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Age and Sex as Moderators of Sleep Architecture in Schizophrenia, Bipolar Disorder and Unipolar Depression: A Case-Control Polysomnographic Systematic Review and Meta-Regression

Submitted:

06 July 2026

Posted:

07 July 2026

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Abstract
Sleep architecture alterations are consistently reported across major psychiatric disorders, but the contribution of demographic factors to between-study heterogeneity remains insufficiently characterized. Age and sex have not been systematically examined as study-level moderators across harmonized case-control polysomnographic evidence spanning psychotic and mood disorders. A transdiagnostic systematic review and meta-regression of case-control polysomnographic studies compared schizophrenia, bipolar disorder and unipolar depression with healthy controls. Eligible studies reported diagnostic criteria, treatment status, age and sex. Standardized mean differences were synthesized using random-effects models with REML estimation. Univariable mixed-effects meta-regressions assessed age and sex as moderators within each diagnostic group. Age and sex moderated delta sleep-related effect sizes in schizophrenia and unipolar depression. Age also moderated sleep continuity and sleep duration effect sizes in unipolar depression and REM sleep-related effect sizes across all disorders. Sex moderated delta sleep duration and REM density in bipolar mania. No significant effects were observed in bipolar depression. These effects were heterogeneous and not consistently observed across disorders or sleep parameters. Overall, demographic composition explains only a modest proportion of heterogeneity. Interpretation is limited by the ecological nature of study-level meta-regression and residual between-study heterogeneity.
Keywords: 
sleep
;  
polysomnography
;  
schizophrenia
;  
bipolar disorder
;  
major depressive disorder
;  
meta-regression
Subject: 
Medicine and Pharmacology  -   Neuroscience and Neurology

Introduction

Sleep architecture undergoes several changes across the lifespan, as demonstrated by polysomnographic (PSG) studies. Aging is associated with reductions in total sleep time (TST), sleep efficiency (SE), delta sleep percentage (%DELTA), REM sleep time (REMT), REM sleep percentage (%REM) and REM latency (REML), whereas sleep onset latency (SOL), wake after sleep onset (WASO) and the percentage of stage 1 (%ST1) and stage 2 (%ST2) sleep generally increase with age [1,2]. These findings are broadly supported by an earlier meta-analysis including both PSG and actigraphic measures [3], although age-related changes in %ST2, %DELTA and %REM remain debated [4].
Sex-related differences in sleep architecture have also been consistently reported. Compared with age-matched males, females generally exhibit deeper and more consolidated sleep, characterized by higher %DELTA and lower %ST1, %ST2 and wakefulness after sleep onset [1]. Male sex has additionally been associated with shorter REML [4]. Similarly, a meta-analysis including both PSG and actigraphic data reported longer TST, albeit with slightly prolonged SOL, in females compared with age-matched men [3].
Large-scale normative data for REM density (REMD) remain scarce and scoring methodologies have varied considerably across studies. Although some investigations reported an age-related reduction in REMD [5,6], others failed to replicate this finding [7,8]. Likewise, evidence regarding sex differences in REMD is limited, with one study reporting no significant differences between males and females [8]. More generally, healthy adults exhibit substantial intra- and inter-individual variability across polysomnographic sleep measures [1].
Meta-analyses have consistently identified abnormalities in sleep architecture across several major psychiatric disorders, including schizophrenia (SCZ) [9,10], schizoaffective disorder (SZA) [11], major depressive disorder (MDD) [12,13,14,15] and bipolar disorder (BD) [16,17]. However, the extent to which demographic characteristics contribute to between-study variability in these abnormalities has received comparatively little attention. A comprehensive systematic review qualitatively assessed demographic subgroup differences, reporting altered REM sleep measures in studies comparing men with MDD and healthy controls (HC), whereas studies of women with MDD more frequently reported impaired sleep continuity and prolonged stage 1 sleep. Among men with SCZ, prolonged REMT but normal REML were described. The same review found only limited evidence supporting age-related differences in sleep architecture among patients with MDD [18].
The present study aimed to investigate age and sex as study-level moderators of case-control differences in polysomnographic sleep architecture across schizophrenia, bipolar disorder and unipolar depression using harmonized mixed-effects meta-regression analyses.

