LU505705B1 - Construction method for classification model of resistant hypertension group based on metabolic biomarkers and application thereof - Google Patents
Construction method for classification model of resistant hypertension group based on metabolic biomarkers and application thereof Download PDFInfo
- Publication number
- LU505705B1 LU505705B1 LU505705A LU505705A LU505705B1 LU 505705 B1 LU505705 B1 LU 505705B1 LU 505705 A LU505705 A LU 505705A LU 505705 A LU505705 A LU 505705A LU 505705 B1 LU505705 B1 LU 505705B1
- Authority
- LU
- Luxembourg
- Prior art keywords
- ascorbate
- citric acid
- patients
- acid
- samples
- Prior art date
Links
- 208000015658 resistant hypertension Diseases 0.000 title claims abstract description 86
- 239000000090 biomarker Substances 0.000 title claims abstract description 28
- 238000013145 classification model Methods 0.000 title claims abstract description 15
- 230000002503 metabolic effect Effects 0.000 title claims abstract description 14
- 238000010276 construction Methods 0.000 title claims abstract description 11
- KRKNYBCHXYNGOX-UHFFFAOYSA-N citric acid Chemical compound OC(=O)CC(O)(C(O)=O)CC(O)=O KRKNYBCHXYNGOX-UHFFFAOYSA-N 0.000 claims abstract description 105
- CIWBSHSKHKDKBQ-JLAZNSOCSA-N Ascorbic acid Natural products OC[C@H](O)[C@H]1OC(=O)C(O)=C1O CIWBSHSKHKDKBQ-JLAZNSOCSA-N 0.000 claims abstract description 61
- 239000002207 metabolite Substances 0.000 claims abstract description 29
- CIWBSHSKHKDKBQ-JLAZNSOCSA-M L-ascorbate Chemical compound OC[C@H](O)[C@H]1OC(=O)C(O)=C1[O-] CIWBSHSKHKDKBQ-JLAZNSOCSA-M 0.000 claims abstract description 11
- 239000002211 L-ascorbic acid Substances 0.000 claims abstract description 11
- 235000000069 L-ascorbic acid Nutrition 0.000 claims abstract description 11
- 239000002253 acid Substances 0.000 claims description 13
- 239000011668 ascorbic acid Substances 0.000 claims description 12
- 238000000034 method Methods 0.000 claims description 12
- 230000035945 sensitivity Effects 0.000 claims description 12
- 208000007530 Essential hypertension Diseases 0.000 claims description 11
- 229940072107 ascorbate Drugs 0.000 claims description 11
- 230000008569 process Effects 0.000 claims description 10
- 238000012545 processing Methods 0.000 claims description 8
- 238000004458 analytical method Methods 0.000 claims description 7
- 230000037361 pathway Effects 0.000 claims description 7
- 238000007477 logistic regression Methods 0.000 claims description 6
- 238000003745 diagnosis Methods 0.000 claims description 5
- 238000012216 screening Methods 0.000 claims description 5
- 239000008280 blood Substances 0.000 claims description 4
- 210000004369 blood Anatomy 0.000 claims description 4
- 238000005516 engineering process Methods 0.000 claims description 4
- CCEKAJIANROZEO-UHFFFAOYSA-N sulfluramid Chemical group CCNS(=O)(=O)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)C(F)(F)F CCEKAJIANROZEO-UHFFFAOYSA-N 0.000 claims description 4
- 238000001195 ultra high performance liquid chromatography Methods 0.000 claims description 4
- 235000010323 ascorbic acid Nutrition 0.000 claims description 3
- 238000003766 bioinformatics method Methods 0.000 claims description 3
- 150000001875 compounds Chemical class 0.000 claims description 3
- 238000004896 high resolution mass spectrometry Methods 0.000 claims description 3
- 238000004895 liquid chromatography mass spectrometry Methods 0.000 claims description 3
- 238000001294 liquid chromatography-tandem mass spectrometry Methods 0.000 claims description 3
