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

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Abstract

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.

Introduction

There have been various attempts on assessing biological age (BA) by a range of computational algorithms. However, few studies are reported on direct comparison of these algorithms, revealing which algorithm is most reliable and acceptable as to be implemented on a BA estimation system.

BA is an abstract concept. Although the concept of BA can be found in many scientific papers throughout last half a century, a concrete and precise definition that can be generally accepted is difficult to find. Klemera and Doubal (2006) explained it as a quantity expressing the “true global state” of ageing organism, or “true life expectancy” of the individual better than corresponding chronological age (CA). As it is obvious, CA does not correlate perfectly with BA. Two people may be of the same age, but differ in their mental and physical capacities. BA describes a person's general condition at a particular time of the CA. It is determined by the individual's physical, psychological, and social characteristics, rather than chronology. Thus, the BA of a person could be defined as the CA at which ‘most normal’ people are in the physical state of that person.

The common assess to BA estimation is through measurements of various age-dependent variables, so-called biomarkers, and aggregating these measures into a value in units of years with some computational algorithms. The algorithms include multiple linear regression (MLR) methods, principal component analysis (PCA), and somewhat unique and novel methods as Hochschild, 1989a, Hochschild, 1989b, Hochschild, 1994 and Klemera and Doubal had proposed. Using the data obtained from experiments, this paper examines these methods as well as some variations of them, and ultimately, proposes the most suitable method for valid BA estimation. The details of each method are discussed in the following sections.

Validation of BA has always been a controversial issue, as the term itself is an abstract concept, and the absence of the true value in the reality makes it difficult to evaluate the validity of the estimated BA. (This is not to deny the existence of the conceptual value of BA.) Ingram (1988) and McClearn (1997) offered inspiration how to deal with the validation issue. They asserted that the validation of BA is ascertained by examining the validity of biomarkers from which the BA is estimated. Ingram pointed out that two types of validity should be established in the development of biomarkers of ageing; they are construct validity and predictive validity. Construct validity refers to how well a candidate biomarker reflects the construct, and predictive validity refers to the usefulness of a biomarker in longitudinal studies (Ingram, 1988).

The focus of this paper is not on the selection of biomarkers (which is still very important), but rather on the validity of the final estimated value of BA with various computational algorithms. Therefore, instead of examining whether individual biomarkers are actually measuring ageing process, the present authors propose the use of Work Ability Index (WAI) as an alternative measure taken in the real world to be examined for the correspondence with the attributes of BA estimates (BAE). The objective of this study is to find the most appropriate method of BA estimation by examining the association between WAI and the differences of each algorithm's BAE from CA.

Section snippets

Selection of biomarkers

Anstey et al. (1996) published an outstanding review of extensive studies related to measurement of BA and empirical findings of the correlation of more than 170 biomarkers with the ageing rates. According to the authors, the biomarkers are classified into seven categories—anthropometric/morphologic, sensorimotor, cognitive, psychosocial, physiological/biomedical, behavioral and dentition. From author to author of BA-related research, the reasons given for the choice of biomarkers vary. Some of

Descriptive statistics of the data

Table 3 shows the mean and standard deviations of the biomarker variables derived from the experiment. From one-way ANOVA, the results of the significance tests on differences of means among 5-year age groups were obtained. They indicated that all physical and cognitive function tests are statistically significant at or beyond the 0.1% level. Visual and muscular reaction time (rVRT and rMRT) tests showed relatively less significant level, whereas acoustic reaction time (rART) test was not

Discussion

The Klemera and Doubal's methods, KD1 and KD2, turned out to be most correlated with WAI, indicating that the estimates of the methods adequately correspond to the health status of the individuals. The two methods both presented satisfactory values for all the criteria used in this study, including (i) Pearson correlation with WAI scores, (ii) the standard deviation (s(BAECA)) and (iii) discrimination ability between healthy and unhealthy individuals. Although KD1 showed a little higher

Acknowledgments

The authors wish to thank Dr. Kyung Chul Chae, professor, Stochastic Modeling Laboratory, Department of Industrial & Systems Engineering, KAIST, for valuable advice and constructive comments. We would also like to thank Dr. Sung-Taek Chung, professor, Integrated Media System Laboratory, Department of Computer Engineering, Korea Polytechnic University, for his kind assistance in the execution of the experiment. We also thank the referee for helpful comments that considerably improved the

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