CN115840050A - Application of reagent for detecting metabolic marker in preparation of sleep disorder screening or diagnosis product - Google Patents

Abstract

The invention provides application of a reagent for detecting a metabolic marker in preparation of a sleep disorder screening or diagnosis product, and relates to the field of biomedicine. The metabolic markers are selected from one or more of triglyceride metabolites, phosphatidylcholine metabolites, cholesterol lipid metabolites, phosphatidylethanolamine metabolites, ceramide metabolites, amino acids, polypeptides and analogues thereof, benzoic acid and derivatives thereof, fatty acid metabolites, fatty acid ester metabolites, steroids and derivatives metabolites thereof, and other metabolites. The invention discloses risk metabolites of sleep disorders and researches the relationship between the risk metabolites and the co-morbidity of the sleep disorders. The risk metabolites can be used as markers for screening or diagnosing sleep disorders, can be used for screening or diagnosing sleep disorders, and particularly has good application prospects for screening or diagnosing sleep disorders affected by different comorbidities.

Description

Application of reagent for detecting metabolic marker in preparation of sleep disorder screening or diagnosis product

Technical Field

The invention relates to the field of biomedicine, in particular to application of a reagent for detecting a metabolic marker in preparation of a sleep disorder screening or diagnosis product.

Background

Sleep is important to humans, about one third of the time a human spends in sleep during their lifetime, sleep being closely related to human health. Firstly, insufficient sleep and sleep disorder can be caused by certain diseases, and certain health problems occur in response to human bodies; meanwhile, insufficient sleep and sleep disorder also have remarkable influence on the cognitive function and mood of people, and anxiety symptoms such as inattention, vigilance reduction, memory decline, slow movement, slow response, visual-visual hallucinations, irritability, mania and the like are easily caused, and even more serious diseases are caused. Investigations have shown that many people suffer from sleep disorders or sleep-related illnesses, with sleep disorders occurring in up to 30% of adults, and more commonly in the elderly.

At present, aiming at the diagnosis of sleep disorder, the most important method is to apply an electroencephalogram multi-lead tracing device to monitor the whole night sleep process; and meanwhile, the sleep quality is evaluated by combining various scales. However, sleep monitoring is expensive and time-consuming, and the sleep quality assessment by various scales is influenced by the subjectivity of the patient, so that the sleep disorder cannot be diagnosed quickly and accurately; people with high risk of suffering from sleep disorder cannot be accurately and effectively screened and prevented.

In addition, sleep disorders in the elderly may be due to age-related comorbidities and increased use of drugs, as well as changes in sleep architecture. Although the close association between sleep disorders, associated comorbidities and aging is well known, the association between them cannot be explained from a molecular level. Therefore, the real cause of the sleep disorder cannot be correctly screened or diagnosed, and thus the sleep disorder cannot be prevented or treated in a targeted manner.

Patent WO2016044338A3 discloses a method and system for diagnosing sleep disorders that discloses a variety of biomarkers that can be used to diagnose sleep disorders, which, while useful to diagnose sleep disorders, do not allow for further diagnosis of the type of sleep disorder, do not allow for the determination of a co-morbidity of the sleep disorder, and find a cause for the sleep disorder. The method finds a marker capable of judging the co-morbidity of the sleep disorders, determines the reason causing the sleep disorders, is used for screening or diagnosing the sleep disorders, and has important significance for pertinently preventing or treating the sleep disorders.

Disclosure of Invention

In order to solve the above problems, the present invention provides a use of a reagent for detecting a metabolic marker in the preparation of a sleep disorder screening or diagnosis product. The invention aims to provide a metabolic marker related to sleep disorder, and the level of the metabolic marker is detected to judge whether a patient suffers from the sleep disorder or not or risks the sleep disorder, and the sleep disorder caused by the metabolic marker, so that a new means is provided for early screening and diagnosis of the sleep disorder.

