scholarly journals A toolkit for quantification of biological age from blood-chemistry and organ-function-test data: BioAge

2021 ◽  
Author(s):  
Dayoon Kwon ◽  
Daniel W Belsky
Keyword(s):  
Dna Methylation ◽  
Controlled Trial ◽  
Blood Chemistry ◽  
R Package ◽  
Biological Age ◽  
Superior Performance ◽  
Biological Aging ◽  
Measurement Tools ◽  
Population Science ◽  
Surrogate Measures

Methods to quantify biological aging are emerging as new measurement tools for epidemiology and population science and have been proposed as surrogate measures for healthy lifespan extension in geroscience clinical trials. Publicly available software packages to compute biological aging measurements from DNA methylation data have accelerated dissemination of these measures and generated rapid gains in knowledge about how different measures perform in a range of datasets. Biological age measures derived from blood chemistry data were introduced at the same time as the DNA methylation measures and, in multiple studies, demonstrate superior performance to these measures in prediction of healthy lifespan. However, their dissemination has been slow by comparison, resulting in a significant gap in knowledge. We developed a software package to help address this knowledge gap. The BioAge R package, available for download at GitHub (http://github.com/dayoonkwon/BioAge), implements three published methods to quantify biological aging based on analysis of chronological age and mortality risk: Klemera-Doubal Biological Age, PhenoAge, and homeostatic dysregulation. The package allows users to parametrize measurement algorithms using custom sets of biomarkers, to compare the resulting measurements to published versions of the Klemera-Doubal method and PhenoAge algorithms, and to score the measurements in new datasets. We applied BioAge to safety lab data from the CALERIETM randomized controlled trial, the first-ever human trial of long-term calorie restriction in healthy, non-obese adults, to test effects of intervention on biological aging. Results contribute evidence that CALERIE intervention slowed biological aging. BioAge is a toolkit to facilitate measurement of biological age for geroscience.

scholarly journals Predictors of Biological Age: The Implications for Wellness and Aging Research

2021 ◽  
Vol 7 ◽  
pp. 233372142110464
Author(s):  
Trevor Lohman ◽  
Gurinder Bains ◽  
Lee Berk ◽  
Everett Lohman
Keyword(s):  
Dna Methylation ◽  
Lifestyle Modification ◽  
Biological Age ◽  
Current Evidence ◽  
Biological Aging ◽  
Clinical Value ◽  
Aging Research ◽  
Mortality And Morbidity ◽  
Morbidity Risk ◽  
Pharmaceutical Interventions

As healthspan and lifespan research breakthroughs have become more commonplace, the need for valid, practical markers of biological age is becoming increasingly paramount. The accessibility and affordability of biological age predictors that can reveal information about mortality and morbidity risk, as well as remaining years of life, has profound clinical and research implications. In this review, we examine 5 groups of aging biomarkers capable of providing accurate biological age estimations. The unique capabilities of these biomarkers have far reaching implications for the testing of both pharmaceutical and non-pharmaceutical interventions designed to slow or reverse biological aging. Additionally, the enhanced validity and availability of these tools may have increasingly relevant clinical value. The authors of this review explore those implications, with an emphasis on lifestyle modification research, and provide an overview of the current evidence regarding 5 biological age predictor categories: Telomere length, composite biomarkers, DNA methylation “epigenetic clocks,” transcriptional predictors of biological age, and functional age predictors.

scholarly journals Association of Blood Chemistry Quantifications of Biological Aging With Disability and Mortality in Older Adults

2019 ◽  
Vol 75 (9) ◽  
pp. 1671-1679 ◽  
Author(s):  
Daniel C Parker ◽  
Bryce N Bartlett ◽  
Harvey J Cohen ◽  
Gerda Fillenbaum ◽  
Janet L Huebner ◽  
...  
Keyword(s):  
Older Adults ◽  
African American ◽  
Activities Of Daily Living ◽  
Daily Living ◽  
Rate Ratio ◽  
Incidence Rate Ratio ◽  
Blood Chemistry ◽  
Biological Age ◽  
Biological Aging ◽  
Reference Samples

