Biological age is a novel biomarker to predict stroke recurrence

The Association Between Accelerated Biological Aging and Cardiovascular Outcomes in Older Adults with Hypertension

BACKGROUND

Current hypertension treatments rely on chronological age, which may not reflect individual differences in aging and its impact on cardiovascular health. This study aimed to determine whether biological age can predict adverse outcomes in older adults with hypertension, independent of traditional risk factors including chronological age.

METHODS

An analysis of a prospective cohort was conducted using data from the Health and Retirement Study, a longitudinal survey of older adults in the United States. The Klemera-Doubal method was employed to calculate the biological age of the participants with hypertension at the time of enrollment in 2016. Discrete-time survival analysis was conducted to analyze the relationship between accelerated biological aging and the risk of mortality, heart disease, and stroke over four years of follow-up.

RESULTS

A total of 4,442 individuals were analyzed. Of these, 2,438 showed decelerated aging, whereas 2,004 experienced accelerated aging (biological age > chronological age). The median age of the patients in both groups was around 70 years. Both groups had similar racial and ethnic distributions and predominantly consisted of non-Hispanic whites. The accelerated aging group had a higher prevalence of chronic diseases, lower education levels, and less wealth than the decelerated aging group. After adjustment for these differences, accelerated aging was associated with a higher risk of a composite outcome of death, heart disease, and stroke, with an adjusted hazard ratio (a-HR) of 1.62 (95% confidence interval: 1.27-2.06, P = .001).

CONCLUSIONS

Accelerated biological age is a predictor of cardiovascular outcomes and death in patients with hypertension.

How epigenetics impacts stroke risk and outcomes through DNA methylation: A systematic review

The impact of DNA methylation (DNAm) on epigenetics has gained prominence in recent years due to its potential influence on ischemic stroke (IS) and treatment outcomes. DNAm is reversible and a better understanding of its role in IS could help identify novel therapeutic targets. The aim of this systematic review was to compile the available data on DNAm in the risk and prognosis of IS and to explore its therapeutic potential. The review process followed the PRISMA criteria. We searched the Pubmed and Cochrane databases to identify studies that used hypothesis free methodological approaches. Of the 459 studies identified, 34 met the inclusion criteria. The studies were categorized as follows: risk of IS; outcomes; and DNAm age. Most studies used genotyping array technology rather than whole-genome sequencing. DNAm testing was mainly based on blood samples. Most studies involved European cohorts. Most of the studies were performed at a single-center with recruitment at the time of stroke. In a few studies, health status was determined longitudinally. This systematic review shows that IS patients are biologically older than expected and present characteristic DNAm patterns related to stroke risk and outcomes. These patterns could be used to develop new treatments with epidrugs.

Association of phenotypic age and accelerated aging with severity and disability in patients with acute ischemic stroke

OBJECTIVE

Biological age may be more accurate than chronological age in determining chronic health outcomes. However, few studies have shown the association between biological age and acute ischemic stroke (AIS). In this study we showed the association between phenotypic age (PhenoAge) or accelerated aging and severity and disability in patients with AIS.

DESIGN

Retrospective study.

SETTING AND SUBJECTS

936 patients with AIS during January 2019 to July 2021 and 512 patients during June 2022 to July 2023 for a validation.

METHODS

Stroke severity was evaluated based on the National Institute of Health stroke scale (NIHSS) questionnaire scale. Disability was evaluated by modified Rankin Scale. PhenoAge was calculated based on chronological age and 9 clinical chemistry biomarkers. Logistic regression analyses were applied to estimate the relationship between PhenoAge and the severity and disability.

