Assessing the rate of aging to monitor aging itself

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.

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

Introduction

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

Methods

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

Results

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

Discussion

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

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).

Human PBMC scRNA-seq-based aging clocks reveal ribosome to inflammation balance as a single-cell aging hallmark and super longevity

Quantifying aging rate is important for evaluating age-associated decline and mortality. A blood single-cell RNA sequencing dataset for seven supercentenarians (SCs) was recently generated. Here, we generate a reference 28-sample aging cohort to compute a single-cell level aging clock and to determine the biological age of SCs. Our clock model placed the SCs at a blood biological age to between 80.43 and 102.67 years. Compared to the model-expected aging trajectory, SCs display increased naive CD8⁺ T cells, decreased cytotoxic CD8⁺ T cells, memory CD4⁺ T cells, and megakaryocytes. As the most prominent molecular hallmarks at the single-cell level, SCs contain more cells and cell types with high ribosome level, which is associated with and, according to Bayesian network inference, contributes to a low inflammation state and slow aging of SCs. Inhibiting ribosomal activity or translation in monocytes validates such translation against inflammation balance revealed by our single-cell aging clock.

Current Trends in Anti-Aging Strategies

The process of aging manifests from a highly interconnected network of biological cascades resulting in the degradation and breakdown of every living organism over time. This natural development increases risk for numerous diseases and can be debilitating. Academic and industrial investigators have long sought to impede, or potentially reverse, aging in the hopes of alleviating clinical burden, restoring functionality, and promoting longevity. Despite widespread investigation, identifying impactful therapeutics has been hindered by narrow experimental validation and the lack of rigorous study design. In this review, we explore the current understanding of the biological mechanisms of aging and how this understanding both informs and limits interpreting data from experimental models based on these mechanisms. We also discuss select therapeutic strategies that have yielded promising data in these model systems with potential clinical translation. Lastly, we propose a unifying approach needed to rigorously vet current and future therapeutics and guide evaluation toward efficacious therapies.

Heterogeneous aging across multiple organ systems and prediction of chronic disease and mortality

Biological aging of human organ systems reflects the interplay of age, chronic disease, lifestyle and genetic risk. Using longitudinal brain imaging and physiological phenotypes from the UK Biobank, we establish normative models of biological age for three brain and seven body systems. Here we find that an organ's biological age selectively influences the aging of other organ systems, revealing a multiorgan aging network. We report organ age profiles for 16 chronic diseases, where advanced biological aging extends from the organ of primary disease to multiple systems. Advanced body age associates with several lifestyle and environmental factors, leukocyte telomere lengths and mortality risk, and predicts survival time (area under the curve of 0.77) and premature death (area under the curve of 0.86). Our work reveals the multisystem nature of human aging in health and chronic disease. It may enable early identification of individuals at increased risk of aging-related morbidity and inform new strategies to potentially limit organ-specific aging in such individuals.

Progress in biological age research

Biological age (BA) is a common model to evaluate the function of aging individuals as it may provide a more accurate measure of the extent of human aging than chronological age (CA). Biological age is influenced by the used biomarkers and standards in selected aging biomarkers and the statistical method to construct BA. Traditional used BA estimation approaches include multiple linear regression (MLR), principal component analysis (PCA), Klemera and Doubal's method (KDM), and, in recent years, deep learning methods. This review summarizes the markers for each organ/system used to construct biological age and published literature using methods in BA research. Future research needs to explore the new aging markers and the standard in select markers and new methods in building BA models.

Interpretable machine learning prediction of all-cause mortality

Background

Unlike linear models which are traditionally used to study all-cause mortality, complex machine learning models can capture non-linear interrelations and provide opportunities to identify unexplored risk factors. Explainable artificial intelligence can improve prediction accuracy over linear models and reveal great insights into outcomes like mortality. This paper comprehensively analyzes all-cause mortality by explaining complex machine learning models.

Methods

We propose the IMPACT framework that uses XAI technique to explain a state-of-the-art tree ensemble mortality prediction model. We apply IMPACT to understand all-cause mortality for 1-, 3-, 5-, and 10-year follow-up times within the NHANES dataset, which contains 47,261 samples and 151 features.

Results

We show that IMPACT models achieve higher accuracy than linear models and neural networks. Using IMPACT, we identify several overlooked risk factors and interaction effects. Furthermore, we identify relationships between laboratory features and mortality that may suggest adjusting established reference intervals. Finally, we develop highly accurate, efficient and interpretable mortality risk scores that can be used by medical professionals and individuals without medical expertise. We ensure generalizability by performing temporal validation of the mortality risk scores and external validation of important findings with the UK Biobank dataset.

