TWI725582B - METHOD FOR ESTIMATING CHANGES IN CHOLESTEROL AND γ-GLUTAMYLTRANSPEPTIDASE LEVELS IN THE BLOOD OF A BELUGA WHALE INDIVIDUAL - Google Patents

Abstract

This invention discloses a method for estimating changes in cholesterol and γ-glutamyltranspeptidase levels in the blood of a beluga whale ( Delphinapterus leucas) individual. This invention also discloses a method for predicting changes in the liver function index relating to cholesterol metabolism in a beluga whale individual.

Description

Method for estimating the change of cholesterol level in the blood of a beluga individual and the level change of γ-glutamine converting enzyme

The present invention relates to a method for estimating the level change of cholesterol and the level change of γ-glutamyltranspeptidase in the blood of a beluga (Delphinapterus leucas) individual. The present invention also relates to a method for predicting the liver function index changes of a beluga individual related to cholesterol metabolism.

The method currently used in cetacean physiology to assess the physiology and health of cetaceans mainly refers to human medical technology and knowledge, and tests and diagnoses through serum chemistry analysis .

γ-glutamyltranspeptidase (GGT) is a liver function index commonly used in serum chemical analysis, and it has been widely used to initially diagnose whether a human individual has liver function abnormalities. Many factors have been found to cause abnormal liver function, including, for example: (i) Parasitic or viral infections may develop into infectious hepatitis, including hepatitis A, hepatitis B and hepatitis C; (ii) Immune system abnormality may develop into autoimmune hepatitis; (iii) Genetic defects, which may cause liver damage by accumulating different substances in the liver, including hemochromatosis, hyperoxaluria and oxalosis , Wilson's disease (Wilson's disease) and alpha-1 antitrypsin deficiency (AATD); (iv) Alcohol intoxication may develop into alcoholic liver disease; and (v) Metabolic syndrome (including hypertension, abnormal cholesterol metabolism, obesity, diabetes, etc.) may develop into non-alcoholic fatty liver disease (NAFLD).

Therefore, if you want to further confirm the cause of the abnormal liver function of an individual with a high GGT level, further detailed pathological examinations are required.

After research, the applicant unexpectedly discovered that the specific lipids in the blood of beluga individuals can be used as biomarkers to simultaneously estimate the blood cholesterol level and the GGT level changes, and therefore are expected to be used to predict liver function index related to cholesterol metabolism The change.

Summary of the invention

Therefore, in the first aspect, the present invention provides a method for estimating changes in cholesterol level and γ-glutamine converting enzyme level in the blood of a beluga individual, which includes: Detect a lipid level in a blood sample taken from the beluga individual, where the lipid is selected from the group consisting of: PC(0:0/20:4), PC(0:0/23:2), PC(0:0/22:6), PC(13:1/16:0), PC(14:0/ 17:1), PC(15:0/20:4), PC(16:0/10:0), PC(16:0/18:1), PC(17:1/17:0), PC (16:0/23:2), PC(18:0/21:2), PC(16:1/20:4), PC(16:1/22:6), PC(16:2/20 :4), PC(17:0/18:1), PC(18:0/18:1), PC(16:0/20:1), PC(18:0/22:4), PC( 18:0/22:5), PC(20:1/20:4), PC(18:0/22:6), PC(18:1/20:4), PC(16:0/22: 5), PC(22:5/18:0), PC(O-21:0/18:0), PC(O-18:0/20:4), PC(O-20:0/18: 1), PC(P-16:0/18:1), PC(P-17:0/18:1), PC(P-18:0/18:1), SM(14:1/18: 0), SM(16:1/16:0), SM(17:0/15:1), SM(16:1/19:0), SM(15:1/20:0), SM(18 :1/17:0), SM(16:1/20:0), SM(18:1/18:0), SM(16:1/24:1), SM(17:1/16:0) ), SM(18:1/15:0), SM(17:1/24:1), SM(18:1/24:1), SM(18:2/24:1), and their combinations ;as well as The cholesterol level and the gamma-glutamine converting enzyme level in the blood of the beluga individual are predicted based on the detected lipid level, where: When the lipid is selected from the group consisting of: PC(13:1/16:0), PC(16:0/18:1), PC(17:1/17:0), PC( 17:0/18:1), PC(18:0/18:1), PC(16:0/20:1), PC(18:0/22:4), PC(18:0/22: 5), PC(20:1/20:4), PC(18:0/22:6), PC(22:5/18:0), PC(O-21:0/18:0), PC (0-18:0/20:4), and their combination, the lipid level is negatively correlated with the cholesterol level, and positively correlated with the γ-glutamine convertase level; as well as When the lipid is selected from the group consisting of: PC(0:0/20:4), PC(0:0/23:2), PC(0:0/22:6), PC( 14:0/17:1), PC(15:0/20:4), PC(16:0/10:0), PC(16:0/23:2), PC(18:0/21: 2), PC(16:1/20:4), PC(16:1/22:6), PC(16:2/20:4), PC(18:1/20:4), PC(16 :0/22:5), PC(O-20:0/18:1), PC(P-16:0/18:1), PC(P-17:0/18:1), PC(P -18:0/18:1), SM(14:1/18:0), SM(16:1/16:0), SM(17:0/15:1), SM(16:1/19 :0), SM(15:1/20:0), SM(18:1/17:0), SM(16:1/20:0), SM(18:1/18:0), SM( 16:1/24:1), SM(17:1/16:0), SM(18:1/15:0), SM(17:1/24:1), SM(18:1/24: 1), SM(18:2/24:1), and their combination, the lipid level is positively correlated with the cholesterol level, and negatively correlated with the γ-glutamine convertase level Sex.

