Biomarker, kit and application of biomarker in early screening of severe cognitive impairment of schizophrenia

Abstract

The invention belongs to the technical field of biomarker detection, and relates to a biomarker, a kit and an application of the biomarker in early screening of severe cognitive impairment of schizophrenia, the biomarker is composed of a metabolite SM C20: 2 and a metabolite PC ae C36: 2, and compared with non-severe cognitive impairment (non-SCI) of schizophrenia, the biomarker has the advantages that the biomarker can be used for early screening of severe cognitive impairment of schizophrenia; the biomarker has an area of 0.685 under an ROC curve in a discovery severe cognitive impairment (SCI) sample and an area of 0.720 under an ROC curve in a verification set. The method can be used for early screening the cognitive severity of schizophrenia patients under the condition of no drug interference, and has good stability, repeatability, sensitivity and specificity. The kit has screening and typing functions when being used for detecting schizophrenia patients, can be used for identifying the schizophrenia patients and a healthy control group, and can also be used for distinguishing SCI (severe cognitive impairment) and non-SCI (non-severe cognitive impairment) groups to assist clinical personalized intervention.

Description

Technical Field

[0001] The present invention belongs to the technical field of biomarker detection, and relates to a biomarker, a kit and their application in early screening of severe cognitive impairment in schizophrenia. Background Art

[0002] Schizophrenia (SZ) is not only characterized by hallucinations and delusions, but one of its core symptoms is widespread and persistent cognitive impairment, encompassing multiple dimensions such as working memory, attention, information processing speed, executive function, and social cognition. Up to 80% of schizophrenia patients experience varying degrees of cognitive impairment in the early stages of the disease. These deficits often persist as the disease progresses and are difficult to significantly improve with conventional antipsychotic drugs. Impaired cognitive function is not only closely related to the patient's social function recovery, career return rate, and quality of life, but can also predict future relapse risk and functional prognosis. Therefore, early identification and intervention of cognitive deficits are of great significance for improving patients' long-term outcomes.

[0003] Metabolomics, through high-throughput mass spectrometry, can comprehensively and quantitatively characterize the overall changes in small molecule metabolites in organisms. In recent years, targeted and non-targeted metabolomics methods have been continuously improved, with significant increases in detection sensitivity, throughput, and data processing capabilities, providing new perspectives for the study of complex disease mechanisms. In the field of mental illness, metabolomics has been used to reveal biomarkers for diseases such as depression, bipolar disorder, and schizophrenia, with discoveries ranging from energy metabolism imbalances and neurotransmitter synthesis to cell membrane lipid composition, providing an objective basis for disease classification, early diagnosis, and personalized treatment strategies.

[0004] Existing metabolomics research on schizophrenia focuses on overall disease diagnosis and screening for drug response markers, such as phosphatidylcholine, sphingomyelin, and amino acid metabolites. Studies have shown that specific lipid metabolic pathways are closely associated with clinical symptoms and drug efficacy; however, research specifically targeting the classification of cognitive impairment is still scarce. Furthermore, existing cognitive assessments primarily rely on neuropsychological scales, which are highly subjective and difficult to screen on a large scale. Using targeted metabolomics to screen plasma small molecule metabolites as objective biomarkers has yet to establish a diagnostic protocol for the classification of cognitive severity in schizophrenia.

[0005] Chinese patent CN119470933A discloses metabolic markers, a kit, and their use in assisting the diagnosis of schizophrenia. The metabolic markers include arachidonic acid, palmitamide, lysophosphatidylcholine LPC (17:0 / 0:0), and lysophosphatidylethanolamine LysoPE (0:0 / 20:4). Although these markers can assist in the diagnosis of schizophrenia, they do not involve the diagnosis of cognitive impairment typing.

[0006] Most existing literature focuses on overall disease diagnostic markers, and there are few targeted studies on cognitive stratification. Therefore, there is an urgent need to develop biomarkers, kits and applications that take into account both sensitivity and specificity for early screening of severe cognitive impairment in schizophrenia. Summary of the Invention

[0007] The purpose of the present invention is to overcome the shortcomings of the existing technology. Based on targeted metabolomics technology, a biomarker, a kit and its application in the early screening of severe cognitive impairment in schizophrenia are provided, which can make an early objective diagnosis of severe cognitive impairment (SCI) in schizophrenia patients with high sensitivity and specificity.

[0008] The technical solution adopted by the present invention to solve the technical problem is:

[0009] In a first aspect, the present invention provides a biomarker, which consists of SM C20:2 (sphingomyelin C20:2) and PC ae C36:2 (ether-type phosphatidylcholine C36:2).

