Accuracy of MRI skeletal age estimation for subjects 12–19. Potential use for subjects of unknown age

In forensic practice, there is a growing need for accurate methods of age estimation, especially in the cases of young individuals of unknown age. Age can be estimated through somatic features that are universally considered associated with chronological age. Unfortunately, these features do not always coincide with the real chronological age: for these reasons that age determination is often very difficult. Our aim is to evaluate accuracy of skeletal age estimation using Tomei's MRI method in subjects between 12 and 19 years old for forensic purposes. Two investigators analyzed MRI images of the left hand and wrist of 77 male and 74 female caucasian subjects, without chronic diseases or developmental disorders, whose age ranged from 12 to 19 years. Skeletal maturation was determined by two operators, who analyzed all MRI images separately, in blinded fashion to the chronological age. Inter-rater agreement was measured with Pearson (R (2)) coefficient. One of the examiners repeated the evaluation after 6 months, and intraobserver variation was analyzed. Bland-Altman plots were used to determine mean differences between skeletal and chronological age. Inter-rater agreement Pearson coefficient showed a good linear correlation, respectively, 0.98 and 0.97 in males and females. Bland-Altman analysis demonstrated that the differences between chronological and skeletal age are not significant. Spearman's correlation coefficient showed good correlation between skeletal and chronological age both in females (R (2) = 0.96) and in males (R (2) = 0.94). Our results show that MRI skeletal age is a reproducible method and has good correlation with chronological age

Moral Case for Legal Age Change

Should a person who feels his legal age does not correspond with his experienced age be allowed to change his legal age? In this paper, I argue that in some cases people should be allowed to change their legal age. Such cases would be when: 1) the person genuinely feels his age differs significantly from his chronological age and 2) the person’s biological age is recognized to be significantly different from his chronological age and 3) age change would likely prevent, stop or reduce ageism, discrimination due to age, he would otherwise face. I also consider some objections against the view that people should be allowed to change their legal age and find them lacking

Exploring the Relationship of Relative Telomere Length and the Epigenetic Clock in the LipidCardio Cohort

Telomere length has been accepted widely as a biomarker of aging. Recently, a novel candidate biomarker has been suggested to predict an individual’s chronological age with high accuracy: The epigenetic clock is based on the weighted DNA methylation (DNAm) fraction of a number of cytosine-phosphate-guanine sites (CpGs) selected by penalized regression analysis. Here, an established methylation-sensitive single nucleotide primer extension method was adapted, to estimate the epigenetic age of the 1005 participants of the LipidCardio Study, a patient cohort characterised by high prevalence of cardiovascular disease, based on a seven CpGs epigenetic clock. Furthermore, we measured relative leukocyte telomere length (rLTL) to assess the relationship between the established and the promising new measure of biological age. Both rLTL (0.79 ± 0.14) and DNAm age (69.67 ± 7.27 years) were available for 773 subjects (31.6% female; mean chronological age= 69.68 ± 11.01 years; mean DNAm age acceleration = −0.01 ± 7.83 years). While we detected a significant correlation between chronological age and DNAm age (n = 779, R = 0.69), we found neither evidence of an association between rLTL and the DNAm age (β = 3.00, p = 0.18) nor rLTL and the DNAm age acceleration (β = 2.76, p = 0.22) in the studied cohort, suggesting that DNAm age and rLTL measure different aspects of biological age

Human Skin, Oral, and Gut Microbiomes Predict Chronological Age.

Human gut microbiomes are known to change with age, yet the relative value of human microbiomes across the body as predictors of age, and prediction robustness across populations is unknown. In this study, we tested the ability of the oral, gut, and skin (hand and forehead) microbiomes to predict age in adults using random forest regression on data combined from multiple publicly available studies, evaluating the models in each cohort individually. Intriguingly, the skin microbiome provides the best prediction of age (mean ± standard deviation, 3.8 ± 0.45 years, versus 4.5 ± 0.14 years for the oral microbiome and 11.5 ± 0.12 years for the gut microbiome). This also agrees with forensic studies showing that the skin microbiome predicts postmortem interval better than microbiomes from other body sites. Age prediction models constructed from the hand microbiome generalized to the forehead and vice versa, across cohorts, and results from the gut microbiome generalized across multiple cohorts (United States, United Kingdom, and China). Interestingly, taxa enriched in young individuals (18 to 30 years) tend to be more abundant and more prevalent than taxa enriched in elderly individuals (>60 yrs), suggesting a model in which physiological aging occurs concomitantly with the loss of key taxa over a lifetime, enabling potential microbiome-targeted therapeutic strategies to prevent aging.IMPORTANCE Considerable evidence suggests that the gut microbiome changes with age or even accelerates aging in adults. Whether the age-related changes in the gut microbiome are more or less prominent than those for other body sites and whether predictions can be made about a person's age from a microbiome sample remain unknown. We therefore combined several large studies from different countries to determine which body site's microbiome could most accurately predict age. We found that the skin was the best, on average yielding predictions within 4 years of chronological age. This study sets the stage for future research on the role of the microbiome in accelerating or decelerating the aging process and in the susceptibility for age-related diseases

