Application of blood lipids in dairy product intake and disease prediction

Abstract

The present invention relates to the use of lipids and / or detection reagents thereof as biomarkers of dairy product intake in preparing a kit for identifying dairy product intake, predicting cardiovascular disease risk, identifying cardiovascular risk factors or their changes, and studying the relationship between dairy products and cardiovascular health.

Description

Technical Field

[0001] The present invention relates to the field of disease prediction and diagnosis, and more specifically to the application of blood lipids in being used as biomarkers for dairy product intake, assessing dairy product intake, and predicting the risk of cardiovascular disease. Background Art

[0002] Cardiovascular disease is the leading cause of death and disability in the world. According to the "China Cardiovascular Health and Disease Report 2019", there are as many as 330 million cardiovascular disease patients in my country, which has imposed a heavy burden on social economy and public health. Its occurrence and development are the result of the long-term interaction of environmental factors such as diet and genetic factors, so it is highly preventable and controllable. Dairy products are part of a healthy dietary pattern and are rich in high-quality whey protein, lipids, vitamins, minerals, etc. They are very likely to become an economical and effective strategy for the prevention and management of cardiovascular diseases. Clarifying the relationship between dairy product exposure and cardiovascular health and its potential mechanisms is the key to prevention and management. However, due to the subjective recall bias of the respondents and the limited coverage of the food ingredient database, the accuracy of the dairy product intake data obtained by questionnaire in population studies is difficult to guarantee, and its relationship with cardiovascular disease and its potential mechanism are still unclear.

[0003] Nutritional metabolomics is a quantitative measurement of small molecule metabolites in blood, urine or tissues. It captures markers that reflect endogenous food exposure or metabolic response by detecting changes in the metabolome of a specific diet or dietary pattern, thereby more accurately and unbiasedly defining the relationship between diet and disease, and providing mechanistic support for the association between diet and disease. At present, some intervention studies abroad have used non-targeted metabolomics to explore changes in the metabolome after a cheese, milk, or whey protein diet. A small number of cross-sectional population studies have explored the relationship between blood metabolomes and dairy product intake obtained from dietary frequency questionnaires. However, due to differences in non-targeted metabolomics detection platforms, rapid dietary changes, and controlled covariates including dietary factors, although some metabolites such as uridine, pantothenate, and plant galactose have been found to be highly correlated with dairy product intake, most metabolites are difficult to verify repeatedly, and there is no evidence reporting the beneficial effects of the above metabolite characteristics on cardiovascular health.

[0004] Dairy products and milk are rich in lipids, of which fresh milk contains about 3%-5% fat and thousands of lipid molecules. Animal and clinical intervention studies suggest that polar lipids in dairy products have a protective effect on cardiovascular health by regulating lipid metabolism disorders, inflammation, and intestinal flora. However, there is a lack of population studies on dairy product intake and blood lipid groups. Whether specific lipid characteristics can reflect dietary dairy product intake and reveal the relationship between dairy products and cardiovascular disease or its risk factors needs to be explored. Summary of the invention

[0005] The present invention provides 4 specific lipids used as biomarkers for dairy product intake by screening 350 plasma lipids in two independent Chinese populations.

[0006] The first aspect of the present invention provides the use of lipids and / or detection reagents thereof as biomarkers of dairy product intake in the preparation of a kit for detecting the lipids to identify dairy product intake, predict cardiovascular disease risk, identify cardiovascular risk factors or changes therein, and study the relationship between dairy products and cardiovascular health. The lipids include sphingomyelin, which contains even-numbered fatty acids with a carbon number ≥12, such as even-numbered fatty acids with a carbon number of 12-22, preferably even-numbered fatty acids with a carbon number of 14-20.

[0007] In one or more embodiments, the sphingomyelin is selected from one or more of SM(OH)C32:2, SM C32:1, SM(2OH)C30:2, and SM(OH)C38:2.

[0008] In one or more embodiments, the sphingomyelin contains C14:0 or C14:1 acyl fatty acids.

[0009] In one or more embodiments, the sphingomyelin includes SM(OH)C32:2, SM C32: 1. In one or more embodiments, the sphingomyelin further includes SM(2OH)C30:2, SM(OH)C38:2.

[0010] In one or more embodiments, the lipids further include C18:1 trans fatty acids.

[0011] In one or more embodiments, the lipid is a lipid present in the blood or urine of a mammal (eg, a human) or a lipid detectable in the blood or urine of a mammal (eg, a human).

[0012] In one or more embodiments, the detection includes auxiliary detection and / or early detection.

[0013] In one or more embodiments, the test is a blood test, a plasma test, a serum test, or a urine test.

[0014] In one or more embodiments, the test sample targeted by the detection includes a blood sample, a plasma sample, a serum sample, or a urine sample.

[0015] In one or more embodiments, the detection method is selected from one or more of the following: chromatography, mass spectrometry, shotgun method, nuclear magnetic resonance. Preferably, the detection method is targeted or non-targeted liquid chromatography-mass spectrometry, flow injection technology-mass spectrometry, shotgun method, nuclear magnetic resonance.

[0016] In one or more embodiments, the detection reagent is a reagent for detecting the lipid content in a sample.

[0017] In one or more embodiments, the detection reagent is selected from the group consisting of butanol, methanol, isopropanol, methyl tert-butyl ether, ethyl acetate, chloroform, acetic acid, 1,2-didodecanoyl-sn-glycerotrioxy-3-phosphocholine, formic acid, acetonitrile, ammonium acetate, and methanol.

[0018] In one or more embodiments, the detection reagent is a chromatography and / or mass spectrometry detection reagent for the lipid. For example: methanol and methyl tert-butyl ether for lipid extraction, isotope-labeled dSM (24:1) used as an internal standard, dichloromethane, methanol, ammonium acetate for dissolving lipids, mobile phase for chromatography (such as ammonium acetate, acetonitrile), stationary phase for chromatography (such as a fully porous support). For example: a reagent for extracting and dissolving C18:1 trans fatty acids.

[0019] In one or more embodiments, the detection is to compare the sphingomyelin content (A1) of the sample from the subject with the corresponding sphingomyelin content (A0) of the normal population. If A1 is significantly higher than A0, it indicates that the subject has a high intake of dairy products, a low risk of cardiovascular disease, and improved cardiovascular risk factors. In one or more embodiments, the "significantly higher" refers to A1 / A0≥1.07, preferably A1 / A0≥1.2, and more preferably A1 / A0≥1.5.

[0020] In one or more embodiments, the number of the normal population is at least 100, preferably at least 300, more preferably at least 500, and most preferably at least 1000.

[0021] In one or more embodiments, the detection is to compare the sphingomyelin content (A1) of a sample from a subject with the subject's previous corresponding sphingomyelin content (A1'). If A1 is significantly higher than A1', it indicates that the subject's dairy product intake is increased, the risk of cardiovascular disease is reduced, and cardiovascular risk factors are improved. Preferably, "significantly higher than" refers to A1 / A1'≥1.07, preferably A1 / A1'≥1.2, and more preferably A1 / A1'≥1.5.

[0022] In one or more embodiments, the dairy product includes one or more selected from the following: fresh milk, yogurt, ice cream, milk powder, milk flakes, cheese, cream, cream cake, condensed milk, and other dairy products.

[0023] In one or more embodiments, the cardiovascular risk factors include one or more, preferably at least 3, of waist circumference, triglycerides, high-density lipoprotein cholesterol, blood pressure, and blood glucose.

[0024] In one or more embodiments, the cardiovascular risk factor change is a 6-year cardiovascular risk factor change.

[0025] In one or more embodiments, the level of sphingomyelin is inversely correlated with the subject's risk of cardiovascular disease and cardiovascular risk factors or changes therein.

[0026] The second aspect of the present invention provides a kit for identifying dairy product intake, detecting cardiovascular disease risk, identifying cardiovascular risk factors or changes thereof, and studying the relationship between dairy products and cardiovascular health, comprising:

[0027] (1) A detection reagent for detecting the lipid content in a sample as a biomarker for dairy product intake, wherein the sample is selected from a blood sample, a plasma sample, a serum sample, or a urine sample, and the lipid comprises sphingomyelin, wherein the sphingomyelin contains an even-numbered fatty acid with a carbon number ≥ 12, such as an even-numbered fatty acid with a carbon number of 12-22;

[0028] (2) optional sphingomyelin standards;

[0029] (3) Optional instruction manual.

