Research Paper Volume 11, Issue 8 pp 2185—2201
Predicting progression from mild cognitive impairment to Alzheimer’s disease on an individual subject basis by applying the CARE index across different independent cohorts
- 1 Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, China
- 2 Institute of Neuropsychiatry, the Affiliated Brain Hospital of Nanjing Medical University, Fourth Clinical College of Nanjing Medical University, Nanjing, China
- 3 Department of Biophysics, Medical College of Wisconsin, Milwaukee, WI 53226, USA
- 4 Department of Neurology, Medical College of Wisconsin, Milwaukee, WI 53226, USA
- 5 Department of Psychology, Xinxiang Medical University, Xinxiang, China
- 6 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
Received: December 1, 2018 Accepted: March 19, 2019 Published: April 29, 2019
https://doi.org/10.18632/aging.101883How to Cite
Copyright: Chen et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
The purposes of this study are to investigate whether the Characterizing Alzheimer’s disease Risk Events (CARE) index can accurately predict progression from mild cognitive impairment (MCI) to Alzheimer’s disease (AD) on an individual subject basis, and to investigate whether this model can be generalized to an independent cohort. Using an event-based probabilistic model approach to integrate widely available biomarkers from behavioral data and brain structural and functional imaging, we calculated the CARE index. We then applied the CARE index to identify which MCI individuals from the ADNI dataset progressed to AD during a three-year follow-up period. Subsequently, the CARE index was generalized to the prediction of MCI individuals from an independent Nanjing Aging and Dementia Study (NADS) dataset during the same time period. The CARE index achieved high prediction performance with 80.4% accuracy, 75% sensitivity, 82% specificity, and 0.809 area under the receiver operating characteristic (ROC) curve (AUC) on MCI subjects from the ADNI dataset over three years, and a highly validated prediction performance with 87.5% accuracy, 81% sensitivity, 90% specificity, and 0.861 AUC on MCI subjects from the NADS dataset. In conclusion, the CARE index is highly accurate, sufficiently robust, and generalized for predicting which MCI individuals will develop AD over a three-year period. This suggests that the CARE index can be usefully applied to select individuals with MCI for clinical trials and to identify which individuals will convert from MCI to AD for administration of early disease-modifying treatment.
Abbreviations
CARE: Characterizing Alzheimer’s disease Risk Events; MCI: mild cognitive impairment; AD: Alzheimer’s disease; ADNI: Alzheimer’s Disease Neuroimaging Initiative; NADS: Nanjing Aging and Dementia Study; ROC: receiver operating characteristic; AUC: receiver operating characteristic curve; PET: positron emission tomography; CSF: cerebrospinal fluid; CF: cognitive function; TOMC: Translational Outpatient Memory Clinic; P-MCI: progressive MCI; N-MCI: non-progressive MCI; FCI: functional connectivity indices; HIPFCI: functional connectivity indices from the hippocampus; PCCFCI: functional connectivity indices from posterior cingulate cortex; FUSFCI: functional connectivity indices from fusiform gyrus; GMI: gray matter concentration indices; HIPGMI: gray matter concentration indice from the hippocampus; FUSGMI: gray matter concentration indice from fusiform gyrus; OR: odds ratio; RR: relative risk; EBP: event-based probabilistic.