Generic placeholder image

Current Alzheimer Research

Editor-in-Chief

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

Research Article

Risk Assessment During Longitudinal Progression of Cognition in Older Adults: A Community-based Bayesian Networks Model

Author(s): Hongjuan Han, Yao Qin, Xiaoyan Ge, Jing Cui, Long Liu, Yanhong Luo, Bei Yang and Hongmei Yu*

Volume 18, Issue 3, 2021

Published on: 23 September, 2021

Page: [232 - 242] Pages: 11

DOI: 10.2174/1567205018666210608110329

Price: $65

Abstract

Background: Cognitive dysfunction, particularly in Alzheimer’s disease (AD), seriously affects the health and quality of life of older adults. Early detection can prevent and slow cognitive decline.

Objective: This study aimed at evaluating the role of socio-demographic variables, lifestyle, and physical characteristics in cognitive decline during AD progression and analyzing the probable causes and predicting stages of the disease.

Methods: By analyzing data of 301 subjects comprising normal elderly and patients with mild cognitive impairment (MCI) or AD from six communities in Taiyuan, China, we identified the influencing factors during AD progression by a Logistic Regression model (LR) and then assessed the associations between variables and cognition using a Bayesian Networks (BNs) model.

Results: The LR revealed that age, sex, family status, education, income, character, depression, hypertension, disease history, physical exercise, reading, drinking, and job status were significantly associated with cognitive decline. The BNs model revealed that hypertension, education, job status, and depression affected cognitive status directly, while character, exercise, sex, reading, income, and family status had intermediate effects. Furthermore, we predicted probable cognitive stages of AD and analyzed probable causes of these stages using a model of causal and diagnostic reasoning.

Conclusion: The BNs model lays the foundation for causal analysis and causal inference of cognitive dysfunction, and the prediction model of cognition in older adults may help the development of strategies to control modifiable risk factors for early intervention in AD.

Keywords: Cognitive evaluation, Alzheimer's disease, causal inference, bayesian networks model, cognitive decline, aging.

[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