Measuring biological age using omics data

Evolutionary screening of precision oncology biomarkers and its applications in prognostic model construction

Biomarker screening is critical for precision oncology. However, one of the main challenges in precision oncology is that the screened biomarkers often fail to achieve the expected clinical effects and are rarely approved by regulatory authorities. Considering the close association between cancer pathogenesis and the evolutionary events of organisms, we first explored the evolutionary feature underlying clinically approved biomarkers, and two evolutionary features of approved biomarkers (Ohnologs and specific evolutionary stages of genes) were identified. Subsequently, we utilized evolutionary features for screening potential prognostic biomarkers in four common cancers: head and neck squamous cell carcinoma, liver hepatocellular carcinoma, lung adenocarcinoma, and lung squamous cell carcinoma. Finally, we constructed an evolution-strengthened prognostic model (ESPM) for cancers. These models can predict cancer patients' survival time across different cancer cohorts effectively and perform better than conventional models. In summary, our study highlights the application potentials of evolutionary information in precision oncology biomarker screening.

Diet, Pace of Biological Aging, and Risk of Dementia in the Framingham Heart Study

OBJECTIVE

People who eat healthier diets are less likely to develop dementia, but the biological mechanism of this protection is not well understood. We tested the hypothesis that healthy diet protects against dementia because it slows the pace of biological aging.

METHODS

We analyzed Framingham Offspring Cohort data. We included participants ≥60 years-old, free of dementia and having dietary, epigenetic, and follow-up data. We assessed healthy diet as long-term adherence to the Mediterranean-Dash Intervention for Neurodegenerative Delay diet (MIND, over 4 visits spanning 1991-2008). We measured the pace of aging from blood DNA methylation data collected in 2005-2008 using the DunedinPACE epigenetic clock. Incident dementia and mortality were defined using study records compiled from 2005 to 2008 visit through 2018.

RESULTS

Of n = 1,644 included participants (mean age 69.6, 54% female), n = 140 developed dementia and n = 471 died over 14 years of follow-up. Greater MIND score was associated with slower DunedinPACE and reduced risks for dementia and mortality. Slower DunedinPACE was associated with reduced risks for dementia and mortality. In mediation analysis, slower DunedinPACE accounted for 27% of the diet-dementia association and 57% of the diet-mortality association.

INTERPRETATION

Findings suggest that slower pace of aging mediates part of the relationship of healthy diet with reduced dementia risk. Monitoring pace of aging may inform dementia prevention. However, a large fraction of the diet-dementia association remains unexplained and may reflect direct connections between diet and brain aging that do not overlap other organ systems. Investigation of brain-specific mechanisms in well-designed mediation studies is warranted. ANN NEUROL 2024;95:1069-1079.

Demographic characteristics and epigenetic biological aging among post-9/11 veterans: Associations of DunedinPACE with sex, race, and age

Measures of epigenetic aging derived from DNA methylation (DNAm) have enabled the assessment of biological aging in new populations and cohorts. In the present study, we used an epigenetic measure of aging, DunedinPACE, to examine rates of aging across demographic groups in a sample of 2,309 United States military veterans from the VISN 6 MIRECC's Post-Deployment Mental Health Study. As assessed by DunedinPACE, female veterans were aging faster than male veterans (β = 0.39, 95 % CI [0.29, 0.48], p < .001), non-Hispanic Black veterans were aging faster than non-Hispanic White veterans (β = 0.58, 95 % CI [0.50, 0.66], p < .001), and older veterans were biologically aging faster than younger veterans (β = 0.21, 95 % CI [0.18, 0.25], p < .001). In secondary analyses, these differences in rates of aging were not explained by a variety of biopsychosocial covariates. In addition, the percentage of European genetic admixture in non-Hispanic Black veterans was not associated with DunedinPACE. Our findings suggest that female and non-Hispanic Black veterans are at greater risk of accelerated aging among post-9/11 veterans. Interventions that slow aging might provide relatively greater benefit among veterans comprising these at-risk groups.

Accelerated Pace of Aging in Schizophrenia: Five Case-Control Studies

BACKGROUND

Schizophrenia is associated with increased risk of developing multiple aging-related diseases, including metabolic, respiratory, and cardiovascular diseases, and Alzheimer's and related dementias, leading to the hypothesis that schizophrenia is accompanied by accelerated biological aging. This has been difficult to test because there is no widely accepted measure of biological aging. Epigenetic clocks are promising algorithms that are used to calculate biological age on the basis of information from combined cytosine-phosphate-guanine sites (CpGs) across the genome, but they have yielded inconsistent and often negative results about the association between schizophrenia and accelerated aging. Here, we tested the schizophrenia-aging hypothesis using a DNA methylation measure that is uniquely designed to predict an individual's rate of aging.

METHODS

We brought together 5 case-control datasets to calculate DunedinPACE (Pace of Aging Calculated from the Epigenome), a new measure trained on longitudinal data to detect differences between people in their pace of aging over time. Data were available from 1812 psychosis cases (schizophrenia or first-episode psychosis) and 1753 controls. Mean chronological age was 38.9 (SD = 13.6) years.

RESULTS

We observed consistent associations across datasets between schizophrenia and accelerated aging as measured by DunedinPACE. These associations were not attributable to tobacco smoking or clozapine medication.

CONCLUSIONS

Schizophrenia is accompanied by accelerated biological aging by midlife. This may explain the wide-ranging risk among people with schizophrenia for developing multiple different age-related physical diseases, including metabolic, respiratory, and cardiovascular diseases, and dementia. Measures of biological aging could prove valuable for assessing patients' risk for physical and cognitive decline and for evaluating intervention effectiveness.

Cell Tree Rings: the structure of somatic evolution as a human aging timer

Biological age is typically estimated using biomarkers whose states have been observed to correlate with chronological age. A persistent limitation of such aging clocks is that it is difficult to establish how the biomarker states are related to the mechanisms of aging. Somatic mutations could potentially form the basis for a more fundamental aging clock since the mutations are both markers and drivers of aging and have a natural timescale. Cell lineage trees inferred from these mutations reflect the somatic evolutionary process, and thus, it has been conjectured, the aging status of the body. Such a timer has been impractical thus far, however, because detection of somatic variants in single cells presents a significant technological challenge. Here, we show that somatic mutations detected using single-cell RNA sequencing (scRNA-seq) from thousands of cells can be used to construct a cell lineage tree whose structure correlates with chronological age. De novo single-nucleotide variants (SNVs) are detected in human peripheral blood mononuclear cells using a modified protocol. A default model based on penalized multiple regression of chronological age on 31 metrics characterizing the phylogenetic tree gives a Pearson correlation of 0.81 and a median absolute error of ~4 years between predicted and chronological ages. Testing of the model on a public scRNA-seq dataset yields a Pearson correlation of 0.85. In addition, cell tree age predictions are found to be better predictors of certain clinical biomarkers than chronological age alone, for instance glucose, albumin levels, and leukocyte count. The geometry of the cell lineage tree records the structure of somatic evolution in the individual and represents a new modality of aging timer. In addition to providing a numerical estimate of "cell tree age," it unveils a temporal history of the aging process, revealing how clonal structure evolves over life span. Cell Tree Rings complements existing aging clocks and may help reduce the current uncertainty in the assessment of geroprotective trials.

