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

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

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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.

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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.

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.

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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

Authors and Affiliations

Authors

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.

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The authors declare no competing interests.

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

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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.

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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

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