Abstract Quantification of biological aging has been proposed for population surveillance of age-related decline in system integrity and evaluation of geroprotective therapies. However, methods of quantifying biological aging have been little studied in geriatric populations. We analyzed three clinical-biomarker-algorithm methods to quantify biological aging. Klemera–Doubal method Biological Age and homeostatic dysregulation algorithms were parameterized from analysis of U.S. National Health and Nutrition Examination Surveys (NHANES) data (N = 36,207) based on published methods. Levine method Biological Age was adapted from published analysis of NHANES data. Algorithms were applied to biomarker data from the Duke Established Populations for Epidemiologic Studies of the Elderly (Duke-EPESE) cohort of older adults (N = 1,374, aged 71–102 years, 35% male, 52% African American). We tested associations of biological aging measures with participant reported Activities of daily living (ADL), instrumental activities of daily living (IADL) dependencies, and mortality. We evaluated the sensitivity of results to the demographic composition of reference samples and biomarker sets used to develop biological aging algorithms. African American and white Duke-EPESE participants with more advanced biological aging reported dependence in more ADLs and IADLs and were at increased risk of death over follow-up through 2017. Effect sizes were similar across algorithms, but were strongest for Levine method Biological Age (per-quintile increase in ADL incidence rate ratio = 1.25, 95% confidence interval [1.17–1.37], IADL incidence rate ratio = 1.23 [1.15–1.32], mortality hazard ratio = 1.12 [1.08–1.16]). Results were insensitive to demographic composition of reference samples, but modestly sensitive to the biomarker sets used to develop biological aging algorithms. Blood-chemistry-based quantifications of biological aging show promise for evaluating the effectiveness of interventions to extend healthy life span in older adults.

scholarly journals Effect of Long-Term Caloric Restriction on DNA Methylation Measures of Biological Aging in Healthy Adults: CALERIETM Trial Analysis

2021 ◽  
Author(s):  
reem waziry ◽  
David L corcoran ◽  
Kim M Huffman ◽  
Michael S Kobor ◽  
Meeraj Kothari ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Controlled Trial ◽  
Model Organisms ◽  
Biological Aging ◽  
Control Group ◽  
Epigenetic Clock ◽  
Long Term Effects ◽  
Cohen’S D ◽  
Cohen's D

Calorie restriction (CR) slows aging and increases healthy lifespan in model organisms. We tested if CR slowed biological aging in humans using DNA methylation analysis of blood samples from N=197 participants in the Comprehensive Assessment of Long-term Effects of Reducing Intake of Energy (CALERIETM) randomized controlled trial. We quantified CR effects on biological aging by comparing change scores for six epigenetic-clock and Pace-of-Aging measures between n=128 CR-group and n=69 ad-libitum-control-group participants at 12- and 24-month follow-ups. CR effects were strongest for DunedinPACE Pace of Aging (12-month Cohen's d=0.3; 24-month Cohen's d=0.2, p<0.01 for both), followed by DunedinPoAm and the GrimAge epigenetic clock, although effects for these measures were not statistically different from zero (p>0.08). CR effects for other epigenetic clocks were in the opposite direction (all p>0.15). CALERIE intervention slowed Pace of Aging but showed minimal effect on epigenetic clocks hypothesized to reflect longer term accumulation of aging burden.

scholarly journals Social mobility and biological aging among older adults in the United States

2021 ◽  
Author(s):  
Gloria Huei-Jong Graf ◽  
Yalu Zhang ◽  
Benjamin W Domingue ◽  
Kathleen Mullan Harris ◽  
Meeraj Kothari ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Social Mobility ◽  
Healthy Aging ◽  
Upward Mobility ◽  
Blood Chemistry ◽  
The United States ◽  
Effect Sizes ◽  
Biological Aging ◽  
Educational Mobility ◽  
System Integrity

