IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v17y2020i3p778-d313395.html
   My bibliography  Save this article

Social Support and Subclinical Coronary Artery Disease in Middle-Aged Men and Women: Findings from the Pilot of Swedish CArdioPulmonary bioImage Study

Author

Listed:
  • Demir Djekic

    (Department of Cardiology, School of Medical Sciences, Örebro University, Örebro University Hospital, 701 85 Örebro, Sweden)

  • Erika Fagman

    (Department of Radiology, Institute of Clinical Sciences, The Sahlgrenska Academy at University of Gothenburg, Sahlgrenska University Hospital, 413 90 Gothenburg, Sweden)

  • Oskar Angerås

    (Department of Molecular and Clinical Medicine, The Sahlgrenska Academy at University of Gothenburg, Sahlgrenska University Hospital, 416 85 Gothenburg, Sweden)

  • George Lappas

    (Department of Molecular and Clinical Medicine, The Sahlgrenska Academy at University of Gothenburg, Sahlgrenska University Hospital, 416 85 Gothenburg, Sweden)

  • Kjell Torén

    (Section of Occupational and Environmental Medicine, Sahlgrenska Academy, University of Gothenburg, 413 90 Gothenburg, Sweden)

  • Göran Bergström

    (Department of Molecular and Clinical Medicine, The Sahlgrenska Academy at University of Gothenburg, Sahlgrenska University Hospital, 416 85 Gothenburg, Sweden)

  • Annika Rosengren

    (Department of Molecular and Clinical Medicine, The Sahlgrenska Academy at University of Gothenburg, Sahlgrenska University Hospital, 416 85 Gothenburg, Sweden)

Abstract

Social support has been associated with coronary artery disease (CAD), particularly in individuals who have sustained a cardiovascular event. This study investigated the relationship between social support and subclinical CAD among 1067 healthy middle-aged men and women. Social support was assessed with validated social integration and emotional attachment measures. Subclinical CAD was assessed as a coronary artery calcium score (CACS) using computed tomography. There was no association between social support and CACS in men. In women, low social support was strongly linked to cardiovascular risk factors, high levels of inflammatory markers, and CACS > 0. In a logistic regression model, after adjustment for 12 cardiovascular risk factors, the odds ratio (95% confidence intervals) for CACS > 0 in women with the lowest social integration, emotional attachment, and social support groups (reference: highest corresponding group) were 2.47 (1.23–5.12), 1.87 (0.93–3.59), and 4.28 (1.52–12.28), respectively. Using a machine learning approach (random forest), social integration was the fourth (out of 12) most important risk factor for CACS > 0 in women. Women with lower compared to higher or moderate social integration levels were about 14 years older in “vascular age”. This study showed an association between lack of social support and subclinical CAD in middle-aged women, but not in men. Lack of social support may affect the atherosclerotic process and identify individuals vulnerable to CAD events.

