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Association of Snoring with Prediabetes and Type 2 Diabetes Mellitus: The Cardiovascular and Metabolic Diseases Etiology Research Center Cohort
So Mi Jemma Cho, Hokyou Lee, Jee-Seon Shim, Hyeon Chang Kim
Diabetes Metab J. 2020;44(5):687-698.   Published online April 16, 2020
DOI: https://doi.org/10.4093/dmj.2019.0128
  • 5,198 View
  • 108 Download
  • 7 Web of Science
  • 7 Crossref
AbstractAbstract PDFSupplementary MaterialPubReader   ePub   
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.

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
So Mi Jemma Cho, Hokyou Lee, Jee-Seon Shim, Hyeon Chang Kim
Diabetes Metab J. 2020;44(5):711-725.   Published online April 16, 2020
DOI: https://doi.org/10.4093/dmj.2019.0119
  • 5,881 View
  • 83 Download
  • 3 Web of Science
  • 3 Crossref
AbstractAbstract PDFSupplementary MaterialPubReader   ePub   
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.

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
  • 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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