A novel approach to risk exposure and epigenetics—the use of multidimensional context to gain insights into the early origins of cardiometabolic and neurocognitive health

Background: Each mother–child dyad represents a unique combination of genetic and environmental factors. This constellation of variables impacts the expression of countless genes. Numerous studies have uncovered changes in DNA methylation (DNAm), a form of epigenetic regulation, in offspring related to maternal risk factors. How these changes work together to link maternal-child risks to childhood cardiometabolic and neurocognitive traits remains unknown. This question is a key research priority as such traits predispose to future non-communicable diseases (NCDs). We propose viewing risk and the genome through a multidimensional lens to identify common DNAm patterns shared among diverse risk profiles. Methods: We identified multifactorial Maternal Risk Profiles (MRPs) generated from population-based data (n = 15,454, Avon Longitudinal Study of Parents and Children (ALSPAC)). Using cord blood HumanMethylation450 BeadChip data, we identified genome-wide patterns of DNAm that co-vary with these MRPs. We tested the prospective relation of these DNAm patterns (n = 914) to future outcomes using decision tree analysis. We then tested the reproducibility of these patterns in (1) DNAm data at age 7 and 17 years within the same cohort (n = 973 and 974, respectively) and (2) cord DNAm in an independent cohort, the Generation R Study (n = 686).Results:We identified twenty MRP-related DNAm patterns at birth in ALSPAC. Four were prospectively related to cardiometabolic and/or neurocognitive childhood outcomes. These patterns were replicated in DNAm data from blood collected at later ages. Three of these patterns were externally validated in cord DNAm data in Generation R. Compared to previous literature, DNAm patterns exhibited novel spatial distribution across the genome that intersects with chromatin functional and tissue-specific signatures. Conclusions: To our knowledge, we are the first to leverage multifactorial population-wide data to detect patterns of variability in DNAm. This context-based approach decreases biases stemming from overreliance on specific samples or variables. We discovered molecular patterns demonstrating prospective and replicable relations to complex traits. Moreover, results suggest that patterns harbour a genome-wide organisation specific to chromatin regulation and target tissues. These preliminary findings warrant further investigation to better reflect the reality of human context in molecular studies of NCDs. Graphical Abstract: [Figure not available: see fulltext.].</p

Etiology of Esophageal Atresia and Tracheoesophageal Fistula: “Mind the Gap”

Esophageal atresia and tracheoesophageal fistula (EA/TEF) are major congenital malformations affecting 1:3500 live births. Current research efforts are focused on understanding the etiology of these defects. We describe well-known animal models, human syndromes, and associations involving EA/TEF, indicating its etiologically heterogeneous nature. Recent advances in genotyping technology and in knowledge of human genetic variation will improve clinical counseling on etiologic factors. This review provides a clinical summary of environmental and genetic factors involved in EA/TEF

Epigenetic age acceleration and cardiovascular outcomes in school-age children:The Generation R Study

BACKGROUND: Hypertension and atherosclerosis may partly originate in early life. Altered epigenetic aging may be a mechanism underlying associations of early-life exposures and the development of cardiovascular risk factors in childhood. A discrepancy between chronological age and age predicted from neonatal DNA methylation data is referred to as age acceleration. It may either be positive, if DNA methylation age is older than clinical age, or negative, if DNA methylation age is younger than chronological age. We examined associations of age acceleration at birth (‘gestational age acceleration’), and of age acceleration at school-age, with blood pressure and with intima-media thickness and distensibility of the common carotid artery, as markers of vascular structure and function, respectively, measured at age 10 years. RESULTS: This study was embedded in the Generation R Study, a population-based prospective cohort study. We included 1115 children with information on cord blood DNA methylation and blood pressure, carotid intima-media thickness or carotid distensibility. Gestational age acceleration was calculated using the Bohlin epigenetic clock, which was developed specifically for cord blood DNA methylation data. It predicts gestational age based on methylation levels of 96 CpGs from HumanMethylation450 BeadChip. We observed no associations of gestational age acceleration with blood pressure, carotid intima-media thickness or carotid distensibility at age 10 years. In analyses among children with peripheral blood DNA methylation measured at age 6 (n = 470) and 10 (n = 449) years, we also observed no associations of age acceleration at these ages with the same cardiovascular outcomes, using the ‘skin and blood clock,’ which predicts age based on methylation levels at 391 CpGs from HumanMethylation450 BeadChip. CONCLUSIONS: Our findings do not provide support for the hypothesis that altered epigenetic aging during the earliest phase of life is involved in the development of cardiovascular risk factors in childhood. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13148-021-01193-4