RESULTS

PhenoAge (odds ratio [OR] = 1.03, 95% confidence interval [CI]: 1.0-1.04, for NIHSS ≥ 5; OR = 1.05, 95%CI: 1.03-1.07, for NIHSS ≥ 10) was independently associated with stroke severity. The probability of NIHSS ≥ 5 or NIHSS ≥ 10 was significantly increased in individuals with accelerated ageing versus individuals with no accelerated aging (age gap: OR = 1.79, 95%CI: 1.18-2.72; OR = 3.53, 95%CI: 1.60-7.77; phenotypically older vs. phenotypically younger: OR = 2.01, 95%CI: 1.21-3.35; OR = 3.69, 95%CI: 1.36-10.0). Similar trends was observed when accelerated aging was defined by residual discrepancies between PhenoAge and chronological age (OR = 1.02, 95%CI: 1.01-1.04, for NIHSS ≥ 5; OR = 1.05, 95%CI: 1.02-1.08, for NIHSS ≥ 10). The area under the curve of PhenoAge was higher than that of chronological age in identifying patients with NIHSS ≥ 5 (0.66, 95%CI:0.62-0.70 vs. 0.61, 95%CI: 0.58-0.65, p < 0.01) and NIHSS ≥ 10 (0.69, 95%CI:0.60-0.77 vs. 0.63, 95%CI: 0.55-0.72, p = 0.05). The probability of severe disability was significantly increased in individuals with accelerated aging versus individuals with no accelerated aging (age gap: OR = 2.87, 95%CI: 1.09-7.53; phenotypically older vs. phenotypically younger: 4.88 (1.20-19.88). Similar results were observed in the validation population.

CONCLUSION

PhenoAge or accelerated aging is associated with stroke severity and disability even after adjusting for chronological age.

Early-life tobacco smoke exposure and stroke risk: a prospective study of 341,783 and 352,737 UK Biobank participants

INTRODUCTION

Stroke is a life-threatening condition that causes a major medical burden globally. The currently used methods for the prevention or prediction of stroke have certain limitations. Exposure to tobacco in early life, including smoking during adolescence and maternal smoking during pregnancy, can affect adolescent development and lead to several negative outcomes. However, the association between early-life tobacco exposure and stroke is not known.

METHODS

In this prospective cohort study, for the analyses involving exposure to maternal smoking during pregnancy and age of smoking initiation, we included 304,984 and 342,893 participants, respectively., respectively from the UK Biobank. Cox proportional hazard regression model and subgroup analyses were performed to investigate the association between early-life tobacco exposure and stroke. Mediation analyses were performed to identify the mediating role of biological aging in the association between early tobacco exposure and stroke.

RESULTS

Compared with participants whose mothers did not smoke during pregnancy, participants whose mothers smoked during pregnancy showed an 11% increased risk of stroke (HR: 1.11, 95% CI: 1.05-1.18, P < 0.001). Compared with participants who never smoked, participants who smoked during adulthood, adolescence and childhood showed a 22%, 24%, and 38% increased risk of stroke during their adulthood, respectively. Mediation analysis indicated that early-life tobacco exposure can cause stroke by increasing biological aging.

CONCLUSION

This study reveals that exposure to tobacco during early life is associated with an increased risk of experiencing a stroke, and increased biological aging can be the underlying mechanism.

Potential Protein Signatures for Recurrence Prediction of Ischemic Stroke

BACKGROUND

Acute ischemic stroke is a major cause of mortality and disability worldwide, with approximately 7.4% to 7.7% recurrence within the first 3 months. This study aimed to identify potential biomarkers for predicting stroke recurrence.

METHODS AND RESULTS

We conducted a nested case-control study using a hospital-based cohort from the Third China National Stroke Registry selecting 214 age- and sex-matched patients with ischemic stroke with hypertension and no history of diabetes or heart disease. Using data-independent acquisition for discovery and multiple reaction monitoring for quantitative validation, we identified 26 differentially expressed proteins in large-artery atherosclerosis (Causative Classification of Ischemic Stroke [CCS]1), 16 in small-artery occlusion (CCS3), and 25 in undetermined causes (CCS5) among patients with recurrent stroke. In the CCS1 and CCS3 subgroups, differentially expressed proteins were associated with platelet aggregation, neuronal death/cerebroprotection, and immune response, whereas differentially expressed proteins in the CCS5 subgroup were linked to altered metabolic functions. Validated recurrence predictors included proteins associated with neutrophil activity and vascular inflammation (TAGLN2 [transgelin 2], ITGAM [integrin subunit α M]/TAGLN2 ratio, ITGAM/MYL9 [myosin light chain 9] ratio, TAGLN2/RSU1 [Ras suppressor protein 1] ratio) in the CCS3 subgroup and proteins associated with endothelial plasticity and blood-brain barrier integrity (ITGAM/MYL9 ratio and COL1A2 [collagen type I α 2 chain]/MYL9 ratio) in the CCS3 and CCS5 subgroups, respectively.

