FAIR data retrieval for sensitive clinical research data in Galaxy

Background: In clinical research, data have to be accessible and reproducible, but the generated data are becoming larger and analysis complex. Here we propose a platform for Findable, Accessible, Interoperable, and Reusable (FAIR) data access and creating reproducible findings. Standardized access to a major genomic repository, the European Genome-Phenome Archive (EGA), has been achieved with API services like PyEGA3. We aim to provide a FAIR data analysis service in Galaxy by retrieving genomic data from the EGA and provide a generalized “omics” platform for FAIR data analysis. Results: To demonstrate this, we implemented an end-to-end Galaxy workflow to replicate the findings from an RD-Connect synthetic dataset Beyond the 1 Million Genomes (synB1MG) available from the EGA. We developed the PyEGA3 connector within Galaxy to easily download multiple datasets from the EGA. We added the gene.iobio tool, a diagnostic environment for precision genomics, to Galaxy and demonstrate that it provides a more dynamic and interpretable view for trio analysis results. We developed a Galaxy trio analysis workflow to determine the pathogenic variants from the synB1MG trios using the GEMINI and gene.iobio tool. The complete workflow is available at WorkflowHub, and an associated tutorial was created in the Galaxy Training Network, which helps researchers unfamiliar with Galaxy to run the workflow. Conclusions: We showed the feasibility of reusing data from the EGA in Galaxy via PyEGA3 and validated the workflow by rediscovering spiked-in variants in synthetic data. Finally, we improved existing tools in Galaxy and created a workflow for trio analysis to demonstrate the value of FAIR genomics analysis in Galaxy.</p

Machine learning in Huntington’s disease:exploring the Enroll-HD dataset for prognosis and driving capability prediction

Background: In biomedicine, machine learning (ML) has proven beneficial for the prognosis and diagnosis of different diseases, including cancer and neurodegenerative disorders. For rare diseases, however, the requirement for large datasets often prevents this approach. Huntington’s disease (HD) is a rare neurodegenerative disorder caused by a CAG repeat expansion in the coding region of the huntingtin gene. The world’s largest observational study for HD, Enroll-HD, describes over 21,000 participants. As such, Enroll-HD is amenable to ML methods. In this study, we pre-processed and imputed Enroll-HD with ML methods to maximise the inclusion of participants and variables. With this dataset we developed models to improve the prediction of the age at onset (AAO) and compared it to the well-established Langbehn formula. In addition, we used recurrent neural networks (RNNs) to demonstrate the utility of ML methods for longitudinal datasets, assessing driving capabilities by learning from previous participant assessments. Results: Simple pre-processing imputed around 42% of missing values in Enroll-HD. Also, 167 variables were retained as a result of imputing with ML. We found that multiple ML models were able to outperform the Langbehn formula. The best ML model (light gradient boosting machine) improved the prognosis of AAO compared to the Langbehn formula by 9.2%, based on root mean squared error in the test set. In addition, our ML model provides more accurate prognosis for a wider CAG repeat range compared to the Langbehn formula. Driving capability was predicted with an accuracy of 85.2%. The resulting pre-processing workflow and code to train the ML models are available to be used for related HD predictions at: https://github.com/JasperO98/hdml/tree/main . Conclusions: Our pre-processing workflow made it possible to resolve the missing values and include most participants and variables in Enroll-HD. We show the added value of a ML approach, which improved AAO predictions and allowed for the development of an advisory model that can assist clinicians and participants in estimating future driving capability.</p

A Systematic Review and Network Meta-Analysis of Pharmacological Treatment of Heart Failure With Reduced Ejection Fraction

Objectives: This study sought to estimate and compare the aggregate treatment benefit of pharmacological therapy for heart failure (HF) with reduced ejection fraction. Background: The estimated treatment effects of various combinations of contemporary HF medical therapies are not well characterized. Methods: We performed a systematic network meta-analysis, using MEDLINE/EMBASE and the Cochrane Central Register of Controlled Trials for randomized controlled trials published between January 1987 and January 2020. We included angiotensin-converting enzyme inhibitors, angiotensin receptor blockers, beta-blockers (BB), mineralocorticoid receptor antagonists (MRAs), digoxin, hydralazine-isosorbide dinitrate, ivabradine, angiotensin receptor–neprilysin inhibitors (ARNi), sodium glucose cotransporter-2 inhibitors (SGLT2i), vericiguat, and omecamtiv-mecarbil. The primary outcome was all-cause death. We estimated the life-years gained in 2 HF populations (BIOSTAT-CHF [BIOlogy Study to TAilored Treatment in Chronic Heart Failure] and ASIAN-HF [Asian Sudden Cardiac Death in Heart Failure Registry]). Results: We identified 75 relevant trials representing 95,444 participants. A combination of ARNi, BB, MRA, and SGLT2i was most effective in reducing all-cause death (HR: 0.39; 95% CI: 0.31-0.49); followed by ARNi, BB, MRA, and vericiguat (HR: 0.41; 95% CI: 0.32-0.53); and ARNi, BB, and MRA (HR: 0.44; 95% CI: 0.36-0.54). Results were similar for the composite outcome of cardiovascular death or first hospitalization for HF (HR: 0.36; 95% CI: 0.29-0.46 for ARNi, BB, MRA, and SGLT2i; HR: 0.44; 95% CI: 0.35-0.56 for ARNi, BB, MRA, and omecamtiv-mecarbil; and HR: 0.43; 95% CI: 0.34-0.55 for ARNi, BB, MRA, and vericiguat). The estimated additional number of life-years gained for a 70-year-old patient on ARNi, BB, MRA, and SGLT2i was 5.0 years (2.5-7.5 years) compared with no treatment in secondary analyses. Conclusions: In patients with HF with reduced ejection fraction, the estimated aggregate benefit is greatest for a combination of ARNi, BB, MRA, and SGLT2i

