Biology subjects

Contaifer, D.

Publications and source records attributed to Contaifer, D..

4 recordsLinked to original sources

bioRxiv2019

Detecting Direct Oral Anticoagulants in Trauma Patients using Liquid Chromatography-Mass Spectrometry: a Novel Approach to Medication Reconciliation

BackgroundAccurate medication reconciliation in trauma patients is essential but difficult. Currently there is no established clinical method of detecting direct oral anticoagulants (DOACs) in trauma patients. We hypothesized that a liquid chromatography-mass spectrometry (LCMS) based assay can be used to accurately detect DOACs in trauma patients upon hospital arrival.\n\nMethodsPlasma samples were collected from 356 patients who provided informed consent including-10 healthy controls, 19 known positive or negative controls and 327 trauma patients over 65 years of age who were evaluated at our large, urban Level 1 Trauma Center. The assay methodology was developed in healthy and known controls to detect apixaban, rivaroxaban and dabigatran using LCMS and then applied to 327 samples from trauma patients. Standard medication reconciliation processes in the electronic medical record documenting DOAC usage was compared with LCMS results to determine overall accuracy, sensitivity, specificity and positive and negative predictive values (PPV, NPV) of the assay.\n\nResultsOf 356 patients, 39 were on DOACs (10.96%): 21 were on Apixaban, 14 on rivaroxaban and 4 on dabigatran. The overall accuracy of the assay for detecting any DOAC was 98.60%, with a sensitivity of 94.87% and specificity of 99.06%, (PPV 92.50% and NPV 99.37%). The assay detected apixaban with a sensitivity of 90.48% and specificity of 99.11% (PPV 86.36% and NPV 99.40%). There were three false positive results and two false negative LCMS results for apixaban. Dabigatran and rivaroxaban were detected with 100% sensitivity and specificity.\n\nConclusionsThis LCMS-based assay was highly accurate in detecting DOACs in trauma patients. Further studies need to confirm the clinical efficacy of this LCMS assay and its value for medication reconciliation in trauma patients.\n\nStudy type: diagnostic test\n\nBasic Science paper: therefore does not require a level of evidence.

pharmacology and toxicology↗

bioRxiv2019

Metabolic Modulation Predicts Heart Failure Tests Performance

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.

biochemistry↗

bioRxiv2018

Risk stratification of allogeneic stem cell recipients with respect to the potential for development of GVHD via their pre-transplant plasma lipid and metabolic signature

The clinical outcome of allogeneic hematopoietic stem cell transplantation (SCT) is strongly influenced from the complications arising during the post-transplant immune restoration and has been well studied and described. However, the metabolic status of the recipient pre-transplant also has the potential to influence this outcome and has never been studied before and has the potential to enable risk stratification with respect to the development of transplant associated complications such as graft vs. host disease (GVHD). In order to better understand this aspect of transplant related complications we investigated the pre-transplantation metabolic signature to assess the possibility of pre-transplant risk stratification. This pilot study was composed of 14 patients undergoing myeloablative conditioning followed by either HLA matched related, unrelated donor, or autologous stem cell transplantation. Blood samples were taken prior to transplant and the plasma was comprehensively characterized with respect to its lipidome and metabolome via LCMS and GCMS. The results indicated a significantly pro-inflammatory metabolic profile in patients who eventually developed Graft vs. Host Disease (GVHD). The data revealed 5 potential pre-transplant biomarkers (1-monopalmitin, diacylglycerol (DG) 38:5, DG 38:6, 2-aminobutyric acid, and fatty acid (FA) 20:1) that demonstrated high sensitivity and specificity towards predicting post-transplant GVHD development. The predictive model developed demonstrated an estimated predictive accuracy of risk stratification of 100%, with an Area under the Curve of the ROC of 0.995 with 100%. The likelihood ratio of 1-monopalmitin (infinity), DG 38:5 (6.0) and DG 38:6 (6.0) also demonstrated that a patient with a positive test result for these biomarkers pre-transplant will likely have very high odds of developing GVHD post-transplant. Collectively the data demonstrates the possibility of using pre-transplant metabolic signature for risk stratification of SCT recipients with respect to development of GVHD.

biochemistry↗

bioRxiv2018

A Pre-transplant Blood-based Lipid Signature for Prediction of Antibody-mediated Rejection in Kidney Transplant Patients

There is a lack of biomarkers for pre-kidney transplant immune risk stratification to avoid over- or under-immunosuppression, despite substantial advances in kidney transplant management. Since the circulating lipidome is integrally involved in various inflammatory process and pathophysiology of several immune response, we hypothesized that the lipidome may provide biomarkers that are helpful in the prediction of kidney rejection. Serial plasma samples collected over 1-year post-kidney transplant from a prospective, observational cohort of 45 adult Kidney Transplant [antibody-mediated rejection (AMR)=16; stable controls (SC) =29] patients, were assayed for 210 unique lipid metabolites by quantitative mass spectrometry. A stepwise regularized linear discriminant analysis (RLDA) was used to generate models of predictors of rejection and multivariate statistics was used to identify metabolic group differences. The RLDA models include lipids as well as of calculated panel reactive antibody (cPRA) and presence of significant donor-specific antibody (DSA) at the time of transplant. Analysis of lipids on day of transplant (T1) samples revealed a 7-lipid classifier (lysophosphatidylethanolamine and phosphatidylcholine species) which discriminated between AMR and SC with a misclassification rate of 8.9% [AUC = 0.95 (95% CI = 0.84-0.98), R2 = 0.63]. A clinical model using cPRA and DSA was inferior and produced a misclassification rate of 15.6% [AUC = 0.82 (95% CI = 0.69-0.93), R2 = 0.41]. A stepwise combined model using 4 lipid classifiers and DSA improved the AUC further to 0.98 (95% CI = 0.89-1.0, R2 = 0.83) with a misclassification of only 2.2%. Specific classes of lipids were lower in AMR compared with SC. Serial analysis of SC patients demonstrated metabolic changes between T1 and 6 months (T2) post-transplant, but not between 6 and 12 (T3) months post-transplant. There were no overtime changes in AMR patients. Analysis of SC T1 vs AMR T3 (that at time of AMR) showed sustained decreased levels of lipids in AMR at the time of rejection. These findings suggest that lack of anti-inflammatory polyunsaturated phospholipids differentiate SC from AMR pre-transplant and at the time of rejection, and a composite model using a 4-lipid classifier along with DSA could be used for prediction of antibody-mediated rejection before transplant. HighlightsO_LIDespite significant advancements in kidney transplant treatment and intensive clinical follow-up monitoring, all rejection events are unlikely to be recognized at the beginning. As a result, efforts have been made to identify new biomarkers for kidney rejection detection. C_LIO_LIWhile lipids are known to be potent mediators of inflammation, pro-resolving processes, and other cell signaling cascades, lipidomics can be applied to identify reliable biomarkers to monitor disease severity and may also allow prediction of kidney rejection. C_LIO_LIOur lipidomic study shows lipid profile changes between antibody-mediated rejection group and stable control group as a function of different time point, pre and post-kidney transplantation. Furthermore, our study demonstrates that combining lipid and clinical parameters allow prediction of rejection on the day of the transplant. C_LIO_LIThese findings have the potential to change the present paradigm of pre and post-transplant monitoring and management of these patients by implementing an evidence-based risk stratification technique, resulting in a substantial improvement in kidney transplant success. C_LI

biochemistry↗