AD Risk Score for the Early Phases of Disease Based on Unsupervised Machine Learning

Overview

Journal Alzheimers Dement

Specialties Neurology
Psychiatry

Date 2020 Jul 31

PMID 32729964

Citations 9

Authors

Zheyu Wang

Zhuojun Tang

Yuxin Zhu

Corinne Pettigrew

Anja Soldan

Alden Gross

Marilyn Albert

Affiliations

Soon will be listed here.

Abstract

Introduction: Identifying cognitively normal individuals at high risk for progression to symptomatic Alzheimer's disease (AD) is critical for early intervention.

Methods: An AD risk score was derived using unsupervised machine learning. The score was developed using data from 226 cognitively normal individuals and included cerebrospinal fluid, magnetic resonance imaging, and cognitive measures, and validated in an independent cohort.

Results: Higher baseline AD progression risk scores (hazard ratio = 2.70, P < 0.001) were associated with greater risks of progression to clinical symptoms of mild cognitive impairment (MCI). Baseline scores had an area under the curve of 0.83 (95% confidence interval: 0.75 to 0.91) for identifying subjects who progressed to MCI/dementia within 5 years. The validation procedure, using data from the Alzheimer's Disease Neuroimaging Initiative, demonstrated accuracy of prediction across the AD spectrum.

Discussion: The derived risk score provides high predictive accuracy for identifying which individuals with normal cognition are likely to show clinical decline due to AD within 5 years.

Citing Articles

Construction of a novel lower-extremity peripheral artery disease subtype prediction model using unsupervised machine learning and neutrophil-related biomarkers.

Zhang L, Ma Y, Li Q, Long Z, Zhang J, Zhang Z Heliyon. 2024; 10(2):e24189.

PMID: 38293541 PMC: 10827514. DOI: 10.1016/j.heliyon.2024.e24189.

Few-shot prediction of amyloid β accumulation from mainly unpaired data on biomarker candidates.

Yada Y, Naoki H NPJ Syst Biol Appl. 2023; 9(1):59.

PMID: 37993458 PMC: 10665362. DOI: 10.1038/s41540-023-00321-5.

Identification of spinal tuberculosis subphenotypes using routine clinical data: a study based on unsupervised machine learning.

Yao Y, Wu S, Liu C, Zhou C, Zhu J, Chen T Ann Med. 2023; 55(2):2249004.

PMID: 37611242 PMC: 10448834. DOI: 10.1080/07853890.2023.2249004.

Novel methodology for detection and prediction of mild cognitive impairment using resting-state EEG.

Deng J, Sun B, Kavcic V, Liu M, Giordani B, Li T Alzheimers Dement. 2023; 20(1):145-158.

PMID: 37496373 PMC: 10811294. DOI: 10.1002/alz.13411.

Machine Learning Approach Predicts Probability of Time to Stage-Specific Conversion of Alzheimer's Disease.

Wu X, Peng C, Nelson P, Cheng Q J Alzheimers Dis. 2022; 90(2):891-903.

PMID: 36189595 PMC: 9840816. DOI: 10.3233/JAD-220590.

References

Gauthier S, Albert M, Fox N, Goedert M, Kivipelto M, Mestre-Ferrandiz J . Why has therapy development for dementia failed in the last two decades?. Alzheimers Dement. 2015; 12(1):60-4. DOI: 10.1016/j.jalz.2015.12.003. View

Pfeffer R, Kurosaki T, Harrah Jr C, Chance J, Filos S . Measurement of functional activities in older adults in the community. J Gerontol. 1982; 37(3):323-9. DOI: 10.1093/geronj/37.3.323. View

Albert M, DeKosky S, Dickson D, Dubois B, Feldman H, Fox N . 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. PMC: 3312027. DOI: 10.1016/j.jalz.2011.03.008. View

Leoutsakos J, Gross A, Jones R, Albert M, Breitner J . 'Alzheimer's Progression Score': Development of a Biomarker Summary Outcome for AD Prevention Trials. J Prev Alzheimers Dis. 2017; 3(4):229-235. PMC: 5639716. DOI: 10.14283/jpad.2016.120. View

Jack Jr C, Knopman D, Weigand S, Wiste H, Vemuri P, Lowe V . An operational approach to National Institute on Aging-Alzheimer's Association criteria for preclinical Alzheimer disease. Ann Neurol. 2012; 71(6):765-75. PMC: 3586223. DOI: 10.1002/ana.22628. View

