Biological age as a useful index to predict seventeen-year survival and mortality in Koreans

Synergistic impact of 25-hydroxyvitamin D concentrations and physical activity on delaying aging

OBJECTIVE

Our study aims to examine the independent and combined associations of serum 25-hydroxyvitamin D [25(OH)D] concentrations and physical activity (PA) status with phenotypic age (PhenoAge).

METHOD

The analysis included 18,738 participants from the NHANES 2007-2010 & 2015-2018. Phenotypic Age Acceleration (PhenoAgeAccel) was calculated as the residuals from regressing PhenoAge on chronological age. Weighted multivariable logistic regression models were used to analysis the relationship between 25(OH)D and PA with PhenoAgeAccel. Population attributable fraction (PAF) was used to estimate the proportion of PhenoAgeAccel which could be avoided if exposure were eliminated.

RESULTS

The multivariate-adjusted OR (95%CI) for PhenoAgeAccel with high 25(OH)D and adequate PA were 0.657 (0.549,0.787) (p < 0.001) for all, 0.663 (0.538,0.818) (p < 0.001) for participants whose age ≤65years old. Furthermore, there was multiplicative interaction between 25(OH)D and PA in age ≤65 years old group (0.729 (0.542,0.979), p = 0.036). High 25(OH)D level and adequate PA reduced the risk of PhenoAgeAccel by 14.3 % and 14.2 %, respectively. Notably, 30.7 % decrease was attributable to both high 25(OH)D level and engaging in adequate PA concurrently. Combining 25(OH)D above 80.4 nmol/l with PA decreased PhenoAge by 1.291 years (p < 0.001).

CONCLUSION

Higher 25(OH)D level was associated with lower risk of biological ageing. Combining 25(OH)D and PA demonstrated enhanced protective effects, especially in middle or young adults. These findings underscore the importance of outdoor PA in slowing down the aging process.

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

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

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.

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

Background

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

Methods and results

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

Conclusion

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

Supplementary Information

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

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.

Aging Challenges. Perceived Age – a New Predictor of Longevity?

The ageing process is accompanied by the manifestation of many characteristics, so-called biomarkers, which can be quantified and used to assess a patient's health status. One of these signs is the progressive decline of a human's facial look, which is described by the concept of 'perceived age'. Facial aging is the most important parameter of perceived age. However, over the years, researchers have identified risk factors that affect the facial skin, including smoking, systematic consumption of alcoholic beverages, overweight or underweight, environmental conditions, and psychosocial determinants. The influence of psychological state on the appearance and life prognosis is shown. The authors presented data from the international literature on the study of perceived age. The frontiers of using perceived age as a biomarker of aging were Danish scientists who developed the main methodological approaches to determine this indicator. One such methodology used in population studies has been the clinical technique of assessing perceived age through photography. The review presents this methodology in detail, with its advantages and modifications. The authors conclude that the measurement of an individual's perceived age can serve not only as a prognostic indicator,  but also over time can become a   useful marker of the effectiveness of various treatments. Until now perceived age has hardly been studied in population studies, the authors presented data from the works of V.A.  Labunskaya, G.V.  Serikov,  T.A.  Shkurko who develop the direction related to psychology of perceived age  and in their studies use social-psychological approaches of appearance assessment.

A comparison between diffusion tensor imaging and generalized q-sampling imaging in the age prediction of healthy adults via machine learning approaches

OBJECTIVE

Brain age, which is predicted using neuroimaging data, has become an important biomarker in aging research. This study applied diffusion tensor imaging (DTI) and generalized q-sampling imaging (GQI) model to predict age respectively, with the purpose of evaluating which diffusion model is more accurate in estimating age and revealing age-related changes in the brain.

APPROACH

Diffusion MRI data of 125 subjects from two sites were collected. Fractional anisotropy (FA) and quantitative anisotropy (QA) from the two diffusion models were calculated and were used as features of machine learning models. Sequential backward elimination algorithm was used for feature selection. Six machine learning approaches including linear regression, ridge regression, support vector regression (SVR) with linear kernel, quadratic kernel and radial basis function (RBF) kernel and feedforward neural network were used to predict age using FA and QA features respectively.

