Prediction Of Chronological Age Research Articles

DIFFERENCE BETWEEN CHRONOLOGICAL AND VASCULAR AGE AS A PREDICTOR FOR CARDIOVASCULAR EVENTS - IDENTIFICATION OF PATIENTS WITH SUPERNORMAL VASCULAR AGING (SUPERNOVA)

Objective: Pulse wave velocity (PWV) is an established marker of early vascular aging (EVA), but may also help identifying individuals with supernormal vascular aging (SUPERNOVA). Aim of this study is to test the hypothesis that individuals with the largest difference between chronological and vascular age (Delta-age), calculated from classical CV risk factors and PWV, show the lowest rate of CV events, and may thus be defined SUPERNOVA. Design and method: Vascular age was calculated according to the best fitting model for chronological age prediction, in the cross-sectional Reference Values for Arterial stiffness Collaboration Database (n = 11,406), for different age categories. The risk of fatal and non-fatal CV events associated with Delta-age was investigated in the longitudinal Malmö Diet and Cancer Study cohort (n = 2642). Delta-age deciles were used to define EVA (<3.0 years), normal vascular ageing (VA, 3.0 to 8.8 years) and SUPERNOVA (>8.8 years). Results: In the Reference Values Database, the most significant predictor of vascular age (full model r2 = 0.598) was PWV. In the Malmö Diet and Cancer Study Cohort (6.6-year follow-up, 286 events). Delta-age was significantly and inversely associated with CV events. Compared to normal VA, SUPERNOVA had lower risk [HR 0.55 (95%CI 0.36–0.82)], whereas EVA had higher risk [HR 2.88 (95%CI 1.88–4.40)]. There was no significant association with all-cause mortality. Conclusions: PWV proved to be useful to identify SUPERNOVA individuals. Further characterization of this extreme phenotype may help discovering novel protective molecular pathways and providing preventive strategies for successful vascular aging.

Blood-based epigenetic estimators of chronological age in human adults using DNA methylation data from the Illumina MethylationEPIC array

BackgroundEpigenetic clocks have been recognized for their precise prediction of chronological age, age-related diseases, and all-cause mortality. Existing epigenetic clocks are based on CpGs from the Illumina HumanMethylation450 BeadChip (450 K) which has now been replaced by the latest platform, Illumina MethylationEPIC BeadChip (EPIC). Thus, it remains unclear to what extent EPIC contributes to increased precision and accuracy in the prediction of chronological age.ResultsWe developed three blood-based epigenetic clocks for human adults using EPIC-based DNA methylation (DNAm) data from the Norwegian Mother, Father and Child Cohort Study (MoBa) and the Gene Expression Omnibus (GEO) public repository: 1) an Adult Blood-based EPIC Clock (ABEC) trained on DNAm data from MoBa (n = 1592, age-span: 19 to 59 years), 2) an extended ABEC (eABEC) trained on DNAm data from MoBa and GEO (n = 2227, age-span: 18 to 88 years), and 3) a common ABEC (cABEC) trained on the same training set as eABEC but restricted to CpGs common to 450 K and EPIC. Our clocks showed high precision (Pearson correlation between chronological and epigenetic age (r) > 0.94) in independent cohorts, including GSE111165 (n = 15), GSE115278 (n = 108), GSE132203 (n = 795), and the Epigenetics in Pregnancy (EPIPREG) study of the STORK Groruddalen Cohort (n = 470). This high precision is unlikely due to the use of EPIC, but rather due to the large sample size of the training set.ConclusionsOur ABECs predicted adults’ chronological age precisely in independent cohorts. As EPIC is now the dominant platform for measuring DNAm, these clocks will be useful in further predictions of chronological age, age-related diseases, and mortality.

AI-based investigation of molecular biomarkers of longevity.

