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

Social Determinants of Health and Biological Age among Diverse U.S. Adults, NHANES 2011-2018

We examined the sex-specific association between education and income with biological age (BA) and by race/ethnicity. The Klemera-Doubal method was used to calculate BA among 6,213 females and 5,938 males aged 30-75 years who were Hispanic, non-Hispanic (NH) White, NH Black (NHB), or NH Asian (NHA). Compared with a college education, less than a high school education was associated with greater BA by 3.06 years (95% CI: 1.58, 4.54) among females only; associations were strongest among NHB, Hispanic, and NHA females. Compared with an annual income of ≥$75,000, an income <$25,000 was associated with greater BA by 4.95 years (95% CI: 3.42, 6.48) among males and 2.76 years among females (95% CI: 1.51, 4.01); associations were strongest among NHW and NHA adults, and Hispanic males. Targeting upstream sources of structural disadvantage among racial/ethnic minority groups, in conjunction with improvements in income and education, may promote healthy aging in these populations.

Age-Based Heuristics Bias Treatment of Displaced Femoral Neck Fractures in the Elderly

Background

Surgeons performing arthroplasty for femoral neck fractures may rely on mental shortcuts (heuristics) when choosing total hip arthroplasty (THA) vs hemiarthroplasty (HA). We sought to quantify the extent to which age-based heuristics drive decision-making.

Methods

We identified all Medicare beneficiaries from 2017-2018 with femoral neck fractures who underwent THA or HA. We compared the likelihood of THA vs HA among patients admitted within 4 weeks before vs 4 weeks after their birthday for each age under the hypothesis that these cohorts would be similar except for numerical age. We controlled for race/ethnicity, sex, comorbidities, poverty status, and hospital census region in a multivariable regression that included facility-level cluster effects. We generated predicted/adjusted probabilities for THA vs HA for different age transition points.

Results

Thirteen thousand three hundred sixty-six elderly patients were included. One thousand eight hundred sixty-five (14%) received THA and 11,501 (86%) received HA. The likelihood of THA decreased from 50.3% among patients almost 67 to 8% among those ≥85 (P < .001). We found significant decreases in likelihood of THA across age transitions. The largest decrement was at age transition 69 (THA likelihood 28.7% for newly 69 vs 43.3% for almost 69, 33.7% relative change). Female gender, Black race, higher comorbidity burden, and lower socioeconomic status were also associated with a lower likelihood of THA.

Conclusions

Our data demonstrate that patient age transitions seem to influence the choice of THA vs HA. Further research is needed to develop data-driven surgical decision aids for this population.

Higher blood biochemistry-based biological age developed by advanced deep learning techniques is associated with frailty in geriatric rehabilitation inpatients: RESORT

BACKGROUND

Accelerated biological ageing is a major underlying mechanism of frailty development. This study aimed to investigate if the biological age measured by a blood biochemistry-based ageing clock is associated with frailty in geriatric rehabilitation inpatients.

METHODS

Within the REStORing health of acutely unwell adulTs (RESORT) cohort, patients' biological age was measured by an ageing clock based on completed data of 30 routine blood test variables measured at rehabilitation admission. The delta of biological age minus chronological age (years) was calculated. Ordinal logistic regression and multinomial logistic regression were performed to evaluate the association of the delta of ages with frailty assessed by the Clinical Frailty Scale. Effect modification of Cumulative Illness Rating Scale (CIRS) score was tested.

RESULTS

A total of 1187 geriatric rehabilitation patients were included (median age: 83.4 years, IQR: 77.7-88.5; 57.4 % female). The biochemistry-based biological age was strongly correlated with chronological age (Spearman r = 0.883). After adjustment for age, sex and primary reasons for acute admission, higher biological age (per 1 year higher in delta of ages) was associated with more severe frailty at admission (OR: 1.053, 95 % CI:1.012-1.096) in patients who had a CIRS score of <12 not in patients with a CIRS score >12. The delta of ages was not associated with frailty change from admission to discharge. The specific frailty manifestations as cardiac, hematological, respiratory, renal, and endocrine conditions were associated with higher biological age.

CONCLUSION

Higher biological age was associated with severe frailty in geriatric rehabilitation inpatients with less comorbidity burden.

