Sleep quality and depressive symptoms: insights from a genetically informative design

Sleep quality and depressive symptoms: insights from a genetically informative design | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Sleep quality and depressive symptoms: insights from a genetically informative design Juan R Ordoñana, Juan J Madrid-Valero, Josep Pol-Fuster, Lucia Colodro-Conde, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8480411/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract According to the diathesis-stress model, the impact of environmental stressors on depression may vary depending on individual vulnerability (e.g., genetic predisposition). Sleep quality has been consistently linked to depression. However, the nature of this relationship is not yet fully understood. Our hypothesis was that there is a significant interaction between sleep quality and genetic vulnerability to depression that contributes to depressive symptomatology. The sample consisted of 1,147 participants from the Murcia Twin Registry (mean age: 55.8; SD: 7.6; females: 66%). Polygenic scores (PGS) for depression were calculated using LDpred2. The model that provided the best fit included sex, age, age 2 , sleep quality, PGS for depression, and their interactions (AIC = 5637.4). Removing the interaction between sleep quality and PGS for depression resulted in a significant deterioration of model fit (AIC = 5644.7; P = 0.002). Our findings suggest that poor sleep quality may exert a greater influence on depressive symptoms in individuals with a higher genetic vulnerability to depression. Health sciences/Diseases/Psychiatric disorders Biological sciences/Genetics Depression genetics interaction polygenic scores sleep quality twins Figures Figure 1 Introduction Depression is among the most prevalent mood disorders and is a leading cause of disability ( 1 ). The etiology of depression is multifactorial, and many environmental and genetic factors are thought to contribute to its onset and progression. One of the most commonly studied factors in relation to depression is sleep ( 2 ). In fact, sleep disturbances are considered a diagnostic criterion for depression ( 3 ). A substantial body of research supports the association between poor sleep quality and depressive symptoms ( 2 , 4 , 5 ), and some researchers have suggested that diagnosing depression in the absence of sleep complaints should be approached with caution ( 6 ). Several studies and meta-analyses have shown that disturbed sleep increases the risk of depression later in life ( 7 , 8 ). However, the mechanisms underlying this association are not fully understood. Twin studies have consistently shown that both sleep quality and depression are influenced by genetic factors, with estimates of 31–44% for sleep quality ( 9 , 10 ) and 37–49% for depression ( 4 , 11 , 12 ). In addition, significant genetic and environmental correlations have been observed between poor sleep quality and depressive symptoms (ranging from 0.50–0.61 and 0.29–0.41, respectively) ( 4 , 11 ). Molecular studies have explored the genetic architecture of sleep quality and insomnia ( 13 – 16 ), as well as depressive symptoms ( 17 ). These studies have reported a strong genetic correlation between insomnia and major depressive disorder (r g = 0.649) ( 13 ), with five risk loci for insomnia sharing the same causal single nucleotide polymorphisms (SNPs) as those associated with major depressive disorder. A well-established explanatory model for the etiology of depression is the diathesis-stress, or vulnerability-stress, model ( 18 , 19 ). This model suggests that stress interacts with an existing vulnerability (i.e., diathesis) to initiate the onset of the disorder. Using a candidate gene approach, some studies have examined the role of environmental triggers (e.g., early adversity or recent stressors) in interaction with specific genes (e.g., CLOCK ) ( 20 , 21 ). However, complex traits such as depression and sleep quality are highly polygenic ( 22 ) and such limited approaches are discouraged due to conceptual issues and the general lack of replication ( 23 – 25 ). To address this, polygenic scores (PGS) offer some insight into the relative risk of exhibiting a particular phenotype by aggregating the small-scale effects of hundreds to thousands of genetic variants ( 26 , 27 ). For example, while the predictive value of PGSs remains limited ( 26 , 28 ), their application as a measure of genetic vulnerability to depression has enabled direct testing of the diathesis-stress model. This has led to the identification of significant interactions between the PGS for depression and stressful life-events ( 29 , 30 ). Such studies have used general indices of self-reported stress as a measure of environmental risk. However, analysis of the effects of specific triggers associated with depressive symptoms (e.g., childhood trauma) has yielded inconsistent results and requires further investigation ( 31 ). To date, no studies have specifically examined how poor sleep quality–one of the most consistently identified factors associated with depression–interacts with genetic vulnerability to depression in the development of depressive symptoms. In this study, we test the association between poor sleep quality and depression, taking into account an individual’s genetic vulnerability to depression. Consistent with the diathesis-stress model of depression, we hypothesize that poor sleep quality (i.e., stressor) will be more strongly associated with depressive symptoms in people with a higher genetic predisposition for depression, as measured by their polygenic score (i.e., diathesis). Subjects and Methods Participants The sample consisted of 1,147 genotyped individuals (mean age: 55.8; SD: 7.6; 66% female) from the Murcia Twin Registry (MTR). The MTR is a population-based twin registry in the region of Murcia, in southeastern Spain. A detailed description of the MTR’s recruitment and data collection procedures is available elsewhere ( 32 ) and the registry is representative of the adult population in Spain ( 33 ). Participants were treated as individuals, irrespective of zygosity, and appropriate analyses were performed to control for relatedness (see “Statistical Analyses”). The MTR protocols and instruments, as well as the data collection procedures and their analytical derivatives, were approved by the Research Ethics and Biosafety Committees of the University of Murcia and comply with the legal requirements regarding confidentiality and the protection of personal data (2098/2018 and CBE154/2018). Participants provided written informed consent for in-person interviews and verbal consent for telephone interviews. Measures Depressive symptoms were assessed using the Spanish adaptation of the Patient Health Questionnaire (PHQ-8). The PHQ-8 assesses depressive symptoms in line with the diagnostic criteria for depressive disorders outlined in the DSM-IV ( 34 ). This scale has been shown to be a reliable and a valid instrument for measuring depressive symptoms ( 35 ) and has been tested in population and epidemiological studies ( 34 ). Cronbach’s Alpha in the present sample was 0.84. Sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI). This questionnaire is made up of seven sub-scales: 1) subjective sleep quality, 2) sleep latency, 3) sleep duration, 4) habitual sleep efficiency, 5) sleep disturbances, 6) use of sleep medication, and 7) daytime dysfunction ( 36 ). These seven sub-scales yield a global score ranging from zero to 21, with higher scores indicating poorer sleep quality. The Spanish version of this questionnaire has been validated ( 37 , 38 ) and has demonstrated adequate psychometric properties, including high correlations with objective measures of sleep ( 39 , 40 ). Cronbach’s Alpha for the PSQI global score in this sample was 0.77. Genotyping : The MTR has collected DNA samples in two separate waves. Blood or saliva samples were obtained from voluntary donors and stored and processed at the Carlos III National DNA Bank ( www.bancoadn.org ). More detailed information about sample management procedures can be found elsewhere ( 32 ). DNA samples were genotyped using the Illumina GSA chip at Erasmus University in The Netherlands (Wave 1) and the Spanish National Genotyping Center (Wave 2). Samples with non-European ancestry (n = 6), a call rate of less than 98%, minor allele frequency below 1%, Hardy-Weinberg equilibrium test p -values less than1e − 6 , and anomalies in heterozygosity were excluded prior to calculating the polygenic scores. Data imputation was performed using Minimac v4. Polygenic scores for depression. PGS can be defined as the sum of the number of trait-associated allelic genetic variants, weighted by their effect sizes, based on summary statistics that aim to capture an individual’s genetic predisposition to a specific phenotype ( 41 ). We used summary statistical data from a Genome-Wide Association Study (GWAS) involving 807,553 individuals ( 17 ). The PGS were calculated using LDpred2 ( 42 ). This Bayesian method calculates PGS by taking into account trait-heritability, assuming a prior distribution for the polygenicity of a trait and adjusting for linkage disequilibrium. No clumping or pruning is applied to the individual genotyped data, thereby preserving all available information. The PGS were z-transformed and adjusted for the first six