Common and distinct metabolomic markers related to immune aging in Western European and East African populations.

In old age, impaired immunity causes high susceptibility to infections and cancer, higher morbidity and mortality, and poorer vaccination efficiency. Many factors, such as genetics, diet, and lifestyle, impact aging. This study aimed to investigate how immune responses change with age in healthy Dutch and Tanzanian individuals and identify common metabolites associated with an aged immune profile. We performed untargeted metabolomics from plasma to identify age-associated metabolites, and we correlated their concentrations with ex-vivo cytokine production by immune cells, DNA methylation-based epigenetic aging, and telomere length. Innate immune responses were impacted differently by age in Dutch and Tanzanian cohorts. Age-related decline in steroid hormone precursors common in both populations was associated with higher systemic inflammation and lower cytokine responses. Hippurate and 2-phenylacetamide, commonly more abundant in older individuals, were negatively correlated with cytokine responses and telomere length and positively correlated with epigenetic aging. Lastly, we identified several metabolites that might contribute to the stronger decline in innate immunity with age in Tanzanians. The shared metabolomic signatures of the two cohorts suggest common mechanisms of immune aging, revealing metabolites with potential contributions. These findings also reflect genetic or environmental effects on circulating metabolites that modulate immune responses.

11-deoxycortisol positively correlates with T cell immune traits in physiological conditions.

BACKGROUND: Endogenous steroid hormones have significant effects on inflammatory and immune processes, but the immunological activities of steroidogenesis precursors remain largely unexplored. METHODS: We conducted a systematic approach to examine the association between steroid hormones profile and immune traits in a cohort of 534 healthy volunteers. Serum concentrations of steroid hormones and their precursors (cortisol, progesterone, testosterone, androstenedione, 11-deoxycortisol and 17-OH progesterone) were determined by liquid chromatography-tandem mass spectrometry. Immune traits were evaluated by quantifying cellular composition of the circulating immune system and ex vivo cytokine responses elicited by major human pathogens and microbial ligands. An independent cohort of 321 individuals was used for validation, followed by in vitro validation experiments. FINDINGS: We observed a positive association between 11-deoxycortisol and lymphoid cellular subsets numbers and function (especially IL-17 response). The association with lymphoid cellularity was validated in an independent validation cohort. In vitro experiments showed that, as compared to androstenedione and 17-OH progesterone, 11-deoxycortisol promoted T cell proliferation and Candida-induced Th17 polarization at physiologically relevant concentrations. Functionally, 11-deoxycortisol-treated T cells displayed a more activated phenotype (PD-L1high CD25high CD62Llow CD127low) in response to CD3/CD28 co-stimulation, and downregulated expression of T-bet nuclear transcription factor. INTERPRETATION: Our findings suggest a positive association between 11-deoxycortisol and T-cell function under physiological conditions. Further investigation is needed to explore the potential mechanisms and clinical implications. FUNDING: Found in acknowledgements.

Controlled Attenuation Parameter Is Associated with a Distinct Systemic Inflammatory Milieu after Clearance of HCV Infection.

Hepatitis C virus (HCV) infection is closely associated with lipid metabolism defects along with a high prevalence of hepatic steatosis. After HCV clearance, steatosis persists in many patients. However, the reasons behind this phenomenon are not completely clear. To investigate the association between 92 soluble inflammatory mediators (SIMs) and the steatosis grade, we made use of a cohort of 94 patients with chronic HCV infection who cleared HCV after direct-acting antiviral agent (DAA) treatment. Patients were classified into three groups according to their controlled attenuation parameter (CAP). CAP is associated with ALT, γ-GT and liver stiffness after HCV clearance. While stem cell factor (SCF) and tumor necrosis factor ligand superfamily member 12 (TWEAK) levels were significantly reduced in patients with CAP > 299 dB/m, the levels of fibroblast growth factor (FGF)-21 and interleukin-18 receptor 1 (IL-18R1) were higher in those patients at week 96 after virus clearance. These four markers also showed a linear correlation with CAP values. FGF-21 levels correlated with CAP only after HCV clearance. Taken together, these four biomarkers, namely SCF, TWEAK, FGF-21 and IL-18R1, are associated with CAP status after virus clearance. A potential role of these proteins in the pathogenesis of post-sustained viral response (SVR) nonalcoholic steatohepatitis requires further investigation.

