Does the epigenetic clock GrimAge predict mortality independent of genetic influences: an 18 year follow-up study in older female twin pairs

Abstract Background Epigenetic clocks are based on DNA methylation (DNAm). It has been suggested that these clocks are useable markers of biological aging and premature mortality. Because genetic factors explain variations in both epigenetic aging and mortality, this association could also be explained by shared genetic factors. We investigated the influence of genetic and lifestyle factors (smoking, alcohol consumption, physical activity, chronic diseases, body mass index) and education on the association of accelerated epigenetic aging with mortality using a longitudinal twin design. Utilizing a publicly available online tool, we calculated the epigenetic age using two epigenetic clocks, Horvath DNAmAge and DNAm GrimAge, in 413 Finnish twin sisters, aged 63–76 years, at the beginning of the 18-year mortality follow-up. Epigenetic age acceleration was calculated as the residuals from a linear regression model of epigenetic age estimated on chronological age (AAHorvath, AAGrimAge, respectively). Cox proportional hazard models were conducted for individuals and twin pairs. Results The results of the individual-based analyses showed an increased mortality hazard ratio (HR) of 1.31 (CI95: 1.13–1.53) per one standard deviation (SD) increase in AAGrimAge. The results indicated no significant associations of AAHorvath with mortality. Pairwise mortality analyses showed an HR of 1.50 (CI95: 1.02–2.20) per 1 SD increase in AAGrimAge. However, after adjusting for smoking, the HR attenuated substantially and was statistically non-significant (1.29; CI95: 0.84–1.99). Similarly, in multivariable adjusted models the HR (1.42–1.49) was non-significant. In AAHorvath, the non-significant HRs were lower among monozygotic pairs in comparison to dizygotic pairs, while in AAGrimAge there were no systematic differences by zygosity. Further, the pairwise analysis in quartiles showed that the increased within pair difference in AAGrimAge was associated with a higher all-cause mortality risk. Conclusions In conclusion, the findings suggest that DNAm GrimAge is a strong predictor of mortality independent of genetic influences. Smoking, which is known to alter DNAm levels and is built into the DNAm GrimAge algorithm, attenuated the association between epigenetic aging and mortality risk.

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The costs of competition: high social status males experience accelerated epigenetic aging in wild baboons

10.1101/2020.02.22.961052 ◽

2020 ◽

Cited By ~ 1

Author(s):

Jordan A. Anderson ◽

Rachel A. Johnston ◽

Amanda J. Lea ◽

Fernando A. Campos ◽

Tawni N. Voyles ◽

...

Keyword(s):

Dominance Rank ◽

Natural Populations ◽

Life History Strategy ◽

Male Dominance ◽

Biological Aging ◽

Epigenetic Clock ◽

Epigenetic Aging ◽

Epigenetic Age ◽

Aging Rates ◽

Young Life

AbstractAging, for virtually all life, is inescapable. However, within populations, biological aging rates vary. Understanding sources of variation in this process is central to understanding the biodemography of natural populations. We constructed a DNA methylation-based age predictor for an intensively studied wild baboon population in Kenya. Consistent with findings in humans, the resulting “epigenetic clock” closely tracks chronological age, but individuals are predicted to be somewhat older or younger than their known ages. Surprisingly, these deviations are not explained by the strongest predictors of lifespan in this population, early adversity and social integration. Instead, they are best predicted by male dominance rank: high-ranking males are predicted to be older than their true ages, and epigenetic age tracks changes in rank over time. Our results argue that achieving high rank for male baboons—the best predictor of reproductive success—imposes costs consistent with a “live fast, die young” life history strategy.

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Deconvolution of the epigenetic age discloses distinct inter-personal variability in epigenetic aging patterns

10.1101/2021.06.20.449142 ◽

2021 ◽

Author(s):

Tamar Shahal ◽

Elad Segev ◽

Thomas Konstantinovsky ◽

Yonit Marcus ◽

Gabi Shefer ◽

...

