In this paper, we investigated epigenetic aging in individuals with AUD using data from the largest study of epigenetic aging in AUD to date. We also investigated the underlying biological mechanisms of age acceleration in AUD. Our study confirmed that epigenetic age acceleration, derived from Levine’s DNAm PhenoAge algorithm, is increased in the AUD group compared to HC. However, we did not find significant associations of AUD status with age acceleration derived from the Horvath and Hannum epigenetic clocks, possibly due to differences in the methods capturing the age-related CpG sites. Furthermore, our exploratory analyses revealed more pronounced age acceleration in individuals with more severe AUD-associated phenotypes, such as elevated GGT, even when controlling for confounding factors. This implies that individuals with AUD with liver and/or tissue damage, as indicated by elevated liver enzymes, may experience accelerated aging and possibly shorter lifespans. To further elucidate the underlying molecular mechanisms and genetic factors contributing to epigenetic aging in AUD, we conducted GWAS of epigenetic age acceleration in our sample and identified a genome-wide significant SNP, rs916264, located in an intronic region in APOL2 on chromosome 22q12.3 which alters mRNA expression levels of APOL2 in brain.

Our study significantly expands upon a previous study by Rosen et al., which found epigenetic aging in AUD and reported an association between epigenetic aging and excessive alcohol consumption in blood and liver [15]. In the current study, which employed more comprehensive analyses with well-characterized alcohol consumption-related phenotypes and endophenotypes of liver function test enzymes, we utilized the Levine DNAm PhenoAge epigenetic clock as well as Hannum and Horvath’s epigenetic clocks. Interestingly, our findings of EAA in AUD and its associated endophenotypes were found using only Levine’s epigenetic clock. The Levine clock was previously shown to better differentiate morbidity and mortality risk among individuals of the same chronological age than other epigenetic clocks [12]. Although the present study examined methylation in whole blood only, we controlled for pertinent factors (e.g., blood cell-type composition) that the previous study by Rosen et al. did not. These factors have been shown to significantly impact variability in DNA methylation and are likely influenced by heavy alcohol consumption [26, 34]. One possible explanation for the failure to replicate the finding of Rosen et al.’s (2018) using Horvath’s epigenetic clock is that first-generation clocks were designed for Illumina’s 450k DNA methylation array. There have been mixed findings on whether DNAm-based estimations from first-generation clocks are accurate when using the EPIC array [35, 36]. DNAm age estimated by the first-generation approaches in our cohort could be inaccurate due to missing probes between the arrays, despite probe imputation procedures [36]. Furthermore, Levine’s second-generation clock has been found to be more strongly associated with alcohol intake and smoking compared with first-generation clocks [7], which could indicate that different clocks capture different aspects of epigenetic aging. Our finding of age acceleration in individuals with AUD using Levine’s approach but not Horvath’s or Hannum’s is not unexpected because Levine’s clock was designed to include CpG sites associated with biomarkers of physiological dysregulation and chronic diseases. In contrast, the Horvath and Hannum clocks include CpGs that predict chronological age only.

We further employed the Levine clock to understand how the severity of AUD phenotype, characterized by drinking pattern and clinical biomarkers of alcohol use, is associated with epigenetic aging. We found that the most severe AUD (i.e., individuals in the highest quartile for number of drinking days and levels of liver enzymes) exhibited on average greater age acceleration than the least severe AUD cases in the lowest quartile for these clinical characteristics. Our results provide evidence that within AUD populations, clinical heterogeneity could lead to differences in future morbidity and point to the possibility of uncovering shared mechanisms of AUD with aging-related morbidity. We examined the effects of heavy drinking days and liver enzyme levels using both the basic, minimally adjusted model, as well as the full model adjusting for all covariates. Here, we did not observe significant differences in age acceleration between the two models, which supports the robustness of our findings.

