Cardiovascular disease has been the focus of extensive biomedical research, yet it remains a leading cause of morbidity and mortality worldwide. One recent approach to support scientific discovery has been the development of large-scale population-based prospective studies that couple phenotypic data, including outcomes, to collection and analysis of biological samples. Large, data-dense biobanks, particularly those linked to electronic health records (including laboratory, electrocardiographic, imaging, and therapeutic data) and genomic data, have demonstrated value for interdisciplinary research into the epidemiology, cause, treatment, and prevention of disease and yield increasingly refined insights into monogenic and complex traits. One limitation to wider use of these resources is that they may collect phenotypic and biological data in a nonuniform fashion, limiting data harmonization, analysis, and validation. The purpose of this scientific statement is to discuss the current state of available biobanks, endorse biobanks for discovery and implementation science, and propose opportunities for future harmonization to increase the potential for novel studies aimed at understanding mechanisms and developing novel therapies for cardiovascular disease treatment and prevention.
BACKGROUND: PPA2 (pyrophosphatase 2) deficiency is a relatively newly recognized condition caused by biallelic variants in the PPA2 gene. The gene encodes the mitochondrial enzyme PPA2, involved in the production of orthophosphate in the mitochondrial matrix. Affected individuals typically present with sudden cardiac death or acute heart failure following common viral illnesses in infants and toddlers, or following exposure to small amounts of alcohol as teenagers. The condition has a high mortality rate in these situations. Here, we explore the clinical presentations and current management of these patients with a view to guiding future emergency clinical management of cases. METHODS: Case records from published and unpublished affected individuals diagnosed with PPA2 deficiency were compiled in a retrospective cohort study of genotypes, phenotypes and clinical management protocols in individuals with biallelic (likely) pathogenic PPA2 variants. RESULTS: We collated 93 affected individuals from 53 families, 14 of which have not previously been reported. Patient presentation is invariably acute, often associated with fatigue, vomiting, seizures and collapse. Generally, presenting individuals received standard emergency management with cardiopulmonary resuscitation and appropriate management of arrhythmia, and here we present compiled clinical management protocols for emergency and ongoing treatment of PPA2-deficient patients. A pronounced inheritance rate of pathogenic alleles from carrier parents is noted, and further research is warranted to confirm and investigate any selective advantage for gametes carrying pathogenic PPA2 variants. CONCLUSIONS: PPA2 deficiency has a high mortality rate following exposure to common childhood viruses in children around and under the age of 3 and in teenagers exposed to ethanol. We have presented current therapeutic approaches gathered from a worldwide survey of clinicians treating their patients, possible pathogenic mechanisms and inheritance peculiarities of the condition and provide a comprehensive review of the current recommendations surrounding patients’ clinical management.
BACKGROUND: Cardiorespiratory fitness, commonly measured as peak oxygen consumption (peak VO 2 , is a strong predictor of cardiovascular health. Circulating lipids have been linked to exercise responses and metabolic adaptation, but the bioactive lipid species associated with objectively measured aerobic capacity and their dynamic regulation during exercise remain incompletely defined. METHODS: We performed directed, nontargeted liquid chromatography–mass spectrometry profiling of circulating bioactive lipids in adults undergoing clinically indicated cardiopulmonary exercise testing, relating resting lipid levels and acute exercise-induced lipid changes to peak VO 2 . We then examined fitness-associated lipid species across complementary models of physiological stress, including marathon running, chronic voluntary wheel running in mice, and the postprandial Burmese python model. RESULTS: Among 1896 detected lipid species in the primary human cohort, 47 were associated with peak VO 2 after false-discovery rate correction, including 16 metabolites with known molecular identities. Higher levels of several eicosanoids and free fatty acids were associated with greater peak VO 2 , including docosahexaenoic acid, arachidic acid, and 12,13-dihydroxy-9Z-octadecenoic acid. In paired rest and peak exercise samples, 37 of 47 fitness-associated lipid species changed acutely with exercise. Among metabolites with known identities, exercise-induced changes in 12,13-dihydroxy-9Z-octadecenoic acid, 8-hydroxyicosa-9,11,14-trienoic acid, 11,12-epoxy-5Z,8Z,14Z-eicosatrienoic acid, and 8,9-dihydroxy-5Z,11Z,14Z-eicosatrienoic acid were associated with peak VO 2 . In marathon runners, 43 of 47 fitness-associated lipid species changed after prolonged endurance exercise. Exploratory animal analyses demonstrated overlapping lipid regulation in chronic voluntary wheel-running mice and in the postprandial Burmese python model. CONCLUSIONS: Circulating bioactive lipids, particularly eicosanoids and free fatty acids, are associated with human cardiorespiratory fitness and are dynamically regulated during exercise. Overlapping lipid responses across complementary human and animal models identify candidate lipid pathways that may merit future mechanistic study.
The advent of high-throughput sequencing technologies has revolutionized cardiovascular genetics, generating vast amounts of data. A major challenge in the genomic era is the efficient and accurate identification of causative genetic variants associated with cardiovascular disease. Artificial intelligence (AI)-driven computational approaches offer a powerful solution by enabling the automation of variant classification, improving consistency and reproducibility, and enhancing predictive accuracy. These methods may guide not only variant classification, variant effect prediction, and clinical prioritization, but also open the door to data-driven precision medicine, enabling more accurate and individualized diagnoses and targeted therapeutic strategies tailored to each patient's unique genetic and clinical profile. This state-of-the-art review provides a structured overview of AI methodologies applied to cardiovascular genomics, beginning with rule-based expert systems that encode standardized guidelines for consistent variant interpretation. Next, we examine machine learning approaches capable of identifying complex patterns in annotated multimodal clinical and multi-omic datasets. The role of deep learning algorithms is highlighted for their ability to extract features from high-dimensional, unstructured data relevant to cardiovascular disease. In addition, the potential of generative AI is explored, including applications in synthetic data generation, variant impact prediction, and automated summarization of biomedical literature. Despite advances, several challenges remain, including data heterogeneity, the need for explainable AI models to elucidate the decision mechanisms, and the complexity linked to the integration of AI-based tools into clinical workflows. Addressing these issues requires interdisciplinary collaboration among clinicians, geneticists, data scientists, and bioinformaticians to ensure the effective translation of AI-generated insights into clinical practice. This review aims to provide a comprehensive perspective on the evolving role of AI in cardiovascular genomics and its implications for advancing precision medicine.
