Effects of exercise on whole-blood transcriptome profile in children with overweight/obesity.

BACKGROUND: The current knowledge about the molecular mechanisms underlying the health benefits of exercise is still limited, especially in childhood. We set out to investigate the effects of a 20-week exercise intervention on whole-blood transcriptome profile (RNA-seq) in children with overweight/obesity. METHODS: Twenty-four children (10.21 ± 1.33 years, 46% girls) with overweight/obesity, were randomized to either a 20-week exercise program (intervention group; n = 10), or to a no-exercise control group (n = 14). Whole-blood transcriptome profile was analyzed using RNA-seq by STRT technique with GlobinLock technology. RESULTS: Following the 20-week exercise intervention program, 161 genes were differentially expressed between the exercise and the control groups among boys, and 121 genes among girls (p-value <0.05), while after multiple correction, no significant difference between exercise and control groups persisted in gene expression profiles (FDR >0.05). Genes enriched in GO processes and molecular pathways showed different immune response in boys (antigen processing and presentation, infections, and T cell receptor complex) and in girls (Fc epsilon RI signaling pathway) (FDR <0.05). CONCLUSION: These results suggest that 20-week exercise intervention program alters the molecular pathways involved in immune processes in children with overweight/obesity.

Beyond the single-outcome approach: A comparison of outcome-wide analysis methods for exposome research.

Outcome-wide analysis can offer several benefits, including increased power to detect weak signals and the ability to identify exposures with multiple effects on health, which may be good targets for preventive measures. Recently, advanced statistical multivariate techniques for outcome-wide analysis have been developed, but they have been rarely applied to exposome analysis. In this work, we provide an overview of a selection of methods that are well-suited for outcome-wide exposome analysis and are implemented in the R statistical software. Our work brings together six different methods presenting innovative solutions for typical problems arising from outcome-wide approaches in the context of the exposome, including dependencies among outcomes, high dimensionality, mixed-type outcomes, missing data records, and confounding effects. The identified methods can be grouped into four main categories: regularized multivariate regression techniques, multi-task learning approaches, dimensionality reduction approaches, and bayesian extensions of the multivariate regression framework. Here, we compare each technique presenting its main rationale, strengths, and limitations, and provide codes and guidelines for their application to exposome data. Additionally, we apply all selected methods to a real exposome dataset from the Human Early-Life Exposome (HELIX) project, demonstrating their suitability for exposome research. Although the choice of the best method will always depend on the challenges to be faced in each application, for an exposome-like analysis we find dimensionality reduction and bayesian methods such as reduced rank regression (RRR) or multivariate bayesian shrinkage priors (MBSP) particularly useful, given their ability to deal with critical issues such as collinearity, high-dimensionality, missing data or quantification of uncertainty.

Statistical Approaches to Study Exposome-Health Associations in the Context of Repeated Exposure Data: A Simulation Study.

The exposome concept aims to consider all environmental stressors simultaneously. The dimension of the data and the correlation that may exist between exposures lead to various statistical challenges. Some methodological studies have provided insight regarding the efficiency of specific modeling approaches in the context of exposome data assessed once for each subject. However, few studies have considered the situation in which environmental exposures are assessed repeatedly. Here, we conduct a simulation study to compare the performance of statistical approaches to assess exposome-health associations in the context of multiple exposure variables. Different scenarios were tested, assuming different types and numbers of exposure-outcome causal relationships. An application study using real data collected within the INMA mother-child cohort (Spain) is also presented. In the simulation experiment, assessed methods showed varying performance across scenarios, making it challenging to recommend a one-size-fits-all strategy. Generally, methods such as sparse partial least-squares and the deletion-substitution-addition algorithm tended to outperform the other tested methods (ExWAS, Elastic-Net, DLNM, or sNPLS). Notably, as the number of true predictors increased, the performance of all methods declined. The absence of a clearly superior approach underscores the additional challenges posed by repeated exposome data, such as the presence of more complex correlation structures and interdependencies between variables, and highlights that careful consideration is essential when selecting the appropriate statistical method. In this regard, we provide recommendations based on the expected scenario. Given the heightened risk of reporting false positive or negative associations when applying these techniques to repeated exposome data, we advise interpreting the results with caution, particularly in compromised contexts such as those with a limited sample size.

