Improving the precision of biological age determinations. Part 1: A new approach to calculating biological age

A systematic review of phenotypic and epigenetic clocks used for aging and mortality quantification in humans

Aging is the leading driver of disease in humans and has profound impacts on mortality. Biological clocks are used to measure the aging process in the hopes of identifying possible interventions. Biological clocks may be categorized as phenotypic or epigenetic, where phenotypic clocks use easily measurable clinical biomarkers and epigenetic clocks use cellular methylation data. In recent years, methylation clocks have attained phenomenal performance when predicting chronological age and have been linked to various age-related diseases. Additionally, phenotypic clocks have been proven to be able to predict mortality better than chronological age, providing intracellular insights into the aging process. This review aimed to systematically survey all proposed epigenetic and phenotypic clocks to date, excluding mitotic clocks (i.e., cancer risk clocks) and those that were modeled using non-human samples. We reported the predictive performance of 33 clocks and outlined the statistical or machine learning techniques used. We also reported the most influential clinical measurements used in the included phenotypic clocks. Our findings provide a systematic reporting of the last decade of biological clock research and indicate possible avenues for future research.

A novel approach to quantifying individual's biological aging using Korea's national health screening program toward precision public health

Accurate prediction of biological age can inform public health measures to extend healthy lifespans and reduce chronic conditions. Multiple theoretical models and methods have been developed; however, their applicability and accuracy are still not extensive. Here, we report Differential Aging and Health Index (DAnHI), a novel measure of age deviation, developed using physical and serum biomarkers from four million individuals in Korea's National Health Screening Program. Participants were grouped into aging statuses (< 26 vs. ≥ 26, < 27 vs. ≥ 27, …, < 75 vs. ≥ 75 years) as response variables in a binary logistic regression model with thirteen biomarkers as independent variables. DAnHI for each individual was calculated as the weighted mean of their relative probabilities of being classified into each older age status, based on model ages ranging from 26 to 75. DAnHI in our large study population showed a steady increase with the increase in age and was positively associated with death after adjusting for chronological age. However, the effect size of DAnHI on the risk of death varied according to the age group and sex. The hazard ratio was highest in the 50-59-year age group and then decreased as the individuals aged. This study demonstrates that routine health check-up biomarkers can be integrated into a quantitative measure for predicting aging-related health status and death via appropriate statistical models and methodology. Our DAnHI-based results suggest that the same level of aging-related health status does not indicate the same degree of risk for death.

Clarifying the biological and statistical assumptions of cross-sectional biological age predictors: an elaborate illustration using synthetic and real data

BACKGROUND

There is divergence in the rate at which people age. The concept of biological age is postulated to capture this variability, and hence to better represent an individual's true global physiological state than chronological age. Biological age predictors are often generated based on cross-sectional data, using biochemical or molecular markers as predictor variables. It is assumed that the difference between chronological and predicted biological age is informative of one's chronological age-independent aging divergence ∆.

METHODS

We investigated the statistical assumptions underlying the most popular cross-sectional biological age predictors, based on multiple linear regression, the Klemera-Doubal method or principal component analysis. We used synthetic and real data to illustrate the consequences if this assumption does not hold.

RESULTS

The most popular cross-sectional biological age predictors all use the same strong underlying assumption, namely that a candidate marker of aging's association with chronological age is directly informative of its association with the aging rate ∆. We called this the identical-association assumption and proved that it is untestable in a cross-sectional setting. If this assumption does not hold, weights assigned to candidate markers of aging are uninformative, and no more signal may be captured than if markers would have been assigned weights at random.

CONCLUSIONS

Cross-sectional methods for predicting biological age commonly use the untestable identical-association assumption, which previous literature in the field had never explicitly acknowledged. These methods have inherent limitations and may provide uninformative results, highlighting the importance of researchers exercising caution in the development and interpretation of cross-sectional biological age predictors.

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.

Accelerated Aging in LMNA Mutations Detected by Artificial Intelligence ECG-Derived Age

OBJECTIVE

To demonstrate early aging in patients with lamin A/C (LMNA) gene mutations after hypothesizing that they have a biological age older than chronological age, as such a finding impacts care.

PATIENT AND METHODS

We applied a previously trained convolutional neural network model to predict biological age by electrocardiogram (ECG) [Artificial Intelligence (AI)-ECG age] to LMNA patients evaluated by multiple ECGs from January 1, 2003, to December 31, 2019. The age gap was the difference between chronological age and AI-ECG age. Findings were compared with age-/sex-matched controls.

