75 results about "Plasma Metabolism" patented technology

The invention relates to a plasma metabolite composition for auxiliary diagnosis of sub-clinical mastitis of dairy cows and application of the plasma metabolite composition. The plasma metabolite combination is a first combination and / or a second combination, and the first combination is composed of glyceryl monolinoleate, 2-methylthiophene, L-cystine, (S)-(+)-citramalic acid, 9-hydroxy-10, 12-octadecadienoic acid, 16-hydroxyhexadecanoic acid, tyrosine hydroxyproline, stearoyl serotonin and histamine; the first composition is composed of 4, 5-dimethyl-2, 4, 5-trimethyloxazole, 12, 13-epoxy octadecadienoic acid, 2-cyclotetradecene-1-ketone, 4 [alpha]-carboxyl-5 [alpha]-cholest-8-ene-3 [beta]-alcohol, N-arachidonyl dopamine d8, 9 (Z) and 11 (E)-conjugated linoleic acid, and the second composition is composed of 2, 5-dihydro-2, 4, 5-trimethyloxazole, 12, 13-epoxy octadecadienoic acid, 2-cyclotetradecene-1-ketone, 4 [alpha]-carboxyl-5 [alpha]-cholest-8-ene-3 [beta]-alcohol, N-arachidonyl dopamine A product prepared based on the plasma metabolite composition and used for auxiliary diagnosis of cow subclinical mastitis is simple in use method and high in accuracy, can be used for assisting a milk somatic cell number method to diagnose cow subclinical mastitis, and reduces the probability of false negative and false positive.

According to the metabolic aging recognition model construction system, the storage medium and the kit, the metabolic aging recognition model is obtained through machine learning of age and plasma metabolite biomarkers, the metabolic age obtained through calculation shows excellent sensitivity, specificity and accuracy in prediction of all-cause death risks, and the metabolic aging recognition model has the advantages of high sensitivity, high specificity and high accuracy in prediction of all-cause death risks. And the actual age is exceeded. Experiments prove that the metabolic aging acceleration condition can be intuitively reflected through comparison with the actual age. Only a small amount of plasma is needed as a detection sample, the evaluation process is simple and convenient, the cost is low, and the method is easy to accept by subjects and suitable for large-scale popularization. Moreover, due to high sensitivity and specificity, the method can also be used for screening anti-aging drugs.

The invention relates to the technical field of diagnosis markers and diagnosis methods for mental diseases of children and adolescents, in particular to a plasma metabolism diagnosis marker and diagnosis system for depression of children and adolescents. The plasma metabolism diagnosis marker comprises at least one of cyclic adenosine monophosphate, methylpyridine, unsaturated undecylenyl carnitine, histidine, prolyl hydroxyproline, tryptophan and glucuronic acid. According to the scheme, non-target metabonomics data is analyzed, and seven candidate diagnostic markers for depression of children and adolescents are screened and determined from common differential metabolites. Then, through targeted metabonomics research, diagnostic markers and combinations thereof with ideal diagnostic effects are determined, and then a diagnostic model is constructed. And a sex factor is also incorporated into the diagnosis model, so that the diagnosis effect is further improved. According to the technical scheme, the technical problem that in the prior art, reliability and popularization potential of depression plasma metabolism biomarkers of children and adolescents are not ideal can be solved, and the depression plasma metabolism biomarkers have ideal popularization and application prospects.

The invention relates to the field of medical prediction, and provides a recognition model construction system for metabolism unhealthy obesity, a storage medium and a kit. The recognition model construction system comprises an acquisition module, a feature selection module, a training module and a recognition module. By means of machine learning of plasma metabolite biomarkers, obesity patients with unhealthy metabolism can be recognized in an early stage when clinical symptoms do not appear, and doctors and patients are helped to take intervention measures before cardiovascular risks are increased. Compared with a traditional physical examination process, the identification model construction system for metabolism unhealthy obesity is simpler and more convenient, and the participation burden of a patient is reduced.

