Protein marker for diagnosing anxiety of heavy metal related middle-aged and elderly people, application of protein marker, diagnostic kit and risk assessment model

Abstract

The invention discloses a protein marker for diagnosing anxiety of heavy metal related middle-aged and elderly people and application thereof, a kit and a risk assessment model, and belongs to the technical field of biomedicine, and the protein marker comprises one or more of ALOX15, ATP6V1A, HBM, MVP or PCSK2. The expression levels of the markers in the heavy metal related middle-aged and elderly anxiety patients are remarkably reduced compared with the normal level, and early diagnosis of anxiety symptoms of the heavy metal related middle-aged and elderly people can be realized by detecting the expression levels of ALOX15, ATP6V1A, HBM, MVP and PCSK2, so that the sensitivity and efficiency of detection are improved. By means of the early detection method, intervention measures can be taken positively, the progress of anxiety symptoms can be delayed, the disability and fatality rate can be reduced, and even early curing can be achieved.

Description

Technical Field

[0001] The present invention relates to the field of biomedical technology, and in particular to protein markers for diagnosing heavy metal-related anxiety in middle-aged and elderly people, applications thereof, diagnostic kits, and risk assessment models. Background Art

[0002] In my country, with the rapid growth of population aging, the anxiety problems of middle-aged and elderly people caused by environmental factors are becoming increasingly serious, and the resulting disease burden is also increasing year by year. The 2020 census shows that the total number of people aged 60 and above in my country is more than 260 million, and the number of people aged 65 and above is more than 190 million, accounting for 18.70% and 13.50% of the total population, respectively. Compared with the sixth national census in 2010, the proportion of the population aged 60 and above increased by 5.44 percentage points, and the proportion of the population aged 65 and above increased by 4.63 percentage points.

[0003] There are many research papers on the concept of anxiety, among which the psychoanalytic school has conducted a systematic and in-depth discussion on this. They believe that anxiety is a signal of the self and another manifestation of suppressed sexual desire. Freud defines anxiety as an experience of tension or fear that comes from the self but has no clear purpose or reason, and its goal is unconscious and is related to the loss of self-image. Other scholars have different definitions. For example, Sevo D analyzed Kierkegaard's works and believed that anxiety is strange because it comes from nothing, it comes from within human nature, and there is no reason. Chinese scholars Park Songhua and Li Chunyu summarized the research on the concept of anxiety by scholars at home and abroad and summarized it as: Anxiety is a kind of nervous and uneasy emotion that is difficult to deal with because of the inner premonition that some unfavorable situation will happen.

[0004] The current state of anxiety among the middle-aged and elderly is a complex and multidimensional issue. With the intensification of social aging, the anxiety of the middle-aged and elderly people gradually increases with age, mainly in terms of life, health, economy, family relationships and social interaction. Regarding anxiety about adapting to life, the middle-aged and elderly people may feel uneasy and worry about their life arrangements after retirement, changes in their living environment and loneliness. Health anxiety involves fear of chronic diseases, declining physical function, medical expenses and death. Economic anxiety includes concerns about pensions, children's financial situation, uncertainty in pension security and future life security issues. In terms of family relationship anxiety, the middle-aged and elderly people may worry about their children's marriage, their children's health problems, and the pressure brought about by the healthy aging of family members. In terms of spiritual loneliness, reduced communication may lead to social anxiety among the middle-aged and elderly people with their children and friends.

[0005] Considering the large number of middle-aged and elderly people who are prone to anxiety, as well as the rapid growth of the latter in China, finding protein markers for early diagnosis of heavy metal-related anxiety in the middle-aged and elderly population has become an important challenge. In the international and domestic markets, there are no protein markers available for such early diagnosis, which seriously restricts the satisfaction of clinical needs. Therefore, researchers urgently need to work hard to find more accurate and efficient protein markers to discover and improve the current status of anxiety in the middle-aged and elderly population related to heavy metals in a timely manner. Summary of the invention

[0006] This study mainly explored the role of proteins in the process of cadmium exposure-induced anxiety in the middle-aged and elderly population, with a particular focus on protein markers associated with cadmium-induced anxiety. By identifying and analyzing these markers, this study aims to provide a new method and tool for early diagnosis of the possibility of anxiety symptoms in the middle-aged and elderly population due to cadmium exposure.

[0007] On the one hand, the present invention provides protein markers for diagnosing heavy metal-related anxiety in middle-aged and elderly people. The protein markers are a combination of ALOX15, ATP6V1A, HBM, MVP and PCSK2, and one or more of them can be selected.

[0008] Among them, compared with normal levels, the expression levels of ALOX15, ATP6V1A, HBM, MVP and PCSK2 were downregulated in patients with heavy metal-related anxiety in the middle-aged and elderly population.

[0009] The source of the protein marker is tissue or blood, etc. Specifically, it is plasma, serum or blood extract, washing, biopsy or surgically removed tissue or liquid sample from the subject.

[0010] Preferably, the source of the protein markers is blood.

[0011] On the other hand, the present invention discloses the use of the aforementioned protein markers in a kit for early diagnosis of heavy metal-related anxiety in the middle-aged and elderly population.

[0012] Among them, the early diagnostic kit for detecting heavy metal-related anxiety in middle-aged and elderly people includes detection reagents that can detect the expression levels of ALOX15, ATP6V1A, HBM, MVP and PCSK2.

