Personalized Nutrition for Pre-Diabetes
Condition: Pre Diabetes
MeSH terms: Diabetes Mellitus; Prediabetic State; Glucose Intolerance
Study type: Interventional

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.
    

Intervention name(s): Algorithm-based diet; Mediterranean-style low-fat diet
Intervention type(s): Other; Other
Intervention description(s): 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.; The Israeli standard of care dietary guidelines for prediabetes.
Allocation: Randomized
Intervention model: Parallel Assignment
Masking: Single (Participant)
Primary purpose: Prevention
Keywords: Diabetes