Social Isolation, Social Interaction, and Neuroticism: A Mendelian Randomization Study

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Concurrently, observational studies in this domain encounter various hurdles, such as confounding variables and the issue of reverse causality. This study employed Two-sample Mendelian Randomization (TSMR) to explore the genetic basis of the causal relationship between social isolation, social interaction, and neuroticism. Methods Single nucleotide polymorphisms (SNPs) associated with social isolation and social interaction were extracted from an aggregated Genome-Wide Association Study (GWAS) dataset. Instrumental variables conforming to predetermined criteria were selected. The primary TSMR analysis was conducted using the Inverse Variance-Weighted (IVW) method, complemented by robustness checks through the Weighted Median, Weighted Mode, and MR Egger methods. Heterogeneity and pleiotropy tests were performed, along with sensitivity analyses, to enhance the precision and robustness of the results. Results Among five social engagement types analyzed, loneliness (IVW Odds Ratio per Standard Deviation change: 4.230; 95% Confidence Interval: 2.081–8.599; p <0.001) and loneliness (MTAG) (IVW Odds Ratio per Standard Deviation change: 1.670; 95% Confidence Interval: 1.314–2.122; p <0.001) demonstrated a statistically significant association with increased neuroticism risk. The remaining three social engagement types showed no significant association with neuroticism risk. Conclusion The findings suggest a causal relationship between loneliness and loneliness (MTAG) and a heightened risk of neuroticism, warranting further research to understand the underlying mechanisms. Social interaction Social isolation Neuroticism Mendelian randomization Causality Figures Figure 1 Figure 2 1. Introduction Neuroticism, characterized by emotional instability, anxiety, and low self-satisfaction, reflects an individual's perceived threats and disorder in their external environment (Martin et al., 1983 ). Studies have shown that individuals with high neuroticism are prone to experiencing negative emotions, including anxiety and depression (Lyon et al., 2021 ). Such individuals often exhibit psychological stress, unrealistic beliefs, excessive demands, and impulsivity (Friedman, 2019 ). The relationship between social engagement and neuroticism has been a subject of interest (Ozer and Benet-Martinez, 2006 ). Recent research underscores the significant influence of poor social networks and factors on mental health, particularly neuroticism (Mandelli et al., 2015 ). A notable correlation exists between the quality of social environment, the frequency of social activities, and neurotic traits. Factors such as diminished social support, reduced social activities, and heightened feelings of isolation contribute to elevated levels of neuroticism. However, it has also been observed that social participation can sometimes have a detrimental effect, challenging the notion that all social activities are beneficial. Nonaka et al. (Nonaka et al., 2019 ) reported instances where social engagement negatively impacted mental health, Adverse social experiences like social rejection or conflict may exacerbate neurotic tendencies. Consequently, various types of social engagement exert intricate influences on neuroticism. Furthermore, given the susceptibility of traditional epidemiological methods to unmeasured confounding factors and potential biases, a clear assessment of the causal relationship between distinct forms of social activity and neuroticism is challenging. Social interaction, encompassing both verbal and non-verbal exchanges, is a dynamic process fundamental to human connectivity (Yamashita et al., 2023 ). It significantly influences an individual's mental state, with positive interactions linked to improved mental health through providing support, validation, and resilience to stressors. Conversely, social isolation-characterized by reduced or absent social contact – is increasingly identified as a harmful factor for mental health. Extended periods of isolation correlate with numerous negative mental health outcomes, such as heightened risks of depression, anxiety, and cognitive decline (Hawkley and Cacioppo, 2010 ). These findings highlight the critical role of social connections in maintaining psychological health. Recent research indicates that social engagement can influence health via physiological pathways, thereby affecting the onset of diseases (Uchino, 2006 ). Nevertheless, research into how social interaction and isolation contribute to the development of neuroticism is an ongoing endeavor. Observational studies often face challenges with unmeasured confounders and reverse causality, complicating the assessment of causal relationships between social isolation, social interaction, and neuroticism (Taylor et al., 2023 ; Wang et al., 2023 ). To overcome these limitations, further evidence unaffected by potential confounding factors is required to elucidate the causal impacts of social isolation and social interaction on neuroticism. Mendelian randomization (MR) utilizes genetic variation as an instrumental variable to examine causal relationships between exposures and outcomes (Lv et al., 2023 ; Sekula et al., 2016 ). Due to alleles undergoing 'random independent assignment' during fertilization, akin to randomization in clinical trials, and the stability of genetic information post-fertilization, MR provides a robust approach to reduce confounding and reverse causality. This method enables more definitive conclusions regarding the causal relationships between exposure factors and outcomes (Davies et al., 2018b ). In this study, we applied the two-sample MR approach to explore potential causal associations between social isolation, social interaction, and neuroticism 2. Materials and Methods 2.1 Study design Our study is based on summary genetic data from previous studies and the IEU Open GWAS database in a European population using two-sample MR analyses to assess whether social isolation, social interaction (mainly included five categories: regular participation in pub/social club, regular attendance at sports club/gym, regular attendance at religious group, loneliness, and loneliness (MTAG)) is causally associated with neuroticism risk. The impact of social isolation and interaction on neuroticism was examined using single nucleotide polymorphisms (SNPs) significantly linked to these variables as instrumental variables (IVs). Three central hypotheses were posited (Davies et al., 2018b ): (1) A significant correlation exists between the IVs and both social isolation and interaction; (2) The IVs are not associated with confounders in the relationship between social isolation, interaction, and neuroticism; (3) The IVs exert their effect on the outcome solely through their association with social isolation and interaction, as depicted in Fig. 1 . Given the study's reliance on existing research and databases, the need for additional ethical approval or participant consent was obviated. 2.2 The data source of social isolation, social interaction Our research utilized Mendelian Randomization (MR), drawing on data from a recent Genome-Wide Association Study (GWAS) focused on social isolation and interaction within the U.K. Biobank cohort. This study encompassed 452,303 participants from the U.K. Biobank, all contributing individual-level data (Day et al., 2018 ). 