Mingyue Hu1, Xinhui Shu2, Xinyin Wu3, Fenghui Chen4, Hengyu Hu1, Junmei Zhang5, Ping Yan6, Hui Feng7. 1. Department of Nursing, XiangYa School of Medicine, Central South University, Changsha, China. 2. Department of Hematology, Tumor Hospital of Henan Province, Affiliated Tumor Hospital of Zhengzhou University, Zhengzhou, China. 3. Department of Public Health, XiangYa School of Medicine, Central South University, Changsha, China. 4. Department of Nursing, XiangYa School of Medicine, Central South University, Changsha, China; Department of Nursing, Xinjiang Medical University, Xinjiang, China. 5. Henan Provincial People's Hospital, Zhengzhou, China. 6. Department of Nursing, Xinjiang Medical University, Xinjiang, China. 7. Department of Nursing, XiangYa School of Medicine, Central South University, Changsha, China; Oceanwide Health management institute, Central South University, Changsha, China; National Clinical Research Center for Geriatric Disorders Xiangya Hospital, Central South University, Changsha, China. Electronic address: feng.hui@csu.edu.cn.
Abstract
BACKGROUND: Although several neuropsychiatric symptoms (NPSs) have been demonstrated to have value in the prediction of the progression of mild cognitive impairment (MCI) to dementia, these symptoms are less studied for the prediction of the transition from normal cognition (NC) to MCI. METHODS: Prospective cohort studies were included if they reported on at least one NPS at baseline and had MCI as the outcome. RESULTS: We obtained 13 cohort studies with a total population of 33,066. Depression was the most common neuropsychiatric symptom and could significantly predict transition to MCI (RR = 1.49, 95% CI: 1.13-1.86). However, depression was more capable of predicting amnestic MCI (RR=1.43, 95% CI: 1.04-1.83) than non-aMCI (RR= 0.96, 95% CI 95% CI: 0.60-1.33). Subgroup analysis suggested that the association between depression and MCI changed with depression severity, depression criteria, apolipoprotein-E-adjusted status, age, the percentage of females, and follow-up times, but some data were too sparse for a reliable estimate. Regarding other NPSs, there were insufficient data to assess their effect on the development of MCI. However, apathy, anxiety, sleep disturbances, irritability, and agitation might be risk factors for the prediction of NC-MCI transition with strong predictive value. CONCLUSIONS: Depression was associated with an approximately 1.5-fold sincreased risk of the progression to MCI in the population with normal cognition. Other NPSs with underlying predictive value deserve more attention.
BACKGROUND: Although several neuropsychiatric symptoms (NPSs) have been demonstrated to have value in the prediction of the progression of mild cognitive impairment (MCI) to dementia, these symptoms are less studied for the prediction of the transition from normal cognition (NC) to MCI. METHODS: Prospective cohort studies were included if they reported on at least one NPS at baseline and had MCI as the outcome. RESULTS: We obtained 13 cohort studies with a total population of 33,066. Depression was the most common neuropsychiatric symptom and could significantly predict transition to MCI (RR = 1.49, 95% CI: 1.13-1.86). However, depression was more capable of predicting amnestic MCI (RR=1.43, 95% CI: 1.04-1.83) than non-aMCI (RR= 0.96, 95% CI 95% CI: 0.60-1.33). Subgroup analysis suggested that the association between depression and MCI changed with depression severity, depression criteria, apolipoprotein-E-adjusted status, age, the percentage of females, and follow-up times, but some data were too sparse for a reliable estimate. Regarding other NPSs, there were insufficient data to assess their effect on the development of MCI. However, apathy, anxiety, sleep disturbances, irritability, and agitation might be risk factors for the prediction of NC-MCI transition with strong predictive value. CONCLUSIONS:Depression was associated with an approximately 1.5-fold sincreased risk of the progression to MCI in the population with normal cognition. Other NPSs with underlying predictive value deserve more attention.
Authors: Alexandra König; Elisa Mallick; Johannes Tröger; Nicklas Linz; Radia Zeghari; Valeria Manera; Philippe Robert Journal: Eur Psychiatry Date: 2021-10-13 Impact factor: 5.361