[0030] In one or more embodiments, the sphingomyelin is selected from one or more of SM(OH)C32:2, SM C32:1, SM(2OH)C30:2, and SM(OH)C38:2.

[0031] In one or more embodiments, the sphingomyelin contains C14:0 or C14:1 acyl fatty acids.

[0032] In one or more embodiments, the sphingomyelin includes SM(OH)C32:2, SM C32: 1. In one or more embodiments, the sphingomyelin further includes SM(2OH)C30:2, SM(OH)C38:2.

[0033] In one or more embodiments, the lipids further include C18:1 trans fatty acids.

[0034] In one or more embodiments, the instructions for use describe the method according to the third aspect of the present invention.

[0035] In one or more embodiments, the detection reagent for detecting the lipid content in a sample described in item (1) is as described in the first aspect of the present invention.

[0036] A third aspect of the present invention provides a method for identifying dairy product intake, detecting cardiovascular disease risk, identifying cardiovascular risk factors or changes thereof, comprising the following steps:

[0037] (1) detecting the content of lipids as biomarkers of dairy product intake in a sample of a subject, wherein the lipids include sphingomyelin, and the sphingomyelin contains even-numbered fatty acids with ≥12 carbon atoms (e.g., even-numbered fatty acids with 12-22 carbon atoms), and

[0038] (2) Determine total dairy product intake, milk intake, cardiovascular disease risk, cardiovascular risk factors, or changes therein based on the measured lipid content.

[0039] In one or more embodiments, the sphingomyelin is selected from one or more of SM(OH)C32:2, SM C32:1, SM(2OH)C30:2, and SM(OH)C38:2.

[0040] In one or more embodiments, the sphingomyelin contains C14:0 or C14:1 acyl fatty acids.

[0041] In one or more embodiments, the sphingomyelin includes SM(OH)C32:2, SM C32: 1. In one or more embodiments, the sphingomyelin further includes SM(2OH)C30:2, SM(OH)C38:2.

[0042] In one or more embodiments, the lipids further include C18:1 trans fatty acids.

[0043] In one or more embodiments, the level of the lipid is inversely correlated with the subject's cardiovascular disease risk and cardiovascular risk factors or changes therein.

[0044] In one or more embodiments, step (2) comprises:

[0045] (2.1) comparing the sphingomyelin content (A1) of step (1) with the sphingomyelin content (A0) of a normal population sample; if A1 is significantly higher than A0, it indicates that the subject has a high intake of dairy products, a low risk of cardiovascular disease, and improved cardiovascular risk factors. Preferably, the “significantly higher” refers to A1 / A0 ≥ 1.07, preferably A1 / A0 ≥ 1.2, and more preferably A1 / A0 ≥ 1.5; or

[0046] (2.2) Compare the sphingomyelin content (A1) of step (1) with the corresponding sphingomyelin content (A1') of the subject. If A1 is significantly higher than A1', it means that the subject's dairy product intake is increased, the risk of cardiovascular disease is reduced, and the cardiovascular risk factors are improved. Preferably, the "significantly higher than" refers to A1 / A1'≥1.07, preferably A1 / A1'≥1.2, and more preferably A1 / A1'≥1.5.

[0047] In one or more embodiments, the sample comprises a blood sample, a plasma sample, a serum sample, or a urine sample.

[0048] In one or more embodiments, the detection method is selected from one or more of the following: chromatography, mass spectrometry, shotgun method, nuclear magnetic resonance. Preferably, the detection method is targeted or non-targeted liquid chromatography-mass spectrometry, flow injection technology-mass spectrometry, shotgun method, nuclear magnetic resonance.

[0049] In one or more embodiments, the dairy product includes one or more selected from the following: fresh milk, yogurt, ice cream, milk powder, milk flakes, cheese, cream, cream cake, condensed milk, and other dairy products.

[0050] In one or more embodiments, the cardiovascular risk factors include one or more, preferably at least 3, of waist circumference, triglycerides, high-density lipoprotein cholesterol, blood pressure, and blood glucose.

[0051] In one or more embodiments, the cardiovascular risk factor change is a 6-year cardiovascular risk factor change.

[0052] The present invention also provides a device for identifying dairy product intake, detecting cardiovascular disease risk, identifying cardiovascular risk factors or changes thereof, the device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the following steps when executing the program:

[0053] (1) detecting the content of lipids as biomarkers of dairy product intake in a sample of a subject, wherein the lipids include sphingomyelin, and the sphingomyelin contains even-numbered fatty acids with ≥12 carbon atoms (e.g., even-numbered fatty acids with 12-22 carbon atoms), and

[0054] (2) Determine dairy product intake, cardiovascular disease risk, cardiovascular risk factors, or changes therein based on the measured lipid content.

[0055] In one or more embodiments, other features of the device are as described in the method of the third aspect herein.

[0056] The present invention also provides a method for constructing a dairy product intake prediction model, comprising:

[0057] (1) Obtaining the subject's dairy product intake, such as the daily dairy product intake,

[0058] (2) obtaining the content of lipids in a sample of the subject after ingestion of a dairy product, wherein the lipids include sphingomyelin, and the sphingomyelin contains an even-numbered fatty acid having ≥12 carbon atoms (e.g., an even-numbered fatty acid having 12-22 carbon atoms), and

[0059] (3) Based on the data from (1) and (2), a mathematical method is used to construct a prediction model for total dairy product intake or milk intake.

[0060] In one or more embodiments, step (1) comprises obtaining the subject's dairy product intake using a questionnaire.

[0061] In one or more embodiments, the mathematical method is selected from generalized linear regression, logistic regression, random forest, or support vector machine.

[0062] In one or more embodiments, the dairy product includes one or more selected from the following: fresh milk, yogurt, ice cream, milk powder, milk flakes, cheese, cream, cream cake, condensed milk, and other dairy products.

[0063] In one or more embodiments, the intake of dairy products is measured in servings, and the weight of one serving of dairy products is as follows: fresh milk, 250 grams; yogurt, 250 grams; milk ice cream, 150 grams; milk powder, 40 grams; milk flakes, 40 grams; cheese, 30 grams; cream, 30 grams; cream cake, 100 grams; condensed milk, 125 grams.

[0064] In one or more embodiments, the model is as shown in formula (I) or formula (II):

[0065] Total dairy product intake (servings / day) = 0.009 × ln[SM(OH)C32:1] + 0.361 × ln[SM C32:1] + 0.178 × ln[SM(OH)C38:2] - 0.199 × ln[SM(2OH)C30:1] + 0.461 (I)

[0066] Milk intake (portions / day) = 0.023 × ln[SM(OH)C32:1] + 0.240 × ln[SM C32:1] + 0.116 × ln[SM(OH)C38:2] - 0.131 × ln[SM(2OH)C30:1] + 0.323 (II)

[0067] The present invention also provides the use of lipids and / or dairy products in preparing a kit for reducing the risk of cardiovascular diseases and improving cardiovascular risk factors. The lipids include sphingomyelin, and the sphingomyelin contains an even-numbered carbon fatty acid with a carbon number ≥12, such as an even-numbered carbon fatty acid with a carbon number of 12-22.

[0068] In one or more embodiments, the sphingomyelin is selected from one or more of SM(OH)C32:2, SM C32:1, SM(2OH)C30:2, and SM(OH)C38:2.

[0069] In one or more embodiments, the sphingomyelin contains C14:0 or C14:1 acyl fatty acids.

[0070] In one or more embodiments, the sphingomyelin includes SM(OH)C32:2, SM C32: 1. In one or more embodiments, the sphingomyelin further includes SM(2OH)C30:2, SM(OH)C38:2.

[0071] In one or more embodiments, the lipids further include C18:1 trans fatty acids.

[0072] In one or more embodiments, the dairy product includes one or more selected from the following: fresh milk, yogurt, ice cream, milk powder, milk flakes, cheese, cream, cream cake, condensed milk, and other dairy products.

[0073] In one or more embodiments, the cardiovascular risk factors include one or more, preferably at least 3, of waist circumference, triglycerides, high-density lipoprotein cholesterol, blood pressure, and blood glucose.

