Pre-diabetes
Conditions
Brief summary
Diabetes/ prediabetes is a substantial problem in India not only because it itself can be associated with morbidities such as coronary artery disease but also because it is a point of importance for the prevention of other diseases. It is not clear if a high protein calorie diet in the Indian population associated with a heightened tendency for prediabetes, metabolic syndrome, atherosclerosis and dysmetabolic state etc. could, besides lifestyle factors, be related to diet, or interaction between the two. The analysis of whole blood transcriptomes and plasma metabolomics profiles may be a potentially useful tool for the assessment of metabolic health status. The proposed study is the first to perform a detailed gene expression profiling with the use of next-generation sequencing technology to assess the differences in molecular mechanisms in the peripheral blood of subjects with prediabetes.
Detailed description
This study will be conducted at the outpatient department of the National Diabetes, Obesity and Cholesterol Foundation (N-DOC), Diabetes Foundation (India) (DFI)and Fortis-C-DOC, Centre of Excellence for Diabetes, Metabolic Diseases and Endocrinology, New Delhi, India. Experimental Protocol: In this study, a total of 60 subjects with pre-diabetes and 60 age and body mass index, and gender-matched controls (age 18-40 years) will be recruited. They will be randomized to an equicaloric normal diet (carbohydrates 60%, protein 15%, and fat 25%) or a high protein diet (carbohydrates 45%, protein 29%, and fat 26%) over 8-week periods. All assessments as done on day 1 will be repeated at day 60.
Interventions
OTHERIntervention
high protein diet (carbohydrates 45%, protein 29%, and fat 26%) over 8-week periods.
Sponsors
Diabetes Foundation, India
Study design
Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
PREVENTION
Masking
DOUBLE (Subject, Investigator)
Eligibility
Sex/Gender
ALL
Age
18 Years to 50 Years
Healthy volunteers
Yes
Exclusion criteria
* Acute infections and advanced end-organ damage. * History of hepatitis or pancreatitis, abnormal liver and renal function tests. * Recent (\<3 months) changes in weight (≥5%) and/or weight changing medications. * Subjects with metallic implants, pacemaker leads, radioactive seeds or surgical staples in the body. Inclusion Criteria Cases: * Age 18 to 50 years. Subjects with pre-diabetes on oral glucose tolerance test (OGTT) * Fasting blood glucose≥100mg/dl and \<126 mg/dl or 2-h plasma glucose ≥140mg/dl and \<200mg/dl (after ingestion of 75 grams of anhydrous oral glucose) or both. Controls: * Age 18 to 50 years. Normoglycemia by OGTT * Fasting blood glucose \<100mg/dl and 2-h plasma glucose \<140mg/dl (after ingestion of 75 gram of anhydrous oral glucose).
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Body Mass Index | 6 Months | BMI will be calculated by using formula weight (Kg)/height (m2). |
| Body Fat | 2 MONTHS | Body estimations will be done by DEXA Scan |
| Liver Fat | 2 Months | Liver fat will be done by MRI Scan |
| Next Generation sequencing | 06 MONTHS | — |
| Pancreatic fat | 06 MONTHS | Pancreatic fat estimation will be done by MRI |
| RNA Extraction | 06 MONTHS | — |
| Metabolomics profile | 06 MONTHS | — |
Contacts
Primary ContactAnoop Misra, MD
Backup ContactSurya Prakash, PhD+Post Doc
Outcome results
None listed