Longitudinal BMI change and outcomes in Chronic Obstructive Pulmonary Disease: a nationwide population-based cohort study

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Abstract

Background

The association between longitudinal body mass index (BMI) change and clinical outcomes in patients with chronic obstructive pulmonary disease (COPD) has not fully investigated.

Methods

This retrospective cohort study included 116,463 COPD patients aged ≥ 40, with at least two health examinations, one within 2 years before and another within 3 years after COPD diagnosis (January 1, 2014, to December 31, 2019). Associations between BMI percentage change with all-cause mortality, primary endpoint, and initial severe exacerbation were assessed.

Results

BMI decreased > 5% in 14,728 (12.6%), while maintained in 80,689 (69.2%), and increased > 5% in 21,046 (18.1%) after COPD diagnosis. Compared to maintenance group, adjusted hazard ratio (aHR) for all-cause mortality was 1.70 in BMI decrease group (95% CI:1.61, 1.79) and 1.13 in BMI increase group (95% CI:1.07, 1.20). In subgroup analysis, decrease in BMI showed a stronger effect on mortality as baseline BMI was lower, while an increase in BMI was related to an increase in mortality only in obese COPD patients with aHRs of 1.18 (95% CI: 1.03, 1.36). The aHRs for the risk of severe exacerbation (BMI decrease group and increase group vs. maintenance group) were 1.30 (95% CI:1.24, 1.35) and 1.12 (95% CI:1.07, 1.16), respectively.

Conclusions

A decrease in BMI was associated with an increased risk of all-cause mortality in a dose-dependent manner in patients with COPD. This was most significant in underweight patients. Regular monitoring for weight loss might be an important component for COPD management.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12931-024-02788-0.

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Most cited references 28

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Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies.

(2004)

A WHO expert consultation addressed the debate about interpretation of recommended body-mass index (BMI) cut-off points for determining overweight and obesity in Asian populations, and considered whether population-specific cut-off points for BMI are necessary. They reviewed scientific evidence that suggests that Asian populations have different associations between BMI, percentage of body fat, and health risks than do European populations. The consultation concluded that the proportion of Asian people with a high risk of type 2 diabetes and cardiovascular disease is substantial at BMIs lower than the existing WHO cut-off point for overweight (> or =25 kg/m2). However, available data do not necessarily indicate a clear BMI cut-off point for all Asians for overweight or obesity. The cut-off point for observed risk varies from 22 kg/m2 to 25 kg/m2 in different Asian populations; for high risk it varies from 26 kg/m2 to 31 kg/m2. No attempt was made, therefore, to redefine cut-off points for each population separately. The consultation also agreed that the WHO BMI cut-off points should be retained as international classifications. The consultation identified further potential public health action points (23.0, 27.5, 32.5, and 37.5 kg/m2) along the continuum of BMI, and proposed methods by which countries could make decisions about the definitions of increased risk for their population.

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Updating and validating the Charlson comorbidity index and score for risk adjustment in hospital discharge abstracts using data from 6 countries.

Hude Quan, Bing Li, Chantal M Couris … (2011)

With advances in the effectiveness of treatment and disease management, the contribution of chronic comorbid diseases (comorbidities) found within the Charlson comorbidity index to mortality is likely to have changed since development of the index in 1984. The authors reevaluated the Charlson index and reassigned weights to each condition by identifying and following patients to observe mortality within 1 year after hospital discharge. They applied the updated index and weights to hospital discharge data from 6 countries and tested for their ability to predict in-hospital mortality. Compared with the original Charlson weights, weights generated from the Calgary, Alberta, Canada, data (2004) were 0 for 5 comorbidities, decreased for 3 comorbidities, increased for 4 comorbidities, and did not change for 5 comorbidities. The C statistics for discriminating in-hospital mortality between the new score generated from the 12 comorbidities and the Charlson score were 0.825 (new) and 0.808 (old), respectively, in Australian data (2008), 0.828 and 0.825 in Canadian data (2008), 0.878 and 0.882 in French data (2004), 0.727 and 0.723 in Japanese data (2008), 0.831 and 0.836 in New Zealand data (2008), and 0.869 and 0.876 in Swiss data (2008). The updated index of 12 comorbidities showed good-to-excellent discrimination in predicting in-hospital mortality in data from 6 countries and may be more appropriate for use with more recent administrative data. © The Author 2011. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved.