Maternal Early-Pregnancy Glucose Concentrations and Liver Fat Among School-Age Children

Background and Aims: Gestational diabetes seems to be associated with offspring NAFLD. We hypothesized that maternal glucose concentrations across the full range may have persistent effects on offspring liver fat accumulation. Approach and Results: In a multiethnic, population-based, prospective cohort study among 2,168 women and their offspring, maternal early-pregnancy glucose concentrations were measured at a median of 13.1 weeks’ gestation (95% CI, 9.6-17.2). Liver fat fraction was measured at 10 years by MRI. NAFLD was defined as liver fat fraction ≥5.0%. We performed analyses among all mothers with different ethnic backgrounds and those of European ancestry only. The multiethnic group had a median maternal early-pregnancy glucose concentration of 4.3 mmol/L (interquartile range, 3.9-4.9) and a 2.8% (n = 60) prevalence of NAFLD. The models adjusted for child age and sex only showed that in the multiethnic group, higher maternal early-pregnancy glucose concentrations were associated with higher liver fat accumulation and higher odds of NAFLD, but these associations attenuated into nonsignificance after adjustment for potential confounders. Among mothers of European ancestry only, maternal early-pregnancy glucose concentrations were associated with increased odds of NAFLD (OR, 1.95; 95% CI, 1.32; 2.88, after adjustment for confounders) per 1-mmol/L increase in maternal early-pregnancy glucose concentration. These associations were not explained by maternal prepregnancy and childhood body mass index, visceral fat, and metabolic markers. Conclusions: In this study, maternal early-pregnancy glucose concentrations were only among mothers of European ancestry associated with offspring NAFLD. The associations of higher maternal early-pregnancy glucose concentrations with offspring NAFLD may differ between ethnic groups.</p

Risk factors and cardio-metabolic outcomes associated with metabolic-associated fatty liver disease in childhood

Background: Non-Alcoholic Fatty Liver Disease (NAFLD) is defined as increased liver fat percentage, and is the most common chronic liver disease in children. Rather than NAFLD, Metabolic-Associated Fatty Liver Disease (MAFLD), defined as increased liver fat with presence of adverse cardio-metabolic measures, might have more clinical relevance in children. We assessed the prevalence, risk-factors and cardio-metabolic outcomes of MAFLD at school-age. Methods: This cross-sectional analysis was embedded in an ongoing population-based prospective cohort study started in 2001, in the Netherlands. In 1910 children of 10 years, we measured liver fat fraction by magnetic resonance imaging (MRI), body mass index (BMI), blood pressure, and lipids, insulin, and glucose concentrations. Childhood lifestyle factors were obtained through questionnaires. MAFLD was defined as ≥2% liver fat in addition to excess adiposity (BMI or visceral adiposity), presence of metabolic risk (blood pressure, triglycerides and HDL-concentrations) or prediabetes (glucose). Findings: Of all children, 49.6% had ≥2% liver fat, and 25.2% fulfilled the criteria of MAFLD. Only non-European descent was associated with increased odds of MAFLD at nominal significance (Odds Ratio 1.38, 95% Confidence Interval 1.04, 1.82). Compared to children with &lt;2% liver fat, those with MAFLD had increased odds of cardio-metabolic-risk-factor clustering (Odds Ratio 7.65, 95% Confidence Interval 5.04, 11.62). Interpretation: In this study, no NAFLD-associated childhood risk factors were associated with increased odds of childhood MAFLD, yet the findings suggest that ethnicity could be, despite mostly explained by socio-economic factors. Use of MAFLD criteria, rather than NAFLD, may identify children at risk for impaired cardio-metabolic health. Funding: Erasmus University MC, the Netherlands Organisation for Health Research and Development, the Ministry of Health, Welfare, and Sport, and the European Research Council.</p

