Generic placeholder image

Current Alzheimer Research

Editor-in-Chief

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

Research Article

Estimating Dementia Onset: AT(N) Profiles and Predictive Modeling in Mild Cognitive Impairment Patients

Author(s): Carlos Platero*, Jussi Tohka and Bryan Strange

Volume 20, Issue 11, 2023

Published on: 28 February, 2024

Page: [778 - 790] Pages: 13

DOI: 10.2174/0115672050295317240223162312

Price: $65

Abstract

Background: Mild Cognitive Impairment (MCI) usually precedes the symptomatic phase of dementia and constitutes a window of opportunities for preventive therapies.

Objectives: The objective of this study was to predict the time an MCI patient has left to reach dementia and obtain the most likely natural history in the progression of MCI towards dementia.

Methods: This study was conducted on 633 MCI patients and 145 subjects with dementia through 4726 visits over 15 years from Alzheimer Disease Neuroimaging Initiative (ADNI) cohort. A combination of data from AT(N) profiles at baseline and longitudinal predictive modeling was applied. A data-driven approach was proposed for categorical diagnosis prediction and timeline estimation of cognitive decline progression, which combined supervised and unsupervised learning techniques.

Results: A reduced vector of only neuropsychological measures was selected for training the models. At baseline, this approach had high performance in detecting subjects at high risk of converting from MCI to dementia in the coming years. Furthermore, a Disease Progression Model (DPM) was built and also verified using three metrics. As a result of the DPM focused on the studied population, it was inferred that amyloid pathology (A+) appears about 7 years before dementia, and tau pathology (T+) and neurodegeneration (N+) occur almost simultaneously, between 3 and 4 years before dementia. In addition, MCI-A+ subjects were shown to progress more rapidly to dementia compared to MCI-A- subjects.

Conclusion: Based on proposed natural histories and cross-sectional and longitudinal analysis of AD markers, the results indicated that only a single cerebrospinal fluid sample is necessary during the prodromal phase of AD. Prediction from MCI into dementia and its timeline can be achieved exclusively through neuropsychological measures.

Keywords: Mild cognitive impairment, Alzheimer’s disease, AT(N) biomarkers, predictive models, disease progression modeling, dementia.

