Biomarker signatures of aging

The longitudinal trajectory of CSF sTREM2: the alzheimer's disease neuroimaging initiative

BACKGROUND

The soluble triggering receptor expressed on myeloid cells 2 (sTREM2) in cerebrospinal fluid (CSF) is considered a biomarker of microglia activity. The objective of this study was to investigate the trajectory of CSF sTREM2 levels over time and examine its association with sex.

METHODS

A total of 1,017 participants from the Alzheimer's Disease Neuroimaging Initiative Study (ADNI) with at least one CSF sTREM2 record were included. The trajectory of CSF sTREM2 was analyzed using a growth curve model. The association between CSF sTREM2 levels and sex was assessed using linear mixed-effect models.

RESULTS

CSF sTREM2 levels were increased with age over time (P < 0.0001). No significant sex difference was observed in sTREM2 levels across the entire sample; however, among the APOE ε4 allele carriers, women exhibited significantly higher sTREM2 levels than men (β = 0.146, P = 0.002).

CONCLUSION

Our findings highlight the association between CSF sTREM2 levels and age-related increments, underscoring the potential influence of aging on sTREM2 dynamics. Furthermore, our observations indicate a noteworthy association between sex and CSF sTREM2 levels, particularly in individuals carrying the APOE ε4 allele.

Towards Healthy Longevity: Comprehensive Insights from Molecular Targets and Biomarkers to Biological Clocks

Aging is a complex and time-dependent decline in physiological function that affects most organisms, leading to increased risk of age-related diseases. Investigating the molecular underpinnings of aging is crucial to identify geroprotectors, precisely quantify biological age, and propose healthy longevity approaches. This review explores pathways that are currently being investigated as intervention targets and aging biomarkers spanning molecular, cellular, and systemic dimensions. Interventions that target these hallmarks may ameliorate the aging process, with some progressing to clinical trials. Biomarkers of these hallmarks are used to estimate biological aging and risk of aging-associated disease. Utilizing aging biomarkers, biological aging clocks can be constructed that predict a state of abnormal aging, age-related diseases, and increased mortality. Biological age estimation can therefore provide the basis for a fine-grained risk stratification by predicting all-cause mortality well ahead of the onset of specific diseases, thus offering a window for intervention. Yet, despite technological advancements, challenges persist due to individual variability and the dynamic nature of these biomarkers. Addressing this requires longitudinal studies for robust biomarker identification. Overall, utilizing the hallmarks of aging to discover new drug targets and develop new biomarkers opens new frontiers in medicine. Prospects involve multi-omics integration, machine learning, and personalized approaches for targeted interventions, promising a healthier aging population.

Principal component-based clinical aging clocks identify signatures of healthy aging and targets for clinical intervention

Clocks that measure biological age should predict all-cause mortality and give rise to actionable insights to promote healthy aging. Here we applied dimensionality reduction by principal component analysis to clinical data to generate a clinical aging clock (PCAge) identifying signatures (principal components) separating healthy and unhealthy aging trajectories. We found signatures of metabolic dysregulation, cardiac and renal dysfunction and inflammation that predict unsuccessful aging, and we demonstrate that these processes can be impacted using well-established drug interventions. Furthermore, we generated a streamlined aging clock (LinAge), based directly on PCAge, which maintains equivalent predictive power but relies on substantially fewer features. Finally, we demonstrate that our approach can be tailored to individual datasets, by re-training a custom clinical clock (CALinAge), for use in the Comprehensive Assessment of Long-term Effects of Reducing Intake of Energy (CALERIE) study of caloric restriction. Our analysis of CALERIE participants suggests that 2 years of mild caloric restriction significantly reduces biological age. Altogether, we demonstrate that this dimensionality reduction approach, through integrating different biological markers, can provide targets for preventative medicine and the promotion of healthy aging.

The oral organ: A new vision of the mouth as a whole for a gerophysiological approach to healthy aging

This article brings a new perspective on oral physiology by presenting the oral organ as an integrated entity within the entire organism and its surrounding environment. Rather than considering the mouth solely as a collection of discrete functions, this novel approach emphasizes its role as a dynamic interphase, supporting interactions between the body and external factors. As a resilient ecosystem, the equilibrium of mouth ecological niches is the result of a large number of interconnected factors including the heterogeneity of different oral structures, diversity of resources, external and internal pressures and biological actors. The manuscript seeks to deepen the understanding of age-related changes within the oral cavity and throughout the organism, aligning with the evolving field of gerophysiology. The strategic position and fundamental function of the mouth make it an invaluable target for early prevention, diagnosis, treatment, and even reversal of aging effects throughout the entire organism. Recognizing the oral cavity capacity for sensory perception, element capture and information processing underscores its vital role in continuous health monitoring. Overall, this integrated understanding of the oral physiology aims at advancing comprehensive approaches to the oral healthcare and promoting broader awareness of its implications on the overall well-being.

Biomarker signatures associated with ageing free of major chronic diseases: results from a population-based sample of the EPIC-Potsdam cohort

BACKGROUND

A number of biomarkers denoting various pathophysiological pathways have been implicated in the aetiology and risk of age-related diseases. Hence, the combined impact of multiple biomarkers in relation to ageing free of major chronic diseases, such as cancer, cardiovascular disease and type 2 diabetes, has not been sufficiently explored.

METHODS

We measured concentrations of 13 biomarkers in a random subcohort of 2,500 participants in the European Prospective Investigation into Cancer and Nutrition Potsdam study. Chronic disease-free ageing was defined as reaching the age of 70 years within study follow-up without major chronic diseases, including cardiovascular disease, type 2 diabetes or cancer. Using a novel machine-learning technique, we aimed to identify biomarker clusters and explore their association with chronic disease-free ageing in multivariable-adjusted logistic regression analysis taking socio-demographic, lifestyle and anthropometric factors into account.

RESULTS

Of the participants who reached the age of 70 years, 321 met our criteria for chronic-disease free ageing. Machine learning analysis identified three distinct biomarker clusters, among which a signature characterised by high concentrations of high-density lipoprotein cholesterol, adiponectin and insulin-like growth factor-binding protein 2 and low concentrations of triglycerides was associated with highest odds for ageing free of major chronic diseases. After multivariable adjustment, the association was attenuated by socio-demographic, lifestyle and adiposity indicators, pointing to the relative importance of these factors as determinants of healthy ageing.

CONCLUSION

These data underline the importance of exploring combinations of biomarkers rather than single molecules in understanding complex biological pathways underpinning healthy ageing.

Association between biological aging and periodontitis using NHANES 2009-2014 and mendelian randomization

Aging is a recognized risk factor for periodontitis, while biological aging could provide more accurate insights into an individual's functional status. This study aimed to investigate the potential association between biological aging and periodontitis. Epidemiological data from 9803 participants in the 2009-2014 National Health and Nutrition Examination Survey were analyzed at a cross-sectional level to assess this link. Three biological ages [Klemera-Doubal method (KDM), PhenoAge, and homeostatic dysregulation (HD)] and two measures of accelerated biological aging (BioAgeAccel and PhenoAgeAccel) were set as primary exposure and were calculated. Logistic regression and restricted cubic spline regression were employed to examine the relationship between biological aging and periodontitis. Additionally, Mendelian randomization analysis was conducted to explore the causal connection between accelerated biological aging and periodontitis. After adjusting for age, gender, race, educational level, marital status, ratio of family income, and disease conditions, this study, found a significant association between subjects with older higher biological ages, accelerated biological aging, and periodontitis. Specifically, for a per year increase in the three biological ages (HD, KDM, and PhenoAge), the risk of periodontitis increases by 15%, 3%, and 4% respectively. Individuals who had positive BioAgeAccel or PhenoAgeAccel were 20% or 37% more likely to develop periodontitis compared with those who had negative BioAgeAccel or PhenoAgeAccel. Furthermore, a significant non-linear positive relationship was observed between the three biological ages, accelerated biological aging, and periodontitis. However, the Mendelian randomization analysis indicated no causal effect of accelerated biological aging on periodontitis. Our findings suggest that biological aging may contribute to the risk of periodontitis, highlighting the potential utility of preventive strategies targeting aging-related pathways in reducing periodontitis risk among older adults.

Association of inflammatory markers with muscle and cognitive function in early and late-aging older adults

OBJECTIVES

Age-related loss in muscle and cognitive function is common in older adults. Numerous studies have suggested that inflammation contributes to the decline in physical performance and increased frailty in older adults. We sought to investigate the relationship of inflammatory markers, including CRP, IL-6, IL-10, TNF-α, TNFR1, and TNFR2, with muscle and cognitive function in frail early-aging and non-frail late-aging older adults.

DESIGN

Secondary analysis of a cross-sectional study.

SETTINGS AND PARTICIPANTS

Two hundred community-dwelling older men and women were included. They had been recruited in two groups based on age and functional status: 100 early-agers (age 65-75, who had poor functional status, and more co-morbidities) and 100 late-agers (older than 75 years, who were healthier and had better functional status).

