Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

The brain structure and genetic mechanisms underlying the nonlinear association between sleep duration, cognition and mental health

Nature Aging volume 2, pages 425–437 (2022)Cite this article

Subjects

An Author Correction to this article was published on 09 May 2022

This article has been updated

Abstract

Sleep duration, psychiatric disorders and dementias are closely interconnected in older adults. However, the underlying genetic mechanisms and brain structural changes are unknown. Using data from the UK Biobank for participants primarily of European ancestry aged 38–73 years, including 94% white people, we identified a nonlinear association between sleep, with approximately 7 h as the optimal sleep duration, and genetic and cognitive factors, brain structure, and mental health as key measures. The brain regions most significantly underlying this interconnection included the precentral cortex, the lateral orbitofrontal cortex and the hippocampus. Longitudinal analysis revealed that both insufficient and excessive sleep duration were significantly associated with a decline in cognition on follow up. Furthermore, mediation analysis and structural equation modeling identified a unified model incorporating polygenic risk score (PRS), sleep, brain structure, cognition and mental health. This indicates that possible genetic mechanisms and brain structural changes may underlie the nonlinear relationship between sleep duration and cognition and mental health.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Guideline of the study.
Fig. 2: Nonlinear association of sleep duration with mental health and with cognitive function.
Fig. 3: Nonlinear association between sleep duration and brain structure.
Fig. 4: The interaction between age and sleep duration.
Fig. 5: Nonlinear association between sleep variability, mental health, cognitive function and brain structures.
Fig. 6: Structural equation model, longitudinal analysis and mediation analysis.

Similar content being viewed by others

Data availability

This project corresponds to UK Biobank application ID 19542. Neuroimaging, genotype and behavioral data from the UK Biobank dataset are available at https://biobank.ndph.ox.ac.uk/ by application. The variables used here are detailed in Supplementary Table 1. The previously published GWAS of sleep duration was downloaded from http://www.t2diabetesgenes.org/data/. European ancestral background LD scores from the 1000 Genomes Project were downloaded from https://alkesgroup.broadinstitute.org/LDSCORE/.

Code availability

MATLAB 2018b was used to perform nonlinear association analysis. FreeSurfer version 6.0 was used to process imaging data. PLINK 1.90 and PRSice (http://www.prsice.info) were used to perform genome-wide association analysis and calculate the PRS, respectively. lavaan 0.8 in R version 3.6.0 was used to perform longitudinal and mediation analyses and make the structural equation model. AER 1.2-9 in R version 3.6.0 was used to perform the interaction test; rms 6.2-0 was used to conduct restricted cubic spine analysis; GenomicSEM version 0.0.3 was used to calculate heritability and genetic correlation; two-lines test version 0.52 was used to identify the breakpoints of the nonlinear model. Scripts used to perform the analyses are available at https://github.com/yuzhulineu/UKB_sleep.

Change history

References

  1. Stickgold, R. Sleep-dependent memory consolidation. Nature 437, 1272–1278 (2005).

    Article  CAS  PubMed  Google Scholar 

  2. Walker, M. P. & van der Helm, E. Overnight therapy? The role of sleep in emotional brain processing. Psychol. Bull. 135, 731–748 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  3. Xie, L. L. et al. Sleep drives metabolite clearance from the adult brain. Science 342, 373–377 (2013).

    Article  CAS  PubMed  Google Scholar 

  4. He, Q., Zhang, P., Li, G. X., Dai, H. X. & Shi, J. P. The association between insomnia symptoms and risk of cardio-cerebral vascular events: a meta-analysis of prospective cohort studies. Eur. J. Prev. Cardiol. 24, 1071–1082 (2017).

    Article  PubMed  Google Scholar 

  5. Spira, A. P., Chen-Edinboro, L. P., Wu, M. N. & Yaffe, K. Impact of sleep on the risk of cognitive decline and dementia. Curr. Opin. Psychiatry 27, 478–483 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  6. Sabia, S. et al. Association of sleep duration in middle and old age with incidence of dementia. Nat. Commun. 12, 2289 (2021).

  7. Ikehara, S. et al. Association of sleep duration with mortality from cardiovascular disease and other causes for Japanese men and women: the JACC study. Sleep 32, 295–301 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  8. Westwood, A. J. et al. Prolonged sleep duration as a marker of early neurodegeneration predicting incident dementia. Neurology 88, 1172–1179 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Crowley, K. Sleep and sleep disorders in older adults. Neuropsychol. Rev. 21, 41–53 (2011).