The invention provides an application of a reagent for detecting a metabolic marker in preparing a sleep disorder screening or diagnosis product;

the metabolic markers are selected from one or more of triglyceride metabolites, phosphatidyl choline metabolites, cholesterol lipid metabolites, phosphatidyl ethanolamine metabolites, ceramide metabolites, amino acids, polypeptides and analogues thereof, benzoic acid and derivatives thereof, fatty acid metabolites, fatty acid ester metabolites, steroids and derivatives metabolites thereof and other metabolites.

Further, the air conditioner is characterized in that,

the triglyceride metabolite is selected from one or more of TG (53), TG (52);

and/or the phosphatidylcholine-type metabolite is selected from one or more of PC (37;

and/or, the cholesterol lipid metabolite is selected from one or more of ChE (22), chE (20), chE (18;

and/or the phosphatidylethanolamine-like metabolite is selected from one or more of PE (42), PE (40;

and/or, the ceramide-like metabolite is selected from one or more of Cer (42), cer (40);

and/or the amino acid, polypeptide and analogue thereof is selected from one or more of L-serine, glutamyl glycine, creatine, diaminocaproate, 2-phenylglycine, L-aspartic acid, guanidinoacetic acid, 3-hydroxy-N6, N6, N6-trimethyl-L-lysine;

and/or the benzoic acid and the derivatives thereof are selected from one or more of salicylic uric acid, hexyl resorcinol, adenylic succinic acid and hydroxybenzoic acid;

and/or, the fatty acid metabolite is selected from one or more of undecanoic acid and tridecanoic acid;

and/or the fatty acid ester metabolite is selected from one or more of stearoyl carnitine, palmitoyl carnitine, linoleic acid carnitine and hydroxybutyryl carnitine;

and/or the steroids and derivatives metabolites are selected from one or more of calcium glycol and pregnenolone;

and/or, the other metabolites are selected from one or more of L-kynurenine, lysoPE (16.

Further, the air conditioner is characterized in that,

one or more of the metabolic markers ChE (20), chE (22), PC (44;

and/or one or more of the metabolic markers PE (42) 10, L-kynurenine, 5-hydroxyindolacetic acid, pregnenolone are metabolic markers for screening or diagnosing the occurrence of age-related sleep disorders;

and/or, one or more of the metabolic markers ChE (18);

and/or one or more of the metabolic markers PC (38), PE (40).

Further, the metabolic marker content is up-or down-regulated in humans.

Further, the reagent is used for enzyme-linked immunosorbent assay;

and/or the reagent is a western blot reagent;

and/or the reagent is used for a protein chip detection method.

Further, the reagent detects metabolite levels by one or more of the following methods: chromatography, spectroscopy, mass spectrometry, chemical analysis.

Further, the sample detected by the reagent is human peripheral blood.

Further, the sample detected by the reagent is blood plasma.

Further, the air conditioner is provided with a fan,

the reagent for detecting one or more of the metabolic markers ChE (20), chE (22), PC (44;

and/or the reagent for detecting one or more of the metabolic markers PE (42);

and/or, the reagent for detecting one or more of the metabolic markers ChE (18);

and/or the reagent for detecting one or more of the metabolic markers PC (38), PE (40.

The invention discloses risk metabolites of sleep disorders and researches the relationship between the risk metabolites and the co-morbidity of the sleep disorders. The risk metabolites can be used as markers for screening or diagnosing sleep disorders, can be used for screening or diagnosing sleep disorders, and particularly has good application prospects for screening or diagnosing sleep disorders affected by different comorbidities.

Obviously, many modifications, substitutions, and variations are possible in light of the above teachings of the invention, without departing from the basic technical spirit of the invention, as defined by the following claims.

The present invention will be described in further detail with reference to the following examples. This should not be understood as limiting the scope of the above-described subject matter of the present invention to the following examples. All the technologies realized based on the above contents of the present invention belong to the scope of the present invention.

