Genetic Drivers of Heterogeneity in Type 2 Diabetes Pathophysiology

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. 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 \u3c 5 × 10-8) 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 scores5 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

Genetic drivers of heterogeneity in type 2 diabetes pathophysiology

Type 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes1,2 and molecular mechanisms that are often specific to cell type3,4. 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 &lt; 5 × 10-8) 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 scores5 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.</p

Attributable Hospital Cost and Length of Stay Associated with Health Care-Associated Infections Caused by Antibiotic-Resistant Gram-Negative Bacteria▿

Determination of the attributable hospital cost and length of stay (LOS) are of critical importance for patients, providers, and payers who must make rational and informed decisions about patient care and the allocation of resources. The objective of the present study was to determine the additional total hospital cost and LOS attributable to health care-associated infections (HAIs) caused by antibiotic-resistant, gram-negative (GN) pathogens. A single-center, retrospective, observational comparative cohort study was performed. The study involved 662 patients admitted from 2000 to 2008 who developed HAIs caused by one of following pathogens: Acinetobacter spp., Enterobacter spp., Escherichia coli, Klebsiella spp., or Pseudomonas spp. The attributable total hospital cost and LOS for HAIs caused by antibiotic-resistant GN pathogens were determined by comparison with the hospital costs and LOS for a control group with HAIs due to antibiotic-susceptible GN pathogens. Statistical analyses were conducted by using univariate and multivariate analyses. Twenty-nine percent of the HAIs were caused by resistant GN pathogens, and almost 16% involved a multidrug-resistant GN pathogen. The additional total hospital cost and LOS attributable to antibiotic-resistant HAIs caused by GN pathogens were 29.3% (P < 0.0001; 95% confidence interval, 16.23 to 42.35) and 23.8% (P = 0.0003; 95% confidence interval, 11.01 to 36.56) higher than those attributable to HAIs caused by antibiotic-susceptible GN pathogens, respectively. Significant covariates in the multivariate analysis were age ≥12 years, pneumonia, intensive care unit stay, and neutropenia. HAIs caused by antibiotic-resistant GN pathogens were associated with significantly higher total hospital costs and increased LOSs compared to those caused by their susceptible counterparts. This information should be used to assess the potential cost-efficacy of interventions aimed at the prevention of such infections

Estimating the Length of Hospitalization Attributable to Multidrug Antibiotic Resistance

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