Anna C. Reisetter

Maternal short-chain fatty acids are associated with metabolic parameters in mothers and newborns

During the course of pregnancy, dynamic remodeling of the gut microbiota occurs and contributes to maternal metabolic changes through an undefined mechanism. Because short chain fatty acids (SCFAs) are a major product of gut microbiome fermentation, we investigated whether serum SCFA levels during pregnancy are related to key metabolic parameters in mothers and newborns. In this prospective study, 20 pregnant women without gestational diabetes were evaluated at 36-38 weeks of gestation, and their newborns were assessed after parturition. In this cohort, which included normal (n = 10) and obese (n = 10) subjects based on prepregnancy body mass index, serum levels of SCFAs (acetate, propionate, and butyrate), maternal adipokines, maternal glucose, and C-peptide were measured at 36-38 weeks of gestation. Maternal weight gain and newborn anthropometrics were also determined. Data were analyzed using linear regression to test for associations, adjusting for prepregnancy obesity. In this cohort, serum acetate levels were associated with maternal weight gain and maternal adiponectin levels. In addition, serum propionate correlated negatively with maternal leptin levels, newborn length, and body weight. Taken together, this study observed that novel relationships exist among maternal SCFA levels and multiple interrelated maternal/newborn metabolic parameters.

Maternal BMI Associations with Maternal and Cord Blood Vitamin D Levels in a North American Subset of Hyperglycemia and Adverse Pregnancy Outcome (HAPO) Study Participants

Objective Obesity in pregnancy may be associated with reduced placental transfer of 25-hydroxyvitamin D (25-OHD). The objective of this study was to examine associations between maternal BMI and maternal and cord blood levels of 25-OHD in full term neonates born to a single racial cohort residing at similar latitude. Secondary objectives were to examine associations between maternal glucose tolerance with maternal levels of 25-OHD and the relationship between cord blood 25-OHD levels and neonatal size. Methods This study was conducted among participants of the Hyperglycemia and Adverse Pregnancy Outcomes (HAPO) Study meeting the following criteria: residing at latitudes 41–43°, maternal white race, and gestational age 39–41 weeks. Healthy pregnant women underwent measures of height, weight, and a 75-g fasting oral glucose tolerance test (OGTT) at approximately 28 weeks gestation. Maternal and cord blood sera were analyzed for total 25-OHD by HPLC tandem mass spectrometry. Statistical analyses included ANOVA and linear regression models. Results Maternal and cord blood (N = 360) mean levels (sd) of 25-OHD were 37.2 (11.2) and 23.4 (9.2) ng/ml, respectively, and these levels were significantly different among the 3 field centers (ANOVA p< 0.001). Maternal serum 25-OHD was lower by 0.40 ng/ml for BMI higher by 1 kg/m2 (p<0.001) in an adjusted model. Maternal fasting plasma glucose, insulin sensitivity, and presence of GDM were not associated with maternal serum 25-OHD level when adjusted for maternal BMI. Cord blood 25-OHD was lower by 0.26 ng/ml for maternal BMI higher by 1 kg/m2 (p<0.004). With adjustment for maternal age, field center, birth season and maternal serum 25-OHD, the association of cord blood 25-OHD with maternal BMI was attenuated. Neither birth weight nor neonatal adiposity was significantly associated with cord blood 25-OHD levels. Conclusion These results suggest that maternal levels of 25-OHD are associated with maternal BMI. The results also suggest that interpretation of neonatal 25-OHD levels may need to incorporate specific maternal factors in addition to season of birth and latitude.

