Method for predicting and identifying high fat deposition risk of liver of laying hen by using blood parameters

Abstract

The invention relates to the technical field of poultry health assessment, in particular to a method for predicting and identifying the high fat deposition risk of the liver of a laying hen based on blood amino acid parameters. The method comprises the following steps: collecting a blood sample when the laying hen is in a metabolic state after absorption, detecting the total amino acid content and the alanine content in blood, and substituting blood parameters into a pre-established liver triglyceride prediction model to obtain the predicted liver triglyceride content, and comparing the prediction results to judge the high fat deposition risk of the liver of the laying hen. According to the method, the laying hens do not need to be slaughtered and sampled, the fat deposition state of the livers of the living laying hens can be rapidly evaluated, and the method is suitable for early screening and risk early warning of high fat deposition of the livers of the laying hens under the large-scale breeding condition.

Description

Technical Field

[0001] This invention belongs to the field of poultry nutrition and healthy breeding technology, and relates to a method for predicting and identifying the risk of high fat deposition in the liver of laying hens using blood parameters. Background Technology

[0002] The information disclosed in this background section is intended only to enhance understanding of the overall background of the invention and is not necessarily to be construed as an admission or in any way implying that such information constitutes prior art known to those skilled in the art.

[0003] In laying hen farming, excessive fat deposition in the liver can easily lead to fatty liver syndrome, thereby affecting the laying hen's production performance, health status, and farming efficiency. Currently, the determination of the fat deposition status in the laying hen's liver mainly relies on histological observation of the liver or direct measurement of liver triglyceride content. These methods usually require slaughtering and sampling of laying hens, making it difficult to achieve dynamic monitoring of live laying hens and unsuitable for early screening and risk warning under large-scale farming conditions. Furthermore, the inventors' research found that there is no stable and consistent relationship between blood triglyceride levels and liver tissue triglyceride content in laying hens. Figure 1 Relying solely on blood triglyceride levels is insufficient to accurately reflect the degree of fat deposition in the liver.

[0004] Existing studies have reported correlations between certain blood biochemical or metabolic indicators and hepatic steatosis, especially under conditions of restricted energy intake or post-absorption metabolic states, where the association between amino acid metabolism and hepatic lipid synthesis and deposition is more significant. However, current techniques mostly remain at the level of single-indicator analysis or correlation description, and a technical solution for quantitatively predicting hepatic steatosis in laying hens based on blood amino acid parameters under specific metabolic states has not yet been established. Therefore, there is an urgent need to develop a method that does not require slaughter, is applicable to live laying hens, and can predict and identify the risk of hepatic steatosis under defined metabolic states. Summary of the Invention

[0005] To address the aforementioned issues, this invention provides a method for predicting and identifying the risk of high fat deposition in the liver of laying hens using blood parameters. By collecting blood samples from laying hens in their post-absorption metabolic state, a liver triglyceride prediction model is established based on blood amino acid parameters, enabling non-invasive prediction of the fat deposition status in the liver of laying hens. This solves the current problems of relying on slaughter for fatty liver diagnosis, the inability to conduct live screening, and the lack of early warning.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: A first aspect of the present invention provides a method for predicting and identifying the risk of high fat deposition in the liver of laying hens using blood parameters, comprising: Blood samples were collected from laying hens during their post-absorption and metabolic state. The blood parameters in the blood sample are detected, including: the total amino acid content (TAA) and the alanine content (Ala) in the blood. The detected blood parameters are substituted into the liver triglyceride prediction model to obtain the predicted liver triglyceride content; The predicted liver triglyceride content is compared with a preset threshold. When the predicted value is higher than the threshold, it is determined that the laying hen has a risk of high fat deposition in the liver. The liver triglyceride prediction model is a regression model based on blood amino acid parameters.

[0007] A second aspect of the present invention provides a system for predicting and identifying the risk of high fat deposition in the liver of laying hens using blood parameters, comprising: a blood collection device, a blood parameter measuring device, a prediction system, and an identification system; The prediction system is used to execute a liver triglyceride prediction model to calculate the predicted liver triglyceride content; The identification system is used to compare the predicted liver triglyceride content with a preset endpoint threshold. If the predicted value is higher than the preset endpoint threshold, it is determined that the laying hen has a risk of high fat deposition in the liver.

[0008] A third aspect of the present invention provides the application of the above-described system in the early screening and risk warning of high fat deposition in the liver of laying hens.

