Deep biomarkers of aging and longevity: from research to applications

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.

p53/MDM2 signaling pathway in aging, senescence and tumorigenesis

A wealth of evidence has emerged that there is an association between aging, senescence and tumorigenesis. Senescence, a biological process by which cells cease to divide and enter a status of permanent cell cycle arrest, contributes to aging and aging-related diseases, including cancer. Aging populations have the higher incidence of cancer due to a lifetime of exposure to cancer-causing agents, reduction of repairing DNA damage, accumulated genetic mutations, and decreased immune system efficiency. Cancer patients undergoing cytotoxic therapies, such as chemotherapy and radiotherapy, accelerate aging. There is growing evidence that p53/MDM2 (murine double minute 2) axis is critically involved in regulation of aging, senescence and oncogenesis. Therefore, in this review, we describe the functions and mechanisms of p53/MDM2-mediated senescence, aging and carcinogenesis. Moreover, we highlight the small molecular inhibitors, natural compounds and PROTACs (proteolysis targeting chimeras) that target p53/MDM2 pathway to influence aging and cancer. Modification of p53/MDM2 could be a potential strategy for treatment of aging, senescence and tumorigenesis.

Targeting Aging and Longevity with Exogenous Nucleotides (TALENTs): Rationale, Design, and Baseline Characteristics from a Randomized Controlled Trial in Older Adults

Nucleotides (NTs), important biomolecules involved in numerous cellular processes, have been proposed as potential candidates for anti-aging interventions. However, whether nucleotides can act as an anti-aging supplement in older adults remains unclear. TALENTs is a randomized, double-blinded, placebo-controlled trial that evaluates the efficacy and safety of NTs as an anti-aging supplement in older adults by exploring the effects of NTs on multiple dimensions of aging in a rigorous scientific setting. Eligible community-dwelling adults aged 60-70 years were randomly assigned equally to two groups: nucleotides intervention group and placebo control group. Comprehensive geriatric health assessments were performed at baseline, 2-months, and 4-months of the intervention. Biological specimens were collected and stored for age-related biomarker testing and multi-omics sequencing. The primary outcome was the change from baseline to 4 months on leukocyte telomere length and DNA methylation age. The secondary aims were the changes in possible mechanisms underlying aging processes (immunity, inflammatory profile, oxidative stress, gene stability, endocrine, metabolism, and cardiovascular function). Other outcomes were changes in physical function, body composition and geriatric health assessment (including sleep quality, cognitive function, fatigue, frailty, and psychology). In the RCT, 301 participants were assessed for eligibility and 122 were enrolled. Participants averaged 65.65 years of age, and were predominately female (67.21%). All baseline characteristics were well-balanced between groups, as expected due to randomization. The majority of participants were pre-frailty and had at least one chronic condition. The mean scores for physical activity, psychological, fatigue and quality of life were within the normal range. However, nearly half of the participants still had room for improvement in cognitive level and sleep quality. This TALENTs trial will represent one of the most comprehensive experimental clinical trials in which supplements are administered to elderly participants. The findings of this study will contribute to our understanding of the anti-aging effects of NTs and provide insights into their potential applications in geriatric healthcare.

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).

Why the United States needs a multifaceted definition of health

How health is conceived and operationalized is an unrecognized contributor to poor health outcomes in the United States. The United States lacks an explicit definition of health, yielding a de facto, implicit biomedical definition in research and in health care that contrasts with how many people define health for themselves. This biomedical conceptualization has led to the development of lifesaving drugs, vaccines, and procedures, but has also resulted in critical underinvestment in people across their lives, beginning in early childhood, in behavioral, environmental, and social determinants. This underinvestment across the entire lifespan in people's health traps the United States in a vicious cycle of chronic disease and unsustainable health care costs. A movement towards holistic definitions of health represents an escape by defining health in more meaningful terms that reflect people's early development, agency, functioning, adaptive capacity, well-being, and lifelong development-that is, the capability for every person to thrive. Adopting and implementing a multifaceted, holistic health definition by federal research and health agencies could transform and humanize health in the United States and advance health equity.

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.

