Biohorology and biomarkers of aging: Current state-of-the-art, challenges and opportunities

Explainable machine learning framework for biomarker discovery by combining biological age and frailty prediction

Biological age (BA) and frailty represent two distinct health measures that offer valuable insights into the aging process. Comparing and analyzing blood-based biomarkers from the machine learning (ML) predictors of BA and frailty helps deepen our understanding of aging. This study aimed to develop a novel framework to identify biomarkers of aging by combining BA and frailty ML predictors with eXplainable Artificial Intelligence (XAI) techniques. We utilized data from middle-aged and older Chinese adults (≥ 45 years) in the 2011/2012 wave (n = 9702) and the 2015/2016 wave (n = 9455, as test set validation) of the China Health and Retirement Longitudinal Study (CHARLS). Sixteen blood-based biomarkers were used to predict BA and frailty. Four tree-based ML algorithms were employed in the training and validation, and performance metrics were compared to select the best models. Then, SHapley Additive exPlanations (SHAP) analysis was conducted on the selected models. CatBoost performed the best in the BA predictor, and Gradient Boosting performed the best in the frailty predictor. Traditional ML feature importance identified cystatin C and glycated hemoglobin as the major contributors for their respective models. However, subsequent SHAP analysis demonstrated that only cystatin C was the primary contributor in both models. The proposed framework can easily incorporate additional biomarkers, providing a scalable and comprehensive toolset that offers a quantitative understanding of biomarkers of aging.

Associations of exposure to volatile organic compounds with biological aging: a cross-sectional study

BACKGROUND

Volatile organic compounds (VOCs) are recognized as potentially linked to various health damages. However, the effects of VOCs exposure on biological aging remain unknown. This study aimed to investigate this potential association through cross-sectional analyses.

METHODS

This study analyzed data from the National Health and Nutrition Examination Surveys (2011-2016). There was a total of 29,902 participants and 3,205 participants were finally included. Biological aging was represented by PhenoAge acceleration calculated through multiple indicators including albumin and alkaline phosphatase and so on. We employed weighted multivariate logistic regression to examine the correlation between individual VOC exposure and biological aging. The least absolute shrinkage and selection operator regression was utilized to identify key VOCs for the weighted quantile sum (WQS) regression, which assessed the association between mixed exposure to VOCs and biological aging. In addition, subgroup analyses were conducted using data from the same database on individuals' daily behaviors, such as physical activity levels, smoking, and alcohol consumption, to explore the influence of daily behaviors on the above associations.

RESULTS

Logistic regression analysis indicated that exposure to various individual VOCs was associated with biological aging. The WQS results revealed a significant positive association between mixed exposure to VOCs and biological aging (P < 0.001, OR = 1.523). Additionally, we found that participants with drinking, smoking, and lower levels of physical activity were more affected by exposure to individual VOCs. Mixed VOCs exposures differed only between smoking (P < 0.001, OR = 1.422) and non-smoking populations (P = 0.216, OR = 1.158).

CONCLUSION

VOCs exposure was associated with biological aging, and daily behaviors may influence an individual's susceptibility to such exposure. This discovery provided a new way of thinking about slowing down the aging process and improving overall health.

Biological age prediction using a DNN model based on pathways of steroidogenesis

Aging involves the progressive accumulation of cellular damage, leading to systemic decline and age-related diseases. Despite advances in medicine, accurately predicting biological age (BA) remains challenging due to the complexity of aging processes and the limitations of current models. This study introduces a method for predicting BA using a deep neural network (DNN) based on pathways of steroidogenesis. We analyzed 22 steroids from 148 serum samples of individuals aged 20 to 73, using 98 samples for model training and 50 for validation. Our model reflects the often-overlooked fact that aging heterogeneity expands over time and uncovers sex-specific variations in steroidogenesis. This study leveraged key markers, including cortisol (COL), which underscore the role of stress-related and sex-specific steroids in aging. The resulting model establishes a biologically meaningful and robust framework for predicting BA across diverse datasets, offering fresh insights and supporting more targeted strategies in aging research and disease management.

Cryptic mitochondrial DNA mutations coincide with mid-late life and are pathophysiologically informative in single cells across tissues and species

Ageing is associated with a range of chronic diseases and has diverse hallmarks. Mitochondrial dysfunction is implicated in ageing, and mouse-models with artificially enhanced mitochondrial DNA mutation rates show accelerated ageing. A scarcely studied aspect of ageing, because it is invisible in aggregate analyses, is the accumulation of somatic mitochondrial DNA mutations which are unique to single cells (cryptic mutations). We find evidence of cryptic mitochondrial DNA mutations from diverse single-cell datasets, from three species, and discover: cryptic mutations constitute the vast majority of mitochondrial DNA mutations in aged post-mitotic tissues, that they can avoid selection, that their accumulation is consonant with theory we develop, hitting high levels coinciding with species specific mid-late life, and that their presence covaries with a majority of the hallmarks of ageing including protein misfolding and endoplasmic reticulum stress. We identify mechanistic links to endoplasmic reticulum stress experimentally and further give an indication that aged brain cells with high levels of cryptic mutations show markers of neurodegeneration and that calorie restriction slows the accumulation of cryptic mutations.

Association between cardiometabolic index and biological ageing among adults: a population-based study

BACKGROUND

Cardiovascular health (CVH) is closely associated with ageing. This study aimed to investigate the association between cardiometabolic index (CMI), a novel indicator of cardiometabolic status, and biological ageing.

METHODS

Cross-sectional data were obtained from participants with comprehensive CMI and biological age data in the National Health and Nutrition Examination Survey from 2011 to 2018. Biological age acceleration (BioAgeAccel) is calculated as the differences between biological age and chronological age, and that biological age is derived from a model incorporating eight biomarkers. Weighted multivariable regression, sensitivity analysis, and smoothing curve fitting were performed to explore the independent association between CMI and the acceleration of biological age. Subgroup and interaction analyses were performed to investigate whether this association was consistent across populations.

RESULTS

In 4282 subjects ≥ 20 years of age, there was a positive relationship between CMI and biological age. The BioAgeAccel increased 1.16 years for each unit CMI increase [1.16 (1.02, 1.31)], and increased 0.99 years for per SD increase in CMI [0.99 (0.87, 1.11)]. Participants in the highest CMI quartile had a BioAgeAccel that was 2.49 years higher than participants in the lowest CMI quartile [2.49 (2.15, 2.83)]. In stratified studies, the positive correlation between CMI and biological age acceleration was not consistent across strata. This positive correlation was stronger in female, diabetes, and non-hypertension populations.

CONCLUSIONS

CMI is positively correlated with biological ageing in adults in the United States. Prospective studies with larger sample sizes are required to validate our findings.

