Effects of chronic HBV infection on lipid metabolism in non-alcoholic fatty liver disease: A lipidomic analysis.

INTRODUCTION AND OBJECTIVES: Chronic hepatitis B virus (HBV) infection exerts an impact on lipid metabolism, but its interaction with dysmetabolism-based non-alcoholic fatty liver disease (NAFLD) remains uncertain. The purpose of the study was to investigate the effects of HBV infection on lipid metabolism, hepatic steatosis and related impairments of NAFLD patients. METHODS: Biopsy-proven Chinese NAFLD patients with (NAFLD-HBV group, n = 21) or without chronic HBV infection (NAFLD group, n = 41) were enrolled in the case-control study. Their serum lipidomics was subjected to individual investigation by ultra-performance liquid chromatography-tandem mass spectrometry. Steatosis, activity, and fibrosis (SAF) scoring revealed the NAFLD-specific pathological characteristics. RESULTS: Chronic HBV infection was associated with global alteration of serum lipidomics in NAFLD patients. Upregulation of phosphatidylcholine (PCs), choline plasmalogen (PC-Os) and downregulation of free fatty acids (FFAs), lysophosphatidylcholine (LPCs) dominated the HBV-related lipidomic characteristics. Compared to those of NAFLD group, the levels of serum hepatoxic lipids (FFA16:0, FFA16: 1, FFA18:1, FFA18:2) were significantly lowered in the NAFLD-HBV group. These low-level FFAs demonstrated correlation to statistical improvements in aspartate aminotransferase activity (FFA16:0, r = 0.33; FFA16:1, r = 0.37; FFA18:1, r = 0.32; FFA18:2, r = 0.42), hepatocyte steatosis (FFA16: 1, r = 0.39; FFA18:1, r = 0.39; FFA18:2, r = 0.32), and ballooning (FFA16:0, r = 0.30; FFA16:1, r = 0.45; FFA18:1, r = 0.36; FFA18:2, r = 0.30) (all P < 0.05). CONCLUSION: Chronic HBV infection may impact on the serum lipidomics and steatosis-related pathological characteristics of NAFLD.

A Classification Tool for Differentiation of Kawasaki Disease from Other Febrile Illnesses.

OBJECTIVE: To develop and validate a novel decision tree-based clinical algorithm to differentiate Kawasaki disease (KD) from other pediatric febrile illnesses that share common clinical characteristics. STUDY DESIGN: Using clinical and laboratory data from 801 subjects with acute KD (533 for development, and 268 for validation) and 479 febrile control subjects (318 for development, and 161 for validation), we developed a stepwise KD diagnostic algorithm combining our previously developed linear discriminant analysis (LDA)-based model with a newly developed tree-based algorithm. RESULTS: The primary model (LDA) stratified the 1280 subjects into febrile controls (n = 276), indeterminate (n = 247), and KD (n = 757) subgroups. The subsequent model (decision trees) further classified the indeterminate group into febrile controls (n = 103) and KD (n = 58) subgroups, leaving only 29 of 801 KD (3.6%) and 57 of 479 febrile control (11.9%) subjects indeterminate. The 2-step algorithm had a sensitivity of 96.0% and a specificity of 78.5%, and correctly classified all subjects with KD who later developed coronary artery aneurysms. CONCLUSION: The addition of a decision tree step increased sensitivity and specificity in the classification of subject with KD and febrile controls over our previously described LDA model. A multicenter trial is needed to prospectively determine its utility as a point of care diagnostic test for KD.

Urine protein biomarkers for the diagnosis and prognosis of necrotizing enterocolitis in infants.

OBJECTIVES: To test the hypothesis that an exploratory proteomics analysis of urine proteins with subsequent development of validated urine biomarker panels would produce molecular classifiers for both the diagnosis and prognosis of infants with necrotizing enterocolitis (NEC). STUDY DESIGN: Urine samples were collected from 119 premature infants (85 NEC, 17 sepsis, 17 control) at the time of initial clinical concern for disease. The urine from 59 infants was used for candidate biomarker discovery by liquid chromatography/mass spectrometry. The remaining 60 samples were subject to enzyme-linked immunosorbent assay for quantitative biomarker validation. RESULTS: A panel of 7 biomarkers (alpha-2-macroglobulin-like protein 1, cluster of differentiation protein 14, cystatin 3, fibrinogen alpha chain, pigment epithelium-derived factor, retinol binding protein 4, and vasolin) was identified by liquid chromatography/mass spectrometry and subsequently validated by enzyme-linked immunosorbent assay. These proteins were consistently found to be either up- or down-regulated depending on the presence, absence, or severity of disease. Biomarker panel validation resulted in a receiver-operator characteristic area under the curve of 98.2% for NEC vs sepsis and an area under the curve of 98.4% for medical NEC vs surgical NEC. CONCLUSIONS: We identified 7 urine proteins capable of providing highly accurate diagnostic and prognostic information for infants with suspected NEC. This work represents a novel approach to improving the efficiency with which we diagnose early NEC and identify those at risk for developing severe, or surgical, disease.

Point-of-care differentiation of Kawasaki disease from other febrile illnesses.

OBJECTIVE: To test whether statistical learning on clinical and laboratory test patterns would lead to an algorithm for Kawasaki disease (KD) diagnosis that could aid clinicians. STUDY DESIGN: Demographic, clinical, and laboratory data were prospectively collected for subjects with KD and febrile controls (FCs) using a standardized data collection form. RESULTS: Our multivariate models were trained with a cohort of 276 patients with KD and 243 FCs (who shared some features of KD) and validated with a cohort of 136 patients with KD and 121 FCs using either clinical data, laboratory test results, or their combination. Our KD scoring method stratified the subjects into subgroups with low (FC diagnosis, negative predictive value >95%), intermediate, and high (KD diagnosis, positive predictive value >95%) scores. Combining both clinical and laboratory test results, the algorithm diagnosed 81.2% of all training and 74.3% of all testing of patients with KD in the high score group and 67.5% of all training and 62.8% of all testing FCs in the low score group. CONCLUSIONS: Our KD scoring metric and the associated data system with online (http://translationalmedicine.stanford.edu/cgi-bin/KD/kd.pl) and smartphone applications are easily accessible, inexpensive tools to improve the differentiation of most children with KD from FCs with other pediatric illnesses.