Biomarkers of aging in women and the rate of longitudinal changes

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.

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 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.

Extracellular Vesicles from Child Gut Microbiota Enter into Bone to Preserve Bone Mass and Strength

Recently, the gut microbiota (GM) has been shown to be a regulator of bone homeostasis and the mechanisms by which GM modulates bone mass are still being investigated. Here, it is found that colonization with GM from children (CGM) but not from the elderly (EGM) prevents decreases in bone mass and bone strength in conventionally raised, ovariectomy (OVX)-induced osteoporotic mice. 16S rRNA gene sequencing reveals that CGM reverses the OVX-induced reduction of Akkermansia muciniphila (Akk). Direct replenishment of Akk is sufficient to correct the OVX-induced imbalanced bone metabolism and protect against osteoporosis. Mechanistic studies show that the secretion of extracellular vesicles (EVs) is required for the CGM- and Akk-induced bone protective effects and these nanovesicles can enter and accumulate into bone tissues to attenuate the OVX-induced osteoporotic phenotypes by augmenting osteogenic activity and inhibiting osteoclast formation. The study identifies that gut bacterium Akk mediates the CGM-induced anti-osteoporotic effects and presents a novel mechanism underlying the exchange of signals between GM and host bone.

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.

Selection of a set of biomarkers and comparisons of biological age estimation models for Korean men

Biological age (BA) represents the rate of the senescence with a set of biomarkers. The BA prediction models have not been compared to obtain an optimal BA prediction model with BA biomarkers for Korean men. The study aims to obtain a set of BA biomarkers and compare three of the reported statistical approaches for an optimal BA prediction model. The Korea National Health and Nutrition Examination Surveys data of 2009 to 2011 were used to select six BA biomarkers from 940 healthy subjects aged between 30 to 80 years. The multiple linear regression (MLR), principal component analysis (PCA), and Klemera and Doubal methods (KDM) were used to obtain three BA prediction models. Correlation coefficients (r) with 95% confidence intervals (CI) and regression slopes were assessed. One of the Euro Quality of Life-5 Dimensions, mobility, was compared for feasibility test of each BA models. KDM showed greatest correlation (r=0.88 [P<0.05]) with smallest 95% CI and regression slope (1.00). PCA also showed strong correlation (r=0.79 [P<0.05]) with small 95% CI and regression slope (0.94). MLR (r=0.68 [P<0.05]) showed over and underestimated BA results at the end of the age spectrum. Estimations of BA were most reliable with KDM. The PCA and MLR approaches were comparatively simple to devise for Korean men.

Risk stratification by means of biological age-related factors better predicts cancer-specific survival than chronological age in patients with upper tract urothelial carcinoma: a multi-institutional database study

Background:

Chronological age is an important factor in determining the treatment options and clinical response of patients with upper tract urothelial carcinoma (UTUC). Much evidence suggests that chronological age alone is an inadequate indicator to predict the clinical response to radical nephroureterectomy (RNU).

Patients and methods:

We retrospectively reviewed the data from 1510 patients with UTUC (Ta-4) treated by surgery. White blood cell (WBC) count, neutrophil-to-lymphocyte ratio, hemoglobin (Hb), platelets, albumin, alkaline phosphatase, lactate dehydrogenase, creatinine, and corrected calcium were tested by the Spearman correlation to indicate the direction of association with chronological age, which yielded significant, negative associations of Hb (p < 0.001) and WBC (p = 0.010) with chronological age. For scoring, we assigned points for these categories as follows; point ‘0’ for Hb >14 (reference) and 13–13.9 [odds ratio (OR): 1.533], point ‘1’ for 12–12.9 (OR: 2.391), point ‘2’ for 11–11.9 (OR: 3.015), and point ‘3’ for <11 (OR: 3.584). For WBC, point ‘1’ was assigned for >9200 (OR: 2.541) and ‘0’ was assigned for the rest; 9200–8500 (reference), 8499–6000 (OR: 0.873), 5999–4500 (OR: 0.772), 4499–3200 (OR: 0.486), and <3200 (OR: 1.277).

