Common methods of biological age estimation

Protocol for the San Diego Nathan Shock Center Clinical Cohort: a new resource for studies of human aging

INTRODUCTION

While it is well recognised that aging is a heterogeneous process, our understanding of the determinants of biological aging and its heterogeneity remains unclear. The San Diego Nathan Shock Center (SD-NSC) Clinical Cohort aims to establish a resource of biospecimens and extensive donor clinical data such as physical, cognitive and sensory function to support other studies that aim to explore the heterogeneity of normal human aging and its biological underpinnings.

METHODS AND ANALYSIS

The SD-NSC Clinical Cohort is composed of 80 individuals across the adult human lifespan. Strict inclusion and exclusion criteria are implemented to minimise extrinsic factors that may impede the study of normal aging. Across three visits, participants undergo extensive phenotyping for collection of physical performance, body composition, cognitive function, sensory ability, mental health and haematological data. During these visits, we also collected biospecimens including plasma, platelets, peripheral blood mononuclear cells and fibroblasts for banking and future studies on aging.

ETHICS AND DISSEMINATION

Ethics approval from the UC San Diego School of Medicine Institutional Review Board (IRB #201 141 SHOCK Center Clinical Cohort, PI: Molina) was obtained on 11 November 2020. Written informed consent is obtained from all participants after objectives and procedures of the study have been fully explained. Congruent with the goal of establishing a core resource, biological samples and clinical data are made available to the research community through the SD-NSC.

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.

A Comparison of 5 Measures of Accelerated Biological Aging and Their Association With Incident Cardiovascular Disease: The CARDIA Study

BACKGROUND

Accelerated biological aging is an increasingly popular way to track the acceleration of biology over time that may not be captured by calendar time. Biological aging has been linked to external and internal chronic stressors and has the potential to be used clinically to understand a person's personalized functioning and predict future disease. We compared the association of different measures of biological aging and incident cardiovascular disease (CVD) overall and by race.

METHODS AND RESULTS

We used multiple informants models to compare the strength of clinical marker-derived age acceleration, 5 measures of epigenetic age acceleration (intrinsic and extrinsic epigenetic age acceleration, GrimAge acceleration, and PhenoAge acceleration), and 1 established clinical predictor of future CVD, Framingham 10-year risk score, with incident CVD over an 11-year period (2007-2018). Participants were 913 self-identified Black or White (41% and 59%, respectively) female or male (51% and 49%, respectively) individuals enrolled in the US-based CARDIA (Coronary Artery Risk Development in Young Adults) cohort study. The analytic baseline for this study was the 20-year follow-up examination (2005-2006; median age 45 years). We also included race-specific analysis. We found that all measures were modestly correlated with one another. However, clinical marker-derived age acceleration and Framingham 10-year risk score were more strongly associated with incident CVD than all the epigenetic measures. Clinical marker-derived age acceleration and Framingham 10-year risk score were not significantly different than one another in their association with incident CVD.

CONCLUSIONS

The type of accelerated aging measure should be taken into consideration when comparing their association with clinical outcomes. A multisystem clinical composite shows associations with incident CVD equally to a well-known clinical predictor.

Assessing Biological Age: The Potential of ECG Evaluation Using Artificial Intelligence: JACC Family Series

Biological age may be a more valuable predictor of morbidity and mortality than a person's chronological age. Mathematical models have been used for decades to predict biological age, but recent developments in artificial intelligence (AI) have led to new capabilities in age estimation. Using deep learning methods to train AI models on hundreds of thousands of electrocardiograms (ECGs) to predict age results in a good, but imperfect, age prediction. The error predicting age using ECG, or the difference between AI-ECG-derived age and chronological age (delta age), may be a surrogate measurement of biological age, as the delta age relates to survival, even after adjusting for chronological age and other covariates associated with total and cardiovascular mortality. The relative affordability, noninvasiveness, and ubiquity of ECGs, combined with ease of access and potential to be integrated with smartphone or wearable technology, presents a potential paradigm shift in assessment of biological age.

Clarifying the biological and statistical assumptions of cross-sectional biological age predictors: an elaborate illustration using synthetic and real data

BACKGROUND

There is divergence in the rate at which people age. The concept of biological age is postulated to capture this variability, and hence to better represent an individual's true global physiological state than chronological age. Biological age predictors are often generated based on cross-sectional data, using biochemical or molecular markers as predictor variables. It is assumed that the difference between chronological and predicted biological age is informative of one's chronological age-independent aging divergence ∆.

METHODS

We investigated the statistical assumptions underlying the most popular cross-sectional biological age predictors, based on multiple linear regression, the Klemera-Doubal method or principal component analysis. We used synthetic and real data to illustrate the consequences if this assumption does not hold.

RESULTS

The most popular cross-sectional biological age predictors all use the same strong underlying assumption, namely that a candidate marker of aging's association with chronological age is directly informative of its association with the aging rate ∆. We called this the identical-association assumption and proved that it is untestable in a cross-sectional setting. If this assumption does not hold, weights assigned to candidate markers of aging are uninformative, and no more signal may be captured than if markers would have been assigned weights at random.

CONCLUSIONS

Cross-sectional methods for predicting biological age commonly use the untestable identical-association assumption, which previous literature in the field had never explicitly acknowledged. These methods have inherent limitations and may provide uninformative results, highlighting the importance of researchers exercising caution in the development and interpretation of cross-sectional biological age predictors.

Age estimation from alveolar bone loss, re-evaluation of Ruquet's method

There are many dental age estimation methods, but all the methods do not correspond, especially for aging methods for adults and mature individuals, to the reality of the forensic field, which favors simple, effective, and easy-to-use methods. Ruquet (2015) developed a method based on alveolar bone loss that predicts age for individuals between 25 and 60 years old and is even more accurate for those 25-40 years old. This study re-evaluated Ruquet's alveolar bone loss method using three-dimensional imaging of individuals whose age and sex were known, without taking into account their medical conditions. Digital measurements, from the cemento-enamel junction (CEJ) to the alveolar bone crest (ABC), were performed on the mesial and distal surfaces of teeth on 243 patients, independent of the tridimensional imaging test. With these measurements, two alveolar bone loss averages (ABL) were calculated, one with all the teeth present on the arches and another with only Ramfjörd's teeth. Bone loss showed a significant correlation with age (p < 0.001). The age estimation with all teeth and with only Ramfjörd's teeth showed a statistically significant difference, and age estimation was more accurate when all teeth were used. The assessment of alveolar resorption appears to be an interesting tool for age estimation in adult individuals. However, the method still lacks precision, and the mean absolute errors (MAEs) obtained by age group were all greater than 5 years, except for the age group 35-39 years old, for the age estimation with all teeth. Further studies should explore this existing correlation between alveolar bone loss and age and refine this method to make it more accurate.

