Predictive variable related to type 2 diabetes mellitus bone mass abnormality risk and application thereof

Abstract

The invention discloses a predictive variable related to a bone mass abnormality risk of type 2 diabetes mellitus, which is characterized in that the predictive variable is AST / ALT. A new insight is provided for the relationship between the liver enzyme and the diabetic complications, and the importance of the AST / ALT ratio in clinical evaluation of diabetic patients is emphasized. A simply obtained prediction index is provided for the diabetes bone diseases, and clinical comprehensive management of T2D patients is facilitated.

Description

Technical Field

[0001] The present invention belongs to the field of risk prediction, and particularly relates to a predictive variable related to the risk of abnormal bone mass in patients with type 2 diabetes and an application thereof. Background Art

[0002] Type 2 diabetes (T2D) is a chronic metabolic disease characterized by high blood sugar levels. Its prevalence is increasing year by year and has become a major global public health issue. In recent years, diabetes-related skeletal complications have received increasing attention. Osteoporosis (OP) is a systemic bone disease characterized by low bone mass, damaged bone tissue microstructure, increased bone fragility, and susceptibility to fractures. Bone loss is an early manifestation of OP, and osteoporotic fractures are a serious consequence of OP. There is a close connection between T2D and OP. Diabetes is one of the important risk factors for osteoporotic fractures, leading to a higher disability and mortality rate of osteoporotic fractures.

[0003] There are still significant differences in the diagnosis and treatment of osteoporosis between regions and between urban and rural areas in my country. At present, the diagnostic criteria for osteoporosis recognized at home and abroad are based on the results of dual-energy X-ray (DXA) detection. However, this detection method may not be able to provide timely diagnosis and treatment in primary medical institutions. Therefore, the discovery of easily accessible biomarkers will help identify high-risk OP patients in clinical diagnosis and treatment, and provide guidance for the prevention and treatment of T2D patients with OP.

[0004] Liver enzymes are a routine biochemical test indicator, among which alanine aminotransferase (ALT) and aspartate aminotransferase (AST) are the most common serum biomarkers, which can be detected quickly, cheaply and repeatedly. ALT is mainly present in the liver, while AST can be expressed in the heart, liver, skeletal muscle, kidney, red blood cells and brain, and is mainly used to evaluate liver function. AST / ALT, also known as the De Ritis ratio, was proposed by Fernando DeRitis in 1957 to estimate the severity of viral hepatitis. Summary of the invention

[0005] Based on this, the present invention aims to confirm the association between AST / ALT and the risk of OP in the T2D population, provide a simple and reliable predictive indicator for the early detection of OP, and provide a predictive variable related to the risk of abnormal bone mass in patients with type 2 diabetes and its application. The specific scheme is as follows: In one aspect, the present invention provides a predictive variable associated with the risk of abnormal bone mass in type 2 diabetes, wherein the predictive variable is AST / ALT.

[0006] In a second aspect, the present invention provides an application of a predictive variable associated with the risk of abnormal bone mass in type 2 diabetes, and an application of the predictive variable in a model for predicting the risk of abnormal bone mass in patients with type 2 diabetes.

[0007] In a third aspect, the present invention provides a risk prediction model for abnormal bone mass in patients with type 2 diabetes, comprising the following steps: S1. Collect T2D patient data: including basic information, metabolic data, and bone density data; S2. Obtaining predictive variables: Performing SPSS analysis on the data collected in step S1 to obtain variables with significant differences between the two groups, using AST / ALT as a continuous variable to explore the relationship between the AST / ALT ratio and the risk of bone mass abnormalities; grouping according to AST / ALT quartiles to explore the relationship between the AST / ALT ratio of each group and the risk of bone mass abnormalities at different quartiles; S3. Data modeling: Using a machine learning algorithm, a T2D prediction model formula based on the AST / ALT ratio diagnostic predictor variable obtained in step S2 is constructed. The formula is: Logit score = 6.580*(AST / ALT)-6.886; S4. Model evaluation: Plot the receiver operating characteristic (ROC) curve and calculate the area under the ROC curve to evaluate the effectiveness of the prediction model.

[0008] Preferably, the basic information in S1 includes: age, whether male, whether smoking, and whether drinking.

[0009] Preferably, the metabolic data in S1 include: systolic blood pressure SBP, diastolic blood pressure DBP, body mass index BMI, duration of diabetes, fasting blood glucose FPG, 2h postprandial blood glucose 2hPG, fasting C-peptide FCP, glycosylated hemoglobin HbA1c, alanine aminotransferase ALT, aspartate aminotransferase AST, calculated AST / ALT ratio, urea nitrogen BUN, creatinine Cr, uric acid UA, triglyceride TG, total cholesterol TC, osteocalcin OC, type 1 collagen cross-linked carboxyl terminal peptide β-CTX, type 1 procollagen N-terminal propeptide PINP, and 25-hydroxyvitamin D25(OH)D.

[0010] Preferably, the bone density data in S1 include: femoral neck density, left hip density, and lumbar vertebrae L1-L4 bone density. Beneficial Effects

[0011] 1. This invention provides new insights into the relationship between liver enzymes and diabetic complications, emphasizing the importance of the AST / ALT ratio in the clinical assessment of diabetic patients.

[0012] 2. The present invention provides an easily obtainable predictive index for diabetic bone disease, which is helpful for the comprehensive clinical management of T2D patients. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 ROC curve to evaluate the diagnostic value of the prediction model. DETAILED DESCRIPTION

[0014] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0015] Example 1: Data Collection The clinical data of 589 patients with T2D who visited the Department of Endocrinology, Drum Tower Hospital Affiliated to Nanjing University Medical School from January 2022 to December 2023 were retrospectively collected, including basic information, metabolic data, and bone density data.

