IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v18y2021i18p9602-d633910.html
   My bibliography  Save this article

Hypertension in Children with Obstructive Sleep Apnea Syndrome—Age, Weight Status, and Disease Severity

Author

Listed:
  • Hai-Hua Chuang

    (Department of Family Medicine, Chang Gung Memorial Hospital, Taipei Branch and Linkou Main Branch, Taoyuan 33305, Taiwan
    Department of Industrial Engineering and Management, National Taipei University of Technology, Taipei 10608, Taiwan
    Obesity Institute, Genomic Medicine Institute, Geisinger Health, Danville, PA 17822, USA
    College of Medicine, Chang Gung University, Taoyuan 33302, Taiwan)

  • Jen-Fu Hsu

    (College of Medicine, Chang Gung University, Taoyuan 33302, Taiwan
    Department of Pediatrics, Chang Gung Memorial Hospital, Linkou Main Branch, Taoyuan 33305, Taiwan)

  • Chao-Yung Wang

    (Department of Cardiology, Chang Gung Memorial Hospital, Linkou Main Branch, Chang Gung University, Taoyuan 33305, Taiwan)

  • Li-Pang Chuang

    (College of Medicine, Chang Gung University, Taoyuan 33302, Taiwan
    Department of Pulmonary and Critical Care Medicine, Chang Gung Memorial Hospital, Linkou Main Branch, Taoyuan 33305, Taiwan)

  • Min-Chi Chen

    (College of Medicine, Chang Gung University, Taoyuan 33302, Taiwan
    Department of Public Health and Biostatistics, Consulting Center, Chang Gung University, Taoyuan 33302, Taiwan)

  • Ning-Hung Chen

    (College of Medicine, Chang Gung University, Taoyuan 33302, Taiwan
    Department of Pulmonary and Critical Care Medicine, Chang Gung Memorial Hospital, Linkou Main Branch, Taoyuan 33305, Taiwan)

  • Yu-Shu Huang

    (College of Medicine, Chang Gung University, Taoyuan 33302, Taiwan
    Department of Child Psychiatry, Chang Gung Memorial Hospital, Linkou Main Branch, Taoyuan 33305, Taiwan)

  • Hsueh-Yu Li

    (College of Medicine, Chang Gung University, Taoyuan 33302, Taiwan
    Department of Otorhinolaryngology-Head and Neck Surgery, Chang Gung Memorial Hospital, Linkou Main Branch, Taoyuan 33305, Taiwan)

  • Li-Ang Lee

    (College of Medicine, Chang Gung University, Taoyuan 33302, Taiwan
    Department of Otorhinolaryngology-Head and Neck Surgery, Chang Gung Memorial Hospital, Linkou Main Branch, Taoyuan 33305, Taiwan)

Abstract

Older age, obesity, and obstructive sleep apnea syndrome (OSAS) are known to increase the risk of hypertension in adults. However, data for children are scarce. This study aimed to investigate the relationships between hypertension, age, weight status, and disease severity in 396 children with OSAS. The prevalence rates of hypertension, obesity, and severe OSAS (apnea-hypopnea index ≥10) were 27.0%, 28.0%, and 42.9%, respectively. Weight z-score and apnea-hypopnea index were independently correlated with systolic blood pressure z-score, and minimal blood oxygen saturation (SpO 2 ) was independently associated with diastolic blood pressure z-score. Overall, late childhood/adolescence (odds ratio (OR) = 1.72, 95% CI = 1.05–2.81), obesity (OR, 2.58, 95% CI = 1.58–4.22), and severe OSAS (OR = 2.38, 95% CI = 1.48–3.81) were independent predictors of pediatric hypertension. Furthermore, late childhood/adolescence (OR = 2.50, 95% CI = 1.10–5.71) and abnormal SpO 2 (mean SpO 2 < 95%; OR = 4.91, 95% CI = 1.81–13.27) independently predicted hypertension in obese children, and severe OSAS (OR = 2.28, 95% CI = 1.27–4.10) independently predicted hypertension in non-obese children. In conclusion, obesity, OSAS severity, and abnormal SpO 2 are potentially modifiable targets to improve hypertension while treating children with OSAS.

