Applying computation biology and "big data" to develop multiplex diagnostics for complex chronic diseases such as osteoarthritis

Unraveling the molecular landscape of osteoarthritis: A comprehensive review focused on the role of non-coding RNAs

Osteoarthritis (OA) poses a significant global health challenge, with its prevalence anticipated to increase in the coming years. This review delves into the emerging molecular biomarkers in OA pathology, focusing on the roles of various molecules such as metabolites, noncoding RNAs (ncRNAs), including microRNAs (miRNAs) and long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs). Advances in omics technologies have transformed biomarker identification, enabling comprehensive analyses of the complex pathways involved in OA pathogenesis. Notably, ncRNAs, especially miRNAs and lncRNAs, exhibit dysregulated expression patterns in OA, presenting promising opportunities for diagnosis and therapy. Additionally, the intricate interplay between epigenetic modifications and OA progression highlights the regulatory role of epigenetics in gene expression dynamics. Genome-wide association studies have pinpointed key genes undergoing epigenetic changes, providing insights into the inflammatory processes and chondrocyte hypertrophy typical of OA. Understanding the molecular landscape of OA, including biomarkers and epigenetic mechanisms, holds significant potential for developing innovative diagnostic tools and therapeutic strategies for OA management.

Biomarkers of Osteoarthritis—A Narrative Review on Causal Links with Metabolic Syndrome

Development of OA (OA) is multifactorial and is strongly associated with risk factors such as aging, trauma, metabolic disorders, and obesity. Metabolic Syndrome (MetS)-associated OA, collectively coined MetS-OA, is an increasingly recognized entity in which metabolic disorders and low-grade inflammation play a key mechanistic role in the disruption of joint homeostasis and cartilage degradation. Although there have been enormous efforts to discover biomarkers of MetS and OA, studies investigating a pathophysiological link between MetS and OA are relatively limited, and no serum blood marker has proved diagnostic so far. OA biomarkers that are necessary to discriminate and diagnose early disease remain to be elicited, explained in part by limited prospective studies, and therefore limited tools available to utilize in any prognostic capacity. Biomarker validation projects have been established by the Biomarker Consortium to determine biochemical markers demonstrating predictive validity for knee OA. Given that the metabolic constituents of MetS are treatable to varying extents, it stands to reason that treating these, and monitoring such treatment, may help to mitigate deleterious links with OA development. This narrative review will describe the current state of biomarker identification and utility in OA associated with MetS. We discuss the pathophysiological mechanisms of disease according to constituent pathologies of MetS and how identification of biomarkers may guide future investigation of novel targets.

Big data analytics in medical engineering and healthcare: methods, advances and challenges

Big data analytics are gaining popularity in medical engineering and healthcare use cases. Stakeholders are finding big data analytics reduce medical costs and personalise medical services for each individual patient. Big data analytics can be used in large-scale genetics studies, public health, personalised and precision medicine, new drug development, etc. The introduction of the types, sources, and features of big data in healthcare as well as the applications and benefits of big data and big data analytics in healthcare is key to understanding healthcare big data and will be discussed in this article. Major methods, platforms and tools of big data analytics in medical engineering and healthcare are also presented. Advances and technology progress of big data analytics in healthcare are introduced, which includes artificial intelligence (AI) with big data, infrastructure and cloud computing, advanced computation and data processing, privacy and cybersecurity, health economic outcomes and technology management, and smart healthcare with sensing, wearable devices and Internet of things (IoT). Current challenges of dealing with big data and big data analytics in medical engineering and healthcare as well as future work are also presented.

Osteoarthritis phenotypes and novel therapeutic targets

The success of disease-modifying osteoarthritis drug (DMOAD) development is still elusive. While there have been successes in preclinical and early clinical studies, phase 3 clinical trials have failed so far and there is still no approved, widely available DMOAD on the market. The latest research suggests that, among other causes, poor trial outcomes might be explained by the fact that osteoarthritis (OA) is a heterogeneous disease with distinct phenotypes. OA trials might be more successful if they would address and target a specific phenotype. The increasing availability of advanced techniques to detect particular OA characteristics expands the possibilities to distinguish between such potential OA phenotypes. Magnetic resonance imaging is among the key imaging techniques to stratify and monitor patients with changes in bone, cartilage and inflammation. Biochemical markers have mainly used as secondary parameters and could further delineate phenotypes. Moreover, post-hoc analyses of trial data have suggested the existence of distinct pain phenotypes and their relevance in the design of clinical trials. Although ongoing work in the field supports the concept of OA heterogeneity, this has not yet resulted in more effective treatment options. This paper reviews the current knowledge about potential OA phenotypes and suggests that combining patient clinical data, quantitative imaging, biochemical markers and utilizing data-driven approaches in patient selection and efficacy assessment will allow for more successful development of effective DMOADs.

Biochemical Markers for the Early Identification of Osteoarthritis: Systematic Review and Meta-Analysis

BACKGROUND

There is a desperate need for the reliable detection of osteoarthritis (OA) at the early stage when patients are likely to benefit most from disease interventions. A variety of biochemical markers have been proposed, but their reliability varies among studies.

OBJECTIVE

In this review, we aimed to answer the following questions: (1) are there biochemical markers that are differentially expressed in early OA versus healthy subjects, and (2) if so, what is the diagnostic value of these biomarkers for early OA?

