Reagent and system for diagnosing obstructive sleep apnea and application

Abstract

The invention belongs to the field of medical health industry, and particularly relates to a reagent and system for diagnosing obstructive sleep apnea and application. The reagent for diagnosing the obstructive sleep apnea comprises a gene expression level detection reagent for ADRB1 and TRIM49. The reagent for diagnosing the obstructive sleep apnea only contains two genes, diagnosis of the obstructive sleep apnea can be achieved by detecting expression levels of a small number of genes, the detection cost is low, and the efficiency is high.

Description

Technical Field

[0001] The present invention belongs to the field of the medical and health industry, and particularly relates to a reagent, a system and an application for diagnosing obstructive sleep apnea. Background Art

[0002] Obstructive Sleep Apnea (OSA) is a common sleep disorder characterized by repeated episodes of apnea or hypopnea during sleep, usually caused by partial or complete obstruction of the upper airway. The clinical manifestations of Obstructive Sleep Apnea (OSA) typically include severe snoring, nocturnal apnea (observed by family members or partners as breathing interruptions), frequent nocturnal awakenings, morning headache, and dry mouth. Patients often cannot obtain deep sleep due to repeated apnea, resulting in symptoms such as daytime sleepiness, fatigue, inattention, and memory loss. Untreated OSA for a long time may also increase the risk of chronic diseases such as hypertension, heart disease, stroke, and diabetes.

[0003] Polysomnography (PSG) is a comprehensive examination method currently used for diagnosing sleep disorders. By simultaneously monitoring multiple physiological parameters, it comprehensively evaluates sleep quality. Its principle is based on recording and analyzing physiological data such as electroencephalogram activity, eye movement, electromyogram, respiration, electrocardiogram, and blood oxygen during sleep to help doctors identify various sleep problems, such as obstructive sleep apnea, periodic limb movement disorder, etc. Its implementation is that before the patient falls asleep in the sleep center, technicians attach various sensors and electrodes to the patient's scalp, face, chest, abdomen, legs and other parts, monitor and record the data in real time during the patient's normal sleep process. After the examination, professional doctors analyze each physiological data and make a diagnosis in combination with the symptoms. It requires ensuring the patient's comfort to minimize interference with sleep, accurately placing the electrodes to ensure data quality, and paying attention to important parameters such as respiration, heart rate, and blood oxygen changes at night.

[0004] Although polysomnography (PSG) is the gold standard for diagnosing sleep disorders, it also has some drawbacks that cannot be ignored. First of all, PSG usually needs to be carried out in a professional sleep center, which makes it not suitable as a routine screening tool. To perform a complete PSG examination, the patient needs to stay overnight in the sleep center and wear a series of electrodes and sensors. This operation is not only relatively troublesome for the patient, but also requires relatively complex equipment and technical support, so its cost is relatively high.

[0005] Secondly, the PSG examination process is not very comfortable for patients. During the entire monitoring process, patients need to wear multiple electrodes, sensors, and strap devices, which may restrict body movements and even interfere with normal sleep. Such an environment and the devices themselves may affect the patients' sleep quality, resulting in the examination results not necessarily fully reflecting the patients' sleep states in daily life. Especially for some patients with mild sleep disorders, their symptoms may be most obvious in the natural sleep state, so the severity of the problem may not be fully reflected in the examination environment of the sleep center. At this time, false negatives may occur in the examination results, bringing certain difficulties to the diagnosis.

[0006] In addition, PSG is only a one-time sleep monitoring, usually only providing single-night sleep data and unable to conduct long-term and continuous tracking. Many sleep disorders, especially problems such as nocturnal apnea, may be intermittent or only appear in certain specific sleep stages. If only through one-night monitoring, it is very likely to miss some abnormal conditions with low attack frequencies or related to specific sleep stages. In addition, the examination results of PSG need to be interpreted by professional doctors, and the whole process may take a long time to analyze and confirm. Therefore, for some patients in urgent need of a clear diagnosis, the waiting time may also bring certain inconveniences.

