Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2025 Oct 16:12:1670309.
doi: 10.3389/fmed.2025.1670309. eCollection 2025.

Body roundness index and its role in predicting COPD risk: insights from the English Longitudinal Study of Aging and the health and retirement study

Affiliations

Body roundness index and its role in predicting COPD risk: insights from the English Longitudinal Study of Aging and the health and retirement study

Longqian Li et al. Front Med (Lausanne). .

Abstract

Background: Chronic obstructive pulmonary disease (COPD) is one of the most common chronic respiratory diseases worldwide. This study aims to investigate the relationship between the Body Roundness Index (BRI) and COPD in individuals aged 45 and older.

Methods: This study included 5818 participants from waves 2 to 9 (2004-2019) of the English Longitudinal Study of Aging (ELSA) and 6928 participants from waves 8 to 10 (2006-2021) of the Health and Retirement Study (HRS). Initially, univariate analysis, univariate Cox regression analysis, and trend analysis were conducted to preliminarily screen the variables. The variance inflation factor (VIF) was used to detect multicollinearity and ensure the independence of the selected variables. Subsequently, multivariate logistic regression and multivariate Cox regression models were employed to assess the relationship between the Body Roundness Index (BRI) and chronic obstructive pulmonary disease (COPD). Restricted cubic spline (RCS) analysis was applied to further explore the nonlinear relationship between BRI and COPD. Finally, sensitivity analysis was performed to validate the robustness of the model results.

Results: The results from both datasets indicate a significant association between the Body Roundness Index (BRI) and chronic obstructive pulmonary disease (COPD) (ELSA: OR (95% CI) = 1.193 (1.074-1.321), P = 0.001; HRS: OR (95% CI) = 1.160 (1.094-1.228), P < 0.001). As BRI increases, the incidence of newly diagnosed COPD significantly rises (ELSA: HR (95% CI) = 1.149 (1.034-1.273), P = 0.009; HRS: HR (95% CI) = 1.114 (1.054-1.177), P < 0.001). The optimal cutoff analysis revealed a significant difference in COPD risk between the high and low BRI groups (ELSA: P = 0.0037; HRS: P = 0.0085). Restricted cubic spline (RCS) analysis further demonstrated a "J-shaped" relationship between BRI and COPD.

Conclusion: This study demonstrates a significant association between the Body Roundness Index (BRI) and chronic obstructive pulmonary disease (COPD). The increase in BRI is significantly associated with both the incidence of COPD and newly diagnosed cases. Restricted cubic spline (RCS) analysis further reveals a "J-shaped" relationship between BRI and COPD, suggesting that BRI may serve as a potential predictive tool for COPD risk.

Keywords: Body roundness index; Chronic obstructive pulmonary disease; restricted cubic spline; the English Longitudinal Study of Aging; the Health and Retirement Study.

PubMed Disclaimer

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Flowchart showing participant selection from HRS and ELSA databases. HRS begins with 18,469 participants, excluding 169 under age forty-five, 1,087 with missing WC/height, and 258 with missing covariates. Result: 6,048 final study subjects after excluding 880. ELSA starts with 9,432 participants, excluding 30 under age forty-five, 2,136 with missing WC/height, and 1,389 with missing covariates. Result: 4,984 final study subjects after excluding 834.
FIGURE 1
Flowchart of the selection of the study population. HRS indicates the Health and Retirement Study. ELSA indicates the English Longitudinal Study on Aging.
Graphs depicting relationships between BRI and risk metrics. (A) and (B) show line graphs with a rising trend in odds ratios (OR) with increasing BRI, shaded for confidence intervals. (C) and (D) display histograms with overlaid line graphs illustrating hazard ratios (HR) linked to BRI, also with confidence intervals.
FIGURE 2
Smooth curve fitting (RCS analysis) between BRI and COPD. (A) Shows the relationship between BRI and COPD in the ELSA dataset; (B) illustrates the relationship between BRI and COPD in the HRS dataset; (C) presents the relationship between BRI and newly diagnosed COPD in the ELSA dataset; and (D) demonstrates the relationship between BRI and newly diagnosed COPD in the HRS dataset.
Kaplan-Meier curves for BRI are shown in two graphs, A and B, reflecting the probability of not having COPD over time. Both graphs compare low BRI and high BRI groups, with A showing a p-value of 0.00037 and B showing a p-value of 0.0085. Time is plotted on the x-axis, and the probability of not having COPD is on the y-axis. Tables below each graph indicate the number of individuals at risk at different time points.
FIGURE 3
Kaplan Meier curves of BRI and newly diagnosed COPD. (A) Shows the Kaplan Meier curves of BRI and newly diagnosed COPD in ELSA data. The samples were divided into low BRI group and high BRI group according to the threshold of 4.813; (B) shows the Kaplan Meier curves of BRI and newly diagnosed COPD in HRS data. The samples were divided into low BRI group and high BRI group according to the threshold of 5.273. The results indicate that with the increase of BRI, the risk of developing new COPD significantly increases.

References

    1. Webber E, Lin J, Thomas R. Screening for chronic obstructive pulmonary disease: updated evidence report and systematic review for the US Preventive Services Task Force. JAMA. (2022) 327:1812–6. 10.1001/jama.2022.4708 - DOI - PubMed
    1. Wang Y, Wang J, Lu Z, Zhou Q, Cao Y, Du Y, et al. Global, regional, and national burden of lower respiratory infections and chronic obstructive pulmonary disease, 1990-2021: a systematic analysis from the global burden of disease study 2021. Infection. (2025) [Online ahead of print]. 10.1007/s15010-025-02566-0 - DOI - PubMed
    1. GBD 2016 Causes of Death Collaborators. Global, regional, and national age-sex specific mortality for 264 causes of death, 1980-2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet. (2017) 390:1151–210. 10.1016/S0140-6736(17)32152-9 - DOI - PMC - PubMed
    1. Hua X, Liu Y, Xiao X. Association between lipid accumulation product and chronic obstructive pulmonary disease: a cross-sectional study based on U.S. adults. Front Nutr. (2024) 11:1517108. 10.3389/fnut.2024.1517108 - DOI - PMC - PubMed
    1. Iheanacho I, Zhang S, King D, Rizzo M, Ismaila A. Economic Burden of Chronic Obstructive Pulmonary Disease (COPD): a systematic literature review. Int J Chron Obstruct Pulmon Dis. (2020) 15:439–60. 10.2147/COPD.S234942 - DOI - PMC - PubMed

LinkOut - more resources