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.

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.

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.

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.

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.

eXplainable Artificial Intelligence (XAI) for the identification of biologically relevant gene expression patterns in longitudinal human studies, insights from obesity research.

Until date, several machine learning approaches have been proposed for the dynamic modeling of temporal omics data. Although they have yielded impressive results in terms of model accuracy and predictive ability, most of these applications are based on "Black-box" algorithms and more interpretable models have been claimed by the research community. The recent eXplainable Artificial Intelligence (XAI) revolution offers a solution for this issue, were rule-based approaches are highly suitable for explanatory purposes. The further integration of the data mining process along with functional-annotation and pathway analyses is an additional way towards more explanatory and biologically soundness models. In this paper, we present a novel rule-based XAI strategy (including pre-processing, knowledge-extraction and functional validation) for finding biologically relevant sequential patterns from longitudinal human gene expression data (GED). To illustrate the performance of our pipeline, we work on in vivo temporal GED collected within the course of a long-term dietary intervention in 57 subjects with obesity (GSE77962). As validation populations, we employ three independent datasets following the same experimental design. As a result, we validate primarily extracted gene patterns and prove the goodness of our strategy for the mining of biologically relevant gene-gene temporal relations. Our whole pipeline has been gathered under open-source software and could be easily extended to other human temporal GED applications.

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.

Genetic Factors and Molecular Mechanisms of Vitamin D and Obesity Relationship.

Vitamin D (vitD) deficiency is associated with a wide range of chronic diseases and conditions, including obesity, and with an increasing severity of metabolic dysregulation, such as insulin resistance, hyperlipidemia, liver disease, and hypertension, both in children and adults. However, the nature of the association between low vitD status and obesity remains unclear. This fact has motivated the scientific community to conduct genetic association analyses between 25-hydroxyvitamin D (25[OH]D)-related genes and obesity traits. In this line, the variation in the vitD receptor (VDR) gene represents the bulk of the findings. Specifically, polymorphisms in the VDR gene have been associated with obesity traits in some but not all, studies. Thus, results regarding this matter remain inconclusive. Other genes aside from VDR have also been investigated in relation to obesity-related traits. However, again, findings have been inconsistent. In general, results point to the fact that the DBP/GC gene could be an important protein-linking obesity and vitD status. On the other hand, several studies have attempted to determine the molecular mechanism of the relationship between 25(OH)-D levels and obesity. Some of these studies suggest that vitD, due to its fat-soluble characteristic, is retained by the adipose tissue and has the capacity to metabolize 25(OH)-D locally, and this can be altered during obesity. Additionally, vitD is capable of regulating the gene expression related to adipogenesis process, inflammation, oxidative stress, and metabolism in mature adipocytes. Therefore, the aim of the present review was to evaluate the association between obesity and vitD deficiency describing the main molecular mechanism of the relationship and the link with genetic factors. Key Messages: Low serum 25(OH)-D is positively associated with obesity or BMI in adults and children. Circulating vitD concentrations are, at least, partially determined by genetic factors. VitD plays an important role in the adipogenesis process and inflammation status in adipocytes and adipose tissue.

Association of prenatal exposure to phthalates and synthetic phenols with pubertal development in three European cohorts.

