Subjective Well-being: A Key to Bridge Urbanization, Brain and Mental Health

Subjective Well-being: A Key to Bridge Urbanization, Brain and Mental Health | 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 Subjective Well-being: A Key to Bridge Urbanization, Brain and Mental Health Wen Qin, Zhen Zhao, Luli Wei, Liyuan Lin, Xin Li, Yingying Xie, and 17 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5794364/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The pursuit of happiness is a lifelong endeavor for everyone; nevertheless, elucidating its etiology, neurobiological substrates, and implications for mental health continues to pose significant challenges in contemporary research. This study sought to delineate the causal relationships among subjective well-being (SWB), urbanization, brain, and mental health, and to explore the protective role of SWB against prevalent psychiatric disorders. Utilizing data from 198,823 adults in the UK Biobank, including SWB questionnaires (five items), urban living environments (121 variables), neuroimaging data (2,413 measures), mental health assessments (39 indicators), and ICD-10 psychiatric diagnoses (10 disorders), we initially identified two robust SWB components using ten-fold cross-validated factor analysis: internal subjective well-being (ISWB) and social subjective well-being (SSWB). Phenome-wide association studies (PheWAS) revealed significant associations between urbanization variables and both ISWB (78/121) and SSWB (59/121); between neuroimaging indicators and both ISWB (416/2,413 measures) and SSWB (77/2,413); and between mental health assessments and both ISWB (38/39 indicators) and SSWB (37/39) (P < 0.05, Bonferroni corrected). Sequential mediation analysis uncovered 28 causal pathways from urbanization to brain to SWB to mental health (ISWB: 16 pathways, SSWB: 12 pathways), while the moderated mediation analysis revealed 19 pathways where SWB significantly moderated the urbanization → brain → mental health pathways (14 for ISWB, 5 for SSWB). Finally, Cox proportional hazards survival analysis demonstrated that individuals in the highest ISWB sextile had a 76% reduction in the overall risk of developing 10 mental disorders compared with those in the lowest sextile (Z = -29.49, Hazard Ratio [HR] = 0.24, P = 3.93e-191), and SSWB showed a 36% risk reduction (Z = -9.42, HR = 0.64, P = 4.50e-2). Moreover, both SWB components demonstrated the highest protective effects against depression (ISWB: HR = 0.13, SSWB: HR = 0.39). By systematically uncovering the causal pathways through which SWB components differentially participate in the regulation of urban living environments on the human brain, thereby affecting mental health, this study thus provides biological evidence and modifiable SWB indicators for the prevention of common psychiatric disorders. Biological sciences/Psychology Health sciences/Diseases/Psychiatric disorders Scientific community and society/Social sciences/Interdisciplinary studies Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Aristotle, one of the preeminent philosophical architects of eudaimonic theory, postulated in his magnum opus, Nicomachean Ethics: “Happiness, then, is obviously something complete and self-sufficient, in that it is the end of what is done.” 1 . As a subjective measure of “eudaimonia” or happiness, subjective well-being (SWB) is proposed to people’s own evaluations of their life satisfaction, positive affect, and low levels of negative affect 2 , 3 . Research has demonstrated that higher levels of SWB are associated with improved health outcomes and increased longevity 3 , 4 . Several theories have been proposed to explain the determinants and mechanisms of SWB 3 , 5 . However, as a subjective psychological cognitive measure, SWB still faces several critical challenges in contemporary research, especially for its complex etiologies 6 , 7 , neurobiological substrates 8 , and implications for mental health 9 . Addressing these challenges is essential for advancing the understanding and application of SWB in both research and practical contexts. SWB is shaped by the complex interplay of genetic and environmental factors 6 , 7 . Genetic variations contribute significantly to individual differences in SWB, with heritability of 30 ~ 50% by twin-family studies 10 , 11 , and teens of significant genomic loci by genome-wide association studies 6 , 12 . Concurrently, environmental influences such as socioeconomic status, social relationships, and physical environments play crucial roles in determining one’s SWB 3 . In recent years, urbanization has gained attention as a pivotal environmental factor impacting SWB 7 , 13 . Studies indicate that the rapid progression of urbanization 14 brings both benefits to SWB, like improved access to healthcare and better socioeconomic status(SES) 15 , 16 , and challenges, such as increased stress, noise pollution, and reduced green spaces 17 – 19 . However, a significant gap remains in literature due to the lack of interdisciplinary research examining the exposome-level urbanization factors affecting SWB and their neurobiological underpinnings. Addressing this gap is vital for developing informed urban policies and self-regulation strategies that promote healthier, happier urban populations. Recent studies have sought to elucidate the neural correlates of SWB through advanced neuroimaging techniques 20 . As a widely used non-invasive neuroimaging method, magnetic resonance imaging (MRI) can quantify large-scale, multi-dimensional brain structural and functional organizations in vivo, including gray matter volume (GMV), cortical thickness (CT), and cortical surface area (SA) via structural MRI (sMRI) 21 , white matter microstructural integrity and anatomical connectivity through diffusion MRI (dMRI) 22 , and brain regional activity and functional connectivity (FC) via resting-state functional MRI (rfMRI) 23 . Early studies have reported significant associations between SWB and various MRI measures, such as GMV 24 , 25 , white matter microstructural integrity 26 , brain regional activity 27 , 28 and functional connectivity 29 . However, findings regarding the neural correlates of SWB have been largely heterogeneous across studies, potentially due to small sample sizes, diverse definitions of SWB, and varying neuroimaging methods 20 , and so on. Furthermore, few studies have examined the causal pathways among environmental exposure (i.e., urbanization), brain, SWB, and mental outcomes. Recent studies have highlighted the intricate and bidirectional relationship between SWB and mental health 4 , 5 , 30 , 31 , indicating that SWB is not only the consequence of various mental illnesses 5 , 32 , 33 , but also serve as an influential protective factor against mental disorders 34 , 35 . Current studies emphasize the potential of enhancing SWB as a viable intervention strategy to prevent mental health issues. Empirical evidence suggests that interventions aimed at increasing SWB—such as more physical activity 36 , green spaces 37 , and social connections 38 —can also significantly improve mental health outcomes 39 – 41 . However, this field faces ongoing challenges, particularly in establishing clear causal relationships between SWB and mental health due to the necessity of long-term longitudinal big data that can disentangle the directionality of effects. Additionally, there is an urgent need for more precise quantification of the differential preventive potentials of SWB and their causal roles for various types of mental disorders. Addressing these challenges is essential for advancing our understanding of how and to what extent SWB can be leveraged to prevent different types of mental disorders, as well as which modifiable environmental interventions can reduce the risk of mental illnesses through the promotion of subjective well-being. Thus, we proposed that exposure to urbanization impacts SWB through its effects on brain structures and functions, subsequently contributing to mental health outcomes. To test this hypothesis, this study utilized longitudinal multi-modal data from 198,823 adults in the UK Biobank, including SWB questionnaires, urban living exposome factors, neuroimaging data, mental health assessments, and ICD-10 psychiatric diagnoses. Our study focused on three key objectives: (1) identifying the latent factors underlying diverse SWB measures; (2) delineating the multivariate causal relationships among SWB, urban living exposome factors, brain neuroimaging indicators, and mental health status; and (3) quantifying the distinct protective role of SWB against major common mental disorders. The study design and workflow are illustrated in Fig. 1 a. Result Latent Components of Subjective Well-Being Among five SWB questionnaire items of 198,823 qualified adults, ten-fold cross-validated exploratory factor analysis (EFA) indicated two latent components among the five SWB items by parallel analysis 42 (KMO = 0.77, Cronbach’s Alpha = 0.714) ( Fig. 1 b, Supplementary Table 5) , which was further validated in the predict dataset by confirmatory factor analysis (CFA) (CFI = 0.987 ± 0.002, TLI = 0.968 ± 0.005, RMSEA = 0.056 ± 0.004, SRMR = 0.217 ± 0.001). Split-half EFA and CFA also reliably replicated the two latent factors (CFI = 0.987, TLI = 0.968, RMSEA = 0.056, SRMR = 0.022) ( Fig. 1 c ). The first latent factor of EFA mainly captures self-status evaluation, encompassing happiness (loading = 0.503 ± 0.002), health satisfaction (loading = 0.557 ± 0.001) and financial situation satisfaction (loading = 0.465 ± 0.002), thus designated as internal SWB (ISWB). The second factor mainly reflects social relationship evaluation, including friendship satisfaction (loading = 0.642 ± 0.002) and family relationship satisfaction (loading = 0.694 ± 0.002), termed social SWB (SSWB) ( Fig. 1 d, Supplementary Table 6) . Spearman association analysis showed a medium correlation between the ISWB and SSWB scores (r = 0.415, P < 0.001), indicating the relatively impendence between these two factors. Finally, a normal score transformation was applied to the harmonized SWB scores to enhance Gaussianity 43 (Supplementary Fig. 2) . Association between SWB and urban living environments Among the 121 urban living exposure variables from 13 environmental categories, Phenome-wide association study (PheWAS) using linear regression model revealed 78 variables showing significant association with ISWB ((P < 0.05 / [121 exposures + 2,413 brain phenotypes + 39 mental health scores] / 2 SWB factors = 9.701e-6, Bonferroni corrected), with the index of multiple deprivation (IMD) demonstrating the strongest positive association (t = 58.543, P = 2.23e-308). Besides, 59 out of 121 urbanization variables showed significant associations with SSWB, with 2010 nitrogen dioxide air pollution levels exhibiting the strongest association (t = 18.968, P = 3.69e-80) (Fig. 2 a, Supplementary Table 7–8 ) (P < 9.70e-6, Bonferroni corrected). Comparison of urban-SWB association t-values revealed a significant skew towards ISWB, indicating that urbanization has a stronger impact on ISWB than on SSWB (Fig. 2 b). Hierarchical clustering of the 78 ISWB-related urban living variables identified two optimal components, ISWB-urban-1 and ISWB-urban-2, based on the Calinski-Harabasz index (CHI = 221) (Fig. 2 c). Canonical correlation analysis (CCA) showed that ISWB-urban-2 (primarily reflecting the IMD, air pollution and street network accessibility) exhibited a stronger association with ISWB (r = 0.138, Pperm < 0.001) compared to ISWB-urban-1 (preferably reflecting destination accessibility and greenspace proximity) (r = 0.080, Pperm < 0.001) (Fig. 2 d-g, Supplementary Table 9 ). Besides, we also identified two optimal components, SSWB-urban-1 and SSWB-urban-2, among the 59 SSWB-related urbanization variables (CHI = 378) (Fig. 2 h). CCA demonstrated comparable significant but weak associations for both components: SSWB-urban-1 (r = 0.044, p < 0.001), which is related to destination accessibility and greenspace proximity, and SSWB-urban-2 (r = 0.053, p < 0.001), which is associated with air pollution, coast proximity, and land use density (Fig. 2 i-l, Supplementary Table 10 ). Association between SWB and brain neuroimaging phenotypes PheWAS across 2,413 brain neuroimaging phenotypes revealed significant correlations with both ISWB and SSWB (P < 9.70e-6, Bonferroni corrected) (Fig. 3 a, Supplementary Table 11–12 ). Specifically, 416 brain phenotypes correlated significantly with ISWB, and 77 phenotypes correlated significantly with SSWB. A comparison of the neuroimaging-SWB association t-values demonstrated a significant bias towards ISWB, suggesting that brain variations are more strongly correlated with ISWB than SSWB (Fig. 3 b). Correlations of SWB with white matter microstructure Of the 432 white matter microstructural measures derived from dMRI, 242 (56.0%) showed significantly associated with ISWB, primary involving fractional anisotropy (FA) in 72.9% (35/48), minimum eigenvalue (L3) in 70.8% (34/48), medium eigenvalue (L2) in 68.8% (33/48), and mean diffusivity in 66.7% (32/48) of white matter regions, with the strongest association observed for mean orientation dispersion (OD) in the left cerebral peduncle (t = 10.512, p = 2.53e-30). In contrast, only 17/432 (3.9%) microstructural measures correlated significantly with SSWB, mainly involving intra-cellular volume fraction (ICVF) 10.4% (5/48), L3 in 8.3% (4/48), FA in 4.2% (2/48), and MD in 4.2% (2/48) of regions, with the strongest association observed for mean L3 in splenium of corpus callosum (t = 5.686, p = 1.31e-8) ( Fig. 3 c-d, Supplementary Table 11–12) . Correlations of SWB with brain morphometry Of the 52 brain volume phenotypes derived from automated segmentation (ASEG), 16 (30.8%) demonstrated significant associations with the ISWB, with ventral diencephalon (DC) volume showing the strongest associations (t = -6.338, P = 2.36e-10) (Supplementary Table 11, Supplementary Fig. 3) ; 10/52 (17.3%) displayed significant correlations with the SSWB, primary affecting global measures, with the strongest associated one being right cortical volume (t = 7.582, P = 3.49e-14) (Supplementary Table 12, Supplementary Fig. 4) . Additionally, 87/444 (19.6%) area-wise cortical phenotypes derived from Destrieux Atlas demonstrated significantly associated with the ISWB, including 30/148 (20.3%) cortical volume (CV) measures, 30/148 (20.3%) surface areas (SA) measures, and 27/148 (18.2%) cortical thickness (CT) measures, with the strongest association observed for the CT of right occipital pole (t = 9.622, P = 6.82e-22). In contrast, only 6/148 (4.1%) area-wise CT phenotypes exhibited significantly associations with the SSWB, with the strongest association observed for the CT of right cuneus (t = 5.246, P = 1.56e-7) (Fig. 3 e-f, Supplementary Table 11–12 ). Correlations of SWB with resting-state functional connectivity. Of the 1,485 functional connectivity (FC) measures derived from rfMRI and independent component analysis (ICA), we observed 71/1,485 (4.8%) FCs presented significant associations with ISWB, with the FC (ICA_FC345) between independent component (IC)-21(left primary sensorimotor subnet) and IC-28 (supplementary motor subnet) showing strongest association (t=-8.386, P = 5.19e-17). Moreover, 44/1,485 (3.0%) FCs exhibited significant associations with SSWB, with the FC (ICA_FC663) between IC-34 (right frontoparietal subnet) and IC-38 (bilateral prefrontal subnet) showing strongest association (t = -7.442, P = 1.01e-13) (Fig. 3 g-h, Supplementary Table 11–12 ). Integrated neuroimaging components associated with SWB Hierarchical clustering of the 416 ISWB-related brain phenotypes identified four optimal components (CHI = 526) (Fig. 4 a). CCA showed that ISWB-brain-2 exhibited the highest explained variance for ISWB (r = 0.183, Pperm < 0.001) among the four neuroimaging components, which was mainly contributed by the dMRI-derived microstructural measures of pontine and cerebellar peduncles, (Fig. 4 c, 4 e, 4 g, 4 i, Supplementary