Biological age estimation using circulating blood biomarkers

Machine learning for predicting all-cause mortality of metabolic dysfunction-associated fatty liver disease: a longitudinal study based on NHANES

BACKGROUND

The mortality burden of metabolic dysfunction-associated fatty liver disease (MAFLD) is rising, making it crucial to predict mortality and identify the factors influencing it. While advanced machine learning algorithms are gaining recognition as effective tools for clinical prediction, their ability to predict all-cause mortality of MAFLD individuals remains uncertain. This study aimed to develop different machine learning models to predict all-cause mortality of MAFLD individuals, compare the predictive performance of these models, and identify the risk factors contributing all-cause mortality, which is crucial for management of MAFLD individuals.

METHODS

We included 3921 MAFLD individuals in NHANES III. After a median follow-up time of 310 months, 1815 (46.3%) deaths were recorded. The data (demographic, behavioral factors and laboratory indicators) were utilized to construct machine learning models (Coxnet, RSF, GBS) after feature selection. Time-dependent AUC, time-dependent brier and C-index were then evaluated the performance of models. We identified the top five factors that contributed significantly to all-cause mortality and further explore the association with all-cause mortality using RCS and Kaplan-Meier survival curves.

RESULTS

Coxnet showed the best performance in short-term and long-term predictions with time-dependent AUC of 0.82 at 5 years and 0.88 at 25 years. Age, FORNS, waist circumstance, AAR, FLI were associated positively with all-cause mortality. Compared to the individuals who smoked more than 100 cigarettes, those below 100 had better survival outcome (P < 0.0001).

CONCLUSIONS

Machine learning has a promising application in predicting all-cause mortality in MAFLD individuals. Combined the results of interpretable machine learning and association analyses, we found risk factors which contributing to the all-cause mortality. These findings provide insights for community health practitioners to intervene in modifiable risk factors, thereby improving the survival and quality of life of MAFLD individuals.

Association of proBNPage with all-cause and cardiovascular mortality among US adults: an analysis of data from the National Health and Nutrition Examination Survey

OBJECTIVE

Biological age assessed by the Klemera and Doubal method (KDM) and phenotypic age (PhenoAge) was considered as a marker for ageing-related outcomes because it reflects different aspects of biological ageing and health, which are associated with increased risk of death. proBNPage based on N-terminal pro-B-type natriuretic peptide (NT-proBNP) is a novel index for biological age estimation. However, the independence of its relationship with clinical outcomes from established risk factors, KDM or PhenoAge remains uncertain. Their identification could provide valuable information to prognosis.

DESIGN, SETTING AND PARTICIPANTS

This study analysed data from the general population included in the National Health and Nutrition Examination Survey (NHANES). Participants who took part in the cross-sectional survey from 1999 to 2004 were included, and all-cause as well as cardiovascular mortality was recorded (up to 31 December 2019).

OUTCOME MEASURES

All-cause and cardiovascular mortality were considered as outcomes. Clinical risk factors were collected, and biological age was estimated by proBNPage, KDM and PhenoAge. Cox proportional hazards models were used to determine the relationship between proBNPage and outcomes with adjustment for risk factors or other biological age indexes. Restricted cubic spline (RCS) analysis based on multivariate Cox regressions was performed to examine whether there was a non-linear relationship between proBNPage and outcomes.

RESULTS

A total of 9 925 participants were included in this study. The association between proBNPage and outcomes remained significant after adjusting for risk factors, including NT-proBNP (for all-cause mortality, HR 1.14; 95% CI 1.10 to 1.17; for cardiovascular mortality, HR 1.20; 95% CI 1.14 to 1.27). Similar results were obtained after adjusting for KDM plus NT-proBNP (for all-cause mortality, HR 1.31; 95% CI 1.22 to 1.41; for cardiovascular mortality, HR 1.21; 95% CI 1.11 to 1.28) or PhenoAge plus NT-proBNP (for all-cause mortality, HR 1.21; 95% CI 1.16 to 1.28; for cardiovascular mortality, HR 1.35; 95% CI 1.24 to 1.47). These findings were confirmed in most subgroups. A non-linear relationship was observed between proBNPage and all-cause and cardiovascular mortality with an inflection point.

