CN113624868A - Biomarker combination for predicting or evaluating cognitive function of bipolar affective disorder patient and application thereof - Google Patents
Biomarker combination for predicting or evaluating cognitive function of bipolar affective disorder patient and application thereof Download PDFInfo
- Publication number
- CN113624868A CN113624868A CN202110861488.7A CN202110861488A CN113624868A CN 113624868 A CN113624868 A CN 113624868A CN 202110861488 A CN202110861488 A CN 202110861488A CN 113624868 A CN113624868 A CN 113624868A
- Authority
- CN
- China
- Prior art keywords
- cognitive function
- bipolar affective
- biomarker
- predicting
- information
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Withdrawn
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
- G01N30/02—Column chromatography
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
- G01N30/02—Column chromatography
- G01N30/04—Preparation or injection of sample to be analysed
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
- G01N30/02—Column chromatography
- G01N30/04—Preparation or injection of sample to be analysed
- G01N30/06—Preparation
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
- G01N30/02—Column chromatography
- G01N30/26—Conditioning of the fluid carrier; Flow patterns
- G01N30/28—Control of physical parameters of the fluid carrier
- G01N30/34—Control of physical parameters of the fluid carrier of fluid composition, e.g. gradient
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
- G01N30/02—Column chromatography
- G01N30/62—Detectors specially adapted therefor
- G01N30/72—Mass spectrometers
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
- G01N30/02—Column chromatography
- G01N30/86—Signal analysis
- G01N30/8693—Models, e.g. prediction of retention times, method development and validation
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
- G01N30/02—Column chromatography
- G01N30/86—Signal analysis
- G01N30/8696—Details of Software
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
- G01N30/02—Column chromatography
- G01N30/88—Integrated analysis systems specially adapted therefor, not covered by a single one of the groups G01N30/04 - G01N30/86
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
- G01N30/02—Column chromatography
- G01N30/04—Preparation or injection of sample to be analysed
- G01N2030/042—Standards
- G01N2030/045—Standards internal
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
- G01N30/02—Column chromatography
- G01N30/88—Integrated analysis systems specially adapted therefor, not covered by a single one of the groups G01N30/04 - G01N30/86
- G01N2030/8809—Integrated analysis systems specially adapted therefor, not covered by a single one of the groups G01N30/04 - G01N30/86 analysis specially adapted for the sample
- G01N2030/8813—Integrated analysis systems specially adapted therefor, not covered by a single one of the groups G01N30/04 - G01N30/86 analysis specially adapted for the sample biological materials
- G01N2030/8818—Integrated analysis systems specially adapted therefor, not covered by a single one of the groups G01N30/04 - G01N30/86 analysis specially adapted for the sample biological materials involving amino acids
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
- G01N30/02—Column chromatography
- G01N30/88—Integrated analysis systems specially adapted therefor, not covered by a single one of the groups G01N30/04 - G01N30/86
- G01N2030/8809—Integrated analysis systems specially adapted therefor, not covered by a single one of the groups G01N30/04 - G01N30/86 analysis specially adapted for the sample
- G01N2030/8813—Integrated analysis systems specially adapted therefor, not covered by a single one of the groups G01N30/04 - G01N30/86 analysis specially adapted for the sample biological materials
- G01N2030/8822—Integrated analysis systems specially adapted therefor, not covered by a single one of the groups G01N30/04 - G01N30/86 analysis specially adapted for the sample biological materials involving blood
Landscapes
- Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Other Investigation Or Analysis Of Materials By Electrical Means (AREA)
- Investigating Or Analysing Biological Materials (AREA)
Abstract
The invention discloses a biomarker combination for predicting or evaluating cognitive function of a bipolar affective disorder patient and application thereof, and relates to the technical field of molecular diagnosis. The biomarker panel disclosed by the invention comprises: hypoxanthine, creatine, arginine, citrulline, and phenylalanine; the biomarker group is adopted to predict or evaluate the cognitive function of the bipolar affective disorder patient, has higher accuracy, provides an objective technical means for predicting or evaluating the cognitive function of the bipolar affective disorder patient, and avoids the problem of fatigue of a subject caused by RBANS evaluation.
Description
Technical Field
The invention relates to the technical field of molecular diagnosis, in particular to a biomarker combination for predicting or evaluating cognitive function of a bipolar affective disorder patient and application thereof.
