利用增长曲线模型对糖尿病和非糖尿病患者血糖动态的纵向研究:Sabzevar 波斯队列研究》。

IF 0.7 Q4 MEDICINE, RESEARCH & EXPERIMENTAL
Advanced biomedical research Pub Date : 2024-04-27 eCollection Date: 2024-01-01 DOI:10.4103/abr.abr_406_23
Yaser Tabarraei, Abbas Ali Keshtkar, Mir Saeed Yekaninejad, Najme Rahimi, Yousef Dowlatabadi, Kamal Azam
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引用次数: 0

摘要

背景:糖尿病是一种慢性代谢性疾病,对公众健康有重大影响。了解血糖波动的影响因素对于有效管理和预防糖尿病至关重要。本研究采用生长曲线模型,旨在评估参加 Sabzevar 波斯队列中心的糖尿病患者和健康人的血糖变化相关因素:研究使用了参加 Sabzevar 波斯队列研究的 589 名糖尿病患者和 589 名非糖尿病患者的相关数据。由于每个人的血糖测量值随时间的推移而重复,我们使用条件潜增长曲线模型来研究个体内部的变化以及影响这些变化的变量:利用自变量拟合的线性潜伏增长曲线模型显示出较好的拟合效果。糖尿病组的线斜率为 1.78,而非糖尿病组的线斜率估计为-0.29。在糖尿病组中,年龄、脂肪肝和先天性心脏病(CHD)对基线(截距)有显著影响,体重指数(BMI)对响应变量的变化趋势(斜率)也有显著影响。在非糖尿病组中,年龄变量、体重指数、糖尿病家族史和家族中风史均有显著影响:总体而言,线性潜增长曲线模型在评估糖尿病患者和健康人血糖变化相关因素方面表现良好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Longitudinal Examination of Blood Sugar Dynamics in Diabetes and Non-Diabetes Using Growth Curve Model: The Sabzevar Persian Cohort Study.

Background: Diabetes mellitus is a chronic metabolic disorder with substantial implications for public health. Understanding the factors influencing blood sugar fluctuations is crucial for effective diabetes management and prevention. This study aimed to evaluate factors associated with blood sugar changes in diabetic patients and healthy individuals attending the Sabzevar Persian Cohort Center, employing the growth curve model.

Materials and methods: Data related to 589 diabetic patients and 589 non-diabetic patients participating in the Persian cohort study of Sabzevar were used. Due to the repetition of blood sugar measurements for each individual over time, we use the conditional latent growth curve model to examine intra-individual changes and variables that affect these changes over time.

Results: The linear latent growth curve model, fitted with independent variables, exhibited a superior fit. The slope of the line for the diabetic group was measured at 1.78, while for the non-diabetic group, it was estimated to be -0.29. Within the diabetic group, the influence of age, the presence of fatty liver, and history of congenital heart disease (CHD) had a significant impact on the baseline (the intercept), and the effect of body mass index (BMI) on the changing trend of the response variable (slope) was also significant. In the non-diabetic group, significant effects were observed for age variables, BMI, family history of diabetes, and history of stroke in the family.

Conclusion: Overall, the linear latent growth curve model showed good performance in the evaluation of the factors related to blood sugar changes in diabetic patients and healthy people.

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