In a second aspect, the present invention provides a method for predicting liver function index changes related to cholesterol metabolism in a beluga individual, which comprises: Detect a lipid level in a blood sample taken from the beluga individual, where the lipid is selected from the group consisting of: PC(0:0/20:4), PC(0:0/ 23:2), PC(0:0/22:6), PC(13:1/16:0), PC(14:0/17:1), PC(15:0/20:4), PC (16:0/10:0), PC(16:0/18:1), PC(17:1/17:0), PC(16:0/23:2), PC(18:0/21 :2), PC(16:1/20:4), PC(16:1/22:6), PC(16:2/20:4), PC(17:0/18:1), PC( 18:0/18:1), PC(16:0/20:1), PC(18:0/22:4), PC(18:0/22:5), PC(20:1/20: 4), PC(18:0/22:6), PC(18:1/20:4), PC(16:0/22:5), PC(22:5/18:0), PC(O -21:0/18:0), PC(O-18:0/20:4), PC(O-20:0/18:1), PC(P-16:0/18:1), PC (P-17:0/18:1), PC(P-18:0/18:1), SM(14:1/18:0), SM(16:1/16:0), SM(17 :0/15:1), SM(16:1/19:0), SM(15:1/20:0), SM(18:1/17:0), SM(16:1/20:0) ), SM(18:1/18:0), SM(16:1/24:1), SM(17:1/16:0), SM(18:1/15:0), SM(17: 1/24:1), SM(18:1/24:1), SM(18:2/24:1), and their combinations; When the lipid is selected from the group consisting of: PC(13:1/16:0), PC(16:0/18:1), PC(17:1/17:0), PC( 17:0/18:1), PC(18:0/18:1), PC(16:0/20:1), PC(18:0/22:4), PC(18:0/22: 5), PC(20:1/20:4), PC(18:0/22:6), PC(22:5/18:0), PC(O-21:0/18:0), PC (O-18:0/20:4), and their combination, the lipid level is positively correlated with the liver function index; and When the lipid is selected from the group consisting of: PC(0:0/20:4), PC(0:0/23:2), PC(0:0/22:6), PC( 14:0/17:1), PC(15:0/20:4), PC(16:0/10:0), PC(16:0/23:2), PC(18:0/21: 2), PC(16:1/20:4), PC(16:1/22:6), PC(16:2/20:4), PC(18:1/20:4), PC(16 :0/22:5), PC(O-20:0/18:1), PC(P-16:0/18:1), PC(P-17:0/18:1), PC(P -18:0/18:1), SM(14:1/18:0), SM(16:1/16:0), SM(17:0/15:1), SM(16:1/19 :0), SM(15:1/20:0), SM(18:1/17:0), SM(16:1/20:0), SM(18:1/18:0), SM( 16:1/24:1), SM(17:1/16:0), SM(18:1/15:0), SM(17:1/24:1), SM(18:1/24: 1) When SM (18:2/24:1), and their combination, the lipid level is negatively correlated with the liver function index.

Detailed description of the invention

It should be understood that if any previous case publication is quoted here, the previous case publication does not constitute a recognition: in Taiwan or any other country, the previous case publication forms a common general in the art. Part of knowledge.

For the purpose of this specification, it will be clearly understood that the word "comprising" means "including but not limited to", and the word "comprises" has a corresponding meaning.

Unless otherwise defined, all technical and scientific terms used in this article have meanings commonly understood by those familiar with the art of the present invention. A person familiar with the art will recognize that many methods and materials similar or equivalent to those described herein can be used to implement the present invention. Of course, the present invention is by no means restricted by the described methods and materials.

In order to find a biomarker that can simultaneously detect the level of cholesterol and γ-glutamyltranspeptidase (GGT) of an individual beluga whale for predicting abnormal liver function caused by abnormal cholesterol metabolism, the applicant analyzed the beluga whale The correlation between various phosphorylcholine-containing lipids and cholesterol and GGT in the blood, and 44 kinds of lipid molecules that are highly correlated with cholesterol and GGT at the same time were screened.

