قيد المعالجة

الرجاء الانتظار...

الإعدادات

الإعدادات

1. WO2017098052 - COMPOSITIONS AND METHODS FOR THE DIAGNOSIS AND TREATMENT OF BONE FRACTURES AND DISORDERS

ملاحظة:نص مبني على العمليات الآلية للتعرف الضوئي على الحروف. الرجاء استخدام إصدار PDF لقيمته القانونية

COMPOSITIONS AND METHODS FOR THE DIAGNOSIS AND TREATMENT OF BONE FRACTURES AND DISORDERS

FIELD OF THE INVENTION

The present invention relates to the therapy, prophylaxis and diagnosis of disorders that are associated with aberrant bone mineral density, in particular osteoporosis.

BACKGROUND OF THE INVENTION

Osteoporosis is characterized by a systemic reduction in bone mass leading to increased bone fragility and an increased risk of bone fracture. Osteoporosis is an asymptomatic disease, which means that patients do not exhibit clear symptoms at the onset or during progression. Consequently, the diagnosis of osteoporosis frequently is made after a patient has sustained a fragility (osteoporotic) fracture. Osteoporotic fractures occur most frequently at the femoral neck, hip, spine or forearm. Osteoporotic fractures are critical outcomes, because they create significant socio-economic burden (Harvey et al., 2010). The risk of osteoporotic fractures can, however, be reduced by therapeutic treatment. Today, two forms of treatment have been shown to reduce fracture-risk by up to 75% during 1 -3 years of treatment: i) anti-resorptive treatments using bisphosphonates or monoclonal antibodies against RANKL impair bone resorption and therefore stabilize bone mass and reduce the risk of fractures (Gronholz et al., 2008, Cummings et al., 2009). ii) anabolic treatments using tropic hormones such as parathyroid hormone or monoclonal antibodies against Sclerostin have been shown to increase bone mass and reduce the risk of fractures (Augustine et al., 2013, Cosman et al., 2016).

These methods of treatments are frequently used to counteract fracture risk in various forms of osteoporosis including type- 1 postmenopausal osteoporosis, type- 11 osteoporosis, idiopathic or male osteoporosis. In addition, primary bone cancer or bone metastases will impact bone metabolism and result in bone lesions. Examples are osteosarcoma, and multiple myeloma.

Accurate tools for early diagnosis and fracture-risk assessment are critical to target the right method of treatment to the patient in need of treatment.

Current methods for the assessment of fracture risk as well as treatment response include non-invasive imaging techniques as well as the analysis of clinical parameters and biochemical markers of bone turnover. Recently, microRNAs have been identified to be secreted into the bloodstream from cells of various tissues, possibly indicating pathological processes in different parts of the body. There is evidence that microRNAs play an important role in the development and function of bone forming and bone resorbing cells, specifically osteoblasts and osteoclasts. Both cell types control the homeostasis between bone anabolism and catabolism, and therefore microRNAs play a pivotal physiological role in bone metabolism. To this day, however, little is known whether an imbalance in bone metabolism, which causes bone diseases, may be reflected in the levels of circulating microRNAs.

Osteoporotic fractures are caused by an increase in bone fragility, which can occur due to low bone mass and microarchitectural changes in bone tissue. Such fractures are the critical hard outcome of osteoporosis, which affects more than 75 million people in the United States, Europe and Japan (Kanis et al., 2013). With a lifetime risk of 30% - 40% to be affected by vertebral or non-vertebral fractures in developed countries, osteoporosis has an incidence rate similar to that of coronary heart disease. Furthermore, with the exception of forearm fractures, osteoporotic fractures are associated with increased mortality. Most fractures cause acute pain and lead to patient hospitalization, immobilization and often slow recovery.

In addition, osteoporotic symptoms are frequently observed in patients with type 2 diabetes, who overall suffer from an elevated risk of fragility fractures. Diabetes mellitus refers to a group of metabolic diseases in which a subject has high blood sugar. Type 2 diabetes results from insulin resistance, a condition in which cells fail to use insulin properly, sometimes also with an absolute insulin deficiency. This form was previously referred to as non insulin-dependent diabetes mellitus (NIDDM) or "adult-onset diabetes".

In the prophylaxis, diagnosis and management of osteoporosis, the assessment of fracture risk and monitoring of treatment response are two of the most important aspects. Therefore, analysis of bone mass by measuring bone mineral density (BMD) is currently the only clinical parameter of the skeleton that is routinely analyzed in clinical practice and part of the WHO FRAX questionnaire (Kanis et al., 2013).

However, due to the lacking correlation with bone strength and bone metabolism (Cefalu, 2004), age- and site-dependent differences in bone density, the assessment of the T-Score (i.e. a comparison of a patient's BMD to that of a healthy thirty-year-old) in combination with other established clinical scores of fracture risk (Rubin et al., 2013) often does not improve the prediction of fracture risk. Particularly in case of patients suffering from type-2 diabetes there is no evidence for correlation between BMD and fracture risk, which demonstrates the need for alternative markers of fracture risk.

In order to estimate the rate of bone formation, bone resorption and therapeutic treatment response, few biochemical bone turnover markers (BTM) have been identified (Vasikaran et al., 20 1 ), such as serum procollagen type I N propeptide (s-PINP), serum C-terminal teiopeptide of type I collagen (s-CTX). While the correlation of these markers with bone metabolism has been established, their specificity and sensitivity for fracture risk prediction needs to be further validated. Therefore, only few countries have recommended to incorporate these biochemical markers into clinical practice (Vasikaran et al., 201 1 ).

Other potential markers of bone metabolism may be derived from the signaling pathways that are known to play a major role in bone formation and resorption, such as WNT, BMP-2 or RANKL. For example, proteins derived from Dickkopf-1 (DKK-1 ) or Sclerostin (SOST) genes can act as binding partners of WNT and WNT-receptors, thereby regulating its activity and subsequently bone formation (Canalis, 2013).

However, the pre-analytical stability of these proteins in serum/plasma in response to diet, exercise and circadian rhythm is questionable, and so is the general significance for bone metabolism due to the fact that these proteins are produced in other tissues as well and might be regulated in response to other diseases. Especially in respect to certain types of cancer, WNT-signalling has been shown to drive the progression of disease (Anastas & Moon, 2013).

Recently, increased attention has been attributed to the importance of microRNAs (miRNAs), small non-coding RNAs that regulate gene expression (Bartel, 2009), in the control of bone metabolism (Dong, Yang, Guo, & Kang, 2012; Zhao et al., 2013). Several miRNAs have been shown to silence osteogenic inhibitors during stem cell differentiation into osteocytes (Trom peter et al., 2013), to regulate BMP2-mediated osteoblast proliferation and differentiation (Li et al., 2008), or to orchestrate the activity of WNT-signalling (Kapinas, Kessler, & Delany, 2009). Therefore, the potential of miRNAs as therapeutic agents for accelerating bone regeneration and/or as diagnostic tools for evaluating bone metabolism and fracture risk has recently been

acknowledged (van Wijnen et al., 2013). The impressive stability of miRNA in serum and plasma even after being subjected to harsh conditions, the limited number of miRNAs (< 500 found secreted in plasma/serum), their simple chemical composition,

the lack of posttranscriptional modification and the availability of advanced and well established, highly sensitive screening techniques define miRNAs as excellent candidates for biomarkers. In fact, blood-circulating miRNAs have already been analyzed in the context of disease (Keller et al., 201 1 ), especially cancer and cardiovascular disease, or non-pathological processes such as ageing (Weiner et al., 2013). A combination of miRNAs that can control the onset and progression of osteoporosis or can serve as surrogate markers for this pathological process, is a specific osteoporosis signature whose use would represent a non-invasive approach to predict the fracture risk as well as targets for therapeutic control of the progression of osteoporosis.

Recently, five freely circulating miRNAs and bone tissue miRNAs have been identified and implicated with osteoporotic fractures (Seeliger et al., 2014).

Recently, miR-188-5p was found to be up-regulated in the bone of aged mice, to induce adipogenic differentiation, and to inhibit osteogenic differentiation (Li et al., 2015).

SUMMARY OF THE INVENTION

It is the objective of the present invention, to broaden the scope, specificity and validity in diagnosing osteoporosis or osteopenia and predicting fractures, and to provide novel agents for the therapy of osteoporosis by stabilizing bone homeostasis and accelerating fracture healing.

The problem is solved by the present invention.

The inventors have detected specific miRNAs that are up- or down-regulated in blood samples derived from patients with recent as well as non-recent osteoporotic fractures.

The present invention specifically provides a selected set of miRNAs that are specifically up- or down-regulated and are thus useful as valuable biomarkers and represent a diagnostic signature applicable both over a broad range of bone disease stages and age groups.

According to the invention there is provided an in vitro method of diagnosing osteoporosis, determining the risk of osteoporotic fractures or monitoring of treatment success in a subject, which can be, but is not limited to a post-menopausal female or an osteoporosis or osteopenia patient, suffering from or being at risk of developing bone fractures, comprising the steps of:

a) providing a sample, specifically a blood sample, from said subject, b) measuring the level of two or more miRNAs, specifically 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 26, 27, 28, 29 or 30 mRNAs, selected from any of miRNAs consisting of hsa-miR-188-3p, hsa-miR-382-3p, hsa-miR-127-3p, hsa-miR-144-3p, hsa-miR-155-5p, hsa-miR-181 a-3p, hsa-miR-1908, hsa-miR-190a, hsa-miR-199b-5p, hsa-miR-203a, hsa-miR-214-3p, hsa-miR-29b-3p, hsa-miR-330-3p, hsa-miR-31 -5p, hsa-miR-335-5p, hsa-let-7b-5p, hsa-miR-342-5p, hsa-miR-369-3p, hsa-miR-377-3p, hsa-miR-378a-5p, hsa-miR-495-3p, hsa-miR-500a-5p, hsa-miR-502-5p, hsa-miR-542-5p, hsa-miR-550a-5p, hsa-miR-576-3p, hsa-miR-582-3p, hsa-miR-642a-5p, hsa-miR-942 and hsa-miR- 181 d or isoforms or variants thereof, and

c) comparing the level of said miRNAs to a reference level,

wherein the magnitude of difference in said level when compared to the reference level is indicative of osteoporosis or the risk of fractures.

According to an alternative embodiment, there is provided an in vitro method of diagnosing osteoporosis or determining the risk of osteoporotic fractures or monitoring of treatment in a subject, comprising the steps of:

a) providing a sample from said subject,

b) measuring the level of one, 2, 3, 4, 5, 6, or 7 miRNAs selected from any of miRNAs consisting of hsa-miR-144-3p, hsa-miR-214-3p, hsa-miR-29b-3p, hsa-miR- 335-5p, hsa-miR-31 -5p, hsa-let-7b-5p and hsa-miR-181 d or isoforms or variants thereof, and

c) measuring the level of one, 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, or 23 miRNAs selected from any of miRNAs consisting of hsa-miR-188-3p, hsa-miR-382-3p, hsa-miR-127-3p, hsa-miR-155-5p, hsa-miR-181 a-3p, hsa-miR-1908, hsa-miR-190a, hsa-miR-199b-5p hsa-miR-203a, hsa-miR-330-3p, hsa-miR-342-5p, hsa-miR-369-3p, hsa-miR-377-3p, hsa-miR-378a-5p, hsa-miR-495-3p, hsa-miR-500a-5p, hsa-miR-502-5p, hsa-miR-542-5p, hsa-miR-550a-5p, hsa-miR-576-3p, hsa-miR-582-3p, hsa-miR-642a-5p and hsa-miR-942, or isoforms or variants thereof, and

d) comparing the level of said miRNAs to a reference level,

wherein the magnitude of difference in said level when compared to the reference level is indicative of osteoporosis or the risk of fractures.

According to a specific embodiment, the level of 3, 4, 5, 6, 7, 8 miRNAs selected from the group consisting of hsa-miR-203a, hsa-miR-330-3p, hsa-miR- 88-3p, hsa-miR-550a-5p, hsa-miR-335-5p, hsa-miR-29b-3p, hsa-miR-214-3p and hsa-miR-31 -5p is measured.

According to a specific embodiment, the level of all of hsa-miR-203a, hsa-miR- 330-3p, hsa-miR-188-3p, hsa-miR-550a-5p, hsa-miR-335-5p, hsa-miR-29b-3p, hsa-miR-2 4-3p and hsa-miR-31 -5p is measured.

According to a specific embodiment of the invention there is provided a method wherein at least one, 2, 3, 4, 5, 6,7, 8 miRNA selected from the group consisting of hsa-miR-188-3p, hsa-miR-382-3p, hsa-miR-203a, hsa-miR-181 a-3p, hsa-miR-29b-3p, hsa-miR-330-3p, hsa-miR-335-5p and hsa-miR-369-3p and at least one, 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24 miRNAs selected from the group consisting of hsa-miR-127-3p, hsa-miR-155-5p, hsa-miR-1908, hsa-miR-190a, hsa-miR-199b-5p, hsa-miR-144-3p, hsa-miR-335-5p, hsa-miR-31 -5p, hsa-miR-214-3p, hsa-miR-29b-4p, hsa-let-7b-5p, hsa-miR-342-5p, hsa-miR-377-3p, hsa-miR-378a-5p, hsa-miR-495-3p, hsa-miR-500a-5p, hsa-miR-502-5p, hsa-miR-542-5p, hsa-miR-550a-5p, hsa-miR-576-3p, hsa-miR-582-3p, hsa-miR-642a-5p, hsa-miR-942, and hsa-miR-181 d are measured.

According to a specific embodiment of the invention there is provided a method wherein 1 or 2 miRNAs selected from the group consisting of hsa-miR-29b-3p, hsa-miR-335-5p and and at least one, 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 26, 27, 28, 29 or 30 miRNAs selected from the group consisting of hsa-miR-188-3p, hsa-miR-382-3p, hsa-miR-203a, hsa-miR-127-3p, hsa-miR-155-5p, hsa-miR-181 a-3p, hsa-miR-1908, hsa-miR-190a, hsa-miR-199b-5p, hsa-miR-144-3p, hsa-miR-330-3p, hsa-miR-335-5p, hsa-miR-31 -5p, hsa-miR-214-3p, hsa-miR-29b-4p, hsa-let-7b-5p, hsa-miR-342-5p, hsa-miR-369-3p hsa-miR-377-3p, hsa-miR-378a-5p, hsa-miR-495-3p, hsa-miR-500a-5p, hsa-miR-502-5p, hsa-miR-542-5p, hsa-miR-550a-5p, hsa-miR-576-3p, hsa-miR-582-3p, hsa-miR-642a-5p, hsa-miR-942, and hsa-miR-181 d are measured.

According to a further embodiment, a method for determining whether a therapeutic amount of a pharmaceutical reduces the risk of developing bone fractures is also provided, comprising

a) providing a sample, specifically a blood sample, from said subject,

b) measuring the level of two or more miRNAs, specifically of 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 26, 27, 28, 29, 30 miRNAs selected from any of miRNAs consisting of hsa-miR-188-3p, hsa-miR-382-3p, hsa-miR-127-3p, hsa-miR-144-3p, hsa-miR-155-5p, hsa-miR-181 a-3p, hsa-miR-1908, hsa-miR-190a, hsa-miR-199b-5p, hsa-miR-203a, hsa-miR-214-3p, hsa-miR-29b-3p, hsa-miR-330-3p, hsa-miR-335-5p, hsa-miR-31 -5p, hsa-miR-342-5p, hsa-miR-369-3p, hsa-miR-377-3p, hsa-miR-378a-5p, hsa-miR-495-3p, hsa-miR-500a-5p, hsa-miR-502-5p, hsa-miR-542-5p, hsa-miR-550a-5p, hsa-miR-576-3p, hsa-miR-582-3p, hsa-miR-642a-5p, hsa-miR-942, hsa-let-7b-5p and has-miR-181 d or isoforms or variants thereof, or

measuring the level of two or more miRNAs, specifically of 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 26 miRNAs selected from any of miRNAs consisting of hsa-miR-32-3p, hsa-let-7b-5p, hsa-let-7g-5p, hsa-let-7i-5p, hsa-miR-141 -3p, hsa-miR-143-5p, hsa-miR-16-2-3p, hsa-miR-181 c-3p, hsa-miR-181 c-5p, hsa-miR-191 -5p, hsa-miR-19b-1 -5p, hsa-miR-203a, hsa-miR-21 -3p, hsa-miR-323a-3p, hsa-miR-375, hsa-miR-486-5p, hsa-miR-500a-5p, hsa-miR-550a-5p, hsa-miR-7-5p, hsa-miR-92a-3p, hsa-miR-942, hsa-miR-96-5p, hsa-miR-330-3p, hsa-miR-31 -5p, hsa-miR-144-3p and hsa-miR-382-3p, and

c) comparing the level of said miRNAs to a reference level, which optionally may be the miRNA level measured before treatment,

wherein the magnitude of difference in said level when compared to the reference level is indicative of treatment success.

According to a further embodiment, a method for determining whether a therapeutic amount of a pharmaceutical reduces the risk of developing bone fractures is also provided, comprising

a) providing a sample, specifically a blood sample, from said subject,

b) measuring the level of one, 2, 3, 4, 5, 6, or 7 miRNAs selected from any of miRNAs consisting of hsa-miR-144-3p, hsa-miR-214-3p, hsa-miR-29b-3p, hsa-miR-335-5p, hsa-miR-31 -5p, hsa-let-7b-5p and hsa-miR-181d or isoforms or variants thereof, and

c) measuring the level of one, 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, or 23 miRNAs selected from any of miRNAs consisting of hsa-miR-188-3p, hsa-miR-382-3p, hsa-miR-127-3p, hsa-miR-155-5p, hsa-miR-181 a-3p, hsa-

miR-1908, hsa-miR-190a, hsa-miR-199b-5p hsa-miR-203a, hsa-miR-330-3p, hsa-miR-342-5p, hsa-miR-369-3p, hsa-miR-377-3p, hsa-miR-378a-5p, hsa-miR-495-3p, hsa-miR-500a-5p, hsa-miR-502-5p, hsa-miR-542-5p, hsa-miR-550a-5p, hsa-miR-576-3p, hsa-miR-582-3p, hsa-miR-642a-5p and hsa-miR-942, or isoforms or variants thereof, and

d) comparing the level of said miRNAs to a reference level, which optionally may be the miRNA level measured before treatment,

wherein the magnitude of difference in said level when compared to the reference level is indicative of treatment success. According to a further specific embodiment of the invention, 1 , 2, 3, 4, 5, 6 or 7 of hsa-miR- 188-3p, hsa-miR-382-3p, hsa-miR-181 a-3p, has-miR-29b-3p, hsa-miR-330-3p, has-miR-335-5p and hsa-miR-369-3p are measured in combination with at least one miRNA, specifically 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 26 miRNAs, selected from hsa-miR-127-3p, hsa-miR-155-5p, hsa-miR-1908, hsa-miR-190a, hsa-miR-199b-5p, hsa-miR-144-3p, hsa-miR-335-5p, hsa-miR-31 -5p, hsa-miR-214-3p, hsa-miR-29b-4p, hsa-let-7b-5p,hsa-miR-342-5p, hsa-miR-377-3p, hsa-miR-378a-5p, hsa-miR-495-3p, hsa-miR-500a-5p, hsa-miR-502-5p, hsa-miR-542-5p, hsa-miR-550a-5p, hsa-miR-576-3p, hsa-miR-582-3p, hsa-miR-642a-5p, hsa-miR-942, hsa-miR-96-5p, hsa-miR-330-3p, and hsa-miR-181 d.

According to a specific embodiment, the level of all of or 2, 3,4, 5, 6 or 7 of hsa-miR-203a, hsa-miR-330-3p, hsa-miR-188-3p, hsa-miR-550a-5p, hsa-miR-335-5p, hsa-miR-29b-3p, hsa-miR-214-3p and hsa-miR-31 -5p is measured for determining whether a therapeutic amount of a pharmaceutical reduces the risk of developing bone fractures.

According to a specific embodiment of the invention there is provided a method, wherein hsa-miR-188-3p is measured in combination with at least one miRNA, specifically 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 26, 27 miRNAs, selected from the group consisting of hsa-miR-127-3p, hsa-miR-155-5p, hsa-miR-1908, hsa-miR-190a, hsa-miR-199b-5p, hsa-miR-144-3p, hsa-miR-335-5p, hsa-miR-31 -5p, hsa-miR-214-3p, hsa-miR-29b-4p, hsa-let-7b-5p, hsa-miR-203a, hsa-miR-342-5p, hsa-miR-377-3p, hsa-miR-378a-5p, hsa-miR-495-3p, hsa-miR-500a-5p, hsa-miR-502-5p, hsa-miR-542-5p, hsa-miR-550a-5p, hsa-miR-576-3p, hsa-miR-582-3p, hsa-miR-642a-5p, hsa-miR-942, hsa-miR-96-5p, hsa-miR-330-3p, and hsa-miR-181 d.

According to a specific embodiment of the invention there is provided a method, wherein hsa-miR- 88-3p is measured in combination with at least one miRNA, specifically 2, 3, 4 miRNAs, selected from the group consisting of hsa-miR-335-5p, hsa-miR-31 -5p, hsa-miR-29b-4p, and hsa-miR-181 d, and optionally with at least one miRNA, specifically 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 26, 27 miRNAs, selected from the group consisting of hsa-miR-127-3p, hsa-miR-155-5p, hsa-miR-1908, hsa-miR-190a, hsa-miR-199b-5p, hsa-miR-144-3p, hsa-miR-214-3p, hsa-let-7b-5p, hsa-miR-203a, hsa-miR-342-5p, hsa-miR-377-3p, hsa-miR-378a-5p, hsa-miR-495-3p, hsa-miR-500a-5p, hsa-miR-502-5p, hsa-miR-542-5p, hsa-miR-550a-5p, hsa-miR-576-3p, hsa-miR-582-3p, hsa-miR-642a-5p, hsa-miR-942, hsa-miR-96-5p, hsa-miR-330-3p.