Living in poverty and accelerated biological aging: evidence from population-representative sample of U.S. adults

BACKGROUND

Biological aging reflects a decline in the functions and integrity of the human body that is closely related to chronological aging. A variety of biomarkers have been found to predict biological age. Biological age higher than chronological age (biological age acceleration) indicates an accelerated state of biological aging and a higher risk of premature morbidity and mortality. This study investigated how socioeconomic disadvantages influence biological aging.

METHODS

The data from the National Health and Nutrition Examination Survey (NHANES) IV, including 10 nationally representative cross-sectional surveys between 1999-2018, were utilized. The analytic sample consisted of N = 48,348 individuals (20-84 years). We used a total of 11 biomarkers for estimating the biological age. Our main outcome was biological age acceleration, indexed by PhenoAge acceleration (PAA) and Klemera-Doubal biological age acceleration (KDM-A). Poverty was measured as a ratio of family income to the poverty thresholds defined by the U.S. Census Bureau, adjusted annually for inflation and family size (5 categories). The PAA and KDM-A were regressed on poverty levels, age, their interaction, education, sex, race, and a data collection wave. Sample weights were used to make the estimates representative of the U.S. adult population.

RESULTS

The results showed that higher poverty was associated with accelerated biological aging (PAA: unstandardized coefficient B = 1.38 p <.001, KDM: B = 0.96, p = .026 when comparing the highest and the lowest poverty level categories), above and beyond other covariates. The association between PAA and KDM-A and age was U-shaped. Importantly, there was an interaction between poverty levels and age (p <.001), as the effect of poverty was most pronounced in middle-aged categories while it was modest in younger and elderly groups.

CONCLUSION

In a nationally representative US adult population, we found that higher poverty was positively associated with the acceleration of biological age, particularly among middle-aged persons.

A Review: Multi-Omics Approach to Studying the Association between Ionizing Radiation Effects on Biological Aging

Multi-omics studies have emerged as powerful tools for tailoring individualized responses to various conditions, capitalizing on genome sequencing technologies' increasing affordability and efficiency. This paper delves into the potential of multi-omics in deepening our understanding of biological age, examining the techniques available in light of evolving technology and computational models. The primary objective is to review the relationship between ionizing radiation and biological age, exploring a wide array of functional, physiological, and psychological parameters. This comprehensive review draws upon an extensive range of sources, including peer-reviewed journal articles, government documents, and reputable websites. The literature review spans from fundamental insights into radiation effects to the latest developments in aging research. Ionizing radiation exerts its influence through direct mechanisms, notably single- and double-strand DNA breaks and cross links, along with other critical cellular events. The cumulative impact of DNA damage forms the foundation for the intricate process of natural aging, intersecting with numerous diseases and pivotal biomarkers. Furthermore, there is a resurgence of interest in ionizing radiation research from various organizations and countries, reinvigorating its importance as a key contributor to the study of biological age. Biological age serves as a vital reference point for the monitoring and mitigation of the effects of various stressors, including ionizing radiation. Ionizing radiation emerges as a potent candidate for modeling the separation of biological age from chronological age, offering a promising avenue for tailoring protocols across diverse fields, including the rigorous demands of space exploration.

Foregoing elective neck dissection for elderly patients with oral cavity squamous cell carcinoma

OBJECTIVE

Elective neck dissection (END) improves outcomes among clinically node-negative patients with oral cavity squamous cell carcinoma (OCSCC). However, END is of questionable value, considering the potentially higher comorbidities and operative risks in elderly patients.

METHODS

A retrospective review of all patients older than 65 years of age who were treated for OCSCC at a tertiary care centre between 2005 and 2020 was conducted.

RESULTS

Fifty-three patients underwent primary tumour resection alone, and 71 had simultaneous END. Most primary tumours were located on the mobile tongue. The patients who did not undergo END had a higher mean age (81.2 vs. 75.1 years, P < 0.00001), significantly shorter surgeries, and shorter hospitalizations. Occult cervical metastases were found in 24% of the patients who underwent END. The two groups showed no significant differences in overall survival or recurrence rates. Similar results were shown in a subpopulation analysis of patients older than 75 years.

CONCLUSION

Foregoing END in elderly patients with no clinical evidence of neck metastases did not result in lower survival rates or higher recurrence rates.

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.

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.

Mechanistic role and potential of Ayurvedic herbs as anti-aging therapies

INTRODUCTION

Medicinal plants and herbs are the most important part of the Ayurveda. The term Rasayana in Charaka Samhita confers long life, youthfulness, strong body, freedom from diseases and the plants mentioned in Rsayana possess antiaging property. Aging is the collective term used for the complex detrimental physiological changes that reduce the functional ability of the cell. Oxidative stress, telomeres shortening, inflammation, and mitochondrial dysfunction are the main factors that regulate the aging process. Chronological aging is an irreversible process but the factors causing biological aging can be controlled. Ayurvedic herbs are better for the management of age-related problems. There are several natural bioactive agents present in plants that can delay the aging process in humans. They trigger actions like enhancing gene longevity and telomerase activity, ROS scavenging furthermore regeneration of tissues.

CONTENT

The plants mentioned in the Rasayana of Ayurveda have antiaging potential and can be used to solve modern problems related to aging. Some Ayurvedic plants and their antiaging potential has explained in this review. The main causes of aging, medicinal plants and their use as potential antiaging mediator are covered in this study.

SUMMARY

The process of aging is still an enigma. It is a complex, irretrievable, dynamic process that involves a number of factors and is subject to a number of environmental and genetic influences. Rasayana aspect has not been much investigated in clinical trials. Aging is considered to result from free radical damage. According to Charaka, Rasayana drugs open the partially or fully blocked channels. Many Rasayanas show free radical scavenging activity and has the potential to mitigate the effects of aging. It gives an overview of the significance of Ayurvedic medicinal plants as a source of inspiration and the use of these plants as remedies for antiaging.

OUTLOOK

This study briefly outlooks the causes of aging and how medicinal plants can be used to reverse the aging process. In this study, we discussed the antiaging potential and mechanistic roles of Ayurvedic herbs. These herbs have the properties to slow down the natural process of aging and can successfully manage common age-related problems.

Cross-sectionally Calculated Metabolic Aging Does Not Relate to Longitudinal Metabolic Changes-Support for Stratified Aging Models

CONTEXT

Aging varies between individuals, with profound consequences for chronic diseases and longevity. One hypothesis to explain the diversity is a genetically regulated molecular clock that runs differently between individuals. Large human studies with long enough follow-up to test the hypothesis are rare due to practical challenges, but statistical models of aging are built as proxies for the molecular clock by comparing young and old individuals cross-sectionally. These models remain untested against longitudinal data.

OBJECTIVE

We applied novel methodology to test if cross-sectional modeling can distinguish slow vs accelerated aging in a human population.