This invention belongs to the field of diagnostic testing technology. Addressing the problem that existing gestational diabetes screening methods may miss the optimal time for early intervention, leading to the failure to promptly detect and manage some potentially hyperglycemic pregnant women, increasing the risk of adverse maternal and fetal outcomes, this invention proposes a plasma metabolic biomarker for the early diagnosis or prediction of gestational diabetes and its application. This plasma metabolic biomarker is at least one of L-lysine, propargylglycine, N6-acetyl-L-lysine, cyclic leucine, and glycine-deoxycholic acid. The five plasma metabolic biomarkers provided by this invention can be applied to the early diagnosis or prediction of gestational diabetes, and are of great significance for the prevention or treatment of gestational diabetes.

The invention provides a biomarker composition based on plasma metabonomics and application of the biomarker composition in early diagnosis of nasal extrinsic N / KT cell lymphoma, and relates to the technical field of biomarkers. According to the present invention, for the first time, it is provided that the 2-Methylbutyl beta-D-glucose acid, the 4-(2, 3-dihydro-1, 4-benzodioxin-6-yl) glucose acid and the 4-(octyloxyl) glucose acid can be adopted as the biomarkers for distinguishing the chronic sinusitis patient from the nasal knot external N / KT cell lymphoma patient, and the application of the 2-Methylbutyl beta-D-glucose acid, the 4-(2, 3-dihydro-1, 4-benzodioxin-6-yl) glucose acid, the 4-(2, 3-dihydro-1, 4-benzodioxin-6-yl) glucose acid, the 4-(2, 3-dihydro-1, 4-benzodioxin-6-yl) glucose acid and the 4-( According to the invention, a single-factor prediction model / combined factor prediction model NKTCL early diagnosis model AUCgt is constructed by taking the three biomarkers as characteristics and combining a random forest algorithm; 0.9, sensitivity gt; the specificity is gt; and the kit has relatively high accuracy, sensitivity and specificity for diagnosing the N / KT cell lymphoma outside the nasal knots, and is high in disease diagnosis and prediction capability. According to the method, stepped improvement of diagnosis efficiency is directly achieved through the synergistic effect of the multi-dimensional plasma metabonomics markers and algorithm suitability optimization, and a new normal form with high precision and clinical feasibility is provided for early screening of tumors.

The present invention relates to plasma metabolite markers for gastric cancer detection and their applications. Based on the metabolomics method, the present invention screened and obtained 15 metabolite markers that can distinguish between health, gastric non-cancer diseases and gastric cancer by mass spectrometry analysis of plasma samples of gastric cancer patients, gastric non-cancerous diseases and healthy controls. Multivariate multi-classification ROC curve analysis showed that the AUC value reached 0.852, and the 15 differential metabolite markers had good diagnostic efficacy in distinguishing health, gastric non-cancerous diseases and gastric cancer. Further selecting 11, 7 or 4 combinations from the 15 markers also had the same good diagnostic effect. And the above combination was further confirmed in the validation group. The metabolite combination provided by the present invention can accurately predict whether it is a healthy group, a patient with a gastric non-cancerous disease, or a patient with gastric cancer, which will help in the early prevention, treatment and prognosis of gastric cancer, etc., with good treatment effect and reduced treatment cost.

The present invention relates to the field of medical prediction, and provides a system for building an identification model for metabolically unhealthy obesity, a storage medium, and a kit. The identification model building system includes: an acquisition module, a feature selection module, a training module, and an identification module. Through machine learning of plasma metabolite biomarkers, it is possible to identify patients with metabolically unhealthy obesity early before clinical symptoms appear, helping doctors and patients take intervention measures before cardiovascular risks increase. The identification model building system for metabolically unhealthy obesity of the present invention is simpler than the traditional physical examination process, reducing the burden of patient participation.