Results:

The 10-year cancer-specific survival (CSS) in the higher risk group with scores of 4 or higher in patients age <60 years was worse than a score of 0, or 1 in age >80 years [mean estimated survival 69.7 months, confidence interval (CI): 33.3–106 versus 103.5. CI: 91–115.9]. The concordance index between biological age scoring and chronological age was 0.704 for CSS and 0.798 for recurrence-free survival. The limitation of the present study is the retrospective nature of the cohort included.

Conclusions:

The biological age scoring developed for patients with UTUC undergoing RNU. It was applicable to those with localized disease and performed well in diverse age populations.

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.

Biological age as a useful index to predict seventeen-year survival and mortality in Koreans

Background

Many studies have been conducted to quantitatively estimate biological age using measurable biomarkers. Biological age should function as a valid proxy for aging, which is closely related with future work ability, frailty, physical fitness, and/or mortality. A validation study using cohort data found biological age to be a superior index for disease-related mortality than chronological age. The purpose of this study is to demonstrate the validity of biological age as a useful index to predict a person’s risk of death in the future.

Methods

The data consists of 13,106 cases of death from 557,940 Koreans at 20–93 years old, surveyed from 1994 to 2011. Biological ages were computed using 15 biomarkers measured in general health check-ups using an algorithm based on principal component analysis. The influence of biological age on future mortality was analyzed using Cox proportional hazards regression considering gender, chronological age, and event type.

Results

In the living subjects, the average biological age was almost the same as the average chronological age. In the deceased, the biological age was larger than the chronological age: largest increment of biological age over chronological age was observed when their baseline chronological age was within 50–59 years. The death rate significantly increased as biological age became larger than chronological age (linear trend test, p value < 0.0001). The largest hazard ratio was observed in subjects whose baseline chronological age was within 50–59 years when the cause was death from non-cancerous diseases (HR = 1.30, 95% confidence intervals = 1.26 - 1.34). The survival probability, over the 17 year term of the study, was significantly decreased in the people whose biological age was larger than chronological age (log rank test, p value < 0.001).

Conclusions

Biological age could be used to predict future risk of death, and its effect size varied according to gender, chronological age, and cause of death.

Electronic supplementary material

The online version of this article (doi:10.1186/s12877-016-0407-y) contains supplementary material, which is available to authorized users.

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.

Analysis of nonlinear gene expression progression reveals extensive pathway and age-specific transitions in aging human brains

Several recent gene expression studies identified hundreds of genes that are correlated with age in brain and other tissues in human. However, these studies used linear models of age correlation, which are not well equipped to model abrupt changes associated with particular ages. We developed a computational algorithm for age estimation in which the expression of each gene is treated as a dichotomized biomarker for whether the subject is older or younger than a particular age. In addition, for each age-informative gene our algorithm identifies the age threshold with the most drastic change in expression level, which allows us to associate genes with particular age periods. Analysis of human aging brain expression datasets from three frontal cortex regions showed that different pathways undergo transitions at different ages, and the distribution of pathways and age thresholds varies across brain regions. Our study reveals age-correlated expression changes at particular age points and allows one to estimate the age of an individual with better accuracy than previously published methods.

Reliability and variability of sleep and activity as biomarkers of ageing in Drosophila

There are currently no reliable biomarkers of ageing. A biomarker should indicate biological age, that is, the amount of an animal's total lifespan it has lived and, therefore, the amount of time it has remaining. Some potential biomarkers cannot be validated as their measurement involves harm or death of the animal, such that its ultimate lifespan cannot be determined. A non-destructive biomarker would allow us to test molecular markers potentially involved directly in the ageing process, to monitor the effectiveness of therapeutic interventions to delay ageing, and provide a useful measure of general health of the organism. In the model organism Drosophila, various behavioural phenotypes change directionally with age, but we do not know whether they predict lifespan. Here we measure activity and sleep parameters in 64 wild type male flies from two recently wild-caught populations over the course of their natural lives, and determine whether such measures may predict biological age and ultimate lifespan. Indices of sleep fragmentation and circadian rhythm were the best predictors of lifespan, though population differences were evident. However, when used to predict a biological age of 50 % lifespan elapsed our best behavioural measure was slightly less accurate and less precise compared with using chronological age as predictor.