Suggested Citation

  • Hai-Hua Chuang & Jen-Fu Hsu & Chao-Yung Wang & Li-Pang Chuang & Min-Chi Chen & Ning-Hung Chen & Yu-Shu Huang & Hsueh-Yu Li & Li-Ang Lee, 2021. "Hypertension in Children with Obstructive Sleep Apnea Syndrome—Age, Weight Status, and Disease Severity," IJERPH, MDPI, vol. 18(18), pages 1-17, September.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:18:p:9602-:d:633910
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/18/18/9602/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/18/18/9602/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hai-Hua Chuang & Jen-Fu Hsu & Li-Pang Chuang & Ning-Hung Chen & Yu-Shu Huang & Hsueh-Yu Li & Jau-Yuan Chen & Li-Ang Lee & Chung-Guei Huang, 2020. "Differences in Anthropometric and Clinical Features among Preschoolers, School-Age Children, and Adolescents with Obstructive Sleep Apnea—A Hospital-Based Study in Taiwan," IJERPH, MDPI, vol. 17(13), pages 1-14, June.
    2. Margaret Pepe & Holly Janes & Gary Longton & Wendy Leisenring & Polly Newcomb, 2004. "Limitations of the Odds Ratio in Gauging the Performance of a Diagnostic or Prognostic Marker," UW Biostatistics Working Paper Series 1035, Berkeley Electronic Press.
    3. Chih-Yu Hsu & Rong-Ho Lin & Yu-Ching Lin & Jau-Yuan Chen & Wen-Cheng Li & Li-Ang Lee & Keng-Hao Liu & Hai-Hua Chuang, 2020. "Are Body Composition Parameters Better than Conventional Anthropometric Measures in Predicting Pediatric Hypertension?," IJERPH, MDPI, vol. 17(16), pages 1-11, August.
    4. Hiroshi Kimura & Hiroyo Ota & Yuya Kimura & Shin Takasawa, 2019. "Effects of Intermittent Hypoxia on Pulmonary Vascular and Systemic Diseases," IJERPH, MDPI, vol. 16(17), pages 1-13, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Debashis Ghosh & Michael S. Sabel, 2022. "A Weighted Sample Framework to Incorporate External Calculators for Risk Modeling," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 14(3), pages 363-379, December.
    2. Aljoscha Benjamin Hwang & Guido Schuepfer & Mario Pietrini & Stefan Boes, 2021. "External validation of EPIC’s Risk of Unplanned Readmission model, the LACE+ index and SQLape as predictors of unplanned hospital readmissions: A monocentric, retrospective, diagnostic cohort study in," PLOS ONE, Public Library of Science, vol. 16(11), pages 1-33, November.
    3. Anna-Karin Ivert & Marie Torstensson Levander & Juan Merlo, 2013. "Adolescents' Utilisation of Psychiatric Care, Neighbourhoods and Neighbourhood Socioeconomic Deprivation: A Multilevel Analysis," PLOS ONE, Public Library of Science, vol. 8(11), pages 1-1, November.
    4. Margaret Sullivan Pepe & Tianxi Cai & Gary Longton, 2006. "Combining Predictors for Classification Using the Area under the Receiver Operating Characteristic Curve," Biometrics, The International Biometric Society, vol. 62(1), pages 221-229, March.
    5. Holly Janes & Margaret S. Pepe, 2008. "Matching in Studies of Classification Accuracy: Implications for Analysis, Efficiency, and Assessment of Incremental Value," Biometrics, The International Biometric Society, vol. 64(1), pages 1-9, March.
    6. Carlos A Labarrere & John R Woods & James W Hardin & Beate R Jaeger & Marian Zembala & Mario C Deng & Ghassan S Kassab, 2014. "Early Inflammatory Markers Are Independent Predictors of Cardiac Allograft Vasculopathy in Heart-Transplant Recipients," PLOS ONE, Public Library of Science, vol. 9(12), pages 1-18, December.
    7. Pia Kjær Kristensen & Raquel Perez-Vicente & George Leckie & Søren Paaske Johnsen & Juan Merlo, 2020. "Disentangling the contribution of hospitals and municipalities for understanding patient level differences in one-year mortality risk after hip-fracture: A cross-classified multilevel analysis in Swed," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-14, June.