Table 13 ). We also revealed four optimal components among the 77 SSWB-related brain phenotypes based on hierarchical clustering (CHI = 50.9) (Fig. 4 b). CCA demonstrated that SSWB-brain-3 exhibited the highest explained variance for SSWB (r = 0.119, Pperm < 0.001) among the four neuroimaging components, which was mainly contributed by resting-state FC measures (Fig. 4 d, 4 f, 4 h, 4 j, Supplementary Table 14 ). Association between SWB and mental health status PheWAS revealed strong correlations between 38 out of 39 mental health indicators and ISWB, with the strongest association observed for tiredness (t = 188.470, P < 2.48e-324). Additionally, 37/39 mental health indicators demonstrated significant associations with SSWB, with the strongest association for loneliness and isolation (t = 139.423, P < 2.48e-324) (Fig. 5 a, Supplementary Table 15–16 ). Mental-SWB association t-values distribution demonstrated a significant skew towards ISWB, suggesting that ISWB accounts for mental health more strongly than SSWB (Fig. 5 b). We further identified two optimal components (ISWB-MH-1 and ISWB-MH-2) from the 38 ISWB-associated mental health indicators by hierarchical clustering (CHI = 34.7) (Fig. 5 c). CCA showed that ISWB-MH-2 (top 3 weights: tiredness, depressed mood and fed up feelings) exhibited a stronger association with ISWB ((r = 0.514, Pperm < 0.001) compared to ISWB-MH-1 (top 3 weights: grief and stress, recurrent major depression and manic/irritable episodes) (r = 0.251, Pperm < 0.001) (Fig. 5 e, 5 f, 5 i, 5 k, Supplementary Table 17 ). Moreover, two optimal SSWB-associated components were also identified (CHI = 33.2) ( Fig. 5 d ) , including SSWB-MH-2 that preferably represents loneliness and isolation, depressed mood and irritability (r = 0.387, Pperm < 0.001), and SSWB-MH-1 that primary reflects severity of manic/irritable episodes, severe and moderate recurrent major depression (r = 0.154, Pperm < 0.001) (Fig. 5 g, 5 h, 5 j, 5 l, Supplementary Table 18 ). Causal pathways among SWB, urban environment, brain, and mental health Sequential mediation pathways (Model 6) Sequential mediation analysis uncovered 28 causal pathways from urbanization to brain to SWB to mental health, including 16 ISWB-related pathways and 12 SSWB-related pathways (Fig. 6 a, 6 c, Supplementary Table 19–20 ). For instance, greater exposure to ISWB-urban-2 factors (primarily IMD and air pollution) is associated with increased changes in ISWB-brain-2 (microstructures of the pontine and cerebellar peduncles), leading to lower ISWB and ultimately higher ISWB-MH-2 symptoms (i.e., tiredness) (Fig. 6 e). Similarly, greater exposure to SSWB-urban-1 factors (primarily urban destination accessibility) is associated with changes in SSWB-brain-3 (functional connectivity), leading to lower SSWB and ultimately higher SSWB-MH-2 symptoms (i.e., loneliness and isolation) (Fig. 6 f). Moderated mediation pathways (Model59) We further explored whether SWB could serve as a moderator in the causal pathways from urbanization to mental health. Our analysis revealed 19 indirect (mediation) pathways (urbanization → brain → mental health) that were significantly moderated by SWB, including 14 ISWB-related pathways and 5 SSWB-related pathways (Fig. 6 b, 6 d, Supplementary Table 21–22 ). Specifically, in the 14 ISWB-related pathways, ISWB moderated both the urbanization → brain sub-path (moderationA) and the brain → mental health sub-path (moderationB) in 8 pathways; while in 6 pathways, it only exerts moderationB effects. For the 5 SSWB-related pathways, we identified 2 pathways with both moderationA & moderationB effects, and 3 pathways with only moderationB effects. Additionally, we identified 20 direct pathways (urbanization → mental health) that were significantly moderated by SWB (moderationC) (16 for ISWB, 4 for SSWB) ( Fig. 6 d ) . One typical example of SWB’s moderation effect is in the ISWB-urban-2 (primarily IMD and air pollution) → ISWB-brain-2 (microstructures of the pontine and cerebellar peduncles) → ISWB-MH-2 (tiredness) pathway, where ISWB exerts both moderationA and moderationB effects, resulting in the indirect effect significant only in the lower sextile ISWB population (Fig. 6 g). Another example is in the SSWB-urban-1 (destination accessibility) → SSWB-brain-3 (functional connectivity) → SSWB-MH-2 (loneliness and isolation) pathway, where SSWB only exerts a moderationB effect, resulting in opposite indirect effects between the upper and lower sextile SSWB populations (Fig. 6 h). SWB's Protective Value in Psychiatric Prevention We finally evaluated the potential of SWB component scores in predicting future occurrence of 10 common mental disorders categorized by ICD10. Cox proportional hazards survival analysis demonstrated that individuals in the highest ISWB sextile (extremely happy) had a 76% reduction in the overall risk of developing at least one of the ten mental disorders compared to those in the lowest sextile (extremely unhappy) (Z = -29.49, Hazard Ratio [HR] = 0.24, P = 3.93e-191), with SSWB showing a 36% risk reduction (Z = -9.42, HR = 0.64, P = 4.50e-21) ( Fig. 7 a ) . Kaplan-Meier survival analysis revealed that the overall prevalence (1-survival probability) of general mental disorders was declining with increasing SWB levels. After 10 years, the mental disorders overall prevalence in the ISWB highest sextile was only 9%, compared to 34% in the ISWB lowest sextile. SSWB showed similar but weaker protective effects over mental disorders, with 10-year prevalence of 13% and 20% in the highest and lowest sextiles, respectively ( Fig. 7 b ) . In terms of disease specificity, ISWB exhibited significant protective effects across all 10 mental disorders for the highest than the lower sextile individuals, with the strongest protection observed for depression (HR = 0.13, 95%CI = 0.10–0.15). SSWB demonstrated significant protective effects for 4 mental disorders, with depression again showing the strongest protective benefit (HR = 0.39, 95%CI = 0.33–0.47) (Fig. 7 c, Supplementary Table 23 ). To test the joint predictive role of ISWB and SSWB, we split the populations into four subgroups based on the medium of the two SWB scores (Fig. 7 d). Survival analyses revealed the strongest protective effect for subgroups with both high ISWB and high SSWB (HR = 0.42, 95%CI = 0.40–0.46), followed by subgroup with high ISWB and low SSWB (HR = 0.48, 95%CI = 0.45–0.52), and a weaker protective effect for subgroup with low ISWB and high SSWB (HR = 0.91, 95%CI = 0.85–0.98) (Fig. 7 e). After 10 years, the overall mental disorder prevalence in high ISWB (10.9%) was 11.6% lower than that of low ISWB (22.5%), indicating a strong protective role of ISWB against mental disorders. Additionally, the prevalences of two subgroups with high SSWB (low ISWB: 21.0%, high ISWB: 10.2%) were also lower (low ISWB: 2.4%, high ISWB: 1.6%) than those with low SSWB (low ISWB: 23.4%, high ISWB: 11.8%) (Fig. 7 f), indicating that SSWB also contributes to a weak protective role against mental disorders. Discussion Based on a large-scale longitudinal cohort, we identified two components of SWB: Internal SWB (ISWB) (general happiness, health satisfaction, and financial satisfaction) and Social SWB (SSWB) (friendship satisfaction and family relationship satisfaction). Moreover, we found that compared to SSWB, ISWB shows stronger overall associations with urbanization exposure, neuroimaging phenotypes, and mental health outcomes, and it plays a more pronounced role in preventing ten common mental disorders. Furthermore, the two forms of SWB are shaped by different urbanization exposure factors, exhibit distinct neural correlates, and regulate unique urbanization–brain–mental health pathways. By systematically uncovering the causal pathways through which urban living environments regulate the human brain, thereby affecting SWB and mental health, this study provides biological evidence and modifiable SWB indicators for the prevention of common psychiatric disorders. One key contribution of this study is that we systematically explained how multidimensional urbanization exposure factors exert effects on different dimensions of SWB. We uncovered several common environmental factors that exert significant regulatory effects on both ISWB and Social SSWB, such as air pollution 44 , 45 , greenspace proximity 46 – 48 , destination accessibility 49 , 50 , and street network accessibility 50 , 51 , which had been supported by existing literature, affirming their pivotal roles in influencing SWB. More importantly, we found multifaceted urbanization exposure factors exert differential impacts on distinct components of SWB: Firstly, the impact of urbanization on ISWB is generally more pronounced than on SSWB. This suggests that urban environment has a more pronounced impact on individual's internal mental states than social ones. Furthermore, the top environmental variables that show notably stronger effects on ISWB compared to SSWB include IMD 52 , 53 , PM2.5 54,55 , and nitrogen dioxide/oxide air pollution 56 . These exposures primarily reflect individuals' socio-economic status (IMD) and natural pollution levels (PM2.5, air pollution), directly correlating with material living conditions and physical health. Conversely, there also exist urban living variables showing stronger effects on SSWB than ISWB, such as non-driving accessibility for university, coast proximity 51 , 57 , and the LandUse density of restaurants/cafeterias 58 and holidays/campsites 59 , which provide essential venues for social interactions. Thus, we speculated that urbanization influences on SWB may operate through distinct pathways based on the personal versus social nature of the environmental factors: urbanization impacts ISWB predominantly through socio-economic and natural environmental channels, whereas that influences SSWB primary through interpersonal interactions. Finally, the study identifies two covarying latent urbanization factors for each of the ISWB and SSWB. These synergic variables not only quantify the overall impact of urban environments on SWB but also provide objective evidence for complex covariance among various urbanization environmental factors 60 – 62 . In summary, this study offers critical insights into how multidimensional urbanization exposures differentially regulate internal and social aspects of SWB, highlighting the importance of tailored urban planning and policy interventions to enhance overall quality of life in urban settings. The second contribution of this study lies in leveraging a large-sample (N = 39,291) multimodal MRI dataset to systematically investigate the neural underpinnings of SWB. While previous studies have explored the relationship between SWB and brain structure and function, limitations such as small sample sizes, varying SWB definitions, and diverse neuroimaging metrics have hindered consistent conclusions 63 – 66 . Through the involvement of 2,413 neuroimaging phenotypes, we demonstrated that ISWB exhibited stronger overall associations with these phenotypes compared to SSWB, suggesting ISWB as a more stable and biologically meaningful endophenotype. Furthermore, white matter microstructure demonstrated the strongest correlation with SWB (56% of phenotypes), followed by gray matter structure (20.7%), with resting-state functional connectivity showing the weakest association (4.8%). This highlights the crucial role of white matter microstructural integrity in SWB 65 , 67 , 68 . Moreover, ISWB and SSWB exhibited distinct association patterns with the brain imaging phenotypes. Specifically, the neuroimaging metrics accounting for the largest explained variance in ISWB predominantly comprised dMRI-derived microstructural measures of the pontine and cerebellar peduncles, while SSWB was most strongly associated with resting-state functional connectivity within frontoparietal subnetworks. These divergent patterns suggest that ISWB may be more sensitive to alterations in sensorimotor-related white matter circuits 69 , 70 , whereas SSWB may be more closely linked to functional networks supporting higher-order cognitive processes 71 , 72 . Prior investigations into the causal relationships of SWB have primarily focused on the pairwise associations by various strategies, such as: urban-SWB 73 – 75 , brain-SWB 76 , and SWB-mental health 77 , with limited research exploring tripartite pathways such as urban-brain-SWB 78 , urban-SWB-mental health 79 , or brain-SWB-mental health 63 . The third major contribution of this study is that we have uncovered multiple dozens of causal pathways among urban living exposure, brain, SWB, and mental health outcomes. We revealed that SWB serves not only as a directly relay mediating the living exposure → brain → mental health pathways (sequential mediation), but also as a indirectly moderator (moderated mediation) for these pathways, presenting a complex mechanisms of SWB in protecting peoples from mental disorders. Besides, we also found diverse roles and pathways of different SWB components on mental health. Specifically, ISWB showed stronger associations with mental health compared to SSWB and was involved in more urbanization-regulated pathways affecting mental health, supporting its more crucial role in mental health. Moreover, ISWB and SSWB participated in relatively specific regulatory pathways. For instance, elevated exposure to the multiple deprivation (a measure for poorer socio-economic status) and air pollution was associated with poorer pontine and cerebellar microstructural integrity 80 , 81 , subsequently leading to worse ISWB 82 – 84 and elevated tiredness 85 – 88 . Conversely, greater exposure to social-related urban destination accessibility was associated with modifications in frontoparietal functional connectivity 89 , resulting in reduced SSWB 90 , 91 and heightened levels of loneliness and social isolation 92 – 94 . Thus, the SWB-related causal pathways identified in this study not only advance our understanding of how urban living modulates mental health through neurobiological remolding, but also provide specific environmental variables and neural targets amenable to intervention for enhancing both SWB and mental health outcomes. The final contribution of this study is the systematic investigation of the protective effects of SWB against ten common mental disorders utilizing a large-scale longitudinal cohort of 31,337 participants (with 5,498 incident cases during follow-up) and a maximum follow-up period of 4606 days. Because of the bidirectional relationship between SWB and diseases 95 , 96 , we excluded individuals with any ICD-10 recorded diseases prior to the SWB assessment. This approach effectively minimizes the potential confounding effects of pre-existing psychological and physical illnesses on SWB, thereby allowing us to specifically examine whether SWB can prevent the onset of psychological disorders in a healthy population. To ensure robust statistical power, we categorized mental disorders at the second-level classification of ICD-10 and ensured that each disorder had a minimum of 50 future cases. Our findings indicate that both ISWB and SSWB can significantly protect against the development of mental disorders in healthy individuals, aligning with previous research 97 . Notably, ISWB demonstrated a substantially greater protective effect compared to SSWB, with ISWB reducing the risk by 76% and SSWB by 36% when comparing the highest sextile to the lowest sextile populations. Over a ten-year follow-up, the incidence of psychological disorders was as high as 22.5% in the lower half SSWB individuals, whereas it decreased to 10.9% in lower half ones. Furthermore, ISWB provided significant protective effects across all ten mental disorders studied, whereas SSWB was significantly protective for five specific disorders. Finally, both aspects of SWB exhibited the strongest protective effects against depressive disorders 98 , 99 (ISWB reducing by 87% risk and SSWB by 61% for the highest versus lowest sextile). These findings underscore the substantial