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We recommend acromegaly to be ruled in all patients with characteristic changes in appearance (A3). In all patients without characteristic changes in appearance, we recommend to rule out acromegaly, if several clinical signs suspicious for acromegaly are present (B3). We recommend acromegaly to be ruled out in all patients with pituitary adenoma (B3). We recommend to rule out acromegaly in all patients with prolactin-secreting adenomas (C4). For laboratory confirmation of acromegaly, we recommend to measure serum insulin-like growth factor-1 (IGF-1, somatomedin C) level (A3). In patients without obvious clinical signs of acromegaly and/or with a moderate increase in IGF-1 levels (IGF-1 index less than 1.3), we recommend to assess the response of somatotropic hormone (GH) to hyperglycemia (GH during an oral glucose tolerance test) (B3). In all patients with biochemically confirmed acromegaly, magnetic resonance imaging of the pituitary gland with intravenous contrast is recommended to assess pituitary adenoma size, location and growth pattern (A3). We recommend to measure blood glucose and glycated hemoglobin in all patients with confirmed acromegaly (B3). We recommend to measure serum prolactin levels in all patients with acromegaly (B3).In all patients with acromegaly and pituitary adenoma, transnasal transsphenoidal adenomectomy is recommended as first-line treatment if the patient’s consent is given and there are no contraindications (A1). For all patients with acromegaly for whom neurosurgical treatment is indicated, surgical intervention is recommended in medical centers, specializing in pituitary diseases, by neurosurgeons who perform at least 50 such operations per year (A3). We recommend that morphological and immunohistochemical examination of the removed pituitary adenoma is performed in all patients with acromegaly (A3). We do not recommend routine medical therapy for acromegaly before surgery as a mean to reduce tumor size (B2).For patients with acromegaly in whom neurosurgical treatment is not indicated or ineffective or while patient awaiting for the effect of radiation therapy, we recommend medical therapy (A1). Long-acting first-generation somatostatin analogues are recommended as first-line drug therapy for acromegaly. In case of partial/complete resistance to or intolerance to somatostatin analogs, pegvisomant is recommended as a second-line medical therapy for acromegaly (A1). We recommend starting pegvisomant from a single dose of 40–80 mg per day subcutaneously, then continue injections at a starting dose of 10 or 15 mg per day with IGF-1 monitoring after 4–6 weeks and dose adjustment if necessary (B2). In patients with acromegaly and a moderate increase in IGF-1 levels, we recommend to initiate cabergoline as medical monotherapy or, if somatostatin analogs are ineffective, in combination with them (B2).In patients with acromegaly, if the disease remains active after surgical treatment, if drug treatment is unavailable, intolerable or ineffective, and repeated neurosurgical intervention is inappropriate, it is we recommend radiation therapy (A1). A multidisciplinary approach is recommended in the management of acromegaly complications (C3).

The most common causes of death in acromegaly are cardiovascular diseases (about 60%). Heart arrhythmias and conduction disorders lead to sudden cardiac death (SCD). In this article, we described a clinical case about preventing SCD in a patient with acromegaly. We identified in this patient predictors of SCD: severe left ventricular hypertrophy, the signs of myocardial fibrosis, decreased systolic function of the left ventricular myocardium, ventricular rhythm disturbances, and heart failure. Patients with acromegaly have higher risk of heart arrhythmias due to development acromegalic cardiomyopathy with includes: left ventricular hypertrophy, diastolic and systolic dysfunction, myocardial fibrosis and electrical disturbances of the myocardium. The main limitation is the lack of special clinical recommendations for the management of this group of patients. Current recommendations based on a standard algorithm and do not consider specificity of acromegalic cardiomyopathy.

