CN116626275B - Severe liver fibrosis prognosis risk assessment system related to metabolism-related fatty liver and application thereof - Google Patents
Severe liver fibrosis prognosis risk assessment system related to metabolism-related fatty liver and application thereof Download PDFInfo
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Abstract
The invention discloses a severe hepatic fibrosis prognosis risk assessment system related to metabolic-related fatty liver and application thereof, and belongs to the field of bioinformatics analysis. The system comprises a calculation unit which calculates risk probability by using a severe hepatic fibrosis prognosis risk assessment model; the severe hepatic fibrosis prognosis risk assessment model takes serum index level, age, sex and whether the severe hepatic fibrosis prognosis risk assessment model belongs to type 2 diabetes as input variables; the serum indicators include monocyte to lymphocyte ratios, alanine aminotransferases, triglycerides, high density proteins and glycosylated hemoglobin. According to the invention, modeling is performed based on inflammation and metabolic indexes related to occurrence and development of MAFLD, liver fibrosis related to a MAFLD patient is graded through a non-invasive detection means, and the risk association of the liver fibrosis related to the severe liver fibrosis is studied in depth, so that the severe liver fibrosis condition of the MAFLD patient can be identified as early as possible.
Description
Technical Field
The invention relates to the field of bioinformatics analysis, in particular to a severe hepatic fibrosis prognosis risk assessment system related to metabolic-related fatty liver and application thereof.
Background
Metabolic dysfunction-related fatty liver (MAFLD) is a newly proposed disease category derived from non-alcoholic fatty liver, known as liver manifestation of metabolic syndrome; it proposes to lighten the importance of alcohol in the definition of nonalcoholic fatty liver disease (NAFLD) and underscores the metabolic risk factors behind the pathological progression associated with NAFLD. MAFLD is associated with an increase in total mortality in the United states. At present, many studies suggest that the triggering factor of inflammation is rooted in the liver (lipid overload, lipotoxicity, oxidative stress) and extrahepatic (intestinal-hepatic axis, adipose tissue, skeletal muscle) systems, leading to unique immune-mediated pathological mechanisms in MAFLD. The development of MAFLD has a certain correlation with indexes such as blood sugar, blood fat and liver functions. The prevalence of significant fibrosis is higher in lean patients with fatty liver and an abnormal risk of ≡2 metabolism. Liver inflammation is a marker in patients with fatty liver disease; many studies have mechanically demonstrated that immune cells are involved in promoting the development of steatohepatitis and fibrosis associated with fatty liver disease. The mechanism of inflammation involves the entire fatty liver spectrum, but especially in more advanced stages of disease, including cirrhosis and transition to HCC. Inflammation involves several cellular and molecular mechanisms, including cellular senescence, mitochondrial dysfunction, autophagy and mitochondrial autophagy defects, activation of inflammatory bodies, deregulation of the ubiquitin-proteasome system, activation of DNA damaging responses and dysbiosis (changes in host microbiota composition). From the perspective of steatohepatitis development, excessive adipose tissue in the liver is the core of inflammatory metastasis. Adipocytes exposed to a nutritionally dense diet increase in size until they reach a state of structural crisis. Thus, vascularization of adipose tissue is reduced and adipose cells are exposed to hypoxic conditions to further cause inflammation, which in turn leads to the transition from pure liver steatosis to liver fibrosis, cirrhosis and even hepatocellular carcinoma.
Liver fibrosis is a key step in the progression of various chronic liver diseases to cirrhosis and an important link affecting prognosis of patients with chronic liver diseases. Although there is currently no definitive and effective intervention for liver fibrosis, there is still an urgent need for early diagnosis and assessment of liver fibrosis, both by clinicians and by patients. Therefore, there is a need to enhance studies of early diagnosis and assessment of liver fibrosis, providing better basis for clinical diagnosis and prognosis. Despite the extensive research efforts of many scholars over the years on diagnosis and assessment of liver fibrosis, especially in terms of non-invasive diagnosis and assessment, including non-invasive detection methods such as non-invasive serum diagnostic models and imaging examinations, the accuracy is still poor. The current gold standard for liver fibrosis diagnosis is to carry out liver puncture pathological biopsy under B ultrasonic, but as liver puncture is an invasive operation, patients are not easy to accept, and the process is complicated and takes a long time. At present, the liver puncture and pathology report needs to take a long time, liver tissues are required to be acquired from a patient and then the next operation is performed, and the time for completing all the processes until the report is obtained is at least 4-5 working days, which increases the time cost and anxiety of the patient to a certain extent, and the liver biopsy is difficult to popularize because of the invasiveness, so that the method for conveniently, economically and minimally traumatically screening the metabolic-related fatty liver severe fibrosis is very important.
