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

Primary Purpose

PreDiabetes

Status
Recruiting
Phase
Not Applicable
Locations
Italy
Study Type
Interventional
Intervention
Digital Health
Standard care
Sponsored by
University of Pisa
About
Eligibility
Locations
Arms
Outcomes
Full info

About this trial

This is an interventional prevention trial for PreDiabetes focused on measuring Prediabetes, Diabetes prevention, Precision medicine, Circulating miRNA, Wereable device, Data integration, Obesity, Gestational Diabetes, Personalized risk estimation, mHealth

Eligibility Criteria

18 Years - 70 Years (Adult, Older Adult)All SexesAccepts Healthy Volunteers

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

Sites / Locations

  • Azienda Ospedaliero-Universitaria PisanaRecruiting
  • Stefano Del Prato

Arms of the Study

Arm 1

Arm 2

Arm Type

Experimental

Active Comparator

Arm Label

mHealth

Standard care

Arm Description

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)

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

Outcomes

Primary Outcome Measures

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

Secondary Outcome Measures

Economic evaluation
Cost-effectiveness of mHealth as compared to traditional approach for implementation of preventive measures
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
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.
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.

Full Information

First Posted
September 13, 2021
Last Updated
November 7, 2022
Sponsor
University of Pisa
Collaborators
University of Florence, Azienda Ospedaliero, Universitaria Pisana
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1. Study Identification

Unique Protocol Identification Number
NCT05147961
Brief Title
Precision Medicine for Preventing Type 2 Diabetes: a Step Forward
Acronym
PRE-MED2
Official Title
Precision Medicine for Preventing Type 2 Diabetes: a Step Forward (PRE-MED2)
Study Type
Interventional

2. Study Status

Record Verification Date
November 2022
Overall Recruitment Status
Recruiting
Study Start Date
May 25, 2022 (Actual)
Primary Completion Date
October 1, 2024 (Anticipated)
Study Completion Date
April 1, 2025 (Anticipated)

3. Sponsor/Collaborators

Responsible Party, by Official Title
Principal Investigator
Name of the Sponsor
University of Pisa
Collaborators
University of Florence, Azienda Ospedaliero, Universitaria Pisana

4. Oversight

Studies a U.S. FDA-regulated Drug Product
No
Studies a U.S. FDA-regulated Device Product
No
Data Monitoring Committee
No

5. Study Description

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.

6. Conditions and Keywords

Primary Disease or Condition Being Studied in the Trial, or the Focus of the Study
PreDiabetes
Keywords
Prediabetes, Diabetes prevention, Precision medicine, Circulating miRNA, Wereable device, Data integration, Obesity, Gestational Diabetes, Personalized risk estimation, mHealth

7. Study Design

Primary Purpose
Prevention
Study Phase
Not Applicable
Interventional Study Model
Parallel Assignment
Masking
None (Open Label)
Allocation
Randomized
Enrollment
300 (Anticipated)

8. Arms, Groups, and Interventions

Arm Title
mHealth
Arm Type
Experimental
Arm Description
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)
Arm Title
Standard care
Arm Type
Active Comparator
Arm Description
Traditional recommendations (lifestyle modification) (phase 4 of the project)
Intervention Type
Other
Intervention Name(s)
Digital Health
Intervention Description
Automated behavioral intervention via e-mail, web, and mobile phone
Intervention Type
Other
Intervention Name(s)
Standard care
Intervention Description
Conventional recommendations on diet and exercise
Primary Outcome Measure Information:
Title
Development of type 2 diabetes, diagnosed by fasting or post-challenge plasma glucose concentrations meeting the American Diabetes Association criteria.
Description
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
Time Frame
9 months
Secondary Outcome Measure Information:
Title
Economic evaluation
Description
Cost-effectiveness of mHealth as compared to traditional approach for implementation of preventive measures
Time Frame
9 months
Title
Identification of clustering by a machine learning approach
Description
Rate of subjects with a different risk factor to develop type 2 diabetes identified by splitting the collected data by a machine learning algorithms
Time Frame
9 months
Title
Identification of abnormal microbiome and metabolome
Description
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.
Time Frame
9 months
Title
Bioinformatics and systems biology methodologies
Description
Number of subjects estimated at risk of type 2 diabetes on the basis of the genomic profiles of the individuals.
Time Frame
9 months

10. Eligibility

Sex
All
Minimum Age & Unit of Time
18 Years
Maximum Age & Unit of Time
70 Years
Accepts Healthy Volunteers
Accepts Healthy Volunteers
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
Central Contact Person:
First Name & Middle Initial & Last Name or Official Title & Degree
Stefano Del Prato, MD
Phone
+39050995103
Email
stefano.delprato@unipi.it
First Name & Middle Initial & Last Name or Official Title & Degree
Angela Dardano, MD, PhD
Phone
+39050995146
Email
angela.dardano@unipi.it
Overall Study Officials:
First Name & Middle Initial & Last Name & Degree
Stefano Del Prato, MD
Organizational Affiliation
Università di Pisa
Official's Role
Principal Investigator
Facility Information:
Facility Name
Azienda Ospedaliero-Universitaria Pisana
City
Pisa
ZIP/Postal Code
56124
Country
Italy
Individual Site Status
Recruiting
Facility Contact:
First Name & Middle Initial & Last Name & Degree
Angela Dardano, MD
Phone
+39 050995146
Email
angela.dardano@unipi.it
Facility Name
Stefano Del Prato
City
Pisa
ZIP/Postal Code
56124
Country
Italy
Individual Site Status
Active, not recruiting

12. IPD Sharing Statement

Plan to Share IPD
No

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Precision Medicine for Preventing Type 2 Diabetes: a Step Forward

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