protective role of ISWB in mental disorder prevention by quantitative analyses, particularly regarding depressive disorders, establishing ISWB as a robust and readily modifiable indicator for both intervention and monitoring purposes. Possible Limitations and Future Directions of the Study included: this study focused only on the effects of urbanization on SWB and its pathways to provide modifiable indicators for mental health. However, we did not examine the genetic influences on SWB or the underlying molecular mechanisms 82 , 100 , 101 . Second, the impact of urbanization on SWB and its relationship with mental health may vary across cultures and regions 102 – 104 . As the study was limited to a UK population, the generalizability of the findings to other countries and regions requires further validation. Lastly, the SWB classification used in this study differs from previous research 100 , and its reproducibility across diverse populations and datasets needs further investigation. In conclusion, this study reinforces Aristotle's timeless philosophical insight on subjective well-being: happiness is not only a lifelong pursuit but also a remedy for healing the mind. By providing biological evidence from the natural sciences, this study highlights the potential of enhancing subjective well-being as a potential psychotherapy strategy against the increasing burden of mental disorders in the context of urbanization. Method UKB cohort The participants of the present study were enrolled from the UKB cohort under application number 75556 105 , encompassing approximately 500k samples of extensive questionnaire items and over 40k sample of brain MRI data released on December 8, 2021. The UK Biobank received approval from its Research Ethics Committee, the Human Tissue Management Agency Research Organization Bank, and the National Health Service (NHS) Centre. Detailed data screening processes are shown in Supplementary Fig. 1 . Subjective well-being data We reviewed all the UKB touchscreen questionnaires completed at the Assessment Centre and selected five items related to SWB, including “Happiness” (Data-field 4526), “Health satisfaction” (Data-field 4548), “Family relationship satisfaction” (Data-field 4559), “Friendships satisfaction” (Data-field 4570), and “Financial situation satisfaction” (Data-field 4581). Although “Work/job satisfaction” (Data-field 4537) is also an important aspect of SWB, we did not choose it because about 30% participants answered, “I don't have a job.” We selected participants who completed the questionnaire for the first time from 2006–2010 (Instance 0), 2012–2013(Instance 1), and 2014+(Instance 2). Responses were on a 6-point Likert scale with 1-point representing extremely happy and 6-point representing extremely unhappy ( Supplementary Table 1 ). We excluded participants with the following conditions: missing SWB questionaries (participants answered “don't know” or “would rather not answer”), genetic sex missing (Data-field 22001), sex mismatch (discrepancy between genetic and self-reported sex [Data-field 31]), outliers for heterozygosity or missing rate (Data-field 22027), and within three-generation relatedness (KING kinship coefficient > 0.0884). We initially retained 200,802 qualified participants with complete five-item SWB scales. Urban living environments data We referred to previous studies that defined urban living environments based on 13 categories and 121 variables 17 , including: EIMD (Data-field 26410), SIMD (Data-field 26427), WIMD (Data-field 26426), Residential air pollution (Data-field 24003–24008), Traffic (Data-field 24009–24015), Residential noise pollution (Data-field 24020), Greenspace proximity (Data field 24503, Data-field 24504, Data-field 24507), water proximity (Data-field 24505), coastal proximity (Data-field 24508) and UK Biobank Urban Morphometric Platform (Category 100115) (Supplementary Table 2) . We excluded environmental variables with missing values ​greater than 50% and removed participants with missing values more than 60% environment variables. Finally, a total of 198,823 participants were left for formal analyses. Neuroimaging data Neuroimaging data were acquired using Siemens Skyra 3T MRI scanners ( https://biobank.ctsu.ox.ac.uk/crystal/crystal/docs/brain_mri.pdf . ) , including brain sMRI, dMRI, and rfMRI. This study included 39,291 participants who underwent at least one of these imaging protocols, yielding 2,413 neuroimaging phenotypes derived from these three modalities 106 . Brain sMRI phenotypes The T1-weighted sMRI data included 38,875 participants and were processed using FreeSurfer version 6 ( https://surfer.nmr.mgh.harvard.edu/ ). We extracted 444 cortical surface phenotypes (Category ID 197) defined by Destrieux Atlas (FreeSurfer a2009s), comprising 148 measures each of cortical volume (CV), cortical thickness (CT), and surface area (SA) 107 , 108 . Additionally, 52 brain volume phenotypes (Category ID 190) were obtained using FreeSurfer's Automatic Segmentation (ASEG) tools 109 . Brain dMRI phenotypes The dMRI data contained 36,180 participants and were preprocessed using FSL version 5.0.10 ( https://fsl.fmrib.ox.ac.uk/fsl ). Following preprocessing, two modeling approaches were applied to derive white matter microstructural phenotypes: Diffusion Tensor Imaging (DTI) and Neurite Orientation Dispersion and Density Imaging (NODDI). For DTI modeling, the b = 1000 shell (50 directions) data were fitted using the DTIFIT toolbox, generating six metrics: fractional anisotropy (FA), mean diffusivity (MD), mode of anisotropy (MO), axial diffusivity (L1), median diffusivity (L2), and minimum diffusivity (L3). For NODDI modeling, data from all three shells were fitted using the AMICO toolbox 110 , 111 , producing three metrics: intra-cellular volume fraction (ICVF), orientation dispersion (OD), and isotropic volume fraction (ISOVF). Finally, all metrics were skeletonized using the tract-based spatial statistics (TBSS) pipeline 112 , and 432 white matter microstructural phenotypes were extract from these nine dMRI metric skeletons using ICBM-DTI-81 white-matter labels atlas (Category 134) 113 . Brain rfMRI phenotypes The fMRI dataset comprised 36,911 participants and was processed using FSL version 5.0.10. Following preprocessing, we performed a 100-dimension spatial independent component analysis (ICA) using FSL's MELODIC tool to derive spatially-independent components ( Supplementary Table 3 ), referred to as resting-state functional networks (RSNs) 114 . After discarding components clearly identified as artifacts (non-neuronal), 55 RSNs with clear biological significance remained. Functional connectivity (FC) between RSN pairs was then quantified using Pearson correlation coefficients, which were converted to Fisher r-to-z scores, yielding 1,485 FC phenotypes (Data-Field 25751) 106 . Mental health score data Mental health (MH) score data were collected from 198,823 participants through the Mental Health category (Category ID 100060) of psychosocial factors assessment, administered via touchscreen devices at assessment centers. Data collection coincided with the completion instance of SWB questionnaires ( Supplementary Table 4 ). After excluding missing responses marked as "Do not know" or "Prefer not to answer," each categorical variable was binary encoded. This process yielded 39 distinct MH variables. Mental disorder records data Using the International Statistical Classification of Diseases and Related Health Problems 10th Revision (ICD10) (Data-field 41270) and the date of first recording in hospital records (Data-field 41280), we excluded individuals with a prior mental disorder (Chapter V of ICD10) diagnosis before the SWB evaluation date. Participants with any non-mental disorders were also excluded. With the SWB evaluation date as the baseline, we screened patients with a certain of mental disorder who were recorded after the SWB evaluation and were closest to the baseline date. Diseases with more than 50 cases were included, resulting in 10 mental disorders: Dementia in Alzheimer's disease (F00); Dementia in Alzheimer's disease (F03); Delirium, not induced by alcohol and other psychoactive substances (F05); Mental and behavioral disorders due to use of alcohol (F10); Mental and behavioral disorders due to use of tobacco (F17); Schizophrenia, schizotypal and delusional disorders (F2); Bipolar affective disorder(F31); Depressive episode disorder (F32), recurrent depressive disorder(F33); Phobic anxiety disorders(F40); Other anxiety disorders (F41). The final dataset included 5,498 patients with future-occurring mental disorders and 25,839 healthy controls with no recorded disorders during follow-up. Confounding variables Age (Data-field 21003), sex (Data-field 31), and 40 genetic principal components (Data-field 22009) were taken as confounding covariates and were adjusted in subsequent association analyses. Besides, the estimated total intracranial volume (Data-field 26521) was considered as an additional confounder for neuroimaging phenotypes. Statistical analysis Factors analysis for subjective well-being A Ten-fold cross-validated factor analysis was applied to identify the latent factors among the five SWB items. Specifically, for each fold, 90% of the 198,823 participants (train dataset) underwent exploratory factor analysis (EFA) to train the optimal model using “psych” R package 115 , and 10% of the participants (predict dataset) underwent confirmatory factor analysis (CFA) to test the model using “lavaan” R package 116 . EFA is a statistical method used to uncover the latent factors of observed variables, which begins by accessing data suitability using the Kaiser-Meyer-Olkin (KMO) measure for sampling adequacy (desire if KMO > 0.7) 117 and Cronbach’s Alpha for internal consistency (good reliability if Alpha > 0.7 ) 118 . A covariance matrix is used to analyze relationships between variables. The optimal number of factors is determined through parallel analysis, comparing eigenvalues from the data with those from random datasets (300 shuffles) 42 . The factors are fitted using the Maximum Likelihood method and are rotated using “Varimax” to improve interpretability 117 . Then the goodness of fit of the identified factors in the train dataset was validated by CFA in predict dataset using Tucker–Lewis index (TSI) (> 0.9), comparative fit index (CFI) (> 0.9), root mean square error of approximation (RMSEA) (< 0.06) and standard root mean square residual (SRMR) (< 0.05) 119 . The SWB factor scores for each individual were calculated by multiplying the predicted dataset with the estimated factor loadings from the optimal EFA model, using the “predict” function in the “psych” package. The above steps are repeated until factor scores of all individuals in 10 folds were obtained. To evaluate the stability of the model estimated through 10-fold cross-validation, the data was split in half, and the same steps were repeated. Finally, a normal score transformation was applied to the harmonized SWB factor scores to enhance Gaussianity 43 . PheWAS analyses between SWB and urbanization, brain and mental health A univariate phenome-wide association study (PheWAS) analysis was used to test the correlation between each SWB factor score and each of the urbanization, brain and mental health variables using a linear regression model, with SWB factor score as the dependent variable, urbanization exposures, brain phenotypes, mental health scores and confounders as independent variables. Bonferroni corrected was used to correct the type-I error caused by the multiple comparisons (P < 0.05 / [121 exposures + 2,413 brain phenotypes + 39 mental health scores / 2 SWB factors] = 9.701e − 6 ). Multivariate associations analyses between SWB and urbanization, brain and mental health Regarding the numerous variables for urbanization, brain, and mental health, as well as the complex interrelationships (collinearity) among them, we further sought to identify potential components within each variable category using hierarchical clustering. We then assessed the combined contributions of each synthetic component to each SWB factor using canonical correlation analysis (CCA). Specifically, for each variable category (urbanization, brain, or mental health), we conducted the following analysis: (1) extracted variables with statistical significance in univariate PheWAS for each category and imputed missing values using mean imputation; (2) applied a hierarchical clustering method to identify the tiered structure within the category variables using a grid search strategy to determine the hyperparameters, including six distance metrics ("euclidean," "maximum," "manhattan," "canberra," "binary," or "minkowski"), seven linkage methods ("ward.D," "ward.D2," "complete," "average," "mcquitty," "median," or "centroid"), and nine cluster numbers (2–10). The Calinski-Harabasz Index (CHI) was used to evaluate the optimal hyperparameters. (3) After regreasing out confounding variables, we performed CCA to estimate the synthetic association between all variables in each cluster and each SWB factor score, followed by a permutation test (n = 1000) to assess the significance of each CCA model (p < 0.05, Bonferroni corrected). Pathway analyses between SWB, urbanization, brain and mental health Using the “processR” R package 120 , a sequential mediation analysis (Model 6) was conducted to identify potential causal pathways from urbanization to brain, to subjective well-being (SWB), and finally to mental health. In this model, urban living canonical variables (Urban) were treated as the exposure (X), brain imaging canonical variables (IMA) as the first mediator (M1), SWB factors as the second mediator (M2), and mental health canonical variables as the outcome (Y). Additionally, a moderated mediation analysis (Model 59) was performed to explore whether SWB could act as a moderator in the causal pathway from urbanization (exposure) to brain (mediator) to mental health (outcome). To enhance interpretation, each mental health (MH) canonical variable was categorized into three levels: low (0), medium (1), and severe (2) mental symptoms. Given the ordinal nature of the outcome variable, a diagonally weighted least squares (DWLS) model was employed to estimate the model parameters 121 . Furthermore, a bias-corrected and accelerated (BCa) bootstrapping method (n = 1000) was used to estimate the mediation effects and their 95% confidence intervals (95%CI). For the moderated medication analysis, if a significant moderation effect was detected, post-hoc analyses were conducted to estimate the mediation effects within sub-populations scoring below the 16th percentile and above the 84th percentile of SWB scores, respectively. Survival analysis on the protective effect of SWB against common mental disorders To facilitate explanation and interpretation, SWB factor scores were categorized into six levels, with 1 representing "extremely unhappy" and 6 representing "extremely happy". Taken the “extremely unhappy” level as the reference, Cox proportional hazards regression models were applied to evaluate the predictive potential of SWB levels for the future occurrence of at least one of 10 common mental disorders classified under ICD-10, adjusted for confounders, using “coxph” function from the “survival” package. Subsequently, we used the “survfit” function from the “survminer” package to plot the Kaplan-Meier survival curves for each SWB level, providing a visualization of overall mental disorder progression over time. We repeated the Cox regression analysis to assess the disease-specific preventive effects of SWB levels on each mental disorder. To test the joint protective role of ISWB and SSWB, we split the populations into four subgroups based on the median split of the two SWB scores: individuals with low ISWB and low SSWB, low ISWB and high SSWB, high ISWB and low SSWB, and high ISWB and high SSWB. With the low ISWB and low SSWB subgroup as the reference, Cox proportional hazards regression models were applied to quantify the protective effects of each SWB subgroup. Additionally, the 10-year (3650 days) conversion prevalence of overall mental disorders was estimated for each SWB subgroup. Declarations Acknowledgements This study was supported by the National Natural Science Foundation of China (No. 82472052 [Wen Qin], No. 82430063 [Chunshui Yu], No. 81971599 [Wen Qin], No. 82030053 [Chunshui Yu], No. 82371924 [Jiayuan Xu]), National Key Research and Development Program of China (No. 2018YFC1314300 [Chunshui Yu]), National Key Project of "Inter-governmental International Scientific and Technological Innovation Cooperation" (No. 2023YFE0199700 [Jiayuan Xu]), Natural Science Foundation of Tianjin City(19JCYBJC25100 [Wen Qin]) and Tianjin Key Medical Discipline (Specialty) Construction Project (TJYXZDXK-001A [Chunshui Yu]), the Tianjin Young Talents in Science and Technology (No. QN20230336 [Jiayuan Xu]), the Tianjin Applied Basic Research Diversified Investment Foundation (No. 21JCYBJC01360 [Jiayuan Xu]), Science&Technology Development Fund of Tianjin Education Commission for Higher Education (No. 2019KJ195 [Jiayuan Xu]) and the Tianjin Medical University "Clinical Talent Training 123 Climbing Plan" [Jiayuan Xu], China Postdoctoral Science Foundation (2023M742623 [Nana Liu]). 