Conclusions

IMPACT's unique strength is the explainable prediction, which provides insights into the complex, non-linear relationships between mortality and features, while maintaining high accuracy. Our explainable risk scores could help individuals improve self-awareness of their health status and help clinicians identify patients with high risk. IMPACT takes a consequential step towards bringing contemporary developments in XAI to epidemiology.

The landscape of aging

Aging is characterized by a progressive deterioration of physiological integrity, leading to impaired functional ability and ultimately increased susceptibility to death. It is a major risk factor for chronic human diseases, including cardiovascular disease, diabetes, neurological degeneration, and cancer. Therefore, the growing emphasis on “healthy aging” raises a series of important questions in life and social sciences. In recent years, there has been unprecedented progress in aging research, particularly the discovery that the rate of aging is at least partly controlled by evolutionarily conserved genetic pathways and biological processes. In an attempt to bring full-fledged understanding to both the aging process and age-associated diseases, we review the descriptive, conceptual, and interventive aspects of the landscape of aging composed of a number of layers at the cellular, tissue, organ, organ system, and organismal levels.

Hibiscus, Rooibos, and Yerba Mate for Healthy Aging: A Review on the Attenuation of In Vitro and In Vivo Markers Related to Oxidative Stress, Glycoxidation, and Neurodegeneration

The world is currently undergoing a demographic change towards an increasing number of elderly citizens. Aging is characterized by a temporal decline in physiological capacity, and oxidative stress is a hallmark of aging and age-related disorders. Such an oxidative state is linked to a decrease in the effective mechanisms of cellular repair, the incidence of post-translational protein glycation, mitochondrial dysfunction, and neurodegeneration, just to name some of the markers contributing to the establishment of age-related reduction-oxidation, or redox, imbalance. Currently, there are no prescribed therapies to control oxidative stress; however, there are strategies to elevate antioxidant defenses and overcome related health challenges based on the adoption of nutritional therapies. It is well known that herbal teas such, as hibiscus, rooibos, and yerba mate, are important sources of antioxidants, able to prevent some oxidation-related stresses. These plants produce several bioactive metabolites, have a pleasant taste, and a long-lasting history as safe foods. This paper reviews the literature on hibiscus, rooibos, and yerba mate teas in the context of nutritional strategies for the attenuation of oxidative stress-related glycoxidation and neurodegeneration, and, here, Alzheimer’s Disease is approached as an example. The focus is given to mechanisms of glycation inhibition, as well as neuroprotective in vitro effects, and, in animal studies, to frame interest in these plants as nutraceutical agents related to current health concerns.

Systematic transcriptome analysis associated with physiological and chronological aging in Caenorhabditis elegans

Aging is associated with changes in a variety of biological processes at the transcriptomic level, including gene expression. Two types of aging occur during a lifetime: chronological and physiological aging. However, dissecting the difference between chronological and physiological ages at the transcriptomic level has been a challenge because of its complexity. We analyzed the transcriptomic features associated with physiological and chronological aging using Caenorhabditis elegans as a model. Many structural and functional transcript elements, such as noncoding RNAs and intron-derived transcripts, were up-regulated with chronological aging. In contrast, mRNAs with many biological functions, including RNA processing, were down-regulated with physiological aging. We also identified an age-dependent increase in the usage of distal 3' splice sites in mRNA transcripts as a biomarker of physiological aging. Our study provides crucial information for dissecting chronological and physiological aging at the transcriptomic level.

Epidemiological and genetic overlap among biological aging clocks: New challenges in biogerontology

Estimators of biological age (BA) - defined as the hypothetical underlying age of an organism - have attracted more and more attention in the last years, especially after the advent of new algorithms based on machine learning and genetic markers. While different aging clocks reportedly predict mortality in the general population, very little is known on their overlap. Here we review the evidence reported so far to support the existence of a partial overlap among different BA acceleration estimators, both from an epidemiological and a genetic perspective. On the epidemiological side, we review evidence supporting shared and independent influence on mortality risk of different aging clocks - including telomere length, brain, blood and epigenetic aging - and provide an overview of how an important exposure like diet may affect the different aging systems. On the genetic side, we apply linkage disequilibrium score regression analyses to support the existence of partly shared genomic overlap among these aging clocks. Through multivariate analysis of published genetic associations with these clocks, we also identified the most associated variants, genes, and pathways, which may affect common mechanisms underlying biological aging of different systems within the body. Based on our analyses, the most implicated pathways were involved in inflammation, lipid and carbohydrate metabolism, suggesting them as potential molecular targets for future anti-aging interventions. Overall, this review is meant as a contribution to the knowledge on the overlap of aging clocks, trying to clarify their shared biological basis and epidemiological implications.