Proteomic Insights into Osteoporosis: Unraveling Diagnostic Markers and Therapeutic Targets for the Metabolic Bone Disease

1. Introduction

Osteoporosis (OP), a prevalent and debilitating skeletal disorder, poses a significant public health concern globally, particularly among aging populations [1,2,3]. Characterized by compromised bone strength and increased fracture risk, OP not only diminishes individuals’ quality of life but also imposes substantial economic burdens on healthcare systems. The traditional diagnostic methods and treatment strategies for OP have long served as the cornerstone of clinical management. Conventional diagnostic approaches primarily rely on bone mineral density (BMD) measurements obtained through dual-energy X-ray absorptiometry (DXA), supplemented by clinical risk assessment tools such as FRAX [4,5,6,7]. While DXA remains the gold standard for OP diagnosis, its limitations in assessing bone quality and fracture risk highlight the need for complementary approaches. Likewise, current treatment modalities predominantly focus on anti-resorptive agents such as bisphosphonates and anabolic agents like teriparatide, aimed at mitigating bone loss and enhancing bone formation. However, despite their efficacy in reducing fracture risk, these treatments are not devoid of limitations, including potential adverse effects and suboptimal response rates. Thus, there exists a pressing demand for novel diagnostic methodologies and therapeutic interventions that can provide more accurate risk stratification, personalized treatment strategies, and improved patient outcomes.

The application of proteomic approaches in OP research presents a compelling rationale rooted in the complex and dynamic nature of bone biology [8,9]. Traditional methods often fail to capture the intricate molecular mechanisms underlying bone metabolism and homeostasis, limiting our understanding of disease pathogenesis and hindering the development of effective diagnostic markers and therapeutic targets. Proteomics, with its ability to comprehensively analyze the entire repertoire of proteins expressed in a biological system, offers unprecedented insights into the intricate protein networks governing bone health [10]. By elucidating the proteomic landscape of OP, researchers can identify potential biomarkers for early disease detection, unravel novel pathways implicated in bone remodeling, and pinpoint therapeutic targets for precision medicine interventions. Furthermore, proteomic technologies enable the exploration of post-translational modifications (PTMs) and protein-protein interactions, providing a deeper understanding of the molecular events driving disease [11,12,13,14]. This review will delineate the rationale for leveraging proteomic approaches in OP research, highlighting their potential to revolutionize diagnosis, prognosis, and treatment strategies for this debilitating disease.

2. Proteomic Technologies in Osteoporosis Research

2.1. Mass Spectrometry-Based Proteomics

Mass spectrometry (MS) has emerged as a powerful tool in OP research, enabling comprehensive analysis of the bone proteome. MS-based proteomics facilitates the identification and quantification of proteins involved in bone metabolism, providing valuable insights into disease mechanisms and potential therapeutic targets. For instance, liquid chromatography-tandem mass spectrometry (LC-MS/MS) allows for the identification of hundreds of proteins in bone tissues and biofluids, uncovering novel biomarkers associated with OP pathogenesis [15,16,17]. Moreover, targeted MS techniques, such as selected reaction monitoring (SRM) and parallel reaction monitoring (PRM), enable precise quantification of specific proteins of interest, offering opportunities for biomarker validation and personalized medicine approaches in OP researches [18,19,20]. MS-based proteomics also facilitates the characterization of post-translational modifications (PTMs) in bone proteins, providing insights into their functional roles and regulatory mechanisms in OP [21,22,23]. Overall, MS-based proteomics holds great promise for advancing our understanding of osteoporosis and identifying diagnostic markers and therapeutic targets for the disease.

