Machine-learning to Stratify Diabetic Patients Using Novel Cardiac Biomarkers and Integrative Genomics

Background: Diabetes mellitus is a chronic disease that impacts an increasing percentage of people each year. Among its comorbidities, diabetics are two to four times more likely to develop cardiovascular diseases. While HbA1c remains the primary diagnostic for diabetics, its ability to predict long-term, health outcomes across diverse demographics, ethnic groups, and at a personalized level are limited. The purpose of this study was to provide a model for precision medicine through the implementation of machine-learning algorithms using multiple cardiac biomarkers as a means for predicting diabetes mellitus development. Methods: Right atrial appendages from 50 patients, 30 non-diabetic and 20 type 2 diabetic, were procured from the WVU Ruby Memorial Hospital. Machine-learning was applied to physiological, biochemical, and sequencing data for each patient. Supervised learning implementing SHapley Additive exPlanations (SHAP) allowed binary (no diabetes or type 2 diabetes) and multiple classifcation (no diabetes, prediabetes, and type 2 diabetes) of the patient cohort with and without the inclusion of HbA1c levels. Findings were validated through Logistic Regression (LR), Linear Discriminant Analysis (LDA), Gaussian Naïve Bayes (NB), Support Vector Machine (SVM), and Classifcation and Regression Tree (CART) models with tenfold cross validation. Results: Total nuclear methylation and hydroxymethylation were highly correlated to diabetic status, with nuclear methylation and mitochondrial electron transport chain (ETC) activities achieving superior testing accuracies in the predictive model (~84% testing, binary). Mitochondrial DNA SNPs found in the D-Loop region (SNP-73G, -16126C, and -16362C) were highly associated with diabetes mellitus. The CpG island of transcription factor A, mitochondrial (TFAM) revealed CpG24 (chr10:58385262, P=0.003) and CpG29 (chr10:58385324, P=0.001) as markers correlating with diabetic progression. When combining the most predictive factors from each set, total nuclear methylation and CpG24 methylation were the best diagnostic measures in both binary and multiple classifcation sets. Conclusions: Using machine-learning, we were able to identify novel as well as the most relevant biomarkers associated with type 2 diabetes mellitus by integrating physiological, biochemical, and sequencing datasets. Ultimately, this approach may be used as a guideline for future investigations into disease pathogenesis and novel biomarker discover

Evaluation of ion exchange processes in drug-eluting embolization beads by use of an improved flow-through elution method.

n improved method for evaluating drug release behaviour of drug-eluting embolization beads (DEBs) was developed utilizing an open-loop flow-through system, in which the beads were packed into an occlusive mass within the system and extracted with a flowing elution medium over time. Glass beads were introduced into the beads mass in order to ensure laminar flow, reduce dead volume and improve reproducibility by compensating for swelling phenomena. The effects of glass bead ratio, elution medium flow rate and ion concentration, DEB size and drug concentration and drug type (doxorubicin and irinotecan) were evaluated using DEB composed of a sulfonate-modified polyvinyl alcohol hydrogel (DC Bead™) as the test article. The rate and amount of drug elution from the packed beads was affected by flow rate, the bead size and initial loading dose. The raw data from the concentration profile analysis provided valuable information to reveal the drug elution behaviour akin to the pharmacokinetic data observed for embolized beads (yielding in vitro Cmax and tmax data) which was complementary to the normal cumulative data obtained. A good correlation with historical reported in vivo data validated the usefulness of the method for predicting in vivo drug elution behaviour

Developmental morphology of cover crop species exhibit contrasting behaviour to changes in soil bulk density, revealed by X-ray computed tomography

Plant roots growing through soil typically encounter considerable structural heterogeneity, and local variations in soil dry bulk density. The way the in situ architecture of root systems of different species respond to such heterogeneity is poorly understood due to challenges in visualising roots growing in soil. The objective of this study was to visualise and quantify the impact of abrupt changes in soil bulk density on the roots of three cover crop species with contrasting inherent root morphologies, viz. tillage radish (Raphanus sativus), vetch (Vicia sativa) and black oat (Avena strigosa). The species were grown in soil columns containing a two-layer compaction treatment featuring a 1.2 g cm-3 (uncompacted) zone overlaying a 1.4 g cm-3 (compacted) zone. Three-dimensional visualisations of the root architecture were generated via X-ray computed tomography, and an automated root-segmentation imaging algorithm. Three classes of behaviour were manifest as a result of roots encountering the compacted interface, directly related to the species. For radish, there was switch from a single tap-root to multiple perpendicular roots which penetrated the compacted zone, whilst for vetch primary roots were diverted more horizontally with limited lateral growth at less acute angles. Black oat roots penetrated the compacted zone with no apparent deviation. Smaller root volume, surface area and lateral growth were consistently observed in the compacted zone in comparison to the uncompacted zone across all species. The rapid transition in soil bulk density had a large effect on root morphology that differed greatly between species, with major implications for how these cover crops will modify and interact with soil structure

World guidelines for falls prevention and management for older adults: a global initiative

Background: falls and fall-related injuries are common in older adults, have negative effects on functional independence and quality of life and are associated with increased morbidity, mortality and health related costs. Current guidelines are inconsistent, with no up-to-date, globally applicable ones present. Objectives: to create a set of evidence- and expert consensus-based falls prevention and management recommendations applicable to older adults for use by healthcare and other professionals that consider: (i) a person-centred approach that includes the perspectives of older adults with lived experience, caregivers and other stakeholders; (ii) gaps in previous guidelines; (iii) recent developments in e-health and (iv) implementation across locations with limited access to resources such as low- and middle-income countries. Methods: a steering committee and a worldwide multidisciplinary group of experts and stakeholders, including older adults, were assembled. Geriatrics and gerontological societies were represented. Using a modified Delphi process, recommendations from 11 topic-specific working groups (WGs), 10 ad-hoc WGs and a WG dealing with the perspectives of older adults were reviewed and refined. The final recommendations were determined by voting. Recommendations: all older adults should be advised on falls prevention and physical activity. Opportunistic case finding for falls risk is recommended for community-dwelling older adults. Those considered at high risk should be offered a comprehensive multifactorial falls risk assessment with a view to co-design and implement personalised multidomain interventions. Other recommendations cover details of assessment and intervention components and combinations, and recommendations for specific settings and populations. Conclusions: the core set of recommendations provided will require flexible implementation strategies that consider both local context and resources