Biological Age Affecting Attrition and Tooth Loss in a Follow-up Study

In population-based longitudinal studies, bias caused by nonresponse among eligible participants and attrition during follow-up thwarts conclusions. As this issue is not commonly addressed in dental studies, it is the aim of this study to examine the consequences of attrition with respect to tooth loss and mortality in a 10-y follow-up study. From the Study of Health in Pomerania (SHIP-0), a biological age (BA) score was constructed from 10 systemic biomarkers and related to one's actual chronological age (CA). The 3,417 dentate participants were stratified according to their BA-CA scores into tertiles: individuals with younger BA than their CA, those with concurrent BA and CA, and those with older BA than their CA. Baseline characteristics and propensity of leaving or remaining in the study were compared across these tertiles. We compared the characteristics within BA strata in the remainers of SHIP-2 (10-y follow-up) and their impact on tooth loss. Besides dropout by those who died, the attrition propensity of baseline study participants was dose dependent as related to BA-CA scores and socioeconomic factors. BA younger participants were underrepresented in dropouts but overrepresented in remaining follow-up participants. BA younger participants had a more favorable risk profile, better oral health, and a lower mortality rate than BA older participants. For the BA older participants, the opposite was observed. Remainers attaining the follow-up SHIP-2 were healthier and more health conscious. After 10 y, their tooth retention was still directed by BA constructed at baseline. The results support the assumption that individual risk profiles aggregated in BA constitute characteristic susceptibility patterns affecting perseverance or attrition in long-term follow-up studies. Attrition, which is common to follow-up studies, changes the study composition of participants depending on their BA and hence the transferability of results to the baseline population. The baseline BA gradient persists even after a long time.

Deep learning and generative artificial intelligence in aging research and healthy longevity medicine

With the global population aging at an unprecedented rate, there is a need to extend healthy productive life span. This review examines how Deep Learning (DL) and Generative Artificial Intelligence (GenAI) are used in biomarker discovery, deep aging clock development, geroprotector identification and generation of dual-purpose therapeutics targeting aging and disease. The paper explores the emergence of multimodal, multitasking research systems highlighting promising future directions for GenAI in human and animal aging research, as well as clinical application in healthy longevity medicine.

Proteomics-based aging clocks in midlife and late-life and risk of dementia

Background

Biological age can be quantified by composite proteomic scores, called aging clocks. We investigated whether biological age acceleration (a discrepancy between chronological and biological age) in midlife and late-life is associated with cognitive function and risk of dementia.

Methods

We used two population-based cohort studies: Atherosclerosis Risk in Communities (ARIC) Study and Multi-Ethnic Study of Atherosclerosis (MESA). Proteomics-based aging clocks (PACs) were created in ARIC at midlife (mean age: 58 years, n=11,758) and late-life (mean age: 77 years, n=4,934) using elastic net regression models in two-thirds of dementia-free participants and validated in the remaining one-third of participants. Age acceleration (AA) was calculated as residuals after regressing PACs on chronological age. We validated the midlife PAC in the MESA cohort (mean age: 62 years, n=5,829). We used multivariable linear and Cox proportional hazards regression to assess the association of AA with cognitive function and dementia incidence, respectively.

Results

In ARIC, every five years AA was associated with lower global cognitive function: difference: -0.11, 95% confidence interval (CI): -0.16, -0.06) using midlife AA and difference: -0.17, CI: -0.23, -0.12 using late-life AA. Consistently, midlife AA was associated with higher risk of dementia (hazard ratio [HR]: 1.20 [CI: 1.04, 1.36]) and more prominently when using late-life AA (HR: 2.14 [CI:1.67, 2.73]). Similar findings were observed in the MESA study: every five years AA was associated with lower global cognitive function (difference: -0.08 [CI: -0.14, -0.03]) and higher risk of dementia (HR:1.23 [CI: 1.04, 1.46]).

Conclusion

Accelerated biological age - as defined by the plasma proteome - is associated with lower cognitive function and predicts a higher risk of dementia in midlife and more prominently in late-life.

Decoding aging clocks: New insights from metabolomics

Chronological age is a crucial risk factor for diseases and disabilities among older adults. However, individuals of the same chronological age often exhibit divergent biological aging states, resulting in distinct individual risk profiles. Chronological age estimators based on omics data and machine learning techniques, known as aging clocks, provide a valuable framework for interpreting molecular-level biological aging. Metabolomics is an intriguing and rapidly growing field of study, involving the comprehensive profiling of small molecules within the body and providing the ultimate genome-environment interaction readout. Consequently, leveraging metabolomics to characterize biological aging holds immense potential. The aim of this review was to provide an overview of metabolomics approaches, highlighting the establishment and interpretation of metabolomic aging clocks while emphasizing their strengths, limitations, and applications, and to discuss their underlying biological significance, which has the potential to drive innovation in longevity research and development.

The Association between Proteomic Aging Clocks and the Risk of Cancer in Midlife Individuals

Background

To measure the aging process before a cancer diagnosis, we developed the first cancer-specific proteomic aging clock (CaPAC) and examined its association with cancer risk in the Atherosclerosis Risk in Communities (ARIC) and Multi-Ethnic Study of Atherosclerosis (MESA) studies.

Methods

Using the SomaScan assay, ARIC measured 4,712 proteins in plasma samples collected in 1990-92 from 3,347 participants who developed cancer over follow-up until 2015 and 7,487 who remained cancer-free, all aged 46-70. We constructed CaPAC0 using elastic net regression among two-thirds randomly selected cancer-free participants (N=4,991, training set) and calculated age acceleration for CaPAC0 (CaPAA0) as residuals of CaPAC0 on chronological age in all remaining ARIC participants. We used multivariable-adjusted Cox proportional hazards regression to calculate hazard ratios (HRs) for the risk of overall, obesity-related, smoking-related, and the most common cancers (prostate, lung, breast, colorectal) with CaPAA0 using a case-cohort design. We replicated the analysis in 3,893 MESA participants aged 46-70 at Exam 1 (456 incident cancer).

Results

CaPAC0 was correlated with chronological age in ARIC and MESA (r=0.82 and 0.86, respectively). In both ARIC and MESA, CaPAA0 was significantly (p<0.05) associated with the risk of overall [HRs per 5-years=1.08 and 1.23, respectively], smoking-related [HRs=1.30 and 1.54, respectively], and lung cancers [HRs=1.54 and 1.94, respectively]. CaPAA0 was also significantly associated with colorectal cancer risk in ARIC [HR=1.31], but not in MESA. CaPAA0 was not associated with obesity-related, breast, or prostate cancers.

Conclusion

CaPAA0 was associated with several types of cancer with the strongest association observed for lung cancer risk.

Histone mark age of human tissues and cell types

Aging is a complex and multifaceted process involving many epigenetic alterations. One key area of interest in aging research is the role of histone modifications, which can dynamically regulate gene expression. Here, we conducted a pan-tissue analysis of the dynamics of seven key histone modifications during human aging. Our histone-specific age prediction models showed surprisingly accurate performance, proving resilient to experimental and artificial noise. Simulation experiments for comparison with DNA methylation age predictors revealed competitive performance. Moreover, gene set enrichment analysis uncovered several critical developmental pathways for age prediction. Different from DNA methylation age predictors, genes known to be involved in aging biology are among the most important ones for the models. Last, we developed a pan-tissue pan-histone age predictor, suggesting that age-related epigenetic information is degenerated across the epigenome. This research highlights the power of histone marks as input for creating robust age predictors and opens avenues for understanding the role of epigenetic changes during aging.

Biological Aging and Venous Thromboembolism: A Review of Telomeres and Beyond

Although venous thromboembolism (VTE) is the third most common cardiovascular disease, and the risk of VTE increases sharply with advancing age, approximately 40% of VTE cases are currently classified as unprovoked, highlighting the importance of risk factor research. While chronological aging is associated with the risk of VTE, the association with biological aging remains unclear. Biological aging is highly complex, influenced by several dysregulated cellular and biochemical mechanisms. In the last decade, advancements in omics methodologies provided insights into the molecular complexity of biological aging. Techniques such as high-throughput genomics, epigenomics, transcriptomics, proteomics, and metabolomics analyses identified and quantified numerous epigenetic markers, transcripts, proteins, and metabolites. These methods have also revealed the molecular alterations organisms undergo as they age. Despite the progress, there is still a lack of consensus regarding the methods for assessing and validating these biomarkers, and their application lacks standardization. This review gives an overview of biomarkers of biological aging, including telomere length, and their potential role for VTE. Furthermore, we critically examine the advantages and disadvantages of the proposed methods and discuss possible future directions for investigating biological aging in VTE.

