Apolipoprotein Particle and Cardiovascular Risk Prediction (from a Prospective Cohort Study).


Journal

The American journal of cardiology
ISSN: 1879-1913
Titre abrégé: Am J Cardiol
Pays: United States
ID NLM: 0207277

Informations de publication

Date de publication:
15 08 2023
Historique:
received: 24 03 2023
revised: 20 05 2023
accepted: 31 05 2023
medline: 31 7 2023
pubmed: 23 6 2023
entrez: 23 6 2023
Statut: ppublish

Résumé

The present study aimed to examine the association between discordant apolipoprotein B (Apo B) with low-density lipoprotein cholesterol (LDL-C) or non-high-density lipoprotein cholesterol (non-HDL-C) and cardiovascular disease (CVD) risk in the Chinese population and to determine whether adding information on Apo B to LDL-C and HDL-C improves CVD risk prediction. This study collected data from the China Health and Nutrition Survey from 2009 to 2015. Discordant Apo B with LDL-C and non-HDL-C were defined based on residual differences and medians. Logistic regression was used to examine the association between discordant Apo B with LDL-C or non-HDL-C and CVD risk. Areas under the receiver operating characteristic curve and categorical net reclassification improvement were utilized to assess the incremental predictive value of Apo B levels for CVD risk. A total of 7,117 participants were included, the mean age was 50.8 ± 14.3 years, 53.6% were female. During the 6-year follow-up, 207 CVD cases were identified. Participants with discordant high Apo B relative to LDL-C or non-HDL-C were at higher risk of CVD than those with the concordant group (odds ratio 1.38, 95% confidence interval 1.01 to 1.87; odds ratio 1.40, 95% confidence interval 1.01 to 1.94, respectively). However, Apo B had no significant contribution to the predictive value of the China atherosclerotic CVD (ASCVD) risk score (areas under the receiver operating characteristic curve 0.788 for China ASCVD score alone vs 0.790 for China ASCVD score plus Apo B). In conclusion, Apo B has the strongest association with CVD risk in healthy Chinese participants than LDL-C and non-HDL-C. However, it has minimal value in CVD risk assessment and discrimination.

Identifiants

pubmed: 37352662
pii: S0002-9149(23)00350-8
doi: 10.1016/j.amjcard.2023.05.052
pii:
doi:

Substances chimiques

Cholesterol, LDL 0
Cholesterol, HDL 0
Apolipoproteins 0
Apolipoproteins B 0
Cholesterol 97C5T2UQ7J
Lipoproteins 0

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

34-41

Informations de copyright

Copyright © 2023 Elsevier Inc. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of Competing Interest The authors have no conflicts of interest to declare. Data availability. The data that support the findings of this study are available from the China Health and Nutrition Survey, but restrictions apply to the availability of these data, which were used under license for the present study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of the China Health and Nutrition Survey.

Auteurs

Xin Su (X)

Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, China; Municipal Key Laboratory of Clinical Epidemiology, Beijing, China; School of Public Health, Baotou Medical College, Baotou, China.

Meiping Wang (M)

Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, China.

Yingting Zuo (Y)

Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, China.

Jing Wen (J)

Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, China.

Qi Zhai (Q)

Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, China.

Yibo Zhang (Y)

Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, China.

Zhang Xia (Z)

Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, China.

Yuhao Li (Y)

Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, China.

Yan He (Y)

Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, China; Municipal Key Laboratory of Clinical Epidemiology, Beijing, China. Electronic address: yanhe118@sina.com.

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Classifications MeSH