PreDiabetesPRE-MED2NCT05147961
Precision Medicine for Preventing Type 2 Diabetes: a Step Forward
A study testing Digital Health in people with PreDiabetes, run by University of Pisa at 2 sites in Italy.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 to 70 yearsHealthy volunteers can take part
- Conditions the study is for
- PreDiabetes
- What is being tested?
- Digital Health
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 300 participants
- Where
- 2 sitesItaly
- Study dates
- 25 May 2022to 1 Apr 2025
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 70 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- 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
Who cannot join
- 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
Show all 11Show fewer
- 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
Trial locations and contacts
2 sites in 1 country.
Italy2 sites, 1 recruiting
- Recruiting
Azienda Ospedaliero-Universitaria Pisana
Pisa, 56124
Angela Dardano, MD, +39 050995146, angela.dardano@unipi.it
- Active, not recruiting
Stefano Del Prato
Pisa, 56124
About this study
Official title: 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.
Read the 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
- Being tested
- Digital Health, Standard care
- Keywords
- Prediabetes, Diabetes prevention, Precision medicine, Circulating miRNA, Wereable device, Data integration, Obesity, Gestational Diabetes, Personalized risk estimation, mHealth
- Medical subject headings
- Prediabetic State, Obesity, Diabetes, Gestational
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Digital HealthOther
Automated behavioral intervention via e-mail, web, and mobile phone
Standard careOther
Conventional recommendations on diet and exercise
| Study group | Role | What they receive |
|---|---|---|
| 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) Receives: Other: Digital Health |
| Standard care | Active comparator | Traditional recommendations (lifestyle modification) (phase 4 of the project) Receives: Other: Standard care |
What is being measured
The primary outcome is the main question the study is designed to answer.
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
Measured: 9 months
Secondary outcomes (4)
Economic evaluation
Cost-effectiveness of mHealth as compared to traditional approach for implementation of preventive measures
Measured: 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
Measured: 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.
Measured: 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.
Measured: 9 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted7 Dec 2021
- Study start25 May 2022
- Primary completion1 Oct 2024 (estimated)
- Study completion1 Apr 2025 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for PreDiabetesStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Prevention
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Enrolment
- 300 participants (estimated)
Sponsor and investigators
- Lead sponsor
- University of Pisa
- Sponsor type
- University, hospital or other
- Collaborators
- University of Florence, Azienda Ospedaliero, Universitaria Pisana
- Responsible party
- Principal investigator, Prof. Stefano Del Prato, University of Pisa
- Investigators
- Stefano Del Prato, MD, Principal investigator, Università di Pisa
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05147961
- Sponsor's protocol ID
- University of Pisa _ Diabetes
- Acronym
- PRE-MED2
- Status last verified
- Dec 2023
- First submitted
- 13 Sep 2021
- Last updated
- 5 Dec 2023
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 5 Dec 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Precision Medicine for Preventing Type 2 Diabetes: a Step Forward (PRE-MED2)
- ClinicalTrials.gov ID
- NCT05147961
- Acronym / sponsor ID
- PRE-MED2 / University of Pisa _ Diabetes
- Conditions
- PreDiabetes
- Keywords
- Prediabetes, Diabetes prevention, Precision medicine, Circulating miRNA, Wereable device, Data integration, Obesity, Gestational Diabetes
- Study type
- Interventional
- Recruitment status
- Unknown status
- Enrolment
- 300 participants (estimated)
- Lead sponsor
- University of PisaUniversity, hospital or other
- Collaborators
- University of Florence, Azienda Ospedaliero, Universitaria Pisana
- Study start
- 25 May 2022 (actual)
- Primary completion
- 1 Oct 2024 (estimated)
- Study completion
- 1 Apr 2025 (estimated)
- First posted
- 7 Dec 2021 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Primary purpose
- Prevention
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| mHealth | Experimental | Other: Digital HealthA 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) |
| Standard care | Active comparator | Other: Standard careTraditional recommendations (lifestyle modification) (phase 4 of the project) |
Who may qualify
- Ages
- 18 to 70 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (5 listed)
- 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
Key exclusion criteria (11 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Digital HealthAutomated behavioral intervention via e-mail, web, and mobile phone | Other | mHealth |
| Standard careConventional recommendations on diet and exercise | Other | Standard care |
Primary outcome
Development of type 2 diabetes, diagnosed by fasting or post-challenge plasma glucose concentrations meeting the American Diabetes Association criteria.
Time frame: 9 months
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
4 secondary outcomes
Economic evaluation
Time frame: 9 months
Identification of clustering by a machine learning approach
Time frame: 9 months
Identification of abnormal microbiome and metabolome
Time frame: 9 months
Bioinformatics and systems biology methodologies
Time frame: 9 months
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 5 Dec 2023.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Stefano Del Prato, MD principal investigator
Università di Pisa
2 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Digital Health in people with PreDiabetes, run by University of Pisa at 2 sites in Italy.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 5 inclusion and 11 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 5 Dec 2023. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 2 sites in Italy. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Stefano Del Prato, MD (+39050995103, stefano.delprato@unipi.it), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Development of type 2 diabetes, diagnosed by fasting or post-challenge plasma glucose concentrations meeting the American Diabetes Association criteria. (time frame: 9 months). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 5 Dec 2023.