Screening method of dairy cow with ketosis based on metabolite fingerprint spectrum

Abstract

The invention relates to the field of cow breeding, in particular to a screening method of ketosis cows based on metabolite fingerprints. The method comprises the following steps: establishing a model; verifying the model; and screening the dairy cows: inputting metabolite fingerprint data of the dairy cows to be detected into the verified diagnosis model, and judging whether the dairy cows suffer from ketosis or not by the model according to preset judgment rules and threshold values, so as to screen the dairy cows with ketosis. Metabolite fingerprint data of a new sample is clinically substituted into the model to judge whether the dairy cow suffers from ketosis or not, and the method can also be used for monitoring disease development and treatment effect.

Description

Technical Field

[0001] The present invention relates to the field of dairy cattle breeding, and in particular to a method for screening dairy cows with ketosis based on metabolite fingerprints. Background Art

[0002] Ketosis in dairy cows is a metabolic disease that is prevalent during the peripartum period. Its traditional diagnostic technology system has three significant limitations:

[0003] Technical and economic bottlenecks are prominent: Traditional testing relies on high-precision equipment such as microplate readers (a single device generally costs 100,000 to 500,000 yuan), and the cost of reagent kit consumables is as high as 20-50 yuan per sample. The annual testing cost for large-scale ranches exceeds 10,000 yuan. Although blood ketone meters improve convenience, the unit price of test strips is still 8-15 yuan per test, making it difficult to achieve high-frequency monitoring.

[0004] Stringent operational requirements: Laboratory testing requires complex pre-processing procedures, including blood centrifugation (3000 rpm for 15 minutes), cryopreservation (-80°C), and column activation (1-2 hours), requiring operators to possess molecular biology laboratory qualifications. Even the ketone powder method for rapid on-site testing requires precise control of the color development reaction time (reading within 30 seconds), resulting in an 18% misjudgment rate for ranch personnel.

[0005] Biomarker specificity is limited; traditional indicators such as β-hydroxybutyrate (BHBA) are susceptible to interference from nutritional manipulation: a high-concentrate diet increases the false-positive rate of BHBA by 23%, while supplementing with calcium propionate increases the risk of false-negative results by 15%. Hormonal fluctuations during pregnancy can also cause physiological fluctuations in blood ketone concentrations of ±0.5 mmol / L, creating a diagnostic overlap with subclinical ketosis.

[0006] In general: Ketosis in dairy cows is a common metabolic disease in the peripartum period. Traditional diagnostic methods have limitations, and new technologies for accurate, convenient, and early diagnosis are urgently needed to ensure the healthy development of the dairy industry.

[0007] Disadvantages: The detection equipment is expensive and the detection cost is high; the sample processing and analysis techniques are demanding and require professional personnel to operate; some metabolites are affected by factors such as diet and environment, which may interfere with the diagnostic results.

[0008] On May 1, 2025, a search was conducted in the China Patent Publication Database using "ketosis and cattle and metabolism and fingerprint and atlas" as abstract keywords, with the option to allow synonym expansion checked, but no relevant literature was found.

[0009] On May 1, 2025, an abstract search for "ketosis and cattle and metabolism and fingerprint and atlas" was conducted on CNKI, and the following was found: 2023-05-01 "Study on the Analysis and Influencing Factors of Raw Milk Flavor Active Substances"

[0010] 2017-09-12 "Bovine Endometrial Epithelial Cells Scale Their Pro-inflammatory Response In vitro to Pathogenic Trueperella pyogenes Isolated from the Bovine Uterus in a Strain-Specific Manner";

[0011] 2014-06-01 "Effects of lysozyme on the diversity of rectal microbiota in periparturient dairy cows";

[0012] 2012-01-01 "Changes in the blood indicators and body condition of high yielding Holstein cows with retained placenta and ketosis";

[0013] 2021-01-26 "A Multi-Platform Metabolomics Approach Identifies Urinary Metabolite Signatures That Differentiate Ketotic From Healthy Dairy Cows".

[0014] However, none of these technologies have planned to build models to conduct advance analysis of ketosis cows.