Reverse inflammaging: Long-term effects of HCV cure on biological age.

BACKGROUND & AIMS: Chronic hepatitis C virus (HCV) infection can be cured with direct-acting antivirals (DAAs). However, not all sequelae of chronic hepatitis C appear to be completely reversible after sustained virologic response (SVR). Recently, chronic viral infections have been shown to be associated with biological age acceleration defined by the epigenetic clock. The aim of this study was to investigate whether chronic HCV infection is associated with epigenetic changes and biological age acceleration and whether this is reversible after SVR. METHODS: We included 54 well-characterized individuals with chronic hepatitis C who achieved SVR after DAA therapy at three time points: DAA treatment initiation, end of treatment, and long-term follow-up (median 96 weeks after end of treatment). Genome-wide DNA methylation status was determined in peripheral blood mononuclear cells (PBMCs) and used to calculate epigenetic age acceleration (EAA) using Horvath's clock. RESULTS: Individuals with HCV had an overall significant EAA of 3.12 years at baseline compared with -2.61 years in the age- and sex-matched reference group (p <0.00003). HCV elimination resulted in a significant long-term increase in DNA methylation dominated by hypermethylated CpGs in all patient groups. Accordingly, EAA decreased to 1.37 years at long-term follow-up. The decrease in EAA was significant only between the end of treatment and follow-up (p = 0.01). Interestingly, eight individuals who developed hepatocellular carcinoma after SVR had the highest EAA and showed no evidence of reversal after SVR. CONCLUSIONS: Our data contribute to the understanding of the biological impact of HCV elimination after DAA therapy and demonstrate that HCV elimination can lead to "reverse inflammaging". In addition, our data support the potential use of biological age as a biomarker for HCV sequelae after SVR. IMPACT AND IMPLICATIONS: Chronic hepatitis C virus infection is now curable with direct-acting antivirals, but it remains unclear whether hepatitis C sequelae are fully reversible after viral elimination. Our results suggest that epigenetic changes or acceleration of biological age are reversible in principle, but this requires time, while a lack of reversibility appears to be associated with the development of hepatocellular carcinoma. While most clinical risk scores now take chronological age into account, it may be worthwhile to explore how biological age might improve these scores in the future. Biological age may be a cornerstone for the individualized clinical assessment of patients in the future, as it better reflects patients' lifestyle and environmental exposures over decades.

Targeted proteomics identifies circulating biomarkers associated with active COVID-19 and post-COVID-19.

The ongoing Coronavirus Disease 2019 (COVID-19) pandemic is caused by the highly infectious Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). There is an urgent need for biomarkers that will help in better stratification of patients and contribute to personalized treatments. We performed targeted proteomics using the Olink platform and systematically investigated protein concentrations in 350 hospitalized COVID-19 patients, 186 post-COVID-19 individuals, and 61 healthy individuals from 3 independent cohorts. Results revealed a signature of acute SARS-CoV-2 infection, which is represented by inflammatory biomarkers, chemokines and complement-related factors. Furthermore, the circulating proteome is still significantly affected in post-COVID-19 samples several weeks after infection. Post-COVID-19 individuals are characterized by upregulation of mediators of the tumor necrosis (TNF)-α signaling pathways and proteins related to transforming growth factor (TGF)-ß. In addition, the circulating proteome is able to differentiate between patients with different COVID-19 disease severities, and is associated with the time after infection. These results provide important insights into changes induced by SARS-CoV-2 infection at the proteomic level by integrating several cohorts to obtain a large disease spectrum, including variation in disease severity and time after infection. These findings could guide the development of host-directed therapy in COVID-19.

Reprogramming of myeloid cells and their progenitors in patients with non-medullary thyroid carcinoma.

Myeloid cells, crucial players in antitumoral defense, are affected by tumor-derived factors and treatment. The role of myeloid cells and their progenitors prior to tumor infiltration is poorly understood. Here we show single-cell transcriptomics and functional analyses of the myeloid cell lineage in patients with non-medullary thyroid carcinoma (TC) and multinodular goiter, before and after treatment with radioactive iodine compared to healthy controls. Integrative data analysis indicates that monocytes of TC patients have transcriptional upregulation of antigen presentation, reduced cytokine production capacity, and overproduction of reactive oxygen species. Interestingly, these cancer-related pathological changes are partially removed upon treatment. In bone marrow, TC patients tend to shift from myelopoiesis towards lymphopoiesis, reflected in transcriptional differences. Taken together, distinct transcriptional and functional changes in myeloid cells arise before their infiltration of the tumor and are already initiated in bone marrow, which suggests an active role in forming the tumor immune microenvironment.

