Epigenetic age is associated with baseline and 3-year change in frailty in the Canadian Longitudinal Study on Aging

Background The trajectory of frailty in older adults is important to public health; therefore, markers that may help predict this and other important outcomes could be beneficial. Epigenetic clocks have been developed and are associated with various health-related outcomes and sociodemographic factors, but associations with frailty are poorly described. Further, it is uncertain whether newer generations of epigenetic clocks, trained on variables other than chronological age, would be more strongly associated with frailty than earlier developed clocks. Using data from the Canadian Longitudinal Study on Aging (CLSA), we tested the hypothesis that clocks trained on phenotypic markers of health or mortality (i.e., Dunedin PoAm, GrimAge, PhenoAge and Zhang in Nat Commun 8:14617, 2017) would best predict changes in a 76-item frailty index (FI) over a 3-year interval, as compared to clocks trained on chronological age (i.e., Hannum in Mol Cell 49:359–367, 2013, Horvath in Genome Biol 14:R115, 2013, Lin in Aging 8:394–401, 2016, and Yang Genome Biol 17:205, 2016). Results We show that in 1446 participants, phenotype/mortality-trained clocks outperformed age-trained clocks with regard to the association with baseline frailty (mean = 0.141, SD = 0.075), the greatest of which is GrimAge, where a 1-SD increase in ΔGrimAge (i.e., the difference from chronological age) was associated with a 0.020 increase in frailty (95% CI 0.016, 0.024), or ~ 27% relative to the SD in frailty. Only GrimAge and Hannum (Mol Cell 49:359–367, 2013) were significantly associated with change in frailty over time, where a 1-SD increase in ΔGrimAge and ΔHannum 2013 was associated with a 0.0030 (95% CI 0.0007, 0.0050) and 0.0028 (95% CI 0.0007, 0.0050) increase over 3 years, respectively, or ~ 7% relative to the SD in frailty change. Conclusion Both prevalence and change in frailty are associated with increased epigenetic age. However, not all clocks are equally sensitive to these outcomes and depend on their underlying relationship with chronological age, healthspan and lifespan. Certain clocks were significantly associated with relatively short-term changes in frailty, thereby supporting their utility in initiatives and interventions to promote healthy aging.Medicine, Faculty ofOther UBCNon UBCReviewedFacult

Maternal epigenetic clocks measured during pregnancy do not predict gestational age at delivery or offspring birth outcomes: a replication study in metropolitan Cebu, Philippines

Adverse birth outcomes, such as early gestational age and low birth weight, can have lasting effects on morbidity and mortality, with impacts that persist into adulthood. Identifying the maternal factors that contribute to adverse birth outcomes in the next generation is thus a priority. Epigenetic clocks, which have emerged as powerful tools for quantifying biological aging and various dimensions of physiological dysregulation, hold promise for clarifying relationships between maternal biology and infant health, including the maternal factors or states that predict birth outcomes. Nevertheless, studies exploring the relationship between maternal epigenetic age and birth outcomes remain few. Here, we attempt to replicate a series of analyses previously reported in a US-based sample, using a larger similarly aged sample (n = 296) of participants of a long-running study in the Philippines. New pregnancies were identified prospectively, dried blood spot samples were collected during the third trimester, and information was obtained on gestational age at delivery and offspring weight after birth. Genome-wide DNA methylation was assessed with the Infinium EPIC array. Using a suite of 15 epigenetic clocks, we only found one significant relationship: advanced age on the epigenetic clock trained on leptin predicted a significantly earlier gestational age at delivery (β = − 0.15, p = 0.009). Of the other 29 relationships tested predicting gestational age and offspring birth weight, none were statistically significant. In this sample of Filipino women, epigenetic clocks capturing multiple dimensions of biology and health do not predict birth outcomes in offspring.Medicine, Faculty ofNon UBCMedical Genetics, Department ofReviewedFacultyResearche

Systematic evaluation of DNA methylation age estimation with common preprocessing methods and the Infinium MethylationEPIC BeadChip array

Background: The capacity of technologies measuring DNA methylation (DNAm) is rapidly evolving, as are the options for applicable bioinformatics methods. The most commonly used DNAm microarray, the Illumina Infinium HumanMethylation450 (450K array), has recently been replaced by the Illumina Infinium HumanMethylationEPIC (EPIC array), nearly doubling the number of targeted CpG sites. Given that a subset of 450K CpG sites is absent on the EPIC array and that several tools for both data normalization and analyses were developed on the 450K array, it is important to assess their utility when applied to EPIC array data. One of the most commonly used 450K tools is the pan-tissue epigenetic clock, a multivariate predictor of biological age based on DNAm at 353 CpG sites. Of these CpGs, 19 are missing from the EPIC array, thus raising the question of whether EPIC data can be used to accurately estimate DNAm age. We also investigated a 71-CpG epigenetic age predictor, referred to as the Hannum method, which lacks 6 probes on the EPIC array. To evaluate these epigenetic clocks in EPIC data properly, a prior assessment of the effects of data preprocessing methods on DNAm age is also required. Methods: DNAm was quantified, on both the 450K and EPIC platforms, from human primary monocytes derived from 172 individuals. We calculated DNAm age from raw, and three different preprocessed data forms to assess the effects of different processing methods on the DNAm age estimate. Using an additional cohort, we also investigated DNAm age of peripheral blood mononuclear cells, bronchoalveolar lavage, and bronchial brushing samples using the EPIC array. Results: Using monocyte-derived data from subjects on both the 450K and EPIC, we found that DNAm age was highly correlated across both raw and preprocessing methods (r > 0.91). Thus, the correlation between chronological age and the DNAm age estimate is largely unaffected by platform differences and normalization methods. However, we found that the choice of normalization method and measurement platform can lead to a systematic offset in the age estimate which in turn leads to an increase in the median error. Comparing the 450K and EPIC DNAm age estimates, we observed that the median absolute difference was 1.44–3.10 years across preprocessing methods. Conclusions: Here, we have provided evidence that the epigenetic clock is resistant to the lack of 19 CpG sites missing from the EPIC array as well as highlighted the importance of considering the technical variance of the epigenetic when interpreting group differences below the reported error. Furthermore, our study highlights the utility of epigenetic age acceleration measure, the residuals from a linear regression of DNAm age on chronological age, as the resulting values are robust with respect to normalization methods and measurement platforms.Medicine, Faculty ofOther UBCNon UBCMedical Genetics, Department ofMedicine, Department ofRespiratory Medicine, Division ofReviewedFacult