Modelling Decline in Cognition to Decline in Function in Alzheimer’s Disease

Author(s): Helene Karcher, Marina Savelieva, Luyuan Qi, Noemi Hummel, Angelika Caputo, Valery Risson*, Gorana Capkun, Alzheimer’s Disease Neuroimaging Initiative

Journal Name: Current Alzheimer Research

Volume 17 , Issue 7 , 2020


DOI : 10.2174/1567205017666201008105429

  Journal Home
Translate in Chinese
Become EABM
Become Reviewer
Call for Editor

Abstract:

Objectives: The study aimed to evaluate and quantify the temporal link between cognitive and functional decline, and assess the impact of the apolipoprotein E4 (APOE-e4) genotype on Alzheimer’s disease (AD) progression.

Methods: A nonlinear mixed-effects Emax model was developed using longitudinal data from 659 patients with dementia due to AD sourced from the Alzheimer's disease neuroimaging initiative (ADNI) database. A cognitive decline model was first built using a cognitive subscale of the AD assessment scale (delayed word recall) as the endpoint, followed by a functional decline model, using the functional assessment questionnaire (FAQ) as the endpoint. Individual and population cognitive decline from the first model drove a functional decline in the second model. The impact of the APOE-e4 genotype status on the dynamics of AD progression was evaluated using the model.

Results: Mixed-effects Emax models adequately quantified population average and individual disease trajectories. The model captured a higher initial cognitive impairment and final functional impairment in APOE-e4 carriers than non-carriers. The age at cognitive decline and diagnosis of dementia due to AD was significantly lower in APOE-e4 carriers than that of non-carriers. The average [standard deviation] time shift between cognitive and functional decline, i.e. the time span between half of the maximum cognitive decline and half of the maximum functional decline, was estimated as 1.5 [1.6] years.

Conclusion: The present analysis quantifies the temporal link between a cognitive and functional decline in AD progression at the population and individual level, and provides information about the potential benefits of pre-clinical AD treatments on both cognition and function.

Keywords: Alzheimer's disease, cognitive decline, functional decline, APOE-e4, mixed-effects model, ADAS-Cog, FAQ, ADNI.

