One Year of Yoga Training Alters Ghrelin Axis in Centrally Obese Adults With Metabolic Syndrome

Introduction: Metabolic syndrome (MetS) is a multiplex cardiometabolic manifestation associated with type 2 diabetes mellitus and cardiovascular diseases. Yoga training has been shown to alleviate MetS. Recently, circulatory ghrelin profile was demonstrated to be associated with MetS. This study examined the effects of 1 year of yoga training on β-cell function and insulin resistance, and the involvement of metabolic peptides, including unacylated ghrelin (UnAG), acylated ghrelin (AG), obestatin, growth hormone (GH), and insulin, in the beneficial effects of yoga training in centrally obese adults with MetS.Methods: This was a follow up study, in which data of risk factors of MetS, physical performance tests [resting heart rate (HR), chair stand test (CS), chair sit and reach test (CSR), back scratch test (BS), and single leg stand tests (SLS)] and serum samples of 79 centrally obese MetS subjects aged 58 ± 8 years (39 subjects received 1-year yoga training and 40 subjects received no training) were retrieved for analyses. β-cell function and insulin resistance were examined by Homeostasis Model Assessment (HOMA). Circulating levels of UnAG, AG, obestatin, GH, and insulin were determined by enzyme-linked immunosorbent assay using fasting serum samples. Generalized estimating equation analysis and Mann–Whitney U-test were used to detect statistically significant differences between groups.Results: Waist circumference (WC) was significantly decreased after yoga intervention (control: +2%; yoga: -4%). Significant improvements in HR (control: +2%; yoga: -5%), CS (control: -1%; yoga: +24%), CSR left (control: worsen by 0.90 cm; yoga: improved by 4.21 cm), CSR right (control: worsen by 0.75 cm; yoga: improved by 4.28 cm), right side of BS (control: improved by 0.19 cm; yoga: improved by 4.31 cm), SLS left (control: -10%; yoga: +86%), and SLS right (control: -6%; yoga: +47%) were observed after 1-year yoga training. No significant difference was found between the two groups in insulin, HOMA indices, and disposition index. Yoga training significantly increased circulating GH (control: -3%; yoga: +22%), total circulating ghrelin (control: -26%; yoga: +13%), and UnAG (control: -27%; yoga: +14%), whereas decreased AG (control: -7%; yoga: -33%) and obestatin (control: +24%; yoga: -29%).Conclusion: One-year of yoga training modulated total ghrelin, UnAG, AG, obestatin, and GH while exerting beneficial effects on physical functions and central obesity in adults with MetS. The beneficial effects of yoga may be associated with the alteration of ghrelin gene product and GH

HDAC9 is implicated in atherosclerotic aortic calcification and affects vascular smooth muscle cell phenotype.

Aortic calcification is an important independent predictor of future cardiovascular events. We performed a genome-wide association meta-analysis to determine SNPs associated with the extent of abdominal aortic calcification (n = 9,417) or descending thoracic aortic calcification (n = 8,422). Two genetic loci, HDAC9 and RAP1GAP, were associated with abdominal aortic calcification at a genome-wide level (P < 5.0 × 10-8). No SNPs were associated with thoracic aortic calcification at the genome-wide threshold. Increased expression of HDAC9 in human aortic smooth muscle cells promoted calcification and reduced contractility, while inhibition of HDAC9 in human aortic smooth muscle cells inhibited calcification and enhanced cell contractility. In matrix Gla protein-deficient mice, a model of human vascular calcification, mice lacking HDAC9 had a 40% reduction in aortic calcification and improved survival. This translational genomic study identifies the first genetic risk locus associated with calcification of the abdominal aorta and describes a previously unknown role for HDAC9 in the development of vascular calcification

Identification of physicochemical selective pressure on protein encoding nucleotide sequences

BACKGROUND: Statistical methods for identifying positively selected sites in protein coding regions are one of the most commonly used tools in evolutionary bioinformatics. However, they have been limited by not taking the physiochemical properties of amino acids into account. RESULTS: We develop a new codon-based likelihood model for detecting site-specific selection pressures acting on specific physicochemical properties. Nonsynonymous substitutions are divided into substitutions that differ with respect to the physicochemical properties of interest, and those that do not. The substitution rates of these two types of changes, relative to the synonymous substitution rate, are then described by two parameters, γ and ω respectively. The new model allows us to perform likelihood ratio tests for positive selection acting on specific physicochemical properties of interest. The new method is first used to analyze simulated data and is shown to have good power and accuracy in detecting physicochemical selective pressure. We then re-analyze data from the class-I alleles of the human Major Histocompatibility Complex (MHC) and from the abalone sperm lysine. CONCLUSION: Our new method allows a more flexible framework to identify selection pressure on particular physicochemical properties

Genetic feature engineering enables characterisation of shared risk factors in immune-mediated diseases

