- ICH GCP
- US Clinical Trials Registry
- Clinical Trial NCT00609271
Study of Macronutrients and Heart Disease Risk (MACRO)
Macronutrient Composition of Diet and Risk Factors for Cardiovascular Disease
The objective of this trial is to examine the long-term effects of a diet low in carbohydrates, as compared to one low in fat, on cardiovascular disease risk factors, including blood pressure (BP), body weight and composition, serum lipids, plasma glucose, insulin, adipocytokines (adiponectin, leptin, resistin), and C-reactive protein (CRP) among obese adults.
The investigators will test the following hypotheses:
Hypothesis 1: Compared to a low fat diet, a diet low in carbohydrates will reduce systolic and diastolic BP over 12 months; Hypothesis 2: Compared to a low fat diet, a diet low in carbohydrates will reduce body weight, total percent body fat, and waist circumference over 12 months; Hypothesis 3: Compared to a low fat diet, a diet low in carbohydrates will reduce serum levels of LDL-cholesterol and triglycerides and increase serum levels of HDL-cholesterol over 12 months; Hypothesis 4: Compared to a low fat diet, a diet low in carbohydrates will reduce plasma levels of glucose and insulin levels over 12 months; and Hypothesis 5: Compared to a low fat diet, a diet low in carbohydrates will reduce plasma levels of leptin, resistin, and CRP and increase plasma levels of adiponectin over 12 months.
Study Overview
Status
Intervention / Treatment
Detailed Description
Study Type
Enrollment (Actual)
Phase
- Not Applicable
Contacts and Locations
Study Locations
-
-
Louisiana
-
New Orleans, Louisiana, United States, 70112
- Tulane University, Office of Health Research
-
-
Participation Criteria
Eligibility Criteria
Ages Eligible for Study
Accepts Healthy Volunteers
Genders Eligible for Study
Description
Inclusion Criteria:
- Men or women aged 22 - 75 years, any race/ethnicity
- BMI of 30 - 45 k/m2
- Willing and able to provide informed consent
Exclusion Criteria:
- History of self-reported clinical CVD (angina/myocardial infarction, coronary revascularization, heart failure, stroke/transient ischemic attack, peripheral arterial disease)
- Medical condition in which a low-carbohydrate diet may not be advised (diabetes, renal disease, cancer requiring treatment during the past year, osteoporosis, untreated thyroid disease, gout)
- Current use of more than 2 antihypertensive or more than 2 cholesterol-lowering medications
- For women, current pregnancy or breastfeeding or plans to become pregnant during the study period
- Consumption of more than 21 alcoholic beverages per week
- Currently on a diet or using prescription weight loss medications, underwent weight loss surgery, and/or experienced weight loss >15 pounds within 6 months of study entry
- Plans to move out of the study area (>1 hour from study site) or difficulty to come to the study site
- Participation of another household member in the study; employees or persons living with employees of the study
- Participation in other lifestyle intervention trials currently
- At the discretion of the study coordinator
Study Plan
How is the study designed?
Design Details
- Primary Purpose: Prevention
- Allocation: Randomized
- Interventional Model: Parallel Assignment
- Masking: Single
Arms and Interventions
Participant Group / Arm |
Intervention / Treatment |
|---|---|
|
Experimental: 1
low carbohydrate diet
|
<40 grams carbohydrate/day
|
|
Active Comparator: 2
low fat diet
|
<30% fat, <7% saturated fat
|
What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Predicted Mean Difference in Body Weight From Baseline, by Assigned Dietary Group
Time Frame: 12 months
|
Predicted mean difference from random-effects models that included diet, time, and diet-by-time interaction term.
Markov-chain Monte Carlo techniques were used to impute missing values.
|
12 months
|
|
Predicted Mean Differences in Lean Mass From Baseline, by Assigned Dietary Group
Time Frame: 12 months
|
Mean Difference in Lean Mass predicted from random-effects models that included diet, time, and diet-by-time interaction term.
Markov-chain Monte Carlo techniques were used to impute missing values.
|
12 months
|
|
Predicted Mean Differences in Fat Mass From Baseline, by Assigned Dietary Group
Time Frame: 12 months
|
Mean Difference in Fat Mass predicted from random-effects models that included diet, time, and diet-by-time interaction term.
Markov-chain Monte Carlo techniques were used to impute missing values.
|
12 months
|
|
Predicted Mean Differences of Waist Circumference From Baseline, by Assigned Dietary Group
Time Frame: 12 months
|
Predicted from random-effects models that included diet, time, and diet-by-time interaction term.
Markov-chain Monte Carlo techniques were used to impute missing values.
|
12 months
|
|
Predicted Mean Differences in Total Cholesterol Level From Baseline by Assigned Dietary Group
Time Frame: 12 months
|
Predicted from random-effects models that included diet, time, and diet-by-time interaction term.
