Precision Medicine for Preventing Type 2 Diabetes: a Step Forward (PRE-MED2)

Precision Medicine for Preventing Type 2 Diabetes: a Step Forward (PRE-MED2)

The prevalence of type 2 diabetes (T2D) has been rising rapidly with an increased burden to the healthcare system. As such T2D prevention is highly recommendable, and, theoretically, it can definitely be successful. However, though feasible T2D prevention is difficult to implement due to the heterogeneity of the disease that make response to population intervention (and treatment) only partially successful. Precision medicine aims to prevent chronic diseases by tailoring interventions or recommendations to a combination of a genetic background, metabolic profile, and lifestyle. Classification of individuals at risk into clusters that differ in their susceptibility to develop T2D may foster the identification of preventive interventions. Recent advances in omics technologies have offered opportunities as well as challenges in the use of precision medicine to prevent T2D. Moreover, new mobile health (mHealth) technologies have enhanced how diabetes is managed. However, little is still known about the effectiveness of mHealth technology as intervention tools for reducing diabetes risk.

Study Overview

• Status: Recruiting
• Conditions: PreDiabetes
• Intervention / Treatment: Other: Digital Health; Other: Standard care

Detailed Description

Multicenter, interventional study (mHealth automated behavioral intervention versus traditional recommendations) designed: 1. toexplore the potential of more accurate subgroup distinction in prediabetes that may help to deliver a more effective preventive strategy with the final goal to enhance the possibility to prevent or delay the development of type 2 diabetes; 2. toexplore the use of mHealth to modify lifestyle in a subgroup of subjects known for their elevated risk of developing type 2 diabetes (i.e. obese and women with previous gestational diabetes) and to determine the impact of such strategies on the basis of individual characterization.

Phase 1: 1200 subjects at high risk of developing type 2 diabetes will be enrolled based on an opportunistic approach (FINDRISK questionnaire).The questionnaire will be made available at GP's offices, Pharmacies as well as through media.Moreover, the infrastructure for data collection and patient interventions will be developed.

Phase 2: all individuals will be characterized on the basis of diet habits (EPIC questionnaire; Binge Eating Scale) and physical activity (by a wrist-worn wearable device) as well metabolic profile (complete blood count, creatinine, plasma glucose and insulin, HbA1c, liver function tests, total cholesterol, HDL cholesterol, triglycerides, urine test, auto-antibody anti-GAD, and A/C ratio on urine spot sample; 75-g oral glucose tolerance test; HOMA-B and HOMA-IR)for identification of special subgroups.Circulating RNA and miRNAwill be extracted from lymphocytes and plasmafor identification ofbiomarkers for prediction of risk of disease and new targets for preventive intervention. A biobank of serum, urine and stool samples will be also collected genetic characterization and for omics profiling.

Phase 3, all lab determination and cluster analysis will be performed. All data will be integrated in the infrastructurefor the identification of new relevant factors and indicators useful for better understanding health conditions and outcomesand for the analysis of discrete risk subtypes (cluster).

Phase 4: the validity of themHealth approach on the metabolic and lifestyle attitude as a function of the individual characterization as obtained in Phase 3 will be tested in the exploratory clinical trial.ThemHealth automated behavioral intervention via E-mail, web, and mobile phone will be developed and tested in a trial in two high-risk populations of obese non-diabetic subjects (n=150) and women with previous gestational diabetes (n=150). These subjects will be randomized 1:1 to either 9-month conventional recommendation for correct lifestyle based on the procedures described in the Diabetes Prevention Programme or mHealth automated behavioral intervention via E-mail, web, and mobile phone. Subjects will be seen at 3-month interval for recording of anthropometric measurements and determination of fasting plasma insulin and glucose as well as lipid profile. During the last two weeks of the intervention trial all subjects will be provided with the same wearable device used for initial characterization for recording of the same initial parameters. At completion of the follow-up all initial measurements will be repeated.Data will then be analyzed as changes vs. baselines between the two groups as well as according to any sub-group.

