Reprogramming to recover youthful epigenetic information and restore vision.

Ageing is a degenerative process that leads to tissue dysfunction and death. A proposed cause of ageing is the accumulation of epigenetic noise that disrupts gene expression patterns, leading to decreases in tissue function and regenerative capacity1-3. Changes to DNA methylation patterns over time form the basis of ageing clocks4, but whether older individuals retain the information needed to restore these patterns-and, if so, whether this could improve tissue function-is not known. Over time, the central nervous system (CNS) loses function and regenerative capacity5-7. Using the eye as a model CNS tissue, here we show that ectopic expression of Oct4 (also known as Pou5f1), Sox2 and Klf4 genes (OSK) in mouse retinal ganglion cells restores youthful DNA methylation patterns and transcriptomes, promotes axon regeneration after injury, and reverses vision loss in a mouse model of glaucoma and in aged mice. The beneficial effects of OSK-induced reprogramming in axon regeneration and vision require the DNA demethylases TET1 and TET2. These data indicate that mammalian tissues retain a record of youthful epigenetic information-encoded in part by DNA methylation-that can be accessed to improve tissue function and promote regeneration in vivo.

Epigenetic-based age acceleration in a representative sample of older Americans: Associations with aging-related morbidity and mortality.

Biomarkers developed from DNA methylation (DNAm) data are of growing interest as predictors of health outcomes and mortality in older populations. However, it is unknown how epigenetic aging fits within the context of known socioeconomic and behavioral associations with aging-related health outcomes in a large, population-based, and diverse sample. This study uses data from a representative, panel study of US older adults to examine the relationship between DNAm-based age acceleration measures in the prediction of cross-sectional and longitudinal health outcomes and mortality. We examine whether recent improvements to these scores, using principal component (PC)-based measures designed to remove some of the technical noise and unreliability in measurement, improve the predictive capability of these measures. We also examine how well DNAm-based measures perform against well-known predictors of health outcomes such as demographics, SES, and health behaviors. In our sample, age acceleration calculated using "second and third generation clocks," PhenoAge, GrimAge, and DunedinPACE, is consistently a significant predictor of health outcomes including cross-sectional cognitive dysfunction, functional limitations and chronic conditions assessed 2 y after DNAm measurement, and 4-y mortality. PC-based epigenetic age acceleration measures do not significantly change the relationship of DNAm-based age acceleration measures to health outcomes or mortality compared to earlier versions of these measures. While the usefulness of DNAm-based age acceleration as a predictor of later life health outcomes is quite clear, other factors such as demographics, SES, mental health, and health behaviors remain equally, if not more robust, predictors of later life outcomes.

Aging biomarkers and the brain.

Quantifying biological aging is critical for understanding why aging is the primary driver of morbidity and mortality and for assessing novel therapies to counter pathological aging. In the past decade, many biomarkers relevant to brain aging have been developed using various data types and modeling techniques. Aging involves numerous interconnected processes, and thus many complementary biomarkers are needed, each capturing a different slice of aging biology. Here we present a hierarchical framework highlighting how these biomarkers are related to each other and the underlying biological processes. We review those measures most studied in the context of brain aging: epigenetic clocks, proteomic clocks, and neuroimaging age predictors. Many studies have linked these biomarkers to cognition, mental health, brain structure, and pathology during aging. We also delve into the challenges and complexities in interpreting these biomarkers and suggest areas for further innovation. Ultimately, a robust mechanistic understanding of these biomarkers will be needed to effectively intervene in the aging process to prevent and treat age-related disease.

Epigenetic age acceleration, fatigue, and inflammation in patients undergoing radiation therapy for head and neck cancer: A longitudinal study.