Background
Bipolar affective disorder refers to a class of mood disorders with both manic or hypomanic episodes and depressive episodes. Has the characteristics of complex disease course, high recurrence rate, high disability rate, low diagnosis rate and low treatment rate. Has been receiving high attention and wide attention from mental health workers in various countries for a long time. Studies have shown that impaired cognitive function is a significant cause of the inability of bipolar disorder patients to recover social function during remission.
The repetitive set of neuropsychological state assessment tools (RBANS) have been widely used in psychiatric clinical research at home and abroad since the completion of Randolph's compilation in 1998. The tool is a simple and single-person operated test, the whole test takes no more than 30 minutes, the cooperation of patients can be obtained to the maximum extent, and the influence of fatigue on the test result is reduced as much as possible. The test difficulty is moderate, the applicable population range is from normal people to moderate dementia, the test comprises 12 tests, and the test can be summarized into 5 groups of neuropsychological states: instant memory (immediate memory), attention (attention), visual breadth (visual/relational), language function (language function), and delayed memory (delayed memory). Immediate memory: subject's ability to remember in the short term after exposure to information, scores derived from story recall and vocabulary learning tests; attention is paid to: the subjects' ability to remember and to present information in short-term memory controlled by their vision and mouth, the scores coming from code and numerical breadth tests; language function: subjects demonstrated their ability to respond in language by recalling or naming known materials, with scores derived from language fluency and picture naming tests; visual breadth: the ability of the subject to be examined to perceive space and to construct a spatial copy of a particular drawing, the scores resulting from line location and graphic replication tests; and (3) delayed memory: the subjects were examined for antegrade memory, scores from recall of words, recognitions of words, memory of stories and recall of graphics.
However, when the cognitive function of the subject is evaluated by using RBANS, fatigue of the subject still can be caused, and at present, no objective technical means for evaluating the cognitive function of the subject exists.
In view of this, the invention is particularly proposed.
Disclosure of Invention
The invention aims to provide a biomarker combination for predicting or evaluating cognitive function of a patient with bipolar affective disorder and application thereof. The biomarker group provided by the invention can reflect the cognitive function damage state of the bipolar affective disorder patient, has higher accuracy by adopting the biomarker group to predict or evaluate the cognitive function of the bipolar affective disorder patient, provides a more objective technical means for evaluating the cognitive function of the bipolar affective disorder patient, and effectively avoids the fatigue problem of a subject caused by RBANS evaluation.
The invention is realized by the following steps:
in one aspect, the invention provides a biomarker panel for predicting or assessing cognitive function in a patient with bipolar affective disorder, comprising a first marker set comprising the following compounds: hypoxanthine, creatine, arginine, citrulline, and phenylalanine.
The study of the embodiment of the invention shows that the biomarker combination can reflect the cognitive function of the bipolar disorder patients on the whole; the detection of the biomarkers can realize the overall prediction or evaluation of the cognitive function of the bipolar affective disorder patient, and has high accuracy.
Alternatively, in some embodiments of the invention, the cognitive function may be predicted or assessed by the immediate memory, attention, visual breadth, language function, or delayed memory dimensions.
Optionally, in some embodiments of the invention, when the cognitive function is predicted or assessed by the immediate memory dimension, the biomarker combination further comprises a second marker panel comprising the following compounds: creatine, ornithine, taurine, arginine, lysine, citrulline, phenylalanine, and glutamic acid.
Optionally, in some embodiments of the invention, when the cognitive function is predicted or assessed by the attention dimension, the biomarker combination further comprises a third marker panel comprising the following compounds: hypoxanthine, cystine, taurine, isoleucine, arginine, tryptophan, citrulline, glutamic acid, and histidine.
Optionally, in some embodiments of the invention, when the cognitive function is predicted or assessed by the visual breadth dimension, the biomarker combination further comprises a fourth marker panel comprising the following compounds: hypoxanthine, creatine, ornithine, proline, tryptophan, methionine, citrulline, phenylalanine, and histidine.
Optionally, in some embodiments of the invention, when the cognitive function is predicted or assessed by a language functional dimension, the biomarker panel further comprises a fifth marker panel comprising the following compounds: hypoxanthine, ornithine, taurine, valine, tryptophan, citrulline, and phenylalanine.
Optionally, in some embodiments of the invention, when the cognitive function is predicted or assessed by delayed memory dimensions, the biomarker panel further comprises a sixth marker panel comprising the following compounds: hypoxanthine, creatine, ornithine, isoleucine, serine, lysine, tryptophan, citrulline, phenylalanine, glutamic acid, and histidine.