AI-powered therapeutic target discovery

Disease modeling and target identification are the most crucial initial steps in drug discovery, and influence the probability of success at every step of drug development. Traditional target identification is a time-consuming process that takes years to decades and usually starts in an academic setting. Given its advantages of analyzing large datasets and intricate biological networks, artificial intelligence (AI) is playing a growing role in modern drug target identification. We review recent advances in target discovery, focusing on breakthroughs in AI-driven therapeutic target exploration. We also discuss the importance of striking a balance between novelty and confidence in target selection. An increasing number of AI-identified targets are being validated through experiments and several AI-derived drugs are entering clinical trials; we highlight current limitations and potential pathways for moving forward.

Glycemic status and its association with retinal age gap: Insights from the UK biobank study

OBJECTIVE

To investigate associations between different glycemic status and biological age indexed by retinal age gap.

METHODS

A total of 28,919 participants from the UK Biobank study with available glycemic status and qualified retinal imaging data were included in the present analysis. Glycemic status included type 2 diabetes mellitus (T2D) disease status and glycemic indicators of plasma glycated hemoglobin (HbA1c) and glucose. Retinal age gap was defined as the difference between the retina-predicted age and chronological age. Linear regression models estimated the association of different glycemic status with retinal age gap.

RESULTS

Prediabetes and T2D was significantly associated with higher retinal age gaps compared to normoglycemia (regression coefficient [β] = 0.25, 95% confidence interval [CI]: 0.11-0.40, P = 0.001; β = 1.06, 95% CI: 0.83-1.29, P < 0.001; respectively). Multi-variable linear regressions further found an increase of HbA1c was independently associated with higher retinal age gaps among all subjects or subjects without T2D. Significant positive associations were noted across the increasing HbA1c and glucose groups with retinal age gaps compared to the normal level group. These findings remained significant after excluding diabetic retinopathy.

CONCLUSIONS

Dysglycemia was significantly associated with accelerated ageing indexed by retinal age gaps, highlighting the importance of maintaining glycemic status.

Stress, diet, exercise: Common environmental factors and their impact on epigenetic age

Epigenetic aging clocks have gained significant attention as a tool for predicting age-related health conditions in clinical and research settings. They have enabled geroscientists to study the underlying mechanisms of aging and assess the effectiveness of anti-aging therapies, including diet, exercise and environmental exposures. This review explores the effects of modifiable lifestyle factors' on the global DNA methylation landscape, as seen by aging clocks. We also discuss the underlying mechanisms through which these factors contribute to biological aging and provide comments on what these findings mean for people willing to build an evidence-based pro-longevity lifestyle.

Precious1GPT: multimodal transformer-based transfer learning for aging clock development and feature importance analysis for aging and age-related disease target discovery

Aging is a complex and multifactorial process that increases the risk of various age-related diseases and there are many aging clocks that can accurately predict chronological age, mortality, and health status. These clocks are disconnected and are rarely fit for therapeutic target discovery. In this study, we propose a novel approach to multimodal aging clock we call Precious1GPT utilizing methylation and transcriptomic data for interpretable age prediction and target discovery developed using a transformer-based model and transfer learning for case-control classification. While the accuracy of the multimodal transformer is lower within each individual data type compared to the state of art specialized aging clocks based on methylation or transcriptomic data separately it may have higher practical utility for target discovery. This method provides the ability to discover novel therapeutic targets that hypothetically may be able to reverse or accelerate biological age providing a pathway for therapeutic drug discovery and validation using the aging clock. In addition, we provide a list of promising targets annotated using the PandaOmics industrial target discovery platform.

Discovery of senolytics using machine learning

Cellular senescence is a stress response involved in ageing and diverse disease processes including cancer, type-2 diabetes, osteoarthritis and viral infection. Despite growing interest in targeted elimination of senescent cells, only few senolytics are known due to the lack of well-characterised molecular targets. Here, we report the discovery of three senolytics using cost-effective machine learning algorithms trained solely on published data. We computationally screened various chemical libraries and validated the senolytic action of ginkgetin, periplocin and oleandrin in human cell lines under various modalities of senescence. The compounds have potency comparable to known senolytics, and we show that oleandrin has improved potency over its target as compared to best-in-class alternatives. Our approach led to several hundred-fold reduction in drug screening costs and demonstrates that artificial intelligence can take maximum advantage of small and heterogeneous drug screening data, paving the way for new open science approaches to early-stage drug discovery.

