Risk scores of incident mild cognitive impairment in a Beijing community-based older cohort
Objective: It is very important to identify individuals who are at greatest risk for mild cognitive impairment (MCI) to potentially mitigate or minimize risk factors early in its course. We created a practical MCI risk scoring system and provided individualized estimates of MCI risk.Methods: Using d...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-10-01
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| Series: | Frontiers in Aging Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnagi.2022.976126/full |
| _version_ | 1811238536068726784 |
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| author | Xin Li Xin Li Jianan Xia Jianan Xia Yumeng Li Yumeng Li Kai Xu Kai Xu Kewei Chen Kewei Chen Junying Zhang Junying Zhang He Li He Li Zhanjun Zhang Zhanjun Zhang |
| author_facet | Xin Li Xin Li Jianan Xia Jianan Xia Yumeng Li Yumeng Li Kai Xu Kai Xu Kewei Chen Kewei Chen Junying Zhang Junying Zhang He Li He Li Zhanjun Zhang Zhanjun Zhang |
| author_sort | Xin Li |
| collection | DOAJ |
| description | Objective: It is very important to identify individuals who are at greatest risk for mild cognitive impairment (MCI) to potentially mitigate or minimize risk factors early in its course. We created a practical MCI risk scoring system and provided individualized estimates of MCI risk.Methods: Using data from 9,000 older adults recruited for the Beijing Ageing Brain Rejuvenation Initiative, we investigated the association of the baseline demographic, medical history, lifestyle and cognitive data with MCI status based on logistic modeling and established risk score (RS) models 1 and 2 for MCI. We evaluated model performance by computing the area under the receiver operating characteristic (ROC) curve (AUC). Finally, RS model 3 was further confirmed and improved based on longitudinal outcome data from the progression of MCI in a sub-cohort who had an average 3-year follow-up.Results: A total of 1,174 subjects (19.8%) were diagnosed with MCI at baseline, and 72 (7.8%) of 849 developed MCI in the follow-up. The AUC values of RS models 1 and 2 were between 0.64 and 0.70 based on baseline age, education, cerebrovascular disease, intelligence and physical activities. Adding baseline memory and language performance, the AUC of RS model 3 more accurately predicted MCI conversion (AUC = 0.785).Conclusion: A combination of risk factors is predictive of the likelihood of MCI. Identifying the RSs may be useful to clinicians as they evaluate their patients and to researchers as they design trials to study possible early non-pharmaceutical interventions to reduce the risk of MCI and dementia. |
| first_indexed | 2024-04-12T12:43:17Z |
| format | Article |
| id | doaj.art-d38323e61fff4fed84b9b86f9264dcc4 |
| institution | Directory Open Access Journal |
| issn | 1663-4365 |
| language | English |
| last_indexed | 2024-04-12T12:43:17Z |
| publishDate | 2022-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Aging Neuroscience |
| spelling | doaj.art-d38323e61fff4fed84b9b86f9264dcc42022-12-22T03:32:43ZengFrontiers Media S.A.Frontiers in Aging Neuroscience1663-43652022-10-011410.3389/fnagi.2022.976126976126Risk scores of incident mild cognitive impairment in a Beijing community-based older cohortXin Li0Xin Li1Jianan Xia2Jianan Xia3Yumeng Li4Yumeng Li5Kai Xu6Kai Xu7Kewei Chen8Kewei Chen9Junying Zhang10Junying Zhang11He Li12He Li13Zhanjun Zhang14Zhanjun Zhang15State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, ChinaBABRI Centre, Beijing Normal University, Beijing, ChinaBABRI Centre, Beijing Normal University, Beijing, ChinaSchool of Computer and Information Technology, Beijing Jiaotong University, Beijing, ChinaState Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, ChinaBABRI Centre, Beijing Normal University, Beijing, ChinaState Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, ChinaBABRI Centre, Beijing Normal University, Beijing, ChinaBABRI Centre, Beijing Normal University, Beijing, ChinaBanner Alzheimer’s Institute, Phoenix, AZ, United StatesBABRI Centre, Beijing Normal University, Beijing, ChinaInstitute of Basic Research in Clinical Medicine, China Academy of Traditional Chinese Medicine, Beijing, ChinaBABRI Centre, Beijing Normal University, Beijing, ChinaInstitute of Basic Research in Clinical Medicine, China Academy of Traditional Chinese Medicine, Beijing, ChinaState Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, ChinaBABRI Centre, Beijing Normal University, Beijing, ChinaObjective: It is very important to identify individuals who are at greatest risk for mild cognitive impairment (MCI) to potentially mitigate or minimize risk factors early in its course. We created a practical MCI risk scoring system and provided individualized estimates of MCI risk.Methods: Using data from 9,000 older adults recruited for the Beijing Ageing Brain Rejuvenation Initiative, we investigated the association of the baseline demographic, medical history, lifestyle and cognitive data with MCI status based on logistic modeling and established risk score (RS) models 1 and 2 for MCI. We evaluated model performance by computing the area under the receiver operating characteristic (ROC) curve (AUC). Finally, RS model 3 was further confirmed and improved based on longitudinal outcome data from the progression of MCI in a sub-cohort who had an average 3-year follow-up.Results: A total of 1,174 subjects (19.8%) were diagnosed with MCI at baseline, and 72 (7.8%) of 849 developed MCI in the follow-up. The AUC values of RS models 1 and 2 were between 0.64 and 0.70 based on baseline age, education, cerebrovascular disease, intelligence and physical activities. Adding baseline memory and language performance, the AUC of RS model 3 more accurately predicted MCI conversion (AUC = 0.785).Conclusion: A combination of risk factors is predictive of the likelihood of MCI. Identifying the RSs may be useful to clinicians as they evaluate their patients and to researchers as they design trials to study possible early non-pharmaceutical interventions to reduce the risk of MCI and dementia.https://www.frontiersin.org/articles/10.3389/fnagi.2022.976126/fullmild cognitive impairmentlifestyle-related diseaserisk scorecognitionprevention |
| spellingShingle | Xin Li Xin Li Jianan Xia Jianan Xia Yumeng Li Yumeng Li Kai Xu Kai Xu Kewei Chen Kewei Chen Junying Zhang Junying Zhang He Li He Li Zhanjun Zhang Zhanjun Zhang Risk scores of incident mild cognitive impairment in a Beijing community-based older cohort Frontiers in Aging Neuroscience mild cognitive impairment lifestyle-related disease risk score cognition prevention |
| title | Risk scores of incident mild cognitive impairment in a Beijing community-based older cohort |
| title_full | Risk scores of incident mild cognitive impairment in a Beijing community-based older cohort |
| title_fullStr | Risk scores of incident mild cognitive impairment in a Beijing community-based older cohort |
| title_full_unstemmed | Risk scores of incident mild cognitive impairment in a Beijing community-based older cohort |
| title_short | Risk scores of incident mild cognitive impairment in a Beijing community-based older cohort |
| title_sort | risk scores of incident mild cognitive impairment in a beijing community based older cohort |
| topic | mild cognitive impairment lifestyle-related disease risk score cognition prevention |
| url | https://www.frontiersin.org/articles/10.3389/fnagi.2022.976126/full |
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