METHODS

We trained a machine learning model to predict age from 153 clinical and cardiometabolic traits. The model was tested against longitudinal data from another cohort. The training data came from cross-sectional surveys of the Finnish population (n = 9708; ages 25-74 years). The validation data included 3 time points across 10 years in the Young Finns Study (YFS; n = 1009; ages 24-49 years). Predicted metabolic age in 2007 was compared against observed aging rate from the 2001 visit to the 2011 visit in the YFS dataset and correlation between predicted vs observed metabolic aging was determined.

RESULTS

The cross-sectional proxy failed to predict longitudinal observations (R2 = 0.018%, P = 0.67).

CONCLUSION

The finding is unexpected under the clock hypothesis that would produce a positive correlation between predicted and observed aging. Our results are better explained by a stratified model where aging rates per se are similar in adulthood but differences in starting points explain diverging metabolic fates.

Conceptual Overview of Biological Age Estimation

Chronological age is an imperfect measure of the aging process, which is affected by a wide range of genetic and environmental exposures. Biological age estimates may be derived using mathematical modelling with biomarkers set as predictors and chronological age as the output. The difference between biological and chronological age is denoted the "age gap" and considered a complementary indicator of aging. The utility of the "age gap" metric is assessed through examination of its associations with exposures of interest and the demonstration of additional information provided by this metric over chronological age alone. This paper reviews the key concepts of biological age estimation, the age gap metric, and approaches to assessment of model performance in this context. We further discuss specific challenges for the field, in particular the limited generalisability of effect sizes across studies owing to dependency of the age gap metric on pre-processing and model building methods. The discussion will be centred on brain age estimation, but the concepts are transferable to all biological age estimation.

Exercise and epigenetic ages in older adults with myeloid malignancies

BACKGROUND

Older adults with myeloid malignancies are susceptible to treatment-related toxicities. Accelerated DNAm age, or the difference between DNA methylation (DNAm) age and chronological age, may be used as a biomarker of biological age to predict individuals at risk. In addition, cancer treatment can also lead to accelerated DNAm age. Exercise is a promising intervention to reduce or prevent functional, psychological, and cognitive impairments in older patients with myeloid malignancies, yet there is little evidence of the effects of exercise on DNAm age. We explored (1) the associations of accelerated DNAm age with physical, psychological, and cognitive functions at baseline; (2) changes in DNAm age from baseline to post-intervention; and (3) the associations of changes in accelerated DNAm age with changes in functions from baseline to post-intervention.

METHODS

We enrolled older patients with myeloid malignancies to a single-arm pilot study testing a mobile health (mHealth) exercise intervention that combines an exercise program (EXCAP©®) with a mobile application over 2 cycles of chemotherapy (8-12 weeks). Patients completed measures of physical, psychological, and cognitive functions and provided blood samples for analyses of DNAm age at baseline and post-intervention. Paired t-tests or Wilcoxon signed rank tests assessed changes in DNAm ages, and Spearman's correlation assessed the relationships between accelerated ages and functions.

RESULTS

We included 20 patients (mean age: 72 years, range 62-80). Accelerated GrimAge, accelerated PhenoAge, and DunedinPACE were stable from baseline to post-intervention. At baseline, DunedinPACE was correlated with worse grip strength (r = -0.41, p = 0.08). From baseline to post-intervention, decreases in accelerated GrimAge (r = -0.50, p = 0.02), accelerated PhenoAge (r = - 0.39, p = 0.09), and DunedinPace (r = - 0.43, p = 0.06) were correlated with increases in distance walked on 6-min walk test. Decreases in accelerated GrimAge (r = - 0.49, p = 0.03), accelerated PhenoAge (r = - 0.40, p = 0.08), and DunedinPace (r = - 0.41, p = 0.07) were correlated with increases in in grip strength.

CONCLUSIONS

Among older adults with myeloid malignancies receiving chemotherapy, GrimAge and PhenoAge on average are stable after a mHealth exercise intervention. Decreases in accelerated GrimAge, accelerated PhenoAge, and DunedinPACE over 8-12 weeks of exercise were correlated with increased physical performance. Future trials assessing the effects of exercise on treatment-related toxicities should evaluate DNAm age. Trial registration Clinicaltrials.gov identifier: NCT04981821.

Novel Strategy in Searching for Natural Compounds with Anti-Aging and Rejuvenating Potential

We suggest a novel approach for searching natural compounds with anti-aging and rejuvenation potential using cell cultures, with a high potential for the further in vivo applications. The present paper discusses ways of defining age for cell populations with large numbers of cells and suggests a method of assessing how young or old a cell population is based on a cell age profile approach. This approach uses experimental distributions of the cells over the cell cycle stages, acquired using flow cytometry. This paper discusses how such a profile should evolve under homeostatic maintenance of cell numbers in the proliferation niches. We describe promising results from experiments on a commercial substance claiming rejuvenating and anti-aging activity acting upon the cultures of human mononuclear cells and dermal fibroblasts. The chosen substance promotes a shift towards larger proportion of cells in synthesis and proliferation stages, and increases cell culture longevity. Further, we describe promising in vivo testing results of a selected food supplement. Based on the described concept of cell age profile and available test results, a strategy to search for natural compounds with regenerative, anti-aging and rejuvenation potential is suggested and proposed for wider and thorough testing. Proposed methodology of age assessment is rather generic and can be used for quantitative assessment of the anti-aging and rejuvenation potential of different interventions. Further research aimed at the tests of the suggested strategy using more substances and different interventions, and the thorough studies of molecular mechanisms related to the action of the substance used for testing the suggested search methodology, are needed.

How can we modulate aging through nutrition and physical exercise? An epigenetic approach

The World Health Organization predicts that by 2050, 2.1 billion people worldwide will be over 60 years old, a drastic increase from only 1 billion in 2019. Considering these numbers, strategies to ensure an extended "healthspan" or healthy longevity are urgently needed. The present study approaches the promotion of healthspan from an epigenetic perspective. Epigenetic phenomena are modifiable in response to an individual's environmental exposures, and therefore link an individual's environment to their gene expression pattern. Epigenetic studies demonstrate that aging is associated with decondensation of the chromatin, leading to an altered heterochromatin structure, which promotes the accumulation of errors. In this review, we describe how aging impacts epigenetics and how nutrition and physical exercise can positively impact the aging process, from an epigenetic point of view. Canonical histones are replaced by histone variants, concomitant with an increase in histone post-translational modifications. A slight increase in DNA methylation at promoters has been observed, which represses transcription of previously active genes, in parallel with global genome hypomethylation. Aging is also associated with deregulation of gene expression - usually provided by non-coding RNAs - leading to both the repression of previously transcribed genes and to the transcription of previously repressed genes. Age-associated epigenetic events are less common in individuals with a healthy lifestyle, including balanced nutrition, caloric restriction and physical exercise. Healthy aging is associated with more tightly condensed chromatin, fewer PTMs and greater regulation by ncRNAs.

