Predicting clinical progression trajectories of early Alzheimer's disease patients.
clinical trial enrichment
disease progression
machine learning
mild cognitive impairment
prognosis
Journal
Alzheimer's & dementia : the journal of the Alzheimer's Association
ISSN: 1552-5279
Titre abrégé: Alzheimers Dement
Pays: United States
ID NLM: 101231978
Informations de publication
Date de publication:
Mar 2024
Mar 2024
Historique:
revised:
06
09
2023
received:
26
04
2023
accepted:
07
11
2023
medline:
18
3
2024
pubmed:
13
12
2023
entrez:
13
12
2023
Statut:
ppublish
Résumé
Models for forecasting individual clinical progression trajectories in early Alzheimer's disease (AD) are needed for optimizing clinical studies and patient monitoring. Prediction models were constructed using a clinical trial training cohort (TC; n = 934) via a gradient boosting algorithm and then evaluated in two validation cohorts (VC 1, n = 235; VC 2, n = 421). Model inputs included baseline clinical features (cognitive function assessments, APOE ε4 status, and demographics) and brain magnetic resonance imaging (MRI) measures. The model using clinical features achieved R Our validated prediction models enable baseline prediction of clinical progression trajectories in early AD, benefiting clinical trial enrichment and various applications.
Sections du résumé
BACKGROUND
BACKGROUND
Models for forecasting individual clinical progression trajectories in early Alzheimer's disease (AD) are needed for optimizing clinical studies and patient monitoring.
METHODS
METHODS
Prediction models were constructed using a clinical trial training cohort (TC; n = 934) via a gradient boosting algorithm and then evaluated in two validation cohorts (VC 1, n = 235; VC 2, n = 421). Model inputs included baseline clinical features (cognitive function assessments, APOE ε4 status, and demographics) and brain magnetic resonance imaging (MRI) measures.
RESULTS
RESULTS
The model using clinical features achieved R
DISCUSSION
CONCLUSIONS
Our validated prediction models enable baseline prediction of clinical progression trajectories in early AD, benefiting clinical trial enrichment and various applications.
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
1725-1738Subventions
Organisme : NIH HHS
ID : U01 AG024904
Pays : United States
Organisme : NIA NIH HHS
Pays : United States
Organisme : NIBIB NIH HHS
Pays : United States
Organisme : CIHR
Pays : Canada
Organisme : NIH HHS
ID : U01 AG024904
Pays : United States
Organisme : NIA NIH HHS
Pays : United States
Organisme : NIBIB NIH HHS
Pays : United States
Organisme : CIHR
Pays : Canada
Informations de copyright
© 2023 Eisai Inc. Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.
Références
Mills EA, Begay JA, Fisher C, Mao-Draayer Y. Impact of trial design and patient heterogeneity on the identification of clinically effective therapies for progressive MS. Mult Scler. 2018;24:1795-1807.
van Eijk RPA, Nikolakopoulos S, Roes KCB, et al. Innovating clinical trials for amyotrophic lateral sclerosis: challenging the established order. Neurology. 2021;97:528-536.
Catenacci DV. Next-generation clinical trials: novel strategies to address the challenge of tumor molecular heterogeneity. Mol Oncol. 2015;9:967-996.
Duara R, Barker W. Heterogeneity in Alzheimer's disease diagnosis and progression rates: implications for therapeutic trials. Neurotherapeutics. 2022;19:8-25.
Manton KG, Vertrees JC, Woodbury MA. Functionally and medically defined subgroups of nursing home populations. Health Care Financ Rev. 1990;12:47-62.
Dammann M, Staudacher S, Simon M, Jeitziner MM. Insights into the challenges faced by chronically critically ill patients, their families and healthcare providers: an interpretive description. Intensive Crit Care Nurs. 2022;68:103135.
Abdelnour C, Agosta F, Bozzali M, et al. Perspectives and challenges in patient stratification in Alzheimer's disease. Alzheimers Res Ther. 2022;14:112.
Hampel H, Au R, Mattke S, et al. Designing the next-generation clinical care pathway for Alzheimer's disease. Nature Aging. 2022;2:692-703.
