Measuring changes in transmission of neglected tropical diseases, malaria, and enteric pathogens from quantitative antibody levels

Abstract

BackgroundSerological antibody levels are a sensitive marker of pathogen exposure, and advances in multiplex assays have created enormous potential for large-scale, integrated infectious disease surveillance. Most methods to analyze antibody measurements reduce quantitative antibody levels to seropositive and seronegative groups, but this can be difficult for many pathogens and may provide lower resolution information than quantitative levels. Analysis methods have predominantly maintained a single disease focus, yet integrated surveillance platforms would benefit from methodologies that work across diverse pathogens included in multiplex assays.Methods/Principal findingsWe developed an approach to measure changes in transmission from quantitative antibody levels that can be applied to diverse pathogens of global importance. We compared age-dependent immunoglobulin G curves in repeated cross-sectional surveys between populations with differences in transmission for multiple pathogens, including: lymphatic filariasis (Wuchereria bancrofti) measured before and after mass drug administration on Mauke, Cook Islands, malaria (Plasmodium falciparum) before and after a combined insecticide and mass drug administration intervention in the Garki project, Nigeria, and enteric protozoans (Cryptosporidium parvum, Giardia intestinalis, Entamoeba histolytica), bacteria (enterotoxigenic Escherichia coli, Salmonella spp.), and viruses (norovirus groups I and II) in children living in Haiti and the USA. Age-dependent antibody curves fit with ensemble machine learning followed a characteristic shape across pathogens that aligned with predictions from basic mechanisms of humoral immunity. Differences in pathogen transmission led to shifts in fitted antibody curves that were remarkably consistent across pathogens, assays, and populations. Mean antibody levels correlated strongly with traditional measures of transmission intensity, such as the entomological inoculation rate for P. falciparum (Spearman’s rho = 0.75). In both high- and low transmission settings, mean antibody curves revealed changes in population mean antibody levels that were masked by seroprevalence measures because changes took place above or below the seropositivity cutoff.Conclusions/SignificanceAge-dependent antibody curves and summary means provided a robust and sensitive measure of changes in transmission, with greatest sensitivity among young children. The method generalizes to pathogens that can be measured in high-throughput, multiplex serological assays, and scales to surveillance activities that require high spatiotemporal resolution. Our results suggest quantitative antibody levels will be particularly useful to measure differences in exposure for pathogens that elicit a transient antibody response or for monitoring populations with very high- or very low transmission, when seroprevalence is less informative. The approach represents a new opportunity to conduct integrated serological surveillance for neglected tropical diseases, malaria, and other infecti...

Enteropathogen seroepidemiology among children in low-resource settings

“…All included antigens have been characterized extensively with respect to patent infections among adults and children in other settings (details in Materials and Methods). Furthermore, we observed high levels of consistency across cohorts in seroepidemiologic patterns, and consistency with general age-dependent patterns documented across diverse pathogens for which transmission begins early in life [15,33,34,36] . Together, these observations suggest that the antibody dynamics and force of infection estimates from IgG responses reflect actual pathogen transmission in these cohorts, but paired stool and blood testing would provide a more definitive test.…”

“…For most pathogens, mean IgG levels and seroprevalence rose quickly and plateaued by ages 1 to 3 years in Haiti ( Figure 3 ), Kenya ( Figure 3 -supplement 1 ), and Tanzania ( Figure 3 supplement 2 ). Despite enormous individual-level variation, age-dependent mean IgG curves exhibited characteristic shapes seen across diverse pathogens, and reflected high levels of early-life exposure [15] . In Haiti, seroprevalence ranged from 66% ( E. histolytica ) to 100% (ETEC LT β subunit) by age 3 years ( Figure 3B ), and in Tanzania, the majority of 1 year olds were already seropositive for Giardia (77%) and Cryptosporidium (85%) ( Figure 3B supplement 2 ).…”

“…Serologically derived force of infection is useful for pathogens that commonly present asymptomatically, such as many enteric infections. Across diverse pathogens, steeper age-seroprevalence curves typically reflect higher transmission intensity [34,35] , and overall seroprevalence equals the area under the age-seroprevalence curve (a summary measure) [15] . We therefore hypothesized that seroprevalence and prospectively estimated force of infection should embed similar information about infection heterogeneity across pathogens.…”

“…Antigens All antigens used have been well-characterized in previous studies. Lipopolysaccharides (LPS) from Group D Salmonella enterica serotype Enteritidis [51] , LPS from Group B S. enterica serotype Typhimurium [51] , and recombinant heat labile toxin β subunit protein [15,52,53] from enterotoxigenic Escherichia coli (ETEC LT β subunit) were purchased from Sigma Chemical (St. Louis, MO). Recombinant Giardia VSP-3 and VSP-5 [54,55] and Cryptosporidium Cp17 and Cp23 antigens [56][57][58] were expressed and purified as previously described [25,59] .…”

“…At the same time, large-scale serological surveillance platforms create new opportunities for expanded enteropathogen surveillance alongside other infectious diseases [10,11] . These challenges and opportunities have generated interest in antibody-based measurement as a complement to PCR for population-based enteropathogen surveillance [12][13][14][15] , and for endpoints in observational and randomized studies [16][17][18][19][20][21][22] .…”