不同MMSE评分下阿尔兹海默病发病风险因素的贝叶斯分位数回归联合模型分析

doi:10.13481/j.1671-587X.20230216

Abstract

Abstract:

Objective To discuss the influencing factors for the risk of Alzheimer’s disease （AD） after correction the Mini-Mental State Examination （MMSE） score trajectory， and to clarify the risk factors for of AD in the people with different MMSE scores. Methods The follow-up data from 2005－2016 were collected based on the AD Neuroimaging Program Database. After screening， the follow-up data of 425 people were finally included， and the variables were screened by LASSO method； the influencing factors of MMSE scores at different quantiles were analyzed by Bayesian quantile regression model， and COX model and Bayesian quantile regression joint model were used to analyze the main risk factors of AD. Results After screening， 10 variables were included， including albumin， total cholesterol， and blood glucose concentration and so on. The results of longitudinal sub-model analysis of the Bayesian quantile regression joint model showed that the influencing factors which affecting the change of MMSE score trajectory at different quartiles were the same， such as albumin， blood glucose concentration， age， gender，apolipoprotein E（APOE4） gene， race， and educational score. The Cox regression sub-model anslysis of the joint model results showed that race and APOE4 gene had the effects on the AD at all quartiles， and the risk ratios of APOE4 gene were 2.188（（95%CI：1.775，2.620）， 1.751（95%CI：1.422，2.042）， 1.706（95%CI：1.391，2.102）， and 2.056（95%CI：1.439，3.206）.The total cholesterol level and history had the effects on the AD only at parts of quartiles. Conclusion The risk factors of AD are different in the people with different MMSE score distributions， and the degree of influencing are also different. Both APOE4 gene and whiteness are the risk factors of AD at different quartiles， and the total cholesterol level and history are the risk factors of AD only at parts of quartiles.

Key words: Bayesian quantile regression joint model, Quantile regression model, Cox model, Alzheimer’s disease, Mini-Mental State Examination, Risk factor

CLC Number:

R749.16

Lianyuan WANG,Yi YANG,Huiwen CONG,Haohua WANG,Qihan BAO,Chengsheng LI,Liwen ZHOU,Zichen DING,Yanli LI,Fuyan SHI,Suzhen WANG. Bayesian quantile regression joint model analysis on risk factors of Alzheimer’s disease in people with different MMSE scores[J].Journal of Jilin University(Medicine Edition), 2023, 49(2): 395-401.

Figures/Tables 7

Tab.1

Fig. 1

Tab.2

Fig. 2

Tab. 3

Tab.4

Tab.5

Longitudinal sub-model of bayesian quantile regression joint model analysis on 425 followers"

Model	β（95%CI）
Model	τ=0.15	τ=0.25	τ=0.75	τ=0.85
Albumin	0.084(0.066－0.098)^*	0.069(0.050－0.082)^*	0.063(0.038－0.081)^*	0.063(0.049－0.076)^*
Glucose	－0.327(－0.353－－0.290)^*	－0.175(－0.209－－0.148)^*	－0.212(－0.267－－0.182)^*	－0.205(－0.262－－0.143)^*
Age	－0.057(－0.070－－0.047)^*	－0.060(－0.068－－0.052)^*	－0.033(－0.041－－0.021)^*	－0.030(－0.036－－0.023)^*
APOE4 gene(Yes vs No)	－0.586(－0.625－－0.532)^*	－0.535(－0.584－－0.471)^*	－0.607(－0.651－－0.570)^*	－0.602(－0.662－－0.537)^*
Gender(Male vs Female)	0.712(0.683－0.742)^*	0.696(0.625－0.759)^*	0.659(0.601－0.705)^*	0.646(0.616－0.664)^*
Educational score	0.159(0.132－0.186)^*	0.168(0.149－0.186)^*	0.109(0.087－0.134)^*	0.104(0.085－0.127)^*
Race(White vs Other)	0.662(0.638－0.693)^*	0.540(0.456－0.623)^*	0.683(0.653－0.712)^*	0.626(0.580－0.677)^*
Survival
Age at baseline	－0.001(－0.025－0.022)	－0.001(－0.027－0.024)	0.022(－0.013－0.060)	0.004(－0.023－0.032)
APOE4 gene(Yes vs No)	0.783(0.574－0.963)^*	0.560(0.352－0.714)^*	0.534(0.330－0.743)^*	0.721(0.364－1.165)^*
Race(White vs Other)	－1.582(－1.873－－1.425)^*	－1.473(－1.787－－1.199)^*	－1.607(－1.803－－1.440)^*	－1.542(－1.951－－1.193)^*
History(Yes vs No)	0.036(－0.224－0.346)	0.166(－0.053－0.378)	0.545(0.143－0.947)^*	0.224(0.089－0.397)^*
Systolic pressure at baseline	－0.001(－0.014－0.009)	－0.004(－0.016－0.009)	0.002(－0.013－0.016)	－0.002(－0.014－0.012)
Blood glucose at baseline	－0.168(－0.317－0.002)	－0.079(－0.184－0.042)	－0.091(－0.228－0.030)	－0.016(－0.157－0.160)
Total cholesterol at baseline	－0.157(－0.368－0.046)	－0.302(－0.472－－0.126)^*	－0.265(－0.463－－0.064)^*	－0.251(－0.402－－0.095)^*
Albumin at baseline	－0.056(－0.108－0.003)	－0.045(－0.101－0.013)	0.023(－0.055－0.129)	－0.036(－0.098－0.046)