Magnitude and Durability of the Antibody Response to mRNA-Based Vaccination Among SARS-CoV-2 Seronegative and Seropositive Health Care Personnel

Few studies have described changes in SARS-CoV-2 antibody levels in response to infection and vaccination at frequent intervals and over extended follow-up periods. The purpose of this study was to assess changes in SARS-CoV-2–specific antibody responses among a prospective cohort of health care personnel over 18 months with up to 22 samples per person. Antibody levels and live virus neutralization were measured before and after mRNA-based vaccination with results stratified by (1) SARS-CoV-2 infection status prior to initial vaccination and (2) SARS-CoV-2 infection at any point during follow-up. We found that the antibody response to the first dose was almost 2-fold higher in individuals who were seropositive prior to vaccination, although neutralization titers were more variable. The antibody response induced by vaccination appeared to wane over time but generally persisted for 8 to 9 months, and those who were infected at any point during the study had slightly higher antibody levels over time vs those who remained uninfected. These findings underscore the need to account for SARS-CoV-2 natural infection as a modifier of vaccine responses, and they highlight the importance of frequent testing of longitudinal antibody titers over time. Together, our results provide a clearer understanding of the trajectories of antibody response among vaccinated individuals with and without prior SARS-CoV-2 infection

Functional annotation of human long noncoding RNAs via molecular phenotyping

Long noncoding RNAs (lncRNAs) constitute the majority of transcripts in the mammalian genomes, and yet, their functions remain largely unknown. As part of the FANTOM6 project, we systematically knocked down the expression of 285 lncRNAs in human dermal fibroblasts and quantified cellular growth, morphological changes, and transcriptomic responses using Capped Analysis of Gene Expression (CAGE). Antisense oligonucleotides targeting the same lncRNAs exhibited global concordance, and the molecular phenotype, measured by CAGE, recapitulated the observed cellular phenotypes while providing additional insights on the affected genes and pathways. Here, we disseminate the largest-todate lncRNA knockdown data set with molecular phenotyping (over 1000 CAGE deep-sequencing libraries) for further exploration and highlight functional roles for ZNF213-AS1 and lnc-KHDC3L-2.Peer reviewe

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 < 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

Trans-ancestry genome-wide association meta-analysis of prostate cancer identifies new susceptibility loci and informs genetic risk prediction.

Prostate cancer is a highly heritable disease with large disparities in incidence rates across ancestry populations. We conducted a multiancestry meta-analysis of prostate cancer genome-wide association studies (107,247 cases and 127,006 controls) and identified 86 new genetic risk variants independently associated with prostate cancer risk, bringing the total to 269 known risk variants. The top genetic risk score (GRS) decile was associated with odds ratios that ranged from 5.06 (95% confidence interval (CI), 4.84-5.29) for men of European ancestry to 3.74 (95% CI, 3.36-4.17) for men of African ancestry. Men of African ancestry were estimated to have a mean GRS that was 2.18-times higher (95% CI, 2.14-2.22), and men of East Asian ancestry 0.73-times lower (95% CI, 0.71-0.76), than men of European ancestry. These findings support the role of germline variation contributing to population differences in prostate cancer risk, with the GRS offering an approach for personalized risk prediction

Characterizing Prostate Cancer Risk Through Multi-Ancestry Genome-Wide Discovery of 187 Novel Risk Variants

The transferability and clinical value of genetic risk scores (GRSs) across populations remain limited due to an imbalance in genetic studies across ancestrally diverse populations. Here we conducted a multi-ancestry genome-wide association study of 156,319 prostate cancer cases and 788,443 controls of European, African, Asian and Hispanic men, reflecting a 57% increase in the number of non-European cases over previous prostate cancer genome-wide association studies. We identified 187 novel risk variants for prostate cancer, increasing the total number of risk variants to 451. An externally replicated multi-ancestry GRS was associated with risk that ranged from 1.8 (per standard deviation) in African ancestry men to 2.2 in European ancestry men. The GRS was associated with a greater risk of aggressive versus non-aggressive disease in men of African ancestry (P = 0.03). Our study presents novel prostate cancer susceptibility loci and a GRS with effective risk stratification across ancestry groups

Germline variation at 8q24 and prostate cancer risk in men of European ancestry

Chromosome 8q24 is a susceptibility locus for multiple cancers, including prostate cancer. Here we combine genetic data across the 8q24 susceptibility region from 71,535 prostate cancer cases and 52,935 controls of European ancestry to define the overall contribution of germline variation at 8q24 to prostate cancer risk. We identify 12 independent risk signals for prostate cancer (p < 4.28 × 10−15), including three risk variants that have yet to be reported. From a polygenic risk score (PRS) model, derived to assess the cumulative effect of risk variants at 8q24, men in the top 1% of the PRS have a 4-fold (95%CI = 3.62–4.40) greater risk compared to the population average. These 12 variants account for ~25% of what can be currently explained of the familial risk of prostate cancer by known genetic risk factors. These findings highlight the overwhelming contribution of germline variation at 8q24 on prostate cancer risk which has implications for population risk stratification