In this paper, I build deep neural networks of various structures and hyperparameters in order to predict human chronological age based on open-access biochemical indicators and their specifications from the NHANES database. In total, 1152 neural networks are trained and tested. The algorithms are trained and tested on incomplete data: missing values in data records are extrapolated by mean or median values for each parameter. I select the best neural networks in terms of validation accuracy (coefficient of determination and mean absolute error). It turns out that the most accurate results are delivered by multilayer networks (6 layers) with recurrent layers. Neural network types are selected by trial and error. The algorithms reached an accuracy of 78% in terms of coefficient of determination and 6.5 in terms of mean absolute error. I also list empirically determined features of neural networks that increase accuracy for the task of chronological age prediction. Obtained results can be considered as an approximation of human biological age. Parameters in training datasets are selected the most broadly: all potentially relevant parameters (926) from the NHANES database are used. Although the networks are trained on the incomplete data, they demonstrated the ability to make reasonable predictions (with R2 > 0.7) based on no more than 100 biochemical indicators. Hence, for practical reasons the full data on each of 926 indicators are not required, although the analysis of the impact of each indicator is useful for theoretical developments.

Evaluation of marker selection methods and statistical models for chronological age prediction based on DNA methylation.

In forensic investigation, retrieving biological information from DNA evidence is a promising field of interest. One of the applications is on the estimation of the age of the donor based on DNA methylation. A large number of studies focused on age prediction using the 450K Human Methylation Beadchip. Various marker selection methods and prediction models have been considered. However, there is a lack of research evaluating different high-dimensional variable selection methods of CpG sites with various models for age prediction. The aim of this study is to evaluate four variable selection methods (forward selection, LASSO, elastic net and SCAD) combined with a classical statistical model and sophisticated machine learning models based on the mean absolute deviation (MAD) and the root-mean-square error (RMSE). We used publicly available 450K data set containing 991 whole blood samples (age 19-101years). We found that the multiple linear regression model with 16 markers selected from the forward selection method performed very well in age prediction (MAD=3.76years and RMSE=5.01years). On the other hand, the highly advanced ultrahigh dimensional variable selection methods and sophisticated machine learning algorithms appeared unnecessary for age prediction based on DNA methylation.

The Reliability of Fishman and Nolla Methods in Prediction of Chronological Age of Yemeni Children

Objective: This cross-sectional school based study aimed to assess the reliability of Fishman and Nolla methods in predicting the chronological age for Yemeni children. Material and Methods: Orthopantomographs and left handwrist radiographs were taken for 358 Yemeni children (193 boys and 165 girls) 8 - 16 years. Skeletal age estimated by Fishman method and dental age estimated by Nolla method were compared to chronological age using SPSS version 21, statistical significance was predetermined at P < 0.05 , using Intraclass Correlation Coefficient- ICC and Wilcoxon signed rank test. Results: The mean chronological, skeletal and dental ages were 12.00 ± 2.25 years, 12.39 ± 1.65 years and 11.32 ± 2.65 years, respectively. Intraclass correlation coefficient showed strong correlation between chronological age and skeletal and dental ages. Wilcoxon test showed Fishman method significantly underestimated the chronological age by 0.44 ± 1.26 years in boys and non-significantly underestimated the chronological age by 0.02 ± 1.08 years in girls. Nolla method significantly underestimated the chronological age by 0.59 ± 1.28 years in boys and 0.78 ± 1.21 years in girls. Conclusions: Chronological age of Yemeni children is highly correlated to skeletal age estimated by Fishman method and dental age estimated by Nolla method. However, the two methods underestimate the chronological age of Yemeni children.KeywordsAge determination by teeth; Orthodontics; Age determination by skeleton; Forensic dentistry; Yemen.

From a deep learning model back to the brain-Identifying regional predictors and their relation to aging.

We present a Deep Learning framework for the prediction of chronological age from structural magnetic resonance imaging scans. Previous findings associate increased brain age with neurodegenerative diseases and higher mortality rates. However, the importance of brain age prediction goes beyond serving as biomarkers for neurological disorders. Specifically, utilizing convolutional neural network (CNN) analysis to identify brain regions contributing to the prediction can shed light on the complex multivariate process of brain aging. Previous work examined methods to attribute pixel/voxel‐wise contributions to the prediction in a single image, resulting in “explanation maps” that were found noisy and unreliable. To address this problem, we developed an inference scheme for combining these maps across subjects, thus creating a population‐based, rather than a subject‐specific map. We applied this method to a CNN ensemble trained on predicting subjects' age from raw T1 brain images in a lifespan sample of 10,176 subjects. Evaluating the model on an untouched test set resulted in mean absolute error of 3.07 years and a correlation between chronological and predicted age of r = 0.98. Using the inference method, we revealed that cavities containing cerebrospinal fluid, previously found as general atrophy markers, had the highest contribution for age prediction. Comparing maps derived from different models within the ensemble allowed to assess differences and similarities in brain regions utilized by the model. We showed that this method substantially increased the replicability of explanation maps, converged with results from voxel‐based morphometry age studies and highlighted brain regions whose volumetric variability correlated the most with the prediction error.