RESULTS

Thirty-one LMNA patients who had a total of 271 ECGs were studied. The median age at symptom onset was 22 years (range, <1-53 years; n=23 patients); eight patients were asymptomatic family members carrying the LMNA mutation. Cardiac involvement was detected by ECG and echocardiogram in 16 patients and consisted of ventricular arrhythmias (13), atrial fibrillation (12), and cardiomyopathy (6). Four patients required cardiac transplantation. Fourteen patients had neurological manifestations, mainly muscular dystrophy. LMNA mutation carriers, including asymptomatic carriers, were 16 years older by AI-ECG than non-LMNA carriers, suggesting accelerated biological age. Most LMNA patients had an age gap of more than 10 years, compared with controls (P<.001). Consecutive AI-ECG analysis showed accelerated aging in the LMNA group compared with controls (P<.0001). There were no significant differences in age-gap among LMNA patients based on phenotype.

CONCLUSION

AI-ECG predicted that LMNA patients have a biological age older than chronological age and accelerated aging even in the absence of cardiac abnormalities by traditional methods. Such a finding could translate into early medical intervention and serve as a disease biomarker.

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.

Familial aggregation of the aging process: biological age measured in young adult offspring as a predictor of parental mortality

Measures of biological age (BA) integrate information across organ systems to quantify "biological aging," i.e., inter-individual differences in aging-related health decline. While longevity and lifespan aggregate in families, reflecting transmission of genes and environments across generations, little is known about intergenerational continuity of biological aging or the extent to which this continuity may be modified by environmental factors. Using data from the Jerusalem Perinatal Study (JPS), we tested if differences in offspring BA were related to mortality in their parents. We measured BA using biomarker data collected from 1473 offspring during clinical exams in 2007-2009, at age 32 ± 1.1. Parental mortality was obtained from population registry data for the years 2004-2016. We fitted parametric survival models to investigate the associations between offspring BA and parental all-cause and cause-specific mortality. We explored potential differences in these relationships by socioeconomic position (SEP) and offspring sex. Participants' BAs widely varied (SD = 6.95). Among those measured to be biologically older, parents had increased all-cause mortality (HR = 1.10, 95% CI: 1.08, 1.13), diabetes mortality (HR = 1.19, 95% CI: 1.08, 1.30), and cancer mortality (HR = 1.07, 95% CI: 1.02, 1.13). The association with all-cause mortality was stronger for families with low compared with high SEP (P_interaction = 0.04) and for daughters as compared to sons (P_interaction < 0.001). Using a clinical-biomarker-based BA estimate, observable by young adulthood prior to the onset of aging-related diseases, we demonstrate intergenerational continuity of the aging process. Furthermore, variation in this familial aggregation according to household socioeconomic position (SEP) at offspring birth and between families of sons and daughters proposes that the environment alters individuals' aging trajectory set by their parents.

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.

Biomarkers of geroprotection and cardiovascular health: An overview of omics studies and established clinical biomarkers in the context of diet

The slowdown, inhibition, or reversal of age-related decline (as a composite of disease, dysfunction, and, ultimately, death) by diet or natural compounds can be defined as dietary geroprotection. While there is no single reliable biomarker to judge the effects of dietary geroprotection, biomarker signatures based on omics (epigenetics, gene expression, microbiome composition) are promising candidates. Recently, omic biomarkers started to supplement established clinical ones such as lipid profiles and inflammatory cytokines. In this review, we focus on human data. We first summarize the current take on genetic biomarkers based on epidemiological studies. However, most of the remaining biomarkers that we describe, whether omics-based or clinical, are related to intervention studies. Then, because of their promising potential in the context of dietary geroprotection, we focus on the effects of berry-based interventions, which up to now have been mostly described employing clinical markers. We provide an aggregation and tabulation of all the recent systematic reviews and meta-analyses that we could find related to this topic. Finally, we present evidence for the importance of the "nutribiography," that is, the influence that an individual's history of diet and natural compound consumption can have on the effects of dietary geroprotection.