Drawings

Figure 1 is the results of the at-risk metabolite screening for sleep disorders: a is PSQI score distribution of 500 persons randomly drawn from the chinese western health and aging trend research cohort (WCHAT) for metabonomic testing; b is the result of drawing a principal component map by using metabonomics data; c is the result of plotting principal component map using lipidomics data; d are 52 metabolites showing significant correlation with sleep quality after logistic regression analysis, 28 hydrophobic lipid metabolites on the left and 24 hydrophilic metabolites on the right, the metabolites are ordered from top to bottom in the graph according to the crude ratio (OR), the width of each metabolite represents the 95% confidence interval, TG represents triglyceride, lysoPE represents lysophosphatidylethanolamine, chE represents cholesterol ester, cer represents ceramide, PC represents phosphatidylcholine, PE represents phosphatidylethanolamine; 5-HIAA represents 5-hydroxyindoleacetic acid; HTMLA represents 3-hydroxy-N6, N6-trimethyl-L-lysine.

Fig. 2 is the results of the correlation between risk metabolites and PSQI components, a network diagram showing the correlation between risk metabolites of sleep disorders and 6 components of PSQI, including subjective sleep quality, sleep duration, sleep latency, habitual sleep efficiency, daytime dysfunction and sleep disorders; spearman correlation coefficients obtained by WGCNA package of R software are expressed AS connecting lines (edges) between PSQI components and metabolite nodes (correlation coefficients >0.1 or <0.1, algorithm AS 89, p-straw 0.05); the edge width is proportional to the correlation strength; the size of the node is proportional to the number of connections to other nodes; the red line segment represents positive correlation and the green line segment represents negative correlation; node colors represent the class of metabolites and the composition of PSQI; the graph is generated in Gephi 0.9.2; chE is cholesterol ester; PC is phosphatidylcholine; PE is phosphatidylethanolamine; lysoPE is lysophosphatidylethanolamine; TG is triglyceride; 5-HIAA is 5-oxindole acetic acid.

Fig. 3 is a predictive model for identifying sleep disorders: a is a coefficient for visualizing each penalty; b and c are the area under the curves (AUC) for the training set (b) and the test set (c); establishing a prediction model through lasso regression analysis of 52 sleep disorder risk metabolites; d and e principal component analysis plots of the external validation set (34 human), with two outliers removed outside the 95% confidence interval; f is the AUC of the outer validation set.

Fig. 4 is a graph of the effect of disease and symptoms on sleep quality assessment: a is the grouping of all participants into four groups based on questionnaire assessment and metabolic model prediction: the first group of PSQI assesses good sleep quality, which is predicted by metabolomics; the second group of PSQI assesses poor sleep quality, metabolomics predicts good sleep quality; the third group of PSQI assesses good sleep quality, metabolomics predicts poor sleep quality; the fourth group of PSQI was assessed as poor sleep quality, which was predicted by metabolomics; b is the difference of the age and the blood routine index among four groups; all y-axis values are scaled to the range of 0 to 1 by "min-max" (min-max) normalization; c is the difference in the number of chronic diseases between four groups; d and e are the differences in epidemiology between metabolomic predictions for hypertension (d) and osteoarthropathy (e). f is the epidemiological difference between metabonomics prediction groups of cognitive dysfunction participants, and the cognitive dysfunction is evaluated by a 10-minute short-term portable psychological condition questionnaire (SPMSQ) combined with culture level; g is the difference in clinical symptoms between four groups. The p-values for blood biochemical tests, diseases and clinical symptoms have been adjusted for age and gender, p <0.05, p <0.01, p <0.001.

FIG. 5 is the analysis of the difference in intensity of dangerous metabolites between groups I and IV: box a plot shows the intensity difference of the risk metabolites between group i (PSQI assessed as good, metabolomics predicted as good) and group iv (PSQI assessed as poor, metabolomics predicted as poor); b is the intensity of HTMLA of groups I-IV, HTMLA is 3-hydroxy-N6, N6, N6-trimethyl-L-lysine.

