Biomarker C10757 for detecting health status and application

Abstract

The invention discloses a biomarker C10757 for detecting a health state and application thereof, relates to the technical field of biology, and solves the technical problems that traditional psychological stress assessment depends on behavior observation (such as animal depression-like behaviors) and has the defects of strong subjectivity, low sensitivity, obvious hysteresis and the like. According to the technical scheme, the biomarker C10757 of the health status is provided, the molecular formula of the biomarker C10757 is C20H27N3O3, the health status is caused by the psychological stress generated by the wild macaque, and the application of the biomarker C10757 in preparation of a detection product for the health status caused by the psychological stress generated by the wild macaque is provided. Through the metabolic function of intestinal microorganisms, non-targeted metabonomics analysis, pathway enrichment and ROC analysis determination, the effects of accurately distinguishing individuals affected by psychological stress from normal individuals and remarkably improving the sensitivity and specificity of detection are achieved.

Description

Technical Field

[0001] The present invention relates to the field of biotechnology, and more specifically, to a biomarker C10757 for detecting health status and an application thereof. Background Art

[0002] In recent years, more and more studies have revealed the complex interactions between psychological stress and intestinal microbiota. Whether in humans or animal models, short-term or long-term psychological stress has been shown to significantly change the structure of the intestinal microbiota, thereby affecting the physiological and psychological health of the host. For example, in the prior art, some studies have proposed that psychological stress affects the function of the intestinal Brunner glands, leading to microbial imbalance, especially the reduction of Lactobacillus, and ultimately triggering enteritis and decreased immune function. In addition, other studies have pointed out that chronic social frustration stress can change the composition of the intestinal microbiota, activate intestinal γδ T cells, and then affect brain function and induce depression-like behavior. These studies highlight the potential mechanism of intestinal microbiota in stress-related diseases. Psychological stress can affect intestinal homeostasis by regulating intestinal microbiota. However, the mechanism by which distal brain stress signals are perceived in the intestine and the signaling molecules involved in this communication remain unclear.

[0003] The gut-brain axis is a complex bidirectional communication system between the gut and the brain, involving multiple pathways, including neural, endocrine, immune, and metabolic mechanisms. Gut microbes influence the central nervous system through the vagus nerve and enteric nervous system, regulating emotions and behaviors. They also influence the hypothalamic-pituitary-adrenal (HPA) axis by regulating the release of hormones from enteroendocrine cells, thereby regulating stress responses. In addition, gut microbes affect systemic inflammation and brain function by regulating the immune system and producing metabolites such as short-chain fatty acids and tryptophan metabolites. Together, these mechanisms maintain the integrity of the intestinal barrier and prevent harmful substances from triggering systemic and neuroinflammation, and their dysregulation is closely associated with the development of a variety of neuropsychiatric disorders. However, the brain-gut-sweat gland axis under psychological stress remains unclear, and it is necessary to study whether the disruption of the gut microbiota and its metabolites caused by psychological stress further affects brain function.

[0004] Currently, most studies rely on rodent models, which have significant physiological and behavioral differences from humans, limiting the direct extrapolation of their findings. In contrast, non-human primates, as close relatives of humans, have more similar physiological, immune, and behavioral characteristics, making them ideal models for studying human-related diseases and physiological processes. In addition, wild non-human primates develop a unique gut microbiota structure in their natural habitats and are not domesticated, which enables them to better mimic the physiological state of humans in the natural environment. When these wild monkeys are introduced into a captive environment, the changes in their living conditions can simulate the psychological stress scenarios faced by humans in modern society, providing a more realistic and reliable model for studying the impact of psychological stress on the gut microbiota.