[0013] Among them, detection reagents include reagents for detecting protein levels by immunoblotting, enzyme-linked immunosorbent assay, mass spectrometry, radioimmunoassay, radioimmunodiffusion, immunoelectrophoresis, tissue immunostaining, immunoprecipitation analysis, complement fixation analysis, fluorescence activated cell sorting, mass analysis or protein microarray methods.

[0014] Specifically, the detection reagent is a binding agent that specifically binds to proteins encoded by ALOX15, ATP6V1A, HBM, MVP and PCSK2.

[0015] More specifically, protein binders include peptides, peptide mimetics, aptamers, spiegelmers, darpins, ankyrin repeat proteins, Kunitz-type domains, antibodies, single domain antibodies and / or monovalent antibody fragments, and the like.

[0016] On the other hand, the present invention discloses an assessment model for the anxiety risk of middle-aged and elderly people related to heavy metals. The risk assessment model uses the expression levels of the aforementioned protein markers (a combination of ALOX15, ATP6V1A, HBM, MVP and PCSK2) as variables (corresponding to corresponding scores), and uses a nomogram to visualize the model results. The single scores corresponding to each protein marker under different values ​​are added together to obtain a total score, which is then corresponded to the probability of anxiety events occurring in middle-aged and elderly people related to heavy metals.

[0017] The steps of associating marker levels with a certain probability or risk can be implemented and realized in different ways by those skilled in the art. Preferably, the measured concentrations of one or more protein markers are mathematically combined and the combined value is associated with the early diagnosis question. The determination of marker values ​​can be combined by any suitable prior art mathematical method.

[0018] The function for associating the marker combination with the disease preferably uses an algorithm developed and obtained by applying statistical methods. For example, suitable statistical methods are discriminant analysis (DA) (i.e., linear, quadratic, regular DA), Kernel methods (i.e., SVM), non-parametric methods (i.e., k-nearest neighbor classifiers), PLS (partial least squares), tree-based methods (i.e., logistic regression, CART, random forest methods, boosting / bagging methods), generalized linear models (i.e., logarithmic regression), principal component-based methods (i.e., SIMCA), generalized superposition models, fuzzy logic-based methods, neural network- and genetic algorithm-based methods, etc. A skilled person will have no problem selecting a suitable statistical method to evaluate the marker combination of the present invention and thereby derive a suitable mathematical algorithm.

[0019] Specifically, in the nomogram, the score range of ATP6V1A is 10.5-7, corresponding to 0-86 in the score scale; the score range of HBM is 10-6.5, corresponding to 0-52 in the score scale; the score range of MVP is 12-8, corresponding to 0-80 in the score scale; the score range of ALOX15 is 10.5-7, corresponding to 0-65 in the score scale; the score range of PCSK2 is 12-7.5, corresponding to 0-100 in the score scale; the probability of heavy metal-related anxiety events in the middle-aged and elderly population is 0.1, 0.3, 0.5, 0.7 and 0.9, corresponding to 146-148, 159-161, 166-168, 174-176 and 186-188 in the total score scale, respectively.

[0020] Beneficial effects of the present invention: The present invention can achieve early diagnosis of cadmium-induced anxiety in the elderly by detecting the expression levels of ALOX15, ATP6V1A, HBM, MVP and PCSK2, increase the sensitivity of detection, improve the detection ability and efficiency, and actively take intervention measures, which can not only delay the progression of anxiety symptoms and reduce the disability and mortality rates, but may even achieve the effect of early cure. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 Flowchart for constructing an early diagnosis model for anxiety symptoms in middle-aged and elderly people; Figure 2 These are the differentially expressed proteins screened between the anxiety group and the healthy control group; Figure 3 is the principal component analysis diagram; Figure 4 It is a quantitative heat map of differentially expressed proteins; Figure 5 is the relationship between the fitting error and log(λ); Figure 6 is the LASSO coefficient distribution diagram of differentially expressed proteins; Figure 7 It is a box plot of protein marker ALOX15 protein expression; Figure 8 It is a box plot of protein marker ATP6V1A protein expression; Fig. 9 It is a box plot of the protein marker HBM protein expression; Fig.10 It is a box plot of the protein marker MVP protein expression; Fig.11 It is a box plot of protein marker PCSK2 protein expression; Fig.12The receiver operating characteristic curve (ROC) diagram of the early diagnosis model; Fig.13 This is the confusion matrix diagram of the early diagnosis model; Fig.14 This is a calibration curve diagram for the early diagnosis model; Fig.15 It is the decision curve (DCA) diagram of the early diagnosis model; Fig.16 A nomogram for predicting the risk of heavy metal-related anxiety in middle-aged and elderly people. DETAILED DESCRIPTION

[0022] In the present invention, the term "middle-aged and elderly people" refers to people aged 55 and above, which usually includes retirees, the elderly and the elderly. They may face physiological and psychological changes in society, such as health problems, retirement life, family relationships, social participation, etc.

[0023] As used herein, the term "anxiety" refers to a persistent, irrational worry and fear that may stem from stress in life, uncertainty about the future, health problems, or other personal circumstances. It may manifest as psychological tension, insomnia, compulsive behavior, or physical discomfort. Anxiety usually requires professional assessment and treatment, such as counseling or medication.

