Experimental Gerontology, Vol. 23, pp. 429-434, 1988 0531-5565/88 $3.00 + .00 Printed in the USA. All rights reserved. Copyright © 1988 Pergamon Press plc

K E Y Q U E S T I O N S IN D E V E L O P I N G B I O M A R K E R S O F A G I N G

DONALD K . INGRAM

Molecular Physiology and Genetics Section, Laboratory of Cellular and Molecular Biology, Gerontology Research Center, National Institute on Aging, Baltimore, Maryland 21224

Abstract -- A series of questions is presented regarding a logical strategy for devel- oping biomarkers of aging. The questions pertain to the conceptualization process in determining bow to define aging and what extraneous and possibly confounding variables must be controlled in measuring this epiphenomenon. In addition, the in- vestigator must consider the degree of generalization that is intended to apply to a candidate biomarker of aging. Empirical questions are also to be considered. Specif- ically, how will reliability and validity of the candidate biomarker be quantified and assessed? What statistical methods will be applied in this process? The need for biomarkers of aging as research tools in gerontology is argued, but the greater need for agreement on how to direct the conceptualization of this effort is also emphasized.

Key Words: aging, biomarkers, validity, reliability

INTRODUCTION

THE OPTIMISTIC interest in developing biomarkers of aging is manifest in the current proceedings and others in the past (Reff and Schneider, 1982). This must be contrasted to other pessimistic views of this enterprise which see development of this field as either futile or useless (Adelman, 1987; Costa and McCrae, 1980, 1985). From a per- spective that has been argued before (Ingram, 1983, 1984), the most pressing need is to agree on a logical strategy for developing biomarkers of aging. This development will proceed by posing the interogatives before proceeding with the investigation.

It appears that this field of gerontological investigation has been guided in an induc- tive fashion primarily by only one question: What biological parameters are highly correlated with chronological age? To focus upon this question exclusively is to over- simplify both the conceptual and measurement problems at hand. Indeed, the identifi- cation of such parameters provides neither sufficient nor even necessary evidence that such a parameter represents a useful biomarker of aging. Other conceptualizations of the measurement problem involved must be generated, and evidence that the selected parameter meets this conceptualization must be demonstrated.

Thus, the recommended approach is deductive. First, the investigator must develop a conceptualization of how aging is to be measured. Then the investigator must provide an empirical demonstration of the reliability and validity of measures to support this

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conceptualization. This logic puts the horse in front of the cart. The following key questions that should guide an investigator's pursuit of biomarkers of aging are offered.

DEFINITION OF AGING

How will aging be defined?

As discussed previously, aging is a hypothetical construct, an epiphenomenon (In- gram, 1983; Ludwig and Smoke, 1980). Before generating a biomarker of aging, the investigator must be guided by a particular definition of aging. A biomarker can be defined simply as a biological measure; but aging may be more difficult to define. Many definitions can be offered, but it is imperative that the investigator conceptualize and communicate the construct that is going to be measured.

The key question is how is the construct of aging going to be operationalized? Ulti- mately it might be operationalized by a battery of biomarkers. But it is clearly prema- ture to consider that a set of tests that correlate highly with chronological age in a particular organism under a specific set of conditions represents this concept merely by demonstrating this correlation. Much more quantitative evidence would be demanded to determine how well the generated results fit the definition offered by the investigator. This represents the burden of demonstrating the reliability and validity of the suggested biomarkers.

Along with the task of defining aging, the investigator should make clear to what level of biological organization candidate biomarkers apply. Will the definition in a par- ticular conceptualization of aging apply to the whole organism, or to a specified organ system only, or to a particular cell population, or to a particular cell culture system, etc.? Related to this specification will be the conceptualization of whether aging is viewed as unidimensional or multidimensional within the model system applied.

There are questions of how the investigator will deal with extraneous or confounding variables. The identification of such variables is integrated with the conceptualization process. As a major example, how will the investigator deal with the issue of disease? Ignore it? Define it as separate from the concept of aging and define methods of teasing out effects attributed to disease? Many other such variables can be identified, including effects due to motivation, cohort and period differences, fatigue, ability, procedural variations, experimenter differences, etc. If an individual sample is to be measured with multiple tests, the effects of test order should also be carefully considered to avoid confounding. All these variables are likely extraneous to the construct of aging being measured and should be controlled to prevent conceptual confounding. Therefore, the careful investigator will identify those variables important to control in assessing how well data fit the definition offered.

GENERALIZATION

To what extent can the results be generalized?

