Classification and prediction of cognitive trajectories of cognitively unimpaired individuals
ObjectivesEfforts 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...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-03-01
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| Series: | Frontiers in Aging Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnagi.2023.1122927/full |
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| author | Young Ju Kim Young Ju Kim Si Eun Kim Alice Hahn Hyemin Jang Hyemin Jang Jun Pyo Kim Jun Pyo Kim Jun Pyo Kim Hee Jin Kim Hee Jin Kim Duk L. Na Duk L. Na Duk L. Na Duk L. Na Juhee Chin Juhee Chin Sang Won Seo Sang Won Seo Sang Won Seo Sang Won Seo Sang Won Seo Sang Won Seo Sang Won Seo |
| author_facet | Young Ju Kim Young Ju Kim Si Eun Kim Alice Hahn Hyemin Jang Hyemin Jang Jun Pyo Kim Jun Pyo Kim Jun Pyo Kim Hee Jin Kim Hee Jin Kim Duk L. Na Duk L. Na Duk L. Na Duk L. Na Juhee Chin Juhee Chin Sang Won Seo Sang Won Seo Sang Won Seo Sang Won Seo Sang Won Seo Sang Won Seo Sang Won Seo |
| author_sort | Young Ju Kim |
| collection | DOAJ |
| description | ObjectivesEfforts 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.MethodsA 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.ResultsGrowth 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).ConclusionOur 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. |
| first_indexed | 2024-04-10T04:06:28Z |
| format | Article |
| id | doaj.art-16e2382522984e5aaca344184c943d10 |
| institution | Directory Open Access Journal |
| issn | 1663-4365 |
| language | English |
| last_indexed | 2024-04-10T04:06:28Z |
| publishDate | 2023-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Aging Neuroscience |
| spelling | doaj.art-16e2382522984e5aaca344184c943d102023-03-13T05:22:59ZengFrontiers Media S.A.Frontiers in Aging Neuroscience1663-43652023-03-011510.3389/fnagi.2023.11229271122927Classification and prediction of cognitive trajectories of cognitively unimpaired individualsYoung Ju Kim0Young Ju Kim1Si Eun Kim2Alice Hahn3Hyemin Jang4Hyemin Jang5Jun Pyo Kim6Jun Pyo Kim7Jun Pyo Kim8Hee Jin Kim9Hee Jin Kim10Duk L. Na11Duk L. Na12Duk L. Na13Duk L. Na14Juhee Chin15Juhee Chin16Sang Won Seo17Sang Won Seo18Sang Won Seo19Sang Won Seo20Sang Won Seo21Sang Won Seo22Sang Won Seo23Department of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of KoreaNeuroscience Center, Samsung Medical Center, Seoul, Republic of KoreaDepartment of Neurology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, Republic of KoreaDepartment of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United StatesDepartment of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of KoreaNeuroscience Center, Samsung Medical Center, Seoul, Republic of KoreaDepartment of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of KoreaNeuroscience Center, Samsung Medical Center, Seoul, Republic of KoreaCenter for Neuroimaging, Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, United StatesDepartment of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of KoreaNeuroscience Center, Samsung Medical Center, Seoul, Republic of KoreaDepartment of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of KoreaNeuroscience Center, Samsung Medical Center, Seoul, Republic of KoreaInstitute of Stem Cell and Regenerative Medicine, Seoul, Republic of KoreaSamsung Alzheimer Research Center, Samsung Medical Center, Seoul, Republic of KoreaDepartment of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of KoreaNeuroscience Center, Samsung Medical Center, Seoul, Republic of KoreaDepartment of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of KoreaNeuroscience Center, Samsung Medical Center, Seoul, Republic of KoreaInstitute of Stem Cell and Regenerative Medicine, Seoul, Republic of KoreaSamsung Alzheimer Research Center, Samsung Medical Center, Seoul, Republic of KoreaCenter for Clinical Epidemiology, Samsung Medical Center, Seoul, Republic of KoreaDepartment of Health Sciences and Technology, Seoul, Republic of Korea0Clinical Research Design and Evaluation, Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sungkyunkwan University, Seoul, Republic of KoreaObjectivesEfforts 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.MethodsA 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.ResultsGrowth 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).ConclusionOur 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.https://www.frontiersin.org/articles/10.3389/fnagi.2023.1122927/fullcognitive trajectorycognitively unimpairednomogrampredictionclassification |
| spellingShingle | Young Ju Kim Young Ju Kim Si Eun Kim Alice Hahn Hyemin Jang Hyemin Jang Jun Pyo Kim Jun Pyo Kim Jun Pyo Kim Hee Jin Kim Hee Jin Kim Duk L. Na Duk L. Na Duk L. Na Duk L. Na Juhee Chin Juhee Chin Sang Won Seo Sang Won Seo Sang Won Seo Sang Won Seo Sang Won Seo Sang Won Seo Sang Won Seo Classification and prediction of cognitive trajectories of cognitively unimpaired individuals Frontiers in Aging Neuroscience cognitive trajectory cognitively unimpaired nomogram prediction classification |
| title | Classification and prediction of cognitive trajectories of cognitively unimpaired individuals |
| title_full | Classification and prediction of cognitive trajectories of cognitively unimpaired individuals |
| title_fullStr | Classification and prediction of cognitive trajectories of cognitively unimpaired individuals |
| title_full_unstemmed | Classification and prediction of cognitive trajectories of cognitively unimpaired individuals |
| title_short | Classification and prediction of cognitive trajectories of cognitively unimpaired individuals |
| title_sort | classification and prediction of cognitive trajectories of cognitively unimpaired individuals |
| topic | cognitive trajectory cognitively unimpaired nomogram prediction classification |
| url | https://www.frontiersin.org/articles/10.3389/fnagi.2023.1122927/full |
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