The evaluation of age-related histomorphometric variables in a cadaver sample of lower socioeconomic status: implications for estimating age at death

Abstract

Estimating age at death from adult skeletal remains is a daunting task for human osteologists. For this reason, the evaluation of micro-structural changes in bone with advancing age has become a popular method. However, factors such as nutrition, chronic disease, population group and sex have been suggested to influence the rate of bone turnover, and thus the use of histological methods in providing an accurate age at death has been questioned. The purpose of this study was to evaluate the accuracy and repeatability of 10 histomorphometric traits used to estimate age. The sample comprised of 146 dissection room cadavers of known sex, age and ancestry (105 males and 41 females). A 0.2 cm × 1.0 cm sample was removed from the anterior surface of the mid-shaft of the femur (opposite the linea aspera), and slides were prepared according to standard methodology. The total osteon count ( r = 0.50), the percentage unremodelled bone ( r = −0.50), the total number of non-Haversian canals ( r = −0.50) and the average percentage of fragmental bone ( r = 0.55) had moderate correlations with age, while the total number of measurable osteons ( r = 0.43), the total number of osteonal fragments ( r = 0.40), the percentage of fragmentary bone ( r = 0.37) the average number of lamellae per osteon ( r = 0.29), the minimum diameter of the Haversian canals ( r = 0.14) and resorption spaces ( r = 0.11) had little to no relationship with age. Despite poor correlations with age, eight variables were shown to be highly repeatable ( r = 0.74–0.93). Moderate to low correlations with age may be attributed to these variables not being related to age in a progressive and predictable fashion, activity patterns or possible chronic disease in the sample. A databank needs to be compiled from larger samples from various populations in order to more holistically assess the relationship between these variables and age as well as other mitigating factors such as disease, nutrition and population group.