Recalibrating the epigenetic clock: implications for assessing biological age in the human cortex

The immunological age prediction of monocytes indicates that gestational diabetes mellitus accelerates the aging of monocytes in offspring

BACKGROUND

Recent studies have suggested that gestational diabetes mellitus (GDM) can accelerate cellular aging in multiple cell types in offspring, but its impact on immune senescence remains uncertain. Our prior study reveals GDM increased the secretion of inflammatory factors by monocytes in offspring. This study discovered the transcriptome characteristics of aging monocytes at the single-cell level and explore the impact of GDM on the progression of monocyte aging in offspring.

METHOD

Single-cell sequencing data from 56 healthy individuals (aged 0-100 years), comprising self-measured samples (n = 6) and publicly available datasets from the Gene Expression Omnibus (GEO, n = 50), were analyzed to characterize monocyte senescence. Linear mixed-effects modeling was used to screen for age-related genes. A random forest model was created to predict immune age in monocytes, allowing for quantitative assessment of aging.

RESULTS

We detected an increase in the number of inflammatory monocytes expressing IL1B and CXCL8 with age. Two age-related gene expression patterns were identified in monocytes. Analysis of offspring monocytes from mothers with GDM suggested that exposure to a GDM environment in the womb may lead to increased expression of aging-related genes, a hindered cell cycle, and increased immune age. The immune age of monocytes at birth is significantly linked to maternal weight gain, high fasting blood glucose levels, and cord blood C-peptide levels during pregnancy.

CONCLUSIONS

Exposure to GDM during pregnancy accelerates aging in offspring immune cells. Monitoring maternal weight and blood sugar during GDM can help prevent negative effects on the offspring immune system.

Designing Epigenetic Clocks for Wildlife Research

The applications of epigenetic clocks - statistical models that predict an individual's age based on DNA methylation patterns - are expanding in wildlife conservation and management. This growing interest highlights the need for field-specific design best practices. Here, we provide recommendations for two main applications of wildlife epigenetic clocks: estimating the unknown ages of individuals and assessing their biological ageing rates. Epigenetic clocks were originally developed to measure biological ageing rates of human tissues, which presents challenges for their adoption in wildlife research. Most notably, the estimated chronological ages of sampled wildlife can be unreliable, and sampling restrictions limit the number and variety of tissues with which epigenetic clocks can be constructed, reducing their accuracy. To address these challenges, we present a detailed workflow for designing, validating applying accurate wildlife epigenetic clocks. Using simulations and analyses applied to an extensive polar bear dataset from across the Canadian Arctic, we demonstrate that accurate epigenetic clocks for wildlife can be constructed and validated using a limited number of samples, accommodating projects with small budgets and sampling constraints. The concerns we address are critical for clock design, whether researchers or third-party service providers perform the bioinformatics. With our workflow and examples, we hope to support the accessible and widespread use of epigenetic clocks in wildlife conservation and management.

Epigenetic ageing clocks: statistical methods and emerging computational challenges

Over the past decade, epigenetic clocks have emerged as powerful machine learning tools, not only to estimate chronological and biological age but also to assess the efficacy of anti-ageing, cellular rejuvenation and disease-preventive interventions. However, many computational and statistical challenges remain that limit our understanding, interpretation and application of epigenetic clocks. Here, we review these computational challenges, focusing on interpretation, cell-type heterogeneity and emerging single-cell methods, aiming to provide guidelines for the rigorous construction of interpretable epigenetic clocks at cell-type and single-cell resolution.

Association between epigenetic aging acceleration and amyloid biomarkers in bipolar disorder

Objectives

Bipolar disorder (BD) has been associated with an elevated risk of Alzheimer's Disease (AD). We assessed AD biomarkers in BD and tested whether epigenetic aging (EA) acceleration is a potential mechanism driving variability in these markers.

Design Setting Participants

Cross-sectional study of n=59 living individuals with BD and n=20 age- and sex-equated control participants, as well as analyses of postmortem brain samples (Brodmann area 9/46) from n=46 individuals with BD.

Measurements

Amyloid beta (Aβ)40, Aβ42, and total Tau levels were measured in plasma from individuals with BD and controls, and Aβ42 levels were measured in brains. EA and its acceleration (blood: GrimAge and DunedinPACE; brains: DNAmClockCortical) were estimated for all samples. Individuals with BD were split into quartiles with accelerated or slower EA if they were in the first or fourth quartiles for GrimAge acceleration (AgeAccelGrim), DunedinPACE, or DNAmClockCortical acceleration (DNAmClockCorticalAccel).

Results

Individuals with BD showed an increase in Aβ40 (p=.049) and a decrease in the Aβ42/40 ratio (p=.035) compared to controls. A decrease in the Aβ42/40 ratio was also found in individuals with BD with high versus low AgeAccelGrim (p=.028). Brain Aβ42 levels significantly correlated with DNAmClockCorticalAccel (r2=.270, p=.007), with those with high EA acceleration showing higher brain Aβ42 after controlling for confounders (p=.008).

Conclusions

Our results provide preliminary evidence that EA may explain the variability in AD risk in individuals with BD and could act as a target for preventing dementia and AD in BD.

Quantification of Epigenetic Aging in Public Health

Estimators of biological age hold promise for use in preventive medicine, for early detection of chronic conditions, and for monitoring the effectiveness of interventions aimed at improving population health. Among the promising biomarkers in this field are DNA methylation-based biomarkers, commonly referred to as epigenetic clocks. This review provides a survey of these clocks, with an emphasis on second-generation clocks that predict human morbidity and mortality. It explores the validity of epigenetic clocks when considering factors such as race, sex differences, lifestyle, and environmental influences. Furthermore, the review addresses the current challenges and limitations in this research area.

A doubly robust estimator for continuous treatments in high dimensions

BACKGROUND

Generalized propensity score (GPS) methods have become popular for estimating causal relationships between a continuous treatment and an outcome in observational studies with rich covariate information. The presence of rich covariates enhances the plausibility of the unconfoundedness assumption. Nonetheless, it is also crucial to ensure the correct specification of both marginal and conditional treatment distributions, beyond the assumption of unconfoundedness.

METHOD

We address limitations in existing GPS methods by extending balance-based approaches to high dimensions and introducing the Generalized Outcome-Adaptive LASSO and Doubly Robust Estimate (GOALDeR). This novel approach integrates a balance-based method that is robust to the misspecification of distributions required for GPS methods, a doubly robust estimator that is robust to the misspecification of models, and a variable selection technique for causal inference that ensures an unbiased and statistically efficient estimation.

RESULTS

Simulation studies showed that GOALDeR was able to generate nearly unbiased estimates when either the GPS model or the outcome model was correctly specified. Notably, GOALDeR demonstrated greater precision and accuracy compared to existing methods and was slightly affected by the covariate correlation structure and ratio of sample size to covariate dimension. Real data analysis revealed no statistically significant dose-response relationship between epigenetic age acceleration and Alzheimer's disease.

CONCLUSION

In this study, we proposed GOALDeR as an advanced GPS method for causal inference in high dimensions, and empirically demonstrated that GOALDeR is doubly robust, with improved accuracy and precision compared to existing methods. The R package is available at https://github.com/QianGao-SXMU/GOALDeR .