[1]
Hyman BT, Phelps CH, Beach TG, et al. National institute on aging–alzheimer’s association guidelines for the neuropathologic assessment of alzheimer’s disease. Alzheimers Dement 2012; 8(1): 1-13.
[http://dx.doi.org/10.1016/j.jalz.2011.10.007] [PMID: 22265587]
[2]
Jack CR Jr, Bennett DA, Blennow K, et al. NIA‐AA research framework: Toward a biological definition of Alzheimer’s disease. Alzheimers Dement 2018; 14(4): 535-62.
[http://dx.doi.org/10.1016/j.jalz.2018.02.018] [PMID: 29653606]
[3]
Gauthier S, Reisberg B, Zaudig M, et al. Mild cognitive impairment. Lancet 2006; 367(9518): 1262-70.
[http://dx.doi.org/10.1016/S0140-6736(06)68542-5] [PMID: 16631882]
[4]
Petersen RC, Roberts RO, Knopman DS, et al. Mild cognitive impairment: Ten years later. Arch Neurol 2009; 66(12): 1447-55.
[http://dx.doi.org/10.1001/archneurol.2009.266] [PMID: 20008648]
[5]
Manly JJ, Tang MX, Schupf N, Stern Y, Vonsattel JPG, Mayeux R. Frequency and course of mild cognitive impairment in a multiethnic community. Ann Neurol 2008; 63(4): 494-506.
[http://dx.doi.org/10.1002/ana.21326] [PMID: 18300306]
[6]
Sperling RA, Rentz DM, Johnson KA, et al. The A4 study: Stopping AD before symptoms begin? Sci Transl Med 2014; 6: 228-8.
[http://dx.doi.org/10.1126/scitranslmed.3007941]
[7]
Falahati F, Westman E, Simmons A. Multivariate data analysis and machine learning in Alzheimer’s disease with a focus on structural magnetic resonance imaging. J Alzheimers Dis 2014; 41(3): 685-708.
[http://dx.doi.org/10.3233/JAD-131928] [PMID: 24718104]
[8]
Vermunt L, Sikkes SAM, van den Hout A, et al. Duration of preclinical, prodromal, and dementia stages of Alzheimer’s disease in relation to age, sex, and APOE genotype. Alzheimers Dement 2019; 15(7): 888-98.
[http://dx.doi.org/10.1016/j.jalz.2019.04.001] [PMID: 31164314]
[9]
van Maurik IS, Vos SJ, Bos I, et al. Biomarker-based prognosis for people with mild cognitive impairment (ABIDE): A modelling study. Lancet Neurol 2019; 18(11): 1034-44.
[http://dx.doi.org/10.1016/S1474-4422(19)30283-2] [PMID: 31526625]
[10]
Blazhenets G, Frings L, Ma Y, et al. Validation of the Alzheimer disease dementia conversion-related pattern as an ATN biomarker of neurodegeneration. Neurology 2021; 96(9): e1358-68.
[http://dx.doi.org/10.1212/WNL.0000000000011521] [PMID: 33408150]
[11]
Korolev IO, Symonds LL, Bozoki AC. Predicting progression from mild cognitive impairment to Alzheimer’s dementia using clinical, MRI, and plasma biomarkers via probabilistic pattern classification. PLoS One 2016; 11(2): e0138866.
[http://dx.doi.org/10.1371/journal.pone.0138866] [PMID: 26901338]
[12]
Steenland K, Zhao L, John SE, et al. A ‘framingham-like’ algorithm for predicting 4-year risk of progression to amnestic mild cognitive impairment or Alzheimer’s disease using multidomain information. J Alzheimers Dis 2018; 63(4): 1383-93.
[http://dx.doi.org/10.3233/JAD-170769] [PMID: 29843232]
[13]
Jang H, Park J, Woo S, et al. Prediction of fast decline in amyloid positive mild cognitive impairment patients using multimodal biomarkers. Neuroimage Clin 2019; 24: 101941.
[http://dx.doi.org/10.1016/j.nicl.2019.101941] [PMID: 31376643]
[14]
Cullen NC, Leuzy A, Palmqvist S, et al. Individualized prognosis of cognitive decline and dementia in mild cognitive impairment based on plasma biomarker combinations. Nature Aging 2020; 1(1): 114-23.
[http://dx.doi.org/10.1038/s43587-020-00003-5] [PMID: 37117993]
[15]
Palmqvist S, Tideman P, Cullen N, et al. Prediction of future Alzheimer’s disease dementia using plasma phospho-tau combined with other accessible measures. Nat Med 2021; 27(6): 1034-42.
