P. falciparum-specific antigens and identify a predictive signature of naturally acquired immunity to malaria. Our results show that an individual's immune status can be accurately predicted by measuring IgG antibody responses to a parsimonious set of 15 target antigens. The identified immune signature is highly versatile and capable of providing precise and accurate estimates of clinical protection from malaria in demographically distinct populations.We have established a predictive modelling framework to systematically analyze IgG antibody responses against a large panel of  HighlightsP. falciparum-specific antigens to identify a specific antigen signature of NAI.A predictive modelling framework has been established to analyze IgG antibody responses against a large panel of An individual's immune status can be accurately predicted by measuring IgG responses against a small set of 15 defined parasite antigens.Proteins identified in the 15-antigen signature represent potential candidates for next-generation malaria vaccines or biomarkers for monitoring the impact of malaria interventions.The developed predictive framework can be adapted for developing novel surveillance and intervention tools for other infectious diseases. Plasmodium spp. parasite in the development of naturally acquired immunity to malaria, however an antigen signature capable of predicting protective immunity against Plasmodium remains to be identified. Key challenges for the identification of a predictive immune signature include the high dimensionality of data produced by high-throughput technologies and the limitation of standard statistical tests in accounting for synergetic interactions between immune responses to multiple targets. In this study, using samples collected from young children in Ghana at multiple time points during a longitudinal study, we adapted a predictive modeling framework which combines feature selection and machine learning techniques to identify an antigen signature of clinical immunity to malaria. Our results show that an individual's immune status can be accurately predicted by measuring antibody responses to a small defined set of 15 target antigens. We further demonstrate that the identified immune signature is highly versatile and capable of providing precise and accurate estimates of clinical protection from malaria in an independent geographic community. Our findings pave the way for the development of a robust point-of-care test to identify individuals at high risk of disease and which could be applied to monitor the impact of vaccinations and other interventions. This approach could be also translated to biomarker discovery for other infectious diseases.A large body of evidence supports the role of antibodies directed against the  P. falciparum antigens in protective immunity to malaria  with high accuracy (86%). We further validated this signature in a distinct epidemiological and demographical setting, among 2–10 year-old children and 18–25 year-old adults in Mali. The predictive modeling framework presented here proved to be a powerful approach to identify a sensitive and specific immune signature of NAI to malaria.Herein, we have established a predictive modeling framework combining feature selection and machine learning to systematically analyze IgG antibody responses against a large panel of supplemental Fig. S1). The characteristics of the area and study details have been published elsewhere  of the Upper East region of northern Ghana. In this region malaria transmission occurs throughout the year with two main seasons: a dry season from approximately October to April, and a wet season from approximately May to October , or predicted to be in the blood-stage secretome (PlasmoDB.org). The array was fabricated as previously described ) genomic DNA and cloned into the PXT7 plasmid with a T7 transcription terminator, and tagged with 5′ polyhistidine (HIS) and 3′ hemagglutinin (HA) epitopes. Recombinant proteins were expressed using E. coli cell-free in vitro transcription and translation reactions  according to the manufacturer's instructions. Protein arrays were printed as previously described, with each recombinant protein spotted once in each array (E. coli lysate in protein array blocking buffer (1:100 dilution) and then 500 μl of the pre-absorbed plasma was added to each protein array and incubated overnight at 4 °C. Serum antibodies were detected with biotin-conjugated goat anti-human IgG secondary antibody (1:1000 dilution) and visualized with a streptavidin P-3-conjugated antibody (1:200 dilution). Air-dried slides were scanned on an Axon GenePix 4300A array scanner  and results read as raw fluorescence signal intensities (SI) were quantified using the Axon GenePix Pro 7 software after correction for spot-specific background. IgG antibody reactivity to the positive control (EBNA-1 fragment) was detected in all individuals, whereas there was no detectable reactivity to the no-DNA (data not shown).Total IgG antibody reactivity against escribed . Brieflych array . Once prch array . Epsteinhttp://www.r-project.org) and the GMine data-mining server (http://cgenome.net/gmine). Raw data were measured as the mean pixel signal intensity for each spot. After subtracting slide background, the mean intensity of negative-control spots (no DNA) was subtracted from each test spot to adjust for any cross-reaction effects from the E. coli vector used to express the proteins. Next, the corrected data were transformed using the variance stabilizing normalization (vsn) method in GMine using the VSN Bioconductor package. Proteins were defined as positive if the transformed signal intensity was larger than the mean plus 2 standard deviations (SDs) of the transformed negative-control spots (no DNA). For each child, the breadth of the antibody responses was calculated as the number of positive responses against the 1080 P. falciparum proteins. Antibody profiles against the 1080 P. falciparum proteins were associated with multiple explanatory variables using the multivariate statistical methods redundancy analysis (RDA). Age group (young/old children), time of the malaria season (dry/wet), parasite positive (yes/no), hemoglobin level (g/dL), fever (yes/no) and gender (M/F) were included as explanatory variables and the antibody responses matrix was included as dependent variable.Protein array data were analyzed primarily in R , age and parasite status (parasite positive/parasite negative) at baseline were included as explanatory variables (predictors) and baseline antibody intensity (SI) against individual P. falciparum proteins was modeled as dependent (response) variable. The regression model had the form: antibody signal intensity = protection status (resistant/susceptible) + age group (younger/older) + parasite status at baseline (positive/negative). Individuals were categorized as susceptible if they had a recorded episode of symptomatic malaria  within our one-year study period, and resistant otherwise. Symptomatic malaria was defined as febrile illness (axillary temperature ≥37.5 °C) with concurrent P. falciparum parasitemia >2500 parasites/μl. To minimize the misclassification bias that arises because of difficulties in ascertaining exposure to the P. falciparum parasite  and signal intensity higher in the resistant group. The immune status (susceptibility or resistance) of children during the one-year study period was included as a response variable. Longitudinal changes in antibody responses against individual antigens were identified by mixed-effects linear regression  . The higgression , which cP. falciparum parasitemia >2500 parasites/μl. To assess the effect that different clinical case definitions have on the performance of the identified antigen signature in predicting the immune status of an individual, we performed a sensitivity and specificity analysis as described below. We considered a positive episode of symptomatic malaria to be one in which there was both fever and parasitemia greater than a specified threshold. For each parasitemia threshold, we counted the number of individuals who had at least one episode of symptomatic malaria during the study period, and we randomly selected an equal number of resistant individuals from the entire cohort. For each parasite threshold, we trained SVM on the baseline signal intensities of the selected antigens and evaluated the performance by a leave-one-out cross-validation. The process was repeated 5 times for each parasite threshold, each time with a different random subset of resistant children from the cohort as a control group. Each time, sensitivity and specificity were calculated using the following formula:Sensitivity: TP/(TP+FN)Specificity: TN/(TN+FP)We defined symptomatic malaria  as febrile illness (axillary temperature ≥37.5 °C) with concurrent i.e. the number of resistant individuals correctly classified as resistant).TP: true positives .FP: false positives .TN: true negatives .FN: false negatives  to profile host immune response (IgG) against malaria parasite proteins. Plasma samples were collected from each individual before and after the 8-month malaria season. Individuals were classified as resistant  or susceptible . In the current study, we included the subset of 194 individuals between the ages of 2–10 years and 18–25 years as an independent validation set. Of these, 128 experienced clinical malaria during the follow up (susceptible) and 66 individuals remained asymptomatic (resistant). Signal intensities were transformed and normalized as previously described  and a SVM was trained and validated by Monte Carlo cross-validation on this subset. Finally, we counted the number of times that our predictive antigen signature performed better than the 1000 random subsets.The predictive immune antigen signature discovered by sPLS-DA was next validated in an independent cohort of children exposed to malaria. As a validation dataset, we used our previously published protein microarray data generated in a prospective study investigating host immune response to the malaria parasite lciparum  in 225 iet al.  were classified as resistant and as susceptible otherwise. The optimal z value was determined by calculating predictive accuracy, sensitivity and specificity for all possible z in the range 1 to n.First, signal intensities of the n-antigen signature were converted to a binary output by assigning 1 (high) to signal intensities above a selected threshold and 0 (low) to signal intensities below the threshold. The level threshold for each antigen was defined as the mean signal intensity of that antigen in the resistant group minus 1 standard deviation for the Ghana dataset and 0.5 standard deviations for the Mali dataset; the cut-offs were different to account for differences in the variance of signal intensities in the two datasets. Next, an “immunity score” was defined as the sum of the binary outputs for the n-antigen signature. Samples with an immunity score greater than Plasmodium (Plasmodium taxid:5820) using protein-protein BLAST, v2.2.29+. P-P BLAST was run without the filter and an E-value cutoff of 1e-15.The amino acid sequences from the proteins identified as predictive immune signature were blasted against the entire NCBI dataset for Plasmodium falciparum strains (n = 15) and 11 human-infective Plasmodium species. Species and strains included are listed as follows: Plasmodium falciparum 7G8, Plasmodium falciparum CAMP Malaysia, Plasmodium falciparum Dd2, Plasmodium falciparum FCH 4, Plasmodium falciparum HB3, Plasmodium falciparum IGH CR14, Plasmodium falciparum MaliPS096_E11, Plasmodium falciparum NF135 5C10, Plasmodium falciparum NF54, Plasmodium falciparum Palo Alto Uganda, Plasmodium falciparum RAJ116, Plasmodium falciparum Santa Lucia, Plasmodium falciparum Tanzania 200070, Plasmodium falciparum UGT51, Plasmodium falciparum Viet0m Oak Knoll FVO, Plasmodium knowlesi, Plasmodium knowlesi strain H, Plasmodium malariae, Plasmodium ovale, Plasmodium ovale curtisi, Plasmodium ovale wallikeri, Plasmodium vivax, Plasmodium vivax Brazil I, Plasmodium vivax India VII, Plasmodium vivax Mauritania I, Plasmodium vivax North Korean.The genomes included in the analysis of conservation are all publicly available supplemental Table S1). P. falciparum infection was prevalent during the entire year, with 58–69% of younger children and 69–95% of older children carrying parasites throughout the study period . In general, older children showed concurrent parasitemia at the time of sampling more frequently than younger children  , but with a similar level of parasite density  . As expected, parasite prevalence and density were significantly higher during the wet season  . Younger children were more frequently anemic than older children , with a higher hemoglobin level during the dry season compared with the wet season  .A total of 80 children were randomly selected from a previously described longitudinal study in Ghana : 39 younn = 80) were collected every two months during the malaria season  and examined for IgG reactivity against 1,080 individual P. falciparum proteins (A). As expected, a high degree of variability in antibody responses was observed across individuals, and within individuals longitudinally during the study period. The overall mean of antibody intensity was significantly higher in older children (p = 4.85E-05 Wilcoxon rank-sum test), and the mean intensities were higher during the wet season compared with the dry season (p = 6.93E-5 Wilcoxon paired rank-test) .Blood samples from each included child  consistent with other studies . Conversely, antibody responses against the leading liver-stage vaccine candidates included in the array, LSA-1 and LSA-3, were surprisingly higher in younger children compared with older children . Other blood-stage vaccine candidates included in the array showed similar levels of reactivity across the two study groups or higher levels