MAIN RESULTS

Age predictions using FA features were more accurate than predictions using QA features for all the 6 machine learning algorithms. Post-hoc analysis revealed that FA was more sensitive to age-related white matter alterations in the brain. In addition, SVR with RBF kernel based on FA features achieved better performances than the competing algorithms with MAE ranging from 7.74 to 10.54, MSE ranging from 87.79 to 150.86, and nMSE ranging from 0.05 to 0.14 Significance: FA from DTI model was more suitable than QA from GQI model in age prediction. FA metric was more sensitive to age-related white matter changes in the brain and FA of several brain regions could be used as white matter biomarkers in aging.

Screening of osteoporosis and sarcopenia in individuals aged 50 years and older at different altitudes in Yunnan province: Protocol of a longitudinal cohort study

INTRODUCTION

Musculoskeletal system gradually degenerates with aging, and a hypoxia environment at a high altitude may accelerate this process. However, the comprehensive effects of high-altitude environments on bones and muscles remain unclear. This study aims to compare the differences in bones and muscles at different altitudes, and to explore the mechanism and influencing factors of the high-altitude environment on the skeletal muscle system.

METHODS

This is a prospective, multicenter, cohort study, which will recruit a total of 4000 participants over 50 years from 12 research centers with different altitudes (50m~3500m). The study will consist of a baseline assessment and a 5-year follow-up. Participants will undergo assessments of demographic information, anthropomorphic measures, self-reported questionnaires, handgrip muscle strength assessment (HGS), short physical performance battery (SPPB), blood sample analysis, and imaging assessments (QCT and/or DXA, US) within a time frame of 3 days after inclusion. A 5-year follow-up will be conducted to evaluate the changes in muscle size, density, and fat infiltration in different muscles; the muscle function impairment; the decrease in BMD; and the osteoporotic fracture incidence. Statistical analyses will be used to compare the research results between different altitudes. Multiple linear, logistic regression and classification tree analyses will be conducted to calculate the effects of various factors (e.g., altitude, age, and physical activity) on the skeletal muscle system in a high-altitude environment. Finally, a provisional cut-off point for the diagnosis of sarcopenia in adults at different altitudes will be calculated.

ETHICS AND DISSEMINATION

The study has been approved by the institutional research ethics committee of each study center (main center number: KHLL2021-KY056). Results will be disseminated through scientific conferences and peer-reviewed publications, as well as meetings with stakeholders.

CLINICAL TRIAL REGISTRATION NUMBER

http://www.chictr.org.cn/index.aspx, identifier ChiCTR2100052153.

Frailty Status Among the Elderly of Different Genders and the Death Risk: A Follow-Up Study

Background: Frailty in the elderly population is currently a frontier and focus in the field of health and aging. The goal of this study was to explore the frailty status among the elderly of different genders and its influence on the risk of death during 11 years.

Methods: Frailty index (FI) was used to evaluate the frailty status in the elderly based on the baseline data conducted in 2009; and death as outcome variables collected in 2020 were analyzed. The difference of the frailty level and mortality of different genders was compared. Cox regression and Kaplan–Meier curves were applied to evaluate the influence on the risk of death and the 11-year survival of the elderly at different level of frailty, respectively.

Results: Totally, 1,246 elderly people were recruited. The mortality in men (43.7%, 227/519) was statistically higher than that in women (34.3%, 249/727) (x² = 11.546, P = 0.001). Deficits accumulated exponentially with age, and at all ages, women accumulated more deficits than do men on average (B = 0.030 vs. 0.028, t = 4.137, P = 0.023). For any given level of frailty, the mortality rate is higher in men than in women, and the difference in mortality between genders reached the peak when FI value was 0.26. Cox regression analysis showed that FI value had a greater impact on the risk of death in older men (HR = 1.171, 95%CI: 1.139~1.249)than that in older women (HR = 1.119, 95%CI: 1.039~1.137). Survival analysis showed that the median 11-year survival time in women was longer than that in men (95.26 vs. 89.52 months, Log rank = 9.249, P = 0.002). Kaplan–Meier curves showed that the survival rate decreased with the increase of frailty, and at the same level of frailty, survival time in older women was longer than that in older men, except for severe frailty (FI ≥ 0.5).

