Population Specific Biomarkers of Human Aging: A Big Data Study Using South Korean, Canadian, and Eastern European Patient Populations

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

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

New insights into healthy ageing, inflammageing and frailty using metabolomics

Human ageing is a normal process and does not necessarily result in the development of frailty. A mix of genetic, environmental, dietary, and lifestyle factors can have an impact on ageing, and whether an individual develops frailty. Frailty is defined as the loss of physiological reserve both at the physical and cellular levels, where systemic processes such as oxidative stress and inflammation contribute to physical decline. The newest "omics" technology and systems biology discipline, metabolomics, enables thorough characterisation of small-molecule metabolites in biological systems at a particular time and condition. In a biological system, metabolites-cellular intermediate products of metabolic reactions-reflect the system's final response to genomic, transcriptomic, proteomic, epigenetic, or environmental alterations. As a relatively newer technique to characterise metabolites and biomarkers in ageing and illness, metabolomics has gained popularity and has a wide range of applications. We will give a comprehensive summary of what is currently known about metabolomics in studies of ageing, with a focus on biomarkers for frailty. Metabolites related to amino acids, lipids, carbohydrates, and redox metabolism may function as biomarkers of ageing and/or frailty development, based on data obtained from human studies. However, there is a complexity that underpins biological ageing, due to both genetic and environmental factors that play a role in orchestrating the ageing process. Therefore, there is a critical need to identify pathways that contribute to functional decline in people with frailty.

A novel approach to quantifying individual's biological aging using Korea's national health screening program toward precision public health

Accurate prediction of biological age can inform public health measures to extend healthy lifespans and reduce chronic conditions. Multiple theoretical models and methods have been developed; however, their applicability and accuracy are still not extensive. Here, we report Differential Aging and Health Index (DAnHI), a novel measure of age deviation, developed using physical and serum biomarkers from four million individuals in Korea's National Health Screening Program. Participants were grouped into aging statuses (< 26 vs. ≥ 26, < 27 vs. ≥ 27, …, < 75 vs. ≥ 75 years) as response variables in a binary logistic regression model with thirteen biomarkers as independent variables. DAnHI for each individual was calculated as the weighted mean of their relative probabilities of being classified into each older age status, based on model ages ranging from 26 to 75. DAnHI in our large study population showed a steady increase with the increase in age and was positively associated with death after adjusting for chronological age. However, the effect size of DAnHI on the risk of death varied according to the age group and sex. The hazard ratio was highest in the 50-59-year age group and then decreased as the individuals aged. This study demonstrates that routine health check-up biomarkers can be integrated into a quantitative measure for predicting aging-related health status and death via appropriate statistical models and methodology. Our DAnHI-based results suggest that the same level of aging-related health status does not indicate the same degree of risk for death.

Higher blood biochemistry-based biological age developed by advanced deep learning techniques is associated with frailty in geriatric rehabilitation inpatients: RESORT

BACKGROUND

Accelerated biological ageing is a major underlying mechanism of frailty development. This study aimed to investigate if the biological age measured by a blood biochemistry-based ageing clock is associated with frailty in geriatric rehabilitation inpatients.

METHODS

Within the REStORing health of acutely unwell adulTs (RESORT) cohort, patients' biological age was measured by an ageing clock based on completed data of 30 routine blood test variables measured at rehabilitation admission. The delta of biological age minus chronological age (years) was calculated. Ordinal logistic regression and multinomial logistic regression were performed to evaluate the association of the delta of ages with frailty assessed by the Clinical Frailty Scale. Effect modification of Cumulative Illness Rating Scale (CIRS) score was tested.

RESULTS

A total of 1187 geriatric rehabilitation patients were included (median age: 83.4 years, IQR: 77.7-88.5; 57.4 % female). The biochemistry-based biological age was strongly correlated with chronological age (Spearman r = 0.883). After adjustment for age, sex and primary reasons for acute admission, higher biological age (per 1 year higher in delta of ages) was associated with more severe frailty at admission (OR: 1.053, 95 % CI:1.012-1.096) in patients who had a CIRS score of <12 not in patients with a CIRS score >12. The delta of ages was not associated with frailty change from admission to discharge. The specific frailty manifestations as cardiac, hematological, respiratory, renal, and endocrine conditions were associated with higher biological age.

CONCLUSION

Higher biological age was associated with severe frailty in geriatric rehabilitation inpatients with less comorbidity burden.

Biomarkers of Aging and Relevant Evaluation Techniques: A Comprehensive Review

The risk of developing chronic illnesses and disabilities is increasing with age. To predict and prevent aging, biomarkers relevant to the aging process must be identified. This paper reviews the known molecular, cellular, and physiological biomarkers of aging. Moreover, we discuss the currently available technologies for identifying these biomarkers, and their applications and potential in aging research. We hope that this review will stimulate further research and innovation in this emerging and fast-growing field.

Association between biological aging and diabetic retinopathy

The impact of aging on diabetic retinopathy (DR) remains underestimated. The current study aimed to investigate the association between biological aging and DR, in contrast to chronological age (CA). Using the National Health and Nutrition Survey data from 2005 to 2008. Biological aging was evaluated through the biological age (BA) and phenotypic age (PA), which were calculated from clinical markers. DR was identified in participants with diabetes mellitus (DM) when they exhibited one or more retinal microaneurysms or retinal blot hemorrhages under retinal imaging, with or without the presence of more severe lesions. Survey-weighted multivariable logistic regression was performed, and the regression model was further fitted using restricted cubic splines. The discriminatory capability and clinical utility of the model were evaluated using receiver operating characteristic (ROC) curves and decision curve analysis (DCA). Based on weighted analyses, of the 3100 participants included in this study, of which 162 had DR. In the adjusted model, BA (odds ratio [OR] = 1.12, 95% CI, 1.06-1.18) and PA (OR = 1.11, 95% CI, 1.07-1.14) were associated with DR, while CA was not significantly (OR = 1.01, 95% CI, 0.99-1.03). Narrowing the analysis to DM participants and adjusting for factors like insulin showed similar results. ROC and DCA analyses indicate that BA/PA predicted DR better than CA and offer greater clinical utility. The positive association between BA/PA and DR was consistent across subgroups despite potential interactions. Biological aging heightens DR risk, with BA/PA showing a stronger association than CA. Our findings underscored the importance of timely anti-aging interventions for preventing DR.

