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，人们一直在寻求更好地了解导致我们骨折的原因，尤其是作为一种毁灭性的骨折。 以及骨质疏松症的广泛后果。除了评估骨量（例如骨矿物质密度）之外， 众所周知，组织质量在决定骨骼强度和脆性方面起着关键作用。我们和其他人 已表明微观组织成分与骨骼疾病有着内在的相关性；然而，有一个限制 这种方法的缺点是它无法捕获骨质量和完整性的纳米级构建块的特征 （矿化胶原原纤维和束约为 500 nm）。因此，高骨成分分析 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 缺乏对成分特性对骨组织质量的综合作用的研究，而这一点至关重要 揭示骨质疏松症中不良骨骼特征背后的多因素本质。为了解决这些关键差距 在知识方面，我们的目标是应用创新的方法来启发知识之间的多因素关系 骨质疏松症中骨纳米级成分和骨组织质量降低。我们假设 骨的纳米级成分特性与预测组织学诊断和骨具有重要相关性 与骨质疏松症相关的形态学特征。在目标 1 中，我们建议确定和量化纳米级 健康骨骼和骨质疏松骨骼的成分特性。首先将进行现成的临床骨活检 通过标准组织病理学诊断以及骨显微计算机断层扫描 (microCT) 进行评估 形态测量。然后，样品将通过最先进的光学光热红外 (O-PTIR) 进行评估 光谱和成像，可在 500 nm 空间对完整组织成分进行突破性分析 解决。我们的支持性初步数据显示了不同骨纳米级的采集和量化 单个骨和小梁内矿物质和胶原蛋白的成分特性。此外，矿物质 化学计量将通过扫描电子显微镜和能量色散 X 射线光谱法确定 (SEM-EDX)。借助这个丰富的数据集，我们将进行比较和相关性的全面分析 分析健康骨骼和骨质疏松骨骼的骨骼特征，以确定相关的纳米级成分特性 与典型的骨质疏松骨质量相关。在目标 2 中，我们建议应用机器学习方法 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 基于预测正常和骨质疏松骨骼的组织病理学诊断和形态特征 输入组合纳米级成分特性。我们首先将应用多变量偏最小二乘法 （PLS）交叉验证，然后关注前沿的深度学习方法。这种创新方法将打破 阐明哪些骨纳米级成分特性是骨质量差的基础 骨质疏松症，并将为未来的临床研究提供新的治疗组织目标，以改善骨骼健康。