[0007] Although the accuracy and comprehensiveness of PSG are very high, it is not a tool suitable for large-scale screening, especially for those patients with mild sleep disorders, and more convenient and economical diagnostic methods may be needed. Summary of the Invention

[0008] The present invention provides a reagent, a system and an application for diagnosing obstructive sleep apnea.

[0009] The technical solution provided by the present invention is as follows:

[0010] Application of a reagent for detecting the gene expression levels of ADRB1 and TRIM49 in the preparation of a product for diagnosing obstructive sleep apnea.

[0011] Further, the product for diagnosing obstructive sleep apnea is used to detect the mRNA levels of the gene expressions of ADRB1 and TRIM49.

[0012] The present invention also provides a reagent for diagnosing obstructive sleep apnea, and the reagent for diagnosing obstructive sleep apnea includes a reagent for detecting the gene expression levels of ADRB1 and TRIM49.

[0013] Further, the test sample of the detection reagent is set as a peripheral blood sample.

[0014] The present invention also provides a detection system for obstructive sleep apnea, including a gene expression level acquisition module and a judgment module;

[0015] The gene expression level acquisition module is used to acquire the gene expression levels of ADRB1 and TRIM49 in a sample to be tested and submit them to the judgment module;

[0016] The judgment module uses the acquired gene expression levels of ADRB1 and TRIM49 as the input of a classifier to judge the obstructive sleep apnea level of the sample to be tested.

[0017] Further, the judgment value index used by the judgment module is: 916.666 - 83.333 * ADRB1 expression level - 20.833 * TRIM49 expression level.

[0018] Further, if the judgment value index ≥ 92.210, it is diagnosed as obstructive sleep apnea; if the judgment value index < 92.210, it is diagnosed as non-obstructive sleep apnea.

[0019] Further, the detection system further includes an analysis module, which is used to generate personalized diagnostic suggestions by combining the results of the judgment module and the obstructive sleep apnea training set, and obtain personalized diagnostic assistance suggestions through comprehensive analysis by combining the detailed data of obstructive sleep apnea.

[0020] Further, the analysis module is used to integrate the analysis results and provide personalized diagnostic suggestions to assist doctors in formulating treatment plans. By performing data integration, it assists doctors in formulating personalized treatment plans and diagnostic suggestions to obtain an analysis report on obstructive sleep apnea.

[0021] The present invention also provides a kit for diagnosing obstructive sleep apnea, and the kit includes reagents for detecting the gene expression levels of ADRB1 and TRIM49.

[0022] Beneficial effects

[0023] Currently, there is no method for diagnosing sleep apnea based on peripheral blood. The reagent, system, and application for diagnosing obstructive sleep apnea described in the present invention are based on the detection of gene expression values in peripheral blood. Peripheral blood sampling is simple and relatively non-invasive. Blood samples can be collected in medical institutions at any level without special equipment. In addition, the reagent, system, and application for diagnosing obstructive sleep apnea in the present invention are based on patients with simple snoring and obstructive sleep apnea, with patients with simple snoring as the control. The development and validation populations are all Chinese. This innate advantage helps to better distinguish between Chinese patients with simple snoring and obstructive sleep apnea. In addition, the reagent, system, and application for diagnosing obstructive sleep apnea described in the present invention have been verified by an external cohort and only contain 2 genes. The diagnosis of obstructive sleep apnea can be achieved by detecting the expression levels of a small number of genes, with a relatively low detection cost and high efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 It is a flow chart for classifier opening;

[0025] Figure 2 It is the classifier development process; Figure A is a volcano plot of differentially expressed genes (DEGs) identified by the differential analysis method, and the red dots indicate significant differentially expressed genes, obtaining a preliminary candidate gene group; Figure B is the further screening of the candidate gene group using LASSO regression; Figure C is the cross-validation for selecting the tuning parameters of the LASSO model; Figure D is the final determination of 2 genes by Logistic regression and the construction of a Nomogram diagnostic model;

[0026] Figure 3 It is to establish a diagnostic model based on this classifier; Figure A is the ROC curve of the classifier model in the training, internal validation, and external validation cohorts, and the corresponding AUC values are 0.946, 0.869, and 0.889 respectively; Figure B is the calibration plot of the training set. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0027] The present invention will be further described below in conjunction with the drawings and embodiments.