Therefore, the present invention provides a method for estimating the change in cholesterol level and the change in γ-glutamine converting enzyme level in the blood of a beluga individual, which includes: Detect a lipid level in a blood sample taken from the beluga individual, where the lipid is selected from the group consisting of: PC(0:0/20:4), PC(0:0/23:2), PC(0:0/22:6), PC(13:1/16:0), PC(14:0/ 17:1), PC(15:0/20:4), PC(16:0/10:0), PC(16:0/18:1), PC(17:1/17:0), PC (16:0/23:2), PC(18:0/21:2), PC(16:1/20:4), PC(16:1/22:6), PC(16:2/20 :4), PC(17:0/18:1), PC(18:0/18:1), PC(16:0/20:1), PC(18:0/22:4), PC( 18:0/22:5), PC(20:1/20:4), PC(18:0/22:6), PC(18:1/20:4), PC(16:0/22: 5), PC(22:5/18:0), PC(O-21:0/18:0), PC(O-18:0/20:4), PC(O-20:0/18: 1), PC(P-16:0/18:1), PC(P-17:0/18:1), PC(P-18:0/18:1), SM(14:1/18: 0), SM(16:1/16:0), SM(17:0/15:1), SM(16:1/19:0), SM(15:1/20:0), SM(18 :1/17:0), SM(16:1/20:0), SM(18:1/18:0), SM(16:1/24:1), SM(17:1/16:0) ), SM(18:1/15:0), SM(17:1/24:1), SM(18:1/24:1), SM(18:2/24:1), and their combinations ;as well as The cholesterol level and the gamma-glutamine converting enzyme level in the blood of the beluga individual are predicted based on the detected lipid level, where: When the lipid is selected from the group consisting of: PC(13:1/16:0), PC(16:0/18:1), PC(17:1/17:0), PC( 17:0/18:1), PC(18:0/18:1), PC(16:0/20:1), PC(18:0/22:4), PC(18:0/22: 5), PC(20:1/20:4), PC(18:0/22:6), PC(22:5/18:0), PC(O-21:0/18:0), PC (0-18:0/20:4), and their combination, the lipid level is negatively correlated with the cholesterol level, and positively correlated with the γ-glutamine convertase level; as well as When the lipid is selected from the group consisting of: PC(0:0/20:4), PC(0:0/23:2), PC(0:0/22:6), PC( 14:0/17:1), PC(15:0/20:4), PC(16:0/10:0), PC(16:0/23:2), PC(18:0/21: 2), PC(16:1/20:4), PC(16:1/22:6), PC(16:2/20:4), PC(18:1/20:4), PC(16 :0/22:5), PC(O-20:0/18:1), PC(P-16:0/18:1), PC(P-17:0/18:1), PC(P -18:0/18:1), SM(14:1/18:0), SM(16:1/16:0), SM(17:0/15:1), SM(16:1/19 :0), SM(15:1/20:0), SM(18:1/17:0), SM(16:1/20:0), SM(18:1/18:0), SM( 16:1/24:1), SM(17:1/16:0), SM(18:1/15:0), SM(17:1/24:1), SM(18:1/24: 1), SM(18:2/24:1), and their combination, the lipid level is positively correlated with the cholesterol level, and negatively correlated with the γ-glutamine convertase level Sex.

Preferably, the lipid is selected from the group consisting of: PC(0:0/23:2), PC(16:0/10:0), PC(16:1/20:4) , PC(16:2/20:4), PC(16:0/18:1), PC(17:1/17:0), PC(18:0/18:1), PC(16:0 /20:1), PC(18:0/22:4), PC(18:0/22:5), PC(20:1/20:4), PC(18:0/22:6), PC(18:1/20:4), PC(16:0/22:5), PC(O-21:0/18:0), PC(P-16:0/18:1), SM( 16:1/19:0), SM(15:1/20:0), SM(18:1/17:0), SM(16:1/20:0), SM(18:1/18: 0), SM(16:1/24:1), SM(17:1/16:0), SM(18:1/15:0), SM(17:1/24:1), SM(18 :1/24:1), and their combinations; more preferably, the lipid is selected from PC(16:0/18:1), PC(17:1/17:0), PC(18:0 /18:1), PC(16:0/20:1), and their combination.

Preferably, the change in the cholesterol level is closely related to the change in the level of the gamma-glutamine converting enzyme. More preferably, the change in the level of the γ-glutamine converting enzyme is caused by the change in the cholesterol level (for example, abnormal cholesterol metabolism).

According to the present invention, the blood sample can be obtained by collecting the beluga individual by methods well known in the art, including, but not limited to: venous blood sampling. In a preferred embodiment of the present invention, the blood sample is obtained by sampling the dorsal fluke veins of the beluga individual.

According to the present invention, the blood sample can be obtained by collecting fasted or non-fasted beluga individuals at any point in time. In a preferred embodiment of the present invention, the blood sample is obtained by collecting a beluga individual who is fasting overnight between 8 am and 10 am.

According to the present invention, the blood sample can be subjected to a blood separation process before being used for detection. In a preferred embodiment of the present invention, the blood sample is subjected to a blood separation process to separate plasma from the blood sample.

According to the present invention, the lipid level can be detected by any method known to those skilled in the art for quantifying lipids. Preferably, the lipid level is detected using at least one of the following methodologies: ultra-high performance liquid chromatography (ultra-high performance liquid chromatography), high-performance liquid chromatography (high-performance liquid chromatography) performance liquid chromatography), mass spectrometry (mass spectrometry), and their combinations. The setting of the operating conditions of these methods and the selection of reagents fall within the scope of professionalism and routine techniques of those who are familiar with this technology.