Keyword(s):

Healthy Subjects ◽

Interindividual Variability ◽

Chronological Age ◽

Biological Aging ◽

Epigenetic Clock ◽

Cpg Sites ◽

Epigenetic Aging ◽

Epigenetic Age ◽

Shed Light

Epigenetic age not only correlates with chronological age but predicts morbidity and mortality. We assumed that deconvolution of epigenetic age to its individual components could shed light on the diversity of epigenetic, and by inference, biological aging. Using the Horvath original epigenetic clock, we identified several CpG sites linked to distinct genes that quantitatively explain much of the interpersonal variability in epigenetic aging, with secretagogin and malin showing the most dominant effects. The analysis shows that the same epigenetic age for any given chronological age can be accounted for by variable contributions of identifiable CpG sites; that old epigenetic relative to chronological age is mostly explained by the same CpG sites, mapped to genes showing the highest interindividual variability differences in healthy subjects but not in subjects with type 2 diabetes. This paves the way to form personalized aging cards indicating the sources of accelerated/decelerated epigenetic aging in each examinee, en route to targeting specific sites as indicators, and perhaps treatment targets of personal undesirable age drifting.

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High social status males experience accelerated epigenetic aging in wild baboons

eLife ◽

10.7554/elife.66128 ◽

2021 ◽

Vol 10 ◽

Author(s):

Jordan A Anderson ◽

Rachel A Johnston ◽

Amanda J Lea ◽

Fernando A Campos ◽

Tawni N Voyles ◽

...

Keyword(s):

Dominance Rank ◽

Natural Populations ◽

Life History Strategy ◽

Male Dominance ◽

Biological Aging ◽

Epigenetic Clock ◽

Epigenetic Aging ◽

Epigenetic Age ◽

Aging Rates ◽

Young Life

Aging, for virtually all life, is inescapable. However, within populations, biological aging rates vary. Understanding sources of variation in this process is central to understanding the biodemography of natural populations. We constructed a DNA methylation-based age predictor for an intensively studied wild baboon population in Kenya. Consistent with findings in humans, the resulting 'epigenetic clock' closely tracks chronological age, but individuals are predicted to be somewhat older or younger than their known ages. Surprisingly, these deviations are not explained by the strongest predictors of lifespan in this population, early adversity and social integration. Instead, they are best predicted by male dominance rank: high-ranking males are predicted to be older than their true ages, and epigenetic age tracks changes in rank over time. Our results argue that achieving high rank for male baboons—the best predictor of reproductive success—imposes costs consistent with a 'live fast, die young' life history strategy.

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A New Monocyte Epigenetic Clock Reveals Effects of Alcohol Consumption on Epigenetic Aging in Three Independent Cohorts

10.1101/2021.03.22.436488 ◽

2021 ◽

Author(s):

Xiaoyu Liang ◽

Rajita Sinha ◽

Amy C. Justice ◽

Mardge H. Cohen ◽

Bradley E. Aouizerat ◽

...

Keyword(s):

