Accelerated Epigenetic Aging in Peripheral Blood does not Predict Dementia Risk

DNA methylation age acceleration is associated with incident cognitive impairment in the health and retirement study

BackgroundDNA methylation clocks have emerged as promising biomarkers for cognitive impairment and dementia. Longitudinal studies exploring the association between DNA methylation clocks and cognitive decline have been constrained by limited sample sizes and a lack of diversity.ObjectiveOur study aimed to investigate associations between DNA methylation clocks and incident cognitive impairment using a larger and US nationally-representative sample from the Health and Retirement Study.MethodsWe measured DNA methylation age acceleration in 2016 by regressing the DNA methylation clocks, including GrimAge, against chronological age. Cognitive change over time was determined by Langa-Weir cognition status from 2016 to 2018. Multivariable logistic regression evaluated the association between DNA methylation age acceleration and cognitive change, adjusting for cell-type proportions, demographic, and health factors. We also applied inverse probability weighting to address potential selection bias from varying loss-to-follow-up rates.ResultsThe analytic sample (N = 2713) was 54% female, 8.4% Black/African American, 86% White, 7.5% Hispanic, and 68 years old at baseline. During the two years of follow-up, 12% experienced cognitive change and had higher baseline GrimAge (mean = 1.2 years) acceleration compared to those maintaining normal cognition (mean = -0.8 years). A one-year increase in GrimAge acceleration was associated with 1.05 times higher adjusted and survey-weighted odds of cognitive change during follow-up (95% CI: 1.01-1.10). This association was consistent after accounting for loss-to-follow-up (OR = 1.07, 95% CI: 1.04-1.11).ConclusionsOur study offers insights into DNA methylation age acceleration associated with cognitive change over time, suggesting avenues for improved prevention, diagnosis, and treatment.

Complementary value of molecular, phenotypic, and functional aging biomarkers in dementia prediction

DNA methylation age (MA), brain age (BA), and frailty index (FI) are putative aging biomarkers linked to dementia risk. We investigated their relationship and combined potential for prediction of cognitive impairment and future dementia risk using the ADNI database. Of several MA algorithms, DunedinPACE and GrimAge2, associated with memory, were combined in a composite MA alongside BA and a data-driven FI in predictive analyses. Pairwise correlations between age- and sex-adjusted measures for MA (aMA), aBA, and aFI were low. FI outperformed BA and MA in all diagnostic tasks. A model including age, sex, and aFI achieved an area under the curve (AUC) of 0.94 for differentiating cognitively normal controls (CN) from dementia patients in a held-out test set. When combined with clinical biomarkers (apolipoprotein E ε4 allele count, memory, executive function), a model including aBA and aFI predicted 5-year dementia risk among MCI patients with an out-of-sample AUC of 0.88. In the prognostic model, BA and FI offered complementary value (both βs 0.50). The tested MAs did not improve predictions. Results were consistent across FI algorithms, with data-driven health deficit selection yielding the best performance. FI had a stronger adverse effect on prognosis in males, while BA's impact was greater in females. Our findings highlight the complementary value of BA and FI in dementia prediction. The results support a multidimensional view of dementia, including an intertwined relationship between the biomarkers, sex, and prognosis. The tested MA's limited contribution suggests caution in their use for individual risk assessment of dementia.

Epigenetic age acceleration predicts subject-specific white matter degeneration in the human brain