[0074] In one or more embodiments, the cardiovascular risk factor change is a 6-year cardiovascular risk factor change.

[0075] The present invention also provides a pharmaceutical composition for reducing the risk of cardiovascular disease and improving cardiovascular risk factors, comprising lipids and / or dairy products and pharmaceutically acceptable excipients, wherein the lipids include sphingomyelin, and the sphingomyelin contains an even-numbered fatty acid with a carbon number ≥12, such as an even-numbered fatty acid with a carbon number of 12-22;.

[0076] In one or more embodiments, the sphingomyelin is selected from one or more of SM(OH)C32:2, SM C32:1, SM(2OH)C30:2, and SM(OH)C38:2.

[0077] In one or more embodiments, the sphingomyelin contains C14:0 or C14:1 acyl fatty acids.

[0078] In one or more embodiments, the sphingomyelin includes SM(OH)C32:2, SM C32: 1. In one or more embodiments, the sphingomyelin further includes SM(2OH)C30:2, SM(OH)C38:2.

[0079] In one or more embodiments, the lipids further include C18:1 trans fatty acids.

[0080] In one or more embodiments, the dairy product includes one or more selected from the following: fresh milk, yogurt, ice cream, milk powder, milk flakes, cheese, cream, cream cake, condensed milk, and other dairy products.

[0081] In one or more embodiments, the cardiovascular risk factors include one or more, preferably at least 3, of waist circumference, triglycerides, high-density lipoprotein cholesterol, blood pressure, and blood glucose.

[0082] In one or more embodiments, the cardiovascular risk factor change is a 6-year cardiovascular risk factor change.

[0083] Advantages of the present invention:

[0084] 1. The existing technology detects few types of sphingomyelin (SM), and mainly focuses on sphingomyelin containing d18:1 and saturated fatty acid structures, such as SM (d18:1 / 16:0), while this article covers more types of sphingomyelin (46 types);

[0085] 2. Dairy products are a widely consumed healthy food. The four sphingomyelins of the present invention can objectively and unbiasedly reflect people's intake of dairy products. The present invention first discovered that the four sphingomyelins can be used as sensitive and stable biomarkers to reflect dairy product intake, accurately reflect dairy product intake, and be used as biomarkers to study the relationship between dairy product intake and cardiovascular risk factors. BRIEF DESCRIPTION OF THE DRAWINGS

[0086] Figure 1 , Spearman correlation coefficients between lipid profile markers of dairy products and various dietary intakes.

[0087] Model: Adjusted for age, sex, region, urban or rural area, education level, smoking, drinking, physical activity, family history of chronic diseases, lipid-lowering drug use, dietary factors (red meat, eggs, aquatic products, soy milk, vegetables, fruits, dietary fiber), and BMI. **, Bonferroni multiple correction was significant. *, P<0.05.

[0088] Figure 2 , Receiver operating characteristic curves for lipid profile markers and dairy product intake.

[0089] A, total population: trans fatty acid 18:1 isomer AUC = 0.75 (95% CI 0.74-0.77), lipid feature combination AUC = 0.87 (95% CI 0.86-0.88), both Delong test P value < 0.001. Lipid feature combination combined with trans fatty acid 18:1 isomer AUC = 0.89 (95% CI 0.88-0.90);

[0090] B, Beijing area: trans fatty acid 18:1 isomer AUC = 0.69 (95% CI 0.67-0.71), lipid feature combination AUC = 0.81 (95% CI 0.79-0.83), both Delong test P value < 0.001. Lipid feature combination combined with trans fatty acid 18:1 isomer AUC = 0.82 (95% CI 0.81-0.84);

[0091] C, Shanghai area: trans fatty acid 18:1 isomer AUC = 0.79 (95% CI 0.77-0.81), lipid feature combination AUC = 0.89 (95% CI 0.88-0.91), both Delong test P value < 0.001. Lipid feature combination combined with trans fatty acid 18:1 isomer AUC = 0.91 (95% CI 0.90-0.92). The lipid feature combination was obtained by weighted summation of the concentrations of SM(OH)C32:2, SM C32:1, SM(2OH)C30:2, and SM(OH)C38:2.

[0092] Figure 3 , the relationship between dairy products and their lipid characteristic markers and cardiovascular risk factors over a 6-year period.

[0093] Model 1: adjusted for age, sex, region, urban or rural area, education level, smoking, drinking, physical activity, family history of chronic diseases, lipid-lowering drug use, dietary factors (red meat, eggs, aquatic products, soy milk, vegetables, fruits, dietary fiber), and baseline levels of each indicator;

[0094] Model 2: Model 1 was further adjusted for BMI, baseline triglycerides, and low-density lipoprotein cholesterol levels. The lipid profile was obtained by weighted summation of the concentrations of SM(OH)C32:2, SM C32:1, SM(2OH)C30:2, and SM(OH)C38:2. ***, P<0.001. **, P<0.01. *, P<0.05. DETAILED DESCRIPTION

[0095] By applying high-throughput targeted lipidomics to quantitatively detect plasma lipids, the inventors discovered and verified that four sphingomyelins, SM(OH)C32:2, SM C32:1, SM(2OH)C30:2, SM(OH)C38:2 and their combination, were significantly associated with dairy product intake and cardiovascular disease risk.

[0096] Lipids and sphingomyelin

[0097] The term "lipid" as used herein refers to oils, fats, and lipids. S Phingo m yelin) is composed of ceramide ( Cer Sphingomyelin and ceramide are both sphingolipids shown in the following formula (I):

[0098]

[0099] For ceramide, R1 is C 10 -C 30 Alkyl or C 10 -C 30 Alkenyl (e.g. C 12 -C 26 Alkyl or C 12 -C 26 Alkenyl, or C 16 -C 22 Alkyl or C 16 -C 22 Alkenyl), R2 is H, and X is CH=CH or CH2-CH2. Saturated ceramide refers to ceramides in which R1 is an alkyl group, such as Cer (d18:1 / 20:0). Unsaturated ceramide refers to ceramides in which R1 contains an unsaturated bond, such as Cer (d18:1 / 18:1). In ceramides such as Cer (d18:a / b:c), a represents the unsaturation degree (number of double bonds) of X, b represents the number of carbon atoms of R1, and c represents the unsaturation degree of R1. For example, in ceramides such as Cer (d18:1 / 14:0), R1 is C 14 alkyl, R2 is H, and X is CH=CH; for example, in the ceramide represented by Cer (d18:1 / 20:1), R1 is C containing a double bond 20 alkenyl, R2 is H, and X is CH=CH; for example, in the ceramide represented by Cer (d18:0 / 24:1), R1 is C containing a double bond 24 Alkenyl, R2 is H, and X is CH2-CH2.

[0100] For sphingomyelin, R1 is an alkyl or alkenyl group, R2 is phosphorylcholine, and X is an alkylene or alkenylene group. Saturated sphingomyelin refers to sphingomyelin in which R1 is an alkyl group and X is an alkylene group, such as SM C34:0. Unsaturated sphingomyelin refers to sphingomyelin in which R1 and / or X contain an unsaturated bond (e.g., a double bond), such as SM C36:1. For sphingomyelin as shown in SM Ca:b, a represents the total carbon number of the sphingomyelin, and b represents the total unsaturation (double bond number) of R1 and X, so the total carbon number of R1 and X is a-16. For example, in the sphingomyelin represented by SM C34:0, R1 is an alkyl group, R2 is phosphorylcholine, X is an alkylene group, the total carbon number of R1 and X is 18, and the total unsaturation of R1 and X is 0, that is, it does not contain a double bond; for another example, in the sphingomyelin represented by SM C36:1, R2 is phosphorylcholine, the total carbon number of R1 and X is 20, and the total unsaturation of R1 and X is 1, that is, R1 and X contain a double bond. In addition, hydroxysphingomyelin refers to a derivative with one (SM(OH)) or two hydroxyl groups (SM(2OH)) added to the sphingosine skeleton (X) or fatty acid residue (R1) of sphingomyelin, for example, SM(OH)C44:3, SM(2OH)C30:2.

[0101] Prediction methods and models

[0102] The inventors have found that measuring blood levels of the following lipids can be used to identify total dairy product intake / milk intake: SM(OH)C32:2, SM C32:1, SM(2OH)C30:2, SM(OH)C38:2. The lipids may also include C18:1 trans fatty acids. Therefore, these lipids can be used as biomarkers for dairy product intake.