[0024] In the present invention, protein markers include ALOX15, ATP6V1A, HBM, MVP and PCSK2. Protein markers such as ALOX15 (UniprotID: P16050), ATP6V1A (UniprotID: P38606), HBM (UniprotID: Q6B0K9), MVP (UniprotID: Q14764), PCSK2 (UniprotID: P16519); including genes and proteins encoded therein and homologs, mutations, and isoforms thereof. The term encompasses full-length, unprocessed protein markers, as well as any form of protein markers derived from processing in cells. The term encompasses naturally occurring variants of protein markers (e.g., splice variants or allelic variants). UniprotID is available at https: / / www.uniprot.org / .

[0025] The term "expression level" generally refers to the amount of a polynucleotide or amino acid product or protein in a biological sample. "Expression" generally refers to the process by which the information encoded by a gene is converted into a structure that exists and operates in a cell. Therefore, "expression" as used herein refers to transcription into polynucleotides, translation into proteins, or post-translational modifications to proteins. Fragments of transcribed polynucleotides, translated proteins, or post-translationally modified proteins are also considered to be products of expression, whether they are derived from transcripts produced by alternative splicing or degraded transcripts, or from post-translational modifications of proteins.

[0026] The term "diagnosis" is used herein to refer to the identification or classification of a molecular or pathological state, disease or condition. For example, "diagnosis" may refer to the identification of the risk of developing anxiety by the tissue / organ involved (e.g., the nervous system), or by a molecular signature (e.g., characterized by the expression of one or a combination of proteins encoded by specific genes).

[0027] The term "early diagnosis" refers to diagnosis of a disease before symptoms or at the early stages of disease. For example, a method for early diagnosis of heavy metal-related anxiety in the elderly may include measuring the expression of certain proteins in a biological sample from an individual.

[0028] The protein expression level described herein can be detected using measurement methods known in the art, including but not limited to methods for detecting the amount of polypeptides encoded by protein markers.

[0029] In this article, the amount of polypeptide can be detected by, for example, proteomics or reagents. Preferably, the sequencing technology is proteomics sequencing, more preferably targeted proteomics sequencing technology. Any suitable protein quantification method can be used in the method provided herein. Exemplary methods that can be used include, but are not limited to, immunoblotting (western blot), enzyme-linked immunosorbent assay (ELISA) and mass spectrometry, etc.

[0030] A "kit" is a manufactured article (e.g., package or container) containing probes for specific detection of a protein marker of the invention. Such kits may include a carrier means that is compartmentalized to tightly contain one or more container means (e.g., vials, tubes, etc.), each container means containing one of the separate components to be used in the method. For example, one of the container segments may contain a probe that is detectably labeled or can be detectably labeled.

[0031] The kit will typically include the above container and one or more other containers containing materials desired from a commercial and user perspective, including buffers, diluents, filters, needles, syringes, and package inserts containing instructions for use. A label may be present on the container to indicate that the composition is used for a specific therapeutic or non-therapeutic application, and may also indicate directions for in vivo or in vitro use, such as those described above. Other optional components in the kit include one or more buffers (e.g., blocking buffer, wash buffer, substrate buffer, etc.), other reagents (e.g., substrates chemically altered by enzyme labeling), epitope retrieval solutions, control samples (positive and / or negative controls), control sections, etc.

[0032] In the prior art, receiver operating characteristic (ROC) curves, confusion matrices, calibration curves and decision curves are often used to evaluate diagnostic models.

[0033] Receiver Operating Characteristic curve, with false positive rate (1-specificity) on the horizontal axis and true positive rate (sensitivity) on the vertical axis. Each point on the ROC line plot represents a sensitivity / 1-specificity pair corresponding to a specific decision threshold. A test with perfect discrimination (no overlap in the two outcome distributions) has an ROC line plot through the upper left corner, where the true positive score is 1.0, or 100% (perfect sensitivity), and the false positive score is 0 (perfect specificity). The theoretical line plot for a test that does not discriminate (the two groups have the same outcome distributions) is a 45° diagonal line from the lower left corner to the upper right corner. Most line plots fall between these two extremes. Qualitatively, the closer the curve is to the upper left corner, the larger the area under the curve (AUC), and the higher the overall accuracy of the model. It is generally believed that when 0.90≤ AUC <1.00, the model has excellent distinguishing ability; 0.75≤ AUC<0.90, the model has good distinguishing ability; 0.60≤AUC<0.75, the model has certain distinguishing ability, but it is not recommended; AUC<0.60, the model has poor distinguishing ability.

[0034] The confusion matrix, also known as the error matrix, is a standard format for expressing accuracy evaluation. It is commonly expressed in a 2-row, 2-column matrix. Each row of the confusion matrix represents the predicted category, and the total number of each row represents the number of data predicted to be in that category; each column represents the true category of the data, and the total number of data in each column represents the number of data instances in that category. So after obtaining the confusion matrix of the model, you need to see how many observations are in the corresponding positions of the second and fourth quadrants. The more values ​​here, the better; conversely, the fewer observations appearing in the corresponding positions of the first and third quadrants, the better.