Disclosure of Invention
The invention aims to provide a severe liver fibrosis prognosis risk assessment system related to metabolic-related fatty liver and application thereof, so as to solve the problems of the prior art. The invention models based on inflammation and metabolic indexes related to occurrence and development of MAFLD, and provides a basis for prognosis diagnosis of severe hepatic fibrosis related to MAFLD.
In order to achieve the above object, the present invention provides the following solutions:
the invention provides a product useful for prognosis of severe liver fibrosis, the product comprising reagents for detecting serum indicators including monocyte to lymphocyte ratio, alanine aminotransferase, triglycerides, high density proteins and glycosylated hemoglobin;
the severe liver fibrosis is severe liver fibrosis associated with metabolic-related fatty liver.
The invention also provides application of the reagent for detecting the serum markers in preparation of a prognosis diagnosis product for severe hepatic fibrosis, wherein the serum markers comprise monocyte to lymphocyte ratio, alanine aminotransferase, triglyceride, high density protein and glycosylated hemoglobin; the severe liver fibrosis is severe liver fibrosis associated with metabolic-related fatty liver.
The invention also provides a system for severe liver fibrosis prognosis risk assessment, which comprises a calculation unit, wherein the calculation unit calculates risk probability by using a severe liver fibrosis prognosis risk assessment model;
the severe hepatic fibrosis prognosis risk assessment model takes serum index level, age, sex and whether the severe hepatic fibrosis prognosis risk assessment model belongs to type 2 diabetes as input variables to obtain single scores;
monocyte to lymphocyte ratio score = 22.528533945 x MLR value in the single score; age score = 2.5 x Age-87.5; gender score: female score 0, male 12.65466; BMI score = 0.270370445 x BMI value-4.055556673; glycosylated hemoglobin score = -2.299808066 x HbA1c value +25.297888721; high density protein score = 4.322856547 x HDL; type 2 diabetes score: none, score 0, score 8.616423; triglyceride score = -1.668173996 ×tg+20.01808795; alanine aminotransferase score = 0.509161731 x ALT value. Adding the single scores to obtain a total score, and marking the total score as R;
the serum indicators include monocyte to lymphocyte ratios, alanine aminotransferase, triglycerides, high density proteins and glycosylated hemoglobin;
the criterion for whether the subject is in type 2 diabetes is that the subject is judged to be in type 2 diabetes if the subject meets any one of the following:
(1) A history of diabetes mellitus, including type 2 diabetes, ICD-10E11 diabetes, and E14 diabetes;
(2) Currently insulin therapy and/or the use of oral hypoglycemic agents;
(3) The serum glucose level is more than or equal to 11.1mmol/l;
(4) The glycosylated hemoglobin level is more than or equal to 48mmol/mol;
the model calculates the risk probability using the following equation:
risk probability = 5.85 x 10-7 xr 3 -0.000186804×R 2 +0.019725652×R-0.677104617；
The severe liver fibrosis is severe liver fibrosis associated with metabolic-related fatty liver.
Further, the system further comprises a detection unit for detecting the serum indicator level.
Further, the system further includes an information acquisition unit for performing an operation of acquiring subject detection information including the serum index level, age, sex, and whether or not it belongs to type 2 diabetes.
Further, the system further comprises an evaluation unit for performing a judgment of risk probability of prognosis of severe hepatic fibrosis of the subject according to the calculation result of the calculation unit, giving rational prevention and treatment advice.
Further, the system also comprises a result display unit for displaying the conclusion drawn by the evaluation unit.
Further, the result display unit displays the result in a mode of screen display, voice broadcasting or printing.
The invention also provides a computer readable storage medium comprising a stored computer program, wherein the computer readable storage medium is controlled to execute the severe liver fibrosis prognosis risk assessment model when the computer program runs.
The invention also provides application of the physiological index in constructing a severe hepatic fibrosis prognosis risk assessment model, wherein the physiological index comprises the serum index level, the age, the sex and whether the blood serum index belongs to type 2 diabetes; the severe liver fibrosis is severe liver fibrosis associated with metabolic-related fatty liver.
The invention discloses the following technical effects:
the invention provides an index which can be used for prognosis diagnosis of severe hepatic fibrosis related to MAFLD, and provides a basis for prognosis diagnosis of severe hepatic fibrosis related to MAFLD.
The invention also provides a severe hepatic fibrosis prognosis risk assessment system related to MAFLD, and the system can be used for assessing the severe hepatic fibrosis patient prognosis risk related to MAFLD.
