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Relations of Lifestyle Behavior Clusters to Dyslipidemia in China: A Compositional Data Analysis

Author

Listed:
  • Xiaona Na

    (Department of Social Medicine and Health Education, School of Public Health, Peking University Health Science Center, Beijing 100191, China)

  • Yangyang Chen

    (Department of Social Medicine and Health Education, School of Public Health, Peking University Health Science Center, Beijing 100191, China)

  • Xiaochuan Ma

    (Department of Social Medicine and Health Education, School of Public Health, Peking University Health Science Center, Beijing 100191, China)

  • Dongping Wang

    (Wuhai Center for Disease Control and Prevention, Inner Mongolia 016099, China)

  • Haojie Wang

    (Wuhai Center for Disease Control and Prevention, Inner Mongolia 016099, China)

  • Yang Song

    (Wuhai Center for Disease Control and Prevention, Inner Mongolia 016099, China)

  • Yumeng Hua

    (Department of Social Medicine and Health Education, School of Public Health, Peking University Health Science Center, Beijing 100191, China)

  • Peiyu Wang

    (Department of Social Medicine and Health Education, School of Public Health, Peking University Health Science Center, Beijing 100191, China)

  • Aiping Liu

    (Department of Social Medicine and Health Education, School of Public Health, Peking University Health Science Center, Beijing 100191, China)

Abstract

Dyslipidemia is associated with lifestyle behaviors, while several lifestyle behaviors exist collectively among some populaitons. This study aims to identify lifestyle behavior clusters and their relations to dyslipidemia. This cross-sectional study was conducted in Wuhai City, China. Cluster analysis combined with compositional data analysis was conducted, with 24-h time-use on daily activities and dietary patterns as input variables. Multiple logistic regression was conducted to compare dyslipidemia among clusters. A total of 4306 participants were included. A higher prevalence of newly diagnosed dyslipidemia was found among participants in cluster 1 (long sedentary behavior (SB) and the shortest sleep, high-salt and oil diet) /cluster 5 (the longest SB and short sleep), relative to the other clusters in both age groups (<50 years and ≥50 years). In conclusion, unhealthy lifestyle behaviors may exist together among some of the population, suggesting that these people are potential subjects of health education and behavior interventions. Future research should be conducted to investigate the relative significance of specific lifestyle behaviors in relation to dyslipidemia.

Suggested Citation

  • Xiaona Na & Yangyang Chen & Xiaochuan Ma & Dongping Wang & Haojie Wang & Yang Song & Yumeng Hua & Peiyu Wang & Aiping Liu, 2021. "Relations of Lifestyle Behavior Clusters to Dyslipidemia in China: A Compositional Data Analysis," IJERPH, MDPI, vol. 18(15), pages 1-13, July.
  • Handle: RePEc:gam:jijerp:v:18:y:2021:i:15:p:7763-:d:599023
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    References listed on IDEAS

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    1. Li Qi & Xianbin Ding & Wenge Tang & Qin Li & Deqiang Mao & Yulin Wang, 2015. "Prevalence and Risk Factors Associated with Dyslipidemia in Chongqing, China," IJERPH, MDPI, vol. 12(10), pages 1-11, October.
    2. Javier Palarea-Albaladejo & Josep Martín-Fernández & Jesús Soto, 2012. "Dealing with Distances and Transformations for Fuzzy C-Means Clustering of Compositional Data," Journal of Classification, Springer;The Classification Society, vol. 29(2), pages 144-169, July.
    3. Janina Janurek & Sascha Abdel Hadi & Andreas Mojzisch & Jan Alexander Häusser, 2018. "The Association of the 24 Hour Distribution of Time Spent in Physical Activity, Work, and Sleep with Emotional Exhaustion," IJERPH, MDPI, vol. 15(9), pages 1-14, September.
    4. Charlotte Lund Rasmussen & Javier Palarea-Albaladejo & Adrian Bauman & Nidhi Gupta & Kirsten Nabe-Nielsen & Marie Birk Jørgensen & Andreas Holtermann, 2018. "Does Physically Demanding Work Hinder a Physically Active Lifestyle in Low Socioeconomic Workers? A Compositional Data Analysis Based on Accelerometer Data," IJERPH, MDPI, vol. 15(7), pages 1-23, June.
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