Effects Of Clinical Covariates Research Articles

Effects of clinical covariates on serum miRNA expression among women without ovarian cancer.

Serum microRNAs (miRNAs) are potential biomarkers for ovarian cancer; however, many factors may influence miRNA expression. To understand potential confounders in miRNA analysis, we examined how sociodemographic factors and comorbidities, including known ovarian cancer risk factors, influence serum miRNA levels in women without ovarian cancer. Data from 1,576 women from the Mass General Brigham Biobank collected between 2012 and 2019, excluding subjects previously or subsequently diagnosed with ovarian cancer, were examined. Using a focused panel of 179 miRNA probes optimized for serum profiling, miRNA expression was measured by flow cytometry using the Abcam Fireplex® assay and correlated with subjects' electronic medical records. The study population broadly reflected the New England population. The median age of subjects was 49 years, 34% were current or prior smokers, 33% were obese (BMI >30kg/m2), 49% were postmenopausal, and 11% had undergone prior bilateral oophorectomy. Significant differences in miRNA expression were observed among ovarian risk factors such as age, obesity, menopause, BRCA1 or BRCA2 germline mutations or breast cancer in family history. Additionally, miRNA expression was significantly altered by prior bilateral oophorectomy, hypertension, and hypercholesterolemia. Other variables, such as smoking, parity, age at menarche, hormonal replacement therapy, oral contraception, breast, endometrial, or colon cancer, and diabetes were not associated with significant changes in the panel when corrected for multiple testing. Serum miRNA expression patterns are significantly affected by patient demographics, exposure history, and medical comorbidities. Understanding confounders in serum miRNA expression is important for refining clinical assays for cancer screening.

Population Pharmacokinetics of Caffeine in Neonates with Congenital Heart Disease and Associations with Acute Kidney Injury.

Cardiac surgery-associated acute kidney injury (CS-AKI) occurs in approximately 65% of neonates undergoing cardiac surgery on cardiopulmonary bypass and contributes to morbidity and mortality. Caffeine may reduce CS-AKI by counteracting adenosine receptor upregulation after bypass, but pharmacokinetics (PK) in this population are unknown. The goal of our analysis is to address knowledge gaps in age-, disease-, and bypass-related effects on caffeine disposition and explore preliminary associations between caffeine exposure and CS-AKI using population PK modeling techniques and an opportunistic, electronic health record-integrated trial design. We prospectively enrolled neonates receiving preoperative caffeine per standard of care and collected PK samples. We retrospectively identified neonates without caffeine exposure undergoing surgery on bypass as a control cohort. We followed US Food and Drug Administration guidance for population PK model development using NONMEM. Effects of clinical covariates on PK parameters were evaluated. We simulated perioperative exposures and used multivariable logistic regression to evaluate the association between caffeine exposure and CS-AKI. Twenty-seven neonates were included in model development. A 1-compartment model with bypass time as a covariate on clearance and volume of distribution best fit the data. Twenty-three neonates with caffeine exposure and 109 controls were included in the exposure-response analysis. Over half of neonates developed CS-AKI. On multivariable analysis, there were no significant differences between CS-AKI with and without caffeine exposure. Neonates with single-ventricle heart disease without CS-AKI had consistently higher simulated caffeine exposures. Our results highlight areas for further study to better understand disease- and bypass-specific effects on drug disposition and identify populations where caffeine may be beneficial.

Mechanisms of Metabolism Interaction Between p-Cresol and Mycophenolic Acid.

