Burden and predictors of hypertension and diabetes among people living with HIV in an integration care study in Kenya

Burden and predictors of hypertension and diabetes among people living with HIV in an integration care study in Kenya. Background High coverage of antiretroviral therapy (ART) has led to increased life expectancy among people living with HIV (PLHIV), consequently raising the risk of non-communicable diseases (NCDs). Cardiovascular diseases (CVDs) have become a growing concern among PLHIV due to ART-related metabolic changes and aging. We evaluated the burden and predictors of hypertension and diabetes comorbidity among PLHIV at baseline in an HIV/NCD integration study conducted across three counties in Kenya. Methods The EIS study, a cluster-randomized trial in Kiambu, Nairobi, and Kitui, evaluates integrated HIV/NCD care versus standard care. This analysis used baseline data from PLHIV, abstracted from medical records and managed in REDCap. We used multivariate logistic regression analysis to determine the burden and predictors of comorbidity (defined as HIV plus hypertension and/or diabetes) among participants at baseline. Results Of 2,165 PLHIV enrolled, 447 (20.7%) had comorbidity: 418 (19.3%) were hypertensive, 15 (0.7%) diabetic, and 14 (0.7%) had both conditions. Increasing age was associated with higher odds of comorbidity [OR = 1.32, 95% CI 1.25–1.40, p < 0.001]. Higher BMI was significantly associated with comorbidity [OR = 1.54, 95% CI 1.33–1.78, p < 0.001]. Longer duration on ART was also associated with increased odds of comorbidity [OR = 2.14, 95% CI: 1.64–2.79, p < 0.001]. Presence of other comorbid conditions were associated with HIV/NCD comorbidity [OR = 16.94, 95% CI 10.99–26.13, p < 0.001]. Sex, viral suppression, and current ART regimen were not statistically significant predictors (p > 0.05). Conclusions Advanced age, elevated BMI, prolonged ART treatment duration and additional comorbidities were associated with higher risk of hypertension and/or diabetes among PLHIV. These factors can guide risk profiling and integrated patient management.