Model Construction for Biological Age Based on a Cross-Sectional Study of a Healthy Chinese Han population

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.

Association of clinical biomarker-based biological age and aging trajectory with cardiovascular disease and all-cause mortality in Chinese adults: a population-based cohort study

BACKGROUND

Evidence on the association of clinical biomarker-based biological age (BA) with cardiovascular disease (CVD) and mortality remains insufficient, particularly concerning aging trajectories' relationship with these two outcomes.

METHODS

Seventy-five thousand five hundred thirty-seven Chinese adults from the Kailuan study who participated in the first checkup (2006-2007) were included. BA was predicted by 32 clinical indicators using deep neural networks models. Aging status was divided into decelerated, accelerated, and normal aging based on BA in the first checkup. Six aging trajectories were developed in the initial three checkups. CVD and mortality were followed up till December 31, 2021.

RESULTS

After adjusting for chronological age, sex, education level, occupation, physical activity, smoking status, alcohol consumption, salt consumption habit, history of hypertension, diabetes, and dyslipidemia, as well as the use of antihypertensive, antidiabetic, and lipid-lowering drugs, Cox proportional hazard models showed that relative to normal aging, accelerated aging was a risk factor for CVD (adjusted hazard ratio [aHR], 1.17 [95% CI 1.11-1.23]) and mortality (aHR, 1.17 [1.12-1.22]), while participants with decelerated aging had a lower risk for CVD (aHR, 0.85 [0.80-0.90]) and mortality (aHR, 0.86 [0.82-0.90]). Relative to low-stable trajectory, other aging trajectories associated with higher risk of CVD and death, and high-stable trajectory associated with the highest risk of CVD (aHR, 1.62 [1.45-1.81]) and mortality (aHR, 1.55 [1.41-1.71]). Relative to high-stable trajectory, high-decreasing trajectory was associated with lower risk of CVD (aHR, 0.76 [0.67-0.86]) and death (aHR, 0.78 [0.70-0.87]), and decreasing-increasing trajectory was associated with lower risk of death (aHR, 0.86 [0.75-0.98]).

CONCLUSIONS

Accelerated BA aging is associated with a higher risk of CVD and mortality, whereas decelerated aging is associated with a lower risk compared to normal aging. Those persistently at high aging levels are at the highest risk for both CVD and death; conversely, it is the act of lowering and continually maintaining a reduced aging state that effectively mitigates these risks.

Towards a Novel Frontier in the Use of Epigenetic Clocks in Epidemiology

Health problems associated with aging are a major public health concern for the future. Aging is a complex process with wide intervariability among individuals. Therefore, there is a need for innovative public health strategies that target factors associated with aging and the development of tools to assess the effectiveness of these strategies accurately. Novel approaches to measure biological age, such as epigenetic clocks, have become relevant. These clocks use non-sequential variable information from the genome and employ mathematical algorithms to estimate biological age based on DNA methylation levels. Therefore, in the present study, we comprehensively review the current status of the epigenetic clocks and their associations across the human phenome. We emphasize the potential utility of these tools in an epidemiological context, particularly in evaluating the impact of public health interventions focused on promoting healthy aging. Our review describes associations between epigenetic clocks and multiple traits across the life and health span. Additionally, we highlighted the evolution of studies beyond mere associations to establish causal mechanisms between epigenetic age and disease. We explored the application of epigenetic clocks to measure the efficacy of interventions focusing on rejuvenation.

Adjuvant chemotherapy and survival outcomes in older women with HR+/HER2- breast cancer: a propensity score-matched retrospective cohort study using the SEER database

OBJECTIVES

This study aimed to investigate the impact of adjuvant chemotherapy (ACT) on survival outcomes in older women with hormone receptor-positive and human epidermal growth factor receptor 2-negative (HR+/HER2-) breast cancer (BC).

DESIGN

A retrospective cohort study using data from the Surveillance, Epidemiology, and End Results database, which contains publicly available information from US cancer registries.

SETTING AND PARTICIPANTS

The study included 45 762 older patients with BC aged over 65 years diagnosed between 2010 and 2015.

METHODS

Patients were divided into two groups based on age: 65-79 years and ≥80 years. Propensity score matching (PSM) was employed to balance clinicopathological characteristics between patients who received ACT and those who did not. Data analysis used the χ2 test and Kaplan-Meier method, with a subgroup analysis conducted to identify potential beneficiaries of ACT.

OUTCOME MEASURES

Overall survival (OS) and cancer-specific survival (CSS).

