Background: There is evidence that complex diseases and mortality are associated with DNA methylation and age acceleration. Numerous epigenetic clocks, including Horvath, Hannum, DNA PhenoAge, DNA GrimAge, and Dunedin Pace of Aging Methylation, continue to be developed in this young scientific field. The most well-known epigenetic clocks are presented here, along with information about how they relate to chronic disease. Summary: We examined all the literature until January 2023, investigating associations between measures of age acceleration and complex and age-related diseases. We focused on the scientific literature and research that are most strongly associated with epigenetic clocks and that have shown promise as biomarkers for obesity, cardiovascular illness, type 2 diabetes, and neurodegenerative disease. Key Messages: Understanding the complex interactions between accelerated epigenetic clocks and chronic diseases may have significant effects on both the early diagnosis of disease and health promotion. Additionally, there is a lot of interest in developing treatment plans that can delay the onset of illnesses or, at the very least, alter the underlying causes of such disorders.

Aging, which is described as a gradual loss of physical integrity, is a significant risk factor for the onset of numerous complex diseases, including diabetes, cardiovascular, neurocognitive, and metabolic disorders [1]. Not everyone ages in the same manner; indeed, individuals of the same chronological age often vary markedly in their age-related physical, physiological, and cognitive decline. Therefore, the scientific community looked for aging-related biomarkers that can be used to predict, monitor, and provide insight into age-associated disease. A growing body of evidence has reported a correlation between DNA methylation (DNAm), aging, and complex disease [2]. Epigenetics describes chemical modifications of the genome that do not modify the DNA sequence but regulate gene expression and cellular processes such as proliferation, division, and differentiation in eukaryotic cells [3]. The ability to detect and quantify DNAm efficiently and accurately has become essential in the study of aging which has been linked to complex and age-related diseases such as metabolic disease and cancer [4‒6]. Horvath et al. [7] proposed a bioinformatic tool in order to calculate DNA methylation age (DNAm age) assessing the overall impact of an epigenetic maintenance system and detecting tissues that exhibit signs of epigenetic age acceleration as a result of illness. Several bioinformatic tools based on the study of DNAm biomarkers, described as “epigenetic clocks” have been developed [8, 9]. Epigenetic clocks can track biological age. Biological age is different from chronological age because it takes into account more than just the amount of time that has passed. It also takes into account a number of different biological and physiological developmental factors. The relationship between epigenetic aging and several pathological traits, such as frailty, cancer, lung function, and physical and cognitive fitness, has been studied using a biomarker called “epigenetic age acceleration,” which is defined as the “difference between the biological age measure and chronological age” [10‒15]. Overall, positive measures of epigenetic age acceleration (faster aging), meaning a greater biological age than chronological age, have also been strongly correlated to some health parameters, such as increased body mass index (BMI) or obesity, low physical activity, fat-rich diets, alcohol, and smoking abuse [16, 17]. Comparing the chronological age resulting from these epigenetic clocks yields an estimation of age acceleration and age deceleration, and they are among the most promising biomarkers of aging. In this review, we focus our attention on epigenetic clocks that by using specific algorithms based on DNAm, are able to clearly predict biological age. Original measures of age acceleration were reported, wherever possible, as these were the most commonly reported values. Finally, we reported the difference between chronological and biological ages, summarizing the most relevant studies demonstrating their correlation with various traits such as obesity, diabetes, cardiovascular and complex neurodegenerative diseases.

First-Generation Clocks

The first epigenetic clocks, Hannum et al.’s whole blood clock [18] and Horvath’s pan-tissue clock [7], were developed using a machine learning algorithm trained to predict chronological age. Hannum and Horvath’s clocks became the two most cited DNAm clocks in the literature [7, 18]. Both Horvath and Hannum clocks were developed using penalized regression methods to train a predictor of chronological age based on the DNAm age levels using Illumina Infinium arrays in several CpG sites throughout the human genome [7]. Applying Illumina 450 k array (Illumina) data from 656 people (19–101 years), Hannum developed an age predictor that included 71 CpGs and showed an age correlation of 0.96 (mean accuracy, 3.9) in the validation data [18]. Since the Hannum’s clock algorithm was only trained on whole-blood samples and adults, estimates derived from non-blood tissues and children exhibited skewed results [19, 20]. Horvath’s clock was developed using approximately 7,844 noncancer samples from 82 datasets, both children and adults [7]. DNAm levels were assessed in 51 different tissues and cell types, using Illumina 27 K or Illumina 450 K arrays. An epigenetic clock was developed for multiple tissues and cell types in order to build a multi-tissue age predictor [7]. It includes 353 age-related CpG sites chosen automatically by a mathematical algorithm in its prediction, of which 193 CpGs were positively correlated with age, while another 160 CpGs showed a negative correlation with age and demonstrated similarly high age correlations of 0.97 (mean accuracy, 2.9) and 0.96 (mean accuracy, 3.6) in the training and testing datasets, respectively. Nevertheless, the absolute median difference between DNAm age and chronological age was only 3.6 years [7, 21]. The model is greatly appreciated for its high accuracy, wide range of lifespans, and high adaptability to different tissues and cell types. As of today, hundreds of data have verified the model’s correctness [22].

