Blood metabolism marker for evaluating lipid metabolism improvement effect of intestinal strain intervention and application of blood metabolism marker
By using 13 common differential metabolites and 6 target bile acid metabolites as blood metabolic markers, combined with a broad-targeted metabolomics approach, this study addresses the lag in evaluating the effects of gut microbiota intervention on lipid metabolism in existing technologies, enabling early and precise efficacy assessment and providing a standardized evaluation tool.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-05
- Publication Date
- 2026-03-24
AI Technical Summary
Existing technologies lack precise and specific indicators for evaluating the improvement of lipid metabolism after intervention with gut microbiota strains. Traditional biochemical indicators cannot capture subtle metabolic changes induced by strain intervention in an early and accurate manner, resulting in lag and uncertainty in the evaluation.
Using 13 common differential metabolites and 6 target bile acid metabolites as blood metabolic biomarkers, and combining broad-targeted metabolomics methods, a detection kit was developed. The effect of intestinal strain intervention on lipid metabolism was evaluated by detecting the biomarker levels in the plasma samples to be tested.
It enables early and accurate assessment of the lipid metabolism effects of gut microbiota intervention, avoids non-specific interference from traditional indicators, and provides a standardized assessment tool suitable for clinical and research scenarios.
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Figure CN121721284A_ABST
Abstract
Description
Technical Field
[0001] This application belongs to the field of biomarker technology, and specifically relates to a blood metabolic marker for evaluating the effect of intestinal strain intervention on lipid metabolism and its application. Background Technology
[0002] Lipid metabolism disorders are closely related to diseases such as obesity, hyperlipidemia, and atherosclerosis. Gut microbiota intervention, as a novel approach to regulating lipid metabolism, has attracted widespread attention due to its high safety and mild effects. However, there is currently a lack of precise and specific indicators to assess the lipid metabolism improvement effects of gut microbiota intervention. Traditional biochemical indicators (such as blood glucose and total cholesterol) can only reflect the overall metabolic state and cannot accurately capture the subtle metabolic changes induced by microbiota intervention in the early stages, leading to a lag and uncertainty in the assessment of the intervention's effectiveness.
[0003] Blood metabolomics can directly reflect the dynamic changes in the host's metabolic state, and differentially metabolites can serve as potential biomarkers for assessing the effectiveness of interventions. However, current research has not yet identified specific blood metabolic biomarkers in the process of lipid metabolism intervention by gut microbiota strains, thus failing to provide precise evidence for evaluating the effectiveness of strain interventions. Summary of the Invention
[0004] To address the aforementioned issues, in a first aspect, this application proposes a blood metabolic biomarker for evaluating the effect of gut microbiota strain intervention on lipid metabolism, the biomarker comprising 13 common differential metabolites; The 13 common differential metabolites are (12E)-10-hydroxytetradec-12-enoylcarnitine, 1-Phenyl-III-Pyrrole-2,5-Dione, and 2-(4-methylphenyl)-2-oxoethyl thiocyanate. Thiocyanate, 3-hydroxy-1-(4-hydroxyphenyl)-propan-1-one, 3-methylheptanedioylcarnitine, 4-(3-Chlorophenyl)-3-(pyridin-3-yl)-1H-1,2,4-triazole-5-thione, Cholest-4-En-3-One, Cinnamoylglycine, Equol 4'-O-Glucuronide, Kaempfetrin, Naringenin-7-sulfate 7-Sulfate), penicillin G, and penillotic acid.
[0005] Furthermore, the biomarkers also include six target bile acid metabolites; The six target bile acid metabolites are bile acid, 7-ketodeoxycholic acid, chenodeoxycholic acid, deoxycholic acid, 3-hydroxy-12-oxocholic-24-acid, and 3-oxocholic acid.
[0006] Furthermore, the intestinal bacteria strain is one or more of the following: Akkermansia myxophila, Parabacterium difficile, Parabacterium guilloché, Christensenella spp., Prevotella foetida, and Oscillatoris ruminantis.
[0007] Furthermore, the biomarker was detected using the subject's plasma.
[0008] Secondly, this application proposes the application of the aforementioned blood metabolism markers in the preparation of detection reagents or kits for evaluating the effects of intestinal strain intervention on lipid metabolism.