Fig. 6 is a common risk metabolite between sleep disorders, aging and related comorbidities: correlation of 52 sleep disorder-associated risk metabolites with age and comorbidity; adjusting gender through correlation analysis of metabolites and ages, and respectively analyzing by linear regression logistic regression, wherein the logistic regression is divided into groups with the age of 69 as the median age; the relevance of the 52 sleep-related risk metabolites to chronic diseases, cognitive disorders, anxiety, depression and anxio-depressive disorders is adjusted by age and gender; SD is sleep disorder, CI is cognitive disorder, HP is hypertension, DIA is diabetes, CHD is coronary heart disease, STR is stroke, COPD is chronic obstructive pulmonary disease, ANX is anxiety, DEP is depression, ANX-DEP is anxiety-depressive disorder, GD is gastrointestinal disease, LD is liver disease, KD is kidney disease, OST is osteoarthritis, the number of participants for correlation analysis is shown in parentheses.

FIG. 7 is a diagram of: different ages of sleep risk metabolites and changes in sleep related disorders. a is a boxplot showing the difference in at-risk metabolite intensity in participants under 69 years of age and over 69 years of age. And b-l are box plot diagrams respectively showing the intensity difference of the metabolites at risk between the participants suffering from the diseases such as cognitive disorder, hypertension, diabetes, stroke, chronic obstructive pulmonary disease, depression, anxiety and depression, gastrointestinal diseases, liver diseases, kidney diseases, osteoarthropathy and the like and the participants without the diseases.

Detailed Description

The raw materials and equipment used in the embodiment of the present invention are known products and obtained by purchasing commercially available products.

Example 1 determination of biomarkers for diagnosing sleep disorders

1. Method of producing a composite material

(1) Declaration of Lorentz theory of Helsinki

Study samples were from the baseline of the western health and aging trend (WCHAT) study in china, an ongoing prospective cohort study aimed at assessing health and its contributing factors in the western region of china. The study was registered in the Chinese clinical trial registry (ChiCTR 1800018895) and approved by the ethical Committee of the Wash Hospital, sichuan university (accession number: 2017-445), beginning in 2018. All participants signed written informed consent to participate in the trial according to the declaration of helsinki ethics. The subject information is collected through a face-to-face interview. Pittsburgh Sleep Quality Index (PSQI) comprises 7 aspects (subjective sleep quality, sleep latency, sleep duration, habitual sleep efficiency, sleep disturbance, sleep medication use and daytime dysfunction) to assess sleep quality, with a total score of 21. In the study, PSQI >5.0 was defined to indicate poor sleep quality. A higher proportion of comorbidities is observed in participants with sleep disorders including coronary heart disease, hypertension, chronic Obstructive Pulmonary Disease (COPD), diabetes, gastrointestinal disorders, stroke, liver disorders, kidney disorders, osteoarthritis, cognitive disorders, depression, anxiety and anxiety-depression.

(2) Blood sample collection

When the subjects arrived at the study center in the morning, three tubes of fasting blood were collected by a trained nurse, routine testing of the blood was performed the same day, and two additional tubes of blood were centrifuged at 3500g for 15 minutes within 30 minutes after venipuncture, wherein plasma was collected and stored in an environment at-80 ℃. The entire blood processing procedure is performed under a strictly standardized protocol.

(3) Non-targeted metabolomics analysis

The hydrophilic metabolites were extracted with methanol, and the lipids were liquid-liquid extracted with dichloromethane/methanol (v/v = 2. Adding into ¹³ C ₆ L-lysine salt powders (Silantes) and ¹³ C ₆ ¹⁵ N ₄ l-arginine salt powder (silates) was monitored for the extraction efficiency of hydrophilic metabolites, and the addition of PE (16. Mass spectrometric metabolomics and lipidomics analysis was performed by the university of qinghua metabolomics and lipidomics research center. BEH amide columns (Waters, USA) and BEH C18 columns (Waters, USA) were used for metabolomics analysis in positive and negative ion mode, respectively. CORTECS C18 column (Waters, USA) was used for lipidomics in positive mode. Mix Quality Control (QCs) was added for each 15-20 injections of plasma samples. Based on the internal database, a tracker (Thermo, CA) was used to assign polar metabolites. The database includes 1500 standard MS/MS spectra of various metabolites. Lipid was identified using lipid research (Thermo, CA) software. The following statistical analysis only used lipids with reliable MS/MS.

