Epigenetic testing refers to testing for epigenetic modifications such as DNA methylation in order to gain information about health, disease, and aging. Epigenetics refers to reversible molecular modifications that do not change the genetic code, but change the way genes work and how one's body reads the DNA sequence. Epigenetic changes can result in positive or negative effects on gene expression. For example, epigenetic changes can cause the levels of gene product or protein encoded by the gene can be higher or lower. Organisms begin as a single cell, but as they grow epigenetic changes mark genes in a tissue-specific manner. This allows different cell types like neurons and skin cells to have different gene expression patterns, even though they all contain the same DNA. Throughout life, epigenetic changes can result from behaviors and interactions with the environment.
Epigenetic changes correlate with diseases and the aging process, which is why epigenetic testing can provide information about the health of an individual. A number of cancer diagnostic tests use epigenetic testing. Epigenetic clocks have been developed based on DNA methylation changes that occur with aging. They can predict chronological age and act as biomarkers to predict mortality in humans. Epigenetic clocks are used in aging research to study the role of DNA methylation in the molecular mechanisms of aging. Products and services based on epigenetic clocks are marketed as an assessment of biological age and health. Epigenetic clocks have applications in forensics for predicting age on DNA samples.
DNA methylation is the addition of a methyl group to DNA. DNA methylation can regulate gene expression by blocking transcription factors from binding to the DNA. DNA methylation also recruits proteins that repress gene expression. In gene regulatory regions called promoters, hypomethylated DNA is associated with gene activity and hypermethylated DNA is associated with gene repression.
About 2 percent of CpG sites show changes in DNA methylation with age. The functional consequences of age-related changes in DNA methylation are not clear. There are correlations between DNA methylation and gene expression that occur with age, but it is not clear if age-specific changes in methylation cause gene expression changes .
Inside the nucleus of the cell, DNA is wound around and packaged in proteins called histones. Together the DNA, histones, and other packaging proteins are referred to as chromatin. Chemical modifications to histones change how tightly the DNA is packaged and how accessible it is to proteins that transcribe the genes. Histone modifications can cause a tighter chromatin structure which turns off genes by preventing access to transcriptional machinery. Other histone modifications turn on genes by opening up the chromatin structure to allow transcription to take place.
Non-coding RNA is a type of RNA that does not code for protein. Non-coding RNA has roles in controlling the stability of coding RNA and also in the recruitment of histone modifying proteins to control gene expression.
Epigenetic clocks are based on CpG methylation at sites that are known to change with age. Epigenetic clocks are built using machine learning methods such as elastic net with datasets that contain DNA methylation and chronological age information in order to "learn" which CpG sites predict chronological age most accurately. The predictive accuracy of these trained models are validated on independent cohorts of subjects of various ages. Penalized regression models, such as elastic net or LASSO, have been used to select a group of CpGs that have a constant or monotonically increasing relationship with age in a given training data set. Epigenetic clocks are accurate in predicting chronological age.
Chronological age is based on when you were born and the amount of time that you have been alive. Biological age, also known as epigenetic age, is the age your cells, tissues, or organs appear to be at the biochemistry level. Age acceleration is the difference between biological age and chronological age and is calculated as the mean absolute deviation (MAD) or median absolute deviation between biological age and chronological age. Age acceleration may also be referred to as the residual, difference between the observed and predicted from the linear regression between biological age and chronological age. The DNA methylation data is often analyzed using microarray and pyrosequencing technologies.
Epigenetic clocks have been developed to analyze multiple tissue types or to focus on single tissues. While testing at more CpG sites can increase accuracy, clocks that can predict age using the least CpG sites are attractive for affordability in clinical and forensic fields. In drug-discovery, multiplexing of thousands of samples is more important the accuracy of the arrays. In the field of forensics, minimized epigenetic clocks use the age-associated CpGs, at the ELOVL2 and FHL2 gene loci and are designed for tissues such as blood, saliva, buccal cells, and semen, which are commonly found at crime scenes.
The Horvath or Pan-Tissue clock was the first multi-tissue age predictor. The Horvath clock utilized 353 CpGs and with a mean error or 3.6 years. This clock was built with a training set of 8000 samples from 82 studies, which included 51 healthy tissues and cell types. The Horvath clock demonstrated that tissues may age at different rates. Brain tissue appears to age slower relative to other tissues. The association between age acceleration and a healthy or diseased state was first shown with obesity using the Horvath clock. That one of the most severe premature aging syndromes, Hutchinson-Gilford Progeria Syndrome (HGPS) did not show age acceleration with the Horvath clock, demonstrates that it has some limitations. The Horvath clock also does not show deceleration in eAge for children with multifocal developmental dysfunctions (syndrome X), a condition in which children appear to age slower. The Horvath clock did not show reliable predictions associated with replicative senescence in primary fibroblasts, a tissue culture model system for aging.