Methods

Studies included in the present analysis were derived from previously published systematic reviews and meta-analyses conducted by the authors, investigating sleep architecture in schizophrenia (SCZ) and bipolar disorder (BD) [10,17]. These datasets were re-assembled and harmonized according to updated inclusion criteria. All eligible studies were re-screened to ensure consistency with current requirements for reporting of age, sex and treatment status. Studies not meeting these criteria were excluded prior to analysis.
Previous meta-analyses on major depressive disorder (MDD) have applied heterogeneous inclusion criteria, including restriction to early-onset samples, focus on selected sleep parameters (e.g., REM sleep), or incomplete reporting of polysomnographic outcomes [12,13,14,15]. In addition, several syntheses have combined heterogeneous depressive phenotypes, including seasonal affective disorder and broadly defined or non-operationalized MDD, resulting in substantial diagnostic variability across included samples.
To address these limitations, a harmonized dataset of recurrent, non-psychotic, drug-free unipolar depression (UD) case-control studies was additionally assembled, with the aim of improving diagnostic specificity and methodological consistency. This dataset provided a unified basis for subsequent meta-regression analyses examining age and sex as study-level moderators of sleep architecture. Inclusion criteria for UD were aligned with those applied in SCZ and BD datasets to ensure comparability across diagnostic groups.
Detailed characteristics of individual studies are provided in the Supplementary Material (Supplementary File S1).

Database Search (UD vs HC)

A systematic literature search was conducted in PubMed and PsycINFO from inception to 2026. The search strategy combined the terms “(sleep OR sleep*) AND (polysomnogra* OR EEG) AND depress*”. The search yielded 4358 records from PubMed and 3046 from PsycINFO. After removal of duplicates (4059), 3345 records remained for screening. Full-text assessment identified 68 eligible studies. Of these, 3277 were excluded due to absence of polysomnographic sleep data. Seven studies were excluded due to overlapping patient samples with previously published datasets. Ultimately, 61 studies were included in the UD vs HC meta-analysis. Study eligibility was assessed independently by both authors (DM and GB). The PRISMA flow diagram is presented in Figure 1 and the PRISMA checklist is provided in Supplementary File S2.

Eligibility Criteria

Studies were included in the meta-regression only if they met all of the following criteria:
1) Case-control studies including at least one patient sample composed exclusively of individuals with SCZ, SZA, UD or BD.
2) Case-control studies including at least one healthy control (HC) sample.
3) Studies clearly reporting the diagnosis, age, sex and treatment status of the patient sample.
4) Studies in which the patient sample was entirely drug-naive or drug-free.
5) Studies using polysomnography (PSG) for sleep staging.
6) Studies in which patients were diagnosed according to DSM or iCD criteria or according to Research Diagnostic Criteria (RDC).
All included studies scored at least 6 points on the Newcastle–Ottawa Scale.
Studies were excluded from the meta-analysis if they met at least one of the following criteria:
1) Absence of a healthy control (HC) sample.
2) Patient samples not composed exclusively of individuals with SCZ, SZA, UD or BD.
3) Failure to clearly report the diagnosis, age, sex, or treatment status of the patient sample.
4) Patient samples including treated participants or participants who were not entirely drug-naive or drug-free.
5) Studies including patients with other psychiatric, neurological, sleep-related or substance use comorbidities (e.g., ADHD, obstructive sleep apnea, substance abuse, etc.).
6) Studies not using polysomnography (PSG) for sleep staging (e.g., actigraphy, sleep diaries)

Data Extraction

Sample size, age, sex (as male proportion), diagnosis and medication status were extracted from the studies assessed as eligible, together with mean and standard deviation (SD) of the following sleep parameters: TST, SE, SOL, WASO, ST1, %ST1, ST2, %ST2, DELTA, %DELTA, REMT, %REM, REML, REMD.

