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

Methods for the assessment of biological age - A systematic review

Biological age has long been proposed to complement chronological age because it has the potential to provide a more accurate assessment of someone's ageing process and functional status. At present, there are several methods to determine an individual's biological age through the measurement of biomarkers of ageing. This review compares methods for assessing biological age in adults, analyses biomarkers of ageing, and determines the goals for which biological age can be calculated, in order to help determine a gold standard for measuring biological age. Articles were eligible if studies included a test battery and statistical method to calculate biological age. Literature research included the databases Medline, Embase, Cochrane Library, Web of Science and ClinicalTrials.gov. In total, 56 studies were included and the risk of bias in each of them was assessed. The most commonly used methods to assess biological age are Klemera and Doubal's method, principal component analysis, multiple linear regression, PhenoAge and Hochschild's method. Klemera and Doubal's method has proved the most reliable. Apart from using different statistical methods, the difference between the biological ageing scores lies in the choice of biomarkers of ageing, especially the inclusion of chronological age as a biomarker of ageing. Most of the included studies aimed to establish a new biological ageing score or compare biological age to different measurements of functionality of the human body. In conclusion, there is still no consensus on a gold standard and more research on this topic is necessary. Study protocol PROSPERO ID: CRD42021287548.

Decoding brain aging trajectory: predictive discrepancies, genetic susceptibilities, and emerging therapeutic strategies

The global extension of human lifespan has intensified the focus on aging, yet its underlying mechanisms remain inadequately understood. The article highlights aspects of genetic susceptibility to impaired brain bioenergetics, trends in age-related gene expression related to neuroinflammation and brain senescence, and the impact of stem cell exhaustion and quiescence on accelerated brain aging. We also review the accumulation of senescent cells, mitochondrial dysfunction, and metabolic disturbances as central pathological processes in aging, emphasizing how these factors contribute to inflammation and disrupt cellular competition defining the aging trajectory. Furthermore, we discuss emerging therapeutic strategies and the future potential of integrating advanced technologies to refine aging assessments. The combination of several methods including genetic analysis, neuroimaging techniques, cognitive tests and digital twins, offer a novel approach by simulating and monitoring individual health and aging trajectories, thereby providing more accurate and personalized insights. Conclusively, the accurate estimation of brain aging trajectories is crucial for understanding and managing aging processes, potentially transforming preventive and therapeutic strategies to improve health outcomes in aging populations.

A biological age model based on physical examination data to predict mortality in a Chinese population

Biological age could be reflective of an individual's health status and aging degree. Limited estimations of biological aging based on physical examination data in the Chinese population have been developed to quantify the rate of aging. We developed and validated a novel aging measure (Balanced-AGE) based on readily available physical health examination data. In this study, a repeated sub-sampling approach was applied to address the data imbalance issue, and this approach significantly improved the performance of biological age (Balanced-AGE) in predicting all-cause mortality with a 10-year time-dependent AUC of 0.908 for all-cause mortality. This mortality prediction tool was found to be effective across different subgroups by age, sex, smoking, and alcohol consumption status. Additionally, this study revealed that individuals who were underweight, smokers, or drinkers had a higher extent of age acceleration. The Balanced-AGE may serve as an effective and generally applicable tool for health assessment and management among the elderly population.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

Accuracy of a Clinical Applicable Method for Prediction of VO2max Using Seismocardiography

Cardiorespiratory fitness measured as ˙VO2max is considered an important variable in the risk prediction of cardiovascular disease and all-cause mortality. Non-exercise ˙VO2max prediction models are applicable, but lack accuracy. Here a model for the prediction of ˙VO2max using seismocardiography (SCG) was investigated. 97 healthy participants (18-65 yrs., 51 females) underwent measurement of SCG at rest in the supine position combined with demographic data to predict ˙VO2max before performing a graded exercise test (GET) on a cycle ergometer for determination of ˙VO2max using pulmonary gas exchange measurements for comparison. Accuracy assessment revealed no significant difference between SCG and GET ˙VO2max (mean±95% CI; 38.3±1.6 and 39.3±1.6 ml·min-1·kg-1, respectively. P=0.075). Further, a Pearson correlation of r=0.73, a standard error of estimate (SEE) of 5.9 ml·min-1·kg-1, and a coefficient of variation (CV) of 8±1% were found. The SCG ˙VO2max showed higher accuracy, than the non-exercise model based on the FRIENDS study, when this was applied to the present population (bias=-3.7±1.3 ml·min-1·kg-1, p<0.0001. r=0.70. SEE=7.4 ml·min-1·kg-1, and CV=12±2%). The SCG ˙VO2max prediction model is an accurate method for the determination of ˙VO2max in a healthy adult population. However, further investigation on the validity and reliability of the SCG ˙VO2max prediction model in different populations is needed for consideration of clinical applicability.

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.