Effects of osteoarthritis on age-at-death estimates from the human pelvis

Evaluating the accuracy of root transparency and periodontosis age estimation models in a Portuguese population

This study aims to evaluate the accuracy of existing dental age estimation models, including the Lamendin, Prince & Ubelaker, Fialho, and modified Fialho methods, within a Portuguese population. Dental techniques, particularly those involving root transparency and periodontosis, are examined due to their relevance in forensic age estimation. A sample of 166 single-rooted teeth from individuals aged 30 to 86 was analyzed. Measurements included root transparency, periodontosis, and tooth and root length. Statistical methods were applied to assess the reliability and accuracy of each model in estimating age, considering the potential influence of environmental and lifestyle factors on dental aging. Root transparency emerged as a robust age indicator, consistently correlating with chronological age. In contrast, periodontosis introduced variability due to external influences, reducing its reliability. The modified Fialho model, which focuses solely on root transparency, showed the highest accuracy, suggesting that eliminating periodontosis from the estimation process may enhance reliability in populations where environmental factors heavily affect dental aging. The findings underscore the importance of population-specific adjustments in dental age estimation models. By refining methods like the modified Fialho model, forensic investigations can achieve more accurate results, particularly in populations where external factors influence periodontosis.

Adult Skeletal Age-at-Death Estimation through Deep Random Neural Networks: A New Method and Its Computational Analysis

Age-at-death assessment is a crucial step in the identification process of skeletal human remains. Nonetheless, in adult individuals this task is particularly difficult to achieve with reasonable accuracy due to high variability in the senescence processes. To improve the accuracy of age-at-estimation, in this work we propose a new method based on a multifactorial macroscopic analysis and deep random neural network models. A sample of 500 identified skeletons was used to establish a reference dataset (age-at-death: 19–101 years old, 250 males and 250 females). A total of 64 skeletal traits are covered in the proposed macroscopic technique. Age-at-death estimation is tackled from a function approximation perspective and a regression approach is used to infer both point and prediction interval estimates. Based on cross-validation and computational experiments, our results demonstrate that age estimation from skeletal remains can be accurately (~6 years mean absolute error) inferred across the entire adult age span and informative estimates and prediction intervals can be obtained for the elderly population. A novel software tool, DRNNAGE, was made available to the community.

Conditional independence assumption and appropriate number of stages in dental developmental age estimation

When estimating the age of an individual it is critical that 1) age ranges are as narrow as possible while still capturing the true age of the individual with an acceptable frequency, and 2) this frequency is known. When multiple traits are used to produce a single age estimate, the simplest practice is to assume that the traits are conditionally independent from one another given age. Unfortunately, if the traits are correlated once the effect of age is accounted for, the resulting age intervals will be too narrow. The frequency at which the age interval captures the true age of the individual will be decreased below the expected value to some unknown degree. It is therefore critical that age estimation methods that include multiple traits incorporate the possible correlations between them. Moorrees et al. (1963) [1] scores of the permanent mandibular dentition from 2607 individuals between 2 and 23 years were used to produce and cross-validate a cumulative probit model for age estimation with an optimal number of stages for each tooth. Two correction methods for covariance of development between teeth were tested: the variance-covariance matrix for a multivariate normal, and the Boldsen et al. (2002) [2] ad-hoc method. Both correction methods successfully decreased age interval error rates from 21% to 23% in the uncorrected model to the expected value of 5%. These results demonstrate both the efficacy of these correction methods and the need to move away from assuming conditional independence in multi-trait age estimation.

Economic access influences degenerative spine disease outcomes at rural Late Medieval Villamagna (Lazio, IT)

OBJECTIVES

Degenerative joint disease in the spine is heavily influenced by genetic, environmental, and epigenetic factors, as well as exacerbated by physical activity and injury. The objective of this study was to investigate the multivariate relationship between known predictors of degenerative joint disease in the spine, such as age and sex, with mortuary indicators of economic access such as grave inclusions, burial location, and burial type.

MATERIALS AND METHODS

The presence and severity of vertebral osteophytosis (VO) and vertebral osteoarthritis (VOA) was recorded for the vertebral columns of N = 106 adult individuals from the Late Medieval period at the rural monastery of San Pietro at Villamagna in Lazio, Italy (1300-1450 AD). Multiple skeletal indicators of degenerative joint disease, morphological sex, and age were compared with differences in mortuary treatment across four regions of the spine.