principal components of genetic ancestry and the chip used to account for population stratification and/or genotyping effects. A total of 587,774 SNPs were available for calculation of the PGS. We also used summary statistics from the largest GWAS on insomnia ( 13 ) to calculate PGS for insomnia, which were subsequently used in our sensitivity analysis. Statistical analyses Twins in the sample were treated as individuals for all analyses. We fitted a mixed-effect regression model, including family as a random effect to control for relatedness within the sample ( 43 ). To test whether the impact of poor sleep quality on depressive symptoms varies according to the degree of genetic predisposition, we fitted a series of regression models. First, we fitted the full model including sleep quality, PGS for depression (PGS DEP ), age, age 2 , sex, and the interaction between sleep quality and PGS DEP , as well as the interaction between age, age 2 , sex, age, and sleep quality and PGS DEP (Model I), as is standard practice in this type of design ( 44 ). We then compared this full model to a series of nested models, each omitting one predictor at a time, to determine whether one or more components could be excluded without a significant deterioration in model fit. Models successively excluded the interaction between sleep quality and PGS DEP (Model II), sex and its interactions (Model III), age and its interactions (Model IV), and both sex and age (Model V). We then examined the individual significance of variables in Model V (sleep quality, PGS DEP , and their interaction) by first excluding the interaction (Model VI), followed by PGS DEP (Model VII) and sleep quality (Model VIII). Model fit was compared using the Akaike Information Criterion (AIC). For the sake of completeness, we also reported p -values to provide additional information, although their use in mixed-effects models is not strictly required and the methods for obtaining them have certain limitations ( 45 ). Continuous variables were mean-centered prior to inclusion in the regression models to reduce the risk of multicollinearity between predictor variables and the constructed cross-product term ( 46 ). All models were fitted using the lme4 ( 47 ) package in R v4.2.2 ( 48 ). Results Descriptive statistics are shown in Table 1 . Females reported higher levels of both poor sleep quality (߂=5.4 vs ߂=3.8) and depressive symptoms (߂=3.7 vs ߂=1.8) compared to males (p < 0.05). Thirty-six percent of the sample was classified as having poor sleep quality based on the standard PSQI cut-off point (i.e., a score higher than five points) ( 36 ). Table 1 Descriptive statistics Male Female Total Total, N (%) 395 ( 34 ) 752 (66) 1147 (100) Age (SD) 55.5 (7.4) 55.9 (7.7) 55.8 (7.6) Mean PSQI (SD) 3.8 (3.5) 5.4 (4.0) 4.86 (3.9) Mean PHQ-8 (SD) 1.8 (3.3) 3.7 (4.4) 3.1 (4.1) Mean PGS DEP (SD) 0.02 (1.0) -0.01 (1.0) 0 (1.0) PHQ-8: Patient Health Questionnaire; PSQI: Pittsburgh Sleep Quality Index; PGS DEP : Polygenic score for depression Note: Higher scores represent poorer sleep quality, more depressive symptoms, or a higher genetic predisposition. Mixed-effect regression models and model comparison are shown in Table 2 . The best fit was provided by the full model (AIC = 5637.4; R 2 = 0.457), which included sleep quality, PGS DEP , age, sex, and the interaction terms as predictors (Model I). In Model II, removal of the interaction term between sleep quality and PGS DEP resulted in a significantly poorer model fit (AIC = 5644.7; p = 0.002) and a reduction in explained variance (0.457 to 0.452). Similarly, compared to the full model (I), models excluding covariates (sex – Model III; age/age 2 – Model IV; sex and age/age 2 – Model V) showed significant reductions in model fit. Thus, younger age ( p = 0.004) and identifying as female ( p < 0.001) emerged as significant predictors of depressive symptomatology. Moreover, removing the interaction term from the model without covariates also led to a deterioration in model fit (AIC model VI =5669.6 vs. AIC model V =5663.9; p = < 0.001). Finally, both poor sleep quality (Model VII; R 2 = 0.428; p < 0.001) and PGS DEP (Model VIII; R 2 = 0.010; p = 0.033) were significant individual predictors of depressive symptoms, each accounting for part of the total variance. Table 2 Regression models for depressive symptomatology (PHQ-8 score) Model (model for comparison) Estimate SE t-value Standardized estimate AIC R 2 explained by fixed effects Model comparison (p-value) I 5637.4 0.457 Sleep quality 0.693 0.028 24.358 0.659 PGS DEP 0.222 0.118 1.882 0.054 Sex -0.991 0.199 -4.983 -0.115 Age -0.148 0.175 -0.847 -0.277 Age 2 0.875 1.335 0.655 0.214 Sleep quality* PGS DEP 0.078 0.026 3.042 0.070 Sex* PGS DEP -0.026 0.196 -0.134 -0.076 Sex* Sleep quality -0.148 0.053 -2.779 -0.076 Age* PGS DEP -0.084 0.174 -0.484 -0.161 Age* Sleep quality -0.049 0.044 -1.111 -0.369 Age 2 * PGS DEP 0.830 1.318 0.630 0.210 Age 2 * Sleep quality 0.306 0.330 0.926 0.308 II (I) 5644.7 0.452 0.002 Sleep quality 0.700 0.029 24.567 0.665 PGS DEP 0.271 0.117 2.312 0.066 Sex -0.978 0.200 -4.897 -0.113 Age -0.162 0.176 -0.922 -0.302 Age 2 0.991 1.339 0.740 0.243 Sex* PGS DEP -0.169 0.191 -0.887 -0.025 Sex* Sleep quality -0.149 0.053 -2.792 -0.076 Age* PGS DEP -0.051 0.174 -0.291 -0.097 Age* Sleep quality -0.050 0.044 -1.140 -0.380 Age 2 * PGS DEP 0.556 1.320 0.421 0.141 Age 2 * Sleep quality 0.321 0.332 0.967 0.323 III (I) 5660.0 0.442 < 0.001 Sleep quality 0.672 0.024 28.066 0.639 PGS DEP 0.197 0.094 2.099 0.048 Age -0.182 0.178 -1.021 -0.339 Age 2 1.163 1.358 0.857 0.285 Sleep quality* PGS DEP 0.078 0.025 3.098 0.070 Age* PGS DEP -0.108 0.176 -0.615 -0.208 Age* Sleep quality -0.052 0.044 -1.165 -0.391 Age 2 * PGS DEP 1.014 1.335 0.759 0.257 Age 2 * Sleep quality 0.339 0.334 1.015 0.341 IV (I) 5644.8 0.447 0.004 Sleep quality 0.698 0.029 24.440 0.663 PGS DEP 0.230 0.119 1.938 0.056 Sex -0.939 0.201 -4.680 -0.108 Sleep quality* PGS DEP 0.068 0.026 2.673 0.062 Sex * PGS DEP -0.038 0.197 -0.191 -0.006 Sex * Sleep quality -0.135 0.053 -2.536 -0.069 V (I) 5663.9 0.434 < 0.001 Sleep quality 0.679 0.024 28.272 0.645 PGS DEP 0.206 0.094 2.183 0.050 Sleep quality* PGS DEP 0.070 0.025 2.779 0.063 VI (V) 5669.6 0.430 0.006 Sleep quality 0.684 0.024 28.480 0.650 PGS DEP 0.201 0.095 2.127 0.049 VII (VI) 5672.2 0.428 0.033 Sleep quality 0.688 0.024 28.662 0.654 VIII (VI) 6273.5 0.010 < 0.001 PGS DEP 0.408 0.124 3.285 0.099 Note: higher scores represent poorer sleep quality or a higher genetic predisposition. PGS: polygenic scores Model I: Symptoms of depression predicted by sleep quality, PGS DEP , sex, age, sleep quality* PGS DEP , sex* PGS DEP , sex* Sleep quality, age* PGS DEP , and age* sleep quality. Model II: Symptoms of depression predicted by sleep quality, PGS DEP , sex, age, sex* PGS DEP , sex* Sleep quality, age* PGS DEP , and age* sleep quality. Model III: Symptoms of depression predicted by sleep quality, PGS DEP , age, sleep quality* PGS DEP , age* PGS DEP , and age* sleep quality. Model IV: Symptoms of depression predicted by sleep quality, PGS DEP , sex, sleep quality* PGS DEP , sex* PGS DEP , and sex* sleep quality. Model V: Symptoms of depression predicted by sleep quality, PGS DEP , and sleep quality* PGS DEP . Model VI: Symptoms of depression predicted by sleep quality, and PGS DEP . Model VII: Symptoms of depression predicted by sleep quality. Model VIII: Symptoms of depression predicted by PGS DEP . AIC: Akaike Information Criterion; PGS DEP : Polygenic score for depression; SE: standard error. Model in bold represents the model with the lowest AIC value, selected as the best fit. As the PHQ-8 questionnaire includes a question inquiring about sleep (i.e. “Trouble falling or staying asleep, or sleeping too much”), we run a sensitivity analysis excluding this item. Results were very similar, and the interaction between sleep quality and PGS DEP was a significant predictor. Its removal also resulted in a significantly worse fit (AIC = 5569.5; p = 0.001) and a reduction in explained variance (from 0.332 to 0.326) (Supplementary Table 1). As an additional check, given the genetic overlap between depression and sleep quality, we conducted a sensitivity analysis by including a polygenic score for insomnia as a covariate in the model (results not reported in tables). The results replicated the same pattern, with a significant interaction between sleep quality and PGS DEP (p = 0.002). The interaction between sleep quality and PGS DEP on depressive symptoms is illustrated in Fig. 1, which displays results for three groups: high genetic predisposition (PGS DEP +1SD), medium genetic predisposition (mean), and low genetic predisposition (PGS DEP -1SD). The figure shows that as sleep quality deteriorates, the divergence between groups increases, with differences reaching approximately 2.5 points in depressive symptoms for participants with very poor sleep quality. There may be two different components to the observed interaction between genetic vulnerability and sleep quality on depressive symptoms. Specifically, genetic predisposition to depression may interact directly with genes influencing sleep (i.e., GxG - genotype x genotype interaction). Alternatively, poor sleep quality may act as an environmental trigger for genetic vulnerability (i.e., GxE - genotype x environment interaction). To further explore the nature of the interaction (GxG vs