[0028] Example 1

[0029] The occurrence and development of diseases is a multi-step process involving dynamic changes in gene expression. These gene changes can affect cell behavior, reveal disease characteristics, and also make them potential biomarkers. Transcriptome analysis based on peripheral blood has been widely used in clinical and scientific research to identify valuable diagnostic and prognostic markers. This detection method is not only less invasive but also relatively convenient.

[0030] In this invention, we utilized two publicly available datasets released in the Gene Expression Omnibus (GEO), namely the GSE75097 dataset and the GSE75097 dataset, to develop and validate an application for diagnosing sleep apnea patients based on gene expression levels measured in peripheral blood. The GSE75097 dataset is a publicly available dataset containing the gene expression profiles of peripheral blood mononuclear cells from 6 patients with primary snoring and 42 patients with sleep apnea (OSA) in Taiwan Province, China. The OSA patients could be divided into 2 groups: patients with simple OSA (28 cases) and OSA patients treated with a ventilator (14 cases). The GSE226379 dataset is a publicly available dataset containing the gene expression profiles of peripheral blood mononuclear cells from 3 healthy controls and 6 patients with sleep apnea (OSA) in Guangdong Province, China.

[0031] By using multiple packages based on the R language (hereinafter referred to as "packages" for short), we analyzed the gene expression datasets of blood samples from 34 snoring patients (6 snoring patients and 28 obstructive sleep apnea patients), obtained the differential genes between obstructive sleep apnea and simple snoring patients using differential gene analysis methods, and then developed a classifier consisting of 2 genes (ADRB1 and TRIM49) based on this candidate gene group using machine learning algorithms (LASSO analysis and multivariate logistic regression analysis) (see Figure 1 , Figure 2 A – 2C). This classifier demonstrated good accuracy and performance in both internal validation and external validation. It provides a new idea for the current diagnosis and differentiation of obstructive sleep apnea.

[0032] Specifically, after downloading the data of the two datasets from the Gene Expression Omnibus (GEO database), we selected 6 patients with primary snoring and 28 patients with simple OSA in the GSE75097 dataset to form a training set, 6 patients with primary snoring and OSA patients treated with a ventilator (14 cases) to form an internal validation set, and the GSE226379 data as an external validation set.

[0033] We divided the gene expression dataset of blood samples from 34 snoring patients (6 snoring patients and 28 obstructive sleep apnea patients) in the training set into 2 groups according to their disease status, namely the snoring group (6 snoring patients) and the obstructive sleep apnea patient group (28 obstructive sleep apnea patients). We used the "LIMMA" package in R language for differential gene analysis. By taking the P-value and the base-2 logarithm fold change (Log2 fold change, log2 FC) of the gene as the boundary value method, we obtained the differential genes between obstructive sleep apnea patients and simple snoring patients. In this invention, the boundary value of the P-value was set to 0.05, and the boundary value of log2 FC was set to the value outside the 90% confidence interval of log2 FC (that is, outside log2 FC ± 1.645 standard deviations). Genes that simultaneously met the above boundary values formed a preliminary candidate gene group. Then, based on this candidate gene group, we first used the machine learning algorithm - LASSO regression ("glmnet" package) to further screen genes related to obstructive sleep apnea. In this process, the family parameter was set to "binomial", the nfolds parameter was set to 3, and the alpha parameter was set to 1. We selected the genes in the result of lambda.1se as the final candidate gene group, which finally contained 3 genes (TRIM49, ADRB1, and LOC653497). Based on the final candidate gene group, we used the "rms" package to establish logistic models one by one through an enumeration method, and finally developed a classifier composed of 2 genes (ADRB1 and TRIM49) (see Figure 1 , Figure 2 A–2C). Based on these 2 genes, we used the "rms" package to construct a Nomogram prediction model (see Figure 2 D), which showed good accuracy and performance in the training set, internal validation dataset, and external validation set ( Figure 3). The diagnostic value was evaluated by the receiver operating characteristic (ROC) curve, a recognized standard evaluation index. The results showed that the area under the curve (AUC) values of the ROC curves for the training set, internal validation dataset, and external validation set were 0.946, 0.869, and 0.889, respectively (generally, a diagnostic test with an AUC between 0.5 - 0.7 has low diagnostic value, between 0.7 - 0.9 has moderate diagnostic value, and greater than 0.9 has high diagnostic value), indicating its good diagnostic value. The calibration ability of the Nomogram prediction model was evaluated by the Brier score, and the result was 0.104, which was good (usually, a Brier score of 0 - 0.1 is considered excellent for the model, 0.1 - 0.25 is good, and higher than 0.25 indicates that the prediction model is not very accurate). Similarly, the P value obtained by the Hosmer-Lemeshow goodness-of-fit test was 1, indicating excellent predictive performance of the model. This method provides a new idea for the current diagnosis of obstructive sleep apnea differentiation.

