search
Back to results

Personalized Nutrition for Diabetes Type 2

Primary Purpose

Diabetes Type 2

Status
Unknown status
Phase
Not Applicable
Locations
Israel
Study Type
Interventional
Intervention
Algorithm-based diet
ADA- based diet
Sponsored by
DayTwo
About
Eligibility
Locations
Arms
Outcomes
Full info

About this trial

This is an interventional treatment trial for Diabetes Type 2 focused on measuring Diabetes Type 2

Eligibility Criteria

18 Years - 85 Years (Adult, Older Adult)All SexesDoes not accept healthy volunteers

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

Sites / Locations

  • The Edith Wolfson Medical CenterRecruiting
  • Diabetes Medical CenterRecruiting

Arms of the Study

Arm 1

Arm 2

Arm Type

Experimental

Other

Arm Label

Algorithm-based diet

ADA- based diet

Arm Description

Subjects randomized to this arm will receive personally tailored dietary recommendations based on their predicted glycemic responses according to the study algorithm.

Subjects randomized to this arm will receive nutritional recommendations according to the standard American dietary approach for treating diabetes

Outcomes

Primary Outcome Measures

Mean change in HbA1C from the baseline level
HbA1C
Evaluation of the total daily time of plasma glucose levels
Time in Range ▪ CGM glucose levels are between 70 to 180 mg/dl

Secondary Outcome Measures

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

Full Information

First Posted
September 2, 2018
Last Updated
February 6, 2019
Sponsor
DayTwo
search

1. Study Identification

Unique Protocol Identification Number
NCT03662217
Brief Title
Personalized Nutrition for Diabetes Type 2
Official Title
Personalized Nutrition for Diabetes Type 2
Study Type
Interventional

2. Study Status

Record Verification Date
February 2019
Overall Recruitment Status
Unknown status
Study Start Date
October 28, 2018 (Actual)
Primary Completion Date
September 2019 (Anticipated)
Study Completion Date
March 2020 (Anticipated)

3. Sponsor/Collaborators

Responsible Party, by Official Title
Sponsor
Name of the Sponsor
DayTwo

4. Oversight

Studies a U.S. FDA-regulated Drug Product
No
Studies a U.S. FDA-regulated Device Product
No
Data Monitoring Committee
No

5. Study Description

Brief Summary
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
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.

6. Conditions and Keywords

Primary Disease or Condition Being Studied in the Trial, or the Focus of the Study
Diabetes Type 2
Keywords
Diabetes Type 2

7. Study Design

Primary Purpose
Treatment
Study Phase
Not Applicable
Interventional Study Model
Parallel Assignment
Masking
None (Open Label)
Allocation
Randomized
Enrollment
200 (Anticipated)

