Chronological Age Estimation Under the Guidance of Age-Related Facial Attributes

2019 ◽  
Vol 14 (9) ◽  
pp. 2500-2511 ◽  
Author(s):  
Jiu-Cheng Xie ◽  
Chi-Man Pun
Keyword(s):  
Age Estimation ◽  
Chronological Age ◽  
Age Related ◽  
Facial Attributes

scholarly journals Exploring Epigenetic Age in Response to Intensive Relaxing Training: A Pilot Study to Slow Down Biological Age

2019 ◽  
Vol 16 (17) ◽  
pp. 3074 ◽  
Author(s):  
Pavanello ◽  
Campisi ◽  
Tona ◽  
Lin ◽  
Iliceto
Keyword(s):  
Myocardial Infarction ◽  
Age Estimation ◽  
Healthy Subjects ◽  
Molecular Mechanisms ◽  
Biological Age ◽  
Chronological Age ◽  
Biological Aging ◽  
Epigenetic Clock ◽  
Age Related ◽  
Epigenetic Age

DNA methylation (DNAm) is an emerging estimator of biological aging, i.e., the often-defined “epigenetic clock”, with a unique accuracy for chronological age estimation (DNAmAge). In this pilot longitudinal study, we examine the hypothesis that intensive relaxing training of 60 days in patients after myocardial infarction and in healthy subjects may influence leucocyte DNAmAge by turning back the epigenetic clock. Moreover, we compare DNAmAge with another mechanism of biological age, leucocyte telomere length (LTL) and telomerase. DNAmAge is reduced after training in healthy subjects (p = 0.053), but not in patients. LTL is preserved after intervention in healthy subjects, while it continues to decrease in patients (p = 0.051). The conventional negative correlation between LTL and chronological age becomes positive after training in both patients (p < 0.01) and healthy subjects (p < 0.05). In our subjects, DNAmAge is not associated with LTL. Our findings would suggest that intensive relaxing practices influence different aging molecular mechanisms, i.e., DNAmAge and LTL, with a rejuvenating effect. Our study reveals that DNAmAge may represent an accurate tool to measure the effectiveness of lifestyle-based interventions in the prevention of age-related diseases.

Age-related changes in the hyoid bone: An autopsy-based radiological analysis

2021 ◽  
pp. 002580242110202
Author(s):  
Devendra Jadav ◽  
Rutwik Shedge ◽  
Tanuj Kanchan ◽  
Vikas Meshram ◽  
Pawan Kumar Garg ◽  
...  
Keyword(s):  
Age Estimation ◽  
Hyoid Bone ◽  
The Body ◽  
Forensic Age Estimation ◽  
Chronological Age ◽  
Post Mortem ◽  
Post Mortem Examination ◽  
Bone Fusion ◽  
Age Related ◽  
Age Related Changes

Forensic age estimation is a crucial aspect of the biological profile of unidentified cadavers. The utility of age-related changes of hyoid bone fusion in forensic age estimation has not been explored much in the past. These age-related changes can be visualised in both the living and the dead using conventional radiography. These changes can assist medico-legal professionals and forensic anthropologists in the identification of unknown deceased, especially when the cadaver is mutilated or charred or when the other well-established indicators of skeletal and dental maturity are absent. The aims of this study were to evaluate age-related changes in the hyoid bone and to ascertain whether these changes may be utilised for age estimation in forensic examinations. The hyoid bone was carefully dissected using a standard procedure from 75 cadavers during post-mortem examination. The hyoid bone was radiographed, and the bone was replaced in the body cavity before the post-mortem examination was completed. Hyoid bone fusion was studied by using a standard grading method. Spearman’s correlation coefficient was calculated between the fusion scores and chronological age to assess their relationship. Box and whisker plots of fusion stage-wise age distribution were constructed to demonstrate the gradual linear relationship between hyoid bone fusion and the chronological age of the study participants. The present study concludes that hyoid bone fusion is an indicator of the chronological age of an individual and can be used in conjunction with other methods of age estimation such as the skeletal and dental age.

COMPARISION OF DENTAL SCORING SYSTEM WITH RADIOGRAPHIC SKELETAL SYSTEM IN AGE ESTIMATION

JKCD ◽  
2019 ◽  
Vol 9 (1) ◽  
pp. 9-11
Author(s):  
Sadaf Ambreen
Keyword(s):  
Age Estimation ◽  
Scoring System ◽  
Skeletal Age ◽  
Chronological Age ◽  
Age Group ◽  
Dental Age ◽  
Scoring Method ◽  
Cross Sectional ◽  
Study Results ◽  
Male Subjects

