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Background <p>Age-related diseases are associated with a wide range of risk factors that are complex and interconnected. Thus, we examined how lifestyle-related factors and physiological markers relate in impacting the risk of healthspan termination (HST).</p> Methods <p>The study recruited 217,412 participants of the UK Biobank prospective cohort study, aged between 37 and 73&#xa0;years. We studied 20 lifestyle-related factors and 19 physiological factors, and defined healthspan by 80 outcome events reported to account for 83.1% of total Disability Adjusted Life Years (DALYs) in 2019. We further implemented Cox proportional hazard models, Weighted Quantile Sum regression (WQS), Lasso regression, and Structural Equation Modeling (SEM) to illustrate the pathways linking sleep, lifestyle, and physiological markers with healthspan.</p> Results <p>During a median follow-up of 8&#xa0;years, 95,408 (43.8%) participants had terminated healthspan. The majority of the lifestyle-related factors and physiological markers were statistically significantly associated with HST. WQS analysis showed that collectively unhealthy lifestyle-related factors (OR = 2.77; 95%CI: 2.65, 2.89; <i>P</i> &lt; 0.01) and physiological markers (OR = 3.27; 95%CI: 3.06, 3.50; <i>P</i> &lt; 0.01) increased the risk of a shortened healthspan. Path analysis further demonstrated that the risk factors were related to one another. Also, for every one standard deviation (SD) increase in biomarkers (β = 0.03; <i>P</i> &lt; 0.01), sleep behavior (β = 0.14; <i>P</i> &lt; 0.01), and lifestyle (β = 0.16; <i>P</i> &lt; 0.01), there was a small positive effect on healthspan. Conversely, one SD increase in mental health predicted negatively on healthspan (β = -0.12; <i>P</i> &lt; 0.01). Additionally, biomarkers (β = 0.05; <i>P</i> &lt; 0.01) and sleep behavior (β = 0.17; <i>P</i> &lt; 0.01) mediated between lifestyle and healthspan. The total indirect effect was also significant (β = 0.22; <i>P</i> = 0.01).</p> Conclusion <p>Healthspan is determined by a host of interconnected factors, including sleep, lifestyle, and physiological markers. Future research and public health initiatives can be guided by this finding.</p>

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Associations of sleep, lifestyle, and physiological markers with healthspan termination: a prospective cohort analysis

  • 期刊论文

  • 发表日期：

    2025年12月3日

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  • Muhammed Lamin Sambou,
  • Yifan Wang,
  • Feifei Xu,
  • Salimata Yakubu,
  • Shuang Liang,
  • Juncheng Dai

摘要

Background

Age-related diseases are associated with a wide range of risk factors that are complex and interconnected. Thus, we examined how lifestyle-related factors and physiological markers relate in impacting the risk of healthspan termination (HST).

Methods

The study recruited 217,412 participants of the UK Biobank prospective cohort study, aged between 37 and 73 years. We studied 20 lifestyle-related factors and 19 physiological factors, and defined healthspan by 80 outcome events reported to account for 83.1% of total Disability Adjusted Life Years (DALYs) in 2019. We further implemented Cox proportional hazard models, Weighted Quantile Sum regression (WQS), Lasso regression, and Structural Equation Modeling (SEM) to illustrate the pathways linking sleep, lifestyle, and physiological markers with healthspan.

Results

During a median follow-up of 8 years, 95,408 (43.8%) participants had terminated healthspan. The majority of the lifestyle-related factors and physiological markers were statistically significantly associated with HST. WQS analysis showed that collectively unhealthy lifestyle-related factors (OR = 2.77; 95%CI: 2.65, 2.89; P < 0.01) and physiological markers (OR = 3.27; 95%CI: 3.06, 3.50; P < 0.01) increased the risk of a shortened healthspan. Path analysis further demonstrated that the risk factors were related to one another. Also, for every one standard deviation (SD) increase in biomarkers (β = 0.03; P < 0.01), sleep behavior (β = 0.14; P < 0.01), and lifestyle (β = 0.16; P < 0.01), there was a small positive effect on healthspan. Conversely, one SD increase in mental health predicted negatively on healthspan (β = -0.12; P < 0.01). Additionally, biomarkers (β = 0.05; P < 0.01) and sleep behavior (β = 0.17; P < 0.01) mediated between lifestyle and healthspan. The total indirect effect was also significant (β = 0.22; P = 0.01).

Conclusion

Healthspan is determined by a host of interconnected factors, including sleep, lifestyle, and physiological markers. Future research and public health initiatives can be guided by this finding.