in younger children  .Antibody responses to some of the malaria vaccine candidate antigens included in the protein array (erythrocyte binding antigen-175 (EBA175), thrombospondin-related anonymous protein (TRAP), and circumsporozoite protein (CSP)) were generally higher in older children . Not surprisingly, we observed a positive correlation between the breadth of antibody responses (i.e. the number of proteins to which IgG reacted in each subject) and the mean intensity of all IgG reactivity on the array  (B). However, unexpectedly, we observed a significant inverse correlation between the breadth of antibody responses and the maximum antibody reactivity  (C). The inverse correlation was stronger for younger children  compared with older children . These results suggest that high antibody responses against individual dominant proteins may suppress the response to other protein, resulting in either a low number of highly reactive proteins or a broader number of less reactive antigens. The inverse correlation between breadth and intensity may represent somatic hypermutation and affinity maturation occurring after long term exposure to the parasite. In early years, the antibody responses may be relatively low affinity against a large collection of antigens whereas as the immune response matures over years it may become more focused with higher affinity against a few antigens.Antibody responses showed a gradient of reactivity across the population with some individuals displaying stronger signals and broader recognition compared with others A. Not suelation) B. Howeveelation) C. The inA), probably as a result of a co-acquisition of antibodies to parasite antigens. However, antibody profiles were highly subject-specific and clustered by age group, and by the malaria season (wet/dry) (B). The multivariate statistical method redundancy analysis (RDA) was applied to test the significance of the observed associations. In both the dry and wet seasons antibody responses were significantly associated with age  and hemoglobin level (hemoglobin level <9 g/dL) . The presence or absence of fever, gender and parasite density were not associated with antibody reactivity in either of the two seasons .Antibody responses to the 1,080 P. falciparum proteins were positively correlated A, probabwet/dry) B. The muP. falciparum proteins could prospectively discriminate resistant from susceptible children, we compared the baseline antibody profiles of children who did not experience any malaria episode during the following one-year study period (n = 56) (resistant) compared with those who experienced at least one episode (n = 16) (susceptible). Among the 16 susceptible individuals, 13 reported one episode of symptomatic malaria during the year whereas 3 individuals reported two episodes. No significant differences were observed in parasite density , breadth  or hemoglobin level  between resistant and susceptible children at baseline  . Of note, among the antibody responses against the leading malaria vaccine candidates included in the protein array , merozoite surface protein 1 (MSP1), merozoite surface protein 2 (MSP2), erythrocyte binding antigen-175 (EBA175)), thrombospondin-related anonymous protein (TRAP), liver-stage antigen 1 and 3 (LSA1-LSA3) and circumsporozoite protein (CSP), none were within the top 100 proteins in discriminating resistant and susceptible children  consistent with recently published findings by us and others . As expected, Pearson's correlation of the antibody responses against the 15-antigen signature showed a null or very low positive correlation between antigens  and a significantly higher intensity in the “resistant” group compared with the “susceptible” group . Using linear mixed-effects regression, we observed no significant longitudinal changes in antibody reactivity to the antigens in the signature , suggesting stability of the antibody signature over different time points of the malaria transmission season.Based on our previous observations , 9, 10 a5A). This predictive performance dropped substantially when the model was applied only to younger children (1–2 years)  but remained significantly high when only older children (4–5 years) were included  (B). Because the predictive performance may depend on the clinical case definition used in the study, we considered how an over- or under-estimation of clinical malaria cases might affect the performance of the 15-antigen signature in predicting immunity. As described under Experimental procedures, we considered a series of clinical case definitions of symptomatic malaria. Each definition required the presence of an axillary temperature ≥37.5 °C and a parasitemia above a specified threshold. We modeled the effect of changing parasitemia thresholds on the predictive performance of the 15-antigen signature. Supplemental Fig. S6 shows the sensitivity and specificity of the performance of the 15-antigen signature in predicting symptomatic and resistant individuals as a function of the parasitemia threshold. For resistant individuals, varying the parasitemia threshold across a wide range had a relatively minor effect on sensitivity  whereas susceptible individuals were more sensitive to the threshold chosen, varying from 58% to 96% as the threshold varied from any detectable parasitemia to up to 4000 parasites/μl. The sensitivity decreased to 88% at 5000 parasites/μl. The specificity varied greatly for both resistant and susceptible individuals , increasing from 64% to 94% and from 71% to 85% from 1 to 4000 parasites/μl respectively, for resistant and susceptible individuals and then decreased to 88 and 79%, respectively, at 5000 parasites/μl.The discriminatory power of the 15-antigen signature to correctly predict immunity to malaria was examined using a Support Vector Machine (SVM) evaluated by Monte Carlo cross-validation. When all children (1–5 years) were included, the SVM correctly predicted immunity to malaria using the baseline antibody responses against the 15 selected antigens with 86% accuracy, 88% sensitivity, 82% specificity and an AUC of 0.94 A. This p = 0.97) B. Becausn = 66), or susceptible if they did (n = 128). A SVM trained on the antibody responses at baseline against the 15-antigen signature and evaluated by Monte Carlo cross-validation was able to predict the immune status of Mali individuals with an accuracy of 87%, sensitivity 68%, specificity 82% and an AUC of 0.82. When the model training only included children, the average performance of the model maintained an accuracy 86% and specificity of 73% but with a lower AUC (0.74) and lower sensitivity (33%), as a result of misclassification of resistant individuals.We used previously published protein microarray data from Malian children  aged 2–1P. falciparum proteins spotted onto the array, we randomly selected 15 proteins and tested the accuracy in classifying resistant individuals in the Mali dataset by SVM with Monte Carlo cross-validation as described above. The analysis was repeated 1000 times using 1000 random sets of 15 proteins. The 15-antigen signature identified in the Ghana cohort performed better than 984 of the 1000 random sets (p = 0.016) (p = 0.075), suggesting that the predictive power of the signature increases with the age and/or exposure, as reported in a previous study  . When thus study , 30. Thei.e. the sum of binary values was greater or equal z). Our 15-antigen signature achieved the best performance for z = 11 with 87% accuracy, 76% sensitivity, and 100% specificity, in our Ghana cohort . The performance of the proposed decision rule was evaluated on an independent cohort of individuals from Mali, achieving 72% accuracy, 71% sensitivity and 72% specificity . The high accuracy observed for the independent validation set demonstrates the robustness of our decision rule in identifying resistant individuals in a different geographical population.We developed a simple decision rule based on our 15-antigen signature that could be used to distinguish between resistant or susceptible individuals and aid in diagnostic classification. Signal intensities were transformed into binary values (0/1 = low/high) based on defined antigen-specific threshold and individuals were classified as resistant if more than z of the 15 antigens had high intensities  as well as the presence and degree of homology for orthologous genes in other human Plasmodium species  (n = 11 strains). Our 15-antigen signature is ∼96% conserved in all P. falciparum strains . Nine antigens showed more than 90% of similarity with all (100%) the P. falciparum isolated analyzed  and 10 antigens shown at least 50% of similarity against the majority of other human infected parasite species (>50%)  . Only one protein (PF3D7_0406100) had a high sequence identity to the human proteome (>50% of identity with more than 50% of coverage) whereas the other 11 proteins showed null or very low degree of homology indicating a low propensity of induction of autoimmune or cross-reactivity .For the purpose of developing vaccines or monitoring tools toward highly variable parasite, the use of conserved antigens across different species and strains is desired. We therefore determined the degree of sequence conservation of our signature among all available Plasmodium spp has proved challenging. Major issues arise from the complexity of the parasite which expressed ∼5300 proteins, the complexity of the host immune response, the high dimensionality of data generated by high-throughput technologies, the often small sample sizes of malaria cohort studies, and the limitations of standard statistical tests in accounting for interactions between different immune responses . These antigens are also conserved among different P. falciparum strains and other human Plasmodium species making them very suitable for the purpose of developing vaccines or monitoring tools toward highly variable parasite.The proteins in our 15-antigen signature, identified based on their association with clinical immunity, represent very promising candidates for next-generation malaria vaccines. None of these antigens has yet been studied as a malaria vaccine candidate. Moreover, this specific combination of 15 antigens was a much better predictor of immunity than single antigen, indicating that a vaccine comprising all the 15 antigens identified in our study would most likely induce a synergistic immune response against the parasite. Consistent with the concept that an effective malaria vaccine will likely need to target multiple stages of the One strength of our study is that subjects were regularly contacted every two months, ensuring that most, if not all, clinical malaria episodes were detected and recorded. However, the identification of susceptible children in this age group is problematic as the presence of asymptomatic malaria parasite carriers is common, the clinical signs of malaria are nonspecific, and parasitemia accompanied by a fever may not be sufficient to indicate an episode of clinical malaria . We, theProtein microarrays have proven to be powerful tools for detecting serum antibodies against a vast repertoire of parasite proteins thus offering the opportunity to correlate protective immunity with seroreactivity profiles. Classical univariate approaches to data analysis suggest that a single parasite antigen is probably not enough to elicit (predict) protective immunity, as demonstrated in different epidemiological settings –7. TheseP. falciparum genome remained unexplored. How to advance beyond those earlier studies has not been obvious. In this study we employed a predictive modeling framework that allowed us to explore antibody responses to 1080 P. falciparum proteins. In the computational sciences, machine-learning methods were introduced because of their power to infer associations between correlated features that could not otherwise be made using conventional statistical methodologies   –40. SuppWe therefore incorporated a features selection step before building a SVM-based classifier to reduce dimensionality and to make the model less complex and easier to interpret, avoid overfitting and consequently providing a more robust model. Features were selected by sparse partial least squares discriminant analysis (sPLS-DA), which has proven to be particularly appropriate for data with small sample sizes and a large number of correlated variables . AlthougP. falciparum, in order to identify a specific antigen signature of NAI. In this comprehensive analysis, we have demonstrated that a signature of only a few antigens can be used to predict the immune status at an individual level. Specifically, we have shown that the simultaneous detection of antibody responses to a specific set of 15 antigens is sufficient to discriminate clinically resistant from susceptible children with a very high degree of accuracy and we further validated this in an independent and geographically and demographically distinct cohort of individuals naturally exposed to malaria. We also developed a simple decision rule based on the 15- antigen signature that could be potentially used for seroepidemiological surveillance.In summary, we have demonstrated the utility of a predictive modeling framework to decode the host antibody responses to the human malaria parasite Further evaluation of this signature in multicohort studies would support the utility of this 15-antigen signature as a tool for identifying individuals at high risk of clinical malaria and for monitoring the acquisition and maintenance of NAI. This study provides proof-of-concept for the power of applying advanced analytical approaches such as feature selections and machine learning algorithms to identify a predictive signature of clinical immunity. It paves the way for the development of a robust point-of-care test to identify individuals at high risk of disease and monitor the impact of vaccinations and other malaria interventions.


