cholesterol, 1.33 [95% CI, 1.22 to 1.44] mg/dL; EAC for triglyceride, 2.28 [95% CI, 1.94 to 2.62] mg/dL; and EAC for LDL-C, 0.94 [95% CI, 0.84 to 1.03] mg/dL). Change in levels of blood lipids were also associated with polygenic risk. Participants at low polygenic risk tended to shift toward lower blood lipid levels, with EACs of -0.16 (95% CI, -0.25 to -0.07) mg/dL; -1.58 (95% CI, -1.78 to -1.37) mg/dL; and -0.13 (95% CI, -0.21 to -0.06) mg/dL for total cholesterol, triglyceride, and LDL-C, respectively. Participants with high polygenic risk had the greatest rates of change for total cholesterol, triglyceride, and LDL-C (EAC, 1.12 [95% CI, 1.03 to 1.21] mg/dL; EAC, 3.57 [95% CI, 3.24 to 3.91] mg/dL; and EAC, 0.73 [95% CI, 0.65 to 0.81] mg/dL, respectively). Similar patterns were also observed across sex and age groups. Conclusions and Relevance: In this cohort study, EACs of blood lipids were significantly associated with age and polygenic risk, suggesting that prevention strategies for lipids should focus on individuals with high genetic risk and in the critical age window.

Indexed as

AtherosclerosisLipidsAdultCholesterol, HDLCholesterol, LDLCohort StudiesFemaleHumansMaleMiddle AgedRisk FactorsTriglyceridesCholesterol, HDLCholesterol, LDLLipidsTriglycerides

Identifiers

PMID36988954
PMCPMC10061238
OpenAlexW4361284352

What Socratic holds

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LicenceCC BY
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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 Socratic graph.