Osteopontin (OPN) is a bone-derived phosphoglycoprotein related to physiological and pathological mechanisms that nowadays has gained relevance due to its role in the immune system response to chronic degenerative diseases, including rheumatoid arthritis (RA) and osteoarthritis (OA). OPN is an extracellular matrix (ECM) glycoprotein that plays a critical role in bone remodeling. Therefore, it is an effector molecule that promotes joint and cartilage destruction observed in clinical studies, in vitro assays, and animal models of RA and OA. Since OPN undergoes multiple modifications, including posttranslational changes, proteolytic cleavage, and binding to a wide range of receptors, the mechanisms by which it produces its effects, in some cases, remain unclear. Although there is strong evidence that OPN contributes significantly to the immunopathology of RA and OA when considering it as a common denominator molecule, some experimental trial results argue for its protective role in rheumatic diseases. Elucidating in detail OPN involvement in bone and cartilage degeneration is of interest to the field of rheumatology. This review aims to provide evidence of the OPN’s multifaceted role in promoting joint and cartilage destruction and propose it as a common denominator of AR and OA immunopathology.

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.

Adipose tissue is responsible for fat storage and is an important producer of extracellular vesicles (EVs). The biological content of exosomes, one kind of EV, provides information on aspects such as immunometabolic alterations. This study aimed to compare three plasma exosome isolation methods—using a commercial kit (CK), size exclusion chromatography (SEC), and differential centrifugation (DC)—and select the best one. Individuals categorized by normal and high body fat percentages were used. The DC and CK were proven to be the most advantageous out of the exosome isolation methods, so we suggest these methods for further protein and molecular analyses, respectively. Still, we emphasize the importance of selecting an appropriate methodology depending on the specific research objectives. At the same time, no statistical differences in exosome quality, morphology, total protein, or microRNA concentration were observed between individuals categorized by body fat percentage, so we suggest that the exosomal cargo varies in individuals with normal and high fat percentages.

Background: The pathophysiology of hemophilic arthropathy is complex and not completely understood. In this study, we aimed to identify biomarkers that can affect the hemophilic arthropathy severity.Methods: Fifty patients were analyzed for biomarker frequencies; in 37 patients, articular symptoms were evaluated based on the physical joint examination score, and in 18, it was based on magnetic resonance imaging. Eight polymorphisms, namely FV 1691G>A, FII 20210G>A, MTHFR 677C>T and 1298A>C, TNFα‐308G>A and ‐238G>A, ACAN VNTR, and IL1RN*2‐VNTR were identified.Results: Patients with the MTHFR 677TT genotype showed a higher number of affected joints (1.83 ± 0.9 vs. 0.55 ± 0.7 for CC; p = .023), whereas those with the MTHFR 1298AC genotype exhibited higher effusion according to two radiologists (0.90 ± 0.31/1.20 ± 0.63 vs. 0.38 ± 0.52/0.50 ± 0.53 for AA genotype; p = .043/0.036, respectively). In addition, patients with the TNFα‐308GA genotype had more subchondral cysts (0.75 ± 0.95 vs. 0.07 ± 0.26 for GG genotype; p = .041).Conclusions: The distribution of risk genotypes for MTHFR and TNFα‐308GA suggests their association with clinical parameters of hemophilic arthropathy. Cohort studies are essential to verify these associations.

It has been reported that the Postpartum Depression (PPD)'s symptomatology increases during the first 72 h after delivery. Although PPD's screening is recommended during this period, there is few evidence about the associated Risk Factors (RF) that determine this disorder in women who exclusively underwent an Elective Cesarean Delivery (ECD). Hence, our objective was to determine the RF for PPD's positive screening in the early postoperative stage after an ECD. An analytical cross-sectional study was conducted in 123 women, rooming-in at Guadalajara's Civil Hospital Fray Antonio Alcalde, during the average hospital length of stay after an ECD (2.1 ± 1.02 days). To screen the PPD's symptomatology, we applied the Edinburgh Postpartum Depression Scale (EPDS). To identify possible RF, we checked the women's sociodemographic characteristics, obstetric/pregnancy complications, and neonatal comorbidities in their Medical Records. Subsequently, we carried out Odds Ratio (OR) analysis, followed by logistic regression that determined adjusted OR (aOR) and 95% Confidence Intervals (95% CI). By applying the EPDS, we identified 32.5% of PPD's positive screening, associated with miscarriages (OR 2.6, 95% CI: 1.06-6.30), and multiparity (OR 2.6, 95% CI: 1.08-6.43) and age ≥ 30 years (OR 2.3, 95% CI: 1.04-5.20). Other associations were with fewer prenatal visits (OR 2.4, 95% CI: 1.10-5.35), unplanned pregnancy (OR 2.7, 95%CI: 1.29-5.98), newborn comorbidities (OR 2.7, 95% CI: 1.18-6.03), and obstetric complications (OR 3.1, 95% CI: 1.37-7.07). Nevertheless, the logistic regression determined that the history of miscarriages (aOR 4.8, 95% CI: 1.14-9.01), previous history of depression (aOR 6.0, 95% CI: 1.24-10.25), obstetric complications (aOR 7.2, 95% CI: 1.40-8.41), and newborn comorbidities (aOR 8.1, 95% CI: 1.58-12.52) were RF for PPD. Newborn and obstetric complications, along with previous depression and possibly miscarriages, were main causes for the presence of depressive symptoms in women with an ECD. By considering not only the latter, but also that the PPD's prevalence that we found was higher than national report's results, we suggest that surgical and health professionals seek these RF -Specifically during this early postoperative period- in order to prevent any negative long-term effects on the mother's mental well-being.

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.