---
title: "Two-year clustered changes in body shape linked to fat, muscle, sleep, and activity in Korean adul"
id: "plos-one-8-lifestyle-factors-and-clustered-patterns-of-body-shape-change-in-korean-adults"
canonical_url: "https://medichelpline.com/clinical-feed/plos-one-8-lifestyle-factors-and-clustered-patterns-of-body-shape-change-in-korean-adults"
content_type: "clinical_feed_article"
specialty: "Endocrinology"
source_name: "PLOS ONE (Medicine)"
source_url: "https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360853"
published_at: "2026-10-15T14:00:00.000Z"
evidence_level: "Journal Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Two-year clustered changes in body shape linked to fat, muscle, sleep, and activity in Korean adul
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/plos-one-8-lifestyle-factors-and-clustered-patterns-of-body-shape-change-in-korean-adults
- **Specialty:** [Endocrinology](https://medichelpline.com/clinical-feed/endocrinology.md)
- **Primary Source:** PLOS ONE (Medicine)
- **Source URL:** [Original Journal Publication](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360853)
- **Published At:** 2026-10-15T14:00:00.000Z
- **Evidence Rating:** Journal Feed
## Executive GIST (TL;DR)
- The study used 2-year follow-up data from the Korean Medicine Daejeon Citizen Cohort (KDCC) and included 1,404 adults who completed both baseline and first follow-up visits. - Investigators applied k-means clustering to two-year changes in six body-shape indicators: **BMI**, waist circumference (WC), hip circumference (HC), waist-to-hip ratio (WHR), waist-to-height ratio (**WHtR**), and body weight. - Three trajectory clusters emerged: Stable (48.0%), Adiposity-Increasing (31.0%), and Adiposity-Decreasing (21.0%). - Baseline **body fat mass** was significantly associated with later cluster membership; higher baseline fat mass correlated with non-stable trajectories. - Over follow-up, increases in **body fat mass** and decreases in **skeletal muscle mass** were strongly associated with assignment to the Adiposity-Increasing cluster. - Worsening sleep quality (≥2.5-point increase in the **PSQI**) and reduced physical activity (declines on the **GPAQ**) were significantly associated with the Adiposity-Increasing trajectory. - The analytic sample had mean age 44.74 years (SD 6.67); 30% male and 70% female. Follow-up occurred June 2020–March 2022, overlapping the COVID-19 pandemic period. - The dataset for the study is not publicly available due to confidentiality; de-identified minimal datasets may be available via institutional request to the Korean Medicine Data Center. - The authors used multinomial logistic regression to identify variables associated with cluster membership; the source reported complete-case analysis and advised selection bias cannot be excluded. - The study supports evaluating combined body-shape indicators and modifiable lifestyle factors when characterizing adiposity-related trajectories and designing tailored interventions.
## Clinical Analysis & Structured Key Points
[ Skip to main content ](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360853#main-content) Advertisement * [plos.org](https://plos.org/) * [Create account](https://community.plos.org/registration/new) * [Sign in](https://journals.plos.org/user/secure/login?page=%2Fplosone%2Farticle%3Fid%3D10.1371%2Fjournal.pone.0360853) * * About * Browse * Publish * [](https://journals.plos.org/plosone/ "PLOS One") * Search [advanced search](https://journals.plos.org/plosone/search) * [Browse Topics](https://journals.plos.org/plosone/subjectAreaBrowse) Browse Subject Areas ? Click through the PLOS taxonomy to find articles in your field. For more information about PLOS Subject Areas, click [here](https://github.com/PLOS/plos-thesaurus/blob/master/README.md "Link opens in new window"). [](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360853) [](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360853) * 0 [Save](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0360853#savedHeader) [Total Mendeley and Citeulike bookmarks.](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0360853#savedHeader) * 0 [Citation](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0360853#citedHeader) [Paper's citation count computed by Dimensions.](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0360853#citedHeader) * 45 [View](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0360853#viewedHeader) [PLOS views and downloads.](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0360853#viewedHeader) * 0 [Share](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0360853#discussedHeader) [Sum of Facebook, Twitter, Reddit and Wikipedia activity.](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0360853#discussedHeader) Open Access Peer-reviewed Research Article # Lifestyle factors and clustered patterns of body shape change in Korean adults: A longitudinal observational study * Jaeho Lee, Roles Conceptualization, Formal analysis, Writing – original draft, Writing – review & editing Affiliation Division of Korean Medicine Data, Korea Institute of Oriental Medicine, Daejeon, Republic of Korea [ ![ORCID logo](https://journals.plos.org/resource/img/orcid_16x16.png) https://orcid.org/0009-0006-7647-6111 ](https://orcid.org/0009-0006-7647-6111 "ORCID Registry") ⨯ * Kyoungsik Jeong, Roles