Rib biomechanical properties exhibit diagnostic potential for accurate ageing in forensic investigations

Abstract

Age estimation remains one of the most challenging tasks in forensic practice when establishing a biological profile of unknown skeletonised remains. Morphological methods based on developmental markers of bones can provide accurate age estimates at a young age, but become highly unreliable for ages over 35 when all developmental markers disappear. This study explores the changes in the biomechanical properties of bone tissue and matrix, which continue to change with age even after skeletal maturity, and their potential value for age estimation. As a proof of concept we investigated the relationship of 28 variables at the macroscopic and microscopic level in rib autopsy samples from 24 individuals. Stepwise regression analysis produced a number of equations one of which with seven variables showed an R2 = 0.949; a mean residual error of 2.13 yrs ±0.4 (SD) and a maximum residual error value of 2.88 yrs. For forensic purposes, by using only bench top machines in tests which can be carried out within 36 hrs, a set of just 3 variables produced an equation with an R2 = 0.902 a mean residual error of 3.38 yrs ±2.6 (SD) and a maximum observed residual error 9.26yrs. This method outstrips all existing age-at-death methods based on ribs, thus providing a novel lab based accurate tool in the forensic investigation of human remains. The present application is optimised for fresh (uncompromised by taphonomic conditions) remains, but the potential of the principle and method is vast once the trends of the biomechanical variables are established for other environmental conditions and circumstances.

Fig 1. Detail of cortical bone matrix showing micro- and nano-indentations in an interstitial bone area.

Fig 2. Pictures of a cortical bone section after conversion into 16-bit and the application of the threshold mask with ImageJ.

Fig 3. Examples of verified in-vivo damage micro-cracks (white arrows) visualised in a fluorescence microscope.

Fig 4. Plot of real age vs. predicted age for E1-E6.

(Regression line with the 95% prediction interval for the data).

Fig 5. Plot of real age vs. predicted age for E1 in the cross-validation.

Line of 1:1 equality with the 95% prediction interval for the data. Residual errors are shown on the x-axis for each donor (+ for overestimation;—for underestimated values).