Second-Generation Clocks

Clinical indicators like blood pressure, blood lipids, and glucose indicate a weak connection with the first-generation epigenetic clock. To improve the epigenetic clock algorithm in order to assess clinical biomarkers over the years, second-generation clocks have been developed utilizing slightly different techniques than what has been done by Horvath and Hannum. Second-generation clocks are based on the correlation between methylation and health risks score [23‒25]. Levine and colleagues, using an innovative two-step process, developed a novel DNAm bioinformatics tool, called DNAm PhenoAge, capable of making predictions for a variety of aging outcomes, including all-cause mortality, cancers, duration of health status, physical functioning, and Alzheimer’s disease (AD) [24]. The model uses clinical data from the USA’s Third National Health and Nutritional Survey to combine 10 clinical factors (chronological age, albumin, creatinine, glucose, C-reactive protein levels, lymphocyte percentage, mean cell volume, red blood cell distribution width, alkaline phosphatase, and white blood cell count). These data were then regressed using a penalized regression model on blood DNAm levels. Based on 513 age-related CpG sites, DNAm PhenoAge also exhibits strong association with subsequent mortality risk in all studies, such that, a 1-year increase in DNAm PhenoAge is associated with a 4.5% increase in the risk of all-cause mortality [24]. Afterward, Lu’s research group developed an innovative epigenetic clock called GrimAge clock, that has been very successful in predicting all-cause mortality [25]. The GrimAge clock is made up of 7 plasma protein indicators based on DNAm and pack-years of smoking that have been linked to morbidity or death [25]. The definition and validation of surrogate DNAm-based biomarkers for plasma protein levels and smoking pack-years was done by using 2,356 blood samples divided in a training and testing dataset from the Framingham Heart Study [25]. These biomarkers reflect inflammation [26], cardiovascular diseases (CVDs) [27], kidney [28], and cognitive functions [29]. Plasma protein substitutes include cystatin C, leptin, tissue inhibitor of metalloproteinase 1, adrenomedullin, beta-2-microglobulin (B2M), growth differentiation factor 15, and plasminogen activation inhibitor 1 (PAI-1). Time-to-death was regressed on the biomarkers using the elastic net Cox regression model. According to 10-year Lu’s study, people who had an acceleration of biological age, measured by GrimAge, were twice as likely to die, and this association was also valid despite other factors being monitored. Among the clocks, second-generation clocks such as DNAm, PhenoAge, and GrimAge were trained on a combination of biomarkers to estimate biological age and mortality risk. Studies show that GrimAge clock is a better predictor of lifespan than previously available DNAm-based predictors, showing that mortality is the outcome most reliably correlated with epigenetic age acceleration [25]. Defining the epigenetic age acceleration by calculating the difference between the observed value of DNAm GrimAge minus its expected value based on chronological age. Thus, a positive (or negative) value of epigenetic age acceleration indicates that the biological age is higher (or lower) than chronological age [25].

Third-Generation Clock

Recently, Belsky et al. [23] developed the Dunedin Pace of Aging Methylation (DunedinPoAm) clock, a third-generation epigenetic clock. In DunedinPoAm, the longitudinal change in 18 biomarkers (such as BMI, LTL, and HDL cholesterol) was examined to determine the subject’s rate of aging [23]. In Belsky’s work, 954 white blood cell samples were evaluated as part of a long-term health investigation known as “The Dunedin Study.” Using these data, the authors developed an algorithm for identifying people with an accelerated or slowed aging rate. Subsequently, they used the algorithm on participants in three other long-term studies, demonstrating that people identified by the algorithm as having “accelerated aging” had a higher risk of ill health, developing complex diseases, or dying earlier. Similarly, those with “decelerated aging” performed better on tests of balance, strength, walking speed, and mental capacity, looking younger than trained raters.

Despite the original purpose of epigenetic clocks, which was to estimate chronological age, different studies showed that the deviation between chronological age and epigenetic age is a good predictor of complex diseases. Here, we present an overview of current research on the acceleration or deceleration of the epigenetic age and the correlation with complex diseases.

