Peak bone mass is the amount of bone tissue that is formed when a stable skeletal state is achieved at a young age. To date, there are no established peak bone mass standards nor clear data on the age at which peak bone mass occurs. At the same time, the level of peak bone mass at a young age is an important predictor of the onset of primary osteoporosis. The purpose of this review is to analyze the results of studies of levels of peak bone mass in general, the age of its onset, as well as factors influencing its formation. Factors such as hormonal levels, body composition, physical activity, nutrition, heredity, smoking, lifestyle, prenatal predictors, intestinal microbiota, and vitamin and micronutrient status were considered, and a comprehensive scheme of the influence of these factors on the level of peak bone mass was created. Determining the standards and timing of the formation of peak bone mass, and the factors affecting it, will help in the development of measures to prevent its shortage and the consequent prevention of osteoporosis and concomitant diseases.

Omics technologies accumulated an enormous amount of data that advanced knowledge about the molecular pathogenesis of type 1 diabetes mellitus and identified a number of fundamental problems focused on the transition to personalized diabetology in the future. Among them, the most significant are the following: (1) clinical and genetic heterogeneity of type 1 diabetes mellitus; (2) the prognostic significance of DNA markers beyond the HLA genes; (3) assessment of the contribution of a large number of DNA markers to the polygenic risk of disease progress; (4) the existence of ethnic population differences in the distribution of frequencies of risk alleles and genotypes; (5) the infancy of epigenetic research into type 1 diabetes mellitus. Disclosure of these issues is one of the priorities of fundamental diabetology and practical healthcare. The purpose of this review is the systemization of the results of modern molecular genetic, transcriptomic, and epigenetic investigations of type 1 diabetes mellitus in general, as well as its individual forms. The paper summarizes data on the role of risk HLA haplotypes and a number of other candidate genes and loci, identified through genome-wide association studies, in the development of this disease and in alterations in T cell signaling. In addition, this review assesses the contribution of differential DNA methylation and the role of microRNAs in the formation of the molecular pathogenesis of type 1 diabetes mellitus, as well as discusses the most currently central trends in the context of early diagnosis of type 1 diabetes mellitus.

Osteoporosis is a complex disease that is affected by a variety of factors, including genetic and epigenetic influences. While DNA markers for osteoporosis have been identified, they do not fully explain the hereditary basis of the disease. Epigenetic factors, such as small microRNAs (miRNAs), may provide a missing link in understanding the molecular mechanisms underlying osteoporosis. miRNAs are a class of non-coding RNAs that play a role in the epigenetic regulation of gene expression. They are known to be involved in various biological processes, including bone formation and remodelling. Differential expression of miRNAs has been linked to the pathological decrease in bone mineral density associated with osteoporosis. It has been shown that an abnormal miRNA expression pattern leads to a decrease in osteoblast activity and an increase in osteoclast activity. Further research into the role of miRNAs in osteoporosis may help to better understand this disease and identify potential therapeutic targets for treatment. Based on these assumptions, the study of miRNA expression patterns in osteoblasts, osteoclasts, and their precursors under normal and osteoporotic conditions is a rapidly growing field of scientific research. Although the results of this research are still incomplete and sometimes contradictory, they require additional scientific analysis to better understand the complex mechanisms involved. The purpose of this paper is to review the current research on miRNAs specifically expressed in osteoblasts and osteoclasts under both normal and pathological conditions. We will also discuss the potential applications of these miRNAs as biomarkers for osteoporosis diagnosis and as targets for osteoporosis treatment.

Autoimmune diabetes encompasses rapidly progressive type 1 diabetes mellitus (T1D) and indolent latent autoimmune diabetes in adults (LADA), which represent distinct inflammatory set points along a shared autoimmune spectrum. Yet, the immunological mechanisms that determine these divergent inflammatory states remain unresolved. We performed single-cell RNA sequencing with paired T and B cell receptor profiling on over 400,000 PBMCs from patients with LADA, newly diagnosed T1D, and individuals acting as healthy controls. PBMC composition was comparable across cohorts, indicating that qualitative rather than quantitative immune differences underlie disease heterogeneity. In T1D, pan-lineage activation of NF-κB, EGFR, MAPK, and hypoxia pathways, coupled with a TNF-centered communication hub, enhanced MHC signaling, disrupted adhesion, and promoted systemic inflammation. LADA, by contrast, exhibited global suppression of NF-κB/EGFR activity, retention of moderate JAK/STAT tone, reinforced NK cell inhibitory checkpoints via HLA-C–KIR2DL3/3DL1 interaction, and stabilized CD8 ⁺ T cell synapses through HLA-C–CD8 binding, collectively restraining effector activation. Single-cell V(D)J analysis revealed multiclonal, patient-unique adaptive repertoires, emphasizing the primacy of signaling context over receptor convergence. These findings define autoimmune diabetes as an inflammatory-inhibitory set-point continuum, positioning the NF-κB/EGFR–JAK/STAT gradient and HLA-C–KIR axis as potential therapeutic targets to preserve residual β cell function.