2.2. Gel-Based and Gel-Free Proteomic Techniques

Gel-based and gel-free proteomic techniques complement MS-based approaches by enabling protein separation, enrichment, and analysis [24,25,26,27]. Bone metabolism is balanced by bone-forming osteoblasts and bone-resorbing osteoclasts. Gel-based and gel-free proteomic approaches help enhance our comprehension of post-genomic mechanisms underlying bone metabolic dysfunctions in OP [28,29,30,31]. Researchers using two-dimensional gel electrophoresis (2-DE) and isotope-coded affinity tags (ICAT) analysis, and identified 23 spots in 2-DE and 19 proteins in ICAT which were expressed differently during osteoclast differentiation. These two methods gave us closely related but different information about proteins, suggesting they are complementary or at least supplementary methods [32]. Additionally, gel-free techniques such as shotgun proteomics and label-free quantification methods offer high-throughput analysis of complex protein mixtures, expanding the coverage of the bone proteome [33,34,35]. These techniques enable the discovery of novel biomarkers and signaling pathways associated with OP pathogenesis, enhancing our understanding of disease mechanisms. Furthermore, gel-based and gel-free proteomic approaches provide complementary information on protein abundance, PTMs, and protein-protein interactions, contributing to a comprehensive characterization of the bone proteome in health and disease.

2.3. Bioinformatics Tools for Proteomic Data Analysis and Interpretation

The vast amount of data generated from proteomic experiments requires sophisticated bioinformatics tools for data processing, analysis, and interpretation. Bioinformatics platforms offer solutions for protein identification, quantification, functional annotation, and pathway analysis, facilitating the extraction of meaningful insights from complex proteomic datasets. Database search algorithms, including Mascot [36,37] SEQUEST [38,39], and MaxQuant [40,41], are commonly used for peptide identification, while label-free or stable isotope labeling-based quantification methods rely on bioinformatics tools for accurate protein quantification. Moreover, bioinformatics tools enable the functional annotation and pathway analysis of identified proteins, shedding light on the biological processes perturbed in OP. Gene Ontology (GO) [42,43] analysis categorizes proteins based on their biological process (BP), molecular function (MF), and cellular component (CC), while pathway analysis tools such as Kyoto Encyclopedia of Genes and Genomes (KEGG) [44,45] and Reactome [46,47] elucidate the interconnected signaling pathways dysregulated in OP. Ingenuity Pathway Analysis (IPA) [48,49], STRING [50,51], and Database for Annotation, Visualization and Integrated Discovery (DAVID) [52] provide functional annotation and enrichment analysis, revealing biological pathways and protein-protein interaction (PPI) networks associated with OP pathogenesis. Integrating bioinformatics tools with proteomic technologies enhances the discovery of diagnostic markers and therapeutic targets for osteoporosis, facilitating translational research efforts aimed at improving patient care.

3. Proteomic Insights into Bone Metabolism

3.1. Identification of Key Proteins Involved in Bone Formation and Resorption

Proteomic approaches have greatly facilitated the identification and characterization of key proteins involved in bone formation and resorption, which are critical processes in maintaining bone homeostasis. Various studies have utilized MS-based proteomic techniques to analyze bone tissues, cell cultures, and biofluids to identify proteins associated with osteoblastogenesis, osteoclastogenesis, and bone remodeling pathways. For instance, in a study exploring the proteins bound to the osteocalcin OSE2 sequence of the mouse osteocalcin promoter, TRPS1 was identified as a crucial regulator of osteocalcin transcription [53]. Human mesenchymal stem cells from bone marrow (BM-hMSCs) are widely recognized as ideal candidates for bone tissue engineering. Proteomic analysis revealed that extracellular calcium ions (Ca(2+)) notably enhanced the proliferation of BM-hMSCs. Furthermore, it suggested that the MAPKs signaling pathway might be involved in Ca(2+)-induced osteogenic differentiation of BM-hMSCs [54]. Estrogen is known to play a significant role in inhibiting osteoclast differentiation and protecting against osteoporosis-related bone loss, particularly in postmenopausal women. A proteomic analysis elucidated estrogen’s inhibitory effect on osteoclasts, highlighting its role in promoting apoptosis, suppressing differentiation, and preventing polarization of osteoclasts [55]. The proteomic profile of osteoclast membrane proteins identified Nhedc2 as a key player in proton transport within osteoclasts, thereby regulating their function [56]. Similarly, a proteomic analysis of serum-derived exosomes from individuals with neurofibromatosis type 1 congenital tibial pseudarthrosis demonstrated their detrimental impact on bone by promoting osteoclastogenesis and inhibiting osteoblastogenesis [57]. Additionally, a proteomic investigation identified LBP and CD14 as pivotal proteins involved in interactions between blood and biphasic calcium phosphate microparticles [58]. These proteomic insights provide valuable knowledge of the molecular mechanisms underlying bone metabolism, offering potential targets for therapeutic interventions in OP [59,60,61,62].

