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DNA methylation variations are prevalent in human obesity but evidence of a causative role in disease pathogenesis is limited. Here, we combine epigenome-wide association and integrative genomics to investigate the impact of adipocyte DNA methylation variations in human obesity. We discover extensive DNA methylation changes that are robustly associated with obesity (N = 190 samples, 691 loci in subcutaneous and 173 loci in visceral adipocytes, P < 1 × 10-7). We connect obesity-associated methylation variations to transcriptomic changes at >500 target genes, and identify putative methylation-transcription factor interactions. Through Mendelian Randomisation, we infer causal effects of methylation on obesity and obesity-induced metabolic disturbances at 59 independent loci. Targeted methylation sequencing, CRISPR-activation and gene silencing in adipocytes, further identifies regional methylation variations, underlying regulatory elements and novel cellular metabolic effects. Our results indicate DNA methylation is an important determinant of human obesity and its metabolic complications, and reveal mechanisms through which altered methylation may impact adipocyte functions. DNA methylation variation is associated with human obesity but a whether it plays a causal role in disease pathogenesis is unclear. Here, the authors perfom an integrative genomic study in human adipocytes to show that DNA methylation variations contribute to obesity and type 2 diabetes susceptibility, revealing underlying genomic and molecular mechanisms. [ABSTRACT FROM AUTHOR]

Purpose: Involuntary hospitalisations for mental health care are rising in many high income countries, including England. Looking at variation between areas can help us understand why rates are rising and how this might be reversed. This cross-sectional, ecological study aimed to better understand variation in involuntary hospitalisations across England.The unit of analysis was Clinical Commissioning Groups (CCGs), NHS bodies responsible for delivering healthcare to local areas in England. 205 CCGs were included in the analysis. Demographic, clinical, and socioeconomic variables at CCG-level were extracted from national, open access data bases. The outcome variable was the rate of involuntary hospitalisation for psychiatric care under the 1983 Mental Health Act in 2021/22.There was a four-fold difference between the CCGs with the highest and lowest involuntary hospitalisations. In an adjusted analysis, CCGs with a higher percentage of severe mental illness in the population, higher percentage of male population, and higher community and outpatient mental health care use showed a higher rate of involuntary hospitalisation. Depression, urbanicity, deprivation, ethnicity, and age were not strongly associated with involuntary hospitalisation after adjustment. These variables explained 10.68% of the variation in involuntary hospitalisations across CCGs.Some demographic and clinical variables explained variation in involuntary hospitalisation between areas in England, however, most of the variance was unexplained. Complex relationships between urbanicity, deprivation, age, and ethnicity need to be further explored. The role of other influences, such as variation in service organisation or clinical practice, also need to be better understood.Method: Involuntary hospitalisations for mental health care are rising in many high income countries, including England. Looking at variation between areas can help us understand why rates are rising and how this might be reversed. This cross-sectional, ecological study aimed to better understand variation in involuntary hospitalisations across England.The unit of analysis was Clinical Commissioning Groups (CCGs), NHS bodies responsible for delivering healthcare to local areas in England. 205 CCGs were included in the analysis. Demographic, clinical, and socioeconomic variables at CCG-level were extracted from national, open access data bases. The outcome variable was the rate of involuntary hospitalisation for psychiatric care under the 1983 Mental Health Act in 2021/22.There was a four-fold difference between the CCGs with the highest and lowest involuntary hospitalisations. In an adjusted analysis, CCGs with a higher percentage of severe mental illness in the population, higher percentage of male population, and higher community and outpatient mental health care use showed a higher rate of involuntary hospitalisation. Depression, urbanicity, deprivation, ethnicity, and age were not strongly associated with involuntary hospitalisation after adjustment. These variables explained 10.68% of the variation in involuntary hospitalisations across CCGs.Some demographic and clinical variables explained variation in involuntary hospitalisation between areas in England, however, most of the variance was unexplained. Complex relationships between urbanicity, deprivation, age, and ethnicity need to be further explored. The role of other influences, such as variation in service organisation or clinical practice, also need to be better understood.Results: Involuntary hospitalisations for mental health care are rising in many high income countries, including England. Looking at variation between areas can help us understand why rates are rising and how this might be reversed. This cross-sectional, ecological study aimed to better understand variation in involuntary hospitalisations across England.The unit of analysis was Clinical Commissioning Groups (CCGs), NHS bodies responsible for delivering healthcare to local areas in England. 205 CCGs were included in the analysis. Demographic, clinical, and socioeconomic variables at CCG-level were extracted from national, open access data bases. The outcome variable was the rate of involuntary hospitalisation for psychiatric care under the 1983 Mental Health Act in 2021/22.There was a four-fold difference between the CCGs with the highest and lowest involuntary hospitalisations. In an adjusted analysis, CCGs with a higher percentage of severe mental illness in the population, higher percentage of male population, and higher community and outpatient mental health care use showed a higher rate of involuntary hospitalisation. Depression, urbanicity, deprivation, ethnicity, and age were not strongly associated with involuntary hospitalisation after adjustment. These variables explained 10.68% of the variation in involuntary hospitalisations across CCGs.Some demographic and clinical variables explained variation in involuntary hospitalisation between areas in England, however, most of the variance was unexplained. Complex relationships between urbanicity, deprivation, age, and ethnicity need to be further explored. The role of other influences, such as variation in service organisation or clinical practice, also need to be better understood.Conclusion: Involuntary hospitalisations for mental health care are rising in many high income countries, including England. Looking at variation between areas can help us understand why rates are rising and how this might be reversed. This cross-sectional, ecological study aimed to better understand variation in involuntary hospitalisations across England.The unit of analysis was Clinical Commissioning Groups (CCGs), NHS bodies responsible for delivering healthcare to local areas in England. 205 CCGs were included in the analysis. Demographic, clinical, and socioeconomic variables at CCG-level were extracted from national, open access data bases. The outcome variable was the rate of involuntary hospitalisation for psychiatric care under the 1983 Mental Health Act in 2021/22.There was a four-fold difference between the CCGs with the highest and lowest involuntary hospitalisations. In an adjusted analysis, CCGs with a higher percentage of severe mental illness in the population, higher percentage of male population, and higher community and outpatient mental health care use showed a higher rate of involuntary hospitalisation. Depression, urbanicity, deprivation, ethnicity, and age were not strongly associated with involuntary hospitalisation after adjustment. These variables explained 10.68% of the variation in involuntary hospitalisations across CCGs.Some demographic and clinical variables explained variation in involuntary hospitalisation between areas in England, however, most of the variance was unexplained. Complex relationships between urbanicity, deprivation, age, and ethnicity need to be further explored. The role of other influences, such as variation in service organisation or clinical practice, also need to be better understood. [ABSTRACT FROM AUTHOR]

Linking epigenetic marks to clinical outcomes improves insight into molecular processes, disease prediction, and therapeutic target identification. Here, a statistical approach is presented to infer the epigenetic architecture of complex disease, determine the variation captured by epigenetic effects, and estimate phenotype-epigenetic probe associations jointly. Implicitly adjusting for probe correlations, data structure (cell-count or relatedness), and single-nucleotide polymorphism (SNP) marker effects, improves association estimates and in 9,448 individuals, 75.7% (95% CI 71.70–79.3) of body mass index (BMI) variation and 45.6% (95% CI 37.3–51.9) of cigarette consumption variation was captured by whole blood methylation array data. Pathway-linked probes of blood cholesterol, lipid transport and sterol metabolism for BMI, and xenobiotic stimuli response for smoking, showed >1.5 times larger associations with >95% posterior inclusion probability. Prediction accuracy improved by 28.7% for BMI and 10.2% for smoking over a LASSO model, with age-, and tissue-specificity, implying associations are a phenotypic consequence rather than causal. Linking epigenetic marks to clinical outcomes promises insight into the underlying processes. Here, the authors introduce a statistical approach to estimate associations between a phenotype and all epigenetic probes jointly, and to estimate the proportion of variation captured by epigenetic effects. [ABSTRACT FROM AUTHOR]

An amendment to this paper has been published and can be accessed via a link at the top of the paper. [ABSTRACT FROM AUTHOR]