[0015] On May 1, 2025, a search for "Ketosis with cows with metabolic with fingerprintwith spectrum" was conducted on the website of the United States Patent and Trademark Office, but no relevant literature was found; the search URL is https: / / ppubs.uspto.gov / pubwebapp / .

[0016] On May 1, 2025, a search was conducted on WIPO's https: / / patentscope2.wipo.int / for "Ketosis and cows and metadata and fingerprint and spectrum", but no relevant documents were found.

[0017] On May 1, 2025, a search for "Ketosis and cows and metabol ic and fingerprint and spectrum" was conducted on the Japan Patent Office website https: / / www.j-platpat.inpit.go.jp / , but no relevant literature was found.

[0018] It is completely different from the conception of this patent. Summary of the Invention

[0019] Purpose of the invention: To provide a more effective method for screening ketotic cows based on metabolite fingerprints. For specific purposes, see the multiple substantial technical effects in the specific implementation section.

[0020] In order to achieve the above purpose, the present invention adopts the following technical solutions:

[0021] The method for screening ketosis dairy cows based on metabolite fingerprint is characterized in that:

[0022] The following steps are included:

[0023] Model building: Using the selected characteristic metabolites as variables, machine learning algorithms such as support vector machines (SVMs) and random forests were used to construct a ketosis diagnosis model. The optimal combination and weights of the characteristic metabolites were determined to accurately distinguish between healthy and ketosis cows.

[0024] Model validation: Use an independent validation dataset to validate the constructed diagnostic model, evaluate the model's accuracy, sensitivity, specificity and other indicators, and optimize the model through methods such as cross-validation to ensure the model's reliability and stability;

[0025] Screening dairy cows: The metabolite fingerprint data of the cow to be tested is input into a verified diagnostic model. The model determines whether the cow has ketosis based on the preset discrimination rules and thresholds, thereby screening cows with ketosis.

[0026] A further technical solution of the present invention is that constructing a ketosis diagnostic model involves collecting blood, urine, and milk from healthy and diseased cows at different stages, analyzing metabolites using liquid chromatography-mass spectrometry (LC-MS), screening for characteristic metabolites of ketosis, and constructing a fingerprint spectrum.

[0027] A further technical solution of the present invention is to collect blood samples when the cow shows suspected symptoms of ketosis or regularly during a specific period of time after delivery to improve the accuracy of diagnosis.

[0028] A further technical solution of the present invention is that the sample type is: blood, urine, and milk samples are often collected. Blood can reflect the metabolic state of the whole body, and the content of metabolites such as ketone bodies in urine is relatively high, which is more convenient to detect;

[0029] Processing method: After collection, serum or plasma must be centrifuged and separated promptly. Urine must be free of impurities. Some samples may need to be frozen to prevent metabolite degradation.

[0030] Data processing and analysis: After pre-processing, the metabolite data obtained were reduced in dimension using principal component analysis to identify the differential metabolites that best distinguish diseased from healthy cows.

[0031] Model establishment: Diagnostic models were constructed using partial least squares discriminant analysis, and model accuracy, sensitivity, and specificity were evaluated through cross-validation.

[0032] Application: The metabolite fingerprint data of new samples is substituted into the model to determine whether the cow has ketosis. It can also be used to monitor the progression of the disease and the effectiveness of treatment.

[0033] A further technical solution of the present invention is that model establishment includes characteristic metabolite screening, which includes the following aspects:

[0034] Fold Difference Analysis: Calculate the fold difference of each metabolite between the healthy group and the ketosis group to screen out metabolites that are significantly upregulated or downregulated in ketosis cows;

[0035] Statistical test: Statistical tests such as t-test and analysis of variance were performed to determine the significance of the difference in metabolite content between the two groups. A P value of less than 0.05 or 0.01 was usually set to indicate statistical significance. Metabolites with significant differences were screened as potential characteristic metabolites.

[0036] Analyze whether the screened metabolites are related to the pathophysiological mechanism of ketosis, and further determine the characteristic metabolites closely related to the occurrence and development of ketosis.