[1]
2017 Alzheimer’s disease facts and figures. Alzheimers Dement 2017; 13(4): 325-73.
[http://dx.doi.org/10.1016/j.jalz.2017.02.001]
[2]
Prince M, Comas-Herrera A, Knapp M, Guerchet M, Karagiannidou M. World Alzheimer report 2016: Improving healthcare for people living with dementia: Coverage, quality and costs now and in the future Alzheimer’s Disease International. London, UK: ADI 2016.
[3]
Ashford JW, Mortimer JA. Non-familial Alzheimer’s disease is mainly due to genetic factors. J Alzheimers Dis 2002; 4(3): 169-77.
[http://dx.doi.org/10.3233/JAD-2002-4307] [PMID: 12226536]
[4]
Lopez Lopez C, Caputo A, Liu F, et al. The Alzheimer’s prevention initiative generation program: Evaluating CNP520 efficacy in the prevention of Alzheimer’s disease. J Prev Alzheimers Dis 2017; 4(4): 242-6.
[PMID: 29181489]
[5]
Sperling RA, Aisen PS, Beckett LA, et al. Toward defining the preclinical stages of Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 2011; 7(3): 280-92.
[http://dx.doi.org/10.1016/j.jalz.2011.03.003] [PMID: 21514248]
[6]
Caselli RJ, Reiman EM. Characterizing the preclinical stages of Alzheimer’s disease and the prospect of presymptomatic intervention. J Alzheimers Dis 2013; 33(1): S405-16.
[PMID: 22695623]
[7]
McKhann GM, Knopman DS, Chertkow H, et al. The diagnosis of dementia due to Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 2011; 7(3): 263-9.
[http://dx.doi.org/10.1016/j.jalz.2011.03.005] [PMID: 21514250]
[8]
Albert MS, DeKosky ST, Dickson D, et al. The diagnosis of mild cognitive impairment due to Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 2011; 7(3): 270-9.
[http://dx.doi.org/10.1016/j.jalz.2011.03.008] [PMID: 21514249]
[9]
Kelley BJ, Petersen RC. Alzheimer’s disease and mild cognitive impairment. Neurol Clin 2007; 25(3): 577-609.
[http://dx.doi.org/10.1016/j.ncl.2007.03.008] [PMID: 17659182]
[10]
Besser LM, Gill DP, Monsell SE, et al. Body mass index, weight change, and clinical progression in mild cognitive impairment and Alzheimer disease. Alzheimer Dis Assoc Disord 2014; 28(1): 36-43.
[http://dx.doi.org/10.1097/WAD.0000000000000005] [PMID: 24126214]
[11]
Delor I, Charoin JE, Gieschke R, Retout S, Jacqmin P. Initiative AsDN. Modeling Alzheimer’s disease progression using disease onset time and disease trajectory concepts applied to CDR‐SoB scores from ADNI. CPT Pharmacometrics Syst Pharmacol 2013; 2(10): 1-10.
[http://dx.doi.org/10.1038/psp.2013.54]
[12]
Ito K, Hutmacher MM. Predicting the time to clinically worsening in mild cognitive impairment patients and its utility in clinical trial design by modeling a longitudinal clinical dementia rating sum of boxes from the ADNI database. J Alzheimers Dis 2014; 40(4): 967-79.
[http://dx.doi.org/10.3233/JAD-132090] [PMID: 24531162]
[13]
Samtani MN, Raghavan N, Novak G, Nandy P, Narayan VA. Disease progression model for clinical dementia rating–sum of boxes in mild cognitive impairment and Alzheimer’s subjects from the Alzheimer’s disease Neuroimaging initiative. Neuropsychiatr Dis Treat 2014; 10: 929-52.
[http://dx.doi.org/10.2147/NDT.S62323] [PMID: 24926196]
[14]
Baker E, Iqbal E, Johnston C, et al. Trajectories of dementia-related cognitive decline in a large mental health records derived patient cohort. PLoS One 2017; 12(6)e0178562
[http://dx.doi.org/10.1371/journal.pone.0178562] [PMID: 28591196]
[15]
Leoutsakos JM, Muthen BO, Breitner JC, Lyketsos CG, Team AR. ADAPT Research Team. Effects of non-steroidal anti-inflammatory drug treatments on cognitive decline vary by phase of pre-clinical Alzheimer disease: Findings from the randomized controlled Alzheimer’s disease anti-inflammatory prevention trial. Int J Geriatr Psychiatry 2012; 27(4): 364-74.
[PMID: 21560159]
[16]
Nakagawa R, Ohnishi T, Kobayashi H, et al. Long-term effect of galantamine on cognitive function in patients with Alzheimer’s disease versus a simulated disease trajectory: An observational study in the clinical setting. Neuropsychiatr Dis Treat 2017; 13: 1115-24.
[http://dx.doi.org/10.2147/NDT.S133145] [PMID: 28458553]
[17]
