CN-121995063-A - Plasma protein marker for risk of heart failure onset of type 2 diabetes patient and risk prediction system

CN121995063ACN 121995063 ACN121995063 ACN 121995063ACN-121995063-A

Abstract

The invention belongs to the technical field of plasma protein markers, and discloses a plasma protein marker for predicting the risk of heart failure onset of type 2 diabetes patients and a risk prediction system, wherein the protein marker combination is applied to preparation of a kit for predicting the risk of heart failure onset of type 2 diabetes patients, and comprises C-C motif chemokine 24, C-C motif chemokine 7, carcinoembryonic antigen-related cell adhesion molecule 6, fiber gel protein 1, follistatin, polypeptide N-acetylgalactosamine transferase 5, ADP ribosylation factor binding protein GGA1, kallikrein-4, nerve bundle protein, renin, plasma proteinase C1 inhibitor, cardiac troponin I, N terminal brain natriuretic peptide precursor, apolipoprotein C1, tetramer connexin, meprin A beta subunit and multi-ligand proteoglycan-4. The invention also constructs a risk score and a prediction model for predicting the risk of developing heart failure of the type 2 diabetes patient based on the protein markers.

Inventors

LIU GANG
PAN AN
YU HANCHENG
ZHANG JIJUAN
GENG TINGTING
LI RUI
XIA MENGYANG

Assignees

华中科技大学

Dates

Publication Date: 20260508
Application Date: 20260206

Claims (9)