The present invention also provides a method for predicting liver function index changes related to cholesterol metabolism in a beluga individual, which includes: Detect a lipid level in a blood sample taken from the beluga individual, where the lipid is selected from the group consisting of: PC(0:0/20:4), PC(0:0/ 23:2), PC(0:0/22:6), PC(13:1/16:0), PC(14:0/17:1), PC(15:0/20:4), PC (16:0/10:0), PC(16:0/18:1), PC(17:1/17:0), PC(16:0/23:2), PC(18:0/21 :2), PC(16:1/20:4), PC(16:1/22:6), PC(16:2/20:4), PC(17:0/18:1), PC( 18:0/18:1), PC(16:0/20:1), PC(18:0/22:4), PC(18:0/22:5), PC(20:1/20: 4), PC(18:0/22:6), PC(18:1/20:4), PC(16:0/22:5), PC(22:5/18:0), PC(O -21:0/18:0), PC(O-18:0/20:4), PC(O-20:0/18:1), PC(P-16:0/18:1), PC (P-17:0/18:1), PC(P-18:0/18:1), SM(14:1/18:0), SM(16:1/16:0), SM(17 :0/15:1), SM(16:1/19:0), SM(15:1/20:0), SM(18:1/17:0), SM(16:1/20:0) ), SM(18:1/18:0), SM(16:1/24:1), SM(17:1/16:0), SM(18:1/15:0), SM(17: 1/24:1), SM(18:1/24:1), SM(18:2/24:1), and their combinations; When the lipid is selected from the group consisting of: PC(13:1/16:0), PC(16:0/18:1), PC(17:1/17:0), PC( 17:0/18:1), PC(18:0/18:1), PC(16:0/20:1), PC(18:0/22:4), PC(18:0/22: 5), PC(20:1/20:4), PC(18:0/22:6), PC(22:5/18:0), PC(O-21:0/18:0), PC (O-18:0/20:4), and their combination, the lipid level is positively correlated with the liver function index; and When the lipid is selected from the group consisting of: PC(0:0/20:4), PC(0:0/23:2), PC(0:0/22:6), PC( 14:0/17:1), PC(15:0/20:4), PC(16:0/10:0), PC(16:0/23:2), PC(18:0/21: 2), PC(16:1/20:4), PC(16:1/22:6), PC(16:2/20:4), PC(18:1/20:4), PC(16 :0/22:5), PC(O-20:0/18:1), PC(P-16:0/18:1), PC(P-17:0/18:1), PC(P -18:0/18:1), SM(14:1/18:0), SM(16:1/16:0), SM(17:0/15:1), SM(16:1/19 :0), SM(15:1/20:0), SM(18:1/17:0), SM(16:1/20:0), SM(18:1/18:0), SM( 16:1/24:1), SM(17:1/16:0), SM(18:1/15:0), SM(17:1/24:1), SM(18:1/24: 1) When SM (18:2/24:1), and their combination, the lipid level is negatively correlated with the liver function index.

Preferably, the lipid is selected from the group consisting of: PC(0:0/23:2), PC(16:0/10:0), PC(16:1/20:4) , PC(16:2/20:4), PC(16:0/18:1), PC(17:1/17:0), PC(18:0/18:1), PC(16:0 /20:1), PC(18:0/22:4), PC(18:0/22:5), PC(20:1/20:4), PC(18:0/22:6), PC(18:1/20:4), PC(16:0/22:5), PC(O-21:0/18:0), PC(P-16:0/18:1), SM( 16:1/19:0), SM(15:1/20:0), SM(18:1/17:0), SM(16:1/20:0), SM(18:1/18: 0), SM(16:1/24:1), SM(17:1/16:0), SM(18:1/15:0), SM(17:1/24:1), SM(18 :1/24:1), and their combinations; more preferably, the lipid is selected from PC(16:0/18:1), PC(17:1/17:0), PC(18:0 /18:1), PC(16:0/20:1), and their combination.

Preferably, the liver function index change related to cholesterol metabolism is abnormal liver function caused by abnormal cholesterol metabolism. More preferably, the liver function index changes related to cholesterol metabolism are non-alcoholic fatty liver caused by abnormal cholesterol metabolism.

According to the present invention, the blood sample collection method, blood separation processing, and lipid level detection method are as described above.

