Generic placeholder image

当代阿耳茨海默病研究

Editor-in-Chief

ISSN (Print): 1567-2050
ISSN (Online): 1875-5828

Research Article

老年人认知纵向发展过程中的风险评估:一个基于社区的贝叶斯网络模型

卷 18, 期 3, 2021

发表于: 23 September, 2021

页: [232 - 242] 页: 11

弟呕挨: 10.2174/1567205018666210608110329

价格: $65

conference banner
摘要

背景:认知功能障碍,特别是阿尔茨海默病(AD),严重影响老年人的健康和生活质量。早期发现可以预防和减缓认知能力下降。 目的:本研究旨在评估社会人口统计学变量、生活方式和身体特征在AD进展过程中认知能力下降中的作用,并分析疾病的可能原因和预测阶段。 方法:通过分析301名受试者的数据包括正常老年人和轻度认知障碍(MCI)或广告从6个社区,太原,我们确定影响因素在逻辑回归模型(LR),然后评估变量和认知之间的关联使用贝叶斯网络(BNs)模型。 结果:LR结果显示,年龄、性别、家庭状况、受教育程度、收入、性格、抑郁、高血压、病史、体育锻炼、阅读、饮酒和工作状况与认知能力下降显著相关。BNs模型显示,高血压、受教育程度、工作状态和抑郁直接影响认知状态,而性格、运动、性别、阅读、收入和家庭地位有中等影响。此外,我们预测了AD可能的认知阶段,并使用因果推理和诊断推理模型分析了这些阶段的可能原因。 结论:BNs模型为认知功能障碍的因果分析和因果推理奠定了基础,老年人认知的预测模型可能有助于制定控制AD早期干预可改变危险因素的策略。

