Biological Age Prediction Research Articles

Prediction of biological age and all-cause mortality by 12-lead electrocardiogram in patients without structural heart disease

BackgroundThere is a well-established relationship between 12-lead electrocardiogram (ECG) and age and mortality. Furthermore, there is increasing evidence that ECG can be used to predict biological age. However, the utility of biological age from ECG for predicting mortality remains unclear.MethodsThis was a single-center cohort study from a cardiology specialized hospital. A total of 19,170 patients registered in this study from February 2010 to March 2018. ECG was analyzed in a final 12,837 patients after excluding those with structural heart disease or with pacing beats, atrial or ventricular tachyarrhythmia, or an indeterminate axis (R axis > 180°) on index ECG. The models for biological age were developed by principal component analysis (BA) and the Klemera and Doubal’s method (not adjusted for age [BAE] and adjusted for age [BAEC]) using 438 ECG parameters. The predictive capability for all-cause death and cardiovascular death by chronological age (CA) and biological age using the three algorithms were evaluated by receiver operating characteristic analysis.ResultsDuring the mean follow-up period of 320.4 days, there were 55 all-cause deaths and 23 cardiovascular deaths. The predictive capabilities for all-cause death by BA, BAE, and BAEC using area under the curves were 0.731, 0.657, and 0.685, respectively, which were comparable to 0.725 for CA (p = 0.760, 0.141, and 0.308, respectively). The predictive capabilities for cardiovascular death by BA, BAE, and BAEC were 0.682, 0.685, and 0.692, respectively, which were also comparable to 0.674 for CA (p = 0.775, 0.839, and 0.706, respectively). In patients aged 60–74 years old, the area under the curves for all-cause death by BA, BAE, and BAEC were 0.619, 0.702, and 0.697, respectively, which tended to be or were significantly higher than 0.482 for CA (p = 0.064, 0.006, and 0.005, respectively).ConclusionBiological age by 12-lead ECG showed a similar predictive capability for mortality compared to CA among total patients, but partially showed a significant increase in predictive capability among patients aged 60–74 years old.

Biological Age Prediction From Wearable Device Movement Data Identifies Nutritional and Pharmacological Interventions for Healthy Aging.

Intervening in aging processes is hypothesized to extend healthy years of life and treat age-related disease, thereby providing great benefit to society. However, the ability to measure the biological aging process in individuals, which is necessary to test for efficacy of these interventions, remains largely inaccessible to the general public. Here we used NHANES physical activity accelerometer data from a wearable device and machine-learning algorithms to derive biological age predictions for individuals based on their movement patterns. We found that accelerated biological aging from our “MoveAge” predictor is associated with higher all-cause mortality. We further searched for nutritional or pharmacological compounds that associate with decelerated aging according to our model. A number of nutritional components peak in their association to decelerated aging later in life, including fiber, magnesium, and vitamin E. We additionally identified one FDA-approved drug associated with decelerated biological aging: the alpha-blocker doxazosin. We show that doxazosin extends healthspan and lifespan in C. elegans. Our work demonstrates how a biological aging score based on relative mobility can be accessible to the wider public and can potentially be used to identify and determine efficacy of geroprotective interventions.

Prediction of biological age and evaluation of genome-wide dynamic methylomic changes throughout human aging.

The use of DNA methylation signatures to predict chronological age and aging rate is of interest in many fields, including disease prevention and treatment, forensics, and anti-aging medicine. Although a large number of methylation markers are significantly associated with age, most age-prediction methods use a few markers selected based on either previously published studies or datasets containing methylation information. Here, we implemented reproducing kernel Hilbert spaces (RKHS) regression and a ridge regression model in a Bayesian framework that utilized phenotypic and methylation profiles simultaneously to predict chronological age. We used over 450,000 CpG sites from the whole blood of a large cohort of 4409 human individuals with a range of 10–101 years of age. Models were fitted using adjusted and un-adjusted methylation measurements for cell heterogeneity. Un-adjusted methylation scores delivered a significantly higher prediction accuracy than adjusted methylation data, with a correlation between age and predicted age of 0.98 and a root mean square error (RMSE) of 3.54 years in un-adjusted data, and 0.90 (correlation) and 7.16 (RMSE) years in adjusted data. Reducing the number of predictors (CpG sites) through subset selection improved predictive power with a correlation of 0.98 and an RMSE of 2.98 years in the RKHS model. We found distinct global methylation patterns, with a significant increase in the proportion of methylated cytosines in CpG islands and a decreased proportion in other CpG types, including CpG shore, shelf, and open sea (P < 5e-06). Epigenetic drift seemed to be a widespread phenomenon as more than 97% of the age-associated methylation sites had heteroscedasticity. Apparent methylomic aging rate (AMAR) had a sex-specific pattern, with an increase in AMAR in females with age related to males.