Multiomic profiling reveals timing of menopause predicts prefrontal cortex aging and cognitive function

A new case of dementia is diagnosed every 3 s. Beyond age, risk prediction of dementia is challenging. There is growing evidence of underlying processes that connect aging across organ systems and may provide insight for early detection, and there is a need to identify early biomarkers at an age when action can be taken to mitigate cognitive decline. We hypothesized that timing of menopause, a marker of ovarian aging, predicts brain age decades later. We used 2086 subjects with multiple "omics" measurements from post-mortem brain samples. Age at menopause (AAM) is positively correlated with cognitive function and negatively correlated with pre-frontal cortex aging acceleration (calculated as estimated biological age from DNA methylation minus chronological age). Genetic correlations showed that at least part of these associations is derived from shared heritability. To dissect the mechanism linking AAM to cognitive decline, we turned to transcriptomic data which confirmed that later AAM was associated with gene expression in pre-frontal cortex consistent with better cognition, and among those who reached menopause naturally, decreased gene expression of pathways implicated in aging. Those with surgical menopause displayed different molecular changes, including perturbed nicotinamide adenine dinucleotide (NAD+) activity, validated by metabolomics. Bile acid metabolism was perturbed in both groups, although different bile acid ratios were associated with AAM in each. Together, our data suggest that AAM is predictive of brain aging and cognition, with potential mediation by the gut, although through different mechanisms depending on the type of menopause.

From bench to bedside: translational insights into aging research

Aging research has rapidly advanced from fundamental discoveries at the molecular and cellular levels to promising clinical applications. This review discusses the critical translational insights that bridge the gap between bench research and bedside applications, highlighting key discoveries in the mechanisms of aging, biomarkers, and therapeutic interventions. It underscores the importance of interdisciplinary approaches and collaboration among scientists, clinicians, and policymakers to address the complexities of aging and improve health span.

Epigenetic Clock: DNA Methylation as a Marker of Biological Age and Age-Associated Diseases

Age is one of the key criteria of human health used in practical medicine to predict the risk of common chronic diseases. However, biological age, which reflects the state of an individual organism, functional capabilities, social well-being, and risk of premature death from various causes, often does not coincide with chronological age. To determine biological age of a particular individuals and the rate of their aging, specific panels of DNA methylation markers called "epigenetic clock" (EC) were proposed. This review summarizes the data about the main types of ECs developed to date and their key characteristics. We described the results of works studying individual aging rates in common age-associated diseases and outlined main directions, development of which could expand application of ECs in fundamental and practical medicine. There is no doubt that revealing complex mechanisms underlying interaction between the rate of epigenetic aging and the risk of age-associated diseases could play a key role for prediction and early diagnosis, as well as for the development of preventive measures that could delay onset of the disease.

Accelerated biological aging increases the risk of short- and long-term stroke prognosis in patients with ischemic stroke or TIA

BACKGROUND

Biological age (BA), an integrated measure of physiological aging, has a clear link to stroke. There is a paucity of long-term longitudinal studies about the association between accelerated biological age and stroke prognosis in patients with previous strokes, and the differences in the predictive ability of various BA indicators calculated from clinical biochemistry biomarkers for future stroke outcomes are still unknown. To evaluate the role of three accelerated BA indicators for short- and long-term prognosis of patients with ischemic stroke or transient ischemic attack (TIA), and to identify the most appropriate predictor.

METHODS

This study included 7396 patients from the Third China National Stroke Registry (CNSR-III), a prospective national registry of patients with acute ischemic stroke or TIA between August 2015 and March 2018 in China. We constructed accelerated BA using three widely recognized algorithms: PhenoAge, Klemera-Doubal, and HD method. To ascertain the association of accelerated BA with the risk of short- and long-term stroke outcomes, a Cox or logistic regression model was conducted for the analysis. The net reclassification index and integrated discrimination improvement were used to evaluate the added model improvement ability of BA acceleration.

FINDINGS

Compared to those with the lowest of PhenoAge acceleration, patients with the highest were more likely to have a higher risk of stroke (HR 1.98, 95% CI 1.49-2.63, P < 0.001), ischemic stroke (HR 1.88, 95% CI 1.41-2.53, P < 0.001), composite vascular events (HR 2.03, 95% CI 1.53-2.68, P < 0.001), all-cause death (HR 7.02, 95% CI 3.41-14.47, P < 0.001) and the modified Rankin scale of 3-6 (OR 2.55, 95% CI 2.05-3.16, P < 0.001) at three months, and the association observed within one year and five years was similar to that within three months. The risk of all stroke outcomes for HDAge was consistent with PhenoAge acceleration, but KDMAge acceleration was the same, except for stroke within one year (HR 1.24, 95% CI 1.00-1.53, P = 0.053). PhenoAge acceleration provided a better improvement in the model's predictive ability for stroke prognosis, compared to BA determined by other algorithms.

INTERPRETATION

In this prospective cohort study, BA acceleration, particularly PhenoAge, may help identify stroke patients with risks of short- and long-term poor outcomes, potentially enabling subclinical prevention and early intervention.

FUNDING

This work was supported by grants from Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences (2019-I2M-5-029), the National Natural Science Foundation of China (U20A20358), Beijing Hospitals Authority Clinical Medicine Development of special funding support (ZLRK202312), the National Key R&D Program of China (No. 2022YFC3602500, 2022YFC3602505), Outstanding Young Talents Project of Capital Medical University (A2105), and Beijing High-Level Public Health Technical Personnel Construction Project (Discipline leader -03-12).

Cell-type specific epigenetic clocks to quantify biological age at cell-type resolution

The ability to accurately quantify biological age could help monitor and control healthy aging. Epigenetic clocks have emerged as promising tools for estimating biological age, yet they have been developed from heterogeneous bulk tissues, and are thus composites of two aging processes, one reflecting the change of cell-type composition with age and another reflecting the aging of individual cell-types. There is thus a need to dissect and quantify these two components of epigenetic clocks, and to develop epigenetic clocks that can yield biological age estimates at cell-type resolution. Here we demonstrate that in blood and brain, approximately 39% and 12% of an epigenetic clock's accuracy is driven by underlying shifts in lymphocyte and neuronal subsets, respectively. Using brain and liver tissue as prototypes, we build and validate neuron and hepatocyte specific DNA methylation clocks, and demonstrate that these cell-type specific clocks yield improved estimates of chronological age in the corresponding cell and tissue-types. We find that neuron and glia specific clocks display biological age acceleration in Alzheimer's Disease with the effect being strongest for glia in the temporal lobe. Moreover, CpGs from these clocks display a small but significant overlap with the causal DamAge-clock, mapping to key genes implicated in neurodegeneration. The hepatocyte clock is found accelerated in liver under various pathological conditions. In contrast, non-cell-type specific clocks do not display biological age-acceleration, or only do so marginally. In summary, this work highlights the importance of dissecting epigenetic clocks and quantifying biological age at cell-type resolution.

Epigenetic Clocks: Beyond Biological Age, Using the Past to Predict the Present and Future

Predicting health trajectories and accurately measuring aging processes across the human lifespan remain profound scientific challenges. Assessing the effectiveness and impact of interventions targeting aging is even more elusive, largely due to the intricate, multidimensional nature of aging-a process that defies simple quantification. Traditional biomarkers offer only partial perspectives, capturing limited aspects of the aging landscape. Yet, over the past decade, groundbreaking advancements have emerged. Epigenetic clocks, derived from DNA methylation patterns, have established themselves as powerful aging biomarkers, capable of estimating biological age and assessing aging rates across diverse tissues with remarkable precision. These clocks provide predictive insights into mortality and age-related disease risks, effectively distinguishing biological age from chronological age and illuminating enduring questions in gerontology. Despite significant progress in epigenetic clock development, substantial challenges remain, underscoring the need for continued investigation to fully unlock their potential in the science of aging.

