Multiple sclerosis (MS) is a chronic autoimmune inflammatory disease that affects the nervous system. Peripheral blood leukocyte telomere length (LTL) and mitochondrial DNA copy number (mtDNA-CN) are potential biomarkers of neurological disability and neural damage. Our objective was to assess the LTL and mtDNA-CN in relapsing-remitting MS (RRMS). We included 10 healthy controls, 75 patients with RRMS, 50 of whom had an Expanded Disability Status Scale (EDSS) from 0 to 3 (mild to moderate disability), and 25 had an EDSS of 3.5 to 7 (severe disability). We use the Real-Time Polymerase Chain Reaction (qPCR) technique to quantify absolute LTL and absolute mtDNA-CN. ANOVA test show differences between healthy control vs. severe disability RRMS and mild-moderate RRMS vs. severe disability RRMS (p = 0.0130). LTL and mtDNA-CN showed a linear correlation in mild-moderate disability RRMS (r = 0.378, p = 0.007). Furthermore, we analyzed LTL between RRMS groups with a ROC curve, and LTL can predict severe disability (AUC = 0.702, p = 0.0018, cut-off < 3.0875 Kb, sensitivity = 75%, specificity = 62%), whereas the prediction is improved with a logistic regression model including LTL plus age (AUC = 0.762, p = 0.0001, sensitivity = 79.17%, specificity = 80%). These results show that LTL is a biomarker of disability in RRMS and is correlated with mtDNA-CN in mild-moderate RRMS patients.

Metabolic syndrome (MetS) is a complex condition characterized by a group of interconnected metabolic abnormalities. Due to its increasing prevalence, better predictive markers are needed. Therefore, this study aims to develop predictive models for MetS by integrating adipokines, metabolic and cardiovascular risk factors, and anthropometric indices. Data were collected from 381 subjects aged 20 to 59 years (242 women and 139 men) from Guadalajara, Jalisco, Mexico, who were classified as having MetS or non-MetS based on the ATP-III criteria. Four supervised machine learning models were developed—Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost)—and their performance was evaluated using the Area under the Curve (AUC), calibration curves, Decision Curve Analysis (DCA), and local interpretability analysis. The RF and XGBoost models achieved the highest AUCs (0.940 and 0.954). The RF and LR models were the best calibrated and showed the highest net benefit in DCA. Key variables included age, anthropometric indices (BRI and DAI), insulin resistance measures (HOMA-IR), lipid profiles (sdLDL-C and LDL-C), and high-molecular-weight adiponectin, used to classify the presence of MetS. The results highlight the usefulness of specific models and the importance of anthropometric variables, cardiovascular risk factors, metabolic profiles, and adiponectin as indicators of MetS.

In Mexico, hepatitis B and C infections are a significant burden on the health system. The aim of this narrative review was to analyze the state of the art on hepatitis B and C in Mexico by searching and studying available data in academic articles and government reports and statements on epidemiology, prevention, treatment, and elimination strategies undertaken by the Mexican government. Even where the government has implemented a hepatitis B vaccination strategy to reduce its incidence, a very low proportion of people complete the vaccination schedule. Regarding hepatitis C, there is a National Elimination Program that emphasizes the importance of screening, diagnosis, and treatment focused on the population at risk. With the implementation of this program, more than a million fast tests have been carried out and the positive cases have been verified by viral load. Infected patients are tested to determine liver function, fibrosis stage, and coinfection with HBV and/or HIV. Patients without cirrhosis and/or coinfections are treated in first-level care centers, while those with cirrhosis and/or comorbidities are referred to specialists. The possibility of hepatitis C eradication in Mexico seems more likely than eradication of hepatitis B; however, major challenges remain to be overcome to reach both infections’ elimination.

Ulcerative colitis is a chronic inflammatory bowel disease characterized by persistent inflammation, immune dysregulation, gut microbiota alterations, and impaired epithelial barrier function. Lupinus albus is a legume rich in galactooligosaccharides (GOS) that functions as a prebiotic capable of modulating the gut microbiota and mitigating ulcerative colitis-related damage. This study aimed to elucidate the effect of GOS on gut microbiota modulation and the molecular mechanisms involved in epithelial restoration and inflammation reduction. Fifteen C57BL/6 mice were randomly assigned to three groups (n = 5 per group): control (CTL), ulcerative colitis (UC), and ulcerative colitis + GOS (UC + GOS). UC was induced by administering 2% dextran sulfate sodium (DSS) in drinking water for seven days. The UC + GOS group received 2.5 g/kg BW of GOS via gavage for 14 days. GOS administration improved mucus layer thickness, regulated the expression of tight junction proteins, reduced pro-inflammatory cytokine levels, and modulated the gut microbiota, preventing the loss of richness and diversity. Additionally, the expression of monocarboxylate transporters (MCTs) MCT1 and MCT4 was evaluated, and significant differences were observed between the groups across colon and cecum tissues. These findings suggest that GOS supplementation may play a potential role in attenuating ulcerative colitis by regulating the gut microbiota and the metabolic state of intestinal cells.

Extracellular vesicles (EVs), particularly exosomes, are key mediators of intercellular communication, transporting biomolecules such as nucleic acids, lipids, and proteins that influence immune and metabolic pathways. In adipose tissue (AT), adipocyte-derived EVs (AdEVs) play a crucial role in maintaining metabolic homeostasis and have been implicated in obesity-related dysfunction. Among their bioactive cargo, microRNAs regulate post-transcriptional gene expression and participate in immunometabolic regulation. This study aimed to determine whether miR-34a expression in serum and circulating EVs varies according to body fat percentage, to explore its potential utility as a non-invasive biomarker of AT dysfunction. A total of 142 adults (mean age 36 ± 11 years) were classified by body fat percentage (≥25% in men, ≥35% in women). Exosomes were isolated (Invitrogen^®) and characterized by cryo-TEM, and miR-34a expression was quantified by qRT-PCR. miR-34a expression correlated negatively with Total Cholesterol, Triglycerides, LDLc/HDLc, TG/HDLc, BMI, C3, CRP, fasting insulin, HOMA-IR, HOMA-B, Body adiposity, Chemerin, CCL2, AdipoQT, and AdipoQ-H, but positively with HDLc and QUICKI. Notably, LDLc, sdLDLc, sdLDLc/LDLc, TC/HDLc, and fasting glucose showed opposite correlation patterns between serum and exosomes. Overall, serum miR-34a levels were higher than in exosomes, suggesting its potential as a biomarker of metabolic dysfunction and insulin resistance.