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For most animals, events that occur early in life can have a lasting impact on individuals’ health. In humans, adolescence is a particularly vulnerable time when rapid growth and development collide with growing independence and experimentation. An unhealthy lifestyle during this period of rapid cell growth can contribute to later health problems like heart disease, lung disease, and premature death. This is due partly to accelerated biological aging, where the body deteriorates faster than what would be expected for an individual’s chronological age. One way to track the effects of lifestyle on biological aging is by measuring epigenetic changes. Epigenetic changes consist on adding or removing chemical ‘tags’ on genes. These tags can switch the genes on or off without changing their sequences. Scientists can measure certain epigenetic changes by measuring the levels of methylated DNA – DNA with a chemical ‘tag’ known as a methyl group – in blood samples. Several algorithms – known as ‘epigenetic clocks’ – are available that estimate how fast an individual is aging biologically based on DNA methylation. Kankaanpää et al. show that unhealthy lifestyles during adolescence may lead to accelerated aging in early adulthood. For their analysis, Kankaanpää et al. used data on the levels of DNA methylation in blood samples from 824 twins between 21 and 25 years old. The twins were participants in the FinnTwin12 study and had completed a survey about their lifestyles at ages 12, 14, and 17. Kankaanpää et al. classified individuals into five groups depending on their lifestyles. The first three groups, which included most of the twins, contained individuals that led relatively healthy lives. The fourth group contained individuals with a higher body mass index based on their height and weight. Finally, the last group included individuals with unhealthy lifestyles who binge drank, smoked and did not exercise. After estimating the biological ages for all of the participants, Kankaanpää et al. found that both the individuals with higher body mass indices and those in the group with unhealthy lifestyles aged faster than those who reported healthier lifestyles. However, the results varied depending on which epigenetic clock Kankaanpää et al. used to measure biological aging: clocks that had been developed earlier showed fewer differences in aging between groups; while newer clocks consistently found that individuals in the higher body mass index and unhealthy groups were older. Kankaanpää et al. also showed that shared genetic factors explained both unhealthy lifestyles and accelerated biological aging. The experiments performed by Kankaanpää et al. provide new insights into the vital role of an individual’s genetics in unhealthy lifestyles and cellular aging. These insights might help scientists identify at risk individuals early in life and try to prevent accelerated aging.

Reproductive history is closely linked to health, yet its relationship with biological aging and survival remains uncertain. We investigated this in the Finnish Twin Cohort, a population-based study that enables modeling of full childbearing history while controlling for common risk factors, through questionnaires and civil registries. We model the association between reproductive trajectories and survival in 14,836 women, and assess biological aging in a subset of 1054 participants using the PCGrimAge, an algorithm trained to predict biological aging and mortality risk from DNA methylation. We identify six distinct reproductive trajectories describing different timing and number of childbearing events. Women with the most live births throughout their lives (mean 6.8, SD 2.4) and nulliparous women showed accelerated aging and elevated mortality risk. These findings support the disposable soma theory of aging in modern humans, and provide valuable insights into the genetic and lifestyle-related determinants of lifespan. This study identifies reproductive trajectories in the Finnish Twin Cohort, and shows that having either many or no live births is linked to faster biological aging and higher mortality risk, supporting the disposable theory of aging.

Background Metabolic syndrome (MetS) is associated with premature aging, but whether this association is driven by genetic or lifestyle factors remains unclear. Methods Two independent discovery cohorts, consisting of twins and unrelated individuals, were examined ( N  = 268, aged 23–69 years). The findings were replicated in two cohorts from the same base population. One consisted of unrelated individuals ( N  = 1 564), and the other of twins ( N  = 293). Participants’ epigenetic age, estimated using blood DNA methylation data, was determined using the epigenetic clocks GrimAge and DunedinPACE. The individual-level linear regression models for investigating the associations of MetS and its components with epigenetic aging were followed by within-twin-pair analyses using fixed-effects regression models to account for genetic factors. Results In individual-level analyses, GrimAge age acceleration was higher among participants with MetS ( N  = 56) compared to participants without MetS ( N  = 212) (mean 2.078 [95% CI = 0.996,3.160] years vs. −0.549 [−1.053,−0.045] years, between-group p  = 3.5E-5). Likewise, the DunedinPACE estimate was higher among the participants with MetS compared to the participants without MetS (1.032 [1.002,1.063] years/calendar year vs. 0.911 [0.896,0.927] years/calendar year, p  = 4.8E-11). An adverse profile in terms of specific MetS components was associated with accelerated aging. However, adjustments for lifestyle attenuated these associations; nevertheless, for DunedinPACE, they remained statistically significant. The within-twin-pair analyses suggested that genetics explains these associations fully for GrimAge and partly for DunedinPACE. The replication analyses provided additional evidence that the association between MetS components and accelerated aging is independent of the lifestyle factors considered in this study, however, suggesting that genetics is a significant confounder in this association. Conclusions The results of this study suggests that MetS is associated with accelerated epigenetic aging, independent of physical activity, smoking or alcohol consumption, and that the association may be explained by genetics.

