ECG-based Cardiac Screening Programs: Legal, Ethical and Logistical Considerations

Screening asymptomatic people with a resting electrocardiogram (ECG) has been theorised to detect latent cardiovascular disease. However, resting ECG screening is not recommended for numerous populations, such as asymptomatic middle-aged (sedentary) people, as it is not sufficiently sensitive to detect coronary artery disease. While the issues raised in this article are largely common to all screening programs, this review focuses on two distinct programs: (1) screening elite athletes for conditions associated with sudden cardiac death (SCD); and (2) screening people aged ?65 years for atrial fibrillation (AF). These two settings have recently gained attention for their promise and concerns regarding prevention of SCD and stroke, respectively. If screening is done, it must be done well. Organisations conducting screening must consider a range of legal, ethical and logistical responsibilities which arise from the beginning to end of the process. This includes consideration of who to screen, timing of screening, whether it is mandatory, consent issues, and auditing systems to ensure quality control. Good infrastructure for interpretation of ECG results according to expert guidelines, and follow-up testing for abnormal screening results, including a pathway to treatment, are essential. Finally, there may be significant implications for those diagnosed with cardiac disease, including insurance, employment, the ability to play sport and mental health issues. There are several legal risks, and the best protective measures are good communication systems, thorough clinical records, careful handling of eligibility questions for those diagnosed, and reference to expert guidelines as the standard of ca

In a large primary care data set, the CHA₂DS₂-VASc score leads to an almost universal recommendation for anticoagulation treatment in those aged ≥65 years with atrial fibrillation

From 2012 to 2016, the oral anticoagulant (OAC) treatment determination for atrial fibrillation (AF) patients moved from the CHADS2 score to the CHA2DS2-VASc score. A data set collated during previous studies (2011–19) with de-identified data extracted from clinical records at a single timepoint for active adult patients (n = 285 635; 8294 with AF) attending 164 general practices in Australia was analysed. The CHA2DS2-VASc threshold (score ≥2 men/≥3 women) captured a significantly higher proportion than CHADS2≥2 (all ages: 85 vs. 68%, P < 0.0001; ≥65 years: 96 vs. 76%, P < 0.0001). The change from CHADS2 to CHA2DS2-VASc resulted in a significantly higher proportion of AF patients being recommended OAC, driven by the revised scoring for age

Albuminuria Changes and Cardiovascular and Renal Outcomes in Type 1 Diabetes: The DCCT/EDIC Study.

Background and objectives In trials of people with type 2 diabetes, albuminuria reduction with renin-angiotensin system inhibitors is associated with lower risks of cardiovascular events and CKD progression. We tested whether progression or remission of microalbuminuria is associated with cardiovascular and renal risk in a well characterized cohort of type 1 diabetes. Design, setting, participants, & measurements We studied 1441 participants in the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications study. Albumin excretion rate (AER) was quantified annually or biennially for up to 30 years. For each participant, albuminuria status was defined over time as normoalbuminuria (AER continuously \u3c30 mg/d), sustained microalbuminuria (AER, 30–299 mg/d on two consecutive visits), macroalbuminuria (AER≥300 mg/d), or remitted microalbuminuria (transition from sustained microalbuminuria to AER\u3c30 mg/d on two consecutive visits). We tested associations of time-updated albuminuria status with adjudicated clinical cardiovascular events, the development of reduced GFR (\u3c60 ml/min per 1.73 m2 on two consecutive visits), and subclinical cardiovascular disease. Results At least one cardiovascular event occurred in 184 participants, and 98 participants developed reduced eGFR. Compared with normoalbuminuria, sustained microalbuminuria, remitted microalbuminuria, and macroalbuminuria were each associated with higher risk of cardiovascular events (adjusted hazard ratios [HRs] and 95% confidence intervals [95% CIs]: 1.79 [1.13 to 2.85], 2.62 [1.68 to 4.07], and 2.65 [1.68 to 4.19], respectively) and reduced eGFR (adjusted HRs [95% CIs], 5.26 [2.43 to 11.41], 4.36 [1.80 to 10.57], and 54.35 [30.79 to 95.94], respectively). Compared with sustained microalbuminuria, remission to normoalbuminuria was not associated with reduced risk of cardiovascular events (adjusted HR, 1.33; 95% CI, 0.68 to 2.59) or reduced eGFR (adjusted HR, 1.75; 95% CI, 0.56 to 5.49). Compared with normoalbuminuria, sustained microalbuminuria, remitted microalbuminuria, and macroalbuminuria were associated with greater carotid intima-media thickness, and macroalbuminuria was associated with a greater degree of coronary artery calcification. Conclusions In type 1 diabetes, microalbuminuria and macroalbuminuria are associated with higher risks of cardiovascular disease and reduced eGFR, but achieving a remission of established microalbuminuria to normoalbuminuria does not appear to improve outcomes

