Testing the Geroscience Hypothesis: Early Days

In this issue, Espinoza and colleagues present the design of a 2-year randomized, double-blind, controlled, clinical trial testing whether 2,000 mg/day of metformin retards the advancement of frailty in prediabetic older adults. Demonstrating that any drug can deflect the progression of frailty would be a major milestone in geriatrics, but this study is also important within the larger frame of geroscience which hypothesizes that human health can be improved by directly targeting the biology of aging (1). Support for this hypothesis is found in studies of model organisms, which show that targeting these pathways in a variety of ways—including the administration of metformin—can increase health span and lifespan (2). The study by Espinoza is one of the few rigorously designed trials to test a drug in a context that has direct implications for evaluating the geroscience hypothesis. The geroscience hypothesis is relatively new, and there is uncertainty regarding how to optimally design studies to test it. A key uncertainty is end-point selection. Espinoza and colleagues selected the Fried Cardiovascular Health Study (CHS) frailty phenotype as the primary end-point (3). This measure has good face validity with geriatricians and its use is supported by a great deal of observational data underscoring its robust prediction of health outcomes in older adults. However, the frailty phenotype domains were originally operationalized using data from the CHS study, an on-going observational study. The approach which has worked so well for observational research has limitations from a clinical trial perspective. The Fried CHS frailty phenotype may not be sensitive to change. Participants receive a point for having a value below a threshold cut-point for each of its five components: unintentional weight loss of at least 10 pounds in the past year, slowness, weakness, lack of energy, and low physical activity. This approach makes it relatively insensitive to change because only participants whose measurements actually cross a cut-point during follow-up contribute information. Trials using relatively insensitive measures require more participants or longer follow-up times to detect effects. A person’s classification may change simply due to the passage of time. The phenotype operationalizes the construct of shrinkage as unexpected weight loss over the past year. It is possible that someone who unexpectedly lost weight 11 months previously would no longer meet these criteria at a subsequent follow-up visit even in the absence of any underlying change. Prior weights may not be available when a person enrolls in a trial, so it may be hard to determine if this criterion is met at the baseline of a frailty prevention trial. Interventions could affect component measures without affecting the underlying physiology of frailty. An exercise intervention is likely to show improvement in the physical activity component even if the underlying frailty pathophysiology is unaffected. Conversely, metformin frequently leads to weight loss so unexplained weight loss may end-up being more common in the treated group even if frailty is not exacerbated. There are potential options for adapting the Fried frailty construct for clinical trial use. Ceiling effects and sensitivity to change can be addressed by rescaling the component measures. Sanders and colleagues published a Vigor scale (0–10) which scores the frailty components on an expanded ordinal scale, allowing for better discrimination among non- and pre-frail participants (4). One could adopt an approach similar to that used by Simonsick and colleagues when they adapted the Established Populations for Epidemiologic Studies of the Elderly short physical performance battery (SPPB) for the well-functioning Health Aging and Body Composition Study population (5). The SPPB is based on the performance of three lower extremity tasks, each of which is scored on an ordinal scale from 0 to 4. This results in an SBBP score with integer values from 0 to 12. The SPPB is reasonably sensitive for those with poor mobility function but is insensitive in persons with good function. To adapt the battery, the investigators scored the SPPB components on a continuous 0–1 scale assigning scores based on the ratio of the measured value to the best possible score. The rescaling led to a continuous summary score without a ceiling, better distributional characteristics, and better sensitivity to change (6). The time dependency issue with respect to the shrinkage measure could be addressed in various ways. One might use weight loss since age 50 or 25. This would still be a problem for weight loss interventions, however. One could also consider height loss since age 25. Self-reported adult height is reasonably accurate and height loss would certainly be unintentional and unlikely to be affected by any potential intervention. For trials involving a physical activity intervention, one might consider using accelerometry to quantify sedentary time as an alternative to using a questionnaire to estimate kcals of energy expenditure. These alternatives do not have the depth of data supporting the use of the original measure, but such adaptions could make trials relevant to the underlying physiology of frailty more efficient. An adapted frailty phenotype measure is by no means the only option for geroscience-relevant trials. Potential outcomes fall on a continuum from those that directly test the hypothesis (e.g. emergence of age-related diseases or life-span) to indirect biomarker-based assessments (Figure 1). The National Insitute on Aging’s (NIA) Intervention Test Program evaluates pharmacological agents in mice for their effects at the far right of this continuum (e.g. median lifespan) (7). This end-point is a translational challenge. For example, the Targeting Aging with Metformin (TAME) study, a randomized trial designed to test metformin’s ability to retard the incidence of multiple age-related disease