Obesity Disease and Epigenetic Clocks

Obesity is a chronic disease with a multifactorial etiology characterized by excessive body weight due to the accumulation of adipose tissues. It represents the most common nutritional disorder in the Western world, and its prevalence is progressively increasing, especially in developing countries. To study the relationship between obesity and DNAm age, Horvath et al. [30] analyzed data on DNAm related to biological age from different biological tissues (n = 141 liver, n = 274 blood, n = 726 adipose tissues, n = 74 muscle) of 1,215 individuals profiled by the Illumina Infinium arrays (450 K and 27 K). The authors showed a significant age acceleration correlated with a high BMI (only in DNA from liver tissue) yielding on average ∼3.3 y of additional DNAm age for each 10 BMI units (kg/m2) (Table 1). A significant correlation between BMI and epigenetic age acceleration could only be observed for the liver. Thus, a liver that exhibits positive age acceleration appears to be older than expected. Moreover, other studies, such as de Toro-Martín et al. [17] using the same Horvath clock, correlated accelerated epigenetic aging in visceral adipose tissue to obesity status. In particular, the authors show a strongly significant and positive correlation between chronological age and epigenetic age in both blood (r = 0.78, p = 9.4 × 10−12) and visceral adipose tissue (r = 0.80, p = 1.1 × 10−12), comparable to those previously obtained by Horvath clock in the liver [30]. To improve our understanding of the role of obesity in epigenetic aging, Nevalainen et al. [31] examined BMI and accelerated epigenetic aging in blood cells in order to determine the relationship between BMI and epigenetic age in three different age groups: young, middle-aged, and nonagenarian individuals. The authors specifically analyzed this population using the Horvath clock algorithm for epigenetic age calculation (https://dnamage.genetics.ucla.edu/home), defining the acceleration of epigenetic age by calculating the deviation of chronological age from epigenetic age (ΔAGE = difference between chronological and epigenetic age). Intriguingly, there was no significant association between ΔAGE and BMI in the young adult subgroup (r = 0.110, p = 0.138), showing that BMI did not affect the epigenetic clock when these individuals approached adulthood. Among the middle-aged group, however, there was a significant association between increased BMI and ΔAGE (r = 0.281, p = 0.0001). The outcome was also significant when analyzed by gender. The authors demonstrated that there was no significant connection between ΔAGE and increased BMI in the nonagenarian subpopulation (r = 0.115, p = 0.211). These findings suggested that a higher BMI is associated with faster epigenetic aging only in middle-aged individuals, and that gender had no effect on this correlation. Similarly, positive associations were found in a large sample of adult women (2,758 non-Hispanic White women) between multiple measures of epigenetic age acceleration using second-generation clocks (Horvath, Hannum, PhenoAge, and GrimAge), BMI, and physical activity level [32]. Recent research examining socioeconomic disadvantage in children has found an association between increased BMI and biological age as measured by the third-generation clock DunedinPoAm [33]. In particular, the authors reported that children and adolescents growing up in more disadvantaged families and neighborhoods exhibited a faster pace of aging as measured by DunedinPoAm clock. Using second- and third-generation clocks, Etzel et al. [34] more recently examined the relationship between BMI and accelerated epigenetic aging in a group of high-risk children (N = 273, ages ranged from 8 to 14 years, 82% of the enrolled children were investigated for maltreatment). Increased BMI was correlated with older chronological age, maltreatment status, household income, blood cell counts, and three of the accelerated epigenetic aging measures: GrimAge (r = 0.31, p < 0.0001), PhenoAge (r = 0.24, p < 0.0001), and DunedinPoAm (r = 0.38, p < 0.0001) [34] (Table 1). If accelerated epigenetic aging related to obesity is detected in early life, DNAm programming may present a prospective and innovative intervention opportunity with consequences for health promotion and the reduction of future disease risk factors. In fact, chronic conditions such as type 2 diabetes (T2D), hypertension, atherosclerosis, and other health outcomes are more frequent in subject with childhood obesity [35‒37]. Changes in epigenetic acceleration are related to variations in BMI, indicating that epigenetic aging may respond to changes in lifestyle, at least with regard to variations in obesity [38]. In this regard, it is noteworthy to recall the recent study of Fitzgerald et al. [39] focused on the reversal of DNAm age, showing that dietary recommendations, relaxation practices, and daily exercise can reduce the epigenetic age and lead to setting back the epigenetic clock by 3.23 years according to the Horvath clock [39]. Finally, numerous studies support the theory that dietary variables can reverse DNAm changes that contribute to the aging process. For instance, folic acid + vitamin B12 as a methyl donor and flavanols as DNA methyltransferase inhibitors have been discovered to diminish epigenetic age [40]. It is required to do additional study to get a deeper understanding of the physiological significance of epigenetic aging and its part in the development of lifestyle-related disorders such as obesity.

