Multi-organ metabolic connectivity mapping in advanced pulmonary arterial hypertension: a group-level 18 F-FDG PET-controlled study.

BACKGROUND

To explore potential systemic alterations in carbohydrate metabolism associated with advanced pulmonary arterial hypertension (PAH) using group-level PET-based metabolic connectivity mapping.

METHODS

This retrospective, controlled study analysed 18F-FDG PET-CT scans from 75 individuals (29 with PAH, 46 controls). Eleven major organs were segmented using AI-based tools and voxel-level PET data were extracted. Inter-organ metabolic profiles in PAH and control groups were evaluated using a Maximum mean discrepancy (MMD) framework with extensive permutation testing (1,000,000 permutations) to assess intra-group homogeneity and detect between-group distributional differences. Upon confirmation of within-group homogeneity, organ-level metabolic connectomes were derived from Spearman correlation matrices, with Holm-adjusted multiple testing correction and significance filtering (t-test, p < 0.05). Between-group comparisons were similarly performed using MMD, flowed by post-hoc organ-pair analyses and construction of differential connectomes to localize statistical metabolic networks divergence.

RESULTS

Standard SUV analysis revealed no significant intergroup differences across most organs except for increased uptake in the right heart in PAH patients. MMD testing confirmed intra-group homogeneity in both controls (0,0048) and PAH (0,0465), with no rejection of H0 at α = 0.05, while demonstrating significant between-group differences (H0 rejected). Spearman-based PET connectomes, retaining only significant correlations (𝜌 ≠ 0; p < 0.05) revealed perturbated metabolic network in PAH. This altered network involved the heart, adipose tissue, liver, spleen, muscle, and bone marrow.

CONCLUSION

Group-level whole-body 18F-FDG PET connectivity analysis may provide additional insights into systemic metabolic alterations in advanced PAH that are not readily captured by conventional regional SUV assessments. These findings suggest that PET-based connectomics could complement existing methods for assessing metabolic involvement in PAH.