Mann UM, Mohr E, Gearing M, Chase TN. Heterogeneity in Alzheimer's disease: progression rate segregated by distinct neuropsychological and cerebral metabolic profiles. J Neurol Neurosurg Psychiatry. 1992;55:956-959.
Hampel H, Lista S. Dementia: the rising global tide of cognitive impairment. Nat Rev Neurol. 2016;12:131-132.
Goyal D, Tjandra D, Migrino RQ, Giordani B, Syed Z, Wiens J. Characterizing heterogeneity in the progression of Alzheimer's disease using longitudinal clinical and neuroimaging biomarkers. Alzheimers Dement. 2018;10:629-637.
Devi G, Scheltens P. Heterogeneity of Alzheimer's disease: consequence for drug trials? Alzheimers Res Ther. 2018;10:122.
Hampel H, Hardy J, Blennow K, et al. The Amyloid-beta pathway in Alzheimer's disease. Mol Psychiatry. 2021;26:5481-5503.
Hersi M, Irvine B, Gupta P, Gomes J, Birkett N, Krewski D. Risk factors associated with the onset and progression of Alzheimer's disease: a systematic review of the evidence. Neurotoxicology. 2017;61:143-187.
Fleisher AS, Sowell BB, Taylor C, et al. Clinical predictors of progression to Alzheimer disease in amnestic mild cognitive impairment. Neurology. 2007;68:1588-1595.
Iddi S, Li D, Aisen PS, et al. Predicting the course of Alzheimer's progression. Brain Inform. 2019;6:6.
Franzmeier N, Koutsouleris N, Benzinger T, et al. Predicting sporadic Alzheimer's disease progression via inherited Alzheimer's disease-informed machine-learning. Alzheimers Dement. 2020;16:501-511.
Doody RS, Raman R, Farlow M, et al. A phase 3 trial of semagacestat for treatment of Alzheimer's disease. N Engl J Med. 2013;369:341-350.
Siemers ER, Sundell KL, Carlson C, et al. Phase 3 solanezumab trials: secondary outcomes in mild Alzheimer's disease patients. Alzheimers Dement. 2016;12:110-120.
Vandenberghe R, Rinne JO, Boada M, et al. Bapineuzumab for mild to moderate Alzheimer's disease in two global, randomized, phase 3 trials. Alzheimers Res Ther. 2016;8:18.
Honig LS, Vellas B, Woodward M, et al. Trial of solanezumab for mild dementia due to Alzheimer's disease. N Engl J Med. 2018;378:321-330.
Wessels AM, Tariot PN, Zimmer JA, et al. Efficacy and Safety of Lanabecestat for Treatment of Early and Mild Alzheimer Disease: the AMARANTH and DAYBREAK-ALZ Randomized Clinical Trials. JAMA Neurol. 2020;77:199-209.
Budd Haeberlein S, Aisen PS, Barkhof F, et al. Two randomized phase 3 studies of aducanumab in early Alzheimer's disease. J Prev Alzheimers Dis. 2022;9:197-210.
Hampel H, O'Bryant SE, Molinuevo JL, et al. Blood-based biomarkers for Alzheimer disease: mapping the road to the clinic. Nat Rev Neurol. 2018;14:639-652.
Hampel H, Shaw LM, Aisen P, et al. State-of-the-art of lumbar puncture and its place in the journey of patients with Alzheimer's disease. Alzheimers Dement. 2022;18:159-177.
Jutten RJ, Sikkes SAM, Van der Flier WM, et al. Finding treatment effects in alzheimer trials in the face of disease progression heterogeneity. Neurology. 2021;96:e2673-e2684.
Mattsson-Carlgren N, Salvado G, Ashton NJ, et al. Prediction of longitudinal cognitive decline in preclinical Alzheimer disease using plasma biomarkers. JAMA Neurol. 2023;80(4):360-369.
Hampel H, Frank R, Broich K, et al. Biomarkers for Alzheimer's disease: academic, industry and regulatory perspectives. Nat Rev Drug Discov. 2010;9:560-574.
