CN113160983A - Metabolism-related fatty liver disease clinical prediction model - Google Patents
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Abstract
The invention discloses a clinical prediction model for metabolic-related fatty liver diseases, which is characterized by comprising the following steps: the abscissa shows the risk relationship between metabolic-related fatty liver disease and Hsp90 alpha, BMI, HbA1c and ALT respectively; the vertical coordinate displays a corresponding 'Point' value vertically corresponding to each risk factor; the 'TotalPoints' is the sum of 'Point' addition corresponding to all risk factors, vertically corresponds to 'RiskofMAFLD', namely a prediction value of the risk of the metabolism-related fatty liver disease, and in the prediction model, a prediction model of the metabolism-related fatty liver disease is established by combining Hsp90 alpha, BMI, HbA1c and ALT, and the equation is-20.283 +1.206 xHsp 90 alpha (ng/mL) +1.449 xBMI (kg/m2) +5.521 xHbA 1c +1.081 xALT (U/L). The prediction model has better sensitivity and specificity when used for predicting the metabolism-related fatty liver disease, the Hsp90 alpha specificity reflects the severity of the metabolism-related fatty liver disease, and the prediction model is used for predicting the non-invasive metabolism-related fatty liver disease, is simple, convenient and economic, and is suitable for clinical screening.
Description
Technical Field
The invention relates to the field of metabolic-related fatty liver disease prevention and treatment, in particular to a metabolic-related fatty liver disease clinical prediction model.
Background
Fatty liver does not have any clinical symptoms and is usually diagnosed when abnormal or imaging liver enzymes are detected. Before progressing to cirrhosis, fatty liver undergoes two important pathological processes, simple hepatic steatosis and steatohepatitis. The hepatic steatosis area is more than or equal to 5 percent, and when no balloon-like change exists, the hepatic steatosis is simple; the area of the hepatic cell steatosis is more than or equal to 5 percent, and the hepatic cell steatosis is accompanied with inflammation and hepatic cell injury (such as balloon-like degeneration), and is steatohepatitis when accompanied or not accompanied with fibrosis. In steatohepatitis, the body is difficult to repair itself, and if there is no intervention, it may progress to hepatic fibrosis. More and more studies suggest that fatty liver is a metabolic disease and is a hepatic manifestation of systemic metabolic disorders. The management concept of metabolic-related fatty liver disease is also continuously updated, and not only is limited to reducing the risk of liver-related complications (such as liver cirrhosis and liver cancer) and death, but also is used for further controlling the risk of metabolic syndrome and related complications.
In 2020, a new definition of metabolic-related fatty liver disease was published by an international panel of 30 experts from 22 countries, non-alcoholic fatty liver disease (NAFLD) was proposed to be changed into metabolic-related fatty liver disease (MAFLD) (herein, "fatty liver" is all metabolic-related fatty liver disease), "consensus" middle panel updated the recommendations for comprehensive management of metabolic-related fatty liver disease, and current dichotomy assessment method for fatty liver presence or absence was no longer proposed, and the assessment of disease and risk factor stratification management were proposed according to the degree of liver inflammation activity and the degree of liver fibrosis of patients with metabolic-related fatty liver disease. The degree of inflammation in metabolic-related fatty liver disease is a significant challenge in the field of diagnosis of fatty liver. At present, the common image examination such as CT, ultrasound and the like can only distinguish fatty liver with steatosis of 20-33%, and can not carry out quantitative diagnosis on the fatty liver, which can cause missed diagnosis of patients with steatosis less than 20%. To date, MRI-PDFF and MRE are the most accurate non-invasive methods for detecting the degree of liver steatosis and fibrosis, respectively. However, these methods are limited in cost and cannot be widely used as a routine inspection item. Relatively reliable non-invasive methods for detecting steatohepatitis are also limited. General research methods fall into two broad categories: serum biomarkers and predictive models. ALT is often used as an index for evaluating inflammation in clinical practice, but has poor predictive value for steatohepatitis. In fatty liver, the elevation of ALT does not accurately reflect the severity of