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Metabolic Modulation Predicts Heart Failure Tests Performance bioRxiv preprint doi: https://doi.org/10.1101/555417; this version posted February 20, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. 1 Metabolic Modulation Predicts Heart Failure Tests Performance 2 Daniel Contaifer Jr1, Leo F. Buckley1, George Wohlford1, Naren G Kumar1,2, Joshua M. 3 Morriss1, Asanga D. Ranasinghe3, Salvatore Carbone4, Justin M Canada4, Cory Trankle4, 4 Antonio Abbate4,5, *Benjamin W. Van Tassell1, *Dayanjan S Wijesinghe1,6,7 5 1 Department of Pharmacotherapy and Outcomes Sciences, School of Pharmacy, Virginia Commonwealth 6 University, Richmond VA 23298 7 2 Department of Microbiology, School of Medicine, Virginia Commonwealth University, Richmond VA 8 23298 9 3 Department of Physical and Biological Sciences, Amarillo College TX 79109 10 4 Pauley Heart Center, Virginia Commonwealth University, Richmond, VA 23298 11 5 Department of Internal Medicine, School of Medicine, Virginia Commonwealth University, Richmond VA 12 23298 13 6 Da Vinci Center, Virginia Commonwealth University, Richmond VA 23284 14 7 Institute for Structural Biology Drug Discovery and Development (ISB3D), VCU School of Pharmacy, 15 Richmond VA 23298 16 17 * = Co-senior authors, equal contribution 18 Please send correspondences to: 19 Dayanjan S Wijesinghe, Ph.D. 20 Department of Pharmacotherapy and Outcomes Sciences 21 School of Pharmacy, Virginia Commonwealth University, 22 PO Box 980533 23 Robert Blackwell Smith Bldg. Rm. 652A 24 410 North 12th Street 25 Richmond, VA 23298-5048 26 USA 27 P: (804) 628 3316 F: (804) 828 0343 28 Email: [email protected] 29 And 30 Benjamin Van Tassell, PharmD, BCPS, FCCP, ASH-CHC. 31 Department of Pharmacotherapy and Outcomes Sciences 32 410 N 12th Street 33 P.O. Box 980533 34 Richmond, VA 23298-5048 35 USA 36 P (804) 828-4583 F: (804) 828-0343 37 Email: [email protected] bioRxiv preprint doi: https://doi.org/10.1101/555417; this version posted February 20, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. 38 1. Abstract 39 Cardiopulmonary testing (CPET) and biomarkers such as NT-proBNP, Galectin-3, and C- 40 reactive protein (CRP) have shown great promise for characterizing the heart failure (HF) 41 phenotypes. However, the underlying metabolic changes that cause, predict and accompany 42 changes in these parameters are not well known. In depth knowledge of these metabolic changes 43 has the ability to provide new insights towards the clinical management of HF as well as providing 44 better biomarkers for the assessment of disease progression and measurement of success from 45 clinical interventions. To address this knowledge gap, we undertook a deep metabolomic and 46 lipidomic phenotyping of a cohort of HF patients and utilized Multiple Regression Analysis 47 (MRA) to identify associations between the patient’s plasma metabolome and the lipidome to 48 parameters of CPET and the above mentioned HF biomarkers (HFBio). A total of 49 subjects were 49 submitted to CPET and HFBio evaluation with deep metabolomic and lipidomic profiling of the 50 plasma. Stepwise MRA and Standard Least Squares methods were used to determine models of 51 best fit. Metabolic Pathway Analysis was utilized to detect what metabolites were over-represented 52 and significantly enriched, and Metabolite Set Enrichment Analysis was used to explore pre- 53 existing biological knowledge from studies of human metabolism. The study cohort was 54 predominantly males of African American decent, presenting a high frequency of diabetes, 55 hyperlipidemia, and hypertension with a low mean left ventricular ejection fraction and a high left 56 ventricular end-diastolic and end-systolic volume. VE/VCO2 and Peak VO2 (CV=0.07 and 0.08, 57 respectively) showed the best metabolic predictive model for test performance, while CRP and 58 NT-ProBNP (CV=0.31 and 0.29, respectively) were the least fitted models. Aminoacyl-tRNA and 59 amino acid biosynthesis, amino acid metabolism, nitrogen metabolism, pantothenate and CoA 60 biosynthesis, sphingolipid and glycerolipid metabolism, fatty acid biosynthesis, glutathione 61 metabolism, and pentose phosphate pathway (PPP) were revealed to be the most relevant 62 pathways. Principal related diseases were brain dysfunction and enzyme deficiencies associated bioRxiv preprint doi: https://doi.org/10.1101/555417; this version posted February 20, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. 