Biological age as a health index for mortality and major age-related disease incidence in Koreans: National Health Insurance Service - Health screening 11-year follow-up study

EchoAGE: Echocardiography-based Neural Network Model Forecasting Heart Biological Age

Biological age is a personalized measure of the health status of an organism, organ, or system, as opposed to simply accounting for chronological age. To date, there have been known attempts to create estimators of biological age based on various biomedical data. In this work, we focused on developing an approach for assessing heart biological age using echocardiographic data. The current study included echocardiographic data from more than 5,000 different cases. As a result, we created EchoAGE - neural network model to determine heart biological age, that was tested on echocardiographic data from patients with age-related diseases, patients with multimorbidity, children with progeria syndrome, and diachronic data series. The model estimates biological age with a Mean Absolute Error of approximately 3.5 years, an R-squared value of around 0.88, and a Spearman's rank correlation coefficient greater than 0.9 in men and women. EchoAGE uses indicators such as E/A ratio of maximum flow rates in the first and second phases, thicknesses of the interventricular septum and the posterior left ventricular wall, cardiac output, and relative wall thickness. In addition, we have applied an AI explanation algorithm to improve understanding of how the model performs an assessment.

Loss of the Y Chromosome: A Review of Molecular Mechanisms, Age Inference, and Implications for Men's Health

Until a few years ago, it was believed that the gradual mosaic loss of the Y chromosome (mLOY) was a normal age-related process. However, it is now known that mLOY is associated with a wide variety of pathologies in men, such as cardiovascular diseases, neurodegenerative disorders, and many types of cancer. Nevertheless, the mechanisms that generate mLOY in men have not been studied so far. This task is of great importance because it will allow focusing on possible methods of prophylaxis or therapy for diseases associated with mLOY. On the other hand, it would allow better understanding of mLOY as a possible marker for inferring the age of male samples in cases of human identification. Due to the above, in this work, a comprehensive review of the literature was conducted, presenting the most relevant information on the possible molecular mechanisms by which mLOY is generated, as well as its implications for men's health and its possible use as a marker to infer age.

Standardized multi-institutional data analysis of fixed and removable prosthesis: estimation of life expectancy with regards to variable risk factors

PURPOSE

This study aims to assess and predict lifespan of dental prostheses using newly developed Korean Association of Prosthodontics (KAP) criteria through a large-scale, multi-institutional survey.

MATERIALS AND METHODS

Survey was conducted including 16 institutions. Cox proportional hazards model and principal component analysis (PCA) were used to find out relevant factors and predict life expectancy.

RESULTS

1,703 fixed and 815 removable prostheses data were collected and evaluated. Statistically significant factors in fixed prosthesis failure were plaque index and material type, with a median survival of 10 to 18 years and 14 to 20 years each. In removable prosthesis, factors were national health insurance coverage, antagonist type, and prosthesis type (complete or partial denture), with median survival of 10 to 13 years, 11 to 14 years, and 10 to 15 years each. For still-usable prostheses, PCA analysis predicted an additional 3 years in fixed and 4.8 years in removable prosthesis.

CONCLUSION

Life expectancy of a prosthesis differed significantly by factors mostly controllable either by dentist or a patient. Overall life expectancy was shown to be longer than previous research.

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.

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

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

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.

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.

Biological Age Predictors: The Status Quo and Future Trends

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

Predicting the incidence of age-related diseases based on biological age: The 11-year national health examination data follow-up

PURPOSE

As the population ages rapidly, the incidence of age-related diseases (ARDs) is also increasing fast. Predicting the incidence of ARDs is a challenge since the rates of individual aging vary, and objective assessments of the stages of aging based on chronological age (CA) may be inaccurate. Thus, in this study, we developed a biological age (BA) model based on the National Health Examination (NHE) data and analyzed the model prediction results for the incidence of 16 ARDs.

