体型指数轨迹与全因和死因特异性死亡率的关联：18 年随访研究。

Association of trajectory of body shape index with all-cause and cause-specific mortality: 18 years follow-up.

机构信息

Non-Communicable Diseases Research Center, Alborz University of Medical Sciences, Karaj, Iran.

Endocrine Research Center, Research Institute for Endocrine Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.

出版信息

Front Endocrinol (Lausanne). 2023 Dec 8;14:1259849. doi: 10.3389/fendo.2023.1259849. eCollection 2023.

DOI:10.3389/fendo.2023.1259849

PMID:38144570

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10739500/

Abstract

OBJECTIVES

The current study aimed to examine how the trajectory of a body shape index (ABSI) could predict mortality in a prospective cohort of 5587 participants.

METHODS

A Growth Mixture Model (GMM) was employed to identify ABSI and body shape trajectories spanning from 2000 to 2018. Multivariate Cox regression models with hazard ratio (HR) and 95% confidence intervals (CIs) were built to assess the association of death from all-cause and cardiovascular disease (CVD) with ABSI and body shape trajectories.

RESULTS

We found that individuals with a low ABSI-marked increase (Class II) and high ABSI-marked increase trajectory (Class III) had a higher risk of all-cause (adjusted HR for Class II, 1.37; 95%CI, 1.04-1.79; adjusted HR for Class III, 1.42; 95%CI, 1.05-1.91) and non- CVD mortality (adjusted HR for Class II, 1.38; 95%CI, 1.00-1.91; adjusted HR for Class III, 1.42; 95%CI, 1.00-2.05) as well as an increased risk of CVD (adjusted HR for Class II, 1.40; 95%CI, 1.14-1.71; adjusted HR for Class III, 1.42; 95%CI, 1.13-1.78) and coronary heart disease (CHD) (adjusted HR for Class II, 1.52; 95%CI, 1.18-1.96; adjusted HR for Class III, 1.47; 95%CI, 1.11-1.95. The trajectories of body shape phenotypes did not show any significant associations with mortality, CVD, or CHD events.

CONCLUSIONS

ABSI trajectories might be associated with subsequent risk of mortality and CVD events.

摘要

目的

本研究旨在通过对 5587 名参与者的前瞻性队列研究，探讨身体形态指数（ABSI）的变化轨迹如何预测死亡率。

方法

采用增长混合模型（GMM）识别 2000 年至 2018 年的 ABSI 和身体形态轨迹。使用多变量 Cox 回归模型评估全因死亡率和心血管疾病（CVD）与 ABSI 和身体形态轨迹的相关性，计算风险比（HR）和 95%置信区间（CI）。

结果

我们发现，ABSI 呈低幅度增加（Ⅱ类）和高幅度增加轨迹（Ⅲ类）的个体全因（调整后的 HR 为Ⅱ类，1.37；95%CI，1.04-1.79；调整后的 HR 为Ⅲ类，1.42；95%CI，1.05-1.91）和非 CVD 死亡率（调整后的 HR 为Ⅱ类，1.38；95%CI，1.00-1.91；调整后的 HR 为Ⅲ类，1.42；95%CI，1.00-2.05）风险更高，CVD（调整后的 HR 为Ⅱ类，1.40；95%CI，1.14-1.71；调整后的 HR 为Ⅲ类，1.42；95%CI，1.13-1.78）和冠心病（CHD）（调整后的 HR 为Ⅱ类，1.52；95%CI，1.18-1.96；调整后的 HR 为Ⅲ类，1.47；95%CI，1.11-1.95）风险也更高。身体形态表型的轨迹与死亡率、CVD 或 CHD 事件无显著相关性。