Diabetes Risk Diagnosis and Management

NCT03949504 | Completed

• 1 other identifier: DEP3_2019
• Study Type: observational
• Target: 1,500
• Locations: 1 country
• Sites: 1

Brief Summary

Considering the multifactorial nature of the complications of Type 2 diabetes, such as cardiovascular and neurological complications and therefore the multiplicity of risk factors that contribute to their development, it is assumed that the use of a dedicated function of the MyStar Connect software (beta version), that allows the querying of the application through specific queries (presence of risk factors) and the calculation of specific risk scores in order to extract the patients most at risk of developing such complications, can provide support to the diabetologist to optimize management of the patient at risk and complicated through, for example, a more intensive visit program and this then translates into an improvement in the parameters related to these risk factors.

Conditions

Diabetes Mellitus, Type 2

Keywords

Diabetes; neurological complications; cardiovascular complications

Outcome Measures

Primary Outcomes (3)

• Number of subjects at high risk for Diabetes

  Risk for type 2 diabetes measured by a structured questionnaires with 11 questions scoring from -1 to 27 and classified in 5 risk categories: Low risk (score\>6); Low-medium risk (score 7-11); Medium-high risk (score 12-14); high risk (score 15-20); very high risk (score \>20).

  12 months

• Number of subjects with diabetes

  diabetes diagnosis will be based on fasting plasma glucose (FPG)≥126 mg/dl. The testswill be performed in a laboratory using a certified method. Fasting is defined as no caloric intake for at least 8 h.

  18 months

• Concentration of glucose in plasma

  Glucose control in patients with diabetes and cardiovascular or neurological complications, measured with plasma glucose criteria, defined as FPG \<126 mg/dL (7.0 mmol/L).

  24 months

Study Arms (2)

General population

Male and Female subjects (n=1000) over 34 years of age randomly recruited from the participants to the recall phase of the Moli-sani study

Procedure: New Find-risk questionnaire software

Patients with type 2 Diabetes

Male and Female patients with type 2 diabetes (n=550) without (n=200) or with cardiovascular (n=200) or neurological (n=150) complications consecutively admitted to the IRCCS Neuromed

Interventions

New Find-risk questionnaire software PROCEDURE

FINDrisk: software with 12 questions to define the risk of diabetes in the general population MyStar Connect: software to guide the diagnosis and the management of diabetes and its complications

Also known as: MyStar Connect software (version beta)

General population

Eligibility Criteria

• Age: 34 Years - 100 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Consecutive male and female subjects, over 34 years of age recruited in teh framework of the recall phase of the Moli-sani study. Male and Female patients with type 2 diabetes without or with cardiovascular or neurological complications

You may qualify if:

• Signature of informed consent;
• Subjects recruited consecutively during the recall phase of the Moli-sani project.
• Patients with type 2 diabetes mellitus and vascular or neurological complications attending the IRCCS Neuromed

You may not qualify if:

• Inability to understand and to want
• Refusal to sign informed consent.
• Type 1 diabetes
• Gestational diabetes.
• For subjects of the general population, a previous diagnosis of diabetes.

Sponsors & Collaborators

• Neuromed IRCCS: lead

Study Sites (1)

IRCCS INM Neuromed, Department of Epidemiology and Prevention

Pozzilli, IS, 86077, Italy

Location

Related Publications (5)

• Knowler WC, Barrett-Connor E, Fowler SE, Hamman RF, Lachin JM, Walker EA, Nathan DM; Diabetes Prevention Program Research Group. Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med. 2002 Feb 7;346(6):393-403. doi: 10.1056/NEJMoa012512.

  PMID: 11832527 RESULT

• Lindstrom J, Tuomilehto J. The diabetes risk score: a practical tool to predict type 2 diabetes risk. Diabetes Care. 2003 Mar;26(3):725-31. doi: 10.2337/diacare.26.3.725.

  PMID: 12610029 RESULT

• Bergmann A, Li J, Wang L, Schulze J, Bornstein SR, Schwarz PE. A simplified Finnish diabetes risk score to predict type 2 diabetes risk and disease evolution in a German population. Horm Metab Res. 2007 Sep;39(9):677-82. doi: 10.1055/s-2007-985353.

  PMID: 17846976 RESULT

• Li J, Bergmann A, Reimann M, Bornstein SR, Schwarz PE. A more simplified Finnish diabetes risk score for opportunistic screening of undiagnosed type 2 diabetes in a German population with a family history of the metabolic syndrome. Horm Metab Res. 2009 Feb;41(2):98-103. doi: 10.1055/s-0028-1087191. Epub 2008 Oct 29.

  PMID: 18975253 RESULT

• American Diabetes Association. Diagnosis and classification of diabetes mellitus. Diabetes Care. 2010 Jan;33 Suppl 1(Suppl 1):S62-9. doi: 10.2337/dc10-S062. No abstract available.

  PMID: 20042775 RESULT

MeSH Terms

Conditions

Diabetes Mellitus, Type 2; Diabetes Mellitus

Condition Hierarchy (Ancestors)

Glucose Metabolism Disorders; Metabolic Diseases; Nutritional and Metabolic Diseases; Endocrine System Diseases

Study Officials

• Licia iacoviello, MD, PhD

  IRCCS Neuromed

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: CASE ONLY
• Time Perspective: CROSS SECTIONAL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: MD, PhD

Study Record Dates

• First Submitted: May 9, 2019
• First Posted: May 14, 2019
• Study Start: January 9, 2019
• Primary Completion: July 30, 2021
• Study Completion: December 31, 2021
• Last Updated: January 12, 2022

Record last verified: 2022-01

Data Sharing

• IPD Sharing: Will not share

Locations

IT (1)