Fetal exposure to phthalates and bisphenols and DNA methylation at birth:the Generation R Study

BACKGROUND: Phthalates and bisphenols are non-persistent endocrine disrupting chemicals that are ubiquitously present in our environment and may have long-lasting health effects following fetal exposure. A potential mechanism underlying these exposure–outcome relationships is differential DNA methylation. Our objective was to examine the associations of maternal phthalate and bisphenol concentrations during pregnancy with DNA methylation in cord blood using a chemical mixtures approach. METHODS: This study was embedded in a prospective birth cohort study in the Netherlands and included 306 participants. We measured urine phthalates and bisphenols concentrations in the first, second and third trimester. Cord blood DNA methylation in their children was processed using the Illumina Infinium HumanMethylation450 BeadChip using an epigenome-wide association approach. Using quantile g-computation, we examined the association of increasing all mixture components by one quartile with cord blood DNA methylation. RESULTS: We did not find evidence for statistically significant associations of a maternal mixture of phthalates and bisphenols during any of the trimesters of pregnancy with DNA methylation in cord blood (all p values > 4.01 * 10(–8)). However, we identified one suggestive association (p value < 1.0 * 10(–6)) of the first trimester maternal mixture of phthalates and bisphenols and three suggestive associations of the second trimester maternal mixture of phthalates and bisphenols with DNA methylation in cord blood. CONCLUSIONS: Although we did not identify genome-wide significant results, we identified some suggestive associations of exposure to a maternal mixture of phthalates and bisphenols in the first and second trimester with DNA methylation in cord blood that need further exploration in larger study samples. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13148-022-01345-0

Animal and plant protein intake during infancy and childhood DNA methylation:a meta-analysis in the NutriPROGRAM consortium

Background: Higher early-life animal protein intake is associated with a higher childhood obesity risk compared to plant protein intake. Differential DNA methylation may represent an underlying mechanism. Methods: We analysed associations of infant animal and plant protein intakes with DNA methylation in early (2−6 years, N = 579) and late (7̄−12 years, N = 604) childhood in two studies. Study-specific robust linear regression models adjusted for relevant confounders were run, and then meta-analysed using a fixed-effects model. We also performed sex-stratified meta-analyses. Follow-up analyses included pathway analysis and eQTM look-up. Results: Infant animal protein intake was not associated with DNA methylation in early childhood, but was associated with late-childhood DNA methylation at cg21300373 (P = 4.27 × 10¯8, MARCHF1) and cg10633363 (P = 1.09 × 10¯7, HOXB9) after FDR correction. Infant plant protein intake was associated with early-childhood DNA methylation at cg25973293 (P = 2.26 × 10−7, C1orf159) and cg15407373 (P = 2.13 × 10−7, MBP) after FDR correction. There was no overlap between the findings from the animal and plant protein analyses. We did not find enriched functional pathways at either time point using CpGs associated with animal and plant protein. These CpGs were not previously associated with childhood gene expression. Sex-stratified meta-analyses showed sex-specific DNA methylation associations for both animal and plant protein intake. Conclusion: Infant animal protein intake was associated with DNA methylation at two CpGs in late childhood. Infant plant protein intake was associated with DNA methylation in early childhood at two CpGs. A potential mediating role of DNA methylation at these CpGs between infant protein intake and health outcomes requires further investigation.</p

DNA methylation at birth and lateral ventricular volume in childhood:a neuroimaging epigenetics study