- 238000011002 quantification Methods 0.000 claims description 3
- 230000009467 reduction Effects 0.000 claims description 3
- 238000012549 training Methods 0.000 claims description 2
- 238000011282 treatment Methods 0.000 description 8
- 206010020772 Hypertension Diseases 0.000 description 6
- 230000036772 blood pressure Effects 0.000 description 6
- 229940079593 drug Drugs 0.000 description 6
- 239000003814 drug Substances 0.000 description 6
- 208000037849 arterial hypertension Diseases 0.000 description 3
- 230000035487 diastolic blood pressure Effects 0.000 description 3
- 230000004060 metabolic process Effects 0.000 description 3
- 230000035488 systolic blood pressure Effects 0.000 description 3
- 201000004239 Secondary hypertension Diseases 0.000 description 2
- 230000006860 carbon metabolism Effects 0.000 description 2
- 238000003759 clinical diagnosis Methods 0.000 description 2
- 201000010099 disease Diseases 0.000 description 2
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 description 2
- 238000010201 enrichment analysis Methods 0.000 description 2
- 230000007717 exclusion Effects 0.000 description 2
- 208000032873 genetic essential hypertension Diseases 0.000 description 2
- RWSXRVCMGQZWBV-WDSKDSINSA-N glutathione Chemical compound OC(=O)[C@@H](N)CCC(=O)N[C@@H](CS)C(=O)NCC(O)=O RWSXRVCMGQZWBV-WDSKDSINSA-N 0.000 description 2
- HHLFWLYXYJOTON-UHFFFAOYSA-N glyoxylic acid Chemical compound OC(=O)C=O HHLFWLYXYJOTON-UHFFFAOYSA-N 0.000 description 2
- 230000007246 mechanism Effects 0.000 description 2
- 238000012544 monitoring process Methods 0.000 description 2
- 230000008506 pathogenesis Effects 0.000 description 2
- 238000006467 substitution reaction Methods 0.000 description 2
- 230000004102 tricarboxylic acid cycle Effects 0.000 description 2
- 239000004475 Arginine Substances 0.000 description 1
- 206010003210 Arteriosclerosis Diseases 0.000 description 1
- 208000017667 Chronic Disease Diseases 0.000 description 1
- RFSUNEUAIZKAJO-VRPWFDPXSA-N D-Fructose Natural products OC[C@H]1OC(O)(CO)[C@@H](O)[C@@H]1O RFSUNEUAIZKAJO-VRPWFDPXSA-N 0.000 description 1
- 206010059866 Drug resistance Diseases 0.000 description 1
- RFSUNEUAIZKAJO-ARQDHWQXSA-N Fructose Chemical compound OC[C@H]1O[C@](O)(CO)[C@@H](O)[C@@H]1O RFSUNEUAIZKAJO-ARQDHWQXSA-N 0.000 description 1
- 108010024636 Glutathione Proteins 0.000 description 1
- 102100032742 Histone-lysine N-methyltransferase SETD2 Human genes 0.000 description 1
- 101000654725 Homo sapiens Histone-lysine N-methyltransferase SETD2 Proteins 0.000 description 1
- OFOBLEOULBTSOW-UHFFFAOYSA-N Malonic acid Chemical compound OC(=O)CC(O)=O OFOBLEOULBTSOW-UHFFFAOYSA-N 0.000 description 1
- 229910019142 PO4 Inorganic materials 0.000 description 1
- 108700005075 Regulator Genes Proteins 0.000 description 1
- 230000002159 abnormal effect Effects 0.000 description 1
- 238000010521 absorption reaction Methods 0.000 description 1
- 150000001299 aldehydes Chemical class 0.000 description 1
- RPMNUQRUHXIGHK-PYXJVEIZSA-N apigenin 7-O-neohesperidoside Chemical compound O[C@@H]1[C@H](O)[C@@H](O)[C@H](C)O[C@H]1O[C@@H]1[C@@H](O)[C@H](O)[C@@H](CO)O[C@H]1OC1=CC(O)=C2C(=O)C=C(C=3C=CC(O)=CC=3)OC2=C1 RPMNUQRUHXIGHK-PYXJVEIZSA-N 0.000 description 1
- 229930034861 apigenin-7-O-neohesperidoside Natural products 0.000 description 1
- ODKSFYDXXFIFQN-UHFFFAOYSA-N arginine Natural products OC(=O)C(N)CCCNC(N)=N ODKSFYDXXFIFQN-UHFFFAOYSA-N 0.000 description 1
- 208000011775 arteriosclerosis disease Diseases 0.000 description 1