Progress in the study of aging marker criteria in human populations

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

Measures of Aging Biology in Saliva and Blood as Novel Biomarkers for Stroke and Heart Disease in Older Adults

BACKGROUND AND OBJECTIVES

The role of aging biology as a novel risk factor and biomarker for vascular outcomes in different accessible body tissues such as saliva and blood remain unclear. We aimed to (1) assess the role of aging biology as a risk factor of stroke and heart disease among individuals of same chronologic age and sex and (2) compare aging biology biomarkers measured in different accessible body tissues as novel biomarkers for stroke and heart disease in older adults.

METHODS

This study included individuals who consented for blood and saliva draw in the Venous Blood Substudy and Telomere Length Study of the Health and Retirement Study (HRS). The HRS is a population-based, nationally representative longitudinal survey of individuals aged 50 years and older in the United States. Saliva-based measures included telomere length. Blood-based measures included DNA methylation and physiology biomarkers. Propensity scores-matched analyses and Cox regression models were conducted.

RESULTS

This study included individuals aged 50 years and older, who consented for blood (N = 9,934) and saliva (N = 5,808) draw in the HRS. Blood-based biomarkers of aging biology showed strong associations with incident stroke as follows: compared with the lowest tertile of blood-based biomarkers of aging, biologically older individuals had significantly higher risk of stroke based on DNA methylation Grim Age clock (adjusted hazard ratio [aHR] = 2.64, 95% CI 1.90-3.66, p < 0.001) and Physiology-based Phenotypic Age clock (aHR = 1.75, 95% CI 1.27-2.42, p < 0.001). In secondary analysis, biologically older individuals had increased risk of heart disease as follows: DNA methylation Grim Age clock (aHR = 1.77, 95% CI 1.49-2.11, p < 0.001) and Physiology-based Phenotypic Age clock (aHR = 1.61, 95% CI 1.36-1.90, p < 0.001).

DISCUSSION

Compared with saliva-based telomere length, blood-based aging physiology and some DNA methylation biomarkers are strongly associated with vascular disorders including stroke and are more precise and sensitive biomarkers of aging. Saliva-based telomere length and blood-based DNA methylation and physiology biomarkers likely represent different aspects of biological aging and accordingly vary in their precision as novel biomarkers for optimal vascular health.

Can We Slow Down Biological Age Progression? Study Protocol for the proBNPage Reduction (PBAR) Randomized, Double-Blind, Placebo-Controlled Trial (Effects of 4 "Anti-Aging" Food Supplements in Healthy Older Adults)

Purpose

The availability of a simple and reliable marker of biological age might allow an acceleration of the research in the field of longevity extension. Previous studies suggest that this marker might be the N-terminal of B-type natriuretic peptide precursor (NT-proBNP), from which proBNPage, a biological age surrogate, can be calculated. Objectives of the study: 1) To fine-tune the method of proBNPage progression assessment and 2) To establish whether 4 "anti-aging" treatments, which provided promising results in previous studies, can modify proBNPage progression.

Patients and Methods

This is a double-blind randomized placebo-controlled clinical trial on 120 adults aged 65-80 years, free of cardiovascular diseases. Participants will be randomized into 3 groups: A) Coenzyme Q10 100 mg bid + Selenium 100 mcg; B) Resveratrol 350 mg bid + TA-65 (Astragalus Membranaceus extract) 100U; C) Placebo-1 bid + Placebo-2. They will be followed for 2 years and checked 8 times, to assess both proBNPage progression and treatment safety. Secondary variables (handgrip strength, aerobic capacity at the step test and quality of life) will also be assessed. Primary outcome will be the demonstration of significant changes of proBNPage, compared to baseline, in the 3 groups at 6, 12, 18 and 24 months. Secondary outcome will be the demonstration of similar changes of secondary variables. Statistical analyses will be mainly performed by repeated measures ANOVA (both according to intention to treat and per protocol) and paired t tests. The study was approved by the Ethics Committee Area Vasta Emilia Centro, Emilia-Romagna Region, ID: 64/2022/Sper/AOUBo. Trial registration: ClinicalTrials.gov, NCT05500742.

Conclusion

The use of proBNPage as a surrogate of biological age may prove an easy method to select anti-aging treatments worthy of further, more complex assessments.

