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Alcohol use disorder (AUD) is a major component in the etiology of cognitive decline and dementia. Underlying mechanisms by which long-term alcohol abuse causes cognitive dysfunction include excessive oxidative stress and inflammation in the brain, activated by increased reactive oxygen/nitrogen species (ROS/RNS), advanced glycation end-products (AGEs) and high-mobility group box 1 protein (HMGB1). In a pilot study, we examine the potential clinical value of circulating biomarkers of oxidative stress including ROS/RNS, HMGB1, the soluble receptor for AGE (sRAGE), the brain biomarker of aging apolipoprotein D (ApoD), and the antioxidant regulator nuclear factor erythroid 2-related factor 2 (NRF2) as predictive indices for cognitive impairment (CI) in abstinent patients with AUD (n = 25) compared to patients with established Alzheimer’s disease (AD, n = 26) and control subjects (n = 25). Plasma concentrations of sRAGE were evaluated with immunoblotting; ROS/RNS with a fluorometric kit; and HMGB1, ApoD, and NRF2 by ELISA. Abstinent AUD patients had higher sRAGE, ROS/RNS (p < 0.05), and ApoD (p < 0.01) concentrations, similar to those of AD patients, and lower NRF2 (p < 0.01) concentrations, compared to controls. These changes were remarkable in AUD patients with CI. HMGB1, and sRAGE correlated positively with duration of alcohol use (rho = 0.398, p = 0.022; rho = 0.404, p = 0.018), whereas sRAGE correlated negatively with periods of alcohol abstinence (rho = −0.340, p = 0.045). A predictive model including ROS/RNS, HMGB1, sRAGE, alcohol use duration, and alcohol abstinence periods was able to differentiate AUD patients with CI (92.3% of correct predictions, ROC-AUC= 0.90) from those without CI. In conclusion, we propose ROS/RNS, HMGB1, and sRAGE as stress biomarkers capable of predicting cognitive impairment in AUD patients. 

With the collection and availability of data on student academic performance and academic background, higher education institutions have recently stepped up initiatives in and infrastructure for learning analytics, leveraging this deluge of data to inform student success. With definitions of student success varying from analyses of what predicts levels of specific career readiness competencies to degree completion, the environment is a fertile ground for statistical practice and collaboration among a statistically savvy yet diverse clientele of instructors, programme advisors and administrators. In this paper, we discuss our experiences to this end through a consulting project evaluating the impact of writing course class size on students achieving a graduation writing requirement. In detailing the workflow for and challenges in this project, we share aspects of statistical communication and reporting, applications of innovative statistical methodology developed by our research group for handling confounding factors and correlated inputs and training through an interdisciplinary applied institutional research professional development programme. This paper illustrates how instilling an appreciation for statistical inference through each of these components is invaluable for capturing institutional buy‐in for data‐informed decision‐making in general statistical practice.