154 results about "Healthy control" patented technology

The invention relates to a metabolic marker for diagnosing diabetic secondary osteoporosis and application of the metabolic marker, and belongs to the technical field of biological medicine. The method comprises the following steps: firstly, screening out obviously different metabolites between osteoporosis (DOP) and osteoporosis-free groups (DM) in diabetic patients; and secondly, screening out significant difference metabolites between the primary osteoporosis patient and the healthy control group, and excluding the intersection of the two groups of difference metabolites to obtain metabolites which exclude the influence of the primary osteoporosis. Metabolic markers, namely cytosine nucleotide, ketoglutaric acid and triethylamine, are obtained through further screening, and when the three metabolic markers are independently used, AUCgt; the AUC during combined use is 0.99, so that the limitation that the sensitivity of the traditional bone mineral density detection on the osteoporosis recognition in the diabetic population is insufficient is effectively made up.

The invention discloses a method for constructing a differential diagnosis model for lupus nephritis and membranous nephropathy, and belongs to the technical field of intelligent medical treatment. The modeling method comprises the following steps: S1, respectively collecting flow cytometry detection data of lupus nephritis patients and healthy control personnel; s2, performing data cleaning and conversion on the flow cytometry detection data, and converting non-numerical features into digits; carrying out implication on the missing value by adopting a k nearest neighbor algorithm from an implication packet; s3, random sampling is carried out on the cleaned and converted data set, and samples are divided into a training set and a verification set according to the proportion of 7: 3; s4, dividing a training subset and a test set from the training set, and iteratively selecting the types of cells incorporated into the constructed model as pDC, CD4T, effector CD4T, Th2, CD8T, CD38 + HLA-DR + CD8T, and CD38 + PD-1 + CD8T by adopting an RFE method, wherein the types of the cells incorporated into the constructed model are pDC, CD4T, effector CD4T, Th2, CD8T, CD38 + HLA-DR + CD8T and CD38 + PD-1 + CD8T; and S5, performing a classification task by adopting TabPFNClassifier, performing training from features selected from the training data set, then evaluating the performance of the model, performing model training by utilizing detection data, and performing evaluation to construct a lupus nephritis prediction model with high accuracy.

The invention discloses an esophageal adenocarcinoma plasma circulation microbial marker combination, a reagent, a kit, a risk prediction model and application thereof, and belongs to the cross technical field of liquid biopsy and microbiomics. The esophageal adenocarcinoma plasma circulating microbial marker combination disclosed by the invention comprises enterobacteriaceae, cilia, prevotella, ackermania, lactobacillus fermentum and streptococcus. When the esophageal adenocarcinoma plasma circulating microbial marker combination is used for verifying esophageal adenocarcinoma patients and healthy controls, the AUC is 0.952, the sensitivity is 100%, the specificity is 80.00%, the accuracy is 91.43%, the positive predictive value is 86.96%, and the negative predictive value is 100%; the esophageal adenocarcinoma plasma circulating microbial marker combination can effectively distinguish esophageal adenocarcinoma patients from healthy people, is suitable for early screening of esophageal adenocarcinoma, and has remarkable clinical screening application value.

The invention discloses a gastric cancer biomarker composition as well as a screening method and application thereof, and belongs to the technical field of bioinformatics. The screening method of the gastric cancer biomarker composition comprises the following steps: firstly, collecting blood specimens of a gastric cancer patient and a healthy control, carrying out high-throughput plasma proteomics detection, and screening out differential expression proteins; and verifying the candidate protein markers by using a prospective queue in the British biological sample library, and finally determining 17 core proteins to obtain the gastric cancer biomarker composition. A gastric cancer risk prediction model is constructed by combining clinical risk factors and protein expression levels of gastric cancer biomarkers. The gastric cancer risk prediction model constructed by the invention can realize accurate layering of gastric cancer risks, decision curve analysis shows that the net benefit of the model is obviously superior to that of a traditional screening method, and an efficient tool is provided for early warning and personalized intervention of gastric cancer.