Immune cells are associated with mortality: the Health and Retirement Study

Introduction

Age-related immunosenescence is characterized by changes in immune cell subsets and is associated with mortality. However, since immunosenescence is associated with other concurrent age-related changes such as inflammation and multi-organ dysfunction, it is unclear whether the association between age-related immunosenescence and mortality is independent of other concurrent age-related changes. To address these limitations, we evaluated the independent association between immune cell subsets and mortality after adjustment for age-related inflammation and biologic age.

Methods

Data for this study was obtained from the 2016 interview of the Health and Retirement Study (N=6802). Cox proportional hazards regression models were used to estimate the association between 25 immune cell subsets (11 T-cell subsets, 4 B-cell subsets, 3 monocyte subsets, 3 natural killer cell subsets, 3 dendritic cell subsets, and neutrophils) and 4-year mortality adjusting for covariates such as the Klemera-Doubal algorithm biological age, chronological age, gender, race/ethnicity, BMI, smoking status, comorbidity index, CMV seropositivity, and inflammatory latent variable comprising C-reactive protein, and 4 cytokines (interleukin-10, interleukin-1 receptor antagonist, interleukin-6, and soluble tumor necrosis factor).

Results

Four hundred and seventy-six participants died during the study period with an overall median follow up time of 2.5 years. After controlling for covariates and adjustment for sample-weights, total T cells [HR: 0.86, p=0.004], NK CD56LO cells [HR: 0.88, p=0.005], and neutrophils [HR: 1.22, p=0.004] were significantly associated with mortality.

Conclusions

These findings support the idea that an aging immune system is associated with short-term mortality independent of age-related inflammation or other age-related measures of physiological dysfunction. If replicated in other external cohorts, these findings could identify novel targets for both monitoring and intervention to reduce the age-related mortality.

Connections between cross-tissue and intra-tissue biomarkers of aging biology in older adults

Background

Saliva measures are generally more accessible than blood, especially in vulnerable populations. However, connections between aging biology biomarkers in different body tissues remain unknown.

Methods

The present study included individuals (N = 2406) who consented for saliva and blood draw in the Health and Retirement Telomere length study in 2008 and the Venous blood study in 2016 who had complete data for both tissues. We assessed biological aging based on telomere length in saliva and DNA methylation and physiology measures in blood. DNA methylation clocks combine information from CpGs to produce the aging measures representative of epigenetic aging in humans. We analyzed DNA methylation clocks proposed by Horvath (353 CpG sites), Hannum (71 CpG sites), Levine or PhenoAge, (513 CpG sites), GrimAge, (epigenetic surrogate markers for select plasma proteins), Horvath skin and blood (391 CpG sites), Lin (99 CpG sites), Weidner (3 CpG sites), and VidalBralo (8 CpG sites). Physiology measures (referred to as phenotypic age) included albumin, creatinine, glucose, [log] C-reactive protein, lymphocyte percent, mean cell volume, red blood cell distribution width, alkaline phosphatase, and white blood cell count. The phenotypic age algorithm is based on parametrization of Gompertz proportional hazard models. Average telomere length was assayed using quantitative PCR (qPCR) by comparing the telomere sequence copy number in each patient's sample (T) to a single-copy gene copy number (S). The resulting T/S ratio was proportional to telomere length, mean. Within individual, relationships between aging biology measures in blood and saliva and variations according to sex were assessed.

Results

Saliva-based telomere length showed inverse associations with both physiology-based and DNA methylation-based aging biology biomarkers in blood. Longer saliva-based telomere length was associated with 1 to 4 years slower biological aging based on blood-based biomarkers with the highest magnitude being Weidner (β = - 3.97, P = 0.005), GrimAge (β = - 3.33, P < 0.001), and Lin (β = - 3.45, P = 0.008) biomarkers of DNA methylation.

Conclusions

There are strong connections between aging biology biomarkers in saliva and blood in older adults. Changes in telomere length vary with changes in DNA methylation and physiology biomarkers of aging biology. We observed variations in the relationship between each body system represented by physiology biomarkers and biological aging, particularly at the DNA methylation level. These observations provide novel opportunities for integration of both blood-based and saliva-based biomarkers in clinical care of vulnerable and clinically difficult to reach populations where either or both tissues would be accessible for clinical monitoring purposes.