Shared gene expression signatures between visceral adipose and skeletal muscle tissues are associated with cardiometabolic traits in children with obesity.

Obesity in children is related to the development of cardiometabolic complications later in life, where molecular changes of visceral adipose tissue (VAT) and skeletal muscle tissue (SMT) have been proven to be fundamental. The aim of this study is to unveil the gene expression architecture of both tissues in a cohort of Spanish boys with obesity, using a clustering method known as weighted gene co-expression network analysis. For this purpose, we have followed a multi-objective analytic pipeline consisting of three main approaches; identification of gene co-expression clusters associated with childhood obesity, individually in VAT and SMT (intra-tissue, approach I); identification of gene co-expression clusters associated with obesity-metabolic alterations, individually in VAT and SMT (intra-tissue, approach II); and identification of gene co-expression clusters associated with obesity-metabolic alterations simultaneously in VAT and SMT (inter-tissue, approach III). In both tissues, we identified independent and inter-tissue gene co-expression signatures associated with obesity and cardiovascular risk, some of which exceeded multiple-test correction filters. In these signatures, we could identify some central hub genes (e.g., NDUFB8, GUCY1B1, KCNMA1, NPR2, PPP3CC) participating in relevant metabolic pathways exceeding multiple-testing correction filters. We identified the central hub genes PIK3R2, PPP3C and PTPN5 associated with MAPK signaling and insulin resistance terms. This is the first time that these genes have been associated with childhood obesity in both tissues. Therefore, they could be potential novel molecular targets for drugs and health interventions, opening new lines of research on the personalized care in this pathology. This work generates interesting hypotheses about the transcriptomics alterations underlying metabolic health alterations in obesity in the pediatric population.

Environmental exposures in early-life and general health in childhood.

BACKGROUND: Early-life environmental exposures are suspected to be involved in the development of chronic diseases later in life. Most studies conducted so far considered single or few exposures and single-health parameter. Our study aimed to identify a childhood general health score and assess its association with a wide range of pre- and post-natal environmental exposures. METHODS: The analysis is based on 870 children (6-12 years) from six European birth cohorts participating in the Human Early-Life Exposome project. A total of 53 prenatal and 105 childhood environmental factors were considered, including lifestyle, social, urban and chemical exposures. We built a general health score by averaging three sub-scores (cardiometabolic, respiratory/allergy and mental) built from 15 health parameters. By construct, a child with a low score has a low general health status. Penalized multivariable regression through Least Absolute Shrinkage and Selection Operator (LASSO) was fitted in order to identify exposures associated with the general health score. FINDINGS: The results of LASSO show that a lower general health score was associated with maternal passive and active smoking during pregnancy and postnatal exposure to methylparaben, copper, indoor air pollutants, high intake of caffeinated drinks and few contacts with friends and family. Higher child's general health score was associated with prenatal exposure to a bluespace near residency and postnatal exposures to pets, cobalt, high intakes of vegetables and more physical activity. Against our hypotheses, postnatal exposure to organochlorine compounds and perfluorooctanoate were associated with a higher child's general health score. CONCLUSION: By using a general health score summarizing the child cardiometabolic, respiratory/allergy and mental health, this study reinforced previously suspected environmental factors associated with various child health parameters (e.g. tobacco, air pollutants) and identified new factors (e.g. pets, bluespace) warranting further investigations.

Explainable artificial intelligence to predict and identify prostate cancer tissue by gene expression.

