Mass deworming for improving health and cognition of children in endemic helminth areas: A systematic review and individual participant data network meta‐analysis

BackgroundSoil transmitted (or intestinal) helminths and schistosomes affect millions of children worldwide.ObjectivesTo use individual participant data network meta‐analysis (NMA) to explore the effects of different types and frequency of deworming drugs on anaemia, cognition and growth across potential effect modifiers.Search MethodsWe developed a search strategy with an information scientist to search MEDLINE, CINAHL, LILACS, Embase, the Cochrane Library, Econlit, Internet Documents in Economics Access Service (IDEAS), Public Affairs Information Service (PAIS), Social Services Abstracts, Global Health CABI and CAB Abstracts up to March 27, 2018. We also searched grey literature, websites, contacted authors and screened references of relevant systematic reviews.Selection CriteriaWe included randomised and quasirandomised deworming trials in children for deworming compared to placebo or other interventions with data on baseline infection.Data Collection and AnalysisWe conducted NMA with individual participant data (IPD), using a frequentist approach for random‐effects NMA. The covariates were: age, sex, weight, height, haemoglobin and infection intensity. The effect estimate chosen was the mean difference for the continuous outcome of interest.ResultsWe received data from 19 randomized controlled trials with 31,945 participants. Overall risk of bias was low. There were no statistically significant subgroup effects across any of the potential effect modifiers. However, analyses showed that there may be greater effects on weight for moderate to heavily infected children (very low certainty evidence).Authors' ConclusionsThis analysis reinforces the case against mass deworming at a population‐level, finding little effect on nutritional status or cognition. However, children with heavier intensity infections may benefit more. We urge the global community to adopt calls to make data available in open repositories to facilitate IPD analyses such as this, which aim to assess effects for the most vulnerable individuals.</div

A possible schematic role of inflammatory cells networks on T2D and CVD.

<p>(A) The situation in animal models of T2D and CVD without the presence of helminths and in humans living in affluent areas not endemic for helminths. (B) The situation in animal models in the presence of helminths and in humans living in areas endemic for helminths. Although it is known that genes and lifestyle factors are involved in the development of insulin resistance and cardiovascular disease, it is becoming increasingly accepted that the immune system and inflammation play an important role as well. Obese people with metabolic syndrome have a higher degree of inflammation, characterized by increased TNF, a cytokine associated with insulin resistance. When the balance of T cell subsets is disturbed, increased frequencies of pro-inflammatory T cells such as T helper (Th) 1 and Th17 can drive classically activated macrophages (CAMs) which release TNF and, when in metabolic organs such as the adipose tissue, can interfere with insulin signaling. There is also evidence that, under inflammatory conditions, mast cells (MCs) contribute to the pathogenesis of metabolic disorders. High-affinity IgE, present on MCs, can lead to degranulation and initiate inflammation when cross-linked (A). However, the immune system is also endowed with cells such as Th2 and Treg that can exert anti-inflammatory activity and counterbalance the effects of TNF. The balance between pro- and anti-inflammatory activities in the immune system would determine insulin sensitivity. The situation seems to be different in rural areas of LMICs where helminth infections are highly prevalent. Helminths need nutrients from their host for their growth and reproduction, and this might use the energy of their host and therefore forestall obesity and insulin resistance. However, helminths can also lead to the expansion of alternatively activated Th2 and Treg. Th2 cytokines result in increased eosinophilia (EO) and, when in adipose tissue, can lead to the alternative activation of macrophages in this metabolically active organ; the AAMs in turn release anti-inflammatory cytokines such as IL-10. The signaling pathways are currently being dissected, but so far there is evidence that this cascade of events involves the activation of PPAR, STAT6, and/or Akt. Moreover, it has been noted that when the immune system is exposed to chronic helminth infections, EO and MCs no longer behave as pro-inflammatory immune cells, and IgE under these conditions appears to be of low affinity showing poor functional activity in terms of inducing MC degranulation. Thus, in the presence of helminth infections, the immune system is in an anti-inflammatory mode that is considered to be disadvantageous to the development of T2D and CVD (B). The solid lines represent associations based on data available, while dotted lines represent theoretical associations that are yet to be tested.</p

The association between combination of bacteria taxa with treatment and infection at post-treatment.

<p>The association between combination of bacteria taxa with treatment and infection at post-treatment.</p

Characteristics of pregnant mothers and their infants included in the logistic regression models.

<p>*any helminth infection: either single or mixed infections of intestinal helminth and filarial.</p><p>IH  =  Intestinal helminth, IP  =  Intestinal protozoan.</p

The association between each bacteria phylum with treatment and infection at post-treatment.

<p>The association between each bacteria phylum with treatment and infection at post-treatment.</p

The profile of the microbiome study.

<p>The chart shows the number of subjecs infected with at least one of the prevalent soil transmitted helminths (Helminth (+)) or free of helminth infection (Helminth (-)) that belonged to either the placebo or albendazole treatment group, at pre-treatment and 21 months after treatment.</p

Heatmaps showing the relative abundance of the 29 most abundant genera of each sample at pre-treatment (A) and post-treatment (B).

<p>Each column in the heatmap represents a specific sample and each row represents a genera. Colors represent the scaled relative abundance of genera with green and red representing low and high abundance, respectively. Samples and genera were clustered hierarchically (using the Ward method [<a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0006620#pntd.0006620.ref038" target="_blank">38</a>]) based on Euclidean distance of the relative abundance profiles and were depicted on the top and left dendrogram, respectively. The infection status, treatment allocation, Shannon and richness indices for all samples were annotated above the heatmap. Circles in Shannon and richness represents the diversity indices for each sample. There is no clustering of samples or genera based on infection or treatment status.</p

Characteristics of the study subjects at baseline and at 21 months post-treatment.

<p>Characteristics of the study subjects at baseline and at 21 months post-treatment.</p

The microbiome composition at pre-treatment (A) and post-treatment (B) stratified by helminth infection.

<p>The stacked bar plots represent the relative abundance for each of the most abundant phyla where the Unclassified represents the category of sequences that could not be assigned to a phyla, and the pooled category consists of the remaining 13 phyla with average relative abundance less than 1%. The numbers inside the stacked bar plots show the relative abundance of the specific taxa. The microbiome compositions were depicted for group of helminth-uninfected (Uninfected), any helminth infected (Any), single helminth infection (<i>A</i>. <i>lumbricoides</i> (Al), hookworm (Hw) or <i>T</i>. <i>trichiura</i> (Tt)), double infection (Al—Hw, Al—Tt and Hw—Tt) or triple infections (Al—Hw—Tt).</p

Recombinant proteins and antibodies used in multiple bead-based assay^*.

<p>*All reagents listed were obtained from the sources indicated from the following abbreviations: Sanquin  =  Stichting Sanquin Bloedvoorziening (Amsterdam, The Netherlands); BS  =  BioSource (Nivelles, Belgium); NIBSC  =  National Institute for Biological Standards & Controls (Potters Bar, UK), with catalogue numbers (Cat. No.) for each reagent given.</p