Biophysical Parameters of Plasma-Derived Extracellular Vesicles as Potential Biomarkers of Bone Disturbances in Breast Cancer Patients Receiving an Individualized Nutrition Intervention.

Extracellular vesicles (EVs) are implicated in several biological conditions, including bone metabolism disturbances in breast cancer patients (BCPs). These disorders hinder the adjustment of nutrition interventions due to changes in bone mineral density (BMD). The biophysical properties of EVs (e.g., size or electrostatic repulsion) affect their cellular uptake, however, their clinical relevance is unclear. In this study, we aimed to investigate the association between the biophysical properties of the plasma-derived EVs and BMDs in BCPs who received an individualized nutrition intervention during the first six months of antineoplastic treatment. As part of the nutritional assessment before and after the intervention, body composition including bone densitometry and plasma samples were obtained. In 16 BCPs, EVs were isolated using ExoQuick® and their biophysical properties were analyzed using light-scattering techniques. We found that the average hydrodynamic diameter of large EVs was associated with femoral neck bone mineral content, lumbar spine BMD, and neoplasms' molecular subtypes. These results provide evidence that EVs play a role in BCPs' bone disorders and suggest that the biophysical properties of EVs may serve as potential nutritional biomarkers. Further studies are needed to evaluate EVs' biophysical properties as potential nutritional biomarkers in a clinical context.

Extracellular Vesicles and Their Zeta Potential as Future Markers Associated with Nutrition and Molecular Biomarkers in Breast Cancer.

A nutritional intervention promotes the loss of body and visceral fat while maintaining muscle mass in breast cancer patients. Extracellular vesicles (EVs) and their characteristics can be potential biomarkers of disease. Here, we explore the changes in the Zeta potential of EVs; the content of miRNA-30, miRNA-145, and miRNA-155; and their association with body composition and biomarkers of metabolic risk in breast cancer patients, before and 6 months after a nutritional intervention. Clinicopathological data (HER2neu, estrogen receptor, and Ki67), anthropometric and body composition data, and plasma samples were available from a previous study. Plasma EVs were isolated and characterized in 16 patients. The expression of miRNA-30, miRNA-145, and miRNA-155 was analyzed. The Zeta potential was associated with HER2neu (ß = 2.1; p = 0.00), Ki67 (ß = -1.39; p = 0.007), estrogen positive (ß = 1.57; p = 0.01), weight (ß = -0.09; p = 0.00), and visceral fat (ß = 0.004; p = 0.00). miRNA-30 was associated with LDL (ß = -0.012; p = 0.01) and HDL (ß = -0.02; p = 0.05). miRNA-155 was associated with visceral fat (ß = -0.0007; p = 0.05) and Ki67 (ß = -0.47; p = 0.04). Our results reveal significant associations between the expression of miRNA-30 and miRNA-155 and the Zeta potential of the EVs with biomarkers of metabolic risk and disease prognosis in women with breast cancer; particularly, the Zeta potential of EVs can be a new biomarker sensitive to changes in the nutritional status and breast cancer progression.

Extracellular Vesicles from Adipose Tissue Could Promote Metabolic Adaptation through PI3K/Akt/mTOR.

Extracellular vesicles (EVs) are nanoparticles secreted by cells under physiological and pathological conditions, such as metabolic diseases. In this context, EVs are considered potential key mediators in the physiopathology of obesity. It has been reported that EVs derived from adipose tissue (ADEVs) contribute to the development of a local inflammatory response that leads to adipose tissue dysfunction. In addition, it has been proposed that EVs are associated with the onset and progression of several obesity-related metabolic diseases such as insulin resistance. In particular, characterizing the molecular fingerprint of obesity-related ADEVs can provide a bigger picture that better reflects metabolic adaptation though PI3K/Akt/mTOR. Hence, in this review we describe the possible crosstalk communication of ADEVs with metabolically active organs and the intracellular response in the insulin signaling pathway.

Malnutrition and Biomarkers: A Journey through Extracellular Vesicles.