Statistical Analysis

Statistical analyses were performed using Jamovi 2.6.26 (Windows x64). Effect sizes were expressed as standardized mean differences (SMD) as reported in the original studies. Random-effects models were fitted using restricted maximum-likelihood estimation. Univariable mixed-effects meta-regression models were conducted to examine the association between study-level mean age and proportion of male participants and effect sizes within each sleep parameter and psychiatric diagnostic group. Moderators were entered separately as continuous predictors based on sample-size weighted study-level aggregates. Results are reported as regression coefficients with corresponding p-values. No multivariable models or interaction terms were tested.
The analytical strategy was based on harmonized datasets derived from previously published meta-analyses of sleep in SCZ and BD, with standardized inclusion criteria across diagnostic groups and restriction to drug-naive or drug-free samples. The primary objective was estimation of age and sex as study-level moderators of sleep architecture differences.
Between-study heterogeneity, publication bias and influence diagnostics were not uniformly re-estimated in the transdiagnostic SCZ and BD analyses, in order to maintain consistency with the analytical structure of the original datasets. An exception was made for the UD subgroup, for which a fully harmonized meta-analysis was additionally conducted. In this subgroup, the primary outcome was a standard mean difference (SMD) and data were fitted with a random-effects model. Heterogeneity assessment, influence diagnostics and publication bias analyses were performed, including funnel plot inspection, Begg and Mazumdar rank correlation test and Egger-type regression of effect sizes on their standard errors.

Results

A total of 132 studies were included after application of the updated inclusion criteria and were stratified according to diagnostic group [19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126], with some studies providing separate datasets for different diagnoses and therefore contributing to multiple meta-analyses. Study-level characteristics, including sample sizes for patients and healthy controls, mean age and proportion of male participants, are summarized in Table 1. Results of univariable meta-regression analyses examining age and sex as moderators of each sleep parameter are reported in Table 2 and Table 3, respectively.
Of these 132 studies, 61 investigated sleep in UD versus that of HC. The corresponding harmonized meta-analysis results are presented in Table 4, while assessments of between-study heterogeneity, identification of potential outliers and influential studies and evaluation of publication bias using funnel plot inspection, Begg and Mazumdar rank correlation test and Egger-type regression of effect sizes on their standard errors are reported in Table 5.

Age and Sex as Moderators

Both age (p=0.043) and sex (p=0.026) were significantly associated with %DELTA in drug-naive patients with SCZ, whereas no significant associations were observed for other sleep parameters in drug-free SCZ. In BD, no significant associations between age or sex and sleep parameters were observed during the depressive phase. During the manic phase in BD, age was significantly associated with REMT (p=0.043), while sex was significantly associated with DELTA (p=0.002) and REMD (p=0.008). In drug-free UD, age was significantly associated with TST (p=0.001), SE (p=0.002), WASO (p < 0.001), ST2 (p=0.004), DELTA (p<0.001), %DELTA (p=0.021) and REMT (p=0.026). Sex was significantly associated with DELTA (p=0.034) and %DELTA (p=0.049) in UD.

Drug-Free UD vs HC Meta-Analysis

The meta-analysis of sleep parameters in drug-free UD versus HC revealed significant sleep continuity alterations with decreased TST (p<0.001) and SE (p<0.001) and prolonged SOL (p<0.001) and WASO (p<0.001). Patients with UD showed reduced ST2 (p<0.001), %ST2 (p=0.045) and DELTA (p=0.02), as well as increased %REM (p<0.001) and REMD (p<0.001) and shortened REML (p<0.001) compared to HC.

