Rationale:: MicroRNAs (miRNAs) have been implicated in the epigenetic regulation of key metabolic, inflammatory, and antiangiogenic pathways in type 2 diabetes (DM) and may contribute to common disease complications. Objective:: In this study, we explore plasma miRNA profiles in patients with DM. Methods and Results:: Total RNA was extracted from plasma samples of the prospective population-based Bruneck study. A total of 13 candidate miRNAs identified by microarray screening and miRNA network inference were quantified by quantitative PCR in all diabetic patients of the Bruneck study and age- and sex-matched controls (1995 evaluation, n=80 each). Quantitative PCR assessment revealed lower plasma levels of miR-20b, miR-21, miR-24, miR-15a, miR-126, miR-191, miR-197, miR-223, miR-320, and miR-486 in prevalent DM, but a modest increase of miR-28-3p. Findings emerged as robust in multivariable analysis and were independent of the standardization procedure applied. For endothelial miR-126, results were confirmed in the entire Bruneck cohort (n=822) in univariate (odds ratio [95% confidence interval], 0.38 [0.26 to 0.55]; P =2.72×10 ⁻⁷ ) and multivariate analyses (0.57 [0.37 to 0.86]; P =0.0082). Importantly, reduced miR-15a, miR-29b, miR-126, miR-223, and elevated miR-28-3p levels antedated the manifestation of disease. Most differences in miRNA levels were replicated in plasma obtained from hyperglycemic Lep ^ob mice. High glucose concentrations reduced the miR-126 content of endothelial apoptotic bodies. Similarly in patients with DM, the reduction of miR-126 was confined to circulating vesicles in plasma. Conclusions:: We reveal a plasma miRNA signature for DM that includes loss of endothelial miR-126. These findings might explain the impaired peripheral angiogenic signaling in patients with DM.

Providing therapies tailored to each patient is the vision of precision medicine, enabled by the increasing ability to capture extensive data about individual patients. In this position paper, we argue that the second enabling pillar towards this vision is the increasing power of computers and algorithms to learn, reason, and build the ‘digital twin’ of a patient. Computational models are boosting the capacity to draw diagnosis and prognosis, and future treatments will be tailored not only to current health status and data, but also to an accurate projection of the pathways to restore health by model predictions. The early steps of the digital twin in the area of cardiovascular medicine are reviewed in this article, together with a discussion of the challenges and opportunities ahead. We emphasize the synergies between mechanistic and statistical models in accelerating cardiovascular research and enabling the vision of precision medicine.

In response to stress, the heart undergoes extensive cardiac remodeling that results in cardiac fibrosis and pathological growth of cardiomyocytes (hypertrophy), which contribute to heart failure. Alterations in microRNA (miRNA) levels are associated with dysfunctional gene expression profiles associated with many cardiovascular disease conditions; however, miRNAs have emerged recently as paracrine signaling mediators. Thus, we investigated a potential paracrine miRNA crosstalk between cardiac fibroblasts and cardiomyocytes and found that cardiac fibroblasts secrete miRNA-enriched exosomes. Surprisingly, evaluation of the miRNA content of cardiac fibroblast–derived exosomes revealed a relatively high abundance of many miRNA passenger strands (“star” miRNAs), which normally undergo intracellular degradation. Using confocal imaging and coculture assays, we identified fibroblast exosomal–derived miR-21_3p (miR-21*) as a potent paracrine-acting RNA molecule that induces cardiomyocyte hypertrophy. Proteome profiling identified sorbin and SH3 domain-containing protein 2 (SORBS2) and PDZ and LIM domain 5 (PDLIM5) as miR-21* targets, and silencing SORBS2 or PDLIM5 in cardiomyocytes induced hypertrophy. Pharmacological inhibition of miR-21* in a mouse model of Ang II–induced cardiac hypertrophy attenuated pathology. These findings demonstrate that cardiac fibroblasts secrete star miRNA–enriched exosomes and identify fibroblast-derived miR-21* as a paracrine signaling mediator of cardiomyocyte hypertrophy that has potential as a therapeutic target.

Background—: The bulk of cardiovascular disease risk is not explained by traditional risk factors. Recent advances in mass spectrometry allow the identification and quantification of hundreds of lipid species. Molecular lipid profiling by mass spectrometry may improve cardiovascular risk prediction. Methods and Results—: Lipids were extracted from 685 plasma samples of the prospective population-based Bruneck Study (baseline evaluation in 2000). One hundred thirty-five lipid species from 8 different lipid classes were profiled by shotgun lipidomics with the use of a triple-quadrupole mass spectrometer. Levels of individual species of cholesterol esters (CEs), lysophosphatidylcholines, phosphatidylcholines, phosphatidylethanolamines (PEs), sphingomyelins, and triacylglycerols (TAGs) were associated with cardiovascular disease over a 10-year observation period (2000–2010, 90 incident events). Among the lipid species with the strongest predictive value were TAGs and CEs with a low carbon number and double-bond content, including TAG(54:2) and CE(16:1), as well as PE(36:5) ( P =5.1×10 ⁻⁷ , 2.2×10 ⁻⁴ , and 2.5×10 ⁻³ , respectively). Consideration of these 3 lipid species on top of traditional risk factors resulted in improved risk discrimination and classification for cardiovascular disease (cross-validated ΔC index, 0.0210 [95% confidence interval, 0.0010-0.0422]; integrated discrimination improvement, 0.0212 [95% confidence interval, 0.0031-0.0406]; and continuous net reclassification index, 0.398 [95% confidence interval, 0.175-0.619]). A similar shift in the plasma fatty acid composition was associated with cardiovascular disease in the UK Twin Registry (n=1453, 45 cases). Conclusions—: This study applied mass spectrometry-based lipidomics profiling to population-based cohorts and identified molecular lipid signatures for cardiovascular disease. Molecular lipid species constitute promising new biomarkers that outperform the conventional biochemical measurements of lipid classes currently used in clinics.