BackgroundAffective (i.e. depressive and anxiety) disorders often co-occur with immunometabolic diseases and related biological pathways. Although many large population-based and meta-analytic... Show moreBackgroundAffective (i.e. depressive and anxiety) disorders often co-occur with immunometabolic diseases and related biological pathways. Although many large population-based and meta-analytic studies have confirmed this link in community and clinical samples, studies in at-risk samples of siblings of persons with affective disorders are lacking. Furthermore, this somatic-mental co-occurrence may be partially explained by familial clustering of the conditions. First, we examined whether the association between a wide range of immunometabolic diseases and related biomarker based risk-profiles with psychological symptoms replicates in at-risk siblings of probands with affective disorders. Second, leveraging on a sibling-pair design, we disentangled and quantified the effect of probands’ immunometabolic health on siblings’ psychological symptoms and on the association between immunometabolic health and these symptoms in siblings.MethodsThe sample included 636 participants (Mage = 49.7; 62.4% female) from 256 families, each including a proband with lifetime depressive and/or anxiety disorders and at least one of their sibling(s) (N = 380 proband-sibling pairs). Immunometabolic health included cardiometabolic and inflammatory diseases, body mass index (BMI), and composite metabolic (based on the five metabolic syndrome components) and inflammatory (based on interleukin-6 and C-reactive protein) biomarker indices. Overall affective symptoms and specific atypical, energy-related depressive symptoms were derived from self-report questionnaires. Mixed-effects analyses were used to model familial clustering.ResultsIn siblings, inflammatory disease (γ = 0.25, p = 0.013), higher BMI (γ = 0.10, p = 0.033) and metabolic index (γ = 0.28, p < 0.001) were associated with higher affective symptoms, with stronger associations for atypical, energy-related depressive symptoms (additionally associated with cardiometabolic disease; γ = 0.56, p = 0.048). Immunometabolic health in probands was not independently associated with psychological symptoms in siblings nor did it moderate the association between immunometabolic health and psychological symptoms estimated in siblings.ConclusionsOur findings demonstrate that the link between later life immunometabolic health and psychological symptoms is consistently present also in adult siblings at high risk for affective disorders. Familial clustering did not appear to have a substantial impact on this association. Instead, individual lifestyle, rather than familial factors, may have a relatively higher impact in the clustering of later life immunometabolic conditions with psychological symptoms in at-risk adult individuals. Furthermore, results highlighted the importance of focusing on specific depression profiles when investigating the overlap with immunometabolic health. Show less
Calster, B. van; Steyerberg, E.W.; Wynants, L.; Smeden, M. van 2023
Background Clinical prediction models should be validated before implementation in clinical practice. But is favorable performance at internal validation or one external validation sufficient to... Show moreBackground Clinical prediction models should be validated before implementation in clinical practice. But is favorable performance at internal validation or one external validation sufficient to claim that a prediction model works well in the intended clinical context? Main body We argue to the contrary because (1) patient populations vary, (2) measurement procedures vary, and (3) populations and measurements change over time. Hence, we have to expect heterogeneity in model performance between locations and settings, and across time. It follows that prediction models are never truly validated. This does not imply that validation is not important. Rather, the current focus on developing new models should shift to a focus on more extensive, well-conducted, and well-reported validation studies of promising models. Conclusion Principled validation strategies are needed to understand and quantify heterogeneity, monitor performance over time, and update prediction models when appropriate. Such strategies will help to ensure that prediction models stay up-to-date and safe to support clinical decision-making. Show less
Durieux, J.; Rombouts, S.A.R.B.; Vos, F. de; Koini, M.; Wilderjans, T.F. 2022