Genetic Prediction of Biological Age: A Meta-Analysis on Telomere Length, DNA Methylation, and All-Cause Mortality

Aging is a biological process during lifespan with accumulation of mutations and damages, lowering fitness of at older ages and increasing hazards to survival...

Seagull: lasso, group lasso and sparse-group lasso regularization for linear regression models via proximal gradient descent

BackgroundStatistical analyses of biological problems in life sciences often lead to high-dimensional linear models. To solve the corresponding system of equations, penalization approaches are often the methods of choice. They are especially useful in case of multicollinearity, which appears if the number of explanatory variables exceeds the number of observations or for some biological reason. Then, the model goodness of fit is penalized by some suitable function of interest. Prominent examples are the lasso, group lasso and sparse-group lasso. Here, we offer a fast and numerically cheap implementation of these operators via proximal gradient descent. The grid search for the penalty parameter is realized by warm starts. The step size between consecutive iterations is determined with backtracking line search. Finally, seagull -the R package presented here- produces complete regularization paths.ResultsPublicly available high-dimensional methylation data are used to compare seagull to the established R package SGL. The results of both packages enabled a precise prediction of biological age from DNA methylation status. But even though the results of seagull and SGL were very similar (R2 > 0.99), seagull computed the solution in a fraction of the time needed by SGL. Additionally, seagull enables the incorporation of weights for each penalized feature.ConclusionsThe following operators for linear regression models are available in seagull: lasso, group lasso, sparse-group lasso and Integrative LASSO with Penalty Factors (IPF-lasso). Thus, seagull is a convenient envelope of lasso variants.

DNA methylation profile is a quantitative measure of biological aging in children.

DNA methylation changes within the genome can be used to predict human age. However, the existing biological age prediction models based on DNA methylation are predominantly adult-oriented. We established a methylation-based age prediction model for children (9-212 months old) using data from 716 blood samples in 11 DNA methylation datasets. Our elastic net model includes 111 CpG sites, mostly in genes associated with development and aging. The model performed well and exhibited high precision, yielding a 98% correlation between the DNA methylation age and the chronological age, with an error of only 6.7 months. When we used the model to assess age acceleration in children based on their methylation data, we observed the following: first, the aging rate appears to be fastest in mid-childhood, and this acceleration is more pronounced in autistic children; second, lead exposure early in life increases the aging rate in boys, but not in girls; third, short-term recombinant human growth hormone treatment has little effect on the aging rate of children. Our child-specific methylation-based age prediction model can effectively detect epigenetic changes and health imbalances early in life. This may thus be a useful model for future studies of epigenetic interventions for age-related diseases.

Centroid of Age Neighborhoods: A New Approach to Estimate Biological Age

Estimation of human biological age is an important and difficult challenge. Different biomarkers and numerous approaches have been studied for biological age prediction, each with its advantages and limitations. In this paper, we propose a new biological age estimation method, and investigate the performance of the new method. We introduce a centroid based approach, using the notion of age neighborhoods. Specifically, we develop a model, based on which we compute biological age using blood biomarkers, by considering the centroid or mediod of specially selected age neighborhoods. Experiments were performed on the National Health and Human Nutrition Examination Survey dataset with biomarkers (21451 individuals). Compared with current popular methods for biological age prediction, our experiments show that the proposed age neighborhood model results in an improved performance in human biological age estimation.

Measuring biological age to assess colony demographics in honeybees.