MEASUREMENTS

We assessed CRP, IL-6, IL-10, TNF-α, TNFR1, TNFR2, grip strength, Short Physical Performance Battery (SPPB) score, and cognitive function. We used correlation coefficients, partial correlations, and regression modeling adjusted for age, BMI, gender, and exercise frequency.

RESULTS

The mean age in the two groups were 70.4 and 83.2, respectively. In regression models adjusting for age, BMI, gender and exercise frequency, early-agers demonstrated significant associations between inflammatory markers and outcomes. Each mg/dl of CRP was associated with (regression coefficient ± standard error) -0.6 ± 0.2 kg in grip strength (p = 0.0023). Similarly, each pg/mL of TNF-α was associated with -1.4 ± 0.7 (p = 0.0454), each 500 pg/mL of TNFR1 was associated with -1.9 ± 0.6 (p = 0.0008), and each 500 pg/mL of TNFR2 was associated with -0.5 ± 0.2 (p = 0.0098) in grip strength. Each 500 pg/mL of TNFR1 was associated with -0.4 ± 0.2 point in SPPB (p = 0.0207) and each pg/mL in IL-10 with 0.2 ± 0.1 point in MoCA (p = 0.0475). In late-agers, no significant correlation was found between any of the inflammatory markers and functional outcomes.

CONCLUSION

In early-agers with frailty and more co-morbidities, the inflammatory markers CRP, TNF-α, TNFR1, and TNFR2 were associated with grip strength, TNFR1 was correlated with physical performance, and IL-10 was correlated with cognitive function. However, in healthier late-agers, no relationship was found between inflammatory markers and muscle or cognitive function. Our findings suggest presence of a relationship between inflammation and loss of muscle performance and cognitive function in frailer and sicker individuals, regardless of their chronological age.

Dissecting unique and common variance across body and brain health indicators using age prediction

Ageing is a heterogeneous multisystem process involving different rates of decline in physiological integrity across biological systems. The current study dissects the unique and common variance across body and brain health indicators and parses inter-individual heterogeneity in the multisystem ageing process. Using machine-learning regression models on the UK Biobank data set (N = 32,593, age range 44.6-82.3, mean age 64.1 years), we first estimated tissue-specific brain age for white and gray matter based on diffusion and T1-weighted magnetic resonance imaging (MRI) data, respectively. Next, bodily health traits, including cardiometabolic, anthropometric, and body composition measures of adipose and muscle tissue from bioimpedance and body MRI, were combined to predict 'body age'. The results showed that the body age model demonstrated comparable age prediction accuracy to models trained solely on brain MRI data. The correlation between body age and brain age predictions was 0.62 for the T1 and 0.64 for the diffusion-based model, indicating a degree of unique variance in brain and bodily ageing processes. Bayesian multilevel modelling carried out to quantify the associations between health traits and predicted age discrepancies showed that higher systolic blood pressure and higher muscle-fat infiltration were related to older-appearing body age compared to brain age. Conversely, higher hand-grip strength and muscle volume were related to a younger-appearing body age. Our findings corroborate the common notion of a close connection between somatic and brain health. However, they also suggest that health traits may differentially influence age predictions beyond what is captured by the brain imaging data, potentially contributing to heterogeneous ageing rates across biological systems and individuals.

Assessing Biological Age: The Potential of ECG Evaluation Using Artificial Intelligence: JACC Family Series

Biological age may be a more valuable predictor of morbidity and mortality than a person's chronological age. Mathematical models have been used for decades to predict biological age, but recent developments in artificial intelligence (AI) have led to new capabilities in age estimation. Using deep learning methods to train AI models on hundreds of thousands of electrocardiograms (ECGs) to predict age results in a good, but imperfect, age prediction. The error predicting age using ECG, or the difference between AI-ECG-derived age and chronological age (delta age), may be a surrogate measurement of biological age, as the delta age relates to survival, even after adjusting for chronological age and other covariates associated with total and cardiovascular mortality. The relative affordability, noninvasiveness, and ubiquity of ECGs, combined with ease of access and potential to be integrated with smartphone or wearable technology, presents a potential paradigm shift in assessment of biological age.

A biological age model based on physical examination data to predict mortality in a Chinese population

Biological age could be reflective of an individual's health status and aging degree. Limited estimations of biological aging based on physical examination data in the Chinese population have been developed to quantify the rate of aging. We developed and validated a novel aging measure (Balanced-AGE) based on readily available physical health examination data. In this study, a repeated sub-sampling approach was applied to address the data imbalance issue, and this approach significantly improved the performance of biological age (Balanced-AGE) in predicting all-cause mortality with a 10-year time-dependent AUC of 0.908 for all-cause mortality. This mortality prediction tool was found to be effective across different subgroups by age, sex, smoking, and alcohol consumption status. Additionally, this study revealed that individuals who were underweight, smokers, or drinkers had a higher extent of age acceleration. The Balanced-AGE may serve as an effective and generally applicable tool for health assessment and management among the elderly population.

Accelerated Aging in Cancer Survivors: Cellular Senescence, Frailty, and Possible Opportunities for Interventions

The population of cancer survivors has markedly increased due to the rapid improvements in cancer treatment. However, cancer survivors experience accelerated aging, which leads to chronic diseases and other age-related conditions, such as frailty. Those conditions may persist years after cancer diagnosis and treatment. Cellular senescence, a hallmark of aging, is one of the mechanisms that contribute to accelerated aging in cancer survivors. Several aging measures, including measures based on clinical markers and biomarkers, have been proposed to estimate the aging process, and some of them have shown associations with mortality and frailty in cancer survivors. Several anti-aging interventions, including lifestyle changes and anti-aging drugs, have been proposed. Future research, particularly in large-scale studies, is needed to determine the efficiency of these aging measures and anti-aging interventions before considering their application in clinics. This review focuses on the mechanisms of cellular senescence and accelerated aging in cancer survivors, assessment of the aging process using clinical markers and biomarkers, and the high prevalence of frailty in that population, as well as possible opportunities for anti-aging interventions. A deeper understanding of aging measures and anti-aging interventions in cancer survivors will contribute to the development of effective strategies to mitigate accelerated aging in cancer survivors and improve their quality of life.

Validation of biomarkers of aging

The search for biomarkers that quantify biological aging (particularly 'omic'-based biomarkers) has intensified in recent years. Such biomarkers could predict aging-related outcomes and could serve as surrogate endpoints for the evaluation of interventions promoting healthy aging and longevity. However, no consensus exists on how biomarkers of aging should be validated before their translation to the clinic. Here, we review current efforts to evaluate the predictive validity of omic biomarkers of aging in population studies, discuss challenges in comparability and generalizability and provide recommendations to facilitate future validation of biomarkers of aging. Finally, we discuss how systematic validation can accelerate clinical translation of biomarkers of aging and their use in gerotherapeutic clinical trials.

Cellular senescence and frailty: a comprehensive insight into the causal links

Senescent cells may have a prominent role in driving inflammation and frailty. The impact of cellular senescence on frailty varies depending on the assessment tool used, as it is influenced by the criteria or items predominantly affected by senescent cells and the varying weights assigned to these items across different health domains. To address this challenge, we undertook a thorough review of all available studies involving gain- or loss-of-function experiments as well as interventions targeting senescent cells, focusing our attention on those studies that examined outcomes based on the individual frailty phenotype criteria or specific items used to calculate two humans (35 and 70 items) and one mouse (31 items) frailty indexes. Based on the calculation of a simple "evidence score," we found that the burden of senescent cells related to musculoskeletal and cerebral health has the strongest causal link to frailty. We deem that insight into these mechanisms may not only contribute to clarifying the role of cellular senescence in frailty but could additionally provide multiple therapeutic opportunities to help the future development of a desirable personalized therapy in these extremely heterogeneous patients.

Newborn DNA methylation age differentiates long-term weight trajectory: The Boston Birth Cohort

Background

Gestational age (GEAA) estimated by newborn DNA methylation (GAmAge) is associated with maternal prenatal exposures and immediate birth outcomes. However, the association of GAmAge with long-term overweight or obesity (OWO) trajectories is yet to be determined.

Methods

GAmAge was calculated for 831 children from a US predominantly urban, low-income, multi-ethnic birth cohort using Illumina EPIC array and cord-blood DNA samples. Repeated anthropometric measurements aligned with pediatric primary care schedule allowed us to calculate body-mass-index percentiles (BMIPCT) at specific age and to define long-term weight trajectories from birth to 18 years.

Results

Four BMIPCT trajectory groups described the long-term weight trajectories: stable (consistent OWO: "early OWO"; constant normal weight: "NW") or non-stable (OWO by year 1 of follow-up: "late OWO"; OWO by year 6 of follow-up: "NW to very late OWO") BMIPCT. were used GAmAge was a predictor of long-term obesity, differentiating between group with consistently high BMIPCT and group with normal BMIPCT patterns and groups with late OWO development. Such differentiation can be observed in the age periods of birth to 1year, 3years, 6years, 10years, and 14years (p<0.05 for all; multivariate models adjusted for GEAA, maternal smoking, delivery method, and child's sex). Birth weight was a mediator for the GAmAge effect on OWO status for specific groups at multiple age periods.