[0034] The diagnostic indicators for OSA are:

[0035] 916.666 - 83.333 * ADRB1 expression level - 20.833 * TRIM49 expression level;

[0036] If the above result ≥ 92.210, it is diagnosed as obstructive sleep apnea; if the above result < 92.210, it is diagnosed as non-obstructive sleep apnea.

[0037] Alternatively, the corresponding scores can be compared in D according to the ADRB1 expression level and TRIM49 expression level. Figure 2 The sum of the scores of the two genes is the total score. According to the total score, the corresponding risk value is compared. If the corresponding risk is greater than 0.5, it is diagnosed as obstructive sleep apnea; if the risk corresponding to the total score is less than 0.5, it is diagnosed as non-obstructive sleep apnea.

[0038] ADRB1 gene sequence (SEQ ID NO.1)

[0039] agaaacatgc tgaagtcccg gcggctcttc cagcagcggc agcggctcca gcagcagcggcggcggcggc ggcggcggca gcggcagcga cagcgctcgg ctcctgcggg aaaggcgccc ggcgcccatgcctccggccc cgcgccgcgg ctgccctgac ccggccgcga cctccctctg cgcaccacgc cgcccgggcttctggggtgt tccccaacca cggcccagcc ctgccacacc ccccgccccc ggcctccgca gctcggcatgggcgcggggg tgctcgtcct gggcgcctcc gagcccggta acctgtcgtc ggccgcaccg ctccccgacggcgcggccac cgcggcgcgg ctgctggtgc ccgcgtcgcc gcccgcctcg ttgctgcctc ccgccagcgaaagccccgag ccgctgtctc agcagtggac agcgggcatg ggtctgctga tggcgctcat cgtgctgctcatcgtggcgg gcaatgtgct ggtgatcgtg gccatcgcca agacgccgcg gctgcagacg ctcaccaacctcttcatcat gtccctggcc agcgccgacc tggtcatggg gctgctggtg gtgccgttcg gggccaccatcgtggtgtgg ggccgctggg agtacggctc cttcttctgc gagctgtgga cctcagtgga cgtgctgtgcgtgacggcca gcatcgagac cctgtgtgtc attgccctgg accgctacct cgccatcacc tcgcccttccgctaccagag cctgctgacg cgcgcgcggg cgcggggcct cgtgtgcacc gtgtgggcca tctcggccctggtgtccttc ctgcccatcc tcatgcactg gtggcgggcg gagagcgacg aggcgcgccg ctgctacaacgaccccaagt gctgcgacttcgtcaccaac cgggcctacg ccatcgcctc gtccgtagtc tccttctacgtgcccctgtg catcatggcc ttcgtgtacc tgcgggtgtt ccgcgaggcc cagaagcagg tgaagaagatcgacagctgc gagcgccgtt tcctcggcgg cccagcgcgg ccgccctcgc cctcgccctc gcccgtccccgcgcccgcgc cgccgcccgg acccccgcgc cccgccgccg ccgccgccac