Machine-learning derived measures of vascular and brain age are independently associated with cognitive function

Abstract

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.