(4) Data processing and statistical analysis

All retained metabolites were present in at least 80% of the discovery set samples, and the Coefficient of Variation (CV) of the Quality Control (QC) samples was below 30%. The total relative intensities of the metabolites were normalized. The intensity values are log2 transformed to reduce skewness and stabilize variance. Statistical analysis was performed using R or SPSS software version 26 (IBM Corporation, chicago, IL, USA). The classification variables are expressed in counts and percentages. Continuous variables of the normal distribution were investigated using the kolmogorov-smiloff test and the variables of the normal distribution were expressed as mean ± standard deviation. The variables of the non-normal distribution are represented by median and quartile range (IQR). Differences between the two groups were measured using the pearson chi-square test (categorical variable), the independent sample T test (normally distributed continuous variable) and the mannich U test (non-normally distributed continuous variable). The differences between 3 and above groups were examined using isovariance analysis and the kruskal-wallis test. In a prediction model, 433 persons are divided into a training set and a test set according to the proportion of 8.

2. Results

(1) Clinical features of sleep disorder in WCHAT cohort

6887 WCHAT cohort participants contained complete Pittsburgh Sleep Quality Index (PSQI) information. They ranged in age from 50 to 95 years with an average age of 62.43 ± 8.27 years. Statistical analysis showed that sleep disturbance was present in 47.2% of 6887 participants, with a PSQI score above 5.0, indicating that sleep disturbance was very common in the elderly. In addition, there is a high proportion of complications in participants with sleep disorders, including coronary artery disease, hypertension, chronic obstructive pulmonary disease, diabetes, gastrointestinal disorders, stroke, liver disease, renal disease, osteoarthritis, cognitive disorders, depression, anxiety and anxiety-depression.

(2) Identifying at-risk metabolites of sleep disorders

To reveal the relationship between sleep disorders and metabolic disorders, 500 Han old people were randomly drawn as an initial discovery set for metabolic analysis. The PSQI score distribution of these 500 people is consistent with the WCHAT cohort distribution (fig. 1 a), indicating that this initial discovery set is well representative. After removing 51 outliers in 16 dosing participants and Principal Component Analysis (PCA) (fig. 1b-1 c), the metabolic data of 433 participants was used to screen metabolites and to establish a risk prediction model for sleep disorders. Metabolites significantly correlated with PSQI scores were determined using Logistic regression analysis, and the abundance of all metabolites was log2 transformed and Z-score normalized. As a result, 52 metabolites (including 28 hydrophobic and 24 hydrophilic metabolites) were significantly correlated with sleep quality (p < 0.05) by using the glm function in R (fig. 1 d). Among the hydrophobic metabolites, triglycerides (TGs) are risk factors for sleep disorders, while Phosphatidylcholine (PC), cholesterol esters (ChEs), phosphatidylethanolamine (PEs) and ceramides (Cers) are protective factors for sleep disorders (fig. 1d left). For hydrophilic metabolites, some such as L-kynurenine, 5-hydroxyindoleacetic acid, calcium diol, stearoylcarnitine increased the risk of sleep disorders, others such as creatine, L-serine, undecanoic acid protected participants from sleep disorders (FIG. 1d right).

Next, it was investigated how 52 risk metabolites obtained by logistic regression analysis affect sleep quality. The relationship between at-risk metabolites and the respective areas of PSQI except drug treatment was analyzed by Spearman correlation analysis in the WGCNA package (algorithm AS 89, p-straw 0.05, correlation coefficient >0.1 or < -0.1) (FIG. 2), respectively. Studies have found that more at-risk metabolites are associated with subjective sleep quality, sleep delay (sleep latency) and daytime dysfunction. Specifically, metabolites of TG (53); TG (52), PC (36), PC (37), PC (44; metabolites TG (50). Among these, some risk metabolites such as salicylic acid, tridecanoic acid, undecanoic acid, lysoPE (16. The above classification section reveals a role for metabolites in sleep regulation.