Prediction of chronological age based on Demirjian dental age using robust ridge regression method

Prediction of chronological age based on Demirjian dental age using robust ridge regression method

Improved precision of epigenetic clock estimates across tissues and its implication for biological ageing

BackgroundDNA methylation changes with age. Chronological age predictors built from DNA methylation are termed ‘epigenetic clocks’. The deviation of predicted age from the actual age (‘age acceleration residual’, AAR) has been reported to be associated with death. However, it is currently unclear how a better prediction of chronological age affects such association.MethodsIn this study, we build multiple predictors based on training DNA methylation samples selected from 13,661 samples (13,402 from blood and 259 from saliva). We use the Lothian Birth Cohorts of 1921 (LBC1921) and 1936 (LBC1936) to examine whether the association between AAR (from these predictors) and death is affected by (1) improving prediction accuracy of an age predictor as its training sample size increases (from 335 to 12,710) and (2) additionally correcting for confounders (i.e., cellular compositions). In addition, we investigated the performance of our predictor in non-blood tissues.ResultsWe found that in principle, a near-perfect age predictor could be developed when the training sample size is sufficiently large. The association between AAR and mortality attenuates as prediction accuracy increases. AAR from our best predictor (based on Elastic Net, https://github.com/qzhang314/DNAm-based-age-predictor) exhibits no association with mortality in both LBC1921 (hazard ratio = 1.08, 95% CI 0.91–1.27) and LBC1936 (hazard ratio = 1.00, 95% CI 0.79–1.28). Predictors based on small sample size are prone to confounding by cellular compositions relative to those from large sample size. We observed comparable performance of our predictor in non-blood tissues with a multi-tissue-based predictor.ConclusionsThis study indicates that the epigenetic clock can be improved by increasing the training sample size and that its association with mortality attenuates with increased prediction of chronological age.

Human epigenetic ageing is logarithmic with time across the entire lifespan

ABSTRACTEpigenetic changes during ageing have been characterized by multiple epigenetic clocks that allow the prediction of chronological age based on methylation status. Despite their accuracy and utility, epigenetic age biomarkers leave many questions about epigenetic ageing unanswered. Specifically, they do not permit the unbiased characterization of non-linear epigenetic ageing trends across entire life spans, a critical question underlying this field of research. Here we provide an integrated framework to address this question. Our model, inspired from evolutionary models, is able to account for acceleration/deceleration in epigenetic changes by fitting an individual’s model age, the epigenetic age, which is related to chronological age in a non-linear fashion. Application of this model to DNA methylation data measured across broad age ranges, from before birth to old age, and from two tissue types, suggests a universal logarithmic trend characterizes epigenetic ageing across entire lifespans.

Assessment of DNA methylation markers for forensic applications

ABSTRACTDNA methylation displays promise in a variety of forensic applications, including chronological age estimation and identical twin differentiation. Knowledge of the age of a body fluid donor would offer intelligence information, as well as provide context to physical characteristic markers. Differentiation of identical twins would also be of considerable benefit as the DNA profile of identical twins cannot be distinguished when a correspondence between DNA profiles recovered from crime scene samples and a person is identified. Our research investigates and develops a methodology for DNA methylation analysis that could be implemented into forensic casework. Our results show that the differentiation of identical twins and chronological age prediction are both possible, with similar accuracies to other international research. Targeted multiplexing of bisulphite converted DNA in combination with massively parallel sequencing is demonstrated as a viable alternative to traditional methylation analysis techniques.

