Metabolomic identification of α-ketoglutaric acid elevation in pediatric chronic graft-versus-host disease

Abstract

Chronic graft versus host disease (cGvHD) is the most common cause for non-relapse mortality post allogenic hematopoietic stem cell transplant (HSCT). However, there are no well-defined biomarkers for cGvHD or late acute GvHD (aGvHD). This study is a longitudinal evaluation of metabolomic patterns of cGvHD and late aGvHD in pediatric HSCT recipients. A quantitative analysis of plasma metabolites was performed on 222 evaluable pediatric subjects from the ABLE/PBMTC1202 study. We performed a risk-assignment analysis at day+100 on subjects who later developed either cGvHD or late aGvHD after day 114 to non-cGvHD controls. A second analysis at diagnosis used fixed and mixed multiple regression to compare cGvHD at onset to time matched non-cGvHD controls. A metabolomic biomarker was considered biologically relevant only if it met all three selection criteria: a) p value ≤0.05, b) effect ratio of ≥1.3 or ≤0.75, and c) receiver operator characteristic AUC ≥0.60. We found a consistent elevation in plasma alpha-ketoglutaric acid before (Day + 100) and at the onset of cGvHD, not impacted by cGvHD severity, pubertal status, or previous aGvHD. In addition, late aGvHD had a unique metabolomic pattern at day+100 compared to cGvHD. Additional metabolomic correlation patterns were seen with the clinical presentation of pulmonary, de novo, and progressive cGvHD. Alpha-ketoglutaric acid emerged as the single most significant metabolite associated with cGvHD, both in the day +100 risk-assignment and later diagnostic onset analysis. These distinctive metabolic patterns may lead to improved subclassification of cGvHD. Future validation of these exploratory results are needed.

Inflammatory CD4/CD8 double-positive human T cells arise from reactive CD8 T cells and are sufficient to mediate GVHD pathology

“…While pretransplant prognostic variables such as HLA matching, conditioning regimens, and graft source all influence allo-HCT outcomes, these population-based variables are not able to predict individual patient outcomes (17)(18)(19). Several posttransplant predictive variables have been found, including sST2, REG3α, elafin, and others (20)(21)(22)(23)(24)(25)(26). These highly promising plasma-based biomarkers are generally released because of host damage and have shown efficacy in predicting the development of steroid-resistant GVHD and nonrelapse mortality either individually or combined into a predictive algorithm (20)(21)(22)(23)(24)(25)(26).…”

“…Several posttransplant predictive variables have been found, including sST2, REG3α, elafin, and others (20)(21)(22)(23)(24)(25)(26). These highly promising plasma-based biomarkers are generally released because of host damage and have shown efficacy in predicting the development of steroid-resistant GVHD and nonrelapse mortality either individually or combined into a predictive algorithm (20)(21)(22)(23)(24)(25)(26). To date, though, these biomarkers have not been able to predict an allo-HCT recipients' chance of relapse as they measure the degree of host damage rather than donor T cell alloreactivity.…”

UPLC/Q-TOF MS-Based Urine Metabonomics Study to Identify Diffuse Axonal Injury Biomarkers in Rat

“…Thus, urine is an optimal specimen to develop biomarkers to diagnose and monitor DAI survival noninvasively. Metabolomics, which focuses on providing an unbiased view of changes in endogenous metabolites, has been successfully applied for identifying diagnostic biomarkers of many diseases [ 9 – 12 ]. At present, one of the most powerful analytical technologies for nontargeted metabonomic mapping is ultraperformance liquid chromatography quadrupole-time-of-flight hybrid mass spectrometry (UPLC/Q-TOF MS) technology, which could accurately quantify and discover the remarkably altered metabolites in biofluids or tissues.…”

Inflammatory CD4/CD8 double positive human T cells arise from reactive CD8 T cells and are sufficient to mediate GVHD pathology

“…While pre-transplant prognostic variables such as HLA-matching, conditioning regimens and graft source all influence allo-HSCT outcomes, these population-based variables are not able to predict individual patient outcomes [17][18][19] . Several post-transplant predictive variables have been discovered including sST2, REG3α, elafin and others [20][21][22][23][24][25][26] . These highly promising plasma-based biomarkers are generally released due to host damage and have shown efficacy in predicting the development of steroid-resistant GVHD and non-relapse mortality (NRM) either individually or combined into a predictive algorithm [20][21][22][23][24][25][26] .…”

“…Several post-transplant predictive variables have been discovered including sST2, REG3α, elafin and others [20][21][22][23][24][25][26] . These highly promising plasma-based biomarkers are generally released due to host damage and have shown efficacy in predicting the development of steroid-resistant GVHD and non-relapse mortality (NRM) either individually or combined into a predictive algorithm [20][21][22][23][24][25][26] . To date though, these biomarkers have not been able to predict an allo-HSCT recipients' chance of relapse as they measure the degree of host damage rather than donor T cell alloreactivity.…”