Epigenetic clocks provide powerful tools for estimating health and lifespan but their ability to predict brain degeneration and neuronal damage during the aging process is unknown. In this study, we use GrimAge, an epigenetic clock correlated to several blood plasma proteins, to longitudinally investigate brain cellular microstructure in axonal white matter from a cohort of healthy aging individuals. A specific focus was made on white matter hyperintensities, a visible neurological manifestation of small vessel disease, and the axonal pathways throughout each individual's brain affected by their unique white matter hyperintensity location and volume. 98 subjects over 55 years of age were scanned at baseline with 41 returning for a follow-up scan 2 years later. Using diffusion MRI lesionometry, we reconstructed subject-specific networks of affected axonal tracts and examined the diffusion cellular microstructure composition of these areas, both at baseline and longitudinally, for evidence of cellular degeneration. A chronological age-adjusted version of GrimAge was significantly correlated with baseline WMH volume and markers of neuronal decline, indicated by increased extracellular free water, increased intracellular signal, and decreased axonal signal within WMH. By isolating subject-specific axonal regions "lesioned" by crossing through a WMH, age-adjusted GrimAge was also able to predict longitudinal development of similar patterns of neuronal decline throughout the brain. This study is the first to demonstrate WMH lesionometry as a subject-specific precision imaging technique to study degeneration in aging and the first to establish a relationship between accelerated epigenetic GrimAge and brain cellular microstructure in humans.

EpiAge: a next-generation sequencing-based ELOVL2 epigenetic clock for biological age assessment in saliva and blood across health and disease

This study introduces EpiAgePublic, a new method to estimate biological age using only three specific sites on the gene ELOVL2, known for its connection to aging. Unlike traditional methods that require complex and extensive data, our model uses a simpler approach that is well-suited for next-generation sequencing technology, which is a more advanced method of analyzing DNA methylation. This new model overcomes some of the common challenges found in older methods, such as errors due to sample quality and processing variations. We tested EpiAgePublic with a large and varied group of over 4,600 people to ensure its accuracy. It performed on par with, and sometimes better than, more complicated models that use much more data for age estimation. We examined its effectiveness in understanding how factors like HIV infection and stress affect aging, confirming its usefulness in real-world clinical settings. Our results prove that our simple yet effective model, EpiAgePublic, can capture the subtle signs of aging with high accuracy. We also used this model in a study involving patients with Alzheimer's Disease, demonstrating the practical benefits of next-generation sequencing in making precise age-related assessments. This study lays the groundwork for future research on aging mechanisms and assessing how different interventions might impact the aging process using this clock.

DNA methylation age acceleration is associated with incident cognitive impairment in the Health and Retirement Study

Background

DNA methylation clocks have emerged as promising biomarkers for cognitive impairment and dementia. Longitudinal studies exploring the link between DNA methylation clocks and cognitive decline have been constrained by limited sample sizes and a lack of diversity.

Objective

Our study aimed to investigate the longitudinal associations between DNA methylation clocks and incident cognitive impairment using a larger sample size encompassing a US nationally representative sample from the Health and Retirement Study.

Methods

We measured DNA methylation age acceleration in 2016 by comparing the residuals of DNA methylation clocks, including GrimAge, against chronological age. Cognitive decline was determined by the change in Langa-Weir cognition status from 2016 to 2018. Using multivariable logistic regression, we evaluated the link between DNA methylation age acceleration and cognitive decline, adjusting for cell-type proportions, demographic, and health factors. We also conducted an inverse probability weighting analysis to address potential selection bias from varying loss-to-follow-up rates.

Results

The analytic sample (N=2,713) at baseline had an average of 68 years old, and during the two years of follow-up, 12% experienced cognitive decline. Participants who experienced cognitive decline during follow-up had higher baseline GrimAge (mean = 1.2 years) acceleration compared to those who maintained normal cognitive function (mean = -0.8 years, p < 0.001). A one-year increase in GrimAge acceleration was associated with 1.05 times higher adjusted and survey-weighted odds of cognitive decline during follow-up (95% CI: 1.01-1.10). This association was consistent after accounting for loss-to-follow-up (OR = 1.07, 95% CI: 1.04-1.11).

Conclusion

Our study offers insights into DNA methylation age acceleration associated with cognitive decline, suggesting avenues for improved prevention, diagnosis, and treatment.