- 229960005070 ascorbic acid Drugs 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 238000009530 blood pressure measurement Methods 0.000 description 1
- 208000020832 chronic kidney disease Diseases 0.000 description 1
- HNEGQIOMVPPMNR-IHWYPQMZSA-N citraconic acid Chemical compound OC(=O)C(/C)=C\C(O)=O HNEGQIOMVPPMNR-IHWYPQMZSA-N 0.000 description 1
- 229940018557 citraconic acid Drugs 0.000 description 1
- 238000013480 data collection Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000029087 digestion Effects 0.000 description 1
- 239000001177 diphosphate Substances 0.000 description 1
- XPPKVPWEQAFLFU-UHFFFAOYSA-J diphosphate(4-) Chemical compound [O-]P([O-])(=O)OP([O-])([O-])=O XPPKVPWEQAFLFU-UHFFFAOYSA-J 0.000 description 1
- 235000011180 diphosphates Nutrition 0.000 description 1
- 239000002934 diuretic Substances 0.000 description 1
- 229940030606 diuretics Drugs 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 230000003203 everyday effect Effects 0.000 description 1
- 230000002068 genetic effect Effects 0.000 description 1
- 229960003180 glutathione Drugs 0.000 description 1
- 230000001404 mediated effect Effects 0.000 description 1
- 238000010197 meta-analysis Methods 0.000 description 1
- 230000004066 metabolic change Effects 0.000 description 1
- 239000000203 mixture Substances 0.000 description 1
- 230000008816 organ damage Effects 0.000 description 1
- 238000010239 partial least squares discriminant analysis Methods 0.000 description 1
- 230000001575 pathological effect Effects 0.000 description 1
- 230000002093 peripheral effect Effects 0.000 description 1
- 239000010452 phosphate Substances 0.000 description 1
- 230000002265 prevention Effects 0.000 description 1
- 238000003672 processing method Methods 0.000 description 1
- 238000004393 prognosis Methods 0.000 description 1
- 108091006091 regulatory enzymes Proteins 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- RPMNUQRUHXIGHK-SBDOOABHSA-N rhoifolin Natural products O([C@@H]1[C@@H](O)[C@H](O)[C@H](CO)O[C@H]1Oc1cc(O)c2C(=O)C=C(c3ccc(O)cc3)Oc2c1)[C@H]1[C@H](O)[C@@H](O)[C@@H](O)[C@H](C)O1 RPMNUQRUHXIGHK-SBDOOABHSA-N 0.000 description 1
- 230000000630 rising effect Effects 0.000 description 1
- 238000000926 separation method Methods 0.000 description 1
- 230000019491 signal transduction Effects 0.000 description 1
- 210000002784 stomach Anatomy 0.000 description 1
- 239000006228 supernatant Substances 0.000 description 1
- 238000002626 targeted therapy Methods 0.000 description 1
- 238000011269 treatment regimen Methods 0.000 description 1
- 229940088594 vitamin Drugs 0.000 description 1
- 229930003231 vitamin Natural products 0.000 description 1
- 239000011782 vitamin Substances 0.000 description 1
- 235000013343 vitamin Nutrition 0.000 description 1
- 150000003722 vitamin derivatives Chemical class 0.000 description 1
Classifications
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
- G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
- G16H10/40—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for data related to laboratory analysis, e.g. patient specimen analysis
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
Landscapes
- Engineering & Computer Science (AREA)
- Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
- General Health & Medical Sciences (AREA)
- Public Health (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Epidemiology (AREA)
- Primary Health Care (AREA)
- Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Biomedical Technology (AREA)