[0010] Furthermore, the biomarker can be used for early screening of severe cognitive impairment in schizophrenia.

[0011] A second aspect of the present invention provides a use of the above-mentioned biomarker in the early screening of severe cognitive impairment in schizophrenia without the purpose of diagnosing and treating the disease.

[0012] Furthermore, the application detects the content of the biomarker in the subject's plasma sample; compared with non-severe cognitive impairment in schizophrenia, if the SM C20:2 metabolite content is increased and the PC ae C36:2 metabolite content is decreased, it indicates that the subject suffers from severe cognitive impairment (SCI).

[0013] Furthermore, relative to non-severe cognitive impairment (non-SCI) in schizophrenia, the area under the ROC curve of the biomarker in severe cognitive impairment (SCI) samples in the discovery set was 0.685, and the area under the ROC curve in the validation set was 0.720.

[0014] Furthermore, the method for detecting the biomarker content includes one of mass spectrometry, chromatography, and nuclear magnetic resonance spectroscopy, with mass spectrometry being more preferred.

[0015] A third aspect of the present invention provides a kit for early screening of severe cognitive impairment in patients with schizophrenia, the kit comprising reagents for detecting the biomarker, wherein the biomarker consists of the metabolite SM C20:2 and the metabolite PC ae C36:2.

[0016] The advantages and positive effects of the present invention are:

[0017] The present invention discloses a biomarker composed of two metabolites, SM C20:2 and PC ae C36:2, which can be used for early screening of severe cognitive impairment in patients with schizophrenia. The assay is simple and low-cost. It can be used to screen for cognitive severity in schizophrenia patients at an early stage without drug interference, and exhibits excellent stability, repeatability, sensitivity, and specificity.

[0018] 3. When used to detect schizophrenia patients, the biomarkers of the present invention have both screening and typing functions. They can not only distinguish schizophrenia patients from healthy controls, but also distinguish SCI (severe cognitive impairment) from non-SCI (non-severe cognitive impairment) groups, assisting in clinical personalized intervention. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 Scatter plots of the scores of the OPLS-DA model, where (a) is the comparison between the schizophrenia severe cognitive impairment (SCI) group and the healthy control group, and (b) is the comparison between the schizophrenia non-severe cognitive impairment (non-SCI) group and the healthy control group;

[0020] Figure 2 Permutation test plots of the OPLS-DA model, where (a) is the comparison between the schizophrenia severe cognitive impairment (SCI) group and the healthy control group, and (b) is the comparison between the schizophrenia non-severe cognitive impairment (non-SCI) group and the healthy control group;

[0021] Figure 3 Bar graph of the screened differential metabolites, red indicates upregulation and blue indicates downregulation, where (a) is the comparison between the schizophrenia severe cognitive impairment (SCI) group and the healthy control group, and (b) is the comparison between the schizophrenia non-severe cognitive impairment (non-SCI) group and the healthy control group;

[0022] Figure 4 Venn diagram comparing differential metabolites between the two groups, red indicates upregulation and blue indicates downregulation;

[0023] Figure 5 The difference heat map obtained after cluster analysis of differential metabolites;

[0024] Figure 6 Pathway enrichment analysis diagram of differential metabolites;

[0025] Figure 7ROC curves for the metabolite combinations of SM C20:2 and PC ae C36:2 in the discovery and validation sets. (A) ROC analysis of differential metabolites between patients with severe cognitive impairment and patients without severe cognitive impairment in the discovery set. (B) ROC analysis of differential metabolites between patients with schizophrenia and healthy controls in the discovery set. (C) ROC analysis of differential metabolites between patients with severe cognitive impairment and patients without severe cognitive impairment in the validation set. (D) ROC analysis of differential metabolites between patients with schizophrenia and healthy controls in the validation set. DETAILED DESCRIPTION

[0026] The present invention will be further described in detail below through specific examples. The following examples are only illustrative and not restrictive, and the scope of protection of the present invention cannot be limited thereto.

[0027] Example 1: Sample collection and metabolomics analysis

[0028] 1. Experimental Subjects

[0029] Cohort Design: 108 drug-naive patients with schizophrenia (SZ) (divided into severe cognitive impairment (SCI) and non-SCI) and 55 healthy controls (HC) were enrolled in a discovery cohort (88 patients: 58 SZ + 30 HC) and a validation cohort (75 patients: 50 SZ + 25 HC). All patients were recruited from Tianjin Anding Hospital. Healthy controls were selected from a matched community population.

[0030] Grouping criteria:

[0031] SCI group: MCCB cognitive deficit score ≥ 3 (moderate or above cognitive impairment).

[0032] Non-SCI group: MCCB score <3.

