Phenotyping Patients With Type 2 Diabetes Mellitus and Cancer
NCT ID: NCT06299800
Last Updated: 2024-03-08
Study Results
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Basic Information
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RECRUITING
779 participants
OBSERVATIONAL
2024-02-04
2025-02-04
Brief Summary
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Primary objective of the study:
\- identify the risk factors affecting the occurrence of cancer in the population affected by type 2 diabetes mellitus;
Secondary objectives of the study:
* description of the demographic, clinical and first-line therapy characteristics of patients diagnosed with type 2 diabetes mellitus;
* assess risk factors for recurrence, presence of a second tumour not related to the first and the presence of both events in patients who have had a tumor within 10 years of diagnosis of diabetes;
* assess the relationship between the characteristics of patients and the time to the onset of cancer.
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Detailed Description
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Participating centre Surgery of Endocrinology and Diabetology of the SCDU of Novara, University of Eastern Piedmont. Responsible: Prof.ssa Flavia Prodam
Subjects Will be included in the study all patients visited at the Endocrinology and Diabetology surgery of the AOU Major of the Charity of Novara for an initial diagnosis of diabetes mellitus type 2 between 1990 and 2010.
Inclusion criteria
* Legal age
* Diagnosis of type 2 diabetes mellitus Exclusion criteria
* Diagnosis of cancer before diagnosis of type 2 diabetes
* Diagnosis of diabetes secondary to other diseases
* Diagnosis of diabetes secondary to other drugs
* Diagnosis of diabetes following surgery
Duration of study: 24 months
Follow-up and events of interest Patients included in the study will be followed from the date of diagnosis of type 2 diabetes mellitus until the date of the last available examination.
During the follow-up, for all patients included, the year of onset of the first cancer after the diagnosis of diabetes and the type of tumor will be detected. From this information it will be possible to calculate the time between the diagnosis of diabetes and the onset of cancer (measured in years).
For patients who have developed a first tumor will also be detected:
1. Recurrence of cancer (year of onset of recurrence)
2. Diagnosis of second tumour not related to primary tumour (year of onset, type of tumour) The diagnosis of cancer will be identified through the extraction system from the electronic medical record Smart Digital Clinic (Meteda Srl), using key words relevant to the area of cancer, identified in the section of the medical history. The key words will be: metastasis, adenocarcinoma, carcinoma, neoplasm, secondary/s, sarcoma, tumor, adenoma, lymphoma, leukemia, glioma, glioblastoma, ependymoma, basalioma, epithelium, melanoma, mesothelioma, cordoma, anaplastic, differentiated, undifferentiated, meningioma, multiple myeloma, small cell carcinoma, timoma, craniopharyngioma, neuroendocrine, LH, LNH, K, GIST, HCC and NET.
Data collection
For each patient, the following variables will be extracted from the Smart Digital Clinic electronic medical record for all visits available after diagnosis, where possible:
* Gender and age;
* Smoking habits and alcohol consumption (units per day);
* Weight and body mass index (BMI) at first (T0) and last visit (T1);
* Given by the diagnosis of type 2 diabetes mellitus;
* Treatment of type 2 diabetes mellitus at T0 and T1;
* Levels of glycated hemoglobin (hba1c) at T0 and T1, mean hba1c and mean fasting blood sugar;
* Creatinine clearance at T0 and T1;
* Liver enzymes at T0 and T1: alanine aminotransferase (ALT) and aspartate aminotransferase (AST);
* T0 and T1 lipid profile: low density lipoproteins (LDL-c) and triglycerides (TG);
* Complications of type 2 diabetes mellitus;
* Treatment of cancer;
* Family history of cancer; The diagnosis of type 2 diabetes mellitus is confirmed at diabetological centres or, in some cases, by general practitioners who refer patients to specialised diabetological centres. The accuracy of the diagnosis will be confirmed by crossing the Piedmont Diabetic Registry (PDR) and involving a second person to ensure a precise assessment.
BMI categories will be divided into underweight (BMI \<18.5 Kg/m2), normal weight (BMI 18.5 - 25 Kg/m2), and overweight (BMI \>25 Kg/m2).
