Central Role of Hypertension in HIV Comorbidity Networks: A Population-Based Study of Age and Sex-Specific Patterns in Southwest China.

BACKGROUND

The increasing life expectancy among people living with HIV (PLWH) has transformed HIV management into chronic disease care. This transformation has introduced complex patterns of comorbidities that challenge current health care approaches. A deeper understanding of the interactions between these conditions and their distribution across demographic groups remains essential for optimizing care.

METHODS

This study analyzed electronic health records of 13 884 adult people living with HIV in Luzhou, China (2001-2022). Network analysis identified central comorbidities and their interactions. Hierarchical clustering revealed disease patterns, while time series analysis tracked temporal trends. A nomogram-based prediction model underwent development and validation using bootstrap resampling.

RESULTS

The analysis identified comorbidities in 34.58% of the cohort. Network analysis revealed hypertension as the most central condition (Strength: 0.30, Betweenness: 82, Closeness: 0.0007), followed by metabolic disorders and peripheral vascular diseases. Four distinct comorbidity clusters emerged, with infectious and metabolic diseases forming the core cluster. Demographic patterns showed that younger, female, and homosexual people living with HIV exhibited patterns dominated by neoplasms and sexually transmitted diseases, contrasting with cardiovascular-metabolic patterns in older, male, and heterosexual individuals. Age >50 years (odds ratio, 2.220 [95% CI, 2.024-2.436]) and male sex (odds ratio, 1.145 [95% CI, 1.053-1.246]) emerged as significant predictors of comorbidity risk. The prediction model demonstrated acceptable calibration (=13.784, =0.088) and discrimination (AUC, 0.666 [95% CI, 0.657-0.675]).

CONCLUSIONS

The findings establish hypertension as a central node in HIV comorbidity networks, with patterns varying by demographic characteristics. This evidence-based framework supports the development of targeted screening strategies and personalized interventions focused on central network conditions.