Differences in GlycA and lipoprotein particle parameters may help distinguish acute kawasaki disease from other febrile illnesses in children.

BackgroundGlycosylation patterns of serum proteins, such as α1-acid glycoprotein, are modified during an acute phase reaction. The response of acute Kawasaki disease (KD) patients to IVIG treatment has been linked to sialic acid levels on native IgG, suggesting that protein glycosylation patterns vary during the immune response in acute KD. Additionally, the distribution and function of lipoprotein particles are altered during inflammation. Therefore, the aim of this study was to explore the potential for GlycA, a marker of protein glycosylation, and the lipoprotein particle profile to distinguish pediatric patients with acute KD from those with other febrile illnesses.MethodsNuclear magnetic resonance was used to quantify GlycA and lipoprotein particle classes and subclasses in pediatric subjects with acute KD (n = 75), post-treatment subacute (n = 36) and convalescent (n = 63) KD, as well as febrile controls (n = 48), and age-similar healthy controls (n = 48).ResultsGlycA was elevated in acute KD subjects compared to febrile controls with bacterial or viral infections, IVIG-treated subacute and convalescent KD subjects, and healthy children (P <0.0001). Acute KD subjects had increased total and small low density lipoprotein particle numbers (LDL-P) (P <0.0001) and decreased total high density lipoprotein particle number (HDL-P) (P <0.0001) compared to febrile controls. Consequently, the ratio of LDL-P to HDL-P was higher in acute KD subjects than all groups tested (P <0.0001). While GlycA, CRP, erythrocyte sedimentation rate, LDL-P and LDL-P/HDL-P ratio were able to distinguish patients with KD from those with other febrile illnesses (AUC = 0.789-0.884), the combinations of GlycA and LDL-P (AUC = 0.909) or GlycA and the LDL-P/HDL-P ratio (AUC = 0.910) were best at discerning KD in patients 6-10 days after illness onset.ConclusionsHigh levels of GlycA confirm enhanced protein glycosylation as part of the acute phase response in KD patients. When combined with common laboratory tests and clinical characteristics, GlycA and NMR-measured lipoprotein particle parameters may be useful for distinguishing acute KD from bacterial or viral illnesses in pediatric patients

Gene-expression patterns reveal underlying biological processes in Kawasaki disease

Background: Kawasaki disease (KD) is an acute self-limited vasculitis and the leading cause of acquired heart disease in children in developed countries. No etiologic agent(s) has been identified, and the processes that mediate formation of coronary artery aneurysms and abatement of fever following treatment with intravenous immunoglobulin (IVIG) remain poorly understood. Results: In an initial survey, we used DNA microarrays to examine patterns of gene expression in peripheral whole blood from 20 children with KD; each was sampled during the acute, subacute, and convalescent phases of the illness. Acute KD was characterized by increased relative abundance of gene transcripts associated with innate immune and proinflammatory responses and decreased abundance of transcripts associated with natural killer cells and CD8+ lymphocytes. There was significant temporal variation in transcript levels during the acute disease phase and stabilization thereafter. We confirmed these temporal patterns in a second cohort of 64 patients, and identified additional inter-individual differences in transcript abundance. Notably, higher levels of transcripts of the gene for carcinoembryonic antigen-related cell adhesion molecule 1 (CEACAM1) were associated with an increased percentage of unsegmented neutrophils, fewer days of illness, higher levels of C-reactive protein, and subsequent non-response to IVIG; this last association was confirmed by quantitative reverse transcription PCR in a third cohort of 33 patients, and was independent of day of illness. Conclusion: Acute KD is characterized by dynamic and variable gene-expression programs that highlight the importance of neutrophil activation state and apoptosis in KD pathogenesis. Our findings also support the feasibility of extracting biomarkers associated with clinical prognosis from gene-expression profiles of individuals with systemic inflammatory illnesses

A diagnostic algorithm combining clinical and molecular data distinguishes Kawasaki disease from other febrile illnesses