GxE), we calculated environmental and genetic factor scores for sleep quality (SQ-E and SQ-A, respectively) using an independent pathway model (Supplementary Fig. 1), which was fitted to the seven sub-scales of the PSQI ( 49 ). A detailed explanation of the methods is provided in the Supplementary Material. The correlation between the sum of SQ-E and SQ-A and the PSQI total score was 0.98 (Supplementary Fig. 2). These variables (SQ-E and SQ-A) were used to replace sleep quality in the fitted models (Supplementary Tables 2 and 3). The interaction between PGS and sleep quality was significant for both variables (i.e., SQ-A and SQ-E), indicating that both the GxG and GxE interactions were significant (models including the interaction yielded the lowest AIC values). The GxG model provided the best fit as indicated by a lower AIC (AIC = 5901.4 for the GxE model; AIC = 5819.7 for the GxG model). The variance explained by the GxG interaction was higher than that explained by the GxE interaction (0.60% and 0.20%, respectively). Discussion This study demonstrates that poor sleep quality interacts with genetic vulnerability to depression and is predictive of depressive symptomatology. We found a significant interaction between poor sleep quality and genetic risk for depression. The interaction remained significant even after controlling for genetic risk associated with insomnia. The genetic component underlying sleep quality was the primary source of the interaction. These findings confirm the link and complex relationship between poor sleep quality and depression. In other words, our results suggest that genetic risk for depression interacts with genes influencing poor sleep quality, thereby increasing the likelihood of developing depressive symptoms. At the same time, the presence of sleep difficulties may act as an environmental factor that interacts with genetic predisposition to further increase the risk of depression, in what would be a classical diathesis-stress model. Our findings are consistent with previous literature showing that poor sleep quality and sleep disorders are associated with depressive symptoms ( 8 , 50 ). In this study, poor sleep quality was a significant predictor of depressive symptoms, accounting for a substantial portion of the model variance (42%). The PGS for depression was also a significant predictor, explaining approximately1% of the model variance. This is comparable to the proportion of variance reported by Howard et al.( 17 ) which ranged from 1.5% to 3.2%. Importantly, the interaction between genetic risk for depression and sleep quality was predictive of depressive symptoms, confirming our hypothesis. Specifically, the association between poor sleep quality and depressive symptoms is more pronounced in individuals with a higher genetic vulnerability to depression compared to those with a lower genetic vulnerability. The relationship between depression and sleep is complex, and the underlying mechanisms of this association remain unclear. Existing research suggests a bidirectional relationship between depression and sleep quality or sleep disorders ( 2 ). However, sleep problems appear to be a more significant predictor of depression than depression is of sleep problems ( 51 ). For example, Rieman and Voderholzer ( 52 ) found that the presence of insomnia predicted an increased risk of depression over the following one to three years. In another longitudinal study, Buysse et al. ( 53 ) also found insomnia to be a significant predictor of subsequent depressive episodes and major depressive disorder. The diathesis-stress model of depression suggests that stress interacts with an existing vulnerability (i.e., diathesis) to initiate the onset of the disorder. In this study, we observed that the impact of poor sleep quality on depressive symptomatology is more pronounced in individuals with greater genetic vulnerability to depression. This pattern becomes detectable from a score of five on the PSQI questionnaire (see Fig. 1), which is the instrument’s cut-off point for poor sleep quality ( 36 ). These results are consistent with a diathesis-stress model of depression and poor sleep. Additionally, our findings support the presence of GxG interactions between sleep quality and depression with effects in the same direction. In other words, poor sleep quality, whether from a genetic and environmental standpoint, may trigger or exacerbate depressive symptoms, with this effect being more pronounced in individuals with greater genetic vulnerability. Various mechanisms have been proposed to explain the relationship between poor sleep quality and depressive symptoms. For example, dysregulation of monoamine neurotransmitters–such as serotonin, norepinephrine, and dopamine–has been implicated in depression, whereas REM sleep requires a decrease in monoaminergic tone and increased cholinergic tone ( 54 ). Glutamate signaling has also been linked with both sleep (particularly during slow oscillations of non-REM sleep) and depressive symptoms (glutamate deficiency) ( 55 ). High levels of cortisol are associated with depressive symptoms and poor sleep quality ( 54 , 55 ). The nature of the link between these constructs is likely to be quite complex, requiring further research to elucidate the underlying pathways, directionality, and mechanisms involved. This study has a number of implications for both basic research and clinical practice. First, it provides corroboration that a PGS for depression, in combination with sleep problems, is a significant predictor of the onset of depressive symptoms in a population-based sample of middle-aged individuals. This knowledge may help to identify people at higher risk of developing depressive symptoms–specifically, those with a high genetic vulnerability to depression who are also experiencing sleep disturbances. Second, this study provides insight into the complex relationship between sleep disturbances and mood disorders by demonstrating that poor sleep quality may act as a trigger for depression in individuals with high genetic vulnerability. This aligns with previous studies suggesting that sleep problems may be a stronger predictor of subsequent depression than depression is of sleep problems ( 2 , 53 ). Third, this study also highlights the potential role of sleep interventions in preventing the onset and progression of depression, especially in individuals with a higher genetic predisposition. Our results are consistent with previous literature indicating that treating insomnia may improve depressive symptoms ( 56 ), as well as a recent meta-analysis of randomized controlled trials showing that improving sleep quality leads to better mental health, particularly in relation to depression ( 57 ). Strengths and limitations The strengths of this study include its large representative sample, the use of two widely used and validated questionnaires to measure sleep quality and depression, and a genetically informative design that enabled this type of analysis. In addition, the use of PGS made it possible to test the interaction between genetic vulnerability to depression and poor sleep quality. There are, however, a number of limitations that must be taken into account in the interpretation of this study. First, both sleep quality and depressive symptoms were assessed using self-report measures. Second, the cross-sectional nature of this study limits our ability to infer causal relationships between risk factors and outcomes. Third, the predictive value of PGS remains limited, resulting in a relatively small proportion of explained variance. Conclusions This study shows a significant interaction between poor sleep quality and genetic vulnerability to depression, with the former acting as a trigger for depressive symptomatology. The effects vary according to genetic predisposition to the disorder. Further research is needed to clarify the underlying mechanisms of this relationship. Declarations Acknowledgments: We thank all the participants from the Murcia Twin Registry. We are also grateful to Prof Sarah Medland for sharing analysis scripts. Conflict of interest: The MTR is funded by CARM-Regional Program for Scientific and Technical Research -Fundación Seneca-Regional Agency for Science and Technology, Murcia, Spain (03082/PHCS/05, Ref.100/2005; 08633/PHCS/08, Ref.359/2008; 15302/PHCS/10, Ref.617/2012; 19479/PI/14, Ref.1213/2016; and FSRM/10.13039/100007801. Ref 22649/PI/24) and the Spanish Ministry of Science and Innovation (PSI2009–11560, Ref.417/2019; PSI2014-56680-R, Ref.1032/2015; and RTI2018-095185-B-I00, Ref.2098/2018), co-funded by the European Regional Development Fund (FEDER) (PI: Juan R Ordoñana). The funder had no role in the study design, data collection, data analyses, writing, or the decision to submit this manuscript for publication. J.J.M-V. was supported by the Conselleria d’Educació, Investigació, Cultura i Esport de la Generalitat Valenciana (Proyectos I+D+i desarrollados por grupos de investigación emergentes) (CIGE/2021/103) (PI: Juan J Madrid-Valero). Ethical Standards: The authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008. The MTR protocols and instruments, as well as the data collection procedures and their analytical derivatives, were approved by the Research Ethics and Biosafety Committees of the University of Murcia and comply with the legal requirements regarding confidentiality and the protection of personal data (2098/2018 and CBE154/2018). Participants provided written informed consent for in-person interviews and verbal consent for telephone interviews. Data availability: By application. References Wang J, Wu X, Lai W, Long E, Zhang X, Li W, et al. Prevalence of depression and depressive symptoms among outpatients: a systematic review and meta-analysis. BMJ Open. 2017;7(8):e017173. Alvaro PK, Roberts RM, Harris JK. 