CONCLUSIONS

A non-linear positive relationship was observed between proBNPage and clinical outcomes. After adjusting for established risk factors and other biological age estimation indices (KDM or PhenoAge), proBNPage was significantly associated with mortality. The results remain similar after further adjustment for NT-proBNP. These results suggest that proBNPage is a useful surrogate for biological age estimation.

Accelerated Metabolomic Aging and Its Association with Social Determinants of Health in Multiple Sclerosis

Objectives

Biological age may better capture differences in disease course among people with multiple sclerosis (PwMS) of identical chronological age. We investigated biological age acceleration through metabolomic age (mAge) in PwMS and its association with social determinants of health (SDoH) measured by area deprivation index (ADI).

Methods

mAge was calculated for three cohorts: 323 PwMS and 66 healthy controls (HCs); 101 HCs and 71 DMT-naïve PwMS; and 64 HCs and 67 pediatric-onset MS/clinically isolated syndrome patients, using an aging clock derived from 11,977 healthy adults. mAge acceleration, the difference between mAge and chronological age, was compared between groups using generalized linear and mixed-effects models, and its association with ADI was assessed via linear regression.

Results

Cross-sectionally, PwMS had higher age acceleration than HCs: 9.77 years in adult PwMS (95% CI:6.57-12.97, p=5.3e-09), 4.90 years in adult DMT-naïve PwMS (95% CI:0.85-9.01, p=0.02), and 6.98 years (95% CI:1.58-12.39, p=0.01) in pediatric-onset PwMS. Longitudinally, PwMS aged 1.19 mAge years per chronological year (95% CI:0.18, 2.20; p=0.02), faster than HCs. In PwMS, a 10-percentile increase in ADI was associated with a 0.63-year (95% CI:0.10-1.18; p=0.02) increase in age acceleration.

Discussion

We demonstrated accelerated mAge in adult and pediatric-onset PwMS and its association with social disadvantage.

Equation Built by Multiple Adaptive Regression Spline to Estimate Biological Age in Healthy Postmenopausal Women in Taiwan

Background: Biological age (BA) is a better representative of health status than chronological age (CA), as it uses different biological markers to quantify cellular and systemic change status. However, BA can be difficult to accurately estimate using current methods. This study uses multiple adaptive regression spline (MARS) to build an equation to estimate BA among healthy postmenopausal women, thereby potentially improving the efficiency and accuracy of BA assessment. Methods: A total of 11,837 healthy women were enrolled (≥51 years old), excluding participants with metabolic syndrome variable values outside two standard deviations. MARS was applied, with the results compared to traditional multiple linear regression (MLR). The method with the smaller degree of estimation error was considered to be more accurate. The lower prediction errors yielded by MARS compared to the MLR method suggest that MARS performs better than MLR. Results: The equation derived from MARS is depicted. It could be noted that BA could be determined by marriage, systolic blood pressure (SBP), diastolic blood pressure (DBP), waist-hip ratio (WHR), alkaline phosphatase (ALP), lactate dehydrogenase (LDH), creatinine (Cr), carcinoembryonic antigen (CEA), bone mineral density (BMD), education level, and income. The MARS equation is generated. Conclusions: Using MARS, an equation was built to estimate biological age among healthy postmenopausal women in Taiwan. This equation could be used as a reference for calculating BA in general. Our equation showed that the most important factor was BMD, followed by WHR, Cr, marital status, education level, income, CEA, blood pressure, ALP, and LDH.

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

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

An Expert Consensus Statement on Biomarkers of Aging for Use in Intervention Studies

Biomarkers of aging serve as important outcome measures in longevity-promoting interventions. However, there is limited consensus on which specific biomarkers are most appropriate for human intervention studies. This work aimed to address this need by establishing an expert consensus on biomarkers of aging for use in intervention studies via the Delphi method. A 3-round Delphi study was conducted using an online platform. In Round 1, expert panel members provided suggestions for candidate biomarkers of aging. In Rounds 2 and 3, they voted on 500 initial statements (yes/no) relating to 20 biomarkers of aging. Panel members could abstain from voting on biomarkers outside their expertise. Consensus was reached when there was ≥70% agreement on a statement/biomarker. Of the 460 international panel members invited to participate, 116 completed Round 1, 87 completed Round 2, and 60 completed Round 3. Across the 3 rounds, 14 biomarkers met consensus that spanned physiological (eg, insulin-like growth factor 1, growth-differentiating factor-15), inflammatory (eg, high sensitivity C-reactive protein, interleukin-6), functional (eg, muscle mass, muscle strength, hand grip strength, Timed-Up-and-Go, gait speed, standing balance test, frailty index, cognitive health, blood pressure), and epigenetic (eg, DNA methylation/epigenetic clocks) domains. Expert consensus identified 14 potential biomarkers of aging which may be used as outcome measures in intervention studies. Future aging research should identify which combination of these biomarkers has the greatest utility.