According to a specific embodiment of the invention there is provided a method, wherein hsa-miR-188-3p is measured in combination with at least one miRNA.

specifically 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 26, 27, 28, 29, 30, 31 , 32 miRNAs, selected from the group consisting of hsa-miR-382-3p, hsa-miR-203a, hsa-miR-181 a-3p, hsa-miR-29b-3p, hsa-miR-330-3p, hsa-miR-335-5p, hsa-miR-369-3p, hsa-miR-127-3p, hsa-miR-155-5phsa-miR-1908, hsa-miR-190a, hsa-miR-199b-5p, hsa-miR-144-3p, hsa-miR-335-5p, hsa-miR-31 -5p, hsa-miR-214-3p, hsa-miR-29b-4p, hsa-let-7b-5p, hsa-miR-342-5p, hsa-miR-377-3p, hsa-miR-378a-5p, hsa-miR-495-3p, hsa-miR-500a-5p, hsa-miR-502-5p, hsa-miR-542-5p, hsa-miR-550a-5p, hsa-miR-576-3p, hsa-miR-582-3p, hsa-miR-642a-5p, hsa-miR-942, hsa-miR-96-5p, and hsa-miR-181d.

According to a specific embodiment of the invention there is provided a method, wherein hsa-miR-188-3p is measured in combination with at least one miRNA, specifically 2, 3, 4, 5, 6, 7 miRNAs, selected from the group consisting of hsa-miR-29b-3p, hsa-miR-335-5p, hsa-miR-144-3p, hsa-miR-31 -5p, hsa-miR-29b-4p, and hsa-miR-181d. and optionally with at least 1 , 2, 3, 4, 5, 6, 7, 89, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 26 miRNAs, selected from the group consisting of hsa-miR-382-3p, hsa-miR-203a, hsa-miR-181 a-3p, hsa-miR-330-3p, hsa-miR-369-3p, hsa-miR-127-3p, hsa-miR-155-5p, hsa-miR-1908, hsa-miR-190a, hsa-miR-199b-5p, hsa-miR-214-3p, hsa-let-7b-5p, hsa-miR-342-5p, hsa-miR-377-3p, hsa-miR-378a-5p, hsa-miR-495-3p, hsa-miR-500a-5p, hsa-miR-502-5p, hsa-miR-542-5p, hsa-miR-550a-5p, hsa-miR-576-3p, hsa-miR-582-3p, hsa-miR-642a-5p, hsa-miR-942, hsa-miR-96-5p, and hsa-miR-181 d.

According to a specific embodiment of the invention there is provided a method, wherein hsa-miR-382-3p is measured in combination with at least one miRNA, specifically 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 26, 27, 28 miRNAs, selected from the group consisting of hsa-miR-127-3p, hsa-miR-155-5p, hsa-miR-188-3p, hsa-miR-1908, hsa-miR-190a, hsa-miR-199b-5p, hsa-miR-335-5p, hsa-miR-214-3p, hsa-miR-29b-4p, hsa-let-7b-5p, hsa-miR-203a, hsa-miR-342-5p, hsa-miR-377-3p, hsa-miR-378a-5p, hsa-miR-495-3p, hsa-miR-500a-5p, hsa-miR-502-5p, hsa-miR-542-5p, hsa-miR-550a-5p, hsa-miR-576-3p, hsa-miR-582-3p, hsa-miR-642a-5p, hsa-miR-942, hsa-miR-96-5p, hsa-miR-330-3p, hsa-miR-31 -5p, hsa-miR-144-3p and hsa-miR-181d.

According to a specific embodiment of the invention there is provided a method, wherein hsa-miR-382-3p is measured in combination with at least one miRNA, specifically 2, 3, 4, 5 miRNAs, selected from the group consisting of hsa-miR-188-3p, hsa-miR-335-5p, hsa-miR-29b-4p, hsa-miR-31 -5p, and hsa-miR-181 d and optionally in combination with with at least one miRNA, specifically 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 26, 27, 28 miRNAs, selected from the group consisting of hsa-miR-127-3p, hsa-miR-155-5p, hsa-miR-1908, hsa-miR-190a, hsa-miR-199b-5p, hsa-miR-214-3p, hsa-let-7b-5p, hsa-miR-203a, hsa-miR-342-5p, hsa-miR-377-3p, hsa-miR-378a-5p, hsa-miR-495-3p, hsa-miR-500a-5p, hsa-miR-502-5p, hsa-miR-542-5p, hsa-miR-550a-5p, hsa-miR-576-3p, hsa-miR-582-3p, hsa-miR-642a-5p, hsa-miR-942, hsa-miR-96-5p, hsa-miR-330-3p, hsa-miR-144-3p and hsa-miR-181 d.

According to a specific embodiment of the invention there is provided a method, wherein hsa-miR-382-3p is measured in combination with at least one miRNA, specifically 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 26, 27, 28, 29 miRNAs, selected from the group consisting of hsa-miR-144-3p, hsa-miR-188-3p, hsa-miR-181 a-3p, hsa-miR-330-3p, hsa-miR-369-3p, hsa-miR-127-3p, hsa-miR-155-5p, hsa-miR-1908, hsa-miR-190a, hsa-miR-199b-5p, hsa-miR-203a, hsa-miR-214-3p, hsa-miR-29b-3p, hsa-miR-342-5p, hsa-miR-335-5p, hsa-miR-31 -5p, hsa-miR-377-3p, hsa-miR-378a-5p, hsa-miR-495-3p, hsa-miR-500a-5p, hsa-miR-502-5p, hsa-miR-542-5p, hsa-miR-550a-5p, hsa-miR-576-3p, hsa-miR-582-3p, hsa-miR-642a-5p, hsa-miR-942, hsa-let-7b-5p and hsa-miR-181 d.

According to a specific embodiment of the invention there is provided a method, wherein hsa-miR-382-3p is measured in combination with at least one miRNA,

specifically 2, 3, 4, 5 miRNAs, selected from the group consisting of hsa-miR-181 a-3p, hsa-miR-29b-3p, hsa-miR-335-5p, hsa-miR-31 -5p, and hsa-miR-181 d, optionally in combination with at least one miRNA, specifically 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24 miRNAs, selected from the group consisting of hsa-miR-144-3p, hsa-miR-188-3p, hsa-miR-330-3p, hsa-miR-369-3p, hsa-miR-127-3p, hsa-miR-155-5p, hsa-miR-1908, hsa-miR-190a, hsa-miR-199b-5p, hsa-miR-203a, hsa-miR-214-3p, hsa-miR-342-5p, hsa-miR-377-3p, hsa-miR-378a-5p, hsa-miR-495-3p, hsa-miR-500a-5p, hsa-miR-502-5p, hsa-miR-542-5p, hsa-miR-550a-5p, hsa-miR-576-3p, hsa-miR-582-3p, hsa-miR-642a-5p, hsa-miR-942, hsa-let-7b-5p.

According to a specific embodiment of the invention there is provided a method, wherein hsa-miR-181 a-3p is measured in combination with at least one miRNA, specifically 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 26, 27 miRNAs, selected from the group consisting of hsa-miR-127-3p, hsa-miR-155-5p, hsa-miR-188-3p, hsa-miR-1908, hsa-miR-190a, hsa-miR-199b-5p, hsa-miR-144-3p, hsa-miR-335-5p, hsa-miR-31 -5p, hsa-miR-214-3p, hsa-miR-29b-4p, hsa-let-7b-5p, hsa-miR-203a, hsa-miR-342-5p, hsa-miR-377-3p, hsa-miR-378a-5p, hsa-miR-495-3p, hsa-miR-500a-5p, hsa-miR-502-5p, hsa-miR-542-5p, hsa-miR-550a-5p, hsa-miR-576-3p, hsa-miR-582-3p, hsa-miR-642a-5p, hsa-miR-942 and hsa-miR-181 d.

According to a specific embodiment of the invention there is provided a method, wherein hsa-miR-181 a-3p is measured in combination with at least one miRNA, specifically 2, 3, 4, 5 miRNAs, selected from the group consisting of, hsa-miR-144-3p, hsa-miR-335-5p, hsa-miR-31 -5p, hsa-miR-29b-4p and hsa-miR-181 d, and optionally in combination with at least one miRNA, specifically 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 miRNAs, selected from the group consisting of hsa-miR-188-3p hsa-miR-127-3p, hsa-miR-155-5p, hsa-miR-1908, hsa-miR-190a, hsa-miR-199b-5p, hsa-miR-214-3p, hsa-let-7b-5p, hsa-miR-203a, hsa-miR-342-5p, hsa-miR-377-3p, hsa-miR-378a-5p, hsa-miR-495-3p, hsa-miR-500a-5p, hsa-miR-502-5p, hsa-miR-542-5p, hsa-miR-550a-5p, hsa-miR-576-3p, hsa-miR-582-3p, hsa-miR-642a-5p and hsa-miR-942.

According to a specific embodiment of the invention there is provided a method, wherein hsa-miR-181 a-3p is measured in combination with at least one miRNA, specifically 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 26, 27, 28, 29 miRNAs, selected from the group consisting of hsa-miR-144-3p, hsa-miR-188-3p, hsa-miR-382-3p, hsa-miR-330-3p, hsa-miR-369-3p, hsa-miR-127-3p,

hsa-miR-155-5p, hsa-miR-1908, hsa-miR-190a, hsa-miR-199b-5p, hsa-miR-203a, hsa-miR-214-3p, hsa-miR-29b-3p, hsa-miR-342-5p, hsa-miR-335-5p, hsa-miR-31 -5p, hsa-miR-377-3p, hsa-miR-378a-5p, hsa-miR-495-3p, hsa-miR-500a-5p, hsa-miR-502-5p, hsa-miR-542-5p, hsa-miR-550a-5p, hsa-miR-576-3p, hsa-miR-582-3p, hsa-miR-642a-5p, hsa-miR-942, hsa-let-7b-5p, and hsa-miR-181 d.

According to a specific embodiment of the invention there is provided a method, wherein hsa-miR-181 a-3p is measured in combination with at least one miRNA, specifically 2, 3, 4, 5, 6 miRNAs, selected from the group consisting of hsa-miR-144-3p, hsa-miR-199b-5p, hsa-miR-29b-3p, hsa-miR-335-5p, hsa-miR-31 -5p, and hsa-miR-181 d, and optionally in combination with at least one miRNA, specifically 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23 miRNAs, selected from the group consisting of hsa-miR-188-3p, hsa-miR-382-3p, hsa-miR-330-3p, hsa-miR-369-3p, hsa-miR-127-3p, hsa-miR-155-5p, hsa-miR-1908, hsa-miR-190a, hsa-miR-203a, hsa-miR-214-3p, hsa-miR-342-5p, hsa-miR-377-3p, hsa-miR-378a-5p, hsa-miR-495-3p, hsa-miR-500a-5p, hsa-miR-502-5p, hsa-miR-542-5p, hsa-miR-550a-5p, hsa-miR-576-3p, hsa-miR-582-3p, hsa-miR-642a-5p, hsa-miR-942 and hsa-let-7b-5p.

According to a specific embodiment of the invention there is provided a method, wherein hsa-miR-330-3p is measured in combination with at least one miRNA, specifically 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 26, 27 miRNAs, selected from the group consisting of hsa-miR-127-3p, hsa-miR-155-5p, hsa-miR-188-3p, hsa-miR-1908, hsa-miR-190a, hsa-miR-199b-5p, hsa-miR-144-3p, hsa-miR-335-5p, hsa-miR-31 -5p, hsa-miR-214-3p, hsa-miR-29b-4p, hsa-let-7b-5p, hsa-miR-203a, hsa-miR-342-5p, hsa-miR-377-3p, hsa-miR-378a-5p, hsa-miR-495-3p, hsa-miR-500a-5p, hsa-miR-502-5p, hsa-miR-542-5p, hsa-miR-550a-5p, hsa-miR-576-3p, hsa-miR-582-3p, hsa-miR-642a-5p, hsa-miR-942 and hsa- miR-181d.

According to a specific embodiment of the invention there is provided a method, wherein hsa-miR-330-3p is measured in combination with at least one miRNA, specifically 2, 3, 4, 5, 6 miRNAs, selected from the group consisting of hsa-miR-188-3p, hsa-miR-144-3p, hsa-miR-335-5p, hsa-miR-31 -5p, hsa-miR-29b-4p, and hsa- miR-181 d and optionally in combination with at least one miRNA, specifically 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20 miRNAs, selected from the group consisting of hsa-miR-127-3p, hsa-miR-155-5p, hsa-miR-1908, hsa-miR-190a, hsa-miR-199b-5p, hsa-miR-214-3p, hsa-let-7b-5p, hsa-miR-203a, hsa-miR-342-5p, hsa-

miR-377-3p, hsa-miR-378a-5p, hsa-miR-495-3p, hsa-miR-500a-5p, hsa-miR-502-5p, hsa-miR-542-5p, hsa-miR-550a-5p, hsa-miR-576-3p, hsa-miR-582-3p, hsa-miR-642a-5p and hsa-miR-942.

According to a specific embodiment of the invention there is provided a method, wherein hsa-miR-330-3p is measured in combination with at least one miRNA, specifically 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 26, 27, 28, 29 miRNAs, selected from the group consisting of hsa-miR-144-3p, hsa-miR-188-3p, hsa-miR-382-3p, hsa-miR-181 a-3p, hsa-miR-369-3p, hsa-miR-127-3p, hsa-miR-155-5p, hsa-miR-1908, hsa-miR-190a, hsa-miR-199b-5p, hsa-miR-203a, hsa-miR-214-3p, hsa-miR-29b-3p, hsa-miR-342-5p, hsa-miR-335-5p, hsa-miR-31 -5p, hsa-miR-377-3p, hsa-miR-378a-5p, hsa-miR-495-3p, hsa-miR-500a-5p, hsa-miR-502-5p, hsa-miR-542-5p, hsa-miR-550a-5p, hsa-miR-576-3p, hsa-miR-582-3p, hsa-miR-642a-5p, hsa-miR-942, hsa-let-7b-5p, and hsa-miR-181d.

According to a specific embodiment of the invention there is provided a method, wherein hsa-miR-330-3p is measured in combination with at least one miRNA, specifically 2, 3, 4, miRNAs, selected from the group consisting of hsa-miR-144-3p, hsa-miR-29b-3p, hsa-miR-335-5p and hsa-miR-31 -5p, and hsa-miR-181 d and optinally in combination with at least one miRNA, specifically 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24 miRNAs, selected from the group consisting of hsa-miR-188-3p, hsa-miR-382-3p, hsa-miR-181 a-3p, hsa-miR-369-3p, hsa-miR-127-3p, hsa-miR-155-5p, hsa-miR-1908, hsa-miR-190a, hsa-miR-199b-5p, hsa-miR-203a, hsa-miR-214-3p, hsa-miR-342-5p, hsa-miR-377-3p, hsa-miR-378a-5p, hsa-miR-495-3p, hsa-miR-500a-5p, hsa-miR-502-5p, hsa-miR-542-5p, hsa-miR-550a-5p, hsa-miR-576-3p, hsa-miR-582-3p, hsa-miR-642a-5p, hsa-miR-942 and hsa-let-7b-5p.

According to a specific embodiment of the invention there is provided a method,, wherein hsa-miR-369-3p is measured in combination with at least one miRNA, specifically 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 26 miRNAs, selected from the group consisting of hsa-miR-127-3p, hsa-miR-155-5p, hsa-miR-188-3p, hsa-miR-1908, hsa-miR-190a, hsa-miR-199b-5p, hsa-miR-144-3p, hsa-miR-335-5p, hsa-miR-31 -5p, hsa-miR-214-3p, hsa-miR-29b-4p, hsa-let-7b-5p, hsa-miR-203a, hsa-miR-342-5p, hsa-miR-377-3p, hsa-miR-378a-5p, hsa-miR-495-3p, hsa-miR-500a-5p, hsa-miR-502-5p, hsa-miR-542-5p, hsa-miR-550a-5p, hsa-miR-576-3p, hsa-miR-582-3p, hsa-miR-642a-5p, hsa-miR-942 and hsa-miR-181d.

According to a specific embodiment of the invention there is provided a method,, wherein hsa-miR-369-3p is measured in combination with at least one miRNA, specifically 2, 3, 4, 5 miRNAs, selected from the group consisting of hsa-miR-144-3p, hsa-miR-335-5p, hsa-miR-31 -5p, hsa-miR-29b-4p and hsa-miR-181 d and optionally in combination with at least one miRNA, specifically 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 miRNAs, selected from the group consisting of hsa-miR-127-3p, hsa-miR-155-5p, hsa-miR-188-3p, hsa-miR-1908, hsa-miR-190a, hsa-miR-199b-5p, hsa-miR-214-3p, hsa-let-7b-5p, hsa-miR-203a, hsa-miR-342-5p, hsa-miR-377-3p, hsa-miR-378a-5p, hsa-miR-495-3p, hsa-miR-500a-5p, hsa-miR-502-5p, hsa-miR-542-5p, hsa-miR-550a-5p, hsa-miR-576-3p, hsa-miR-582-3p, hsa-miR-642a-5p and hsa-miR-942.

According to a specific embodiment of the invention there is provided a method, wherein hsa-miR-369-3p is measured in combination with at least one miRNA, specifically 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 26, 27, 28, 29 miRNAs, selected from the group consisting of hsa-miR-144-3p, hsa-miR-188-3p, hsa-miR-382-3p, hsa-miR-181 a-3p, hsa-miR-330-3p, hsa-miR-127-3p, hsa-miR-155-5p, hsa-miR-1908, hsa-miR-190a, hsa-miR-199b-5p, hsa-miR-203a, hsa-miR-214-3p, hsa-miR-29b-3p, hsa-miR-342-5p, hsa-miR-335-5p, hsa-miR-31 -5p, hsa-miR-377-3p, hsa-miR-378a-5p, hsa-miR-495-3p, hsa-miR-500a-5p, hsa-miR-502-5p, hsa-miR-542-5p, hsa-miR-550a-5p, hsa-miR-576-3p, hsa-miR-582-3p, hsa-miR-642a-5p, hsa-miR-942, hsa-let-7b-5p, and hsa-miR-181d.

According to a specific embodiment of the invention there is provided a method, wherein hsa-miR-369-3p is measured in combination with at least one miRNA, specifically 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25 miRNAs, selected from the group consisting of hsa-miR-144-3p, hsa-miR-29b-3p, hsa-miR-335-5p, hsa-miR-31 -5p and hsa-miR-181d and in combination with at least one miRNA, specifically 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 26, 27, 28, 29 miRNAs, selected from the group consisting of hsa-miR-144-3p, hsa-miR-188-3p, hsa-miR-382-3p, hsa-miR-181 a-3p, hsa-miR-330-3p, hsa-miR-127-3p, hsa-miR-155-5p, hsa-miR-1908, hsa-miR-190a, hsa-miR-199b-5p, hsa-miR-203a, hsa-miR-214-3p, hsa-miR-342-5p, hsa-miR-377-3p, hsa-miR-378a-5p, hsa-miR-495-3p, hsa-miR-500a-5p, hsa-miR-502-5p, hsa-miR-542-5p, hsa-miR-550a-5p, hsa-miR-576-3p, hsa-miR-582-3p, hsa-miR-642a-5p, hsa-miR-942 and hsa-let-7b-5p.

According to a specific embodiment of the invention there is provided a method, wherein hsa-miR-203a is measured in combination with at least one miRNA, specifically 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23 miRNAs, selected from the group consisting of hsa-miR-127-3p, hsa-miR-155-5p, hsa-miR-188-3p, hsa-miR-1908, hsa-miR-190a, hsa-miR-335-5p, hsa-miR-31 -5p, hsa-miR-214-3p, hsa-miR-29b-4p, hsa-let-7b-5p, hsa-miR-342-5p, hsa-miR-377-3p, hsa-miR-378a-5p, hsa-miR-495-3p, hsa-miR-500a-5p, hsa-miR-502-5p, hsa-miR-542-5p, hsa-miR-550a-5p, hsa-miR-576-3p, hsa-miR-582-3p, hsa-miR-642a-5p, hsa-miR-942 and miR-181 d.

According to a specific embodiment of the invention there is provided a method, wherein hsa-miR-203a is measured in combination with at least one miRNA, specifically 2, 3, 4 miRNAs, selected from the group consisting of hsa-miR-335-5p, hsa-miR-31 -5p, hsa-miR-29b-4p and miR-181d and optionally in combination with at least one miRNA, specifically 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19 miRNAs, selected from the group consisting of hsa-miR-127-3p, hsa-miR-155-5p, hsa-miR-188-3p, hsa-miR-1908, hsa-miR-190a, hsa-miR-214-3p, hsa-let-7b-5p, hsa-miR-342-5p, hsa-miR-377-3p, hsa-miR-378a-5p, hsa-miR-495-3p, hsa-miR-500a-5p, hsa-miR-502-5p, hsa-miR-542-5p, hsa-miR-550a-5p, hsa-miR-576-3p, hsa-miR-582-3p, hsa-miR-642a-5p and hsa-miR-942.

According to a specific embodiment of the invention there is provided a method, wherein hsa-miR-203a is measured in combination with at least one miRNA, specifically 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 26, 27, 28, 29 miRNAs, selected from the group consisting of hsa-miR-144-3p, hsa-miR-188-3p, hsa-miR-382-3p, hsa-miR-181 a-3p, hsa-miR-330-3p, hsa-miR-127-3p, hsa-miR-155-5p, hsa-miR-1908, hsa-miR-190a, hsa-miR-199b-5p, hsa-miR-203a, hsa-miR-214-3p, hsa-miR-29b-3p, hsa-miR-342-5p, hsa-miR-335-5p, hsa-miR-31 -5p, hsa-miR-377-3p, hsa-miR-378a-5p, hsa-miR-495-3p, hsa-miR-500a-5p, hsa-miR-502-5p, hsa-miR-542-5p, hsa-miR-550a-5p, hsa-miR-576-3p, hsa-miR-582-3p, hsa-miR-642a-5p, hsa-miR-942, hsa-let-7b-5p, and hsa-miR-181 d.