- Biophysics (AREA)
- Evolutionary Biology (AREA)
- Genetics & Genomics (AREA)
- Molecular Biology (AREA)
- Proteomics, Peptides & Aminoacids (AREA)
- Bioinformatics & Computational Biology (AREA)
- Biotechnology (AREA)
- Analytical Chemistry (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Theoretical Computer Science (AREA)
- Chemical & Material Sciences (AREA)
- Data Mining & Analysis (AREA)
- Databases & Information Systems (AREA)
- Pathology (AREA)
- Investigating Or Analysing Biological Materials (AREA)
Abstract
The present invention provides a construction method for a classification model of resistant hypertension (RH) group based on metabolic biomarkers and an application thereof. According to the present invention, a RH group and related controls are analyzed by using differential metabolites, a biomarker combination of L-Ascorbate and Citric acid in RH patients is obtained, and a classification model of the RH group is constructed. The classification model and the biomarker combination of the RH group provided by the present invention can be applied to early and rapid classification of the RH group.
Description
CONSTRUCTION METHOD FOR CLASSIFICATION MODEL OF RESISTANT 0505705
HYPERTENSION GROUP BASED ON METABOLIC BIOMARKERS AND
APPLICATION THEREOF
The present invention relates to the medical field, in particular to a construction method for a classification model of resistant hypertension (RH) group based on metabolic biomarkers and an application thereof.
RH, a special type of hypertension, is a complex disease caused by a variety of genetic and environmental factors and has become a "chronic disease” that puzzles the prevention and treatment of hypertension at present. À meta-analysis of 91 studies from 1991 to 2017 showed that the real prevalence of RH was 10.3% (95% CI 7.6%-13.2%). In terms of diagnosis, many factors may affect the accurate diagnosis of RH, such as incorrect blood pressure measurement method, poor treatment compliance, taking drugs affecting blood pressure, unhealthy lifestyle, inadequate drug treatment and secondary hypertension, so that it 1s difficult to exclude false RH.
Due to the lack of targeted therapy in treatment, even the combination of multiple drugs has little effect. Therefore, it is an urgent problem to improve the clinical diagnosis and treatment ability of RH.
Metabonomics technology may study the composition and content changes of all small molecular metabolites in human body before and after abnormal blood pressure disturbance to characterize the metabolic characteristics of the collective in the stage of rising blood pressure, lock in the differential metabolites and find out the key regulatory enzymes and genes to complete the research on the pathogenesis of diseases and the mechanism of effective preparation treatment. However, there is still a lack of metabonomics analysis for RH.
In order to solve the above technical defects, the present invention provides a construction method for a classification model of resistant hypertension (RH) group based on metabolic biomarkers and an application thereof.
Focusing on a key problem that there are no characteristic metabolic biomarkers in the current clinical diagnosis, treatment and prognosis of RH, the present invention conducts metabonomics analysis on RH, and screens metabolic biomarkers with capability to classify 905/05 people whose clinical blood pressure is difficult to be controlled, thus providing strong support for precise treatment of RH.