cgccccgctg gccaacgggcgtgcgggtaa gcggcggccc tcgcgcctcg tggccctgcg cgagcagaag gcgctcaaga cgctgggcatcatcatgggc gtcttcacgc tctgctggct gcccttcttc ctggccaacg tggtgaaggc cttccaccgcgagctggtgc ccgaccgcct cttcgtcttc ttcaactggc tgggctacgc caactcggcc ttcaaccccatcatctactg ccgcagcccc gacttccgca aggccttcca gggactgctc tgctgcgcgc gcagggctgcccgccggcgc cacgcgaccc acggagaccg gccgcgcgcc tcgggctgtc tggcccggcc cggacccccgccatcgcccg gggccgcctc ggacgacgac gacgacgatg tcgtcggggc cacgccgccc gcgcgcctgctggagccctg ggccggctgc aacggcgggg cggcggcgga cagcgactcg agcctggacg agccgtgccgccccggcttc gcctcggaat ccaaggtgta gggcccggcg cggggcgcgg actccgggca cggcttcccaggggaacgag gagatctgtg tttacttaag accgatagca ggtgaactcg aagcccacaa tcctcgtctgaatcatccga ggcaaagaga aaagccacggaccgttgcac aaaaaggaaa gtttgggaag ggatgggagagtggcttgct gatgttcctt gttgtttttt ttttcttttc ttttctttct tcttcttttt tttttttttttttttttctg tttgtggtcc ggccttcttt tgtgtgtgcg tgtgatgcat ctttagattt ttttcccccaccaggtggtt tttgacactc tctgagagga ccggagtgga agatgggtgg gttaggggaa gggagaagcattaggagggg attaaaatcg atcatcgtgg ctcccatccc tttcccggga acaggaacac actaccagccagagagagga gaatgacagt ttgtcaagac atatttcctt ttgctttcca gagaaatttc attttaatttctaagtaatg atttctgctg ttatgaaagc aaagagaaag gatggaggca aaataaaaaa aaatcacgtttcaagaaatg ttaagctctt cttggaacaa gccccacctt gctttccttg tgtagggcaa acccgctgtcccccgcgcgc ctgggtggtc aggctgaggg atttctacct cacactgtgc atttgcacag cagatagaaagacttgttta tattaaacag cttatttatg tatcaatatt agttggaagg accaggcgca gagcctctctctgtgacatg tgactctgtc aattgaagac aggacattaa aagagagcga gagagagaaa cagttcagattactgcacat gtggataaaa acaaaaacaa aaaaaaggag tggttcaaaa tgccattttt gcacagtgttaggaattaca aaatccacag aagatgttac ttgcacaaaa agaaattaaa tattttttaa agggagaggggctgggcaga tcttaaataa aattcaaact ctacttctgttgtctagtat gttattgagc taatgattcattgggaaaat acctttttat actcctttat catggtactg taactgtatc catattataa atataattatcttaaggatt ttttattttt ttttatgtcc aagtgcccac gtgaatttgc tggtgaaagt tagcacttgtgtgtaaattc tacttcctct tgtgtgtttt accaagtatt tatactctgg tgcaactaac tactgtgtgaggaattggtc catgtgcaat aaataccaat gaagcacaa。

[0040] TRIM49 gene sequence (SEQ ID NO.2)