Contextualizing aging clocks and properly describing biological age

Usage of the phrase "biological age" has picked up considerably since the advent of aging clocks and it has become commonplace to describe an aging clock's output as biological age. In contrast to this labeling, biological age is also often depicted as a more abstract concept that helps explain how individuals are aging internally, externally, and functionally. Given that the bulk of molecular aging is tissue-specific and aging itself is a remarkably complex, multifarious process, it is unsurprising that most surveyed scientists agree that aging cannot be quantified via a single metric. We share this sentiment and argue that, just like it would not be reasonable to assume that an individual with an ideal grip strength, VO2 max, or any other aging biomarker is biologically young, we should be careful not to conflate an aging clock with whole-body biological aging. To address this, we recommend that researchers describe the output of an aging clock based on the type of input data used or the name of the clock itself. Epigenetic aging clocks produce epigenetic age, transcriptomic aging clocks produce transcriptomic age, and so forth. If a clock has a unique name, such as our recently developed epigenetic aging clock CheekAge, the name of the clock can double as the output. As a compromise solution, aging biomarkers can be described as indicators of biological age. We feel that these recommendations will help scientists and the public differentiate between aging biomarkers and the much more elusive concept of biological age.

Estimation of physiological aging based on routine clinical biomarkers: a prospective cohort study in elderly Chinese and the UK Biobank

BACKGROUND

Chronological age (CA) does not reflect individual variation in the aging process. However, existing biological age predictors are mostly based on European populations and overlook the widespread nonlinear effects of clinical biomarkers.

METHODS

Using data from the prospective Dongfeng-Tongji (DFTJ) cohort of elderly Chinese, we propose a physiological aging index (PAI) based on 36 routine clinical biomarkers to measure aging progress. We first determined the optimal level of each biomarker by restricted cubic spline Cox models. For biomarkers with a U-shaped relationship with mortality, we derived new variables to model their distinct effects below and above the optimal levels. We defined PAI as a weighted sum of variables predictive of mortality selected by a LASSO Cox model. To measure aging acceleration, we defined ΔPAI as the residual of PAI after regressing on CA. We evaluated the predictive value of ΔPAI on cardiovascular diseases (CVD) in the DFTJ cohort, as well as nine major chronic diseases in the UK Biobank (UKB).

RESULTS

In the DFTJ training set (n = 12,769, median follow-up: 10.38 years), we identified 25 biomarkers with significant nonlinear associations with mortality, of which 11 showed insignificant linear associations. By incorporating nonlinear effects, we selected CA and 17 clinical biomarkers to calculate PAI. In the DFTJ testing set (n = 15,904, 5.87 years), PAI predict mortality with a concordance index (C-index) of 0.816 (95% confidence interval, [0.796, 0.837]), better than CA (C-index = 0.771 [0.755, 0.788]) and PhenoAge (0.799 [0.784, 0.814]). ΔPAI was predictive of incident CVD and its subtypes, independent of traditional risk factors. In the external validation set of UKB (n = 296,931, 12.80 years), PAI achieved a C-index of 0.749 (0.746, 0.752) to predict mortality, remaining better than CA (0.706 [0.702, 0.709]) and PhenoAge (0.743 [0.739, 0.746]). In both DFTJ and UKB, PAI calibrated better than PhenoAge when comparing the predicted and observed survival probabilities. Furthermore, ΔPAI outperformed any single biomarker to predict incident risks of eight age-related chronic diseases.

CONCLUSIONS

Our results highlight the potential of PAI and ΔPAI as integrative biomarkers to evaluate aging acceleration and facilitate the development of targeted intervention strategies for healthy aging.

Epistemic uncertainty challenges aging clock reliability in predicting rejuvenation effects

Epigenetic aging clocks have been widely used to validate rejuvenation effects during cellular reprogramming. However, these predictions are unverifiable because the true biological age of reprogrammed cells remains unknown. We present an analytical framework to consider rejuvenation predictions from the uncertainty perspective. Our analysis reveals that the DNA methylation profiles across reprogramming are poorly represented in the aging data used to train clock models, thus introducing high epistemic uncertainty in age estimations. Moreover, predictions of different published clocks are inconsistent, with some even suggesting zero or negative rejuvenation. While not questioning the possibility of age reversal, we show that the high clock uncertainty challenges the reliability of rejuvenation effects observed during in vitro reprogramming before pluripotency and throughout embryogenesis. Conversely, our method reveals a significant age increase after in vivo reprogramming. We recommend including uncertainty estimation in future aging clock models to avoid the risk of misinterpreting the results of biological age prediction.

Epigenetic clocks and programmatic aging

The last decade has seen remarkable progress in the characterization of methylation clocks that can serve as indicators of biological age in humans and many other mammalian species. While the biological processes of aging that underlie these clocks have remained unclear, several clues have pointed to a link to developmental mechanisms. These include the presence in the vicinity of clock CpG sites of genes that specify development, including those of the Hox (homeobox) and polycomb classes. Here we discuss how recent advances in programmatic theories of aging provide a framework within which methylation clocks can be understood as part of a developmental process of aging. This includes how such clocks evolve, how developmental mechanisms cause aging, and how they give rise to late-life disease. The combination of ideas from evolutionary biology, biogerontology and developmental biology open a path to a new discipline, that of developmental gerontology (devo-gero). Drawing on the properties of methylation clocks, we offer several new hypotheses that exemplify devo-gero thinking. We suggest that polycomb controls a trade-off between earlier developmental fidelity and later developmental plasticity. We also propose the existence of an evolutionarily-conserved developmental sequence spanning ontogenesis, adult development and aging, that both constrains and determines the evolution of aging.

Aging at scale: Younger dogs and larger breeds from the Dog Aging Project show accelerated epigenetic aging

Dogs exhibit striking within-species variability in lifespan, with smaller breeds often living more than twice as long as larger breeds. This longevity discrepancy also extends to health and aging-larger dogs show higher rates of age-related diseases. Despite this well-established phenomenon, we still know little about the biomarkers and molecular mechanisms that might underlie breed differences in aging and survival. To address this gap, we generated an epigenetic clock using DNA methylation from over 3 million CpG sites in a deeply phenotyped cohort of 864 companion dogs from the Dog Aging Project, including some dogs sampled annually for 2-3 years. We found that the largest breed size tends to have epigenomes that are, on average, 0.37 years older per chronological year compared to the smallest breed size. We also found that higher residual epigenetic age was significantly associated with increased mortality risk, with dogs experiencing a 34% higher risk of death for each year increase in residual epigenetic age. These findings not only broaden our understanding of how aging manifests within a diverse species but also highlight the significant role that demographic factors play in modulating the biological mechanisms underlying aging. Additionally, they highlight the utility of DNA methylation as both a biomarker for healthspan-extending interventions, a mortality predictor, and a mechanism for understanding inter-individual variation in aging in dogs.