BACKGROUND AND OBJECTIVE: Prostate cancer is one of the most prevalent forms of cancer in men worldwide. Traditional screening strategies such as serum PSA levels, which are not necessarily cancer-specific, or digital rectal exams, which are often inconclusive, are still the screening methods used for the disease. Some studies have focused on identifying biomarkers of the disease but none have been reported for diagnosis in routine clinical practice and few studies have provided tools to assist the pathologist in the decision-making process when analyzing prostate tissue. Therefore, a classifier is proposed to predict the occurrence of PCa that provides physicians with accurate predictions and understandable explanations. METHODS: A selection of 47 genes was made based on differential expression between PCa and normal tissue, GO gene ontology as well as the literature to be used as input predictors for different machine learning methods based on eXplainable Artificial Intelligence. These methods were trained using different class-balancing strategies to build accurate classifiers using gene expression data from 550 samples from 'The Cancer Genome Atlas'. Our model was validated in four external cohorts with different ancestries, totaling 463 samples. In addition, a set of SHapley Additive exPlanations was provided to help clinicians understand the underlying reasons for each decision. RESULTS: An in-depth analysis showed that the Random Forest algorithm combined with majority class downsampling was the best performing approach with robust statistical significance. Our method achieved an average sensitivity and specificity of 0.90 and 0.8 with an AUC of 0.84 across all databases. The relevance of DLX1, MYL9 and FGFR genes for PCa screening was demonstrated in addition to the important role of novel genes such as CAV2 and MYLK. CONCLUSIONS: This model has shown good performance in 4 independent external cohorts of different ancestries and the explanations provided are consistent with each other and with the literature, opening a horizon for its application in clinical practice. In the near future, these genes, in combination with our model, could be applied to liquid biopsy to improve PCa screening.

Omics Data Preprocessing for Machine Learning: A Case Study in Childhood Obesity.

The use of machine learning techniques for the construction of predictive models of disease outcomes (based on omics and other types of molecular data) has gained enormous relevance in the last few years in the biomedical field. Nonetheless, the virtuosity of omics studies and machine learning tools are subject to the proper application of algorithms as well as the appropriate pre-processing and management of input omics and molecular data. Currently, many of the available approaches that use machine learning on omics data for predictive purposes make mistakes in several of the following key steps: experimental design, feature selection, data pre-processing, and algorithm selection. For this reason, we propose the current work as a guideline on how to confront the main challenges inherent to multi-omics human data. As such, a series of best practices and recommendations are also presented for each of the steps defined. In particular, the main particularities of each omics data layer, the most suitable preprocessing approaches for each source, and a compilation of best practices and tips for the study of disease development prediction using machine learning are described. Using examples of real data, we show how to address the key problems mentioned in multi-omics research (e.g., biological heterogeneity, technical noise, high dimensionality, presence of missing values, and class imbalance). Finally, we define the proposals for model improvement based on the results found, which serve as the bases for future work.

Serum levels of the novel adipokine isthmin-1 are associated with obesity in pubertal boys.

OBJECTIVES: To evaluate whether there is an association between the serum levels of the novel insulin-like adipokine isthmin-1 (ISM1) and obesity-related phenotypes in a population of Spanish children and to investigate the plausible molecular alterations behind the alteration of the serum levels of this protein in children with obesity. METHODS: The study population is a sub-cohort of the PUBMEP research project, consisting of a cross-sectional population of 119 pubertal children with overweight (17 boys, 19 girls), obesity (20 boys, 25 girls), and normal weight (17 boys, 21 girls). All subjects were classified into experimental groups according to their sex, obesity, and insulin resistance (IR) status. They were counted anthropometry, glucose and lipid metabolism, inflammation and cardiovascular biomarkers as well as isthmin-1 (ISM1) serum levels. This population was intended as a discovery population to elucidate the relationship between obesity and ISM1 levels in children. Furthermore, the study population had blood whole-genome DNA methylation examined, allowing deepening into the obesity-ISM1 molecular relationship. RESULTS: Higher serum ISM1 levels were observed in boys with obesity than in normal weight (P = 0.004) and overweight (P = 0.007) boys. ISM1 serum levels were positively associated with body mass index (BMI) Z-score (P = 0.005) and fat mass (P = 0.058) and negatively associated with myeloperoxidase (MPO) (P = 0.043) in boys. Although we did not find associations between ISM1 serum levels and metabolic outcomes in girls, which may indicate a putative sexual dimorphism, fat mass was positively associated in all children, including boys and girls (P = 0.011). DNA methylation levels in two-enhancer-related CpG sites of ISM1 (cg03304641 and cg14269097) were associated with serum levels of ISM1 in children. CONCLUSIONS: ISM1 is associated with obesity in boys at the pubertal stage, elucidating how this protein might be of special relevance as a new biomarker of obesity in children. Further studies including a longitudinal design during puberty are needed.

