Socioeconomic inequalities in type 2 diabetes : mediation through status anxiety?

Published online: 02 October 2023-- Objectives: While status anxiety has received attention as a potential mechanism generating health inequalities, empirical evidence is still limited. Studies have been ecological and have largely focused on mental and not physical health outcomes. -- Methods: We conducted individual-level analyses to assess status anxiety (feelings of inferiority resulting from social comparisons) and resources (financial difficulties) as mediators of the relationship between socioeconomic status (SES) (education/occupation/employment status) and type 2 diabetes (T2D). We used cross-sectional data of 21,150 participants (aged 18–70 years) from the Amsterdam-based HELIUS study. We estimated associations using logistic regression models and estimated mediated proportions using natural effect modelling. -- Results: Odds of status anxiety were higher among participants with a low SES [e.g., OR = 2.66 (95% CI: 2.06–3.45) for elementary versus academic occupation]. Odds of T2D were 1.49 (95% CI: 1.12–1.97) times higher among participants experiencing status anxiety. Proportion of the SES–T2D relationship mediated was 3.2% (95% CI: 1.5%–7.0%) through status anxiety and 10.9% (95% CI: 6.6%–18.0%) through financial difficulties. -- Conclusion: Status anxiety and financial difficulties played small but consistent mediating roles. These individual-level analyses underline status anxiety’s importance and imply that status anxiety requires attention in efforts to reduce health inequalities

Understanding the impact of exposure to adverse socioeconomic conditions on chronic stress from a complexity science perspective

Background: Chronic stress increases chronic disease risk and may underlie the association between exposure to adverse socioeconomic conditions and adverse health outcomes. The relationship between exposure to such conditions and chronic stress is complex due to feedback loops between stressor exposure and psychological processes, encompassing different temporal (acute stress response to repeated exposure over the life course) and spatial (biological/psychological/social) scales. We examined the mechanisms underlying the relationship between exposure to adverse socioeconomic conditions and chronic stress from a complexity science perspective, focusing on amplifying feedback loops across different scales. Methods: We developed a causal loop diagram (CLD) to interpret available evidence from this perspective. The CLD was drafted by an interdisciplinary group of researchers. Evidence from literature was used to confirm/contest the variables and causal links included in the conceptual framework and refine their conceptualisation. Our findings were evaluated by eight independent researchers. Results: Adverse socioeconomic conditions imply an accumulation of stressors and increase the likelihood of exposure to uncontrollable childhood and life course stressors. Repetition of such stressors may activate mechanisms that can affect coping resources and coping strategies and stimulate appraisal of subsequent stressors as uncontrollable. We identified five feedback loops describing these mechanisms: (1) progressive deterioration of access to coping resources because of repeated insolvability of stressors; (2) perception of stressors as uncontrollable due to learned helplessness; (3) tax on cognitive bandwidth caused by stress; (4) stimulation of problem avoidance to provide relief from the stress response and free up cognitive bandwidth; and (5) susceptibility to appraising stimuli as stressors against a background of stress. Conclusions: Taking a complexity science perspective reveals that exposure to adverse socioeconomic conditions implies recurrent stressor exposure which impacts chronic stress via amplifying feedback loops that together could be conceptualised as one vicious cycle. This means that in order for individual-level psychological interventions to be effective, the context of exposure to adverse socioeconomic conditions also needs to be addressed

Inferring temporal dynamics from cross-sectional data using Langevin dynamics

Cross-sectional studies are widely prevalent since they are more feasible to conduct compared with longitudinal studies. However, cross-sectional data lack the temporal information required to study the evolution of the underlying dynamics. This temporal information is essential to develop predictive computational models, which is the first step towards causal modelling. We propose a method for inferring computational models from cross-sectional data using Langevin dynamics. This method can be applied to any system where the data-points are influenced by equal forces and are in (local) equilibrium. The inferred model will be valid for the time span during which this set of forces remains unchanged. The result is a set of stochastic differential equations that capture the temporal dynamics, by assuming that groups of data-points are subject to the same free energy landscape and amount of noise. This is a 'baseline' method that initiates the development of computational models and can be iteratively enhanced through the inclusion of domain expert knowledge as demonstrated in our results. Our method shows significant predictive power when compared against two population-based longitudinal datasets. The proposed method can facilitate the use of cross-sectional datasets to obtain an initial estimate of the underlying dynamics of the respective systems