Proteomic aging clock (PAC) predicts age-related outcomes in middle-aged and older adults

Beyond mere prognostication, optimal biomarkers of aging provide insights into qualitative and quantitative features of biological aging and might, therefore, offer useful information for the testing and, ultimately, clinical use of gerotherapeutics. We aimed to develop a proteomic aging clock (PAC) for all-cause mortality risk as a proxy of biological age. Data were from the UK Biobank Pharma Proteomics Project, including 53,021 participants aged between 39 and 70 years and 2923 plasma proteins assessed using the Olink Explore 3072 assay®. 10.9% of the participants died during a mean follow-up of 13.3 years, with the mean age at death of 70.1 years. The Spearman correlation between PAC proteomic age and chronological age was 0.77. PAC showed robust age-adjusted associations and predictions for all-cause mortality and the onset of various diseases in general and disease-free participants. The proteins associated with PAC proteomic age deviation were enriched in several processes related to the hallmarks of biological aging. Our results expand previous findings by showing that biological age acceleration, based on PAC, strongly predicts all-cause mortality and several incident disease outcomes. Particularly, it facilitates the evaluation of risk for multiple conditions in a disease-free population, thereby, contributing to the prevention of initial diseases, which vary among individuals and may subsequently lead to additional comorbidities.

[Genetics, epigenetics, and environmental factors in life expectancy-What role does nature-versus-nurture play in aging?]

In Germany and worldwide, the average age of the population is continuously rising. With this general increase in chronological age, the focus on biological age, meaning the actual health and fitness status, is becoming more and more important. The key question is to what extent the age-related decline in fitness is genetically predetermined or malleable by environmental factors and lifestyle.Many epigenetic studies in aging research have provided interesting insights in this nature-versus-nurture debate. In most model organisms, aging is associated with specific epigenetic changes, which can be countered by certain interventions like moderate caloric restriction or increased physical activity. Since these interventions also have positive effects on lifespan and health, epigenetics appears to be the interface between environmental factors and the aging process. This notion is supported by the fact that an epigenetic drift occurs through the life course of identical twins, which is related to the different manifestations of aging symptoms. Furthermore, biological age can be determined with high precision based on DNA methylation patterns, further emphasizing the importance of epigenetics in aging.This article provides an overview of the importance of genetic and epigenetic parameters for life expectancy. A major focus will be on the possibilities of maintaining a young epigenome through lifestyle and environmental factors, thereby slowing down biological aging.

Organ-specific aging in the plasma proteome predicts disease

In their recent Nature paper, Oh et al. use 4979 plasma proteins collected across multiple cohorts, publicly available gene expression data, and machine learning models to identify 11 organ-specific aging scores that are linked to organ-specific disease and mortality risk, including heart failure, cognitive decline, and Alzheimer's disease.

Associations of combined phenotypic aging and genetic risk with incident cancer: A prospective cohort study

Background

Age is the most important risk factor for cancer, but aging rates are heterogeneous across individuals. We explored a new measure of aging-Phenotypic Age (PhenoAge)-in the risk prediction of site-specific and overall cancer.

Methods

Using Cox regression models, we examined the association of Phenotypic Age Acceleration (PhenoAgeAccel) with cancer incidence by genetic risk group among 374,463 participants from the UK Biobank. We generated PhenoAge using chronological age and nine biomarkers, PhenoAgeAccel after subtracting the effect of chronological age by regression residual, and an incidence-weighted overall cancer polygenic risk score (CPRS) based on 20 cancer site-specific polygenic risk scores (PRSs).

Results

Compared with biologically younger participants, those older had a significantly higher risk of overall cancer, with hazard ratios (HRs) of 1.22 (95% confidence interval, 1.18-1.27) in men, and 1.26 (1.22-1.31) in women, respectively. A joint effect of genetic risk and PhenoAgeAccel was observed on overall cancer risk, with HRs of 2.29 (2.10-2.51) for men and 1.94 (1.78-2.11) for women with high genetic risk and older PhenoAge compared with those with low genetic risk and younger PhenoAge. PhenoAgeAccel was negatively associated with the number of healthy lifestyle factors (Beta = -1.01 in men, p<0.001; Beta = -0.98 in women, p<0.001).

Conclusions

Within and across genetic risk groups, older PhenoAge was consistently related to an increased risk of incident cancer with adjustment for chronological age and the aging process could be retarded by adherence to a healthy lifestyle.

Funding

This work was supported by the National Natural Science Foundation of China (82230110, 82125033, 82388102 to GJ; 82273714 to MZ); and the Excellent Youth Foundation of Jiangsu Province (BK20220100 to MZ).

Proteomic aging clock (PAC) predicts age-related outcomes in middle-aged and older adults

Beyond mere prognostication, optimal biomarkers of aging provide insights into qualitative and quantitative features of biological aging and might, therefore, offer useful information for the testing and, ultimately, clinical use of gerotherapeutics. We aimed to develop a proteomic aging clock (PAC) for all-cause mortality risk as a proxy of biological age. Data were from the UK Biobank Pharma Proteomics Project, including 53,021 participants aged between 39 and 70 years and 2,923 plasma proteins assessed using the Olink Explore 3072 assay®. The Spearman correlation between PAC proteomic age and chronological age was 0.77. A total of 10.9% of the participants died during a mean follow-up of 13.3 years, with the mean age at death 70.1 years. We developed a proteomic aging clock (PAC) for all-cause mortality risk as a surrogate of BA using a combination of least absolute shrinkage and selection operator (LASSO) penalized Cox regression and Gompertz proportional hazards models. PAC showed robust age-adjusted associations and predictions for all-cause mortality and the onset of various diseases in general and disease-free participants. The proteins associated with PAC were enriched in several processes related to the hallmarks of biological aging. Our results expand previous findings by showing that age acceleration, based on PAC, strongly predicts all-cause mortality and several incident disease outcomes. Particularly, it facilitates the evaluation of risk for multiple conditions in a disease-free population, thereby, contributing to the prevention of initial diseases, which vary among individuals and may subsequently lead to additional comorbidities.

The association between blood biological age at rehabilitation admission and physical activity during rehabilitation in geriatric inpatients: RESORT

Geriatric rehabilitation inpatients have high levels of sedentary behaviour (SB) and low levels of physical activity (PA). Biological age predicted by blood biomarkers is indicative of adverse outcomes. The objective was to determine the association between blood biological age at rehabilitation admission and levels of SB and PA during rehabilitation in geriatric inpatients. Inpatients admitted to geriatric rehabilitation wards at the Royal Melbourne Hospital (Melbourne, Australia) from October 22, 2019, to March 29, 2020, in the REStORing health of acute unwell adulTs (RESORT) observational cohort were included. Blood biological age was predicted using SenoClock-BloodAge, a hematological ageing clock. Patients wore an inertial sensor to measure SB and PA. Logistic regression analyses were conducted. A total of 111 patients (57.7% female) with mean age 83.3 ± 7.5 years were included in the analysis. The mean blood biological age was 82.7 ± 8.4 years. Patients with 1-year higher blood biological age had higher odds of having high SB measured as non-upright time greater than 23 h/day (odds ratio (OR): 1.050, 95% confidence interval (CI): 1.000-1.102). Individuals having 1-year higher age deviation trended towards lower odds of having high levels of PA measured as stepping time greater than 7.4 min/day (OR: 0.916, CI: 0.836-1.005) and as greater than 19.5 sit-to-stand transitions/day (OR: 0.915, CI: 0.836-1.002). In conclusion, higher biological age was associated with higher levels of SB and trended towards lower PA. Incorporating blood biological age could facilitate resource allocation and the development of more tailored rehabilitation plans.