BACKGROUND: The authors measured epigenetic age acceleration (EAA) during and after cancer treatment and its association with inflammation and fatigue, which is a debilitating symptom in patients with cancer. METHODS: Patients who had head and neck cancer without distant metastases were assessed before, immediately after, and at 6 months and 12 months postradiotherapy. Blood DNA methylation was assessed using a proprietary bead chip (the Illumina MethylationEPIC BeadChip). EAA was calculated using the Levine epigenetic clock (DNAmPhenoAge), adjusted for chronological age. Fatigue was assessed using the Multidimensional Fatigue Inventory-20. Inflammatory markers were measured using standard techniques. RESULTS: Most patients (N = 133) were men, White, had advanced disease, and received concurrent chemoradiation. EAA changes over time were significant, with the largest increase (4.9 years) observed immediately after radiotherapy (P < .001). Increased EAA was associated with elevated fatigue (P = .003) over time, and patients who had severe fatigue experienced 3.1 years higher EAA than those who had low fatigue (P < .001), which was more prominent (5.6 years; P = .018) for patients who had human papillomavirus-unrelated disease at 12 months posttreatment. EAA was also positively associated with inflammatory markers, including C-reactive protein (CRP) and interleukin-6 (IL-6), over time (P < .001), and patients who had high CRP and IL-6 levels exhibited increases of 4.6 and 5.9 years, respectively, in EAA compared with those who had low CRP and IL-6 levels (P < .001). CRP and IL-6 mediated the association between EAA and fatigue (CRP: 95% CI, 0.060-0.279; IL-6: 95% CI, 0.024-0.220). CONCLUSIONS: Patients with head and neck cancer experienced increased EAA, especially immediately after treatment completion. EAA was associated with greater fatigue and inflammation, including 1 year after treatment. Inflammation may be a target to reduce the impact of age acceleration on poor functional outcomes.

Midlife Study of the Louisville Twins: Connecting Cognitive Development to Biological and Cognitive Aging.

The Louisville Twin Study (LTS) began in 1958 and became a premier longitudinal twin study of cognitive development. The LTS continuously collected data from twins through 2000 after which the study closed indefinitely due to lack of funding. Now that the majority of the sample is age 40 or older (61.36%, N = 1770), the LTS childhood data can be linked to midlife cognitive functioning, among other physical, biological, social, and psychiatric outcomes. We report results from two pilot studies in anticipation of beginning the midlife phase of the LTS. The first pilot study was a participant tracking study, in which we showed that approximately 90% of the Louisville families randomly sampled (N = 203) for the study could be found. The second pilot study consisted of 40 in-person interviews in which twins completed cognitive, memory, biometric, and functional ability measures. The main purpose of the second study was to correlate midlife measures of cognitive functioning to a measure of biological age, which is an alternative index to chronological age that quantifies age as a function of the breakdown of structural and functional physiological systems, and then to relate both of these measures to twins' cognitive developmental trajectories. Midlife IQ was uncorrelated with biological age (- .01) while better scores on episodic memory more strongly correlated with lower biological age (- .19 to - .31). As expected, midlife IQ positively correlated with IQ measures collected throughout childhood and adolescence. Additionally, positive linear rates of change in FSIQ scores in childhood significantly correlated with biological age (- .68), physical functioning (.71), and functional ability (- .55), suggesting that cognitive development predicts lower biological age, better physical functioning, and better functional ability. In sum, the Louisville twins can be relocated to investigate whether and how early and midlife cognitive and physical health factors contribute to cognitive aging.

Correction: A new aging measure captures morbidity and mortality risk across diverse subpopulations from NHANES IV: A cohort study.

[This corrects the article DOI: 10.1371/journal.pmed.1002718.].

Associations of genetics, behaviors, and life course circumstances with a novel aging and healthspan measure: Evidence from the Health and Retirement Study.

BACKGROUND: An individual's rate of aging directly influences his/her susceptibility to morbidity and mortality. Thus, quantifying aging and disentangling how various factors coalesce to produce between-person differences in the rate of aging, have important implications for potential interventions. We recently developed and validated a novel multi-system-based aging measure, Phenotypic Age (PhenoAge), which has been shown to capture mortality and morbidity risk in the full US population and diverse subpopulations. The aim of this study was to evaluate associations between PhenoAge and a comprehensive set of factors, including genetic scores, childhood and adulthood circumstances, and health behaviors, to determine the relative contributions of these factors to variance in this aging measure. METHODS AND FINDINGS: Based on data from 2,339 adults (aged 51+ years, mean age 69.4 years, 56% female, and 93.9% non-Hispanic white) from the US Health and Retirement Study, we calculated PhenoAge and evaluated the multivariable associations for a comprehensive set of factors using 2 innovative approaches-Shapley value decomposition (the Shapley approach hereafter) and hierarchical clustering. The Shapley approach revealed that together all 11 study domains (4 childhood and adulthood circumstances domains, 5 polygenic score [PGS] domains, and 1 behavior domain, and 1 demographic domain) accounted for 29.2% (bootstrap standard error = 0.003) of variance in PhenoAge after adjustment for chronological age. Behaviors exhibited the greatest contribution to PhenoAge (9.2%), closely followed by adulthood adversity, which was suggested to contribute 9.0% of the variance in PhenoAge. Collectively, the PGSs contributed 3.8% of the variance in PhenoAge (after accounting for chronological age). Next, using hierarchical clustering, we identified 6 distinct subpopulations based on the 4 childhood and adulthood circumstances domains. Two of these subpopulations stood out as disadvantaged, exhibiting significantly higher PhenoAges on average. Finally, we observed a significant gene-by-environment interaction between a previously validated PGS for coronary artery disease and the seemingly most disadvantaged subpopulation, suggesting a multiplicative effect of adverse life course circumstances coupled with genetic risk on phenotypic aging. The main limitations of this study were the retrospective nature of self-reported circumstances, leading to possible recall biases, and the unrepresentative racial/ethnic makeup of the population. CONCLUSIONS: In a sample of US older adults, genetic, behavioral, and socioenvironmental circumstances during childhood and adulthood account for about 30% of differences in phenotypic aging. Our results also suggest that the detrimental effects of disadvantaged life course circumstances for health and aging may be further exacerbated among persons with genetic predisposition to coronary artery disease. Finally, our finding that behaviors had the largest contribution to PhenoAge highlights a potential policy target. Nevertheless, further validation of these findings and identification of causal links are greatly needed.