BACKGROUND: There is limited epidemiological evidence on the association of prenatal exposure to phthalates and synthetic phenols with altered pubertal timing. OBJECTIVE: To examine the association of prenatal exposure to phthalates, bisphenol A (BPA), parabens, benzophenone 3 (BP-3), and triclosan (TCS) with pubertal development in girls and boys from three European cohorts. METHODS: Urinary metabolites of six different phthalate diesters (DEP, DiBP, DnBP, BBzP, DEHP, and DiNP), BPA, methyl- (MePB), ethyl- (EtPB), propyl- (PrPB), and butyl-paraben (BuPB), BP-3, and TCS were quantified in one or two (1st and 3rd trimester) urine samples collected during pregnancy (1999-2008) from mothers in three birth cohorts: INMA (Spain), EDEN (France), and MoBa (Norway). Pubertal development of their children was assessed at a single visit at age 7-12 years (579 girls, 644 boys) using the parent-reported Pubertal Development Scale (PDS). Mixed-effect Poisson and g-computation and Bayesian Kernel Machine Regression (BKMR) were employed to examine associations of individual and combined prenatal chemical exposure, respectively, with the probability of overall pubertal onset, adrenarche, and gonadarche (stage 2+) in girls and boys. Effect modification by child body mass index (BMI) was also assessed. RESULTS: Maternal concentrations of the molar sum of DEHP and of DiNP metabolites were associated with a slightly higher probability of having started puberty in boys (relative risk, RR [95% CI] = 1.13 [0.98-1.30] and 1.20 [1.06-1.34], respectively, for a two-fold increase in concentrations), with a stronger association for DiNP in boys with overweight or obesity. In contrast, BPA, BuPB, EtPB, and PrPB were associated with a lower probability of pubertal onset, adrenarche, and/or gonadarche in all boys (e.g. overall puberty, BPA: RR [95% CI] = 0.93 [0.85-1.01] and BuPB: 0.95 [0.90-1.00], respectively), and the association with BPA was stronger in boys with underweight/normal weight. In girls, MEHP and BPA were associated with delayed gonadarche in those with underweight/normal weight (RR [95% CI] = 0.86 [0.77-0.95] and 0.90 [0.84-0.97], respectively). Most of these associations were trimester specific. However, the chemical mixture was not associated with any pubertal outcome in boys or girls. CONCLUSIONS: Prenatal exposure to certain phthalates and synthetic phenols such as BPA may impact the pubertal development of boys, and weight status may modify this effect. BPA may also alter the pubertal development of girls.

Multiomics and eXplainable artificial intelligence for decision support in insulin resistance early diagnosis: A pediatric population-based longitudinal study.

Pediatric obesity can drastically heighten the risk of cardiometabolic alterations later in life, with insulin resistance standing as the cornerstone linking adiposity to the increased cardiovascular risk. Puberty has been pointed out as a critical stage after which obesity-associated insulin resistance is more difficult to revert. Timely prediction of insulin resistance in pediatric obesity is therefore vital for mitigating the risk of its associated comorbidities. The construction of effective and robust predictive systems for a complex health outcome like insulin resistance during the early stages of life demands the adoption of longitudinal designs for more causal inferences, and the integration of factors of varying nature involved in its onset. In this work, we propose an eXplainable Artificial Intelligence-based decision support pipeline for early diagnosis of insulin resistance in a longitudinal cohort of 90 children. For that, we leverage multi-omics (genomics and epigenomics) and clinical data from the pre-pubertal stage. Different data layers combinations, pre-processing techniques (missing values, feature selection, class imbalance, etc.), algorithms, training procedures were considered following good practices for Machine Learning. SHapley Additive exPlanations were provided for specialists to understand both the decision-making mechanisms of the system and the impact of the features on each automatic decision, an essential issue in high-risk areas such as this one where system decisions may affect people's lives. The system showed a relevant predictive ability (AUC and G-mean of 0.92). A deep exploration, both at the global and the local level, revealed promising biomarkers of insulin resistance in our population, highlighting classical markers, such as Body Mass Index z-score or leptin/adiponectin ratio, and novel ones such as methylation patterns of relevant genes, such as HDAC4, PTPRN2, MATN2, RASGRF1 and EBF1. Our findings highlight the importance of integrating multi-omics data and following eXplainable Artificial Intelligence trends when building decision support systems.

Prenatal Exposure to Chemical Mixtures and Metabolic Syndrome Risk in Children.