RESULTS

Due to clinicopathological characteristic imbalances between patients with BC aged 65-79 years and those aged ≥80 years, PSM was used to categorise the population into two groups for analysis: the 65-79 years age group (n=38 128) and the ≥80 years age group (n=7634). Among patients aged 65-79 years, Kaplan-Meier analysis post-PSM indicated that ACT was effective in improving OS (p<0.05, HR=0.80, 95% CI 0.73 to 0.88), particularly in those with advanced disease stages, but did not show a significant benefit in CSS (p=0.09, HR=1.13, 95% CI 0.98 to 1.31). Conversely, for patients aged ≥80 years, ACT did not demonstrate any improvement in OS (p=0.79, HR=1.04, 95% CI 0.79 to 1.36) or CSS (p=0.09, HR=1.46, 95% CI 0.69 to 2.26) after matching. Subgroup analysis also revealed no positive impact on OS and CSS.

CONCLUSIONS

Patients with HR+/HER2- BC ≥80 years of age may be considered exempt from ACT because no benefits were found in terms of OS and CSS.

The biological age model for evaluating the degree of aging in centenarians

BACKGROUND

Biological age (BA) has been used to assess individuals' aging conditions. However, few studies have evaluated BA models' applicability in centenarians.

METHODS

Important organ function examinations were performed in 1798 cases of the longevity population (80∼115 years old) in Hainan, China. Eighty indicators were selected that responded to nutritional status, cardiovascular function, liver and kidney function, bone metabolic function, endocrine system, hematological system, and immune system. BA models were constructed using multiple linear regression (MLR), principal component analysis (PCA), Klemera and Doubal method (KDM), random forest (RF), support vector machine (SVM), extreme gradient boosting (XGBoost), and light gradient boosting machine (lightGBM) methods. A tenfold crossover validated the efficacy of models.

RESULTS

A total of 1398 participants were enrolled, of whom centenarians accounted for 49.21%. Seven aging markers were obtained, including estimated glomerular filtration rate, albumin, pulse pressure, calf circumference, body surface area, fructosamine, and complement 4. Eight BA models were successfully constructed, namely MLR, PCA, KDM1, KDM2, RF, SVM, XGBoost and lightGBM, which had the worst R2 of 0.45 and the best R2 of 0.92. The best R2 for cross-validation was KDM2 (0.89), followed by PCA (0.62).

CONCLUSION

In this study, we successfully applied eight methods, including traditional methods and machine learning, to construct models of biological age, and the performance varied among the models.

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.

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.

HIV-Infected Patients as a Model of Aging

We appraised the relationship between the biological and the chronological age and estimated the rate of biological aging in HIV-infected patients. Two independent biomarkers, the relative telomere length and iron metabolism parameters, were analyzed in younger (<35) and older (>50) HIV-infected and uninfected patients (control group). In our control group, telomeres of younger patients were significantly longer than telomeres of older ones. However, in HIV-infected participants, the difference in the length of telomeres was lost. By combining the length of telomeres with serum iron, ferritin, and transferrin iron-binding capacity, a new formula for determination of the aging process was developed. The life expectancy of the healthy population was related to their biological age, and HIV-infected patients were biologically older. The effect of antiretroviral HIV drug therapies varied with respect to the biological aging process. IMPORTANCE This article is focused on the dynamics of human aging. Moreover, its interdisciplinary approach is applicable to various systems that are aging.

Biological age models based on a healthy Han Chinese population

BACKGROUND

Biological age (BA) may reflect the actual aging state in humans better than chronological age (CA). The study aimed to construct BA models suitable for the Chinese Han population by selecting appropriate aging markers and evaluation methods.

METHODS

A total of 1207 individuals (21∼91 years) from the Han Chinese population in Beijing were examined for essential organ functions, and 156 cardiovascular, pulmonary function, and atherosclerotic indices and clinical and genetic factors were used as candidate markers of aging. BA models were constructed using multiple linear regression (MLR), principal component analysis (PCA), and the Klemera and Doubal method (KDM). Models were internally and externally validated using cross-validation and disease populations.

RESULTS

Nine aging markers were selected. Two MLR, three PCA, and three KDM models were successfully constructed. External validation showed that the difference between CA and BA was most significant in the PCA3 and KDM2 models, while there was no significant difference in the MLR1 and MLR2 models; the fitted lines for BA in the disease population were higher than those in the healthy population in the MLR1, MLR2, KDM1, and KDM2 models, while the other models showed the opposite.

CONCLUSIONS

Based on a healthy population in Beijing, nine markers representing multiple organ/system functions were screened from the candidate markers, eight methods were successfully used to construct BA models, and the KDM2 model was found to potentially be more appropriate for assessing BA in the Chinese Han population.