[0037] A further technical solution of the present invention is that the metabolites in the difference fold analysis are β-hydroxybutyric acid and acetoacetic acid, and the levels of β-hydroxybutyric acid and acetoacetic acid are usually significantly increased in ketotic cows;

[0038] The characteristic metabolites that are closely related to the occurrence and development of ketosis are metabolites of fatty acid metabolism and sugar metabolism.

[0039] The method for screening ketosis dairy cows based on metabolite fingerprint is characterized in that:

[0040] Sample collection and analysis:

[0041] Blood samples were collected from all cows and analyzed using liquid chromatography-mass spectrometry. After data processing, a total of 500 metabolites were identified;

[0042] Characteristic metabolite screening:

[0043] Multivariate statistical analysis: Principal component analysis revealed that the metabolite data of healthy cows and ketosis cows showed a clear separation trend in the score graph;

[0044] Fold difference and statistical test: Fold difference analysis and t-test showed that 30 metabolites were significantly different between the two groups;

[0045] Build and validate models;

[0046] Model construction: Characteristic metabolites are used as variables, and the support vector machine algorithm is used to build a diagnostic model to determine the weight of each metabolite;

[0047] Validation of the model: 50 additional cow samples with known health conditions were used to validate the model, which achieved 92% accuracy, 90% sensitivity, and 94% specificity.

[0048] A further technical solution of the present invention is that the average content of β-hydroxybutyrate in ketotic cows is 3 times that of healthy cows, with a P value of less than 0.01; the average content of acetoacetate is 2.5 times that of healthy cows, with a P value of less than 0.05;

[0049] Combined with the pathological mechanism of ketosis, β-hydroxybutyrate, glucose, calcium, and free fatty acid metabolites were identified as characteristic metabolites.

[0050] A further technical solution of the present invention is to collect blood tests from 10 dairy cows of unknown health status, input the data into the model, and 3 cows were judged to be in ketosis. After clinical diagnosis, 2 of them were confirmed to have ketosis. The model screening results were highly consistent with the clinical diagnosis.

[0051] A further technical solution of the present invention is that β-hydroxybutyric acid, glucose, calcium, and free fatty acid metabolites are used as comprehensive diagnostic indicators.

[0052] The present invention, employing the above technical solution, offers the following advantages over existing technologies: it enables early diagnosis, detecting metabolic abnormalities before cows show obvious symptoms; it comprehensively reflects the metabolic status of dairy cows, providing multi-dimensional diagnostic information; and it is non-invasive or minimally invasive, causing minimal stress on the cows. Clinically, the metabolite fingerprint data of new samples is fed into the model to determine whether the cow has ketosis, and can also be used to monitor the progression of the disease and the effectiveness of treatment. DETAILED DESCRIPTION

[0053] The following specific embodiments are presented to further illustrate the present invention. It should be understood that the following specific embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention.

[0054] The present invention involves chemometric methods to identify differential metabolites and establish a diagnostic model. Its main applications are: 1. Gas chromatography-mass spectrometry (GC-MS): It can separate and identify a variety of volatile and semi-volatile metabolites, and can detect ketone bodies such as β-hydroxybutyric acid and acetoacetic acid, as well as other metabolites such as organic acids and amino acids related to ketosis in dairy cows.

[0055] 2. Liquid chromatography-mass spectrometry (LC-MS): Suitable for analyzing polar and heat-labile metabolites, it can detect a wider variety of metabolites, such as triglycerides, free fatty acids, and other substances that change in ketosis in dairy cows.

[0056] 3. Nuclear magnetic resonance (NMR): It can detect multiple metabolites simultaneously, provide information on the molecular structure of metabolites, and has a good detection effect on metabolites such as ketone bodies, glucose, and amino acids in blood and urine. It is also simple to operate and has good repeatability.

[0057] advantage:

[0058] Ketosis in dairy cows causes an imbalance in energy metabolism, excessive fat breakdown, and enhanced gluconeogenesis. This leads to changes in the levels and ratios of various metabolites in biological samples such as blood and urine, forming a specific metabolite fingerprint. By analyzing the metabolite fingerprints of biological samples from diseased and healthy dairy cows and using chemometric methods, we can identify differential metabolites and establish a diagnostic model that can distinguish between diseased and healthy cows.

