A SAS macro for stepwise correlated binary regression

Metabolite Triplet in Serum Improves the Diagnostic Accuracy of Prediabetes and Diabetes Screening

Large-scale population screenings are not feasible by applying laborious oral glucose tolerance tests, but using fasting blood glucose (FPG) and glycated hemoglobin (HbA_1c), a considerable number of diagnoses are missed. A novel marker is urgently needed to improve the diagnostic accuracy of broad-scale diabetes screening in easy-to-collect blood samples. In this study, by applying a novel knowledge-based, multistage discovery and validation strategy, we scaled down from 108 diabetes-associated metabolites to a diagnostic metabolite triplet (Met-T), namely hexose, 2-hydroxybutyric/2-hydroxyisobutyric acid, and phenylalanine. Met-T showed in two independent cohorts, each comprising healthy controls, prediabetic, and diabetic individuals, distinctly higher diagnostic sensitivities for diabetes screening than FPG alone (>79.6 vs <68%). Missed diagnoses decreased from >32% using fasting plasma glucose down to <20.4%. Combining Met-T and fasting plasma glucose further improved the diagnostic accuracy. Additionally, a positive association of Met-T with future diabetes risk was found (odds ratio: 1.41; p = 1.03 × 10^-6). The results reveal that missed prediabetes and diabetes diagnoses can be markedly reduced by applying Met-T alone or in combination with FPG and it opens perspectives for higher diagnostic accuracy in broad-scale diabetes-screening approaches using easy to collect sample materials.

Divergent Six Month Functional Recovery Trajectories and Predictors after Traumatic Brain Injury: Novel Insights from the Citicoline Brain Injury Treatment Trial Study

Cross-sectional approaches to outcome assessment may not adequately capture heterogeneity in recovery after traumatic brain injury (TBI). Using latent class mixed models (LCMM), a data-driven analytic that identifies groups of patients with similar trajectories, we identified distinct 6 month functional recovery trajectories in a large cohort (n = 1046) of adults 18-70 years of age with complicated mild to severe TBI who participated in the Citicoline Brain Injury Treatment Trial (COBRIT). We used multinomial logistic fixed effect models and backward elimination, forward selection, and forward stepwise selection with several stopping rules to explore baseline predictors of functional recovery trajectory. Based on statistical and clinical considerations, the seven-class model was deemed superior. Visualization of these seven functional recovery trajectories revealed that each trajectory class started at one of three recovery levels at 1 month, which, for ease of reference we labeled groups A-C: Group A, good recovery (two classes; A1 and A2); Group B, moderate disability (two classes; B1 and B2); and Group C, severe disability (three classes; C1, C2, and C3). By 6 months, these three groups experienced dramatically divergent trajectories. Group A experienced stable good recovery (A1, n = 115) or dramatic decline (A2, n = 4); Group B experienced rapid complete recovery (B1, n = 71) or gradual recovery (B2, n = 742); Group C experienced dramatic rapid recovery (C1, n = 12), no recovery (C2, n = 91), or death (C3, n = 11). Trajectory class membership was not predicted by citicoline treatment (p = 0.57). The models identified demographic, pre-injury, and injury-related predictors of functional recovery trajectory, including: age, race, education, pre-injury employment, pre-injury diabetes, pre-injury psychiatric disorder, site, Glasgow Coma Scale (GCS) score, post-traumatic amnesia, TBI mechanism, major extracranial injury, hemoglobin, and acute computed tomographic (CT) findings. GCS was the most consistently selected predictor across all models. All models also selected at least one demographic or pre-injury medical predictor. LCMM successfully identified dramatically divergent, clinically meaningful 6 month recovery trajectories with utility to inform clinical trial design.

A Large-scale, multicenter serum metabolite biomarker identification study for the early detection of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the third most lethal cancer worldwide. The lack of effective biomarkers for the early detection of HCC results in unsatisfactory curative treatments. Here, metabolite biomarkers were identified and validated for HCC diagnosis. A total of 1,448 subjects, including healthy controls and patients with chronic hepatitis B virus infection, liver cirrhosis, and HCC, were recruited from multiple centers in China. Liquid chromatography-mass spectrometry-based metabolomics methods were used to characterize the subjects' serum metabolic profiles and to screen and validate the HCC biomarkers. A serum metabolite biomarker panel including phenylalanyl-tryptophan and glycocholate was defined. This panel had a higher diagnostic performance than did α-fetoprotein (AFP) in differentiating HCC from a high-risk population of cirrhosis, such as an area under the receiver-operating characteristic curve of 0.930, 0.892, and 0.807 for the panel versus 0.657, 0.725, and 0.650 for AFP in the discovery set, test set, and cohort 1 of the validation set, respectively. In the nested case-control study, this panel had high sensitivity (range 80.0%-70.3%) to detect preclinical HCC, and its combination with AFP provided better risk prediction of preclinical HCC before clinical diagnosis. Besides, this panel showed a larger area under the receiver-operating characteristic curve than did AFP (0.866 versus 0.682) to distinguish small HCC, and 80.6% of the AFP false-negative patients with HCC were correctly diagnosed using this panel in the test set, which was corroborated by the validation set. The specificity and biological relevance of the identified biomarkers were further evaluated using sera from another two cancers and HCC tissue specimens, respectively. Conclusion: The discovered and validated serum metabolite biomarker panel exhibits good diagnostic performance for the early detection of HCC from at-risk populations. (Hepatology 2018;67:662-675).

