Post-stroke depression biomarker and application thereof

Abstract

The invention relates to the technical field of biological medicine, and discloses a post-stroke depression biomarker and application thereof, the post-stroke depression specific biomarker is analyzed by comparing differential metabolites of depression and PSD, and the post-stroke depression specific biomarker comprises stearic acid (Stearic Acid), oleic acid (Oleic Acid) and heptadecanoic acid (Heptadecanoic Acid); data verification shows that stearic acid, oleic acid and heptadecanoic acid have extremely high detection sensitivity and can be used for diagnosis of post-stroke depression and differential diagnosis of post-stroke depression and depression, so that a new thought and method can be provided for early diagnosis and treatment of PSD. The method has great popularization and application values.

Description

Technical Field

[0001] The present invention relates to the field of biomedicine technology, and in particular to biomarkers of post-stroke depression and applications thereof. Background Art

[0002] The statements in this section merely provide background information related to the present disclosure and may not constitute prior art.

[0003] About 33% of stroke patients develop secondary depression, or PSD, one year later. Currently, there is a lack of drugs that specifically target the pathophysiology of PSD. Drug treatment is still mainly based on antidepressants such as selective serotonin reuptake inhibitors (SRRIs), serotonin and noradrenaline reuptake inhibitors (SNRIs), and noradrenaline reuptake inhibitors (NRIs). Although they can alleviate the depressive symptoms of PSD patients to a certain extent, they are ineffective for the patient's motor function, cognitive function and other symptoms, and may cause adverse reactions such as bleeding. The pathophysiology of PSD is extremely complex, superimposed with various neurobiological dysfunctions induced by stroke in the context of social and psychological distress. Therefore, finding biomarkers and drug targets that distinguish PSD from depression is of great significance for the early differential diagnosis and drug development of PSD.

[0004] Metabolic profile analysis is used to study complex diseases with multifactorial pathology. It can reflect the life activities that are taking place at a certain moment, and more accurately and directly reflect the metabolic characteristics of organisms under physiological and pathological conditions. It is the type of omics that is closest to biological phenotypes. Metabolomics research can be used to discover metabolites associated with specific diseases, and used as biomarkers for early diagnosis and prediction of diseases and the formulation of individualized medication strategies; further, by studying the interaction between drugs and specific metabolites, new drug targets may be discovered. Cerebrospinal fluid (CSF) is in direct contact with the central nervous system, and its metabolites can reflect metabolic changes associated with the presence and progression of PSD. Therefore, compared with other biological samples, CSF has significant advantages in the study of central nervous system diseases. The present invention aims to reveal specific differential metabolites in the pathological state of PSD and depression, and provide specific biomarkers and potential drug targets for the early differential diagnosis of PSD and depression. Summary of the invention

[0005] The purpose of the present invention is to provide biomarkers for post-stroke depression and their applications in response to the current difficulty in early differential diagnosis of PSD, to provide specific biomarkers and possible drug targets that can identify PSD at an early stage, and to apply them to the preparation of diagnostic kits or therapeutic drugs, which have great value.

[0006] The technical solution of the present invention is as follows:

[0007] In one aspect, the present invention provides a biomarker for post-stroke depression, comprising a combination of stearic acid, oleic acid and margaric acid.

[0008] Preferably, the stroke is ischemic stroke.

[0009] Another aspect of the present invention provides the use of any one or more of the above-mentioned biomarkers of post-stroke depression in the preparation of a product for detecting, predicting, diagnosing or identifying post-stroke depression.

[0010] According to a preferred embodiment, the diagnosis is to determine whether the subject suffers from post-stroke depression based on the expression level of the biomarker in the biological sample of the subject.

[0011] According to a preferred embodiment, the detection, prediction, diagnosis or identification product is a reagent, a chip or a kit.

[0012] According to a preferred embodiment, the determination based on the expression level of the biomarker in the biological sample of the diagnosis object is: determination based on the increase in the expression level of the biomarker in the biological sample of the diagnosis object.

[0013] According to a preferred embodiment, the biological sample of the diagnostic subject is collected from cerebrospinal fluid.

[0014] According to a preferred embodiment, the one or more are:

[0015] Testing stearic acid, oleic acid or margaric acid individually; or,

[0016] Detect any two-by-two combinations of stearic acid, oleic acid and margaric acid; or,

[0017] Three biomarkers, stearic acid, oleic acid and heptadecanoic acid, were detected simultaneously.