The present invention adopts the following technical solution: a construction method for a classification model of resistant hypertension (RH) group based on metabolic biomarkers includes the following steps: (1) selecting and dividing patients diagnosed as RH, patients with essential hypertension (EH) and healthy people (N) into three groups, and collecting blood samples and data; (2) metabonomics data processing and analysis extracting and detecting metabolites by using ultra-high performance liquid chromatography and high resolution mass spectrometry, namely, namely, an LC-MS technology (LC-MS/MS), importing an offline data file into a compound discoverer database search software for processing to obtain identification and relative quantification results of the metabolites, screening differential metabolites based on the following criteria: VIP > 1, P value < 0.05 and FC > 1.2 or FC < 0.833, and performing data processing based on a Linux operating system and R and Python software; (3) bioinformatics analysis annotating identified metabolites by using a KEGG database, an HMDB database and an
LIPIDMaps database, and analyzing metabolite pathway enrichment by using the KEGG database; and drawing a volcano plot, a clustering heatmap and a bubble chart by the R software; (4) construction of classification model of RH metabolic biomarkers evaluating 50 selected differential metabolites by using a ROC curve and determining their diagnostic values, then screening 13 markers with AUC > 0.9 to evaluate the ROC curve by pairwise combinations, and finally selecting an optimal biomarker combination, i.e., L-Ascorbate and Citric acid; standardizing the original contents of the L-Ascorbate and the Citric acid by using a
Z-Score with the significant reduction of the original contents of the L-Ascorbate and the Citric acid in RH patients, with standardization processes as follows:
PA Ri asvorbate — 1749726
Fem 8222927
Jo = Reinicacia - 82708526 31457852 where, Ri-ascorbate and Rcitric acid are original contents of the L-Ascorbate and the Citric acid 00700 respectively, and ZI-Ascorbate and Zcitric acid are contents of the L-Ascorbate and the Citric acid after
Z-Score standardization; training a logistic regression model based on standardized L-Ascorbate and Citric acid contents, establishing a corresponding ROC curve by using R-package pROC, and calculating
AUC to be 0.93, showing that the model is highly accurate, with a specific process of the logistic regression model as follows: 2-7 0.4126—4.2429% Ze asporbate 0.353 Zeurie acid where, ZI-Ascorbate and ZCitric acid are the contents of the L-Ascorbate and the Citric acid after the Z-Score standardization, and p represents a probability that the samples belong to RH, ranging from 0 to 1; an optimal cutoff value of the model being 0.33 according to a maximum Youden index, namely, when p > 0.33, the sample is classified as suffering from or having susceptibility to hypertension, with specific processes as follows:
Sensitivity = IP
TP+FN
Specificity = IN
TN+FP
Foner = Max {sensitivity{t} + specificity(£) — 1} where, Sensitivity represents a degree of sensitivity, Specificity represents a degree of specificity, TP represents the number of correctly classified RH samples, FN represents the number of incorrectly classified non-RH samples, TN represents the number of correctly classified non-RH samples, FP represents the number of incorrectly classified RH samples, Jmax represents a maximum Youden index, and t represents a classification threshold at the maximum
Youden index, namely, the optimal cutoff value.
The present invention provides a biomarker combination for RH patients, the biomarker combination includes L-Ascorbate and Citric acid, and original contents of the L-Ascorbate and the Citric acid in the RH patients are significantly reduced.
The present invention further provides an application of a biomarker combination for RH patients in diagnosis of resistant hypertension, the biomarker combination includes L-Ascorbate and Citric acid, and original contents of the L-Ascorbate and the Citric acid in the RH patients are significantly reduced. 4505705
The present invention has the following beneficial effects: the present invention provides a biomarker combination for RH patients, i.e., L-Ascorbate and Citric acid, and constructs a classification model of RH group. The classification model and biomarker combination of resistant hypertension (RH) group provided by the present invention may be applied to early and rapid classification of RH group.
FIG. 1 is a non-targeted metabonomic analysis of RH;
FIG. 2 is a comparison of L-Ascorbate content;
FIG. 3 is a comparison of Citric acid content; and
FIG. 4 is a ROC curve.
In order to make the objective, technical solutions and advantages of the application more clear, the examples of the application are introduced below.
Example 1 Construction method for a classification model of resistant hypertension (RH) group based on metabolic biomarkers and a process thereof.
The present invention was included into the Affiliated Hospital of Shandong University of
Traditional Chinese Medicine, and 10 people were definitely diagnosed as RH patients, 10 people were diagnosed as essential hypertension (EH) patients and 10 people were diagnosed as healthy people (N). The present invention has been approved by the Ethics Committee of
Shandong University of Traditional Chinese Medicine, and all the subjects agreed to participate in the present invention and signed an informed consent form.