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 (AA Horvath , AA GrimAge , 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 (CI 95 : 1.13–1.53) per one standard deviation (SD) increase in AA GrimAge . The results indicated no significant associations of AA Horvath with mortality. Pairwise mortality analyses showed an HR of 1.50 (CI 95 : 1.02–2.20) per 1 SD increase in AA GrimAge . However, after adjusting for smoking, the HR attenuated substantially and was statistically non-significant (1.29; CI 95 : 0.84–1.99). Similarly, in multivariable adjusted models the HR (1.42–1.49) was non-significant. In AA Horvath , the non-significant HRs were lower among monozygotic pairs in comparison to dizygotic pairs, while in AA GrimAge there were no systematic differences by zygosity. Further, the pairwise analysis in quartiles showed that the increased within pair difference in AA GrimAge 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.

Epigenetic research involves examining the mitotically heritable processes that regulate gene expression, independent of changes in the DNA sequence. Recent technical advances such as whole-genome bisulfite sequencing and affordable epigenomic array-based technologies, allow researchers to measure epigenetic profiles of large cohorts at a genome-wide level, generating comprehensive high-dimensional datasets that may contain important information for disease development and treatment opportunities. The epigenomic profile for a certain disease is often a result of the complex interplay between multiple genetic and environmental factors, which poses an enormous challenge to visualize and interpret these data. Furthermore, due to the dynamic nature of the epigenome, it is critical to determine causal relationships from the many correlated associations. In this review we provide an overview of recent data analysis approaches to integrate various omics layers to understand epigenetic mechanisms of complex diseases, such as obesity and cancer. We discuss the following topics: (i) advantages and limitations of major epigenetic profiling techniques, (ii) resources for standardization, annotation and harmonization of epigenetic data, and (iii) statistical methods and machine learning methods for establishing data-driven hypotheses of key regulatory mechanisms. Finally, we discuss the future directions for data integration that shall facilitate the discovery of epigenetic-based biomarkers and therapies.

Background Machine learning (ML) classifiers are increasingly used for predicting cardiovascular disease (CVD) and related risk factors using omics data, although these outcomes often exhibit categorical nature and class imbalances. However, little is known about which ML classifier, omics data, or upstream dimension reduction strategy has the strongest influence on prediction quality in such settings. Our study aimed to illustrate and compare different machine learning strategies to predict CVD risk factors under different scenarios. Methods We compared the use of six ML classifiers in predicting CVD risk factors using blood-derived metabolomics, epigenetics and transcriptomics data. Upstream omic dimension reduction was performed using either unsupervised or semi-supervised autoencoders, whose downstream ML classifier performance we compared. CVD risk factors included systolic and diastolic blood pressure measurements and ultrasound-based biomarkers of left ventricular diastolic dysfunction (LVDD; E/e' ratio, E/A ratio, LAVI) collected from 1,249 Finnish participants, of which 80% were used for model fitting. We predicted individuals with low, high or average levels of CVD risk factors, the latter class being the most common. We constructed multi-omic predictions using a meta-learner that weighted single-omic predictions. Model performance comparisons were based on the F1 score. Finally, we investigated whether learned omic representations from pre-trained semi-supervised autoencoders could improve outcome prediction in an external cohort using transfer learning. Results Depending on the ML classifier or omic used, the quality of single-omic predictions varied. Multi-omics predictions outperformed single-omics predictions in most cases, particularly in the prediction of individuals with high or low CVD risk factor levels. Semi-supervised autoencoders improved downstream predictions compared to the use of unsupervised autoencoders. In addition, median gains in Area Under the Curve by transfer learning compared to modelling from scratch ranged from 0.09 to 0.14 and 0.07 to 0.11 units for transcriptomic and metabolomic data, respectively. Conclusions By illustrating the use of different machine learning strategies in different scenarios, our study provides a platform for researchers to evaluate how the choice of omics, ML classifiers, and dimension reduction can influence the quality of CVD risk factor predictions.

