BackgroundPrevious studies have developed models predicting methylation age from DNA methylation in blood and other tissues (epigenetic clock) and suggested the difference between DNA methylation and chronological ages as a marker of healthy aging. The goal of this study was to confirm and expand such observations by investigating whether different concepts of the epigenetic clocks in a population-based cohort are associated with cancer, cardiovascular, and all-cause mortality.ResultsDNA methylation age was estimated in a cohort of 1863 older people, and the difference between age predicted by DNA methylation and chronological age (Δage) was calculated. A case-cohort design and weighted proportional Cox hazard models were used to estimate associations of Δage with cancer, cardiovascular, and all-cause mortality. Hazard ratios for Δage (per 5 years) calculated using the epigenetic clock developed by Horvath were 1.23 (95 % CI 1.10–1.38) for all-cause mortality, 1.22 (95 % CI 1.03–1.45) for cancer mortality, and 1.19 (95 % CI 0.98–1.43) for cardiovascular mortality after adjustment for batch effects, age, sex, educational level, history of chronic diseases, hypertension, smoking status, body mass index, and leucocyte distribution. Associations were similar but weaker for Δage calculated using the epigenetic clock developed by Hannum.ConclusionsThese results show that age acceleration in terms of the difference between age predicted by DNA methylation and chronological age is an independent predictor of all-cause and cause-specific mortality and may be useful as a general marker of healthy aging.Electronic supplementary materialThe online version of this article (doi:10.1186/s13148-016-0228-z) contains supplementary material, which is available to authorized users.

DNA methylation (DNAm) has been revealed to play a role in various diseases. Here we performed epigenome-wide screening and validation to identify mortality-related DNAm signatures in a general population-based cohort with up to 14 years follow-up. In the discovery panel in a case-cohort approach, 11,063 CpGs reach genome-wide significance (FDR<0.05). 58 CpGs, mapping to 38 well-known disease-related genes and 14 intergenic regions, are confirmed in a validation panel. A mortality risk score based on ten selected CpGs exhibits strong association with all-cause mortality, showing hazard ratios (95% CI) of 2.16 (1.10–4.24), 3.42 (1.81–6.46) and 7.36 (3.69–14.68), respectively, for participants with scores of 1, 2–5 and 5+ compared with a score of 0. These associations are confirmed in an independent cohort and are independent from the ‘epigenetic clock'. In conclusion, DNAm of multiple disease-related genes are strongly linked to mortality outcomes. The DNAm-based risk score might be informative for risk assessment and stratification.

Prostate cancer is a highly heritable disease with large disparities in incidence rates across ancestry populations. We conducted a multiancestry meta-analysis of prostate cancer genome-wide association studies (107,247 cases and 127,006 controls) and identified 86 new genetic risk variants independently associated with prostate cancer risk, bringing the total to 269 known risk variants. The top genetic risk score (GRS) decile was associated with odds ratios that ranged from 5.06 [95% confidence interval (CI) 4.84–5.29] for men of European ancestry to 3.74 [95% CI 3.36–4.17] for men of African ancestry. Men of African ancestry were estimated to have a mean GRS that was 2.18-times higher [95% CI 2.14–2.22], and men of East Asian ancestry 0.73-times lower [95% CI 0.71–0.76], than men of European ancestry. These findings support the role of germline variation contributing to population differences in prostate cancer risk, with the GRS offering an approach for personalized risk prediction.

Objectives To develop and validate a genetic tool to predict age of onset of aggressive prostate cancer (PCa) and to guide decisions of who to screen and at what age. Design Analysis of genotype, PCa status, and age to select single nucleotide polymorphisms (SNPs) associated with diagnosis. These polymorphisms were incorporated into a survival analysis to estimate their effects on age at diagnosis of aggressive PCa (that is, not eligible for surveillance according to National Comprehensive Cancer Network guidelines; any of Gleason score ≥7, stage T3-T4, PSA (prostate specific antigen) concentration ≥10 ng/L, nodal metastasis, distant metastasis). The resulting polygenic hazard score is an assessment of individual genetic risk. The final model was applied to an independent dataset containing genotype and PSA screening data. The hazard score was calculated for these men to test prediction of survival free from PCa. Setting Multiple institutions that were members of international PRACTICAL consortium. Participants All consortium participants of European ancestry with known age, PCa status, and quality assured custom (iCOGS) array genotype data. The development dataset comprised 31 747 men; the validation dataset comprised 6411 men. Main Outcome Measures Prediction with hazard score of age of onset of aggressive cancer in validation set. Results In the independent validation set, the hazard score calculated from 54 single nucleotide polymorphisms was a highly significant predictor of age at diagnosis of aggressive cancer (z=11.2, P<10−16). When men in the validation set with high scores (>98th centile) were compared with those with average scores (30th-70th centile), the hazard ratio for aggressive cancer was 2.9 (95% confidence interval 2.4 to 3.4). Inclusion of family history in a combined model did not improve prediction of onset of aggressive PCa (P=0.59), and polygenic hazard score performance remained high when family history was accounted for. Additionally, the positive predictive value of PSA screening for aggressive PCa was increased with increasing polygenic hazard score. Conclusions Polygenic hazard scores can be used for personalised genetic risk estimates that can predict for age at onset of aggressive PCa.

