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.

Principal component-based clinical aging clocks identify signatures of healthy aging and targets for clinical intervention

Clocks that measure biological age should predict all-cause mortality and give rise to actionable insights to promote healthy aging. Here we applied dimensionality reduction by principal component analysis to clinical data to generate a clinical aging clock (PCAge) identifying signatures (principal components) separating healthy and unhealthy aging trajectories. We found signatures of metabolic dysregulation, cardiac and renal dysfunction and inflammation that predict unsuccessful aging, and we demonstrate that these processes can be impacted using well-established drug interventions. Furthermore, we generated a streamlined aging clock (LinAge), based directly on PCAge, which maintains equivalent predictive power but relies on substantially fewer features. Finally, we demonstrate that our approach can be tailored to individual datasets, by re-training a custom clinical clock (CALinAge), for use in the Comprehensive Assessment of Long-term Effects of Reducing Intake of Energy (CALERIE) study of caloric restriction. Our analysis of CALERIE participants suggests that 2 years of mild caloric restriction significantly reduces biological age. Altogether, we demonstrate that this dimensionality reduction approach, through integrating different biological markers, can provide targets for preventative medicine and the promotion of healthy aging.

Gene-Environment Interactions and Gene-Gene Interactions on Two Biological Age Measures: Evidence from Taiwan Biobank Participants

PhenoAge and BioAge are two commonly used biological age (BA) measures. The author here searched for gene-environment interactions (GxE) and gene-gene interactions (GxG) on PhenoAgeAccel (age-adjusted PhenoAge) and BioAgeAccel (age-adjusted BioAge) of 111,996 Taiwan Biobank (TWB) participants, including a discovery set of 86,536 TWB2 individuals and a replication set of 25,460 TWB1 individuals. Searching for variance quantitative trait loci (vQTLs) provides a convenient way to evaluate GxE and GxG. A total of 4 nearly independent (linkage disequilibrium measure r2 < 0.01) PhenoAgeAccel-vQTLs are identified from 5,303,039 autosomal TWB2 SNPs (p < 5E-8), whereas no vQTLs are found from BioAgeAccel. These 4 PhenoAgeAccel-vQTLs (rs35276921, rs141927875, rs10903013, and rs76038336) are further replicated by TWB1 (p < 5E-8). They are located in the OR51B5, FAM234A, and AXIN1 genes. All 4 PhenoAgeAccel-vQTLs are significantly associated with PhenoAgeAccel (p < 5E-8). A phylogenetic heat map of the GxE analyses showed that smoking exacerbated the PhenoAgeAccel-vQTLs' aging effects, while higher educational attainment attenuated the PhenoAgeAccel-vQTLs' aging effects. Body mass index, chronological age, alcohol consumption, and sex do not prominently modulate PhenoAgeAccel-vQTLs' aging effects. Based on these vQTL results, rs141927875-rs35276921 interaction (p = 4.7E-61) and rs76038336-rs10903013 interaction (p = 3.3E-116) on PhenoAgeAccel are detected.

BrainAGE, brain health, and mental disorders: A systematic review

The imaging-based method of brainAGE aims to characterize an individual's vulnerability to age-related brain changes. The present study systematically reviewed brainAGE findings in neuropsychiatric conditions and discussed the potential of brainAGE as a marker for biological age. A systematic PubMed search (from inception to March 6th, 2023) identified 273 articles. The 30 included studies compared brainAGE between neuropsychiatric and healthy groups (n≥50). We presented results qualitatively and adapted a bias risk assessment questionnaire. The imaging modalities, design, and input features varied considerably between studies. While the studies found higher brainAGE in neuropsychiatric conditions (11 mild cognitive impairment/ dementia, 11 schizophrenia spectrum/ other psychotic and bipolar disorder, six depression/ anxiety, two multiple groups), the associations with clinical characteristics were mixed. While brainAGE is sensitive to group differences, limitations include the lack of diverse training samples, multi-modal studies, and external validation. Only a few studies obtained longitudinal data, and all have used algorithms built solely to predict chronological age. These limitations impede the validity of brainAGE as a biological age marker.

A biological age model based on physical examination data to predict mortality in a Chinese population

Biological age could be reflective of an individual's health status and aging degree. Limited estimations of biological aging based on physical examination data in the Chinese population have been developed to quantify the rate of aging. We developed and validated a novel aging measure (Balanced-AGE) based on readily available physical health examination data. In this study, a repeated sub-sampling approach was applied to address the data imbalance issue, and this approach significantly improved the performance of biological age (Balanced-AGE) in predicting all-cause mortality with a 10-year time-dependent AUC of 0.908 for all-cause mortality. This mortality prediction tool was found to be effective across different subgroups by age, sex, smoking, and alcohol consumption status. Additionally, this study revealed that individuals who were underweight, smokers, or drinkers had a higher extent of age acceleration. The Balanced-AGE may serve as an effective and generally applicable tool for health assessment and management among the elderly population.