Longitudinal associations between cardiovascular biomarkers and metabolic syndrome during puberty: the PUBMEP study.

Puberty has been described as a life stage of considerable metabolic risk specially for those with obesity. The low-grade systemic inflammatory status associated with obesity could be one of the connections with metabolic syndrome (MetS). Thus, we aimed to assess the relationship between inflammatory and cardiovascular biomarkers and the development of MetS during puberty. Seventy-five children from the PUBMEP study (33 females), aged 4-18 years, were included. Cardiovascular and inflammatory biomarkers were measured in the prepubertal and pubertal stage, including high-sensitivity C-reactive protein (CRP), leptin, tumor necrosis factor-alpha (TNFα), interleukin 8 (IL8), monocyte chemoattractant protein 1 (MCP-1), total plasminogen activator inhibitor-1 (tPAI), resistin, adiponectin, myeloperoxidase (MPO), and soluble intercellular adhesion molecule-1 (sICAM-1). MetS was diagnosed at each measurement point. Mixed-effects and logistic regressions were performed. Those children with MetS in puberty presented higher prepubertal values of several cardiometabolic biomarkers in comparison to those without MetS (z-score body mass index (zBMI), waist circumference, insulin, HOMA-IR, leptin, and tPAI (p < 0.05)). For prepubertal children with obesity, the odds of developing MetS in puberty were significantly higher in those having high zBMI (OR = 4.27; CI: 1.39-22.59) or high concentrations of tPAI (OR = 1.19; CI: 1.06-1.43). CONCLUSION: Those with obesity with higher prepubertal tPAI plasma levels had 19% higher odds of having MetS at puberty highlighting the existence of association between MetS, obesity, and inflammation already in puberty. Thus, assessing cardiometabolic and inflammatory status in children with obesity already at prepuberty is key to avoiding future comorbidities. WHAT IS KNOWN: • Inflammation, metabolic syndrome, and obesity may have their onset in childhood. • Puberty is a life stage characterized for an increased cardiovascular risk. WHAT IS NEW: • Prepuberty state could be an early indicator of future cardiometabolic risk. • Children with obesity and high total plasminogen have higher odds of future metabolic syndrome.

Prepubertal Children With Metabolically Healthy Obesity or Overweight Are More Active Than Their Metabolically Unhealthy Peers Irrespective of Weight Status: GENOBOX Study.

