Uncovering the biological toll of neighborhood disorder trajectories: a machine learning based weighting analysis of biomarkers in older adults

ObjectivesNeighborhood physical disorder has been linked to adverse health outcomes, but less is known about longitudinal patterns of disorder trajectories and their associations with biological markers. This study examined associations between neighborhood physical disorder trajectories and metabolic and inflammatory biomarkers in U.S. older adults.MethodsWe analyzed data from community-dwelling Medicare beneficiaries in the National Health and Aging Trends Study (n = 2,284, 2011–2017). Neighborhood physical disorder was assessed annually through interviewer observations over six years. Latent class analysis was used to identify exposure trajectory subgroups. Machine learning-based inverse probability weighted (IPW) regression models were conducted to estimate associations with five biomarkers, including body mass index (BMI), waist circumference, hemoglobin A1c (HbA1c), high-sensitivity C-reactive protein (hsCRP), and interleukin-6 (IL-6).ResultsWe identified four subgroup trajectories: stable low exposure (85%), increased exposure (3%), decreased exposure (9%), and stable high exposure (3%). Regression models revealed significant associations with HbA1c and hsCRP. Compared to the stable low exposure group, older adults with increased exposure (b = 0.04, 95% CI: 0.01–0.08), decreased exposure (b = 0.02, 95% CI: 0.01–0.05), and stable high exposure (b = 0.10, 95% CI: 0.03–0.17) exhibited higher levels of HbA1c. Only stable high exposure was associated with increased hsCRP (b = 0.25, 95% CI: 0.05–0.45). No significant associations were found for other biomarkers.DiscussionResidential environments play an important role in shaping the biological risk of aging. Incorporating routine screening for neighborhood environmental risks and implementing community-level interventions are pivotal in promoting healthy aging in place.