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Zhao Y, Liu J, Xia JH, Li C, Ma XQ. Dose-response relationship between sleep duration and cardiovascular metabolic multimorbidity among older adults in China: A nationwide survey. J Affect Disord 2024; 354:75-81. [PMID: 38479505 DOI: 10.1016/j.jad.2024.03.051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Revised: 03/04/2024] [Accepted: 03/09/2024] [Indexed: 03/17/2024]
Abstract
AIMS AND OBJECTIVES The purpose of this study was to explore the relationship between the duration of sleep per day and cardiovascular metabolic multimorbidity (CMM) in older adults and to identify how many hours of sleep per day can lead to a lower risk of CMM in older adults. BACKGROUND CMM are a common syndrome in the older adults. There may be an association between sleep duration and CMM in older adults, with both insomnia and sleep deprivation having an impact on the health of older adults. Therefore, it is important to explore the possibility that older adults who sleep for a few hours per day may have a lower prevalence of CMM. METHODS The study included 9710 older adults. The sleep duration in this study was assessed by the question "How many hours of sleep do you currently get in a day? ". Older adults were defined as having CMM when they had two or more of the five categories of hypertension, diabetes, heart disease, stroke or cardiovascular disease, dyslipidemia. We used multivariate logistic regression analysis to explore the association among sleep duration and CMM. Restrictive cubic splines were used to examine the shape of the association among sleep duration and the CMM. The STROBE checklist was used for this cross-sectional study. RESULTS The mean age was 84.78 ± 11.73 years, with 55.5 % being female. Of the total sample, 21.3 % were CMM. When all covariates were adjusted, there was dose-response relationship between sleep duration and CMM. The dose-response relationship between CMM and sleep duration showed that older adults had a lower risk of cardiovascular and metabolic multimorbidity when they slept 9 h and 10 h per day. CONCLUSION With the increasing population of older adults, the number of older adults suffering from CMM continues to rise, and adequate sleep time can effectively prevent the occurrence of CMM. We should pay attention to the sleep problem of the older adults. RELEVANCE TO CLINICAL PRACTICE This study provided information for healthcare providers to identify circumstances that increase cardiovascular metabolic multimorbidity and suggest the appropriate sleep duration per day to reduce the risk of disease in older adults. PATIENT OR PUBLIC CONTRIBUTION Because of the public database data used in this study, all data were collected by survey agency personnel, so this section is not applicable to this study.
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Affiliation(s)
- Yu Zhao
- HanZhong Central Hospital, HanZhong, China
| | - Juan Liu
- HanZhong Central Hospital, HanZhong, China
| | | | - Cui Li
- HanZhong Central Hospital, HanZhong, China
| | - Xiu-Qin Ma
- HanZhong Central Hospital, HanZhong, China.
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He H, Tu R, Chen H, Wang C, Wu S, Wang S. Longitudinal trajectories of disability among Chinese adults: the role of cardiometabolic multimorbidity. Aging Clin Exp Res 2024; 36:79. [PMID: 38520484 PMCID: PMC10960913 DOI: 10.1007/s40520-024-02732-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2023] [Accepted: 03/04/2024] [Indexed: 03/25/2024]
Abstract
BACKGROUND Cardiometabolic multimorbidity (CM) has been found to be associated with higher mortality and functional limitations. However, few studies have investigated the longitudinal association between CM and disability in the Chinese population and whether these associations vary by smoking status. METHODS The study included 16,754 participants from four waves (2011, 2013, 2015, and 2018) of China Health and Retirement Longitudinal Study (CHARLS) (mean age: 59, female: 51%). CM was assesed at baseline and defined as having two or more of diabetes, stroke, or heart disease. Disability was repeatedly measured by summing the number of impaired activities of daily living (ADL) and instrumental activities of daily living (IADL) during the 7-year follow-up. Linear mixed-effects model was used to determine the association of CM and trajectories of disability and to assess the modification effect of smoking status in these associations. RESULTS Participants with CM at baseline had a faster progression of disability compared to those without CM (CM: β = 0.13, 95% CI: 0.05 to 0.21). Current smokers with CM developed disability faster than their counterparts (Pinteraction for smoking=0.011). In addition, there was a significant association between CM and the annual change of disability in current smokers (β = 0.34, 95% CI: 0.17 to 0.50) while no such association was observed in current non-smokers (β = 0.08, 95% CI: -0.02 to 0.17). CONCLUSION CM was associated with more a rapid disability progression. Notably, being current smokers may amplify the adverse effects of CM on disability progression.