Urinary MicroRNA Profiling in the Nephropathy of Type 1 Diabetes

Background: Patients with Type 1 Diabetes (T1D) are particularly vulnerable to development of Diabetic nephropathy (DN) leading to End Stage Renal Disease. Hence a better understanding of the factors affecting kidney disease progression in T1D is urgently needed. In recent years microRNAs have emerged as important post-transcriptional regulators of gene expression in many different health conditions. We hypothesized that urinary microRNA profile of patients will differ in the different stages of diabetic renal disease. Methods and Findings: We studied urine microRNA profiles with qPCR in 40 T1D with >20 year follow up 10 who never developed renal disease (N) matched against 10 patients who went on to develop overt nephropathy (DN), 10 patients with intermittent microalbuminuria (IMA) matched against 10 patients with persistent (PMA) microalbuminuria. A Bayesian procedure was used to normalize and convert raw signals to expression ratios. We applied formal statistical techniques to translate fold changes to profiles of microRNA targets which were then used to make inferences about biological pathways in the Gene Ontology and REACTOME structured vocabularies. A total of 27 microRNAs were found to be present at significantly different levels in different stages of untreated nephropathy. These microRNAs mapped to overlapping pathways pertaining to growth factor signaling and renal fibrosis known to be targeted in diabetic kidney disease. Conclusions: Urinary microRNA profiles differ across the different stages of diabetic nephropathy. Previous work using experimental, clinical chemistry or biopsy samples has demonstrated differential expression of many of these microRNAs in a variety of chronic renal conditions and diabetes. Combining expression ratios of microRNAs with formal inferences about their predicted mRNA targets and associated biological pathways may yield useful markers for early diagnosis and risk stratification of DN in T1D by inferring the alteration of renal molecular processes. © 2013 Argyropoulos et al

International Olympic Committee consensus statement: Methods for recording and reporting of epidemiological data on injury and illness in sport 2020 (including STROBE Extension for Sport Injury and Illness Surveillance (STROBE-SIIS))

Injury and illness surveillance, and epidemiological studies, are fundamental elements of concerted efforts to protect the health of the athlete. To encourage consistency in the definitions and methodology used, and to enable data across studies to be compared, research groups have published 11 sport-specific or setting-specific consensus statements on sports injury (and, eventually, illness) epidemiology to date. Our objective was to further strengthen consistency in data collection, injury definitions and research reporting through an updated set of recommendations for sports injury and illness studies, including a new Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) checklist extension. The IOC invited a working group of international experts to review relevant literature and provide recommendations. The procedure included an open online survey, several stages of text drafting and consultation by working groups and a 3-day consensus meeting in October 2019. This statement includes recommendations for data collection and research reporting covering key components: defining and classifying health problems; severity of health problems; capturing and reporting athlete exposure; expressing risk; burden of health problems; study population characteristics and data collection methods. Based on these, we also developed a new reporting guideline as a STROBE Extension -the STROBE Sports Injury and Illness Surveillance (STROBE-SIIS). The IOC encourages ongoing in-and out-of-competition surveillance programmes and studies to describe injury and illness trends and patterns, understand their causes and develop measures to protect the health of the athlete. Implementation of the methods outlined in this statement will advance consistency in data collection and research reporting. © Author(s) (or their employer(s)) 2020