Cardiovascular and non-cardiovascular death distinction:the utility of troponin beyond N-terminal pro-B-type natriuretic peptide. Findings from the BIOSTAT-CHF study

Aims Heart failure (HF) patients are at high‐risk of cardiovascular (CV) events, including CV death. Nonetheless, a substantial proportion of these patients die from non‐CV causes. Identifying patients at higher risk for each individual event may help selecting patients for clinical trials and tailoring cardiovascular therapies. The aims of the present study are to: (i) characterize patients according to CV vs. non‐CV death; (ii) develop models for the prediction of the respective events; (iii) assess the models' performance to differentiate CV from non‐CV death. Methods and results This study included 2309 patients with HF from the BIOSTAT‐CHF (a systems BIOlogy Study to TAilored Treatment in Chronic Heart Failure) study. Competing‐risk models were used to assess the best combination of variables associated with each cause‐specific death. Results were validated in an independent cohort of 1738 HF patients. The best model to predict CV death included low blood pressure, estimated glomerular filtration rate ≤ 60 mL/min, peripheral oedema, previous HF hospitalization, ischaemic HF, chronic obstructive pulmonary disease, elevated N‐terminal pro‐B‐type natriuretic peptide (NT‐proBNP), and troponin (c‐index = 0.73). The non‐CV death model incorporated age &gt; 75 years, anaemia and elevated NT‐proBNP (c‐index = 0.71). Both CV and non‐CV death rose by quintiles of the risk scores; yet these models allowed the identification of patients in whom absolute CV death rates clearly outweigh non‐CV death ones. These findings were externally replicated, but performed worse in a less severely diseased population. Conclusions Risk models for predicting CV and non‐CV death allowed the identification of patients at higher absolute risk of dying from CV causes (vs. non‐CV ones). Troponin helped in predicting CV death only, whereas NT‐proBNP helped in the prediction of both CV and non‐CV death. These findings can be useful both for tailoring therapies and for patient selection in HF trials in order to attain CV event enrichment

Readmissions, death and its associated predictors in heart failure with preserved versus reduced ejection fraction

BACKGROUND: Data on rehospitalizations for heart failure (HF) in Asia are scarce. We sought to determine the burden and predictors of HF (first and recurrent) rehospitalizations and all‐cause mortality in patients with HF and preserved versus reduced ejection fraction (preserved EF, ≥50%; reduced EF, <40%), in the multinational ASIAN‐HF (Asian Sudden Cardiac Death in Heart Failure) registry. METHODS AND RESULTS: Patients with symptomatic (stage C) chronic HF were followed up for death and recurrent HF hospitalizations for 1 year. Predictors of HF hospitalizations or all‐cause mortality were examined with Cox regression for time to first event and other methods for recurrent events analyses. Among 1666 patients with HF with preserved EF (mean age, 68±12 years; 50% women), and 4479 with HF with reduced EF (mean age, 61±13 years; 22% women), there were 642 and 2302 readmissions, with 28% and 45% attributed to HF, respectively. The 1‐year composite event rate for first HF hospitalization or all‐cause death was 11% and 21%, and for total HF hospitalization and all‐cause death was 17.7 and 38.7 per 100 patient‐years in HF with preserved EF and HF with reduced EF, respectively. In HF with preserved EF, consistent independent predictors of these clinical end points included enrollment as an inpatient, Southeast Asian location, and comorbid chronic kidney disease or atrial fibrillation. The same variables were predictive of outcomes in HF with reduced EF except atrial fibrillation, and also included Northeast Asian location, older age, elevated heart rate, decreased systolic blood pressure, diabetes, smoking, and non‐usage of beta blockers. CONCLUSIONS: One‐year HF rehospitalization and mortality rates were high among Asian patients with HF. Predictors of outcomes identified in this study could aid in risk stratification and timely interventions. REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT01633398

Comparative Genomics and Physiology of Akkermansia muciniphila Isolates from Human Intestine Reveal Specialized Mucosal Adaptation

