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Original Article
The association between metabolic syndrome and heart failure in middle-aged male and female: Korean population-based study of 2 million individuals
Tae-Eun Kim, Hyeongsu Kim, JiDong Sung, Duk-Kyung Kim, Myoung-Soon Lee, Seong Woo Han, Hyun-Joong Kim, Sung Hea Kim, Kyu-Hyung Ryu
Epidemiol Health. 2022;44:e2022078.   Published online September 21, 2022
DOI: https://doi.org/10.4178/epih.e2022078
  • 3,505 View
  • 66 Download
  • 3 Web of Science
  • 3 Crossref
AbstractAbstract PDF
Abstract
OBJECTIVES
Although an association is known to exist between metabolic syndrome (MetS) and heart failure (HF) risk, large longitudinal studies are limited. We investigated metabolic status as a risk factor for HF in middle-aged male and female and considered sex differences in various risk factors for HF using nationwide real-world data.
METHODS
Data obtained from the Korean National Health Insurance Service from 2009 to 2016 were analyzed. A total of 2,151,597 middle-aged subjects (between 50 and 59 years old) were enrolled. Subjects were divided into 3 groups (normal, pre‐ MetS, and MetS). Cox proportional hazard models were used to estimate the association between MetS and incident HF after adjusting for clinical risk factors.
RESULTS
At baseline, MetS existed in 23.77% of male and 10.58% of female. Pre-MetS and MetS increased the risk of HF: the hazard ratios of pre-MetS for incident HF were 1.508 (95% confidence interval [CI], 1.287 to 1.767) in male and 1.395 (95% CI, 1.158 to 1.681) in female, and those of MetS were 1.711 (95% CI, 1.433 to 2.044) in male and 2.144 (95% CI, 1.674 to 2.747) in female. Current smoking, a low hemoglobin level, underweight (body mass index < 18.5 kg/m2), a high creatinine level, and acute myocardial infarction were also predictors of HF in both sexes.
CONCLUSIONS
Pre-MetS and MetS were identified as risk factors for HF in middle-aged male and female. The effect of MetS on the occurrence of HF was stronger in female than in male. Pre-MetS was also a predictor of HF, but was associated with a lower risk than MetS.
Summary

Citations

Citations to this article as recorded by  
  • Integrating machine learning and nontargeted plasma lipidomics to explore lipid characteristics of premetabolic syndrome and metabolic syndrome
    Xinfeng Huang, Qing He, Haiping Hu, Huanhuan Shi, Xiaoyang Zhang, Youqiong Xu
    Frontiers in Endocrinology.2024;[Epub]     CrossRef
  • 17-year follow-up of association between telomere length and all-cause mortality, cardiovascular mortality in individuals with metabolic syndrome: results from the NHANES database prospective cohort study
    Lijiao Xiong, Guangyan Yang, Tianting Guo, Zhaohao Zeng, Tingfeng Liao, Yanchun Li, Ying Li, Fujuan Chen, Shu Yang, Lin Kang, Zhen Liang
    Diabetology & Metabolic Syndrome.2023;[Epub]     CrossRef
  • Modifications of long-term heart rate variability produced in an experimental model of diet-induced metabolic syndrome
    W. M. Lozano, J. E. Ortiz-Guzmán, O. Arias-Mutis, A. Bizy, P. Genovés, L. Such-Miquel, A. Alberola, F. J. Chorro, M. Zarzoso, C. J. Calvo
    Interface Focus.2023;[Epub]     CrossRef
Review Paper
Population data science: advancing the safe use of population data for public benefit
Kerina Helen Jones, David Vincent Ford
Epidemiol Health. 2018;40:e2018061.   Published online December 25, 2018
DOI: https://doi.org/10.4178/epih.e2018061
  • 11,003 View
  • 155 Download
  • 8 Web of Science
  • 9 Crossref
AbstractAbstract PDF
Abstract
The value of using population data to answer important questions for individual and societal benefit has never been greater. Governments and research funders world-wide are recognizing this potential and making major investments in data-intensive initiatives. However, there are challenges to overcome so that safe, socially-acceptable data sharing can be achieved. This paper outlines the field of population data science, the International Population Data Linkage Network (IPDLN), and their roles in advancing data-intensive research. We provide an overview of core concepts and major challenges for data-intensive research, with a particular focus on ethical, legal, and societal implications (ELSI). Using international case studies, we show how challenges can be addressed and lessons learned in advancing the safe, socially-acceptable use of population data for public benefit. Based on the case studies, we discuss the common ELSI principles in operation, we illustrate examples of a data scrutiny panel and a consumer panel, and we propose a set of ELSI-based recommendations to inform new and developing data-intensive initiatives.We conclude that although there are many ELSI issues to be overcome, there has never been a better time or more potential to leverage the benefits of population data for public benefit. A variety of initiatives, with different operating models, have pioneered the way in addressing many challenges. However, the work is not static, as the ELSI environment is constantly evolving, thus requiring continual mutual learning and improvement via the IPDLN and beyond.
Summary