According to a specific embodiment of the invention there is provided a method, wherein hsa-miR-203a is measured in combination with at least one miRNA, specifically 2, 3, 4miRNAs, selected from the group consisting of hsa-miR-144-3p, hsa-miR-29b-3p, hsa-miR-335-5p, hsa-miR-31 -5p and optionally in combination with at least one miRNA, specifically 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25 miRNAs, selected from the group consisting of hsa-miR-188-3p, hsa-miR-382-3p, hsa-miR-181 a-3p, hsa-miR-330-3p, hsa-miR-127-3p, hsa-miR-155-5p, hsa-miR-1908, hsa-miR-190a, hsa-miR-199b-5p, hsa-miR-203a, hsa-miR-214-3p, hsa-miR-342-5p, hsa-miR-377-3p, hsa-miR-378a-5p, hsa-miR-495-3p, hsa-miR-500a-5p, hsa-miR-502-5p, hsa-miR-542-5p, hsa-miR-550a-5p, hsa-miR-576-3p, hsa-miR-582-3p, hsa-miR-642a-5p, hsa-miR-942, hsa-let-7b-5p, and hsa-miR-181 d.

According to a further embodiment of the invention there is provided an in vitro method of diagnosing osteoporosis or determining the risk of osteoporotic fractures or monitoring of treatment in a subject, which can be but is not limited to a patient being at risk of or suffering from type 2 diabetes mellitus, comprising the steps of:

a) providing a sample, specifically a blood sample, from said subject, b) measuring the level of two or more miRNAs, specifically 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25, 26 miRNAs, selected from any of miRNAs consisting of hsa-miR-32-3p, hsa-let-7b-5p, hsa-let-7g-5p, hsa-let-7i-5p, hsa-miR-141 -3p, hsa-miR-143-5p, hsa-miR-16-2-3p, hsa-miR-181 c-3p, hsa-miR-181 c-5p, hsa-miR-191 -5p, hsa-miR-19b-1 -5p, hsa-miR-203a, hsa-miR-21 -3p, hsa-miR-323a-3p, hsa-miR-375, hsa-miR-486-5p, hsa-miR-500a-5p, hsa-miR-550a-5p, hsa-miR-7-5p, hsa-miR-92a-3p, hsa-miR-942, hsa-miR-96-5p hsa-miR-330-3p, hsa-miR-31 -5p, hsa-miR-144-3p, and hsa-miR-382-3p, and

c) comparing the level of said miRNAs to a reference level,

wherein the magnitude of difference in said level when compared to the reference level is indicative of osteoporosis or the risk of fractures.

According to a further embodiment of the invention there is provided an in vitro method of diagnosing osteoporosis or determining the risk of osteoporotic fractures or monitoring of treatment in a subject, comprising the steps of:

a) providing a sample from said subject,

b) measuring the level of 1 , 2, 3, 4 or 5 miRNAs selected from any of miRNAs consisting of hsa-miR-486-5p, hsa-miR-330-3p, hsa-miR-31 -5p, hsa-miR-144-3p, and hsa-miR-382-3p, and

c) measuring the level of 1 , 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17,

18, 19, 20, 21 miRNAs selected from any of miRNAs consisting of miRNAs consisting of hsa-miR-32-3p, hsa-let-7b-5p, hsa-let-7g-5p, hsa-let-7i-5p, hsa-miR-141 -3p, hsa-miR-143-5p, hsa-miR-16-2-3p, hsa-miR-181 c-3p, hsa-miR-181 c-5p, hsa-miR-191 -5p, hsa-miR-19b-1 -5p, hsa-miR-203a, hsa-miR-21 -3p, hsa-miR-323a-3p, hsa-miR-375,

hsa-miR-500a-5p, hsa-miR-550a-5p, hsa-miR-7-5p, hsa-miR-92a-3p, hsa-miR-942, hsa-miR-96-5p, and

d) comparing the level of said miRNAs to a reference level,

wherein the magnitude of difference in said levels when compared to the reference levels is indicative of osteoporosis or the risk of fractures.

According to a specific embodiment of the invention there is provided a method, wherein at least one miRNA selected from the group consisting of hsa-miR-32-3p, hsa-miR-181 c-3p, hsa-miR-323a-3p, hsa-miR-550a-5p and hsa-miR-96-5p and at least one miRNA, specifically 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 miRNAs, selected from the group consisting of hsa-let-7b-5p, hsa-let-7g-5p, hsa-let-7i-5p, hsa-miR-141 -3p, hsa-miR-143-5p, hsa-miR-16-2-3p, hsa-miR-181 c-5p, hsa-miR-191 -5p, hsa-miR-19b-1 -5p, hsa-miR-203a, hsa-miR-21 -3p, hsa-miR-375, hsa-miR-486-5p, hsa-miR-500a-5p, hsa-miR-7-5p, hsa-miR-92a-3p, hsa-miR-942, hsa-miR-330-3p, hsa-miR-31 -5p, hsa-miR-144-3p and hsa-miR-382-3p are measured.

According to a further specific embodiment of the invention, 2, 3, 4 or 5 of hsa-miR-32-3p, hsa-miR-181 c-3p, hsa-miR-323a-3p, hsa-miR-550a-5p and hsa-miR-96-5p can be measured in combination with at least one miRNA, specifically 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 miRNAs, selected from hsa-let-7b-5p, hsa-let-7g-5p, hsa-let-7i-5p, hsa-miR-141 -3p, hsa-miR-143-5p, hsa-miR-16-2-3p, hsa-miR-181 c-5p, hsa-miR-191 -5p, hsa-miR-19b-1 -5p, hsa-miR-203a, hsa-miR-21 -3p, hsa-miR-375, hsa-miR-486-5p, hsa-miR-500a-5p, hsa-miR-7-5p, hsa-miR-92a-3p, hsa-miR-942, hsa-miR-330-3p, hsa-miR-31 -5p, hsa-miR-144-3p and hsa-miR-382-3p.

According to a specific embodiment of the invention there is provided a method,, wherein hsa-miR-32-3p is measured in combination with at least one miRNA, specifically 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 miRNAs, selected from the group consisting of hsa-let-7b-5p, hsa-let-7g-5p, hsa-let-7i-5p, hsa-miR-141 -3p, hsa-miR-143-5p, hsa-miR-16-2-3p, hsa-miR-181 c-5p, hsa-miR-191 -5p, hsa-miR-19b-1 -5p, hsa-miR-203a, hsa-miR-21 -3p, hsa-miR-375, hsa-miR-486-5p, hsa-miR-500a-5p, hsa-miR-7-5p, hsa-miR-92a-3p, hsa-miR-942, hsa-miR-330-3p, hsa-miR-31 -5p, hsa-miR-144-3p and hsa-miR-382-3p.

According to a specific embodiment of the invention there is provided a method,, wherein hsa-miR-32-3p is measured in combination with hsa-miR-31 -5p and optionally in combination with at least one miRNA, specifically 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20 miRNAs, selected from the group consisting of hsa-let-7b-5p,

hsa-let-7g-5p, hsa-let-7i-5p, hsa-miR-141 -3p, hsa-miR-143-5p, hsa-miR-16-2-3p, hsa-miR-181 c-5p, hsa-miR-191 -5p, hsa-miR-19b-1 -5p, hsa-miR-203a, hsa-miR-21 -3p, hsa-miR-375, hsa-miR-486-5p, hsa-miR-500a-5p, hsa-miR-7-5p, hsa-miR-92a-3p, hsa-miR-942, hsa-miR-330-3p, hsa-miR-144-3p and hsa-miR-382-3p. According to a specific embodiment of the invention there is provided a method,, wherein hsa-miR-32-3p is measured in combination with at least one miRNA, specifically 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25 miRNAs, selected from the group consisting of hsa-miR-181 c-3p, hsa-miR-323a-3p, hsa-miR-550a-5p, hsa-miR-96-5p hsa-let-7b-5p, hsa-let-7g-5p, hsa-let-7i-5p, hsa-miR-141 -3p, hsa-miR-143-5p, hsa-miR- 16-2-3p, hsa-miR- 181 c-5p, hsa-miR-191 -5p, hsa-miR- 19b-1 -5p, hsa-miR-203a, hsa-miR-21 -3p, hsa-miR-375, hsa-miR-486-5p, hsa-miR-500a-5p, hsa-miR-7-5p, hsa-miR-92a-3p, hsa-miR-942, hsa-miR-330-3p, hsa-miR-31 -5p, hsa-miR-144-3p and hsa-miR-382-3p.

According to a specific embodiment of the invention there is provided a method,, wherein hsa-miR-32-3p is measured in combination with hsa-miR-31 -5p, and

optionally in combination with at least one miRNA, specifically 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24 miRNAs, selected from the group consisting of hsa-miR-181 c-3p, hsa-miR-323a-3p, hsa-miR-550a-5p, hsa-miR-96-5p hsa-let-7b-5p, hsa-let-7g-5p, hsa-let-7i-5p, hsa-miR-141 -3p, hsa-miR-143-5p, hsa-miR-16-2-3p, hsa-miR-181 c-5p, hsa-miR-191 -5p, hsa-miR-19b-1 -5p, hsa-miR-203a, hsa-miR-21 -3p, hsa-miR-375, hsa-miR-486-5p, hsa-miR-500a-5p, hsa-miR-7-5p, hsa-miR-92a-3p, hsa-miR-942, hsa-miR-330-3p, hsa-miR-144-3p and hsa-miR-382-3p. According to a specific embodiment of the invention there is provided a method, wherein hsa-miR-181 c-3p is measured in combination with at least one miRNA, specifically 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 miRNAs, selected from the group consisting of hsa-let-7b-5p, hsa-let-7g-5p, hsa-let-7i-5p, hsa-miR-141 -3p, hsa-miR-143-5p, hsa-miR-16-2-3p, hsa-miR-181 c-5p, hsa-miR-191 -5p, hsa-miR-19b-1 -5p, hsa-miR-203a, hsa-miR-21 -3p, hsa-miR-375, hsa-miR-486-5p, hsa-miR-500a-5p, hsa-miR-7-5p, hsa-miR-92a-3p, hsa-miR-942, hsa-miR-330-3p, hsa-miR-31 -5p, hsa-miR-144-3p and hsa-miR-382-3p.

According to a specific embodiment of the invention there is provided a method, wherein hsa-miR-181 c-3p is measured in combination with hsa-miR-31 -5p and optionally in combination with at least one miRNA, specifically 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20 miRNAs, selected from the group consisting of

hsa-let-7b-5p, hsa-let-7g-5p, hsa-let-7i-5p, hsa-miR-141 -3p, hsa-miR-143-5p, hsa-miR-16-2-3p, hsa-miR-181 c-5p, hsa-miR-191 -5p, hsa-miR-19b-1 -5p, hsa-miR-203a, hsa-miR-21 -3p, hsa-miR-375, hsa-miR-486-5p, hsa-miR-500a-5p, hsa-miR-7-5p, hsa-miR-92a-3p, hsa-miR-942, hsa-miR-330-3p, hsa-miR-144-3p and hsa-miR-382-3p.

According to a specific embodiment of the invention there is provided a method, wherein hsa-miR-181 c-3p is measured in combination with at least one miRNA, specifically 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24 miRNAs, selected from the group consisting of hsa-miR-323a-3p, hsa-miR-550a-5p, hsa-miR-96-5p, hsa-let-7b-5p, hsa-let-7g-5p, hsa-let-7i-5p, hsa-miR-141 -3p, hsa-miR-143-5p, hsa-miR-16-2-3p, hsa-miR-181 c-5p, hsa-miR-191 -5p, hsa-miR-19b-1 -5p, hsa-miR-203a, hsa-miR-21 -3p, hsa-miR-375, hsa-miR-486-5p, hsa-miR-500a-5p, hsa-miR-7-5p, hsa-miR-92a-3p, hsa-miR-942, hsa-miR-330-3p, hsa-miR-31 -5p, hsa-miR-144-3p and hsa-miR-382-3p.

According to a specific embodiment of the invention there is provided a method, wherein hsa-miR-181 c-3p is measured in combination with hsa-miR-31 -5p and optionally in combination with at least one miRNA, specifically 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23 miRNAs, selected from the group consisting of hsa-miR-323a-3p, hsa-miR-550a-5p, hsa-miR-96-5p, hsa-let-7b-5p, hsa-let-7g-5p, hsa-let-7i-5p, hsa-miR-141 -3p, hsa-miR-143-5p, hsa-miR-16-2-3p, hsa-miR-181 c-5p, hsa-miR-191 -5p, hsa-miR-19b-1 -5p, hsa-miR-203a, hsa-miR-21 -3p, hsa-miR-375, hsa-miR-486-5p, hsa-miR-500a-5p, hsa-miR-7-5p, hsa-miR-92a-3p, hsa-miR-942, hsa-miR-330-3p, hsa-miR-144-3p and hsa-miR-382-3p.

According to a specific embodiment of the invention there is provided a method, wherein hsa-miR-323a-3p is measured in combination with at least one miRNA, specifically 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 miRNAs, selected from the group consisting of hsa-let-7b-5p, hsa-let-7g-5p, hsa-let-7i-5p, hsa-miR-141 -3p, hsa-miR-143-5p, hsa-miR-16-2-3p, hsa-miR-181 c-5p, hsa-miR-191 -5p, hsa-miR-19b-1 -5p, hsa-miR-203a, hsa-miR-21 -3p, hsa-miR-375, hsa-miR-486-5p, hsa-miR-500a-5p, hsa-miR-7-5p, hsa-miR-92a-3p, hsa-miR-942, hsa-miR-330-3p, hsa-miR-31 -5p, hsa-miR-144-3p and hsa-miR-382-3p.

According to a specific embodiment of the invention there is provided a method, wherein hsa-miR-323a-3p is measured in combination with hsa-miR-31 -5p and optionally in combination with at least one miRNA, specifically 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20 miRNAs, selected from the group consisting of

hsa-let-7b-5p, hsa-let-7g-5p, hsa-let-7i-5p, hsa-miR-141 -3p, hsa-miR-143-5p, hsa-miR-16-2-3p, hsa-miR-181 c-5p, hsa-miR-191 -5p, hsa-miR- 9b-1 -5p, hsa-miR-203a, hsa-miR-21 -3p, hsa-miR-375, hsa-miR-486-5p, hsa-miR-500a-5p, hsa-miR-7-5p, hsa-miR-92a-3p, hsa-miR-942, hsa-miR-330-3p, hsa-miR-144-3p and hsa-miR-382-3p.

According to a specific embodiment of the invention there is provided a method, wherein hsa-miR-323a-3p is measured in combination with at least one miRNA, specifically 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25 miRNAs, selected from the group consisting of hsa-miR-32-3p, hsa-miR-323a-3p, hsa-miR-550a-5p, hsa-miR-96-5p, hsa-let-7b-5p, hsa-let-7g-5p, hsa-let-7i-5p, hsa-miR-141 -3p, hsa-miR-143-5p, hsa-miR-16-2-3p, hsa-miR-181 c-5p, hsa-miR-191 -5p, hsa-miR-19b-1 -5p, hsa-miR-203a, hsa-miR-21 -3p, hsa-miR-375, hsa-miR-486-5p, hsa-miR-500a-5p, hsa-miR-7-5p, hsa-miR-92a-3p, hsa-miR-942, hsa-miR-330-3p, hsa-miR-31 -5p, hsa-miR-144-3p and hsa-miR-382-3p.

According to a specific embodiment of the invention there is provided a method, wherein hsa-miR-323a-3p is measured in combination with hsa-miR-31 -5p, and optionally in combination with at least one miRNA, specifically 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24 miRNAs, selected from the group consisting of hsa-miR-32-3p, hsa-miR-323a-3p, hsa-miR-550a-5p, hsa-miR-96-5p, hsa-let-7b-5p, hsa-let-7g-5p, hsa-let-7i-5p, hsa-miR-141 -3p, hsa-miR-143-5p, hsa-miR-16-2-3p, hsa-miR-181 c-5p, hsa-miR-191 -5p, hsa-miR-19b-1 -5p, hsa-miR-203a, hsa-miR-21 -3p, hsa-miR-375, hsa-miR-486-5p, hsa-miR-500a-5p, hsa-miR-7-5p, hsa-miR-92a-3p, hsa-miR-942, hsa-miR-330-3p, hsa-miR-144-3p and hsa-miR-382-3p.

According to a specific embodiment of the invention there is provided a method .wherein hsa-miR-550a-5p is measured in combination with at least one miRNA, specifically 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 miRNAs, selected from the group consisting, hsa-let-7b-5p, hsa-let-7g-5p, hsa-let-7i-5p, hsa-miR-141 -3p, hsa-miR-143-5p, hsa-miR-16-2-3p, hsa-miR-181 c-5p, hsa-miR-191 -5p, hsa-miR-19b-1 -5p, hsa-miR-203a, hsa-miR-21 -3p, hsa-miR-375, hsa-miR-486-5p, hsa-miR-500a-5p, hsa-miR-7-5p, hsa-miR-92a-3p, hsa-miR-942, hsa-miR-330-3p, hsa-miR-31 -5p, hsa-miR-144-3p and hsa-miR-382-3p.

According to a specific embodiment of the invention there is provided a method .wherein hsa-miR-550a-5p is measured in combination with hsa-miR-31 -5p, and optionally in combination with at least one miRNA, specifically 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 miRNAs, selected from the group

consisting, hsa-let-7b-5p, hsa-let-7g-5p, hsa-let-7i-5p, hsa-miR-141 -3p, hsa-miR-143-5p, hsa-miR- 6-2-3p, hsa-miR-181 c-5p, hsa-miR-191 -5p, hsa-miR-19b-1 -5p, hsa-miR-203a, hsa-miR-21 -3p, hsa-miR-375, hsa-miR-486-5p, hsa-miR-500a-5p, hsa-miR-7-5p, hsa-miR-92a-3p, hsa-miR-942, hsa-miR-330-3p, hsa-miR-31 -5p, hsa-miR-144-3p and hsa-miR-382-3p.

According to a specific embodiment of the invention there is provided a method .wherein hsa-miR-550a-5p is measured in combination with at least one miRNA, specifically 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 23, 24, 25 miRNAs, selected from the group consisting of hsa-miR-32-3p, hsa-miR-181 c-3p, hsa-miR-323a-3p, hsa-miR-96-5p, hsa-let-7b-5p, hsa-let-7g-5p, hsa-let-7i-5p, hsa-miR-141 -3p, hsa-miR-143-5p, hsa-miR-16-2-3p, hsa-miR-181 c-5p, hsa-miR-191 -5p, hsa-miR-19b-1 -5p, hsa-miR-203a, hsa-miR-21 -3p, hsa-miR-375, hsa-miR-486-5p, hsa-miR-500a-5p, hsa-miR-7-5p, hsa-miR-92a-3p, hsa-miR-942, hsa-miR-330-3p, hsa-miR-31 -5p, hsa-miR-144-3p and hsa-miR-382-3p.

According to a specific embodiment of the invention there is provided a method .wherein hsa-miR-550a-5p is measured in combination with hsa-miR-31 -5p and optionally in combination with at least one miRNA, specifically 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 23, 24 miRNAs, selected from the group consisting of hsa-miR-32-3p, hsa-miR-181 c-3p, hsa-miR-323a-3p, hsa-miR-96-5p, hsa-let-7b-5p, hsa-let-7g-5p, hsa-let-7i-5p, hsa-miR-141 -3p, hsa-miR-143-5p, hsa-miR-16-2-3p, hsa-miR-181 c-5p, hsa-miR-191 -5p, hsa-miR-19b-1 -5p, hsa-miR-203a, hsa-miR-21 -3p, hsa-miR-375, hsa-miR-486-5p, hsa-miR-500a-5p, hsa-miR-7-5p, hsa-miR-92a-3p, hsa-miR-942, hsa-miR-330-3p, hsa-miR-144-3p and hsa-miR-382-3p.

According to a specific embodiment of the invention there is provided a method, wherein hsa-miR-96-5p is measured in combination with at least one miRNA, specifically 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 miRNAs, selected from the group consisting of hsa-let-7b-5p, hsa-let-7g-5p, hsa-let-7i-5p, hsa-miR-141 -3p, hsa-miR-143-5p, hsa-miR-16-2-3p, hsa-miR-181 c-5p, hsa-miR-191 -5p, hsa-miR-19b-1 -5p, hsa-miR-203a, hsa-miR-21 -3p, hsa-miR-375, hsa-miR-486-5p, hsa-miR-500a-5p, hsa-miR-7-5p, hsa-miR-92a-3p, hsa-miR-942, hsa-miR-330-3p, hsa-miR-31 -5p, hsa-miR-144-3p and hsa-miR-382-3p.

According to a specific embodiment of the invention there is provided a method, wherein hsa-miR-96-5p is measured in combination with hsa-miR-31 -5p, and optionally in combination with at least one miRNA, specifically 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20 miRNAs, selected from the group consisting of hsa-let-7b-5p, hsa-let-7g-5p, hsa-let-7i-5p, hsa-miR-141 -3p, hsa-miR-143-5p, hsa-miR-16-2-3p, hsa-miR-181 c-5p, hsa-miR-191 -5p, hsa-miR-19b-1 -5p, hsa-miR-203a, hsa-miR-21 -3p, hsa-miR-375, hsa-miR-486-5p, hsa-miR-500a-5p, hsa-miR-7-5p, hsa-miR-92a-3p, hsa-miR-942, hsa-miR-330-3p, hsa-miR-144-3p and hsa-miR-382-3p.

According to a specific embodiment of the invention there is provided a method, wherein hsa-miR-96-5p is measured in combination with at least one miRNA, specifically 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24, 25 miRNAs, selected from the group consisting of hsa-miR-32-3p, hsa-miR-181 c-3p, hsa-miR-323a-3p, hsa-miR-550a-5p, hsa-let-7b-5p, hsa-let-7g-5p, hsa-let-7i-5p, hsa-miR-141 -3p, hsa-miR-143-5p, hsa-miR-16-2-3p, hsa-miR-181 c-5p, hsa-miR-191 -5p, hsa-miR-19b-1 -5p, hsa-miR-203a, hsa-miR-21 -3p, hsa-miR-375, hsa-miR-486-5p, hsa-miR-500a-5p, hsa-miR-7-5p, hsa-miR-92a-3p, hsa-miR-942, hsa-miR-330-3p, hsa-miR-31 -5p, hsa-miR-144-3p and hsa-miR-382-3p.