Alcohol Consumption ◽

Hiv Infection ◽

Clinical Decision Making ◽

Clinical Decision ◽

Biological Age ◽

Biological Aging ◽

Epigenetic Clock ◽

Excessive Alcohol Consumption ◽

Epigenetic Age ◽

The Impact

AbstractBackgroundExcessive alcohol consumption increases the risk of aging-related comorbidities and mortality. Assessing the impact of alcohol consumption on biological age is important for clinical decision-making and prevention. Evidence shows that alcohol alters monocyte function, and age is associated with DNA methylome and transcriptomic changes among monocytes. However, no monocyte-based epigenetic clock is currently available. In this study, we developed a new monocyte-based DNA methylation clock (MonoDNAmAge) by using elastic net regularization. The MonoDNAmAge was validated by benchmarking using epigenetic age acceleration (EAA) in HIV infection. Using MonoDNAmAge clock as well as four established clocks (i.e., HorvathDNAmAge, HannumDNAmAge, PhenoDNAmAge, GrimDNAmAge), we then evaluated the effect of alcohol consumption on biological aging in three independent cohorts (N=2,242).ResultsMonoDNAmAge, comprised of 186 CpG sites, was highly correlated with chronological age (rtraining=0.96, p<2.20E-16; rtesting=0.86, p=1.55E-141). The MonoDNAmAge clock predicted an approximately 10-year age acceleration from HIV infection in two cohorts. Quadratic regression analysis showed a nonlinear relationship between MonoDNAmAge and alcohol consumption in the Yale Stress Center Community Study (YSCCS, pmodel=4.55E-08, px2 =7.80E-08) and in the Veteran Aging Cohort Study (VACS, pmodel=1.85E-02, px2 =3.46E-02). MonoDNAmAge and light alcohol consumption showed a negative linear relationship in the Women’s Interagency HIV Study (WIHS, β=-2.63, px=2.82E-06). Heavy consumption increased EAAMonoDNAmAge up to 1.60 years in the VACS while light consumption decreased EAAMonoDNAmAge to 2.66 years in the WIHS. These results were corroborated by the four established epigenetic clocks.ConclusionsWe observed a nonlinear effect of alcohol consumption on epigenetic age that is estimated by a novel monocyte-based “clock” in three distinct cohorts, highlighting the complex effects of alcohol consumption on biological age.

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

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph16173074 ◽

2019 ◽

Vol 16 (17) ◽

pp. 3074 ◽

Cited By ~ 5

Author(s):

Pavanello ◽

Campisi ◽

Tona ◽

Lin ◽

Iliceto

Keyword(s):

Myocardial Infarction ◽

Age Estimation ◽

Healthy Subjects ◽

Molecular Mechanisms ◽

Biological Age ◽

Chronological Age ◽

Biological Aging ◽

Epigenetic Clock ◽

Age Related ◽

Epigenetic Age

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

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Association between Phenotypic Age and Mortality in Patients with Multivessel Coronary Artery Disease

Disease Markers ◽

10.1155/2022/4524032 ◽

2022 ◽

Vol 2022 ◽

pp. 1-11

Author(s):

Qiong Ma ◽

Bo-Lin Li ◽

Lei Yang ◽

Miao Zhang ◽

Xin-Xin Feng ◽

...

Keyword(s):

Coronary Artery Disease ◽

Mortality Risk ◽

Proportional Hazards ◽

Roc Curves ◽

Cox Proportional Hazards ◽

Biological Aging ◽

Multivessel Coronary Artery Disease ◽

All Cause Mortality ◽

Artery Disease

Background. Chronological age (CA) is not a perfect proxy for the true biological aging status of the body. A new biological aging measure, phenotypic age (PhenoAge), has been shown to capture morbidity and mortality risk in the general US population and diverse subpopulations. This study was aimed at evaluating the association between PhenoAge and long-term outcome of patients with multivessel coronary artery disease (CAD). Methods. A total of 609 multivessel CAD patients who received PCI attempt and with follow-up were enrolled. The clinical outcome was all-cause mortality on follow-up. PhenoAge was calculated using an equation constructed from CA and 9 clinical biomarkers. Cox proportional hazards regression models and receiver operating characteristic (ROC) curves were performed to evaluate the association between PhenoAge and mortality. Results. Overall, patients with more diseases had older PhenoAge and phenotypic age acceleration (PhenoAgeAccel). After a median follow-up of 33.5 months, those with positive PhenoAgeAccel had a significantly higher incidence of all-cause mortality ( P = 0.001 ). After adjusting for CA, Cox proportional hazards models showed that both PhenoAge and PhenoAgeAccel were significantly associated with all-cause mortality. Even after further adjusting for confounding factors, each 10-year increase in PhenoAge was also associated with a 51% increased mortality risk. ROC curves revealed that PhenoAge, with an area under the curve of 0.705, significantly outperformed CA, the individual clinical chemistry measure, and other risk factors. When reexamining the ROC curves using various combinations of variables, we found that PhenoAge provides additional predictive power to all models. Conclusions. In conclusion, PhenoAge was strongly associated with all-cause mortality even after adjusting for CA. Our findings suggest that PhenoAge measure may be complementary in predicting mortality risk for patients with multivessel CAD.