Background: Lateral ventricular volume (LVV) enlargement has been repeatedly linked to schizophrenia; yet, what biological factors shape LVV during early development remain unclear. DNA methylation (DNAm), an essential process for neurodevelopment that is altered in schizophrenia, is a key molecular system of interest. Methods:In this study, we conducted the first epigenome-wide association study of neonatal DNAm in cord blood with LVV in childhood (measured using T1-weighted brain scans at 10 years), based on data from a large population-based birth cohort, the Generation R Study (N = 840). Employing both probe-level and methylation profile score (MPS) approaches, we further examined whether epigenetic modifications identified at birth in cord blood are: (a) also observed cross-sectionally in childhood using peripheral blood DNAm at age of 10 years (Generation R, N = 370) and (b) prospectively associated with LVV measured in young adulthood in an all-male sample from the Avon Longitudinal Study of Parents and Children (ALSPAC, N = 114). Results: At birth, DNAm levels at four CpGs (annotated to potassium channel tetramerization domain containing 3, KCTD3; SHH signaling and ciliogenesis regulator, SDCCAG8; glutaredoxin, GLRX) prospectively associated with childhood LVV after genome-wide correction; these genes have been implicated in brain development and psychiatric traits including schizophrenia. An MPS capturing a broader epigenetic profile of LVV – but not individual top hits – showed significant cross-sectional associations with LVV in childhood in Generation R and prospectively associated with LVV in early adulthood within ALSPAC. Conclusions: This study finds suggestive evidence that DNAm at birth prospectively associates with LVV at different life stages, albeit with small effect sizes. The prediction of MPS on LVV in a childhood sample and an independent male adult sample further underscores the stability and reproducibility of DNAm as a potential marker for LVV. Future studies with larger samples and comparable time points across development are needed to further elucidate how DNAm associates with this clinically relevant brain structure and risk for neuropsychiatric disorders, and what factors explain the identified DNAm profile of LVV at birth.</p

Cardiometabolic risk estimation using exposome data and machine learning

Background: The human exposome encompasses all exposures that individuals encounter throughout their lifetime. It is now widely acknowledged that health outcomes are influenced not only by genetic factors but also by the interactions between these factors and various exposures. Consequently, the exposome has emerged as a significant contributor to the overall risk of developing major diseases, such as cardiovascular disease (CVD) and diabetes. Therefore, personalized early risk assessment based on exposome attributes might be a promising tool for identifying high-risk individuals and improving disease prevention. Objective: Develop and evaluate a novel and fair machine learning (ML) model for CVD and type 2 diabetes (T2D) risk prediction based on a set of readily available exposome factors. We evaluated our model using internal and external validation groups from a multi-center cohort. To be considered fair, the model was required to demonstrate consistent performance across different sub-groups of the cohort. Methods: From the UK Biobank, we identified 5,348 and 1,534 participants who within 13 years from the baseline visit were diagnosed with CVD and T2D, respectively. An equal number of participants who did not develop these pathologies were randomly selected as the control group. 109 readily available exposure variables from six different categories (physical measures, environmental, lifestyle, mental health events, sociodemographics, and early-life factors) from the participant's baseline visit were considered. We adopted the XGBoost ensemble model to predict individuals at risk of developing the diseases. The model's performance was compared to that of an integrative ML model which is based on a set of biological, clinical, physical, and sociodemographic variables, and, additionally for CVD, to the Framingham risk score. Moreover, we assessed the proposed model for potential bias related to sex, ethnicity, and age. Lastly, we interpreted the model's results using SHAP, a state-of-the-art explainability method. Results: The proposed ML model presents a comparable performance to the integrative ML model despite using solely exposome information, achieving a ROC-AUC of 0.78±0.01 and 0.77±0.01 for CVD and T2D, respectively. Additionally, for CVD risk prediction, the exposome-based model presents an improved performance over the traditional Framingham risk score. No bias in terms of key sensitive variables was identified. Conclusions: We identified exposome factors that play an important role in identifying patients at risk of CVD and T2D, such as naps during the day, age completed full-time education, past tobacco smoking, frequency of tiredness/unenthusiasm, and current work status. Overall, this work demonstrates the potential of exposome-based machine learning as a fair CVD and T2D risk assessment tool.</p