According to a specific embodiment of the invention there is provided a method, wherein hsa-miR-96-5p is measured in combination with hsa-miR-31 -5p and optionally in combination with at least one miRNA, specifically 2, 3, 4, 5, 6, 7, 8, 9, 10, 1 1 , 12, 13, 14, 15, 16, 17, 18, 19, 20, 21 , 22, 23, 24 miRNAs, selected from the group consisting of hsa-miR-32-3p, hsa-miR-181 c-3p, hsa-miR-323a-3p, hsa-miR-550a-5p, hsa-let-7b-5p, hsa-let-7g-5p, hsa-let-7i-5p, hsa-miR-141 -3p, hsa-miR-143-5p, hsa-miR-16-2-3p, hsa-miR-181 c-5p, hsa-miR-191 -5p, hsa-miR-19b-1 -5p, hsa-miR-203a, hsa-miR-21 -3p, hsa-miR-375, hsa-miR-486-5p, hsa-miR-500a-5p, hsa-miR-7-5p, hsa-miR-92a-3p, hsa-miR-942, hsa-miR-330-3p, hsa-miR-144-3p and hsa-miR-382-3p.

In case the present invention is used for monitoring the treatment of a subject, the treatment may be adjusted respectively.

In an alternative embodiment of the invention, the level of said miRNAs can be compared with the average level of corresponding miRNAs in healthy subjects, specifically in a pool of samples derived from healthy subjects, wherein a difference by more than one standard deviations, specifically by about 1 .5, 1 .6, 1 .7, 1 .8, 1 .9, specifically about 2 standard deviations or more is indicative of osteoporosis with increased risk of future fractures, specifically of osteoporotic fractures.

According to a further embodiment, a difference by more than 2.5 standard deviations, specifically about 3, specifically about 3.5, specifically more than 3.5

standard deviations is indicative of osteoporosis with high risk of future fractures, specifically of osteoporotic fractures.

Thus it is within the embodiment of the invention to use either a single reference sample from a healthy subject or a pool of samples derived from healthy subjects for comparison with the respective sample from a subject to be diagnosed. Said pool can consist of 2, 3, 4, 5, 6, 7, or more samples, specifically up to 10, 100 or more than 100 samples from different individuals.

According to a further embodiment a method is provided, wherein the level of said miRNAs is compared with the average level of corresponding miRNAs in healthy subjects, wherein a difference by more than one standard deviation is indicative of osteoporosis with increased risk of future fractures.

According to a specific embodiment, a method is provided, wherein the level of at least 3, preferably at least 4, preferably at least 5, specifically up to 30 miRNAs of hsa-miR-188-3p, hsa-miR-382-3p, hsa-miR-127-3p, hsa-miR-144-3p, hsa-miR-155-5p, hsa-miR-181 a-3p, hsa-miR-1908, hsa-miR-190a, hsa-miR-199b-5p, hsa-miR-203a, hsa-miR-214-3p, hsa-miR-29b-3p, hsa-miR-330-3p, hsa-miR-335-5p, hsa-miR-31 -5p, hsa-miR-342-5p, hsa-miR-369-3p, hsa-miR-377-3p, hsa-miR-378a-5p, hsa-miR-495-3p, hsa-miR-500a-5p, hsa-miR-502-5p, hsa-miR-542-5p, hsa-miR-550a-5p, hsa-miR-576-3p, hsa-miR-582-3p, hsa-miR-642a-5p, hsa-miR-942, hsa-let-7b-5p and hsa-miR-181 d are measured, specifically for diagnosing non-diabetic subjects.

According to a specific embodiment a method is provided, wherein the level of at least 3, preferably at least 4, preferably at least 5, specifically up to 26 miRNAs of hsa-miR-32-3p, hsa-miR-181 c-3p, hsa-miR-323a-3p, hsa-miR-550a-5p, hsa-miR-96-5p, hsa-let-7b-5p, hsa-let-7g-5p, hsa-let-7i-5p, hsa-miR-141 -3p, hsa-miR-143-5p, hsa-miR-16-2-3p, hsa-miR-181 c-5p, hsa-miR-191 -5p, hsa-miR-19b-1 -5p, hsa-miR-203a, hsa-miR-21 -3p, hsa-miR-375, hsa-miR-486-5p, hsa-miR-500a-5p, hsa-miR-7-5p, hsa-miR-92a-3p, hsa-miR-942, hsa-miR-330-3p, hsa-miR-31 -5p, hsa-miR-144-3p and miR-382-3p are measured, specifically for diagnosing Type ll-diabetic subjects.

According to a specific embodiment, a single diagnostic assay is provided for diagnosing post-menopausal females, osteoporosis or osteopenia patients, suffering from or being at risk of developing bone fractures, ad patients being at risk or suffering from diabetes mellitus Type 2 wherein more than one and up to 30hsa-miRNAs of the inventive list of miRNAs are measured according to the present invention.

Specifically, at least one of hsa-miR-188-3p, hsa-miR-382-3p, hsa-miR-181 a-3p, hsa-miR-330-3p and hsa-miR-369-3p, hsa-miR-32-3p, hsa-miR- 81 c-3p, hsa-miR-323a-3p, hsa-miR-550a-5p and hsa-miR-96-5p is measured together with at least one of hsa-miR-127-3p, hsa-miR-155-5p, hsa-miR-1908, hsa-miR-190a, hsa-miR-203a, hsa-miR-342-5p, hsa-miR-377-3p, hsa-miR-378a-5p, hsa-miR-495-3p, hsa-miR-500a-5p, hsa-miR-502-5p, hsa-miR-542-5p, hsa-miR-576-3p, hsa-miR-582-3p, hsa-miR-642a-5p, hsa-miR-94, miR181d, hsa-let-7b-5p, hsa-let-7g-5p, hsa-let-7i-5p, hsa-miR-141 -3p, hsa-miR-143-5p, hsa-miR-16-2-3p, hsa-miR-181 c-5p, hsa-miR-191 -5p, hsa-miR-19b-1 -5p, hsa-miR-203a, hsa-miR-21 -3p, hsa-miR-375, hsa-miR-486-5p, hsa-miR-500a-5p, hsa-miR-7-5p, hsa-miR-92a-3p, hsa-miR-942, hsa-miR-31 -5p, hsa-miR-144-3p and hsa-miR-382-3p

According to a specific embodiment a method is provided, wherein the level of 24, 25, 26, 27, 28, 29, 30, 31 , 32, 33, 34, 35, 36, 37, 38, 39, 40, 41 , 42, 43, 45, 46 miRNAs is measured, specifically for providing a diagnostic system wherein non-diabetic and diabetic subjects can be diagnosed according to the invention.

According to a specific embodiment a method is provided, wherein one or more further miRNAs are detected, wherein said miRNAs are

a) differentially regulated in osteoporotic individuals as compared to healthy individuals and are

b) involved in osteogenic differentiation and/or in osteoclastogenic activation.

According to a specific embodiment of the invention, there is provided the use of the inventive method for monitoring a subject, specifically for the prognosis of bone fraction.

According to a specific embodiment of the invention, there is provided the use of the inventive method for monitoring a subject, specifically for measuring the

responseof a subject to anti-osteoporotic treatments.

According to a specific embodiment a method is provided, wherein the difference in miRNA levels is determined by quantitative or digital PGR, sequencing, microarray, Luminex nucleic acid assays, or other hybridization-based techniques.

The present invention also provides a composition for use in treating or preventing osteoporosis or fractures comprising

a) at least two synthetic human miRNAs from miRNAs as described above, and/or

b) an antagonist/inhibitor of at least two of miRNAs as described above, that i. decreases the level of said miRNAs; and/or

ii. inhibits or down-regulates expression of the sequences coding for said miRNAs or degrades or cleaves said miRNAs.

The present invention also provides a composition for use in treating or preventing osteoporosis or fractures comprising

a) at least one synthetic human miRNA sleeted from hsa- miR-188-3p, hsa-miR-382-3p und hsa-miR-550a-5p and/or

b) an antagonist/inhibitor of at least one of miRNAs hsa- miR-188-3p, hsa-miR-382-3p und hsa-miR-550a-5p, that

i. decreases the level of said miRNAs; and/or

ii. inhibits or down-regulates expression of the sequences coding for said miRNAs or degrades or cleaves said miRNAs.

According to an embodiment of the invention, the use of a composition comprising at least one, specifically at least two synthetic human miRNAs as specified above and/or an antagonist/inhibitor of at least two of miRNAs as specified above that decreases the level of said miRNAs; and/or inhibits or down -regulates expression of the sequences coding for said miRNAs or degrades or cleaves said miRNAsfor preparing a medicament.

In a further embodiment, the present invention provides a method for treating a subject which was diagnosed osteoporosis or the risk of osteoporotic fractures using any one of the in vitro methods as described herein, wherein the treatment comprises administering a drug to said subject, said drug being known by the skilled artisan for treating osteoporosis or prophylactic treatment of bone fractures. Said drugs can be, but are not limited to calcium (calcitonin, Fortical®, Miacalcin®, Calcichew®,

Osteofos®, Caltrate Plus®), vitamin D, bisphosphonates like alendronate (Fosamax®, Reclast®), etidronate, risedronate (Actonel®), ibandronate (Boniva®), zoledronic acid (Reclast), hormones, such as estrogen, and hormone-like medications approved for preventing and treating osteoporosis, such as estrogen agonists/antagonists, raloxifene (Evista®), strontium ranelate, monoclonal antibodies, e.g. monoclonal antibodies against RANKL or Sclerostin, like e.g. denosumab (Prolia®), teriparatide (Forteo®), bazedoxifine acetate (Combriza®).

FIGURES

Figure 1 : Multi-parameter models for the classification of post-menopausal women with osteoporosis. A classification of osteoporotic cases versus controls was performed by testing any possible combination of at least 2 up to 10 microRNAs (red boxplots) selected from 23 microRNAs against a negative control data set (grey boxplots). The data clearly show, that the combinations of at least 2 microRNAs yield significantly better results for the classification of post-menopausal women with fractures than random.

Figure 2: Multi-parameter models for the classification of diabetic postmenopausal women with osteoporosis. A classification of diabetic osteoporotic cases versus controls was performed by testing any possible combination of at least 2 up to 10 microRNAs (black boxplots) selected from 23 microRNAs against a negative control data set (grey boxplots). The data clearly show, that the combinations of at least 2 microRNAs yield significantly better results for the classification of diabetic postmenopausal women with fractures than random.

Figure 3: Four-parameter models for the classification of diabetic (A) and non-diabetic (B) post-menopausal women with osteoporosis. The data show the results from testing any combination of four microRNAs out of the list of 23 pre-selected microRNAs for the classification of diabetic (A) and non-diabetic (B) patients with osteoporosis.

Figure 4: Serum levels of microRNAs in diabetic (A) and non-diabetic (B) osteoporotic cases and controls. Shown are the Cq levels of microRNAs, which were determined by quantitative PGR, that were part of the top-performing classification model for diabetic (A) or non-diabetic (B) osteoporosis, respectively.

Figure 5: Therapeutic efficacy of three selected microRNAs: miR-188-3p, miR-382-3p, miR-550a-5p. (A) microRNAs were overexpressed in mesenchymal stem cells through electroporation. High levels of overexpression were achieved for all three microRNAs. (B) The impact of microRNA overexpression on cell proliferation. (C) The impact of microRNA overexpression on Alkaline Phosphatase activity, which is indicative of bone formation, was tested. miR-188-3p and miR-382-3p significantly improved osteogenic differentiation, while miR-550a-5p inhibited osteogenic

differentiation. (D) Effects on Calcium deposition are shown, which is another important characteristic of stem cells undergoing osteogenic differentiation.

DETAILED DESCRIPTION OF THE INVENTION

Osteogenic differentiation is defined as the process during which a mesenchymal stem cell or adipose tissue derived stem cell becomes activated to proliferate and differentiate into an osteoblast. This process is characterized by secretion of alkaline phosphatase (ALP), changes in gene expression such as

Osteocalcin, RUNX2, ALP, and elevated calcium incorporation.

Osteoclastogenic formation is defined as the process during which monocytes (i.e. macrophages) are activated by RANKL and M-CSF to form osteoclasts, which are characterized by release of H+, specific proteases and other enzymes such as tartrate resistant acidic phosphatase (TRAP), Cathepsin K, which assist in bone resorption.

As used in this specification and the appended claims, the singular forms "a",

"an", and "the" include plural referents, unless the content clearly dictates otherwise.

As used herein, the term "blood sample" refers to serum, plasma, whole blood and its components, blood derived products or preparations. Plasma and serum are very useful as shown in the examples.

The term "sample" generally refers to blood sample and urine, saliva and cerebrospinal fluid.

As used herein, the term "subject" or "individual" or "patient" shall refer to a warm-blooded mammalian, particularly a human being.

The term "patient" includes human and other mammalian subjects that receive either prophylactic or therapeutic treatment or are diagnosed of a specific disease, like but not limited to osteoporosis or diabetes mellitus.

Specifically, a change in the level of miRNAs selected from of hsa-miR-188-3p, hsa-miR-382-3p, hsa-miR-127-3p, hsa-miR-144-3p, hsa-miR-155-5p, hsa-miR-181 a-3p, hsa-miR-1908, hsa-miR-190a, hsa-miR-199b-5p, hsa-miR-203a, hsa-miR-214-3p, hsa-miR-29b-3p, hsa-miR-330-3p, hsa-miR-31 -5p, hsa-miR-335-5p, hsa-miR-342-5p, hsa-miR-369-3p, hsa-miR-377-3p, hsa-miR-378a-5p, hsa-miR-495-3p, hsa-miR-500a-5p, hsa-miR-502-5p, hsa-miR-542-5p, hsa-miR-550a-5p, hsa-miR-576-3p, hsa-miR-582-3p, hsa-miR-642a-5p, hsa-miR-942, hsa-let-7b-5p and miR181 d compared to the level of these miRNAs in healthy subjects are highly indicative for the risk of

osteoporotic fractures in post-menopausal women or osteoporosis or osteopenia patients, suffering from or being at risk of developing bone fractures.

Specifically, a change in the level of miRNAs selected from hsa-miR-32-3p, hsa-let-7b-5p, hsa-let-7g-5p, hsa-let-7i-5p, hsa-miR-141 -3p, hsa-miR-143-5p, hsa-miR-16-2-3p, hsa-miR-181 c-3p, hsa-miR-181 c-5p, hsa-miR-191 -5p, hsa-miR-19b-1 -5p, hsa-

miR-203a, hsa-miR-21 -3p, hsa-miR-323a-3p, hsa-miR-375, hsa-miR-486-5p, hsa-miR-500a-5p, hsa-miR-550a-5p, hsa-miR-7-5p, hsa-miR-92a-3p, hsa-miR-942, hsa-miR-96-5p, hsa-miR-330-3p, hsa-miR-31 -5p, hsa-miR- 44-3p, and miR382-3p compared to the level of these miRNAs in healthy subjects are highly indicative for the risk of osteoporotic fractures in patients being at risk of or suffering from type 2 diabetes mellitus.

The term "treatment" relates to any treatment which improves the health status, reduces or inhibits unwanted weight loss and/or prolongs and/or increases the lifespan of an individual. Said treatment may eliminate the disorder in an individual, arrest or slow the development of a disease in an individual, inhibit or slow the development of a disease in an individual, decrease the frequency or severity of symptoms in an individual, and/or decrease the recurrence in an individual who currently has or who previously has had a disorder. The terms "prophylactic treatment" or "preventive treatment" can be used interchangeably and relate to any treatment that is intended to prevent a disease from occurring in a subject.

As used herein, "preventing" or "prevention" of a disease, disorder or condition refers to the reduction of the occurrence of the disorder or condition in a treated subject relative to an untreated control subject, or delays the onset or reduces the severity of one or more symptoms of the disorder or condition relative to the untreated control subject. The terms "protect", "prevent", "prophylactic", "preventive", or

"protective" relate to the prevention and/or treatment of the occurrence and/or the propagation of a disease.

The term "post-menopausal" women refer to women who had not had their period for at least one year. The term "type- 11 diabetic" refers to individuals who have received antidiabetic medication for at least 3 years in the form of oral medication and/or insulin, and/or who fulfil the diagnostic criteria of the American Diabetes

Association (American Diabetes Association, Diagnosis and classification of diabetes mellitus, Diabetes Care. 2014;37 Suppl 1 :S81 -90).

As used therein, the term "cohort of individuals" or "pool of individuals" shall refer to a group of healthy individuals and may specifically refer to the samples received from said individuals. The number of individuals of a cohort can vary, i.e. it may comprise 2, 3, 4, 5, 6, 7 or more individuals, however it also may be a larger group of subjects, like for example but not limited to 10, 50, 100 or more individuals.

According to the embodiment of the invention the cohort may also comprise large cohorts of 500 or more individuals.

According to the invention, the term "about" encompasses the explicitly recited values as well as small deviations therefrom. Accordingly, a deviation from a recited value for 0%, preferably 5%, preferably 1 % is encompassed by the term "about". According to the invention, subjects with primary osteoporosis (post-menopausal) with mean ages of about 60 years were assessed, which stands in contrast to bone loss and fracture risk due to senile osteoporosis, which affects subjects of about 70 years or older.

The term "treatment success" as used herein is defined as maintaining the bone density or delaying the process of osteoporosis and decreasing the risk of breaking a bone (osteoporotic fracture) as a result of osteoporosis. Hence, a marker that predicts treatment success should be preferentially related to the clinical outcome for a patient, i.e. the reduction in fracture risk. Moderate treatment success reduces fracture risk by about 25% up to about 50%. High treatment success results in a risk reduction by more than 50%.

A "control", "control sample", or "reference value" are terms which can be used interchangeably herein, and are to be understand as a sample or standard used for comparison with the experimental sample. The control may include a sample obtained from a healthy subject or a subject, which is not at risk of or suffering from developing bone fracture or type 2 diabetes mellitus. Additionally, a control may also be a standard reference value or range of values, i.e. such as stable expressed miRNAs in the samples, for example the endogenous control cel-miR-39.

The present invention provides selected miRNAs for use in a method for diagnosing osteoporosis, determining the risk of developing osteoporotic leasons or fractures or monitoring the treatment in subjects undergoing therapy, specifically osteoporosis or diabetes treatment.

Said first group of miRNAs are, preferably for diagnosing osteoporosis or determining the risk of osteoporotic fractures or monitoring of treatment in post-menopausal females or osteoporosis or osteopenia patients, suffering from or being at risk of developing bone fractures, hsa-miR-188-3p, hsa-miR-382-3p, hsa-miR-127-3p, hsa-miR-144-3p, hsa-miR-155-5p, hsa-miR-181 a-3p, hsa-miR-1908, hsa-miR-190a, hsa-miR-199b-5p, hsa-miR-203a, hsa-miR-214-3p, hsa-miR-29b-3p, hsa-miR-330-3p, hsa-miR-31 -5p, hsa-miR-335-5p, hsa-miR-342-5p, hsa-miR-369-3p, hsa-miR-377-3p,

hsa-miR-378a-5p, hsa-miR-495-3p, hsa-miR-500a-5p, hsa-miR-502-5p, hsa-miR-542-5p, hsa-miR-550a-5p, hsa-miR-576-3p, hsa-miR-582-3p, hsa-miR-642a-5p, hsa-miR-942, hsa-let-7b-5p and hsa-miR181 d or isoforms or variants thereof.

Said second group of miRNAs are, preferably for diagnosing osteoporosis or determining the risk of osteoporotic fractures or monitoring of treatment in patients being at risk of or suffering from type 2 diabetes mellitus, hsa-miR-32-3p, hsa-let-7b-5p, hsa-let-7g-5p, hsa-let-7i-5p, hsa-miR-141 -3p, hsa-miR- 43-5p, hsa-miR-16-2-3p, hsa-miR- 81 c-3p, hsa-miR-18 c-5p, hsa-miR-191 -5p, hsa-miR-19b-1 -5p, hsa-miR-203a, hsa-miR-21 -3p, hsa-miR-323a-3p, hsa-miR-375, hsa-miR-486-5p, hsa-miR-500a-5p, hsa-miR-550a-5p, hsa-miR-7-5p, hsa-miR-92a-3p, hsa-miR-942, hsa-miR-96-5p, hsa-miR-330-3p, hsa-miR-31 -5p, hsa-miR- 44-3p, and miR-382-3p or isoforms or variants thereof.

According to the inventive method, the magnitude of difference in the respective circulating miRNA level in a body fluid, specifically in blood serum or plasma when compared to the reference or control level is indicative of osteoporosis or the risk of fractures.

The detection of an increase or decrease of the level of two or more of said miRNAs compared to the level in healthy subjects can be for example used for predicting a risk of osteoporosis or fractures in a subject.

Alternatively, as a read out, the level of miRNAs in a sample to determine the selected miRNAs may be measured and correlated to the risk of said patients, which can be low, medium or high, or else prediction rules established in order to

discriminate between the binary outcome stable or progressive disease. For example, the ability of a prediction rule can be assessed by calculating the area under the ROC curve (AUC) using the Sommer's D statistic. The relation between the area under the ROC and Sommer's D is the following:

AUC= ( +Sommer's D)/2.

It is preferred to employ a miRNA marker according to the invention either as single predictor of progression with an AUC value of at least 0.5, preferably at least 0.6, more preferred 0.7, 0.8 or even at least 0.9. Preferred marker combinations reach AUC values of at least 0.6, preferably at least 0.7, 0.8 or even at least 0.9, up to 1 .0.

With reference to a healthy subject, the preferred method according to the invention qualifies a significant risk when an increase of the amount of two or more miRNAs according to the invention by at least 50%, preferably at least 60%, more

preferred at least 70%, more preferably at least 80%, more preferably at least 100% is determined.

The high risk for osteoporosis or risk of fractures is preferably indicated, if the amount of at least two miRNAs or the combination of more miRNAs as indicated in above lists is increased at least 1 .5 times the reference value of subjects not suffering from the disease, preferably being healthy subjects or subjects suffering from a different disease not correlated to increased risk of osteoporosis or bone fracture.