Metabolomics study of hepatocellular carcinoma: discovery and validation of serum potential biomarkers by using capillary electrophoresis-mass spectrometry

Hepatocellular carcinoma (HCC) is one of the most lethal malignancies. The lack of effective screening methods for early diagnosis has been a longstanding bottleneck to improve the survival rate. In the present study, a capillary electrophoresis-time-of-flight mass spectrometry (CE-TOF/MS)-based metabolomics method was employed to discover novel biomarkers for HCC. A total of 183 human serum specimens (77 sera in discovery set and 106 sera in external validation set) were enrolled in this study, and a "serum biomarker model" including tryptophan, glutamine, and 2-hydroxybutyric acid was finally established based on the comprehensive screening and validation workflow. This model was evaluated as an effective tool in that area under the receiver operating characteristic curve reached 0.969 in the discovery set and 0.99 in the validation set for diagnosing HCC from non-HCC (health and cirrhosis). Furthermore, this model enabled the discrimination of small HCC from precancer cirrhosis with an AUC of 0.976, highlighting the potential of early diagnosis. The biomarker model is effective for those a-fetoprotein (AFP) false-negative and false-postive subjects, indicating the complementary function to conventional tumor marker AFP. This study demonstrates the promising potential of CE-MS-based metabolomics approach in finding biomarkers for disease diagnosis and providing special insights into tumor metabolism.

Predicting risk of perioperative death and stroke after carotid endarterectomy in asymptomatic patients: derivation and validation of a clinical risk score

BACKGROUND AND PURPOSE

National guidelines on carotid endarterectomy (CEA) for asymptomatic patients state that the procedure should be performed with a ≤ 3% risk of perioperative death or stroke. We developed and validated a multivariate model of risk of death or stroke within 30 days of CEA for asymptomatic disease and a related clinical prediction rule.

METHODS

We analyzed asymptomatic cases in a population-based cohort of CEAs performed in Medicare beneficiaries in New York State. Medical records were abstracted for sociodemographics, neurologic history, disease severity, diagnostic imaging data, comorbidities, and deaths and strokes within 30 days of surgery. We used multivariate logistic regression to identify independent predictors of perioperative death or stroke. The CEA-8 clinical risk score was derived from the final model.

RESULTS

Among the 6553 patients, the mean age was 74 years, 55% were male, 62% had coronary artery disease, and 22% had a history of distant stroke or transient ischemic attack. The perioperative rate of death or stroke was 3.0%. Multivariable predictors of perioperative events were female sex (odds ratio [OR] = 1.5; 95% CI, 1.1 to 1.9), nonwhite race (OR = 1.8; 95% CI, 1.1 to 2.9), severe disability (OR = 3.7; 95% CI, 1.8 to 7.7), congestive heart failure (OR = 1.6; 95% CI, 1.1 to 2.4), coronary artery disease (OR = 1.6; 95% CI, 1.2 to 2.2), valvular heart disease (OR = 1.5; 95% CI, 1.1 to 2.3), a distant history of stroke or transient ischemic attack (OR = 1.5; 95% CI, 1.1 to 2.0), and a nonoperated stenosis ≥ 50% (OR = 1.8; 95% CI, 1.3 to 2.3). The CEA-8 risk score stratified patients with a predicted probability of death or stroke rate from 0.6% to 9.6%.

CONCLUSIONS

Several sociodemographic, neurologic severity, and comorbidity factors predicted the risk of perioperative death or stroke in asymptomatic patients. The CEA-8 risk score can help clinicians calculate a predicted probability of complications for an individual patient to help inform the decision about revascularization.

Variable selection in high-dimensional multivariate binary data with application to the analysis of microbial community DNA fingerprints

In order to understand the relevance of microbial communities on crop productivity, the identification and characterization of the rhizosphere soil microbial community is necessary. Characteristic profiles of the microbial communities are obtained by denaturing gradient gel electrophoresis (DGGE) of polymerase chain reaction (PCR) amplified 16S rDNA from soil extracted DNA. These characteristic profiles, commonly called community DNA fingerprints, can be represented in the form of high-dimensional binary vectors. We address the problem of modeling and variable selection in high-dimensional multivariate binary data and present an application of our methodology in the context of a controlled agricultural experiment.

A comparative study for stepwise correlated binary regression

Real-time monitored binary data are often recorded along with a large amount of associated covariates for biomedical image processing. Serially measured binary outcomes and covariates could be autocorrelated. Appropriate variable selection schemes are necessary to find a set of influential covariates on the changes in the correlated binary outcomes. Selected variables can be used as feedback information to reduce the dimension of the database. In this context, we examine the performance of the stepwise correlated binary regression. Several realistic situations of the real-time monitored binary data are considered in Monte-Carlo simulation. Results of a simulation study are discussed.