Plasma metabolome predicts trained immunity responses after antituberculosis BCG vaccination.

The antituberculosis vaccine Bacillus Calmette-Guérin (BCG) induces nonspecific protection against heterologous infections, at least partly through induction of innate immune memory (trained immunity). The amplitude of the response to BCG is variable, but the factors that influence this response are poorly understood. Metabolites, either released by cells or absorbed from the gut, are known to influence immune responses, but whether they impact BCG responses is not known. We vaccinated 325 healthy individuals with BCG, and collected blood before, 2 weeks and 3 months after vaccination, to assess the influence of circulating metabolites on the immune responses induced by BCG. Circulating metabolite concentrations after BCG vaccination were found to have a more pronounced impact on trained immunity responses, such as the increase in IL-1ß and TNF-α production upon Staphylococcus aureus stimulation, than on specific adaptive immune memory, assessed as IFN-γ production in response to Mycobacterium tuberculosis. Circulating metabolites at baseline were able to predict trained immunity responses at 3 months after vaccination and enrichment analysis based on the metabolites positively associated with trained immunity revealed enrichment of the tricarboxylic acid (TCA) cycle and glutamine metabolism, both of which were previously found to be important for trained immunity. Several new metabolic pathways that influence trained immunity were identified, among which taurine metabolism associated with BCG-induced trained immunity, a finding validated in functional experiments. In conclusion, circulating metabolites are important factors influencing BCG-induced trained immunity in humans. Modulation of metabolic pathways may be a novel strategy to improve vaccine and trained immunity responses.

Multi-omics examination of Q fever fatigue syndrome identifies similarities with chronic fatigue syndrome.

BACKGROUND: Q fever fatigue syndrome (QFS) is characterised by a state of prolonged fatigue that is seen in 20% of acute Q fever infections and has major health-related consequences. The molecular mechanisms underlying QFS are largely unclear. In order to better understand its pathogenesis, we applied a multi-omics approach to study the patterns of the gut microbiome, blood metabolome, and inflammatory proteome of QFS patients, and compared these with those of chronic fatigue syndrome (CFS) patients and healthy controls (HC). METHODS: The study population consisted of 31 QFS patients, 50 CFS patients, and 72 HC. All subjects were matched for age, gender, and general geographical region (South-East part of the Netherlands). The gut microbiome composition was assessed by Metagenomic sequencing using the Illumina HiSeq platform. A total of 92 circulating inflammatory markers were measured using Proximity Extension Essay and 1607 metabolic features were assessed with a high-throughput non-targeted metabolomics approach. RESULTS: Inflammatory markers, including 4E-BP1 (P = 9.60-16 and 1.41-7) and MMP-1 (P = 7.09-9 and 3.51-9), are significantly more expressed in both QFS and CFS patients compared to HC. Blood metabolite profiles show significant differences when comparing QFS (319 metabolites) and CFS (441 metabolites) patients to HC, and are significantly enriched in pathways like sphingolipid (P = 0.0256 and 0.0033) metabolism. When comparing QFS to CFS patients, almost no significant differences in metabolome were found. Comparison of microbiome taxonomy of QFS and CFS patients with that of HC, shows both in- and decreases in abundancies in Bacteroidetes (with emphasis on Bacteroides and Alistiples spp.), and Firmicutes and Actinobacteria (with emphasis on Ruminococcus and Bifidobacterium spp.). When we compare QFS patients to CFS patients, there is a striking resemblance and hardly any significant differences in microbiome taxonomy are found. CONCLUSIONS: We show that QFS and CFS patients are similar across three different omics layers and 4E-BP1 and MMP-1 have the potential to distinguish QFS and CFS patients from HC.

Nasal DNA methylation profiling of asthma and rhinitis.

