Steroids are present in all animals and plants, from mammals to prokaryotes. In the medical field, steroids are commonly classified as glucocorticoids, mineralocorticoids, and gonadal steroid hormones. Monitoring of hormones is useful in clinical and research fields for the assessment of physiological changes associated with aging, disease risk, and the diagnostic and therapeutic effects of various diseases. Since the discovery and isolation of steroid hormones, measurement methods for steroid hormones in biological samples have advanced substantially. Although immunoassays (IAs) are widely used in daily practice, mass spectrometry (MS)-based methods have been reported to be more specific. Steroid hormone measurement based on MS is desirable in clinical practice; however, there are several drawbacks, including the purchase and maintenance costs of the MS instrument and the need for specialized training of technicians. In this review, we discuss IA- and MS-based methods currently in use and briefly present the history of steroid hormone measurement. In addition, we describe recent advances in IA- and MS-based methods and future applications and considerations.

Aldosterone and cortisol serve important roles in the pathogenesis of cardiovascular diseases and metabolic disorders. Epigenetics is a mechanism to control enzyme expression by genes without changing the gene sequence. Steroid hormone synthase gene expression is regulated by transcription factors specific to each gene, and methylation has been reported to be involved in steroid hormone production and disease. Angiotensin II or potassium regulates the aldosterone synthase gene, CYP11B2. The adrenocorticotropic hormone controls the 11b-hydroxylase, CYP11B1. DNA methylation negatively controls the CYP11B2 and CYP11B1 expression and dynamically changes the expression responsive to continuous stimulation of the promoter gene. Hypomethylation status of the CYP11B2 promoter region is seen in aldosterone-producing adenomas. Methylation of recognition sites of transcription factors, including cyclic AMP responsive element binding protein 1 or nerve growth factor-induced clone B, diminish their DNA-binding activity. A methyl-CpG-binding protein 2 cooperates directly with the methylated CpG dinucleotides of CYP11B2. A low-salt diet, treatment with angiotensin II, and potassium increase the CYP11B2 mRNA levels and induce DNA hypomethylation in the adrenal gland. A close association between a low DNA methylation ratio and an increased CYP11B1 expression is seen in Cushing’s adenoma and aldosterone-producing adenoma with autonomous cortisol secretion. Epigenetic control of CYP11B2 or CYP11B1 plays an important role in autonomic aldosterone or cortisol synthesis.

Background: Plasma aldosterone-to-renin ratio (ARR) is popularly used for screening primary aldosteronism (PA). Some medications, including diuretics, are known to have an effect on ARR and cause false-negative and false-positive results in PA screening. Currently, there are no studies on the effects of sodium–glucose cotransporter-2 (SGLT2) inhibitors, which are known to have diuretic effects, on ARR. We aimed to investigate the effects of SGLT2 inhibitors on ARR.Methods: We employed a retrospective design; the study was conducted from April 2016 to December 2018 and carried out in three hospitals. Forty patients with diabetes and hypertension were administered SGLT2 inhibitors. ARR was evaluated before 2 to 6 months after the administration of SGLT2 inhibitors to determine their effects on ARR.Results: No significant changes in the levels of ARR (90.9 ± 51.6 vs. 81.4 ± 62.9) were found. Body mass index, diastolic blood pressure, heart rate, fasting plasma glucose, and hemoglobin A1c were significantly decreased by SGLT2 inhibitors. Serum creatinine was significantly increased.Conclusion: SGLT2 inhibitor administration yielded minimal effects on ARR and did not increase false-negative results in PA screening in patients with diabetes and hypertension more than 2 months after administration.

Hyperuricemia (HUA) is a symptom of high blood uric acid (UA) levels, which causes disorders such as gout and renal urinary calculus. Prolonged HUA is often associated with hypertension, atherosclerosis, diabetes mellitus, and chronic kidney disease. Studies have shown that gut microbiota (GM) affect these chronic diseases. This study aimed to determine the relationship between HUA and GM. The microbiome of 224 men and 254 women aged 40 years was analyzed through next-generation sequencing and machine learning. We obtained GM data through 16S rRNA-based sequencing of the fecal samples, finding that alpha-diversity by Shannon index was significantly low in the HUA group. Linear discriminant effect size analysis detected a high abundance of the genera Collinsella and Faecalibacterium in the HUA and non-HUA groups. Based on light gradient boosting machine learning, we propose that HUA can be predicted with high AUC using four clinical characteristics and the relative abundance of nine bacterial genera, including Collinsella and Dorea. In addition, analysis of causal relationships using a direct linear non-Gaussian acyclic model indicated a positive effect of the relative abundance of the genus Collinsella on blood UA levels. Our results suggest abundant Collinsella in the gut can increase blood UA levels.