The AccelerAge framework: a new statistical approach to predict biological age based on time-to-event data

Aging is a multifaceted and intricate physiological process characterized by a gradual decline in functional capacity, leading to increased susceptibility to diseases and mortality. While chronological age serves as a strong risk factor for age-related health conditions, considerable heterogeneity exists in the aging trajectories of individuals, suggesting that biological age may provide a more nuanced understanding of the aging process. However, the concept of biological age lacks a clear operationalization, leading to the development of various biological age predictors without a solid statistical foundation. This paper addresses these limitations by proposing a comprehensive operationalization of biological age, introducing the "AccelerAge" framework for predicting biological age, and introducing previously underutilized evaluation measures for assessing the performance of biological age predictors. The AccelerAge framework, based on Accelerated Failure Time (AFT) models, directly models the effect of candidate predictors of aging on an individual's survival time, aligning with the prevalent metaphor of aging as a clock. We compare predictors based on the AccelerAge framework to a predictor based on the GrimAge predictor, which is considered one of the best-performing biological age predictors, using simulated data as well as data from the UK Biobank and the Leiden Longevity Study. Our approach seeks to establish a robust statistical foundation for biological age clocks, enabling a more accurate and interpretable assessment of an individual's aging status.

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

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

Organ-specific biological clocks: Ageotyping for personalized anti-aging medicine

Aging is a complex multidimensional, progressive remodeling process affecting multiple organ systems. While many studies have focused on studying aging across multiple organs, assessment of the contribution of individual organs to overall aging processes is a cutting-edge issue. An organ's biological age might influence the aging of other organs, revealing a multiorgan aging network. Recent data demonstrated a similar yet asynchronous inter-organs and inter-individuals progression of aging, thereby providing a foundation to track sources of declining health in old age. The integration of multiple omics with common clinical parameters through artificial intelligence has allowed the building of organ-specific aging clocks, which can predict the development of specific age-related diseases at high resolution. The peculiar individual aging-trajectory, referred to as ageotype, might provide a novel tool for a personalized anti-aging, preventive medicine. Here, we review data relative to biological aging clocks and omics-based data, suggesting different organ-specific aging rates. Additional research on longitudinal data, including young subjects and analyzing sex-related differences, should be encouraged to apply ageotyping analysis for preventive purposes in clinical practice.

The 'middle-aging' brain

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

Body Size, Diet Quality, and Epigenetic Aging: Cross-Sectional and Longitudinal Analyses

Epigenetic age is an emerging marker of health that is highly predictive of disease and mortality risk. There is a lack of evidence on whether lifestyle changes are associated with changes in epigenetic aging. We used data from 1 041 participants in the Melbourne Collaborative Cohort Study with blood DNA methylation measures at baseline (1990-1994, mean age: 57.4 years) and follow-up (2003-2007, mean age: 68.8 years). The Alternative Healthy Eating Index-2010 (AHEI-2010), the Mediterranean Dietary Score, and the Dietary Inflammatory Index were used as measures of diet quality, and weight, waist circumference, and waist-to-hip ratio as measures of body size. Five age-adjusted epigenetic aging measures were considered: GrimAge, PhenoAge, PCGrimAge, PCPhenoAge, and DunedinPACE. Multivariable linear regression models including restricted cubic splines were used to assess the cross-sectional and longitudinal associations of body size and diet quality with epigenetic aging. Associations between weight and epigenetic aging cross-sectionally at both time points were positive and appeared greater for DunedinPACE (per SD: β ~0.24) than for GrimAge and PhenoAge (β ~0.10). The longitudinal associations with weight change were markedly nonlinear (U-shaped) with stable weight being associated with the lowest epigenetic aging at follow-up, except for DunedinPACE, for which only weight gain showed a positive association. We found negative, linear associations for AHEI-2010 both cross-sectionally and longitudinally. Other adiposity measures and dietary scores showed similar results. In middle-aged to older adults, declining diet quality and weight gain may increase epigenetic age, while the association for weight loss may require further investigation. Our study sheds light on the potential of weight management and dietary improvement in slowing aging processes.

The transition from genomics to phenomics in personalized population health

Modern health care faces several serious challenges, including an ageing population and its inherent burden of chronic diseases, rising costs and marginal quality metrics. By assessing and optimizing the health trajectory of each individual using a data-driven personalized approach that reflects their genetics, behaviour and environment, we can start to address these challenges. This assessment includes longitudinal phenome measures, such as the blood proteome and metabolome, gut microbiome composition and function, and lifestyle and behaviour through wearables and questionnaires. Here, we review ongoing large-scale genomics and longitudinal phenomics efforts and the powerful insights they provide into wellness. We describe our vision for the transformation of the current health care from disease-oriented to data-driven, wellness-oriented and personalized population health.

Application of AI in biological age prediction

The development of anti-aging interventions requires quantitative measurement of biological age. Machine learning models, known as "aging clocks," are built by leveraging diverse aging biomarkers that vary across lifespan to predict biological age. In addition to traditional aging clocks harnessing epigenetic signatures derived from bulk samples, emerging technologies allow the biological age estimating at single-cell level to dissect cellular diversity in aging tissues. Moreover, imaging-based aging clocks are increasingly employed with the advantage of non-invasive measurement, making it suitable for large-scale human cohort studies. To fully capture the features in the ever-growing multi-modal and high-dimensional aging-related data and uncover disease associations, deep-learning based approaches, which are effective to learn complex and non-linear relationships without relying on pre-defined features, are increasingly applied. The use of big data and AI-based aging clocks has achieved high accuracy, interpretability and generalizability, guiding clinical applications to delay age-related diseases and extend healthy lifespans.

Mitochondrial respiration atlas reveals differential changes in mitochondrial function across sex and age

Organ function declines with age, and large-scale transcriptomic analyses have highlighted differential aging trajectories across tissues. The mechanisms underlying shared and organ-selective functional changes across the lifespan, however, still remains poorly understood. Given the central role of mitochondria in powering cellular processes needed to maintain tissue health, we therefore undertook a systematic assessment of respiratory activity across 33 different tissues in young (2.5 months) and old (20 months) mice of both sexes. Our high-resolution mitochondrial respiration atlas reveals: 1) within any group of mice, mitochondrial activity varies widely across tissues, with the highest values consistently seen in heart, brown fat, and kidney; 2) biological sex is a significant but minor contributor to mitochondrial respiration, and its contributions are tissue-specific, with major differences seen in the pancreas, stomach, and white adipose tissue; 3) age is a dominant factor affecting mitochondrial activity, especially across different fat depots and skeletal muscle groups, and most brain regions; 4) age-effects can be sex- and tissue-specific, with some of the largest effects seen in pancreas, heart, adipose tissue, and skeletal muscle; and 5) while aging alters the functional trajectories of mitochondria in a majority of tissues, some are remarkably resilient to age-induced changes. Altogether, our data provide the most comprehensive compendium of mitochondrial respiration and illuminate functional signatures of aging across diverse tissues and organ systems.

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.

