Background Deoxycholic acid (DCA) is a secondary bile acid that may promote vascular calcification in experimental settings. Higher DCA levels were associated with prevalent coronary artery calcification (CAC) in a small group of individuals with advanced chronic kidney disease. Whether DCA levels are associated with CAC prevalence, incidence, and progression in a large and diverse population of individuals with chronic kidney disease stages 2 to 4 is unknown. Methods and Results In the CRIC (Chronic Renal Insufficiency Cohort) study, we evaluated cross-sectional (n=1057) and longitudinal (n=672) associations between fasting serum DCA levels and computed tomographic CAC using multivariable-adjusted regression models. The mean age was 57±12 years, 47% were women, and 41% were Black. At baseline, 64% had CAC (CAC score >0 Agatston units). In cross-sectional analyses, models adjusted for demographics and clinical factors showed no association between DCA levels and CAC >0 compared with no CAC (prevalence ratio per 1-SD higher log DCA, 1.08 [95% CI, 0.91-1.26). DCA was not associated with incident CAC (incidence per 1-SD greater log DCA, 1.08 [95% CI, 0.85-1.39]) or CAC progression (risk for increase in ≥100 and ≥200 Agatston units per year per 1-SD greater log DCA, 1.05 [95% CI, 0.84-1.31] and 1.26 [95% CI, 0.77-2.06], respectively). Conclusions Among CRIC study participants, DCA was not associated with prevalent, incident, or progression of CAC.
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http://dx.doi.org/10.1161/JAHA.121.022891 | DOI Listing |
Front Cardiovasc Med
January 2025
Shengli Clinical Medical College of Fujian Medical University, Fujian Medical University, Fuzhou, Fujian, China.
Background: Depression is being increasingly acknowledged as an important risk factor contributing to coronary heart disease (CHD). Currently, there is no predictive model specifically designed to evaluate the risk of coronary heart disease among individuals with depression. We aim to develop a machine learning (ML) model that will analyze risk factors and forecast the probability of coronary heart disease in individuals suffering from depression.
View Article and Find Full Text PDFJ Cardiothorac Surg
January 2025
Department of Anesthesiology, The First People's Hospital of Chenzhou, Chenzhou, Hunan Province, 423000, China.
Background: Chronic post-surgical pain (CPSP) is a common complication following video-assisted thoracoscopic surgery (VATS) that significantly impacts the quality of life of patients. Although multiple risk factors have been identified, no systematically validated prediction model exists to guide clinical decision-making.
Objectives: This study aimed to develop and validate a risk prediction model for CPSP in patients undergoing VATS for lung cancer.
Heliyon
January 2025
Clinical Laboratory, Chest Hospital, Tianjin University, Tianjin, 300222, China.
Objective: The aim of this paper is to discover differentially expressed genes related to ferroptosis (DEFRGs) in patients with ST-segment elevation myocardial infarction (STEMI) and to construct a reliable prognostic signature that incorporates key DEFRGs and easily accessible clinical factors.
Methods: We did a systematic review of Gene Expression Omnibus datasets and picked datasets SE49925, GSE60993, and GSE61144 for analysis. We applied GEO2R to find DEFRGs and overlapped them among the picked datasets.
BMC Pharmacol Toxicol
January 2025
Faculty of Medicine, Department of Physiology, Istanbul Demiroglu Bilim University, Istanbul, Turkey.
Background: Diabetic neuropathy (DN) is a heterogeneous condition characterized by complex pathophysiological changes affecting both autonomic and somatic components of the nervous system. Inflammation and oxidative stress are recognized contributors to the pathogenesis of DN. This study aims to evaluate the therapeutic potential of dichloroacetic acid (DCA) in alleviating DN symptoms, focusing on its anti-inflammatory and antioxidant properties.
View Article and Find Full Text PDFFront Med (Lausanne)
January 2025
Department of Nephrology, Chaohu Hospital of Anhui Medical University, Hefei, China.
Objectives: The annual growth in the population of maintenance hemodialysis (MHD) patients is accompanied by a trend towards younger age groups among new cases. Despite the escalating mortality risk observed in MHD patients, there remains a dearth of research focused on young and middle-aged individuals in this cohort, leading to a deficiency in specialized predictive instruments for this demographic. This research seeks to explore the critical determinants impacting mortality risk in young and middle-aged MHD patients and to construct a prediction model accordingly.
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