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Metabolic syndrome is a cluster of risk factors for cardiovascular disease afflicting more than 1 billion people worldwide and is increasingly being identified in younger age groups and in socioeconomically disadvantaged settings in the global south. Enteropathogen exposure and environmental enteropathy in infancy may contribute to metabolic syndrome by disrupting the metabolic profile in a way that is detectable in cardiometabolic markers later in childhood. A total of 217 subjects previously enrolled in a birth cohort in Amazonian Peru were monitored annually from ages 2 to 5 years. A total of 197 blood samples collected in later childhood were analyzed for 37 cardiometabolic biomarkers, including adipokines, apolipoproteins, cytokines, which were matched to extant early-life markers of enteropathy ascertained between birth and 2 years. Multivariate and multivariable regression models were fitted to test for associations, adjusting for confounders. Fecal and urinary markers of intestinal permeability and inflammation (myeloperoxidase, lactulose, and mannitol) measured in infancy were associated with later serum concentrations of soluble CD40-ligand, a proinflammatory cytokine correlated with adverse metabolic outcomes. Fecal myeloperoxidase was also associated with later levels of omentin-1. Enteric protozoa exposure showed stronger associations with later cardiometabolic markers than viruses, bacteria, and overall diarrheal episodes. Early-life enteropathy markers were associated with altered adipokine, apolipoprotein, and cytokine profiles later in childhood consistent with an adverse cardiometabolic disease risk profile in this cohort. Markers of intestinal permeability and inflammation measured in urine (lactulose, mannitol) and stool (myeloperoxidase, protozoal infections) during infancy may predict metabolic syndrome in adulthood.

BackgroundDiarrheal disease remains a leading cause of childhood illness and mortality and Shigella is a major etiological contributor for which a vaccine may soon be available. This study aimed to model the spatiotemporal variation in pediatric Shigella infection and map its predicted prevalence across low- and middle-income countries (LMICs).MethodsIndependent participant data on Shigella positivity in stool samples collected from children aged ≤59 months were sourced from multiple LMIC-based studies. Covariates included household- and subject-level factors ascertained by study investigators and environmental and hydrometeorological variables extracted from various data products at georeferenced child locations. Multivariate models were fitted, and prevalence predictions obtained by syndrome and age stratum.Findings20 studies from 23 countries contributed 66,563 sample results. Age, symptom status, and study design contributed most to model performance followed by temperature, wind speed, relative humidity, and soil moisture. Shigella probability exceeded 20% when both precipitation and soil moisture were above average and had a 43% peak in uncomplicated diarrhea cases at 33°C temperatures, above which it decreased. Improved sanitation and open defecation decreased Shigella odds by 19% and 18% respectively compared to unimproved sanitation.InterpretationThe distribution of Shigella is more sensitive to climatological factors like temperature than previously recognized. Conditions in much of sub-Saharan Africa are particularly propitious for Shigella transmission, though hotspots also occur in South and Central America, the Ganges–Brahmaputra Delta, and New Guinea. These findings can inform prioritization of populations for future vaccine trials and campaigns.FundingNASA 16-GEO16-0047; NIH-NIAID 1R03AI151564-01; BMGF OPP1066146.

BackgroundThe COVID-19 pandemic has caused societal disruption globally and South America has been hit harder than other lower-income regions. This study modeled effects of 6 weather variables on district-level SARS-CoV-2 reproduction numbers (Rt) in three contiguous countries of Tropical Andean South America (Colombia, Ecuador, and Peru), adjusting for environmental, policy, healthcare infrastructural and other factors.MethodsDaily time-series data on SARS-CoV-2 infections were sourced from health authorities of the three countries at the smallest available administrative level. Rt values were calculated and merged by date and unit ID with variables from a Unified COVID-19 dataset and other publicly available sources for May – December 2020. Generalized additive mixed effects models were fitted.FindingsRelative humidity and solar radiation were inversely associated with SARS-CoV-2 Rt. Days with radiation above 1,000 KJ/m2 saw a 1.3%, and those with humidity above 50%, a 1.0% reduction in Rt. Transmission was highest in densely populated districts, and lowest in districts with poor healthcare access and on days with least population mobility. Temperature, region, aggregate government policy response and population age structure had little impact. The fully adjusted model explained 3.9% of Rt variance.InterpretationDry atmospheric conditions of low humidity increase, and higher solar radiation decrease district-level SARS-CoV-2 reproduction numbers, effects that are comparable in magnitude to population factors like lockdown compliance. Weather monitoring could be incorporated into disease surveillance and early warning systems in conjunction with more established risk indicators and surveillance measures.FundingNASA’s Group on Earth Observations Work Programme (16-GEO16-0047).

BackgroundMetabolic syndrome is a cluster of risk factors for cardiovascular disease thought to afflict over a billion people worldwide and is increasingly being identified in younger age groups and socio-economically disadvantaged settings in the global south. Enteropathogen exposure and environmental enteropathy in infancy may lead to metabolic syndrome by disrupting the metabolic profile in a way that is detectable in cardiometabolic markers later in childhood.Methods217 subjects previously enrolled in a birth cohort in Amazonian Peru were followed up annually from ages 2 to 5 years. Blood samples collected in later childhood were analyzed for a panel of 37 cardiometabolic biomarkers, including adipokines, apolipoproteins, cytokines, and other analytes. These were matched to extant early-life markers of enteropathy ascertained between birth and 2 years of age. Multivariate and multivariable regression models were fitted to test for associations adjusting for confounders.ResultsFecal and urinary markers of intestinal permeability and inflammation (myeloperoxidase, lactulose and mannitol) measured from birth to 2 years of age were independently associated with later serum concentrations of soluble CD40-ligand, a proinflammatory cytokine correlated with adverse metabolic outcomes. Fecal myeloperoxidase was also strongly, directly associated with later levels of the anti-inflammatory adipocytokine omentin-1. Cumulative enteric protozoa exposure before 2 years of age showed stronger associations with later cardiometabolic markers than enteric viruses and bacteria and overall diarrheal episodes.ConclusionEarly-life markers of enteric infection and enteropathy were associated with numerous changes in adipokine, apolipoprotein and cytokine profiles later in childhood consistent with those of an adverse cardiometabolic disease risk profile in this Peruvian birth cohort. Markers of intestinal permeability and inflammation measured in urine (lactulose, mannitol) and stool (myeloperoxidase, protozoal infections) during infancy, may predict disruptions to cytokine and adipocytokine production in later childhood that are precursors to metabolic syndrome in adulthood. Chronic enteric infections, such as by protozoan pathogens, may be more important drivers of these changes than symptomatic diarrhea or growth faltering.FundingBill & Melinda Gates Foundation OPP1066146 and OPP1152146.