RESULTS

There are marked differences in severe joint disease outcome between groups with more and less economic access. Relative risk ratios suggest that males and females with less economic access have elevated risk for VO and VOA in specific spine regions, although this effect is reduced among females.

DISCUSSION

Current research on the consequences of economic and social inequality point to the important role of economic inequality in shaping disease outcomes. Our results suggest that biocultural effects of reduced economic access at the intraclass level may increase vulnerability to the downstream effects of risk exposure (e.g., biomechanical injure, physical activity, biochemical imbalance), and ultimately increase the risk and prevalence for severe degenerative disease outcomes in medieval Italy.

Reconsidering osteoarthritis as a skeletal indicator of age at death

OBJECTIVES

Osteoarthritis (OA) generally increases with age, but it is a complex, multifactorial disease. This study investigated whether obesity, physical activity, and antemortem trauma preclude the use of OA for skeletal age estimation.

MATERIALS AND METHODS

The temporomandibular joint (TMJ) and all appendicular joints were scored for skeletal indicators of OA in 408 modern European-Americans (Bass Collection, TN). Binomial generalized linear models (GLMs) assessed the contributions of self-reported demographic data to OA, including: age; body mass index (BMI); and metabolic values for physical activities. Repeated resampling tested whether observed mean OA scores for joints with trauma consistently exceeded mean scores for unaffected joints. Single-variable GLM probit models were generated for OA presence/absence data in relevant joints.

RESULTS

Age was the only statistically significant predictor of OA in most multivariable GLMs. Occupation and age were both significant predictors of male hand OA; BMI was the only significant predictor of female ankle OA. Trauma significantly affected OA in most joints. Age cut-offs calculated from the single-variable probit models (representing ages of transition to "OA present") ranged from 29.7 to 77.3 years (90%) and 32.7 to 96.6 years (95%), but were problematic for the male TMJ.

DISCUSSION

Ankle OA should not be used to age females; TMJ OA should not be used to age males. For other joints, using OA to inform age estimates appears valid (in absence of antemortem trauma). While skeletal evidence of OA is not a primary age indicator, its presence can refine age ranges and provide essential age data in fragmentary cases.

Bone Mineral Density Adult Age Estimation in Forensic Anthropology: A Test of the DXAGE Application

Estimating age-at-death of individuals represented only by skeletonized human remains is a fundamental aspect of forensic anthropological casework. Recently, several researchers have proposed that bone mineral density (BMD) is a useful predictor of age-at-death in forensic contexts. Navega et al. (JFS 63(2):497-503) developed an online application called DXAGE for calculating age-at-death from BMD parameters. This study tests the utility of DXAGE by utilizing data from the National Health and Nutrition Examination Survey (NHANES). BMD data from a female subsample (n = 470) of the NHANES 2007-2008 dataset were analyzed, and the relationship between predicted age and real age was examined. Inaccuracy was 14.25 years, and bias was -7.20 years. Results show that there is a weak correlation between predicted and actual age (r = 0.47) using the DXAGE application. While BMD data are potentially useful for predicting age age-at-death, the DXAGE application should be used cautiously in forensic anthropological contexts.

Validation of the Acetabulum As a Skeletal Indicator of Age at Death in Modern European-Americans

Progressive changes in the acetabulum have been used in modern skeletal age estimation, but they have not been completely understood. If their age correlations are weakened by the influence of factors like physical activity and obesity, acetabular changes should not be used for age estimation. To investigate their utility for aging, the acetabular variables of Rissech et al. (2006) were analyzed in 409 modern European-Americans (Bass Collection, Tennessee). Correlation tests assessed potential associations between acetabular data, osteoarthritis scores (collected per Jurmain, 1990), and documented demographic information (age, body mass index [BMI], metabolic intensity of physical activities). Acetabular changes had statistically significant, positive correlations with osteoarthritis (p < 0.001 in most joints/regions) and age (p < 0.001), indicating their degenerative nature and relevance for age estimation. Acetabular changes showed no associations with BMI or metabolic values, suggesting resistance to obesity and activity effects. These results suggest that acetabular degeneration is a valid skeletal age-at-death indicator.