[0005] Therefore, establishing a wild non-human primate stress model, determining the differentiating substances or means between animal individuals with health problems caused by psychological stress and normal individuals based on the relationship of the microbiota-metabolite-neural regulation network, and providing a theoretical basis for developing microbiota-targeted intervention strategies and a standardized wildlife mental health monitoring system are urgent problems for us to solve. Summary of the Invention

[0006] To solve the deficiencies in the prior art, the purpose of the present invention is to provide an application of a biomarker C10757 in detecting health risks caused by psychological stress, achieving the purpose of being able to quickly and accurately distinguish animal individuals affected by psychological stress from normal individuals.

[0007] The above technical purpose of the present invention is achieved through the following technical solutions:

[0008] On the one hand, the present invention provides a biomarker C10757 for detecting the health state caused by psychological stress generated by wild Hainan macaques. The Latin name of the wild Hainan macaque is Macaca mulatta brevicaudus, and the molecular formula of the biomarker C10757 is C 20 H 27 N3O3, and the CAS number is 6874-80-2.

[0009] On the other hand, the present invention also provides an application of the biomarker C10757 as described in the first aspect in the preparation of a detection product for the health state caused by psychological stress generated by wild Hainan macaques.

[0010] Furthermore, the psychological stress generated by the wild Hainan macaques is a stress response caused by the captive environment.

[0011] On the other hand, the present invention also provides a reagent or kit for determining or screening the expression level of the biomarker C10757 as described in the first aspect.

[0012] On the other hand, the present invention also provides the use of a reagent for detecting the expression level of biomarker C10757 in a biological sample as described in the first aspect in the preparation of a detection product for determining the health status caused by psychological stress generated by wild Hainan macaques.

[0013] On the other hand, the present invention also provides a method for detecting the health status caused by psychological stress generated by wild Hainan macaques using the biomarker C10757 as described in the first aspect, including:

[0014] Obtaining a biological sample of an individual to be tested;

[0015] Performing pretreatment on the biological sample;

[0016] Detecting the expression level of biomarker C10757 in the pretreated biological sample;

[0017] Determining the psychological stress health condition by comparing the experimentally measured results with a standard database.

[0018] Compared with the prior art, the present invention has the following beneficial effects:

[0019] 1. By revealing the application value of C10757 as a highly specific biomarker, the present invention can quickly and accurately distinguish animal individuals affected by psychological stress from normal individuals;

[0020] 2. The present invention clarifies the molecular mechanism by which psychological stress causes metabolic disorders through the microbial-host co-metabolic pathway (such as dopaminergic synapses, retrograde endocannabinoid signaling), and proposes detection reagents and products for related psychological stress based on biomarker C10757, providing a standardized tool for wildlife protection and captive management;

[0021] 3. As a stress exposure biomarker, biomarker C10757 can be used to evaluate the stress impact of the captive environment on animals, optimize feeding conditions, and promote the scientific and ethical management of wildlife protection and laboratory animals;

[0022] 4. The present invention forms a closed loop from biomarker discovery, mechanism analysis to application transformation, filling the gap in non-invasive stress monitoring technology and having the potential for cross-species promotion. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, form a part of this application, and do not limit the embodiments of the present invention. In the drawings:

[0024] Figure 1 It is the chemical structure diagram of biomarker C10757 in Example 1 of the present invention;

[0025] Figure 2 It is the KEGG metabolic pathway classification diagram in Example 6 of the present invention;

[0026] Figure 3 It is a stacked bar chart of the classification composition of common metabolites (the top 20 most abundant metabolites) in Example 6 of the present invention; Figure 4 It is a bar chart showing important differential metabolites in Example 6 of the present invention;

[0027] Figure 5 It is a volcano plot of differential metabolites in Example 6 of the present invention;

[0028] Figure 6 It is a heat map of the abundance analysis of differential metabolites in Example 6 of the present invention;

[0029] Figure 7 It is a chord diagram of metabolite correlations in Example 6 of the present invention;

[0030] Figure 8 It is a bar chart of enrichment analysis in Example 6 of the present invention;

[0031] Figure 9 It is the ROC curve diagram of C10757 in Example 6 of the present invention. Detailed implementation manners

[0032] To make the objectives, technical solutions and advantages of the present invention more clear and understandable, the present invention will be further described in detail below in conjunction with examples and drawings. The illustrative embodiments of the present invention and their descriptions are only used to explain the present invention and do not limit the present invention.

