Characterization of rheumatoid arthritis subtypes using symptom profiles, clinical chemistry and metabolomics measurements

Convergent Lines of Descent: Symptoms, Patterns, Constellations, and the Emergent Interface of Systems Biology and Chinese Medicine

During the first decade of the twenty-first century, a network composed of politicians, regulators, bioscientists, clinical researchers, and Chinese medicine specialists has emerged that seeks to bridge an imagined gulf between the modern West and ancient China in order to create a new type of personalized medicine. The central building block of this bridge is the Chinese medical concept of zheng /, variously translated into English as syndrome, pattern, or type. My paper places side by side two different genealogies of how zheng assumed this central role. The first genealogy examines the process by means of which zheng came to be considered as something shared by both ancient China and cutting-edge biological science and, by extension, how it manages to hold together the entire institutional, political, and economic framework into which this bridge is embedded and which it co-creates. The second genealogy shows zheng to be central to a much older series of redefinitions of Chinese medicine and Chinese medical practice that extend from the eleventh century to the present. Read together, these two genealogies-neither of which should be seen as exhaustive-raise three important issues that are further discussed in the conclusion of the paper. First, I explore how the concept of zheng has come to tie a medical tradition derided by its adversaries for being a pseudoscience to one of the most cutting-edge fields of bioscience research. I ask what is at stake in this synthesis, for whom, and why, and how it transforms Chinese medicine and/or systems biology along the way. Second, I am interested in finding out how and why the very same concept can be at the heart of two apparently agonistic visions of Chinese medicine's future as it is popularly imagined in China today. Finally, I insist that the medical humanities need to become actively involved in the construction of emergent articulations such as the ones I am exploring. Merely writing a history of the present will not be productive unless its critique can somehow be articulated into the very processes of emergence that historians or anthropologists seek to examine.

Evaluation of symptom, clinical chemistry and metabolomics profiles during Rehmannia six formula (R6) treatment: an integrated and personalized data analysis approach

ETHNOPHARMACOLOGICAL RELEVANCE

Rehmannia Six Formula (R6, Chinese name is Liu Wei Di Huang Wan) is one of the most important classic Chinese medicine formula used to treat metabolic disorders related to aging. It was first reported in the Chinese medicine book titled 'Xiao Er Yao Zheng Zhi Jue by Qian Yi' (Chinese Song dynasty: 1035-1117). In modern times it is therefore often used to treat diabetes, pre-diabetes, fatigue and people with metabolic syndrome. The aim of this study is to measure changes in symptoms, clinical parameters and serum metabolite profiles during R6 treatment of human subjects with features of metabolic syndrome.

MATERIALS AND METHODS

Symptoms, clinical parameters and serum metabolites were measured before and after 4 and 8 weeks of R6 treatment. Nonlinear Principal Component Analysis was applied for the first time to conduct an integrated analysis of the three data sets. Correlation structures were compared before treatment and after 4 and 8 weeks of treatment. Additionally, a State Space Grid approach was used to study personalized changes in symptom profiles.

RESULTS

The symptoms 'hectic fever' and 'spontaneous sweating' were found to be most relieved during R6 treatment. Most of the symptoms were less correlated with other variables after 8 weeks of R6 treatment. LDL-C, total cholesterol, systolic blood pressure and waist size were found to decrease during R6 treatment. Additionally, 10 of the 15 measured phosphatidylcholines were found to decrease. Personalized symptom profiles as described by Chinese medical terms show that most Yin deficiencies are addressed first by R6 treatment. However, in subjects with reduced or less Yin deficiency but which do have a substantial Qi deficiency a reduction of Qi deficiency is subsequently observed.

CONCLUSIONS

R6 treatment was shown to improve the lipid profile indicating a reduction of cardiovascular risk. Additionally, the changes observed in correlation structure indicate a different angle of looking at treatment effects. Less strong correlations between symptoms and metabolites suggest a healthier situation after R6 treatment. A State Space Grid analysis showed that the effect of R6 was different for the Yin deficiency subjects and the Qi deficiency subjects. The observed decrease of Yin deficiency related symptoms is in agreement with the use of R6 in Chinese medicine to nourish Yin. Observing individual differences in treatment effects is therefore an essential step in the development of personalized medicine.