Objectives: To compare Demirjian Dental scoring method with Greulich-Pyle (GP) Skeletal method of age estimation in pubertal children. Materials and Methods: Sample of the study included 267 male healthy subjects of 11-16 years of age group.. Demirjian Scoring system was utilized to evaluate the orthopantomograms to assess their Dental age and the Hand-Wrist radiographs were analyzed to calculate the skeletal age by utilizing GP atlas. Chronological age was obtained from the date of birth of the subject .Both methods were compared with one another and with the chronological age. It was a cross-sectional study and only healthy male subjects without any clinical abnormalities were included in the study. Results: A total of 267 male subjects of 11-16 years of age group were assessed by Demirjian and Greulich Pyle Methods. Both were compared with Chronological Age. Data obtained was statistically analyzed and the Student “t” test was applied in the study population. The mean difference between Chronolgical age and dental age was 0.69years and that of chronological age and skeletal age was 0.87 years. It was observed from dental age assessment that it does not differ much from the skeletal age. Conclusion: It was concluded that Demirjian method of Age Estimation is more precise than Greulich Pyle method of Age Estimation. Furthermore both methods can be used selectively in Medicolegal cases to access bone age which can be easily correlated to chronological age.

scholarly journals A Biomarker-based Biological Age in UK Biobank: Composition and Prediction of Mortality and Hospital Admissions

2021 ◽  
Author(s):  
Mei Sum Chan ◽  
Matthew Arnold ◽  
Alison Offer ◽  
Imen Hammami ◽  
Marion Mafham ◽  
...  
Keyword(s):  
Principal Components ◽  
Hospital Admissions ◽  
Later Life ◽  
Chronological Age ◽  
Uk Biobank ◽  
Hand Grip ◽  
Cox Proportional Hazard ◽  
Prediction Of Mortality ◽  
Age Related ◽  
Cox Proportional Hazard Models

Abstract Background Chronological age is the strongest risk factor for most chronic diseases. Developing a biomarker-based age and understanding its most important contributing biomarkers may shed light on the effects of age on later-life health and inform opportunities for disease prevention. Methods A subpopulation of 141 254 individuals healthy at baseline were studied, from among 480 019 UK Biobank participants aged 40–70 recruited in 2006–2010, and followed up for 6–12 years via linked death and secondary care records. Principal components of 72 biomarkers measured at baseline were characterized and used to construct sex-specific composite biomarker ages using the Klemera Doubal method, which derived a weighted sum of biomarker principal components based on their linear associations with chronological age. Biomarker importance in the biomarker ages was assessed by the proportion of the variation in the biomarker ages that each explained. The proportions of the overall biomarker and chronological age effects on mortality and age-related hospital admissions explained by the biomarker ages were compared using likelihoods in Cox proportional hazard models. Results Reduced lung function, kidney function, reaction time, insulin-like growth factor 1, hand grip strength, and higher blood pressure were key contributors to the derived biomarker age in both men and women. The biomarker ages accounted for &gt;65% and &gt;84% of the apparent effect of age on mortality and hospital admissions for the healthy and whole populations, respectively, and significantly improved prediction of mortality (p &lt; .001) and hospital admissions (p &lt; 1 × 10−10) over chronological age alone. Conclusions This study suggests that a broader, multisystem approach to research and prevention of diseases of aging warrants consideration.

scholarly journals A Novel Ground Metric for Optimal Transport-Based Chronological Age Estimation

2021 ◽  
pp. 1-14
Author(s):  
Ali Akbari ◽  
Muhammad Awais ◽  
Soroush Fatemifar ◽  
Syed Safwan Khalid ◽  
Josef Kittler
Keyword(s):  
Age Estimation ◽  
Optimal Transport ◽  
Chronological Age

A metabolomic aging clock using human CSF

2021 ◽  
Author(s):  
Nathan Hwangbo ◽  
Xinyu Zhang ◽  
Daniel Raftery ◽  
Haiwei Gu ◽  
Shu-Ching Hu ◽  
...  
Keyword(s):  
Cerebrospinal Fluid ◽  
Healthy Aging ◽  
Prediction Models ◽  
Mass Spectrometry Analysis ◽  
Chronological Age ◽  
Healthy Human ◽  
Missing Data Imputation ◽  
Spectrometry Analysis ◽  
Residual Variation ◽  
Age Related

Abstract Quantifying the physiology of aging is essential for improving our understanding of age-related disease and the heterogeneity of healthy aging. Recent studies have shown that in regression models using “-omic” platforms to predict chronological age, residual variation in predicted age is correlated with health outcomes, and suggest that these “omic clocks” provide measures of biological age. This paper presents predictive models for age using metabolomic profiles of cerebrospinal fluid from healthy human subjects, and finds that metabolite and lipid data are generally able to predict chronological age within 10 years. We use these models to predict the age of a cohort of subjects with Alzheimer’s and Parkinson’s disease and find an increase in prediction error, potentially indicating that the relationship between the metabolome and chronological age differs with these diseases. However, evidence is not found to support the hypothesis that our models will consistently over-predict the age of these subjects. In our analysis of control subjects, we find the carnitine shuttle, sucrose, biopterin, vitamin E metabolism, tryptophan, and tyrosine to be the most associated with age. We showcase the potential usefulness of age prediction models in a small dataset (n = 85), and discuss techniques for drift correction, missing data imputation, and regularized regression, which can be used to help mitigate the statistical challenges that commonly arise in this setting. To our knowledge, this work presents the first multivariate predictive metabolomic and lipidomic models for age using mass spectrometry analysis of cerebrospinal fluid.

