Trace elements as potential modulators of puberty-induced amelioration of oxidative stress and inflammation in childhood obesity.

Although puberty is known to influence obesity progression, the molecular mechanisms underlying the role of sexual maturation in obesity-related complications remains largely unexplored. Here, we delve into the impact of puberty on the most relevant pathogenic hallmarks of obesity, namely oxidative stress and inflammation, and their association with trace element blood status. To this end, we studied a well-characterized observational cohort comprising prepubertal (N = 46) and pubertal (N = 48) children with obesity. From all participants, plasma and erythrocyte samples were collected and subjected to metallomics analysis and determination of classical biomarkers of oxidative stress and inflammation. Besides the expected raise of sexual hormones, pubertal children displayed better inflammatory and oxidative control, as reflected by lower levels of C-reactive protein and oxidative damage markers, as well as improved antioxidant defense. This was in turn accompanied by a healthier multielemental profile, with increased levels of essential elements involved in the antioxidant system and metabolic control (metalloproteins containing zinc, molybdenum, selenium, and manganese) and decreased content of potentially deleterious species (total copper, labile free iron). Therefore, our findings suggest that children with obesity have an exacerbated inflammatory and oxidative damage at early ages, which could be ameliorated during pubertal development by the action of trace element-mediated buffering mechanisms.

Characterization of the Plasmatic and Erythroid Multielemental Biodistribution in Childhood Obesity Using a High-Throughput Method for Size Fractionation of Metal Species.

In this chapter, we describe a metallomics method based on protein precipitation under non-denaturing conditions and further analysis by inductively coupled plasma mass spectrometry for high-throughput metal speciation in plasma and erythrocyte samples. This methodology enables to study the total multielemental profile of these biological matrices, as well as to quantify the metal fractions conforming the metallometabolome and the metalloproteome. Furthermore, the analytical coverage comprises several essential and toxic metal elements, namely aluminum, arsenic, cadmium, cobalt, chromium, copper, iron, lithium, manganese, molybdenum, nickel, lead, selenium, vanadium, and zinc. Altogether, the metallomics method here proposed represents an excellent approach to comprehensively characterize the metal biodistribution in human peripheral blood, which would enable to decipher the role of metal homeostasis in health and disease, and particularly in childhood obesity.

Exploring the association between circulating trace elements, metabolic risk factors, and the adherence to a Mediterranean diet among children and adolescents with obesity.

Diet is one of the most important modifiable lifestyle factors for preventing and treating obesity. In this respect, the Mediterranean diet (MD) has proven to be a rich source of a myriad of micronutrients with positive repercussions on human health. Herein, we studied an observational cohort of children and adolescents with obesity (N = 26) to explore the association between circulating blood trace elements and the degree of MD adherence, as assessed through the KIDMED questionnaire. Participants with higher MD adherence showed better glycemic/insulinemic control and a healthier lipid profile, as well as raised plasma levels of selenium, zinc, cobalt, molybdenum, and arsenic, and increased erythroid content of selenium. Interestingly, we found that these MD-related mineral alterations were closely correlated with the characteristic metabolic complications behind childhood obesity, namely hyperglycemia, hyperinsulinemia, and dyslipidemia (p < 0.05, |r| > 0.35). These findings highlight the pivotal role that dietary trace elements may play in the pathogenesis of obesity and related disorders.

FOBI: an ontology to represent food intake data and associate it with metabolomic data.

Nutrition research can be conducted by using two complementary approaches: (i) traditional self-reporting methods or (ii) via metabolomics techniques to analyze food intake biomarkers in biofluids. However, the complexity and heterogeneity of these two very different types of data often hinder their analysis and integration. To manage this challenge, we have developed a novel ontology that describes food and their associated metabolite entities in a hierarchical way. This ontology uses a formal naming system, category definitions, properties and relations between both types of data. The ontology presented is called FOBI (Food-Biomarker Ontology) and it is composed of two interconnected sub-ontologies. One is a 'Food Ontology' consisting of raw foods and 'multi-component foods' while the second is a 'Biomarker Ontology' containing food intake biomarkers classified by their chemical classes. These two sub-ontologies are conceptually independent but interconnected by different properties. This allows data and information regarding foods and food biomarkers to be visualized in a bidirectional way, going from metabolomics to nutritional data or vice versa. Potential applications of this ontology include the annotation of foods and biomarkers using a well-defined and consistent nomenclature, the standardized reporting of metabolomics workflows (e.g. metabolite identification, experimental design) or the application of different enrichment analysis approaches to analyze nutrimetabolomic data. Availability: FOBI is freely available in both OWL (Web Ontology Language) and OBO (Open Biomedical Ontologies) formats at the project's Github repository (https://github.com/pcastellanoescuder/FoodBiomarkerOntology) and FOBI visualization tool is available in https://polcastellano.shinyapps.io/FOBI_Visualization_Tool/.

Multi-Chemical Profiling of Strawberry as a Traceability Tool to Investigate the Effect of Cultivar and Cultivation Conditions.