Conclusion: The frailty status and its influence on mortality are different among the older people of different genders; therefore, specific interventions for frailty should be conducted in the elderly population of different genders, as well as of different degrees of frailty.

Prediction of biological age and all-cause mortality by 12-lead electrocardiogram in patients without structural heart disease

Background

There is a well-established relationship between 12-lead electrocardiogram (ECG) and age and mortality. Furthermore, there is increasing evidence that ECG can be used to predict biological age. However, the utility of biological age from ECG for predicting mortality remains unclear.

Methods

This was a single-center cohort study from a cardiology specialized hospital. A total of 19,170 patients registered in this study from February 2010 to March 2018. ECG was analyzed in a final 12,837 patients after excluding those with structural heart disease or with pacing beats, atrial or ventricular tachyarrhythmia, or an indeterminate axis (R axis > 180°) on index ECG. The models for biological age were developed by principal component analysis (BA) and the Klemera and Doubal’s method (not adjusted for age [BA_E] and adjusted for age [BA_EC]) using 438 ECG parameters. The predictive capability for all-cause death and cardiovascular death by chronological age (CA) and biological age using the three algorithms were evaluated by receiver operating characteristic analysis.

Results

During the mean follow-up period of 320.4 days, there were 55 all-cause deaths and 23 cardiovascular deaths. The predictive capabilities for all-cause death by BA, BA_E, and BA_EC using area under the curves were 0.731, 0.657, and 0.685, respectively, which were comparable to 0.725 for CA (p = 0.760, 0.141, and 0.308, respectively). The predictive capabilities for cardiovascular death by BA, BA_E, and BA_EC were 0.682, 0.685, and 0.692, respectively, which were also comparable to 0.674 for CA (p = 0.775, 0.839, and 0.706, respectively). In patients aged 60–74 years old, the area under the curves for all-cause death by BA, BA_E, and BA_EC were 0.619, 0.702, and 0.697, respectively, which tended to be or were significantly higher than 0.482 for CA (p = 0.064, 0.006, and 0.005, respectively).

Conclusion

Biological age by 12-lead ECG showed a similar predictive capability for mortality compared to CA among total patients, but partially showed a significant increase in predictive capability among patients aged 60–74 years old.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12877-021-02391-8.

The association of proBNPage with manifestations of age-related cardiovascular, physical, and psychological impairment in community-dwelling older adults

NT-proB-type natriuretic peptide (NT-proBNP) serum concentration can be transformed by simple formulas into proBNPage, a surrogate of biological age strongly associated with chronological age, all-cause mortality, and disease count. This cross-sectional study aimed to assess whether proBNPage is also associated with other manifestations of the aging process in comparison with other variables. The study included 1117 noninstitutionalized older adults (73.1 ± 5.6 years, 537 men). Baseline measurements of serum NT-proBNP, erythrocyte sedimentation rate, hemoglobin, lymphocytes, and creatinine, which have previously been shown to be highly associated with both age and all-cause mortality, were performed. These variables were compared between subjects with and without manifestations of cardiovascular impairment (myocardial infarction (MI), stroke, peripheral artery disease (PAD), arterial revascularizations (AR)), physical impairment (long step test duration (LSTD), walking problems, falls, deficit in one or more activities of daily living), and psychological impairment (poor self-rating of health (PSRH), anxiety/depression, Mini Mental State Examination (MMSE) score < 24). ProBNPage (years) was independently associated (OR, 95% CI) with MI (1.08, 1.07-1.10), stroke (1.02, 1.00-1.05), PAD (1.04, 1.01-1.06), AR (1.06, 1.04-1.08), LSTD (1.03, 1.02-1.04), walking problems (1.02, 1.01-1.03), and PSRH (1.02, 1.01-1.02). For 5 of these 7 associations, the relationship was stronger than that of chronological age. In addition, proBNPage was univariately associated with MMSE score < 24, anxiety/depression, and falls. None of the other variables provided comparable performances. Thus, in addition to the known associations with mortality and disease count, proBNPage is also associated with cardiovascular manifestations as well as noncardiovascular manifestations of the aging process.