CONCLUSIONS

These findings provide a foundation for developing a blood-based biomarker panel, using causative classifications, which may be used in routine clinical practice to predict stroke recurrence.

Association between kidney function and biological age: a China Health and Retirement Longitudinal Study

Introduction

The chronological age (CA) cannot precisely reflect the health status. Our study aimed to establish a model of kidney biological age to evaluate kidney function more elaborately.

Methods

The modeling group was used to establish the model, consisting of 1,303 respondents of the China Health and Retirement Longitudinal Study (CHARLS). The biological age of the kidney (BA) was constructed by principal component analysis (PCA) and Klemera and Doubal's method (KDM) with the 1,303 health respondents.

Results

PCA was chosen as the best method for our research step by step. The test group was used to apply the model. (a) BA of the kidney can distinguish respondents with from without kidney disease. (b) BA of the kidney was significantly different in various levels of kidney function. The BA of the eGFR <60 group and 60 ≤ eGFR <90 group were older than GFR ≥90 group. (c) The group with younger BA of kidney at baseline had a lower risk of kidney function decreased. (d) The risk of decreased kidney function caused by increasing BA every additional year is higher than CA.

Discussion

The BA of the kidney is a parameter negatively correlated with decreased kidney function and fills the blank of evaluation among people in the middle of heathy and kidney diseases.

Plaque enhancement predicts recurrence in acute ischemic stroke patients with large artery intracranial atherosclerosis

BACKGROUND

The association between the degree of plaque enhancement and ischemic brain stroke recurrence remains unclear. We aimed to establish models to predict plaque enhancement and stroke recurrence.

METHODS

Seventy-eight participants with acute ischemic brain stroke due to intracranial arterial stenosis were recruited and divided into high enhancement (HE) and non-HE groups. The relationship between imaging characteristics (degree of stenosis, minimal lumen area, intraplaque hemorrhage, and plaque burden) and the degree of plaque contrast enhancement was analyzed. Inflammatory cytokine expression was examined by flow cytometry. Independent predictors of stroke recurrence were investigated via multivariate Cox proportional hazards regression analysis. Nomogram was used to construct a prediction model. Harrell's concordance indices (c-indices) and calibration curves were used to assess the discrimination of the nomogram. A risk prediction nomogram for prognosis was constructed.

RESULTS

Thirty-three participants were assigned to the HE group and 45 to the non-HE group. The degree of stenosis and plaque burden in the HE group was higher than that in the non-HE group (P<0.05). Multiple linear regression analysis showed the degree of stenosis was associated with HE (β=0.513; P=0.000). After adjusting for confounding factors, age (HR=1.115; 95%CI=1.034-1.203, P=0.005) and HE plaques (HR=10.457; 95%CI=1.176-93.018; P=0.035) were independent risk factors of stroke recurrence, whereas cytokine levels were not statistically significant between two group.

CONCLUSIONS

HE of intracranial atherosclerosis plaques is an independent factor for ischemic brain stroke recurrence.

Protein Biomarkers in Blood Reflect the Interrelationships Between Stroke Outcome, Inflammation, Coagulation, Adhesion, Senescence and Cancer