Lower socioeconomic status is associated with faster biological aging, the gradual and progressive decline in system integrity that accumulates with advancing age. Efforts to promote upward social mobility may therefore extend healthy lifespan. However, recent studies suggest that upward mobility may also have biological costs related to the stresses of crossing social boundaries. We analyzed blood-chemistry and DNA methylation (DNAm) data from n=9286 participants in the 2016 Health and Retirement Study (HRS) Venous Blood Study to test associations of life-course social mobility with biological aging. We quantified social mobility from childhood to later-life using data on childhood family characteristics, educational attainment, and wealth accumulation. We quantified biological aging using three DNA methylation "clocks" and three blood-chemistry algorithms. We observed substantial social mobility among study participants. Those who achieved upward mobility exhibited less-advanced and slower biological aging. Associations of upward mobility with less-advanced and slower aging were consistent for blood-chemistry and DNAm measures of biological aging and were similar for men and women and for Black and White Americans (Pearson-r effect-sizes ~0.2 for blood-chemistry measures and the DNAm GrimAge clock and DunedinPoAm pace-of-aging measures; effect-sizes were smaller for the DNAm PhenoAge clock). Analysis restricted to educational mobility revealed differential effects by racial identity, suggesting that mediating links between educational mobility and healthy aging may be disrupted by structural racism. In contrast, mobility producing accumulation of wealth appeared to benefit White and Black Americans equally, suggesting economic intervention to reduce wealth inequality may have potential to heal disparities in healthy aging.

scholarly journals Testing DNA-methylation and blood-chemistry measures of biological aging in models of Black-White disparities in healthspan characteristics

2021 ◽  
Author(s):  
GH Graf ◽  
CL Crowe ◽  
M Kothari ◽  
D Kwon ◽  
JJ Manly ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Health Disparities ◽  
Daily Living ◽  
Blood Chemistry ◽  
The United States ◽  
Biological Aging ◽  
White Americans ◽  
Proof Of Concept ◽  
Black And White ◽  
Retirement Study

ABSTRACTBiological aging is a proposed mechanism through which social determinants drive health disparities. We conducted proof-of-concept testing of eight DNA-methylation and blood-chemistry quantifications of biological aging as mediators of disparities in healthspan between Black and White participants in the United States Health and Retirement Study (HRS; n=8231). We quantified biological aging from four DNA-methylation “clocks” (Horvath, Hannum, PhenoAge, and GrimAge), a DNA-methylation Pace of Aging (DunedinPoAm), and three blood-chemistry measures (PhenoAge, Klemera-Doubal method Biological Age, and homeostatic dysregulation). We quantified Black-White disparities in healthspan from tests of physical-performance, self-reported limitations to activities of daily living (ADLs), and physician-diagnosed chronic diseases. DNA-methylation and blood-chemistry quantifications of biological aging were moderately correlated (Pearson-r range 0.1-0.4). GrimAge, DunedinPoAm and all three blood-chemistry measures were associated with healthspan characteristics (10-25% increase in risk per SD of biological aging) and showed evidence of more advanced/faster biological aging in Black compared with White participants (Cohen’s d=.3-.5). In mediation analysis, these measures accounted for 19-48% of Black-White differences in healthspan-related characteristics. Evidence that Black Americans are both biologically older and aging more rapidly than White Americans of the same chronological age suggests that differences in aging may represent a novel pathway to understand and eliminate health disparities.

scholarly journals Association of Facial Aging with DNA Methylation and Epigenetic Age Predictions

2018 ◽  
Author(s):  
Riccardo E Marioni ◽  
Daniel W Belsky ◽  
Ian J Deary ◽  
Wolfgang Wagner
Keyword(s):  
Dna Methylation ◽  
Healthy Aging ◽  
Later Life ◽  
Biological Age ◽  
Chronological Age ◽  
Biological Aging ◽  
Facial Aging ◽  
Epigenetic Age ◽  
All Cause Mortality ◽  
Facial Images

AbstractEvaluation of biological age, as opposed to chronological age, is of high relevance for interventions to increase healthy aging. Highly reproducible age-associated DNA methylation (DNAm) changes can be integrated into algorithms for epigenetic age predictions. These predictors have mostly been trained to correlate with chronological age, but they are also indicative for biological aging. For example accelerated epigenetic age of blood is associated with higher risk of all-cause mortality in later life. The perceived age of facial images (face-age) is also associated with all-cause mortality and other aging-associated traits. In this study, we therefore tested the hypothesis that an epigenetic predictor for biological age might be trained on face-age as surrogate for biological age, rather than on chronological age. Our data demonstrate that facial aging and DNAm changes in blood provide two independent measures for biological aging.