Llano DA, Laforet G, Devanarayan V, Alzheimer's Disease Neuroimaging I. Derivation of a new ADAS-cog composite using tree-based multivariate analysis: prediction of conversion from mild cognitive impairment to Alzheimer disease. Alzheimer Dis Assoc Disord. 2011;25:73-84.
Desikan RS, Cabral HJ, Settecase F, et al. Automated MRI measures predict progression to Alzheimer's disease. Neurobiol Aging. 2010;31:1364-1374.
Llano DA, Bundela S, Mudar RA, Devanarayan V, Alzheimer's Disease Neuroimaging I. A multivariate predictive modeling approach reveals a novel CSF peptide signature for both Alzheimer's Disease state classification and for predicting future disease progression. PLoS One. 2017;12:e0182098.
Devanarayan P, Devanarayan V, Llano DA, Alzheimer's Disease Neuroimaging I. Identification of a simple and novel cut-point based cerebrospinal fluid and MRI signature for predicting Alzheimer's disease progression that reinforces the 2018 NIA-AA research framework. J Alzheimers Dis. 2019;68:537-550.
Wattmo C, Wallin AK, Minthon L. Progression of mild Alzheimer's disease: knowledge and prediction models required for future treatment strategies. Alzheimers Res Ther. 2013;5:44.
Desikan RS, Segonne F, Fischl B, et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage. 2006;31:968-980.
Dale AM, Fischl B, Sereno MI. Cortical surface-based analysis. I. Segmentation and surface reconstruction. Neuroimage. 1999;9:179-194.
Fischl B, Sereno MI, Dale AM. Cortical surface-based analysis. II: inflation, flattening, and a surface-based coordinate system. Neuroimage. 1999;9:195-207.
Fischl B, Sereno MI, Tootell RB, Dale AM. High-resolution intersubject averaging and a coordinate system for the cortical surface. Hum Brain Mapp. 1999;8:272-284.
Song WM, Zhang B. Multiscale embedded gene co-expression network analysis. PLoS Comput Biol. 2015;11:e1004574.
Friedman JH. Greedy function approximation: a gradient boosting machine. Annals of Statistics. 2001;29:1189-1232.
Natekin A, Knoll A. Gradient boosting machines, a tutorial. Front Neurorobot. 2013;7:21.
Goldstein A, Kapelner A, Bleich J, Pitkin E. Peeking inside the black box: visualizing statistical learning with plots of individual conditional expectation. J Comput Graph Statist. 2015;24:44-65.
Shi L, Campbell G, Jones WD, et al. The MicroArray Quality Control (MAQC)-II study of common practices for the development and validation of microarray-based predictive models. Nat Biotechnol. 2010;28:827-838.
O'Bryant SE, Waring SC, Cullum CM, et al. Staging dementia using clinical dementia rating scale sum of boxes scores: a Texas Alzheimer's research consortium study. Arch Neurol. 2008;65:1091-1095.
R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing; 2022.
Song W, Zhang B, MEGENA: Multiscale Clustering of Geometrical Network. 2018. R package, version 1.3.7, https://CRAN.R-project.org/package=MEGENA
Greenwell B, Boehmke B, Cunningham J, Developers GBM, gbm: Generalized Boosted Regression Models. 2022. R package version 2.1.8.1, https://CRAN.R-project.org/package=gbm
Greenwell BM. pdp: an R Package for constructing partial dependence plots. R Journal. 2017;9:421-436.
Maheux E, Koval I, Ortholand J, et al. Forecasting individual progression trajectories in Alzheimer's disease. Nat Commun. 2023;14:761.
Kuhnel L, Bouteloup V, Lespinasse J, et al. Personalized prediction of progression in pre-dementia patients based on individual biomarker profile: a development and validation study. Alzheimers Dement. 2021;17:1938-1949.
U.S. Department of Health and Human Services, Food and Drug Administration, Center for Drug Evaluation and Research, Center for Biologics Evaluation and Research. Guidance for Industry: Enrichment Strategies for Clinical Trials to Support Approval of Human Drugs and Biological Products. 2019. https://www.fda.gov/media/121320/download
Tam A, Laurent C, Gauthier S, Dansereau C. Prediction of cognitive decline for enrichment of Alzheimer's disease clinical trials. J Prev Alzheimers Dis. 2022;9:400-409.