[0035] Calibration curves are often used to evaluate the consistency between the model's predicted probability and the actual probability of occurrence, also known as goodness of fit. Generally, the categories directly determined by the model (such as whether or not a person is ill) are called relative risk predictions; if it is assumed that everyone has a risk of illness, the difference is that the risk of illness is different, then the specific value of the risk of illness determined by the model, that is, the model's predicted probability, is called an absolute risk prediction. Calibration is an evaluation indicator for evaluating the accuracy of the model's absolute risk prediction. At present, the Hosmer-Lemeshow goodness of fit test method is commonly used to evaluate the calibration of binary classification models. The basic idea of ​​this method is to divide the observed samples into several groups according to the size of the predicted probability, and then calculate the observed frequency (the number of people who are actually ill in the group) and the predicted frequency (the number of people in the group predicted by the model to be ill) of each group, and finally calculate a fitting straight line, and use the fitting straight line to test the difference between the observed frequency and the predicted frequency. The calibration curve is the visualization of the results of the Hosmer-Lemeshow test. The higher the consistency between the model's predicted probability and the actual observation results, the better the calibration of the model.

[0036] The decision curve is a method used to evaluate the clinical application value of a prediction model. It draws a decision curve to compare the clinical benefits of different prediction models at different treatment decision thresholds, helping medical researchers and clinicians make the best choice between different prediction models to achieve better clinical prediction results. In the figure, the probability threshold is the horizontal axis and the net benefit rate is the vertical axis. The red line is the decision curve, which is the change in the net benefit rate brought about by the intervention based on the prediction results of the logistic model with the probability threshold, while the bottom black horizontal line None represents the net benefit rate of 0 when no treatment is performed, and the gray curve All represents the change in the net benefit rate of all treatments with the probability threshold. Within a certain range, the higher the net benefit rate of the model, the greater its clinical utility.

[0037] The present invention will be further described in detail below in conjunction with the accompanying drawings and examples. The following examples are only used to illustrate the present invention and are not intended to limit the scope of the present invention. The experimental methods in the examples where specific conditions are not specified are usually carried out under conventional conditions or under conditions recommended by the manufacturer.

[0038] 1. Sample collection and storage Serum samples were collected from 25 elderly patients with anxiety (anxiety group) and 25 elderly healthy controls (healthy control group). The basic information of the patients and healthy controls and related laboratory test indicators were recorded in detail when collecting samples, including age, gender, weight, BMI, education, triglycerides, total cholesterol, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol and fasting blood glucose, etc. There was no statistical difference in age, gender, BMI and fasting blood glucose between the anxiety group and the healthy control group.

[0039] Inclusion criteria for the middle-aged and elderly population: People aged 55 and above, this group usually includes retirees, the elderly population and the very old. They may face physical and psychological changes in society, such as health problems, retirement life, family relationships, social participation, etc.

[0040] Inclusion criteria for the case group: 1. The participant is over 55 years old; 2. Anxiety exists. According to the Hamilton Anxiety Rating Scale (HAMA) score, if the total score is ≥29, it may be severe anxiety; if the total score is ≥21, it is definitely obvious anxiety; if the total score is ≥14, it is definitely anxiety; if the total score is more than 7, it may be anxiety; 3. Consent to participate: Participants or their legal guardians must agree to participate in this study and sign the informed consent form; 4. No use of antidepressants or antipsychotics for at least 3 months; 5. Language comprehension ability: Participants have basic language comprehension and expression skills and are able to take tests and communicate; 6. Living environment: The participants were permanent residents of Gangkou Town, Jiujiang City, Jiangxi Province, a cadmium-contaminated area.

[0041] Inclusion criteria for the healthy group: 1. Age: Participants must be over 55 years old; 2. Participants have no anxiety symptoms. According to the HAMA Anxiety Scale, if the score is less than 7, there is no anxiety symptom; 3. Consent to participate: Participants or their legal guardians must agree to participate in this study and sign the informed consent form; 4. Language comprehension ability: Participants have basic language comprehension and expression skills and are able to take tests and communicate; 5. Living environment: The participants were also permanent residents of Gangkou Town, Jiujiang City, Jiangxi Province, a cadmium-contaminated area.

[0042] Exclusion criteria: 1. Age: People below 55 years old will be excluded.

[0043] 2. Health status: Severe heart disease, lung disease, kidney disease or other serious physical illness, or mental illness Participants with schizophrenia, severe depression, etc. will be excluded.

[0044] 3. If the consent of the participant or his / her legal guardian is not obtained, the participant will be excluded.

[0045] 4. Language comprehension ability: Participants with severely impaired language comprehension and expression ability and inability to communicate effectively will be excluded. Venipuncture, collect 2mL of peripheral blood in a red blood collection tube or a golden blood collection tube (coagulant tube), gently invert to mix, and let stand for 30 minutes; within 60 minutes, centrifuge the blood collection tube at 2500×g for 10 minutes, transfer the upper serum to a new centrifuge tube; dispense serum samples in 100μL per tube, and label the sample tubes; quickly freeze the dispensed serum samples in a -20℃ refrigerator for short-term storage, or in a -80℃ refrigerator for long-term storage, and avoid repeated freezing and thawing of the samples.

