Folate, but not vitamin B-12 status, predicts respiratory morbidity in north Indian children

Background: Vitamin deficiencies are often part of malnutrition, which predisposes to acute lower respiratory tract infections. Objective: The objective was to measure the association between cobalamin and folate status and subsequent respiratory morbidity. Design: A prospective cohort study was conducted in 2482 children aged 6-30 mo nested in a zinc supplementation trial. We measured plasma concentrations of folate, cobalamin, methylmalonic acid, and total homocysteine (tHcy) and followed the children for 4 mo. Results: We observed 1176 episodes of acute lower respiratory tract infections. Children with folate concentrations in the lowest quartile (interquartile range: 6.4-20.0 nmol/L) had a 44% higher incidence [adjusted incidence rate ratio (IRR): 1.44; 95% CI: 1.23, 1.70] of acute lower respiratory tract infections than did children in the other 3 quartiles. For tHcy, the IRR was 1.24 (1.07, 1.40) in a comparison of those in the highest quartile with those in the other quartiles. Breastfeeding was associated with high folate concentrations and protection against subsequent respiratory tract infections. This protection was significantly and substantially reduced after adjustment for plasma folate concentrations at baseline. Compared with the children in the other 3 quartiles, the IRR for being in the lowest quartile of cobalamin was 1.13 (0.76, 1.03) and for being in the highest quartile of methylmalonic acid was 1.12 (0.96, 1.31). Conclusions: Poor folate status appears to be an independent risk factor for lower respiratory tract infections in young children. This study also suggests that the protective effect of breastfeeding is partly mediated by folate provided through breast milk

An EPIC predictor of gestational age and its application to newborns conceived by assisted reproductive technologies

Background Gestational age is a useful proxy for assessing developmental maturity, but correct estimation of gestational age is difficult using clinical measures. DNA methylation at birth has proven to be an accurate predictor of gestational age. Previous predictors of epigenetic gestational age were based on DNA methylation data from the Illumina HumanMethylation 27 K or 450 K array, which have subsequently been replaced by the Illumina MethylationEPIC 850 K array (EPIC). Our aims here were to build an epigenetic gestational age clock specific for the EPIC array and to evaluate its precision and accuracy using the embryo transfer date of newborns from the largest EPIC-derived dataset to date on assisted reproductive technologies (ART). Methods We built an epigenetic gestational age clock using Lasso regression trained on 755 randomly selected non-ART newborns from the Norwegian Study of Assisted Reproductive Technologies (START)-a substudy of the Norwegian Mother, Father, and Child Cohort Study (MoBa). For the ART-conceived newborns, the START dataset had detailed information on the embryo transfer date and the specific ART procedure used for conception. The predicted gestational age was compared to clinically estimated gestational age in 200 non-ART and 838 ART newborns using MM-type robust regression. The performance of the clock was compared to previously published gestational age clocks in an independent replication sample of 148 newborns from the Prediction and Prevention of Preeclampsia and Intrauterine Growth Restrictions (PREDO) study-a prospective pregnancy cohort of Finnish women. Results Our new epigenetic gestational age clock showed higher precision and accuracy in predicting gestational age than previous gestational age clocks (R-2 = 0.724, median absolute deviation (MAD) = 3.14 days). Restricting the analysis to CpGs shared between 450 K and EPIC did not reduce the precision of the clock. Furthermore, validating the clock on ART newborns with known embryo transfer date confirmed that DNA methylation is an accurate predictor of gestational age (R-2 = 0.767, MAD = 3.7 days). Conclusions We present the first EPIC-based predictor of gestational age and demonstrate its robustness and precision in ART and non-ART newborns. As more datasets are being generated on the EPIC platform, this clock will be valuable in studies using gestational age to assess neonatal development.Peer reviewe

Blood-based epigenetic estimators of chronological age in human adults using DNA methylation data from the Illumina MethylationEPIC array

