BACKGROUND AND AIMS:Frailty is associated with cardiovascular disease (CVD) through shared pathophysiology and risk factors, and frailty is a known modifiable risk factor for CVD. Statins reduce CVD risk and have anti-inflammatory properties that may lower the risk of frailty, though this has not been comprehensively examined. METHODS:Older US veterans (aged ≥67 years) who were statin naïve and received regular care in the Veteran Affairs (VA) medical system from 2002 to 2018 were included. Veterans who were frail at baseline based on a validated 31-item VA-Frailty Index (VA-FI) were excluded (scores >0.2). Data were linked to Medicare and Medicaid. Overlap propensity score weighting (PSW) was used to address confounding by indication. Cox regression models were fit to examine the association of statin use with the composite outcome of incident frailty with censoring at death. Similar analyses were conducted on pre-frail veterans (VA-FI score of 0.1-0.2). RESULTS:Of 987 301 veterans included in the study population (age 72 ± 6 years; 98% men; 87% white), 290 729 initiated statins during the study period. During a mean follow-up of 5.3 (SD 4.1) years, 636 195 incident frailty events occurred, representing unadjusted event rates of 153.1 events per 1000 person-years among statin initiators and 111.4 events per 1000 person-years in non-initiators. After PSW, new statin initiators were less likely to experience incident frailty (hazard ratio 0.76, 95% confidence interval 0.75-0.76) compared to non-initiators. Similar results were seen in pre-frail veterans. CONCLUSIONS:Statin initiation was associated with a significantly lower risk of incident frailty or death among older US veterans including those who were pre-frail at baseline.

Hypoglycemia is a preventable adverse treatment effect in diabetes patients, but genetic markers to identify those with increased susceptibility are lacking. We performed a case/control genome-wide association study (GWAS) of hypoglycemia in US Million Veteran Program (MVP) participants with medication-treated diabetes mellitus. Cases had an outpatient random serum/plasma glucose <70 mg/dL or an emergency department visit for hypoglycemia. GWAS was stratified by race/ethnicity, adjusted for age at MVP enrollment, sex, and top 10 population-specific principal components, followed by multi-population meta-analysis. Secondary analyses examined genetic associations with hypoglycemia stratified by diabetes medication exposure as well as replication in UK Biobank and the Action to Control Cardiovascular Risk in Diabetes clinical trial. The study included 72,244 (22,045 cases) non-Hispanic White participants, 24,162 (10,441 cases) non-Hispanic Black participants, and 9,196 (2,800 cases) Hispanic participants. Four loci had genome-wide significant associations with hypoglycemia in multi-population meta-analysis: rs12712928 (chromosome 2, SIX2/SIX3 locus), rs1064173 (chromosome 6, HLA-DQB1/DQA2 locus), rs35198068 (chromosome 10, TCF7L2 locus), and rs113748381 (chromosome 17, SCL16A11 locus). All four loci replicated in at least one independent cohort, and the magnitude of associations with hypoglycemia varied by diabetes type. Genome-wide analyses may complement candidate pharmacogenetic studies to identify risk markers of adverse drug effects.

The high burden of dilated cardiomyopathy (DCM) in individuals of African descent remains incompletely explained. Here, to explore a genetic basis, we conducted a genome-wide association study in 1,802 DCM cases and 93,804 controls of African genetic ancestry (AFR). A nonsense variant ( rs3211938 :G) in CD36 was associated with increased risk of DCM. This variant, believed to be under positive selection due to a protective role in malaria resistance, is present in 17% of AFR individuals but <0.1% of European genetic ancestry (EUR) individuals. Homozygotes for the risk allele, who comprise ~1% of the AFR population, had approximately threefold higher odds of DCM. Among those without clinical cardiomyopathy, homozygotes exhibited an 8% absolute reduction in left ventricular ejection fraction. In AFR, the DCM population attributable fraction for the CD36 variant was 8.1%. This single variant accounted for approximately 20% of the excess DCM risk in individuals of AFR compared to those of EUR. Experiments in human induced pluripotent stem cell-derived cardiomyocytes demonstrated that CD36 loss of function impairs fatty acid uptake and disrupts cardiac metabolism and contractility. These findings implicate CD36 loss of function and suboptimal myocardial energetics as a prevalent cause of DCM in individuals of African descent.