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An epigenetic aging analysis of randomized metformin and weight loss interventions in overweight postmenopausal breast cancer survivors

Clinical Epigenetics ◽

10.1186/s13148-021-01218-y ◽

2021 ◽

Vol 13 (1) ◽

Author(s):

Jamaji C. Nwanaji-Enwerem ◽

Felicia Fei-Lei Chung ◽

Lars Van der Laan ◽

Alexei Novoloaca ◽

Cyrille Cuenin ◽

...

Keyword(s):

Breast Cancer ◽

Weight Loss ◽

Cancer Survivors ◽

Breast Cancer Survivors ◽

Controlled Trial ◽

Postmenopausal Breast Cancer ◽

Biological Aging ◽

Epigenetic Aging ◽

Epigenetic Age ◽

Registration Number

AbstractMetformin and weight loss relationships with epigenetic age measures—biological aging biomarkers—remain understudied. We performed a post-hoc analysis of a randomized controlled trial among overweight/obese breast cancer survivors (N = 192) assigned to metformin, placebo, weight loss with metformin, or weight loss with placebo interventions for 6 months. Epigenetic age was correlated with chronological age (r = 0.20–0.86; P < 0.005). However, no significant epigenetic aging associations were observed by intervention arms. Consistent with published reports in non-cancer patients, 6 months of metformin therapy may be inadequate to observe expected epigenetic age deceleration. Longer duration studies are needed to better characterize these relationships.Trial Registration: Registry Name: ClincialTrials.Gov.Registration Number: NCT01302379.Date of Registration: February 2011.URL:https://clinicaltrials.gov/ct2/show/NCT01302379

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Association of Epigenetic Age and p16 INK4a With Markers of T-Cell Composition in a Healthy Cohort

The Journals of Gerontology Series A ◽

10.1093/gerona/glaa108 ◽

2020 ◽

Vol 75 (12) ◽

pp. 2299-2303

Author(s):

Christin E Burd ◽

Juan Peng ◽

Bryon F Laskowski ◽

Jennifer L Hollyfield ◽

Suohui Zhang ◽

...

Keyword(s):

Peripheral Blood ◽

Immune Checkpoints ◽

Epigenetic Clock ◽

P16ink4a Expression ◽

Cell Composition ◽

Epigenetic Aging ◽

Epigenetic Age ◽

Healthy Cohort ◽

Subtype Differentiation ◽

P16 Ink4a

Abstract How the measurement of aging biomarkers in peripheral blood T-lymphocytes (PBTLs) is influenced by cell composition is unclear. Here, we collected peripheral blood and isolated CD3+ PBTLs from 117 healthy couples between the ages of 21 and 72. Each sample was profiled for Horvath epigenetic clock (DNAm), p16INK4a expression, cytomegalovirus (CMV) seropositivity and 74 mRNA markers of PBTL subtype, differentiation, immune checkpoints, and cytokine production. Correlations between individual aging biomarkers (DNAm or p16INK4a) and PBTL mRNAs were corrected for chronological age, sex, and couple. DNAm measurements correlated with CMV seropositivity as well as PBTL mRNAs indicative of effector function (CD8A, EOMES, TBX21, GZMB), poor proliferative capacity (KLRG1, CD57), differentiation (CD45RO, CD45RA), and immune checkpoints (PDCD1, TIGIT, LAG3, CD160, CD244). In contrast, only three PBTL mRNAs, CD28, CD244, and p14ARF, showed a significant association with p16INK4a. p16INK4a expression also showed a weaker association with immunosenescent PBTL subsets than DNAm in flow cytometry analyses. These data suggest that PBTL composition has a greater influence on DNAm than p16INK4a and link accelerated epigenetic aging to immunosenescent phenotypes.

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Dysfunctional epigenetic aging of the normal colon and colorectal cancer risk

Clinical Epigenetics ◽

10.1186/s13148-019-0801-3 ◽

2020 ◽

Vol 12 (1) ◽

Cited By ~ 11

Author(s):

Ting Wang ◽

Sean K. Maden ◽

Georg E. Luebeck ◽

Christopher I. Li ◽

Polly A. Newcomb ◽

...