G-quadruplex DNA and RNA in cellular senescence

Normal cells divide, are damaged, and are repaired across their lifetime. As cells age, they enter cellular senescence, characterized by a permanent state of cell-cycle arrest triggered by various stressors. The molecular mechanisms that regulate senescent phenotypes have been actively investigated over the last several decades; however, one area that has been neglected is how G-quadruplex (G4) DNA and RNA (G4-DNA and G4-RNA) mediate senescence. These non-canonical four-stranded DNA and RNA structures regulate most normative DNA and RNA-dependent processes, such as transcription, replication, and translation, as well as pathogenic mechanisms, including genomic instability and abnormal stress granule function. This review also highlights the contribution of G4s to sex differences in age-associated diseases and emphasizes potential translational approaches to target senescence and anti-aging mechanisms through G4 manipulation.

Accumulation of DNA G-quadruplex in mitochondrial genome hallmarks mesenchymal senescence

Searching for biomarkers of senescence remains necessary and challenging. Reliable and detectable biomarkers can indicate the senescence condition of individuals, the need for intervention in a population, and the effectiveness of that intervention in controlling or delaying senescence progression and senescence-associated diseases. Therefore, it is of great importance to fulfill the unmet requisites of senescence biomarkers especially when faced with the growing global senescence nowadays. Here, we established that DNA G-quadruplex (G4) in mitochondrial genome was a reliable hallmark for mesenchymal senescence. Via developing a versatile and efficient mitochondrial G4 (mtG4) probe we revealed that in multiple types of senescence, including chronologically healthy senescence, progeria, and replicative senescence, mtG4 hallmarked aged mesenchymal stem cells. Furthermore, we revealed the underlying mechanisms by which accumulated mtG4, specifically within respiratory chain complex (RCC) I and IV loci, repressed mitochondrial genome transcription, finally impairing mitochondrial respiration and causing mitochondrial dysfunction. Our findings endowed researchers with the visible senescence biomarker based on mitochondrial genome and furthermore revealed the role of mtG4 in inhibiting RCC genes transcription to induce senescence-associated mitochondrial dysfunction. These findings depicted the crucial roles of mtG4 in predicting and controlling mesenchymal senescence.

Development, characterization, and replication of proteomic aging clocks: Analysis of 2 population-based cohorts

BACKGROUND

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 this study, we created de novo PACs and compared their performance to published PACs at 2 different time points in the Atherosclerosis Risk in Communities (ARIC) study of white and black participants (around 75% white and 25% black).

MEDTHODS AND FINDINGS

A total of 4,712 plasma proteins were measured using SomaScan in blood samples collected in 1990 to 1992 from 11,761 midlife participants (aged 46 to 70 years) and in 2011 to 2013 from 5,183 late-life participants (aged 66 to 90 years). The de novo ARIC PACs were constructed by training them against chronological age using elastic net regression in two-thirds of healthy participants in midlife and late life and validated in the remaining one-third of healthy participants at the corresponding time point. We also computed 3 published PACs. We estimated age acceleration for each PAC as residuals after regressing each PAC on chronological age. We also calculated the change in age acceleration from midlife to late life. We examined the associations of age acceleration and change in age acceleration with mortality through 2019 from all-cause, cardiovascular disease (CVD), cancer, and lower respiratory disease (LRD) using Cox proportional hazards regression in participants (irrespective of health) after excluding the training set. The model was adjusted for chronological age, smoking, body mass index (BMI), and other confounders. We externally validated the midlife PAC using the Multi-Ethnic Study of Atherosclerosis (MESA) Exam 1 data. 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 PACs and published PACs. For late-life and midlife age acceleration for the ARIC PACs, respectively, hazard ratios (HRs) per 1 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 = 0.028) and 1.04 (p = 0.280) for cancer mortality, and 1.68 and 1.36 (both p < 0.001) for LRD mortality. For the change in age acceleration, HRs for all-cause, CVD, and LRD mortality were comparable to the HRs for late-life age acceleration. The association between the change in age acceleration and cancer mortality was not significant. The external validation of the midlife PAC in MESA showed significant associations with mortality, as observed for midlife participants in ARIC. The main limitation is that our PACs were constructed in midlife and late-life participants. It is unknown whether these PACs could be applied to young individuals.

CONCLUSIONS

In this longitudinal study, we found that the ARIC PACs and published PACs were similarly associated with an increased risk of mortality. These findings suggested that PACs show promise as biomarkers of biological age. PACs may be serve as tools to predict mortality and evaluate the effect of anti-aging lifestyle and therapeutic interventions.

Proteomic Aging Clocks and the Risk of Mortality among Longer-Term Cancer Survivors in the Atherosclerosis Risk in Communities (ARIC) Study

Background

We constructed a new proteomic aging clock (PAC) and computed the published Lehallier's PAC to estimate biological age. We tested PACs' associations with mortality in longer-term cancer survivors and cancer-free participants.

Methods

ARIC measured 4,712 proteins using SomaScan in plasma samples collected at multiple visits, including Visit 5 (2011-13), from 806 cancer survivors and 3,699 cancer-free participants (aged 66-90). In the training set (N=2,466 randomly selected cancer-free participants), we developed the new PAC using elastic net regression and computed Lehallier's PAC. Age acceleration was calculated as residuals after regressing each PAC on chronological age after excluding the training set. We used multivariable-adjusted Cox proportional hazards regression to examine the associations of age acceleration with all-cause, cardiovascular disease (CVD), and cancer mortality.

Results

Both PACs were correlated with chronological age [r=0.70-0.75]. Age acceleration for these two PACs was similarly associated with all-cause mortality in cancer survivors [hazard ratios (HRs) per 1 SD=1.40-1.42, p<0.01]. The associations with all-cause mortality were similar in cancer survivors and cancer-free participants for both PACs [p-interactions=0.20-0.62]. There were also associations with all-cause mortality in breast cancer survivors for both PACs [HRs=1.54-1.72, p<0.01] and colorectal cancer survivors for the new PAC [HR=1.96, p=0.03]. Additionally, the new PAC was associated with cancer mortality in all cancer survivors. Finally, HRs=1.42-1.61 [p<0.01] for CVD mortality in cancer-free participants for two PACs but the association was insignificant in cancer survivors perhaps due to a limited number of outcomes.

Conclusion

PACs hold promise as potential biomarkers for premature mortality in cancer survivors.

Aging atlas reveals cell-type-specific effects of pro-longevity strategies

Organismal aging involves functional declines in both somatic and reproductive tissues. Multiple strategies have been discovered to extend lifespan across species. However, how age-related molecular changes differ among various tissues and how those lifespan-extending strategies slow tissue aging in distinct manners remain unclear. Here we generated the transcriptomic Cell Atlas of Worm Aging (CAWA, http://mengwanglab.org/atlas ) of wild-type and long-lived strains. We discovered cell-specific, age-related molecular and functional signatures across all somatic and germ cell types. We developed transcriptomic aging clocks for different tissues and quantitatively determined how three different pro-longevity strategies slow tissue aging distinctively. Furthermore, through genome-wide profiling of alternative polyadenylation (APA) events in different tissues, we discovered cell-type-specific APA changes during aging and revealed how these changes are differentially affected by the pro-longevity strategies. Together, this study offers fundamental molecular insights into both somatic and reproductive aging and provides a valuable resource for in-depth understanding of the diversity of pro-longevity mechanisms.

