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Wang Y, Chen P, Wang J, Lin Q, Li H, Izci-Balserak B, Yuan J, Zhao R, Zhu B. Sleep health predicted glucose metabolism among pregnant women: A prospective cohort study. Diabetes Res Clin Pract 2024; 209:111570. [PMID: 38341040 DOI: 10.1016/j.diabres.2024.111570] [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: 11/02/2023] [Revised: 01/23/2024] [Accepted: 02/05/2024] [Indexed: 02/12/2024]
Abstract
AIMS To examine whether sleep health in the first trimester could predict glucose metabolism in the second trimester. METHODS Pregnant women (N = 127) during the first trimester were recruited (August 2022 to March 2023). Overall sleep health was assessed by the Sleep Health Index. Various dimensions of sleep health were measured using a 7-day sleep diary and questionnaires. The outcomes, including diagnosis of gestational diabetes mellitus (GDM) and HbA1c, were obtained from the medical records in the second trimester. Poisson regression analysis and multiple linear regression were used for data analysis. RESULTS The average age of the participants was 32.6 years. The incidence of GDM was 28.3 % and the mean HbA1c was 5.2 % (33 mmol/mol). Sleep duration regularity (RR = 1.808; 95 %CI 1.023, 3.196) was associated with GDM after controlling for confounders. SHI total score (β = -0.278; 95 %CI -0.022, -0.005) and sleep duration regularity (β = 0.243; 95 %CI 0.057, 0.372) were associated with HbA1c. CONCLUSIONS Worse sleep health, particularly lower sleep regularity, predicted worse glucose metabolism among pregnant women. Healthcare professional may consider adding sleep-related assessment to prenatal care. Maintaining regular sleep should be encouraged. Studies examining the impact of sleep intervention on glucose metabolism among pregnant women are warranted.
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Affiliation(s)
- Yueying Wang
- School of Nursing, Shanghai Jiao Tong University, Shanghai, China
| | - Pei Chen
- College of Nursing, University of Illinois Chicago, Chicago, IL, USA
| | - Jinle Wang
- School of Nursing, Shanghai Jiao Tong University, Shanghai, China
| | - Qin Lin
- International Peace Maternity and Child Health Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Hong Li
- International Peace Maternity and Child Health Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | | | - Jinjin Yuan
- School of Nursing, Shanghai Jiao Tong University, Shanghai, China
| | - Ruru Zhao
- Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China
| | - Bingqian Zhu
- School of Nursing, Shanghai Jiao Tong University, Shanghai, China.
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Diao T, Liu K, Wang Q, Lyu J, Zhou L, Yuan Y, Wang H, Yang H, Wu T, Zhang X. Bedtime, sleep pattern, and incident cardiovascular disease in middle-aged and older Chinese adults: The dongfeng-tongji cohort study. Sleep Med 2023; 110:82-88. [PMID: 37544277 DOI: 10.1016/j.sleep.2023.08.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 07/12/2023] [Accepted: 08/01/2023] [Indexed: 08/08/2023]
Abstract
OBJECTIVES To investigate the associations of bedtime and a low-risk sleep pattern with incident cardiovascular disease (CVD). METHODS A total of 31,500 retirees were included from the Dongfeng-Tongji cohort in 2008-2010 and 2013. Sleep information was collected by questionnaires. CVD events were identified through the health care system until December 31, 2018. Cox proportional hazards regression models were performed to estimate hazard ratios (HRs) and 95% confidence intervals (CIs). RESULTS During an average follow-up of 7.2 years, 8324 cases of incident CVD, including 6557 coronary heart disease (CHD) and 1767 stroke, were documented. U-shaped associations of bedtime with the risks of incident CVD and stroke were observed. Compared with bedtime between 10:01 p.m.-11:00 p.m., the HR (95% CI) for CVD was 1.10 (1.01-1.20) for ≤9:00 p.m., 1.07 (1.01-1.13) for 9:01 p.m.-10:00 p.m., and 1.32 (1.11-1.58) for >12:00 a.m., respectively, mainly driven by stroke risk (22%, 14%, and 70% higher for ≤9:00 p.m., 9:01 p.m.-10:00 p.m., and >12:00 a.m., respectively). The number of low-risk sleep factors, namely bedtime between 10:01 p.m.-12:00 a.m., sleep duration of 7-< 8 h/night, good/fair sleep quality, and midday napping ≤60 min, exhibited dose-dependent relationships with CVD, CHD, and stroke risks. Participants with 4 low-risk sleep factors had a respective 24%, 21%, and 30% lower risk of CVD, CHD, and stroke than those with 0-1 low-risk sleep factor. CONCLUSIONS Individuals with early or late bedtimes had a higher CVD risk, especially stroke. Having low-risk sleep habits is associated with lower CVD risks.
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Affiliation(s)
- Tingyue Diao
- Department of Occupational and Environmental Health, Key Laboratory of Environment and Health, Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Kang Liu
- Department of Occupational and Environmental Health, Key Laboratory of Environment and Health, Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China; School of Public Health, Guangzhou Medical University, Guangzhou, China
| | - Qiuhong Wang
- Department of Occupational and Environmental Health, Key Laboratory of Environment and Health, Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Junrui Lyu
- Department of Occupational and Environmental Health, Key Laboratory of Environment and Health, Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Lue Zhou
- Department of Occupational and Environmental Health, Key Laboratory of Environment and Health, Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yu Yuan
- Department of Occupational and Environmental Health, Key Laboratory of Environment and Health, Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Hao Wang
- Department of Occupational and Environmental Health, Key Laboratory of Environment and Health, Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Handong Yang
- Department of Cardiovascular Diseases, Sinopharm Dongfeng General Hospital, Hubei University of Medicine, Shiyan, China
| | - Tangchun Wu
- Department of Occupational and Environmental Health, Key Laboratory of Environment and Health, Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
| | - Xiaomin Zhang
- Department of Occupational and Environmental Health, Key Laboratory of Environment and Health, Ministry of Education and State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
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