This review discusses the implications of frameworks leveraging genetic admixture and multiomics data for advancing gene discovery in cardiovascular and kidney disease research. By broadening gene discovery efforts to additional populations that have a disproportionately high risk of disease and leveraging genetic diversity in admixed populations, studies can identify population-enriched risk variants that traditionally have been missed in genome-wide association studies. The use of multiomics approaches, including the transcriptome, proteome, and metabolome, advances a mechanistic understanding of disease beyond associations. As single-cell omics technologies continue to improve, their integration into gene discovery may help uncover cell-type-specific regulatory pathways and more precise biological contexts. The full potential of these approaches depends on sustained investment in diverse, well-characterized omics data sets, methodological innovation in multiancestry statistical approaches, and interdisciplinary collaboration bridging genomics, epidemiology, and clinical medicine. These efforts will need to be translated into clinically actionable insights, including ancestry-informed risk stratification and targeted therapeutics, to improve outcomes for cardiovascular and kidney diseases.

Primary aldosteronism is a common and potentially curable cause of secondary hypertension, yet it remains substantially underdiagnosed. Current diagnostic pathways rely on the aldosterone-to-renin ratio, confirmatory tests, and subtype classification by imaging and adrenal venous sampling. However, these approaches are limited by assay variability, medication effects, imperfect diagnostic accuracy, and the absence of a true reference standard, particularly for bilateral disease. Recent molecular imaging and histopathologic data further challenge the traditional binary distinction between unilateral and bilateral primary aldosteronism and instead support a biological continuum of renin-independent aldosteronism. In this context, novel biomarkers may improve both detection and biological subtyping. Steroidomics is currently the most clinically advanced approach, with plasma and urinary steroid profiles showing promise for primary aldosteronism detection, identification of KCNJ5-mutated adenomas, and subtype prediction. Proteomics and circulating microRNAs provide complementary information on systemic effects, upstream regulation, and lesion biology, while multiomics strategies increasingly demonstrate that integrated models outperform single-modality approaches. Nevertheless, translation into clinical practice remains constrained by small and selected cohorts, heterogeneous analytical platforms, inconsistent reference standards, and limited external validation. Future progress will depend on standardized assays, interpretable machine learning models, and prospective multicenter implementation studies linking biomarker-guided diagnosis to improved clinical outcomes.