[0018] According to a preferred embodiment, a substance capable of detecting any one or more of the biomarkers of post-stroke depression as described above is used in the preparation of a product for detecting, predicting, diagnosing or identifying post-stroke depression.

[0019] Another aspect of the present invention provides the use of any one or more of the above-mentioned biomarkers of post-stroke depression in the preparation of drugs for preventing, improving and treating post-stroke depression.

[0020] Compared with the prior art, the present invention has the following beneficial effects:

[0021] 1. The present invention provides specific biomarkers for early cerebrospinal fluid of PSD, including a combination of stearic acid, oleic acid and heptadecanoic acid, which provides a possible way for early detection and diagnosis of PSD. Compared with existing PSD blood metabolites, cerebrospinal fluid metabolites have stronger specificity and representativeness;

[0022] 2. The present invention also provides the use of specific biomarkers of cerebrospinal fluid in the early stage of PSD in the preparation of PSD detection products, which provides new ideas and methods for the early detection and treatment of PSD;

[0023] 3. The present invention also provides the use of specific biomarkers of cerebrospinal fluid in the early stage of PSD in the preparation of drugs for improving PSD, providing new ideas for the treatment and improvement of PSD. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 The results of the sugar water preference experiment in the fourth week of the PSD rat modeling of the present invention;

[0025] Figure 2 The results of the open field experiment in the fourth week of PSD rat modeling of the present invention;

[0026] Figure 3 The results of the weight changes of the PSD rats in the fourth week of the modeling of the present invention;

[0027] Figure 4 The results of the variants of hippocampal neurotransmitter levels in the fourth week of PSD rat modeling of the present invention;

[0028] Figure 5 PCA score diagrams of positive (a) and negative (b) ion modes of CSF samples of PSD rats in the fourth week of modeling;

[0029] Figure 6 The positive (a) and negative (b) PLS-DA model score diagrams of CSF samples of PSD rats in the fourth week of modeling;

[0030] Figure 7 The positive (a) and negative (b) OPLS-DA model score graphs and the positive (c) and negative (d) 200 permutation test graphs of CSF samples of PSD rats in the fourth week of modeling;

[0031] Figure 8 Heat map of CSF differential metabolites in depression group and PSD group in the fourth week of modeling and their repeated biomarkers related to mental illness.

[0032] Fig. 9The expression abundance (A) and area under the ROC curve (B) of three repeated biomarkers related to mental illness in CSF differential metabolites in the depression group and PSD group in the fourth week of modeling;

[0033] Fig.10 The results of the analysis of the three repeated biomarkers related to mental illness in pairs (a), (b), (c) and in full combination (D);

[0034] Fig.11 is the optimal indicator threshold for the three replicate biomarkers. DETAILED DESCRIPTION

[0035] The specific embodiments listed in the present invention are only examples of the present invention, and the present invention is not limited to the specific embodiments described below. For those skilled in the art, any equivalent modifications and substitutions to the embodiments described below are also within the scope of the present invention. Therefore, the equal transformations and modifications made without departing from the spirit and scope of the present invention should be included in the scope of the present invention. If the specific conditions are not specified in the embodiments, they are carried out according to the conventional conditions or the conditions recommended by the manufacturer. If the manufacturers are not specified for all reagents or instruments, they are all conventional products that can be purchased commercially. In order to better illustrate the present invention, numerous specific details are given in the specific embodiments below. It should be understood by those skilled in the art that the present invention can also be implemented without certain specific details. In other embodiments, the methods, means, equipment and steps well known to those skilled in the art are not described in detail in order to highlight the purport of the present invention.

[0036] Unless otherwise defined, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art. Unless otherwise specified, the units used in this specification are all international standard units, and the numerical values ​​and numerical ranges appearing in the present invention should be understood to include the inevitable systematic errors in industrial production.

[0037] The features and performance of the present invention are further described in detail below in conjunction with the embodiments.

[0038] Example 1

[0039] 1. Experimental Materials

[0040] 1. Experimental Animals

[0041] Male SD rats, SPF grade, weighing 250-300 g. Provided by Hunan Slake Jingda Experimental Animal Co., Ltd., production license number: SCXK (Xiang) 2019-0004, breeding conditions: room temperature 20-26 ° C, relative humidity about 50%.