[0009] A fourth aspect of the present invention provides a method for predicting liver triglyceride levels using blood parameters, comprising: Blood samples were collected from laying hens during their post-absorption and metabolic state. The blood parameters in the blood sample are detected, and the blood parameters include at least the total amino acid content and alanine content in the blood; The detected blood parameters are substituted into the liver triglyceride prediction model to obtain the predicted liver triglyceride content; The liver triglyceride prediction model satisfies the following relationship: TG_pred = c0 + c1 × TAA c2×Ala, where TG_pred is the predicted liver triglyceride content, TAA is the total amino acid content in the blood, Ala is the alanine content, and c0–c2 are the regression coefficients obtained through modeling.

[0010] Beneficial effects of the present invention (1) This invention predicts the fat deposition status of laying hen liver based on blood amino acid parameters, thereby achieving non-invasive risk identification of fatty liver in laying hens; (2) The present invention establishes a prediction model under a defined post-absorption metabolic state, thereby improving the stability and reliability of the prediction results; (3) This invention does not require slaughtering and sampling, and is suitable for early screening and risk warning of high fat deposition in the liver of laying hens under large-scale breeding conditions. It has good promotion and application value. Attached Figure Description

[0011] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. Exemplary embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0012] Figure 1 To determine the liver triglyceride content of aged laying hens grouped according to serum triglyceride and intrahepatic triglyceride levels, (A) animal screening, (B) liver morphology images of livers with intrahepatic triglyceride levels of 0.28 mmol / gprot and 0.44 mmol / gprot, (C) liver triglyceride content of aged laying hens grouped according to serum triglyceride levels, and (D) liver triglyceride content of aged laying hens grouped according to intrahepatic triglyceride levels.

[0013] Among them, LSTG (Low Serum Triglyceride): low serum triglycerides; HSTG (High Serum Triglyceride): high serum triglycerides; NORM (Normal): normal group; FL (Fatty Liver): high fat deposition group. Detailed Implementation

[0014] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of the invention. Unless otherwise specified, all technical and scientific terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0015] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of skill in the art. The reagents and raw materials used in this invention are readily available through conventional means, and unless otherwise specified, they are used in accordance with conventional methods in the art or product instructions. Similarly, unless otherwise specified, the test methods of this invention are performed in accordance with conventional methods in the art or industry-standard methods or practices. Furthermore, any methods and materials similar to or equivalent to those described herein may be applied to the methods of this invention. The preferred embodiments and materials described herein are for illustrative purposes only.

[0016] This invention provides a method for predicting and identifying the risk of high fat deposition in the liver of laying hens using blood parameters, including: Blood samples were collected from laying hens during their post-absorption and metabolic state. The blood parameters in the blood sample are detected, including: the total amino acid content (TAA) and the alanine content (Ala) in the blood. The detected blood parameters are substituted into the liver triglyceride prediction model to obtain the predicted liver triglyceride content; The predicted liver triglyceride content is compared with a preset threshold. When the predicted value is higher than the threshold, it is determined that the laying hen has a risk of high fat deposition in the liver. The liver triglyceride prediction model is a regression model based on blood amino acid parameters.

[0017] Preferably, the post-absorption metabolic state is the physiological stage in which the hen, after ceasing feed intake, primarily derives its nutrients from endogenous metabolism, and the blood amino acid levels reliably reflect the liver's metabolic state. Under this metabolic state, the correlation between blood amino acid parameters and liver fat synthesis and deposition is enhanced, which helps improve the accuracy and stability of the prediction model.

[0018] Preferably, the liver triglyceride prediction model satisfies the following relationship: TG_pred = c0 + c1 × TAA c2×Ala, where TG_pred is the predicted liver triglyceride content, TAA is the total amino acid content in the blood, Ala is the alanine content, and c0–c2 are regression coefficients obtained through modeling. It should be noted that the above model expression and regression coefficients are merely examples obtained based on specific sample data in this embodiment, used to illustrate the feasibility and effectiveness of the method of the present invention; the regression coefficients can be adjusted according to different laying hen breeds, rearing stages, detection platforms, and modeling datasets, and their changes do not affect the technical essence of the method of the present invention for predicting the risk of high hepatic fat deposition.

[0019] Preferably, c0 = 0.279, c1 = 0.003, and c2 = 0.036.

[0020] Preferably, liver triglyceride content is used as the endpoint evaluation index. When the liver triglyceride content is higher than a preset endpoint threshold, it is determined that the endpoint of liver hyperlipidemia has been reached. The endpoint evaluation is used to verify or control the prediction results, and does not constitute a limitation on the prediction steps.

[0021] Preferably, the laying hen is a laying hen in the late stage of its peak egg production.

