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Jin S, Li C, Miao J, Sun J, Yang Z, Cao X, Sun K, Liu X, Ma L, Xu X, Liu Z. Sociodemographic Factors Predict Incident Mild Cognitive Impairment: A Brief Review and Empirical Study. J Am Med Dir Assoc 2023; 24:1959-1966.e7. [PMID: 37716705 DOI: 10.1016/j.jamda.2023.08.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2023] [Revised: 08/15/2023] [Accepted: 08/17/2023] [Indexed: 09/18/2023]
Abstract
OBJECTIVES Mild cognitive impairment (MCI) is a transitional stage between normal cognitive aging and dementia that increases the risk of progressive cognitive decline. Early prediction of MCI could be beneficial for identifying vulnerable individuals in the community and planning primary and secondary prevention to reduce the incidence of MCI. DESIGN A narrative review and cohort study. SETTING AND PARTICIPANTS We review the MCI prediction based on the assessment of sociodemographic factors. We included participants from 3 surveys: 8915 from wave 2011/2012 of the China Health and Retirement Longitudinal Study (CHARLS), 9765 from the 2011 Chinese Longitudinal Healthy Longevity Survey (CLHLS), and 1823 from the 2014 Rugao Longevity and Ageing Study (RuLAS). METHODS We searched in PubMed, Embase, and Web of Science Core Collection between January 1, 2019, and December 30, 2022. To construct the composite risk score, a multivariate Cox proportional hazards regression model was used. The performance of the score was assessed using receiver operating characteristic (ROC) curves. Furthermore, the composite risk score was validated in 2 longitudinal cohorts, CLHLS and RuLAS. RESULTS We concluded on 20 articles from 892 available. The results suggested that the previous models suffered from several defects, including overreliance on cross-sectional data, low predictive utility, inconvenient measurement, and inapplicability to developing countries. Our empirical work suggested that the area under the curve for a 5-year MCI prediction was 0.861 in CHARLS, 0.797 in CLHLS, and 0.823 in RuLAS. We designed a publicly available online tool for this composite risk score. CONCLUSIONS AND IMPLICATIONS Attention to these sociodemographic factors related to the incidence of MCI can be beneficially incorporated into the current work, which will set the stage for better early prediction of MCI before its incidence and for reducing the burden of the disease.
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Affiliation(s)
- Shuyi Jin
- Institute of Wenzhou, Second Affiliated Hospital, and School of Public Health, the Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Chenxi Li
- Institute of Wenzhou, Second Affiliated Hospital, and School of Public Health, the Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Jiani Miao
- Institute of Wenzhou, Second Affiliated Hospital, and School of Public Health, the Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Jingyi Sun
- Institute of Wenzhou, Second Affiliated Hospital, and School of Public Health, the Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Zhenqing Yang
- Institute of Wenzhou, Second Affiliated Hospital, and School of Public Health, the Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Xingqi Cao
- Institute of Wenzhou, Second Affiliated Hospital, and School of Public Health, the Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Kaili Sun
- Institute of Wenzhou, Second Affiliated Hospital, and School of Public Health, the Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Xiaoting Liu
- School of Public Affairs, Zhejiang University, Hangzhou, Zhejiang, China
| | - Lina Ma
- Department of Geriatrics, Xuanwu Hospital Capital Medical University, National Clinical Research Center for Geriatric Diseases, Beijing, China
| | - Xin Xu
- Department of Big Data in Health Science School of Public Health, and Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, School of Medicine, Zhejiang University, China.
| | - Zuyun Liu
- Institute of Wenzhou, Second Affiliated Hospital, and School of Public Health, the Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China.
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Roh M, Dan H, Kim O. Influencing Factors of Subjective Cognitive Impairment in Middle-Aged and Older Adults. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph182111488. [PMID: 34770002 PMCID: PMC8583411 DOI: 10.3390/ijerph182111488] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 09/27/2021] [Revised: 10/28/2021] [Accepted: 10/29/2021] [Indexed: 11/25/2022]
Abstract
The purpose of this study was to identify the factors affecting subjective cognitive impairment. We analyzed data from the 2019 Korea Community Health Survey and enrolled 68,546 middle-aged adults, aged 50 to 64 years, and 74,547 older adults, aged 65 years and older, in this study. Multiple logistic regression analysis was performed to identify factors influencing subjective cognitive impairment. Of the participants, 11,926 (17.4%) middle-aged and 21,880 (29.4%) older adults living in the community reported subjective cognitive impairment. Major factors that influenced subjective cognitive impairment in both middle-aged and older adults were gender, subjective stress, depressive symptoms, and alcohol drinking. In contrast to middle-aged adults, the marital status of older adults affected subjective cognitive impairment. Therefore, the factors affecting subjective cognitive impairment in middle-aged and older adults need to be considered for screening and management to prevent cognitive impairment and dementia. In particular, it is necessary to evaluate and manage stress and depressive symptoms from middle age to prevent subjective cognitive impairment.
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Affiliation(s)
| | | | - Oksoo Kim
- Correspondence: ; Tel.: +82-02-3277-3703
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