Plasmodium falciparum the deadliest species of the parasite that causes this disease. Plasmodium falciparum is characterized by an extensive genetic diversity that makes vaccine development challenging. In this study, the diversity of P. falciparum isolates was analysed from asymptomatic children residing in the district of Toubacouta, Senegal.Malaria is a leading cause of mortality and morbidity in tropical countries, especially in sub-Saharan Africa. In Senegal, a control plan implemented in the beginning of the 2000s has enabled a substantial reduction of mortality and morbidity due to malaria. However, eradication of malaria requires a vaccine that protects against msp-1 and msp-2 loci in samples from 87 asymptomatic children infected with P. falciparum, collected during a cross sectional survey in November and December 2010. Parasite densities in blood samples were determined by microscopic examination and statistical analyses were used to identify association of parasite genotype and parasitaemia.A nested PCR approach was used to perform genotyping of the msp-1 and msp-2, respectively. A strong genetic diversity was found with a total of 15 and 21 different alleles identified for msp-1 and msp-2, respectively. RO33 was the most frequent allelic family of msp-1 followed by MAD20, then by K1. Regarding msp-2 allelic families, 3D7 was more common than FC27. Multiple infections were predominant, since 69% and 89% of the samples genotyped for msp-1 and msp-2 showed more than one clone of P. falciparum with complexity of infection (COI) of 2.5 and 4.7, respectively. Expected heterozygosity (HE) was 0.57 and 0.55 for msp-1 and msp-2, respectively. Interestingly, polyclonal infections were significantly associated with higher parasitaemia.Genotyping was successful in 84/87 and 82/87 samples for P. falciparum clones and the association of polyclonal infection with high parasitaemia call for a multi-allelic approach in the design of vaccine candidates for efficient malaria eradication.The strong genetic diversity of The online version of this article (10.1186/s12936-019-2684-3) contains supplementary material, which is available to authorized users. Malaria is a leading cause of mortality and morbidity in tropical countries. In 2016, more than 212 million of clinical cases and 429,000 deaths were reported worldwide. Sub-Saharan African countries support 92% of the global malaria burden, with children younger than 5 years, pregnant women, and non-immune visitors to endemic areas being people the most at risk of developing severe or fatal malaria , insectiPlasmodium falciparum, which is the deadliest of the five human malaria parasites. A major obstacle toward this goal is the extensive genetic diversity of natural parasite populations that complicate the design of an effective vaccine against all P. falciparum strains. The difficulty related to P. falciparum genetic diversity has been clearly illustrated in clinical trials that tested different vaccine candidates. For example, the malaria vaccine combination B, designed as a three-component blood-stage product targeting the merozoite surface proteins MSP-1 (K1 parasite line) and MSP-2 (3D7), and the ring-infected erythrocyte surface antigen (RESA) was efficient against homologous parasites harboring the 3D7 allelic family of MSP-2, but failed to protect against those containing the FC27 allelic family, and was even associated with an increased rate of morbidity [P. falciparum did not provide significant protection against clinical malaria, but showed a strain-specific efficacy [P. falciparum polymorphism as an important step toward the identification of efficient malaria vaccine candidates.Achieving malaria elimination requires the development of an effective vaccine, especially against orbidity . More re MSP-2 3D, and theefficacy . CollectP. falciparum isolates from asymptomatic children in the selected study site by PCR-amplification of polymorphic regions of the two marker genes msp-1 and msp-2. The generated data are used to analyse the potential relationship between the levels of parasitaemia and specific parasite’s allelic types.The study presented here falls within the framework of preliminary investigations aimed at establishing a malaria clinical trial site in the district of Toubacouta . It follows epidemiological , entomolAnopheles gambiae complex. Malaria is mainly caused by P. falciparum with an entomological inoculation rate (EIR) of four infective bites per person from July to December 2011 in seven of the eight villages. Toubanding, the eighth village had an EIR of 30 infective bites per person owing to its proximity to the river Nema [The study was conducted using samples from the rural community of Toubacouta situated in the region of Fatick . The study area included eight villages: Haïdara, Daga Ndoup, Keur Ndianko, Nemanding, Passy Ndinderling, Keur Saloly Bouya, Keur Samba Gueye and Toubanding Fig. . Malariaver Nema . The popver Nema  and ACT A total of 1316 children under 10 years of age were enrolled in the study from November to December 2010. Children were examined by a medical doctor and clinical parameters were properly recorded. A venous blood sample was taken and used for blood typing, measurement of biological parameters, and for preparation of thick and thin blood smears for malaria diagnosis. A symptomatic case of malaria was defined as an axillary temperature ≥ 37.5 °C or any reported history of fever within the last 24 h confirmed by RDT . These cThick and thin smears were stained with 10% Giemsa for 25 min and microscopically examined for determination of parasite density and identification of species and developmental stages respectively. The number of parasites per 200 white blood cells (WBC) in thick-film was recorded and parasite density was estimated by counting the number of leucocytes by field examined and by arbitrarily considering that 8000 leucocytes were present in 1 µl of blood. At least 200 thick-film fields were examined before a slide was declared negative. Two experienced microscopists read the blood smears and in the case of a discrepancy, a third microscopist examined the slide. A slide was considered positive after two concordant readings by two different microscopists. Parasitaemias were classified into five levels including F1 (< 50 parasites/µl blood), F2 (50–499 parasites/µl), F3 (500–4999 parasites/µl), F4  and F5  .Genomic DNA (gDNA) was extracted from 100 μl packed red blood cells using QiaAmp DNA mini kit  according to the manufacturer’s instructions. The DNA was recovered in 50 μl of elution buffer provided with the kit into a properly labelled Eppendorf tube. DNA concentration and purity were estimated using a NanoDrop Lite Spectrophotometer (Thermo Scientific). Extracted DNAs were stored at − 20 °C until used.P. falciparum isolates was performed using a nested PCR approach targeting msp-1 and msp-2, two highly polymorphic region of the parasite’s genome as previously described [msp-1 block 2 and msp-2 block 3 respectively. The initial amplification was followed by individual nested PCR reactions using primers specific for K1, MAD20, and RO33 allelic types of msp-1, and FC27 and 3D7 allelic types of msp-2. In details, in the first PCR, 2.0 μl of DNA was amplified with 12.5 μl of 2× GMM , 1 μl of forward primer (10 μM), 1 μl of reverse primer (10 μM) and sterile ultrapure water to a final volume of 25 μl. For the second round of PCR, 2.0 μl of first-round PCR products was amplified with 12.5 μl of 2× GMM , 1.0 μl of forward primer (10 μM), 1.0 μl of reverse primer (10 μM) and sterile ultrapure water to a final volume of 25 μl. Primers and the reaction conditions are shown in Additional file P. falciparum msp-1 or msp-2 gene.Genotyping of escribed . All priE) was defined as the probability of being infected by two parasites with different alleles at a given locus. It is ranged between 0 and 1 was calculated by using the following formula: P value was < 0.05. All analyses were performed using R software (version 3.0.2).Asymptomatic malaria infection was defined as the presence of parasitaemia in the absence of symptoms or treatment. The complexity of infection (COI) also referred as multiplicity of infection (MOI) represented by the number of fragments per infected person, is the average number, for each gene, of distinct fragments per PCR positive sample. Expected heterozygosity  was 0.74  were positive for msp-1 gene, 15 different alleles were identified including 6, 5 and 4 belonging to the K1, MAD20 and RO33 allelic families respectively. The total number of msp-1 alleles detected in all samples was 219. Allele frequencies were 40.2% (88/219), 31.1% (68/219), and 28.7% (63/219) for RO33, MAD20 and K1, respectively (Table E) was 0.67 for msp-1 loci. Of msp-1 positive samples, 69% (58/84) were polyclonal. The mean complexity of infection (COI) was 2.5. These polyclonal infections mostly involved trimorphic and dimorphic allelic family combinations: 27.4% (23/84) of K1/MAD20/RO33, 16.7% (14/84) of MAD20/RO33, 14.3% (12/84) of K1/RO33 and 5.9% (5/84) of K1/MAD20. The maximum number of genotypes belonging to the same allelic family detected in a single isolate was 3, 5, and 4, for K1, MAD20 and RO33, respectively.For msp-2 gene, 21 different alleles were detected with 10 for 3D7 allelic family and 11 for FC27. Allelic family frequencies showed that the 3D7 allelic type was predominant  over FC27  (Table E) was 0.50 for msp-2 loci. The prevalence of multiple infections (MI) was 89% (73/82). The mean complexity of infection was 4.7. The frequency of infections involving the two allelic families of msp-2 was 63% (52/82). The maximum number of genotypes to belong to the same allelic family detected in a single isolate was 5 and 7 for 3D7 and FC27, respectively.For msp-1 and msp-2 allelic families are shown in Additional file Ranges of fragment sizes for msp-1 positives isolates . In contrast, parasitaemia were significantly and negatively correlated with MAD20 monoallelic infections . Regarding msp-2, FC27 monoallelic infections were negatively associated with parasitaemia .To further analyse the effect of polyclonal infection on parasitaemia, the influence of specific allele combinations on parasite density was assessed. For this purpose, the samples were classified according to their parasite density and five levels of parasitaemia were set. Analysis of the relationship between mia