Dose-response relationship between sleep duration and mediation of phenotypic age acceleration: A cross-sectional study.

This study aimed to explore the dose-response relationship between sleep duration and phenotypic age acceleration (PhenoAgeAccel), as well as the potential impact of different sleep duration patterns on the biological aging process. Utilizing data from the National Health and Nutrition Examination Survey from 2001 to 2010, this cross-sectional study included 8992 adult participants. Sleep duration data were collected via self-report and categorized into "<7 hours/day," "7-9 hours/day," and ">9 hours/day" groups. PhenoAgeAccel was calculated by combining actual age with 9 biomarkers. Weighted generalized linear regression models and unrestricted cubic spline analyses were employed to examine the relationship between sleep duration and PhenoAgeAccel. Interaction effects were assessed to evaluate the influence of different demographic and health characteristics. In unadjusted analyses, the 7 to 9 hours/day sleep group showed a significant deceleration in phenotypic aging compared to the <7 hours/day group (β = -1.207, P < .0001). However, this association was substantially attenuated and no longer statistically significant after full adjustment for demographic, lifestyle, and comorbidity factors. A significant nonlinear dose-response relationship was confirmed, with an inflection point at approximately 6.7 hours. Interaction effect tests revealed that this relationship was significantly influenced by an individual's smoking and diabetes status (P < .01). This study suggests that moderate sleep duration of 7 to 9 hours/day is associated with a deceleration in phenotypic aging, with a critical inflection point for sleep and aging health at approximately 6.7 hours. Both insufficient and excessive sleep durations may be detrimental to slowing the aging process. The results of the interaction effect tests emphasize the need to consider individual smoking and diabetes status when developing targeted health interventions. These findings provide new insights into the complex relationship between sleep and biological aging, offering a scientific basis for public health guidance and the optimization of individual sleep habits. Future research should employ longitudinal designs and objective sleep monitoring tools to further explore the causal relationship and underlying mechanisms between sleep and biological aging.