Biological age and rates of aging of patients with vibration disease and bilateral sensorineural hearing loss

2020 ◽  
pp. 624-624
Author(s):  
S. I. Ereniev ◽  
O. V. Plotnikova
Keyword(s):  
Hearing Loss ◽  
Sensorineural Hearing Loss ◽  
Sensorineural Hearing ◽  
Biological Age ◽  
Biological Aging ◽  
Vibration Disease ◽  
Bilateral Sensorineural Hearing Loss

Biological age and rates of aging of patients with vibration disease and bilateral sensorineural hearing loss were studied. The biological age of patients exceeded the calendar age by an average of 7.36±0.36 years and the proper biological age by 10.79±0.72 years. The rate of biological aging of the examined patients was 1.14±0.08 times higher than the rate of aging of their healthy peers.

scholarly journals Genome-wide association studies identify 137 genetic loci for DNA methylation biomarkers of aging

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Daniel L. McCartney ◽  
Josine L. Min ◽  
Rebecca C. Richmond ◽  
Ake T. Lu ◽  
Maria K. Sobczyk ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Genome Wide Association Study ◽  
Association Studies ◽  
Genome Wide Association ◽  
Biological Aging ◽  
Genome Wide Association Studies ◽  
Genetic Loci ◽  
Biomarkers Of Aging ◽  
Genome Wide ◽  
Shared Genetic

Abstract Background Biological aging estimators derived from DNA methylation data are heritable and correlate with morbidity and mortality. Consequently, identification of genetic and environmental contributors to the variation in these measures in populations has become a major goal in the field. Results Leveraging DNA methylation and SNP data from more than 40,000 individuals, we identify 137 genome-wide significant loci, of which 113 are novel, from genome-wide association study (GWAS) meta-analyses of four epigenetic clocks and epigenetic surrogate markers for granulocyte proportions and plasminogen activator inhibitor 1 levels, respectively. We find evidence for shared genetic loci associated with the Horvath clock and expression of transcripts encoding genes linked to lipid metabolism and immune function. Notably, these loci are independent of those reported to regulate DNA methylation levels at constituent clock CpGs. A polygenic score for GrimAge acceleration showed strong associations with adiposity-related traits, educational attainment, parental longevity, and C-reactive protein levels. Conclusion This study illuminates the genetic architecture underlying epigenetic aging and its shared genetic contributions with lifestyle factors and longevity.

A zero-inflated non-negative matrix factorization for the deconvolution of mixed signals of biological data

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Yixin Kong ◽  
Ariangela Kozik ◽  
Cindy H. Nakatsu ◽  
Yava L. Jones-Hall ◽  
Hyonho Chun
Keyword(s):  
Matrix Factorization ◽  
Factor Model ◽  
R Package ◽  
Biological Data ◽  
Superior Performance ◽  
Sequencing Data ◽  
Fecal Microbiome ◽  
Brain Gene Expression ◽  
Cell Transcriptome ◽  
Non Negative Matrix Factorization

Abstract A latent factor model for count data is popularly applied in deconvoluting mixed signals in biological data as exemplified by sequencing data for transcriptome or microbiome studies. Due to the availability of pure samples such as single-cell transcriptome data, the accuracy of the estimates could be much improved. However, the advantage quickly disappears in the presence of excessive zeros. To correctly account for this phenomenon in both mixed and pure samples, we propose a zero-inflated non-negative matrix factorization and derive an effective multiplicative parameter updating rule. In simulation studies, our method yielded the smallest bias. We applied our approach to brain gene expression as well as fecal microbiome datasets, illustrating the superior performance of the approach. Our method is implemented as a publicly available R-package, iNMF.

Sign in / Sign up

Export Citation Format

Share Document