The invention models based on inflammation and metabolic indexes related to the occurrence and development of MAFLD, indexes liver fibrosis related to the MAFLD patient by a non-invasive detection means, and deeply researches the risk association of the liver fibrosis to help to recognize the liver fibrosis condition of the MAFLD patient as early as possible.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of selection of populations of UK Biobank (UK Biobank) metabolism associated fatty liver disease;
FIG. 2 is a diagram of a nonogram predictive prognosis model established using age, sex, type 2 diabetes, hbA1c, HDL, TG, ALT and MLR;
fig. 3 is a graph of the work curves of subjects plotted using R-package "pROC".
Detailed Description
Various exemplary embodiments of the invention will now be described in detail, which should not be considered as limiting the invention, but rather as more detailed descriptions of certain aspects, features and embodiments of the invention.
It is to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. In addition, for numerical ranges in this disclosure, it is understood that each intermediate value between the upper and lower limits of the ranges is also specifically disclosed. Every smaller range between any stated value or stated range, and any other stated value or intermediate value within the stated range, is also encompassed within the invention. The upper and lower limits of these smaller ranges may independently be included or excluded in the range.
Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Although only preferred methods and materials are described herein, any methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention. All documents mentioned in this specification are incorporated by reference for the purpose of disclosing and describing the methods and/or materials associated with the documents. In case of conflict with any incorporated document, the present specification will control.
It will be apparent to those skilled in the art that various modifications and variations can be made in the specific embodiments of the invention described herein without departing from the scope or spirit of the invention. Other embodiments will be apparent to those skilled in the art from consideration of the specification of the present invention. The specification and examples of the present invention are exemplary only.
As used herein, the terms "comprising," "including," "having," "containing," and the like are intended to be inclusive and mean an inclusion, but not limited to.
The meanings represented by the english abbreviations in the examples of the invention are as follows:
MLR: monocyte to lymphocyte ratio;
ALT: alanine aminotransferases;
BMI: body mass index;
TG: triglycerides;
HDL: high density lipoprotein;
HbA1c: glycosylated hemoglobin;
AST: aspartic acid aminotransferases;
FLI: fatty liver index;
FIB-4: liver fibrosis score-4;
MAFLD: metabolic-related fatty liver.
Example 1
1. Experimental data Source and pretreatment
(1) 117393 populations of metabolic-related fatty liver disease (see FIG. 1 for specific flow) from Biobank of UK (UK Biobank) were selected and liver fibrosis of MAFLD was defined according to FIB-4 index.
FIB-4 index (Fibrosis 4 Score) is a method for noninvasively assessing liver Fibrosis in chronic liver disease patients, and the values required include age, ALT (glutamic pyruvic transaminase), AST (aspartate aminotransferase), PLT (platelets). The calculation formula is as follows:
the liver fibrosis critical values defined by different liver disease FIB-4 indexes are different, and for non-alcoholic fatty liver, the liver fibrosis critical values below grade 2 or above grade 3-4 are FIB-4 < 1.3 and FIB-4 > 2.67 respectively. Metabolic dysfunction-related fatty liver (MAFLD) is a newly proposed disease category derived from non-alcoholic fatty liver, known as liver manifestation of metabolic syndrome; it proposes to lighten the importance of alcohol in the definition of nonalcoholic fatty liver disease (NAFLD) and underscores the metabolic risk factors behind the pathological progression associated with NAFLD. The relationship between FIB-4 index and liver fibrosis level and fibrosis degree is shown in Table 1.
TABLE 1 comparison of FIB-4 index and liver fibrosis level versus fibrosis degree Table
| FIB-4 index | <1.3 | 1.3～2.67 | >2.67 |
| Grade of hepatic fibrosis | Below grade 2 | Grade 2 to grade 3 | 3-4 grade or above |
| Degree of liver fibrosis | No/mild fibrosis | Moderate fibrosis | Severe fibrosis |
(2) Acquisition of other variable factor information
Physical activity level: this variable is derived from a baseline questionnaire, using a metabolic equivalent Score (Metabolic equivalent task Score, MET Score) to reflect parameters derived from participants' integrated physical activity levels, taking into account factors such as daily commute, effort, and fitness exercise. The study used the number of hours each participant engaged in more than moderate physical activity per week (MET-hours/week) to measure each participant's physical activity level. According to the results, the classification into two categories: one is "the individual weekly physical activity level is higher than the recommended medium-height physical labor level", the other group is "the individual weekly physical labor level is lower than the recommended medium-strength physical labor level", these two criteria are met, and the baseline table is defined as "yes".