Blood Test-Based Age Acceleration Is Inversely Associated with High-Volume Sports Activity

PURPOSE

We develop blood test-based aging clocks and examine how these clocks reflect high-volume sports activity.

METHODS

We use blood tests and body metrics data of 421 Hungarian athletes and 283 age-matched controls (mean age, 24.1 and 23.9 yr, respectively), the latter selected from a group of healthy Caucasians of the National Health and Nutrition Examination Survey (NHANES) to represent the general population ( n = 11,412). We train two age prediction models (i.e., aging clocks) using the NHANES dataset: the first model relies on blood test parameters only, whereas the second one additionally incorporates body measurements and sex.

RESULTS

We find lower age acceleration among athletes compared with the age-matched controls with a median value of -1.7 and 1.4 yr, P < 0.0001. BMI is positively associated with age acceleration among the age-matched controls ( r = 0.17, P < 0.01) and the unrestricted NHANES population ( r = 0.11, P < 0.001). We find no association between BMI and age acceleration within the athlete dataset. Instead, age acceleration is positively associated with body fat percentage ( r = 0.21, P < 0.05) and negatively associated with skeletal muscle mass (Pearson r = -0.18, P < 0.05) among athletes. The most important blood test features in age predictions were serum ferritin, mean cell volume, blood urea nitrogen, and albumin levels.

CONCLUSIONS

We develop and apply blood test-based aging clocks to adult athletes and healthy controls. The data suggest that high-volume sports activity is associated with slowed biological aging. Here, we propose an alternative, promising application of routine blood tests.

Blood-based biological ageing and red cell distribution width are associated with prevalent Parkinson's disease: findings from a large Italian population cohort

Background

Aging clocks tag the actual underlying age of an organism and its discrepancy with chronological age and have been reported to predict incident disease risk in the general population. However, the relationship with neurodegenerative risk and in particular with Parkinson's Disease (PD) remains unclear, with few discordant findings reporting associations with both incident and prevalent PD risk.

Objective

To clarify this relationship, we computed a common aging clock based on blood markers and tested the resulting discrepancy with chronological age (ΔPhenoAge) for association with both incident and prevalent PD risk.

Methods

In a large Italian population cohort - the Moli-sani study (N=23,437; age ≥ 35 years; 52% women) - we carried out both Cox Proportional Hazards regressions modelling ΔPhenoAge as exposure and incident PD as outcome, and linear models testing prevalent PD as exposure and ΔPhenoAge as outcome. All models were incrementally adjusted for age, sex, education level completed and other risk/protective factors previously associated with PD risk in the same cohort (prevalent dysthyroidism, hypertension, diabetes, use of oral contraceptives, exposure to paints, daily coffee intake and cigarette smoking).

Results

No significant association between incident PD risk (209 cases, median (IQR) follow-up time 11.19 (2.03) years) and PhenoAging was observed (Hazard Ratio [95% Confidence Interval] = 0.98 [0.71; 1.37]). However, a small but significant increase of ΔPhenoAge was observed in prevalent PD cases vs healthy subjects (β (Standard Error) = 1.39 (0.70)). An analysis of each component biomarker of PhenoAge revealed a significant positive association of prevalent PD status with red cell distribution width (RDW; β (SE) = 0.46 (0.18)). All the remaining markers did not show any significant evidence of association.

Conclusion

The reported evidence highlights systemic effects of prevalent PD status on biological aging and red cell distribution width. Further cohort and functional studies may help shedding a light on the related pathways altered at the organism level in prevalent PD, like red cells variability, inflammatory and oxidative stress mechanisms.

Deep neural network-estimated age using optical coherence tomography predicts mortality

The concept of biological age has emerged as a measurement that reflects physiological and functional decline with ageing. Here we aimed to develop a deep neural network (DNN) model that predicts biological age from optical coherence tomography (OCT). A total of 84,753 high-quality OCT images from 53,159 individuals in the UK Biobank were included, among which 12,631 3D-OCT images from 8,541 participants without any reported medical conditions at baseline were used to develop an age prediction model. For the remaining 44,618 participants, OCT age gap, the difference between the OCT-predicted age and chronological age, was calculated for each participant. Cox regression models assessed the association between OCT age gap and mortality. The DNN model predicted age with a mean absolute error of 3.27 years and showed a strong correlation of 0.85 with chronological age. After a median follow-up of 11.0 years (IQR 10.9-11.1 years), 2,429 deaths (5.44%) were recorded. For each 5-year increase in OCT age gap, there was an 8% increased mortality risk (hazard ratio [HR] = 1.08, CI:1.02-1.13, P = 0.004). Compared with an OCT age gap within ± 4 years, OCT age gap less than minus 4 years was associated with a 16% decreased mortality risk (HR = 0.84, CI: 0.75-0.94, P = 0.002) and OCT age gap more than 4 years showed an 18% increased risk of death incidence (HR = 1.18, CI: 1.02-1.37, P = 0.026). OCT imaging could serve as an ageing biomarker to predict biological age with high accuracy and the OCT age gap, defined as the difference between the OCT-predicted age and chronological age, can be used as a marker of the risk of mortality.