Background: Incidence of diabetes in the US has more than doubled in the last 20 years, affecting more than 30 million adults. It is unclear what the combined effect of lifestyle habits and GLP-1 receptor agonists (GLP-1 RAs) has on the risk of cardiovascular outcomes. This study aims to assess the independent and combined impact of protective lifestyle factors and GLP-1 RAs on risk of major cardiovascular events (MACE) in a large cohort of US veterans with diabetes. Methods: A prospective cohort study of individuals with type 2 diabetes within the Million Veteran Program with no previous history of myocardial infarction, stroke, cancers or advanced chronic kidney disease. Risk of MACE (i.e., non-fatal stroke, non-fatal myocardial infarction, cardiovascular death) was assessed in the setting of GLP-1 RA usage and healthy lifestyle habits (healthy eating, physically active, not smoking, restful sleep, no to moderate alcohol intake, good stress management, social connection and support, and no opioid addiction) using Cox proportional hazard regression models. Results: A total of 63,656 participants were included in the study with 418,513 person-years of follow-up. The multivariable-adjusted hazard ratio (HR) of MACE was 0.37 (95% confidence interval (CI): 0.24-0.56) comparing participants adhering to all lifestyle habits to those adopting one lifestyle factor or less. When examined individually, all protective lifestyle factors were significantly and independently associated with a lower likelihood of MACE. Multivariable-adjusted HR of MACE was 0.80 (0.71-0.90) comparing users of GLP-1 RA with those not taking GLP-1 RA. The combination of adherence to healthy lifestyle and use of GLP-1 RAs together was associated with a 50% reduction in MACE compared to veterans with a lower adherence to a healthy lifestyle and no GLP-1 RAs usage with standard diabetes care, HR: 0.50 (95% CI: 0.38-0.65). Conclusions: Healthy lifestyle habits in combination with GLP-1 RAs have a greater association than either therapy alone with reduced risk of MACE emphasizing the importance of lifestyle modification with pharmacotherapy.

Diabetes complications occur at higher rates in individuals of African ancestry. Glucose-6-phosphate dehydrogenase deficiency (G6PDdef), common in some African populations, confers malaria resistance, and reduces hemoglobin A1c (HbA1c) levels by shortening erythrocyte lifespan. In a combined-ancestry genome-wide association study of diabetic retinopathy, we identified nine loci including a G6PDdef causal variant, rs1050828 -T (Val98Met), which was also associated with increased risk of other diabetes complications. The effect of rs1050828 -T on retinopathy was fully mediated by glucose levels. In the years preceding diabetes diagnosis and insulin prescription, glucose levels were significantly higher and HbA1c significantly lower in those with versus without G6PDdef. In the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial, participants with G6PDdef had significantly higher hazards of incident retinopathy and neuropathy. At the same HbA1c levels, G6PDdef participants in both ACCORD and the Million Veteran Program had significantly increased risk of retinopathy. We estimate that 12% and 9% of diabetic retinopathy and neuropathy cases, respectively, in participants of African ancestry are due to this exposure. Across continentally defined ancestral populations, the differences in frequency of rs1050828 -T and other G6PDdef alleles contribute to disparities in diabetes complications. Diabetes management guided by glucose or potentially genotype-adjusted HbA1c levels could lead to more timely diagnoses and appropriate intensification of therapy, decreasing the risk of diabetes complications in patients with G6PDdef alleles.

Objectives To develop, validate, and implement algorithms to identify diabetic retinopathy (DR) cases and controls from electronic health care records (EHRs).Materials and Methods We developed and validated electronic health record (EHR)-based algorithms to identify DR cases and individuals with type I or II diabetes without DR (controls) in 3 independent EHR systems: Vanderbilt University Medical Center Synthetic Derivative (VUMC), the VA Northeast Ohio Healthcare System (VANEOHS), and Massachusetts General Brigham (MGB). Cases were required to meet 1 of the following 3 criteria: (1) 2 or more dates with any DR ICD-9/10 code documented in the EHR, (2) at least one affirmative health-factor or EPIC code for DR along with an ICD9/10 code for DR on a different day, or (3) at least one ICD-9/10 code for any DR occurring within 24 hours of an ophthalmology examination. Criteria for controls included affirmative evidence for diabetes as well as an ophthalmology examination.Results The algorithms, developed and evaluated in VUMC through manual chart review, resulted in a positive predictive value (PPV) of 0.93 for cases and negative predictive value (NPV) of 0.91 for controls. Implementation of algorithms yielded similar metrics in VANEOHS (PPV = 0.94; NPV = 0.86) and lower in MGB (PPV = 0.84; NPV = 0.76). In comparison, the algorithm for DR implemented in Phenome-wide association study (PheWAS) in VUMC yielded similar PPV (0.92) but substantially reduced NPV (0.48). Implementation of the algorithms to the Million Veteran Program identified over 62 000 DR cases with genetic data including 14 549 African Americans and 6209 Hispanics with DR.Conclusions/Discussion We demonstrate the robustness of the algorithms at 3 separate healthcare centers, with a minimum PPV of 0.84 and substantially improved NPV than existing automated methods. We strongly encourage independent validation and incorporation of features unique to each EHR to enhance algorithm performance for DR cases and controls.