    Article  PubMed  Google Scholar 

  10. Ohayon, M. M., Carskadon, M. A., Guilleminault, C. & Vitiello, M. V. Meta-analysis of quantitative sleep parameters from childhood to old age in healthy individuals: developing normative sleep values across the human lifespan. Sleep 27, 1255–1273 (2004).

    Article  PubMed  Google Scholar 

  11. Leng, Y. et al. Self-reported sleep patterns in a British population cohort. Sleep Med. 15, 295–302 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  12. Gulia, K. K. & Kumar, V. M. Sleep disorders in the elderly: a growing challenge. Psychogeriatrics 18, 155–165 (2018).

    Article  PubMed  Google Scholar 

  13. Irwin, M. R. & Vitiello, M. V. Implications of sleep disturbance and inflammation for Alzheimer’s disease dementia. Lancet Neurol. 18, 296–306 (2019).

    Article  CAS  PubMed  Google Scholar 

  14. Ma, Y. J. et al. Association between sleep duration and cognitive decline. JAMA Netw. Open 3, e2013573 (2020).

  15. Xu, W. et al. Sleep characteristics and cerebrospinal fluid biomarkers of Alzheimer’s disease pathology in cognitively intact older adults: the CABLE study. Alzheimers Dement. 16, 1146–1152 (2020).

    Article  PubMed  Google Scholar 

  16. Lo, J. C., Loh, K. K., Zheng, H., Sim, S. K. Y. & Chee, M. W. L. Sleep duration and age-related changes in brain structure and cognitive performance. Sleep 37, 1171–1178 (2014).

    PubMed Central  Google Scholar 

  17. Kocevska, D. et al. The prospective association of objectively measured sleep and cerebral white matter microstructure in middle-aged and older persons. Sleep 42, zsz140 (2019).

    Article  PubMed  Google Scholar 

  18. Wu, Y. H. & Swaab, D. F. Disturbance and strategies for reactivation of the circadian rhythm system in aging and Alzheimer’s disease. Sleep Med. 8, 623–636 (2007).

    Article  PubMed  Google Scholar 

  19. Grumbach, P. et al. Sleep duration is associated with white matter microstructure and cognitive performance in healthy adults. Hum. Brain Mapp. 41, 4397–4405 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  20. Shi, H. Y. et al. Sleep duration and snoring at midlife in relation to healthy aging in women 70 years of age or older. Nat. Sci. Sleep 13, 411–422 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  21. El-Sheikh, M., Philbrook, L. E., Kelly, R. J., Hinnant, J. B. & Buckhalt, J. A. What does a good night’s sleep mean? Nonlinear relations between sleep and children’s cognitive functioning and mental health. Sleep 42, zsz078 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  22. Kendall, K. M. et al. Cognitive performance among carriers of pathogenic copy number variants: analysis of 152,000 UK Biobank subjects. Biol. Psychiatry 82, 103–110 (2017).

    Article  PubMed  Google Scholar 

  23. Lutsey, P. L. et al. Sleep characteristics and risk of dementia and Alzheimer’s disease: the Atherosclerosis Risk in Communities Study. Alzheimers Dement. 14, 157–166 (2018).

    Article  PubMed  Google Scholar 

  24. Liang, Y., Qu, L. B. & Liu, H. Non-linear associations between sleep duration and the risks of mild cognitive impairment/dementia and cognitive decline: a dose–response meta-analysis of observational studies. Aging Clin. Exp. Res. 31, 309–320 (2019).

    Article  PubMed  Google Scholar 

  25. Helfrich, R. F., Mander, B. A., Jagust, W. J., Knight, R. T. & Walker, M. P. Old brains come uncoupled in sleep: slow wave–spindle synchrony, brain atrophy, and forgetting. Neuron 97, 221–230 (2018).

    Article  CAS  Google Scholar 

  26. Klinzing, J. G., Niethard, N. & Born, J. Mechanisms of systems memory consolidation during sleep. Nat. Neurosci. 22, 1598–1610 (2019).