Wilkosz PA, Seltman HJ, Devlin B, et al. Trajectories of cognitive decline in Alzheimer’s disease. Int Psychogeriatr 2010; 22(2): 281-90.
[http://dx.doi.org/10.1017/S1041610209991001] [PMID: 19781112]
[18]
Conrado DJ, Denney WS, Chen D, Ito K. An updated Alzheimer’s disease progression model: incorporating non-linearity, beta regression, and a third-level random effect in NONMEM. J Pharmacokinet Pharmacodyn 2014; 41(6): 581-98.
[http://dx.doi.org/10.1007/s10928-014-9375-z] [PMID: 25168488]
[19]
Gomeni R, Simeoni M, Zvartau-Hind M, Irizarry MC, Austin D, Gold M. Modeling Alzheimer’s disease progression using the disease system analysis approach. Alzheimers Dement 2012; 8(1): 39-50.
[http://dx.doi.org/10.1016/j.jalz.2010.12.012] [PMID: 21782528]
[20]
Holford NH, Peace KE. Methodologic aspects of a population pharmacodynamic model for cognitive effects in Alzheimer patients treated with tacrine. Proc Natl Acad Sci USA 1992; 89(23): 11466-70.
[http://dx.doi.org/10.1073/pnas.89.23.11466] [PMID: 1454835]
[21]
Ito K, Ahadieh S, Corrigan B, French J, Fullerton T, Tensfeldt T. Alzheimer’s Disease Working Group. Disease progression meta-analysis model in Alzheimer’s disease. Alzheimers Dement 2010; 6(1): 39-53.
[http://dx.doi.org/10.1016/j.jalz.2009.05.665] [PMID: 19592311]
[22]
Ito K, Corrigan B, Zhao Q, et al. Alzheimer’s Disease Neuroimaging Initiative. Disease progression model for cognitive deterioration from Alzheimer’s Disease Neuroimaging Initiative database. Alzheimers Dement 2011; 7(2): 151-60.
[http://dx.doi.org/10.1016/j.jalz.2010.03.018] [PMID: 20810324]
[23]
Rogers JA, Polhamus D, Gillespie WR, et al. Combining patient-level and summary-level data for Alzheimer’s disease modeling and simulation: Aβ regression meta-analysis. J Pharmacokinet Pharmacodyn 2012; 39(5): 479-98.
[http://dx.doi.org/10.1007/s10928-012-9263-3] [PMID: 22821139]
[24]
Samtani MN, Farnum M, Lobanov V, et al. Alzheimer’s Disease Neuroimaging Initiative. An improved model for disease progression in patients from the Alzheimer’s disease neuroimaging initiative. J Clin Pharmacol 2012; 52(5): 629-44.
[http://dx.doi.org/10.1177/0091270011405497] [PMID: 21659625]
[25]
Samtani MN, Raghavan N, Shi Y, et al. Alzheimer’s Disease Neuroimaging Initiative. Disease progression model in subjects with mild cognitive impairment from the Alzheimer’s disease neuroimaging initiative: CSF biomarkers predict population subtypes. Br J Clin Pharmacol 2013; 75(1): 146-61.
[http://dx.doi.org/10.1111/j.1365-2125.2012.04308.x] [PMID: 22534009]
[26]
William-Faltaos D, Chen Y, Wang Y, Gobburu J, Zhu H. Quantification of disease progression and dropout for Alzheimer’s disease. Int J Clin Pharmacol Ther 2013; 51(2): 120-31.
[http://dx.doi.org/10.5414/CP201787] [PMID: 23211395]
[27]
Langbaum JB, Hendrix S, Ayutyanont N, et al. Establishing composite cognitive endpoints for use in Preclinical Alzheimer’s disease trials. J Prev Alzheimers Dis 2015; 2(1): 2-3.
[PMID: 26273569]
[28]
Duff K, Hobson VL, Beglinger LJ, O’Bryant SE. Diagnostic accuracy of the RBANS in mild cognitive impairment: Limitations on assessing milder impairments. Arch Clin Neuropsychol 2010; 25(5): 429-41.
[http://dx.doi.org/10.1093/arclin/acq045] [PMID: 20570820]
[29]
Sheehan B. Assessment scales in dementia. Ther Adv Neurol Disorder 2012; 5(6): 349-58.
[http://dx.doi.org/10.1177/1756285612455733] [PMID: 23139705]
[30]
Pereira FS, Yassuda MS, Oliveira AM, et al. Profiles of functional deficits in mild cognitive impairment and dementia: Benefits from objective measurement. J Int Neuropsychol Soc 2010; 16(2): 297-305.
[http://dx.doi.org/10.1017/S1355617709991330] [PMID: 20175938]
[31]
Ito K, Hutmacher MM, Corrigan BW. Modeling of Functional Assessment Questionnaire (FAQ) as continuous bounded data from the ADNI database. J Pharmacokinet Pharmacodyn 2012; 39(6): 601-18.
[http://dx.doi.org/10.1007/s10928-012-9271-3] [PMID: 22990808]
[32]
Liu-Seifert H, Siemers E, Price K, et al. Alzheimer’s Disease Neuroimaging Initiative. Cognitive impairment precedes and predicts functional impairment in mild Alzheimer’s disease. J Alzheimers Dis 2015; 47(1): 205-14.