Clarifying the biological and statistical assumptions of cross-sectional biological age predictors: an elaborate illustration using synthetic and real data

BACKGROUND

There is divergence in the rate at which people age. The concept of biological age is postulated to capture this variability, and hence to better represent an individual's true global physiological state than chronological age. Biological age predictors are often generated based on cross-sectional data, using biochemical or molecular markers as predictor variables. It is assumed that the difference between chronological and predicted biological age is informative of one's chronological age-independent aging divergence ∆.

METHODS

We investigated the statistical assumptions underlying the most popular cross-sectional biological age predictors, based on multiple linear regression, the Klemera-Doubal method or principal component analysis. We used synthetic and real data to illustrate the consequences if this assumption does not hold.

RESULTS

The most popular cross-sectional biological age predictors all use the same strong underlying assumption, namely that a candidate marker of aging's association with chronological age is directly informative of its association with the aging rate ∆. We called this the identical-association assumption and proved that it is untestable in a cross-sectional setting. If this assumption does not hold, weights assigned to candidate markers of aging are uninformative, and no more signal may be captured than if markers would have been assigned weights at random.

CONCLUSIONS

Cross-sectional methods for predicting biological age commonly use the untestable identical-association assumption, which previous literature in the field had never explicitly acknowledged. These methods have inherent limitations and may provide uninformative results, highlighting the importance of researchers exercising caution in the development and interpretation of cross-sectional biological age predictors.

Heterogeneity of aging and mortality risk among individuals with hypertension: Insights from phenotypic age and phenotypic age acceleration

OBJECTIVES

Hypertension, a key contributor to mortality, is impacted by biological aging. We investigated the relationship between novel biological aging metrics - Phenotypic Age (PA) and Phenotypic Age Acceleration (PAA) - and mortality in individuals with hypertension, exploring the mediating effects of arterial stiffness (estimated Pulse Wave Velocity, ePWV), and Heart/Vascular Age (HVA).

METHODS

Using data from 62,160 National Health and Nutrition Examination Survey (NHANES) participants (1999-2010), we selected 4,228 individuals with hypertension and computed PA, PAA, HVA, and ePWV. Weighted, multivariable Cox regression analysis yielded Hazard Ratios (HRs) relating PA, PAA to mortality, and mediation roles of ePWV, PAA, HVA were evaluated. Mendelian randomization (MR) analysis was employed to investigate causality between genetically inferred PAA and hypertension.

RESULTS

Over a 12-year median follow-up, PA and PAA were tied to increased mortality risks in individuals with hypertension. All-cause mortality hazard ratios per 10-year PA and PAA increments were 1.96 (95% CI, 1.81-2.11) and 1.67 (95% CI, 1.52-1.85), respectively. Cardiovascular mortality HRs were 2.32 (95% CI, 1.97-2.73) and 1.93 (95% CI, 1.65-2.26) for PA and PAA, respectively. ePWV, PAA, and HVA mediated 42%, 30.3%, and 6.9% of PA's impact on mortality, respectively. Mendelian randomization highlighted a causal link between PAA genetics and hypertension (OR = 1.002; 95% CI, 1.000-1.003).

CONCLUSION

PA and PAA, enhancing cardiovascular risk scores by integrating diverse biomarkers, offer vital insights for aging and mortality evaluation in individuals with hypertension, suggesting avenues for intensified aging mitigation and cardiovascular issue prevention. Validations in varied populations and explorations of underlying mechanisms are warranted.

Rapid simulation of glycoprotein structures by grafting and steric exclusion of glycan conformer libraries

Most membrane proteins are modified by covalent addition of complex sugars through N- and O-glycosylation. Unlike proteins, glycans do not typically adopt specific secondary structures and remain very mobile, shielding potentially large fractions of protein surface. High glycan conformational freedom hinders complete structural elucidation of glycoproteins. Computer simulations may be used to model glycosylated proteins but require hundreds of thousands of computing hours on supercomputers, thus limiting routine use. Here, we describe GlycoSHIELD, a reductionist method that can be implemented on personal computers to graft realistic ensembles of glycan conformers onto static protein structures in minutes. Using molecular dynamics simulation, small-angle X-ray scattering, cryoelectron microscopy, and mass spectrometry, we show that this open-access toolkit provides enhanced models of glycoprotein structures. Focusing on N-cadherin, human coronavirus spike proteins, and gamma-aminobutyric acid receptors, we show that GlycoSHIELD can shed light on the impact of glycans on the conformation and activity of complex glycoproteins.

Sevoflurane Alters Serum Metabolites in Elders and Aging Mice and Increases Inflammation in Hippocampus

Purpose

Postoperative cognitive dysfunction (POCD) is a central nervous system complication that occurs after anesthesia, particularly among the elderly. However, the neurological pathogenesis of postoperative cognitive dysfunction remains unclear. The aim of this study was to evaluate the effects of sevoflurane exposure on serum metabolites and hippocampal gene expression in elderly patients and aging mice by metabolomics and transcriptomic analysis and to explore the pathogenesis of sevoflurane induced POCD.

Patients and Methods

Human serum samples from five patients over 60 years old were collected before sevoflurane anesthesia and 1 hour after anesthesia. Besides, mice aged at 12 months (n=6 per group) were anesthetized with sevoflurane for 2 hours or with sham procedure. Subsequently, serum and hippocampal tissues were harvested for analysis. Further investigation into the relationship between isatin and neuroinflammation was conducted using BV2 microglial cells.

Results

Sevoflurane anesthesia led to the activation of inflammatory pathways, an increased presence of hippocampal astrocytes and microglia, and elevated expression of neuroinflammatory cytokines. Comparative analysis identified 12 differential metabolites that exhibited changes in both human and mouse serum post-sevoflurane anesthesia. Notably, isatin levels were significantly decreased after anesthesia. Notably, isatin levels significantly decreased after anesthesia, a factor known to stimulate proliferation and proinflammatory gene expression in microglia-the pivotal cell type in inflammatory responses.

Conclusion

Sevoflurane-induced alterations in serum metabolites in both elderly patients and aging mice, subsequently contributing to increased inflammation in the hippocampus.

A Review: Multi-Omics Approach to Studying the Association between Ionizing Radiation Effects on Biological Aging

Multi-omics studies have emerged as powerful tools for tailoring individualized responses to various conditions, capitalizing on genome sequencing technologies' increasing affordability and efficiency. This paper delves into the potential of multi-omics in deepening our understanding of biological age, examining the techniques available in light of evolving technology and computational models. The primary objective is to review the relationship between ionizing radiation and biological age, exploring a wide array of functional, physiological, and psychological parameters. This comprehensive review draws upon an extensive range of sources, including peer-reviewed journal articles, government documents, and reputable websites. The literature review spans from fundamental insights into radiation effects to the latest developments in aging research. Ionizing radiation exerts its influence through direct mechanisms, notably single- and double-strand DNA breaks and cross links, along with other critical cellular events. The cumulative impact of DNA damage forms the foundation for the intricate process of natural aging, intersecting with numerous diseases and pivotal biomarkers. Furthermore, there is a resurgence of interest in ionizing radiation research from various organizations and countries, reinvigorating its importance as a key contributor to the study of biological age. Biological age serves as a vital reference point for the monitoring and mitigation of the effects of various stressors, including ionizing radiation. Ionizing radiation emerges as a potent candidate for modeling the separation of biological age from chronological age, offering a promising avenue for tailoring protocols across diverse fields, including the rigorous demands of space exploration.

