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In epidemiology, traditional statistical methods such as logistic regression, linear regression, and other parametric models are commonly employed to investigate associations between predictors and health outcomes. However, non-parametric machine learning techniques, such as deep neural networks (DNNs), coupled with explainable AI (XAI) tools, offer new opportunities for this task. Despite their potential, these methods face challenges due to the limited availability of high-quality, high-quantity data in this field. To address these challenges, we introduce SEANN, a novel approach for informed DNNs that leverages a prevalent form of domain-specific knowledge: Pooled Effect Sizes (PES). PESs are commonly found in published Meta-Analysis studies, in different forms, and represent a quantitative form of a scientific consensus. By integrating PES into the training loss, we demonstrate—under controlled simulations—significant improvements in predictive generalization and in the epidemiological plausibility of the learned relationships, relative to a domain-knowledge agnostic neural network.

Background:Pregnant women and children are especially vulnerable to exposures to food contaminants, and a balanced diet during these periods is critical for optimal nutritional status.Objectives:Our objective was to study the association between diet and measured blood and urinary levels of environmental contaminants in mother–child pairs from six European birth cohorts (n=818 mothers and 1,288 children).Methods:We assessed the consumption of seven food groups and the blood levels of organochlorine pesticides, polybrominated diphenyl ethers, polychlorinated biphenyls (PCBs), per- and polyfluoroalkyl substances (PFAS), and heavy metals and urinary levels of phthalate metabolites, phenolic compounds, and organophosphate pesticide (OP) metabolites. Organic food consumption during childhood was also studied. We applied multivariable linear regressions and targeted maximum likelihood based estimation (TMLE).Results:Maternal high (≥4  times/week) versus low (<2  times/week) fish consumption was associated with 15% higher PCBs [geometric mean (GM) ratio=1.15; 95% confidence interval (CI): 1.02, 1.29], 42% higher perfluoroundecanoate (PFUnDA) (GM   ratio=1.42; 95% CI: 1.20, 1.68), 89% higher mercury (Hg) (GM   ratio=1.89; 95% CI: 1.47, 2.41) and a 487% increase in arsenic (As) (GM   ratio=4.87; 95% CI: 2.57, 9.23) levels. In children, high (≥3  times/week) versus low (<1.5  times/week) fish consumption was associated with 23% higher perfluorononanoate (PFNA) (GM   ratio=1.23; 95% CI: 1.08, 1.40), 36% higher PFUnDA (GM   ratio=1.36; 95% CI: 1.12, 1.64), 37% higher perfluorooctane sulfonate (PFOS) (GM   ratio=1.37; 95% CI: 1.22, 1.54), and >200% higher Hg and As [GM   ratio=3.87 (95% CI: 1.91, 4.31) and GM   ratio=2.68 (95% CI: 2.23, 3.21)] concentrations. Using TMLE analysis, we estimated that fish consumption within the recommended 2–3 times/week resulted in lower PFAS, Hg, and As compared with higher consumption. Fruit consumption was positively associated with OP metabolites. Organic food consumption was negatively associated with OP metabolites.Discussion:Fish consumption is related to higher PFAS, Hg, and As exposures. In addition, fruit consumption is a source of exposure to OPs. https://doi-org.proxy.insermbiblio.inist.fr/10.1289/EHP5324

Although age is generally measured by the number of years since birth, many factors contribute to the rate at which a person physically ages. In adults, linking these measurements to age gives a measure of overall health and resilience. This ‘biological age’ offers a better prediction of remaining life and disease risk than the number of years lived. Multiple factors can be used to calculate biological age, such as measuring the length of telomeres – protective caps on the end of chromosomes – which shorten as people age. The rate at which they shorten can give an indication of how quickly someone is ageing. Researchers can also study epigenetic factors: these mechanisms lead to certain genes being switched on or off, and they can be combined into a ‘epigenetic clock’ to assess biological age. However, compared with adults, the relationship between biological age and child health and developmental maturity is less well understood. Robinson et al. studied 1,173 school-aged children from six European countries, measuring telomere length, epigenetic factors and other biological indicators related to metabolism and the immune system. The relationships between these factors and an array of child developmental measures such as height, weight, behaviour and the age of onset of puberty were established. The findings showed that biological age indicators are only weakly linked to each other in children. Despite this, biological age was related to greater amount of body fat across all tested indicators – which is also associated with biological age in adults and is an important determinant of lifespan. Among several observed effects on development, analysis found that shorter telomere length and older epigenetic age were associated with greater behavioural problems, suggesting they may be detrimental to child development. On the other hand, a greater age due to metabolic and immune related changes was associated with greater cognitive and behavioural maturity. Environmental factors were also linked to biological ageing, with children exposed to smoking in their homes or while their mother was pregnant displaying an older epigenetic age. Robinson et al. showed that biological ageing in children is multifaceted and can have both beneficial and harmful impacts on development. This knowledge is important for identifying early life risk factors that might influence healthy ageing in later life. Future work will help researchers to understand these complex interactions and the long-term consequences for health and well-being.