Sex and education differences in trajectories of physiological ageing: longitudinal analysis of a prospective English cohort study

BACKGROUND

Physiological age (PA) derived from clinical indicators including blood-based biomarkers and tests of physiological function can be compared with chronological age to examine disparities in health between older adults of the same age. Though education interacts with sex to lead to inequalities in healthy ageing, their combined influence on longitudinally measured PA has not been explored. We derived PA based on longitudinally measured clinical indicators and examined how sex and education interact to inform PA trajectories.

METHODS

Three waves of clinical indicators (2004/05-2012/13) drawn from the English Longitudinal Study of Ageing (ages 50-100 years) were used to estimate PA, which was internally validated by confirming associations with incident chronic conditions, functional limitations and memory impairment after adjustment for chronological age and sex. Joint models were used to construct PA trajectories in 8891 English Longitudinal Study of Ageing participants to examine sex and educational disparities in PA.

FINDINGS

Amongst the least educated participants, there were negligible sex differences in PA until age 60 (sex difference [men-women] age 50 = -0.6 years [95% confidence interval = -2.2 to 0.6]; age 60 = 0.4 [-0.6 to 1.4]); at age 70, women were 1.5 years (0.7-2.2) older than men. Amongst the most educated participants, women were 3.8 years (1.6-6.0) younger than men at age 50 and 2.7 years (0.4-5.0) younger at age 60, with a nonsignificant sex difference at age 70.

INTERPRETATION

Higher education provides a larger midlife buffer to physiological ageing for women than men. Policies to promote gender equity in higher education may contribute to improving women's health across a range of ageing-related outcomes.

Beyond hallmarks of aging - biological age and emergence of aging networks

The hallmarks of aging have contributed immensely to the systematization of research on aging and have influenced the emergence of geroscience. The developments that led to the concepts of the hallmarks and geroscience were first marked by the proliferation of 'theories' of aging, mostly based on the experimental predilections of practitioners of aging research. Deeper consideration of the concepts of hallmarks of aging and geroscience leads to the quandary of whether a biological aging process exists beyond disease itself. To address this difficulty, a metric of biological age as opposed to calendar age is necessary. Several examples of biological age measured using similar assumptions, but different methods, exist. One of these, the frailty index was the first to successfully characterize aging in terms of loss of integrated function, and it is simpler than and superior to other constructs for measuring biological age. Though relatively simple in construction, the frailty index is rich conceptually, however, pointing to a network model of the aging organism. This network functions as a nonlinear complex system that is governed by stochastic thermodynamics, in which loss of integration leads to increasing entropy. Its structure transcends all levels of biological organization, such that its parts form hierarchies that are self-similar (fractal). The hallmarks of aging are simply nodes in the aging network, which can be found repetitively in various locations of the network. Stochastic thermodynamics implies that the aging system with higher entropy can exist in a multitude of possible microstates that are tantamount to high disorder with a high probability to assume a certain state. This explains the observed variability among aging individuals.

Predicting cardiovascular morbidity and mortality with SCORE2 (OP) and Framingham risk estimates in combination with indicators of biological ageing

BACKGROUND AND OBJECTIVE

Previous research assessing whether biological ageing (BA) indicators can enhance the risk assessment of cardiovascular disease (CVD) outcomes beyond established CVD risk indicators, such as Framingham Risk Score (FRS) and Systematic Coronary Risk Evaluation (SCORE2)/SCORE2-Older Persons (OP), is scarce. We explored whether BA indicators, namely the Rockwood Frailty Index (FI) and leukocyte telomere length (TL), improve predictive accuracy of CVD outcomes beyond the traditional CVD risk indicators in general population of middle-aged and older CVD-free individuals.