Suggested Citation

  • Demir Djekic & Erika Fagman & Oskar Angerås & George Lappas & Kjell Torén & Göran Bergström & Annika Rosengren, 2020. "Social Support and Subclinical Coronary Artery Disease in Middle-Aged Men and Women: Findings from the Pilot of Swedish CArdioPulmonary bioImage Study," IJERPH, MDPI, vol. 17(3), pages 1-16, January.
  • Handle: RePEc:gam:jijerp:v:17:y:2020:i:3:p:778-:d:313395
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/17/3/778/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1660-4601/17/3/778/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ishwaran, Hemant & Kogalur, Udaya B. & Gorodeski, Eiran Z. & Minn, Andy J. & Lauer, Michael S., 2010. "High-Dimensional Variable Selection for Survival Data," Journal of the American Statistical Association, American Statistical Association, vol. 105(489), pages 205-217.
    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. Zemin Zheng & Jie Zhang & Yang Li, 2022. "L 0 -Regularized Learning for High-Dimensional Additive Hazards Regression," INFORMS Journal on Computing, INFORMS, vol. 34(5), pages 2762-2775, September.
    2. Xiang, Pengcheng & Zhou, Ling & Tang, Lu, 2024. "Transfer learning via random forests: A one-shot federated approach," Computational Statistics & Data Analysis, Elsevier, vol. 197(C).
    3. Jung-sik Hong & Hyeongyu Yeo & Nam-Wook Cho & Taeuk Ahn, 2018. "Identification of Core Suppliers Based on E-Invoice Data Using Supervised Machine Learning," JRFM, MDPI, vol. 11(4), pages 1-13, October.
    4. Shang-Ming Zhou & Fabiola Fernandez-Gutierrez & Jonathan Kennedy & Roxanne Cooksey & Mark Atkinson & Spiros Denaxas & Stefan Siebert & William G Dixon & Terence W O’Neill & Ernest Choy & Cathie Sudlow, 2016. "Defining Disease Phenotypes in Primary Care Electronic Health Records by a Machine Learning Approach: A Case Study in Identifying Rheumatoid Arthritis," PLOS ONE, Public Library of Science, vol. 11(5), pages 1-14, May.
    5. Youngjoo Cho & Debashis Ghosh, 2021. "Quantile-Based Subgroup Identification for Randomized Clinical Trials," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 13(1), pages 90-128, April.
    6. Ismael Ahrazem Dfuf & José Manuel Mira McWilliams & María Camino González Fernández, 2019. "Multi-Output Conditional Inference Trees Applied to the Electricity Market: Variable Importance Analysis," Energies, MDPI, vol. 12(6), pages 1-24, March.
    7. Han, Dongxiao & Huang, Jian & Lin, Yuanyuan & Shen, Guohao, 2022. "Robust post-selection inference of high-dimensional mean regression with heavy-tailed asymmetric or heteroskedastic errors," Journal of Econometrics, Elsevier, vol. 230(2), pages 416-431.
    8. Makariou, Despoina & Barrieu, Pauline & Chen, Yining, 2021. "A random forest based approach for predicting spreads in the primary catastrophe bond market," Insurance: Mathematics and Economics, Elsevier, vol. 101(PB), pages 140-162.
    9. Christine Porzelius & Martin Schumacher & Harald Binder, 2011. "The benefit of data-based model complexity selection via prediction error curves in time-to-event data," Computational Statistics, Springer, vol. 26(2), pages 293-302, June.
    10. Foucher Yohann & Danger Richard, 2012. "Time Dependent ROC Curves for the Estimation of True Prognostic Capacity of Microarray Data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(6), pages 1-22, November.
    11. J. Choi & S. Ye & K. H. Eng & K. Korthauer & W. H. Bradley & J. S. Rader & C. Kendziorski, 2017. "IPI59: An Actionable Biomarker to Improve Treatment Response in Serous Ovarian Carcinoma Patients," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 9(1), pages 1-12, June.
    12. Rossella Tatoli & Luisa Lampignano & Rossella Donghia & Alfredo Niro & Fabio Castellana & Ilaria Bortone & Roberta Zupo & Sarah Tirelli & Madia Lozupone & Francesco Panza & Giovanni Alessio & Francesc, 2023. "Retinal Microvasculature and Neural Changes and Dietary Patterns in an Older Population in Southern Italy," IJERPH, MDPI, vol. 20(6), pages 1-17, March.
    13. Peter Calhoun & Melodie J. Hallett & Xiaogang Su & Guy Cafri & Richard A. Levine & Juanjuan Fan, 2020. "Random forest with acceptance–rejection trees," Computational Statistics, Springer, vol. 35(3), pages 983-999, September.
    14. Hoora Moradian & Denis Larocque & François Bellavance, 2017. "$$L_1$$ L 1 splitting rules in survival forests," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 23(4), pages 671-691, October.
    15. Yiwei Fan & Gang Wang & Xiaoling Lu & Gaobin Wang, 2019. "Distributed forecasting and ant colony optimization for the bike-sharing rebalancing problem with unserved demands," PLOS ONE, Public Library of Science, vol. 14(12), pages 1-26, December.
    16. Eiran Z Gorodeski & Emer Joyce & Benjamin T Gandesbery & Eugene H Blackstone & David O Taylor & W H Wilson Tang & Randall C Starling & Rory Hachamovitch, 2017. "Discordance between 'actual' and 'scheduled' check-in times at a heart failure clinic," PLOS ONE, Public Library of Science, vol. 12(11), pages 1-13, November.
    17. Makariou, Despoina & Barrieu, Pauline & Chen, Yining, 2021. "A random forest based approach for predicting spreads in the primary catastrophe bond market," LSE Research Online Documents on Economics 111529, London School of Economics and Political Science, LSE Library.
    18. Kim, Dongwoo, 2024. "Corporate loan duration, macroeconomic environments, and COVID-19," International Review of Economics & Finance, Elsevier, vol. 93(PB), pages 1088-1103.
    19. Mao, Xiaojun & Peng, Liuhua & Wang, Zhonglei, 2022. "Nonparametric feature selection by random forests and deep neural networks," Computational Statistics & Data Analysis, Elsevier, vol. 170(C).
    20. Julia Gilhodes & Florence Dalenc & Jocelyn Gal & Christophe Zemmour & Eve Leconte & Jean Marie Boher & Thomas Filleron, 2020. "Comparison of Variable Selection Methods for Time-to-Event Data in High-Dimensional Settings," Post-Print hal-02934793, HAL.

    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:17:y:2020:i:3:p:778-:d:313395. 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.