Benavente, Ernest Diez

Abstract Background and purpose A heart age biomarker has been developed using deep neural networks applied to electrocardiograms. Whether this biomarker is associated with cognitive function was investigated. Methods Using 12‐lead electrocardiograms, heart age was estimated for a population‐based sample ( N = 7779, age 40–85 years, 45.3% men). Associations between heart delta age (HDA) and cognitive test scores were studied adjusted for cardiovascular risk factors. In addition, the relationship between HDA, brain delta age (BDA) and cognitive test scores was investigated in mediation analysis. Results Significant associations between HDA and the Word test, Digit Symbol Coding Test and tapping test scores were found. HDA was correlated with BDA (Pearson's r = 0.12, p = 0.0001). Moreover, 13% (95% confidence interval 3–36) of the HDA effect on the tapping test score was mediated through BDA. Discussion Heart delta age, representing the cumulative effects of life‐long exposures, was associated with brain age. HDA was associated with cognitive function that was minimally explained through BDA.

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): UiT The Arctic University of Norway, Northern Norway Regional Health Authority. Background/Introduction Machine learning models have been applied to magnetic resonance images of the brain to estimate brain age gap as a biomarker of biological aging. Because of its association with cognitive performance and neurological disorders, brain age gap has become an indicator of general brain health. Among others, cardiovascular risk factors are associated with brain health. Recently a novel non-invasive biomarker of vascular ageing has been proposed based on the application of a deep-learning algorithm to digitised 12-lead electrocardiograms. The difference between electrocardiogram predicted age and chronological age is defined as vascular age gap and has been shown to be associated with an adverse cardiovascular profile. However, the association of vascular age gap with cognitive health and dementias has not yet been examined. Purpose We apply a causal mediation framework to vascular age gap, brain age gap and cognitive function data obtained in the same individuals who took part in a population-based study in Norway. We hypothesise that cardiovascular risk factors encapsulated in vascular age gap can indirectly affect cognitive function by increasing the brain age gap. Methods We applied a deep learning algorithm (1) to electrocardiograms (N=7779, age 40-85 years) collected from the population-based sample in Norway (mean age 63.8 years, 45.3% men) to obtain a vascular age gap. For a subsample (N=1693) magnetic resonance images of the brain were also available and another deep neural network algorithm (2) was applied to obtain a biomarker of brain age. For 1678 participants with both vascular age gap and brain age gap available we used mediation analysis to test whether brain age gap mediates the effects of vascular age gap on cognitive function (as measured by mini mental status test, word memory test, digit–symbol coding test, tapping test) by estimating total, direct and indirect effects. Results We found a weak correlation (Pearson’s r = 0.12) between vascular age gap and brain age gap. Both age gaps were associated with cognitive scores capturing psychomotor speed, attention and mental flexibility. For a 1 standard deviation (7.3 years) increase in vascular age gap digit-symbol coding test score decreased by 0.63 (95%CI: -0.89, -0.36; p<0.001) points, and tapping test decreased by 0.36 (95%CI: -0.56, -0.14; p=0.001). Only 13% of effect of vascular age gap on cognitive test results was mediated by brain age gap. Conclusion(s) Vascular age gap was weakly associated with poorer cognitive functioning, and only 13% of this association was mediated by brain age gap. This suggests that cognitive decline linked to accumulated damage of the vascular system is not fully captured by structural changes in the brain. Further investigations of novel measures of biological aging in relation to cognitive decline are warranted as these may hold promise for improved understanding of mechanisms and clinical prediction.

Abstract 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 as recent work has found differences from chronological age ("delta age") to be associated with mortality and co-morbidities. However, the genetic underpinning of delta age is unknown, but crucial for understanding underlying individual risk. By performing a genome-wide association study using UK Biobank data (n=34,432), we identified 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.

Abstract Background Left ventricular systolic dysfunction (LVSD) is associated with increased morbidity and mortality. Although there are effective treatments for patients with LVSD to prevent mortality, heart failure and to improve symptoms, many patients remain undetected and untreated. We have recently derived a deep learning algorithm to detect LVSD using the electrocardiogram (ECG) which could have an important screening role, particularly in limited resources settings. We evaluated the accuracy of this algorithm for the first time in Africa in a sample of subjects attending a cardiology clinic. Methods We conducted a retrospective study in a general cardiac clinic in Uganda. Consecutive patients ≥18 years who had a digital ECG and echocardiogram done within two days of each other were included. We excluded patients with pacemakers or missing information regarding left ventricular ejection fraction (LVEF). Routine 10-second, twelve-lead surface rest ECG were performed using an Edan PC ECG Model SE-1515, Hamburg, Germany. The probability of LVSD was estimated with the Mayo Clinic artificial intelligence (AI) ECG algorithm. LVEF was calculated by the MMode (Teichholz method) using a Philips Ultrasound system, HD7XE, Bothel, Washington, USA. LVSD was defined as a LVEF≤35%. We assessed the overall diagnostic performance of the algorithm to identify LVSD in this population with the area under the receiver operating curve (AUC), and estimated sensitivity, specificity and accuracy using a pre-specified cut-off based on the probability for LVSD generated by the algorithm. We conducted secondary analyses using different LVEF cutoff values. Results We included 634 subjects, 32% (200) of whom had hypertension and 12% (77) clinical heart failure. Mean age was 57±18.8 years, 58% were women and the overall prevalence of LVSD was 4%. The AI-ECG had an AUC of 0.866 (see figure below), sensitivity 73.08%, specificity 91.10%, negative predictive value 98.75%, positive predictive value 26.03% and an accuracy of 90.96% using the original threshold. Using the optimal cutoff based on the AUCs, the sensitivity was 80.77% and specificity was 81.05% with a negative predictive value of 98.99%. The ROC for the detection of LVEF of 40% or below was 0.821. Conclusion The Mayo AI-ECG algorithm demonstrated good accuracy, sensitivity and specificity to detect LVSD in patients seen in a clinical setting in Uganda. This tool may facilitate the identification of people at a high risk for LVSD in settings with low resources. ROC Funding Acknowledgement Type of funding source: None