[0042] 2. Reagents

[0043] Citalopram (H. Lundbeck A / S, CN-107-22-10-13005).

[0044] 2. Experimental Methods

[0045] 1. Establishment of rat tMCAO model

[0046] Male SD rats weighing 280-300 g were selected, and the tMCAO model was established by the suture method. The rats' middle cerebral artery was blocked, and the suture was removed 2 hours after ischemia for reperfusion. The degree of neurological deficit in the rats was evaluated with reference to the Longa 5 scoring method. Rats with a score of 2-3 were selected for subsequent experiments. The same surgical procedure was used, but only the common carotid artery and vagus nerve were separated as sham-operated rats.

[0047] Table 1 Scoring criteria for neurological deficit

[0048]

[0049] 2. Replication of rat PSD model

[0050] The depression model was established 7 days after the rat tMCAO surgery. The specific method was as follows: based on the cerebral ischemia model, the rats were placed in a rat fixator for 6 hours every day, and then kept in a single cage for 28 consecutive days to establish the rat PSD model.

[0051] 3. Replication of depression model

[0052] The sham-operated rats were placed in a rat holder for 6 hours every day and then kept in a single cage for 28 consecutive days, which was the depression group. Another batch of sham-operated rats were kept in a cage with 4 rats. They were free to move when the rats in the PSD and depression groups were restrained, but they were deprived of water and food, which was used as the control group.

[0053] 4. Animal grouping and drug administration

[0054] At the beginning of the depression model, rats in the PSD group were divided into a PSD group and a treatment group by random block design according to the Longa 5 score and body weight. The treatment group was given citalopram (PSD+Cit. group), and the control group, PSD group, and depression group were given equal volumes of solvent. There were 6-10 animals in each group.

[0055] 5. Behavioral testing

[0056] In the fourth week after solitary confinement, the depressive-like behavior and body weight changes of the rats were measured to determine whether the depression model was successful.

[0057] 5.1 Sugar water preference experiment

[0058] The preference for sugar water experiment includes the following two parts: Adaptation training part - first put two bottles of 1% (w / v) sucrose solution on both sides of each frame, and replace one of the bottles with pure water after 24 hours. After 24 hours, the positions of the sucrose water bottle and the pure water bottle are swapped every 6 hours to avoid the interference of the water bottle position on the experiment. Then the rats are deprived of water and food for 24 hours. Test part - put two bottles of water measured in advance on both sides of each frame: one bottle is 1% (w / v) sucrose solution, and the other bottle is pure water. Fasting, 2 hours (the position of the bottle is swapped once every 1 hour). During this period, all rats are kept in separate cages and can freely use two bottles, one containing 200mL 1% sucrose solution and the other containing 200mL water. After the end, take away the two bottles and weigh them, and record the sugar water and pure water consumption data of each rat. Sugar water preference index calculation formula: Sugar water preference index % = sugar water consumption / (sugar water consumption + pure water consumption) × 100%.

[0059] The sugar water preference test is a test indicator for reflecting rodent anhedonia. Anhedonia refers to the loss of interest in reward stimulation in animals and is one of the core symptoms of depressive disorders. The results suggest that after 4 weeks of stress stimulation, Figure 1 As shown, compared with the control group, the sugar water preference rate of rats in the PSD group and depression group was reduced (P < 0.01, P < 0.001), indicating that the animals suffered from anhedonia; compared with PSD, the sugar water preference rate of rats in the treatment group was increased, and the symptoms of PSD were alleviated.

[0060] 5.2 Open field experiment

[0061] The open field test evaluates the autonomous and exploratory behavior of rats in an unfamiliar environment. The reduction in motor activity levels indicates the occurrence of anxiety-like behaviors associated with depression. The experiment was conducted in a quiet environment. The rats were placed in the center of the bottom of the behavioral experimental box, and the time was set for 5 minutes and video was taken. The motor activity of the rats per unit time (movement distance, average movement speed, number of grids crossed) was set on the computer software.

