Personalized Nutrition for Diabetes Type 2

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.

Study Overview

• Status: Unknown
• Conditions: Diabetes Type 2
• Intervention / Treatment: Other: Algorithm-based diet; Other: ADA- based diet

Detailed Description

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.

• Study Type: Interventional
• Enrollment (Anticipated): 200
• Phase: Not Applicable

Contacts and Locations

• Study Contact: Name: Rony Bikovsky; Phone Number: +972542299300; Email: rony.bikovsky@daytwo.com
• Study Contact Backup: Name: Tal Ofek, Ph.d; Phone Number: +972505658786; Email: tal.ofek@daytwo.com

Study Locations

• Israel
  • H̱olon, Israel, 5822012
    • Recruiting
    • The Edith Wolfson Medical Center
    • Contact: Tali Ganz; Phone Number: +972523374030; Email: taliganz@wmc.gov.il
    • Principal Investigator: Julio Weinstaine, MD
  • Tel Aviv, Israel, 6937947
    • Recruiting
    • Diabetes Medical Center
    • Contact: Vered Sason; Phone Number: +97236900333; Email: vered.sason@dmc.org.il

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years to 85 years (Adult, Older Adult)
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: All

Description

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

Study Plan

How is the study designed?

Design Details

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

Number of Arms

2

Arms and Interventions

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

What is the study measuring?

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

Other Outcome Measures

Outcome Measure	Measure Description	Time Frame
Patients satisfaction evaluation using Satisfaction questionnaire	Patients fill out Satisfaction questionnaire	3 months intervention period
Patients Diet compliance evaluation	Diet Compliance measure using food logging application	3 months intervention period

Collaborators and Investigators

• Sponsor: DayTwo
• Investigators: Study Director: Davidi Bachrach, DayTwo COO

Publications and helpful links

General Publications

• Zeevi D, Korem T, Zmora N, Israeli D, Rothschild D, Weinberger A, Ben-Yacov O, Lador D, Avnit-Sagi T, Lotan-Pompan M, Suez J, Mahdi JA, Matot E, Malka G, Kosower N, Rein M, Zilberman-Schapira G, Dohnalova L, Pevsner-Fischer M, Bikovsky R, Halpern Z, Elinav E, Segal E. Personalized Nutrition by Prediction of Glycemic Responses. Cell. 2015 Nov 19;163(5):1079-1094. doi: 10.1016/j.cell.2015.11.001.

Helpful Links

• Personalized Nutrition by Prediction of Glycemic Responses

Study record dates

Study Major Dates

• Study Start (Actual): October 28, 2018
• Primary Completion (Anticipated): September 1, 2019
• Study Completion (Anticipated): March 1, 2020

Study Registration Dates

• First Submitted: September 2, 2018
• First Submitted That Met QC Criteria: September 5, 2018
• First Posted (Actual): September 7, 2018

Study Record Updates

• Last Update Posted (Actual): February 7, 2019
• Last Update Submitted That Met QC Criteria: February 6, 2019
• Last Verified: February 1, 2019

More Information

Terms related to this study

• Keywords: Diabetes Type 2
• Additional Relevant MeSH Terms: Glucose Metabolism Disorders; Metabolic Diseases; Endocrine System Diseases; Diabetes Mellitus; Diabetes Mellitus, Type 2
• Other Study ID Numbers: 003577

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: No

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No