Herman van Wietmarschen

Systems Biology Guided by Chinese Medicine Reveals New Markers for Sub-typing Rheumatoid Arthritis Patients

Background:Complex chronic diseases such as rheumatoid arthritis have become a major challenge in medicine and for the pharmaceutical industry. New impulses for drug development are needed. Objective:A systems biology approach is explored to find subtypes of rheumatoid arthritis patients enabling a development towards more personalized medicine. Methods:Blood samples of 33 rheumatoid arthritis (RA) patients and 16 healthy volunteers were collected. The RA patients were diagnosed according to Chinese medicine (CM) theory and divided into 2 groups, the RA Heat and RA Cold group. CD4+ T-cells were used for a total gene expression analysis. Metabolite profiles were measured in plasma using gas chromatography/mass spectrometry. Multivariate statistics was employed to find potential biomarkers for the RA Heat and RA Cold phenotype. A comprehensive biologic interpretation of the results is discussed. Results:The genomics and metabolomics analysis showed statistically relevant different gene expression and metabolite profiles between healthy controls and RA patients as well as between the RA Heat and RA Cold group. Differences were found in the regulation of apoptosis. In the RA Heat group caspase 8 activated apoptosis seems to be stimulated while in the RA Cold group apoptosis seems to be suppressed through the Nrf2 pathway. Conclusions:RA patients could be divided in 2 groups according to CM theory. Molecular differences between the RA Cold and RA Heat groups were found which suggest differences in apoptotic activity. Subgrouping of patients according to CM diagnosis has the potential to provide opportunities for better treatment outcomes by targeting Western or CM treatment to specific groups of patients.

Systems Biology-Based Diagnostic Principles as Pillars of the Bridge between Chinese and Western Medicine

Innovative systems approaches to develop medicine and health care are emerging from the integration of Chinese and Western medicine strategies, philosophies and practices. The two medical systems are highly complementary as the reductionist aspects of Western medicine are favourable in acute disease situations and the holistic aspects of Chinese medicine offer more opportunities in chronic conditions and for prevention. In this article we argue that diagnosis plays a key role in building the bridge between Chinese and Western medicine. Recent advances in the study of health, healing, placebo effects and patient-physician interactions will be discussed pointing out the development of a system-based diagnosis. Especially, a system biology-based diagnosis can be used to capture phenotype information, leading towards a scientific basis for a more refined patient characterization, new diagnostic tools and personalized heath strategies. Subtyping of rheumatoid arthritis patients based on Chinese diagnostic principles is discussed as an example. New insights from this process of integrating Western and Chinese medicine will pave the way for a patient-centred health care ecosystem.

Characterization of Rheumatoid Arthritis Subtypes Using Symptom Profiles, Clinical Chemistry and Metabolomics Measurements

Objective The aim is to characterize subgroups or phenotypes of rheumatoid arthritis (RA) patients using a systems biology approach. The discovery of subtypes of rheumatoid arthritis patients is an essential research area for the improvement of response to therapy and the development of personalized medicine strategies. Methods In this study, 39 RA patients are phenotyped using clinical chemistry measurements, urine and plasma metabolomics analysis and symptom profiles. In addition, a Chinese medicine expert classified each RA patient as a Cold or Heat type according to Chinese medicine theory. Multivariate data analysis techniques are employed to detect and validate biochemical and symptom relationships with the classification. Results The questionnaire items ‘Red joints’, ‘Swollen joints’, ‘Warm joints’ suggest differences in the level of inflammation between the groups although c-reactive protein (CRP) and rheumatoid factor (RHF) levels were equal. Multivariate analysis of the urine metabolomics data revealed that the levels of 11 acylcarnitines were lower in the Cold RA than in the Heat RA patients, suggesting differences in muscle breakdown. Additionally, higher dehydroepiandrosterone sulfate (DHEAS) levels in Heat patients compared to Cold patients were found suggesting that the Cold RA group has a more suppressed hypothalamic-pituitary-adrenal (HPA) axis function. Conclusion Significant and relevant biochemical differences are found between Cold and Heat RA patients. Differences in immune function, HPA axis involvement and muscle breakdown point towards opportunities to tailor disease management strategies to each of the subgroups RA patient.

Towards whole-body ultra-weak photon counting and imaging with a focus on human beings: A review

For decades, the relationship between ultra-weak photon emission (UPE) and the health state of the body is being studied. With the advent of systems biology, attention shifted from the association between UPE and reactive oxygen species towards UPE as a reflection of changed metabolic networks. Essential for this shift in thinking is the development of novel photon count statistical methods that more reflect the dynamics of the systems organization. Additionally, efforts to combine and correlate UPE data with other types of measurements such as metabolomics be key to understand the complexity of the human body. This review describes the history and developments in the area of human UPE research from a technical - methodological perspective, an experimental perspective and a theoretical perspective. There is ample evidence that human UPE research will allow a better understanding of the body as a complex dynamical system. The future lies in the further development of an integrated UPE and metabolomics platform for a personalized monitoring of changes of the system towards health or disease.

