Genetic and epigenetic analyses of aldosterone-producing adenoma with hypercortisolemia.

DNA methylation is associated with excess cortisol and aldosterone. The DNA encoding aldosterone synthase (CYP11B2) and 11ß-hydroxylase (CYP11B1), which catalyzes the final step of cortisol biosynthesis, is less methylated in aldosterone-producing adenomas (APA) and cortisol-producing adenomas (CPA), respectively. Several studies have reported specific gene mutations in APA and CPA, and some APAs also cause hypercortisolemia. The aim of this study was to clarify the molecular mechanisms of cortisol co-production in APA using genetic and epigenetic analyses. We evaluated 16 patients with APA between 2011 and 2018 at Kanazawa University Hospital (Ishikawa, Japan). The diagnostic criteria for hypercortisolemia were based on the guideline from the Endocrine Society. Gene mutation and DNA methylation analyses of the CYP11B2 and CYP11B1 promoters in APA were performed. Of the 16 patients with APA, six also had hypercortisolemia. In the genetic analysis, all six APAs with hypercortisolemia as well as eight of the 10 APAs without hypercortisolemia had a KCNJ5 mutation. In the epigenetic analyses, the methylation status of the CYP11B2 promoter was similar in the APAs with and without hypercortisolemia. However, in the APAs with hypercortisolemia, the CYP11B1 promoter was significantly less methylated, especially at two CpG sites near the Ad1/cAMP response element binding site within the CYP11B1 promoter. In conclusion, the genetic analysis revealed no association between hypercortisolemia and the evaluated gene mutations. However, the epigenetic analysis suggested that DNA methylation of the CYP11B1 promoter plays a role in concurrent hypercortisolemia and APA.

Study Protocol for the Effects of Artificial Intelligence (AI)-Supported Automated Nutritional Intervention on Glycemic Control in Patients with Type 2 Diabetes Mellitus.

INTRODUCTION: Nutritional intervention is effective in improving glycemic control in patients with type 2 diabetes but requires large inputs of manpower. Recent improvements in photo analysis technology facilitated by artificial intelligence (AI) and remote communication technologies have enabled automated evaluations of nutrient intakes. AI- and mobile-supported nutritional intervention is expected to be an alternative approach to conventional in-person nutritional intervention, but with less human resources, although supporting evidence is not yet complete. The aim of this study is to test the hypothesis that AI-supported nutritional intervention is as efficacious as the in-person, face-to-face method in terms of improving glycemic control in patients with type 2 diabetes. METHODS: This is a multicenter, unblinded, parallel, randomized controlled study comparing the efficacy of AI-supported automated nutrition therapy with that of conventional human nutrition therapy in patients with type 2 diabetes. Patients with type 2 diabetes mainly controlled with diet are to be recruited and randomly assigned to AI-supported nutrition therapy (n = 50) and to human nutrition therapy (n = 50). Asken, a mobile application whose nutritional evaluation has been already validated to that by the classical method of weighted dietary records, has been specially modified for this study so that it follows the recommendations of Japan Diabetes Society (total energy restriction with proportion of carbohydrates to fat to protein of 50-60, 20, and 20-30%, respectively). PLANNED OUTCOMES: The primary outcome is the change in glycated hemoglobin levels from baseline to 12 months, and this outcome is to be compared between the two groups. The secondary outcomes are changes in fasting plasma glucose, plasma lipid profile, body weight, body mass index, waist circumference, blood pressures, and urinary albumin excretion. The results of this randomized controlled trial will fill the gap between the demand for support of AI in nutritional interventions and the scientific evidence on its efficacy. TRIAL REGISTRATION: UMIN000032231.