[0103] The inventors also found that the correlation between the levels of the above lipids and the risk of cardiovascular disease is similar to the correlation between dairy product intake and the risk of cardiovascular disease. Therefore, detecting the blood levels of these lipids can also predict the risk of cardiovascular disease, identify cardiovascular risk factors or their changes, and play a role in studying the relationship between dairy products and cardiovascular health. The dairy products described herein can be any milk-containing product, including but not limited to fresh milk, yogurt, ice cream, milk powder, milk slices, cheese, cream, cream cakes, condensed milk, and other dairy products.

[0104] The "cardiovascular diseases" mentioned in this article refer to a series of diseases involving the circulatory system, including but not limited to: coronary heart disease, angina pectoris, hypertension, hyperlipidemia, hyperglycemia, myocardial infarction and related diseases.

[0105] The "cardiovascular risk factors" mentioned in this article include one or more of waist circumference, triglycerides, high-density lipoprotein, blood pressure and blood glucose. According to the criteria for Asians in the National Cholesterol Education Program Adult Treatment Program III (NCEP-ATPIII) revised in 2005 in the United States, individuals who meet three or more of the following five components are defined as having ≥3 cardiovascular risk factors: central obesity: waist circumference ≥90 cm for men and ≥80 cm for women; hypertriglyceridemia: triglycerides ≥1.7 mmol / L; low high-density lipoprotein-cholesterol: high-density lipoprotein-cholesterol <1.03 mmol / L for men and high-density lipoprotein-cholesterol <1.30 mmol / L for women; elevated blood pressure: systolic blood pressure ≥130 mmHg or diastolic blood pressure ≥85 mmHg or use of antihypertensive drugs; high blood sugar: fasting blood sugar ≥5.6 mmol / L or have been diagnosed with type 2 diabetes or use of oral hypoglycemic drugs or insulin.

[0106] Therefore, the present invention provides a method for identifying dairy product intake, detecting cardiovascular disease risk, identifying cardiovascular risk factors or changes thereof, comprising the following steps: (1) detecting the content of lipids as dairy product intake biomarkers in a sample of a subject, wherein the lipids include sphingomyelin, and the sphingomyelin contains even-numbered carbon fatty acids with carbon atoms ≥ 12 (e.g., even-numbered carbon fatty acids with carbon atoms of 12-22), and (2) determining dairy product intake, cardiovascular disease risk, cardiovascular risk factors or changes thereof based on the measured lipid content. Herein, lipids associated with dairy product intake and cardiovascular disease risk include one or more sphingomyelins selected from SM(OH)C32:2, SM C32:1, SM(2OH)C30:2, SM(OH)C38:2, and optionally C18:1 trans fatty acids. Preferably, the sphingomyelin includes sphingomyelin containing C14:0 or C14:1 acyl fatty acids. For example, the sphingomyelin includes SM(OH)C32:2, SM C32:1, and optionally includes SM(2OH)C30:2, SM(OH)C38:2. The content of the lipid is negatively correlated with cardiovascular disease risk and cardiovascular risk factors or changes thereof.

[0107] In the embodiment of comparing with samples from a normal population, step (2) comprises: (2.1) comparing the sphingomyelin content (A1) of step (1) with the sphingomyelin content (A0) of samples from a normal population. If A1 is significantly higher than A0, it indicates that the subject has a high intake of dairy products, a low risk of cardiovascular disease, and improved cardiovascular risk factors. Preferably, the "significantly higher" refers to A1 / A0≥1.07, preferably A1 / A0≥1.2, and more preferably A1 / A0≥1.5. In the embodiment of comparing with samples from the same subject at different times, step (2) comprises: (2.2) comparing the sphingomyelin content (A1) of step (1) with the corresponding sphingomyelin content (A1') of the subject. If A1 is significantly higher than A1', it indicates that the subject has an increased intake of dairy products, a reduced risk of cardiovascular disease, and improved cardiovascular risk factors. Preferably, the "significantly higher" refers to A1 / A1'≥1.07, preferably A1 / A1'≥1.2, and more preferably A1 / A1'≥1.5.

[0108] Alternatively, step (2) comprises determining the total dairy product intake / milk intake level, cardiovascular disease risk, and cardiovascular risk factor level according to the model shown in formula (I) or formula (II):

[0109] Total dairy product intake (servings / day) = 0.009 × ln[SM(OH)C32:1] + 0.361 × ln[SM C32:1] + 0.178 × ln[SM(OH)C38:2] - 0.199 × ln[SM(2OH)C30:1] + 0.461 (I)

[0110] Milk intake (portions / day) = 0.023 × ln[SM(OH)C32:1] + 0.240 × ln[SM C32:1] + 0.116 × ln[SM(OH)C38:2] - 0.131 × ln[SM(2OH)C30:1] + 0.323 (II).

[0111] The above model is a model constructed by the inventor based on the relationship between the lipids and dairy products. The method for constructing a dairy product intake prediction model includes: (1) obtaining the subject's dairy product intake, such as the daily dairy product intake weight, by using a meal frequency questionnaire, a 24-hour meal review, or a food record, (2) obtaining the lipid content in the subject's sample after dairy product intake, wherein the lipid includes sphingomyelin, and the sphingomyelin contains an even-numbered fatty acid with a carbon number ≥12 (e.g., an even-numbered fatty acid with a carbon number of 12-22), and (3) constructing a dairy product intake prediction model based on the data of (1) and (2) using a mathematical method. The mathematical method can be any method known in the art, such as generalized linear regression, logistic regression, random forest, or support vector machine.

[0112] In an exemplary embodiment, the intake weight of dairy products is the daily intake of dairy products. In this embodiment, there are 5 dairy food items, including fresh milk, yogurt, ice cream, milk powder, and other dairy products. Among them, fresh milk, yogurt, and ice cream are recorded in "portions", and the weight of one portion is 250 grams, 250 grams, and 150 grams respectively. The intake of milk powder and other dairy products is recorded in "grams", and the weight of one portion is: milk powder, 40 grams; milk slices, 40 grams; cheese, 30 grams; cream, 30 grams; cream cake, 100 grams; condensed milk, 125 grams.

[0113] In addition, the present invention also discloses a computer-readable storage medium storing a computer program, and the computer program stored on the storage medium executes the method for identifying milk product intake, detecting cardiovascular disease risk, identifying cardiovascular risk factors or their changes described herein after running. The steps of the method or algorithm described in conjunction with the embodiments disclosed herein can be directly embodied in hardware, in a software module executed by a processor, or in a combination of the two. If implemented as a computer program product in software, each function can be stored on a computer-readable medium or transmitted by it as one or more instructions or codes. Computer-readable media include both computer storage media and communication media, including any medium that facilitates the transfer of computer programs from one place to another. The storage medium can be any available medium that can be accessed by a computer. As an example and not limitation, such a computer-readable medium may include RAM, ROM, EEPROM, CD-ROM or other optical disk storage, disk storage or other magnetic storage device, or any other medium that can be used to carry or store a desired program code in the form of an instruction or data structure and can be accessed by a computer. An exemplary storage medium is coupled to a processor so that the processor can read and write information from / to the storage medium. In an alternative, the storage medium can be integrated into the processor. The processor and storage medium may reside in an ASIC. The ASIC may reside in a user terminal. In the alternative, the processor and storage medium may reside in a user terminal as discrete components.

[0114] Detection reagents and detection methods

[0115] The present invention relates to quantitative and qualitative detection of human blood lipid levels. The blood level of lipids can be detected using any method known in the art, such as high-performance targeted liquid chromatography-electrospray tandem mass spectrometry (LC-ESI-MS / MS). Exemplary detection steps include: collecting plasma, extracting lipids (e.g., using methyl tert-butyl ether method), extraction, centrifugation, dissolving after drying, separation by high-performance liquid chromatography (e.g., ACQUITY UPLC BEH HILIC column), and mass spectrometry detection (e.g., electrospray ionization, positive and negative modes, multiple reaction monitoring mode). The test results can be quantitatively calculated using conventional data analysis methods (e.g., Analyst 1.6.3 software and MultiQuant 3.0 software). All lipids are data corrected according to internal standards, such as using isotope-labeled dSM (24:1) standards as internal standards for sphingomyelin. In addition, the method can also be other methods commonly used in the art for detecting lipids, such as flow injection technology-mass spectrometry, shotgun method, nuclear magnetic resonance method, and methods based on antigen-antibody reactions.