Progress in the study of aging marker criteria in human populations

The use of human aging markers, which are physiological, biochemical and molecular indicators of structural or functional degeneration associated with aging, is the fundamental basis of individualized aging assessments. Identifying methods for selecting markers has become a primary and vital aspect of aging research. However, there is no clear consensus or uniform principle on the criteria for screening aging markers. Therefore, we combine previous research from our center and summarize the criteria for screening aging markers in previous population studies, which are discussed in three aspects: functional perspective, operational implementation perspective and methodological perspective. Finally, an evaluation framework has been established, and the criteria are categorized into three levels based on their importance, which can help assess the extent to which a candidate biomarker may be feasible, valid, and useful for a specific use context.

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

Introduction

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

Methods

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

Results

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

Discussion

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

Association of biological age with health outcomes and its modifiable factors

Identifying the clinical implications and modifiable and unmodifiable factors of aging requires the measurement of biological age (BA) and age gap. Leveraging the biomedical traits involved with physical measures, biochemical assays, genomic data, and cognitive functions from the healthy participants in the UK Biobank, we establish an integrative BA model consisting of multi-dimensional indicators. Accelerated aging (age gap >3.2 years) at baseline is associated incident circulatory diseases, related chronic disorders, all-cause, and cause-specific mortality. We identify 35 modifiable factors for age gap (p < 4.81 × 10-4 ), where pulmonary functions, body mass, hand grip strength, basal metabolic rate, estimated glomerular filtration rate, and C-reactive protein show the most significant associations. Genetic analyses replicate the possible associations between age gap and health-related outcomes and further identify CST3 as an essential gene for biological aging, which is highly expressed in the brain and is associated with immune and metabolic traits. Our study profiles the landscape of biological aging and provides insights into the preventive strategies and therapeutic targets for aging.

Discordant Biological and Chronological Age: Implications for Cognitive Decline and Frailty

BACKGROUND

Older adults with discordant biological and chronological ages (BA and CA) may vary in cognitive and physical function from those with concordant BA and CA.

METHODS

To make our approach clinically accessible, we created easy-to-interpret participant groups in the Health, Aging, and Body Composition Study (N = 2 458, 52% female participants, 65% White participants, age: 73.5 ± 2.8) based on medians of CA, and a previously validated BA index comprised of readily available clinical tests. Joint models estimated associations of BA-CA group with cognition (Modified Mini-Mental State Examination [3MS] and Digit Symbol Substitution Test [DSST]) and frailty over 10 years.

RESULTS

The sample included the following: 32%, Young group (BA and CA < median); 21%, Prematurely Aging group (BA ≥ median, CA < median), 27%, Old group (BA and CA ≥ median), and 20%, Resilient group (BA < median, CA ≥ median). In education-adjusted models of cognition, among those with CA < median, the Prematurely Aging group performed worse than the Young at baseline (3MS and DSST p < .0001), but among those with CA ≥ median, the Resilient group did not outperform the Old group (3MS p = .31; DSST p = .25). For frailty, the Prematurely Aging group performed worse than the Young group at baseline (p = .0001), and the Resilient group outperformed the Old group (p = .003). For all outcomes, groups did not differ on change over time based on the same pairwise comparisons (p ≥ .40).

CONCLUSIONS

Discordant BA and CA identify groups who have greater cognitive and physical functional decline or are more protected than their CA would suggest. This information can be used for risk stratification.

Biomarkers selection and mathematical modeling in biological age estimation

Biological age (BA) is important for clinical monitoring and preventing aging-related disorders and disabilities. Clinical and/or cellular biomarkers are measured and integrated in years using mathematical models to display an individual's BA. To date, there is not yet a single or set of biomarker(s) and technique(s) that is validated as providing the BA that reflects the best real aging status of individuals. Herein, a comprehensive overview of aging biomarkers is provided and the potential of genetic variations as proxy indicators of the aging state is highlighted. A comprehensive overview of BA estimation methods is also provided as well as a discussion of their performances, advantages, limitations, and potential approaches to overcome these limitations.

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

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

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.

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.

Longitudinal association of lung function with frailty among older adults: the English Longitudinal Study of Ageing

PURPOSE

To investigate the effect of baseline lung function on the trajectory of frailty over time.