(3) Predictive model for sleep disorders

To establish a metabolic model for diagnosing sleep disorder participants, ridge regression analysis was performed using R middle glmnet encompassing 52 risk metabolites. 443 samples were randomly divided into training and testing sets in a ratio of 8. The quality of the model was trained by 10-fold cross validation to optimize the penalty parameters (fig. 3 a), and the model quality was assessed by calculating the sensitivity, specificity and area under the receiver operating characteristic curve (ROC). The area under the curve (AUC) of the training set was 72.24% (95% ci. 32 of 34 participants were used as external validation set after removing outliers (fig. 3d-3 e), with AUC statistic of 75.42% (95% ci. Cronbach's alpha is a statistical indicator commonly used to measure confidence. The accuracy of the metabolic model prediction was found to be consistent (0.70 to 0.83) with the Cronbach' salpha value of PSQI 37. It is demonstrated that the metabolites of the present invention can be used to construct predictive models of sleep disorders.

(4) Effect of comorbidities and symptoms on sleep quality assessment

To assess the effect of co-morbidity on sleep quality, participants were divided into four groups. Group I (n = 133), evaluated as good by questionnaire, predicted as good by the model; group II (n = 73), evaluated as poor, predicted as good; group III (n = 71), evaluated good, predicted poor; group IV (n = 156), evaluated as difference, predicted as difference (fig. 4 a).

Statistical analysis showed that participants who were assessed as having poor sleep but predicted to have good sleep (group II) were much younger in age (fig. 4 b) and had much fewer chronic diseases (fig. 4 c). Whereas participants who were evaluated as good but predicted to be sleepy (group III) had higher TG and VLDL, lower HDL, indicating that sleep quality is closely related to plasma lipid profile. In addition, they suffered from more types of complications (fig. 4 c). Among the associated comorbidities, subjects predicted to be good sleepers had much lower prevalence of hypertension, osteoarthrosis, and cognitive dysfunction (fig. 4d-4 f), suggesting that comorbidities may significantly affect metabolic balance and the prediction accuracy of the model. Next, all participants were examined for clinical symptoms and it was found that the sleeper who was evaluated as poor but predicted to be good (group II) had more headaches, palpitations, chest tightness, precordial pain, diarrhea, bitterness, dry eyes, back pain and toothache, indicating that mild symptoms may affect questionnaire-based assessments, but had less impact on sleep disorder risk metabolite balance (fig. 4 g).

Furthermore, comparing the abundance of 52 risk metabolites in group I and group IV (fig. 5 a), it was found that most of the metabolites except 3-hydroxy-N6, N6-trimethyl-L-lysine (HTMLA) were significantly different between the two groups. Interestingly, those participants who were assessed as poor but predicted to be poor had much lower levels of HTMLA (supplementary fig. 5 b). Overall, studies reveal the effects of aging, co-morbidities and clinical symptoms on metabolic balance and sleep quality assessment.

(5) Metabolic marker study of sleep disorders with aging and related comorbidities

To establish a direct link between sleep disorders, associated complications and aging, fusion analysis was next performed on 52 at-risk metabolites of sleep disorders (fig. 6 and 7). Research has found that the risk metabolites ChE (20), chE (22.

At the same time, many of the at-risk metabolites that are closely related to sleep disorders have also been shown to be closely related to aging and/or certain related complications.

It was found that changes in 4 metabolites among the 52 sleep disorder risk metabolites, including PE (42. These metabolic markers are useful for screening or diagnosing aging-related sleep disorders.