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Med Sci Sports Exerc. 2020;52(7):1518–24. Keller MC. Gene × environment interaction studies have not properly controlled for potential confounders: the problem and the (simple) solution. Biol Psychiatry. 2014;75(1):18–24. Luke SG. Evaluating significance in linear mixed-effects models in R. Behav Res Methods. 2017;49(4):1494–502. Shieh G. Clarifying the role of mean centring in multicollinearity of interaction effects. Br J Math Stat Psychol. 2011;64(3):462–77. Bates DaMMaBBaWS. Fitting Linear Mixed-Effects Models Using lme4. Journal of Statistical Software. 2015;67(1):1–48. R Core Team. R: A Language and Environment for Statistical Computing R Foundation for Statistical Computing. 2021. Boomsma DI, Molenaar PC, Orlebeke JF. Estimation of individual genetic and environmental factor scores. Genet Epidemiol. 1990;7(1):83–91. Becker NB, Jesus SN, João KADR, Viseu JN, Martins RIS. Depression and sleep quality in older adults: a meta-analysis. Psychol Health Med. 2017;22(8):889–95. O'Leary K, Bylsma LM, Rottenberg J. Why might poor sleep quality lead to depression? A role for emotion regulation. Cogn Emot. 2017;31(8):1698–706. Riemann D, Voderholzer U. Primary insomnia: a risk factor to develop depression? J Affect Disord. 2003;76(1–3):255–9. Buysse DJ, Angst J, Gamma A, Ajdacic V, Eich D, Rössler W. Prevalence, course, and comorbidity of insomnia and depression in young adults. Sleep. 2008;31(4):473–80. Peterson MJ, Benca RM. Sleep in mood disorders. Psychiatr Clin North Am. 2006;29(4):1009–32; abstract ix. Murphy MJ, Peterson MJ. Sleep Disturbances in Depression. Sleep Med Clin. 2015;10(1):17–23. Ho FY, Chan CS, Lo WY, Leung JC. The effect of self-help cognitive behavioral therapy for insomnia on depressive symptoms: An updated meta-analysis of randomized controlled trials. J Affect Disord. 2020;265:287–304. Scott AJ, Webb TL, Martyn-St James M, Rowse G, Weich S. Improving sleep quality leads to better mental health: A meta-analysis of randomised controlled trials. Sleep Med Rev. 2021;60:101556. Additional Declarations The authors have declared there is NO conflict of interest to disclose Supplementary Files SupplementaryInformationXTables.docx Supplementary methods Cite Share Download PDF Status: Under Review Version 1 posted Review # 2 received at journal 05 May, 2026 Reviewer # 2 agreed at journal 16 Apr, 2026 Reviewer # 1 agreed at journal 05 Feb, 2026 Reviewers invited by journal 21 Jan, 2026 Editor assigned by journal 09 Jan, 2026 Submission checks completed at journal 09 Jan, 2026 First submitted to journal 08 Jan, 2026 Unknown event 07 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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design","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDepression is among the most prevalent mood disorders and is a leading cause of disability (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). The etiology of depression is multifactorial, and many environmental and genetic factors are thought to contribute to its onset and progression. One of the most commonly studied factors in relation to depression is sleep (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). In fact, sleep disturbances are considered a diagnostic criterion for depression (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). A substantial body of research supports the association between poor sleep quality and depressive symptoms (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e), and some researchers have suggested that diagnosing depression in the absence of sleep complaints should be approached with caution (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Several studies and meta-analyses have shown that disturbed sleep increases the risk of depression later in life (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). However, the mechanisms underlying this association are not fully understood.\u003c/p\u003e \u003cp\u003eTwin studies have consistently shown that both sleep quality and depression are influenced by genetic factors, with estimates of 31\u0026ndash;44% for sleep quality (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e) and 37\u0026ndash;49% for depression (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). In addition, significant genetic and environmental correlations have been observed between poor sleep quality and depressive symptoms (ranging from 0.50\u0026ndash;0.61 and 0.29\u0026ndash;0.41, respectively) (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Molecular studies have explored the genetic architecture of sleep quality and insomnia (\u003cspan additionalcitationids=\"CR14 CR15\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e), as well as depressive symptoms (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). These studies have reported a strong genetic correlation between insomnia and major depressive disorder (r\u003csub\u003eg\u003c/sub\u003e = 0.649) (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e), with five risk loci for insomnia sharing the same causal single nucleotide polymorphisms (SNPs) as those associated with major depressive disorder.\u003c/p\u003e \u003cp\u003eA well-established explanatory model for the etiology of depression is the diathesis-stress, or vulnerability-stress, model (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). This model suggests that stress interacts with an existing vulnerability (i.e., diathesis) to initiate the onset of the disorder. Using a candidate gene approach, some studies have examined the role of environmental triggers (e.g., early adversity or recent stressors) in interaction with specific genes (e.g., \u003cem\u003eCLOCK\u003c/em\u003e) (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). However, complex traits such as depression and sleep quality are highly polygenic (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e) and such limited approaches are discouraged due to conceptual issues and the general lack of replication (\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). To address this, polygenic scores (PGS) offer some insight into the relative risk of exhibiting a particular phenotype by aggregating the small-scale effects of hundreds to thousands of genetic variants (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). For example, while the predictive value of PGSs remains limited (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e), their application as a measure of genetic vulnerability to depression has enabled direct testing of the diathesis-stress model. This has led to the identification of significant interactions between the PGS for depression and stressful life-events (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Such studies have used general indices of self-reported stress as a measure of environmental risk. However, analysis of the effects of specific triggers associated with depressive symptoms (e.g., childhood trauma) has yielded inconsistent results and requires further investigation (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). To date, no studies have specifically examined how poor sleep quality\u0026ndash;one of the most consistently identified factors associated with depression\u0026ndash;interacts with genetic vulnerability to depression in the development of depressive symptoms.\u003c/p\u003e \u003cp\u003eIn this study, we test the association between poor sleep quality and depression, taking into account an individual\u0026rsquo;s genetic vulnerability to depression. Consistent with the diathesis-stress model of depression, we hypothesize that poor sleep quality (i.e., stressor) will be more strongly associated with depressive symptoms in people with a higher genetic predisposition for depression, as measured by their polygenic score (i.e., diathesis).\u003c/p\u003e"},{"header":"Subjects and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eThe sample consisted of 1,147 genotyped individuals (mean age: 55.8; SD: 7.6; 66% female) from the Murcia Twin Registry (MTR). The MTR is a population-based twin registry in the region of Murcia, in southeastern Spain. A detailed description of the MTR\u0026rsquo;s recruitment and data collection procedures is available elsewhere (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e) and the registry is representative of the adult population in Spain (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Participants were treated as individuals, irrespective of zygosity, and appropriate analyses were performed to control for relatedness (see \u0026ldquo;Statistical Analyses\u0026rdquo;).\u003c/p\u003e \u003cp\u003e The MTR protocols and instruments, as well as the data collection procedures and their analytical derivatives, were approved by the Research Ethics and Biosafety Committees of the University of Murcia and comply with the legal requirements regarding confidentiality and the protection of personal data (2098/2018 and CBE154/2018). Participants provided written informed consent for in-person interviews and verbal consent for telephone interviews.