An interpretable machine learning-based cerebrospinal fluid proteomics clock for predicting age reveals novel insights into brain aging

Machine learning can be used to create "biologic clocks" that predict age. However, organs, tissues, and biofluids may age at different rates from the organism as a whole. We sought to understand how cerebrospinal fluid (CSF) changes with age to inform the development of brain aging-related disease mechanisms and identify potential anti-aging therapeutic targets. Several epigenetic clocks exist based on plasma and neuronal tissues; however, plasma may not reflect brain aging specifically and tissue-based clocks require samples that are difficult to obtain from living participants. To address these problems, we developed a machine learning clock that uses CSF proteomics to predict the chronological age of individuals with a 0.79 Pearson correlation and mean estimated error (MAE) of 4.30 years in our validation cohort. Additionally, we analyzed proteins highly weighted by the algorithm to gain insights into changes in CSF and uncover novel insights into brain aging. We also demonstrate a novel method to create a minimal protein clock that uses just 109 protein features from the original clock to achieve a similar accuracy (0.75 correlation, MAE 5.41). Finally, we demonstrate that our clock identifies novel proteins that are highly predictive of age in interactions with other proteins, but do not directly correlate with chronological age themselves. In conclusion, we propose that our CSF protein aging clock can identify novel proteins that influence the rate of aging of the central nervous system (CNS), in a manner that would not be identifiable by examining their individual relationships with age.

Epigenetic age acceleration and cognitive performance over time in older adults

INTRODUCTION

This study investigated whether epigenetic age acceleration (AA) is associated with the change in cognitive function and the risk of incident dementia over 9 years, separately in males and females.

METHODS

Six epigenetic AA measures, including GrimAge, were estimated in baseline blood samples from 560 Australians aged ≥70 years (50.7% female). Cognitive assessments included global function, episodic memory, executive function, and psychomotor speed. Composite cognitive scores were also generated. Dementia (Diagnostic and Statistical Manual for Mental Disorders - IV [DSM-IV] criteria) was adjudicated by international experts.

RESULTS

Associations between epigenetic AA and cognitive performance over-time varied by sex. In females only, GrimAA/Grim2AA was associated with worse delayed recall, composite cognition, and composite memory (adjusted-beta ranged from -0.1372 to -0.2034). In males only, GrimAA/Grim2AA was associated with slower processing speed (adjusted-beta, -0.3049) and increased dementia risk (adjusted hazard ratios [HRs], 1.78 and 2.00, respectively).

DISCUSSION

Epigenetic AA is associated with cognitive deterioration in later life but with evidence of sex-specific associations.

Highlights

Epigenetic age acceleration was associated with cognitive deterioration over time.However, these associations differed by sex.In females, accelerated GrimAge appeared to be a better marker of decline in memory.In males, accelerated GrimAge was associated with slower processing speed over time.Association between accelerated GrimAge and dementia risk was found only in males.

Epigenetic age acceleration and the risk of frailty, and persistent activities of daily living (ADL) disability

BACKGROUND

Epigenetic ageing is among the most promising ageing biomarkers and may be a useful marker of physical function decline, beyond chronological age. This study investigated whether epigenetic age acceleration (AA) is associated with the change in frailty scores over 7 years and the 7-year risk of incident frailty and persistent Activities of Daily Living (ADL) disability among 560 Australians (50.7% females) aged ≥70 years.

METHODS

Seven AA indices, including GrimAge, GrimAge2, FitAge and DunedinPACE, were estimated from baseline peripheral-blood DNA-methylation. Frailty was assessed using both the 67-item deficit-accumulation frailty index (FI) and Fried phenotype (Fried). Persistent ADL disability was defined as loss of ability to perform one or more basic ADLs for at least 6 months. Linear mixed models and Cox proportional-hazard regression models were used as appropriate.

RESULTS

Accelerated GrimAge, GrimAge2, FitAge and DunedinPACE at baseline were associated with increasing FI scores per year (adjusted-Beta ranged from 0.0015 to 0.0021, P < 0.05), and accelerated GrimAge and GrimAge2 were associated with an increased risk of incident FI-defined frailty (adjusted-HRs 1.43 and 1.39, respectively, P < 0.05). The association between DunedinPACE and the change in FI scores was stronger in females (adjusted-Beta 0.0029, P 0.001 than in males (adjusted-Beta 0.0002, P 0.81). DunedinPACE, but not the other AA measures, was also associated with worsening Fried scores (adjusted-Beta 0.0175, P 0.04). No associations were observed with persistent ADL disability.