[http://dx.doi.org/10.1038/s41591-021-01348-z] [PMID: 34031605]
[16]
Zheng C, Xia Y, Pan Y, Chen J. Automated identification of dementia using medical imaging: A survey from a pattern classification perspective. Brain Inform 2016; 3(1): 17-27.
[http://dx.doi.org/10.1007/s40708-015-0027-x] [PMID: 27747596]
[17]
Rathore S, Habes M, Iftikhar MA, Shacklett A, Davatzikos C. A review on neuroimaging-based classification studies and associated feature extraction methods for Alzheimer’s disease and its prodromal stages. Neuroimage 2017; 155: 530-48.
[http://dx.doi.org/10.1016/j.neuroimage.2017.03.057] [PMID: 28414186]
[18]
Grueso S, Viejo-Sobera R. Machine learning methods for predicting progression from mild cognitive impairment to Alzheimer’s disease dementia: A systematic review. Alzheimers Res Ther 2021; 13(1): 162.
[http://dx.doi.org/10.1186/s13195-021-00900-w] [PMID: 34583745]
[19]
Ansart M, Epelbaum S, Bassignana G, et al. Predicting the progression of mild cognitive impairment using machine learning: A systematic, quantitative and critical review. Med Image Anal 2021; 67: 101848.
[http://dx.doi.org/10.1016/j.media.2020.101848] [PMID: 33091740]
[20]
Lee E, Giovanello KS, Saykin AJ, et al. Single‐nucleotide polymorphisms are associated with cognitive decline at Alzheimer’s disease conversion within mild cognitive impairment patients. Alzheimers Dement 2017; 8(1): 86-95.
[http://dx.doi.org/10.1016/j.dadm.2017.04.004] [PMID: 28560309]
[21]
Bi X, Xing Z, Zhou W, Li L, Xu L. Pathogeny detection for mild cognitive impairment via weighted evolutionary random forest with brain imaging and genetic data. IEEE J Biomed Health Inform 2022; 26(7): 3068-79.
[http://dx.doi.org/10.1109/JBHI.2022.3151084] [PMID: 35157601]
[22]
Donohue MC, Jacqmin-Gadda H, Le Goff M, et al. Estimating long‐term multivariate progression from short‐term data. Alzheimers Dement 2014; 10(5S): S400-10.
[http://dx.doi.org/10.1016/j.jalz.2013.10.003] [PMID: 24656849]
[23]
Guerrero R, Schmidt-Richberg A, Ledig C, Tong T, Wolz R, Rueckert D. Instantiated mixed effects modeling of Alzheimer’s disease markers. Neuroimage 2016; 142: 113-25.
[http://dx.doi.org/10.1016/j.neuroimage.2016.06.049] [PMID: 27381077]
[24]
Schmidt-Richberg A, Ledig C, Guerrero R, et al. Learning biomarker models for progression estimation of Alzheimer’s disease. PLoS One 2016; 11(4): e0153040.
[http://dx.doi.org/10.1371/journal.pone.0153040] [PMID: 27096739]
[25]
Li D, Iddi S, Thompson WK, Donohue MC, Initiative ADN. Bayesian latent time joint mixed effect models for multicohort longitudinal data. Stat Methods Med Res 2019; 28(3): 835-45.
[http://dx.doi.org/10.1177/0962280217737566] [PMID: 29168432]
[26]
Lorenzi M, Filippone M, Frisoni GB, Alexander DC, Ourselin S, Initiative ADN, et al. Probabilistic disease progression modeling to characterize diagnostic uncertainty: Application to staging and prediction in Alzheimer’s disease. Neuroimage 2019; 190: 56-68.
[http://dx.doi.org/10.1016/j.neuroimage.2017.08.059] [PMID: 29079521]
[27]
Wyman BT, Harvey DJ, Crawford K, et al. Standardization of analysis sets for reporting results from ADNI MRI data. Alzheimers Dement 2013; 9(3): 332-7.
[http://dx.doi.org/10.1016/j.jalz.2012.06.004] [PMID: 23110865]
[28]
The ADNI team: ADNIMERGE: Alzheimer’s disease neuroimaging initiative. 2021. Available from: https://adni.loni.usc.edu/
[29]
Petersen RC, Aisen PS, Beckett LA, et al. Alzheimer’s disease neuroimaging initiative (ADNI): Clinical characterization. Neurology 2010; 74(3): 201-9.
[http://dx.doi.org/10.1212/WNL.0b013e3181cb3e25] [PMID: 20042704]
[30]