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMeasures\u003c/h3\u003e\n\u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eDepressive symptoms\u003c/span\u003e were assessed using the Spanish adaptation of the Patient Health Questionnaire (PHQ-8). The PHQ-8 assesses depressive symptoms in line with the diagnostic criteria for depressive disorders outlined in the DSM-IV (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). This scale has been shown to be a reliable and a valid instrument for measuring depressive symptoms (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e) and has been tested in population and epidemiological studies (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). Cronbach\u0026rsquo;s Alpha in the present sample was 0.84.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSleep quality\u003c/span\u003e was assessed using the Pittsburgh Sleep Quality Index (PSQI). This questionnaire is made up of seven sub-scales: 1) subjective sleep quality, 2) sleep latency, 3) sleep duration, 4) habitual sleep efficiency, 5) sleep disturbances, 6) use of sleep medication, and 7) daytime dysfunction (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). These seven sub-scales yield a global score ranging from zero to 21, with higher scores indicating poorer sleep quality. The Spanish version of this questionnaire has been validated (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e) and has demonstrated adequate psychometric properties, including high correlations with objective measures of sleep (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). Cronbach\u0026rsquo;s Alpha for the PSQI global score in this sample was 0.77.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eGenotyping\u003c/span\u003e: The MTR has collected DNA samples in two separate waves. Blood or saliva samples were obtained from voluntary donors and stored and processed at the Carlos III National DNA Bank (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.bancoadn.org\u003c/span\u003e\u003c/span\u003e). More detailed information about sample management procedures can be found elsewhere (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). DNA samples were genotyped using the Illumina GSA chip at Erasmus University in The Netherlands (Wave 1) and the Spanish National Genotyping Center (Wave 2). Samples with non-European ancestry (n\u0026thinsp;=\u0026thinsp;6), a call rate of less than 98%, minor allele frequency below 1%, Hardy-Weinberg equilibrium test \u003cem\u003ep\u003c/em\u003e-values less than1e\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e, and anomalies in heterozygosity were excluded prior to calculating the polygenic scores. Data imputation was performed using Minimac v4.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ePolygenic scores for depression.\u003c/span\u003e PGS can be defined as the sum of the number of trait-associated allelic genetic variants, weighted by their effect sizes, based on summary statistics that aim to capture an individual\u0026rsquo;s genetic predisposition to a specific phenotype (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). We used summary statistical data from a Genome-Wide Association Study (GWAS) involving 807,553 individuals (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). The PGS were calculated using LDpred2 (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). This Bayesian method calculates PGS by taking into account trait-heritability, assuming a prior distribution for the polygenicity of a trait and adjusting for linkage disequilibrium. No clumping or pruning is applied to the individual genotyped data, thereby preserving all available information. The PGS were z-transformed and adjusted for the first six principal components of genetic ancestry and the chip used to account for population stratification and/or genotyping effects. A total of 587,774 SNPs were available for calculation of the PGS. We also used summary statistics from the largest GWAS on insomnia (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e) to calculate PGS for insomnia, which were subsequently used in our sensitivity analysis.\u003c/p\u003e\n\u003ch3\u003eStatistical analyses\u003c/h3\u003e\n\u003cp\u003eTwins in the sample were treated as individuals for all analyses. We fitted a mixed-effect regression model, including family as a random effect to control for relatedness within the sample (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). To test whether the impact of poor sleep quality on depressive symptoms varies according to the degree of genetic predisposition, we fitted a series of regression models. First, we fitted the full model including sleep quality, PGS for depression (PGS \u003csub\u003eDEP\u003c/sub\u003e), age, age\u003csup\u003e2\u003c/sup\u003e, sex, and the interaction between sleep quality and PGS \u003csub\u003eDEP\u003c/sub\u003e, as well as the interaction between age, age\u003csup\u003e2\u003c/sup\u003e, sex, age, and sleep quality and PGS \u003csub\u003eDEP\u003c/sub\u003e (Model I), as is standard practice in this type of design (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). We then compared this full model to a series of nested models, each omitting one predictor at a time, to determine whether one or more components could be excluded without a significant deterioration in model fit. Models successively excluded the interaction between sleep quality and PGS \u003csub\u003eDEP\u003c/sub\u003e (Model II), sex and its interactions (Model III), age and its interactions (Model IV), and both sex and age (Model V). We then examined the individual significance of variables in Model V (sleep quality, PGS \u003csub\u003eDEP\u003c/sub\u003e, and their interaction) by first excluding the interaction (Model VI), followed by PGS \u003csub\u003eDEP\u003c/sub\u003e (Model VII) and sleep quality (Model VIII). Model fit was compared using the Akaike Information Criterion (AIC). For the sake of completeness, we also reported \u003cem\u003ep\u003c/em\u003e-values to provide additional information, although their use in mixed-effects models is not strictly required and the methods for obtaining them have certain limitations (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). Continuous variables were mean-centered prior to inclusion in the regression models to reduce the risk of multicollinearity between predictor variables and the constructed cross-product term (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). All models were fitted using the lme4 (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e) package in R v4.2.2 (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eDescriptive statistics are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Females reported higher levels of both poor sleep quality (߂=5.4 vs ߂=3.8) and depressive symptoms (߂=3.7 vs ߂=1.8) compared to males (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Thirty-six percent of the sample was classified as having poor sleep quality based on the standard PSQI cut-off point (i.e., a score higher than five points) (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal, N (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e395 (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e752 (66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1147 (100)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55.5 (7.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55.9 (7.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55.8 (7.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean PSQI (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.8 (3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.4 (4.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.86 (3.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean PHQ-8 (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.8 (3.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.7 (4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.1 (4.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean PGS \u003csub\u003eDEP\u003c/sub\u003e (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.02 (1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.01 (1.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0 (1.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003ePHQ-8: Patient Health Questionnaire; PSQI: Pittsburgh Sleep Quality Index; PGS \u003csub\u003eDEP\u003c/sub\u003e: Polygenic score for depression\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNote: Higher scores represent poorer sleep quality, more depressive symptoms, or a higher genetic predisposition.