Menopause accelerates biological aging.

Although epigenetic processes have been linked to aging and disease in other systems, it is not yet known whether they relate to reproductive aging. Recently, we developed a highly accurate epigenetic biomarker of age (known as the "epigenetic clock"), which is based on DNA methylation levels. Here we carry out an epigenetic clock analysis of blood, saliva, and buccal epithelium using data from four large studies: the Women's Health Initiative (n = 1,864); Invecchiare nel Chianti (n = 200); Parkinson's disease, Environment, and Genes (n = 256); and the United Kingdom Medical Research Council National Survey of Health and Development (n = 790). We find that increased epigenetic age acceleration in blood is significantly associated with earlier menopause (P = 0.00091), bilateral oophorectomy (P = 0.0018), and a longer time since menopause (P = 0.017). Conversely, epigenetic age acceleration in buccal epithelium and saliva do not relate to age at menopause; however, a higher epigenetic age in saliva is exhibited in women who undergo bilateral oophorectomy (P = 0.0079), while a lower epigenetic age in buccal epithelium was found for women who underwent menopausal hormone therapy (P = 0.00078). Using genetic data, we find evidence of coheritability between age at menopause and epigenetic age acceleration in blood. Using Mendelian randomization analysis, we find that two SNPs that are highly associated with age at menopause exhibit a significant association with epigenetic age acceleration. Overall, our Mendelian randomization approach and other lines of evidence suggest that menopause accelerates epigenetic aging of blood, but mechanistic studies will be needed to dissect cause-and-effect relationships further.

A new aging measure captures morbidity and mortality risk across diverse subpopulations from NHANES IV: A cohort study.

BACKGROUND: A person's rate of aging has important implications for his/her risk of death and disease; thus, quantifying aging using observable characteristics has important applications for clinical, basic, and observational research. Based on routine clinical chemistry biomarkers, we previously developed a novel aging measure, Phenotypic Age, representing the expected age within the population that corresponds to a person's estimated mortality risk. The aim of this study was to assess its applicability for differentiating risk for a variety of health outcomes within diverse subpopulations that include healthy and unhealthy groups, distinct age groups, and persons with various race/ethnic, socioeconomic, and health behavior characteristics. METHODS AND FINDINGS: Phenotypic Age was calculated based on a linear combination of chronological age and 9 multi-system clinical chemistry biomarkers in accordance with our previously established method. We also estimated Phenotypic Age Acceleration (PhenoAgeAccel), which represents Phenotypic Age after accounting for chronological age (i.e., whether a person appears older [positive value] or younger [negative value] than expected, physiologically). All analyses were conducted using NHANES IV (1999-2010, an independent sample from that originally used to develop the measure). Our analytic sample consisted of 11,432 adults aged 20-84 years and 185 oldest-old adults top-coded at age 85 years. We observed a total of 1,012 deaths, ascertained over 12.6 years of follow-up (based on National Death Index data through December 31, 2011). Proportional hazard models and receiver operating characteristic curves were used to evaluate all-cause and cause-specific mortality predictions. Overall, participants with more diseases had older Phenotypic Age. For instance, among young adults, those with 1 disease were 0.2 years older phenotypically than disease-free persons, and those with 2 or 3 diseases were about 0.6 years older phenotypically. After adjusting for chronological age and sex, Phenotypic Age was significantly associated with all-cause mortality and cause-specific mortality (with the exception of cerebrovascular disease mortality). Results for all-cause mortality were robust to stratifications by age, race/ethnicity, education, disease count, and health behaviors. Further, Phenotypic Age was associated with mortality among seemingly healthy participants-defined as those who reported being disease-free and who had normal BMI-as well as among oldest-old adults, even after adjustment for disease prevalence. The main limitation of this study was the lack of longitudinal data on Phenotypic Age and disease incidence. CONCLUSIONS: In a nationally representative US adult population, Phenotypic Age was associated with mortality even after adjusting for chronological age. Overall, this association was robust across different stratifications, particularly by age, disease count, health behaviors, and cause of death. We also observed a strong association between Phenotypic Age and the disease count an individual had. These findings suggest that this new aging measure may serve as a useful tool to facilitate identification of at-risk individuals and evaluation of the efficacy of interventions, and may also facilitate investigation into potential biological mechanisms of aging. Nevertheless, further evaluation in other cohorts is needed.