关键词: 认知评估,阿尔茨海默氏病,因果推理,贝叶斯网络模型,认知衰退,衰老。

[1]
Fukuhara M, Matsumura K, Ansai T, et al. Prediction of cognitive function by arterial stiffness in the very elderly. Circ J 2006; 70(6): 756-61.
[http://dx.doi.org/10.1253/circj.70.756] [PMID: 16723799]
[2]
Scheltens P, Blennow K, Breteler MM, et al. Alzheimer's disease. Lancet 2016; 388(10043): 505-17.
[http://dx.doi.org/10.1016/S0140-6736(15)01124-1] [PMID: 26921134]
[3]
Cummings JL, Doody R, Clark C. Disease-modifying therapies for Alzheimer disease: challenges to early intervention. Neurology 2007; 69(16): 1622-34.
[http://dx.doi.org/10.1212/01.wnl.0000295996.54210.69] [PMID: 17938373]
[4]
Olazarán J, Muñiz R, Reisberg B, et al. Benefits of cognitive-motor intervention in MCI and mild to moderate Alzheimer disease. Neurology 2004; 63(12): 2348-53.
[http://dx.doi.org/10.1212/01.WNL.0000147478.03911.28] [PMID: 15623698]
[5]
Vassallo M, Poynter L, Kwan J, Sharma JC, Allen SC. A prospective observational study of outcomes from rehabilitation of elderly patients with moderate to severe cognitive impairment. Clin Rehabil 2016; 30(9): 901-8.
[http://dx.doi.org/10.1177/0269215515611466] [PMID: 27496699]
[6]
Bagai A, Chen AY, Udell JA, et al. Association of Cognitive Impairment With Treatment and Outcomes in Older Myocardial Infarction Patients: A Report From the NCDR Chest Pain-MI Registry. J Am Heart Assoc 2019; 8(17): e012929.
[http://dx.doi.org/10.1161/JAHA.119.012929] [PMID: 31462138]
[7]
Domenech-Cebrían P, Martinez-Martinez M, Cauli O. Relationship between mobility and cognitive impairment in patients with Alzheimer’s disease. Clin Neurol Neurosurg 2019; 179: 23-9.
[http://dx.doi.org/10.1016/j.clineuro.2019.02.015] [PMID: 30798193]
[8]
Davis M, O Connell T, Johnson S, et al. Estimating Alzheimer’s Disease Progression Rates from Normal Cognition Through Mild Cognitive Impairment and Stages of Dementia. Curr Alzheimer Res 2018; 15(8): 777-88.
[http://dx.doi.org/10.2174/1567205015666180119092427] [PMID: 29357799]
[9]
Qin Y, Tian Y, Han H, et al. Risk classification for conversion from mild cognitive impairment to Alzheimer’s disease in primary care. Psychiatry Res 2019; 278: 19-26.
[http://dx.doi.org/10.1016/j.psychres.2019.05.027] [PMID: 31132572]
[10]
Kaye J, Gregor M, Matteck N, et al. Social biomarkers for early signs of dementia: Increased spoken word counts among older adults with mild cognitive impairment (MIC). Alzheimers Dement 2014; 10: 915-6.
[http://dx.doi.org/10.1016/j.jalz.2014.07.118]
[11]
Tokuchi R, Hishikawa N, Kurata T, et al. Clinical and demographic predictors of mild cognitive impairment for converting to Alzheimer’s disease and reverting to normal cognition. J Neurol Sci 2014; 346(1-2): 288-92.
[http://dx.doi.org/10.1016/j.jns.2014.09.012] [PMID: 25248955]
[12]
Pereira T, Ferreira FL, Cardoso S, et al. Neuropsychological predictors of conversion from mild cognitive impairment to Alzheimer’s disease: a feature selection ensemble combining stability and predictability. BMC Med Inform Decis Mak 2018; 18(1): 137.
[http://dx.doi.org/10.1186/s12911-018-0710-y] [PMID: 30567554]
[13]
Asgari M, Kaye J, Dodge H. Predicting mild cognitive impairment from spontaneous spoken utterances. Alzheimers Dement (N Y) 2017; 3(2): 219-28.
[http://dx.doi.org/10.1016/j.trci.2017.01.006] [PMID: 29067328]
[14]
Davatzikos C, Xu F, An Y, Fan Y, Resnick SM. Longitudinal progression of Alzheimer’s-like patterns of atrophy in normal older adults: the SPARE-AD index. Brain 2009; 132(Pt 8): 2026-35.
[http://dx.doi.org/10.1093/brain/awp091] [PMID: 19416949]
[15]