Deep learning for biological age estimation

Modern machine learning techniques (such as deep learning) offer immense opportunities in the field of human biological aging research. Aging is a complex process, experienced by all living organisms. While traditional machine learning and data mining approaches are still popular in aging research, they typically need feature engineering or feature extraction for robust performance. Explicit feature engineering represents a major challenge, as it requires significant domain knowledge. The latest advances in deep learning provide a paradigm shift in eliciting meaningful knowledge from complex data without performing explicit feature engineering. In this article, we review the recent literature on applying deep learning in biological age estimation. We consider the current data modalities that have been used to study aging and the deep learning architectures that have been applied. We identify four broad classes of measures to quantify the performance of algorithms for biological age estimation and based on these evaluate the current approaches. The paper concludes with a brief discussion on possible future directions in biological aging research using deep learning. This study has significant potentials for improving our understanding of the health status of individuals, for instance, based on their physical activities, blood samples and body shapes. Thus, the results of the study could have implications in different health care settings, from palliative care to public health.

Aging Fits the Disease Criteria of the International Classification of Diseases

The disease criteria used by the World Health Organization (WHO) were applied to human biological aging in order to assess whether aging can be classified as a disease. These criteria were developed for the 11th revision of the International Classification of Diseases (ICD) and included disease diagnostics, mechanisms, course and outcomes, known interventions, and linkage to genetic and environmental factors. RESULTS: Biological aging can be diagnosed with frailty indices, functional, blood-based biomarkers. A number of major causal mechanisms of human aging involved in various organs have been described, such as inflammation, replicative cellular senescence, immune senescence, proteostasis failures, mitochondrial dysfunctions, fibrotic propensity, hormonal aging, body composition changes, etc. We identified a number of clinically proven interventions, as well as genetic and environmental factors of aging. Therefore, aging fits the ICD-11 criteria and can be considered a disease. Our proposal was submitted to the ICD-11 Joint Task force, and this led to the inclusion of the extension code for "Ageing-related" (XT9T) into the "Causality" section of the ICD-11. This might lead to greater focus on biological aging in global health policy and might provide for more opportunities for the new therapy developers.

Deep Learning using Convolutional LSTM estimates Biological Age from Physical Activity

Human age estimation is an important and difficult challenge. Different biomarkers and numerous approaches have been studied for biological age estimation, each with its advantages and limitations. In this work, we investigate whether physical activity can be exploited for biological age estimation for adult humans. We introduce an approach based on deep convolutional long short term memory (ConvLSTM) to predict biological age, using human physical activity as recorded by a wearable device. We also demonstrate five deep biological age estimation models including the proposed approach and compare their performance on the NHANES physical activity dataset. Results on mortality hazard analysis using both the Cox proportional hazard model and Kaplan-Meier curves each show that the proposed method for estimating biological age outperforms other state-of-the-art approaches. This work has significant implications in combining wearable sensors and deep learning techniques for improved health monitoring, for instance, in a mobile health environment. Mobile health (mHealth) applications provide patients, caregivers, and administrators continuous information about a patient, even outside the hospital.

When will my mouse die? Life span prediction based on immune function, redox and behavioural parameters in female mice at the adult age

The identification of predictive markers of life span would help to unravel the underlying mechanisms influencing ageing and longevity. For this aim, 30 variables including immune functions, inflammatory-oxidative stress state and behavioural characteristics were investigated in ICR-CD1 female mice at the adult age (N = 38). Mice were monitored individually until they died and individual life spans were registered. Multiple linear regression was carried out to construct an Immunity model (adjusted R² = 75.8%) comprising Macrophage chemotaxis and phagocytosis and Lymphoproliferation capacity, a Redox model (adjusted R² = 84.4%) involving Reduced Glutathione and Malondialdehyde concentrations and Glutathione Peroxidase activity and a Behavioural model (adjusted R² = 79.8%) comprising Internal Locomotion and Time spent in open arms indices. In addition, a Combined model (adjusted R² = 92.4%) and an Immunity-Redox model (adjusted R² = 88.7%) were also constructed by combining the above-mentioned selected variables. The models were also cross-validated using two different sets of female mice (N = 30; N = 40). Correlation between predicted and observed life span was 0.849 (P < 0.000) for the Immunity model, 0.691 (P < 0.000) for the Redox, 0.662 (P < 0.000) for the Behavioural and 0.840 (P < 0.000) for the Immunity-Redox model. Thus, these results provide a new perspective on the use of immune function, redox and behavioural markers as prognostic tools in ageing research.

