| Title: | Artificial intelligence-enhanced electrocardiography derived body mass index as a predictor of future cardiometabolic disease |
| Journal: | npj Digital Medicine |
| Published: | 25 Jun 2024 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/38918595/ |
| DOI: | https://doi.org/10.1038/s41746-024-01170-0 |
| URL: | https://www.nature.com/articles/s41746-024-01170-0.pdf |
Publication 11793
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Abstract
The electrocardiogram (ECG) can capture obesity-related cardiac changes. Artificial intelligence-enhanced ECG (AI-ECG) can identify subclinical disease. We trained an AI-ECG model to predict body mass index (BMI) from the ECG alone. Developed from 512,950 12-lead ECGs from the Beth Israel Deaconess Medical Center (BIDMC), a secondary care cohort, and validated on UK Biobank (UKB) (n = 42,386), the model achieved a Pearson correlation coefficient (r) of 0.65 and 0.62, and an R2 of 0.43 and 0.39 in the BIDMC cohort and UK Biobank, respectively for AI-ECG BMI vs. measured BMI. We found delta-BMI, the difference between measured BMI and AI-ECG-predicted BMI (AI-ECG-BMI), to be a biomarker of cardiometabolic health. The top tertile of delta-BMI showed increased risk of future cardiometabolic disease (BIDMC: HR 1.15, p < 0.001; UKB: HR 1.58, p < 0.001) and diabetes mellitus (BIDMC: HR 1.25, p < 0.001; UKB: HR 2.28, p < 0.001) after adjusting for covariates including measured BMI. Significant enhancements in model fit, reclassification and improvements in discriminatory power were observed with the inclusion of delta-BMI in both cohorts. Phenotypic profiling highlighted associations between delta-BMI and cardiometabolic diseases, anthropometric measures of truncal obesity, and pericardial fat mass. Metabolic and proteomic profiling associates delta-BMI positively with valine, lipids in small HDL, syntaxin-3, and carnosine dipeptidase 1, and inversely with glutamine, glycine, colipase, and adiponectin. A genome-wide association study revealed associations with regulators of cardiovascular/metabolic traits, including SCN10A, SCN5A, EXOG and RXRG. In summary, our AI-ECG-BMI model accurately predicts BMI and introduces delta-BMI as a non-invasive biomarker for cardiometabolic risk stratification.</p>
15 Authors
- Libor Pastika
- Arunashis Sau
- Konstantinos Patlatzoglou
- Ewa Sieliwonczyk
- Antônio H. Ribeiro
- Kathryn A. McGurk
- Sadia Khan
- Danilo Mandic
- William R. Scott
- James S. Ware
- Nicholas S. Peters
- Antonio Luiz P. Ribeiro
- Daniel B. Kramer
- Jonathan W. Waks
- Fu Siong Ng
Enabling scientific discoveries that improve human health