steatohepatitis. Research shows that the ALT concentration is increased by more than 2 times (>70U/L) to predict steatohepatitis, the sensitivity is only 50 percent, and the specificity is 61 percent. In addition, ALT levels may also be normal during progression of fatty liver. Therefore, neither elevated ALT levels nor steatohepatitis can be ruled out. The currently more accepted non-invasive biological marker for assessing steatohepatitis is the hydrolyzed fragment of serum Cytokeratin 18 (CK-18). CK-18 is an intracellular protein expressed in a variety of epithelial cells. When steatohepatitis is caused, liver cells are necrosed and apoptotic, CK-18 is released by caspase catalytic hydrolysis, and CK-18 hydrolysis fragments in serum are increased. In steatohepatitis, CK-18 has relevance to NAS (NAFLD activity score, NAS) classification. However, CK-18 has certain limitations, and has a specificity of 82% for diagnosing metabolic-related fatty liver diseases, but has a sensitivity of only 66%. CK-18 has also been reported in the literature for the prediction of simple hepatic steatosis and steatohepatitis, respectively, with areas under the ROC curve of 0.77 (95% CI. RTM. 0.710.84) and 0.65 (95% CI. RTM. 0.590.71); sensitivity/specificity were 63% (57-70%)/83% (69-92%) and 58% (51-65%)/68% (59-76%), respectively.
Heat shock proteins (Hsp) are a group of proteins that have unique cytoprotective and highly conserved functions in species evolution and are induced under specific circumstances, such as oxidative stress. The heat shock protein can assist the protein to refold and regulate the function of the protein under the stress environment, and protect cells from oxidative stress damage. One study of circulating blood Hsp90 a in obese children found that serum Hsp90 a in children with combined fatty liver was significantly reduced in obese children [5 ]. Tsutsui M et al compared the levels of non-steatohepatitis and steatohepatitis serum Hsp90 a, and found that Hsp90 a is elevated in steatohepatitis patients and has correlation with the pathologic inflammation activity score of liver tissue, and the level is significantly correlated with the steatosis grade, the sink area inflammation grade and the hepatocyte ballooning degeneration grade. However, in the study of Zheng ZG et al, no significant difference was found in the quantification of Hsp90 a protein in liver tissues of patients with fatty liver and mice with high-fat diet by immunohistochemical method. Normally, cells do not secrete Hsp90 a. The cells express and secrete Hsp90 alpha under various stress environments such as active oxygen, heat, hypoxia, radiation and cytokine release caused by tissue injury. Thus, serum Hsp90 a levels may be a reflection of the oxidative stress status of the liver.
In fatty liver, serum Hsp90 a is elevated, possibly reflecting the degree of oxidative stress of liver tissue. However, the single index of Hsp90 alpha is used for predicting metabolism-related fatty liver diseases, and both sensitivity and specificity are not ideal. In addition to Hsp90 a, BMI, HbA1c, ALT, HDL, LDL are risk factors for the MAFLD.
For this case, a predictive model was established combining Hsp90 a, BMI, HbA1c, ALT, in which Hsp90 a reflects oxidative stress, BMI reflects body fat levels, HbA1c reflects sugar load, and ALT serves as a marker of liver damage. The metabolic correlation fatty liver disease established by combining Hsp90 alpha, BMI, HbA1c and ALT has better sensitivity and specificity when being predicted. A clinical prediction model of metabolic-related fatty liver disease is proposed.
Disclosure of Invention
The invention aims to provide a clinical prediction model of metabolic-related fatty liver disease, the prediction model has better sensitivity and specificity when used for predicting metabolic-related fatty liver disease, Hsp90 alpha specifically reflects the severity of metabolic-related fatty liver disease, and is used for non-invasive prediction of metabolic-related fatty liver disease, the method is simple, convenient and economic, and is suitable for clinical screening.
The purpose of the invention can be realized by the following technical scheme:
a clinical prediction model of metabolic-related fatty liver disease, which is shown as follows:
the abscissa shows the risk relationship between metabolic-related fatty liver disease and Hsp90 alpha, BMI, HbA1c and ALT respectively;
the vertical coordinate displays a corresponding 'Point' value vertically corresponding to each risk factor;
"Total Points" is the sum of the "Points" corresponding to all Risk factors, and vertically corresponds to "Risk of MAFLD", namely the Risk prediction value of the metabolic-related fatty liver disease.
Furthermore, a metabolism-related fatty liver disease prediction model is established by combining Hsp90 alpha, BMI, HbA1c and ALT in the prediction model, and the equation is-20.283 +1.206 × Hsp90 alpha (ng/mL) +1.449 × BMI (kg/m2) +5.521 × HbA1c +1.081 × ALT (U/L).
The invention has the beneficial effects that:
1. the prediction model has better sensitivity and specificity when used for predicting metabolic-related fatty liver diseases;
2. the invention utilizes Hsp90 alpha specificity to reflect the severity of metabolic-related fatty liver disease;
3. the method is used for non-invasive metabolism-related fatty liver disease prediction, is simple, convenient and economical, and is suitable for clinical screening.
Drawings
The invention will be further described with reference to the accompanying drawings.
FIG. 1 is a graph of the positive correlation of serum Hsp90 a concentration with the severity of steatohepatitis in accordance with the present invention;
FIG. 2 is a graph of the use of serum Hsp90 a of the invention for the diagnosis of metabolic-related fatty liver disease;
FIG. 3 is a diagram of a prediction model Nomogram according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
A clinical prediction model of metabolic-related fatty liver disease is established by the following research:
first, animal research
1. Animal model and treatment:
1.1, establishing a metabolic-related fatty liver disease animal model: male C57BL/6 mice (clean grade) at 4-6 weeks of age, all experimental animals were housed at the animal laboratory center of Nanjing medical university (clean grade). All mice were fed with high-fat-fructose-cholesterol diet for 12 weeks after 1 week with normal diet (ingredient);
1.2, grouping and processing: after the mice were fed for 12 weeks, they were divided into 4 groups:
1) and a control group: feeding high-fat-fructose-cholesterol feed and NS;
2) and a treatment group: feeding high-fat-fructose-cholesterol feed and GGA;
GGA preparation and administration method: GGA is dissolved in 0.9% physiological saline to prepare suspension, the treatment group is fed at 200mg/kg/d, the suspension is fully mixed before each mouse is fed, and the control group is fed with the 0.9% physiological saline (NS) solution with the same volume for 12 weeks.
2. Biochemical analysis:
TG, CHO, LDL, HDL and ALT were detected using an Olympus AU5400 automatic biochemical analyzer (Olympus, Tokyo, Japan);
3. histopathological staining
3.1 oil Red O staining
3.1.1 sample treatment:
fixing a mouse liver sample by using 4% paraformaldehyde, embedding the mouse liver sample in paraffin, cutting the mouse liver sample into sections with the thickness of 4 microns, washing a cell sample by using precooled PBS for 2 times, and fixing the cell sample by using the 4% paraformaldehyde for 10 minutes;
3.1.2, oil red dyeing:
1) oil red dyeing: putting the slices into oil red dye liquor for dip dyeing for 8-10min (covering and keeping out of the sun);
2) background differentiation: taking out the slices, standing for 3s, sequentially immersing the slices in two cylinders of 60% isopropanol for differentiation for 3s and 5s respectively, and sequentially immersing the slices in 2 cylinders of pure water for immersion cleaning for 10s respectively;
3) hematoxylin staining: taking out the slices, standing for 3s, immersing in hematoxylin for counterstaining for 3-5min, and washing with distilled water for 3 times, each for 5s, 10s, and 30 s. Differentiating the differentiation solution (1% hydrochloric acid alcohol) for 2-8s, and washing with distilled water for 3 times, 10s each time;
4) sealing: sealing with glycerol gelatin sealing agent;
3.2, HE staining:
1) dewaxing: xylene I for 20 minutes; xylene II for 20 minutes; 100% ethanol I for 5 min; 100% ethanol II for 5 min; 75% ethanol for 5 minutes; washing with tap water;