The most important predictors for outcomes after ischemic stroke, that is, for health deterioration and death, are chronological age and stroke severity; gender, genetics and lifestyle/environmental factors also play a role. Of all these, only the latter can be influenced after the event. Recurrent stroke may be prevented by antiaggregant/anticoagulant therapy, angioplasty of high-grade stenoses, and treatment of cardiovascular risk factors. Blood cell composition and protein biomarkers such as C-reactive protein or interleukins in serum are frequently considered as biomarkers of outcome. Here we aim to provide an up-to-date protein biomarker signature that allows a maximum of mechanistic understanding, to predict health deterioration following stroke. We thus surveyed protein biomarkers that were reported to be predictive for outcome after ischemic stroke, specifically considering biomarkers that predict long-term outcome (≥ 3 months) and that are measured over the first days following the event. We classified the protein biomarkers as immune‑inflammatory, coagulation-related, and adhesion-related biomarkers. Some of these biomarkers are closely related to cellular senescence and, in particular, to the inflammatory processes that can be triggered by senescent cells. Moreover, the processes that underlie inflammation, hypercoagulation and cellular senescence connect stroke to cancer, and biomarkers of cancer-associated thromboembolism, as well as of sarcopenia, overlap strongly with the biomarkers discussed here. Finally, we demonstrate that most of the outcome-predicting protein biomarkers form a close-meshed functional interaction network, suggesting that the outcome after stroke is partially determined by an interplay of molecular processes relating to inflammation, coagulation, cell adhesion and cellular senescence.

Machine Learning Approximations to Predict Epigenetic Age Acceleration in Stroke Patients

Age acceleration (Age-A) is a useful tool that is able to predict a broad range of health outcomes. It is necessary to determine DNA methylation levels to estimate it, and it is known that Age-A is influenced by environmental, lifestyle, and vascular risk factors (VRF). The aim of this study is to estimate the contribution of these easily measurable factors to Age-A in patients with cerebrovascular disease (CVD), using different machine learning (ML) approximations, and try to find a more accessible model able to predict Age-A. We studied a CVD cohort of 952 patients with information about VRF, lifestyle habits, and target organ damage. We estimated Age-A using Hannum's epigenetic clock, and trained six different models to predict Age-A: a conventional linear regression model, four ML models (elastic net regression (EN), K-Nearest neighbors, random forest, and support vector machine models), and one deep learning approximation (multilayer perceptron (MLP) model). The best-performing models were EN and MLP; although, the predictive capability was modest (R² 0.358 and 0.378, respectively). In conclusion, our results support the influence of these factors on Age-A; although, they were not enough to explain most of its variability.

Association of Accelerometer-Measured Physical Activity and Sedentary Time with Epigenetic Markers of Aging

INTRODUCTION/PURPOSE

Physical activity may influence chronic disease risk, in part, through epigenetic mechanisms. Previous studies have demonstrated that an acute bout of physical activity can influence DNA methylation status. Few studies have explored the relationship between habitual, accelerometer-measured physical activity or sedentary time with epigenetic markers of aging.

METHODS

We used linear regression to examine cross-sectional associations of accelerometer-measured physical activity and sedentary time with extrinsic and intrinsic epigenetic age acceleration (EEAA and IEAA) models and GrimAge measured from blood samples from Framingham Heart Study participants with accelerometry and DNA methylation data ( n = 2435; mean age, 54.9 ± 14.3; 46.0% men). Residuals of Hannum-, Horvath-, and GrimAge-predicted epigenetic age were calculated by regressing epigenetic age on chronological age. We took into account blood cell composition for EEAA, IEAA, and AdjGrimAge. Moderate to vigorous physical activity was log-transformed to normalize its distribution. Adjustment models accounted for family structure, age, sex, smoking status, cohort-laboratory indicator, and accelerometer wear time. We additionally explored adjustment for body mass index (BMI).

RESULTS

Walking 1500 more steps per day or spending 3 fewer hours sedentary was associated with >10 months lower GrimAge biological age (or ~1 month lower AdjGrimAge, after adjusting for blood cells, P < 0.05). Every 5 min·d -1 more moderate to vigorous physical activity was associated with 19-79 d of lower GrimAge (4-23 d lower using EEAA or AdjGrimAge, P < 0.01). Adjusting for BMI attenuated these results, but all statistically significant associations with AdjGrimAge remained.

CONCLUSIONS

Greater habitual physical activity and lower sedentary time were associated with lower epigenetic age, which was partially explained by BMI. Further research should explore whether changes in physical activity influence methylation status and whether those modifications influence chronic disease risk.