Biological age and environmental risk factors for dementia and stroke: Molecular mechanisms

Since the development of antibiotics and vaccination, as well as major improvements in public hygiene, the main risk factors for morbidity and mortality are age and chronic exposure to environmental factors, both of which can interact with genetic predispositions. As the average age of the population increases, the prevalence and costs of chronic diseases, especially neurological conditions, are rapidly increasing. The deleterious effects of age and environmental risk factors, develop chronically over relatively long periods of time, in contrast to the relatively rapid deleterious effects of infectious diseases or accidents. Of particular interest is the hypothesis that the deleterious effects of environmental factors may be mediated by acceleration of biological age. This hypothesis is supported by evidence that dietary restriction, which universally delays age-related diseases, also ameliorates deleterious effects of environmental factors. Conversely, both age and environmental risk factors are associated with the accumulation of somatic mutations in mitotic cells and epigenetic modifications that are a measure of "biological age", a better predictor of age-related morbidity and mortality than chronological age. Here we review evidence that environmental risk factors such as smoking and air pollution may also drive neurological conditions, including Alzheimer's Disease, by the acceleration of biological age, mediated by cumulative and persistent epigenetic effects as well as somatic mutations. Elucidation of such mechanisms could plausibly allow the development of interventions which delay deleterious effects of both aging and environmental risk factors.

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 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.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Modeling biological age and its link with the aging process

Differences in health status at older ages are a result of genetic predispositions and physiological responses to exposure accumulation over the lifespan. These vary across individuals and lead to health status heterogeneity as people age. Chronological age (CA) is a standard indicator that reflects overall risks of morbidity and mortality. However, CA is only a crude proxy for individuals' latent physiological deterioration. An alternative to CA is biological age (BA), an indicator of accumulated age-related biological change reflected in markers of major physiological systems. We propose and validate two BA estimators that improve upon existing ones. These estimators (i) are based on a structural equation model (SEM) that represents the relation between BA and CA, (ii) circumvent the need to impose arbitrary assumptions about the relation between CA and BA, and (iii) provide tools to empirically test the validity of assumptions the researcher may wish to invoke. We use the US National Health and Nutrition Examination Survey 1988-1994 and compare results with three commonly used methods to compute BA (principal components-PCA, multiple regression-MLR, and Klemera-Doubal's method-KD). We show that SEM-based estimates of BA differ significantly from those generated by PCA and MLR and are comparable to, but have better predictive power than KD's. The proposed estimators are flexible, allow testing of assumptions about functional forms relating BA and CA, and admit a rich interpretation as indicators of accelerated aging.

Evaluation of T-cell aging-related immune phenotypes in the context of biological aging and multimorbidity in the Health and Retirement Study

BACKGROUND

Cellular changes in adaptive immune system accompany the process of aging and contribute to an aging-related immune phenotype (ARIP) characterized by decrease in naïve T-cells (TN) and increase in memory T-cells (TM). A population-representative marker of ARIP and its associations with biological aging and age-related chronic conditions have not been studied previously.

METHODS

We developed two ARIP indicators based on well understood age-related changes in T cell distribution: TN/(TCM (Central Memory) + TEM (Effector Memory) + TEFF (Effector)) (referred as TN/TM) in CD4 + and CD8 + T-cells. We compared them with existing ARIP measures including CD4/CD8 ratio and CD8 + TN cells by evaluating associations with chronological age and the Klemera Doubal measure of biological age (measured in years) using linear regression, multimorbidity using multinomial logistic regression and two-year mortality using logistic regression.

RESULTS

CD8 + TN and CD8 + TN/TM had the strongest inverse association with chronological age (beta estimates: -3.41 and -3.61 respectively; p-value < 0.0001) after adjustment for sex, race/ethnicity and CMV status. CD4 + TN/TM and CD4 + TN had the strongest inverse association with biological age (β = -0.23; p = 0.003 and β = -0.24; p = 0.004 respectively) after adjustment for age, sex, race/ethnicity and CMV serostatus. CD4/CD8 ratio was not associated with chronological age or biological age. CD4 + TN/TM and CD4 + TN was inversely associated with multimorbidity. For CD4 + TN/TM, people with 2 chronic conditions had an odds ratio of for 0.74 (95%CI: 0.63-0.86 p = 0.0003) compared to those without any chronic conditions while those with 3 chronic conditions had an odds ratio of 0.75 (95% CI: 0.63-0.90; p = 0.003) after adjustment for age, sex, race/ethnicity, CMV serostatus, smoking, and BMI. The results for the CD4 + TN subset were very similar to the associations seen with the CD4 + TN/TM. CD4 + TN/TM and CD4 + TN were both associated with two-year mortality (OR = 0.80 (95% CI: 0.67-0.95; p = 0.01) and 0.81 (0.70-0.94; p = 0.01), respectively).