METHODS

Embase, PubMed, and Web of Science were searched to obtain all relevant studies up to March 2018, and studies comparing the biochemical markers between early OA and healthy controls were selected. The Downs and Black checklist was used to assess the risk of bias. Biomarkers that were investigated in five or more different populations were pooled for meta-analysis. A meta-regression analysis was performed to explore possible explanations for the heterogeneity of studies.

RESULT

In total, 26 articles met the criteria for the qualitative synthesis and 17 articles for the final quantitative synthesis. N-terminal crosslinked telopeptide of type I collagen (NTX-I) was the only biomarker found to be differently expressed in patients with early OA versus controls, without significant heterogeneity among studies (I2 = 0%, [Formula: see text] = 1.695, p = 0.792). The meta-regression analysis identified that sample size and affected joint possibly explained the heterogeneity among studies.

CONCLUSION

Although a wide range of biomarkers has been previously investigated in early OA, the diagnostic value of these biomarkers could not be determined because due to a low number of studies regarding any given biomarker. Large prospective and adequately powered studies are therefore required to validate these (and other) biomarkers for identifying early OA.

Systems approaches in osteoarthritis: Identifying routes to novel diagnostic and therapeutic strategies

Systems orientated research offers the possibility of identifying novel therapeutic targets and relevant diagnostic markers for complex diseases such as osteoarthritis. This review demonstrates that the osteoarthritis research community has been slow to incorporate systems orientated approaches into research studies, although a number of key studies reveal novel insights into the regulatory mechanisms that contribute both to joint tissue homeostasis and its dysfunction. The review introduces both top-down and bottom-up approaches employed in the study of osteoarthritis. A holistic and multiscale approach, where clinical measurements may predict dysregulation and progression of joint degeneration, should be a key objective in future research. The review concludes with suggestions for further research and emerging trends not least of which is the coupled development of diagnostic tests and therapeutics as part of a concerted effort by the osteoarthritis research community to meet clinical needs. © 2017 The Authors. Journal of Orthopaedic Research Published by Wiley Periodicals, Inc. on behalf of Orthopaedic Research Society. J Orthop Res 35:1573-1588, 2017.

MRI and biomechanics multidimensional data analysis reveals R2 -R1ρ as an early predictor of cartilage lesion progression in knee osteoarthritis

PURPOSE

To couple quantitative compositional MRI, gait analysis, and machine learning multidimensional data analysis to study osteoarthritis (OA). OA is a multifactorial disorder accompanied by biochemical and morphological changes in the articular cartilage, modulated by skeletal biomechanics and gait. While we can now acquire detailed information about the knee joint structure and function, we are not yet able to leverage the multifactorial factors for diagnosis and disease management of knee OA.

MATERIALS AND METHODS

We mapped 178 subjects in a multidimensional space integrating: demographic, clinical information, gait kinematics and kinetics, cartilage compositional T_1ρ and T₂ and R₂ -R_1ρ (1/T₂ -1/T_1ρ ) acquired at 3T and whole-organ magnetic resonance imaging score morphological grading. Topological data analysis (TDA) and Kolmogorov-Smirnov test were adopted for data integration, analysis, and hypothesis generation. Regression models were used for hypothesis testing.

RESULTS

The results of the TDA showed a network composed of three main patient subpopulations, thus potentially identifying new phenotypes. T₂ and T_1ρ values (T₂ lateral femur P = 1.45*10^-8 , T_1ρ medial tibia P = 1.05*10^-5 ), the presence of femoral cartilage defects (P = 0.0013), lesions in the meniscus body (P = 0.0035), and race (P = 2.44*10^-4 ) were key markers in the subpopulation classification. Within one of the subpopulations we observed an association between the composite metric R₂ -R_1ρ and the longitudinal progression of cartilage lesions.

CONCLUSION

The analysis presented demonstrates some of the complex multitissue biochemical and biomechanical interactions that define joint degeneration and OA using a multidimensional approach, and potentially indicates that R₂ -R_1ρ may be an imaging biomarker for early OA.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:78-90.

Biomarkers of (osteo)arthritis

Arthritic diseases are a major cause of disability and morbidity, and cause an enormous burden for health and social care systems globally. Osteoarthritis (OA) is the most common form of arthritis. The key risk factors for the development of OA are age, obesity, joint trauma or instability. Metabolic and endocrine diseases can also contribute to the pathogenesis of OA. There is accumulating evidence to suggest that OA is a whole-organ disease that is influenced by systemic mediators, inflammaging, innate immunity and the low-grade inflammation induced by metabolic syndrome. Although all joint tissues are implicated in disease progression in OA, articular cartilage has received the most attention in the context of aging, injury and disease. There is increasing emphasis on the early detection of OA as it has the capacity to target and treat the disease more effectively. Indeed it has been suggested that this is the era of "personalized prevention" for OA. However, the development of strategies for the prevention of OA require new and sensitive biomarker tools that can detect the disease in its molecular and pre-radiographic stage, before structural and functional alterations in cartilage integrity have occurred. There is also evidence to support a role for biomarkers in OA drug discovery, specifically the development of disease modifying osteoarthritis drugs. This Special Issue of Biomarkers is dedicated to recent progress in the field of OA biomarkers. The papers in this Special Issue review the current state-of-the-art and discuss the utility of OA biomarkers as diagnostic and prognostic tools.