Those skilled in the art know that dimensions reflecting cognitive function include: immediate memory, attention, visual breadth, language functionality, and delayed memory. In this regard, the present invention also provides a biomarker set for use in predicting or assessing cognitive function with respect to different dimensions, e.g., a second marker set may be used when cognitive function of a bipolar affective disorder patient needs to be predicted or assessed from the memorial dimensions; when it is desired to predict or assess cognitive function in a patient with bipolar affective disorder from the attention dimension, a third marker set may be employed; a fourth marker panel may be used when it is desired to predict or assess cognitive function in a patient with bipolar affective disorder from the visual breadth dimension; a fifth marker panel may be used when it is desired to predict or assess cognitive function in a patient with bipolar affective disorder from the linguistic functional dimension; a sixth marker panel may be used when it is desired to predict or assess cognitive function in a patient with bipolar affective disorder from the delayed memory dimension. Each marker group has higher accuracy, and the invention also provides a more objective technical means for evaluating different dimensions of the cognitive function of the bipolar affective disorder patient.
In another aspect, the invention provides the use of a biomarker combination as defined in any of the above in the manufacture of a kit for predicting or assessing cognitive function in a patient with bipolar affective disorder.
In another aspect, the invention provides the use of a reagent for detecting a biomarker combination as defined in any of the above in the manufacture of a kit for predicting or assessing cognitive function in a patient with bipolar affective disorder.
It is to be noted that the above-mentioned compounds are all well-known compounds in the art, and those skilled in the art will readily appreciate that the detection can be achieved by means of conventional techniques in the art, and that the reagents and/or consumables for detecting the biomarker combinations are also well-known in the art.
In another aspect, the invention provides a kit for predicting or assessing cognitive function in a patient with bipolar disorder, comprising: reagents and/or consumables for detecting the concentration of a biomarker combination as defined in any one of the above.
Optionally, in some embodiments of the invention, the reagents and/or consumables are suitable for use in LC-MS/MS techniques for detecting the concentration of the biomarker combination.
Alternatively, in some embodiments of the invention, the test sample of the kit is plasma or serum.
In another aspect, the invention provides an apparatus for predicting or assessing cognitive function in a patient with bipolar affective disorder, comprising:
an information acquisition module for acquiring evaluation information including concentration information of the biomarker combination according to any one of claims 1 to 3 in a test sample from a target bipolar affective patient, and age information and education level information of the target bipolar affective patient;
and the evaluation module is used for processing the information to be evaluated by using an evaluation model to obtain an evaluation result.
It should be noted that, those skilled in the art may select the corresponding biomarker concentration information according to the index to be evaluated.
Alternatively, in some embodiments of the invention, it comprises: the evaluation model is obtained by training an initial model by an evaluation information standard sample;
the assessment information standard samples comprise concentration information samples, age information, education degree information and corresponding RBANS scale score samples of the biomarker combinations of a plurality of bipolar affective disorder patients.
It should be noted that the specific number of the "multiple cases" can be reasonably selected by those skilled in the art according to actual situations, but is at least 3 cases, and of course, the larger the number, the more reliable the obtained prediction result, preferably more than 10 cases. Alternatively, in some embodiments of the invention, 62 cases are selected for use in the invention.
The education level information indicates the education period of the target bipolar affective disorder patient.
Optionally, in some embodiments of the invention, the initial model is a support vector machine, a K-nearest neighbor algorithm, or a neural network algorithm.
Alternatively, in some embodiments of the invention, the concentration information is detected by an amino acid autoanalyzer, capillary electrophoresis, gas chromatography-mass spectrometry tandem, liquid chromatography, or liquid chromatography-mass spectrometry tandem.
Knowing the specific compound, those skilled in the art can readily detect its concentration by means of techniques that are conventional in the art, such as amino acid autoanalyzer, capillary electrophoresis, gas chromatography, tandem gas chromatography-mass spectrometry, liquid chromatography, or tandem liquid chromatography-mass spectrometry.
Optionally, in some embodiments of the invention, the concentration information is detected using LC-MS/MS techniques.
Alternatively, in some embodiments of the invention, the concentration information is represented by the concentration value of each marker, the ion abundance value, or the ratio thereof to an internal standard.
