Amer M. Johri, Marie-France Hétu, Daren K. Heyland, Julia E. Herr, Jennifer Korol, Shawna Froese, Patrick A. Norman, Andrew G. Day, Murray F. Matangi, Erin D. Michos, Stephen A. LaHaye, Fraser W. Saunders & J. David Spence 

Nutrition & Metabolism volume 19, Article number: 26 (2022) Cite this article

Abstract

Background

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).

Methods

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.

Results

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.

Conclusions

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:

https://healthsci.queensu.ca/stories/feature/queens-project-brings-interactive-ultrasound-training-remote-communities

Amer M Johri, Laura E Mantella, Ankush D Jamthikar, Luca Saba, John R Laird, Jasjit S Suri

https://pubmed.ncbi.nlm.nih.gov/34050838/

Abstract

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.

Congratulations to Laura Mantella who successfully defended her PhD thesis last week (future MD/PhD)! Impressive work on carotid plaque neovascularization associated with coronary artery disease.

Stress echo (SE) is one of the most accessible tests used to select patients for angiography, but suffers from moderate sensitivity and specificity for cardiovascular (CV) outcomes. In Ontario alone, of the >100,000 SEs performed every year, 90% are normal, and patients may have little follow-up. It is estimated that 3-5% (~4,000 patients/year) of those with a negative SE have a major adverse cardiovascular event(MACE) within 3 years. We have demonstrated that adding carotid plaque assessment by ultrasound to the SE is feasible and improves testing with a net reclassification improvement (NRI) of 25% for coronary artery disease (CAD). We reported that activity of plaque adds further discriminatory power for predicting CAD and MACE. Plaques can be assessed by intraplaque neovascularization (IPN), which occurs in response to hypoxia or inflammation within the lesion. IPN occurs when vessels grow from the vasa vasorum into the lesion, resulting in a fragile and leaky network at risk of rupture and hemorrhage. IPN of carotid arterial plaque can now be quantified using ultrasound contrast. We hypothesize that addition of carotid IPN detection at the time of SE will enhance risk prediction for MACE. We plan to demonstrate that plaque inflammatory activity, detected by IPN serves as a powerful imaging biomarker of CAD and CV events to improve the sensitivity of SE.

This is a prospective, parallel, 6-centre study assessing IPN + SE in 1500 consecutive outpatients referred for SE. Patients will be recruited from a community-based cardiac clinic, and the Universities of Queen’s, Dalhousie, Calgary, Toronto, and RUMC(Chicago). The NPV and sensitivity of IPN + SE for ruling out CAD will be determined. Follow-up will be 3-year MACE. The proposal is founded upon a simple, inexpensive, and safe addition to the workflow of an existing non-invasive test. Extensive work to date indicates that plaque assessment added to SE will enhance stratification to reduce referral for unnecessary angiography and better identify patients at risk. This multi-center study of IPN + SE is expected to show increased predictive power for MACE, establishing a new standard for CV risk stratification.

The CIRCE study was the top ranked proposal in its committee and awarded the 2020/2021 CIHR Project grant led by Queen’s University.