Systems Medicine Approach Research Articles

Systems medicine and metabolic modelling

Several complex diseases are caused by the malfunction of human metabolism, and deciphering the underlying molecular mechanisms can elucidate their aetiology. Systems biology is an integrative approach combining experimental and computational biology to identify and describe the molecular mechanisms of complex biological systems. Systems medicine has the potential to elucidate the onset and progression of complex metabolic diseases through the use of computational approaches. Advances in biotechnology have resulted in the provision of high-throughput data, which provide information about different metabolic processes. The systems medicine approach can utilize such data to reconstruct genome-scale metabolic models that can be used to study the function of specific enzymes and pathways in the context of the complete metabolic network. In this review, we outline the importance of genome-scale models in systems medicine and discuss how they may contribute towards the development of personalized medicine.

A personalized systems medicine approach to refractory rumination

As with many functional disorders, rumination syndrome poses a great dilemma when approached via standard of care. This case report illustrates how rumination syndrome may be effectively approached using the systems medicine. The patient's treatment involved two distinctively different treatment cycles. Initially she was treated in an academic tertiary inpatient and outpatient multidisciplinary program with a primary symptom-based focus with little improvement. She subsequently sought care at a systems-based integrative medicine clinic within an academic family medicine centre, which identified the inciting events, diagnosed the current pathology and developed a stepwise treatment plan. The patient is now rumination free. Chronic or refractory diseases, especially when regarded as 'functional' may be approached by a systems medicine methodology, which allows physicians to fine-tune the vast amount of specific pieces of knowledge to achieve an integrated approach to managing the whole person.

Pathways of the Heart

Disease heterogeneity presents a formidable challenge for clinical medicine. We are unlikely to develop successful, targeted treatments unless diagnostic schemes begin to reflect the biological complexity underlying superficially similar disease phenotypes. To address heterogeneity, there is an increasing recognition that realistic disease models need to be built on a foundation of quantitative molecular information.1 Such a “systems medicine” approach should ultimately allow classification into biologically more homogeneous groups, with similar prognosis and treatment response. Article see p 371 Oncology has been among the first specialties to embrace molecular profiling to improve clinical decision making. Today, a wealth of expression profiling information exists for tumors, and early efforts are being made to classify patients into likely treatment responders for specific chemotherapeutics.2 The investigation of complex multisystem pathologies, such as cardiovascular disease, has been slower to incorporate molecular profiling, in part, because of the involvement of multiple tissues, many of which are not readily accessible. Moreover, cardiovascular disease, unlike cancer, is not clonal in origin, making experimental analyses more challenging. It is clear, however, that broadly defined diseases such as the cardiomyopathies are the product of diverse genetic and environmental agents3; one can expect that quantitative molecular profiling will shed light on distinct, pathological activities underlying these conditions, thus allowing more meaningful classification schemes beyond those that are solely based on anatomic or hemodynamic considerations. The article by Barth et al4 in this issue of Circulation: Cardiovascular Genetics represents such a broadened search for molecular correlates of cardiovascular disease. The authors focus on the problem of heart failure with mechanical dyssynchrony (DHF) and its treatment by cardiac resynchronization therapy (CRT). Clinically, CRT has been a remarkable success, with significant gains in quality of life and mortality.5 Multiple studies have investigated the physiological consequences of dyssynchrony and …

Family systems medicine: Systemic-constructivist perspectives

In this paper the Family Systems Medicine approach to counselling is presented, which have a baring upon the major theoretical concepts and postulates, and practical issues related to assessment procedures, goals and therapeutic techniques. Emphasis is placed on current constructivist contributions to the development of the model. Also, the strengths and limitations of the above model are discussed.

Captopril and hypertension in black diabetics.

<h3>Introduction</h3> Psoriatic arthritis (PsA) is a chronic, inflammatory, musculoskeletal disease that affects up to 30% of patients with psoriasis. Current challenges in clinical care and research include personalised treatment, understanding the divergence of therapy response and unravelling the multifactorial pathophysiology of this complex disease. Moreover, there is an urgent clinical need to predict, assess and understand the cellular and molecular pathways underlying the response to disease-modifying antirheumatic drugs (DMARDs). The TOFA-PREDICT clinical trial addresses this need. Our primary objective is to determine key immunological factors predicting tofacitinib efficacy and drug-free remission in PsA. <h3>Methods and analysis</h3> In this investigator-initiated, phase III, multicentre, open-label, four-arm randomised controlled trial, we plan to integrate clinical, molecular and imaging parameters of 160 patients with PsA. DMARD-naïve patients are randomised to methotrexate or tofacitinib. Additionally, patients who are non-responsive to conventional synthetic (cs)DMARDs continue their current csDMARD and are randomised to etanercept or tofacitinib. This results in four arms each with 40 patients. Patients are followed for 1 year. Treatment response is defined as minimal disease activity at week 16. Clinical data, biosamples and images are collected at baseline, 4 weeks and 16 weeks; at treatment failure (treatment switch) and 52 weeks. For the first 80 patients, we will use a systems medicine approach to assess multiomics biomarkers and develop a prediction model for treatment response. Subsequently, data from the second 80 patients will be used for validation. <h3>Ethics and dissemination</h3> The study was approved by the Medical Research Ethics Committee in Utrecht, Netherlands, is registered in the European Clinical Trials Database and is carried out in accordance with the Declaration of Helsinki. The study’s progress is monitored by Julius Clinical, a science-driven contract research organisation. <h3>Trial registration number</h3> EudraCT: 2017-003900-28.