Monozygotic (MZ) twins and higher-order multiples arise when a zygote splits during pre-implantation stages of development. The mechanisms underpinning this event have remained a mystery. Because MZ twinning rarely runs in families, the leading hypothesis is that it occurs at random. Here, we show that MZ twinning is strongly associated with a stable DNA methylation signature in adult somatic tissues. This signature spans regions near telomeres and centromeres, Polycomb-repressed regions and heterochromatin, genes involved in cell-adhesion, WNT signaling, cell fate, and putative human metastable epialleles. Our study also demonstrates a never-anticipated corollary: because identical twins keep a lifelong molecular signature, we can retrospectively diagnose if a person was conceived as monozygotic twin. The mechanisms underlying how monozygotic (or identical) twins arise are yet to be determined. Here, the authors investigate this in an epigenome-wide association study, showing that monozygotic twinning has a characteristic DNA methylation signature in adult somatic tissues.

Individuals with fast nicotine metabolism typically smoke more and thus have a greater risk for smoking-induced diseases. Further, the efficacy of smoking cessation pharmacotherapy is dependent on the rate of nicotine metabolism. Our objective was to use nicotine metabolite ratio (NMR), an established biomarker of nicotine metabolism rate, in a genome-wide association study (GWAS) to identify novel genetic variants influencing nicotine metabolism. A heritability estimate of 0.81 (95% CI 0.70-0.88) was obtained for NMR using monozygotic and dizygotic twins of the FinnTwin cohort. We performed a GWAS in cotinine-verified current smokers of three Finnish cohorts (FinnTwin, Young Finns Study, FINRISK2007), followed by a meta-analysis of 1518 subjects, and annotated the genome-wide significant SNPs with methylation quantitative loci (meQTL) analyses. We detected association on 19q13 with 719 SNPs exceeding genome-wide significance within a 4.2 Mb region. The strongest evidence for association emerged for CYP2A6 (min p = 5.77E-86, in intron 4), the main metabolic enzyme for nicotine. Other interesting genes with genome-wide significant signals included CYP2B6, CYP2A7, EGLN2, and NUMBL. Conditional analyses revealed three independent signals on 19q13, all located within or in the immediate vicinity of CYP2A6. A genetic risk score constructed using the independent signals showed association with smoking quantity (p = 0.0019) in two independent Finnish samples. Our meQTL results showed that methylation values of 16 CpG sites within the region are affected by genotypes of the genome-wide significant SNPs, and according to causal inference test, for some of the SNPs the effect on NMR is mediated through methylation. To our knowledge, this is the first GWAS on NMR. Our results enclose three independent novel signals on 19q13.2. The detected CYP2A6 variants explain a strikingly large fraction of variance (up to 31%) in NMR in these study samples. Further, we provide evidence for plausible epigenetic mechanisms influencing NMR.

A growing body of evidence supports the role of nutrient sensing and metabolism pathways in regulating ageing rate and healthspan, but the diversity of human lifestyles challenges our ability to identify the mechanisms of this age acceleration. Here, we examine how the transition of wild King penguins to zoo husbandry can closely mimic the shift to a Western lifestyle in humans, and shed light on conserved epigenetic changes in responses to sedentary conditions. We show that, just like modern humans, zoo-housed male King penguins experience an extended lifespan, but this comes at the cost of accelerated epigenetic ageing throughout life. This accelerated ageing is associated with differential methylation in key growth and maintenance pathways, including the mTOR and PI3K/Akt networks. Our results demonstrate the conserved link between lifestyle and age acceleration. Such evolutionary evidence may help us to improve risk detection and, ultimately, therapeutics for lifestyle-induced age acceleration in humans. For calorie-restriction-adapted King penguins, living a sedentary life at the zoo increases life expectancy; but by suppressing metabolic challenges, it also accelerates the epigenetic markers of ageing, decoupling lifespan from health.

Whether differences in lifestyle between co-twins are reflected in differences in their internal or external exposome profiles remains largely underexplored. We therefore investigated whether within-pair differences in lifestyle were associated with within-pair differences in exposome profiles across four domains: the external exposome, proteome, metabolome and epigenetic age acceleration (EAA). For each domain, we assessed the similarity of co-twin profiles using Gaussian similarities in up to 257 young adult same-sex twin pairs (54% monozygotic). We additionally tested whether similarity in one domain translated into greater similarity in another. Results suggest that a lower degree of similarity in co-twins' exposome profiles was associated with greater differences in their behavior and substance use. The strongest association was identified between excessive drinking behavior and the external exposome. Overall, our study demonstrates how social behavior and especially substance use are connected to the internal and external exposomes, while controlling for familial confounders.