[0033] Example 1

[0034] A biomarker C10757 for detecting the health status, where the health status is triggered by the psychological stress generated by wild Hainan macaques. The molecular formula of the biomarker C10757 is C 20 H 27 N3O3, and the CAS number is 6874 - 80 - 2.

[0035] The Chinese name of the biomarker C10757 is Calpurnine, and the English name is Calpurnine. The molecular formula is C 20 H 27 N3O3, and the chemical structure is as Figure 1 shown. It belongs to quinolizidine alkaloids, is a lysine-derived alkaloid, has a molecular weight of 357 g / mol, and has a typical bicyclic quinolizidine skeleton.

[0036] The psychological stress generated by wild Hainan macaques is a stress response triggered by the captive environment.

[0037] Psychological stress is a stress response caused by the captive environment, and the animal individuals are wild Hainan macaques among non-human primates. In order to establish an animal model of psychological stress, wild Hainan macaques that were born in the wild and not accustomed to captivity were selected in the present invention, and in cooperation with wildlife protection organizations, a captive environment was provided for them. After being captive, these monkeys showed obvious depression or emotional instability.

[0038] Example 2

[0039] The present invention also provides an application of the biomarker C10757 as described in Example 1 in the preparation of a detection product for the health status caused by psychological stress generated by wild Hainan macaques.

[0040] Example 3

[0041] The present invention also provides a reagent or kit for determining or screening the expression level of the biomarker C10757 as described in Example 1.

[0042] Example 4

[0043] The present invention also provides an application of a reagent for detecting the expression level of the biomarker C10757 as described in Example 1 in a biological sample in the preparation of a detection product for the health status caused by psychological stress generated by wild Hainan macaques.

[0044] Example 5

[0045] The present invention also provides a method for the biomarker C10757 to detect the health status caused by psychological stress generated by wild Hainan macaques, including:

[0046] Obtaining a biological sample of the individual to be tested;

[0047] Performing pretreatment on the biological sample;

[0048] Detecting the expression level of the biomarker C10757 in the pretreated biological sample;

[0049] Comparing the results measured in the experiment with the standard database to determine the psychological stress health condition.

[0050] Example 6

[0051] Experimental treatment and analysis process of the biomarker C10757:

[0052] Step 1: Metabolomics analysis:

[0053] (1) Perform metabolomics sample preparation and detection, and the process is as follows:

[0054] Wild rhesus macaques from Nanwan Monkey Island in Lingshui, Hainan, as part of a macaque nature reserve, 100 fresh fecal samples (LS) were collected from them as experimental samples for the wild group. These samples were from 30 individuals. Considering wildlife welfare and conservation ethics, captive Hainan macaques were selected from a wildlife rescue center. These Hainan macaques were matched with wild macaques in terms of age, weight, health status, and diet (plants, fruits, etc.). 90 fresh fecal samples (BT) were collected as experimental samples for the captive group. These samples were from 30 individuals. Each sample was collected within 2 minutes after defecation. The uncontaminated internal part was placed into a sterile cryotube, then stored in liquid nitrogen and transported to the laboratory for storage at -80°C. Throughout the sampling process, methods that might be harmful to macaques were not adopted. This study complied with relevant laws and regulations, including the Wildlife Protection Law and the Regulations on the Administration of Nature Reserves.

[0055] The samples were thawed at 4°C and then processed by grinding, ultrasonic treatment in a 4°C water bath, standing at -20°C, centrifugation, and resuspension. An appropriate amount of the supernatant was taken as a quality control sample to evaluate the repeatability and stability of LC-MS analysis.