METHODS

This study was based on the 2002-2019 National Health Information Databases of the National Health Insurance Service (NHIS-NHID). The data from a total of 10,002,494 subjects were selected between 2009 and 2010, and the principal component analysis (PCA) was performed to develop the BA model. The Cox-proportional hazard model was used to perform predictive analysis of the ARD incidence.

RESULTS

For the unit increase in the difference between corrected biological age (cBA) and chronological age (CA), the hazard ratios (HRs) of ARDs increased significantly for both sexes (p < 0.001). In descending order, the corresponding ARDs' HRs were obesity (1.655), chronic renal failure (1.362), hypertension (1.301), hyperlipidemia (1.264), diabetes mellitus (1.261), fracture (1.119), dementia (1.163), cataract (1.116), myocardial infarction (1.097), stroke (1.169), macular degeneration (1.075), osteoarthritis (1.059), osteoporosis (1.124), Parkinson's disease (1.048), and chronic obstructive pulmonary disease (1.026).

CONCLUSIONS

In this study, the incidence of 16 ARDs were analyzed based on BA. Therefore, conducting the NHIS health examination can facilitate the prevention of ARDs by estimating HRs for at least 16 diseases.

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.

Discordance in Bone Mineral Density between the Lumbar Spine and Femoral Neck Is Associated with Renal Dysfunction

PURPOSE

Bone mineral density (BMD) determined by dual-energy X-ray absorptiometry is considered a gold standard for diagnosing osteoporosis. Some people show discordance in BMD values measured at the femur and that at the lumbar spine (LS). The aim of the present study was to investigate whether differences in BMD T-scores between the LS and femur neck (FN) are associated with renal dysfunction in the general population of Korea.

MATERIALS AND METHODS

We analyzed national data for 17306 adults from the Korean National Health and Nutrition Examination Survey conducted between 2008 and 2011. BMD T-score differences between LS and FN (termed BMD offset) were calculated by subtracting FN T-scores from LS T-scores. Diminished renal function was defined as estimated glomerular filtration rates (eGFR) less than 60 mL/min/1.73 m².

RESULTS

Among those aged ≥50 years, BMD offset was negatively associated with eGFR levels. Additionally, eGFR levels decreased linearly across increasing BMD offset quartiles. Men and women with an offset of >1.5 showed a 4.79-times and 2.51-times higher risk of renal dysfunction, respectively, compared to individuals with an offset of ≤0, after adjusting for age, body mass index, educational level, current smoking, and physical activity. In contrast, there was little evidence of an association between renal dysfunction and BMD offset in subjects aged <50 years.

CONCLUSION

Discordance between LS and FN BMDs was significantly associated with renal dysfunction in subjects aged ≥50 years. When assessing bone health in older chronic kidney disease patients, physicians should consider the possibility of BMD discordance between LS and FN.

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.

The effectiveness of body age-based intervention in workplace health promotion: Results of a cohort study on 9851 Danish employees

INTRODUCTION

The aging population emphasize the need for effective health promotion interventions. The workplace is a prioritized setting for health promotion to reach widely within a population. Body age can be used as a health-risk estimate and as a motivational tool to change health behavior. In this study we investigate body age-based intervention including motivational interview and its effect on health, when applied to real life workplace health promotion.

MATERIAL AND METHODS

Body age-based intervention was performed in 90 companies on 9851 Danish employees from 2011-2017. Metabolic risk factors were assessed, body age score was determined and an individualized motivational interview was conducted at baseline and follow-up. Change in body age score, single risk factors, smoking habits and metabolic syndrome were analyzed. The body age score is a composite score comprising 11 weighted variables. A body age score ≤ 0 is preferred, as this elicit a younger/healthier or equal body age compared to chronological age.

RESULTS

At 1.3 year follow-up the unhealthiest employees were less likely to participate. Within follow-up participants (39%, n = 3843) body age had improved by a decline in mean body age score of -0.6 and -0.7 years for men and women, respectively (p<0.001). Number of employees with metabolic syndrome had decreased from 646 at baseline to 557 at follow-up (p = 0.005) and 42% of smokers had quit smoking (p<0.001).

