Study of Macronutrients and Heart Disease Risk

NCT00609271 | Completed Results DMC

• 1 other identifier: 07-00111
• Study Type: interventional
• Target: 148
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

Obesity; Body Composition; Blood Pressure; Cardiovascular Diseases

Outcome Measures

Primary Outcomes (16)

• Predicted Mean Difference in Body Weight From Baseline, by Assigned Dietary Group

  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

  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

  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

  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

  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

  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

  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

  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

  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

  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

  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

  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

  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

  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

  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

  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

Study Arms (2)

1

EXPERIMENTAL

low carbohydrate diet

Behavioral: low carbohydrate diet

2

ACTIVE COMPARATOR

low fat diet

Behavioral: low fat diet

Interventions

low carbohydrate diet BEHAVIORAL

\<40 grams carbohydrate/day

1

low fat diet BEHAVIORAL

\<30% fat, \<7% saturated fat

2

Eligibility Criteria

• Age: 22 Years - 75 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• Men or women aged 22 - 75 years, any race/ethnicity
• BMI of 30 - 45 k/m2
• Willing and able to provide informed consent

You may not qualify if:

• 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

Sponsors & Collaborators

• Tulane University Health Sciences Center: lead

Study Sites (1)

Tulane University, Office of Health Research

New Orleans, Louisiana, 70112, United States

Location

Related Publications (2)

• 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.

  PMID: 26803589 DERIVED

• 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.

  PMID: 25178568 DERIVED

MeSH Terms

Conditions

Obesity; Cardiovascular Diseases

Interventions

Diet, Carbohydrate-Restricted; Diet, Fat-Restricted

Condition Hierarchy (Ancestors)

Overweight; Overnutrition; Nutrition Disorders; Nutritional and Metabolic Diseases; Body Weight; Signs and Symptoms; Pathological Conditions, Signs and Symptoms

Intervention Hierarchy (Ancestors)

Diet Therapy; Nutrition Therapy; Therapeutics; Diet; Nutritional Physiological Phenomena; Diet, Food, and Nutrition; Physiological Phenomena

Results Point of Contact

• Title: Dr. Lydia Bazzano
• Organization: Tulane University

lbazzano@tulane.edu

5049887323

Study Officials

• Lydia A Bazzano, MD, PhD

  Tulane University

  PRINCIPAL INVESTIGATOR

Publication Agreements

• PI is Sponsor Employee: Yes

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: SINGLE
• Who Masked: OUTCOMES ASSESSOR
• Purpose: PREVENTION
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Associate Professor Epidemiology

Study Record Dates

• First Submitted: January 24, 2008
• First Posted: February 7, 2008
• Study Start: January 1, 2008
• Primary Completion: January 1, 2011
• Study Completion: January 1, 2012
• Last Updated: November 19, 2018
• Results First Posted: November 19, 2018

Record last verified: 2018-04

Locations

US (1)