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.

10-year follow-up study on medical expenses and medical care use according to biological age: National Health Insurance Service Health Screening Cohort (NHIS-HealS 2002~2019)

OBJECTIVES

The world is witnessing a sharp increase in its elderly population, accelerated by longer life expectancy and lower birth rates, which in turn imposes enormous medical burden on society. Although numerous studies have predicted medical expenses based on region, gender, and chronological age (CA), any attempt has rarely been made to utilize biological age (BA)-an indicator of health and aging-to ascertain and predict factors related to medical expenses and medical care use. Thus, this study employs BA to predict factors that affect medical expenses and medical care use.

MATERIALS AND METHODS

Referring to the health screening cohort database of the National Health Insurance Service (NHIS), this study targeted 276,723 adults who underwent health check-ups in 2009-2010 and kept track of the data on their medical expenses and medical care use up to 2019. The average follow-up period is 9.12 years. Twelve clinical indicators were used to measure BA, while the total annual medical expenses, total annual number of outpatient days, total annual number of days in hospital, and average annual increases in medical expenses were used as the variables for medical expenses and medical care use. For statistical analysis, this study employed Pearson correlation analysis and multiple regression analysis.

RESULTS

Regression analysis of the differences between corrected biological age (cBA) and CA exhibited statistically significant increases (p<0.05) in all the variables of the total annual medical expenses, total annual number of outpatient days, total annual number of days in hospital, and average annual increases in medical expenses.

CONCLUSIONS

This study quantified decreases in the variables for medical expenses and medical care use based on improved BA, thereby motivating people to become more health-conscious. In particular, this study is significant in that it is the first of its kind to predict medical expenses and medical care use through BA.

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.

Accelerated Aging in LMNA Mutations Detected by Artificial Intelligence ECG-Derived Age

OBJECTIVE

To demonstrate early aging in patients with lamin A/C (LMNA) gene mutations after hypothesizing that they have a biological age older than chronological age, as such a finding impacts care.

PATIENT AND METHODS

We applied a previously trained convolutional neural network model to predict biological age by electrocardiogram (ECG) [Artificial Intelligence (AI)-ECG age] to LMNA patients evaluated by multiple ECGs from January 1, 2003, to December 31, 2019. The age gap was the difference between chronological age and AI-ECG age. Findings were compared with age-/sex-matched controls.

RESULTS

Thirty-one LMNA patients who had a total of 271 ECGs were studied. The median age at symptom onset was 22 years (range, <1-53 years; n=23 patients); eight patients were asymptomatic family members carrying the LMNA mutation. Cardiac involvement was detected by ECG and echocardiogram in 16 patients and consisted of ventricular arrhythmias (13), atrial fibrillation (12), and cardiomyopathy (6). Four patients required cardiac transplantation. Fourteen patients had neurological manifestations, mainly muscular dystrophy. LMNA mutation carriers, including asymptomatic carriers, were 16 years older by AI-ECG than non-LMNA carriers, suggesting accelerated biological age. Most LMNA patients had an age gap of more than 10 years, compared with controls (P<.001). Consecutive AI-ECG analysis showed accelerated aging in the LMNA group compared with controls (P<.0001). There were no significant differences in age-gap among LMNA patients based on phenotype.

CONCLUSION

AI-ECG predicted that LMNA patients have a biological age older than chronological age and accelerated aging even in the absence of cardiac abnormalities by traditional methods. Such a finding could translate into early medical intervention and serve as a disease biomarker.

Modulatory effect of exogenous Coenzyme Q10 on redox and inflammatory biomarkers during aging in rats

An impaired redox homeostasis is an important hallmark of biological aging. Coenzyme Q₁₀ is an endogenous lipophilic antioxidant that decreases with age and has been linked to oxidative stress. The purpose of this study was to evaluate the effect of CoQ₁₀ supplementation on redox homeostasis and levels of inflammatory cytokines in young and old rats. Male Wistar rats (young and old) were randomly divided into four groups (n = 6). Group I: young control, Group II: young rats treated with CoQ₁₀, Group III: old control, Group IV: old rats treated with CoQ₁₀. CoQ₁₀ (20 mg/kg) was administered daily to Group II and IV via oral gavage. After 28 days of treatment, rats were sacrificed and biomarkers of oxidative stress and inflammatory cytokines were evaluated. Results demonstrated a significant (p ≤ 0.05) increase in malondialdehyde, protein carbonyl oxidation, advanced oxidation protein products, inflammatory cytokines: CRP, IL-6, TNF-α, and a decline in levels of superoxide dismutase, catalase, reduced glutathione, ferric reducing antioxidant potential in plasma and plasma membrane redox system in old rats when compared to young rats. After treatment with CoQ₁₀ significant decrease in the level of MDA, PCO, AOPP, CRP, IL-6, and TNF-α was observed. Also, significant up-regulation of SOD, CAT, GSH, FRAP, and PMRS was observed. The results show that supplementing rats with CoQ₁₀ aids in the maintenance of redox equilibrium with replenishment of antioxidant reserves and down-regulation of inflammatory biomarkers. Thus CoQ₁₀ supplementation could be a potential anti-aging therapy.

Nutrition and stem cell integrity in aging

Adult stem cells (SCs) represent the regenerative capacity of organisms throughout their lifespan. The maintenance of robust SC populations capable of renewing organs and physiological systems is one hallmark of healthy aging. The local environment of SCs, referred to as the niche, includes the nutritional milieu, which is essential to maintain the quantity and quality of SCs available for renewal and regeneration. There is increased recognition that SCs have unique metabolism and conditional nutrient needs compared to fully differentiated cells. However, the contribution of SC nutrition to overall human nutritional requirements is an understudied and underappreciated area of investigation. Nutrient needs vary across the lifespan and are modified by many factors including individual health, disease, physiological states including pregnancy, age, sex, and during recovery from injury. Although current nutrition guidance is generally derived for apparently healthy populations and to prevent nutritional deficiency diseases, there are increased efforts to establish nutrient-based and food-based recommendations based on reducing chronic disease. Understanding the dynamics of SC nutritional needs throughout the life span, including the role of nutrition in extending biological age by blunting biological systems decay, is fundamental to establishing food and nutrient guidance for chronic disease reduction and health maintenance. This review summarizes a 3-day symposium of the Marabou Foundation (www.marabousymposium.org) held to examine the metabolic properties and unique nutritional needs of adult SCs and their role in healthy aging and age-related chronic disease.

Epigenetic age stratifies the risk of blood pressure elevation related to short-term PM2.5 exposure in older adults

BACKGROUND

Exposure to fine particulate matter (PM_2.5) is a prominent risk factor for cardiovascular aging in older adults and causes mild syndromes or other comorbidities in otherwise healthy older adults. Accordingly, a precise tool for PM_2.5 exposure risk stratification is urgently needed. We aimed to address this need by comparing the performances of seven types of epigenetic age and chronological age to classify the effects of short-term PM_2.5 exposure on blood pressure (BP), a typical clinical surrogate marker of cardiovascular aging.