Short-term memory and vocabulary development in children with Down syndrome and children with specific language impairment

A longitudinal comparison was made between development of verbal and visuo-spatial short-term memory and vocabulary in children with Down syndrome (DS), children with specific language impairment (SLI), and typically developing children as a control group. Participants were 12 children with DS (6 males, 6 females; mean chronological age 9y 9mo [SD 2.8 mo], range 8y 6mo to 11y 4mo); nine children with SLI (4 males, 5 females; mean chronological age 3y 9mo [SD 4.8mo], range 3y 3mo to 4y 5mo); and 12 typically developing children (5 males, 7 females; mean chronological age 4y 4mo [SD 3.9mo], range 3y 3mo to 4y 3mo). Participants were matched on mental age (mean mental age 4y 3mo). All participants completed verbal short-term memory, visuo-spatial short-term memory, and expressive and receptive vocabulary tasks on three occasions over 1 year. Similarities were seen in the clinical groups for verbal short-term memory. There was some evidence of difficulty in visuo-spatial short-term memory in the children with SLI relative to the other groups, but all three groups showed overlap in visuo-spatial short-term memory performance. At the final time-point vocabulary performance in the clinical groups was similar; the typically developing children showed higher vocabulary abilities than both clinical groups

The Influence of Physiological Status on age Prediction of Anopheles Arabiensis Using Near Infra-red spectroscopy

Determining the age of malaria vectors is essential for evaluating the impact of interventions that reduce the survival of wild mosquito populations and for estimating changes in vectorial capacity. Near infra-red spectroscopy (NIRS) is a simple and non-destructive method that has been used to determine the age and species of Anopheles gambiae s.l. by analyzing differences in absorption spectra. The spectra are affected by biochemical changes that occur during the life of a mosquito and could be influenced by senescence and also the life history of the mosquito, i.e., mating, blood feeding and egg-laying events. To better understand these changes, we evaluated the influence of mosquito physiological status on NIR energy absorption spectra. Mosquitoes were kept in individual cups to permit record keeping of each individual insect’s life history. Mosquitoes of the same chronological age, but at different physiological stages, were scanned and compared using cross-validations. We observed a slight trend within some physiological stages that suggest older insects tend to be predicted as being physiologically more mature. It was advantageous to include mosquitoes of different chronological ages and physiological stages in calibrations, as it increases the robustness of the model resulting in better age predictions. Progression through different physiological statuses of An. arabiensis influences the chronological age prediction by the NIRS. Entomologists that wish to use NIR technology to predict the age of field-caught An. gambiae s.l from their study area should use a calibration developed from their field strain using mosquitoes of diverse chronological ages and physiological stages to increase the robustness and accuracy of the predictions.\u

Brain Age from the Electroencephalogram of Sleep

The human electroencephalogram (EEG) of sleep undergoes profound changes with age. These changes can be conceptualized as "brain age", which can be compared to an age norm to reflect the deviation from normal aging process. Here, we develop an interpretable machine learning model to predict brain age based on two large sleep EEG datasets: the Massachusetts General Hospital sleep lab dataset (MGH, N = 2,621) covering age 18 to 80; and the Sleep Hearth Health Study (SHHS, N = 3,520) covering age 40 to 80. The model obtains a mean absolute deviation of 8.1 years between brain age and chronological age in the healthy participants in the MGH dataset. As validation, we analyze a subset of SHHS containing longitudinal EEGs 5 years apart, which shows a 5.5 years difference in brain age. Participants with neurological and psychiatric diseases, as well as diabetes and hypertension medications show an older brain age compared to chronological age. The findings raise the prospect of using sleep EEG as a biomarker for healthy brain aging

Defining Dental Age for Chronological Age Determination

Dental age assessment is one of the most reliable methods of chronological age estimation used for criminal, forensic and anthropologic purposes. Visual, radiographic, chemical and histological techniques can be used for dental age estimation. Visual method is based on the sequence of eruption of the teeth and morphological changes that are caused due to function such as attrition, changes in color that are indicators of aging. Radiographs of the dentition can be used to determine the stage of dental development of the teeth from initial mineralization of a tooth, crown formation to root apex maturation. Histological methods require the preparation of the tissues for detailed microscopic examination. The chemical analysis of dental hard tissues determines alterations in ion levels with age, whereas the histological and chemical methods are invasive methods requiring extraction/sectioning of the tooth. In this chapter, the different techniques and considered studies were overviewed in conjunction with their advantages and disadvantages. It needs to be taken into consideration that rather than restricting on one age estimation technique, using the other available techniques additionally and performing repetitive measurements may be beneficial for accurate age estimation