With regard to the treatment of type 2 diabetes mellitus, participants will be categorised and grouped for statistical purposes in the following classes:
* Dietary therapy;
* Metformin/Acarbosio;
* Sulfanilurea;
* Metformin + GLP1/ DDPIV inhibitors (DDPIVi) or only GLP1 or only DDPIVi;
* Metformin + SGLT2i inhibitors (SGLT2i) or only SGLT2i;
* Basal insulin + GLP1/DDPIVi +/- Metformin;
* Basal insulin +/- Metformin +/- SGLT2i;
* Insulin basal bolus;
* Basal insulin bolus +/- Metformin +/- SGLT2i. In addition, complications of metabolic pathology will be extracted from the dedicated section of the program, where are systematically recorded and organized, and the diagnosis of which is conducted in accordance with the appropriate guidelines. These complications will be divided into the following categories: vasculopathy, neuropathy, hypertension, heart disease, kidney failure and retinopathy.
Finally, as regards cancer pathology, a categorization of treatment will be carried out in three classes: chemotherapy, surgery and radiation therapy.
In addition, the types of cancer will be divided in order to ensure greater homogeneity between groups, including the nervous system, head and neck, thorax, gastrointestinal, gynecological, urinary tract, male genital system, skin, blood, breast, soft tissues, endocrine glands and neuroendocrine tumors. This information will be collected at the diabetes diagnosis visit and at the last visit before the onset of cancer for the subjects experiencing the event and at the last available visit for the remaining subjects.
EXPECTED RESULTS Through this research, the investigators aim to obtain new information on the study population in order to better understand the possible correlation between the two pathologies. This will allow us to identify the risk factors associated with metabolic pathology that can affect the development time of cancer pathologies, as well as to identify those that could contribute to carcinogenesis itself.
This knowledge will allow us to define the role of hyperglycemia, obesity and other factors or behaviors at risk in the field of cancer in order to act with appropriate prevention strategies, thus considering the tumor pathology as one of the possible complications of type 2 diabetes mellitus.
Conditions
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Study Design
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COHORT
RETROSPECTIVE
Study Groups
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Diabetes mellitus type 2 and cancer
779 patients with cancer and diagnosis of type 2 diabetes mellitus diagnosed between 1990 and 2010
phenotyping of patients followed at a third-level diabetes centre with cancer
Collection of the following data for each patient enrolled, where possible:
* Gender and age;
* Smoking habits and alcohol consumption (units per day);
* Weight and body mass index (BMI) at first (T0) and last visit (T1);
* Given by the diagnosis of type 2 diabetes mellitus;
* Treatment of type 2 diabetes at T0 and T1;
* Levels of glycated hemoglobin (hba1c) at T0 and T1, mean hba1c and mean fasting blood sugar;
* Creatinine clearance at T0 and T1;
* Liver enzymes at T0 and T1: alanine aminotransferase (ALT) and aspartate aminotransferase (AST);
* T0 and T1 lipid profile: low density lipoproteins (LDL-c) and triglycerides (TG);
* Complications of type 2 diabetes;
* Treatment of cancer;
* Family history of cancer.
Interventions
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phenotyping of patients followed at a third-level diabetes centre with cancer
Collection of the following data for each patient enrolled, where possible:
* Gender and age;
* Smoking habits and alcohol consumption (units per day);
* Weight and body mass index (BMI) at first (T0) and last visit (T1);
* Given by the diagnosis of type 2 diabetes mellitus;
* Treatment of type 2 diabetes at T0 and T1;
* Levels of glycated hemoglobin (hba1c) at T0 and T1, mean hba1c and mean fasting blood sugar;
* Creatinine clearance at T0 and T1;
* Liver enzymes at T0 and T1: alanine aminotransferase (ALT) and aspartate aminotransferase (AST);
* T0 and T1 lipid profile: low density lipoproteins (LDL-c) and triglycerides (TG);
* Complications of type 2 diabetes;
* Treatment of cancer;
* Family history of cancer.
