Association and Pathways of Baseline and Longitudinal Hemoglobin A1c with the Risk of Incident Stroke: A Nationwide Prospective Cohort Study.

Xue Tian,Xue Xia,Yijun Zhang,Qin Xu,Yanxia Luo,Anxin Wang

Diabetes Research and Clinical Practice（2024）

Capital Med Univ

Cited 0|Views29

Abstract

Aims: To investigate the association of baseline and long-term mean hemoglobin A1c (HbA1c) with the risk of stroke. Methods: A total of 11,220 participants aged over 45 years and without stroke at baseline were enrolled from the China Health and Retirement Longitudinal Study. Mean HbA1c was calculated as the mean of HbA1c at all previous visits before stroke occurred or the end of follow-up. Multivariable-adjusted Cox regressions and Bayesian network were used for the analysis. Results: During a median follow-up of 7.50 years, a total of 626 cases of stroke occurred. The risk of stroke increased with quintiles of baseline and mean HbA1c, the hazard ratio (HR) in Q5 versus Q1 was 1.30 (95 % confidence interval [CI],1.03-1.64) and 1.79 (95 % CI, 1.38-2.34), respectively. Per 1 unit increase in baseline and mean HbA1c was associated with 10 % (HR, 1.10; 95 % CI, 1.02-1.18) an 12 % (HR, 1.12; 95 % CI, 1.05-1.19) higher risk of stroke. Bayesian network analysis showed that the pathway from HbA1c to stroke was through hypertension, dyslipidemia, obesity, and inflammation. Conclusions: Elevated levels of both baseline and long-term HbA1c were associated with increased risk of stroke, and hypertension and obesity played an important role in the pathway.

Translated text

Key words

Stroke,hemoglobin A1c,Longitudinal analysis,Bayesian network

求助PDF

上传PDF

Bibtex

AI Read Science

AI Summary

AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.

Example

Background

Key content

Introduction

Methods

Results

Related work

Fund

Key content

Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

Upload PDF to Generate Summary

Must-Reading Tree

Example

Generate MRT to find the research sequence of this paper