Towards personalized nicotinamide mononucleotide (NMN) supplementation: Nicotinamide adenine dinucleotide (NAD) concentration

Nicotinamide mononucleotide (NMN) is a precursor of nicotinamide adenine dinucleotide (NAD), which declines with age. Supplementation of NMN has been shown to improve blood NAD concentration. However, the optimal NMN dose remains unclear. This is a post-hoc analysis of a double-blinded clinical trial involving 80 generally healthy adults aged 40-65 years. The participants received a placebo or daily 300 mg, 600 mg, or 900 mg NMN for 60 days. Blood NAD concentration, blood biological age, homeostatic model assessment for insulin resistance, 6-minute walk test, and 36-item short-form survey (SF-36) were measured at baseline and after supplement. A significant dose-dependent increase in NAD concentration change (NADΔ) was observed following NMN supplementation, with a large coefficient of variation (29.2-113.3%) within group. The increase in NADΔ was associated with an improvement in the walking distance of 6-minute walk test and the SF-36 score. The median effect dose of NADΔ for the 6-minute walk test and SF-36 score was 15.7 nmol/L (95% CI: 10.9-20.5 nmol/L) and 13.5 nmol/L (95% CI; 10.5-16.5 nmol/L), respectively. Because of the high interindividual variability of the NADΔ after NMN supplementation, monitoring NAD concentration can provide valuable insights for tailoring personalized dosage regimens and optimizing NMN utilization.

Application of AI in biological age prediction

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

Validation of biomarkers of aging

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

An accurate aging clock developed from large-scale gut microbiome and human gene expression data

Accurate measurement of the biological markers of the aging process could provide an "aging clock" measuring predicted longevity and enable the quantification of the effects of specific lifestyle choices on healthy aging. Using machine learning techniques, we demonstrate that chronological age can be predicted accurately from (1) the expression level of human genes in capillary blood and (2) the expression level of microbial genes in stool samples. The latter uses a very large metatranscriptomic dataset, stool samples from 90,303 individuals, which arguably results in a higher quality microbiome-aging model than prior work. Our analysis suggests associations between biological age and lifestyle/health factors, e.g., people on a paleo diet or with IBS tend to have higher model-predicted ages and people on a vegetarian diet tend to have lower model-predicted ages. We delineate the key pathways of systems-level biological decline based on the age-specific features of our model.

Accurate estimation of biological age and its application in disease prediction using a multimodal image Transformer system

Aging in an individual refers to the temporal change, mostly decline, in the body's ability to meet physiological demands. Biological age (BA) is a biomarker of chronological aging and can be used to stratify populations to predict certain age-related chronic diseases. BA can be predicted from biomedical features such as brain MRI, retinal, or facial images, but the inherent heterogeneity in the aging process limits the usefulness of BA predicted from individual body systems. In this paper, we developed a multimodal Transformer-based architecture with cross-attention which was able to combine facial, tongue, and retinal images to estimate BA. We trained our model using facial, tongue, and retinal images from 11,223 healthy subjects and demonstrated that using a fusion of the three image modalities achieved the most accurate BA predictions. We validated our approach on a test population of 2,840 individuals with six chronic diseases and obtained significant difference between chronological age and BA (AgeDiff) than that of healthy subjects. We showed that AgeDiff has the potential to be utilized as a standalone biomarker or conjunctively alongside other known factors for risk stratification and progression prediction of chronic diseases. Our results therefore highlight the feasibility of using multimodal images to estimate and interrogate the aging process.

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.

Developing an interpretable machine learning model for predicting COVID-19 patients deteriorating prior to intensive care unit admission using laboratory markers

Coronavirus disease (COVID-19) remains a significant global health challenge, prompting a transition from emergency response to comprehensive management strategies. Furthermore, the emergence of new variants of concern, such as BA.2.286, underscores the need for early detection and response to new variants, which continues to be a crucial strategy for mitigating the impact of COVID-19, especially among the vulnerable population. This study aims to anticipate patients requiring intensive care or facing elevated mortality risk throughout their COVID-19 infection while also identifying laboratory predictive markers for early diagnosis of patients. Therefore, haematological, biochemical, and demographic variables were retrospectively evaluated in 8,844 blood samples obtained from 2,935 patients before intensive care unit admission using an interpretable machine learning model. Feature selection techniques were applied using precision-recall measures to address data imbalance and evaluate the suitability of the different variables. The model was trained using stratified cross-validation with k=5 and internally validated, achieving an accuracy of 77.27%, sensitivity of 78.55%, and area under the receiver operating characteristic (AUC) of 0.85; successfully identifying patients at increased risk of severe progression. From a medical perspective, the most important features of the progression or severity of patients with COVID-19 were lactate dehydrogenase, age, red blood cell distribution standard deviation, neutrophils, and platelets, which align with findings from several prior investigations. In light of these insights, diagnostic processes can be significantly expedited through the use of laboratory tests, with a greater focus on key indicators. This strategic approach not only improves diagnostic efficiency but also extends its reach to a broader spectrum of patients. In addition, it allows healthcare professionals to take early preventive measures for those most at risk of adverse outcomes, thereby optimising patient care and prognosis.

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

BACKGROUND

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

METHODS

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

FINDINGS

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

INTERPRETATION

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

FUNDING

National Science Foundation and National Institutes of Health.