BackgroundEpigenetic clocks have been recognized for their precise prediction of chronological age, age-related diseases, and all-cause mortality. Existing epigenetic clocks are based on CpGs from the Illumina HumanMethylation450 BeadChip (450K) which has now been replaced by the latest platform, Illumina MethylationEPIC BeadChip (EPIC). Thus, it remains unclear to what extent EPIC contributes to increased precision and accuracy in the prediction of chronological age.ResultsWe developed three blood-based epigenetic clocks for human adults using EPIC-based DNA methylation (DNAm) data from the Norwegian Mother, Father and Child Cohort Study (MoBa) and the Gene Expression Omnibus (GEO) public repository: 1) an Adult Blood-based EPIC Clock (ABEC) trained on DNAm data from MoBa (n=1592, age-span: 19 to 59years), 2) an extended ABEC (eABEC) trained on DNAm data from MoBa and GEO (n=2227, age-span: 18 to 88years), and 3) a common ABEC (cABEC) trained on the same training set as eABEC but restricted to CpGs common to 450K and EPIC. Our clocks showed high precision (Pearson correlation between chronological and epigenetic age (r)>0.94) in independent cohorts, including GSE111165 (n=15), GSE115278 (n=108), GSE132203 (n=795), and the Epigenetics in Pregnancy (EPIPREG) study of the STORK Groruddalen Cohort (n=470). This high precision is unlikely due to the use of EPIC, but rather due to the large sample size of the training set.ConclusionsOur ABECs predicted adults' chronological age precisely in independent cohorts. As EPIC is now the dominant platform for measuring DNAm, these clocks will be useful in further predictions of chronological age, age-related diseases, and mortality.Peer reviewe

The association of gastric cancer risk with plasma folate, cobalamin, and Methylenetetrahydrofolate reductase polymorphisms in the European prospective investigation into cancer and nutrition

Previous studies have shown inconsistent associations of folate intake and polymorphisms of the methylenetetrahydrofolate reductase (MTHFR) gene with gastric cancer risk. Our nested case-control study within the European Prospective Investigation into Cancer and Nutrition cohort is the first prospective study of blood folate levels and gastric cancer. Gastric cancer cases (n = 247) and controls (n = 631) were matched for study center, age, sex, and time of blood donation. Two common single nucleotide polymorphisms of the MTHFR gene were determined, as were plasma concentrations of folate, cobalamin (vitamin B12), total homocysteine, and methylmalonic acid (cobalamin deficiency marker) in prediagnostic plasma. Risk measures were calculated with conditional logistic regression. Although no relations were observed between plasma folate or total homocysteine concentrations and gastric cancer, we observed a trend toward lower risk of gastric cancer with increasing cobalamin concentrations (odds ratio, 0.79 per SD increase in cobalamin; P = 0.01). Further analyses showed that the inverse association between cobalamin and gastric cancer was confined to cancer cases with low pepsinogen A levels (marker of severe chronic atrophic gastritis) at the time of blood sampling. The 677 C -> T MTHFR polymorphism was not associated with gastric cancer, but we observed an increased risk with the variant genotype of the 1298 A -> C polymorphism (odds ratio, 1.47 for CC versus AA; P = 0.04). In conclusion, we found no evidence of a role of folate in gastric cancer etiology. However, we observed increased gastric cancer risk at low cobalamin levels that was most likely due to compromised cobalamin status in atrophic gastritis preceding gastric cancer

The association between lower educational attainment and depression owing to shared genetic effects? Results in ∼25 000 subjects: Results in ~25,000 subjects

An association between lower educational attainment (EA) and an increased risk for depression has been confirmed in various western countries. This study examines whether pleiotropic genetic effects contribute to this association. Therefore, data were analyzed from a total of 9662 major depressive disorder (MDD) cases and 14 949 controls (with no lifetime MDD diagnosis) from the Psychiatric Genomics Consortium with additional Dutch and Estonian data. The association of EA and MDD was assessed with logistic regression in 15 138 individuals indicating a significantly negative association in our sample with an odds ratio for MDD 0.78 (0.75-0.82) per standard deviation increase in EA. With data of 884 105 autosomal common single-nucleotide polymorphisms (SNPs), three methods were applied to test for pleiotropy between MDD and EA: (i) genetic profile risk scores (GPRS) derived from training data for EA (independent meta-analysis on ∼120 000 subjects) and MDD (using a 10-fold leave-one-out procedure in the current sample), (ii) bivariate genomic-relationship-matrix restricted maximum likelihood (GREML) and (iii) SNP effect concordance analysis (SECA). With these methods, we found (i) that the EA-GPRS did not predict MDD status, and MDD-GPRS did not predict EA, (ii) a weak negative genetic correlation with bivariate GREML analyses, but this correlation was not consistently significant, (iii) no evidence for concordance of MDD and EA SNP effects with SECA analysis. To conclude, our study confirms an association of lower EA and MDD risk, but this association was not because of measurable pleiotropic genetic effects, which suggests that environmental factors could be involved, for example, socioeconomic status