Precision Diets for Diabetes Prevention
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 入组人数
- 115
- 试验地点
- 2
- 主要终点
- Change in glycemic control as measured by change blood sugar values
研究概览
简要总结
With this study the investigators want to understand the physiological differences for people developing pre-diabetes and diabetes. The investigators hypothesize that different individuals go through different paths in the development of the disease. By understanding the personal mechanism for developing disease, the investigators will find a personalized approach to prevent that development. The investigators are also hoping to be able to find a biomarker that will pinpoint to the particular defect and thus, diagnose the problem at an earlier stage and have the information to give personalized diet recommendations to prevent the development of diabetes more effectively.
详细描述
At present, individuals with prediabetes or diabetes are grouped together as a single entity, but almost certainly they represent a mix of different gene-environment interactions that lead to one of four dominant physiologic mechanisms underlying their dysglycemia. 1- liver insulin resistance, 2- muscle insulin resistance, 3- impaired insulin secretion, 4- impaired incretin hormone secretion. Gaps that we are addressing here are extremely important - first, we will define a composite biomarker to identify different subphenotypes of prediabetes based on the four known physiologic mechanisms that contribute differentially in each individual to glucose elevations, which we hypothesize will also be reflected in their "glucotype". Importantly, because both continuous glucose monitor and administration of standardized meal testing and metabolic tests are not practical in the clinic, the development of a composite biomarker comprised of select multi-omics measures and clinical variables will enable clinicians and possibly patients (without clinician) to easily identify the specific diet that will yield optimal health results.
研究设计
- 研究类型
- Interventional
- 分配方式
- Na
- 干预模型
- Single Group
- 主要目的
- Prevention
- 盲法
- None
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •Be 18 years of age or older;
- •Not be pregnant, if female;
排除标准
- •Have major organ disease, hypertension defined as >160/100, pregnant/lactating, diabetogenic medications, malabsorptive disorders like celiac sprue, others, heavy alcohol use, use of weight loss medications or specific diets, weight change > 2 kg in the last three weeks, history of bariatric surgery.
- •Any medical condition that physicians believe would interfere with study participation or evaluation of results.
- •Mental incapacity a nd/or cognitive impairment on the part of the patient that would preclude adequate understanding of, or cooperation with, the study protocol.
研究组 & 干预措施
Optimizing Diet for Glycemic Control
Phase 1: Metabolic testing will include 3 metabolic tests:
- The Oral Glucose Tolerance Test. The participant will wear the CGM while undergoing the OGTT + will be asked to repeat the test at home twice.
- The Insulin Sensitivity Test (Steady State Plasma Glucose). This test is designed to measure how well cells remove glucose from the blood in response to insulin.
- The Isoglycemic Intravenous Glucose Infusion (IIGI). This test is designed to measure the incretin hormone effect.
Phase 2: Participants follow their own diet while using the CGM. Participants are provided with 5-10 standardized foods to test during this phase.
Phase 3: Participants are provided with additional standardized foods and counseled to continue their own diet during this phase.
Phase 4: Participants are counseled on reducing or limiting the foods that caused glucose spikes and they are also counseled on macronutrient composition of their diet based on lipid profile.
干预措施: Dietary (Other)
结局指标
主要结局
Change in glycemic control as measured by change blood sugar values
时间窗: Three years
Change in glycemic control measured from baseline through all phases of study. Glycemic control is derived from continuous glucose monitor (CGM) data and expressed in milligrams/deciliter.
Classification of metabolic subphenotype
时间窗: Four years
Classify metabolic subphenotype in individuals without diabetes using a machine learning algorithm applied to the glucose time-series response generated by a 16-point (blood draws) OGTT done in the clinical research center and at home (using CGM)
次要结局
- Change in area under the curve (AUC) of blood glucose level(Three years)
研究者
Michael Snyder
Chair, Genetics Department
Stanford University