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

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

Central obesity and its association with retinal age gap: insights from the UK Biobank study

BACKGROUND

Conflicting evidence exists on the association between ageing and obesity. Retinal age derived from fundus images has been validated as a novel biomarker of ageing. In this study, we aim to investigate the association between different anthropometric phenotypes based on body mass index (BMI) and waist circumference (WC) and the retinal age gap (retinal age minus chronological age).

METHODS

A total of 35,550 participants with BMI, WC and qualified retinal imaging data available were included to investigate the association between anthropometric groups and retinal ageing. Participants were stratified into 7 different body composition groups based on BMI and WC (Normal-weight/Normal WC, Overweight/Normal WC, Mild obesity/Normal WC, Normal-weight/High WC, Overweight/High WC, Mild obesity/High WC, and Severe obesity/High WC). Linear regression and logistic regression models were fitted to investigate the association between the seven anthropometric groups and retinal age gap as continuous and categorical outcomes, respectively.

RESULTS

A total of 35,550 participants (55.6% females) with a mean age 56.8 ± 8.04 years were included in the study. Individuals in the Overweight/High WC, Mild obesity/High WC and Severe obesity/High WC groups were associated with an increase in the retinal age gap, compared with those in the Normal Weight/Normal WC group (β = 0.264, 95% CI: 0.105-0.424, P =0.001; β = 0.226, 95% CI: 0.082-0.371, P = 0.002; β = 0.273, 95% CI: 0.081-0.465, P = 0.005; respectively) in fully adjusted models. Similar findings were noted in the association between the anthropometric groups and retinal ageing process as a categorical outcome.

CONCLUSION

A significant positive association exists between central obesity and accelerated ageing indexed by retinal age gaps, highlighting the significance of maintaining a healthy body shape.

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.

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.

Small immunological clocks identified by deep learning and gradient boosting

Background

The aging process affects all systems of the human body, and the observed increase in inflammatory components affecting the immune system in old age can lead to the development of age-associated diseases and systemic inflammation.

Results

We propose a small clock model SImAge based on a limited number of immunological biomarkers. To regress the chronological age from cytokine data, we first use a baseline Elastic Net model, gradient-boosted decision trees models, and several deep neural network architectures. For the full dataset of 46 immunological parameters, DANet, SAINT, FT-Transformer and TabNet models showed the best results for the test dataset. Dimensionality reduction of these models with SHAP values revealed the 10 most age-associated immunological parameters, taken to construct the SImAge small immunological clock. The best result of the SImAge model shown by the FT-Transformer deep neural network model has mean absolute error of 6.94 years and Pearson ρ = 0.939 on the independent test dataset. Explainable artificial intelligence methods allow for explaining the model solution for each individual participant.

Conclusions

We developed an approach to construct a model of immunological age based on just 10 immunological parameters, coined SImAge, for which the FT-Transformer deep neural network model had proved to be the best choice. The model shows competitive results compared to the published studies on immunological profiles, and takes a smaller number of features as an input. Neural network architectures outperformed gradient-boosted decision trees, and can be recommended in the further analysis of immunological profiles.

Explainable machine learning framework to predict personalized physiological aging

Attaining personalized healthy aging requires accurate monitoring of physiological changes and identifying subclinical markers that predict accelerated or delayed aging. Classic biostatistical methods most rely on supervised variables to estimate physiological aging and do not capture the full complexity of inter-parameter interactions. Machine learning (ML) is promising, but its black box nature eludes direct understanding, substantially limiting physician confidence and clinical usage. Using a broad population dataset from the National Health and Nutrition Examination Survey (NHANES) study including routine biological variables and after selection of XGBoost as the most appropriate algorithm, we created an innovative explainable ML framework to determine a Personalized physiological age (PPA). PPA predicted both chronic disease and mortality independently of chronological age. Twenty-six variables were sufficient to predict PPA. Using SHapley Additive exPlanations (SHAP), we implemented a precise quantitative associated metric for each variable explaining physiological (i.e., accelerated or delayed) deviations from age-specific normative data. Among the variables, glycated hemoglobin (HbA1c) displays a major relative weight in the estimation of PPA. Finally, clustering profiles of identical contextualized explanations reveal different aging trajectories opening opportunities to specific clinical follow-up. These data show that PPA is a robust, quantitative and explainable ML-based metric that monitors personalized health status. Our approach also provides a complete framework applicable to different datasets or variables, allowing precision physiological age estimation.

Age prediction from human blood plasma using proteomic and small RNA data: a comparative analysis

Aging clocks, built from comprehensive molecular data, have emerged as promising tools in medicine, forensics, and ecological research. However, few studies have compared the suitability of different molecular data types to predict age in the same cohort and whether combining them would improve predictions. Here, we explored this at the level of proteins and small RNAs in 103 human blood plasma samples. First, we used a two-step mass spectrometry approach measuring 612 proteins to select and quantify 21 proteins that changed in abundance with age. Notably, proteins increasing with age were enriched for components of the complement system. Next, we used small RNA sequencing to select and quantify a set of 315 small RNAs that changed in abundance with age. Most of these were microRNAs (miRNAs), downregulated with age, and predicted to target genes related to growth, cancer, and senescence. Finally, we used the collected data to build age-predictive models. Among the different types of molecules, proteins yielded the most accurate model (R² = 0.59 ± 0.02), followed by miRNAs as the best-performing class of small RNAs (R² = 0.54 ± 0.02). Interestingly, the use of protein and miRNA data together improved predictions (R2 = 0.70 ± 0.01). Future work using larger sample sizes and a validation dataset will be necessary to confirm these results. Nevertheless, our study suggests that combining proteomic and miRNA data yields superior age predictions, possibly by capturing a broader range of age-related physiological changes. It will be interesting to determine if combining different molecular data types works as a general strategy to improve future aging clocks.

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.

