Lindekleiv, Haakon

Objective To assess the association between educational level and cardiovascular age acceleration metric derived from ECG, and to determine whether this association is mediated by established cardiovascular disease (CVD) risk factors. Design Prospective population-based cohort study (the Tromsø Study). Setting General population of the Tromsø municipality, Norway. Participants The study sample consisted of 4367 participants of the Tromsø Study, who took part in both Tromsø6 (2007–2008) and Tromsø7 (2015–2016), had a 12-lead ECG obtained at Tromsø7 and did not report a history of heart attack, stroke or atrial fibrillation. Primary outcome measures δ-age, a biomarker of cardiovascular ageing, is defined as the difference (in years) between an individual’s ECG-predicted heart age and their chronological age. ECG-predicted heart age was estimated using a previously validated deep neural network. Results Our findings indicate an inverse association between education and δ-age, with a regression coefficient per increment increase in education of −0.24 (95% CI −0.41 to –0.07) in the overall sample, −0.38 (95% CI −0.59 to –0.16) for women and −0.04 (95% CI −0.31 to 0.23) for men. Participants with the highest level of education (university/college for 4 or more years) had the lowest estimated δ-age with a regression coefficient of −0.69 years (95% CI −1.23 to –0.16) compared with the group with primary education for the overall sample, −1.05 years (95% CI −1.73 to –0.37) for women and −0.15 years (95% CI −1.03 to 0.73) for men. CVD risk factors mediated up to 75% of the association between overall education and δ-age, and 80% of the association among those with the highest education level (university/college for 4 or more years). Among women, 50% of the effect of overall education on δ-age was mediated by CVD risk factors, rising to 53% in the category with the highest level of education. However, in the subsample of men, there was no significant association between education and δ-age, and the mediation analysis produced natural direct and indirect effects pointing in opposite directions. Conclusions Cardiovascular ageing is inversely associated with educational level, an effect that appears to be largely mediated through established risk factors.

Introduction: Deep neural networks are increasingly used to generate diagnostic algorithms in cardiovascular medicine. A biomarker of cardiovascular aging, derived from a deep-learning algorithm applied to digitized 12-lead electrocardiograms (ECGs), has recently been introduced. This biomarker, delta age (δ-age), is defined as the difference between predicted ECG age and chronological age. Hypthesis: We hypothesized that δ-age will improve the prediction of incident fatal and non-fatal myocardial infarction (MI) and stroke on top of what contemporary CVD prediction tools would do. Methods: In this cohort study, we included 7,111 men and women from the Norwegian Tromsø Study conducted in 2015-16, with follow-up through 2021 for incident fatal and non-fatal myocardial infarction (MI) and hemorrhagic or cerebral stroke. We used Cox proportional hazards regression models to assess the independent effect of δ-age on MI and stroke. Discrimination was evaluated using Harrell’s concordance statistic (C-index) and the net reclassification improvement (NRI). Results: During a median follow-up of 5.9 years, we observed 155 incident cases of MI, 141 cases stroke and 290 cases with either MI or stroke. δ-age was observed with a mean of 0 and standard deviation of 6.2 years. In men and women combined, hazard ratios (HRs) per standard deviation increase in δ-age, after adjustment for traditional risk factors, were 1.24 (95% confidence interval (CI) 1.09, 1.41) for the combined outcome, 1.26 (1.06, 1.49) for MI and 1.25 (1.03, 1.50) for stroke. In men, the corresponding HRs were 1.27 (1.09, 1.49), 1.46 (1.18, 1.79) and 1.02 (0.80, 1.31), respectively, and in women, 1.20 (0.97, 1.49), 0.87 (0.64, 1.20) and 1.58 (1.19, 2.11), respectively. The C-index increased modestly when δ-age was added to a model with traditional risk factors. The 95% CI for the C-index increase excluded zero for the combined outcome overall, and for MI in men and for stroke in women. The NRI was 26.0% (13.3%, 38.1%) for the combined outcome, 17.5% (0.6%, 33.5%) for MI and 37.2% (20.1%, 53.0%) for stroke. Conclusions: Incorporating δ-age into primary prevention risk prediction models significantly improved performance beyond traditional cardiovascular risk factors for the combined outcome and separately for MI and stroke.

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.