Precision Medicine for Preventing Type 2 Diabetes: a Step Forward

NCT05147961 | Unknown

• 1 other identifier: University of Pisa _ Diabetes
• Study Type: interventional
• Target: 300
• Locations: 1 country
• Sites: 2

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.

Conditions

PreDiabetes

Keywords

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

Outcome Measures

Primary Outcomes (1)

• 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 Outcomes (4)

• Economic evaluation

  9 months

• Identification of clustering by a machine learning approach

  9 months

• Identification of abnormal microbiome and metabolome

  9 months

• Bioinformatics and systems biology methodologies

  9 months

Study Arms (2)

mHealth

EXPERIMENTAL

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

Standard care

ACTIVE COMPARATOR

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

Other: Standard care

Interventions

Digital Health OTHER

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

mHealth

Standard care OTHER

Conventional recommendations on diet and exercise

Standard care

Eligibility Criteria

• Age: 18 Years - 70 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• age of 18-70 years
• 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

You may not qualify if:

• 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

Sponsors & Collaborators

• University of Pisa: lead
• University of Florence: collaborator
• Azienda Ospedaliero, Universitaria Pisana: collaborator

Study Sites (2)

Azienda Ospedaliero-Universitaria Pisana

Pisa, 56124, Italy

RECRUITING

Stefano Del Prato

Pisa, 56124, Italy

ACTIVE NOT RECRUITING

MeSH Terms

Conditions

Prediabetic State; Obesity; Diabetes, Gestational

Interventions

Digital Health; Standard of Care

Condition Hierarchy (Ancestors)

Diabetes Mellitus; Glucose Metabolism Disorders; Metabolic Diseases; Nutritional and Metabolic Diseases; Endocrine System Diseases; Overweight; Overnutrition; Nutrition Disorders; Body Weight; Signs and Symptoms; Pathological Conditions, Signs and Symptoms; Pregnancy Complications; Female Urogenital Diseases and Pregnancy Complications; Urogenital Diseases

Intervention Hierarchy (Ancestors)

Delivery of Health Care; Patient Care Management; Health Services Administration; Health Care Quality, Access, and Evaluation; Quality Indicators, Health Care; Quality of Health Care

Study Officials

• Stefano Del Prato, MD

  Università di Pisa

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Stefano Del Prato, MD

CONTACT

+39050995103; stefano.delprato@unipi.it

Angela Dardano, MD, PhD

CONTACT

+39050995146; angela.dardano@unipi.it

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: NONE
• Purpose: PREVENTION
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Principal Investigator

Study Record Dates

• First Submitted: September 13, 2021
• First Posted: December 7, 2021
• Study Start: May 25, 2022
• Primary Completion: October 1, 2024
• Study Completion: April 1, 2025
• Last Updated: December 5, 2023

Record last verified: 2023-12

Data Sharing

• IPD Sharing: Will not share

Locations

IT (2)