Towards Improving Point-of-Care Diagnosis of Non-malaria Febrile Illness: A Metabolomics Approach

Abstract

Introduction: Non-malaria febrile illnesses such as bacterial bloodstream infections (BSI) are a leading cause of disease and mortality in the tropics. However, there are no reliable, simple diagnostic tests for identifying BSI or other severe non-malaria febrile illnesses. We hypothesized that different infectious agents responsible for severe febrile illness would impact on the host metabolome in different ways, and investigated the potential of plasma metabolites for diagnosis of non-malaria febrile illness.

Methodology: We conducted a comprehensive mass-spectrometry based metabolomics analysis of the plasma of 61 children with severe febrile illness from a malaria-endemic rural African setting. Metabolite features characteristic for non-malaria febrile illness, BSI, severe anemia and poor clinical outcome were identified by receiver operating curve analysis.

Principal findings: The plasma metabolome profile of malaria and non-malaria patients revealed fundamental differences in host response, including a differential activation of the hypothalamic-pituitary-adrenal axis. A simple corticosteroid signature was a good classifier of severe malaria and non-malaria febrile patients (AUC 0.82, 95% CI: 0.70-0.93). Patients with BSI were characterized by upregulated plasma bile metabolites; a signature of two bile metabolites was estimated to have a sensitivity of 98.1% (95% CI: 80.2-100) and a specificity of 82.9% (95% CI: 54.7-99.9) to detect BSI in children younger than 5 years. This BSI signature demonstrates that host metabolites can have a superior diagnostic sensitivity compared to pathogen-detecting tests to identify infections characterized by low pathogen load such as BSI.

Conclusions: This study demonstrates the potential use of plasma metabolites to identify causality in children with severe febrile illness in malaria-endemic settings.

Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1. Spiderplot showing the results of the 27 regression analyses modeling the co-variance between metabolome measurements and the listed patient characteristics.

The plotted validation metric Q2 allows assessing the relative impact of the patient characteristics on the metabolome. The following 27 patient variables were included in the analysis from center top in clockwise direction: BSI = confirmed or no confirmed BSI diagnosis; malaria RDT = positive or negative malaria rapid diagnostic test; parasite density = number of asexual Plasmodium parasites in the bloodstream (x103/mL); prior AB Rx = reported or no reported antibiotic treatment in the past 48 hrs; prior AM Rx = reported or no reported antimalarial treatment in the past 48 hrs; temp. = axillary temperature at time of hospital admission (°C); duration illness = duration illness prior to hospital admission (days); survival = clinical outcome survival or no survival; sex = male or female; age = age expressed in months; weight = weight expressed in kg; height = height expressed in cm; malnutrition = presence or absence of severe malnutrition defined as weight for height score < 70% according to national guidelines or report of kwashiorkor; blood glucose = blood glucose level (mg/dL); WBC = white blood cell count in blood (x103/μl); RBC = red blood cell count in blood (x106/μl); hemoglobin = blood hemoglobin level (g/dL); hematocrit = blood hematocrit (%); MCV = mean corpuscular volume (fL); MCH = mean corpuscular hemoglobin (pg); MCHC = mean corpuscular hemoglobin concentration (g/dl); platelet count = platelet blood count (x103/μL); % neutr. = neutrophils as % of total WBC; % lymph. = lymphocytes as % of total WBC; % monoc. = monocytes as % of total WBC; % eosin. = eosinophils as % of total WBC; % basoph. = basophils as % of total WBC.

Fig 2. Plot showing correlation between clinical patient data and the metabolite features characterizing non-malaria illness.

The plot consists of 2 panels: (i) in the lower panel, the color and size of the circles correspond to the strength of the correlation, with increasing circle size and color intensity indicating increasing correlation; shades of blue are used for negative correlations and shades of red for positive correlations, crosses indicate correlations that were statistically insignificant (p-value Plasmodium parasites in the bloodstream (x103/mL); disease duration = duration illness prior to hospital admission expressed in days; non-survival = clinical outcome survival or no survival; age = age expressed in months; Hb = blood hemoglobin level (g/dL); % neutrophils = neutrophils as % of total WBC. Lipids are abbreviated with PC, PE, SL, DG or TG for phosphatidylcholines, phosphatidylethanolamines, sphingolipids, diacylglycerides and triacylglycerides respectively, and the total number of acyl side-chain carbons and the double bonds in the side-chains.

Fig 3. Heatmap of the top 26 features that characterize non-malaria and malaria febrile illness.

