Bethany Armentrout

Type 1 diabetes (T1D) is a complex chronic condition that places young adults aged 18-31 years at high risk for general and diabetes-related distress and poor cardiovascular health. Both general and diabetes distress are linked to higher A1C, a known risk factor for cardiovascular disease (CVD). The purpose of this cross-sectional quantitative descriptive study was to examine the associations between distress symptoms (general and diabetes) and cardiovascular health while considering covariates in young adults ages 18-31 years with T1D. One-hundred and sixty-five young adults with T1D, recruited from specialty clinics through two major health systems and online platforms, completed a demographic and clinical survey along with the 8-item PROMIS Emotional Distress Scale and 17-item Diabetes Distress Scale. Higher diabetes distress and higher general emotional distress were associated with lower cardiovascular health scores. Associations remained statistically significant after adjusting for age, T1D duration, sex at birth, race, and continuous subcutaneous insulin infusion. In young adults with type 1 diabetes, addressing both diabetes and general emotional distress may be important to improve cardiovascular health. However, longitudinal and experimental studies are needed to clarify underlying mechanisms and evaluate the effectiveness of interventions like cognitive behavioral therapy.

Multiple individual sleep health dimensions (satisfaction, regularity, and duration) are associated with diabetes symptoms, precursors to micro-and macrovascular complications, among young adults with type 1 diabetes mellitus (T1DM). Nearly half of young adults with T1DM develop vascular complications; however, modifiable contributors of diabetes symptoms, including sleep health, have been understudied.

It is increasingly evident that blood biomarkers have potential to improve the diagnosis and management of both acute and chronic neurological conditions. The most well-studied candidates, and arguably those with the broadest utility, are proteins that are highly enriched in neural tissues and released into circulation upon cellular damage. It is currently unknown how the brain expression levels of these proteins is influenced by demographic factors such as sex, race, and age. Given that source tissue abundance is likely a key determinant of the levels observed in the blood during neurological pathology, understanding such influences is important in terms of identifying potential clinical scenarios that could produce diagnostic bias. In this study, we leveraged existing mRNA sequencing data originating from 2,642 normal brain specimens harvested from 382 human donors to examine potential demographic variability in the expression levels of genes which code for 28 candidate blood biomarkers of neurological damage. Existing mass spectrometry data originating from 26 additional normal brain specimens harvested from 26 separate human donors was subsequently used to tentatively assess whether observed transcriptional variance was likely to produce corresponding variance in terms of protein abundance. Genes associated with several well-studied or emerging candidate biomarkers including neurofilament light chain (NfL), ubiquitin carboxyl-terminal hydrolase isozyme L1 (UCH-L1), neuron-specific enolase (NSE), and synaptosomal-associated protein 25 (SNAP-25) exhibited significant differences in expression with respect to sex, race, and age. In many instances, these differences in brain expression align well with and provide a mechanistic explanation for previously reported differences in blood levels.

Background: The development of tools that could help emergency department clinicians recognize stroke during triage could reduce treatment delays and improve patient outcomes. Growing evidence suggests that stroke is associated with several changes in circulating cell counts. The aim of this study was to determine whether machine-learning can be used to identify stroke in the emergency department using data available from a routine complete blood count with differential.

Methods: Red blood cell, platelet, neutrophil, lymphocyte, monocyte, eosinophil, and basophil counts were assessed in admission blood samples collected from 160 stroke patients and 116 stroke mimics recruited from three geographically distinct clinical sites, and an ensemble artificial neural network model was developed and tested for its ability to discriminate between groups.

Results: Several modest but statistically significant differences were observed in cell counts between stroke patients and stroke mimics. The counts of no single cell population alone were adequate to discriminate between groups with high levels of accuracy; however, combined classification using the neural network model resulted in a dramatic and statistically significant improvement in diagnostic performance according to receiver-operating characteristic analysis. Furthermore, the neural network model displayed superior performance as a triage decision making tool compared to symptom-based tools such as the Cincinnati Prehospital Stroke Scale (CPSS) and the National Institutes of Health Stroke Scale (NIHSS) when assessed using decision curve analysis.

Conclusions: Our results suggest that algorithmic analysis of commonly collected hematology data using machine-learning could potentially be used to help emergency department clinicians make better-informed triage decisions in situations where advanced imaging techniques or neurological expertise are not immediately available, or even to electronically flag patients in which stroke should be considered as a diagnosis as part of an automated stroke alert system.