[0046] 2. Experimental methods This patent uses the next generation of label-free quantitative proteomics technology to complete the analysis. In the data independent acquisition (DIA) mode, the latest high-resolution mass spectrometry is used to simultaneously collect peptide ion characteristics in terms of mass number and retention time. Compared with the traditional method of extracting a single ion for fragmentation analysis, the mass spectrometer in the DIA mode is set to a wide parent ion window for cyclic acquisition and simultaneous fragmentation of multiple peptide ions. The complete collection of all detectable protein spectral peak information in the sample is achieved, so that a large number of samples can be analyzed with high repeatability. The DIA process provides an ideal platform for qualitative analysis of differentially expressed proteomes or quantitative analysis of proteomes of massive samples.

[0047] The detection and analysis process steps are as follows: 1) Sample preparation The sample was enriched using superparamagnetic iron oxide nanoparticles. Take 20 microliters of serum sample, dilute it with loading buffer (10mM Tris-HCl, 1mM EDTA, 150mM KCl, 0.05% CHAPS), mix it evenly with 1 mg nanoparticle suspension, and incubate it at 37°C for 1 hour. Wash the magnetic beads twice with loading buffer, and then wash the magnetic beads once with loading buffer without CHAPS (10mMTris-HCl, 1mM EDTA, 150mM KCl). Adsorb the magnetic beads on the magnetic stand, discard the supernatant, and obtain the protein-enriched nanoparticles. Add lysis buffer (1% SDC / 100 mM Tris-HCl, pH=8.5 / 10mM TCEP / 40mM CAA) to the sample and incubate at 60°C for 30 minutes for reduction alkylation reaction. Add an equal volume of ddH2O to dilute the SDC concentration to less than 0.5%, add 1 μg of trypsin, and incubate at 37°C with shaking overnight for enzyme digestion. The next day, add TFA to terminate the digestion, take the supernatant and desalt it with an SDB-RPS desalting column, vacuum dry it, and freeze it at -20°C for later use.

[0048] Reagent Description: Tris-HCl: tris(hydroxymethyl)aminomethane hydrochloride; EDTA: ethylenediaminetetraacetic acid; KCl: potassium chloride; CHAPS: 3-[(3-cholamidopropyl)dimethylamino]propanesulfonic acid inner salt; TCEP: tris(2-carbonylethyl)phosphine hydrochloride; CAA: calcium acetylacetonate; ddH2O: double deionized water; SDC: sodium deoxycholate; TFA: trifluoroacetic acid; SDB-RPS: Sulfonated styrene-vinylbenzene copolymer.

[0049] 2) Mass spectrometry The mass spectrometry of the samples was detected using an UltiMate 3000 RSLCnano nanoliter liquid phase (Thermo) in series with a timsTOFPro mass spectrometer (Bruker). The peptide samples were injected by an autosampler, coupled to a C18 trapping column (75 µm*2cm, 3 µmparticle size, 100 Å pore size, Thermo), and then separated in an analytical column (75 µm*25cm, 1.6 µmparticle size, 100 Å pore size, IonOpticks). The analytical gradient was established using mobile phase A (0.1% formic acid) and mobile phase B (0.1% formic acid in ACN). The flow rate of the analysis was set to 300 nL / min. The mass spectrometer was used for data acquisition in diaPASEF mode. The capillary voltage was set to 1400 V. The scan range of the MS1 and MS2 spectra was set to 100-1700 m / z. The ion mobility range was set to 0.6-1.6 Vs / cm2. Accumulation time and ramp time were set to 100 ms. The diaPASEF acquisition window was set using timsControl software according to the distribution law of mass-to-charge ratio-ion mobility. The collision energy was set to increase linearly from 59 eV at 1 / K0=1.6 Vs / cm2 to 20 eV at 1 / K0=0.6 Vs / cm2 according to the ion mobility.

[0050] 3) Data analysis ① Database search DIA raw data were analyzed using the library-free mode of DIA-NN (1.8.1) software. The database used for the search was the HUMAN protein sequence database downloaded from Uniprot. The search parameters were mainly set by default, and the key parameters are described as follows: the Precursor ion generation option was enabled for theoretical spectrum library prediction; Trypsin / P was used, and a maximum of 1 missed cleavage site was allowed; Carbamidomethyl (C) modification was set as fixed modification; Oxidaton (M) and Acetylation (protein N-terminal) were set as variable modifications; MS1 and MS2 mass tolerances were set to 15ppm; MBR was enabled; Heuristic protein inference was enabled; FDR was set to 1%. Protein quantitative information was normalized using the MaxLFQ algorithm.

[0051] ② Data cleaning, filtering, conversion and filling Contaminant proteins in the quantitative protein matrix were removed, and proteins with a missing ratio of more than 70% in the samples were filtered and logarithmically transformed. Then, missing values ​​were filled with values ​​representing a normal distribution near the detection limit of the mass spectrometer. To do this, we determined the mean and standard deviation of the actual intensity distribution, and then created a new distribution with a downward shift of 1.8 standard deviations and a width of 0.25 standard deviations. These values ​​were used to estimate the missing values ​​in the protein matrix. Finally, proteins with a missing ratio of more than 50% in both groups were removed, and the remaining proteins were used for subsequent analysis.

[0052] ③ Differential expression analysis The protein quantitative data of the anxiety group of the middle-aged and elderly people were compared with the healthy control group of the middle-aged and elderly people. The fold change (FC) and difference test (bilateral P <0.05) were used as the criteria to screen the differentially expressed proteins between the two groups. The screening criteria were |log2FC|≥0.26, p <0.05. The results are as follows Figure 2 shown.