METHODS

We conducted a panel study of the Chinese healthy adults aged 60-69 years through five monthly visits. Personal PM_2.5 exposures were measured using wearable monitoring devices for three consecutive days, and DNA methylation was determined by the Illumina MethylationEPIC BeadChip using blood samples collected at each visit. Systolic BP, diastolic BP, mean arterial pressure and pulse pressure were measured by the electronic BP monitor. Linear mixed models with interaction terms between PM_2.5 and different ages were used to assess their potential usefulness for stratification.

RESULTS

DNAmPhenoAge, Skin & blood clock, DNAmGrimAge acceleration, and DunedinPoAm had significant modifying effects on the relationship between PM_2.5 and BP. For instance, a 10-μg/m³ increase in the 72-h moving mean PM_2.5 was significantly associated with 0.30% (95% CI: 0.10%, 0.51%) and -0.07% (95% CI: -0.32%, 0.18%) increases in systolic BP at higher and lower DNAmPhenoAge acceleration, respectively. Joint models further revealed that using a combination of epigenetic ages could more precisely stratify the effect of PM_2.5 on BP.

CONCLUSIONS

Our research indicates that epigenetic age may be a useful tool for evaluating the effect of short-term PM_2.5 exposure on cardiovascular aging status.

Where Are Populations Aging Better? A Global Comparison of Healthy Aging Across Organization for Economic Cooperation and Development Countries

OBJECTIVES

Global comparisons and large samples are needed to inform policy makers about aging trends among people aged older than 60 years. Using harmonized data gathered from the Gateway to Global Aging data, we introduce a new framework to measure healthy aging across 13 OECD countries.

METHODS

First, we developed an original measure of physiological age (PA), that is, a measure of age weighted for the influence of frailty, activities of daily living limitations, and comorbidities. Second, we compared healthy aging measures across 13 countries based on a ranking of the countries according to the discrepancy between estimated PA and chronological age (CA). Third, we explored the socioeconomic factors associated with healthy aging.

RESULTS

We found a strong correlation between our PA measure and biological age. Italy, Israel, and the United States are the 3 countries where PA is the highest (independent of CA), thus indicating aging in poor health. In contrast, Switzerland, The Netherlands, Greece, Sweden, and Denmark have much lower PA than CA, thus indicating healthy aging. Finally, the PA-CA discrepancy is higher among poorer, less educated, and single older individuals.

CONCLUSIONS

Countries with higher PA need to implement or reinforce healthy aging measures and target the disadvantaged populations.

Biological age for chronic kidney disease patients using index model

The estimation of biological age (BA) is an important asymptomatic measure that can be used to understand the physical changes and the aging process of a living being. Factors that contribute towards profiling the human biological age can be diverse. Therefore, this study focuses on developing a BA model for patients with Chronic Kidney Disease (CKD). The procedure commences with the selection of significant biomarkers using a correlation test. Appropriate weighting is then assigned to each selected biomarker using the indexing method to produce a BA index. The BA index is matched to the age variation within the sample to acquire additional terms for the chronological age leading ultimately to the estimated BA. From a sample of 190 patients (133 trained data and 57 testing data) obtained from the University of Malaya Medical Centre (UMMC), Malaysia, the intensity of the BA is found to be between three to nine years from the chronological age. Visual observations further validate the high similarities between the training and testing data sets.

Perceived facial age and biochemical indicators of glycemia in adult men and women

Glycemia is linked with one of the key mechanisms underlying the aging process and inter-individual differences in biological age. Previous research showed that glucose level is linked with perceived age in elder individuals. This study aimed to verify if glycemia is related to perceived facial age in healthy adult individuals as interventions in younger and healthy cohorts are crucial for preventing the onset of age-related diseases. The study sample consisted of 116 healthy men of mean age 35.53 ± 3.54 years (29.95–44.29) and 163 healthy women of mean age 28.38 ± 2.40 (24.25–34.17) years. Glycemia was evaluated by fasting glucose, insulin, HOMA-IR, and glycated hemoglobin level. BMI, facial sexual dimorphism, estradiol, testosterone, and hsCRP levels were controlled. Perceived age was evaluated based on standardized facial photos in an online survey. Additionally perceived facial aging was calculated as a difference between perceived age and chronological age. No relationship between the levels of biochemical indicators of glycemia and perceived facial age or aging was found both in men and women, also when controlled for possible confounders. This study shows that perceived facial age in adult individuals is rather linked with body adiposity of sexual dimorphism but not with glycemic markers.

A Model for Estimating Biological Age From Physiological Biomarkers of Healthy Aging: Cross-sectional Study

BACKGROUND

Individual differences in the rate of aging and susceptibility to disease are not accounted for by chronological age alone. These individual differences are better explained by biological age, which may be estimated by biomarker prediction models. In the light of the aging demographics of the global population and the increase in lifestyle-related morbidities, it is interesting to invent a new biological age model to be used for health promotion.

OBJECTIVE

This study aims to develop a model that estimates biological age based on physiological biomarkers of healthy aging.

METHODS

Carefully selected physiological variables from a healthy study population of 100 women and men were used as biomarkers to establish an estimate of biological age. Principal component analysis was applied to the biomarkers and the first principal component was used to define the algorithm estimating biological age.

RESULTS

The first principal component accounted for 31% in women and 25% in men of the total variance in the biological age model combining mean arterial pressure, glycated hemoglobin, waist circumference, forced expiratory volume in 1 second, maximal oxygen consumption, adiponectin, high-density lipoprotein, total cholesterol, and soluble urokinase-type plasminogen activator receptor. The correlation between the corrected biological age and chronological age was r=0.86 (P<.001) and r=0.81 (P<.001) for women and men, respectively, and the agreement was high and unbiased. No difference was found between mean chronological age and mean biological age, and the slope of the regression line was near 1 for both sexes.

CONCLUSIONS

Estimating biological age from these 9 biomarkers of aging can be used to assess general health compared with the healthy aging trajectory. This may be useful to evaluate health interventions and as an aid to enhance awareness of individual health risks and behavior when deviating from this trajectory.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03680768; https://clinicaltrials.gov/ct2/show/NCT03680768.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.2196/19209.

