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SES and Anxiety Symptoms in Emerging Adulthood: Gene-Environment Correlation (rGE) and Interaction (G x E)
No full textAnxiety is one of the most common mental health conditions and has been increasing in young adults in the UK (Dykxhoorn et al. 2025). One reason for this increase could be the challenging socio-economic climate young people are now facing. This is supported by previous research which found significant associations between SES indicators and mental health problems among emerging adults in Europe (Bacigalupe, Esnaola, & Martın, 2016; Fiori et al., 2016).
There are two key theories seeking to explain the relationship between SES and mental health: social selection and social causation (Dohrenwend et al. 1992). The social selection hypothesis proposes that individuals with health conditions are more likely to drift into or remain in lower SES because of reduced employment (Marwaha & Johnson, 2004), discrimination (Ameri et al. 2015), reduced economic and educational productivity (Haas, 2006), as a result of their health conditions during the critical developmental stage. The social causation hypothesis argues that exposures to socio-economic environmental stressors associated with low SES (e.g., childhood maltreatment, lack of access to medical care/health insurance) increases the risk for developing physical and mental health conditions (Dohrenwend et al. 1992).
Extensive evidence has shown that gene-environmental interplay might have contributed to the process of social selection and causation. Genetic influences on mental health conditions could shape individuals’ likelihood of exposing to certain environments (rGE) (Plomin, DeFries & Loehlin, 1977; Rutter et al. 2006). For example, previous research has found genetic correlations between education levels and anxiety (Tambs et al. 2011; Yuan et al., 2021). In the context of social selection theory, we might be able to explain the associations between anxiety and SES in emerging adults by rGE, that individuals with anxiety would often find themselves prone to SES disadvantages because they were genetically predisposed to do so (South & Krueger, 2011). Alternatively, genetic influences on mental health conditions might be amplified or attenuated corresponding to the levels of exposure to a certain environmental trait (GxE) (Plomin, DeFries & Loehlin, 1977; Jaffee & Price, 2007), explaining the social causation theory that SES stressors might increase the risk for developing anxiety symptoms (South, Hamdi & Krueger, 2017).
To date, several studies have examined relationships between SES and mental health psychopathology through the approach of gene-environment interplay, but very few specifically focused on anxiety. Existing biometrical evidence indicates a potential joint mechanism of social selection and social causation underlying the association between SES and mental health conditions. Studies examine the rGE between SES and mental health conditions have found shared genetic basis (Badini et al. 2023; Garrison & Lee Rodgers, 2019; Tambs et al. 2011; Yuan et al., 2021). For anxiety specifically, one study found a significant genetic overlaps between internalizing disorders (including Generalized Anxiety Disorders) and SES, could be suggesting a form of rGE (South & Krueger, 2011). Similarly, another study found substantial genetic correlations between anxiety disorder and a range of SES indicators such as household income and educational attainment (Marees et al. 2021). Moreover, two previous studies found that individuals who are genetically predisposed to higher education levels also tend to be at greater risk of developing anxiety disorders (Tambs et al. 2011; Yuan et al., 2021). This association is explained primarily by common genetic factors rather than shared environments within twin pairs (Tambs et al. 2011).
To our knowledge, no study to date has focused on gene-environment interaction between SES and anxiety symptoms specifically. Supporting a GxE interpretation, a US study found that low household income amplified non-shared environmental influences on internalizing syndromes (including GAD), while the genetic variance in internalizing syndromes increased across low to high levels of income (South & Krueger, 2011). This finding suggests that economic hardship moderates genetic effects on the aetiology of internalizing syndromes and therefore make genetic effects easier to detect in enriched environments (South & Krueger, 2011). Consistent with this, a Sri Lanka study found that individuals with lower SES exhibited greater variability depression symptoms. This association arose because depression-specific familial and nonshared environmental influences were stronger in low-SES contexts, rather than due to influences that overlap with SES (Badini et al. 2023).
