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Abstract
We developed a novel interpretable biological heart age estimation model using cardiovascular magnetic resonance radiomics measures of ventricular shape and myocardial character. We included 29,996 UK Biobank participants without cardiovascular disease. Images were segmented using an automated analysis pipeline. We extracted 254 radiomics features from the left ventricle, right ventricle, and myocardium of each study. We then used Bayesian ridge regression with tenfold cross-validation to develop a heart age estimation model using the radiomics features as the model input and chronological age as the model output. We examined associations of radiomics features with heart age in men and women, observing sex-differential patterns. We subtracted actual age from model estimated heart age to calculate a “heart age delta”, which we considered as a measure of heart aging. We performed a phenome-wide association study of 701 exposures with heart age delta. The strongest correlates of heart aging were measures of obesity, adverse serum lipid markers, hypertension, diabetes, heart rate, income, multimorbidity, musculoskeletal health, and respiratory health. This technique provides a new method for phenotypic assessment relating to cardiovascular aging; further studies are required to assess whether it provides incremental risk information over current approaches.
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1 Queen Mary University of London, William Harvey Research Institute, NIHR Barts Biomedical Research Centre, London, UK (GRID:grid.4868.2) (ISNI:0000 0001 2171 1133); Barts Health NHS Trust, Barts Heart Centre, St Bartholomew’s Hospital, London, UK (GRID:grid.139534.9) (ISNI:0000 0001 0372 5777)
2 University of Verona, Department of Computer Science, Verona, Italy (GRID:grid.5611.3) (ISNI:0000 0004 1763 1124); University of Barcelona, Dept. de Matematiques I Informatica, Barcelona, Spain (GRID:grid.5841.8) (ISNI:0000 0004 1937 0247)
3 University of Barcelona, Dept. de Matematiques I Informatica, Barcelona, Spain (GRID:grid.5841.8) (ISNI:0000 0004 1937 0247)
4 University of Verona, Department of Computer Science, Verona, Italy (GRID:grid.5611.3) (ISNI:0000 0004 1763 1124)
5 University of Southampton, MRC Lifecourse Epidemiology Centre, Southampton, UK (GRID:grid.5491.9) (ISNI:0000 0004 1936 9297); University of Southampton and University Hospital Southampton NHS Foundation Trust, NIHR Southampton Biomedical Research Centre, Southampton, UK (GRID:grid.430506.4) (ISNI:0000 0004 0465 4079)
6 Queen Mary University of London, William Harvey Research Institute, NIHR Barts Biomedical Research Centre, London, UK (GRID:grid.4868.2) (ISNI:0000 0001 2171 1133); Barts Health NHS Trust, Barts Heart Centre, St Bartholomew’s Hospital, London, UK (GRID:grid.139534.9) (ISNI:0000 0001 0372 5777); Health Data Research UK, London, UK (GRID:grid.507332.0) (ISNI:0000 0004 9548 940X); Alan Turing Institute, London, UK (GRID:grid.499548.d) (ISNI:0000 0004 5903 3632)