[0059] Develop a new metabolomics-based diagnostic technology to accurately predict ketosis within one week before delivery, with an accuracy rate of ≥95%. Build an intelligent, portable diagnostic device that can provide results on-site within 15 minutes.

[0060] Sample collection and processing

[0061] Sample type: Blood and urine are commonly collected. Blood can reflect the body's metabolic status, while urine contains higher levels of metabolites such as ketone bodies, making it easier to detect.

[0062] Collection time: Generally, the sample is collected when the cow shows suspected symptoms of ketosis, or regularly during a specific period of time after calving to improve diagnostic accuracy.

[0063] Processing method: After collection, serum or plasma must be centrifuged and separated promptly. Urine must be free of impurities. Some samples may need to be frozen to prevent metabolite degradation.

[0064] Diagnostic model establishment and application

[0065] Data processing and analysis: After pre-processing, the metabolite data obtained from the test are reduced in dimension using methods such as principal component analysis to identify the differential metabolites that can best distinguish between diseased and healthy cows.

[0066] Model building: Diagnostic models are constructed using partial least squares discriminant analysis and other methods, and model accuracy, sensitivity, and specificity are evaluated through cross-validation and other methods.

[0067] Clinical application: The metabolite fingerprint data of new samples is substituted into the model to determine whether the cow has ketosis. It can also be used to monitor the progression of the disease and the effectiveness of treatment.

[0068] 1. Metabolite fingerprint construction: Blood, urine, and milk were collected from healthy and diseased dairy cows at different stages of development. Metabolites were analyzed using liquid chromatography-mass spectrometry (LC-MS) to screen for characteristic metabolites of ketosis and construct a fingerprint.

[0069] 2. Sensor R&D: Based on characteristic metabolites, we will develop sensors that are compatible with electrochemical biosensors, nano-optics, and other technologies to enable rapid detection of trace samples; we will also optimize the sensors' anti-interference and stability. This approach primarily aims to enable rapid parameter acquisition and is a next-step improvement.

[0070] 3. Intelligent diagnostic model development: Input sensor data into machine learning algorithms, train diagnostic models, and achieve automatic identification; develop supporting apps to remotely transmit and analyze data.

[0071] Example 1:

[0072] Data processing and analysis

[0073] Peak extraction and identification: Import the raw data obtained by LC-MS and other technologies into professional data processing software to extract and identify the signal peaks of metabolites, and determine the mass-to-charge ratio, retention time and other information of the metabolites corresponding to each peak.

[0074] Data normalization: Normalize the extracted data to eliminate possible errors in sample collection, processing, and instrument testing, making the data of different samples comparable.

[0075] Multivariate statistical analysis: We used multivariate statistical methods such as principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) to analyze the normalized data, reduce the dimensionality of the data, and identify differential patterns and regularities in the metabolite data of healthy and ketotic cows, visually demonstrating the separation trends of the two groups of samples.

[0076] Characteristic metabolite screening

[0077] Fold Difference Analysis: Calculate the fold difference in the content of each metabolite between the healthy and ketosis groups to screen out metabolites that are significantly upregulated or downregulated in ketosis cows. For example, β-hydroxybutyrate and acetoacetic acid are often significantly increased in ketosis cows.

[0078] Statistical tests: Statistical tests such as t-test and analysis of variance were performed to determine the significance of the difference in metabolite content between the two groups. A P value of less than 0.05 or 0.01 was usually set to indicate statistical significance. Metabolites with significant differences were screened as potential characteristic metabolites.

[0079] Integrate with biological knowledge: Refer to existing biological research results and metabolic pathway knowledge to analyze whether the screened metabolites are related to the pathophysiological mechanism of ketosis, and further identify characteristic metabolites closely related to the occurrence and development of ketosis, such as key metabolites involved in fatty acid metabolism, sugar metabolism and other pathways.

