Classification and prediction of cognitive trajectories of cognitively unimpaired individuals

Young Ju Kim; Si Eun Kim; Alice Hahn; Hyemin Jang; Jun Pyo Kim; Hee Jin Kim; Duk L Na; Juhee Chin; Sang Won Seo

doi:10.3389/fnagi.2023.1122927

Classification and prediction of cognitive trajectories of cognitively unimpaired individuals

Front Aging Neurosci. 2023 Mar 13:15:1122927. doi: 10.3389/fnagi.2023.1122927. eCollection 2023.

Authors

Young Ju Kim^{1

2}, Si Eun Kim³, Alice Hahn⁴, Hyemin Jang^{1

2}, Jun Pyo Kim^{1

2

5}, Hee Jin Kim^{1

2}, Duk L Na^{1

2

6

7}, Juhee Chin^{1

2}, Sang Won Seo^{1

2

6

7

8

9

10}

Affiliations

¹ Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
² Neuroscience Center, Samsung Medical Center, Seoul, Republic of Korea.
³ Department of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Republic of Korea.
⁴ Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States.
⁵ Center for Neuroimaging, Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, United States.
⁶ Institute of Stem Cell and Regenerative Medicine, Seoul, Republic of Korea.
⁷ Samsung Alzheimer Research Center, Samsung Medical Center, Seoul, Republic of Korea.
⁸ Center for Clinical Epidemiology, Samsung Medical Center, Seoul, Republic of Korea.
⁹ Department of Health Sciences and Technology, Seoul, Republic of Korea.
¹⁰ Clinical Research Design and Evaluation, Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sungkyunkwan University, Seoul, Republic of Korea.

Abstract

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 modeling and developed the predictive model.

Results: Growth mixture modeling 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 modeling showed that increased amyloid-β (Aβ) uptake (β [SE]: 4.852 [0.862], p < 0.001), low baseline cognitive composite scores (β [SE]: -0.274 [0.070], p < 0.001), 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 < 0.001) and low baseline cognitive composite scores (β [SE]: -4.464 [0.758], p < 0.001) 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.

Keywords: classification; cognitive trajectory; cognitively unimpaired; nomogram; prediction.

Grants and funding

This research was supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare and Ministry of Science and ICT, Republic of Korea (HI19C1132, HU20C0111, and HU22C0170), the National Research Foundation of Korea (NRF) grant funded by the Korea Government (MSIT) (NRF-2019R1A5A2027340), Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korea Government (MSIT) (No. 2021-0-02068, Artificial Intelligence Innovation Hub), Future Medicine 20*30 Project of the Samsung Medical Center (#SMX1230081), and the “National Institutes of Health” Research Project (2021-ER1006-02).