Metabolomics profiling of the free and total oxidised lipids in urine by LC-MS/MS: application in patients with rheumatoid arthritis

Oxidised lipids, covering enzymatic and auto-oxidation-synthesised mediators, are important signalling metabolites in inflammation while also providing a readout for oxidative stress, both of which are prominent physiological processes in a plethora of diseases. Excretion of these metabolites via urine is enhanced through the phase-II conjugation with glucuronic acid, resulting in increased hydrophilicity of these lipid mediators. Here, we developed a bovine liver-β-glucuronidase hydrolysing sample preparation method, using liquid chromatography coupled to tandem mass spectrometry to analyse the total urinary oxidised lipid profile including the prostaglandins, isoprostanes, dihydroxy-fatty acids, hydroxy-fatty acids and the nitro-fatty acids. Our method detected more than 70 oxidised lipids biosynthesised from two non-enzymatic and three enzymatic pathways in urine samples. The total oxidised lipid profiling method was developed and validated for human urine and was demonstrated for urine samples from patients with rheumatoid arthritis. Pro-inflammatory mediators PGF2α and PGF3α and oxidative stress markers iPF2α- IV, 11-HETE and 14-HDoHE were positively associated with improvement of disease activity score. Furthermore, the anti-inflammatory nitro-fatty acids were negatively associated with baseline disease activity. In conclusion, the developed methodology expands the current metabolic profiling of oxidised lipids in urine, and its application will enhance our understanding of the role these bioactive metabolites play in health and disease.

Sub-Typing of Rheumatic Diseases Based on a Systems Diagnosis Questionnaire

Background The future of personalized medicine depends on advanced diagnostic tools to characterize responders and non-responders to treatment. Systems diagnosis is a new approach which aims to capture a large amount of symptom information from patients to characterize relevant sub-groups. Methodology 49 patients with a rheumatic disease were characterized using a systems diagnosis questionnaire containing 106 questions based on Chinese and Western medicine symptoms. Categorical principal component analysis (CATPCA) was used to discover differences in symptom patterns between the patients. Two Chinese medicine experts where subsequently asked to rank the Cold and Heat status of all the patients based on the questionnaires. These rankings were used to study the Cold and Heat symptoms used by these practitioners. Findings The CATPCA analysis results in three dimensions. The first dimension is a general factor (40.2% explained variance). In the second dimension (12.5% explained variance) ‘anxious’, ‘worrying’, ‘uneasy feeling’ and ‘distressed’ were interpreted as the Internal disease stage, and ‘aggravate in wind’, ‘fear of wind’ and ‘aversion to cold’ as the External disease stage. In the third dimension (10.4% explained variance) ‘panting s’, ‘superficial breathing’, ‘shortness of breath s’, ‘shortness of breath f’ and ‘aversion to cold’ were interpreted as Cold and ‘restless’, ‘nervous’, ‘warm feeling’, ‘dry mouth s’ and ‘thirst’ as Heat related. ‘Aversion to cold’, ‘fear of wind’ and ‘pain aggravates with cold’ are most related to the experts Cold rankings and ‘aversion to heat’, ‘fullness of chest’ and ‘dry mouth’ to the Heat rankings. Conclusions This study shows that the presented systems diagnosis questionnaire is able to identify groups of symptoms that are relevant for sub-typing patients with a rheumatic disease.

Exploring the Inflammatory Metabolomic Profile to Predict Response to TNF-α Inhibitors in Rheumatoid Arthritis

In clinical practice, approximately one-third of patients with rheumatoid arthritis (RA) respond insufficiently to TNF-α inhibitors (TNFis). The aim of the study was to explore the use of a metabolomics to identify predictors for the outcome of TNFi therapy, and study the metabolomic fingerprint in active RA irrespective of patients’ response. In the metabolomic profiling, lipids, oxylipins, and amines were measured in serum samples of RA patients from the observational BiOCURA cohort, before start of biological treatment. Multivariable logistic regression models were established to identify predictors for good- and non-response in patients receiving TNFi (n = 124). The added value of metabolites over prediction using clinical parameters only was determined by comparing the area under receiver operating characteristic curve (AUC-ROC), sensitivity, specificity, positive- and negative predictive value and by the net reclassification index (NRI). The models were further validated by 10-fold cross validation and tested on the complete TNFi treatment cohort including moderate responders. Additionally, metabolites were identified that cross-sectionally associated with the RA disease activity score based on a 28-joint count (DAS28), erythrocyte sedimentation rate (ESR) or C-reactive protein (CRP). Out of 139 metabolites, the best-performing predictors were sn1-LPC(18:3-ω3/ω6), sn1-LPC(15:0), ethanolamine, and lysine. The model that combined the selected metabolites with clinical parameters showed a significant larger AUC-ROC than that of the model containing only clinical parameters (p = 0.01). The combined model was able to discriminate good- and non-responders with good accuracy and to reclassify non-responders with an improvement of 30% (total NRI = 0.23) and showed a prediction error of 0.27. For the complete TNFi cohort, the NRI was 0.22. In addition, 88 metabolites were associated with DAS28, ESR or CRP (p<0.05). Our study established an accurate prediction model for response to TNFi therapy, containing metabolites and clinical parameters. Associations between metabolites and disease activity may help elucidate additional pathologic mechanisms behind RA.