Background Telomere length is an important indicator of biological age and a complex multi-factor trait. To date, the telomere interactome for comprehending the high-dimensional biological aspects linked to telomere regulation during childhood remains unexplored. Here we describe the multi-omics signatures associated with childhood telomere length. Methods This study included 1001 children aged 6 to 11 years from the Human Early-life Exposome (HELIX) project. Telomere length was quantified via qPCR in peripheral blood of the children. Blood DNA methylation, gene expression, miRNA expression, plasma proteins and serum and urinary metabolites were measured through microarrays or (semi-) targeted assays. The association between each individual omics feature and telomere length was assessed in omics-wide association analyses. In addition, a literature-guided, sparse supervised integration method was applied to multiple omics, and latent components were extracted as predictors of child telomere length. The association of these latent components with early-life aging risk factors (child lifestyle, body mass index (BMI), exposure to smoking, etc.), were interrogated. Results After multiple-testing correction, only two CpGs (cg23686403 and cg16238918 at PARD6G gene) out of all the omics features were significantly associated with child telomere length. The supervised multi-omics integration approach revealed robust associations between latent components and child BMI, with metabolites and proteins emerging as the primary contributing features. In these latent components, the contributing molecular features were known as involved in metabolism and immune regulation-related pathways. Conclusions Findings of this multi-omics study suggested an intricate interplay between telomere length, metabolism and immune responses, providing valuable insights into the molecular underpinnings of the early-life biological aging.

The exposome represents the totality of life course environmental exposures (including lifestyle and other non-genetic factors), from the prenatal period onwards. This holistic concept of exposure provides a new framework to advance the understanding of complex and multifactorial diseases. Prospective pregnancy and birth cohort studies provide a unique opportunity for exposome research as they are able to capture, from prenatal life onwards, both the external (including lifestyle, chemical, social and wider community-level exposures) and the internal (including inflammation, metabolism, epigenetics, and gut microbiota) domains of the exposome. In this paper, we describe the steps required for applying an exposome approach, describe the main strengths and limitations of different statistical approaches and discuss their challenges, with the aim to provide guidance for methodological choices in the analysis of exposome data in birth cohort studies. An exposome approach implies selecting, pre-processing, describing and analyzing a large set of exposures. Several statistical methods are currently available to assess exposome-health associations, which differ in terms of research question that can be answered, of balance between sensitivity and false discovery proportion, and between computational complexity and simplicity (parsimony). Assessing the association between many exposures and health still raises many exposure assessment issues and statistical challenges. The exposome favors a holistic approach of environmental influences on health, which is likely to allow a more complete understanding of disease etiology.