The application relates to the technical field of targeted drugs, in particular to an organic compound and application of the organic compound in preparation of a PET probe for targeting 18kDa translocation protein and in pharmacy. 18 Or F. Compared with a conventional PET probe, the organic compound has the targeting TSPO characteristic, can be used for preparing the PET probe for targeting TSPO, is stable in in-vivo plasma metabolism, has lower non-specific uptake, and can be used for diagnosis, scientific research and drug preparation of neuroinflammation.

The invention belongs to the technical field of traditional Chinese medicine constitution classification, and particularly relates to a traditional Chinese medicine constitution classification method based on plasma metabolism profiles. The method comprises the following steps: constructing a multiple linear regression prediction model of a physique by taking a plasma metabolism profile as an independent variable and an original score of one physique in traditional Chinese medicine physiques as a dependent variable; according to the same method, multiple linear regression prediction models of the other eight physiques are constructed respectively; respectively inputting plasma metabolism profiles of healthy people with unknown traditional Chinese medicine constitution types into the constructed multiple linear regression prediction model of the nine constitution types, and predicting metabolic constitution original scores of the nine constitution types; converting the original metabolic constitution score to obtain a conversion score, and judging the traditional Chinese medicine constitution classification according to the conversion score. The method has the advantages that the traditional Chinese medicine theory and the modern biological technology can be combined, an objective scientific basis is provided for traditional Chinese medicine constitution classification, the accuracy and credibility of traditional Chinese medicine constitution classification are improved, and the defects of traditional subjective evaluation are overcome.

The present invention belongs to the field of pharmaceutical technology and discloses the use of 1-Oleoyl-2-palmitoy-sn-glycero-3-phosphocholine (OPPC) combined with γ-Linolenic acid in the preparation of a medicament for treating autoimmune liver disease (ALD). The medicament for treating autoimmune liver disease according to the present invention comprises OPPC and / or γ-Linolenic acid, as well as derivatives of OPPC and / or γ-Linolenic acid. By administering the medicament of the present invention to treat MRL / MpJ-Fas<supgt;lpr< / supgt; (MRL / lpr) mice, the plasma metabolic level and liver function of MRL / lpr mice can be improved, providing a new direction for the clinical treatment of autoimmune liver disease.

The invention relates to the technical field of health assessment, in particular to a dynamic metabolism difference modeling method for a premature infant and a full-term infant, a marker and application, and the method comprises the following steps: obtaining a data set; the data set comprises neonatal plasma samples of a to-be-detected group and a control group; preprocessing the plasma sample in the data set; carrying out non-targeted metabonomics detection on the pretreated plasma sample by adopting ultra-high performance liquid chromatography-time-of-flight mass spectrometry to obtain mass spectrometry original data; preprocessing the mass spectrum original data, performing multivariate statistical analysis on the preprocessed mass spectrum data, and constructing a plasma metabolite discrimination model; and based on the plasma metabolite discrimination model, evaluating the metabolic disorder degree and health risk of the premature infant. According to the method, the metabolic disorder degree and the health risk of the premature infant are evaluated by constructing the plasma metabolite judgment model and based on the differential metabolite characteristics in the plasma metabolite judgment model, and the early screening of the health condition of the premature infant is realized.

The invention discloses application of a reagent for detecting a plasma metabolism marker combination in preparation of a product for diagnosing breast cancer, and belongs to the field of breast cancer markers. According to the application of the reagent for detecting the plasma metabolism marker combination in preparing the product for diagnosing the breast cancer, the plasma metabolism marker combination comprises dehydroepiandrosterone sulfate, acylcarnitine 12: 0 and taurodeoxycholic acid. The plasma metabolism marker combination has high specificity, high sensitivity and non-invasiveness when being used for breast cancer detection, so that a subject can conveniently and safely receive detection, and the plasma metabolism marker combination is suitable for screening and detection of large-scale common people and is beneficial to early screening, early diagnosis and early treatment of breast cancer.

