{"doi":"10.1111/jgs.18496","title":"Medical characterization of cognitive <scp>SuperAgers</scp> : Investigating the medication profile of <scp>SuperAgers</scp>","abstract":"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","journal":"Journal of the American Geriatrics Society","year":2023,"id":355692,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":5,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9584,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1104495,"name":"Alice Kerr","orcid":null,"position":1,"is_corresponding":false},{"id":938505,"name":"Beth Makowski‐Woidan","orcid":null,"position":2,"is_corresponding":false},{"id":878387,"name":"Nathan Gill","orcid":"0000-0002-0815-785X","position":3,"is_corresponding":false},{"id":455455,"name":"Marsel Mesulam","orcid":"0000-0001-5731-851X","position":4,"is_corresponding":false},{"id":49969,"name":"Sandra Weıntraub","orcid":"0000-0003-2605-5205","position":5,"is_corresponding":false},{"id":1104063,"name":"Hui Zhang","orcid":"0000-0001-9476-4244","position":6,"is_corresponding":false},{"id":302259,"name":"Lee A. Lindquist","orcid":"0000-0002-4290-5081","position":7,"is_corresponding":false},{"id":253934,"name":"Emily Rogalskı","orcid":"0000-0002-6472-1363","position":8,"is_corresponding":false},{"id":1104062,"name":"Janessa Engelmeyer","orcid":"0000-0002-3973-6256","position":0,"is_corresponding":true}],"reference_count":10,"raw_metadata":null,"created_at":"2026-07-19T01:13:21.145955Z","pmid":"37395468","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}