[0062] After 4 weeks of stress stimulation, the total movement distance of rats in the depression group and PSD group was reduced compared with that in the control group ( Figure 2 A) The average speed of movement decreases ( Figure 2 B) (P<0.05), the number of grid crossings decreased ( Figure 2 C). The above results suggest that the lack of interest in the surroundings caused the decreased activity in the depression group and PSD group. Decreased activity is one of the core symptoms of depressive disorders, suggesting that animals have depressive-like behaviors. Compared with PSD, the above indicators of the treatment group rats did not improve significantly, which is consistent with the clinical fact that SSRIs cannot improve the motor function of PSD patients.

[0063] 5.3 Weight changes

[0064] Weight loss is one of the physical symptoms of depressive disorders. After 4 weeks of stress stimulation after surgery, the body weight of rats in the PSD group and depression group was ( Figure 3 A) and weight gain ( Figure 3 B) decreased significantly (P<0.001, P<0.001); compared with PSD, the weight gain of rats in the treatment group was improved to a certain extent.

[0065] 6. Determination of 5-HT and NE levels in hippocampus of PSD rats

[0066] Four weeks after modeling, rats were anesthetized by intraperitoneal injection of chloral hydrate, and the brain was removed after cardiac perfusion with 0.9% saline, and the hippocampal tissue was separated. The tissue was cut into pieces after weighing, and pre-cooled PBS (0.01M, pH=7.4) (weight-to-volume ratio of 1:9) was added for ultrasonic disruption, and the homogenate was centrifuged at 5000×g for 5-10 minutes, and the supernatant was tested. The ELISA test kit was placed at room temperature for 30 minutes, and the 5-HT and NE contents were tested according to the instructions.

[0067] Results: Compared with the control group, the 5-HT( Figure 4 A) NE Figure 4 B) The content of neurotransmitters was significantly decreased (P<0.05 or P<0.01). Compared with the PSD group, the 5-HT ( Figure 4 A) NE Figure 4 B) Neurotransmitter content increased significantly (P < 0.05 or P < 0.01). Currently, the generally accepted monoamine neurotransmitter hypothesis holds that depression is caused by a relative or absolute deficiency of monoamine neurotransmitters such as 5-HT and NE in the brain. The above changes in neurotransmitters support the depressive phenotype of the model animals.

[0068] Example 2

[0069] 1. CSF metabolomics testing

[0070] 1.1 Preparation of CSF samples

[0071] CSF was extracted from the rats with PSD and depression models successfully established in Example 1 and placed in a centrifuge tube. After ice bathing for 30 minutes, the tube was centrifuged at 3500g for 15 minutes at 4°C. 100 μL of the supernatant was taken, 100 μL of pre-cooled water and 800 μL of pre-cooled methanol / acetonitrile (1:1, v / v) were added, and ultrasonicated for 1 hour in an ice bath. After standing at -20°C for 2 hours, the tube was centrifuged at 16000g for 20 minutes at 4°C, the supernatant was taken, and evaporated to dryness in a high-speed vacuum concentrator centrifuge. 100 μL of methanol-water solution (1:1, v / v) was added for re-dissolution, and the supernatant was taken for mass spectrometry analysis.

[0072] 1.2 UPLC-MS / MS analysis

[0073] Metabolomics testing was commissioned to Shanghai Baipu Biotechnology. Each sample was detected by electrospray ionization (ESI) in positive (+) and negative (-) modes. After UPLC separation, the samples were analyzed by mass spectrometry using a QE Plus mass spectrometer (ThermoScientific), using a HESI source for ionization under the following conditions: Spray Voltage: 3.8 kv (+) and 3.2 kv (-); Capillary Temperature: 320 (±); Sheath Gas: 30 (±); Aux Gas: 5 (±); Probe Heater Temp: 350 (±); S-Lens RF Level: 50.

[0074] Mass spectrometry acquisition time: 15min. Precursor ion scanning range: 70-1050m / z, primary mass spectrometry resolution: 70,000@m / z 200, AGC target: 3e6, primary Maximum IT: 100ms. Secondary mass spectrometry analysis was acquired according to the following method: after each full scan, trigger the acquisition of the secondary mass spectra (MS2 scan) of the 10 highest intensity precursor ions, secondary mass spectrometry resolution: 17,500@m / z 200, AGC target: 1e5, secondary Maximum IT: 50ms, MS2 Activation Type: HCD, Isolation window: 2m / z, Normalized collision energy (Setpped): 20, 30, 40.