Gross A, Walker K, Moghekar A, Pettigrew C, Soldan A, Albert M . Plasma Markers of Inflammation Linked to Clinical Progression and Decline During Preclinical AD. Front Aging Neurosci. 2019; 11:229. PMC: 6742958. DOI: 10.3389/fnagi.2019.00229. View

Desikan R, Segonne F, Fischl B, Quinn B, Dickerson B, Blacker D . An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage. 2006; 31(3):968-80. DOI: 10.1016/j.neuroimage.2006.01.021. View

Gross A, Mungas D, Leoutsakos J, Albert M, Jones R . Alzheimer's disease severity, objectively determined and measured. Alzheimers Dement (Amst). 2016; 4:159-168. PMC: 5078784. DOI: 10.1016/j.dadm.2016.08.005. View

Soldan A, Pettigrew C, Lu Y, Wang M, Selnes O, Albert M . Relationship of medial temporal lobe atrophy, APOE genotype, and cognitive reserve in preclinical Alzheimer's disease. Hum Brain Mapp. 2015; 36(7):2826-41. PMC: 4478167. DOI: 10.1002/hbm.22810. View

10.

Albert M, Zhu Y, Moghekar A, Mori S, Miller M, Soldan A . Predicting progression from normal cognition to mild cognitive impairment for individuals at 5 years. Brain. 2018; 141(3):877-887. PMC: 5837651. DOI: 10.1093/brain/awx365. View

11.

Sperling R, Aisen P, Beckett L, Bennett D, Craft S, Fagan A . 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. PMC: 3220946. DOI: 10.1016/j.jalz.2011.03.003. View

12.

Resnick S, Pham D, Kraut M, Zonderman A, Davatzikos C . Longitudinal magnetic resonance imaging studies of older adults: a shrinking brain. J Neurosci. 2003; 23(8):3295-301. PMC: 6742337. View

13.

Miller M, Younes L, Ratnanather J, Brown T, Trinh H, Postell E . The diffeomorphometry of temporal lobe structures in preclinical Alzheimer's disease. Neuroimage Clin. 2013; 3:352-60. PMC: 3863771. DOI: 10.1016/j.nicl.2013.09.001. View

14.

Reuter M, Schmansky N, Rosas H, Fischl B . Within-subject template estimation for unbiased longitudinal image analysis. Neuroimage. 2012; 61(4):1402-18. PMC: 3389460. DOI: 10.1016/j.neuroimage.2012.02.084. View

15.

Shaw L, Vanderstichele H, Knapik-Czajka M, Clark C, Aisen P, Petersen R . Cerebrospinal fluid biomarker signature in Alzheimer's disease neuroimaging initiative subjects. Ann Neurol. 2009; 65(4):403-13. PMC: 2696350. DOI: 10.1002/ana.21610. View

16.

Jack Jr C, Knopman D, Jagust W, Petersen R, Weiner M, Aisen P . Tracking pathophysiological processes in Alzheimer's disease: an updated hypothetical model of dynamic biomarkers. Lancet Neurol. 2013; 12(2):207-16. PMC: 3622225. DOI: 10.1016/S1474-4422(12)70291-0. View

17.

Moghekar A, Li S, Lu Y, Li M, Wang M, Albert M . CSF biomarker changes precede symptom onset of mild cognitive impairment. Neurology. 2013; 81(20):1753-8. PMC: 3821715. DOI: 10.1212/01.wnl.0000435558.98447.17. View

18.

Albert M, Soldan A, Gottesman R, McKhann G, Sacktor N, Farrington L . Cognitive changes preceding clinical symptom onset of mild cognitive impairment and relationship to ApoE genotype. Curr Alzheimer Res. 2014; 11(8):773-84. PMC: 4163954. DOI: 10.2174/156720501108140910121920. View

19.

Dale A, Fischl B, Sereno M . Cortical surface-based analysis. I. Segmentation and surface reconstruction. Neuroimage. 1999; 9(2):179-94. DOI: 10.1006/nimg.1998.0395. View

20.

Fischl B, Dale A . Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proc Natl Acad Sci U S A. 2000; 97(20):11050-5. PMC: 27146. DOI: 10.1073/pnas.200033797. View