FRAILTY AND CARDIOVASCULAR DISEASE EVENTS IN COMMUNITY-DWELLING HEALTHY OLDER ADULTS

Abstract BACKGROUND Frailty is associated with adverse outcomes, but whether it independently increases cardiovascular disease (CVD) risk requires clarification. METHODS This study examined the association between frailty in a cohort with no previous CVD events and subsequent CVD outcomes in 19,114 community-dwelling older people from the ASPREE trial. Frailty was assessed using the modified Fried phenotype, comprising weakness, exhaustion, low body mass index (BMI), slowness and low physical activity, and a deficit accumulation frailty index (FI) of 66 items. CVD event was defined as a composite of CVD death, non-fatal myocardial infarction, non-fatal stroke and hospitalization for heart failure. Results Over a median 4.7-years of follow-up (interquartile range: 3.6 to 5.7 years), pre-frail/frail participants were more likely to develop CVD events (Hazard ratio (HR): 1.33; 95% Confidence Interval (CI): 1.16, 1.53 for pre-frail and HR: 1.68; 95% CI: 1.19, 2.38 for frail participants) according to Fried phenotype. Subtypes of CVD (fatal/non-fatal myocardial infarction and heart failure hospitalization) similarly increased HRs except fatal or non-fatal stroke. These effect sizes were more prominent when frailty was assessed using the FI than that assessed by Fried phenotype. CONCLUSION Pre-frail and frail participants were at significantly increased risk of developing CVD and its sub-types (particularly fatal/non-fatal myocardial infarction and hospitalization for heart failure). Addressing pre-frailty and frailty in older people could contribute to CVD prevention strategies.

score was created using 15 social factors and was classified as low (0-29), intermediate (30)(31)(32)(33)(34)(35)(36)(37)(38)(39)(40)(41)(42), and high (43+). We used the Poisson regression to estimate the risk of incident frailty by three polysocial score categories. We found that 444 (34.5%), 651 (17.9%), and 108 (9.4%) cases of incident frailty at the 4-year follow-up among participants with a low, intermediate, and high polysocial score, respectively. In the multivariable-adjusted Poisson model, the risk of frailty among participants in the intermediate and high polysocial score categories was 35% and 59% lower than those in the low polysocial score category, respectively. We found a universal association between polysocial scores and frailty across race/ethnicity and sex subgroups. The polysocial approach may offer possible solutions to monitor social environments and suggestions for older people to improve their social status for specific health outcomes. Sedentary behavior (SB) is a significant health risk. Emerging research suggests that mentally active SB (such as computer use and reading) were associated with better health than mentally passive SB (such as watching TV). However, this has not been examined among the oldest old (age ≥80). The aims of this study were to (1) identify distinct profiles of oldest old adults based on six domains of SB (watching TV, using a computer/tablet, talking to friends or family members, doing hobby or other activities, transportation, and resting/napping); and (2) compare health-related outcomes across identified profiles, using the National Health and Aging Trends Study (NHATS) dataset. Latent profile analysis was used to identify distinct profiles of SB. Design-based linear and logistic regressions were used to examine associations between different profiles and health outcomes, accounting for sociodemographic characteristics. We identified four profiles and named them based on total sedentary time (ST) and passive/active pattern (n=852, "Low ST", "High ST-passive", "Medium ST-TV", "High total ST-mentally active"). Compared to the "High ST-passive" group, "Low ST" group was associated with fewer difficulties with activities of daily living, fewer problems limiting activities and higher cognitive function; "High ST-mentally active" group was associated with the above outcomes, as well as lower anxiety and depression. This study, with a national representative sample of the oldest old population, suggests that both total ST and SB pattern matter when evaluating health outcomes of being sedentary. Interventions should encourage oldest old adults to reduce ST and especially target mentally passive ST. BACKGROUND: Frailty is associated with adverse outcomes, but whether it independently increases cardiovascular disease (CVD) risk requires clarification.