Retinal age gap as a predictive biomarker for mortality risk

AIM

To develop a deep learning (DL) model that predicts age from fundus images (retinal age) and to investigate the association between retinal age gap (retinal age predicted by DL model minus chronological age) and mortality risk.

METHODS

A total of 80 169 fundus images taken from 46 969 participants in the UK Biobank with reasonable quality were included in this study. Of these, 19 200 fundus images from 11 052 participants without prior medical history at the baseline examination were used to train and validate the DL model for age prediction using fivefold cross-validation. A total of 35 913 of the remaining 35 917 participants had available mortality data and were used to investigate the association between retinal age gap and mortality.

RESULTS

The DL model achieved a strong correlation of 0.81 (p<0·001) between retinal age and chronological age, and an overall mean absolute error of 3.55 years. Cox regression models showed that each 1 year increase in the retinal age gap was associated with a 2% increase in risk of all-cause mortality (hazard ratio (HR)=1.02, 95% CI 1.00 to 1.03, p=0.020) and a 3% increase in risk of cause-specific mortality attributable to non-cardiovascular and non-cancer disease (HR=1.03, 95% CI 1.00 to 1.05, p=0.041) after multivariable adjustments. No significant association was identified between retinal age gap and cardiovascular- or cancer-related mortality.

CONCLUSIONS

Our findings indicate that retinal age gap might be a potential biomarker of ageing that is closely related to risk of mortality, implying the potential of retinal image as a screening tool for risk stratification and delivery of tailored interventions.

Biological age models based on a healthy Han Chinese population

BACKGROUND

Biological age (BA) may reflect the actual aging state in humans better than chronological age (CA). The study aimed to construct BA models suitable for the Chinese Han population by selecting appropriate aging markers and evaluation methods.

METHODS

A total of 1207 individuals (21∼91 years) from the Han Chinese population in Beijing were examined for essential organ functions, and 156 cardiovascular, pulmonary function, and atherosclerotic indices and clinical and genetic factors were used as candidate markers of aging. BA models were constructed using multiple linear regression (MLR), principal component analysis (PCA), and the Klemera and Doubal method (KDM). Models were internally and externally validated using cross-validation and disease populations.

RESULTS

Nine aging markers were selected. Two MLR, three PCA, and three KDM models were successfully constructed. External validation showed that the difference between CA and BA was most significant in the PCA3 and KDM2 models, while there was no significant difference in the MLR1 and MLR2 models; the fitted lines for BA in the disease population were higher than those in the healthy population in the MLR1, MLR2, KDM1, and KDM2 models, while the other models showed the opposite.

CONCLUSIONS

Based on a healthy population in Beijing, nine markers representing multiple organ/system functions were screened from the candidate markers, eight methods were successfully used to construct BA models, and the KDM2 model was found to potentially be more appropriate for assessing BA in the Chinese Han population.

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.

Association between the Inflammatory Potential of the Diet and Biological Aging: A Cross-Sectional Analysis of 4510 Adults from the Moli-Sani Study Cohort

Chronological age (CA) may not accurately reflect the health status of an individual. Rather, biological age (BA) or hypothetical underlying "functional" age has been proposed as a relevant indicator of healthy aging. Observational studies have found that decelerated biological aging or Δage (BA-CA) is associated with a lower risk of disease and mortality. In general, CA is associated with low-grade inflammation, a condition linked to the risk of the incidence of disease and overall cause-specific mortality, and is modulated by diet. To address the hypothesis that diet-related inflammation is associated with Δage, a cross-sectional analysis of data from a sub-cohort from the Moli-sani Study (2005-2010, Italy) was performed. The inflammatory potential of the diet was measured using the Energy-adjusted Dietary Inflammatory Index (E-DII^TM) and a novel literature-based dietary inflammation score (DIS). A deep neural network approach based on circulating biomarkers was used to compute BA, and the resulting Δage was fit as the dependent variable. In 4510 participants (men 52.0%), the mean of CA (SD) was 55.6 y (±11.6), BA 54.8 y (±8.6), and Δage -0.77 (±7.7). In a multivariable-adjusted analysis, an increase in E-DII^TM and DIS scores led to an increase in Δage (β = 0.22; 95%CI 0.05, 0.38; β = 0.27; 95%CI 0.10, 0.44, respectively). We found interaction for DIS by sex and for E-DII^TM by BMI. In conclusion, a pro-inflammatory diet is associated with accelerated biological aging, which likely leads to an increased long-term risk of inflammation-related diseases and mortality.

Machine learning-based prediction of infarct size in patients with ST-segment elevation myocardial infarction: A multi-center study

BACKGROUND

Cardiac magnetic resonance imaging (CMR) is the gold standard for measuring infarct size (IS). However, this method is expensive and requires a specially trained technologist to administer. We therefore sought to quantify the IS using machine learning (ML) based analysis on clinical features, which is a convenient and cost-effective alternative to CMR.

METHODS AND RESULTS

We included 315 STEMI patients with CMR examined one week after morbidity in final analysis. After feature selection by XGBoost on fifty-six clinical features, we used five ML algorithms (random forest (RF), light gradient boosting decision machine, deep forest, deep neural network, and stacking) to predict IS with 26 (selected by XGBoost with information gain greater than average level of 56 features) and the top 10 features, during which 5-fold cross-validation were used to train and optimize models. We then evaluated the value of actual and ML-IS for the prediction of adverse remodeling. Our finding indicates that MLs outperform the linear regression in predicting IS. Specifically, the RF with five predictors identified by the exhaustive method performed better than linear regression (LR) with 10 indicators (R² of RF: 0.8; LR: 0). The finding also shows that both actual and ML-IS were independently associated with adverse remodeling. ML-IS ≥ 21% was associated with a twofold increase in the risk of LV remodeling (P < 0.01) compared with patients with reference IS (1st tertile).