    8. Diego Tomassi & Liliana Forzani & Efstathia Bura & Ruth Pfeiffer, 2017. "Sufficient dimension reduction for censored predictors," Biometrics, The International Biometric Society, vol. 73(1), pages 220-231, March.
    9. Osamu Komori, 2011. "A boosting method for maximization of the area under the ROC curve," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 63(5), pages 961-979, October.
    10. Quang Bao Le & Boubaker Dhehibi, 2019. "A Typology-Based Approach for Assessing Qualities and Determinants of Adoption of Sustainable Water Use Technologies in Coping with Context Diversity: The Case of Mechanized Raised-Bed Technology in E," Sustainability, MDPI, vol. 11(19), pages 1-21, September.
    11. Kenichi Hayashi & Shinto Eguchi, 2024. "A new integrated discrimination improvement index via odds," Statistical Papers, Springer, vol. 65(8), pages 4971-4990, October.
    12. Lin Zhou & Wei Liang & Yuxiu He & Yanping Duan & Ryan E. Rhodes & Hao Liu & Hongmei Liang & Xiaowei Shi & Jun Zhang & Yingzhe Cheng, 2022. "Relationship of 24-Hour Movement Behaviors with Weight Status and Body Composition in Chinese Primary School Children: A Cross-Sectional Study," IJERPH, MDPI, vol. 19(14), pages 1-14, July.
    13. Chiara Stipa & Serena Incerti-Parenti & Matteo Cameli & Daniela Rita Ippolito & Antonio Gracco & Giulio Alessandri-Bonetti, 2023. "Antero-Posterior Mandibular Excursion in Obstructive Sleep Apnea Patients Treated with Mandibular Advancement Device: A Retrospective Cohort Study," IJERPH, MDPI, vol. 20(4), pages 1-9, February.
    14. Tianle Chen & Yuanjia Wang & Huaihou Chen & Karen Marder & Donglin Zeng, 2014. "Targeted Local Support Vector Machine for Age-Dependent Classification," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(507), pages 1174-1187, September.
    15. Hajime Uno & Tianxi Cai & Lu Tian & L. J. Wei, 2011. "Graphical Procedures for Evaluating Overall and Subject-Specific Incremental Values from New Predictors with Censored Event Time Data," Biometrics, The International Biometric Society, vol. 67(4), pages 1389-1396, December.
    16. Anna Persmark & Maria Wemrell & Sofia Zettermark & George Leckie & S V Subramanian & Juan Merlo, 2019. "Precision public health: Mapping socioeconomic disparities in opioid dispensations at Swedish pharmacies by Multilevel Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA)," PLOS ONE, Public Library of Science, vol. 14(8), pages 1-21, August.
    17. Rebecca Yates Coley & Aaron J. Fisher & Mufaddal Mamawala & Herbert Ballentine Carter & Kenneth J. Pienta & Scott L. Zeger, 2017. "A Bayesian hierarchical model for prediction of latent health states from multiple data sources with application to active surveillance of prostate cancer," Biometrics, The International Biometric Society, vol. 73(2), pages 625-634, June.
    18. Juan Merlo & Philippe Wagner & Nermin Ghith & George Leckie, 2016. "An Original Stepwise Multilevel Logistic Regression Analysis of Discriminatory Accuracy: The Case of Neighbourhoods and Health," PLOS ONE, Public Library of Science, vol. 11(4), pages 1-31, April.
    19. Michael Lebenbaum & Osvaldo Espin-Garcia & Yi Li & Laura C Rosella, 2018. "Development and validation of a population based risk algorithm for obesity: The Obesity Population Risk Tool (OPoRT)," PLOS ONE, Public Library of Science, vol. 13(1), pages 1-11, January.
    20. Michael King & Louise Marston & Igor Švab & Heidi-Ingrid Maaroos & Mirjam I Geerlings & Miguel Xavier & Vicente Benjamin & Francisco Torres-Gonzalez & Juan Angel Bellon-Saameno & Danica Rotar & Anu Al, 2011. "Development and Validation of a Risk Model for Prediction of Hazardous Alcohol Consumption in General Practice Attendees: The PredictAL Study," PLOS ONE, Public Library of Science, vol. 6(8), pages 1-10, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:18:y:2021:i:18:p:9602-:d:633910. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.