Group, subgroup, and person specific symptom associations in individuals at different levels of risk for psychosis:A combination of theory-based and data-driven approaches

Introduction Dynamics between symptoms may reveal insights into mechanisms underlying the development of psychosis. We combined a top-down (theory-based) and bottom-up (data-driven) approach to examine which symptom dynamics arise on group-level, on subgroup levels, and on individual levels in early clinical stages. We compared data-driven subgroups to theory-based subgroups, and explored how the data-driven subgroups differed from each other. Methods Data came from N = 96 individuals at risk for psychosis divided over four subgroups (n1 = 25, n2 = 27, n3 = 24, n4 = 20). Each subsequent subgroup represented a higher risk for psychosis (clinical stages 0-1b). All individuals completed 90 days of daily diaries, totaling 8640 observations. Confirmatory Subgrouping Group Iterative Multiple Model Estimation (CS-GIMME) and subgrouping (S-)-GIMME were used to examine group-level associations, respectively, theory-based and data-driven subgroups associations, and individual-specific associations between daily reports of depression, anxiety, stress, irritation, psychosis, and confidence. Results One contemporaneous group path between depression and confidence was identified. CS-GIMME identified several subgroup-specific paths and some paths that overlapped with other subgroups. S-GIMME identified two data-driven subgroups, with one subgroup reporting more psychopathology and lower social functioning. This subgroup contained most individuals from the higher stages and those with more severe psychopathology from the lower stages, and shared more connections between symptoms. Discussion Although subgroup-specific paths were recovered, no clear ordering of symptom patterns was found between different early clinical stages. Theory-based subgrouping distinguished individuals based on psychotic severity, whereas data-driven subgrouping distinguished individuals based on overall psychopathological severity. Future work should compare the predictive value of both methods

Dynamic symptom networks across different at-risk stages for psychosis:An individual and transdiagnostic perspective

The clinical staging model distinguishes different stages of mental illness. Early stages, are suggested to be more mild, diffuse and volatile in terms of expression of psychopathology than later stages. This study aimed to compare individual transdiagnostic symptom networks based on intensive longitudinal data between individuals in different early clinical stages for psychosis. It was hypothesized that with increasing clinical stage (i) density of symptom networks would increase and (ii) psychotic experiences would be more central in the symptom networks. Data came from a 90-day diary study, resulting in 8640 observations within N = 96 individuals, divided over four subgroups representing different early clinical stages (n1 = 25, n2 = 27, n3 = 24, n4 = 20). Sparse Time Series Chain Graphical Models were used to create individual contemporaneous and temporal symptom networks based on 10 items concerning symptoms of depression, anxiety, psychosis, non-specific and vulnerability domains. Network density and symptom centrality (strength) were calculated individually and compared between and within the four subgroups. Level of psychopathology increased with clinical stage. The symptom networks showed large between-individual variation, but neither network density not psychotic symptom strength differed between the subgroups in the contemporaneous (pdensity = 0.59, pstrength > 0.51) and temporal (pdensity = 0.75, pstrength > 0.35) networks. No support was found for our hypothesis that higher clinical stage comes with higher symptom network density or a more central role for psychotic symptoms. Based on the high inter-individual variability, our results highlight the importance of individualized assessment of symptom networks

Transforming waste management methods: a Dutch Airport’s journey toward a circular economy through baseline measurements and strategic priority setting

Airports, the essential hubs of global travel, have to cater for the increasing demands for air travel, with growing passenger numbers and the associated growth in resource consumption. While the aviation sector prioritizes reducing environmental impact in the air, substantial waste is generated at airports. This necessitates a critical examination of waste management practices, especially since a Circular Economy (CE) approach is gaining momentum within the aviation sector. This article introduces the Baseline Circular Airports Method (BCAM), a methodology developed and rigorously tested at Schiphol Amsterdam airport. BCAM systematically analyzes resource streams, considering composition and relevant stakeholders, treatment processes, and environmental impact. By doing so, it establishes strategic prioritization of resource streams for airports to perform focused and effective interventions. BCAM analysis reveals that the highest impact of operational resource streams are Residual, Plastic, Swill, Paper, and International Catering Waste (CAT1), and that corresponding waste management efficiencies can be determined. These outcomes serve as a baseline for ongoing monitoring, offering airports a starting point for strategic planning and assessing progress towards sustainable waste management and CE transitions

M2 Macrophages Activate WNT Signaling Pathway in Epithelial Cells: Relevance in Ulcerative Colitis