Diagnostic criteria of RH: the diagnostic criteria of RH were based on the diagnostic criteria of RH in ESC-ESH 2018
Guidelines for the Management of Arterial Hypertension. When a treatment strategy including appropriate lifestyle measures and three or more drugs (including diuretics) with optimum tolerance dose is ineffective in reducing systolic blood pressure and diastolic blood pressure to < 140 mmHg and/or < 90 mmHg respectively, it is considered as RH. The diagnosis of RH was confirmed by home blood pressure monitoring (HBPM) or ambulatory blood pressure monitoring (ABPM).
Diagnostic criteria of EH: 4505705 the diagnostic criteria of EH were based on ESC-ESH 2018 Guidelines for the
Management of Arterial Hypertension, systolic blood pressure (SBP) > 140 mmHg and/or diastolic blood pressure (DBP) > 90 mmHg, regardless of age, gender and comorbidity. 5 Exclusion criteria: the exclusion criteria were based on ESC-ESH 2018 Guidelines for the Management of
Arterial Hypertension, secondary hypertension and organ damage mediated by advanced hypertension, especially chronic kidney disease or arteriosclerosis, were excluded.
Data collection: blood samples were collected on an empty stomach at the same time every day (between 6: 00 and 8: 00 a.m.), Sml of peripheral venous blood was extracted from participants and centrifuged at 4000rpm for 10min, and supernatant is packed in 1.5ml of EP tubes, each of the
EP tubes is filled with 600ul, labeled and stored in a refrigerator at -80°C for later use.
Metabonomics data processing and analysis methods: metabolites were extracted and detected by using ultra-high performance liquid chromatography (UHPLC) and high resolution mass spectrometry (Q Exactive™ HF), namely, an LC-MS technology (LC-MS/MS), an offline data (.raw) file was imported into a compound discoverer database search software (CD, version 3.1) for processing to obtain identification and relative quantification results of the metabolites, differential metabolites were screened based on the following criteria: VIP > 1, P value < 0.05 and FC > 1.2 or FC < 0.833, and data processing was performed based on a Linux operating system (CentOS version 6.6) and R software (version 3.4.3) and Python software (version 3.5.0).
Bioinformatics analysis method:
A KEGG database (https://www.genome.jp/kegg/pathway html), an HMDB database (https://hmdb.ca/metabolites) and an LIPIDMaps database (http://www lipidmaps.org/) were used to annotate identified metabolites, and metabolite pathway enrichment analysis was analyzed by using the KEGG database; and a volcano plot, a clustering heatmap and a bubble chart were drawn by the R software (version 3.4.3). 1. Identification and analysis of differential metabolites
RH is a special type of hypertension, so it inevitably has certain characteristics of hypertension, but it also has characteristics of drug resistance. In the present invention, starting 0° 0° from excavating pathological mechanism of RH, a common point between two comparison groups, i.e., RH and N, and RH and FH, are mainly studied. A total of 972 metabolites are identified by non-targeted metabonomics. Partial Least Squares Discriminant Analysis (PLS-DA) shows comprehensive metabolic changes between the two groups, and there is obvious separation between RH group and EH group and between RH group and N group, indicating that the metabonomic characteristics among the groups are different (A and B in FIG.1). According to screening criteria of VIP > 1.0, FC > 1.2 or FC < 0.833 and P < 0.05, significantly different metabolites are screened, as shown in Veen diagram, heatmap and volcano plot (C, D, E and F in
FIG.1), with 225 species of RH.vs.N and 78 species of RH.vs.EH. There are 50 major significantly different metabolites representing RH, ranked according to P value, and the top 20 are shown in Table 1. In order to explore pathogenesis of RH by changes of metabolites, KEGG pathway enrichment analysis is conducted to evaluate potential roles of these differentially expressed metabolites. In RH.vs. N group, there are three pathways of significant enrichment, involving ascorbic acid and aldehyde metabolism, carbon metabolism and arginine biosynthesis; and in RH.vs.EH group, there are seven pathways of significant enrichment, involving carbon metabolism, glutathione metabolism, HIF-1 signaling pathway, vitamin digestion and absorption, glyoxylic acid, dicarboxylic acid metabolism and citric acid cycle (TCA cycle), as shown in the bubble chart (G and H in FIG.1, sorting by P value, only showing the first 20 results).