Kahan BC, Jairath V, Dore CJ, Morris TP. The risks and rewards of covariate adjustment in randomized trials: an assessment of 12 outcomes from 8 studies. Trials. 2014;15:139.
Zhang Z, Ma S. Machine learning methods for leveraging baseline covariate information to improve the efficiency of clinical trials. Stat Med. 2019;38:1703-1714.
Schuler A, Walsh D, Hall D, et al. Increasing the efficiency of randomized trial estimates via linear adjustment for a prognostic score. Int J Biostat. 2022;18:329-356.
U.S. Department of Health and Human Services, Food and Drug Administration, Center for Drug Evaluation and Research, Center for Biologics Evaluation and Research. Guidance for Industry: Adjusting for Covariates in Randomized Clinical Trials for Drugs and Biological Products. 2023. https://www.fda.gov/media/148910/download
Huang X, Sun Y, Trow P, et al. Patient subgroup identification for clinical drug development. Stat Med. 2017;36:1414-1428.
Hampel H, Gao P, Cummings J, et al. The foundation and architecture of precision medicine in neurology and psychiatry. Trends Neurosci. 2023;46:176-198.
Llano DA, Devanarayan P, Devanarayan V, Alzheimer's Disease Neuroimaging I. CSF peptides from VGF and other markers enhance prediction of MCI to AD progression using the ATN framework. Neurobiol Aging. 2023;121:15-27.
Friedman J, Hastie T, Tibshirani R. Regularization paths for generalized linear models via coordinate descent. J Stat Softw. 2010;33:1-22.
Hong F, Tian L, Devanarayan V. Improving the robustness of variable selection and predictive performance of regularized generalized linear models and cox proportional hazard models. mathematics. 2023;11:557.
Maruff P, Lim YY, Darby D, et al. Clinical utility of the cogstate brief battery in identifying cognitive impairment in mild cognitive impairment and Alzheimer's disease. BMC Psychol. 2013;1:30.
Saxton J, Morrow L, Eschman A, Archer G, Luther J, Zuccolotto A. Computer assessment of mild cognitive impairment. Postgrad Med. 2009;121:177-185.
Barnett JH, Blackwell AD, Sahakian BJ, Robbins TW. The paired associates learning (PAL) test: 30 years of CANTAB translational neuroscience from laboratory to bedside in dementia research. Curr Top Behav Neurosci. 2016;28:449-474.
Staffaroni AM, Tsoy E, Taylor J, Boxer AL, Possin KL. Digital Cognitive Assessments for Dementia: digital assessments may enhance the efficiency of evaluations in neurology and other clinics. Pract Neurol (Fort Wash Pa). 2020;2020:24-45.
Jack CR Jr, Bennett DA, Blennow K, et al. NIA-AA research framework: toward a biological definition of Alzheimer's disease. Alzheimers Dement. 2018;14:535-562.
Hampel H, Cummings J, Blennow K, Gao P, Jack CR Jr, Vergallo A. Developing the ATX(N) classification for use across the Alzheimer disease continuum. Nat Rev Neurol. 2021;17:580-589.
Leuzy A, Mattsson-Carlgren N, Palmqvist S, Janelidze S, Dage JL, Hansson O. Blood-based biomarkers for Alzheimer's disease. EMBO Mol Med. 2022;14:e14408.
Varesi A, Carrara A, Pires VG, et al. Blood-Based biomarkers for Alzheimer's disease diagnosis and progression: an overview. Cells. 2022;11:1367.
Stevenson-Hoare J, Heslegrave A, Leonenko G, et al. Plasma biomarkers and genetics in the diagnosis and prediction of Alzheimer's disease. Brain. 2023;146:690-699.
Shi L, Buckley NJ, Bos I, et al. Plasma proteomic biomarkers relating to Alzheimer's disease: a Meta-analysis based on our own studies. Front Aging Neurosci. 2021;13:712545.
Jiang Y, Zhou X, Ip FC, et al. Large-scale plasma proteomic profiling identifies a high-performance biomarker panel for Alzheimer's disease screening and staging. Alzheimers Dement. 2022;18:88-102.