<p>Abstract</p> <p>Background</p> <p>Kawasaki disease is an acute vasculitis of infants and young children that is recognized through a constellation of clinical signs that can mimic other benign conditions of childhood. The etiology remains unknown and there is no specific laboratory-based test to identify patients with Kawasaki disease. Treatment to prevent the complication of coronary artery aneurysms is most effective if administered early in the course of the illness. We sought to develop a diagnostic algorithm to help clinicians distinguish Kawasaki disease patients from febrile controls to allow timely initiation of treatment.</p> <p>Methods</p> <p>Urine peptidome profiling and whole blood cell type-specific gene expression analyses were integrated with clinical multivariate analysis to improve differentiation of Kawasaki disease subjects from febrile controls.</p> <p>Results</p> <p>Comparative analyses of multidimensional protein identification using 23 pooled Kawasaki disease and 23 pooled febrile control urine peptide samples revealed 139 candidate markers, of which 13 were confirmed (area under the receiver operating characteristic curve (ROC AUC 0.919)) in an independent cohort of 30 Kawasaki disease and 30 febrile control urine peptidomes. Cell type-specific analysis of microarrays (csSAM) on 26 Kawasaki disease and 13 febrile control whole blood samples revealed a 32-lymphocyte-specific-gene panel (ROC AUC 0.969). The integration of the urine/blood based biomarker panels and a multivariate analysis of 7 clinical parameters (ROC AUC 0.803) effectively stratified 441 Kawasaki disease and 342 febrile control subjects to diagnose Kawasaki disease.</p> <p>Conclusions</p> <p>A hybrid approach using a multi-step diagnostic algorithm integrating both clinical and molecular findings was successful in differentiating children with acute Kawasaki disease from febrile controls.</p

Diagnosis of childhood febrile illness using a multi-class blood RNA molecular signature

Background: Appropriate treatment and management of children presenting with fever depend on accurate and timely diagnosis, but current diagnostic tests lack sensitivity and specificity and are frequently too slow to inform initial treatment. As an alternative to pathogen detection, host gene expression signatures in blood have shown promise in discriminating several infectious and inflammatory diseases in a dichotomous manner. However, differential diagnosis requires simultaneous consideration of multiple diseases. Here, we show that diverse infectious and inflammatory diseases can be discriminated by the expression levels of a single panel of genes in blood. Methods: A multi-class supervised machine-learning approach, incorporating clinical consequence of misdiagnosis as a ‘‘cost’’ weighting, was applied to a whole-blood transcriptomic microarray dataset, incorporating 12 publicly available datasets, including 1,212 children with 18 infectious or inflammatory diseases. The transcriptional panel identifiedwas further validated in a new RNA sequencing dataset comprising 411 febrile children. Findings: We identified 161 transcripts that classified patients into 18 disease categories, reflecting individual causative pathogen and specific disease, as well as reliable prediction of broad classes comprising bacterial infection, viral infection, malaria, tuberculosis, or inflammatory disease. The transcriptional panel was validated in an independent cohort andbenchmarked against existingdichotomousRNA signatures. Conclusions: Our data suggest that classification of febrile illness can be achieved with a single blood sample and opens the way for a new approach for clinical diagnosis. Funding: European Union’s Seventh Framework no. 279185; Horizon2020 no. 668303 PERFORM; Wellcome Trust (206508/Z/17/Z); Medical Research Foundation (MRF-160-0008-ELP-KAFO-C0801); NIHR Imperial BRC

Predicting Hemolytic Uremic Syndrome and Renal Replacement Therapy in Shiga Toxin-producing Escherichia coli-infected Children.

BACKGROUND: Shiga toxin-producing Escherichia coli (STEC) infections are leading causes of pediatric acute renal failure. Identifying hemolytic uremic syndrome (HUS) risk factors is needed to guide care. METHODS: We conducted a multicenter, historical cohort study to identify features associated with development of HUS (primary outcome) and need for renal replacement therapy (RRT) (secondary outcome) in STEC-infected children without HUS at initial presentation. Children agedeligible. RESULTS: Of 927 STEC-infected children, 41 (4.4%) had HUS at presentation; of the remaining 886, 126 (14.2%) developed HUS. Predictors (all shown as odds ratio [OR] with 95% confidence interval [CI]) of HUS included younger age (0.77 [.69-.85] per year), leukocyte count ≥13.0 × 103/μL (2.54 [1.42-4.54]), higher hematocrit (1.83 [1.21-2.77] per 5% increase) and serum creatinine (10.82 [1.49-78.69] per 1 mg/dL increase), platelet count \u3c250 \u3e× 103/μL (1.92 [1.02-3.60]), lower serum sodium (1.12 [1.02-1.23 per 1 mmol/L decrease), and intravenous fluid administration initiated ≥4 days following diarrhea onset (2.50 [1.14-5.46]). A longer interval from diarrhea onset to index visit was associated with reduced HUS risk (OR, 0.70 [95% CI, .54-.90]). RRT predictors (all shown as OR [95% CI]) included female sex (2.27 [1.14-4.50]), younger age (0.83 [.74-.92] per year), lower serum sodium (1.15 [1.04-1.27] per mmol/L decrease), higher leukocyte count ≥13.0 × 103/μL (2.35 [1.17-4.72]) and creatinine (7.75 [1.20-50.16] per 1 mg/dL increase) concentrations, and initial intravenous fluid administration ≥4 days following diarrhea onset (2.71 [1.18-6.21]). CONCLUSIONS: The complex nature of STEC infection renders predicting its course a challenge. Risk factors we identified highlight the importance of avoiding dehydration and performing close clinical and laboratory monitoring

Diagnosis of Kawasaki Disease Using a Minimal Whole-Blood Gene Expression Signature.