Fig.  showed aP. falciparum, the only species diagnosed, was analysed in samples from asymptomatic children collected through a cross-sectional survey. An important allelic diversity was observed with 15 and 21 different alleles, and 69% and 89% of multiple infections, for msp-1 and msp-2 genes, respectively. The high genetic diversity in our study is in agreement with previous findings on asymptomatic children in the mesoendemic malaria site of Niakhar in Senegal. In Niakhar, genotyping with the msp-2 polymorphic marker revealed a range from 2 to 7 different fragments per carrier with 64% of multiclonal infections [P. falciparum isolates in Ndiop (1994) [P. falciparum genetic diversity does not only rely on parasite transmission rate. Thus, it has been demonstrated that the genotypic profile changes within hours or days in asymptomatic infections [Preliminary studies for a clinical trial site for malaria have been undertaken in the district of Toubacouta . Eight villages were involved. The genetic diversity of fections . Similarfections . Comparip (1994)  and our p (1994) . This obfections .msp-1 and msp-2 markers [Unlike genetic diversity, complexity of infection (COI) is more commonly associated with the level of malaria transmission. In line with this observation, Konate et al.  found th markers , 20, 21.Association between the number of clones per sample and the level of parasitaemia has been investigated. The results show that polyclonality is likely to be associated with high parasitaemia. This is consistent with previous studies , 21–23. msp-1 gene and parasitaemia (Fig. Moreover, this study emphasizes the significant positive correlation between frequencies of trimorphic infections K1/MAD20/RO33 of P. falciparum clones circulating in Toubacouta, Senegal.This study has some technical limitations. Firstly, PCR cannot discriminate between alleles of differing sequences with similar size and can thus underestimate the number of distinct alleles , 31. SecP. falciparum strains are substantially circulating in this study site and display a high genetic diversity. The level of this diversity is comparable to that found in malaria endemic and mesoendemic areas, therefore, making the site interesting for potential vaccine and therapeutic clinical trials. The positive correlation between msp-1 trimorphic infection and parasitaemia suggests the use of field information on genetic diversity as starting blocks for designing new malaria vaccines.Despite the general decline in malaria prevalence observed following the implementation of various malaria control strategies, Additional file 1: Table S1. Primers and the reaction conditions used to amplify msp-1 and msp-2 genes.Additional file 2: Fig. S1. Photographs of migration profiles of msp-1 and msp-2 allelic families in 1.5% agarose gel.Additional file 3: Table S2. Ranges of fragment sizes for msp-1 and msp-2 allelic families.Additional file 4: Fig. S2. Box plot of parasitaemia in samples from asymptomatic children infected with Plasmodium falciparum. The eight outliers removed prior to comparison of monoclonal and polyclonal infections are shown.


An automated method for measuring arterial path length with devices that determine pulse wave velocity (PWV) in peripheral arteries is frequently applied. We aimed to compare arterial path length measurements based on mathematical height-based formulas with those measured manually and to assess whether the ankle-brachial difference (abD-PWV) measured with the VOPITB device is comparable to that obtained by manual measurements. In 245 patients, a metric measuring tape was used to determine the arterial path length from the suprasternal notch to the midpoint of the VOPITB cuffs wrapped around the extremities, and the results were compared with those obtained with height-based formulas. We examined the relationship between the abD-PWV measured with both methods. The arterial path length measured manually was shorter than that calculated automatically by 5 ± 2 and 30 ± 4 cm—of 13% and 21% for the arms and legs, respectively (difference of 13% and 21%). As a result, the abD-PWV calculated with the automatic method was greater . The Blant Altman plot showed a percentage error of: 15,2%, 7,5% and 17,3% for heart-brachial, heart-ankle length and abD-PWV respectively. In conclusion there were significant differences between manual and automated arterial length measurements and it translates into difference abD-PWV calculate from both methods. However, the Bland-Alman plot showed that abD-PWV was comparable for both techniques. The advantages of height-based formulas for the calculation of arterial path lengths suggest that they may be the recommended method for measuring the abD-PWV. More than half of cardiovascular events occur in individuals who were previously asymptomatic; therefore, identifying at-risk subjects is of utmost importance for prevention of these events . GeneralThe study of pulse wave velocity (PWV) is recognised by the European Society of Hypertension and the European Society of Cardiology as a useful tool to assess target organ damage . MeasureThe PWV is assessed by dividing the travelled distance by the travel time. The transit time or time delay of the arterial pulse along the arterial pathway can be measured accurately, but distances are usually estimated from measurements obtained from the body surface. The travel distance should also be measured accurately because even small differences may influence the absolute value of the PWV ,13. To eOur group has developed a device (VOPITB) that independently measures the brachial PWV (b-PWV) and the ankle PWV (a-PWV), allowing the comparison of the PWV between both extremities . Since t2 (m2). All participants were required to fast for the determination of total cholesterol, HDL-cholesterol, LDL-cholesterol, triglycerides, glucose, and HbA1c values. Using the relevant data, the patients were stratified by vascular risk according to the Framingham tables [This is a cross-sectional descriptive study conducted at the Hospital San Pedro de Alcántara, Cáceres (Spain), between April 2014 and May 2016. A total of 245 subjects were recruited. One hundred fifty subjects were referred from the vascular risk clinic at the Department of Internal Medicine, and 95 were recruited among Hospital workers without vascular risk factors, who were selected during their annual medical examination. The study protocol was approved by the local hospital ethical committee , according to the principles of the Declaration of Helsinki. All subjects provided written consent for participation. All patients underwent a medical history and physical examination that included recording age, sex, height, weight and waist circumference. The body mass index (BMI) was calculated according to the following formula: weight (kg)/heightPWV measurement was performed with the VOPITB device according to a previously described technique . BrieflyContinuous variables are expressed as the mean ± standard deviation, and categorical variables are expressed as percentages. The Kolmogorov-Smirnov test was used to determine whether the variables followed a normal distribution. Continuous variables were compared using Student's t-test or the Mann-Whitney U test, as appropriate, and categorical variables were compared using the chi-square test. Equivalency between arterial path length and abD-PWV measured manually or mathematical height-based formulas was assessed using Bland-Altman analysis. To be considered clinically acceptable, we have used both methods if the percentage error or limits of agreements would have ranged ±20%, as Critchley LA suggested .A value of p < 0.05 was considered statistically significant. Data were analysed with IBM SPSS Statistics version 24 statistical software.Table 1. Differences in weight and height were found between males and females: 85 ± 13 vs. 71 ± 16 kg (p < 0.001) and 170 ± 8 vs. 155 ± 8 cm (p < 0.001), respectively. The abdominal circumference in men compared to women was 102 ± 12 vs. 95 ± 15 cm (p < 0.001). Males, compared with females, had a higher 10-year cardiovascular risk of presenting ischaemic heart disease according to the Framingham scale, 46% vs. 17%; (p < 0.001). No significant differences were found in the other variables studied.Of the 245 patients examined, 147 (60%) were males. The mean age of the population was 57 ± 14 years (mean ± SD). The main clinical characteristics by sex are shown in Table 2 shows the differences between manual measurement of arterial path lengths and height-based formulas and the abD-PWV resulting from the measurement of the arterial path length with each method. The difference of the arterial path length measured manually from that calculated automatically was shorter for the arm than for the leg: 5 ± 2 cm and 30 ± 4 cm, respectively. Manual measurements resulted in 13% and 21% lower than the height-based values for the arm and leg, respectively, and these values were similar for both men and women.Fig 1 displays Bland-Altman plots for manual measured and automatic estimated arterial path length for heart-brachial and heart-ankle. A mean differences between the two measure was 5 cm with the limits of agreement from  for heart-brachial length and 30 cm  for heart-ankle length. The percentage error was 15,2% and 7,5% respectively. Fig 2 shows Bland-Altman plots for abD-PWV measured with height-based formulas and manual. A mean differences between the two methods was 345,9 cm/s with the limits of agreement from . The percentage error was 17,3%.In the present study, we found that arterial path length estimation from the heart to the arm and ankle is higher when calculated with mathematical height-based formulas than when measured manually. The differences between the two methods were proportionally higher for the arterial path in the leg than in the arm. As a result, the abD-PWV was greater when using arterial path lengths obtained with the proposed formulas than with those measured manually. However, the Bland-Altman plot showed that abD-PWV was comparable for both techniques, suggesting that both can be used to calculate the abD-PWV without evidence to favour any technique because true arterial path was not studied.The most suitable method for arterial path length estimation for the calculation of the PWV has been widely debated . DependiOur group has developed a device—VOPITB Click here for additional data file.