Markov-chain Monte Carlo techniques were used to impute missing values.
|
12 months
|
|
Predicted Mean Differences in LDL Cholesterol Level From Baseline, by Assigned Dietary Group
Time Frame: 12 months
|
Predicted from random-effects models that included diet, time, and diet-by-time interaction term.
Markov-chain Monte Carlo techniques were used to impute missing values.
|
12 months
|
|
Predicted Mean Differences in HDL Cholesterol From Baseline, by Assigned Dietary Group
Time Frame: 12 months
|
Predicted from random-effects models that included diet, time, and diet-by-time interaction term.
Markov-chain Monte Carlo techniques were used to impute missing values.
|
12 months
|
|
Predicted Mean Differences in Total-HDL Cholesterol Ratio From Baseline, by Assigned Dietary Group
Time Frame: 12 months
|
Predicted from random-effects models that included diet, time, and diet-by-time interaction term.
Markov-chain Monte Carlo techniques were used to impute missing values.
|
12 months
|
|
Predicted Mean Differences in Triglycerides From Baseline, by Assigned Dietary Group
Time Frame: 12 months
|
Predicted from random-effects models that included diet, time, and diet-by-time interaction term.
Markov-chain Monte Carlo techniques were used to impute missing values.
|
12 months
|
|
Predicted Mean Differences in Systolic Blood Pressure From Baseline, by Assigned Dietary Group
Time Frame: 12 months
|
Predicted from random-effects models that included diet, time, and diet-by-time interaction term.
Markov-chain Monte Carlo techniques were used to impute missing values.
|
12 months
|
|
Predicted Mean Difference in Diastolic Blood Pressure, by Assigned Dietary Group
Time Frame: 12 Months
|
Mean Difference in Diastolic Blood Pressure predicted from random-effects models that included diet, time, and diet-by-time interaction term.
Markov-chain Monte Carlo techniques were used to impute missing values
|
12 Months
|
|
Predicted Mean Difference in Plasma Glucose Level, by Assigned Dietary Group
Time Frame: 12 months
|
Mean Difference in Plasma Glucose Level predicted from random-effects models that included diet, time, and diet-by-time interaction term.
Markov-chain Monte Carlo techniques were used to impute missing values.
|
12 months
|
|
Predicted Mean Differences in Serum Insulin Level From Baseline, by Assigned Dietary Group
Time Frame: 12 months
|
Mean Difference in Serum Insulin Level predicted from random-effects models that included diet, time, and diet-by-time interaction term.
Markov-chain Monte Carlo techniques were used to impute missing values.
|
12 months
|
|
Predicted Mean Differences in C-reactive Protein Level From Baseline, by Assigned Dietary Group
Time Frame: 12 Months
|
Mean Difference in C-reactive Protein Level predicted from random-effects models that included diet, time, and diet-by-time interaction term.
Markov-chain Monte Carlo techniques were used to impute missing values
|
12 Months
|
|
Predicted Mean Differences in Serum Creatinine Level From Baseline, by Assigned Dietary Group
Time Frame: 12 Months
|
Mean Difference in Serum Creatinine Level predicted from random-effects models that included diet, time, and diet-by-time interaction term.
Markov-chain Monte Carlo techniques were used to impute missing values.
|
12 Months
|
|
Predicted Mean Differences of 10-y Framingham Risk Score From Baseline, by Assigned Dietary Group
Time Frame: 12 Months
|
Mean Difference in 10-y Framingham Risk Score predicted from random-effects models that included diet, time, and diet-by-time interaction term.
Markov-chain Monte Carlo techniques were used to impute missing values.
|
12 Months
|
Collaborators and Investigators
Investigators
- Principal Investigator: Lydia A Bazzano, MD, PhD, Tulane University
Publications and helpful links
General Publications
- Hu T, Yao L, Reynolds K, Niu T, Li S, Whelton P, He J, Bazzano L. The effects of a low-carbohydrate diet on appetite: A randomized controlled trial. Nutr Metab Cardiovasc Dis. 2016 Jun;26(6):476-88. doi: 10.1016/j.numecd.2015.11.011. Epub 2015 Dec 12.
- Bazzano LA, Hu T, Reynolds K, Yao L, Bunol C, Liu Y, Chen CS, Klag MJ, Whelton PK, He J. Effects of low-carbohydrate and low-fat diets: a randomized trial. Ann Intern Med. 2014 Sep 2;161(5):309-18. doi: 10.7326/M14-0180.
Study record dates
Study Major Dates
Study Start
Primary Completion (Actual)
Study Completion (Actual)
Study Registration Dates
First Submitted
First Submitted That Met QC Criteria
First Posted (Estimate)
Study Record Updates
Last Update Posted (Actual)
Last Update Submitted That Met QC Criteria
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
- 07-00111
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
product manufactured in and exported from the U.S.
This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.
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