Past or Present; Which Exposures Predict Metabolomic Aging Better? The Doetinchem Cohort Study

People age differently. Differences in aging might be reflected by metabolites, also known as metabolomic aging. Predicting metabolomic aging is of interest in public health research. However, the added value of longitudinal over cross-sectional predictors of metabolomic aging is unknown. We studied exposome-related exposures as potential predictors of metabolomic aging, both cross-sectionally and longitudinally in men and women. We used data from 4 459 participants, aged 36-75 of Round 4 (2003-2008) of the long-running Doetinchem Cohort Study (DCS). Metabolomic age was calculated with the MetaboHealth algorithm. Cross-sectional exposures were demographic, biological, lifestyle, and environmental at Round 4. Longitudinal exposures were based on the average exposure over 15 years (Round 1 [1987-1991] to 4), and trend in these exposure over time. Random Forest was performed to identify model performance and important predictors. Prediction performances were similar for cross-sectional and longitudinal exposures in both men (R2 6.8 and 5.8, respectively) and women (R2 14.8 and 14.4, respectively). Biological and diet exposures were most predictive for metabolomic aging in both men and women. Other important predictors were smoking behavior for men and contraceptive use and menopausal status for women. Taking into account history of exposure levels (longitudinal) had no added value over cross-sectionally measured exposures in predicting metabolomic aging in the current study. However, the prediction performances of both models were rather low. The most important predictors for metabolomic aging were from the biological and lifestyle domain and differed slightly between men and women.

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.

The association between methylmalonic acid, a biomarker of mitochondria dysfunction, and phenotypic age acceleration: A population-based study

Phenotypic age acceleration (PAA) is a sensitive marker of biological aging. Circulating methylmalonic acid (MMA) is a novel biomarker of mitochondrial dysfunction and has been associated with age-related disorders. Our study aimed to investigate to what extent circulating MMA was associated with PAA, and whether the association was independent of vitamin B12 status and renal function in the general population. We analyzed cross-sectional data from 13,023 participants across a wide age range (mean age: 38.9 years, range: 12 - 85 years, 51.1% women) from the US National Health and Nutrition Examination Survey (NHANES). PAA was calculated based on the published algorithm. Linear regression models were performed to assess the association between circulating MAA and PAA. Only 31% of the variation in MMA levels was explained by age, sex, race/ethnicity, social economic status, vitamin B12 status, and renal function. Per unit increase in circulating MAA (1.0 nmol/L) was associated with 1.59 years increase in PAA (β = 1.59, 95% CI: 1.17, 2.00, p < 0.001) after adjusting for multiple confounders. Importantly, PAA increased with circulating MMA levels independent of vitamin B12, creatine, and homocysteine levels. The association was more pronounced in subgroups of age ≥ 65 years, women, underweight, vitamin B12 < 400 μmol/L, and homocysteine ≥ 10 μmol/L. The association was much stronger among participants with cardiovascular diseases (CVDs) than without CVDs. In conclusion, our current population-based study showed that mitochondria-derived circulating MMA was associated with increased phenotypic age acceleration in the general population.

Telomere length is not a useful tool for chronological age estimation in animals

Telomeres are short repetitive DNA sequences capping the ends of chromosomes. Telomere shortening occurs during cell division and may be accelerated by oxidative damage or ameliorated by telomere maintenance mechanisms. Consequently, telomere length changes with age, which was recently confirmed in a large meta-analysis across vertebrates. However, based on the correlation between telomere length and age, it was concluded that telomere length can be used as a tool for chronological age estimation in animals. Correlation should not be confused with predictability, and the current data and studies suggest that telomeres cannot be used to reliably predict individual chronological age. There are biological reasons for why there is large individual variation in telomere dynamics, which is mainly due to high susceptibility to a wide range of environmental, but also genetic factors, rendering telomeres unfeasible as a tool for age estimation. The use of telomeres for chronological age estimation is largely a misguided effort, but its occasional reappearance in the literature raises concerns that it will mislead resources in wildlife conservation.

Associations of combined phenotypic ageing and genetic risk with incidence of chronic respiratory diseases in the UK Biobank: a prospective cohort study

BACKGROUND

Accelerated biological ageing has been associated with an increased risk of several chronic respiratory diseases. However, the associations between phenotypic age, a new biological age indicator based on clinical chemistry biomarkers, and common chronic respiratory diseases have not been evaluated.

METHODS

We analysed data from 308 592 participants at baseline in the UK Biobank. The phenotypic age was calculated from chronological age and nine clinical chemistry biomarkers, including albumin, alkaline phosphatase, creatinine, glucose, C-reactive protein, lymphocyte percent, mean cell volume, red cell distribution width and white blood cell count. Furthermore, phenotypic age acceleration (PhenoAgeAccel) was calculated by regressing phenotypic age on chronological age. The associations of PhenoAgeAccel with incident common chronic respiratory diseases and cross-sectional lung function were investigated. Moreover, we constructed polygenic risk scores and evaluated whether PhenoAgeAccel modified the effect of genetic susceptibility on chronic respiratory diseases and lung function.

RESULTS

The results showed significant associations of PhenoAgeAccel with increased risk of idiopathic pulmonary fibrosis (IPF) (hazard ratio (HR) 1.52, 95% CI 1.45-1.59), COPD (HR 1.54, 95% CI 1.51-1.57) and asthma (HR 1.18, 95% CI 1.15-1.20) per 5-year increase and decreased lung function. There was an additive interaction between PhenoAgeAccel and the genetic risk for IPF and COPD. Participants with high genetic risk and who were biologically older had the highest risk of incident IPF (HR 5.24, 95% CI 3.91-7.02), COPD (HR 2.99, 95% CI 2.66-3.36) and asthma (HR 2.07, 95% CI 1.86-2.31). Mediation analysis indicated that PhenoAgeAccel could mediate 10∼20% of the associations between smoking and chronic respiratory diseases, while ∼10% of the associations between particulate matter with aerodynamic diameter <2.5 µm and the disorders were mediated by PhenoAgeAccel.

CONCLUSION

PhenoAgeAccel was significantly associated with incident risk of common chronic respiratory diseases and decreased lung function and could serve as a novel clinical biomarker.

A unified framework for evolutionary genetic and physiological theories of aging

Why and how we age are 2 intertwined questions that have fascinated scientists for many decades. However, attempts to answer these questions remain compartmentalized, preventing a comprehensive understanding of the aging process. We argue that the current lack of knowledge about the evolution of aging mechanisms is due to a lack of clarity regarding evolutionary theories of aging that explicitly involve physiological processes: the disposable soma theory (DST) and the developmental theory of aging (DTA). In this Essay, we propose a new hierarchical model linking genes to vital rates, enabling us to critically reevaluate the DST and DTA in terms of their relationship to evolutionary genetic theories of aging (mutation accumulation (MA) and antagonistic pleiotropy (AP)). We also demonstrate how these 2 theories can be incorporated in a unified hierarchical framework. The new framework will help to generate testable hypotheses of how the hallmarks of aging are shaped by natural selection.

The biological age model for evaluating the degree of aging in centenarians

BACKGROUND

Biological age (BA) has been used to assess individuals' aging conditions. However, few studies have evaluated BA models' applicability in centenarians.