Epigenetic Clock Explains White Matter Hyperintensity Burden Irrespective of Chronological Age

In this manuscript we studied the relationship between WMH and biological age (B-age) in patients with acute stroke. We included in this study 247 patients with acute stroke recruited at Hospital del Mar having both epigenetic (DNA methylation) and magnetic resonance imaging data. WMH were measured using a semi-automated method. B-age was calculated using two widely used methods: the Hannum and Horvath formulas. We used multiple linear regression models to interrogate the role of B-age on WMH volume after adjusting for chronological age (C-age) and other covariables. Average C-age of the sample was 68.4 (±11.8) and we observed a relatively high median WMH volume (median = 8.8 cm³, Q1-Q3 = 4.05-18.8). After adjusting for potential confounders, we observed a significant effect of B-age_Hannum on WMH volume (β_Hannum = 0.023, p-value = 0.029) independently of C-age, which remained significant (β_C-age = 0.021, p-value = 0.036). Finally, we performed a mediation analysis, which allowed us to discover that 42.7% of the effect of C-age on WMH is mediated by B-age_Hannum. On the other hand, B-age_Hoarvath showed no significant associations with WMH after being adjusted for C-age. In conclusion, we show for the first time that biological age, measured through DNA methylation, contributes substantially to explain WMH volumetric burden irrespective of chronological age.

Retinal age gap as a predictive biomarker of stroke risk

BACKGROUND

The aim of this study is to investigate the association of retinal age gap with the risk of incident stroke and its predictive value for incident stroke.

METHODS

A total of 80,169 fundus images from 46,969 participants in the UK Biobank cohort met the image quality standard. A deep learning model was constructed based on 19,200 fundus images of 11,052 disease-free participants at baseline for age prediction. Retinal age gap (retinal age predicted based on the fundus image minus chronological age) was generated for the remaining 35,917 participants. Stroke events were determined by data linkage to hospital records on admissions and diagnoses, and national death registers, whichever occurred earliest. Cox proportional hazards regression models were used to estimate the effect of retinal age gap on risk of stroke. Logistic regression models were used to estimate the predictive value of retinal age and well-established risk factors in 10-year stroke risk.

RESULTS

A total of 35,304 participants without history of stroke at baseline were included. During a median follow-up of 5.83 years, 282 (0.80%) participants had stroke events. In the fully adjusted model, each one-year increase in the retinal age gap was associated with a 4% increase in the risk of stroke (hazard ratio [HR] = 1.04, 95% confidence interval [CI]: 1.00-1.08, P = 0.029). Compared to participants with retinal age gap in the first quintile, participants with retinal age gap in the fifth quintile had significantly higher risks of stroke events (HR = 2.37, 95% CI: 1.37-4.10, P = 0.002). The predictive capability of retinal age alone was comparable to the well-established risk factor-based model (AUC=0.676 vs AUC=0.661, p=0.511).

CONCLUSIONS

We found that retinal age gap was significantly associated with incident stroke, implying the potential of retinal age gap as a predictive biomarker of stroke risk.

Predicting the incidence of age-related diseases based on biological age: The 11-year national health examination data follow-up

PURPOSE

As the population ages rapidly, the incidence of age-related diseases (ARDs) is also increasing fast. Predicting the incidence of ARDs is a challenge since the rates of individual aging vary, and objective assessments of the stages of aging based on chronological age (CA) may be inaccurate. Thus, in this study, we developed a biological age (BA) model based on the National Health Examination (NHE) data and analyzed the model prediction results for the incidence of 16 ARDs.

METHODS

This study was based on the 2002-2019 National Health Information Databases of the National Health Insurance Service (NHIS-NHID). The data from a total of 10,002,494 subjects were selected between 2009 and 2010, and the principal component analysis (PCA) was performed to develop the BA model. The Cox-proportional hazard model was used to perform predictive analysis of the ARD incidence.

RESULTS

For the unit increase in the difference between corrected biological age (cBA) and chronological age (CA), the hazard ratios (HRs) of ARDs increased significantly for both sexes (p < 0.001). In descending order, the corresponding ARDs' HRs were obesity (1.655), chronic renal failure (1.362), hypertension (1.301), hyperlipidemia (1.264), diabetes mellitus (1.261), fracture (1.119), dementia (1.163), cataract (1.116), myocardial infarction (1.097), stroke (1.169), macular degeneration (1.075), osteoarthritis (1.059), osteoporosis (1.124), Parkinson's disease (1.048), and chronic obstructive pulmonary disease (1.026).

