Construction of an integral formula of biological age for a healthy Chinese population using principle component analysis

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.

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.

Conceptual Overview of Biological Age Estimation

Chronological age is an imperfect measure of the aging process, which is affected by a wide range of genetic and environmental exposures. Biological age estimates may be derived using mathematical modelling with biomarkers set as predictors and chronological age as the output. The difference between biological and chronological age is denoted the "age gap" and considered a complementary indicator of aging. The utility of the "age gap" metric is assessed through examination of its associations with exposures of interest and the demonstration of additional information provided by this metric over chronological age alone. This paper reviews the key concepts of biological age estimation, the age gap metric, and approaches to assessment of model performance in this context. We further discuss specific challenges for the field, in particular the limited generalisability of effect sizes across studies owing to dependency of the age gap metric on pre-processing and model building methods. The discussion will be centred on brain age estimation, but the concepts are transferable to all biological age estimation.

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.

Integrated Multi-Omics for Novel Aging Biomarkers and Antiaging Targets

Aging is closely related to the occurrence of human diseases; however, its exact biological mechanism is unclear. Advancements in high-throughput technology provide new opportunities for omics research to understand the pathological process of various complex human diseases. However, single-omics technologies only provide limited insights into the biological mechanisms of diseases. DNA, RNA, protein, metabolites, and microorganisms usually play complementary roles and perform certain biological functions together. In this review, we summarize multi-omics methods based on the most relevant biomarkers in single-omics to better understand molecular functions and disease causes. The integration of multi-omics technologies can systematically reveal the interactions among aging molecules from a multidimensional perspective. Our review provides new insights regarding the discovery of aging biomarkers, mechanism of aging, and identification of novel antiaging targets. Overall, data from genomics, transcriptomics, proteomics, metabolomics, integromics, microbiomics, and systems biology contribute to the identification of new candidate biomarkers for aging and novel targets for antiaging interventions.

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.

A model for comprehensive oral biological age score with oral and systemic clinical parameters

BACKGROUND

Biological age reflects the functional status of an individual. The purpose of the study was to develop a model for estimating oral biological age with oral and systemic parameters.

METHODS

A total of 248 subjects who had a routine health check were assessed with oral and general clinical examination. Chi-square test was performed to screen oral clinical candidate indicators. General parameters were analyzed by Pearson correlation coefficient and principal component analysis to develop a general biological age score. A final comprehensive model of oral biological age score was established by combining oral and general biological age score.

RESULTS

A total of eight oral indicators (mucosal blood blister, mucosal dryness, impacted tooth, missing teeth, residual crowns, dental calculus, gingival hyperemia, and gingival recession) and 10 general clinical indicators (triglyceride, creatinine, blood urea nitrogen, glucose, total cholesterol, mean erythrocyte hemoglobin concentration, mean erythrocyte hemoglobin, uric acid, body weight, and systolic blood pressure) were selected for oral and general biological age score, respectively (r > 0.25, P < 0.05). A model of comprehensive oral biological age score was then formed by principal component analysis: 0.046 triglyceride + 0.010 creatinine + 0.141 blood urea nitrogen + 0.048 glucose + 0.068 total cholesterol + 0.014 mean erythrocyte hemoglobin concentration + 0.082 mean erythrocyte hemoglobin + 0.001 uric acid + 0.020 body weight + 0.005 systolic blood pressure + 0.037 oral biological age score -10.908. The score was increased accordingly with CA.

CONCLUSION

Oral biological age can be easily estimated clinically by the model of comprehensive oral biological age score using oral and systemic clinical parameters by general practitioners.

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.

Quantitative characterization of biological age and frailty based on locomotor activity records

We performed a systematic evaluation of the relationships between locomotor activity and signatures of frailty, morbidity, and mortality risks using physical activity records from the 2003-2006 National Health and Nutrition Examination Survey (NHANES) and UK BioBank (UKB). We proposed a statistical description of the locomotor activity tracks and transformed the provided time series into vectors representing physiological states for each participant. The Principal Component Analysis of the transformed data revealed a winding trajectory with distinct segments corresponding to subsequent human development stages. The extended linear phase starts from 35-40 years old and is associated with the exponential increase of mortality risks according to the Gompertz mortality law. We characterized the distance traveled along the aging trajectory as a natural measure of biological age and demonstrated its significant association with frailty and hazardous lifestyles, along with the remaining lifespan and healthspan of an individual. The biological age explained most of the variance of the log-hazard ratio that was obtained by fitting directly to mortality and the incidence of chronic diseases. Our findings highlight the intimate relationship between the supervised and unsupervised signatures of the biological age and frailty, a consequence of the low intrinsic dimensionality of the aging dynamics.

Measurement of biological age may help to assess the risk of colorectal adenoma in screening colonoscopy

AIM

To investigate the clinical utility of biological age (BA) measurement in screening colonoscopy for the detection of colorectal adenomas in the average-risk population.

METHODS

A consecutive series of asymptomatic subjects aged ≥ 30 years who underwent colonoscopy in routine check-ups were enrolled. Colorectal adenoma was classified according to size, number, and location. BAs were calculated using the MEDIAGE^TM Biological Age Measurement System.