BACKGROUND: Epigenetic signatures in the nasal epithelium, which is a primary interface with the environment and an accessible proxy for the bronchial epithelium, might provide insights into mechanisms of allergic disease. OBJECTIVE: We aimed to identify and interpret methylation signatures in nasal epithelial brushes associated with rhinitis and asthma. METHODS: Nasal epithelial brushes were obtained from 455 children at the 16-year follow-up of the Dutch Prevention and Incidence of Asthma and Mite Allergy birth cohort study. Epigenome-wide association studies were performed on children with asthma, rhinitis, and asthma and/or rhinitis (AsRh) by using logistic regression, and the top results were replicated in 2 independent cohorts of African American and Puerto Rican children. Significant CpG sites were related to environmental exposures (pets, active and passive smoking, and molds) during secondary school and were correlated with gene expression by RNA-sequencing (n = 244). RESULTS: The epigenome-wide association studies identified CpG sites significantly associated with rhinitis (n = 81) and AsRh (n = 75), but not with asthma. We significantly replicated 62 of 81 CpG sites with rhinitis and 60 of 75 with AsRh, as well as 1 CpG site with asthma. Methylation of cg03565274 was negatively associated with AsRh and positively associated with exposure to pets during secondary school. DNA methylation signals associated with AsRh were mainly driven by specific IgE-positive subjects. DNA methylation related to gene transcripts that were enriched for immune pathways and expressed in immune and epithelial cells. Nasal CpG sites performed well in predicting AsRh. CONCLUSIONS: We identified replicable DNA methylation profiles of asthma and rhinitis in nasal brushes. Exposure to pets may affect nasal epithelial methylation in relation to asthma and rhinitis.

Differential DNA methylation in bronchial biopsies between persistent asthma and asthma in remission.

Approximately 40% of asthmatics experience remission of asthma symptoms. A better understanding of biological pathways leading to asthma remission may provide insight into new therapeutic targets for asthma. As an important mechanism of gene regulation, investigation of DNA methylation provides a promising approach. Our objective was to identify differences in epigenome wide DNA methylation levels in bronchial biopsies between subjects with asthma remission and subjects with persistent asthma or healthy controls.We analysed differential DNA methylation in bronchial biopsies from 26 subjects with persistent asthma, 39 remission subjects and 70 healthy controls, using the limma package. The comb-p tool was used to identify differentially methylated regions. DNA methylation of CpG-sites was associated to expression of nearby genes from the same biopsies to understand function.Four CpG-sites and 42 regions were differentially methylated between persistent asthma and remission. DNA methylation at two sites was correlated i n cis with gene expression at ACKR2 and DGKQ Between remission subjects and healthy controls 1163 CpG-sites and 328 regions were differentially methylated. DNA methylation was associated with expression of a set of genes expressed in ciliated epithelium.CpGs differentially methylated between remission and persistent asthma identify genetic loci associated with resolution of inflammation and airway responsiveness. Despite the absence of symptoms, remission subjects have a DNA methylation profile that is distinct from that of healthy controls, partly due to changes in cellular composition, with a higher gene expression signal related to ciliated epithelium in remission versus healthy controls.

Development of Normal Tissue Complication Probability Model for Trismus in Head and Neck Cancer Patients Treated With Radiotherapy: The Role of Dosimetric and Clinical Factors.

BACKGROUND/AIM: The aim of this study was to develop a normal tissue complication probability (NTCP) model for trismus in head and neck cancer (HNC) patients treated with radiotherapy (RT). PATIENTS AND METHODS: Prospective measurements of maximum inter-incisal opening (MIO) were performed at baseline and 6 months after definitive RT in 132 HNC patients. The primary endpoint of this study was defined when a patient fulfilled both of the following criteria: 1) MIO at 6 months after RT ≤35 mm and 2) MIO at 6 months after RT ≤80% of baseline MIO. Eleven clinical factors and a wide range of dosimetric factors (mean dose, maximum dose, V5, V10, V20, and V40) in twelve organs at risk (OARs) were chosen as candidate prognostic variables. RESULTS: Thirty out of 132 patients (23%) developed the primary endpoint. Multivariate logistic regression analysis revealed that the mean dose to the contralateral mandible joint (p=0.001) and baseline MIO (p=0.027) were independent prognostic factors. CONCLUSION: A multivariable NTCP model for trismus in HNC patients treated with RT was established including the mean dose to contralateral mandible joint and baseline MIO.