The mechanisms, hallmarks, and therapies for brain aging and age-related dementia

Age-related cognitive decline and dementia are significant manifestations of brain aging. As the elderly population grows rapidly, the health and socio-economic impacts of cognitive dysfunction have become increasingly significant. Although clinical treatment of dementia has faced considerable challenges over the past few decades, with limited breakthroughs in slowing its progression, there has been substantial progress in understanding the molecular mechanisms and hallmarks of age-related dementia (ARD). This progress brings new hope for the intervention and treatment of this disease. In this review, we categorize the latest findings in ARD biomarkers into four stages based on disease progression: Healthy brain, pre-clinical, mild cognitive impairment, and dementia. We then systematically summarize the most promising therapeutic approaches to prevent or slow ARD at four levels: Genome and epigenome, organelle, cell, and organ and organism. We emphasize the importance of early prevention and detection, along with the implementation of combined treatments as multimodal intervention strategies, to address brain aging and ARD in the future.

Impact of coffee intake on human aging: Epidemiology and cellular mechanisms

The conception of coffee consumption has undergone a profound modification, evolving from a noxious habit into a safe lifestyle actually preserving human health. The last 20 years also provided strikingly consistent epidemiological evidence showing that the regular consumption of moderate doses of coffee attenuates all-cause mortality, an effect observed in over 50 studies in different geographic regions and different ethnicities. Coffee intake attenuates the major causes of mortality, dampening cardiovascular-, cerebrovascular-, cancer- and respiratory diseases-associated mortality, as well as some of the major causes of functional deterioration in the elderly such as loss of memory, depression and frailty. The amplitude of the benefit seems discrete (17 % reduction) but nonetheless corresponds to an average increase in healthspan of 1.8 years of lifetime. This review explores evidence from studies in humans and human tissues supporting an ability of coffee and of its main components (caffeine and chlorogenic acids) to preserve the main biological mechanisms responsible for the aging process, namely genomic instability, macromolecular damage, metabolic and proteostatic impairments with particularly robust effects on the control of stress adaptation and inflammation and unclear effects on stem cells and regeneration. Further studies are required to detail these mechanistic benefits in aged individuals, which may offer new insights into understanding of the biology of aging and the development of new senostatic strategies. Additionally, the safety of this lifestyle factor in the elderly prompts a renewed attention to recommending the maintenance of coffee consumption throughout life as a healthy lifestyle and to further exploring who gets the greater benefit with what schedules of which particular types and doses of coffee.

Genetic architecture of epigenetic cortical clock age in brain tissue from older individuals: alterations in CD46 and other loci

The cortical epigenetic clock was developed in brain tissue as a biomarker of brain aging. As one way to identify mechanisms underlying aging, we conducted a GWAS of cortical age. We leveraged postmortem cortex tissue and genotyping array data from 694 participants of the Rush Memory and Aging Project and Religious Orders Study (ROSMAP; 11000,000 SNPs), and meta-analysed ROSMAP with 522 participants of Brains for Dementia Research (5,000,000 overlapping SNPs). We confirmed results using eQTL (cortical bulk and single nucleus gene expression), cortical protein levels (ROSMAP), and phenome-wide association studies (clinical/neuropathologic phenotypes, ROSMAP). In the meta-analysis, the strongest association was rs4244620 (p = 1.29 × 10-7), which also exhibited FDR-significant cis-eQTL effects for CD46 in bulk and single nucleus (microglia, astrocyte, oligodendrocyte, neuron) cortical gene expression. Additionally, rs4244620 was nominally associated with lower cognition, faster slopes of cognitive decline, and greater Parkinsonian signs (n ~ 1700 ROSMAP with SNP/phenotypic data; all p ≤ 0.04). In ROSMAP alone, the top SNP was rs4721030 (p = 8.64 × 10-8) annotated to TMEM106B and THSD7A. Further, in ROSMAP (n = 849), TMEM106B and THSD7A protein levels in cortex were related to many phenotypes, including greater AD pathology and lower cognition (all p ≤ 0.0007). Overall, we identified converging evidence of CD46 and possibly TMEM106B/THSD7A for potential roles in cortical epigenetic clock age.

Robust double machine learning model with application to omics data

BACKGROUND

Recently, there has been a growing interest in combining causal inference with machine learning algorithms. Double machine learning model (DML), as an implementation of this combination, has received widespread attention for their expertise in estimating causal effects within high-dimensional complex data. However, the DML model is sensitive to the presence of outliers and heavy-tailed noise in the outcome variable. In this paper, we propose the robust double machine learning (RDML) model to achieve a robust estimation of causal effects when the distribution of the outcome is contaminated by outliers or exhibits symmetrically heavy-tailed characteristics.

RESULTS

In the modelling of RDML model, we employed median machine learning algorithms to achieve robust predictions for the treatment and outcome variables. Subsequently, we established a median regression model for the prediction residuals. These two steps ensure robust causal effect estimation. Simulation study show that the RDML model is comparable to the existing DML model when the data follow normal distribution, while the RDML model has obvious superiority when the data follow mixed normal distribution and t-distribution, which is manifested by having a smaller RMSE. Meanwhile, we also apply the RDML model to the deoxyribonucleic acid methylation dataset from the Alzheimer's disease (AD) neuroimaging initiative database with the aim of investigating the impact of Cerebrospinal Fluid Amyloid β 42 (CSF A β 42) on AD severity.

CONCLUSION

These findings illustrate that the RDML model is capable of robustly estimating causal effect, even when the outcome distribution is affected by outliers or displays symmetrically heavy-tailed properties.

Epigenetic signatures of regional tau pathology and cognition in the aging and pathological brain

Primary age-related tauopathy (PART) and Alzheimer's disease (AD) share hippocampal phospho-tau (p-tau) pathology but differ in p-tau extent and amyloid presence. As a result, PART uniquely enables investigation of amyloid-independent p-tau mechanisms during brain aging. We conducted the first epigenome-wide association (EWAS) study of PART, which yielded 13 new and robust p-tau/methylation associations. We then jointly analyzed PART and AD epigenomes to develop "TauAge", novel epigenetic clocks that predict p-tau severity in region-specific, age-, and amyloid-independent manners. Integrative transcriptomic analyses revealed that genes involved in synaptic transmission are related to hippocampal p-tau severity in both PART and AD, while neuroinflammatory genes are related to frontal cortex p-tau severity in AD only. Further, a machine learning classifier based on PART-vs-AD epigenetic differences discriminates neuropathological diagnoses and stratifies indeterminate cases into subgroups with disparity in cognitive impairment. Together, these findings demonstrate the brain epigenome's substantial role in linking tau pathology to cognitive outcomes in aging and AD.

Examining epigenetic aging in the post-mortem brain in attention deficit hyperactivity disorder

Mathematical algorithms known as "epigenetic clocks" use methylation values at a set of CpG sites to estimate the biological age of an individual in a tissue-specific manner. These clocks have demonstrated both acceleration and delays in epigenetic aging in multiple neuropsychiatric conditions, including schizophrenia and neurodevelopmental disorders such as autism spectrum disorder. However, no study to date has examined epigenetic aging in ADHD despite its status as one of the most prevalent neurodevelopmental conditions, with 1 in 9 children having ever received an ADHD diagnosis in the US. Only a handful of studies have examined epigenetic age in brain tissue from neurodevelopmental conditions, with none focused on ADHD, despite the obvious relevance to pathogenesis. Thus, here we asked if post-mortem brain tissue in those with lifetime histories of ADHD would show accelerated or delayed epigenetic age, as has been found for other neurodevelopmental conditions. We applied four different epigenetic clocks to estimate epigenetic age in individuals with ADHD and unaffected controls from cortical (anterior cingulate cortex, N = 55) and striatal (caudate, N = 56) post-mortem brain tissue, as well as peripheral blood (N = 84) and saliva (N = 112). After determining which epigenetic clock performed best in each tissue, we asked if ADHD was associated with altered biological aging in corticostriatal brain and peripheral tissues. We found that a range of epigenetic clocks accurately predicted chronological age in all tissues. We also found that a diagnosis of ADHD was not significantly associated with differential epigenetic aging, neither for the postmortem ACC or caudate, nor for peripheral tissues. These findings held when accounting for comorbid psychiatric diagnoses, substance use, and stimulant medication. Thus, in this study of epigenetic clocks in ADHD, we find no evidence of altered epigenetic aging in corticostriatal brain regions nor in peripheral tissue. We consider reasons for this unexpected finding, including the limited sampling of brain regions, the age range of individuals studied, and the possibility that processes that accelerate epigenetic age may be counteracted by the developmental delay posited in some models of ADHD.