RESULTS

A total of 2696 subjects were investigated (1876 men and 820 women). The mean chronological age (CA) was 46.0 years and the mean BA was 44.7 years. Metabolic syndrome (MS) was diagnosed in 218 subjects (8.1%). The prevalence of overall colorectal adenoma was 23.1% (622/2,696). When the subjects were divided into four groups based on BA (≤ 39 years; 40-49 years; 50-59 years; ≥ 60 years), the prevalence of colorectal adenoma was increased as BA increased (P < 0.001). Colorectal adenoma located in the proximal colon was more prevalent in the BA-dominant group (BA-CA ≥ 5 years) than the CA-dominant group (CA-BA ≥ 5 years) (P = 0.034). When the subjects were categorized into four groups according to MS and age gap between BA and CA, the incidence of colorectal adenoma increased with MS and BA-dominance (P < 0.05).

CONCLUSION

Measurement of BA may help to assess the risk of colorectal adenoma in screening colonoscopy.

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.

Aging-Related Correlation between Serum Sirtuin 1 Activities and Basal Metabolic Rate in Women, but not in Men

Sirtuin (SIRT) is a main regulator of metabolism and lifespan, and its importance has been implicated in the prevention against aging-related diseases. The purpose of this study was to identify the pattern of serum SIRT1 activity according to age and sex, and to investigate how serum SIRT1 activity is correlated with other metabolic parameters in Korean adults. The Biobank of Jeju National University Hospital, a member of the Korea Biobank Network, provided serum samples from 250 healthy adults. Aging- and metabolism-related factors were analyzed in serum, and the data were compared by the stratification of age and sex. Basal metabolic rate (BMR) decreased with age and was significantly lower in men in their fifties and older and in women in their forties and older compared with twenties in men and women, respectively. SIRT1 activities were altered by age and sex. Especially, women in their thirties showed the highest SIRT1 activities. Correlation analysis displayed that SIRT1 activity is positively correlated with serum triglyceride (TG) in men, and with waist circumference, systolic blood pressure, diastolic blood pressure, and serum TG in women. And, SIRT1 activity was negatively correlated with aspartate aminotransferase/alanine aminotransferase ratio in women (r = −0.183, p = 0.039). Positive correlation was observed between SIRT1 activity and BMR in women (r = 0.222, p = 0.027), but not in men. Taken together, these findings suggest the possibility that serum SIRT1 activities may be utilized as a biomarker of aging. In addition, positive correlation between SIRT1 activity and BMR in women suggests that serum SIRT1 activity may reflect energy expenditure well in human.

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

OBJECTIVES

Biological age (BA) has been proposed to evaluate the aging status in an objective way instead of chronological age (CA). The purpose of our study is to construct a more precise formula of BA in the cross-sectional study based on a largest-ever sample of our studies. This formula aims at better evaluation of body function and exploring the disciplines of aging in different genders and age stages.

METHODS

A total of 1,373 healthy Chinese Han (age range, 19-93 years) were recruited from five cities in China, including 581 males and 792 females. Physical examination, blood routine, blood chemistry, and other lab tests were performed to obtain a total of 74 clinical variables. Then, the principal component analysis (PCA) was used to select variables and estimate BA. The BA formula was further validated in a population with some diseases (n=266), including cardiovascular diseases, type 2 diabetes, kidney diseases, pulmonary diseases, cancer and disorders in nervous system.

RESULTS

The BA formula was constructed as follows: BA = 0.358 (pulse pressure) + 0.258 (trail making test) - 11.552 (mitral valve E/A peak) + 26.383 (minimum intima-media thickness) + 31.965 (Cystatin C) + 0.163 (CA) - 3.902. In validation of the formula, BAs of patients were older than those of healthy persons. The BA accelerates faster in the middle-aged population than in the elderly population (>75 years old).

CONCLUSION

This BA formula can reflect health condition changes of aging better than CA in a Chinese Han population.

Construction Formula of Biological Age Using the Principal Component Analysis

The biological age (BA) equation is a prediction model that utilizes an algorithm to combine various biological markers of ageing. Different from traditional concepts, the BA equation does not emphasize the importance of a golden index but focuses on using indices of vital organs to represent the senescence of whole body. This model has been used to assess the ageing process in a more precise way and may predict possible diseases better as compared with the chronological age (CA). The principal component analysis (PCA) is applied as one of the common and frequently used methods in the construction of the BA formula. Compared with other methods, PCA has its own study procedures and features. Herein we summarize the up-to-date knowledge about the BA formula construction and discuss the influential factors, so as to give an overview of BA estimate by PCA, including composition of samples, choices of test items, and selection of ageing biomarkers. We also discussed the advantages and disadvantages of PCA with reference to the construction mechanism, accuracy, and practicability of several common methods in the construction of the BA formula.

Select aging biomarkers based on telomere length and chronological age to build a biological age equation

The purpose of this study is to build a biological age (BA) equation combining telomere length with chronological age (CA) and associated aging biomarkers. In total, 139 healthy volunteers were recruited from a Chinese Han cohort in Beijing. A genetic index, renal function indices, cardiovascular function indices, brain function indices, and oxidative stress and inflammation indices (C-reactive protein [CRP]) were measured and analyzed. A BA equation was proposed based on selected parameters, with terminal telomere restriction fragment (TRF) and CA as the two principal components. The selected aging markers included mitral annulus peak E anterior wall (MVEA), intima-media thickness (IMT), cystatin C (CYSC), D-dimer (DD), and digital symbol test (DST). The BA equation was: BA = −2.281TRF + 26.321CYSC + 0.025DD − 104.419MVEA + 34.863IMT − 0.265DST + 0.305CA + 26.346. To conclude, telomere length and CA as double benchmarks may be a new method to build a BA.