\u003c/p\u003e\u003cp\u003eMixed-effect regression models and model comparison are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The best fit was provided by the full model (AIC\u0026thinsp;=\u0026thinsp;5637.4; R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.457), which included sleep quality, PGS \u003csub\u003eDEP\u003c/sub\u003e, age, sex, and the interaction terms as predictors (Model I). In Model II, removal of the interaction term between sleep quality and PGS \u003csub\u003eDEP\u003c/sub\u003e resulted in a significantly poorer model fit (AIC\u0026thinsp;=\u0026thinsp;5644.7; p\u0026thinsp;=\u0026thinsp;0.002) and a reduction in explained variance (0.457 to 0.452). Similarly, compared to the full model (I), models excluding covariates (sex \u0026ndash; Model III; age/age\u003csup\u003e2\u003c/sup\u003e \u0026ndash; Model IV; sex and age/age\u003csup\u003e2\u003c/sup\u003e \u0026ndash; Model V) showed significant reductions in model fit. Thus, younger age (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004) and identifying as female (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) emerged as significant predictors of depressive symptomatology. Moreover, removing the interaction term from the model without covariates also led to a deterioration in model fit (AIC \u003csub\u003emodel VI\u003c/sub\u003e=5669.6 vs. AIC \u003csub\u003emodel V\u003c/sub\u003e=5663.9; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Finally, both poor sleep quality (Model VII; R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.428; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and PGS \u003csub\u003eDEP\u003c/sub\u003e (Model VIII; R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.010; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.033) were significant individual predictors of depressive symptoms, each accounting for part of the total variance.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRegression models for depressive symptomatology (PHQ-8 score)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel (model for comparison)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003et-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eStandardized estimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAIC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e explained by fixed effects\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eModel comparison (p-value)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e5637.4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e0.457\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSleep quality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.693\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24.358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.659\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePGS \u003csub\u003eDEP\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.882\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-4.983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSleep quality* PGS \u003csub\u003eDEP\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSex* PGS \u003csub\u003eDEP\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSex* Sleep quality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-2.779\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge* PGS \u003csub\u003eDEP\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.484\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge* Sleep quality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge\u003csup\u003e2\u003c/sup\u003e* PGS \u003csub\u003eDEP\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.830\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.318\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.630\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge\u003csup\u003e2\u003c/sup\u003e* Sleep quality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.926\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII (I)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5644.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSleep quality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24.567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.665\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePGS \u003csub\u003eDEP\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.978\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-4.897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.922\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.740\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSex* PGS \u003csub\u003eDEP\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSex* Sleep quality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-2.792\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge* PGS \u003csub\u003eDEP\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.097\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge* Sleep quality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge\u003csup\u003e2\u003c/sup\u003e* PGS \u003csub\u003eDEP\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.556\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge\u003csup\u003e2\u003c/sup\u003e* Sleep quality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.967\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.323\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII (I)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5660.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSleep quality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e28.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.639\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePGS \u003csub\u003eDEP\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSleep quality* PGS \u003csub\u003eDEP\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge* PGS \u003csub\u003eDEP\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.615\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge* Sleep quality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge\u003csup\u003e2\u003c/sup\u003e* PGS \u003csub\u003eDEP\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.759\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge\u003csup\u003e2\u003c/sup\u003e* Sleep quality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIV (I)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5644.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.447\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSleep quality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.698\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24.440\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.663\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePGS \u003csub\u003eDEP\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.939\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-4.680\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSleep quality* PGS \u003csub\u003eDEP\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.673\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSex * PGS \u003csub\u003eDEP\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSex * Sleep quality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-2.536\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eV (I)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5663.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSleep quality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.679\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e28.272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.645\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePGS \u003csub\u003eDEP\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSleep quality* PGS \u003csub\u003eDEP\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.779\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVI (V)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5669.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.430\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSleep quality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.684\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e28.480\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePGS \u003csub\u003eDEP\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVII (VI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5672.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.428\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSleep quality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.688\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e28.662\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVIII (VI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6273.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePGS \u003csub\u003eDEP\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.408\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eNote: higher scores represent poorer sleep quality or a higher genetic predisposition. PGS: polygenic scores\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eModel I: Symptoms of depression predicted by sleep quality, PGS \u003csub\u003eDEP\u003c/sub\u003e, sex, age, sleep quality* PGS \u003csub\u003eDEP\u003c/sub\u003e, sex* PGS \u003csub\u003eDEP\u003c/sub\u003e, sex* Sleep quality, age* PGS \u003csub\u003eDEP\u003c/sub\u003e, and age* sleep quality.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eModel II: Symptoms of depression predicted by sleep quality, PGS \u003csub\u003eDEP\u003c/sub\u003e, sex, age, sex* PGS \u003csub\u003eDEP\u003c/sub\u003e, sex* Sleep quality, age* PGS \u003csub\u003eDEP\u003c/sub\u003e, and age* sleep quality.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eModel III: Symptoms of depression predicted by sleep quality, PGS \u003csub\u003eDEP\u003c/sub\u003e, age, sleep quality* PGS \u003csub\u003eDEP\u003c/sub\u003e, age* PGS \u003csub\u003eDEP\u003c/sub\u003e, and age* sleep quality.