The invention discloses a biomarker composition for predicting the biological age or aging state of an individual, and the biomarker composition comprises tetracosyl saturated fatty acid / tetracosyl monounsaturated fatty acid phosphatidylcholine (PC 24: 0 / 14: 1), cysteine-glycine-cysteine tripeptide (Cys-Gly-Cys), cysteine-glycine-cysteine tripeptide (Cys-Gly-Cys), cysteine-glycine-cysteine tripeptide (Cys-Gly-Cys), cysteine-glycine-cysteine tripeptide (Cys-Gly-Cys), cysteine-glycine-cysteine tripeptide (Cys-Gly-Cys), cysteine-glycine- The invention relates to a pharmaceutical composition for the preparation of a pharmaceutical composition, which comprises the following components in parts by weight: 20: 0 / 22: 5, 20 / docosapentaenoic acid monohexosyl sphingosine amide (HexCer 20: 0 / 22: 5), trigonelline, lithocholic acid glycine (Lithocholic acid glycine), and octadecanoic saturated fatty acid / octadecanoic monounsaturated fatty acid phosphatidylcholine (PC 18: 0 / 18: 1). The biomarker can be used for accurately characterizing the aging state of an organism, and provides a strategy for early screening, intervention evaluation, accurate health management and the like of related diseases of the elderly.

The present application relates to the field of medical informatics, and provides a paliperidone efficacy prediction method and system based on plasma metabolomics. The method comprises the following steps: collecting basic data of a target patient; performing feature selection on metabolites through the basic data to obtain relevant metabolites corresponding to paliperidone efficacy; training a neural network based on the relevant metabolites and baseline data in the basic data to obtain a prediction model; and performing paliperidone efficacy prediction on a patient to be predicted through the prediction model to obtain a prediction result. The present application can predict the probability of patient response to paliperidone, thereby more accurately evaluating the efficacy of paliperidone before medication, and providing an auxiliary decision for individualized medication.

The invention belongs to the field of marker detection, and particularly relates to a marker for diagnosing or identifying potential causes of migraine, application, a kit, a device and an evaluation model. The invention provides a marker for diagnosing or identifying potential causes of migraine. The marker comprises indoleacrylic acid and / or hypersensitive C-reactive protein. The invention reveals that PFO is combined with migraine metabonomics maps, and branched chain amino acid metabolism, neurosteroid metabolism and tryptophan metabolism disorder are core pathological mechanisms for the first time. On the basis of plasma metabonomics characteristics of a PFO-combined migraine patient and a healthy control, 211 metabolites with significant difference are screened out, it is found that various metabolites are significantly related to the symptom severity of migraine, and two factors [indoleacrylic acid and hypersensitive C-reactive protein (hs-CRP)] significantly related to migraine related to patent foramen ovale are confirmed. In addition, the concentration of indolecrylic acid (IA) is found to have a relatively good prediction value on symptom relief after occlusion.

The invention discloses a combined biomarker for drug-induced liver injury diagnosis, a diagnosis model and application, plasma metabolites with abundance difference are obtained on the basis of plasma metabolite data related to intestinal flora, and the metabolites show excellent differential diagnosis value for distinguishing drug-induced liver injury from healthy people and have good application prospects. A high-specificity and high-accuracy diagnosis model is constructed by combining the plasma metabolite and baseline clinical information, the model can effectively and accurately diagnose the drug-induced liver injury and healthy people, and an auxiliary decision-making tool with conversion application value is provided for objective evaluation of the drug-induced liver injury in clinical practice.