CONCLUSION

CD4 + TN/TM and CD4 + TN had a stronger association with biological age, age-related morbidity and mortality compared to other ARIP measures. Future longitudinal studies are needed to evaluate the utility of the CD4 + subsets in predicting the risk of aging-related outcomes.

Biological aging for risk prediction of first-ever intracerebral hemorrhage and cerebral infarction in advanced age

BACKGROUND AND OBJECTIVES

successful interventions to prevent cerebrovascular disease and stroke require early identification of persons at risk before clinical manifestation of disease. The literature remains to be sparse on accessible plasma-based biomarkers for monitoring brain health and cerebrovascular disease in advanced age. We assessed the predictive value of biological age (BA) as an early indicator for cerebrovascular disease and risk of first-ever intracerebral hemorrhage (ICH) and cerebral infarction (CI) in advanced age and compared these relationships with chronological age (CA) and commonly used biomarkers including tau and Aβ40 and Aβ42.

METHODS

The study included Individuals who consented for blood draw and follow-up. We computed biological age using structural equation modelling. The criteria for the biomarkers included their representability of the various body systems; their availability in the Rotterdam study and their pre-hypothesized reflection of aging in other populations. The algorithm integrates biomarkers that represent six body systems involved in overall cerebrovascular health including metabolic function, cardiac function, lung function, kidney function, liver function, immunity, and inflammation. Time to event analysis was conducted using Cox-regression models. Prediction analysis was conducted using Harrel's C and Area under the receiver operating characteristic curve.

RESULTS

The sample included a total of 1699 individuals at baseline followed up over a median of 11 years. During a period of 15, 780 and 16, 172 person-years, a total of 17 first-ever intracerebral hemorrhage and 83 cerebral infarction cases occurred. In time-to-event analysis, BA showed higher magnitude of associations with ICH compared to CA (HR_BA-ICH: 2.30, 95% CI: 1.20, 4.30; HR_CA-ICH: 1.40, 95% CI: 0.76, 2.53) and higher precision with CI (HR_BA-CI: 1.30, 95% CI: 1.01,1.75; HR_CA-CI:1.90, 95% CI: 1.48, 2.66). BA outperformed CA for prediction of ICH (AUC: 0.68 vs 0.53; Harrel's C: 0.72 vs 0.53) and for CI (AUC:0.63 vs 0.62; Harrel's C: 0.68 vs 0.67).

CONCLUSIONS

Biological aging (delta biological aging) based on integrated physiology biomarkers provides a novel tool for monitoring and identification of persons at highest risk of cerebrovascular disease in advanced age with varying degrees of precision and magnitude for stroke subtypes. These variations are likely related to differences in pathophysiology of intracerebral hemorrhage and cerebral infarction. Wider validation and applicability require extension of these findings in other comparable samples and in clinical settings.

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.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

Understanding the role of telomere attrition and epigenetic signatures in COVID-19 severity

Within the past several decades, the emergence and spread of infectious diseases with pandemic potential have endangered human lives. Coronavirus disease 2019 (COVID-19) outbreak represents an unprecedented threat for all health systems worldwide. The clinical spectrum of COVID-19 is highly heterogeneous, ranging from asymptomatic and mild upper respiratory tract illness to severe interstitial pneumonia with respiratory failure and even death. Highly age-dependent patterns of immune response potentially explain the higher rates of the severe forms of COVID-19 in elderly patients. However, genetic and epigenetic architecture can influence multiple biological processes during the lifespan, therefore as far as our knowledge shows, vulnerability to viral infection concerning telomere length and epigenetic signature is not a new idea. This review aims is to summarize the current understanding of the role of telomere length and epigenetic mechanisms on the severity of COVID-19. The current knowledge highlights the significant association between the shorter telomere length and the higher risk of developing severe COVID-19. Differential DNA methylation patterns and miRNA expression profiles imply that these hallmarks can play a pivotal role in COVID- 19 pathogenesis. Understanding the causes of inter-individual variations in COVID-19 outcomes could provide clues to the development of the personalized therapeutic intervention.

