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      Association of Depressive Symptoms With Incident Cardiovascular Diseases in Middle-Aged and Older Chinese Adults

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          Key Points

          Question

          Are depressive symptoms associated with incident cardiovascular disease among middle-aged and older Chinese adults?

          Findings

          In this cohort study of 12 417 Chinese adults, participants with depressive symptoms at baseline had higher incident rates of cardiovascular disease compared with those without such symptoms. Elevated depressive symptoms as a whole and 2 individual symptoms (restless sleep and loneliness) were significantly associated with increased risk of cardiovascular disease after adjusting for potential confounders.

          Meaning

          This study suggests that depressive symptoms, particularly restless sleep and loneliness, may be associated with incident cardiovascular disease among middle-aged and older Chinese adults.

          Abstract

          This cohort study examines the association between depressive symptoms and incident cardiovascular disease among middle-aged and older Chinese adults.

          Abstract

          Importance

          The prevalence of depressive symptoms among older adults has become an increasingly important public health priority. Elevated depressive symptoms are well documented among elderly people with cardiovascular disease (CVD), but studies conducted among Chinese adults are scarce.

          Objective

          To estimate the association between depressive symptoms and incident CVD among middle-aged and older Chinese adults.

          Design, Setting, and Participants

          The China Health and Retirement Longitudinal Study is an ongoing nationally representative prospective cohort study that was initiated in 2011. This cohort study included 12 417 middle-aged and older Chinese adults without heart disease and stroke at baseline. Statistical analysis was conducted from April 25, 2018, to December 13, 2018.

          Exposure

          Depressive symptoms were assessed using the validated 10-item of Center for Epidemiologic Studies Depression Scale.

          Main Outcomes and Measures

          Incident CVD (ie, self-reported physician-diagnosed heart disease and stroke combined) was followed-up from June 1, 2011, to June 31, 2015. The Center for Epidemiologic Studies Depression Scale total score ranges from 0 to 30, with a score of 12 or more indicating elevated depressive symptoms.

          Results

          Of the 12 417 participants (mean [SD] age at baseline, 58.40 [9.51] years), 6113 (49.2%) were men. During the 4 years of follow-up, 1088 incident CVD cases were identified. Elevated depressive symptoms were independently associated with an increased CVD risk (adjusted hazard ratio, 1.39; 95% CI, 1.22-1.58) after adjusting for age, sex, residence, marital status, educational level, smoking status, drinking status, systolic blood pressure, and body mass index; history of diabetes, hypertension, dyslipidemia, and chronic kidney disease; and use of hypertension medications, diabetes medications, and lipid-lowering therapy. Of the 10 individual depressive symptoms measured by the Center for Epidemiologic Studies Depression Scale, only 2 symptoms, restless sleep (adjusted hazard ratio, 1.21; 95% CI, 1.06-1.39) and loneliness (adjusted hazard ratio, 1.21; 95% CI, 1.02-1.44), were significantly associated with incident CVD.

          Conclusions and Relevance

          Elevated depressive symptoms overall and 2 individual symptoms (restless sleep and loneliness) were significantly associated with incident CVD among middle-aged and older Chinese adults.

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          Most cited references40

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          Screening for depression in well older adults: evaluation of a short form of the CES-D (Center for Epidemiologic Studies Depression Scale).

          We derived and tested a short form of the Center for Epidemiologic Studies Depression Scale (CES-D) for reliability and validity among a sample of well older adults in a large Health Maintenance Organization. The 10-item screening questionnaire, the CESD-10, showed good predictive accuracy when compared to the full-length 20-item version of the CES-D (kappa = .97, P or = 16 for the full-length questionnaire and > or = 10 for the 10-item version. We discuss other potential cutoff values. The CESD-10 showed an expected positive correlation with poorer health status scores (r = .37) and a strong negative correlation with positive affect (r = -.63). Retest correlations for the CESD-10 were comparable to those in other studies (r = .71). We administered the CESD-10 again after 12 months, and scores were stable with strong correlation of r = .59.
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            Slow-wave sleep and the risk of type 2 diabetes in humans.

            There is convincing evidence that, in humans, discrete sleep stages are important for daytime brain function, but whether any particular sleep stage has functional significance for the rest of the body is not known. Deep non-rapid eye movement (NREM) sleep, also known as slow-wave sleep (SWS), is thought to be the most "restorative" sleep stage, but beneficial effects of SWS for physical well being have not been demonstrated. The initiation of SWS coincides with hormonal changes that affect glucose regulation, suggesting that SWS may be important for normal glucose tolerance. If this were so, selective suppression of SWS should adversely affect glucose homeostasis and increase the risk of type 2 diabetes. Here we show that, in young healthy adults, all-night selective suppression of SWS, without any change in total sleep time, results in marked decreases in insulin sensitivity without adequate compensatory increase in insulin release, leading to reduced glucose tolerance and increased diabetes risk. SWS suppression reduced delta spectral power, the dominant EEG frequency range in SWS, and left other EEG frequency bands unchanged. Importantly, the magnitude of the decrease in insulin sensitivity was strongly correlated with the magnitude of the reduction in SWS. These findings demonstrate a clear role for SWS in the maintenance of normal glucose homeostasis. Furthermore, our data suggest that reduced sleep quality with low levels of SWS, as occurs in aging and in many obese individuals, may contribute to increase the risk of type 2 diabetes.
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              Depression sum-scores don’t add up: why analyzing specific depression symptoms is essential