Malarial acute renal failure (MARF) is a component of the severe malaria syndrome, and complicates 1–5% of malaria infections. This form of renal failure has not been well characterized by histopathology.Plasmodium falciparum. His creatinine was 616 µmol/L coming from a normal baseline of 89 µmol/L. He had a urine protein:creatinine ratio of 346 mg/mmol (4.4 g/L). He required dialysis. A renal biopsy showed acute interstitial nephritis with podocyte foot-process effacement. He was treated with artesunate and his renal function improved. At 1 year follow-up his creatinine had plateaued at 120 µmol/L with persistent low-grade proteinuria.A 44 year-old male presented to the emergency department with a 5-day history of fever and malaise after returning from Nigeria. A blood film was positive for Acute interstitial nephritis and podocyte foot-process effacement might be under-recognized lesions in MARF. Studying the mechanisms of MARF could give insight into the immunopathology of severe malaria. Plasmodium falciparum is nearly always the infecting pathogen in MARF; rarely, Plasmodium vivax has been implicated [P. falciparum infection [Acute renal failure complicates 1–5% of malaria infections and this syndrome is referred to as malarial acute renal failure (MARF). MARF is a criterion for severe malaria and its associated mortality rate is 15–45% . Plasmodplicated , 3. MARFplicated , 4. Acutplicated , 5. Recoplicated , endotheplicated  and toxiplicated . Proteinnfection , 10 howeA 44 year-old male presented to the emergency department with a 5-day history of fever and malaise. He had recently returned to Ireland (his country of residence for 10 years) from Nigeria (his native country) after visiting friends and relatives, without taking malaria prophylaxis. He had a history of hypertension, for which he took ramipril, amlodipine and bendroflumethiazide throughout the previous year. There was no family history of renal disease. He reported having taken over the counter paracetamol during the 5 days prior to presentation, and a single 400 mg dose of ibuprofen on the day of presentation. Consumption of non-steroidal anti-inflammatory drugs (NSAIDs) beyond the day of presentation was repeatedly denied. He had not taken any other medications commonly associated with AIN such as beta-lactams, fluoroquinolones, sulfonamides or proton pump inhibitors prior to presentation.On examination, he was euvolaemic, his blood pressure was 169/77 mmHg and he produced 1580 mls of dark urine during the first 24 h. Urinalysis revealed 4+ protein and 3+ blood. He did not have a rash and had no peripheral oedema.9/L, eosinophil count 0.1 × 109/L, serum albumin 26 g/L, total serum bilirubin 15 μmol/L and lactate dehydrogenase 960 U/L. A blood film was positive for P. falciparum with 0.4% parasitaemia. Initial urine protein–creatinine ratio was 346 mg/mmol (absolute proteinuria = 4448 mg/L).Initial routine blood tests included creatinine 616 µmol/L , haemoglobin 11.2 g/dL, platelet count 70 × 10Tests for HIV, HBV, HCV, ANA and ANCA were all negative. C3 was normal and C4 was low (0.09 g/L). His haptoglobin was low (0.24 g/L) and G6PD enzyme activity was normal. A renal ultrasound described diffusely echogenic kidneys with the right kidney measuring 130 mm and the left kidney measuring 143 mm.A renal biopsy performed 10 days after presentation demonstrated acute interstitial nephritis with numerous eosinophils, particularly at the cortico-medullary junction  explains the heavy proteinuria. The relationship between these two lesions is unclear, although they both likely arose through the same inflammatory process. Parasitic infections can have a causative role in both AIN  and MCD A critical point in this case is to discern whether the interstitial nephritis was triggered by a drug or by the infecting pathogen. The patient had normal renal function documented while taking his anti-hypertensives, and there was no relapse after re-challenge with these medications. His renal failure was well established at presentation and renal function began to improve during his admission, so the medications he received in hospital cannot be held accountable. The consumption of a non-steroidal anti-inflammatory drug (NSAID) mandates further discussion.NSAID induced interstitial nephritis occurs 2 weeks to 18 months after initial exposure to the drug . A lymphP. falciparum were found to have renal malaria antigen deposition and interstitial nephritis [Reports of renal biopsies from patients with MARF are scarce and mostly originate from India , 18. Oneephritis .Babesia microti, a protozoan closely related to Plasmodium. In this case the inflammatory infiltrate also contained eosinophils and there was focal podocyte foot-process effacement [Many infective organisms have been associated with interstitial nephritis , includifacement , very siP. falciparum infection ranging from 0.15 to 5 g/day; there were no accompanying renal biopsies and, based on urine electrophoresis, they concluded that both glomerular and tubular sources could account for the proteinuria. More recently Ogbadoyi et al. [P. falciparum infection.Proteinuria is not uncommon in malaria infection , 10. EhrP. falciparum, malaria is overwhelmingly a disease of children, in whom MARF is unusual [P. falciparum infection. Given its absence in Asian case-series however, AIN is unlikely to be a common cause of MARF.MARF is predominantly a disease of partially- or non-immune adults in low-transmission endemic regions —a popula unusual . However unusual , and MAR unusual . Malaria unusual . A limitPeople living in malaria endemic regions develop an asymptomatic, non-sterile immunity to malaria after repeated exposure to infection, known as “premunition” , 24. It P. falciparum antigen deposited in his kidney. Plasmodium falciparum antigen deposition in renal tubules has previously been described in a post-mortem study [The patient had no prior history of renal failure associated with malaria infection, despite coming from an endemic-region. He could have suffered interstitial nephritis with associated podocyte foot-process effacement on this occasion due to a loss of tolerance to malaria, leading to an unbridled cell-mediated inflammatory response to em study . The locThe low level of C4 in this patient can be attributed to activation of the classical complement pathway by malaria infection  and the Regarding potential treatment options, corticosteroids have been shown to accelerate the rate of recovery from AIN, however they have not been shown to improve long term outcome . CorticoP. falciparum infection is supported firstly by the biological precedent in animal models, secondly, because the natural history of the illness and all clinical features are best explained by the malaria infection and, thirdly, because NSAID induced nephropathy does not fit with the patient’s drug history, histology or outcome. The principles of managing MARF include effective treatment of the infection according to WHO guidelines [The conclusion that this case of AIN was caused by idelines , avoidanDescribing the underlying renal pathology found in MARF is important as it can inform treatment strategies and improve our understanding of immunopathology in severe malaria.


Plasmodium parasite infection in a human induces a wide range of cytokine responses, including anti-inflammatory, pro-inflammatory as well as regulatory cytokines. Although immunological interactions have been suggested to occur during a concurrent infection of helminths and Plasmodium parasites, different conclusions have been drawn on the influence this co-infection has on cytokine production. This review briefly discusses patterns of selected cytokine  responses associated with infections caused by Plasmodium, intestinal parasites as well as a Plasmodium-helminth co-infection.Co-infection of malaria and intestinal parasites is widespread in sub-Saharan Africa and causes severe disease especially among the poorest populations. It has been shown that an intestinal parasite (helminth), mixed intestinal helminth or  Plasmodium spp.) and helminths coincide [Plasmodium spp., the interdependence between pro- and anti-inflammatory mediators of immunity can influence the survival of the Plasmodium parasite and subsequent development of disease [Plasmodium infection enhances gametocyte production [Plasmodium parasite development and survival in the host.Malaria, a significant contributor to high childhood mortality, is endemic in the tropical regions as are pathogenic helminth infections. The distribution patterns of malaria parasites  co-infection on the outcome of malaria [N. americanus, A. lumbricoides T. trichiura or S. mansoni or S. haematobium [S. mansoni eggs and larvae [Plasmodium-helminth co-infections are naturally expected to differ, depending on the form (stage) of the infecting helminth . This then could account for the variations in results obtained in the reported studies. The risk of anemia has been suggested to be high in STH infections as is an increase in the prevalence of malaria during a Plasmodium-Schistosoma spp. co-infection [Plasmodium falciparum parasite, some developmental life-stages (trophozoites and schizonts) are sequestered and evade immune recognition [Plasmodium-helminth co-infections.It is evident from several human epidemiological surveys that have assessed variations in cytokine responses during parasitic infections that contradictory findings have been reported on responses identified during  disease –10. Othe malaria –13. Some malaria , 15. Twomatobium , 17. It d larvae . As suchnfection . During ognition . The 3 tognition . This maPlasmodium infection, T-cell mediated inflammatory responses contribute to reduced parasite density as well as the pathology of the disease [via regulating inflammatory processes remains poorly understood [Plasmodium infection in both humans and small rodents [P. falciparum infection [During a  disease , 23. Thederstood , althougderstood . An optiderstood  to ensur rodents . The Th1 rodents  are crucnfection , 30.P. falciparum infection is usually associated with an increase in pro-inflammatory responses, which subsequently cause an increase in the secretion of IFN-γ and TNF-α [P. falciparum infection, cytokines including IL-12, IFN-γ and TNF-α can induce adverse immunopathology if not well regulated [An acute nd TNF-α . An earlnd TNF-α –34. Howeegulated . The indegulated , 36, 37 P. falciparum infections , found that IL-6, IL-8, IL-12 and IFN-γ levels in uninfected Fulani children were much higher than in uninfected Dogon children. The levels of IL-8 in Fulani infected with P. falciparum were significantly lower than an uninfected Fulani; however, the same was not observed among the Dogon people. They also identified IFN-γ as the only cytokine that was significantly higher in P. falciparum-infected Fulani children compared to matched Dogon children [Plasmodium parasites was found to produce 10-times higher IFN-γ responses compared to similar cells from the Dogon [A study conducted on the Dogon and the Fulani people of Mali, who live within the same environment but respond very differently to children . Stimulahe Dogon .Plasmodium infection [. IL-6 has been suggested to be an essential component of the immune response during the acute phase [P. falciparum malaria [Plasmodium infections.TNF-α is predominantly secreted by activated macrophages and its pyrogenic properties are central to the immune response generated during a nfection . IL-6 hate phase  and in c malaria . Elevate malaria . This su malaria . AdditioPlasmodium infection [Interleukin 10 in combination with other cytokines play an important role as immune regulators by neutralizing the Th1 effects associated with the more severe forms of nfection . Plasma nfection . Elevatenfection , whilst nfection . Similarnfection . The devnfection . Furthernfection .in vitro and in vivo human studies relating to the use of cytokines in the control of malaria parasites. Murine models of malaria demonstrate a regulation of balance between IL-10 and inflammatory cytokines such as IFN-γ and TNF-α [Animal studies have been used to buttress some findings observed in nd TNF-α . IL-10 pnd TNF-α  and the nd TNF-α . Althougnd TNF-α , early pnd TNF-α .Plasmodium infections. Increased levels of IFN-γ, TNF-α, IL-12 