Detailed description of the preferred embodiment

The present invention will be further described with respect to the following embodiments, but it should be understood that these embodiments are for illustrative purposes only, and should not be construed as limitations on the implementation of the present invention. Examples General experimental materials: 1. Experimental animals:

Two female beluga whales (Latin scientific name: Delphinapterus leucas ; English common name: Beluga whale) used in the following examples (11 and 14 years old, body length about 3.6-4.3 m, weight about 900-1,200 kg) It comes from the National Museum of Marine Biology and Aquarium, Taiwan ROC, and is kept in an ^{aquarium with natural lighting and containing natural seawater with a volume of 3,566 m 3} and a depth of 8 m. The temperature is maintained at 15-17°C, and feed and nutritional supplements are adequately supplied. The handling of laboratory animals and all experimental procedures are in compliance with international laboratory animal management standards. They have also been reviewed by the Agricultural Committee of the Executive Yuan and are based on the Regulations Governing Raising and Breeding of Protected or Dangerous Wildlife (Regulations Governing Raising and Breeding of Protected or Dangerous Wildlife). ) To proceed. General experimental methods: 1. Determination of γ-glutamyltranspeptidase (GGT) level and cholesterol (cholesterol) level in plasma

The GGT level and cholesterol level are measured by using Fuji Dri-Chem 3500s system. 2. Phosphorylcholine-containing lipids profile analysis in plasma

To 10 μL of plasma sample, add 800 μL of chloroform-methanol mixture (2:1, v/v) for extraction, then add 200 μL of 0.15 M sodium chloride aqueous solution and mix well, then It takes 30 minutes to stand still to cause delamination, and then collect the formed lower layer of liquid to obtain a sample to be tested. The lipid composition of the sample to be tested is with reference to Tang CH et al., (2012), Anal. Bioanal. Chem. , 404(10): 2949-61 and by using high-performance liquid chromatography-tandem mass spectrometry ( high performance liquid chromatography-tandem mass spectrometry, HPLC-MS/MS) for analysis. The HPLC analytical instruments used are as follows: Agilent 1100 series HPLC system (Agilent Technologies, USA). The various operating conditions related to this HPLC analysis are shown in Table 1 below. Table 1. Operating parameters and conditions of HPLC analysis Operating parameters condition Separation column Kinetex C ₁₈ 2.6μm (Phenomenex, USA) String specifications 100 mm × 2.1 mm Column temperature 50℃ Mobile phase Solvent A: 5% acetonitrile (acetonitrile) aqueous solution, solvent B: acetonitrile-methanol mixture (40:60, v/v) [Add 1.0% 2.0 M ammonium acetate (NH ₄ Ac)] Gradient elution of mobile phase From 0 to 1 minute, solvent B changed from 70% to 100%; from 1 minute to 7 minutes, solvent B remained at 100%. Flow rate (mL/min) 0.5

Tandem mass spectrometry is measured using Applied Biosystems API 4000 triple quadrupole mass spectrometer (Applied Biosystems, USA).

After that, use the obtained mass spectrometry data to identify the type of lipid molecules containing -phosphocholine in the plasma (create a database), and then use the Analyst 1.4 software suite (Applied Biosystems Instrument Co., USA) for analysis and processing , And further estimate the content ratio of the lipid molecules containing phosphocholine based on the signal peak area of each molecule and the sum of all signals. Example 1. γ- bran converting enzyme acyl amine containing cholesterol level, and - a lipid composition choline phosphate (phosphorylcholine-containing lipids profile) of the associated method of evaluation experiments:

The beluga whales were subjected to overnight fasting, and then blood was collected from the dorsal fluke veins, and collected in a heparin-treated vacuum blood collection tube (Vacutainer ^® , Becton, Dickinson and Co., USA ), followed by centrifugation at 3000 rpm at 4°C for 5 minutes to obtain a plasma sample. After that, within 24 hours, the plasma samples obtained were subjected to the determination of γ-glutamine converting enzyme level, cholesterol level, and lipid composition containing -phosphocholine according to the method described in the "General Experimental Methods" above. analysis. The above blood collection and analysis steps were repeated once a month for a total of 19 months.

Then, SIMCA-P 13 software (Umetrics AB, Sweden) was used to perform orthogonal projections to latent structures (OPLS) analysis on the analytical data obtained every month to initially screen out the data that can be separated from GGT Level and cholesterol level to establish a good model [ie, cross-validated predictive abilities (cross-validated predictive abilities) Q ² Y> 0.5] and higher variable importance for the projection (VIP) contains- Phosphocholine lipids.

Based on the preliminary screening results, the applicant further selected 100 lipid molecules containing phosphocholine whose level changes were significant within 19 months and the molecular structure has been identified, and then these lipid molecules containing phosphocholine The level and the corresponding GGT level and cholesterol level are used again for OPLS analysis, and an OPLS model of important lipids related to GGT level and an OPLS model of important lipids related to cholesterol level are established respectively . result:

Figure 1 shows that in the OPLS model of an important lipid-phosphocholine-containing lipid related to cholesterol levels, there is a quantitative relationship between the variation of the lipid composition of-phosphocholine-containing lipids and the cholesterol level (quantitative relationship). the scores plot tu (tu score plot), wherein the total variation interpretable ^{(total variance explained) R 2 X} = 0.215, R 2 Y = 0.887, cross-validation predictive capability Q ² Y = 0.689, p - value> 0.0001, Shows that this model has good explanatory power and predictive power.

Figure 2 shows the quasi-GGT bit associated with important containing - OPLS model lipid phosphorylcholine, showing containing - tu score map quantitative relationship between the variation of the lipid composition and phosphorylcholine GGT level, wherein The interpretable total variation R ² X = 0.311, R ² Y = 0.955, the predictive power of cross-validation Q ² Y = 0.801, and the p -value> 0.0001, showing that this model has good explanatory power and predictive power.