Conclusions

GAmAge is associated with BMI trajectories from birth to age 18 years, independent of GEAA and birth weight. If further confirmed, GAmAge may serve as an early biomarker for future OWO risk.

Relationship between an ageing measure and chronic obstructive pulmonary disease, lung function: a cross-sectional study of NHANES, 2007-2010

OBJECTIVES

Chronic obstructive pulmonary disease (COPD) is a disease associated with ageing. However, actual age does not accurately reflect the degree of biological ageing. Phenotypic age (PhenoAge) is a new indicator of biological ageing, and phenotypic age minus actual age is known as phenotypic age acceleration (PhenoAgeAccel). This research aimed to analyse the relationship between PhenoAgeAccel and lung function and COPD.

DESIGN

A cross-sectional study.

PARTICIPANTS

Data for the study were obtained from the National Health and Nutrition Examination Survey (NHANES) 2007-2010. We defined people with forced expiratory volume in 1 s/forced vital capacity <0.70 after inhaled bronchodilators as COPD and the rest of the population as non-COPD. Adults aged 40 years or older were enrolled in the study.

PRIMARY AND SECONDARY OUTCOME MEASURES

Linear and logistic regression were used to investigate the relationship between PhenoAgeAccel, lung function and COPD. Subgroup analysis was performed by gender, age, ethnicity and smoking index COPD. In addition, we analysed the relationship between the smoking index, respiratory symptoms and PhenoAgeAccel. Multiple models were used to reduce confounding bias.

RESULTS

5397 participants were included in our study, of which 1042 had COPD. Compared with PhenoAgeAccel Quartile1, Quartile 4 had a 52% higher probability of COPD; elevated PhenoAgeAccel was also significantly associated with reduced lung function. Further subgroup analysis showed that high levels of PhenoAgeAccel had a more significant effect on lung function in COPD, older adults and whites (P for interaction <0.05). Respiratory symptoms and a high smoking index were related to higher indicators of ageing.

CONCLUSIONS

Our study found that accelerated ageing is associated with the development of COPD and impaired lung function. Smoking cessation and anti-ageing therapy have potential significance in COPD.

Brain macrophage development, diversity and dysregulation in health and disease

Brain macrophages include microglia in the parenchyma, border-associated macrophages in the meningeal-choroid plexus-perivascular space, and monocyte-derived macrophages that infiltrate the brain under various disease conditions. The vast heterogeneity of these cells has been elucidated over the last decade using revolutionary multiomics technologies. As such, we can now start to define these various macrophage populations according to their ontogeny and their diverse functional programs during brain development, homeostasis and disease pathogenesis. In this review, we first outline the critical roles played by brain macrophages during development and healthy aging. We then discuss how brain macrophages might undergo reprogramming and contribute to neurodegenerative disorders, autoimmune diseases, and glioma. Finally, we speculate about the most recent and ongoing discoveries that are prompting translational attempts to leverage brain macrophages as prognostic markers or therapeutic targets for diseases that affect the brain.

Biological age estimation using circulating blood biomarkers

Biological age captures physiological deterioration better than chronological age and is amenable to interventions. Blood-based biomarkers have been identified as suitable candidates for biological age estimation. This study aims to improve biological age estimation using machine learning models and a feature-set of 60 circulating biomarkers available from the UK Biobank (n = 306,116). We implement an Elastic-Net derived Cox model with 25 selected biomarkers to predict mortality risk (C-Index = 0.778; 95% CI [0.767-0.788]), which outperforms the well-known blood-biomarker based PhenoAge model (C-Index = 0.750; 95% CI [0.739-0.761]), providing a C-Index lift of 0.028 representing an 11% relative increase in predictive value. Importantly, we then show that using common clinical assay panels, with few biomarkers, alongside imputation and the model derived on the full set of biomarkers, does not substantially degrade predictive accuracy from the theoretical maximum achievable for the available biomarkers. Biological age is estimated as the equivalent age within the same-sex population which corresponds to an individual's mortality risk. Values ranged between 20-years younger and 20-years older than individuals' chronological age, exposing the magnitude of ageing signals contained in blood markers. Thus, we demonstrate a practical and cost-efficient method of estimating an improved measure of Biological Age, available to the general population.

Alcohol consumption and epigenetic age acceleration across human adulthood

The alcohol-associated biological aging remains to be studied across adulthood. We conducted linear regression analyses to investigate the associations between alcohol consumption and two DNA methylation-based biological age acceleration metrics in 3823 Framingham Heart Study participants (24-92 years and 53.8% women) adjusting for covariates. We also investigated whether the two epigenetic aging metrics mediated the association of alcohol consumption with hypertension. We found that higher long-term average alcohol consumption was significantly associated with biological age acceleration assessed by GrimAge acceleration (GAA) and PhenoAge acceleration (PAA) in middle-aged (45-64 years, n = 1866) and older (65-92 years, n = 1267) participants while not in young participants (24-44 years, n = 690). For example, one additional standard drink of alcohol (~14 grams of ethanol per day) was associated with a 0.71 ± 0.15-year (p = 2.1e-6) and 0.60 ± 0.18-year (p = 7.5e-4) increase in PAA in middle-aged and older participants, respectively, but the association was not significant in young participants (p = 0.23). One additional standard serving of liquor (~14 grams of ethanol) was associated with a greater increase in GAA (0.82-year, p = 4.8e-4) and PAA (1.45-year, p = 7.4e-5) than beer (GAA: 0.45-year, p = 5.2e-4; PAA: 0.48-year, p = 0.02) and wine (GAA: 0.51-year, p = 0.02; PAA: 0.91-year, p = 0.008) in middle-aged participant group. We observed that up to 28% of the association between alcohol consumption and hypertension was mediated by GAA or PAA in the pooled sample. Our findings suggest that alcohol consumption is associated with greater biological aging quantified by epigenetic aging metrics, which may mediate the association of alcohol consumption with quantitative traits, such as hypertension.

Relationship between Composite Dietary Antioxidant Index and Aging

BACKGROUND

Numerous studies have demonstrated a close relationship between antioxidant-rich diets and comorbidities as well as mortality. However, the relationship between such diets and aging remains unclear. The purpose of this study was to investigate the association between the Composite Dietary Antioxidant Index (CDAI) and aging.

METHODS

All participants were from the National Health and Nutrition Examination Survey (NHANES) 2001-2010. Phenotypic age was calculated using a formula and subtracted from the chronological age to determine the aging. When the phenotypic age exceeded the chronological age, it was considered as aging. A weighted logistic regression model was employed to explore the relationship between CDAI and aging. Restricted cubic splines (RCSs) were used to examine the potential nonlinear relationship between them. Subgroup analysis and joint analysis were conducted to explore the effect of modifiers in these relationships.

RESULTS

A total of 19,212 participants (weighted: 165,285,442 individuals) were included in this study. The weighted logistic regression model showed a significant correlation between CDAI and the risk of aging (OR = 0.90, 95% CI: 0.84-0.96). RCS analysis revealed an L-shaped dose-response relationship between CDAI and the risk of aging. Subgroup analysis indicated that the association between CDAI and aging was more pronounced in middle-aged individuals and non-smokers. The joint analysis demonstrated that although smoking accelerated aging among participants, a high CDAI diet could still offset these damages.

CONCLUSIONS

The association between high CDAI and reduced risk of aging is particularly significant in young and middle-aged individuals and non-smokers. Consuming foods rich in CDAI components may potentially lower the risk of aging.

Natural Animal Populations as Model Systems for Understanding Early Life Adversity Effects on Aging

Adverse experiences in early life are associated with aging-related disease risk and mortality across many species. In humans, confounding factors, as well as the difficulty of directly measuring experiences and outcomes from birth till death, make it challenging to identify how early life adversity impacts aging and health. These challenges can be mitigated, in part, through the study of non-human animals, which are exposed to parallel forms of adversity and can age similarly to humans. Furthermore, studying the links between early life adversity and aging in natural populations of non-human animals provides an excellent opportunity to better understand the social and ecological pressures that shaped the evolution of early life sensitivities. Here, we highlight ongoing and future research directions that we believe will most effectively contribute to our understanding of the evolution of early life sensitivities and their repercussions.

