A ‘Framingham-like’ Algorithm for Predicting 4-Year Risk of Progression to Amnestic Mild Cognitive Impairment or Alzheimer’s Disease Using Multidomain Information

Article type: Research Article

Authors: Steenland, Kyle^{a; *} | Zhao, Liping^b | John, Samantha E.^c | Goldstein, Felicia C.^c | Levey, Allan^c | Alvaro, Alonso^d | for the Alzheimer’s Disease Neuroimaging Initiative¹

Affiliations: [a] Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA | [b] Department of Biostatistics, Rollins School of Public Health, Emory University, Atlanta, GA, USA | [c] Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA | [d] Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, GA, USA

Correspondence: [*] Correspondence to: Kyle Steenland, Emory University, Rollins School Public Health, 1518 Clifton Road, Atlanta, GA 30322, USA. E-mail: [email protected].

Note: [1] Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.ucla.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf

Abstract: Background:There are no agreed-upon variables for predicting progression from unimpaired cognition to amnestic mild cognitive impairment (aMCI), or from aMCI to Alzheimer’s disease (AD). Objective:Use ADNI data to develop a ‘Framingham-like’ prediction model for a 4-year period. Methods:We developed models using the strongest baseline predictors from six domains (demographics, neuroimaging, CSF biomarkers, genetics, cognitive tests, and functional ability). We chose the best predictor from each domain, which was dichotomized into more versus less harmful. Results:There were 224 unimpaired individuals and 424 aMCI subjects with baseline data on all predictors, of whom 37 (17% ) and 150 (35% ) converted to aMCI and AD, respectively, during 4 years of follow-up. For the unimpaired, CSF tau/Aβ ratio, hippocampal volume, and a memory score predicted progression. For those aMCI at baseline, the same predictors plus APOE4 status and functional ability predicted progression. Demographics and family history were not important predictors for progression for either group. The fit statistic was good for the unimpaired-aMCI model (C-statistic 0.80) and very good for the aMCI-AD model (C-statistic 0.91). Among the unimpaired, those with no harmful risk factors had a 4-year predicted 2% risk of progression, while those with the most harmful risk factors had a predicted 35% risk. The aMCI subjects with no harmful risk factors had a predicted 1% risk of progression those with all six harmful risk factors had a predicted 90% risk. Conclusion:Our parsimonious model accurately predicted progression from unimpaired to aMCI with three variables, and from aMCI to AD with five variables.

Keywords: Alzheimer’s disease, biomarkers, cerebrospinal fluid, dementia, imaging, mild cognitive impairment

DOI: 10.3233/JAD-170769

Journal: Journal of Alzheimer's Disease, vol. 63, no. 4, pp. 1383-1393, 2018