Age and Sex Imbalance Assessment

Across the included literature, demographic composition shows substantial and diagnosis-dependent heterogeneity. In SCZ, mean study-level age is similar between drug-naive (29.7 ± 10.5 years, range 22.2-70.9) and drug-free samples (29.6 ± 4.9 years, range 16.2-44.6), although dispersion is markedly reduced in drug-free studies, indicating tighter age selection at the expense of representativeness. Sex distribution is consistently male-biased but more extreme in drug-free SCZ (81.7 ± 22.1% male, median 100%) compared to drug-naive samples (72.8 ± 14.2% male). BD samples show higher mean age in depressive (39.4 ± 7.7 years) than manic episodes (36.2 ± 8.4 years), with moderate-to-high variability in sex composition in both groups (71.8 ± 24.6% and 74.8 ± 26.7% male, respectively). UD exhibits the greatest demographic heterogeneity, with a broad lifespan age distribution (29.5 ± 11.5 years, range 9.4-69.5) and the widest variability in sex composition (57.1 ± 28.8% male, range 0-100%), indicating fundamentally heterogeneous recruitment across pediatric, adult and late-life populations.
Table 1. Included studies with numerosity of samples, age and sex composition.
Table 1. Included studies with numerosity of samples, age and sex composition.
N. datasets N. Patients/HC Average Age (Patients/HC) Average %Males
(Patients/HC)
SCZ (drug-naive) 14 263/232 29.74 72.80
SCZ (drug-free) 40 726/633 29.57 81.70
BD (drug-free, manic) 5 62/67 36.16 71.80
BD (drug-free, depressed) 12 138/178 39.41 74.80
MDD (drug-free) 61 1823/1343 29.53 57.10
Table 2. Results of age as sleep parameters moderator.
Table 2. Results of age as sleep parameters moderator.
Age as moderator
SCZ (drug-naive) SCZ (drug-free) BD (depressed, drug-free) BD (manic, drug-free) MDD (drug-free)
Estimate p-value Estimate p-value Estimate p-value Estimate p-value Estimate p-value
TST 0.008 (-0.038 to 0.054) 0.721 0.058 (-0.072 to 0.187) 0.385 -0.04 (-0.132 to 0.052) 0.344 0.025 (-0.060 to 0.110) 0.337 -0.020 (-0.033 to -0.008) 0.001
SOL 0.019 (-0.013 to 0.052) 0.240 -0.039 (-0.099 to 0.020) 0.192 0.075 (-0.053 to 0.202) 0.203 0.034 (-0.317 to 0.385) 0.780 0.005 (-0.006 to 0.016) 0.353
SE -0.080 (-0.271 to 0.107) 0.394 0.015 (-0.060 to 0.090) 0.702 0.045 (-0.407 to 0.497) 0.810 -0.022 (-0.348 to 0.305) 0.848 -0.020 (-0.033 to -0.007) 0.002
WASO -0.013 (-0.041 to 0.015) 0.358 0.049 (-0.018 to 0.115) 0.154 -0.002 (-0.085 to 0.082) 0.965 -0.035 (-0.133 to 0.063) 0.265 0.024 (0.015 to 0.032) <0.001
ST1 -0.024 (-0.076 to 0.029) 0.380 0.026 (-0.058 to 0.111) 0.541 -0.022 (-0.124 to 0.079) 0.535 -0.058 (-0.181 to 0.065) 0.181 -0.003 (-0.016 to 0.009) 0.605
%ST1 -0.014 (-0.033 to 0.005) 0.160 0.011 (-0.056 to 0.077) 0.758 -0.032 (-0.166 to 0.102) 0.505 -0.033 (-0.222 to 0.156) 0.618 0.007 (-0.004 to 0.017) 0.202
ST2 0.007 (-0.044 to 0.057) 0.793 -0.011 (-0.078 to 0.056) 0.743 -0.031 (-0.180 to 0.118) 0.552 0.014 (-0.071 to 0.098) 0.557 -0.015 (-0.025 to -0.005) 0.004
%ST2 0.000 (-0.048 to 0.048) 0.989 -0.014 (-0.065 to 0.037) 0.597 -0.025 (-0.136 to 0.087) 0.530 0.050 (-0.203 to 0.304) 0.572 0.000 (-0.015 to 0.014) 0.928
DELTA 0.012 (-0.109 to 0.134) 0.841 0.002 (-0.065 to 0.068) 0.964 -0.004 (-0.043 to 0.035) 0.814 -0.032 (-0.131 to 0.068) 0.304 -0.014 (-0.021 to -0.006) <0.001