3.2. Quantitative Proteomics in Assessing Dynamic Changes in Bone Proteome

Quantitative proteomic techniques have emerged as powerful tools for assessing dynamic changes in the bone proteome under physiological and pathological conditions. Stable isotope labeling-based methods such as tandem mass tag (TMT) [63,64] and isobaric tags for the relative and absolute quantitation (iTRAQ) [65] allow for the simultaneous quantification of thousands of proteins across multiple samples, enabling comprehensive analysis of bone proteome alterations [66,67,68]. Several quantitative proteomic studies have investigated changes in protein expression profiles during bone development, aging, and osteoporosis progression [18,59,69]. Recently, a novel mass spectrometric workflow is introduced to explore the bone proteome, offering deep coverage and quantification strategies. This approach unveils key components like extracellular matrix proteins, bone-specific proteins, and signaling molecules, while also identifying post-translational modifications and senescence factors relevant to age-related bone diseases [70]. Moreover, quantitative proteomic analyses have been instrumental in elucidating the effects of therapeutic interventions, such as bisphosphonate treatment, on the bone proteome, providing insights into their mechanisms of action and potential side effects [71].

3.3. Proteomic Studies on the Bone Extracellular Matrix

Extracellular matrix (ECM) dynamics represent an emerging yet understudied hallmark of aging and longevity [72]. The bone ECM encompasses mineral deposits on extensively crosslinked collagen fibrils alongside numerous non-collagenous proteins. Many of these proteins are pivotal in governing bone formation and regeneration through signaling pathways, contributing crucial regulatory and structural functions [73]. Proteomic studies focusing on the bone ECM have shed light on the composition, organization, and remodeling dynamics of this essential structural component. The bone ECM provides a dynamic scaffold for bone cells and plays crucial roles in regulating bone architecture, mineralization, and mechanical properties. Proteomic analyses of the bone ECM have identified numerous ECM-associated proteins, including collagens [74,75], proteoglycans [76,77], glycoproteins [76,78], and matricellular proteins [79], which contribute to ECM integrity and function. For instance, studies have identified collagen isoforms and post-translational modifications that influence bone matrix assembly and mineralization [80,81]. Additionally, proteomic analyses have revealed ECM proteins involved in cell-ECM interactions and signaling pathways, such as integrins and focal adhesion kinases, which play pivotal roles in regulating osteoblast and osteoclast behavior [82,83,84,85,86]. Understanding the composition and regulation of the bone ECM through proteomic approaches provides valuable insights into the pathogenesis of osteoporosis and offers potential targets for ECM-targeted therapies to enhance bone strength and integrity.