Alternatively, in some embodiments of the invention, the chromatographic conditions for detecting said concentration information using LC-MS/MS techniques are as follows:
a chromatographic column: BEH Amide,1.7 μm,2.1 mm. times.100 mm, mobile phase A: water (containing 5mM ammonium formate, 5mM ammonium acetate and 0.2% formic acid), mobile phase B: acetonitrile (containing 1mM ammonium formate, 1mM ammonium acetate and 0.2% formic acid), flow rate 0.4mL/min, column temperature 35 ℃, gradient elution conditions:
0-3min,95％B；3-11min,95％B-70％B；11-13min,70％B-55％B；13-14min,55％B。
the numerical parameters of the above conditions may fluctuate within a range of ± 3%.
Alternatively, in some embodiments of the invention, the mass spectrometric conditions for detecting said concentration information using LC-MS/MS techniques are as follows:
the instrument comprises the following steps: triple quadrupole mass spectrometry, scan mode: multiple reaction monitoring (MRM/SRM), electrospray voltage: positive ion 3.5kv, negative ion 3kv, sheath gas: 35Arb, assist gas: 10Arb, ion source temperature: at 350 ℃.
The numerical parameters of the above conditions may fluctuate within a range of ± 3%.
Alternatively, in some embodiments of the invention, the sample to be tested is plasma or serum.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
FIG. 1 is a graph of the results of prediction of immediate memory using biomarker combinations 1 and the regression model of example 1 in an example of the present invention; a: comparing the predicted score with the score of the scale, wherein the abscissa represents the serial number of the subject, the ordinate represents the predicted score, the dot represents the score of the scale, the triangle represents the predicted score, the light gray line represents the difference from the score of the scale is +/-10, and the dark gray line represents the difference from the score of the scale is +/-20; b: the difference value between the predicted score and the scale score is within 10 and is more than 10; fig. 2-a and B in fig. 6 are illustrated as such.
FIG. 2 is a graph showing the prediction results of the prediction of attention using the biomarker combinations 2 in the examples of the present invention and the regression model in example 2.
Fig. 3 is a result of predicting visual span using biomarker combinations 3 and the regression model of example 3 in an example of the present invention.
FIG. 4 is a graph of the results of a regression model prediction language function using biomarker combinations 4 and example 4 in an example of the present invention.
FIG. 5 is a graph of the prediction of delayed memory using biomarker combinations 5 in an example of the present invention and the regression model of example 5.
Fig. 6 is a prediction result of predicting cognitive function using the biomarker combinations 6 and the regression model of example 6 in the example of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. The examples, in which specific conditions are not specified, were conducted under conventional conditions or conditions recommended by the manufacturer. The reagents or instruments used are not indicated by the manufacturer, and are all conventional products available commercially.
The features and properties of the present invention are described in further detail below with reference to examples.
Example 1
Screening and modeling of biomarkers for predicting or assessing immediate memory in bipolar affective patients.
(1) Collecting peripheral venous blood of 62 samples (information shown in table 1) of a training set of bipolar disorder patients, extracting blood plasma, and storing at-80 ℃ for later use;
taking 90 microliters of plasma, adding 10 microliters of internal standard solution, adding 400 microliters of extraction solution, performing vortex for 30 seconds, performing ultrasonic treatment for 5 minutes, performing centrifugation for 10 minutes at 13000rpm, and taking supernatant to obtain a sample to be detected;
preparation of internal standard solution: accurately weighing L-2-chlorophenylalanine, dissolving in methanol, and preparing into 10 μ g/mL solution;
preparation of extraction solution: mixing chromatographic grade dichloromethane and methanol according to the volume ratio of 2: 1;
quality control of plasma: taking the anticoagulation blood of the bipolar affective disorder patient, centrifuging at normal temperature for 10 minutes, transferring the supernatant into a new centrifugal tube, and mixing the supernatant with the plasma: the internal standard solution was added at a ratio of 9:1 and mixed.
(2) Carrying out qualitative and quantitative analysis on main metabolites in plasma by a metabonomics method and an LC-MS/MS technology;
injecting the metabolic extract into a chromatographic column by an automatic sample injector for separation, wherein the specific chromatographic conditions are as follows:
packing and specification of chromatographic column: BEH Amide,1.7 μm,2.1mm X100 mm, mobile phase a: water (containing 5mM ammonium formate, 5mM ammonium acetate and 0.2% formic acid), mobile phase B: acetonitrile (containing 1mM ammonium formate, 1mM ammonium acetate and 0.2% formic acid), flow rate 0.4mL/min, column temperature 35 ℃, gradient elution conditions as follows:
0-3min,95％B；3-11min,95％B-70％B；11-13min,70％B-55％B；13-14min,55％B。
the metabolites after chromatographic separation are injected into a mass spectrum for detection, and the specific mass spectrum conditions are as follows: the instrument comprises the following steps: triple quadrupole mass spectrometry, scan mode: multiple reaction monitoring (MRM/SRM), electrospray voltage: positive ion 3.5kv, negative ion 3kv, sheath gas: 35Arb, assist gas: 10Arb, ion source temperature: at 350 ℃.