Bittner T, Zetterberg H, Teunissen CE, et al. Technical performance of a novel, fully automated electrochemiluminescence immunoassay for the quantitation of β‐amyloid (1–42) in human cerebrospinal fluid. Alzheimers Dement 2016; 12(5): 517-26.
[http://dx.doi.org/10.1016/j.jalz.2015.09.009] [PMID: 26555316]
[31]
Hansson O, Seibyl J, Stomrud E, et al. CSF biomarkers of Alzheimer’s disease concord with amyloid‐β PET and predict clinical progression: A study of fully automated immunoassays in BioFINDER and ADNI cohorts. Alzheimers Dement 2018; 14(11): 1470-81.
[http://dx.doi.org/10.1016/j.jalz.2018.01.010] [PMID: 29499171]
[32]
Landau SM, Mintun MA, Joshi AD, et al. Amyloid deposition, hypometabolism, and longitudinal cognitive decline. Ann Neurol 2012; 72(4): 578-86.
[http://dx.doi.org/10.1002/ana.23650] [PMID: 23109153]
[33]
Platero C. Categorical predictive and disease progression modeling in the early stage of Alzheimer’s disease. J Neurosci Methods 2022; 374: 109581.
[http://dx.doi.org/10.1016/j.jneumeth.2022.109581] [PMID: 35346695]
[34]
Kleinbaum DG, Klein M. Survival analysis. Springer 2010; p. 3.
[35]
Sabuncu MR, Bernal-Rusiel JL, Reuter M, Greve DN, Fischl B. Event time analysis of longitudinal neuroimage data. Neuroimage 2014; 97: 9-18.
[http://dx.doi.org/10.1016/j.neuroimage.2014.04.015] [PMID: 24736175]
[36]
Platero C, Tobar MC. Longitudinal survival analysis and two-group comparison for predicting the progression of mild cognitive impairment to Alzheimer’s disease. J Neurosci Methods 2020; 341: 108698.
[http://dx.doi.org/10.1016/j.jneumeth.2020.108698] [PMID: 32534272]
[37]
Bernal-Rusiel JL, Greve DN, Reuter M, Fischl B, Sabuncu MR. Statistical analysis of longitudinal neuroimage data with linear mixed effects models. Neuroimage 2013; 66: 249-60.
[http://dx.doi.org/10.1016/j.neuroimage.2012.10.065] [PMID: 23123680]
[38]
Bernal-Rusiel JL, Reuter M, Greve DN, Fischl B, Sabuncu MR. Spatiotemporal linear mixed effects modeling for the mass-univariate analysis of longitudinal neuroimage data. Neuroimage 2013; 81: 358-70.
[http://dx.doi.org/10.1016/j.neuroimage.2013.05.049] [PMID: 23702413]
[39]
Kuhn M, Johnson K. Applied predictive modeling. Springer 2013; p. 26.
[http://dx.doi.org/10.1007/978-1-4614-6849-3]
[40]
Hanchuan Peng, Fuhui Long, Ding C. Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell 2005; 27(8): 1226-38.
[http://dx.doi.org/10.1109/TPAMI.2005.159] [PMID: 16119262]
[41]
Cuingnet R, Gerardin E, Tessieras J, et al. Automatic classification of patients with Alzheimer’s disease from structural MRI: Automatic classification of patients with Alzheimer’s disease from structural MRI. Neuroimage 2011; 56: 766-81.
[42]
Vos SJB, Verhey F, Frölich L, et al. Prevalence and prognosis of Alzheimer’s disease at the mild cognitive impairment stage. Brain 2015; 138(5): 1327-38.
[http://dx.doi.org/10.1093/brain/awv029] [PMID: 25693589]
[43]
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]
[44]
Roberts RO, Knopman DS, Mielke MM, et al. Higher risk of progression to dementia in mild cognitive impairment cases who revert to normal. Neurology 2014; 82(4): 317-25.
[http://dx.doi.org/10.1212/WNL.0000000000000055] [PMID: 24353333]
[45]
Ward A, Tardiff S, Dye C, Arrighi HM. Rate of conversion from prodromal Alzheimer’s disease to Alzheimer’s dementia: A systematic review of the literature. Dement Geriatr Cogn Disord Extra 2013; 3(1): 320-32.
[http://dx.doi.org/10.1159/000354370] [PMID: 24174927]
[46]
Chen Y, Denny KG, Harvey D, et al. Progression from normal cognition to mild cognitive impairment in a diverse clinic‐based and community‐based elderly cohort. Alzheimers Dement 2017; 13(4): 399-405.