CONCLUSION

Epigenetic AA in later life is associated with increasing frailty scores per year and the risk of incident FI-defined frailty.

Sex differences in biological aging and the association with clinical measures in older adults

Females live longer than males, and there are sex disparities in physical health and disease incidence. However, sex differences in biological aging have not been consistently reported and may differ depending on the measure used. This study aimed to determine the correlations between epigenetic age acceleration (AA), and other markers of biological aging, separately in males and females. We additionally explored the extent to which these AA measures differed according to socioeconomic characteristics, clinical markers, and diseases. Epigenetic clocks (HorvathAge, HannumAge, PhenoAge, GrimAge, GrimAge2, and DunedinPACE) were estimated in blood from 560 relatively healthy Australians aged ≥ 70 years (females, 50.7%) enrolled in the ASPREE study. A system-wide deficit accumulation frailty index (FI) composed of 67 health-related measures was generated. Brain age and subsequently brain-predicted age difference (brain-PAD) were estimated from neuroimaging. Females had significantly reduced AA than males, but higher FI, and there was no difference in brain-PAD. FI had the strongest correlation with DunedinPACE (range r: 0.21 to 0.24 in both sexes). Brain-PAD was not correlated with any biological aging measures. Significant correlations between AA and sociodemographic characteristics and health markers were more commonly found in females (e.g., for DunedinPACE and systolic blood pressure r = 0.2, p < 0.001) than in males. GrimAA and Grim2AA were significantly associated with obesity and depression in females, while in males, hypertension, diabetes, and chronic kidney disease were associated with these clocks, as well as DunedinPACE. Our findings highlight the importance of considering sex differences when investigating the link between biological age and clinical measures.

The Causal Relationship between Genetically Predicted Biological Aging, Alzheimer's Disease and Cognitive Function: A Mendelian Randomisation Study

Aging is one of the most important risk factors for Alzheimer's disease (AD). Biological aging is a better indicator of the body's functional state than age (chronological aging). Leukocyte telomere length (LTL) and epigenetic clocks constructed from DNA methylation patterns have emerged as reliable markers of biological aging. Recent studies have shown that it may be possible to slow down or even reverse biological aging, offering promising prospects for treating AD. Several observational studies have reported an association between biological aging, AD, and cognitive function, but the causality behind this association and the effects of different biological aging markers on AD risk and cognitive function remain unclear. Therefore, we explored the causal relationship between them by Mendelian randomization (MR) study. Inverse-variance weighted (IVW) method is the most dominant analytical method in MR studies, which is a weighted average of estimates from different genotype combinations, and this weighted average provides an overall estimate of the causal effect. The results of the IVW analyses showed that HannumAge acceleration and LTL shortening were able to increase the risk of late-onset AD (LOAD), but not early-onset AD (EOAD). Excellent prospective memory and fluid intelligence are potentially protective against GrimAge acceleration. GrimAge acceleration and HorvathAge acceleration increase the risk of LOAD through effects on LTL. Our findings provide important insights into the role of biological aging in the pathogenesis of AD, while also highlighting the interplay of different biological aging markers and their complexity in different AD subtypes.

