Related Experiment Video
Updated: Jun 22, 2025

Modeling and Evaluation of Murine Diabetic Cardiomyopathy Model
Published on: November 29, 2024
Construction of a 3-year risk prediction model for developing diabetes in patients with pre-diabetes
Jianshu Yang1, Dan Liu1, Qiaoqiao Du2
1Health Management Center, The First Affiliated Hospital of Soochow University, Suzhou, China.
Insights
This study identified key risk factors for prediabetes progression to diabetes within three years, developing a predictive model to assess individual risk. The model aids in early intervention for prediabetes patients.
Area of Science:
- Endocrinology
- Metabolic Diseases
- Public Health
Background:
- Prediabetes (PreDM) is a critical stage preceding type 2 diabetes.
- Identifying progression factors is vital for timely intervention.
- A robust prediction model can improve patient outcomes.
Purpose of the Study:
- To analyze factors influencing the progression from newly diagnosed prediabetes to diabetes within three years.
- To establish a prediction model for assessing the three-year risk of diabetes development in prediabetes patients.
Main Methods:
- A cohort of newly diagnosed prediabetes patients was followed for three years.
- Baseline demographic and clinical data were collected.
- Logistic regression analysis was used to build a predictive model, with nomogram visualization and calibration.
Main Results:
- Ageing, elevated BMI, male gender, high fasting blood glucose, increased LDL-C, fatty liver, and liver dysfunction were significant risk factors.
- HDL-C emerged as a protective factor.
- The developed prediction model demonstrated good predictive ability with an AUC of 0.787.
Conclusions:
- A risk prediction model based on eight factors (age, BMI, gender, fasting blood glucose, LDL-C, HDL-C, fatty liver, liver dysfunction) was successfully developed.
- The model shows good discrimination and calibration for predicting diabetes development within three years in prediabetes patients.
Introduction:
To analyze the influencing factors for progression from newly diagnosed prediabetes (PreDM) to diabetes within 3 years and establish a prediction model to assess the 3-year risk of developing diabetes in patients with PreDM.
Methods:
Subjects who were diagnosed with new-onset PreDM at the Physical Examination Center of the First Affiliated Hospital of Soochow University from October 1, 2015 to May 31, 2023 and completed the 3-year follow-up were selected as the study population. Data on gender, age, body mass index (BMI), waist circumference, etc. were collected. After 3 years of follow-up, subjects were divided into a diabetes group and a non-diabetes group. Baseline data between the two groups were compared. A prediction model based on logistic regression was established with nomogram drawn. The calibration was also depicted.
Results:
Comparison between diabetes group and non-diabetes group: Differences in 24 indicators including gender, age, history of hypertension, fatty liver, BMI, waist circumference, systolic blood pressure, diastolic blood pressure, fasting blood glucose, HbA1c, etc. were statistically significant between the two groups (P<0.05). Differences in smoking, creatinine and platelet count were not statistically significant between the two groups (P>0.05). Logistic regression analysis showed that ageing, elevated BMI, male gender, high fasting blood glucose, increased LDL-C, fatty liver, liver dysfunction were risk factors for progression from PreDM to diabetes within 3 years (P<0.05), while HDL-C was a protective factor (P<0.05). The derived formula was: In(p/1-p)=0.181×age (40-54 years old)/0.973×age (55-74 years old)/1.868×age (≥75 years old)-0.192×gender (male)+0.151×blood glucose-0.538×BMI (24-28)-0.538×BMI (≥28)-0.109×HDL-C+0.021×LDL-C+0.365×fatty liver (yes)+0.444×liver dysfunction (yes)-10.038. The AUC of the model for predicting progression from PreDM to diabetes within 3 years was 0.787, indicating good predictive ability of the model.
Conclusions:
The risk prediction model for developing diabetes within 3 years in patients with PreDM constructed based on 8 influencing factors including age, BMI, gender, fasting blood glucose, LDL-C, HDL-C, fatty liver and liver dysfunction showed good discrimination and calibration.
More Related Videos
Related Concept Videos
Diabetes Mellitus: Type 2 and Gestational
Diabetes: Symptoms, Diagnosis, and Complications
Pathophysiology of Diabetes
Type 1 diabetes is characterized by autoimmune-mediated destruction of pancreatic β cells, with environmental factors potentially triggering this process in genetically susceptible individuals. Despite many not having a family history, certain genes increase susceptibility,...
Diabetes Mellitus: Overview and Type I Subtype
Type 1 diabetes is an autoimmune disease in which the immune system mistakenly attacks and destroys the insulin-producing beta cells in the pancreas. As a result, the body is unable to produce sufficient insulin, and individuals with...
Carbohydrate Metabolism
Starch accounts for approximately 60% of the carbohydrates consumed by humans. Since amylase enzymes cannot function in the stomach's acidic environment, starch can only be digested in the mouth and small intestine. Simple sugars are found naturally in milk and fruits in...
Diabetes: Management and Pharmacotherapy
Insulin remains the cornerstone of treatment for most patients with type 1 and many...

