A large study maps sleep duration against multi-organ aging clocks, revealing why the healthiest biological aging profiles may cluster around the familiar 6 to 8 hour sleep window.
Study: Sleep chart of biological ageing clocks in middle and late life. Image Credit: Gorodenkoff / Shutterstock
In a recent study published in the journal Nature, researchers from the MULTI consortium describe the development and potential of the “Sleep Chart,” a comprehensive framework developed using large-scale population data to assess the correlation between self-reported sleep duration and 23 biological aging clocks.
The study’s findings revealed a pervasive, U-shaped relationship between sleep and biological age gaps across nine of the 23 aging clocks spanning brain and body systems, and found that the lowest observed biological age gaps occurred within a range of 6.4 to 7.8 hours of self-reported daily sleep duration. Furthermore, the results indicate that both insufficient and excessive sleep are associated with increased systemic disease risk and biological aging, as well as elevated all-cause mortality, underscoring the potential relevance of sleep duration to long-term health outcomes.
Sleep Duration and Biological Aging Background
Traditional metrics of aging, particularly chronological age, the number of candles on a birthday cake, often fail to capture the granular, organ-specific decline that precedes clinical disease, collectively termed “biological age.” While previous neuroimaging studies have identified nonlinear associations between sleep and brain phenotypes, it remained unclear if these patterns generalized to peripheral body systems and molecular layers.
The relatively recent confluence of Magnetic Resonance Imaging (MRI) with high-throughput next-generation (“next-gen”) plasma proteomics and metabolomics now enables researchers to quantify an individual’s organ biological age relative to their calendar age.
Researchers now aim to identify sample-specific minimum values in biological age gap curves to develop personalized, potentially modifiable targets to extend human longevity and reduce the burden of age-related systemic disorders.
UK Biobank Sleep Chart Study Design
The present study was carried out by the MULTI Consortium and utilized data from the UK Biobank (UKBB), encompassing more than 500,000 participants (37 to 84 years). The study’s primary exposure was questionnaire-derived, participant self-reported, sleep duration (Field ID: 1160). The analyses were notably restricted to UKBB individuals reporting 4 to 10 hours to minimize outlier influence.
The MULTI Consortium subsequently developed 23 organ-specific biological age gaps (BAGs) using a nested cross-validation machine learning framework:
- MRI-based clocks (MRIBAG; n = 7) were used to quantify structural integrity in the brain, heart, liver, pancreas, spleen, adipose tissue, and kidney.
- Proteomic clocks (ProtBAG; n = 11), which leveraged Olink-based plasma proteomics to track aging signatures in circulating proteins, provide organ-specific resolution.
- Metabolomic clocks (MetBAG; n = 5) were used to analyze plasma metabolomic profiles derived from the Nightingale Health dataset.
Generalized Additive Models (GAMs) with cubic regression splines were subsequently used to model nonlinear associations without prior assumptions regarding curve shape. Nonlinearity was quantified statistically using the effective degrees of freedom (e.d.f.) curve-complexity metric.
U-Shaped Sleep and Aging Findings
The study’s GAM analyses revealed a U-shaped relationship in 9 of the 23 clocks (p < 0.05), indicating that either too much (> 8 hours) or too little (< 6 hours) sleep was associated with higher biological age gaps in these BAGs. The “youngest” biological ages were observed among participants who reported 6.4-7.8 hours of sleep.
The brain ProtBAG showed the most robust U-shaped association with sleep (e.d.f. = 3.61, P1 < 1 x 10-20). The sample-specific minimum, representing the “youngest” biological state, was 7.82 hours for females and 7.70 hours for males. The endocrine MetBAG also demonstrated a significant U-shaped relationship (e.d.f. = 1.04, P1 = 3.97 x 10-5), with estimated minima at 6.67 hours for females and 6.06 hours for males.
Simultaneously, the brain MRIBAG was observed to reach its minimum at approximately 6.48 hours in females and 6.42 hours in males (e.d.f. = 1.94, P1 = 3.85 x 10-7).
Furthermore, short sleep (<6 h) was genetically correlated with heart failure (gc = 0.31), depression (gc = 0.37), and type 2 diabetes (T2D), whereas long sleep showed a more focused genetic correlation profile, mainly involving brain-related and psychiatric traits.
In contrast, long sleep duration was hypothesized to serve as a "marker" of underlying subclinical disease, informed by Structural Equation Modeling (SEM) data, suggesting that excessive sleep may be a marker of underlying physiological compensation or subclinical disease processes, potentially including neurodegeneration.
Finally, mortality and disease evaluations revealed that both extremes were associated with a roughly 40-50% increased risk of all-cause mortality (long sleep hazard ratio [HR] = 1.40; short sleep HR = 1.50; P < 1 x 10-20).
Sleep Optimization and Healthy Aging Implications
This study’s results and consequential Sleep Chart demonstrate that sleep duration is associated with systemic patterns of biological aging across diverse organ systems and omics technologies. The consistent U-shaped associations observed in both structural imaging and circulating molecular markers suggest that maintaining sleep within the 6–8-hour window may be relevant to healthier organ aging profiles.
The study notably revealed that while women generally required slightly more sleep than men to achieve the lowest biological age in certain categories, such as the brain's proteomic clock (7.82 hours for females vs. 7.70 hours for males), sleep optimization is a potential target for systemic health management and the promotion of healthy aging trajectories across both sexes.
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