Exploring a Diabetic Registry for Cardiovascular Risk Factors

Background: Cardiovascular disease is the leading cause of death in the United States. There were over 18 million people diagnosed with diabetes in 2002. These disease processes together combine for significant health burden on society (American Diabetes Association, 2008). The purpose of the study was to describe the relationship between select demographics, and clinical characteristics to determine risk factors for cardiovascular disease in a diabetic population. Methods: A retrospective descriptive study was conducted using a diabetic registry database containing patients diagnosed with diabetes from January 1, 2011 to December 31, 2012. Study variables included age, gender, socio-economic status, glycosylated hemoglobin levels (HgbAlc), micro-albumin levels, and low density lipoprotein levels (LDL). Descriptive and inferential statistics were conducted using SPSS Windows version 22. Results: For the total cases (N=292) age ranging 35 years to 97 years, 57% female, the analysis revealed only one independent variable, gender, demonstrated a relationship with the dependent variable, LDL (r =.138; p =\u3e0.009). For the regression analysis, combined variability of the independent variables (age, gender, socio-economic status, HgbAlc, and micro-albumin) accounted for only three percent variance in the dependent variable (p =0.134). The overall model was not a good fit to the sample data. Conclusions: The diabetic registry used for this study was designed to meet regulatory and accreditation requirements and as such had limited categories of data. The practicality of using a database for research has benefits, but can also impose significant limitations. In this study the data categories were limited, possibly accounting for the lack of model fit. However, the findings did indicate a premise for future research using a diabetic registry if changes could be made to collect more categories of data such that the findings could provide full characterization of the sample and generalizability of the findings

Mental flexibility depends on a largely distributed white matter network: Causal evidence from connectome-based lesion-symptom mapping.

Mental flexibility (MF) refers to the capacity to dynamically switch from one task to another. Current neurocognitive models suggest that since this function requires interactions between multiple remote brain areas, the integrity of the anatomic tracts connecting these brain areas is necessary to maintain performance. We tested this hypothesis by assessing with a connectome-based lesion-symptom mapping approach the effects of white matter lesions on the brain's structural connectome and their association with performance on the trail making test, a neuropsychological test of MF, in a sample of 167 first unilateral stroke patients. We found associations between MF deficits and damage of i) left lateralized fronto-temporo-parietal connections and interhemispheric connections between left temporo-parietal and right parietal areas; ii) left cortico-basal connections; and iii) left cortico-pontine connections. We further identified a relationship between MF and white matter disconnections within cortical areas composing the cognitive control, default mode and attention functional networks. These results for a central role of white matter integrity in MF extend current literature by providing causal evidence for a functional interdependence among the regional cortical and subcortical structures composing the MF network. Our results further emphasize the necessity to consider connectomics in lesion-symptom mapping analyses to establish comprehensive neurocognitive models of high-order cognitive functions