Sickle cell disease (SCD) is a monogenic disease caused by mutations in the β-globin gene. The complications related to the disease are systemic as they impact multiple organ systems. Our goal in this study was to identify metabolome changes contributing to SCD-related severity. Employing both targeted and untargeted approaches, we profiled the plasma of 706 SCD patients using liquid chromatography tandem mass spectrometry. The cohort included 406 French patients of recent African descent and 300 African Americans from the southeastern US. In total, we measured the levels of 61 known and 2,100 unknown metabolites. We applied weighted gene correlation network analysis (WGCNA) algorithms to account for correlations among metabolites and identify specific metabolomic modules associated with SCD-related complications. Finally, we incorporated genetic data from 30 million SNPs to the modules in order to identify the biological pathways implicated by the unknown metabolites. We constructed 66 modules containing at least 7 metabolites per module. Correlating these modules to 15 clinically important phenotypes (6 complications and 11 hematological traits), we found a module strongly associated with increased risks of gall bladder removal. That particular module contained 4 known metabolites (glycocholate, glycodeoxycholate, taurocholate, and taurodeoxycholate) involved in bile acid metabolism, and 20 unknown metabolites. Additionally, we found another module of metabolites strongly correlated with estimated glomerular filtration rate (eGFR). The module contained several carboxylic acids metabolites (citrulline, 4-acetamidobutanoate, symmetric dimethylarginine) as well as metabolites involved in purine and pyrimidine metabolism. Moreover, for eGFR, the same module included 114 unknown metabolites. We performed a GWAS for each of the 39 most robust modules, which resulted in two modules strongly associated with SNPs (FDR < 0.05). We found that one of these two modules was significantly associated (P < 8.0 x 10-10) with multiple SNPs near the gene encoding for hepatic triglyceride lipase (LIPC). Using metabolomics, we identified metabolomic signatures of liver, gall bladder, and kidney complications in SCD. Although hemolysis is the key determinant of organ damage in SCD, understanding which specific metabolite or metabolic pathway plays a role in organ dysfunction can be exploited to predict SCD severity. The module we found to be associated with the LIPC variants suggest that triglycerides could play an important role in the progression of SCD. Determining the causal role of the metabolites involved within the module and how they relate to complications may be key to our understanding of the role of blood lipids in SCD.