Table 1 Significantly different metabolites representing RH ~~ RHwN RHAvwsEH
Name FC pvalue VIP Tred FC Pvalue VIP Trend
L-Ascorbate 0.350418707 1.61398E-05 2461038106 Down 0.51005498 0.000583845 2220238567 Down
Citric acid 1.89963E-05 2.398615519 Down 0.589568597 0.000724787 2.330542665 Down
Citraconic acid 0.538615518 0.003184425 Down 0.674743096 0.001933207 1.779424719 Down
D-Fructose 0282928052 0.000111457 2.478323719 Down 0451817726 0.002719254 2241060025 Down 6-phosphate (+)-alpha-Lipoic 0.267396206 0.000191111 2455860101 Down = 0449097242 0.004203529 2.192553088 Down
SHEE 0.724047863 0.005115222 1461592993 Down 0.658705764 0.004361226 2.403302499 Down
SM 0.776427812 0.004396743 1445595611 Down 0.70621686 0.007203891 2.421061157 Down (d26:0/12:1)
RPK 2.381013349 0.00012198 2252661718 Up 1694603177 0.008885222 1.794053593 Up farensyl 11.48521546 0.000251441 2245578325 Up 5261033757 0.010788568 1901664031 14505705 diphosphate
SM 0.621181423 0.002609334 1975447817 Down 0.705988066 0.015948448 2.006211802 Down (d28:1/12:1)
Rhoifolin 0.635532704 0.003836541 1.779557842 Down 0.727882929 0.018486901 Down 2. Construction of classification model of RH metabolic biomarkers 50 selected differential metabolites are evaluated by using a ROC curve and their diagnostic values are determined, then 13 markers with AUC > 0.9 are screened to evaluate the ROC curve by pairwise combinations, and finally an optimal biomarker combination, i.e., L-Ascorbate and
Citric acid, is selected.
The original contents of the L-Ascorbate and the Citric acid are standardized by using a
Z-Score with the significant reduction of the original contents of the L-Ascorbate and the Citric acid in RH patients (FIGs. 2 and 3), with standardization processes as follows: 7 _ Ri _Ascarbate — 1749726
L>Ascorbate — 8727977 7 Reitvie acid - 82708526
Citric acid — C0 314578%2 7 where, RL-Ascorbate and Rcitric acid are original contents of the L-Ascorbate and the Citric acid, respectively, and ZI-Ascorbate and Zcitric acid are contents of the L-Ascorbate and the Citric acid after
Z-Score standardization.
A logistic regression model was trained based on standardized L-Ascorbate and Citric acid content, a corresponding ROC curve is constructed by using R-package pROC, and AUC is calculated to be 0.93, showing that the model is highly accurate, with a specific process of the logistic regression model as follows: op OA120- 224295 _Ascocbarr 7 0.353 XZ Citric acid
B= 02126 2319XEL score 0.353 XE Cin utd where, ZI-Ascorbate and Zcitric acid are the contents of the L-Ascorbate and the Citric acid after the Z-Score standardization, and p represents a probability that the samples belong to RH, ranging from 0 to 1; an optimal cutoff value of the model is 0.33 according to a maximum Youden index, namely, when p > 0.33, the sample is classified as suffering from or having susceptibility to hypertension, with specific processes as follows:
Sensitivity = TP
ERSILIVULY = TP + FN soocificiey + TN
Specifici y= TN + FP
Foner = max, {sensitivity(t) + specificity(t) — 1} LUS05705 where, Sensitivity represents a degree of sensitivity, Specificity represents a degree of specificity, TP represents the number of correctly classified RH samples, FN represents the number of incorrectly classified non-RH samples, TN represents the number of correctly classified non-RH samples, FP represents the number of incorrectly classified RH samples, Jmax represents a maximum Youden index, and t represents a classification threshold at the maximum
Youden index, namely, the optimal cutoff value.