The role of epigenetics in the development of childhood asthma.

Introduction: The development of childhood asthma is caused by a combination of genetic factors and environmental exposures. Epigenetics describes mechanisms of (heritable) regulation of gene expression that occur without changes in DNA sequence. Epigenetics is strongly related to aging, is cell-type specific, and includes DNA methylation, noncoding RNAs, and histone modifications.Areas covered: This review summarizes recent epigenetic studies of childhood asthma in humans, which mostly involve studies of DNA methylation published in the recent five years. Environmental exposures, in particular cigarette smoking, have significant impact on epigenetic changes, but few of these epigenetic signals are also associated with asthma. Several asthma-associated genetic variants relate to DNA methylation. Epigenetic signals can be better understood by studying their correlation with gene expression, which revealed higher presence and activation of blood eosinophils in asthma. Strong associations of nasal methylation signatures and atopic asthma were identified, which were replicable across different populations.Expert commentary: Epigenetic markers have been strongly associated with asthma, and might serve as biomarker of asthma. The causal and longitudinal relationships between epigenetics and disease, and between environmental exposures and epigenetic changes need to be further investigated. Efforts should be made to understand cell-type-specific epigenetic mechanisms in asthma.

Comparison of smoking-related DNA methylation between newborns from prenatal exposure and adults from personal smoking.

Aim: Cigarette smoking influences DNA methylation genome wide, in newborns from pregnancy exposure and in adults from personal smoking. Whether a unique methylation signature exists for in utero exposure in newborns is unknown. Materials & methods: We separately meta-analyzed newborn blood DNA methylation (assessed using Illumina450k Beadchip), in relation to sustained maternal smoking during pregnancy (9 cohorts, 5648 newborns, 897 exposed) and adult blood methylation and personal smoking (16 cohorts, 15907 participants, 2433 current smokers). Results & conclusion: Comparing meta-analyses, we identified numerous signatures specific to newborns along with many shared between newborns and adults. Unique smoking-associated genes in newborns were enriched in xenobiotic metabolism pathways. Our findings may provide insights into specific health impacts of prenatal exposure on offspring.

Meta-analysis of epigenome-wide association studies in neonates reveals widespread differential DNA methylation associated with birthweight.

Birthweight is associated with health outcomes across the life course, DNA methylation may be an underlying mechanism. In this meta-analysis of epigenome-wide association studies of 8,825 neonates from 24 birth cohorts in the Pregnancy And Childhood Epigenetics Consortium, we find that DNA methylation in neonatal blood is associated with birthweight at 914 sites, with a difference in birthweight ranging from -183 to 178 grams per 10% increase in methylation (PBonferroni < 1.06 x 10-7). In additional analyses in 7,278 participants, <1.3% of birthweight-associated differential methylation is also observed in childhood and adolescence, but not adulthood. Birthweight-related CpGs overlap with some Bonferroni-significant CpGs that were previously reported to be related to maternal smoking (55/914, p = 6.12 x 10-74) and BMI in pregnancy (3/914, p = 1.13x10-3), but not with those related to folate levels in pregnancy. Whether the associations that we observe are causal or explained by confounding or fetal growth influencing DNA methylation (i.e. reverse causality) requires further research.

Profiling of healthy and asthmatic airway smooth muscle cells following interleukin-1ß treatment: a novel role for CCL20 in chronic mucus hypersecretion.

Chronic mucus hypersecretion (CMH) contributes to the morbidity and mortality of asthma, and remains uncontrolled by current therapies in the subset of patients with severe, steroid-resistant disease. Altered cross-talk between airway epithelium and airway smooth muscle cells (ASMCs), driven by pro-inflammatory cytokines such as interleukin (IL)-1ß, provides a potential mechanism that influences CMH. This study investigated mechanisms underlying CMH by comparing IL-1ß-induced gene expression profiles between asthma and control-derived ASMCs and the subsequent paracrine influence on airway epithelial mucus production in vitroIL-1ß-treated ASMCs from asthmatic patients and healthy donors were profiled using microarray analysis and ELISA. Air-liquid interface (ALI)-cultured CALU-3 and primary airway epithelial cells were treated with identified candidates and mucus production assessed.The IL-1ß-induced CCL20 expression and protein release was increased in ASMCs from moderate compared with mild asthmatic patients and healthy controls. IL-1ß induced lower MIR146A expression in asthma-derived ASMCs compared with controls. Decreased MIR146A expression was validated in vivo in bronchial biopsies from 16 asthmatic patients versus 39 healthy donors. miR-146a-5p overexpression abrogated CCL20 release in ASMCs. CCL20 treatment of ALI-cultured CALU-3 and primary airway epithelial cells induced mucus production, while CCL20 levels in sputum were associated with increased levels of CMH in asthmatic patients.Elevated CCL20 production by ASMCs, possibly resulting from dysregulated expression of the anti-inflammatory miR-146a-5p, may contribute to enhanced mucus production in asthma.