and IL-10 are known to be associated with a reduced risk of malaria; however, those who become infected with malaria parasites have an elevated risk of symptomatic malaria [Plasmodium parasite infection can help develop malaria vaccines as well as help in designing other strategies for malaria control interventions such as intermittent preventive treatment (IPT) or seasonal malaria chemoprevention (SMC).In summary, human and murine models have both suggested IFN-γ to be essential for parasite clearance and reduced parasite multiplication rates –53. Howe malaria . These c malaria . These sS. mansoni parasites. The role played by TNF-α in the metabolism of the adult S. mansoni parasite also remains controversial [Helminths survive by modulating the host’s immune system . The inioversial . A comprS. mansoni, S. haematobium and A. lumbricoides, influence immune responses in a patient. There are only a few studies that have tried to understand the effect of multiple helminths and intestinal protozoan parasite co-infections on immune responses in children [Entamoeba histolytica) were stimulated with helminth-specific antigens, they produced higher IL-10 levels compared with PBMCs from adults without the triple infection [ex vivo frequency of CD4+ cells producing IL-10 and IL-4 compared with those without the infection [A. lumbricoides and T. trichiura was associated with an acute production of IL-10 in response to stimulation with A. lumbricoides antigen [A. lumbricoides and T. trichiura, identified similar levels of IL-10 in the uninfected control, the mono- and poly-parasite infected groups [Giardia lamblia and Ascaris lumbricoides. Higher levels of TNF-α was found in the co-infected children compared to matched children with only a Giardia infection and the IL-10/IFN-γ ratio in these co-infected children was higher than in uninfected children as well as children with only an Ascaris infection [Although concurrent multiple (mixed) helminth infections are frequent occurrences, little is known about how concurrent parasite infections, such as children –71. The children . Similarnfection . Filarianfection . IL-10 cnfection  as well nfection , 75. An nfection , 76. A c antigen . Howeverd groups . The balnfection . These rSchistosoma spp. may affect the immune response of a host to a malaria parasite infection and lead to increased susceptibility and disease severity [Plasmodium parasites in the study population [The precise impact of polyparasitism on the immunopathology of malaria is not known even though malaria co-infections with intestinal parasites in tropical regions are common . Severalseverity –83. Howeseverity . The varpulation , 86.Ascaris infections are more likely to afford protection against severe malaria while infections with hookworm or Schistosoma spp. are more likely to enhance the incidence of malaria [S. haematobium and P. falciparum co-infected children compared with those infected with only P. falciparum. When blood from Ghanaian children with and without a S. haematobium infection was stimulated with P. falciparum antigens, a significantly higher level of IL-10 was recorded [P. falciparum schizont lysate compared with the asymptomatic patients without filarial infection [S. haematobium infection were found to have significantly higher levels of IFN-γ but similar levels of IL-10 when compared to matched children without S. haematobium infection [Schistosoma haematobium was found to protect against P. falciparum in children between 4 and 8 years-old [S. haematobium who developed P. falciparum malaria when compared with Schistosoma-negative children who developed malaria. The effect was more pronounced in children aged between 9 and 14 years relative to those between 4 and 8 years [Schistosoma-positive and -negative children aged between 4 and 14 years who developed acute malaria, a significantly lower mean IL-10/TNF-α ratio was identified in Schistosoma-positive children aged between 9 and 14 years who developed acute malaria compared with matched Schistosoma negative children [Plasmodium and S. haematobium did not demonstrate any influence of the co-infecting helminth on the immune response generated by the host against the Plasmodium parasite as a similar increase in both innate and adaptive immune responses was observed in the P. falciparum-infected and P. falciparum-S. haematobium co-infected children [ malaria . The und malaria  reportedrecorded . Similarnfection . Childrenfection . Schistoears-old . IL-6 an 8 years . Whilst children . Howeverchildren .Plasmodium infection or an intestinal parasite  infection or concurrent Plasmodium-helminth  co-infection were compared to uninfected individuals, the levels of TNF-α, IL-2, IL-10, IL-6 in the Plasmodium and Plasmodium-helminth co-infected group were similar, and both significantly higher than the uninfected group. These results corroborated the absence of additional stimulation of cytokine responses by the co-infecting helminth [Plasmodium, intestinal parasite and Plasmodium-helminth co-infected) groups compared to the uninfected group [Plasmodium-helminth co-infection as such information is very limited.Helminth infections induce immuno-regulatory responses such as Th2 responses in the human host. These immune-regulatory responses can inhibit the ability of the host to mount an effective Th1 response , which ahelminth . The meded group . In malaPlasmodium-helminth co-infections [Plasmodium and helminths, their main focus, which has been captured in a review by Mpairwe et al. [Pregnancy naturally causes alterations in immune responses and results in the dominance of Th2 immune responses. Increased IL-10 and a decrease in IFN-γ production have been associated with successful pregnancies in humans –94. Howefections . Althouge et al. , has beeP. falciparum and different helminths. Diverse effects of a helminth infection on the susceptibility, severity and the pathology or the risk of cerebral malaria have been observed in several human studies conducted in Africa. These disparities could have arisen as a result of a number of different covariates, including the choice and nutritional status of the cohort(s) used in the study, the level of exposure, the infecting parasite species and the population and composition of microbial communities (microbiota) resident in the hosts’ intestines to name a few. In P. falciparum immunology, age is a strong confounder to antibody and cytokine analysis; however, in many of the studies monitoring the cytokine profiles of people with a malaria helminth co-infection, children with ages ranging from a few months with a low age range [We noticed the existence of several disparities relating to the association between various cytokine levels found in individuals co-infected with ge range , to 19 yge range  were useP. falciparum and helminth infection differed between the studies, making the definition for a malaria helminth co-infection very different. A few studies defined malaria as P. falciparum positivity by real time PCR [Plasmodium-helminth co-infections under different infecting parasite conditions.The definition of a time PCR  and othetime PCR , 109. Sotime PCR  or by obtime PCR , 91. Thetime PCR  or both Nutrition directly impacts immunity as malnutrition and obesity have been found to result in immunodeficiency and reduced immunity, respectively , and indPlasmodium and helminth infections and co-infections is shown in Table These properties of the infection status all greatly influence the cytokine profile of the individual infections and likely would affect the cytokine profile of the co-infection. A list of cytokine responses associated with the innate and adaptive immune responses to Plasmodium antigens in adults and children above 4 years old [Plasmodium antigen  in RTS,S and subsequently increase the effectiveness of the vaccine. This increased immune responsiveness may also result in RTS,S efficacy in older children as well.Anthelmintic treatment (deworming) in humans has been suggested to enhance immune responsiveness to vaccines, especially to ears old . A majorears old . DewormiExperimental results obtained from the mice studies discussed in this review that have not been validated in human studies are to be taken as suggestive and to an extent that is probable but not definitive. These cautions are relevant because despite the genetic and physciological similarities between humans and mice , 121 andPlasmodium intestinal parasite co-infections; however, due to varying confounding factors, different mechanisms have been reported for the protection provided by different cytokines. A major source of variation in the results reported by the numerous studies could be the use of different cohorts and population (ethnic groups and age), stimulant (live parasite or recombinant antigen) and cell type (PBMC or plasma) as well as the different methods used to determine parasite prevalence and density.Several studies have provided evidence of immunological perturbations occurring during Plasmodium and intestinal worms.Increased national efforts to reduce parasitic worm infections with frequent mass drug treatments may result in the modulation of the helminth induced cytokine response. It is thus imperative that the precise contribution intestinal parasites add on to immune responses generated during a malaria infection is fully understood. This will enable a better understanding of immune modulation when such a mass drug treatment is implemented. Increased efforts to obtain an effective malaria vaccine require a complete understanding of immune moderations generated during the co-infections of


Severe falciparum malaria is a medical emergency characterised by potentially lethal vital organ dysfunction. Patient fatality rates even with parenteral artesunate treatment remain high. Despite considerable research into adjuvant therapies targeting organ and tissue dysfunction, none have shown efficacy apart from renal replacement therapy. Understanding the causal contributions of clinical and laboratory abnormalities to mortality is essential for the design and evaluation of novel therapeutic interventions.n = 5,635) and adults  with severe falciparum malaria from 15 countries in Africa and Asia who were studied prospectively over the past 35 years. On admission, patient covariates associated with increased in-hospital mortality were severity of acidosis (odds ratio [OR] 2.10 for a 7-mEq/L increase in base deficit [95% CI 1.93–2.28]), renal impairment , coma (OR 3.59 [95% CI 3.07–4.21]), seizures (OR 1.40 [95% CI 1.16–1.68]), shock (OR 1.51 [95% CI 1.14–1.99]), and presumed pulmonary oedema (OR 1.58 [95% CI 1.04–2.39]). Lower in-hospital mortality was associated with moderate anaemia (OR 0.87 for a decrease of 10 percentage points in haematocrit [95% CI 0.80–0.95]). Circulating parasite density was not associated with mortality (OR 1.02 for a 6-fold increase [95% CI 0.94–1.11]), so the pathological effects of parasitaemia appear to be mediated entirely by the downstream effects of sequestration. Treatment with an artemisinin derivative decreased mortality compared with quinine (OR 0.64 [95% CI 0.56–0.74]). These estimates were consistent across children and adults . Using inverse probability weighting, transfusion was not estimated to be beneficial in children with admission haematocrit values between 15% and 25% (OR 0.99 [95% CI 0.97–1.02]). Except for the effects of artemisinin treatment and transfusion, causal interpretations of these estimates could be biased by unmeasured confounding from severe bacterial sepsis, immunity, and duration of illness.We used a structural model causal inference approach to investigate causal relationships between epidemiological, laboratory, and clinical variables in patients with severe falciparum malaria enrolled in clinical trials and their in-hospital mortality. Under this causal model, we analysed records from 9,040 hospitalised children .In the context of severe falciparum malaria, moderate anaemia may protect against death, and a conservative approach to blood transfusion might be warranted.The current haemoglobin threshold of 5 g/dL used to define severe malarial anaemia may be too high and should be reconsidered.In order to refute or confirm these findings, randomised studies are needed to assess optimal transfusion thresholds. Severe falciparum malaria remains an important cause of premature death in malaria-endemic countries ,2. The oAssessing the predictive value of admission ‘phenotypes’, as defined by clinical and laboratory covariates, on the outcome of severe malaria  may improve the recognition of patients who are likely to deteriorate and thus help identify those who would benefit most from escalation of care . This doIn the absence of randomisation, estimating the direction and magnitude of a causal contribution relies on multiple assumptions that are nonstatistical in nature . The pasIn this work, we take a pragmatic approach to the important question, ‘Which clinical or pathological features of severe falciparum malaria cause death, and by how much?’ We attempt to estimate the causal contributions to mortality of key, routinely measured, clinical variables in severe falciparum malaria. Our approach is based on the structural model causal inference framework , underpihttp://dagitty.net/development/dags.html?id=HJ1OW3). DAGitty is an online platform for the analysis of causal diagrams and implements causal calculus (d-separation rules). Conditional on the causal diagram under consideration, this determines the necessary adjustment sets to allow for unbiased estimation of the total or direct causal effects on the outcome variable. We make the distinction between direct effects and total effects. The direct effect is the effect of the exposure  on death at a fixed level of the mediator . This is termed a controlled direct effect in the causal inference literature. The total effect includes indirect effects via mediating variables .We used a structural model causal inference approach . All ourWe based our DAG  on the gThe goal of this analysis was to determine the individual total and direct causal effects on mortality (represented by the vertex ‘Death’) of anaemia , acute kidney injury , coma , seizures, shock, acidosis (base deficit), pulmonary oedema , and circulating parasitaemia .The important unmeasured variables are ‘Sepsis’ and ‘Lactate’ . A significant proportion of unmeasured bacterial sepsis caused by the malarial infection would result in confounding for all exposure–outcome relationships. Bacteraemia, particularly with enteropathogens, is common in children diagnosed with severe malaria, but it is uncommon in adults. Although severe malaria does predispose to sepsis, many cases in children in endemic areas diagnosed as severe malaria are likely to represent primary septicaemia with incidental malaria ) assuminThe total effect of anaemia on death cannot be estimated; however, the direct effect can be estimated with adjustments required for pulmonary oedema, AKI, shock, acidosis, hypoglycaemia, seizures, and coma.The direct effect of coma on death can be estimated with adjustments required for pulmonary oedema, AKI, shock, acidosis, hypoglycaemia, anaemia, and seizures (mediator).The total  of acidosis on death can be estimated with adjustments required for AKI, anaemia, coma, seizures, hypoglycaemia, pulmonary oedema, and shock.The direct effect of shock on death can be estimated with adjustments required for AKI, acidosis (mediator), anaemia, coma, hypoglycaemia, pulmonary oedema, and seizures.The direct effect of AKI on death can be estimated with adjustments required for acidosis (mediator), anaemia, coma, seizures, hypoglycaemia, pulmonary oedema, and shock.The total  of pulmonary oedema in death can be estimated with adjustments required for AKI, acidosis, anaemia, coma, hypoglycaemia, seizures, parasitaemia, and shock.The direct effect of parasitaemia on death can be estimated with adjustments required for AKI, acidosis, anaemia, coma, hypoglycaemia, pulmonary oedema, seizures, and shock.The total  of seizures on death can be estimated with adjustments required for AKI, acidosis, anaemia, coma, hypoglycaemia, pulmonary oedema, and shock.Any resulting association between these factors and mortality may then be interpreted as causal. This causal interpretation relies on three key assumptions, known as conditional exchangeability (no unmeasured confounding), consistency, and positivity. Consistency means that the probability of the outcome is the same whether the value of a variable was simply observed or was intervened upon. For example, in this context, consistency implies that the probabilities of death in a patient with 35% haematocrit on admission and in an identical patient but with 10% haematocrit on admission who received a blood transfusion leading to 35% haematocrit are equal . Positivity means that every value of the exposure variable  has a nonzero probability of occurring in all patient strata.In the statistical analyses, we adjusted for antimalarial treatment (‘Drug’) in order to reduce the variance in the final estimates of the causal effects. We also adjusted for age and access to care (using the proxies study site and country) which explains large differences between trials and countries .A reasonable criticism of the causal DAG shown in We argue that the exposure variables in We included in this analysis all randomised trials and observational studies conducted or coordinated by the Mahidol Oxford Tropical Medicine Research Unit, Bangkok, Thailand, and the Oxford University Clinical Research Unit, Hospital for Tropical Disease, Ho Chi Minh City, Vietnam, between 1980 and 2016. The pooled dataset was made up of records for 9,040 patients . Each study included in this pooled analysis was approved by a local or national ethics committee and by the Oxford tropical research ethics committee. All patients, or their attendant relative or guardian, provided written informed consent. For further details, see the references provided below.n = 683) [‘Core Malaria’, Thailand: observational and treatment studies of severe-malaria patients enrolled in hospital-based studies conducted between 1980 and 2016 in Chantaburi, Kanchanaburi, and Tak provinces (Thailand) (n = 683) .n = 424) [‘Core Malaria’, Bangladesh: observational and treatment studies of patients with severe malaria enrolled at the Chittagong Medical College Hospital, Chittagong, Bangladesh, carried out between 2003 and 2016 n = 424) –24. –24.n = n = 48) [The quinine versus chloroquine trial in The Gambia (QC): a randomised controlled trial of quinine versus chloroquine in children with severe malaria enrolled at the Royal Victoria Hospital and the United Kingdom Medical Research Council laboratories and wards, The Gambia, from September to October 1988 (n = 48) .n = 370) [The artemether versus artesunate trial in Vietnam (AAV): a randomised controlled trial of artesunate versus artemether in Vietnamese adults with severe falciparum malaria conducted at the Hospital for Tropical diseases, Ho Chi Minh City, Vietnam, between May 1996 and June 2003 (n = 370) .n = 560) [The artemether versus quinine trial in Vietnam (AQ): a randomised controlled trial of parenteral artemether versus quinine in Vietnamese adults with severe falciparum malaria conducted at the Hospital for Tropical diseases, Ho Chi Minh City, Vietnam, between May 1991 and January 1996 (n = 560) .n = 1,461) [The South East Asian multinational quinine versus artesunate malaria trial (SEAQUAMAT): a multicentre, open-label, randomised trial of artesunate versus quinine for treatment of severe falciparum malaria conducted in Papua, Myanmar, Bangladesh, and India between June 2003 and May 2005  [The Africa multinational quinine versus artesunate in severe-malaria trial (AQUAMAT): a multicentre, open-label, randomised controlled trial of artesunate versus quinine in the treatment of severe falciparum malaria in African children conducted in 11 sites located in nine countries between October 3, 2005, and July 14, 2010  .Details of the study populations and methods used in these studies can be found in the individual publications . All analyses are fully reproducible and publicly available as an RMarkdown notebook on GitHub, which can be found at In the pooled dataset, several variables were missing in a significant proportion of records. Coma status was missing from 229 records (3%), haematocrit from 870 (10%), parasite density from 1,882 (21%), base deficit from 2,375 (26%), plasma bicarbonate from 3,679 (41%), respiratory rate from 369 (4%), venous blood or plasma lactate from 7,423 (82%), blood urea nitrogen from 1,544 (17%), plasma creatinine from 7,242 (80%), transfusion information from 2,342 (22%), and information on hypoglycaemia from 115 records (1%).Imputation of missing variables was done using known mechanistic relationships with other recorded variables and was done sequentially, processing from the most-informative recorded variable to the least informative.We used the auxiliary variables bicarbonate, lactate, and respiratory rate to impute missing base deficit values. For base deficit, this was done with Gaussian error linear models, first using bicarbonate, and then, if that value was also missing, we used lactate and then finally respiratory rate for the remaining missing values. Remaining missing values were then modelled from the marginal age-adjusted distributions.We used plasma creatinine to impute missing blood urea nitrogen values. Imputation for missing blood urea nitrogen values was also done with a Gaussian linear model on the log scale, with creatinine used as the predictive variable. Remaining missing values were imputed from the marginal distribution.Missing pulmonary oedema values were imputed from respiratory rate with Gaussian error.Missing haematocrit values were imputed from age with Gaussian error.Missing parasitaemia values were imputed from age on the log scale with Gaussian error.https://github.com/Stije/SevereMalariaAnalysis.Every imputation model had random-effect terms for country and study. A total of 100 imputed datasets were created for the final analysis. The full imputation code is available online at N = 9,040, yi is the outcome by end of hospitalisation (died/survived), and xi is the set of explanatory variables as given above: haematocrit, coma, blood urea nitrogen, base deficit, pulmonary oedema, hypoglycaemia, seizures, shock, parasitaemia, age, treatment drug, study ID, country, and study site.Patient data are denoted z ∈ ‘haematocrit, coma, base deficit, blood urea nitrogen, pulmonary oedema, seizures’, and survival yi from severe-malaria clinical trial data was done as a two-stage process:The estimation of the causal relationship between the set of baseline covariates j = 1…100, we fitted a mixed-effects logistic regression model, whereby yi ~ L(xi|β), which characterised the relationship between survival yi and the observed covariates xi. Patients were indexed by i. This model adjusts for confounders and blocks ‘back-door paths’ conditional on the structural causal model shown in x as fixed effects is the set of necessary adjustment variables. One exception is ‘Access to care’, which is modelled as an independent random effect using two proxy variables:In the first stage, we did the following: For each imputed dataset studycountryThis model fit estimates the set of parameters e ‘lme4’ .In a second stage, the sets of parameters erTools’ ) and obtThe global estimate n = 3,498). This analysis used 100 imputed datasets, with the same imputation procedures as above. We believe that data from Asian adults will not be significantly confounded by the presence of sepsis, as this has been investigated prospectively, and the estimated prevalence in adult severe falciparum malaria at presentation is less than 2%. This is based on observations from the AQ and AAV studies done in Vietnam, whereby admission blood cultures were performed systematically. In the AQ study, eight patients had positive blood cultures out of 560 (1.4%), and in the AAV study, six out of 220 were positive, but two of these were coagulase-negative staphylococci, which were likely contaminants, giving a global estimate of 1.8% (the remaining 150 records in the AAV study could not be matched).We performed a sensitivity analysis using only the patient records from studies done in Asia . For many of the missing key variables of interest (such as base deficit), values will not be ‘missing completely at random’, but it can be assumed that they are ‘missing at random’ . In most cases, data are missing because the necessary