Macro and micro sleep architecture and cognitive performance in older adults

Abstract

We sought to determine which facets of sleep neurophysiology were most strongly linked to cognitive performance in 3,819 older adults from two independent cohorts, using whole-night electroencephalography. From over 150 objective sleep metrics, we identified 23 that predicted cognitive performance, and processing speed in particular, with effects that were broadly independent of gross changes in sleep quality and quantity. These metrics included rapid eye movement duration, features of the electroencephalography power spectra derived from multivariate analysis, and spindle and slow oscillation morphology and coupling. These metrics were further embedded within broader associative networks linking sleep with aging and cardiometabolic disease: individuals who, compared with similarly aged peers, had better cognitive performance tended to have profiles of sleep metrics more often seen in younger, healthier individuals. Taken together, our results point to multiple facets of sleep neurophysiology that track coherently with underlying, age-dependent determinants of cognitive and physical health trajectories in older adults.

Figure 1.. Demographic profiles of primary sleep and cognitive measures.

a) Mean cognitive measure values in MESA, stratified by decade of age and sex, for DSCT, CASI, DS(F) (=DS_F) and DS(B) (=DS_B), and mean cognitive measure values in MrOS stratified by decade of age, for Trails B, 3MS and DVT. Note that here we present the raw values of Trails B and DVT, but association results are given for Trails B* and DVT*, which are those variables multiplied by −1. Error bars represent 95% confidence intervals around the estimate of the mean. b) Key sleep macro-architecture mean values stratified by decade of age, study and sex for N1, N2, N3 and REM duration (in minutes, and as a proportion of TST), TST, WASO (in minutes), sleep efficiency and REM latency. c) Absolute log-scaled N2 spectral band power stratified by age decade, study and sex for slow (<1 Hz), delta (1–4 Hz), theta (4–8 Hz), alpha (8–11 Hz), sigma (11–15 Hz) and beta (15–30 Hz) power, each divided by total power to yield relative power metrics. d) As for c), but for N3 sleep.

Figure 2.. Spindles, slow oscillations and their coupling.

a) Spindle wavelet amplitude (in arbitrary units) when using the default bandwidth setting (7 cycles) for F_C = 11 Hz and F_C =15 Hz wavelets, where each wavelet detects spindles primarily within approximately +/− 2 Hz of the target frequency. b) Key N2 spindle metrics stratified by age (decade), cohort and sex for spindle density, amplitude, duration and frequency, separately for slow (top row) and fast (bottom row) spindles. c) Distributions of individuals’ mean SO phase at spindle peak for slow spindles (SS) and fast spindles (FS) in MESA (left) and MrOS (right). SO phase angle is oriented on the x-axis from one negative peak (N) to the subsequent one; spindles tend to cluster just after (slow spindles, SS) or just before (fast spindles, FS) the positive SO peak (P). d) Key SO occurrence and morphology mean values stratified by age (decade), cohort and sex for SO count, density, amplitude, duration and slope, in both N3 (top row) and N2+N3 combined (bottom row). e) Spindle/SO coupling metrics (magnitude Z score and normalized SO angle) for slow spindles (SS) and fast spindles (SS), in N2+N3 sleep.

Figure 3.. Principal spectral component (PSC) analysis.

a) Median absolute log-scaled power for five groups defined by the quintiles of the corresponding PSC component during N2 sleep, showing the first four components (see Supplementary Figures 6 and 7 for the first ten, in both N2 and N3 sleep). b) Absolute correlations from an intra-MrOS comparison of the top ten N2 PSC components, comparing those derived from analysis within MrOS versus those projected from the MESA-derived components. The results show a very strong correspondence between the PSC structures in MESA and MrOS. c) Within-cohort, between sleep stage comparisons for N2-N3 (top row) and N2-REM (bottom row) PSC for MESA, showing absolute Pearson correlation coefficients. Whereas N2 and N3 show a clear correspondence of PSCs as ordered by the rank of singular values, REM only shows strong correspondence for the first three PSCs. d) Same as c) but showing results from MrOS. For clarity of presentation, in all correlation plots (b-d), only correlations p <10⁻¹⁰ and |r| > 0.25 are shown.

Figure 4.. Primary sleep-cognition association results for omnibus tests and the 23 selected objective metrics, in MESA and MrOS.

a) Domain-based omnibus results in MESA and MrOS. Color-coded empirical significance values based on 20,000 permutations, calculated using the baseline covariate model and using the max test statistic to control for multiple testing. See Methods for further details. b) Correspondence of cognition/sleep associations in MESA and MrOS, showing log₁₀-scaled p-values signed by the direction of effect, for DSCT in MESA (x-axis) versus Trails B* in MrOS (y-axis). Labeled orange points indicate the 23 metrics that replicated across cohorts. c) Correlation coefficients (Pearson’s r) in MESA between key sleep metrics, adjusted for baseline covariates, and only showing cells where p < 0.01 and |r| > 0.1. d) As above, for MrOS.

