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Identification of a Novel Clinical Phenotype of Severe Malaria using a Network-Based Clustering Approach.

ABSTRACT: The parasite Plasmodium falciparum is the main cause of severe malaria (SM). Despite treatment with antimalarial drugs, more than 400,000 deaths are reported every year, mainly in African children. The diversity of clinical presentations associated with SM highlights important differences in disease pathogenesis that often require specific therapeutic options. The clinical heterogeneity of SM is largely unresolved. Here we report a network-based analysis of clinical phenotypes associated with SM in 2,915 Gambian children admitted to hospital with Plasmodium falciparum malaria. We used a network-based clustering method which revealed a strong correlation between disease heterogeneity and mortality. The analysis identified four distinct clusters of SM and respiratory distress that departed from the WHO definition. Patients in these clusters characteristically presented with liver enlargement and high concentrations of brain natriuretic peptide (BNP), giving support to the potential role of circulatory overload and/or right-sided heart failure as a mechanism of disease. The role of heart failure is controversial in SM and our work suggests that standard clinical management may not be appropriate. We find that our clustering can be a powerful data exploration tool to identify novel disease phenotypes and therapeutic options to reduce malaria-associated mortality.

SUBMITTER: Cominetti O

PROVIDER: S-EPMC6110866 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature

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Identification of a Novel Clinical Phenotype of Severe Malaria using a Network-Based Clustering Approach.

Cominetti Ornella O Smith David D Hoffman Fred F Jallow Muminatou M Thézénas Marie L ML Huang Honglei H Kwiatkowski Dominic D Maini Philip K PK Casals-Pascual Climent C

Scientific reports 20180827 1

The parasite Plasmodium falciparum is the main cause of severe malaria (SM). Despite treatment with antimalarial drugs, more than 400,000 deaths are reported every year, mainly in African children. The diversity of clinical presentations associated with SM highlights important differences in disease pathogenesis that often require specific therapeutic options. The clinical heterogeneity of SM is largely unresolved. Here we report a network-based analysis of clinical phenotypes associated with SM ...[more]

PMID: 30150696

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Project description:Malaria is a disease that generates a broad spectrum of clinical features. The purpose of this study was to evaluate the clinical spectrum of malaria in semi-immune populations. Patients were recruited in Mâncio Lima, a city situated in the Brazilian Amazon region. The study included 171 malaria cases, which were diagnosed via the use of a thick blood smear and confirmed by molecular methods. A questionnaire addressing 19 common symptoms was administered to all patients. Multiple correspondence analysis and hierarchical cluster analysis were performed to identify clusters of symptoms, and logistic regression was used to identify factors associated with the occurrence of symptoms. The cluster analysis revealed five groups of symptoms: the first cluster, which included algic- and fever-related symptoms, occurred in up to 95.3% of the cases. The second cluster, which comprised gastric symptoms (nausea, abdominal pain, inappetence, and bitter mouth), occurred in frequencies that ranged between 35.1% and 42.7%, and at least one of these symptoms was observed in 71.9% of the subjects. All respiratory symptoms were clustered and occurred in 42.7% of the malaria cases, and diarrhea occurred in 9.9% of the cases. Symptoms constituting the fifth cluster were vomiting and pallor, with a 14.6% and 11.7% of prevalence, respectively. A higher parasitemia count (more than 300 parasites/mm(3)) was associated with the presence of fever, vomiting, dizziness, and weakness (P < 0.05). Arthralgia and myalgia were associated with patients over the age of 14 years (P < 0.001). Having experienced at least eight malaria episodes prior to the study was associated with a decreased risk of chills and fever and an increased risk of sore throat (P < 0.05). None of the symptoms showed an association with gender or with species of Plasmodium. The clinical spectrum of malaria in semi-immune individuals can have a broad range of symptoms, the frequency and intensity of which are associated with age, past exposure to malaria, and parasitemia. Understanding the full spectrum of nonsevere malaria is important in endemic areas to guide both passive and active case detection, for the diagnosis of malaria in travelers returning to non-endemic areas, and for the development of vaccines aimed to decrease symptom severity.

Project description:BackgroundVariation in genes involved in ethanol metabolism has been shown to influence risk for alcohol dependence (AD) including protective loss of function alleles in ethanol metabolizing genes. We therefore hypothesized that people with severe AD would exhibit different patterns of rare functional variation in genes with strong prior evidence for influencing ethanol metabolism and response when compared to genes not meeting these criteria.ObjectiveLeverage a novel case only design and Whole Exome Sequencing (WES) of severe AD cases from the island of Ireland to quantify differences in functional variation between genes associated with ethanol metabolism and/or response and their matched control genes.MethodsFirst, three sets of ethanol related genes were identified including those a) involved in alcohol metabolism in humans b) showing altered expression in mouse brain after alcohol exposure, and altering ethanol behavioral responses in invertebrate models. These genes of interest (GOI) sets were matched to control gene sets using multivariate hierarchical clustering of gene-level summary features from gnomAD. Using WES data from 190 individuals with severe AD, GOI were compared to matched control genes using logistic regression to detect aggregate differences in abundance of loss of function, missense, and synonymous variants, respectively.ResultsThree non-independent sets of 10, 117, and 359 genes were queried against control gene sets of 139, 1522, and 3360 matched genes, respectively. Significant differences were not detected in the number of functional variants in the primary set of ethanol-metabolizing genes. In both the mouse expression and invertebrate sets, we observed an increased number of synonymous variants in GOI over matched control genes. Post-hoc simulations showed the estimated effects sizes observed are unlikely to be under-estimated.ConclusionThe proposed method demonstrates a computationally viable and statistically appropriate approach for genetic analysis of case-only data for hypothesized gene sets supported by empirical evidence.

Dataset Information

Identification of a Novel Clinical Phenotype of Severe Malaria using a Network-Based Clustering Approach.

Publications

Identification of a Novel Clinical Phenotype of Severe Malaria using a Network-Based Clustering Approach.

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