Extracellular vesicles (EVs) have been identified as active components in cellular communication, which are easily altered both morphologically and chemically by the cellular environment and metabolic state of the body. Due to this sensitivity to the conditions of the cellular microenvironment, EVs have been found to be associated with disease conditions, including those associated with obesity and undernutrition. The sensitivity that EVs show to changes in the cellular microenvironment could be a reflection of early cellular alterations related to conditions of malnutrition, which could eventually be used in the routine monitoring and control of diseases or complications associated with it. However, little is known about the influence of malnutrition alone; that is, without the influence of additional diseases on the heterogeneity and specific content of EVs. To date, studies in "apparently healthy" obese patients show that there are changes in the size, quantity, and content of EVs, as well as correlations with some metabolic parameters (glucose, insulin, and serum lipids) in comparison with non-obese individuals. In light of these changes, a direct participation of EVs in the development of metabolic and cardiovascular complications in obese subjects is thought to exist. However, the mechanisms through which this process might occur are not yet fully understood. The evidence on EVs in conditions of undernutrition is limited, but it suggests that EVs play a role in the maintenance of homeostasis and muscle repair. A better understanding of how EVs participate in or promote cellular signaling in malnutrition conditions could help in the development of new strategies to treat them and their comorbidities.

Bioelectric Impedance Vector Analysis (BIVA) in Breast Cancer Patients: A Tool for Research and Clinical Practice.

Background and objectives: Body composition assessment can provide information associated with breast cancer patients' (BCP) prognosis, that can lead interventions to improve survival outcomes. The aim of this study was to evaluate the effect of an individualized nutrition intervention program on breast cancer patients using bioelectrical impedance vector analysis (BIVA). Materials and Methods: This is a pretest-posttest study in recently diagnosed nonmetastatic BCP undergoing antineoplastic treatment, free of co-morbidities and dietary supplementation. Body composition was assessed at baseline and 6 months after an individualized nutrition intervention program, by dual-energy X-ray absorptiometry and BIVA. According to BIVA, each participant was located in the bivariate tolerance ellipses for Mexican population (50%, 75%, and 95%). In clinical practice, the 50% and 75% ellipses are considered within normality ranges. Results: Nine nonmetastatic BCP completed the intervention and were included in the analysis. After the intervention, they decreased by 5.8 kg of body weight (IQR, 3-6; p < 0.05), 3.8 kg of fat mass (IQR, 0.1-4.2; p < 0.05), and 1.4 kg of fat-free mass (IQR, -0.1 to 4; p < 0.05) while appendicular skeletal muscle mass remained unchanged (-0.2 kg, IQR, -0.8 to 2.3; p = 0.4). Using BIVA at baseline, five participants were among the 50% and 75% ellipses, mainly located in the area corresponding to edema and low lean tissue, two in the cachexia quadrant and two in the athletic quadrant (≥95% ellipse). After 6 months of intervention, six out of nine participants were in the athletic quadrant and eight of nine BCP were above the 5° phase angle cut-off point. One patient initially presented cachexia (≥95% ellipse); at postintervention her vector changed to the 50% ellipse. Conclusions: An individualized nutrition intervention program designed for nonmetastatic BCP was effective to improve the nutritional status of BCP as assessed by BIVA, therefore BIVA can be a useful tool to monitor changes in nonmetastatic BCP body composition in research and clinical practice.

[MODEL FOR ESTIMATING STANDING HEIGHT IN MEXICAN ADULTS FOR 20-59 YEARS, BASED ON KNEE LENGTH]. / MODELO PARA LA ESTIMACIÓN DE LA TALLA DE PIE EN ADULTOS MEXICANOS DE 20-59 AÑOS BASADO EN LA LONGITUD RODILLA-TALÓN.

Currently, bone distances are used to predict standing height in adults that might not be able to achieve a correct standing position. Knee length based algorithms for estimating standing height have been proposed and designed for specific populations. However, equations for other populations may not necessarily reflect environmental and genetic factors for the group of interest. The aim of this study was to develop and validate predictive models for standing height in Mexican adults. For this purpose, 240 male and female adults aged 20 to 59 years, with no apparent spine problems were measured. We measured weight, height and knee length, using an anthropometer of our own design and a glass fiber metric measuring tape. A predictive model for each measuring instrument was developed. Model selection and development of equations were carried out by "all possible regressions and multiple regression" procedures. The predictive models for standing height by the anthropometer and by the measuring tape did not show significant differences between measured and estimated height. The R2 for the two models were 0.93 and 0.92, with a standard error of the estimator (EE) of 2.30 and 2.40 cm, for the anthropometer and the measuring tape, respectively. Both methods were acceptable in terms of concordance, accuracy and precision; however, at very high and low predicted height values, both models showed significant bias, which should be considered when applying these algorithms in different populations.

