Intestinal Microbiome and Extremes of Atherosclerosis

Renal Function Clinical Trial

Official title:

Intestinal Microbiome and Extremes of Atherosclerosis

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT03398889
• Other study ID #: 105166
• Status: Completed
• Phase: N/A
• First received: December 12, 2017
• Last updated: January 11, 2018
• Start date: July 1, 2014
• Est. completion date: October 15, 2016

Study information

• Verified date: December 2017
• Source: Lawson Health Research Institute
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial Summary

Patients attending stroke prevention clinics and a premature atherosclerosis clinic at University Hospital in London, ON, Canada were recruited to the study. They completed a dietary questionnaire, provided stool samples and had blood drawn to measure plasma levels of metabolites produced by the intestinal bacteria.

Description:

Patients were phenotyped by their residual score in linear multiple regression with measured carotid plaque burden as the dependent variable and coronary risk factors were predictors. The residual score essentially represents the distance off the regression line of predicted plaque. They were grouped into three categories: Unexplained atherosclerosis (with more plaque than predicted by risk factors; residual score >2); Explained (the amount of plaque predicted by risk factors, residual score >-2 and <2); and Protected (less plaque than predicted by risk factors, residual score <-2).

DNA was extracted from stool samples in the lab of Dr. Allen-Vercoe at University of Guelph. RNA makeup of the intestinal microbiome was assessed in the lab of Dr. Gregory Gloor at Western. Plasma levels of trimethylamine n-oxide, p-cresylsulfate, hippuric acid, p-cresyl glucuronide, pheny acetyl glutamine and phenyl sulfate were measured by ultra-performance liquid chromatography coupled to quadrupole time-of-flight mass spectrometry in the lab of Dr. Bradley Urquhart at Western.

Nutrient intake over the past year was calculated at the Harvard School of Public Health from the 131 item self-reported and semi-quantitative Harvard Food Frequency Questionnaire (FFQ).

Estimated glomerular filtration rate was calculated from the Chronic Kidney Disease Epidemiological (CKD-EPI) equations.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 316
• Est. completion date: October 15, 2016
• Est. primary completion date: October 15, 2016
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years and older

Eligibility

Inclusion Criteria:

• Patients attending stroke prevention clinics and a Premature Atherosclerosis Clinic at University Hospital, London, ON, Canada,

• with measurements of carotid plaque burden and the risk factors used in the linear regression model.

• Willing to consent to the protocol approved by the Ethics board

Exclusion Criteria:

• Missing data on variables used in the regression model,

Related Conditions & MeSH terms

• Atherosclerosis
• Gastrointestinal Microbiome, Atherosclerosis, Nutrients
• Renal Function

Intervention

Diagnostic Test:
• Plasma levels of metabolites
plasma levels of metabolites

Behavioral:
• Dietary questionnaire
Harvard food frequency questionnaire

Diagnostic Test:
• Intestinal microbiome
amplification and sequencing of 16S rRNA gene variable regions in stool samples

Locations

Country	Name	City	State
Canada	Stroke Prevention & Atherosclerosis Research Centre	London	Ontario

Sponsors (1)

Lead Sponsor	Collaborator
Lawson Health Research Institute

Country where clinical trial is conducted

Canada, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Plasma levels of metabolites in the three phenotypes	Plasma levels of trimethylamine n-oxide, p-cresylsulfate, hippuric acid, p-cresyl glucuronide, pheny acetyl glutamine and phenyl sulfate	Day 1
Secondary	Bacterial profile of the intestinal microbiome	Amplification and sequencing of 16S rRNA gene variable regions	Day 1
Secondary	Effect of nutrient intake on plasma levels of metabolites	Nutrient analysis from food frequency questionnaire	Day 1
Secondary	Effect of renal function on plasma levels of metabolites	eGFR from CKD-EPI equations	Day 1