The sarcomeric interaction of α-myosin heavy chain (α-MHC) with Titin is vital for cardiac structure and contraction. However, the mechanism regulating this interaction in normal and failing hearts remains unknown. Lactate is a crucial energy substrate of the heart. Here, we identify that α-MHC undergoes lactylation on lysine 1897 to regulate the interaction of α-MHC with Titin. We observed a reduction of α-MHC K1897 lactylation in mice and patients with heart failure. Loss of K1897 lactylation in α-MHC K1897R knock-in mice reduces α-MHC–Titin interaction and leads to impaired cardiac structure and function. Furthermore, we identified that p300 and Sirtuin 1 act as the acyltransferase and delactylase of α-MHC, respectively. Decreasing lactate production by chemical or genetic manipulation reduces α-MHC lactylation, impairs α-MHC–Titin interaction and worsens heart failure. By contrast, upregulation of the lactate concentration by administering sodium lactate or inhibiting the pivotal lactate transporter in cardiomyocytes can promote α-MHC K1897 lactylation and α-MHC–Titin interaction, thereby alleviating heart failure. In conclusion, α-MHC lactylation is dynamically regulated and an important determinant of overall cardiac structure and function. Excessive lactate efflux and consumption by cardiomyocytes may decrease the intracellular lactate level, which is the main cause of reduced α-MHC K1897 lactylation during myocardial injury. Our study reveals that cardiac metabolism directly modulates the sarcomeric structure and function through lactate-dependent modification of α-MHC.

The global burden of cardiovascular diseases continues to rise, making their prevention, diagnosis and treatment increasingly critical. With advancements and breakthroughs in omics technologies such as high-throughput sequencing, multi-omics approaches can offer a closer reflection of the complex physiological and pathological changes in the body from a molecular perspective, providing new microscopic insights into cardiovascular diseases research. However, due to the vast volume and complexity of data, accurately describing, utilising, and translating these biomedical data demands substantial effort. Researchers and clinicians are actively developing artificial intelligence (AI) methods for data-driven knowledge discovery and causal inference using various omics data. These AI approaches, integrated with multi-omics research, have shown promising outcomes in cardiovascular studies. In this review, we outline the methods for integrating machine learning, one of the most successful applications of AI, with omics data and summarise representative AI models developed that leverage various omics data to facilitate the exploration of cardiovascular diseases from underlying mechanisms to clinical practice. Particular emphasis is placed on the effectiveness of using AI to extract potential molecular information to address current knowledge gaps. We discuss the challenges and opportunities of integrating omics with AI into routine diagnostic and therapeutic practices and anticipate the future development of novel AI models for wider application in the field of cardiovascular diseases.

Objectives: Adiposity, defined by higher cardiometabolic index (CMI), lipid accumulation product (LAP), and body adiposity index (BAI), has conferred increased metabolic risk. However, the incremental utility of CMI, LAP, and BAI in association with prevalent hypertension has not been well described in a population-based setting. We hypothesized that CMI, LAP, and BAI would provide important insight into hypertension risk. Methods: Blood pressure (BP), fasting lipid profiles, and anthropometric parameters were recorded in a cross-sectional study of 11,400 participants (mean age, 54 years; 53% women) from China. Logistic regression models were used to assess associations of CMI, LAP, and BAI with prevalent hypertension. BAI was evaluated according to hip (cm)/[height (m)^1.5]-18; LAP was calculated separately for men [(WC-65) × TG] and women [(WC-58) × TG]; and CMI was defined by TG/HDL-C × waist-to-height ratio. Results: CMI, LAP, and BAI were independently correlated with higher SBP and DBP, with nonstandardized (B) coefficients ranging from 1.827 to 4.590 mmHg and 1.475 to 2.210 mmHg (all P < 0.001). After adjustment for hypertension risk factors and potential confounders, CMI, LAP, and BAI, modeled as continuous measures, carried hypertension odds (95% CI) of 1.356 (1.259–1.459), 1.631 (1.501–1.771), and 1.555 (1.454–1.662) in women, respectively, per SD increment. In men, each SD increase in CMI, LAP, and BAI experienced a 31%, 65%, and 53% higher hypertension risk, respectively. Moreover, among women, the odds ratio (95% CI) for hypertension were 2.318 (1.956–2.745), 3.548 (2.985–4.217), and 3.004 (2.537–3.557) in the 4th quartile vs the first quartile of CMI, LAP, and BAI, respectively. For men, the corresponding figures were 2.200 (1.838–2.635), 3.892 (3.238–4.677), and 3.288 (2.754–3.927), respectively. Conclusion: Measurements of CMI, LAP, and BAI provide a more complete understanding of hypertension risk related to variation in body fat distribution and pinpoint hypertensive participants in great risk of cardiovascular disease in the future.