Text

Background: Manually extracted body composition (BC) measures from a single-slice area (A) are associated with cardiometabolic outcomes. Artificial intelligence enables automated volumetric (V) segmentation, but it is unknown whether BC carries prognostic value to predict mortality in the general population. We developed and tested a deep learning framework to quantify volumetric BC from whole-body magnetic resonance imaging (MRI) and investigated their prognostic value to predict mortality in a large Western population.

Methods: The framework was developed using data from the UK Biobank (UKBB) and the German National Cohort (NAKO). BC was defined as subcutaneous (SAT), visceral (VAT), intramuscular adipose tissue (IMAT), skeletal muscle (SM), and SM fat fraction (SMFF). Cox regression assessed the prognostic value of BC in the UKBB. Additionally, we extracted BC areas for every level of the thoracic and lumbar spine (i) to compare the volumetric to the established area-based BC approach and (ii) to investigate their correlation on the level of each vertebra.

Findings: In 36,317 UKBB participants (mean age 65.1±7.8 y, 51.7% female; 1.7% all-cause deaths; median follow-up 4.8 y), Cox regression revealed an independent association between VSM (adjusted hazard ratio [aHR]: 0.88, 95% confidence interval [CI] [0.81–0.91], p=0.00023), VSMFF (aHR:1.06, 95%CI [1.02–1.10], p=0.0043), and VIMAT(aHR:1.19, 95%CI [1.05–1.35], p=0.0056) and mortality after adjustment for age, sex, BMI, race, alcohol consumption, smoking status, hypertension, diabetes, history of cancer, and blood serum markers. This association was attenuated when using single-slice area measures. Highest correlation coefficients (R) between volumetric and single-slice area BC measures were located at vertebra L5 for SAT (R = 0.820) and SMFF (R=0.95), at L3 for VAT (R=0.89), SM (R=0.94), and at L4 for IMAT (R=0.55) (all p<0.0001). Results were similar in 23,725 NAKO participants (mean age 53.9±8.3 y, 44.9% female).

Interpretation: Volumetric BC assessment predicted mortality in a large Western population beyond clinical risk factors. Single slice areas were highly correlated with volumetric BC measures but their association with mortality attenuated after multivariable adjustment. As volumetric BC measures are increasingly accessible, identifying high-risk individuals may help to improve personalised prevention and lifestyle interventions.

Competing interests: Declaration of interests Vineet K. Raghu: Grants or contracts from any entity: Norn Group, Johnson and Johnson Innovation, National Academy of Medicine. Tobias Pischon: Leadership in board: Member of the board of directors of the NAKO e.V., who are leading the NAKO study (unpaid position). Hans-Ulrich Kauczor: Payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events: Siemens Healthineers, Philips, Boehringer Ingelheim Participation on a Data Safety Monitoring Board or Advisory Board: Median, contextflow. Christopher L. Schlett: Payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events: Siemens Healthineers, Bayer Healthcare. Michael T. Lu: Grants or contracts from any entity: American Heart Association, AstraZeneca, Ionis, Johnson & Johnson Innovation, Kowa Pharmaceuticals America, MedImmune, National Academy of Medicine, National Heart, Lung, and Blood Institute, and Risk Management Foundation of the Harvard Medical Institutions. Fabian Bamberg: Grants or contracts from any entity: Siemens Healthineers, Bayer Healthcare Payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing or educational events: Siemens Healthineers, Bayer Healthcare. Leadership or fiduciary role in other board, society, committee or advocacy group, paid or unpaid: German Roentgen Society. Jakob Weiss: Consulting fees: Onc.AI.