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Psychol Methods 21 , 369-387 (2016). https://doi.org:10.1037/met0000093 Additional Declarations There is NO Competing Interest. Supplementary Files SupplementaryTables.xlsx Supplementary Tables SupplementaryInformation.docx Supplementary Information Cite Share Download PDF Status: Posted Version 1 posted 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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07:55:10","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-5794364/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5794364/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":75883019,"identity":"a12b5868-5d8d-48d1-ab4f-454cb1e118ff","added_by":"auto","created_at":"2025-02-10 08:49:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":294674,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStudy Design and latent structure of SWB. \u0026nbsp;(a) \u003c/strong\u003eA schematic overview of the study design. (\u003cstrong\u003eb)\u003c/strong\u003eTen-fold and \u003cstrong\u003e(c)\u003c/strong\u003esplit-half cross-validation factor analyses identified two replicable latent factors for SWB.\u003cstrong\u003e (d) \u003c/strong\u003eThe recognized items for ISWB and SSWB, respectively.\u003cstrong\u003e (e)\u003c/strong\u003e the correlation between ISWB and SSWB. Abbreviations: CFA = confirmatory factor analysis; EFA = explanatory factor analysis; SWB = subjective well-being; ISWB = Internal subjective well-being; SSWB = Social subjective well-being.\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-5794364/v1/116dfc9bcf6c0c7387fd2c8e.png"},{"id":75884963,"identity":"3718073d-46c3-4aa2-bf7e-34c3c2e3f01e","added_by":"auto","created_at":"2025-02-10 09:05:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":663604,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation between SWB factors and urban living exposures.\u003c/strong\u003e (\u003cstrong\u003ea) \u003c/strong\u003eUnivariate correlation between 121 urban living variables and SWB factors by linear regression. The red dotted line represents the threshold of Bonferroni correction ((P \u0026lt; 0.05 / [121 exposures + 2,413 brain phenotypes + 39 mental health scores / 2 SWB factors] = 9.701e\u003csup\u003e-6\u003c/sup\u003e). (\u003cstrong\u003eb)\u003c/strong\u003e Spearman correlation between the absolute t-values of ISWB and SSWB associated with urban living exposures. Hierarchical clustering results of urban living variables associated with (\u003cstrong\u003ec)\u003c/strong\u003e ISWB and (\u003cstrong\u003ed\u003c/strong\u003e) SSWB based on the Calinski-Harabasz Index criteria. (\u003cstrong\u003ee-h\u003c/strong\u003e) shows the canonical correlation coefficient between the urbanization canonical scores and ISWB (\u003cstrong\u003ee, f\u003c/strong\u003e), and the top ten contributed variables (\u003cstrong\u003eg, h\u003c/strong\u003e).\u0026nbsp; (\u003cstrong\u003ei-l\u003c/strong\u003e) represents canonical correlation coefficient between the urbanization canonical scores and SSWB (\u003cstrong\u003ei, j\u003c/strong\u003e), and the top ten contributed variables (\u003cstrong\u003ek, l\u003c/strong\u003e). \u0026nbsp;\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-5794364/v1/0b8ba5ea4701e41f7df81955.png"},{"id":75883018,"identity":"5fc3eb6a-624e-4a49-871a-4220503a94d8","added_by":"auto","created_at":"2025-02-10 08:49:32","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":984636,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation between SWB factors and brain neuroimaging phenotypes\u003c/strong\u003e. (\u003cstrong\u003ea\u003c/strong\u003e) Univariate correlation between 2,413 brain phenotypes and SWB factors by linear regression. The red dotted line represents the threshold of Bonferroni correction. (\u003cstrong\u003eb)\u003c/strong\u003eSpearman correlation between the absolute t-values of ISWB and SSWB associated with brain phenotypes. (\u003cstrong\u003ec-d\u003c/strong\u003e) illustrate the specific white matter microstructure phenotypes\u003cstrong\u003e (c)\u003c/strong\u003e and their counts \u003cstrong\u003e(d) \u003c/strong\u003eassociated with different SWB factors. (\u003cstrong\u003ee-f\u003c/strong\u003e) show the specific cortical morphometric phenotypes\u003cstrong\u003e (e)\u003c/strong\u003e and their counts \u003cstrong\u003e(f) \u003c/strong\u003erelated to different SWB factors. (\u003cstrong\u003eg-h\u003c/strong\u003e) show the specific functional connectivity phenotypes\u003cstrong\u003e (g)\u003c/strong\u003e and their counts \u003cstrong\u003e(h) \u003c/strong\u003erelated to different SWB factors.\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-5794364/v1/22b4c7c3e1d3b97b05a5f836.png"},{"id":75883004,"identity":"de178a37-072e-45ed-824d-4fa078b1b7aa","added_by":"auto","created_at":"2025-02-10 08:49:31","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":796720,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHierarchical Organization of Brain Phenotypes and Their Synergistic Association with Two SWB Factors. \u003c/strong\u003e\u0026nbsp;(\u003cstrong\u003ea-b) \u003c/strong\u003erepresent the hierarchical clustering results of brain neuroimaging phenotypes related to (\u003cstrong\u003ea)\u003c/strong\u003e ISWB and (\u003cstrong\u003eb\u003c/strong\u003e) SSWB based on the Calinski-Harabasz Index criteria. Canonical correlation analyses demonstrate significant associations of the neuroimaging canonical scores with ISWB (\u003cstrong\u003ec, e, g, i\u003c/strong\u003e) and SSWB (\u003cstrong\u003ed, f, h, j\u003c/strong\u003e), in which first column shows top ten contributed phenotypes and the second column shows the correlation scatter plot.\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-5794364/v1/2838bc6dd19a42e55e6d3c8a.png"},{"id":75883006,"identity":"c897b761-fe92-467c-99ce-1b9aad3da77a","added_by":"auto","created_at":"2025-02-10 08:49:31","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":685130,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation between SWB factors and mental health indicators\u003c/strong\u003e. (\u003cstrong\u003ea\u003c/strong\u003e) Univariate correlation between 39 mental health indicators and SWB factors by linear regression. The red dotted line represents the threshold of Bonferroni correction. (\u003cstrong\u003eb)\u003c/strong\u003eSpearman correlation between the absolute t-values of ISWB and SSWB associated with mental health indicators. (\u003cstrong\u003ec-d)\u003c/strong\u003e illustrate the hierarchical clustering results of mental health indicators associated with (\u003cstrong\u003ec)\u003c/strong\u003e ISWB and (\u003cstrong\u003ed\u003c/strong\u003e) SSWB based on the Calinski-Harabasz Index criteria. Canonical correlation analyses demonstrate significant associations of the mental health canonical scores with ISWB (\u003cstrong\u003ee, f\u003c/strong\u003e) and SSWB (\u003cstrong\u003ei, j\u003c/strong\u003e) and the top-ten contributed mental health indicators for ISWB (\u003cstrong\u003eg, h\u003c/strong\u003e) and SSWB (\u003cstrong\u003ek, l\u003c/strong\u003e).\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-5794364/v1/c9c87f39319caca9decadf8f.png"},{"id":75884604,"identity":"7c258c8a-8182-4030-b600-2cd40c3c868d","added_by":"auto","created_at":"2025-02-10 08:57:31","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":300233,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCausal pathways among SWB, urban environment, brain, and mental health\u003c/strong\u003e. Two types of candidate mediation pathways are predefined, including (\u003cstrong\u003ea\u003c/strong\u003e) the sequential mediation pathway (Model 6) and (\u003cstrong\u003eb\u003c/strong\u003e) the moderated mediation pathway (Model59). (\u003cstrong\u003ec\u003c/strong\u003e) shows the counts of significant sequential mediation pathways, and (\u003cstrong\u003ed\u003c/strong\u003e) illustrates the counts of significant sequential mediation pathways for ISWB and SSWB, respectively. (\u003cstrong\u003ee-f)\u003c/strong\u003e provide an example of sequential mediation effects for ISWB \u003cstrong\u003e(e)\u003c/strong\u003e and SSWB\u003cstrong\u003e (f)\u003c/strong\u003e, respectively. (\u003cstrong\u003eg-h\u003c/strong\u003e) provide an example of moderated mediation effects for ISWB \u003cstrong\u003e(g) \u003c/strong\u003eand SSWB\u003cstrong\u003e (h)\u003c/strong\u003e, respectively. Please referred to Supplementary Table 19-22 for the detailed statistics for all pathways.\u003c/p\u003e","description":"","filename":"Fig6.png","url":"https://assets-eu.researchsquare.com/files/rs-5794364/v1/8ac7be929c68d427309abe33.png"},{"id":75884603,"identity":"6ab0288c-f0ea-457e-840d-47bc11c06e2c","added_by":"auto","created_at":"2025-02-10 08:57:31","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":493407,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSWB's Protective Value in Psychiatric Prevention. \u0026nbsp;(a)\u003c/strong\u003e Cox proportional hazards survival analysis results of the hazard ratio (HR) of six-level ISWB and SSWB on the future prevalence of overall mental disorders, with extremely unhappy as a reference. Dots represent hazard ratios; horizontal lines indicate corresponding 95% CIs. (\u003cstrong\u003eb\u003c/strong\u003e) Kaplan-Meier survival plot of the overall conversion of overall mental disorders during 10+ years follow-up in each of the six SWB levels, with shaded colors representing 95% CIs. (\u003cstrong\u003ec\u003c/strong\u003e) represents the disease-specific HRs for 10 mental disorders with extremely unhappy as a reference. (\u003cstrong\u003ed-f\u003c/strong\u003e) show the joint preventive effects of ISWB and SSWB, recoding them into high SWB (happy) and low SWB (unhappy), with quantities including (\u003cstrong\u003ed\u003c/strong\u003e) Kaplan-Meier survival plots, (\u003cstrong\u003ee\u003c/strong\u003e) HRs, and (\u003cstrong\u003ef\u003c/strong\u003e) ten-years conversion in each ISWB-SSWB combination.\u003c/p\u003e","description":"","filename":"Fig7.png","url":"https://assets-eu.researchsquare.com/files/rs-5794364/v1/7858f14b3d54876975012434.png"},{"id":78980518,"identity":"7ef4a115-979c-4adc-b4b7-9cac2d645865","added_by":"auto","created_at":"2025-03-21 16:08:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5535494,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5794364/v1/6337bcfb-5a1f-420c-a014-ec668764af54.pdf"},{"id":75884599,"identity":"ef1cec52-594b-40bc-8383-1d5d6c22f155","added_by":"auto","created_at":"2025-02-10 08:57:31","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":585955,"visible":true,"origin":"","legend":"Supplementary Tables","description":"","filename":"SupplementaryTables.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5794364/v1/3df01fedf12926328c3d5caf.xlsx"},{"id":75883023,"identity":"37382943-898a-43b5-ab63-46f56e36a7bc","added_by":"auto","created_at":"2025-02-10 08:49:32","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":2451101,"visible":true,"origin":"","legend":"Supplementary Information","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-5794364/v1/a1b4df5628ba963080d496fc.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Subjective Well-being: A Key to Bridge Urbanization, Brain and Mental Health","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAristotle, one of the preeminent philosophical architects of eudaimonic theory, postulated in his magnum opus, Nicomachean Ethics: \u0026ldquo;Happiness, then, is obviously something complete and self-sufficient, in that it is the end of what is done.\u0026rdquo;\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. As a subjective measure of \u0026ldquo;eudaimonia\u0026rdquo; or happiness, subjective well-being (SWB) is proposed to people\u0026rsquo;s own evaluations of their life satisfaction, positive affect, and low levels of negative affect\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Research has demonstrated that higher levels of SWB are associated with improved health outcomes and increased longevity\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Several theories have been proposed to explain the determinants and mechanisms of SWB\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. However, as a subjective psychological cognitive measure, SWB still faces several critical challenges in contemporary research, especially for its complex etiologies\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, neurobiological substrates\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e, and implications for mental health\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Addressing these challenges is essential for advancing the understanding and application of SWB in both research and practical contexts.\u003c/p\u003e \u003cp\u003eSWB is shaped by the complex interplay of genetic and environmental factors\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Genetic variations contribute significantly to individual differences in SWB, with heritability of 30\u0026thinsp;~\u0026thinsp;50% by twin-family studies\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, and teens of significant genomic loci by genome-wide association studies\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Concurrently, environmental influences such as socioeconomic status, social relationships, and physical environments play crucial roles in determining one\u0026rsquo;s SWB\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. In recent years, urbanization has gained attention as a pivotal environmental factor impacting SWB\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Studies indicate that the rapid progression of urbanization\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e brings both benefits to SWB, like improved access to healthcare and better socioeconomic status(SES)\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, and challenges, such as increased stress, noise pollution, and reduced green spaces\u003csup\u003e\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. However, a significant gap remains in literature due to the lack of interdisciplinary research examining the exposome-level urbanization factors affecting SWB and their neurobiological underpinnings. Addressing this gap is vital for developing informed urban policies and self-regulation strategies that promote healthier, happier urban populations.