Macrophages, which exhibit great plasticity, are important components of the inflamed tissue and constitute an essential element of regenerative responses. Epithelial Wnt signalling is involved in mechanisms of proliferation and differentiation and expression of Wnt ligands by macrophages has been reported. We aim to determine whether the macrophage phenotype determines the expression of Wnt ligands, the influence of the macrophage phenotype in epithelial activation of Wnt signalling and the relevance of this pathway in ulcerative colitis. Human monocyte-derived macrophages and U937-derived macrophages were polarized towards M1 or M2 phenotypes and the expression of Wnt1 and Wnt3a was analyzed by qPCR. The effects of macrophages and the role of Wnt1 were analyzed on the expression of β-catenin, Tcf-4, c-Myc and markers of cell differentiation in a co-culture system with Caco-2 cells. Immunohistochemical staining of CD68, CD206, CD86, Wnt1, β-catenin and c-Myc were evaluated in the damaged and non-damaged mucosa of patients with UC. We also determined the mRNA expression of Lgr5 and c-Myc by qPCR and protein levels of β-catenin by western blot. Results show that M2, and no M1, activated the Wnt signaling pathway in co-culture epithelial cells through Wnt1 which impaired enterocyte differentiation. A significant increase in the number of CD206+ macrophages was observed in the damaged mucosa of chronic vs newly diagnosed patients. CD206 immunostaining co-localized with Wnt1 in the mucosa and these cells were associated with activation of canonical Wnt signalling pathway in epithelial cells and diminution of alkaline phosphatase activity. Our results show that M2 macrophages, and not M1, activate Wnt signalling pathways and decrease enterocyte differentiation in co-cultured epithelial cells. In the mucosa of UC patients, M2 macrophages increase with chronicity and are associated with activation of epithelial Wnt signalling and diminution in enterocyte differentiation

Zooming in on the development of psychotic experiences:insights from daily diaries

Psychosis often does not emerge abruptly, but rather gradually. Psychotic experiences, which are subclinical psychotic symptoms, are often present before a psychosis diagnosis. Early identification of those with an enhanced risk to develop psychosis can aid early intervention practices which in turn decreases the risk of worsening of symptoms. The development of psychosis is complex and it is not yet clear why certain people get more symptoms while others do not. One possibility to get more insight into this is by the use of diary data. With diary data, we can investigate the associations between symptoms. These associations can provide information on how risk and protective factors influence the daily development of psychotic symptoms. I investigated the association between psychotic experiences and respectively sleep problems and positive affect. In addition, with diary data, we can investigate the associations between multiple symptoms. The network theory assumes that psychopathology develops due to interactions between symptoms. I used several methods to investigate what symptom networks can learn us. I found no evidence that stronger connected networks are associated with a higher vulnerability for developing (more) psychopathology. With this thesis I show that daily diary data had added value in describing, understanding and predicting the early development of psychosis. With a diary period of only 7 days, the prediction of psychotic experiences and psychopathology can be improved. I am convinced that researching whether a (short) diary period in clinical practice has added value should be high on the research agenda

Mental health, risk and protective factors at micro- and macro-levels across early at-risk stages for psychosis: The Mirorr study

Background: The clinical staging model states that psychosis develops through subsequent stages of illness severity. To better understand what drives illness progression, more extensive comparison across clinical stages is needed. The current paper presents an in-depth characterization of individuals with different levels of risk for psychosis (i.e., different early clinical stages), using a multimethod approach of cross-sectional assessments and daily diary reports. Methods: Data came from the Mirorr study that includes N = 96 individuals, divided across four subgroups (n1 = 25, n2 = 27, n3 = 24, and n4 = 20). These subgroups, each with an increasing risk for psychosis, represent clinical stages 0-1b. Cross-sectional data and 90-day daily diary data on psychopathology, well-being, psychosocial functioning, risk and protective factors were statistically compared across subgroups (stages) and descriptively compared across domains and assessment methods. Results: Psychopathology increased across subgroups, although not always linearly and nuanced differences were seen between assessment methods. Well-being and functioning differed mostly between subgroup 1 and the other subgroups, suggesting differences between non-clinical and clinical populations. Risk and protective factors differed mostly between the two highest and lowest subgroups, especially regarding need of social support and coping, suggesting differences between those with and without substantial psychotic experiences. Subgroup 4 (stage 1b) reported especially high levels of daily positive and negative psychotic experiences. Conclusions: Risk for psychosis exists in larger contexts of mental health and factors of risk and protection that differ across stages and assessment methods. Taking a broad, multi-method approach is an important next step to understand the complex development of youth mental health problems

Relating stability of individual dynamical networks to change in psychopathology.

One hypothesis flowing from the network theory of psychopathology is that symptom network structure is associated with psychopathology severity and in turn, one may expect that individual network structure changes with the level of psychopathology severity. However, this expectation has rarely been addressed directly. This study aims to examine (1) the stability of individual contemporaneous symptom networks over a one-year period and (2) whether network stability is associated with a change in psychopathology. We used daily diary data of n = 66 individuals, located along the psychosis severity continuum, from two separate 90-day periods, one year apart (t = 180). Based on the newly developed Individual Network Invariance Test (INIT) to assess symptom-network stability, participants were divided into two groups with stable and unstable networks and we tested whether these groups differed in their absolute change in psychopathology severity. The majority of the sample (n = 51, 77.3%) showed a stable network over time while most individuals showed a decrease in psychopathological severity. We found no significant association between a change in psychopathology severity and individual network stability. Our results call for further critical evaluation of the association between networks and psychopathology to optimize the implementation of clinical applications based on current methods