CONCLUSIONS

In this study, the incidence of 16 ARDs were analyzed based on BA. Therefore, conducting the NHIS health examination can facilitate the prevention of ARDs by estimating HRs for at least 16 diseases.

Epigenetic mechanisms and potential therapeutic targets in stroke

Accumulating evidence indicates a central role for epigenetic modifications in the progression of stroke pathology. These epigenetic mechanisms are involved in complex and dynamic processes that modulate post-stroke gene expression, cellular injury response, motor function, and cognitive ability. Despite decades of research, stroke continues to be classified as a leading cause of death and disability worldwide with limited clinical interventions. Thus, technological advances in the field of epigenetics may provide innovative targets to develop new stroke therapies. This review presents the evidence on the impact of epigenomic readers, writers, and erasers in both ischemic and hemorrhagic stroke pathophysiology. We specifically explore the role of DNA methylation, DNA hydroxymethylation, histone modifications, and epigenomic regulation by long non-coding RNAs in modulating gene expression and functional outcome after stroke. Furthermore, we highlight promising pharmacological approaches and biomarkers in relation to epigenetics for translational therapeutic applications.

A bibliometric analysis of DNA methylation in cardiovascular diseases from 2001 to 2021

BACKGROUND

DNA methylation is a dynamically reversible form of epigenetics. Dynamic regulation plays an important role in cardiovascular diseases (CVDs). However, there have been few bibliometric studies in this field. We aimed to visualize the research results and hotspots of DNA methylation in CVDs using a bibliometric analysis to provide a scientific direction for future research.

METHODS

Publications related to DNA methylation in CVDs from January 1, 2001, to September 15, 2021, were searched and confirmed from the Web of Science Core Collection. CiteSpace 5.7 and VOSviewer 1.6.15 were used for bibliometric and knowledge-map analyses.

RESULTS

A total of 2617 publications were included in 912 academic journals by 15,584 authors from 963 institutions from 85 countries/regions. Among them, the United States of America, China, and England were the top 3 countries contributing to the field of DNA methylation. Harvard University, Columbia University, and University of Cambridge were the top 3 contributing institutions in terms of publications and were closely linked. PLoS One was the most published and co-cited journal. Baccarelli Andrea A published the most content, while Barker DJP had the highest frequency of co-citations. The keyword cluster focused on the mechanism, methyl-containing substance, exposure/risk factor, and biomarker. In terms of research hotspots, references with strong bursts, which are still ongoing, recently included "epigenetic clock" (2017-2021), "obesity, smoking, aging, and DNA methylation" (2017-2021), and "biomarker and epigenome-wide association study" (2019-2021).

CONCLUSIONS

We used bibliometric and visual methods to identify research hotspots and trends in DNA methylation in CVDs. Epigenetic clocks, biomarkers, environmental exposure, and lifestyle may become the focus and frontier of future research.

Biological Age Acceleration Is Lower in Women With Ischemic Stroke Compared to Men

BACKGROUND

Stroke onset in women occurs later in life compared with men. The underlying mechanisms of these differences have not been established. Epigenetic clocks, based on DNA methylation (DNAm) profiles, are the most accurate biological age estimate. Epigenetic age acceleration (EAA) measures indicate whether an individual is biologically younger or older than expected. Our aim was to analyze whether sexual dichotomy at age of stroke onset is conditioned by EAA.

METHODS

We used 2 DNAm datasets from whole blood samples of case-control genetic studies of ischemic stroke (IS), a discovery cohort of 374 IS patients (N women=163, N men=211), from GRECOS (Genotyping Recurrence Risk of Stroke) and SEDMAN (Dabigatran Study in the Early Phase of Stroke, New Neuroimaging Markers and Biomarkers) studies and a replication cohort of 981 IS patients (N women=411, N men=570) from BASICMAR register. We compared chronological age, 2 DNAm-based biomarkers of aging and intrinsic and extrinsic epigenetic age acceleration EAA (IEAA and extrinsic EAA, respectively), in IS as well as in individual IS etiologic subtypes. Horvath and Hannum epigenetic clocks were used to assess the aging rate. A proteomic study using the SOMAScan multiplex assay was performed on 26 samples analyzing 1305 proteins.

