Huge congratulations to 5 of our lab members on their acceptance to medical school! We’re incredibly proud and wish them all the best in their next big adventure.
Pictured below (L to R) Top: Georgia Kersche, Braeden Hill Bottom: Natalie Kearn, Sara Pollanen, Rohaan Syan.
Logan C. Barr, Julia E. Herr, Marie-France Hétu, Graeme N. Smith, Amer M. Johri
First published: 19 April 2022 https://doi.org/10.14814/phy2.15276
Preeclampsia (PE) is a hypertensive obstetrical complication associated with increased cardiovascular disease risk. Carotid artery functional assessments allow for identification of subclinical vascular dysfunction. This cross-sectional study measured carotid artery functional indices in healthy women with a recent pregnancy complicated by PE, versus women with a prior uncomplicated pregnancy. Women with a history of PE (N = 30) or an uncomplicated pregnancy (N = 30), were recruited between 6 months and 5 years postpartum. Left and right carotid artery ultrasound measured carotid intima media thickness, plaque burden, peak systolic velocity, end diastolic flow velocity and carotid far-wall circumferential strain (FWCS). Carotid FWCS is inversely related to vessel stiffness, where a decrease in FWCS indicates increased vessel stiffness. Right-side FWCS did not differ between women with a history of PE versus normotensive pregnancy. Left carotid artery FWCS was lower in formerly preeclamptic women after adjustment for diameter, pulse pressure, and heart rate compared to women following an uncomplicated pregnancy (3.35 ± 1.08 × 10−3 vs. 4.46 ± 1.40 × 10−3; p = 0.003). Those with prior severe PE had the greatest decrease in FWCS adjusted to diameter, pulse pressure, and heart rate compared to healthy controls (p = 0.02). Adjusted FWCS and total serum cholesterol were independent indicators of PE history when present in a logistic regression model with confounding variables including age, body mass index, and resting blood pressure. Further investigation is needed to elucidate if FWCS can be used as a risk stratification tool for future cardiovascular disease following a pregnancy complicated by PE. A history of PE is associated with decreased left FWCS (increased left carotid artery stiffness).
Nutrition & Metabolism volume 19, Article number: 26 (2022) Cite this article
Abstract
L-carnitine (L-C), a ubiquitous nutritional supplement, has been investigated as a potential therapy for cardiovascular disease, but its effects on human atherosclerosis are unknown. Clinical studies suggest improvement of some cardiovascular risk factors, whereas others show increased plasma levels of pro-atherogenic trimethylamine N-oxide. The primary aim was to determine whether L-C therapy led to progression or regression of carotid total plaque volume (TPV) in participants with metabolic syndrome (MetS).
This was a phase 2, prospective, double blinded, randomized, placebo-controlled, two-center trial. MetS was defined as ≥ 3/5 cardiac risk factors: elevated waist circumference; elevated triglycerides; reduced HDL-cholesterol; elevated blood pressure; elevated glucose or HbA1c; or on treatment. Participants with a baseline TPV ≥ 50 mm3 were randomized to placebo or 2 g L-C daily for 6 months.
The primary outcome was the percent change in TPV over 6 months. In 157 participants (L-C N = 76, placebo N = 81), no difference in TPV change between arms was found. The L-C group had a greater increase in carotid atherosclerotic stenosis of 9.3% (p = 0.02) than the placebo group. There was a greater increase in total cholesterol and LDL-C levels in the L-C arm.
Though total carotid plaque volume did not change in MetS participants taking L-C over 6-months, there was a concerning progression of carotid plaque stenosis. The potential harm of L-C in MetS and its association with pro-atherogenic metabolites raises concerns for its further use as a potential therapy and its widespread availability as a nutritional supplement.
Trial registration: ClinicalTrials.gov, NCT02117661, Registered April 21, 2014, https://clinicaltrials.gov/ct2/show/NCT02117661.
https://nutritionandmetabolism.biomedcentral.com/articles/10.1186/s12986-022-00661-9
FEATURE STORIES | 29 Mar 2022
“We remotely teach physicians outside of urban centres so that if a patient comes into a remote clinic they get the same level of care as a patient walking into a clinic in Toronto,” explains Dr. Johri, an Associate Professor in the Department of Medicine and the Founder and Director of the Cardiovascular Imaging Network at Queen’s (CINQ).
READ MORE:
Amer M Johri, Laura E Mantella, Ankush D Jamthikar, Luca Saba, John R Laird, Jasjit S Suri
https://pubmed.ncbi.nlm.nih.gov/34050838/
The aim of this study was to compare machine learning (ML) methods with conventional statistical methods to investigate the predictive ability of carotid plaque characteristics for assessing the risk of coronary artery disease (CAD) and cardiovascular (CV) events. Focused carotid B-mode ultrasound, contrast-enhanced ultrasound, and coronary angiography were performed on 459 participants. These participants were followed for 30 days. Plaque characteristics such as carotid intima-media thickness (cIMT), maximum plaque height (MPH), total plaque area (TPA), and intraplaque neovascularization (IPN) were measured at baseline. Two ML-based algorithms-random forest (RF) and random survival forest (RSF) were used for CAD and CV event prediction. The performance of these algorithms was compared against (i) univariate and multivariate analysis for CAD prediction using the area-under-the-curve (AUC) and (ii) Cox proportional hazard model for CV event prediction using the concordance index (c-index). There was a significant association between CAD and carotid plaque characteristics [cIMT (odds ratio (OR) = 1.49, p = 0.03), MPH (OR = 2.44, p < 0.0001), TPA (OR = 1.61, p < 0.0001), and IPN (OR = 2.78, p < 0.0001)]. IPN alone reported significant CV event prediction (hazard ratio = 1.24, p < 0.0001). CAD prediction using the RF algorithm reported an improvement in AUC by ~ 3% over the univariate analysis with IPN alone (0.97 vs. 0.94, p < 0.0001). Cardiovascular event prediction using RSF demonstrated an improvement in the c-index by ~ 17.8% over the Cox-based model (0.86 vs. 0.73). Carotid imaging phenotypes and IPN were associated with CAD and CV events. The ML-based system is superior to the conventional statistically-derived approaches for CAD prediction and survival analysis.
Keywords: And cardiovascular event prediction; Coronary artery disease; Focused carotid ultrasound; Intraplaque neovascularization; Machine learning; Risk prediction.