Liangtao Luo

Metabolomic identification of diagnostic plasma biomarkers in humans with chronic heart failure.

Chronic heart failure (CHF), as a progressive clinical syndrome, is characterized by failure of enough blood supply from the heart to meet the bodys metabolic demands, and there is intense interest in identifying novel biomarkers that could make contributions to the diagnosis of CHF. Metabolomics, compared with current diagnostic approaches, could investigate many metabolic perturbations within biological systems. The overarching goal of the work discussed here is to apply a high-throughput approach to identify metabolic signatures and plasma diagnostic biomarkers underlying CHF by 1H-NMR spectroscopy. Plasma samples from 39 patients with CHF and 15 controls were analyzed by NMR spectroscopy. After processing the data, orthogonal partial least square discriminant analysis (OPLS-DA) was performed. The statistical model revealed good explained variance and predictability, and the diagnostic performance assessed by leave-one-out analysis exhibited 92.31% sensitivity and 86.67% specificity. The OPLS-DA score plots of spectra revealed good separation between case and control on the level of metabolites, and multiple biochemical changes indicated hyperlipidemia, alteration of energy metabolism and other potential biological mechanisms underlying CHF. It was concluded that the NMR-based metabolomics approach demonstrated good performance to identify diagnostic plasma markers and provided new insights into metabolic process related to CHF.

Clinical Data Mining of Phenotypic Network in Angina Pectoris of Coronary Heart Disease

Coronary heart disease (CHD) is the leading causes of morbidity and mortality in China. The diagnosis of CHD in Traditional Chinese Medicine (TCM) was mainly based on experience in the past. In this paper, we proposed four MI-based association algorithms to analyze phenotype networks of CHD, and established scale of syndromes to automatically generate the diagnosis of patients based on their phenotypes. We also compared the change of core syndromes that CHD were combined with other diseases, and presented the different phenotype spectra.

The Effects of Jiang-Zhi-Ning and Its Main Components on Cholesterol Metabolism

To examine how Jiang-Zhi-Ning (JZN) regulates cholesterol metabolism and compare the role of its four main components. We established a beagle model of hyperlipidemia, fed with JZN extract and collected JZN-containing serum 0, 1, 2, 4, and 6 h later. Human liver cells Bel-7402 were stimulated with 10% JZN-containing serum as well as the four main components of JZN and Atorvastatin. The mRNA expression of LDL receptor (LDL-R), 3-hydroxy-3-methyl-glutaryl-CoA reductase (HMG-CoAR), cytochrome P450 7A1 (CYP7A1), and acetyl-Coenzyme A acetyltransferase 2 (ACAT2) was measured by real-time PCR. LDL-R surface expression and LDL-binding and internalization were examined by flow cytometry. The results showed that JZN-containing serum significantly increased the mRNA expression of LDL-R, HMG-CoAR, and CYP7A1 in Bel-7402 cells. All the four components significantly increased the mRNA and protein expression of LDL-R and HMG-CoAR and decreased the mRNA and protein expression of ACAT2 in Bel-7402 cells. Hyperinand chrysophanol also markedly increased the mRNA expression of CYP7A1. Stimulation with stilbene glycosidesignificantly increased the surface expression of LDL-R and the binding and internalization of LDL. In conclusion, JZN and its four components have close relationship with the process of cholesterol metabolism, emphasizing their promising application as new drug candidates in the treatment of hyperlipidemia.

Chinese Herbal Medicine in the Treatment of Chronic Heart Failure: Three-Stage Study Protocol for a Randomized Controlled Trial.

Background. Chinese herbal medicine (CHM) has been used in the treatment of chronic heart failure (CHF) for a long time. Treatment based on syndrome differentiation and the main characteristic of TCM is the fundamental principle of TCM practice. In this study protocol, we have designed a trial to assess the efficacy and safety of CHM on CHF based on syndrome differentiation. Methods/Design. This is a three-stage trial of CHM in the treatment of CHF. The first stage is a literature review aiming to explore the common syndromes of CHF. The second is a multicentral, randomized, placebo-controlled trial to evaluate the efficacy and safety of CHM for the treatment of CHF. The third is a multicentral, randomized controlled clinical trial aiming to make cost-effectiveness analysis and evaluate the feasibility, compliance, and universality of CHM on CHF. Discussion. This trial will evaluate the efficacy, safety, feasibility, compliance, and universality of CHM on CHF. The expected outcome is to provide evidence-based recommendations for CHM on CHF and develop a prescription of CHM in the treatment of CHF. This trial is registered with NCT01939236 (Stage Two of the whole trial).

The active ingredients of Jiang-Zhi-Ning: study of the Nelumbo nucifera alkaloids and their main bioactive metabolites.