%DELTA 0.113 (0.003 to 0.222) 0.043 0.000 (-0.012 to 0.010) 0.862 -0.006 (-0.047 to 0.034) 0.727 -0.041 (-0.129 to 0.047) 0.237 -0.013 (-0.023 to -0.002) 0.021
REMT 0.008 (-0.041 to 0.056) 0.753 0.068 (0.005 to 0.131) 0.036 -0.011 (-0.073 to 0.051) 0.737 -0.024 (-0.046 to -0.002) 0.043 -0.012 (-0.022 to -0.001) 0.026
%REM 0.002 (-0.070 to 0.073) 0.964 0.049 (-0.011 to 0.110) 0.108 0.010 (-0.035 to 0.055) 0.657 -0.035 (-0.105 to 0.035) 0.208 0.001 (-0.014 to 0.016) 0.884
REML -0.019 (-0.081 to 0.043) 0.547 0.034 (-0.019 to 0.087) 0.205 -0.046 (-0.177 to 0.085) 0.489 0.059 (-0.089 to 0.207) 0.294 -0.008 (-0.020 to 0.004) 0.187
REMD -0.034 (-0.110 to 0.043) 0.385 -0.004 (-0.048 to 0.040) 0.869 0.042 (-0.035 to 0.119) 0.282 -0.070 (-0.195 to 0.055) 0.137 0.014 (-0.010 to 0.037) 0.255
Table 3. Results of sex as sleep parameters moderator.
Table 3. Results of sex as sleep parameters moderator.
Sex as moderator
SCZ (drug-naive) SCZ (drug-free) BD (depressed, drug-free) BD (manic, drug-free) MDD (drug-free)
Estimate p-value Estimate p-value Estimate p-value Estimate p-value Estimate p-value
TST 0.031 (-0.003 to 0.065) 0.074 0.005 (-0.010 to 0.019) 0.522 0.121 (-0.383 to 0.626) 0.594 -0.225 (1.004 to 0.555) 0.341 0.002 (-0.004 to 0.008) 0.450
SOL -0.010 (-0.036 to 0.016) 0.448 -0.010 (-0.022 to 0.002) 0.102 -0.090 (-0.898 to 0.719) 0.796 -0.148 (2.436 to 2.140) 0.850 -0.000 (-0.005 to 0.005) 0.765
SE 0.037 (-0.020 to 0.095) 0.206 0.005 (-0.010 to 0.019) 0.522 0.222 (-1.235 to 1.678) 0.712 0.067 (-2.053 to 2.189) 0.925 0.003 (-0.003 to 0.009) 0.334
WASO -0.005 (-0.033 to 0.024) 0.732 0.000 (-0.011 to 0.010) 0.931 -0.233 (-0.709 to 0.242) 0.275 0.372 (-0.352 to 1.095) 0.158 0.003 (-0.003 to 0.008) 0.305
ST1 0.010 (-0.036 to 0.055) 0.679 -0.007 (-0.021 to 0.007) 0.332 0.010 (-0.025 to 0.044) 0.446 0.187 (-1.811 to 2.185) 0.726 0.000 (-0.006 to 0.005) 0.922
%ST1 0.000 (-0.015 to 0.013) 0.890 0.000 (-0.014 to 0.014) 0.991 -0.436 (-1.424 to 0.552) 0.255 0.032 (-1.224 to 1.289) 0.940 0.001 (-0.003 to 0.006) 0.579
ST2 0.014 (-0.028 to 0.055) 0.525 -0.001 (-0.013 to 0.010) 0.800 0.602 (-0.122 to 1.326) 0.077 -0.233 (-0.740 to 0.274) 0.187 0.000 (-0.004 to 0.006) 0.763
%ST2 -0.014 (-0.053 to 0.026) 0.500 -0.002 (-0.012 to 0.008) 0.687 0.424 (-0.100 to 0.949) 0.082 -0.402 (-1.975 to 1.172) 0.476 0.000 (-0.006 to 0.006) 0.927
DELTA 0.081 (-0.009 to 0.172) 0.079 -0.019 (-0.229 to 0.192) 0.863 0.068 (-0.144 to 0.281) 0.463 0.421 (0.343 to 0.499) 0.002 -0.004 (-0.008 to -0.000) 0.034
%DELTA 0.096 (0.011 to 0.181) 0.026 -0.017 (-0.058 to 0.024) 0.424 0.023 (-0.210 to 0.255) 0.820 0.317 (-0.123 to 0.757) 0.106 -0.005 (-0.010 to -0.000) 0.049
REMT 0.028 (-0.010 to 0.065) 0.150 0.006 (-0.007 to 0.020) 0.363 -0.100 (-0.500 to 0.299) 0.623 0.119 (-0.465 to 0.704) 0.472 0.000 (-0.005 to 0.006) 0.843
%REM 0.034 (-0.002 to 0.090) 0.238 0.011 (-0.002 to 0.025) 0.106 -0.078 (-0.356 to 0.200) 0.584 0.217 (-0.211 to 0.645) 0.205 0.000 (-0.005 to 0.007) 0.822
REML 0.028 (-0.021 to 0.077) 0.268 0.008 (-0.004 to 0.021) 0.188 0.229 (-0.471 to 0.928) 0.521 -0.336 (-1.294 to 0.623) 0.346 -0.003 (-0.008 to 0.002) 0.283
REMD 0.004 (-0.021 to 0.029) 0.760 0.009 (-0.005 to 0.022) 0.216 0.144 (-0.288 to 0.575) 0.513 0.578 (0.356 to 0.799) 0.008 -0.004 (-0.016 to 0.008) 0.516
Table 4. Sleep parameters in UD versus HC meta-analysis results.