The skin and blood clock was developed by Horvath and colleagues, which does predict age in a range of in vivo and in vitro tissues and cells. The Hannum clock was the first developed for blood with the aim at increasing accuracy by focusing on a single tissue.
DNAm PhenoAge is an epigenetic clock designed to assess the risk of major diseases as well as overall lifespan. This epigenetic clock was developed by Morgan Levine, who previously worked in the laboratory of Steve Horvath, who invented the Horvath clock. DNAm PhenoAge is sold commercially from Elysium Health, where Levine is the head of bioinformatics.
DNAm PhenoAge is considered a second-generation clock that has two steps. First, the measurement of creatine, c-reactive protein and 8 other markers were used to develop a phenotypic age estimator. This data was used to identify 513 CpG sites that are biomarkers for disease and mortality among individuals of the same chronological age.
GrimAge was an epigenetic clock developed by Steve Horvath that predicts mortality risk from person to person. To construct the clock, DNA methylation of the genes encoding 12 plasma proteins were analyzed at biomarkers of physiological risk factors and stress factors. A DNAm-based estimator of lifetime tobacco exposure (smoking pack years) was also included since it is a significant risk factor of mortality and morbidity.
The Zhang clock was primarily trained on blood samples but is able to predict ages of breast, liver, adipose and muscle tissue as accurately as the Horvath clock and predicts blood age better than the Horvath and Hannum clocks. The Zhang clock was created using a much larger training data set of over 13,000 samples.
eAge acceleration is associated with physical and cognitive fitness and neuropathy. eAge predictors have found age acceleration associated with the following diseases:
- Down’s syndrome
- HIV
- Obesity
- Huntington’s disease
- Werner syndrome
- Sotos syndrome
- Frailty and the frailty-related outcome of grip strength
- Type II diabetes
- Heroin use
- Depression
- Horvath clock was not associated with cognitive decline in monozygotic twins
- Frailty-related standing balance and chair-rise time
Epigenetic aging rates have been shown to vary depending on the following factors:
- Sex
- Race/ethnicity
- Vitamin D sufficiency (lower eAge)
- Smoking (higher eAge)
Some direct-to-consumer epigenetic tests make claims that certain lifestyle changes can reverse a person’s epigenetic age/biological age, but it is not yet known how reversible epigenetic age is. Studies on mice have shown evidence that interventions such as caloric restriction increased lifespan and also decelerated epigenetic aging.
- DNAm PhenoAge (Elysium Health)
- DNAge Epigenetic Age Analysis Service (Zymo Research and Epimorphy)
- Chronomics
- EpigenCare
- Muhdo
- MyDNAge
- TruAge Epigenetic Test (TruMe)
The following biomarkers are used with liquid biopsies. Liquid biopsies are utilized with body fluids, such as small samples of blood or urine, which have the advantage of being minimally invasive. Circulating tumor DNA (ctDNA) originates in the cancer and can be analyzed in liquid biopsies for mutations and epigenetic marks.
- AssureMDx (MDXHealth)
- Bladder CARE (Pangea)
- Bladder EpiCheck (Nucleix)
- therascreen PTXQ RGQ (Qiagen)
- Human MGMT Gene Methylation Detection (Xiamen SpacegenCo)
- MGMT Pyro (Qiagen)
- IvyGene (Laboratory for Advanced Medicine)
- GynTech (Oncgnostics)
- QIAsure (Qiagen)
- Cologuard (Exact Sciences)
- ColoSure (LabCorp)
- COLVERA (Clinical Genomics)
- EpiProColon (Epigenomics)
- PredictMDx (LabCorp)
- HCCBloodTest (Epigenomics)
- ConfirmMDx (MdxHealth)
- EPICUP (Ferrer)
MicroRNAs (miRNAs) are a class of small non-coding RNA that function in gene regulation and have a key role in the development and physiology of the cardiovascular system. MicroRNAs are associated with pathophysiology in many cardiovascular diseases. It has been proposed that miRNAs could be detected as circulating biomarkers for different cardiovascular pathologies.
- Methylation sequencing—next generation sequencing
- Methylation microarrays
- methyLight
- methyl-specific PCR
- methylation-sensitive high-resolution melting
- pyrosequencing
- Chromatin immunoprecipitation (ChIP) combined with massively parallel sequencing (ChIP-Seq) to survey interactions between protein, DNA, and RNA
- Transposase-accessible chromatin with sequencing (ATAC-Seq) is a method for determining chromatin accessibility across the genome
- Direct miRNA analysis (DMA)
