BORIS Theses

BORIS Theses
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Quantifying bone extracellular matrix properties for improved clinical fracture risk prediction

Kochetkova, Tatiana (2023). Quantifying bone extracellular matrix properties for improved clinical fracture risk prediction. (Thesis). Universität Bern, Bern

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Metabolic bone diseases like osteoporosis lead to increased bone fragility and consequent implications for the patient lifestyle and health expenses. In the present aging society, fragility fractures pose significant health and economic burden. Current clinical methods to assess bone health status (dual-energy X-ray absorptiometry, FRAX, quantitative computed tomography variations) depend mostly on bone mineral density (BMD) measurements. However, BMD alone only accounts for about 70% of the variance in bone strength. It is therefore of high interest and potential societal impact to investigate bone quality, i.e. measures other than BMD influencing bone strength and toughness. In the present thesis, novel laboratory methods were developed for high-throughput investigation of bone properties, with the ultimate goal to define combinations of measurements that can be used as a proxy for bone quality in a fracture risk analysis. Firstly, a novel method for quantifying mineralized collagen fibril orientation based on polarized Raman spectroscopy (qPRS) was calibrated and validated on a natural material (mineralized turkey leg tendon). This method enables the quantitative estimation of the local degree of mineralization and 3D collagen fibril orientation non-destructively at submicron resolution. It was then applied to the cortex of bovine bone samples in combination with micropillar compression, allowing to reliably determine structure-property relationships of bone at the microscale. Later, a multimodal framework for bone characterization was developed in another animal bone model (minipig jawbone). This included the development of a novel femtosecond laser ablation protocol for bone micropillar fabrication allowing high-throughput and site-matched testing without exposure to high vacuum. The key part of the research was then carried out on a set of femoral neck samples collected from patients who underwent the hip arthroplasty due to osteoarthritis or fragility fracture. The femoral neck cortex from the inferomedial region was analyzed ex vivo in a site-matched manner using a combination of micromechanical testing (nanoindentation, micropillar compression) together with micro-computed tomography and quantitative polarized Raman spectroscopy for both morphological and compositional characterization. The output bone properties were correlated with the clinical information about age, gender, and primary diagnosis (coxarthrosis or hip fracture) of the participating patients. Patient gender and diagnosis did not influence any of the investigated bone properties. Moreover, all mechanical properties as well as the tissue-level mineral density were nearly constant over all ages (45-89 y.o.). Only local tissue composition was found to change significantly with age: decline in mineral to matrix ratio and increase in collagen cross-link ratio. Site-matched microscale analysis confirmed that all investigated mechanical properties except yield strain demonstrate a positive correlation with the mineral fraction of bone. The large dataset of experimentally assessed microscale bone properties together with the available clinical information of the patients allowed the application of machine learning algorithms for fracture prediction in silico. Logistic regression classification suggests that indentation hardness, relative mineralization and micropillar yield stress are the most perspective parameters for bone fracture risk prediction. As a result of this thesis, the output database of experimental measurements is the first to integrate microscale mechanical, chemical, morphological, and clinical information about the patients. In future, it can be used to compare existing methods of bone quality assessment. Moreover, the presented data and analysis approaches may be used to improve the prediction of fracture risk in the elderly.

Item Type: Thesis
Granting Institution: Faculty of Medicine, University of Bern
Dissertation Type: Cumulative
Date of Defense: 23 March 2023
Subjects: 500 Science > 530 Physics
500 Science > 570 Life sciences; biology
600 Technology > 610 Medicine & health
600 Technology > 620 Engineering
Institute / Center: 04 Faculty of Medicine
Depositing User: Tatiana Kochetkova
Date Deposited: 14 Nov 2023 17:58
Last Modified: 23 Mar 2024 23:25
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