Maternal BMI and glycemia impact the fetal metabolome

OBJECTIVE We used targeted metabolomics to determine associations of maternal BMI and glucose levels with cord blood metabolites and associations of cord blood metabolites with newborn birth weight and adiposity in mother-offspring dyads. RESEARCH DESIGN AND METHODS Targeted metabolomic assays were performed on cord blood plasma samples from European ancestry, Afro-Caribbean, Thai, and Mexican American newborns (400 from each ancestry group) whose mothers participated in the Hyperglycemia and Adverse Pregnancy Outcome (HAPO) Study and who had anthropometric measurements at birth. RESULTS Meta-analysis across the four cohorts demonstrated significant correlation of all cord blood metabolites analyzed with maternal fasting levels of the same metabolites at ∼28 weeks’ gestation except for triglycerides, asparagine/aspartate, arginine, and the acylcarnitine C14-OH/C12-DC. Meta-analyses also demonstrated that maternal BMI with or without adjustment for maternal glucose was associated with cord blood metabolites including the branched-chain amino acids and their metabolites as well as phenylalanine. One-hour but not fasting glucose was associated with cord blood 3-hydroxybutyrate and its carnitine ester, a medium-chain acylcarnitine, and glycerol. A number of cord blood metabolites were associated with newborn birth weight and sum of skinfolds, including a negative association of triglycerides and positive association of 3-hydroxybutyrate, its carnitine ester, and serine with both newborn outcomes. CONCLUSIONS Maternal BMI and glycemia are associated with different components of the newborn metabolome, consistent with their independent effects on newborn size at birth. Maternal BMI is associated with a newborn metabolic signature characteristic of insulin resistance and risk of type 2 diabetes in adults.

Genetic Risk Score for Prediction of Newborn Adiposity and Large-for-Gestational-Age Birth

CONTEXT Macrosomic infants are at increased risk for adverse metabolic outcomes. Improving prediction of large-for-gestational-age (LGA) birth may help prevent these outcomes. OBJECTIVE This study sought to determine whether genes associated with obesity-related traits in adults are associated with newborn size, and whether a genetic risk score (GRS) predicts LGA birth. SETTING AND DESIGN Single nucleotide polymorphisms (SNPs) in 40 regions associated with adult obesity-related traits were tested for association with newborn size. GRSs for birth weight and sum of skinfolds (SSF) specific to ancestry were calculated using the most highly associated SNP for each ancestry in genomic regions with one or more SNPs associated with birth weight and/or SSF in at least one ancestry group or meta-analyses. PARTICIPANTS Newborns from the Hyperglycemia Adverse Pregnancy Outcomes Study were studied (942 Afro-Caribbean, 1294 Northern European, 573 Mexican-American, and 1182 Thai). OUTCOME MEASURES Birth weight >90th percentile (LGA) and newborn SSF >90th percentile were primary outcomes. RESULTS After adjustment for ancestry, sex, gestational age at delivery, parity, maternal genotype, maternal smoking/alcohol intake, age, body mass index, height, blood pressure and glucose, 25 and 23 SNPs were associated (P < .001) with birth weight and newborn SSF, respectively. The GRS was highly associated with both phenotypes as continuous variables across all ancestries (P ≤ 1.6 × 10(-19)) and improved prediction of birth weight and SSF >90th percentile when added to a baseline model incorporating the covariates listed above. CONCLUSIONS A GRS comprised of SNPs associated with adult obesity-related traits may provide an approach for predicting LGA birth and newborn adiposity beyond established risk factors.

Metabomxtr: an R package for mixture-model analysis of non-targeted metabolomics data

Summary: Non-targeted metabolomics technologies often yield data in which abundance for any given metabolite is observed and quantified for some samples and reported as missing for other samples. Apparent missingness can be due to true absence of the metabolite in the sample or presence at a level below detectability. Mixture-model analysis can formally account for metabolite ‘missingness’ due to absence or undetectability, but software for this type of analysis in the high-throughput setting is limited. The R package metabomxtr has been developed to facilitate mixture-model analysis of non-targeted metabolomics data in which only a portion of samples have quantifiable abundance for certain metabolites. Availability and implementation: metabomxtr is available through Bioconductor. It is released under the GPL-2 license. Contact: dscholtens@northwestern.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Mixture model normalization for non-targeted gas chromatography/mass spectrometry metabolomics data