1. Use of a protein marker combination for the preparation of a kit for predicting the risk of heart failure onset in a type 2 diabetic patient, wherein the protein marker combination comprises both C-C motif chemokine 24, C-C motif chemokine 7, carcinoembryonic antigen-related cell adhesion molecule 6, fiber-gel protein 1, follistatin, polypeptide N-acetylgalactosamine transferase 5, ADP ribosylating factor binding protein GGA1, kallikrein-4, neuropilin, renin, plasmin C1 inhibitor, cardiac troponin I, N terminal brain natriuretic peptide precursor, and apolipoprotein C1, tetrameric connexin, meprin A beta subunit, and multi-ligand proteoglycan-4 that are negatively associated with heart failure onset risk.
2. The use of claim 1, wherein any one of the protein markers in the combination of protein markers is derived from plasma.
3. A method for constructing a predictive model for predicting the risk of developing heart failure in a type 2 diabetic patient based on protein markers, comprising the steps of: S1, preparing a data set aiming at a group of type 2 diabetics, wherein the data set comprises Z-valued expression values of 17 specific protein markers obtained by detection of the individual type 2 diabetics and whether heart failure occurs before a preset subsequent time point; S2, constructing a Cox proportion risk model introducing a minimum absolute shrinkage and selection operator (LASSO) penalty term by taking Z-valued expression values of 17 specific protein markers as variables in a data set to obtain protein risk scores for predicting the heart failure incidence risk of type 2 diabetics and constructing a prediction model; Wherein the 17 specific protein markers simultaneously comprise C-C motif chemokine 24, C-C motif chemokine 7, carcinoembryonic antigen-related cell adhesion molecule 6, fiber gel protein 1, follistatin, polypeptide N-acetylgalactosamine transferase 5, ADP ribosylating factor binding protein GGA1, kallikrein-4, nerve bundle protein, renin, plasma proteinase C1 inhibitor, cardiac troponin I, N terminal brain natriuretic peptide precursor, and apolipoprotein C1, tetrameric connexin, meprin A beta subunit, multi-ligand proteoglycan-4 negatively associated with heart failure risk.
4. The method of claim 3, wherein step S2 is performed to obtain a predictive model for predicting heart failure risk of type 2 diabetic patients by calculating a protein risk score (ProRS) of the subject and constructing a predictive model for predicting heart failure risk of type 2 diabetic patients, wherein the protein risk score (ProRS) is calculated as follows: wherein j=17, A Z-valued expression value for an i-th specific protein marker corresponding to the subject; Representing the weight coefficient of the i-th specific protein marker, wherein, for C-C motif chemokine 24, C-C motif chemokine 7, carcinoembryonic antigen-related cell adhesion molecule 6, fiber gel protein 1, follistatin, polypeptide N-acetylgalactosamine transferase 5, ADP ribosylating factor binding protein GGA1, kallikrein-4, neuropilin, renin, plasma proteinase C1 inhibitor, cardiac troponin I, N terminal brain natriuretic peptide precursor, Positive values are taken, for apolipoprotein C1, tetrameric connexin, meprin A beta subunit, multi-ligand proteoglycan-4, Taking a negative value; Preferably, the method comprises the steps of, The values are as follows: 。
5. the method of claim 3, wherein in step S1, the data set further comprises clinical variable data of an individual with type 2 diabetes when obtaining biological samples for detecting 17 specific protein markers; Correspondingly, in the step S2, simultaneously taking Z-valued expression values of 17 specific protein markers and clinical variable data as variables, constructing a Cox proportional risk model introducing a minimum absolute shrinkage and selection operator (LASSO) penalty term, obtaining a protein risk score for predicting the heart failure incidence risk of the type 2 diabetes patient, and constructing a prediction model; preferably, the clinical variables include both age, body mass index, smoking status, history of coronary heart disease, history of atrial fibrillation, and diabetic medication status.
6. A scoring system for predicting the risk of developing heart failure in type 2 diabetics based on protein markers, characterized in that it is used to obtain a protein risk score (profs) of a subject using Z-valued expression values of 17 specific protein markers correspondingly contained in the subject's plasma; The 17 specific protein markers simultaneously comprise C-C motif chemokine 24, C-C motif chemokine 7, carcinoembryonic antigen-related cell adhesion molecule 6, fiber gel protein 1, follistatin, polypeptide N-acetylgalactosamine transferase 5, ADP ribosylating factor binding protein GGA1, kallikrein-4, nerve bundle protein, renin, plasma proteinase C1 inhibitor, cardiac troponin I, N terminal brain natriuretic peptide precursor, and apolipoprotein C1, tetrameric connexin, meprin A beta subunit, multi-ligand proteoglycan-4 that are negatively associated with heart failure risk.
7. The scoring system of claim 6, wherein the protein risk score (profs) is calculated as follows: wherein j=17, A Z-valued expression value for an i-th specific protein marker corresponding to the subject; Representing the weight coefficient of the i-th specific protein marker, wherein, for C-C motif chemokine 24, C-C motif chemokine 7, carcinoembryonic antigen-related cell adhesion molecule 6, fiber gel protein 1, follistatin, polypeptide N-acetylgalactosamine transferase 5, ADP ribosylating factor binding protein GGA1, kallikrein-4, neuropilin, renin, plasma proteinase C1 inhibitor, cardiac troponin I, N terminal brain natriuretic peptide precursor, Positive values are taken, for apolipoprotein C1, tetrameric connexin, meprin A beta subunit, multi-ligand proteoglycan-4, Taking a negative value; Preferably, the method comprises the steps of, The values are as follows: 。
8. the scoring system of claim 6, wherein the scoring system further incorporates clinical variables as scoring items for comprehensive scoring along with the protein risk score (profs); preferably, the clinical variables include both age, body mass index, smoking status, history of coronary heart disease, history of atrial fibrillation, and diabetic medication status.
9. The scoring system of claim 6, wherein the Z-valued expression values are obtained by first providing a detection value for a specific protein marker contained in the plasma of each subject in the population to be tested as a normalized protein expression value (NPX), and calculating the mean and standard deviation of the normalized protein expression values for the specific protein marker; then, for a certain subject in the tested population, subtracting the mean value from the standardized protein expression value (NPX) of the specific protein marker contained in the plasma of the subject, and dividing the mean value by the standard deviation to obtain the Z-value expression value of the specific protein marker contained in the plasma of the subject; The subject population is a type 2 diabetic population and heart failure disease has not occurred.

