- Complications
- Association of Snoring with Prediabetes and Type 2 Diabetes Mellitus: The Cardiovascular and Metabolic Diseases Etiology Research Center Cohort
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So Mi Jemma Cho, Hokyou Lee, Jee-Seon Shim, Hyeon Chang Kim
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Diabetes Metab J. 2020;44(5):687-698. Published online April 16, 2020
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DOI: https://doi.org/10.4093/dmj.2019.0128
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Abstract
PDFSupplementary MaterialPubReader ePub
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Background
Evidence suggests that habitual snoring is an independent risk factor for poor glycemic health. We examined the associations between snoring with prediabetes and diabetes in Korean population.
Methods
Self-reported snoring characteristics were collected from 3,948 middle-aged adults without prior cardiovascular diseases. Multivariable linear regression assessed the association of snoring intensity, frequency, disruptiveness, and disrupted breathing with fasting glucose and glycosylated hemoglobin (HbA1c) level. Then, multinomial regression evaluated how increasing snoring symptoms are associated with the risk for prediabetes and diabetes, adjusting for socioeconomic and behavioral risk factors of diabetes, obesity, hypertension, and other sleep variables.
Results
Higher snoring intensity and frequency were positively associated with fasting glucose and HbA1c levels. Participants presenting the most severe snoring were at 1.84 times higher risk (95% confidence interval [CI], 1.09 to 2.29) for prediabetes and 2.24 times higher risk (95% CI, 1.84 to 2.95) for diabetes, compared to non-snorers. Such graded association was also observed amongst the most frequent snorers with higher risk for prediabetes (odds ratio [OR], 1.78; 95% CI, 1.29 to 2.22) and diabetes (OR, 2.03; 95% CI, 1.45 to 2.85). Disruptive snoring (OR, 1.60; 95% CI, 1.12 to 2.28) and near-daily disruptive breathing (OR, 2.18; 95% CI, 1.02 to 4.19) were associated with higher odds for diabetes. Such findings remained robust after additional adjustment for sleep duration, excessive daytime sleepiness, unwakefulness, and sleep-deprived driving.
Conclusion
Snoring is associated with impaired glucose metabolism even in otherwise metabolically healthy adults. Habitual snorers may require lifestyle modifications and pharmacological treatment to improve glycemic profile.
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Citations
Citations to this article as recorded by
- Does seasonality affect snoring? A study based on international data from the past decade
Ping Wang, Cai Chen, Xingwei Wang, Ningling Zhang, Danyang Lv, Wei Li, Fulai Peng, Xiuli Wang Sleep and Breathing.2023; 27(4): 1297. CrossRef - Association Between Snoring and Diabetes Among Pre- and Postmenopausal Women
Yun Yuan, Fan Zhang, Jingfu Qiu, Liling Chen, Meng Xiao, Wenge Tang, Qinwen Luo, Xianbin Ding, Xiaojun Tang International Journal of General Medicine.2022; Volume 15: 2491. CrossRef - Elevated fasting insulin results in snoring: A view emerged from causal evaluation of glycemic traits and snoring
Minhan Yi, Quanming Fei, Kun Liu, Wangcheng Zhao, Ziliang Chen, Yuan Zhang European Journal of Clinical Investigation.2022;[Epub] CrossRef - Sleeping Duration, Napping and Snoring in Association with Diabetes Control among Patients with Diabetes in Qatar
Hiba Bawadi, Asma Al Sada, Noof Al Mansoori, Sharifa Al Mannai, Aya Hamdan, Zumin Shi, Abdelhamid Kerkadi International Journal of Environmental Research and Public Health.2021; 18(8): 4017. CrossRef - Changes in creatinine‐to‐cystatin C ratio over 4 years, risk of diabetes, and cardiometabolic control: The China Health and Retirement Longitudinal Study
Shanhu Qiu, Xue Cai, Yang Yuan, Bo Xie, Zilin Sun, Tongzhi Wu Journal of Diabetes.2021; 13(12): 1025. CrossRef - Association Between Self-Reported Snoring and Metabolic Syndrome: A Systematic Review and Meta-Analysis
Jinsha Ma, Huifang Zhang, Hui Wang, Qian Gao, Heli Sun, Simin He, Lingxian Meng, Tong Wang Frontiers in Neurology.2020;[Epub] CrossRef - Early Development of Bidirectional Associations between Sleep Disturbance and Diabetes
Yongin Cho Diabetes & Metabolism Journal.2020; 44(5): 668. CrossRef
- Metabolic Risk/Epidemiology
- Sex-, Age-, and Metabolic Disorder-Dependent Distributions of Selected Inflammatory Biomarkers among Community-Dwelling Adults
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So Mi Jemma Cho, Hokyou Lee, Jee-Seon Shim, Hyeon Chang Kim
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Diabetes Metab J. 2020;44(5):711-725. Published online April 16, 2020
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DOI: https://doi.org/10.4093/dmj.2019.0119
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7,442
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90
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4
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Abstract
PDFSupplementary MaterialPubReader ePub
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Background
Inflammatory cytokines are increasingly utilized to detect high-risk individuals for cardiometabolic diseases. However, with large population and assay methodological heterogeneity, no clear reference currently exists.