Towards a Novel Frontier in the Use of Epigenetic Clocks in Epidemiology

Health problems associated with aging are a major public health concern for the future. Aging is a complex process with wide intervariability among individuals. Therefore, there is a need for innovative public health strategies that target factors associated with aging and the development of tools to assess the effectiveness of these strategies accurately. Novel approaches to measure biological age, such as epigenetic clocks, have become relevant. These clocks use non-sequential variable information from the genome and employ mathematical algorithms to estimate biological age based on DNA methylation levels. Therefore, in the present study, we comprehensively review the current status of the epigenetic clocks and their associations across the human phenome. We emphasize the potential utility of these tools in an epidemiological context, particularly in evaluating the impact of public health interventions focused on promoting healthy aging. Our review describes associations between epigenetic clocks and multiple traits across the life and health span. Additionally, we highlighted the evolution of studies beyond mere associations to establish causal mechanisms between epigenetic age and disease. We explored the application of epigenetic clocks to measure the efficacy of interventions focusing on rejuvenation.

U-shaped association between sleep duration and biological aging: Evidence from the UK Biobank study

Previous research on sleep and aging largely has failed to illustrate the optimal dose-response curve of this relationship. We aimed to analyze the associations between sleep duration and measures of predicted age. In total, 241,713 participants from the UK Biobank were included. Habitual sleep duration was collected from the baseline questionnaire. Four indicators, homeostatic dysregulation (HD), phenoAge (PA), Klemera-Doubal method (KDM), and allostatic load (AL), were chosen to assess predicted age. Multivariate linear regression models were utilized. The association of sleep duration and predicted age followed a U-shape (All p for nonlinear <0.05). Compared with individuals who sleep for 7 h/day, the multivariable-adjusted beta of ≤5 and ≥9 h/day were 0.05 (95% CI 0.03, 0.07) and 0.03 (95% CI 0.02, 0.05) for HD, 0.08 (95% CI 0.01, 0.14) and 0.36 (95% CI 0.31, 0.41) for PA, and 0.21 (95% CI 0.12, 0.30) and 0.30 (95% CI 0.23, 0.37) for KDM. Significant independent and joint effects of sleep and cystatin C (CysC) and gamma glutamyltransferase (GGT) on predicted age metrics were future found. Similar results were observed when conducting stratification analyses. Short and long sleep duration were associated with accelerated predicted age metrics mediated by CysC and GGT.

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.

Dietary diversity score and the acceleration of biological aging: a population-based study of 88,039 participants

OBJECTIVES

Our study aimed to investigate the association of dietary diversity score (DDS), as reflected by five dietary categories, with biological age acceleration.

DESIGN

A cross-sectional study.

SETTING AND PARTICIPANTS

This study included 88,039 individuals from the UK Biobank.

METHODS

Biological age (BA) was assessed using Klemerae-Doubal (KDM) and PhenoAge methods. The difference between BA and chronological age represents the age acceleration (AgeAccel), termed as "KDMAccel" and "PhenoAgeAccel". AgeAccel > 0 indicates faster aging. Generalized linear regression models were performed to assess the associations of DDS with AgeAccel. Similar analyses were performed for the five dietary categories.

RESULTS

After adjusting for multiple variables, DDS was inversely associated with KDMAccel (βHigh vs Low= -0.403, 95%CI: -0.492 to -0.314, P < 0.001) and PhenoAgeAccel (βHigh vs Low= -0.545, 95%CI: -0.641 to -0.450, P < 0.001). Each 1-point increment in the DDS was associated with a 4.4% lower risk of KDMAccel and a 5.6% lower risk of PhenoAgeAccel. The restricted cubic spline plots demonstrated a non-linear dose-response association between DDS and the risk of AgeAccel. The consumption of grains (βKDMAccel = -0.252, βPhenoAgeAccel = -0.197), vegetables (βKDMAccel = -0.044, βPhenoAgeAccel = -0.077) and fruits (βKDMAccel = -0.179, βPhenoAgeAccel = -0.219) was inversely associated with the two AgeAccel, while meat and protein alternatives (βKDMAccel = 0.091, βPhenoAgeAccel = 0.054) had a positive association (All P < 0.001). Stratified analysis revealed stronger accelerated aging effects in males, smokers, and drinkers. A strengthening trend in the association between DDS and AgeAccel as TDI quartiles increased was noted.

CONCLUSIONS

This study suggested that food consumption plays a role in aging process, and adherence to a higher diversity dietary is associated with the slowing down of the aging process.

CheekAge: a next-generation buccal epigenetic aging clock associated with lifestyle and health

Epigenetic aging clocks are computational models that predict age using DNA methylation information. Initially, first-generation clocks were developed to make predictions using CpGs that change with age. Over time, next-generation clocks were created using CpGs that relate to both age and health. Since existing next-generation clocks were constructed in blood, we sought to develop a next-generation clock optimized for prediction in cheek swabs, which are non-invasive and easy to collect. To do this, we collected MethylationEPIC data as well as lifestyle and health information from 8045 diverse adults. Using a novel simulated annealing approach that allowed us to incorporate lifestyle and health factors into training as well as a combination of CpG filtering, CpG clustering, and clock ensembling, we constructed CheekAge, an epigenetic aging clock that has a strong correlation with age, displays high test-retest reproducibility across replicates, and significantly associates with a plethora of lifestyle and health factors, such as BMI, smoking status, and alcohol intake. We validated CheekAge in an internal dataset and multiple publicly available datasets, including samples from patients with progeria or meningioma. In addition to exploring the underlying biology of the data and clock, we provide a free online tool that allows users to mine our methylomic data and predict epigenetic age.

The role of quality of life data as an endpoint for collecting real-world evidence within geroscience clinical trials

With geroscience research evolving at a fast pace, the need arises for human randomized controlled trials to assess the efficacy of geroprotective interventions to prevent age-related adverse outcomes, disease, and mortality in normative aging cohorts. However, to confirm efficacy requires a long-term and costly approach as time to the event of morbidity and mortality can be decades. While this could be circumvented using sensitive biomarkers of aging, current molecular, physiological, and digital endpoints require further validation. In this review, we discuss how collecting real-world evidence (RWE) by obtaining health data that is amenable for collection from large heterogeneous populations in a real-world setting can help speed up validation of geroprotective interventions. Further, we propose inclusion of quality of life (QoL) data as a biomarker of aging and candidate endpoint for geroscience clinical trials to aid in distinguishing healthy from unhealthy aging. We highlight how QoL assays can aid in accelerating data collection in studies gathering RWE on the geroprotective effects of repurposed drugs to support utilization within healthy longevity medicine. Finally, we summarize key metrics to consider when implementing QoL assays in studies, and present the short-form 36 (SF-36) as the most well-suited candidate endpoint.

Aging as a loss of morphostatic information: A developmental bioelectricity perspective

Maintaining order at the tissue level is crucial throughout the lifespan, as failure can lead to cancer and an accumulation of molecular and cellular disorders. Perhaps, the most consistent and pervasive result of these failures is aging, which is characterized by the progressive loss of function and decline in the ability to maintain anatomical homeostasis and reproduce. This leads to organ malfunction, diseases, and ultimately death. The traditional understanding of aging is that it is caused by the accumulation of molecular and cellular damage. In this article, we propose a complementary view of aging from the perspective of endogenous bioelectricity which has not yet been integrated into aging research. We propose a view of aging as a morphostasis defect, a loss of biophysical prepattern information, encoding anatomical setpoints used for dynamic tissue and organ homeostasis. We hypothesize that this is specifically driven by abrogation of the endogenous bioelectric signaling that normally harnesses individual cell behaviors toward the creation and upkeep of complex multicellular structures in vivo. Herein, we first describe bioelectricity as the physiological software of life, and then identify and discuss the links between bioelectricity and life extension strategies and age-related diseases. We develop a bridge between aging and regeneration via bioelectric signaling that suggests a research program for healthful longevity via morphoceuticals. Finally, we discuss the broader implications of the homologies between development, aging, cancer and regeneration and how morphoceuticals can be developed for aging.

