Diabetes Type 2
Conditions
Keywords
Diabetes Type 2
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.
Interventions
OTHERAlgorithm-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
OTHERADA- based diet
The American standard of care dietary guidelines for diabetes.
Sponsors
DayTwo
Study design
Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
TREATMENT
Masking
NONE
Eligibility
Sex/Gender
ALL
Age
18 Years to 85 Years
Healthy volunteers
No
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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Evaluation of the total daily time of plasma glucose levels | 3 months intervention period | Time in Range ▪ CGM glucose levels are between 70 to 180 mg/dl |
| Mean change in HbA1C from the baseline level | 3 months intervention period | HbA1C |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Mean change in ADRR from the baseline level | 3 months intervention period | ADRR |
| Mean change in BGRI from the baseline level | 3 months intervention period | BGRI |
| Mean change in LBGI from the baseline level | 3 months intervention period | LBGI |
| Mean change in HBGI from the baseline level | 3 months intervention period | HBGI |
| Mean change in MAGE from the baseline level | 3 months intervention period | MAGE |
| Mean change in CV glucose % from the baseline level | 3 months intervention period | CV glucose % |
| Mean change in Glucose from the baseline level | 3 months intervention period | Mean glucose |
| Mean change in CONGA from the baseline level | 3 months intervention period | CONGA |
| Change in Weight from baseline | 3 months intervention period | Weight |
| Change in HbA1C from the baseline level | 3 months intervention period | Percentage of patients with HbA1C \<8% |
| change in HbA1C from the baseline level | 3 months intervention period | Percentage of patients with HbA1C \<6.5% |
| Change in Lipid profile parameters | 3 months intervention period | Lipid profile |
| Change in Liver function parameters | 3 months intervention period | Liver function test |
| Change in Creatinine parameter | 3 months intervention period | Creatinine |
| Change in Fructosamin parameter | 3 months intervention period | Fructosamin |
| Mean change in Standard deviation of glucose from the baseline level | 3 months intervention period | Standard deviation of glucose |
| Evaluation of the total daily time of plasma glucose levels | 3 months intervention period | Total daily time of CGM glucose levels below 70 mg/dl (Hypoglycemia incidents) |
Other
| Measure | Time frame | Description |
|---|---|---|
| Patients satisfaction evaluation using Satisfaction questionnaire | 3 months intervention period | Patients fill out Satisfaction questionnaire |
| Patients Diet compliance evaluation | 3 months intervention period | Diet Compliance measure using food logging application |
Countries
Israel
Contacts
Primary ContactRony Bikovsky
Backup ContactTal Ofek, Ph.d
Outcome results
None listed