Honeybee colonies are increasingly exposed to environmental stress factors, which can lead to their decline or failure. However, there are major gaps in stressor risk assessment due to the difficulty of assessing the honeybee colony state and detecting abnormal events. Since stress factors usually induce a demographic disturbance in the colony (e.g. loss of foragers, early transition from nurse to forager state), we suggest that disturbances could be revealed indirectly by measuring the age- and task-related physiological state of bees, which can be referred to as biological age (an indicator of the changes in physiological state that occur throughout an individual lifespan). We therefore estimated the biological age of bees from the relationship between age and biomarkers of task specialization (vitellogenin and the adipokinetic hormone receptor). This relationship was determined from a calibrated sample set of known-age bees and mathematically modelled for biological age prediction. Then, we determined throughout the foraging season the evolution of the biological age of bees from colonies with low (conventional apiary) or high Varroa destructor infestation rates (organic apiary). We found that the biological age of bees from the conventional apiary progressively decreased from the spring (17 days) to the fall (6 days). However, in colonies from the organic apiary, the population aged from spring (13 days) to summer (18.5 days) and then rejuvenated in the fall (13 days) after Varroa treatment. Biological age was positively correlated with the amount of brood (open and closed cells) in the apiary with low Varroa pressure, and negatively correlated with Varroa infestation level in the apiary with high Varroa pressure. Altogether, these results show that the estimation of biological age is a useful and effective method for assessing colony demographic state and likely detrimental effects of stress factors.

Biological Brain Age Prediction Using Cortical Thickness Data: A Large Scale Cohort Study.

Brain age estimation from anatomical features has been attracting more attention in recent years. This interest in brain age estimation is motivated by the importance of biological age prediction in health informatics, with an application to early prediction of neurocognitive disorders. It is well-known that normal brain aging follows a specific pattern, which enables researchers and practitioners to predict the age of a human's brain from its degeneration. In this paper, we model brain age predicted by cortical thickness data gathered from large cohort brain images. We collected 2,911 cognitively normal subjects (age 45–91 years) at a single medical center and acquired their brain magnetic resonance (MR) images. All images were acquired using the same scanner with the same protocol. We propose to first apply Sparse Group Lasso (SGL) for feature selection by utilizing the brain's anatomical grouping. Once the features are selected, a non-parametric non-linear regression using the Gaussian Process Regression (GPR) algorithm is applied to fit the final age prediction model. Experimental results demonstrate that the proposed method achieves the mean absolute error of 4.05 years, which is comparable with or superior to several recent methods. Our method can also be a critical tool for clinicians to differentiate patients with neurodegenerative brain disease by extracting a cortical thinning pattern associated with normal aging.

Development of Biological Age Prediction Model Based on the Osseous Parameters and the Effects of Endurance Exercise Duration and Falling on Aging

PURPOSE: The aims of this study were to identify bone health related biomarkers, develop an osseous health based biological age (BA) prediction model, and investigate its applicability. METHODS: Data of a total of 4,644 eligible male participants (age≥30 years) examined for bone health from the 2010 and 2008 Korea National Health and Nutrition Examination Survey were used in the study. Among 15 bone health related variables, 5 biomarkers were selected through a parsimonious exclusion procedure and correlation analysis between the parameters and chronological age. Principal component analysis was applied to extract the principal component factors. Through application of CA and adjustment procedure, a BA prediction model based on osseous biomarkers was obtained. Applicability of the model was assessed. First, history of fall was compared between the fall and no-fall groups. Second, three groups were compared for their continuous walking durations. All groups were compared by three age groups. RESULTS: BA was significantly greater than CA in the fall group by 5.60 years for the older group. As for the walking duration, BA was significantly less by 5.14 and more by 6.51 years than CA for the older groups with more than 2 hours and less than 1 hour of walking durations, respectively. CONCLUSIONS: BA prediction model based on five non-invasive osseous biomarkers may be applicable to predict bone health related conditions. It may be used as a health promotional tool in a health related field to deter or even reverse health deterring factors. 색인어: principal component analysis, bone mineral density, biological age, chronological age, endurance exercise Keywords: Principal component analysis, Bone mineral density, Fall, Biological age, Chronological age, Endurance exercise

Dental age estimation and different predictive ability of various tooth types in the Czech population: data mining methods