As used herein, the term "microRNA" or "miRNA" or "miR" designates a non-coding RNA molecule having a length of about 17 to 25 nucleotides, specifically having a length of 17, 18, 19, 20, 21 , 22, 23, 24 or 25 nucleotides which hybridizes to and regulates the expression of a coding messenger RNA. The term "miRNA molecule" refers to any nucleic acid molecule representing the miRNA, including natural miRNA molecules, i.e. the mature miRNA, pre-miRNA, pri-miRNA.

"miR precursor", "pre-miRNA" or "pre-miR" designates a non-coding RNA having a hairpin structure, which contains a miRNA. A pre-miRNA is the product of cleavage of a primary mi-RNA transcript, or "pri-miR" by the double-stranded RNA-specific ribonuclease known as Drosha. The precursors may be forms of the respective polynucleotides as they occur during maturation of the respective polynucleotides. Specifically, examples of said precursors are listed in tables 3 and 4 specifically they are of SEQ ID Nos 24 to 46, 48 and 72 to 94.

Nucleotide sequences of mature miRNAs and their respective precursors are known in the art and available from the database miRBase at http://www.mirbase.org/ index.shtml or from Sanger database at http://microrna.sanqer.ac.uk/sequences/ ftp.shtml. The nucleotide sequences are also specifically disclosed in tables 3 and 4 including reference to the respective gene bank accession numbers.

Identical polynucleotides as used herein in the context of a polynucleotide to be detected or inhibited in contect of the present invention may have a nucleic acid sequence with an identity of at least 90%, 95%, 97%, 98% or 99% to a polynucleotide comprising or consisting of the nucleotide sequence of any one of SEQ ID Nos. 1 to 23, 47 and 49 to 71 .

Furthermore, identical polynucleotides as used herein in the context of a polynucleotide to be detected or inhibited in context of the present invention may have a nucleic acid sequence with an identity of at least 90%, 95%, 97%, 98% or 99% to a polynucleotide comprising or consisting of the nucleotide sequence of any one of SEQ ID Nos. 24 to 46, 48 and 72 to 94 including one, two, three or more nucleotides of the corresponding pre-miRNA sequence at the 5'end and/or the 3'end of the respective seed sequence.

All of the specified miRNAs used according to the invention also encompass isoforms and variants thereof.

For the purpose of the invention, the terms "isoforms and variants" (which have also be termed "isomirs") of a reference miRNA include trimming variants (5' trimming variants in which the 5' dicing site is upstream or downstream from the reference miRNA sequence; 3' trimming variants: the 3' dicing site is upstream or downstream from the reference miRNA sequence), or variants having one or more nucleotide modifications (3' nucleotide addition to the 3' end of the reference miRNA; nucleotide substitution by changing nucleotides from the miRNA precursor), or the

complementary mature microRNA strand including its isoforms and variants (for example for a given 5' mature microRNA the complementary 3' mature microRNA and vice-versa). With regard to nucleotide modification, the nucleotides relevant for

RNA/RNA binding, i.e. the 5'-seed region and nucleotides at the cleavage/anchor side are exempt from modification.

In the following, if not otherwise stated, the term "miRNA" encompasses 3p and 5p strands and also its isoforms and variants.

Specifically, the term "miR-respective_number-3p" as used herein in the specification also encompasses its complementary 5p miRNA and vice versa.

In specific embodiments, the miRNAs of interest are detected using a nucleotide that hybridizes, preferably under stringent conditions, with said miRNA of interest and measuring the hybridization signal.

Circulating microRNAs in cell-free blood such as serum or plasma are a minimal or non-invasive source of biomarkers allowing minimal-invasive detection and therefore a broad applicability in clinics and research repositories. This is especially advantageous for diseases affecting tissues that are not easily accessible for biopsies such as bone. Mitchell and others demonstrated that miRNA measurements in serum and plasma collected from the same healthy individuals from the same blood draw are highly correlated (Chen et al., 2008; Mitchell et al., 2008). However, the choice of anticoagulant (Heparin, EDTA, sodium citrate or Natriumfluorid/potassium oxalat (NaF/KOx)) for plasma sample collection is important, since heparin interferes with enzyme activity in PCR-based assays (Boeckel et al., 2013; Garcia et al., 2002).

Similar to heparin, also citrate exhibits an inhibitory effect on qPCR and both are therefore not recommended for miRNA quantitation by qPCR. Unlike heparin, EDTA is removable from the PGR mastermix and is therefore considered the anticoagulant of choice for PCR-based miRNA profiling ( Zampetaki & Mayr, 2012). The use of

NaF/KOx as anticoagulant resulted in increased miRNA detection rates and may be a suitable alternative if serum or EDTA blood is not at disposition (Kim et al., 2012). The time to centrifugation as well as centrifugation speeds have been shown to critically impact the detection of miRNAs in EDTA plasma samples since it affects the contamination with platelet-derived miRNAs (Cheng et al., 2013). In contrast, miRNA detection in serum was shown to be less sensitive towards pre-processing variations. In view of the state of the art, selecting the appropriate conditions for mi-RNA diagnosis can be done by the skilled person without undue burden.

In a preferred embodiment, the level of the miRNAs of interest is determined by polymerase chain reaction (PGR). PGR methods are well known in the art and widely used, they include quantitative real time PGR, semi-quantitative PGR, multiplex PGR, digital PGR, or any combination thereof. In a particularly preferred embodiment, the levels of miRNAs are determined by quantitative real time PGR (qRT-PCR). Methods of determining the levels of miRNAs using qRT-PCR are known in the art, and are usually preceded by reverse transcription of a miRNA into a cDNA.

In the PGR methods useful in the present invention, the primers are usually based on the mature miRNA molecule, but may include chemical modifications to optimize hybridization behavior.

qRT-PCR methods may determine an absolute level of expression of a miRNA. Alternatively, qRT-PCR methods may determine the relative quantity of a miRNA. The relative quantity of a miRNA may be determined by normalizing the level of the miRNA to the level of one or more internal standard nucleic acid sequences. In general, such internal standard nucleic acid sequences should have a constant level in the analyzed blood or serum sample. For instance, internal standard nucleic acid sequences may be constitutively transcribed RNA nucleic acid sequences such as mRNAs like

glyceraldehyde-3-phosphate-dehydrogenase (GAPDH), beta-actin (ACTB), or non-coding RNAs such as 5S and 18S ribosomal RNA, RNU48, RNU44, and RNU6. In addition, miRNAs that have constant and high levels in serum or plasma, such as miR-23a-3p, miR-23b-3p, miR-15-5p or miR-16-5p can be used as references for relative quantification. In addition, synthetic RNA sequences added in an equimolar amount

during RNA isolation or cDNA synthesis can be used as references for relative quantification of specific miRNAs.

An overview of real time PGR quantification methods useful in the present invention is given by Schmittgen et a I., 2008, Methods. January; 44(1 ): 31-38.

Primers for detection of miRNAs are commercially available, e.g. as microRNA

LNA™ PGR primer sets from Exiqon.

Since miRNAs are relatively short molecules, it may be useful, as suggested, e.g. in WO201 1/14476, to lengthen them by adding adenosine monomers to the strand (a technique known as polyadenylation) before reverse transcription and amplification. Briefly, the RNA may be extracted from the sample by a suitable reagent (e.g.Trizol reagent), polyadenylated in the presence of ATP and poly(A) polymerase, reverse transcribed into cDNA using a poly(T) adapter and 5' RACE sequence, and amplified using a forward primer derived from the 3' end of the miRNA and a reverse RACE primer. Improvements of this technique include designing the RACE primer with a nucleotide at its 3' end (constituting an A, C, or G, but not a T, so to exclude priming anywhere on the polyA sequence and enforce priming on the miRNA sequence) or RACE primers which are anchored at the 3' cDNA end of a specific microRNA using 2, 3, 4, or more nucleotides with or without chemical modification.

The detection of a miRNA may also be achieved by other methods known in the art, e.g. those described in WO201 1/14476, like by the deep sequencing method, bead-based quantification, e.g. Illumina bead -arrays, hydrogel-particle based quantification, e.g. Firefly™, by microarray technology, e.g. the Ncode™ human miRNA array available from Invitrogen, chip arrays available from Affymetrix, Agilent, or microarrays which employ LNA-backbone capture probes (miRCURY LNA™ arrays), e.g., from Exiqon.

The difference in miRNA levels can also be determined using multiplex chemiluminescence-based nucleic acid assays such as Panomics, or reporter plasmid assays ("biosensors") containing reporter proteins with microRNA-complementary regulatory sites, or other hybridization-based techniques known in the art.

The use of miRNAs in a method of the invention is useful for diagnosing bone disorders associated with low bone mineral density (due to aberrant bone metabolism which is reflected in secreted miRNAs) like osteoporosis and, in particular, for assessing the risk of osteoporotic fractures.

The present invention specifically provides a set of miRNAs that represent a diagnostic signature applicable both over a broad range of bone disease stages and age groups. In particular, detection of miRNAs, a), which are differentially regulated in the blood or serum of younger patients than those recruited by Seeliger et al., supra, b), which are differentially regulated in patients with non-recent fractures, and/or c), which are differentially regulated in type-2 diabetes patients with non-recent fractures, provides a diagnostic and predictive tool that has a higher significance for early diagnosis, long-term prognosis, and screening of patients with high risk of fractures.

Biomarkers with prognostic value for disease progression are of utmost importance to minimize the occurrence of severe osteoporotic fractures. Currently, a high incidence in osteoporotic fractures can be attributed to unspecific diagnostic methods that are largely based on bone imaging and routine clinical parameters and overt characteristics such as sex, age, life style and family history and FRAX™ scores. Evaluation of these parameters are however, not directly relevant for bone metabolism and osteoblast/osteoclast activity. Therefore high variation in the individual fracture-risk persists, albeit general guidelines that involve FRAX™ and BMD. Early diagnosis using microRNAs relies on a read out of bone metabolism and thus the pathophysiology of the diseases itself. This analysis is therefore more specific to the individual patient.

According to another aspect, the invention relates to therapeutical compositions for the treatment of bone fractures and bone disorders like osteoporosis, or in the context of type-2 diabetes, in particular for the prevention or healing of fractures.

Specifically, a composition can comprise at least one, specifically at least two isolated or synthetic human miRNAs from miRNAs hsa-miR-188-3p, hsa-miR-382-3p, hsa-miR-127-3p, hsa-miR-144-3p, hsa-miR-155-5p, hsa-miR- 8 a-3p, hsa-miR-1908, hsa-miR-190a, hsa-miR-199b-5p, hsa-miR-203a, hsa-miR-214-3p, hsa-miR-29b-3p, hsa-miR-330-3p, hsa-miR-335-5p, hsa-miR-31 -5p, hsa-miR-342-5p, hsa-miR-369-3p, hsa-miR-377-3p, hsa-miR-378a-5p, hsa-miR-495-3p, hsa-miR-500a-5p, hsa-miR-502-5p, hsa-miR-542-5p, hsa-miR-550a-5p, hsa-miR-576-3p, hsa-miR-582-3p, hsa-miR-642a-5p, hsa-miR-942, sa-let-7b-5p, hsa-miR181 d, hsa-miR-32-3p, hsa-let-7b-5p, hsa-let-7g-5p, hsa-let-7i-5p, hsa-miR-141 -3p, hsa-miR-143-5p, hsa-miR- 6-2-3p, hsa-miR-181 c-3p, hsa-miR-181 c-5p, hsa-miR-191 -5p, hsa-miR-19b-1 -5p, hsa-miR-203a, hsa-miR-21 -3p, hsa-miR-323a-3p, hsa-miR-375, hsa-miR-486-5p, hsa-miR-500a-5p, hsa-miR-7-5p, hsa-miR-92a-3p, hsa-miR-942, hsa-miR-330-3p, hsa-miR-31 -5p, hsa-miR-144-3p , hsa-miR-96-5p and hsa-miR382-3p or isoforms or variants thereof.

According to a specific embodiment, the composition comprises at least one of specifically at least two isolated or synthetic human miRNAs from hsa-miR-188, hsa-miR-382 und hsa-miR-550a. These miRNAs or their inhibitors/antagonists respectively, may be used in combination with the miRNAs listed in the paragraph above or as single components or in any combination thereof.

Whether the miRNA itself or an inhibitor/antagonist thereof is incorporated as the active ingredient in the therapeutical composition not only depends on whether such miRNA is up- or down-regulated in a patient at risk of an osteoporotic fracture, but on its specific function in osteogenic differentiation or in osteoclastogenic

activation. By way of example, if a miRNA, which functions as an inhibitor of

osteogenic differentiation, is found upregulated in osteoporosis, as shown in tables 1 and 2 or specifically, as known for miR 31 -5p, or if it functions as a promoter of osteoclastogenesis like miR-148a-5p, an inhibitor/antagonist of such miRNA will be the active ingredient in the composition of the invention.

In the following, if not otherwise stated, the term "miRNA therapeutic" is used for both the miRNA itself and the respective miRNA inhibitor/antagonist.

A miRNA therapeutic is generally based on the sequence of the targeted mature miRNA. Therapeutics for miRNA replacement therapies need to share most of the sequence of the mature miRNA which is substituted. Exact sequence homology is required in the 5' seed region of the miRNA. Therapeutics designed to specifically inhibit miRNA function (anti-microRNA oligonucleotides, AfvIO) need to be

complementary to the targeted sequence so that a stable hybridization and hence sequestration of the miRNA is achieved. AMOs may contain chemical modifications which cause stable RNA duplex formation, such as a phosphorothioate backbone, or LNA and 2'OMe modifications of the sugar residues, respectively.

Whether a miRNA that is up- or downregulated in serum/plasma of subjects with bone disorders, may be causally related to the disease due to its function in bone formation, can be determined by assessing the effect of these miRNAs on osteogenic differentiation: synthetic microRNA transfection in mesenchymal stem cells is performed prior to the initiation of osteogenic differentiation. Using assays that quantitate the early osteogenic marker alkaline phosphatase (ALP), e.g. by qPCR, western blot, or enzymatically, or assays determining calcium deposition, e.g. by Alizarin staining, as described by Deng et al. (Deng et al., 2014), conclusions about the importance of a miRNA for bone formation can be drawn.

Alternatively, a miRNA therapeutic may be routinely tested for usefulness in the present invention by transfecting MSCs or, as a model for SCs, adipose tissue-derived stem cells (ASCs), with mammalian vector constructs containing the DNA sequence encoding the miRNA therapeutic and determining its effect on osteogenic differentiation as described above.

MSCs and ASCs may be obtained by known methods, e.g. as described by Wolbank et ai., 2007 (Tissue Eng 13, 1 173-1 183) and Wolbank et ai., 2009 (Tissue Eng Part A 15, 1843-1854).

A miRNA that is confirmed to be involved in bone regeneration and thus to promote bone healing, is useful as the active ingredient in a pharmaceutical composition of the invention.

Whether a miRNA that is up- or downregulated in serum/plasma of subjects with bone disorders, may be causally related to the disease due to its function in bone resorption, can be determined by assessing the effect of these miRNAs on osteoclast formation: synthetic microRNA transfection in CD14+ peripheral blood mononuclear cells is performed prior to the initiation of osteoclast formation through RANKL and M-CSF. Using assays that quantitate osteoclast markers such as tartrate-resistant acid phosphatase (TRAP) activity, Calcitonin receptor and RANK expression, conclusions about the importance of a miRNA for bone resorption can be drawn. A miRNA can be obtained from a miR precursor using intact cells or cell lysates or it can be produced in vitro using isolated processing enzymes, such as isolated Drosha/Dgcr8 and Dicer. A miRNA may also be produced by chemical synthesis, without having been processed from a miR precursor.

Antagonists/inhibitors of miRNAs are well known in the art and customized miRNA inhibitors are commercially available. For example, antagonists/inhibitors of in context of the present invention may be nucleic acid molecules such as antagomiRs (Kriitzfeldt, Nature (2005), 438: 685-689) or any other T-O-methyl-RNA oligonucleotide having phosphoroth ioates bonds and a cholesterol tail, miRCURY LNA™ microRNA inhibitors (Exiqon), in vivo LNA™ miRNA inhibitors (Exiqon), tiny LNAs (Obad, Nat Genet (201 1 ), 43(4): 371 -378), miR-decoys or miR-sponges (Ebert, Nat Methods (2007), 4: 721 -726; Bond, Nat Med (2008), 14: 1271 -1277) or the like. An

antagonist/inhibitor might also be or derived from miRNA degrading enzymes as described in Chatterjee, Nature (2009), 461 : 546-9, hammerhead ribozymes as described in Tedeschi, Drug Discov Today (2009), 14: 776-783, or antogomirzymes as

described in Jadhav, Angew Chem Int Ed Engl (2009), 48(14 : 2557-2560. In context of the present invention, the antagmiRs, miCURY LNA™ microRNA inhibitors, in vivo LNA™ miR inhibitors, tiny LNAs, miR decoys or miR sponges.

In a further embodiment, the active ingredient of the pharmaceutical

composition is selected according to the principles of so-called "personalized medicine", i.e. correlated with the results of the diagnostic method of the invention, which would, in this case, be a so-called "companion" diagnostic. This means that the decision over therapeutic administration of a miRNA with the aim to either substitute or inhibit a specific miRNA, is closely linked to an accompanying diagnostic procedure where the level of the specific miRNA is analyzed in an individual.

Osteoclast-specific promoters such as Calcitonin receptor (CalcR), RANK (receptor activator of NFkB), colony stimulating factor 1 receptor (c-Fms), and

Cathepsin K (CathK) may be used.

In embodiments of local administration, e.g. for accelerating bone healing after a fracture, the nucleic acid molecule encoding the miRNA therapeutic may be delivered to the site of interest by means of viral or nonvirai vectors or as naked DNA or RNA. As reviewed by Pelled et al., 2010 (Tissue Engineering: Part B, Volume 16, No.1 , 13-20), localization of the therapeutic molecule within the fracture site may be assured either by physical placement at the target site or by gene release from a three dimensional biomaterial implanted at or near the defect area, including biological glues such as polymers of fibrinogen and thrombin. Useful physical placement methods include direct injection of the miRNA, or lipid-microRNA complexes formed from agents such as Polyethylenimine (PEI) therapeutic into the fracture site. Preferably, in order for the nucleic acid molecule to penetrate cells in situ, it is delivered in complexed state using such as liposomes or PEI. Alternatively, the miRNA could be transcribed by a virus. Preferably, an adenoviral vector is used, as described for expressing bone

morphogenetic protein (BMP) by Egermann et al., 2006 (Hum Gene Ther. May; 17 (5):507-17).

Alternatively to using a vector, in vivo electroporation or sonoporation may be used to deliver the therapeutic locally. Using these methods, the miRNA or the miRNA-encoding DNA molecule is directly injected into a fracture and an electric pulse or ultrasonic wave is applied to the site either trans- or percutaneously. Said miRNAs or antagonists/inhibitors thereof may also be part of fibrin sealants, specifically used for bone repair and regeneration.

In a further embodiment, mesenchymal stem cells derived from any source, including but not limited to bone marrow, adipose tissue, umbilical tissue, urine, or placenta, genetically engineered to overexpress or suppress the therapeutic miRNA may be implanted at the defect site (Marie, 201 1 , Osteoporos Int 22:2023-2026, Deng et ai, supra).

In an alternative embodiment, localizing the miRNA therapeutic at the site of interest, e.g. the fracture site, e.g. by transgene expression, is achieved by first binding the miRNA therapeutic DNA or RNA to a delivery system (e.g. by adsorption, entrapment or immobilization, or by covalent binding; Luginbuehl et ai., 2004, Eur J Pharm Biopharm 58:197-208) and then implanting the gene-activated matrix (GAM) into the defect site, e.g. as described by Fang et al., 1996 (Proc Natl Acad Sci USA 93, 5753).

Useful matrices (GAMs, "gene-activated matrices") have been described in the context with matrices for the delivery of the miRNA. Also when the therapeutically active miRNA or an inhibitor/antagonist thereof is administered locally, either as such or incorporated in a matrix, it may advantageously be linked to a bone-targeting molecule. This may be accomplished by linking the delivery vehicle, e.g. a liposome, which is used to complex the miRNA therapeutic, with the bone-targeting molecule. In the case that a nucleic acid molecule is to be administered locally, incorporation of the bone-targeting molecule is achieved by linking it to the surface of the delivery vehicle. The same applies for a CPP.

Any miRNA therapeutic of the invention, either containing the miRNA molecule or the nucleic acid molecule encoding it, or an antisense inhibitor, may be combined with one or more other agents e.g. teriparatide, denosumab, blosozumab,

romosozumab, bisphosphonates such as alendronate, zolendronate, or one or more bone growth factors or the respective encoding nucleic acid molecules, e.g. a BMP like BMP-2 and/or BMP-7, or RNAs, like e.g. RNAs antagonizing miR-31 .

The present invention also encompasses following items:

1 . An in vitro method of diagnosing osteoporosis or determining the risk of osteoporotic fractures or monitoring of treatment in a subject, comprising the steps of: a) providing a sample from said subject,

b) measuring the level of two or more miRNAs selected from any of miRNAs consisting of hsa-miR-144-3p, hsa-miR-188-3p, hsa-miR-382-3p, hsa-miR-127-3p, hsa-miR-155-5p, hsa-miR-181 a-3p, hsa-miR-1908, hsa-miR-190a, hsa-miR-199b-5p

hsa-miR-203a, hsa-miR-214-3p, hsa-miR-29b-3p, hsa-miR-330-3p, hsa-miR-335-5p, hsa-miR-31 -5p, hsa-miR-342-5p, hsa-miR-369-3p, hsa-miR-377-3p, hsa-miR-378a-5p, hsa-miR-495-3p, hsa-miR-500a-5p, hsa-miR-502-5p, hsa-miR-542-5p, hsa-miR-550a-5p, hsa-miR-576-3p, hsa-miR-582-3p, hsa-miR-642a-5p, hsa-miR-942, hsa-let-7b-5p and hsa-miR-181d or isoforms or variants thereof, and

c) comparing the level of said miRNAs to a reference level,

wherein the magnitude of difference in said level when compared to the reference level is indicative of osteoporosis or the risk of fractures.