Abstract: Background: Genome-wide association studies (GWAS) have identified pervasive sharing of genetic architectures across multiple immune-mediated diseases (IMD). By learning the genetic basis of IMD risk from common diseases, this sharing can be exploited to enable analysis of less frequent IMD where, due to limited sample size, traditional GWAS techniques are challenging. Methods: Exploiting ideas from Bayesian genetic fine-mapping, we developed a disease-focused shrinkage approach to allow us to distill genetic risk components from GWAS summary statistics for a set of related diseases. We applied this technique to 13 larger GWAS of common IMD, deriving a reduced dimension “basis” that summarised the multidimensional components of genetic risk. We used independent datasets including the UK Biobank to assess the performance of the basis and characterise individual axes. Finally, we projected summary GWAS data for smaller IMD studies, with less than 1000 cases, to assess whether the approach was able to provide additional insights into genetic architecture of less common IMD or IMD subtypes, where cohort collection is challenging. Results: We identified 13 IMD genetic risk components. The projection of independent UK Biobank data demonstrated the IMD specificity and accuracy of the basis even for traits with very limited case-size (e.g. vitiligo, 150 cases). Projection of additional IMD-relevant studies allowed us to add biological interpretation to specific components, e.g. related to raised eosinophil counts in blood and serum concentration of the chemokine CXCL10 (IP-10). On application to 22 rare IMD and IMD subtypes, we were able to not only highlight subtype-discriminating axes (e.g. for juvenile idiopathic arthritis) but also suggest eight novel genetic associations. Conclusions: Requiring only summary-level data, our unsupervised approach allows the genetic architectures across any range of clinically related traits to be characterised in fewer dimensions. This facilitates the analysis of studies with modest sample size by matching shared axes of both genetic and biological risk across a wider disease domain, and provides an evidence base for possible therapeutic repurposing opportunities

Federated Learning on Heterogenous Data using Chest CT

Large data have accelerated advances in AI. While it is well known that population differences from genetics, sex, race, diet, and various environmental factors contribute significantly to disease, AI studies in medicine have largely focused on locoregional patient cohorts with less diverse data sources. Such limitation stems from barriers to large-scale data share in medicine and ethical concerns over data privacy. Federated learning (FL) is one potential pathway for AI development that enables learning across hospitals without data share. In this study, we show the results of various FL strategies on one of the largest and most diverse COVID-19 chest CT datasets: 21 participating hospitals across five continents that comprise >10,000 patients with >1 million images. We present three techniques: Fed Averaging (FedAvg), Incremental Institutional Learning (IIL), and Cyclical Incremental Institutional Learning (CIIL). We also propose an FL strategy that leverages synthetically generated data to overcome class imbalances and data size disparities across centers. We show that FL can achieve comparable performance to Centralized Data Sharing (CDS) while maintaining high performance across sites with small, underrepresented data. We investigate the strengths and weaknesses for all technical approaches on this heterogeneous dataset including the robustness to non-Independent and identically distributed (non-IID) diversity of data. We also describe the sources of data heterogeneity such as age, sex, and site locations in the context of FL and show how even among the correctly labeled populations, disparities can arise due to these biases

Genomic and molecular characterization of preterm birth.

Preterm birth (PTB) complications are the leading cause of long-term morbidity and mortality in children. By using whole blood samples, we integrated whole-genome sequencing (WGS), RNA sequencing (RNA-seq), and DNA methylation data for 270 PTB and 521 control families. We analyzed this combined dataset to identify genomic variants associated with PTB and secondary analyses to identify variants associated with very early PTB (VEPTB) as well as other subcategories of disease that may contribute to PTB. We identified differentially expressed genes (DEGs) and methylated genomic loci and performed expression and methylation quantitative trait loci analyses to link genomic variants to these expression and methylation changes. We performed enrichment tests to identify overlaps between new and known PTB candidate gene systems. We identified 160 significant genomic variants associated with PTB-related phenotypes. The most significant variants, DEGs, and differentially methylated loci were associated with VEPTB. Integration of all data types identified a set of 72 candidate biomarker genes for VEPTB, encompassing genes and those previously associated with PTB. Notably, PTB-associated genes RAB31 and RBPJ were identified by all three data types (WGS, RNA-seq, and methylation). Pathways associated with VEPTB include EGFR and prolactin signaling pathways, inflammation- and immunity-related pathways, chemokine signaling, IFN-γ signaling, and Notch1 signaling. Progress in identifying molecular components of a complex disease is aided by integrated analyses of multiple molecular data types and clinical data. With these data, and by stratifying PTB by subphenotype, we have identified associations between VEPTB and the underlying biology

An interdisciplinary team communication framework and its application to healthcare 'e-teams' systems design

<p>Abstract</p> <p>Background</p> <p>There are few studies that examine the processes that interdisciplinary teams engage in and how we can design health information systems (HIS) to support those team processes. This was an exploratory study with two purposes: (1) To develop a framework for interdisciplinary team communication based on structures, processes and outcomes that were identified as having occurred during weekly team meetings. (2) To use the framework to guide 'e-teams' HIS design to support interdisciplinary team meeting communication.</p> <p>Methods</p> <p>An ethnographic approach was used to collect data on two interdisciplinary teams. Qualitative content analysis was used to analyze the data according to structures, processes and outcomes.</p> <p>Results</p> <p>We present details for team meta-concepts of structures, processes and outcomes and the concepts and sub concepts within each meta-concept. We also provide an exploratory framework for interdisciplinary team communication and describe how the framework can guide HIS design to support 'e-teams'.</p> <p>Conclusion</p> <p>The structures, processes and outcomes that describe interdisciplinary teams are complex and often occur in a non-linear fashion. Electronic data support, process facilitation and team video conferencing are three HIS tools that can enhance team function.</p