Included in the process of defining aging for the purpose of developing a particular set of biomarkers are questions pertaining to the generalization of the results. To what population will the data apply? Will they be intended as measurements of aging in all species under all conditions?

More likely, an investigator will intend their use to more specified conditions. A restricted age range, for example, 60 to 90 years in humans, might be the limit of

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generalization. The tests may apply to only one species, or to only one genotype of a species, or to only one genotype under only one environmental condition. Answers to these questions are part of the conceptualization process. They should be used to assist the investigator and, eventually, the targeted scientific audience, in better comprehend- ing the intended use of a specified set of biomarkers.

RELIABILITY

How will reliability be determined?

Once the conceptualization process has been initiated, the empirical process has some rationale for proceeding. One of the first steps is to establish the degree of reliability for candidate biomarkers of aging.

To some investigators, reliability has referred to the magnitude of the correlation between the biomarker and chronological age. The higher the correlation, the greater the reliability. As argued previously (Costa and McCrae, 1980; Ingram, 1983), this perspective is a bit short sighted for some purposes. A perfect correlation between a biomarker and chronological age yields the biomarker as perfectly useless as an alter- native to chronological age as a predictor of anything. This is patently true if the investigator is interested in assessing individual differences in aging among individuals of the same chronological age. Biomarkers that do not generate variability among individuals of the same chronological age will not be useful in this case. The empirical question to be addressed is: How reliable is this variability?

Thus, reliability more properly refers to the degree of measurement error present when the biomarker is applied. How much of the variability can be attributed to genuine individual differences and how much represents error of measurement? There are sev- eral statistical procedures for addressing this question, including the estimation of test-retest correlations and split-halves correlations, that will not be discussed in detail here. A quantitative estimate of reliability should be generated through some appropri- ate means.

Even before estimates of reliability are known, the investigator must make a decision about what degree of reliability can be accepted. If the candidate biomarkers are being used to distinguish between individuals in small groups, then a very high degree of reliability (e.g., r > 0.95) might be required. If the investigator will use the biomarkers to test for differences between large groups of individuals, then less degrees of reliabil- ity may be acceptable (e.g., r > 0.50). Thus, questions of reliability, although empiri- cally derived, are also part of the conceptualization process for developing biomarkers of aging.

VALIDITY

How will validity be demonstrated?

Validation of candidate biomarkers of aging is an empirical process that can be approached by producing several different, but convergent lines of evidence. At least two specific types of validity apply to the development of biomarkers of a g i n g - construct and predictive validity (Ingram, 1983, 1984).

At the heart of this process is the production of evidence to support the construct validity of the selected measure. This involves an empirical demonstration of how well

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a candidate biomarker reflects the construct. Strong support of this type of validity would be to demonstrate that the biomarker could differentiate between groups with established differences in the rate of aging and/or life span.

Several quest!ons relate to the conceptualization of this process. What will serve as the standard population for this process? The answer to this question also pertains to the issues of generalization mentioned earlier. What will serve as the test population for this process? Finally, will the assessment of the test population represent a true test of the construct?

As an example, an investigator could offer the specific intent to use male C57BL/6J mice housed five per cage in standard laboratory cages and fed a particular diet ad libitum as the standard population. The generalization of the results might be intended for all genotypes of laboratory mice also fed ad libitum. The candidate biomarkers of aging developed in the standard population might then be applied to a population of mice of the same genotype but undergoing a regimen of dietary restriction (e.g., 70% of caloric intake of the ad libitum standard population). The investigator could provide evidence that the life span and rate of aging as measured by Gompertz analysis (Sacher, 1977) were altered by this manipulation. If the candidate biomarkers could differentiate between the standard and test populations, then initial evidence for the construct valid- ity of these measures would thus be provided.

However, in order to address the issue of construct validity thoroughly, the possibility of extraneous variables would also need to be addressed. The investigator would need to offer evidence that the difference in biomarker performance between the standard and test populations was not due to specified extraneous variables that had been identified. For example, the performance of diet-restricted mice in the candidate biomarker test should not be due exclusively to their lighter weight or to a physiological state of hunger.

Such possibilities could be controlled experimentally or statistically. For example, experimental groups in the test population could be returned to the control diet prior to biomarker testing or control groups could be put on the experimental diet shortly before testing (Ingram et al., 1987; Roth et al., 1984). Or as a statistical control, the inves- tigator might apply analysis of covariance, with possible extraneous variables such as body weight as the covariates (Ingram, 1983; Ingram et al., 1987).