Frailty biomarkers in humans and rodents: Current approaches and future advances

Even though they would have great benefit across research and clinical fields, currently there are no accepted biomarkers of frailty. Cross-sectional studies in humans have identified promising candidates including inflammatory markers such as IL-6, immune markers such as WBC count, clinical markers such as albumin, endocrine markers such as vitamin D, oxidative stress markers such as isoprostanes, proteins such as BDNF and epigenetic markers such as DNA methylation, but there are limitations to the current state of the research. Future approaches to the identification of frailty biomarkers should include longitudinal studies, studies using animal models of frailty, studies incorporating novel biomarkers combined into composite panels, and studies investigating sex differences and potential overlap between markers of biological age and frailty.

Heterogeneity of Human Aging and Its Assessment

Understanding the heterogeneity in health of older adults is a compelling question in the biology of aging. We analyzed the performance of five measures of health heterogeneity, judging them by their ability to predict mortality. Using clinical and biomarker data on 1,013 participants of the Canadian Study of Health and Aging who were followed for up to 6 years, we calculated two indices of biological age using the Klemera and Doubal method, which controversially includes using chronological age as a "biomarker," and three frailty indices (FIs) that do not include chronological age: a standard clinical FI, an FI from standard laboratory blood tests and blood pressure, and their combination (FI-combined). Predictive validity was tested using Cox proportional hazards analysis and discriminative ability by the area under the receiver-operating characteristic curves. All five measures showed moderate performance that was improved by combining measures to evaluate larger numbers of items. The greatest addition in explanatory power came from the FI-combined that showed the best mortality prediction in an age-adjusted model. More extensive comparisons across different databases are required, but these results do not support including chronological age as a biomarker.

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.

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.

An empirical comparative study on biological age estimation algorithms with an application of Work Ability Index (WAI)

In this study, we described the characteristics of five different biological age (BA) estimation algorithms, including (i) multiple linear regression, (ii) principal component analysis, and somewhat unique methods developed by (iii) Hochschild, (iv) Klemera and Doubal, and (v) a variant of Klemera and Doubal's method. The objective of this study is to find the most appropriate method of BA estimation by examining the association between Work Ability Index (WAI) and the differences of each algorithm's estimates from chronological age (CA). The WAI was found to be a measure that reflects an individual's current health status rather than the deterioration caused by a serious dependency with the age. Experiments were conducted on 200 Korean male participants using a BA estimation system developed principally under the concept of non-invasive, simple to operate and human function-based. Using the empirical data, BA estimation as well as various analyses including correlation analysis and discriminant function analysis was performed. As a result, it had been confirmed by the empirical data that Klemera and Doubal's method with uncorrelated variables from principal component analysis produces relatively reliable and acceptable BA estimates.

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.

A Note on Age-Related Biomarkers

Forty-eight randomly selected age-related human biological functions were analyzed in order to establish whether or not their (linear) regressions were modified by age-related standard errors. Possible reasons for this are advanced. Statistically significant multiple regressions were obtained in 25%, and 21% of the functions yielded statistically significant changes in the correlations between data and age when partial correlation coefficients were calculated. The conclusion is that age-related data need to be subjected to the above tests in order to minimize confounding factors.

Biological age--what is it and can it be measured?

Biological age is a concept used loosely and with little objectivity to describe a shortfall between a population cohort average life expectancy and the perceived life expectancy of an individual of the same age. Many biomarkers decline roughly linearly with age with a slope of <1% per annum. The use of a battery of 16 biomarkers is described as a method of calculating an individual biological age. They include: the concentration of prostacyclin in fibroblasts, cell membrane viscosity, the electroretinogram, baroreflex regulation of the heart rate, the concentration of lymphocytes, leucocyte density and velocity, grip strength, cells of the corneal endothelium and the buccal epithelium, neck muscle mobility, and vital capacity. Although not subjected to a prospective validation, the method might provide an objective approach to this widely used concept.

Gastric cancer in the elderly

The elderly population is expanding and, from the early 1990s, one-quarter of newly diagnosed gastric cancer patients are over 80 years of age. The main risk factors for post-operative complications and mortality are total gastrectomy, radical lymphadenectomy, splenectomy and/or pancreatectomy and these should, therefore, not be practised routinely. Good long-term results can be achieved with careful monitoring of concomitant disease.

