Personalized Nutrition for Diabetes Type 2

Study Overview
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Brief Summary
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The study will investigate the effect of personalized diet on blood glucose control in individuals with diabetes as compared with ADA diet. The primary objective is to test whether personalized diets based on DayTwo's algorithm can improve glycemic control and metabolic health compared to standard ADA acceptable dietary approach for diabetes at the end of a 3-month intervention period.

Detailed Description
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The prevalence of diabetes type 2 estimated to 628 Million people in the world by 2045 and was announced by the International Diabetes Federation (IDF) as one of the biggest epidemics in the history. Complications of diabetics Type 2 can range from high blood sugar include heart disease, strokes, diabetic retinopathy which can result in blindness, kidney failure, and poor blood flow in the limbs which may lead to amputations. 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 postprandial 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, we should build a personally tailored diet which takes into account various factors. 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. Recently, DayTwo developed a highly accurate algorithm for predicting the personalized glucose response to food for each person based on the PNP Study conducted by the Weizmann Institute. 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 by the Weizmann Institute using the algorithm, the researchers 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 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 diabetes.

Official Title
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Personalized Nutrition for Diabetes Type 2

Conditions
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Diabetes Type 2

Intervention / Treatment
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* Other: Algorithm-based diet
* Other: ADA- based diet


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Diabetes Type 2 for at least 1 year (diagnosed by ADA criteria) and up to 20 years 7.5 <= HbA1C <= 9.5 Stable dose of meds for 3 months Stable diet and lifestyle for 3 months Age -between 18 to 85 BMI - between 25 to 35 Capable of working with smartphone application At least 5 days of the food logging in screening week: At least 60% reported Kcals out of the recommended daily consumption At least 2 reported meals a day Exclusion Criteria: Short-acting insulin treatment Bariatric surgery Antibiotics/antifungal treatment in the last 3 months Use of weight-loss medication for less than 6 months Use of GLP-1 and SGLT-2 for less than 6 months People under another diet regime that is different from the ADA recommended diet Pregnancy or 3 months after giving birth, fertility treatments Chronic disease (e.g. HIV, Cushing syndrome, CKD, acromegaly, active hyperthyroidism etc.) Cancer and anticancer treatment in the last 5 years Psychiatric disorders (that in the eyes of the investigator should exclude the participant) Life-threatening food allergy Have received DayTwo nutrition recommendations in the past have been continuously using CGM\FGM Any disorder, which in the investigator's opinion might jeopardize subject's safety or compliance with the protocol

Ages Eligible for Study
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Minimum Age: 18 Years
Maximum Age: 85 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Treatment
Allocation: Randomized
Intervention Model: Parallel Assignment
Masking: None (Open Label)


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Experimental: Algorithm-based diet<br>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<br>* 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<br>|
| Other: ADA- based diet<br>Subjects randomized to this arm will receive nutritional recommendations according to the standard American dietary approach for treating diabetes | Other: ADA- based diet<br>* The American standard of care dietary guidelines for diabetes.<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Mean change in HbA1C from the baseline level | HbA1C | 3 months intervention period | 
| Evaluation of the total daily time of plasma glucose levels | Time in Range ▪ CGM glucose levels are between 70 to 180 mg/dl | 3 months intervention period | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Evaluation of the total daily time of plasma glucose levels | Total daily time of CGM glucose levels below 70 mg/dl (Hypoglycemia incidents) | 3 months intervention period | 
| Evaluation of the total daily time of plasma glucose levels | Time in Range ▪ CGM glucose levels are between 70 to 140 mg/dl | 3 months intervention period | 
| Mean change in ADRR from the baseline level | ADRR | 3 months intervention period | 
| Mean change in BGRI from the baseline level | BGRI | 3 months intervention period | 
| Mean change in LBGI from the baseline level | LBGI | 3 months intervention period | 
| Mean change in HBGI from the baseline level | HBGI | 3 months intervention period | 
| Mean change in MAGE from the baseline level | MAGE | 3 months intervention period | 
| Mean change in CV glucose % from the baseline level | CV glucose % | 3 months intervention period | 
| Mean change in Glucose from the baseline level | Mean glucose | 3 months intervention period | 
| Mean change in Standard deviation of glucose from the baseline level | Standard deviation of glucose | 3 months intervention period | 
| Mean change in CONGA from the baseline level | CONGA | 3 months intervention period | 
| Change in Weight from baseline | Weight | 3 months intervention period | 
| Change in HbA1C from the baseline level | Percentage of patients with HbA1C <8% | 3 months intervention period | 
| Change in HbA1C from the baseline level | Percentage of patients with HbA1C <7% | 3 months intervention period | 
| change in HbA1C from the baseline level | Percentage of patients with HbA1C <6.5% | 3 months intervention period | 
| Change in Lipid profile parameters | Lipid profile | 3 months intervention period | 
| Change in Liver function parameters | Liver function test | 3 months intervention period | 
| Change in Creatinine parameter | Creatinine | 3 months intervention period | 
| Change in Fructosamin parameter | Fructosamin | 3 months intervention period | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Diabetes Type 2