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eModel IV: Symptoms of depression predicted by sleep quality, PGS \u003csub\u003eDEP\u003c/sub\u003e, sex, sleep quality* PGS \u003csub\u003eDEP\u003c/sub\u003e, sex* PGS \u003csub\u003eDEP\u003c/sub\u003e, and sex* sleep quality.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eModel V: Symptoms of depression predicted by sleep quality, PGS \u003csub\u003eDEP\u003c/sub\u003e, and sleep quality* PGS \u003csub\u003eDEP\u003c/sub\u003e.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eModel VI: Symptoms of depression predicted by sleep quality, and PGS \u003csub\u003eDEP\u003c/sub\u003e.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eModel VII: Symptoms of depression predicted by sleep quality.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eModel VIII: Symptoms of depression predicted by PGS \u003csub\u003eDEP\u003c/sub\u003e.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eAIC: Akaike Information Criterion; PGS \u003csub\u003eDEP\u003c/sub\u003e: Polygenic score for depression; SE: standard error.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eModel in bold represents the model with the lowest AIC value, selected as the best fit.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAs the PHQ-8 questionnaire includes a question inquiring about sleep (i.e. \u0026ldquo;Trouble falling or staying asleep, or sleeping too much\u0026rdquo;), we run a sensitivity analysis excluding this item. Results were very similar, and the interaction between sleep quality and PGS \u003csub\u003eDEP\u003c/sub\u003e was a significant predictor. Its removal also resulted in a significantly worse fit (AIC\u0026thinsp;=\u0026thinsp;5569.5; p\u0026thinsp;=\u0026thinsp;0.001) and a reduction in explained variance (from 0.332 to 0.326) (Supplementary Table\u0026nbsp;1). As an additional check, given the genetic overlap between depression and sleep quality, we conducted a sensitivity analysis by including a polygenic score for insomnia as a covariate in the model (results not reported in tables). The results replicated the same pattern, with a significant interaction between sleep quality and PGS \u003csub\u003eDEP\u003c/sub\u003e (p\u0026thinsp;=\u0026thinsp;0.002).\u003c/p\u003e \u003cp\u003eThe interaction between sleep quality and PGS \u003csub\u003eDEP\u003c/sub\u003e on depressive symptoms is illustrated in Fig.\u0026nbsp;1, which displays results for three groups: high genetic predisposition (PGS \u003csub\u003eDEP\u003c/sub\u003e +1SD), medium genetic predisposition (mean), and low genetic predisposition (PGS \u003csub\u003eDEP\u003c/sub\u003e -1SD). The figure shows that as sleep quality deteriorates, the divergence between groups increases, with differences reaching approximately 2.5 points in depressive symptoms for participants with very poor sleep quality.\u003c/p\u003e \u003cp\u003eThere may be two different components to the observed interaction between genetic vulnerability and sleep quality on depressive symptoms. Specifically, genetic predisposition to depression may interact directly with genes influencing sleep (i.e., GxG - genotype x genotype interaction). Alternatively, poor sleep quality may act as an environmental trigger for genetic vulnerability (i.e., GxE - genotype x environment interaction). To further explore the nature of the interaction (GxG vs GxE), we calculated environmental and genetic factor scores for sleep quality (SQ-E and SQ-A, respectively) using an independent pathway model (Supplementary Fig.\u0026nbsp;1), which was fitted to the seven sub-scales of the PSQI (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). A detailed explanation of the methods is provided in the Supplementary Material. The correlation between the sum of SQ-E and SQ-A and the PSQI total score was 0.98 (Supplementary Fig.\u0026nbsp;2). These variables (SQ-E and SQ-A) were used to replace sleep quality in the fitted models (Supplementary Tables\u0026nbsp;2 and 3). The interaction between PGS and sleep quality was significant for both variables (i.e., SQ-A and SQ-E), indicating that both the GxG and GxE interactions were significant (models including the interaction yielded the lowest AIC values). The GxG model provided the best fit as indicated by a lower AIC (AIC\u0026thinsp;=\u0026thinsp;5901.4 for the GxE model; AIC\u0026thinsp;=\u0026thinsp;5819.7 for the GxG model). The variance explained by the GxG interaction was higher than that explained by the GxE interaction (0.60% and 0.20%, respectively).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study demonstrates that poor sleep quality interacts with genetic vulnerability to depression and is predictive of depressive symptomatology. We found a significant interaction between poor sleep quality and genetic risk for depression. The interaction remained significant even after controlling for genetic risk associated with insomnia. The genetic component underlying sleep quality was the primary source of the interaction. These findings confirm the link and complex relationship between poor sleep quality and depression. In other words, our results suggest that genetic risk for depression interacts with genes influencing poor sleep quality, thereby increasing the likelihood of developing depressive symptoms. At the same time, the presence of sleep difficulties may act as an environmental factor that interacts with genetic predisposition to further increase the risk of depression, in what would be a classical diathesis-stress model. Our findings are consistent with previous literature showing that poor sleep quality and sleep disorders are associated with depressive symptoms (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). In this study, poor sleep quality was a significant predictor of depressive symptoms, accounting for a substantial portion of the model variance (42%). The PGS for depression was also a significant predictor, explaining approximately1% of the model variance. This is comparable to the proportion of variance reported by Howard et al.(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e) which ranged from 1.5% to 3.2%. Importantly, the interaction between genetic risk for depression and sleep quality was predictive of depressive symptoms, confirming our hypothesis. Specifically, the association between poor sleep quality and depressive symptoms is more pronounced in individuals with a higher genetic vulnerability to depression compared to those with a lower genetic vulnerability.\u003c/p\u003e \u003cp\u003eThe relationship between depression and sleep is complex, and the underlying mechanisms of this association remain unclear. Existing research suggests a bidirectional relationship between depression and sleep quality or sleep disorders (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). However, sleep problems appear to be a more significant predictor of depression than depression is of sleep problems (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e). For example, Rieman and Voderholzer (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e) found that the presence of insomnia predicted an increased risk of depression over the following one to three years. In another longitudinal study, Buysse et al. (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e) also found insomnia to be a significant predictor of subsequent depressive episodes and major depressive disorder. The diathesis-stress model of depression suggests that stress interacts with an existing vulnerability (i.e., diathesis) to initiate the onset of the disorder. In this study, we observed that the impact of poor sleep quality on depressive symptomatology is more pronounced in individuals with greater genetic vulnerability to depression. This pattern becomes detectable from a score of five on the PSQI questionnaire (see Fig.\u0026nbsp;1), which is the instrument\u0026rsquo;s cut-off point for poor sleep quality (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). These results are consistent with a diathesis-stress model of depression and poor sleep. Additionally, our findings support the presence of GxG interactions between sleep quality and depression with effects in the same direction. In other words, poor sleep quality, whether from a genetic and environmental standpoint, may trigger or exacerbate depressive symptoms, with this effect being more pronounced in individuals with greater genetic vulnerability.\u003c/p\u003e \u003cp\u003eVarious mechanisms have been proposed to explain the relationship between poor sleep quality and depressive symptoms. For example, dysregulation of monoamine neurotransmitters\u0026ndash;such as serotonin, norepinephrine, and dopamine\u0026ndash;has been implicated in depression, whereas REM sleep requires a decrease in monoaminergic tone and increased cholinergic tone (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e). Glutamate signaling has also been linked with both sleep (particularly during slow oscillations of non-REM sleep) and depressive symptoms (glutamate deficiency) (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e). High levels of cortisol are associated with depressive symptoms and poor sleep quality (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e). The nature of the link between these constructs is likely to be quite complex, requiring further research to elucidate the underlying pathways, directionality, and mechanisms involved.