The chemical composition of foods is tightly regulated by multiple genotypic and agronomic factors, which can thus serve as potential descriptors for traceability and authentication purposes. In the present work, we performed a multi-chemical characterization of strawberry fruits from five varieties (Aromas, Camarosa, Diamante, Medina, and Ventana) grown in two cultivation systems (open/closed soilless systems) during two consecutive campaigns with different climatic conditions (rainfall and temperature). For this purpose, we analyzed multiple components closely related to the sensory and health characteristics of strawberry, including sugars, organic acids, phenolic compounds, and essential and non-essential mineral elements, and various complementary statistical approaches were applied for selecting chemical descriptors of cultivar and agronomic conditions. Anthocyanins, phenolic acids, sucrose, and malic acid were found to be the most discriminant variables among cultivars, while climatic conditions and the cultivation system were behind changes in polyphenol contents. These results thus demonstrate the utility of combining multi-chemical profiling approaches with advanced chemometric tools in food traceability research.

Combination of complementary data mining methods for geographical characterization of extra virgin olive oils based on mineral composition.

This work explores the potential of multi-element fingerprinting in combination with advanced data mining strategies to assess the geographical origin of extra virgin olive oil samples. For this purpose, the concentrations of 55 elements were determined in 125 oil samples from multiple Spanish geographic areas. Several unsupervised and supervised multivariate statistical techniques were used to build classification models and investigate the relationship between mineral composition of olive oils and their provenance. Results showed that Spanish extra virgin olive oils exhibit characteristic element profiles, which can be differentiated on the basis of their origin in accordance with three geographical areas: Atlantic coast (Huelva province), Mediterranean coast and inland regions. Furthermore, statistical modelling yielded high sensitivity and specificity, principally when random forest and support vector machines were employed, thus demonstrating the utility of these techniques in food traceability and authenticity research.

Application of Targeted Metabolomics to Investigate Optimum Growing Conditions to Enhance Bioactive Content of Strawberry.

A simple, sensitive, and rapid assay based on liquid chromatography coupled to tandem mass spectrometry was designed for simultaneous quantitation of secondary metabolites in order to investigate the influence of variety and agronomic conditions on the biosynthesis of bioactive compounds in strawberry. For this purpose, strawberries belonging to three varieties with different sensitivity to environmental conditions ('Camarosa', 'Festival', 'Palomar') were grown in a soilless system under multiple agronomic conditions (electrical conductivity, substrate type, and coverage). Targeted metabolomic analysis of polyphenolic compounds, combined with advanced chemometric methods based on learning machines, revealed significant differences in multiple bioactives, such as chlorogenic acid, ellagic acid rhamnoside, sanguiin H10, quercetin 3-O-glucuronide, catechin, procyanidin B2, pelargonidin 3-O-glucoside, cyanidin 3-O-glucoside, and pelargonidin 3-O-rutinoside, which play a pivotal role in organoleptic properties and beneficial healthy effects of these polyphenol-rich foods.

Application of metabolomics based on direct mass spectrometry analysis for the elucidation of altered metabolic pathways in serum from the APP/PS1 transgenic model of Alzheimer's disease.

Metabolomic analysis of brain tissue from transgenic mouse models of Alzheimer's disease has demonstrated a great potential for the study of pathological mechanisms and the development of new therapies and biomarkers for diagnosis. However, in order to translate these investigations to the clinical practice it is necessary to corroborate these findings in peripheral samples. To this end, this work considers the application of a novel metabolomic platform based on the combination of a two-steps extraction procedure with complementary analysis by direct infusion electrospray mass spectrometry and flow infusion atmospheric pressure photoionization mass spectrometry for a holistic investigation of metabolic abnormalities in serum samples from APP/PS1 mice. A number of metabolites were found to be perturbed in this mouse model, including increased levels of di- and tri-acylglycerols, eicosanoids, inosine, choline and glycerophosphoethanolamine; reduced content of cholesteryl esters, free fatty acids, lysophosphocholines, amino acids, energy-related metabolites, phosphoethanolamine and urea, as well as abnormal distribution of phosphocholines depending on the fatty acid linked to the molecular moiety. This allowed the elucidation of possible pathways disturbed underlying to disease (abnormal homeostasis of phospholipids leading to membrane breakdown, energy-related failures, hyperammonemia and hyperlipidemia, among others), thus demonstrating the utility of peripheral samples to investigate pathology in the APP/PS1 model.

Homeostasis of metals in the progression of Alzheimer's disease.

In order to study the involvement of metals in the progression of Alzheimer's disease, serum samples from patients with Alzheimer and mild cognitive impairment were investigated. For this purpose, metal content was analyzed after size-fractionation of species and then, inter-element and inter-fraction ratios were computed. In this way, the analysis allowed discovering changes that could be used as markers of disease, but also provided a new insight into the interactions in the homeostasis of elements in neurodegeneration and its progression. Aluminum and labile forms of iron and copper were increased in demented patients, while manganese, zinc and selenium were reduced. Interestingly, levels of different elements, principally iron, aluminum and manganese, were closely inter-related, which could evidence a complex interdependency between the homeostasis of the different metals in this disorder. On the other hand, imbalances in metabolism of copper, zinc and selenium could be associated to abnormal redox status. Therefore, this study may contribute to our understanding of the pathological mechanisms related to metals in Alzheimer's disease.