Citations

Citations to this article as recorded by  
  • ‘What about the dads?’ Linking fathers and children in administrative data: A systematic scoping review
    Irina Lut, Katie Harron, Pia Hardelid, Margaret O’Brien, Jenny Woodman
    Big Data & Society.2022; 9(1): 205395172110692.     CrossRef
  • The Social Data Foundation model: Facilitating health and social care transformation throughdatatrust services
    Michael Boniface, Laura Carmichael, Wendy Hall, Brian Pickering, Sophie Stalla-Bourdillon, Steve Taylor
    Data & Policy.2022;[Epub]     CrossRef
  • Fostering trustworthy data sharing: Establishing data foundations in practice
    Sophie Stalla-Bourdillon, Laura Carmichael, Alexsis Wintour
    Data & Policy.2021;[Epub]     CrossRef
  • Interdisciplinary data science to advance environmental health research and improve birth outcomes
    Jeanette A. Stingone, Sofia Triantafillou, Alexandra Larsen, Jay P. Kitt, Gary M. Shaw, Judit Marsillach
    Environmental Research.2021; 197: 111019.     CrossRef
  • Considerations for an integrated population health databank in Africa: lessons from global best practices
    Jude O. Igumbor, Edna N. Bosire, Marta Vicente-Crespo, Ehimario U. Igumbor, Uthman A. Olalekan, Tobias F. Chirwa, Sam M. Kinyanjui, Catherine Kyobutungi, Sharon Fonn
    Wellcome Open Research.2021; 6: 214.     CrossRef
  • The road to hell is paved with good intentions: the experience of applying for national data for linkage and suggestions for improvement
    Julie A Taylor, Sonya Crowe, Ferran Espuny Pujol, Rodney C Franklin, Richard G Feltbower, Lee J Norman, James Doidge, Doug William Gould, Christina Pagel
    BMJ Open.2021; 11(8): e047575.     CrossRef
  • Panel 4: Recent advances in understanding the natural history of the otitis media microbiome and its response to environmental pressures
    Robyn L. Marsh, Celestine Aho, Jemima Beissbarth, Seweryn Bialasiewicz, Michael Binks, Anders Cervin, Lea-Ann S. Kirkham, Katherine P. Lemon, Mary P.E. Slack, Heidi C. Smith-Vaughan
    International Journal of Pediatric Otorhinolaryngology.2020; 130: 109836.     CrossRef
  • Age-Dependent and Seasonal Changes in Menstrual Cycle Length and Body Temperature Based on Big Data
    Takayuki Tatsumi, Makiko Sampei, Kazuki Saito, Yuka Honda, Yuka Okazaki, Naoko Arata, Kanako Narumi, Naho Morisaki, Tomonori Ishikawa, Satoshi Narumi
    Obstetrics & Gynecology.2020; 136(4): 666.     CrossRef
  • Public Views on Models for Accessing Genomic and Health Data for Research: Mixed Methods Study
    Kerina H Jones, Helen Daniels, Emma Squires, David V Ford
    Journal of Medical Internet Research.2019; 21(8): e14384.     CrossRef
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