Advanced biological ageing predicts future risk for neurological diagnoses and clinical examination findings

Age is a dominant risk factor for some of the most common neurological diseases. Biological ageing encompasses interindividual variation in the rate of ageing and can be calculated from clinical biomarkers or DNA methylation data amongst other approaches. Here, we tested the hypothesis that a biological age greater than one's chronological age affects the risk of future neurological diagnosis and the development of abnormal signs on clinical examination. We analysed data from the Swedish Adoption/Twin Study of Aging (SATSA): a cohort with 3175 assessments of 802 individuals followed-up over several decades. Six measures of biological ageing were generated: two physiological ages (created from bedside clinical measurements and standard blood tests) and four blood methylation age measures. Their effects on future stroke, dementia or Parkinson's disease diagnosis, or development of abnormal clinical signs, were determined using survival analysis, with and without stratification by twin pairs. Older physiological ages were associated with ischaemic stroke risk; for example one standard deviation advancement in baseline PhenoAgePhys or KDMAgePhys residual increased future ischaemic stroke risk by 29.2% [hazard ratio (HR): 1.29, 95% confidence interval (CI) 1.06-1.58, P = 0.012] and 42.9% (HR 1.43, CI 1.18-1.73, P = 3.1 × 10-4), respectively. In contrast, older methylation ages were more predictive of future dementia risk, which was increased by 29.7% (HR 1.30, CI 1.07-1.57, P = 0.007) per standard deviation advancement in HorvathAgeMeth. Older physiological ages were also positively associated with future development of abnormal patellar or pupillary reflexes, and the loss of normal gait. Measures of biological ageing can predict clinically relevant pathology of the nervous system independent of chronological age. This may help to explain variability in disease risk between individuals of the same age and strengthens the case for trials of geroprotective interventions for people with neurological disorders.

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.

Epigenetic analysis suggests aberrant cerebellum brain aging in old-aged adults with autism spectrum disorder and schizophrenia

The aberrant aging hypothesis of schizophrenia (SCZ) and autism spectrum disorder (ASD) has been proposed, and the DNA methylation (DNAm) clock, which is a cumulative evaluation of DNAm levels at age-related CpGs, could serve as a biological aging indicator. This study evaluated epigenetic brain aging of ASD and SCZ using Horvath's epigenetic clock, based on two public genome-wide DNA methylation datasets of post-mortem brain samples (NASD = 222; NSCZ = 142). Total subjects were further divided into subgroups by gender and age. The epigenetic age acceleration (AgeAccel) for each sample was calculated as the residual value resulting from the regression model and compared between groups. Results showed DNAm age has a strong correlation with chronological age in both datasets across multiple brain regions (P < 0.05). When divided into equally sized age groups, the AgeAccel of the cerebellum (CB) region from people over 45 years of age was greater compared to the control sample (AgeAccel of ASD vs control: 5.069 vs -6.249; P < 0.001). And a decelerated epigenetic aging process was observed in the CB region of individuals with SCZ aged 50-70 years (AgeAccel of SCZ vs control: -3.171 vs 2.418; P < 0.05). However, our results showed no significant difference in AgeAccel between ASD and control groups, and between SCZ and control groups in the total and gender-specific groups (P > 0.05). This study's results revealed some evidence for aberrant epigenetic CB brain aging in old-aged patients with ASD and SCZ, indicating a different pattern of CB aging in older adults with these two diseases. However, further studies of larger ASD and SCZ cohorts are necessary to make definitive conclusions on this observation.

Biological Age Acceleration and Motoric Cognitive Risk Syndrome

OBJECTIVE

Motoric cognitive risk (MCR) syndrome, a predementia syndrome characterized by slow gait and subjective cognitive concerns, is associated with multiple age-related risk factors. We hypothesized that MCR is associated with biological age acceleration. We examined the associations of biological age acceleration with MCR, and mortality risk in MCR cases.

METHODS

Biological age was determined using proteomic and epigenetic clocks in participants aged 65 years and older in the LonGenity study (N = 700, females = 57.9%) and Health and Retirement Study (HRS; N = 1,043, females = 57.1%) cohorts. Age acceleration (AgeAccel) was operationally defined as the residual from regressing predicted biological age (from both clocks separately) on chronological age. Association of AgeAccel with incident MCR in the overall sample as well as with mortality risk in MCR cases was examined using Cox models and reported as hazard ratios (HRs).

