Evidence map›Paper›PMID 34527522›Full record

ArticleCardiovascular diagnosis and therapy2021

Non-fasting lipid profile for cardiovascular risk assessments using China ASCVD risk estimator and Europe SCORE risk charts in Chinese participants.

Shi-Lan Zhang, Xiao Du, Jin Xu, Qun-Yan Xiang, Ling Liu

Open access · diamondAbstract read
In one paragraph

Article in Cardiovascular diagnosis and therapy, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.4field-weighted citation impact, top 35% 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

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 3 citations in OpenAlex.

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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

5 authors at 2 institutions in 1 country.

Shi-Lan ZhangDepartment of Cardiovascular Medicine, The Second Xiangya Hospital, Central South University, Changsha, China.
Xiao DuDepartment of Cardiovascular Medicine, The Second Xiangya Hospital, Central South University, Changsha, China.
Jin XuDepartment of Cardiovascular Medicine, The Second Xiangya Hospital, Central South University, Changsha, China.
Qun-Yan XiangDepartment of Cardiovascular Medicine, The Second Xiangya Hospital, Central South University, Changsha, China.
Ling LiuDepartment of Cardiovascular Medicine, The Second Xiangya Hospital, Central South University, Changsha, China.
Central South University · CNHunan Provincial Center for Disease Control and Prevention · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPrevious studies have shown that non-fasting lipids have similar values in cardiovascular risk estimation as fasting, but it is not clear whether this could also be applicable to Chinese participants.

methodsA total of 127 (76 men, 51 women) participants without atherosclerotic cardiovascular diseases (ASCVD) were enrolled in the study. Serum levels of blood lipids were monitored at 0 h, 2 h and 4 h after a daily breakfast. Ten-year cardiovascular disease (CVD) risk was estimated with China ASCVD risk estimator and European SCORE risk charts. Kappa statistic was used to determine agreement among estimators.

resultsChina ASCVD risk estimator assessed half of the participants as low risk, while European risk charts assessed half of the participants as moderate risk in the same participants. Reliability analysis in China ASCVD risk estimator and Europe SCORE risk charts based on fasting and or non-fasting lipids profile were relatively high (Kappa =0.731 or 0.718, P<0.001), (Kappa =0.922 or 0.935, P<0.001) (Kappa =0.886 or 0.874, P<0.001), but agreement between two were relatively poor in both fasting and non-fasting states (Kappa =0.339 or 0.300, P<0.001), (Kappa =0.364 or 0.286, P<0.001).

conclusionsPromoting use of non-fasting lipids in diagnosis, evaluation, and prediction of CVD are feasible. Furthermore, non-fasting lipids could be used in China ASCVD risk estimator to evaluate 10-year risk of ASCVD among Chinese general participants.

Indexed as

cardiovascular disease (CVD)China atherosclerotic cardiovascular diseases risk estimator (China ASCVD risk estimator)European Systemic Coronary Risk Estimation risk charts (European SCORE risk charts)Non-fasting lipid profilerisk estimation

Identifiers

PMID34527522
PMCPMC8410488
OpenAlexW3179843117

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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.