Inferring temporal dynamics from cross-sectional data using Langevin dynamics

Cross-sectional studies are widely prevalent since they are more feasible to conduct compared to longitudinal studies. However, cross-sectional data lack the temporal information required to study the evolution of the underlying processes. Nevertheless, this is essential to develop predictive computational models which is the first step towards causal modelling. We propose a method for inferring computational models from cross-sectional data using Langevin dynamics. This method can be applied to any system that can be described as effectively following a free energy landscape, such as protein folding, stem cell differentiation and reprogramming, and social systems involving human interaction and social norms. A crucial assumption in our method is that the data-points are gathered from a system in (local) equilibrium. The result is a set of stochastic differential equations which capture the temporal dynamics, by assuming that groups of data-points are subject to the same free energy landscape and amount of noise. Our method is a 'baseline' method which initiates the development of computational models which can be iteratively enhanced through the inclusion of expert knowledge. We validate the proposed method against two population-based longitudinal datasets and observe significant predictive power in comparison with random choice algorithms. We also show how the predictive power of our 'baseline' model can be enhanced by incorporating domain expert knowledge. Our method addresses an important obstacle for model development in fields dominated by cross-sectional datasets.Comment: 17 pages, 3 figures, "The code for the proposed method is written in Mathematica programming language and is available at https://github.com/Pritha17/langevin-crosssectional

Lifestyle clusters related to type 2 diabetes and diabetes risk in a multi-ethnic population: The HELIUS study

Little is known about how health-related behaviours cluster across different populations and how lifestyle clusters are associated with type 2 diabetes (T2D) risk. We investigated lifestyle clusters and their association with T2D in a multi-ethnic population. 4396 Dutch, 2850 South-Asian Surinamese, 3814 African Surinamese, 2034 Ghanaian, 3328 Turkish, and 3661 Moroccan origin participants of the HELIUS study were included (2011–2015). K-medoids cluster analyses were used to identify lifestyle clusters. Logistic and cox regression analyses were performed to investigate the association of clusters with prevalent and incident T2D, respectively. Pooled analysis revealed three clusters: a ‘healthy’, ‘somewhat healthy’, and ‘unhealthy’ cluster. Most ethnic groups were unequally distributed: Dutch participants were mostly present in the ‘healthy’ cluster, Turkish and Moroccan participants in the ‘somewhat healthy’ cluster, while the Surinamese and Ghanaian participants were equally distributed across clusters. When stratified for ethnicity, analysis revealed three clusters per ethnic group. While the ‘healthy’ and ‘somewhat healthy’ clusters were similar to those of the pooled analysis, we observed considerable differences in the ethnic-specific ‘unhealthy’ clusters. Fruit consumption (3–4 days/week) was the only behaviour that was consistent across all ethnic-specific ‘unhealthy’ clusters. The pooled ‘unhealthy’ cluster was positively associated with prediabetes (OR: 1.34, 95%CI 1.21–1.48) and incident T2D (OR: 1.23, 95%CI 0.89–1.69), and negatively associated with prevalent T2D (OR: 0.80, 95%CI 0.69–0.93). Results were similar for most, but not all, ethnic-specific clusters. This illustrates that targeting multiple behaviours is relevant in prevention of T2D but that ethnic differences in lifestyle clusters should be taken into account

Using network analysis to identify leverage points based on causal loop diagrams leads to false inference.

Acknowledgements: This study was funded by the Netherlands Organization for Health Research and Development (ZonMw) under project number 531003015. We gratefully acknowledge Nina de Boer and Karoline Huth for their thoughtful comments on a previous version of this paper.Network analysis is gaining momentum as an accepted practice to identify which factors in causal loop diagrams (CLDs)-mental models that graphically represent causal relationships between a system's factors-are most likely to shift system-level behaviour, known as leverage points. This application of network analysis, employed to quantitatively identify leverage points without having to use computational modelling approaches that translate CLDs into sets of mathematical equations, has however not been duly reflected upon. We evaluate whether using commonly applied network analysis metrics to identify leverage points is justified, focusing on betweenness- and closeness centrality. First, we assess whether the metrics identify the same leverage points based on CLDs that represent the same system but differ in inferred causal structure-finding that they provide unreliable results. Second, we consider conflicts between assumptions underlying the metrics and CLDs. We recognise six conflicts suggesting that the metrics are not equipped to take key information captured in CLDs into account. In conclusion, using betweenness- and closeness centrality to identify leverage points based on CLDs is at best premature and at worst incorrect-possibly causing erroneous identification of leverage points. This is problematic as, in current practice, the results can inform policy recommendations. Other quantitative or qualitative approaches that better correspond with the system dynamics perspective must be explored

How exposure to chronic stress contributes to the development of type 2 diabetes: A complexity science approach

Chronic stress contributes to the onset of type 2 diabetes (T2D), yet the underlying etiological mechanisms are not fully understood. Responses to stress are influenced by earlier experiences, sex, emotions and cognition, and involve a complex network of neurotransmitters and hormones, that affect multiple biological systems. In addition, the systems activated by stress can be altered by behavioral, metabolic and environmental factors. The impact of stress on metabolic health can thus be considered an emergent process, involving different types of interactions between multiple variables, that are driven by non-linear dynamics at different spatiotemporal scales. To obtain a more comprehensive picture of the links between chronic stress and T2D, we followed a complexity science approach to build a causal loop diagram (CLD) connecting the various mediators and processes involved in stress responses relevant for T2D pathogenesis. This CLD could help develop novel computational models and formulate new hypotheses regarding disease etiology