[0116] The "reagent" described herein is a reagent for detecting one or more lipids described herein in blood or urine. The reagents for detecting lipids described herein include any reagent involved in the lipid detection process, such as reagents for extracting lipids from samples (such as blood) (such as reagents used in the methyl tert-butyl ether method); reagents for purifying lipids (such as mobile phases, stationary phases, buffers, etc. in chromatography); reagents for mass spectrometry detection of lipids (such as nebulizer gas, auxiliary gas, collision gas, etc. used for electrospray tandem mass spectrometry). Exemplarily, the reagent is a reagent used for detecting lipids using liquid chromatography-mass spectrometry. For example: chloroform, methanol, butanol, isopropanol, formic acid, acetic acid, acetonitrile, ethyl acetate, ammonium acetate, methyl tert-butyl ether, isotope-labeled dSM (24:1), mobile phases for chromatography (such as ammonium acetate, acetonitrile), and stationary phases for chromatography (such as fully porous supports). In addition, the reagent also includes reagents used in methods for detecting lipids commonly used in the art, such as butanol / methanol, isopropanol, methyl tert-butyl ether / methanol / isopropanol, isopropanol / ethyl acetate, chloroform / methanol / acetic acid for lipid extraction, methanol / isopropanol, chloroform / methanol for lipid dissolution, formic acid / acetonitrile / isopropanol, ammonium acetate / formic acid, methanol / acetic acid, ammonium acetate / methanol / acetic acid used as mobile phase. In one or more embodiments, the reagent is a reagent for detecting the lipids in blood or urine. For example: methanol and / or methyl tert-butyl ether for lipid extraction, isotope-labeled dSM (24:1) used as an internal standard, 50% dichloromethane-methanol / ammonium acetate 50% (v:v) for dissolving lipids, mobile phase for chromatography (such as 10mM ammonium acetate / pure acetonitrile), stationary phase for chromatography (such as a fully porous support).

[0117] Reagent test kit

[0118] The present invention also provides a kit for identifying dairy product intake, detecting cardiovascular disease risk, identifying cardiovascular risk factors or changes thereof, and studying the relationship between dairy products and cardiovascular health, wherein the lipids described herein are used as biomarkers for dairy product intake. The kit that can be used for the present invention generally includes reagents for detecting lipid content in samples as described above, lipid standards and / or instructions. Among them, the lipid standards are isotope-labeled lipids. The instructions describe the detection method and the method for judging the risk of cardiovascular disease in an individual based on the measured values ​​A1 of different samples. The lipids include sphingomyelin containing even-numbered carbon fatty acids with a carbon number ≥ 12 (e.g., one or more of SM(OH)C32:2, SM C32:1, SM(2OH)C30:2, SM(OH)C38:2) and optional C18:1 trans fatty acids.

[0119] An exemplary method for determining individual cardiovascular disease risk is the method for identifying dairy product intake, detecting cardiovascular disease risk, identifying cardiovascular risk factors or changes thereof as described above. A typical kit of the present invention can be used to detect human blood samples, plasma samples, or urine samples. Wherein, the blood or plasma samples can be from peripheral blood.

[0120] Pharmaceutical composition

[0121] Since the inventors have found that SM(OH)C32:2, SM C32:1, SM(2OH)C30:2 and SM(OH)C38:2 or dairy products are consistently negatively correlated with cardiovascular disease, the present invention also provides the use of the lipids described herein in the preparation of drugs or kits for reducing the risk of cardiovascular disease and improving cardiovascular risk factors.

[0122] The pharmaceutical composition of the present invention comprises any of the above-mentioned lipids and / or dairy products and pharmaceutically acceptable excipients. In the present invention, "pharmaceutically acceptable excipients" are pharmaceutically or food acceptable carriers, solvents, suspending agents or excipients for delivering the lipids and / or dairy products of the present invention to animals or humans. Herein, pharmaceutically acceptable excipients are non-toxic to the recipient of the composition at the dose and concentration used. Various types of carriers or excipients commonly used to deliver Kang Bio in treatments known in the art may be included. Exemplary excipients may be liquid or solid, including but not limited to: pH regulators, surfactants, carbohydrates, adjuvants, antioxidants, chelating agents, ionic strength enhancers, preservatives, carriers, glidants, sweeteners, dyes / colorants, flavor enhancers, wetting agents, dispersants, suspending agents, stabilizers, isotonic agents, solvents or emulsifiers. In some embodiments, pharmaceutically acceptable excipients may include one or more inactive ingredients, including but not limited to: stabilizers, preservatives, additives, adjuvants, sprays, compressed air or other suitable gases, or other suitable inactive ingredients for use with the pharmacological compound. More specifically, suitable excipients may be excipients commonly used in the art for transposition systems or cell administration containing them. Examples of excipients include various lactose, mannitol, oils such as corn oil, buffers such as PiggyBacS, saline, polyethylene glycol, glycerol, polypropylene glycol, dimethyl sulfoxide, amides such as dimethylacetamide, proteins such as albumin, and detergents such as Tween 80, monosaccharides and oligosaccharides such as glucose, lactose, cyclodextrin and starch.

[0123] Other pharmaceutical compositions will be apparent to those skilled in the art, including formulations comprising the lipids and / or milk products described herein in sustained or controlled release delivery formulations. Techniques for preparing a variety of other sustained or controlled delivery modes (such as liposome carriers, bioerodible microparticles or porous beads, and depot injections) are also known to those skilled in the art.

[0124] Pharmaceutical compositions for in vivo administration are usually provided in the form of sterile preparations. Sterilization is achieved by filtering through a sterile filtration membrane. When the composition is freeze-dried, this method can be used for sterilization before or after freeze-drying and rehydration. Compositions for parenteral administration can be stored in freeze-dried form or in solution. Parenteral compositions are usually placed in containers with sterile access holes, such as intravenous solution strips or bottles with stoppers that can be pierced by hypodermic injection needles.

[0125] Typically, the composition contains a therapeutically effective amount of lipids and / or dairy products described herein. A therapeutically effective amount refers to a dose that can achieve treatment, prevention, alleviation and / or relief of a disease or condition in a subject. These effects can be achieved by inserting exogenous genes with corresponding functions, and the exogenous genes with corresponding functions have functions corresponding to specific uses, such as therapeutic functions or inducing functions. The therapeutically effective amount can be determined based on factors such as the patient's age, gender, the disease and its severity, and other physical conditions of the patient. The therapeutically effective amount can be administered as a single dose, or it can be administered in multiple doses according to an effective treatment regimen. Herein, a subject or patient generally refers to a mammal, particularly a human. Exemplarily, the composition contains, for example, 0.001-50%, preferably 0.01-30%, and more preferably 0.05-10% of the lipids and / or dairy products described herein in a weight ratio.

[0126] The composition described herein can be used in combination with other agents having functions similar to or corresponding to the functions achieved by the exogenous gene. For example, it can be used in combination with agents for treating the disease or condition treated by the exogenous gene. The dosage of other agents can be determined by those skilled in the art.

[0127] The dosage form of the pharmaceutical composition of the present invention can be varied, as long as it is a dosage form that can effectively make the active ingredient reach the mammalian body, it can be made into a unit dosage form. The dosage form can be selected from: gel, aerosol, tablet, capsule, powder, granule, syrup, solution, suspension, injection, powder, pill, controlled rapid release agent, infusion, suspension, etc. According to the disease type prevented and treated by lipid and / or dairy products described herein, those skilled in the art can choose a dosage form that is convenient to use. From the standpoint of easy preparation and storage, the preferred composition is a solid composition, especially a tablet and a solid-filled or liquid-filled capsule. Lipid and / or dairy products described herein or their compositions can also be stored in a sterilizing apparatus suitable for injection or instillation. Lipid and / or dairy products described herein or their compositions can also be stored in a suitable container and placed in a test kit or a medicine box.