Transfer learning improves resting-state functional connectivity pattern analysis using convolutional neural networks

BackgroundDeep learning is gaining importance in the prediction of cognitive states and brain pathology based on neuroimaging data. Including multiple hidden layers in artificial neural networks enables unprecedented predictive power; however, the proper training of deep neural networks requires thousands of exemplars. Collecting this amount of data is not feasible in typical neuroimaging experiments. A handy solution to this problem, which has largely fallen outside the scope of deep learning applications in neuroimaging, is to repurpose deep networks that have already been trained on large datasets by fine-tuning them to target datasets/tasks with fewer exemplars. Here, we investigated how this method, called transfer learning, can aid age category classification and regression based on brain functional connectivity patterns derived from resting-state functional magnetic resonance imaging. We trained a connectome-convolutional neural network on a larger public dataset and then examined how the knowledge learned can be used effectively to perform these tasks on smaller target datasets collected with a different type of scanner and/or imaging protocol and pre-processing pipeline.ResultsAge classification on the target datasets benefitted from transfer learning. Significant improvement (∼9%–13% increase in accuracy) was observed when the convolutional layers’ weights were initialized based on the values learned on the public dataset and then fine-tuned to the target datasets. Transfer learning also appeared promising in improving the otherwise poor prediction of chronological age.ConclusionsTransfer learning is a plausible solution to adapt convolutional neural networks to neuroimaging data with few exemplars and different data acquisition and pre-processing protocols.

Correlation between chronological age and computed tomography attenuation of trabecular bone from the os coxae

Abstract Objective: Fields such as biological anthropology, forensic anthropology, archaeology, and mummy studies have increased the use medical imaging, such as computed tomography (CT), to analyze human remains through virtual examination. When predicting chronological age from virtual anatomy, methods developed on skeletal collections do not always produce accurate age predictions when applied to virtual anatomy. Age estimation methods developed specifically for medical imaging can improve accuracy of chronological age prediction when examining virtual anatomy. The present study examines the relationship between attenuation of trabecular bone from the os coxae and chronological age using CT scanning data. Materials and Methods: A sample of 324 CT scans of living and cadaveric individuals were randomly selected from a CT scan database and used to identify a correlation between trabecular bone CT attenuation in four regions of the os coxae and chronological age. The four regions included trabecular bone deep to the auricular surface, pubic symphysis, posterior superior iliac spine, and the ischial tuberosity. Results: Through cross validation, the trabecular bone deep to the pubic symphysis, posterior superior iliac spine, and ischial tuberosity resulted in the best prediction models based on model testing. Model testing identified models with adjusted R2 values of 0.83 and 0.86 for combine male/female bone attenuation and only female bone density, respectively. Discussion: The resulting models, when used in relation with CT scanning data, provide a quantitative method that predicts chronological age and can be used in situations when remains are recently deceased fleshed individuals.

An epigenetic pacemaker is detected via a fast conditional expectation maximization algorithm.

DNA methylation has proven to be a remarkably accurate biomarker for human age, allowing the prediction of chronological age to within a couple of years. Recently, we proposed that the Universal PaceMaker (UPM), a flexible paradigm for modeling evolution, could be applied to epigenetic aging. Nevertheless, application to real data was restricted to small datasets for technical limitations. We partition the set of variables into to two subsets and optimize the likelihood function on each set separately. This yields an extremely efficient Conditional Expectation Maximization algorithm, alternating between the two sets while increasing the overall likelihood. Using the technique, we could reanalyze datasets of larger magnitude and show significant advantage to the UPM approach. The UPM more faithfully models epigenetic aging than the time linear approach while methylated sites accelerate and decelerate jointly.

An unbiased data-driven age-related structural brain parcellation for the identification of intrinsic brain volume changes over the adult lifespan

This study aims to elucidate age-related intrinsic brain volume changes over the adult lifespan using an unbiased data-driven structural brain parcellation. Anatomical brain images from a cohort of 293 healthy volunteers ranging in age from 21 to 86 years were analyzed using independent component analysis (ICA). ICA-based parcellation identified 192 component images, of which 174 (90.6%) showed a significant negative correlation with age and with some components being more vulnerable to aging effects than others. Seven components demonstrated a convex slope with aging; 3 components had an inverted U-shaped trajectory, and 4 had a U-shaped trajectory. Linear combination of 86 components provided reliable prediction of chronological age with a mean absolute prediction error of approximately 7.2 years. Structural co-variation analysis showed strong interhemispheric, short-distance positive correlations and long-distance, inter-lobar negative correlations. Estimated network measures either exhibited a U- or an inverted U-shaped relationship with age, with the vertex occurring at approximately 45–50 years. Overall, these findings could contribute to our knowledge about healthy brain aging and could help provide a framework to distinguish the normal aging processes from that associated with age-related neurodegenerative diseases.