[0075] 2. Data preprocessing and screening

[0076] The raw data were aligned, corrected for retention time, and extracted for peak area using MSDIAL software. Metabolite structures were identified by accurate mass matching (mass tolerance < 20 ppm) and secondary spectrum matching (mass tolerance < 0.02 Da), searching public databases such as HMDB 5.0, MassBank 2.2.7, GNPS, and the self-built Baipu metabolite standard database (BP-DB). For the extracted data, ion peaks with missing values ​​> 50% within the group were deleted and not involved in subsequent statistical analysis; the total peak area of ​​positive and negative ion data was normalized, and the positive and negative ion peaks were integrated and pattern recognition was performed using Python3.6 software. The data was preprocessed by Unit variance scaling (UV) before subsequent data analysis.

[0077] 3. Data statistical processing

[0078] R4.0.3 software package was used for all multivariate data analysis and modeling, and principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA) and orthogonal partial least squares discriminant analysis (OPLS-DA) of the overall sample were performed. Differential metabolites were obtained using the statistically significant variable projection importance value (VIP) threshold obtained from the OPLS-DA model and the two-tailed Student's t test (P value) of the original data normalized by the univariate analysis level. P values ​​were calculated by one-way analysis of variance for multi-group analysis. Metabolites with VIP>1.5 and P<0.05 were considered statistically significant biomarkers.

[0079] 4. Metabolomics analysis results

[0080] 4.1 Pattern recognition of differential metabolic products in rat CSF

[0081] 4.1.1 PCA Results

[0082] MSDIAL software was used to extract metabolite ion peaks. The ion peaks extracted from all experimental samples and all QC samples were subjected to PCA analysis after UV treatment. The PCA model score diagram obtained after 7 cycles of interactive verification ( Figure 5 ), proving that the positive ion mode (R 2 X=0.56,Q 2 =0.811) and negative ion mode (R 2 X=0.516,Q 2 =0.398) metabolites in the depression group (C4) and PSD group (D4) were closely clustered, indicating that the intra-group sample variation was small and the experimental repeatability was good; while the depression group and PSD group were significantly separated, indicating that the metabolite variation between the two groups was large and the compound information was significantly different. In the fourth week of modeling, the depression group and PSD group were the C4 and D4 samples of all samples, respectively.

[0083] 4.1.2PLS-DA Results

[0084] PLS-DA uses partial least squares regression to establish a relationship model between metabolite expression and sample category to predict sample category. Figure 6 ), which proves that in positive ion mode (R 2 X=0.349,Q 2 =0.678) and in negative ion mode (R 2 X=0.359,Q 2=0.948) There was a significant trend of classification between the depression group and the PSD group. 2 If it is greater than 0.5, it indicates that the prediction ability of this model is good.

[0085] 4.1.3 OPLS-DA results

[0086] OPLS-DA is a supervised discriminant analysis statistical method. It uses partial least squares regression to construct a relationship model between metabolite expression and sample category. It takes into account group information while achieving dimensionality reduction, which can better distinguish differences between groups, improve the effectiveness and analytical ability of the model, and achieve prediction of sample category. OPLS-DA models for each comparison group were established. The model score graph and permutation test graph are shown in Figure 7 The horizontal axis in the figure represents the correlation between the random grouping Y and the original grouping Y, and the vertical axis represents R 2 and Q 2 Q 2 The intercept of the regression line is less than 0, or the Q of 200 permutation tests is 2 The scores are lower than the original model Q 2 The score that satisfies any of the two represents that the model is very reliable, indicating that the OPLS-DA model established by the experimental data is not overfitted.

[0087] 4.2 Screening of PSD biomarkers in CSF

[0088] Based on the P value of the independent sample t-test (P<0.05) of the MAD normalization method, there were 12 differential variables in the positive ion mode and 91 differential variables in the negative ion mode among the CSF differential metabolites between the depression group and the PSD group. Based on the VIP value of the OPLS-DA model (VIP>1.5) and the p value of the independent sample t-test (P<0.05), there were 5 differential variables in the positive ion mode and 71 differential variables in the negative ion mode. Among them, 17 metabolites were downregulated in PSD rats and 12 metabolites were upregulated (Table 1). The heat map showed a clear separation of 29 biomarkers between the depression group and the PSD group. The above 29 differential metabolites were intersected with the endogenous differential metabolites in the blood related to human mental illness screened out in the HMDB database, and 3 repeated differential metabolites Stearic Acid, Oleic Acid, Heptadecanoic Acid ( Figure 8 The present invention firstly found that compared with depression, the contents of stearic acid, oleic acid and heptadecanoic acid in the cerebrospinal fluid (CSF) of PSD rats were significantly increased.