(3) And performing partial least squares discriminant regression (PLS) on the obtained data and the immediate memory score, screening by VIP >1, and comparing and identifying with a reference substance to obtain the alternative metabolic marker.
(4) Taking the ion abundance ratio of the alternative metabolic markers and the internal standard of 62 samples and the corresponding immediate memory score (which is scored by a professional physician according to a RBANS scale), age and education age as input data, establishing a regression model capable of predicting the immediate memory score by using a Support Vector Machine (SVM), and resampling method: repeatedcv, number of iterations of resampling: 10 times; finally, a group of biomarker combinations 1 with high prediction accuracy and a regression model for predicting the immediate memory are obtained through machine learning screening, and are used for predicting or evaluating the immediate memory score of a schizophrenia patient, and the biomarker combinations specifically comprise the following compounds: creatine, ornithine, taurine, arginine, lysine, citrulline, phenylalanine, and glutamic acid.
TABLE 1
| Training set | Test set | P value | |
| Quantity (example) | 62 | 30 | |
| Age (year of old) | 33.3±11.8 | 31.6±8.9 | 0.499 |
| Percentage of women (%) | 61.3 | 70.0 | 0.420 |
| Education time limit (year) | 11.6±3.2 | 10.9±2.6 | 0.288 |
| Body Mass Index (BMI) | 23.6±3.5 | 22.9±5.5 | 0.478 |
| Heart rate | 79.6±14.4 | 79.8±14.3 | 0.967 |
| Percentage of smoking (%) | 20.0 | 26.7 | 0.596 |
| Percentage of drinking (%) | 11.9 | 14.3 | 0.191 |
| Glycated hemoglobin (%) | 5.3±0.4 | 5.4±0.5 | 0.355 |
| Blood sugar (mmol/L) | 4.9±0.8 | 5.4±3.3 | 0.243 |
| Triglyceride (mmol/L) | 1.5±0.9 | 1.7±2.7 | 0.657 |
| Total cholesterol (mmol/L) | 4.4±1.0 | 4.3±1.0 | 0.962 |
| High density lipoprotein (mmol/L) | 1.3±0.6 | 1.3±0.5 | 0.785 |
| Low density lipoprotein (mmol/L) | 2.6±0.9 | 2.3±0.8 | 0.198 |
Note: the score data in the table are expressed as mean ± standard deviation, and the education years are counted from grade 1 of primary school.
In other embodiments, a person skilled in the art can directly detect the concentration of the biomarker panel obtained in the embodiments of the present invention by using multiple cases of bipolar affective patients (more than 10 cases are recommended) as samples according to the biomarker panel obtained in the embodiments of the present invention, and then establish a regression model capable of predicting the immediate memory by combining the scoring results of the RBANS scale, such as the immediate memory; under the condition, the screening step of the marker is not needed, the workload is reduced, and the prediction efficiency is improved.
Example 2
Screening and model recommendation of biomarkers for predicting or assessing attention of bipolar affective disorder patients.
Referring to the method of example 1, using the ages, educational years, ion abundance ratios of alternative metabolic markers to internal standards and corresponding attention scores (scored by a specialist according to the RBANS scale) of the above 62 samples of bipolar affective patients as input data, a set of biomarker combinations 2 with higher prediction accuracy and regression models for predicting attention were obtained, which were used to predict or assess the attention scores of patients with schizophrenia, biomarker combinations 2 specifically comprising the following compounds: hypoxanthine, cystine, taurine, isoleucine, arginine, tryptophan, citrulline, glutamic acid, and histidine.
Example 3
Screening and model recommendation of biomarkers for predicting or assessing the extent of vision in bipolar disorder patients.
Referring to the method of example 1, using the ages, educational years, ion abundance ratios of alternative metabolic markers to internal standards and corresponding visual breadth scores (scored by a professional physician according to the RBANS scale) of the 62 samples of bipolar affective patients as input data, a set of biomarker combinations 3 with higher prediction accuracy and regression models for predicting visual breadth were obtained, which were used to predict or evaluate the visual breadth scores of patients with schizophrenia, biomarker combinations 3 specifically including the following compounds: hypoxanthine, creatine, ornithine, proline, tryptophan, methionine, citrulline, phenylalanine, and histidine.