[0033] Baseline data: There were no significant differences between the two groups in age, gender, BMI, years of education, and PANSS scores (P>0.05). However, the MCCB total score and social cognition dimension score in the SCI group were significantly lower than those in the non-SCI group (P<0.05) (see Table 1).

[0034] Table 1 Demographic and clinical characteristics of the discovery and validation sets

[0035]

[0036] Note: BMI: body mass index; MCCB: Schizophrenia Cognitive Battery; PANSS: Positive and Negative Syndrome Scale; SCI: Severe Cognitive Impairment in Schizophrenia; non-SCI: Non-Severe Cognitive Impairment in Schizophrenia; Pa : Comparison of severe cognitive impairment between healthy controls and schizophrenia patients; P b : Comparison between healthy controls and schizophrenia patients with non-severe cognitive impairment; bold indicates significance at P < 0.05.

[0037] 2. Sample Collection and Targeted Metabolomics Analysis

[0038] Plasma Collection: After fasting for 8 hours, collect 5 mL of cubital venous blood into EDTA-anticoagulant tubes. Centrifuge the fasting venous blood at 1000 rpm for 13 minutes at 4°C to separate the plasma. Aliquot 300 μL / tube and immediately store in a -80°C ultra-low temperature freezer (avoid repeated freezing and thawing).

[0039] Targeted metabolomics analysis:

[0040] (1) Experimental reagents and instruments:

[0041] The Biocrates MxP Quant 500 kit (for the detection of 630 metabolites) (purchased from Beijing BGI Protein Research Center Co., Ltd.) was used in combination with a 6500QTRAP mass spectrometer (AB Sciex) equipped with an electrospray ionization source (ESI) and Analyst 1.7 software, and a chromatographic column: Waters ACQUITY UPLC BEH C18 (2.1×100 mm, 1.7 μm). In the “sample preparation” step, the Biocrates MxP The Quant 500 kit provides a 96-well filter plate and supporting reagents.

[0042] (2) Metabolite extraction and derivatization:

[0043] Sample pretreatment: First, thaw the plasma sample and then vortex mix for 30 seconds. Take 10 μL of plasma and add it to a 96-well filter plate (Biocrates). Add 20 μL of internal standard mixture (plasma, containing isotope ( 13 C. 15 N or D, carbon, nitrogen or deuterium) mark the internal standard mixture).

[0044] Derivatization: Add 50 μL of 5% phenylisothiocyanate (PITC) solution to the sample and incubate at room temperature in the dark for 30 minutes. Remove excess reagent with nitrogen purge, and reconstitute the residue with 300 μL of methanol-water (80:20, v / v).

[0045] (3) Mass spectrometry detection parameters

[0046] HPLC conditions: Mobile phase A: 0.1% formic acid in water; Mobile phase B: 0.1% formic acid in acetonitrile. Gradient: 2% B from 0-2 minutes, linearly increasing to 95% B from 2-15 minutes, maintaining 95% B from 15-18 minutes, returning to initial conditions at 18.1 minutes, and equilibration for 5 minutes. Flow rate: 0.3 mL / min. Column temperature: 40°C.

[0047] Mass spectrometry conditions: ion source temperature: 550°C, spray voltage: 5500 V (positive ion mode), multiple reaction monitoring (MRM) mode, collision energy optimized according to metabolites (example: SM C20:2, parent ion m / z 703.6→184.1, CE=35 eV).

[0048] (4) Quality Control (QC) QC samples: All subject plasma was pooled and one QC sample was inserted for every 10 test samples. Data filtering: Metabolites with a relative standard deviation (RSD) > 25% in the QC samples were eliminated. Metabolites missing > 50% in the test samples were eliminated, and the remaining metabolites were imputed with the mean of the adjacent QC samples.

[0049] 3. Mass Spectrometry Data Analysis

[0050] Differential metabolite screening:

