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Connections Between the Gut Microbiome and Metabolic Hormones in Early Pregnancy in Overweight and Obese Women

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Connections Between the Gut Microbiome and Metabolic Hormones in Early Pregnancy in Overweight and Obese Women 2214 Diabetes Volume 65, August 2016 Luisa F. Gomez-Arango,1,2 Helen L. Barrett,1,2,3 H. David McIntyre,1,4 Leonie K. Callaway,1,2,3 Mark Morrison,5 and Marloes Dekker Nitert,1,2 for the SPRING Trial Group Connections Between the Gut Microbiome and Metabolic Hormones in Early Pregnancy in Overweight and Obese Women Diabetes 2016;65:2214–2223 | DOI: 10.2337/db16-0278 Overweight and obese women are at a higher risk for concern and a major challenge for obstetrics practice. In gestational diabetes mellitus. The gut microbiome could early pregnancy, overweight and obese women are at an modulate metabolic health and may affect insulin resis- increased risk of metabolic complications that affect placen- tance and lipid metabolism. The aim of this study was to tal and embryonic development (1). Metabolic adjustments, reveal relationships between gut microbiome composition such as a decline in insulin sensitivity and an increase in and circulating metabolic hormones in overweight and nutrient absorption, are necessary to support a healthy ’ obese pregnant women at 16 weeks gestation. Fecal pregnancy (2,3); however, these metabolic changes occur fi microbiota pro les from overweight (n =29)andobese on top of preexisting higher levels of insulin resistance (n = 41) pregnant women were assessed by 16S rRNA in overweight and obese pregnant women, increasing the sequencing. Fasting metabolic hormone (insulin, C-peptide, risk of overt hyperglycemia because of a lack of sufficient glucagon, incretin, and adipokine) concentrations were insulin secretion to compensate for the increased insulin measured using multiplex ELISA. Metabolic hormone lev- METABOLISM resistance (3). elsaswellasmicrobiomeprofiles differed between over- weight and obese women. Furthermore, changes in some The potential role of the gut microbiome (the com- metabolic hormone levels were correlated with alterations posite of the bacteria present in the gastrointestinal tract) in the relative abundance of specific microbes. Adipokine in pregnancy has become the subject of considerable levels were strongly correlated with Ruminococcaceae interest. In normal pregnancy, the maternal gut microbiota fi and Lachnospiraceae, which are dominant families in en- changes from rst to third trimester with a decline in ergy metabolism. Insulin was positively correlated with the butyrate-producing bacteria and an increase in Bifidobacteria, genus Collinsella. Gastrointestinal polypeptide was posi- Proteobacteria, and lactic acid–producing bacteria. In- tively correlated with the genus Coprococcus but nega- flammationandweightgainthatoccursduringpreg- tively with family Ruminococcaceae. This study shows nancy might be the result of microbe-driven processes novel relationships between gut microbiome composition to increase energy supply for the fetus (4). These alter- and the metabolic hormonal environment in overweight ations might also be linked with the maternal metabolic and obese pregnant women at 16 weeks’ gestation. These profile and thereby contribute to the development of results suggest that manipulation of the gut microbiome pregnancy complications (5,6) as well as affect the meta- composition may influence pregnancy metabolism. bolic and immunological health of the offspring (7). In summation, modifications in the metabolic hormone The increasing prevalence of maternal obesity and its milieu during gestation are proposed to be linked with subsequent health outcomes are a significant public health changes in the maternal microbiota; however, no studies 1School of Medicine, The University of Queensland, Brisbane, Queensland, Australia Received 28 February 2016 and accepted 11 May 2016. 2 The University of Queensland Centre for Clinical Research, Brisbane, Queensland, Clinical trial reg. no. ANZCTR12611001208998, www.anzctr.org.au. Australia This article contains Supplementary Data online at http://diabetes 3Obstetric Medicine, Royal Brisbane and Women’s Hospital, Brisbane, Queensland, .diabetesjournals.org/lookup/suppl/doi:10.2337/db16-0278/-/DC1. Australia 4Mater Research, The University of Queensland, Brisbane, Queensland, Australia © 2016 by the American Diabetes Association. Readers may use this article as 5Faculty of Medicine and Biomedical Sciences, The University of Queensland long as the work is properly cited, the use is educational and not for profit, and Diamantina Institute, Brisbane, Queensland, Australia the work is not altered. Corresponding author: Marloes Dekker Nitert, [email protected]. diabetes.diabetesjournals.org Gomez-Arango and Associates 2215 have reported a possible maternal hormonal-microbial in- with a NanoDrop