CONCLUSION

On the basis of this study, we suggest that body age assessment motivates to participate in workplace health promotion, affect high risk behavior such as smoking thus have potential in public health promotion.

Osteoporosis and cognitive impairment interwoven warning signs: community-based study on older adults-Bushehr Elderly Health (BEH) Program

Cognitive impairment and osteoporosis are frequently seen to coincide in clinical practice. Osteoporosis was higher in elderly populations with cognitive impairment, especially in postmenopausal women. Thus, prophylaxis for osteoporosis, falls, and fractures should be considered as part of the treatment of patients with cognitive impairment.

INTRODUCTION

Cognitive impairment and osteoporosis are two important health concerns among older adults that their possible relationship, concurrent occurrence, and linking mechanism have recently been highlighted. The purpose of this study was to assess the sex-independent association of these two conditions.

MATERIALS AND METHODS

From among 2331 individuals aged ≥ 60 years selected in Bushehr Elderly Health (BEH) Program, Iran; data of 1508 participants were analyzed. Cognitive status was assessed using Category Fluency Test and Mini-cog assessment instrument. Association between osteopenia-osteoporosis and cognitive impairment were assessed using uni- and multivariable logistic regression models.

RESULTS

Osteoporosis was diagnosed in 598 (39.6%) of the participants (58.3% female and 21.9% male, P < 0.001). From among them, 677 (44.9%) had evidence of cognitive impairment (64.5% female and 31.0% male, P < 0.001). Multivariate logistic regressions showed spinal and total hip osteoporosis was associated with 1.83 (CI 95% 1.13-2.96) and 2.24-fold (CI 95% 1.28-3.89) increase in the risk of cognitive impairment among female subjects, respectively. Ordinal logistic regression, on the other hand, revealed cognitive impairment to be associated with 1.42-fold (CI 95% 1.04-1.92) increase in the risk of spinal osteopenia-osteoporosis, 1.5-fold increase in total hip osteoporosis (CI 95% 1.09-2.05), and 1.48-fold increase in general osteoporosis (CI 95% 1.06-2.0).

CONCLUSION

Different degrees of bone loss and cognitive impairment may be a risk factor for each other among women but not in men. It is suggested that the screening, adopting preventive measures for the other condition and regular follow-ups, if needed, could be of utmost importance.

Is Sleep Duration Associated with Biological Age (BA)?: Analysis of (2010⁻2015) South Korean NHANES Dataset South Korea

(1) Background: South Korea ranked worst in sleep duration compared to other countries, but there are no clear healthcare programs to guarantee sufficient sleep. Studies are needed to suggest evidence and arouse public awareness of the negative effects of abnormal sleep duration. In this study, we investigated the relationship between biological age (BA) and sleep duration. (2) Methods: We used data from the Korea National Health and Nutrition Examination Surveys (KNHANES V-VI; 2010–2015, which is an annually cross-sectional study including 29,309 participants). We performed multiple linear regression to investigate the associations between sleep duration and differences in BA and chronological age (CA). (3) Results: A total of 14.22% of respondents had short sleep duration (less than 6 h per day) and 7.10% of respondents had long sleep duration (more than 8 h per day). People with long sleep duration had a positive correlation with difference between BA and CA (>8 h per day, β = 1.308, p-value = 0.0001; ref = 6~8 h per day, normal). Short sleep duration had an inverse trend with the difference, although the result was not statically significant. Associations were greater in vulnerable populations, such as low income, obese, or people with chronic diseases. (4) Conclusions: Excess sleep duration that is greater than the normal range was associated with increased BA. In particular, such relationships that are related to worsening BA were greater in patients with low income, obesity, and chronic diseases. Based on our findings, healthcare professionals should also consider the negative effects of excess sleep, not only insufficient sleep. Alternatives for controlling optimal sleep duration should be reviewed, especially with vulnerable populations.