scholarly journals The Gut Microbiota and Healthy Aging: A Mini-Review

Gerontology ◽  
2018 ◽  
Vol 64 (6) ◽  
pp. 513-520 ◽  
Author(s):  
Sangkyu Kim ◽  
S. Michal Jazwinski
Keyword(s):  
Gut Microbiota ◽  
Individual Variation ◽  
Gut Microbiome ◽  
Chronological Age ◽  
Low Grade ◽  
Beneficial Effects ◽  
Gut Dysbiosis ◽  
Age Related ◽  
Host Health ◽  
Nutrient Signaling

The gut microbiota shows a wide inter-individual variation, but its within-individual variation is relatively stable over time. A functional core microbiome, provided by abundant bacterial taxa, seems to be common to various human hosts regardless of their gender, geographic location, and age. With advancing chronological age, the gut microbiota becomes more diverse and variable. However, when measures of biological age are used with adjustment for chronological age, overall richness decreases, while a certain group of bacteria associated with frailty increases. This highlights the importance of considering biological or functional measures of aging. Studies using model organisms indicate that age-related gut dysbiosis may contribute to unhealthy aging and reduced longevity. The gut microbiome depends on the host nutrient signaling pathways for its beneficial effects on host health and lifespan, and gut dysbiosis disrupting the interdependence may diminish the beneficial effects or even have reverse effects. Gut dysbiosis can trigger the innate immune response and chronic low-grade inflammation, leading to many age-related degenerative pathologies and unhealthy aging. The gut microbiota communicates with the host through various biomolecules, nutrient signaling-independent pathways, and epigenetic mechanisms. Disturbance of these communications by age-related gut dysbiosis can affect the host health and lifespan. This may explain the impact of the gut microbiome on health and aging.

scholarly journals Estimating Chronological Age by Dental Measurements on the Periapical Radiographs of an ‎Iranian Adult Population

2020 ◽  
Vol 10 (3) ◽  
pp. 32551
Author(s):  
Mitra Akhlaghi ◽  
Zahra Ghoncheh ◽  
Lida Hatami
Keyword(s):  
Root Length ◽  
Age Estimation ◽  
Adult Population ◽  
Chronological Age ◽  
Maxillary Canine ◽  
Regression Formula ◽  
Dental Measurements ◽  
Maximum Root ◽  
Tooth Length

Background: This study aimed to assess the accuracy of chronological age estimation based on dental measurements made on the Periapical (PA) radiographs of an Iranian adult population.   Methods: This study evaluated 90 parallel PA radiographs of sound maxillary canine teeth of 39 males and 51 females. Tooth length, root length, pulp length, pulp width, and root width at points A, B, and C according to Kvaal’s method were measured on PA radiographs using Scanora software. The collected data were analyzed using SPSS. Results: Maximum root width at point A provided the highest accuracy for gender estimation (77.7%). A significant correlation was noted between maximum pulp width at points B and C with age. Besides, a regression formula for age estimation was obtained. Conclusion: Maximum pulp width at points B and C could be used for age estimation in the adult population besides other parameters.

Abstract 160: DNA-Methylation and Aging. Contribution of Biological Age to Recovery After Ischemic Stroke

Stroke ◽  
2017 ◽  
Vol 48 (suppl_1) ◽  
Author(s):  
Jordi Jimenez-Conde ◽  
Carolina Soriano-Tarraga ◽  
Eva Giralt-Steinhauer ◽  
Marina Mola ◽  
Rosa Vivanco-Hidalgo ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Ischemic Stroke ◽  
Functional Status ◽  
Biological Age ◽  
Chronological Age ◽  
Bivariate Analysis ◽  
Stroke Severity ◽  
Age Related ◽  
Recovery Capacity ◽  
The Impact

Background: Stroke has a great impact in functional status of patients, although there are substantial interindividual differences in recovery capacity. Apart from stroke severity, age is considered an important predictor of outcome after stroke, but aging is not only due to chronological age. There are age-related DNA-methylation changes in multiple CpG sites across the genome that can be used to estimate the biological age (b-Age), and we seek to analyze the impact of this b-Age in recovery after an ischemic stroke. Methods: We include 600 individuals with acute ischemic stroke assessed in Hospital del Mar (Barcelona). Demographic and clinical data such as chronological age (c-Age), vascular risk factors, NIHSS at admission, recanalization treatment (rtPA or endovascular treatment), previous modified Rankin scale (p-mRS) and 3 months post stroke functional status (3-mRS) were registered. Biological age (b-Age) was estimated with Hannumm algorithm, based on DNA methylation in 71 CpGs. Results: The bivariate analyses for association with 3-mRS showed a significant results of NIHSS, c-Age, b-Age, p-mRS, and current smoking (all with p<0.001). Recanalization treatment showed no significant differences in bivariate analysis. In multivariate ordinal models, b-Age kept its significance (p=0.025) nullifying c-Age (p=0.84). Initial NIHSS, p-mRS and recanalization treatment kept also significant results (p<0.001). Conclusions: Biological Age, estimated by DNA methylation, is an independent predictor of stroke prognosis, irrespective to chronological age. "Healthy aging” affects the capacity of recovering after an ischemic stroke.

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