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.

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.

The adipose tissue is responsible for fat storage and is an important producer of extracellular vesicles (EVs). The biological content of exosomes, one kind of EVs, provides information such as immunometabolic alterations. This study aimed to compare three plasma exosome isolation methods and select the best. Plasma from 118 individuals, categorized by normal and high body fat percentage, and developing a 3T3-L1 cell line as an obesogenic model through continuous glucose exposure and a prolonged hypoxic microenvironment, were used. We perform three exo-some isolation methods: commercial kit (CK), size exclusion chromatography (SEC), and differ-ential centrifugation (DC), and characterized by DLS, cryo-TEM, TEM, and western blot CD9 and CD81 markers. In the DLS, cryo-TEM, and TEM analysis showed similar quality and mor-phology of exosomes. The CK and DC proved to comply with most of the advantages of an exo-some isolation method. Still, we emphasize the importance of selecting the appropriate method-ology depending on the specific research objectives. At the same time, no differences in exosome morphology, total protein, nor microRNAs concentration were observed between individuals categorized by body fat percentage, so we suggest that exosome cargo is the one that varies in in-dividuals with normal and high-fat percentages.

Plant in vitro culture has emerged as a biotechnological approach for the sustainable production of high-value secondary metabolites. Despite advances in plant tissue culture and phytochemical research, converting biologically productive in vitro systems into reliable production platforms remains a major challenge because metabolite production alone does not necessarily reflect technological maturity. This review evaluates plant in vitro production platforms for secondary metabolites in tropical and subtropical plant species by distinguishing validated, partially validated, and emerging platforms according to their level of technological development. To support this assessment, we propose a technological validation framework based on complementary experimental evidence that distinguishes species-level scientific knowledge from platform-level technological validation. The analysis reveals substantial differences in platform development and identifies production reliability as the principal limitation preventing many systems from reaching more advanced stages of technological maturity. These findings indicate that technological maturity should be understood as a progressive process driven by the accumulation of complementary experimental evidence rather than isolated experimental achievements. Future advances in plant in vitro production will depend on integrating mechanistic understanding of secondary metabolite biosynthesis with process engineering approaches to develop robust, reproducible, and scalable production platforms, thereby facilitating the sustainable biotechnological exploitation of tropical and subtropical plant biodiversity.