Identifying underlying patterns in Alzheimer's disease trajectory: a deep learning approach and Mendelian randomization analysis

Fan Yi; Yaoyun Zhang; Jing Yuan; Ziyue Liu; Feifei Zhai; Ankai Hao; Fei Wu; Judith Somekh; Mor Peleg; Yi-Cheng Zhu; Zhengxing Huang

doi:10.1016/j.eclinm.2023.102247

Identifying underlying patterns in Alzheimer's disease trajectory: a deep learning approach and Mendelian randomization analysis

EClinicalMedicine. 2023 Sep 28:64:102247. doi: 10.1016/j.eclinm.2023.102247. eCollection 2023 Oct.

Authors

Fan Yi¹, Yaoyun Zhang², Jing Yuan³, Ziyue Liu³, Feifei Zhai³, Ankai Hao¹, Fei Wu¹, Judith Somekh⁴, Mor Peleg⁴, Yi-Cheng Zhu³, Zhengxing Huang¹

Affiliations

¹ College of Computer Science and Technology, Zhejiang University, China.
² DAMO Academy, Alibaba Group, China.
³ Department of Neurology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, 100730, China.
⁴ Department of Information Systems, University of Haifa, Haifa, Israel.

Abstract

Background: Alzheimer's disease (AD) is a heterogeneously progressive neurodegeneration disorder with varied rates of deterioration, either between subjects or within different stages of a certain subject. Estimating the course of AD at early stages has treatment implications. We aimed to analyze disease progression to identify distinct patterns in AD trajectory.

Methods: We proposed a deep learning model to identify underlying patterns in the trajectory from cognitively normal (CN) to a state of mild cognitive impairment (MCI) to AD dementia, by jointly predicting time-to-conversion and clustering out distinct subgroups characterized by comprehensive features as well as varied progression rates. We designed and validated our model on the ADNI dataset (1370 participants). Prediction of time-to-conversion in AD trajectory was used to validate the expression of the identified patterns. Causality between patterns and time-to-conversion was further inferred using Mendelian randomization (MR) analysis. External validation was performed on the AIBL dataset (233 participants).

Findings: The proposed model clustered out patterns characterized by significantly different biomarkers and varied progression rates. The discovered patterns also showed a strong prediction ability, as indicated by hazard ratio (CN→MCI, HR = 3.51, p < 0.001; MCI→AD, HR = 8.11, p < 0.001), C-Index (CN→MCI, 0.618; MCI→AD, 0.718), and AUC (CN→MCI, 3 years 0.802, 5 years 0.876; MCI→AD, 3 years 0.914, 5 years 0.957). In the external validation cohort, our model demonstrated competitive performance on conversion time prediction (CN→MCI, C-Index = 0.693; MCI→AD, C-Index = 0.752). Moreover, suggestive associations between CN→MCI/MCI→AD patterns with four/three SNPs were mediated and MR analysis indicated a causal link between MCI→AD patterns and time-to-conversion in the first three years.

Interpretation: Our proposed model identifies biologically and clinically meaningful patterns from real-world data and provides promising performance on time-to-conversion prediction in AD trajectory, which could promote the understanding of disease progression, facilitate clinical trial design, and provide potential for decision-making.

Funding: The National Key Research and Development Program of China, the Key R&D Program of Zhejiang, and the National Nature Science Foundation of China.

Keywords: Alzheimer's disease; Deep learning; Mendelian randomization; Progression pattern; Time-to-conversion prediction.