Table 4. Sleep parameters in UD versus HC meta-analysis results.
Meta-analysis
N. of studies N. of subjects Mean ± SD RE Model p-value
TST 55 1614 VS 1185 410.08 ± 53.91 VS 433.23 ± 37.13 -0.372 (-0.555 to -0.188) <0.001
SOL 59 1734 VS 1282 26.64 ± 21.96 VS 16.91 ± 12.45 0.547 (0.407 to 0.687) <0.001
SE 49 1468 VS 1028 86.27 ± 8.75 VS 91.39 ± 5.54 -0.633 (-0.828 to -0.438) <0.001
WASO 48 1488 VS 1011 34.98 ± 47.34 VS 24.29 ± 20.96 0.375 (0.221 to 0.529) <0.001
ST1 48 1525 VS 1052 29.73 ± 17.66 VS 31.78 ± 15.02 0.067 (-0.103 to 0.236) 0.441
%ST1 51 1592 VS 1141 7.41 ± 4.45 VS 7.36 ± 4.36 0.138 (-0.005 to 0.280) 0.059
ST2 49 1535 VS 1062 220.33 ± 44.26 VS 236.53 ± 41.45 -0.454 (-0.606 to -0.302) <0.001
%ST2 52 1602 VS 1151 54.01 ± 8.73 VS 54.89 ± 7.18 -0.195 (-0.385 to -0.004) 0.045
DELTA 31 972 VS 789 66.34 ± 31.55 VS 72.85 ± 32.50 -0.144 (-0.266 to -0.022) 0.020
%DELTA 52 1588 VS 1102 13.51 ± 7.47 VS 14.51 ± 6.95 -0.138 (-0.287 to 0.010) 0.068
REMT 32 1167 VS 776 93.83 ± 26.15 VS 92.03 ± 20.91 0.135 (-0.022 to 0.2919 0.092
%REM 44 1233 VS 799 23.01 ± 5.88 VS 20.66 ± 4.30 0.369 (0.196 to 0.543) <0.001
REML 58 1776 VS 1312 75.20 ± 34.42 VS 88.85 ± 34.69 -0.441 (-0.601 to -0.280) <0.001
REMD 43 1459 VS 962 5.15 ± 1.41 VS 4.56 ± 1.37 0.610 (0.288 to 0.931) <0.001
Table 5. Heterogeneity, publications bias, outlier and overly influential studies and funnel plot asymmetry analyses (UD vs HC).
Table 5. Heterogeneity, publications bias, outlier and overly influential studies and funnel plot asymmetry analyses (UD vs HC).
Heterogeneity Publication Bias Funnel plot asymmetry
Q i² p-value Fail-Safe N p-value Outlier Overly influential Rank correlation (p-value) Regression test (p-value)
TST 204.42 78.92% <0.001 1453 <0.001 Arana-Lechuga et al., 2008 Arana-Lechuga et al., 2008 0.718 0.010*
SOL 153.42 65.29% <0.001 3555 <0.001 Arana-Lechuga et al., 2008
Asaad et al., 2016		 Arana-Lechuga et al., 2008
Asaad et al., 2016		 0.567 0.043*
SE 178.15 78.32% <0.001 3215 <0.001 Asaad et al., 2016 Asaad et al., 2016 0.455 0.071
WASO 141.65 65.56% <0.001 1035 <0.001 - - 0.565 0.448
ST1 159.29 73.24% <0.001 0 0.071 Arana-Lechuga et al., 2008 Hoffman et al., 2000
Arana-Lechuga et al., 2008		 0.577 0.021*
%ST1 143.49 64.50% <0.001 144 <0.001 Hubain et al., 2006
Asaad et al., 2016		 Hubain et al., 2006
Asaad et al., 2016		 0.224 0.444
ST2 156.72 68.01% <0.001 1896 <0.001 - Reynolds et al., 1985
Lauer et al., 1991		 0.051 0.060
%ST2 190.89 80.38% <0.001 328 <0.001 McCracken et al., 1997
Asaad et al., 2016		 McCracken et al., 1997
Landolt & Gillin, 2005
Asaad et al., 2016		 0.117 0.682
DELTA 42.89 31.74% 0.06 68 0.020 - - 0.145 0.507
%DELTA 133.52 66.21% <0.001 116 0.002 Asaad et al., 2016 Asaad et al., 2016 0.969 0.840
REMT 91.80 56.36% <0.001 59 0.003 Arana-Lechuga et al., 2008 Emslie et al., 1990
Arana-Lechuga et al., 2008		 0.530 0.003*
%REM 112.12 66.18% <0.001 817 <0.001 Landolt & Gillin, 2005 Landolt & Gillin, 2005 0.193 0.056
REML 200.94 74.80% <0.001 2314 <0.001 Landolt & Gillin, 2005
Arana-Lechuga et al., 2008		 Landolt & Gillin, 2005
Arana-Lechuga et al., 2008		 0.521 0.133
REMD 304.06 91.78% <0.001 1976 <0.001 Armitage et al., 2000.3
Asaad et al., 2016		 Emslie et al., 1987
McCracken et al., 1997
Armitage et al., 2000.3
Asaad et al., 2016		 0.260 0.957
* Asymmetry found

