基于联合潜在类别模型对阿尔兹海默病发病危险因素的研究  

Research on risk factors for Alzheimer′s disease based on a joint latent class model

作　　者：徐雅琪 王爱民 王凤琳 黄一铭 张文婧 丛慧文 杨毅 王廉源 石福艳 王素珍 XU Yaqi;WANG Aimin;WANG Fenglin;HUANG Yiming;ZHANG Wenjing;CONG Huiwen;YANG Yi;WANG Lianyuan;SHI Fuyan;WANG Suzhen(Department of Health Statistics,School of Public Health,Shandong Second Medical University,Weifang,Shandong261053,China)

机构地区：[1]山东第二医科大学公共卫生学院卫生统计学系,山东潍坊261053

出　　处：《中国预防医学杂志》2025年第1期44-49,共6页Chinese Preventive Medicine

基　　金：山东省自然科学基金项目(ZR2023MH313)。

摘　　要：目的分析轻度认知功能障碍(mild cognitive impairment,MCI)患者的简易精神状态检查(minimental state examination,MMSE)得分轨迹及阿尔兹海默病(Alzheimer's disease,AD)发病风险,分析MCI向AD转化的危险因素,为疾病干预提供参考。方法基于AD神经影像学计划数据库2005—2016年的随访数据,采用联合潜在类别模型(joint latent class modle,JLCM)分析不同类别MCI患者MMSE得分变化轨迹及AD发病风险因素。结果共纳入324例患者,随访后113例转化为AD,211例为MCI,两组临床痴呆评分总和量表(clinical dementia rating scale sum of boxes,CDR-SB)得分、功能活动评估(functional activities questionnaire,FAQ)得分、MMSE得分、听觉语言学习测试(rey auditory-verbal learning test,RAVLT)得分、年龄、体质量指数(body mass index,BMI)差异有统计学意义(t=-17.14、-16.97、11.33、11.42、-2.41、2.98,P<0.05)。根据MMSE的动态变化轨迹将人群划分为高危组和低危组,JLCM分析发现,在高危组中,CDR-SB得分(HR=1.55,95%CI:1.05~2.29)和FAQ得分(HR=1.10,95%CI:1.03~1.18)越高,BMI(HR=0.91,95%CI:0.85~0.97)越低,AD发病风险越高;在低危组中,CDR-SB得分(HR=1.30,95%CI:1.03~1.65)、糖蛋白-N-乙酰(glycoproteinN-acetyl,GlycA)(HR=13.30,95%CI:3.46~51.14)和FAQ得分(HR=1.06,95%CI:1.01~1.11)越高,RAVLT得分越低(HR=0.95,95%CI:0.93~0.97),AD发病风险越高。相较于女性,男性高危组中BMI越低(HR=0.91,95%CI:0.85~0.97),AD发病风险越高;而男性低危组中GlycA越高(HR=13.32,95%CI:3.46~51.42),AD发病风险越高。结论JLCM模型能识别MCI人群中MMSE评分变化的异质性,发现不同风险MCI人群发生AD的危险因素,从而实现AD的个性化预防和干预,为AD的有效防控提供实践依据。Objective To explore the trajectory of mini-mental state examination(MMSE)scores and the risk factors for Alzheimer's disease(AD)in patients with mild cognitive impairment(MCI),analyzing the transition from MCI to AD to inform disease intervention strategies.Methods Utilizing follow-up data from the Alzheimer's Disease Neuroimaging Initiative Database from 2005 to 2016.A latent class joint model was used to analyze the changes in MMSE scores across different MCI categories and identify risk factors for AD onset.Results Among the 324 patients,113 transitioned to AD,while 211 remained as MCI.Significant differences were observed in clinical dementia rating scale sum of boxes(CDR-SB)(t=-17.14,P<0.01),functional activities questionnaire(FAQ)(t=-16.97,P<0.01),MMSE scores(t=11.33,P<0.01),rey auditory-verbal learning test(RAVLT)(t=11.42,P<0.01),age(t=-2.41,P=0.02),and body mass index(BMI)(t=2.98,P=0.003).According to the dynamic change trajectory of the MMSE score,the population was divided into a high-risk group and a low-risk group.The latent class joint model found that in the high-risk group,the higher the CDR-SB scores(HR=1.55,95%CI:1.05-2.29)and FAQ scores(HR=1.10,95%CI:1.03-1.18)and the lower BMI(HR=0.91,95%CI:0.85-0.97),the higher the risk of AD.In the low-risk group,higher CDR-SB scores(HR=1.30,95%CI:1.03-1.65),glycoprotein N-acetyl(GlycA)(HR=13.30,95%CI:3.46-51.14)and FAQ scores(HR=1.06,95%CI:1.01-1.11),and lower RAVLT scores(HR=0.95,95%CI:0.93-0.97)were associated with a higher risk of AD.In comparison to females,the risk of AD was higher in the male high-risk group with low BMI(HR=0.91,95%CI:0.85-0.97),while the risk of AD was higher in the male low-risk group with high GlycA(HR=13.32,95%CI:3.46-51.42).Conclusions The latent class joint model effectively identifies the heterogeneity of MMSE score changes in MCI populations and uncovers distinct risk factors for AD among different MCI risk groups.This approach facilitates personalized prevention and intervention strategies for AD,providing practical evidence

关 键 词：联合潜在类别模型 阿尔兹海默病 轻度认知功能障碍 危险因素 

分 类 号：R18[医药卫生—流行病学]