Discovering small-molecule senolytics with deep neural networks

The accumulation of senescent cells is associated with aging, inflammation and cellular dysfunction. Senolytic drugs can alleviate age-related comorbidities by selectively killing senescent cells. Here we screened 2,352 compounds for senolytic activity in a model of etoposide-induced senescence and trained graph neural networks to predict the senolytic activities of >800,000 molecules. Our approach enriched for structurally diverse compounds with senolytic activity; of these, three drug-like compounds selectively target senescent cells across different senescence models, with more favorable medicinal chemistry properties than, and selectivity comparable to, those of a known senolytic, ABT-737. Molecular docking simulations of compound binding to several senolytic protein targets, combined with time-resolved fluorescence energy transfer experiments, indicate that these compounds act in part by inhibiting Bcl-2, a regulator of cellular apoptosis. We tested one compound, BRD-K56819078, in aged mice and found that it significantly decreased senescent cell burden and mRNA expression of senescence-associated genes in the kidneys. Our findings underscore the promise of leveraging deep learning to discover senotherapeutics.

Turning Back the Clock: A Retrospective Single-Blind Study on Brain Age Change in Response to Nutraceuticals Supplementation vs. Lifestyle Modifications

BACKGROUND

There is a growing consensus that chronological age (CA) is not an accurate indicator of the aging process and that biological age (BA) instead is a better measure of an individual's risk of age-related outcomes and a more accurate predictor of mortality than actual CA. In this context, BA measures the "true" age, which is an integrated result of an individual's level of damage accumulation across all levels of biological organization, along with preserved resources. The BA is plastic and depends upon epigenetics. Brain state is an important factor contributing to health- and lifespan.

METHODS AND OBJECTIVE

Quantitative electroencephalography (qEEG)-derived brain BA (BBA) is a suitable and promising measure of brain aging. In the present study, we aimed to show that BBA can be decelerated or even reversed in humans (N = 89) by using customized programs of nutraceutical compounds or lifestyle changes (mean duration = 13 months).

RESULTS

We observed that BBA was younger than CA in both groups at the end of the intervention. Furthermore, the BBA of the participants in the nutraceuticals group was 2.83 years younger at the endpoint of the intervention compared with their BBA score at the beginning of the intervention, while the BBA of the participants in the lifestyle group was only 0.02 years younger at the end of the intervention. These results were accompanied by improvements in mental-physical health comorbidities in both groups. The pre-intervention BBA score and the sex of the participants were considered confounding factors and analyzed separately.

CONCLUSIONS

Overall, the obtained results support the feasibility of the goal of this study and also provide the first robust evidence that halting and reversal of brain aging are possible in humans within a reasonable (practical) timeframe of approximately one year.

A Data-Mining Approach to Identify NF-kB-Responsive microRNAs in Tissues Involved in Inflammatory Processes: Potential Relevance in Age-Related Diseases

The nuclear factor NF-kB is the master transcription factor in the inflammatory process by modulating the expression of pro-inflammatory genes. However, an additional level of complexity is the ability to promote the transcriptional activation of post-transcriptional modulators of gene expression as non-coding RNA (i.e., miRNAs). While NF-kB’s role in inflammation-associated gene expression has been extensively investigated, the interplay between NF-kB and genes coding for miRNAs still deserves investigation. To identify miRNAs with potential NF-kB binding sites in their transcription start site, we predicted miRNA promoters by an in silico analysis using the PROmiRNA software, which allowed us to score the genomic region’s propensity to be miRNA cis-regulatory elements. A list of 722 human miRNAs was generated, of which 399 were expressed in at least one tissue involved in the inflammatory processes. The selection of “high-confidence” hairpins in miRbase identified 68 mature miRNAs, most of them previously identified as inflammamiRs. The identification of targeted pathways/diseases highlighted their involvement in the most common age-related diseases. Overall, our results reinforce the hypothesis that persistent activation of NF-kB could unbalance the transcription of specific inflammamiRNAs. The identification of such miRNAs could be of diagnostic/prognostic/therapeutic relevance for the most common inflammatory-related and age-related diseases.