\u003c/p\u003e \u003cp\u003eRecent studies have sought to elucidate the neural correlates of SWB through advanced neuroimaging techniques\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. As a widely used non-invasive neuroimaging method, magnetic resonance imaging (MRI) can quantify large-scale, multi-dimensional brain structural and functional organizations in vivo, including gray matter volume (GMV), cortical thickness (CT), and cortical surface area (SA) via structural MRI (sMRI)\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e, white matter microstructural integrity and anatomical connectivity through diffusion MRI (dMRI)\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e, and brain regional activity and functional connectivity (FC) via resting-state functional MRI (rfMRI)\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Early studies have reported significant associations between SWB and various MRI measures, such as GMV\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e, white matter microstructural integrity\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e, brain regional activity\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e and functional connectivity\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. However, findings regarding the neural correlates of SWB have been largely heterogeneous across studies, potentially due to small sample sizes, diverse definitions of SWB, and varying neuroimaging methods\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e, and so on. Furthermore, few studies have examined the causal pathways among environmental exposure (i.e., urbanization), brain, SWB, and mental outcomes.\u003c/p\u003e \u003cp\u003eRecent studies have highlighted the intricate and bidirectional relationship between SWB and mental health\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e, indicating that SWB is not only the consequence of various mental illnesses \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e, but also serve as an influential protective factor against mental disorders\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Current studies emphasize the potential of enhancing SWB as a viable intervention strategy to prevent mental health issues. Empirical evidence suggests that interventions aimed at increasing SWB\u0026mdash;such as more physical activity\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e, green spaces\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e, and social connections\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e\u0026mdash;can also significantly improve mental health outcomes\u003csup\u003e\u003cspan additionalcitationids=\"CR40\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. However, this field faces ongoing challenges, particularly in establishing clear causal relationships between SWB and mental health due to the necessity of long-term longitudinal big data that can disentangle the directionality of effects. Additionally, there is an urgent need for more precise quantification of the differential preventive potentials of SWB and their causal roles for various types of mental disorders. Addressing these challenges is essential for advancing our understanding of how and to what extent SWB can be leveraged to prevent different types of mental disorders, as well as which modifiable environmental interventions can reduce the risk of mental illnesses through the promotion of subjective well-being.\u003c/p\u003e \u003cp\u003eThus, we proposed that exposure to urbanization impacts SWB through its effects on brain structures and functions, subsequently contributing to mental health outcomes. To test this hypothesis, this study utilized longitudinal multi-modal data from 198,823 adults in the UK Biobank, including SWB questionnaires, urban living exposome factors, neuroimaging data, mental health assessments, and ICD-10 psychiatric diagnoses. Our study focused on three key objectives: (1) identifying the latent factors underlying diverse SWB measures; (2) delineating the multivariate causal relationships among SWB, urban living exposome factors, brain neuroimaging indicators, and mental health status; and (3) quantifying the distinct protective role of SWB against major common mental disorders. The study design and workflow are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Result","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eLatent Components of Subjective Well-Being\u003c/h2\u003e \u003cp\u003eAmong five SWB questionnaire items of 198,823 qualified adults, ten-fold cross-validated exploratory factor analysis (EFA) indicated two latent components among the five SWB items by parallel analysis\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e (KMO\u0026thinsp;=\u0026thinsp;0.77, Cronbach\u0026rsquo;s Alpha\u0026thinsp;=\u0026thinsp;0.714) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb, \u003cb\u003eSupplementary Table\u0026nbsp;5)\u003c/b\u003e, which was further validated in the predict dataset by confirmatory factor analysis (CFA) (CFI\u0026thinsp;=\u0026thinsp;0.987\u0026thinsp;\u0026plusmn;\u0026thinsp;0.002, TLI\u0026thinsp;=\u0026thinsp;0.968\u0026thinsp;\u0026plusmn;\u0026thinsp;0.005, RMSEA\u0026thinsp;=\u0026thinsp;0.056\u0026thinsp;\u0026plusmn;\u0026thinsp;0.004, SRMR\u0026thinsp;=\u0026thinsp;0.217\u0026thinsp;\u0026plusmn;\u0026thinsp;0.001). Split-half EFA and CFA also reliably replicated the two latent factors (CFI\u0026thinsp;=\u0026thinsp;0.987, TLI\u0026thinsp;=\u0026thinsp;0.968, RMSEA\u0026thinsp;=\u0026thinsp;0.056, SRMR\u0026thinsp;=\u0026thinsp;0.022) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec\u003cb\u003e).\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe first latent factor of EFA mainly captures self-status evaluation, encompassing happiness (loading\u0026thinsp;=\u0026thinsp;0.503\u0026thinsp;\u0026plusmn;\u0026thinsp;0.002), health satisfaction (loading\u0026thinsp;=\u0026thinsp;0.557\u0026thinsp;\u0026plusmn;\u0026thinsp;0.001) and financial situation satisfaction (loading\u0026thinsp;=\u0026thinsp;0.465\u0026thinsp;\u0026plusmn;\u0026thinsp;0.002), thus designated as internal SWB (ISWB). The second factor mainly reflects social relationship evaluation, including friendship satisfaction (loading\u0026thinsp;=\u0026thinsp;0.642\u0026thinsp;\u0026plusmn;\u0026thinsp;0.002) and family relationship satisfaction (loading\u0026thinsp;=\u0026thinsp;0.694\u0026thinsp;\u0026plusmn;\u0026thinsp;0.002), termed social SWB (SSWB) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed, \u003cb\u003eSupplementary Table\u0026nbsp;6)\u003c/b\u003e. Spearman association analysis showed a medium correlation between the ISWB and SSWB scores (r\u0026thinsp;=\u0026thinsp;0.415, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating the relatively impendence between these two factors. Finally, a normal score transformation was applied to the harmonized SWB scores to enhance Gaussianity\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e \u003cb\u003e(Supplementary Fig.\u0026nbsp;2)\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAssociation between SWB and urban living environments\u003c/h3\u003e\n\u003cp\u003eAmong the 121 urban living exposure variables from 13 environmental categories, Phenome-wide association study (PheWAS) using linear regression model revealed 78 variables showing significant association with ISWB ((P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 / [121 exposures\u0026thinsp;+\u0026thinsp;2,413 brain phenotypes\u0026thinsp;+\u0026thinsp;39 mental health scores] / 2 SWB factors\u0026thinsp;=\u0026thinsp;9.701e-6, Bonferroni corrected), with the index of multiple deprivation (IMD) demonstrating the strongest positive association (t\u0026thinsp;=\u0026thinsp;58.543, P\u0026thinsp;=\u0026thinsp;2.23e-308). Besides, 59 out of 121 urbanization variables showed significant associations with SSWB, with 2010 nitrogen dioxide air pollution levels exhibiting the strongest association (t\u0026thinsp;=\u0026thinsp;18.968, P\u0026thinsp;=\u0026thinsp;3.69e-80) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea, \u003cb\u003eSupplementary Table\u0026nbsp;7\u0026ndash;8\u003c/b\u003e) (P\u0026thinsp;\u0026lt;\u0026thinsp;9.70e-6, Bonferroni corrected). Comparison of urban-SWB association t-values revealed a significant skew towards ISWB, indicating that urbanization has a stronger impact on ISWB than on SSWB (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eHierarchical clustering of the 78 ISWB-related urban living variables identified two optimal components, ISWB-urban-1 and ISWB-urban-2, based on the Calinski-Harabasz index (CHI\u0026thinsp;=\u0026thinsp;221) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). Canonical correlation analysis (CCA) showed that ISWB-urban-2 (primarily reflecting the IMD, air pollution and street network accessibility) exhibited a stronger association with ISWB (r\u0026thinsp;=\u0026thinsp;0.138, Pperm\u0026thinsp;\u0026lt;\u0026thinsp;0.001) compared to ISWB-urban-1 (preferably reflecting destination accessibility and greenspace proximity) (r\u0026thinsp;=\u0026thinsp;0.080, Pperm\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed-g, \u003cb\u003eSupplementary Table\u0026nbsp;9\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eBesides, we also identified two optimal components, SSWB-urban-1 and SSWB-urban-2, among the 59 SSWB-related urbanization variables (CHI\u0026thinsp;=\u0026thinsp;378) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eh). CCA demonstrated comparable significant but weak associations for both components: SSWB-urban-1 (r\u0026thinsp;=\u0026thinsp;0.044, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), which is related to destination accessibility and greenspace proximity, and SSWB-urban-2 (r\u0026thinsp;=\u0026thinsp;0.053, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), which is associated with air pollution, coast proximity, and land use density (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ei-l, \u003cb\u003eSupplementary Table\u0026nbsp;10\u003c/b\u003e).\u003c/p\u003e\n\u003ch3\u003eAssociation between SWB and brain neuroimaging phenotypes\u003c/h3\u003e\n\u003cp\u003ePheWAS across 2,413 brain neuroimaging phenotypes revealed significant correlations with both ISWB and SSWB (P\u0026thinsp;\u0026lt;\u0026thinsp;9.70e-6, Bonferroni corrected) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea, \u003cb\u003eSupplementary Table\u0026nbsp;11\u0026ndash;12\u003c/b\u003e). Specifically, 416 brain phenotypes correlated significantly with ISWB, and 77 phenotypes correlated significantly with SSWB. A comparison of the neuroimaging-SWB association t-values demonstrated a significant bias towards ISWB, suggesting that brain variations are more strongly correlated with ISWB than SSWB (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eCorrelations of SWB with white matter microstructure\u003c/h3\u003e\n\u003cp\u003eOf the 432 white matter microstructural measures derived from dMRI, 242 (56.0%) showed significantly associated with ISWB, primary involving fractional anisotropy (FA) in 72.9% (35/48), minimum eigenvalue (L3) in 70.8% (34/48), medium eigenvalue (L2) in 68.8% (33/48), and mean diffusivity in 66.7% (32/48) of white matter regions, with the strongest association observed for mean orientation dispersion (OD) in the left cerebral peduncle (t\u0026thinsp;=\u0026thinsp;10.512, p\u0026thinsp;=\u0026thinsp;2.53e-30). In contrast, only 17/432 (3.9%) microstructural measures correlated significantly with SSWB, mainly involving intra-cellular volume fraction (ICVF) 10.4% (5/48), L3 in 8.3% (4/48), FA in 4.2% (2/48), and MD in 4.2% (2/48) of regions, with the strongest association observed for mean L3 in splenium of corpus callosum (t\u0026thinsp;=\u0026thinsp;5.686, p\u0026thinsp;=\u0026thinsp;1.31e-8) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec-d, \u003cb\u003eSupplementary Table\u0026nbsp;11\u0026ndash;12)\u003c/b\u003e.\u003c/p\u003e\n\u003ch3\u003eCorrelations of SWB with brain morphometry\u003c/h3\u003e\n\u003cp\u003eOf the 52 brain volume phenotypes derived from automated segmentation (ASEG), 16 (30.8%) demonstrated significant associations with the ISWB, with ventral diencephalon (DC) volume showing the strongest associations (t = -6.338, P\u0026thinsp;=\u0026thinsp;2.36e-10) \u003cb\u003e(Supplementary Table\u0026nbsp;11, Supplementary Fig.\u0026nbsp;3)\u003c/b\u003e; 10/52 (17.3%) displayed significant correlations with the SSWB, primary affecting global measures, with the strongest associated one being right cortical volume (t\u0026thinsp;=\u0026thinsp;7.582, P\u0026thinsp;=\u0026thinsp;3.49e-14) \u003cb\u003e(Supplementary Table\u0026nbsp;12, Supplementary Fig.\u0026nbsp;4)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eAdditionally, 87/444 (19.6%) area-wise cortical phenotypes derived from Destrieux Atlas demonstrated significantly associated with the ISWB, including 30/148 (20.3%) cortical volume (CV) measures, 30/148 (20.3%) surface areas (SA) measures, and 27/148 (18.2%) cortical thickness (CT) measures, with the strongest association observed for the CT of right occipital pole (t\u0026thinsp;=\u0026thinsp;9.622, P\u0026thinsp;=\u0026thinsp;6.82e-22). In contrast, only 6/148 (4.1%) area-wise CT phenotypes exhibited significantly associations with the SSWB, with the strongest association observed for the CT of right cuneus (t\u0026thinsp;=\u0026thinsp;5.246, P\u0026thinsp;=\u0026thinsp;1.56e-7) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee-f, \u003cb\u003eSupplementary Table\u0026nbsp;11\u0026ndash;12\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003cb\u003eCorrelations of SWB with resting-state functional connectivity.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eOf the 1,485 functional connectivity (FC) measures derived from rfMRI and independent component analysis (ICA), we observed 71/1,485 (4.8%) FCs presented significant associations with ISWB, with the FC (ICA_FC345) between independent component (IC)-21(left primary sensorimotor subnet) and IC-28 (supplementary motor subnet) showing strongest association (t=-8.386, P\u0026thinsp;=\u0026thinsp;5.19e-17). Moreover, 44/1,485 (3.0%) FCs exhibited significant associations with SSWB, with the FC (ICA_FC663) between IC-34 (right frontoparietal subnet) and IC-38 (bilateral prefrontal subnet) showing strongest association (t = -7.442, P\u0026thinsp;=\u0026thinsp;1.01e-13) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eg-h, \u003cb\u003eSupplementary Table\u0026nbsp;11\u0026ndash;12\u003c/b\u003e).