利用线性混合效应模型分析男男性行为人群中HIV疾病进程

唐林; 凌倩; 吕繁; 汤后林; 李培龙; 葛琳; 陈方方; 蔡畅; 李东民

Abstract

唐林,凌倩,吕繁,汤后林,李培龙,葛琳,陈方方,蔡畅,李东民.利用线性混合效应模型分析男男性行为人群中HIV疾病进程[J].Chinese journal of Epidemiology,2020,41(6):861-865

利用线性混合效应模型分析男男性行为人群中HIV疾病进程

Using linear mixed-effects model to analyze the progression of HIV disease, among men who have sex with men

Received:September 18, 2019

DOI：10.3760/cma.j.cn112338-20190918-00679

KeyWord: 男男性行为人群 CD₄⁺T淋巴细胞线性混合效应模型艾滋病病毒/艾滋病

English Key Word: Men who have sex with men CD₄⁺ T cells Linear mixed-effects model HIV/AIDS

FundProject:国家科技重大专项（2017ZX10201101-002-005）

Author Name	Affiliation	E-mail
Tang Lin	Department of Epidemiology, National Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China
Ling Qian	Department of Epidemiology, National Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China
Lyu Fan	Department of Epidemiology, National Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China
Tang Houlin	Department of Epidemiology, National Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China
Li Peilong	Department of Epidemiology, National Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China
Ge Lin	Department of Epidemiology, National Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China
Chen Fangfang	Department of Epidemiology, National Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China
Cai Chang	Department of Epidemiology, National Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China
Li Dongmin	Department of Epidemiology, National Center for AIDS/STD Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing 102206, China	lidongmin@chinaaids.cn

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Abstract:

目的了解MSM不同年龄组中CD₄⁺T淋巴细胞计数（CD₄）的进展变化，进一步探索HIV感染的疾病进程。方法利用我国艾滋病综合防治基本信息系统截至2019年5月31日、≥15岁、感染途径为男男性行为、抗病毒治疗前CD₄检测次数≥2的HIV/AIDS作为研究对象，采用线性混合效应模型拟合抗病毒治疗前的CD₄平方根与感染时间之间的线性消除关系，利用含有末次HIV阴性检测日期和首次阳性检测日期的CD₄值估计截距，采用t检验和Z检验对模型参数进行检验，并反向估计从HIV阳转到达CD₄<500、<350、<200个/μl的中位时间。结果纳入研究对象共计26 754例，含有HIV末次阴性检测日期的共146例；年龄为M=27（P₂₅～P₇₅：23～35）岁；线性消除模型中，15～、25～和≥35岁年龄组的截距24.84（95% CI：23.76～25.92）、23.94（95% CI：22.86～25.02）、23.44（95% CI：21.91～24.96）；15～、25～、35～和≥45岁年龄组的斜率为-1.31（95% CI：-1.33～-1.25）、-1.37（95% CI：-1.40～-1.33）、-1.53（95% CI：-1.58～-1.47）、-1.59（95% CI：-1.68～-1.51）；从HIV抗体阳转到CD₄<500、<350、<200个/μl的中位时间分别为1.29（95% CI：0.79～1.81）、3.92（95% CI：3.36～4.48）和7.21（95% CI：6.58～7.81）年，其中15～岁年龄组到达3个CD₄阈值的中位时间最长，分别为1.89（95% CI：1.05～2.85）、4.68（95% CI：3.80～5.77）、8.17（95% CI：7.23～9.42）年，≥45岁年龄组到达3个CD₄阈值的中位时间最短，分别为0.68（95% CI：0.00～1.72）、2.98（95% CI：1.91～4.14）、5.85（95% CI：4.62～7.16）年。结论 MSM中CD₄的消除率随着年龄的增大而进展加快，高年龄组从HIV阳转到达不同CD₄阈值的进展时间比低年龄组更短，提示MSM中高年龄组受HIV感染的影响更大，早诊断并及早开展治疗有助于延缓疾病进程。

English Abstract:

Objective To understand the progression of CD₄⁺ T cells (CD₄) declining rate in different age groups among MSM and to further explore the pathogenesis of HIV infection. Methods Data regarding MSM who were diagnosed as HIV positive, aged ≥15 years, with homosexual route of transmission and with more than two records of CD₄ count retained before antiretroviral therapy (ART), were collected from the National AIDS Comprehensive Prevention Information System until May 31, 2019. Linear mixed effect model was used to fit the linear elimination relationship between the square root of CD₄ cell count and infection time before taking up the ART. To get the intercept estimation, we used the results from CD₄ count which containing the dates of last negativity and first positivity on HIV antibody testing. Both t test and Z test were used to test the model parameters. Median intervals from HIV seroconversion to CD₄<500, <350, <200 cells/μl were estimated. Results A total of 26 754 individuals were included in the study including 146 of them having records on the last date of being test negative. Their median age was 27 years old (M=27, P₂₅-P₇₅:23-35). The intercept of the liner mixed models among 15-, 25- and ≥35 year olds were 24.84 (95%CI: 23.76-25.92), 23.94 (95%CI: 22.86-25.02), 23.44 (95%CI: 21.91-24.96) and the slope of the liner mixed models among the 15-24, 25-34, 35-44 and ≥45 year olds were -1.31 (95%CI: -1.33- -1.25), -1.37(95%CI: -1.40- -1.33), -1.53 (95%CI: -1.58- -1.47) and -1.59 (95%CI:-1.68- -1.51), respectively. Estimation on the median intervals from HIV seroconversion to CD₄ <500, <350, <200 cells/μl counts were 1.29 (95%CI: 0.79-1.81), 3.92 (95%CI: 3.36-4.48) and 7.21 (95%CI: 6.58-7.81), respectively. The median time of 15-24 age group from HIV seroconversion to reach the three CD₄ thresholds appeared the longest, as 1.89 (95%CI: 1.05-2.85), 4.68(95%CI: 3.80-5.77) and 8.17 (95%CI: 7.23-9.42) years, respectively, the median time of ≥45 age group from HIV seroconversion to reach the three CD₄ thresholds appeared the shortest, as 0.68 (95%CI: 0.00-1.72)、2.98 (95%CI: 1.91-4.14)、5.85 (95%CI: 4.62-7.16) years, respectively. Conclusions Our findings suggested that the CD₄ declining rate had been accelerated along with ageing. Progression time from HIV seroconversion to different CD₄ thresholds appeared different, which was shorter in the older age group. Again, these findings showed the great impact of HIV infection among older age groups in the MSM population. Early diagnosis and treatment were bound to delay the progression of the disease.

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