Human biological decline and mortality rates

Intercepts on the x (age)-axis of 107 normalized declining human biological functions were determined and assembled in 3 histograms, being placed in increasing order within each decade (10 year period). The histograms were classed accordingly as they contained properties associated with dividing cells, sensory properties and non-dividing cells respectively. Their cumulants were determined, multiple regressions calculated and compared with current death-rates for women and men respectively, for 10 amongst the longest living populations in the World. An alternative verification based on risk theory led to an estimate of an optimal life expectancy of 96 years. The survival curve turns out to be of the form (See text: Formula) where the inner integral represents the cumulant dimension (t') and the outer one age (t"). The premises underlying this study are compatible with the notion of a probable life-span, rather than a fixed one.

Why does the human visual system age in the way it does?

The tentative observation that the decrease of lenticular glutathione in man and in cattle may be under genetic control is extended to other biological functions which show a systematic reduction with age. Ocular and visual parameters are shown to decline consistently with the view that the human eye has evolved in keeping with other biological functions sustaining a life-span of approximately 120 years. Analysis of the data suggests that presbyopia represents an outlier in the distribution of ocular attributes, and should not be used as a biomarker for ageing.

Human sensory functions and longevity

Age-related variations of human sensory properties have been expressed in a manner which permits them to be validly compared with other biological functions presented on a similar basis. They are found to be representative of much larger data-base, and it is tentatively suggested that they may have evolved in support of a life-span of approximately 120 years. Some theoretical arguments are advanced to explain this.

Have human biological functions evolved in support of a life-span?

Published data on age-related human biological functions are surveyed. For the many showing a linear decrease, regressions were calculated, or existing functions extrapolated, to yield an intercept on the abscissa. The values of chi(0), the age at which a function ceases, provide a tentative common means of comparison. The distribution of chi(0) is skewed because of the apparent longevity of nervous and cerebral functions. The database is sufficiently large to enable one to distinguish those biological functions which appear to have immediate survival value from those which do not. Such an approach may link biomarkers to estimates of the life-span of species.

Effect of neonatal exposure to 5-bromo-2'-deoxyuridine on life span, estrus function and tumor development in rats--an argument in favor of the mutation theory of aging?

Outbred LIO rats were exposed to subcutaneous injections (3.2 mg) of a synthetic analogue of thymidine, 5-bromo-2'-deoxyuridine (BrdUrd), on days 1 and 3, or days 1, 3, 7 and 21 of postnatal life. The mean life span decreased by 31% and 38% in male and by 14% and 27% in female rats that received 2 and 4 injections of BrdUrd, respectively, in comparison to untreated controls. The opening of the vagina was delayed, whereas age-related changes in the length of the estrous cycle and in the incidence of persistent estrus and/or anestrus were observed earlier in BrdUrd-injected female rats than in untreated ones. Inhibition of compensatory ovarian hypertrophy induced by hemiovariectomy at the age of 3 months was found in females exposed neonatally to BrdUrd as compared to untreated rats, while the uterus weight increase induced by the administration of human chorionic gonadotropin was similar in both control and BrdUrd-treated infantile rats. These data suggest that exposure to BrdUrd in early life impairs pituitary gonadotropic function in female rats. It was also shown that neonatal administration of BrdUrd to rats doubles the incidence of chromosome aberrations in peripheral blood lymphocytes in comparison to controls and is followed by a dose-related increase in tumor incidence. Our observations on the decrease in mean and maximum life span, acceleration of age-related changes in reproductive system function, increase in chromosome aberration and tumor incidence and decrease in tumor latency in rats exposed to BrdUrd in early life suggest that this model could be used as a model of accelerated aging and that some of the results can be interpreted as arguments in favor of the mutation theory of aging.

Improving the precision of biological age determinations. Part 2: Automatic human tests, age norms and variability

In order to eliminate variability due to test operators, procedures for measuring 12 physiological functions that are candidate biomarkers of aging have been automated. Data was collected from a norm group of 2462 male and female office workers using an instrument which requires no test operators, administers all 12 tests in about 45 min. per subject, computes biological age, prints out results, and stores data on floppy disks for transfer to other computers for analysis. This report a) describes the instrumentation and test procedures, b) presents normal age/sex standards for each of the 12 biomarkers, c) reports the variance of the data for each biomarker by sex, d) lists sources of biomarker variance, e) discusses criteria for biomarker selection and f) examines implications for information loss when biomarker data is combined to calculate biological age. After eliminating chronological age as a variable, the standard deviations of the frequency distributions of predicted age for individual biomarkers were found to vary from .226 to 1.075, a range of more than 4 to 1. Procedures are discussed for improving the ratio of useful-to-useless variance in calculating biological age.