Table 1.

Summary of the relevant studies on epigenetic clocks and complex diseases

Association with diseaseEpigenetic clockAnalysis of DNAm (#CpGs)FindingsReferences
Obesity First-generation Horvath 353 CpGs For each 10 BMI units, 3.3-year increase was detected in epigenetic age Horvath et al. [30] (2014) 
First-generation Horvath 353 CpGs There is an association between BMI and age acceleration in the whole blood of middle-aged adults Nevalainen et al. [31] (2017) 
First-generation Horvath 353 CpGs Association between BMI with age acceleration in VAT Juan de Toro-Martín et al. [17] (2019) 
DunedinPoAm 46 CpGs Correlation between BMI and pace of aging in salivary DNA in socioeconomically disadvantaged children Raffington et al. [33] (2020) 
PhenoAge, GrimAge 513 CpGs, 1,030 CpGs Associations between BMI and age acceleration Kresovich et al. [32] (2021) 
PhenoAge, GrimAge, DunedinPoAm 513 CpGs, 1,030 CpGs, 46 CpGs Association between BMI, an older chronological age, maltreatment status, household income, blood cell counts, and three of the accelerated epigenetic aging Etzel et al. [34] (2022) 
T2D GrimAge 1,030 CpGs A positive association between T2D patients and the plasma protein PAI-1 selected as biomarker in the epigenetic clock Lu et al. [19] (2019) 
GrimAge, first-generation Hannum and Horvath, PhenoAge 513 CpGs, 1,030 CpGs, 46 CpGs, 71 CpGs A positive association between age and acceleration was observed in incident T2D cases Fraszczyk et al. [45] (2022) 
PhenoAge 513 CpGs The phenotypic age of DNAm age increased by 10 years in whole blood and by 15 years in pancreatic islets in patients with diabetes Briana et al. [46] (2022) 
First-generation Horvath 353 CpGs The metformin-treated group has an acceleration age of less than 2.77 years compared to the metformin-free group Li et al. [48] (2022) 
Hannum 71 CpGs The metformin-treated group has an acceleration age of less than 3.43 years compared to the metformin-free group Li et al. [48] (2022) 
CVD PhenoAge 513 CpGs Higher PhenoAge was associated with a 10% increase in the risk of CVD mortality Levine et al. [24] (2018) 
GrimAge, Hannum, Horvath, PhenoAge 513 CpGs, 1,030 CpGs, 46 CpGs CVD was significantly related to all four epigenetic clocks Oblack et al. [21] (2021) 
First-generation Horvath and Hannum 353 CpGs, 71 CpGs Higher CVH score had lower epigenetic age acceleration with respect to participants with lower CVH score Pottinger et al. [50] (2021) 
GrimAge, Hannum, Horvath, PhenoAge 513 CpGs, 1,030 CpGs, 46 CpGs Correlation between higher CVH score and lower epigenetic age acceleration determinated by the second-generation clocks Lo et al. [54] (2022) 
AD First-generation Horvath 353 CpGs AD participants had dorsolateral prefrontal cortex more than 1 year older than same-aged people who were not postmortem diagnosed with AD Levine et al. [24] (2018) 
First-generation Horvath 353 CpGs Observed correlations of chronologic age to clock age ranged from 0.61 to 0.99 within six brain regions Lu et al. [61] (2017) 
PhenoAge 513 CpGs PhenoAge was positively associated with neuropathological indicators of AD, including amyloid load (r = 0.094, p = 0.012), neuritic plaques (r = 0.11, p = 0.0032), and neurofibrillary tangles (r = 0.10, p = 0.0073) Levine et al. [24] (2018) 
First-generation Hannum, Horvath, and PhenoAge 71 CpGs, 353 CpGs, 513 CpGs Positive correlation between AD and accelerated epigenetics age Grodstein et al. [59] (2021) 
PD First-generation Horvath 353 CpGs An increase in epigenetic age has been observed in patients with PD Horvath et al. [22] (2018) 
First-generation Horvath 353 CpGs The authors observed that a greater DNAm age acceleration was associated with an earlier PD onset. Picillo et al. [64] (2018) 
First-generation Horvath 353 CpGs An increase in epigenetic age has been observed in patients with PD. Tang et al. [65] (2022) 