Progress in biological age research

Biological age (BA) is a common model to evaluate the function of aging individuals as it may provide a more accurate measure of the extent of human aging than chronological age (CA). Biological age is influenced by the used biomarkers and standards in selected aging biomarkers and the statistical method to construct BA. Traditional used BA estimation approaches include multiple linear regression (MLR), principal component analysis (PCA), Klemera and Doubal's method (KDM), and, in recent years, deep learning methods. This review summarizes the markers for each organ/system used to construct biological age and published literature using methods in BA research. Future research needs to explore the new aging markers and the standard in select markers and new methods in building BA models.

A Machine Learning-Based Aging Measure Among Middle-Aged and Older Chinese Adults: The China Health and Retirement Longitudinal Study

Objective: Biological age (BA) has been accepted as a more accurate proxy of aging than chronological age (CA). This study aimed to use machine learning (ML) algorithms to estimate BA in the Chinese population. Materials and methods: We used data from 9,771 middle-aged and older Chinese adults (≥45 years) in the 2011/2012 wave of the China Health and Retirement Longitudinal Study and followed until 2018. We used several ML algorithms (e.g., Gradient Boosting Regressor, Random Forest, CatBoost Regressor, and Support Vector Machine) to develop new measures of biological aging (ML-BAs) based on physiological biomarkers. R-squared value and mean absolute error (MAE) were used to determine the optimal performance of these ML-BAs. We used logistic regression models to examine the associations of the best ML-BA and a conventional aging measure-Klemera and Doubal method-BA (KDM-BA) we previously developed-with physical disability and mortality, respectively. Results: The Gradient Boosting Regression model performed the best, resulting in an ML-BA with an R-squared value of 0.270 and an MAE of 6.519. This ML-BA was significantly associated with disability in basic activities of daily living, instrumental activities of daily living, lower extremity mobility, and upper extremity mobility, and mortality, with odds ratios ranging from 1 to 7% (per 1-year increment in ML-BA, all P < 0.001), independent of CA. These associations were generally comparable to that of KDM-BA. Conclusion: This study provides a valid ML-based measure of biological aging for middle-aged and older Chinese adults. These findings support the application of ML in geroscience research and may help facilitate preventive and geroprotector intervention studies.

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

BACKGROUND

Actions to improve healthy aging and delay morbidity are crucial, given the global aging population. We believe that biological age estimation can help promote the health of the general population. Biological age reflects the heterogeneity in functional status and vulnerability to disease that chronological age cannot. Thus, biological age assessment is a tool that provides an intuitively meaningful outcome for the general population, and as such, facilitates our understanding of the extent to which lifestyle can increase health span.

OBJECTIVE

This interdisciplinary study intends to develop a biological age model and explore its usefulness.

METHODS

The model development comprised three consecutive phases: (1) conducting a cross-sectional study to gather candidate biomarkers from 100 individuals representing normal healthy aging people (the derivation cohort); (2) estimating the biological age using principal component analysis; and (3) testing the clinical use of the model in a validation cohort of overweight adults attending a lifestyle intervention course.

RESULTS

We completed the data collection and analysis of the cross-sectional study, and the initial results of the principal component analysis are ready. Interpretation and refinement of the model is ongoing. Recruitment to the validation cohort is forthcoming. We expect the results to be published by December 2021.

CONCLUSIONS

We expect the biological age model to be a useful indicator of disease risk and metabolic risk, and further research should focus on validating the model on a larger scale.

TRIAL REGISTRATION

ClinicalTrials.gov NCT03680768, https://clinicaltrials.gov/ct2/show/NCT03680768 (Phase 1 study); NCT04279366 https://clinicaltrials.gov/ct2/show/NCT04279366 (Phase 3 study).

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/19209.

Development and Validation of 2 Composite Aging Measures Using Routine Clinical Biomarkers in the Chinese Population: Analyses From 2 Prospective Cohort Studies

BACKGROUND

This study aimed to: (i) develop 2 composite aging measures in the Chinese population using 2 recent advanced algorithms (the Klemera and Doubal method and Mahalanobis distance); and (ii) validate the 2 measures by examining their associations with mortality and disease counts.

METHODS

Based on data from the China Nutrition and Health Survey (CHNS) 2009 wave (N = 8119, aged 20-79 years, 53.5% women), a nationwide prospective cohort study of the Chinese population, we developed Klemera and Doubal method-biological age (KDM-BA) and physiological dysregulation (PD, derived from Mahalanobis distance) using 12 biomarkers. For the validation analysis, we used Cox proportional hazard regression models (for mortality) and linear, Poisson, and logistic regression models (for disease counts) to examine the associations. We replicated the validation analysis in the China Health and Retirement Longitudinal Study (CHARLS, N = 9304, aged 45-99 years, 53.4% women).

