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Personalized Nutrition for Diabetes Type 2

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ClinicalTrials.gov Identifier: NCT03662217
Recruitment Status : Recruiting
First Posted : September 7, 2018
Last Update Posted : February 7, 2019
Sponsor:
Information provided by (Responsible Party):
DayTwo

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.


Condition or disease Intervention/treatment Phase
Diabetes Type 2 Other: Algorithm-based diet Other: ADA- based diet Not Applicable

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.


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Study Type : Interventional  (Clinical Trial)
Estimated Enrollment : 200 participants
Allocation: Randomized
Intervention Model: Parallel Assignment
Masking: None (Open Label)
Primary Purpose: Treatment
Official Title: Personalized Nutrition for Diabetes Type 2
Actual Study Start Date : October 28, 2018
Estimated Primary Completion Date : September 2019
Estimated Study Completion Date : March 2020

Resource links provided by the National Library of Medicine


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

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.




Primary Outcome Measures :
  1. Mean change in HbA1C from the baseline level [ Time Frame: 3 months intervention period ]
    HbA1C

  2. Evaluation of the total daily time of plasma glucose levels [ Time Frame: 3 months intervention period ]
    Time in Range ▪ CGM glucose levels are between 70 to 180 mg/dl


Secondary Outcome Measures :
  1. Evaluation of the total daily time of plasma glucose levels [ Time Frame: 3 months intervention period ]
    Total daily time of CGM glucose levels below 70 mg/dl (Hypoglycemia incidents)

  2. Evaluation of the total daily time of plasma glucose levels [ Time Frame: 3 months intervention period ]
    Time in Range ▪ CGM glucose levels are between 70 to 140 mg/dl

  3. Mean change in ADRR from the baseline level [ Time Frame: 3 months intervention period ]
    ADRR

  4. Mean change in BGRI from the baseline level [ Time Frame: 3 months intervention period ]
    BGRI

  5. Mean change in LBGI from the baseline level [ Time Frame: 3 months intervention period ]
    LBGI

  6. Mean change in HBGI from the baseline level [ Time Frame: 3 months intervention period ]
    HBGI

  7. Mean change in MAGE from the baseline level [ Time Frame: 3 months intervention period ]
    MAGE

  8. Mean change in CV glucose % from the baseline level [ Time Frame: 3 months intervention period ]
    CV glucose %

  9. Mean change in Glucose from the baseline level [ Time Frame: 3 months intervention period ]
    Mean glucose

  10. Mean change in Standard deviation of glucose from the baseline level [ Time Frame: 3 months intervention period ]
    Standard deviation of glucose

  11. Mean change in CONGA from the baseline level [ Time Frame: 3 months intervention period ]
    CONGA

  12. Change in Weight from baseline [ Time Frame: 3 months intervention period ]
    Weight

  13. Change in HbA1C from the baseline level [ Time Frame: 3 months intervention period ]
    Percentage of patients with HbA1C <8%

  14. Change in HbA1C from the baseline level [ Time Frame: 3 months intervention period ]
    Percentage of patients with HbA1C <7%

  15. change in HbA1C from the baseline level [ Time Frame: 3 months intervention period ]
    Percentage of patients with HbA1C <6.5%

  16. Change in Lipid profile parameters [ Time Frame: 3 months intervention period ]
    Lipid profile

  17. Change in Liver function parameters [ Time Frame: 3 months intervention period ]
    Liver function test

  18. Change in Creatinine parameter [ Time Frame: 3 months intervention period ]
    Creatinine

  19. Change in Fructosamin parameter [ Time Frame: 3 months intervention period ]
    Fructosamin


Other Outcome Measures:
  1. Patients satisfaction evaluation using Satisfaction questionnaire [ Time Frame: 3 months intervention period ]
    Patients fill out Satisfaction questionnaire

  2. Patients Diet compliance evaluation [ Time Frame: 3 months intervention period ]
    Diet Compliance measure using food logging application



Information from the National Library of Medicine

Choosing to participate in a study is an important personal decision. Talk with your doctor and family members or friends about deciding to join a study. To learn more about this study, you or your doctor may contact the study research staff using the contacts provided below. For general information, Learn About Clinical Studies.


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Ages Eligible for Study:   18 Years to 85 Years   (Adult, Older Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   No
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

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT03662217


Contacts
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Contact: Rony Bikovsky +972542299300 rony.bikovsky@daytwo.com
Contact: Tal Ofek, Ph.d +972505658786 tal.ofek@daytwo.com

Locations
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Israel
The Edith Wolfson Medical Center Recruiting
H̱olon, Israel, 5822012
Contact: Tali Ganz    +972523374030    taliganz@wmc.gov.il   
Principal Investigator: Julio Weinstaine, MD         
Diabetes Medical Center Recruiting
Tel Aviv, Israel, 6937947
Contact: Vered Sason    +97236900333    vered.sason@dmc.org.il   
Sponsors and Collaborators
DayTwo
Investigators
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Study Director: Davidi Bachrach DayTwo COO

Additional Information:
Publications of Results:
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Responsible Party: DayTwo
ClinicalTrials.gov Identifier: NCT03662217     History of Changes
Other Study ID Numbers: 003577
First Posted: September 7, 2018    Key Record Dates
Last Update Posted: February 7, 2019
Last Verified: February 2019
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: No

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No

Keywords provided by DayTwo:
Diabetes Type 2

Additional relevant MeSH terms:
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Diabetes Mellitus
Diabetes Mellitus, Type 2
Glucose Metabolism Disorders
Metabolic Diseases
Endocrine System Diseases