The emerging landscape of dynamic DNA methylation in early childhood.

BACKGROUND: DNA methylation has been found to associate with disease, aging and environmental exposure, but it is unknown how genome, environment and disease influence DNA methylation dynamics in childhood. RESULTS: By analysing 538 paired DNA blood samples from children at birth and at 4-5 years old and 726 paired samples from children at 4 and 8 years old from four European birth cohorts using the Illumina Infinium Human Methylation 450 k chip, we have identified 14,150 consistent age-differential methylation sites (a-DMSs) at epigenome-wide significance of p < 1.14 × 10-7. Genes with an increase in age-differential methylation were enriched in pathways related to 'development', and were more often located in bivalent transcription start site (TSS) regions, which can silence or activate expression of developmental genes. Genes with a decrease in age-differential methylation were involved in cell signalling, and enriched on H3K27ac, which can predict developmental state. Maternal smoking tended to decrease methylation levels at the identified da-DMSs. We also found 101 a-DMSs (0.71%) that were regulated by genetic variants using cis-differential Methylation Quantitative Trait Locus (cis-dMeQTL) mapping. Moreover, a-DMS-associated genes during early development were significantly more likely to be linked with disease. CONCLUSION: Our study provides new insights into the dynamic epigenetic landscape of the first 8 years of life.

Genome-Wide Interaction Analysis of Air Pollution Exposure and Childhood Asthma with Functional Follow-up.

RATIONALE: The evidence supporting an association between traffic-related air pollution exposure and incident childhood asthma is inconsistent and may depend on genetic factors. OBJECTIVES: To identify gene-environment interaction effects on childhood asthma using genome-wide single-nucleotide polymorphism (SNP) data and air pollution exposure. Identified loci were further analyzed at epigenetic and transcriptomic levels. METHODS: We used land use regression models to estimate individual air pollution exposure (represented by outdoor NO2 levels) at the birth address and performed a genome-wide interaction study for doctors' diagnoses of asthma up to 8 years in three European birth cohorts (n = 1,534) with look-up for interaction in two separate North American cohorts, CHS (Children's Health Study) and CAPPS/SAGE (Canadian Asthma Primary Prevention Study/Study of Asthma, Genetics and Environment) (n = 1,602 and 186 subjects, respectively). We assessed expression quantitative trait locus effects in human lung specimens and blood, as well as associations among air pollution exposure, methylation, and transcriptomic patterns. MEASUREMENTS AND MAIN RESULTS: In the European cohorts, 186 SNPs had an interaction P < 1 × 10-4 and a look-up evaluation of these disclosed 8 SNPs in 4 loci, with an interaction P < 0.05 in the large CHS study, but not in CAPPS/SAGE. Three SNPs within adenylate cyclase 2 (ADCY2) showed the same direction of the interaction effect and were found to influence ADCY2 gene expression in peripheral blood (P = 4.50 × 10-4). One other SNP with P < 0.05 for interaction in CHS, rs686237, strongly influenced UDP-Gal:betaGlcNAc ß-1,4-galactosyltransferase, polypeptide 5 (B4GALT5) expression in lung tissue (P = 1.18 × 10-17). Air pollution exposure was associated with differential discs, large homolog 2 (DLG2) methylation and expression. CONCLUSIONS: Our results indicated that gene-environment interactions are important for asthma development and provided supportive evidence for interaction with air pollution for ADCY2, B4GALT5, and DLG2.

DNA Methylation in Newborns and Maternal Smoking in Pregnancy: Genome-wide Consortium Meta-analysis.

