Prediction of 7‐year's conversion from subjective cognitive decline to mild cognitive impairment

Subjective cognitive decline (SCD) is a high‐risk yet less understood status before developing Alzheimer's disease (AD). This work included 76 SCD individuals with two (baseline and 7 years later) neuropsychological evaluations and a baseline T1‐weighted structural MRI. A machine learning‐based model was trained based on 198 baseline neuroimaging (morphometric) features and a battery of 25 clinical measurements to discriminate 24 progressive SCDs who converted to mild cognitive impairment (MCI) at follow‐up from 52 stable SCDs. The SCD progression was satisfactorily predicted with the combined features. A history of stroke, a low education level, a low baseline MoCA score, a shrunk left amygdala, and enlarged white matter at the banks of the right superior temporal sulcus were found to favor the progression. This is to date the largest retrospective study of SCD‐to‐MCI conversion with the longest follow‐up, suggesting predictable far‐future cognitive decline for the risky populations with baseline measures only. These findings provide valuable knowledge to the future neuropathological studies of AD in its prodromal phase.
In this article, we used a community‐based cohort of subjective cognitive decline (SCD) elderly subjects with 7‐year follow‐up and retrospectively identified individual SCD who had converted to mild cognitive impairment (MCI) during the follow‐up. After identifying progressive SCDs from stable SCDs, we constructed a machine learning‐based prediction model that successfully identified most progressive SCD individuals based on 198 morphometric features derived from structural MRI and 25 comprehensive clinical features. The striking finding is that as few as five neuroimaging and clinical features derived from baseline were able to predict cognitive impairment occurred 7 years later.