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An Effortless Lifestyle Narrows Health Inequality in Modern Society

28 Pages Posted: 12 Nov 2024

See all articles by Fan Pu

Fan Pu

Zhejiang University - Department of Big Data in Health Science

Jiening Yu

Zhejiang University - Department of Big Data in Health Science

Zhiyong Zhao

Zhejiang University

Xucheng Wu

Zhejiang University - Department of Big Data in Health Science

Weiran Chen

Zhejiang University - Department of Big Data in Health Science

Ruike Chen

Zhejiang University

Jiayao Fan

Zhejiang University - Department of Big Data in Health Science

Xueqing Jia

Zhejiang University - Department of Big Data in Health Science; Zhejiang University

Dan Zhou

Zhejiang University - Department of Big Data in Health Science

John S. Ji

Tsinghua University - Vanke School of Public Health

Dan Wu

Zhejiang University

Zuyun Liu

Zhejiang University - Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province

More...

Abstract

Background: To examine associations of a new effortless lifestyle with multiple health outcomes and whether it narrows health inequality among disadvantaged populations. 

Methods: An effortless lifestyle score was constructed based on nonlinear associations of four effortless behaviors (i.e., tea intake, sunbathing, sleeping, and sedentary time) with all-cause mortality in 407,333 UK Biobank participants. We examined its associations with multiple health outcomes (e.g., dementia, cardiovascular disease, diabetes mellitus, depression and aging). Mediation analyses were performed to examine whether this effortless lifestyle narrows health inequality among disadvantaged populations (i.e., those of low socioeconomic status or obesity). By further incorporating multimodal data (i.e., brain phenotypes and peripheral markers), we investigated the underlying mechanisms.

Findings: A higher effortless lifestyle score was significantly associated with reduced risks of all health outcomes (P < 0.001). The effortless lifestyle significantly mediated associations of disadvantaged populations with all health outcomes, with the proportion of 8.2% (4.7% to 16.1%) of the overweight population experiencing anxiety compared to those with a normal weight. Moreover, a higher effortless lifestyle score was associated with brain structure and function in several stress-related regions or networks, increased white matter integrity in superior fronto-occipital fasciculus (β = 0.026, P = 0.008), and decreased functional connectivity between sensorimotor and frontoparietal networks (IC12-IC21, β = -0.026, P = 0.006). Lipid metabolic biomarkers were significantly associated with a higher effortless lifestyle score.

Interpretation: The effortless lifestyle we proposed might be an alternative way to further narrow health inequality in modern society.

Funding: This research was supported by grants from the National Natural Science Foundation of China (72374180), the Research Center of Prevention and Treatment of Senescence Syndrome, School of Medicine Zhejiang University (2022010002), the Fundamental Research Funds for the Central Universities, Zhejiang Key Laboratory of Intelligent Preventive Medicine (2020E10004), Zhejiang University Global Partnership Fund and Zhejiang University School of Public Health Interdisciplinary Research Innovation Team Development Project.

Declaration of Interest: The authors declare no competing interests.

Ethical Approval: The use of UK Biobank data was performed under application 61856.

Keywords: cohort study, effortless lifestyle, health inequality, socioeconoic status, brain magnetic resonance imaging

Suggested Citation

Pu, Fan and Yu, Jiening and Zhao, Zhiyong and Wu, Xucheng and Chen, Weiran and Chen, Ruike and Fan, Jiayao and Jia, Xueqing and Zhou, Dan and Ji, John S. and Wu, Dan and Liu, Zuyun, An Effortless Lifestyle Narrows Health Inequality in Modern Society. Available at SSRN: https://ssrn.com/abstract=5016423 or http://dx.doi.org/10.2139/ssrn.5016423

Fan Pu

Zhejiang University - Department of Big Data in Health Science ( email )

Jiening Yu

Zhejiang University - Department of Big Data in Health Science ( email )

Zhiyong Zhao

Zhejiang University ( email )

38 Zheda Road
Hangzhou, 310058
China

Xucheng Wu

Zhejiang University - Department of Big Data in Health Science ( email )

Weiran Chen

Zhejiang University - Department of Big Data in Health Science ( email )

Ruike Chen

Zhejiang University ( email )

38 Zheda Road
Hangzhou, 310058
China

Jiayao Fan

Zhejiang University - Department of Big Data in Health Science ( email )

Xueqing Jia

Zhejiang University - Department of Big Data in Health Science ( email )

Zhejiang University ( email )

Dan Zhou

Zhejiang University - Department of Big Data in Health Science ( email )

John S. Ji

Tsinghua University - Vanke School of Public Health ( email )

Beijing
China

Dan Wu

Zhejiang University ( email )

38 Zheda Road
Hangzhou, 310058
China

Zuyun Liu (Contact Author)

Zhejiang University - Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province ( email )