Patients are shown along the y-axis, the four color-coded bars right of the heatmap indicate their malaria status, survival outcome, age and severe anemia status. The annotation right of the heatmap shows which pathogenic bacteria were identified by blood culture (BC) and/or by 16S sequencing (16S) along with the pathogen species detected (in case of 16S results, only the most abundant pathogen is shown with the matching number of sequencing reads); NA = incomplete BSI diagnosis. The 26 metabolites are shown along the x-axis, their putative identity and feature ID as listed in S1 data are shown below the heatmap. Unsupervised hierarchical clustering of the patients (the tree right of the y-axis) reveals that the shown metabolite intensity profiles differ sufficiently to distinguish non-malaria (upper branch) and malaria patients (lower branch). Clustering of the metabolites according to similarity in intensity profiles (the tree above the x-axis) reveals 3 major groups of metabolite features: the right cluster are plasma phospholipids and fatty acids typically appearing in malaria patients, the middle cluster are the malaria triglycerides, and the left cluster are metabolites characteristic for non-malaria febrile illness. (Abbreviations: LAMA = left against medical advice, mo. = months, Hb = hemoglobin, lipids are abbreviated with PC, PE, SL, DG or TG for phosphatidylcholines, phosphatidylethanolamines, sphingolipids, diacylglycerides and triacylglycerides respectively, and the total number of acyl side-chain carbons and the double bonds in the side-chains.)

Fig 4. Plot showing correlation between clinical patient data and the metabolite features characterizing BSI.

The plot consists of 2 panels: (i) in the lower panel, the color and size of the circles correspond to the strength of the correlation, with increasing circle size and color intensity indicating increasing correlation; shades of blue are used for negative correlations and shades of red for positive correlations, crosses indicate correlations that were statistically insignificant (p-value Plasmodium parasites in the bloodstream (x103/mL); disease duration = duration illness prior to hospital admission expressed in days; non-survival = clinical outcome survival or no survival; age = age expressed in months; Hb = blood hemoglobin level (g/dL); % neutrophils = neutrophils as % of total WBC. Lipids are abbreviated with PC, PE, SL, DG or TG for phosphatidylcholines, phosphatidylethanolamines, sphingolipids, diacylglycerides and triacylglycerides respectively, and the total number of acyl side-chain carbons and the double bonds in the side-chains.

Fig 5. Diagnostic performance of corticosteroid signature for non-malaria febrile illness (panel A) and non-survival (panel B).

The metabolite signature consists of 2 corticosteroid compounds detected by C18-UHPLC-MS: a C21-corticosteroid (ID_2094_ON, m/z M-H = 377.196, putative ID: C21H30O6 18-hydroxycortisol) and a C19-steroid-glucuronide (ID_3388_ON, m/z M-H = 481.243, putative ID: C25H38O9 11-beta-hydroxyandrosterone-3-glucuronide). Each panel shows: (i) a ROC curve along with the optimal cut-off value (the non-malaria analysis excluded BSI/malaria co-infection patients; the non-survival analysis excluded patients that left hospital against medical advice), (ii) check for over-fitting by Monte-Carlo cross-validation based on 200 rounds of balanced subsampling in all cases and controls and the significance permutation test, and (iii) a boxplot with the test result for all 61 patients (x-axis categories indicate the patient groups based on the study case definitions, y-axis shows the test-value of the diagnostic test which is based on the sum of the signal intensity of the two metabolites, here called composite signal intensity, included in each model, the orange dashed line indicates the cut-off value determined in the ROC analysis). (Abbreviations: LAMA = left against medical advice)

Fig 6. Diagnostic performance of bile metabolite signature for BSI.

The metabolite signature consists of 2 bile metabolites detected by C18-UHPLC-MS: a C26 bile alcohol (ID_4741_ON, m/z M+FA-H = 483.331, putative ID: C26H46O5 27-Nor-5b-cholestane-3a,7a,12a,24,25-pentol bile alcohol) and a C27 bile acid (ID_6663_ON, m/z M+FA-H = 493.316, putative ID: C27H44O5 C27 bile acid). The figure includes: (i) a ROC curve along with the optimal cut-off value (excluding patients with BSI/malaria co-infection, incomplete or possible BSI diagnosis), (ii) check for over-fitting by Monte-Carlo cross-validation based on 200 rounds of balanced subsampling in all cases and controls and the significance permutation test, and (iii) a boxplot with the test result for all 61 patients (x-axis categories indicate the patient groups based on the study case definitions, y-axis shows the test-value of the diagnostic test which is based on the sum of the signal intensity of the two metabolites, here called composite signal intensity, included in each model, the orange dashed line indicates the cut-off value determined in the ROC analysis).