Epigenetic clocks have been employed to study age acceleration in other neuropsychiatric disorders in addition to AUD. In major depressive disorder, patients with depression experience a 0.64-year increase in EAA after adjusting for sex, education level, body mass index, cotinine levels, alcohol use, physical activity, and number of chronic diseases [37]. A study investigating post-traumatic stress disorder (PTSD) among veterans found a 1.97-year increase in EAA in individuals exhibiting PTSD symptoms, but the study is limited by a small sample size of 96 [38]. Both studies used blood to obtain methylation levels. Mixed results have been seen in schizophrenia. One study found no accelerated aging in the brain or blood of schizophrenia patients [39]. Another study reported a decrease in age acceleration in the blood of schizophrenia patients in one cohort but no effect in another [40]. To the best of our knowledge, no studies have been done on epigenetic aging in individuals with substance use disorders. Our finding of a 1.38-year age acceleration (full model) in AUD is significant as our effect size is the same as or greater than EAA in other neuropsychiatric disorders. It is interesting that a similar range of EAA is observed across psychiatric disorders, as psychiatric comorbidity is common and cross-disorder genetic susceptibility has previously been suggested [41]. One possible speculation could be that shared genetic or epigenetic susceptibility could also play a role in biological aging in these psychiatric disorders.

Given that DNA methylation is sensitive to both genetic and environmental factors, our findings do not determine whether DNA methylation differences and epigenetic age acceleration are predisposing factors for addiction or if they are consequences of long-term alcohol use. To address this limitation of our cross-sectional case-control study, future studies may collect methylation data from multiple time points to investigate how substance exposure and epigenetic aging interact. It would be beneficial to determine whether epigenetic age acceleration can be a biomarker that tracks changes in drinking patterns over time and whether the removal of harmful factors like chronic, heavy alcohol consumption can decelerate aging. Furthermore, although methylation, and thus epigenetic aging, in peripheral blood and brain has been shown to be highly correlated [8, 42], future studies should confirm the association between EAA in the blood or EAA derived from postmortem brain tissue. Our study was also limited in the covariates representing environmental factors that were not available to include in the analyses. While we accounted for gender, blood-cell composition, BMI, race, and smoking status, we did not collect data on other environmental factors that can impact DNA methylation levels and epigenetic aging. Covariates such as exercise, diet, and environmental toxin exposure, like air pollution, which have been shown to impact epigenetic aging should be included in future studies with larger sample sizes [25, 43].

In order to investigate the genetic contributing factors underlying EAA in AUD, we conducted GWAS analyses and identified in our meta-analysis, a genome-wide significant SNP, rs916264 in the gene encoding apolipoprotein L2 (APOL2). The apolipoprotein family of proteins facilitate the transport of lipids and lipophilic substrates [44]. APOL2 is related to cholesterol biosynthesis and trafficking and has been suggested to mediate cell death induced by interferon-gamma or viral infection. However, the role of APOL2 in cell death remains unclear [45]. Moreover, differential expression of APOL2 has been observed in individuals with substance use disorders (cocaine, cannabis, and phencyclidine) [46] as well as schizophrenia and bipolar disorders [47]. APOL2 is highly expressed in several brain regions. The BRAINEAC dataset confirmed that the top significant variant, rs916264, is significantly associated with expression of APOL2 in the hippocampus as well as in intralobular white matter and the medulla. Among the three brain regions, the hippocampus showed the strongest correlation with rs916264 genotype. This suggests rs916264 may influence regulation of APOL2 expression in human hippocampus. However, the biological function of APOL2 in the brain remains unclear [44, 46]. Furthermore, three SNPs in LINC01317 and CWC27 showed suggestive association with EAA. LINC01317 is a non-protein coding RNA that is transcribed from DNA but not translated into proteins and is generally associated with regulation of gene expression. To date, no study has published on the gene. CWC27 plays a role in pre-mRNA splicing and codes for a cyclophilin, which is part of the spliceosome and has been implicated in retinal degeneration, including age-related macular degeneration [48,49,50].

To the best of our knowledge, our study was the first GWAS analysis of epigenetic age acceleration in an AUD population. Previous GWAS on age acceleration have been done in brain and blood and have implicated genes coding for telomerase reverse transcriptase as well as loci (17q11.2 and 1p36.12) associated with the expression levels of multiple genes including EFCAB5, involved in calcium ion binding, and CRLF3, which regulates cell cycle progression [51, 52]. Our GWAS analysis identified several other variants whose effects are mild to moderate but did not meet a statistically significant genome-wide threshold after correcting for multiple comparisons. These variants are usually polygenic, meaning they could be related to functionally relevant biological pathways.