Impact of genetic ancestry on chronological age prediction using DNA methylation analysis

Several forensic age predictors based on DNA methylation have been developed, reporting a considerable number of CpG loci highly correlated with chronological age. Most of the published age prediction models so far were based on European samples, and therefore there is a need to explore additional worldwide populations. We performed a comprehensive analysis of methylation profiles from buccal mucosa samples of three independent population groups from Middle East, West Africa and Central Europe. For each population approx. 50 samples with an equal distribution of donor ages were collected. Using bisulfite pyrosequencing we analyzed the age dependent methylation changes at five CpG sites in the genes of ASPA, ITGA2B, PDE4C and ELOVL2 and predicted the donor ages using two previously reported age prediction models. Our results display significantly lower dispersions of methylations and thus errors of predicted ages in the Middle East population compared to the Central Europe and West African populations. Interestingly, the overall methylation variation was highly similar between the three populations at all investigated CpGs.

Chronological age prediction based on DNA methylation: Massive parallel sequencing and random forest regression

The use of DNA methylation (DNAm) to obtain additional information in forensic investigations showed to be a promising and increasing field of interest. Prediction of the chronological age based on age-dependent changes in the DNAm of specific CpG sites within the genome is one such potential application. Here we present an age-prediction tool for whole blood based on massive parallel sequencing (MPS) and a random forest machine learning algorithm. MPS allows accurate DNAm determination of pre-selected markers and neighboring CpG-sites to identify the best age-predictive markers for the age-prediction tool. 15 age-dependent markers of different loci were initially chosen based on publicly available 450K microarray data, and 13 finally selected for the age tool based on MPS (DDO, ELOVL2, F5, GRM2, HOXC4, KLF14, LDB2, MEIS1-AS3, NKIRAS2, RPA2, SAMD10, TRIM59, ZYG11A). Whole blood samples of 208 individuals were used for training of the algorithm and a further 104 individuals were used for model evaluation (age 18–69). In the case of KLF14, LDB2, SAMD10, and GRM2, neighboring CpG sites and not the initial 450K sites were chosen for the final model. Cross-validation of the training set leads to a mean absolute deviation (MAD) of 3.21 years and a root-mean square error (RMSE) of 3.97 years. Evaluation of model performance using the test set showed a comparable result (MAD 3.16 years, RMSE 3.93 years). A reduced model based on only the top 4 markers (ELOVL2, F5, KLF14, and TRIM59) resulted in a RMSE of 4.19 years and MAD of 3.24 years for the test set (cross validation training set: RMSE 4.63 years, MAD 3.64 years). The amplified region was additionally investigated for occurrence of SNPs in case of an aberrant DNAm result, which in some cases can be an indication for a deviation in DNAm.Our approach uncovered well-known DNAm age-dependent markers, as well as additional new age-dependent sites for improvement of the model, and allowed the creation of a reliable and accurate epigenetic tool for age-prediction without restriction to a linear change in DNAm with age.

Exploration of unsupervised feature selection methods to predict chronological age of individuals by utilising CpG dinucleotics from whole blood.

Identification of the age of individuals from epigenetic biomarkers can reveal vital information for criminal investigation, disease prevention, and extension of life. DNA methylation changes are highly associated with chronological age and the process of disease development. Computational methods such as clustering, feature selection and regression can be utilised to construct quantitative model of aging. In this study, we utilised 473034 CpG biomarkers from whole blood of 656 individuals aged 19 to 101 to construct predictive models and we treat the development of this age predictive model as extremely high-dimensional regression problem that is relatively understudied. Unlike semi-supervised and supervised feature selection methods, unsupervised feature selection methods are generally good at removing irrelevant features that can act as noise. In this study, along with the entire feature set, four different unsupervised feature selection methods (USFSMs) are therefore considered for the quantitative prediction of human ages. Since USFSMs are independent of any predictive method, support vector regression is then used to evaluate the prediction performances of the unsupervised feature selection methods. We proposed a novel k-means based unsupervised feature selection method to predict human ages by utilising CpG dinucleotides. Experimental results have validated the effectiveness of the proposed method as the optimum number of the CpG dinucleotides is found to be only 41 that corresponds to only 0.0087% of the entire feature space. To the best of our knowledge, this is the first study that presents exploration and comprehensive comparison of USFSMs in very high dimensional regression problems, particularly in epigenetic biomedical domain for the prediction of chronological age from changes in DNA methylation.