[0041] aaacatgaat tctggaatct tacaggtctt tcagggggaa ctcatctgcc ccctgtgcatgaactacttc atagacccgg tcaccataga ctgtgggcac agcttttgca ggccttgttt ctacctcaactggcaagaca tcccatttct tgtccagtgc tctgaatgca caaagtcaac cgagcagata aacctcaaaaccaacattca tttgaagaag atggcttctc ttgccagaaa agtcagtctc tggctattcc tgagctctgaggagcaaatg tgtggcactc acagggagac aaagaagata ttctgtgaag tggacaggag cctgctctgtttgctgtgct ccagctctca ggagcaccgg tatcacagac accgtcccat tgagtgggct gctgaggaacaccgggagaa gcttttacag aaaatgcagt ctttgtggga aaaagcttgt gaaaatcaca gaaacctgaatgtggaaacc accagaacca gatgctggaa ggcttttgga gacatattac acaggagtga gtccgtgctgctgcacatgc cccagcctct gaatccagag ctcagtgcag ggcccatcac tggactgagg gacaggctcaaccaattccg agtgcatatt actctgcatc atgaagaagc caacaatgat atctttctgt atgaaattttgagaagcatg tgtattggat gtgaccatca agatgtaccc tatttcactg caacacctag aagttttcttgcatggggtg ttcagacttt cacctcgggc aaatattact gggaggtcca tgtaggggac tcctggaattgggcttttgg tgtctgtaat atgtatcgga aagagaagaa tcagaatgag aagatagatg gaaaggcgggactctttctt cttgggtgtgttaagaatga cattcaatgc agtctcttta ccacctcccc acttatgctgcaatatatcc caaaacctac cagccgagta ggattattcc tggattgtga ggctaagact gtgagctttgttgatgttaa tcaaagctcc ctaatataca ccatccctaa ttgctctttc tcacctcctc tcaggcttatcttttgctgt attcacttct gaccagagac aaatcagaaa tgtgttcaca tgctgtggga acccctttatcccaggaagt cctcttcctt gtgccttaac atacaggaca aataggctct attttatgtc ttgaattgccttctaatgtt atcaaaactc atttattgtg ttactattaa atatgctgaa aacgctaaaa gtatacgtattggttcttta ttaaataatt tttgaaaaat cattattcat gatcatggca tacagtatat tctcttttttttctttattt atgactgtca ctgagtgaaa taatagatga cagacatgtc tgaatgaagt aaaaatcaatggaagacagt cgggatcttt tgcttcatgc aaaaaacttg gagtgaagtc tcaatgataa ctgggaaatgtttttcttcc tctttatcta actatattac acttatccat caggtttcat tgtattaatc tatcctttgaggtaata。

Claims

1. Application of reagents for detecting gene expression levels of ADRB1 and TRIM49 in the preparation of products for diagnosing obstructive sleep apnea.

2. The use according to claim 1, characterized in that: The obstructive sleep apnea diagnostic product is used to detect the mRNA levels of ADRB1 and TRIM49 gene expression.

3. A reagent for diagnosing obstructive sleep apnea, characterized in that: The reagent for diagnosing obstructive sleep apnea includes a reagent for detecting the gene expression level of ADRB1 and TRIM49.

4. The reagent for diagnosing obstructive sleep apnea according to claim 3, characterized in that: The sample to be tested of the detection reagent is set as a peripheral blood sample.

5. A detection system for obstructive sleep apnea, characterized in that: It includes a gene expression level acquisition module and a judgment module; The gene expression level acquisition module is used to obtain the gene expression levels of ADRB1 and TRIM49 of the sample to be tested and submit them to the judgment module; The judgment module uses the acquired ADRB1 and TRIM49 gene expression levels as the input of the classifier to judge the obstructive sleep apnea level of the sample to be tested.

6. The obstructive sleep apnea detection system according to claim 5, characterized in that: The judgment value index adopted by the judgment module is: 916.666-83.333*ADRB1 expression level-20.833*TRIM49 expression level.

7. The obstructive sleep apnea detection system according to claim 6, characterized in that: If the judgment value index is ≥92.210, it is diagnosed as obstructive sleep apnea. If the judgment value index is <92.210, it is diagnosed as non-obstructive sleep apnea.

8. The obstructive sleep apnea detection system according to claim 6, characterized in that: The detection system also includes an analysis module, which is used to generate personalized diagnostic suggestions by combining the results of the judgment module and the obstructive sleep apnea training set, and obtain personalized diagnostic auxiliary suggestions by performing comprehensive analysis in combination with obstructive sleep apnea detailed data.

9. The obstructive sleep apnea detection system according to claim 6, characterized in that: The analysis module is used to integrate the analysis results and provide personalized diagnostic suggestions to assist doctors in formulating treatment plans. By integrating data, it assists doctors in specifying personalized treatment plans and diagnostic suggestions to obtain an obstructive sleep apnea analysis report.

10. A kit for diagnosing obstructive sleep apnea, characterized in that: The kit comprises reagents for detecting the gene expression levels of ADRB1 and TRIM49.

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