Previous research using the MZ differences approach found that after controlling for familial influences (i.e. shared genetics and environment), within-twin pair associations remained significant only for composite SES indicators, NEET (Not in Education, Employment or Training) and SES composite score. This finding potentially suggests that the phenotypic associations between individual SES indicators and anxiety symptoms were due to shared genetic and environmental influences. Although attenuated in magnitude, the phenotypic association between SES composite score and anxiety symptoms remained within-twin pairs, suggesting that the association was not solely confounded by genetic or environmental influences shared with anxiety. The aims of the present study therefore are:
To examine the extent to which associations between individual and composite SES indicators and concurrent anxiety symptoms reflect shared genetic and environmental influences (rGE) at ages 21 and 26, and to determine whether these associations persist after accounting for such shared aetiology.
To explore whether composite SES indicators associated with concurrent anxiety symptoms at age 21 and 26, moderate genetic and environmental influences on anxiety through the form of GxE
Operationalizing Ethical AI
No full textThis paper proposes an empirically grounded framework for evaluating the ethical performance of AI-based decision support systems in high stakes institutional contexts. Drawing on sociotechnical systems theory, it reframes AI ethics as observable human–AI interaction patterns. Ethical risks such as accountability gaps, bias, over-reliance and responsibility diffusion are operationalized through measurable indicators derived from simulations and human-in-the-loop experiments. The framework enables continuous, data-driven assessment of ethical performance and supports adaptive governance in real AI deployments
Anthropomorphic Design Influences Purchase Intention: Mediating and Moderating
No full textThis OSF project contains the survey data and related materials used in the study titled "How Anthropomorphic Design Influences Purchase Intention: The Mediating Role of Cognitive Trust and the Differential Moderating Effect of AI Identity Disclosure." The dataset includes responses to the questionnaire measuring constructs such as anthropomorphic design, cognitive trust, AI identity disclosure, and purchase intention
Impact of pesticide use on gut microbiota and health: a sys-tematic review of findings in humans and animal models
No full textThis work is a systematic review of the impact of pesticide use on human health. This review integrates systematic reviews of observational studies conducted in humans and also experimental studies carried out in animal models with genetic, biological or behavioral similarities to humans, and offers a translational perspective on this important public health issue
A Generalized Symmetric Group
No full textWe formalize a generalization of the classical symmetric group by replacing point-wise bijections of a set with bijections acting on fixed-measure subsets
Smart Dumbbells: AI-Driven Innovation for Smarter,Personalized Workout
No full textSmart dumbbells combine traditional strength-training equipment with digital technologies to improve fitness and health monitoring. Embedded sensors measure parameters such as repetitions, weight, speed, force, and range of motion. Real-time data is transmitted via Bluetooth or Wi-Fi to mobile applications for performance tracking and feedback. This system supports personalized workouts and is suitable for home fitness, rehabilitation, and remote health monitoring
Affinity voting in Europe: The impact of religion, migration background and gender on preferences for in-group politicians
No full textTo what extent does sharing the same religion, migration background and gender versus policy positions impact voting? Most evidence comes from the US and majority populations, but European minorities may respond differently. In this study, respondents from France, Germany and the Netherlands choose between profiles of hypothetical politicians with randomised politician religion, migration background, gender and policy positions. Oversampling voters with a migration background (N=1,889/3,058), among which a portion identifies as Muslim (N=649/3,058), reveals that instead of minorities, majorities are just as, if not more, likely to engage in affinity voting. Shared religion is the most influential affinity impacting Muslim and non-religious voters. Sharing the same migration background or gender has no positive impact on voting likelihood; on the contrary, the findings suggest that voters with a migration background tend to prefer politicians without a migration background. Non-religious voters exhibit an in-group preference both when voter and politician agree but especially when voter and politician disagree about policy. These findings reveal the electoral challenges to achieving diversity in politics and minority representation, particularly concerning the political inclusion of Muslims
Narrative Identity and Personality Functioning: Triangulating Traditional Narrative Analysis with Computational Linguistic Methods