[0128] The present invention will be further described below in conjunction with specific examples. It should be understood that these examples are only used to illustrate the present invention and are not intended to limit the scope of the present invention. The experimental methods in the following examples that do not specify specific conditions are usually carried out according to conventional conditions in the art, or according to the conditions recommended by the manufacturer. Unless otherwise defined, all professional and scientific terms used in the text have the same meaning as those familiar to those skilled in the art. In addition, any method and material similar or equivalent to the recorded content can be applied to the present invention. The preferred implementation methods and materials described in the text are for demonstration purposes only.

[0129] Example

[0130] 1. Research Methods

[0131] 1. Study population

[0132] Biomarkers of dairy product intake were established from samples from two independent Chinese populations. The discovery population was based on a population-designed prospective epidemiological study, the Nutrition and Health Study of the Aging Chinese Population, which was designed to investigate the role of environmental, genetic, and their interactions in the development of cardiometabolic diseases. In 2005, 3289 subjects aged 50–70 years in community residents in Beijing and Shanghai were recruited by multistage sampling and followed up. In 2011, 760 of the 3289 baseline subjects were lost to follow-up (23.1%), of whom 554 could not be contacted and 206 refused to participate. Therefore, a total of 2529 subjects participated in the follow-up study. The validation population was derived from a randomized, parallel-design nutritional intervention study that recruited 253 overweight or obese individuals with impaired fasting glucose at baseline. The protocol was registered with the ClinicalTrials.gov (registration number NCT03856762). At the baseline of the two studies, face-to-face home visits were used to collect demographic data, lifestyle and health information. In the study of the relationship between plasma lipid group and dairy product intake, respondents who met one of the following conditions were excluded: daily average energy intake exceeded the reasonable range (800 to 4000 kcal for men and 500 to 3500 kcal for women), no complete dietary data, no blood lipid group data. After exclusion, the final discovery and verification population samples included 2140 and 212 people, respectively.

[0133] In analyzing the relationship between dairy products and their lipid characteristic markers and certain cardiovascular risk factors, a prospective design was used. The research samples came from the "Nutrition and Health Status of the Aging Population in China Study". After excluding people who took hypoglycemic, antihypertensive, and lipid-lowering drugs at baseline and follow-up, the final number of people included in the analysis was: 1,205 people with blood pressure, 1,829 people with blood sugar, 1,673 people with blood lipids, 2,135 people with waist circumference, 1,097 people with abdominal obesity, and 1,243 people with cardiovascular risk factors ≥3.

[0134] Both groups of sample source projects have passed the review of the Ethics Committee of the Institute of Nutritional Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, and all survey subjects have signed written informed consent.

[0135] 2. Dietary data evaluation

[0136] The dietary data of the discovery population were collected using a food frequency questionnaire containing 74 food items, which was adjusted based on the questionnaire used in the 2002 National Nutrition Survey. The dietary information of the validation population was collected using a three-day dietary record form, which recorded all the foods consumed on two working days and one weekend day in the past week. There were five dairy products, including fresh milk, yogurt, ice cream, milk powder, and other dairy products. Among them, milk, yogurt, and ice cream were recorded in "portions", and the weight of one portion was 250 grams, 250 grams, and 150 grams respectively. The intake of milk powder and other dairy products was recorded in "grams", and the weight of one portion was: milk powder, 40 grams; milk slices, 40 grams; cheese, 30 grams; cream, 30 grams; cream cake, 100 grams; condensed milk, 125 grams. Dietary nutrients were estimated based on the Chinese Food Composition Table, and energy correction was performed using the residual method.

[0137] 3. Lipidome detection

[0138] Plasma lipidome was quantitatively detected by high-performance targeted liquid chromatography-electrospray ionization tandem mass spectrometry (LC-ESI-MS / MS). Lipids were extracted using a modified methyl tert-butyl ether (MTBE) method. After extraction, centrifugation, and drying, the lipid extracts were dissolved and internal standards (Sciex, Foster City, CA, USA, see Tables 1 and 2 for the specific internal standards of each lipid) were added before the samples were tested. Lipidomics analysis was performed using an ACQUITY UPLC BEH HILIC column for chromatographic separation and mass spectrometry detection using a Nexera X2 LC-30AD system (Shimadzu Scientific Instruments, Marlborough, MA, USA) combined with a Sciex 5500QTRAP triple quadrupole mass spectrometer (Applied Biosystems / Sciex). Mass spectrometry analysis was performed using electrospray ionization in positive and negative modes, and data were acquired in multiple reaction monitoring (MRM) mode. Analyst 1.6.3 software (Sciex, Foster City, CA, USA) was used for feature alignment, signal detection and signal integration, and MultiQuant 3.0 software (Sciex, Foster City, CA, USA) was used for quantitative calculation. Taking into account the systematic error, a quality control sample was added for data monitoring for every 10 samples tested, and all lipids should be corrected according to the internal standard. After excluding lipid species with more than 20% missing metabolite values ​​or a coefficient of variation greater than 30%, the quantitative analysis of 350 lipids was finally completed (see Tables 1 and 2).

[0139] Table 1 Dynamic MRM parameters for isotope internal standard detection of plasma lipid groups

[0140]

[0141]

[0142]

[0143]

[0144]

[0145]

[0146]

[0147] Note: Q1 represents the molecular mass of the parent ion; Q3 represents the molecular mass of the secondary ion.

[0148] Table 2 Dynamic MRM parameters for isotope internal standard detection of plasma glycerides

[0149]

[0150]

[0151]

[0152]

[0153]

[0154]

[0155]

[0156]

[0157]

[0158] Note: Q1 represents the molecular mass of the parent ion; Q3 represents the molecular mass of the secondary ion.

[0159] 4. Assessment of cardiovascular risk factors

[0160] Weight, height, waist-hip circumference, and blood pressure were collected by standardized physical examination, and body mass index (BMI) was calculated by dividing weight (kg) by height (m) squared. Fasting venous blood was collected at baseline and follow-up studies, and the supernatant was collected after centrifugation and stored at -80 degrees. Fasting blood glucose and blood lipids (total cholesterol, low-density lipoprotein-cholesterol, high-density lipoprotein-cholesterol, and triglycerides) were detected using an automatic biochemical analyzer (Hitachi 7080, Tokyo, Japan), and the reagents were purchased from Wako Pure Chemical Industries (Osaka, Japan). According to the criteria for Asians in the National Cholesterol Education Program Adult Treatment Program III (NCEP-ATPIII) revised in 2005, individuals who met three or more of the following five components were defined as having ≥3 cardiovascular risk factors: central obesity: waist circumference ≥90 cm for men and ≥80 cm for women; hypertriglyceridemia: triglycerides ≥1.7 mmol / L; low high-density lipoprotein-cholesterol: HDL-cholesterol <1.03 mmol / L for men and HDL-cholesterol <1.30 mmol / L for women; elevated blood pressure: systolic blood pressure ≥130 mmHg or diastolic blood pressure ≥85 mmHg or use of antihypertensive drugs; hyperglycemia: fasting blood glucose ≥5.6 mmol / L or diagnosed with type 2 diabetes or use of oral hypoglycemic drugs or insulin.

[0161] 5. Statistical Analysis

[0162] The concentration of some lipids was too low to be filled with 1 / 2 minimum detection value, and natural logarithm transformation and standardization were performed. Multiple linear regression and Spearman rank correlation were used to analyze the relationship between lipids and dairy product intake and other dietary factors. The model was adjusted for age, gender, region, urban and rural areas, education level, smoking, drinking, physical activity, family history of chronic diseases, lipid-lowering drug use, dietary factors (red meat, eggs, aquatic products, soy milk, vegetables, fruits, dietary fiber) and BMI, and Bonferroni correction was used for multiple tests. The lipid combination markers used the weighted value of lipid concentration, and the weight came from the β effect value of lipid and dairy product intake. The area under the receiver operating characteristic curve (AUC) was used to evaluate the predictive effect of the screened lipid markers on dairy product intake in the discovery population and its subpopulation (Beijing or Shanghai). No intake of any dairy products was marked as "0", and dairy product intake greater than 1 serving / day was marked as "1". The markers were compared with trans fatty acid markers previously discovered by this study group. General linear models were used to evaluate the relationship between lipid biomarkers and changes in cardiovascular risk factors over 6 years, with age, sex, region, urban / rural area, education level, smoking, drinking, physical activity, family history of chronic diseases, lipid-lowering drug use, dietary factors (red meat, eggs, aquatic products, soy milk, vegetables, fruits, dietary fiber), baseline levels of each indicator, BMI, baseline triglycerides, and low-density lipoprotein-cholesterol as covariates.