[0009] Furthermore, the kit employs a broadly targeted metabolomics approach to detect biomarker levels.
[0010] Furthermore, the kit also includes reagents for detecting biochemical indicators related to lipid metabolism, including blood glucose and total cholesterol.
[0011] Thirdly, this application proposes a method for evaluating the effect of intestinal strain intervention on lipid metabolism by detecting the level of biomarkers in the plasma sample to be tested.
[0012] Furthermore, the assessment by detecting the level of biomarkers in the plasma sample to be tested includes the following steps: Plasma samples were collected from subjects before and after the intervention with gut microbiota strains. The levels of the biomarkers described were detected in plasma samples, and the changes in blood biomarker levels before and after the intervention were compared. If there are significant differences among the 13 common differential metabolites (P<0.05; VIP>1), and the levels of the 6 target bile acid metabolites are significantly reduced (P<0.05), then the intervention of intestinal strains is considered to have an improving effect on lipid metabolism.
[0013] Fourthly, this application proposes the application of the aforementioned blood metabolic markers in evaluating the improvement effect of lipid metabolism and monitoring the intervention effect of intestinal bacteria.
[0014] Compared with the prior art, this application has the following advantages: The 13 common differential metabolites in the biomarkers of this application are common differential metabolites after intervention with 6 candidate intestinal strains. The 6 target bile acid metabolites are directly related to cholesterol metabolism and can specifically reflect the improvement effect of the strains on lipid metabolism, avoiding the non-specific interference of traditional indicators.
[0015] This application utilizes broadly targeted metabolomics technology to detect subtle changes in plasma metabolites, enabling earlier capture of the metabolic response to bacterial intervention compared to traditional biochemical indicators, thus facilitating early assessment of intervention efficacy. Based on biomarkers, diagnostic kits can be developed; these kits are easy to use and allow for rapid quantitative analysis of biomarker levels, making them suitable for various scenarios including clinical and research applications. This provides a standardized tool for evaluating the efficacy of gut microbiota-related interventions in lipid metabolism.
[0016] Other features and advantages of this application will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the application. The objectives and other advantages of this application may be realized and obtained by means of the structures pointed out in the description, claims and drawings. Attached Figure Description
[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0018] Figure 1 PCA plots of metabolites from each group of mice are shown; Figure 2 The OPLS-DA plots of plasma metabolites in mice from each group are shown (in order: comparison between the intervention and control groups of *Ackermania myxophila*, *Pseudomonas difficile*, *Pseudomonas guilloché*, *Christsenia minora*, *Bacillus predominus*, and *Oxidobacterium ruminantum*). Figure 3 Venn diagrams showing the differential metabolites in mice from each bacterial intervention group are presented. Figure 4 The heatmap shows 13 common differential metabolites in mice from each bacterial culture intervention group; Figure 5 A heatmap of differential metabolites between *Ackermania glutinis* and the control group is shown. Figure 6 A heatmap of differential metabolites between *Pseudomonas dilatatus* and the control group is shown. Figure 7 A heatmap of differential metabolites between *Pseudomonas aeruginosa* and the control group is shown. Figure 8 A heatmap of differential metabolites between *Christsenium spp.* and the control group is shown. Figure 9 A heatmap of differential metabolites between *Proteus vulgaris* and the control group is shown. Figure 10 A heatmap of differential metabolites between *Oxidative ruminant* and the control group is shown. Figures 11A-11F The differences in plasma levels of cholic acid, 7-ketodeoxycholic acid, hyocholic acid, chenodeoxycholic acid, 3-hydroxy-12-oxochol-24-Oic acid, and 3-oxocholic acid were shown among the groups of mice. Figure 12 The results of the correlation analysis between differential metabolites and biochemical indicators are shown. Detailed Implementation
[0019] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0020] The experimental animals and bacterial strains involved in the embodiments of this application are as follows: A total of 64 male and female WT and Apoe- / - mice under the C57BL / 6J genetic background were purchased from Cyagen Biotech Ltd., including 4 male and 4 female WT mice and 28 male and 28 female Apoe- / - mice.
