Environmental exposures during early life play a critical role in life-course health, yet the molecular phenotypes underlying environmental effects on health are poorly understood. In the Human Early Life Exposome (HELIX) project, a multi-centre cohort of 1301 mother-child pairs, we associate individual exposomes consisting of >100 chemical, outdoor, social and lifestyle exposures assessed in pregnancy and childhood, with multi-omics profiles (methylome, transcriptome, proteins and metabolites) in childhood. We identify 1170 associations, 249 in pregnancy and 921 in childhood, which reveal potential biological responses and sources of exposure. Pregnancy exposures, including maternal smoking, cadmium and molybdenum, are predominantly associated with child DNA methylation changes. In contrast, childhood exposures are associated with features across all omics layers, most frequently the serum metabolome, revealing signatures for diet, toxic chemical compounds, essential trace elements, and weather conditions, among others. Our comprehensive and unique resource of all associations (https://helixomics.isglobal.org/) will serve to guide future investigation into the biological imprints of the early life exposome.

Animal and human studies indicate that exposure to air pollution and natural environments might modulate the gut microbiota, but epidemiological evidence is very scarce. To assess the potential impact of pre- and postnatal exposure to air pollution and green spaces on infant gut microbiota assembly and trajectories during the first year of life. MAMI ("MAternal MIcrobes") birth cohort (Valencia, Spain, N=162) was used to study the impact of environmental exposure (acute and chronic) on infant gut microbiota during the first year of life (amplicon-based 16S rRNA sequencing). At 7 days and at 1, 6 and 12 months, residential pre- and postnatal exposure to air pollutants (NO₂, black carbon -BC-, PM_2.5 and O₃) and green spaces indicators (NDVI and area of green spaces at 300, 500 and 1000 m buffers) were obtained. For the association between exposures and alpha diversity indicators linear regression models (cross-sectional analyses) and mixed models, including individual as a random effect (longitudinal analyses), were applied. For the differential taxon analysis, the ANCOM-BC package with a log count transformation and multiple-testing corrections were used. Acute exposure in the first week of life and chronic postnatal exposure to NO₂ were associated with a reduction in microbial alpha diversity, while the effects of green space exposure were not evident. Acute and chronic (prenatal or postnatal) exposure to NO₂ resulted in increased abundance of Haemophilus, Akkermansia, Alistipes, Eggerthella, and Tyzerella populations, while increasing green space exposure associated with increased Negativicoccus, Senegalimassilia and Anaerococcus and decreased Tyzzerella and Lachnoclostridium populations. We observed a decrease in the diversity of the gut microbiota and signs of alteration in its composition among infants exposed to higher levels of NO₂. Increasing green space exposure was also associated with changes in gut microbial composition. Further research is needed to confirm these findings.

Steroid hormones (SHs), including sex steroids and corticosteroids, are crucial for a healthy pregnancy. We aimed to comprehensively characterize the maternal SH metabolome in late pregnancy and identify clinical, lifestyle, and sociodemographic determinants influencing SH metabolism with a replication in an independent cohort. Urinary SH metabolites were analyzed in 1221 third-trimester pregnant women (aged 28 to 37 years) from two Spanish cohorts, BiSC (2018–2021, n = 721) and INMA-Sabadell (2004–2006, n = 500), using targeted UHPLC-MS/MS. We quantified 50 SH metabolites, resulting in 13 hormone groups, 9 sulfate/glucuronide ratios, and 17 estimated steroid enzymatic activities across steroidogenesis pathways. We applied elastic net regression to identify determinants, and multivariable linear regression models to estimate variance explained. Among the 47 and 28 determinants from BiSC and INMA-Sabadell, respectively, 10 determinant-SH metabolome pairs showed statistically significant associations (p < 0.05), supporting robust replication. Maternal BMI was the main determinant linked to higher corticosteroid and androgen metabolites. Higher physical activity was associated with lower glucocorticoids and progestogen metabolites, while older maternal age was related with lower levels of androgen and corticosteroid metabolites. Tobacco exposure in the first trimester predicted higher levels of cortisol metabolites. Latin American women had lower cortolone levels compared with Spanish women. Parity, dietary fat intake, sleep, alcohol intake, and sex of the fetus contributed to smaller variations in different SHs. This dual-cohort analysis provides the most detailed and replicated evidence to date of how clinical, lifestyle, and sociodemographic factors shape the maternal SH metabolome during late pregnancy.

(1) Background: Steroid hormones (SH), including sex steroids and corticosteroids, are crucial for a healthy pregnancy. We identified and replicated the main determinants associated with a wide range of SH molecular features during later pregnancy. (2) Methods: We analyzed urinary SH metabolites in 1,221 pregnant women (aged 28 to 37 years) in the 3rd trimester from two cohorts: BiSC (2018-2021, n=721) and INMA-Sabadell (2004-2006, n=500). Using targeted UHPLC-MS/MS, we quantified 50 SH metabolites, resulting in 13 hormone groups, 9 sulfate/glucuronide (S/G) ratios, and 17 estimated steroid enzymatic activities across steroidogenesis pathways including progestogens, androgens, estrogens and corticosteroids metabolites. In total 89 SH molecular features were considered in the study. We applied Elastic Net Regression to identify determinants, and multivariable linear regression models to estimate variance explained (3) Results: Pregnant women with gestational age at sample collection of BiSC: 34.7 (SD 1.5) and INMA-Sabadell 34.3 (SD 1.6) gestational weeks were considered in the study. Among the 47 and 28 determinants from BiSC and INMA-Sabadell, respectively, 10 determinant-SH metabolome pairs showed statistically significant associations (P< 0.05) and were concordant in most cases in effect direction across the cohorts. Maternal BMI was the most influential predictor, accounting for 38% of the all associations, followed by maternal age (25%), maternal ethnicity (11%), physical activity (7%), smoking (5%), parity (4%), sex of the fetus (4%), dietary fat intake (4%), sleep (2%), and alcohol intake (2%). (4) Conclusions: Our findings provide evidence that clinical, lifestyle and sociodemographic factors shape the maternal SH metabolome during late pregnancy.

A framework for 'scalable' statistical computing on large on-disk matrices stored in 'HDF5' files. It provides efficient block-wise implementations of core linear-algebra operations (matrix multiplication, SVD, PCA, QR decomposition, and canonical correlation analysis) written in C++ and R. These building blocks are designed not only for direct use, but also as foundational components for developing new statistical methods that must operate on datasets too large to fit in memory. The package supports data provided either as 'HDF5' files or standard R objects, and is intended for high-dimensional applications such as 'omics' and precision-medicine research.