The differential effects of palmitic acid and oleic acid on the metabolic response of hypothalamic astrocytes from male and female mice.

Diets rich in saturated fats are more detrimental to health than those containing mono- or unsaturated fats. Fatty acids are an important source of energy, but they also relay information regarding nutritional status to hypothalamic metabolic circuits and when in excess can be detrimental to these circuits. Astrocytes are the main site of central fatty acid ß-oxidation, and hypothalamic astrocytes participate in energy homeostasis, in part by modulating hormonal and nutritional signals reaching metabolic neurons, as well as in the inflammatory response to high-fat diets. Thus, we hypothesized that how hypothalamic astrocytes process-specific fatty acids participates in determining the differential metabolic response and that this is sex dependent as males and females respond differently to high-fat diets. Male and female primary hypothalamic astrocyte cultures were treated with oleic acid (OA) or palmitic acid (PA) for 24 h, and an untargeted metabolomics study was performed. A clear predictive model for PA exposure was obtained, while the metabolome after OA exposure was not different from controls. The observed modifications in metabolites, as well as the expression levels of key metabolic enzymes, indicate a reduction in the activity of the Krebs and glutamate/glutamine cycles in response to PA. In addition, there were specific differences between the response of astrocytes from male and female mice, as well as between hypothalamic and cerebral cortical astrocytes. Thus, the response of hypothalamic astrocytes to specific fatty acids could result in differential impacts on surrounding metabolic neurons and resulting in varied systemic metabolic outcomes.

Identifying subgroups of childhood obesity by using multiplatform metabotyping.

Introduction: Obesity results from an interplay between genetic predisposition and environmental factors such as diet, physical activity, culture, and socioeconomic status. Personalized treatments for obesity would be optimal, thus necessitating the identification of individual characteristics to improve the effectiveness of therapies. For example, genetic impairment of the leptin-melanocortin pathway can result in rare cases of severe early-onset obesity. Metabolomics has the potential to distinguish between a healthy and obese status; however, differentiating subsets of individuals within the obesity spectrum remains challenging. Factor analysis can integrate patient features from diverse sources, allowing an accurate subclassification of individuals. Methods: This study presents a workflow to identify metabotypes, particularly when routine clinical studies fail in patient categorization. 110 children with obesity (BMI > +2 SDS) genotyped for nine genes involved in the leptin-melanocortin pathway (CPE, MC3R, MC4R, MRAP2, NCOA1, PCSK1, POMC, SH2B1, and SIM1) and two glutamate receptor genes (GRM7 and GRIK1) were studied; 55 harboring heterozygous rare sequence variants and 55 with no variants. Anthropometric and routine clinical laboratory data were collected, and serum samples processed for untargeted metabolomic analysis using GC-q-MS and CE-TOF-MS and reversed-phase U(H)PLC-QTOF-MS/MS in positive and negative ionization modes. Following signal processing and multialignment, multivariate and univariate statistical analyses were applied to evaluate the genetic trait association with metabolomics data and clinical and routine laboratory features. Results and Discussion: Neither the presence of a heterozygous rare sequence variant nor clinical/routine laboratory features determined subgroups in the metabolomics data. To identify metabolomic subtypes, we applied Factor Analysis, by constructing a composite matrix from the five analytical platforms. Six factors were discovered and three different metabotypes. Subtle but neat differences in the circulating lipids, as well as in insulin sensitivity could be established, which opens the possibility to personalize the treatment according to the patients categorization into such obesity subtypes. Metabotyping in clinical contexts poses challenges due to the influence of various uncontrolled variables on metabolic phenotypes. However, this strategy reveals the potential to identify subsets of patients with similar clinical diagnoses but different metabolic conditions. This approach underscores the broader applicability of Factor Analysis in metabotyping across diverse clinical scenarios.

Insulin Resistance in Obese Children: What Can Metabolomics and Adipokine Modelling Contribute?

The evolution of obesity and its resulting comorbidities differs depending upon the age of the subject. The dramatic rise in childhood obesity has resulted in specific needs in defining obesity-associated entities with this disease. Indeed, even the definition of obesity differs for pediatric patients from that employed in adults. Regardless of age, one of the earliest metabolic complications observed in obesity involves perturbations in glucose metabolism that can eventually lead to type 2 diabetes. In children, the incidence of type 2 diabetes is infrequent compared to that observed in adults, even with the same degree of obesity. In contrast, insulin resistance is reported to be frequently observed in children and adolescents with obesity. As this condition can be prerequisite to further metabolic complications, identification of biological markers as predictive risk factors would be of tremendous clinical utility. Analysis of obesity-induced modifications of the adipokine profile has been one classic approach in the identification of biomarkers. Recent studies emphasize the utility of metabolomics in the analysis of metabolic characteristics in children with obesity with or without insulin resistance. These studies have been performed with targeted or untargeted approaches, employing different methodologies. This review summarizes some of the advances in this field while emphasizing the importance of the different techniques employed.