No full textPre-registration document for the study: Narrative Identity and Personality Functioning: Triangulating Traditional Narrative Analysis with Computational Linguistic Methods. This project is theoretically underpinned by the life story model of identity pioneered by Dan McAdams (e.g., McAdams, 2011), which asserts that individuals gain unity and meaning from their lives by constructing internalised and evolving narratives of the self (i.e., life stories). Investigating identity and self is crucial, especially as disturbances in identity play a significant role in dysfunction (e.g., Bach & First, 2018). Accordingly, the overarching objective of this work is to improve understanding of self-processes (i.e., identity) in the context of personality functioning. Although some research has been conducted in the realm of narrative identity and personality functioning (e.g., see Lind et al., 2020), this has exclusively been done using traditional narrative analysis methods (i.e., manual annotation of life story data to extract narrative and structural themes) – as with almost all narrative identity studies. The exclusive use of standardised narrative analysis methods constrains the scalability of the findings and limits the scope of what can be understood about identity to a small range of measures. Thus, we propose to study narrative identity using computational linguistic methods, permitting larger scale, naturalistic analyses of identity leveraging a broad range of measurement tools. Importantly, we will use such computational methods to supplement, rather than replace, traditional methods. The primary aim of this study is to yield novel, ecologically valid, generalisable insights into the relationship between narrative identity and personality functioning from a new perspective. We have three central research objectives: 1) Examine interactions between narrative identity and personality functioning on a large scale using naturalistic life story data. 2) Triangulate traditional narrative analysis methods with automated linguistic analysis of life stories to better understand narrative identity using a broader range of measures. 3) Incorporate interpretable machine learning to identify complex non-linear and interactional relationships between narrative identity and personality functioning
Characterizing the hierarchical structure of eating pathology and related mental disorders in youths across multiple timepoints
No full textThe prevalence of mental disorders is high among youths, and comorbidities are common (Costello et al., 2011; Merikangas et al., 2010). The hierarchical taxonomy of psychopathology (HiTOP) is a promising approach to studying the co-occurrence of mental disorders by modeling their underlying latent dimensions (Cicero et al., 2024; Kotov et al., 2022). Although the HiTOP framework was initially proposed and validated in models of adult psychopathology, recent research suggests utility for this framework among youth (Forbes et al., 2024; Tackett, 2022).
However, development has not been critically integrated into HiTOP. In particular, the process in which the latent structure of youth mental disorders evolve over time is poorly understood. Similarly, eating disorder psychopathology has been less integrated into HiTOP models among youth. A prior study among adolescents showed that eating pathology is part of the Internalizing factor, consistent with the HiTOP model (Castellanos-Ryan et al., 2016; Forbes & Wright, 2024; Watson et al., 2022). However, a more recent study among youths suggested that eating pathology formed a unique factor outside of Internalizing (Forbes, 2024). The placement of eating pathology within HiTOP among youths, therefore, is unclear, and may be particularly sensitive to developmental influences, as etiological processes underlying eating pathology are developmentally sensitive (e.g., social exposure, pubertal timing) (Rodgers, 2015; Kaltiala-Heino et al., 2001), its onset demonstrates a sharp peak in adolescence (Uhlhaas, 2023; López-Gil et al., 2023), and its symptoms rarely present in isolation. Thus, incorporating eating pathology is critical in a developmentally-informed structural model of youth psychopathology.
On the other hand, many studies of structural models of mental disorders demonstrate strong homotypic stability (association of the same construct between timepoints) and weaker heterotypic stability (association of a construct with another construct between timepoints) of higher-order factors, such as Internalizing and Externalizing (Pocuca et al., 2023; Snyder et al., 2017; Oldehinkel & Ormel, 2023). However, developmental changes in lower-order factors are rarely investigated. Lower-level factors may exhibit distinct etiology and offer unique predictive values for future functioning and impairment (Michelini et al., 2019, 2022; Vize et al., 2025). Thus, it is important to characterize how development impacts the structure of mental disorders across all hierarchical levels in HiTOP. The need for studies, especially those using longitudinal designs is a particular gap. Advancement in this area of research can refine the etiological understanding of youth psychopathology via a developmental perspective.
This project aims to fill the two gaps highlighted above. We will examine the structure of eating pathology and related mental disorders among youth at multiple timepoints, from childhood into adolescence. Specifically, we will conduct extensive exploratory and confirmatory factor analyses of symptom-level data obtained via a semi-structured clinical interview in the Adolescent Brain Cognitive Development (ABCD) Study