Individualized Biological Age as a Predictor of Disease: Korean Genome and Epidemiology Study (KoGES) Cohort

Chronological age (CA) predicts health status but its impact on health varies with anthropometry, socioeconomic status (SES), and lifestyle behaviors. Biological age (BA) is, therefore, considered a more precise predictor of health status. We aimed to develop a BA prediction model from self-assessed risk factors and validate it as an indicator for predicting the risk of chronic disease. A total of 101,980 healthy participants from the Korean Genome and Epidemiology Study were included in this study. BA was computed based on body measurements, SES, lifestyle behaviors, and presence of comorbidities using elastic net regression analysis. The effects of BA on diabetes mellitus (DM), hypertension (HT), combination of DM and HT, and chronic kidney disease were analyzed using Cox proportional hazards regression. A younger BA was associated with a lower risk of DM (HR = 0.63, 95% CI: 0.55–0.72), hypertension (HR = 0.74, 95% CI: 0.68–0.81), and combination of DM and HT (HR = 0.65, 95% CI: 0.47–0.91). The largest risk of disease was seen in those with a BA higher than their CA. A consistent association was also observed within the 5-year follow-up. BA, therefore, is an effective tool for detecting high-risk groups and preventing further risk of chronic diseases through individual and population-level interventions.

Exploring the problem of determining human age from fingermarks using MALDI MS-machine learning combined approaches

For over a century fingerprints have been predominantly used as a means of biometric identification. Notwithstanding, the unique pattern of lines that can contribute to identifying a suspect is made up of molecules originating from touch chemistry (contaminants) as well as from within the body. It is the latter class of molecules that could provide additional information about a suspect, such as lifestyle, as well as physiological, pharmacological and pathological states. An example of the physiological state (and semi-biometric information) is the sex of an individual; recent investigations have demonstrated the opportunity to determine the sex of an individual with an 86% accuracy of prediction based on the peptidic/protein profile of their fingerprints. In the study presented here, the first of its kind, a range of supervised learning predictive methods have been evaluated to explore the depth of the issue connected to human age determination from fingermarks exploiting again the differential presence of peptides and small proteins. A number of observations could be made providing (i) an understanding of the more appropriate study design for this kind of investigation, (ii) the most promising prediction model to test within future work and (iii) the deeper issues relating to this type of determination and concerning a mismatch between chronological and biological ages. Particularly resolving point (iii) is crucial to the success in determining the age of an individual from the molecular composition of their fingermark.

Biological vascular age and its relationship with cardiovascular risk factors

Aim. To study of the relationship between cardiovascular risk factors and biological vascular age.

Material and methods. The biological vascular age was estimated using models based on the arterial wall parameters. Using multiple logistic and linear regression, we studied the relationship between the biological vascular age and cardiovascular risk factors in 143 people without cardiovascular disease (CVD). Persons with a positive difference between the vascular and chronological age were assigned to the “old” vascular group, and persons with no or negative difference between the vascular and chronological age were assigned to the “young” vascular group.

Results. Linear regression in the “young” vascular group showed an inverse relationship between the difference between the vascular and chronological age with the levels of low-density lipoprotein cholesterol (p=0,001; β±SE=-1,67±0,47), triglycerides (p=0,017; β±SE=-1,66±0,68), urea (p=0,025; β±SE=-0,89±0,39) and insulin resistance index (p=0,001; β±SE=-1,22±0,36). In the “old” vascular group, a direct relationship was found between the difference between the vascular and chronological age and central systolic blood pressure (p=0,015; β±SE=0,10±0,04). According to logistic regression, the likelihood of having “old” vessels increased by 1,23 times with an increase in blood glucose levels by 0,5 mmol/l (p=0,044; odds ratio (OR)=1,23; 95% confidence interval (CI): 1,011,51), the presence of hypertension (p=0,034; OR=3,11; 95% CI: 1,09-8,86) and type 2 diabetes (p=0,025; OR=3,61; 95% CI: 1,1711,09), as well as decreased by 2 times with an increase in high-density lipoprotein cholesterol by 0,3 mmol/l (p=0,003; OR=0,5; 95% CI: 0,32-0,79).

Conclusion. The difference between the biological vascular age and chronological age is associated with traditional CVD risk factors.

Urinary Biomarkers of Oxidative Stress in Aging: Implications for Prediction of Accelerated Biological Age in Prospective Cohort Studies

Background

Aging is a major risk factor for a range of chronic diseases. Oxidative stress theory of aging has been previously proposed as one of the mechanisms responsible for the age-related decline in organ/tissue function and the development of age-related diseases. Urine contains rich biological information on the health status of every major organ system and can be an important noninvasive source for biomarkers of systemic oxidative stress in aging.

Aims

The objective of this cross-sectional study was to validate a novel panel of urinary oxidative stress biomarkers.

Methods

Nucleic acid oxidation adducts and oxidative damage markers of lipids and proteins were assessed in urine samples from nondiabetic and currently nonsmoking subjects (n = 198) across different ages (20 to 89 years old). Urinary parameters and chronological age were correlated then the biological age of enrolled individuals was determined from the urinary oxidative stress markers using the algorithm of Klemera and Doubal.

Results

Our findings showed that 8-oxo-7,8-deoxyguanosine (8-oxoG), 8-oxo-7,8-dihydroguanosine (8-OHdG), and dityrosine (DTyr) positively correlated with chronological age, while the level of an F₂-isoprostane (iPF₂ α-VI) correlated negatively with age. We found that 8-oxoG, DTyr, and iPF₂ α-VI were significantly higher among accelerated agers compared to nonaccelerated agers and that a decision tree model could successfully identify accelerated agers with an accuracy of >92%. Discussion. Our results indicate that 8-oxoG and iPF₂ α-VI levels in the urine reveal biological aging.

Conclusion

Assessing urinary biomarkers of oxidative stress may be an important approach for the evaluation of biological age by identifying individuals at accelerated risk for the development of age-related diseases.

Effect of Diet, Exercise, or Both on Biological Age and Healthy Aging in Older Adults with Obesity: Secondary Analysis of a Randomized Controlled Trial

OBJECTIVES

To determine the effect of diet, exercise, and diet-exercise in combination on measures of biological age.

DESIGN

Secondary analysis of a 1-year randomized, controlled trial.

SETTING

University-based Medical Center.

PARTICIPANTS

One-hundred-seven older (age≥65 yrs.) adults with obesity (BMI≥30 kg/m2) were randomized and 93 completed the study. Analyses used intention-to-treat.

INTERVENTIONS

Participants were randomized to a control group, a weight-management (diet) group, an exercise group, or a weight-management-plus-exercise (diet-exercise) group.

MAIN OUTCOME MEASURES

We calculated Klemera-Doubal Method (KDM) biological age, Homeostatic Dysregulation (HD) score, and Health Aging Index (HAI) score at baseline, and changes at 6- and 12-months.

RESULTS

Diet and diet-exercise decreased KDM biological age more than exercise and control (-2.4±0.4, -2.2±0.3, -0.2±0.4, and 0.2±0.5, respectively, P<0.05 for the between group-differences). Diet and diet-exercise also decreased HD score more than exercise and control (-1.0±0.3, -1.1±0.3, 0.1±0.3, and 0.3±0.3 respectively, P<0.05). Moreover, diet-exercise decreased HAI score more than exercise, diet, or control (-1.1±0.2, -0.5±0.2, -0.5±0.2, and 0.0±0.2, respectively, P<0.05).