An Updated Overview on the Role of Small Molecules and Natural Compounds in the "Young Science" of Rejuvenation

Aging is a gradual process that occurs over time which leads to a progressive decline of cells and tissues. Telomere shortening, genetic instability, epigenetic alteration, and the accumulation of misfolded proteins represent the main hallmarks that cause perturbed cellular functions; this occurs in conjunction with the progression of the so-called "aging clocks". Rejuvenation aims to influence the natural evolution of such aging clocks and to enhance regenerative capacity, thus overcoming the limitations of common anti-aging interventions. Current rejuvenation processes are based on heterochronic parabiosis, cell damage dilution through asymmetrical cell division, the excretion of extracellular vesicles, the modulation of genetic instability involving G-quadruplexes and DNA methylation, and cell reprogramming using Yamanaka factors and the actions of antioxidant species. In this context, we reviewed the most recent contributions that report on small molecules acting as senotherapeutics; these molecules act by promoting one or more of the abovementioned processes. Candidate drugs and natural compounds that are being studied as potential rejuvenation therapies act by interfering with CDGSH iron-sulfur domain 2 (CISD2) expression, G-quadruplex structures, DNA methylation, and mitochondrial decay. Moreover, direct and indirect antioxidants have been reported to counteract or revert aging through a combination of mixed mechanisms.

Towards AI-driven longevity research: An overview

While in the past technology has mostly been utilized to store information about the structural configuration of proteins and molecules for research and medical purposes, Artificial Intelligence is nowadays able to learn from the existing data how to predict and model properties and interactions, revealing important knowledge about complex biological processes, such as aging. Modern technologies, moreover, can rely on a broader set of information, including those derived from the next-generation sequencing (e.g., proteomics, lipidomics, and other omics), to understand the interactions between human body and the external environment. This is especially relevant as external factors have been shown to have a key role in aging. As the field of computational systems biology keeps improving and new biomarkers of aging are being developed, artificial intelligence promises to become a major ally of aging research.

The efficacy and safety of β-nicotinamide mononucleotide (NMN) supplementation in healthy middle-aged adults: a randomized, multicenter, double-blind, placebo-controlled, parallel-group, dose-dependent clinical trial

In animal studies, β-nicotinamide mononucleotide (NMN) supplementation increases nicotinamide adenine dinucleotide (NAD) concentrations and improves healthspan and lifespan with great safety. However, it is unclear if these effects can be transferred to humans. This randomized, multicenter, double-blind, placebo-controlled, parallel-group, dose-dependent clinical trial included 80 middle-aged healthy adults being randomized for a 60-day clinical trial with once daily oral dosing of placebo, 300 mg, 600 mg, or 900 mg NMN. The primary objective was to evaluate blood NAD concentration with dose-dependent regimens. The secondary objectives were to assess the safety and tolerability of NMN supplementation, next to the evaluation of clinical efficacy by measuring physical performance (six-minute walking test), blood biological age (Aging.Ai 3.0 calculator), Homeostatic Model Assessment for Insulin Resistance (HOMA-IR), and subjective general health assessment [36-Item Short Form Survey Instrument (SF-36)]. Statistical analysis was performed using the Per Protocol analysis with significant level set at p = 0.05. All 80 participants completed the trial without trial protocol violation. Blood NAD concentrations were statistically significantly increased among all NMN-treated groups at day 30 and day 60 when compared to both placebo and baseline (all p ≤ 0.001). Blood NAD concentrations were highest in the groups taking 600 mg and 900 mg NMN. No safety issues, based on monitoring adverse events (AEs), laboratory and clinical measures, were found, and NMN supplementation was well tolerated. Walking distance increase during the six-minute walking test was statistically significantly higher in the 300 mg, 600 mg, and 900 mg groups compared to placebo at both days 30 and 60 (all p < 0.01), with longest walking distances measured in the 600 mg and 900 mg groups. The blood biological age increased significantly in the placebo group and stayed unchanged in all NMN-treated groups at day 60, which resulted in a significant difference between the treated groups and placebo (all p < 0.05). The HOMA-IR showed no statistically significant differences for all NMN-treated groups as compared to placebo at day 60. The change of SF-36 scores at day 30 and day 60 indicated statistically significantly better health of all three treated groups when compared to the placebo group (p < 0.05), except for the SF-36 score change in the 300 mg group at day 30. NMN supplementation increases blood NAD concentrations and is safe and well tolerated with oral dosing up to 900 mg NMN daily. Clinical efficacy expressed by blood NAD concentration and physical performance reaches highest at a dose of 600 mg daily oral intake. This trial was registered with ClinicalTrials.gov, NCT04823260, and Clinical Trial Registry - India, CTRI/2021/03/032421.