Background: FMRI resting state networks (RSNs) are used to characterize brain disorders. They also show extensive heterogeneity across patients. Identifying systematic differences between RSNs in... Show moreBackground: FMRI resting state networks (RSNs) are used to characterize brain disorders. They also show extensive heterogeneity across patients. Identifying systematic differences between RSNs in patients, i.e. discovering neurofunctional subtypes, may further increase our understanding of disease heterogeneity. Currently, no methodology is available to estimate neurofunctional subtypes and their associated RSNs simultaneously. New method: We present an unsupervised learning method for fMRI data, called Clusterwise Independent Component Analysis (C-ICA). This enables the clustering of patients into neurofunctional subtypes based on differences in shared ICA-derived RSNs.The parameters are estimated simultaneously, which leads to an improved estimation of subtypes and their associated RSNs. Results: In five simulation studies, the C-ICA model is successfully validated using both artificially and realistically simulated data (N = 30-40). The successful performance of the C-ICA model is also illustrated on an empirical data set consisting of Alzheimer's disease patients and elderly control subjects (N = 250). C-ICA is able to uncover a meaningful clustering that partially matches (balanced accuracy = .72) the diagnostic labels and identifies differences in RSNs between the Alzheimer and control cluster. Comparison with other methods: Both in the simulation study and the empirical application, C-ICA yields better results compared to competing clustering methods (i.e., a two step clustering procedure based on single subject ICA's and a Group ICA plus dual regression variant thereof) that do not simultaneously estimate a clustering and associated RSNs. Indeed, the overall mean adjusted Rand Index, a measure for cluster recovery, equals 0.65 for C-ICA and ranges from 0.27 to 0.46 for competing methods. Conclusions: The successful performance of C-ICA indicates that it is a promising method to extract neuro-functional subtypes from multi-subject resting state-fMRI data. This method can be applied on fMRI scans of patient groups to study (neurofunctional) subtypes, which may eventually further increase understanding of disease heterogeneity. Show less
The overarching goal of this dissertation was to investigate parenting processes in relation to affective well-being in families in the daily flow of life from different perspectives (i.e.,... Show moreThe overarching goal of this dissertation was to investigate parenting processes in relation to affective well-being in families in the daily flow of life from different perspectives (i.e., adolescent, mother, father), on different levels (i.e., objective, momentary, and daily), and in a clinical sample (families with an adolescent with a depression) in addition to community samples. Our findings underline the importance of daily parental warmth and criticism of mothers and fathers for adolescents’ well-being. In addition, we showed that adolescents, parents, and their perceptions of parenting influence each other and that becoming more attuned to each other’s intention and needs is essential. Importantly, we consistently demonstrated that not only the extent and direction of the dynamic processes between parenting and affect in daily life differs between, but also within persons and families. This stresses the need for research focusing on individual processes and combining quantitative with qualitative measures. The use of more idiographic approaches would not only enable gaining more insight into these differences between individuals, but also contribute to identifying parenting practices that work for almost all adolescents. This would facilitate the development of interventions combining universal parenting principles with suggestions for tailoring it to individual- or family-specific situations. Show less
Exposed to the natural light-dark cycle, living beings show robust 24 h rhythms in physiology and behavior. Interestingly, even in the absence of a light-dark cycle, for example in constant... Show moreExposed to the natural light-dark cycle, living beings show robust 24 h rhythms in physiology and behavior. Interestingly, even in the absence of a light-dark cycle, for example in constant conditions, such as under the constant darkness or the constant light, living beings maintain a robust rhythm of which the endogenous period (named free running period, FRP) is close to 24 h. The endogenous rhythms are regulated by a master clock located in the suprachiasmatic nucleus (SCN) of mammals, where the SCN neurons show heterogeneity in the sensitivity to the light. In this article, we examined how this heterogeneity influences the FRP under constant light. Using a Poincare model for the SCN network it is shown that the FRP increases with the increase of the degree of heterogeneity in the sensitivity of neuronal subpopulations to light. Moreover, the presence of a critical value where the periods of the subpopulation diverge, presents a mechanism dictating how some animals remain rhythmic under constant light conditions, while others lose their rhythms completely. Our findings help to understand how the neuronal heterogeneity to light sensitivity in the SCN influences the circadian behavior of the animal. (C) 2022 Elsevier B.V. All rights reserved. Show less
