Extracellular vesicles (EVs) are spherical structures of cell membrane origin, ranging in the size from 40 nm to 5000 nm. They are involved in the horizontal transfer of many proteins and microRNAs. The mechanisms EV internalization include clathrin-dependent endocytosis, caveolin-dependent endocytosis, raft-mediated endocytosis, and macropinocytosis. Type 2 diabetes mellitus (T2DM) is a common group of metabolic disorders in adults; the incidence and prevalence increase in parallel with the obesity epidemic. Since adipose tissue plays a crucial role in the development of insulin resistance, EVs secreted by adipose tissue can be a kind of information transmitter in this process. EVs of adipocytic origin are predominantly absorbed by tissue macrophages, adipocytes themselves, hepatocytes, and skeletal muscles. This contributes to the M1 polarization of macrophages, a decrease in glucose uptake by hepatocytes and myocytes due to the transfer of functionally active microRNAs by these EVs, which affect carbohydrate and lipid metabolism. Patients with T2DM and impaired glucose tolerance have significantly higher levels of CD235a-positive (erythrocyte) EVs, as well as a tendency to increase CD68-positive (leukocyte) and CD62p-positive (platelets/endothelial cells) EVs. The levels of CD31+/CD146-positive BB (endothelial cells) were comparable between diabetic and euglycemic patients. EVs from diabetic patients were preferably internalized by monocytes (mainly classical and intermediate monocyte fractions and to a lesser extent by non-classical monocyte fractions) and B cells compared to euglycemic patients. Internalization of EVs from patients with T2DM by monocytes leads to decreased apoptosis, changes in differentiation, and suppression of reactions controlling oxidative stress in monocytes. Thus, insulin resistance increases secretion of EVs, which are preferentially internalized by monocytes and influence their function. EVs are considered as sources of promising clinical markers of insulin resistance, complications of diabetes mellitus (endothelial dysfunction, retinopathy, nephropathy, neuropathy), and markers of EVs can also be used to monitor the effectiveness of therapy for these complications.

Background: Sarcopenia is a complex, multifactorial condition characterized by progressive loss of muscle mass, strength, and function. Despite growing awareness, the early diagnosis and pathophysiological characterization of this condition remain challenging due to the lack of integrative biomarkers. Objective: This study aimed to conduct a comprehensive multilevel profiling of clinical parameters, immune cell phenotypes, extracellular vesicle (EV) signatures, and biochemical markers to elucidate biological gradients associated with different stages of sarcopenia. Materials and Methods: A prospective cohort study enrolled adults aged 45–85 years classified as control, presarcopenic, or sarcopenic based on European Working Group on Sarcopenia in Older People 2 (EWGSOP2) criteria. Clinical evaluation included anthropometry, muscle strength, sarcopenia screening (SARC-F) questionnaire/Short Physical Performance Battery (SPPB) questionnaires, and quality-of-life assessment. Flow cytometry was used to characterize blood monocyte/macrophage subsets (cluster of differentiation 14 (CD14), CD68, CD163, CD206). EVs were isolated from plasma and profiled for surface tetraspanins and matrix metalloproteinases (MMP2, MMP9, tissue inhibitor of metalloproteinase-1 (TIMP-1)) using bead-based flow cytometry. Biochemical assays measured metabolic, inflammatory, and extracellular matrix (ECM)-related markers. Data were analyzed via Kruskal–Wallis testing, discriminant analysis, and principal component analysis (PCA). Results: Sarcopenia, a muscle-wasting condition linked to aging, is characterized by chronic inflammation, proteolytic imbalance, and metabolic disturbances. Clinical deterioration is evident through reduced appendicular lean mass (ALM), appendicular skeletal muscle index (ASMI), SPPB scores, and sarcopenia quality of life (SarQoL) domains. Principal component analysis (PCA) identified four functional marker clusters: ECM degradation (MMP-positive EVs), inflammatory and homeostasis-stabilizing macrophages, and metabolic disruption (glucose, asprosin, triglycerides). Discriminant analysis emphasized vesicular and immune markers with significant classification potential, even when univariate differences were non-significant. Metabolic destabilization and inflammatory activation are detectable in presarcopenia stages. Chronic inflammation, characterized by CD14–CD163+206+ cells releasing pro-inflammatory cytokines, accelerates muscle degradation. Proteolytic dysfunction, with an imbalance between proteases and inhibitors, further contributes to muscle loss. Metabolic disorders impair energy production and nutrient utilization, exacerbating muscle wasting. A comprehensive assessment, including anthropometric, functional, physical activity, and QoL measures, is crucial for identifying high-risk individuals and understanding sarcopenia’s mechanisms. Vesicular biomarkers, regulating tissue remodeling and inflammation, provide valuable insights. Standardized assessment methods are essential for enhancing diagnostic accuracy and intervention effectiveness. Future research should focus on developing and refining biomarkers to improve specificity and sensitivity, enabling targeted therapies and better QoL. Conclusions: Integrating clinical, immunological, and biochemical markers with EVs helps stratify sarcopenia effectively. Our data shows that EVs and macrophage profiles reflect systemic changes and metabolic stress. However, age- and gender-related variability in our cohort warrants caution in generalizing the findings. Artificial intelligence (AI) enhances patient clustering by combining these data types, enabling precise, personalized sarcopenia management, predicting disease progression, and identifying high-risk patients. AI also standardizes and optimizes analytical protocols, improving diagnostic and monitoring reliability and reproducibility.