BackgroundThe epigenetic clock, in particular epigenetic pre-aging quantified by the so-called DNA methylation age acceleration, has recently been suggested to closely correlate with a variety of disease phenotypes. There remains a dearth of data, however, on its association with telomere length and frailty, which can be considered major correlates of age on the genomic and clinical level, respectively.ResultsIn this cross-sectional observational study on altogether 1820 subjects from two subsets (n = 969 and n = 851; mean ± standard deviation age 62.1 ± 6.5 and 63.0 ± 6.7 years, respectively) of the ESTHER cohort study of the elderly general population in Germany, DNA methylation age was calculated based on a 353 loci predictor previously developed in a large meta-study, and the difference-based epigenetic age acceleration was calculated as predicted methylation age minus chronological age. No correlation of epigenetic age acceleration with telomere length was found in our study (p = 0.63). However, there was an association of DNA methylation age acceleration with a comprehensive frailty measure, such that the accumulated deficits significantly increased with increasing age acceleration. Quantitatively, about half an additional deficit was added per 6 years of methylation age acceleration (p = 0.0004). This association was independent from age, sex, and estimated leukocyte distribution, as well as from a variety of other confounding variables considered.ConclusionsThe results of the present study suggest that epigenetic age acceleration is correlated with clinically relevant aging-related phenotypes through pathways unrelated to cellular senescence as assessed by telomere length. Innovative approaches like Mendelian randomization will be needed to elucidate whether epigenetic age acceleration indeed plays a causal role for the development of clinical phenotypes.Electronic supplementary materialThe online version of this article (doi:10.1186/s13148-016-0186-5) contains supplementary material, which is available to authorized users.

OBJECTIVES: To investigate the relationship between polypharmacy and frailty. DESIGN: Longitudinal, observational cohort study. SETTING: Saarland, Germany. PARTICIPANTS: 3,058 community-dwelling adults aged between 57 and 84 years. MEASUREMENTS: Frailty was assessed according to the frailty phenotype, described by Fried et al. Polypharmacy and hyperpolypharmacy were defined as the concomitant use of five or more and 10 or more drugs, respectively. We assessed associations between polypharmacy and prevalent and incident frailty within 3 years of follow-up by logistic regression models controlled for multiple potential confounders including comorbidity. Additionally, cubic splines were used to assess dose-response associations. RESULTS: Polypharmacy was reported in 39.1% (n = 1,194), and hyperpolypharmacy in 8.9% (n = 273) of participants. Prevalent frailty was present in 271 (8.9%) participants; 186 (9.3%) of 1,998 non-frail participants with follow-up data became frail within 3 years. After adjustment, polypharmacy and hyperpolypharmacy were associated with prevalent frailty with adjusted odds ratios (95% confidence interval) of 2.30 (1.60-3.31) and 4.97 (2.97-8.32), respectively. Polypharmacy (odds ratio (OR) 1.51 (1.05-2.16)) and hyperpolypharmacy (OR 1.90 (1.10-3.28)) were also independent predictors of incident frailty. Furthermore, there was a moderate exposure-response relationship between the number of medicines and prevalent as well as incident frailty.CONCLUSION: Our study showed that polypharmacy is associated with frailty. Further research should address the potential benefit of reducing of inappropriate polypharmacy and better pharmacotherapeutic management for preventing medication-associated frailty. J Am Geriatr Soc 65:e27-e32, 2017.

BackgroundThe concept of frailty is rapidly gaining attention as an independent syndrome with high prevalence in older adults. Thereby, frailty is often related to certain adverse outcomes like mortality or disability. Another adverse outcome discussed is increased health care utilization. However, only few studies examined the impact of frailty on health care utilization and corresponding costs. The aim of this study was therefore to investigate comprehensively the relationship between frailty, health care utilization and costs.MethodsCross sectional data from 2598 older participants (57–84 years) recruited in the Saarland, Germany, between 2008 and 2010 was used. Participants passed geriatric assessments that included Fried’s five frailty criteria: weakness, slowness, exhaustion, unintentional weight loss, and physical inactivity. Health care utilization was recorded in the sectors of inpatient treatment, outpatient treatment, pharmaceuticals, and nursing care.ResultsPrevalence of frailty (≥3 symptoms) was 8.0 %. Mean total 3-month costs of frail participants were €3659 (4 or 5 symptoms) and €1616 (3 symptoms) as compared to €642 of nonfrail participants (no symptom). Controlling for comorbidity and general socio-demographic characteristics in multiple regression models, the difference in total costs between frail and non-frail participants still amounted to €1917; p < .05 (4 or 5 symptoms) and €680; p < .05 (3 symptoms). Among the 5 symptoms of frailty, weight loss and exhaustion were significantly associated with total costs after controlling for comorbidity.ConclusionsThe study provides evidence that frailty is associated with increased health care costs. The analyses furthermore indicate that frailty is an important factor for health care costs independent from pure age and comorbidity. Costs were rather attributable to frailty (and comorbidity) than to age. This stresses that the overlapping concepts of multimorbidity and frailty are both necessary to explain health care use and corresponding costs among older adults.Electronic supplementary materialThe online version of this article (doi:10.1186/s12913-016-1360-3) contains supplementary material, which is available to authorized users.

our data stress the economic relevance of frailty in late life. Postponing or reducing frailty might be fruitful in order to reduce healthcare costs.