The leading cause of drug-induced liver injury in the developed world is overdose with N -acetyl- p -aminophenol (APAP). A comparative metabonomic approach was applied to the study of both xenobiotic and endogenous metabolic profiles reflective of in vivo exposure to APAP (300 mg/kg) and its structural isomer N -acetyl- m -aminophenol (AMAP; 300 mg/kg) in C57BL/6J mice, which was anchored with histopathology. Liver and urine samples were collected at 1 h, 3 h and 6 h post-treatment and analyzed by 1 H nuclear magnetic resonance (NMR) spectroscopy and gas chromatography–mass spectrometry (liver only). Histopathology revealed the presence of centrilobular necrosis from 3 h post-APAP treatment, while an AMAP-mediated necrotic endpoint was not observed within the timescale of this study, yet two of five treated mice showed minimal centrilobular eosinophilia. The 1 H-NMR xenobiotic metabolic profile of APAP-treated animals comprised of mercapturate (urine and liver) and glutathionyl (liver) conjugates detected at 1 h post-treatment. This finding corroborated the hepatic endogenous metabolic profile which showed depletion of glutathione from 1 h onwards. In contrast, AMAP glutathionyl conjugates were not detected, nor was AMAP-induced depletion of hepatic glutathione observed. APAP administration induced significant endogenous hepatic metabolic perturbations, primarily linked to oxidative and energetic stress, and perturbation of amino acid metabolism. Early depletion of glutathione was followed by depletion of additional sulfur-containing metabolites, while altered levels of mitochondrial and glycolytic metabolites indicated a disruption of energy homeostasis. In contrast, AMAP administration caused minimal, transient, distinct metabolic perturbations and by 6 h the metabolic profiles of AMAP-treated mice were indistinguishable from those of controls.

Chemical and nonchemical environmental exposures are increasingly suspected to influence the development of obesity, especially during early life, but studies mostly consider single exposure groups. Our study aimed to systematically assess the association between a wide array of early-life environmental exposures and childhood obesity, using an exposome-wide approach. The HELIX (Human Early Life Exposome) study measured child body mass index (BMI), waist circumference, skinfold thickness, and body fat mass in 1,301 children from six European birth cohorts age 6-11 y. We estimated 77 prenatal exposures and 96 childhood exposures (cross-sectionally), including indoor and outdoor air pollutants, built environment, green spaces, tobacco smoking, and biomarkers of chemical pollutants (persistent organic pollutants, metals, phthalates, phenols, and pesticides). We used an exposure-wide association study (ExWAS) to screen all exposure-outcome associations independently and used the deletion-substitution-addition (DSA) variable selection algorithm to build a final multiexposure model. The prevalence of overweight and obesity combined was 28.8%. Maternal smoking was the only prenatal exposure variable associated with higher child BMI ( -score increase of 0.28, 95% confidence interval: 0.09, 0.48, for active vs. no smoking). For childhood exposures, the multiexposure model identified particulate and nitrogen dioxide air pollution inside the home, urine cotinine levels indicative of secondhand smoke exposure, and residence in more densely populated areas and in areas with fewer facilities to be associated with increased child BMI. Child blood levels of copper and cesium were associated with higher BMI, and levels of organochlorine pollutants, cobalt, and molybdenum were associated with lower BMI. Similar results were found for the other adiposity outcomes. This first comprehensive and systematic analysis of many suspected environmental obesogens strengthens evidence for an association of smoking, air pollution exposure, and characteristics of the built environment with childhood obesity risk. Cross-sectional biomarker results may suffer from reverse causality bias, whereby obesity status influenced the biomarker concentration. https://doi.org/10.1289/EHP5975.

IntroductionMetabolomics may identify biological pathways predisposing children to the risk of overweight and obesity. In this study, we have investigated the cord blood metabolic signatures of rapid growth in infancy and overweight in early childhood in four European birth cohorts.MethodsUntargeted liquid chromatography-mass spectrometry metabolomic profiles were measured in cord blood from 399 newborns from four European cohorts (ENVIRONAGE, Rhea, INMA and Piccolipiu). Rapid growth in the first year of life and overweight in childhood was defined with reference to WHO growth charts. Metabolome-wide association scans for rapid growth and overweight on over 4500 metabolic features were performed using multiple adjusted logistic mixed-effect models and controlling the false discovery rate (FDR) at 5%. In addition, we performed a look-up analysis of 43 pre-annotated metabolites, previously associated with birthweight or rapid growth.ResultsIn the Metabolome-Wide Association Study analysis, we identified three and eight metabolites associated with rapid growth and overweight, respectively, after FDR correction. Higher levels of cholestenone, a cholesterol derivative produced by microbial catabolism, were predictive of rapid growth (p = 1.6 × 10−3). Lower levels of the branched-chain amino acid (BCAA) valine (p = 8.6 × 10−6) were predictive of overweight in childhood. The area under the receiver operator curve for multivariate prediction models including these metabolites and traditional risk factors was 0.77 for rapid growth and 0.82 for overweight, compared with 0.69 and 0.69, respectively, for models using traditional risk factors alone. Among the 43 pre-annotated metabolites, seven and five metabolites were nominally associated (P < 0.05) with rapid growth and overweight, respectively. The BCAA leucine, remained associated (1.6 × 10−3) with overweight after FDR correction.ConclusionThe metabolites identified here may assist in the identification of children at risk of developing obesity and improve understanding of mechanisms involved in postnatal growth. Cholestenone and BCAAs are suggestive of a role of the gut microbiome and nutrient signalling respectively in child growth trajectories.