BackgroundMetabolomics offers a unique integrative perspective for health research, reflecting genetic and environmental contributions to disease-related phenotypes. Identifying robust associations in population-based or large-scale clinical studies demands large numbers of subjects and therefore sample batching for gas-chromatography/mass spectrometry (GC/MS) non-targeted assays. When run over weeks or months, technical noise due to batch and run-order threatens data interpretability. Application of existing normalization methods to metabolomics is challenged by unsatisfied modeling assumptions and, notably, failure to address batch-specific truncation of low abundance compounds.ResultsTo curtail technical noise and make GC/MS metabolomics data amenable to analyses describing biologically relevant variability, we propose mixture model normalization (mixnorm) that accommodates truncated data and estimates per-metabolite batch and run-order effects using quality control samples. Mixnorm outperforms other approaches across many metrics, including improved correlation of non-targeted and targeted measurements and superior performance when metabolite detectability varies according to batch. For some metrics, particularly when truncation is less frequent for a metabolite, mean centering and median scaling demonstrate comparable performance to mixnorm.ConclusionsWhen quality control samples are systematically included in batches, mixnorm is uniquely suited to normalizing non-targeted GC/MS metabolomics data due to explicit accommodation of batch effects, run order and varying thresholds of detectability. Especially in large-scale studies, normalization is crucial for drawing accurate conclusions from non-targeted GC/MS metabolomics data.

Targeted Metabolomics Demonstrates Distinct and Overlapping Maternal Metabolites Associated With BMI, Glucose, and Insulin Sensitivity During Pregnancy Across Four Ancestry Groups

OBJECTIVE We used targeted metabolomics in pregnant mothers to compare maternal metabolite associations with maternal BMI, glycemia, and insulin sensitivity. RESEARCH DESIGN AND METHODS Targeted metabolomic assays of clinical metabolites, amino acids, and acylcarnitines were performed on fasting and 1-h postglucose serum samples from European ancestry, Afro-Caribbean, Thai, and Mexican American mothers (400 from each ancestry group) who participated in the Hyperglycemia and Adverse Pregnancy Outcome (HAPO) Study and underwent an oral glucose tolerance test at ∼28 weeks gestation. RESULTS K-means clustering, which identified patterns of metabolite levels across ancestry groups, demonstrated that, at both fasting and 1-h, levels of the majority of metabolites were similar across ancestry groups. Meta-analyses demonstrated association of a broad array of fasting and 1-h metabolites, including lipids and amino acids and their metabolites, with maternal BMI, glucose levels, and insulin sensitivity before and after adjustment for the different phenotypes. At fasting and 1 h, a mix of metabolites was identified that were common across phenotypes or associated with only one or two phenotypes. Partial correlation estimates, which allowed comparison of the strength of association of different metabolites with maternal phenotypes, demonstrated that metabolites most strongly associated with different phenotypes included some that were common across as well as unique to each phenotype. CONCLUSIONS Maternal BMI and glycemia have metabolic signatures that are both shared and unique to each phenotype. These signatures largely remain consistent across different ancestry groups and may contribute to the common and independent effects of these two phenotypes on adverse pregnancy outcomes.

Erratum. Maternal BMI and Glycemia Impact the Fetal Metabolome. Diabetes Care 2017;40:902–910

In the article cited above, the authors have requested that three sentences be corrected: 1. In the research design and methods section of the abstract, the sentence was corrected to read: “Targeted metabolic assays were performed on cord blood plasma samples from European ancestry, Afro-Caribbean, Thai, and …

The chromosome 3q25 locus associated with fetal adiposity is not associated with childhood adiposity

Increased newborn adiposity is associated with later adverse metabolic outcomes. Previous genome-wide association studies (GWAS) demonstrated strong association of a locus on chromosome 3 (3q25.31) with newborn sum of skinfolds, a measure of overall adiposity. Whether this locus is associated with childhood adiposity is unknown. Genotype and sum of skinfolds data were available for 293 children at birth and age 2, and for 350 children at birth and age 6 from a European cohort (Belfast, UK) who participated in the Hyperglycemia and Adverse Pregnancy Outcome GWAS. We examined single nucleotide polymorphisms (SNPs) at the 3q25.31 locus associated with newborn adiposity. Linear regression analyses under an additive genetic model adjusting for maternal body mass index were performed. In both cohorts, a positive association was observed between all SNPs and sum of skinfolds at birth (P=2.3 × 10−4, β=0.026 and P=4.8 × 10−4, β=0.025). At the age of 2 years, a non-significant negative association was observed with sum of skinfolds (P=0.06; β =−0.015). At the age of 6 years, there was no evidence of association (P=0.86; β=0.002). The 3q25.31 locus strongly associated with newborn adiposity had no significant association with childhood adiposity suggesting that its impact may largely be limited to fetal fat accretion.