Estimating Biological Age in the Singapore Longitudinal Aging Study

Abstract

Background Biological age (BA) is a more accurate measure of the rate of human aging than chronological age (CA). However, there is limited consensus regarding measures of BA in life span and healthspan. Methods This study investigated measurement sets of 68 physiological biomarkers using data from 2,844 Chinese Singaporeans in two age subgroups (55–70 and 71–94 years) in the Singapore Longitudinal Aging Study (SLAS-2) with 8-year follow-up frailty and mortality data. We computed BA estimate using three commonly used algorithms: Principal Component Analysis (PCA), Multiple Linear Regression (MLR), and Klemera and Doubal (KD) method, and additionally, explored the use of machine learning methods for prediction of mortality and frailty. The most optimal algorithmic estimate of BA compared to CA was evaluated for their associations with risk factors and health outcome. Results Stepwise selection procedures resulted in the final selection of 8 biomarkers in males and 10 biomarkers in females. The highest-ranking biomarkers were estimated glomerular filtration rate for both genders, and the forced expiratory volume in 1 second in males and females. The BA estimates robustly predicted frailty and mortality and outperformed CA. The best performing KD measure of BA was notably predictive in the younger group (aged 55–70 years). BA estimates obtained using a machine learning train-test method were not more accurate than conventional BA estimates in predicting mortality and frailty in most situations. Biologically older people with the same CA as biologically younger individuals had higher prevalence of frailty and 8-year mortality, and worse health, behavioral, and functional characteristics. Conclusions BA is better than CA for measuring life span (mortality) and healthspan (frailty). This measurement set of physiological markers of biological aging among Chinese robustly differentiate biologically old from younger individuals with the same CA.

Biological age in UK Biobank: biomarker composition and prediction of mortality, coronary heart disease and hospital admissions

“…20 The present study provides additional detail on the relative importance on ageing of biomarkers within body system groups, such as cystatin C over other renal biomarkers (creatinine and creatinine-based eGFR), 21 as previous studies each assessed only one of these biomarkers. 8,9,19,20 Several key biomarkers in this study (blood pressure, blood lipids, height and lung function) have each been shown to be associated observationally, and in some cases causally, in randomised trials and Mendelian randomisation studies, with a range of age-related diseases (Table 2). Associations for other key determinants such as cystatin C and hand grip strength have been less extensively researched, and available studies have focused on mortality and cardiovascular outcomes.…”

“…6 The most important contributors to biological age have varied between studies, in part due to differences in the set of biomarkers available. 8,9 The main methods of estimating biological ages using clinical biomarkers are multiple linear regression, principal component analysis (PCA) and the Klemera Doubal method (KDM), which derives a weighted sum of biomarkers based on the strengths of their associations with chronological age 10 and is favoured by comparison studies. 9,11,12 KDM allows comparison of the predictive value of its biological age with that of chronolological age for health outcomes, when chronological age has not been included as a constituent 'biomarker'.…”

“…8,9 The main methods of estimating biological ages using clinical biomarkers are multiple linear regression, principal component analysis (PCA) and the Klemera Doubal method (KDM), which derives a weighted sum of biomarkers based on the strengths of their associations with chronological age 10 and is favoured by comparison studies. 9,11,12 KDM allows comparison of the predictive value of its biological age with that of chronolological age for health outcomes, when chronological age has not been included as a constituent 'biomarker'. However, KDM biological ages have typically included chronological age 13 and the predictive values of biological ages have only occasionally been compared with those of chronological age 13 or mortality-based scores.…”

“…However, KDM biological ages have typically included chronological age 13 and the predictive values of biological ages have only occasionally been compared with those of chronological age 13 or mortality-based scores. 2,14 Multiple linear regression has been used in many studies 5,9,12 and both multiple linear regression and KDM have been extended to principal components of biomarkers instead of individual biomarkers. 11 The UK Biobank is a richly phenotyped resource with 0.5 million participants 15 that provides an unrivalled opportunity to investigate earlier stages of ageing through biological, lifestyle and environmental factors, compared to previous studies of typically 100-10,000 participants with panels of fewer than 30 biomarkers.…”

Are We Ill Because We Age?

“…However, a new approach is needed and should be oriented at a systemic conceptualization of the aging process and not at the fragmentation of its different components. Thus, better assessment of the biological aging against the chronological aging holds promises to be able (e.g., by significant biomarkers) to assess the physiological aging processes in their complexity and act on them specifically and jointly (Zhong et al, 2019). The concept that aging does not always lead to ARD, but that the same processes may lead to either ARD or successful aging in older persons depending on the homeodynamics, will also help to individualize the interventions (Franceschi et al, 2018).…”