Prediction of chronological age from hierarchical brain volumes using a random forest regression can provide a personalized lifetime metric of aging. (P6.078)

Prediction of chronological age from hierarchical brain volumes using a random forest regression can provide a personalized lifetime metric of aging. (P6.078)

DNA methylation-based forensic age prediction using artificial neural networks and next generation sequencing

The ability to estimate the age of the donor from recovered biological material at a crime scene can be of substantial value in forensic investigations. Aging can be complex and is associated with various molecular modifications in cells that accumulate over a person’s lifetime including epigenetic patterns. The aim of this study was to use age-specific DNA methylation patterns to generate an accurate model for the prediction of chronological age using data from whole blood. In total, 45 age-associated CpG sites were selected based on their reported age coefficients in a previous extensive study and investigated using publicly available methylation data obtained from 1156 whole blood samples (aged 2–90 years) analysed with Illumina’s genome-wide methylation platforms (27K/450K). Applying stepwise regression for variable selection, 23 of these CpG sites were identified that could significantly contribute to age prediction modelling and multiple regression analysis carried out with these markers provided an accurate prediction of age (R2=0.92, mean absolute error (MAE)=4.6 years). However, applying machine learning, and more specifically a generalised regression neural network model, the age prediction significantly improved (R2=0.96) with a MAE=3.3 years for the training set and 4.4 years for a blind test set of 231 cases. The machine learning approach used 16 CpG sites, located in 16 different genomic regions, with the top 3 predictors of age belonged to the genes NHLRC1, SCGN and CSNK1D. The proposed model was further tested using independent cohorts of 53 monozygotic twins (MAE=7.1 years) and a cohort of 1011 disease state individuals (MAE=7.2 years). Furthermore, we highlighted the age markers’ potential applicability in samples other than blood by predicting age with similar accuracy in 265 saliva samples (R2=0.96) with a MAE=3.2 years (training set) and 4.0 years (blind test). In an attempt to create a sensitive and accurate age prediction test, a next generation sequencing (NGS)-based method able to quantify the methylation status of the selected 16 CpG sites was developed using the Illumina MiSeq® platform. The method was validated using DNA standards of known methylation levels and the age prediction accuracy has been initially assessed in a set of 46 whole blood samples. Although the resulted prediction accuracy using the NGS data was lower compared to the original model (MAE=7.5years), it is expected that future optimization of our strategy to account for technical variation as well as increasing the sample size will improve both the prediction accuracy and reproducibility.

Prediction of gestational age based on genome-wide differentially methylated regions.

BackgroundWe explored the association between gestational age and cord blood DNA methylation at birth and whether DNA methylation could be effective in predicting gestational age due to limitations with the presently used methods. We used data from the Norwegian Mother and Child Birth Cohort study (MoBa) with Illumina HumanMethylation450 data measured for 1753 newborns in two batches: MoBa 1, n = 1068; and MoBa 2, n = 685. Gestational age was computed using both ultrasound and the last menstrual period. We evaluated associations between DNA methylation and gestational age and developed a statistical model for predicting gestational age using MoBa 1 for training and MoBa 2 for predictions. The prediction model was additionally used to compare ultrasound and last menstrual period-based gestational age predictions. Furthermore, both CpGs and associated genes detected in the training models were compared to those detected in a published prediction model for chronological age.ResultsThere were 5474 CpGs associated with ultrasound gestational age after adjustment for a set of covariates, including estimated cell type proportions, and Bonferroni-correction for multiple testing. Our model predicted ultrasound gestational age more accurately than it predicted last menstrual period gestational age.ConclusionsDNA methylation at birth appears to be a good predictor of gestational age. Ultrasound gestational age is more strongly associated with methylation than last menstrual period gestational age. The CpGs linked with our gestational age prediction model, and their associated genes, differed substantially from the corresponding CpGs and genes associated with a chronological age prediction model.Electronic supplementary materialThe online version of this article (doi:10.1186/s13059-016-1063-4) contains supplementary material, which is available to authorized users.