CONCLUSION

ML-based methods can predict IS with widely available clinical features, which provide a proof-of-concept tool to quantitatively assess acute phase IS.

Blood Inflammatory Biomarkers Differentiate Inpatient and Outpatient Coronavirus Disease 2019 From Influenza

Background

The ongoing circulation of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) poses a diagnostic challenge because symptoms of coronavirus disease 2019 (COVID-19) are difficult to distinguish from other respiratory diseases. Our goal was to use statistical analyses and machine learning to identify biomarkers that distinguish patients with COVID-19 from patients with influenza.

Methods

Cytokine levels were analyzed in plasma and serum samples from patients with influenza and COVID-19, which were collected as part of the Centers for Disease Control and Prevention's Hospitalized Adult Influenza Vaccine Effectiveness Network (inpatient network) and the US Flu Vaccine Effectiveness (outpatient network).

Results

We determined that interleukin (IL)-10 family cytokines are significantly different between COVID-19 and influenza patients. The results suggest that the IL-10 family cytokines are a potential diagnostic biomarker to distinguish COVID-19 and influenza infection, especially for inpatients. We also demonstrate that cytokine combinations, consisting of up to 3 cytokines, can distinguish SARS-CoV-2 and influenza infection with high accuracy in both inpatient (area under the receiver operating characteristics curve [AUC] = 0.84) and outpatient (AUC = 0.81) groups, revealing another potential screening tool for SARS-CoV-2 infection.

Conclusions

This study not only reveals prospective screening tools for COVID-19 infections that are independent of polymerase chain reaction testing or clinical condition, but it also emphasizes potential pathways involved in disease pathogenesis that act as potential targets for future mechanistic studies.

Blood Markers of Biological Age Evaluates Clinic Complex Medical Spa Programs

BACKGROUND

Kivach Clinic has developed a special medical spa program to prevent aging-related conditions in metabolic, cardio-vascular, and neurological states. Spa programs modify diet, physical activity, and lymphatic drainage, as it deteriorates with aging. We investigated its influence on the blood markers of biological age of patients during their stay to objectify the potential of spa treatment for influencing the risk of age-related events.

METHODS

The artificial deep learning model Aging.ai 3.0 was based on blood parameters. The change in the biological age of 43 patients was assessed after their 14-day spa treatment at Kivach Clinic.

RESULTS

Biological age decreased in 29 patients (median decrease: 8 years, mean: 8.83 years), increased in 10 patients (median increase: 3 years, mean: 5.33 years) and remained unchanged in 4 patients. Overall mean values for the entire patient group were as follows: median value was -3 years, and mean was -4.79 ± 1.2 years (p-value = 0.00025, t-test).

CONCLUSIONS

The capability of specially selected medical spa treatment to reduce human biological age (assessed by Aging.AI 3.0) has been established.

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.

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.

Reverse causal relationship between periodontitis and shortened telomere length: Bidirectional two-sample Mendelian random analysis

Background

Observational studies have demonstrated a link between shortened telomere lengths(TL) and chronic periodontitis. However, whether the shortened TL is the cause or the result of periodontitis is unknown.Therefore, our objective was to investigate a bidirectional causal relationship between periodontitis and TL using a two-sample Mendel randomized (MR) study.

Methods

A two-sample bidirectional MR analysis using publicly available genome-wide association study (GWAS) data was used. As the primary analysis, inverse variance weighting (IVW) was employed. To identify pleiotropy, we used leave-one-out analysis, MR-Egger, Weighted median, Simple mode, Weighted mode, and MR pleiotropy residual sum and outlier (MR-PRESSO).

Results

In reverse MR results, a genetic prediction of short TL was causally associated with a higher risk of periodontitis (IVW: odds ratio [OR]: 1.0601, 95% confidence interval [CI]: 1.0213 to 1.1002; P =0.0021) and other complementary MR methods. In the forward MR analysis, periodontitis was shown to have no significant effect on TL (IVW: p = 0.7242), with consistent results for the remaining complementary MR. No pleiotropy was detected in sensitivity analysis (all P>0.05).

Conclusion

Our MR studies showed a reverse causal relationship, with shorten TL being linked to a higher risk of periodontitis, rather than periodontitis shorten that TL. Future research is needed to investigate the relationship between cell senescence and the disease.

Biological Age Predictors: The Status Quo and Future Trends

There is no single universal biomarker yet to estimate overall health status and longevity prospects. Moreover, a consensual approach to the very concept of aging and the means of its assessment are yet to be developed. Markers of aging could facilitate effective health control, more accurate life expectancy estimates, and improved health and quality of life. Clinicians routinely use several indicators that could be biomarkers of aging. Duly validated in a large cohort, models based on a combination of these markers could provide a highly accurate assessment of biological age and the pace of aging. Biological aging is a complex characteristic of chronological age (usually), health-to-age concordance, and medically estimated life expectancy. This study is a review of the most promising techniques that could soon be used in routine clinical practice. Two main selection criteria were applied: a sufficient sample size and reliability based on validation. The selected biological age calculators were grouped according to the type of biomarker used: (1) standard clinical and laboratory markers; (2) molecular markers; and (3) epigenetic markers. The most accurate were the calculators, which factored in a variety of biomarkers. Despite their demonstrated effectiveness, most of them require further improvement and cannot yet be considered for use in standard clinical practice. To illustrate their clinical application, we reviewed their use during the COVID-19 pandemic.