2. The method according to item 1 , wherein the level of all of hsa-miR-203a, hsa-miR-330-3p, hsa-miR- 88-3p, hsa-miR-550a-5p, hsa-miR-335-5p, hsa-miR-29b- 3p, hsa-miR-214-3p and hsa-miR-31 -5p is measured.

3. The method according to item 1 , wherein at least one miRNA selected from the group consisting of hsa-miR-188-3p, hsa-miR-382-3p, hsa-miR-203a, hsa-miR-181 a-3p, hsa-miR-330-3p and hsa-miR-369-3p and at least one miRNA selected from the group consisting of hsa-miR-127-3p, hsa-miR-155-5p, hsa-miR-1908, hsa-miR-190a, hsa-miR-199b-5p, hsa-miR-144-3p, hsa-miR-335-5p, hsa-miR-31 -5p, hsa-miR-214-3p, hsa-miR-29b-4p, hsa-let-7b-5p, hsa-miR-342-5p, hsa-miR-377-3p, hsa-miR-378a-5p, hsa-miR-495-3p, hsa-miR-500a-5p, hsa-miR-502-5p, hsa-miR-542-5p, hsa-miR-550a-5p, hsa-miR-576-3p, hsa-miR-582-3p, hsa-miR-642a-5p, hsa-miR-942, and hsa-miR-181 d are measured.

4. The method according to item 1 or 3, wherein hsa-miR-188-3p is measured in combination with at least one miRNA selected from the group consisting of hsa-miR-127-3p, hsa-miR-155-5p, hsa-miR-1908, hsa-miR-190a, hsa-miR-199b-5p, hsa-miR-144-3p, hsa-miR-335-5p, hsa-miR-31 -5p, hsa-miR-214-3p, hsa-miR-29b-4p, hsa-let-7b-5p, hsa-miR-203a, hsa-miR-342-5p, hsa-miR-377-3p, hsa-miR-378a-5p, hsa-miR-495-3p, hsa-miR-500a-5p, hsa-miR-502-5p, hsa-miR-542-5p, hsa-miR-550a-5p, hsa-miR-576-3p, hsa-miR-582-3p, hsa-miR-642a-5p, hsa-miR-942 and hsa-miR-181d.

5. The method according to item 1 or 3, wherein hsa-miR-382-3p is measured in combination with at least one miRNA selected from the group consisting of hsa-miR-127-3p, hsa-miR-155-5p, hsa-miR-188-3p, hsa-miR-1908, hsa-miR-190a, hsa-miR-199b-5p, hsa-miR-144-3p, hsa-miR-335-5p, hsa-miR-31 -5p, hsa-miR-214-3p, hsa-miR-29b-4p, hsa-let-7b-5p, hsa-miR-203a, hsa-miR-342-5p, hsa-miR-377-3p, hsa-miR-378a-5p, hsa-miR-495-3p, hsa-miR-500a-5p, hsa-miR-502-5p, hsa-miR-542-5p,

hsa-miR-550a-5p, hsa-miR-576-3p, hsa-miR-582-3p, hsa-miR-642a-5p, hsa-miR-942 and hsa-miR-181 d.

6. The method according to item 1 or 3, wherein hsa-miR-181 a-3p is measured in combination with at least one miRNA selected from the group consisting of hsa-miR-127-3p, hsa-miR-155-5p, hsa-miR-188-3p, hsa-miR-1908, hsa-miR-190a, hsa-miR-199b-5p, hsa-miR-144-3p, hsa-miR-335-5p, hsa-miR-31 -5p, hsa-miR-214-3p, hsa-miR-29b-4p, hsa-let-7b-5p, hsa-miR-203a, hsa-miR-342-5p, hsa-miR-377-3p, hsa-miR-378a-5p, hsa-miR-495-3p, hsa-miR-500a-5p, hsa-miR-502-5p, hsa-miR-542-5p, hsa-miR-550a-5p, hsa-miR-576-3p, hsa-miR-582-3p, hsa-miR-642a-5p, hsa-miR-942 and hsa-miR-181 d.

7. The method according to item 1 or 3, wherein hsa-miR-330-3p is measured in combination with at least one miRNA selected from the group consisting of hsa-miR-127-3p, hsa-miR-155-5p, hsa-miR-188-3p, hsa-miR-1908, hsa-miR-190a, hsa-miR-199b-5p, hsa-miR-144-3p, hsa-miR-335-5p, hsa-miR-31 -5p, hsa-miR-214-3p, hsa-miR-29b-4p, hsa-let-7b-5p, hsa-miR-203a, hsa-miR-342-5p, hsa-miR-377-3p, hsa-miR-378a-5p, hsa-miR-495-3p, hsa-miR-500a-5p, hsa-miR-502-5p, hsa-miR-542-5p, hsa-miR-550a-5p, hsa-miR-576-3p, hsa-miR-582-3p, hsa-miR-642a-5p, hsa-miR-942 and hsa-miR-181 d.

8. The method according to item 1 or 3, wherein hsa-miR-369-3p is measured in combination with at least one miRNA selected from the group consisting of hsa-miR-127-3p, hsa-miR-155-5p, hsa-miR-188-3p, hsa-miR-1908, hsa-miR-190a, hsa-miR-199b-5p, hsa-miR-144-3p, hsa-miR-335-5p, hsa-miR-31 -5p, hsa-miR-214-3p, hsa-miR-29b-4p, hsa-let-7b-5p, hsa-miR-203a, hsa-miR-342-5p, hsa-miR-377-3p, hsa-miR-378a-5p, hsa-miR-495-3p, hsa-miR-500a-5p, hsa-miR-502-5p, hsa-miR-542-5p, hsa-miR-550a-5p, hsa-miR-576-3p, hsa-miR-582-3p, hsa-miR-642a-5p, hsa-miR-942 and hsa-miR-181 d.

9. The method according to item 1 or 3, wherein hsa-miR-203a is measured in combination with at least one miRNA selected from the group consisting of hsa-miR-127-3p, hsa-miR-155-5p, hsa-miR-188-3p, hsa-miR-1908, hsa-miR-190a, hsa-miR-335-5p, hsa-miR-31 -5p, hsa-miR-214-3p, hsa-miR-29b-4p, hsa-let-7b-5p, hsa-miR-342-5p, hsa-miR-377-3p, hsa-miR-378a-5p, hsa-miR-495-3p, hsa-miR-500a-5p, hsa-miR-502-5p, hsa-miR-542-5p, hsa-miR-550a-5p, hsa-miR-576-3p, hsa-miR-582-3p, hsa-miR-642a-5p, hsa-miR-942 and hsa-miR-181d.

10. The method according to any one of items 1 to 9, wherein the subject is a postmenopausal female or an osteoporosis or osteopenia patient, suffering from or being at risk of developing bone fractures.

1 1 . The method according to any one of items 1 to 10, wherein the level of at least 3, preferably at least 4, preferably at least 5, specifically up to 30 miRNAs is measured.

12. An in vitro method of diagnosing osteoporosis or determining the risk of osteoporotic fractures or monitoring of treatment in a subject, comprising the steps of: a) providing a sample from said subject,

b) measuring the level of two or more miRNAs selected from any of miRNAs consisting of hsa-miR-32-3p, hsa-let-7b-5p, hsa-let-7g-5p, hsa-let-7i-5p, hsa-miR-141 -3p, hsa-miR-143-5p, hsa-miR-16-2-3p, hsa-miR-181 c-3p, hsa-miR-181 c-5p, hsa-miR-191 -5p, hsa-miR-19b-1 -5p, hsa-miR-203a, hsa-miR-21 -3p, hsa-miR-323a-3p, hsa-miR-375, hsa-miR-486-5p, hsa-miR-500a-5p, hsa-miR-550a-5p, hsa-miR-7-5p, hsa-miR-92a-3p, hsa-miR-942, hsa-miR-96-5p, hsa-miR-330-3p, hsa-miR-31 -5p, hsa-miR-144-3p, and hsa-miR-382-3p, and

c) comparing the level of said miRNAs to a reference level,

wherein the magnitude of difference in said level when compared to the reference level is indicative of osteoporosis or the risk of fractures.

13. The method according to item 12, wherein at least one miRNA selected from the group consisting of hsa-miR-32-3p, hsa-miR-181 c-3p, hsa-miR-323a-3p, hsa-miR-550a-5p and hsa-miR-96-5p and at least one miRNA selected from the group consisting of hsa-let-7b-5p, hsa-let-7g-5p, hsa-let-7i-5p, hsa-miR-141 -3p, hsa-miR-143-5p, hsa-miR-16-2-3p, hsa-miR-181 c-5p, hsa-miR-191 -5p, hsa-miR-19b-1 -5p, hsa-miR-203a, hsa-miR-21 -3p, hsa-miR-375, hsa-miR-486-5p, hsa-miR-500a-5p, hsa-miR-7-5p, hsa-miR-92a-3p, hsa-miR-942, hsa-miR-330-3p, hsa-miR-31 -5p, hsa-miR-144-3p and hsa-miR-382-3p are measured.

14. The method according to item 12 or 13, wherein hsa-miR-32-3p is measured in combination with at least one miRNA selected from the group consisting of hsa-let-7b-5p, hsa-let-7g-5p, hsa-let-7i-5p, hsa-miR-141 -3p, hsa-miR-143-5p, hsa-miR-16-2-3p, hsa-miR-181 c-5p, hsa-miR-191 -5p, hsa-miR-19b-1 -5p, hsa-miR-203a, hsa-miR-21 -3p, hsa-miR-375, hsa-miR-486-5p, hsa-miR-500a-5p, hsa-miR-7-5p, hsa-miR-92a-3p, hsa-miR-942, hsa-miR-330-3p, hsa-miR-31 -5p, hsa-miR-144-3p and hsa-miR-382-3p.

15. The method according to item 12 or 13, wherein hsa-miR-181 c-3p is measured in combination with at least one miRNA selected from the group consisting of hsa-let-7b-5p, hsa-let-7g-5p, hsa-let-7i-5p, hsa-miR-141 -3p, hsa-miR-143-5p, hsa-miR-16-2-3p, hsa-miR-181 c-5p, hsa-miR-191 -5p, hsa-miR-19b-1 -5p, hsa-miR-203a, hsa-miR-21 -3p, hsa-miR-375, hsa-miR-486-5p, hsa-miR-500a-5p, hsa-miR-7-5p, hsa-miR-92a-3p, hsa-miR-942, hsa-miR-330-3p, hsa-miR-31 -5p, hsa-miR-144-3p and hsa-miR-382-3p.

16. The method according to item 12 or 13, wherein hsa-miR-323a-3p is measured in combination with at least one miRNA selected from the group consisting of hsa-let-7b-5p, hsa-let-7g-5p, hsa-let-7i-5p, hsa-miR-141 -3p, hsa-miR-143-5p, hsa-miR-16-2-3p, hsa-miR-181 c-5p, hsa-miR-191 -5p, hsa-miR-19b-1 -5p, hsa-miR-203a, hsa-miR-21 -3p, hsa-miR-375, hsa-miR-486-5p, hsa-miR-500a-5p, hsa-miR-7-5p, hsa-miR-92a-3p, hsa-miR-942, hsa-miR-330-3p, hsa-miR-31 -5p, hsa-miR-144-3p and hsa-miR-382-3p.

17. The method according to item 12 or 13, wherein hsa-miR-550a-5p is measured in combination with at least one miRNA selected from the group consisting of hsa-let-7b-5p, hsa-let-7g-5p, hsa-let-7i-5p, hsa-miR-141 -3p, hsa-miR-143-5p, hsa-miR-16-2-3p, hsa-miR-181 c-5p, hsa-miR-191 -5p, hsa-miR-19b-1 -5p, hsa-miR-203a, hsa-miR-21 -3p, hsa-miR-375, hsa-miR-486-5p, hsa-miR-500a-5p, hsa-miR-7-5p, hsa-miR-92a-3p, hsa-miR-942, hsa-miR-330-3p, hsa-miR-31 -5p, hsa-miR-144-3p and hsa-miR382-3p.

18. The method according to item 12 or 13, wherein hsa-miR-96-5p is measured in combination with at least one miRNA selected from the group consisting of hsa-let-7b-5p, hsa-let-7g-5p, hsa-let-7i-5p, hsa-miR-141 -3p, hsa-miR-143-5p, hsa-miR-16-2-3p, hsa-miR-181 c-5p, hsa-miR-191 -5p, hsa-miR-19b-1 -5p, hsa-miR-203a, hsa-miR-21 -3p, hsa-miR-375, hsa-miR-486-5p, hsa-miR-500a-5p, hsa-miR-7-5p, hsa-miR-92a-3p, hsa-miR-942, , hsa-miR-330-3p, hsa-miR-31 -5p, hsa-miR-144-3p and hsa-miR-382-3p.

19. The method according to any one of items 12 to 18, wherein the subject is a patient being at risk of or suffering from type 2 diabetes mellitus.

20. The method according to any one of items 12 to 19, wherein the sample is a blood sample.

21 . The method according to any one of items 12 to 20, wherein the level of at least 3, preferably at least 4, preferably at least 5, specifically up to 26 miRNAs is measured.

22. The method according to any one of items 1 to 21 , wherein the level of said miRNAs is compared with the average level of corresponding miRNAs in healthy subjects, wherein a difference by more than one standard deviations is indicative of osteoporosis with increased risk of future fractures.

23. The method according to items 1 to 22, wherein the level of all miRNAs is measured.

24. The method according to items 1 to 23, wherein one or more further miRNAs are detected, wherein said miRNAs are

a) differentially regulated in osteoporotic individuals as compared to healthy individuals and are

b) involved in osteogenic differentiation and/or in osteoclastogenic activation. 25. Use of a method according to any one of claims 1 to 24 for monitoring a subject, specifically for the prognosis of bone fraction.

26. Use of a method according to any one of items 1 to 24 for monitoring a subject, specifically for measuring the response of a subject to anti-osteoporotic treatments.

27. The method according to any one of items 1 to 25, wherein the difference in miRNA levels is determined by quantitative or digital PGR, sequencing, microarray, Luminex nucleic acid assays, or other hybridization-based techniques.

28. Composition for use in treating or preventing osteoporosis or fractures comprising

a) at least two synthetic human miRNAs from miRNAs of items 1 and/or 12 and/or

b) an antagonist/inhibitor of at least two of miRNAs of items 1 and/or 12 that i. decreases the level of said miRNAs; and/or

ii. inhibits or down-regulates expression of the sequences coding for said miRNAs or degrades or cleaves said miRNAs.

29. Use of a composition comprising at least one, specifically at least two synthetic human miRNAs of items 1 and/or 12 and/or an antagonist/inhibitor of at least two of miRNAs of claims 1 and/or 12 that decreases the level of said miRNAs; and/or inhibits or down-regulates expression of the sequences coding for said miRNAs or degrades or cleaves said miRNAsfor preparing a medicament.

The examples described herein are illustrative of the present invention and are not intended to be limitations thereon. Different embodiments of the present invention have been described according to the present invention. Many modifications and variations may be made to the techniques described and illustrated herein without departing from the spirit and scope of the invention. Accordingly, it should be

understood that the examples are illustrative only and are not limiting upon the scope of the invention.

EXAMPLES

Example 1 : Circulating microRNAs in response to recent femoral-neck fractures

Study Design

For the analysis of miRNAs in recent fractures the focus was put on patients younger than those selected by Seeliger et al., 2014, supra, since diagnosis

preferentially occurs early during disease development, i.e. at younger age.

Ethical approval was granted by the upper Austrian ethics committee for the collection of serum samples from 14 subjects by centrifugation at room temperature at 2000xg for 15 minutes after incubation at room temperature for 30 minutes. Subjects were classified into two groups (n=7) based on prior occurrence of osteoporotic femoral fractures (Figure 1 a). Of the analyzed characteristics such as age, body mass index (BMI), sampling interval after surgery, BMD T-Score, Vitamin D and PTH, only BMI showed significant differences.

RNA Isolation

Serum samples were frozen at -80°C for long term storage. Upon RNA isolation, serum was thawed at 37°C, centrifuged at 12.000xg for 5 minutes to remove cellular debris and 200 μΙ serum were homogenized in 750 μΙ Qiazol containing 35 fmol synthetic cel-miR-39-3p spike-in control. RNA isolation was performed using

chloroform and the miRNeasy isolation kit (Qiagen, Germany) for RNA precipitation and purification with the following deviations from the standard protocol: 200 μΙ plasma were homogenized in 750 μΙ Qiazol. Exactly 500 μΙ aqueous phase were taken, 1 μΙ

Glycogen (Ambion, TX) was added to a final concentration of 50 g/ml and precipitated with 750 μΙ 100% Ethanol. Columns were washed three times with RPE buffer and plasma-RNA was eluted once in 30 μΙ nuclease-free water and stored at -80°C.

Quantitation of cel-miR-39-3p was performed in quadruplicates on a Qiagen Rotorgene using the respective Taqman microRNA Assay Kit and Mastermix (Applied

Biosystems).

qPCR Analysis

Screening of miRNA expression was performed by Exiqon Inc. in Denmark using 384-weli serum/plasma focus panels, which cover 175 distinct human miRNAs that have been repeatedly found to circulate in serum or plasma. First, 4 μΙ of isolated RNA were reverse transcribed in 20 μΙ reactions using the miRCURY LNA Universal RT reaction kit. UniSp3 and UniSp6 are synthetic controls that were added at this step and subsequently analyzed to detect presence of enzyme inhibitors. RT-reactions were diluted 50-fold prior to qPCR analysis and each miRNA was assayed once per sample in a 10 μΙ reaction using the Roche LC 480 Real-Time PGR System (Roche, Germany).

Data Analysis

Melting curve analysis was performed and miRNA PGR reactions with more than one peak were excluded from the analysis. Amplification efficiencies were calculated using algorithms similar to the linreg software package. Efficiencies ranged between 1 .8 and 2.1 for most miRNAs. Individual reactions that gave efficiencies < 1 .6 were excluded from the dataset. Background levels for each miRNA were generated by assaying a "no template" cDNA synthesis control on a full serum/plasma focus panel plate. The majority of miRNA assays did not yield any signal and background Cp was set to 42. We required every miRNA assay to exhibit signals > 5 Cps lower than the background value to be included in the analysis. Normalization of Cp-values was performed based on the average Cp of the miRNA assays detected across all 14 samples (124 assays). Normfinder software was used to confirm that the stability of the average Cp was higher than the stability of any individual miRNA assay in the data set. The following equation was used for normalization: normalized Cp (dCp) = average Cp (124 assays) - assay Cp (sample). This results in a delta Cp (dCp) value, which is a relative log2-transformed measure for expression where higher values indicate a higher concentration and lower dCp values indicate lower concentration in plasma.

Non-parametric t-statistics were calculated using the Mann-Whitney U test and fold changes between the average expression values for each group were calculated. In total, fifteen miRNAs showed a high difference (Fold Change > 1 .5) between recent fracture and control samples.

Example 2: Circulating microRNAs in patients with prevalent or incident non-recent osteoporotic fractures with and without type-2 diabetes

Study Design for prevalent osteoporotic fractures

Serum samples of 74 postmenopausal women (17 controls without fracture history (Co), 19 controls with history of fragility fractures (Fx), 19 type 2 diabetic women without fractures (DM) and 19 type 2 diabetic women with history of fragility fractures (DMFx)) have been collected during the study conduct. To be included in the study, all women had to be postmenopausal, aged 50-75 with a body mass index ranging from 18 to 37 kg/m2. All subjects were required to be mobile and able to move without walkers. For subjects enrolled in the diabetic group, a minimum of 3 years history of treatment for type 2 diabetes by oral medications and/or insulin was required. Caucasian, Asian and African-American women were included. Subjects with fractures were only included if the fractures were caused by a low energy trauma such as falls from standing height or less and if they were sustained after menopause. Patients with pathologic fractures of other origin such as local tumors, tumour-like lesions or focal demineralizations as visualized on radiographs were excluded from the study.

Exclusion criteria comprised all medical conditions that could affect bone metabolism such as severe neuropathic disease, juvenile or premenopausal idiopathic osteoporosis, hyperthyroidism, hyperparathyroidsm, a recent history of longer (>3 months) periods of immobilization, chronic drug use, alcoholism, chronic

gastrointestinal disease, significant chronic renal impairment (CKD stages IV and V), significant chronic hepatic impairment, unstable cardiovascular disease or uncontrolled hypertension. In addition any chronic treatment over the last six months with adrenal or anabolic steroids, estrogens, antacids, anticonvulsants, anticoagulants,

pharmacological doses of Vitamin A, fluorides, bisphosphonates, calcitonin, tamoxifen or parathyroid hormone (PTH) was considered a criterion for exclusion. Due to their proven impact on bone mass and bone structure subjects who were on anti-diabetic agents such as rosiglitazone or pioglitazone were also excluded from the study.

The study protocol was approved by the UCSF Committee of Human Research (CHR) and all patients gave written informed consent before participation. Blood specimens were collected between 8 and 1 1 am after 12 hours of overnight fasting according to the laboratory's handling instructions. For serum samples, blood was allowed to clot in an upright position for 40 minutes and then centrifuged at 2500 rpm for 15 min within one hour of collection. None of the samples showed signs of hemolysis on visual inspection. Serum was subsequently transferred to 1 .5 ml plastic screw-cap vials and stored at - 80° C until further analysis.

