Exosomes are extracellular vesicles released by cells, both constitutively and after cell activation, and are present in different types of biological fluid. Exosomes are involved in the pathogenesis of diseases, such as cancer, neurodegenerative diseases, pregnancy disorders and cardiovascular diseases, and have emerged as potential non-invasive biomarkers for the detection, prognosis and therapeutics of a myriad of diseases. In this review, we describe recent advances related to the regulatory mechanisms of exosome biogenesis, release and molecular composition, as well as their role in health and disease, and their potential use as disease biomarkers and therapeutic targets. In addition, the advantages and disadvantages of their main isolation methods, characterization and cargo analysis, as well as the experimental methods used for exosome-mediated drug delivery, are discussed. Finally, we present potential perspectives for the use of exosomes in future clinical practice.

Alzheimer’s disease (AD) is a neurodegenerative disorder characterized by progressive cognitive decline and memory loss. While the precise causes of AD remain unclear, emerging evidence suggests that messenger RNA (mRNA) dysregulation contributes to AD pathology and risk. This study examined exosomal mRNA expression profiles of 15 individuals diagnosed with AD and 15 healthy controls from Barranquilla, Colombia. Utilizing advanced bioinformatics and machine learning (ML) techniques, we identified differentially expressed mRNAs and assessed their predictive power for AD diagnosis and AD age of onset (ADAOO). Our results showed that ENST00000331581 (CADM1) and ENST00000382258 (TNFRSF19) were significantly upregulated in AD patients. Key predictors for AD diagnosis included ENST00000311550 (GABRB3), ENST00000278765 (GGTLC1), ENST00000331581 (CADM1), ENST00000372572 (FOXJ3), and ENST00000636358 (ACY1), achieving > 90% accuracy in both training and testing datasets. For ADAOO, ENST00000340552 (LIMK2) expression correlated with a delay of ~12.6 years, while ENST00000304677 (RNASE6), ENST00000640218 (HNRNPU), ENST00000602017 (PPP5D1), ENST00000224950 (STN1), and ENST00000322088 (PPP2R1A) emerged as the most important predictors. ENST00000304677 (RNASE6) and ENST00000602017 (PPP5D1) showed promising predictive accuracy in unseen data. These findings suggest that mRNA expression profiles may serve as effective biomarkers for AD diagnosis and ADAOO, providing a cost-efficient and minimally invasive tool for early detection and monitoring. Further research is needed to validate these results in larger, diverse cohorts and explore the biological roles of the identified mRNAs in AD pathogenesis.

Alzheimer’s disease (AD), a neurodegenerative disorder characterized by progressive cognitive decline, is the most common form of dementia. Currently, there is no single test that can diagnose AD, especially in understudied populations and developing countries. Instead, diagnosis is based on a combination of medical history, physical examination, cognitive testing, and brain imaging. Exosomes are extracellular nanovesicles, primarily composed of RNA, that participate in physiological processes related to AD pathogenesis such as cell proliferation, immune response, and neuronal and cardiovascular function. However, the identification and understanding of the potential role of long non-coding RNAs (lncRNAs) in AD diagnosis remain largely unexplored. Here, we clinically, cognitively, and genetically characterized a sample of 15 individuals diagnosed with AD (cases) and 15 controls from Barranquilla, Colombia. Advanced bioinformatics, analytics and Machine Learning (ML) techniques were used to identify lncRNAs differentially expressed between cases and controls. The expression of 28,909 lncRNAs was quantified. Of these, 18 were found to be differentially expressed and harbored in pivotal genes related to AD. Two lncRNAs, ENST00000608936 and ENST00000433747, show promise as diagnostic markers for AD, with ML models achieving > 95% sensitivity, specificity, and accuracy in both the training and testing datasets. These findings suggest that the expression profiles of lncRNAs could significantly contribute to advancing personalized AD diagnosis in this community, offering promising avenues for early detection and follow-up.

