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Metabolic syndrome (MetS) is a clustering of at least three of the following five medical conditions: abdominal obesity, high blood pressure, high blood sugar, high serum triglycerides, and low serum high-density lipoprotein (HDL). Antipsychotic (AP)-induced MetS (AIMetS) is the most common adverse drug reaction (ADR) of psychiatric pharmacotherapy. Herein, we review the results of studies of blood (serum and plasma) and urinary biomarkers as predictors of AIMetS in patients with schizophrenia (Sch). We reviewed 1440 studies examining 38 blood and 19 urinary metabolic biomarkers, including urinary indicators involved in the development of AIMetS. Among the results, only positive associations were revealed. However, at present, it should be recognized that there is no consensus on the role of any particular urinary biomarker of AIMetS. Evaluation of urinary biomarkers of the development of MetS and AIMetS, as one of the most common concomitant pathological conditions in the treatment of patients with psychiatric disorders, may provide a key to the development of strategies for personalized prevention and treatment of the condition, which is considered a complication of AP therapy for Sch in clinical practice.

Study the impact of various combinations of comorbid original diseases in patients infected with COVID-19 later on the disease progression and outcomes of the new coronavirus infection.The ACTIV registry was created on the Eurasian Association of Therapists initiative. 5,808 patients have been included in the registry: men and women with COVID-19 treated at hospital or at home.gov ID NCT04492384.Most patients with COVID-19 have original comorbid diseases (oCDs). Polymorbidity assessed by way of simple counting of oCDs is an independent factor in negative outcomes of COVID-19. Search for most frequent combinations of 2, 3 and 4 oCDs has revealed absolute domination of cardiovascular diseases (all possible variants). The most unfavorable combination of 2 oCDs includes atrial hypertension (AH) and chronic heart failure (CHF). The most unfavorable combination of 3 oCDs includes AH, coronary heart disease (CHD) and CHF; the worst combination of 4 oCDs includes AH, CHD, CHF and diabetes mellitus. Such combinations increased the risk of lethal outcomes 3.963, 4.082 and 4.215 times respectively.Polymorbidity determined by way of simple counting of diseases may be estimated as a factor in the lethal outcome risk in the acute phase of COVID-19 in real practice. Most frequent combinations of 2, 3 and 4 diseases in patients with COVID-19 primarily include cardiovascular diseases (AH, CHD and CHF), diabetes mellitus and obesity. Combinations of such diseases increase the COVID-19 lethal outcome risk.Цель. Изучить влияние различных комбинаций сопутствующих заболеваний, существовавших у пациентов исходно, до инфицирования вирусом SARS-CoV-2, на тяжесть течения и исходы новой коронавирусной инфекции. Материалы и методы. Регистр АКТИВ создан по инициативе Евразийской ассоциации терапевтов. В регистр включены 5808 пациентов: мужчины и женщины с диагнозом COVID-19, находящиеся на лечении в стационаре или получающие лечение на дому. Регистрация регистра: идентификатор ClinicalTrials.gov: NCT04492384. Результаты. Подавляющее большинство пациентов с COVID-19 имеют исходные сопутствующие заболевания (иСЗ). Полиморбидность, оцененная методом простого подсчета иСЗ, является самостоятельным фактором негативного прогноза больных COVID-19. Поиск наиболее часто встречающихся комбинаций из 2, 3, 4 и более СЗ выявил абсолютное доминирование сердечно-сосудистых заболеваний во всех возможных вариантах. Наиболее неблагоприятной при сочетании 2 иСЗ была комбинация, включающая артериальную гипертензию (АГ) и хроническую сердечную недостаточность (ХСН), при сочетании 3 иСЗ комбинация, включающая АГ, ишемическую болезнь сердца (ИБС), ХСН, и при сочетании 4 иСЗ комбинация, включающая АГ, ИБС, ХСН и сахарный диабет. Наличие таких комбинаций приводило к росту риска летального исхода в 3,963, 4,082, 4,215 раза соответственно. Заключение. Полиморбидность, определенная методом простого подсчета заболеваний, может использоваться в реальной практике как фактор риска летальности в остром периоде COVID-19. В состав наиболее часто встречающихся комбинаций из 2, 3, 4 заболеваний у пациентов с COVID-19 входят преимущественно сердечно-сосудистые заболевания (АГ, ИБС, ХСН), сахарный диабет и ожирение. Комбинации этих заболеваний повышают риск летального исхода при COVID-19.

Gestational diabetes mellitus (GDM) and its consequences for the mother and child represent a serious not only medical, but also an economic problem. The urgency of studying this problem also lies in the fact that the number of pregnant women suffering from this disease is progressively increasing. The aim of the study was to study the risk factors and features of the course of pregnancy and childbirth in women with gestational diabetes mellitus, as well as to improve methods for diagnosing and predicting the development of diabetic fetopathy (DF). The work was carried out in 3 stages: 1 — prospective study of the course of pregnancy and childbirth in 104 women with GDM and in 50 pregnant women without GDM (control group); 2 — determination in the peripheral blood of the pregnant women of both groups of the level of glycemia, C-peptide, insulin, calculation of the insulin resistance index (HOMA-IR) with an assessment of the prognostic significance of these markers in the development of diabetic fetopathy; 3 — prediction of the development of diabetic fetopathy using a mathematical model that includes risk factors, the results of laboratory and instrumental research methods for this pathology. It was found that the violation of carbohydrate metabolism during pregnancy promotes the development of a large number of obstetric complications and in 36.5% of cases leads to the birth of children with diabetic fetopathy, which is manifested to a greater degree by macrosomia (30%), which increases the risk of perinatal complications and worsens the course of the period newborn in the future. An increase in the level of C-peptide was diagnosed in 87% of pregnant women with gestational diabetes, and an increase in the insulin resistance index in 93%, in contrast to the control group, where these indicators were 4 and 6%, respectively (p < 0.05). Using discriminant analysis, it was determined that the threshold for predicting the development of DF should be considered an increase in HOMA-IR above 7 with a confidence of 73%, but the level of C- peptide does not have a predictive value. The mathematical model, including risk factors, data of laboratory and instrumental methods for studying carbohydrate metabolism in the mother and fetus, created using regression analysis, reflects the probability of development of diabetic fetopathy in pregnant women with gestational diabetes with an accuracy of 91.4%, which will help to prevent this complication in at an earlier stage through insulin therapy.