Background and Aim: The association of a metabolically healthy status with the practice of physical activity (PA) remains unclear. Sedentarism and low PA have been linked to increased cardiometabolic risk. The aim of this study was to evaluate the PA levels in metabolically healthy (MH) or unhealthy (MU) prepubertal children with or without overweight/obesity. Methods: A total 275 children (144 boys) with 9 ± 2 years old were selected for the GENOBOX study. PA times and intensities were evaluated by accelerometry, and anthropometry, blood pressure, and blood biochemical markers were analyzed. Children were considered to have normal weight or obesity, and further classified as MH or MU upon fulfillment of the considered metabolic criteria. Results: Classification resulted in 119 MH children (21% with overweight/obesity, referred to as MHO) and 156 MU children (47% with overweight/obesity, referred to as MUO). Regarding metabolic profile, MHO showed lower blood pressure levels, both systolic and diastolic and biochemical markers levels, such as glucose, Homeostatic Model Assessment of Insulin Resistance, triglycerides and higher HDL-c levels than MUO (P < 0.001). In addition, MHO children spent more time in PA of moderate intensity compared with MUO children. In relation to vigorous PA, MH normal weight (MHN) children showed higher levels than MUO children. Considering sex, boys spent more time engaged in moderate, vigorous, and moderate-vigorous (MV) PA than girls, and the number of boys in the MH group was also higher. Conclusion: Prepubertal MHO children are less sedentary, more active, and have better metabolic profiles than their MUO peers. However, all children, especially girls, should increase their PA engagement, both in terms of time and intensity because PA appears to be beneficial for metabolic health status itself.

Body mass index interacts with a genetic-risk score for depression increasing the risk of the disease in high-susceptibility individuals.

Depression is strongly associated with obesity among other chronic physical diseases. The latest mega- and meta-analysis of genome-wide association studies have identified multiple risk loci robustly associated with depression. In this study, we aimed to investigate whether a genetic-risk score (GRS) combining multiple depression risk single nucleotide polymorphisms (SNPs) might have utility in the prediction of this disorder in individuals with obesity. A total of 30 depression-associated SNPs were included in a GRS to predict the risk of depression in a large case-control sample from the Spanish PredictD-CCRT study, a national multicentre, randomized controlled trial, which included 104 cases of depression and 1546 controls. An unweighted GRS was calculated as a summation of the number of risk alleles for depression and incorporated into several logistic regression models with depression status as the main outcome. Constructed models were trained and evaluated in the whole recruited sample. Non-genetic-risk factors were combined with the GRS in several ways across the five predictive models in order to improve predictive ability. An enrichment functional analysis was finally conducted with the aim of providing a general understanding of the biological pathways mapped by analyzed SNPs. We found that an unweighted GRS based on 30 risk loci was significantly associated with a higher risk of depression. Although the GRS itself explained a small amount of variance of depression, we found a significant improvement in the prediction of depression after including some non-genetic-risk factors into the models. The highest predictive ability for depression was achieved when the model included an interaction term between the GRS and the body mass index (BMI), apart from the inclusion of classical demographic information as marginal terms (AUC = 0.71, 95% CI = [0.65, 0.76]). Functional analyses on the 30 SNPs composing the GRS revealed an over-representation of the mapped genes in signaling pathways involved in processes such as extracellular remodeling, proinflammatory regulatory mechanisms, and circadian rhythm alterations. Although the GRS on its own explained a small amount of variance of depression, a significant novel feature of this study is that including non-genetic-risk factors such as BMI together with a GRS came close to the conventional threshold for clinical utility used in ROC analysis and improves the prediction of depression. In this study, the highest predictive ability was achieved by the model combining the GRS and the BMI under an interaction term. Particularly, BMI was identified as a trigger-like risk factor for depression acting in a concerted way with the GRS component. This is an interesting finding since it suggests the existence of a risk overlap between both diseases, and the need for individual depression genetics-risk evaluation in subjects with obesity. This research has therefore potential clinical implications and set the basis for future research directions in exploring the link between depression and obesity-associated disorders. While it is likely that future genome-wide studies with large samples will detect novel genetic variants associated with depression, it seems clear that a combination of genetics and non-genetic information (such is the case of obesity status and other depression comorbidities) will still be needed for the optimization prediction of depression in high-susceptibility individuals.

Progression of metabolic syndrome and associated cardiometabolic risk factors from prepuberty to puberty in children: The PUBMEP study.