An evaluation of aging measures: from biomarkers to clocks

With the increasing number of aged population and growing burden of healthy aging demands, a rational standard for evaluation aging is in urgent need. The advancement of medical testing technology and the prospering of artificial intelligence make it possible to evaluate the biological status of aging from a more comprehensive view. In this review, we introduced common aging biomarkers and concluded several famous aging clocks. Aging biomarkers reflect changes in the organism at a molecular or cellular level over time while aging clocks tend to be more of a generalization of the overall state of the organism. We expect to construct a framework for aging evaluation measurement from both micro and macro perspectives. Especially, population-specific aging clocks and multi-omics aging clocks may better fit the demands to evaluate aging in a comprehensive and multidimensional manner and make a detailed classification to represent different aging rates at tissue/organ levels. This framework will promisingly provide a crucial basis for disease diagnosis and intervention assessment in geroscience.

A machine learning-based data mining in medical examination data: a biological features-based biological age prediction model

Background

Biological age (BA) has been recognized as a more accurate indicator of aging than chronological age (CA). However, the current limitations include: insufficient attention to the incompleteness of medical data for constructing BA; Lack of machine learning-based BA (ML-BA) on the Chinese population; Neglect of the influence of model overfitting degree on the stability of the association results.

Methods and results

Based on the medical examination data of the Chinese population (45–90 years), we first evaluated the most suitable missing interpolation method, then constructed 14 ML-BAs based on biomarkers, and finally explored the associations between ML-BAs and health statuses (healthy risk indicators and disease). We found that round-robin linear regression interpolation performed best, while AutoEncoder showed the highest interpolation stability. We further illustrated the potential overfitting problem in ML-BAs, which affected the stability of ML-Bas’ associations with health statuses. We then proposed a composite ML-BA based on the Stacking method with a simple meta-model (STK-BA), which overcame the overfitting problem, and associated more strongly with CA (r = 0.66, P < 0.001), healthy risk indicators, disease counts, and six types of disease.

Conclusion

We provided an improved aging measurement method for middle-aged and elderly groups in China, which can more stably capture aging characteristics other than CA, supporting the emerging application potential of machine learning in aging research.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04966-7.

Artificial Intelligence Confirming Treatment Success: The Role of Gender- and Age-Specific Scales in Performance Evaluation

In plastic surgery and cosmetic dermatology, photographic data are an invaluable element of research and clinical practice. Additionally, the use of before and after images is a standard documentation method for procedures, and these images are particularly useful in consultations for effective communication with the patient. An artificial intelligence (AI)-based approach has been proven to have significant results in medical dermatology, plastic surgery, and antiaging procedures in recent years, with applications ranging from skin cancer screening to 3D face reconstructions, the prediction of biological age and perceived age. The increasing use of AI and computer vision methods is due to their noninvasive nature and their potential to provide remote diagnostics. This is especially helpful in instances where traveling to a physical office is complicated, as we have experienced in recent years with the global coronavirus pandemic. However, one question remains: how should the results of AI-based analysis be presented to enable personalization? In this paper, the author investigates the benefit of using gender- and age-specific scales to present skin parameter scores calculated using AI-based systems when analyzing image data.

Psychological factors substantially contribute to biological aging: evidence from the aging rate in Chinese older adults

We have developed a deep learning aging clock using blood test data from the China Health and Retirement Longitudinal Study, which has a mean absolute error of 5.68 years. We used the aging clock to demonstrate the connection between the physical and psychological aspects of aging. The clock detects accelerated aging in people with heart, liver, and lung conditions. We demonstrate that psychological factors, such as feeling unhappy or being lonely, add up to 1.65 years to one's biological age, and the aggregate effect exceeds the effects of biological sex, living area, marital status, and smoking status. We conclude that the psychological component should not be ignored in aging studies due to its significant impact on biological age.

Systematic Selection of Age-Associated mRNA Markers and the Development of Predicted Models for Forensic Age Inference by Three Machine Learning Methods

Aging is usually accompanied by the decline of physiological function and dysfunction of cellular processes. Genetic markers related to aging not only reveal the biological mechanism of aging but also provide age information in forensic research. In this study, we aimed to screen age-associated mRNAs based on the previously reported genome-wide expression data. In addition, predicted models for age estimations were built by three machine learning methods. We identified 283 differentially expressed mRNAs between two groups with different age ranges. Nine mRNAs out of 283 mRNAs showed different expression patterns between smokers and non-smokers and were eliminated from the following analysis. Age-associated mRNAs were further screened from the remaining mRNAs by the cross-validation error analysis of random forest. Finally, 14 mRNAs were chosen to build the model for age predictions. These 14 mRNAs showed relatively high correlations with age. Furthermore, we found that random forest showed the optimal performance for age prediction in comparison to the generalized linear model and support vector machine. To sum up, the 14 age-associated mRNAs identified in this study could be viewed as valuable markers for age estimations and studying the aging process.

Factors associated with early biological aging in older people with HIV

Advances and availability of antiretroviral treatment has enabled a longer life expectancy for HIV population. However, with its chronicity, premature aging challenges the management of people living with HIV. This study, conducted between 2018-2020, aimed to identify an association between risk factors and premature aging, using the biological age estimated by artificial intelligence (AI) based on deep learning (Aging 3.0). This was a cross-sectional, analytical study, involving older people living with HIV (OPLHIV), 66.1% of whom were men. Premature aging was identified in 67.8%. The presence of cannabis and diabetes were significant (p = 0.045 and p = 0.042, respectively). For current and nadir CD4 + cell counts, participants were divided into groups comparing biological age (BA) and chronological age (CA). Just one group presented no premature aging, whereas the group with premature aging was subdivided into BA > CA up to 4 years and BA > CA in 5 or more years. In conclusion, premature aging was present in most of the OPLHIV. The use of cannabis was self-reported in those with higher BAs and those with a lower BA presented a higher prevalence of diabetes. Factors directly linked to HIV infection, lower current and nadir CD4 + counts were associated with premature aging.

