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   Nov 22

Mechanism of Chinese Medicine Herbs Effects on Chronic Heart Failure Based on Metabolic Profiling

ORIGINAL: https://doi.org/10.3389/fphar.2017.00864

• 1School of Preclinical Medicine, Beijing University of Chinese Medicine, Beijing, China

• 2Beijing University of Chinese Medicine, Dongfang Hospital, Beijing, China

• 3FengNing Chinese Medicine Hospital, FengNing, China

Chronic heart failure (CHF) is a major public health problem in huge population worldwide. The detailed understanding of CHF mechanism is still limited. Zheng (syndrome) is the criterion of diagnosis and therapeutic in Traditional Chinese Medicine (TCM). Syndrome prediction may be a better approach for understanding of CHF mechanism basis and its treatment. The authors studied disturbed metabolic biomarkers to construct a predicting mode to assess the diagnostic value of different syndrome of CHF and explore the Chinese herbal medicine (CHM) efficacy on CHF patients. A cohort of 110 patients from 11 independent centers was studied and all patients were divided into 3 groups according to TCM syndrome differentiation: group of Qi deficiency syndrome, group of Qi deficiency and Blood stasis syndrome, and group of Qi deficiency and Blood stasis and Water retention syndrome. Plasma metabolomic profiles were determined by UPLC-TOF/MS and analyzed by multivariate statistics. About 6 representative metabolites were highly possible to be associated with CHF, 4, 7, and 5 metabolites with Qi deficiency syndrome, Qi deficiency and Blood stasis syndrome, and Qi deficiency and Blood stasis and Water retention syndrome (VIP > 1, p < 0.05). The diagnostic model was further constructed based on the metabolites to diagnose other CHF patients with satisfying sensitivity and specificity (sensitivity and specificity are 97.1 and 80.6% for CHF group vs. NH group; 97.1 and 80.0% for QD group vs. NH group; 97.1 and 79.5% for QB group vs. NH group; 97.1 and 88.9% for QBW group vs. NH group), validating the robustness of plasma metabolic profiling to diagnostic strategy. By comparison of the metabolic profiles, 9 biomarkers, 2-arachidonoylglycerophosphocholine, LysoPE 16:0, PS 21:0, LysoPE 20:4, LysoPE 18:0, linoleic acid, LysoPE 18:2, 4-hydroxybenzenesulfonic acid, and LysoPE 22:6, may be especially for the effect of CHM granules. A predicting model was attempted to construct and predict patient based on the related symptoms of CHF and the potential biomarkers regulated by CHM were explored.

This trial was registered with NCT01939236 (https://clinicaltrials.gov/).


Chronic heart failure (CHF), as a complex clinical syndrome, results from any structural or functional cardiac disorder that impairs the ability of the ventricle to fill with or eject blood (Hunt et al., 2005). In 2000, an international cooperation research program on cardiovascular disease in Asia (InterASIA) was made, which included 15,518 urban or rural residents from 35 to 74 years old. The results showed that the prevalence of heart failure was 0.9% for the general population, 0.7% for the males, and 1.0% for the females, indicating that there were ~4 million heart failure targets in China (Gu et al., 2003). CHF is the only cardiovascular disease with an increasing hospitalization burden and an ongoing drain on health care expenditures, for its complex molecular mechanisms cannot be easily deciphered (Ramani et al., 2010). As a complementary alternative, Traditional Chinese Medicine (TCM) may be to improve the state of CHF. Zheng (syndrome) is the key pathological principle of TCM. All diagnostic and therapeutic methods in TCM are based on the differentiations of syndrome, and this concept has been used for thousands of years in China (Gu, 1956; Li et al., 2007). Syndrome differentiation was based on information from traditional four diagnostic methods. For the complexity and multilevel relationships of four diagnostic methods, a mode which could distinguish syndrome differentiation and be verified is imperative. Many techniques of data mining are applied to syndromes in TCM (Lukman et al., 2007; Shi and Zhou, 2007; Gao et al., 2010; Yao et al., 2011). Chen et al. proposed a discovery algorithm based on revised mutual information to discover syndromes for chronic renal failure (Chen et al., 2007). In regards to coronary artery disease, Liu et al. designed standardization scale on inquiry diagnosis and constructed this diagnostic model by using the method of multi-label learning (Liu et al., 2010). With many achievements have been made in syndrome differentiation, there are still some problems left, deserving further discussion (Wang et al., 2009; Lu et al., 2012). Syndromes-identified techniques such as multi-label learning could identify syndrome information in TCM more effectively, and solve the multi-label problems of one sample with several syndromes. While there are many research challenges of multi-label learning to be addressed in the future, such as multi-label data usually suffers from inherent class imbalance and unequal misclassification costs. Metabolomics is distinctive for its overall concept and dynamic character, which may be an effective tool to reveal the scientific connotation of TCM. Metabolic alterations are measured in response to disease progression (Cheng et al., 2015). LC-MS based methods provide more compatible technique and high quality data for sensitive detection of small-molecule metabolites with robust reliability and reproducibility (Gika et al., 2007). Abnormal metabolism may characterize syndrome differentiation. Our previous metabolomics study on blood stasis syndrome of CHF showed glucose metabolism and lipid metabolism disorders reinforce each other, which results a deterioration of coronary artery disease with blood stasis syndrome. These metabolites maybe used as indicators of clinical diagnosis and treatment of coronary artery disease (Wang et al., 2011). Wang et al. concluded that NMR-based metabolomics approach demonstrated alteration of energy metabolism and other potential biological mechanisms underlying CHF (Wang et al., 2013b). In this paper, we try to discover comprehensive metabolomic characteristics of CHF and its syndrome differentiation for accurate clinical diagnosis. A syndrome predictive method for CHF was to build with non-targeted metabolomics and potential biomarkers related to CHF syndrome differentiation were to explore. The predictive performance would be validated and these potential biomarkers would be used as predictors to diagnose the patients. Besides, special metabolic characteristic of CMH on CHF would also be elaborated.