Actualmente las distancias óseas se utilizan para predecir la talla en adultos que no pueden tener una adecuada bipedestación o no cumplen con la posición adecuada para la obtención de la talla. Existen algoritmos para la estimación de la talla basados en la longitud rodilla-talón, diseñados para poblaciones específicas; sin embargo, existen poblaciones que aún no cuentan con esta herramienta, por lo que se usan ecuaciones ya existentes, que no necesariamente reflejan los factores genético-ambientales para la población de interés. El objetivo de este estudio fue desarrollar y validar modelos predictivos para la talla de pie en adultos mexicanos, con antropómetro de diseño propio y cinta métrica. Se midieron 240 adultos de 20 a 59 años, de ambos sexos, sin problemas aparentes en la columna vertebral. Se midió el peso, la talla de pie y la longitud rodilla-talón. Se desarrolló un modelo predictivo para cada instrumento de medición. La selección de modelos y el desarrollo de las ecuaciones se llevó a cabo mediante los métodos de "Todas las Regresiones Posibles" y "Regresión Múltiple". Se obtuvieron dos algoritmos que no mostraron diferencias significativas entre la talla medida y estimada tras un proceso de validación cruzada. Las R2 para los modelos fueron de 0,93 y 0,92, con un error estándar del estimador (EE) de 2,30 y 2,40 cm, para el antropómetro y la cinta respectivamente. Ambos métodos resultaron aceptables en términos de concordancia, exactitud y precisión. A tallas extremas (altas o bajas), ambos modelos presentaron sesgo, lo cual debe considerarse al aplicar estos algoritmos.

Modelo para la estimación de la talla de pie en adultos mexicanos de 20-59 años basado en la longitud rodilla-talón / Model for estimating standing height in Mexican adults for 20-59 years, based on knee length

Actualmente las distancias óseas se utilizan para predecir la talla en adultos que no pueden tener una adecuada bipedestación o no cumplen con la posición adecuada para la obtención de la talla. Existen algoritmos para la estimación de la talla basados en la longitud rodilla-talón, diseñados para poblaciones específicas; sin embargo, existen poblaciones que aún no cuentan con esta herramienta, por lo que se usan ecuaciones ya existentes, que no necesariamente reflejan los factores genético-ambientales para la población de interés. El objetivo de este estudio fue desarrollar y validar modelos predictivos para la talla de pie en adultos mexicanos, con antropómetro de diseño propio y cinta métrica. Se midieron 240 adultos de 20 a 59 años, de ambos sexos, sin problemas aparentes en la columna vertebral. Se midió el peso, la talla de pie y la longitud rodilla-talón. Se desarrolló un modelo predictivo para cada instrumento de medición. La selección de modelos y el desarrollo de las ecuaciones se llevó a cabo mediante los métodos de 'Todas las Regresiones Posibles' y 'Regresión Múltiple'. Se obtuvieron dos algoritmos que no mostraron diferencias significativas entre la talla medida y estimada tras un proceso de validación cruzada. Las R2 para los modelos fueron de 0,93 y 0,92, con un error estándar del estimador (EE) de 2,30 y 2,40 cm, para el antropómetro y la cinta respectivamente. Ambos métodos resultaron aceptables en términos de concordancia, exactitud y precisión. A tallas extremas (altas o bajas), ambos modelos presentaron sesgo, lo cual debe considerarse al aplicar estos algoritmos (AU)

Currently, bone distances are used to predict standing height in adults that might not be able to achieve a correct standing position. Knee length based algorithms for estimating standing height have been proposed and designed for specific populations. However, equations for other populations may not necessarily reflect environmental and genetic factors for the group of interest. The aim of this study was to develop and validate predictive models for standing height in Mexican adults. For this purpose, 240 male and female adults aged 20 to 59 years, with no apparent spine problems were measured. We measured weight, height and knee length, using an anthropometer of our own design and a glass fiber metric measuring tape. A predictive model for each measuring instrument was developed. Model selection and development of equations were carried out by 'all possible regressions and multiple regression' procedures. The predictive models for standing height by the anthropometer and by the measuring tape did not show significant differences between measured and estimated height. The R2 for the two models were 0.93 and 0.92, with a standard error of the estimator (EE) of 2.30 and 2.40 cm, for the anthropometer and the measuring tape, respectively. Both methods were acceptable in terms of concordance, accuracy and precision; however, at very high and low predicted height values, both models showed significant bias, which should be considered when applying these algorithms in different populations (AU)