Single-nucleus transcriptomic profiling of human orbitofrontal cortex reveals convergent effects of aging and psychiatric disease

Aging is a complex biological process and represents the largest risk factor for neurodegenerative disorders. The risk for neurodegenerative disorders is also increased in individuals with psychiatric disorders. Here, we characterized age-related transcriptomic changes in the brain by profiling ~800,000 nuclei from the orbitofrontal cortex from 87 individuals with and without psychiatric diagnoses and replicated findings in an independent cohort with 32 individuals. Aging affects all cell types, with LAMP5+LHX6+ interneurons, a cell-type abundant in primates, by far the most affected. Disrupted synaptic transmission emerged as a convergently affected pathway in aged tissue. Age-related transcriptomic changes overlapped with changes observed in Alzheimer's disease across multiple cell types. We find evidence for accelerated transcriptomic aging in individuals with psychiatric disorders and demonstrate a converging signature of aging and psychopathology across multiple cell types. Our findings shed light on cell-type-specific effects and biological pathways underlying age-related changes and their convergence with effects driven by psychiatric diagnosis.

Towards a Novel Frontier in the Use of Epigenetic Clocks in Epidemiology

Health problems associated with aging are a major public health concern for the future. Aging is a complex process with wide intervariability among individuals. Therefore, there is a need for innovative public health strategies that target factors associated with aging and the development of tools to assess the effectiveness of these strategies accurately. Novel approaches to measure biological age, such as epigenetic clocks, have become relevant. These clocks use non-sequential variable information from the genome and employ mathematical algorithms to estimate biological age based on DNA methylation levels. Therefore, in the present study, we comprehensively review the current status of the epigenetic clocks and their associations across the human phenome. We emphasize the potential utility of these tools in an epidemiological context, particularly in evaluating the impact of public health interventions focused on promoting healthy aging. Our review describes associations between epigenetic clocks and multiple traits across the life and health span. Additionally, we highlighted the evolution of studies beyond mere associations to establish causal mechanisms between epigenetic age and disease. We explored the application of epigenetic clocks to measure the efficacy of interventions focusing on rejuvenation.

Epigenetics in Alzheimer's Disease: A Critical Overview

Epigenetic modifications have been implicated in a number of complex diseases as well as being a hallmark of organismal aging. Several reports have indicated an involvement of these changes in Alzheimer's disease (AD) risk and progression, most likely contributing to the dysregulation of AD-related gene expression measured by DNA methylation studies. Given that DNA methylation is tissue-specific and that AD is a brain disorder, the limitation of these studies is the ability to identify clinically useful biomarkers in a proxy tissue, reflective of the tissue of interest, that would be less invasive, more cost-effective, and easily obtainable. The age-related DNA methylation changes have also been used to develop different generations of epigenetic clocks devoted to measuring the aging in different tissues that sometimes suggests an age acceleration in AD patients. This review critically discusses epigenetic changes and aging measures as potential biomarkers for AD detection, prognosis, and progression. Given that epigenetic alterations are chemically reversible, treatments aiming at reversing these modifications will be also discussed as promising therapeutic strategies for AD.

Increased hippocampal epigenetic age in the Ts65Dn mouse model of Down Syndrome

Down syndrome (DS) is a segmental progeroid genetic disorder associated with multi-systemic precocious aging phenotypes, which are particularly evident in the immune and nervous systems. Accordingly, people with DS show an increased biological age as measured by epigenetic clocks. The Ts65Dn trisomic mouse, which harbors extra-numerary copies of chromosome 21 (Hsa21)-syntenic regions, was shown to recapitulate several progeroid features of DS, but no biomarkers of age have been applied to it so far. In this pilot study, we used a mouse-specific epigenetic clock to measure the epigenetic age of hippocampi from Ts65Dn and euploid mice at 20 weeks. Ts65Dn mice showed an increased epigenetic age in comparison with controls, and the observed changes in DNA methylation partially recapitulated those observed in hippocampi from people with DS. Collectively, our results support the use of the Ts65Dn model to decipher the molecular mechanisms underlying the progeroid DS phenotypes.

The impact of ageing mechanisms on musculoskeletal system diseases in the elderly

Ageing is an inevitable process that affects various tissues and organs of the human body, leading to a series of physiological and pathological changes. Mechanisms such as telomere depletion, stem cell depletion, macrophage dysfunction, and cellular senescence gradually manifest in the body, significantly increasing the incidence of diseases in elderly individuals. These mechanisms interact with each other, profoundly impacting the quality of life of older adults. As the ageing population continues to grow, the burden on the public health system is expected to intensify. Globally, the prevalence of musculoskeletal system diseases in elderly individuals is increasing, resulting in reduced limb mobility and prolonged suffering. This review aims to elucidate the mechanisms of ageing and their interplay while exploring their impact on diseases such as osteoarthritis, osteoporosis, and sarcopenia. By delving into the mechanisms of ageing, further research can be conducted to prevent and mitigate its effects, with the ultimate goal of alleviating the suffering of elderly patients in the future.

A multi-organization epigenetic age prediction based on a channel attention perceptron networks

DNA methylation indicates the individual's aging, so-called Epigenetic clocks, which will improve the research and diagnosis of aging diseases by investigating the correlation between methylation loci and human aging. Although this discovery has inspired many researchers to develop traditional computational methods to quantify the correlation and predict the chronological age, the performance bottleneck delayed access to the practical application. Since artificial intelligence technology brought great opportunities in research, we proposed a perceptron model integrating a channel attention mechanism named PerSEClock. The model was trained on 24,516 CpG loci that can utilize the samples from all types of methylation identification platforms and tested on 15 independent datasets against seven methylation-based age prediction methods. PerSEClock demonstrated the ability to assign varying weights to different CpG loci. This feature allows the model to enhance the weight of age-related loci while reducing the weight of irrelevant loci. The method is free to use for academics at www.dnamclock.com/#/original.

A blood biomarker of the pace of aging is associated with brain structure: replication across three cohorts

Biological aging is the correlated decline of multi-organ system integrity central to the etiology of many age-related diseases. A novel epigenetic measure of biological aging, DunedinPACE, is associated with cognitive dysfunction, incident dementia, and mortality. Here, we tested for associations between DunedinPACE and structural MRI phenotypes in three datasets spanning midlife to advanced age: the Dunedin Study (age=45 years), the Framingham Heart Study Offspring Cohort (mean age=63 years), and the Alzheimer's Disease Neuroimaging Initiative (mean age=75 years). We also tested four additional epigenetic measures of aging: the Horvath clock, the Hannum clock, PhenoAge, and GrimAge. Across all datasets (total N observations=3380; total N individuals=2322), faster DunedinPACE was associated with lower total brain volume, lower hippocampal volume, greater burden of white matter microlesions, and thinner cortex. Across all measures, DunedinPACE and GrimAge had the strongest and most consistent associations with brain phenotypes. Our findings suggest that single timepoint measures of multi-organ decline such as DunedinPACE could be useful for gauging nervous system health.