Development and Characterization of Proteomic Aging Clocks in the Atherosclerosis Risk in Communities (ARIC) Study

Biological age may be estimated by proteomic aging clocks (PACs). Previous published PACs were constructed either in smaller studies or mainly in White individuals, and they used proteomic measures from only one-time point. In the Atherosclerosis Risk in Communities (ARIC) study of about 12,000 persons followed for 30 years (around 75% White, 25% Black), we created de novo PACs and compared their performance to published PACs at two different time points. We measured 4,712 plasma proteins by SomaScan in 11,761 midlife participants, aged 46-70 years (1990-92), and 5,183 late-life pariticpants, aged 66-90 years (2011-13). All proteins were log2-transformed to correct for skewness. We created de novo PACs by training them against chronological age using elastic net regression in two-thirds of healthy participants in midlife and late life and compared their performance to three published PACs. We estimated age acceleration (by regressing each PAC on chronological age) and its change from midlife to late life. We examined their associations with mortality from all-cause, cardiovascular disease (CVD), cancer, and lower respiratory disease (LRD) using Cox proportional hazards regression in all remaining participants irrespective of health. The model was adjusted for chronological age, smoking, body mass index (BMI), and other confounders. The ARIC PACs had a slightly stronger correlation with chronological age than published PACs in healthy participants at each time point. Associations with mortality were similar for the ARIC and published PACs. For late-life and midlife age acceleration for the ARIC PACs, respectively, hazard ratios (HRs) per one standard deviation were 1.65 and 1.38 (both p<0.001) for all-cause mortality, 1.37 and 1.20 (both p<0.001) for CVD mortality, 1.21 (p=0.03) and 1.04 (p=0.19) for cancer mortality, and 1.46 and 1.68 (both p<0.001) for LRD mortality. For the change in age acceleration, HRs for all-cause, CVD, and LRD mortality were comparable to those observed for late-life age acceleration. The association between the change in age acceleration and cancer mortality was insignificant. In this prospective study, the ARIC and published PACs were similarly associated with an increased risk of mortality and advanced testing in relation to various age-related conditions in future studies is suggested.

Resolving the Temporal Splenic Proteome during Fungal Infection for Discovery of Putative Dual Perspective Biomarker Signatures

Fungal pathogens are emerging threats to global health with the rise of incidence associated with climate change and increased geographical distribution; factors also influencing host susceptibility to infection. Accurate detection and diagnosis of fungal infections is paramount to offer rapid and effective therapeutic options. For improved diagnostics, the discovery and development of protein biomarkers presents a promising avenue; however, this approach requires a priori knowledge of infection hallmarks. To uncover putative novel biomarkers of disease, profiling of the host immune response and pathogen virulence factor production is indispensable. In this study, we use mass-spectrometry-based proteomics to resolve the temporal proteome of Cryptococcus neoformans infection of the spleen following a murine model of infection. Dual perspective proteome profiling defines global remodeling of the host over a time course of infection, confirming activation of immune associated proteins in response to fungal invasion. Conversely, pathogen proteomes detect well-characterized C. neoformans virulence determinants, along with novel mapped patterns of pathogenesis during the progression of disease. Together, our innovative systematic approach confirms immune protection against fungal pathogens and explores the discovery of putative biomarker signatures from complementary biological systems to monitor the presence and progression of cryptococcal disease.

Accelerometer-measured physical activity patterns are associated with phenotypic age: Isotemporal substitution effects

Prolonged sitting appears to accelerate aging, while optimal physical activity patterns have been found to delay the process. It is an emerging topic, and no conclusions have been reached regarding the relationship between physical activity patterns and biomarkers-measured aging. Hence, the aim of this study was to investigate the association between sensor-based objectively measured physical activity and phenotypic age using a nationwide population from the National Health and Nutrition Examination Survey (NHANES) in the United States. Weighted linear regression models were performed to evaluate the association between sedentary behavior, light-intensity physical activity (LPA), moderate-to-vigorous physical activity (MVPA) and phenotypic age. A total of 6439 eligible participants were included and the weighted respondents were 49,964,300. Results showed that prolonged sitting was positively associated with phenotypic age in the fully adjusted model [β (95% CI): 0.009(0.007,0.011), p < 0.001], while increasing volume of LPA and MVPA was associated with younger phenotypic age using the fully adjusted model [β (95% CI): -0.010(-0.013,-0.006), p < 0.001; -0.062(-0.075,-0.048), p < 0.001]. By utilizing the Isotemporal Substitution Model, it was found that replacing 30 min of sedentary behavior with 30 min of LPA or MVPA per day was associated with estimated 0.4 or 1.9 years of phenotypic age reduction. According to the study's findings, maintaining a certain level of physical activity could delay the process of aging and intensity matters.

Associations between cadmium exposure and whole-body aging: mediation analysis in the NHANES

INTRODUCTION

Even though cadmium (Cd) exposure and cellular senescence (telomere length) have been linked in previous studies, composite molecular aging biomarkers are more significant and reliable factors to consider when examining the connection between metal exposure and health outcomes. The purpose of this research was to assess the association between urinary cadmium (U-Cd) and whole-body aging (phenotypic age).

METHODS

Phenotypic age was calculated from chronological age and 9 molecular biomarkers. Multivariate linear regression models, subgroup analysis, and smoothing curve fitting were used to explore the linear and nonlinear relationship between U-Cd and phenotypic age. Mediation analysis was performed to explore the mediating effect of U-Cd on the association between smoking and phenotypic age.

RESULTS

This study included 10,083 participants with a mean chronological age and a mean phenotypic age of 42.24 years and 42.34 years, respectively. In the fully adjusted model, there was a positive relationship between U-Cd and phenotypic age [2.13 years per 1 ng/g U-Cd, (1.67, 2.58)]. This association differed by sex, age, and smoking subgroups (P for interaction < 0.05). U-Cd mediated a positive association between serum cotinine and phenotypic age, mediating a proportion of 23.2%.

CONCLUSIONS

Our results suggest that high levels of Cd exposure are associated with whole-body aging.

Birth size and the serum level of biological age markers in men

Previous studies showed that intrauterine growth restrictions, resulting in smaller body size at birth, are associated with altered development and the risk of age-related diseases in adult life. Thus, prenatal development may predict aging trajectories in humans. The study aimed to verify if body size at birth is related to biological age in adult men. The study sample consisted of 159 healthy, non-smoking men with a mean age of 35.24 (SD 3.44) years. Birth weight and length were taken from medical records. The ponderal index at birth was calculated. Biological age was evaluated based on serum levels of s-Klotho, hsCRP, DHEA/S, and oxidative stress markers. Pregnancy age at birth, lifestyle, weight, cortisol, and testosterone levels were controlled. The results showed no relationship between birth size and s-Klotho, DHEA/S level, inflammation, or oxidative stress. Also, men born as small-for-gestational-age (N = 49) and men born as appropriate-for-gestational-age (N = 110) did not differ in terms of biological age markers levels. The results were similar when controlled for pregnancy week at birth, chronological age, BMI, testosterone, or cortisol level. The results suggest that there is no relationship between intrauterine growth and biomarkers of aging in men aged 30-45 years from the affluent population.

Frailty Among Breast Cancer Survivors: Evidence From Swedish Population Data

Incidence and survival of breast cancer, the most common cancer among women, have been increasing, leaving survivors at risk of aging-related health conditions. In this matched cohort study, we examined frailty risk with the Hospital Frailty Risk Score among breast cancer survivors (n = 34,900) and age-matched comparison subjects (n = 290,063). Women born in 1935-1975, registered in the Swedish Total Population Register (1991-2015), were eligible for inclusion. Survivors had a first breast cancer diagnosis in 1991-2005 and survived ≥5 years after initial diagnosis. Death date was determined by linkage to the National Cause of Death Registry (through 2015). Cancer survivorship was weakly associated with frailty (subdistribution hazard ratio (SHR) = 1.04, 95% confidence interval (CI): 1.00, 1.07). In age-stratified models, those diagnosed at younger ages (<50 years) had higher risk of frailty (SHR = 1.12, 95% CI: 1.00, 1.24) than those diagnosed at ages 50-65 (SHR = 1.03, 95% CI: 0.98, 1.07) or >65 (SHR = 1.09, 95% CI: 1.02, 1.17) years. Additionally, there was increased risk of frailty for diagnoses in 2000 or later (SHR = 1.15, 95% CI: 1.09, 1.21) compared with before 2000 (SHR = 0.97, 95% CI: 0.93, 1.17). This supports work from smaller samples showing that breast cancer survivors have increased frailty risk, particularly when diagnosed at younger ages.