[0021] Six candidate strains were identified, namely Akkermansia myxophila ( Akkermansia muciniphila ), Parabacterium dilatatum ( Parabacteroides distasonis ), Parabacteroides archaea ( Parabacteroides goldsteinii ), Christensenella smallis ( Christensenellaceae minuta ), Prevotella coli ( Faecalibacterium prausnitzii ) and ruminant bacteria ( Oscillibacter ruminantium The sources and cultivation conditions of these organisms are shown in the table below.
[0022] Table 1. Sources and culture conditions of the strains
[0023] Example 1 (a) Activation and culture of microbial strains: ① Take 0.3 mL of sterile water and drop it into the inoculum tube. Gently shake to dissolve the freeze-dried bacteria into a suspension.
[0024] ② After dipping a disposable inoculation loop into the bacterial suspension, streak it onto a three-zone agar plate. Simultaneously, inoculate the remaining bacterial suspension into liquid culture medium. Then, according to the characteristics of each bacterial species, anaerobically culture it at a specific temperature and time. Pick single colonies and preserve them through liquid culture.
[0025] Strain expansion: ① Preparation and sterilization of liquid culture media: Liquid thioglycolate medium, RCM medium, PY+X medium and GAM medium are prepared in anaerobic bottles according to the formula, sterilized at 121℃ for 20 min and ready for use.
[0026] ② Glycerin inoculation: Inoculate 6 strains of anaerobic bacteria with glycerin culture: Take out the glycerin tubes from the -80℃ freezer, thaw them, centrifuge at 4000rpm for 5 min, remove the supernatant, resuspend the culture medium in the anaerobic bottle, and culture anaerobically at a certain temperature and time according to the characteristics of each bacteria.
[0027] McFarland turbidimetric assay for bacterial concentration and aliquoting: After culturing, the concentration of each bacterium was determined using McFarland turbidimetric tubes. The tubes were centrifuged at 4000 rpm to remove the supernatant, then 20% glycerol culture medium was added, and aliquoted into 5 × 10⁻⁶ tubes. 9 CFU / mL, temporarily stored at -80℃. After purchasing the strain, revive it, plate it, and if there are no contaminants, pick a single colony for inoculation. After anaerobic culture, transfer the seed culture to 250mL of liquid medium for expansion. After expansion, take a portion of the bacterial culture, dilute it, plate it, and count the CFU. If the density reaches 5.0×10⁻⁶, the inoculum is considered a positive result. 9 If the concentration is CFU / mL, add 20% glycerol and aliquot into 1 mL tubes, then store at -80°C. For each gavage administration to mice, take one tube of bacterial solution, centrifuge at 6000 rpm for 10 min, remove the supernatant glycerol in an anaerobic apparatus, and resuspend in 1 mL of PBS.
[0028] (II) Animal grouping and intervention: Six-week-old C57BL / 6J mice and Apoe- / - mice were selected for the experiment. Ear tags were inserted into the inner side of the right ear of each mouse using a punch, and the mice were numbered sequentially. The mice were kept under 12-hour light cycles, allowed free access to food and water, and had their bedding changed regularly. After acclimatization to a standard diet for 1 to 7 weeks, the mice were randomly divided into 8 groups of 8 mice each. Each group consisted of 4 males and 4 females. For example, in group B, the first, second, third, and fourth female mice were designated BF1, BF2, BF3, and BF4, respectively, and the first, second, third, and fourth male mice were designated BM1, BM2, BM3, and BM4, respectively. The naming of mice in the remaining groups followed the same principle. The randomization groups are as follows: Group A (normal high-fat control group CP): C57BL / 6J mice, fed a high-fat diet + PBS by gavage; Group B (Apoe- / - model control group AP): Apoe- / - mice, fed a high-fat diet + PBS by gavage; Group C (Akkermansia muciniphila group Am): Apoe- / - mice, high-fat diet + 5.0 × 10⁻⁶ mice 9 CFU / mL bacterial solution administered by gavage; Group D (Parabacteroides distasonis group Pd): Apoe- / - mice, high-fat diet + 5.0 × 10⁻⁶ mcgs 9 CFU / mL bacterial solution administered by gavage; Group E (Parabacteroides goldsteinii group Pg): Apoe- / - mice, high-fat diet + 5.0 × 10⁻⁶ mcgs 9CFU / mL bacterial solution administered by gavage; Group F (Christensenellaceae minuta group Cm): Apoe- / - mice, high-fat diet + 5.0 × 10⁻⁶ mice 9 CFU / mL bacterial solution administered by gavage; Group G (Faecalibacterium prausnitzii Fp): Apoe- / - mice, high-fat diet + 5.0 × 10⁻⁶ mcgs 9 CFU / mL bacterial solution administered by gavage; Group H (Oscillibacter ruminantium group Or): Apoe- / - mice, high-fat diet + 5.0 × 10⁻⁶ mice 9 CFU / mL bacterial solution administered by gavage; 0.2 mL was administered by gavage daily at regular intervals in the morning for 35 consecutive days (5 weeks). Mice were sacrificed at 12 weeks of age after the intervention. The mice's activity, coat color, and mortality were observed daily. The mice were weighed daily, and blood samples were collected for quantitative analysis of plasma metabolites.