[0056] Metabolites were separated and detected using Waters UPLC I-Class Plus coupled with a Q Exactive high-resolution mass spectrometer. The chromatographic conditions were as follows: in positive ion mode, mobile phase A (0.1% formic acid in water) and mobile phase B (0.1% formic acid in methanol); in negative ion mode, mobile phase A (10 mM ammonium formate in water) and mobile phase B (10 mM ammonium formate in 95% methanol). The solvents were eluted in a gradient, the flow rate was 0.35 ml / min, the column temperature was 45°C, and the injection volume was 5 μl. The mass spectrometry conditions included scanning the mass-to-charge ratio range of 70 - 1050, and selecting the top three precursor ions for fragmentation at 20 eV, 40 eV, and 60 eV energies. The ion source parameters were as follows: sheath gas flow rate 40, auxiliary gas flow rate 10, spray voltage 3.80 in positive ion mode, spray voltage 3.20 in negative ion mode, ion transfer tube temperature 320°C, and auxiliary gas heater temperature 350°C.

[0057] Metabolomics data analysis:

[0058] The original mass spectrometry data was imported into Compound Discoverer 3.3 (Thermo Fisher Scientific, USA) software. The exported results were further preprocessed and analyzed subsequently in metaX. The preprocessing included probabilistic quotient normalization (PQN) to obtain relative peak areas, and batch effect correction using locally weighted polynomial regression fitting (QC-RLSC). In the QC samples, compounds with a relative peak area coefficient of variation (CV) greater than 30% were removed.

[0059] Principal component analysis was performed in R to reduce the data dimension and ensure data quality. Metabolites were classified and annotated using the HMDB and KEGG databases. The VIP values were calculated to assist in the selection of metabolic biomarkers. The data were log2-transformed, and a PLS-DA model was established for inter-group comparison and differential metabolite screening. Differential metabolites were identified by a VIP value greater than 1 combined with univariate statistical analysis (P < 0.05). The results were visualized using R. The differential metabolites were traced through the MetOrigin platform. Spearman and Pearson correlation analyses were performed in R to examine the relationships between differential microorganisms and metabolites in the feces of wild Hainan macaques.

[0060] Differences were statistically significant at P < 0.05 and highly significant at P < 0.01. Spearman and Pearson correlation analyses were performed using R software to analyze the relationships between different microorganisms and metabolites in the feces of wild Hainan macaques, and the results were plotted.

[0061] Through the above operations, the following can be obtained: high-quality metabolome raw data and annotation information; a list of differential metabolites and their metabolic pathway annotations; a microorganism-metabolite correlation network; potential biomarkers and host-microorganism interaction clues. These results provide a data basis for in-depth understanding of the intestinal metabolic characteristics of wild Hainan macaques and their association with the microbiome.

[0062] Research process and result analysis:

[0063] (1) Abnormal intestinal microbial function caused by psychological stress:

[0064] To study the effects of different psychological stress levels on the functional distribution of intestinal microorganisms in macaques, the present invention performed functional annotation on the non-redundant genes predicted from the fecal microbiota of captive and wild macaques. Functional enrichment analysis showed that the metabolic pathways of the two groups were mainly concentrated in global and overview maps, carbohydrate metabolism, amino acid metabolism, membrane transport, and the metabolism of cofactors and vitamins. Based on the Bray-Curtis distance, the genes of the captive group and the wild group formed different clusters (Adonis test, R 2= 0.08, p = 0.04), indicating a significant difference between the two groups. Changes occurred in the abundance of metabolic pathways based on functional annotation, especially in the pathways with the highest relative abundance, showing obvious differences. High-abundance pathways such as K07133 and K21572 showed higher abundances in the captive group. To further explore the specific functional differences between the two groups, we performed enrichment analysis using the module databases of the captive group and the wild group. The results showed that compared with the captive group, the gut microbiota of the wild group showed higher gene enrichment in 11 pathways, including sphingolipid biosynthesis, globin and globin-like proteins, methane metabolism, O-antigen nucleotide sugar biosynthesis, leucine, aspartate, and glutamate metabolism, cysteine and methionine metabolism, production of various plant secondary metabolites, and amino sugar and nucleotide sugar metabolism. In contrast, the gut microbiota of the captive group showed higher gene enrichment in five pathways, including photosynthesis, phosphate and phosphonate metabolism, butyrate metabolism, triazine degradation, and secondary bile acid biosynthesis. Therefore, captivity significantly affects the metabolic functions of the gut microbiota of macaques, and obvious abundance differences are observed in several key metabolic pathways.