    Article  CAS  PubMed  Google Scholar 

  27. Olsson, M., Arlig, J., Hedner, J., Blennow, K. & Zetterberg, H. Sleep deprivation and cerebrospinal fluid biomarkers for Alzheimer’s disease. Sleep 41, zsy025 (2018).

  28. Winer, J. R. et al. Sleep disturbance forecasts β-amyloid accumulation across subsequent years. Curr. Biol. 30, 4291–4298 (2020).

    Article  CAS  Google Scholar 

  29. Holth, J. K. et al. The sleep–wake cycle regulates brain interstitial fluid tau in mice and CSF tau in humans. Science 363, 880–884 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Grandner, M. A. & Kripke, D. F. Self-reported sleep complaints with long and short sleep: a nationally representative sample. Psychosom. Med. 66, 239–241 (2004).

    Article  PubMed  PubMed Central  Google Scholar 

  31. Kroenke, K., Spitzer, R. L. & Williams, J. B. The PHQ-9: validity of a brief depression severity measure. J. Gen. Intern. Med. 16, 606–613 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Gehrman, P. R. et al. Heritability of insomnia symptoms in youth and their relationship to depression and anxiety. Sleep 34, 1641–1646 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  33. Gregory, A. M. et al. A longitudinal twin and sibling study of associations between insomnia and depression symptoms in young adults. Sleep 39, 1985–1992 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  34. Freeman, D., Sheaves, B., Waite, F., Harvey, A. G. & Harrison, P. J. Sleep disturbance and psychiatric disorders. Lancet Psychiatry 7, 628–637 (2020).

    Article  PubMed  Google Scholar 

  35. Lim, A. S. P. et al. Regional neocortical gray matter structure and sleep fragmentation in older adults. Sleep 39, 227–235 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  36. Spano, G. et al. Sleeping with hippocampal damage. Curr. Biol. 30, 523–529 (2020).

    Article  CAS  Google Scholar 

  37. Fjell, A. M. et al. Self-reported sleep relates to hippocampal atrophy across the adult lifespan: results from the Lifebrain consortium. Sleep 43, zsz280 (2020).

    Article  PubMed  Google Scholar 

  38. Cheng, W., Rolls, E. T., Ruan, H. T. & Feng, J. F. Functional connectivities in the brain that mediate the association between depressive problems and sleep quality. JAMA Psychiatry 75, 1052–1061 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  39. Mezick, E. J. et al. Intra-individual variability in sleep duration and fragmentation: associations with stress. Psychoneuroendocrinology 34, 1346–1354 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  40. Okun, M. L. et al. Sleep variability, health-related practices, and inflammatory markers in a community dwelling sample of older adults. Psychosom. Med. 73, 142–150 (2011).

    Article  PubMed  Google Scholar 

  41. Vetter, C., Fischer, D., Matera, J. L. & Roenneberg, T. Aligning work and circadian time in shift workers improves sleep and reduces circadian disruption. Curr. Biol. 25, 907–911 (2015).

    Article  CAS  PubMed  Google Scholar 

  42. Mander, B. A., Winer, J. R. & Walker, M. P. Sleep and human aging. Neuron 94, 19–36 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Musiek, E. S., Xiong, D. D. & Holtzman, D. M. Sleep, circadian rhythms, and the pathogenesis of Alzheimer disease. Exp. Mol. Med. 47, e148 (2015).

  44. Skene, D. J. & Swaab, D. F. Melatonin rhythmicity: effect of age and Alzheimer’s disease. Exp. Gerontol. 38, 199–206 (2003).

    Article  CAS  PubMed  Google Scholar 

  45. Jones, S. E. et al. Genetic studies of accelerometer-based sleep measures yield new insights into human sleep behaviour. Nat. Commun. 10, 1585 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  46. Fry, A. et al. Comparison of sociodemographic and health-related characteristics of UK Biobank participants with those of the general population. Am. J. Epidemiol. 186, 1026–1034 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  47. Davis, K. A. S. et al. Mental health in UK Biobank—development, implementation and results from an online questionnaire completed by 157 366 participants: a reanalysis. BJPsych Open 6, e18 (2020).

  48. Desikan, R. S. et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage 31, 968–980 (2006).

    Article  PubMed  Google Scholar 

  49. Fischl, B. et al. Whole brain segmentation: automated labeling of neuroanatomical structures in the human brain. Neuron 33, 341–355 (2002).