Vaccination as a preventative measure contributing to immune fitness

The primary goal of vaccination is the prevention of pathogen-specific infection. The indirect consequences may include maintenance of homeostasis through prevention of infection-induced complications; trained immunity that re-programs innate cells to respond more efficiently to later, unrelated threats; slowing or reversing immune senescence by altering the epigenetic clock, and leveraging the pool of memory B and T cells to improve responses to new infections. Vaccines may exploit the plasticity of the immune system to drive longer-term immune responses that promote health at a broader level than just the prevention of single, specific infections. In this perspective, we discuss the concept of “immune fitness” and how to potentially build a resilient immune system that could contribute to better health. We argue that vaccines may contribute positively to immune fitness in ways that are only beginning to be understood, and that life-course vaccination is a fundamental tool for achieving healthy aging.

Comparing Biological Age Estimates Using Domain-Specific Measures From the Canadian Longitudinal Study on Aging

Many studies have shown that estimates of biological age (BA) can predict health-related outcomes in older adults. Often, researchers employ multiple measures belonging to a variety of biological/physiological systems, and assess the validity of BA estimates by how well they approximate chronological age (CA). However, it is not clear whether this is the best approach for judging a BA estimate, or whether certain groups of measures are more informative to this end. Using data from the Canadian Longitudinal Study on Aging, we composed panels of biological measures based on the physiological systems/domains they belong to (blood, organ function, physical/cognitive performance), and also composed a panel of measures that optimized the association of BA with CA. We then compared BA estimates for each according to their association with CA and health-related outcomes, including frailty, multimorbidity, chronic condition domains, disability, and health care utilization. Although BA estimated using all 40 measures (r = 0.74) or our age-optimized panel (r = 0.77) most closely approximated CA, the strength of associations to health-related outcomes was comparable or weaker than that of our panel composed only of physical performance measures (CA r = 0.59). All BA estimates were significantly associated to the outcomes considered, with exception to the neurological and musculoskeletal disease domains, and only varied slightly by sex. In summary, while the approximation of CA is important to consider when estimating BA, the strength of associations to prospective outcomes may be of greater importance. Hence, the context in which BA is estimated should be influenced by an investigator's specific research goals.

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.

Dietary Polyphenol Intake Is Associated with Biological Aging, a Novel Predictor of Cardiovascular Disease: Cross-Sectional Findings from the Moli-Sani Study

Biological aging, or the discrepancy between biological and chronological age of a subject (Δage), has been associated with a polyphenol-rich Mediterranean diet and represents a new, robust indicator of cardiovascular disease risk. We aimed to disentangle the relationship of dietary polyphenols and total antioxidant capacity with Δage in a cohort of Italians. A cross-sectional analysis was performed on a sub-cohort of 4592 subjects (aged ≥ 35 y; 51.8% women) from the Moli-sani Study (2005-2010). Food intake was recorded by a 188-item food-frequency questionnaire. The polyphenol antioxidant content (PAC)-score was constructed to assess the total dietary content of polyphenols. Total antioxidant capacity was measured in foods by these assays: trolox equivalent antioxidant capacity (TEAC), total radical-trapping antioxidant parameter (TRAP) and ferric reducing-antioxidant power (FRAP). A deep neural network, based on 36 circulating biomarkers, was used to compute biological age and the resulting Δage, which was tested as outcome in multivariable-adjusted linear regressions. Δage was inversely associated with the PAC-score (β = -0.31; 95%CI -0.39, -0.24) but not with total antioxidant capacity of the diet. A diet rich in polyphenols, by positively contributing to deceleration of the biological aging process, may exert beneficial effects on the long-term risk of cardiovascular disease and possibly of bone health.