[0080] Building a diagnostic model

[0081] Model building: Using the selected characteristic metabolites as variables, machine learning algorithms such as support vector machines (SVMs) and random forests are used to build a ketosis diagnosis model. The optimal combination and weights of the characteristic metabolites are determined to accurately distinguish between healthy and ketosis cows.

[0082] Model validation: Use an independent validation dataset to validate the constructed diagnostic model, evaluate the model's accuracy, sensitivity, specificity, and other indicators, and optimize the model through methods such as cross-validation to ensure its reliability and stability.

[0083] Screening dairy cows: The metabolite fingerprint data of the cow to be tested is input into a verified diagnostic model. The model determines whether the cow has ketosis based on the preset discrimination rules and thresholds, thereby screening cows with ketosis.

[0084] Example 2:

[0085] Experimental design

[0086] The researchers selected 200 dairy cows, 100 of which were clinically diagnosed with ketosis and the other 100 were healthy cows. The study covered different stages of the peripartum period: two weeks before calving, one week before calving, one week after calving, and two weeks after calving, with two groups of 25 cows in each stage.

[0087] Sample collection and analysis

[0088] Blood samples were collected from all cows and analyzed using liquid chromatography-mass spectrometry. After data processing, a total of 500 metabolites were identified.

[0089] Characteristic metabolite screening

[0090] Multivariate statistical analysis: Principal component analysis revealed that the metabolite data of healthy cows and cows with ketosis showed a clear separation trend in the score plot.

[0091] Fold Difference and Statistical Tests: Fold difference analysis and t-tests showed that 30 metabolites were significantly different between the two groups. For example, the average level of β-hydroxybutyrate in ketotic cows was three times that of healthy cows, with a P value less than 0.01; the average level of acetoacetate was 2.5 times that of healthy cows, with a P value less than 0.05.

[0092] Integrating biological knowledge: Based on the pathological mechanism of ketosis, 10 metabolites including β-hydroxybutyrate, glucose, calcium, and free fatty acids were identified as characteristic metabolites.

[0093] Building and validating the model

[0094] Model construction: Using these 10 characteristic metabolites as variables, a diagnostic model was constructed using the support vector machine algorithm to determine the weight of each metabolite.

[0095] Validation of the model: Validation of the model with samples from an additional 50 cows of known health status demonstrated 92% accuracy, 90% sensitivity, and 94% specificity.

[0096] Actual screening

[0097] Blood tests were collected from 10 cows of unknown health status, and the data was input into the model. Three cows were diagnosed with ketosis. After clinical diagnosis, two of them were confirmed to have ketosis. The model screening results were highly consistent with the clinical diagnosis.

[0098]

[0099]

[0100]

[0101]

[0102] The basic principles, main features, and advantages of the present invention are shown and described above. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The above embodiments and descriptions are merely illustrative of the principles of the present invention. Various changes and modifications may be made to the present invention without departing from the spirit and scope of the present invention, and such changes and modifications are intended to fall within the scope of the claims.

Claims

1. A method for screening dairy cows with ketosis based on metabolite fingerprints, characterized in that: The following steps are included: Model building: Using the selected characteristic metabolites as variables, machine learning algorithms such as support vector machines (SVMs) and random forests were used to construct a ketosis diagnosis model. The optimal combination and weights of the characteristic metabolites were determined to accurately distinguish between healthy and ketosis cows. Model validation: Use an independent validation dataset to validate the constructed diagnostic model, evaluate the model's accuracy, sensitivity, specificity and other indicators, and optimize the model through methods such as cross-validation to ensure the model's reliability and stability; Screening dairy cows: The metabolite fingerprint data of the cow to be tested is input into a verified diagnostic model. The model determines whether the cow has ketosis based on the preset discrimination rules and thresholds, thereby screening cows with ketosis.

2. The method for screening ketosis cows based on metabolite fingerprints according to claim 1, characterized in that: Constructing a ketosis diagnostic model involves collecting blood, urine, and milk from healthy and diseased cows at different stages, analyzing metabolites using liquid chromatography-mass spectrometry (LC-MS), screening for characteristic metabolites of ketosis, and constructing a fingerprint map.