[0065] The psychological stress induced by the captive environment significantly changed the functional structure of the gut microbiota, involving abundance differences in core pathways such as amino acid metabolism and secondary bile acid synthesis, indicating that the microbiota may adapt to the stress brought by captivity by changing its own metabolic mode. However, gene function annotation can only speculate on metabolic potential, and whether the abnormal microbial functions directly lead to changes in the levels of host metabolites still needs to be verified. Therefore, the comprehensive analysis of fecal metabolites was continued by non-targeted metabolomics.

[0066] (2) Differences in metabolite composition under different stress environments:

[0067] The present invention further performed non-targeted metabolomics analysis on fecal samples of captive and wild macaques to study the differences in metabolite composition under different stress environments. A sufficient number of metabolites were analyzed in the samples. The identified metabolites were counted and classified and counted according to the main metabolic pathways (super pathways) they participated in. For lipid metabolites, the counting was further classified by sub-pathways, such as Figure 2 shown, the y-axis represents the category to which the metabolic pathway belongs, and the x-axis represents the number of metabolites. Different colors represent different metabolic pathway classification entries, and the percentage represents the proportion of metabolites in each category relative to the total number of metabolites with pathway information. The results showed that among the fecal metabolites of all macaques, the metabolites produced by amino acid metabolism were the most numerous, with a total of 121, accounting for 22.92% of the total metabolites. There were 98 (18.56%) for the biosynthesis of other secondary metabolites and 74 (14.02%) for lipid metabolism. Statistical analysis of the relative abundances of metabolites in the captive group and the wild group showed that the most abundant metabolites were amino acids, peptides and their analogs, and fatty acyls, such asFigure 3 As shown, the y-axis represents the proportion of metabolites. These findings indicate that amino acid metabolism and the biosynthesis of other secondary metabolites are the most prominent metabolic pathways, which may reflect the characteristics of gut microbial metabolism and host-microbe interactions, providing an important basis for further study of their physiological functions and metabolic characteristics.

[0068] The above metabolomics analysis revealed a significant differentiation in the abundances of amino acids, lipids, and secondary metabolites under stress conditions, indicating that the composition of host metabolites is profoundly affected by abnormal microbial functions. However, it is difficult to locate key differential molecules and their dynamic interaction relationships based on the distribution analysis of major metabolite categories (such as amino acids). Therefore, to deeply analyze the molecular mechanisms of metabolic disorders, the study on the impact of psychological stress on the composition of gut metabolites was continued.

[0069] (3)Psychological stress leads to changes in the composition of gut metabolites:

[0070] The present invention established principal component analysis (PCA) models for wild and captive populations and verified these models through permutation tests. It was found that the two groups of samples clustered tightly and had a high correlation, indicating that the experimental systematic error was extremely small and had good reproducibility. Subsequently, univariate analysis was performed to compare the contents of differential metabolites in the feces of wild and captive populations. The results showed that compared with the wild population, 999 metabolites had significantly increased levels and 959 metabolites had significantly decreased levels in the captive population, as Figure 4 , Figure 5 shown. Figure 4 In, the X-axis represents the comparison between groups; the Y-axis represents the number of differential metabolites, with red indicating significantly upregulated metabolites and green indicating significantly downregulated metabolites; Figure 5 In, the x-axis represents the logarithm-transformed fold change, and the y-axis represents the -logarithm-transformed q-value (p-value). Blue indicates significantly downregulated metabolites, red indicates significantly upregulated metabolites, and gray indicates non-significant metabolites. To further analyze the expression patterns of these differential metabolites, we performed cluster analysis on their expression levels. The results showed that a total of 1,958 differential metabolites in the two groups of fecal samples showed significant inter-group differences in expression levels, and these differential metabolites showed consistent expression patterns in the samples of wild and captive populations, as Figure 6 shown. Each row in the heatmap represents a differential metabolite, with green to red corresponding to low to high expression levels. To more intuitively reveal the synergistic or antagonistic relationships between differential metabolites, for different types of differential metabolites, we constructed a chord diagram of differential metabolite associations based on the Spearman correlation coefficient (|r|>0.8 and p<0.05) between metabolites. The results showed that the expression levels of coumarin and its derivatives and 1,4-dioxane were negatively correlated, while the remaining groups generally showed positive correlations, as Figure 7As shown, the starting point of the inner - circle link represents significantly different metabolites, and the arcs on the outer circle indicate the classification of these metabolites. The colored lines represent the correlations within each metabolite category, with brown indicating positive correlation and green indicating negative correlation. To further explore the differences in highly abundant metabolites between the two groups, 20 metabolites with the largest differences between the two groups were analyzed. The results showed that the highly expressed metabolites in the wild group included HMDB0029218 (urolithin C) and 9.028_315.19611 (15 - deoxy - 12,14 - prostaglandin A2), while the highly expressed metabolites in the captive group included 3.512_206.11780 (2 - piperidinebenzoic acid), PB00581 (angolensin), and HMDB0002085 (sorbic acid).

[0071] A large number of differential metabolites were screened out through the above - mentioned research, but how these molecules participate in stress - related physiological and pathological processes is not yet clear. To analyze their biological significance, the present invention continues with metabolic pathway enrichment analysis.

[0072] (4)Metabolic function changes caused by different stress environments

[0073] The present invention performed metabolic pathway enrichment analysis on the differential metabolites. The results showed that many differential metabolites were enriched in pathways such as dopaminergic synapse, retrograde endocannabinoid signaling, bile secretion, and steroid hormone biosynthesis (p < 0.01, as Figure 8 shown, in the metabolic pathway, the p - value higher than the red auxiliary line is less than 0.01, and the p - value higher than the blue auxiliary line is less than 0.05. The numbers on the bars represent the number of differential metabolites in the corresponding metabolic pathway). To further analyze the overall changes of these differential metabolites in each pathway, we analyzed the top 10 significantly enriched metabolic pathways in the two groups. The results showed that in the alanine, aspartate, and glutamate metabolism, alcohol dependence, amphetamine addiction, and cocaine addiction pathways, all metabolites were significantly up - regulated in group C. On the contrary, in the linoleic acid metabolism and retrograde endocannabinoid signaling pathways, all metabolites were significantly down - regulated in group C. In addition, the overall expression level of metabolites in the dopaminergic synapse pathway was up - regulated, while the metabolites in the steroid hormone biosynthesis, tyrosine metabolism, and bile secretion pathways were down - regulated.

[0074] Finally, to screen out biomarkers with diagnostic value and predictive ability for different stress environments, ROC curve analysis was continued. ROC (Receiver Operating Characteristic) curve analysis is a statistical method used to evaluate the diagnostic performance of differential metabolites in distinguishing different groups (such as disease group vs. healthy group). Its core is to quantify the classification ability of metabolites through sensitivity and specificity, and generate the area under the curve (AUC) as a comprehensive evaluation index. Through ROC curve analysis, differential metabolites can be quickly screened as potential biomarkers and provide a statistical basis for subsequent translational research.