FRAILTY AND CARDIOVASCULAR DISEASE EVENTS IN COMMUNITY-DWELLING HEALTHY OLDER ADULTS
METHODS: This study examined the association between frailty in a cohort with no previous CVD events and subsequent CVD outcomes in 19,114 community-dwelling older people from the ASPREE trial. Frailty was assessed using the modified Fried phenotype, comprising weakness, exhaustion, low body mass index (BMI), slowness and low physical activity, and a deficit accumulation frailty index (FI) of 66 items. CVD event was defined as a composite of CVD death, non-fatal myocardial infarction, non-fatal stroke and hospitalization for heart failure.
Results: Over a median 4.7-years of follow-up (interquartile range: 3.6 to 5.7 years), pre-frail/frail participants were more likely to develop CVD events (Hazard ratio (HR): 1.33; 95% Confidence Interval (CI): 1.16, 1.53 for pre-frail and HR: 1.68; 95% CI: 1.19, 2.38 for frail participants) according to Fried phenotype. Subtypes of CVD (fatal/non-fatal myocardial infarction and heart failure hospitalization) similarly increased HRs except fatal or non-fatal stroke. These effect sizes were more prominent when frailty was assessed using the FI than that assessed by Fried phenotype.
CONCLUSION: Pre-frail and frail participants were at significantly increased risk of developing CVD and its subtypes (particularly fatal/non-fatal myocardial infarction and hospitalization for heart failure). Addressing pre-frailty and frailty in older people could contribute to CVD prevention strategies.

TRANSLATION OF A MEDICARE CLAIMS-BASED FRAILTY ALGORITHM FROM ICD-9-CM TO ICD-10-CM
Keturah Faurot 1 , Emilie Duchesneau 1 , Shahar Shmuel 2 , Jihye Park 1 , Til Stürmer 2 , Michele L. Jonsson-Funk 1 , and Jennifer L lund 1 , 1. University of North Carolina at Chapel Hill,Chapel Hill,North Carolina,United States,2. Gillings School of Global Public Health,Chapel Hill,North Carolina,United States Clinical trials often have insufficient power to estimate drug effects in older adult populations. Medicare claims data are a valuable resource for studying healthcare delivery for older adults. The Faurot frailty index (FFI) is a validated algorithm that uses demographic, enrollment, and ICD-9-CM-based claims information to predict ADL dependency as a proxy for frailty. The original FFI consists of 20 constructs, including those related to geriatric syndromes, of which 16 are based on ICD-9-CM diagnosis codes (e.g., arthritis, dementia). In October 2015, the US healthcare system transitioned to ICD-10-CM. In this study, we updated and validated the FFI for the ICD-10-CM era . We used General Equivalence Mapping (GEM) to translate the ICD-9-CM codes to ICD-10-CM. For each construct, we manually reviewed the ICD-10-CM codes after GEM, assessed the suitability, and plotted the monthly prevalence before and after the transition (2013-2017). Code lists for all but 1 construct required editing after translation using GEM (adding and/or removing codes). We observed increasing monthly prevalence for several constructs, although trend lines were consistent across the ICD-9-CM and ICD-10-CM eras: bladder dysfunction (1.8-2.5%), weakness (2.7-3.9%), and psychiatric illnesses (6.9-9.1%). Rehabilitation services did not translate well using diagnosis codes, however, adding CPT codes improved capture. The updated FFI was strongly associated with frailty-related geriatric outcomes (1-year mortality, hospitalization, SNF admission). Studies of drug effects in older adults should use validated indices, such as the FFI, to reduce bias. Thorough evaluation of claims-based algorithms is essential to support healthcare services research using these measures.

MOVING, THINKING, AND SLEEPING: NOVEL INSIGHTS INTO PHYSICAL AND COGNITIVE HEALTH FROM ACCELEROMETRY DATA
Chair: Jennifer Schrack Discussant: Amal Wanigatunga Physical activity and sleep are well-established predictors of health and longevity with aging. Wrist accelerometers, that produce high-frequency time series data, capture multiple aspects of daily physical activity and sleep 24-hours/ day. Historically, the majority of accelerometry-based activity research has employed summary metrics to understand the associations of total daily physical activity and sleep with physical and cognitive health. Although these measures are important for understanding conformity with physical activity and sleep recommendations, they underutilize the potential of these data. Further, the summary metrics may differ by accelerometer type/brand, making it difficult to translate results across device types and studies. This symposium will examine the associations between accelerometry-derived physical activity and various aging-related health outcomes, and compare the measurement properties of two commonly used accelerometers for measuring sleep. Ms. Marino will discuss the association of physical activity volume and fragmentation with the presence of the Apolipoprotein-ε4 genotype in the Baltimore Longitudinal Study of Aging (BLSA), overall and by time of day. Dr. Wanigatunga will present evidence on the association of physical activity patterns with beta amyloid plaques in the BLSA. Dr. Schrack will present the association of physical activity fragmentation and diurnal patterns with peripheral artery disease in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL). Finally, Ms. Liu will compare measurement of sleep variables derived from two commonly used accelerometers. Collectively, these presentations highlight ways to utilize the richness of accelerometry data to illuminate more sensitive associations between movement and health outcomes to advance prevention science and promote health aging.