BackgroundLow birth weight (LBW; <2,500 g) affects approximately 15 to 20 percent of global births annually and is associated with suboptimal child development. Recent studies suggest a link between the maternal gut microbiome and poor obstetric and perinatal outcomes. The goal of this study was to examine relationships between maternal microbial taxa, fecal metabolites, and maternal anthropometry on incidence of LBW in resource-limited settings.MethodsThis was a secondary analysis of the Women First trial conducted in a semi-rural region of Guatemala. Maternal weight was measured at 12 and 34 weeks (wk) of gestation. Infant anthropometry measures were collected within 48 h of delivery. Maternal fecal samples at 12 and 34 weeks were used for microbiome (16S rRNA gene amplicon sequencing) and metabolomics analysis (34 wk). Linear mixed models using the MaAslin2 package were utilized to assess changes in microbiome associated with LBW. Predictive models using gradient boosted machines (XGBoost) were developed using the H2o.ai engine.ResultsNo differences in β-diversity were observed at either time point between mothers with LBW infants relative to normal weight (NW) infants. Simpson diversity at 12 and 34 weeks was lower in mothers with LBW infants. Notable differences in genus-level abundance between LBW and NW mothers (p < 0.05) were observed at 12 weeks with increasing abundances of Barnesiella, Faecalibacterium, Sutterella, and Bacterioides. At 34 weeks, there were lower abundances of Magasphaera, Phascolarctobacterium, and Turicibacter and higher abundances of Bacteriodes, and Fusobacterium in mothers with LBW infants. Fecal metabolites related to bile acids, tryptophan metabolism and fatty acid related metabolites changed in mothers with LBW infants. Classification models to predict LBW based on maternal anthropometry and predicted microbial functions showed moderate performance.ConclusionCollectively, the findings indicate that alterations in the maternal microbiome and metabolome were associated with LBW. Future research should target functional and predictive roles of the maternal gut microbiome in infant birth outcomes including birthweight.

The influenza pandemic of 1918, known as the Spanish Flu, was one of the deadliest and widespread pandemics in human history. The scale of the outbreak, as well as limitations in technology, medicine and communication, create difficulties when trying to uncover accurate figures relating to the pandemic. Estimates suggest that the virus, known as the H1N1 influenza virus, infected more than one quarter of the global population, which equated to approximately 500 million people in 1920. It was responsible for roughly 25 million fatalities, although some projections suggest that it could have caused double this number of deaths. The exact origins of this strain of influenza remain unclear to this day, however it was first noticed in Western Europe in the latter stages of the First World War. Wartime censorship in Europe meant that the severity of the pandemic was under-reported, while news outlets in neutral Spain were free to report openly about the impact of the virus; this gave the illusion that the virus was particularly strong in Spain, giving way to the term "Spanish Flu".

Effects of the virus

By late summer 1918, the pandemic had spread across the entire continent, and the H1N1 virus had mutated into a deadlier strain that weakened the infected's immune system more than traditional influenzas. Some studies suggest that, in contrast to these traditional influenza viruses, having a stronger immune system was actually a liability in the case of the H1N1 virus as it triggered what is known as a "cytokine storm". This is where white blood cells release proteins called cytokines, which signal the body to attack the virus, in turn releasing more white blood cells which release more cytokines. This cycle over-works and greatly weakens the immune system, often giving way to other infections; most commonly pneumonia in the case of the Spanish Flu. For this reason, the Spanish Flu had an uncommonly high fatality rate among young adults, who are traditionally the healthiest group in society. Some theories for the disproportionate death-rate among young adults suggest that the elderly's immune systems benefitted from exposure to earlier influenza pandemics, such as the "Asiatic/Russian Flu" pandemic of 1889.

Decrease in life expectancy As the war in Europe came to an end, soldiers returning home brought the disease to all corners of the world, and the pandemic reached global proportions. Isolated and under-developed nations were especially vulnerable; particularly in Samoa, where almost one quarter of the population died within two months and life expectancy fell to just barely over one year for those born in 1918; this was due to the arrival of a passenger ship from New Zealand in November 1918, where the infected passengers were not quarantined on board, allowing the disease to spread rapidly. Other areas where life expectancy dropped below ten years for those born in 1918 were present-day Afghanistan, the Congo, Fiji, Guatemala, Kenya, Micronesia, Serbia, Tonga and Uganda. The British Raj, now Bangladesh, India and Pakistan, saw more fatalities than any other region, with as many as five percent of the entire population perishing as a result of the pandemic. The pandemic also had a high fatality rate among pregnant women and infants, and greatly impacted infant mortality rates across the world. There were several waves of the pandemic until late 1920, although they decreased in severity as time progressed, and none were as fatal as the outbreak in 1918. A new strain of the H1N1 influenza virus did re-emerge in 2009, and was colloquially known as "Swine Flu"; thankfully it had a much lower fatality rate due to medical advancements across the twentieth century.

Low birth weight (babies born at less than 2,500 grams) affects approximately 15 to 20 percent of global births annually and is associated with poor child development. The goal of this study was to examine relationships between maternal microbial taxa, fecal metabolites, and maternal anthropometry on incidence of LBW in resource-limited settings. This was a secondary analysis of the Women First trial conducted in a semi-rural region of Guatemala. Maternal weight was measured at 12 and 34 weeks (wk) of gestation. Infant anthropometry measures were collected within 48 h of delivery. Maternal fecal samples at 12 and 34 wk were used for microbiome (16S rRNA gene amplicon sequencing) and metabolomics analysis (34 wk only). Linear mixed models using the MaAslin2 package were utilized to assess changes in microbiome associated with LBW. Predictive models using gradient boosted machines (XGBoost) were developed using the H2o.ai engine. No differences in β-diversity were observed at either time point between mothers with LBW infants relative to normal weight (NW) infants. Simpson diversity at 12 and 34 wk was lower in mothers with LBW infants. Notable differences in genus-level abundance between LBW and NW mothers (p< 0.05) were observed at 12 weeks with increasing abundances of Barnesiella, Faecalibacterium, Sutterella, and Bacterioides. At 34 weeks, there were lower abundances of Magasphaera, Phascolarctobacterium, and Turicibacter and higher abundances of Bacteriodes, and Fusobacterium in mothers with LBW infants. Fecal metabolites related to bile acids, tryptophan metabolism and fatty acid related metabolites changed in mothers with LBW infants. Classification models to predict LBW based on maternal anthropometry and predicted microbial functions showed moderate performance. Collectively, the findings indicate that less beneficial gut microbes and circulating metabolites of the mother is associated with low birth weight infants compared to normal weight. Future research should target functional and predictive roles of the maternal gut microbiome in infant birth outcomes including birthweight.

Estimates of proportion of small-for-gestational age (SGA) infants by LMP and Ballard compared to ultrasound.

Guatemala GT: Completeness of Birth Registration: Rural data was reported at 96.200 % in 2015. This records a decrease from the previous number of 96.900 % for 2009. Guatemala GT: Completeness of Birth Registration: Rural data is updated yearly, averaging 96.550 % from Dec 2009 (Median) to 2015, with 2 observations. The data reached an all-time high of 96.900 % in 2009 and a record low of 96.200 % in 2015. Guatemala GT: Completeness of Birth Registration: Rural data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Guatemala – Table GT.World Bank.WDI: Population and Urbanization Statistics. Completeness of birth registration is the percentage of children under age 5 whose births were registered at the time of the survey. The numerator of completeness of birth registration includes children whose birth certificate was seen by the interviewer or whose mother or caretaker says the birth has been registered.; ; UNICEF's State of the World's Children based mostly on household surveys and ministry of health data.; ;