Data analysis and creation of epigenetics database

Indiana University-Purdue University Indianapolis (IUPUI)This thesis is aimed at creating a pipeline for analyzing DNA methylation epigenetics data and creating a data model structured well enough to store the analysis results of the pipeline. In addition to storing the results, the model is also designed to hold information which will help researchers to decipher a meaningful epigenetics sense from the results made available. Current major epigenetics resources such as PubMeth, MethyCancer, MethDB and NCBI’s Epigenomics database fail to provide holistic view of epigenetics. They provide datasets produced from different analysis techniques which raises an important issue of data integration. The resources also fail to include numerous factors defining the epigenetic nature of a gene. Some of the resources are also struggling to keep the data stored in their databases up-to-date. This has diminished their validity and coverage of epigenetics data. In this thesis we have tackled a major branch of epigenetics: DNA methylation. As a case study to prove the effectiveness of our pipeline, we have used stage-wise DNA methylation and expression raw data for Lung adenocarcinoma (LUAD) from TCGA data repository. The pipeline helped us to identify progressive methylation patterns across different stages of LUAD. It also identified some key targets which have a potential for being a drug target. Along with the results from methylation data analysis pipeline we combined data from various online data reserves such as KEGG database, GO database, UCSC database and BioGRID database which helped us to overcome the shortcomings of existing data collections and present a resource as complete solution for studying DNA methylation epigenetics data

Valsartan for attenuating disease evolution in early sarcomeric hypertrophic cardiomyopathy: the design of the Valsartan for Attenuating Disease Evolution in Early Sarcomeric Hypertrophic Cardiomyopathy (VANISH) trial

Background: Hypertrophic cardiomyopathy (HCM) is often caused by sarcomere gene mutations, resulting in left ventricular hypertrophy (LVH), myocardial fibrosis, and increased risk of sudden cardiac death and heart failure. Studies in mouse models of sarcomeric HCM demonstrated that early treatment with an angiotensin receptor blocker (ARB) reduced development of LVH and fibrosis. In contrast, prior human studies using ARBs for HCM have targeted heterogeneous adult cohorts with well-established disease. The VANISH trial is testing the safety and feasibility of disease-modifying therapy with an ARB in genotyped HCM patients with early disease. Methods: A randomized, placebo-controlled, double-blind clinical trial is being conducted in sarcomere mutation carriers, 8 to 45 years old, with HCM and no/minimal symptoms, or those with early phenotypic manifestations but no LVH. Participants are randomly assigned to receive valsartan 80 to 320 mg daily (depending on age and weight) or placebo. The primary endpoint is a composite of 9 z-scores in domains representing myocardial injury/hemodynamic stress, cardiac morphology, and function. Total z-scores reflecting change from baseline to final visits will be compared between treatment groups. Secondary endpoints will assess the impact of treatment on mutation carriers without LVH, and analyze the influence of age, sex, and genotype. Conclusions: The VANISH trial is testing a new strategy of disease modification for treating sarcomere mutation carriers with early HCM, and those at risk for its development. In addition, further insight into disease mechanisms, response to therapy, and phenotypic evolution will be gained

VORTEX: Physics-Driven Data Augmentations Using Consistency Training for Robust Accelerated MRI Reconstruction

Deep neural networks have enabled improved image quality and fast inference times for various inverse problems, including accelerated magnetic resonance imaging (MRI) reconstruction. However, such models require a large number of fully-sampled ground truth datasets, which are difficult to curate, and are sensitive to distribution drifts. In this work, we propose applying physics-driven data augmentations for consistency training that leverage our domain knowledge of the forward MRI data acquisition process and MRI physics to achieve improved label efficiency and robustness to clinically-relevant distribution drifts. Our approach, termed VORTEX, (1) demonstrates strong improvements over supervised baselines with and without data augmentation in robustness to signal-to-noise ratio change and motion corruption in data-limited regimes; (2) considerably outperforms state-of-the-art purely image-based data augmentation techniques and self-supervised reconstruction methods on both in-distribution and out-of-distribution data; and (3) enables composing heterogeneous image-based and physics-driven data augmentations. Our code is available at https://github.com/ad12/meddlr.Comment: Accepted to MIDL 202