Keyword(s):

Dna Methylation ◽

Risk Group ◽

Risk Groups ◽

Biological Age ◽

Chronological Age ◽

Epigenetic Clock ◽

Normal Colon ◽

Epigenetic Aging ◽

Epigenetic Age ◽

Epigenetic Age Acceleration

Abstract Background Chronological age is a prominent risk factor for many types of cancers including colorectal cancer (CRC). Yet, the risk of CRC varies substantially between individuals, even within the same age group, which may reflect heterogeneity in biological tissue aging between people. Epigenetic clocks based on DNA methylation are a useful measure of the biological aging process with the potential to serve as a biomarker of an individual’s susceptibility to age-related diseases such as CRC. Methods We conducted a genome-wide DNA methylation study on samples of normal colon mucosa (N = 334). Subjects were assigned to three cancer risk groups (low, medium, and high) based on their personal adenoma or cancer history. Using previously established epigenetic clocks (Hannum, Horvath, PhenoAge, and EpiTOC), we estimated the biological age of each sample and assessed for epigenetic age acceleration in the samples by regressing the estimated biological age on the individual’s chronological age. We compared the epigenetic age acceleration between different risk groups using a multivariate linear regression model with the adjustment for gender and cell-type fractions for each epigenetic clock. An epigenome-wide association study (EWAS) was performed to identify differential methylation changes associated with CRC risk. Results Each epigenetic clock was significantly correlated with the chronological age of the subjects, and the Horvath clock exhibited the strongest correlation in all risk groups (r > 0.8, p < 1 × 10−30). The PhenoAge clock (p = 0.0012) revealed epigenetic age deceleration in the high-risk group compared to the low-risk group. Conclusions Among the four DNA methylation-based measures of biological age, the Horvath clock is the most accurate for estimating the chronological age of individuals. Individuals with a high risk for CRC have epigenetic age deceleration in their normal colons measured by the PhenoAge clock, which may reflect a dysfunctional epigenetic aging process.

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Phenotypic frailty and multimorbidity are independent 18-year mortality risk indicators in older men

European Geriatric Medicine ◽

10.1007/s41999-021-00472-w ◽

2021 ◽

Author(s):

Timo E. Strandberg ◽

Linda Lindström ◽

Satu Jyväkorpi ◽

Annele Urtamo ◽

Kaisu H. Pitkälä ◽

...

Keyword(s):

Mortality Risk ◽

Cox Regression ◽

Reference Group ◽

Strong Predictor ◽

Cognitive Status ◽

Risk Indicators ◽

Adl Disability ◽

Long Term Prognosis

Abstract Purpose Multimorbidity, prefrailty, and frailty are frequent in ageing populations, but their independent relationships to long-term prognosis in home-dwelling older people are not well recognised. Methods In the Helsinki Businessmen Study (HBS) men with high socioeconomic status (born 1919–1934, n = 3490) have been followed-up from midlife. In 2000, multimorbidity (≥ 2 conditions), phenotypic prefrailty and frailty were determined in 1365 home-dwelling men with median age of 73 years). Disability was assessed as a possible confounder. 18-year mortality follow-up was established from registers and Cox regression used for analyses. Results Of the men, 433 (31.7%) were nonfrail and without multimorbidity at baseline (reference group), 500 (36.6%) and 82 (6.0%) men had prefrailty or frailty, respectively, without multimorbidity, 84 (6.2%) men had multimorbidity only, and 201 (14.7%) and 65 (4.8%) men had prefrailty or frailty together with multimorbidity. Only 30 (2.2%) and 86 (6.3%) showed signs of ADL or mobility disability. In the fully adjusted analyses (including ADL disability, mental and cognitive status) of 18-year mortality, frailty without multimorbidity (hazard ratio 1.62, 95% confidence interval 1.13–2.31) was associated with similar mortality risk than multimorbidity without frailty (1.55, 1.17–2.06). The presence of both frailty and multimorbidity indicated a strong mortality risk (2.93, 2.10–4.07). Conclusion Although multimorbidity is generally considered a substantial health problem, our long-term observational study emphasises that phenotypic frailty alone, independently of disability, may be associated with a similar risk, and a combination of multimorbidity and frailty is an especially strong predictor of mortality.

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