RT Journal Article
SR Electronic
T1 Metabolic Modulation Predicts Heart Failure Tests Performance
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 555417
DO 10.1101/555417
A1 Daniel Contaifer, Jr.
A1 Leo F Buckley
A1 George Wohlford
A1 Naren G Kumar
A1 Joshua M Morriss
A1 Asanga D Ranasinghe
A1 Salvatore Carbone
A1 Justin M Canada
A1 Cory Trankle
A1 Antonio Abbate
A1 Benjamin W Van Tassell
A1 Dayanjan Wijesinghe
YR 2019
UL http://biorxiv.org/content/early/2019/02/20/555417.abstract
AB Cardiopulmonary testing (CPET) and biomarkers such as NT-proBNP, Galectin-3, and C-reactive protein (CRP) have shown great promise for characterizing the heart failure (HF) phenotypes. However, the underlying metabolic changes that cause, predict and accompany changes in these parameters are not well known. In depth knowledge of these metabolic changes has the ability to provide new insights towards the clinical management of HF as well as providing better biomarkers for the assessment of disease progression and measurement of success from clinical interventions. To address this knowledge gap, we undertook a deep metabolomic and lipidomic phenotyping of a cohort of HF patients and utilized Multiple Regression Analysis (MRA) to identify associations between the patients plasma metabolome and the lipidome to parameters of CPET and the above mentioned HF biomarkers (HFBio). A total of 49 subjects were submitted to CPET and HFBio evaluation with deep metabolomic and lipidomic profiling of the plasma. Stepwise MRA and Standard Least Squares methods were used to determine models of best fit. Metabolic Pathway Analysis was utilized to detect what metabolites were over-represented and significantly enriched, and Metabolite Set Enrichment Analysis was used to explore pre-existing biological knowledge from studies of human metabolism. The study cohort was predominantly males of African American decent, presenting a high frequency of diabetes, hyperlipidemia, and hypertension with a low mean left ventricular ejection fraction and a high left ventricular end-diastolic and end-systolic volume. VE/VCO2 and Peak VO2 (CV=0.07 and 0.08, respectively) showed the best metabolic predictive model for test performance, while CRP and NT-ProBNP (CV=0.31 and 0.29, respectively) were the least fitted models. Aminoacyl-tRNA and amino acid biosynthesis, amino acid metabolism, nitrogen metabolism, pantothenate and CoA biosynthesis, sphingolipid and glycerolipid metabolism, fatty acid biosynthesis, glutathione metabolism, and pentose phosphate pathway (PPP) were revealed to be the most relevant pathways. Principal related diseases were brain dysfunction and enzyme deficiencies associated with lactic acidosis. Considering the modulations in HF poor test performance, our results indicate an overall profile of oxidative stress, lactic acidosis, and metabolic syndrome coupled with mitochondria dysfunction. Overall, the insights resulting from this studys MRA coincides with what has previously been discussed in parts in existing literature thereby confirming the validity of our findings while at the same time thoroughly characterizing the metabolic underpinning of CPET and HFBio results.