Multifactorial contribution of bone nanoscale composition to tissue quality in osteoporosis

骨纳米级成分对骨质疏松症组织质量的多因素贡献

• 批准号: 10575979
• 负责人: William Querido
• 金额: $ 20.44万
• 依托单位: TEMPLE UNIV OF THE COMMONWEALTH
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-01 至 2024-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10575979
• 关键词: 3-Dimensional; Address; Age; Biopsy; Bone Density; Bone Tissue; Clinical; Clinical Management; Clinical Research; Clinical assessments; Collagen; Collagen Fibril; Complex; Data; Data Set; Diagnosis; Disease; Elements; Evaluation; Fracture; Future; Goals; Heterogeneity; Histologic; Image; Impairment; Incidence; Individual; Knowledge; Least-Squares Analysis; Machine Learning; Minerals; Modeling; Morphology; Nature; Optics; Osteon; Osteoporosis; Outcome; Outcomes Research; Pathogenesis; Performance; Persons; Pharmacotherapy; Play; Polishes; Polymethyl Methacrylate; Property; Public Health; Research; Resolution; Role; Sampling; Scanning Electron Microscopy; Specificity; Spectroscopy, Fourier Transform Infrared; Standard Preparations; Structure; Techniques; Tissues; Validation; X ray spectroscopy; aging population; artificial neural network; bone; bone fragility; bone health; bone quality; bone strength; crystallinity; deep learning; fracture risk; imaging system; improved; infrared spectroscopy; innovation; insight; learning strategy; machine learning method; markov model; meter; microCT; mineralization; morphometry; nanoscale; novel therapeutics; osteoporotic bone; recurrent neural network; research study; skeletal disorder; stoichiometry; submicron

SUMMARY There is a long-standing quest to better understand what causes our bones to break, especially as a devastating and widespread consequence of osteoporosis. Beyond assessment of bone quantity (e.g., bone mineral density), it is well-know that tissue-level quality plays a key role in determining bone strength and fragility. We and others have shown that microscale tissue composition is intrinsically related to skeletal diseases; however, a limitation of this approach is that it cannot capture features of the nanoscale building blocks of bone quality and integrity (mineralized collagen fibrils and bundles on the order of 500 nm). Thus, analysis of bone composition at high spatial resolution is needed to elucidate the nanoscale origins of impaired bone health. Additionally, there is a paucity of research into the combined role of compositional properties in bone tissue quality, which is essential to reveal the multifactorial nature underlying poor bone features in osteoporosis. To address these critical gaps in knowledge, we aim to apply innovative approaches to enlighten the multifactorial relationship between bone nanoscale composition and reduced bone tissue quality in osteoporosis. We hypothesize that nanoscale compositional properties of bone are significant correlates to predict histological diagnosis and bone morphologic features associated with osteoporosis. In Aim 1, we propose to determine and quantify nanoscale compositional properties of healthy and osteoporotic bones. Readily available clinical bone biopsies will first be evaluated for standard histopathological diagnosis, as well as by micro-computed tomography (microCT) of bone morphometry. The samples will then be assessed by state-of-the-art optical photothermal infrared (O-PTIR) spectroscopy and imaging, which allows breakthrough analysis of intact tissue composition at 500 nm spatial resolution. Our supportive preliminary data show the acquisition and quantification of diverse bone nanoscale compositional properties of mineral and collagen within individual osteon and trabeculae. Additionally, mineral stoichiometry will be determined by scanning electron microscopy with energy dispersive X-ray spectroscopy (SEM-EDX). With this rich dataset, we will perform a comprehensive analysis of comparisons and correlations among bone features in healthy and osteoporotic bones to identify relevant nanoscale compositional properties associated with typical osteoporotic bone quality. In Aim 2, we propose to apply machine learning methods to elucidate the multifactorial relationship between bone nanoscale composition and osteoporosis. The goal will be to predict histopathological diagnosis and morphometric features of normal and osteoporotic bones based on input of combined nanoscale compositional properties. We will initially apply multivariable partial least square (PLS) cross-validation, then focus on cutting-edge deep learning methods. This innovative approach will break new ground towards elucidating which bone nanoscale compositional properties underlie poor bone quality in osteoporosis and will inform future clinical studies into new therapeutic tissue targets to improve bone health.

概括 长期以来，有一个长期的追求，以更好地了解导致骨骼破裂的是什么，尤其是作为毁灭性的 以及骨质疏松症的广泛结果。除了评估骨骼数量（例如骨矿物质密度）， 众所周知，组织水平的质量在确定骨骼强度和脆弱性方面起着关键作用。我们和其他人 已经表明，微观组织组成与骨骼疾病本质上有关。但是，这是一个限制 这种方法是它无法捕获纳米级骨质和完整性的构建块的特征 （矿化胶原原纤维和捆绑式500 nm）。因此，分析高骨成分 需要空间分辨率来阐明骨骼健康受损的纳米级起源。此外，还有一个 对组成特性在骨组织质量中的综合作用的研究很少，这是必不可少的 揭示骨质疏松症中骨骼特征的多因素性质。解决这些关键差距 在知识中，我们旨在采用创新的方法来启发多因素之间的关系 骨纳米级组成和骨质疏松症的骨组织质量降低。我们假设这一点 骨骼的纳米级成分特性与预测组织学诊断和骨骼是显着的相关性 形态学特征与骨质疏松症相关。在AIM 1中，我们建议确定和量化纳米级 健康和骨质疏松骨的组成特性。容易获得的临床骨活检将首先 评估了标准的组织病理学诊断，以及通过骨的微观层析成像（Microct） 形态计量学。然后，样品将通过最新的光学光热红外（O-PTIR）进行评估 光谱和成像，可以突破性分析完整的组织组成在500 nm空间上 解决。我们的支持性初步数据表明，各种骨纳米级的获取和量化 矿物和胶原蛋白在单个Osteon和小梁中的组成特性。另外，矿物质 化学计量法将通过用能量色散X射线光谱扫描电子显微镜来确定 （SEM-EDX）。有了这个丰富的数据集，我们将对比较和相关性进行全面分析 在健康和骨质疏松骨骼中的骨骼特征中，以鉴定相关的纳米级成分特性 与典型的骨质疏松骨质质量相关。在AIM 2中，我们建议将机器学习方法应用于 阐明骨纳米级成分与骨质疏松症之间的多因素关系。目标将是 预测基于正常和骨质疏松骨骼的组织病理学诊断和形态特征 组合纳米级成分特性的输入。我们最初将应用多变量的偏最不正方形 （PLS）交叉验证，然后专注于尖端的深度学习方法。这种创新的方法将打破 阐明哪种骨纳米级成分特性的新基础是骨质不良的基础 骨质疏松症，并将为未来的临床研究提供新的治疗性组织靶标，以改善骨骼健康。