The present invention relates to a method for identifying immune-related microorganisms in colorectal cancer based on multi-omics features, and belongs to the field of biomedicine technology. The method includes analyzing the diversity and composition of intestinal flora in colorectal cancer patients and healthy controls; identifying intestinal microorganisms significantly associated with patients through the LASSO algorithm; selecting characteristic intestinal microorganisms of colorectal cancer by the SVM-RFE method; obtaining overlapping genes between the two algorithms, and incorporating the overlapping genes into ROC and DCA analysis verification; the method includes screening key microorganisms for immune regulation, predictive analysis of immune-related intestinal microbial functions, analysis of immune-related microorganisms and immune microenvironment, and prediction of the mechanism of action of immune-related intestinal microorganisms; the method of the present invention can screen out key microorganisms with immune regulatory effects in colorectal cancer, laying the foundation for studying the mechanism of action of intestinal flora and formulating personalized precision medication plans.

The invention discloses a novel tRNA (transfer ribonucleic acid) derived small RNA biomarker tRF5-23-GlyTCC-2 and application thereof, and relates to the technical field of biological medicines. The nucleotide sequence of the biomarker tRF5-23-GlyTCC-2 disclosed by the invention is as shown in SEQ ID NO. 1, and the nucleotide sequence of the biomarker tRF5-23-GlyTCC-2 is as shown in SEQ ID NO. Results of embodiments show that the gene has significant low expression in colorectal cancer tissues and serum, when the gene is independently used for colorectal cancer diagnosis, AUC for distinguishing a patient from a healthy control reaches 0.8628, the sensitivity is 74%, the specificity is 92%, and the AUC, the sensitivity and the specificity are both superior to CEA; the AUC is improved to 0.9077 and the sensitivity reaches 90% when the compound is used in combination with CEA (Carcino Embryonic Acid). Meanwhile, low expression of the marker is related to poor prognosis characteristics such as late T staging and low differentiation of a patient, and the marker can be used for prognosis evaluation. Functional experiments prove that overexpression can inhibit colorectal cancer cell proliferation and tumor growth, and a new scheme is provided for colorectal cancer diagnosis and treatment.

The present application relates to the field of biological medicine, and particularly relates to biomarkers for migraine diagnosis, kits and application thereof. The biomarkers related to migraine disease according to the present application include COL4A2, and also include any one or a combination of multiple of MMP-14, LCAT, ADAMTS13 and CPXM2. The present application is based on proteomic data of clinical healthy controls and migraine patients, and uses bioinformatics analysis and machine learning methods to deeply mine protein combinations with the most early warning value for migraine disease diagnosis, and to verify in a clinical cohort with expanded samples, to provide a good prospect for clinical transformation of newly discovered biomarkers, and also to lay a foundation for subsequent mechanism research.

The invention discloses an application of a substance for detecting a piRNA marker. The application comprises one or more of the following applications: A1) an application in preparation of a product for diagnosing vascular aging; a2) is applied to preparation of products for screening vascular senescence; a3) in preparation of a product for evaluating vascular aging risk; a4) in preparation of a vascular aging prognosis evaluation product; a5) application in preparation of products for identifying and distinguishing vascular senescence and other diseases; the piRNA marker is hsapiR017724, and the nucleotide sequence of the piRNA marker is as shown in SEQ ID No. 1. The expression quantity of the hsapiR017724 marker provided by the invention in blood plasma of a vascular aging patient is obviously increased compared with that of a healthy control person, so that the hsapiR017724 marker is indicated to be a potential vascular aging biomarker. The ROC curve of the efficacy of the hsapiR017724 in the aspect of diagnosing vascular aging patients shows that the hsapiR017724 has good diagnosis efficacy.

The application discloses a Bifidobacterium longum subsp.longum strain for relieving pathological characteristics of mice with chronic kidney disease induced by adenine, and belongs to the technical fields of microorganisms and medicines.The application provides a biomarker combination related to end-stage renal disease of peritoneal dialysis, wherein the corresponding species abundance in the biomarker combination presents significant difference in matched healthy control population and peritoneal dialysis population, can effectively distinguish the end-stage renal disease population of peritoneal dialysis and the healthy population, and has high sensitivity and specificity.The application also provides a Bifidobacterium longum subsp.longum strain CCFM1375 capable of delaying the progression of renal failure, which can be used for preparing products for improving chronic kidney disease.

A method of assessing expression profiles of miRNA markers using predictive classification models to distinguish between non-diseased and diseased mitral valve disease, non-diseased and diseased DCM, non-diseased and diseased HCM. Additionally, an assessment of the same method is provided to discriminate pre-clinical from clinical MMVD or DCM patients. Also provided is a method of differentially diagnosing MMVD patients from DCM patients or from healthy controls.