[0053] ④ Key protein screening The total samples were randomly divided into training set and test set in a ratio of 4:1. In the training set, the Lasso regression model was used for five-fold cross validation to screen out N differential protein features; then, these N proteins were screened by three-fold cross validation, and the optimal diagnostic model was selected by the discrimination index AUC and calibration index Brill score calculated by three-fold cross validation to select the optimal protein marker combination for constructing the logistic regression model. The differentially expressed proteins in the optimal combination are the disease-related protein markers.

[0054] ⑤ Model construction and evaluation Logistic regression and cross-validation were used to randomly select protein combinations from protein markers in the training set to construct multiple prediction models, and the optimal model was selected based on the area under the receiver operating characteristic curve (AUC) and the Brill score. The optimal prediction model was then evaluated. The discrimination, calibration, and clinical utility performance of the model in the training set and the test set were evaluated by the receiver operating characteristic (ROC) curve, calibration curve, and decision curve in the test set and the training set, respectively. It is generally believed that when 0.90≤AUC<1.00, the model has excellent discrimination ability; when 0.75≤AUC<0.90, the model has good discrimination ability; when 0.60≤AUC<0.75, the model has certain discrimination ability, but it is not recommended; when AUC<0.60, the model has poor discrimination ability. All statistical analyses were performed using R (version 4.3.2) and Python (version 3.10.12).

[0055] 3. Results and analysis 1) From the population of middle-aged and elderly questionnaires, 25 pairs of information-matched middle-aged and elderly anxiety patients and middle-aged and elderly healthy controls were selected for proteomic research, and an early diagnosis model was established. Figure 1 A flowchart describing the construction of an early diagnosis model for anxiety symptoms in middle-aged and elderly people is presented.

[0056] 2) The demographic and clinical characteristics of the middle-aged and elderly population participating in this study were summarized, and the results are shown in Table 1.

[0057] Table 1 Demographic and clinical characteristics of the middle-aged and elderly population Variables class class Total Anxiety0 Anxiety1 n 50 25 25 HAMD(median [lQR]) 7.50 [4.00,13.00] 4.0 [0.0,6.0] 12.0 [9.0,13.0] Gender (%) 0 28 (56.0) 12(48.00) 16 (64.00) 1 22(44.0) 13 (52.00) 9 (36.00) Age (median [lQR]) 63.00 [57.00,67.00] 64.0 [58.0,67.0] 62.0 [55.0,67.0] Education (median [lQR]) 12.00 [9.00,14.25] 12.0 [9.0,12.0] 12.0 [9.0,15.0] BMI (mean (SD)) 23.98(2.76) 24.1 (2.6) 23.8(2.9) Smoking(%) 0 35(70.0) 19(76.00) 16 (64.00) 1 15(30.0) 6 (24.00) 9 (36.00) Hypertension(%) 0 16 (32.0) 13 (52.00) 3(12.00) 1 34 (68.0) 12(48.00) 22(88.00) DM(%) 0 35(70.0) 17(68.00) 18(72.00) 1 15 (30.0) 8 (32.00) 7(28.00) Hyperlipidemia(%) 0 33 (66.0) 14 (56.00) 19(76.00) 1 17(34.0) 11(44.00) 6 (24.00) VRF (mean (SD)) 9.32(3.85) 8.8(3.5) 9.8(4.2) HAMA (median [lQR]) 6.50 [4.00,10.75] 4.0 [3.0,6.0] 10.0[7.0,13.0] MMSE (median [lQR]) 23.50 [22.00,27.00] 26.0 [23.0,28.0] 23.0 [21.0,24.0] VFT (mean (SD)) 30.86 (9.97) 33.6(10.3) 28.1 (9.0) TMT_A(median [lQR]) 63.47 [45.15,80.63] 56.0 [44.8,70.3] 77.0 [47.4,114.9] TMT_B(median [IQR]) 101.89 [75.22,180.64] 96.2 [68.3,196.0] 112.1 [76.3,179.0] Stroop_word(median [IQR]) 23.73[18.94,31.60] 22.4[18.9,27.1] 25.8[20.0,41.0] Stroop_color(median [IQR]) 26.60[21.23,42.25] 26.4[19.2,34.5] 31.8[22.0,43.8] Stroop_color_word(median [IQR]) 45.28[32.08,74.75] 38.9 [29.1,55.6] 50.7 [33.0,82.6] VST(median [IQR]) 19.05[8.02,28.91] 17.9 [8.1,28.6] 20.1 [8.0,29.0] IADL (median [IQR]) 12.00 [12.00,15.75] 12.0 [12.0,14.0] 13.0[12.0,17.0] GUL(median [IQR]) 5.17[4.72,6.33] 5.2 [4.8,6.4] 5.2 [4.6,6.2] TC (mean (SD)) 4.13(1.12) 4.0(1.2) 4.2(1.0) TG (median [lQR]) 1.46 [0.99,1.95] 1.4[1.0,2.0] 1.5[1.0,2.0] LDL_C(mean (SD)) 0.99[0.85,1.25] 2.3(0.9) 2.5 (0.7) HDL_C (median [lQR]) 0.99[0.85,1.25] 1.0[0.8,1.1] 1.1 [0.9,1.3] Hg (median [lQR]) 4.32[2.5,5.66] 3.8[2.1,53] 4.9 [2.9,5.7] apoE4(%) 0 20(40.0) 13(52.00) 7(28.00) 1 30(60.0) 12(48.00) 18(72.00) apoE3(%) 0 23(46.0) 8(32.00) 15(60.00) 1 27(54.0) 17(68.00) 10(40.00) Lead (mean(SD)) 5.41(2.82) 6.2(3.0) 4.6(2.4) Cd(median [lQR]) 3.88[2.15,7.40] 2.8[1.5,5.2] 6.4[3.4,9.0] (1) t: t-test, Z: Mann-Whitney test; (2) SD: standard deviation; M: median; Q1: first quartile; Q3: third quartile.