Effects of High-Intensity Interval Training on Melatonin Function and Cellular Lymphocyte Apoptosis in Sedentary Middle-Aged Men

Background: Physical performance increased by controlled interventions of high-intensity intermittent training (HIIT); however, little is known about their influence as anti-aging and antioxidant effects, or their role in mitochondrial biogenesis. Purpose: This study aimed to determine the effects of HIIT for 12 weeks on melatonin function, lymphocyte cell apoptosis, oxidative stress on aging, and physical performance. Methods: Eighty healthy male subjects aged 18-65 years randomly participated in a HIIT-exercise training program for 12 weeks. Anthropometric analysis, cardiovascular fitness, total antioxidant capacity (TAC), lymphocyte count and apoptosis, and serum melatonin and cytochrome c oxidase (COX), were estimated for all subjects before and after HIIT-exercise training. HIIT training was performed in subjects for 12 weeks. Results: Data analysis showed a significant increase in the expression levels of the melatonin hormone (11.2 ± 2.3, p < 0.001), TAC (48.7 ± 7.1, p < 0.002), COX (3.7 ± 0.75, p < 0.001), and a higher percentage of lymphocyte apoptosis (5.2 ± 0.31, p < 0.003). In addition, there was an improvement in fitness scores (W; 196.5 ± 4.6, VO₂max; 58.9 ± 2.5, p < 0.001), adiposity markers (p < 0.001); BMI, WHtR, and glycemic control parameters (p < 0.01); FG, HbA1c (%), FI, and serum C-peptide were significantly improved following HIIT intervention. Both melatonin and lymphocyte apoptosis significantly correlated with the studied parameters, especially TAC and COX. Furthermore, the correlation of lymphocyte apoptosis with longer exercise duration was significantly associated with increased serum melatonin following exercise training. This association supports the mechanistic role of melatonin in promoting lymphocyte apoptosis either via the extrinsic mediator pathway or via inhibition of lymphocyte division in the thymus and lymph nodes. Additionally, the correlation between melatonin, lymphocyte apoptosis, TAC, and COX activities significantly supports their role in enhancing physical performance. Conclusions: The main findings of this study were that HIIT exercise training for 12 weeks significantly improved adiposity markers, glycemic control parameters, and physical performance of sedentary older adult men. In addition, melatonin secretion, % of lymphocyte apoptosis, COX activities, and TAC as biological aging markers were significantly increased following HIIT exercise training interventions for 12 weeks. The use of HIIT exercise was effective in improving biological aging, which is adequate for supporting chronological age, especially regarding aging problems. However, subsequent studies are required with long-term follow-up to consider HIIT as a modulator for several cardiometabolic health problems in older individuals with obesity.

Association of the dietary inflammatory index with phenotypic age in the United States adults

OBJECTIVES

One of the underlying mechanisms of aging is chronic inflammation, which has been closely associated with daily diet. Phenotypic age (PhenoAge) has been used as an index to track the aging process before diseases show clinical symptoms. The present study aimed to explore the association between the dietary inflammatory index (DII) and PhenoAge.

METHODS

In total, 9,275 adults aged 20 years old and over in the National Health and Nutrition Examination Survey were involved in this study. Dietary patterns were classified as pro-inflammatory or anti-inflammatory according to the DII. PhenoAge was regarded as a continuous variable, and linear regression was used to explore its association with dietary inflammation. Stratified analyses by sex, age, race, physical exercise, smoking status, drinking status, and body mass index were used to test the sensitivity of these associations.

RESULTS

The median value of PhenoAge was 38.60 years and 39.76 years for the participants with anti-inflammatory and pro-inflammatory diets, respectively. A pro-inflammatory diet was positively associated with PhenoAge (β=0.73; 95% confidence interval, 0.31 to 1.14), compared with participants who had an anti-inflammatory diet. There was an interaction between dietary inflammation and age for PhenoAge (pinteraction<0.001). The strength of the association between a pro-inflammatory diet and PhenoAge was stronger as age increased.

CONCLUSIONS

A pro-inflammatory diet was associated with a higher PhenoAge, and the association was strongest in the elderly. We recommended reducing dietary inflammation to delay phenotypic aging, especially for the elderly.

Acute-phase response following one-stage full-mouth versus quadrant non-surgical periodontal treatment in subjects with comorbid type 2 diabetes: A randomized clinical trial

AIM

To compare the level of inflammatory markers and endothelial function 24 h (Day 1) and 90 days (Day 90) after conventional quadrant-wise scaling and root planing (Q-SRP) versus one-stage full-mouth SRP (FM-SRP) in patients affected by type 2 diabetes mellitus (T2DM).

MATERIALS AND METHODS

Patients affected by periodontitis and T2DM were randomly allocated to receive FM-SRP or Q-SRP and followed up at Day 1 and Day 90. Serum samples, vital signs, and flow-mediated dilation (FMD) parameters were collected at baseline, Day 1, and Day 90. Periodontal variables were collected at baseline and Day 90. The primary outcome was the C-reactive protein (CRP) concentration at Day 1 after periodontal treatment. Student's t-test for independent samples was used for between-group comparisons (Mann-Whitney U test for non-normal data), while analysis of variance with post hoc Tukey tests (Kruskal-Wallis and Dunn tests for non-normal data) were used for intra-group comparisons.

RESULTS

Forty subjects were included in the study. FM-SRP produced a significant increase in CRP and a significant reduction in FMD at Day 1 compared to Q-SRP (p < .05). The absolute change in HbA1c (mmol/mol) from baseline to Day 90 was significantly improved in the Q-SRP (ΔHbA1c = -1.59 [SD = 1.20]) compared to the FM-SRP group (ΔHbA1c = -0.8 [SD = 0.95]) (p = .04).

CONCLUSIONS

FM-SRP triggers a robust acute-phase response at 24 h after treatment compared to Q-SRP. Such systemic acute perturbations may offset the beneficial systemic effects of periodontal treatment in terms of HbA1c reduction and improvement in endothelial function in T2DM subjects.

A time-series analysis of blood-based biomarkers within a 25-year longitudinal dolphin cohort

Causal interactions and correlations between clinically-relevant biomarkers are important to understand, both for informing potential medical interventions as well as predicting the likely health trajectory of any individual as they age. These interactions and correlations can be hard to establish in humans, due to the difficulties of routine sampling and controlling for individual differences (e.g., diet, socio-economic status, medication). Because bottlenose dolphins are long-lived mammals that exhibit several age-related phenomena similar to humans, we analyzed data from a well controlled 25-year longitudinal cohort of 144 dolphins. The data from this study has been reported on earlier, and consists of 44 clinically relevant biomarkers. This time-series data exhibits three starkly different influences: (A) directed interactions between biomarkers, (B) sources of biological variation that can either correlate or decorrelate different biomarkers, and (C) random observation-noise which combines measurement error and very rapid fluctuations in the dolphin's biomarkers. Importantly, the sources of biological variation (type-B) are large in magnitude, often comparable to the observation errors (type-C) and larger than the effect of the directed interactions (type-A). Attempting to recover the type-A interactions without accounting for the type-B and type-C variation can result in an abundance of false-positives and false-negatives. Using a generalized regression which fits the longitudinal data with a linear model accounting for all three influences, we demonstrate that the dolphins exhibit many significant directed interactions (type-A), as well as strong correlated variation (type-B), between several pairs of biomarkers. Moreover, many of these interactions are associated with advanced age, suggesting that these interactions can be monitored and/or targeted to predict and potentially affect aging.

Shared Activities With Parents During Adolescence Predicts Health Risk Across Multiple Biological Systems 22 Years Later

OBJECTIVE

Although affectively focused dimensions of social relationships are associated with differences in health risk, less research has considered nonaffective features of relationships, such as engaging in shared activities. This study sought to test whether adolescents who engaged in more shared activities with their parents had lower health risk in early midlife across multiple biological markers.

METHODS

Using data from a nationally representative study ( N = 4801), prospective associations between shared activities with parents during adolescence and health risk classifications for measures of inflammation, renal function, glucose homeostasis, and cholesterol 22 years later were examined, along with the potentially confounding roles of childhood socioeconomic status and parent-child relationship satisfaction. Exploratory analyses considered possible indirect effects of cigarette use, alcohol use, and body mass index in adulthood.

RESULTS

Engaging in more shared activities with parents was associated with a reduced likelihood of being classified in a high-risk health category for markers of inflammation ( B = -0.02, standard error [SE] = 0.01, p = .040), renal function ( B = -0.08, SE = 002, p = .001), glucose ( B = -0.06, SE = 0.23, p = .011), and high-density lipoprotein ( B = - 0.03, SE = 0.01, p = .021), and overall allostatic load ( B = - 0.02, SE = 0.02, p = .001), beyond demographic and health covariates. Controlling for parental income and relationship satisfaction largely did not affect observed associations. Exploratory tests of indirect effects imply that health behaviors in adulthood may partially account for observed associations.

CONCLUSIONS

Engaging in more shared activities predicted more optimal health classifications 22 years later, suggesting that the amount of contact between parents and teenagers may have long-lasting beneficial health effects. Furthermore, consideration of nonaffective dimensions of family relationships may provide additional insight into associations between social relationships and health.

Artificial intelligence-estimated biological heart age using a 12-lead electrocardiogram predicts mortality and cardiovascular outcomes

Background

There is a paucity of data on artificial intelligence-estimated biological electrocardiography (ECG) heart age (AI ECG-heart age) for predicting cardiovascular outcomes, distinct from the chronological age (CA). We developed a deep learning-based algorithm to estimate the AI ECG-heart age using standard 12-lead ECGs and evaluated whether it predicted mortality and cardiovascular outcomes.

Methods

We trained and validated a deep neural network using the raw ECG digital data from 425,051 12-lead ECGs acquired between January 2006 and December 2021. The network performed a holdout test using a separate set of 97,058 ECGs. The deep neural network was trained to estimate the AI ECG-heart age [mean absolute error, 5.8 ± 3.9 years; R-squared, 0.7 (r = 0.84, p < 0.05)].