Example 4
Screening and model recommendation of biomarkers for predicting or assessing language function in bipolar affective disorder patients.
Referring to the method of example 1, using the ages, educational years, ion abundance ratios of alternative metabolic markers to internal standards and corresponding language function scores (scored by a specialist according to the RBANS scale) of the above 62 samples of bipolar affective patients as input data, a set of biomarker combinations 4 with higher prediction accuracy and regression models for predicting language function were obtained for predicting or assessing the language function scores of patients with schizophrenia, biomarker combinations 4 specifically comprising the following compounds: hypoxanthine, ornithine, taurine, valine, tryptophan, citrulline, and phenylalanine.
Example 5
Screening and model recommendation for biomarkers for predicting or assessing delayed memory in bipolar affective patients.
Referring to the method of example 1, using the ages, educational years, ion abundance ratios of alternative metabolic markers to internal standards and corresponding delayed memory scores (scored by a physician in accordance with the RBANS scale) of the 62 samples of bipolar affective patients as input data, a set of biomarker combinations 5 with higher prediction accuracy and regression models for predicting delayed memory were obtained for predicting or assessing delayed memory scores of patients with schizophrenia, the biomarker combinations 5 specifically comprising the following compounds: hypoxanthine, creatine, ornithine, isoleucine, serine, lysine, tryptophan, citrulline, phenylalanine, glutamic acid, and histidine.
Example 6
Screening and model recommendation for biomarkers for predicting or assessing cognitive function in bipolar affective disorder patients.
Referring to the method of example 1, using the ages, educational years, ion abundance ratios of alternative metabolic markers to internal standards and corresponding cognitive function scores (i.e., total scores obtained by a professional according to the RBANS scale) of the 62 samples of bipolar affective patients as input data, a set of biomarker combinations 6 with higher prediction accuracy and regression models for predicting cognitive function were obtained for predicting or assessing cognitive function scores of schizophrenic patients, biomarker combinations 6 specifically including the following compounds: hypoxanthine, creatine, arginine, citrulline, and phenylalanine.
Experimental example 1
The evaluation effect of the prediction regression model established in example 1 on the cognitive function and each sub-dimension was evaluated.
Selecting blood randomly derived from outpatients or inpatients of a certain hospital and blood of enrolled bipolar affective disorder patients (30 samples of a test set of bipolar affective disorder patients (see table 1 for information), wherein all samples have complete scale data and are signed with informed consent), directly detecting ion abundance ratios of biomarkers and internal standards by referring to the methods of examples 1-6, inputting data (age, education degree of a subject and ion abundance ratio (ratio of biomarker of corresponding biomarker group of dimension to be predicted to internal standard)) into the prediction regression models established in examples 1-6 to obtain prediction scores of different dimensions of cognitive function and evaluation of cognitive function, as represented by triangles in figures 1-6, comparing the prediction scores with the scores diagnosed by a professional doctor on the 30 samples through RBANS scale, see A in the figure; and counting the data, wherein the predicted value is within the range of +/-10 of the RBANS scale value, the predicted result is considered to be correct, and the statistical result is shown as B in the figure.
It can be seen that 90.00% accuracy can be achieved in predicting the immediate memory dimension of bipolar affective patients using the predictive regression model of example 1 (figure 1).
It can be seen that an accuracy of 86.67% can be achieved in predicting the attention dimension of bipolar affective patients using the predictive regression model of example 2 (figure 2).
It can be seen that 86.67% accuracy was achieved in the dimension of predicting the visual breadth of bipolar affective patients using the predictive regression model of example 3 (figure 3).
It can be seen that an accuracy of 83.33% can be achieved in predicting the language function dimension of bipolar affective patients using the predictive regression model of example 4 (figure 4).
It can be seen that an accuracy of 76.67% can be achieved in predicting the dimension of delayed memory in patients with bipolar affective disorder using the predictive regression model of example 5 (figure 5).
It can be seen that an accuracy of 96.67% was achieved in predicting cognitive function in bipolar affective patients using the predictive regression model of example 6 (figure 6).