RESULTS

Women present lower Hannum-extrinsic EAA values, whereas men have higher Hannum-extrinsic EAA values (women=-0.64, men=1.24, P=1.34×10^-2); the same tendency was observed in the second cohort (women=-0.57, men=0.79, P=0.02). These differences seemed to be specific to cardioembolic and undetermined stroke subtypes. Additionally, 42 blood protein levels were associated with Hannum-extrinsic EAA (P<0.05), belonging to the immune effector process (P=1.54×10^-6) and platelet degranulation (P<8.74×10^-6) pathways.

CONCLUSIONS

This study shows that sex-specific underlying biological mechanisms associated with stroke onset could be due to differences in biological age acceleration between men and women.

Dynamic epigenetic age mosaicism in the human atherosclerotic artery

Accelerated epigenetic ageing, a promising marker of disease risk, has been detected in peripheral blood cells of atherosclerotic patients, but evidence in the vascular wall is lacking. Understanding the trends of epigenetic ageing in the atheroma may provide insights into mechanisms of atherogenesis or identify targets for molecular therapy. We surveyed DNA methylation age in two human artery samples: a set of donor-matched, paired atherosclerotic and healthy aortic portions, and a set of carotid artery atheromas. The well-characterized pan-tissue Horvath epigenetic clock was used, together with the Weidner whole-blood-specific clock as validation. For the first time, we document dynamic DNA methylation age mosaicism of the vascular wall that is atherosclerosis-related, switches from acceleration to deceleration with chronological ageing, and is consistent in human aorta and carotid atheroma. At CpG level, the Horvath epigenetic clock showed modest differential methylation between atherosclerotic and healthy aortic portions, weak association with atheroma histological grade and no clear evidence for participation in atherosclerosis-related cellular pathways. Our data suggest caution when assigning a unidirectional DNA methylation age change to the atherosclerotic arterial wall. Also, the results support previous conclusions that epigenetic ageing reflects non-disease-specific cellular alterations.

The Predicted Value of Kidney Injury Molecule-1 (KIM-1) in Healthy People

Purpose

Recent studies have focused on whether kidney injury molecule-1 (KIM-1) might serve as a marker of acute kidney tubular injury. Our study analyzed the levels of KIM-1 in the healthy population of different ages to explore the correlation between KIM-1 and age. Moreover, we constructed a model to predict kidney age.

Methods

A cross-sectional study was conducted by Huashan Hospital, Shanghai, China, between April 2020 and December 2020. KIM-1 and other kidney biomarkers were measured in 176 healthy individuals ranging from 26 to 91 years old. Statistical correlated analyses for urinary KIM-1, creatinine (uCREA), potassium (K), sodium (Na) and chlorine (Cl), plasmic renin, angiotensin-2 (AngII) and aldosterone (ALD), and serum microalbuminuria (MALB), β2-microglobulin (B2MG), cystatin C (CYSC), urea nitrogen (BUN), creatinine (CREA), and glucose (GLU) were performed to assess the correlation between age and kidney biomarkers. All variables were selected as independent variables for the prediction of age by multiple linear regression.

Results

KIM-1 positively correlated with age in kidney healthy people (r = 0.41, p < 0.05), whether among females (r = 0.51, p < 0.05) or males (r = 0.27, p < 0.05). It was much related to K (r = 0.34), B2MG (r = 0.28), and CL (r = 0.23). The predicted model was constructed with eGFR, Cl, ALD, CYSC, KIM-1, BUN, GLU and AngII, reaching an adjusted R² of 69.5% and a standard error of the estimated 7.84 years.

Conclusion

The level of urinary KIM-1 increases with age in healthy people. The model constructed by KIM-1 and the other 7 biomarkers can predict kidney age in healthy people.

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.