The invention relates to the technical field of biologic detection of metabolomics, in particular to an OSAHS metabolism marker, a diagnosis model and application of the OSAHS metabolism marker and the diagnosis model. The metabolic marker comprises a red blood cell metabolite and / or a plasma metabolite; erythrocyte metabolites comprise L-aspartic acid, D-ribose-5-diphosphoric acid, cadaverine, 12, 13-epoxy octadecenoic acid, L-arginine, N-acetylmethionine, 5-hydroxyeicosatetraenoic acid, 13-oxo-9Z, 9, 10-tetrahydroxy-9Z, 9, 10-tetrahydroxy-9Z, 9, 10-tetrahydroxy-9Z, 9, 10-tetrahydroxy-9Z, 9, 10-tetrahydroxy-9Z, 10-tetrahydroxy-9Z, the component A is one or more of 11E-octadecadienoic acid, inosine monophosphate, nicotinamide, alpha-lactose, sphingosine, hydroxy octadecadienoyl or glyceraldehyde-3-phosphoric acid; the plasma metabolite is prepared from one or more of fumaric acid, cadaverine, L-aspartic acid, malic acid, sphingosine-1-phosphoric acid, L-arginine, cysteine-S-sulfate, thiosulfate, gamma-glutamyl alanine, galacturonic acid or vitamin B2.

The invention relates to the technical field of biological medicines, and provides a plasma metabolite-based ALD non-invasive differential diagnosis model, which is characterized in that five key metabolites in plasma, namely vecuronium bromide, N-(3-amino-3-oxopropyl)-L-valine, geranyl citronellol, phenylpropanolamine and S-allylcysteine, are used as core diagnosis indexes; or by taking alanine aminotransferase as an auxiliary index at the same time, calculating the ALD disease probability through a logistic regression equation. The invention further provides a construction method and application of the model. The method has the advantages that by accurately capturing plasma metabolite differences and combining clinical indexes, efficient identification of healthy people and ALD patients is achieved, and a reliable tool is provided for early screening and clinical diagnosis of ALD.

The present invention discloses metabolic markers for predicting the risk of liver cancer and their applications. The development and utilization of such metabolic markers will provide technical support for risk prediction and early diagnosis and treatment of liver cancer. The present invention uses a non-targeted metabolomics detection method to screen and verify 44 plasma metabolites associated with the risk of liver cancer, and identifies a group of 18 plasma metabolites that can significantly improve the risk prediction level of liver cancer.

The invention discloses screening and application of an endometrial cancer plasma metabolism marker composition. Based on metabonomics research, a group of metabolic marker compositions with significant difference are screened out by analyzing and comparing metabolites in plasma of endometrial cancer patients and healthy people. By means of the screened markers, early, rapid and accurate detection of endometrial cancer can be achieved, so that treatment measures can be taken in time, and the survival rate and life quality of patients are improved. The metabolic marker obtained by the invention has the advantages of non-invasive detection, high sensitivity and specificity, high throughput analysis, cost effectiveness and early screening. Early discovery and treatment of diseases such as cancers are facilitated, and prognosis is improved. In practical application, excellent performance and huge potential are shown, and a new direction is provided for disease diagnosis and health management in the future.

The invention provides application of a group of plasma metabolism markers in preparation of a reagent product for pulmonary nodule diagnosis, and relates to the technical field of disease diagnosis reagents. The metabolic marker is prepared from trigonelline, 4-methylcatechol, 2, 4-dinitrophenol, 3, 4-dihydroxyphenylacetic acid, N-(2-furfuryl) glycine, mevalonic acid and formononetin, and the metabolic marker is prepared from the following raw materials: the trigonelline, the 4-methylcatechol, the 2, 4-dinitrophenol, the 3, 4-dihydroxyphenylacetic acid, the N-( According to the application, ultra-high performance liquid chromatography-high resolution mass spectrometry (UPLC / MS) is adopted to carry out metabonomics analysis on plasma samples before and after an early pulmonary nodule operation, and a group of plasma metabolites (trigonelline, 4-methylcatechol, 2, 4-dinitrophenol, 3, 4-dihydroxyphenylacetic acid, N-(2-furfuryl) glycine, mevalonic acid and formononetin) are identified; and the group of metabolites is utilized to form a diagnosis model, so that benign and malignant pulmonary nodules can be accurately diagnosed, and early-stage differential diagnosis and risk assessment are facilitated.