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eIntegrated neuroimaging components associated with SWB\u003c/h2\u003e \u003cp\u003eHierarchical clustering of the 416 ISWB-related brain phenotypes identified four optimal components (CHI\u0026thinsp;=\u0026thinsp;526) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea). CCA showed that ISWB-brain-2 exhibited the highest explained variance for ISWB (r\u0026thinsp;=\u0026thinsp;0.183, Pperm\u0026thinsp;\u0026lt;\u0026thinsp;0.001) among the four neuroimaging components, which was mainly contributed by the dMRI-derived microstructural measures of pontine and cerebellar peduncles, (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec,\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ee, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eg, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ei, \u003cb\u003eSupplementary Table\u0026nbsp;13\u003c/b\u003e). We also revealed four optimal components among the 77 SSWB-related brain phenotypes based on hierarchical clustering (CHI\u0026thinsp;=\u0026thinsp;50.9) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). CCA demonstrated that SSWB-brain-3 exhibited the highest explained variance for SSWB (r\u0026thinsp;=\u0026thinsp;0.119, Pperm\u0026thinsp;\u0026lt;\u0026thinsp;0.001) among the four neuroimaging components, which was mainly contributed by resting-state FC measures (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed,\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ef, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eh, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ej, \u003cb\u003eSupplementary Table\u0026nbsp;14\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAssociation between SWB and mental health status\u003c/h3\u003e\n\u003cp\u003ePheWAS revealed strong correlations between 38 out of 39 mental health indicators and ISWB, with the strongest association observed for tiredness (t\u0026thinsp;=\u0026thinsp;188.470, P\u0026thinsp;\u0026lt;\u0026thinsp;2.48e-324). Additionally, 37/39 mental health indicators demonstrated significant associations with SSWB, with the strongest association for loneliness and isolation (t\u0026thinsp;=\u0026thinsp;139.423, P\u0026thinsp;\u0026lt;\u0026thinsp;2.48e-324) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea, \u003cb\u003eSupplementary Table\u0026nbsp;15\u0026ndash;16\u003c/b\u003e). Mental-SWB association t-values distribution demonstrated a significant skew towards ISWB, suggesting that ISWB accounts for mental health more strongly than SSWB (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe further identified two optimal components (ISWB-MH-1 and ISWB-MH-2) from the 38 ISWB-associated mental health indicators by hierarchical clustering (CHI\u0026thinsp;=\u0026thinsp;34.7) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec). CCA showed that ISWB-MH-2 (top 3 weights: tiredness, depressed mood and fed up feelings) exhibited a stronger association with ISWB ((r\u0026thinsp;=\u0026thinsp;0.514, Pperm\u0026thinsp;\u0026lt;\u0026thinsp;0.001) compared to ISWB-MH-1 (top 3 weights: grief and stress, recurrent major depression and manic/irritable episodes) (r\u0026thinsp;=\u0026thinsp;0.251, Pperm\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ee,\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ef,\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ei,\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ek, \u003cb\u003eSupplementary Table\u0026nbsp;17\u003c/b\u003e). Moreover, two optimal SSWB-associated components were also identified (CHI\u0026thinsp;=\u0026thinsp;33.2) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed\u003cb\u003e)\u003c/b\u003e, including SSWB-MH-2 that preferably represents loneliness and isolation, depressed mood and irritability (r\u0026thinsp;=\u0026thinsp;0.387, Pperm\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and SSWB-MH-1 that primary reflects severity of manic/irritable episodes, severe and moderate recurrent major depression (r\u0026thinsp;=\u0026thinsp;0.154, Pperm\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eg, \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eh, \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ej, \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003el, \u003cb\u003eSupplementary Table\u0026nbsp;18\u003c/b\u003e).\u003c/p\u003e\n\u003ch3\u003eCausal pathways among SWB, urban environment, brain, and mental health\u003c/h3\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSequential mediation pathways (Model 6)\u003c/h2\u003e \u003cp\u003eSequential mediation analysis uncovered 28 causal pathways from urbanization to brain to SWB to mental health, including 16 ISWB-related pathways and 12 SSWB-related pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ea, \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec, \u003cb\u003eSupplementary Table\u0026nbsp;19\u0026ndash;20\u003c/b\u003e). For instance, greater exposure to ISWB-urban-2 factors (primarily IMD and air pollution) is associated with increased changes in ISWB-brain-2 (microstructures of the pontine and cerebellar peduncles), leading to lower ISWB and ultimately higher ISWB-MH-2 symptoms (i.e., tiredness) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ee). Similarly, greater exposure to SSWB-urban-1 factors (primarily urban destination accessibility) is associated with changes in SSWB-brain-3 (functional connectivity), leading to lower SSWB and ultimately higher SSWB-MH-2 symptoms (i.e., loneliness and isolation) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ef).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eModerated mediation pathways (Model59)\u003c/h2\u003e \u003cp\u003eWe further explored whether SWB could serve as a moderator in the causal pathways from urbanization to mental health. Our analysis revealed 19 indirect (mediation) pathways (urbanization \u0026rarr; brain \u0026rarr; mental health) that were significantly moderated by SWB, including 14 ISWB-related pathways and 5 SSWB-related pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eb, \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ed, \u003cb\u003eSupplementary Table\u0026nbsp;21\u0026ndash;22\u003c/b\u003e). Specifically, in the 14 ISWB-related pathways, ISWB moderated both the urbanization \u0026rarr; brain sub-path (moderationA) and the brain \u0026rarr; mental health sub-path (moderationB) in 8 pathways; while in 6 pathways, it only exerts moderationB effects. For the 5 SSWB-related pathways, we identified 2 pathways with both moderationA \u0026amp; moderationB effects, and 3 pathways with only moderationB effects. Additionally, we identified 20 direct pathways (urbanization \u0026rarr; mental health) that were significantly moderated by SWB (moderationC) (16 for ISWB, 4 for SSWB) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ed\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eOne typical example of SWB\u0026rsquo;s moderation effect is in the ISWB-urban-2 (primarily IMD and air pollution) \u0026rarr; ISWB-brain-2 (microstructures of the pontine and cerebellar peduncles) \u0026rarr; ISWB-MH-2 (tiredness) pathway, where ISWB exerts both moderationA and moderationB effects, resulting in the indirect effect significant only in the lower sextile ISWB population (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eg). Another example is in the SSWB-urban-1 (destination accessibility) \u0026rarr; SSWB-brain-3 (functional connectivity) \u0026rarr; SSWB-MH-2 (loneliness and isolation) pathway, where SSWB only exerts a moderationB effect, resulting in opposite indirect effects between the upper and lower sextile SSWB populations (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eh).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eSWB's Protective Value in Psychiatric Prevention\u003c/h2\u003e \u003cp\u003eWe finally evaluated the potential of SWB component scores in predicting future occurrence of 10 common mental disorders categorized by ICD10. Cox proportional hazards survival analysis demonstrated that individuals in the highest ISWB sextile (extremely happy) had a 76% reduction in the overall risk of developing at least one of the ten mental disorders compared to those in the lowest sextile (extremely unhappy) (Z = -29.49, Hazard Ratio [HR]\u0026thinsp;=\u0026thinsp;0.24, P\u0026thinsp;=\u0026thinsp;3.93e-191), with SSWB showing a 36% risk reduction (Z = -9.42, HR\u0026thinsp;=\u0026thinsp;0.64, P\u0026thinsp;=\u0026thinsp;4.50e-21) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eKaplan-Meier survival analysis revealed that the overall prevalence (1-survival probability) of general mental disorders was declining with increasing SWB levels. After 10 years, the mental disorders overall prevalence in the ISWB highest sextile was only 9%, compared to 34% in the ISWB lowest sextile. SSWB showed similar but weaker protective effects over mental disorders, with 10-year prevalence of 13% and 20% in the highest and lowest sextiles, respectively \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eb\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eIn terms of disease specificity, ISWB exhibited significant protective effects across all 10 mental disorders for the highest than the lower sextile individuals, with the strongest protection observed for depression (HR\u0026thinsp;=\u0026thinsp;0.13, 95%CI\u0026thinsp;=\u0026thinsp;0.10\u0026ndash;0.15). SSWB demonstrated significant protective effects for 4 mental disorders, with depression again showing the strongest protective benefit (HR\u0026thinsp;=\u0026thinsp;0.39, 95%CI\u0026thinsp;=\u0026thinsp;0.33\u0026ndash;0.47) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ec, \u003cb\u003eSupplementary Table\u0026nbsp;23\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eTo test the joint predictive role of ISWB and SSWB, we split the populations into four subgroups based on the medium of the two SWB scores (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ed). Survival analyses revealed the strongest protective effect for subgroups with both high ISWB and high SSWB (HR\u0026thinsp;=\u0026thinsp;0.42, 95%CI\u0026thinsp;=\u0026thinsp;0.40\u0026ndash;0.46), followed by subgroup with high ISWB and low SSWB (HR\u0026thinsp;=\u0026thinsp;0.48, 95%CI\u0026thinsp;=\u0026thinsp;0.45\u0026ndash;0.52), and a weaker protective effect for subgroup with low ISWB and high SSWB (HR\u0026thinsp;=\u0026thinsp;0.91, 95%CI\u0026thinsp;=\u0026thinsp;0.85\u0026ndash;0.98) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ee). After 10 years, the overall mental disorder prevalence in high ISWB (10.9%) was 11.6% lower than that of low ISWB (22.5%), indicating a strong protective role of ISWB against mental disorders. Additionally, the prevalences of two subgroups with high SSWB (low ISWB: 21.0%, high ISWB: 10.2%) were also lower (low ISWB: 2.4%, high ISWB: 1.6%) than those with low SSWB (low ISWB: 23.4%, high ISWB: 11.8%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ef), indicating that SSWB also contributes to a weak protective role against mental disorders.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eBased on a large-scale longitudinal cohort, we identified two components of SWB: Internal SWB (ISWB) (general happiness, health satisfaction, and financial satisfaction) and Social SWB (SSWB) (friendship satisfaction and family relationship satisfaction). Moreover, we found that compared to SSWB, ISWB shows stronger overall associations with urbanization exposure, neuroimaging phenotypes, and mental health outcomes, and it plays a more pronounced role in preventing ten common mental disorders. Furthermore, the two forms of SWB are shaped by different urbanization exposure factors, exhibit distinct neural correlates, and regulate unique urbanization\u0026ndash;brain\u0026ndash;mental health pathways. By systematically uncovering the causal pathways through which urban living environments regulate the human brain, thereby affecting SWB and mental health, this study provides biological evidence and modifiable SWB indicators for the prevention of common psychiatric disorders.\u003c/p\u003e \u003cp\u003eOne key contribution of this study is that we systematically explained how multidimensional urbanization exposure factors exert effects on different dimensions of SWB. We uncovered several common environmental factors that exert significant regulatory effects on both ISWB and Social SSWB, such as air pollution\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e, greenspace proximity\u003csup\u003e\u003cspan additionalcitationids=\"CR47\" citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e, destination accessibility\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e,\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e, and street network accessibility\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e,\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e, which had been supported by existing literature, affirming their pivotal roles in influencing SWB. More importantly, we found multifaceted urbanization exposure factors exert differential impacts on distinct components of SWB: Firstly, the impact of urbanization on ISWB is generally more pronounced than on SSWB. This suggests that urban environment has a more pronounced impact on individual's internal mental states than social ones. Furthermore, the top environmental variables that show notably stronger effects on ISWB compared to SSWB include IMD\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e,\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e, PM2.5\u003csup\u003e54,55\u003c/sup\u003e, and nitrogen dioxide/oxide air pollution\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. These exposures primarily reflect individuals' socio-economic status (IMD) and natural pollution levels (PM2.5, air pollution), directly correlating with material living conditions and physical health. Conversely, there also exist urban living variables showing stronger effects on SSWB than ISWB, such as non-driving accessibility for university, coast proximity\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e,\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e, and the LandUse density of restaurants/cafeterias\u003csup\u003e58\u003c/sup\u003e and holidays/campsites\u003csup\u003e59\u003c/sup\u003e, which provide essential venues for social interactions. Thus, we speculated that urbanization influences on SWB may operate through distinct pathways based on the personal versus social nature of the environmental factors: urbanization impacts ISWB predominantly through socio-economic and natural environmental channels, whereas that influences SSWB primary through interpersonal interactions. Finally, the study identifies two covarying latent urbanization factors for each of the ISWB and SSWB. These synergic variables not only quantify the overall impact of urban environments on SWB but also provide objective evidence for complex covariance among various urbanization environmental factors\u003csup\u003e\u003cspan additionalcitationids=\"CR61\" citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e. In summary, this study offers critical insights into how multidimensional urbanization exposures differentially regulate internal and social aspects of SWB, highlighting the importance of tailored urban planning and policy interventions to enhance overall quality of life in urban settings.\u003c/p\u003e \u003cp\u003eThe second contribution of this study lies in leveraging a large-sample (N\u0026thinsp;=\u0026thinsp;39,291) multimodal MRI dataset to systematically investigate the neural underpinnings of SWB. While previous studies have explored the relationship between SWB and brain structure and function, limitations such as small sample sizes, varying SWB definitions, and diverse neuroimaging metrics have hindered consistent conclusions\u003csup\u003e\u003cspan additionalcitationids=\"CR64 CR65\" citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e. Through the involvement of 2,413 neuroimaging phenotypes, we demonstrated that ISWB exhibited stronger overall associations with these phenotypes compared to SSWB, suggesting ISWB as a more stable and biologically meaningful endophenotype. Furthermore, white matter microstructure demonstrated the strongest correlation with SWB (56% of phenotypes), followed by gray matter structure (20.7%), with resting-state functional connectivity showing the weakest association (4.8%). This highlights the crucial role of white matter microstructural integrity in SWB\u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e,\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e,\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e\u003c/sup\u003e. Moreover, ISWB and SSWB exhibited distinct association patterns with the brain imaging phenotypes. Specifically, the neuroimaging metrics accounting for the largest explained variance in ISWB predominantly comprised dMRI-derived microstructural measures of the pontine and cerebellar peduncles, while SSWB was most strongly associated with resting-state functional connectivity within frontoparietal subnetworks. These divergent patterns suggest that ISWB may be more sensitive to alterations in sensorimotor-related white matter circuits\u003csup\u003e\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e,\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e, whereas SSWB may be more closely linked to functional networks supporting higher-order cognitive processes\u003csup\u003e\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e,\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePrior investigations into the causal relationships of SWB have primarily focused on the pairwise associations by various strategies, such as: urban-SWB\u003csup\u003e\u003cspan