2) hematoxylin staining was performed for 3-5 minutes and rinsed with tap water. Then processing the slices with hematoxylin differentiation solution, washing with tap water, processing the slices with hematoxylin-scott tap water, and washing with tap water;
3) soaking in 85% ethanol for 5 min; soaking in 95% ethanol for 5 min; finally, the sections were stained with eosin dye for 5 minutes;
4) and (3) dehydrating: 100% ethanol I, 100% ethanol II, 100% ethanol III, xylene, 100% ethanol, 5 min;
4. fatty liver Activity Score (NAFLD Activity Score, NAS):
5. serum enzyme-linked immunosorbent assay (ELISA)
5.1, preparing all reagents, working standards and samples;
5.2, referring to an analysis layout table, determining the number of holes to be used, putting the rest holes and the drying agent into the bag, sealing, and storing at 4 ℃;
5.3 Add 100. mu.l of Standard and sample to each well, cover with tape supplied with kit, incubate for 2h at 37 ℃;
5.4, removing the liquid in each hole without flushing;
5.5. mu.l of Biotin-antibody (1X) was added to each well, covered with fresh tape, and incubated at 37 ℃ for 1 hour;
5.6, adding Wash Buffer (1 x) (200 mu l) into each hole, cleaning for 3 times, each time for 2min, and after the last cleaning, covering liquid in the holes on absorbent paper;
5.7 Add 100. mu.l of HRP-avidin (1X) to each well, cover with fresh tape, incubate for 1h at 37 ℃;
5.8, as shown in the step 5.6, washing for 5 times;
5.9 Add 90. mu.l of TMB Substrate to each well and incubate at 37 ℃ for 30min, protected from light;
5.10 Add 50. mu.l of Stop Solution to each well and gently shake to ensure adequate mixing. Reading OD value at 450nm by using an enzyme-labeling instrument;
animal experiment results:
serum Hsp90 a concentration was positively correlated with the severity of steatohepatitis.
Correlation analysis results showed significant positive correlation of serum Hsp90 α concentration with ALT (r ═ 0.827, P ═ 0.000), liver weight (r ═ 0.800, P ═ 0.000), liver steatosis area (r ═ 0.695, P ═ 0.006), steatosis score (r ═ 0.667, P ═ 0.009), NAS score (r ═ 0.661, P ═ 0.010); the association with leaflet inflammation, balloon-like degeneration score was not significant as shown in figure 1.
Second, clinical research
1. Content of research
1.1, study subject, inclusion criteria:
1) and the age is 18-70 years old;
2) without basic diseases which seriously affect the functions of heart, lung and kidney;
3) fatty liver with clear ultrasound;
1.2 exclusion criteria
1) Inflammatory diseases including infections, noninfectious;
2) the use of drugs that cause liver lipid accumulation, including glucocorticoids, tamoxifen, amiodarone, methotrexate, and the like;
3) other diseases that cause hepatic steatosis;
4) history of surgery or other trauma within the last 1 year;
5) acute myocardial infarction or acute cerebral infarction occurs in the last 1 year;
6) pregnant or lactating women;
7) and malignant tumors;
8) serious heart, brain, liver, kidney diseases;
9) working in a high temperature environment;
10) history of alcohol consumption (> 20g of alcohol consumed daily);
2. diagnostic criteria for MAFLD:
liver biopsy, imaging or blood biomarkers provide evidence of the presence of liver steatosis and meet one of the following 3 conditions:
1) overweight or obese (BMI ≥ 23kg/m2)；
2) Type 2 diabetes;
3) metabolic dysfunction;
2.1.2, diagnostic criteria for diabetes:
2.2, definition of metabolic dysfunction:
metabolic dysfunction is defined as fulfilling at least two of the following conditions:
1) the waistline of the male is more than or equal to 90cm, and the waistline of the female is more than or equal to 80 cm;
2) the blood pressure is more than or equal to l30/85mmHg, or the treatment is carried out by using a blood pressure lowering medicine;
3) triglyceride is more than or equal to l.7mmol/L, or a hypolipidemic drug;
4) high density lipoprotein cholesterol: male < l.0mmol/l, female < l.3mmol/l, or receiving lipid-regulating drug therapy;