Background: Childhood trauma (CT) is a risk factor for depressive and anxiety disorders. However, whether CT is more strongly linked to specific clinical features of these disorders remains... Show moreBackground: Childhood trauma (CT) is a risk factor for depressive and anxiety disorders. However, whether CT is more strongly linked to specific clinical features of these disorders remains inconclusive. The current study comprehensively examined cross-sectional and longitudinal associations between CT and depressive/anxiety symptomatology in a large adult sample with current and remitted depressive and/or anxiety disorders. Methods: Baseline (n = 1803), 2-year (n = 1735), 4-year (n = 1585), and 6-year follow-up (n = 1475) data from the Netherlands Study of Depression and Anxiety were used. CT (emotional neglect, emotional/physical/sexual abuse) was assessed at baseline, while depressive/anxiety symptomatology with relevant dimensions (e.g., mood/cognitive, melancholic, general distress, and somatic depression) was assessed at each wave using selfreported questionnaires. Linear regressions and linear mixed models determined cross-sectional and longitudinal associations. Results: Individuals with CT, especially, severe CT, compared to those without CT, had significantly higher scores in overall depressive symptomatology (Cohen's d = 0.674), mood/cognitive depression (d = 0.691), melancholic depression (d = 0.587), general distress (d = 0.561), and somatic depression severity (d = 0.549). Differences were lower, but still highly significant for anxiety (d = 0.418), worry (d = 0.362), and fear/phobic symptomatology (d = 0.359). Effects were consistent across CT types and maintained over six years. Limitations: Retrospectively-reported CT. Conclusions: CT is a risk factor for depressive and anxiety symptomatology across all dimensions and enduring over multiple years. Screening for CT is essential to identify individuals at risk for more severe and chronic manifestations of affective disorders. Show less
Fungal food spoilage often starts with a contamination with spores. Experimental data strongly indicate the existence of subpopulations of spores with different levels of resistance to preservation... Show moreFungal food spoilage often starts with a contamination with spores. Experimental data strongly indicate the existence of subpopulations of spores with different levels of resistance to preservation methods. In this thesis, the extent of this heterogeneity and the underlying mechanisms using fungal model systems is studied. The role of the genetic background, environmental conditions and the developmental state of the spores were studied, using quantitative imaging, genome and RNA/protein sequencing as well as functional gene analysis. The role of transcription factors in weak acid stress resistance of Aspergillus niger is described. Next, heat resistance of fungal spores of three food spoilage species was quantified and compared. The genomes of Aspergillus niger strains were sequenced and compared revealing the existence of a possible sexual cycle. Melanin of fungal spores impacts UV-C resistance, but not heat resistance and a functional CRISPR/Cas9 genome editing system for Paecilomyces variotii and Penicillium roqueforti is described. Older spores are more heat resistant than younger spores, which can be contributed to differences in compatible solute composition. Additionally, a high cultivation temperature results in fungal spores with high heat resistance, possibly due to heat shock proteins. Show less
Lange, M.J. de; Nell, R.J.; Velden, P.A. van der 2021
Here, we discuss the presence and roles of heterogeneity in the development of uveal melanoma. Both genetic and cellular heterogeneity are considered, as their presence became undeniable due to... Show moreHere, we discuss the presence and roles of heterogeneity in the development of uveal melanoma. Both genetic and cellular heterogeneity are considered, as their presence became undeniable due to single cell approaches that have recently been used in uveal melanoma analysis. However, the presence of precursor clones and immune infiltrate in uveal melanoma have been described as being part of the tumour already decades ago. Since uveal melanoma grow in the corpus vitreous, they present a unique tumour model because every cell present in the tumour tissue is actually part of the tumour and possibly plays a role. For an effective treatment of uveal melanoma metastasis, it should be clear whether precursor clones and normal cells play an active role in progression and metastasis. We propagate analysis of bulk tissue that allows analysis of tumour heterogeneity in a clinical setting. Show less
Nell, R.J.; Menger, N.V.; Versluis, M.; Luyten, G.P.M.; Verdijk, R.M.; Madigan, M.C.; ... ; Velden, P.A. van der 2021