    Article  CAS  PubMed  Google Scholar 

  50. Bycroft, C. et al. The UK Biobank resource with deep phenotyping and genomic data. Nature 562, 203–209 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Kang, J. et al. Increased brain volume from higher cereal and lower coffee intake: shared genetic determinants and impacts on cognition and metabolism. Cereb. Cortex https://doi.org/10.1093/cercor/bhac005 (2022).

  52. Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Cooper, H. M., Hedges, L. V. & Valentine, J. C. The Handbook of Research Synthesis and Meta-Analysis 3rd edn (Russell Sage Foundation, 2019).

  54. Simonsohn, U. Two lines: a valid alternative to the invalid testing of U-shaped relationships with quadratic regressions. Adv. Methods Pract. Psychol. Sci. 1, 538–555 (2018).

    Article  Google Scholar 

  55. Kleiber, C. & Zeileis, A. Applied Econometrics with R (Springer, 2008).

Download references

Acknowledgements

This study used the UK Biobank Resource under application number 19542. We thank all participants and researchers from the UK Biobank. J.F. was supported by the National Key R&D Program of China (nos. 2018YFC1312900 and 2019YFA0709502), the Shanghai Municipal Science and Technology Major Project (no. 2018SHZDZX01), the ZJ Lab, Shanghai Center for Brain Science and Brain-Inspired Technology and the 111 Project (no. B18015). W.C. was supported by grants from the National Natural Sciences Foundation of China (no. 82071997) and the Shanghai Rising-Star Program (no. 21QA1408700).

Author information

Author notes

  1. These authors contributed equally: Yuzhu Li, Barbara J. Sahakian.

Authors and Affiliations

  1. Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China

    Yuzhu Li, Barbara J. Sahakian, Jujiao Kang, Christelle Langley, Wei Zhang, Chao Xie, Shitong Xiang, Jintai Yu, Wei Cheng & Jianfeng Feng

  2. Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China

    Yuzhu Li, Jujiao Kang, Wei Zhang, Chao Xie, Shitong Xiang, Wei Cheng & Jianfeng Feng

  3. Department of Psychiatry, University of Cambridge, Cambridge, UK

    Barbara J. Sahakian & Christelle Langley

  4. Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK

    Barbara J. Sahakian & Christelle Langley

  5. MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China

    Jujiao Kang, Wei Zhang & Jianfeng Feng

  6. Department of Neurology and Institute of Neurology, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China

    Jintai Yu

  7. Fudan ISTBI—ZJNU Algorithm Centre for Brain-Inspired Intelligence, Zhejiang Normal University, Jinhua, China

    Wei Cheng & Jianfeng Feng

  8. Shanghai Medical College and Zhongshan Hosptital Immunotherapy Technology Transfer Center, Shanghai, China

    Wei Cheng

  9. Department of Neurology, Huashan Hospital, Fudan University, Shanghai, China

    Wei Cheng

  10. Zhangjiang Fudan International Innovation Center, Shanghai, China

    Jianfeng Feng

  11. Department of Computer Science, University of Warwick, Coventry, UK

    Jianfeng Feng

  12. School of Data Science, Fudan University, Shanghai, China

    Jianfeng Feng

Authors
  1. Yuzhu Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Barbara J. Sahakian
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jujiao Kang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Christelle Langley
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Wei Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Chao Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Shitong Xiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Jintai Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Wei Cheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Jianfeng Feng
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

J.F. and W.C. proposed the study. Y.L., J.K. and W.Z. analyzed data. S.X. preprocessed data. W.C., J.F. and B.J.S. contributed to interpretation of results. Y.L. drafted the manuscript. B.J.S., C.L., J.Y. and W.C. edited the manuscript. Y.L., C.X. and W.C. contributed to visualization. All authors considered how to analyze data and approved the manuscript.

Corresponding authors

Correspondence to Wei Cheng or Jianfeng Feng.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Aging thanks Naiara Demnitz, Cathryn Lewis and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Histograms of sleep duration in baseline and imaging assessment.

The sleep duration data from the baseline assessment (2006–2010, n = 498,277) and neuroimaging visit (2014+, n = 48,511) were used in the analyses. Sleep duration assessed at baseline was utilized to determine the association between cognitive function and online follow-up mental health assessments. Sleep duration assessed at the neuroimaging visit was used to determine the association with brain structure.

Extended Data Fig. 2 Covariates utilized in the statistical analyses.