Study Design for incident osteoporotic fractures

A prospective nested case-control study-design with 443 postmenopausal women over age 66 from the AGES-Reykjavik cohort was generated. The aim of this study was the identification of circulating microRNAs for the prediction of first osteoporotic fractures (incident fracture) or additional osteoporotic fractures. For that purpose blood samples are analyzed at baseline for serum microRNA levels and correlated with the patient outcome after the first follow up at 5.4 years. In total the study design included 4 groups: a control group comprising 100 healthy individuals without prevalent fractures and who did not sustain fractures during the 5.4 year follow-up, a fracture group comprising 172 patients of which 100 had sustained a first incident fracture during the follow up and 72 patients who already had one or more prevalent fractures before sustaining an additional fracture during the follow-up period, a control diabetic group comprising 100 individuals that had been diagnosed with type-2 diabetes but did not have prevalent or sustain incident fractures during the follow up, and a diabetic fracture group consisting of 71 patients of which 35 had sustained a first incident fracture within the 5.4 year follow-up and 36 patients who had prevalent fracture at baseline and one or more additional incident fractures during the follow up period.

In fracture groups, patients with high energy trauma and stressfractures were excluded. Prevalent fractures that had happened 18 months before study visit or less were excluded. Only subjects were included that exhibited kidney functions above 30 ml/min (eGFR), a BMI of > 20 kg/m2, no history of longstanding or recent

immobilization, no current intake of bone affecting medications and no self-reported or medical record based evidence of kidney disease, liver disease, chronic gastrointestinal disease, hyperparathyroidism, ovariectomy, chornic alcoholism, or idiopathic osteoporosis.

RNA Isolation

For RNA isolation, 200 μΙ serum were thawed at 37°C, centrifuged at 12,000 xg for 5 minutes and homogenized in 1000 μΙ Qiazol containing synthetic RNA spike-in controls (Exiqon, Denmark) at three different concentrations to monitor the efficiency of small RNA purification. RNA isolation was performed using chloroform extraction and the miRNeasy isolation kit (Qiagen, Germany) for RNA precipitation and purification with the following deviations from the standard protocol: exactly 650 μΙ aqueous phase after extraction were taken, and 1 μΙ Glycogen (Ambion, TX, USA) was added to a final concentration of 50 μg/ml and precipitated with 975 μΙ 100% Ethanol. Columns were washed three times with RPE buffer and RNA was eluted once in 30 μΙ nuclease -free water and stored at -80°C.

PCR Analysis

The qPCR-based high-throughput quantification of miRNAs was performed in 384-well plate using reagents by Exiqon. First, 10 μΙ of isolated RNA were reverse transcribed in 50 μΙ reactions using the Universal cDNA Synthesis Kit II. UniSp6 and cel-miR-39-3p were added during this step to monitor the presence of enzyme inhibitors. cDNA samples ere diluted 100-fold prior to qPCR analysis in pre-coated Pick&Mix 384-well plates with custom design. Using an epMotion P5073 liquid handling robot (Eppendorf, Germany), 10 μΙ of qPCR mix were distributed to each well of the qPCR palte. Each miRNA is assayed once per sample in a 10 μΙ reaction using the Roche LC 480 Real-Time PGR System (Roche, Germany).

Data Analysis

Melting curve analysis was performed and miRNA PGR reactions with more than one peak were excluded from the analysis. Amplification efficiencies were calculated using algorithms similar to the linreg software package. Efficiencies ranged between 1 .8 and 2.1 for most miRNAs. Individual reactions that gave efficiencies < 1 .6 were excluded from the dataset. Background levels for each miRNA were generated by assaying a "no template" cDNA synthesis control on a full serum/plasma focus

panel plate. The majority of miRNA assays did not yield any signal and background Cp was set to 42. The expression data was prefiltered according to the following criteria: i) features with more than 50% empty-values were excluded; ii) features with a p-value of < 0.05 in a single-factor AN OVA analysis between any of the 4 groups were selected; iii) features with chi-square test p-value < 0.1 , indicating unequal distribution of negative signals between fracture and non-fracture samples or diabetes and non-diabetes samples, were selected. The aim of this step was to allow only features with trend towards regulation in any of the 4 groups to be further processed. The Ct-values of the remaining 146 features were corrected for the global mean of spike-in control levels and finally, empty values were replaced by imputed values, based on the assumption of normall distributed values.

Gene-wise linear models were fitted incorporating class information, e.g.

fracture vs. no fracture, or diabetic fracture vs diabetic control, by generalized least squares. The p-values of the test whether the class coefficient is different from 0 were adjusted for multiple testing using the method proposed by Benjamini and Hochberg. The limma package from the Bioconductor repository was used. Every single model was evaluated by means of the AUC values and misclassification rates of a 5- fold cross validation using the support vector machine as a base classifier. The smallest model size that obtained an AUC value close to the maximum AUC value was chosen.

The entire procedure was repeated with simulated data that incorporated the same dimensionality and correlation structure as the original data but exhibited no difference in means between classes. The maximal resulting AUC value was used as a reference point characterized by zero reproducibility. All models selected using the two step method described above clearly yielded superior results as compared to the reference point.

Results

For the classification of non-diabetic fracture patients, a combination of 4 microRNAs (Table 5) was identified that yielded an AUC value of 1 .0. This combination consisted of miR-188-3p, miR-550a-5p, miR-582-3p, and miR-382-3p. In addition, several other combinations of four microRNAs performed similar well (Table 5, Figure 3B).

For the classification of diabetic fracture patients, a combination of 4 microRNAs (Table 6) was identified that yielded an AUC value of 0.961 . This combination

consisted of miR-550a-5p, miR-96-5p, miR-32-3p, and miR-375. In addition, several other combinations of four microRNAs performed similar well (Table 6, Figure 3A).

Example 3: Analysis of microRNA function in the context of osteogenic differentiation

Human adipose-derived stem cells (ASCs) were obtained from subcutaneous adipose tissue, which was derived from outpatient tumescence liposuction under local anesthesia with patient consent. ASCs were isolated as described before (Wolbank et al., 2007a; Wolbank et al., 2007b; Wolbank et al., 2009a) and cultured in DMEM-low glucose/HAM 's F-12 supplemented with 4mM L-glutamine, 10% fetal calf serum (FCS, PAA) and 1 ng/ml_ recombinant human basic fibroblast growth factor (rhFGF, R&D Systems) at 37°C, 5% CO2 and 95% air humidity. Cells were passaged once or twice a week at a split ratio of 1 :2 according to the growth rate.

Induction of osteogenic differentiation in ASCs

All differentiation protocols were carried out in 24 well cell culture plates. For osteogenic differentiation ASCs were seeded at a density of 2x103 cell per well. 72 hours after seeding cells were incubated with osteogen ic differentiation medium

(DMEM-low glucose, 10% FCS, 4 mM L-glutamine, 10 nM dexamethasone, 150 μΜ ascorbate-2-phosphat, 10 mM β-glycerolphosphate and 10 nM vitamine-D3) up to 4 weeks.

Alizarin Red S Staining

For Alizarin staining of calcified structures, cells were fixed for 1 hour in 70% ethanol at -20°C. After brief rinsing, cells were stained for 20 minutes with 40 mM Alizarin Red solution (Sigma) and washed with PBS. For quantification Alizarin was extracted for 30 minutes using 200 μΙ 0.1 M HCL/0.5% SDS solution. The extracted dye was measured at 425 nm.

Transfections

ASCs were transfected using siPORT™ NeoFX™ transfection reagent (Applied Biosystems). Cells were transfected with 10nM precursor microRNA, or scrambled miRNA control #2 (Ambion) according to the manufacturer's protocol. Three days after transfection, differentiation was started as described above.

Results

Transfections were performed for miR-188-3p, miR-382-3p, and miR-550a-5p in biological triplicates (i.e. three different human donors for adipose tissue derived mesenchymal stem cells). Overexpression was successful and was significant for all three miRNAs (Figure 5A). Proliferation was analyzed one week after the transfection. A slightly positive effect could be detected for miR-188-3p, while miR-382-3p and miR-550a-5p did not affect cell proliferation (Figure 5B). All three microRNAs significantly impacted osteogenic differentiation (Figure 5C/D) as indicated by the induction of alkaline phosphatase activity and calcium deposition through miR-188-3p and miR-382-3p, and the inhibition of alkaline phosphatase and Calcium deposition by miR-550a-5p.

Example 4: Circulating microRNA signatures in patients with idiopathic and postmenopausal osteoporosis and fragility fractures

Study Design

A case-control study with cross-sectional design was performed. A total of 36 adult female and male patients with idiopathic osteoporosis (mean age 46.6 +/-13.0 years) were included in the study: 10 premenopausal women (mean age 39.0 +/- 8.6), 10 postmenopausal women (mean age 59.0 +/- 1 1 .4) and 16 men (mean age 43.7 +/-1 1 .1 ) with low-traumatic fractures. Vertebral fractures and peripheral low-traumatic fractures were observed in 61 % and 50% of the patients, respectively. Twenty-two percent of patients sustained both vertebral and peripheral fractures. In total, 152 fractures were recorded (mean number of fractures 4.2). Thirty -one percent of patients had received either anticatabolic (bisphosphonates, denosumab, n = 13) or

osteoanabolic (teriparatide, n = 2) drugs within the last year. miRNAs were compared to 39 subjects without low-traumatic fractures (mean age 46.6 +/- 9.4 years): 12 premenopausal women (mean age 42.5 +/- 6.8), 1 1 post- menopausal women (mean age 54.3 +/- 7.4) and 16 men (mean age 44.4 +/- 9.6). Patients with osteoporosis and healthy controls had similar age, height, weight and therefore BMI.

microRNA qPCR analysis

In total 187 miRNAs and 5 controls were analyzed in the entire cohort of 75 individuals over a period of 4 weeks. All miRNAs were selected based on the results from previous studies (Example 1 and 2). The miRNeasy Mini Kit (Qiagen, Germany)

was used to perform RNA-lsolation. Frozen serum samples at -80°C were thawed on ice and centrifuged at 12,000g for 5 minutes. After centrifugation, 200 μΙ serum were mixed by vortexing with 1000 μΙ Qiazol, to which a mix of 3 synthetic spike-in controls (UniSp 2,4,5) had been added. After incubation at room temperature for 10 minutes, 200 μΙ chloroform were added to the homogenized sample, vigorously vortexed and incubated for 3 minutes. After centrifugation at 12 000 g for 15 minutes at 4°C, exactly 650 μΙ of upper aqueous phase were taken and glycogen (Ambion.USA) was added to a final concentration of 50 g/ml. Samples were then transferred to columns and further processed using theQIA- Cube liquid handling robot. RNA was precipated with 750 μΙ Ethanol, triple washed with RPE-buffer, followed by RNA-elu tion in 30 μΙ nuclease free water and storage at -80°C.

For qPCR analysis,4 μΙ of isolated RNA were reversed transcribed using the Universal cDNA Synthesis Kit II (Exiqon, Denmark). Synthetic cel-miR-39-3p was spiked at this step to control for enzyme inhibition. The reaction was incubated at 42°C for 60 minutes, and then heat inactivated at 95°C for 5 minutes. cDNA samples were stored at -20°C. Real Time quantitative PGR (RT-qPCR) analysis of 187 circulating miRNAs (see Supporting Data 1 ) was conducted using custom 384-well panels (Exiqon, Denmark). For RT-qPCR analysis, cDNA samples were diluted 50-fold and 5 μΙ were used in individual 10 μΙ PGR reactions using ExiLENT SYBR® Green master mix and LNA-enhanced miRNA primer assays (Exiqon, Denmark). PGR conditions were 95°C for 10 minutes, 45 cycles of denaturation (95°C, 10 seconds) and

annealing/elongation (60°C, 60 seconds), and melting curve analysis on LC 480 Real Time PGR system (Roche, Germany). The second derivative method was used to calculate the cycle of quantification values (Cq-values).

Results

19 miRNAs, which were significantly regulated between patients with low-traumatic fractures and controls in all 3 subgroups (postmenopausal, premenopausal and male patients with fractures). Three out of 19 miRNAs (15%) were commonly up-regulated (miR-152- 5p, miR-335-5p, miR-320a), while 16 (85%) were down-regulated. miRNAs have been found to be statistically significant discriminators between patients with low- traumatic fractures and controls. Eight miRNAs (miR-140-5p, miR-152-3p, miR-30e-5p, miR-324-3p, miR- 335-3p, miR-19a-3p, miR-19b-3p, miR-550a-3p) had AUCvalues > 0.9 for the classification of fracture patients (see Table 7).

TABLES

Table 1 :

MicroRNA regulation in serum of postmenopausal women with fracture

Fold Change

miRNA ID (log2- AveExpr t P.Value adj.P.Val transformed)

hsa-miR-188-3p -1,84 36,69 3,52 0,001 0,112 hsa-miR-576-3p -1,43 37,26 3,23 0,002 0,139 hsa-miR-181a-3p -1,99 38,47 3,06 0,003 0,156 hsa-miR-942 -1,64 37,02 2,56 0,013 0,391 hsa-miR-155-5p -1,11 34,42 2,55 0,013 0,391 hsa-miR-378a-5p -1,15 35,37 2,41 0,019 0,406 hsa-miR-582-3p -1,51 35,5 2,35 0,021 0,406 hsa-miR-550a-5p 2,08 39,97 -2,36 0,021 0,406 hsa-miR-502-5p -1,46 36,15 2,23 0,029 0,424 hsa-miR-642a-5p -1,61 37,58 2,21 0,030 0,424 hsa-miR-382-3p -2,11 38,8 2,12 0,037 0,471 hsa-miR-342-5p -1,05 35,66 2,07 0,042 0,476 hsa-miR-369-3p -1,04 35,73 2,05 0,044 0,476 hsa-miR-190a -1,23 35,52 1,9 0,061 0,587 hsa-miR-377-3p -1,05 35,23 1,84 0,070 0,624 hsa-miR-495-3p -1,02 33,77 1,71 0,092 0,624 hsa-miR-500a-5p -1 36,19 1,68 0,097 0,624 hsa-miR-181d 1,52 39,21 -1,65 0,103 0,624 hsa-miR-330-3p 1 36,37 -1,65 0,104 0,624 hsa-miR-127-3p -1,59 36,79 1,45 0,150 0,642 hsa-miR-1908 -1,12 37,55 1,27 0,208 0,666 hsa-miR-542-5p -1,46 36,75 1,23 0,222 0,666 hsa-miR-203a 1,11 37,77 -1,15 0,256 0,689