2.3 The data source of neuroticism We used GWAS summary data on neuroticism from the IEU Open GWAS database, published in 2017, which encompasses 6,949,615 SNPs from 160,958 European participants (Nagel et al., 2018 ) ( https://gwas.mrcieu.ac.uk/datasets/ieu-a-118/ ). 2.4 Selection of instrumental variables To address the first essential hypothesis, known as the correlation hypothesis, IVs must exhibit significant correlation with exposure factors (Davies et al., 2018a ). Consequently, we selected SNPs surpassing the genome-wide significance threshold ( p < 5 × 10 − 8 ) for preliminary analysis (Wei et al., 2022 ). Subsequently, SNPs exhibiting linkage disequilibrium were excluded using the PLINK algorithm (r 2 threshold < 5 × 10 − 8 , window size = 5000kb) (Shen et al., 2021 ). To fulfill the second MR hypothesis, the independence hypothesis, we utilized the PhenoScannerV2 database ( http://www.phenoscanner.medschl.cam.ac.uk ) to assess each IV and its proxy traits, eliminating SNPs linked to confounders at an R 2 threshold greater than 0.8 (Kamat et al., 2019 ). Additionally, to confirm a robust correlation between IVs and exposure factors, we ensured that the F-statistic for each SNP exceeded 10, calculated using the formula F = β 2 /SE 2 (Zou et al., 2023 ). 2.5 Statistical analysis In our study, we utilized the TwoSampleMR package for MR analysis in R 4.2.1 software. To mitigate potential pleiotropic effects, we employed three MR methods - MR-Egger regression, the weighted median, and inverse-variance weighted (IVW) - to assess the causal relationship between social isolation, social interaction, and neuroticism. We designated the IVW method's results as the primary outcome, supplementing these with MR-Egger and weighted median estimates. The IVW method, a prevalent approach in MR studies, was the primary analysis tool due to its efficiency. To validate the robustness of our findings, we conducted additional analyses using the weighted median (WM) method and simple mode and weighted mode (Verbanck et al., 2018 ) in further sensitivity analyses. The IVW method provides consistent results only when incorporating all genetic variants. Although MR-Egger regression can identify and adjust for pleiotropy, its accuracy is relatively low (Bowden et al., 2015 ). The weighted median method offers precise estimates under the assumption that a minimum of 50% of the IVs are valid (Bowden et al., 2016 ). To ascertain the reliability of our results, we performed sensitivity analyses, including the Cochran's Q test and funnel plot, to detect horizontal pleiotropy and heterogeneity. We considered heterogeneity significant if the Cochran's Q statistic indicated p < 0.05. Additionally, MR-Egger Intercept tests were conducted to estimate horizontal pleiotropy (with p < 0.05 indicating significant pleiotropy) (Burgess and Thompson, 2017 ). A leave-one-out analysis was also executed to determine if any single SNP biased or influenced causality. A fundamental principle of MR asserts that Single Nucleotide Polymorphisms (SNPs) should not exhibit associations with multiple factors (vertical pleiotropy) or pathways (horizontal pleiotropy). To investigate directional pleiotropy, the MR Egger regression method was employed, with an intercept close to zero suggesting its absence. Cochran's Q test was also conducted to evaluate heterogeneity among Instrumental Variables (IVs) derived from various genetic variants, a key aspect in identifying pleiotropy. Furthermore, genetic variants used as instruments for studying social interaction and isolation were required to demonstrate a connection to the risk factor, as indicated by an F-statistic exceeding 10. In our study, F-statistics ranged between 30.07 and 106.77, as elaborated in Supplementary Table 1 . Additionally, a comprehensive array of sensitivity analyses was performed, including visual evaluations of scatter, forest, funnel, and leave-one-out plots, to reinforce the reliability of our results. 3. Results In our investigation of five potential risk factors (mainly included five categories: regular participation in pub/social club, regular attendance at sports club/gym, regular attendance at religious group, loneliness, and loneliness (MTAG)), while genetically predicted neuroticism was associated with loneliness and loneliness (MTAG), we found no association between other three types of social engagement and neuroticism, as detailed in Fig. 2 . Additionally, we extracted the results from the observational analysis for a comparative evaluation against the MR effect estimates. It should be noted that our observational data predominantly focus on overall social participation and lack of social support, rather than specific social behaviors. As a result, these findings provide a broad overview rather than a precise delineation of cause-and-effect relationships. We further repeated the analysis of the GWAS statistics, presenting these results in Fig. 2 . Loneliness was found (IVW: 4.23 per SD change; 95% CI: 2.08–8.60; p < 0.001), loneliness (MTAG) (IVW: 1.67 per SD change; 95% CI: 1.31–2.12; p < 0.001) and the risk of neuroticism, suggesting a harmful impact on neurotic traits. Conversely, other social factors such as frequent gym or sports club visits (IVW: 0.62 per SD change; 95% CI: 0.25–1.54; p = 0.301), regular pub or social club attendance (IVW: 1.43 per SD change; 95% CI: 0.75–2.74; p = 0.279), and consistent participation in religious groups (IVW: 0.73 per SD change; 95% CI: 0.27–1.94; p = 0.528) did not demonstrate a significant association with neuroticism. To affirm the robustness of our finding that loneliness adversely impacts and increases the risk of neuroticism, we executed a range of analytical procedures. The MR Egger intercept test for loneliness, which yielded an intercept of 0.0043, standard error of 0.0101, and a p -value of 0.682, suggested no horizontal pleiotropy. Meanwhile, the MR Egger intercept test for loneliness (MTAG), which yielded an intercept of 0.0056, standard error of 0.0089, and a p -value of 0.539, suggested no horizontal pleiotropy (see in Supplementary Table 4). Moreover, Cochran's Q statistic for loneliness (Q = 5.621; p = 0.846) and loneliness (MTAG) (Q = 6.240; p = 0.937) indicated a lack of significant heterogeneity among the instrumental SNPs' effects (detailed in Supplementary Table 3). Our sensitivity analyses uniformly reinforced the link between loneliness, loneliness (MTAG) and neuroticism. The MR-Egger regression revealed the odds ratio (OR) between loneliness and neuroticism is 1.944, 95% confidence interval (CI) is from 0.049 to 76.51, and a p -value is 0.731. Likewise, the weighted median method showed an OR of 3.884, with a 95% CI of 1.484 to 10.164, and a p -value of 0.006. MR-Egger regression also revealed the odds ratio (OR) between loneliness (MTAG) and neuroticism is 1.127, 95% confidence interval (CI) is from 0.325 to 3.904, and a p -value is 0.853. Likewise, the weighted median method showed an OR of 1.481, with a 95% CI of 1.080 to 2.033, and a p -value of 0.016 (see in Supplementary Table 2) . For a detailed review of all sensitivity analyses, including leave-one-out results and funnel plots, refer to Supplementary Fig. 1. Consequently, we propose that experiencing loneliness increases the risk of developing neuroticism. To corroborate this hypothesis, we utilized an additional GWAS statistic to explore the relationship between loneliness and neuroticism. 