Stratification of candidate genes for Parkinson’s disease using weighted protein interaction network analysis

Genome wide association studies (GWAS) have helped identify large numbers of genetic loci that significantly associate with increased risk of developing diseases. However, translating genetic knowledge into understanding of the molecular mechanisms underpinning disease (i.e. disease-specific impacted biological processes) has to date proved to be a major challenge. This is primarily due to difficulties in confidently defining candidate genes at GWAS-risk loci. The goal of this study was to better characterize candidate genes within GWAS loci using a protein interactome based approach and with Parkinson's disease (PD) data as a test case.We applied a recently developed Weighted Protein-Protein Interaction Network Analysis (WPPINA) pipeline as a means to define impacted biological processes, risk pathways and therein key functional players. We used previously established Mendelian forms of PD to identify seed proteins, and to construct a protein network for genetic Parkinson's and carried out functional enrichment analyses. We isolated PD-specific processes indicating 'mitochondria stressors mediated cell death', 'immune response and signaling', and 'waste disposal' mediated through 'autophagy'. Merging the resulting protein network with data from Parkinson's GWAS we confirmed 10 candidate genes previously selected by pure proximity and were able to nominate 17 novel candidate genes for sporadic PD.With this study, we were able to better characterize the underlying genetic and functional architecture of idiopathic PD, thus validating WPPINA as a robust pipeline for the in silico genetic and functional dissection of complex disorders

Genome, transcriptome and proteome: the rise of omics data and their integration in biomedical sciences

Advances in the technologies and informatics used to generate and process large biological data sets (omics data) are promoting a critical shift in the study of biomedical sciences. While genomics, transcriptomics and proteinomics, coupled with bioinformatics and biostatistics, are gaining momentum, they are still, for the most part, assessed individually with distinct approaches generating monothematic rather than integrated knowledge. As other areas of biomedical sciences, including metabolomics, epigenomics and pharmacogenomics, are moving towards the omics scale, we are witnessing the rise of inter-disciplinary data integration strategies to support a better understanding of biological systems and eventually the development of successful precision medicine. This review cuts across the boundaries between genomics, transcriptomics and proteomics, summarizing how omics data are generated, analysed and shared, and provides an overview of the current strengths and weaknesses of this global approach. This work intends to target students and researchers seeking knowledge outside of their field of expertise and fosters a leap from the reductionist to the global-integrative analytical approach in research

How Much Rugby is Too Much? A Seven-Season Prospective Cohort Study of Match Exposure and Injury Risk in Professional Rugby Union Players.

INTRODUCTION: Numerous studies have documented the incidence and nature of injuries in professional rugby union, but few have identified specific risk factors for injury in this population using appropriate statistical methods. In particular, little is known about the role of previous short-term or longer-term match exposures in current injury risk in this setting. OBJECTIVES: Our objective was to investigate the influence that match exposure has upon injury risk in rugby union. METHOD: We conducted a seven-season (2006/7-2012/13) prospective cohort study of time-loss injuries in 1253 English premiership professional players. Players' 12-month match exposure (number of matches a player was involved in for ≥20 min in the preceding 12 months) and 1-month match exposure (number of full-game equivalent [FGE] matches in preceding 30 days) were assessed as risk factors for injury using a nested frailty model and magnitude-based inferences. RESULTS: The 12-month match exposure was associated with injury risk in a non-linear fashion; players who had been involved in fewer than ≈15 or more than ≈35 matches over the preceding 12-month period were more susceptible to injury. Monthly match exposure was linearly associated with injury risk (hazard ratio [HR]: 1.14 per 2 standard deviation [3.2 FGE] increase, 90% confidence interval [CI] 1.08-1.20; likely harmful), although this effect was substantially attenuated for players in the upper quartile for 12-month match exposures (>28 matches). CONCLUSION: A player's accumulated (12-month) and recent (1-month) match exposure substantially influences their current injury risk. Careful attention should be paid to planning the workloads and monitoring the responses of players involved in: (1) a high (>≈35) number of matches in the previous year, (2) a low (<≈15) number of matches in the previous year, and (3) a low-moderate number of matches in previous year but who have played intensively in the recent past. These findings make a major contribution to evidence-based policy decisions regarding match workload limits in professional rugby union