5) impaired glucose regulation: 5.6 to 6.9mmol/l of fasting blood sugar, 7.8 to 11.0mmol/l of blood sugar after 2 hours of meal, or 5.7 to 6.4 percent of glycosylated hemoglobin;
6) steady state model insulin resistance index: HOMA-IR is more than or equal to 2.5;
7) hypersensitivity C-reactive protein: >2 mg/L;
3. general information acquisition: age, sex, height, weight, history of disease and medication;
4. biochemical examination: ALT, AST, BUN, Cr, HbA1c, CHO, TG, HDL, LDL;
5. venous blood collection for serum Hsp90 a detection:
collecting blood of all subjects on an empty stomach in the early morning, collecting venous blood by using an inert separation gel blood collection tube, standing at 4 ℃ for 30 minutes, centrifuging at 1000rpm for 10 minutes, sucking upper serum, subpackaging, and placing at-80 ℃ for frozen storage to be detected;
6. serum Hsp90 a assay: using an enzyme-linked immunosorbent assay (ELISA) method (described in detail in the second section);
7. statistical analysis
Analyzing data by adopting SPSS 23 software, performing difference comparison between two groups by using t test according to positive-Tai distribution and U test according to non-normal distribution for continuous variables, and performing chi-square test on binary variables, wherein the test level P is less than 0.05; analysis of correlation between variables Using either the Spearman test, or the Pearson test, test level P < 0.05, multiple comparisons for FDR correction, binary Logistic regression for multifactor analysis, area under ROC curve to assess diagnostic efficacy, and R language to plot nomogrm of predictive models.
8. Results
First, general characteristics of population studies:
table 1 summarizes clinical features of two groups of 113 metabolic-related fatty liver diseases and 72 healthy people, wherein the age, BMI, alanine Aminotransferase (ALT), aspartate Aminotransferase (AST), Cholesterol (CHO), Triglyceride (TG), low-density lipoprotein (LDL), Uric Acid (UA), and glycated hemoglobin (HbA1c) of the metabolic-related fatty liver disease group are all significantly higher than those of the healthy people, and the high-density lipoprotein (HDL) is lower than those of the healthy people.
TABLE 1 comparison of biochemical indicators for healthy persons and metabolic-related fatty liver disease patients
The values shown in the table are mean values (median)
χ2: checking a chi square; u: Mann-Whitney U test; t, Student's t test.
*P＜0.05；**P＜0.01；***P＜0.001。
Secondly, the level of serum Hsp90 alpha of the patient with metabolism-related fatty liver is increased
Serum Hsp90 a levels varied from 0.615ng/ml to 23.33ng/ml, median (Q25, Q27): 3.502ng/ml (2.088, 6.077); serum Hsp90 a median (Q25, Q27) of patients with metabolic-related fatty liver disease: 4.653ng/ml (3.00, 8.284), median healthy control (Q25, Q27): 2.384ng/ml (1.213, 3.500), serum Hsp90 a of patients with metabolic-related fatty liver disease is significantly higher than that of healthy control group (figure 2).
Third, serum Hsp90 alpha used for predicting metabolism-related fatty liver disease
Blood Hsp90 a was used to predict the area under ROC curve AUC of metabolic-related fatty liver disease 0.77 (95% CI 0.702-0.837, P0.000) (Figure 1C). The cutoff value calculated by john's index was 2.86ng/mL, sensitivity 77.9%, specificity 66.7%, positive predictive value 70.1%, negative predictive value 75.1% (table 2).
TABLE 2 serum Hsp90 alpha, Joint diagnostic model predictive value used for the diagnosis of metabolic-related fatty liver disease threshold values, respectively
Four, analysis of metabolic-related fatty liver disease risk multifactor
Serum Hsp90 a levels were significantly positively correlated with age (r 0.275, P0.000), BMI (r 0.203, P0.005), ALT (r 0.317, P0.000), AST (r 0.207, P0.005), TG (r 0.345, P0.000) by Spearman correlation analysis; significantly negatively correlated with HDL (r ═ 0.360, P ═ 0.000); there was no significant correlation with CHO, LDL, UA (table 3).