Introduction: Metabolic syndrome (MetS) is a cluster of clinical and metabolic alterations related to the risk of cardiovascular diseases (CVD). Metabolic changes occurring during puberty, especially in children with overweight and obesity, can influence the risk of developing chronic diseases, especially CVD. Methods: Longitudinal study based on the follow-up until puberty of a cohort of 191 prepubertal Spanish boys and girls without congenital, chronic, or inflammatory diseases: undernutrition: or intake of any drug that could alter blood glucose, blood pressure, or lipid metabolism. The following parameters were used to determine the presence of MetS: obesity, hypertension, hyperglycemia, hypertriglyceridemia, and low HDL-c. Results: A total of 75·5% of participants stayed in the same BMI category from prepuberty to puberty, whereas 6·3% increased by at least one category. The prevalence of MetS was 9·1% (prepubertal stage) and 11·9% (pubertal stage). The risk of presenting alterations in puberty for systolic blood pressure (SBP), plasma triacylglycerols, HDL cholesterol (HDL-c), and HOMA-IR was significantly higher in those participants who had the same alterations in prepuberty. MetS prevalence in puberty was predicted by sex and levels of HOMA-IR, BMI-z, and waist circumference in the prepubertal stage, in the whole sample: in puberty, the predictors were levels of HOMA-IR, BMI-z, and diastolic blood pressure in participants with obesity. Two fast-and-frugal decision trees were built to predict the risk of MetS in puberty based on prepuberty HOMA-IR (cutoff 2·5), SBP (cutoff 106 mm of Hg), and TAG (cutoff 53 mg/dl). Discussion: Controlling obesity and cardiometabolic risk factors, especially HOMA-IR and blood pressure, in children during the prepubertal stage appears critical to preventing pubertal MetS effectively.

Impact of Physical Activity Intensity Levels on the Cardiometabolic Risk Status of Children: The Genobox Study.

Childhood obesity has been related to metabolic syndrome and low-grade chronic inflammation. This study aimed to evaluate the impact of physical activity intensities and practice on inflammation, endothelial damage, and cardiometabolic risk factors in children. There were 513 participants, aged 6-14 years, recruited for the study. Physical activity was measured by accelerometry, and the children were classified into four groups according to quartiles of moderate to vigorous physical activity (MVPA) practice as very low active, low active, moderate active, and high active. Anthropometric measures, blood pressure, and plasma metabolic and proinflammatory parameters were analyzed. Very low active group presented a worse lipid profile and higher insulin, leptin, adiponectin, resistin, matrix metallopeptidase-9, and tissue plasminogen activator inhibitor-1, while lower levels of tumor necrosis factor-alpha, Type 1 macrophages, and interleukin 8 than high-active children. Regression analyses showed that a higher MVPA practice was associated with lower levels of triacylglycerols (ß: -0.118; p = .008), resistin (ß: -0.151; p = .005), tPAI (ß: -0.105; p = .046), and P-selectin (ß: -0.160; p = .006), independently of sex, age, and body mass index (BMI). In contrast, a higher BMI was associated with higher levels of insulin (ß: 0.370; p < .001), Homeostasis Model Assessment (ß: 0.352; p < .001), triacylglycerols (ß: 0.209; p < .001), leptin (ß: 0.654; p < .001), tumor necrosis factor-alpha (ß: 0.182; p < .001), Type 1macrophages (ß: 0.181; p < .001), and tissue plasminogen activator inhibitor (ß: 0.240; p < .001), independently of sex, age, and MVPA. A better anthropometric, metabolic, and inflammatory profile was detected in the most active children; however, these differences were partly due to BMI. These results suggest that a higher MVPA practice and a lower BMI in children may lead to a better cardiometabolic status.

The Vitamin D Decrease in Children with Obesity Is Associated with the Development of Insulin Resistance during Puberty: The PUBMEP Study.