How much is too much? (Part 2) International Olympic Committee consensus statement on load in sport and risk of illness

The modern-day athlete participating in elite sports is exposed to high training loads and increasingly saturated competition calendar. Emerging evidence indicates that inappropriate load management is a significant risk factor for acute illness and the overtraining syndrome. The IOC convened an expert group to review the scientific evidence for the relationship of load - including rapid changes in training and competition load, competition calendar congestion, psychological load and travel - and health outcomes in sport. This paper summarises the results linking load to risk of illness and overtraining in athletes, and provides athletes, coaches and support staff with practical guidelines for appropriate load management to reduce the risk of illness and overtraining in sport. These include guidelines for prescription of training and competition load, as well as for monitoring of training, competition and psychological load, athlete well-being and illness. In the process, urgent research priorities were identified

The Protein-Protein Interaction tasks of BioCreative III: classification/ranking of articles and linking bio-ontology concepts to full text

BACKGROUND: Determining usefulness of biomedical text mining systems requires realistic task definition and data selection criteria without artificial constraints, measuring performance aspects that go beyond traditional metrics. The BioCreative III Protein-Protein Interaction (PPI) tasks were motivated by such considerations, trying to address aspects including how the end user would oversee the generated output, for instance by providing ranked results, textual evidence for human interpretation or measuring time savings by using automated systems. Detecting articles describing complex biological events like PPIs was addressed in the Article Classification Task (ACT), where participants were asked to implement tools for detecting PPI-describing abstracts. Therefore the BCIII-ACT corpus was provided, which includes a training, development and test set of over 12,000 PPI relevant and non-relevant PubMed abstracts labeled manually by domain experts and recording also the human classification times. The Interaction Method Task (IMT) went beyond abstracts and required mining for associations between more than 3,500 full text articles and interaction detection method ontology concepts that had been applied to detect the PPIs reported in them.RESULTS:A total of 11 teams participated in at least one of the two PPI tasks (10 in ACT and 8 in the IMT) and a total of 62 persons were involved either as participants or in preparing data sets/evaluating these tasks. Per task, each team was allowed to submit five runs offline and another five online via the BioCreative Meta-Server. From the 52 runs submitted for the ACT, the highest Matthew's Correlation Coefficient (MCC) score measured was 0.55 at an accuracy of 89 and the best AUC iP/R was 68. Most ACT teams explored machine learning methods, some of them also used lexical resources like MeSH terms, PSI-MI concepts or particular lists of verbs and nouns, some integrated NER approaches. For the IMT, a total of 42 runs were evaluated by comparing systems against manually generated annotations done by curators from the BioGRID and MINT databases. The highest AUC iP/R achieved by any run was 53, the best MCC score 0.55. In case of competitive systems with an acceptable recall (above 35) the macro-averaged precision ranged between 50 and 80, with a maximum F-Score of 55. CONCLUSIONS: The results of the ACT task of BioCreative III indicate that classification of large unbalanced article collections reflecting the real class imbalance is still challenging. Nevertheless, text-mining tools that report ranked lists of relevant articles for manual selection can potentially reduce the time needed to identify half of the relevant articles to less than 1/4 of the time when compared to unranked results. Detecting associations between full text articles and interaction detection method PSI-MI terms (IMT) is more difficult than might be anticipated. This is due to the variability of method term mentions, errors resulting from pre-processing of articles provided as PDF files, and the heterogeneity and different granularity of method term concepts encountered in the ontology. However, combining the sophisticated techniques developed by the participants with supporting evidence strings derived from the articles for human interpretation could result in practical modules for biological annotation workflows