Inflammation is a risk factor for both depression and cardiovascular disease. Depressed mood is also a cardiovascular risk factor. To date, research into mechanisms through which inflammation impacts cardiovascular health rarely takes into account central effects on autonomic cardiovascular control, instead emphasizing direct effects of peripheral inflammatory responses on endothelial reactivity and myocardial function. However, brain responses to inflammation engage neural systems for motivational and homeostatic control and are expressed through depressed mood state and changes in autonomic cardiovascular regulation. Here we combined an inflammatory challenge, known to evoke an acute reduction in mood, with neuroimaging to identify the functional brain substrates underlying potentially detrimental changes in autonomic cardiovascular control. We first demonstrated that alterations in the balance of low to high frequency (LF/HF) changes in heart rate variability (a measure of baroreflex sensitivity) could account for some of the inflammation-evoked changes in diastolic blood pressure, indicating a central (rather than solely local endothelial) origin. Accompanying alterations in regional brain metabolism (measured using (18)FDG-PET) were analysed to localise central mechanisms of inflammation-induced changes in cardiovascular state: three discrete regions previously implicated in stressor-evoked blood pressure reactivity, the dorsal anterior and posterior cingulate and pons, strongly mediated the relationship between inflammation and blood pressure. Moreover, activity changes within each region predicted the inflammation-induced shift in LF/HF balance. These data are consistent with a centrally-driven component originating within brain areas supporting stressor evoked blood pressure reactivity. Together our findings highlight mechanisms binding psychological and physiological well-being and their perturbation by peripheral inflammation.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3701839 | PMC |
http://dx.doi.org/10.1016/j.bbi.2013.02.001 | DOI Listing |
Best Pract Res Clin Anaesthesiol
September 2024
Division of Maternal-Fetal Medicine, Brigham and Women's Hospital, Harvard Medical School, 75 Francis St, L1, Boston, MA, 02115, USA. Electronic address:
Preeclampsia is a life-threatening complication that develops in 2-8% of pregnancies. It is characterized by elevated blood pressure after 20 weeks of gestation and may progress to multiorgan dysfunction, leading to severe maternal and fetal morbidity and mortality. The only definitive treatment is delivery, and efforts are focused on early risk prediction, surveillance, and severity mitigation.
View Article and Find Full Text PDFInt J Numer Method Biomed Eng
January 2025
College of Chemistry and Life Science, Beijing University of Technology, Beijing, China.
The accurate non-invasive detection and estimation of central aortic pressure waveforms (CAPW) are crucial for reliable treatments of cardiovascular system diseases. But the accuracy and practicality of current estimation methods need to be improved. Our study combines a meta-learning neural network and a physics-driven method to accurately estimate CAPW based on personalized physiological indicators.
View Article and Find Full Text PDFEClinicalMedicine
October 2024
Toronto 3D Knowledge Synthesis and Clinical Trials Unit, Clinical Nutrition and Risk Factor Modification Center, St. Michael's Hospital, Unity Health Toronto, Toronto, ON M5B 1W8, Canada.
Background: Use of health applications (apps) to support healthy lifestyles has intensified. Different app features may support effectiveness, including gamification defined as the use of game elements in a non-game situation. Whether health apps with gamification can impact behaviour change and cardiometabolic risk factors remains unknown.
View Article and Find Full Text PDFWorld J Clin Cases
January 2025
Department of Gastroenterology, Laiko General Hospital, National and Kapodistrian University of Athens, Athens 11527, Greece.
Machine learning (ML) is a type of artificial intelligence that assists computers in the acquisition of knowledge through data analysis, thus creating machines that can complete tasks otherwise requiring human intelligence. Among its various applications, it has proven groundbreaking in healthcare as well, both in clinical practice and research. In this editorial, we succinctly introduce ML applications and present a study, featured in the latest issue of the .
View Article and Find Full Text PDFPatient Prefer Adherence
January 2025
Division of Hypertension, Department of Medicine, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand.
Introduction: Self-care practices are crucial for optimizing blood pressure control and are influenced by multilevel factors.
Objective: To examine the influences of multilevel factors on hypertension self-care practices among individuals with uncontrolled hypertension and to determine the relationship between hypertension self-care practices and blood pressure.
Methods: The study was conducted in primary, secondary, and tertiary care settings in Bangkok, selected for convenience, where individuals with uncontrolled hypertension were recruited using a convenience sampling method based on specific inclusion criteria.
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!