METHODS

Important organ function examinations were performed in 1798 cases of the longevity population (80∼115 years old) in Hainan, China. Eighty indicators were selected that responded to nutritional status, cardiovascular function, liver and kidney function, bone metabolic function, endocrine system, hematological system, and immune system. BA models were constructed using multiple linear regression (MLR), principal component analysis (PCA), Klemera and Doubal method (KDM), random forest (RF), support vector machine (SVM), extreme gradient boosting (XGBoost), and light gradient boosting machine (lightGBM) methods. A tenfold crossover validated the efficacy of models.

RESULTS

A total of 1398 participants were enrolled, of whom centenarians accounted for 49.21%. Seven aging markers were obtained, including estimated glomerular filtration rate, albumin, pulse pressure, calf circumference, body surface area, fructosamine, and complement 4. Eight BA models were successfully constructed, namely MLR, PCA, KDM1, KDM2, RF, SVM, XGBoost and lightGBM, which had the worst R2 of 0.45 and the best R2 of 0.92. The best R2 for cross-validation was KDM2 (0.89), followed by PCA (0.62).

CONCLUSION

In this study, we successfully applied eight methods, including traditional methods and machine learning, to construct models of biological age, and the performance varied among the models.

Progress in the study of aging marker criteria in human populations

The use of human aging markers, which are physiological, biochemical and molecular indicators of structural or functional degeneration associated with aging, is the fundamental basis of individualized aging assessments. Identifying methods for selecting markers has become a primary and vital aspect of aging research. However, there is no clear consensus or uniform principle on the criteria for screening aging markers. Therefore, we combine previous research from our center and summarize the criteria for screening aging markers in previous population studies, which are discussed in three aspects: functional perspective, operational implementation perspective and methodological perspective. Finally, an evaluation framework has been established, and the criteria are categorized into three levels based on their importance, which can help assess the extent to which a candidate biomarker may be feasible, valid, and useful for a specific use context.

Posttraumatic stress disorder, trauma, and accelerated biological aging among post-9/11 veterans

People who experience trauma and develop posttraumatic stress disorder (PTSD) are at increased risk for poor health. One mechanism that could explain this risk is accelerated biological aging, which is associated with the accumulation of chronic diseases, disability, and premature mortality. Using data from 2309 post-9/11 United States military veterans who participated in the VISN 6 MIRECC's Post-Deployment Mental Health Study, we tested whether PTSD and trauma exposure were associated with accelerated rate of biological aging, assessed using a validated DNA methylation (DNAm) measure of epigenetic aging-DunedinPACE. Veterans with current PTSD were aging faster than those who did not have current PTSD, β = 0.18, 95% CI [0.11, 0.27], p < .001. This effect represented an additional 0.4 months of biological aging each year. Veterans were also aging faster if they reported more PTSD symptoms, β = 0.13, 95% CI [0.09, 0.16], p < 0.001, or higher levels of trauma exposure, β = 0.09, 95% CI [0.05, 0.13], p < 0.001. Notably, veterans with past PTSD were aging more slowly than those with current PTSD, β = -0.21, 95% CI [-0.35, -0.07], p = .003. All reported results accounted for age, gender, self-reported race/ethnicity, and education, and remained when controlling for smoking. Our findings suggest that an accelerated rate of biological aging could help explain how PTSD contributes to poor health and highlights the potential benefits of providing efficacious treatment to populations at increased risk of trauma and PTSD.

HALL: a comprehensive database for human aging and longevity studies

Diverse individuals age at different rates and display variable susceptibilities to tissue aging, functional decline and aging-related diseases. Centenarians, exemplifying extreme longevity, serve as models for healthy aging. The field of human aging and longevity research is rapidly advancing, garnering significant attention and accumulating substantial data in recent years. Omics technologies, encompassing phenomics, genomics, transcriptomics, proteomics, metabolomics and microbiomics, have provided multidimensional insights and revolutionized cohort-based investigations into human aging and longevity. Accumulated data, covering diverse cells, tissues and cohorts across the lifespan necessitates the establishment of an open and integrated database. Addressing this, we established the Human Aging and Longevity Landscape (HALL), a comprehensive multi-omics repository encompassing a diverse spectrum of human cohorts, spanning from young adults to centenarians. The core objective of HALL is to foster healthy aging by offering an extensive repository of information on biomarkers that gauge the trajectory of human aging. Moreover, the database facilitates the development of diagnostic tools for aging-related conditions and empowers targeted interventions to enhance longevity. HALL is publicly available at https://ngdc.cncb.ac.cn/hall/index.

The ticking of aging clocks

Computational models that measure biological age and aging rate regardless of chronological age are called aging clocks. The underlying counting mechanisms of the intrinsic timers of these clocks are still unclear. Molecular mediators and determinants of aging rate point to the key roles of DNA damage, epigenetic drift, and inflammation. Persistent DNA damage leads to cellular senescence and the senescence-associated secretory phenotype (SASP), which induces cytotoxic immune cell infiltration; this further induces DNA damage through reactive oxygen and nitrogen species (RONS). I discuss the possibility that DNA damage (or the response to it, including epigenetic changes) is the fundamental counting unit of cell cycles and cellular senescence, that ultimately accounts for cell composition changes and functional decline in tissues, as well as the key intervention points.

eXplainable Artificial Intelligence (XAI) in aging clock models

XAI is a rapidly progressing field of machine learning, aiming to unravel the predictions of complex models. XAI is especially required in sensitive applications, e.g. in health care, when diagnosis, recommendations and treatment choices might rely on the decisions made by artificial intelligence systems. AI approaches have become widely used in aging research as well, in particular, in developing biological clock models and identifying biomarkers of aging and age-related diseases. However, the potential of XAI here awaits to be fully appreciated. We discuss the application of XAI for developing the "aging clocks" and present a comprehensive analysis of the literature categorized by the focus on particular physiological systems.

Patterns and Life Course Determinants of Black-White Disparities in Biological Age Acceleration: A Decomposition Analysis

Despite the prominence of the weathering hypothesis as a mechanism underlying racialized inequities in morbidity and mortality, the life course social and economic determinants of Black-White disparities in biological aging remain inadequately understood. This study uses data from the Health and Retirement Study (n = 6,782), multivariable regression, and Kitagawa-Blinder-Oaxaca decomposition to assess Black-White disparities across three measures of biological aging: PhenoAge, Klemera-Doubal biological age, and homeostatic dysregulation. It also examines the contributions of racial differences in life course socioeconomic and stress exposures and vulnerability to those exposures to Black-White disparities in biological aging. Across the outcomes, Black individuals exhibited accelerated biological aging relative to White individuals. Decomposition analyses showed that racial differences in life course socioeconomic exposures accounted for roughly 27% to 55% of the racial disparities across the biological aging measures, and racial disparities in psychosocial stress exposure explained 7% to 11%. We found less evidence that heterogeneity in the associations between social exposures and biological aging by race contributed substantially to Black-White disparities in biological aging. Our findings offer new evidence of the role of life course social exposures in generating disparities in biological aging, with implications for understanding age patterns of morbidity and mortality risks.

ExplaiNAble BioLogical Age (ENABL Age): an artificial intelligence framework for interpretable biological age

BACKGROUND

Biological age is a measure of health that offers insights into ageing. The existing age clocks, although valuable, often trade off accuracy and interpretability. We introduce ExplaiNAble BioLogical Age (ENABL Age), a computational framework that combines machine-learning models with explainable artificial intelligence (XAI) methods to accurately estimate biological age with individualised explanations.