[0163] The above statistical analyses were performed using SAS V9.4 and R V3.4.4 software. A two-sided P < 0.05 was considered statistically significant.

[0164] 2. Research Results

[0165] 1. Crowd characteristics

[0166] Table 3 shows the characteristics of the population divided into four groups according to the amount of dairy product intake: no intake, 0-0.5 servings / day, 0.5-1 servings / day, and >1 serving / day. The proportion of people who did not consume dairy products in the discovery and validation sample sets was 40.7% and 5.7%, respectively, and the proportion of people who consumed more than 1 serving / day was 16.6% and 29.2%, respectively. In both sample sets, people with higher dairy product intake also had higher egg intake. Compared with the discovery population, the validation population was younger, drank more, had a higher education level, and had lower physical activity. In addition, the population also had a relatively higher intake of red meat, aquatic products, and soy products.

[0167] 2. Correlation between plasma lipids and dairy product intake

[0168] As shown in Table 4, after controlling for age, sex, region, urban and rural areas, education level, smoking, drinking, physical activity, family history of chronic diseases, lipid-lowering drug use, dietary factors, BMI, and multiple corrections, 26 lipids were screened and found to be significantly associated with dairy product intake in the discovery population, among which the sphingomyelin (SM) subclass had the strongest relationship with dairy product intake. In the validation population, 4 of the 26 lipids, namely SM(OH)C32:2, SM C32:1, SM(2OH)C30:2, and SM(OH)C38:2, were strongly associated with dairy product intake (effect value range: 0.126-0.146; Table 4). When further exploring the relationship between the 4 lipid characteristic markers and various dietary intakes, the 4 lipid characteristics showed consistent and strong positive correlations with dairy products (partial correlation coefficient r s : 0.21 to 0.28), and with red meat (partial correlation coefficient r s :-0.06 to 0.07), eggs (partial correlation coefficient r s :-0.14 to -0.05), aquatic products (partial correlation coefficient r s :0.01 to 0.12), soy milk (partial correlation coefficient r s :-0.15 to -0.01), vegetables (partial correlation coefficient r s :-0.08 to 0.02), fruit (partial correlation coefficient r s :0 to 0.06), dietary fiber (partial correlation coefficient r s :-0.04 to 0.02) show weak correlation or no correlation ( Figure 1 ). In the sensitivity analysis, when 137 individuals who took lipid-lowering drugs at baseline were excluded, the relationship between these four lipid characteristics and dairy products remained basically unchanged (effect value range: 0.128-0.149; see Table 5). At the same time, in the analysis of the relationship between the intake of milk and other dairy products other than milk and plasma lipids, three lipid characteristics [SM(OH)C32:2, SM C32:1, SM(2OH)C30:2] had consistent and strong correlations with milk, suggesting that the relationship between the above four lipid characteristics and dairy product intake may be mainly related to milk (effect value range: 0.111-0.123; see Table 6).

[0169] 3. Dairy product intake prediction model

[0170] Compared with the traditional trans fatty acid marker 18:1, the addition of these four sphingomyelins to the model significantly improved the prediction effect of dairy product intake, with the AUC value increasing from 0.75 to 0.89 ( Figure 2). At the same time, the predictive effect of the above lipid characteristic markers on dairy product intake was verified in two sub-populations, namely Beijing and Shanghai. In the Beijing population: the AUC value of the trans fatty acid 18:1 marker was 0.69, the AUC values ​​of the four lipid characteristic markers ranged from 0.74 to 0.76, and the AUC value of the four lipid characteristic markers combined with the trans fatty acid 18:1 marker was 0.82. In the Shanghai population: the AUC value of trans fatty acids was 0.79, the AUC values ​​of the four lipid markers ranged from 0.81 to 0.83, and the AUC value of the four lipid characteristic markers combined with the trans fatty acid 18:1 marker was 0.91. The above results reveal that the newly discovered lipid characteristic markers and their combinations have a better predictive effect on dairy product intake.

[0171] 4. Relationship between dairy products and their lipid profile markers and cardiovascular risk factors over a 6-year period

[0172] After controlling for age, sex, region, urban and rural areas, education level, smoking, drinking, physical activity, family history of chronic diseases, lipid-lowering drug use, dietary factors, BMI, baseline values ​​of corresponding indicators and baseline triglycerides and low-density lipoprotein-cholesterol, dairy product intake was significantly negatively correlated with the 6-year changes in multiple cardiovascular risk factors: systolic blood pressure: -1.27 (95% confidence interval: -2.26, -0.29), P value = 0.012; diastolic blood pressure: -0. 56 (95% confidence interval: -1.12, -0.01), P value = 0.047; fasting blood glucose: -0.14 (95% confidence interval: -0.21, -0.07), P value = 0.00001; abdominal obesity: 0.84 (95% confidence interval: 0.72, 0.97), P value = 0.020; and cardiovascular risk factors ≥ 3: 0.90 (95% confidence interval: 0.82, 0.99), P value = 0.031. Consistently, lipid profile markers associated with dairy intake showed the same relationships with the above risk factors: systolic blood pressure: -2.68 (95% confidence interval: -4.92, -0.44), P value = 0.019; diastolic blood pressure: -1.88 (95% confidence interval: -3.14, -0.62), P value = 0.003; fasting blood glucose: -0.28 (95% confidence interval: -0.41, -0.08), P value = 0.003; abdominal obesity: 0.71 (95% confidence interval: 0.53, 0.94), P value = 0.018; and cardiovascular risk factors ≥ 3: 0.68 (95% confidence interval: 0.56, 0.83), P value = 0.0002 ( Figure 3 ).

[0173]

[0174]

[0175]

[0176]

[0177] Discussion

[0178] The present invention discovered and verified that 4 SMs can be used as lipid characteristic markers reflecting dietary dairy product intake. Like dairy products, the 4 dairy product-related lipid characteristic markers are significantly negatively correlated with the 6-year change values ​​of multiple cardiovascular risk factors, including systolic blood pressure, diastolic blood pressure, fasting blood sugar, obesity, etc. The above findings suggest that specific sphingomyelins or their combinations can be used as stable biomarkers reflecting dairy product intake and partially explain the relationship between dairy products and cardiovascular health.

[0179] Dairy products are an important food source for many bioactive lipids. Using high-throughput targeted omics to accurately quantify more than 350 lipids, the present invention reveals for the first time that the SM subclass has the strongest relationship with dairy product intake. Since the milk fat globule membrane of dairy products is rich in phospholipids such as SM, the relationship between SM and dairy products in the present invention is explained. Among the four SMs found, the two SMs containing 14:0 or 14:1 fatty acids, namely SM(OH)C32:2 and SM C32:1, have the strongest relationship with dairy products. Among them, the relationship between SM(OH)C38:2 and dairy products is still significant after additional correction of the two strongest lipid markers, suggesting that this lipid can reflect independent dairy product intake. Previous lipidomics analysis of bovine milk suggests that milk is rich in SM containing C20-C22 acyl chains, which partially explains the relationship between the discovered SM(OH)C38:2 and dairy products.

[0180] Previous population studies have found that odd-chain fatty acids (15:0, 17:0, 17:1), trans fatty acids (16:1n-7, 18:1), 14:0 and 14:1 are strongly correlated with dairy products, and an association between blood fatty acids and dairy product intake has been established. However, recent evidence shows that odd-chain fatty acids may also be derived from dietary fish, ruminant meat, eggs, and even produced by endogenous fermentation of human intestinal flora, and therefore lack sufficient specificity. For example, in a study involving 3,000 Europeans, Lankinen et al. found that for people with a high intake of fish, trans fatty acids 15:0 and 17:0 were strongly correlated with the intake of fish and docosahexaenoic acid, but not with dairy products. Interestingly, the present invention also found that odd-chain triglycerides such as TAG (49:2) and TAG (53:3) were positively correlated with dairy product intake in the population. However, this type of relationship was not found in the validation set. In comparison, SM can be used as a more stable and specific dairy product marker, supported by the following: (1) The population in this discovery included two independent populations with an age span of 20-30 years and different dietary habits. The correlation coefficients between SM and dairy products in the two populations were almost the same; (2) The four SMs discovered were not correlated or had a weak relationship with other dietary factors including red meat, aquatic products, eggs, soy milk, vegetables, fruits and dietary fiber, confirming that dairy products are the main food source of these SMs; (3) Compared with the trans fatty acid marker 18:1 discovered by our research group in the early stage, the four SMs can better distinguish between people who do not consume dairy products and people with high dairy product intake.