[0029] (III) Collection and processing of mouse blood samples: ① Mouse Blood Sample Collection: After collecting blood from the mouse eyeballs, drop the collected blood into a sterile EP tube without anticoagulant. Place the EP tube at room temperature (20-25 ℃) for 1 hour to allow the blood to fully coagulate. After coagulation, the blood clot will shrink, separating a pale yellow serum. Place the coagulated blood in a centrifuge at 4 ℃ and centrifuge at 3000 rpm for 10 minutes. After centrifugation, the serum will accumulate at the top of the centrifuge tube, while cell debris and other components will precipitate at the bottom. Carefully aspirate the serum from the centrifuge tube using a sterile pipette or pipette, being careful not to aspirate the precipitate. Transfer the separated serum to a new sterile EP tube. Aliquot the separated serum into new sterile EP tubes and store the aliquoted serum samples at -80 ℃.
[0030] ② Mouse serum sample processing: 50 μL of blood sample was placed in a 1.5 mL centrifuge tube, and 150 μL of extraction buffer (acetonitrile:methanol = 1:1 (v:v)) containing four internal standards (L-2-chlorophenylalanine (0.02 mg / mL), etc.) was added. After vortexing for 30 s, the sample was extracted by low-temperature sonication for 30 min (5 ℃, 40 kHz). The sample was then placed at -20 ℃ for 30 min. After centrifugation for 15 min (13000 g, 4 ℃), the supernatant was collected, dried under nitrogen, and reconstituted with 100 µL of reconstitution solution (acetonitrile:water = 1:1). The sample was extracted by low-temperature sonication for 5 min (5 ℃, 40 kHz), and centrifuged for 10 min (13000 g, 4 ℃). The supernatant was transferred to a vial with an inner tube for analysis. In addition, 20 μL of the supernatant from each sample was mixed and used as a quality control sample.
[0031] Example 2 To identify the metabolite characteristics altered by the candidate strain intervention in lipid metabolism, a broad-targeted metabolomics approach was used to quantitatively analyze the metabolites in all blood samples from Example 1. The specific procedure is as follows: Observing differences in metabolic profiles through principal component analysis (PCA) (e.g.) Figure 1 As shown in the figure, the overall difference between the plasma metabolite profiles of Apoe- / - mice and the control group (AP) after gavage administration of six candidate intestinal strains (Akermansia myxophila, Parabacterium difficile, etc.) is evident. The results show that the sample points from each group exhibit significant clustering and separation in the PCA space, indicating that intervention with candidate strains can significantly alter the overall composition of mouse plasma metabolites, providing a basis for subsequent differential metabolite screening at the overall metabolite profile level.
[0032] Orthogonal partial least squares discriminant analysis (OPLS-DA) was used to identify differentially isolated metabolites that could explain the discriminative isolation between the bacterial intervention group and the control group. Figure 2 (See Table 2). The sample points of the intervention group and the blank group were completely separated in the figure, indicating that this analysis can effectively identify differentially differentiated metabolites between the two groups. Variable importance for the projection (VIP) was used for feature selection, with P<0.05 and VIP>1 as the screening criteria for significantly differentially differentiated metabolites.
[0033] Table 2. Number of differentially metabolites in the metabolome of experimental mice
[0034] As shown in Table 2, the levels of 777, 312, 268, 316, 407, 442, and 555 metabolites, respectively, differed significantly among mice administered *Ackermania mucilaginosa*, *Peribrio digilei*, *Peribrio guleri*, *Kristensenia spp.*, *Peribrio proteobacterium*, and *Oxidative ruminant* via gavage (P<0.05; VIP>1).