To further test the limits of generalization, the investigator might then proceed to assess other genotypes, or even species, in biomarker performance. Such proof that a candidate biomarker was related to differencs in the rates of aging among different genotypes would provide additional support for the construct validity of the measure. However, such proof, while desirable and useful, would not be necessary if the inves- tigator intended to limit the generalization of the test results.

Additional evidence of predictive validity would lend further support to the demon- stration of construct validity (Ingram and Reynolds, 1986). Predictive validity refers to the accuracy with which the candidate biomarker predicts future standing on a crite- rion. This again is an empirical challenge; however, there are relevant questions related to this component of the conceptualization process. Is there an identifiable criterion? The criterion can be one that directly supports the construct as defined or one more external to it but which is useful nonetheless. As example of the former possibility, the ability of a candidate biomarker to predict life span would be useful and might support the construct of aging provided. As an example of a latter possibility, other candidate biomarkers might be useful only for their ability to predict future performance.

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The key word here appears to be "useful ." Predictive validity emphasizes the utliity of the candidate biomarkers for a specific purpose provided by their developer. Other useful criteria suggested might be the ability to withstand a specific stressor or toxin or the age of onset of specific age-related diseases. There are potentially many other useful criteria.

STATISTICS

What statistical analysis will be applied?

Whether dealing with one or hundreds of candidate biomarkers of aging, the inves- tigator will also be faced with questions pertaining to the statistical treatment of the data generated. This treatment can range from simple linear correlations and t-test compari- sons requiring only hand calculators, to complex multivariate analysis requiring main- frame computers.

When assessing multiple candidate biomarkers, the investigator must consider how to deal with data from different tests. Will they be treated as independent measures or as related measures? Will the scores from the different biomarker tests be combined? If so, how? Would multiple regression be useful for assessing criterion-related validity? Factor analysis, or related analyses, would be useful in discerning the underlying struc- ture or dimensions of interrelationship among the scores of the candidate biomarker tests. Planning appropriate statistical analysis is thus a fundamental component of the conceptualization process in developing biomarkers of aging.

CONCLUSIONS

Is this effort worthwhile?

As a coordinated scientific effort, the field of biomarkers of aging can be considered in its infancy (Reff and Schneider, 1982). This is not to deny the wealth of data that describes age-related alterations in a wide variety of biological parameters or the many previous efforts to measure functional, biological, or physiological age. What is repre- sented by this new effort is the desire to foster the standardization of reliable and valid measures of aging such that purported interventions into aging could be assessed in animal models in a cost- and time-efficient manner (Reff and Schneider, 1982).

As such, this effort can be branded as applied research with all appropriate misgiv- ings expressed about large government efforts of this type (Adelman, 1987). On the more positive side, however, such undertakings are long overdue in gerontology. The call for standardization of techniques would never hurt any science. But more impor- tantly, the research generated in this pursuit will aim directly at addressing fundamental questions in gerontology. How can aging can be defined? How can aging be operationalized? How many biological dimensions of aging are there? One? Several? Many? Too many to be useful? What commonalities in aging exist across species? At what levels of biological organization can aging be observed to interact? And finally, what interventions, if any, will prove useful in altering aging rate in animal models? Pursuit of answers to these questions cannot help but bump into fundamental questions pertaining to mechanisms of aging in support of basic gerontological research.

There are historical parallels between the current effort and those designed to de- velop intelligence tests in the early part of this century. The hypothetical constructs of

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intelligence and aging are equally complex and difficult to measure. Yet major govern- ment programs to develop intelligence tests, like the Binet and Simon scale (1905) and the Army Alpha and Beta tests (Yerkes, 1921), provided the scientific rationale, impetus, and demonstration for research of this hypothetical construct. To be sure, the applied nature of this science remains controversial, but tremendously valuable basic research has followed which analyzed the structure of intelligence and variables that affect and modify it. Moreover, the application of these research tools, that is, intelli- gence tests, has proven fundamentally important to addressing important questions about aging (Schaie, 1983).

Thus, the effort to develop biomarkers of aging as measurement tools will likely advance gerontology rather than impede its progress. Physicists need linear ac- celerators, astronomers need radio telescopes, molecular geneticists need restriction enzymes, gerontologists need longitudinal studies, psychologists need standardized intelligence tests. All are research instruments that can further the advance of basic science.

If research in biomarkers of aging is to develop past infancy, however, interested investigators will benefit from addressing the questions posed above. To elaborate further on the adage mentioned earlier, it seems obvious that the cart must have wheels on it, the load must be well-balanced, and the horse must be well-secured in front. Only in this fashion will the fruits of this labor be delivered to the scientific market place in good shape and on time. And it is precisely in this market place where the value of this effort will be decided.

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