Combining Polygenic Hazard Score With Volumetric MRI and Cognitive Measures Improves Prediction of Progression From Mild Cognitive Impairment to Alzheimer's Disease

被引:35
作者
Kauppi, Karolina [1 ,2 ]
Fan, Chun Chieh [1 ,3 ]
McEvoy, Linda K. [1 ]
Holland, Dominic [4 ]
Tan, Chin Hong [5 ]
Chen, Chi-Hua [1 ]
Andreassen, Ole A. [6 ,7 ]
Desikan, Rahul S. [5 ]
Dale, Anders M. [1 ,3 ,4 ]
机构
[1] Univ Calif San Diego, Dept Radiol, La Jolla, CA 92093 USA
[2] Univ Umea, Dept Radiat Sci, Umea, Sweden
[3] Univ Calif San Diego, Dept Cognit Sci, La Jolla, CA 92093 USA
[4] Univ Calif San Diego, Dept Neurosci, La Jolla, CA 92093 USA
[5] Univ Calif San Francisco, Dept Radiol & Biomed Imaging, Neuroradiol Sect, San Francisco, CA 94143 USA
[6] Univ Oslo, Oslo Univ Hosp, NORMENT, Inst Clin Med,Div Mental Hlth & Addict, Oslo, Norway
[7] Oslo Univ Hosp, Div Mental Hlth & Addict, Oslo, Norway
基金
瑞典研究理事会; 美国国家卫生研究院; 加拿大健康研究院;
关键词
pHs; MCI; AD prediction; MRI; genetics; CLASSIFICATION; DEMENTIA; MCI;
D O I
10.3389/fnins.2018.00260
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
Improved prediction of progression to Alzheimer's Disease (AD) among older individuals with mild cognitive impairment (MCI) is of high clinical and societal importance. We recently developed a polygenic hazard score (PHS) that predicted age of AD onset above and beyond APOE. Here, we used data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) to further explore the potential clinical utility of PHS for predicting AD development in older adults with MCI. We examined the predictive value of PHS alone and in combination with baseline structural magnetic resonance imaging (MRI) data on performance on the Mini-Mental State Exam (MMSE). In survival analyses, PHS significantly predicted time to progression from MCI to AD over 120 months (p = 1.07e-5), and PHS was significantly more predictive than APOE alone (p = 0.015). Combining PHS with baseline brain atrophy score and/or MMSE score significantly improved prediction compared to models without PHS (three-factor model p = 4.28e-17). Prediction model accuracies, sensitivities and area under the curve were also improved by including PHS in the model, compared to only using atrophy score and MMSE. Further, using linear mixed-effect modeling, PHS improved the prediction of change in the Clinical Dementia Rating-Sum of Boxes (CDR-SB) score and MMSE over 36 months in patients with MCI at baseline, beyond both APOE and baseline levels of brain atrophy. These results illustrate the potential clinical utility of PHS for assessment of risk for AD progression among individuals with MCI both alone, or in conjunction with clinical measures of prodromal disease including measures of cognitive function and regional brain atrophy.
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页数:7
相关论文
共 27 条
[1]  
Alzheimers Association, 2015, Alzheimers Dement, V11, P332
[2]  
Brewer JB, 2009, BEHAV NEUROL, V21, P21, DOI [10.1155/2009/616581, 10.3233/BEN-2009-0226]
[3]   Fully-Automated Quantification of Regional Brain Volumes for Improved Detection of Focal Atrophy in Alzheimer Disease [J].
Brewer, J. B. ;
Magda, S. ;
Airriess, C. ;
Smith, M. E. .
AMERICAN JOURNAL OF NEURORADIOLOGY, 2009, 30 (03) :578-580
[4]   Predicting Alzheimer's disease development: a comparison of cognitive criteria and associated neuroimaging biomarkers [J].
Callahan, Brandy L. ;
Ramirez, Joel ;
Berezuk, Courtney ;
Duchesne, Simon ;
Black, Sandra E. .
ALZHEIMERS RESEARCH & THERAPY, 2015, 7
[5]   Genetic assessment of age-associated Alzheimer disease risk: Development and validation of a polygenic hazard score [J].
Desikan, Rahul S. ;
Fan, Chun Chieh ;
Wang, Yunpeng ;
Schork, Andrew J. ;
Cabra, Howard J., I ;
Cupples, L. Adrienne ;
Thompson, Wesley K. ;
Besser, Lilah ;
Kukull, Walter A. ;
Holland, Dominic ;
Chen, Chi-Hua ;
Brewer, James B. ;
Karow, David S. ;
Kauppi, Karolina ;
Witoelar, Aree ;
Karch, Celeste M. ;
Bonham, Luke W. ;
Yokoyama, Jennifer S. ;
Rosen, Howard J. ;
Miller, Bruce L. ;
Dillon, William P. ;
Wilson, David M. ;
Hess, Christopher P. ;
Pericak-Vance, Margaret ;
Haines, Jonathan L. ;
Farrer, Lindsay A. ;
Mayeux, Richard ;
Hardy, John ;
Goate, Alison M. ;
Hyman, Bradley T. ;
Schellenberg, Gerard D. ;
McEvoy, Linda K. ;
Andreassen, Ole A. ;
Dale, Anders M. .
PLOS MEDICINE, 2017, 14 (03)
[6]   Accurate Prediction of Conversion to Alzheimer's Disease using Imaging, Genetic, and Neuropsychological Biomarkers [J].
Dukart, Juergen ;
Sambataro, Fabio ;
Bertolino, Alessandro .
JOURNAL OF ALZHEIMERS DISEASE, 2016, 49 (04) :1143-1159
[7]   Multimodal Prediction of Dementia with up to 10 Years Follow Up: The Gothenburg MCI Study [J].
Eckerstrom, Carl ;
Olsson, Erik ;
Klasson, Niklas ;
Berge, Josef ;
Nordlund, Arto ;
Bjerke, Maria ;
Wallin, Anders .
JOURNAL OF ALZHEIMERS DISEASE, 2015, 44 (01) :205-214
[8]   MINI-MENTAL STATE - PRACTICAL METHOD FOR GRADING COGNITIVE STATE OF PATIENTS FOR CLINICIAN [J].
FOLSTEIN, MF ;
FOLSTEIN, SE ;
MCHUGH, PR .
JOURNAL OF PSYCHIATRIC RESEARCH, 1975, 12 (03) :189-198
[9]   Predicting MCI outcome with clinically available MRI and CSF biomarkers [J].
Heister, D. ;
Brewer, J. B. ;
Magda, S. ;
Blennow, K. ;
McEvoy, L. K. .
NEUROLOGY, 2011, 77 (17) :1619-1628
[10]   Enrichment and Stratification for Predementia Alzheimer Disease Clinical Trials [J].
Holland, Dominic ;
McEvoy, Linda K. ;
Desikan, Rahul S. ;
Dale, Anders M. .
PLOS ONE, 2012, 7 (10)