CONCLUSIONS

These findings suggest that diet and diet-exercise are both effective methods of improving biological age, and that biological age may be a valuable method of assessing geroprotective interventions in older humans.

Linking Brain Age Gap to Mental and Physical Health in the Berlin Aging Study II

From a biological perspective, humans differ in the speed they age, and this may manifest in both mental and physical health disparities. The discrepancy between an individual's biological and chronological age of the brain ("brain age gap") can be assessed by applying machine learning techniques to Magnetic Resonance Imaging (MRI) data. Here, we examined the links between brain age gap and a broad range of cognitive, affective, socioeconomic, lifestyle, and physical health variables in up to 335 adults of the Berlin Aging Study II. Brain age gap was assessed using a validated prediction model that we previously trained on MRI scans of 32,634 UK Biobank individuals. Our statistical analyses revealed overall stronger evidence for a link between higher brain age gap and less favorable health characteristics than expected under the null hypothesis of no effect, with 80% of the tested associations showing hypothesis-consistent effect directions and 23% reaching nominal significance. The most compelling support was observed for a cluster covering both cognitive performance variables (episodic memory, working memory, fluid intelligence, digit symbol substitution test) and socioeconomic variables (years of education and household income). Furthermore, we observed higher brain age gap to be associated with heavy episodic drinking, higher blood pressure, and higher blood glucose. In sum, our results point toward multifaceted links between brain age gap and human health. Understanding differences in biological brain aging may therefore have broad implications for future informed interventions to preserve mental and physical health in old age.

Diabetes mellitus correlates with increased biological age as indicated by clinical biomarkers

Chronological age (CA) is determined by time of birth, whereas biological age (BA) is based on changes on a cellular level and strongly correlates with morbidity, mortality, and longevity. Type 2 diabetes (T2D) associates with increased morbidity and mortality; thus, we hypothesized that BA would be increased and calculated it from biomarkers collected at routine clinical visits. Deidentified data was obtained from three cohorts of patients (20–80 years old)—T2D, type 1 diabetes (T1D), and prediabetes—and compared to gender- and age-matched non-diabetics. Eight clinical biomarkers that correlated with CA in people without diabetes were used to calculate BA using the Klemera and Doubal method 1 (KDM1) and multiple linear regression (MLR). The phenotypic age (PhAge) formula was used with its predetermined biomarkers. BA of people with T2D was, on average, 12.02 years higher than people without diabetes (p < 0.0001), while BA in T1D was 16.32 years higher (p < 0.0001). Results were corroborated using MLR and PhAge. The biomarkers with the strongest correlation to increased BA in T2D using KDM were A1c (R² = 0.23, p < 0.0001) and systolic blood pressure (R² = 0.21, p < 0.0001). BMI had a positive correlation to BA in non-diabetes subjects but disappeared in those with diabetes. Mortality data using the ACCORD trial was used to validate our results and showed a significant correlation between higher BA and decreased survival. In conclusion, BA is increased in people with diabetes, irrespective of pathophysiology, and to a lesser extent in prediabetes.

Association between body temperature and leukocyte telomere length in Korean middle-aged and older adults

OBJECTIVES

Data on associations between body temperature (BT) and leukocyte telomere length (LTL), which has been widely used as a biomarker of cellular senescence in recent epidemiological studies, are limited. Therefore, this study aimed to explore the associations between a normal BT range (35.0-37.5°C) and LTL via 6-year longitudinal observations of 2,004 male and female adults aged 50 or older.

METHODS

BT was obtained by measuring the tympanic temperature, and relative LTL was determined by real-time polymerase chain reaction. Robust regression analysis was used to evaluate the association between the baseline and follow-up LTL values and their differences.

RESULTS

A significant inverse association was found between BT and LTL at baseline. The regression coefficient estimate was -0.03 (95% confidence interval, -0.07 to -0.001; p<0.05). This association was stronger in participants with a body mass index >25 kg/m² and males (p<0.01). However, there were no associations between BT and LTL at follow-up or BT and 6-year longitudinal differences in LTL.

CONCLUSIONS

These findings suggest that having a high BT between 35°C and 37.5°C (95°F and 99°F) may be detrimental for obese individuals in terms of biological aging.

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.

Age-Net: An MRI-Based Iterative Framework for Brain Biological Age Estimation

The concept of biological age (BA) - although important in clinical practice - is hard to grasp mainly due to the lack of a clearly defined reference standard. For specific applications, especially in pediatrics, medical image data are used for BA estimation in a routine clinical context. Beyond this young age group, BA estimation is mostly restricted to whole-body assessment using non-imaging indicators such as blood biomarkers, genetic and cellular data. However, various organ systems may exhibit different aging characteristics due to lifestyle and genetic factors. Thus, a whole-body assessment of the BA does not reflect the deviations of aging behavior between organs. To this end, we propose a new imaging-based framework for organ-specific BA estimation. In this initial study we focus mainly on brain MRI. As a first step, we introduce a chronological age (CA) estimation framework using deep convolutional neural networks (Age-Net). We quantitatively assess the performance of this framework in comparison to existing state-of-the-art CA estimation approaches. Furthermore, we expand upon Age-Net with a novel iterative data-cleaning algorithm to segregate atypical-aging patients (BA [Formula: see text] CA) from the given population. We hypothesize that the remaining population should approximate the true BA behavior. We apply the proposed methodology on a brain magnetic resonance image (MRI) dataset containing healthy individuals as well as Alzheimer's patients with different dementia ratings. We demonstrate the correlation between the predicted BAs and the expected cognitive deterioration in Alzheimer's patients. A statistical and visualization-based analysis has provided evidence regarding the potential and current challenges of the proposed methodology.

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.

Individual predisposition and the intricate interplay between systemic biomarkers and periodontal risk in a general population

BACKGROUND

Increasing age is associated with systemic diseases as well as with periodontal diseases. We wondered whether a biological age score constructed exclusively from systemic biomarkers would reflect periodontal risk factors at baseline and tooth loss as well as periodontal outcome during 10 years follow-up.

METHODS

From the Study of Health in Pomerania (SHIP) 2256 participants (1072 male, 1184 female) were studied for the relationship of the systemic biomarkers glycated hemoglobin (HbA1c), low density lipoprotein cholesterol (LDL), fibrinogen, white blood cell count, blood pressure, and waist circumference to their age. Construction of a biological age (BA) score allowed its comparison with the participants' actual chronological age (CA) and their predisposition to periodontal disease.