METHODS

Data included 14 118 individuals from three population-based cohorts: TwinGene, Health 2000 (H2000), and the Helsinki Birth Cohort Study, grouped by baseline age (<70, 70+). The outcomes were incident CVD and CVD mortality with 10-year follow-up. Risk estimations were assessed using Cox regression and predictive accuracies with Harrell's C-index.

RESULTS

Across the three study cohorts and age groups: (i) a higher FI, but not TL, was associated with a higher occurrence of incident CVD (P < .05), (ii) also when considering simultaneously the baseline CVD risk according to FRS or SCORE2/SCORE2-OP (P < .05) (iii) adding FI to the FRS or SCORE2/SCORE2-OP model improved the predictive accuracy of incident CVD. Similar findings were seen for CVD mortality, but less consistently across the cohorts.

CONCLUSIONS

We show robust evidence that a higher FI value at baseline is associated with an increased risk of incident CVD in middle-aged and older CVD-free individuals, also when simultaneously considering the risk according to the FRS or SCORE2/SCORE2-OP. The FI improved the predictive accuracy of CVD outcomes beyond the traditional CVD risk indicators and demonstrated satisfactory predictive accuracy even when used independently.

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

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

Systemic immune inflammation mediates the association of serum omega-3 and omega-6 polyunsaturated fatty acids with biological aging: a national population-based study

OBJECTIVE

This study aimed to explore the association between serum omega-3 (n-3) and omega-6 (n-6) polyunsaturated fatty acids (PUFAs) and biological aging, along with the potential mediating role of systemic immune inflammation (SII).

METHODS

Data from the National Health and Nutrition Examination Survey (NHANES) 2011-2014 were used for analyses. Accelerated aging in participants was assessed by calculating the difference between phenotypic age (PhenoAge) and chronological age. Weighted multivariate linear regression models and subgroup analysis were used to investigate the correlation between serum n-3 and n-6 PUFAs and accelerated aging, and restricted cubic spline (RCS) model was applied to explore potential nonlinear relationships. We further conducted mediation analyses to assess the role of SII in these relationships. Additionally, weighted quantile sum (WQS) regression and quantile g-computation (QGC) models were conducted to investigate the mixed effects of serum PUFAs and identify the key contributor.

RESULTS

A total of 3376 participants were enrolled in this study. In multivariate linear regression models, eight of the twelve individual serum PUFAs showed a significantly negative association with PhenoAge acceleration, Specifically, per-unit increases in linoleic acid (LA), gamma-linolenic acid (GLA), arachidonic acid (AA), alpha-linolenic acid (ALA), stearidonic acid (SDA), eicosapentaenoic acid (EPA), docosapentaenoic acid (n-3 DPA), and docosahexaenoic acid (DHA) were all associated with reduced PhenoAge acceleration (P < 0.05, respectively). Subgroup analysis demonstrated robust consistence results when stratified by age, sex, and race/ethnicity. L-shaped nonlinear relationships were observed between PhenoAge acceleration with total n-6 PUFAs, LA and ALA (all P for nonlinear < 0.05). Mediation analyses indicated that SII mediated the relationship between serum PUFAs and reduced PhenoAge acceleration. Mixed-effects analysis using WQS and QGC models revealed that the combined effect of serum PUFAs on reducing PhenoAge acceleration, with DHA showing the strongest significant contribution.

CONCLUSIONS

This study demonstrated that higher levels of certain PUFAs were associated with a reduction in PhenoAge acceleration either individually or in combination, with DHA having the most prominent effect in mixed effects. The SII mediated these relationships, suggesting that PUFAs may slow biological aging by reducing inflammation. These findings highlighted the potential role of PUFAs in mitigating accelerated aging and their implications for aging-related health interventions.

Identifying potential predictors of the risk of surgical site infection following cardiac surgery: a scoping review

OBJECTIVES

This scoping review was undertaken to identify risk prediction models and pre-operative predictors of surgical site infection (SSI) in adult cardiac surgery. A particular focus was on the identification of novel predictors that could underpin the future development of a risk prediction model to identify individuals at high risk of SSI, and therefore guide a national SSI prevention strategy.