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.

Pediococcus acidilactici Promotes the Longevity of C. elegans by Regulating the Insulin/IGF-1 and JNK/MAPK Signaling, Fat Accumulation and Chloride Ion

Probiotics are known to contribute to the anti-oxidation, immunoregulation, and aging delay. Here, we investigated the extension of lifespan by fermented pickles-origin Pediococcus acidilactici (PA) in Caenorhabditis elegans (C. elegans), and found that PA promoted a significantly extended longevity of wild-type C. elegans. The further results revealed that PA regulated the longevity via promoting the insulin/IGF-1 signaling, JNK/MAPK signaling but not TOR signaling in C. elegans, and that PA reduced the reactive oxygen species (ROS) levels and modulated expression of genes involved in fatty acids uptake and lipolysis, thus reducing the fat accumulation in C. elegans. Moreover, this study identified the nrfl-1 as the key regulator of the PA-mediated longevity, and the nrfl-1/daf-18 signaling might be activated. Further, we highlighted the roles of one chloride ion exchanger gene sulp-6 in the survival of C. elegans and other two chloride ion channel genes clh-1 and clh-4 in the prolonged lifespan by PA-feeding through the modulating expression of genes involved in inflammation. Therefore, these findings reveal the detailed and novel molecular mechanisms on the longevity of C. elegans promoted by PA.

A Biomarker-based Biological Age in UK Biobank: Composition and Prediction of Mortality and Hospital Admissions

Background

Chronological age is the strongest risk factor for most chronic diseases. Developing a biomarker-based age and understanding its most important contributing biomarkers may shed light on the effects of age on later-life health and inform opportunities for disease prevention.

Methods

A subpopulation of 141 254 individuals healthy at baseline were studied, from among 480 019 UK Biobank participants aged 40–70 recruited in 2006–2010, and followed up for 6–12 years via linked death and secondary care records. Principal components of 72 biomarkers measured at baseline were characterized and used to construct sex-specific composite biomarker ages using the Klemera Doubal method, which derived a weighted sum of biomarker principal components based on their linear associations with chronological age. Biomarker importance in the biomarker ages was assessed by the proportion of the variation in the biomarker ages that each explained. The proportions of the overall biomarker and chronological age effects on mortality and age-related hospital admissions explained by the biomarker ages were compared using likelihoods in Cox proportional hazard models.

Results

Reduced lung function, kidney function, reaction time, insulin-like growth factor 1, hand grip strength, and higher blood pressure were key contributors to the derived biomarker age in both men and women. The biomarker ages accounted for >65% and >84% of the apparent effect of age on mortality and hospital admissions for the healthy and whole populations, respectively, and significantly improved prediction of mortality (p < .001) and hospital admissions (p < 1 × 10⁻¹⁰) over chronological age alone.

Conclusions

This study suggests that a broader, multisystem approach to research and prevention of diseases of aging warrants consideration.

Ranking Biomarkers of Aging by Citation Profiling and Effort Scoring

Aging affects most living organisms and includes the processes that reduce health and survival. The chronological and the biological age of individuals can differ remarkably, and there is a lack of reliable biomarkers to monitor the consequences of aging. In this review we give an overview of commonly mentioned and frequently used potential aging-related biomarkers. We were interested in biomarkers of aging in general and in biomarkers related to cellular senescence in particular. To answer the question whether a biological feature is relevant as a potential biomarker of aging or senescence in the scientific community we used the PICO strategy known from evidence-based medicine. We introduced two scoring systems, aimed at reflecting biomarker relevance and measurement effort, which can be used to support study designs in both clinical and research settings.

