Precision Medicine for Preventing Type 2 Diabetes: a Step Forward

NCT ID: NCT05147961

Last Updated: 2023-12-05

Basic Information

• Recruitment Status: RECRUITING
• Clinical Phase: NA
• Total Enrollment: 300 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2022-05-25
• Study Completion Date: 2025-04-01

Brief Summary

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.

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.

Conditions

PreDiabetes

Keywords

Prediabetes; Diabetes prevention; Precision medicine; Circulating miRNA; Wereable device; Data integration; Obesity; Gestational Diabetes; Personalized risk estimation; mHealth

Study Design

• Allocation Method: RANDOMIZED
• Intervention Model: PARALLEL
• Primary Study Purpose: PREVENTION
• Blinding Strategy: NONE

Study Groups

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)

Group Type: EXPERIMENTAL

Digital Health

Intervention Type: OTHER

Automated behavioral intervention via e-mail, web, and mobile phone

Standard care

Traditional recommendations (lifestyle modification) (phase 4 of the project)

Group Type: ACTIVE_COMPARATOR

Standard care

Intervention Type: OTHER

Conventional recommendations on diet and exercise

Interventions

Digital Health

Automated behavioral intervention via e-mail, web, and mobile phone

Intervention Type: OTHER

Standard care

Conventional recommendations on diet and exercise

Intervention Type: OTHER

Eligibility Criteria

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

• Minimum Eligible Age: 18 Years
• Maximum Eligible Age: 70 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

University of Florence

OTHER

Sponsor Role: collaborator

Azienda Ospedaliero, Universitaria Pisana

OTHER

Sponsor Role: collaborator

University of Pisa

OTHER

Sponsor Role: lead

Responsible Party

Prof. Stefano Del Prato

Principal Investigator

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

Stefano Del Prato, MD

Role: PRINCIPAL_INVESTIGATOR

Università di Pisa

Locations

Azienda Ospedaliero-Universitaria Pisana

Pisa, , Italy

Site Status: RECRUITING

Stefano Del Prato

Pisa, , Italy

Site Status: ACTIVE_NOT_RECRUITING

Countries

Italy

Central Contacts

Stefano Del Prato, MD

Role: CONTACT

Phone: +39050995103

Email: stefano.delprato@unipi.it

Angela Dardano, MD, PhD

Role: CONTACT

Phone: +39050995146

Email: angela.dardano@unipi.it

Facility Contacts

Angela Dardano, MD

Role: primary

Other Identifiers

University of Pisa _ Diabetes

Identifier Type: -

Identifier Source: org_study_id