[0058] 3) Mass spectrometry quality control information (1) Using data-independent acquisition (DIA) technology, we obtained the serum proteome profiles of 25 middle-aged and elderly patients with anxiety and 25 middle-aged and elderly healthy controls.

[0059] (2) The expression of 126 proteins in the middle-aged and elderly patients with anxiety was significantly different from that in the healthy control group. Compared with the healthy control group, the expression of 27 proteins in the anxiety group was upregulated and the expression of 99 proteins was downregulated. Figure 2 shown.

[0060] (3) The results of principal component analysis (PCA) showed the intensity of 126 differentially expressed proteins, showing significant differences between middle-aged and elderly people with anxiety and the healthy control group. Figure 3 shown.

[0061] (4) A heat map was created using the expression levels of 126 differentially expressed proteins, showing that there were significant differences in the expression levels of differentially expressed proteins between middle-aged and elderly people with anxiety and the healthy control group. Figure 4 shown.

[0062] 4) Protein screening The total samples were randomly divided into training set and test set in a ratio of 4:1. In the training set, the Lasso regression model was used for five-fold cross validation to screen out 8 important proteins from 126 differentially expressed proteins. These 8 important proteins were randomly combined for Logistic regression analysis to construct a diagnostic model, and the optimal diagnostic model was selected by the discrimination index AUC and calibration index Brier score calculated by three-fold cross validation. The screening results are shown in Figure 2. Figure 5 and Figure 6 Finally, ALOX15, ATP6V1A, HBM, MVP and PCSK2 were selected as the best protein marker combination, and a clinical risk model was established. The model results are shown in Table 2.

[0063] Table 2 Logistic regression model Unnamed:0 Estimate Std.Error z value Pr(>|z|) (Intercept) 100.67 46.50 2.17 0.03 ATP6V1A -2.62 2.06 -1.28 0.20 HBM -1.59 1.29 -1.23 0.22 MVP -2.14 1.24 -1.73 0.08 ALOX15 -1.99 1.21 -1.65 0.10 PCSK2 -2.38 1.18 -2.02 0.04 “Estimate” is the regression coefficient, “Std.Error” is the standard error of the regression coefficient, “z value” is the test statistic of the Z test on the regression coefficient, and “Pr(>|z)” is the P value of the Z test on the regression coefficient.

[0064] The expression levels of the five protein markers ALOX15, ATP6V1A, HBM, MVP and PCSK2 in the two groups of people were plotted as box plots to visualize the expression differences of these five protein markers in the serum of middle-aged and elderly people with anxiety and the healthy control group. The box plots showed that compared with the healthy control group, the expression levels of ALOX15, ATP6V1A, HBM, MVP and PCSK2 in middle-aged and elderly people with anxiety were downregulated. The results are as follows Figure 7-11 shown.

[0065] 4. Model Evaluation When evaluating the early diagnosis model composed of protein markers ALOX15, ATP6V1A, HBM, MVP and PCSK2, 37 samples were divided into two sets according to the proportion. 80% of the data was used as a training set (n=29), and 20% of the data was used as a test set (n=8). The training set and the test set were used for model evaluation.

[0066] 1) Receiver operating characteristic curve (ROC) plot The results are as follows Fig.12 As shown in the figure, the ROC curve is used to evaluate the discrimination of the early diagnosis model. The area under the ROC curve (AUC) is generally used to evaluate the model prediction performance. Evaluation criteria: AUC ≥ 0.90, the model has excellent discrimination ability; 0.90> AUC ≥ 0.75, the model has good discrimination ability; 0.75> AUC ≥ 0.60, the model has a certain discrimination ability, but it is not recommended; AUC < 0.60, the model has poor discrimination ability. The ROC curve shows that the AUC (95% CI) of the training set is 0.948 (0.871-1), and the AUC (95% CI) of the test set is 0.938 (0764-1). The ROC curve shows that the early diagnosis model for anxiety symptoms in the middle-aged and elderly population obtained this time has good discrimination ability.

[0067] 2) Confusion Matrix The results are as follows Fig.13 As shown in the figure, the confusion matrix diagram shows the classification of the test set by the early diagnosis model. The horizontal axis represents the actual category, and the vertical axis represents the predicted category. The people in the upper left quadrant and the lower right quadrant are classified into the correct category. The confusion matrix diagram results show that the model sensitivity is 94.4% (17 / 18) and the specificity is 73.4% (14 / 19), indicating that the early diagnosis model has good predictive ability.