[0089] Table 1 Differential metabolites between PSD group and depression group

[0090]

[0091]

[0092] Example 3 Evaluation of biomarker prediction performance

[0093] 1. ROC analysis

[0094] The predictive performance of three differential metabolites, stearic acid, oleic acid, and heptadecanoic acid, was verified by receiver operating characteristic (ROC) curve to evaluate the accuracy of the diagnostic test.

[0095] SPSS statistical software was used to draw ROC curves with sensitivity (Sensitivity) or true positive rate (TPR) as the ordinate and specificity (Specificity) or false positive rate (FPR) as the abscissa. TPR counts the proportion of correct predictions in all positive samples, and FPR counts the proportion of incorrect predictions in all negative samples, reflecting the relationship between sensitivity and specificity. The area under the ROC curve (AUC) can intuitively evaluate the prediction accuracy of the model. The larger the AUC value, the higher the prediction accuracy. The ROC of stearic acid, oleic acid, and heptadecanoic acid was 1.00, indicating that the three biomarkers screened had strong differential diagnostic ability for PSD and depression. In order to further improve the diagnostic ability, logistic regression was used to combine several different variables into a multivariate parameter for the three markers. The results showed that when the three markers were used in combination, the AUC of two or three markers was 1.00, which can further improve the differential diagnostic ability compared with a single biomarker ( Fig. 9 , Fig.10 ).

[0096] 2. Optimal Threshold Point (Cutoff) Selection

[0097] The optimal threshold point has both high TPR and low FPR, with the least classification error, and this threshold can be used as the decision threshold for classification. The optimal threshold point was determined by Youden's index (Youden's index = TPR-FPR). The results showed that the optimal threshold point for stearic acid was 383021400.4, the optimal threshold point for oleic acid was 7857157.77, and the optimal threshold point for heptadecanoic acid was 3841231.005; the sensitivity of the three biomarkers was ≈100%, and the specificity was ≈1-0 = 100% ( Fig.11 ), can be used as the optimal diagnostic threshold to improve the sensitivity and specificity of diagnosis.

[0098] The above-mentioned embodiments only express the specific implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the protection scope of the present application. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the technical solution concept of the present application, and these all belong to the protection scope of the present application.

Claims

1. A biomarker for post-stroke depression, characterized in that: Includes stearic acid, oleic acid and margaric acid alone or in combination.

2. The biomarker for post-stroke depression according to claim 1, characterized in that The stroke is ischemic stroke.

3. Use of any one or more post-stroke depression biomarkers as described in claim 1 in the preparation of post-stroke depression detection, prediction, diagnosis or identification products.

4. The use according to claim 3, characterized in that: The diagnosis is to determine whether the subject suffers from post-stroke depression based on the expression level of the biomarker in the biological sample of the subject.

5. The use according to claim 3, characterized in that: The detection, prediction, diagnosis or identification product is a reagent, a chip or a kit.

6. The use according to claim 3, characterized in that: The biological sample of the diagnostic subject can be collected from cerebrospinal fluid or blood.

7. The use according to claim 3, characterized in that: The determining according to the expression level of the biomarker in the biological sample of the diagnostic object is: determining according to the increase in the expression level of the biomarker in the biological sample of the diagnostic object.

8. The use according to claim 3, characterized in that: The one or more are: Testing stearic acid, oleic acid or margaric acid individually; or, Detect any two-by-two combinations of stearic acid, oleic acid and margaric acid; or, Three biomarkers, stearic acid, oleic acid and heptadecanoic acid, were detected simultaneously.

9. Use of a substance capable of detecting any one or more biomarkers of post-stroke depression as claimed in claim 1 in the preparation of a product for detecting, predicting, diagnosing or identifying post-stroke depression.

10. Use of any one or more biomarkers of post-stroke depression as claimed in claim 1 in the preparation of drugs for preventing, improving and treating post-stroke depression.

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