Personalized Nutrition for Pre-Diabetes

Not Applicable

Completed

• Conditions: Pre Diabetes
• Interventions: Other: Algorithm-based diet; Other: Mediterranean-style low-fat diet

• Registration Number: NCT03222791
• Lead Sponsor: Weizmann Institute of Science

Brief Summary

The Personalized Nutrition Project for Prediabetes (PNP3) study will investigate whether personalized diet intervention will improve postprandial blood glucose levels and other metabolic health factors in individuals with prediabetes as compared with the standard low-fat diet.

Detailed Description

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 Timeline

• Study Start: 2017-02-01
• Study First Submitted: 2017-07-13
• Study First Submitted QC: 2017-07-18
• Study First Posted: 2017-07-19
• Primary Completion: 2019-09-10
• Study Completion: 2020-03-01
• Status Verified: 2020-04
• Last Update Submitted: 2020-04-21
• Last Update Posted: 2020-04-22

Recruitment & Eligibility

• Status: COMPLETED
• Sex: All
• Target Recruitment: 244

Inclusion Criteria

• HbA1C 5.7 - 6.4
• Fasting Glucose 100 - 125 mg/dl
• Age - 18-55
• Capable of working with smartphone application

Exclusion Criteria

• Antibiotics/antifungal in the last 3 month
• Use of anti-diabetic and/or weight-loss medication
• People under another diet regime and/or a dietitian consultation/another study
• Pregnancy, fertility treatments
• Chronic disease (e.g. HIV, Cushing syndrome, CKD, acromegaly, hyperthyroidism etc.)
• Cancer and recent anticancer treatment
• Psychiatric disorders
• Coagulation disorders
• IBD (inflammatory bowel diseases)
• Bariatric surgery
• Alcohol or substance abuse

Study & Design

• Study Type: INTERVENTIONAL
• Study Design: PARALLEL

Arm && Interventions

Group	Intervention	Description
Algorithm-based diet	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.
Mediterranean-style low-fat diet	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.

Primary Outcome Measures

Name	Time	Method
Mean change in Glucose Tolerance Test from the baseline level	6 months	GTT glucose values (mg/dl)
Evaluation of the total daily time of plasma glucose levels below 140 mg/dl	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	6 months	Difference of at least 0.1% in the reduction of HbA1C between control group and experimental group

Secondary Outcome Measures

Name	Time	Method
Change is Fasting plasma glucose from baseline	6 months	Fasting glucose values (mg/dl)
Change in HOMA-IR from baseline	6 months	Change in insulin sensitivity from baseline to 6 months will be measured via HOMA-IR

Trial Locations

Locations (1)

The Weizmann Institute of Science; 🇮🇱 Rehovot, Israel