Importance: To date, there is no diagnostic test for Kawasaki disease (KD). Diagnosis is based on clinical features shared with other febrile conditions, frequently resulting in delayed or missed treatment and an increased risk of coronary artery aneurysms. Objective: To identify a whole-blood gene expression signature that distinguishes children with KD in the first week of illness from other febrile conditions. Design, Setting, and Participants: The case-control study comprised a discovery group that included a training and test set and a validation group of children with KD or comparator febrile illness. The setting was pediatric centers in the United Kingdom, Spain, the Netherlands, and the United States. The training and test discovery group comprised 404 children with infectious and inflammatory conditions (78 KD, 84 other inflammatory diseases, and 242 bacterial or viral infections) and 55 healthy controls. The independent validation group comprised 102 patients with KD, including 72 in the first 7 days of illness, and 130 febrile controls. The study dates were March 1, 2009, to November 14, 2013, and data analysis took place from January 1, 2015, to December 31, 2017. Main Outcomes and Measures: Whole-blood gene expression was evaluated using microarrays, and minimal transcript sets distinguishing KD were identified using a novel variable selection method (parallel regularized regression model search). The ability of transcript signatures (implemented as disease risk scores) to discriminate KD cases from controls was assessed by area under the curve (AUC), sensitivity, and specificity at the optimal cut point according to the Youden index. Results: Among 404 patients in the discovery set, there were 78 with KD (median age, 27 months; 55.1% male) and 326 febrile controls (median age, 37 months; 56.4% male). Among 202 patients in the validation set, there were 72 with KD (median age, 34 months; 62.5% male) and 130 febrile controls (median age, 17 months; 56.9% male). A 13-transcript signature identified in the discovery training set distinguished KD from other infectious and inflammatory conditions in the discovery test set, with AUC of 96.2% (95% CI, 92.5%-99.9%), sensitivity of 81.7% (95% CI, 60.0%-94.8%), and specificity of 92.1% (95% CI, 84.0%-97.0%). In the validation set, the signature distinguished KD from febrile controls, with AUC of 94.6% (95% CI, 91.3%-98.0%), sensitivity of 85.9% (95% CI, 76.8%-92.6%), and specificity of 89.1% (95% CI, 83.0%-93.7%). The signature was applied to clinically defined categories of definite, highly probable, and possible KD, resulting in AUCs of 98.1% (95% CI, 94.5%-100%), 96.3% (95% CI, 93.3%-99.4%), and 70.0% (95% CI, 53.4%-86.6%), respectively, mirroring certainty of clinical diagnosis. Conclusions and Relevance: In this study, a 13-transcript blood gene expression signature distinguished KD from other febrile conditions. Diagnostic accuracy increased with certainty of clinical diagnosis. A test incorporating the 13-transcript disease risk score may enable earlier diagnosis and treatment of KD and reduce inappropriate treatment in those with other diagnoses

Diagnosis of childhood febrile illness using a multi-class blood RNA molecular signature.

BackgroundAppropriate treatment and management of children presenting with fever depend on accurate and timely diagnosis, but current diagnostic tests lack sensitivity and specificity and are frequently too slow to inform initial treatment. As an alternative to pathogen detection, host gene expression signatures in blood have shown promise in discriminating several infectious and inflammatory diseases in a dichotomous manner. However, differential diagnosis requires simultaneous consideration of multiple diseases. Here, we show that diverse infectious and inflammatory diseases can be discriminated by the expression levels of a single panel of genes in blood.MethodsA multi-class supervised machine-learning approach, incorporating clinical consequence of misdiagnosis as a "cost" weighting, was applied to a whole-blood transcriptomic microarray dataset, incorporating 12 publicly available datasets, including 1,212 children with 18 infectious or inflammatory diseases. The transcriptional panel identified was further validated in a new RNA sequencing dataset comprising 411 febrile children.FindingsWe identified 161 transcripts that classified patients into 18 disease categories, reflecting individual causative pathogen and specific disease, as well as reliable prediction of broad classes comprising bacterial infection, viral infection, malaria, tuberculosis, or inflammatory disease. The transcriptional panel was validated in an independent cohort and benchmarked against existing dichotomous RNA signatures.ConclusionsOur data suggest that classification of febrile illness can be achieved with a single blood sample and opens the way for a new approach for clinical diagnosis.FundingEuropean Union's Seventh Framework no. 279185; Horizon2020 no. 668303 PERFORM; Wellcome Trust (206508/Z/17/Z); Medical Research Foundation (MRF-160-0008-ELP-KAFO-C0801); NIHR Imperial BRC