Photo Credit: iStock.com/ojos de hojalata

Blood-based protein signatures showed high accuracy in classifying forms of hypertension, supporting a shift toward precision diagnostics.  

As per the study published in June 2025 in the issue of Endocrine, Primary aldosteronism (PA) was known as a common yet underdiagnosed cause of hypertension (HT), with limited noninvasive diagnostic tools despite its impact on cardiovascular health.  

Researchers conducted a retrospective study to identify serum proteomic markers that could distinguish between HT, PA, unilateral PA (uPA), and bilateral PA (bPA).  

They evaluated 88 individuals with HT for PA and classified them as essential HT (n = 30), bPA (n = 29), and uPA (n = 29). Serum from all participants was analyzed using the Olink^®  Explore 384 Cardiometabolic Panel. A machine learning model employing ridge logistic regression with stratified 5-fold cross-validation was applied to differentiate among HT, PA, bPA, and uPA.  

The results showed that 56 circulating proteins differed significantly across the study groups, including 4 between PA and HT, 3 between bPA and uPA, 1 between bPA and HT, 9 between uPA and HT, and 1 across HT, bPA, and uPA. Coagulation factor IX (PA vs HT), dipeptidyl peptidase 4 (uPA vs HT and bPA), and heat shock protein B1 (bPA vs uPA) demonstrated the strongest discrimination and were validated using enzyme-linked immunosorbent assay. The machine learning model correctly classified 95% of HT, bPA, and uPA samples.  

Investigators concluded that serum protein biomarkers showed potential in distinguishing HT, PA, uPA, and bPA, warranting further validation through additional studies. 

Source: link.springer.com/article/10.1007/s12020-025-04302-y