[0022] The threshold can be obtained based on the statistical distribution characteristics of liver triglyceride content in experimental samples, the risk of fatty liver in production practice, or through empirical setting of historical data, and is not limited to the specific value used in this embodiment. This determination method is used to achieve rapid screening and risk warning of liver fat deposition status in laying hens, rather than absolute quantitative determination of liver triglyceride content. Preferably, the threshold is 0.40. The threshold is an example threshold determined based on the statistical characteristics of the sample, used to illustrate the determination idea of ​​the method of the present invention. Its specific value can be adjusted according to the detection platform, sample source and application scenario. Differences between the predicted value and the measured value at the numerical level do not affect the consistency judgment in liver fat deposition risk classification.

[0023] The present invention will be further described in detail below with reference to specific embodiments. It should be noted that the specific embodiments are explanations of the present invention and not limitations thereof.

[0024] Example 1: A method for predicting the risk of high fat deposition in the liver of laying hens based on blood amino acid parameters (post-absorption metabolic state) Twenty-eight laying hens (69 weeks old) in the late peak laying period were selected and housed individually under the same feeding conditions, with free access to commercial laying hen feed and water. After an acclimatization period, samples were collected from the hens under a metabolically restricted energy intake state (fasting for 12 hours). During sampling, blood samples were collected via the wing vein, and serum and plasma were prepared for subsequent blood parameter testing. After blood collection, the hens were euthanized, and liver samples were collected to determine the liver triglyceride (TG) content, which served as the endpoint evaluation indicator for liver fat deposition.

[0025] (1) Blood parameter detection The following blood amino acid parameters were measured in the blood samples: total amino acid content (TAA) and alanine content (Ala) in plasma. These blood parameters, as important indicators reflecting the body's protein metabolism status and liver metabolic load, were used to construct a subsequent model for predicting the risk of liver fat deposition.

[0026] (2) Sample grouping Laying hens were grouped into endpoint groups based on the measured liver triglyceride levels, such as... Figure 1As shown in Figure B, a liver TG level of 0.28 mmol / gprot indicates a normal liver, while a liver TG level of 0.44 mmol / gprot clearly indicates a liver with high fat deposition. Laying hens with liver TG levels within the normal physiological range were classified as the normal group (<0.30 mmol / gprot), and laying hens with significantly elevated liver TG levels were classified as the high fat deposition group (>0.45 mmol / gprot). The normal group (NORM) consisted of 20 hens, and the high fat deposition group (FL) consisted of 8 hens. These grouping results are only used to verify the correspondence between blood amino acid parameters and the endpoint indicators of liver fat deposition and are not necessary conditions for constructing a predictive model.

[0027] (3) Establishment of the prediction model Using liver triglyceride levels as an output indicator to characterize the degree of liver fat deposition, and total amino acid (TAA) and alanine (Ala) levels detected in the blood as input parameters, regression modeling analysis is performed on the collected experimental data to construct a predictive model between blood amino acid parameters and the degree of liver fat deposition. In this embodiment, the predictive model can be expressed as: TG_pred = c0 + c1 × TAA c2×Ala Wherein, TG_pred represents the predicted liver triglyceride content, TAA and Ala represent the corresponding blood amino acid parameters, and c0, c1, and c2 are regression coefficients obtained through modeling analysis. Under the conditions of this embodiment, the regression coefficients are exemplarily: c0 = 0.279, c1 = 0.003, and c2 = 0.036. Further analysis results show that the prediction model can effectively reflect the quantitative relationship between blood amino acid metabolism characteristics and liver fat deposition endpoint indicators.

[0028] like Figure 1 As shown in Figure C, comparing liver TG levels according to serum TG groups, the P-value was greater than 0.05, indicating no significant difference in liver TG levels. This suggests that relying solely on blood TG levels is insufficient to accurately reflect the degree of hepatic steatosis. Figure 1 As shown in Table D, the liver TG levels were grouped into a normal group (NORM) and a high-fat deposition group (FL). There was a significant difference in liver TG levels between the two groups (P<0.001). The results in Table 1 are analyzed and presented according to this grouping method.

[0029] Table 1. Blood amino acid parameters of laying hens at different hepatic steatosis endpoints under energy-restricted metabolic states.

[0030] Note: The amino acid data are the average value of all samples in the group ± standard error.

[0031] (4) Methods for determining high fat deposition in the liver In this embodiment, the predicted liver triglyceride content (TG_pred) for each laying hen is calculated based on the prediction model, and this predicted value is compared with a preset threshold (0.4 mmol / gprot) to determine the risk of high hepatic fat deposition in the laying hen. When the predicted TG_pred is higher than the threshold (0.4 mmol / gprot), the laying hen is determined to be in a state of high hepatic fat deposition risk; when the predicted value is lower than the threshold, it is determined to be in a state of normal hepatic fat deposition.

