OBJECTIVES: To study the influence of symptom experience and clinical markers on a multidimensional Health-related quality of life (HRQL) questionnaire specific to HIV using sparse PLS regression and Canonical Correlation Analysis (RGCCA). METHODS: Self-reports were collected during the Vespa2 national survey, including: the 8-dimension PROQOL-HIV questionnaire, a 22-symptom checklist, and 22 binary clinical indicators. Complete cases for all three blocks of data were considered in this analysis: N=1524 patients undergoing ART, age = 47, 77% men, 51% MSM, 80% undetectable viral load. Data were analysed using bootstrapped sparse PLS (HRQL and symptoms only) and RGCCA (three blocks), with hyper-parameters (number of components, κ, and L1 penalty, λ) optimised through 10-fold cross-validation. The HRQL block was never penalised. RESULTS: Sparse PLS (κ=4, λ=0.65) selected 15 symptoms where sexual dysfunction, lipodystrophy, depression had the highest negative loadings on relevant dimensions of the PROQOL-HIV dimensions (sexual relationships, body changes, emotional distress). Fatigue, anxiety, and sleep disorder cross-loaded on all but the body changes dimension. Using three-block RGCCA (λ=0.7), the top symptoms (loadings) on the first component (44% of average variance explained) were anxiety (0.52), depression (0.51), and fatigue (0.48), while gender (-0.68), chronic HCV (0.61) and living mode (0.30) were the most important clinical predictors. Model-based cluster analysis of first component scores highlighted four equal-size clusters of patients accounting for important individual factors: chronic HCV, MSM vs. women, living alone, disease duration. CONCLUSIONS: Using sparse multi-block modelling of HRQL, symptom experience, and clinical markers allows to study both variables relationships and individual profiles of respondents. Accounting for the differential impact of symptoms and clinical factors on several dimensions of a specific HRQL questionnaire provides interesting perspectives in health management and treatment monitoring for HIV patients.