Development and application of biological age prediction models with physical fitness and physiological components in Korean adults

BACKGROUND

Several biological age (BA) prediction models have been suggested with a variety of biomarkers. Valid models should be able to measure BA in a relatively short time period and predict subsequent physiological capability. Physiological and physical fitness variables have been shown to be distinctive markers for predicting BA and morbidity. The practical and noninvasive nature of such variables makes them useful as clinical assessment tools in estimating BA for in-depth diagnosis and corresponding intervention.

OBJECTIVE

To identify, develop and evaluate biomarkers and BA prediction models and validate their clinical usefulness for the practical diagnosis of functional aging.

METHODS

Fourteen variables were measured in 3,112 male and 1,233 female participants aged 30 and older between the years 2004 and 2007. Through a series of parsimonious stepwise elimination processes, two sets of 8 gender-specific variables were selected as candidate biomarkers for 1,604 men and 760 women. Principal component analysis, linear regression analysis and adjustment methods were further applied to obtain two sets of true BA (TBA) prediction models. The TBA models were examined for validity by comparing TBA to the corresponding chronological age (CA) with clinical risk factors.

RESULTS

TBA prediction models with r(2) values of 0.638 and 0.672 were developed, each unique to men and women, respectively. The overall mean TBA and CA of the participants were 53.9 and 51.8 years, respectively, with a marginal difference of -2.1 and -1.3 years. The regression slopes or rates of TBA as a function of CA were 1.00 and 1.28 for men and women with r values of 0.799 and 0.820 (p < 0.001), respectively. In comparing TBA to CA rates between healthy and clinical risk groups, both sarcopenic and obese groups showed significant increases in TBA.

CONCLUSIONS

The selected biomarkers encompass various complex physiopathological factors related to intrinsic and extrinsic physiological and functional aging. The BA prediction models based on the selected biomarkers could be practical in assessing BA for Korean adults.

Age-Related Changes in Biomarkers: Longitudinal Data from a Population-Based Sample

Identifying how biological parameters change with age can provide insights into the physiological determinants of disease, and ultimately, death. Most prior studies of age-related change in biomarkers are based on cross-sectional data, small or selective samples, or a limited number of biomarkers. We use data from a nationally-representative longitudinal sample of 639 Taiwanese aged 54 and older in 2000 to assess changes over a six-year period in a wide range of biomarkers. Markers that increased most with age were glycoslyated hemoglobin, interleukin-6, and norepinephrine. Markers that decreased most with age were diastolic blood pressure and creatinine clearance. For example, glycoslyated hemoglobin increased by 8-13%, on average, over this six-year period. Several standard clinical risk factors exhibited little evidence of age-related change. Further research is needed to determine whether the observed variation between individuals in biomarker changes represents differences in underlying physiological function that are predictive of future health and survival.

Development of a serum profile for healthy aging

Increasing numbers of Americans are reaching 85 years of age or older, yet there are no reliable biomarkers to predict who will live this long. The goal of this pilot study therefore was: (1) to identify a potential serum pattern that could identify proteins involved in longevity and (2) to determine if this pattern was a marker of longevity in an independent sample of individuals. Serum samples were analyzed in three cohorts of individuals (n = 12 in each) aged 20-34, 60-74, and ≥ 90 years who participated in The Louisiana Healthy Aging Study. The 12 most abundant proteins were removed and the remaining proteins separated by two-dimensional gel electrophoresis. Gels were matched and the intensity of each spot quantified. Multivariate discriminant analysis was used to identify a serum pattern that could separate these three age cohorts. Seven protein spots were found that correctly distinguished the subjects into the three groups. However, these spots were not as successful in discriminating the ages in a second set of 15 individuals as only eight of these subjects were placed into their correct group. These preliminary results show that the proteomics approach can be used to identify potential proteins or markers that may be involved in the aging process and/or be important determinants of longevity.