METHODS

This longitudinal study included 3,658 adults aged 60 and over (average age 70.4 years old and 46.4% males) at baseline from the English Longitudinal Study of Ageing. Lung function indicators included forced vital capacity (FVC) and forced expiratory volume in the first second (FEV1), both measured at baseline examination. Frailty was defined based on Fried's frailty phenotype criteria, the measurement was repeated for four times. Linear mixed-effect regression model was applied to estimate the association of baseline lung function with the trajectory of frailty over time.

RESULTS

Frailty score increased significantly over time (β = 0.030, P < 0.001). Linear mixed-effect regression model identified significant interactions between FVC (β =- 0.018, P < 0.001) or FEV1 (β =- 0.022, P < 0.001) and time on frailty.

CONCLUSION

Poor baseline lung function might accelerate the speed of frailty. Lung function might be an important predictor of the development and progression of frailty among older adults.

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.

Beyond Chronological Age: A Multidimensional Approach to Survival Prediction in Older Adults

BACKGROUND

There is a growing interest in generating precise predictions of survival to improve the assessment of health and life-improving interventions. We aimed to (a) test if observable characteristics may provide a survival prediction independent of chronological age; (b) identify the most relevant predictors of survival; and (c) build a metric of multidimensional age.

METHODS

Data from 3 095 individuals aged ≥60 from the Swedish National Study on Aging and Care in Kungsholmen. Eighty-three variables covering 5 domains (diseases, risk factors, sociodemographics, functional status, and blood tests) were tested in penalized Cox regressions to predict 18-year mortality.

RESULTS

The best prediction of mortality at different follow-ups (area under the receiver operating characteristic curves [AUROCs] 0.878-0.909) was obtained when 15 variables from all 5 domains were tested simultaneously in a penalized Cox regression. Significant prediction improvements were observed when chronological age was included as a covariate for 15- but not for 5- and 10-year survival. When comparing individual domains, we find that a combination of functional characteristics (ie, gait speed, cognition) gave the most accurate prediction, with estimates similar to chronological age for 5- (AUROC 0.836) and 10-year (AUROC 0.830) survival. Finally, we built a multidimensional measure of age by regressing the predicted mortality risk on chronological age, which displayed a stronger correlation with time to death (R = -0.760) than chronological age (R = -0.660) and predicted mortality better than widely used geriatric indices.

CONCLUSIONS

Combining easily accessible characteristics can help in building highly accurate survival models and multidimensional age metrics with potentially broad geriatric and biomedical applications.

All-Subset Analysis Improves the Predictive Accuracy of Biological Age for All-Cause Mortality in Chinese and U.S. Populations

BACKGROUND

Klemera-Doubal's method (KDM) is an advanced and widely applied algorithm for estimating biological age (BA), but it has no uniform paradigm for biomarker processing. This article proposed all subsets of biomarkers for estimating BAs and assessed their association with mortality to determine the most predictive subset and BA.

METHODS

Clinical biomarkers, including those from physical examinations and blood assays, were assessed in the China Health and Nutrition Survey (CHNS) 2009 wave. Those correlated with chronological age (CA) were combined to produce complete subsets, and BA was estimated by KDM from each subset of biomarkers. A Cox proportional hazards regression model was used to examine and compare each BA's effect size and predictive capacity for all-cause mortality. Validation analysis was performed in the Chinese Longitudinal Healthy Longevity Survey (CLHLS) and National Health and Nutrition Examination Survey (NHANES). KD-BA and Levine's BA were compared in all cohorts.

RESULTS

A total of 130 918 panels of BAs were estimated from complete subsets comprising 3-17 biomarkers, whose Pearson coefficients with CA varied from 0.39 to 1. The most predictive subset consisted of 5 biomarkers, whose estimated KD-BA had the most predictive accuracy for all-cause mortality. Compared with Levine's BA, the accuracy of the best-fitting KD-BA in predicting death varied among specific populations.

CONCLUSION

All-subset analysis could effectively reduce the number of redundant biomarkers and significantly improve the accuracy of KD-BA in predicting all-cause mortality.

Retinal photograph-based deep learning predicts biological age, and stratifies morbidity and mortality risk

BACKGROUND

ageing is an important risk factor for a variety of human pathologies. Biological age (BA) may better capture ageing-related physiological changes compared with chronological age (CA).

OBJECTIVE

we developed a deep learning (DL) algorithm to predict BA based on retinal photographs and evaluated the performance of our new ageing marker in the risk stratification of mortality and major morbidity in general populations.