4. Discussion To our knowledge, this is the first study using genetic variation to investigate the causal relationship between social interaction, social isolation, and the risk of neuroticism employing MR. And the results genetically predicted neuroticism was associated with loneliness and loneliness (MTAG). However, little evidence was found to suggest that other factors related to social interaction and isolation causally influence neuroticism. The MR analysis findings corroborate the notion that loneliness and loneliness (MTAG) significantly contributes to neuroticism. We employed the IVW method as the primary analytical approach, complemented by MR-Egger and weighted median estimates. All SNPs included in our investigation demonstrated F-values exceeding 10, ensuring the absence of weak instrumental variable bias. The pleiotropy of SNPs was assessed through MR-Egger regression analysis, revealing no evidence of pleiotropy bias at the gene level within our analytical outcomes. Concurrently, no significant heterogeneity was detected in the heterogeneity analysis. Furthermore, the "leave-one-out" sensitivity analysis revealed no single SNP significantly influencing the estimation of the overall effect. Collectively, these analyses affirm the robustness and reliability of our research findings. Numerous observational studies have historically investigated the relationship between social isolation, social interaction and neuroticism. These studies predominantly examined the negative consequences of social participation rather than the advantages conferred by social status. Moreover, they lacked specificity regarding the types of social activities involved. Research indicates that frequent, positive social interactions, particularly with close partners, can mitigate neuroticism (Zhaoyang et al., 2022 ). Lower levels of social integration, characterized by infrequent real-world social interactions, smaller social networks, and weaker social connections, correlate with increased neuroticism (Kalish and Robins, 2006 ). Our findings are in line with the epidemiological association between neuroticism and loneliness. A meta-analysis indicated that higher loneliness levels are associated with increased neuroticism (Buecker et al., 2020 ). And a study linked lower social interaction frequency with reduced total brain volume and an increase in white matter damage (Hirabayashi et al., 2023 ), suggesting a direct impact of decreased social activity on brain health, potentially influencing conditions like neuroticism. Contrasting these findings, MR estimates on loneliness suggest a reverse effect, offering greater insights for developing strategies to prevent neuroticism. Additionally, our MR results also found no causal link between other three types of social engagement and neuroticism, providing a basis for further recommendations. The origins of loneliness and neuroticism can be traced to both biological and social factors. According to the social deficit model, loneliness arises from inadequate social relationships and support systems, potentially leading to emotional distress and psychological stress, thereby heightening the risk of developing neurotic traits (Hutten et al., 2021 ). Loneliness not only amplifies depressive symptoms but also augments perceived stress, anxiety, anger, the fear of negative judgment, and diminishes optimism and self-esteem, all of which exhibit a robust correlation with neuroticism (Cacioppo et al., 2006 ). Mechanistically, to elucidate the causal link between loneliness and neuroticism, three neurobiological hypotheses based on this model offer further insights. Firstly, research indicates that loneliness can activate the hypothalamic-pituitary-adrenal axis, intensify sympathetic nerve activity, hinder parasympathetic functioning, and provoke pro-inflammatory immune responses and oxidative stress (Li and Xia, 2020b ). Chronic inflammation and oxidative stress have been associated with a range of health issues, including mental health disorders. These physiological alterations can foster an environment conducive to the emergence or exacerbation of neurological symptoms (Li and Xia, 2020a ). Secondly, a potential biological connection between loneliness and neuroticism is suggested, with loneliness possibly contributing to alterations in brain structure and function that augment the likelihood of neurotic traits. This includes changes in areas crucial for emotional regulation and social cognition, such as the prefrontal cortex and amygdala. These alterations might lead to dysfunctions in these brain regions, impacting emotional and social information processing (Kong et al., 2015 ). Lastly, several studies have identified a significant role for neurotransmitters in the interplay between loneliness and neuroticism (Bowirrat et al., 2023 ; Gao et al., 2017 ). Neurotransmitters like dopamine and serotonin, crucial for mood regulation and mental health, may become imbalanced due to chronic loneliness, thus exacerbating neurotic traits. Consequently, these pathways suggest that loneliness intensifies the risk of neuroticism. Our results demonstrate a distinct genetic correlation at the genetic interpretation level. Research has demonstrated a notable genetic correlation between loneliness and neuroticism, quantified at 0.71, signifying a substantial genetic connection at the molecular level (Abdellaoui et al., 2019 ). Such a pronounced correlation suggests that loneliness and neuroticism manifestations might be influenced in part by shared genetic factors. Notably, genetic studies have identified specific gene variants impacting these traits, illustrating a profound genetic link. Employing a large sample, this research elucidated the genetic underpinnings of the loneliness-neuroticism relationship. Similarly, another investigation also revealed a strong genetic correlation between these traits (Gao et al., 2017 ). Further in-depth genetic analysis reinforced this conclusion, illuminating potential biological mechanisms, such as the role of specific gene variants in both loneliness and neuroticism. These findings imply that loneliness and neuroticism could share common genetic pathways or be influenced by similar genetic variants, underscoring the critical role of genetics in their interplay and offering insights into their biological foundations. Our study inevitably presents several limitations. Primarily, the participant pool comprised exclusively European individuals, necessitating additional research to ascertain the applicability of our findings across diverse populations. Secondly, a limited dataset of only 11 SNPs associated with loneliness and 14 SNPs associated with loneliness (MTAG) may undermine the robustness of causal inferences. The identification of additional independent genetic variants associated with this trait would substantiate our findings more convincingly. Lastly, despite employing various MR methods to address potential pleiotropy, the possibility of unaccounted confounding factors related to social interaction or isolation and neuroticism cannot be discounted, a recognized constraint of the MR methodology. Despite the aforementioned limitations, our study represents, to the best of our knowledge, the inaugural and most extensive MR analysis encompassing social interaction, social isolation, and neuroticism. The findings indicate that loneliness is a contributing factor to neuroticism, potentially offering a theoretical foundation for its treatment. Moreover, given the escalating prevalence of neuroticism and the absence of a definitive cure, mitigating loneliness emerges as a viable preventive approach, chiefly due to its feasibility and practicability. 