[0068] 3) Calibration curve The results are as follows Fig.14 As shown in the figure, the calibration curve is used to evaluate the calibration of the early diagnosis model, that is, the consistency between the probability of the predicted outcome and the actual observed frequency of occurrence. The dotted line is the ideal curve, the red line is the actual curve of the model, and the blue line is the curve after resampling calibration. The closer the calibration curve is to the ideal curve, the better the model effect. The results of the calibration curve show that the consistency between the predicted probability and the observed probability of the early diagnosis model obtained this time is good.

[0069] 4) Decision curve The results are as follows Fig.15 As shown in the figure, the decision curve is used to evaluate the clinical utility of the model. The net rate of return reflects the clinical value of the model. The larger the net rate of return, the better the model effect. In the decision curve, the black horizontal line indicates that all people are not treated, and the net benefit is 0; the gray oblique line indicates that all people are treated, and the net benefit is a reverse oblique line with a negative slope; the red curve is the net benefit curve of the current early diagnosis model. The higher the net rate of return of the model, the greater its clinical utility. The decision curve shows that within a certain range, the net rate of return of the early diagnosis model is higher, and is higher than the two options of all treatment and no treatment, proving that the clinical utility of the early diagnosis model obtained this time is greater.

[0070] 5. Model Visualization The results are as follows Fig.16As shown in the figure, the model results are visualized using a nomogram to facilitate the prediction of disease risk. The individual scores (Points) corresponding to each protein marker at different values ​​are added together to obtain the total score (Total Points), which is then converted to the Risk score, which is the probability of anxiety events occurring in the middle-aged and elderly population. Specifically, in the nomogram, the score range of ATP6V1A is 10.5-7, corresponding to 0-86 in the score scale; the score range of HBM is 10-6.5, corresponding to 0-52 in the score scale; the score range of MVP is 12-8, corresponding to 0-80 in the score scale; the score range of ALOX15 is 10.5-7, corresponding to 0-65 in the score scale; the score range of PCSK2 is 12-7.5, corresponding to 0-100 in the score scale; the probability of non-alcoholic fatty liver disease events in sedentary people is 0.1, 0.3, 0.5, 0.7 and 0.9, corresponding to 146-148, 159-161, 166-168, 174-176 and 186-188 in the total score scale, respectively. As can be seen from the figure, the upper limit of the predicted probability can reach 0.9 when the five markers are combined for risk prediction.

[0071] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

Claims

1. A protein marker for diagnosing heavy metal-related anxiety in the elderly, characterized in that: The protein marker is one or more of ALOX15, ATP6V1A, HBM, MVP or PCSK2.

2. An application of the protein marker for diagnosing heavy metal-related anxiety in the elderly according to claim 1, characterized in that: One or more of the protein markers ALOX15, ATP6V1A, HBM, MVP or PCSK2 are used for early diagnosis of heavy metal-related anxiety in middle-aged and elderly people, and the expression levels of ALOX15, ATP6V1A, HBM, MVP or PCSK2 are downregulated compared to normal levels.

3. The use according to claim 2, characterized in that: The protein markers are extracted from tissue or blood.

4. The use according to claim 3, characterized in that: The protein markers are derived from blood.

5. A diagnostic kit for diagnosing heavy metal-related anxiety in the elderly, characterized in that: Contains a detection reagent capable of detecting the expression level of the protein marker according to claim 1.

6. The diagnostic kit according to claim 5, characterized in that The detection reagents include reagents for detecting protein levels by immunoblotting, enzyme-linked immunosorbent assay, mass spectrometry, radioimmunoassay, radioimmunodiffusion, immunoelectrophoresis, tissue immunostaining, immunoprecipitation analysis, complement fixation analysis, fluorescence activated cell sorting, mass analysis or protein microarray.

7. The diagnostic kit according to claim 5, characterized in that The detection reagent can specifically bind to one or more of ALOX15, ATP6V1A, HBM, MVP or PCSK2.

8. The diagnostic kit according to claim 7, characterized in that The detection reagents comprise peptides, peptide mimetics, aptamers, spiegelmers, darpins, ankyrin repeat proteins, Kunitz-type domains, antibodies, single domain antibodies and / or monovalent antibody fragments.

9. A risk assessment model for anxiety in the middle-aged and elderly population related to heavy metals, characterized in that: The method comprises the following steps: using the expression level of the protein marker described in claim 1 as a variable, visualizing the model results by using a nomogram, summing up the individual scores corresponding to each protein marker at different values ​​to obtain a total score, and then corresponding the total score to the probability of heavy metal-related anxiety events occurring in the middle-aged and elderly population.

10. The risk assessment model according to claim 9, characterized in that: The score range of ATP6V1A is 10.5-7, corresponding to 0-86 in the score scale; the score range of HBM is 10-6.5, corresponding to 0-52 in the score scale; the score range of MVP is 12-8, corresponding to 0-80 in the score scale; the score range of ALOX15 is 10.5-7, corresponding to 0-65 in the score scale; the score range of PCSK2 is 12-7.5, corresponding to 0-100 in the score scale; the probability of heavy metal-related anxiety events in the elderly population is 0.1, 0.3, 0.5, 0.7 and 0.9, corresponding to 146-148, 159-161, 166-168, 174-176 and 186-188 in the total score scale, respectively.

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