ND-1000 spectrophotometer (NanoDrop teraction to date. Technologies). The aim of this study was to evaluate the relationships between gut microbiome composition and the metabolic Microbiome Profiling hormonal milieu in overweight and obese pregnant women Fecal microbiome profiles were assessed by 16S rRNA in early gestation (,16 weeks). To address this relation- gene amplicon sequencing by using the Illumina MiSeq ship, fecal microbiota profiles as assessed by 16S rRNA system at The University of Queensland Australian Centre gene amplicon sequencing were correlated with the serum for Ecogenomics. Bacterial 16S rRNA gene sequences concentrations of nine metabolic hormones involved in covering variable regions (V6–V8) were PCR amplified glucose and energy metabolism. from purified genomic DNA by using the primers 926F (59-TCG TCG GCA GCG TCA GAT GTG TAT AAG AGA RESEARCH DESIGN AND METHODS CAG AAA CTY AAA KGA ATT GRC GG-39) and 1392R Study Population and Sample Collection (59-GTC TCG TGG GCT CGG AGA TGT GTA TAA GAG This study included a subset of 29 overweight and 41 obese ACA GAC GGG CGG TGW GTR C-39) with overhang pregnant women from the SPRING (Study of Probiotics in adapters, generating a 500-base pair product. In each the Prevention of Gestational Diabetes Mellitus) cohort (8). PCR run, a positive control (Escherichia coli JM109) and Women with known preexisting diabetes, impaired fasting negative control (sterile deionized water) were added. The glucose, or impaired glucose tolerance and/or early-onset V6–V8 PCR amplicons were cleaned using AMPure XP gestational diabetes mellitus (GDM) were excluded. The beads to remove unbound primers, primer dimer species, women in this subset were selected based on completion and nucleotides. Thirty-two self-correction bar codes were of the pregnancy with two stool samples collected. Of the added to the primers using Nextera XT Index Kit followed women meeting these criteria, all with GDM at 28 weeks’ by a second PCR cleanup step. Library quantification, nor- gestation were included and those with normoglycemia malization, and pooling were performed according to the were matched for BMI, maternal age, and ethnicity to en- manufacturer’s instructions. compass a broad spectrum of metabolic health. Clinical Data files were processed using QIIME (Quantitative characteristics and biological samples at ,16 weeks’ gestation Insights Into Microbial Ecology) version 1.9.1 software are summarized in Table 1. Fasting blood glucose, HbA1c, (www.qiime.org). Forward and reverse sequence reads for triglyceride, total cholesterol, HDL, LDL, and VLDL levels each individual sample were demultiplexed, quality filtered, were analyzed immediately after collection, with further and joined (10). Sequences were assigned to operational serum aliquots stored at 280°C. HOMA of insulin resis- taxonomic units (OTUs) with a pairwise identity threshold tance (HOMA-IR) was calculated for each participant. of 97% by using the GG (Greengenes) reference database Refrigerated fecal samples were self-collected by each par- (11). Sequences were mapped, yielding an OTU table, and ticipant at home and stored within 1 day after collection a phylogenetic tree was constructed using FastTree (12), at 280°C until DNA extraction. which was then used to generate unweighted and weighted UniFrac distance metrics. Any OTUs with overall relative Hormone Measurements abundance ,0.0001 were excluded from further analysis. Stored serum samples were used to measure insulin, A total of 2,086,048 reads representing 787 OTUs were C-peptide, glucagon, gastrointestinal polypeptide (GIP), retained (median 26,049 reads/sample, range 3,180– GLP-1, ghrelin, leptin, resistin, and visfatin by using the 88,127). To standardize sequence reads across samples, Bio-Plex Pro human diabetes immunoassay (Bio-Rad data were randomly rarefied to 3,000 sequences per sample. Laboratories, Hercules, CA) at room temperature accord- The relative abundances of OTUs were summarized across ing to the manufacturer’s instructions. Serum samples various taxonomic levels using default settings in QIIME. were allowed to thaw overnight at 4°C. Fifty microliters of undiluted serum were loaded in each well in dupli- Statistical Analysis cate. Standard curves were generated for all hormones. Pregnant women were classified according to BMI cutoff Hormone concentrations were collected and analyzed by values of 25.0–30.0 kg/m2 (overweight) and .30.0 kg/m2 using a Bio-Rad Bio-Plex 200 instrument equipped with (obese). Normally distributed variables are reported as Bio-Plex Manager 6.1 software (Bio-Rad Laboratories). mean 6 SEM; otherwise, median with interquartile range is reported (Table 1). Comparisons between the two BMI Fecal DNA Extraction categories were performed with the Mann-Whitney U test. The repeated bead beating and column method (9) fol- Correlations between hormonal and anthropometric var- lowed by
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