5. Conclusion In this study, the MR method was adopted to assess the potential causal association between social interaction, social isolation, and neuroticism. Our results provide substantial evidence supporting a causal relationship between loneliness and neuroticism. These findings underscore the significance of enhancing active social engagement and reducing loneliness as preventive approaches against neuroticism. Future research should focus on elucidating the underlying mechanisms and developing potential therapeutic strategies to decrease the risk of neuroticism. Declarations Data availability Publicly available datasets were analyzed in this MR study. Funding This work is supported by National Natural Science Foundation of China (Grant No.81601330) and Natural Science Foundation of Hubei Province, China (Grant No.2022CFB178). Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Acknowledgment Not applicable. Appendix A. 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Supplementary Files SupplementaryFigure1.docx SupplementaryTable1.docx SupplementaryTable2.docx SupplementaryTable3.docx SupplementaryTable4.docx 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. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4168624","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":285382911,"identity":"b0e92236-c304-4338-a42c-ce029f261d65","order_by":0,"name":"Jinjin Guo","email":"","orcid":"","institution":"School of Nursing, Tongji Medical College, Huazhong University of Science and Technology, Wuhan","correspondingAuthor":false,"prefix":"","firstName":"Jinjin","middleName":"","lastName":"Guo","suffix":""},{"id":285382912,"identity":"84886595-6145-4738-af5c-fe29c2152cf7","order_by":1,"name":"Keqin Liu","email":"","orcid":"","institution":"School 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Su","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA30lEQVRIie3OsQrCMBCA4ZNCJ9E1HYo+wkEgLuK7hEKnIE7iJIKr6BrfoiA4HxTsUnEt1MHJyaHgoiBoxMkldnTIDyEZ7gsH4HL9bQjQZp9nY1abBNrcVJ+8VVGXYLbfsWo0lZtywa8V9MOEvPPJSvJhHGhM5faYC0YQ84T8HloJKVE2keS2UMIslsqEmj6zksNFlA80i2nFK4JnDWI+LwE9mTCFZjH6TYLi0rsvMOW6iMcsx4ivU19YSeugON4e03Clo001mQzCZTY/W0mXvvc0x7PNmzqzHwMul8vlghfL2lBhFjzPCQAAAABJRU5ErkJggg==","orcid":"","institution":"School of Nursing, Tongji Medical College, Huazhong University of Science and Technology, Wuhan","correspondingAuthor":true,"prefix":"","firstName":"Yanwei","middleName":"","lastName":"Su","suffix":""}],"badges":[],"createdAt":"2024-03-26 09:20:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4168624/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4168624/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54010890,"identity":"f09825ef-a1bd-4b87-af92-a54adc79ac2e","added_by":"auto","created_at":"2024-04-03 10:45:42","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":231006,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4168624/v1/926c948b457b08d82b1dd29d.jpg"},{"id":54010495,"identity":"4d073559-164e-45d5-9935-84f205b3a5ca","added_by":"auto","created_at":"2024-04-03 10:37:42","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":396810,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4168624/v1/d03d7e3344308dd8ad04e474.jpg"},{"id":55265644,"identity":"3dcf85a6-dba7-4bba-b5de-deecce967bba","added_by":"auto","created_at":"2024-04-25 02:11:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":496849,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4168624/v1/150235e4-084d-464f-af24-d35758f21cea.pdf"},{"id":54010500,"identity":"9c12374a-efd0-434f-92fe-db59d50e3f56","added_by":"auto","created_at":"2024-04-03 10:37:43","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":253687,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigure1.docx","url":"https://assets-eu.researchsquare.com/files/rs-4168624/v1/626e20decb9c472853782355.docx"},{"id":54011418,"identity":"9dee6b23-2dfc-4d75-a3d5-50130a6d8876","added_by":"auto","created_at":"2024-04-03 10:53:42","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":29396,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-4168624/v1/eec7863fb7a8eeb163aeb31c.docx"},{"id":54010499,"identity":"abb7df58-6308-4025-bb35-d3a25f671598","added_by":"auto","created_at":"2024-04-03 10:37:42","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":21171,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable2.docx","url":"https://assets-eu.researchsquare.com/files/rs-4168624/v1/b799716834ffa0b2ffdf7fc1.docx"},{"id":54011862,"identity":"4c6863ac-9a16-4b5b-b42d-238756222da8","added_by":"auto","created_at":"2024-04-03 11:01:42","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":17824,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable3.docx","url":"https://assets-eu.researchsquare.com/files/rs-4168624/v1/add7af747741b5e8b17392cf.docx"},{"id":54010893,"identity":"92cfab9d-03ce-4df5-8b6c-a33d5eb15fab","added_by":"auto","created_at":"2024-04-03 10:45:43","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":17092,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable4.docx","url":"https://assets-eu.researchsquare.com/files/rs-4168624/v1/681e84708261fd3b982ba5fe.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Social Isolation, Social Interaction, and Neuroticism: A Mendelian Randomization Study","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eNeuroticism, characterized by emotional instability, anxiety, and low self-satisfaction, reflects an individual's perceived threats and disorder in their external environment (Martin et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1983\u003c/span\u003e). Studies have shown that individuals with high neuroticism are prone to experiencing negative emotions, including anxiety and depression (Lyon et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Such individuals often exhibit psychological stress, unrealistic beliefs, excessive demands, and impulsivity (Friedman, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The relationship between social engagement and neuroticism has been a subject of interest (Ozer and Benet-Martinez, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Recent research underscores the significant influence of poor social networks and factors on mental health, particularly neuroticism (Mandelli et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). A notable correlation exists between the quality of social environment, the frequency of social activities, and neurotic traits. Factors such as diminished social support, reduced social activities, and heightened feelings of isolation contribute to elevated levels of neuroticism. However, it has also been observed that social participation can sometimes have a detrimental effect, challenging the notion that all social activities are beneficial. Nonaka et al. (Nonaka et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) reported instances where social engagement negatively impacted mental health, Adverse social experiences like social rejection or conflict may exacerbate neurotic tendencies. Consequently, various types of social engagement exert intricate influences on neuroticism. Furthermore, given the susceptibility of traditional epidemiological methods to unmeasured confounding factors and potential biases, a clear assessment of the causal relationship between distinct forms of social activity and neuroticism is challenging.