Findings

In the Cox proportional hazards models, after adjusting for relevant comorbidity factors, the patients with an AI ECG-heart age of 6 years older than the CA had higher all-cause mortality (hazard ratio (HR) 1.60 [1.42-1.79]) and more major adverse cardiovascular events (MACEs) [HR: 1.91 (1.66-2.21)], whereas those under 6 years had an inverse relationship (HR: 0.82 [0.75-0.91] for all-cause mortality; HR: 0.78 [0.68-0.89] for MACEs). Additionally, the analysis of ECG features showed notable alterations in the PR interval, QRS duration, QT interval and corrected QT Interval (QTc) as the AI ECG-heart age increased.

Conclusion

Biological heart age estimated by AI had a significant impact on mortality and MACEs, suggesting that the AI ECG-heart age facilitates primary prevention and health care for cardiovascular outcomes.

AnthropoAge, a novel approach to integrate body composition into the estimation of biological age

Aging is believed to occur across multiple domains, one of which is body composition; however, attempts to integrate it into biological age (BA) have been limited. Here, we consider the sex-dependent role of anthropometry for the prediction of 10-year all-cause mortality using data from 18,794 NHANES participants to generate and validate a new BA metric. Our data-driven approach pointed to sex-specific contributors for BA estimation: WHtR, arm and thigh circumferences for men; weight, WHtR, thigh circumference, subscapular and triceps skinfolds for women. We used these measurements to generate AnthropoAge, which predicted all-cause mortality (AUROC 0.876, 95%CI 0.864-0.887) and cause-specific mortality independently of ethnicity, sex, and comorbidities; AnthropoAge was a better predictor than PhenoAge for cerebrovascular, Alzheimer, and COPD mortality. A metric of age acceleration was also derived and used to assess sexual dimorphisms linked to accelerated aging, where women had an increase in overall body mass plus an important subcutaneous to visceral fat redistribution, and men displayed a marked decrease in fat and muscle mass. Finally, we showed that consideration of multiple BA metrics may identify unique aging trajectories with increased mortality (HR for multidomain acceleration 2.43, 95%CI 2.25-2.62) and comorbidity profiles. A simplified version of AnthropoAge (S-AnthropoAge) was generated using only BMI and WHtR, all results were preserved using this metric. In conclusion, AnthropoAge is a useful proxy of BA that captures cause-specific mortality and sex dimorphisms in body composition, and it could be used for future multidomain assessments of aging to better characterize the heterogeneity of this phenomenon.

Measuring biological age using omics data

Age is the key risk factor for diseases and disabilities of the elderly. Efforts to tackle age-related diseases and increase healthspan have suggested targeting the ageing process itself to 'rejuvenate' physiological functioning. However, achieving this aim requires measures of biological age and rates of ageing at the molecular level. Spurred by recent advances in high-throughput omics technologies, a new generation of tools to measure biological ageing now enables the quantitative characterization of ageing at molecular resolution. Epigenomic, transcriptomic, proteomic and metabolomic data can be harnessed with machine learning to build 'ageing clocks' with demonstrated capacity to identify new biomarkers of biological ageing.

Using blood test parameters to define biological age among older adults: association with morbidity and mortality independent of chronological age validated in two separate birth cohorts

Biomarkers defining biological age are typically laborious or expensive to assess. Instead, in the current study, we identified parameters based on standard laboratory blood tests across metabolic, cardiovascular, inflammatory, and kidney functioning that had been assessed in the Berlin Aging Study (BASE) (n = 384) and Berlin Aging Study II (BASE-II) (n = 1517). We calculated biological age using those 12 parameters that individually predicted mortality hazards over 26 years in BASE. In BASE, older biological age was associated with more physician-observed morbidity and higher mortality hazards, over and above the effects of chronological age, sex, and education. Similarly, in BASE-II, biological age was associated with physician-observed morbidity and subjective health, over and above the effects of chronological age, sex, and education as well as alternative biomarkers including telomere length, DNA methylation age, skin age, and subjective age but not PhenoAge. We discuss the importance of biological age as one indicator of aging.

Microbiota from patients with ulcerative colitis promote colorectal carcinogenesis in mice

OBJECTIVES

Long-term ulcerative colitis (UC) is associated with both dysbiosis in intestinal microbiota and predisposition to colorectal cancer. In this study, we investigated whether microbiota from patients with UC could increase colorectal carcinogenesis in mice, generated by azoxymethane through intraperitoneal injection.

METHODS

Mice were gavaged twice per week with intestinal microbiota from patients with UC or healthy individuals. Intestinal tissues were collected from mice and compared by histology, immunohistochemistry, expression microarray, quantitative polymerase chain reaction, Western blot, and flow cytometry analyses. Quantification of bacteria in feces was performed using 16 S ribosomal RNA gene selective quantitative polymerase chain reaction.

RESULTS

Compared with mice fed microbiota from healthy controls, increased tumorigenesis was observed in mice gavaged with microbiota from patients with UC, including a higher number of colon adenoma and a significantly higher proportion of grade dysplasia. Consistent with tumorigenesis, mice gavaged with microbiota from patients with UC showed an increased expression of Ki67 and proliferating cell nuclear antigen. In addition, an increased expression of cytokines and more abundant presence of T helper cells types 1 and 17 was observed in mice receiving microbiota from patients with UC. Moreover, a decrease in the abundance of short-chain fatty acids was detected in the feces, as well as an altered intestinal microbial composition in mice fed with microbiota from patients with UC.

CONCLUSIONS

Fecal microbiota from patients with UC exacerbate tumorigenesis in mice. The disturbance of intestinal microbiota and activation of T helper cells types 1 and 17 cytokines caused by gavaging microbiota from patients with UC both contributed to intestinal carcinogenesis.

Integrative analysis of DNA methylation and gene expression identifies genes associated with biological aging in Alzheimer's disease

Introduction

The acceleration of biological aging is a risk factor for Alzheimer's disease (AD). Here, we performed weighted gene co-expression network analysis (WGCNA) to identify modules and dysregulated genesinvolved in biological aging in AD.

Methods

We performed WGCNA to identify modules associated with biological clocks and hub genes of the module with the highest module significance. In addition, we performed differential expression analysis and association analysis with AD biomarkers.

Results

WGCNA identified five modules associated with biological clocks, with the module designated as "purple" showing the strongest association. Functional enrichment analysis revealed that the purple module was related to cell migration and death. Ten genes were identified as hub genes in purple modules, of which CX3CR1 was downregulated in AD and low levels of CX3CR1 expression were associated with AD biomarkers.

Conclusion

Network analysis identified genes associated with biological clocks, which suggests the genetic architecture underlying biological aging in AD.

Highlights

Examine links between Alzheimer's disease (AD) peripheral transcriptome and biological aging changes.Weighted gene co-expression network analysis (WGCNA) found five modules related to biological aging.Among the hub genes of the module, CX3CR1 was downregulated in AD.The CX3CR1 expression level was associated with cognitive performance and brain atrophy.

Serum neurofilament light levels are predictive of all-cause mortality in late middle-aged individuals

Background

Blood biomarkers can offer valuable and easily accessible indicators of normal biological processes, pathogenic conditions, and responses to therapeutic interventions. Recent studies found that levels of neurofilament light chain (NfL) in the blood are associated with mortality in three European cohorts of older adults (median ages 73, 93, and 100 years). Whether similar associations exist in younger adults and in other ethnic groups is currently not known.

Methods

We utilized a cohort study that included 294 African Americans (baseline ages 49–65). Serum NfL levels were measured using a Meso Scale Discovery-based assay. Vital status was determined by matching through the National Death Index.

Findings

Seventy-two participants (24.5%) died during the 14–15 years of follow up (2000–2014). Baseline serum NfL levels were significantly higher in the decedent group (86.1±65.7 pg/ml vs. 50.1±28.0 pg/ml, p < 0·001). In binomial logistic regression models adjusted for age, gender, education, baseline smoking status, BMI, and total comorbidities (0–11), serum NfL levels remained a strong predictor of all-cause mortality, and sensitivity analyses employing multiple additional covariates did not substantively change the relationship. Further, Kaplan-Meier curves based on serum NfL quartiles showed reduced survival in groups with higher serum NfL levels.

Interpretation

This study found a positive association between serum NfL levels and mortality in late middle-aged and older individuals. While our findings support that serum NfL levels may be a useful biomarker for all-cause mortality, further studies are needed to understand the biological mechanisms underlying this association.

Funding

National Institute on Aging, Saint Louis University.

