Peiyuan Qiu1,2,3,4, Miao Zeng1, Weihong Kuang5, Steven Siyao Meng4, Yan Cai1, Huali Wang6,7, Yang Wan1. 1. West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China. 2. West China Research Center for Rural Health Development, Sichuan University, Chengdu, China. 3. Social System Design Lab, George Warren Brown School of Social Work, Washington University in St. Louis, St. Louis, Missouri, USA. 4. Department of Psychiatry, University of Rochester Medical Center, Rochester, New York, USA. 5. West China Hospital, Sichuan University, Chengdu, China. 6. Dementia Care & Research Center, Peking University Institute of Mental Health (Sixth Hospital), Beijing, China. 7. Beijing Dementia Key Lab, National Clinical Research Center for Mental Disorders, Key Laboratory for Mental Health, National Health Commission, Beijing, China.
Abstract
OBJECTIVES: Our aim is to distinguish different trajectories of cognitive change in Chinese geriatric population and identify risk factors for cognitive decline in each subpopulation. METHODS: We obtained data from five waves (2002, 2005, 2008, 2011, 2014) of the Chinese Longitudinal Health Longevity Survey, using the Chinese Mini-Mental State Examination (C-MMSE) as a proxy for cognitive function. We applied growth mixture modeling (GMM) to identify heterogeneous subpopulations and potential risk factors. RESULTS: Our sample included 3859 older adults, 1387 (48.7%) male and 1974 (51.2%) female with age range of 62 to 108 (average of 74.5) at initial survey. Using GMM and best fit statistics, we identified two distinct subgroups in respect to their longitudinal cognitive function: (a) cognitively stable (87.8%) group with 0.49 C-MMSE points decline per 3 years, and (b) cognitively declining (12.2%) group with 6.03 C-MMSE points decline per 3 years. Of note, cognitive activities were protective, and hearing and visual impairments were risk factors in both groups. Diabetes, hypertension, stroke and cardiovascular disease were associated with cognitive decline in the cognitively declining group. Physical activities, and intake of fresh vegetables, fruits, and fish products were protective in the cognitively stable group. CONCLUSIONS: Using GMM, we identified heterogeneity in trajectories of cognitive change in older Chinese people. Moreover, we found risk factors specific to each subgroup, which should be considered in future studies.
OBJECTIVES: Our aim is to distinguish different trajectories of cognitive change in Chinese geriatric population and identify risk factors for cognitive decline in each subpopulation. METHODS: We obtained data from five waves (2002, 2005, 2008, 2011, 2014) of the Chinese Longitudinal Health Longevity Survey, using the Chinese Mini-Mental State Examination (C-MMSE) as a proxy for cognitive function. We applied growth mixture modeling (GMM) to identify heterogeneous subpopulations and potential risk factors. RESULTS: Our sample included 3859 older adults, 1387 (48.7%) male and 1974 (51.2%) female with age range of 62 to 108 (average of 74.5) at initial survey. Using GMM and best fit statistics, we identified two distinct subgroups in respect to their longitudinal cognitive function: (a) cognitively stable (87.8%) group with 0.49 C-MMSE points decline per 3 years, and (b) cognitively declining (12.2%) group with 6.03 C-MMSE points decline per 3 years. Of note, cognitive activities were protective, and hearing and visual impairments were risk factors in both groups. Diabetes, hypertension, stroke and cardiovascular disease were associated with cognitive decline in the cognitively declining group. Physical activities, and intake of fresh vegetables, fruits, and fish products were protective in the cognitively stable group. CONCLUSIONS: Using GMM, we identified heterogeneity in trajectories of cognitive change in older Chinese people. Moreover, we found risk factors specific to each subgroup, which should be considered in future studies.
Authors: Niranjani Nagarajan; Lama Assi; V Varadaraj; Mina Motaghi; Yi Sun; Elizabeth Couser; Joshua R Ehrlich; Heather Whitson; Bonnielin K Swenor Journal: BMJ Open Date: 2022-01-06 Impact factor: 3.006