Figure 5.. Empirical clustering of sleep metrics.

a) Results from UMAP dimension reduction (based on the 1-|r_ij| where r_ij is the Pearson correlation coefficient between the i^th and j^th sleep metrics), extracting 2 components for visualization, in MESA and MrOS separately. Colors correspond to the cluster assignment from HDBSCAN cluster analysis based on 10 UMAP components. b) Approximate labels of clusters from HDBSCAN analysis, see Supplementary Figure 10b for a full tabulation. MESA yielded a 10-cluster solution, MrOS an 8-cluster solution; clusters that are “similar” between MESA and MrOS (based on assigned metrics) are listed next to each other. c) Indication of cognition-associated sleep metrics in the UMAP 2-dimensional projection of MESA and MrOS sleep metrics. Red: one of the 23 replicated hits. Orange: metrics with p < 10⁻³ for DSCT (MESA) or Trails B (MrOS). d) Omnibus association empirical significance values for groupings of sleep metrics based on data-driven clustering, for the max-statistic.

Figure 6.. Correlations between cognition-associated metrics and other putative confounding and mediating variables.

Pearson correlations between sleep metrics with age and sex, and other factors. Correlations with variables other than age and sex are adjusted for age, sex, race/ethnicity and collection site (being based on the baseline model residuals rather than raw sleep metrics). Only significant (p<0.05) correlations are shown. a) Correlations with age, sex, AI and AHI in MESA. b) As above, for MrOS. c) Correlations with other health and behavioral measures in MESA. Note, for clarity of presentation, as age, sex, AI and AHI show large correlations with some sleep metrics, the scale is reduced here (+/−0.2 versus +/−0.6). d) As above, for MrOS. e) Association results (direction-of-effect signed -log₁₀ p-values) in MESA for the 23 selected metrics, adjusted for ‘gross’ sleep metrics, namely Epworth Sleepiness Scale (ESS), the Women’s Health Initiative Insomnia Rating Scale (WHIIRS), and two MESA Sleep Questionnaire items in MESA (see Supplementary Methods). f) As above, in MrOS, adjusting for ESS, Pittsburgh Sleep Quality Index and the Functional Outcomes of Sleep Questionnaire. g) Fully adjusted association results for the selected 23 metrics under the augmented covariate model and including medication-use covariates in MESA. h) As above, for MrOS.

Figure 7.. Patterns of associations between sleep metrics and age, cognition, cardiometabolic disease and sex.

a) Standardized regression coefficients for age (per year), cognitive performance (DSCT and Trails B* in MESA and MrOS respectively, per SD unit), a binary indicator variable denoting the presence of hypertension or diabetes, and sex (1 male, 0 female). Rows correspond to the 23 replicated cognition-associated sleep metrics, ordered by increasing effect size of the cognition/sleep association in MrOS. Positive effects are in red, negative effects are in blue; effects not nominally significant (p>0.05) are white/gray. For the DSCT/Trails B*, cognition was the dependent variable and the sleep metric was the predictor (i.e. as per the primary analyses). For the other variables, the sleep metric was the dependent variable. All models included baseline covariates. b) Results for all 155 sleep metrics in common between MESA and MrOS (all objective metrics plus ESS). Unlike a), here effect estimates for age, cognition and cardiometabolic disease were all jointly estimated in a single multiple linear regression, with the sleep metric as the dependent variable, and age, cognitive performance (DSCT or Trails B*) and cardiometabolic disease state as predictors (along with other baseline model covariates). All effect estimates are plotted in red/blue to denote the direction of effect (b>0 or b<0), regardless of statistical significance. Rows/sleep metrics are sorted by the cognitive effect estimate, separately for MESA and MrOS. Across all sleep metrics, effect estimates were highly negatively correlated for cognition and cardiometabolic in both MESA (Pearson’s r = −0.66, 95% CI −0.74, −0.57, p < 10⁻¹⁵) and MrOS (r = −0.65, 95% CI −0.73, −0.55, p < 10⁻¹⁵) as well as age in MESA (r = −0.39, 95% CI −0.52, −0.25, p < 10⁻¹⁵) and MrOS (r = −0.69, 95% CI −0.76, −0.59, p < 10⁻¹⁵) respectively.