Abstract Body: Introduction: Randomized clinical trials (RCTs) are the gold standard for evaluating treatment effects, but they are costly, time-consuming, and complex. Therefore, selecting the most promising novel treatments for RCTs is vital. Genetic variants, randomly distributed during meiosis, create true 'experiements of nature' for the traits they influence. Drugs backed by genomic evidence are twice as likely to gain FDA approval, yet genomics have not been used to formally emulate RCTs. In this study, we present a genomic proof-of-concept emulation of the SPRINT RCT.

Methods: We conducted SPRINT-e, a genomic emulation of the SPRINT RCT, using UK Biobank data. SPRINT originally randomized 9,361 hypertensive individuals without diabetes into two groups: intensive treatment (SBP<120 mmHg, n=4,678) and standard treatment (SBP<140 mmHg, n=4,683). We selected participants meeting SPRINT’s criteria and used a validated polygenic risk score for SBP to emulate treatment effects. Starting with the 4,678 participants with the lowest genetic risk, we iteratively replaced low-risk participants with higher-risk ones until we matched SPRINT’s SBP difference (15 mmHg) and standard treatment arm size. Finally, we used Cox models, as in SPRINT, to assess whether intensive treatment reduced the risk of stroke, myocardial infarction, coronary artery disease, and cardiovascular death.

Results: Like SPRINT, SPRINT-e included 4678 participants assigned to intensive genetic treatment, 4683 participants assigned to standard genetic treatment (both mean age 63, 42% female), and a follow-up time of 4.8 years. The number of events in the intensive treatment group of SPRINT and SPRINT-e was 243(5.2%) and 220(4.7%), respectively, while in the standard treatment groups was 319(6.8%) and 270(5.8%), respectively. In SPRINT and SPRINT-e, Cox proportional hazard models estimated that intensive versus standard treatment led to risk reductions in the composite outcome of 35%(HR: 0.75, 95%CI: 0.64-0.89) and 34%(HR: 0.76, 95%CI: 0.62–0.92), respectively (Figure 1).

Conclusion: Our genomic-based RCT emulation framework accurately reproduced the primary result of SPRINT. Although the absolute event rate in SPRINT-e was lower than in SPRINT, the relative risk reductions were nearly identical. This framework could be valuable for simulating RCTs that target continuous and highly heritable physiological variables. Utilizing these genomic tools could significantly improve the success rate of future RCTs.