From the above results, it can be seen that the biomarker set provided by the embodiments of the present invention and the regression model established therefrom can predict the cognitive function of the bipolar affective disorder patient and evaluate the dimensional indexes of the cognitive function with high accuracy, including: immediate memory, attention, visual breadth, language function, delayed memory; an objective technical means is provided for evaluating the cognitive function of a bipolar affective disorder patient, the fatigue problem of a test subject caused by RBANS measurement can be effectively avoided, namely, a person skilled in the art only needs to inquire age information, education degree information and collected blood samples of the test subject, concentration information of a biomarker can be obtained through instrument detection, and the detected marker concentration result data is input into a pre-established regression model to obtain the cognitive function and the prediction scores of different dimensions of the test subject. Whether or not the cognition is impaired based on the obtained prediction score, for example, when the cognitive function prediction is less than 70 points (corresponding to RBANS total points), it is considered that there is cognitive impairment, and 70 points or more are considered to be normal.
The predicted cognitive function and scores of different dimensions predicted by the regression model are those skilled in the art, and the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and variations may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A biomarker panel for predicting or assessing cognitive function in a patient with bipolar affective disorder, comprising a first marker set comprising the following compounds: hypoxanthine, creatine, arginine, citrulline, and phenylalanine.
2. The biomarker combination according to claim 1, wherein when the cognitive function is predicted or assessed by the immediate memory dimension, the biomarker combination further comprises a second marker panel comprising the following compounds: creatine, ornithine, taurine, arginine, lysine, citrulline, phenylalanine, and glutamic acid.
3. The biomarker combination according to claim 1 or 2, characterized in that when the cognitive function is predicted or assessed by the attention dimension, the biomarker combination further comprises a third marker panel comprising the following compounds: hypoxanthine, cystine, taurine, isoleucine, arginine, tryptophan, citrulline, glutamic acid, and histidine;
preferably, when the cognitive function is predicted or assessed by the visual breadth dimension, the biomarker combination further comprises a fourth marker panel comprising the following compounds: hypoxanthine, creatine, ornithine, proline, tryptophan, methionine, citrulline, phenylalanine, and histidine;
preferably, when the cognitive function is predicted or assessed by language functional dimensions, the biomarker panel further comprises a fifth marker panel of hypoxanthine, guanine, taurine, valine, tryptophan, citrulline and phenylalanine;
preferably, when the cognitive function is predicted or assessed by delayed memory dimensions, the biomarker panel further comprises a sixth marker panel comprising the following compounds: hypoxanthine, creatine, ornithine, isoleucine, serine, lysine, tryptophan, citrulline, phenylalanine, glutamic acid, and histidine.
4. Use of a biomarker combination according to any of claims 1 to 3 in the manufacture of a kit for predicting or assessing cognitive function in a patient with bipolar affective disorder.
5. Use of reagents for detecting a biomarker combination according to any of claims 1 to 3 for the manufacture of a kit for predicting or assessing cognitive function in a patient with bipolar affective disorder.
6. A kit for predicting or assessing cognitive function in a patient with bipolar affective disorder, comprising: a reagent and/or consumable for detecting the concentration of a biomarker combination according to any of claims 1 to 3.
7. The kit according to claim 6, wherein the detection sample of the kit is plasma or serum.
8. An apparatus for predicting or assessing cognitive function in a patient with bipolar affective disorder, comprising:
an information acquisition module for acquiring evaluation information including concentration information of the biomarker combination according to any one of claims 1 to 3 in a test sample from a target bipolar affective patient, and age information and education level information of the target bipolar affective patient;
and the evaluation module is used for processing the information to be evaluated by using an evaluation model to obtain an evaluation result.
9. The device according to claim 8, characterized in that it comprises: the evaluation model is obtained by training an initial model by an evaluation information standard sample;
the assessment information standard samples comprise concentration information samples, age information, education degree information and corresponding RBANS scale score samples of the biomarker combinations of a plurality of bipolar affective disorder patients.