RESULTS

Though nearly identical in CA, participants appearing younger than their true age had a significantly reduced burden of periodontal risk factors. If BA > CA, then risk factors were more frequent including smoking, oral hygiene, dental visits, education, and income. After 10 years, in participants with identical CA, tooth loss followed their BA calculated at baseline, that is, with BA > CA fewer teeth were preserved. Similarly, periodontal measures varied according to BA; sex differences were obvious. Most significant were BA-related differences in inflammatory and anthropometry parameters.

CONCLUSIONS

The results support the assumption that risk profiles aggregated in BA constitute a characteristic susceptibility pattern unique to each individual, common to both systemic and periodontal diseases. Although BA was constructed exclusively from systemic measures at baseline, BA reflects the oral conditions at follow-up.

Age-group determination of living individuals using first molar images based on artificial intelligence

Dental age estimation of living individuals is difficult and challenging, and there is no consensus method in adults with permanent dentition. Thus, we aimed to provide an accurate and robust artificial intelligence (AI)-based diagnostic system for age-group estimation by incorporating a convolutional neural network (CNN) using dental X-ray image patches of the first molars extracted via panoramic radiography. The data set consisted of four first molar images from the right and left sides of the maxilla and mandible of each of 1586 individuals across all age groups, which were extracted from their panoramic radiographs. The accuracy of the tooth-wise estimation was 89.05 to 90.27%. Performance accuracy was evaluated mainly using a majority voting system and area under curve (AUC) scores. The AUC scores ranged from 0.94 to 0.98 for all age groups, which indicates outstanding capacity. The learned features of CNNs were visualized as a heatmap, and revealed that CNNs focus on differentiated anatomical parameters, including tooth pulp, alveolar bone level, or interdental space, depending on the age and location of the tooth. With this, we provided a deeper understanding of the most informative regions distinguished by age groups. The prediction accuracy and heat map analyses support that this AI-based age-group determination model is plausible and useful.

Biological Versus Chronological Aging: JACC Focus Seminar

Aging is the main risk factor for vascular disease and ensuing cardiovascular and cerebrovascular events, the leading causes of death worldwide. In a progressively aging population, it is essential to develop early-life biomarkers that efficiently identify individuals who are at high risk of developing accelerated vascular damage, with the ultimate goal of improving primary prevention and reducing the health care and socioeconomic impact of age-related cardiovascular disease. Studies in experimental models and humans have identified 9 highly interconnected hallmark processes driving mammalian aging. However, strategies to extend health span and life span require understanding of interindividual differences in age-dependent functional decline, known as biological aging. This review summarizes the current knowledge on biological age biomarkers, factors influencing biological aging, and antiaging interventions, with a focus on vascular aspects of the aging process and its cardiovascular disease related manifestations.

The combination of a male donor's high muscle mass and quality is an independent protective factor for graft loss after living donor liver transplantation

We evaluated the hypothesis that grafts from donors with high muscle mass and quality may have a better outcome after living-donor-liver-transplantation (LDLT) than those from usual donors. A total of 376 primary adult-to-adult LDLT cases were enrolled in this study. Donor skeletal muscle mass index (SMI) and intramuscular adipose tissue content (IMAC) were used as markers of muscle mass and quality. In male donor cases (n = 198), those with higher SMI and lower IMAC than age-adjusted values were defined as the "high muscularity donors" (n = 38) and the others were defined as the "control" (n = 160). The high muscularity donor showed better 1-year (97% vs 82%, P = .020) and overall graft survival rate (88% vs 67%, P = .024) than the control group after LDLT. Contrastingly, the influence of the muscularity was not observed in female donor cases. Multivariable analysis including donor age confirmed that a high muscularity donor was an independent protective factor for overall graft survival after LDLT (hazard ratio, 0.337; 95% CI: 0.101-0.838; P = .017). Our study first confirmed that high muscle mass and quality of a male donor is a protective factor of allograft loss after LDLT, independently from donor age.

Biological senescence risk score. A practical tool to predict biological senescence status

BACKGROUND

Ageing and biological senescence, both related to cardiovascular disease, are mediated by oxidative stress and inflammation. We aim to develop a predictive tool to evaluate the degree of biological senescence in coronary patients.

METHODS

Relative telomere length (RTL) of 1002 coronary patients from the CORDIOPREV study (NCT00924937) was determined at baseline in addition to markers of inflammatory response (hs-C-Reactive Protein, monocyte chemoattractant protein-1, IL-6, IL-1β, TNF-α, adiponectin, resistin and leptin) and oxidative stress (nitric oxide, lipid peroxidation products, carbonylated proteins, catalase, total glutathione, reduced glutathione, oxidized glutathione, superoxide dismutase and peroxidated glutathione). Biological senescence was defined using the cut-off value defined by the lower quintile of relative telomere length in our population (RTL = 0.7629). We generated and tested different predictive models based on logistic regression analysis to identify biological senescence. Three models were designed to be used with different sets of information.

RESULTS

We selected those patients with all the variables proposed to develop the predictive models (n = 353). Statistically significant differences between both groups (Biological senescence vs. Nonbiological senescence) were found for total cholesterol, catalase, superoxide dismutase, IL-1β, resistin and leptin. The area under the curve of receiver-operating characteristic to predict biological senescence for our models was 0.65, 0.75 and 0.72.

CONCLUSIONS

These predictive models allow us to calculate the degree of biological senescence in coronary patients, identifying a subgroup of patients at higher risk and who may require more intensive treatment.

A Biological Age Model Designed for Health Promotion Interventions: Protocol for an Interdisciplinary Study for Model Development

BACKGROUND

Actions to improve healthy aging and delay morbidity are crucial, given the global aging population. We believe that biological age estimation can help promote the health of the general population. Biological age reflects the heterogeneity in functional status and vulnerability to disease that chronological age cannot. Thus, biological age assessment is a tool that provides an intuitively meaningful outcome for the general population, and as such, facilitates our understanding of the extent to which lifestyle can increase health span.

OBJECTIVE

This interdisciplinary study intends to develop a biological age model and explore its usefulness.

METHODS

The model development comprised three consecutive phases: (1) conducting a cross-sectional study to gather candidate biomarkers from 100 individuals representing normal healthy aging people (the derivation cohort); (2) estimating the biological age using principal component analysis; and (3) testing the clinical use of the model in a validation cohort of overweight adults attending a lifestyle intervention course.

RESULTS

We completed the data collection and analysis of the cross-sectional study, and the initial results of the principal component analysis are ready. Interpretation and refinement of the model is ongoing. Recruitment to the validation cohort is forthcoming. We expect the results to be published by December 2021.

CONCLUSIONS

We expect the biological age model to be a useful indicator of disease risk and metabolic risk, and further research should focus on validating the model on a larger scale.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03680768, https://clinicaltrials.gov/ct2/show/NCT03680768 (Phase 1 study); NCT04279366 https://clinicaltrials.gov/ct2/show/NCT04279366 (Phase 3 study).

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/19209.

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.