Prolonged hyperglycemia leads to diabetes complications, especially in individuals with African ancestry (AFR) - a health disparity. Glucose-6-phosphate dehydrogenase deficiency (G6PDd) disproportionately affects men with AFR ancestry (prevalence 9.5% vs. 2.2% in the US population) and shortens red cell lifespan, reducing HbA1c with no effect on glucose levels. We investigated whether men with AFR ancestry and G6PDd were at increased risk of diabetes complications. We performed a multi-ethnic genome-wide association study meta-analysis of diabetic retinopathy (DR) using the VA Million Veteran Program (MVP) dataset (nmax = 192,406) and studied clinical impact with the MVP and ACCORD trial datasets. Nine significant loci were associated with DR, including a causal variant for G6PDd [rs1050828-T, OR 1.48 (95% CI 1.45 - 1.51), p = 1.99x10-90)]. Plasma glucose was much higher in those with vs. without G6PDd in the year preceding diabetes diagnosis (168 vs. 137 mg/dL, respectively) and insulin prescription (253 vs. 233), both p <0.001. In a Cox proportional hazards analysis, ACCORD participants with vs. without G6PDd had a higher likelihood of DR [HR 1.78 (1.55 - 2.04), p <0.001] and neuropathy (HR 1.37 (1.23 - 1.54), p <0.005]. A mediation analysis of G6PDd on DR showed that risk was fully attributable to higher glucose levels. In MVP participants, compared to those without G6PDd who were in the top two tertiles of HbA1c regressed onto plasma glucose, the risk of DR was increased with G6PDd (OR 1.40), but lower than the risk of DR in those without G6PDd who were in the lowest tertile of A1c vs. glucose (OR 1.61) - consistent with risk due to inadequate treatment rather than oxidative stress alone. Conclusions: Management based on both glucose and HbA1c, rather than HbA1c alone, might be needed to reduce diabetes complications in both individuals with African ancestry who have G6PDd, and other individuals with low HbA1c levels relative to their glucose levels. Disclosure J.H. Breeyear: None. J. Hellwege: None. J.S. House: None. S.L. Mitchell: None. B. Charest: None. T.B. Basnet: None. P. Reaven: Research Support; Dexcom, Inc. J.B. Meigs: None. M.K. Rhee: Research Support; Kowa Pharmaceuticals America, Inc. Y. Sun: None. O. Wilson: None. A.M. Hung: None. S.K. Iyengar: None. D.M. Rotroff: Consultant; Novo Nordisk. Research Support; Bayer Inc. J.B. Buse: Other Relationship; Novo Nordisk. Consultant; Corcept Therapeutics. Research Support; Corcept Therapeutics, Dexcom, Inc., Insulet Corporation. Consultant; Alkahest, Anji Pharmaceuticals, Aqua Medical, Altimmune Inc., AstraZeneca, Boehringer-Ingelheim, CeQur, Eli Lilly and Company, embecta, GentiBio, Glyscend Inc., Mellitus Health, Metsera, Pendulum Therapeutics, Praetego, LLC, Stability Health, Terns Pharmaceuticals, Insulet Corporation, Vertex Pharmaceuticals Incorporated, vTv Therapeutics. Other Relationship; Medtronic. Stock/Shareholder; Glyscend Inc., Mellitus Health, Pendulum Therapeutics, Praetego, LLC, Stability Health. A. Leong: Other Relationship; Merck & Co., Inc. J.M. Mercader: None. M. Brantley: None. N.S. Peachey: None. A. Motsinger-Reif: None. P.W. Wilson: None. Y. Sun: None. A. Giri: None. L.S. Phillips: Other Relationship; Diasyst, Inc. Research Support; Kowa Pharmaceuticals America, Inc., Janssen Pharmaceuticals, Inc., AbbVie Inc., Novo Nordisk, GlaxoSmithKline plc, Abbott, Sanofi-Aventis U.S., Pfizer Inc. T.L. Edwards: None. Funding NEI (F31EY033663, T32EY021453-10, R01EY025295, R01EY032159, P30-EY026877), NICHD (K12HD043483), NIAMS (K12AR084232-24), NIDDK (R01DK127083, K01DK120631, R21AI156161), NHGRI (U01HG011723, UL1TR002378)

Objective: To characterize high type 1 diabetes (T1D) genetic risk in a population where type 2 diabetes (T2D) predominates. Research Design and Methods: Characteristics typically associated with T1D were assessed in 109,594 Million Veteran Program (MVP) participants with adult-onset diabetes 2011–2021, who had T1D genetic risk scores (GRS) defined as low (0-<45%), medium (45-<90%), high (90-<95%), or highest (≥95%). Results: T1D characteristics increased progressively with higher genetic risk (p<0.001 for trend). A GRS ≥90% was more common with diabetes diagnoses before age 40 years, but 95% of those participants were diagnosed at age ≥40 years, and they resembled T2D in mean age (64.3 years) and BMI (32.3 kg/m2). Compared to the low risk group, the highest risk group was more likely to have diabetic ketoacidosis (DKA) (low 0.9% vs. highest GRS 3.7%), hypoglycemia prompting emergency visits (3.7% vs. 5.8%), outpatient plasma glucose <50 mg/dL (7.5% vs. 13.4%), a shorter median time to start insulin (3.5 vs. 1.4 years), use of a T1D diagnostic code (16.3% vs. 28.1%), low C peptide levels if tested (1.8% vs. 32.4%), and glutamic acid decarboxylase (GAD) antibodies (6.9% vs. 45.2%), all p<0.001. Conclusions: Characteristics associated with T1D were increased with higher genetic risk, and especially with the top 10% of risk. However, the age and BMI of those participants resemble T2D, and a substantial proportion did not have diagnostic testing or use of T1D diagnostic codes. T1D genetic screening could be used to aid identification of adult-onset T1D in settings in which T2D predominates

BackgroundStatins are highly effective for primary prevention of atherosclerotic cardiovascular disease (ASCVD) and mortality. Data on the benefit of statins in adults with heart failure with preserved ejection fraction (HFpEF) and without ASCVD are limited.ObjectivesThe purpose of this study was to determine whether statins are associated with a lower risk of mortality and major adverse cardiovascular events (MACE) in HFpEF.MethodsVeterans Health Administration data from 2002 to 2016, linked to Medicare and Medicaid claims and pharmaceutical data, were collected. Patients had a new HFpEF diagnosis and no known ASCVD or prior statin use at baseline. Cox proportional hazards models were fit to evaluate the association of new statin use with outcomes (all-cause mortality and MACE). Propensity score overlap weighting (PSW) was used to balance baseline characteristics.ResultsAmong 7,970 Veterans, 47% initiated a statin over a mean 6.0-year follow-up. At HFpEF diagnosis, mean age was 69 ± 12 years, 96% were male, 67% were White, 14% were Black, and mean EF was 60% ± 6%. Before PSW, statin users were younger with more prevalent metabolic syndrome, arthritis, and other chronic conditions. All characteristics were balanced after PSW. There were 5,314 deaths and 4,859 MACE events. After PSW, the hazard for all-cause mortality for statin users vs nonusers was 22% lower (HR: 0.78; 95% CI: 0.73-0.83). The HR for MACE was 0.79 (95% CI: 0.74-0.84), 0.69 (95% CI: 0.60-0.80) for all-cause hospitalization, and 0.72 (95% CI: 0.59-0.88) for HF hospitalization.ConclusionsNew statin use was associated with reduced all-cause mortality, MACE, and hospitalization in Veterans with HFpEF without prevalent ASCVD.

Objective: To characterize high type 1 diabetes (T1D) genetic risk in a population where type 2 diabetes (T2D) predominates. Research Design and Methods: Characteristics typically associated with T1D were assessed in 109,594 Million Veteran Program (MVP) participants with adult-onset diabetes 2011–2021, who had T1D genetic risk scores (GRS) defined as low (0-<45%), medium (45-<90%), high (90-<95%), or highest (≥95%). Results: T1D characteristics increased progressively with higher genetic risk (p<0.001 for trend). A GRS ≥90% was more common with diabetes diagnoses before age 40 years, but 95% of those participants were diagnosed at age ≥40 years, and they resembled T2D in mean age (64.3 years) and BMI (32.3 kg/m2). Compared to the low risk group, the highest risk group was more likely to have diabetic ketoacidosis (DKA) (low 0.9% vs. highest GRS 3.7%), hypoglycemia prompting emergency visits (3.7% vs. 5.8%), outpatient plasma glucose <50 mg/dL (7.5% vs. 13.4%), a shorter median time to start insulin (3.5 vs. 1.4 years), use of a T1D diagnostic code (16.3% vs. 28.1%), low C peptide levels if tested (1.8% vs. 32.4%), and glutamic acid decarboxylase (GAD) antibodies (6.9% vs. 45.2%), all p<0.001. Conclusions: Characteristics associated with T1D were increased with higher genetic risk, and especially with the top 10% of risk. However, the age and BMI of those participants resemble T2D, and a substantial proportion did not have diagnostic testing or use of T1D diagnostic codes. T1D genetic screening could be used to aid identification of adult-onset T1D in settings in which T2D predominates

Objective: To characterize high type 1 diabetes (T1D) genetic risk in a population where type 2 diabetes (T2D) predominates. Research Design and Methods: Characteristics typically associated with T1D were assessed in 109,594 Million Veteran Program (MVP) participants with adult-onset diabetes 2011–2021, who had T1D genetic risk scores (GRS) defined as low (0-<45%), medium (45-<90%), high (90-<95%), or highest (≥95%). Results: T1D characteristics increased progressively with higher genetic risk (p<0.001 for trend). A GRS ≥90% was more common with diabetes diagnoses before age 40 years, but 95% of those participants were diagnosed at age ≥40 years, and they resembled T2D in mean age (64.3 years) and BMI (32.3 kg/m2). Compared to the low risk group, the highest risk group was more likely to have diabetic ketoacidosis (DKA) (low 0.9% vs. highest GRS 3.7%), hypoglycemia prompting emergency visits (3.7% vs. 5.8%), outpatient plasma glucose <50 mg/dL (7.5% vs. 13.4%), a shorter median time to start insulin (3.5 vs. 1.4 years), use of a T1D diagnostic code (16.3% vs. 28.1%), low C peptide levels if tested (1.8% vs. 32.4%), and glutamic acid decarboxylase (GAD) antibodies (6.9% vs. 45.2%), all p<0.001. Conclusions: Characteristics associated with T1D were increased with higher genetic risk, and especially with the top 10% of risk. However, the age and BMI of those participants resemble T2D, and a substantial proportion did not have diagnostic testing or use of T1D diagnostic codes. T1D genetic screening could be used to aid identification of adult-onset T1D in settings in which T2D predominates

INTRODUCTION:Diabetes and dementia are diseases of high healthcare burden worldwide. Individuals with diabetes have 1.4 to 2.2 times higher risk of dementia. Our objective was to evaluate evidence of causality between these two common diseases.METHODS:We conducted a one-sample Mendelian randomization (MR) analysis in the U.S. Department of Veterans Affairs Million Veteran program. The study included 334,672 participants ≥65 years of age with type 2 diabetes and dementia case-control status and genotype data.RESULTS:For each standard deviation increase in genetically-predicted diabetes, we found increased odds of three dementia diagnoses in non-Hispanic White participants (all-cause: OR=1.07[1.05-1.08], P =3.40E-18; vascular: OR=1.11[1.07-1.15], P =3.63E-09, Alzheimer's: OR=1.06[1.02-1.09], P =6.84E-04) and non-Hispanic Black participants (all-cause: OR=1.06[1.02-1.10], P =3.66E-03, vascular: OR=1.11[1.04-1.19], P =2.20E-03, Alzheimer's: OR=1.12 [1.02-1.23], P =1.60E-02) but not in Hispanic participants (all P >.05).DISCUSSION:We found evidence of causality between diabetes and dementia using a one-sample MR study, with access to individual level data, overcoming limitations of prior studies utilizing two-sample MR techniques.

Background: Contemporary guidelines emphasize the value of incorporating frailty into clinical decision-making regarding revascularization strategies for coronary artery disease. Yet, there are limited data describing the association between frailty and longer-term mortality among coronary artery bypass grafting (CABG) patients. Methods: We conducted a retrospective cohort study (2016-2020, 40 VA medical centers) of US veterans nationwide that underwent coronary artery bypass grafting (CABG). Frailty was quantified by the Veterans Administration Frailty Index (VA-FI), which applies the cumulative deficit method to render a proportion of 30 pertinent diagnosis codes. Patients were classified as non-frail (VA-FI <= 0.1), pre-frail (0.1 < VA-FI <= 0.2), or frail (VA-FI > 0.2). We used Cox proportional hazards models to ascertain the association of frailty with all-cause mortality. Our primary study outcome was 5-year all-cause mortality; the co-primary outcome was days alive and out of the hospitalwithin the first postoperative year. Results: There were 13,554 CABG patients (median 69 years, 79% White, 1.5% women). The mean pre-operative VA-FI was 0.21 (SD: 0.11); 31% were pre-frail (VA-FI: 0.17) and 47% were frail (VA-FI: 0.31). Frail patients were older and had higher co-morbidity burdens than pre-frail and non-frail patients. Compared with non-frail patients (13.0% [11.4, 14.7]), there was a significant association between frail and pre-frail patients and increased cumulative 5-year all-cause mortality (frail: 24.8% [23.3, 26.1]; HR: 1.75 [95% CI 1.54, 2.00]; pre-frail 16.8% [95% CI 15.3, 18.4]; HR 1.2 [1.08,1.34]). Compared with non-frail patients (mean 362[SD 12]), pre-frail (mean 361 [SD 14]; p < 0.01) and frail patients (mean 358[SD 18]; p < 0.01) spent fewer days alive and out of the hospital in the first postoperative year. Conclusions: Pre-frailty and frailty were prevalent among US veterans undergoing CABG and associated with worse mid-term outcomes. Given the high prevalence of frailty with attendant adverse outcomes, there may be an opportunity to improve outcomes by identifying and mitigating frailty before surgery.

We conduct a large-scale meta-analysis of heart failure genome-wide association studies (GWAS) consisting of over 90,000 heart failure cases and more than 1 million control individuals of European ancestry to uncover novel genetic determinants for heart failure. Using the GWAS results and blood protein quantitative loci, we perform Mendelian randomization and colocalization analyses on human proteins to provide putative causal evidence for the role of druggable proteins in the genesis of heart failure. We identify 39 genome-wide significant heart failure risk variants, of which 18 are previously unreported. Using a combination of Mendelian randomization proteomics and genetic cis-only colocalization analyses, we identify 10 additional putatively causal genes for heart failure. Findings from GWAS and Mendelian randomization-proteomics identify seven ( CAMK2D , PRKD1 , PRKD3 , MAPK3 , TNFSF12 , APOC3 and NAE1 ) proteins as potential targets for interventions to be used in primary prevention of heart failure.

Therapeutic Area: CVD Prevention – Primary and Secondary Background: Frailty is a syndrome of decreased physiologic reserve that is increasingly recognized as a risk factor for cardiovascular disease (CVD), yet no pharmacotherapeutics are available to treat or prevent frailty. Moreover, frailty and CVD have a shared pathophysiology in part through inflammation. We hypothesized that statins, which are known to reduce CVD risk and have anti-inflammatory properties, may also lower the risk of incident frailty. Methods: Study participants were Veterans ≥65 years receiving care in the Veterans Health Administration from 2002 to 2019 who were statin naïve and non-frail. Data were linked to Medicare and Medicaid claims and pharmaceutical data. Frailty was defined according to the VA-Frailty Index (VA-FI) a 31-item validated EHR index (score of ≤0.10 was robust, 0.11-0.20 was pre-frail, and ≥0.21 was frail). A new user design, excluding those with any prior statin use, was employed. Multivariable Cox proportional hazards models were fit to evaluate the association of statin use with the primary composite outcome of frailty or death. Analyses were conducted using propensity score overlap weighting to address confounding by indication. Results: Of 1,253,152 Veterans (aged 72±6 years; 98% men; 87% white), 440,483 (35%) initiated statins. During a mean follow-up of 6±4 years, 335,610 participants developed incident frailty or died in the statin group versus 623,848 in the non-user group. After propensity score overlap weighting was applied, the hazard ratio was 0.89 (95% CI 0.89-0.90) for incident frailty or death when comparing statin users with statin non-users. Results remained consistent for those with or without CVD at baseline, by race, and sex. Results were attenuated among those over age 85 (Table). Conclusions: Among US Veterans aged ≥ 65 years who were statin naïve and free of frailty at baseline, new statin use was significantly associated with a lower risk of incident frailty or death. Given the important role of statin therapy in CVD prevention and the association of frailty with CVD, the present analysis lays the groundwork for future studies, which are needed to further define the role of statin therapy in older adults for the prevention of frailty and downstream CVD.