8. Arms, Groups, and Interventions

Arm Title
Algorithm-based diet
Arm Type
Experimental
Arm Description
Subjects randomized to this arm will receive personally tailored dietary recommendations based on their predicted glycemic responses according to the study algorithm.
Arm Title
ADA- based diet
Arm Type
Other
Arm Description
Subjects randomized to this arm will receive nutritional recommendations according to the standard American dietary approach for treating diabetes
Intervention Type
Other
Intervention Name(s)
Algorithm-based diet
Intervention Description
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
Intervention Type
Other
Intervention Name(s)
ADA- based diet
Intervention Description
The American standard of care dietary guidelines for diabetes.
Primary Outcome Measure Information:
Title
Mean change in HbA1C from the baseline level
Description
HbA1C
Time Frame
3 months intervention period
Title
Evaluation of the total daily time of plasma glucose levels
Description
Time in Range ▪ CGM glucose levels are between 70 to 180 mg/dl
Time Frame
3 months intervention period
Secondary Outcome Measure Information:
Title
Evaluation of the total daily time of plasma glucose levels
Description
Total daily time of CGM glucose levels below 70 mg/dl (Hypoglycemia incidents)
Time Frame
3 months intervention period
Title
Evaluation of the total daily time of plasma glucose levels
Description
Time in Range ▪ CGM glucose levels are between 70 to 140 mg/dl
Time Frame
3 months intervention period
Title
Mean change in ADRR from the baseline level
Description
ADRR
Time Frame
3 months intervention period
Title
Mean change in BGRI from the baseline level
Description
BGRI
Time Frame
3 months intervention period
Title
Mean change in LBGI from the baseline level
Description
LBGI
Time Frame
3 months intervention period
Title
Mean change in HBGI from the baseline level
Description
HBGI
Time Frame
3 months intervention period
Title
Mean change in MAGE from the baseline level
Description
MAGE
Time Frame
3 months intervention period
Title
Mean change in CV glucose % from the baseline level
Description
CV glucose %
Time Frame
3 months intervention period
Title
Mean change in Glucose from the baseline level
Description
Mean glucose
Time Frame
3 months intervention period
Title
Mean change in Standard deviation of glucose from the baseline level
Description
Standard deviation of glucose
Time Frame
3 months intervention period
Title
Mean change in CONGA from the baseline level
Description
CONGA
Time Frame
3 months intervention period
Title
Change in Weight from baseline
Description
Weight
Time Frame
3 months intervention period
Title
Change in HbA1C from the baseline level
Description
Percentage of patients with HbA1C <8%
Time Frame
3 months intervention period
Title
Change in HbA1C from the baseline level
Description
Percentage of patients with HbA1C <7%
Time Frame
3 months intervention period
Title
change in HbA1C from the baseline level
Description
Percentage of patients with HbA1C <6.5%
Time Frame
3 months intervention period
Title
Change in Lipid profile parameters
Description
Lipid profile
Time Frame
3 months intervention period
Title
Change in Liver function parameters
Description
Liver function test
Time Frame
3 months intervention period
Title
Change in Creatinine parameter
Description
Creatinine
Time Frame
3 months intervention period
Title
Change in Fructosamin parameter
Description
Fructosamin
Time Frame
3 months intervention period
Other Pre-specified Outcome Measures:
Title
Patients satisfaction evaluation using Satisfaction questionnaire
Description
Patients fill out Satisfaction questionnaire
Time Frame
3 months intervention period
Title
Patients Diet compliance evaluation
Description
Diet Compliance measure using food logging application
Time Frame
3 months intervention period

10. Eligibility

Sex
All
Minimum Age & Unit of Time
18 Years
Maximum Age & Unit of Time
85 Years
Accepts Healthy Volunteers
No
Eligibility Criteria
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
Central Contact Person:
First Name & Middle Initial & Last Name or Official Title & Degree
Rony Bikovsky
Phone
+972542299300
Email
rony.bikovsky@daytwo.com
First Name & Middle Initial & Last Name or Official Title & Degree
Tal Ofek, Ph.d
Phone
+972505658786
Email
tal.ofek@daytwo.com
Overall Study Officials:
First Name & Middle Initial & Last Name & Degree
Davidi Bachrach
Organizational Affiliation
DayTwo COO
Official's Role
Study Director
Facility Information:
Facility Name
The Edith Wolfson Medical Center
City
H̱olon
ZIP/Postal Code
5822012
Country
Israel
Individual Site Status
Recruiting
Facility Contact:
First Name & Middle Initial & Last Name & Degree
Tali Ganz
Phone
+972523374030
Email
taliganz@wmc.gov.il
First Name & Middle Initial & Last Name & Degree
Julio Weinstaine, MD
Facility Name
Diabetes Medical Center
City
Tel Aviv
ZIP/Postal Code
6937947
Country
Israel
Individual Site Status
Recruiting
Facility Contact:
First Name & Middle Initial & Last Name & Degree
Vered Sason
Phone
+97236900333
Email
vered.sason@dmc.org.il

12. IPD Sharing Statement

Plan to Share IPD
No
Citations:
PubMed Identifier
26590418
Citation
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.
Results Reference
result

Learn more about this trial

Personalized Nutrition for Diabetes Type 2

We'll reach out to this number within 24 hrs