3. The method for screening ketosis cows based on metabolite fingerprints according to claim 1, wherein: The samples can be collected when the cow shows suspected symptoms of ketosis or regularly during a specific period of time after calving to improve diagnostic accuracy.

4. The method for screening ketosis cows based on metabolite fingerprints according to claim 3, characterized in that: Sample type: Blood, urine, and breast milk samples are commonly collected. Blood can reflect the metabolic status of the entire body, and urine contains higher levels of metabolites such as ketone bodies, making it easier to detect. Processing method: After collection, serum or plasma must be centrifuged and separated promptly. Urine must be free of impurities. Some samples may need to be frozen to prevent metabolite degradation. Data processing and analysis: After pre-processing, the metabolite data obtained were reduced in dimension using principal component analysis to identify the differential metabolites that best distinguish diseased from healthy cows. Model establishment: Diagnostic models were constructed using partial least squares discriminant analysis, and model accuracy, sensitivity, and specificity were evaluated through cross-validation. Application: The metabolite fingerprint data of new samples is substituted into the model to determine whether the cow has ketosis. It can also be used to monitor the progression of the disease and the effectiveness of treatment.

5. The method for screening ketosis cows based on metabolite fingerprints according to claim 1, wherein: Model establishment includes characteristic metabolite screening, which includes the following aspects: Fold Difference Analysis: Calculate the fold difference of each metabolite between the healthy group and the ketosis group to screen out metabolites that are significantly upregulated or downregulated in ketosis cows; Statistical test: Statistical tests such as t-test and analysis of variance were performed to determine the significance of the difference in metabolite content between the two groups. A P value of less than 0.05 or 0.01 was usually set to indicate statistical significance. Metabolites with significant differences were screened as potential characteristic metabolites. Analyze whether the screened metabolites are related to the pathophysiological mechanism of ketosis, and further determine the characteristic metabolites closely related to the occurrence and development of ketosis.

6. The method for screening ketosis cows based on metabolite fingerprints according to claim 5, characterized in that: The metabolites in the fold difference analysis were β-hydroxybutyrate and acetoacetate, which are usually significantly increased in ketotic cows. The characteristic metabolites that are closely related to the occurrence and development of ketosis are metabolites of fatty acid metabolism and sugar metabolism.

7. A method for screening dairy cows with ketosis based on metabolite fingerprints, characterized in that: Sample collection and analysis: Blood samples were collected from all cows and analyzed using liquid chromatography-mass spectrometry. After data processing, a total of 500 metabolites were identified; Characteristic metabolite screening: Multivariate statistical analysis: Principal component analysis revealed that the metabolite data of healthy cows and ketosis cows showed a clear separation trend in the score graph; Fold difference and statistical test: Fold difference analysis and t-test showed that 30 metabolites were significantly different between the two groups; Build and validate models; Model construction: Characteristic metabolites are used as variables, and the support vector machine algorithm is used to build a diagnostic model to determine the weight of each metabolite; Validation of the model: 50 additional cow samples with known health conditions were used to validate the model, which achieved 92% accuracy, 90% sensitivity, and 94% specificity.

8. The method for screening ketosis cows based on metabolite fingerprints according to claim 7, characterized in that: The average content of β-hydroxybutyrate in ketotic cows was 3 times that of healthy cows, with a P value less than 0.01; the average content of acetoacetate was 2.5 times that of healthy cows, with a P value less than 0.05; Combined with the pathological mechanism of ketosis, β-hydroxybutyrate, glucose, calcium, and free fatty acid metabolites were identified as characteristic metabolites.

9. The method for screening ketosis cows based on metabolite fingerprints according to claim 7, characterized in that: Blood tests were collected from 10 cows of unknown health status, and the data was input into the model. Three cows were diagnosed with ketosis. After clinical diagnosis, two of them were confirmed to have ketosis. The model screening results were highly consistent with the clinical diagnosis.

10. The method for screening ketosis cows based on metabolite fingerprints according to claim 7, characterized in that: β-Hydroxybutyrate, glucose, calcium, and free fatty acid metabolites were used as comprehensive diagnostic indicators.

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