Discussion

To date, this is the first study to systematically examine age and sex as study-level moderators of polysomnographic case-control differences across schizophrenia, bipolar disorder and unipolar depression.
The present study found an effect of sex on both DELTA and %DELTA in UD versus HC compatible with previous reports of sex differences in SWS in MDD [122,127,128], found also in early-onset samples [129], as depressed women have been reported to live on a higher homeostatic sleep pressure level [130]. Age significantly moderated differences in DELTA, %DELTA, REMT and measures of sleep continuity and duration between UD and HC. These findings are in line with the literature reporting an age-related decline in TST, including SWS and REMT and a disruption of sleep continuity. No significant age-related moderation was observed for SOL in UD, compatible with evidence suggesting only modest age-related changes in this parameter. Along these results, the moderator effect of age on ST2 differences may reflect greater time spent in lighter NREM stages as SWS declines with aging [131]. Our results did not identify a moderating effect of age on REMD in UD, in line with evidence suggesting that REMD may be less sensitive to age-related variation than REML [99]. Early studies suggested a steeper age-related shortening of REML in MDD compared to HC, interpreted as evidence of “premature aging” effects [99,132,133], while more recent evidence challenged this idea by not identifying clear differences in REML patterns across age strata between MDD and HC [123]. Consistently, the present meta-regression did not identify age as a significant moderator of REML differences between drug-free UD and HC, suggesting a lack of robust evidence for age-related modulation of UD-HC differences in REML.
Cross-sectional reports observing age and sex differences in SCZ and BD are scarce, whereas such differences were disproportionately investigated in MDD. Contrary to what found in this study regarding sleep in UD, no effect of age and sex on any sleep parameter in the depressive phase of BD was found. On the opposite, a significant effect of age on REMT was found in the manic phase of BD. Higher REM pressure has recently been proposed as a characteristic feature of manic episodes [17], which may partly explain why demographic moderation was observed for this REM-related measure. No other effect of age on any sleep parameter in mania was found consistently with an early study on the matter [71]. The effect of sex on sleep parameters in mania was tested with no results in one small-sized sample study [72], whereas our study found sex to significantly moderate DELTA also in BD mania. An effect of sex on REMD in mania was further found in the present study. Sex-related differences in the clinical expression, course and neurobiology of bipolar disorder have been reported [134,135] and may contribute to the observed study-level moderation of REMD. REMD has been proposed as a marker associated with antidepressant treatment response in MDD [136]. Whether REMD may similarly reflect treatment response in bipolar mania remains unknown. Given that lithium has been reported to delay REM sleep [137] and reduce REMD [138], future longitudinal studies could examine whether REMD changes during lithium treatment are associated with clinical response.
Regarding SCZ, age and sex significantly moderated %DELTA differences in drug-naive samples, whereas no such effects were observed in drug-free samples. This discrepancy may reflect differences in illness stage and treatment history between the two populations. Drug-naive samples are more likely to represent earlier phases of the disorder, whereas drug-free samples may include individuals with longer illness duration and prior pharmacological exposure or discontinuation effects, all of which may contribute to increased between-study heterogeneity in sleep architecture measures [9]. Such heterogeneity may attenuate detectable demographic moderation in chronic or clinically heterogeneous samples, raising the possibility that demographic influences may be more detectable in early-stage schizophrenia, although differences in study heterogeneity and statistical power may also contribute. Age significantly moderated REMT differences in drug-free SCZ, potentially reflecting greater variability in illness duration and treatment history in these samples.
As reported, high-quality PSG studies in specific populations, particularly drug-naive SCZ and BD, remain limited in the literature. Several analyses may therefore have limited statistical power and negative findings may reflect type II error. Moreover, age and sex are not homogeneously represented across studies within and between diagnostic groups, with a general predominance of male participants and limited variability in demographic distributions. This demographic imbalance limits the ability to robustly assess differential effects of age and sex on PSG parameters. Furthermore, because our meta-regression relies on study-level moderators, such as mean age or the percentage of maleparticipants, our findings are susceptible to the ecological fallacy. Consequently, associations observed at the aggregate study level may not perfectly reflect relationships at the individual patient level. Finally, differences in observed moderation effects across disorders may partly reflect differences in between-study variance of demographic composition rather than disorder-specific mechanisms. In SCZ and BD, restricted variability in sex ratios across studies likely reduced statistical power to detect sex-related moderation effects, potentially attenuating regression estimates.
The meta-analysis of sleep parameters of drug-free UD versus HC confirmed marked abnormalities in sleep continuity and duration, increased REM pressure and reduced SWS [14,18]. Although heterogeneity was moderate to high (>60%) for the majority of analyzed parameters, lowest heterogeneity (31.7%) was observed in the meta-analysis of DELTA in UD patients, supporting SWS reduction in depressed patients regardless of previous exposure to treatment. A meta-regression could not be performed for SZA due to insufficient number of available sleep studies and limited variability in study-level covariates, including age and sex distribution [11], highlighting a gap in literature that currently limits characterization of sleep architecture in this diagnostic group.
Future research should prioritize longitudinal designs and individual participant data meta-analyses to better disentangle biological aging from illness chronicity and pharmacological exposure while reducing ecological bias. A shift toward sleep microstructural characterization is warranted, as sleep microstructure may be more sensitive than macrostructure to age-related differences [139,140,141]. In particular, parameters such as REMD may benefit from this approach, given the heterogeneity in scoring methodologies. Methods aggregating healthy populations to model demographic effects on sleep spindles [142,143] may be extended to psychiatric cohorts to improve characterization of age and sex effects in pathological sleep. Such work should be coupled with improved sampling of underrepresented clinical strata, including drug-naive and drug-free populations with broader and more balanced demographic distributions, alongside stricter stratification of medication history. Finally, integrating circadian timing metrics with sleep microstructural features may improve characterization of dynamic vulnerability across mood and psychotic phases [144], while dimensional approaches across diagnostic boundaries may further enhance explanatory resolution beyond categorical classifications.