63 with lactic acidosis. Considering the modulations in HF poor test performance, our results indicate 64 an overall profile of oxidative stress, lactic acidosis, and metabolic syndrome coupled with 65 mitochondria dysfunction. Overall, the insights resulting from this study’s MRA coincides with 66 what has previously been discussed in parts in existing literature thereby confirming the validity 67 of our findings while at the same time thoroughly characterizing the metabolic underpinning of 68 CPET and HFBio results. 69 70 2. Introduction 71 The prevalence of heart failure (HF) has increased over time with the aging population. In 72 people older than 20, the incidence of HF has increased from 5.7 million Americans between 2009 73 and 2012 to 6.5 million Americans by 2011. Despite aggressive measures to improve HF 74 management, the five-year mortality rate of HF patients remains approximately 40%--comparable 75 to many forms of cancer[1,2]. Investigations into diagnosis of HF has revealed promising 76 cardiopulmonary tests and biomarkers that allow better disease management following 77 diagnosis[3,4]. Combining patient’s metabolic profiles from HF compromised organs and tissues 78 with HF tests has been demonstrated to provide physicians with an efficient source of clinical 79 information used to both manage and diagnose patients[5]. However, the complex association of 80 these HF tests with changes in the peripheral metabolism of compromised individuals is still under 81 insufficient investigation and has failed to reveal the value of circulating metabolites as HF 82 biomarkers[6]. 83 Impaired cardiorespiratory fitness measured during cardiopulmomary exercise testing 84 (CPET) is a hallmark manifestation of heart failure[7] and exercise training reduces all-cause 85 mortality in patients with heart failure and reduced left ventricular ejection fraction (HFrEF)[8]. 86 As such, cardiopulmonary exercise testing (CPET) is an evidence-supported assessment technique 87 routinely used in the functional diagnosis of HF, prognostic evaluation of patients with chronic bioRxiv preprint doi: https://doi.org/10.1101/555417; this version posted February 20, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. 88 Heart Failure (CHF), and is also clinically relevant to supplement other clinical data in patients’ 89 screening for heart transplantation[9,10]. Similarly, HF biomarkers (HFBio) such as NT-proBNP, 90 Galectin-3, and C-reactive protein (CRP) have shown promising results as predictors of 91 mortality[9,11]. However, a potential disadvantage of the CPET and HFBio use is that the 92 evaluation of their response alone is insufficient to indicate risk of heart disease nor is it enough 93 to diagnose a heart problem[12,13]. 94 It is now known that cardiac and peripheral metabolic abnormalities may contribute to the 95 pathogenesis of heart failure[14]. Studies of the metabolic profile of HF patients have indicated a 96 rich metabolic modulation that can be identified and used as putative biomarkers[15–17]. 97 However, little is known about the relationship of cardiorespiratory fitness and global metabolic 98 profile in HFrEF. Efforts to correlate clinical and metabolic data are still necessary to fully 99 integrate metabolomics as a translational medicine apparatus[18]. We hypothesized that deep 100 metabolomic and lipidomic phenotyping would reveal novel metabolic and lipid mediators of 101 cardiorespiratory fitness in patients with HFrEF. To test this hypothesis, we employed a Multiple 102 Regression Analysis (MRA) study on the association of both CPET and HFBio with respects to 103 the plasma lipidome and the metabolome of HF patients demonstrating that the metabolic 104 modulation in HF patients depends on the tests’ performance. Although MRA cannot with 105 certainty imply causality for the HF performance, this analysis intends to reveal the metabolic 106 changes underlying the complex HF pathology to maximize the relevance of the HF tests and its 107 potential for HF prognosis. 108 109 bioRxiv preprint doi: https://doi.org/10.1101/555417; this version posted February 20, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. 110 3. Materials and Methods 111 3.1) Patients 112 A post-hoc analysis was performed on patients who were enrolled in the REDHART study[19], 113 which included patients with a left ventricular ejection fraction < 50% and high-sensitivity C- 114 reactive protein (hsCRP) ≥2 mg/L and who were recently discharged after a hospitalization for 115 HF. The present analysis includes plasma samples collected from 49 patients at baseline prior to
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