Epigenetic modifications, including DNA methylation, represent a potential mechanism for environmental impacts on human disease. Maternal smoking in pregnancy remains an important public health problem that impacts child health in a myriad of ways and has potential lifelong consequences. The mechanisms are largely unknown, but epigenetics most likely plays a role. We formed the Pregnancy And Childhood Epigenetics (PACE) consortium and meta-analyzed, across 13 cohorts (n = 6,685), the association between maternal smoking in pregnancy and newborn blood DNA methylation at over 450,000 CpG sites (CpGs) by using the Illumina 450K BeadChip. Over 6,000 CpGs were differentially methylated in relation to maternal smoking at genome-wide statistical significance (false discovery rate, 5%), including 2,965 CpGs corresponding to 2,017 genes not previously related to smoking and methylation in either newborns or adults. Several genes are relevant to diseases that can be caused by maternal smoking (e.g., orofacial clefts and asthma) or adult smoking (e.g., certain cancers). A number of differentially methylated CpGs were associated with gene expression. We observed enrichment in pathways and processes critical to development. In older children (5 cohorts, n = 3,187), 100% of CpGs gave at least nominal levels of significance, far more than expected by chance (p value < 2.2 × 10(-16)). Results were robust to different normalization methods used across studies and cell type adjustment. In this large scale meta-analysis of methylation data, we identified numerous loci involved in response to maternal smoking in pregnancy with persistence into later childhood and provide insights into mechanisms underlying effects of this important exposure.

Statistical validation of normal tissue complication probability models.

PURPOSE: To investigate the applicability and value of double cross-validation and permutation tests as established statistical approaches in the validation of normal tissue complication probability (NTCP) models. METHODS AND MATERIALS: A penalized regression method, LASSO (least absolute shrinkage and selection operator), was used to build NTCP models for xerostomia after radiation therapy treatment of head-and-neck cancer. Model assessment was based on the likelihood function and the area under the receiver operating characteristic curve. RESULTS: Repeated double cross-validation showed the uncertainty and instability of the NTCP models and indicated that the statistical significance of model performance can be obtained by permutation testing. CONCLUSION: Repeated double cross-validation and permutation tests are recommended to validate NTCP models before clinical use.

Multivariate modeling of complications with data driven variable selection: guarding against overfitting and effects of data set size.

PURPOSE: Multivariate modeling of complications after radiotherapy is frequently used in conjunction with data driven variable selection. This study quantifies the risk of overfitting in a data driven modeling method using bootstrapping for data with typical clinical characteristics, and estimates the minimum amount of data needed to obtain models with relatively high predictive power. MATERIALS AND METHODS: To facilitate repeated modeling and cross-validation with independent datasets for the assessment of true predictive power, a method was developed to generate simulated data with statistical properties similar to real clinical data sets. Characteristics of three clinical data sets from radiotherapy treatment of head and neck cancer patients were used to simulate data with set sizes between 50 and 1000 patients. A logistic regression method using bootstrapping and forward variable selection was used for complication modeling, resulting for each simulated data set in a selected number of variables and an estimated predictive power. The true optimal number of variables and true predictive power were calculated using cross-validation with very large independent data sets. RESULTS: For all simulated data set sizes the number of variables selected by the bootstrapping method was on average close to the true optimal number of variables, but showed considerable spread. Bootstrapping is more accurate in selecting the optimal number of variables than the AIC and BIC alternatives, but this did not translate into a significant difference of the true predictive power. The true predictive power asymptotically converged toward a maximum predictive power for large data sets, and the estimated predictive power converged toward the true predictive power. More than half of the potential predictive power is gained after approximately 200 samples. Our simulations demonstrated severe overfitting (a predicative power lower than that of predicting 50% probability) in a number of small data sets, in particular in data sets with a low number of events (median: 7, 95th percentile: 32). Recognizing overfitting from an inverted sign of the estimated model coefficients has a limited discriminative value. CONCLUSIONS: Despite considerable spread around the optimal number of selected variables, the bootstrapping method is efficient and accurate for sufficiently large data sets, and guards against overfitting for all simulated cases with the exception of some data sets with a particularly low number of events. An appropriate minimum data set size to obtain a model with high predictive power is approximately 200 patients and more than 32 events. With fewer data samples the true predictive power decreases rapidly, and for larger data set sizes the benefit levels off toward an asymptotic maximum predictive power.