Data curation, Resources Affiliation Division of Korean Medicine Data, Korea Institute of Oriental Medicine, Daejeon, Republic of Korea ⨯ * Youngseop Lee, Roles Data curation, Funding acquisition, Resources Affiliation Division of Korean Medicine Data, Korea Institute of Oriental Medicine, Daejeon, Republic of Korea [ ![ORCID logo](https://journals.plos.org/resource/img/orcid_16x16.png) https://orcid.org/0000-0002-8879-142X ](https://orcid.org/0000-0002-8879-142X "ORCID Registry") ⨯ * Younghwa Baek Roles Supervision, Writing – original draft, Writing – review & editing * E-mail: aori79@kiom.re.kr Affiliation Division of Korean Medicine Data, Korea Institute of Oriental Medicine, Daejeon, Republic of Korea [ ![ORCID logo](https://journals.plos.org/resource/img/orcid_16x16.png) https://orcid.org/0000-0002-1827-1701 ](https://orcid.org/0000-0002-1827-1701 "ORCID Registry") ⨯ # Lifestyle factors and clustered patterns of body shape change in Korean adults: A longitudinal observational study * Jaeho Lee, * Kyoungsik Jeong, * Youngseop Lee, * Younghwa Baek ![PLOS](https://journals.plos.org/resource/img/logo-plos-full-color.svg) x * Published: October 15, 2026 * * [Article](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360853) * [Authors](https://journals.plos.org/plosone/article/authors?id=10.1371/journal.pone.0360853) * [Metrics](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0360853) * [Comments](https://journals.plos.org/plosone/article/comments?id=10.1371/journal.pone.0360853) * [Media Coverage](http://plos.altmetric.com/details/doi/10.1371/journal.pone.0360853) * [Abstract](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360853#abstract0) * [Introduction](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360853#sec005) * [Materials and methods](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360853#sec006) * [Results](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360853#sec010) * [Discussion](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360853#sec016) * [Conclusions](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360853#sec018) * [Supporting information](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360853#sec019) * [References](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360853#references) * [Reader Comments](https://journals.plos.org/plosone/article/comments?id=10.1371/journal.pone.0360853) * [Figures](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360853) ## Abstract ### Background Obesity is a major health problem driven by lifestyle habits and changes in body composition, particularly increased body fat mass and reduced skeletal muscle mass. Body mass index alone cannot fully characterize adiposity distribution or changes in muscle mass. Body-shape indicators such as waist circumference, waist-to-hip ratio, and waist-to-height ratio predict metabolic and cardiovascular disease risk; however, few studies have assessed longitudinal changes in these measures together with lifestyle factors. In this study, we aimed to identify clusters of body-shape change using 2-year follow-up data from Korean adults and to elucidate the body-composition and lifestyle factors underlying these changes. ### Methods K-means cluster analysis was applied to 1,404 participants in the Korean Medicine Daejeon Citizen Cohort using 2-year changes in body mass index, waist circumference, hip circumference, waist-to-hip ratio, waist-to-height ratio, and body weight. Baseline body shape, body composition, and lifestyle indicators were compared across clusters, and multinomial logistic regression was used to identify variables associated with cluster membership. ### Results Three clusters were identified: Stable (48.0%), Adiposity-Increasing (31.0%), and Adiposity-Decreasing (21.0%). At baseline, cluster membership was significantly associated with body fat mass. Over follow-up, changes in body fat mass and skeletal muscle mass showed strong associations with cluster assignment. In addition, clinically meaningful worsening in sleep quality (increase of ≥2.5 points in the Pittsburgh Sleep Quality Index) and reduced physical activity (decrease in activity as measured by the Global Physical Activity Questionnaire) were significantly associated with the Adiposity-Increasing cluster. ### Conclusions Changes in body composition, especially body fat mass and skeletal muscle mass, were closely associated with body-shape trajectories, while worsening sleep quality and declining physical activity were associated with the Adiposity-Increasing trajectory. These findings support an integrative approach that considers body shape, body composition, and modifiable lifestyle factors when characterizing adiposity-related trajectories and designing tailored lifestyle interventions. ## Figures ![Table 5](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0360853.t005) ![Fig 1](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0360853.g001) ![Fig 2](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0360853.g002) ![Table 1](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0360853.t001) ![Table 2](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0360853.t002) ![Table 3](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0360853.t003) ![Table 4](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0360853.t004) ![Table 5](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0360853.t005) ![Fig 