Macronutrient Composition of Diet and Risk Factors for Cardiovascular Disease
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 入组人数
- 148
- 试验地点
- 2
- 主要终点
- Predicted Mean Difference in Body Weight From Baseline, by Assigned Dietary Group
研究概览
简要总结
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.
详细描述
Cardiovascular diseases (CVD) remain the leading cause of death globally as well as here in the United States. Manipulations of the macronutrient (protein, carbohydrate and fat) contents of diet have been used extensively for weight loss and weight control in the past several decades. Low carbohydrate diets, in particular, have gained popularity for weight loss. However, few studies have examined the effects of a diet low in carbohydrates on traditional and novel cardiovascular risk factors in the long term, particularly in contrast to the current dietary recommendations for decreased fat intake to reduce risk of CVD. In this proposal, we plan to conduct a 12-month, parallel-arm, randomized controlled trial of a diet low in carbohydrates versus the currently recommended low fat diet to reduce CVD risk factors among obese adults. 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 CVD 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. In order to accomplish these objectives we will randomize 130 eligible participants (n=65 in each group) to consume either a diet low in carbohydrates (≤40 g/d) or a diet low in fat (<7% saturated fat, <30% total fat). Neither of the diets will be energy-restricted. Participants will meet with a dietitian for one-on-one counseling sessions weekly for the first 4 weeks, then bi-monthly in small group sessions for the next 5 months, and monthly in larger group sessions for the final 6 months of the intervention. Data on both traditional and novel CVD risk factors will be collected at baseline, 3, 6, and 12 months. We hypothesize that a diet low in carbohydrates as compared to a diet low in fat will lower systolic and diastolic BP, body weight, total percent body fat, waist circumference, serum levels of triglycerides, and plasma levels of insulin, glucose, leptin, resistin, and CRP, and increase serum levels of HDL-cholesterol and adiponectin. Because CVD is the most common cause of death here in the U.S. and world-wide, this study has important public health implications. It will provide new information on the potential long-term effects of diets low in carbohydrates on both the traditional risk factors for CVD as well as novel risk factors and inflammatory factors. The results from this study will help to determine if a diet low in carbohydrates as compared to the currently recommended low fat diet can decrease the risk of CVD among obese adults.
研究设计
- 研究类型
- Interventional
- 分配方式
- Randomized
- 干预模型
- Parallel
- 主要目的
- Prevention
- 盲法
- Single (Outcomes Assessor)
入排标准
- 年龄范围
- 22 Years 至 75 Years（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •Men or women aged 22 - 75 years, any race/ethnicity
- •BMI of 30 - 45 k/m2
- •Willing and able to provide informed consent
排除标准
- •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
研究组 & 干预措施
1
low carbohydrate diet
干预措施: low carbohydrate diet (Behavioral)
2
low fat diet
干预措施: low fat diet (Behavioral)
结局指标
主要结局
Predicted Mean Difference in Body Weight From Baseline, by Assigned Dietary Group
时间窗: 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.
Predicted Mean Differences in Fat Mass From Baseline, by Assigned Dietary Group
时间窗: 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.
Predicted Mean Differences in HDL Cholesterol From Baseline, by Assigned Dietary Group
时间窗: 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.
Predicted Mean Differences in Serum Insulin Level From Baseline, by Assigned Dietary Group
时间窗: 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.
Predicted Mean Differences in Total-HDL Cholesterol Ratio From Baseline, by Assigned Dietary Group
时间窗: 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.
Predicted Mean Differences in Systolic Blood Pressure From Baseline, by Assigned Dietary Group
时间窗: 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.
Predicted Mean Difference in Diastolic Blood Pressure, by Assigned Dietary Group
时间窗: 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
Predicted Mean Differences of Waist Circumference From Baseline, by Assigned Dietary Group
时间窗: 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.
Predicted Mean Difference in Plasma Glucose Level, by Assigned Dietary Group
时间窗: 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.
Predicted Mean Differences of 10-y Framingham Risk Score From Baseline, by Assigned Dietary Group
时间窗: 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.
Predicted Mean Differences in Lean Mass From Baseline, by Assigned Dietary Group
时间窗: 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.
Predicted Mean Differences in Total Cholesterol Level From Baseline by Assigned Dietary Group
时间窗: 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.
Predicted Mean Differences in LDL Cholesterol Level From Baseline, by Assigned Dietary Group
时间窗: 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.
Predicted Mean Differences in C-reactive Protein Level From Baseline, by Assigned Dietary Group
时间窗: 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
Predicted Mean Differences in Serum Creatinine Level From Baseline, by Assigned Dietary Group
时间窗: 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.
Predicted Mean Differences in Triglycerides From Baseline, by Assigned Dietary Group
时间窗: 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.
次要结局
未报告次要终点
研究者
Lydia A. Bazzano
Associate Professor Epidemiology
Tulane University Health Sciences Center