ADA 2013: New Techniques in Obesity and Pregnancy


New findings from high-throuput techniques looking at the effects of obesity on pregnancy and infants:

Metabolomics and Diabetes in Pregnancy

William L. Lowe, Jr., MD

  • Discussing initial use of metabolomics technology to asses the metabolic profile of mothers in pregnancy and the babies as well
  • A trans-generational cycle of diabetes and obesity
  • Mothers who are obese or have diabetes are at risk for having babies that are large for their gestational age, and that have a greater chance of being obese in adulthood
  • How can we break the cycle?
  • Do break the cycle, we need to understand the interuterine environment and how it impacts fetal health. We will need to look at all the *omes– genome, transcriptome, epigenome, proteome, metabolome, microbiome, etc.
  • This is initial work with the metabolome
  • The model: mothers with decreased insulin output or increased insulin resistance (IR) have increased plasma glucose and other metabolites, which crosses the placenta and induces insulin production in the infant, resulting in macrosomia. And early obesity increases the likelihood of later obesity as well.
  • What is metabolomics? The metabolome is the collection of all metabolites (products of metabolism) in fluids, cells, tissues, or organs.
  • The metabolome is generated by cellular/bio processes in the body, proteins being made and broken down, plus diet and environmental input.
  • Using gas chromatography/mass spectrometry (mass spec) to quantify metabolites of interest.
    • Targeted, in which you look at a defined panel of metabolites
    • More high-throughput: non-targeted. Fragment into peptides, ionize, throw at a wall. Measure the time the peptides take to hit the wall. Calculate mass. Algorithmically determine likely components. Still the Wild West in terms of statistics and analysis, IMO.
  • Hyperglycemia and Adverse Pregnancy Outcomes (HAPO) study
    • 25K mothers, demographically diverse, well-phenotyped
    • Fasting glucose > 105 or 2hr post-meal > 200 mg/dL
    • Take samples from maternal blood, cord blood, cord c-peptide
  • Checking c-pep in umbilical cord glucose shows that increasing maternal glucose increases the amount of c-peptide (== insulin production) visible in the cord blood.
  • The mothers were IR, modest hyperglycemia. Higher levels of triglycerides and many other metabolites (lactate, beta hydroxy butyrate, amino acids, alanine, branch chain amino acids)
  • Branch chain amino acids (BCAA) making news lately as a biomarker for t2d
  • Alanine is a substrate for gluconeogensis
  • Suggests that IR in pregnancy and in non-pregnant IR populations are similar in terms of metabolomics
  • What about the other metabolites?
  • First a warning: mass spec is still a work in progress. Post-translational modifications, algorithmic challenges, missing data for low-level metabolites. Using a mixed model approach
  • Of 309 compounds identified, 130 were detectable in at least half of the samples from both high-glucose and control mothers
  • Non-targeted mass spec confirms findings in targeted (positive control)
  • New findings? Many possible. One story here.
  • One metabolite was lower– piqued interest. 1,5-AG anhydroglucitol.
  • 1,5-AG is normally ingested, not metabolized.
  • 1,5-AG has been suggested as a biomarker for t2d. Recovered from urine by glucose uptake transporters normally. In hyperglycemia, too much glucose, all begins to pass into the urine. Blood levels of 1,5-AG go down immediately.
  • Triglycerides correlated with high BG. Some of the BCAA, too, but less so.
  • In conclusion, metabolomics is able to uncover broad scale dysregulations in hyperglycemia during pregnancy (that is, it’s not just sugar). Gives us new biomarkers, and common mediators of fetal health.
  • Q: How old were the samples? A: 5 or 6 years; frozen at -80C. Could affect detection of certain metabolites.
  • Q: What about the fetus? A: We don’t have enough data yet. We see correlations, and the next study will give us more information. Hoping to be able to say more in the next year.

The Changing Maternal Microbiome and Its Impact on Obesity in the Next Generation

Dominick J. Lemas, PhD

  • Does the maternal microbiome affect infant obesity?
  • 400 yrs ago in Europe, a Dutch merchant used a microscope to look at “small animals,” which he called animacules. Now we use high-throughput sequencing to look at the whole microbiome of a sample
  • Start with DNA samples from an organism. Fragment, sequence, map/assemble. What bacterial species contributed to the DNA sample?
  • Microbe: a tiny living organism such as bacteria, fungus, protozoan, or virus
  • Microbiome: hot topic. Collectively, the microbes in a community, such as in the human body.
  • Fun human microbiome stats
    • In a human, 9 out of 10 cells are microbial
    • The microbiome inside of/on a person encompasses about 150x the genes as the human genome
    • ~3lbs of microbes in the human gut
  • Why do we care?
    • Stimulate the immune system
    • Important for pathogen resistance
    • Many effects on metabolism
  • There is lots of variation among human microbiomes.
    • Different human populations and geographies have different mocriobial populations
    • Different parts of the body have different distributions of microbial populations
  • Lots of things affect the microbiome. Diet, environment, illness, etc.
  • The infant microbiome
    • Infants are born “sterile,” without a microbiome.
    • Does the acquisition of certain microbiomes predispose infants to obesity?
    • Could the environment at the time of birth affect the microbiome and thus fetal outcomes (ie, maternal health, c-sections, breast milk)
  • Study looking at maternal microbiota showed that taking microbes from a woman in her first trimester and exposing a mouse to it had no metabolic effect. The microbiome from women in the third trimester led to a fatter, more insulin desensitized mouse
  • Track one infant over 130 days. Sample each day the microbial community. Not random changes, but changes in populations that seem to correlate with life events (ie, moving to solid foods, adult diet). (Some stats on this would be nice; vague clustering, but also almost linear. Seems reasonable, but I don’t know that this particular data set illustrates this point well enough…)
  • Correlation between early exposure to antibiotics (< 6 weeks) and obesity later in life previously reported. How do these changes in microbes affect obesity rates?
  • Low-level antibiotic exposure in mice increases deposits of adipose tissue. Correlates with changes in the microbiome.
  • Does mode of delivery alter the infant microbiome? Vaginal versus c-section?
    • 10 infants measured over four days
    • PCA clusters out vaginal versus caesarian delivered infants nicely
    • Evidence that c-section babies are more at risk for asthma, obesity
    • Correlation between c-section and infant obesity.
    • But if you stratify according to BMI, then affect is lost. (Correlation != causation; high BMI more likely to have c-section? Many compounding factors not addressed here.)
  • New study: how does maternal obesity and diabetes act to colonize the microbiome of the mother-infant pairs?
    • Recruit normal weight, and overweight/obese mothers.
    • Pilot data. Can be clustered according to BMI. “You can see that the left hand side is a little different from the right hand side.” (Little different indeed… Not sold yet)
    • Correlation between relative abundance of lactate fermentation genes and infant weight gain (r^2=0.56).
    • Another study finds corroborative data. Differences in child size related to lactate levels.
    • Following this up.
  • In conclusion, the human microbiome matters for fetal health. Lots of work to do to tease out the effects of the host (that is, human) genotype, gestational exposure, post-natal environment, probiotics and prebiotics, breastfeeding, etc.
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