Detecting treatment-subgroup interactions in clustered data with generalized linear mixed-effects model trees

Identification of subgroups of patients for whom treatment A is more effective than treatment B, and vice versa, is of key importance to the development of personalized medicine. Tree-based algorithms are helpful tools for the detection of such interactions, but none of the available algorithms allow for taking into account clustered or nested dataset structures, which are particularly common in psychological research. Therefore, we propose the generalized linear mixed-effects model tree (GLMM tree) algorithm, which allows for the detection of treatment-subgroup interactions, while accounting for the clustered structure of a dataset. The algorithm uses model-based recursive partitioning to detect treatment-subgroup interactions, and a GLMM to estimate the random-effects parameters. In a simulation study, GLMM trees show higher accuracy in recovering treatment-subgroup interactions, higher predictive accuracy, and lower type II error rates than linear-model-based recursive partitioning and mixed-effects regression trees. Also, GLMM trees show somewhat higher predictive accuracy than linear mixed-effects models with pre-specified interaction effects, on average. We illustrate the application of GLMM trees on an individual patient-level data meta-analysis on treatments for depression. We conclude that GLMM trees are a promising exploratory tool for the detection of treatment-subgroup interactions in clustered datasets.Article / Letter to editorInstituut Psychologi

Detecting treatment-subgroup interactions in clustered data with generalized linear mixed-effects model trees

Identification of subgroups of patients for whom treatment A is more effective than treatment B, and vice versa, is of key importance to the development of personalized medicine. Tree-based algorithms are helpful tools for the detection of such interactions, but none of the available algorithms allow for taking into account clustered or nested dataset structures, which are particularly common in psychological research. Therefore, we propose the generalized linear mixed-effects model tree (GLMM tree) algorithm, which allows for the detection of treatment-subgroup interactions, while accounting for the clustered structure of a dataset. The algorithm uses model-based recursive partitioning to detect treatment-subgroup interactions, and a GLMM to estimate the random-effects parameters. In a simulation study, GLMM trees show higher accuracy in recovering treatment-subgroup interactions, higher predictive accuracy, and lower type II error rates than linear-model-based recursive partitioning and mixed-effects regression trees. Also, GLMM trees show somewhat higher predictive accuracy than linear mixed-effects models with pre-specified interaction effects, on average. We illustrate the application of GLMM trees on an individual patient-level data meta-analysis on treatments for depression. We conclude that GLMM trees are a promising exploratory tool for the detection of treatment-subgroup interactions in clustered datasets.Multivariate analysis of psychological dat

Panorama Natuur: een visie op natuur door Young Professionals

Het denken over natuur en landschap in Nederland is aan een opfrisbeurt toe. In Panorama Natuur hebben 9 jongeren hun eigen visie daarop ontwikkeld, en dragen daarmee bij aan een nieuw natuurverhaal

Intramolecular Cooperative Effects in Multichromophoric Cavitands Exhibiting Nonlinear Optical Properties

We report on the design, synthesis, and characterization of a new class of multichromophoric cavitands based on resorcin[4]arenes. The novel compounds have exhibited high values of second-order nonlinear optical (NLO) properties, as evidenced by electric-field-induced second harmonic generation (EFISHG) measurements. Theoretical calculations indicate the presence of edge-to-face T-shaped interactions between the aromatic building blocks within these multichromophoric systems, which is further supported by the detection of hypsochromic shifts in UV-vis and upfield aromatic chemical shifts in 1H NMR. We proved for the first time that the gain in the quadratic hyperpolarizabilities of multichromophoric NLO macrocycles, originating from the near parallel orientations of the subchromophores, can be partially suppressed if the distance between the dipolar subunits falls into a specific range, where intramolecular cooperative and/or collective effects are operative. Our finding will contribute to the better understanding of the phenomenon of cooperativity in new molecular materials with promising NLO properties. (Figure Presented). © 2015 American Chemical Society

Genome-Wide Association Study Identifies Risk Loci for Cluster Headache

OBJECTIVE: To identify susceptibility loci for cluster headache and obtain insights into relevant disease pathways. METHODS: We carried out a genome-wide association study, where 852 UK and 591 Swedish cluster headache cases were compared with 5,614 and 1,134 controls, respectively. Following quality control and imputation, single variant association testing was conducted using a logistic mixed model, for each cohort. The two cohorts were subsequently combined in a merged analysis. Downstream analyses, such as gene-set enrichment, functional variant annotation, prediction and pathway analyses, were performed. RESULTS: Initial independent analysis identified two replicable cluster headache susceptibility loci on chromosome 2. A merged analysis identified an additional locus on chromosome 1 and confirmed a locus significant in the UK analysis on chromosome 6, which overlaps with a previously known migraine locus. The lead single nucleotide polymorphisms were rs113658130 (p = 1.92 x 10-17 , OR [95%CI] = 1.51 [1.37-1.66]) and rs4519530 (p = 6.98 x 10-17 , OR = 1.47 [1.34-1.61]) on chromosome 2, rs12121134 on chromosome 1 (p = 1.66 x 10-8 , OR = 1.36 [1.22-1.52]) and rs11153082 (p = 1.85 x 10-8 , OR = 1.30 [1.19-1.42]) on chromosome 6. Downstream analyses implicated immunological processes in the pathogenesis of cluster headache. INTERPRETATION: We identified and replicated several genome-wide-significant associations supporting a genetic predisposition in cluster headache in a genome-wide association study involving 1,443 cases. Replication in larger independent cohorts combined with comprehensive phenotyping, in relation to e.g. treatment response and cluster headache subtypes, could provide unprecedented insights into genotype-phenotype correlations and the pathophysiological pathways underlying cluster headache

Metabolic compartmentalization in the human cortex and hippocampus: evidence for a cell- and region-specific localization of lactate dehydrogenase 5 and pyruvate dehydrogenase

BACKGROUND: For a long time now, glucose has been thought to be the main, if not the sole substrate for brain energy metabolism. Recent data nevertheless suggest that other molecules, such as monocarboxylates (lactate and pyruvate mainly) could be suitable substrates. Although monocarboxylates poorly cross the blood brain barrier (BBB), such substrates could replace glucose if produced locally.The two key enzymatiques systems required for the production of these monocarboxylates are lactate dehydrogenase (LDH; EC1.1.1.27) that catalyses the interconversion of lactate and pyruvate and the pyruvate dehydrogenase complex that irreversibly funnels pyruvate towards the mitochondrial TCA and oxydative phosphorylation. RESULTS: In this article, we show, with monoclonal antibodies applied to post-mortem human brain tissues, that the typically glycolytic isoenzyme of lactate dehydrogenase (LDH-5; also called LDHA or LDHM) is selectively present in astrocytes, and not in neurons, whereas pyruvate dehydrogenase (PDH) is mainly detected in neurons and barely in astrocytes. At the regional level, the distribution of the LDH-5 immunoreactive astrocytes is laminar and corresponds to regions of maximal 2-deoxyglucose uptake in the occipital cortex and hippocampus. In hippocampus, we observed that the distribution of the oxidative enzyme PDH was enriched in the neurons of the stratum pyramidale and stratum granulosum of CA1 through CA4, whereas the glycolytic enzyme LDH-5 was enriched in astrocytes of the stratum moleculare, the alveus and the white matter, revealing not only cellular, but also regional, selective distributions. The fact that LDH-5 immunoreactivity was high in astrocytes and occurred in regions where the highest uptake of 2-deoxyglucose was observed suggests that glucose uptake followed by lactate production may principally occur in these regions. CONCLUSION: These observations reveal a metabolic segregation, not only at the cellular but also at the regional level, that support the notion of metabolic compartmentalization between astrocytes and neurons, whereby lactate produced by astrocytes could be oxidized by neurons

Metabolic effects of a 13-weeks lifestyle intervention in older adults: The Growing Old Together Study

Analytical BioScience