This invention relates to the field of chemical or physical testing technology, specifically to a method for detecting multiple SNARE-related proteins in children with ADHD. The method includes: identifying the best-matching capture antibody for a target protein; mixing the matching capture antibodies for various target proteins with a blood sample, and detecting the initial response value of the target protein in the mixed blood sample; identifying a combination of target proteins with spectral spillover interference by utilizing the correlation between the initial response values ​​of any two target proteins; determining the compensated response value and reference weight of the interfered protein by using the initial response value of the interfered protein in the target protein combination and the corresponding mean response value in the healthy control group; and obtaining the weighted total response value of all target proteins and the expression characteristics of the SNARE complex based on the compensated response value and reference weight. The technical solution of this invention can comprehensively, accurately, and conveniently reflect the overall functional state of the SNARE protein complex expression.

The present invention discloses a method for evaluating and predicting placental transport efficiency, comprising the following steps: S1: respectively measuring and analyzing PFASs in different test samples; S2: TTE estimation: calculating TTE0 and TTE(tem) according to the following formula: TTE0=C PFAS‑脐带血清 / C PFAS‑母体血清 、C PFAS‑母体血 ×V 母体血 ×TTE m =C PFAS ‑ 脐带血 ×V 脐带血 、C PFAS‑血清 / R=C PFAS‑全血 S3: Statistical Analysis. The present invention successfully identified differences in TTE between SGA infants and matched healthy controls using an improved TEM model that considers total PFAS exposure. The improved TEM model exhibited advantages in active transport and facilitated diffusion.

A biomarker for systemic sclerosis, and a detection method therefor and the use thereof. The biomarker is an anti-PRMT5 antibody. The use of PRMT5 in the preparation of a drug or agent for detecting or treating systemic sclerosis, wherein patients with systemic sclerosis are distinguished from healthy controls and patients with other autoimmune diseases by means of detecting the anti-PRMT5 antibody level. The anti-PRMT5 antibody levels in patients with systemic sclerosis are closely correlated with the outcome of skin and pulmonary fibrosis in the patients, and the biomarker has potential value in prognosis evaluation. The detection method using enzyme-linked immunosorbent assay is simple, easy to perform, convenient for translation, and suitable for wide promotion in primary hospitals.

The invention discloses a plasma exosome marker of tuberculosis (TB) and application of the plasma exosome marker, and belongs to the field of biomedical diagnosis, the plasma exosome marker comprises hsa-miR-451a, hsa-miR-1908-5p and hsa-miR-1268b, the expression of hsa-miR-451a in the plasma exosome of an active TB patient is up-regulated, and the expression of the hsa-miR-451a and the expression of the hsa-miR-451a and the expression of the hsa-miR-1908-5p are down-regulated. The expression levels of the markers are detected by using a droplet digital PCR technology, and a multinomial logistic regression diagnosis model is constructed based on a detection result. Experiments prove that when the three miRNA combinations are used for distinguishing active tuberculosis from healthy control (HC), the area (AUC) under a working characteristic curve of a subject reaches 0.970; when latent tuberculosis infection (LTBI) is distinguished from a healthy control, AUC is 0.971; and when the secondary miRNA combination of the hsa-miR-451a and the hsa-miR-1268b is used for distinguishing the asymptomatic tuberculosis (aTB) from the healthy control, the AUC is 0.880. The TB early diagnosis accuracy is remarkably improved, the non-invasive advantage is achieved, wide application prospects are achieved in the aspects of preparation of diagnostic reagents and kits, clinical diagnosis, disease monitoring and the like, and global tuberculosis prevention and control work is powerfully promoted.

The invention discloses an AI intervention analysis method for diabetic renal interstitial fibrosis, and relates to the technical field of biologication.The method comprises the following specific steps of sample collection and multi-omics data acquisition, specifically, kidney tissue samples of diabetic renal interstitial fibrosis patients before and after the diabetic renal interstitial fibrosis patients use glucose kidney health intervention and kidney tissue samples of healthy contrasts are collected; respectively acquiring epigenetic data and single-cell gene expression data, preprocessing, and integrating to construct a comprehensive data set; by combining the AI technology and the high-throughput epigenetic detection technology, the epigenetic modification change on a TGF-beta1 / Smads signal path in the process of intervening diabetic renal interstitial fibrosis by the Sushenkang can be deeply analyzed; a brand-new perspective is provided for understanding the occurrence mechanism of the diabetic renal interstitial fibrosis disease and the drug action mechanism of the TGF-beta1 / Smads pathway, epigenetic data is deeply mined through AI, and the specific mechanism of the TGF-beta1 / Smads pathway related gene expression affected by the TGF-beta1 / Smads pathway through epigenetic regulation is shown.

