Evidence map›Paper›PMID 35369305›Full record

ArticleFrontiers in cardiovascular medicine2022

Negative Risk Markers for Cardiovascular Risk Evaluation in Chinese Adults.

Lizhan Bie, Jingya Niu, Shujing Wu, Ruizhi Zheng, Min Xu, Jieli Lu, Tiange Wang, Zhiyun Zhao, Shuangyuan Wang, Hong Lin and 8 more

Open access · goldAbstract read
In one paragraph

Article in Frontiers in cardiovascular medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.3field-weighted citation impact, top 42% 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

1 citing paper in PubMed, 2 citations in OpenAlex.

  1. 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

18 authors at 2 institutions in 1 country.

Lizhan BieDepartment of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
Jingya NiuJiading District Central Hospital Affiliated Shanghai University of Medicine and Health Sciences, Shanghai, China.
Shujing WuDepartment of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
Ruizhi ZhengDepartment of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
Min XuDepartment of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
Jieli LuDepartment of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
Tiange WangDepartment of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
Zhiyun ZhaoDepartment of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
Shuangyuan WangDepartment of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
Hong LinDepartment of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
Meng DaiDepartment of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
Di ZhangDepartment of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
Yuhong ChenDepartment of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
Yufang BiDepartment of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
Weiqing WangDepartment of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
Guang NingDepartment of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
Mian LiDepartment of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
Yu XuDepartment of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
Ruijin Hospital · CNJiading District Central Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The atherosclerotic cardiovascular disease (ASCVD) risk predicted by traditional risk factors is used to guide preventive treatment. We aimed to investigate whether preferable levels of non-traditional emerging risk factors (i.e., negative risk markers) could downgrade the predicted ASCVD risk beyond traditional risk factors. Methods: A total of 7,568 Chinese adults aged ≥ 40 years were followed up during 2010-2015. Negative risk markers including non-traditional lipids, urinary albumin-to-creatinine ratio, electrocardiogram (ECG), and measurements of atherosclerosis were evaluated using diagnostic likelihood ratio (DLR) and continuous net reclassification index (NRI) for their ability to downshift predicted CVD risk in the overall study population and in participants with intermediate (traditional risk factor predicted ASCVD risk 7.5% to 19.9%) or high risk (≥20%). Results: During a median follow-up of 4.5 years, 416 participants developed CVD events including non-fatal myocardial infarction, non-fatal stroke, and cardiovascular death. Among negative risk markers examined, lipoprotein(a) ≤ 10th percentile (5 mg/dL), normal ECG, and carotid intima-media thickness (CIMT) ≤ 25th percentile (0.5 mm) provided moderate CVD risk reclassification and downward changes in pre- to post-test risk on top of the traditional CVD risk factors, especially in high-risk participants. The DLRs were 0.41, 0.75, and 0.41, and the NRIs were 18, 22, and 14% for lipoprotein(a), ECG, and CIMT, respectively in high-risk participants. Conclusions: Lipoprotein(a) ≤ 5 mg/dL, normal ECG, and CIMT ≤ 0.5 mm might be used as negative non-traditional risk markers to correctly downgrade predicted ASCVD risk in Chinese adults.

Indexed as

cardiovascular diseasecarotid intima-media thicknesselectrocardiogramlipoprotein(a)negative risk marker

Identifiers

PMID35369305
PMCPMC8964789
OpenAlexW4221015917

What OpenQuestion holds

Textmetadata
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.