10. The apparatus of claim 9, wherein the initial model is a support vector machine, a K-nearest neighbor algorithm, or a neural network algorithm;
preferably, the concentration information is detected by an amino acid automatic analyzer, a capillary electrophoresis method, a gas chromatography-mass spectrometry tandem method, a liquid chromatography or a liquid chromatography-mass spectrometry tandem method;
the concentration information is represented by concentration values of all markers, ion abundance values or the ratio of the ion abundance values to the internal standard;
preferably, the sample to be tested is plasma or serum.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110861488.7A CN113624868A (en) | 2021-07-29 | 2021-07-29 | Biomarker combination for predicting or evaluating cognitive function of bipolar affective disorder patient and application thereof |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110861488.7A CN113624868A (en) | 2021-07-29 | 2021-07-29 | Biomarker combination for predicting or evaluating cognitive function of bipolar affective disorder patient and application thereof |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113624868A true CN113624868A (en) | 2021-11-09 |
Family
ID=78381504
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110861488.7A Withdrawn CN113624868A (en) | 2021-07-29 | 2021-07-29 | Biomarker combination for predicting or evaluating cognitive function of bipolar affective disorder patient and application thereof |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113624868A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114002421A (en) * | 2021-12-30 | 2022-02-01 | 佛山市第三人民医院(佛山市精神卫生中心) | Application of exosome metabolite as bipolar affective disorder marker |
| CN116883794A (en) * | 2023-09-07 | 2023-10-13 | 北京智精灵科技有限公司 | Spatial structure cognitive ability evaluation method and system based on graph attention network |
-
2021
- 2021-07-29 CN CN202110861488.7A patent/CN113624868A/en not_active Withdrawn
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114002421A (en) * | 2021-12-30 | 2022-02-01 | 佛山市第三人民医院(佛山市精神卫生中心) | Application of exosome metabolite as bipolar affective disorder marker |
| CN116883794A (en) * | 2023-09-07 | 2023-10-13 | 北京智精灵科技有限公司 | Spatial structure cognitive ability evaluation method and system based on graph attention network |
| CN116883794B (en) * | 2023-09-07 | 2024-05-31 | 北京智精灵科技有限公司 | Spatial structure cognitive ability evaluation method and system based on graph attention network |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106979982B (en) | Method and kit for diabetes risk prediction and treatment evaluation | |
| JP6763770B2 (en) | Biomarker for autism spectrum disorders | |
| JP2015231392A (en) | Metabolic biomarkers for autism | |
| TWI553313B (en) | Method for diagnosing heart failure | |
| CN113624868A (en) | Biomarker combination for predicting or evaluating cognitive function of bipolar affective disorder patient and application thereof | |
| CN111562338B (en) | Application of transparent renal cell carcinoma metabolic marker in renal cell carcinoma early screening and diagnosis product | |
| CN112083111A (en) | Non-invasive diagnosis marker for chronic drug-induced liver injury related cirrhosis and application thereof | |
| EP2756311B1 (en) | Method for diagnosing alzheimer's disease | |
| CN113624870A (en) | Biomarker combination for predicting PANSS scale score of schizophrenia patient and application thereof | |
| CN113624865A (en) | Biomarker combination for predicting or evaluating cognitive function of depression patient and application thereof | |
| CN113624864A (en) | Biomarker panel for predicting or evaluating cognitive function of schizophrenia patient and application thereof | |
| CN116754772A (en) | Peripheral blood protein marker for early diagnosis of senile dementia, application and auxiliary diagnosis system | |
| CN114544790B (en) | Application of reagent for detecting lysophosphatidylethanolamine (22:5) in blood plasma in preparation of depression detection kit | |
| CN113702652A (en) | Biomarker combination for predicting or evaluating cognitive function of healthy individual and application thereof | |
| EP3803410B1 (en) | Method of performing differential diagnosis of neurodegenerative diseases in a subject | |
| CN115701540A (en) | Marker, detection method and kit for evaluating heart damage of schizophrenia patient | |
| CN114544822B (en) | Application of reagent for detecting lysophosphatidylcholine (22:0) in blood plasma in preparation of depression detection kit | |
| Cascant-Vilaplana et al. | Longitudinal perturbations of plasma nuclear magnetic resonance profiles in neonatal encephalopathy | |
| EP4051104A1 (en) | Method to determine cognitive impairment | |
| RU2818128C2 (en) | Method for diagnosing mental disorders by blood lipids | |
| WO2015183917A2 (en) | Metabolic biomarkers for memory loss | |
| CN114544821B (en) | Application of reagent for detecting phosphatidylethanolamine (36:4) in blood plasma in preparation of depression detection kit | |
| CN111830169B (en) | Compound for diagnosing polycystic ovarian syndrome and application thereof | |
| CN113376379A (en) | Biomarkers for diagnosing mental diseases and uses thereof | |
| CN114544826B (en) | Application of reagent for detecting histidine in blood plasma in preparation of depression detection kit |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| WW01 | Invention patent application withdrawn after publication | ||
| WW01 | Invention patent application withdrawn after publication |
Application publication date: 20211109 |