[http://dx.doi.org/10.1016/j.jalz.2016.07.151] [PMID: 27590706]
[47]
Doraiswamy PM, Sperling RA, Johnson K, et al. Florbetapir F 18 amyloid PET and 36-month cognitive decline: A prospective multicenter study. Mol Psychiatry 2014; 19(9): 1044-51.
[http://dx.doi.org/10.1038/mp.2014.9] [PMID: 24614494]
[48]
Okello A, Koivunen J, Edison P, et al. Conversion of amyloid positive and negative MCI to AD over 3 years. Neurology 2009; 73(10): 754-60.
[http://dx.doi.org/10.1212/WNL.0b013e3181b23564] [PMID: 19587325]
[49]
van Rossum IA, Vos SJB, Burns L, et al. Injury markers predict time to dementia in subjects with MCI and amyloid pathology. Neurology 2012; 79(17): 1809-16.
[http://dx.doi.org/10.1212/WNL.0b013e3182704056] [PMID: 23019259]
[50]
Sosa-Ortiz AL, Acosta-Castillo I, Prince MJ. Epidemiology of dementias and Alzheimer’s disease. Arch Med Res 2012; 43(8): 600-8.
[http://dx.doi.org/10.1016/j.arcmed.2012.11.003] [PMID: 23159715]
[51]
Dubois B, Feldman HH, Jacova C, et al. Advancing research diagnostic criteria for Alzheimer’s disease: The IWG-2 criteria. Lancet Neurol 2014; 13(6): 614-29.
[http://dx.doi.org/10.1016/S1474-4422(14)70090-0] [PMID: 24849862]
[52]
Dubois B, Hampel H, Feldman HH, et al. Preclinical Alzheimer’s disease: Definition, natural history, and diagnostic criteria. Alzheimers Dement 2016; 12(3): 292-323.
[http://dx.doi.org/10.1016/j.jalz.2016.02.002] [PMID: 27012484]
[53]
Frisoni GB, Boccardi M, Barkhof F, et al. Strategic roadmap for an early diagnosis of Alzheimer’s disease based on biomarkers. Lancet Neurol 2017; 16(8): 661-76.
[http://dx.doi.org/10.1016/S1474-4422(17)30159-X] [PMID: 28721928]
[54]
Jessen F, Wolfsgruber S, Wiese B, et al. AD dementia risk in late MCI, in early MCI, and in subjective memory impairment. Alzheimers Dement 2014; 10(1): 76-83.
[http://dx.doi.org/10.1016/j.jalz.2012.09.017] [PMID: 23375567]
[55]
Mormino EC, Betensky RA, Hedden T, et al. Synergistic effect of β-amyloid and neurodegeneration on cognitive decline in clinically normal individuals. JAMA Neurol 2014; 71(11): 1379-85.
[http://dx.doi.org/10.1001/jamaneurol.2014.2031] [PMID: 25222039]
[56]
Lim YY, Maruff P, Pietrzak RH, et al. Effect of amyloid on memory and non-memory decline from preclinical to clinical Alzheimer’s disease. Brain 2014; 137(1): 221-31.
[http://dx.doi.org/10.1093/brain/awt286] [PMID: 24176981]
[57]
Duke Han S, Nguyen CP, Stricker NH, Nation DA. Detectable neuropsychological differences in early preclinical Alzheimer’s disease: A meta-analysis. Neuropsychol Rev 2017; 27(4): 305-25.
[http://dx.doi.org/10.1007/s11065-017-9345-5] [PMID: 28497179]
[58]
Morris JC, Roe CM, Xiong C, et al. APOE predicts amyloid‐beta but not tau Alzheimer pathology in cognitively normal aging. Ann Neurol 2010; 67(1): 122-31.
[http://dx.doi.org/10.1002/ana.21843] [PMID: 20186853]
[59]
Gomez-Isla T, West HL, Rebeck GW, et al. Clinical and pathological correlates of apolipoprotein E ε4 in Alzheimer’s disease. Ann Neurol 1996; 39(1): 62-70.
[http://dx.doi.org/10.1002/ana.410390110] [PMID: 8572669]
[60]
Papp KV, Buckley R, Mormino E, et al. Clinical meaningfulness of subtle cognitive decline on longitudinal testing in preclinical AD. Alzheimers Dement 2019; 16(3): 552-60.
[PMID: 31759879]
[61]
Sabbagh MN, Hendrix S, Harrison JE. FDA position statement “Early Alzheimer’s disease: Developing drugs for treatment, Guidance for Industry”. Alzheimers Dement 2019; 5(1): 13-9.
[http://dx.doi.org/10.1016/j.trci.2018.11.004] [PMID: 31650002]
[62]
