Prediction of hepatocellular carcinoma risk in patients with chronic liver disease from dynamic modular networks.

BACKGROUND: Discovering potential predictive risks in the super precarcinomatous phase of hepatocellular carcinoma (HCC) without any clinical manifestations is impossible under normal paradigm but critical to control this complex disease. METHODS: In this study, we utilized a proposed sequential allosteric modules (AMs)-based approach and quantitatively calculated the topological structural variations of these AMs. RESULTS: We found the total of 13 oncogenic allosteric modules (OAMs) among chronic hepatitis B (CHB), cirrhosis and HCC network used SimiNEF. We obtained the 11 highly correlated gene pairs involving 15 genes (r > 0.8, P < 0.001) from the 12 OAMs (the out-of-bag (OOB) classification error rate < 0.5) partial consistent with those in independent clinical microarray data, then a three-gene set (cyp1a2-cyp2c19-il6) was optimized to distinguish HCC from non-tumor liver tissues using random forests with an average area under the curve (AUC) of 0.973. Furthermore, we found significant inhibitory effect on the tumor growth of Bel-7402, Hep 3B and Huh7 cell lines in zebrafish treated with the compounds affected those three genes. CONCLUSIONS: These findings indicated that the sequential AMs-based approach could detect HCC risk in the patients with chronic liver disease and might be applied to any time-dependent risk of cancer.

Small dense low density lipoprotein-cholesterol and cholesterol ratios to predict arterial stiffness progression in normotensive subjects over a 5-year period.

BACKGROUND: Small dense low density lipoprotein-cholesterol (sdLDL-C), cholesterol ratios and carotid-femoral pulse wave velocity (cf-PWV) impart risk for all-cause morbidity and mortality independently of conventional cardiovascular disease (CVD) risk factors. This study was designed to identify feasible indicators for predicting arterial stiffness progression. METHODS: We followed up 816 normotensive participants without diabetes or CVD for nearly 5.0 years. Cholesterol parameters, ratios and other clinical and laboratory data were collected at baseline. cf-PWV were measured at baseline and the end of follow-up. RESULTS: PWV progression subjects had higher levels of PWV parameters, sdLDL-C and TG/HDL-C ratio. sdLDL-C and TG/HDL-C were significantly correlated with all PWV parameters. Multiple regression models showed that sdLDL-C was closely associated with follow-up PWV (ß = 0.222, p < 0.001) and △PWV (ß = 0.275, p < 0.001). TG/HDL-C was only one cholesterol ratios that associated with all PWV parameters. sdLDL-C (OR = 2.070, 95%CI: 1.162 to 3.688, p = 0.014) and TG/HDL-C (OR = 1.355, 95%CI: 1.136 to 1.617, p = 0.001) could significantly determine the progression of PWV after correction for covariates. High sd-LDL-C quantiles subjects were more likely to develop arterial stiffness progression than low quantiles (Tertiles 3 vs Tertiles1, RR = 2.867, 95%CI: 1.106 to 7.434, p = 0.03). CONCLUSION: We founded that sdLDL-C and TG/HDL-C ratio can independently predict arterial stiffness progression in normotensive subjects, and high level sdLDL-C and TG/HDL-C ratio were associated with a higher risk of arterial stiffness.

Autoantibodies against AT1 and α1-adrenergic receptors predict arterial stiffness progression in normotensive subjects over a 5-year period.