Targeting the epigenetically older individuals for geroprotective trials: the use of DNA methylation clocks

Chronological age is the most important risk factor for the incidence of age-related diseases. The pace of ageing determines the magnitude of that risk and can be expressed as biological age. Targeting fundamental pathways of human aging with geroprotectors has the potential to lower the biological age and therewith prolong the healthspan, the period of life one spends in good health. Target populations for geroprotective interventions should be chosen based on the ageing mechanisms being addressed and the expected effect of the geroprotector on the primary outcome. Biomarkers of ageing, such as DNA methylation age, can be used to select populations for geroprotective interventions and as a surrogate outcome. Here, the use of DNA methylation clocks for selecting target populations for geroprotective intervention is explored.

The beginning of becoming a human

According to birth certificates, the life of a child begins once their body comes out of the mother's womb. But when does their organismal life begin? Science holds a palette of answers-depending on how one defines a human life. In 1984, a commission on the regulatory framework for human embryo experimentation opted not to answer this question, instead setting a boundary, 14 days post-fertilization, beyond which any experiments were forbidden. Recently, as the reproductive technologies developed and the demand for experimentation grew stronger, this boundary may be set aside leaving the ultimate decision to local oversight committees. While science has not come closer to setting a zero point for human life, there has been significant progress in our understanding of early mammalian embryogenesis. It has become clear that the 14-day stage does in fact possess features, which make it a foundational time point for a developing human. Importantly, this stage defines the separation of soma from the germline and marks the boundary between rejuvenation and aging. We explore how different levels of life organization emerge during human development and suggest a new meaning for the 14-day stage in organismal life that is grounded in recent mechanistic advances and insights from aging studies.

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.

Heterotopic mineralization (ossification or calcification) in aged musculoskeletal soft tissues: A new candidate marker for aging

Aging can lead to various disorders in organisms and with the escalating impact of population aging, the incidence of age-related diseases is steadily increasing. As a major risk factor for chronic illnesses in humans, the prevention and postponement of aging have become focal points of research among numerous scientists. Aging biomarkers, which mirror molecular alterations at diverse levels in organs, tissues, and cells, can be used to monitor and evaluate biological changes associated with aging. Currently, aging biomarkers are primarily categorized into physiological traits, imaging characteristics, histological features, cellular-level alterations, and molecular-level changes that encompass the secretion of aging-related factors. However, in the context of the musculoskeletal soft tissue system, aging-related biological indicators primarily involve microscopic parameters at the cellular and molecular levels, resulting in inconvenience and uncertainty in the assessment of musculoskeletal soft tissue aging. To identify convenient and effective indicators, we conducted a comprehensive literature review to investigate the correlation between ectopic mineralization and age-related changes in the musculoskeletal soft tissue system. Here, we introduce the concept of ectopic mineralization as a macroscopic, reliable, and convenient biomarker for musculoskeletal soft tissue aging and present novel targets and strategies for the future management of age-related musculoskeletal soft tissue disorders.

Harnessing Genetics to Extend Lifespan and Healthspan: Current Progress and Future Directions

Aging is inevitable, but the lifespan (duration of life) and healthspan (healthy aging) vary greatly among individuals and across species. Unlocking the secrets behind these differences has captivated scientific curiosity for ages. This review presents relevant recent advances in genetics and cell biology that are shedding new light by untangling how subtle changes in conserved genes, pathways, and epigenetic factors influence organismal senescence and associated declines. Biogerontology is a complex and rapidly growing field aimed at elucidating genetic modifications that extend lifespan and healthspan. This review explores gerontogenes, genes influencing lifespan and healthspan across species. Though subtle differences exist, long-lived individuals such as centenarians demonstrate extended healthspans, and numerous studies confirm the heritability of longevity/healthspan genes. Importantly, genes and gerontogenes are directly and indirectly involved in DNA repair, insulin/IGF-1 and mTOR signaling pathways, long non-coding RNAs, sirtuins, and heat shock proteins. The complex interactions between genetics and epigenetics are teased apart. While more research into optimizing healthspan is needed, conserved gerontogenes offer synergistic potential to forestall aging and age-related diseases. Understanding complex longevity genetics brings closer the goal of extending not only lifespan but quality years of life. The primary aim of human Biogerontology is to enhance lifespan and healthspan, but the question remains: are current genetic modifications effectively promoting healthy aging? This article collates the advancements in gerontogenes that enhance lifespan and improve healthspan alongside their potential challenges.

Insights into ageing rates comparison across tissues from recalibrating cerebellum DNA methylation clock

DNA methylation (DNAm)-based age clocks have been studied extensively as a biomarker of human ageing and a risk factor for age-related diseases. Despite different tissues having vastly different rates of proliferation, it is still largely unknown whether they age at different rates. It was previously reported that the cerebellum ages slowly; however, this claim was drawn from a single clock using a relatively small sample size and so warrants further investigation. We collected the largest cerebellum DNAm dataset (N = 752) to date. We found the respective epigenetic ages are all severely underestimated by six representative DNAm age clocks, with the underestimation effects more pronounced in the four clocks whose training datasets do not include brain-related tissues. We identified 613 age-associated CpGs in the cerebellum, which accounts for only 14.5% of the number found in the middle temporal gyrus from the same population (N = 404). From the 613 cerebellum age-associated CpGs, we built a highly accurate age prediction model for the cerebellum named CerebellumClockspecific (Pearson correlation=0.941, MAD=3.18 years). Ageing rate comparisons based on the two tissue-specific clocks constructed on the 201 overlapping age-associated CpGs support the cerebellum has younger DNAm age. Nevertheless, we built BrainCortexClock to prove a single DNAm clock is able to unbiasedly estimate DNAm ages of both cerebellum and cerebral cortex, when they are adequately and equally represented in the training dataset. Comparing ageing rates across tissues using DNA methylation multi-tissue clocks is flawed. The large underestimation of age prediction for cerebellums by previous clocks mainly reflects the improper usage of these age clocks. There exist strong and consistent ageing effects on the cerebellar methylome, and we suggest the smaller number of age-associated CpG sites in cerebellum is largely attributed to its extremely low average cell replication rates.

A metabolomic signature of decelerated physiological aging in human plasma

The degenerative processes that occur during aging increase the risk of disease and impaired health. Meanwhile, interventions that target aging to promote healthy longevity are gaining interest, both academically and in the public. While nutritional and physical interventions exist, efficacy is often difficult to determine. It is therefore imperative that an aging score measuring the biological aging process is available to the wider public. However, simple, interpret, and accessible biological aging scores are lacking. Here, we developed PhysiAge, a physiological aging score based on five accessible parameters that have influence on or reflect the aging process: (1) average daily step count, (2) blood glucose, (3) systolic blood pressure, (4) sex, and (5) age. Here, we found that compared to calendar age alone, PhysiAge better predicts mortality, as well as established muscle aging markers such as decrease in NAD+ levels, increase in oxidative stress, and decline in physical functioning. In order to demonstrate the usefulness of PhysiAge in identifying relevant factors associated with decelerated aging, we calculated PhysiAges for a cohort of aged individuals and obtained mass spectrometry-based blood plasma metabolomic profiles for each individual. Here, we identified a metabolic signature of decelerated aging, which included components of the TCA cycle, including malate, citrate, and isocitrate. Higher abundance of these metabolites was associated with decelerated aging, in line with supplementation studies in model organisms. PhysiAge represents an accessible way for people to track and intervene in their aging trajectories, and identifies a metabolic signature of decelerated aging in human blood plasma, which can be further studied for its causal involvement in human aging.

