Raman spectroscopic determination of bone matrix quantity and quality augments prediction of human cortical bone mechanical properties.
Being independent contributors to bone mechanical resistance at the apparent level, quality and quantity of bone primary constituents are essential factors in better fracture risk assessment. Raman spectroscopy (RS) holds great potential for being a clinical tool with providing quality and quantity measurements of the bone mineralized matrix. Beyond mineral quality and quantity, recent years have revealed newly developed RS-derived bone compositional measurements focusing on organic matrix and water though their associations with bone mechanics have not been fully established yet. Herein, the author reported first thorough characterization study investigating associations between twenty different RS-derived measurements and mechanical properties of human cortical bone (i.e., yield and ultimate strength, elastic modulus, toughness, post-yield toughness, and post-yield strain). Forty-five rectangular human cortical beams harvested from all four anatomical quadrants of two male donors were tested under three-point bending. Raman spectra of each specimen were collected at the spectral range of 800 to 4000 cm-1. While correlations were tested among RS-derived measurements via Spearman's rank correlations, multivariate linear regression using mixed effects were used to determine the best RS-derived measurement or the combination of RS-derived measurements in predicting various mechanical properties of human cortical bone. Most of the RS-derived measurements were associated with the mechanical properties (Rm2 ranges from 8.9 to 68.3%, p < 0.05). The various linear combinations of six RS-derived measurements focusing on different aspects of bone matrix (i.e., ν1PO4/Amide I, ν1PO4/Amide III, Carbonate/ν1PO4, ~I1670/I1640, ~I3453/I2949, ~I3584/I2949) improved the prediction (Rm2 = 43.5 to 70.2%, p < 0.05). While a causal relationship still needs to be investigated, RS has a great potential to establish a robust patient-specific fracture risk prediction with the latest advances in technologies.