Tab.5

References 25

1	宋皓，李玉萍，王松华，等. 阿尔茨海默症突触功能障碍研究进展［J］. 生命科学， 2018， 30（1）： 20-26.
2	PRINCE M， WIMO A， GUERCHET M，et al. World Alzheimer report 2015： the global impact of dementia［M］.London：Alzheimer’s Disease International，2015.
3	周正芳，丁凤琴. 品管圈活动在老年阿尔茨海默症患者护理管理中的应用效果［J］. 实用临床护理学电子杂志， 2018， 3（45）： 16-17.
4	胡薇薇，侯雪芹，廖儒佳，等. 阿尔茨海默病的药物治疗靶点及新药研发进展：我们还有新武器吗？［J］. 中国药理学与毒理学杂志， 2017， 31（7）： 701-706.
5	DOODY R S， MASSMAN P， DUNN J K. A method for estimating progression rates in Alzheimer disease［J］. Arch Neurol， 2001， 58（3）： 449-454.
6	DOODY R S， PAVLIK V， MASSMAN P， et al. Correction： Predicting progression of Alzheimer’s disease［J］. Alzheimers Res Ther， 2010， 2（3）： 14.
7	汪秋燕，祝云龙. 衢州市老年痴呆的患病率及危险因素［J］. 中国现代医生， 2021， 59（8）： 33-38.
8	SCHMIDT-RICHBERG A， LEDIG C， GUERRERO R，et al. Learning biomarker models for progression estimation of Alzheimer’s disease［J］. PLoS One， 2016， 11（4）： e0153040.
9	JACK C R J R， VEMURI P， WISTE H J， et al. Shapes of the trajectories of 5 major biomarkers of Alzheimer disease［J］. Arch Neurol， 2012， 69（7）： 856-867.
10	BORAH B J， BASU A. Highlighting differences between conditional and unconditional quantile regression approaches through an application to assess medication adherence［J］. Heal Econ， 2013， 22（9）： 1052-1070.
11	CHEN Y F， MA X W， SUNDELL K， et al. Quantile regression to characterize solanezumab effects in Alzheimer’s disease trials［J］.Alzheimers Dement （N Y）， 2016， 2（3）： 192-198.
12	PALMA M， TAVAKOLI S， BRETTSCHNEIDER J， et al. Quantifying uncertainty in brain-predicted age using scalar-on-image quantile regression［J］. NeuroImage， 2020， 219： 116938.
13	SHERWOOD B， ZHOU A X H， WEINTRAUB S，et al. Using quantile regression to create baseline norms for neuropsychological tests［J］. Alzheimer’s Dement Diagn Assess Dis Monit， 2016， 2： 12-18.
14	AREVALO-RODRIGUEZ I， SMAILAGIC N， ROQUÉ I FIGULS M， et al. Mini-Mental State Examination （MMSE） for the detection of Alzheimer’s disease and other dementias in people with mild cognitive impairment （MCI）［J］. Cochrane Database Syst Rev， 2015（3）： CD010783.
15	LI K， LUO S. Bayesian functional joint models for multivariate longitudinal and time-to-event data［J］. Comput Stat Data Anal， 2019， 129： 14-29.
16	曾津，周建军. 高维数据变量选择方法综述［J］. 数理统计与管理， 2017， 36（4）： 678-692.
17	BEKRIS L M， YU C G， BIRD T D， et al. Genetics of Alzheimer disease［J］. J Geriatr Psychiatry Neurol，2010， 23（4）： 213-227.
18	GOODMAN R A. Prevalence of dementia subtypes in United States Medicare fee-for-service beneficiaries， 2011-2013［J］.Alzheimer’s Dement，2017，13（1）： 28-37.
19	PEÑA-BAUTISTA C， ROCA M， LÓPEZ-CUEVAS R，et al. Metabolomics study to identify plasma biomarkers in alzheimer disease： ApoE genotype effect［J］. J Pharm Biomed Anal， 2020， 180： 113088.
20	UDEH-MOMOH C， PRICE G， ROPACKI M T，et al. Prospective evaluation of cognitive health and related factors in elderly at risk for developing Alzheimer’s dementia： a longitudinal cohort study［J］. J Prev Alzheimers Dis， 2019， 6（4）： 256-266.
21	陈静华. Logistic回归模型、神经网络模型和决策树模型在轻度认知功能障碍向阿尔茨海默症转归预测中的比较［D］. 南昌：南昌大学， 2018.
22	杨晓敏，鲍天昊，阮冶. 阿尔兹海默病发病危险因素概况［J］. 四川精神卫生， 2020， 33（6）： 560-565.
23	张温. 预防阿尔兹海默病越早越好［J］. 人人健康， 2021（7）： 38-39.
24	YU J T， XU W， TAN C C， et al. Evidence-based prevention of Alzheimer’s disease： systematic review and meta-analysis of 243 observational prospective studies and 153 randomised controlled trials［J］. J Neurol Neurosurg Psychiatry， 2020，91（11）：1201-1209.
25	LITKE R， GARCHARNA L C， JIWANI S， et al. Modifiable risk factors in Alzheimer disease and related dementias： a review［J］.Clin Ther，2021，43（6）：953-965.