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.

Liquid-biopsy proteomics combined with AI identifies cellular drivers of eye aging and disease in vivo

Single-cell analysis in living humans is essential for understanding disease mechanisms, but it is impractical in non-regenerative organs, such as the eye and brain, because tissue biopsies would cause serious damage. We resolve this problem by integrating proteomics of liquid biopsies with single-cell transcriptomics from all known ocular cell types to trace the cellular origin of 5,953 proteins detected in the aqueous humor. We identified hundreds of cell-specific protein markers, including for individual retinal cell types. Surprisingly, our results reveal that retinal degeneration occurs in Parkinson's disease, and the cells driving diabetic retinopathy switch with disease stage. Finally, we developed artificial intelligence (AI) models to assess individual cellular aging and found that many eye diseases not associated with chronological age undergo accelerated molecular aging of disease-specific cell types. Our approach, which can be applied to other organ systems, has the potential to transform molecular diagnostics and prognostics while uncovering new cellular disease and aging mechanisms.

The Potential Role of C-Reactive Protein in Metabolic-Dysfunction-Associated Fatty Liver Disease and Aging

Nonalcoholic fatty liver disease (NAFLD), recently redefined as metabolic-dysfunction-associated fatty liver disease (MASLD), is liver-metabolism-associated steatohepatitis caused by nonalcoholic factors. NAFLD/MASLD is currently the most prevalent liver disease in the world, affecting one-fourth of the global population, and its prevalence increases with age. Current treatments are limited; one important reason hindering drug development is the insufficient understanding of the onset and pathogenesis of NAFLD/MASLD. C-reactive protein (CRP), a marker of inflammation, has been linked to NAFLD and aging in recent studies. As a conserved acute-phase protein, CRP is widely characterized for its host defense functions, but the link between CRP and NAFLD/MASLD remains unclear. Herein, we discuss the currently available evidence for the involvement of CRP in MASLD to identify areas where further research is needed. We hope this review can provide new insights into the development of aging-associated NAFLD biomarkers and suggest that modulation of CRP signaling is a potential therapeutic target.

Intersection clock reveals a rejuvenation event during human embryogenesis

Recent research revealed a rejuvenation event during early development of mice. Here, by examining epigenetic age dynamics of human embryogenesis, we tested whether a similar event exists in humans. For this purpose, we developed an epigenetic clock method, the intersection clock, that utilizes bisulfite sequencing in a way that maximizes the use of informative CpG sites with no missing clock CpG sites in test samples and applied it to human embryo development data. We observed no changes in the predicted epigenetic age between cleavage stage and blastocyst stage embryos; however, a significant decrease was observed between blastocysts and cells representing the epiblast. Additionally, by applying the intersection clock to datasets spanning pre and postimplantation, we found no significant change in the epigenetic age during preimplantation stages; however, the epigenetic age of postimplantation samples was lower compared to the preimplantation stages. We further investigated the epigenetic age of primed (representing early postimplantation) and naïve (representing preimplantation) pluripotent stem cells and observed that in all cases the epigenetic age of primed cells was significantly lower than that of naïve cells. Together, our data suggest that human embryos are rejuvenated during early embryogenesis. Hence, the rejuvenation event is conserved between the mouse and human, and it occurs around the gastrulation stage in both species. Beyond this advance, the intersection clock opens the way for other epigenetic age studies based on human bisulfite sequencing datasets as opposed to methylation arrays.

Biomedical generative pre-trained based transformer language model for age-related disease target discovery

Target discovery is crucial for the development of innovative therapeutics and diagnostics. However, current approaches often face limitations in efficiency, specificity, and scalability, necessitating the exploration of novel strategies for identifying and validating disease-relevant targets. Advances in natural language processing have provided new avenues for predicting potential therapeutic targets for various diseases. Here, we present a novel approach for predicting therapeutic targets using a large language model (LLM). We trained a domain-specific BioGPT model on a large corpus of biomedical literature consisting of grant text and developed a pipeline for generating target prediction. Our study demonstrates that pre-training of the LLM model with task-specific texts improves its performance. Applying the developed pipeline, we retrieved prospective aging and age-related disease targets and showed that these proteins are in correspondence with the database data. Moreover, we propose CCR5 and PTH as potential novel dual-purpose anti-aging and disease targets which were not previously identified as age-related but were highly ranked in our approach. Overall, our work highlights the high potential of transformer models in novel target prediction and provides a roadmap for future integration of AI approaches for addressing the intricate challenges presented in the biomedical field.

Associations between Differential Aging and Lifestyle, Environment, Current, and Future Health Conditions: Findings from Canadian Longitudinal Study on Aging

INTRODUCTION

An aging population will bring a pressing challenge for the healthcare system. Insights into promoting healthy longevity can be gained by quantifying the biological aging process and understanding the roles of modifiable lifestyle and environmental factors, and chronic disease conditions.

METHODS

We developed a biological age (BioAge) index by applying multiple state-of-art machine learning models based on easily accessible blood test data from the Canadian Longitudinal Study of Aging (CLSA). The BioAge gap, which is the difference between BioAge index and chronological age, was used to quantify the differential aging, i.e., the difference between biological and chronological age, of the CLSA participants. We further investigated the associations between the BioAge gap and lifestyle, environmental factors, and current and future health conditions.

RESULTS

BioAge gap had strong associations with existing adverse health conditions (e.g., cancers, cardiovascular diseases, diabetes, and kidney diseases) and future disease onset (e.g., Parkinson's disease, diabetes, and kidney diseases). We identified that frequent consumption of processed meat, pork, beef, and chicken, poor outcomes in nutritional risk screening, cigarette smoking, exposure to passive smoking are associated with positive BioAge gap ("older" BioAge than expected). We also identified several modifiable factors, including eating fruits, legumes, vegetables, related to negative BioAge gap ("younger" BioAge than expected).

CONCLUSIONS

Our study shows that a BioAge index based on easily accessible blood tests has the potential to quantify the differential biological aging process that can be associated with current and future adverse health events. The identified risk and protective factors for differential aging indicated by BioAge gap are informative for future research and guidelines to promote healthy longevity.

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.

The association of blood biomarkers with treatment response and adverse health outcomes in older patients with solid tumors: A systematic review

INTRODUCTION

Blood biomarkers are potentially useful prognostic markers and may support treatment decisions, but it is unknown if and which biomarkers are most useful in older patients with solid tumors. The aim of this systematic review was to evaluate the evidence on the association of blood biomarkers with treatment response and adverse health outcomes in older patients with solid tumors.

MATERIALS AND METHODS

A literature search was conducted in five databases in December 2022 to identify studies on blood biomarkers measured before treatment initiation, not tumor specific, and outcomes in patients with solid tumors aged ≥60 years. Studies on any type or line of oncologic treatment could be included. Titles and abstracts were screened by three authors. Data extraction and quality assessment, using the Quality in Prognosis Studies (QUIPS) checklist, were performed by two authors.