Multifactor analysis showed that Hsp90 α (OR ═ 1.244, 95% CI 1.005-1.541, P ═ 0.045), BMI (OR ═ 1.513, 95% CI 1.200-1.909, P ═ 0.000), HbA1c (OR ═ 5.204, 95% CI 1.525-17.762, P ═ 0.008), ALT (OR ═ 1.071, 95% CI 1.001-1.145, P ═ 0.046), LDL (OR ═ 2.503, 95% CI 1.060-5.909, P ═ 0.036) were independent risk factors for metabolic-related fatty liver disease, HDL (OR ═ 0.170, 95% CI 0.040-0.714, P ═ 0.016) were independent protection factors (fig. 3, table 4).
TABLE 3 correlation analysis of serum Hsp90 alpha with related indices
r is a correlation coefficient;
spearman; p is less than or equal to 0.0055(FDR correction)
TABLE 4 multifactor analysis of Metabolic-related fatty liver disease Risk
*P＜0.05；**P＜0.01；***P＜0.001；
Fifthly, establishing a metabolism-related fatty liver disease prediction model:
the single index of Hsp90 alpha is used for predicting the unsatisfactory MAFLD (myeloproliferative disease) efficacy, and a prediction model is further established by combining multiple risk factors such as Hsp90 alpha, BMI, HbA1c, ALT and the like, wherein the equation of the model is-20.283 +1.206 × Hsp90 alpha (ng/mL) +1.449 × BMI (kg/m2) +5.521 × HbA1c +1.081 × ALT (U/L); ROC area under curve 0.945 (95% CI 0.916-0.974, P ═ 0.000) (fig. 2); the predicted value (Risk of MAFLD) cut-off value is 0.62, the sensitivity is 84.1 percent, and the specificity is 93.1 percent; the positive predictive value was 92.4% and the negative predictive value was 85.4% (table 2); fig. 3 is a graph of a prediction model Nomogram for predictive risk value evaluation.
FIG. 2 is a schematic representation of violins comparing healthy humans with serum Hsp90 a from metabolic-related fatty liver patients,. beta.P < 0.001; B. forest diagram metabolic-related fatty liver disease risk factor c. serum Hsp90 α is used to predict ROC curve of metabolic-related fatty liver disease, AUC 0.77 (95% CI 0.702-0.837, P0.000), Cut-off value 2.86ng/mL, sensitivity 77.9%, specificity 66.7%. D. Serum Hsp90 α, BMI, HbA1c, ALT prediction model ROC curve, AUC 0.94 (95% CI 0.909-0.971, P0.000) Cut-off value 0.62, sensitivity 84.1%, specificity 93.1%.
Fig. 3 shows the Risk relationship between metabolic-related fatty liver disease and Hsp90 α, BMI, HbA1c, ALT, each Risk factor corresponds to the corresponding "Point" value vertically, "Total Points" is the sum of "Points" corresponding to all Risk factors, and "Risk of mfld" corresponds vertically, i.e. the Risk value of metabolic-related fatty liver disease.
In the description herein, references to the description of "one embodiment," "an example," "a specific example" or the like are intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The foregoing shows and describes the general principles, essential features, and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed.
Claims (2)
1. A clinical prediction model of metabolic-related fatty liver disease is characterized by comprising the following components:
the abscissa shows the risk relationship between metabolic-related fatty liver disease and Hsp90 alpha, BMI, HbA1c and ALT respectively;
the vertical coordinate displays a corresponding 'Point' value vertically corresponding to each risk factor;
"Total Points" is the sum of the "Points" corresponding to all Risk factors, and vertically corresponds to "Risk of MAFLD", namely the Risk prediction value of the metabolic-related fatty liver disease.
2. The clinical prediction model of metabolic-related fatty liver disease according to claim 1, wherein the prediction model is established by combining Hsp90 a, BMI, HbA1c and ALT, and the equation is-20.283 +1.206 xHsp 90 a (ng/mL) +1.449 xBMI (kg/m2) +5.521 xHbA 1c +1.081 xALT (U/L).
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