Baseline type 2 diabetes is defined by one of the following criteria: 1) A self-reported history of type 2 diabetes or unspecified diabetes; 2) Hospital diagnosis of type 2 or unspecified diabetes (ICD-10E 11, E14) occurs prior to baseline assessment visit; 3) Currently insulin therapy and/or the use of oral hypoglycemic agents; 4) Serum glucose level is not less than 11.1mmol/l (200 mg/dl); 5) HbA1c is not less than 48mmol/mol (6.5%).
The baseline for each variable is shown in table 2.
Table 2 baseline table
2. Single-and multi-factor analysis of severe liver fibrosis
To explore the risk factors associated with the incorporation of severe liver fibrosis in the MAFLD population, single and multi-factor logistic regression analysis methods were used first to incorporate age, sex, BMI, race, state of drinking, exercise, presence or absence of type 2 diabetes, hbA1c, HDL, TG, ALT and MLR into univariate analysis, with events defined as severe liver fibrosis. Age, sex, BMI, status of drinking, exercise, presence or absence of type 2 diabetes, hbA1c, TG, ALT and MLR were found to be associated with severe liver fibrosis status in the mfld individuals (P < 0.05) in univariate analysis. The race is often taken as an influencing factor in clinical practice, and is combined with the content of single factor and clinical practice, although the race is not different in single factor analysis, the race is also taken into multi-factor analysis, so that the age, sex, BMI, race, drinking state, exercise, whether type 2 diabetes exists or not, hbA1c, HDL, TG, ALT and MLR are taken as covariates and are taken into COX regression analysis simultaneously for multivariate analysis, and the event is defined as severe liver fibrosis. The results suggest that age, sex, type 2 diabetes, hbA1c, HDL, TG, ALT, BMI and MLR are independent risk factors for MAFLD combined severe liver fibrosis (P < 0.05) (table 3).
TABLE 3 relationship of clinical predictive model and MAFLD-associated severe liver fibrosis
3. Establishment of a nonogram predictive prognosis model
A nomogram predictive prognosis model was established using age, sex, type 2 diabetes, hbA1c, HDL, TG, ALT and MLR, and the results are shown in figure 2. The C-index of this model was 0.766. Monocyte to lymphocyte ratio score = 22.528533945 x MLR value using the formula for extracting nomogram from R-pack nomogram ex; age score = 2.5 x Age-87.5; gender score: female score 0, male 12.65466; BMI score = 0.270370445 x BMI value-4.055556673; glycosylated hemoglobin score = -2.299808066 x HbA1c value +25.297888721; high density protein score = 4.322856547 x HDL; type 2 diabetes score: none, score 0, score 8.616423; triglyceride score = -1.668173996 ×tg+20.01808795; alanine aminotransferase score = 0.509161731 x ALT value. The score of each variable is shown in fig. 2, and the score of each variable is added to obtain the total score; probability of severe liver fibrosis = 5.85 x 10 -7 X total score 3 Plus-0.000186804 x total score 2 +0.019725652 ×total fraction-0.677104617. For example, one woman with MAFLD aged 47 years, BMI 45.24kg/m2, ALT 23.84U/L, TG 3.65mmol/L, hbA1c 5.81%, type 2 diabetes, MLR 0.29. The total score is 87.68828, the probability of the severe liver fibrosis is estimated to be less than 0.1% according to a formula, and the severe liver fibrosis does not exist in women according to the diagnosis conditions of the patent.
4. Diagnostic efficacy analysis
Subject work profile analysis was performed using UKBiobank data, and subject work curves were plotted using R-pack "pROC" as in fig. 3, auc=0.766. Results: from fig. 3, it can be seen that the model can accurately predict whether the individual with MAFLD has severe liver fibrosis, and the model index is easy to obtain or detect, so that the further progress of the disease is delayed for clinically and timely screening out patients with severe liver fibrosis with MAFLD and performing corresponding intervention.
In conclusion, the model shows high clinical predictive value for severe liver fibrosis in the uk biobank metabolic liver disease queue.
The above embodiments are only illustrative of the preferred embodiments of the present invention and are not intended to limit the scope of the present invention, and various modifications and improvements made by those skilled in the art to the technical solutions of the present invention should fall within the protection scope defined by the claims of the present invention without departing from the design spirit of the present invention.