1](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0360853.g001) ![Fig 2](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0360853.g002) ![Table 1](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0360853.t001) **Citation:** Lee J, Jeong K, Lee Y, Baek Y (2026) Lifestyle factors and clustered patterns of body shape change in Korean adults: A longitudinal observational study. PLoS One 21(10): e0360853. https://doi.org/10.1371/journal.pone.0360853 **Editor:** Fei Xu, Nanjing Medical University Affiliated Nanjing Municipal Center for Disease Control and Prevention, CHINA **Received:** October 21, 2025; **Accepted:** September 29, 2026; **Published:** October 15, 2026 **Copyright:** © 2026 Lee et al. This is an open access article distributed under the terms of the [Creative Commons Attribution License](http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. **Data Availability:** The datasets analyzed during the current study are not publicly available due to confidentiality and ethical restrictions regarding sensitive participant information. However, de-identified minimal datasets may be made available to qualified researchers who meet the criteria for access to confidential data, subject to institutional review. Requests for data access are managed through an institutional process and should be directed via email to the Korean Medicine Data Center at kdc@kiom.re.kr. **Funding:** This work was supported by the Korea Institute of Oriental Medicine [grant numbers KSN1739121 and KSN25120212]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. **Competing interests:** The authors have declared that no competing interests exist. **Abbreviations:** BMI, body mass index; WC, waist circumference; WHR, waist-to-hip ratio; WHtR, waist-to-height ratio; KDCC, Korean Medicine Daejeon Citizen Cohort; COVID-19, coronavirus disease 2019; HC, hip circumference; BFM, body fat mass; SMM, skeletal muscle mass; PSQI, Pittsburgh Sleep Quality Index; GPAQ, Global Physical Activity Questionnaire; OR, odds ratio; CI, confidence interval; VIF, variance inflation factor; MCID, minimal clinically important difference ## Introduction Overweight and obesity negatively affect nearly all physiological functions through the accumulation of abnormal or excessive adipose tissue, leading to increased health risks. Nearly one-third of the global population is currently classified as either overweight or obese [[1](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360853#pone.0360853.ref001)]. Obesity results from complex interactions among multiple factors, including genetics, body composition and fat distribution, lifestyle habits, and psychological factors. Rather than acting independently, these factors influence one another and may increase or decrease obesity risk [[2](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360853#pone.0360853.ref002),[3](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360853#pone.0360853.ref003)]. In particular, lifestyle factors such as sleep and physical activity are closely associated with body mass index (BMI) and may affect obesity risk [[3](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360853#pone.0360853.ref003),[4](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360853#pone.0360853.ref004)]. However, BMI alone is insufficient to capture the health effects of excess adiposity, and additional measures should be considered [[5](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360853#pone.0360853.ref005)]. Individuals with the same BMI can have different risks of cardiovascular disease, metabolic syndrome, or type 2 diabetes depending on body fat distribution and/or differences in lean mass (including changes in muscle mass) [[6](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360853#pone.0360853.ref006),[7](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360853#pone.0360853.ref007)]. Accordingly, beyond absolute body-weight measures, tracking changes in body shape has been proposed as a more realistic approach with improved predictive power [[8](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360853#pone.0360853.ref008)–[10](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360853#pone.0360853.ref010)]. Changes in body shape may reflect not only physiological changes but also changes in lifestyle habits [[11](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360853#pone.0360853.ref011)]. Decreased sleep quality and physical activity, along with increased sedentary time, are associated with increased fat mass and decreased muscle mass, which may in turn affect body-shape indicators [[12](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360853#pone.0360853.ref012),[13](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360853#pone.0360853.ref013)]. Even individuals with similar body-shape indicators can experience different health effects depending on interactions with lifestyle factors [[14](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360853#pone.0360853.ref014)]. For example, when increased body weight is accompanied by regular resistance exercise, muscle mass is more likely to increase; however, when accompanied by poor sleep quality or prolonged sedentary behavior, the same weight gain may be more consistent with increased fat mass [[13](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360853#pone.0360853.ref013)–[17](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360853#pone.0360853.ref017)]. Given these complex interactions, there is a need to simultaneously analyze changes in body shape and lifestyle factors. Compared with BMI, metrics such as waist-to-height ratio (WHtR), waist-to-hip ratio (WHR), and waist circumference (WC) have been reported to better predict abdominal obesity and metabolic risk [[18](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360853#pone.0360853.ref018)–[20](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360853#pone.0360853.ref020)]. BMI alone has also been shown to be insufficient to fully explain adiposity-related risk, and the use of BMI in parallel with measures such as WC and WHtR has been recommended [[21](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360853#pone.0360853.ref021)]. Thus, combinations of body-shape indicators or patterns of change may improve predictive power for obesity risk and related diseases [[21](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360853#pone.0360853.ref021)–[23](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360853#pone.0360853.ref023)]. Nevertheless, most studies to date have evaluated obesity using cross-sectional body-shape indicators, and relatively few analyses account for changes in body shape over time and their interactions with lifestyle factors [[24](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360853#pone.0360853.ref024)]. Moreover, prior studies have largely focused on linear relationships between a single variable and obesity-related risk, which may not fully reflect the complexity and heterogeneity of health states [[21](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360853#pone.0360853.ref021)]. Therefore, this study aimed to derive clusters based on changes in body shape over a 2-year follow-up period and to analyze their relationships with underlying body-shape indicators and lifestyle factors, with the goal of providing foundational data for predicting obesity risk and informing customized intervention strategies. ## Materials and methods ### Participants This study used data from the Korean Medicine Daejeon Citizen Cohort (KDCC), which is managed by the Korean Medicine Data Center (KDC) of the Korea Institute of Oriental Medicine (KIOM) [[25](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360853#pone.0360853.ref025)]. The KDCC is a prospective cohort study designed to support the prevention and management of chronic diseases and incorporates individual and lifestyle characteristics. The baseline survey was conducted between 2017 and 2019, and follow-up surveys are conducted at 2-year intervals [[25](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360853#pone.0360853.ref025)]. The first follow-up examinations (visit 2, V2) were conducted between June 2020 and March 2022, during a period that overlapped with the coronavirus disease 2019 (COVID-19) pandemic in Korea. Follow-up assessments were performed at the cohort examination center under standardized protocols in accordance with national COVID-19–related public health and infection-control guidelines. Of the 2,000 participants enrolled at baseline (visit 1, V1), 1,404 participants who completed the first follow-up survey (visit 2, V2; June 2020 to March 2022) and had no missing values for the main variables (body-shape indicators and lifestyle habits) were included in the final analysis ([Fig 1](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0360853#pone-0360853-g001)). At baseline, we also compared participants included in the analytic sample (n = 1,404) with those excluded because of loss to follow-up and/or missing data (n = 596; data not shown). There were no significant differences in height, weight, BMI, WC, WHR, WHtR, body fat mass, or SMM between the groups (all _p_ > .05). The analytic sample was slightly older and had a slightly larger hip circumference than excluded participants (both _p_ < .05). All analyses were therefore conducted using a complete-case dataset; selection bias cannot be ruled out if participants with missing data differed systematically from those included. [![thumbnail](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0360853.g001)](https://journals.plos.org/plosone/article/figure/image?size=medium&id=10.1371/journal.pone.0360853.g001 "Click for larger image") Download: * [PNG larger image](https://journals.plos.org/plosone/article/figure/image?download&size=large&id=10.1371/journal.pone.0360853.g001) * [TIFF original image](https://journals.plos.org/plosone/article/figure/image?download&size=original&id=10.1371/journal.pone.0360853.g001) Fig 1. Flowchart of the study. [ https://doi.org/10.1371/journal.pone.0360853.g001](https://doi.org/10.1371/journal.pone.0360853.g001) The mean age of included participants was 44.74 years (SD, 6.67); 421 (30.0%) were male, and 983 (70.0%) were female. All participants provided written informed consent after receiving an explanation of the study objectives and the use of personal information. This study was approved by the KIOM Institutional Review Board (IRB; No. I-1703/002–002, DJDSKH-17-BM-12) and was conducted in accordance with the ethical principles of the Declaration of Helsinki. ### Data collection Anthropometric me
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