Methods
Among participants of the Cardiovascular and Metabolic Diseases Etiology Research Center cohort, of community-dwelling adults aged 30 to 64 without overt cardiovascular diseases, we presented distributions of tumor necrosis factor (TNF)-α and -β, interleukin (IL)-1α, -1β, and 6, monocyte chemoattractant protein (MCP)-1 and -3 and high sensitivity C-reactive protein (hsCRP) with and without non-detectable (ND) measurements using multiplex enzyme-linked immunosorbent assay. Then, we compared each markers by sex, age, and prevalence of type 2 diabetes mellitus, hypertension, and dyslipidemia, using the Wilcoxon Rank-Sum Test.
Results
In general, there were inconsistencies in direction and magnitude of differences in distributions by sex, age, and prevalence of cardiometabolic disorders. Overall, the median and the 99th percentiles were higher in men than in women. Older participants had higher TNF-α, high sensitivity IL-6 (hsIL-6), MCP-1, hsCRP, TNF-β, and MCP-3 median, after excluding the NDs. Participants with type 2 diabetes mellitus had higher median for all assayed biomarkers, except for TNF-β, IL-1α, and MCP-3, in which the medians for both groups were 0.00 due to predominant NDs. Compared to normotensive group, participants with hypertension had higher TNF-α, hsIL-6, MCP-1, and hsCRP median. When stratifying by dyslipidemia prevalence, the comparison varied significantly depending on the treatment of NDs.
Conclusion
Our findings provide sex-, age-, and disease-specific reference values to improve risk prediction and diagnostic performance for inflammatory diseases in both population- and clinic-based settings.
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Citations
Citations to this article as recorded by
- Characterizing CD8+ TEMRA Cells in CP/CPPS Patients: Insights from Targeted Single-Cell Transcriptomic and Functional Investigations
Fei Zhang, Qintao Ge, Jialin Meng, Jia Chen, Chaozhao Liang, Meng Zhang ImmunoTargets and Therapy.2024; Volume 13: 111. CrossRef - Within-subject variation of C-reactive protein and high-sensitivity C-reactive protein: A systematic review and meta-analysis
Alex Gough, Alice Sitch, Erica Ferris, Tom Marshall, Andreas Zirlik PLOS ONE.2024; 19(11): e0304961. CrossRef - Association between physical activity and inflammatory markers in community-dwelling, middle-aged adults
So Mi Jemma Cho, Hokyou Lee, Jee-Seon Shim, Justin Y. Jeon, Hyeon Chang Kim Applied Physiology, Nutrition, and Metabolism.2021; 46(7): 828. CrossRef - The monocyte-to-lymphocyte ratio: Sex-specific differences in the tuberculosis disease spectrum, diagnostic indices and defining normal ranges
Thomas S. Buttle, Claire Y. Hummerstone, Thippeswamy Billahalli, Richard J. B. Ward, Korina E. Barnes, Natalie J. Marshall, Viktoria C. Spong, Graham H. Bothamley, Selvakumar Subbian PLOS ONE.2021; 16(8): e0247745. CrossRef
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