Endocrine causes of hypertension: literature review and practical approach

Effects of sodium intake, age, gender, blood sampling time on distribution of plasma aldosterone, renin activity, deoxycorticosterone, cortisol, cortisone, and 24 h urinary aldosterone levels in normotensive individuals based on LC-MS/MS

PURPOSE

This study aims to analyze the distribution of plasma aldosterone, renin activity, deoxycorticosterone (DOC), cortisol, cortisone, and 24 h urinary aldosterone (24 h-uAld) levels based on liquid chromatography-tandem mass spectrometry (LC-MS/MS) method.

MATERIALS AND METHODS

Plasma and 24 h urine were collected from 129 healthy volunteers in Northeast China. The effect of sodium intake, age, gender, blood sampling time on plasma aldosterone concentration (PAC), plasma renin activity (PRA), PAC to PRA ratio (ARR), DOC, cortisol, cortisone, cortisol to cortisone ratio, and 24 h-uAld were investigated by nonparametric test, multiple linear regression and Harris-Boyd's standard deviate test.

RESULTS

There was no significant difference observed in 24 h-uAld, PAC (AM), PRA(AM), ARR (AM), DOC (AM), cortisol (AM), cortisone (AM), and cortisol to cortisone (AM) between high and low sodium intake group. Significant differences were observed between morning and afternoon sampling groups in terms of PAC, ARR, DOC, cortisol, and cortisone. Reference intervals (RIs) of 24 h-uAld, PAC (AM) were recommended to be partitioned by gender. RI of PRA was recommended age stratification.

CONCLUSION

We recommend that the same reference interval could be used regardless of sodium intake. Gender is the main influence factor for 24 h-uAld, PAC, and ARR. Age is key influence factor for PRA.

Steroid profiling in adrenal disease

The measurement of steroid hormones in blood and urine, which reflects steroid biosynthesis and metabolism, has been recognized as a valuable tool for identifying and distinguishing steroidogenic disorders. The application of mass spectrometry enables the reliable and simultaneous analysis of large panels of steroids, ushering in a new era for diagnosing adrenal diseases. However, the interpretation of complex hormone results necessitates the expertise and experience of skilled clinicians. In this scenario, machine learning techniques are gaining worldwide attention within healthcare fields. The clinical values of combining mass spectrometry-based steroid profiles analysis with machine learning models, also known as steroid metabolomics, have been investigated for identifying and discriminating adrenal disorders such as adrenocortical carcinomas, adrenocortical adenomas, and congenital adrenal hyperplasia. This promising approach is expected to lead to enhanced clinical decision-making in the field of adrenal diseases. This review will focus on the clinical performances of steroid profiling, which is measured using mass spectrometry and analyzed by machine learning techniques, in the realm of decision-making for adrenal diseases.