Biological Age Estimates Research Articles

Clinical Significance of Biological Age in Patients Undergoing Percutaneous Coronary Intervention

ObjectiveTo test whether biological age calculated using deficits, functional impairments, or their combination will provide improved estimation of long-term mortality among older adults undergoing percutaneous coronary intervention. Patients and MethodsCardiovascular deficits, noncardiovascular deficits, and functional impairments were prospectively studied in 535 patients aged 55 years or older from August 1, 2014, to March 31, 2018. Models for biological age included deficits (acquired, increase with age, associated with worse prognosis, did not saturate early), functional impairments (subjective—help with daily activities, difficulty with sensory input, continence, weight, balance, mobility; or objective—timed up and go, functional reach), or their combination. ResultsThe mean ± SD age of the study patients was 72.1±9.5 years. For every 5-year increase in chronological age, the mean number of cardiovascular deficits increased from 2.36 among patients younger than 70 years to 3.44 in nonagenarians. The mean number of functional impairments increased from 2.15 for those younger than 70 years to 6.74 for nonagenarians. During a median follow-up of 2.05 years, 99 patients died. Significant improvement in the Harrell concordance index (C index) for prediction of long-term all-cause mortality was noted with biological age calculated from deficits and impairments compared with chronological age (0.77 vs 0.65; P<.001) and when estimating biological age via functional impairments alone vs chronological age (0.75 vs 0.65; P<.001) but not via deficits alone (0.71 vs 0.65; P=.08). Biological age estimates from subjective functional impairments captured most of the prognostic information related to all-cause and noncardiac mortality, whereas deficit-based estimation favored cardiovascular mortality. ConclusionThe derivation of biological age from deficits and functional impairments provides a major improvement in the estimation of survival as estimated by chronological age.

Effect of long-term caloric restriction on DNA methylation measures of biological aging in healthy adults from the CALERIE trial

The geroscience hypothesis proposes that therapy to slow or reverse molecular changes that occur with aging can delay or prevent multiple chronic diseases and extend healthy lifespan1–3. Caloric restriction (CR), defined as lessening caloric intake without depriving essential nutrients4, results in changes in molecular processes that have been associated with aging, including DNA methylation (DNAm)5–7, and is established to increase healthy lifespan in multiple species8,9. Here we report the results of a post hoc analysis of the influence of CR on DNAm measures of aging in blood samples from the Comprehensive Assessment of Long-term Effects of Reducing Intake of Energy (CALERIE) trial, a randomized controlled trial in which n = 220 adults without obesity were randomized to 25% CR or ad libitum control diet for 2 yr (ref. 10). We found that CALERIE intervention slowed the pace of aging, as measured by the DunedinPACE DNAm algorithm, but did not lead to significant changes in biological age estimates measured by various DNAm clocks including PhenoAge and GrimAge. Treatment effect sizes were small. Nevertheless, modest slowing of the pace of aging can have profound effects on population health11–13. The finding that CR modified DunedinPACE in a randomized controlled trial supports the geroscience hypothesis, building on evidence from small and uncontrolled studies14–16 and contrasting with reports that biological aging may not be modifiable17. Ultimately, a conclusive test of the geroscience hypothesis will require trials with long-term follow-up to establish effects of intervention on primary healthy-aging endpoints, including incidence of chronic disease and mortality18–20.

Conceptual Overview of Biological Age Estimation.

Chronological age is an imperfect measure of the aging process, which is affected by a wide range of genetic and environmental exposures. Biological age estimates may be derived using mathematical modelling with biomarkers set as predictors and chronological age as the output. The difference between biological and chronological age is denoted the "age gap" and considered a complementary indicator of aging. The utility of the "age gap" metric is assessed through examination of its associations with exposures of interest and the demonstration of additional information provided by this metric over chronological age alone. This paper reviews the key concepts of biological age estimation, the age gap metric, and approaches to assessment of model performance in this context. We further discuss specific challenges for the field, in particular the limited generalisability of effect sizes across studies owing to dependency of the age gap metric on pre-processing and model building methods. The discussion will be centred on brain age estimation, but the concepts are transferable to all biological age estimation.