Table 2 shows the important -phosphocholine-containing lipid molecules (including glycerophosphocholines (PCs) and sphingomyelins (SMs) identified based on the modeled covariance in the OPLS model above). )], and the VIP values and loading values of the lipids containing -phosphocholine in the two OPLS models. Wherein, when the load value is greater than 0, it means that there is a positive correlation between the lipid level and the GGT level or cholesterol level, and vice versa, when the load value is less than 0, it means a negative correlation (negative correlation). These phosphocholine-containing lipids are named and abbreviated based on the recommendations in the lipid metabolites and pathway research project [LIPID Metabolites and Pathways Strategy (LIPID MAPS) consortium]. Table 2. VIP value and load value of each lipid in the OPLS model of important lipids associated with GGT and cholesterol levels Lipid Cholesterol level GGT level VIP value Load value VIP value Load value PC(0:0/20:4) 1.4 0.003 1.0 -0.003 PC(0:0/23:2) 1.5 0.000 1.2 0.000 PC(0:0/22:6) 1.1 0.001 0.9 -0.001 PC(13:1/16:0) 0.9 -0.002 1.1 0.002 PC(14:0/17:1) 1.0 0.002 1.2 -0.003 PC(15:0/20:4) 1.4 0.006 0.9 -0.003 PC(16:0/10:0) 1.3 0.001 1.7 -0.001 PC(16:0/18:1) +PC(17:1/17:0) 0.7 -0.080 1.0 0.127 PC(16:0/23:2) +PC(18:0/21:2) 1.1 0.006 1.4 -0.006 PC(16:1/20:4) 1.5 0.008 1.5 -0.009 PC(16:1/22:6) 1.2 0.008 1.4 -0.011 PC(16:2/20:4) 1.1 0.006 1.5 -0.010 PC(17:0/18:1) 1.0 -0.026 1.2 0.031 PC(18:0/18:1) +PC(16:0/20:1) 1.6 -0.263 1.0 0.180 PC(18:0/22:4) 1.7 -0.012 1.2 0.010 PC(18:0/22:5) +PC(20:1/20:4) 1.6 -0.040 1.9 0.046 PC(18:0/22:6) 2.0 -0.114 1.7 0.088 PC(18:1/20:4) +PC(16:0/22:5) 1.6 0.113 1.3 -0.095 PC(22:5/18:0) 1.1 -0.006 1.4 0.009 PC(O-21:0/18:0) ^a 1.5 -0.035 1.2 0.026 PC(O-18:0/20:4) 0.9 -0.003 1.4 0.006 PC(O-20:0/18:1) 0.9 0.003 1.1 -0.007 PC(P-16:0/18:1) ^b 1.4 0.050 1.7 -0.056 PC(P-17:0/18:1) 0.9 0.004 1.2 -0.005 PC(P-18:0/18:1) 1.3 0.008 1.0 -0.005 SM(14:1/18:0) +SM(16:1/16:0) +SM(17:0/15:1) 1.1 0.006 1.4 -0.009 SM(16:1/19:0) +SM(15:1/20:0) +SM(18:1/17:0) 1.7 0.025 1.1 -0.014 SM(16:1/20:0) +SM(18:1/18:0) 1.9 0.118 1.9 -0.107 SM(16:1/24:1) 2.2 0.015 2.4 -0.016 SM(17:1/16:0) +SM(18:1/15:0) 1.7 0.010 1.5 -0.008 SM(17:1/24:1) 1.7 0.009 0.9 -0.003 SM(18:1/24:1) 1.6 0.018 1.6 -0.022 SM(18:2/24:1) 1.1 0.006 1.4 -0.008 a: "O-" means that it is bonded to the glycerin skeleton through an alkyl ether. b: "P-" represents the bond to the glycerin backbone through vinyl ether.

It can be seen from Table 2, PC(13:1/16:0), PC(16:0/18:1), PC(17:1/17:0), PC(17:0/18:1), PC (18:0/18:1), PC(16:0/20:1), PC(18:0/22:4), PC(18:0/22:5), PC(20:1/20 :4), PC(18:0/22:6), PC(22:5/18:0), PC(O-21:0/18:0) and PC(O-18:0/20:4 ) Has a negative correlation with the cholesterol level, and has a positive correlation with the GGT level; on the other hand, PC(0:0/20:4), PC(0:0/23:2), PC (0:0/22:6), PC(14:0/17:1), PC(15:0/20:4), PC(16:0/10:0), PC(16:0/23 :2), PC(18:0/21:2), PC(16:1/20:4), PC(16:1/22:6), PC(16:2/20:4), PC( 18:1/20:4), PC(16:0/22:5), PC(O-20:0/18:1), PC(P-16:0/18:1), PC(P- 17:0/18:1), PC(P-18:0/18:1), SM(14:1/18:0), SM(16:1/16:0), SM(17:0/ 15:1), SM(16:1/19:0), SM(15:1/20:0), SM(18:1/17:0), SM(16:1/20:0), SM (18:1/18:0), SM(16:1/24:1), SM(17:1/16:0), SM(18:1/15:0), SM(17:1/24 :1), SM(18:1/24:1), SM(18:2/24:1) levels are positively correlated with cholesterol levels, and negatively correlated with GGT levels. In particular, the combination of PC(16:0/18:1)+PC(17:1/17:0) and the combination of PC(18:0/18:1)+PC(16:0/20:1) They account for 9.9±1.0% and 8.7±1.2% of phosphorylcholine-containing lipids in the plasma of beluga whales. They are major phosphorylcholine-containing lipids, and are at the GGT or cholesterol level respectively. The associated OPLS models all have higher load values.

