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Updated: Aug 19, 2026

A Novel Method: Super-selective Adrenal Venous Sampling
Published on: September 15, 2017
Biological Variation of Adrenal-Related Endocrine Biomarkers Used for Investigation of Secondary Hypertension
Anna Stylianou Lerpold1, Risha Chowdhury1, Eirik Åsen Røys1,2
1Department of Clinical Science, University of Bergen, Bergen, Norway.
Background:
Adrenal-related biomarkers are central in evaluating endocrine causes of secondary hypertension, but their interpretation is influenced by biological variation (BV). In this study, we aimed to estimate the within-subject (CVI) and between-subject (CVG) BV and derive the reference change values (RCVs), the Index of Individuality (II), and the Harris-Brown heterogeneity ratio (HBR) for aldosterone, renin, and the aldosterone-renin ratio (ARR), ACTH, the cortisol/ACTH ratio, metanephrines, and normetanephrines.
Methods:
Weekly blood samples were collected from 30 healthy volunteers for 10 consecutive weeks under standardized conditions and were analyzed using state-of-the-art analytical methods. A Bayesian hierarchical model was applied to estimate the posterior distributions of the BV components, yielding estimates of the CVI, CVG, RCVs, II, and HBR.
Results:
High BV was observed for aldosterone, renin, and the ARR. Aldosterone showed the highest CVI (48.8%), while metanephrine showed the lowest (13.5%). The CVG was high for most measurands, whereas metanephrines showed comparatively low CVG (30.5%). No major sex differences were observed for the biomarkers after performing sex-stratified estimates. The HBR indicated population homogeneity for ARR (female), ACTH (male), and both metanephrines, while aldosterone, renin, ARR (male and mixed), and ACTH (mixed and female) showed heterogeneity. All biomarkers had an II < 1, and RCVs ranged from -72.3% to +261.2% (aldosterone) and -32.9% to +48.9% (metanephrine).
Conclusions:
This study provides BV estimates for multiple biomarkers relevant to endocrine secondary hypertension. The results underscore the importance of applying BV-derived tools, including RCVs and II, to improve the interpretation of serial measurements in diagnosis and follow-up.
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