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Affiliation(s)
- Huihui He
- Department of Nursing, The First Affiliated Hospital of Wannan Medical College, Wuhu, Anhui, China.
| | - Raoping Tu
- School of Health Management, Fujian Medical University, Fuzhou, Fujian, China
| | - Huahua Chen
- Department of Gastrointestinal surgery, The First Affiliated Hospital of Wannan Medical College, Wuhu, Anhui, China
| | - Chao Wang
- Department of Gastrointestinal surgery, The First Affiliated Hospital of Wannan Medical College, Wuhu, Anhui, China
| | - Shengjuan Wu
- Department of Gastrointestinal surgery, The First Affiliated Hospital of Wannan Medical College, Wuhu, Anhui, China
| | - Suhang Wang
- Anesthesia Surgery and Pain Management, Department Zhongda Hospital, School of Medicine Southeast University, Nanjing, Jiangsu, China
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Jin Y, Xu Z, Zhang Y, Zhang Y, Wang D, Cheng Y, Zhou Y, Fawad M, Xu X. Serum/plasma biomarkers and the progression of cardiometabolic multimorbidity: a systematic review and meta-analysis. Front Public Health 2023; 11:1280185. [PMID: 38074721 PMCID: PMC10701686 DOI: 10.3389/fpubh.2023.1280185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2023] [Accepted: 10/27/2023] [Indexed: 12/18/2023] Open
Abstract
Background The role of certain biomarkers in the development of single cardiometabolic disease (CMD) has been intensively investigated. Less is known about the association of biomarkers with multiple CMDs (cardiometabolic multimorbidity, CMM), which is essential for the exploration of molecular targets for the prevention and treatment of CMM. We aimed to systematically synthesize the current evidence on CMM-related biomarkers. Methods We searched PubMed, Embase, Web of Science, and Ebsco for relevant studies from inception until August 31st, 2022. Studies reported the association of serum/plasma biomarkers with CMM, and relevant effect sizes were included. The outcomes were five progression patterns of CMM: (1) no CMD to CMM; (2) type 2 diabetes mellitus (T2DM) followed by stroke; (3) T2DM followed by coronary heart disease (CHD); (4) T2DM followed by stroke or CHD; and (5) CHD followed by T2DM. Newcastle-Ottawa Quality Assessment Scale (NOS) was used to assess the quality of the included studies. A meta-analysis was conducted to quantify the association of biomarkers and CMM. Results A total of 68 biomarkers were identified from 42 studies, which could be categorized into five groups: lipid metabolism, glycometabolism, liver function, immunity, and others. Lipid metabolism biomarkers were most reported to associate with CMM, including TC, TGs, HDL-C, LDL-C, and Lp(a). Fasting plasma glucose was also reported by several studies, and it was particularly associated with coexisting T2DM with vascular diseases. According to the quantitative meta-analysis, HDL-C was negatively associated with CHD risk among patients with T2DM (pooled OR for per 1 mmol/L increase = 0.79, 95% CI = 0.77-0.82), whereas a higher TGs level (pooled OR for higher than 150 mg/dL = 1.39, 95% CI = 1.10-1.75) was positively associated with CHD risk among female patients with T2DM. Conclusion Certain serum/plasma biomarkers were associated with the progression of CMM, in particular for those related to lipid metabolism, but heterogeneity and inconsistent findings still existed among included studies. There is a need for future research to explore more relevant biomarkers associated with the occurrence and progression of CMM, targeted at which is important for the early identification and prevention of CMM.
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Affiliation(s)
- Yichen Jin
- School of Public Health, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Ziyuan Xu
- School of Public Health, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Yuting Zhang
- School of Public Health, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Yue Zhang
- School of Public Health, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Danyang Wang
- School of Public Health, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Yangyang Cheng
- School of Public Health, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Yaguan Zhou
- School of Public Health, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Muhammad Fawad
- School of Public Health, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, Zhejiang, China
| | - Xiaolin Xu
- School of Public Health, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
- The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Hangzhou, Zhejiang, China
- School of Public Health, Faculty of Medicine, The University of Queensland, Brisbane, QLD, Australia
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