[0075] Finally, ROC analysis was performed on the differential metabolites in the present invention. The results showed that the AUC value of metabolite C10757 was 0.999, as shown in Figure 9. The x-axis represents 1 - specificity, the y-axis represents sensitivity, and the area under the curve is the AUC value, indicating that this metabolite has a high discrimination degree and is suitable as a biomarker for distinguishing wild and captive macaques.

[0076] To reveal the sources and metabolic pathways of differential metabolites, we used the MetOrigin platform to trace the sources of differential metabolites. The results showed that 126 metabolites were annotated to the KEGG and HMDB databases: 4 were from the macaque host, 25 were from gut microbiota, 33 were co-metabolites of microbiota and host, and the remaining 207 metabolites were from other sources. According to the enrichment of metabolic source pathways, there were 5, 24, and 50 enrichment pathways for host, microbiota, and microbiota-host co-metabolism, respectively. Among them, 15 pathways were significantly enriched (log0.05p > 1). The microbial-specific metabolic pathway was polycyclic aromatic hydrocarbon degradation, while the host was significantly enriched only in the steroid hormone biosynthesis pathway. In the host-microbiota co-metabolism pathway, tyrosine metabolism, isoleucine, lanolin, aspartic acid, and glutamic acid metabolism were significantly enriched.

[0077] Summary: In study (1), it was first revealed that psychological stress (captive environment) drives the imbalance of microbe-host interaction by significantly altering the metabolic functions of the gut microbiota in rhesus monkeys (such as abnormal pathways in amino acid metabolism, secondary bile acid synthesis, etc.); on this basis, study (2) verified the actual impact of abnormal microbial functions on host metabolites through non-targeted metabolomics analysis, and found the abundance differences in amino acids, lipids and secondary metabolites (such as coumarins) under stress conditions, providing direct evidence for metabolic disorders; further through study (3), 1,958 differential metabolites (such as urolithin C, sorbic acid) were systematically screened, and a metabolite association network was constructed to reveal the dynamic disruption of the synergistic / antagonistic relationships of metabolites caused by stress; finally, in study (4), through pathway enrichment and ROC analysis, the differential metabolites were mapped to key physiological pathways such as dopaminergic synapses and bile secretion, and high-discrimination markers (such as C10757, AUC = 0.999) were screened, fully elucidating that psychological stress affects host health through the cascade mechanism of "microbial function remodeling → metabolite imbalance → neuroendocrine disorder", providing molecular targets and theoretical basis for diagnosis and intervention.

[0078] The specific embodiments described above further elaborate on the purpose, technical solutions and beneficial effects of the present invention. It should be understood that the above are only specific embodiments of the present invention and are not used to limit the protection scope of the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included in the protection scope of the present invention.

Claims

1. A biomarker C10757 for detecting health status, characterized in that, The health state is triggered by the psychological stress generated by wild Hainan macaques, and the molecular formula of the biomarker C10757 is C 20 H 27 N3O3.

2. Use of biomarker C10757 as described in claim 1 in the preparation of a detection product for the health status triggered by psychological stress generated by wild Hainan macaques.

3. The application according to claim 2, characterized in that The psychological stress generated by the wild Hainan macaques is a stress response triggered by the captive environment.

4. A reagent or kit for determining or screening the expression level of biomarker C10757 as described in claim 1.

5. Use of a reagent for detecting the expression level of biomarker C10757 as described in claim 1 in a biological sample in the preparation of a detection product for determining the health status triggered by psychological stress generated by wild Hainan macaques.

6. A method for detecting the health status triggered by psychological stress generated by wild Hainan macaques using the biomarker C10757 as described in claim 1, characterized in that, Comprising: Obtaining a biological sample of an individual to be tested; Performing pretreatment on the biological sample; Detecting the expression level of biomarker C10757 in the pretreated biological sample; Comparing the result measured in the experiment with a standard database to determine the psychological stress health condition.

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