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Is Open Access

Data Resource Profile: The National Health Information Database of the National Health Insurance Service in South Korea

Sang Cheol Seong, Yeon-Yong Kim, Young-Ho Khang … (2016)

Data resource basics The National Health Information Database (NHID) is a public database on health care utilization, health screening, socio-demographic variables, and mortality for the whole population of South Korea, formed by the National Health Insurance Service. The population included in the data is over 50 million, and the participation rate in the health screening programs was 74.8% in 2014. The NHID covers data between 2002 and 2014. Those insured by NHI pay insurance contributions and receive medical services from their health care providers. The NHIS, as the single insurer, pays costs based on the billing records of health care providers (Figure 1). To govern and carry out these processes in the NHI, the NHIS built a data warehouse to collect the required information on insurance eligibility, insurance contributions, medical history, and medical institutions. In 2012, the NHIS formed the NHID using information from medical treatment and health screening records and eligibility data from an existing database system. Figure 1. The governance of the National Health Insurance of South Korea. Data collected The eligibility database includes information about income-based insurance contributions, demographic variables, and date of death. The national health screening database includes information on health behaviors and bio-clinical variables. The health care utilization database includes information on records on inpatient and outpatient usage (diagnosis, length of stay, treatment costs, services received) and prescription records (drug code, days prescribed, daily dosage). The long-term care insurance database includes information about activities of daily living and service grades. The health care provider database includes data about the types of institutions, human resources, and equipment. In the NHID, de-identified join keys replacing the personal identifiers are used to interlink these databases. Data resource use Papers published covered various diseases or health conditions like infectious diseases, cancer, cardiovascular diseases, hypertension, diabetes mellitus, and injuries and risk factors such as smoking, alcohol consumption, and obesity. The impacts of health care and public health policies on health care utilization have been also explored since the data include all the necessary information reflecting patterns of health care utilization. Reasons to be cautious First, information on diagnosis and disease may not be optimal for identifying disease occurrence and prevalence since the data have been collected for medical service claims and reimbursement. However, the NHID also collects prescription data with secondary diagnosis, so the accuracy of the disease information can be improved. Second, the data linkage with other secondary national data is not widely available due to privacy issues in Korea. Governmental discussions on the statutory reform of data linkage using the NHID are under way. Collaboration and data access Access to the NHID can be obtained through the Health Insurance Data Service home page (http://nhiss.nhis.or.kr). An ethics approval from the researchers’ institutional review board is required with submission of a study proposal, which is reviewed by the NHIS review committee before providing data. Further inquiries on data use can be obtained by contacting the corresponding author. Funding and competing interests This work was supported by the NHIS in South Korea. The authors declare no competing interests.

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Author and article information

Contributors

Danbee Kang:

ORCID: https://orcid.org/0000-0003-0244-7714

dbee.kang@skku.edu

Hye Yun Park:

ORCID: https://orcid.org/0000-0002-5937-9671

hyeyunpark@skku.edu

Journal

Journal ID (nlm-ta): Respir Res

Journal ID (iso-abbrev): Respir Res

Title: Respiratory Research

Publisher: BioMed Central (London )

ISSN (Print): 1465-9921

ISSN (Electronic): 1465-993X

Publication date (Electronic): 30 March 2024

Publication date PMC-release: 30 March 2024

Publication date (Print): 2024

Volume: 25

Electronic Location Identifier: 150

Affiliations

[1 ]GRID grid.411144.5, ISNI 0000 0004 0532 9454, Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, , Kosin University Gospel Hospital, Kosin University College of Medicine, ; Busan, Republic of Korea

[2 ]GRID grid.264381.a, ISNI 0000 0001 2181 989X, Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, , Samsung Medical Center, Sungkyunkwan University School of Medicine, ; 81 Irwon-ro, Seoul, 06351 Republic of Korea

[3 ]Center for Clinical Epidemiology, Samsung Medical Center, ( https://ror.org/05a15z872) Seoul, Republic of Korea

[4 ]Department of Clinical Research Design and Evaluation, SAIHST, Sungkyunkwan University, ( https://ror.org/04q78tk20) 115 Irwon-ro, Seoul, 06335 South Korea

Author information

Taeyun Kim https://orcid.org/0000-0001-7786-5051

Sun Hye Shin https://orcid.org/0000-0003-3164-889X

Hyunsoo Kim https://orcid.org/0000-0001-5241-289X

Juhee Cho https://orcid.org/0000-0001-9081-0266

Danbee Kang https://orcid.org/0000-0003-0244-7714

Hye Yun Park https://orcid.org/0000-0002-5937-9671

Article

Publisher ID: 2788

DOI: 10.1186/s12931-024-02788-0

PMC ID: 10981805

PubMed ID: 38555459

SO-VID: 071b84b2-f0d4-4e19-ad1f-f8c660a8abdd

License:

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

History

Date received : 29 November 2023

Date accepted : 25 March 2024

Funding

Funded by: FundRef http://dx.doi.org/10.13039/501100003725, National Research Foundation of Korea;

Award ID: 2021R1A2C2093987

Custom metadata

ScienceOpen disciplines: Respiratory medicine

Keywords: copd,bmi,mortality,exacerbation,k-nhis

Data availability:

ScienceOpen disciplines: Respiratory medicine

Keywords: copd, bmi, mortality, exacerbation, k-nhis

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Longitudinal BMI change and outcomes in Chronic Obstructive Pulmonary Disease: a nationwide population-based cohort study

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Abstract

Background

Methods

Results

Conclusions

Supplementary Information

Related collections

Biomarkers for Inflammatory Lung Disease

Most cited references 28

Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies.

Updating and validating the Charlson comorbidity index and score for risk adjustment in hospital discharge abstracts using data from 6 countries.

Data Resource Profile: The National Health Information Database of the National Health Insurance Service in South Korea

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