Levy B, Tsoy E, Gable S. Developing cognitive markers of Alzheimer’s disease for primary care: Implications for behavioral and global prevention. J Alzheimers Dis 2016; 54(4): 1259-72.
[http://dx.doi.org/10.3233/JAD-160309] [PMID: 27567831]
[16]
Chapman RM, Mapstone M, McCrary JW, et al. Predicting conversion from mild cognitive impairment to Alzheimer’s disease using neuropsychological tests and multivariate methods. J Clin Exp Neuropsychol 2011; 33(2): 187-99.
[http://dx.doi.org/10.1080/13803395.2010.499356] [PMID: 20711906]
[17]
Dillon C, Serrano CM, Castro D, Leguizamón PP, Heisecke SL, Taragano FE. Behavioral symptoms related to cognitive impairment. Neuropsychiatr Dis Treat 2013; 9: 1443-55.
[http://dx.doi.org/10.2147/NDT.S47133] [PMID: 24092982]
[18]
Whitehouse PJ. Alzheimer’s disease: past, present, and future. Eur Arch Psychiatry Clin Neurosci 1999; 249(3 Suppl. 3): 43-5.
[http://dx.doi.org/10.1007/PL00014173] [PMID: 10654099]
[19]
Livingston G, Sommerlad A, Orgeta V, et al. Dementia prevention, intervention, and care. Lancet 2017; 390(10113): 2673-734.
[http://dx.doi.org/10.1016/S0140-6736(17)31363-6] [PMID: 28735855]
[20]
Song YN, Wang P, Xu W, et al. Risk factors of rapid cognitive decline in Alzheimer’s disease and mild cognitive impairment: A systematic review and meta-analysis. J Alzheimers Dis 2018; 66(2)(Suppl. 11): 497-515.
[http://dx.doi.org/10.3233/JAD-180476] [PMID: 30320579]
[21]
Jia L, Du Y, Chu L, et al. Prevalence, risk factors, and management of dementia and mild cognitive impairment in adults aged 60 years or older in China: a cross-sectional study. Lancet Public Health 2020; 5(12): e661-71.
[http://dx.doi.org/10.1016/S2468-2667(20)30185-7] [PMID: 33271079]
[22]
Lu J, Li D, Li F, et al. Montreal cognitive assessment in detecting cognitive impairment in Chinese elderly individuals: a population-based study. J Geriatr Psychiatry Neurol 2011; 24(4): 184-90.
[http://dx.doi.org/10.1177/0891988711422528] [PMID: 22228824]
[23]
Chu LW, Ng KH, Law AC, Lee AM, Kwan F. Validity of the cantonese Chinese montreal cognitive assessment in southern Chinese. Geriatr Gerontol Int 2015; 15(1): 96-103.
[http://dx.doi.org/10.1111/ggi.12237] [PMID: 24456109]
[24]
Pocklington C, Gilbody S, Manea L, McMillan D. The diagnostic accuracy of brief versions of the Geriatric Depression Scale: a systematic review and meta-analysis. Int J Geriatr Psychiatry 2016; 31(8): 837-57.
[http://dx.doi.org/10.1002/gps.4407] [PMID: 26890937]
[25]
Guidelines NICE. NICE Guidelines. Donepezil, galantamine, rivastigmine (review) and memantine for the treatment of Alzeihmer’s Dementia 2018.
[26]
Petersen RC. Mild cognitive impairment as a diagnostic entity. J Intern Med 2004; 256(3): 183-94.
[http://dx.doi.org/10.1111/j.1365-2796.2004.01388.x] [PMID: 15324362]
[27]
Reiman EM, McKhann GM, Albert MS, Sperling RA, Petersen RC, Blacker D. Clinical impact of updated diagnostic and research criteria for Alzheimer’s disease. J Clin Psychiatry 2011; 72(12): e37.
[http://dx.doi.org/10.4088/JCP.10087tx2c] [PMID: 22244033]
[28]
Daly R, Shen Q, Aitken S. Learning Bayesian networks: approaches and issues. Knowl Eng Rev 2011; 26(02): 99-157.
[http://dx.doi.org/10.1017/S0269888910000251]
[29]
Krob KB, Nicholson AE, Bayesian ENA. Bayesian Artificial Intelligence. Second Edition. 2010. xxiv,364
[30]
Wicker N, Muller J, Kalathur RKR, Poch O. A maximum likelihood approximation method for Dirichlet's parameter estimation. Computational Statistics & Data Analysis 2008; 52(3): 1315-22.
[31]
Nagarajan R, Scutari M, Lèbre S. Bayesian Networks in R, 2013.
[http://dx.doi.org/10.1007/978-1-4614-6446-4]
[32]
Rojas-Guzmán C, Kramer MA. An evolutionary computing approach to probabilistic reasoning on Bayesian networks. Evol Comput 1996; 4(1): 57-85.