[0051] OPLS-DA analysis was performed on the data exported from the 6500QTRAP mass spectrometer using SIMCA 14.0 software. The stability and reliability of the model were verified by 100 permutation tests. The screening criteria were set as VIP value greater than 1 and t-test P value less than 0.05. OPLS-DA model analysis was performed on the data of severe cognitive impairment, non-severe cognitive impairment, and healthy control groups. The scatter plots are shown in Figure 2. Figure 1 As shown, (a) is a comparison diagram of schizophrenia patients with severe cognitive impairment (SCI) and healthy controls (HC). Schizophrenia patients with severe cognitive impairment (SCI) and healthy controls (HC) are almost completely separated on the horizontal axis of the predicted components, and the 95% confidence ellipses hardly overlap, indicating that the metabolic profiles of schizophrenia patients with severe cognitive impairment (SCI) and healthy controls are highly distinguishable. (b) is a comparison diagram of schizophrenia patients with non-severe cognitive impairment (non-SCI) and healthy controls (HC). The non-severe cognitive impairment (non-SCI) group and healthy controls (HC) have obvious overlaps on the same component, and there are more ellipse intersections, suggesting that although the metabolic changes in patients with non-severe cognitive impairment are considerable, they are less significant and less distinguishable than those in the SCI group. The OPLS-DA model was then subjected to a permutation test, and the permutation test diagram is shown in the figure below. Figure 2As shown in the figure, (a) is a comparison between patients with schizophrenia and severe cognitive impairment and the healthy control group, and (b) is a comparison between patients with schizophrenia and non-severe cognitive impairment and the healthy control group. It can be seen that R 2 and Q 2 The results showed good values, and the loading plot showed a small number of high-contribution variables, which was consistent with the biological significance, indicating the feasibility of the OPLS-DA model.

[0052] The screened differential metabolites were analyzed by histogram. Figure 3 As shown, (a) compares schizophrenia patients with severe cognitive impairment with healthy controls, and (b) compares schizophrenia patients with non-severe cognitive impairment with healthy controls. Compared with healthy controls, red indicates upregulated metabolites, and blue indicates downregulated metabolites. The schizophrenia patients with severe cognitive impairment showed a "downregulated > upregulated" trend (19 vs 7), while the opposite was true for schizophrenia patients with non-severe cognitive impairment (4 vs 11), highlighting the association between the severity of cognitive impairment and the direction and breadth of metabolic changes. The widespread downregulation in patients with schizophrenia with severe cognitive impairment may reflect the association between severe cognitive impairment and insufficient brain-peripheral energy supply and impaired neuroprotective mechanisms.

[0053] A total of 26 differential metabolites were screened out in the SCI group vs the HC group, among which Asn, CE (16:1), Cortisol GCA, ProBetaine, SMC20:2 and TG (16:1_38:4) were upregulated, and C2, Cer (d18:1 / 22:0), Cer (d18:1 / 24:0), FA (18:1), GABA, lysoPCa-C18:0, PC aa C34:2, PC aa C36:0, PC aa C36:2, PC aaC36:6, PC-aa C38:0, PC aa C40:2, PC ae C34:3, PC ae C36:2, PC ae C36:3, PC.ae C38:0, PC ae C38:2, SM(OH)C22:1 and SM-C24:0 were downregulated; Fifteen differential metabolites were screened out by HC, among which CE(14:0), CE(15:0), CE(18:3), Cortisol TG(14:0_34:3), TG(16:1_32:1), TG(16:1_36:5), TG(16:1_38:4), TG(18:3_34:1), TG(18:3_34:2) and TG(18:3_34:3) were upregulated, while AconAcid, C2, Cer(d18:1 / 24:0) and GABA were downregulated (see Table 2 ).

[0054] Table 2 Findings There were differences in the expression of metabolites between the SCI and non-SCI groups compared with healthy controls.

[0055]

[0056] Note: SCI: severe cognitive impairment in schizophrenia; non-SCI: non-severe cognitive impairment in schizophrenia; VIP: variable important projection; FC: fold difference; bold indicates significance at P < 0.05.

[0057] Identification of specific metabolites: The intersection of the two groups of differential metabolites resulted in 21 SCI-specific metabolites ( Figure 4 ), as shown in the figure, there are 5 differential metabolites that are upregulated in SCI, shown in red; there are 16 differential metabolites that are downregulated, shown in blue. Based on the MetaboAnalyst 5.0 platform, we performed cluster analysis on the 21 differential metabolites, and only a few metabolites showed similar expression levels, which shows the difference in metabolic profiles between the SCI group and the non-SCI group ( Figure 5 ), Figure 5 It can be seen that the heat map clearly depicts the expression profile differences of 21 differential metabolites in the SCI and non-SCI groups, revealing the close correlation between the severity of cognitive impairment and the imbalance of amino acid, bile acid, sphingomyelin and phosphatidylcholine metabolism, laying the foundation for subsequent mechanism research and biomarker development. Pathway analysis was completed using the MetaboAnalyst 5.0 platform, with the reference database being KEGG. The significance standard was set at a P value of less than 0.05 to screen key metabolic pathways related to differential metabolites. Significant enrichment was found in sphingolipid metabolism and glycerophospholipid metabolism ( Figure 6 ).

