The aim is to provide insight into the occurrence and development of pancreatogenic diabetes. Through early prevention, establishment of diagnostic and predictive models, refinement of type 3c diabetes with different etiology and clinical characteristics, and personalized treatment, the incidence and the misdiagnose of type 3C diabetes can be reduced, and the clinical efficacy can be improved.The following are each of the data required for this project:
1. Demographic data: age, gender and other relevant data, used to group the population and exclude the influence of other research factors.
2. Risk factors: The data related to lifestyle, chronic pancreatitis, etc. are quantified and statistically analyzed to distinguish the high-risk population and explore the public health measures for the prevention of type 3c diabetes.
3. Etiology: Pancreatitis, pancreatic dysplasia, pancreatic tumor, etc. are analyzed. The proportion of patients with type 3c diabetes and the relative proportion of each cause are to be calculated. Therefore, early intervention of these causes can slow down the occurrence and development of type 3c diabetes.
4. Clinical features: Clinical manifestations include weight loss, edema, acromegaly, etc. The laboratory examination data included blood biochemistry, blood routine, urine routine, etc.These data combined with the previous data are to be used to analyze the clinical characteristics of type 3c diabetes, especially for comparison with other types of diabetes. In addition, through big data analysis, a prediction model with diagnostic value can be established.
5. Treatment regimens and clinical outcome data: The former includes, diet therapy, exercise therapy, drug therapy, etc., and the latter includes blood glucose control, islet function changes, and type 3c diabetes related complications, etc. These data are used to compare the treatment effects of various subtypes of type 3c diabetes receiving different medical interventions.
