Emerging Technologies and Future Directions in Interorgan Crosstalk Cardiometabolic Research

Abstract

The heart does not work in isolation, with cardiac health and disease occurring through complex interactions between the heart with multiple organs. Furthermore, the integration of organ-specific lipid metabolism, blood pressure, insulin sensitivity, and inflammation involves a complex network of signaling pathways between many organs. Dysregulation in these communications is now recognized as a key contributor to many manifestations of cardiovascular disease. Mechanistic characterization of specific molecules mediating interorgan signaling has been pivotal in advancing our understanding of cardiovascular disease. The discovery of insulin, glucagon, and other hormones in the early 20th century illustrated the importance of communication between organs in maintaining physiological homeostasis. For example, elegant studies evaluating insulin signaling and its role in regulating glucose metabolism have shed light on its broader impact on cardiovascular health, hypertension, atherosclerosis, and other cardiovascular disease risks. Recent technological advances have revolutionized our understanding of interorgan signaling. Global approaches such as proteomics and metabolomics applications to blood have enabled the simultaneous profiling of thousands of circulating factors, revealing previously unknown signaling molecules and pathways. These large-scale studies have identified biomarkers linked to early stages of heart disease and offered new therapeutic targets. By understanding how specific cells in the heart interact with cells in other organs, such as the kidney or liver, researchers can identify key pathways that, when disrupted, lead to cardiovascular pathology. The ability to capture a more holistic view of the cardiovascular system positions interorgan signaling at the forefront of cardiovascular research. As we continue to refine our tools for mapping these complex networks, the insights gained hold the potential to not only improve early diagnosis but also to develop more targeted and effective treatments for cardiovascular disease. In this review, we discuss current approaches used to enhance our understanding of organ crosstalk with a specific emphasis on cardiac and cardiovascular physiology.

Keywords: blood pressure; cardiovascular diseases; cell communication; endocrine system; heart diseases; hypertension.

Figure 1.

Arteriovenous metabolomics reveals interorgan flux. The workflow of arteriovenous metabolomics involves using large animal models, such as pigs, or human patients. Venous and arterial blood samples collected from sites representing different organs are extracted for metabolite measurement by liquid chromatography-mass spectrometry (LC-MS). By comparing metabolite levels in arterial (A) and venous (V) blood, the net production or consumption of metabolites by each organ can be assessed. A higher venous concentration (V>A) indicates net metabolite release by the organ, whereas a lower venous concentration (V<A) signifies net metabolite uptake. Metabolite exchange between organs can be visualized on the right, with numbers highlighting significant exchanges for each organ.

Figure 2.

Detection of microproteins and small peptides. Simplified workflow for detecting microproteins and small peptides from human samples. The focus is on the translatome, which reflects all actively translated RNA sequences, studied through ribosome profiling. This technique captures ribosome-protected RNA fragments, enabling the identification of actively translated regions, including novel short open reading frames (sORFs). These sORFs, found in untranslated regions of mRNAs and long noncoding RNAs (lncRNAs), can be validated as encoding microproteins and small peptides through proteomic analyses. This combined approach of ribosome profiling and proteomics facilitates high-throughput detection of new potential circulating microproteins and small peptides.

Figure 3.

Population-based approaches for discovery of organ crosstalk mechanisms. In a population where heterogeneity of organ-level omic data (eg, RNA-seq or proteomics) is observed (top), these differences can be analyzed using network-based or statistical modeling approaches (middle) to uncover new modes of tissue communication (bottom).