Introducción. La obesidad se considera un grave problema de salud pública y por ello se hacen esfuerzos en la búsqueda de genes como el LEP, el LEPR y el MC4R del sistema leptina-melanocortina, el cual opera en la regulación neuroendocrina de la ingestión y el equilibrio energético e influye en la patogenia de la enfermedad. Los resultados contradictorios en torno a la asociación de estos genes con la obesidad plantean la necesidad de nuevas investigaciones.Objetivo. Analizar los polimorfismos rs2167270 del gen LEP, rs1137101 del gen LEPR y rs17782313 del gen MC4R asociados con la obesidad y sus variables clínicas y bioquímicas en una muestra de pacientes adultos de Barranquilla.Materiales y métodos. Se estudiaron 111 personas obesas y 155 no obesas como controles. Los polimorfismos se determinaron mediante reacción en cadena de la polimerasa (PCR) en tiempo real. Se tomaron las medidas antropométricas, se evaluó la presión arterial y se hicieron pruebas bioquímicas.Resultados. No se encontraron diferencias estadísticas en la frecuencia alélica y genotípica de los polimorfismos en los grupos estudiados. En cuanto a las variables clínicas y bioquímicas, el genotipo CC del polimorfismo rs17782313 del gen MC4R, se asoció con un aumento de la presión arterial sistólica y, el alelo T y su genotipo homocigoto, con una disminución del colesterol HDL en los obesos. No se evidenció ningún efecto de los otros polimorfismos en estas variables.Conclusiones. Los polimorfismos rs2167270 del gen LEP, rs1137101 del gen LEPR y rs17782313 del gen MC4R, no se asociaron con obesidad en la población analizada. Se encontró que el polimorfismo rs17782313 del gen MC4R influyó en el aumento de la presión arterial sistólica y la disminución del colesterol HDL en las personas obesas.

Alzheimer’s disease (AD) is a neurodegenerative disorder characterized by cognitive decline and complex molecular changes. Extracellular vesicles (EVs), particularly exosomes, play a key role in intercellular communication and disease progression, transporting proteins, lipids, and nucleic acids. While altered exosomal mRNA profiles have emerged as potential biomarkers for AD, the relationship between mRNA expression and AD neuropsychological deficits remains unclear. Here, we investigated the correlation between exosomx10-derived mRNA signatures and neuropsychological performance in a cohort from Barranquilla, Colombia. Expression profiles of 16,585 mRNAs in 15 AD patients and 15 healthy controls were analysed using Generalized Linear Models (GLMs) and the Predictive Power Score (PPS). We identified significant correlations between specific mRNA signatures and key neuropsychological variables, including the Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), Functional Assessment Screening Tool (FAST), Boston Naming Test, and Rey–Osterrieth Figure test. These mRNAs were in key AD-associated genes (i.e., GABRB3 and CADM1), while other genes are novel (i.e., SHROOM3, SLC7A2, GJB4, and XBP1). PPS analyses further revealed predictive relationships between mRNA expression and neuropsychological variables, accounting for non-linear patterns and asymmetric associations. If replicated in more extensive and heterogeneous studies, these findings provide critical insights into the molecular basis governing the natural history of AD, potential personalized and non-invasive diagnosis, prognosis, follow-up, and potential targets for future therapies.

Background/Objectives: Alzheimer’s disease (AD) accounts for ~70% of global dementia cases, with projections estimating 139 million affected individuals by 2050. This increasing burden highlights the urgent need for accessible, cost-effective diagnostic tools, particularly in low- and middle-income countries (LMICs). Traditional neuropsychological assessments, while effective, are resource-intensive and time-consuming. Methods: A total of 760 older adults (394 [51.8%] with AD) were recruited and neuropsychologically evaluated at the Instituto Colombiano de Neuropedagogía (ICN) in collaboration with Universidad del Norte (UN), Barranquilla. Machine learning (ML) algorithms were trained on a screening protocol incorporating demographic data and neuropsychological measures assessing memory, language, executive function, and praxis. Model performance was determined using 10-fold cross-validation. Variable importance analyses identified key predictors to develop optimized, abbreviated ML-based protocols. Metrics of compactness, cohesion, and separation further quantified diagnostic differentiation performance. Results: The eXtreme Gradient Boosting (xgbTree) algorithm achieved the highest diagnostic accuracy (91%) with the full protocol. Five ML-optimized screening protocols were also developed. The most efficient, the ICN-UN battery (including MMSE, Rey–Osterrieth Complex Figure recall, Rey Auditory Verbal Learning, Lawton & Brody Scale, and FAST), maintained strong diagnostic performance while reducing screening time from over four hours to under 25 min. Conclusions: The ML-optimized ICN-UN protocol offers a rapid, accurate, and scalable AD screening solution for LMICs. While promising for clinical adoption and earlier detection, further validation in diverse populations is recommended.