Dental development is frequently used to estimate age in many anthropological specializations. The aim of this study was to extract an accurate predictive age system for the Czech population and to discover any different predictive ability of various tooth types and their ontogenetic stability during infancy and adolescence. A cross-sectional panoramic X-ray study was based on developmental stages assessment of mandibular teeth (Moorrees et al. 1963) using 1393 individuals aged from 3 to 17 years. Data mining methods were used for dental age estimation. These are based on nonlinear relationships between the predicted age and data sets. Compared with other tested predictive models, the GAME method predicted age with the highest accuracy. Age-interval estimations between the 10th and 90th percentiles ranged from -1.06 to +1.01 years in girls and from -1.13 to +1.20 in boys. Accuracy was expressed by RMS error, which is the average deviation between estimated and chronological age. The predictive value of individual teeth changed during the investigated period from 3 to 17 years. When we evaluated the whole period, the second molars exhibited the best predictive ability. When evaluating partial age periods, we found that the accuracy of biological age prediction declines with increasing age (from 0.52 to 1.20 years in girls and from 0.62 to 1.22 years in boys) and that the predictive importance of tooth types changes, depending on variability and the number of developmental stages in the age interval. GAME is a promising tool for age-interval estimation studies as they can provide reliable predictive models.

Models for estimating the biological age of five organs using clinical biomarkers that are commonly measured in clinical practice settings

ObjectivesTo date, no worldwide studies have been conducted to estimate the biological age of five organs using clinical biomarkers that are associated with the aging status. Therefore, we conducted this study to develop the models for estimating the biological age of five organs (heart, lung, liver, pancreas, and kidney) using clinical biomarkers which are commonly measured in clinical practice. DesignA cross sectional study. MethodsSubjects were recruited from the routine health check-up centers in Korea from 2004 through 2010. Data obtained from 121,189 subjects (66,168 men and 55,021 women) were used for clinical evaluation and statistical analysis. We examined the relations between clinical biomarkers associated with five organs and the chronological age and proposed a model for estimating the biological age of five organs. ResultsIn the models for predicting the biological ages of the heart, lung, liver, pancreas and kidney in men, 12, 2, 8, 3, and 5 parameters were respectively included (R2=0.652, 0.427, 0.107, 0.245, and 0.651). In contrast to men, 10, 2, 8, 3, and 5 parameters in women were respectively included (R2=0.780, 0.435, 0.140, 0.384, and 0.501). ConclusionWe first proposed the models for predicting the biological age of five organs in the current study. We developed those using clinical parameters that can be easily obtained in clinical practice settings. Our biological age prediction models may be used as supplementary tools to assess the aging status of five organs in clinical practice settings.

Identification of four novel developmentally regulated gamma hemoglobin mRNA isoforms

In the course of developing assays for the molecular prediction of biological age, we serendipitously discovered four novel isoforms of gamma hemoglobin mRNA, designated HBG1n1, HBG1n2, HBG2n2, and HBG2n3, collectively termed HBGn isoforms. Here we report the molecular characterization and tissue expression of these isoforms. RNA obtained from human peripheral blood and various fetal and adult tissues was amplified with duplex reverse transcription polymerase chain reaction (RT-PCR) assays to determine the expression profiles of the HBGn isoforms. To determine if their molecular origin was either a genomic recombination or an undefined RNA rearrangement event, DNA and RNA samples were pretreated with RNaseI and/or DNaseI and then analyzed with the duplex RT-PCR assays. Alignment analysis indicated that the HBG1n(1/2) and HBG2n(2/3) isoforms were identical to the expected parental HBG1 or HBG2 sequences except for unique deletions that spanned the 3' end of exon two, the entire second intron, and the 5' end of exon three. RT-PCR duplex reactions revealed that the isoforms exhibit restricted expression to fetal hematopoietic tissue and newborn peripheral blood and appear to be temporally regulated during development. Finally, nucleic acid digestion experiments illustrate that the isoforms appear to be created by an RNA rearrangement event between penta- or octanucleotide direct repeats in adjacent exons. The precise genesis and function of these novel isoforms is unknown at present. Interestingly, each of the four isoforms identified maintain an open reading frame as well as 5' and 3' regulatory regions invoking the possibility that they encode a family of related polypeptides.