METHODS

A scoping review to systematically identify and map out existing research evidence on pre-operative predictors of SSI was conducted in two stages. Stage 1 reviewed prediction modelling studies of SSI in cardiac surgery. Stage 2 identified primary studies and systematic reviews of novel cardiac SSI predictors.

RESULTS

The search identified 7887 unique reports; 7154 were excluded at abstract screening and 733 were selected for full-text assessment. Twenty-nine studies (across 30 reports) were included in Stage 1 and reported the development (N=14), validation (N=13), or both development and validation (N=2) of 52 SSI risk prediction models including 67 different pre-operative predictors. The remaining 703 reports were re-assessed in Stage 2; 49 studies met the inclusion criteria, and 56 novel pre-operative predictors that have not been assessed previously in models were identified.

CONCLUSIONS

This review identified 123 pre-operative predictors of the risk of SSI following cardiac surgery, 56 of which have not been included previously in the development of cardiac SSI risk prediction models. These candidate predictors will be a valuable resource in the future development of risk prediction scores, and may be relevant to prediction of the risk of SSI in other surgical specialities.

Video-based Informed Consent in Radiology - Acceptance, Satisfaction, and Effectiveness

Before any medical procedure, including computed tomography (CT), it is crucial to ensure patients are fully informed about the risks and alternative options. Video-based informed consent offers an increased transfer of information in less time.In a monocentric, prospective, questionnaire-based study, video-based informed consent, which included a digital medical history form, was compared to the traditional paper-based consent form. Two groups (doctors and patients) were divided into a control group (traditional informed consent) and one study group (video-based informed consent). Participants rated their satisfaction and acceptance on a scale of 1 to 6 (1: very good). Additionally, patients' understanding of the information provided was evaluated, and the duration of informed consents process was measured.A total of 205 patients in the control group and 150 in the study group were surveyed. Satisfaction ratings of "very good" or "good" were similar for both methods (91% control group, 94% study group). The patients' study group showed a higher recall of the information provided in all six areas, e.g. radiation exposure (73% control group; 86% study group).Among the doctors, 20 from the control group and 11 from the study group were interviewed. Satisfaction was significantly higher in the study group (30% control group, 72% study group).The duration of the traditional informed consent process averaged 270.2 seconds, compared to 228.7 seconds for the video-based informed consent.Satisfaction with video-based information is high among both patients and doctors. Patients retain the content more effectively with video-based informed consent, which also saves time. · Video-based informed consent shows high levels of satisfaction and acceptance among patients and doctors.. · After a video-based informed consent consultation, patients were better able to remember the information provided.. · Compared to conventional informed consent consultations, video-based consultations save time.. · Vogele D, Nedelcu A, Beer M et al. Video-based Informed Consent in Radiology - Acceptance, Satisfaction, and Effectiveness. Rofo 2025; DOI 10.1055/a-2490-1472.

Pan-cancer secreted proteome and skeletal muscle regulation: insight from a proteogenomic data-driven knowledge base

Large-scale, pan-cancer analysis is enabled by data driven knowledge bases that link tumor molecular profiles with phenotypes. A debilitating cancer-related phenotype is skeletal muscle loss, or cachexia, which occurs partly from tumor products secreted into circulation. Using the LinkedOmicsKB knowledge base assembled from the Clinical Proteomics Tumor Analysis Consortium proteogenomic analysis, along with catalogs of human secretome proteins, ligand-receptor pairs and molecular signatures, we sought to identify candidate pan-cancer proteins secreted to blood that could regulate skeletal muscle phenotypes in multiple solid cancers. Tumor proteins having significant pan-cancer associations with muscle were referenced against secretome proteins secreted to blood from the Human Protein Atlas, then verified as increased in paired tumor vs. normal tissues in pan-cancer manner. This workflow revealed seven secreted proteins from cancers afflicting kidneys, head and neck, lungs and pancreas that classified as protein-binding activity modulator, extracellular matrix protein or intercellular signaling molecule. Concordance of these biomarkers with validated molecular signatures of cachexia and senescence supported relevance to muscle and cachexia disease biology, and high tumor expression of the biomarker set associated with lower overall survival. In this article, we discuss avenues by which skeletal muscle and cachexia may be regulated by these candidate pan-cancer proteins secreted to blood, and conceptualize a strategy that considers them collectively as a biomarker signature with potential for refinement by data analytics and radiogenomics for predictive testing of future risk in a non-invasive, blood-based panel amenable to broad uptake and early management.