Among the associated comorbidities, cognitive disorders, hypertension and diabetes have the most common risk metabolites with sleep disorders. Among these, the metabolites ChE (18), PC (33), PC (34.

The metabolites PC (38.

The invention discloses risk metabolites of sleep disorders and researches the relationship between the risk metabolites and the co-morbidity of the sleep disorders. The risk metabolites can be used as markers for screening or diagnosing sleep disorders, can be used for screening or diagnosing sleep disorders, and particularly has good application prospects for screening or diagnosing sleep disorders affected by different comorbidities.

Claims (9)

1. The use of a reagent for detecting a metabolic marker for the manufacture of a product for screening or diagnosing sleep disorders;

the metabolic markers are selected from one or more of triglyceride metabolites, phosphatidyl choline metabolites, cholesterol lipid metabolites, phosphatidyl ethanolamine metabolites, ceramide metabolites, amino acids, polypeptides and analogues thereof, benzoic acid and derivatives thereof, fatty acid metabolites, fatty acid ester metabolites, steroids and derivatives metabolites thereof and other metabolites.

2. Use according to claim 1, characterized in that:

the triglyceride metabolite is selected from one or more of TG (53), TG (52);

and/or the phosphatidylcholine-type metabolite is selected from one or more of PC (37;

and/or, the cholesterol lipid metabolite is selected from one or more of ChE (22), chE (20), chE (18;

and/or the phosphatidylethanolamine-like metabolite is selected from one or more of PE (42), PE (40;

and/or, the ceramide-like metabolite is selected from one or more of Cer (42), cer (40);

and/or the amino acid, polypeptide and analogue thereof is selected from one or more of L-serine, glutamyl glycine, creatine, diaminocaproate, 2-phenylglycine, L-aspartic acid, guanidinoacetic acid, 3-hydroxy-N6, N6, N6-trimethyl-L-lysine;

and/or the benzoic acid and the derivatives thereof are selected from one or more of salicylic uric acid, hexyl resorcinol, adenylic succinic acid and hydroxybenzoic acid;

and/or, the fatty acid metabolite is selected from one or more of undecanoic acid and tridecanoic acid;

and/or the fatty acid ester metabolite is selected from one or more of stearoyl carnitine, palmitoyl carnitine, linoleic acid carnitine and hydroxybutyryl carnitine;

and/or the steroids and derivatives metabolites are selected from one or more of calcium glycol and pregnenolone;

and/or, the other metabolites are selected from one or more of L-kynurenine, lysoPE (16.

3. Use according to claim 1, characterized in that: one or more of the metabolic markers ChE (20), chE (22), PC (44;

and/or one or more of the metabolic markers PE (42) 10, L-kynurenine, 5-hydroxyindolacetic acid, pregnenolone are metabolic markers for screening or diagnosing the occurrence of age-related sleep disorders;

and/or one or more of the metabolic markers ChE (18), PC (33;

and/or one or more of the metabolic markers PC (38), PE (40).

4. Use according to any one of claims 1 to 3, characterized in that: the metabolic marker content is up-or down-regulated in humans.

5. Use according to any one of claims 1 to 3, characterized in that: the reagent is used for enzyme-linked immunosorbent assay;

and/or the reagent is a western blot reagent;

and/or the reagent is used for a protein chip detection method.

6. Use according to any one of claims 1 to 3, characterized in that: the reagent detects metabolite levels by one or more of the following methods: chromatography, spectroscopy, mass spectrometry, chemical analysis.

7. Use according to any one of claims 1 to 4, characterized in that: the sample detected by the reagent is human peripheral blood.

8. Use according to claim 7, characterized in that: the sample detected by the reagent is plasma.

9. Use according to claim 1, characterized in that: the reagent for detecting one or more of the metabolic markers ChE (20), chE (22), PC (44;

and/or the reagent for detecting one or more of the metabolic markers PE (42);

and/or, the reagent for detecting one or more of the metabolic markers ChE (18);

and/or, the reagent for detecting one or more of the metabolic markers PC (38), PE (40).

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