tests or machines were not available for that study. Therefore, a sensitivity analysis only using patient records with full data (for the variables of interest) should not introduce any major biases but will have reduced power.We performed an additional sensitivity analysis only using the patient records for which there were complete data  with propensity scores. The range of 15%–25% haematocrit was chosen, as it is above the AQUAMAT trial threshold for transfusion (15% haematocrit). Below 25% haematocrit, the estimated weights were all less than 10. The sample size was not large enough for an equivalent analysis in adults, as only the AQ and AAV studies reliably recorded transfusion status.The inverse probability weights were computed using the R package ‘ipw’ . This coThis analysis used the data from the AQUAMAT study to estimate the effect of transfusion on death. Patients enrolled in the Democratic Republic of Congo were excluded, as almost all of these patients were given a blood transfusion (only 16 out of 422 did not receive one). The final analysis included 2,214 with baseline haematocrits between 15% and 25%, of which 1,083 were transfused, and 1,131 were not transfused. The effect of transfusion on death in the IPW population was estimated with a robust sandwich estimator using the function ‘svyglm’ from the R package ‘survey’ . BalanceWe pooled individual patient data from all clinical trials in severe falciparum malaria run by or coordinated by the Mahidol Oxford Tropical Medicine Research Unit based in Bangkok, Thailand, and the Oxford University Clinical Research Unit based in Ho Chi Minh City, Vietnam, conducted between 1980 and 2015. The final pooled dataset included records from 9,040 individual patients enrolled in four large randomised controlled trials in severe malaria and a series of observational studies and one small randomised trial (quinine versus chloroquine) conducted in The Gambia. The considerable interdependencies between variables are highlighted in the bivariate scatter plots along with mixed-effects spline fits . Marked The estimated univariate regression models, adjusting only for intersite and intercountry variability and allowing for nonlinear relationships between the four continuous baseline characteristics of interest and in-hospital mortality, are shown in The primary goal of this study was to estimate unbiased causal effects on the subsequent in-hospital mortality from severe falciparum malaria of the absolute admission values of haematocrit (anaemia), base deficit (acidosis), blood urea nitrogen (AKI), and circulating parasitaemia (percent of parasitised red blood cells) and the presence of coma, seizures, shock, and pulmonary oedema measured or observed on enrolment in these prospective studies. Antimalarial treatments have a major impact on outcome, and adequately powered randomised drug trials  should remove any confounding effects and thus allow for direct causal interpretation of observed differences between groups. However, for baseline characteristics such as anaemia, randomisation is not possible. Mendelian randomisation  is aOur proposed causal diagram  posits tAssuming a linear relationship between patient covariates and the log-odds of in-hospital mortality, we estimated that the adjusted mortality odds ratios varied from 0.87 (0.80–0.95), for an absolute decrease of 10 percentage points in haematocrit, to 3.59 (3.07–4.21), for the presence of coma. The full set of estimated causal effects are shown in We performed two subgroup analyses with data only from Africa and only from Asia, respectively . OverallUnder our causal model assumptions, ‘moderate anaemia’ was protective against mortality: a lower absolute admission haematocrit of 10 percentage points corresponded to a mortality odds ratio of 0.87 (95% CI 0.80–0.95). This assumes a linear relationship between haematocrit and the log-odds of death, which must be incorrect for extremely low haematocrit values. A sensitivity analysis removing patients with admission haematocrits less than or equal to 9% (a haemoglobin of approximately 3 g/dL) estimated a stronger protective effect, with an odds ratio of 0.83 (95% CI 0.75–0.91).Blood transfusion could mediate the relationship between anaemia and death. The apparent protective effect of lower haematocrits in severe malaria could result from the beneficial effect provided by blood transfusions given to patients presenting with haematocrits below a certain threshold . However, a naive mediation analysis via a simple adjustment for transfusion status results in selection (‘collider’) bias . This isn = 6,307, with no missing values). An analysis stratified by time to death limited to these three studies did not show a statistically significant protective effect for transfusion, with an adjusted odds ratio of 0.90 (0.72–1.63). This can be compared with the ‘naive’ analysis not accounting for the competing risk of death, which gives a substantially lower adjusted odds ratio of 0.61 (0.49–0.78). The estimate of the direct effect of haematocrit was approximately the same as for the non-mediated analysis but was not statistically significant, with an adjusted odds ratio of 0.91 (0.79–1.04); see Blood transfusion status was only recorded reliably in the AQUAMAT, AAV, and AQ studies , there was no evidence for a beneficial effect of transfusion, with an odds ratio for death in the transfused group of 0.99 (95% CI 0.97–1.02). We explored the relationship between transfusion and death further in a ‘moderate anaemia’ range, defined ad hoc as 15%–25% haematocrit, using the data from the AQUAMAT study. The causal effect of transfusion was estimated using IPW with propensity scores . In thiThe assumption of no unmeasured confounders for the relationship between anaemia and death may be incorrect. A simplified DAG in panel B of In the absence of randomisation, causal inference is difficult and relies on nonstatistical assumptions supported by expert knowledge. Formalising these assumptions using causal diagrams (DAGs), which are readily understood and therefore readily challenged, improves transparency and facilitates investigation. Using a structural causal models framework , direct If it is indeed true that ‘moderate anaemia’  reduces the mortality of patients with severe falciparum malaria, the mechanisms of the protective effect are unclear. In severe malaria, macrocirculatory changes are characterised by an increase in cardiac output and a moderate decrease in peripheral vascular resistance so that severe hypotension is rare . HoweverIf moderate anaemia does confer protection against death, it would have significant implications for patient management and, particularly, for blood transfusion, suggesting a more conservative transfusion strategy may be warranted. It should be noted that, whereas transfused red cells may have less immediate oxygen-carrying capacity (depending on their oxygen dissociation curves), they may be more deformable than the relatively rigid erythrocytes that occur in severe malaria. The transfused plasma may also have antimalarial activity in endemic areas. Thus, the effects of blood transfusion on microvascular perfusion may not be predicted accurately by the relationship between haematocrit and oxygen delivery in severe falciparum malaria. The mediation analysis in this study lacked power to determine whether the protective effect of lower haematocrits was partially mediated by blood transfusion or not. However, in the reduced pooled data with reliably collected transfusion data and stratified by time to death, there was not strong evidence that blood transfusion was associated with a reduced risk of mortality. This stratification is necessary, as patients who receive transfusions are a selected group who by definition have survived long enough to receive the blood. Moribund patients may be less likely to be transfused. Resolution of this important management question requires prospective studies assessing blood transfusion thresholds.Our data do not contain information on host genetic markers, but the hypothesis of a protective effect of anaemia in severe malaria could be tested on recent large pooled datasets that have collected these data . TreatmeThis study estimated causal effects from observational data . The causal interpretation of the relationship between patient covariates and mortality is strongly dependent on the structure of the underlying causal model. The main limitations can be summarised as follows. Unmeasured confounding is plausible, leading to indirect relationships . A key consideration in the DAG is the role of sepsis in the causal pathway of severe malaria. In the large majority of the pooled data analysed, concomitant sepsis was not assessed by blood culture (which itself is an insensitive measure). This could result in contamination of the data. Sepsis can occur independently of the malarial infection in a patient with incidental asymptomatic parasitaemia, or it can be a result of parasite sequestration (particularly in the gut). This is mainly an issue in the higher-transmission settings in sub-Saharan Africa . If sepsFurther clinical investigation is necessary to determine whether there is truly any benefit from moderate anaemia in patients with severe malaria and, if so, to characterise the relationship and its time course, as current therapeutic strategies assume only that it is harmful. It should be emphasised that this observation refers only to patients who present with severe malaria and not to patients with uncomplicated malaria. However, it is possible that the apparent life-saving benefit provided by moderate anaemia has occurred earlier in the course of the illness . A better understanding of the pathological process would inform indications for blood transfusion and hopefully avoid any detrimental effects of overtransfusion in patients with severe falciparum malaria . ClearlyIn conclusion, using a causal inference approach to explore the relationships between clinical features and mortality in patients with severe falciparum malaria, it was found that although six well-established measures of vital organ dysfunction were causally associated with an increased risk of death, the direction of the causal effect of anaemia goes against accepted understanding. Moderate anaemia had a relatively small direct protective effect on outcome. Further clinical investigation is required. However, this observation, derived from a very large prospectively studied population across Asia and Africa, does suggest that the current ‘severe anaemia’ criterion for severe malaria, which is a 5 g/dL threshold for children and 7 g/dL for adults, should be reviewed. The application of causal inference-based methodology may be useful in future studies to investigate the direct effects of major complications of severe malaria on mortality. This could help increase the clinical definition of severe malaria, improve the understanding of its pathophysiology, and provide a sound basis for developing and evaluating adjunctive interventions.S1 FigGreen diamonds (lines): point estimates (95% CI) using data from only Asian patients. Blue diamonds (lines): point estimates (95% CI) using data from only African patients. Black diamonds (lines): point estimates (95% CI) using the whole pooled database, as shown in (TIF)Click here for additional data file.S2 FigThe forest plot shows the estimated effects on survival of main patient characteristics and treatment drug used, stratified by time to death and adjusted for transfusion.(TIF)Click here for additional data file.S3 FigThe black circles show the standardised differences for the original population, and the red triangles show the standardised differences for the IPW population. Standardised differences greater than 0 indicate higher values of that covariate in the transfused group , whereas standardised differences less than 0 indicate higher values in the not-transfused group . The vertical dashed lines show ±10% standardised difference. AQUAMAT, Africa multinational quinine versus artesunate in severe-malaria trial; IPW, inverse probability weighting.(TIF)Click here for additional data file.S1 TableHCT, haematocrit; SB, serum bilirubin; SBP, systolic blood pressure; SC, serum creatinine; UO, urine output.(XLSX)Click here for additional data file.