BackgroundActivating G alpha (q) signalling mutations are considered an early event in the development of uveal melanoma. Whereas most tumours harbour a mutation in GNAQ or GNA11, CYSLTR2 (encoding... Show moreBackgroundActivating G alpha (q) signalling mutations are considered an early event in the development of uveal melanoma. Whereas most tumours harbour a mutation in GNAQ or GNA11, CYSLTR2 (encoding G-protein coupled receptor CysLT(2)R) forms a rare alternative. The role of wild-type CysLT(2)R in uveal melanoma remains unknown.MethodsWe performed a digital PCR-based molecular analysis of benign choroidal nevi and primary uveal melanomas. Publicly available bulk and single cell sequencing data were mined to further study mutant and wild-type CYSLTR2 in primary and metastatic uveal melanoma.Results1/16 nevi and 2/120 melanomas carried the CYSLTR2 mutation. The mutation was found in a subpopulation of the nevus, while being clonal in both melanomas. In the melanomas, secondary, subclonal CYSLTR2 alterations shifted the allelic balance towards the mutant. The resulting genetic heterogeneity was confirmed in distinct areas of both tumours. At the RNA level, further silencing of wild-type and preferential expression of mutant CYSLTR2 was identified, which was also observed in two CYSLTR2 mutant primary melanomas and one metastatic lesion from other cohorts. In CYSLTR2 wild-type melanomas, high expression of CYSLTR2 correlated to tumour inflammation, but expression originated from melanoma cells specifically.ConclusionsOur findings suggest that CYSLTR2 is involved in both early and late development of uveal melanoma. Whereas the CYSLTR2 p.L129Q mutation is likely to be the initiating oncogenic event, various mechanisms further increase the mutant allele abundance during tumour progression. This makes mutant CysLT(2)R an attractive therapeutic target in uveal melanoma. Show less
Background: Major depressive disorder (MDD) is linked to higher cardio-metabolic comorbidity that may in part be due to the low-grade inflammation and poorer metabolic health observed in MDD.... Show moreBackground: Major depressive disorder (MDD) is linked to higher cardio-metabolic comorbidity that may in part be due to the low-grade inflammation and poorer metabolic health observed in MDD. Heterogeneity of MDD is however large, and immune-inflammatory and metabolic dysregulation is present in only part of the MDD cases. We examined the associations of four depression dimensional profilers (atypical energy-related symptom dimension, melancholic symptom dimension, childhood trauma severity, and anxious distress symptom dimension) with immuno-metabolic outcomes, both cross-sectionally and longitudinally.Methods: Three waves covering a 6-year follow-up (>7000 observations) of the Netherlands Study of Depression and Anxiety (NESDA) were used. Depression profilers were based on the Inventory of Depressive Symptomatology, the Beck Anxiety Inventory, and the Childhood Trauma index. An inflammatory index (based on IL-6 and CRP), a metabolic syndrome index (based on the five metabolic syndrome components), and a combination of these two indices were constructed. Mixed models were used for cross-sectional and longitudinal models, controlling for covariates.Results: Of the four depression profilers, only the atypical, energy-related symptom dimension showed robust associations with higher scores on the inflammatory, metabolic syndrome and combined inflammatory-metabolic indexes cross-sectionally, as well as at follow-up. The melancholic symptom dimension was associated with lower scores on the metabolic syndrome index both cross-sectionally and longitudinally.Conclusion: The atypical energy-related symptom dimension was linked to poorer immune-inflammatory and metabolic health, while the melancholic symptom dimension was linked to relatively better metabolic health. Persons with high atypical energy-related symptom burden, representing an immuno-metabolic depression, may be the most important group to target in prevention programs for cardiometabolic disease, and may benefit most from treatments targeting immuno-metabolic pathways. Show less
In meta-analysis, heterogeneity often exists between studies. In such cases, it is essential to investigate the sources of heterogeneity and understand the relationship between effect size and... Show moreIn meta-analysis, heterogeneity often exists between studies. In such cases, it is essential to investigate the sources of heterogeneity and understand the relationship between effect size and study characteristics (i.e., moderators). Applying tree-based methods in meta-analysis is a promising alternative for conventional meta-regression, since trees excel at modeling interactions and non-linear relationships and provide easily interpretable results. In this thesis, we propose a method called meta-CART, which integratesclassification and regression trees (CART) into the framework of meta-analysis. This method identifies subgroups of homogeneous studies by searching influential moderators that can explain the heterogeneity, and performs subgroup analysis to test the significance of the identified moderators and estimate the subgroup effect sizes. Show less
Calster, B. van; McLernon, D.J.; Smeden, M. van; Wynants, L.; Steyerberg, E.W.; STRATOS Initiative 2019
Background: The assessment of calibration performance of risk prediction models based on regression or more flexible machine learning algorithms receives little attention.Main text: Herein, we... Show moreBackground: The assessment of calibration performance of risk prediction models based on regression or more flexible machine learning algorithms receives little attention.Main text: Herein, we argue that this needs to change immediately because poorly calibrated algorithms can be misleading and potentially harmful for clinical decision-making. We summarize how to avoid poor calibration at algorithm development and how to assess calibration at algorithm validation, emphasizing balance between model complexity and the available sample size. At external validation, calibration curves require sufficiently large samples. Algorithm updating should be considered for appropriate support of clinical practice.Conclusion: Efforts are required to avoid poor calibration when developing prediction models, to evaluate calibration when validating models, and to update models when indicated. The ultimate aim is to optimize the utility of predictive analytics for shared decision-making and patient counseling. Show less
Rozendaal, Y.J.W.; Wang, Y.N.; Hilbers, P.A.J.; Riel, N.A.W. van 2019
BackgroundA positive energy balance is considered to be the primary cause of the development of obesity-related diseases. Treatment often consists of a combination of reducing energy intake and... Show moreBackgroundA positive energy balance is considered to be the primary cause of the development of obesity-related diseases. Treatment often consists of a combination of reducing energy intake and increasing energy expenditure. Here we use an existing computational modelling framework describing the long-term development of Metabolic Syndrome (MetS) in APOE3L.CETP mice fed a high-fat diet containing cholesterol with a human-like metabolic system. This model was used to analyze energy expenditure and energy balance in a large set of individual model realizations.ResultsWe developed and applied a strategy to select specific individual models for a detailed analysis of heterogeneity in energy metabolism. Models were stratified based on energy expenditure. A substantial surplus of energy was found to be present during MetS development, which explains the weight gain during MetS development. In the majority of the models, energy was mainly expended in the peripheral tissues, but also distinctly different subgroups were identified.In silico perturbation of the system to induce increased peripheral energy expenditure implied changes in lipid metabolism, but not in carbohydrate metabolism. In silico analysis provided predictions for which individual models increase of peripheral energy expenditure would be an effective treatment.ConclusionThe computational analysis confirmed that the energy imbalance plays an important role in the development of obesity. Furthermore, the model is capable to predict whether an increase in peripheral energy expenditure - for instance by cold exposure to activate brown adipose tissue (BAT) - could resolve MetS symptoms. Show less
The filamentous bacteria Streptomyces are widespread inhabitants of terrestrial soils. Streptomycetes are not only among the most potent producers of valuable secondary metabolites (e.g.... Show moreThe filamentous bacteria Streptomyces are widespread inhabitants of terrestrial soils. Streptomycetes are not only among the most potent producers of valuable secondary metabolites (e.g. antibiotics), but also the source of various industrially relevant hydrolytic enzymes. The mode-of-growth of streptomycetes under industrial conditions is markedly different to that observed in their natural habitat. Most species form dense particles called pellets. Pellets are heterogeneous in size; more specifically at least two populations of differently-sized pellets exist in submerged cultures. Importantly, pellet size and production have been shown to be to be tightly correlated in streptomycetes. The study and control of pellet size heterogeneity in streptomycetes is the subject of the research presented in this thesis. Here, the various phenomena occurring throughout growth are characterized with the aim of understanding the factors underlying this phenomenon. Subsequently, the obtained knowledge is applied to obtain homogeneously-sized pellets of the industrial workhorse Streptomyces lividans. The work described in this thesis also addresses the fate of pellets at late stages of growth and a growth strategy representing a valuable alternative to conventional liquid cultures. Show less
This thesis describes the search for new high-risk breast cancer susceptibility genes by linkage analysis. To date 20-25% of familial breast cancer is explained by mutations in the high-risk BRCA1... Show moreThis thesis describes the search for new high-risk breast cancer susceptibility genes by linkage analysis. To date 20-25% of familial breast cancer is explained by mutations in the high-risk BRCA1 and BRCA2 breast cancer susceptibility genes. For the remaining families the genetic etiology is unknown. It is still possible that other high-penetrant genes play a role. Although a polygenic model with multiple low-penetrant genes acting additive or multiplicative will probable explain most of the BRCA1/2 negative families. Linkage in an international set of 150 high-risk breast cancer families couldn__t identify high-risk genes. However when limiting the linkage analysis to Dutch families only, we identified 9q21-22 as a putative breast cancer susceptibility locus Show less