The present application relates to a kind of molecular markers for early diagnosis of glaucoma, the molecular marker is tsRNA, the tsRNA is selected from one or more of tsRNA-5009b-ValCAC and tsRNA-5003c-GlyGCC.The present application finds that relative to healthy control group, the expression of tsRNA-5009b-ValCAC in POAG group patient is significantly down-regulated, its AUC value in ROC curve is 0.717, and diagnosis performance is good, indicating that the tsRNAs can be used as early diagnosis of POAG molecular marker and applied to clinical.

The invention discloses a screening method of a biomarker for auxiliary diagnosis of small cell lung cancer, and relates to the technical field of biomedicine. The method comprises: acquiring multiple groups of samples; the plurality of groups of samples comprise exosome RNA transcriptome sequencing data of a plurality of SCLC patients and a plurality of healthy controls; each piece of exosome RNA transcriptome sequencing data comprises a plurality of RNA characteristics; carrying out technical quality filtering on RNA characteristics in all exosome RNA transcriptome sequencing data, carrying out differential expression analysis on the filtered characteristics, and determining candidate RNA sets of the SCLC patient and the healthy contrast; performing feature selection on the candidate RNA set through three complementary feature selection modes, and screening an optimal exosome RNA marker combination from different quantities of RNA feature combinations through 20 times of iteration and 10-fold nested cross validation; and the optimal exosome RNA marker combination comprises LINC00989, CXCL5, MAP3K7CL and TUBB1 (Tumor Umbrella Blanket B1). The optimal exosome RNA marker combination screened by the method is beneficial to diagnosis of small cell lung cancer.

The present invention relates to a biomarker for diagnosing pediatric atopic dermatitis and use thereof. As result of comparing immune phenotypes of a healthy control group with those of pediatric atopic dermatitis patients, it has been identified that Th1, Th2, Th17 and Th22 cells or expression of STAT1, CD6 and ALCAM genes are increased or decreased. In addition, in the present invention, it has been identified that the frequencies of monocytes, plasmacytoid dendritic cells, CD4 central memory T cells, and CD4 effector memory T cells are related to the severity of pediatric atopic dermatitis. Therefore, the present invention can ensure an efficient treatment strategy for pediatric atopic dermatitis by accurately diagnosing pediatric atopic dermatitis and the severity thereof.

The invention provides application of an immune response biomarker in prognosis evaluation of mesenchymal stromal cells in treatment of liver cirrhosis patients, and relates to the technical field of biomedicine, the immune response biomarker comprises MX1 positive monocytes or / and LGALS2 positive monocytes, and the MX1 positive monocytes are MX1 positive monocytes or / and LGALS2 positive monocytes. Screening to obtain a potential biomarker LGALS2 positive mononuclear cell and an MX1 positive mononuclear cell for predicting the MSC treatment dose-effect relationship; compared with a healthy control, the proportion of baseline MX1 positive monocytes of a liver cirrhosis patient is remarkably increased (Plt; 0.01), while the proportion of LGALS2 positive monocytes is significantly reduced (Plt; 0.05) of the substrate (1); the change trend of the biomarker under the same MSC dose and the regulation effect on other immune cell subgroups are identified through sequencing analysis, the dose-effect relationship of MSC-mediated immune regulation can be accurately reflected, and patients with low MSC treatment prognosis recurrence risk can be identified.

This invention discloses a method for identifying intrahepatic cholangiocarcinoma (ICC) based on serum peptide characteristics and its application. The invention collects serum samples from three groups: individuals with ICC, those with benign liver disease, and healthy controls. Peptide characteristic peaks associated with ICC are screened, and a model is constructed using a logistic regression algorithm. This model demonstrates excellent discriminative ability on both the training and independent test sets, with AUCs reaching 0.986 and 0.963, respectively, significantly outperforming traditional tumor markers CA19-9 and CEA. The peptide characteristic peak combination and the discriminative model constructed based on it can be used for early detection of ICC, differentiation between benign and malignant cases, and screening of high-risk populations. It offers advantages such as rapid detection, standardized procedures, strong early ICC identification capability, and low false positive rate, providing a highly sensitive, specific, and widely applicable non-invasive screening tool for clinical use.

