Personalising therapies in primary osteoporosis
Primary (post-menopausal) osteoporosis is a systemic disease of the musculoskeletal system resulting in an increased risk for severe fractures at the spine, femoral neck, and peripheral bones. More than 3.5 million osteoporotic fractures are registered every year in Europe. Osteoporotic fractures reduce patient quality-adjusted life years (QALYs) due to patient immobility, comorbidities and increased mortality. As a consequence, fractures due to osteoporosis represent a major health burden, and exert significant economic pressure on the health care systems – the total cost for the treatment of fractures amounted to over EUR 30 billion a year in 2010.1
Post-menopausal osteoporosis usually progresses asymptomatically. Frequently, patients are diagnosed only after the first fracture has occurred. For this reason, population-based fracture risk assessments are essential to prevent fractures and support the management of osteoporosis using for example pharmacologic interventions, dietary measures and exercise. Currently, the standard-of-care for fracture risk assessment mainly includes the evaluation of bone mineral density (BMD) by dual X-ray absorptiometry (DXA). BMD is one important risk factor, and DXA has proven clinical utility for assessing fracture-risk, and can also be used to monitor patient response to treatment.2 However, standalone use of DXA for fracture risk assessment would imply that low bone mass is the only contributor to fracture risk. In reality, the risk of falling is of almost equal importance for fracture risk but more difficult to assess.3 A patient’s tendency to frequent falling can have many reasons, such as reduced muscle strength, cognitive impairment or general frailty, and currently objective and quantitative ways to include all of these factors in our treatment decision process are lacking. Therefore, the puzzle pieces that determine fracture risk need to be assembled individually for each patient, and thus need to be deciphered accurately and in a personalised way (Figure 1A).
Clinical risk scores, such as FRAX™, were developed to better acknowledge the multi-factorial nature of fracture-risk due to post-menopausal osteoporosis.4 FRAX™ has been implemented in many guidelines as a simple and standardised initial step to screening for fracture risk (Figure 1B). While FRAX has clinical utility as a first ‘filter’, it should not be expected that the fracture risk probabilities have sufficient sensitivity and specificity to reliably support treatment decisions, especially for preventing the first fracture. Therefore, there is an urgent clinical need for more sophisticated tests or algorithms that integrate several independent risk factors for osteoporotic fractures.
Small molecules, big hopes
MicroRNAs are small non-coding RNAs,5 which regulate gene expression at the post-transcriptional level through RNA interference.6 Currently, about 2000 different human microRNA species are known.7 The beauty of microRNA-based regulation lies in the fact that single miRNAs can interact with hundreds of protein-coding RNAs. As a consequence, it modulates and regulates biological processes such as differentiation, proliferation and apoptosis.5 Although RNA interference happens intracellularly, microRNAs were found to be constantly released from cells and shuttled to other cells in an endocrine fashion. Extracellularly, microRNAs are protected from degradation by encapsulation in vesicles or association to proteins. MicroRNA detection is therefore possible in liquid biopsies such as serum/plasma, urine or saliva.8 By quantifying the levels of specific microRNAs in liquid biopsies, important information about tissue function for diagnostic purposes can be gained. Therefore, extracellular (circulating) microRNAs have been intensively researched as novel, minimally invasive biomarkers in various disease areas.9
From a diagnostic point of view, parallel analysis of multiple circulating microRNAs (so-called ‘signatures’) is promising. This is because microRNA signatures allow integration of information about pathophysiologic processes from different tissues. This is especially useful for the diagnosis of multi-factorial diseases that involve multiple tissues, but to a varying degree depending on the individual patient. This concept is summarised under the term ‘personalised medicine’ as formulated 2500 years ago by Hippocrates, who said: “It is more important to know which person has a disease, than what disease a person has”.
osteomiRs – microRNA signatures for osteoporosis
Fracture-risk due to osteoporosis falls under the category ‘multifactorial disease’. Novel diagnostic concepts are required to personalise disease management: for example, fracture risk driven by bone fragility should be treated using medications that modulate bone turnover, that is, anti-resorptive or anabolic drugs. Fracture risk due to frequent falling, however, might be better mitigated using exercise and dietary intervention.
