Effect heterogeneity of loneliness and social support on incident hypertension in older, normotensive populations.

Although social isolation is known to impact those with cardiovascular disease, its relationship with incident hypertension is unclear. In this retrospective cohort study, we used data from the All of Us research program. Participants were enrolled between May 2017-April 2023, aged 50+ years, and had at least 60-days of EHR data pre-index date. We identified incident hypertension during follow-up. Those classified as hypertensive at or prior to the index date were excluded. We defined low social support and loneliness as binary variables. Using causal forests, we estimated treatment effects as unweighted and overlap-weighted risk differences (RD). A total of 24,720 participants with a median follow-up of 2.6 years were included. While loneliness and social support were not significantly associated with incident hypertension in the overall population, causal forests revealed meaningful effect heterogeneity, with socially isolated male (RD: 0.038, 95% CI 0.002 to 0.075; overlap-weighted RD: 0.027, 95% CI -0.005 to 0.059) and Gay or Lesbian participants (RD: 0.108, 95% CI 0.011 to 0.205; overlap-weighted RD: 0.11, 95% CI 0.008 to 0.211) experiencing a significantly elevated risk. This illustrates how machine learning, when embedded within epidemiologic frameworks, can detect subgroup vulnerability that other approaches may obscure, enabling targeting of limited resources.