Association between Phenotypic Age and Mortality in Patients with Multivessel Coronary Artery Disease

Background

Chronological age (CA) is not a perfect proxy for the true biological aging status of the body. A new biological aging measure, phenotypic age (PhenoAge), has been shown to capture morbidity and mortality risk in the general US population and diverse subpopulations. This study was aimed at evaluating the association between PhenoAge and long-term outcome of patients with multivessel coronary artery disease (CAD).

Methods

A total of 609 multivessel CAD patients who received PCI attempt and with follow-up were enrolled. The clinical outcome was all-cause mortality on follow-up. PhenoAge was calculated using an equation constructed from CA and 9 clinical biomarkers. Cox proportional hazards regression models and receiver operating characteristic (ROC) curves were performed to evaluate the association between PhenoAge and mortality.

Results

Overall, patients with more diseases had older PhenoAge and phenotypic age acceleration (PhenoAgeAccel). After a median follow-up of 33.5 months, those with positive PhenoAgeAccel had a significantly higher incidence of all-cause mortality (P = 0.001). After adjusting for CA, Cox proportional hazards models showed that both PhenoAge and PhenoAgeAccel were significantly associated with all-cause mortality. Even after further adjusting for confounding factors, each 10-year increase in PhenoAge was also associated with a 51% increased mortality risk. ROC curves revealed that PhenoAge, with an area under the curve of 0.705, significantly outperformed CA, the individual clinical chemistry measure, and other risk factors. When reexamining the ROC curves using various combinations of variables, we found that PhenoAge provides additional predictive power to all models.

Conclusions

In conclusion, PhenoAge was strongly associated with all-cause mortality even after adjusting for CA. Our findings suggest that PhenoAge measure may be complementary in predicting mortality risk for patients with multivessel CAD.

Ageing-related markers and risks of cancer and cardiovascular disease: a prospective study in the EPIC-Heidelberg cohort

Biological age is an important risk factor for chronic diseases. We examined the associations between five markers of unhealthy ageing; Growth Differentiation Factor-15 (GDF-15), N-terminal pro-brain natriuretic peptide (NT-proBNP), glycated hemoglobin A1c (HbA1C), C-Reactive Protein (CRP) and cystatin-C; with risks of cancer and cardiovascular disease (CVD). We used a case-cohort design embedded in the EPIC-Heidelberg cohort, including a subcohort of 3792 participants along with 4867 incident cases of cancer and CVD. Hazard ratios (HRs) were computed and the strongest associations were used to build weighted multi-marker combinations, and their associations with cancer and CVD risks were tested. After adjusting for common confounders, we observed direct associations of GDF-15 with lung cancer risk, NT-proBNP with breast, prostate and colorectal cancers, HbA1C with lung, colorectal, and breast cancer risks, and CRP with lung and colorectal cancer risks. An inverse association was observed for GDF-15 and prostate cancer risk. We also found direct associations of all 5 markers with myocardial infarction (MI) risk, and of GDF-15, NT-proBNP, CRP and cystatin-C with stroke risk. A combination of the independently-associated markers showed a moderately strong association with the risks of cancer and CVD (HR_Q4-Q1 ranged from 1.78[1.36, 2.34] for breast cancer, when combining NT-proBNP and HbA1C, to 2.87[2.15, 3.83] for MI when combining NT-proBNP, HbA1C, CRP and cystatin-C). This analysis suggests that combinations of biomarkers related to unhealthy ageing show strong associations with cancer risk, and corroborates published evidence on CVD risk. If confirmed in other studies, using these biomarkers could be useful for the identification of individuals at higher risk of age-related diseases.

Exploring domains, clinical implications and environmental associations of a deep learning marker of biological ageing

Deep Neural Networks (DNN) have been recently developed for the estimation of Biological Age (BA), the hypothetical underlying age of an organism, which can differ from its chronological age (CA). Although promising, these population-specific algorithms warrant further characterization and validation, since their biological, clinical and environmental correlates remain largely unexplored. Here, an accurate DNN was trained to compute BA based on 36 circulating biomarkers in an Italian population (N = 23,858; age ≥ 35 years; 51.7% women). This estimate was heavily influenced by markers of metabolic, heart, kidney and liver function. The resulting Δage (BA-CA) significantly predicted mortality and hospitalization risk for all and specific causes. Slowed biological aging (Δage < 0) was associated with higher physical and mental wellbeing, healthy lifestyles (e.g. adherence to Mediterranean diet) and higher socioeconomic status (educational attainment, household income and occupational status), while accelerated aging (Δage > 0) was associated with smoking and obesity. Together, lifestyles and socioeconomic variables explained ~48% of the total variance in Δage, potentially suggesting the existence of a genetic basis. These findings validate blood-based biological aging as a marker of public health in adult Italians and provide a robust body of knowledge on its biological architecture, clinical implications and potential environmental influences.