Supplementary Materials

The following supporting information can be downloaded at the website of this paper posted on Preprints.org. Supplementary File S1, Supplementary File S2 (PRISMA checklist).

Funding

No funding received.

Data Availability Statement

No new data were created.

Conflicts of Interest

The authors declare that there are no conflicts of interest.

Conflicts of Interest

No financial conflicts of interest.

Abbreviations

Abbreviation In full
BD Bipolar disorder
DELTA Delta sleep time
%DELTA Delta sleep percentage
HC Healthy control
MDD Major depressive disorder
NREM Non-rapid-eye-movement
PRISMA Preferred reporting items for systematic reviews and meta-analyses
PSG Polysomnography
REM Rapid-eye-movement
REMD Rapid-eye-movement density
REML Rapid-eye-movement latency
REMT Rapid-eye-movement time
%REM Rapid-eye-movement percentage
SCZ Schizophrenia
SD Standard deviation
SE Sleep efficiency
SOL Sleep onset latency
ST1 Stage 1 sleep time
ST2 Stage 2 sleep time
%ST1 Stage 1 sleep percentage
%ST2 Stage 2 sleep percentage
SWS Slow-wave sleep
SZA Schizoaffective disorder
TST Total sleep time
UD Unipolar depression
WASO Wake after sleep onset

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Preprints 221890 g001
Figure 1. PRISMA diagram for the UD vs HC case-control studies search.
Figure 1. PRISMA diagram for the UD vs HC case-control studies search.
Preprints 221890 g001
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