Reimand J, Collij L, Scheltens P, Bouwman F, Ossenkoppele R, Initiative ADN, et al. Association of amyloid-β CSF/PET discordance and tau load 5 years later. Neurology 2020; 95(19): e2648-57.
[http://dx.doi.org/10.1212/WNL.0000000000010739] [PMID: 32913020]
[63]
Fagan AM, Mintun MA, Shah AR, et al. Cerebrospinal fluid tau and ptau 181 increase with cortical amyloid deposition in cognitively normal individuals: Implications for future clinical trials of Alzheimer’s disease. EMBO Mol Med 2009; 1(8-9): 371-80.
[http://dx.doi.org/10.1002/emmm.200900048] [PMID: 20049742]
[64]
Buchhave P, Minthon L, Zetterberg H, Wallin ˚AK, Blennow K, Hansson O. Cerebrospinal fluid levels of β-amyloid 1-42, but not of tau, are fully changed already 5 to 10 years before the onset of Alzheimer dementia. Arch Gen Psychiatry 2012; 69(1): 98-106.
[http://dx.doi.org/10.1001/archgenpsychiatry.2011.155] [PMID: 22213792]
[65]
Tan MS, Ji X, Li JQ, et al. Longitudinal trajectories of Alzheimer’s ATN biomarkers in elderly persons without dementia. Alzheimers Res Ther 2020; 12(1): 55.
[http://dx.doi.org/10.1186/s13195-020-00621-6] [PMID: 32393375]
[66]
Ishida T, Tokuda K, Hisaka A, et al. A novel method to estimate long-term chronological changes from fragmented observations in disease progression. Clin Pharmacol Ther 2019; 105(2): 436-47.
[http://dx.doi.org/10.1002/cpt.1166] [PMID: 29951994]
[67]
Mehdipour GM, Nielsen M, Pai A, et al. Robust parametric modeling of Alzheimer’s disease progression. Neuroimage 2021; 225: 117460.
[http://dx.doi.org/10.1016/j.neuroimage.2020.117460] [PMID: 33075562]
[68]
Koval I, Bône A, Louis M, et al. AD Course Map charts Alzheimer’s disease progression. Sci Rep 2021; 11(1): 8020.
[http://dx.doi.org/10.1038/s41598-021-87434-1] [PMID: 33850174]
[69]
Garbarino S, Lorenzi M, Initiative ADN, et al. Investigating hypotheses of neurodegeneration by learning dynamical systems of protein propagation in the brain. Neuroimage 2021; 235: 117980.
[http://dx.doi.org/10.1016/j.neuroimage.2021.117980] [PMID: 33823273]
[70]
Ghazi MM, Sørensen L, Ourselin S, Nielsen M. Carrnn: A continuous autoregressive recurrent neural network for deep representation learning from sporadic temporal data. IEEE Trans Neural Netw Learn Syst 2022.
[PMID: 35666790]
[71]
Cho SH, Woo S, Kim C, et al. Disease progression modelling from preclinical Alzheimer’s disease (AD) to AD dementia. Sci Rep 2021; 11(1): 4168.
[http://dx.doi.org/10.1038/s41598-021-83585-3] [PMID: 33603015]
[72]
Teng E, Becker BW, Woo E, Knopman DS, Cummings JL, Lu PH. Utility of the functional activities questionnaire for distinguishing mild cognitive impairment from very mild Alzheimer disease. Alzheimer Dis Assoc Disord 2010; 24(4): 348-53.
[http://dx.doi.org/10.1097/WAD.0b013e3181e2fc84] [PMID: 20592580]
[73]
Kukull WA, Larson EB, Teri L, Bowen J, McCormick W, Pfanschmidt ML. The mini-mental state examination score and the clinical diagnosis of dementia. J Clin Epidemiol 1994; 47(9): 1061-7.
[http://dx.doi.org/10.1016/0895-4356(94)90122-8] [PMID: 7730909]
[74]
Yang YW, Hsu KC, Wei CY, Tzeng RC, Chiu PY. Operational determination of subjective cognitive decline, mild cognitive impairment, and dementia using sum of boxes of the clinical dementia rating scale. Front Aging Neurosci 2021; 13: 705782.
[http://dx.doi.org/10.3389/fnagi.2021.705782] [PMID: 34557083]
[75]
Ou YN, Xu W, Li JQ, et al. FDG-PET as an independent biomarker for Alzheimer’s biological diagnosis: A longitudinal study. Alzheimers Res Ther 2019; 11(1): 57.
[http://dx.doi.org/10.1186/s13195-019-0512-1] [PMID: 31253185]

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