[http://dx.doi.org/10.1162/evco.1996.4.1.57]
[33]
Lourenco J, Serrano A, Santos-Silva A, et al. Cardiovascular Risk Factors Are Correlated with Low Cognitive Function among Older Adults Across Europe Based on The SHARE Database. Aging Dis 2018; 9(1): 90-101.
[http://dx.doi.org/10.14336/AD.2017.0128] [PMID: 29392084]
[34]
Sona A, Zhang P, Ames D, et al. Predictors of rapid cognitive decline in Alzheimer’s disease: results from the Australian imaging, biomarkers and lifestyle (AIBL) study of ageing. Int Psychogeriatr 2012; 24(2): 197-204.
[http://dx.doi.org/10.1017/S1041610211001335] [PMID: 21749739]
[35]
Meng X, D’Arcy C. Education and dementia in the context of the cognitive reserve hypothesis: a systematic review with meta-analyses and qualitative analyses. PLoS One 2012; 7(6): e38268.
[http://dx.doi.org/10.1371/journal.pone.0038268] [PMID: 22675535]
[36]
Bickel H, Kurz A. Education, occupation, and dementia: the Bavarian school sisters study. Dement Geriatr Cogn Disord 2009; 27(6): 548-56.
[http://dx.doi.org/10.1159/000227781] [PMID: 19590201]
[37]
Gronek P, Balko S, Gronek J, et al. Physical activity and Alzheimer’s disease: A narrative review. Aging Dis 2019; 10(6): 1282-92.
[http://dx.doi.org/10.14336/AD.2019.0226] [PMID: 31788339]
[38]
Suzuki T, Shimada H, Makizako H, et al. A randomized controlled trial of multicomponent exercise in older adults with mild cognitive impairment. PLoS One 2013; 8(4): e61483.
[http://dx.doi.org/10.1371/journal.pone.0061483] [PMID: 23585901]
[39]
Heymann D, Stern Y, Cosentino S, Tatarina-Nulman O, Dorrejo JN, Gu Y. The association between alcohol use and the progression of Alzheimer’s disease. Curr Alzheimer Res 2016; 13(12): 1356-62.
[http://dx.doi.org/10.2174/1567205013666160603005035] [PMID: 27628432]
[40]
Kim S, Kim Y, Park SM. Association between alcohol drinking behaviour and cognitive function: results from a nationwide longitudinal study of South Korea. BMJ Open 2016; 6(4): e010494.
[http://dx.doi.org/10.1136/bmjopen-2015-010494] [PMID: 27118285]
[41]
Neafsey EJ, Collins MA. Moderate alcohol consumption and cognitive risk. Neuropsychiatr Dis Treat 2011; 7: 465-84.
[http://dx.doi.org/10.2147/NDT.S23159] [PMID: 21857787]
[42]
Dzierzewski Joseph M, Potter Guy G, Jones Richard N, Rostant Ola S, Ayotte Brian. Cognitive functioning throughout the treatment history of clinical late-life depression. Int J Geriatr Psychiatry 2015; 30(10): 1076-84.
[http://dx.doi.org/10.1002/gps.4264] [PMID: 25703072]
[43]
Wilson RS, Barnes LL, de Leon CFM, et al. Depressive symptoms, cognitive decline, and risk of AD in older persons. Neurology 2002; 59(3): 364-70.
[http://dx.doi.org/10.1212/WNL.59.3.364] [PMID: 12177369]
[44]
Gale CR, Allerhand M, Deary IJ. Is there a bidirectional relationship between depressive symptoms and cognitive ability in older people? A prospective study using the English Longitudinal Study of Ageing. Psychol Med 2012; 42(10): 2057-69.
[http://dx.doi.org/10.1017/S0033291712000402] [PMID: 23206378]
[45]
Barnes J, Bartlett JW, Wolk DA, van der Flier WM, Frost C. Disease course varies according to age and symptom length in Alzheimer’s disease. J Alzheimers Dis 2018; 64(2): 631-42.
[http://dx.doi.org/10.3233/JAD-170841] [PMID: 29914016]
[46]
Arora P, Boyne D, Slater JJ, Gupta A, Brenner DR, Druzdzel MJ. Bayesian networks for risk prediction using real-world data: A tool for precision medicine. Value Health 2019; 22(4): 439-45.
[http://dx.doi.org/10.1016/j.jval.2019.01.006] [PMID: 30975395]

Rights & Permissions Print Cite
© 2024 Bentham Science Publishers | Privacy Policy