A Novel Score to Predict Individual Risk for Future Alzheimer’s Disease: A Longitudinal Study of the ADNI Cohort

Article type: Research Article

Authors: Guo, Hongxiu^a | Sun, Shangqi^a | Yang, Yang^b | Ma, Rong^c | Wang, Cailin^a | Zheng, Siyi^a | Wang, Xiufeng^a | Li, Gang^{a; *} | for the Alzheimer’s Disease Neuroimaging Initiative¹ | the Alzheimer’s Disease Metabolomics Consortium²

Affiliations: [a] Department of Neurology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China | [b] Department of General Medicine, Binzhou Medical University Hospital, Binzhou, China | [c] Department of Pharmacology, School of Basic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China

Correspondence: [*] Correspondence to: Gang Li, Department of Neurology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1277 Jiefang Avenue, Wuhan 430022, China. Tel.: +86 13971150606; E-mail: gangli2008@hust.edu.cn.

Note: [1] Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.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.

Note: [2] Data used in preparation of this article were also generated by the Alzheimer’s Disease Metabolomics Consortium (ADMC). As such, the investigators within the ADMC provided data but did not participate in analysis or writing of this report. A complete listing of ADMC investigators can be found at: https://sites.duke.edu/adnimetab/team/

Abstract: Background:Identifying high-risk individuals with mild cognitive impairment (MCI) who are likely to progress to Alzheimer’s disease (AD) is crucial for early intervention. Objective:This study aimed to develop and validate a novel clinical score for personalized estimation of MCI-to-AD conversion. Methods:The data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) study were analyzed. Two-thirds of the MCI patients were randomly assigned to a training cohort (n = 478), and the remaining one-third formed the validation cohort (n = 239). Multivariable logistic regression was performed to identify factors associated with MCI-to-AD progression within 4 years. A prediction score was developed based on the regression coefficients derived from the logistic model and tested in the validation cohort. Results:A lipidomics-signature was obtained that showed a significant association with disease progression. The MCI conversion scoring system (ranged from 0 to 14 points), consisting of the lipidomics-signature and five other significant variables (Apolipoprotein ɛ4, Rey Auditory Verbal Learning Test immediate and delayed recall, Alzheimer’s Disease Assessment Scale delayed recall test, Functional Activities Questionnaire, and cortical thickness of the AD signature), was constructed. Higher conversion scores were associated with a higher proportion of patients converting to AD. The scoring system demonstrated good discrimination and calibration in both the training cohort (AUC = 0.879, p of Hosmer-Lemeshow test = 0.597) and the validation cohort (AUC = 0.915, p of Hosmer-Lemeshow test = 0.991). The risk classification achieved excellent sensitivity (0.84) and specificity (0.75). Conclusions:The MCI-to-AD conversion score is a reliable tool for predicting the risk of disease progression in individuals with MCI.

Keywords: Alzheimer’s disease, conversion score, individual prediction, lipid metabolism, noninvasive biomarkers, progression

DOI: 10.3233/JAD-240532

Journal: Journal of Alzheimer's Disease, vol. 101, no. 3, pp. 923-936, 2024