The Impact of Aging on Multiple Sclerosis

PURPOSE OF REVIEW

Multiple sclerosis (MS) is a chronic, immune-mediated demyelinating disorder of the central nervous system. Age is one of the most important factors in determining MS phenotype. This review provides an overview of how age influences MS clinical characteristics, pathology, and treatment.

RECENT FINDINGS

New methods for measuring aging have improved our understanding of the aging process in MS. New studies have characterized the molecular and cellular composition of chronic active or smoldering plaques in MS. These lesions are important contributors to disability progression in MS. These studies highlight the important role of immunosenescence and the innate immune system in sustaining chronic inflammation. Given these changes in immune function, several studies have assessed optimal treatment strategies in aging individuals with MS. MS phenotype is intimately linked with chronologic age and immunosenescence. While there are many unanswered questions, there has been much progress in understanding this relationship which may lead to more effective treatments for progressive disease.

The 'middle-aging' brain

Middle age has historically been an understudied period of life compared to older age, when cognitive and brain health decline are most pronounced, but the scope for intervention may be limited. However, recent research suggests that middle age could mark a shift in brain aging. We review emerging evidence on multiple levels of analysis indicating that midlife is a period defined by unique central and peripheral processes that shape future cognitive trajectories and brain health. Informed by recent developments in aging research and lifespan studies in humans and animal models, we highlight the utility of modeling non-linear changes in study samples with wide subject age ranges to distinguish life stage-specific processes from those acting linearly throughout the lifespan.

Where are we in the implementation of tissue-specific epigenetic clocks?

Introduction: DNA methylation clocks presents advantageous characteristics with respect to the ambitious goal of identifying very early markers of disease, based on the concept that accelerated ageing is a reliable predictor in this sense. Methods: Such tools, being epigenomic based, are expected to be conditioned by sex and tissue specificities, and this work is about quantifying this dependency as well as that from the regression model and the size of the training set. Results: Our quantitative results indicate that elastic-net penalization is the best performing strategy, and better so when-unsurprisingly-the data set is bigger; sex does not appear to condition clocks performances and tissue specific clocks appear to perform better than generic blood clocks. Finally, when considering all trained clocks, we identified a subset of genes that, to the best of our knowledge, have not been presented yet and might deserve further investigation: CPT1A, MMP15, SHROOM3, SLIT3, and SYNGR. Conclusion: These factual starting points can be useful for the future medical translation of clocks and in particular in the debate between multi-tissue clocks, generally trained on a large majority of blood samples, and tissue-specific clocks.

Deciphering the role of immune cell composition in epigenetic age acceleration: Insights from cell-type deconvolution applied to human blood epigenetic clocks

Aging is a significant risk factor for various human disorders, and DNA methylation clocks have emerged as powerful tools for estimating biological age and predicting health-related outcomes. Methylation data from blood DNA has been a focus of more recently developed DNA methylation clocks. However, the impact of immune cell composition on epigenetic age acceleration (EAA) remains unclear as only some clocks incorporate partial cell type composition information when analyzing EAA. We investigated associations of 12 immune cell types measured by cell-type deconvolution with EAA predicted by six widely-used DNA methylation clocks in data from >10,000 blood samples. We observed significant associations of immune cell composition with EAA for all six clocks tested. Across the clocks, nine or more of the 12 cell types tested exhibited significant associations with EAA. Higher memory lymphocyte subtype proportions were associated with increased EAA, and naïve lymphocyte subtypes were associated with decreased EAA. To demonstrate the potential confounding of EAA by immune cell composition, we applied EAA in rheumatoid arthritis. Our research maps immune cell type contributions to EAA in human blood and offers opportunities to adjust for immune cell composition in EAA studies to a significantly more granular level. Understanding associations of EAA with immune profiles has implications for the interpretation of epigenetic age and its relevance in aging and disease research. Our detailed map of immune cell type contributions serves as a resource for studies utilizing epigenetic clocks across diverse research fields, including aging-related diseases, precision medicine, and therapeutic interventions.

Insights into ageing rates comparison across tissues from recalibrating cerebellum DNA methylation clock

DNA methylation (DNAm)-based age clocks have been studied extensively as a biomarker of human ageing and a risk factor for age-related diseases. Despite different tissues having vastly different rates of proliferation, it is still largely unknown whether they age at different rates. It was previously reported that the cerebellum ages slowly; however, this claim was drawn from a single clock using a relatively small sample size and so warrants further investigation. We collected the largest cerebellum DNAm dataset (N = 752) to date. We found the respective epigenetic ages are all severely underestimated by six representative DNAm age clocks, with the underestimation effects more pronounced in the four clocks whose training datasets do not include brain-related tissues. We identified 613 age-associated CpGs in the cerebellum, which accounts for only 14.5% of the number found in the middle temporal gyrus from the same population (N = 404). From the 613 cerebellum age-associated CpGs, we built a highly accurate age prediction model for the cerebellum named CerebellumClockspecific (Pearson correlation=0.941, MAD=3.18 years). Ageing rate comparisons based on the two tissue-specific clocks constructed on the 201 overlapping age-associated CpGs support the cerebellum has younger DNAm age. Nevertheless, we built BrainCortexClock to prove a single DNAm clock is able to unbiasedly estimate DNAm ages of both cerebellum and cerebral cortex, when they are adequately and equally represented in the training dataset. Comparing ageing rates across tissues using DNA methylation multi-tissue clocks is flawed. The large underestimation of age prediction for cerebellums by previous clocks mainly reflects the improper usage of these age clocks. There exist strong and consistent ageing effects on the cerebellar methylome, and we suggest the smaller number of age-associated CpG sites in cerebellum is largely attributed to its extremely low average cell replication rates.

Integrative multi-omics analysis reveals the critical role of the PBXIP1 gene in Alzheimer's disease

Alzheimer's disease (AD) is a neurodegenerative disorder, and its strongest risk factor is aging. A few studies have explored the relationship between aging and AD, while the underlying mechanism remains unclear. We assembled data across multi-omics (i.e., epigenetics, transcriptomics, and proteomics, based on frozen tissues from the dorsolateral prefrontal cortex) and neuropathological and clinical traits from the Religious Orders Study and Rush Memory and Aging Project (ROSMAP). Aging was assessed using six DNA methylation clocks (including the Horvath clock, Hannum clock, Levine clock, HorvathSkin clock, Lin clock, and Cortical clock) that capture mortality risk in literature. After accounting for age, we first identified a gene module (including 263 genes) that was related to the integrated aging measure of six clocks, as well as three neuropathological traits of AD (i.e., β-amyloid, Tau tangles, and tangle density). Interestingly, among 20 key genes with top intramodular connectivity of the module, PBXIP1 was the only one that was significantly associated with all three neuropathological traits of AD at the protein level after Bonferroni correction. Furthermore, PBXIP1 was associated with the clinical diagnosis of AD in both ROSMAP and three independent datasets. Moreover, PBXIP1 may be related to AD through its role in astrocytes and hippocampal neurons, and the mTOR pathway. The results suggest the critical role of PBXIP1 in AD and support the potential and feasibility of using multi-omics data to investigate mechanisms of complex diseases. However, more validations in different populations and experiments in vitro and in vivo are required in the future.