4. Diagnostic Markers in Osteoporosis

4.1. Blood and Urinary Biomarkers

Blood and urinary biomarkers are pivotal in diagnosing osteoporosis, offering valuable insights into bone turnover and fracture risk prediction. These biomarkers encompass molecules associated with both bone formation, such as osteocalcin (OC) and bone alkaline phosphatase (BALP), and bone resorption, including C-terminal telopeptide of type I collagen (CTx) and N-terminal telopeptide of type I collagen (NTx). Elevated levels of these biomarkers in serum and urine have been consistently correlated with heightened fracture risk and bone loss in individuals with osteoporosis [87,88,89]. Additionally, assessing the ratio of bone resorption to formation markers, such as the osteoprotegerin/receptor activator of nuclear factor kappa-B ligand (OPG/RANKL) ratio, provides further valuable information regarding bone metabolism status and fracture risk [90,91]. This comprehensive analysis of blood and urinary biomarkers serves as a critical component in the multifaceted approach to diagnosing and managing OP, enabling clinicians to better understand disease progression and tailor treatment strategies effectively. Furthermore, this compilation holds significant importance as it consolidates the advancements in proteomic technology applied to OP biomarker research. Proteomic approaches offer the potential to identify novel diagnostic markers and proteomic signatures for early detection of OP. By analyzing the protein expression profiles in serum, urine, and bone tissues, proteomic studies have identified candidate biomarkers associated with OP pathogenesis and fracture risk. Integrating proteomic signatures with traditional diagnostic markers may improve the accuracy and sensitivity of OP diagnosis, enabling early intervention and prevention strategies. By summarizing recent findings in Table 1, researchers and clinicians gain valuable insights into the evolving landscape of diagnostic and prognostic markers for OP. This synthesis facilitates a deeper understanding of the molecular mechanisms underlying bone turnover and fracture risk, paving the way for more precise diagnostic approaches and targeted therapeutic interventions. Additionally, it underscores the potential for proteomic technology to unravel novel biomarkers that may enhance early detection, risk stratification, and personalized management strategies for individuals at risk of OP-related complications.

4.2. Potential Integration with Imaging Techniques for Comprehensive Diagnosis

The integration of proteomic biomarkers with imaging techniques holds promise for comprehensive diagnosis and risk assessment of OP. DXA remains the gold standard for assessing BMD and diagnosing OP; however, it has limitations in predicting fracture risk and assessing bone quality. Complementary imaging techniques such as quantitative computed tomography (QCT) [117,118,119,120], high-resolution peripheral quantitative computed tomography (HR-pQCT) [121,122,123], and magnetic resonance imaging (MRI) [124,125,126] provide additional information on bone microarchitecture, volumetric BMD, and bone strength. By integrating proteomic biomarkers with advanced imaging modalities, clinicians can obtain a comprehensive evaluation of bone health status, including both structural and molecular aspects. For instance, the multicentre, prospective cohort study developed a comprehensive fracture risk assessment tool incorporating proteomic, genomic, and clinical factors, with the objective of enhancing fracture prediction accuracy across diverse populations. By integrating proteomic data from blood samples with imaging techniques, such as HR-pQCT, the study seeks to improve the diagnosis and management of OP and OF, ultimately advancing personalized medicine approaches for fracture prevention [127]. This multidimensional approach may enhance the accuracy of OP diagnosis, risk stratification, and treatment monitoring, ultimately improving patient outcomes.

5. Therapeutic Targets and Drug Discovery

5.1. Proteomic Identification of Novel Therapeutic Targets

Proteomic approaches play a pivotal role in identifying novel therapeutic targets for OP by unraveling the intricate molecular mechanisms underlying bone metabolism [15,128,129]. By analyzing the proteomic landscape of bone tissues, cell cultures, and biofluids, researchers can identify dysregulated proteins and pathways associated with OP pathogenesis. For instance, a study has identified seven circulating proteins, including ANTXR2, cadherin-13 (CDH-13), CD163, COMP, DKK3, periostin, and secretogranin-1, which decrease with age in mice. Among these, CDH-13 was found to inhibit osteoclast differentiation and delay age-related bone loss in aged mice, suggesting its potential therapeutic role in preventing osteopenia [115]. Another study illustrated through proteomic analysis that estrogen promotes autophagy in human osteoblasts during differentiation to promote survival and mineralization by upregulating RAB3 GTPase-activating protein [130,131]. Furthermore, through proteomic techniques and functional validation, ubiquitin C-terminal hydrolase 1 (UCHL1) was shown to stabilize the transcriptional coactivator with PDZ-binding motif (TAZ) by deubiquitination, inhibiting osteoclast formation. These findings collectively highlight UCHL1 as a potential therapeutic target for osteoporosis by modulating bone remodeling processes [132].