BACKGROUND: Pituitary adenomas are the third most common intracranial neoplasm. Clinical significance is determined by the nature of growth and hormonal activity, leading to disruption of various parts of metabolism. Medical registries are a valuable tool for assessing clinical and epidemiological parameters and obtaining data on real-life clinical practice.AIM: To analyze the epidemiological and clinical characteristics of four pituitary disorders in Russian Hypothalamic and Pituitary Tumor Registry (OGGO): acromegaly, Cushing’s disease, prolactin-secreting and non-functioning pituitary adenomasMATERIALS AND METHODS: The object of the study is the database of the OGGO registry, which includes 84 regions of the Russian Federation, as of 01/01/2023.RESULTS: The maximum prevalence of acromegaly in the Russian Federation was noted in Karelia (9.48/100 thousand) and Chuvash Republic (10.8/100 thousand) and in Tyumen region (8.9/100 thousand). The incidence of acromegaly in 2021 was 0.6/1 million population. The maximum prevalence of Cushing’s disease is observed in Karelia Republic (2.33/100 thousand), Chukotka (2.01/100 thousand), Kirov (1.79/100 thousand) and Tver region ( 1.79/100 thousand). The incidence of Cushing’s disease in 2021 was 0.1/1 million population. The maximum prevalence of prolactinomas is observed Karelia (13.33/100 thousand) and Chuvash Republic (12.35/100 thousand) and in Voronezh region (8.66/100 thousand). The incidence of prolactin-secreting adenomas in 2021 was 0.6/1 million population. The maximum prevalence of non-functioning pituitary adenomas is observed in the Karelia (15.08/100 thousand), Voronezh region (8.82/100 thousand), Tyumen region (8.11/100 thousand) and Sakha/Yakutia Republic (7.56 /100 thousand). The incidence of non-functioning pituitary adenomas in 2021 was 0.3/1 million population. The median age for acromegaly was 63.6 [53.2;71.5], for Cushing’s disease 49.2 [38.8;60.8], for prolactin-secreting adenomas 47.5 [38.3;59.7], for patients with non-functioning pituitary adenomas — 58.4 [43.8;67.9] . At the time of the last visit, remission was observed in 42.7% of patients with acromegaly, 62.5% with Cushing’s disease, 39.7% with prolactinomas and 76.6% of patients with non-active adenomas.CONCLUSION: The OGGO registry is a valuable tool for assessing epidemiological, clinical data and treatments used. At the same time, the quality of the analysis directly depends on the quality and completeness of the entered data. Further research in the field of epidemiology of endocrinopathies should be aimed at improving the quality and ease of data entry, which will allow obtaining the most complete patient characterization.

Diabetic nephropathy (DN) is a specific kidney involvement in diabetes mellitus (DM), caused by hemodynamic and metabolic factors. In the kidneys takes place an important step in the metabolism of vitamin D — 1α-hydroxylation, which results in the formation of its biologically active form. Reduced number of functioning nephrons in DN leads to impaired vitamin D metabolism, contributing to the development of a number of complications. In this review, we have focused in detail on both normal vitamin D metabolism and the features of vitamin D metabolism in chronic kidney disease (CKD). DN is the most common cause of CKD and, as a consequence, of kidney transplantation and one of the leading causes of cardiovascular morbidity and mortality in patients with DM. Bone mineral disorders resulting from abnormal vitamin D metabolism are also independent factors of high mortality among patients with DM. The final part of our review briefly highlights current approaches to vitamin D therapy in CKD and, in particular, in DN. It is worth noting that, despite the increasing number of patients with DN, there is currently no unified view on the use of vitamin D as a therapeutic agent in this pathology.

Disclaimer. The EAC Guidelines represent the views of the EAC, and were produced after careful consideration of the scientific and medical knowledge, and the evidence available at the time of their publication. The EAC is not responsible in the event of any contradiction, discrepancy, and/or ambiguity between the EAC Guidelines and any other official recommendations or guidelines issued by the relevant public health authorities, in particular in relation to good use of healthcare or therapeutic strategies. Health professionals are encouraged to take the EAC Guidelines fully into account when exercising their clinical judgment, as well as in the determination and the implementation of preventive, diagnostic, or therapeutic medical strategies; however, the EAC Guidelines do not override, in any way whatsoever, the individual responsibility of health professionals to make appropriate and accurate decisions in consideration of each patient’s health condition and in consultation with that patient and, where appropriate and/or necessary, the patient’s caregiver. Nor do the EAC Guidelines exempt health professionals from taking into full and careful consideration the relevant official updated recommendations or guidelines issued by the competent public health authorities, in order to manage each patient’s case in light of the scientifically accepted data pursuant to their respective ethical and professional obligations. It is also the health professional’s responsibility to verify the applicable rules and regulations relating to drugs and medical devices at the time of prescription.

This paper presents a software package that allows us to generate algorithms for the automatic classification of signals. The software package includes an algorithm that converts records of continuous signals into vector form and a set of machine learning methods, as well as data mining tools aimed at achieving transparency and interpretability of learning. This approach is based on the presentation of differences between the compared classes as a set of relatively simple, statistically significant and interpretable effects, which are graphically represented on two-dimensional diagrams. The performance of the method is illustrated on the problem of assessing the state of a hive by sound signals. The software package can be used in solving applied problems of automatic diagnostics and data analysis. [ABSTRACT FROM AUTHOR]