Biological age in healthy elderly predicts aging-related diseases including dementia

Application of biological age as a measure of an individual´s health status offers new perspectives into extension of both lifespan and healthspan. While algorithms predicting mortality and most aging-related morbidities have been reported, the major shortcoming has been an inability to predict dementia. We present a community-based cohort study of 1930 participants with a mean age of 72 years and a follow-up period of over 7 years, using two variants of a phenotypic blood-based algorithm that either excludes (BioAge1) or includes (BioAge2) neurofilament light chain (NfL) as a neurodegenerative marker. BioAge1 and BioAge2 predict dementia equally well, as well as lifespan and healthspan. Each one-year increase in BioAge1/2 was associated with 11% elevated risk (HR 1.11; 95%CI 1.08-1.14) of mortality and 7% elevated risk (HR 1.07; 95%CI 1.05-1.09) of first morbidities. We additionally tested the association of microRNAs with age and identified 263 microRNAs significantly associated with biological and chronological age alike. Top differentially expressed microRNAs based on biological age had a higher significance level than those based on chronological age, suggesting that biological age captures aspects of aging signals at the epigenetic level. We conclude that accelerated biological age for a given age is a predictor of major age-related morbidity, including dementia, among healthy elderly.

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.

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.

Data-driven identification of ageing-related diseases from electronic health records

Reducing the burden of late-life morbidity requires an understanding of the mechanisms of ageing-related diseases (ARDs), defined as diseases that accumulate with increasing age. This has been hampered by the lack of formal criteria to identify ARDs. Here, we present a framework to identify ARDs using two complementary methods consisting of unsupervised machine learning and actuarial techniques, which we applied to electronic health records (EHRs) from 3,009,048 individuals in England using primary care data from the Clinical Practice Research Datalink (CPRD) linked to the Hospital Episode Statistics admitted patient care dataset between 1 April 2010 and 31 March 2015 (mean age 49.7 years (s.d. 18.6), 51% female, 70% white ethnicity). We grouped 278 high-burden diseases into nine main clusters according to their patterns of disease onset, using a hierarchical agglomerative clustering algorithm. Four of these clusters, encompassing 207 diseases spanning diverse organ systems and clinical specialties, had rates of disease onset that clearly increased with chronological age. However, the ages of onset for these four clusters were strikingly different, with median age of onset 82 years (IQR 82–83) for Cluster 1, 77 years (IQR 75–77) for Cluster 2, 69 years (IQR 66–71) for Cluster 3 and 57 years (IQR 54–59) for Cluster 4. Fitting to ageing-related actuarial models confirmed that the vast majority of these 207 diseases had a high probability of being ageing-related. Cardiovascular diseases and cancers were highly represented, while benign neoplastic, skin and psychiatric conditions were largely absent from the four ageing-related clusters. Our framework identifies and clusters ARDs and can form the basis for fundamental and translational research into ageing pathways.

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.

Objectives, design and main findings until 2020 from the Rotterdam Study

The Rotterdam Study is an ongoing prospective cohort study that started in 1990 in the city of Rotterdam, The Netherlands. The study aims to unravel etiology, preclinical course, natural history and potential targets for intervention for chronic diseases in mid-life and late-life. The study focuses on cardiovascular, endocrine, hepatic, neurological, ophthalmic, psychiatric, dermatological, otolaryngological, locomotor, and respiratory diseases. As of 2008, 14,926 subjects aged 45 years or over comprise the Rotterdam Study cohort. Since 2016, the cohort is being expanded by persons aged 40 years and over. The findings of the Rotterdam Study have been presented in over 1700 research articles and reports. This article provides an update on the rationale and design of the study. It also presents a summary of the major findings from the preceding 3 years and outlines developments for the coming period.