[0035] Differential metabolites in the six bacterial strain intervention groups were analyzed using Venn diagrams. Figure 3 The figure shows that the six circles represent the set of differentially expressed metabolites in each bacterial strain intervention group. The values / labels of the overlapping areas of the circles represent the number of differentially expressed metabolites shared by multiple groups. A total of 13 overlapping metabolites were found to be significantly altered in all six bacterial strain intervention groups of mice, thus identifying 13 shared differentially expressed metabolites. Figure 4 The five compounds are (12E)-10-hydroxytetradec-12-enoylcarnitine, 1-phenyl-1H-pyrrole-2,5-dione, and 2-(4-methylphenyl)-2-oxoethyl thiocyanate. Thiocyanate, 3-hydroxy-1-(4-hydroxyphenyl)-propan-1-one, 3-methylheptanedioylcarnitine, 4-(3-Chlorophenyl)-3-(pyridin-3-yl)-1H-1,2,4-triazole-5-thione, Cholest-4-En-3-One, Cinnamoylglycine, Equol 4'-O-Glucuronide, Kaempfetrin, Naringenin-7-sulfate 7-Sulfate, penicillin G, and penillotic acid.
[0036] Further pairwise comparisons showed that, compared with the control group, gavage administration of *Akkermansia myxophilus* significantly reduced plasma levels of lysophosphatidylcholine (e.g., 1-(18:3)lysophosphatidylcholine) and free fatty acids (e.g., octadecenoic acid) (P<0.05, VIP>1). Figure 5 ).
[0037] Following oral administration of *Pseudomonas diffusa*, plasma levels of bile acids and 3-oxocholic acid were significantly reduced (P<0.05, VIP>1). Figure 6 ); Following oral administration of *Pseudomonas aeruginosa*, plasma levels of steroids (such as corticosterone) were significantly increased (P<0.05, VIP>1), while plasma levels of lysophosphatidylethanolamine (such as 1-(18:2)lysophosphatidylethanolamine) and free fatty acids (such as tetradecanoic acid) were significantly decreased (P<0.05, VIP>1). Figure 7 ); Following oral administration of *Kristensenium valerate*, plasma levels of glycerophosphatidylcholine (phosphatidylcholine (22:6 / 16:0)) were significantly increased (P<0.05, VIP>1), while plasma levels of lysophosphatidylethanolamine (e.g., 1-(18:2)lysophosphatidylethanolamine) and bile acids were significantly decreased (P<0.05, VIP>1). Figure 8 ); Following gavage with *Bacillus prenanti*, plasma levels of phosphatidylethanolamine (such as 1-docosahexaenoyl-sn-glycerol-3-phosphate ethanolamine) were significantly increased (P<0.05, VIP>1), while plasma levels of bile acids were significantly decreased (P<0.05, VIP>1). Figure 9 ); Following oral administration of *Oxidative ruminant*, plasma levels of phosphatidylethanolamine (e.g., 1-docosahexaenoyl-sn-glycerol-3-phosphate ethanolamine) and sphingomyelin (e.g., sphingomyelin (d33:1)) were significantly increased (P<0.05, VIP>1), while plasma levels of bile acids were significantly decreased (P<0.05, VIP>1). Figure 10 ).
[0038] Further comparisons were made of six target differentially expressed bile acid metabolites: cholic acid, 7-ketodeoxycholic acid, hyocholic acid, chenodeoxycholic acid, 3-hydroxy-12-oxochol-24-Oic acid, and 3-oxocholic acid. Results are as follows: Figures 11A-11F As shown, Figures 11A-11FThe differences in plasma levels of cholic acid, 7-ketodeoxycholic acid, porphyrin, chenodeoxycholic acid, 3-hydroxy-12-oxocholic acid-24-acid, and 3-oxocholic acid in each group of mice were shown. It can be seen that compared with the control group (AP), the levels of the target metabolites decreased in all six candidate strain intervention groups. Specifically, the levels of cholic acid, chenodeoxycholic acid, porphyrin, 3-hydroxy-12-oxocholic acid-24-acid, and 3-oxocholic acid were significantly reduced in mice treated with gavage of *Pseudomonas despinipes*, *Kristensenia spp.*, *Follella sesquiterpenes*, and *Oxidobacterium ruminants* (P<0.05); the level of 7-ketodeoxycholic acid was significantly reduced in mice treated with gavage of *Pseudomonas despinipes*, *Follella sesquiterpenes*, and *Oxidobacterium ruminants* (P<0.05). The reduction in these six metabolites suggests that the candidate strains may have influenced cholesterol metabolism, indirectly regulating cholesterol metabolism by reducing the conversion of cholesterol to bile acids or promoting other cholesterol metabolic pathways.