[0058] Example 2: Biomarker Validation and Diagnostic Efficacy

[0059] 1. Biomarker Validation

[0060] Methods: In correlation validation, partial correlation analysis was performed to investigate the independent associations between the 21 differentially expressed metabolites and clinical indicators, controlling for body mass index (BMI) and Positive and Negative Syndrome Scale (PANSS) total score as covariates. All analyses were performed using IBM SPSS 27.0 software. The correlation coefficients (r) and significance (P) are presented in Table 3 as r(P). The results showed that SM C20:2 was negatively correlated with the MCCB total cognitive score (r = -0.399, P = 0.029) and the neurocognitive dimension (r = -0.419, P = 0.021). PC ae C36:2 was negatively correlated with social cognition (r = -0.398, P = 0.029). The metabolites SM C20:2 and PC ae C36:2 were independently and negatively correlated with cognitive function, suggesting that these two metabolites may play an important role in the development and progression of cognitive impairment. These findings provide support for these two metabolites as potential biomarkers of cognitive impairment and may lay the foundation for the development of early diagnosis and intervention strategies.

[0061] Table 3 Correlation between differential plasma metabolites and cognition in SCI and non-SCI patients

[0062]

[0063] Note: BMI: body mass index; PANSS: Positive and Negative Syndrome Scale; SCI: severe cognitive impairment in schizophrenia; non-SCI: non-severe cognitive impairment in schizophrenia; orange in the table indicates differential metabolites related to cognition in SCI, and red indicates significance at P < 0.05.

[0064] 2. ROC curve evaluation of diagnostic efficacy

[0065] In the discovery set, the biomarker combination of SM C20:2 and PC ae C36:2 showed good diagnostic ability in the early screening of severe cognitive impairment in patients with schizophrenia, with a corresponding area under the curve (AUC) value of 0.685 [95% (CI) = 0.543-0.827] ( Figure 7 In addition, in the discovery set, the AUC value of these two differential metabolites in distinguishing SZ patients from HC patients was 0.717 [95% CI = 0.610-0.824] ( Figure 7 To further verify the reliability of these two differential metabolites, ROC curve analysis was performed in the validation set. The results showed that the combination of PC ae C36:2 and SM C20:2 could better predict the severe cognitive symptoms of SZ, with the corresponding AUC value of 0.720 [95% (CI) = 0.567-0.873] ( Figure 7In addition, the AUC value of these two different metabolites in distinguishing SZ patients from HC patients was 0.786 [95% CI = 0.678-0.893] ( Figure 7 (shown in D). This suggests that this differential metabolic profile can serve as a promising biomarker for screening severe cognitive impairment, and its excellent reproducibility and stability were verified. Although SM C20:2 and PC ae C36:2 each have limited diagnostic capabilities for cognitive impairment, their combination demonstrated stable performance in both the discovery and validation sets (AUC ≈ 0.68–0.79), demonstrating their synergistic effect and potential as a biomarker for early screening of severe cognitive impairment in schizophrenia and for distinguishing SZ from HC.

[0066] The above description is only a preferred embodiment of the present invention. It should be pointed out that a person skilled in the art can make several modifications and improvements without departing from the inventive concept, and these all fall within the scope of protection of the present invention.

Claims

1. A biomarker, characterized in that The biomarkers consist of the metabolite SM C20:2 and the metabolite PC aeC36:

2.

2. The biomarker according to claim 1, characterized in that The biomarkers can be used for early screening of severe cognitive impairment in schizophrenia.

3. Use of the biomarker according to claim 1 or 2 in the early screening of severe cognitive impairment in schizophrenia without the purpose of diagnosis and treatment of the disease.

4. The use according to claim 3, characterized in that The application is to detect the content of the biomarker in the subject's plasma sample; compared with non-severe cognitive impairment in schizophrenia, if the content of the metabolite SM C20:2 is increased and the content of the metabolite PC ae C36:2 is decreased, it indicates that the subject suffers from severe cognitive impairment.

5. The use according to claim 3, characterized in that Relative to non-severe cognitive impairment in schizophrenia, the area under the ROC curve of the biomarker in the severe cognitive impairment samples of schizophrenia in the discovery set was 0.685, and the area under the ROC curve in the validation set was 0.

720.

6. The use according to claim 3, characterized in that Methods for detecting the content of biomarkers include one of mass spectrometry, chromatography, and nuclear magnetic resonance spectroscopy.

7. The use according to claim 3, characterized in that The method for detecting the content of biomarkers is mass spectrometry.

8. A kit for early screening of severe cognitive impairment in schizophrenia, characterized in that: The kit comprises reagents for detecting the biomarker according to claim 1, wherein the biomarker consists of metabolite SM C20:2 and metabolite PC ae C36:2.

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