Eligibility Criteria
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Inclusion Criteria
* Diagnosis of type 2 diabetes mellitus
Exclusion Criteria
* Diagnosis of diabetes secondary to other diseases
* Diagnosis of diabetes secondary to other drugs
* Diagnosis of diabetes following surgery
18 Years
ALL
No
Sponsors
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Azienda Ospedaliero Universitaria Maggiore della Carita
OTHER
Responsible Party
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Flavia Prodam
Professor
Principal Investigators
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Flavia Prodam, MD PhD
Role: PRINCIPAL_INVESTIGATOR
AOU Maggiore della Carità di Novara
Locations
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SCDU Endocrinology, AOU Ospedale Maggiore della Carità
Novara, , Italy
Countries
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Central Contacts
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Facility Contacts
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References
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Fu Z, Gilbert ER, Liu D. Regulation of insulin synthesis and secretion and pancreatic Beta-cell dysfunction in diabetes. Curr Diabetes Rev. 2013 Jan 1;9(1):25-53.
Rojas A, Schneider I, Lindner C, Gonzalez I, Morales MA. Association between diabetes and cancer. Current mechanistic insights into the association and future challenges. Mol Cell Biochem. 2023 Aug;478(8):1743-1758. doi: 10.1007/s11010-022-04630-x. Epub 2022 Dec 24.
Cignarelli A, Genchi VA, Caruso I, Natalicchio A, Perrini S, Laviola L, Giorgino F. Diabetes and cancer: Pathophysiological fundamentals of a 'dangerous affair'. Diabetes Res Clin Pract. 2018 Sep;143:378-388. doi: 10.1016/j.diabres.2018.04.002. Epub 2018 Apr 19.
Francisco V, Pino J, Campos-Cabaleiro V, Ruiz-Fernandez C, Mera A, Gonzalez-Gay MA, Gomez R, Gualillo O. Obesity, Fat Mass and Immune System: Role for Leptin. Front Physiol. 2018 Jun 1;9:640. doi: 10.3389/fphys.2018.00640. eCollection 2018.
Chiefari E, Mirabelli M, La Vignera S, Tanyolac S, Foti DP, Aversa A, Brunetti A. Insulin Resistance and Cancer: In Search for a Causal Link. Int J Mol Sci. 2021 Oct 15;22(20):11137. doi: 10.3390/ijms222011137.
Yakar S, Leroith D, Brodt P. The role of the growth hormone/insulin-like growth factor axis in tumor growth and progression: Lessons from animal models. Cytokine Growth Factor Rev. 2005 Aug-Oct;16(4-5):407-20. doi: 10.1016/j.cytogfr.2005.01.010.
Mao Z, Zhang W. Role of mTOR in Glucose and Lipid Metabolism. Int J Mol Sci. 2018 Jul 13;19(7):2043. doi: 10.3390/ijms19072043.
Saxton RA, Sabatini DM. mTOR Signaling in Growth, Metabolism, and Disease. Cell. 2017 Mar 9;168(6):960-976. doi: 10.1016/j.cell.2017.02.004.
Laplante M, Sabatini DM. mTOR signaling in growth control and disease. Cell. 2012 Apr 13;149(2):274-93. doi: 10.1016/j.cell.2012.03.017.
Shlomai G, Neel B, LeRoith D, Gallagher EJ. Type 2 Diabetes Mellitus and Cancer: The Role of Pharmacotherapy. J Clin Oncol. 2016 Dec 10;34(35):4261-4269. doi: 10.1200/JCO.2016.67.4044. Epub 2016 Nov 7.
Cho NH, Shaw JE, Karuranga S, Huang Y, da Rocha Fernandes JD, Ohlrogge AW, Malanda B. IDF Diabetes Atlas: Global estimates of diabetes prevalence for 2017 and projections for 2045. Diabetes Res Clin Pract. 2018 Apr;138:271-281. doi: 10.1016/j.diabres.2018.02.023. Epub 2018 Feb 26.
Provided Documents
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Document Type: Study Protocol and Statistical Analysis Plan
Other Identifiers
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CE385/2023
Identifier Type: -
Identifier Source: org_study_id
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