[http://dx.doi.org/10.3233/JAD-142508] [PMID: 26402769]
[33]
Zahodne LB, Manly JJ, MacKay-Brandt A, Stern Y. Cognitive declines precede and predict functional declines in aging and Alzheimer’s disease. PLoS One 2013; 8(9)e73645
[http://dx.doi.org/10.1371/journal.pone.0073645] [PMID: 24023894]
[34]
Alzheimer’s Disease Neuroimaging Initiative Data Alzheimer’s Disease Neuroimaging Initiative 2003.
[35]
Folstein MF, Folstein SE, McHugh PR. “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 1975; 12(3): 189-98.
[http://dx.doi.org/10.1016/0022-3956(75)90026-6] [PMID: 1202204]
[36]
Pfeffer RI, Kurosaki TT, Harrah CH Jr, Chance JM, Filos S. Measurement of functional activities in older adults in the community. J Gerontol 1982; 37(3): 323-9.
[http://dx.doi.org/10.1093/geronj/37.3.323] [PMID: 7069156]
[37]
Berg L, Miller JP, Storandt M, et al. Mild senile dementia of the Alzheimer type: 2 Longitudinal assessment. Ann Neurol 1988; 23(5): 477-84.
[http://dx.doi.org/10.1002/ana.410230509] [PMID: 3389756]
[38]
Ueckert S, Plan EL, Ito K, Karlsson MO, Corrigan B, Hooker AC. Alzheimer’s Disease Neuroimaging Initiative. Improved utilization of ADAS-cog assessment data through item response theory based pharmacometric modeling. Pharm Res 2014; 31(8): 2152-65.
[http://dx.doi.org/10.1007/s11095-014-1315-5] [PMID: 24595495]
[39]
Health C. Detecting pro-cognitive effects Clinical drug trials: Case studies from Alzheimer’s disease clinical leaders. A CRF Bracket Company 2018.
[40]
Felmlee MA, Morris ME, Mager DE. Mechanism-based pharmacodynamic modelingComputational Toxicology. Springer 2012; pp. 583-600.
[http://dx.doi.org/10.1007/978-1-62703-050-2_21]
[41]
Comets E, Lavenu A, Lavielle M. Parameter estimation in nonlinear mixed effect models using saemix, an r implementation of the saem algorithm. J Stat Softw 2017; 80(3): 1-42.
[http://dx.doi.org/10.18637/jss.v080.i03]
[42]
de Oliveira FF, Pivi GAK, Chen ES, Smith MC, Bertolucci PHF. Risk factors for cognitive and functional change in one year in patients with Alzheimer’s disease dementia from São Paulo, Brazil. J Neurol Sci 2015; 359(1-2): 127-32.
[http://dx.doi.org/10.1016/j.jns.2015.10.051] [PMID: 26671101]
[43]
de Oliveira FF, de Almeida SS, Chen ES, et al. Lifetime risk factors for functional and cognitive outcomes in patients with Alzheimer’s disease. J Alzheimers Dis 2018; 65(4): 1283-99.
[http://dx.doi.org/10.3233/JAD-180303] [PMID: 30149448]
[44]
Black R, Greenberg B, Ryan JM, et al. Scales as outcome measures for Alzheimer’s disease. Alzheimers Dement 2009; 5(4): 324-39.
[45]
Olarte L, Schupf N, Lee JH, et al. Apolipoprotein E ε4 and age at onset of sporadic and familial Alzheimer disease in Caribbean Hispanics. Arch Neurol 2006; 63(11): 1586-90.
[http://dx.doi.org/10.1001/archneur.63.11.1586] [PMID: 17101827]
[46]
Gómez-Tortosa E, Barquero MS, Barón M, et al. Variability of age at onset in siblings with familial Alzheimer disease. Arch Neurol 2007; 64(12): 1743-8.
[http://dx.doi.org/10.1001/archneur.64.12.1743] [PMID: 18071037]
[47]
Ungar L, Altmann A, Greicius MD. Apolipoprotein E, gender, and Alzheimer’s disease: An overlooked, but potent and promising interaction. Brain Imaging Behav 2014; 8(2): 262-73.
[http://dx.doi.org/10.1007/s11682-013-9272-x] [PMID: 24293121]
[48]
Sando SB, Melquist S, Cannon A, et al. APOE epsilon 4 lowers age at onset and is a high risk factor for Alzheimer’s disease; a case control study from central Norway. BMC Neurol 2008; 8: 9-9.
[http://dx.doi.org/10.1186/1471-2377-8-9] [PMID: 18416843]
[49]
Qiu Y, Li L, Zhou TY, Lu W. Alzheimer’s Disease Neuroimaging Initiative. Alzheimer’s disease progression model based on integrated biomarkers and clinical measures. Acta Pharmacol Sin 2014; 35(9): 1111-20.
[http://dx.doi.org/10.1038/aps.2014.57] [PMID: 25088003]


Rights & PermissionsPrintExport Cite as

Article Details

VOLUME: 17
ISSUE: 7
Year: 2020
Published on: 16 November, 2020
Page: [635 - 657]
Pages: 23
DOI: 10.2174/1567205017666201008105429
Price: $65

Article Metrics

PDF: 31
HTML: 3



Related Article(s)

Most Downloaded Article(s)