Deep learning-derived cardiovascular age shares a genetic basis with other cardiac phenotypes

Artifcial intelligence (AI)-based approaches can now use electrocardiograms (ECGs) to provide expertlevel performance in detecting heart abnormalities and diagnosing disease. Additionally, patient age predicted from ECGs by AI models has shown great potential as a biomarker for cardiovascular age, where recent work has found its deviation from chronological age (“delta age”) to be associated with mortality and co-morbidities. However, despite being crucial for understanding underlying individual risk, the genetic underpinning of delta age is unknown. In this work we performed a genome-wide association study using UK Biobank data (n=34,432) and identifed eight loci associated with delta age (p ≤ 5 × 10−8), including genes linked to cardiovascular disease (CVD) (e.g. SCN5A) and (heart) muscle development (e.g. TTN). Our results indicate that the genetic basis of cardiovascular ageing is predominantly determined by genes directly involved with the cardiovascular system rather than those connected to more general mechanisms of ageing. Our insights inform the epidemiology of CVD, with implications for preventative and precision medicine

Studying accelerated cardiovascular ageing in Russian adults through a novel deep-learning ECG biomarker [version 1; peer review: awaiting peer review]

Background: A non-invasive, easy-to-access marker of accelerated cardiac ageing would provide novel insights into the mechanisms and aetiology of cardiovascular disease (CVD) as well as contribute to risk stratification of those who have not had a heart or circulatory event. Our hypothesis is that differences between an ECG-predicted and chronologic age of participants (δage) would reflect accelerated or decelerated cardiovascular ageing. Methods: A convolutional neural network model trained on over 700,000 ECGs from the Mayo Clinic in the U.S.A was used to predict the age of 4,542 participants in the Know Your Heart study conducted in two cities in Russia (2015-2018). Thereafter, δage was used in linear regression models to assess associations with known CVD risk factors and markers of cardiac abnormalities. / Results: The biomarker δage (mean: +5.32 years) was strongly and positively associated with established risk factors for CVD: blood pressure, body mass index (BMI), total cholesterol and smoking. Additionally, δage had strong independent positive associations with markers of structural cardiac abnormalities: N-terminal pro b-type natriuretic peptide (NT-proBNP), high sensitivity cardiac troponin T (hs-cTnT) and pulse wave velocity, a valid marker of vascular ageing. / Conclusion: The difference between the ECG-age obtained from a convolutional neural network and chronologic age (δage) contains information about the level of exposure of an individual to established CVD risk factors and to markers of cardiac damage in a way that is consistent with it being a biomarker of accelerated cardiovascular (vascular) ageing. Further research is needed to explore whether these associations are seen in populations with different risks of CVD events, and to better understand the underlying mechanisms involved

External validation of a deep learning electrocardiogram algorithm to detect ventricular dysfunction

Objective - To validate a novel artificial-intelligence electrocardiogram algorithm (AI-ECG) to detect left ventricular systolic dysfunction (LVSD) in an external population. Background - LVSD, even when asymptomatic, confers increased morbidity and mortality. We recently derived AI-ECG to detect LVSD using ECGs based on a large sample of patients treated at the Mayo Clinic. Methods - We performed an external validation study with subjects from the Know Your Heart Study, a cross-sectional study of adults aged 35–69 years residing in two cities in Russia, who had undergone both ECG and transthoracic echocardiography. LVSD was defined as left ventricular ejection fraction ≤ 35%. We assessed the performance of the AI-ECG to identify LVSD in this distinct patient population. Results - Among 4277 subjects in this external population-based validation study, 0.6% had LVSD (compared to 7.8% of the original clinical derivation study). The overall performance of the AI-ECG to detect LVSD was robust with an area under the receiver operating curve of 0.82. When using the LVSD probability cut-off of 0.256 from the original derivation study, the sensitivity, specificity, and accuracy in this population were 26.9%, 97.4%, 97.0%, respectively. Other probability cut-offs were analysed for different sensitivity values. Conclusions - The AI-ECG detected LVSD with robust test performance in a population that was very different from that used to develop the algorithm. Population-specific cut-offs may be necessary for clinical implementation. Differences in population characteristics, ECG and echocardiographic data quality may affect test performance

Deep learning-derived cardiovascular age shares a genetic basis with other cardiac phenotypes.

Artificial intelligence (AI)-based approaches can now use electrocardiograms (ECGs) to provide expert-level performance in detecting heart abnormalities and diagnosing disease. Additionally, patient age predicted from ECGs by AI models has shown great potential as a biomarker for cardiovascular age, where recent work has found its deviation from chronological age ("delta age") to be associated with mortality and co-morbidities. However, despite being crucial for understanding underlying individual risk, the genetic underpinning of delta age is unknown. In this work we performed a genome-wide association study using UK Biobank data (n=34,432) and identified eight loci associated with delta age ([Formula: see text]), including genes linked to cardiovascular disease (CVD) (e.g. SCN5A) and (heart) muscle development (e.g. TTN). Our results indicate that the genetic basis of cardiovascular ageing is predominantly determined by genes directly involved with the cardiovascular system rather than those connected to more general mechanisms of ageing. Our insights inform the epidemiology of CVD, with implications for preventative and precision medicine

Reduction of the ATPase inhibitory factor 1 (IF1) leads to visual impairment in vertebrates