METHODS

we first trained a DL algorithm using 129,236 retinal photographs from 40,480 participants in the Korean Health Screening study to predict the probability of age being ≥65 years ('RetiAGE') and then evaluated the ability of RetiAGE to stratify the risk of mortality and major morbidity among 56,301 participants in the UK Biobank. Cox proportional hazards model was used to estimate the hazard ratios (HRs).

RESULTS

in the UK Biobank, over a 10-year follow up, 2,236 (4.0%) died; of them, 636 (28.4%) were due to cardiovascular diseases (CVDs) and 1,276 (57.1%) due to cancers. Compared with the participants in the RetiAGE first quartile, those in the RetiAGE fourth quartile had a 67% higher risk of 10-year all-cause mortality (HR = 1.67 [1.42-1.95]), a 142% higher risk of CVD mortality (HR = 2.42 [1.69-3.48]) and a 60% higher risk of cancer mortality (HR = 1.60 [1.31-1.96]), independent of CA and established ageing phenotypic biomarkers. Likewise, compared with the first quartile group, the risk of CVD and cancer events in the fourth quartile group increased by 39% (HR = 1.39 [1.14-1.69]) and 18% (HR = 1.18 [1.10-1.26]), respectively. The best discrimination ability for RetiAGE alone was found for CVD mortality (c-index = 0.70, sensitivity = 0.76, specificity = 0.55). Furthermore, adding RetiAGE increased the discrimination ability of the model beyond CA and phenotypic biomarkers (increment in c-index between 1 and 2%).

CONCLUSIONS

the DL-derived RetiAGE provides a novel, alternative approach to measure ageing.

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.

Lifestyle factors and genetic variants on two biological age measures: evidence from 94,443 Taiwan Biobank participants

BACKGROUND

Biological age (BA) can be estimated by phenotypes and is useful for predicting lifespan and healthspan. Levine et al. proposed a PhenoAge and a BioAge to measure BA. Although there have been studies investigating the genetic predisposition to BA acceleration in Europeans, little has been known regarding this topic in Asians.

METHODS

I here estimated PhenoAgeAccel (age-adjusted PhenoAge) and BioAgeAccel (age-adjusted BioAge) of 94,443 Taiwan Biobank (TWB) participants, wherein 25,460 TWB1 subjects formed a discovery cohort and 68,983 TWB2 individuals constructed a replication cohort. Lifestyle factors and genetic variants associated with PhenoAgeAccel and BioAgeAccel were investigated through regression analysis and a genome-wide association study (GWAS).

RESULTS

A unit (kg/m 2) increase of BMI was associated with a 0.177-year PhenoAgeAccel (95% C.I. = 0.163~0.191, p = 6.0×) and 0.171-year BioAgeAccel (95% C.I. = 0.165~0.177, p = 0). Smokers on average had a 1.134-year PhenoAgeAccel (95% C.I. = 0.966~1.303, p = 1.3×) compared with non-smokers. Drinkers on average had a 0.640-year PhenoAgeAccel (95% C.I. = 0.433~0.847, p = 1.3×) and 0.193-year BioAgeAccel (95% C.I. = 0.107~0.279, p = 1.1×) relative to non-drinkers. A total of 11 and 4 single-nucleotide polymorphisms (SNPs) were associated with PhenoAgeAccel and BioAgeAccel (p<5× in both TWB1 and TWB2), respectively.

CONCLUSIONS

A PhenoAgeAccel-associated SNP (rs1260326 in GCKR) and two BioAgeAccel-associated SNPs (rs7412 in APOE; rs16998073 near FGF5) were consistent with the finding from the UK Biobank. The lifestyle analysis shows that prevention from obesity, cigarette smoking, and alcohol consumption is associated with a slower rate of biological aging.

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

Background

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

Methods

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

Results

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

Conclusions

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

Socio-Environmental, Lifestyle, Behavioural, and Psychological Determinants of Biological Ageing: The Singapore Longitudinal Ageing Study

INTRODUCTION

The identification of modifiable health span-promoting factors is a public health priority.

OBJECTIVE

To explore the socio-environmental, lifestyle, behavioural, and psychological determinants of a clinical phenotypic measure of biological ageing in the Singapore Longitudinal Ageing Study (SLAS) cohort.

METHODS

Using cross-sectional data on 2,844 SLAS-2 adults with a chronological age (CA) ≥55 years, we estimated biological age (BA) using a validated panel of clinical, biochemical, physiological, and functional indicators (8 in men and 10 in women) and calculated the difference between BA and CA (BA - CA in years). Potential determinants included education, housing status, loss of a spouse, living alone, lifestyle and health activity, smoking, alcohol consumption, nutritional risks, consumption of milk, soy, fruit, vegetables, coffee and tea, sleep parameters, and life satisfaction.