Based on the above experimental results, the applicant believes that: the above lipid [especially the combination of PC(16:0/18:1)+PC(17:1/17:0) and PC(18:0/18:1)+ The combination of PC (16:0/20:1)] can be used as a biomarker for estimating the GGT and cholesterol level changes in the blood of a beluga individual, and then used to predict the amount of cholesterol in a beluga individual Changes in liver function index related to metabolism.

All patents and documents cited in this specification are incorporated into this case as reference materials in their entirety. If there is a conflict, the detailed description of the case (including the definition) will prevail.

Although the present invention has been described with reference to the above specific specific examples, it is obvious that many modifications and changes can be made without departing from the scope and spirit of the present invention. Therefore, it is intended that the present invention is only limited by the scope of the patent application attached hereto.

The above and other objectives, features, and advantages of the present invention will become apparent with reference to the following detailed description, preferred embodiments, and accompanying drawings. Among them: Figure 1 shows the importance of cholesterol levels Tu scoring map of the orthogonal projections to latent structures (OPLS) model of the potential structure of lipids containing -phosphocholine, in which the total interpretable variation is R ² X = 0.215, R ² Y = 0.887, cross-validation The predictive power of Q ² Y = 0.689, p -value>0.0001; and Figure 2 shows the tu score map of the OPLS model of important lipid-phosphocholine-containing lipids related to the γ-glutamine converting enzyme level, which can be The explained total variation is R ² X = 0.311, R ² Y = 0.955, the predictive power of cross-validation is Q ² Y = 0.801, and the p -value is> 0.0001.

Claims (9)