In vertebrates, mitochondria are tightly preserved energy producing organelles, which sustain nervous system development and function. The understanding of proteins that regulate their homoeostasis in complex animals is therefore critical and doing so via means of systemic analysis pivotal to inform pathophysiological conditions associated with mitochondrial deficiency. With the goal to decipher the role of the ATPase inhibitory factor 1 (IF1) in brain development, we employed the zebrafish as elected model reporting that the Atpif1a−/− zebrafish mutant, pinotage (pnttq209), which lacks one of the two IF1 paralogous, exhibits visual impairment alongside increased apoptotic bodies and neuroinflammation in both brain and retina. This associates with increased processing of the dynamin-like GTPase optic atrophy 1 (OPA1), whose ablation is a direct cause of inherited optic atrophy. Defects in vision associated with the processing of OPA1 are specular in Atpif1−/− mice thus confirming a regulatory axis, which interlinks IF1 and OPA1 in the definition of mitochondrial fitness and specialised brain functions. This study unveils a functional relay between IF1 and OPA1 in central nervous system besides representing an example of how the zebrafish model could be harnessed to infer the activity of mitochondrial proteins during development

Search for chargino-neutralino production with mass splittings near the electroweak scale in three-lepton final states in √s=13 TeV pp collisions with the ATLAS detector

A search for supersymmetry through the pair production of electroweakinos with mass splittings near the electroweak scale and decaying via on-shell W and Z bosons is presented for a three-lepton final state. The analyzed proton-proton collision data taken at a center-of-mass energy of √s=13  TeV were collected between 2015 and 2018 by the ATLAS experiment at the Large Hadron Collider, corresponding to an integrated luminosity of 139  fb−1. A search, emulating the recursive jigsaw reconstruction technique with easily reproducible laboratory-frame variables, is performed. The two excesses observed in the 2015–2016 data recursive jigsaw analysis in the low-mass three-lepton phase space are reproduced. Results with the full data set are in agreement with the Standard Model expectations. They are interpreted to set exclusion limits at the 95% confidence level on simplified models of chargino-neutralino pair production for masses up to 345 GeV

Clinical phenotypes of acute heart failure based on signs and symptoms of perfusion and congestion at emergency department presentation and their relationship with patient management and outcomes

Objective To compare the clinical characteristics and outcomes of patients with acute heart failure (AHF) according to clinical profiles based on congestion and perfusion determined in the emergency department (ED). Methods and results Overall, 11 261 unselected AHF patients from 41 Spanish EDs were classified according to perfusion (normoperfusion = warm; hypoperfusion = cold) and congestion (not = dry; yes = wet). Baseline and decompensation characteristics were recorded as were the main wards to which patients were admitted. The primary outcome was 1-year all-cause mortality; secondary outcomes were need for hospitalisation during the index AHF event, in-hospital all-cause mortality, prolonged hospitalisation, 7-day post-discharge ED revisit for AHF and 30-day post-discharge rehospitalisation for AHF. A total of 8558 patients (76.0%) were warm+ wet, 1929 (17.1%) cold+ wet, 675 (6.0%) warm+ dry, and 99 (0.9%) cold+ dry; hypoperfused (cold) patients were more frequently admitted to intensive care units and geriatrics departments, and warm+ wet patients were discharged home without admission. The four phenotypes differed in most of the baseline and decompensation characteristics. The 1-year mortality was 30.8%, and compared to warm+ dry, the adjusted hazard ratios were significantly increased for cold+ wet (1.660; 95% confidence interval 1.400-1.968) and cold+ dry (1.672; 95% confidence interval 1.189-2.351). Hypoperfused (cold) phenotypes also showed higher rates of index episode hospitalisation and in-hospital mortality, while congestive (wet) phenotypes had a higher risk of prolonged hospitalisation but decreased risk of rehospitalisation. No differences were observed among phenotypes in ED revisit risk. Conclusions Bedside clinical evaluation of congestion and perfusion of AHF patients upon ED arrival and classification according to phenotypic profiles proposed by the latest European Society of Cardiology guidelines provide useful complementary information and help to rapidly predict patient outcomes shortly after ED patient arrival

Open Access Solutions for Biodiversity Journals: Don’t Replace One Problem with Another

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Open access solutions for biodiversity journals : Do not replace one problem with another

Peer reviewe

Correlates of preclinical cardiovascular disease in Indigenous and Non-Indigenous Australians: a case control study

Background. The high frequency of premature death from cardiovascular disease in indigenous Australians is often attributed to the high prevalence of risk factors, especially type II diabetes mellitus (DM). We evaluated the relationship of ethnicity to atherosclerotic burden, as evidenced by carotid intima-media thickness (IMT), independent of risk factor status. Methods. We studied 227 subjects (147 men; 50 ± 13 y): 119 indigenous subjects with (IDM, n = 54), and without DM (InDM, n = 65), 108 Caucasian subjects with (CDM, n = 52), and without DM (CnDM, n = 56). IMT was measured according to standard methods and compared with clinical data and cardiovascular risk factors. Results. In subjects both with and without DM, IMT was significantly greater in indigenous subjects. There were no significant differences in gender, body mass index (BMI), systolic blood pressure (SBP), or diastolic blood pressure (DBP) between any of the groups, and subjects with DM showed no difference in plasma HbA1c. Cardiovascular risk factors were significantly more prevalent in indigenous subjects. Nonetheless, ethnicity (β = -0.34; p < 0.0001), age (β = 0.48; p < 0.0001), and smoking (β = 0.13; p < 0.007) were independent predictors of IMT in multiple linear regression models. Conclusion. Ethnicity appears to be an independent correlate of preclinical cardiovascular disease, even after correction for the high prevalence of cardiovascular risk factors in indigenous Australians. Standard approaches to control currently known risk factors are vital to reduce the burden of cardiovascular disease, but in themselves may be insufficient to fully address the high prevalence in this population