Eleven Telomere, Epigenetic Clock, and Biomarker-Composite Quantifications of Biological Aging: Do They Measure the Same Thing?

The geroscience hypothesis posits that therapies to slow biological processes of aging can prevent disease and extend healthy years of life. To test such "geroprotective" therapies in humans, outcome measures are needed that can assess extension of disease-free life span. This need has spurred development of different methods to quantify biological aging. But different methods have not been systematically compared in the same humans. We implemented 7 methods to quantify biological aging using repeated-measures physiological and genomic data in 964 middle-aged humans in the Dunedin Study (New Zealand; persons born 1972-1973). We studied 11 measures in total: telomere-length and erosion, 3 epigenetic-clocks and their ticking rates, and 3 biomarker-composites. Contrary to expectation, we found low agreement between different measures of biological aging. We next compared associations between biological aging measures and outcomes that geroprotective therapies seek to modify: physical functioning, cognitive decline, and subjective signs of aging, including aged facial appearance. The 71-cytosine-phosphate-guanine epigenetic clock and biomarker composites were consistently related to these aging-related outcomes. However, effect sizes were modest. Results suggested that various proposed approaches to quantifying biological aging may not measure the same aspects of the aging process. Further systematic evaluation and refinement of measures of biological aging is needed to furnish outcomes for geroprotector trials.

Is 60 the New 50? Examining Changes in Biological Age Over the Past Two Decades.

Increasing life expectancy has been interpreted as improving health of a population. However, mortality is not always a reliable proxy for the pace of aging and could instead reflect achievement in keeping ailing people alive. Using data from NHANES III (1988-1994) and NHANES IV (2007-2010), we examined how biological age, relative to chronological age, changed in the United States between 1988 and 2010, while estimating the contribution of changes in modifiable health behaviors. Results suggest that biological age is lower for more recent periods; however, the degree of improvement varied across age and sex groups. Overall, older adults experienced the greatest improvement or decreases in biological age. Males, especially those in the youngest and oldest groups, experienced greater declines in biological age than females. These differences were partially explained by age- and sex-specific changes in behaviors, such as smoking, obesity, and medication use. Slowing the pace of aging, along with increasing life expectancy, has important social and economic implications; thus, identifying modifiable risk factors that contribute to cohort differences in health and aging is essential.

Quantification of biological aging in young adults.

Antiaging therapies show promise in model organism research. Translation to humans is needed to address the challenges of an aging global population. Interventions to slow human aging will need to be applied to still-young individuals. However, most human aging research examines older adults, many with chronic disease. As a result, little is known about aging in young humans. We studied aging in 954 young humans, the Dunedin Study birth cohort, tracking multiple biomarkers across three time points spanning their third and fourth decades of life. We developed and validated two methods by which aging can be measured in young adults, one cross-sectional and one longitudinal. Our longitudinal measure allows quantification of the pace of coordinated physiological deterioration across multiple organ systems (e.g., pulmonary, periodontal, cardiovascular, renal, hepatic, and immune function). We applied these methods to assess biological aging in young humans who had not yet developed age-related diseases. Young individuals of the same chronological age varied in their "biological aging" (declining integrity of multiple organ systems). Already, before midlife, individuals who were aging more rapidly were less physically able, showed cognitive decline and brain aging, self-reported worse health, and looked older. Measured biological aging in young adults can be used to identify causes of aging and evaluate rejuvenation therapies.