Mycophenolic acid (MPA) is commonly prescribed for preventing graft rejection after kidney transplantation. The primary metabolic pathways of MPA are hepatic glucuronidation through UDP-glucuronosyltransferase (UGT) enzymes in the formation of MPA-glucuronide (MPAG, major pathway) and MPA-acyl glucuronide (AcMPAG). p-Cresol, a potent uremic toxin known to accumulate in patients with renal dysfunction, can potentially interact with MPA via the inhibition of glucuronidation. We hypothesized that the interaction between MPA and p-cresol is clinically relevant and that the estimated exposure changes in the clinic are of toxicological significance. Using in vitro approaches (ie, human liver microsomes and recombinant enzymes), the potency and mechanisms of inhibition by p-cresol towards MPA glucuronidation were characterized. Inter-individual variabilities, effects of clinical co-variates, in vitro-in vivo prediction of likely changes in MPA exposure, and comparison to other toxins were determined for clinical relevance. p-Cresol inhibited MPAG formation in a potent and competitive manner (Ki=5.2 µM in pooled human liver microsomes) and the interaction was primarily mediated by UGT1A9. This interaction was estimated to increase plasma MPA exposure in patients by approximately 1.8-fold, which may result in MPA toxicity. The mechanism of inhibition for AcMPAG formation was noncompetitive (Ki=127.5 µM) and less likely to be clinically significant. p-Cresol was the most potent inhibitor of MPA-glucuronidation compared with other commonly studied uremic toxins (eg, indole-3-acetic acid, indoxyl sulfate, hippuric acid, kynurenic acid, and 3-carboxy-4-methyl-5-propyl-2-furanpropionic acid) and its metabolites (ie, p-cresol sulfate and p-cresol glucuronide). Our findings indicate that the interaction between p-cresol and MPA is of toxicological significance and warrants clinical investigation.

Accounting for clinical covariates and interactions in ranking genomic markers using ROC

ABSTRACTIn biomedical research, profiling is now commonly conducted, generating high-dimensional genomic measurements (without loss of generality, say genes). An important analysis objective is to rank genes according to their marginal associations with a disease outcome/phenotype. Clinical-covariates, including for example clinical risk factors and environmental exposures, usually exist and need to be properly accounted for. In this study, we propose conducting marginal ranking of genes using a receiver operating characteristic (ROC) based method. This method can accommodate categorical, censored survival, and continuous outcome variables in a very similar manner. Unlike logistic-model-based methods, it does not make very specific assumptions on model, making it robust. In ranking genes, we account for both the main effects of clinical-covariates and their interactions with genes, and develop multiple diagnostic accuracy improvement measurements. Using simulation studies, we show that the proposed method is effective in that genes associated with or gene–covariate interactions associated with the outcome receive high rankings. In data analysis, we observe some differences between the rankings using the proposed method and the logistic-model-based method.

INDEPENDENTLY PREDICTIVE PROGNOSTIC VARIABLES AFTER RESECTION FOR COLORECTAL CARCINOMA

Clinical variables such as surgical morbidity, comorbidity and follow-up have been claimed to influence ultimate survival in patients who have resection for colorectal cancer. It is unclear whether the effect of clinical covariates is confounding or independent. We have attempted to build a comprehensive model, which is capable of testing the dependence and importance of prognostic factors. A consecutive series of patients admitted between 1970 and 1988 and followed until 1992 had data recorded about presentation, pathology, hospitalization, aftercare and long-term outcome. The patients were also divided into two approximately equal groups that were cared for by one and seven surgeons, respectively. Clinical and pathological covariates were built into a Cox (multivariate) proportional hazard model of crude survival. This was achieved with the SPSS advanced statistical package version 6.1. Comparison between groups was then performed of clinical and pathological factors and subsequent cancer management. There were 207 patients whose average age was 75 years, median survival was 43 months and operative mortality was 4%. The Cox model was robust. Covariates that had independent survival effects were pathological stage (P = 0.0000), grade (P = 0.014), age (P = 0.018), heart disease (P = 0.001), and group (P = 0.0008). Some of the dependent variables were symptoms, type of surgery, complications and length of stay. The groups, however, were well matched for age, stage, substage and comorbidity. Furthermore there were no substantial differences in mortality, complications or follow-up frequency. There was a significant survival difference (P = 0.0003) between groups, which was restricted to patients who were in clinicopathological stages B and C. Within stages B and C there was a significant (P = 0.008) survival difference between patients who were or were not treated for recurrent disease. Diagnosis of recurrence was pursued more aggressively (P < 0.01), and decisions to treat recurrent disease were made more frequently in group 1 (P = 0.0002). Pathology, comorbidity and management of recurrence all have a significant independent effect upon crude survival after colorectal carcinoma resection.