Age, sex, body mass index, Townsend deprivation index, educational qualification, smoking status and drinking status were adjusted in all analyses. In addition, for analysis involving neuroimaging data and polygenetic risk score, intracranial volumes, neuroimaging scanning sites and PRS components were further added as covariates respectively.

Extended Data Fig. 3 Nonlinear association between sleep duration and cortical area and thickness.

Cortical regions with their a) area and b) thickness significantly and nonlinearly associated with sleep duration adjusted for sleep duration with intracranial volume, age, sex, sex, body mass index, Townsend deprivation index, educational qualification, smoking status and drinking status, imaging scanning sites (Bonferroni corrected, p < 0.05). F-tests were utilized to access statistical significance and derive F-statistics and corresponding one-sided p values adjusted for multiple comparisons.

Extended Data Fig. 4 Sex difference of the association between sleep duration and mental health, cognitive function and brain structure.

The nonlinear association between sleep duration and anxiety symptom was more significant in female participants (F female = 622.6, n = 533, 2878, 15240, 36239, 26712, 4712 and 1000 participants respectively; F male = 417.2, n = 347, 2126, 12587, 29710, 18677, 3229 and 665 participants respectively), whereas mania symptoms showed more significant association with sleep duration for male participants (F female = 140.3, n = 550, 2928, 15466, 36672, 26988, 4774 and 1025 participants respectively; F male = 145.0 respectively, n = 354, 2148, 12673, 29893, 18780, 3252 and 670 participants respectively).Fluid intelligence were found to have a greater nonlinear association with sleep duration in females compared with males (F female = 272.7, n = 940, 3981, 16606, 32724, 25625, 5051 and 1502 participants respectively; F male = 205.4, n = 673, 3144, 14934, 29192, 20019, 4018 and 1223 participants respectively) while pair matching were more associated with sleep duration in males (F female = 85.8, n = 3087, 11892, 48704, 98567, 79070, 15934 and 4922 participants respectively; F male = 104.1, n = 2367, 9356, 44236, 88501, 61182, 12315 and 3980 participants respectively). For brain structure, female participants demonstrated a more significant association between sleep duration and cortical volumes (rh, F female = 29.1, n = 192, 991, 4221, 8375, 5746, 1158 and 249 participants respectively; F male = 14.7, n = 118, 631, 3445, 7523, 5592, 1231 and 238 participants respectively) while cortical thickness was more significantly associated with sleep duration for males (F female = 2.89, n = 192, 991, 4221, 8375, 5746, 1158 and 249 participants respectively; F male = 20.0, n = 118, 631, 3445, 7523, 5592, 1231 and 238 participants respectively). Lines are fitted nonlinear model indicating fitted mean value and error bar is standard error of the mean.

Extended Data Fig. 5 Histograms of the change of variables over time in the longitudinal analysis.

Baseline sleep duration is 0.031 hours longer than the follow-up sleep duration (std = 0.94). At baseline, participants were more depressed compared with the measurement at follow-up (difference = 0.0069, std = 0.11). Fluid intelligence of participants at baseline was also higher than at follow-up (difference = 0.043, std = 1.73).

Extended Data Fig. 6 Structural equation model, longitudinal analysis and mediation analysis for participants with more than 7 hours sleep.

a. The longitudinal association between the sleep duration, depression and fluid intelligence revealed by cross-lagged panel model. The baseline sleep duration (β= 0.025, p = 1.3 × 10−5) and depressive symptom (β= −0.023, p = 1.3 × 10−5) was significantly associated with fluid intelligence in the follow-up. b. Mediation analysis. The mediation models were conducted to analyze the direct relationship between sleep duration and fluid intelligence, with sleep duration, brain structure and both of them as mediator respectively. Brain regions significantly mediated the association between sleep duration and fluid intelligence (β= −0.0046, p = 1.4 × 10−5). These figures utilized participants with sleep duration more than 7 hours. c. Full frame model. Standardized coefficients were showed in the figure. PRS was significantly associated with mental health (β = −0.034, p = 4.7 × 10−5). Brain volumes were a better predictor of cognitive function (β= −0.198, p < 1.0 × 10−20) compared to mental health (β = 0.048, p = 3.5 × 10−6). Sleep duration was the most significant predictor of mental health (β = 0.167, p < 1.0 × 10−20) and brain regions (β= −0.044, p < 1.0 × 10−20). Latent variable including brain structure, mental health and cognitive function were estimated in the model which showed in the figure with orange, green and blue box respectively. Wald tests were utilized to derive the two-sided p value adjusted for multiple comparisons (FDR correction). * represented p < 0.05, ** represented p < 0.01 and *** represented p < 0.001.