Contemporaneous Social Environment and the Architecture of Late-Life Gene Expression Profiles.

Environmental or social challenges can stimulate a cascade of coordinated physiological changes in stress response systems. Unfortunately, chronic activation of these adaptations under conditions such as low socioeconomic status (SES) can have negative consequences for long-term health. While there is substantial evidence tying low SES to increased disease risk and reduced life expectancy, the underlying biology remains poorly understood. Using pilot data on 120 older adults from the Health and Retirement Study (United States, 2002-2010), we examined the associations between SES and gene expression levels in adulthood, with particular focus on a gene expression program known as the conserved transcriptional response to adversity. We also used a bioinformatics-based approach to assess the activity of specific gene regulation pathways involved in inflammation, antiviral responses, and stress-related neuroendocrine signaling. We found that low SES was related to increased expression of conserved transcriptional response to adversity genes and distinct patterns of proinflammatory, antiviral, and stress signaling (e.g., sympathetic nervous system and hypothalamic-pituitary-adrenal axis) transcription factor activation.

Toll-like receptor 4 in glial inflammatory responses to air pollution in vitro and in vivo.

BACKGROUND: Exposure to traffic-related air pollution (TRAP) is associated with accelerated cognitive aging and higher dementia risk in human populations. Rodent brains respond to TRAP with activation of astrocytes and microglia, increased inflammatory cytokines, and neurite atrophy. A role for Toll-like receptor 4 (TLR4) was suggested in mouse TLR4-knockouts, which had attenuated lung macrophage responses to air pollution. METHODS: To further analyze these mechanisms, we examined mixed glial cultures (astrocytes and microglia) for RNA responses to nanoscale particulate matter (nPM; diameter <0.2 µm), a well-characterized nanoscale particulate matter subfraction of TRAP collected from a local freeway (Morgan et al. Environ Health Perspect 2011; 119,1003-1009, 2011). The nPM was compared with responses to the endotoxin lipopolysaccharide (LPS), a classic TLR4 ligand, using Affymetrix whole genome microarray in rats. Expression patterns were analyzed by significance analysis of microarrays (SAM) for fold change and by weighted gene co-expression network analysis (WGCNA) to identify modules of shared responses between nPM and LPS. Finally, we examined TLR4 activation in hippocampal tissue from mice chronically exposed to nPM. RESULTS: SAM and WGCNA analyses showed strong activation of TLR4 and NF-κB by both nPM and LPS. TLR4 siRNA attenuated TNFα and other inflammatory responses to nPM in vitro, via the MyD88-dependent pathway. In vivo, mice chronically exposed to nPM showed increased TLR4, MyD88, TNFα, and TNFR2 RNA, and decreased NF-κB and TRAF6 RNA TLR4 and NF-κB responses in the hippocampus. CONCLUSIONS: These results show TLR4 activation is integral in brain inflammatory responses to air pollution, and warrant further study of TLR4 in accelerated cognitive aging by air pollution.

Effects of Recent Stress and Variation in the Serotonin Transporter Polymorphism (5-HTTLPR) on Depressive Symptoms: A Repeated-Measures Study of Adults Age 50 and Older.

Depending on genetic sensitivity to it, stress may affect depressive symptomatology differentially. Applying the stress-diathesis hypothesis to older adults, we postulate: (1) recent stress will associate with increased depressive symptom levels and (2) this effect will be greater for individuals with at least one short allele of the serotonin transporter gene promoter region (5-HTTLPR). Further, we employ a design that addresses specific limitations of many prior studies that have examined the 5-HTTLPR × SLE relation, by: (a) using a within-person repeated-measures design to address fluctuations that occur within individuals over time, increase power for detecting G × E, and address GE correlation; (b) studying reports of exogenous stressful events (those unlikely to be caused by depression) to help rule out reverse causation and negativity bias, and in order to assess stressors that are more etiologically relevant to depressive symptomatology in older adults. The sample is drawn from the Health and Retirement Study, a U.S. population-based study of older individuals (N = 28,248; mean age = 67.5; 57.3 % female; 80.7 % Non-Hispanic White, 14.9 % Hispanic/Latino, 4.5 % African American; genetic subsample = 12,332), from whom measures of depressive symptoms and exogenous stressors were collected biannually (1994-2010). Variation in the 5-HTTLPR was characterized via haplotype, using two single nucleotide polymorphisms (SNPs). Ordered logit models were constructed to predict levels of depressive symptoms from 5-HTTLPR and stressors, comparing results of the most commonly applied statistical approaches (i.e., comparing allelic and genotypic models, and continuous and categorical predictors) used in the literature. All models were stratified by race/ethnicity. Overall, results show a main effect of recent stress for all ethnic groups, and mixed results for the variation in 5-HTTLPR × stress interaction, contingent upon statistical model used. Findings suggest there may be a differential effect of stressors and 5-HTTLPR on depressive symptoms by ethnicity, but further research is needed, particularly when using a haplotype to characterize variation in 5-HTTLPR in population-based sample with a diverse ethnic composition.