Description

Plasma protein marker for risk of heart failure onset of type 2 diabetes patient and risk prediction system Technical Field The invention belongs to the technical field of plasma protein markers, and particularly relates to a plasma protein marker and a risk prediction system for heart failure incidence risk of type 2 diabetes patients, wherein a risk score (ProRS) is constructed by focusing on 17 protein marker combinations, so that the heart failure incidence risk of type 2 diabetes patients is accurately predicted. Background Heart failure is a serious cardiovascular complication in type 2 diabetics. The risk of heart failure in diabetics is 2-5 times that of the general population, while after heart failure in diabetics, the risk of death is significantly increased by up to about 10 times that of the general population. Early and accurate identification of high risk individuals and timely intervention are key to reducing the heart failure disease burden of type 2 diabetics and improving prognosis. The plasma protein is used as a key molecule for maintaining and regulating various biological activities, is widely involved in pathophysiological processes such as inflammatory reaction, oxidative stress and the like, and plays an important role in the occurrence and development of heart failure. Although the existing researches find that part of plasma proteins (such as NT-proBNP and GDF 15) related to heart failure are focused on early diagnosis of diseases, or a prediction model is built by combining a single biomarker with clinical indexes, the pathophysiological characteristics of type 2 diabetics cannot be fully matched, so that the prediction accuracy and specificity of the heart failure incidence risk of the specific crowd are insufficient, and the actual requirements of clinical secondary prevention are difficult to meet. Disclosure of Invention In view of the above-mentioned drawbacks or improvements of the prior art, it is an object of the present invention to provide a plasma protein marker and a risk prediction system for the risk of developing heart failure in type 2 diabetics, by combining 17 specific protein markers (i.e. 4 proteins of C-C motif chemokine 24, C-C motif chemokine 7, carcinoembryonic antigen-related cell adhesion molecule 6, fiber-gel protein 1, follistatin, polypeptide N-acetylgalactosamine transferase 5, ADP ribosylating factor binding protein GGA1, kallikrein-4, neuropilin, renin, plasmin C1 inhibitor, cardiac troponin I, N terminal brain natriuretic peptide precursor, etc., which are positively correlated with the risk of heart failure) to form a protein marker combination, and the corresponding kit is suitable for type 2 diabetics. Meanwhile, the invention also constructs a risk score and a prediction model for predicting the heart failure onset risk of the type 2 diabetes patients based on protein markers, and the causal time sequence of 17 specific protein markers screened out by aiming at the type 2 diabetes patients is clear, and the standardized expression value and the weighting coefficient of 17 proteins are integrated by constructing a protein risk score (ProRS), so that compared with the traditional method relying on clinical indexes and/or single biomarker, the heart failure onset risk of the type 2 diabetes patients can be predicted more accurately, and the support is provided for clinical primary prevention. To achieve the above object, according to one aspect of the present invention, there is provided the use of a protein marker combination for the preparation of a kit for predicting the risk of heart failure onset in a type 2 diabetic patient, characterized in that the protein marker combination comprises simultaneously C-C motif chemokine 24, C-C motif chemokine 7, carcinoembryonic antigen-related cell adhesion molecule 6, fiber-gel protein 1, follistatin, polypeptide N-acetylgalactosamine transferase 5, ADP ribosylating factor binding protein GGA1, kallikrein-4, neuropilin, renin, plasmin C1 inhibitor, cardiac troponin I, N terminal brain natriuretic peptide precursor, and apolipoprotein C1, tetrameric connexin, meprin A β subunit, and multi-ligand proteoglycan-4 negatively correlated with the heart failure onset risk. As a further preferred aspect of the present invention, any one of the protein markers in the protein marker combination is derived from plasma. According to another aspect of the present invention, there is provided a method for constructing a predictive model for predicting the risk of developing heart failure in a type 2 diabetic patient based on protein markers, characterized by comprising the steps of: S1, preparing a data set aiming at a group of type 2 diabetics, wherein the data set comprises Z-valued expression values of 17 specific protein markers obtained by detection of the individual type 2 diabetics and whether heart failure occurs before a preset subsequent time point; S2, constructing a Cox proportion risk model introducing a