Estimation of Biological Ages with Kvaal Method Using Panoramic Radiography in Semarang City

Introduction: Forensic odontology is a branch of dentistry that has disciplines learn about the examination of evidence derived from teeth, and how to deal with the evidence for legal concerns. Purpose: The aim of the study was to describe biological age estimates by using Kvaal method in Semarang, and the difference between biological and chronological age by using the Kvaal method in Semarang. This study was descriptive with crosssectional design. Materials and Methods: One of the methods in determining the estimated age by using teeth is the Kvaal method. Kvaal method determines the estimated age based on the pulp size using radiography. Result: The results showed the difference between biological and chronological age was ±4,57 years. This result is lower than Kvaal’s previous study result which was ±9,5 years. Conclusion: Based in the findings above, it can be concluded that the difference of chronological and biological age using Kvaal method in Semarang was ±4,57 years within 15-60 years old individuals in Semarang.

Maternal biological age assessed in early pregnancy is associated with gestational age at birth

Maternal age is an established predictor of preterm birth independent of other recognized risk factors. The use of chronological age makes the assumption that individuals age at a similar rate. Therefore, it does not capture interindividual differences that may exist due to genetic background and environmental exposures. As a result, there is a need to identify biomarkers that more closely index the rate of cellular aging. One potential candidate is biological age (BA) estimated by the DNA methylome. This study investigated whether maternal BA, estimated in either early and/or late pregnancy, predicts gestational age at birth. BA was estimated from a genome-wide DNA methylation platform using the Horvath algorithm. Linear regression methods assessed the relationship between BA and pregnancy outcomes, including gestational age at birth and prenatal perceived stress, in a primary and replication cohort. Prenatal BA estimates from early pregnancy explained variance in gestational age at birth above and beyond the influence of other recognized preterm birth risk factors. Sensitivity analyses indicated that this signal was driven primarily by self-identified African American participants. This predictive relationship was sensitive to small variations in the BA estimation algorithm. Benefits and limitations of using BA in translational research and clinical applications for preterm birth are considered.

Abstract 2085: The evolutionary trajectory of epigenomics in adult glioma

Abstract Background: Glioma is the most common malignant tumor of the central nervous system, often behaving very aggressively. Recently, The Cancer Genome Atlas (TCGA) and others have shown that epigenomic alterations in primary glioma tumors have prognostic and predictive roles, but there is a gap in knowledge of the molecular alterations after glioma treatment. In order to fill this gap, the Glioma Longitudinal AnalySiS (GLASS) Consortium, a multi-national collaboration from 13 institutions, is investigating genome-wide molecular data from primary and recurrent matched pairs. The current data freeze has DNA methylation data for 266 primary-recurrent pairs fromIllumina 450K and EPIC array platforms. Methods: We hypothesize that there will be evidence of aggressivity in the DNA methylation profiles of recurrent tumors relative to their matched primary, and we explored this through tumor subtyping, patterns of differentially methylated CpGs (DMPs), and epigenomic aging. Results: The subtype classification in the primary tumors was as follows for IDH wildtype tumors - 43.8%Classical, 47.3% Mesenchymal, 8.7% PA-like, none LGm6-GBM and for IDH mutant tumors 19.1% Codel, 79.4% G-CIMP-high, 1.3% G-CIMP-low. We observed that among IDH wildtype tumors 29.8% changed subtype, 47.1% of which shifted to the more aggressive Mesenchymal-like subtype. In IDH mutant tumors, 26.0% changed at recurrence, of which 57.9% shifted to the aggressive G-CIMP-low subtype. Patterns of DMPs in IDH mutant-code tumors (15 pairs) showed a loss of methylation upon recurrence, with 651 DMPs identified (paired Wilcoxon test, FDR &amp;lt0.05). In unsupervised clustering, recurrent Codel tumors thus move away from IDH mutant tumors and align more closely to IDH wildtype tumors. We do not see the same increases in hypomethylation upon recurrence among IDHwt tumors, with only 17 DMPs at FDR &amp;lt0.05 in the classical subtype (24 pairs). It has been shown that biological age estimates of the tumor using DNA methylation can predict aggressivity of certain tumor types. We examined epigenetic aging changes between primary and recurrent tumors, relative to the patients chronological age, with Horvath (tissue-based) and epiTOC (mitotic-based) DNA methylation clocks. Both clocks showed shifts in age acceleration, that is increased biological age in relation to chronological age. For the Horvath clock, we found increased age acceleration in Classical-like tumors (primary=39.5±8.9, recurrent 41.7± 19.7) and Codel tumors (primary= 57.0±22.3, recurrent= 64.2±31.8). Conclusions: Collectively, we observed glioma tumor epigenetic changes from the primary to recurrent state and these differences tend to reflect a shift to a more aggressive phenotype. Future work will explore the relationship of these findings with clinical treatments received between primary and recurrent states. Citation Format: Indrani Datta, Tathi Malta, Thais Sabedot, Ruicong She, The GLASS Consortium, Antonio Iavarone, Houtan Noushmehr, Laila M. Poisson. The evolutionary trajectory of epigenomics in adult glioma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2085.