Association with diseaseEpigenetic clockAnalysis of DNAm (#CpGs)FindingsReferences
Obesity First-generation Horvath 353 CpGs For each 10 BMI units, 3.3-year increase was detected in epigenetic age Horvath et al. [30] (2014) 
First-generation Horvath 353 CpGs There is an association between BMI and age acceleration in the whole blood of middle-aged adults Nevalainen et al. [31] (2017) 
First-generation Horvath 353 CpGs Association between BMI with age acceleration in VAT Juan de Toro-Martín et al. [17] (2019) 
DunedinPoAm 46 CpGs Correlation between BMI and pace of aging in salivary DNA in socioeconomically disadvantaged children Raffington et al. [33] (2020) 
PhenoAge, GrimAge 513 CpGs, 1,030 CpGs Associations between BMI and age acceleration Kresovich et al. [32] (2021) 
PhenoAge, GrimAge, DunedinPoAm 513 CpGs, 1,030 CpGs, 46 CpGs Association between BMI, an older chronological age, maltreatment status, household income, blood cell counts, and three of the accelerated epigenetic aging Etzel et al. [34] (2022) 
T2D GrimAge 1,030 CpGs A positive association between T2D patients and the plasma protein PAI-1 selected as biomarker in the epigenetic clock Lu et al. [19] (2019) 
GrimAge, first-generation Hannum and Horvath, PhenoAge 513 CpGs, 1,030 CpGs, 46 CpGs, 71 CpGs A positive association between age and acceleration was observed in incident T2D cases Fraszczyk et al. [45] (2022) 
PhenoAge 513 CpGs The phenotypic age of DNAm age increased by 10 years in whole blood and by 15 years in pancreatic islets in patients with diabetes Briana et al. [46] (2022) 
First-generation Horvath 353 CpGs The metformin-treated group has an acceleration age of less than 2.77 years compared to the metformin-free group Li et al. [48] (2022) 
Hannum 71 CpGs The metformin-treated group has an acceleration age of less than 3.43 years compared to the metformin-free group Li et al. [48] (2022) 
CVD PhenoAge 513 CpGs Higher PhenoAge was associated with a 10% increase in the risk of CVD mortality Levine et al. [24] (2018) 
GrimAge, Hannum, Horvath, PhenoAge 513 CpGs, 1,030 CpGs, 46 CpGs CVD was significantly related to all four epigenetic clocks Oblack et al. [21] (2021) 
First-generation Horvath and Hannum 353 CpGs, 71 CpGs Higher CVH score had lower epigenetic age acceleration with respect to participants with lower CVH score Pottinger et al. [50] (2021) 
GrimAge, Hannum, Horvath, PhenoAge 513 CpGs, 1,030 CpGs, 46 CpGs Correlation between higher CVH score and lower epigenetic age acceleration determinated by the second-generation clocks Lo et al. [54] (2022) 
AD First-generation Horvath 353 CpGs AD participants had dorsolateral prefrontal cortex more than 1 year older than same-aged people who were not postmortem diagnosed with AD Levine et al. [24] (2018) 
First-generation Horvath 353 CpGs Observed correlations of chronologic age to clock age ranged from 0.61 to 0.99 within six brain regions Lu et al. [61] (2017) 
PhenoAge 513 CpGs PhenoAge was positively associated with neuropathological indicators of AD, including amyloid load (r = 0.094, p = 0.012), neuritic plaques (r = 0.11, p = 0.0032), and neurofibrillary tangles (r = 0.10, p = 0.0073) Levine et al. [24] (2018) 
First-generation Hannum, Horvath, and PhenoAge 71 CpGs, 353 CpGs, 513 CpGs Positive correlation between AD and accelerated epigenetics age Grodstein et al. [59] (2021) 
PD First-generation Horvath 353 CpGs An increase in epigenetic age has been observed in patients with PD Horvath et al. [22] (2018) 
First-generation Horvath 353 CpGs The authors observed that a greater DNAm age acceleration was associated with an earlier PD onset. Picillo et al. [64] (2018) 
First-generation Horvath 353 CpGs An increase in epigenetic age has been observed in patients with PD. Tang et al. [65] (2022) 

VAT, visceral adipose tissue.