Variable	Data
Time of follow-up (year)	1.85±1.43
Number of mean follow-up	2.55±0.69
MMSE score	27.69±2.69
Age (year）	72.06±7.24
Educational score	16.50±2.61
Diastolic pressure (P/mmHg)	73.75±9.98
Systolic pressure (P/mmHg)	134.14±16.31
Blood glucose[c_B/（mmol·L^－1）]	5.40±1.05
Total cholesterol [c_B/(mmol·L^－1)]	4.95±0.95
Total triglycerides [c_B/(mmol·L^－1)]	1.19±0.54
Albumin [ρ_B/(g·L^－1)]	40.93±3.04
Gender
Male	223（52.47）
Female	202（47.53）
Marital status
Married	312（73.41）
Other	113（26.59）
Race
White	399(93.89)
Other	26(6.11)
APOE4 gene
Yes	152（35.76）
No	273（64.24）
Family history
Yes	243（57.18）
No	182（42.82）

Variable	τ=0.15		τ=0.25		τ=0.75		τ=0.85
Variable	β	P	β	P	β	P	β	P
Albumin	0.093	0.008	0.010	0.001	0.082	0.011	0.084	0.003
Total cholesterol	－0.337	0.064	－0.334	0.113	－0.337	0.083	－0.335	0.062
Glucose	－0.283	0.001	－0.287	0.002	－0.282	0.002	－0.279	0.001
Systolic pressure	－0.004	0.408	0.002	0.687	0.0003	0.951	－0.003	0.581
Age	－0.059	<0.001	－0.066	<0.001	－0.033	0.040	－0.022	0.192
Educational score	0.156	0.002	0.146	0.007	0.155	0.001	0.153	0.003
APOE4 gene (Yes vs No）	－0.655	0.002	－0.651	0.005	－0.652	0.015	－0.651	0.006
History (Yes vs No）	0.306	0.177	0.304	0.219	0.309	0.247	0.310	0.226
Gender (Male vs Female)	0.672	0.017	0.676	0.066	0.674	0.016	0.674	0.036
Race (White vs Other)	0.705	0.068	0.706	0.092	0.705	0.075	0.704	0.034

Variable	β	HR(95%CI)	P
Age	0.012	1.012（0.977－1.048）	0.516
Gender（Male vs Female）	－0.338	0.713（0.399－1.274）	0.253
Educational score	－0.006	0.994（0.901－1.097）	0.909
Race（White vs Other）	－1.466	0.231（0.032－1.675）	0.147
Marry (Married vs Other)	－0.174	0.840（0.445－1.585）	0.591
History（Yes vs No）	0.302	1.352（0.804－2.273）	0.255
APOE4 gene（Yes vs No）	0.776	2.174（1.340－3.527）	0.002
Systolic pressure at baseline	0.001	1.001（0.988－1.014）	0.885
Glucose at baseline	－0.054	0.947（0.751－1.196）	0.650
Total cholesterol at baseline	－0.147	0.863（0.651－1.144）	0.306
Albumin at baseline	－0.036	0.965（0.886－1.051）	0.410