Based on this hypothesis, we launched an ambitious research project to identify those microRNAs in human serum that reflect the function of bone, muscle and neurological tissue, and to combine this microRNA signature into a diagnostic algorithm for improved fracture-risk assessment, and as a consequence targeted treatment.9
Muscle function is highly dependent on a set of muscle-specific microRNAs called ‘myomiRs’, which contribute to the development and regeneration of muscle tissue, such as miR-133, miR-1, or miR-206.10 An example for a bone-related miRNAs is miR-31, which modulates genes of the WNT signaling pathway, the transcription factors Osterix and Satb2, and thereby inhibits the formation of osteoblasts from stem cells.11,12 At the same time, miRNA-31 plays an important role in bone resorption,13 by promoting the restructuring of the cytoskeleton through modulating the differentiation of osteoclasts. So far 50 miRNAs, out of approximately 2000 identified so far in humans, have been shown to play an important role in the homeostasis of bone metabolism.14
Cross-sectional and prospective studies involving more than 700 patients have been conducted to identify circulating microRNAs that can predict fracture risk in post-menopausal women accurately.15 There is strong evidence that fracture patients show characteristic microRNA profiles in serum, which have potential as biomarkers for osteoporotic fractures and for the early detection of osteoporosis.16–19 In total, 11 microRNAs were identified, which are significantly associated with the risk for osteoporotic fractures, referred to as ‘osteomiRs’. Interestingly, it seems that some osteomiRs are also significantly regulated in patients with secondary forms of osteoporosis, who currently cannot be identified through routine diagnostic procedures. An important example is diabetic osteopathy, which cannot be detected by means of routine bone densitometry.20 The biological and clinical relevance of this new biomarker candidate is currently being investigated on the basis of model systems for osteoporosis in the laboratory, and clinical studies.
In order to standardise the analysis of osteomiRs in human serum, a simple procedure was developed using only 200µl of serum. Serum to be used for osteomiR analysis can be either fresh or frozen (–20°C or –80°C), where the stability of frozen samples is at least five years. The company TAmiRNA GmbH was founded in 2013 to validate the clinical utility of osteomiRs in prospective clinical trials and bring a certified ‘in vitro diagnostic medical device’ to the patient.
Through this research, we expect a significant improvement in the early detection of osteoporosis, a targeted application of therapeutic measures and, ultimately, a cost-effective reduction of fracture incidence in Austria and Europe.
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4 Kanis JA et al. FRAX and fracture prediction without bone mineral density. Climacteric 2015;18:2–9.
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9 De Guire V et al. Circulating miRNAs as sensitive and specific biomarkers for the diagnosis and monitoring of human diseases: promises and challenges. Clin Biochem 2013;46:846–60.
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11 Deng Y et al. Effects of a miR-31, Runx2, and Satb2 regulatory loop on the osteogenic differentiation of bone mesenchymal stem cells. Stem Cells Dev 2013;22:2278–86.
12 Weilner S et al. Secreted microvesicular miR-31 inhibits osteogenic differentiation of mesenchymal stem cells. Aging Cell 2016;15(4):744–54.
13 Mizoguchi F et al. miR-31 controls osteoclast formation and bone resorption by targeting RhoA. Arthritis Res Ther 2013;15:R102.
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15 Hackl M et al. Circulating microRNAs as novel biomarkers for bone diseases – Complex signatures for multifactorial diseases? Mol Cell Endocrinol 2016;432:83–95.
16 Weilner S et al. Differentially circulating miRNAs after recent osteoporotic fractures can influence osteogenic differentiation. Bone 2015;79:43–51.
17 Heilmeier U et al. Serum microRNAs are indicative of skeletal fractures in postmenopausal women with and without type 2 diabetes and influence osteogenic and adipogenic differentiation of adipose-tissue derived mesenchymal stem cells in vitro. J Bone Miner Res 2016;31(12):2173–92.
18 Kocijan R et al. Circulating microRNA signatures in patients with idiopathic and postmenopausal osteoporosis and fragility fractures. J Clin Endocrinol Metab 2016;101(11):4125–34.
19 Panach L et al. Serum circulating microRNAs as biomarkers of osteoporotic fracture. Calcif Tissue Int 2015;97(5):495–505.
20 Patsch JM et al. Increased cortical porosity in type 2 diabetic postmenopausal women with fragility fractures. J Bone Miner Res 2013;28:313–24.