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.

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.

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.

The protein S100A4 as a novel marker of insulin resistance in prepubertal and pubertal children with obesity.

BACKGROUND: S100A4 is a metastasis-associated protein also reported as a promising marker for dysfunctional white adipose tissue (WAT) and insulin resistance (IR) in adult and adolescent populations. OBJECTIVE: We aimed to evaluate the association between the protein S100A4 and obesity and IR in children and during pubertal development. DESIGN AND METHODS: The study design consisted of three cross-sectional populations of 249, 11 and 19 prepubertal children respectively (named study population 1, 2 and 3), and a longitudinal population of 53 girls undergoing sexual maturation (study population 4). All subjects were classified into experimental groups according to their sex, obesity and IR status. All study populations counted on anthropometry, glucose, and lipid metabolism, inflammation and cardiovascular biomarkers as well as S100A4 plasma levels measured. The study population 1 was intended as the discovery population in which to elucidate the relationship between Obesity-IR and S100A4 plasma levels in prepubertal children. The cross-sectional populations 2 and 3 further counted on WAT gene expression data for investigating the molecular basis of this association. Instead, the longitudinal study population 4 presented blood whole-genome DNA methylation data at each temporal record, allowing deepening into the Obesity-IR-S1004 relationship during puberty as well as deciphering plausible epigenetic mechanisms altering S100A4 plasma levels. RESULTS: S100A4 plasma levels were strongly associated with several metabolic and anthropometric outcomes, namely IR, in prepubertal non-diabetic obese children. We also found highly significant positive associations during the course of puberty between the increase in S100A4 levels and the increase in HOMA-IR (P = 0.0003, FDR = 0.005) and insulin levels (P = 0.0003, FDR = 0.005). Methylation in two-enhancer related CpG sites of the S100A4 region (cg07245635 and cg10447638) was associated with IR biomarkers at the prepubertal stage and with longitudinal changes in these measurements. We further reported an association between visceral WAT (vWAT) S100A4 expression and HOMA-IR, insulin levels and BMI Z-Score, but not with circulating S100A4. CONCLUSIONS: We report for the first time the association of S100A4 with IR and WAT dysfunction in prepubertal populations as well as how the change in plasma S100A4 levels accompanies longitudinal trajectories of IR in children during pubertal development. Moreover, we propose epigenetic changes in two methylation sites and an altered S100A4 vWAT expression as plausible molecular mechanisms underlying this disturbance in obesity.