Obesity is a chronic metabolic disease characterized by excessive accumulation or uneven distribution of fat in the body, which poses a serious threat to health. Obesity significantly increases the risk of developing сonditions such as type 2 diabetes, coronary heart disease, hypertension, obstructive sleep apnea, and some types of cancer. The prevalence of obesity, especially in childhood, has increased significantly worldwide over the past few decades. The World Health Organization predicts that 250 million children and adolescents aged 5–19 years will be obese by 2030, which indicates a global problem with far‐reaching consequences. Advances in genomic technologies have led to the identification of multiple genetic loci associated with the disease ranging from severe cases with early onset to common multifactorial polygenic forms. Epigenetic changes driven by dietary and lifestyle factors are now recognized as crucial contributors to obesity. These modifications can alter gene expression and thereby link environmental influences to the observable clinical features of the disease. Significant progress has been made in deciphering the genetic architecture of obesity, particularly in pediatric populations. However, further advancement requires integrative multiomics analyses that encompass genomic, epigenomic, transcriptomic, proteomic, metabolomic, and microbiome data. To better understand the complex molecular underpinnings and clinical variability of obesity, researchers are increasingly applying methods from machine learning and artificial intelligence. These technologies help analyze large‐scale genomic and phenotypic datasets, allowing for the identification of biological pathways involved in weight regulation. In the future, this may support the design of individualized diagnostic tools and targeted treatment plans that reflect a patient’s genetic profile, lifestyle, and environmental exposures. To implement the principles of personalized and precision medicine in the treatment of obesity, it is crucial to identify risk profiles by assessing multiple contributing factors. This approach not only enables the prediction of an individual’s risk of obesity and its associated diseases but also facilitates the optimization of treatment based on the patient’s genetic profile. This study provides a comprehensive overview of the current understanding of childhood obesity, including its prevalence, genetic determinants, and pathophysiological mechanisms. It highlights the contribution of genetic factors to hereditary and syndromic forms, the role of gene–environment interactions (including nutrition and environmental pollutants), and the influence of epigenetic modifications on metabolic disturbances associated with polygenic obesity.

Background/Objectives: Cystic fibrosis (CF) is one of the most common autosomal-recessive disorders worldwide. The incidence of CF depends on the prevalence of cystic fibrosis transmembrane conductance regulator gene (CFTR) mutations in the population, which is determined by genetic diversity and ethnicity. Methods: The search for the causes of mutations in the transmembrane conductance regulator gene (CFTR) was carried out using targeted next-generation sequencing (NGS) on the Illumina platform in patients with cystic fibrosis from the Republic of Bashkortostan (Russia), taking into account the ethnic structure of the sample. Results: A total of 35 distinct causal variants were found in 139 cases from 129 families. Five (F508del, E92K, 3849+10kbC>T, CFTRdele2.3, L138ins) explain 78.7% of identified CF causal alleles. Variants N13103K and 394delTT were found in four families each. Variants 2143delT, S1196X, W1282X, Y84X, G194R, and 1525-1G>A, as well as the two previously described complex alleles—c. [S466X; R1070Q] and str.[G509D;E217G]—were found in two or three families each. Twenty additional variants occurred only once. Variant c.3883_3888dup has not been described previously. Thus, regional and ethnic features were identified in the spectrum of frequencies of pathogenic variants of the CFTR gene in the three major sub-groups of patients—Russians, Tatars, and Bashkirs. Conclusions: Taking into account these results, highlighting the genetic specificity of the region, a more efficient search for CFTR mutations in patients can be performed. In particular it is possible to choose certain test kits for quick and effective genetic screening before use of NGS sequencing.