Obesity and cardiometabolic risk have been associated with vitamin D levels even in children. The objective of the present study was to evaluate the association between insulin resistance (IR), cardiometabolic risk factors, and vitamin D in children from prepubertal to pubertal stages. A total of 76 children from the PUBMEP study, aged 4-12 years at baseline, were included. Children were evaluated in prepubertal and pubertal stages. Anthropometric measurements and selected cardiometabolic risk biomarkers, such as plasma glucose, blood lipids, insulin, adiponectin, leptin, and blood pressure, and serum 25-hydroxyvitamin D (25(OH)D) were determined. Children were categorized by obesity degree and IR status combined before and after puberty. Paired t-test and multivariate linear regression analyses were conducted. During puberty, the increase in triacylglycerols, insulin, and HOMA-IR and the decrease in QUICKI were significantly associated with the reduction in 25(OH)D (B = -0.274, p = 0.032; B = -0.219, p = 0.019; B = -0.250, p = 0.013; B = 1.574, p = 0.013, respectively) after adjustment by BMI-z, sex, and pubertal stage. Otherwise, prepubertal non-IR children with overweight/obesity that became IR during puberty showed a significant decrease in 25(OH)D and HDL-c, and an increase in waist circumference and triacylglycerol concentrations (p < 0.05 for all) over time. These results suggest that changes in IR seem to be associated with an effect on 25(OH)D levels during puberty, especially in children with overweight.

Association of Diet, Physical Activity Guidelines and Cardiometabolic Risk Markers in Children.

The aim was to identify different dietary and physical activity (PA) patterns in 5- to 14-year-old children with a high prevalence of overweight and obesity using cluster analysis based on their adherence to the Spanish Society of Community Nutrition dietary guidelines and levels of PA, and to determine their associations with age, sex, body composition, and cardiometabolic risk markers. In 549 children, hierarchical cluster analysis was used to identify subgroups with similar adherence to dietary recommendations and level of PA. Three clusters were identified: Cluster 1, with the lowest level of vigorous PA and adherence to dietary recommendations; Cluster 2, with the lowest levels of moderate and vigorous PA and the highest adherence to dietary recommendations; and Cluster 3, with the highest level of PA, especially vigorous PA and a medium level adherence to dietary recommendations. Cluster 3 had lower total body fat and higher lean body mass percentages than Cluster 2. Cluster 2 had lower high-density lipoprotein cholesterol and higher low-density lipoprotein cholesterol levels than Cluster 1. The results from our study suggest that it is important to consider adherence to PA recommendations together with adherence to dietary guidelines to understand patterns of obesogenic habits in pediatric populations with high prevalence of overweight and obesity.

Omics Approaches in Adipose Tissue and Skeletal Muscle Addressing the Role of Extracellular Matrix in Obesity and Metabolic Dysfunction.

Extracellular matrix (ECM) remodeling plays important roles in both white adipose tissue (WAT) and the skeletal muscle (SM) metabolism. Excessive adipocyte hypertrophy causes fibrosis, inflammation, and metabolic dysfunction in adipose tissue, as well as impaired adipogenesis. Similarly, disturbed ECM remodeling in SM has metabolic consequences such as decreased insulin sensitivity. Most of described ECM molecular alterations have been associated with DNA sequence variation, alterations in gene expression patterns, and epigenetic modifications. Among others, the most important epigenetic mechanism by which cells are able to modulate their gene expression is DNA methylation. Epigenome-Wide Association Studies (EWAS) have become a powerful approach to identify DNA methylation variation associated with biological traits in humans. Likewise, Genome-Wide Association Studies (GWAS) and gene expression microarrays have allowed the study of whole-genome genetics and transcriptomics patterns in obesity and metabolic diseases. The aim of this review is to explore the molecular basis of ECM in WAT and SM remodeling in obesity and the consequences of metabolic complications. For that purpose, we reviewed scientific literature including all omics approaches reporting genetic, epigenetic, and transcriptomic (GWAS, EWAS, and RNA-seq or cDNA arrays) ECM-related alterations in WAT and SM as associated with metabolic dysfunction and obesity.