Optimizing future well-being with artificial intelligence: self-organizing maps (SOMs) for the identification of islands of emotional stability

In this article, we present a deep learning model of human psychology that can predict one’s current age and future well-being. We used the model to demonstrate that one’s baseline well-being is not the determining factor of future well-being, as posited by hedonic treadmill theory. Further, we have created a 2D map of human psychotypes and identified the regions that are most vulnerable to depression. This map may be used to provide personalized recommendations for maximizing one’s future well-being.

Utilization of Host and Microbiome Features in Determination of Biological Aging

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

Integrating DNA Methylation Measures of Biological Aging into Social Determinants of Health Research

PURPOSE OF REVIEW

Acceleration of biological processes of aging is hypothesized to drive excess morbidity and mortality in socially disadvantaged populations. DNA methylation measures of biological aging provide tools for testing this hypothesis.

RECENT FINDINGS

Next-generation DNA methylation measures of biological aging developed to predict mortality risk and physiological decline are more predictive of morbidity and mortality than the original epigenetic clocks developed to predict chronological age. These new measures show consistent evidence of more advanced and faster biological aging in people exposed to socioeconomic disadvantage and may be able to record the emergence of socially determined health inequalities as early as childhood. Next-generation DNA methylation measures of biological aging also indicate race/ethnic disparities in biological aging. More research is needed on these measures in samples of non-Western and non-White populations. New DNA methylation measures of biological aging open opportunities for refining inference about the causes of social disparities in health and devising policies to eliminate them. Further refining measures of biological aging by including more diversity in samples used for measurement development is a critical priority for the field.

Clinical Trials Targeting Aging

The risk of morbidity and mortality increases exponentially with age. Chronic inflammation, accumulation of DNA damage, dysfunctional mitochondria, and increased senescent cell load are factors contributing to this. Mechanistic investigations have revealed specific pathways and processes which, proposedly, cause age-related phenotypes such as frailty, reduced physical resilience, and multi-morbidity. Among promising treatments alleviating the consequences of aging are caloric restriction and pharmacologically targeting longevity pathways such as the mechanistic target of rapamycin (mTOR), sirtuins, and anti-apoptotic pathways in senescent cells. Regulation of these pathways and processes has revealed significant health- and lifespan extending results in animal models. Nevertheless, it remains unclear if similar results translate to humans. A requirement of translation are the development of age- and morbidity associated biomarkers as longitudinal trials are difficult and not feasible, practical, nor ethical when human life span is the endpoint. Current biomarkers and the results of anti-aging intervention studies in humans will be covered within this paper. The future of clinical trials targeting aging may be phase 2 and 3 studies with larger populations if safety and tolerability of investigated medication continues not to be a hurdle for further investigations.

How to Slow down the Ticking Clock: Age-Associated Epigenetic Alterations and Related Interventions to Extend Life Span

Epigenetic alterations pose one major hallmark of organismal aging. Here, we provide an overview on recent findings describing the epigenetic changes that arise during aging and in related maladies such as neurodegeneration and cancer. Specifically, we focus on alterations of histone modifications and DNA methylation and illustrate the link with metabolic pathways. Age-related epigenetic, transcriptional and metabolic deregulations are highly interconnected, which renders dissociating cause and effect complicated. However, growing amounts of evidence support the notion that aging is not only accompanied by epigenetic alterations, but also at least in part induced by those. DNA methylation clocks emerged as a tool to objectively determine biological aging and turned out as a valuable source in search of factors positively and negatively impacting human life span. Moreover, specific epigenetic signatures can be used as biomarkers for age-associated disorders or even as targets for therapeutic approaches, as will be covered in this review. Finally, we summarize recent potential intervention strategies that target epigenetic mechanisms to extend healthy life span and provide an outlook on future developments in the field of longevity research.

A Machine Learning-Based Aging Measure Among Middle-Aged and Older Chinese Adults: The China Health and Retirement Longitudinal Study

Objective: Biological age (BA) has been accepted as a more accurate proxy of aging than chronological age (CA). This study aimed to use machine learning (ML) algorithms to estimate BA in the Chinese population. Materials and methods: We used data from 9,771 middle-aged and older Chinese adults (≥45 years) in the 2011/2012 wave of the China Health and Retirement Longitudinal Study and followed until 2018. We used several ML algorithms (e.g., Gradient Boosting Regressor, Random Forest, CatBoost Regressor, and Support Vector Machine) to develop new measures of biological aging (ML-BAs) based on physiological biomarkers. R-squared value and mean absolute error (MAE) were used to determine the optimal performance of these ML-BAs. We used logistic regression models to examine the associations of the best ML-BA and a conventional aging measure-Klemera and Doubal method-BA (KDM-BA) we previously developed-with physical disability and mortality, respectively. Results: The Gradient Boosting Regression model performed the best, resulting in an ML-BA with an R-squared value of 0.270 and an MAE of 6.519. This ML-BA was significantly associated with disability in basic activities of daily living, instrumental activities of daily living, lower extremity mobility, and upper extremity mobility, and mortality, with odds ratios ranging from 1 to 7% (per 1-year increment in ML-BA, all P < 0.001), independent of CA. These associations were generally comparable to that of KDM-BA. Conclusion: This study provides a valid ML-based measure of biological aging for middle-aged and older Chinese adults. These findings support the application of ML in geroscience research and may help facilitate preventive and geroprotector intervention studies.