              Most measures of depression severity are based on the number of reported symptoms, and threshold scores are often used to classify individuals as healthy or depressed. This method – and research results based on it – are valid if depression is a single condition, and all symptoms are equally good severity indicators. Here, we review a host of studies documenting that specific depressive symptoms like sad mood, insomnia, concentration problems, and suicidal ideation are distinct phenomena that differ from each other in important dimensions such as underlying biology, impact on impairment, and risk factors. Furthermore, specific life events predict increases in particular depression symptoms, and there is evidence for direct causal links among symptoms. We suggest that the pervasive use of sum-scores to estimate depression severity has obfuscated crucial insights and contributed to the lack of progress in key research areas such as identifying biomarkers and more efficacious antidepressants. The analysis of individual symptoms and their causal associations offers a way forward. We offer specific suggestions with practical implications for future research. Electronic supplementary material The online version of this article (doi:10.1186/s12916-015-0325-4) contains supplementary material, which is available to authorized users.
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                Author and article information

                Journal
                JAMA Netw Open
                JAMA Netw Open
                JAMA Netw Open
                JAMA Network Open
                American Medical Association
                2574-3805
                4 December 2019
                December 2019
                4 December 2019
                : 2
                : 12
                : e1916591
                Affiliations
                [1 ]Department of Epidemiology and Health Statistics, Capital Medical University School of Public Health, Beijing, China
                [2 ]Beijing Municipal Key Laboratory of Clinical Epidemiology, Capital Medical University, Beijing, China
                [3 ]Department of Mathematics and Statistics, La Trobe University, Melbourne, Victoria, Australia
                [4 ]Global Health and Genomics, Edith Cowan University School of Medical Health Sciences, Perth, Western Australia, Australia
                [5 ]Global and Community Mental Health Research Group, Department of Psychology, University of Macau, Macao SAR, China
                [6 ]Health, Behavior, and Society, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
                [7 ]Unit of Psychiatry, Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Macao SAR, China
                [8 ]Center for Cognition and Brain Sciences, University of Macau, Macao SAR, China
                Author notes
                Article Information
                Accepted for Publication: October 11, 2019.
                Published: December 4, 2019. doi:10.1001/jamanetworkopen.2019.16591
                Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2019 Li H et al. JAMA Network Open.
                Corresponding Authors: Xiuhua Guo, PhD, Department of Epidemiology and Health Statistics, Capital Medical University School of Public Health, No. 10 Xitoutiao, You’anmen Wai, Fengtai District, Beijing 100069, China ( statguo@ 123456ccmu.edu.cn ); Yu-tao Xiang, MD, PhD, Unit of Psychiatry, Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Avenida da Universidade, Tai Po Hospital, Multicentre Building, G/F, Taipa, Macao SAR, China ( xyutly@ 123456gmail.com ).
                Author Contributions: Drs Guo and Xiang had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.
                Concept and design: H. Li, Zheng, Z. Li, Wu, Feng, Cao, Gao, W. Wang, Hall, Guo.
                Acquisition, analysis, or interpretation of data: H. Li, Zheng, Feng, J. Wang, X. Li, Hall, Xiang, Guo.
                Drafting of the manuscript: H. Li, Zheng, Z. Li, Wu, Cao, J. Wang, Gao, Xiang.
                Critical revision of the manuscript for important intellectual content: H. Li, Zheng, Feng, X. Li, W. Wang, Hall, Xiang, Guo.
                Statistical analysis: H. Li, Zheng, Z. Li, Gao, X. Li, Guo.
                Obtained funding: Guo.
                Administrative, technical, or material support: H. Li, Zheng, Wu, Feng, Cao, J. Wang.
                Supervision: W. Wang, Xiang.
                Conflict of Interest Disclosures: None reported.
                Funding/Support: This work was supported by grants 81530087 and 81703317 from the National Natural Science Foundation of China.
                Role of the Funder/Sponsor: The funder had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
                Additional Information: This analysis uses data or information from the Harmonized China Health and Retirement Longitudinal Study (CHARLS) dataset and Codebook, version C, as of April 2018, which was developed by the Gateway to Global Aging Data ( https:/g2aging.org). The development of the Harmonized CHARLS was funded by the National Institute on Aging (grants R01 AG030153, RC2 AG036619, and R03 AG043052).
                Article
                zoi190629
                10.1001/jamanetworkopen.2019.16591
                6902756
                31800066
                2e781642-0c97-4e38-9a82-148c12096bcf
                Copyright 2019 Li H et al. JAMA Network Open.

                This is an open access article distributed under the terms of the CC-BY License.

                History
                : 21 May 2019
                : 11 October 2019
                Categories
                Research
                Original Investigation
                Online Only
                Cardiology

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