Identifying reliable biomarkers of aging is a major goal in geroscience. While the first generation of epigenetic biomarkers of aging were developed using chronological age as a surrogate for biological age, we hypothesized that incorporation of composite clinical measures of phenotypic age that capture differences in lifespan and healthspan may identify novel CpGs and facilitate the development of a more powerful epigenetic biomarker of aging. Using an innovative two-step process, we develop a new epigenetic biomarker of aging, DNAm PhenoAge, that strongly outperforms previous measures in regards to predictions for a variety of aging outcomes, including all-cause mortality, cancers, healthspan, physical functioning, and Alzheimer's disease. While this biomarker was developed using data from whole blood, it correlates strongly with age in every tissue and cell tested. Based on an in-depth transcriptional analysis in sorted cells, we find that increased epigenetic, relative to chronological age, is associated with increased activation of pro-inflammatory and interferon pathways, and decreased activation of transcriptional/translational machinery, DNA damage response, and mitochondrial signatures. Overall, this single epigenetic biomarker of aging is able to capture risks for an array of diverse outcomes across multiple tissues and cells, and provide insight into important pathways in aging.

Background DNA methylation leaves a long-term signature of smoking exposure and is one potential mechanism by which tobacco exposure predisposes to adverse health outcomes, such as cancers, osteoporosis, lung, and cardiovascular disorders. Methods and Results To comprehensively determine the association between cigarette smoking and DNA methylation, we conducted a meta-analysis of genome-wide DNA methylation assessed using the Illumina BeadChip 450K array on 15,907 blood derived DNA samples from participants in 16 cohorts (including 2,433 current, 6,518 former, and 6,956 never smokers). Comparing current versus never smokers, 2,623 CpG sites (CpGs), annotated to 1,405 genes, were statistically significantly differentially methylated at Bonferroni threshold of p<1×10−7 (18,760 CpGs at False Discovery Rate (FDR)<0.05). Genes annotated to these CpGs were enriched for associations with several smoking-related traits in genome-wide studies including pulmonary function, cancers, inflammatory diseases and heart disease. Comparing former versus never smokers, 185 of the CpGs that differed between current and never smokers were significant p<1×10−7 (2,623 CpGs at FDR<0.05), indicating a pattern of persistent altered methylation, with attenuation, after smoking cessation. Transcriptomic integration identified effects on gene expression at many differentially methylated CpGs. Conclusions Cigarette smoking has a broad impact on genome-wide methylation that, at many loci, persists many years after smoking cessation. Many of the differentially methylated genes were novel genes with respect to biologic effects of smoking, and might represent therapeutic targets for prevention or treatment of tobacco-related diseases. Methylation at these sites could also serve as sensitive and stable biomarkers of lifetime exposure to tobacco smoke.

A genome-wide association study of educational attainment was conducted in a discovery sample of 101,069 individuals and a replication sample of 25,490. Three independent SNPs are genome-wide significant (rs9320913, rs11584700, rs4851266), and all three replicate. Estimated effects sizes are small (R2 ≈ 0.02%), approximately 1 month of schooling per allele. A linear polygenic score from all measured SNPs accounts for ≈ 2% of the variance in both educational attainment and cognitive function. Genes in the region of the loci have previously been associated with health, cognitive, and central nervous system phenotypes, and bioinformatics analyses suggest the involvement of the anterior caudate nucleus. These findings provide promising candidate SNPs for follow-up work, and our effect size estimates can anchor power analyses in social-science genetics.

43Identifying reliable biomarkers of aging is a major goal in geroscience. While the first generation 44 of epigenetic biomarkers of aging were developed using chronological age as a surrogate for 45 biological age, we hypothesized that incorporation of composite clinical measures of phenotypic 46 age that capture differences in lifespan and healthspan may identify novel CpGs and facilitate the 47 development of a more powerful epigenetic biomarker of aging. Using a innovative two-step 48 process, we develop a new epigenetic biomarker of aging, DNAm PhenoAge, that strongly 49 outperforms previous measures in regards to predictions for a variety of aging outcomes, including 50 all-cause mortality, cancers, healthspan, physical functioning, and Alzheimer's disease. While this 51 biomarker was developed using data from whole blood, it correlates strongly with age in every 52 tissue and cell tested. Based on an in-depth transcriptional analysis in sorted cells, we find that 53 increased epigenetic, relative to chronological age, is associated increased activation of pro-54 inflammatory and interferon pathways, and decreased activation of transcriptional/translational 55 machinery, DNA damage response, and mitochondrial signatures. Overall, this single epigenetic 56 biomarker of aging is able to capture risks for an array of diverse outcomes across multiple tissues 57 and cells, and provide insight into important pathways in aging. 58 Keywords: aging; life expectancy; biological age; epigenetic clock; DNA methylation 59 60 61 62 63 64 4 BACKGROUND 65One of the major goals of geroscience research is to define 'biomarkers of aging' [1, 2], which can 66 be thought of as individual-level measures of aging that capture between-person differences in the 67 timing of disease onset, functional decline, and death over the life course. While chronological age 68 is arguably the strongest risk factor for aging-related death and disease, it is important to 69 distinguish chronological time from biological aging. Individuals of the same chronological age 70 may exhibit greatly different susceptibilities to age-related diseases and death, which is likely 71 reflective of differences in their underlying biological aging processes. Such biomarkers of aging 72 will be crucial to enable evaluation of interventions aimed at promoting healthier aging, by 73 providing a measurable outcome, that unlike incidence of death and/or disease, does not require 74 extremely long follow-up observation. 75One potential biomarker that has gained significant interest in recent years is DNA methylation 76 (DNAm). Chronological time has been shown to elicit predictable hypo-and hyper-methylation 77 changes at many regions across the genome [3][4][5][6][7], and as a result, the first generation of DNAm 78 based biomarkers of aging were developed to predict chronological age [8][9][10][11][12][13]. The blood-based 79 algorithm by Hannum[10] and the multi-tissue algorithm by Horvath [14] produce age estimates 80 (DNAm age) that correlate with chronologica...

Chronic kidney disease (CKD) is a significant public health problem, and recent genetic studies have identified common CKD susceptibility variants. The CKDGen consortium performed a meta-analysis of genome-wide association data in 67,093 Caucasian individuals from 20 population-based studies to identify new susceptibility loci for reduced renal function, estimated by serum creatinine (eGFRcrea), cystatin C (eGFRcys), and CKD (eGFRcrea <60 ml/min/1.73m2; n = 5,807 CKD cases). Follow-up of the 23 genome-wide significant loci (p<5×10−8) in 22,982 replication samples identified 13 novel loci for renal function and CKD (in or near LASS2, GCKR, ALMS1, TFDP2, DAB2, SLC34A1, VEGFA, PRKAG2, PIP5K1B, ATXN2, DACH1, UBE2Q2, and SLC7A9) and 7 creatinine production and secretion loci (CPS1, SLC22A2, TMEM60, WDR37, SLC6A13, WDR72, BCAS3). These results further our understanding of biologic mechanisms of kidney function by identifying loci potentially influencing nephrogenesis, podocyte function, angiogenesis, solute transport, and metabolic functions of the kidney.