RESULTS

AgeAccel scores derived from a proteomic clock were associated with prevalent MCR (odds ratio adjusted for age, gender, education years, and chronic illnesses [aOR] = 1.36, 95% confidence interval [CI] = 1.09-1.71) as well as predicted incident MCR (HR = 1.19, 95% CI = 1.00-1.41) in the LonGenity cohort. In HRS, the association of AgeAccel using an epigenetic clock with prevalent MCR was confirmed (aOR = 1.47, 95% CI = 1.16-1.85). Participants with MCR and accelerated aging (positive AgeAccel score) were at the highest risk for mortality in both LonGenity (HR = 3.38, 95% CI = 2.01-5.69) and HRS (HR = 2.47, 95% CI = 1.20-5.10).

INTERPRETATION

Accelerated aging predicts risk for MCR, and is associated with higher mortality in MCR patients. ANN NEUROL 2023;93:1187-1197.

Sixteen-Year Longitudinal Evaluation of Blood-Based DNA Methylation Biomarkers for Early Prediction of Alzheimer's Disease

BACKGROUND

DNA methylation (DNAm), an epigenetic mark reflecting both inherited and environmental influences, has shown promise for Alzheimer's disease (AD) prediction.

OBJECTIVE

Testing long-term predictive ability (>15 years) of existing DNAm-based epigenetic age acceleration (EAA) measures and identifying novel early blood-based DNAm AD-prediction biomarkers.

METHODS

EAA measures calculated from Illumina EPIC data from blood were tested with linear mixed-effects models (LMMs) in a longitudinal case-control sample (50 late-onset AD cases; 51 matched controls) with prospective data up to 16 years before clinical onset, and post-onset follow-up. Novel DNAm biomarkers were generated with epigenome-wide LMMs, and Sparse Partial Least Squares Discriminant Analysis applied at pre- (10-16 years), and post-AD-onset time-points.

RESULTS

EAA did not differentiate cases from controls during the follow-up time (p > 0.05). Three new DNA biomarkers showed in-sample predictive ability on average 8 years pre-onset, after adjustment for age, sex, and white blood cell proportions (p-values: 0.022-<0.00001). Our longitudinally-derived panel replicated nominally (p = 0.012) in an external cohort (n = 146 cases, 324 controls). However, its effect size and discriminatory accuracy were limited compared to APOEɛ4-carriership (OR = 1.38 per 1 SD DNAm score increase versus OR = 13.58 for ɛ4-allele carriage; AUCs = 77.2% versus 87.0%). Literature review showed low overlap (n = 4) across 3275 AD-associated CpGs from 8 published studies, and no overlap with our identified CpGs.

Pilot Study of Second-Generation DNA Methylation Epigenetic Markers in Relation to Cognitive and Neuropsychiatric Symptoms in Older Adults

BACKGROUND

Associations between epigenetic aging with cognitive aging and neuropsychiatric measures are not well-understood.

OBJECTIVE

1) To assess cross-sectional correlations between second-generation DNA methylation (DNAm)-based clocks of healthspan and lifespan (i.e., GrimAge, PhenoAge, and DNAm-based estimator of telomere length [DNAmTL]) and cognitive and neuropsychiatric measures; 2) To examine longitudinal associations between change in DNAm markers and change in cognition over 2 years.

METHODS

Participants were members of VITAL-DEP (VITamin D and OmegA-3 TriaL- Depression Endpoint Prevention) study. From previously ascertained cognitive groups (i.e., cognitively normal and mild cognitive impairment), we randomly selected 45 participants, aged≥60 years, who completed in-person neuropsychiatric assessments at baseline and 2 years. The primary outcome was global cognitive score (averaging z-scores of 9 tests). Neuropsychiatric Inventory severity scores were mapped from neuropsychiatric symptoms (NPS) from psychological scales and structured diagnostic interviews. DNAm was assayed using Illumina MethylationEPIC 850K BeadChip at baseline and 2 years. We calculated baseline partial Spearman correlations between DNAm markers and cognitive and NPS measures. We constructed multivariable linear regression models to examine longitudinal relations between DNAm markers and cognition.