RESULTS

The mean CA was 67.0 (standard deviation [SD] 7.9; range 55-94) years. The estimated BA varied more widely (SD 8.9 years; range 47.5-119.9 years), and BA - CA ranged from -11.3 to 30.0 years. In stepwise selection regression analyses, multiple significant independent determinants in a final model were larger for private housing, being single/divorced/widowed, productivity, cognitive and leisure time activity scores, 10 h/week of moderate-to-vigorous physical activity, unintended loss of weight, life satisfaction, and daily consumption of fruits 1-2 or ≥3 servings and Chinese tea 1-2 or ≥3 cups daily, together explaining 16% of BA - CA variance in men and 14% in women. Associated BA - CA estimates were highest in men with high-end housing status (-1.8 years, effect size 0.015) and unintended weight loss (1.5 years, effect size 0.017).

CONCLUSION

We identified determinants of biological ageing which can promote health span.

The untwining of immunosenescence and aging

From a holistic point of view, aging results from the cumulative erosion of the various systems. Among these, the immune system is interconnected to the rest as immune cells are present in all organs and recirculate through bloodstream. Immunosenescence is the term used to define the remodelling of immune changes during aging. Because immune cells-and particularly lymphocytes-can further differentiate after their maturation in response to pathogen recognition, it is therefore unclear when senescence is induced in these cells. Additionally, it is also unclear which signals triggers senescence in immune cells (i) aging per se, (ii) specific response to pathogens, (iii) underlying conditions, or (iv) inflammaging. In this review, we will cover the current knowledge and concepts linked to immunosenescence and we focus this review on lymphocytes and T cells, which represent the typical model for replicative senescence. With the evidence presented, we propose to disentangle the senescence of immune cells from chronological aging.

Development and Validation of 2 Composite Aging Measures Using Routine Clinical Biomarkers in the Chinese Population: Analyses From 2 Prospective Cohort Studies

BACKGROUND

This study aimed to: (i) develop 2 composite aging measures in the Chinese population using 2 recent advanced algorithms (the Klemera and Doubal method and Mahalanobis distance); and (ii) validate the 2 measures by examining their associations with mortality and disease counts.

METHODS

Based on data from the China Nutrition and Health Survey (CHNS) 2009 wave (N = 8119, aged 20-79 years, 53.5% women), a nationwide prospective cohort study of the Chinese population, we developed Klemera and Doubal method-biological age (KDM-BA) and physiological dysregulation (PD, derived from Mahalanobis distance) using 12 biomarkers. For the validation analysis, we used Cox proportional hazard regression models (for mortality) and linear, Poisson, and logistic regression models (for disease counts) to examine the associations. We replicated the validation analysis in the China Health and Retirement Longitudinal Study (CHARLS, N = 9304, aged 45-99 years, 53.4% women).

RESULTS

Both aging measures were predictive of mortality after accounting for age and gender (KDM-BA, per 1-year, hazard ratio [HR] = 1.14, 95% confidence interval [CI] = 1.08, 1.19; PD, per 1-SD, HR = 1.50, 95% CI = 1.33, 1.69). With few exceptions, these mortality predictions were robust across stratifications by age, gender, education, and health behaviors. The 2 aging measures were associated with disease counts both cross-sectionally and longitudinally. These results were generally replicable in CHARLS although 4 biomarkers were not available.

CONCLUSIONS

We successfully developed and validated 2 composite aging measures-KDM-BA and PD, which have great potentials for applications in early identifications and preventions of aging and aging-related diseases in China.

Are We Ill Because We Age?

Growing elderly populations, sometimes referred to as gray (or silver) tsunami, are an increasingly serious health and socioeconomic concern for modern societies. Science has made tremendous progress in the understanding of aging itself, which has helped medicine to extend life expectancies. With the increase of the life expectancy, the incidence of chronic age-related diseases (ARDs) has also increased. A new approach trying to solve this problem is the concept of geroscience. This concept implies that the aging process itself is the common cause of all ARDs. The corollary and consequence of such thinking is that we can and should treat aging itself as a disease. How to translate this into the medical practice is a big challenge, but if we consider aging as a disease the problem is solved. However, as there is no common definition of what aging is, what its causes are, why it occurs, and what should be the target(s) for interventions, it is impossible to conclude that aging is a disease. On the contrary, aging should be strongly considered not to be a disease and as such should not be treated; nonetheless, aging is likely amenable to optimization of changes/adaptations at an individual level to achieve a better functional healthspan.