\u003c/p\u003e \u003cp\u003eSocial interaction, encompassing both verbal and non-verbal exchanges, is a dynamic process fundamental to human connectivity (Yamashita et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). It significantly influences an individual's mental state, with positive interactions linked to improved mental health through providing support, validation, and resilience to stressors. Conversely, social isolation-characterized by reduced or absent social contact \u0026ndash; is increasingly identified as a harmful factor for mental health. Extended periods of isolation correlate with numerous negative mental health outcomes, such as heightened risks of depression, anxiety, and cognitive decline (Hawkley and Cacioppo, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). These findings highlight the critical role of social connections in maintaining psychological health. Recent research indicates that social engagement can influence health via physiological pathways, thereby affecting the onset of diseases (Uchino, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Nevertheless, research into how social interaction and isolation contribute to the development of neuroticism is an ongoing endeavor. Observational studies often face challenges with unmeasured confounders and reverse causality, complicating the assessment of causal relationships between social isolation, social interaction, and neuroticism (Taylor et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). To overcome these limitations, further evidence unaffected by potential confounding factors is required to elucidate the causal impacts of social isolation and social interaction on neuroticism.\u003c/p\u003e \u003cp\u003eMendelian randomization (MR) utilizes genetic variation as an instrumental variable to examine causal relationships between exposures and outcomes (Lv et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Sekula et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Due to alleles undergoing 'random independent assignment' during fertilization, akin to randomization in clinical trials, and the stability of genetic information post-fertilization, MR provides a robust approach to reduce confounding and reverse causality. This method enables more definitive conclusions regarding the causal relationships between exposure factors and outcomes (Davies et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018b\u003c/span\u003e). In this study, we applied the two-sample MR approach to explore potential causal associations between social isolation, social interaction, and neuroticism\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study design\u003c/h2\u003e \u003cp\u003eOur study is based on summary genetic data from previous studies and the IEU Open GWAS database in a European population using two-sample MR analyses to assess whether social isolation, social interaction (mainly included five categories: regular participation in pub/social club, regular attendance at sports club/gym, regular attendance at religious group, loneliness, and loneliness (MTAG)) is causally associated with neuroticism risk. The impact of social isolation and interaction on neuroticism was examined using single nucleotide polymorphisms (SNPs) significantly linked to these variables as instrumental variables (IVs). Three central hypotheses were posited (Davies et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018b\u003c/span\u003e): (1) A significant correlation exists between the IVs and both social isolation and interaction; (2) The IVs are not associated with confounders in the relationship between social isolation, interaction, and neuroticism; (3) The IVs exert their effect on the outcome solely through their association with social isolation and interaction, as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Given the study's reliance on existing research and databases, the need for additional ethical approval or participant consent was obviated.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 The data source of social isolation, social interaction\u003c/h2\u003e \u003cp\u003eOur research utilized Mendelian Randomization (MR), drawing on data from a recent Genome-Wide Association Study (GWAS) focused on social isolation and interaction within the U.K. Biobank cohort. This study encompassed 452,303 participants from the U.K. Biobank, all contributing individual-level data (Day et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 The data source of neuroticism\u003c/h2\u003e \u003cp\u003eWe used GWAS summary data on neuroticism from the IEU Open GWAS database, published in 2017, which encompasses 6,949,615 SNPs from 160,958 European participants (Nagel et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://gwas.mrcieu.ac.uk/datasets/ieu-a-118/\u003c/span\u003e\u003cspan address=\"https://gwas.mrcieu.ac.uk/datasets/ieu-a-118/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Selection of instrumental variables\u003c/h2\u003e \u003cp\u003eTo address the first essential hypothesis, known as the correlation hypothesis, IVs must exhibit significant correlation with exposure factors (Davies et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018a\u003c/span\u003e). Consequently, we selected SNPs surpassing the genome-wide significance threshold (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;5 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e) for preliminary analysis (Wei et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Subsequently, SNPs exhibiting linkage disequilibrium were excluded using the PLINK algorithm (r\u003csup\u003e2\u003c/sup\u003e threshold\u0026thinsp;\u0026lt;\u0026thinsp;5 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e, window size\u0026thinsp;=\u0026thinsp;5000kb) (Shen et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). To fulfill the second MR hypothesis, the independence hypothesis, we utilized the PhenoScannerV2 database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.phenoscanner.medschl.cam.ac.uk\u003c/span\u003e\u003cspan address=\"http://www.phenoscanner.medschl.cam.ac.uk\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to assess each IV and its proxy traits, eliminating SNPs linked to confounders at an R\u003csup\u003e2\u003c/sup\u003e threshold greater than 0.8 (Kamat et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Additionally, to confirm a robust correlation between IVs and exposure factors, we ensured that the F-statistic for each SNP exceeded 10, calculated using the formula F\u0026thinsp;=\u0026thinsp;β\u003csup\u003e2\u003c/sup\u003e/SE\u003csup\u003e2\u003c/sup\u003e (Zou et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Statistical analysis\u003c/h2\u003e \u003cp\u003eIn our study, we utilized the TwoSampleMR package for MR analysis in R 4.2.1 software. To mitigate potential pleiotropic effects, we employed three MR methods - MR-Egger regression, the weighted median, and inverse-variance weighted (IVW) - to assess the causal relationship between social isolation, social interaction, and neuroticism. We designated the IVW method's results as the primary outcome, supplementing these with MR-Egger and weighted median estimates. The IVW method, a prevalent approach in MR studies, was the primary analysis tool due to its efficiency. To validate the robustness of our findings, we conducted additional analyses using the weighted median (WM) method and simple mode and weighted mode (Verbanck et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) in further sensitivity analyses. The IVW method provides consistent results only when incorporating all genetic variants. Although MR-Egger regression can identify and adjust for pleiotropy, its accuracy is relatively low (Bowden et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The weighted median method offers precise estimates under the assumption that a minimum of 50% of the IVs are valid (Bowden et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo ascertain the reliability of our results, we performed sensitivity analyses, including the Cochran's Q test and funnel plot, to detect horizontal pleiotropy and heterogeneity. We considered heterogeneity significant if the Cochran's Q statistic indicated \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Additionally, MR-Egger Intercept tests were conducted to estimate horizontal pleiotropy (with \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicating significant pleiotropy) (Burgess and Thompson, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). A leave-one-out analysis was also executed to determine if any single SNP biased or influenced causality.\u003c/p\u003e \u003cp\u003eA fundamental principle of MR asserts that Single Nucleotide Polymorphisms (SNPs) should not exhibit associations with multiple factors (vertical pleiotropy) or pathways (horizontal pleiotropy). To investigate directional pleiotropy, the MR Egger regression method was employed, with an intercept close to zero suggesting its absence. Cochran's Q test was also conducted to evaluate heterogeneity among Instrumental Variables (IVs) derived from various genetic variants, a key aspect in identifying pleiotropy. Furthermore, genetic variants used as instruments for studying social interaction and isolation were required to demonstrate a connection to the risk factor, as indicated by an F-statistic exceeding 10. In our study, F-statistics ranged between 30.07 and 106.77, as elaborated \u003cb\u003ein Supplementary Table\u0026nbsp;1\u003c/b\u003e. Additionally, a comprehensive array of sensitivity analyses was performed, including visual evaluations of scatter, forest, funnel, and leave-one-out plots, to reinforce the reliability of our results.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eIn our investigation of five potential risk factors (mainly included five categories: regular participation in pub/social club, regular attendance at sports club/gym, regular attendance at religious group, loneliness, and loneliness (MTAG)), while genetically predicted neuroticism was associated with loneliness and loneliness (MTAG), we found no association between other three types of social engagement and neuroticism, as detailed \u003cb\u003ein\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Additionally, we extracted the results from the observational analysis for a comparative evaluation against the MR effect estimates. It should be noted that our observational data predominantly focus on overall social participation and lack of social support, rather than specific social behaviors. As a result, these findings provide a broad overview rather than a precise delineation of cause-and-effect relationships. We further repeated the analysis of the GWAS statistics, presenting these results \u003cb\u003ein\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eLoneliness was found (IVW: 4.23 per SD change; 95% CI: 2.08\u0026ndash;8.60; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), loneliness (MTAG) (IVW: 1.67 per SD change; 95% CI: 1.31\u0026ndash;2.12; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and the risk of neuroticism, suggesting a harmful impact on neurotic traits. Conversely, other social factors such as frequent gym or sports club visits (IVW: 0.62 per SD change; 95% CI: 0.25\u0026ndash;1.54; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.301), regular pub or social club attendance (IVW: 1.43 per SD change; 95% CI: 0.75\u0026ndash;2.74; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.279), and consistent participation in religious groups (IVW: 0.73 per SD change; 95% CI: 0.27\u0026ndash;1.94; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.528) did not demonstrate a significant association with neuroticism.\u003c/p\u003e \u003cp\u003eTo affirm the robustness of our finding that loneliness adversely impacts and increases the risk of neuroticism, we executed a range of analytical procedures. The MR Egger intercept test for loneliness, which yielded an intercept of 0.0043, standard error of 0.0101, and a \u003cem\u003ep\u003c/em\u003e-value of 0.682, suggested no horizontal pleiotropy. Meanwhile, the MR Egger intercept test for loneliness (MTAG), which yielded an intercept of 0.0056, standard error of 0.0089, and a \u003cem\u003ep\u003c/em\u003e-value of 0.539, suggested no horizontal pleiotropy \u003cb\u003e(see in Supplementary Table\u0026nbsp;4).\u003c/b\u003e Moreover, Cochran's Q statistic for loneliness (Q\u0026thinsp;=\u0026thinsp;5.621; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.846) and loneliness (MTAG) (Q\u0026thinsp;=\u0026thinsp;6.240; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.937) indicated a lack of significant heterogeneity among the instrumental SNPs' effects \u003cb\u003e(detailed in Supplementary Table\u0026nbsp;3).\u003c/b\u003e Our sensitivity analyses uniformly reinforced the link between loneliness, loneliness (MTAG) and neuroticism. The MR-Egger regression revealed the odds ratio (OR) between loneliness and neuroticism is 1.944, 95% confidence interval (CI) is from 0.049 to 76.51, and a \u003cem\u003ep\u003c/em\u003e-value is 0.731. Likewise, the weighted median method showed an OR of 3.884, with a 95% CI of 1.484 to 10.164, and a \u003cem\u003ep\u003c/em\u003e-value of 0.006. MR-Egger regression also revealed the odds ratio (OR) between loneliness (MTAG) and neuroticism is 1.127, 95% confidence interval (CI) is from 0.325 to 3.904, and a \u003cem\u003ep\u003c/em\u003e-value is 0.853. Likewise, the weighted median method showed an OR of 1.481, with a 95% CI of 1.080 to 2.033, and a \u003cem\u003ep\u003c/em\u003e-value of 0.016\u003cb\u003e(see in Supplementary Table\u0026nbsp;2)\u003c/b\u003e. For a detailed review of all sensitivity analyses, including leave-one-out results and funnel plots, refer to \u003cb\u003eSupplementary Fig.\u0026nbsp;1.\u003c/b\u003e\u003c/p\u003e \u003cp\u003eConsequently, we propose that experiencing loneliness increases the risk of developing neuroticism. To corroborate this hypothesis, we utilized an additional GWAS statistic to explore the relationship between loneliness and neuroticism.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eTo our knowledge, this is the first study using genetic variation to investigate the causal relationship between social interaction, social isolation, and the risk of neuroticism employing MR. And the results genetically predicted neuroticism was associated with loneliness and loneliness (MTAG). However, little evidence was found to suggest that other factors related to social interaction and isolation causally influence neuroticism. The MR analysis findings corroborate the notion that loneliness and loneliness (MTAG) significantly contributes to neuroticism.