The object of this study was to identify the major active ingredients of the Chinese Traditional Medicine Jiang-Zhi-Ning (JZN) based on the high performance liquid chromatography (HPLC) profiles of plasma samples obtained from beagle dogs at different times after intragastric administration of JZN, crude JZN extracts, different extracted fractions, different subfractions of the active fraction and different isolated ingredients. 2-Hydroxy-1-methoxyaporphin (2H1M), an alkaloid from Nelumbo nucifera, one of the herbs that make up JZN, was identified as the constituent showing the major pharmacodynamic effect. The major metabolites of 2H1M were analyzed and identified as N-demethyl-2-hydroxy-1-methoxyaporphine-2-O-glycuronic acid, 2-hydroxy-1-methoxy-aporphine-2-O-glycuronic acid and 2-hydroxy-1-methoxy-aporphine-2-O-sulphate. This study provided a comprehensive insight into the active components of JZN.

Metabolomics-Based Study of Clinical and Animal Plasma Samples in Coronary Heart Disease with Blood Stasis Syndrome

The aim of this study is to explore a bridge connecting the mechanism basis and macro syndromes of coronary heart disease with experimental animal models. GC-MS technique was used to detect the metabolites of plasma samples in mini swine models with myocardial infarction (MI) and patients with unstable angina (UA). 30 metabolites were detected in the plasma samples of more than 50 percent of model group and control group in swine, while 37 metabolites were found in the plasma samples of UA patients and healthy control group. 21 metabolites in the plasma samples of swine model and 20 metabolites in patients with UA were found of significant value. Among which, 8 shared metabolites were found of low level expression in both swine model and UA patients. Independent Students t-test, principal component analysis (PCA), and hierarchicalcluster analysis (HCA) were orderly applied to comprehend inner rules of variables in the data. The 8 shared metabolites could take place of the 21 or 20 metabolites in classification of swine model with MI and UA patients, which could be considered as a bridge connecting the mechanism basis and macrosyndromes of swine model with MI and UA patients.

A New Biomarkers Feature Pattern Consisting of TNF-α, IL-10, and IL-8 for Blood Stasis Syndrome with Myocardial Ischemia

Objective. To explore new diagnostic patterns for syndromes to overcome the insufficiency of obtainable macrocharacteristics and specific biomarkers. Methods. Chinese miniswines were subjected to Ameroid constrictor, placed around the proximal left anterior descending branch. On the 4th week, macrocharacteristics, coronary angiography, echocardiography, and hemorheology indices were detected for diagnosis. IL-1, IL-6, IL-8, IL-10, TNF-α, and hsCRP in serum were detected, and Decision Tree was built. Results. According to current official-issued standard, model animals matched the diagnosis of blood stasis syndrome with myocardial ischemia based on findings, including >90% occlusion, attenuated left ventricular segmental motion, dark red or purple tongues, and higher blood viscosity. Significant decrease of IL-10 and increase of TNF-α were found in model animals. However, in the Decision Tree, besides IL-10 and TNF-α, IL-8 helped to increase the accuracy of classification to 86%. Conclusions. The Decision Tree building with TNF-α, IL-10, and IL-8 is helpful for the diagnosis of blood stasis syndrome in myocardial ischemia animals. What is more is that our data set up a new path to the differentiation of syndrome by feature patterns consisting of multiple biomarkers not only for animals but also for patients. We believe that it will contribute to the standardization and international application of syndromes.

PLASMA METABOLOMICS REVEALS NOVEL METABOLIC MARKERS OF CHRONIC HEART FAILURE

Objectives Chronic heart failure (CHF) is a progressive clinical syndrome characterised by inability of the heart to adequately pump blood to meet metabolic demands of the body. There is intense interest in the identification of novel biomarkers which could improve the diagnosis of chronic heart failure. The overarching goal of the work discussed here was to apply a high-throughput approach, using 1H-NMR spectroscopy to identify novel plasma biomarkers and metabolic signatures underlying chronic heart failure. Methods Plasma samples from 30 patients with systolic heart failure (EF<40% plus signs and symptoms of failure) and 15 controls were analysed by nuclear magnetic spectroscopy. Each spectroscopy divided into regions of 0.005 ppm width was integrated. After processing the data, orthogonal partial least square discriminant analysis (OPLS-DA) was performed using SIMCA-P+ software (v11.5, Umetrics, Sweden). Results The score plot of OPLS-DA showed good separation between case and control on the level of metabolites. Several metabolites of chronic heart failure patients were altered, including the increased levels of lactate, creatine, proline, leucine, isoleucine, low-density lipoprotein, very-low-density lipoprotein and the decreased levels of histine, glucose, glutamate, valine as well as high-density lipoprotein. Multiple biochemical changes indicated dyslipidemia, oxidative stress and alteration of energy metabolism in chronic heart failure patients. Some compound could also discriminate between different stages of diseases. These findings revealed potential biological mechanisms underlying chronic heart failure. Conclusions The NMR-based metabolomics approach demonstrates good performance to identify the plasma metabolomic markers and provides new insights into metabolic process related to chronic heart failure.