Materials and Methods

Patients and Study Design

The study included 110 patients with chronic heart failure from 11 hospitals (The Second Affiliated Hospital of Beijing University of Chinese Medicine, Zhengzhou Hospital of TCM in Henan Province, The Affiliated Hospital to Changchun University of CM, Guang’anmen Hospital which was Affiliated to China Academy of Chinese Medicine Sciences, Hubei Provincial Hospital of TCM, Wuhan Hospital of TCM, Hospital of T.C.M.S Shijingshan District, Beijing Changping Hospital, Hospital of T.C.M.S Beijing Miyun County, Beijing Changping Hospital of Integrated Chinese and Western Medicine, TCM Hospital of Beijing Huairou and TCM Hospital of Tongzhou District) in China between May 2010 and December 2014. During the same period, 54 normal healthy participants of matched age were included from medical center of Dongzhimen Hospital as controls.

Diagnostic Criteria

Diagnostic criteria of CHF: 2007 China Guideline for the Diagnosis and Treatment of CHF (Chinese Society of Cardiology of Chinese Medical Association and Editorial Board of Chinese Journal of Cardiology, 2007). Heart function standard: The Criteria for Diagnosis and Treatment of Heart Disease first published by the New York Heart Association (NYHA) (Feinstein, 1964). Syndrome differentiation of CHF patients followed the TCM differentiation standard: according to the Guiding Principles for the Clinical Study of New Drugs in Traditional Chinese Medicine released in 2002 (Zheng, 2002).

Inclusion Criteria

Primary heart disease in this research was Coronary Heart Disease (CHD), which could be diagnosed by coronary computed tomography, coronary angiography, limb-salvage Q wave for electrocardiogram (ECG), history of acute myocardial infarction, ECG test, radionuclide examination support, etc. The included patients also had no history of taking antihypertensive drugs and exhibited a blood pressure under 160/100 mmHg or of hypertension. Following symptoms and signs were observed with a history of CHD: fatigue, difficulty breathing, fluid retention (edema), left ventricular enlargement, NYHA functional classification II or III, and end systolic volume of left ventricular increase and left ventricular ejection fraction (LVEF)<40. All subjects between 40 and 75 years old; If there was a “No” answer for any issue, the subject could not enter into this research.

Exclusion Criteria

Patients with one of the following diseases were excluded: (1) serious valvular heart disease; (2) cardiomyopathy; (3) pericardial disease; (4) congenital heart disease; (5) cardiac shock; (6) acute myocardial infarction (AMI) within 4 weeks; (7) acute myocarditis or serious arrhythmia with variation in hemodynamics. Patients who suffer from pulmonary artery hypertension caused by cor pulmonale, pulmonary embolism, or stroke within a half year were also excluded. Patients who suffer from serious hepatic or renal deficiency. Patients who suffer from diseases of blood system or malignant tumor. Patients who suffer from diabetes mellitus with serious complications, hyperthyrea, or hypothyrea. Patients who suffer from infection. Pregnant women or women in lactation. Patients with mental disorders. Patients with infectious disease or who join in other trials within 2 months of the present study.

