We use cookies to understand how you use our site and to improve your experience. This includes personalizing content and advertising. To learn more, click here. By continuing to use our site, you accept our use of cookies. Cookie Policy.

Features Partner Sites Information LinkXpress hp
Sign In
Advertise with Us
INTEGRA BIOSCIENCES AG

Download Mobile App




Preterm Birth Clues Identified in Cervicovaginal Microbiome

By LabMedica International staff writers
Posted on 03 Apr 2019
Failure to predict and understand the causes of preterm birth, the leading cause of neonatal morbidity and mortality, have limited effective interventions and therapeutics. More...
Preterm birth (PTB) is defined as birth before 37 completed weeks of gestation and is the leading cause of death in neonates and children under the age of 5.

The interaction between microbial communities and their host, in many biological niches, has been found to be mechanistically involved in health and disease pathogenesis. There have been only a few studies that have examined the relationship between cervicovaginal microbial communities and spontaneous preterm birth.

A group of scientists collaborating with the University of Maryland School of Medicine (Baltimore, MD, USA) enrolled within 2,000 pregnant women, and identified a few hundred women for a nested case-control study of spontaneous preterm birth (sPTB), a group that included 107 extensively phenotyped women who gave birth prior to 37 weeks of gestation and 432 unaffected women who delivered their babies at term. Nearly three-quarters of the participants were African-American, and the median age of the mothers was 28 years old. The investigators had access to cervicovaginal swab samples and anthropometric measurements collected at three points during pregnancy: at 16 to 20 weeks gestation, 20 to 24 weeks gestation, and between 24 and 28 weeks gestation.

Amplicons were visualized on a 2% agarose gel, quantified, pooled in equimolar concentration, and purified prior to loading on an Illumina HiSeq 2500 modified to generate 300 bp paired-end reads. When the scientists compared microbial community members in cervicovaginal samples from those cases and controls, which were profiled with 16S ribosomal RNA gene sequencing, they saw seven bacterial taxa that appeared to be associated with spontaneous preterm birth, particularly in women with African-American ancestry. The team studied that association further using enzyme-linked immunosorbent assay (ELISA)-based immunological profiling, focused on host-derived anti-microbial peptides known as beta-defensin-2, that have previously been described in studies of the genital tract, both in healthy women and those suffering from bacterial infections.

The results indicated that the presence of Lactobacillus bacteria that are normally considered beneficial did not necessary coincide with diminished risk of spontaneous preterm birth. Instead, the data suggested that enhanced beta-defensin-2 levels in cervicovaginal samples typically coincided with decreased spontaneous preterm birth risk, while lower-than-usual levels of the protein tended to track with increased risk, even when high levels of bacteria belonging to Lactobacillus species were present. That beta-defensin-2 effect was especially pronounced in the African-American women, but was not significant when the team analyzed data for non-African-American women alone. Likewise, African-American women who delivered their infants at term also had increased beta-defensin-2 levels compared to non-African-American women with at-term deliveries.

The author concluded that higher vaginal levels of β-defensin-2 lowered the risk sPTB associated with cervicovaginal microbiota in an ethnicity-dependent manner. Surprisingly, even in Lactobacillus spp. dominated cervicovaginal microbiota, low β-defensin-2 was associated with increased risk of sPTB. Their findings hold promise for diagnostics to accurately identify women at risk for sPTB early in pregnancy. The study was published on March 21, 2019, in the journal Nature Communications.

Related Links:
University of Maryland School of Medicine


Gold Member
Veterinary Hematology Analyzer
Exigo H400
Verification Panels for Assay Development & QC
Seroconversion Panels
New
C-Reactive Protein Assay
OneStep C-Reactive Protein (CRP) RapiCard InstaTest
New
PSA Test
Humasis PSA Card
Read the full article by registering today, it's FREE! It's Free!
Register now for FREE to LabMedica.com and get access to news and events that shape the world of Clinical Laboratory Medicine.
  • Free digital version edition of LabMedica International sent by email on regular basis
  • Free print version of LabMedica International magazine (available only outside USA and Canada).
  • Free and unlimited access to back issues of LabMedica International in digital format
  • Free LabMedica International Newsletter sent every week containing the latest news
  • Free breaking news sent via email
  • Free access to Events Calendar
  • Free access to LinkXpress new product services
  • REGISTRATION IS FREE AND EASY!
Click here to Register








Channels

Clinical Chemistry

view channel
Image: The GlycoLocate platform uses multi-omics and advanced computational biology algorithms to diagnose early-stage cancers (Photo courtesy of AOA Dx)

AI-Powered Blood Test Accurately Detects Ovarian Cancer

Ovarian cancer ranks as the fifth leading cause of cancer-related deaths in women, largely due to late-stage diagnoses. Although over 90% of women exhibit symptoms in Stage I, only 20% are diagnosed in... Read more

Immunology

view channel
Image: The cancer stem cell test can accurately choose more effective treatments (Photo courtesy of University of Cincinnati)

Stem Cell Test Predicts Treatment Outcome for Patients with Platinum-Resistant Ovarian Cancer

Epithelial ovarian cancer frequently responds to chemotherapy initially, but eventually, the tumor develops resistance to the therapy, leading to regrowth. This resistance is partially due to the activation... Read more

Technology

view channel
Image: The new algorithms can help predict which patients have undiagnosed cancer (Photo courtesy of Adobe Stock)

Advanced Predictive Algorithms Identify Patients Having Undiagnosed Cancer

Two newly developed advanced predictive algorithms leverage a person’s health conditions and basic blood test results to accurately predict the likelihood of having an undiagnosed cancer, including ch... Read more
Copyright © 2000-2025 Globetech Media. All rights reserved.