RESULTS

At baseline, we observed a suggestive negative correlation between GrimAge clock markers and global cognition but no signal between DNAm markers and NPS measures. Over 2 years: each 1-year increase in DNAmGrimAge was significantly associated with faster declines in global cognition; each 100-base pair increase in DNAmTL was significantly associated with better global cognition.

CONCLUSION

We found preliminary evidence of cross-sectional and longitudinal associations between DNAm markers and global cognition.

Female APOE ɛ4 Carriers with Slow Rates of Biological Aging Have Better Memory Performances Compared to Female ɛ4 Carriers with Accelerated Aging

BACKGROUND

Evidence suggests that APOE ɛ4 carriers have worse memory performances compared to APOE ɛ4 non-carriers and effects may vary by sex and age. Estimates of biological age, using DNA methylation may enhance understanding of the associations between sex and APOE ɛ4 on cognition.

OBJECTIVE

To investigate whether associations between APOE ɛ4 status and memory vary according to rates of biological aging, using a DNA methylation age biomarker, in older men and women without dementia.

METHODS

Data were obtained from 1,771 adults enrolled in the 2016 wave of the Health and Retirement Study. A series of ANCOVAs were used to test the interaction effects of APOE ɛ4 status and aging rates (defined as 1 standard deviation below (i.e., slow rate), or above (i.e., fast rate) their sex-specific mean rate of aging on a composite measure of verbal learning and memory.

RESULTS

APOE ɛ4 female carriers with slow rates of GrimAge had significantly better memory performances compared to fast and average aging APOE ɛ4 female carriers. There was no effect of aging group rate on memory in the female non-carriers and no significant differences in memory according to age rate in either male APOE ɛ4 carriers or non-carriers.

CONCLUSION

Slower rates of aging in female APOE ɛ4 carriers may buffer against the negative effects of the ɛ4 allele on memory. However, longitudinal studies with larger sample sizes are needed to evaluate risk of dementia/memory impairment based on rates of aging in female APOE ɛ4 carriers.

Assessing brain and biological aging trajectories associated with Alzheimer's disease

The development of effective treatments to prevent and slow Alzheimer's disease (AD) pathogenesis is needed in order to tackle the steady increase in the global prevalence of AD. This challenge is complicated by the need to identify key health shifts that precede the onset of AD and cognitive decline as these represent windows of opportunity for intervening and preventing disease. Such shifts may be captured through the measurement of biomarkers that reflect the health of the individual, in particular those that reflect brain age and biological age. Brain age biomarkers provide a composite view of the health of the brain based on neuroanatomical analyses, while biological age biomarkers, which encompass the epigenetic clock, provide a measurement of the overall health state of an individual based on DNA methylation analysis. Acceleration of brain and biological ages is associated with changes in cognitive function, as well as neuropathological markers of AD. In this mini-review, we discuss brain age and biological age research in the context of cognitive decline and AD. While more research is needed, studies show that brain and biological aging trajectories are variable across individuals and that such trajectories are non-linear at older ages. Longitudinal monitoring of these biomarkers may be valuable for enabling earlier identification of divergent pathological trajectories toward AD and providing insight into points for intervention.

Epigenetic aging as a biomarker of dementia and related outcomes: a systematic review

Background: Biological aging may be a robust biomarker of dementia or cognitive performance. This systematic review synthesized the evidence for an association between epigenetic aging and dementia, mild cognitive impairment and cognitive function. Methods: A systematic search was conducted according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Results: 30 eligible articles were included. There was no strong evidence that accelerated epigenetic aging was associated with dementia/mild cognitive impairment (n = 7). There was some evidence of an association with poorer cognition (n = 20), particularly with GrimAge acceleration, but this was inconsistent and varied across cognitive domains. A meta-analysis was not performed due to high study heterogeneity. Conclusion: There is insufficient evidence to indicate that current epigenetic aging clocks can be clinically useful biomarkers of dementia or cognitive aging.