Sex and education differences in trajectories of physiological ageing: longitudinal analysis of a prospective English cohort study

Background

Physiological age (PA) derived from clinical indicators including blood-based biomarkers and tests of physiological function can be compared with chronological age to examine disparities in health between older adults of the same age. Though education interacts with sex to lead to inequalities in healthy ageing, their combined influence on longitudinally-measured PA has not been explored. We derived PA based on longitudinally-measured clinical indicators and examined how sex and education interact to inform PA trajectories.

Methods

Three waves of clinical indicators (2004/05-2012/13) drawn from the English Longitudinal Study of Ageing (ages 50-100 years) were used to estimate PA, which was internally validated by confirming associations with incident chronic conditions, functional limitations, and memory impairment after adjustment for chronological age and sex. Joint models were used to construct PA trajectories in 8,891 ELSA participants to examine sex and educational disparities in PA.

Findings

Among the least educated participants, there were negligible sex differences in PA until age 60 (sex difference [men-women] age 50=-0.6 years [95% confidence interval=-2.2-0.6]; age 60=0.4 [-0.6-1.4]); at age 70, women were 1.5 years (0.7-2.2) older than men. Among the most educated participants, women were 3.8 years (1.6-6.0) younger than men at age 50, and 2.7 years (0.4-5.0) younger at age 60, with a non-significant sex difference at age 70.

Interpretation

Higher education provides a larger midlife buffer to physiological ageing for women than men. Policies to promote gender equity in higher education may contribute to improving women's health across a range of ageing-related outcomes.

Multiomics of Aging and Aging-Related Diseases

Despite their astonishing biological diversity, surprisingly few shared traits connect all or nearly all living organisms. Aging, i.e., the progressive and irreversible decline in the function of multiple cells and tissues, is one of these fundamental features of all organisms, ranging from single-cell creatures to complex animals, alongside variability, adaptation, growth, healing, reproducibility, mobility, and, finally, death. Age is a key determinant for many pathologies, shaping the risks of incidence, severity, and treatment outcomes for cancer, neurodegeneration, heart failure, sarcopenia, atherosclerosis, osteoporosis, and many other diseases. In this review, we aim to systematically investigate the age-related features of the development of several diseases through the lens of multiomics: from genome instability and somatic mutations to pathway alterations and dysregulated metabolism.

Evaluating facial dermis aging in healthy Caucasian females with LC-OCT and deep learning

Recent advancements in high-resolution imaging have significantly improved our understanding of microstructural changes in the skin and their relationship to the aging process. Line Field Confocal Optical Coherence Tomography (LC-OCT) provides detailed 3D insights into various skin layers, including the papillary dermis and its fibrous network. In this study, a deep learning model utilizing a 3D ResNet-18 network was trained to predict chronological age from LC-OCT images of 100 healthy Caucasian female volunteers, aged 20 to 70 years. The AI-based protocol focused on regions of interest delineated between the segmented dermal-epidermal junction and the superficial dermis, exploiting complex patterns within the collagen network for age prediction. The model achieved a mean absolute error of 4.2 years and exhibited a Pearson correlation coefficient of 0.937 with actual ages. Furthermore, there was a notable correlation (r = 0.87) between quantified clinical scoring, encompassing parameters such as firmness, elasticity, density, and wrinkle appearance, and the ages predicted by deep learning model. This strong correlation underscores how integrating emerging imaging technologies with deep learning can accelerate aging research and deepen our understanding of how alterations in skin microstructure are related to visible signs of aging.

A Framework for an Effective Healthy Longevity Clinic

In the context of an aging global population and the imperative for innovative healthcare solutions, the concept of longevity clinics emerges as a timely and vital area of exploration. Unlike traditional medical facilities, longevity clinics offer a unique approach to preclinical prevention, focusing on "prevention of prevention" through the utilization of aging clocks and biomarkers from healthy individuals. This article presents a comprehensive overview of longevity clinics, encompassing descriptions of existing models, the development of a proposed framework, and insights into biomarkers, wearable devices, and therapeutic interventions. Additionally, economic justifications for investing in longevity clinics are examined, highlighting the significant growth potential of the global biotechnology market and its alignment with the goals of achieving active longevity. Anchored by an Analytical Center, the proposed framework underscores the importance of data-driven decision-making and innovation in promoting prolonged and enhanced human life. At present, there is no universally accepted standard model for longevity clinics. This absence highlights the need for additional research and ongoing improvements in this field. Through a synthesis of scientific research and practical considerations, this article aims to stimulate further discussion and innovation in the field of longevity clinics, ultimately contributing to the advancement of healthcare practices aimed at extending and enhancing human life.