Extended Data Fig. 7 Mediation analysis.

a. Three mediation analysis were conducted between PRS and sleep duration, 1) PRS →depressive symptoms→brain structure →sleep, 2) PRS →depressive symptoms→sleep, 3) PRS→brain structure →sleep. Depressive symptoms and brain structure serially mediated the association between PRS and sleep duration (β=4.14 × 10−5, p = 0.044). Specifically, with depression significantly associated with PRS (β = −0.033, p = 1.6 × 10−4) and brain volumes positively associated with depression (β = −0.028, p = 1.4 × 10−3), and in addition, brain volumes significantly associated with sleep duration (β = 0.044, p = 1.4 × 10−6). Meanwhile, depressive symptoms and brain structure also separately significantly mediated the association between PRS and sleep duration (β2 = 0.004, p = 3.3 × 10−4, β3 = 0.001, p = 0.01). b. Three mediation pathway analyses were conducted for the cognitive function of fluid intelligence for participants with less than 8 hours sleep duration, 1) sleep duration→brain structure→ depression→ fluid intelligence, 2) sleep duration→ brain structure → fluid intelligence, 3) sleep duration→ depression→ fluid intelligence. Sleep duration showed a significant positive association with fluid intelligence in the model (β = 0.062, p = 2 × 10−15). The serial mediation pathway via brain structure and depression was not significant (β1 = 3 × 10−5, p = 0.06), but brain structure and depression were separately significant mediators for this association. Brain structure accounted for the association between sleep duration and fluid intelligence (β2 = 0.009, p = 2 × 10−7; β3 = 0.004, p = 5.6 × 10−5). Wald tests were utilized to derive the two-sided p value adjusted for multiple comparisons (FDR correction). * represented p < 0.05, ** represented p < 0.01 and *** represented p < 0.001.

Extended Data Fig. 8 Mediation analysis between sleep duration and cognitive functions.

For participants with sleep duration ≤ 7 hours, brain structure related to sleep significantly mediated the association between sleep duration and numeric memory (path β= 0.006, p = 1.4 × 10−11), trail making (path β= −0.003, p = 7.8 × 10−7), prospective memory (path β= −8.8 × 10−4, p = 0.02) and tower rearranging (path β= 0.004, p = 9.5 × 10−9). Meanwhile, sleep duration and brain regions related to sleep significantly mediated the association between PRS of sleep and symbol digit substitution (path β = 1.5 × 10−4, p = 0.001). Specifically, with sleep duration significantly associated with PRS (β = 0.058, p = 4.5 × 10−10) and brain volumes positively associated with sleep duration (β = 0.053, p = 1.1 × 10−8), and in addition, brain volumes significantly associated with symbol digit substitution (β = 0.049, p = 1.1 × 10−7). Sleep duration (β = 0.003, p = 6.3 × 10−5) and brain volumes (β = 0.002, p = 4.2 × 10−3) also separately mediated the association between PRS and symbol digit substitution. The association between these cognitive functions and sleep duration were also significantly mediated by brain structure related to sleep for participants with sleep duration > 7 hours, including symbol digit substitution (path β= −0.002, p = 0.019), numeric memory (path β= −0.003, p = 0.004) and trail making (path β= 0.002, p = 0.017). Reaction time and sleep duration were also mediated by brain structure for participants with sleep duration > 7 hours (path β= 0.001, p = 0.031). Wald tests were utilized to derive the two-sided p value adjusted for multiple comparisons (FDR correction). * represented p < 0.05, ** represented p < 0.01 and *** represented p < 0.001.

Supplementary information

Supplementary Information

Supplementary Figs. 1–8 and Supplementary Tables 1–13

Reporting Summary

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, Y., Sahakian, B.J., Kang, J. et al. The brain structure and genetic mechanisms underlying the nonlinear association between sleep duration, cognition and mental health. Nat Aging 2, 425–437 (2022). https://doi.org/10.1038/s43587-022-00210-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s43587-022-00210-2

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing