Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes and molecular mechanisms that are often specific to cell type. Here, to characterize the genetic contribution...
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Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes and molecular mechanisms that are often specific to cell type. Here, to characterize the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study data from 2,535,601 individuals (39.7% not of European ancestry), including 428,452 cases of T2D. We identify 1,289 independent association signals at genome-wide significance (P < 5 × 10) that map to 611 loci, of which 145 loci are, to our knowledge, previously unreported. We define eight non-overlapping clusters of T2D signals that are characterized by distinct profiles of cardiometabolic trait associations. These clusters are differentially enriched for cell-type-specific regions of open chromatin, including pancreatic islets, adipocytes, endothelial cells and enteroendocrine cells. We build cluster-specific partitioned polygenic scores in a further 279,552 individuals of diverse ancestry, including 30,288 cases of T2D, and test their association with T2D-related vascular outcomes. Cluster-specific partitioned polygenic scores are associated with coronary artery disease, peripheral artery disease and end-stage diabetic nephropathy across ancestry groups, highlighting the importance of obesity-related processes in the development of vascular outcomes. Our findings show the value of integrating multi-ancestry genome-wide association study data with single-cell epigenomics to disentangle the aetiological heterogeneity that drives the development and progression of T2D. This might offer a route to optimize global access to genetically informed diabetes care.
Systemic sclerosis (SSc) remains a challenging and enigmatic systemic autoimmune disease, owing to its complex pathogenesis, clinical and molecular heterogeneity, and the lack of effective disease-modifying treatments...
Systemic sclerosis (SSc) remains a challenging and enigmatic systemic autoimmune disease, owing to its complex pathogenesis, clinical and molecular heterogeneity, and the lack of effective disease-modifying treatments. Despite a century of research in SSc, the interconnections among microvascular dysfunction, autoimmune phenomena and tissue fibrosis in SSc remain unclear. The absence of validated biomarkers and reliable animal models complicates diagnosis and treatment, contributing to high morbidity and mortality. Advances in the past 5 years, such as single-cell RNA sequencing, next-generation sequencing, spatial biology, transcriptomics, genomics, proteomics, metabolomics, microbiome profiling and artificial intelligence, offer new avenues for identifying the early pathogenetic events that, once treated, could change the clinical history of SSc. Collaborative global efforts to integrate these approaches are crucial to developing a comprehensive, mechanistic understanding and enabling personalized therapies. Challenges include disease classification, clinical heterogeneity and the establishment of robust biomarkers for disease activity and progression. Innovative clinical trial designs and patient-centred approaches are essential for developing effective treatments. Emerging therapies, including cell-based and fibroblast-targeting treatments, show promise. Global cooperation, standardized protocols and interdisciplinary research are vital for advancing SSc research and improving patient outcomes. The integration of advanced research techniques holds the potential for important breakthroughs in the diagnosis, treatment and care of individuals with SSc.
The consumption of energy levels has increased in association with economic growth and concurrently increased the energy demand from renewable sources. The need under Sustainable Development Goals (SDG) intends to exp...
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The consumption of energy levels has increased in association with economic growth and concurrently increased the energy demand from renewable sources. The need under Sustainable Development Goals (SDG) intends to explore various technological advancements for the utilization of waste to energy. Municipal Solid Waste (MSW) has been reported as constructive feedstock to produce biofuels, biofuel carriers and biochemicals using energy-efficient technologies in risk freeways. The present review contemplates risk assessment and challenges in sorting and transportation of MSW and different aspects of conversion of MSW into energy are critically analysed. The circular bioeconomy of energy production strategies and management of waste are also analysed. The current scenario on MSW and its impacts on the environment are elucidated in conjunction with various policies and amendments equipped for the competent management of MSW in order to fabricate a sustained environment.
Galaxy clusters, the largest gravitationally bound structures in the Universe, contain vast amounts of dark matter, galaxies, and hot ionised gas known as the intracluster medium (ICM). In relaxed cluster cores, the I...
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With recent Nobel Prizes recognising AI contributions to science, Large Language Models (LLMs) are transforming scientific research by enhancing productivity and reshaping the scientific method. LLMs are now involved ...
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Background: Despite major advances in artificial intelligence (AI) research for healthcare, the deployment and adoption of AI technologies remain limited in clinical practice. In recent years, concerns have been raise...
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We present an analysis of the first two XRISM/Resolve spectra of the well-known Seyfert-1.5 active galactic nucleus in NGC 4151, obtained in December 2023. Our work focuses on the nature of the narrow Fe Kα emission ...
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