Oxidative stress, aging, antioxidant supplementation and their impact on human health: An overview

Aging is characterized by a progressive loss of tissue and organ function due to genetic and environmental factors, nutrition, and lifestyle. Oxidative stress is one the most important mechanisms of cellular senescence and increased frailty, resulting in several age-linked, noncommunicable diseases. Contributing events include genomic instability, telomere shortening, epigenetic mechanisms, reduced proteome homeostasis, altered stem-cell function, defective intercellular communication, progressive deregulation of nutrient sensing, mitochondrial dysfunction, and metabolic unbalance. These complex events and their interplay can be modulated by dietary habits and the ageing process, acting as potential measures of primary and secondary prevention. Promising nutritional approaches include the Mediterranean diet, the intake of dietary antioxidants, and the restriction of caloric intake. A comprehensive understanding of the ageing processes should promote new biomarkers of risk or diagnosis, but also beneficial treatments oriented to increase lifespan.

Treatment of gingivitis is associated with reduction of systemic inflammation and improvement of oral health-related quality of life: A randomized clinical trial

AIM

To compare the level of inflammatory markers, oral health-related quality of life (OHRQoL) and gingival parameters 1 month after introduction of electric toothbrush and intensive oral hygiene maneuvers adaptation (OHI) versus routine habits (no-OHI) in patients affected by generalized gingivitis.

METHODS

140 subjects with generalized gingivitis were randomized to receive either OHI or no-OHI. Full-mouth plaque/bleeding scores (FMPS/FMBS), serum high-sensitivity C-reactive protein (hs-CRP), interleukin-6- (IL-6) and an Oral health impact profile-14 questionnaire (OHIP-14) were collected at baseline and at 1-month follow-up visit.

RESULTS

In the OHI, a significant FMPS and FMPBS reduction (p<0.01), a significant intragroup decrease in hs-CRP and IL-6 (p<0.01) and a significant improvement of OHRQoL (p<0.01) was noted at 1-month. In the no-OHI, lower-magnitude differences were noted only for oral parameters. Resolution of gingivitis varied between OHI and no-OHI (89% versus 7% respectively, p<0.01). A Logistic multivariate regression suggested that FMBS ≤8% was associated with odds ratio of 13 of having both CRP and IL-6 below the selected threshold for healthy young adults (p=0.04).

CONCLUSIONS

Gingivitis resolution determined important reductions of gingival inflammation and plaque levels, as well as systemic inflammatory markers and an improvement of quality of life (NCT03848351).

Biological aging for risk prediction of first-ever intracerebral hemorrhage and cerebral infarction in advanced age

BACKGROUND AND OBJECTIVES

successful interventions to prevent cerebrovascular disease and stroke require early identification of persons at risk before clinical manifestation of disease. The literature remains to be sparse on accessible plasma-based biomarkers for monitoring brain health and cerebrovascular disease in advanced age. We assessed the predictive value of biological age (BA) as an early indicator for cerebrovascular disease and risk of first-ever intracerebral hemorrhage (ICH) and cerebral infarction (CI) in advanced age and compared these relationships with chronological age (CA) and commonly used biomarkers including tau and Aβ40 and Aβ42.

METHODS

The study included Individuals who consented for blood draw and follow-up. We computed biological age using structural equation modelling. The criteria for the biomarkers included their representability of the various body systems; their availability in the Rotterdam study and their pre-hypothesized reflection of aging in other populations. The algorithm integrates biomarkers that represent six body systems involved in overall cerebrovascular health including metabolic function, cardiac function, lung function, kidney function, liver function, immunity, and inflammation. Time to event analysis was conducted using Cox-regression models. Prediction analysis was conducted using Harrel's C and Area under the receiver operating characteristic curve.

RESULTS

The sample included a total of 1699 individuals at baseline followed up over a median of 11 years. During a period of 15, 780 and 16, 172 person-years, a total of 17 first-ever intracerebral hemorrhage and 83 cerebral infarction cases occurred. In time-to-event analysis, BA showed higher magnitude of associations with ICH compared to CA (HR_BA-ICH: 2.30, 95% CI: 1.20, 4.30; HR_CA-ICH: 1.40, 95% CI: 0.76, 2.53) and higher precision with CI (HR_BA-CI: 1.30, 95% CI: 1.01,1.75; HR_CA-CI:1.90, 95% CI: 1.48, 2.66). BA outperformed CA for prediction of ICH (AUC: 0.68 vs 0.53; Harrel's C: 0.72 vs 0.53) and for CI (AUC:0.63 vs 0.62; Harrel's C: 0.68 vs 0.67).

CONCLUSIONS

Biological aging (delta biological aging) based on integrated physiology biomarkers provides a novel tool for monitoring and identification of persons at highest risk of cerebrovascular disease in advanced age with varying degrees of precision and magnitude for stroke subtypes. These variations are likely related to differences in pathophysiology of intracerebral hemorrhage and cerebral infarction. Wider validation and applicability require extension of these findings in other comparable samples and in clinical settings.

Effects of STAT3 on aging-dependent neovascularization impairment following limb ischemia: from bedside to bench

Aging is a major risk factor for ischemic hypoxia-related diseases, including peripheral artery diseases (PADs). Signal transducer and activator of transcription 3 (STAT3) is a critical transcription activator in angiogenesis. Nevertheless, the effect of aging on endothelial cells and their responses to hypoxia are not well studied. Using a hindlimb hypoxic/ischemic model of aged mice, we found that aged mice (80-100-week-old) expressed significantly lower levels of angiogenesis than young mice (10-week-old). In our in vitro study, aged endothelial cells (≥30 passage) showed a significant accumulation of β-galactosidase and a high expression of aging-associated genes, including p16, p21, and hTERT compared with young cells (<10 passage). After 24 hours of hypoxia exposure, proliferation, migration and tube formation were significantly impaired in aged cells compared with young cells. Notably, STAT3 and angiogenesis-associated proteins such as PI3K/AKT were significantly downregulated in aged mouse limb tissues and aged cells. Further, using STAT3 siRNA, we found that suppressing STAT3 expression in endothelial cells impaired proliferation, migration and tube formation under hypoxia. Correspondingly, in patients with limb ischemia we also observed a higher expression of circulating STAT3, associated with a lower rate of major adverse limb events (MALEs). Collectively, STAT3 could be a biomarker reflecting the development of MALE in patients and also a regulator of age-dependent angiogenesis post limb ischemia. Additional studies are required to elucidate the clinical applications of STAT3.

Aging clocks & mortality timers, methylation, glycomic, telomeric and more. A window to measuring biological age

As humans age multiple forms of biological decay ensue, and many aspects of human biology can be measured to determine how far biological machinery has drifted from homeostasis. Research has led to aging clocks being developed that claim to predict biological age as opposed to chronological age. Aging could be regarded as a measured loss of homeostatic biological equilibrium that augments biological decay in fully developed tissues. Measuring aspects of how far various elements of biology have drifted from a youthful state may allow us to make determinations on a subject's health but also make informed predictions on their biological age. As we see across human physiology, many facets that maintain human health taper off such as nicotinamide adenine dinucleotide, glutathione, catalase, super oxide dismutase, and more. Extracellular vesicle density also tapers off during age combined with epigenetic drift, telomere attrition, and stem cell exhaustion, whilst genomic instability and biological insults from environment and lifestyle factors increase. Measuring these types of biomarkers with aging clocks may allow subjects to understand their own health more accurately and enable subjects to better focus on their efforts in the pursuit of longevity and, in addition, allow healthcare practitioners to deliver better health advice.

Association of Markers of Inflammation, the Kynurenine Pathway and B Vitamins with Age and Mortality, and a Signature of Inflammaging

BACKGROUND

Inflammation is a key feature of aging. We aimed to (i) investigate the association of 34 blood markers potentially involved in inflammatory processes with age and mortality and (ii) develop a signature of "inflammaging."

METHODS

Thirty-four blood markers relating to inflammation, B vitamin status, and the kynurenine pathway were measured in 976 participants in the Melbourne Collaborative Cohort Study at baseline (median age = 59 years) and follow-up (median age = 70 years). Associations with age and mortality were assessed using linear and Cox regression, respectively. A parsimonious signature of inflammaging was developed and its association with mortality was compared with 2 marker scores calculated across all markers associated with age and mortality, respectively.

RESULTS

The majority of markers (30/34) were associated with age, with stronger associations observed for neopterin, cystatin C, interleukin (IL)-6, tumor necrosis factor alpha (TNF-α), several markers of the kynurenine pathway and derived indices KTR (kynurenine/tryptophan ratio), PAr index (ratio of 4-pyridoxic acid and the sum of pyridoxal 5'-phosphate and pyridoxal), and HK:XA (3-hydroxykynurenine/xanthurenic acid ratio). Many markers (17/34) showed an association with mortality, in particular IL-6, neopterin, C-reactive protein, quinolinic acid, PAr index, and KTR. The inflammaging signature included 10 markers and was strongly associated with mortality (hazard ratio [HR] per SD = 1.40, 95% CI: 1.24-1.57, p = 2 × 10-8), similar to scores based on all age-associated (HR = 1.38, 95% CI: 1.23-1.55, p = 4 × 10-8) and mortality-associated markers (HR = 1.43, 95% CI: 1.28-1.60, p = 1 × 10-10), respectively. Strong evidence of replication of the inflammaging signature association with mortality was found in the Hordaland Health Study.