RESULTS

Sixty-three studies were included, with a median sample size of 138 patients (Interquartile range [IQR] 99-244) aged 76 years (IQR 72-78). Most studies were retrospective cohort studies (63%). The risk of bias was moderate in 52% and high in 43%. Less than one-third reported geriatric parameters. Eighty-six percent examined mortality outcomes, 37% therapeutic response, and 37% adverse events. In total, 77 unique markers were studied in patients with a large variety of tumor types and treatment modalities. Neutrophil-to-lymphocyte ratio (20 studies), albumin (19), C-reactive protein (16), hemoglobin (14) and (modified) Glasgow Prognostic Score ((m)GPS) (12) were studied most often. The vast majority showed no significant association of these biomarkers with outcomes, except for associations between low albumin and adverse events and high (m)GPS with mortality.

DISCUSSION

Most studies did not find a significant association between blood biomarkers and clinical outcomes. The interpretation of current evidence on prognostic blood biomarkers is hampered by small sample sizes and inconsistent results across heterogeneous studies. The choice for blood biomarkers in the majority of included studies seemed driven by availability in clinical practice in retrospective cohort studies. Ageing biomarkers are rarely studied in older patients with solid tumors. Further research is needed in larger and more homogenous cohorts that combine clinical parameters and biomarkers before these can be used in clinical practice.

DNA methylation markers of age(ing) in non-model animals

Inferring the chronological and biological age of individuals is fundamental to population ecology and our understanding of ageing itself, its evolution, and the biological processes that affect or even cause ageing. Epigenetic clocks based on DNA methylation (DNAm) at specific CpG sites show a strong correlation with chronological age in humans, and discrepancies between inferred and actual chronological age predict morbidity and mortality. Recently, a growing number of epigenetic clocks have been developed in non-model animals and we here review these studies. We also conduct a meta-analysis to assess the effects of different aspects of experimental protocol on the performance of epigenetic clocks for non-model animals. Two measures of performance are usually reported, the R2 of the association between the predicted and chronological age, and the mean/median absolute deviation (MAD) of estimated age from chronological age, and we argue that only the MAD reflects accuracy. R2 for epigenetic clocks based on the HorvathMammalMethylChip4 was higher and the MAD scaled to age range lower, compared with other DNAm quantification approaches. Scaled MAD tended to be lower among individuals in captive populations, and decreased with an increasing number of CpG sites. We conclude that epigenetic clocks can predict chronological age with relatively high accuracy, suggesting great potential in ecological epigenetics. We discuss general aspects of epigenetic clocks in the hope of stimulating further DNAm-based research on ageing, and perhaps more importantly, other key traits.

Targeting epigenetics: A novel promise for Alzheimer's disease treatment

So far, the search for a cure for Alzheimer Disease (AD) has been unsuccessful. The only approved drugs attenuate some symptoms, but do not halt the progress of this disease, which affects 50 million people worldwide and will increase its incidence in the coming decades. Such scenario demands new therapeutic approaches to fight against this devastating dementia. In recent years, multi-omics research and the analysis of differential epigenetic marks in AD subjects have contributed to our understanding of AD; however, the impact of epigenetic research is yet to be seen. This review integrates the most recent data on pathological processes and epigenetic changes relevant for aging and AD, as well as current therapies targeting epigenetic machinery in clinical trials. Evidence shows that epigenetic modifications play a key role in gene expression, which could provide multi-target preventative and therapeutic approaches in AD. Both novel and repurposed drugs are employed in AD clinical trials due to their epigenetic effects, as well as increasing number of natural compounds. Given the reversible nature of epigenetic modifications and the complexity of gene-environment interactions, the combination of epigenetic-based therapies with environmental strategies and drugs with multiple targets might be needed to properly help AD patients.

Biomarkers of aging for the identification and evaluation of longevity interventions

With the rapid expansion of aging biology research, the identification and evaluation of longevity interventions in humans have become key goals of this field. Biomarkers of aging are critically important tools in achieving these objectives over realistic time frames. However, the current lack of standards and consensus on the properties of a reliable aging biomarker hinders their further development and validation for clinical applications. Here, we advance a framework for the terminology and characterization of biomarkers of aging, including classification and potential clinical use cases. We discuss validation steps and highlight ongoing challenges as potential areas in need of future research. This framework sets the stage for the development of valid biomarkers of aging and their ultimate utilization in clinical trials and practice.

A transcriptome-based single-cell biological age model and resource for tissue-specific aging measures

Accurately measuring biological age is crucial for improving healthcare for the elderly population. However, the complexity of aging biology poses challenges in how to robustly estimate aging and interpret the biological significance of the traits used for estimation. Here we present SCALE, a statistical pipeline that quantifies biological aging in different tissues using explainable features learned from literature and single-cell transcriptomic data. Applying SCALE to the "Mouse Aging Cell Atlas" (Tabula Muris Senis) data, we identified tissue-level transcriptomic aging programs for more than 20 murine tissues and created a multitissue resource of mouse quantitative aging-associated genes. We observe that SCALE correlates well with other age indicators, such as the accumulation of somatic mutations, and can distinguish subtle differences in aging even in cells of the same chronological age. We further compared SCALE with other transcriptomic and methylation "clocks" in data from aging muscle stem cells, Alzheimer's disease, and heterochronic parabiosis. Our results confirm that SCALE is more generalizable and reliable in assessing biological aging in aging-related diseases and rejuvenating interventions. Overall, SCALE represents a valuable advancement in our ability to measure aging accurately, robustly, and interpretably in single cells.

Programmed ageing: decline of stem cell renewal, immunosenescence, and Alzheimer's disease

The characteristic maximum lifespan varies enormously across animal species from a few hours to hundreds of years. This argues that maximum lifespan, and the ageing process that itself dictates lifespan, are to a large extent genetically determined. Although controversial, this is supported by firm evidence that semelparous species display evolutionarily programmed ageing in response to reproductive and environmental cues. Parabiosis experiments reveal that ageing is orchestrated systemically through the circulation, accompanied by programmed changes in hormone levels across a lifetime. This implies that, like the circadian and circannual clocks, there is a master 'clock of age' (circavital clock) located in the limbic brain of mammals that modulates systemic changes in growth factor and hormone secretion over the lifespan, as well as systemic alterations in gene expression as revealed by genomic methylation analysis. Studies on accelerated ageing in mice, as well as human longevity genes, converge on evolutionarily conserved fibroblast growth factors (FGFs) and their receptors, including KLOTHO, as well as insulin-like growth factors (IGFs) and steroid hormones, as key players mediating the systemic effects of ageing. Age-related changes in these and multiple other factors are inferred to cause a progressive decline in tissue maintenance through failure of stem cell replenishment. This most severely affects the immune system, which requires constant renewal from bone marrow stem cells. Age-related immune decline increases risk of infection whereas lifespan can be extended in germfree animals. This and other evidence suggests that infection is the major cause of death in higher organisms. Immune decline is also associated with age-related diseases. Taking the example of Alzheimer's disease (AD), we assess the evidence that AD is caused by immunosenescence and infection. The signature protein of AD brain, Aβ, is now known to be an antimicrobial peptide, and Aβ deposits in AD brain may be a response to infection rather than a cause of disease. Because some cognitively normal elderly individuals show extensive neuropathology, we argue that the location of the pathology is crucial - specifically, lesions to limbic brain are likely to accentuate immunosenescence, and could thus underlie a vicious cycle of accelerated immune decline and microbial proliferation that culminates in AD. This general model may extend to other age-related diseases, and we propose a general paradigm of organismal senescence in which declining stem cell proliferation leads to programmed immunosenescence and mortality.