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德宏傣族景颇族自治州HIV感染者抗病毒治疗后新发糖尿病及影响因素分析

Incidence of diabetes and influencing factors in HIV-infected individuals after antiretroviral therapy in Dehong Dai and Jingpo Autonomous Prefecture
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摘要 目的分析德宏傣族景颇族自治州(德宏州)HIV感染者启动抗病毒治疗(ART)后新发糖尿病及影响因素、随访血糖变化趋势及死亡风险。方法资料来源于中国疾病预防控制信息系统,采用回顾性队列研究方法,纳入2004-2020年启动ART的HIV感染者为研究对象。采用Cox比例风险回归模型分析研究对象的糖尿病新发率及影响因素,采用混合线性效应模型分析其FPG变化趋势,预测基线不同糖代谢状态者随访FPG值。采用SAS 9.4软件进行统计学分析。结果在8763例研究对象中,基线糖尿病患者331例,未患糖尿病者占96.2%(8432/8763),新发糖尿病的发病密度为2.31/1000人年。Cox比例风险回归模型多因素分析结果显示,HIV感染者启动ART后新发糖尿病的危险因素包括30~59岁、基线BMI≥24.0 kg/m^(2)、基线ART方案含依非韦伦(EFV)和基线空腹血糖受损(IFG)。混合效应模型分析结果显示,ART后HIV感染者FPG值与ART时长、年龄、基线FPG呈正相关。基线和随访均患糖尿病的HIV感染者死亡风险显著增高。结论2004-2020年德宏州HIV感染者启动ART后,在高龄、超重、基线ART方案使用EFV和IFG的HIV感染者中,糖尿病发病风险较高,应重点监测HIV感染者FPG情况,加强随访和糖尿病的科学防治。 Objective To understand the incidence of diabetes and influencing factors,the trend of FPG change and risk for mortality in HIV-infected individuals after antiretroviral therapy(ART)in Dehong Dai and Jingpo Autonomous Prefecture(Dehong).Methods The HIV/AIDS treatment database was collected from China Information System for Disease Control and Prevention.This retrospective cohort study was conducted in HIV-infected individuals with access to ART in Dehong during 2004-2020.The Cox proportional hazard regression model was used to analyze the incidence density of diabetes,the influencing factors and risk for mortality in HIV-infected individuals with access to ART,mixed linear effects model was used to analyze the trend of FPG change and predict FPG in those with different glucose metabolic status at baseline survey.Statistical analysis was performed using software SAS 9.4.Results A total of 8763 HIV-infected individuals were included,in whom 8432(96.2%)had no diabetes,331 had diabetes.The incidence density of diabetes was 2.31/1000 person years.Multivariate Cox proportional hazard regression analysis revealed that 30-59 years old,BMI≥24.0 kg/m^(2),Efavirenz(EFV)based initial treatment regimen and impaired fasting glucose(IFG)at baseline survey were significantly and positively associated with incidence of diabetes.Mixed effect model revealed that FPG was positively correlated with the duration of ART,age and baseline FPG.Suffering from diabetes was a risk factor for mortality in HIV-infected individuals both at baseline survey and during follow-up.Conclusions The risk for diabetes increased in HIV-infected individuals who were 30-59 years old,baseline BMI≥24.0 kg/m^(2),received EFV based initial treatment,and IFG in HIV-infected individuals after antiretroviral therapy in Dehong,2004-2020.It is important to pay close attention to their blood glucose,and patients with high blood glucose should receive treatment as early as possible.
作者 叶润华 张韵秋 曹东冬 时允 肖桂芳 李品银 许元武 魏华 孙进廷 杨跃诚 唐仁海 王继宝 何纳 丁盈盈 段松 Ye Runhua;Zhang Yunqiu;Cao Dongdong;Shi Yun;Xiao Guifang;Li Pinyin;Xu Yuanwu;Wei Hua;Sun Jinting;Yang Yuecheng;Tang Renhai;Wang Jibao;He Na;Ding Yingying;Duan Song(Dehong Dai and Jingpo Autonomous Prefecture Center for Disease Control and Prevention,Mangshi 678400,China;Department of Epidemiology,Key Laboratory of Public Health Safety of Ministry of Education,School of Public Health,Fudan University,Shanghai 200032,China;Dehong Dai and Jingpo Autonomous Prefecture People's Hospital,Mangshi 678400,China;Mangshi People's Hospital of Dehong Dai and Jingpo Autonomous Prefecture,Mangshi 678400,China;Dehong Dai and Jingpo Autonomous Prefecture Hospital of Traditional Chinese Medicine,Mangshi 678400,China;Ruili City People's Hospital of Dehong Dai and Jingpo Autonomous Prefecture,Ruili 678600,China;Longchuan County People's Hospital of Dehong Dai and Jingpo Autonomous Prefecture,Longchuan 678700,China;Yingjiang County People's Hospital of Dehong Dai and Jingpo Autonomous Prefecture,Yingjiang 679300,China;Lianghe County People's Hospital of Dehong Dai and Jingpo Autonomous Prefecture,Lianghe 679200,China)
出处 《中华流行病学杂志》 CAS CSCD 北大核心 2024年第3期358-364,共7页 Chinese Journal of Epidemiology
基金 国家科技重大专项(2018ZX10715006-001,2018ZX10721102-004) 国家重点研发计划(2022YFC2304901)。
关键词 艾滋病病毒 新发糖尿病 影响因素 HIV Incidence of diabetes Influencing factors
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