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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.
The study aims are:
Through the IT platform made available within the framework of the project, the selected sample will be given the questionnaire to detect the risk of diabetic disease (FINDRISC adapted) and, in the case of a positive outcome, the subject at risk will be assessed with laboratory tests, to confirm or not the condition of prediabetes or diabetes. Therefore, all the subsequent phases of patient care and management will be followed, from the modification of lifestyles for prediabetics to the management of overt diabetic pathology and the complications associated with it, thus experimenting with all the modules of the software platform integrated.
Subjects who have a high diabetic risk score will be referred to the Neuromed laboratories for the analysis of fasting blood glucose and the glycemic load test. Consistent with the diagnostic protocol developed, the subjects will follow a triple address:
At time T0 for diabetic patients without or with cardiovascular and neurological complications who will come to visit as from normal clinical practice, the presence of risk / complication parameters will be checked and risk scores will be applied to ascertain the patient's condition. The patient will then be followed as per normal clinical practice and risk parameters and the derived scores will be re-evaluated.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| 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 |
| |
| 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 |
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| 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 |
| Measure | Description | Time Frame |
|---|---|---|
| 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 |
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Inclusion Criteria:
Exclusion Criteria:
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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
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| Name | Affiliation | Role |
|---|---|---|
| Licia iacoviello, MD, PhD | IRCCS Neuromed | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| IRCCS INM Neuromed, Department of Epidemiology and Prevention | Pozzilli | IS | 86077 | Italy |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 11832527 | Result | 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. | |
| 12610029 | 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. |
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| ID | Term |
|---|---|
| D003924 | Diabetes Mellitus, Type 2 |
| D003920 | Diabetes Mellitus |
| ID | Term |
|---|---|
| D044882 | Glucose Metabolism Disorders |
| D008659 | Metabolic Diseases |
| D009750 | Nutritional and Metabolic Diseases |
| D004700 | Endocrine System Diseases |
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| 17846976 | 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. |
| 18975253 | 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. |
| 20042775 | 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. |