The association between sleep quality and accelerated epigenetic aging with metabolic syndrome in Korean adults

BACKGROUND

Healthy sleep is vital for maintaining optimal mental and physical health. Accumulating evidence suggests that sleep loss and disturbances play a significant role in the biological aging process, early onset of disease, and reduced lifespan. While numerous studies have explored the association between biological aging and its drivers, only a few studies have examined its relationship with sleep quality. In this study, we investigated the associations between sleep quality and epigenetic age acceleration using whole blood samples from a cohort of 692 Korean adults. Sleep quality of each participant was assessed using the validated Pittsburgh Sleep Quality Index (PSQI), which encompassed seven domains: subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbance, use of sleep medication, and daytime dysfunction. Four epigenetic age accelerations (HorvathAgeAccel, HannumAgeAccel, PhenoAgeAccel, and GrimAgeAccel) and the pace of aging, DunedinPACE, were investigated for epigenetic aging estimates.

RESULTS

Among the 692 participants (good sleepers [n = 441, 63.7%]; poor sleepers [n = 251, 36.3%]), DunedinPACE was positively correlated with PSQI scores in poor sleepers ( γ =0.18, p < 0.01). GrimAgeAccel ( β =0.18, p = 0.02) and DunedinPACE ( β =0.01, p < 0.01) showed a statistically significant association with PSQI scores only in poor sleepers by multiple linear regression. In addition, every one-point increase in PSQI was associated with a 15% increase in the risk of metabolic syndrome (MetS) among poor sleepers (OR = 1.15, 95% CI = 1.03-1.29, p = 0.011). In MetS components, a positive correlation was observed between PSQI score and fasting glucose ( γ = 0.19, p < 0.01).

CONCLUSIONS

This study suggests that worsening sleep quality, especially in poor sleepers, is associated with accelerated epigenetic aging for GrimAgeAccel and DundinePACE with risk of metabolic syndrome. This finding could potentially serve as a promising strategy for preventing age-related diseases in the future.

Towards Healthy Longevity: Comprehensive Insights from Molecular Targets and Biomarkers to Biological Clocks

Aging is a complex and time-dependent decline in physiological function that affects most organisms, leading to increased risk of age-related diseases. Investigating the molecular underpinnings of aging is crucial to identify geroprotectors, precisely quantify biological age, and propose healthy longevity approaches. This review explores pathways that are currently being investigated as intervention targets and aging biomarkers spanning molecular, cellular, and systemic dimensions. Interventions that target these hallmarks may ameliorate the aging process, with some progressing to clinical trials. Biomarkers of these hallmarks are used to estimate biological aging and risk of aging-associated disease. Utilizing aging biomarkers, biological aging clocks can be constructed that predict a state of abnormal aging, age-related diseases, and increased mortality. Biological age estimation can therefore provide the basis for a fine-grained risk stratification by predicting all-cause mortality well ahead of the onset of specific diseases, thus offering a window for intervention. Yet, despite technological advancements, challenges persist due to individual variability and the dynamic nature of these biomarkers. Addressing this requires longitudinal studies for robust biomarker identification. Overall, utilizing the hallmarks of aging to discover new drug targets and develop new biomarkers opens new frontiers in medicine. Prospects involve multi-omics integration, machine learning, and personalized approaches for targeted interventions, promising a healthier aging population.