Akkermansia muciniphila is a champion of mucin degradation in the human gastrointestinal tract. Here, we report the isolation of six novel strains from healthy human donors and their genomic, proteomic and physiological characterization in comparison to the type-strains A. muciniphila Muc(T) and A. glycaniphila Pyt(T). Complete genome sequencing revealed that, despite their large genomic similarity (>97.6%), the novel isolates clustered into two distinct subspecies of A. muciniphila: Amuc1, which includes the type-strain Muc(T), and AmucU, a cluster of unassigned strains that have not yet been well characterized. CRISPR analysis showed all strains to be unique and confirmed that single healthy subjects can carry more than one A. muciniphila strain. Mucin degradation pathways were strongly conserved amongst all isolates, illustrating the exemplary niche adaptation of A. muciniphila to the mucin interface. This was confirmed by analysis of the predicted glycoside hydrolase profiles and supported by comparing the proteomes of A. muciniphila strain H2, belonging to the AmucU cluster, to Muc(T) and A. glycaniphila Pyt(T) (including 610 and 727 proteins, respectively). While some intrinsic resistance was observed among the A. muciniphila straind, none of these seem to pose strain-specific risks in terms of their antibiotic resistance patterns nor a significant risk for the horizontal transfer of antibiotic resistance determinants, opening the way to apply the type-strain Muc(T) or these new A. muciniphila strains as next generation beneficial microbes.Peer reviewe

Prevalence and Prognostic Significance of Frailty in Asian Patients With Heart Failure:Insights From ASIAN-HF

Background: Frailty is common in patients with heart failure (HF) and can adversely impact outcomes. Objectives: This study examined the prevalence of frailty among Asian patients with HF, its association with 1-year outcomes, and if race-ethnicity, HF subtypes, and sex modify this relationship. Methods: In the multinational ASIAN-HF (Asian Sudden Cardiac Death in Heart Failure) registry, a baseline frailty index (FI) was constructed using a cumulative deficits approach with 48 baseline variables, and patients were followed for the 1-year primary outcome of all-cause death or HF hospitalization. Results: Among 3,881 participants (age 61 ± 13 years, 27% female), the mean FI was 0.28 ± 0.11, and 69% were frail (FI &gt;0.21). Higher FI was associated with older age, Malay ethnicity, and Southeast Asian residency. While comorbidities were more frequent in frail patients (by definition), body mass index was not different across frailty classes. Compared with FI class 1 (&lt;0.21, nonfrail), FI class 2 (0.21-0.31) and FI class 3 (&gt;0.31) had increased risk of the 1-year composite outcome (hazard ratios of 1.84 [95% confidence interval (CI): 1.42-2.38] and 4.51 [95% CI: 3.59-5.67], respectively), even after multivariable adjustment (adjusted hazard ratios of 1.49 [95% CI: 1.13-1.97] and 2.69 [95% CI: 2.06-3.50], respectively). Race-ethnicity modified the association of frailty with the composite outcome (Pinteraction = 0.0097), wherein the impact of frailty was strongest among Chinese patients. The association between frailty and outcomes did not differ between men and women (Pinteraction = 0.186) or for HF with reduced ejection fraction versus HF with preserved ejection fraction (Pinteraction = 0.094). Conclusions: Most Asian patients with HF are frail despite relatively young age. Our results reveal specific ethnic (Malay) and regional (Southeast Asia) predisposition to frailty and highlight its prognostic importance, especially in Chinese individuals.</p

Prevalence and Prognostic Significance of Frailty in Asian Patients With Heart Failure:Insights From ASIAN-HF

Background: Frailty is common in patients with heart failure (HF) and can adversely impact outcomes. Objectives: This study examined the prevalence of frailty among Asian patients with HF, its association with 1-year outcomes, and if race-ethnicity, HF subtypes, and sex modify this relationship. Methods: In the multinational ASIAN-HF (Asian Sudden Cardiac Death in Heart Failure) registry, a baseline frailty index (FI) was constructed using a cumulative deficits approach with 48 baseline variables, and patients were followed for the 1-year primary outcome of all-cause death or HF hospitalization. Results: Among 3,881 participants (age 61 ± 13 years, 27% female), the mean FI was 0.28 ± 0.11, and 69% were frail (FI &gt;0.21). Higher FI was associated with older age, Malay ethnicity, and Southeast Asian residency. While comorbidities were more frequent in frail patients (by definition), body mass index was not different across frailty classes. Compared with FI class 1 (&lt;0.21, nonfrail), FI class 2 (0.21-0.31) and FI class 3 (&gt;0.31) had increased risk of the 1-year composite outcome (hazard ratios of 1.84 [95% confidence interval (CI): 1.42-2.38] and 4.51 [95% CI: 3.59-5.67], respectively), even after multivariable adjustment (adjusted hazard ratios of 1.49 [95% CI: 1.13-1.97] and 2.69 [95% CI: 2.06-3.50], respectively). Race-ethnicity modified the association of frailty with the composite outcome (Pinteraction = 0.0097), wherein the impact of frailty was strongest among Chinese patients. The association between frailty and outcomes did not differ between men and women (Pinteraction = 0.186) or for HF with reduced ejection fraction versus HF with preserved ejection fraction (Pinteraction = 0.094). Conclusions: Most Asian patients with HF are frail despite relatively young age. Our results reveal specific ethnic (Malay) and regional (Southeast Asia) predisposition to frailty and highlight its prognostic importance, especially in Chinese individuals.</p