The Role of Artificial Intelligence in Managing Multimorbidity and Cancer

Traditional healthcare paradigms rely on the disease-centered approach aiming at reducing human nature by discovering specific drivers and biomarkers that cause the advent and progression of diseases. This reductive approach is not always suitable to understand and manage complex conditions, such as multimorbidity and cancer. Multimorbidity requires considering heterogeneous data to tailor preventing and targeting interventions. Personalized Medicine represents an innovative approach to address the care needs of multimorbid patients considering relevant patient characteristics, such as lifestyle and individual preferences, in opposition to the more traditional “one-size-fits-all” strategy focused on interventions designed at the population level. Integration of omic (e.g., genomics) and non-strictly medical (e.g., lifestyle, the exposome) data is necessary to understand patients’ complexity. Artificial Intelligence can help integrate and manage heterogeneous data through advanced machine learning and bioinformatics algorithms to define the best treatment for each patient with multimorbidity and cancer. The experience of an Italian research hospital, leader in the field of oncology, may help to understand the multifaceted issue of managing multimorbidity and cancer in the framework of Personalized Medicine.

Modeling the human aging transcriptome across tissues, health status, and sex

Aging in humans is an incredibly complex biological process that leads to increased susceptibility to various diseases. Understanding which genes are associated with healthy aging can provide valuable insights into aging mechanisms and possible avenues for therapeutics to prolong healthy life. However, modeling this complex biological process requires an enormous collection of high‐quality data along with cutting‐edge computational methods. Here, we have compiled a large meta‐analysis of gene expression data from RNA‐Seq experiments available from the Sequence Read Archive. We began by reprocessing more than 6000 raw samples—including mapping, filtering, normalization, and batch correction—to generate 3060 high‐quality samples spanning a large age range and multiple different tissues. We then used standard differential expression analyses and machine learning approaches to model and predict aging across the dataset, achieving an R² value of 0.96 and a root‐mean‐square error of 3.22 years. These models allow us to explore aging across health status, sex, and tissue and provide novel insights into possible aging processes. We also explore how preprocessing parameters affect predictions and highlight the reproducibility limits of these machine learning models. Finally, we develop an online tool for predicting the ages of human transcriptomic samples given raw gene expression counts. Together, this study provides valuable resources and insights into the transcriptomics of human aging.

ARDD 2020: from aging mechanisms to interventions

Aging is emerging as a druggable target with growing interest from academia, industry and investors. New technologies such as artificial intelligence and advanced screening techniques, as well as a strong influence from the industry sector may lead to novel discoveries to treat age-related diseases. The present review summarizes presentations from the 7th Annual Aging Research and Drug Discovery (ARDD) meeting, held online on the 1st to 4th of September 2020. The meeting covered topics related to new methodologies to study aging, knowledge about basic mechanisms of longevity, latest interventional strategies to target the aging process as well as discussions about the impact of aging research on society and economy. More than 2000 participants and 65 speakers joined the meeting and we already look forward to an even larger meeting next year. Please mark your calendars for the 8th ARDD meeting that is scheduled for the 31st of August to 3rd of September, 2021, at Columbia University, USA.

PsychoAge and SubjAge: development of deep markers of psychological and subjective age using artificial intelligence

Aging clocks that accurately predict human age based on various biodata types are among the most important recent advances in biogerontology. Since 2016 multiple deep learning solutions have been created to interpret facial photos, omics data, and clinical blood parameters in the context of aging. Some of them have been patented to be used in commercial settings. However, psychological changes occurring throughout the human lifespan have been overlooked in the field of “deep aging clocks”.

In this paper, we present two deep learning predictors trained on social and behavioral data from Midlife in the United States (MIDUS) study: (a) PsychoAge, which predicts chronological age, and (b) SubjAge, which describes personal aging rate perception. Using 50 distinct features from the MIDUS dataset these models have achieved a mean absolute error of 6.7 years for chronological age and 7.3 years for subjective age. We also show that both PsychoAge and SubjAge are predictive of all-cause mortality risk, with SubjAge being a more significant risk factor.

Both clocks contain actionable features that can be modified using social and behavioral interventions, which enables a variety of aging-related psychology experiment designs. The features used in these clocks are interpretable by human experts and may prove to be useful in shifting personal perception of aging towards a mindset that promotes productive and healthy behaviors.