The application discloses application of a reagent for detecting a biomarker in preparation of a product for predicting ulcerative colitis disease activity or a product for diagnosing ulcerative colitis disease, and relates to the technical field of serological markers of inflammatory bowel disease. The serum CD105 of a UC patient is significantly higher than that of a healthy control group, and CD105 has a very high effect of diagnosing ulcerative colitis disease, and CD105 can distinguish disease activity from remission. Therefore, the application provides a novel serum marker which is convenient to sample, has good repeatability and can objectively reflect the colon mucosa inflammation load, and is expected to provide more accurate marker selection for UC disease activity prediction and UC diagnosis.

The application discloses application of a killer cell lectin-like receptor G1 (KLRG1) gene as a marker in preparation of a systemic lupus erythematosus (SLE) secondary hemophagocytic lymphohistiocytosis (HLH) detection preparation, and the preparation is used for detecting the expression level of the KLRG1 gene in a biological sample. Research finds that the expression level of the KLRG1 gene in SLE secondary HLH patients is significantly lower than that of healthy control groups and SLE patients, and has good diagnostic sensitivity and specificity. The application also discloses a preparation for detecting the expression level of the KLRG1 gene in a biological sample of a patient, the preparation contains a primer pair for detecting the mRNA level of the KLRG1 gene expression, or contains an antibody for detecting the protein level of the KLRG1 gene expression, and only needs to extract peripheral blood mononuclear cells (PBMC) after blood drawing to detect whether SLE patients are secondary to HLH, and the preparation is simple, rapid, and accurate in result.

The invention relates to a methylation site marker for assisting in early diagnosis of esophageal squamous cell carcinoma and application of the methylation site marker, and belongs to the technical field of biomedicine. The methylation site marker is one or a combination of human peripheral blood mononuclear cell methylation sites cg05064044, cg03395511 and cg21548813, and the nucleotide sequence of the methylation site marker is as shown in SEQ ID NO. 1-3. According to the present invention, the methylation chip detection results show that the methylation levels of the cg05064044, the cg03395511 and the cg21548813 in the peripheral blood mononuclear cells of the esophageal squamous cell carcinoma patient are significantly increased compared to the health control group; the methylation levels of cg05064044, cg03395511 and cg21548813 sites in PBMC of esophageal squamous cell carcinoma patients in a training queue and a verification queue are all obviously higher than those of a healthy control group through further analysis. Finally, through ROC curve analysis, it is determined that the three methylation sites cg05064044, cg03395511 and cg21548813 have good specificity and sensitivity, have high diagnosis efficiency when being independently or jointly used for esophageal squamous cell carcinoma diagnosis and can be used as potential molecular markers for esophageal squamous cell carcinoma diagnosis.

The invention discloses an application of a Gal-9 + Th cell and / or Gal-9 + Treg as an AIH detection target spot. Researches find that compared with a healthy control group, the peripheral blood Gal-9 + Treg cell proportion and Gal-9 + Th cell proportion of an AIH patient baseline group are remarkably increased, and after ALT of a patient in a treatment group returns to normal, the Gal-9 + Treg cell proportion and the Gal-9 + Th cell proportion are both decreased compared with those before treatment, and it is indicated that the Gal-9 + Treg cell proportion and the Gal-9 + Th cell proportion are effective indexes for monitoring disease improvement. However, the AIH is a chronic disease process, even if the ALT level returns to normal, Gal-9 + Treg cells and Gal-9 + Th cells still do not return to the level of a healthy control group, the Gal-9 + Th cells are taken as detection targets, the progress of the AIH disease course can be monitored more accurately, and disease repetition and biochemical rebounding caused by premature drug withdrawal are prevented.

The invention discloses a multi-modal CWP staging prediction method based on CT images and clinical biochemical data. The method comprises the following steps: S1, data acquisition: acquiring CT images and clinical biochemical data of a healthy control group and CWP different staging patients; s2, data pre-processing: performing different data pre-processing methods on the CT image and the clinical biochemical data to obtain heterogeneous data; s3, constructing a CWP staging prediction model based on a single mode of the CT image: selecting a plurality of deep learning models which are different in model architecture and are relatively wide in application degree in the medical field; s4, constructing a multi-modal staging prediction model: adopting two parallel feature extraction and feature level fusion architecture designs including an image feature extraction branch of the CT image and a biochemical feature extraction branch of the clinical biochemical data; and S5, multi-modal model CWP staging prediction: inputting the heterogeneous data preprocessed in the step S2 into the multi-modal staging prediction model constructed in the step S4 to carry out CWP staging prediction. According to the invention, accurate and comprehensive CWP staging prediction can be realized.

