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.

心电图（ECG）可以捕捉与肥胖相关的心脏变化。人工智能增强心电图（AI-ECG）可以识别亚临床疾病。我们训练了一个 AI-ECG 模型，仅根据 ECG 来预测体重指数 (BMI)。该模型根据二级护理队列贝斯以色列女执事医疗中心 (BIDMC) 的 512,950 份 12 导联心电图开发，并在英国生物银行 (UKB) ( n = 42,386) 上进行了验证，皮尔逊相关系数 (r) 为 0.65，在 BIDMC 队列和 UK Biobank 中，AI-ECG BMI 与测量的 BMI 的 R 2 分别为 0.62，R ²为 0.43 和 0.39。我们发现 delta-BMI，即测量的 BMI 和 AI-ECG 预测的 BMI (AI-ECG-BMI) 之间的差异，是心脏代谢健康的生物标志物。 δ-BMI 的前三分位显示未来心脏代谢疾病（BIDMC：HR 1.15， p < 0.001；UKB：HR 1.58， p < 0.001）和糖尿病（BIDMC：HR 1.25， p < 0.001；UKB：HR 2.28）的风险增加， p < 0.001）在调整包括测量的 BMI 在内的协变量后。在两个队列中纳入 delta-BMI 后，我们观察到模型拟合、重新分类和辨别能力显着增强。表型分析强调了 delta-BMI 与心脏代谢疾病、躯干肥胖的人体测量指标和心包脂肪量之间的关联。代谢和蛋白质组学分析将 delta-BMI 与缬氨酸、小 HDL 中的脂质、突触蛋白 3 和肌肽酶 1 呈正相关，与谷氨酰胺、甘氨酸、辅脂酶和脂联素呈负相关。一项全基因组关联研究揭示了与心血管/代谢特征调节因子的关联，包括SCN10A 、 SCN5A 、 EXOG和RXRG 。 总之，我们的 AI-ECG-BMI 模型可以准确预测 BMI，并引入 delta-BMI 作为心脏代谢风险分层的非侵入性生物标志物。