\u003c/p\u003e \u003cp\u003eWe employed the IVW method as the primary analytical approach, complemented by MR-Egger and weighted median estimates. All SNPs included in our investigation demonstrated F-values exceeding 10, ensuring the absence of weak instrumental variable bias. The pleiotropy of SNPs was assessed through MR-Egger regression analysis, revealing no evidence of pleiotropy bias at the gene level within our analytical outcomes. Concurrently, no significant heterogeneity was detected in the heterogeneity analysis. Furthermore, the \"leave-one-out\" sensitivity analysis revealed no single SNP significantly influencing the estimation of the overall effect. Collectively, these analyses affirm the robustness and reliability of our research findings.\u003c/p\u003e \u003cp\u003eNumerous observational studies have historically investigated the relationship between social isolation, social interaction and neuroticism. These studies predominantly examined the negative consequences of social participation rather than the advantages conferred by social status. Moreover, they lacked specificity regarding the types of social activities involved. Research indicates that frequent, positive social interactions, particularly with close partners, can mitigate neuroticism (Zhaoyang et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Lower levels of social integration, characterized by infrequent real-world social interactions, smaller social networks, and weaker social connections, correlate with increased neuroticism (Kalish and Robins, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Our findings are in line with the epidemiological association between neuroticism and loneliness. A meta-analysis indicated that higher loneliness levels are associated with increased neuroticism (Buecker et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). And a study linked lower social interaction frequency with reduced total brain volume and an increase in white matter damage (Hirabayashi et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), suggesting a direct impact of decreased social activity on brain health, potentially influencing conditions like neuroticism. Contrasting these findings, MR estimates on loneliness suggest a reverse effect, offering greater insights for developing strategies to prevent neuroticism. Additionally, our MR results also found no causal link between other three types of social engagement and neuroticism, providing a basis for further recommendations.\u003c/p\u003e \u003cp\u003eThe origins of loneliness and neuroticism can be traced to both biological and social factors. According to the social deficit model, loneliness arises from inadequate social relationships and support systems, potentially leading to emotional distress and psychological stress, thereby heightening the risk of developing neurotic traits (Hutten et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Loneliness not only amplifies depressive symptoms but also augments perceived stress, anxiety, anger, the fear of negative judgment, and diminishes optimism and self-esteem, all of which exhibit a robust correlation with neuroticism (Cacioppo et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Mechanistically, to elucidate the causal link between loneliness and neuroticism, three neurobiological hypotheses based on this model offer further insights. Firstly, research indicates that loneliness can activate the hypothalamic-pituitary-adrenal axis, intensify sympathetic nerve activity, hinder parasympathetic functioning, and provoke pro-inflammatory immune responses and oxidative stress (Li and Xia, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020b\u003c/span\u003e). Chronic inflammation and oxidative stress have been associated with a range of health issues, including mental health disorders. These physiological alterations can foster an environment conducive to the emergence or exacerbation of neurological symptoms (Li and Xia, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e). Secondly, a potential biological connection between loneliness and neuroticism is suggested, with loneliness possibly contributing to alterations in brain structure and function that augment the likelihood of neurotic traits. This includes changes in areas crucial for emotional regulation and social cognition, such as the prefrontal cortex and amygdala. These alterations might lead to dysfunctions in these brain regions, impacting emotional and social information processing (Kong et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Lastly, several studies have identified a significant role for neurotransmitters in the interplay between loneliness and neuroticism (Bowirrat et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Gao et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Neurotransmitters like dopamine and serotonin, crucial for mood regulation and mental health, may become imbalanced due to chronic loneliness, thus exacerbating neurotic traits. Consequently, these pathways suggest that loneliness intensifies the risk of neuroticism.\u003c/p\u003e \u003cp\u003eOur results demonstrate a distinct genetic correlation at the genetic interpretation level. Research has demonstrated a notable genetic correlation between loneliness and neuroticism, quantified at 0.71, signifying a substantial genetic connection at the molecular level (Abdellaoui et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Such a pronounced correlation suggests that loneliness and neuroticism manifestations might be influenced in part by shared genetic factors. Notably, genetic studies have identified specific gene variants impacting these traits, illustrating a profound genetic link. Employing a large sample, this research elucidated the genetic underpinnings of the loneliness-neuroticism relationship. Similarly, another investigation also revealed a strong genetic correlation between these traits (Gao et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Further in-depth genetic analysis reinforced this conclusion, illuminating potential biological mechanisms, such as the role of specific gene variants in both loneliness and neuroticism. These findings imply that loneliness and neuroticism could share common genetic pathways or be influenced by similar genetic variants, underscoring the critical role of genetics in their interplay and offering insights into their biological foundations.\u003c/p\u003e \u003cp\u003eOur study inevitably presents several limitations. Primarily, the participant pool comprised exclusively European individuals, necessitating additional research to ascertain the applicability of our findings across diverse populations. Secondly, a limited dataset of only 11 SNPs associated with loneliness and 14 SNPs associated with loneliness (MTAG) may undermine the robustness of causal inferences. The identification of additional independent genetic variants associated with this trait would substantiate our findings more convincingly. Lastly, despite employing various MR methods to address potential pleiotropy, the possibility of unaccounted confounding factors related to social interaction or isolation and neuroticism cannot be discounted, a recognized constraint of the MR methodology.