Table 2

MicroRNA regulation in serum of type-2 diabetic women with fracture


Table 3

mature SEQ mature SEQ

mature Seq hairpin hairpin Seq hairpin Acc ID ID Acc ID

CGGGGUGAGGUAGUAGGUUGUGUGG

UGAGGUAGUAG

hsa-let- MIMAT0 hsa- UUUCAGGGCAGUGAUGUUGCCCCUCG

1 GUUGUGUGGU 24 MI0000063

7b-5p 000063 let-7b GAAGAUAACUAUACAACCUACUGCCU

U UCCCUG

AGGCUGAGGUAGUAGUUUGUACAGU

hsa-let- UGAGGUAGUAG hsa- UUGAGGGUCUAUGAUACCACCCGGUA

2 I AT00 25 MI0000433 7g-5p UUUGUACAGUU let-7g CAGGAGAUAACUGUACAGGCCACUGC

00414

CUUGCCA

CUGGCUGAGGUAGUAGUUUGUGCUG

UGAGGUAGUAG

sa-let- MIMATO hsa- UUGGUCGGGUUGUGACAUUGCCCGC

3 UUUGUGCUGU 26 MI0000434 7i-5p 000415 let-7i UGUGGAGAUAACUGCGCAAGCUACUG

U CCUUGCUA

CGGCCGGCCCUGGGUCCAUCUUCCAG

hsa-hsa-miR- UAACACUGUCU MIMATO UACAGUGUUGGAUGGUCUAAUUGUG

4 mir- 27 MI00O0457 141-3p GGUAAAGAUGG 000432 AAGCUCCUAACACUGUCUGGUAAAGA

141

UGGCUCCCGGGUGGGUUC

GUUCCACUCUAGCAGCACGUAAAUAU

hsa-hsa-miR- CCAAUAUUACU MIMATO UGGCGUAGUGAAAUAUAUAUUAAACA

5 mir- 28 MI0000115

16-2-3p GUGCUGCUUUA 004518 CCAAUAUUACUGUGCUGCUUUAGUG

16-2

UGAC

CGGAAAAUUUGCCAAGGGUUUGGGG

hsa- GAACAUUCAACCUGUCGGUGAGUUUG hsa-miR- AACCAUCGACCG MIMATO

6 mir- 29 GGCAGCUCAGGCAAACCAUCGACCGU MI0000271 181c-3p UUGAGUGGAC 004559

181c UGAGUGGACCCUGAGGCCUGGAAUUG

CCAUCCU

CGGAAAAUUUGCCAAGGGUUUGGGG

hsa- GAACAUUCAACCUGUCGGUGAGUUUG hsa-miR- AACAUUCAACC MIMATO

7 mir- 30 GGCAGCUCAGGCAAACCAUCGACCGU MI0000271

181c-5p UGUCGGUGAGU 000258

181c UGAGUGGACCCUGAGGCCUGGAAUUG

CCAUCCU

CGGCUGGACAGCGGGCAACGGAAUCC

hsa-hsa-miR- CAACGGAAUCCC MIMATO CAAAAGCAGCUGUUGUCUCCAGAGCA

8 mir- 31 MI0000465

191-5p AAAAGCAGCUG 000440 UUCCAGCUGCGCUUGGAUUUCGUCCC

191

CUGCUCUCCUGCCU

CACUGUUCUAUGGUUAGUUUUGCAG

AGUUUUGCAGG hsa-hsa-miR- MIMATO GUUUGCAUCCAGCUGUGUGAUAUUC

9 UUUGCAUCCAG mir- 32 MI0000074

19b-l-5p 004491 UGCUGUGCAAAUCCAUGCAAAACUGA

C 19b-l

CUGUGGUAGUG

GUGUUGGGGACUCGCGCGCUGGGUCC

hsa- AGUGGUUCUUAACAGUUCAACAGUUC hsa-miR- GUGAAAUGUUU MIMATO

10 mir- 33 UGUAGCGCAAUUGUGAAAUGUUUAG MI0000283

203a AGGACCACUAG 000264

203a GACCACUAGACCCGGCGGGCGCGGCG

ACAGCGA

UGUCGGGUAGCUUAUCAGACUGAUG

hsa-miR- CAACACCAGUCG MIMATO hsa- 11 34 UUGACUGUUGAAUCUCAUGGCAACAC MI0000077

21-3p AUGGGCUGU 004494 mir-21

CAGUCGAUGGGCUGUCUGACA

GCGCAGCGCCCUG UCUCCCAGCCU

Hsa- GGUGCAG UGC Ml MAT hsa- GAGGUGCAG UGCUGCAUCUCUGG

miR143- 12 UGCAUCUCUG 000459 mir- 35 UCAGU UGGGAGUCUGAGAUGAAG M 10000459

5p GU 9 143 CACUG UAGCUCAGGAAGAGAGAA

GU UGU UCUGCAGC

GCUCCCCCUCUCUAAUCCU UGCUA

UAAUCCUUGC Ml MAT hsa-hsa-miR- CCUGGGUGAGAGUGCUGUCUGAA

13 UACCUGGGUG 000477 mir- 36 M I00031S4

500a-5p UGCAAUGCACCUGGGCAAGGAUU

AGA 3 500a

CUGAGAGCGAGAGC

UUGGUACUUGGAGAGAGGUGGUCCG

hsa-hsa-miR- CACAUUACACG MIMATO UGGCGCGUUCGCUUUAUUUAUGGCG

14 mir- 37 MIOOOO807

323a-3p GUCGACCUCU 000755 CACAUUACACGGUCGACCUCUUUGCA

323a

GUAUCUAAUC

GGAGAUAUUGCACAUUACUAAGUUGC

hsa-miR- CAAUUUAGUGU MIMATO hsa- 15 38 AUGUUGUCACGGCCUCAAUGCAAUUU MI0000090 32-3p GUGUGAUAUUU 004505 mir-32

AGUGUGUGUGAUAUUUUC

hsa- CCCCGCGACGAGCCCCUCGCACAAACC

hsa-miR- UUUGUUCGUUC MIMATO

16 mir- 39 GGACCUGAGCGUUUUGUUCGUUCGG MI0000783

375 GGCUCGCGUGA 000728

375 CUCGCGUGAGGC

hsa- GCAUCCUGUACUGAGCUGCCCCGAGG

hsa-miR- UCCUGUACUGA MIMATO

17 mir- 40 CCCUUCAUGCUGCCCAGCUCGGGGCA MIOOO2470

486-5p GCUGCCCCGAG 002177

486 GCUCAGUACAGGAUAC

GCUCCCCCUCUCUAAUCCUUGCUACCU

UAAUCCUUGCU hsa-hsa-miR- MIMATO GGGUGAGAGUGCUGUCUGAAUGCAA

18 ACCUGGGUGAG mir- MI0003184

500a-5p 004773 41 UGCACCUGGGCAAGGAUUCUGAGAGC

A 500a

GAGAGC

UGAUGCUUUGCUGGCUGGUGCAGUG

AGUGCCUGAGG hsa-hsa-miR- MIMATO CCUGAGGGAGUAAGAGCCCUGUUGUU

19 GAGUAAGAGCC mir- 42 MI0OO36OO 550a-5p 004800 GUAAGAUAGUGUCUUACUCCCUCAGG

C 550a-l

CACAUCUCCAACAAGUCUCU

UUGGAUGUUGGCCUAGUUCUGUGUG

UGGAAGACUAG hsa- GAAGACUAGUGAUUUUGUUGUUUUU

hsa-miR- MIMATO

20 UGAUUUUGUU mir-7- 43 AGAUAACUAAAUCGACAACAAAUCACA MI0000263

7-5p 000252

GU 1 GUCUGCCAUAUGGCACAGGCCAUGCC

UCUACAG

CUUUCUACACAGGUUGGGAUCGGUU

hsa-hsa-miR- UAUUGCACUUG MIMATO GCAAUGCUGUGUUUCUGUAUGGUAU

21 mir- 44 MI0000093

92a-3p UCCCGGCCUGU 000092 UGCACUUGUCCCGGCCUGUUGAGUUU

92a-l

GG

AUUAGGAGAGUAUCUUCUCUGUUUU

hsa-hsa-miR- UCUUCUCUGUU MIMATO GGCCAUGUGUGUACUCACAGCCCCUC

22 mir- 45 MI0O05767 942-5p UUGGCCAUGUG 004985 ACACAUGGCCGAAACAGAGAAGUUAC

942

UUUCCUAAU

UUUGGCACUAG UGGCCGAUUUUGGCACUAGCACAUUU

hsa-miR- MIMATO hsa- 23 CACAUUUUUGC 46 UUGCUUGUGUCUCUCCGCUCUGAGCA MI0000098

96-5p 000095 mir-96

u AUCAUGUGCAGUGCCAAUAUGGGAAA

AAUCAUUCACG hsa- UACUUGAAGAGAAGUUGUUCGUGGU

Hsa-miR- MIMATO

47 GACAACACUU mir- 48 GGAUUCGCUUUACUUAUGACGAAUCA MIOOOO790

382-3p 022697

382 UUCACGGACAACACUUUUUUCAGUA

UGUUUUGAGCGGGGGUCAAGAGCAA MI0000816

UCAAGAGCAAU hsa-hsa-miR- MIMATO UAACGAAAAAUGUUUGUCAUAAACCG

95 AACGAAAAAUG mir- 101

335-5p 000765 UUUUUCAUUAUUGCUCCUGACCUCCU

U 335 CUCAUUUGCUAUAUUCA

102 CUUCAGGAAGCUGGUUUCAUAUGGU MI0000105

UAGCACCAUUU hsa-hsa-miR- MIMATO GGUUUAGAUUUAAAUAGUGAUUGUC

96 GAAAUCAGUGU mir-29b-3p 000100 UAGCACCAUUUGAAAUCAGUGUUCUU

U 29b-l GGGGG

CCAGAGGACACCUCCACUCCGUCUACC MI0000282

CCCAGUGUUUA hsa- CAGUGUUUAGACUAUCUGUUCAGGAC

hsa-miR- MIMATO

97 GACUAUCUGUU mir- 103 UCCCAAAUUGUACAGUAGUCUGCACA

199b-5p 000263

C 199b UUGGUUAGGCUGGGCUGGGUUAGAC

CCUCGG

GGCCUGGCUGGACAGAGUUGUCAUG MIOOOO290 hsa- UGUCUGCCUGUCUACACUUGCUGUGC

hsa-miR- ACAGCAGGCAC MIMATO

98 mir- 104 AGAACAUCCGCUCACCUGUACAGCAG

214-3p AGACAGGCAGU 000271

214 GCACAGACAGGCAGUCACAUGACAACC

CAGCCU

UGGGGCCCUGGCUGGGAUAUCAUCAU MI0000460 hsa-hsa-miR- GGAUAUCAUC Ml MAT AUACUGUAAGUUUGCGAUGAGACACU

99 mir- 105

144- 5 p AUAUACUG UA 000460 ACAGUAUAGAUGAUGUACUAGUCCGG

144

AG 0 GCACCCCC

GGAGAGGAGGCAAGAUGCUGGCAUAG MI0000089 hsa-miR- AGGCAAGAUGC MI ATO hsa- CUGUUGAACUGGGAACCUGCUAUGCC

100 106

31-5p UGGCAUAGCU 000089 miR-31 AACAUAUUGCCAUCUUUCC

Table 4

mature SEQ mature SEQ

mature Seq hairpin hairpin Seq hairpin Acc ID ID Acc ID

UGUGAUCACUGUCUCCAGCCUGCUGA

UCGGAUCCGU

hsa-miR- MIMATOO hsa-mir- AGCUCAGAGGGCUCUGAUUCAGAAAG

49 CUGAGCUUGG 72 M 10000472 127-3p 00446 127 AUCAUCGGAUCCGUCUGAGCUUGGCU

CU GGUCGGAAGUCUCAUCAUC

UUAAUGCUAA CUGUUAAUGCUAAUCGUGAUAGGGG

hsa-miR- MIMATOO hsa-mir- 50 UCGUGAUAGG 73 UUUUUGCCUCCAACUGACUCCUACAU M 10000681

155-5p 00646 155

GGU AUUAGCAUUAACAG

UGAGUUUUGAGGUUGCUUCAGUGAA

ACCAUCGACCG CAUUCAACGCUGUCGGUGAGUUUGG

hsa-miR- MIMATOO hsa-mir- 51 UUGAUUGUAC 74 AAUUAAAAUCAAAACCAUCGACCG U U M 10000289 181a-3p 00270 181a-l

C GAUUGUACCCUAUGGCUAACCAUCAU CUACUCCA

GUCCCCUCCCCUAGGCCACAGCCGAGG

U CACAAU CAACAUUCAUUGUUGUCGG

AACAUUCAUU

hsa-miR- MIMATOO hsa-mir- UGGGUUGUGAGGACUGAGGCCAGAC

52 GUUGUCGGUG 75 MI0003139 181d 02821 181d CCACCGGGGGAUGAAUGUCACUGUGG

GGU

CUGGGCCAGACACGGCUUAAGGGGAA

UGGGGAC

UGCUCCCUCUCUCACAUCCCUUGCAU

hsa-miR- CUCCCACAUGC MIMATOO hsa-mir- GGUGGAGGGUGAGCUUUCUGAAAAC

53 76 M 10000484 188-3p AGGGUUUGCA 04613 188 CCCUCCCACAUGCAGGGUUUGCAGGA

UGGCGAGCC

CGGGAAUGCCGCGGCGGGGACGGCGA

CGGCGGGGAC

hsa-miR- MIMATOO hsa-mir- UUGGUCCGUAUGUGUGGUGCCACCG

54 GGCGAUUGGU 77 M 10008329 1908-5p 07881 1908 GCCGCCGGCUCCGCCCCGGCCCCCGCC

C

CC

UGCAGGCCUCUGUGUGAUAUGUUUG

UGAUAUGUUU

hsa-miR- MIMATOO hsa-mir- AUAUAUUAGGUUGUUAUUUAAUCCA

55 GAUAUAUUAG 78 M 10000486

190a-5p 00458 190a ACUAUAUAUCAAACAUAUUCCUACAG

GU UGUCUUGCC

GUGUUGGGGACUCGCGCGCUGGGUC

GUGAAAUGUU CAGUGGUUCUUAACAGUUCAACAGUU

hsa-miR- MIMATOO hsa-mir- 56 UAGGACCACU 79 CUGUAGCGCAAUUGUGAAAUGUUUA M 10000283

203a 00264 203a

AG GGACCACUAGACCCGGCGGGCGCGGC

GACAGCGA

CUUUGGCGAUCACUGCCUCUCUGGGC

GCAAAGCACAC

hsa-miR- MIMATOO hsa-mir- CUGUGUCUUAGGCUCUGCAAGAUCAA

57 GGCCUGCAGA 80 M 10000803 330-3p 00751 330 CCGAGCAAAGCACACGGCCUGCAGAG

GA AGGCAGCGCUCUGCCC

GAAACUGGGCUCAAGGUGAGGGGUG

AGGGGUGCUA

hsa-miR- MIMATOO hsa-mir- CUAUCUGUGAUUGAGGGACAUGGUU

58 UCUGUGAUUG 81 M 10000805

342-5p 04694 342 AAUGGAAUUGUCUCACACAGAAAUCG

A CACCCGUCACCUUGGCCUACUUA

AAUAAUACAU UUGAAGGGAGAUCGACCGUGUUAUA hsa-miR- MI ATOO hsa-mir- 59 GGUUGAUCUU 82 UUCGCUUUAUUGACUUCGAAUAAUA M 10000777

369-3p 00721 369

U CAUGGUUGAUCUUUUCUCAG

AUCACACAAAG UUGAGCAGAGGUUGCCCUUGGUGAA

hsa-miR- MIMATOO hsa-mir- 60 GCAACUUUUG 83 UUCGCUUUAUUUAUGUUGAAUCACA M 10000785 377-3p 00730 377

U CAAAGGCAACUUUUGUUUG

CUCCUGACUCC AGGGCUCCUGACUCCAGGUCCUGUGU

sa-miR- MIMATOO hsa-mir- 61 AGGUCCUGUG 84 GUUACCUAGAAAUAGCACUGGACUUG M 10000786

378a-5p 00731 378a

u GAGUCAGAAGGCCU

AAUCAUUCAC UACUUGAAGAGAAGUUGUUCGUGGU

hsa-miR- MIMATOO hsa-mir- 62 GGACAACACU 85 GGAUUCGCUUUACUUAUGACGAAUCA M 10000790

382-3p 22697 382

U U U CACG G ACA AC ACU U U U U U CAG U A

UGGUACCUGAAAAGAAGUUGCCCAUG

AAACAAACAUG

hsa-miR- MIMATOO hsa-mir- UUAUUUUCGCUUUAUAUGUGACGAA

63 GUGCACUUCU 86 M10003135 495-3p 02817 495 ACAAACAUGGUGCACUUCUUUUUCGG

U UAUCA

GCUCCCCCUCUCUAAUCCUUGCUACC

UAAUCCUUGC

hsa-miR- MIMATOO hsa-mir- UGGGUGAGAGUGCUGUCUGAAUGCA

64 UACCUGGGUG 87 10003184

500a-5p 04773 500a AUGCACCUGGGCAAGGAUUCUGAGAG

AGA CGAGAGC

UGCUCCCCCUCUCUAAUCCUUGCUAU

AUCCUUGCUA

hsa-miR- MIMATOO sa-mir- CUGGGUGCUAGUGCUGGCUCAAUGC

65 UCUGGGUGCU 88 MI0003186

502-5p 02873 502 AAUGCACCUGGGCAAGGAUUCAGAGA

A GGGGGAGCU

CAGAUCUCAGACAUCUCGGGGAUCAU

UCGGGGAUCA

sa-miR- MIMATOO hsa-mir- CAUGUCACGAGAUACCAGUGUGCACU

66 UCAUGUCACG 89 M 10003686

542-5p 03340 542 UGUGACAGAUUGAUAACUGAAAGGU

AGA CUGGGAGCCACUCAUCUUCA

UGAUGCUUUGCUGGCUGGUGCAGUG

AGUGCCUGAG

sa-miR- MIMATOO hsa-mir- CCUGAGGGAGUAAGAGCCCUGUUGU

67 GGAGUAAGAG 90 M 10003600

550a-5p 04800 550a-l UGUAAGAUAGUGUCUUACUCCCUCAG

ccc

GCACAUCUCCAACAAGUCUCU

UACAAUCCAACGAGGAUUCUAAUUUC

AAGAUGUGGA

hsa-miR- MIMATOO hsa-mir- UCCACGUCUUUGGUAAUAAGGUUUG

68 AAAAUUGGAA 91 M 10003583

576-3p 04796 576 GCAAAGAUGUGGAAAAAUUGGAAUCC

UC UCAUUCGAUUGGUUAUAACCA

AUCUGUGCUCUUUGAUUACAGUUGU

UAACUGGUUG

sa-miR- MIMATOO hsa-mir- UCAACCAGUUACUAAUCUAACUAAUU

69 AACAACUGAAC 92 M 10003589

582-3p 04797 582 GUAACUGGUUGAACAACUGAACCCAA

C AGGGUGCAAAGUAGAAACAUU

AUCUGAGUUGGGAGGGUCCCUCUCCA

GUCCCUCUCCA

hsa-miR- MIMATOO hsa-mir- AAUGUGUCUUGGGGUGGGGGAUCAA

70 AAUGUGUCUU 93 M 10003657 642a-5p 03312 642a GACACAUUUGGAGAGGGAACCUCCCA

G ACUCGGCCUCUGCCAUCAUU

AUUAGGAGAGUAUCUUCUCUGUUUU

UCUUCUCUGU

hsa-miR- MIMATOO hsa-mir- GGCCAUGUGUGUACUCACAGCCCCUC

71 UUUGGCCAUG 94 M 10005767 942 04985 942 ACACAUGGCCGAAACAGAGAAGUUAC

UG

UUUCCUAAU

Table 5

Post-menopausal Fractures: Top 10 Multiparametric Models and AUC values to differentiate fracture patients from controls

Feature 1 Feature 2 Feature 3 Feature 4 Mean AUC (95% CI)

Model 1

miR-382-3p miR-188-3p miR-582-3p miR-550a-5p 1.000 (0.97 to 1.00)

Model 2

miR-382-3p miR-188-3p miR-542-5p miR-550a-5p 1.000 (0.96 to 1.00)

Model 3

miR-382-3p miR-188-3p miR-542-5p miR-330-3p 1.000 (0.96 to 1.00)

Model 4

miR-382-3p miR-188-3p miR-495-3p miR-550a-5p 0.996 (0.95 to 1.00)

Model 5

miR-382-3p miR-188-3p miR-500a-5p miR-550a-5p 0.996 (0.95 to 1.00)

Model 6

miR-382-3p miR-188-3p miR-542-5p miR-500a-5p 0.996 (0.94 to 1.00)

Model 7

miR-382-3p miR-188-3p miR-181d miR-550a-5p 0.993 (0.96 to 1.00)

Model 8

miR-382-3p miR-188-3p miR-330-3p miR-550a-5p 0.990 (0.96 to 1.00)

Model 9

miR-382-3p miR-188-3p miR-642a-5p miR-582-3p 0.981 (0.91 to 1.00)

Model 10

miR-382-3p miR-188-3p miR-495-3p miR-181d 0.975 (0.91 to 1.00)

Table 6

Diabetic Fractures: Top 10 Muitiparametric Models and AUC values to differentiate diabetic fracture patients from controls

Feature 1 Feature 2 Feature 3 Feature 4 Mean AUC (95% CI)

Model 1 miR-550a-5p miR-96-5p miR-32-3p miR-375 0,961 (0,91 to 0,98)

Model 2 miR-550a-5p miR-96-5p miR-141-3p miR-32-3p 0,939 (0,89 to 0,96)

Model 3 miR-550a-5p miR-96-5p miR-375 miR-181c-5p 0,928 (0,87 to 0,95)

Model 4 miR-550a-5p miR-96-5p miR-141-3p miR-375 0,919 (0,86 to 0,95)

Model 5 miR-550a-5p miR-96-5p miR-32-3p miR-16-2-3p 0,919 (0,86 to 0,95)

Model 6 miR-550a-5p miR-7-5p miR-96-5p miR-32-3p 0,914 (0,85 to 0,95)

Model 7 miR-550a-5p miR-96-5p miR-141-3p let-7g-5p 0,908 (0,84 to 0,94)

Model 8 miR-550a-5p miR-96-5p miR-323a-3p miR-181c-5p 0,905 (0,85 to 0,94)

Model 9 miR-550a-5p miR-181c-3p miR-96-5p miR-141-3p 0,903 (0,83 to 0,93)

Model 10 miR-550a-5p miR-500a-5p miR-96-5p miR-181c-5p 0,894 (0,83 to 0,93)

Table 7

Differential expression characteristics and classification performance of 19 microRNAs between patients with fractures and controls

LITERATURE

Anastas, J. N., & Moon, R. T. (2013). WNT signalling pathways as therapeutic targets in cancer. Nature Reviews Cancer, f3(1 ), 1 1 -26. doi:10.1038/nrc3419

Augustine, M., & Horwitz, J. M. (2013). Parathyroid Hormone and Parathyroid

Hormone-related Protein Analogs as Therapies for Osteoporosis. Current Osteoporosis Reports, 11(4), 1 -1 1 .Bartel, D. P. (2009). MicroRNAs: target recognition and regulatory functions. Cell, 136(2), 215-233. doi:10.1016/j.cell.2009.01 .002

Boeckel JN, et al., Heparin selectively affects the quantification of microRNAs in human blood samples, 2013, Clin.Chem. 59, 1 125-1 127

Canalis, E. (2013). Wnt signalling in osteoporosis: mechanisms and novel therapeutic approaches. Nature Reviews. Endocrinology, 9(10), 575-83. doi:10.1038/nrendo.2013.154

Cefalu, C. A. (2004). Is bone mineral density predictive of fracture risk reduction? Current Medical Research and Opinion, 20(3), 341 -349. doi:10.1 185/030079903125003062

Chen C, et al., 2008, Characterization of microRNAs in serum: a novel class of biomarkers for diagnosis of cancer and other diseases, cell res. 18, 997-1006

Cheng HH et al., Plasma processing conditions substantially influence circulating microRNA biomarkerlevels. PLos One 8, 1 -1 1

Cosman, F., Crittenden, D. B., Adachi, J. D., Binkley, N., Czerwinski, E., Ferrari, S., ...

Grauer, A. (2016). Romosozumab Treatment in Postmenopausal Women with Osteoporosis. New England Journal of Medicine, NEJMoal 607948. http://doi.Org/10.1056/NEJMoa 1607948

Cummings, S. R., San Martin, J., McClung, M. R., Siris, E. S., Eastell, R., Reid, I. R., ... Christiansen, C. (2009). Denosumab for prevention of fractures in postmenopausal women with osteoporosis. The New England Journal of Medicine, 361(8), 756-65.

Deng, Y., Bi, X., Zhou, H ., You, Z., Wang, Y., Gu, P., & Fan, X. (2014). Repair of critical-sized bone defects with anti-miR-31 -expressing bone marrow stromal stem cells and poly(glycerol sebacate) scaffolds. European Cells & Materials, 27, 13-24; discussion 24-5. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/244251 57

Dong, S., Yang, B., Guo, H., & Kang, F. (2012). MicroRNAs regulate osteogenesis and chondrogenesis. Biochemical and Biophysical Research Communications, 418(4), 587-591 . doi:10.1016/j.bbrc.2012.01 .075

Garcia, ME et al., 2002, Anticoagulants interfere with PGR used to diagnose invasive aspergillosis. J. Clin. Microbiol. 40, 1567-1 568

Gronholz, M. J. (2008). Prevention, Diagnosis, and Management of Osteoporosis-Related Fracture: A Muitifactoral Osteopathic Approach. The Journal of the American Osteopathic Association, 108(10), S4-585.

Harvey, N., Dennison, E., & Cooper, C. (2010). Osteoporosis: impact on health and economics. Nature Reviews. Rheumatology, 6(2), 99-105.Kanis, J. A., McCloskey, E. V, Johansson, H., Cooper, C, Rizzoli, R., Reginster, J.-Y., & Scientific Advisory Board of the European Society for Clinical and Economic Aspects of Osteoporosis and Osteoarthritis (ESCEO) and the Committee of Scientific Advisors of the International Osteoporosis Foundation (IOF). (2013). European guidance for the diagnosis and management of osteoporosis in postmenopausal women. Osteoporosis International: A Journal Established as Result of Cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA , 24(1 ), 23-57. doi:10.1007/s00198-012-2074-y

Kapinas, K., Kessler, C. B., & Delany, A. M. (2009). miR-29 suppression of osteonectin in osteoblasts: regulation during differentiation and by canonical Wnt signaling. Journal of Cellular Biochemistry, 108(1 ), 216-224. doi:10.1002/jcb.22243

Keller, A., Leidinger, P., Bauer, A., Elsharawy, A., Haas, J., Backes, C, ... Meese, E. (201 1 ). Toward the blood-borne miRNome of human diseases. Nature Methods, 8(10), 841 -3. doi:10.1038/nmeth.1682

Kim DJ et al., 2012, Plasma components affects accuracy of circulating cancer-related microRNA quantitation. J.Mol.Diagn. 14, 71 -80

Li, Z., Hassan, M. Q., Volinia, S., van Wijnen, A. J., Stein, J. L, Croce, C. M., ... Stein, G. S. (2008). A microRNA signature for a BMP2-induced osteoblast lineage commitment program. Proceedings of the National Academy of Sciences of the United States of America, 105(37), 13906-1391 1 . doi:10.1073/pnas.0804438105

Rubin, K. H., Abrahamsen, B., Friis-Holmberg, T., Hjelmborg, J. V. B., Bech, M., Hermann, A. P., ... Brixen, K. (2013). Comparison of different screening tools (FRAX®, OST, ORAI, OSIRIS, SCORE and age alone) to identify women with increased risk of

fracture. A population-based prospective study. Bone, 56(1 ), 16-22. doi:10.1016/j.bone.2013.05.002

Seeliger, C, Karpinski, K., Haug, A., Vester, H., Schmitt, A., Bauer, J., & van Griensven, M. (2014). Five Freely Circulating miRNAs and Bone Tissue miRNAs are Associated with Osteoporotic Fractures. Journal of Bone and Mineral Research : The Official Journal of the American Society for Bone and Mineral Research. doi:10.1002/jbmr.2175

Trom peter, H.-l., Dreesen, J., Hermann, E., Iwaniuk, K. M., Hafner, M., Renwick, N., ... Wernet, P. (2013). MicroRNAs miR-26a, miR-26b, and miR-29b accelerate osteogenic differentiation of unrestricted somatic stem cells from human cord blood. BMC Genomics, 14(1 ), 1 1 1 . doi:10.1 186/1471 -2164-14-1 1 1

Van Wijnen, A. J., van de Peppel, J., van Leeuwen, J. P., Lian, J. B., Stein, G. S., Westendorf, J. J., ... Kakar, S. (2013). MicroRNA functions in osteogenesis and dysfunctions in osteoporosis. Current Osteoporosis Reports, 11(2), 72-82. doi:10.1007/s1 1914-013-0143-6

Vasikaran, S., Eastell, R., Bruyere, O., Foldes, A. J., Garnero, P. , Griesmacher, A., ... lOF-IFCC Bone Marker Standards Working Group. (201 1 ). Markers of bone turnover for the prediction of fracture risk and monitoring of osteoporosis treatment: a need for international reference standards. Osteoporosis International: A Journal Established as Result of Cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA , 22(2), 391—420. doi:10.1007/sOOI 98-010-1501 -1

Weilner, S., Schraml, E., Redl, H., Grillari-Voglauer, R., & Grillari, J. (2013). Secretion of microvesicular miRNAs in cellular and organismal aging. Experimental Gerontology, 48(7), 626-633. doi:10.1016/j.exger.2012.1 1 .017

Zhao, X., Xu, D., Li, Y., Zhang, J., Liu, T., Ji, Y., ... Xie, X. (201 3). MicroRNAs regulate bone metabolism. Journal of Bone and Mineral Metabolism. doi:10.1007/s00774-013-0537-7