Ethnicity, subjective wellbeing, and cardiovascular disease - Insights from path and machine learning analyses using data from the UK Biobank

Aug 5, 2026

Study objectives focus on how ethnicity shapes subjective wellbeing (SWB) and how these associations interact with demographics and cardiovascular disease (CVD), alongside identifying key SWB and CVD predictors using machine learning. Using 296,767 UK Biobank participants in a cross-sectional design, path analysis and machine learning models address missing data via sensitivity analyses. Logistic and linear regression with ROC analysis quantify direct and indirect effects and feature importance, showing ethnicity relates to both outcomes and mediates demographic associations.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages15

Reading Time38 min

AccessFree to download

Introduction

Abstract

Objectives

Design

Results

Conclusion

Keywords

University of Birmingham Ethnicity, subjective wellbeing, and cardiovascular disease Patel, Mubarak; Buchya, Mohammed Aadil; Uthman, Olalekan DOI: 10.1186/s12889-025-25889-x License: Creative Commons: Attribution-NonCommercial-NoDerivs (CC BY-NC-ND) Document Version Publisher's PDF, also known as Version of record Citation for published version (Harvard): Patel, M, Buchya, MA & Uthman, O 2026, 'Ethnicity, subjective wellbeing, and cardiovascular disease: insights from path and machine learning analyses using data from the UK biobank', BMC Public Health, vol. 26, no. 1, 282. [https://doi.org/10.1186/s12889-025-25889-x](https://doi.org/10.1186/s12889-025-25889-x) Link to publication on Research at Birmingham portal General rights Unless a licence is specified above, all rights (including copyright and moral rights) in this document are retained by the authors and/or the copyright holders. The express permission of the copyright holder must be obtained for any use of this material other than for purposes permitted by law. •Users may freely distribute the URL that is used to identify this publication. •Users may download and/or print one copy of the publication from the University of Birmingham research portal for the purpose of private study or non-commercial research. •User may use extracts from the document in line with the concept of ‘fair dealing’ under the Copyright, Designs and Patents Act 1988 (?) •Users may not further distribute the material nor use it for the purposes of commercial gain. Where a licence is displayed above, please note the terms and conditions of the licence govern your use of this document. When citing, please reference the published version. Take down policy While the University of Birmingham exercises care and attention in making items available there are rare occasions when an item has been uploaded in error or has been deemed to be commercially or otherwise sensitive. If you believe that this is the case for this document, [please contact UBIRA@lists.bham.ac.uk](please contact UBIRA@lists.bham.ac.uk) providing details and we will remove access to the work immediately and investigate. Download date: 13. Mar. 2026 Patel et al. BMC Public Health (2026) 26:282 BMC Public Health [https://doi.org/10.1186/s12889-025-25889-x](https://doi.org/10.1186/s12889-025-25889-x) RESEARCH Open Access Ethnicity, subjective wellbeing, and cardiovascular disease: insights from path and machine learning analyses using data from the UK biobank Mubarak Patel 1*, Mohammed Aadil Buchya2 and Olalekan Uthman2 Abstract Objectives The study is centred around two objectives: (1) to elucidate the effects of ethnicity on subjective wellbeing (SWB) and its interrelation with demographic factors and cardiovascular disease (CVD); and (2) to identify key predictors of SWB and CVD using machine learning. Design Employing data from 296,767 UK Biobank participants in a cross-sectional design, we conducted path analysis, and machine learning analysis to investigate the impact of ethnicity on CVD and SWB, addressing missing data through sensitivity analyses. Our models used logistic and linear regression, complemented by receiver operating characteristic analysis, to explore direct and indirect effects, and feature importance. Results Ethnicity was significantly associated to both outcomes directly and acting as a mediating variable when evaluating the association between key demographic variables and the outcomes. Ethnicity influenced CVD and SWB, with non-White groups showing higher CVD odds and lower SWB. Age, BMI, waist circumference, smoking status, depression, and sex were significant predictors of CVD, while factors like handgrip strength and alcohol intake showed protective effects. Conclusion This study underscores the critical need for ethnic-specific health interventions and highlights the complex interplay between demographic factors, CVD, and SWB. Our findings

FAQs

What are the main objectives of the study?

The study investigates how ethnicity affects subjective wellbeing and its relationship with demographics and cardiovascular disease, and it identifies key predictors of SWB and CVD using machine learning.

How were the data and analyses performed?

The study used 296,767 participants from the UK Biobank in a cross-sectional design, applying path analysis and machine learning while using sensitivity analyses to address missing data.

What key findings relate ethnicity to SWB and CVD?

Ethnicity was significantly associated with both SWB and CVD directly and also acted as a mediating variable. Non-White groups showed higher CVD odds and lower SWB in the results.

Aug 5, 2026

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