IntroductionIschaemic heart disease is the main cause of death in developed countries. There are many modifiable risk factors associated with coronary heart disease (CAD). A growing number of studies point to vitamin D deficiency as a risk factor for heart attacks and the conditions associated with cardiovascular disease. This study aimed to analyse the relationship between the level of 25-hydroxyvitamin D (25(OH)D) and the severity of coronary artery atherosclerosis and to study 25(OH)D levels in non-diabetic patients hospitalised due to acute coronary syndrome and those diagnosed with stable CAD.Material and methodsoronary angiography was performed prospectively in 410 successive cardiac patients. The severity of coronary artery atherosclerosis was assessed according to the Coronary Artery Surgery Study Score (CASSS). The plasma 25(OH)D level was assessed with the electrochemiluminescence method.ResultsThe 25(OH)D level proved to be one of the significant determinants of the CASSS (p < 0.05). In subjects without significant lesions in the coronary arteries the 25(OH)D level was significantly higher compared to patients with one- to three-vessel coronary atherosclerosis (p < 0.05). A significantly higher 25(OH)D level was noted in patients diagnosed with stable CAD compared to patients hospitalised due to acute coronary syndrome (p < 0.01).ConclusionsPatients with one- to three-vessel atherosclerosis have a significantly lower 25(OH)D level compared to patients without significant lesions in the coronary arteries. A lower 25(OH)D level was observed in patients hospitalised due to acute coronary syndrome compared to patients diagnosed with stable CAD.
Background: Detecting early-stage Alzheimer’s disease (AD) is still problematic in clinical practice. This work aimed to find T1-weighted MRI-based markers for AD and mild cognitive impairment (MCI) to improve the screening process. Objective: Our assumption was to build a screening model that would be accessible and easy to use for physicians in their daily clinical routine. Methods: The multinomial logistic regression was used to detect status: AD, MCI, and normal control (NC) combined with the Bayesian information criterion for model selection. Several T1-weighted MRI-based radiomic features were considered explanatory variables in the prediction model. Results: The best radiomic predictor was the relative brain volume. The proposed method confirmed its quality by achieving a balanced accuracy of 95.18%, AUC of 93.25%, NPV of 97.93%, and PPV of 90.48% for classifying AD versus NC for the European DTI Study on Dementia (EDSD). The comparison of the two models: with the MMSE score only as an independent variable and corrected for the relative brain value and age, shows that the addition of the T1-weighted MRI-based biomarker improves the quality of MCI detection (AUC: 67.04% versus 71.08%) while maintaining quality for AD (AUC: 93.35% versus 93.25%). Additionally, among MCI patients predicted as AD inconsistently with the original diagnosis, 60% from ADNI and 76.47% from EDSD were re-diagnosed as AD within a 48-month follow-up. It shows that our model can detect AD patients a few years earlier than a standard medical diagnosis. Conclusion: The created method is non-invasive, inexpensive, clinically accessible, and efficiently supports AD/MCI screening.
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