Minimizing Air Pollution Exposure: A Practical Policy to Protect Vulnerable Older Adults from Death and Disability.

Air pollution causes an estimated 200,000 deaths per year in the United States alone. Older adults are at greater risk of mortality caused by air pollution. Here we quantify the number of older adult facilities in Los Angeles County that are exposed to high levels of traffic derived air pollution, and propose policy solutions to reduce pollution exposure to this vulnerable subgroup. Distances between 20,362 intersections and 858 elder care facilities were estimated, and roads or highways within 500 of facilities were used to estimate traffic volume exposure. Of the 858 facilities, 54 were located near at least one major roadway, defined as a traffic volume over 100,000 cars/day. These 54 facilities house approximately 6,000 older adults. Following standards established for schools, we recommend legislation mandating the placement of new elder care facilities a minimum of 500 feet from major roadways in order to reduce unnecessary mortality risk from pollution exposure.

A comparison of methods for assessing mortality risk.

OBJECTIVES: Concepts such as Allostatic Load, Framingham Risk Score, and Biological Age were developed to combine information from multiple measures into a single latent variable that can be used to quantify a person's biological state. Given these varying approaches, the goal of this article is to compare how well these three measures predict subsequent all-cause and disease-specific mortality within a large nationally representative U.S. sample. METHODS: Our study population consisted of 9,942 adults, ages 30 and above from National Health and Nutrition Examination Survey III. Receiver Operating Characteristic curves and Cox Proportional Hazard models for the whole sample and for stratified age groups were used to compare how well Allostatic Load, Framingham Risk Score, and Biological Age predict ten-year all-cause and disease-specific mortality in the sample, for whom there were 1,076 deaths over 96,420 person years of exposure. RESULTS: Overall, Biological Age predicted 10-year mortality more accurately than other measures for the full age range, as well as for participants ages 50 to 69 and 70+. Additionally, out of the three measures, Biological Age had the strongest association with all-cause and cancer mortality, while the Framingham Risk Score had the strongest association with CVD mortality. CONCLUSIONS: Methods for quantifying biological risk provide important approaches to improving our understanding of the causes and consequences of changes in physiological function and dysregulation. Biological Age offers an alternative and, in some cases a more accurate summary approach to the traditionally used methods, such as Allostatic Load and Framingham Risk Score.

Reliable detection of stochastic epigenetic mutations and associations with cardiovascular aging.

Stochastic epigenetic mutations (SEMs) have been proposed as novel aging biomarkers to capture heterogeneity in age-related DNA methylation changes. SEMs are defined as outlier methylation patterns at cytosine-guanine dinucleotide sites, categorized as hypermethylated (hyperSEM) or hypomethylated (hypoSEM) relative to a reference. Because SEMs are defined by their outlier status, it is critical to differentiate extreme values due to technical noise or data artifacts from those due to real biology. Using technical replicate data, we found SEM detection is not reliable: across 3 datasets, 24 to 39% of hypoSEM and 46 to 67% of hyperSEM are not shared between replicates. We identified factors influencing SEM reliability-including blood cell type composition, probe beta-value statistics, genomic location, and presence of SNPs. We used these factors in a training dataset to build a machine learning-based filter that removes unreliable SEMs, and found this filter enhances reliability in two independent validation datasets. We assessed associations between SEM loads and aging phenotypes in the Framingham Heart Study and discovered that associations with aging outcomes were in large part driven by hypoSEMs at baseline methylated probes and hyperSEMs at baseline unmethylated probes, which are the same subsets that demonstrate highest technical reliability. These aging associations were preserved after filtering out unreliable SEMs and were enhanced after adjusting for blood cell composition. Finally, we utilized these insights to formulate best practices for SEM detection and introduce a novel R package, SEMdetectR, which uses parallel programming for efficient SEM detection with comprehensive options for detection, filtering, and analysis.