Traditional Chinese medicine (TCM) has demonstrated efficacy in treating human diseases over more than two millennia, underscoring its enduring value in healthcare. Proteomic studies serve as powerful tools in elucidating the mechanistic basis of TCM therapies, offering valuable insights into the molecular pathways underlying their therapeutic effects. In an osteoporosis study, researchers employed proteomic analysis, specifically iTRAQ technology, to identify potential therapeutic targets associated with deer antler extract. Through comprehensive serum protein profiling and bioinformatics analysis, they revealed a complex interaction network involving various proteins and signaling pathways related to bone formation and remodeling [133]. Another investigation utilized proteomic analysis to explore the effects of Er-Xian Decoction (EXD) on osteoblastic and osteoclastic cells. Their findings demonstrated significant modulation of protein expression associated with osteoblastic proliferation, differentiation, and apoptosis, as well as osteoclastic protein folding and aggregation [134]. Metabolomics combined with proteomics analysis of femur provided a comprehensive interpretation of the changes in PMOP under salidroside treatment [135]. Furthermore, a study investigating the mechanism of action of Bugu Shengsui Decoction in treating osteoporosis revealed that the decoction promotes osteogenesis via the PI3K-AKT pathway, highlighting its potential as a therapeutic target for osteoporosis [136]. By targeting these dysregulated proteins and pathways, novel therapeutic interventions can be developed to restore bone homeostasis and prevent fractures in osteoporotic patients.

5.2. Evaluation of Proteomic Profiles in Drug Development and Personalized Medicine

Proteomic data serve as valuable resources for evaluating the efficacy and safety of potential drug candidates in drug development [137,138]. Proteomic data are uniquely valuable for predicting drug responses and discovering biomarkers, since drugs primarily interact with proteins in target cells rather than with DNA or RNA [139]. By integrating proteomic analyses with preclinical and clinical studies, researchers can assess the effects of candidate drugs on the bone proteome and identify molecular mechanisms underlying their therapeutic actions. For example, studies have identified serum proteome markers associated with the response to antiosteoporosis drugs, teriparatide and denosumab, in osteoporosis patients. Proteomic analysis revealed significant changes in protein levels post-treatment, particularly in pathways related to the insulin like growth factor 1 (IGF-I) and innate immune system, suggesting their involvement in drug response [140]. Another study comprehensively investigated the anti-osteoporotic mechanism of icariin through integrating proteomics and NMR metabolomics, and results revealed significant alterations in proteins and metabolites related to various pathways including bone remodeling, energy metabolism, and signaling pathways [141]. Moreover, proteomic analyses can identify biomarkers of drug response and adverse effects, enabling personalized treatment strategies and improving patient outcomes [142,143,144]. Integrating proteomic data into the drug development process enhances the understanding of drug mechanisms, accelerates the identification of promising candidates, and minimizes the risk of treatment-related complications. Furthermore, genomics or proteomic profiling can identify patients at high risk of treatment failure or adverse drug reactions, facilitating early intervention and personalized monitoring strategies [128,145,146]. Overall, personalized medicine approaches based on proteomic insights have the potential to revolutionize osteoporosis management by improving treatment efficacy, minimizing side effects, and reducing fracture risk in vulnerable patient populations.

6. Challenges and Future Directions

6.1. Limitations and Challenges in Proteomic Studies of Osteoporosis

Despite the promise of proteomic technologies in unraveling the complexities of osteoporosis, several limitations and challenges hinder their application and interpretation. One significant challenge is the heterogeneity of bone tissues and the dynamic nature of bone metabolism, which can introduce variability in proteomic analyses. Sample collection and processing methods, including sample storage conditions and protein extraction protocols, may also impact the reproducibility and reliability of proteomic data [147,148]. Moreover, the complexity of the bone proteome, including the presence of low-abundance proteins and PTMs, poses analytical challenges for protein identification and quantification [149,150]. Additionally, standardization of proteomic workflows, data analysis pipelines, and quality control (QC) measures is essential to ensure the robustness and reproducibility of proteomic studies in osteoporosis research [151,152].