Using either top 5% or 25% significant genetic variants, our gene set enrichment analysis compared our results of GWAS analysis with sets of genes in pre-specified pathways that are relevant with aging or addiction. Our top 5% findings were enriched in a set of genes involved in a pathway of cerebellar long-term depression, a process involving the reduction in synaptic strength between parallel fiber and Purkinje cell [53]. This finding may be significant as acute alcohol consumption has been shown to suppress parallel fiber long-term depression in the cerebellum, which may contribute to alcohol-induced deficits in motor coordination in rats [54, 55]. Furthermore, in mice, chronic alcohol consumption has been shown to impair the sensory stimulation-induced molecular layer interneurons (MLIs) to the Purkinje cell (PC) synapses through the activation of a nitric oxide signaling pathway in the cerebellar cortex, which could lead to a deficit in cerebellar motor learning [56]. Taken together, our findings suggest that chronic alcohol abuse in AUD individuals may impact cerebellar long-term depression, which could have pleiotropic effects leading to accelerated biological aging.

In addition, we found three biological pathways (dopaminergic synapse, cAMP signaling, and mineral absorption) to be nominally significant using the KEGG Pathway database. The dopaminergic synapse pathway includes components of the dopaminergic signaling, including its five receptors (D1, D2, D3, D4, and D5). The dopaminergic reward system has been implicated in alcohol dependence as well as other substance use disorders, such as cocaine dependence [57]. Specifically, there is evidence for the association between genetic variation in the D2 dopamine receptor gene (DRD2) and alcoholism [57, 58]. The Taql A minor (A1) allele of the DRD2 gene reduces the number of D2 dopamine receptors in the brain, reducing the efficiency of the dopaminergic system [57]. It is hypothesized that abuse of substances like alcohol may increase brain dopamine levels. Furthermore, animal studies have shown that drugs of abuse increase extracellular dopamine in the nucleus accumbens [58]. This pathway has also been implicated in other neuropsychiatric disorders including obsessive-compulsive disorder, schizophrenia, and attention deficit hyperactivity disorder [59,60,61]. The cAMP signaling pathway includes cAMP, one of the most common second messengers, adenylyl cyclase (AC), G protein-coupled receptors, protein kinase A (PKA), and other components. This finding is of interest as alcohol and other drugs of abuse have been shown to affect cAMP-PKA signaling in the mesolimbic pathway [62]. Acute alcohol consumption has been shown to elevate extracellular adenosine levels, which plays a role in mediating alcohol preference and its sedative effects [63]. Furthermore, alcohol has been suggested to stimulate AC activity, with a positive family history of alcohol dependence being associated with higher levels of AC activity [64]. Lastly, the mineral absorption pathway consists of the passive or active transport systems that help absorb minerals and transport proteins. Alcohol is known to disrupt osteoblastic activity, which results in decreased bone formation and impaired mineralization [65]. Heavy alcohol use has been associated with decreased bone mineral density and impairs bone remodeling, leading to bone loss and increased risk for osteoporosis and fracture [66,67,68]. This pathway plays a role in bone diseases such as osteoporosis and may suggest the role of disrupted mineral absorption in AUD that could lead to accelerated aging [69]. While these pathways did not reach statistical significance after correction for multiple comparisons, there is evidence of their potential roles in the relationship between aging and AUD. Future studies with larger sample sizes may further explore these biological pathways.

Our study is the largest epigenetic aging study of AUD to date and was adequately powered to detect EAA with 80% power to identify an age acceleration difference of 1.5–2 years between AUD and healthy controls at the 1–5% significance level. Our GWAS of epigenetic aging in AUD would benefit from a larger sample size that would allow the detection of additional variants with stronger significance due to higher statistical power. Our GWAS of EAA focused on and analyzed only common variants with minor allele frequencies greater than 1% that were available in both of the Illumina OmniExpress BeadChips. Detection of rare genetic variants requires a larger sample size. We may also miss rare functional variants with large effects, which could influence EAA. Finally, future studies of epigenetic aging in AUD might consider high-throughput interrogation of a genome-wide study.

In conclusion, our study indicates accelerated biological aging in individuals with AUD and more pronounced acceleration in individuals with more severe clinical AUD phenotypes. In addition, we identified an association between APOL2 and EAA in AUD, suggesting a potential role of apolipoproteins in molecular aging. Our findings warrant further investigation into the shared mechanisms underlying AUD and aging, which can lead to the development of novel methods of detection and treatment for aging-related morbidities.