Arterial stiffness is an independent indicator of cardiovascular risk. Autoantibodies (AAs) against angiotensin AT1 receptor (AT1-AAs) and α1-adrenergic receptor (α1-AAs) are important in the pathogenesis of hypertension. We identified the types of AT1-AAs and α1-AAs in normotensive subjects, with the aim of determining whether these antibodies predict aortic stiffness progression. Carotid-femoral pulse wave velocity (cf-PWV) was used to measure aortic stiffness. Overall, 816 subjects (71% of those invited) underwent a medical examination and evaluation of aortic stiffness. The types of AT1-AAs and α1-AAs were measured at baseline. Meanwhile, plasma renin, angiotensin II (Ang II), and norepinephrine (NE) concentrations were measured at baseline and follow-up. Baseline mean cf-PWV was 9.90 ± 0.84 m/s and follow-up was 10.51 ± 1.12 m/s. The annualized ΔPWV was 0.12 ± 0.08 m/s/year. At the end of follow-up, 129 normotensive subjects developed hypertension and 144 subjects had PWV progression. After adjustment for covariates, AA type was independently associated with ΔPWV, annualized ΔPWV, and abnormal PWV. In our study, the risk of developing hypertension (RR =2.028, 95% CI: 1.227-3.351, P=0.006) and PWV progression (RR =2.910, 95% CI: 1.612-5.253, P<0.001) in AA-positive subjects was significantly higher than that in AA-negative subjects. Receiver operating characteristic (ROC) curve showed AA had an identify power to discriminate subjects with or without PWV and hypertension progression. We have shown for the first time that the types of A1-AAs and α1-AAs are independent predictors for aortic stiffness progression in normotensive subjects. Our data collectively support the utility of these AAs as potential markers of aortic stiffness.

Molecular classification and clinical diagnosis of acute-on-chronic liver failure patients by serum metabolomics.

Prevalence of acute-on-chronic liver failure (ACLF) patients is growing worldwide, associating with multi-organ failure and high short-term mortality rates. ACLF can be of varying entity manifestation, whereas it remains poorly defined. Traditional Chinese medicine (TCM) stratifies ACLF into two types, damp hot (DH) and cold damp (CD), by seasoned TCM practitioners, for specific treatment with different TCMs. The biggest challenge for the outcome of TCM therapy is the accuracy of diagnosis. However, it is difficult to guarantee it due to lack of the molecule classification of ACLF. Herein, we recruited 58 subjects including 34 ACLF patients (18 DH and 16 CD) and 24 healthy controls, and analyzed serum metabolic profiles using untargeted ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF/MS) metabolomics approach. A total of 10 serum metabolites were found as potential biomarkers for diagnosis of ACLF. Among them, taurochenodesoxycholic acid (N3), glycyldeoxycholic acid (N5) and 12-HETE-GABA (N7), varied between two types of ACLF and can be merged as a combination marker to differentiate CD from DH patients with area under the receiver operating curve (AUC) of 0.928 (95 % CI 0.8-1). CD patients possessed comparatively higher bile acid metabolism and lower arachidonic acid metabolism compared with DH patients. The results provide not only serum molecules for early accurate diagnosis of ACLF patients, but also potential clinical biomarkers for classification of CD and DH types. The findings clarify that molecular markers will be objective criteria for diagnosis of clinical types in TCM practice.

Symptom-based network classification identifies distinct clinical subgroups of liver diseases with common molecular pathways.

