Personalized Nutrition for Pre-Diabetes
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|ClinicalTrials.gov Identifier: NCT03222791|
Recruitment Status : Recruiting
First Posted : July 19, 2017
Last Update Posted : November 17, 2017
|Condition or disease||Intervention/treatment||Phase|
|Pre Diabetes||Other: Algorithm-based diet Other: Mediterranean-style low-fat diet||Not Applicable|
Blood glucose levels are rapidly increasing in the population, as evident by the sharp incline in the prevalence of prediabetes and impaired glucose tolerance estimated to affect, in the U.S. alone, 37% of the adult population. Chronic hyperglycaemia is a significant risk factor for type II diabetes mellitus (TIIDM), with up to 70% of prediabetics eventually developing the disease. It is also linked to other manifestations, collectively termed the metabolic syndrome, including obesity, hypertension, non-alcoholic fatty liver disease, hypertriglyceridemia and cardiovascular disease.
As blood glucose levels are mainly affected by food consumption, the growing number of blood glucose abnormalities is likely attributable to nutrition. Indeed, dietary and lifestyle changes normalize blood glucose levels in 55% -80% of the cases. Therefore, maintaining normal blood glucose levels is critical for preventing diabetes and its metabolic complications.
Currently, there are no effective methods for predicting the post prandial glycemic response (PPGR) of people to food. The current practice of using the meal carbohydrate content is a poor predictor of the PPGR and has limited efficacy.The glycemic index (GI), which quantifies PPGR to consumption of a single tested food type, and the derived glycemic load have limited applicability in assessing the PPGR to real-life meals consisting of arbitrary food combinations and varying quantities, consumed at different times of the day, and at different proximity to physical activity and other meals. Indeed, studies examining the effect of diets with a low glycemic index on TIIDM risk, weight loss, and cardiovascular risk factors yielded mixed results. The limited success of GI measure is probably due to the fact that it is a general index, which does not take into consideration the large variation between individuals in their glycemic response to food. It can be concluded, therefore, that in order to control glycemic response of an individual, a personalized tailored diet which takes into account various factors is required. Although genetic factors influence the levels of fasting blood glucose and glycemic response to food, these factors only explain approximately 10% of the variance in the population. Supporting this claim is the fact that the number of people with diabetes is increasing in recent years regardless of patients' genetic background. In contrast, environmental factors such as the composition of the intestinal bacteria and their metabolic activity may affect the glycemic response. The entire bacteria population in the digestive tract (microbiome) consist of ~1,000 species with a genetic repertoire of ~3 million different genes. The microbiome is directly affected by our diet and directly affect the body's response to food. This special relationship between the host and the intestinal flora is reflected by the composition of bacteria unique to type 2 diabetes and in the significant changes in the bacteria composition upon transition from a diet rich in fiber to a "Western" diet rich in simple sugars.
The study is conducted to evaluate a highly accurate algorithm developed at the Weizmann Institute of Science for predicting the personalized glucose response to food for each person. The algorithm‟s predictions are based on many personal measurements, including blood tests, personal lifestyle and gut bacteria. In a small-scale pilot study that was conducted using this algorithm, the investigators personally tailored dietary interventions to healthy and prediabetic people, which resulted in significantly improved PPGRs accompanied by consistent alterations to the gut microbiota. These findings led the investigators to hypothesize that tailoring personalized diets based on PPGRs predictions may achieve better outcomes in terms of controlling blood glucose levels and its metabolic consequences relative to the current standard nutritional therapy for prediabetes.
|Study Type :||Interventional (Clinical Trial)|
|Estimated Enrollment :||200 participants|
|Intervention Model:||Parallel Assignment|
|Official Title:||Personalized Nutrition for Pre-Diabetes|
|Actual Study Start Date :||February 1, 2017|
|Estimated Primary Completion Date :||February 2018|
|Estimated Study Completion Date :||August 2018|
Experimental: Algorithm-based diet
Subjects randomized to this arm will receive personally tailored dietary recommendations based on their predicted glycemic responses according to the study algorithm.
Other: Algorithm-based diet
Personalized nutrition plan based on an algorithm for predicting the personalized glucose response to food. The algorithm's predictions are based on many personal measurements, including blood tests, personal lifestyle and gut bacteria.
Mediterranean-style low-fat diet
Subjects randomized to this arm will receive nutritional recommendations according to the standard Israeli dietary approach for treating pre-diabetes: Mediterranean-style low-fat diet.
Other: Mediterranean-style low-fat diet
The Israeli standard of care dietary guidelines for prediabetes.
- Evaluation of the total daily time of plasma glucose levels below 140 mg/dl [ Time Frame: 6 months ]Total daily plasma glucose levels will be evaluated by using a Continuous glucose monitoring (CGM)
- Mean change in HbA1C from the baseline level [ Time Frame: 6 months ]Difference of at least 0.1% in the reduction of HbA1C between control group and experimental group
- Mean change in Glucose Tolerance Test from the baseline level [ Time Frame: 6 months ]GTT glucose values (mg/dl)
- Change is Fasting plasma glucose from baseline [ Time Frame: 6 months ]Fasting glucose values (mg/dl)
- Change in HOMA-IR from baseline [ Time Frame: 6 months ]Change in insulin sensitivity from baseline to 6 months will be measured via HOMA-IR
- Patients compliance evaluation using a compliance questionnaire [ Time Frame: 6 months, 12 months ]Follow up questionnaire completed independently by the patients
Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT03222791
|Contact: Orly Ben-Yacovfirstname.lastname@example.org|
|Contact: Michal Reinemail@example.com|
|The Weizmann Institute of Science||Recruiting|
|Contact: Orly Ben-Yacov firstname.lastname@example.org|
|Principal Investigator:||Eran Segal, Prof.||Weizmann Institute of Science|
|Principal Investigator:||Eran Elinav, MD||Weizmann Institute of Science|