T2D Disease and Epigenetic Clocks

T2D is characterized by chronically high blood glucose levels caused by insulin resistance and decreased insulin production. It is well established that aging, a sedentary lifestyle, and obesity contribute to insulin resistance in target tissues, such as the skeletal muscle, liver, and adipose tissue [41]. It has recently been recognized that DNAm plays a role in diabetes, both in terms of development, progression, and complications. There have been numerous studies showing how hyperglycemia causes epigenetic modifications of histones and subsequent alterations in the expression of genes in cell cultures, animal models, and human models [42, 43]. Furthermore, these changes may persist even when normal circulating glucose concentrations are restored. Some study suggested that T2D alters the normal metabolism of methyl, folate, homocysteine, and choline donors [44]. A relationship between epigenetic clocks and T2D was first observed by Lu et al. [25], showing a positive association between T2D and the plasma protein PAI-1, chosen as biomarker in the GrimAge clock. DNAm PAI-1 stands out when it comes to associations with T2D status, glucose levels, insulin, triglycerides, BMI, waist-to-hip ratio, and computed tomography data on fatty liver [25]. Most recently, Fraszczyk et al. [45] observed positive association between T2D, and age acceleration based on four clocks (GrimAge, Hannum, Horvath, and PhenoAge). In this study, DNAm age was measured in blood samples using the Infinium MethylationEPIC array (Illumina). The results show that age acceleration measurement was negative (slower aging) in controls but positive (accelerated aging) in T2D patients [45]. The DNAm PhenoAge clock was recently questioned to investigate the prediction of T2D disease [46]. A correlation between T2D and age acceleration was observed in pancreatic islets and whole blood but not in adipose or skeletal muscle. Interestingly, when comparing DNAm age from T2D versus healthy patients DNAm phenotypic age in T2D patients was increased in whole blood by 10 years and pancreatic islets by 15 years [46]. This correlation contributes to highlight the foundation of T2D as a disease of accelerated cellular aging, leading in the future to the discovery of specific pathophysiological pathways and the development for a new treatment target. Identifying therapeutic therapies that can modulate the mechanisms of aging or delay the age at which age-related disorders first appear is becoming an increasingly important area of research [47]. In this context, Li et al. [48] investigated the effects of metformin consumption on the rate of epigenetic aging in the peripheral blood of T2D patients. They used three different epigenetic clocks to determine whether metformin consumption was associated with a slower rate of epigenetic aging. Metformin is a first-line medication for the treatment of T2D and has garnered substantial interest among researchers. In Li et al. [48], examining the relationship between metformin and DNAm age, the Horvath acceleration age of the metformin-free group was 2.77 years greater than that of the metformin group (p = 0.04). The Hannum acceleration age of the metformin-free group exceeded that of the metformin-treated group by 3.43 years (p = 0.04), whereas the DNAm PhenoAge did not demonstrate any statistically significant variations [48]. As of today, studies focused on metformin and the reversal of epigenetic age are limited, and the analysis of DNAm as biomarker of the differences between diabetic patients with and without treatment could be helpful find possible new and more effective intervention targets and therapies.

CVDs and Epigenetic Clocks

CVD is a general term for conditions affecting the heart and blood vessels. The risk of CVDs may be increased by smoking, high blood pressure, high cholesterol, an unhealthy diet, a lack of exercise, and obesity. CVDs affect 471 million people worldwide and are the leading cause of death with approximately 17.6 million deaths per year, with a tendency to increase to 24 million by 2030 [49]. To characterize the significance of CVD in terms of DNAm and aging, Levine et al. [24] studied the association between CVD subjects and PhenoAge clock. The study established the correlation between DNA PhenoAge and CVD status, higher DNA PhenoAge was associated with a 10% increase in the risk of CVD mortality (hazard ratio [HR] = 1,10, p = 5,1−17). More recently, Oblack et al. [21] in a meta-analysis showed that CVD was significantly related to all four epigenetic clocks. The authors investigated several factors, among which CVD showed statistically significant relationships (HR with p < 0.001) with epigenetic age acceleration. In particular, CVD GrimAge (HR = 1.083, p < 0.001), CVD Hannum (HR = 1.024, p < 0.001), CVD Horvath (HR = 1.011, p < 0.001), and CVD Levine (HR = 1,028, p < 0.001). Notably, Pottinger et al. [50] observed the association of cardiovascular health (CVH) score and epigenetic age acceleration by the first-generation epigenetic clocks. Higher CVH is associated with lower incidence of CVD [51], and was defined by the American Heart Association as an integrative algorithm of four lifestyle factors (cigarette smoking, physical activity, BMI, and dietary habits) and three clinical factors (fasting glucose, blood pressure, and total cholesterol level) [52]. A person is considered to have optimal or favorable CVH by meeting a greater number of metrics at the ideal level for each of the four lifestyles and three clinically modifiable risk factors [53]. CVH score was investigated by the two first-generation clocks, Horvath and Hannum in 2,170 postmenopausal women (aged from 50 to 79 years) [50]. The authors showed that participants with a higher CVH score had lower epigenetic age acceleration (∼6-month lower age per 1 point higher CVH score; p < 0.0001) with respect to participants with a lower CVH score [50]. The initial evidence is provided for epigenetic age acceleration to be considered a potential early detection biomarker for CVH. More recently, first- and second-generation clocks were used to investigate the correlation between CVH score and DNAm age status [54]. In this study, the authors show that, even for individuals not affected by CVD, ideal CVH is necessary to delay the rate of epigenetic aging and reduce the risk of age-related diseases [54]. Specifically, the authors demonstrated that a one-point drop in CVH was linked with a 0.350 year PhenoAge age acceleration (p = 4.5E4) and a 0.499 year GrimAge age acceleration (p = 4.2E15). The relationships between CVH scores and age acceleration assessed using Horvath’s [7] and Hannum’s [18] clocks (first-generation epigenetic age) were not statistically significant. Noteworthy, this was one of the first studies to thoroughly examine the relationships between the CVH and the four clocks all together. Ideal CVH was associated with reduced levels of epigenetic age acceleration as measured by second-generation epigenetic clocks, and ideal CVH can minimize the risk of aging-related illnesses. Future research may provide novel information that, in addition to genetic data such as polygenic risk scores, could be utilized to improve predictions of a person's risk for CVD.