BACKGROUND AND OBJECTIVE: Liver disease is a multifactorial complex disease with high global prevalence and poor long-term clinical efficacy and liver disease patients with different comorbidities often incorporate multiple phenotypes in the clinic. Thus, there is a pressing need to improve understanding of the complexity of clinical liver population to help gain more accurate disease subtypes for personalized treatment. METHODS: Individualized treatment of the traditional Chinese medicine (TCM) provides a theoretical basis to the study of personalized classification of complex diseases. Utilizing the TCM clinical electronic medical records (EMRs) of 6475 liver inpatient cases, we built a liver disease comorbidity network (LDCN) to show the complicated associations between liver diseases and their comorbidities, and then constructed a patient similarity network with shared symptoms (PSN). Finally, we identified liver patient subgroups using community detection methods and performed enrichment analyses to find both distinct clinical and molecular characteristics (with the phenotype-genotype associations and interactome networks) of these patient subgroups. RESULTS: From the comorbidity network, we found that clinical liver patients have a wide range of disease comorbidities, in which the basic liver diseases (e.g. hepatitis b, decompensated liver cirrhosis), and the common chronic diseases (e.g. hypertension, type 2 diabetes), have high degree of disease comorbidities. In addition, we identified 303 patient modules (representing the liver patient subgroups) from the PSN, in which the top 6 modules with large number of cases include 51.68% of the whole cases and 251 modules contain only 10 or fewer cases, which indicates the manifestation diversity of liver diseases. Finally, we found that the patient subgroups actually have distinct symptom phenotypes, disease comorbidity characteristics and their underlying molecular pathways, which could be used for understanding the novel disease subtypes of liver conditions. For example, three patient subgroups, namely Module 6 (M6, n = 638), M2 (n = 623) and M1 (n = 488) were associated to common chronic liver disease conditions (hepatitis, cirrhosis, hepatocellular carcinoma). Meanwhile, patient subgroups of M30 (n = 36) and M36 (n = 37) were mostly related to acute gastroenteritis and upper respiratory infection, respectively, which reflected the individual comorbidity characteristics of liver subgroups. Furthermore, we identified the distinct genes and pathways of patient subgroups and the basic liver diseases (hepatitis b and cirrhosis), respectively. The high degree of overlapping pathways between them (e.g. M36 with 93.33% shared enriched pathways) indicates the underlying molecular network mechanisms of each patient subgroup. CONCLUSIONS: Our results demonstrate the utility and comprehensiveness of disease classification study based on community detection of patient network using shared TCM symptom phenotypes and it can be used to other more complex diseases.

The feasibility of two anthropometric indices to identify metabolic syndrome, insulin resistance and inflammatory factors in obese and overweight adults.

OBJECTIVES: A body shape index (ABSI) and body roundness index (BRI) were reported to predict diabetes and hypertension in general population, but their validity was regularly questioned. The aim of this study was to evaluate whether ABSI and BRI are the best anthropometric indices to reflect metabolic syndrome (MetS), insulin resistance (IR), and inflammatory factors in obese and overweight Chinese adults. METHODS: Cross-sectional data on sociodemographic, lifestyle, anthropometric indices, clinical characteristics, and biochemical measurements were collected for 1442 Chinese obese and overweight adults. Logistic regression analysis examined the associations between anthropometric indices with incidences of MetS and IR in both sexes. Furthermore, the correlation between anthropometric indices and inflammatory factors was assessed. RESULTS: Multivariate regression analysis depicting BRI and waist circumference (WC) were associated significantly with MetS and IR. BRI had the highest odds ratios (ORs) for IR and WC had the highest ORs for MetS in all anthropometric indices. However, ABSI did not exhibit any association between the MetS and IR. The ABSI adjusted regression coefficients (ß values) were 0.403 for high-sensitivity C reactive protein, 0.077 for tumor necrosis factor-α, and 0.022 for interleukin-6. BRI and WC were also significantly associated with three inflammatory factors. Comparing the lowest with the highest quintile, BRI had the largest ORs for MetS (OR, 5.778; 95% confidence interval [CI], 2.954-11.303; P < 0.01) and IR (OR, 6.212; 95% CI, 2.912-13.250; P < 0.01). CONCLUSION: Only BRI and WC, not ABSI, can significantly determine the presence of MetS and IR. BRI showed the optimal capability to identify IR in obese and overweight population.

Construction of a shared system-based real-world clinical research system.

Hubei Provincial Hospital of Traditional Chinese Medicine established research outpatient clinics to contribute to the major disease-entity research conducted by the National Traditional Chinese Medicine Clinical Research Base and to the construction of the National Traditional Chinese Medicine Treatment and Clinical Research Information Sharing System. With a view of developing a "real-world traditional Chinese medicine clinical research paradigm," these clinics explored the mode of constructing research outpatient clinics from the aspects of clinical research, health management, and characteristics diagnosis and treatment.