Imbalanced network biomarkers for traditional Chinese medicine Syndrome in gastritis patients

Cold Syndrome and Hot Syndrome are thousand-year-old key therapeutic concepts in traditional Chinese medicine (TCM), which depict the loss of body homeostasis. However, the scientific basis of TCM Syndrome remains unclear due to limitations of current reductionist approaches. Here, we established a network balance model to evaluate the imbalanced network underlying TCM Syndrome and find potential biomarkers. By implementing this approach and investigating a group of chronic superficial gastritis (CSG) and chronic atrophic gastritis (CAG) patients, we found that with leptin as a biomarker, Cold Syndrome patients experience low levels of energy metabolism, while the CCL2/MCP1 biomarker indicated that immune regulation is intensified in Hot Syndrome patients. Such a metabolism-immune imbalanced network is consistent during the course from CSG to CAG. This work provides a new way to understand TCM Syndrome scientifically, which in turn benefits the personalized medicine in terms of the ancient medicine and complex biological systems.

Serum metabolic signatures of four types of human arthritis

Similar symptoms of the different types of arthritis have continued to confound the clinical diagnosis and represent a clinical dilemma making treatment choices with a more personalized or generalized approach. Here we report a mass spectrometry-based metabolic phenotyping study to identify the global metabolic defects associated with arthritis as well as metabolic signatures of four major types of arthritis--rheumatoid arthritis (n = 27), osteoarthritis (n = 27), ankylosing spondylitis (n = 27), and gout (n = 33)--compared with healthy control subjects (n = 60). A total of 196 metabolites were identified from serum samples using a combined gas chromatography coupled with time-of-flight mass spectrometry (GC-TOF MS) and ultraperformance liquid chromatography quadrupole-time-of-flight mass spectrometry (UPLC-QTOF MS). A global metabolic profile is identified from all arthritic patients, suggesting that there are common metabolic defects resulting from joint inflammation and lesion. Meanwhile, differentially expressed serum metabolites are identified constituting an unique metabolic signature of each type of arthritis that can be used as biomarkers for diagnosis and patient stratification. The results highlight the applicability of metabonomic phenotyping as a novel diagnostic tool for arthritis complementary to existing clinical modalities.

Urinary metabolic biomarker and pathway study of hepatitis B virus infected patients based on UPLC-MS system

Hepatitis B virus (HBV) is the fatal consequence of chronic hepatitis, and lack of biomarkers has been a long standing bottleneck in the clinical diagnosis. Metabolomics concerns with comprehensive analysis of small molecules and provides a powerful approach to discover biomarkers in biological systems. Here, we present metabolomics analysis applying ultra-performance liquid chromatography/electrospray ionization quadruple time-of-flight mass spectrometry. (UPLC-Q-TOF-HDMS) to determine metabolite alterations in HBV patients. Most important permutations are elaborated using multivariate statistical analysis and network analysis that was used to select the metabolites for the noninvasive diagnosis of HBV. In this study, the total 11 urinary differential metabolites were identified and contributed to HBV progress involving several key metabolic pathways by using pathway analysis with MetPA, which are promising biomarker candidates for diagnostic research. More importantly, of 11 altered metabolites, 4 metabolite markers were effective for the diagnosis of human HBV, achieved a satisfactory accuracy, sensitivity and specificity, respectively. It demonstrates that metabolomics has the potential as a non-invasive tool to evaluate the potential of these metabolites in the early diagnosis of HBV patients. These findings may be promising to yield a valuable insight into the pathophysiology of HBV and to advance the approaches of diagnosis, treatment, and prevention.

Metabolomics as a tool for personalizing medicine: 2012 update

Numerous factors in conjunction with an individual’s genetic make up will determine predisposition to disease, adverse or beneficial effects of drug treatment or therapy, and disease progression. A major limitation of current clinical measures is that the disease phenotype, which is comprised of the genotype and other environmental factors, is underestimated. Rather, each disease is treated similarly even though the disease process is highly complex. Methods that evaluate the interaction of genotype and environmental factors would likely be a better indicator of patients’ response to medical treatments. The omics technologies, specifically metabolomics, will play a major role in the movement towards personalized medicine. Metabolomics is phenotype driven and should provide better clinical biomarkers. Furthermore, recent studies have shown that associations between genetic variants and downstream metabolite changes can provide a unique description of an individual’s genotype and phenotype, which will further enhance the movement towards personalized medicine.