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- Title
Artificial intelligence-enhanced electrocardiography derived body mass index as a predictor of future cardiometabolic disease.
- Authors
Pastika, Libor; Sau, Arunashis; Patlatzoglou, Konstantinos; Sieliwonczyk, Ewa; Ribeiro, Antônio H.; McGurk, Kathryn A.; Khan, Sadia; Mandic, Danilo; Scott, William R.; Ware, James S.; Peters, Nicholas S.; Ribeiro, Antonio Luiz P.; Kramer, Daniel B.; Waks, Jonathan W.; Ng, Fu Siong
- 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.
- Subjects
RISK assessment; PEARSON correlation (Statistics); BODY mass index; GENOME-wide association studies; RESEARCH funding; ARTIFICIAL intelligence; CARDIOVASCULAR diseases risk factors; RETROSPECTIVE studies; DESCRIPTIVE statistics; ELECTROCARDIOGRAPHY; UK Biobank Ltd.; LONGITUDINAL method; COMPUTER-aided diagnosis; PROTEOMICS; SURVIVAL analysis (Biometry); CONFIDENCE intervals; BIOMARKERS; REGRESSION analysis
- Publication
NPJ Digital Medicine, 2024, Vol 7, Issue 1, p1
- ISSN
2398-6352
- Publication type
Article
- DOI
10.1038/s41746-024-01170-0