Key result
Increased silhouette-predicted VAT/ASAT ratio was associated with an increased risk of prevalent type 2 diabetes (OR 1.78 per 1-SD increase in males) independent of BMI and waist circumference.
Why the study?
Inter-individual variation in fat distribution is clinically important but not routinely assessed in practice, partly because medical imaging has not been practical to deploy at scale.
Does a deep learning model trained on body silhouette images improve estimation of body fat distribution and associated cardiometabolic risk compared to anthropometric and bioimpedance measures?
Observational (n=40,032)
Yes
Does a deep learning model trained on body silhouette images improve estimation of body fat distribution and associated cardiometabolic risk compared to anthropometric and bioimpedance measures?
Body silhouette images can accurately estimate metabolically important fat distribution and predict cardiometabolic risk, offering a scalable alternative to medical imaging.
May enable scalable, imaging-free fat depot estimation; leaves open prospective validation before clinical use.
Inter-individual variation in fat distribution is increasingly recognized as clinically important but is not routinely assessed in clinical practice, in part because medical imaging has not been practical to deploy at scale for this task. Here, we report a deep learning model trained on an individual’s body shape outline—or “silhouette” —that enables accurate estimation of specific fat depots of interest, including visceral (VAT), abdominal subcutaneous (ASAT), and gluteofemoral (GFAT) adipose tissue volumes, and VAT/ASAT ratio. Two-dimensional coronal and sagittal silhouettes are constructed from whole-body magnetic resonance images in 40,032 participants of the UK Biobank and used as inputs for a convolutional neural network to predict each of these quantities. Mean age of the study participants is 65 years and 51% are female. A cross-validated deep learning model trained on silhouettes enables accurate estimation of VAT, ASAT, and GFAT volumes ( R 2 : 0.88, 0.93, and 0.93, respectively), outperforming a comparator model combining anthropometric and bioimpedance measures (Δ R 2 = 0.05–0.13). Next, we study VAT/ASAT ratio, a nearly body-mass index (BMI)—and waist circumference-independent marker of metabolically unhealthy fat distribution. While the comparator model poorly predicts VAT/ASAT ratio ( R 2 : 0.17–0.26), a silhouette-based model enables significant improvement ( R 2 : 0.50–0.55). Increased silhouette-predicted VAT/ASAT ratio is associated with increased risk of prevalent and incident type 2 diabetes and coronary artery disease independent of BMI and waist circumference. These results demonstrate that body silhouette images can estimate important measures of fat distribution, laying the scientific foundation for scalable population-based assessment.
No takes yet. Share an insight, caveat, or question.
Klarqvist et al. (2022) conducted an observational in Cardiometabolic risk and body fat distribution (n=40,032). Silhouette-predicted VAT/ASAT ratio vs. Lower silhouette-predicted VAT/ASAT ratio was evaluated on Prevalent type 2 diabetes (per 1-SD increase in silhouette-predicted VAT/ASAT ratio in males) (OR 1.78, 95% CI 1.69-1.87, p=2.0e-101). Increased silhouette-predicted VAT/ASAT ratio was associated with an increased risk of prevalent type 2 diabetes (OR 1.78 per 1-SD increase in males) independent of BMI and waist circumference.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: