# Informing optimal first-line antihypertensive therapy: A rigorous comparative effectiveness analysis of ARBs vs. ACEIs on long-term risk of dementia, cancer, heart disease, and quality of life > **NIH NIH R01** · UNIVERSITY OF PENNSYLVANIA · 2024 · $763,579 ## Abstract PROJECT SUMMARY Hypertension (HTN) prevalence increases with aging and is a leading risk factor for several chronic illnesses including Alzheimer's disease and related dementias (ADRD), cardiovascular disease (CVD), and several cancers, as well as mortality. Angiotensin II receptor blockers (ARBs) and angiotensin-converting enzyme inhibitors (ACEIs) are two of the most commonly prescribed anti-HTN classes, used by ~40 million US adults. ARBs and ACEIs and have distinctive beneficial downstream effects on physiologic abnormalities in HTN, including vasoconstriction, inflammation, fibrosis, and oxidative stress, which in turn may result in different long-term risks of ADRD and multimorbidity associated with aging. However, current HTN guidelines recommend prescribing ARBs and ACEIs interchangeably due to presumed equivalent benefit and safety. Our goal is to optimize initial anti-HTN medication prescribing by clarifying the optimal first choice RAS-blocker between ARBs vs. ACEIs. Because ~23 million US adults are currently taking an ACEI and physiologic evidence supports differences in downstream effects of these medications, even if ARBs are only 15% more effective, the long-term population health impact of switching first-line RAS-blockade from ACEI to ARB would be enormous. We will leverage data from the Veterans Health Administration (VHA) and Kaiser Permanente Southern California (KP SoCal) to evaluate the effects of ARBs vs. ACEIs on the risk of ADRD, multimorbidity, frailty, and health-adjusted life expectancy (HALE; the amount of time one can expect to live accounting for one's cumulative morbidity burden). The VHA and KP SoCal are ideal data sources to perform this research because they include comprehensive healthcare information for >10 million patients, collect detailed information on medication use and health outcomes, and have high patient retention with >10 years of follow- up. The specific aims are to determine long-term comparative effects, including duration of use, of ARB- vs. ACEI-based anti-HTN medication regimens on (Aim 1) the incidence of ADRD, CVD (stroke, myocardial infarction, coronary revascularization, or heart failure), and cancers, separately and (Aim 2) the patient- centered outcome of frailty and the population-centered outcome of HALE. We will use an active comparator, new-user design accounting for medication adherence, as well as natural language processing to ascertain ADRD more accurately in the electronic health record over using administrative codes alone. Our team is well- suited to perform the study given considerable prior experience analyzing VHA and KP data, including pharmacoepidemiologic analyses of anti-HTN medication use; assessment of ADRD, CVD incidence, cancer incidence, and multimorbidity; and application of causal inference methods. Our project could support a paradigm shift of first-choice RAS-blockade. Current projections indicate that ADRD will affect >115 million people by 2050 and cancer in... ## Key facts - **NIH application ID:** 10788406 - **Project number:** 5R01AG074989-03 - **Recipient organization:** UNIVERSITY OF PENNSYLVANIA - **Principal Investigator:** Adam P Bress - **Activity code:** R01 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2024 - **Award amount:** $763,579 - **Award type:** 5 - **Project period:** 2022-03-15 → 2025-12-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10788406 ## Citation > US National Institutes of Health, RePORTER application 10788406, Informing optimal first-line antihypertensive therapy: A rigorous comparative effectiveness analysis of ARBs vs. ACEIs on long-term risk of dementia, cancer, heart disease, and quality of life (5R01AG074989-03). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10788406. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*