6.2. Integration of Multi-Omics Data for a Holistic Understanding

To overcome the limitations of proteomic studies and achieve a comprehensive understanding of osteoporosis, integration of multi-omics data is imperative. Combining proteomic data with genomics, transcriptomics, metabolomics, and epigenomics datasets can provide a holistic view of the molecular mechanisms underlying osteoporosis pathogenesis and treatment response. Integration of multi-omics data enables the identification of key regulatory pathways, biomolecular networks, and potential therapeutic targets that may not be evident from individual omics analyses alone [153,154,155]. For example, integrating proteomic and transcriptomic data can elucidate the relationship between gene expression and protein abundance, revealing post-transcriptional regulatory mechanisms in osteoporosis [128,156,157]. Similarly, integration of proteomic and metabolomic data can uncover metabolic pathways dysregulated in osteoporosis and identify metabolic biomarkers associated with disease progression and fracture risk [141]. Overall, the integration of multi-omics data holds promise for advancing our understanding of osteoporosis pathophysiology and facilitating the development of personalized therapeutic strategies [158,159].

6.3. Future Directions and Potential Impact on Clinical Practice

Looking ahead, several future directions in proteomic research have the potential to transform clinical practice in osteoporosis management. Firstly, advancements in proteomic technologies, such as improved instrumentation, sample preparation methods, and data analysis algorithms, will enhance the sensitivity, specificity, and throughput of proteomic studies in osteoporosis. Secondly, the development of targeted proteomic assays, including MRM and PRM, will enable the accurate quantification of specific protein biomarkers in clinical samples, facilitating their translation into diagnostic tests and therapeutic monitoring tools [160,161,162]. Additionally, the integration of proteomic biomarkers into clinical decision-making algorithms and risk assessment models may improve the accuracy of osteoporosis diagnosis, fracture prediction, and treatment selection. Finally, the implementation of personalized medicine approaches based on proteomic profiling may enable tailored interventions targeting individual patient needs and disease characteristics, ultimately improving patient outcomes and reducing the burden of osteoporosis-related fractures [163,164].

7. Conclusion

In summary, proteomic research has emerged as a powerful tool for unraveling the complex molecular mechanisms underlying osteoporosis, offering valuable insights into disease pathogenesis and potential therapeutic targets (Figure 1). Through proteomic analyses of bone tissues, biofluids, and circulating cells, researchers have identified novel diagnostic markers, therapeutic targets, and molecular pathways implicated in osteoporosis development and progression. The identification of key proteins involved in bone metabolism, such as regulators of osteoblast and osteoclast activity, has provided a deeper understanding of bone homeostasis and remodeling processes. Moreover, proteomic studies have facilitated the discovery of potential biomarkers for early detection of osteoporosis and prediction of fracture risk, enabling timely intervention and personalized treatment strategies. Despite the challenges and limitations inherent in proteomic studies, including sample heterogeneity and analytical complexity, ongoing advancements in proteomic technologies and data integration approaches hold promise for furthering our understanding of osteoporosis and translating proteomic insights into clinical practice.

The transformative potential of proteomic research in osteoporosis extends beyond diagnosis to encompass therapeutic discovery and personalized medicine approaches. By identifying dysregulated proteins and pathways associated with osteoporosis pathogenesis, proteomic studies have unveiled promising therapeutic targets for drug development and intervention. Integration of multi-omics data, including genomics, transcriptomics, and metabolomics, offers a holistic understanding of osteoporosis at the molecular level, facilitating the development of targeted therapies and personalized treatment regimens. The translation of proteomic biomarkers into clinical practice has the potential to revolutionize osteoporosis management by improving diagnostic accuracy, guiding treatment selection, and monitoring treatment response. Ultimately, proteomic insights into osteoporosis promise to advance patient care, reduce the burden of osteoporotic fractures, and enhance quality of life for individuals affected by this debilitating disease.