Developing a biological age assessment equation using principal component analysis and clinical biomarkers of aging in Korean men

The purpose of the present study is to find clinically useful candidate biomarkers of aging, and using these to develop an equation measuring biological age (BA) in Korean men, then to validate the clinical usefulness of it. Among 4288 men who received medical health examinations, we selected 1588 men who met the normality criteria of each variable. We assumed that chronological ages (CA) of healthy persons represent the BA of them. Variables showing significant correlations with CA were selected. Redundant variables were excluded. We selected 11 variables: VO(2)max, percent body fat (%BF), waist circumference (WC), forced expiratory volume in 1 s (FEV1), systolic blood pressure (SBP), low density cholesterol (LDLCH), blood urea nitrogen (BUN), serum albumin (SA), erythrocyte sedimentation rate(ESR) hearing threshold (HT), and glycosylated hemoglobin (HBA1C). These 11 variables were then submitted into principal component analysis (PCA) and standardized BA scores were obtained. Using them and T-scale idea, the following equation to assess BA was developed: BA=-28.7+0.83(%BF)+0.48(WC)+0.13(SBP)-0.27(VO(2)max)+0.19(HT)-3.1(FEV1)+0.32(BUN)+0.06(LDLCH)-3.0(SA)+0.34(ESR)+4.6(HBA1C). We compared the BA of 3122 men by their fasting glucose and age level. The BA of the higher glucose level group was significantly higher than that of others at all CA levels. The selected 11 biomarkers encompassed known clinically important factors of adult diseases and functional disabilities. This BA assessment equation can be used in the general Korean male population and it proved to be clinically useful.

Postponement of postmenopausal mortality acceleration in low-mortality populations

Mortality analyses commonly disregard postmenopausal acceleration. This study examined period log mortality in World Health Organization (WHO) data for 34 low-mortality countries in 2000, demonstrating significant gradient increases for women (33/34 countries) and men (22/34), from a later age than previously reported, dividing the postmenopausal period into phases. "Break points" were identified as intersects of lines of best fit to these and the same approach was used in analysis of Human Mortality Database data for 19 countries. There has been an upward migration of about 10 years in female age at break point since 1850. Male data flipped from mortality acceleration to deceleration and back in the late 20th century with no apparent shift in break point. Altered age at mortality acceleration appears genuine, gender-specific, and internationally consistent. Its timing prompts the hypothesis that it may relate to falling fertility.

Artificial environments and an aging population: designing for age-related functional losses

Over the past century there has been a large and continuing increase in the frequency of persons aged over 65 years; particularly those aged over 100 years. During the 21st century the number of persons over 100 years will continue to increase. This will occur at such a rapid rate that the 21st century may one day be called the century of centenarians. Frailty and disability secondary to senescence, disease, and trauma have accompanied old age (often defined as age 65 and over) as far back as recorded history. However, during the 20th century, age, frailty, disability, and chronic degenerative diseases have been decoupled to some extant in the most long-lived human populations. Until recently, there was little need to design artificial environments for the unique needs of the elderly due to their low representation in most national populations. Today that need is increasing in concert with the number of persons aged 65 and older. The purpose of this review is to suggest areas wherein physiological anthropologists may have an opportunity to contribute to design trends for this rapidly increasing aging population. Major considerations for design of environments for the elderly are based upon altering the environment to accommodate their declining visual, auditory, and kinesthetic senses, thereby enhancing their declining faculties and improving their autonomy, independence, and self perceptions of well-being. To date most design considerations have been directed toward improving environments for those suffering from Alzheimer's disease or residing within assisted living facilities. Many such design improvements also may be effective in improving life satisfaction and functional abilities of the non-institutionalized elderly.