Importance: Prenatal exposure to ubiquitous endocrine-disrupting chemicals (EDCs) may increase the risk of metabolic syndrome (MetS) in children, but few studies have studied chemical mixtures or explored underlying protein and metabolic signatures. Objective: To investigate associations of prenatal exposure to EDC mixtures with MetS risk score in children and identify associated proteins and metabolites. Design, Setting, and Participants: This population-based, birth cohort study used data collected between April 1, 2003, and February 26, 2016, from the Human Early Life Exposome cohort based in France, Greece, Lithuania, Norway, Spain, and the UK. Eligible participants included mother-child pairs with measured prenatal EDC exposures and complete data on childhood MetS risk factors, proteins, and metabolites. Data were analyzed between October 2022 and July 2023. Exposures: Nine metals, 3 organochlorine pesticides, 5 polychlorinated biphenyls, 2 polybrominated diphenyl ethers (PBDEs), 5 perfluoroalkyl substances (PFAS), 10 phthalate metabolites, 3 phenols, 4 parabens, and 4 organophosphate pesticide metabolites measured in urine and blood samples collected during pregnancy. Main Outcomes and Measures: At 6 to 11 years of age, a composite MetS risk score was constructed using z scores of waist circumference, systolic and diastolic blood pressures, triglycerides, high-density lipoprotein cholesterol, and insulin levels. Childhood levels of 44 urinary metabolites, 177 serum metabolites, and 35 plasma proteins were quantified using targeted methods. Associations were assessed using bayesian weighted quantile sum regressions applied to mixtures for each chemical group. Results: The study included 1134 mothers (mean [SD] age at birth, 30.7 [4.9] years) and their children (mean [SD] age, 7.8 [1.5] years; 617 male children [54.4%] and 517 female children [45.6%]; mean [SD] MetS risk score, -0.1 [2.3]). MetS score increased per 1-quartile increase of the mixture for metals (ß = 0.44; 95% credible interval [CrI], 0.30 to 0.59), organochlorine pesticides (ß = 0.22; 95% CrI, 0.15 to 0.29), PBDEs (ß = 0.17; 95% CrI, 0.06 to 0.27), and PFAS (ß = 0.19; 95% CrI, 0.14 to 0.24). High-molecular weight phthalate mixtures (ß = -0.07; 95% CrI, -0.10 to -0.04) and low-molecular weight phthalate mixtures (ß = -0.13; 95% CrI, -0.18 to -0.08) were associated with a decreased MetS score. Most EDC mixtures were associated with elevated proinflammatory proteins, amino acids, and altered glycerophospholipids, which in turn were associated with increased MetS score. Conclusions and Relevance: This cohort study suggests that prenatal exposure to EDC mixtures may be associated with adverse metabolic health in children. Given the pervasive nature of EDCs and the increase in MetS, these findings hold substantial public health implications.

DNA damage and its association with early-life exposome: Gene-environment analysis in Colombian children under five years old.

Environmental exposures and gene-exposure interactions are the major causes of some diseases. Early-life exposome studies are needed to elucidate the role of environmental exposures and their complex interactions with biological mechanisms involved in childhood health. This study aimed to determine the contribution of early-life exposome to DNA damage and the modifying effect of genetic polymorphisms involved in air pollutants metabolism, antioxidant defense, and DNA repair. We conducted a cohort study in 416 Colombian children under five years. Blood samples at baseline were collected to measure DNA damage by the Comet assay and to determine GSTT1, GSTM1, CYP1A1, H2AX, OGG1, and SOD2 genetic polymorphisms. The exposome was estimated using geographic information systems, remote sensing, LUR models, and questionnaires. The association exposome-DNA damage was estimated using the Elastic Net linear regression with log link. Our results suggest that exposure to PM2.5 one year before the blood draw (BBD) (0.83, 95 %CI: 0.76; 0.91), soft drinks consumption (0.94, 0.89; 0.98), and GSTM1 null genotype (0.05, 0.01; 0.36) diminished the DNA damage, whereas exposure to PM2.5 one-week BBD (1.18, 1.06; 1.32), NO2 lag-5 days BBD (1.27, 1.18; 1.36), in-house cockroaches (1.10, 1.00; 1.21) at the recruitment, crowding at home (1.34, 1.08; 1.67) at the recruitment, cereal consumption (1.11, 1.04; 1.19) and H2AX (AG/GG vs. AA) (1.44, 1.11; 1.88) increased the DNA damage. The interactions between H2AX (AG/GG vs. AA) genotypes with crowding and PM2.5 one week BBD, GSTM1 (null vs. present) with humidity at the first year of life, and OGG1 (SC/CC vs. SS) with walkability at the first year of life were significant. The early-life exposome contributes to elucidating the effect of environmental exposures on DNA damage in Colombian children under five years old. The exposome-DNA damage effect appears to be modulated by genetic variants in DNA repair and antioxidant defense enzymes.

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.

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.

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.

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.

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.

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.

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.