Extracellular vesicles (EVs) are spherical functionally important structures of cell membrane origin ranging in size from 40 nm to 5000 nm. They are involved in the horizontal transfer of mainly proteins and microRNAs. The mechanisms of EV internalization include clathrin-dependent endocytosis, caveolin-dependent endocytosis, raft-mediated endocytosis, and macropinocytosis. Type 2 diabetes mellitus (T2DM) is a common group of metabolic disorders in adults; the incidence and prevalence of T2DM increase in parallel with the obesity epidemic. Since adipose tissue plays a key role in the development of insulin resistance, EVs secreted by adipose tissue may be considered as an information transmitter in this process. EVs of the adipocyte origin are predominantly captured up by tissue macrophages, adipocytes themselves, hepatocytes, and skeletal muscles. The EV uptake promotes M1 polarization of macrophages, a decrease in glucose uptake by hepatocytes and myocytes due to the transfer of functionally active microRNAs influencing carbohydrate and lipid metabolism. Patients with T2DM and impaired glucose tolerance, have a significantly higher level of CD235a-positive (erythrocyte) EVs and a trend to the increase in CD68-positive (leukocyte) and CD62p-positive (platelet/endothelial cells) EVs. The levels of CD31+/CD146-positive EVs (endothelial cells) were comparable between diabetic and euglycemic patients. EVs from diabetic patients were preferentially internalized by monocytes (predominantly classical and transitional, and to a lesser extent nonclassical monocyte fractions) and B cells as compared to euglycemic patients. Internalization of EVs from patients with T2DM by monocytes led to a decrease in their apoptosis, changes in differentiation, and suppression of monocyte reactions controlling oxidative stress. Thus, insulin resistance increases the secretion of EVs, which are preferentially internalized by monocytes and alter their function. EVs are considered as sources of promising clinical markers of insulin resistance, complications of diabetes mellitus (endothelial dysfunction, retinopathy, nephropathy, neuropathy), and EV markers can also be used to monitor the effectiveness of therapy for these complications.

The majority of colorectal cancer patients (CRCPs) develop tumors on the background of “metabolically healthy obesity” or metabolic syndrome. The aim of the work was to study the levels of matrix metalloproteinases (MMPs) and heat shock proteins (HSPs) on the surface of blood plasma CD9-positive and FABP4-positive small extracellular vesicles (sEVs) from CRCPs depending on metabolic status and tumor angiogenesis, as well as to evaluate the sEVs markers as predictors of the effectiveness of thermoradiotherapy. In CRCPs, compared with patients with colorectal polyps (CPPs), the proportion of triple positive EVs and EVs with the MMP9+MMP2-TIMP1+ phenotype increased significantly among FABP4-positive EVs (adipocyte-derived EVs), which in general may indicate the overexpression of MMP9 and TIMP1 by adipocytes or adipose tissue macrophages in CRCPs. The results obtained have prospects for use as markers to clarify cancer risk in CPPs. One can assume that for CRCPs with metabolic syndrome or metabolically healthy obesity, it is the FABP4+MMP9+MMP2-TIMP1- population of circulating sEVs that is the most optimal biomarker reflecting tumor angiogenesis. Determining this population in the blood will be useful in monitoring patients after treatment for the early detection of tumor progression. CD9+MMP9+MMP2-TIMP1- and MMP9+MMP2-TIMP1+ subpopulations of circulating sEVs are the most promising predictors of the efficacy of thermoradiation therapy because their levels at baseline differ significantly in CRCPs with different tumor responses.

Mitochondrial transfer in mammals has been proven to occur both under physiological conditions and during pathological conditions. It has been shown that neighboring cells can exchange mitochondria via nanotunnel tubes. However, there is evidence that free mitochondria, as well as whole mitochondria and individual mitochondrial fragments, can be transported between cells within extracellular vesicles (EVs). This review discusses the methodological aspects of isolation and a minimal set of methods for characterizing mitochondria-rich EVs (mitoEVs), as well as methodological approaches for studying the nucleic acid, protein, and lipid composition. It has been shown that mitoEVs, as well as extracellular mitochondria, contain a characteristic set of nucleic acids of mitochondrial origin. First and foremost, the dominant fraction of mitochondrial nucleic acids is mitochondrial DNA (mtDNA), a circular double-stranded molecule approximately 16.6 thousand base pairs in length. The mechanisms involved in EV internalization include clathrin-dependent endocytosis, caveolin-dependent endocytosis, raft-mediated endocytosis, and macropinocytosis. Mitochondrial-enriched autologous and xenogeneic EVs are thought to be internalized by similar mechanisms. The review also presents the main sources (stem cells, platelet concentrate, peripheral blood mononuclear cells) for obtaining mitochondria-rich EVs for therapeutic purposes.

Circulating small extracellular vesicles (sEVs) are emerging as potential biomarkers for glioblastoma progression. This study aimed to compare the levels of matrix metalloproteinases (MMP2 and MMP9), terminal complement complex (C5b-9), and VEGF-A in circulating sEVs in glioblastoma patients (GBMPs) with and without tumor recurrence. Using differential ultracentrifugation, sEVs were isolated from blood samples of GBMPs with no tumor recurrence for over one year (n = 6) and after first relapse (n = 14). The vesicles were characterized and quantified using flow cytometry. In both groups, C5b-9 was predominantly detected on tumor-specific circulating sEVs (glial fibrillary acidic protein (GFAP)-positive sEVs) with high VEGF-A expression, while C5b-9 was significantly less frequent on sEVs with low VEGF-A expression (p < 0.05). GFAP+VEGF+dimMMP2-C5b-9+ vesicles were rarely detected in GBMPs without relapse, suggesting their potential utility as biomarkers for a favorable relapse-free prognosis. In recurrent GBMPs, a positive correlation was observed between GFAP+VEGF+bright MMP2+C5b-9+ sEVs and MGMT gene promoter methylation levels (r = 0.543, p < 0.05). Additionally, a trend toward a negative correlation was found between GFAP+VEGF+bright MMP2+C5b-9- sEVs and mutant p53 expression in primary tumor tissue (r = −0.44, p = 0.114). These findings suggest that sEV profiles may serve as valuable prognostic markers for glioblastoma recurrence and treatment responses.