Computational protocol: Cytokine Network in Adults with Falciparum Malaria and HIV 1: Increased IL 8 and IP 10 Levels Are Associated with Disease Severity

Similar protocols

Protocol publication

[…] According to the hospital routine and in line with standard procedures in the laboratory of the Central Hospital of Maputo HIV-test (Determine, Abbot Japan Co., Ltd. and Unigold, Trinity Biotech plc, Bray, Ireland), HRP-2 Rapid Diagnostic Test for malaria (RDT) (2010–2011 First Response Malaria antigen P. falciparum, Premium Medical Corporation Ltd., Daman, India; 2011–2012 ICT Malaria P.f., ICT Diagnostics Cape Town, South Africa), malaria thick blood slides (Giemsa 20% for 5 minutes), and other routine laboratory tests were performed. Parasitemia was categorised in a thick smear as 1+ when 1–10 parasites/100 fields, 2+ when 11–100 parasites/100 fields, 3+ when 1–10 parasites/field, 4+ when 11–100/field, and 5+ when >100 parasites/field . In addition, blood samples were taken separately for malaria and HIV PCR analyses, as previously described . An HCG-urine pregnancy test was performed for the female patients of fertile age (Quick Vue, Quidel Corp., San Diego, CA, USA). [...] For the cytokine data, we report the means, medians, and 25th and 75th percentiles. Differences in categorical variables such as sex and HIV-status were tested by chi-square tests. Differences in cytokine distributions between the groups of study participants were tested by Mann-Whitney tests. Spearman rank correlation was used for calculating correlations between cytokines in all malaria patients (). Because this was an exploratory study with many inter-dependent markers, we used no statistical corrections for multiple comparisons. For multivariate analyses, due to high correlations among many of the cytokines, ordinary multivariable logistic regression was not suitable. Instead we used Pelora, a method constructed for supervised clustering in situations with strong correlations among predictors (here i.e. cytokines), and many predictors compared to the number of individuals (Pelora), which is recommended for such cytokine analyses , , i.e. exactly the challenges we have in cytokine data . Pelora finds groups of cytokines characterizing the property we are searching for, as which group of cytokines are characteristic for the difference between all malaria patients compared to healthy controls, for HIV positive malaria patients compared to the HIV negative malaria patients and for those with severe malaria compared to the patients with uncomplicated malaria. The ability of a cytokine group to distinguish between patient groups, or patients versus controls, is given as the “predictive ability”, measured by the area under the operator characteristic (ROC) curve , and where the closer to 1,- the better. Each cytokine in a group may have a positive or a negative effect on the patient group membership probability , . According to the recommendations for this method, we included clusters into the models until there was a levelling off in the increase in predictive ability, and we adjusted for the effect of age, sex and where relevant HIV status. Prior to Pelora, the cytokine values were log transformed to normalize their skewed distribution, and the 1–2 most extreme values of the most skewed cytokines were adjusted down to 3SD from the mean.For analyses involving variables for which there were missing observations for certain patients, these subjects were excluded; the number of included patients is indicated in the tables. In the calculation of malaria severity, missing observations were reckoned as “normal” in cases in which the investigation in question, e.g., bilirubin, was performed only on the clinical suspicion of specific organ involvement. Most of the statistical analyses were performed with SPSS-21 (IBM Corporation 1, New Orchard Road, Armonk, New York 10504–1722, USA, 914–499–1900), but the Pelora analysis and the correlations plots were performed in R version 3.0. ( using the R-packages “supclust“and “corrplot”, respectively. There is a validation of all statistical methods used in . […]

Pipeline specifications

Software tools Trinity, PReMiuM, SPSS, corrplot
Applications Miscellaneous, Gene expression microarray analysis
Organisms Human immunodeficiency virus 1, Homo sapiens
Diseases Fever, Inflammation, Malaria, HIV Infections, Malaria, Falciparum, Coinfection