[0016] Basic information: age, whether male, whether smoking, whether drinking.

[0017] Metabolic data: systolic blood pressure SBP, diastolic blood pressure DBP, body mass index BMI, duration of diabetes, fasting blood glucose FPG, 2h postprandial blood glucose 2hPG, fasting C-peptide FCP, glycosylated hemoglobin HbA1c, alanine aminotransferase ALT, aspartate aminotransferase AST, calculated AST / ALT ratio, urea nitrogen BUN, creatinine Cr, uric acid UA, triglyceride TG, total cholesterol TC, osteocalcin OC, type 1 collagen cross-linked carboxyl terminal peptide β-CTX, type 1 procollagen N-terminal propeptide PINP, 25-hydroxyvitamin D 25(OH)D.

[0018] Bone density data: Femur Neck BMD (g / cm 2 )], Left Hip BMD (g / cm 2 )], Lumbar Spine BMD (g / cm 2 ) ].

[0019] According to the T-value detected by DXA, the subjects were divided into a normal bone mass group (T-≥-1.0) and an abnormal bone mass group (T-<-1.0). SPSS 27.0 software was used for statistical analysis.

[0020] Table 1 Comparison of clinical data and bone density data between the two groups

[0021] Example 2: AST / ALT quartile grouping For the AST / ALT index, p<0.05 indicated that there was a significant difference between the two groups, indicating that AST / ALT may be used as a predictive variable for the risk of abnormal bone mass in T2D patients. According to the quartiles of AST / ALT, they were grouped into Q1 group (0.650, 0.874), Q2 group (0.875, 0.999), Q3 group (1.000, 1.173) and Q4 group (1.174, 1.917). The relationship between AST / ALT as a continuous variable and a categorical variable and the risk of osteopenia / osteoporosis was explored. SPSS27.0 software was used for regression model analysis to construct a T2D bone mass abnormality risk prediction model formula diagnosed by AST / ALT ratio, which is: Logit score = 6.580*(AST / ALT)-6.886.

[0022] Example 3: Model Adjustment Confounding factors can distort the causal relationship between variable factors and results. By controlling confounding factors, bias can be eliminated or reduced, making the model more accurate and reliable. The present invention adjusts the remaining variables with significant differences between the two groups. The results are shown in Table 2. After adjusting the confounding factors, AST / ALT still shows a significant difference.

[0023] Table 2 Relationship between AST / ALT and the risk of osteopenia / osteoporosis in different models

[0024] Model 1 is the unadjusted model. Model 2 adjusted for age, sex, smoking, drinking, and BMI. Model 3 adjusted for age, sex, smoking, drinking, BMI, SBP, DBP, duration of diabetes, ALT, Cr, UA, OC, β-CTX, PINP, 25 (OH) D. The ROC curve of AST / ALT for abnormal bone mass events in T2DM patients was constructed using the "lrm" function and "plot" function in the R software rms package. The results are shown in Figure 2. Figure 1As shown in the figure, the area under the ROC curve (AUC) was 0.81, and the 95% confidence interval (95%CI) was 0.77-0.84, indicating that this indicator has a high diagnostic value.

[0025] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A predictive variable associated with the risk of abnormal bone mass in type 2 diabetes, characterized in that: The predictor variable was AST / ALT.

2. An application of a predictor variable associated with the risk of abnormal bone mass in type 2 diabetes, characterized in that: Application of the predictor variables in the risk prediction model for abnormal bone mass in patients with type 2 diabetes.

3. A risk prediction model for abnormal bone mass in patients with type 2 diabetes, characterized in that: The following steps are involved: S1. Collect T2D patient data: including basic information, metabolic data, and bone density data; S2. Obtaining predictive variables: Performing SPSS analysis on the data collected in step S1 to obtain variables with significant differences between the two groups, using AST / ALT as a continuous variable to explore the relationship between the AST / ALT ratio and the risk of bone mass abnormalities; grouping according to AST / ALT quartiles to explore the relationship between the AST / ALT ratio of each group and the risk of bone mass abnormalities at different quartiles; S3. Data modeling: Using a machine learning algorithm, a T2D prediction model formula based on the AST / ALT ratio diagnostic predictor variable obtained in step S2 is constructed. The formula is: Logit score = 6.580*(AST / ALT)-6.886; S4. Model evaluation: Plot the receiver operating characteristic (ROC) curve and calculate the area under the ROC curve to evaluate the effectiveness of the prediction model.

4. A risk prediction model for abnormal bone mass in patients with type 2 diabetes according to claim 3, characterized in that: The basic information in S1 includes: age, whether male, whether smoking, and whether drinking.

5. The risk prediction model for abnormal bone mass in patients with type 2 diabetes according to claim 3, characterized in that: The metabolic data in S1 include: systolic blood pressure SBP, diastolic blood pressure DBP, body mass index BMI, duration of diabetes, fasting blood glucose FPG, 2h postprandial blood glucose 2hPG, fasting C-peptide FCP, glycosylated hemoglobin HbA1c, alanine aminotransferase ALT, aspartate aminotransferase AST, calculated AST / ALT ratio, urea nitrogen BUN, creatinine Cr, uric acid UA, triglyceride TG, total cholesterol TC, osteocalcin OC, type 1 collagen cross-linked carboxyl terminal peptide β-CTX, type 1 procollagen N-terminal propeptide PINP, and 25-hydroxyvitamin D 25(OH)D.

6. The risk prediction model for abnormal bone mass in patients with type 2 diabetes according to claim 3, characterized in that: The bone density data in S1 include: femoral neck density, left hip density, and lumbar vertebrae L1-L4 bone density.

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