Neurodegenerative Diseases and Epigenetic Clocks

Neurodegenerative disease is a generic term indicating a series of conditions that mainly affect the neurons of the human brain, among the most common are AD and Parkinson disease (PD). Many aspects of neuronal function and development are influenced by epigenetic mechanisms, such as DNAm, chromatin remodeling, and posttranslational histone changes. The most notable risk factor for neurodegenerative diseases is age, and aging itself is associated with a decline in cognitive abilities. The epigenetic clock can be used to determine if individuals with higher levels of neuropathology, lower cognitive performance, and/or a diagnosis of AD or PD will have a higher acceleration age, indicating their brains are biologically older. Following are several reports supporting the relationship between epigenetic clocks and neurodegenerative diseases, such as AD and PD.

Alzheimer’s Disease

AD is the leading cause of dementia in the elderly and a major public health concern, with a current estimate of 5.5 million AD patients in the USA alone. Both amyloid beta (A) plaques and neurofibrillary tangles containing hyperphosphorylated Tau are pathological markers of the disease, and both soluble oligomers and aggregated proteins contribute to neuronal toxicity. Substantial research on DNAm and AD etiology reveals that the most prominent DNAm change in AD occurs at hypomethylated or unmethylated levels [55]. For example, neurons from cortical tissue withdrew from dead patients with AD showed a lower immunoreactivity of 5-methylcytosine compared to normal controls of similar age, suggesting a marked loss of 5-methylcytosine in AD-affected brains [56]. It was also observed that patients with late-onset AD had low-density CpG regions of the hypermethylated apolipoprotein E-e4 (APOE-e4) promoter [56]. Moreover, there is growing evidence suggesting the involvement of bacteria and virus infections such as herpes simplex virus type 1 in AD pathogenesis [57] and a recent study reported that specific DNAm can be caused by herpes simplex virus type 1 infection [58]. Even though, it is clear that DNAm plays a critical role in neurodegenerative processes, there is limited existing literature on DNAm age clocks and AD [24, 25, 59‒61] (Table 1). Initially, epigenetic age acceleration was linked to AD neuropathological indicators including neuritic plaques, diffuse plaques, and amyloid burden in the dorsolateral prefrontal cortex [62]. Afterward, in a publication from Lu et al. [61], seven AD cohorts were analyzed by Horvath clock, and a correlation between chronologic and epigenetic age was observed with a value ranging from 0.61 to 0.99. While using PhenoAge clock, Levine et al. [24] reported correlation values with chronologic age ranging from 0.51 to 0.92 across varying brain regions. Moreover, it was discovered that age-adjusted DNAm PhenoAge was strongly linked with the neuropathological indicators of AD, including amyloid load (r = 0.094, p = 0.012), neuritic plaques (r = 0.11, p = 0.0032), and neurofibrillary tangles (r = 0.10, p = 0.0073) [24]. Finally, authors further demonstrate that AD participants had a dorsolateral prefrontal cortex that seems to be more than 1 year older than the same-aged people who are not postmortem diagnosed with AD (p = 4.6E−4), utilizing data from over 700 postmortem samples [24]. Similarly, Grodstein et al. [59] used postmortem dorsolateral prefrontal cortex from 721 older participants to investigate the link between epigenetic age acceleration and AD by using the four established epigenetic clocks (Hannum, Horvath, PhenoAge, and GrimAge), as well as a specifically developed “cortical clock,” developed in cortical tissue and trained to predict brain chronologic age [59]. Cortical clock uses a set of 347 DNAm sites that has been shown to optimally predict age in the human cortex [60]. A positive correlation between AD and accelerated epigenetics age was found across the Hannum, Horvath, and PhenoAge clocks, although there was no relation using GrimAge clock [59]. Interestingly, findings were stronger for the cortical clock, where each standard deviation increase in clock age was related to a 90% greater likelihood of pathologic AD (OR = 1.91, 95% CI: 1.38, 2.62) [59].