MANOCCA: a robust and computationally efficient test of covariance in high-dimension multivariate omics data

Multivariate analysis is becoming central in studies investigating high-throughput molecular data, yet, some important features of these data are seldom explored. Here, we present MANOCCA (Multivariate Analysis of Conditional CovAriance), a powerful method to test for the effect of a predictor on the covariance matrix of a multivariate outcome. The proposed test is by construction orthogonal to tests based on the mean and variance and is able to capture effects that are missed by both approaches. We first compare the performances of MANOCCA with existing correlation-based methods and show that MANOCCA is the only test correctly calibrated in simulation mimicking omics data. We then investigate the impact of reducing the dimensionality of the data using principal component analysis when the sample size is smaller than the number of pairwise covariance terms analysed. We show that, in many realistic scenarios, the maximum power can be achieved with a limited number of components. Finally, we apply MANOCCA to 1000 healthy individuals from the Milieu Interieur cohort, to assess the effect of health, lifestyle and genetic factors on the covariance of two sets of phenotypes, blood biomarkers and flow cytometry-based immune phenotypes. Our analyses identify significant associations between multiple factors and the covariance of both omics data.

Ratio of Red Blood Cell Distribution Width to Albumin Level and Risk of Mortality

Importance

The ratio of red blood cell distribution width (RDW) to albumin concentration (RAR) has emerged as a reliable prognostic marker for mortality in patients with various diseases. However, whether RAR is associated with mortality in the general population remains unknown.

Objectives

To explore whether RAR is associated with all-cause and cause-specific mortality and to elucidate their dose-response association.

Design, Setting, and Participants

This population-based prospective cohort study used data from participants in the 1998-2018 US National Health and Nutrition Examination Survey (NHANES) and from the UK Biobank with baseline information provided from 2006 to 2010. Included participants had complete data on serum albumin concentration, RDW, and cause of death. The NHANES data were linked to the National Death Index records through December 31, 2019. For the UK Biobank, dates and causes of death were obtained from the National Health Service Information Centre (England and Wales) and the National Health Service Central Register Scotland (Scotland) to November 30, 2022.

Main Outcomes and Measures

Potential associations between RAR and the risk of all-cause and cause-specific mortality were evaluated using Cox proportional hazards regression models. Restricted cubic spline regressions were applied to estimate possible nonlinear associations.

Results

In NHANES, 50 622 participants 18 years of age or older years were included (mean [SD] age, 48.6 [18.7] years; 26 136 [51.6%] female), and their mean (SD) RAR was 3.15 (0.51). In the UK Biobank, 418 950 participants 37 years of age or older (mean [SD], 56.6 [8.1] years; 225 038 [53.7%] female) were included, and their mean RAR (SD) was 2.99 (0.31). The NHANES documented 7590 deaths over a median (IQR) follow-up of 9.4 (5.1-14.2) years, and the UK Biobank documented 36 793 deaths over a median (IQR) follow-up of 13.8 (13.0-14.5) years. According to the multivariate analysis, elevated RAR was significantly associated with greater risk of all-cause mortality (NHANES: hazard ratio [HR], 1.83 [95% CI, 1.76-1.90]; UK Biobank: HR, 2.08 [95% CI, 2.03-2.13]), as well as mortality due to malignant neoplasm (NHANES: HR, 1.89 [95% CI, 1.73-2.07]; UK Biobank: HR, 1.93 [95% CI, 1.86-2.00]), heart disease (NHANES: HR, 1.88 [95% CI, 1.74-2.03]; UK Biobank: HR, 2.42 [95% CI, 2.29-2.57]), cerebrovascular disease (NHANES: HR, 1.35 [95% CI, 1.07-1.69]; UK Biobank: HR, 2.15 [95% CI, 1.91-2.42]), respiratory disease (NHANES: HR, 1.99 [95% CI, 1.68-2.35]; UK Biobank: HR, 2.96 [95% CI, 2.78-3.15]), diabetes (NHANES: HR, 1.55 [95% CI, 1.27-1.90]; UK Biobank: HR, 2.83 [95% CI, 2.35-3.40]), and other causes of mortality (NHANES: HR, 1.97 [95% CI, 1.86-2.08]; UK Biobank: HR, 2.40 [95% CI, 2.30-2.50]) in both cohorts. Additionally, a nonlinear association was observed between RAR levels and all-cause mortality in both cohorts.

Conclusions and Relevance

In this cohort study, a higher baseline RAR was associated with an increased risk of all-cause and cause-specific mortality in the general population. These findings suggest that RAR may be a simple, reliable, and inexpensive indicator for identifying individuals at high risk of mortality in clinical practice.

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

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

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.