additionalcitationids=\"CR74\" citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e\u003c/sup\u003e, brain-SWB\u003csup\u003e\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e\u003c/sup\u003e, and SWB-mental health\u003csup\u003e\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e\u003c/sup\u003e, with limited research exploring tripartite pathways such as urban-brain-SWB\u003csup\u003e\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e\u003c/sup\u003e, urban-SWB-mental health\u003csup\u003e\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e\u003c/sup\u003e, or brain-SWB-mental health\u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e. The third major contribution of this study is that we have uncovered multiple dozens of causal pathways among urban living exposure, brain, SWB, and mental health outcomes. We revealed that SWB serves not only as a directly relay mediating the living exposure \u0026rarr; brain \u0026rarr; mental health pathways (sequential mediation), but also as a indirectly moderator (moderated mediation) for these pathways, presenting a complex mechanisms of SWB in protecting peoples from mental disorders. Besides, we also found diverse roles and pathways of different SWB components on mental health. Specifically, ISWB showed stronger associations with mental health compared to SSWB and was involved in more urbanization-regulated pathways affecting mental health, supporting its more crucial role in mental health. Moreover, ISWB and SSWB participated in relatively specific regulatory pathways. For instance, elevated exposure to the multiple deprivation (a measure for poorer socio-economic status) and air pollution was associated with poorer pontine and cerebellar microstructural integrity\u003csup\u003e\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e,\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e\u003c/sup\u003e, subsequently leading to worse ISWB\u003csup\u003e\u003cspan additionalcitationids=\"CR83\" citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e\u003c/sup\u003e and elevated tiredness\u003csup\u003e\u003cspan additionalcitationids=\"CR86 CR87\" citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e\u003c/sup\u003e. Conversely, greater exposure to social-related urban destination accessibility was associated with modifications in frontoparietal functional connectivity\u003csup\u003e\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e\u003c/sup\u003e, resulting in reduced SSWB\u003csup\u003e\u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e,\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e\u003c/sup\u003e and heightened levels of loneliness and social isolation\u003csup\u003e\u003cspan additionalcitationids=\"CR93\" citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e\u003c/sup\u003e. Thus, the SWB-related causal pathways identified in this study not only advance our understanding of how urban living modulates mental health through neurobiological remolding, but also provide specific environmental variables and neural targets amenable to intervention for enhancing both SWB and mental health outcomes.\u003c/p\u003e \u003cp\u003eThe final contribution of this study is the systematic investigation of the protective effects of SWB against ten common mental disorders utilizing a large-scale longitudinal cohort of 31,337 participants (with 5,498 incident cases during follow-up) and a maximum follow-up period of 4606 days. Because of the bidirectional relationship between SWB and diseases\u003csup\u003e\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e,\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e\u003c/sup\u003e, we excluded individuals with any ICD-10 recorded diseases prior to the SWB assessment. This approach effectively minimizes the potential confounding effects of pre-existing psychological and physical illnesses on SWB, thereby allowing us to specifically examine whether SWB can prevent the onset of psychological disorders in a healthy population. To ensure robust statistical power, we categorized mental disorders at the second-level classification of ICD-10 and ensured that each disorder had a minimum of 50 future cases. Our findings indicate that both ISWB and SSWB can significantly protect against the development of mental disorders in healthy individuals, aligning with previous research\u003csup\u003e\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e\u003c/sup\u003e. Notably, ISWB demonstrated a substantially greater protective effect compared to SSWB, with ISWB reducing the risk by 76% and SSWB by 36% when comparing the highest sextile to the lowest sextile populations. Over a ten-year follow-up, the incidence of psychological disorders was as high as 22.5% in the lower half SSWB individuals, whereas it decreased to 10.9% in lower half ones. Furthermore, ISWB provided significant protective effects across all ten mental disorders studied, whereas SSWB was significantly protective for five specific disorders. Finally, both aspects of SWB exhibited the strongest protective effects against depressive disorders\u003csup\u003e\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e,\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e\u003c/sup\u003e (ISWB reducing by 87% risk and SSWB by 61% for the highest versus lowest sextile). These findings underscore the substantial protective role of ISWB in mental disorder prevention by quantitative analyses, particularly regarding depressive disorders, establishing ISWB as a robust and readily modifiable indicator for both intervention and monitoring purposes.\u003c/p\u003e \u003cp\u003ePossible Limitations and Future Directions of the Study included: this study focused only on the effects of urbanization on SWB and its pathways to provide modifiable indicators for mental health. However, we did not examine the genetic influences on SWB or the underlying molecular mechanisms\u003csup\u003e\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e,\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e,\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e101\u003c/span\u003e\u003c/sup\u003e. Second, the impact of urbanization on SWB and its relationship with mental health may vary across cultures and regions\u003csup\u003e\u003cspan additionalcitationids=\"CR103\" citationid=\"CR102\" class=\"CitationRef\"\u003e102\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e\u003c/sup\u003e. As the study was limited to a UK population, the generalizability of the findings to other countries and regions requires further validation. Lastly, the SWB classification used in this study differs from previous research\u003csup\u003e\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e\u003c/sup\u003e, and its reproducibility across diverse populations and datasets needs further investigation.\u003c/p\u003e \u003cp\u003eIn conclusion, this study reinforces Aristotle's timeless philosophical insight on subjective well-being: happiness is not only a lifelong pursuit but also a remedy for healing the mind. By providing biological evidence from the natural sciences, this study highlights the potential of enhancing subjective well-being as a potential psychotherapy strategy against the increasing burden of mental disorders in the context of urbanization.\u003c/p\u003e "},{"header":"Method","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003eUKB cohort\u003c/h2\u003e \u003cp\u003eThe participants of the present study were enrolled from the UKB cohort under application number 75556\u003csup\u003e105\u003c/sup\u003e, encompassing approximately 500k samples of extensive questionnaire items and over 40k sample of brain MRI data released on December 8, 2021. The UK Biobank received approval from its Research Ethics Committee, the Human Tissue Management Agency Research Organization Bank, and the National Health Service (NHS) Centre. Detailed data screening processes are shown in \u003cb\u003eSupplementary Fig.\u0026nbsp;1\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eSubjective well-being data\u003c/h2\u003e \u003cp\u003eWe reviewed all the UKB touchscreen questionnaires completed at the Assessment Centre and selected five items related to SWB, including \u0026ldquo;Happiness\u0026rdquo; (Data-field 4526), \u0026ldquo;Health satisfaction\u0026rdquo; (Data-field 4548), \u0026ldquo;Family relationship satisfaction\u0026rdquo; (Data-field 4559), \u0026ldquo;Friendships satisfaction\u0026rdquo; (Data-field 4570), and \u0026ldquo;Financial situation satisfaction\u0026rdquo; (Data-field 4581). Although \u0026ldquo;Work/job satisfaction\u0026rdquo; (Data-field 4537) is also an important aspect of SWB, we did not choose it because about 30% participants answered, \u0026ldquo;I don't have a job.\u0026rdquo; We selected participants who completed the questionnaire for the first time from 2006\u0026ndash;2010 (Instance 0), 2012\u0026ndash;2013(Instance 1), and 2014+(Instance 2). Responses were on a 6-point Likert scale with 1-point representing extremely happy and 6-point representing extremely unhappy (\u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eWe excluded participants with the following conditions: missing SWB questionaries (participants answered \u0026ldquo;don't know\u0026rdquo; or \u0026ldquo;would rather not answer\u0026rdquo;), genetic sex missing (Data-field 22001), sex mismatch (discrepancy between genetic and self-reported sex [Data-field 31]), outliers for heterozygosity or missing rate (Data-field 22027), and within three-generation relatedness (KING kinship coefficient\u0026thinsp;\u0026gt;\u0026thinsp;0.0884). We initially retained 200,802 qualified participants with complete five-item SWB scales.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eUrban living environments data\u003c/h2\u003e \u003cp\u003eWe referred to previous studies that defined urban living environments based on 13 categories and 121 variables \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e, including: EIMD (Data-field 26410), SIMD (Data-field 26427), WIMD (Data-field 26426), Residential air pollution (Data-field 24003\u0026ndash;24008), Traffic (Data-field 24009\u0026ndash;24015), Residential noise pollution (Data-field 24020), Greenspace proximity (Data field 24503, Data-field 24504, Data-field 24507), water proximity (Data-field 24505), coastal proximity (Data-field 24508) and UK Biobank Urban Morphometric Platform (Category 100115) \u003cb\u003e(Supplementary Table\u0026nbsp;2)\u003c/b\u003e. We excluded environmental variables with missing values ​greater than 50% and removed participants with missing values more than 60% environment variables. Finally, a total of 198,823 participants were left for formal analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eNeuroimaging data\u003c/h2\u003e \u003cp\u003eNeuroimaging data were acquired using Siemens Skyra 3T MRI scanners (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://biobank.ctsu.ox.ac.uk/crystal/crystal/docs/brain_mri.pdf\u003c/span\u003e\u003cspan address=\"https://biobank.ctsu.ox.ac.uk/crystal/crystal/docs/brain_mri.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e, including brain sMRI, dMRI, and rfMRI. This study included 39,291 participants who underwent at least one of these imaging protocols, yielding 2,413 neuroimaging phenotypes derived from these three modalities\u003csup\u003e\u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e106\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eBrain sMRI phenotypes\u003c/h2\u003e \u003cp\u003eThe T1-weighted sMRI data included 38,875 participants and were processed using FreeSurfer version 6 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://surfer.nmr.mgh.harvard.edu/\u003c/span\u003e\u003cspan address=\"https://surfer.nmr.mgh.harvard.edu/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). We extracted 444 cortical surface phenotypes (Category ID 197) defined by Destrieux Atlas (FreeSurfer a2009s), comprising 148 measures each of cortical volume (CV), cortical thickness (CT), and surface area (SA) \u003csup\u003e\u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e107\u003c/span\u003e,\u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e108\u003c/span\u003e\u003c/sup\u003e. Additionally, 52 brain volume phenotypes (Category ID 190) were obtained using FreeSurfer's Automatic Segmentation (ASEG) tools\u003csup\u003e\u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e109\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eBrain dMRI phenotypes\u003c/h2\u003e \u003cp\u003eThe dMRI data contained 36,180 participants and were preprocessed using FSL version 5.0.10 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://fsl.fmrib.ox.ac.uk/fsl\u003c/span\u003e\u003cspan address=\"https://fsl.fmrib.ox.ac.uk/fsl\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Following preprocessing, two modeling approaches were applied to derive white matter microstructural phenotypes: Diffusion Tensor Imaging (DTI) and Neurite Orientation Dispersion and Density Imaging (NODDI). For DTI modeling, the b\u0026thinsp;=\u0026thinsp;1000 shell (50 directions) data were fitted using the DTIFIT toolbox, generating six metrics: fractional anisotropy (FA), mean diffusivity (MD), mode of anisotropy (MO), axial diffusivity (L1), median diffusivity (L2), and minimum diffusivity (L3). For NODDI modeling, data from all three shells were fitted using the AMICO toolbox \u003csup\u003e\u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e110\u003c/span\u003e,\u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e111\u003c/span\u003e\u003c/sup\u003e, producing three metrics: intra-cellular volume fraction (ICVF), orientation dispersion (OD), and isotropic volume fraction (ISOVF). Finally, all metrics were skeletonized using the tract-based spatial statistics (TBSS) pipeline \u003csup\u003e\u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e112\u003c/span\u003e\u003c/sup\u003e, and 432 white matter microstructural phenotypes were extract from these nine dMRI metric skeletons using ICBM-DTI-81 white-matter labels atlas (Category 134)\u003csup\u003e\u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e113\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eBrain rfMRI phenotypes\u003c/h2\u003e \u003cp\u003eThe fMRI dataset comprised 36,911 participants and was processed using FSL version 5.0.10. Following preprocessing, we performed a 100-dimension spatial independent component analysis (ICA) using FSL's MELODIC tool to derive spatially-independent components (\u003cb\u003eSupplementary Table\u0026nbsp;3\u003c/b\u003e), referred to as resting-state functional networks (RSNs) \u003csup\u003e114\u003c/sup\u003e. After discarding components clearly identified as artifacts (non-neuronal), 55 RSNs with clear biological significance remained. Functional connectivity (FC) between RSN pairs was then quantified using Pearson correlation coefficients, which were converted to Fisher r-to-z scores, yielding 1,485 FC phenotypes (Data-Field 25751) \u003csup\u003e\u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e106\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eMental health score data\u003c/h2\u003e \u003cp\u003eMental health (MH) score data were collected from 198,823 participants through the Mental Health category (Category ID 100060) of psychosocial factors assessment, administered via touchscreen devices at assessment centers. Data collection coincided with the completion instance of SWB questionnaires (\u003cb\u003eSupplementary Table\u0026nbsp;4\u003c/b\u003e). After excluding missing responses marked as \"Do not know\" or \"Prefer not to answer,\" each categorical variable was binary encoded. This process yielded 39 distinct MH variables.