A method for estimating the changes in the level of cholesterol and the level of γ-glutamine converting enzyme in the blood of a beluga individual, which includes: Detect a lipid level in a blood sample taken from the beluga individual, where the lipid is selected from the group consisting of: PC(0:0/20:4), PC(0:0/23:2), PC(0:0/22:6), PC(13:1/16:0), PC(14:0/ 17:1), PC(15:0/20:4), PC(16:0/10:0), PC(16:0/18:1), PC(17:1/17:0), PC (16:0/23:2), PC(18:0/21:2), PC(16:1/20:4), PC(16:1/22:6), PC(16:2/20 :4), PC(17:0/18:1), PC(18:0/18:1), PC(16:0/20:1), PC(18:0/22:4), PC( 18:0/22:5), PC(20:1/20:4), PC(18:0/22:6), PC(18:1/20:4), PC(16:0/22: 5), PC(22:5/18:0), PC(O-21:0/18:0), PC(O-18:0/20:4), PC(O-20:0/18: 1), PC(P-16:0/18:1), PC(P-17:0/18:1), PC(P-18:0/18:1), SM(14:1/18: 0), SM(16:1/16:0), SM(17:0/15:1), SM(16:1/19:0), SM(15:1/20:0), SM(18 :1/17:0), SM(16:1/20:0), SM(18:1/18:0), SM(16:1/24:1), SM(17:1/16:0) ), SM(18:1/15:0), SM(17:1/24:1), SM(18:1/24:1), SM(18:2/24:1), and their combinations ;as well as The cholesterol level and the gamma-glutamine converting enzyme level in the blood of the beluga individual are predicted based on the detected lipid level, where: When the lipid is selected from the group consisting of: PC(13:1/16:0), PC(16:0/18:1), PC(17:1/17:0), PC( 17:0/18:1), PC(18:0/18:1), PC(16:0/20:1), PC(18:0/22:4), PC(18:0/22: 5), PC(20:1/20:4), PC(18:0/22:6), PC(22:5/18:0), PC(O-21:0/18:0), PC (0-18:0/20:4), and their combination, the lipid level is negatively correlated with the cholesterol level, and positively correlated with the γ-glutamine convertase level; as well as When the lipid is selected from the group consisting of: PC(0:0/20:4), PC(0:0/23:2), PC(0:0/22:6), PC( 14:0/17:1), PC(15:0/20:4), PC(16:0/10:0), PC(16:0/23:2), PC(18:0/21: 2), PC(16:1/20:4), PC(16:1/22:6), PC(16:2/20:4), PC(18:1/20:4), PC(16 :0/22:5), PC(O-20:0/18:1), PC(P-16:0/18:1), PC(P-17:0/18:1), PC(P -18:0/18:1), SM(14:1/18:0), SM(16:1/16:0), SM(17:0/15:1), SM(16:1/19 :0), SM(15:1/20:0), SM(18:1/17:0), SM(16:1/20:0), SM(18:1/18:0), SM( 16:1/24:1), SM(17:1/16:0), SM(18:1/15:0), SM(17:1/24:1), SM(18:1/24: 1), SM (18:2/24:1), and their combination, the lipid level is positively correlated with the cholesterol level, and negatively correlated with the γ-glutamine convertase level Sex. The method of claim 1, wherein the level change of the γ-glutamine converting enzyme is related to cholesterol metabolism. A method for predicting the liver function index changes related to cholesterol metabolism in a beluga individual, which includes: Detect a lipid level in a blood sample taken from the beluga individual, where the lipid is selected from the group consisting of: PC(0:0/20:4), PC(0:0/ 23:2), PC(0:0/22:6), PC(13:1/16:0), PC(14:0/17:1), PC(15:0/20:4), PC (16:0/10:0), PC(16:0/18:1), PC(17:1/17:0), PC(16:0/23:2), PC(18:0/21 :2), PC(16:1/20:4), PC(16:1/22:6), PC(16:2/20:4), PC(17:0/18:1), PC( 18:0/18:1), PC(16:0/20:1), PC(18:0/22:4), PC(18:0/22:5), PC(20:1/20: 4), PC(18:0/22:6), PC(18:1/20:4), PC(16:0/22:5), PC(22:5/18:0), PC(O -21:0/18:0), PC(O-18:0/20:4), PC(O-20:0/18:1), PC(P-16:0/18:1), PC (P-17:0/18:1), PC(P-18:0/18:1), SM(14:1/18:0), SM(16:1/16:0), SM(17 :0/15:1), SM(16:1/19:0), SM(15:1/20:0), SM(18:1/17:0), SM(16:1/20:0) ), SM(18:1/18:0), SM(16:1/24:1), SM(17:1/16:0), SM(18:1/15:0), SM(17: 1/24:1), SM(18:1/24:1), SM(18:2/24:1), and their combinations; According to the detected lipid level, the liver function index change in the blood of the beluga individual is predicted, wherein, When the lipid is selected from the group consisting of: PC(13:1/16:0), PC(16:0/18:1), PC(17:1/17:0), PC( 17:0/18:1), PC(18:0/18:1), PC(16:0/20:1), PC(18:0/22:4), PC(18:0/22: 5), PC(20:1/20:4), PC(18:0/22:6), PC(22:5/18:0), PC(O-21:0/18:0), PC (O-18:0/20:4), and their combination, the lipid level is positively correlated with the liver function index; and When the lipid is selected from the group consisting of: PC(0:0/20:4), PC(0:0/23:2), PC(0:0/22:6), PC( 14:0/17:1), PC(15:0/20:4), PC(16:0/10:0), PC(16:0/23:2), PC(18:0/21: 2), PC(16:1/20:4), PC(16:1/22:6), PC(16:2/20:4), PC(18:1/20:4), PC(16 :0/22:5), PC(O-20:0/18:1), PC(P-16:0/18:1), PC(P-17:0/18:1), PC(P -18:0/18:1), SM(14:1/18:0), SM(16:1/16:0), SM(17:0/15:1), SM(16:1/19 :0), SM(15:1/20:0), SM(18:1/17:0), SM(16:1/20:0), SM(18:1/18:0), SM( 16:1/24:1), SM(17:1/16:0), SM(18:1/15:0), SM(17:1/24:1), SM(18:1/24: 1) When SM (18:2/24:1), and their combination, the lipid level is negatively correlated with the liver function index. The method according to any one of claims 1 to 3, wherein the lipid is selected from the group consisting of: PC (0:0/23:2), PC (16:0/10:0), PC(16:1/20:4), PC(16:2/20:4), PC(16:0/18:1), PC(17:1/17:0), PC(18:0/ 18:1), PC(16:0/20:1), PC(18:0/22:4), PC(18:0/22:5), PC(20:1/20:4), PC (18:0/22:6), PC(18:1/20:4), PC(16:0/22:5), PC(O-21:0/18:0), PC(P-16 :0/18:1), SM(16:1/19:0), SM(15:1/20:0), SM(18:1/17:0), SM(16:1/20:0) ), SM(18:1/18:0), SM(16:1/24:1), SM(17:1/16:0), SM(18:1/15:0), SM(17: 1/24:1), SM(18:1/24:1), and their combination. The method according to any one of claims 1 to 3, wherein the lipid is selected from PC (16:0/18:1), PC (17:1/17:0), and combinations thereof. The method according to any one of claims 1 to 3, wherein the lipid is selected from PC (18:0/18:1), PC (16:0/20:1), and combinations thereof. The method according to any one of claims 1 to 3, wherein the blood sample is subjected to a blood separation process before being used for detection, so as to separate the serum for the purpose of detecting the lipid level. The method according to any one of claims 1 to 3, wherein the blood sample is subjected to a blood separation process before being used for detection, so as to separate the plasma for the purpose of detecting the lipid level. The method according to any one of claims 1 to 3, wherein the lipid level is detected using at least one of the following methodologies: ultra high performance liquid chromatography, high performance liquid chromatography, Mass spectrometry, and their combination.

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