Claims (7)
1. A severe liver fibrosis prognosis risk assessment device, characterized in that the device comprises a calculation module that calculates risk probabilities using a severe liver fibrosis prognosis risk assessment model;
the severe hepatic fibrosis prognosis risk assessment model takes serum index level, age, sex, body mass index BMI and whether the severe hepatic fibrosis prognosis risk assessment model belongs to type 2 diabetes as input variables to obtain single scores;
the serum index comprises monocyte-lymphocyte ratio MLR, alanine aminotransferase ALT, triglyceride TG, high density lipoprotein HDL and glycosylated hemoglobin HbA1c;
the criterion for whether the subject is in type 2 diabetes is that the subject is judged to be in type 2 diabetes if the subject meets any one of the following:
(1) There is a history of type 2 diabetes, ICD-10E11 diabetes or E14 diabetes;
(2) Currently insulin therapy and/or the use of oral hypoglycemic agents;
(3) The serum glucose level is more than or equal to 11.1mmol/l;
(4) The glycosylated hemoglobin level is more than or equal to 48mmol/mol;
monocyte to lymphocyte ratio in the single score MLR score = 22.528533945 x MLR value; age score = 2.5 x Age-87.5; gender score: female score 0, male score 12.65466; body mass index BMI score = 0.270370445 x BMI value-4.055556673; glycosylated hemoglobin HbA1c score = -2.299808066 x HbA1c value +25.297888721; high density lipoprotein HDL score = 4.322856547 x HDL value; type 2 diabetes score: not belonging to type 2 diabetes, the score is 0, and the score is 8.616423 when belonging to type 2 diabetes; triglyceride TG score = -1.668173996 ×tg value +20.01808795; alanine aminotransferase ALT score = 0.509161731 x ALT value;
adding the single scores to obtain a total score, and marking the total score as R;
the model calculates the risk probability using the following equation:
risk probability = 5.85 x 10 -7 ×R 3 -0.000186804×R 2 +0.019725652×R-0.677104617；
The severe liver fibrosis is severe liver fibrosis associated with metabolic-related fatty liver.
2. The apparatus of claim 1, further comprising a detection module for detecting the serum indicator level.
3. The apparatus of claim 2, further comprising an information acquisition module for performing an operation of acquiring subject detection information including the serum index level, age, gender, body mass index BMI, and whether or not it belongs to type 2 diabetes.
4. The apparatus of claim 3, further comprising an evaluation module for performing a judgment of risk probability of prognosis of severe liver fibrosis in a subject based on the calculation result of the calculation module, giving rational prevention and treatment advice.
5. The apparatus of claim 4, further comprising a result display module for displaying a conclusion drawn by the evaluation module.
6. The device of claim 5, wherein the results display module displays the results by means of screen display, voice broadcast or printing.
7. A computer readable storage medium, characterized in that the computer readable storage medium comprises a stored computer program, wherein the computer program when run controls a calculation module in an apparatus in which the computer readable storage medium is located to calculate a risk probability using a severe liver fibrosis prognosis risk assessment model;
the severe hepatic fibrosis prognosis risk assessment model takes serum index level, age, sex, body mass index BMI and whether the severe hepatic fibrosis prognosis risk assessment model belongs to type 2 diabetes as input variables to obtain single scores;
the serum index comprises monocyte-lymphocyte ratio MLR, alanine aminotransferase ALT, triglyceride TG, high density lipoprotein HDL and glycosylated hemoglobin HbA1c;
the criterion for whether the subject is in type 2 diabetes is that the subject is judged to be in type 2 diabetes if the subject meets any one of the following:
(1) There is a history of type 2 diabetes, ICD-10E11 diabetes or E14 diabetes;
(2) Currently insulin therapy and/or the use of oral hypoglycemic agents;
(3) The serum glucose level is more than or equal to 11.1mmol/l;
(4) The glycosylated hemoglobin level is more than or equal to 48mmol/mol;
monocyte to lymphocyte ratio in the single score MLR score = 22.528533945 x MLR value; age score = 2.5 x Age-87.5; gender score: female score 0, male score 12.65466; body mass index BMI score = 0.270370445 x BMI value-4.055556673; glycosylated hemoglobin HbA1c score = -2.299808066 x HbA1c value +25.297888721; high density lipoprotein HDL score = 4.322856547 x HDL value; type 2 diabetes score: not belonging to type 2 diabetes, the score is 0, and the score is 8.616423 when belonging to type 2 diabetes; triglyceride TG score = -1.668173996 ×tg value +20.01808795; alanine aminotransferase ALT score = 0.509161731 x ALT value;
adding the single scores to obtain a total score, and marking the total score as R;
the model calculates the risk probability using the following equation:
risk probability = 5.85 x 10 -7 ×R 3 -0.000186804×R 2 +0.019725652×R-0.677104617；
The severe liver fibrosis is severe liver fibrosis associated with metabolic-related fatty liver.
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