[0181] Consistent with previous population studies, the present invention also found that dairy products have a protective effect on multiple cardiovascular risk factors such as blood pressure, blood sugar, and obesity. It is worth noting that this study found that the three SMs related to dairy product intake, namely SM(OH)C32:2, SM C32:1, and SM(2OH)C30:2, were significantly negatively correlated with blood pressure, including systolic and diastolic blood pressure, and obesity, while SM(OH)C38:2 was significantly negatively correlated with 6-year blood sugar changes. More and more experimental evidence has found that polar lipids in milk are not only a structural component, but are increasingly considered to be "functional components." On the one hand, SM disorders will lead to an imbalance in the homeostasis of vascular ions such as calcium ions and sodium ions. At the same time, SM's active metabolites such as S-1-P can directly regulate smooth muscle tension by activating the Rho kinase signaling pathway, thereby regulating blood pressure. For obesity, animal experiments have found that high-fat mice continuously fed with polar lipids containing 25% SM can effectively resist obesity induced by a high-fat diet. The possible explanation is that SM increases the diversity of intestinal bacteria by regulating the ratio of intestinal Firmicutes to Bacteroidetes. Although the current research conclusions on SM and blood sugar regulation are inconsistent, there is evidence that milk contains more saturated SM and is an important source of saturated sphingosine, and saturated sphingosine has been shown to promote the secretion of glucagon-like peptide-1 and inhibit the occurrence of insulin resistance. Based on the above evidence, the four SMs screened by the present invention may provide a new mechanism explanation for dairy products and cardiovascular health.

[0182] This study is the largest and most comprehensive study known to date to evaluate the relationship between lipid metabolites and dietary dairy product intake. It also has the following advantages: (1) The sample size is large, and the study population comes from two completely independent populations with an age difference of 20-30 years. The dietary patterns and lifestyle habits of the two populations are significantly different, which indirectly confirms that the discovered dairy product markers are stable and reliable; (2) High-throughput targeted lipidomics was used to comprehensively and systematically evaluate the relationship between blood lipid molecules and dietary dairy product intake; (3) A large number of covariates including lifestyle, medical history, medication, and dietary factors were collected and carefully controlled; (4) This study used a strict Bonferroni multiple correction method to effectively avoid the emergence of false-positive metabolites.

[0183] In summary, the present invention has established the relationship between blood lipid groups and dietary dairy product intake in two independent Chinese populations. In the Chinese population, where metabolic abnormalities such as cardiovascular disease are frequent and the dietary structure is complex and diverse, the screened lipid group characteristics can be used for large-scale epidemiological surveys to study the relationship between dairy products and cardiovascular health, and more accurately evaluate the relationship between dairy product exposure and disease.

Claims

1. Use of a reagent for detecting lipids in the preparation of a kit for detecting the lipids to identify dairy product intake, wherein the lipids are sphingomyelins, and the sphingomyelins are selected from one or more of SM(OH)C32:2, SM C32:1, SM(2OH)C30:2, and SM(OH)C38:

2.

2. The use according to claim 1, characterized in that Also contains one or more of the following characteristics: The lipids also include C18:1 trans fatty acids, and / or The test is a blood test, a plasma test, a serum test, or a urine test, and / or The detection method is selected from one or more of the following: chromatography, mass spectrometry, shotgun method, nuclear magnetic resonance, and / or The dairy products include one or more selected from the following: fresh milk, yogurt, ice cream, milk powder, milk flakes, cheese, cream, cream cake, condensed milk, and other dairy products, and / or The kit also includes a sphingomyelin standard.

3. The use according to claim 1, characterized in that The detection reagent is a chromatography and / or mass spectrometry detection reagent for the lipid.

4. The use according to any one of claims 1 to 3, characterized in that The test is to compare the content of sphingomyelin (A1) in the sample from the subject with the corresponding sphingomyelin content (A0) in normal people. If A1 is significantly higher than A0, it means that the subject has a high intake of dairy products; and / or The detection is to compare the sphingomyelin content (A1) of the sample from the subject with the previous corresponding sphingomyelin content (A1') of the subject. If A1 is significantly higher than A1', it indicates that the subject's dairy product intake is increased.

5. The use according to claim 4, characterized in that The term "significantly higher than" refers to A1 / A0≥1.

07.

6. The use according to claim 5, characterized in that The term "significantly higher than" refers to A1 / A0≥1.

2.

7. The use according to claim 6, characterized in that The term "significantly higher than" refers to A1 / A0≥1.

5.

8. The use according to claim 4, characterized in that The term "significantly higher than" refers to A1 / A1'≥1.

07.

9. The use according to claim 8, characterized in that The term "significantly higher than" refers to A1 / A1'≥1.

2.

10. The use according to claim 9, characterized in that The term "significantly higher than" refers to A1 / A1'≥1.

5.

11. A method for identifying milk product intake, comprising the steps of: (1) detecting the content of a lipid as a biomarker of dairy product intake in a sample of a subject, wherein the lipid is sphingomyelin, and the sphingomyelin is selected from one or more of SM(OH)C32:2, SM C32:1, SM(2OH)C30:2, SM(OH)C38:2, and (2) Determine total dairy product intake, milk intake, or changes therein based on the measured lipid content.

12. The method according to claim 11, characterized in that Also includes one or more of the following characteristics: The lipids also include C18:1 trans fatty acids, and / or The sample comprises a blood sample, a plasma sample, a serum sample, or a urine sample, and / or The detection method is selected from one or more of the following: chromatography, mass spectrometry, shotgun method, nuclear magnetic resonance, and / or The dairy products include one or more selected from the following: fresh milk, yogurt, ice cream, milk powder, milk flakes, cheese, cream, cream cake, condensed milk, and other dairy products.

13. The method according to claim 12, characterized in that Step (2) comprises: (2.1) comparing the sphingomyelin content (A1) of step (1) with the sphingomyelin content (A0) of a normal population sample; if A1 is significantly higher than A0, it indicates that the subject has a high intake of dairy products, and / or (2.2) Compare the sphingomyelin content (A1) of step (1) with the subject's previous corresponding sphingomyelin content (A1'). If A1 is significantly higher than A1', it indicates that the subject's intake of dairy products has increased.

14. The method according to claim 13, characterized in that The term "significantly higher than" refers to A1 / A0≥1.

07.

15. The method according to claim 14, characterized in that The term "significantly higher than" refers to A1 / A0≥1.

2.

16. The method according to claim 15, characterized in that The term "significantly higher than" refers to A1 / A0≥1.

5.

17. The method according to claim 13, characterized in that The term "significantly higher than" refers to A1 / A1'≥1.

07.

18. The method according to claim 17, characterized in that The term "significantly higher than" refers to A1 / A1'≥1.

2.

19. The method according to claim 18, characterized in that The term "significantly higher than" refers to A1 / A1'≥1.

5.

20. A device for identifying milk product intake, the device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the following steps are implemented: (1) detecting the content of lipids as biomarkers of dairy product intake in a sample of a subject, wherein the lipids are selected from one or more of SM(OH)C32:2, SM C32:1, SM(2OH)C30:2, SM(OH)C38:2, and (2) Determine total dairy product intake and milk intake based on the measured lipid content.

21. The device according to claim 20, characterized in that Also includes one or more of the following characteristics: The lipids also include C18:1 trans fatty acids, and / or The sample comprises a blood sample, a plasma sample, a serum sample, or a urine sample, and / or The detection method is selected from one or more of the following: chromatography, mass spectrometry, shotgun method, nuclear magnetic resonance, and / or The dairy products include one or more selected from the following: fresh milk, yogurt, ice cream, milk powder, milk flakes, cheese, cream, cream cake, condensed milk, and other dairy products.

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