Background Environment and diet in early life can affect development and health throughout the life course. Metabolic phenotyping of urine and serum represents a complementary systems-wide approach to elucidate environment–health interactions. However, large-scale metabolome studies in children combining analyses of these biological fluids are lacking. Here, we sought to characterise the major determinants of the child metabolome and to define metabolite associations with age, sex, BMI and dietary habits in European children, by exploiting a unique biobank established as part of the Human Early-Life Exposome project ( http://www.projecthelix.eu ). Methods Metabolic phenotypes of matched urine and serum samples from 1192 children (aged 6–11) recruited from birth cohorts in six European countries were measured using high-throughput 1 H nuclear magnetic resonance (NMR) spectroscopy and a targeted LC-MS/MS metabolomic assay (Biocrates Absolute IDQ p180 kit). Results We identified both urinary and serum creatinine to be positively associated with age. Metabolic associations to BMI z-score included a novel association with urinary 4-deoxyerythronic acid in addition to valine, serum carnitine, short-chain acylcarnitines (C3, C5), glutamate, BCAAs, lysophosphatidylcholines (lysoPC a C14:0, lysoPC a C16:1, lysoPC a C18:1, lysoPC a C18:2) and sphingolipids (SM C16:0, SM C16:1, SM C18:1). Dietary-metabolite associations included urinary creatine and serum phosphatidylcholines (4) with meat intake, serum phosphatidylcholines (12) with fish, urinary hippurate with vegetables, and urinary proline betaine and hippurate with fruit intake. Population-specific variance (age, sex, BMI, ethnicity, dietary and country of origin) was better captured in the serum than in the urine profile; these factors explained a median of 9.0% variance amongst serum metabolites versus a median of 5.1% amongst urinary metabolites. Metabolic pathway correlations were identified, and concentrations of corresponding metabolites were significantly correlated ( r  > 0.18) between urine and serum. Conclusions We have established a pan-European reference metabolome for urine and serum of healthy children and gathered critical resources not previously available for future investigations into the influence of the metabolome on child health. The six European cohort populations studied share common metabolic associations with age, sex, BMI z-score and main dietary habits. Furthermore, we have identified a novel metabolic association between threonine catabolism and BMI of children.

Childhood obesity poses a significant public health challenge, yet the molecular intricacies underlying its pathobiology remain elusive. Leveraging extensive multi-omics profiling (methylome, miRNome, transcriptome, proteins and metabolites) and a rich phenotypic characterization across two parts of Europe within the population-based Human Early Life Exposome project, we unravel the molecular landscape of childhood obesity and associated metabolic dysfunction. Our integrative analysis uncovers three clusters of children defined by specific multi-omics profiles, one of which characterized not only by higher adiposity but also by a high degree of metabolic complications. This high-risk cluster exhibits a complex interplay across many biological pathways, predominantly underscored by inflammation-related cascades. Further, by incorporating comprehensive information from the environmental risk-scape of the critical pregnancy period, we identify pre-pregnancy body mass index and environmental pollutants like perfluorooctanoate and mercury as important determinants of the high-risk cluster. Overall, our work helps to identify potential risk factors for prevention and intervention strategies early in the life course aimed at mitigating obesity and its long-term health consequences. Obesity encompasses numerous interconnected pathological mechanisms. Here, the authors show that integrating multi-omics data uncovers distinct molecular profiles and prenatal factors linked to childhood obesity and metabolic dysfunction, providing insights for early prevention and intervention strategies.