Classification and prediction of cognitive trajectories of cognitively unimpaired individuals - Kim, Young Ju; Kim, Si Eun; Hahn, Alice; Jang, Hyemin; Kim, Jun Pyo; Kim, Hee Jin; Na, Duk L.; Chin, Juhee; Seo, Sang Won (Frontiers in Aging Neuroscience, 2023)
Objectives: Efforts to prevent Alzheimer’s disease (AD) would benefit from identifying cognitively unimpaired (CU) individuals who are liable to progress to cognitive impairment. Therefore, we aimed to develop a model to predict cognitive decline among CU individuals in two independent cohorts. Methods: A total of 407 CU individuals from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and 285 CU individuals from the Samsung Medical Center (SMC) were recruited in this study. We assessed cognitive outcomes by using neuropsychological composite scores in the ADNI and SMC cohorts. We performed latent growth mixture modelling and developed the predictive model. Results: Growth mixture modelling identified 13.8% and 13.0% of CU individuals in the ADNI and SMC cohorts, respectively, as the ‘declining group’. In the ADNI cohort, multivariable logistic regression modelling showed that increased amyloid-β (Aβ) uptake (β [SE]: 4.852 [0.862], p<.001), low baseline cognitive composite scores (β [SE]: -0.274 [0.070], p<.001), and reduced hippocampal volume (β [SE]: -0.952 [0.302], p=0.002) were predictive of cognitive decline. In the SMC cohort, increased Aβ uptake (β [SE]: 2.007 [0.549], p<.001) and low baseline cognitive composite scores (β [SE]: -4.464 [0.758], p<.001) predicted cognitive decline. Finally, predictive models of cognitive decline showed good to excellent discrimination and calibration capabilities (C-statistic = 0.85 for the ADNI model and 0.94 for the SMC model). Conclusion: Our study provides novel insights into the cognitive trajectories of CU individuals. Furthermore, the predictive model can facilitate the classification of CU individuals in future primary prevention trials.