Medical characterization of cognitive SuperAgers: Investigating the medication profile of SuperAgers.

Aging is associated with decline in cognition, with episodic memory changes representing the most common complaint of older adults.1 SuperAgers are 80+ years with episodic memory capacity at least equal to persons in their 50s to 60s.2 Their youthful memory phenotype offers a unique model for identifying factors for optimizing healthspan. Initial investigations have identified biologic, genetic, and psychosocial features that distinguish SuperAgers from their average episodic memory peers.2-4 However, medications have not been characterized. Medications, both as therapies supporting cognition and as indicators of overall health may contribute to the youthful SuperAging phenotype. Polypharmacy (i.e., use of >5 medications), affects ~40% of US older adults and is associated with increased risk of adverse drug events, falls, and mortality. When considering medication type, opiates, benzodiazepines, and non-benzodiazepine hypnotics are on The American Geriatrics Society (AGS) Beers Criteria list of potentially inappropriate medications (PIMs) for older adults, in part due to their detrimental effects on cognition. Conversely, common medications from antihypertensives to statins to vitamin D have been investigated for possible memory benefits.5, 6 This study examined whether medication profiles differed between SuperAgers and controls. Community-dwelling participants age 80+ were prospectively enrolled as SuperAgers or cognitively average older controls. Detailed inclusion criteria have been previously reported.2 Briefly, SuperAgers must perform at or above average normative values for 50–65-year-olds in episodic memory and at least average-for-age normative values in other cognitive domains. Controls were required to perform average-for-age across cognitive domains. The study received institutional review board approval and informed consent was obtained. Participants reported current medications and supplements, dosage, and duration for each medication/supplement. Staff verified responses. Two physicians independently categorized medications as prescription or OTC; discrepancies were adjudicated by consensus. Secondary analysis further classified participants as users/non-users of 10 medications/medications classes. Aspirin, angiotensin-converting enzyme inhibitors/angiotensin receptor blockers, and statins were highlighted given their roles in cardiovascular health. Using the updated AGS Beers Criteria, diuretics, opiates, benzodiazepines, and non-benzodiazepines hypnotics were examined as PIMs. Vitamin D, metformin, and thyroid hormones were included for their potential role in supporting cognition. Linear regression models were used to analyze differences in the number of medications (prescription, OTC, total medications) used, and logistic regression was used to model binary variables (use versus non-use) for 10 specific medications or medication classes. Race, gender, and age were included as covariates. Uncontrolled t-test and Fisher's exact tests were performed for continuous and binary variables respectively. Significance was set at p = 0.05. Table 1 provides demographics and neuropsychological performance for 96 SuperAgers and 46 controls. No significant difference was detected in total, mean prescription, or OTC medication use between SuperAgers and controls in the uncontrolled t-test or the linear regression controlling for age, gender, and race (Figure 1). The specified medications/medication use categories also showed no significant difference between groups (Figure 1). The medication profiles of SuperAgers, older adults with exceptional episodic memory, showed no significant difference compared to cognitively average-for-age older controls in total medications, prescription medications, OTC medications, or in 10 medications/medication categories of interest. On average, prescription medications were higher in the current study (SuperAgers: 3.48, controls 3.20) than in larger epidemiologic studies like the Bronx Aging Study (BAS: 2.3) and the Monongahela Valley Independent Elders Survey (MoVIES: 2.0).7, 8 Notably, these studies were completed over 20 years prior with younger participants (average age: 79.2, 73.1 years, respectively). Higher use in the current study likely reflects temporal changes rather than intrinsic medical differences, given that prescription use increases over the life span and in recent decades.9 Use of potentially inappropriate medications tended to be lower in this study than National Social Life, Health, and Aging Project (NSHAP) cohort, a representative sample of adults aged 57–85 at enrollment.10 Statins were the most commonly used medications for both SuperAgers (36.5%) and controls (28.3%), while the NSHAP was 46.2%. Similarly, the NSHAP participants reported higher aspirin use (40.2%) compared to SuperAgers (26.0%) and controls (21.7%). Definitive conclusions cannot be drawn without statistical comparison; however, higher use of these medications in larger, representative samples of older adults relative to this study raises the possibility our controls may not represent typical older adult medication use. In summary, while SuperAgers differ in memory performance from controls, their medication use—total, prescription, and pre-specified subclasses of medication use—did not differ. Thus, distinctive medication profiles cannot fully account for memory performance differences between SuperAgers and cognitively average older adults. However, our previous findings point to slower brain atrophy and psychosocial factors, as potential contributors to youthful memory performance.2-4 Janessa R. Engelmeyer contributed to data acquisition, analysis, and drafted the manuscript. Alice Kerr contributed to data analysis, interpretation and drafted the manuscript. Beth A. Makowski-Woidan contributed to data acquisition and critical revision of the manuscript. Nathan P. Gill and Hui Zhang contributed to data analysis, interpretation, and critical revision of the manuscript. Lee Lindquist contributed to analysis, interpretation, and critical revision of the manuscript. M.-Marsel Mesulam, Sandra Weintraub, and Emily J. Rogalski contributed to the study conception and design, data acquisition, and critical revision. All authors gave final approval and agreed to be accountable for all aspects of the work. Emily J. Rogalski, M.-Marsel Mesulam, Hui Zhang, Nathan P. Gill, Lee Lindquist, and Sandra Weintraub report NIH funding. Emily J. Rogalski, M.-Marsel Mesulam, and Sandra Weintraub report receiving honoraria. The sponsor was not involved in the design, methods, subject recruitment, data collection, analysis, or preparation of the manuscript. Research reported in this manuscript was supported by the McKnight Brain Research Foundation; the National Institute on Aging (NIA) under award numbers R01AG045571, R56AG045571, R01AG067781, U19AG073153, P30AG072977, and P30AG13854; and the National Center for Advancing Translational Science (NCATS; award number U54NS092089). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.