Parkinson’s Disease

Few studies have investigated the connection so far between PD and epigenetic age acceleration. Horvath et al. [63], by analyzing the blood of PD subjects, showed that they exhibit increased age acceleration according to both intrinsic epigenetic age acceleration (IEAA) (p = 0.019) and extrinsic epigenetic age acceleration rate (EEAA) (p = 6.1 × 10−3). In particular, IEAA is the IEAA of blood, which is independent of blood cell counts, and EEAA is the EEAA rate of blood, which is associated with age-dependent changes in blood cell counts. After that, DNAm age acceleration was calculated using DNA extracted from patients carrier of mutation linked to an autosomal dominant Parkinsonism form, in order to investigate if epigenetic age phenomena occurs in PD family affected by autosomal dominant Parkinsonism [64]. The authors observed that a greater DNAm age acceleration was associated with an earlier onset. More recently, epigenetic age acceleration in blood samples from 498 patients was assessed [65]. In brief, the Horvath clock was applied to 96 individuals who had been diagnosed with idiopathic PD, and the results revealed that 75% of the patients had increased epigenetic age acceleration (Table 1). Moreover, the authors investigated the impact of PD in 220 patients carrying the G2019S mutation in LRRK2 gene, known to be associated with 1% of PD patients without family history and 4% in familial PD, with a much higher frequency in PD patients of Ashkenazi Jewish origin (∼20%), as well as the Arab and Berber population in North Africa (∼40%) [66]. Accelerated epigenetic age was observed in 78% of the 220 G2019S carriers, with a similar pattern in manifesting PD and non-manifesting PD individuals. The authors propose that epigenetic age could serve as a biomarker to facilitate the selection of patients with early symptoms of PD for trials of potential disease-modifying therapies [65]. This was one of the first studies demonstrating the association between age acceleration and age at onset of PD, demonstrating its probably clinical utility in disease-modifying clinical trials. Future studies should better evaluate the stability of age acceleration over longer time periods.

This article examines the significance of epigenetic clocks in connection to the development of complex and age-related illnesses. As they provide a more comprehensive evaluation of aging, tissue- and disease-specific epigenetic clocks can be used to identify specific risk factors and monitor certain diseases. It is still unknown how DNAm changes combine with other factors, including genetic background, to regulate the aging process, despite recent advances. How environmental influences, lifestyles, physiological and psychological states contribute to epigenetic modifications in the aging process, and the development of complex diseases is also unknown. It has been shown that distinct epigenetic clocks are able to quantify the biological age and identify the risk of complex diseases, such as obesity, T2D, CVD and neurodegenerative diseases. According to Ecker and Beck [67], each epigenetic clock has a different calibration process, highlighting the significance of tissue type, sample size, and statistical approach. The current epigenetic clocks differ not only in terms of CpGs and Illumina profiles but also in terms of tissue origins and ethnicity, which results in significant variation across research and practical application restrictions. Hopefully, by identifying relationships not only at the clock level but also at the level of smaller subsets of CpG sites and by gathering longitudinal experimental data, the epigenetic clocks may help us understand the factors that play a role in disease development. As science improves, it is possible that clocks will continue to evolve in the future. Further advancements are likely to be made in measuring methylation, constructing clocks, and comprehending their biological importance. The availability of these many DNAm age assessments in a publicly accessible data collection ought to promote scientific progress. In fact, biological clocks are excellent tools for understanding how to implement lifestyle changes to reduce age acceleration and biological age, thus representing an important reference point for risk prevention.

The authors declare no conflicts of interest.

This research received no external funding.

Conceptualization, K.M. and F.M., and writing – review and editing, K.M., F.M., M.F., A.M., and C.G. All authors have read and agreed to the published version of the manuscript.

Additional Information

Katia Margiotti and Francesca Monaco contributed equally to this work.

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