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Genetic correlates of socio-economic status influence the pattern of shared heritability across mental health traits.

ABSTRACT: Epidemiological studies show high comorbidity between different mental health problems, indicating that individuals with a diagnosis of one disorder are more likely to develop other mental health problems. Genetic studies reveal substantial sharing of genetic factors across mental health traits. However, mental health is also genetically correlated with socio-economic status (SES), and it is therefore important to investigate and disentangle the genetic relationship between mental health and SES. We used summary statistics from large genome-wide association studies (average N ~ 160,000) to estimate the genetic overlap across nine psychiatric disorders and seven substance use traits and explored the genetic influence of three different indicators of SES. Using genomic structural equation modelling, we show significant changes in patterns of genetic correlations after partialling out SES-associated genetic variation. Our approach allows the separation of disease-specific genetic variation and genetic variation shared with SES, thereby improving our understanding of the genetic architecture of mental health.

SUBMITTER: Marees AT

PROVIDER: S-EPMC8376746 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature

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Genetic correlates of socio-economic status influence the pattern of shared heritability across mental health traits.

Marees Andries T AT Smit Dirk J A DJA Abdellaoui Abdel A Nivard Michel G MG van den Brink Wim W Denys Damiaan D Galama Titus J TJ Verweij Karin J H KJH Derks Eske M EM

Nature human behaviour 20210308 8

Epidemiological studies show high comorbidity between different mental health problems, indicating that individuals with a diagnosis of one disorder are more likely to develop other mental health problems. Genetic studies reveal substantial sharing of genetic factors across mental health traits. However, mental health is also genetically correlated with socio-economic status (SES), and it is therefore important to investigate and disentangle the genetic relationship between mental health and SES ...[more]

PMID: 33686200

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Project description:There is a growing body of literature suggesting that the arts can support mental health. However, both arts participation and cultural engagement are unevenly patterned across the population, with a strong social gradient. This social gradient is also evident in mental health. So it remains unclear whether the relationship between arts engagement and mental health can in fact be explained by socio-economic status (SES). This study explores this question specifically in relation to cultural engagement (e.g. visiting museums/galleries/cinema/theatre/concerts) using data from 8780 adults aged 50 + from the English Longitudinal Study of Ageing. We used a statistical triangulation approach, running three separate sets of analyses that each have different strengths and address different statistical limitations or biases. Using logistic regression, the relationship between cultural engagement and mental health was still present when including covariates relating to SES, and there was no evidence of moderation by SES either through the inclusion of interaction terms or stratification. Using propensity score matching, matching participants based on their SES, we also consistently found evidence of the relationship. Finally, using fixed-effects regression which takes account of all time-invariant factors (which include multiple aspects of SES) even if unobserved, we also found no attenuation of the relationship. Overall, this confirms previous reports that cultural engagement is linked with a lower odds of depression amongst adults aged 50 + by demonstrating a robust association in a nationally-representative sample of older adults. While SES does explain around half of the association between cultural engagement and depression, we found no evidence that it either acts as a moderator or the main explanatory factor, with independent associations maintained across all three approaches. However, the fact that higher SES is associated with more frequent engagement indicates that, in population terms, SES is still an important determinant of the salutogenic impact of culture.

Project description:Since its outbreak, COVID-19 disease has claimed over one hundred thousand lives in the United States, resulting to multiple and complex nation-wide challenges. In this study, we employ global and local regression models to assess the influence of socio-economic and health conditions on COVID-19 mortality in contiguous USA. For a start, stepwise and exploratory regression models were employed to isolate the main explanatory variables for COVID-19 mortality from the ensemble 33 socio-economic and health parameters between January 1st and 16th of September 2020. Preliminary results showed that only five out of the examined variables (case fatality rate, vulnerable population, poverty, percentage of adults that report no leisure-time physical activity, and percentage of the population with access to places for physical activity) can explain the variability of COVID-19 mortality across the Counties of contiguous USA within the study period. Consequently, we employ three global and two local regression algorithms to model the relationship between COVID-19 and the isolated socio-economic and health variables. The outcomes of the regression analyses show that the adopted models can explain 61%-81% of COVID-19 mortality across the contiguous USA within the study period. However, MGWR yielded the highest R2 (0.81) and lowest AICc values (4031), emphasizing that it is the most efficient among the adopted regression models. The computed average adjusted R2 values show that local regression models (mean adj. R2 = 0.80) outperformed the global regression models (mean adj. R2 = 0.64), indicating that the former is ideal for modeling spatial causal relationships. The GIS-based optimized cluster analyses results show that hotspots for COVID-19 mortality as well as socioeconomic variables are mostly delineated in the South, Mid-West and Northeast of contiguous USA. COVID-19 mortality exhibited positive and significant association with black race (0.51), minority (0.48) and poverty (0.34). Whereas, the percentage of persons that attended college was negatively associated with poverty (-0.51), obesity (-0.50) and diabetes (-0.45). Results show that education is crucial to improve socio-economic and health conditions of the Americans. We conclude that investing in people's standard of living would reduce the vulnerability of an entire population.

Project description:BackgroundWe sought to determine whether socio-economic status (SES) is an independent predictor of outcome following total knee (TKR) and hip (THR) replacement in Australians.MethodsIn this prospective cohort study, we included patients undergoing TKR and THR in a public hospital in whom baseline and 12-month follow-up data were available. SES was determined using the Australian Bureau of Statistics 'Index of Relative Advantage and Disadvantage'. Other independent variables included patients' demographics, comorbidities and procedure-related variables. Outcome measures were the International Knee Society Score and Harris Hip Score pain and function subscales, and the Short Form Health Survey (SF-12) physical and mental component scores.ResultsAmong 1,016 patients undergoing TKR and 835 patients undergoing THR, in multiple regression analysis, SES score was not independently associated with pain and functional outcomes. Female sex, older age, being a non-English speaker, higher body mass index and presence of comorbidities were associated with greater post-operative pain and poorer functional outcomes following arthroplasty. Better baseline function, physical and mental health, and lower baseline level of pain were associated with better outcomes at 12 months. In univariate analysis, for TKR, the improvement in SF-12 mental health score post arthroplasty was greater in patients of lower SES (3.8 ± 12.9 versus 1.5 ± 12.2, p=0.008), with a statistically significant inverse association between SES score and post-operative SF-12 mental health score in linear regression analysis (coefficient-0.28, 95% CI: -0.52 to -0.04, p=0.02).ConclusionsWhen adjustments are made for other covariates, SES is not an independent predictor of pain and functional outcome following large joint arthroplasty in Australian patients. However, relative to baseline, patients in lower socioeconomic groups are likely to have greater mental health benefits with TKR than more privileged patients. Large joint arthroplasty should be made accessible to patients of all SES.

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Genetic correlates of socio-economic status influence the pattern of shared heritability across mental health traits.

Publications

Genetic correlates of socio-economic status influence the pattern of shared heritability across mental health traits.

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