All qualified participants had signed informed consent prior to the enrollment. This study was conducted according to the principles of the Declaration of Helsinki and the principles of Good Clinical Practice.

All patients formed the chronic heart failure group, and were further divided into 3 groups according to TCM syndrome differentiation, including group of Qi deficiency syndrome (QD), group of Qi deficiency and Blood stasis syndrome (QB), and group of Qi deficiency and Blood stasis and Water retention syndrome (QBW). The normal healthy participants composed the NH group.

Besides, according to previous research (Wang et al., 2013a), the most syndrome of CHF was Qi deficiency and Blood stasis syndrome, so 64 of the participants with CHF of Qi deficiency and Blood stasis syndrome (QB group) were enrolled in a randomized double-blind controlled clinical trial. According to the 2007 China Guidelines for the Diagnosis and Treatment of Chronic Heart Failure (Chinese Society of Cardiology of Chinese Medical Association and Editorial Board of Chinese Journal of Cardiology, 2007), all patients in the two groups received the standardized western medicine treatment, which includes angiotensin-converting enzyme inhibitors (ACEI) or angiotensin receptor blockers (ARBs), b-blockers, and diuretics. Except the standardized western medicine treatment, subjects with the CHM treatment were defined as the CHM group, and subjects with the placebo intervention were defined as the placebo group. The subjects were treated with CHM granules, made from Astragalus membranaceus (Fisch.) Bge. var. mongholicus (Bge.) Hsiao (huangqi, 60 g), Codonopsis pilosula (Franch.) Nannf (dangshen, 15 g), Salvia miltiorrhiza Bge (danshen 15 g), Paeonia lactiflora Pall (chishao, 15 g), Prunus davidiana (Carr.) Franch. (taoren, 10 g), and Carthamus tinctorius L. (honghua, 10 g) or placebo granules twice per day for 4 weeks. The CHM granules and placebo granules were offered by Beijing Kang Rentang pharmaceutical industry (Beijing, China). Finally, 31 patients were in CHM group, and 33 were in placebo group.

Plasma samples were collected on morning of the first and the twenty-eighth day after enrollment and were immediately frozen at −80°C. Six minutes of walking test (6 MWT) and echocardiography were also detected at the same time. Both the observer of the 6 MWT and the echo technician were blind to the allocation of the patients.

After several preliminary experiments by different mixtures of methanol, acetonitrile, and methanol/acetonitrile (1:1), finally the extraction solvent was acetonitrile. By mixing equal amounts of plasma (30 μL) from all samples, pooled quality control samples were prepared to ensure data quality for metabolic profiling. Detailed sample methods were shown in the Supplementary Material.

This research was approved by the Institutional Committee on Human Research of Beijing University of Chinese Medicine (No. 200807007), and this trial is registered with NCT01939236 (Luo et al., 2015).

Ultra Performance Liquid Chromatography/Time of Flight Mass Spectrometry (UPLC-TOF MS) Metabolomics Study Analysis

The liquid chromatographic separation of processed plasma was conducted on a 100*2.1-mm ACQUITY UPLC ° R BEH C18 1.7-um column (Lot No. 0252350221) using an ACQUITY Ultra Performance LC (Waters Corporation, Milford Massachusetts, USA), whereas mass spectrometry was performed on a SYNAPT G2 Quadrupole-Time of Flight system (Waters Corporation, Milford Massachusetts, USA). With a random-number generator in Excel (Microsoft, Redmond, Washington), 3 sequences for samples in discovery phase, validation phase and placebo group were assigned by the study administrator. During analyses of the sample sequence, after each 20 injections, then 1 quality control sample was run to check the stability of system. The acquired mass spectrometry data were exported to data format (.centroid) files by Masslynx Software (version 4.0, Waters Corporation). Data pre-treatment procedures, such as nonlinear retention time alignment, peak discrimination, filtering, alignment, matching, and identification, were performed in Progenesis QI (34 Maple Street, Milford Massachusetts, 01757, USA), and retention time and the mass-to ratio data pairs as the parameters for each ion. The UPLC-QTOF-MS characteristic chromatogram of CHM granules (6 herbs) was identified, and detailed method was shown in the Supplementary Material. Disturbed metabolites and metabolic pathways were identified by open database sources, including Human Metabolome Database (http://www.hmdb.ca/), METLIN (https://metlin.scripps.edu/), KEGG (http://www.genome.jp/kegg/pathway.html), and MetaboAnalyst (http://www.metaboanalyst.ca/faces/home.xhtml)…

Source: Frontiers

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