A system for early warning of coronary heart disease. A computer device comprises a memory, a processor, and a computer program stored on the memory. The processor executes the computer program to implement the following steps: receiving methylation level data of a DNA methylation marker combination of a potential coronary heart disease patient and a healthy control sample, establishing a mathematical model, and determining a threshold; and inputting methylation level data of the DNA methylation marker combination of a subject to be tested and substituting same into the mathematical model to obtain a detection index, and comparing the detection index with the threshold to obtain a conclusion of whether a sample to be tested belongs to the potential coronary heart disease patient or the healthy control. The present invention has important scientific significance and clinical application value in early warning of coronary heart disease and diagnosis and treatment of coronary heart disease.

The invention discloses an early diagnosis marker for children refractory mycoplasma pneumonia (RMPP) and an RT-qPCR detection method, and relates to the technical field of disease detection. According to the invention, PBMC maps of a healthy control group in the early morbidity stage of RMPP sick children, in the early morbidity stage of mycoplasma pneumoniae pneumonia sick children and in the early morbidity stage of RMPP sick children are drawn for the first time by virtue of an scRNA-seq technology, and IGHM, NEAT1, IL32 and ACTG1 four-gene marker combination for early diagnosis of RMPP in children is innovatively discovered; a peripheral blood sampling scheme is combined to thoroughly solve throat swab sampling obstacles of young children; and a high-precision three-classification diagnosis model is constructed. According to the method disclosed by the invention, the diagnosis time is shortened from 7 days to 24 hours, the sensitivity is improved to 93-96%, the detection cost is reduced by 95%, a first molecular solution with high precision, low cost and clinical accessibility is provided for early RMPP intervention of children, and the severe case rate and medical burden are expected to be obviously reduced.

This invention belongs to the technical field of diabetes diagnosis, specifically the use of reagents for detecting intestinal microorganisms in the preparation of diabetic diagnostic preparations and kits. Through metagenomics and metabolomics, key biomarkers, including key differential gut microorganisms and key differential fecal metabolites, will be screened. Its clinical value lies in the eventual identification of a multi-omics classifier that can distinguish adult-onset T1D from healthy controls (HCs) and T2D. This will facilitate the precise diagnosis and typing of adult-onset T1D and provide novel diagnostic and therapeutic targets.

The invention discloses a young coronary heart disease marker HTRA1 and application thereof, and belongs to the technical field of biomarkers. The expression level of the HTRA1 in serum of young coronary heart disease patients is obviously increased, the ROC value of the HTRA1 in the young coronary heart disease patients is high, healthy control and young coronary heart disease groups can be effectively distinguished, it is prompted that the HTRA1 has high value for predicting and diagnosing the young coronary heart disease, and the HTRA1 can be used for developing corresponding diagnostic reagents and kits and has good application prospects. The kit is used for detecting the content of HTRA1 in a human body sample, so that auxiliary diagnosis of the young coronary heart disease is realized. The diagnostic reagent and the kit have the advantages of being high in specificity, high in sensitivity, easy and convenient to operate and the like, and important reference bases can be provided for early discovery and treatment of the young coronary heart disease.

This invention discloses a set of biomarkers, reagents, kits, and applications for predicting alcoholic liver disease (ALD), belonging to the field of biomarker technology. The key technical points are: This invention uses non-target metabolomics to analyze plasma from ALD patients and healthy controls to identify potential biomarkers and related metabolic pathways in the plasma of ALD patients. Through human fecal metagenomic sequencing, potential biomarkers in the microbiome of ALD patients are identified, providing a scientific basis for the pathogenesis and early screening of ALD. Furthermore, this invention evaluates the therapeutic effect of L-threonine on ALD and explores its molecular mechanism, providing solid preclinical evidence and theoretical basis for L-threonine as a safe and effective natural metabolite-derived drug for the treatment of ALD, opening new directions for the development of innovative ALD therapies.