Feature Selection Algorithms Enhance the Accuracy of Frailty Indexes as Measures of Biological Age.

Biological age captures some of the variance in life expectancy for which chronological age is not accountable, and it quantifies the heterogeneity in the presentation of the aging phenotype in various individuals. Among the many quantitative measures of biological age, the mathematically uncomplicated frailty/deficit index is simply the proportion of the total health deficits in various health items surveyed in different individuals. We used 3 different statistical methods that are popular in machine learning to select 17-28 health items that together are highly predictive of survival/mortality, from independent study cohorts. From the selected sets, we calculated frailty indexes and Klemera-Doubal's biological age estimates, and then compared their mortality prediction performance using Cox proportional hazards regression models. Our results indicate that the frailty index outperforms age and Klemera-Doubal's biological age estimates, especially among the oldest old who are most prone to biological aging-caused mortality. We also showed that a DNA methylation index, which was generated by applying the frailty/deficit index calculation method to 38 CpG sites that were selected using the same machine learning algorithms, can predict mortality even better than the best performing frailty index constructed from health, function, and blood chemistry.

Comparing Biological Age Estimates Using Domain-Specific Measures From the Canadian Longitudinal Study on Aging.

Many studies have shown that estimates of biological age (BA) can predict health-related outcomes in older adults. Often, researchers employ multiple measures belonging to a variety of biological/physiological systems, and assess the validity of BA estimates by how well they approximate chronological age (CA). However, it is not clear whether this is the best approach for judging a BA estimate, or whether certain groups of measures are more informative to this end. Using data from the Canadian Longitudinal Study on Aging, we composed panels of biological measures based on the physiological systems/domains they belong to (blood, organ function, physical/cognitive performance), and also composed a panel of measures that optimized the association of BA with CA. We then compared BA estimates for each according to their association with CA and health-related outcomes, including frailty, multimorbidity, chronic condition domains, disability, and health care utilization. Although BA estimated using all 40 measures (r = 0.74) or our age-optimized panel (r = 0.77) most closely approximated CA, the strength of associations to health-related outcomes was comparable or weaker than that of our panel composed only of physical performance measures (CA r = 0.59). All BA estimates were significantly associated to the outcomes considered, with exception to the neurological and musculoskeletal disease domains, and only varied slightly by sex. In summary, while the approximation of CA is important to consider when estimating BA, the strength of associations to prospective outcomes may be of greater importance. Hence, the context in which BA is estimated should be influenced by an investigator's specific research goals.

THE FRAILTY INDEX CONSTRUCTED USING FEATURE SELECTION METHODS IS THE BEST MEASURE OF BIOLOGICAL AGE

No single biomarker can reliably represent the complexity of aging. One way to overcome this shortcoming is to aggregate multiple biomarkers into a composite index. The frailty index (FI), which is simply the proportion of accumulated deficits among a set of various health markers, reflects functional abilities and risks of adverse outcomes. Furthermore, the FI accounts for the variation in mortality among individuals of the same chronological age (CA). Thus, the FI is a reliable measure of biological age (BA). Unlike the FI, other popular BA-estimating algorithms use CA directly as a biomarker or indirectly to derive model parameters. However, genetic, pharmaceutical, and intervention studies have shown that aging is delayable or reversible, indicating that CA is not the direct cause of aging. The popular Klemera-Doubal (K-D) method proposes two equations for BA estimation: BE uses CA to derive equation parameters, and BEC directly incorporates CA as an additional biomarker. BA estimates by the K-D method, especially by BEC, have been shown to outperform CA. Using Louisiana Healthy Aging Study (LHAS) data, we constructed an FI from a battery of health items selected using machine learning methods for their ability to predict mortality. We compared the FI with CA and the two K-D BA estimates and found that the FI was the better predictor of mortality, especially among nonagenarians. The results were replicable with the FI calculated from different sets of selected health items using NHANES and HRS datasets. These results demonstrate the FI as the best-performing measure of BA.