\u003c/p\u003e \u003cp\u003eDespite the aforementioned limitations, our study represents, to the best of our knowledge, the inaugural and most extensive MR analysis encompassing social interaction, social isolation, and neuroticism. The findings indicate that loneliness is a contributing factor to neuroticism, potentially offering a theoretical foundation for its treatment. Moreover, given the escalating prevalence of neuroticism and the absence of a definitive cure, mitigating loneliness emerges as a viable preventive approach, chiefly due to its feasibility and practicability.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn this study, the MR method was adopted to assess the potential causal association between social interaction, social isolation, and neuroticism. Our results provide substantial evidence supporting a causal relationship between loneliness and neuroticism. These findings underscore the significance of enhancing active social engagement and reducing loneliness as preventive approaches against neuroticism. Future research should focus on elucidating the underlying mechanisms and developing potential therapeutic strategies to decrease the risk of neuroticism.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePublicly available datasets were analyzed in this MR study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work is supported by National Natural Science Foundation of China (Grant No.81601330) and Natural Science Foundation of Hubei Province, China (Grant No.2022CFB178).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of competing interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAppendix A. Supplementary data\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupplementary data to this article can be found online at Supplementary File.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbdellaoui A, Chen HY, Willemsen G, Ehli EA, Davies GE, Verweij KJH, Nivard MG, de Geus EJC, Boomsma DI, Cacioppo JT (2019) Associations between loneliness and personality are mostly driven by a genetic association with Neuroticism. J Pers 87:386\u0026ndash;397\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBowden J, Smith GD, Burgess S (2015) Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol 44:512\u0026ndash;525\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBowden J, Smith GD, Haycock PC, Burgess S (2016) Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator. 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Redox Biol 37\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLv WQ, He J, Shao JJ, Chen YX, Xia LJ, Zhang LJ (2023) Causal relationships between short-chain fatty acids and L-isoleucine biosynthesis and susceptibility and severity of COVID-19: Evidence from Mendelian randomization. J Infect 87:E16\u0026ndash;E18\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLyon KA, Elliott R, Ware K, Juhasz G, Brown LJE (2021) Associations between Facets and Aspects of Big Five Personality and Affective Disorders:A Systematic Review and Best Evidence Synthesis. J Affect Disord 288:175\u0026ndash;188\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMandelli L, Nearchou FA, Vaiopoulos C, Stefanis CN, Vitoratou S, Serretti A, Stefanis NC (2015) Neuroticism, social network, stressful life events: association with mood disorders, depressive symptoms and suicidal ideation in a community sample of women. 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J Alzheimers Dis 87:675\u0026ndash;684\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYamashita M, Abe T, Seino S, Nofuji Y, Sugawara Y, Shinkai S, Kitamura A, Fujiwara Y (2023) Role of personality traits in determining the association between social participation and mental health: A cross-sectional study in Japan. J Health Psychol 28:48\u0026ndash;60\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhaoyang R, Harrington KD, Scott SB, Graham-Engeland JE, Sliwinski MJ (2022) Daily Social Interactions and Momentary Loneliness: The Role of Trait Loneliness and Neuroticism. J Gerontol B Psychol Sci Soc Sci 77:1791\u0026ndash;1802\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZou ML, Zhang W, Shen LL, Xu Y, Zhu Y (2023) Causal association between inflammatory bowel disease and herpes virus infections: a two-sample bidirectional Mendelian randomization study. Front Immunol 14\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"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":"Social interaction, Social isolation, Neuroticism, Mendelian randomization, Causality","lastPublishedDoi":"10.21203/rs.3.rs-4168624/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4168624/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eNeuroticism, as a fundamental personality trait marked by emotional instability, anxiety, and a propensity for negative emotions, presents difficulties in elucidating its developmental underpinnings, especially in the context of its association with social factors. Concurrently, observational studies in this domain encounter various hurdles, such as confounding variables and the issue of reverse causality. This study employed Two-sample Mendelian Randomization (TSMR) to explore the genetic basis of the causal relationship between social isolation, social interaction, and neuroticism.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eSingle nucleotide polymorphisms (SNPs) associated with social isolation and social interaction were extracted from an aggregated Genome-Wide Association Study (GWAS) dataset. Instrumental variables conforming to predetermined criteria were selected. The primary TSMR analysis was conducted using the Inverse Variance-Weighted (IVW) method, complemented by robustness checks through the Weighted Median, Weighted Mode, and MR Egger methods. Heterogeneity and pleiotropy tests were performed, along with sensitivity analyses, to enhance the precision and robustness of the results.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAmong five social engagement types analyzed, loneliness (IVW Odds Ratio per Standard Deviation change: 4.230; 95% Confidence Interval: 2.081\u0026ndash;8.599; \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001) and loneliness (MTAG) (IVW Odds Ratio per Standard Deviation change: 1.670; 95% Confidence Interval: 1.314\u0026ndash;2.122; \u003cem\u003ep\u003c/em\u003e\u0026lt;0.001) demonstrated a statistically significant association with increased neuroticism risk. The remaining three social engagement types showed no significant association with neuroticism risk.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe findings suggest a causal relationship between loneliness and loneliness (MTAG) and a heightened risk of neuroticism, warranting further research to understand the underlying mechanisms.\u003c/p\u003e","manuscriptTitle":"Social Isolation, Social Interaction, and Neuroticism: A Mendelian Randomization Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-03 10:37:38","doi":"10.21203/rs.3.rs-4168624/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":"e866df95-e4b2-4863-8fff-675fb685b704","owner":[],"postedDate":"April 3rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-04-23T17:01:52+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-03 10:37:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4168624","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4168624","identity":"rs-4168624","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}