The invention relates to the technical field of metabonomics analysis, in particular to application of a metabolic marker in preparation of a head and neck squamous cell carcinoma diagnostic product, a kit, a screening method of the metabolic marker of the head and neck squamous cell carcinoma, a diagnostic model and a construction method and application of the diagnostic model. The metabolic marker comprises at least one of lactic acid, sphingosine, cadaverine, uridine diphosphate, allantoic acid, hydroxyproline, sphingosine-1-phosphoric acid, indole-3-acetaldehyde, pantothenic acid, fumaric acid, malic acid, prostaglandin or ornithine in red blood cells and / or plasma. The screened red blood cells and plasma metabolism markers can be used for predicting or diagnosing the head and neck squamous cell carcinoma respectively or independently, particularly, the red blood cell metabolism markers can provide more stable tumor microenvironment information, and the metabolism stability is high and is not easily influenced by diet and circadian rhythm.

The invention discloses a model for evaluating the net energy level of Dingan black pig daily ration based on metabonomics and a construction method of the model, and eight plasma metabolism markers related to the net energy level of daily ration and a change rule of the plasma metabolism markers are found by comparing the difference of plasma metabolomics of daily ration with different net energy levels. A dynamic prediction model between the plasma biomarker and the daily ration net energy level is established, and then the net energy level of the daily ration fed by the Dingan black pigs in the actual production process is predicted. According to the invention, metabonomics is utilized to predict the net energy level of the fed daily ration, so that not only can errors caused by body type evaluation and pig herd stress caused by repeated weight weighing be avoided, but also effective energy value errors caused by heat consumption increase differences of energy feed raw materials or protein feed raw materials can be reduced; application of unconventional feed raw materials such as palm meal and coconut meal in Hainan is facilitated, and the breeding cost is reduced.

The invention discloses a preparation method and application of a porous PtCu alloy nano material, and relates to the technical field of biology. The preparation method comprises the following steps: synthesizing a porous PtCu nano-particle micelle by adopting an improved soft template guiding method, centrifuging, washing and drying to obtain a porous PtCu nano-material; the porous PtCu alloy nano material is applied to LDI MS metabolite detection, a CAP patient plasma metabolism fingerprint spectrogram is obtained, and machine learning is carried out to construct a model. The porous PtCu alloy nano material is synthesized and applied to a laser desorption ionization mass spectrometry technology to enhance the signal sensitivity of the porous PtCu alloy nano material, the plasma metabolism fingerprint spectrum of a CAP patient is obtained through the platform, and severe CAP diagnosis and metabolic marker screening are carried out.

The invention discloses application of a plasma metabolism marker or a detection reagent thereof in preparation of a kit for evaluating third-generation EGFR-TKI drug treatment prognosis of advanced non-small cell lung cancer. The markers comprise daidzein sulfate, N-(2-furfuryl) glycine, N-oleoyl taurine, hexanoyl glutamine, 3-hydroxy oleic acid, myristic acid (14: 1n5), oleic acid (18: 1), palmitic acid (16: 0), palmitoleic acid (16: 1n7), eicosenoic acid (20: 1), docosadienoic acid (22: 2n6), uncosapentaenoic acid (21: 5n3), 3-hydroxybutyric acid, 3-hydroxybutyryl carnitine, hexanoyl carnitine (C6) and 3-hydroxydecanoyl carnitine. 3-hydroxy oleoyl carnitine, 5-dodecenoyl carnitine (C12: 1), decanoyl carnitine (C10), lauroyl carnitine (C12), myristoyl carnitine (C14: 1), myristoyl carnitine (C14), capryloyl carnitine (C8), oleoyl carnitine (C18: 1), palm oil acyl carnitine (C16: 1), 1-linoyl-GPE (18: 2), 1, 2-dilinoyl-GPE (18: 2 / 18: 2), 1-oleoyl-2-linoyl-GPE (18: 1 / 18: 2) or any combination thereof. The prognosis condition of a subject can be judged, and the curative effect of a treatment scheme can be predicted.