METHODS

To construct the ENABL Age clock, we first predicted an age-related outcome (eg, all-cause or cause-specific mortality), and then rescaled these predictions to estimate biological age, using UK Biobank and National Health and Nutrition Examination Survey (NHANES) datasets. We adapted existing XAI methods to decompose individual ENABL Ages into contributing risk factors. For broad accessibility, we developed two versions: ENABL Age-L, based on blood tests, and ENABL Age-Q, based on questionnaire characteristics. Finally, we validated diverse ageing mechanisms captured by each ENABL Age clock through genome-wide association studies (GWAS) association analyses.

FINDINGS

Our ENABL Age clock was significantly correlated with chronological age (r=0·7867, p<0·0001 for UK Biobank; r=0·7126, p<0·0001 for NHANES). These clocks distinguish individuals who are healthy (ie, their ENABL Age is lower than their chronological age) from those who are unhealthy (ie, their ENABL Age is higher than their chronological age), predicting mortality more effectively than existing clocks. Groups of individuals who were unhealthy showed approximately three to 12 times higher log hazard ratio than healthy groups, as per ENABL Age. The clocks achieved high mortality prediction power with an area under the receiver operating characteristic curve of 0·8179 for 5-year mortality and 0·8115 for 10-year mortality on the UK Biobank dataset, and 0·8935 for 5-year mortality and 0·9107 for 10-year mortality on the NHANES dataset. The individualised explanations that revealed the contribution of specific characteristics to ENABL Age provided insights into the important characteristics for ageing. An association analysis with risk factors and ageing-related morbidities and GWAS results on ENABL Age clocks trained on different mortality causes showed that each clock captures distinct ageing mechanisms.

INTERPRETATION

ENABL Age brings an important leap forward in the application of XAI for interpreting biological age clocks. ENABL Age also carries substantial potential in practical settings, assisting medical professionals in untangling the complexity of ageing mechanisms, and potentially becoming a valuable tool in informed clinical decision-making processes.

FUNDING

National Science Foundation and National Institutes of Health.

Association of biological age with health outcomes and its modifiable factors

Identifying the clinical implications and modifiable and unmodifiable factors of aging requires the measurement of biological age (BA) and age gap. Leveraging the biomedical traits involved with physical measures, biochemical assays, genomic data, and cognitive functions from the healthy participants in the UK Biobank, we establish an integrative BA model consisting of multi-dimensional indicators. Accelerated aging (age gap >3.2 years) at baseline is associated incident circulatory diseases, related chronic disorders, all-cause, and cause-specific mortality. We identify 35 modifiable factors for age gap (p < 4.81 × 10-4 ), where pulmonary functions, body mass, hand grip strength, basal metabolic rate, estimated glomerular filtration rate, and C-reactive protein show the most significant associations. Genetic analyses replicate the possible associations between age gap and health-related outcomes and further identify CST3 as an essential gene for biological aging, which is highly expressed in the brain and is associated with immune and metabolic traits. Our study profiles the landscape of biological aging and provides insights into the preventive strategies and therapeutic targets for aging.

Organ aging signatures in the plasma proteome track health and disease

Animal studies show aging varies between individuals as well as between organs within an individual1-4, but whether this is true in humans and its effect on age-related diseases is unknown. We utilized levels of human blood plasma proteins originating from specific organs to measure organ-specific aging differences in living individuals. Using machine learning models, we analysed aging in 11 major organs and estimated organ age reproducibly in five independent cohorts encompassing 5,676 adults across the human lifespan. We discovered nearly 20% of the population show strongly accelerated age in one organ and 1.7% are multi-organ agers. Accelerated organ aging confers 20-50% higher mortality risk, and organ-specific diseases relate to faster aging of those organs. We find individuals with accelerated heart aging have a 250% increased heart failure risk and accelerated brain and vascular aging predict Alzheimer's disease (AD) progression independently from and as strongly as plasma pTau-181 (ref. 5), the current best blood-based biomarker for AD. Our models link vascular calcification, extracellular matrix alterations and synaptic protein shedding to early cognitive decline. We introduce a simple and interpretable method to study organ aging using plasma proteomics data, predicting diseases and aging effects.

Association of Air Pollution with a Urinary Biomarker of Biological Aging and Effect Modification by Vitamin K in the FLEMENGHO Prospective Population Study

BACKGROUND

A recently developed urinary peptidomics biological aging clock can be used to study accelerated human aging. From 1990 to 2019, exposure to airborne particulate matter (PM) became the leading environmental risk factor worldwide.

OBJECTIVES

This study investigated whether air pollution exposure is associated with accelerated urinary peptidomic aging, independent of calendar age, and whether this association is modified by other risk factors.

METHODS

In a Flemish population, the urinary peptidomic profile (UPP) age (UPP-age) was derived from the urinary peptidomic profile measured by capillary electrophoresis coupled with mass spectrometry. UPP-age-R was calculated as the residual of the regression of UPP-age on chronological age, which reflects accelerated aging predicted by UPP-age, independent of chronological age. A high-resolution spatial-temporal interpolation method was used to assess each individual's exposure to PM 10 , PM 2.5 , black carbon (BC), and nitrogen dioxide (NO 2 ). Associations of UPP-age-R with these pollutants were investigated by mixed models, accounting for clustering by residential address and confounders. Effect modifiers of the associations between UPP-age-R and air pollutants that included 18 factors reflecting vascular function, renal function, insulin resistance, lipid metabolism, or inflammation were evaluated. Direct and indirect (via UPP-age-R) effects of air pollution on mortality were evaluated by multivariable-adjusted Cox models.

RESULTS

Among 660 participants (50.2% women; mean age: 50.7 y), higher exposure to PM 10 , PM 2.5 , BC, and NO 2 was associated with a higher UPP-age-R. Studying effect modifiers showed that higher plasma levels of desphospho-uncarboxylated matrix Gla protein (dpucMGP), signifying poorer vitamin K status, steepened the slopes of UPP-age-R on the air pollutants. In further analyses among participants with dpucMGP ≥ 4.26 μ g / L (median), an interquartile range (IQR) higher level in PM 10 , PM 2.5 , BC, and NO 2 was associated with a higher UPP-age-R of 2.03 [95% confidence interval (CI): 0.60, 3.46], 2.22 (95% CI: 0.71, 3.74), 2.00 (95% CI: 0.56, 3.43), and 2.09 (95% CI: 0.77, 3.41) y, respectively. UPP-age-R was an indirect mediator of the associations of mortality with the air pollutants [multivariable-adjusted hazard ratios from 1.094 (95% CI: 1.000, 1.196) to 1.110 (95% CI: 1.007, 1.224)] in participants with a high dpucMGP, whereas no direct associations were observed.

DISCUSSION

Ambient air pollution was associated with accelerated urinary peptidomics aging, and high vitamin K status showed a potential protective effect in this population. Current guidelines are insufficient to decrease the adverse health effects of airborne pollutants, including healthy aging trajectories. https://doi.org/10.1289/EHP13414.

Epigenetic clocks in neurodegenerative diseases: a systematic review

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

PROSPERO REGISTRATION NUMBER

CRD42022365233.

How is Big Data reshaping preclinical aging research?