• Study Type: Interventional
• Enrollment (Estimated): 300
• Phase: Not Applicable

Contacts and Locations

• Study Contact: Name: Stefano Del Prato, MD; Phone Number: +39050995103; Email: stefano.delprato@unipi.it
• Study Contact Backup: Name: Angela Dardano, MD, PhD; Phone Number: +39050995146; Email: angela.dardano@unipi.it

Study Locations

• Italy
  • Pisa, Italy, 56124
    • Recruiting
    • Azienda Ospedaliero-Universitaria Pisana
    • Contact: Angela Dardano, MD; Phone Number: +39 050995146; Email: angela.dardano@unipi.it
  • Pisa, Italy, 56124
    • Active, not recruiting
    • Stefano Del Prato

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years to 70 years (Adult, Older Adult)
• Accepts Healthy Volunteers: Yes

Description

Inclusion Criteria:

• age of 18-70 years
• 12 points or more in the Finnish diabetes risk score or previous gestational diabetes or obese subjects
• technology skills (computers, smartphones, tablets with internet connection)
• absence of language barriers
• ability to provide written informed consent to the study

Exclusion Criteria:

• Established diagnosis of diabetes
• Pregnancy and breastfeeding
• Renal or hepatic failure
• Severe cardiovascular, neurological, hematological, endocrinological, gastrointestinal, nephrological or pneumological affections that may interfere with the study
• Ongoing treatment with antidiabetics, diuretics, glucocorticoids, antypsychoticsoral contraceptives or other drugs known to affect glucose metabolism.
• History of pancreatitis
• Alcohol abuse or abuse of psychoactive substances
• Subjects with mental disorders, or predictably unfit to understand and issue valid written informed consent to the study
• Subjects with mental disorders, or not suitable for understanding and performing the tasks required by the study
• Bariatric surgery
• Current cancer or less than 6 months from the end of cancer treatment

Study Plan

How is the study designed?

Design Details

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

Number of Arms

2

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Experimental: mHealth; A mHealth automated behavioral intervention via E-mail, web, and mobile phone will be developed and tested in the intervention trial trial (phase 4 of the project)	Other: Digital Health; Automated behavioral intervention via e-mail, web, and mobile phone
Active Comparator: Standard care; Traditional recommendations (lifestyle modification) (phase 4 of the project)	Other: Standard care; Conventional recommendations on diet and exercise

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Development of type 2 diabetes, diagnosed by fasting or post-challenge plasma glucose concentrations meeting the American Diabetes Association criteria.	Number of subjects with a fasting glycemia ≥ 126 mg/dl or 2-h glycemia ≥200 mg/dl after ingestion of 75-g oral glucose load	9 months

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Economic evaluation	Cost-effectiveness of mHealth as compared to traditional approach for implementation of preventive measures	9 months
Identification of clustering by a machine learning approach	Rate of subjects with a different risk factor to develop type 2 diabetes identified by splitting the collected data by a machine learning algorithms	9 months
Identification of abnormal microbiome and metabolome	Number of subjects with abnormal microbiome and metabolome evaluated using sample type, feces, and others biosamples, such as urine, plasma/serum and analyzed by by reverse-phase ultra-high performance liquid chromatography-tandem mass spectrometry.	9 months
Bioinformatics and systems biology methodologies	Number of subjects estimated at risk of type 2 diabetes on the basis of the genomic profiles of the individuals.	9 months

Collaborators and Investigators

• Sponsor: University of Pisa
• Collaborators: Azienda Ospedaliero, Universitaria Pisana; University of Florence
• Investigators: Principal Investigator: Stefano Del Prato, MD, Universita di Pisa

Study record dates

Study Major Dates

• Study Start (Actual): May 25, 2022
• Primary Completion (Estimated): October 1, 2024
• Study Completion (Estimated): April 1, 2025

Study Registration Dates

• First Submitted: September 13, 2021
• First Submitted That Met QC Criteria: November 23, 2021
• First Posted (Actual): December 7, 2021

Study Record Updates

• Last Update Posted (Estimated): December 5, 2023
• Last Update Submitted That Met QC Criteria: December 4, 2023
• Last Verified: December 1, 2023

More Information

Terms related to this study

• Keywords: Obesity; Prediabetes; mHealth; Precision medicine; Gestational Diabetes; Diabetes prevention; Circulating miRNA; Wereable device; Data integration; Personalized risk estimation
• Additional Relevant MeSH Terms: Glucose Metabolism Disorders; Metabolic Diseases; Endocrine System Diseases; Diabetes Mellitus; Hyperglycemia; Prediabetic State; Glucose Intolerance
• Other Study ID Numbers: University of Pisa _ Diabetes

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