[0032] (5) Consistency verification of prediction results To verify the effectiveness of the method, the TG_pred output of the prediction model for 28 laying hens was compared with the corresponding measured values ​​of liver triglycerides and the threshold (0.4 mmol / gprot). The results are shown in Table 2. The results show that in most samples, the predicted and measured results are consistent in determining the risk of liver steatosis, effectively distinguishing between individuals with normal liver steatosis and those with significantly elevated liver steatosis. In a few samples, the predicted and measured results were not completely consistent (i.e., the endpoint was reached). This may be related to individual metabolic differences, the dynamic characteristics of liver steatosis, and instantaneous fluctuations in blood indicators, but it does not affect the risk identification effect of the method at the population level and in actual production applications.

[0033] Table 2. Correspondence between the predicted model output and the measured liver TG results under fasting conditions.

[0034] (6) Technical effects As can be seen from this embodiment, the prediction method based on blood amino acid parameters provided by the present invention can achieve non-invasive assessment of the risk of high liver fat deposition without dissecting and sampling laying hens. This method uses easily detectable amino acid parameters in the blood as input, and establishes a mathematical relationship between blood metabolic characteristics and the degree of liver fat deposition to achieve auxiliary identification and risk warning of liver fat deposition status. It has the advantages of simple operation, strong applicability, and good repeatability, and is suitable for monitoring and managing liver health during laying hen production.

[0035] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for predicting and discriminating the risk of high fat deposition in the liver of laying hens using blood parameters, characterized in that, include: Blood samples were collected from laying hens during their post-absorption and metabolic state. The blood parameters in the blood sample are detected, including: the total amino acid content (TAA) and the alanine content (Ala) in the blood. The detected blood parameters are substituted into the liver triglyceride prediction model to obtain the predicted liver triglyceride content; The predicted liver triglyceride content is compared with a preset threshold. When the predicted value is higher than the threshold, it is determined that the laying hen has a risk of high fat deposition in the liver. The liver triglyceride prediction model is a regression model based on blood amino acid parameters.

2. The method for predicting and identifying the risk of high fat deposition in the liver of laying hens using blood parameters as described in claim 1, characterized in that, The metabolic state after absorption refers to the physiological stage in which, after the laying hen stops eating, the nutrients in its body mainly come from endogenous metabolism, and the level of amino acids in the blood can relatively stably reflect the metabolic state of the liver.

3. The method for predicting and identifying the risk of high fat deposition in the liver of laying hens using blood parameters as described in claim 1, characterized in that, The liver triglyceride prediction model satisfies the following relationship: TG_pred = c0 + c1×TAA c2×Ala, where TG_pred is the predicted liver triglyceride content, TAA is the total amino acid content in the blood, Ala is the alanine content, and c0–c2 are the regression coefficients obtained through modeling.

4. The method for predicting and identifying the risk of high fat deposition in the liver of laying hens using blood parameters as described in claim 3, characterized in that, c0 = 0.279, c1 = 0.003, c2 = 0.

036.

5. The method for predicting and identifying the risk of high fat deposition in the liver of laying hens using blood parameters as described in claim 1, characterized in that, Using liver triglyceride content as the endpoint evaluation index, when the liver triglyceride content is higher than the preset endpoint threshold, it is determined that the endpoint of liver hyperlipidemia has been formed.

6. The method for predicting and identifying the risk of high fat deposition in the liver of laying hens using blood parameters as described in claim 1, characterized in that, The laying hens mentioned are those in the late stage of their peak egg production.

7. The method for predicting and identifying the risk of high fat deposition in the liver of laying hens using blood parameters as described in claim 1, characterized in that, The threshold is 0.4 mmol / gprot.

8. A system for predicting and identifying the risk of high fat deposition in the liver of laying hens using blood parameters, characterized in that, include: Blood collection devices, blood parameter measuring devices, prediction systems, and identification systems; The prediction system is used to execute a liver triglyceride prediction model to calculate the predicted liver triglyceride content; The identification system is used to compare the predicted liver triglyceride content with a preset endpoint threshold. If the predicted value is higher than the preset endpoint threshold, it is determined that the laying hen has a risk of high fat deposition in the liver.

9. The application of the system according to claim 8 in early screening and risk warning of high fat deposition in the liver of laying hens.

10. A method for predicting liver triglyceride levels using blood parameters, characterized in that, include: Blood samples were collected from laying hens during their post-absorption and metabolic state. The blood parameters in the blood sample are detected, and the blood parameters include at least the total amino acid content and alanine content in the blood; The detected blood parameters are substituted into the liver triglyceride prediction model to obtain the predicted liver triglyceride content; The liver triglyceride prediction model satisfies the following relationship: TG_pred = c0 + c1×TAA c2×Ala, where TG_pred is the predicted liver triglyceride content, TAA is the total amino acid content in the blood, Ala is the alanine content, and c0–c2 are the regression coefficients obtained through modeling.

Images