High-dimensional generalized median adaptive lasso with application to omics data

Recently, there has been a growing interest in variable selection for causal inference within the context of high-dimensional data. However, when the outcome exhibits a skewed distribution, ensuring the accuracy of variable selection and causal effect estimation might be challenging. Here, we introduce the generalized median adaptive lasso (GMAL) for covariate selection to achieve an accurate estimation of causal effect even when the outcome follows skewed distributions. A distinctive feature of our proposed method is that we utilize a linear median regression model for constructing penalty weights, thereby maintaining the accuracy of variable selection and causal effect estimation even when the outcome presents extremely skewed distributions. Simulation results showed that our proposed method performs comparably to existing methods in variable selection when the outcome follows a symmetric distribution. Besides, the proposed method exhibited obvious superiority over the existing methods when the outcome follows a skewed distribution. Meanwhile, our proposed method consistently outperformed the existing methods in causal estimation, as indicated by smaller root-mean-square error. We also utilized the GMAL method on a deoxyribonucleic acid methylation dataset from the Alzheimer's disease (AD) neuroimaging initiative database to investigate the association between cerebrospinal fluid tau protein levels and the severity of AD.

A bibliometric and visual analysis of epigenetic research publications for Alzheimer's disease (2013-2023)

Background

Currently, the prevalence of Alzheimer's disease (AD) is progressively rising, particularly in developed nations. There is an escalating focus on the onset and progression of AD. A mounting body of research indicates that epigenetics significantly contributes to AD and holds substantial promise as a novel therapeutic target for its treatment.

Objective

The objective of this article is to present the AD areas of research interest, comprehend the contextual framework of the subject research, and investigate the prospective direction for future research development.

Methods

ln Web of Science Core Collection (WOSCC), we searched documents by specific subject terms and their corresponding free words. VOSviewer, CiteSpace and Scimago Graphica were used to perform statistical analysis on measurement metrics such as the number of published papers, national cooperative networks, publishing countries, institutions, authors, co-cited journals, keywords, and visualize networks of related content elements.

Results

We selected 1,530 articles from WOSCC from January 2013 to June 2023 about epigenetics of AD. Based on visual analysis, we could get that China and United States were the countries with the most research in this field. Bennett DA was the most contributed and prestigious scientist. The top 3 cited journals were Journal of Alzheimer's Disease, Neurobiology of Aging and Molecular Neurobiology. According to the analysis of keywords and the frequency of citations, ncRNAs, transcription factor, genome, histone modification, blood DNA methylation, acetylation, biomarkers were hot research directions in AD today.

Conclusion

According to bibliometric analysis, epigenetic research in AD was a promising research direction, and epigenetics had the potential to be used as AD biomarkers and therapeutic targets.

Unsupervised machine learning identifies distinct ALS molecular subtypes in post-mortem motor cortex and blood expression data

Amyotrophic lateral sclerosis (ALS) displays considerable clinical and genetic heterogeneity. Machine learning approaches have previously been utilised for patient stratification in ALS as they can disentangle complex disease landscapes. However, lack of independent validation in different populations and tissue samples have greatly limited their use in clinical and research settings. We overcame these issues by performing hierarchical clustering on the 5000 most variably expressed autosomal genes from motor cortex expression data of people with sporadic ALS from the KCL BrainBank (N = 112). Three molecular phenotypes linked to ALS pathogenesis were identified: synaptic and neuropeptide signalling, oxidative stress and apoptosis, and neuroinflammation. Cluster validation was achieved by applying linear discriminant analysis models to cases from TargetALS US motor cortex (N = 93), as well as Italian (N = 15) and Dutch (N = 397) blood expression datasets, for which there was a high assignment probability (80-90%) for each molecular subtype. The ALS and motor cortex specificity of the expression signatures were tested by mapping KCL BrainBank controls (N = 59), and occipital cortex (N = 45) and cerebellum (N = 123) samples from TargetALS to each cluster, before constructing case-control and motor cortex-region logistic regression classifiers. We found that the signatures were not only able to distinguish people with ALS from controls (AUC 0.88 ± 0.10), but also reflect the motor cortex-based disease process, as there was perfect discrimination between motor cortex and the other brain regions. Cell types known to be involved in the biological processes of each molecular phenotype were found in higher proportions, reinforcing their biological interpretation. Phenotype analysis revealed distinct cluster-related outcomes in both motor cortex datasets, relating to disease onset and progression-related measures. Our results support the hypothesis that different mechanisms underpin ALS pathogenesis in subgroups of patients and demonstrate potential for the development of personalised treatment approaches. Our method is available for the scientific and clinical community at https://alsgeclustering.er.kcl.ac.uk .

Epigenetic clocks in neurodegenerative diseases: a systematic review

BACKGROUND

Biological ageing is one of the principal risk factors for neurodegenerative diseases. It is becoming increasingly clear that acceleration of DNA methylation age, as measured by the epigenetic clock, is closely associated with many age-related diseases.

METHODS

We searched the PubMed and Web of Science databases to identify eligible studies reporting epigenetic clocks in several neurodegenerative diseases, including Alzheimer's disease (AD), Parkinson's disease (PD), amyotrophic lateral sclerosis (ALS) and Huntington's disease (HD).

RESULTS

Twenty-three studies (12 for AD, 4 for PD, 5 for ALS, and 2 for HD) were included. We systematically summarised the clinical utility of 11 epigenetic clocks (based on blood and brain tissues) in assessing the risk factors, age of onset, diagnosis, progression, prognosis and pathology of AD, PD, ALS and HD. We also critically described our current understandings to these evidences, and further discussed key challenges, potential mechanisms and future perspectives of epigenetic ageing in neurodegenerative diseases.

CONCLUSIONS

Epigenetic clocks hold great potential in neurodegenerative diseases. Further research is encouraged to evaluate the clinical utility and promote the application.

PROSPERO REGISTRATION NUMBER

CRD42022365233.

Histopathologic brain age estimation via multiple instance learning

Understanding age acceleration, the discordance between biological and chronological age, in the brain can reveal mechanistic insights into normal physiology as well as elucidate pathological determinants of age-related functional decline and identify early disease changes in the context of Alzheimer's and other disorders. Histopathological whole slide images provide a wealth of pathologic data on the cellular level that can be leveraged to build deep learning models to assess age acceleration. Here, we used a collection of digitized human post-mortem hippocampal sections to develop a histological brain age estimation model. Our model predicted brain age within a mean absolute error of 5.45 ± 0.22 years, with attention weights corresponding to neuroanatomical regions vulnerable to age-related changes. We found that histopathologic brain age acceleration had significant associations with clinical and pathologic outcomes that were not found with epigenetic based measures. Our results indicate that histopathologic brain age is a powerful, independent metric for understanding factors that contribute to brain aging.

A DNA Methylation Perspective on Infertility

Infertility affects a significant number of couples worldwide and its incidence is increasing. While assisted reproductive technologies (ART) have revolutionized the treatment landscape of infertility, a significant number of couples present with an idiopathic cause for their infertility, hindering effective management. Profiling the genome and transcriptome of infertile men and women has revealed abnormal gene expression. Epigenetic modifications, which comprise dynamic processes that can transduce environmental signals into gene expression changes, may explain these findings. Indeed, aberrant DNA methylation has been widely characterized as a cause of abnormal sperm and oocyte gene expression with potentially deleterious consequences on fertilization and pregnancy outcomes. This review aims to provide a concise overview of male and female infertility through the lens of DNA methylation alterations.