RESULTS

Both aging measures were predictive of mortality after accounting for age and gender (KDM-BA, per 1-year, hazard ratio [HR] = 1.14, 95% confidence interval [CI] = 1.08, 1.19; PD, per 1-SD, HR = 1.50, 95% CI = 1.33, 1.69). With few exceptions, these mortality predictions were robust across stratifications by age, gender, education, and health behaviors. The 2 aging measures were associated with disease counts both cross-sectionally and longitudinally. These results were generally replicable in CHARLS although 4 biomarkers were not available.

CONCLUSIONS

We successfully developed and validated 2 composite aging measures-KDM-BA and PD, which have great potentials for applications in early identifications and preventions of aging and aging-related diseases in China.

Aging Biomarkers and Novel Targets for Anti-Aging Interventions

The aging population worldwide is expanding at an increasing rate. By 2050, approximately a quarter of the world population will consist of the elderly. To slow down the aging process, exploration of aging biomarkers and the search for novel antiaging targets have attracted much interest. Nonetheless, because aging research is costly and time-consuming and the aging process is complicated, aging research is considered one of the most difficult biological fields. Here, providing a broader definition of aging biomarkers, we review cutting-edge research on aging biomarkers at the molecular, cellular, and organismal levels, thus shedding light on the relations between aging and telomeres, longevity proteins, a senescence-associated secretory phenotype, the gut microbiota and metabolic patterns. Furthermore, we evaluate the suitability of these aging biomarkers for the development of novel antiaging targets on the basis of the most recent research on this topic. We also discuss the possible implications and some controversies regarding these biomarkers for therapeutic interventions in aging and age-related disease processes. We have attempted to cover all of the latest research on aging biomarkers in our review but there are countless studies on aging biomarkers, and the topic of aging interventions will continue to deepen even further. We hope that our review can serve as a reference for better characterization of aging and as inspiration for the screening of antiaging drugs as well as give some clues to further research into aging biomarkers and antiaging targets.

Application of biological age assessment of Chinese population in potential anti-ageing technology

BACKGROUND

This study aimed to construct a biological age assessment formula for the Chinese population and to explore the effectiveness of double filtration plasmapheresis for anti-ageing and longevity.

METHODS

915 subjects were recruited, including 584 (63.8%) males and 331 females (36.2%). Male age was 50.94±10.60 (mean±SD), and female age was 51.20±11.84 (mean±SD). 34 blood markers were detected in the laboratory. The ageing biomarkers were determined by statistical correlation analysis and redundancy analysis, and the biological age assessment formula was established by multiple linear regression analysis. Paired sample T test was used to analyse the elimination effect of double filtration plasmapheresis on aging biomarkers.

RESULTS

Based on the comprehensive blood test and analysis, the ageing biomarkers were screened, and the male and female biological age assessment formulas were established. Then, the elimination of ageing biomarkers by double filtration plasmapheresis was examined. Double filtration plasmapheresis can eliminate ageing biomarkers, with an average of 4.47 years decrease in age for males and 8.36 years for females.

CONCLUSION

So, biological age provides a scientific tool for assessing ageing, and double filtration plasmapheresis is safe and might be effective for anti-ageing and longevity. However, the effect of plasmapheresis is expected to be transient, so further studies are needed to plan the number and range of the plasmapheresis procedures necessary to consistently lower the parameters under study.

Common methods of biological age estimation

At present, no single indicator could be used as a golden index to estimate aging process. The biological age (BA), which combines several important biomarkers with mathematical modeling, has been proposed for >50 years as an aging estimation method to replace chronological age (CA). The common methods used for BA estimation include the multiple linear regression (MLR), the principal component analysis (PCA), the Hochschild's method, and the Klemera and Doubal's method (KDM). The fundamental differences in these four methods are the roles of CA and the selection criteria of aging biomarkers. In MLR and PCA, CA is treated as the selection criterion and an independent index. The Hochschild's method and KDM share a similar concept, making CA an independent variable. Previous studies have either simply constructed the BA model by one or compared the four methods together. However, reviews have yet to illustrate and compare the four methods systematically. Since the BA model is a potential estimation of aging for clinical use, such as predicting onset and prognosis of diseases, improving the elderly's living qualities, and realizing successful aging, here we summarize previous BA studies, illustrate the basic statistical steps, and thoroughly discuss the comparisons among the four common BA estimation methods.