[1]	Shan GAO,Yutong WANG,Minqiu LU,Lei SHI,Bin CHU,Yuehua DING,Mengzhen WANG,Li BAO. Analysis on causes for early death and its risk factors of patients with multiple myeloma in era of novel drugs [J]. Journal of Jilin University(Medicine Edition), 2022, 48(3): 783-789.
[2]	. Improvement effect of Sanjiao acupuncture on permeability of blood-brain barrier in SAM mice and its mechanismWANG Yu^1,2, KAN Bohong^2,3,ZHAO Lan^2,3,SHI Huiyan^2,3,JIA Yujie^2,3 [J]. Journal of Jilin University(Medicine Edition), 2021, 47(5): 1086-1091.
[3]	LI Jinhua, JIN Ying, LI Junfeng, LI Li. Effects of sesamin on abilities of learning and memory in AD model mice and their mechanisms [J]. Journal of Jilin University(Medicine Edition), 2019, 45(06): 1275-1280.
[4]	ZHANG Haolong, YU Zhentao, GAO Zihan, WU Yuanyu, FANG Xuedong. Related risk factors and clinical treatment of patients with PGS after gastric cancer operation [J]. Journal of Jilin University(Medicine Edition), 2019, 45(03): 673-677.
[5]	JIANG Rui, LIU Fang, PAN Yueji, LU Boyang, HOU Lulu, WANG Chunmei, JIA Hui, SHI Xiaoru. Screening and analysis of dry eye in eye discomfort patients in Jilin Province [J]. Journal of Jilin University Medicine Edition, 2017, 43(04): 832-838.
[6]	ZHOU Hong, LIU Jincai, LUO Guanghua, XIE Peihan, YANG Juan, DONG Yulan, QING Weipeng, ZHANG Jingjing. Application of Coronary Artery Disease-Reporting and Data System in risk factor analysis of patients with coronary artery disease [J]. Journal of Jilin University Medicine Edition, 2017, 43(03): 617-622.
[7]	SI Henan, ZHONG Shuxia, ZHOU Junfeng, YAO Lei, LIU Yuanyuan, SONG Yang, LI Shanshan. Analysis on clinical characteristics and risk factors of 271 children with vitiligo [J]. Journal of Jilin University Medicine Edition, 2017, 43(01): 147-150.
[8]	GUO Li, ZHANG Xiaoxia, YU Meiling, ZHAO Ting, ZHANG Liqun. Preventive effect of hydroxyethyl starch solution on ovarian hyperstimulation syndrome [J]. Journal of Jilin University Medicine Edition, 2016, 42(06): 1147-1150.
[9]	ZHANG Heng, QIN Zhigang, LIANG Huaxin, FANG Xiaoxuan. Influence of delayed traumatic intracranial hemorrhage followed by large decompressive craniectomy in prognosis of patients with severe traumatic brain injury [J]. Journal of Jilin University Medicine Edition, 2016, 42(02): 362-365.
[10]	FENG Huifen, ZHAO Qiumin, DUAN Guangcai, ZHU Guang. Main risk factors of severe hand-foot-mouth disease: A Meta-analysis [J]. Journal of Jilin University Medicine Edition, 2015, 41(05): 974-979.
[11]	JI Guofeng, TAO Youmao, LI Zhuo, MA Chong, XIAO Ling. Analysis on risk factors of infections in patients with gastric cancerafter laparoscope-assisted distal gastrectomy [J]. Journal of Jilin University Medicine Edition, 2015, 41(05): 1076-1079.
[12]	SHI Meirong, JIANG Wei, WANG Zhiyuan, SHI Weidong, SUN Ruoxue, CHEN Enqi, LIU Meihan. Ultrasonic examination of carotid artery and peripheral artery atherosclerosis and their risk factors [J]. Journal of Jilin University Medicine Edition, 2015, 41(05): 1055-1059.
[13]	YANG Fang,LIU Qian-qian,WANG Li-juan,GUO Wei-ying,YAO Yan. Risk factors of vascular cognitive impairment among Chinese population：Meta-analysis [J]. Journal of Jilin University Medicine Edition, 2014, 40(03): 626-632.
[14]	CUI Lan,XIONG Ying-huan,JIN Nai-xing,FANG Jin-nü. Influence of insulin resistance in different metabolic risk factors and their clusters among Yanbian Korean-Chinese and Han nationality people [J]. Journal of Jilin University Medicine Edition, 2014, 40(02): 422-427.
[15]	. Analysis on condition of chronic diseases,distribution of disease spectrum and main risk factors of diseases of elderly in Jilin province [J]. Journal of Jilin University Medicine Edition, 2013, 39(6): 1275-1281.

Bayesian quantile regression joint model analysis on risk factors of Alzheimer’s disease in people with different MMSE scores

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 7

References 25

Related Articles 15

Metrics

Comments

Recommended 0

Variable	Assignment
Age (year）	－
Educational score	－
Systolic pressure	－
Blood glucose	－
Total cholesterol	－
Albumin	－
Gender	1=Male, 2=Female
Race	1=White, 2=Other
APOE4 gene	1=No, 2=Yes
History	1=No, 2=Yes
“－”:Variables were continuous variables.