Cellular senescence in brain aging and cognitive decline

Cellular senescence is a biological aging hallmark that plays a key role in the development of neurodegenerative diseases. Clinical trials are currently underway to evaluate the effectiveness of senotherapies for these diseases. However, the impact of senescence on brain aging and cognitive decline in the absence of neurodegeneration remains uncertain. Moreover, patient populations like cancer survivors, traumatic brain injury survivors, obese individuals, obstructive sleep apnea patients, and chronic kidney disease patients can suffer age-related brain changes like cognitive decline prematurely, suggesting that they may suffer accelerated senescence in the brain. Understanding the role of senescence in neurocognitive deficits linked to these conditions is crucial, especially considering the rapidly evolving field of senotherapeutics. Such treatments could help alleviate early brain aging in these patients, significantly reducing patient morbidity and healthcare costs. This review provides a translational perspective on how cellular senescence plays a role in brain aging and age-related cognitive decline. We also discuss important caveats surrounding mainstream senotherapies like senolytics and senomorphics, and present emerging evidence of hyperbaric oxygen therapy and immune-directed therapies as viable modalities for reducing senescent cell burden.

The Cutting Edge of Epigenetic Clocks: In Search of Mechanisms Linking Aging and Mental Health

Individuals with psychiatric disorders are at increased risk of age-related diseases and early mortality. Recent studies demonstrate that this link between mental health and aging is reflected in epigenetic clocks, aging biomarkers based on DNA methylation. The reported relationships between epigenetic clocks and mental health are mostly correlational, and the mechanisms are poorly understood. Here, we review recent progress concerning the molecular and cellular processes underlying epigenetic clocks as well as novel technologies enabling further studies of the causes and consequences of epigenetic aging. We then review the current literature on how epigenetic clocks relate to specific aspects of mental health, such as stress, medications, substance use, health behaviors, and symptom clusters. We propose an integrated framework where mental health and epigenetic aging are each broken down into multiple distinct processes, which are then linked to each other, using stress and schizophrenia as examples. This framework incorporates the heterogeneity and complexity of both mental health conditions and aging, may help reconcile conflicting results, and provides a basis for further hypothesis-driven research in humans and model systems to investigate potentially causal mechanisms linking aging and mental health.

Cellular specificity is key to deciphering epigenetic changes underlying Alzheimer's disease

Different cell types in the brain play distinct roles in Alzheimer's disease (AD) progression. Late onset AD (LOAD) is a complex disease, with a large genetic component, but many risk loci fall in non-coding genome regions. Epigenetics implicates the non-coding genome with control of gene expression. The epigenome is highly cell-type specific and dynamically responds to the environment. Therefore, epigenetic mechanisms are well placed to explain genetic and environmental factors that are associated with AD. However, given this cellular specificity, purified cell populations or single cells need to be profiled to avoid effect masking. Here we review the current state of cell-type specific genome-wide profiling in LOAD, covering DNA methylation (CpG, CpH, and hydroxymethylation), histone modifications, and chromatin changes. To date, these data reveal that distinct cell types contribute and react differently to AD progression through epigenetic alterations. This review addresses the current gap in prior bulk-tissue derived work by spotlighting cell-specific changes that govern the complex interplay of cells throughout disease progression and are critical in understanding and developing effective treatments for AD.

Evaluating genomic signatures of aging in brain tissue as it relates to Alzheimer's disease

Telomere length (TL) attrition, epigenetic age acceleration, and mitochondrial DNA copy number (mtDNAcn) decline are established hallmarks of aging. Each has been individually associated with Alzheimer's dementia, cognitive function, and pathologic Alzheimer's disease (AD). Epigenetic age and mtDNAcn have been studied in brain tissue directly but prior work on TL in brain is limited to small sample sizes and most studies have examined leukocyte TL. Importantly, TL, epigenetic age clocks, and mtDNAcn have not been studied jointly in brain tissue from an AD cohort. We examined dorsolateral prefrontal cortex (DLPFC) tissue from N = 367 participants of the Religious Orders Study (ROS) or the Rush Memory and Aging Project (MAP). TL and mtDNAcn were estimated from whole genome sequencing (WGS) data and cortical clock age was computed on 347 CpG sites. We examined dementia, MCI, and level of and change in cognition, pathologic AD, and three quantitative AD traits, as well as measures of other neurodegenerative diseases and cerebrovascular diseases (CVD). We previously showed that mtDNAcn from DLPFC brain tissue was associated with clinical and pathologic features of AD. Here, we show that those associations are independent of TL. We found TL to be associated with β-amyloid levels (beta = - 0.15, p = 0.023), hippocampal sclerosis (OR = 0.56, p = 0.0015) and cerebral atherosclerosis (OR = 1.44, p = 0.0007). We found strong associations between mtDNAcn and clinical measures of AD. The strongest associations with pathologic measures of AD were with cortical clock and there were associations of mtDNAcn with global AD pathology and tau tangles. Of the other pathologic traits, mtDNAcn was associated with hippocampal sclerosis, macroscopic infarctions and CAA and cortical clock was associated with Lewy bodies. Multi-modal age acceleration, accelerated aging on both mtDNAcn and cortical clock, had greater effect size than a single measure alone. These findings highlight for the first time that age acceleration determined on multiple genomic measures, mtDNAcn and cortical clock may have a larger effect on AD/AD related disorders (ADRD) pathogenesis than single measures.

Targeting epigenetics: A novel promise for Alzheimer's disease treatment

So far, the search for a cure for Alzheimer Disease (AD) has been unsuccessful. The only approved drugs attenuate some symptoms, but do not halt the progress of this disease, which affects 50 million people worldwide and will increase its incidence in the coming decades. Such scenario demands new therapeutic approaches to fight against this devastating dementia. In recent years, multi-omics research and the analysis of differential epigenetic marks in AD subjects have contributed to our understanding of AD; however, the impact of epigenetic research is yet to be seen. This review integrates the most recent data on pathological processes and epigenetic changes relevant for aging and AD, as well as current therapies targeting epigenetic machinery in clinical trials. Evidence shows that epigenetic modifications play a key role in gene expression, which could provide multi-target preventative and therapeutic approaches in AD. Both novel and repurposed drugs are employed in AD clinical trials due to their epigenetic effects, as well as increasing number of natural compounds. Given the reversible nature of epigenetic modifications and the complexity of gene-environment interactions, the combination of epigenetic-based therapies with environmental strategies and drugs with multiple targets might be needed to properly help AD patients.

Epigenetic Association Analyses and Risk Prediction of RLS

BACKGROUND

As opposed to other neurobehavioral disorders, epigenetic analyses and biomarkers are largely missing in the case of idiopathic restless legs syndrome (RLS).

OBJECTIVES

Our aims were to develop a biomarker for RLS based on DNA methylation in blood and to examine DNA methylation in brain tissues for dissecting RLS pathophysiology.

METHODS

Methylation of blood DNA from three independent cohorts (n = 2283) and post-mortem brain DNA from two cohorts (n = 61) was assessed by Infinium EPIC 850 K BeadChip. Epigenome-wide association study (EWAS) results of individual cohorts were combined by random-effect meta-analysis. A three-stage selection procedure (discovery, n = 884; testing, n = 520; validation, n = 879) established an epigenetic risk score including 30 CpG sites. Epigenetic age was assessed by Horvath's multi-tissue clock and Shireby's cortical clock.

RESULTS

EWAS meta-analysis revealed 149 CpG sites linked to 136 genes (P < 0.05 after Bonferroni correction) in blood and 23 CpG linked to 18 genes in brain (false discovery rate [FDR] < 5%). Gene-set analyses of blood EWAS results suggested enrichments in brain tissue types and in subunits of the kainate-selective glutamate receptor complex. Individual candidate genes of the brain EWAS could be assigned to neurodevelopmental or metabolic traits. The blood epigenetic risk score achieved an area under the curve (AUC) of 0.70 (0.67-0.73) in the validation set, comparable to analogous scores in other neurobehavioral disorders. A significant difference in biological age in blood or brain of RLS patients was not detectable.