CARDIORESPIRATORY SYSTEM AND ANTIHYPOXIC RESISTANCE STATE IN ELDER CORONARY HEART DISEASE PATIENTS

The aim of the study. Evaluation of cardiorespiratory system and microcirculation state in elder CHD patients with different resistance against hypoxia. Methods. The author has carried out a retrospective analysis of 103 CHD 60+-patients, their deaths have occurred due to cardiovascular events. Two patient groups have been formed including patients (68 persons) having kept their resistance to hypoxia (their blood SO2 level was never below 80 %) and patients (35 ones) with decreased resistance to hypoxia, their blood SO2 level having been dropped below 80 %. Results: The life span of patients with decreased hypoxia resistance is lower comparing to ones having kept such resistance. The patients with decreased hypoxia resistance demonstrate decreased ejection fraction, increased mass of left ventricle myocardium as well as increased left ventricle volumes. Such patients show also significant increase of mean daily values of systolic, diastolic, and mean arterial pressure. Generally, the micro-circulation state in patients with decreased anti-hypoxic resistance is lower comparing to persons having kept this resistance. Simultaneously, the endothelial function of persons with decreased anti-hypoxic resistance is significantly worse. The pulmonary ventilation system patency and bronchial patency in these persons are also lowered comparing to these indices in patients with kept anti-hypoxic resistance. Conclusions: The decrease of anti-hypoxic resistance leads to the shorter life span on elderly CHD patients. Such decreased resistance is accompanied by worsened potency of cardio-respiratory system, microcirculation, and endothelial system functioning.

Psychological aging, depression, and well-being

Aging is a multifactorial process, which affects the human body on every level and results in both biological and psychological changes. Multiple studies have demonstrated that a lower subjective age is associated with better mental and physical health, cognitive functions, well-being and satisfaction with life. In this work we propose a list of non-modifiable and modifiable factors that may possibly be influenced by subjective age and its changes across an individual's lifespan. These factors can be used for a future development of individual psychological aging clocks, which may be utilized as a sensitive measure for health status and overall life satisfaction. Furthermore, recent progress in artificial intelligence and biomarkers of biological aging have enabled scientists to discover and evaluate the efficacy of potential aging- and disease-modifying drugs and interventions. We propose that biomarkers of psychological age, which are just as important as those for biological age, may likewise be used for these purposes. Indeed, these two types of markers complement one another. We foresee the development of a broad range of parametric and deep psychological and biopsychological aging clocks, which may have implications for drug development and therapeutic interventions, and thus healthcare and other industries.

Age and life expectancy clocks based on machine learning analysis of mouse frailty

The identification of genes and interventions that slow or reverse aging is hampered by the lack of non-invasive metrics that can predict the life expectancy of pre-clinical models. Frailty Indices (FIs) in mice are composite measures of health that are cost-effective and non-invasive, but whether they can accurately predict health and lifespan is not known. Here, mouse FIs are scored longitudinally until death and machine learning is employed to develop two clocks. A random forest regression is trained on FI components for chronological age to generate the FRIGHT (Frailty Inferred Geriatric Health Timeline) clock, a strong predictor of chronological age. A second model is trained on remaining lifespan to generate the AFRAID (Analysis of Frailty and Death) clock, which accurately predicts life expectancy and the efficacy of a lifespan-extending intervention up to a year in advance. Adoption of these clocks should accelerate the identification of longevity genes and aging interventions.

Machine-Learning Analysis of Voice Samples Recorded through Smartphones: The Combined Effect of Ageing and Gender

Background: Experimental studies using qualitative or quantitative analysis have demonstrated that the human voice progressively worsens with ageing. These studies, however, have mostly focused on specific voice features without examining their dynamic interaction. To examine the complexity of age-related changes in voice, more advanced techniques based on machine learning have been recently applied to voice recordings but only in a laboratory setting. We here recorded voice samples in a large sample of healthy subjects. To improve the ecological value of our analysis, we collected voice samples directly at home using smartphones. Methods: 138 younger adults (65 males and 73 females, age range: 15–30) and 123 older adults (47 males and 76 females, age range: 40–85) produced a sustained emission of a vowel and a sentence. The recorded voice samples underwent a machine learning analysis through a support vector machine algorithm. Results: The machine learning analysis of voice samples from both speech tasks discriminated between younger and older adults, and between males and females, with high statistical accuracy. Conclusions: By recording voice samples through smartphones in an ecological setting, we demonstrated the combined effect of age and gender on voice. Our machine learning analysis demonstrates the effect of ageing on voice.