The International Workshop on Osteoarthritis Imaging Knee MRI Segmentation Challenge: A Multi-Institute Evaluation and Analysis Framework on a Standardized Dataset

Purpose: To organize a knee MRI segmentation challenge for characterizing the semantic and clinical efficacy of automatic segmentation methods relevant for monitoring osteoarthritis progression. Methods: A dataset partition consisting of 3D knee MRI from 88 subjects at two timepoints with ground-truth articular (femoral, tibial, patellar) cartilage and meniscus segmentations was standardized. Challenge submissions and a majority-vote ensemble were evaluated using Dice score, average symmetric surface distance, volumetric overlap error, and coefficient of variation on a hold-out test set. Similarities in network segmentations were evaluated using pairwise Dice correlations. Articular cartilage thickness was computed per-scan and longitudinally. Correlation between thickness error and segmentation metrics was measured using Pearson's coefficient. Two empirical upper bounds for ensemble performance were computed using combinations of model outputs that consolidated true positives and true negatives. Results: Six teams (T1-T6) submitted entries for the challenge. No significant differences were observed across all segmentation metrics for all tissues (p=1.0) among the four top-performing networks (T2, T3, T4, T6). Dice correlations between network pairs were high (>0.85). Per-scan thickness errors were negligible among T1-T4 (p=0.99) and longitudinal changes showed minimal bias (<0.03mm). Low correlations (<0.41) were observed between segmentation metrics and thickness error. The majority-vote ensemble was comparable to top performing networks (p=1.0). Empirical upper bound performances were similar for both combinations (p=1.0). Conclusion: Diverse networks learned to segment the knee similarly where high segmentation accuracy did not correlate to cartilage thickness accuracy. Voting ensembles did not outperform individual networks but may help regularize individual models.Comment: Submitted to Radiology: Artificial Intelligence; Fixed typo

Noise2Recon: Enabling Joint MRI Reconstruction and Denoising with Semi-Supervised and Self-Supervised Learning

Deep learning (DL) has shown promise for faster, high quality accelerated MRI reconstruction. However, supervised DL methods depend on extensive amounts of fully-sampled (labeled) data and are sensitive to out-of-distribution (OOD) shifts, particularly low signal-to-noise ratio (SNR) acquisitions. To alleviate this challenge, we propose Noise2Recon, a model-agnostic, consistency training method for joint MRI reconstruction and denoising that can use both fully-sampled (labeled) and undersampled (unlabeled) scans in semi-supervised and self-supervised settings. With limited or no labeled training data, Noise2Recon outperforms compressed sensing and deep learning baselines, including supervised networks, augmentation-based training, fine-tuned denoisers, and self-supervised methods, and matches performance of supervised models, which were trained with 14x more fully-sampled scans. Noise2Recon also outperforms all baselines, including state-of-the-art fine-tuning and augmentation techniques, among low-SNR scans and when generalizing to other OOD factors, such as changes in acceleration factors and different datasets. Augmentation extent and loss weighting hyperparameters had negligible impact on Noise2Recon compared to supervised methods, which may indicate increased training stability. Our code is available at https://github.com/ad12/meddlr

Growth differentiation factor 15 predicts poor prognosis in patients with heart failure and reduced ejection fraction and anemia: results from RED-HF

Aims - We aimed to assess the value of GDF-15, a stress-responsive cytokine, in predicting clinical outcomes in patients with heart failure (HF) with reduced ejection fraction (HFrEF) and anemia Methods and results - Serum GDF-15 was assessed in 1582 HFrEF and mild-to-moderate anemia patients who where followed for 28 months in the Reduction of Events by Darbepoetin alfa in Heart Failure (RED-HF) trial, an overall neutral RCT evaluating the effect darbepoetin alfa on clinical outcomes in patients with systolic heart failure and mild-to-moderate anemia. Association between baseline and change in GDF-15 during 6 months follow-up and the primary composite outcome of all-cause death or HF hospitalization were evaluated in multivariable Cox-models adjusted for conventional clinical and biochemical risk factors. The adjusted risk for the primary outcome increased with (i) successive tertiles of baseline GDF-15 (tertile 3 HR 1.56 [1.23–1.98] p  Conclusions - In patients with HF and anemia, both higher baseline serum GDF-15 levels and an increase in GDF-15 during follow-up, were associated with worse clinical outcomes. GDF-15 did not identify subgroups of patients who might benefit from correction of anemia but was associated with several indices of anemia and iron status in the HF patients