[0039] To further illustrate the impact of metabolites on blood lipids, a correlation analysis was performed between these metabolites and biochemical indicators. Figure 12 The results showed that lysophosphatidylcholine (18:3-Sn1) was significantly positively correlated with blood glucose and total cholesterol (R = 0.38, P < 0.01; R = 0.54, P < 0.01), while taurocholic acid glycine, (Z)-6-methyl-2-(4-methylpent-3-enyl)hept-2-enedioic acid, and phosphatidylethanolamine (18:1E7-Hdohe) were significantly negatively correlated with blood glucose and total cholesterol (R = -0.47, P < 0.01; R = -0.40, P < 0.01; R = -0.50, P < 0.01; R = -0.36, P < 0.01; R = -0.49, P < 0.01). =-0.48, P<0.01).
[0040] Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.
Claims
1. A blood metabolic biomarker for evaluating the effect of gut microbiota strain intervention on lipid metabolism, characterized in that, The biomarkers include 13 common differentially expressed metabolites; The 13 common differential metabolites are (12E)-10-hydroxytetradecano-12-enoylcarnitine, 1-phenyl-1H-pyrrole-2,5-dione, 2-(4-methylphenyl)-2-oxoethylthiocyanate, 3-hydroxy-1-(4-hydroxyphenyl)-prop-1-one, 3-methylpimecroylcarnitine, 4-(3-chlorophenyl)-3-(pyridin-3-yl)-1H-1,2,4-triazol-5-thione, cholester-4-en-3-one, cinnamoylglycine, estrol 4'-O-glucuronide, kaempferol, naringenin-7-sulfate, penicillin G, and penicillin thiazolic acid.
2. The blood metabolic marker according to claim 1, characterized in that, The biomarkers also include six target bile acid metabolites; The six target bile acid metabolites are bile acid, 7-ketodeoxycholic acid, porcine bile acid, chenodeoxycholic acid, 3-hydroxy-12-oxocholic-24-acid, and 3-oxocholic acid.
3. The blood metabolic marker according to claim 1, characterized in that, The intestinal bacteria strain is one or more of the following: Akkermansia myxophila, Parabacterium difficile, Parabacterium guilloché, Christensenella septembin, Prevotella foetida, and Oscillatoris ruminantis.
4. The blood metabolic marker according to claim 1, characterized in that, The biomarker was detected in the subject's plasma.
5. The use of the blood metabolism markers according to any one of claims 1-4 in the preparation of detection reagents or kits for evaluating the effect of intestinal strain intervention on lipid metabolism.
6. The application according to claim 5, characterized in that, The kit uses a broad-targeted metabolomics approach to detect biomarker levels.
7. The application according to claim 5, characterized in that, The kit also includes reagents for detecting biochemical indicators related to lipid metabolism, including blood glucose and total cholesterol.
8. A method for evaluating the effect of gut microbiota strain intervention on lipid metabolism, characterized in that, The assessment is performed by detecting the levels of the biomarkers of claim 1 or 2 in the plasma sample to be tested.
9. The method according to claim 8, characterized in that, The assessment by detecting the level of the biomarker of claim 1 or 2 in the plasma sample to be tested includes the following steps: Plasma samples were collected from subjects before and after the intervention with gut microbiota strains. The levels of the biomarkers described were detected in plasma samples, and the changes in blood biomarker levels before and after the intervention were compared. If significant differences are found in the 13 common differential metabolites and the levels of the 6 target bile acid metabolites are significantly reduced, then the intervention of intestinal strains is considered to have an improving effect on lipid metabolism.
10. The application of the blood metabolic markers described in any one of claims 1-4 in evaluating the improvement effect of lipid metabolism and monitoring the intervention effect of intestinal bacteria.