The exponential scientific and technological progress during the past 30 years has favored the comprehensive characterization of aging processes with their multivariate nature, leading to the advent of Big Data in preclinical aging research. Spanning from molecular omics to organism-level deep phenotyping, Big Data demands large computational resources for storage and analysis, as well as new analytical tools and conceptual frameworks to gain novel insights leading to discovery. Systems biology has emerged as a paradigm that utilizes Big Data to gain insightful information enabling a better understanding of living organisms, visualized as multilayered networks of interacting molecules, cells, tissues and organs at different spatiotemporal scales. In this framework, where aging, health and disease represent emergent states from an evolving dynamic complex system, context given by, for example, strain, sex and feeding times, becomes paramount for defining the biological trajectory of an organism. Using bioinformatics and artificial intelligence, the systems biology approach is leading to remarkable advances in our understanding of the underlying mechanism of aging biology and assisting in creative experimental study designs in animal models. Future in-depth knowledge acquisition will depend on the ability to fully integrate information from different spatiotemporal scales in organisms, which will probably require the adoption of theories and methods from the field of complex systems. Here we review state-of-the-art approaches in preclinical research, with a focus on rodent models, that are leading to conceptual and/or technical advances in leveraging Big Data to understand basic aging biology and its full translational potential.

Partnership status and positive DNA methylation age acceleration across the adult lifespan in the UK

Although a significant body of research has shown that married people are healthier and live longer, empirical research on sex differences in the link between marital status and health suggests results are mixed. Moreover, the sex disparities in marital status and health relationships vary across adulthood. The literature on partnership status and measures of ageing is largely focused on older age groups and is limited in its view of early adulthood. Data from waves 2 and 3 (2010-2012) of Understanding Society: UKHLS were used to examine the association of current partnership status with epigenetic age acceleration (AA) assessed with DNA methylation (DNAm) algorithms 'Phenoage' and ' DunedinPACE ' in 3492 participants (aged 16-97). Regression models were estimated separately for men and women, and further stratified by age groups. Divorced/separated and widowed people showed positive age acceleration compared to the married/cohabiting people (reference group). Some sex differences were apparent, especially, among the single and divorced/separated groups. Age differences were also apparent, for example in men, being single was negatively associated with DNAmAA in the youngest group, but positively in the oldest group compared to partnered counterparts. These findings illustrate the importance of partnerships on the ageing process, in particular marital change through divorce and widowhood for positive age acceleration in adults. For single groups, observations were heterogenous by age and sex.

Determining a multimodal aging clock in a cohort of Chinese women

BACKGROUND

Translating aging rejuvenation strategies into clinical practice has the potential to address the unmet needs of the global aging population. However, to successfully do so requires precise quantification of aging and its reversal in a way that encompasses the complexity and variation of aging.

METHODS

Here, in a cohort of 113 healthy women, tiled in age from young to old, we identified a repertoire of known and previously unknown markers associated with age based on multimodal measurements, including transcripts, proteins, metabolites, microbes, and clinical laboratory values, based on which an integrative aging clock and a suite of customized aging clocks were developed.

FINDINGS

A unified analysis of aging-associated traits defined four aging modalities with distinct biological functions (chronic inflammation, lipid metabolism, hormone regulation, and tissue fitness), and depicted waves of changes in distinct biological pathways peak around the third and fifth decades of life. We also demonstrated that the developed aging clocks could measure biological age and assess partial aging deceleration by hormone replacement therapy, a prevalent treatment designed to correct hormonal imbalances.

CONCLUSIONS

We established aging metrics that capture systemic physiological dysregulation, a valuable framework for monitoring the aging process and informing clinical development of aging rejuvenation strategies.

FUNDING

This work was supported by the National Natural Science Foundation of China (32121001), the National Key Research and Development Program of China (2022YFA1103700 and 2020YFA0804000), the National Natural Science Foundation of China (81502304), and the Quzhou Technology Projects (2022K46).

NMR metabolomic modelling of age and lifespan: a multi-cohort analysis

Metabolomic age models have been proposed for the study of biological aging, however they have not been widely validated. We aimed to assess the performance of newly developed and existing nuclear magnetic resonance spectroscopy (NMR) metabolomic age models for prediction of chronological age (CA), mortality, and age-related disease. 98 metabolic variables were measured in blood from nine UK and Finnish cohort studies (N ≈ 31,000 individuals, age range 24-86 years). We used non-linear and penalised regression to model CA and time to all-cause mortality. We examined associations of four new and two previously published metabolomic age models, with ageing risk factors and phenotypes. Within the UK Biobank (N≈ 102,000), we tested prediction of CA, incident disease (cardiovascular disease (CVD), type-2 diabetes mellitus, cancer, dementia, chronic obstructive pulmonary disease) and all-cause mortality. Cross-validated Pearson's r between metabolomic age models and CA ranged between 0.47-0.65 in the training set (mean absolute error: 8-9 years). Metabolomic age models, adjusted for CA, were associated with C-reactive protein, and inversely associated with glomerular filtration rate. Positively associated risk factors included obesity, diabetes, smoking, and physical inactivity. In UK Biobank, correlations of metabolomic age with chronological age were modest (r = 0.29-0.33), yet all metabolomic model scores predicted mortality (hazard ratios of 1.01 to 1.06 / metabolomic age year) and CVD, after adjustment for CA. While metabolomic age models were only moderately associated with CA in an independent population, they provided additional prediction of morbidity and mortality over CA itself, suggesting their wider applicability.

LensAge index as a deep learning-based biological age for self-monitoring the risks of age-related diseases and mortality

Age is closely related to human health and disease risks. However, chronologically defined age often disagrees with biological age, primarily due to genetic and environmental variables. Identifying effective indicators for biological age in clinical practice and self-monitoring is important but currently lacking. The human lens accumulates age-related changes that are amenable to rapid and objective assessment. Here, using lens photographs from 20 to 96-year-olds, we develop LensAge to reflect lens aging via deep learning. LensAge is closely correlated with chronological age of relatively healthy individuals (R2 > 0.80, mean absolute errors of 4.25 to 4.82 years). Among the general population, we calculate the LensAge index by contrasting LensAge and chronological age to reflect the aging rate relative to peers. The LensAge index effectively reveals the risks of age-related eye and systemic disease occurrence, as well as all-cause mortality. It outperforms chronological age in reflecting age-related disease risks (p < 0.001). More importantly, our models can conveniently work based on smartphone photographs, suggesting suitability for routine self-examination of aging status. Overall, our study demonstrates that the LensAge index may serve as an ideal quantitative indicator for clinically assessing and self-monitoring biological age in humans.

Accelerated biological aging six decades after prenatal famine exposure

To test the hypothesis that early-life adversity accelerates the pace of biological aging, we analyzed data from the Dutch Hunger Winter Families Study (DHWFS, N=951). DHWFS is a natural-experiment birth-cohort study of survivors of in-utero exposure to famine conditions caused by the German occupation of the Western Netherlands in Winter 1944-5, matched controls, and their siblings. We conducted DNA methylation analysis of blood samples collected when the survivors were aged 58 to quantify biological aging using the DunedinPACE, GrimAge, and PhenoAge epigenetic clocks. Famine survivors had faster DunedinPACE, as compared with controls. This effect was strongest among women. Results were similar for GrimAge, although effect-sizes were smaller. We observed no differences in PhenoAge between survivors and controls. Famine effects were not accounted for by blood-cell composition and were similar for individuals exposed early and later in gestation. Findings suggest in-utero undernutrition may accelerate biological aging in later life.