\u003c/p\u003e \u003cp\u003eThis study has a number of implications for both basic research and clinical practice. First, it provides corroboration that a PGS for depression, in combination with sleep problems, is a significant predictor of the onset of depressive symptoms in a population-based sample of middle-aged individuals. This knowledge may help to identify people at higher risk of developing depressive symptoms\u0026ndash;specifically, those with a high genetic vulnerability to depression who are also experiencing sleep disturbances. Second, this study provides insight into the complex relationship between sleep disturbances and mood disorders by demonstrating that poor sleep quality may act as a trigger for depression in individuals with high genetic vulnerability. This aligns with previous studies suggesting that sleep problems may be a stronger predictor of subsequent depression than depression is of sleep problems (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e). Third, this study also highlights the potential role of sleep interventions in preventing the onset and progression of depression, especially in individuals with a higher genetic predisposition. Our results are consistent with previous literature indicating that treating insomnia may improve depressive symptoms (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e), as well as a recent meta-analysis of randomized controlled trials showing that improving sleep quality leads to better mental health, particularly in relation to depression (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and limitations\u003c/h2\u003e \u003cp\u003eThe strengths of this study include its large representative sample, the use of two widely used and validated questionnaires to measure sleep quality and depression, and a genetically informative design that enabled this type of analysis. In addition, the use of PGS made it possible to test the interaction between genetic vulnerability to depression and poor sleep quality. There are, however, a number of limitations that must be taken into account in the interpretation of this study. First, both sleep quality and depressive symptoms were assessed using self-report measures. Second, the cross-sectional nature of this study limits our ability to infer causal relationships between risk factors and outcomes. Third, the predictive value of PGS remains limited, resulting in a relatively small proportion of explained variance.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study shows a significant interaction between poor sleep quality and genetic vulnerability to depression, with the former acting as a trigger for depressive symptomatology. The effects vary according to genetic predisposition to the disorder. Further research is needed to clarify the underlying mechanisms of this relationship.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u003c/strong\u003e We thank all the participants from the Murcia Twin Registry. We are also grateful to Prof Sarah Medland for sharing analysis scripts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest:\u0026nbsp;\u003c/strong\u003eThe MTR is funded by CARM-Regional Program for Scientific and Technical Research -Fundaci\u0026oacute;n Seneca-Regional Agency for Science and Technology, Murcia, Spain (03082/PHCS/05, Ref.100/2005; 08633/PHCS/08, Ref.359/2008; 15302/PHCS/10, Ref.617/2012; 19479/PI/14, Ref.1213/2016; and FSRM/10.13039/100007801. Ref 22649/PI/24) and the Spanish Ministry of Science and Innovation (PSI2009\u0026ndash;11560, Ref.417/2019; PSI2014-56680-R, Ref.1032/2015; and RTI2018-095185-B-I00, Ref.2098/2018), co-funded by the European Regional Development Fund (FEDER) (PI: Juan R Ordo\u0026ntilde;ana). The funder had no role in the study design, data collection, data analyses, writing, or the decision to submit this manuscript for publication.\u0026nbsp;J.J.M-V. was supported by the Conselleria d\u0026rsquo;Educaci\u0026oacute;, Investigaci\u0026oacute;, Cultura i Esport de la Generalitat Valenciana (Proyectos I+D+i desarrollados por grupos de investigaci\u0026oacute;n emergentes) (CIGE/2021/103) (PI: Juan J Madrid-Valero).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Standards:\u0026nbsp;\u003c/strong\u003eThe authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008. The MTR protocols and instruments, as well as the data collection procedures and their analytical derivatives, were approved by the Research Ethics and Biosafety Committees of the University of Murcia and comply with the legal requirements regarding confidentiality and the protection of personal data (2098/2018 and CBE154/2018). Participants provided written informed consent for in-person interviews and verbal consent for telephone interviews.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability:\u003c/strong\u003e By application.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWang J, Wu X, Lai W, Long E, Zhang X, Li W, et al. Prevalence of depression and depressive symptoms among outpatients: a systematic review and meta-analysis. BMJ Open. 2017;7(8):e017173.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlvaro PK, Roberts RM, Harris JK. 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J Affect Disord. 2020;265:287\u0026ndash;304.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eScott AJ, Webb TL, Martyn-St James M, Rowse G, Weich S. Improving sleep quality leads to better mental health: A meta-analysis of randomised controlled trials. Sleep Med Rev. 2021;60:101556.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"translational-psychiatry","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"tp","sideBox":"Learn more about [Translational Psychiatry](http://www.nature.com/tp/)","snPcode":"41398","submissionUrl":"https://mts-tp.nature.com/cgi-bin/main.plex","title":"Translational Psychiatry","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Depression, genetics, interaction, polygenic scores, sleep quality, twins","lastPublishedDoi":"10.21203/rs.3.rs-8480411/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8480411/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAccording to the diathesis-stress model, the impact of environmental stressors on depression may vary depending on individual vulnerability (e.g., genetic predisposition). Sleep quality has been consistently linked to depression. However, the nature of this relationship is not yet fully understood. Our hypothesis was that there is a significant interaction between sleep quality and genetic vulnerability to depression that contributes to depressive symptomatology. The sample consisted of 1,147 participants from the Murcia Twin Registry (mean age: 55.8; SD: 7.6; females: 66%). Polygenic scores (PGS) for depression were calculated using LDpred2. The model that provided the best fit included sex, age, age\u003csup\u003e2\u003c/sup\u003e, sleep quality, PGS for depression, and their interactions (AIC\u0026thinsp;=\u0026thinsp;5637.4). Removing the interaction between sleep quality and PGS for depression resulted in a significant deterioration of model fit (AIC\u0026thinsp;=\u0026thinsp;5644.7; P\u0026thinsp;=\u0026thinsp;0.002). Our findings suggest that poor sleep quality may exert a greater influence on depressive symptoms in individuals with a higher genetic vulnerability to depression.\u003c/p\u003e","manuscriptTitle":"Sleep quality and depressive symptoms: insights from a genetically informative design","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-24 00:39:08","doi":"10.21203/rs.3.rs-8480411/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"This content is not available.","date":"2026-05-05T15:04:47+00:00","index":2,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2026-04-16T13:58:16+00:00","index":2,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2026-02-05T10:31:52+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewersInvited","content":"","date":"2026-01-21T18:59:33+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-09T12:31:03+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-09T12:30:56+00:00","index":"","fulltext":""},{"type":"submitted","content":"Translational Psychiatry","date":"2026-01-08T08:45:53+00:00","index":"","fulltext":""},{"type":"checksFailed","content":"","date":"2026-01-07T16:03:33+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"translational-psychiatry","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"tp","sideBox":"Learn more about [Translational Psychiatry](http://www.nature.com/tp/)","snPcode":"41398","submissionUrl":"https://mts-tp.nature.com/cgi-bin/main.plex","title":"Translational Psychiatry","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0c98f265-8458-4f96-b161-2c0d53568ccb","owner":[],"postedDate":"January 24th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"This content is not available.","date":"2026-05-05T15:04:47+00:00","index":2,"fulltext":"This content is not available."}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":61534789,"name":"Health sciences/Diseases/Psychiatric disorders"},{"id":61534790,"name":"Biological sciences/Genetics"}],"tags":[],"updatedAt":"2026-01-24T00:39:08+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-24 00:39:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8480411","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8480411","identity":"rs-8480411","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}