Impact of Spironolactone on Longitudinal Changes in Health-Related Quality of Life in the Treatment of Preserved Cardiac Function Heart Failure With an Aldosterone Antagonist Trial

BACKGROUND: Heart failure (HF) with preserved ejection fraction patients have equally impaired health-related quality of life (HRQL) compared with those with HF with reduced ejection fraction, but limited studies have evaluated the impact of therapies on changes in HRQL. METHODS AND RESULTS: Patients ≥50 years of age, with symptomatic HF and left ventricular ejection fraction ≥45%, were enrolled in Treatment of Preserved Cardiac Function Heart Failure With an Aldosterone Antagonist (TOPCAT) and randomized to spironolactone or placebo. Patients completed the Kansas City Cardiomyopathy Questionnaire (KCCQ), which was the primary HRQL instrument, and EQ5D visual analog scale at baseline, 4 months, 12 months, and annually thereafter. McMaster Overall Treatment Evaluation was assessed at 4 and 12 months to assess global change scores. Change scores (+SD) were calculated to determine between-group differences, and multivariable repeated-measures models were created to identify other factors associated with change scores. Paired KCCQ data were available for 91.7% of 3445 TOPCAT patients. By 4 months, the mean change in KCCQ was 7.7±16 and mean change in EQ5D visual analog scale was 4.7±16. Adjusted mean changes in KCCQ for the spironolactone group were significantly better than those for the placebo group at 4-month (1.54 better; P=0.002), 12-month (1.35 better; P=0.02), and 36-month (1.86 better; P=0.02) visits. No between-group differences in EQ5D visual analog scale change scores or McMaster Overall Treatment Evaluation were noted. Older age, obesity, current smoking, New York Heart Association class III/IV, and comorbid illnesses were associated with declines in KCCQ scores. Use of spironolactone was an independent predictor of improved KCCQ scores. CONCLUSIONS: In symptomatic HF with preserved ejection fraction patients, use of spironolactone was associated with an improvement in HF-specific HRQL. Several modifiable risk factors were associated with HRQL deterioration. CLINICAL TRIAL REGISTRATION: URL: http://www.clinicaltrials.gov. Unique identifier: NCT00094302

Influence of ejection fraction on cause‐specific mortality in heart failure with preserved ejection fraction

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/143697/1/ejhf1040.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/143697/2/ejhf1040_am.pd

N-terminal pro-B-type natriuretic peptide by itself predicts death and cardiovascular events in high-risk patients with type 2 diabetes

Background: NT‐proBNP (N‐terminal pro‐B‐type natriuretic peptide) improves the discriminatory ability of risk‐prediction models in type 2 diabetes mellitus (T2DM) but is not yet used in clinical practice. We assessed the discriminatory strength of NT‐proBNP by itself for death and cardiovascular events in high‐risk patients with T2DM. Methods and Results: Cox proportional hazards were used to create a base model formed by 20 variables. The discriminatory ability of the base model was compared with that of NT‐proBNP alone and with NT‐proBNP added, using C‐statistics. We studied 5509 patients (with complete data) of 8561 patients with T2DM and cardiovascular and/or chronic kidney disease who were enrolled in the ALTITUDE (Aliskiren in Type 2 Diabetes Using Cardiorenal Endpoints) trial. During a median 2.6‐year follow‐up period, 469 patients died and 768 had a cardiovascular composite outcome (cardiovascular death, resuscitated cardiac arrest, nonfatal myocardial infarction, stroke, or heart failure hospitalization). NT‐proBNP alone was as discriminatory as the base model for predicting death (C‐statistic, 0.745 versus 0.744, P=0.95) and the cardiovascular composite outcome (C‐statistic, 0.723 versus 0.731, P=0.37). When NT‐proBNP was added, it increased the predictive ability of the base model for death (C‐statistic, 0.779 versus 0.744, P&lt;0.001) and for cardiovascular composite outcome (C‐statistic, 0.763 versus 0.731, P&lt;0.001). Conclusions: In high‐risk patients with T2DM, NT‐proBNP by itself demonstrated discriminatory ability similar to a multivariable model in predicting both death and cardiovascular events and should be considered for risk stratification