CONCLUSIONS

DNA methylation supports the notion of altered neurodevelopment in RLS. Epigenetic risk scores are reliably associated with RLS but require even higher accuracy to be useful as biomarkers. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Epigenetic Age Acceleration in Frontotemporal Lobar Degeneration: A Comprehensive Analysis in the Blood and Brain

Frontotemporal lobar degeneration (FTLD) includes a heterogeneous group of disorders pathologically characterized by the degeneration of the frontal and temporal lobes. In addition to major genetic contributors of FTLD such as mutations in MAPT, GRN, and C9orf72, recent work has identified several epigenetic modifications including significant differential DNA methylation in DLX1, and OTUD4 loci. As aging remains one of the major risk factors for FTLD, we investigated the presence of accelerated epigenetic aging in FTLD compared to controls. We calculated epigenetic age in both peripheral blood and brain tissues of multiple FTLD subtypes using several DNA methylation clocks, i.e., DNAmClockMulti, DNAmClockHannum, DNAmClockCortical, GrimAge, and PhenoAge, and determined age acceleration and its association with different cellular proportions and clinical traits. Significant epigenetic age acceleration was observed in the peripheral blood of both frontotemporal dementia (FTD) and progressive supranuclear palsy (PSP) patients compared to controls with DNAmClockHannum, even after accounting for confounding factors. A similar trend was observed with both DNAmClockMulti and DNAmClockCortical in post-mortem frontal cortex tissue of PSP patients and in FTLD cases harboring GRN mutations. Our findings support that increased epigenetic age acceleration in the peripheral blood could be an indicator for PSP and to a smaller extent, FTD.

Current Trends in Anti-Aging Strategies

The process of aging manifests from a highly interconnected network of biological cascades resulting in the degradation and breakdown of every living organism over time. This natural development increases risk for numerous diseases and can be debilitating. Academic and industrial investigators have long sought to impede, or potentially reverse, aging in the hopes of alleviating clinical burden, restoring functionality, and promoting longevity. Despite widespread investigation, identifying impactful therapeutics has been hindered by narrow experimental validation and the lack of rigorous study design. In this review, we explore the current understanding of the biological mechanisms of aging and how this understanding both informs and limits interpreting data from experimental models based on these mechanisms. We also discuss select therapeutic strategies that have yielded promising data in these model systems with potential clinical translation. Lastly, we propose a unifying approach needed to rigorously vet current and future therapeutics and guide evaluation toward efficacious therapies.

Utility of DNA Methylation as a Biomarker in Aging and Alzheimer's Disease

Epigenetic mechanisms such as DNA methylation have been implicated in a number of diseases including cancer, heart disease, autoimmune disorders, and neurodegenerative diseases. While it is recognized that DNA methylation is tissue-specific, a limitation for many studies is the ability to sample the tissue of interest, which is why there is a need for a proxy tissue such as blood, that is reflective of the methylation state of the target tissue. In the last decade, DNA methylation has been utilized in the design of epigenetic clocks, which aim to predict an individual's biological age based on an algorithmically defined set of CpGs. A number of studies have found associations between disease and/or disease risk with increased biological age, adding weight to the theory of increased biological age being linked with disease processes. Hence, this review takes a closer look at the utility of DNA methylation as a biomarker in aging and disease, with a particular focus on Alzheimer's disease.

Entorhinal cortex epigenome-wide association study highlights four novel loci showing differential methylation in Alzheimer's disease

BACKGROUND

Studies on DNA methylation (DNAm) in Alzheimer's disease (AD) have recently highlighted several genomic loci showing association with disease onset and progression.

METHODS

Here, we conducted an epigenome-wide association study (EWAS) using DNAm profiles in entorhinal cortex (EC) from 149 AD patients and control brains and combined these with two previously published EC datasets by meta-analysis (total n = 337).

RESULTS

We identified 12 cytosine-phosphate-guanine (CpG) sites showing epigenome-wide significant association with either case-control status or Braak's tau-staging. Four of these CpGs, located in proximity to CNFN/LIPE, TENT5A, PALD1/PRF1, and DIRAS1, represent novel findings. Integrating DNAm levels with RNA sequencing-based mRNA expression data generated in the same individuals showed significant DNAm-mRNA correlations for 6 of the 12 significant CpGs. Lastly, by calculating rates of epigenetic age acceleration using two recently proposed "epigenetic clock" estimators we found a significant association with accelerated epigenetic aging in the brains of AD patients vs. controls.

CONCLUSION

In summary, our study represents the hitherto most comprehensive EWAS in AD using EC and highlights several novel differentially methylated loci with potential effects on gene expression.

Biomarkers of aging

Aging biomarkers are a combination of biological parameters to (i) assess age-related changes, (ii) track the physiological aging process, and (iii) predict the transition into a pathological status. Although a broad spectrum of aging biomarkers has been developed, their potential uses and limitations remain poorly characterized. An immediate goal of biomarkers is to help us answer the following three fundamental questions in aging research: How old are we? Why do we get old? And how can we age slower? This review aims to address this need. Here, we summarize our current knowledge of biomarkers developed for cellular, organ, and organismal levels of aging, comprising six pillars: physiological characteristics, medical imaging, histological features, cellular alterations, molecular changes, and secretory factors. To fulfill all these requisites, we propose that aging biomarkers should qualify for being specific, systemic, and clinically relevant.

Decoding the role of transcriptomic clocks in the human prefrontal cortex

Aging is a complex process with interindividual variability, which can be measured by aging biological clocks. Aging clocks are machine-learning algorithms guided by biological information and associated with mortality risk and a wide range of health outcomes. One of these aging clocks are transcriptomic clocks, which uses gene expression data to predict biological age; however, their functional role is unknown. Here, we profiled two transcriptomic clocks (RNAAgeCalc and knowledge-based deep neural network clock) in a large dataset of human postmortem prefrontal cortex (PFC) samples. We identified that deep-learning transcriptomic clock outperforms RNAAgeCalc to predict transcriptomic age in the human PFC. We identified associations of transcriptomic clocks with psychiatric-related traits. Further, we applied system biology algorithms to identify common gene networks among both clocks and performed pathways enrichment analyses to assess its functionality and prioritize genes involved in the aging processes. Identified gene networks showed enrichment for diseases of signal transduction by growth factor receptors and second messenger pathways. We also observed enrichment of genome-wide signals of mental and physical health outcomes and identified genes previously associated with human brain aging. Our findings suggest a link between transcriptomic aging and health disorders, including psychiatric traits. Further, it reveals functional genes within the human PFC that may play an important role in aging and health risk.

Changes in Loneliness, BDNF, and Biological Aging Predict Trajectories in a Blood-Based Epigenetic Measure of Cortical Aging: A Study of Older Black Americans

A recent epigenetic measure of aging has developed based on human cortex tissue. This cortical clock (CC) dramatically outperformed extant blood-based epigenetic clocks in predicting brain age and neurological degeneration. Unfortunately, measures that require brain tissue are of limited utility to investigators striving to identify everyday risk factors for dementia. The present study investigated the utility of using the CpG sites included in the CC to formulate a peripheral blood-based cortical measure of brain age (CC-Bd). To establish the utility of CC-Bd, we used growth curves with individually varying time points and longitudinal data from a sample of 694 aging African Americans. We examined whether three risk factors that have been linked to cognitive decline-loneliness, depression, and BDNFm-predicted CC-Bd after controlling for several factors, including three new-generation epigenetic clocks. Our findings showed that two clocks-DunedinPACE and PoAm-predicted CC-BD, but that increases in loneliness and BDNFm continued to be robust predictors of accelerated CC-Bd even after taking these effects into account. This suggests that CC-Bd is assessing something more than the pan-tissue epigenetic clocks but that, at least in part, brain health is also associated with the general aging of the organism.