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Table 1 Major biological and analytic issues with epigenetic DNA methylation clocks

From: DNA methylation aging clocks: challenges and recommendations

Significant issue Current problem Potential solutions/advances
Biological age measures. No single measure or “gold standard” of biological age is likely to be possible Simultaneously measuring multiple contributing biological processes that are changing with age Focused analysis on specific components of aging biology and/or specific age-related diseases
Prediction versus mechanistic insight Predictors are by design not optimal for mechanistic insight but are nevertheless used Separate prediction (using sparse CpG sets) from mechanistic studies (based on whole DNA methylome/integrated epigenome)
Age-associated changes in non-dividing cells Uncertainty over mechanism and current ability to dissect apart intrinsic (intracellular) from extrinsic (whole tissue) changes Analyze aging in single cells. Also, determine whether construction of single-cell clocks is possible
Confusion between epigenetic correlations of aging and the aging process itself Aging is a complex multi-systemic process. Specific evidence is required that the epigenetic changes observed in DNA methylation clocks are driving the aging process itself To reduce confusion for those outside the epigenomics field, publications need to be clear that epigenetic observations usually only represent a biomarker of aging
Bias of DNA methylation clock training sets Uncertainty of the contribution of deviation between predicted and actual age to biological aging or prediction error. Clocks trained on small samples are prone to confounding by cell composition Larger studies, as well as increasingly focused tissue/disease-relevant clocks and cell type-specific information
Contribution to DNA methylation clock signals of cell type proportions and rare cells and/or clonality Uncertainty whether cell type deconvolution increases or decreases biomarker power for different diseases Refined single-cell analysis to separate tissue cell proportional changes from intrinsic cell changes for specific diseases. Also, purified cell type analyses and further refinement of cell type deconvolution methodology
Pan-tissue aging changes Separation of true pan-tissue changes from cell proportion changes Single-cell analysis to identify cell proportion changes from individual cell changes. Also, purified cell type and statistical cell type deconvolution analyses
Aging-related increased variability in DNA methylation versus directional changes Difficult to deconvolute intrinsic from extrinsic changes in heterogeneous cell type-derived DNA, as well as to delineate technical from biological variation Single-cell analysis to differentiate cell proportion from individual cell changes. Use of multiple technical and statistical methodologies to dissect origin of variability, including deep-targeted BS-seq and third-generation sequencing
Construction of a clock at the single-cell level Currently technically challenging, particularly due to missing data in each individual cell Imputation may be helpful, but ultimately improvements in single-cell analysis will be required
Identification of disease-related changes Uncertainty whether capturing the most disease relevant changes Improved methodology: latest array increased enhancer CpGs focus—improved high-throughput power to identify tissue-specific and disease-specific loci. Also, increased deep BS sequencing
Disease mechanism is unknown Role of aging-related epigenomic deterioration contributing to age-related disease pathology is not appreciated Discovery of disease-related mechanisms through disease-relevant cell and tissue-type epigenomic analysis of aging-related changes
Regulatory role of DNA methylation is more complex than classical models Complex interplay between transcription factors and epigenomic factors impacts on outcome within different functional loci (promoters, enhancers, insulators, transcribed regions, etc.) Detailed experimental evidence within specific genetic loci and in disease-relevant cell types, including appropriate disease stressors, to infer potential repressive and/or activating roles
Differentiation between DNA methylation loss due to reduction in active processes required for maintenance, or active enzymatic-driven loss Firm evidence required in appropriate cell types of decay without cell division. May be more prevalent at dynamic enhancer regions. Neuronal cells have high post-mitotic expression of DNMTs and TETs plus high 5-hydroxymethylcytosine (5hmC) Detailed models studying DNMT and TET expression in disease-appropriate cell types. Assaying the specific products of TET activity, such as 5hmC
Functionality of DNA methylation changes is often assumed Crossing statistical significance thresholds does not infer function. Statistical differences between quantitative and categorial measures Acknowledged functional evidence deficiency in results and that further integrated disease-relevant tissue genomic/epigenomic/transcriptomic analyses in appropriate models are required
Low reproducibility of DNA methylation clocks in model organisms reduces the utility of published clocks Technical issue because of the reliance on low-depth sequencing due to the lack of available commercial DNA methylation arrays in non-human species Higher-depth base-resolution sequencing studies are required to improve portability of DNA methylation clocks between experiments. Also, new methodology robust to stochastic missing data
Aging DNA methylation sites are only partially conserved among different mammalian species Reduced insights to be made from comparative studies Integrative whole epigenome analyses to identify common mechanistic processes
Role of DNA methylation and rare modifications, such as 5-hydroxymethylation (5hmC) in specific functional loci, such as enhancers Large-scale base-resolution analyses currently performed using bisulfite conversion. This does not differentiate between 5hmC and 5mC Oxidative bisulfite sequencing and new methodologies, such as a non-destructive DNA deaminase, and third-generation direct modification analysis
Interconnected role of DNA modifications and chromatin modifications Unknown directionality and causative effects of cross talk between these different epigenomic modifications In vitro, organoid, and model organism evaluation of epigenetic machinery with age. Integration of DNA modifications, histone post-translational marks, and transcriptional data into a single integrated aging model
Population variation in DNA methylation clock measures Genetic variation may be influencing clock measures directly, or impacting on relevant causative factors, such as inflammation and immunological aging Integrating genetic effectors into clock and age-related measures, including haplotypic information. This will also lead to insights into causal or mechanistic pathways
Many different DNA methylation clock models Many available clocks and ad hoc application and interpretation of results can result in suboptimal robustness of findings Systematic evaluation of methods with a priori assumptions about the meaning of associations of various measures
Forensic use of DNA methylation clocks to determine legal age Robustness of DNA methylation clocks across populations, tissues, and environments is unknown. Furthermore, the impact of acute and chronic inflammatory processes needs assessment Assess variability by the analysis of large, diverse, and well-powered datasets in the range of tissues likely to be employed (whole blood, buccal cells, etc.)
DNA methylation clocks as a de facto measure of an individual’s “health” Associations with biological aging are cross-sectional and epidemiological. Accuracy within an individual and in other populations the clock is not derived from is unknown Longitudinal studies required to assess clock changes within an individual over time. Requires appropriately powered studies across diverse populations. Re-commercialization—public must be protected by provision of accurate data regarding estimate/error rates