The invention discloses a biomarker for detecting diabetes mellitus macular edema (DME), a kit for detecting the diabetes mellitus macular edema (DME), and application of the biomarker and the kit for detecting the diabetes mellitus macular edema (DME). The invention provides application of a biomarker in preparation of a reagent or a kit for detecting diabetic macular oedema. The biomarker is selected from one or more of 5-phosphate-beta-D-ribose amine, ascorbic acid glucoside, succinic acid and glutathione disulfide. The potential metabolic marker of DME is identified according to the plasma metabolism type of a DME patient. The biomarker provided by the invention can provide a scientific insight and a new strategy for early diagnosis and intervention of DME.

The application discloses a plasma metabolic marker composition and a screening method and application thereof, and relates to the technical field of biomedicine. The plasma metabolic marker composition comprises glycyl-L-glutamic acid, phosphatidylcholine 38:7, pyrrole-2-formic acid and phosphatidylethanolamine 36:2p. The metabolic marker composition is significantly different in different cancer types, can distinguish different cancer types of cancer patients, and realizes the detection of pan-cancer. The metabolic marker composition is non-invasive and minimally invasive when used for the detection of pan-cancer, is more suitable for the screening and detection of a large number of ordinary people, has high sensitivity and specificity, is more suitable for the early screening of cancer, and is helpful for the early screening, early diagnosis and early treatment of cancer, and is helpful for improving the survival rate and cure rate of cancer patients.

The invention relates to the technical field of prediction models, and particularly discloses a pain and rheumatism syndrome biomarker, a diagnosis / prediction model and a construction method thereof. The pain and rheumatism syndrome biomarker comprises at least one of acetyl vanillin, cyclic adenosine monophosphate, methyl vanillate and uridine succinic acid; the pain and rheumatism syndrome biomarker comprises a pain and rheumatism syndrome diagnosis marker and a pain and rheumatism syndrome recurrence prediction marker; the pain and rheumatism syndrome diagnosis marker comprises at least one of acetyl vanillin, cyclic adenosine monophosphate and methyl vanillate; the pain and rheumatism syndrome recurrence prediction marker comprises cyclic adenosine monophosphate and uridine succinic acid. According to the application, a diagnosis model of the pain and rheumatism syndrome can be constructed on the basis of the expression levels of the three plasma metabolites, namely the acetylvanillin, the cyclic adenosine monophosphate and the methyl vanillate; based on the plasma expression level of cyclic adenosine monophosphate and uridine succinic acid, a gout recurrence prediction model can be developed, and high prediction accuracy is achieved.

Chronological age is an important predictor of morbidity and mortality, however it is unable to account for heterogeneity in the decline of physiological function and health with advancing age. Several attempts have been made to instead define a “biological age” using multiple physiological parameters in order to account for variation in the trajectory of human aging; however, these methods require technical expertise and are likely too time-intensive and costly to be implemented into clinical practice. Accordingly, a metabolomic signature of biological aging was developed that can predict changes in physiological function with the convenience of a blood sample. A weighted model of biological age was generated based on multiple clinical and physiological measures in a large group of healthy adults and was then applied to a cohort of healthy older adults who were tracked longitudinally over a 5-10 year timeframe. Plasma metabolomic signatures were identified that were associated with biological age, including some that could predict whether individuals would age at a faster or slower rate. These results not only have clinical implications by providing a simple blood-based assay of biological aging, but also provide insight into the molecular mechanisms underlying human healthspan.