CONCLUSION

Our study highlights the key role of the kynurenine pathway and vitamin B6 catabolism in aging, along with other well-established inflammation-related markers. A signature of inflammaging based on 10 markers was strongly associated with mortality.

Utilization of Host and Microbiome Features in Determination of Biological Aging

The term ‘old age’ generally refers to a period characterized by profound changes in human physiological functions and susceptibility to disease that accompanies the final years of a person’s life. Despite the conventional definition of old age as exceeding the age of 65 years old, quantifying aging as a function of life years does not necessarily reflect how the human body ages. In contrast, characterizing biological (or physiological) aging based on functional parameters may better reflect a person’s temporal physiological status and associated disease susceptibility state. As such, differentiating ‘chronological aging’ from ‘biological aging’ holds the key to identifying individuals featuring accelerated aging processes despite having a young chronological age and stratifying them to tailored surveillance, diagnosis, prevention, and treatment. Emerging evidence suggests that the gut microbiome changes along with physiological aging and may play a pivotal role in a variety of age-related diseases, in a manner that does not necessarily correlate with chronological age. Harnessing of individualized gut microbiome data and integration of host and microbiome parameters using artificial intelligence and machine learning pipelines may enable us to more accurately define aging clocks. Such holobiont-based estimates of a person’s physiological age may facilitate prediction of age-related physiological status and risk of development of age-associated diseases.

Validation of the Elderly Traumatic Brain Injury Score: an observational case-control study

INTRODUCTION

Traumatic brain injury (TBI) poses a particular health risk for the elderly. The recently developed Elderly Traumatic Brain Injury Score (eTBI Score) combines the prognostic information of the risk factors characteristic of the geriatric population. We aimed to determine its validity and reliability on an independent sample.

METHODS

We present a retrospective analysis of 506 consecutive patients after TBI aged ≥65 years. The previously described nomogram and the eTBI Score were used. The primary outcome measure was mortality or vegetative state at 30 days after hospital admission.

RESULTS

Mortality or vegetative state rate was 21.3%. The nomogram and eTBI Score showed similar predictive performance with accuracy of 83.8% (95%CI 80.2%-87%) and 84.4% (95%CI 80.8%-87.6%), respectively. Based on the Youden index and C4.5 algorithm we divided patients according to the 3-tier pattern into low, high and medium risk groups. The outcome prediction in the first two groups was correct in 93.1% (survival in the low risk group) and 94.4% (mortality in the high risk group). Patients included in the medium risk group usually required surgical treatment (85.3%), and characterized for increased mortality or vegetative state (55%). Among patients with eTBI≥5 (n=221), there was no difference in outcome between those treated conservatively and surgically.

CONCLUSIONS

This is the first study confirming the validity of the eTBI Score and its close association with outcome of geriatric population after TBI. The novel 3-tier risk stratification scheme was applicable to both conservatively and surgically treated patients.

The metabolome as a biomarker of aging in Drosophila melanogaster

Many biomarkers have been shown to be associated not only with chronological age but also with functional measures of biological age. In human populations, it is difficult to show whether variation in biological age is truly predictive of life expectancy, as such research would require longitudinal studies over many years, or even decades. We followed adult cohorts of 20 Drosophila Genetic Reference Panel (DGRP) strains chosen to represent the breadth of lifespan variation, obtain estimates of lifespan, baseline mortality, and rate of aging, and associate these parameters with age‐specific functional traits including fecundity and climbing activity and with age‐specific targeted metabolomic profiles. We show that activity levels and metabolome‐wide profiles are strongly associated with age, that numerous individual metabolites show a strong association with lifespan, and that the metabolome provides a biological clock that predicts not only sample age but also future mortality rates and lifespan. This study with 20 genotypes and 87 metabolites, while relatively small in scope, establishes strong proof of principle for the fly as a powerful experimental model to test hypotheses about biomarkers and aging and provides further evidence for the potential value of metabolomic profiles as biomarkers of aging.

Association between Phenotypic Age and Mortality in Patients with Multivessel Coronary Artery Disease

Background

Chronological age (CA) is not a perfect proxy for the true biological aging status of the body. A new biological aging measure, phenotypic age (PhenoAge), has been shown to capture morbidity and mortality risk in the general US population and diverse subpopulations. This study was aimed at evaluating the association between PhenoAge and long-term outcome of patients with multivessel coronary artery disease (CAD).

Methods

A total of 609 multivessel CAD patients who received PCI attempt and with follow-up were enrolled. The clinical outcome was all-cause mortality on follow-up. PhenoAge was calculated using an equation constructed from CA and 9 clinical biomarkers. Cox proportional hazards regression models and receiver operating characteristic (ROC) curves were performed to evaluate the association between PhenoAge and mortality.

Results

Overall, patients with more diseases had older PhenoAge and phenotypic age acceleration (PhenoAgeAccel). After a median follow-up of 33.5 months, those with positive PhenoAgeAccel had a significantly higher incidence of all-cause mortality (P = 0.001). After adjusting for CA, Cox proportional hazards models showed that both PhenoAge and PhenoAgeAccel were significantly associated with all-cause mortality. Even after further adjusting for confounding factors, each 10-year increase in PhenoAge was also associated with a 51% increased mortality risk. ROC curves revealed that PhenoAge, with an area under the curve of 0.705, significantly outperformed CA, the individual clinical chemistry measure, and other risk factors. When reexamining the ROC curves using various combinations of variables, we found that PhenoAge provides additional predictive power to all models.

Conclusions

In conclusion, PhenoAge was strongly associated with all-cause mortality even after adjusting for CA. Our findings suggest that PhenoAge measure may be complementary in predicting mortality risk for patients with multivessel CAD.

Periodontitis and Accelerated Biological Aging: A Geroscience Approach

As the whole world is epidemically aging, the burden of periodontitis and tooth loss is becoming a major health concern. Growing meta-epidemiological data implicate chronic systemic inflammation/infection due to periodontitis as an independent risk factor for aging-related diseases and mortality. However, because people age differently, chronological age is not a reliable marker of an individual's functional status. Recent advances in geroscience have shown that various biomarker signatures of biological aging are longitudinally associated with declined physical function, morbidity, and mortality due to major age-related diseases, including periodontitis. Here, we emphasize novel research developments bidirectionally linking periodontitis to accelerated biological aging. Using a composite biomarker age estimator, a striking increase in periodontitis and tooth loss was observed in subjects whose biological age at baseline was higher than their chronological age. Moreover, significantly shortened telomeres were encountered in populations affected by severe periodontitis. Second, we elucidate the cellular and molecular pillars of the aging process at the periodontal level. Accumulating evidence suggests that cellular senescence, stem cell exhaustion, and immunoaging are hallmarks of biological aging implicated in the impairment of periodontal homeostasis and the pathophysiology of periodontitis. Indeed, persistent bacteria-derived lipopolysaccharide stimulation influences cellular senescence in osteocytes, driving alveolar bone resorption. Moreover, inflammaging status induced by chronic hyperglycemia elevates the burden of senescent cells in gingival tissues, impairing their barrier function. Lastly, we reviewed a recent breakthrough in senotherapy to directly target the mechanisms of aging at the periodontal level. Physical exercise and intermittent fasting, together with natural compounds, senolytic drugs, and cell therapy, are increasingly being evaluated to rejuvenate the oral cavity. Following these innovations in geroscience, further advancements could provide oral clinicians the chance to intercept biological aging when still "subclinical" and set interventions for halting or delaying the trajectory toward aging-related diseases while patients are still chronologically young.

A machine learning-based biological aging prediction and its associations with healthy lifestyles: the Dongfeng-Tongji cohort

This study aims to establish a biological age (BA) predictor and to investigate the roles of lifestyles on biological aging. The 14,848 participants with the available information of multisystem measurements from the Dongfeng-Tongji cohort were used to estimate BA. We developed a composite BA predictor showing a high correlation with chronological age (CA) (r = 0.82) by using an extreme gradient boosting (XGBoost) algorithm. The average frequency hearing threshold, forced expiratory volume in 1 second (FEV₁ ), gender, systolic blood pressure, and homocysteine ranked as the top five important features for the BA predictor. Two aging indexes, recorded as the AgingAccel (the residual from regressing predicted age on CA) and aging rate (the ratio of predicted age to CA), showed positive associations with the risks of all-cause (HR (95% CI) = 1.12 (1.10-1.14) and 1.08 (1.07-1.10), respectively) and cause-specific (HRs ranged from 1.06 to ∼1.15) mortality. Each 1-point increase in healthy lifestyle score (including normal body mass index, never smoking, moderate alcohol drinking, physically active, and sleep 7-9 h/night) was associated with a 0.21-year decrease in the AgingAccel (95% CI: -0.27 to -0.15) and a 0.4% decrease in the aging rate (95% CI: -0.5% to -0.3%). This study developed a machine learning-based BA predictor in a prospective Chinese cohort. Adherence to healthy lifestyles showed associations with delayed biological aging, which highlights potential preventive interventions.