Relationship between Physical Activity, Oxidative Stress, and Total Plasma Antioxidant Capacity in Spanish Children from the GENOBOX Study.

The World Health Organization has recommended performing at least 60 min a day of moderate-to-vigorous physical activity (MVPA) and reducing sedentarism in children and adolescents to offer significant health benefits and mitigate health risks. Physical fitness and sports practice seem to improve oxidative stress (OS) status during childhood. However, to our knowledge, there are no data regarding the influence of objectively-measured physical activity (PA) and sedentarism on OS status in children and adolescents. The present study aimed to evaluate the influence of moderate and vigorous PA and sedentarism on OS and plasma total antioxidant capacity (TAC) in a selected Spanish population of 216 children and adolescents from the GENOBOX study. PA (light, moderate, and vigorous) and sedentarism (i.e., sedentary time (ST)) were measured by accelerometry. A Physical Activity-Sedentarism Score (PASS) was developed integrating moderate and vigorous PA and ST levels. Urinary 8-hydroxy-2'-deoxyguanosine (8-OHdG) and isoprostane F2α (F2-IsoPs), as markers of OS, were determined by ELISA; and TAC was estimated by colorimetry using an antioxidant kit. A higher PASS was associated with lower plasma TAC and urinary 8-OHdG and F2-IsoPs, showing a better redox profile. Reduced OS markers (8-OHdG and F2-IsoPs) in children with higher PASS may diminish the need of maintaining high concentrations of antioxidants in plasma during rest to achieve redox homeostasis.

Changes in Physical Activity Patterns from Childhood to Adolescence: Genobox Longitudinal Study.

Longitudinal changes of physical activity (PA) from childhood into adolescence have not been accurately described yet for the Spanish population. The aim of this study is to evaluate the changes of PA, assessed by accelerometry and anthropometric measures in a cohort of 213 children from the prepubertal to pubertal period, focusing on those with valid data from both time points (n = 75). Sedentary time (ST) increased about 50%, while all PA intensities declined from the pre-pubertal to pubertal period. Light PA (LPA) was the major contributor, decreasing by about 30%. Boys were more active than girls in both periods, but they showed a higher decline in PA, especially moderate-to-vigorous PA (MVPA). The proportion who reached the recommendation of 60 min of MVPA decreased by 33.3% in boys and 4.6% in girls. Children with obesity or overweight had lower MVPA than those with normal-weight in the pre-pubertal period, but no differences were found in the pubertal period. This study shows a decrease of PA and an increase of sedentarism in the transition from childhood to adolescence, particularly in boys. Regardless of body weight, adolescents tend to be less active. Therefore, prevention programs should be implemented to achieve optimal PA and reduce sedentarism during infancy considering the differences found by sex.

A Multi-Omics Approach Reveals New Signatures in Obese Allergic Asthmatic Children.

Background: Asthma is a multifactorial condition where patients with identical clinical diagnoses do not have the same clinical history or respond to treatment. This clinical heterogeneity is reflected in the definition of two main endotypes. We aimed to explore the metabolic and microbiota signatures that characterize the clinical allergic asthma phenotype in obese children. Methods: We used a multi-omics approach combining clinical data, plasma and fecal inflammatory biomarkers, metagenomics, and metabolomics data in a cohort of allergic asthmatic children. Results: We observed that the obese allergic asthmatic phenotype was markedly associated with higher levels of leptin and lower relative proportions of plasma acetate and a member from the Clostridiales order. Moreover, allergic children with a worse asthma outcome showed higher levels of large unstained cells, fecal D lactate and D/L lactate ratio, and with a higher relative proportion of plasma creatinine and an unclassified family member from the RF39 order belonging to the Mollicutes class. Otherwise, children with persistent asthma presented lower levels of plasma citrate and dimethylsulfone. Conclusion: Our integrative approach shows the molecular heterogeneity of the allergic asthma phenotype while highlighting the use of omics technologies to examine the clinical phenotype at a more holistic level.