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003eMental disorder records data\u003c/h2\u003e \u003cp\u003eUsing the International Statistical Classification of Diseases and Related Health Problems 10th Revision (ICD10) (Data-field 41270) and the date of first recording in hospital records (Data-field 41280), we excluded individuals with a prior mental disorder (Chapter V of ICD10) diagnosis before the SWB evaluation date. Participants with any non-mental disorders were also excluded. With the SWB evaluation date as the baseline, we screened patients with a certain of mental disorder who were recorded after the SWB evaluation and were closest to the baseline date. Diseases with more than 50 cases were included, resulting in 10 mental disorders: Dementia in Alzheimer's disease (F00); Dementia in Alzheimer's disease (F03); Delirium, not induced by alcohol and other psychoactive substances (F05); Mental and behavioral disorders due to use of alcohol (F10); Mental and behavioral disorders due to use of tobacco (F17); Schizophrenia, schizotypal and delusional disorders (F2); Bipolar affective disorder(F31); Depressive episode disorder (F32), recurrent depressive disorder(F33); Phobic anxiety disorders(F40); Other anxiety disorders (F41). The final dataset included 5,498 patients with future-occurring mental disorders and 25,839 healthy controls with no recorded disorders during follow-up.\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section3\"\u003e \u003ch2\u003eConfounding variables\u003c/h2\u003e \u003cp\u003eAge (Data-field 21003), sex (Data-field 31), and 40 genetic principal components (Data-field 22009) were taken as confounding covariates and were adjusted in subsequent association analyses. Besides, the estimated total intracranial volume (Data-field 26521) was considered as an additional confounder for neuroimaging phenotypes.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cdiv id=\"Sec27\" class=\"Section3\"\u003e \u003ch2\u003eFactors analysis for subjective well-being\u003c/h2\u003e \u003cp\u003eA Ten-fold cross-validated factor analysis was applied to identify the latent factors among the five SWB items. Specifically, for each fold, 90% of the 198,823 participants (train dataset) underwent exploratory factor analysis (EFA) to train the optimal model using \u0026ldquo;psych\u0026rdquo; R package\u003csup\u003e115\u003c/sup\u003e, and 10% of the participants (predict dataset) underwent confirmatory factor analysis (CFA) to test the model using \u0026ldquo;lavaan\u0026rdquo; R package\u003csup\u003e116\u003c/sup\u003e. EFA is a statistical method used to uncover the latent factors of observed variables, which begins by accessing data suitability using the Kaiser-Meyer-Olkin (KMO) measure for sampling adequacy (desire if KMO\u0026thinsp;\u0026gt;\u0026thinsp;0.7)\u003csup\u003e117\u003c/sup\u003e and Cronbach\u0026rsquo;s Alpha for internal consistency (good reliability if Alpha\u0026thinsp;\u0026gt;\u0026thinsp;0.7 )\u003csup\u003e118\u003c/sup\u003e. A covariance matrix is used to analyze relationships between variables. The optimal number of factors is determined through parallel analysis, comparing eigenvalues from the data with those from random datasets (300 shuffles)\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. The factors are fitted using the Maximum Likelihood method and are rotated using \u0026ldquo;Varimax\u0026rdquo; to improve interpretability\u003csup\u003e117\u003c/sup\u003e. Then the goodness of fit of the identified factors in the train dataset was validated by CFA in predict dataset using Tucker\u0026ndash;Lewis index (TSI) (\u0026gt;\u0026thinsp;0.9), comparative fit index (CFI) (\u0026gt;\u0026thinsp;0.9), root mean square error of approximation (RMSEA) (\u0026lt;\u0026thinsp;0.06) and standard root mean square residual (SRMR) (\u0026lt;\u0026thinsp;0.05)\u003csup\u003e119\u003c/sup\u003e. The SWB factor scores for each individual were calculated by multiplying the predicted dataset with the estimated factor loadings from the optimal EFA model, using the \u0026ldquo;predict\u0026rdquo; function in the \u0026ldquo;psych\u0026rdquo; package. The above steps are repeated until factor scores of all individuals in 10 folds were obtained. To evaluate the stability of the model estimated through 10-fold cross-validation, the data was split in half, and the same steps were repeated. Finally, a normal score transformation was applied to the harmonized SWB factor scores to enhance Gaussianity\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003ePheWAS analyses between SWB and urbanization, brain and mental health\u003c/h2\u003e \u003cp\u003eA univariate phenome-wide association study (PheWAS) analysis was used to test the correlation between each SWB factor score and each of the urbanization, brain and mental health variables using a linear regression model, with SWB factor score as the dependent variable, urbanization exposures, brain phenotypes, mental health scores and confounders as independent variables. Bonferroni corrected was used to correct the type-I error caused by the multiple comparisons (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 / [121 exposures\u0026thinsp;+\u0026thinsp;2,413 brain phenotypes\u0026thinsp;+\u0026thinsp;39 mental health scores / 2 SWB factors]\u0026thinsp;=\u0026thinsp;9.701e\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003eMultivariate associations analyses between SWB and urbanization, brain and mental health\u003c/h2\u003e \u003cp\u003eRegarding the numerous variables for urbanization, brain, and mental health, as well as the complex interrelationships (collinearity) among them, we further sought to identify potential components within each variable category using hierarchical clustering. We then assessed the combined contributions of each synthetic component to each SWB factor using canonical correlation analysis (CCA). Specifically, for each variable category (urbanization, brain, or mental health), we conducted the following analysis: (1) extracted variables with statistical significance in univariate PheWAS for each category and imputed missing values using mean imputation; (2) applied a hierarchical clustering method to identify the tiered structure within the category variables using a grid search strategy to determine the hyperparameters, including six distance metrics (\"euclidean,\" \"maximum,\" \"manhattan,\" \"canberra,\" \"binary,\" or \"minkowski\"), seven linkage methods (\"ward.D,\" \"ward.D2,\" \"complete,\" \"average,\" \"mcquitty,\" \"median,\" or \"centroid\"), and nine cluster numbers (2\u0026ndash;10). The Calinski-Harabasz Index (CHI) was used to evaluate the optimal hyperparameters. (3) After regreasing out confounding variables, we performed CCA to estimate the synthetic association between all variables in each cluster and each SWB factor score, followed by a permutation test (n\u0026thinsp;=\u0026thinsp;1000) to assess the significance of each CCA model (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Bonferroni corrected).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePathway analyses between SWB, urbanization, brain and mental health\u003c/h3\u003e\n\u003cp\u003eUsing the \u0026ldquo;processR\u0026rdquo; R package\u003csup\u003e120\u003c/sup\u003e, a sequential mediation analysis (Model 6) was conducted to identify potential causal pathways from urbanization to brain, to subjective well-being (SWB), and finally to mental health. In this model, urban living canonical variables (Urban) were treated as the exposure (X), brain imaging canonical variables (IMA) as the first mediator (M1), SWB factors as the second mediator (M2), and mental health canonical variables as the outcome (Y). Additionally, a moderated mediation analysis (Model 59) was performed to explore whether SWB could act as a moderator in the causal pathway from urbanization (exposure) to brain (mediator) to mental health (outcome). To enhance interpretation, each mental health (MH) canonical variable was categorized into three levels: low (0), medium (1), and severe (2) mental symptoms. Given the ordinal nature of the outcome variable, a diagonally weighted least squares (DWLS) model was employed to estimate the model parameters\u003csup\u003e121\u003c/sup\u003e. Furthermore, a bias-corrected and accelerated (BCa) bootstrapping method (n\u0026thinsp;=\u0026thinsp;1000) was used to estimate the mediation effects and their 95% confidence intervals (95%CI). For the moderated medication analysis, if a significant moderation effect was detected, post-hoc analyses were conducted to estimate the mediation effects within sub-populations scoring below the 16th percentile and above the 84th percentile of SWB scores, respectively.\u003c/p\u003e \u003cdiv id=\"Sec31\" class=\"Section2\"\u003e \u003ch2\u003eSurvival analysis on the protective effect of SWB against common mental disorders\u003c/h2\u003e \u003cp\u003eTo facilitate explanation and interpretation, SWB factor scores were categorized into six levels, with 1 representing \"extremely unhappy\" and 6 representing \"extremely happy\". Taken the \u0026ldquo;extremely unhappy\u0026rdquo; level as the reference, Cox proportional hazards regression models were applied to evaluate the predictive potential of SWB levels for the future occurrence of at least one of 10 common mental disorders classified under ICD-10, adjusted for confounders, using \u0026ldquo;coxph\u0026rdquo; function from the \u0026ldquo;survival\u0026rdquo; package. Subsequently, we used the \u0026ldquo;survfit\u0026rdquo; function from the \u0026ldquo;survminer\u0026rdquo; package to plot the Kaplan-Meier survival curves for each SWB level, providing a visualization of overall mental disorder progression over time. We repeated the Cox regression analysis to assess the disease-specific preventive effects of SWB levels on each mental disorder.\u003c/p\u003e \u003cp\u003eTo test the joint protective role of ISWB and SSWB, we split the populations into four subgroups based on the median split of the two SWB scores: individuals with low ISWB and low SSWB, low ISWB and high SSWB, high ISWB and low SSWB, and high ISWB and high SSWB. With the low ISWB and low SSWB subgroup as the reference, Cox proportional hazards regression models were applied to quantify the protective effects of each SWB subgroup. Additionally, the 10-year (3650 days) conversion prevalence of overall mental disorders was estimated for each SWB subgroup.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThis study was supported by the National Natural Science Foundation of China (No. 82472052 [Wen Qin], No. 82430063 [Chunshui Yu], No. 81971599 [Wen Qin], No. 82030053 [Chunshui Yu], No. 82371924 [Jiayuan Xu]), National Key Research and Development Program of China (No. 2018YFC1314300 [Chunshui Yu]), National Key Project of \"Inter-governmental International Scientific and Technological Innovation Cooperation\" (No. 2023YFE0199700 [Jiayuan Xu]), Natural Science Foundation of Tianjin City(19JCYBJC25100 [Wen Qin]) and Tianjin Key Medical Discipline (Specialty) Construction Project (TJYXZDXK-001A [Chunshui Yu]), the Tianjin Young Talents in Science and Technology (No. QN20230336 [Jiayuan Xu]), the Tianjin Applied Basic Research Diversified Investment Foundation (No. 21JCYBJC01360 [Jiayuan Xu]), Science\u0026amp;Technology Development Fund of Tianjin Education Commission for Higher Education (No. 2019KJ195 [Jiayuan Xu]) and the Tianjin Medical University \"Clinical Talent Training 123 Climbing Plan\" [Jiayuan Xu], China Postdoctoral Science Foundation (2023M742623 [Nana Liu]). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. We thank all the participants and professionals contributing to the UK Biobank.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCrisp, R. \u003cem\u003eAristotle: nicomachean ethics\u003c/em\u003e. (Cambridge University Press, 2014).\u003c/li\u003e\n\u003cli\u003eDiener, E. Subjective well-being: The science of happiness and a proposal for a national index. \u003cem\u003eAmerican Psychologist\u003c/em\u003e \u003cstrong\u003e55\u003c/strong\u003e, 34-43 (2000). https://doi.org:10.1037/0003-066x.55.1.34\u003c/li\u003e\n\u003cli\u003eDiener, E., Oishi, S. \u0026amp; Tay, L. Advances in subjective well-being research. \u003cem\u003eNat Hum Behav\u003c/em\u003e \u003cstrong\u003e2\u003c/strong\u003e, 253-260 (2018). https://doi.org:10.1038/s41562-018-0307-6\u003c/li\u003e\n\u003cli\u003eDiener, E. \u0026amp; Chan, M. Y. 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The performance of ML, DWLS, and ULS estimation with robust corrections in structural equation models with ordinal variables. \u003cem\u003ePsychol Methods\u003c/em\u003e \u003cstrong\u003e21\u003c/strong\u003e, 369-387 (2016). https://doi.org:10.1037/met0000093\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5794364/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5794364/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe pursuit of happiness is a lifelong endeavor for everyone; nevertheless, elucidating its etiology, neurobiological substrates, and implications for mental health continues to pose significant challenges in contemporary research. This study sought to delineate the causal relationships among subjective well-being (SWB), urbanization, brain, and mental health, and to explore the protective role of SWB against prevalent psychiatric disorders. Utilizing data from 198,823 adults in the UK Biobank, including SWB questionnaires (five items), urban living environments (121 variables), neuroimaging data (2,413 measures), mental health assessments (39 indicators), and ICD-10 psychiatric diagnoses (10 disorders), we initially identified two robust SWB components using ten-fold cross-validated factor analysis: internal subjective well-being (ISWB) and social subjective well-being (SSWB). Phenome-wide association studies (PheWAS) revealed significant associations between urbanization variables and both ISWB (78/121) and SSWB (59/121); between neuroimaging indicators and both ISWB (416/2,413 measures) and SSWB (77/2,413); and between mental health assessments and both ISWB (38/39 indicators) and SSWB (37/39) (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Bonferroni corrected). Sequential mediation analysis uncovered 28 causal pathways from urbanization to brain to SWB to mental health (ISWB: 16 pathways, SSWB: 12 pathways), while the moderated mediation analysis revealed 19 pathways where SWB significantly moderated the urbanization \u0026rarr; brain \u0026rarr; mental health pathways (14 for ISWB, 5 for SSWB). Finally, Cox proportional hazards survival analysis demonstrated that individuals in the highest ISWB sextile had a 76% reduction in the overall risk of developing 10 mental disorders compared with those in the lowest sextile (Z = -29.49, Hazard Ratio [HR]\u0026thinsp;=\u0026thinsp;0.24, P\u0026thinsp;=\u0026thinsp;3.93e-191), and SSWB showed a 36% risk reduction (Z = -9.42, HR\u0026thinsp;=\u0026thinsp;0.64, P\u0026thinsp;=\u0026thinsp;4.50e-2). Moreover, both SWB components demonstrated the highest protective effects against depression (ISWB: HR\u0026thinsp;=\u0026thinsp;0.13, SSWB: HR\u0026thinsp;=\u0026thinsp;0.39). By systematically uncovering the causal pathways through which SWB components differentially participate in the regulation of urban living environments on the human brain, thereby affecting mental health, this study thus provides biological evidence and modifiable SWB indicators for the prevention of common psychiatric disorders.\u003c/p\u003e","manuscriptTitle":"Subjective Well-being: A Key to Bridge Urbanization, Brain and Mental Health","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-10 08:49:26","doi":"10.21203/rs.3.rs-5794364/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e6b9d180-0d39-4039-b640-92257cf4c6f3","owner":[],"postedDate":"February 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":44005123,"name":"Biological sciences/Psychology"},{"id":44005124,"name":"Health sciences/Diseases/Psychiatric disorders"},{"id":44005125,"name":"Scientific community and society/Social sciences/Interdisciplinary studies"}],"tags":[],"updatedAt":"2025-03-21T16:00:45+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-10 08:49:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5794364","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5794364","identity":"rs-5794364","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}