Between Roundhouse and Villa: Assessing Perinatal and Infant Burials from Piddington, Northamptonshire

AbstractThe discovery of perinatal and infant individuals is common in the excavation of Iron Age and Romano-British domestic sites. In recent years, the discovery of many such burials has led to interpretations of infanticide and unceremonious disposal. Although this has been a widely discussed phenomenon, much of the literature has focused on the funerary context, and the biological age and sex estimates of these individuals, with little consideration of the palaeopathological evidence. This article provides a detailed analysis of 17 perinates/infants from the late Iron Age/early Roman site of Piddington, Northants. It discusses the skeletal evidence for poor health and growth, and highlights the potential of these remains to reveal alternative insights into perinatal and infant death. Evidence of growth changes and pathological lesions were identified, suggesting that these individuals experienced chronic episodes of poor health that affected their skeletal development. The study explores the implications of these findings within the context of Iron Age and Roman Britain. At Piddington, the death of these infants is not associated with the cultural practice of infanticide, but occurred due to poor health, highlighting the precarious nature of infant survival in the past.

Patterns of Growth and Development in Urban and Rural Children of the Northern Part of European Russia

Two thousand children and adolescents of both sexes aged 7–17 were studied in 2009–2010 in Arkhangelsk and several villages of the Arkhangelsk Region. Results were compared with data on 1500 children of the same age collected by the same authors in the same area in 1988–1989. The program included some 50 metric and descriptive characteristics, biological age estimates, and somatotyping. We collected data on parental education and occupation, number of children per family, etc. Lengths of body segments and extremities, body mass index (BMI), and certain other indices were calculated. Statistical analysis included standardization of data and one-way ANOVA. Urban children were shown to be slightly taller than their rural peers but did not differ from them in weight, chest circumference or BMI. Modern children, both urban and rural, showed larger stature, weight, and chest circumference compared to those measured in 1988. Signifi cant changes in body proportions were found in modern children: they had longer trunks, smaller shoulder breadth, and larger pelvic breadth. Also, signifi cant increase in limb circumferences and subcutaneous fat was found. Modern urban and rural children were closer to each other in most physical characteristics than were their peers of the previous generation. The results can be interpreted in terms of the ongoing secular trend in population of the Arkhangelsk Region.

Response to Dr. Mitnitski's and Dr. Rockwood's Letter to the Editor: Biological Age Revisited

Response to Dr. Mitnitski's and Dr. Rockwood's Letter to the Editor: Biological Age Revisited

An empirical comparative study on biological age estimation algorithms with an application of Work Ability Index (WAI)

In this study, we described the characteristics of five different biological age (BA) estimation algorithms, including (i) multiple linear regression, (ii) principal component analysis, and somewhat unique methods developed by (iii) Hochschild, (iv) Klemera and Doubal, and (v) a variant of Klemera and Doubal's method. The objective of this study is to find the most appropriate method of BA estimation by examining the association between Work Ability Index (WAI) and the differences of each algorithm's estimates from chronological age (CA). The WAI was found to be a measure that reflects an individual's current health status rather than the deterioration caused by a serious dependency with the age. Experiments were conducted on 200 Korean male participants using a BA estimation system developed principally under the concept of non-invasive, simple to operate and human function-based. Using the empirical data, BA estimation as well as various analyses including correlation analysis and discriminant function analysis was performed. As a result, it had been confirmed by the empirical data that Klemera and Doubal's method with uncorrelated variables from principal component analysis produces relatively reliable and acceptable BA estimates.

Biological age and its estimation. III. Introduction of a correction to the multiple regression model of biological age and assessment of biological age in cross-sectional and longitudinal studies

In order to eliminate systematic error inherent in assessment of an individual biological age (BA), a correction is suggested for the multiple regression model of BA. The larger the difference between an individual's chronological age and middle age of the sample, the greater the correction value. BA estimates of apparently healthy people of 60 to 100 years were assessed using measurements of three physiological indices: hand grip strength, short-time memory, and vibrotactile sensitivity. Introduction of the correction allowed comparison of BA estimates of the same individual obtained from repeated observations. Preliminary results show there are at least four types of the BA changes with advancing age.

Biological age and its estimation. II. Assessment of biological age of albino rats by multiple regression analysis

Multiple regression model of biological age (BA) theoretically gives agreement with the main concept of BA. When assessment of BA is based on the model, the age being in regression center, the method provides satisfactory results, whereas BA estimates of individuals in extreme age groups are erroneous. Investigation of male and female Wistar rats of age 5–29 months showed the BA estimates calculated from 4–10 physiological indices in young (5–7 mo) animals are overestimated, and in old (24–28 mo) animals are underestimated. Coincidence of average BA in one-age group of animals with its chronological age served as a criterion for the correspondence of the estimate to “real” BA. The paper also examines the following questions: the necessary and sufficient number of physiological indices; the sample size from the intact animal population to establish normal aging standard; the relationship between BA and animal weight.