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

INTRODUCTION

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSION

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

N-terminal pro B-type natriuretic peptide (NT-proBNP): a possible surrogate of biological age in the elderly people

NT-proB-type natriuretic peptide (NT-proBNP) increases with age and is associated with all-cause mortality. With this study, we assessed its possible utilization as a marker of biological age in comparison with other variables. The study included 1079 non-institutionalized elderly subjects (mean age 72.8 ± 5.5 years, 561 women). Baseline measurements were performed of serum NT-proBNP and of some laboratory variables previously utilized to estimate biological age (creatinine, albumin, C-reactive protein, cholesterol, blood glucose, leukocytes, lymphocytes, hemoglobin, mean cell volume). During 7 years of follow-up, 114 all-cause deaths occurred. The logarithm of NT-proBNP was the most age-related parameter (r = 0.35, P < 0.0001). Its relationship with mortality, according to Cox regression and ROC curve (AUC = 0.707, 95% CI 0.654-0.759), was stronger than that of all other variables, including age. In multivariate analysis, only NT-proBNP and age remained independently associated with mortality. The regression lines between age and NT-proBNP (pg/ml) allowed a separate estimation of biological age ("proBNPage") for men (= [log(NT-proBNP) + 1.2068]/0.0827) and for women (= [log(NT-proBNP) - 1.5258]/0.0478). The hazard ratio of all-cause mortality for the fifth quintile of proBNP age (≥ 85 years) compared with the first quintile (< 61 years) was 7.9 (95% CI 3.6-17.5). Similarly, the difference between pro-BNPage and chronological age was associated with a hazard ratio of 3.5 in the fifth quintile (95% CI 1.9-6.4) and was associated with disease count (P for trend = 0.0002). In conclusion, NT-proBNP was the best indicator of biological age, which can be estimated by simple formulas and might be used for prognostic purposes or as a surrogate end point in epidemiological and intervention studies.

Quantification of biological age as a determinant of age-related diseases in the Rotterdam Study: a structural equation modeling approach

Chronological age alone is not a sufficient measure of the true physiological state of the body. The aims of the present study were to: (1) quantify biological age based on a physiological biomarker composite model; (2) and evaluate its association with death and age-related disease onset in the setting of an elderly population. Using structural equation modeling we computed biological age for 1699 individuals recruited from the first and second waves of the Rotterdam study. The algorithm included nine physiological parameters (c-reactive protein, creatinine, albumin, total cholesterol, cytomegalovirus optical density, urea nitrogen, alkaline phosphatase, forced expiratory volume and systolic blood pressure). We assessed the association between biological age, all-cause mortality, all-cause morbidity and specific age-related diseases over a median follow-up of 11 years. Biological age, compared to chronological age or the traditional biomarkers of age-related diseases, showed a stronger association with all-cause mortality (HR 1.15 vs. 1.13 and 1.10), all-cause morbidity (HR 1.06 vs. 1.05 and 1.03), stroke (HR 1.17 vs. 1.08 and 1.04), cancer (HR 1.07 vs. 1.04 and 1.02) and diabetes mellitus (HR 1.12 vs. 1.01 and 0.98). Individuals who were biologically younger exhibited a healthier life-style as reflected in their lower BMI (P < 0.001) and lower incidence of stroke (P < 0.001), cancer (P < 0.01) and diabetes mellitus (P = 0.02). Collectively, our findings suggest that biological age based on the biomarker composite model of nine physiological parameters is a useful construct to assess individuals 65 years and older at increased risk for specific age-related diseases.

Perspectives of Frailty Among African American Men and Women

Frailty is a syndrome of impaired homeostasis and poor response to stressors leading to adverse health outcomes. The aim of the current study was to explore lay perspectives about frailty among African American adults. Thirteen men and women participated in two focus groups. Content analysis yielded six themes: (a) Physical Impairment With Loss of Independence; (b) Can Happen to Anyone, At Any Age, At Any Time; (c) Mind-Body Connection; (d) Affects All Aspects of Life; (e) Positive Attitude and Prayer Guard Against Frailty; and (f) Be In Tune and Stay Connected. Findings suggest psychological and social factors, including a positive attitude and spirituality, are linked to physical function and well-being in aging and are influential in frailty. Culturally tailored interventions that focus not only on promoting physical function but also address psychological, social, and spiritual domains may foster the resilience needed to prevent or alleviate frailty in African American individuals. [Journal of Psychosocial Nursing and Mental Health Services, 56(7), 20-29.].