The hypothalamic-pituitary-adrenal-leptin axis and metabolic health: a systems approach to resilience, robustness and control.

Glucocorticoids contribute to obesity and metabolic syndrome; however, the mechanisms are unclear, and prognostic measures are unavailable. A systems level understanding of the hypothalamic–pituitary–adrenal (HPA)–leptin axis may reveal novel insights. Eighteen obese premenopausal women provided blood samples every 10 min over 24 h, which were assayed for cortisol, adrenocorticotropin releasing hormone (ACTH) and leptin. A published personalized HPA systems model was extended to incorporate leptin, yielding three parameters: (i) cortisol inhibitory feedback signalling, (ii) ACTH–adrenal signalling, and (iii) leptin–cortisol antagonism. We investigated associations between these parameters and metabolic risk profiles: fat and lean body mass (LBM; using dual-energy X-ray absorptiometry), and insulin resistance. Decreased cortisol inhibitory feedback signalling was significantly associated with greater fat (kg; p = 0.01) and insulin resistance (p = 0.03) but not LBM. Leptin significantly antagonized cortisol dynamics in eight women, who exhibited significantly lower 24 h mean leptin levels, LBM and higher ACTH–adrenal signalling nocturnally (all p < 0.05), compared with women without antagonism. Traditional neuroendocrine measures did not predict metabolic health, whereas a dynamic systems approach revealed that lower central inhibitory cortisol feedback signalling was significantly associated with greater metabolic risk. While exploratory, leptin–cortisol antagonism may reflect a ‘neuroendocrine starvation’ response.

Collagen Induced Arthritis in DBA/1J Mice Associates with Oxylipin Changes in Plasma

Oxylipins play important roles in various biological processes and are considered as mediators of inflammation for a wide range of diseases such as rheumatoid arthritis (RA). The purpose of this research was to study differences in oxylipin levels between a widely used collagen induced arthritis (CIA) mice model and healthy control (Ctrl) mice. DBA/1J male mice (age: 6-7 weeks) were selected and randomly divided into two groups, namely, a CIA and a Ctrl group. The CIA mice were injected intraperitoneally (i.p.) with the joint cartilage component collagen type II (CII) and an adjuvant injection of lipopolysaccharide (LPS). Oxylipin metabolites were extracted from plasma for each individual sample using solid phase extraction (SPE) and were detected with high performance liquid chromatography/tandem mass spectrometry (HPLC-ESI-MS/MS), using dynamic multiple reaction monitoring (dMRM). Both univariate and multivariate statistical analyses were applied. The results in univariate Students t-test revealed 10 significantly up- or downregulated oxylipins in CIA mice, which were supplemented by another 6 additional oxylipins, contributing to group clustering upon multivariate analysis. The dysregulation of these oxylipins revealed the presence of ROS-generated oxylipins and an increase of inflammation in CIA mice. The results also suggested that the collagen induced arthritis might associate with dysregulation of apoptosis, possibly inhibited by activated NF-κB because of insufficient PPAR-γ ligands.

Grip on health: A complex systems approach to transform health care

This article addresses the urgent need for a transition in health care to deal with the increasing prevalence of chronic diseases and associated rapid rise of health care costs. Chronic diseases evolve and are predominantly related to lifestyle and environment. A shift is needed from a reductionist repair mode of thinking, toward a more integrated biopsychosocial way of thinking about health. The aim of this article is to discuss the opportunities that complexity science offer for transforming health care toward optimal treatment and prevention of chronic lifestyle diseases. Health and health care is discussed from a complexity science perspective. The benefits of concepts developed in the field of complexity science for stimulating transitions in health care are explored. Complexity science supports the elucidation of the essence of health processes. It provides a unique perspective on health with a focus on the relationships within networks of dynamically interacting factors and the emergence of health out of the organization of those relationships. Novel types of complexity science-based intervention strategies are being developed. The first application in practice is the integrated obesity treatment program currently piloted in the Netherlands, focusing on health awareness and healing relationships. Complexity science offers various theories and methods to capture the path toward unhealthy and healthy states, facilitating the development of a dynamic integrated biopsychosocial perspective on health. This perspective offers unique insights into health processes for patients and citizens. In addition, dynamic models driven by personal data provide simulations of health processes and the ability to detect transitions between health states. Such models are essential for aligning and reconnecting the many institutions and disciplines involved in the health care sector and evolve toward an integrated health care ecosystem.