The above are only the preferred examples of the patent, and it is pointed out that for ordinary skilled in the art, several improvements and substitutions may be made without departing from the technical principle of the patent, and these improvements and substitutions also belong to the protection scope of the patent.
Claims (3)
1. À construction method for a classification model of resistant hypertension (RH) group based on metabolic biomarkers, comprising the following steps: (1) selecting and dividing patients diagnosed as RH, patients with essential hypertension (EH) and healthy people (N) into three groups, and collecting blood samples and data; (2) metabonomics data processing and analysis extracting and detecting metabolites by using ultra-high performance liquid chromatography and high resolution mass spectrometry, namely, an LC-MS technology (LC-MS/MS), importing an offline data file into a compound discoverer database search software for processing to obtain identification and relative quantification results of the metabolites, screening differential metabolites based on the following criteria: VIP > 1, P value <
0.05 and FC > 1.2 or FC < 0.833, and performing data processing based on a Linux operating system and R and Python software; (3) bioinformatics analysis annotating identified metabolites by using a KEGG database, an HMDB database and an LIPIDMaps database, analyzing metabolite pathway enrichment by using the KEGG database, and drawing a volcano plot, a clustering heatmap and a bubble chart by the R software; and (4) construction of classification model of RH metabolic biomarkers evaluating 50 selected differential metabolites by using a ROC curve and determining diagnostic values, then screening 13 markers with AUC > 0.9 to evaluate the ROC curve by pairwise combinations, and finally selecting an optimal biomarker combination, i.e., L-Ascorbate and Citric acid; standardizing original contents of the L-Ascorbate and the Citric acid by using a Z-Score with the significant reduction of the original contents of the L-Ascorbate and the Citric acid in RH patients, with standardization processes as follows: 7 = Ri ascorbate — 1749726 Ferhat 8222927 Tew = Rcitric acid — 82708526 31457852 where, RL-Ascorbate and Rcitric acid are original contents of the L-Ascorbate and the Citric acid, and ZL-Ascorbate and Zcitric acid are contents of the L-Ascorbate and the Citric acid after Z-Score standardization;
training a logistic regression model based on standardized L-Ascorbate and Citric acid 505705 contents, establishing a corresponding ROC curve by using R-package pROC, and calculating AUC to be 0.93, showing that the model is highly accurate, with a specific process of the logistic regression model as follows: 27 94126—4.2420X F1 _accomats 0.353 X Zeigie acid p= 1 + 04126-42429 XÆ;_ascostate D 358% Lrigric seid where, ZI-Ascorbate and Zcitric acid are the contents of the L-Ascorbate and the Citric acid after the Z-Score standardization, and p represents a probability that the samples belong to RH, ranging from 0 to 1; an optimal cutoff value of the model being 0.33 according to a maximum Youden index, namely, when p > 0.33, the sample is classified as suffering from or having susceptibility to RH, with specific processes as follows: Sensitivity = LP TP+FN Specificity = IN TN+FRP fmax = Max {sensitivity{t) + specificity{t} — 1} where, Sensitivity represents a degree of sensitivity, Specificity represents a degree of specificity, TP represents the number of correctly classified RH samples, FN represents the number of incorrectly classified non-RH samples, TN represents the number of correctly classified non-RH samples, FP represents the number of incorrectly classified RH samples, Jmax represents a maximum Youden index, and t represents a classification threshold at the maximum Youden index, namely, the optimal cutoff value.
2. A biomarker combination for RH patients, the biomarker combination comprising L-Ascorbate and Citric acid, and original contents of the L-Ascorbate and the Citric acid in the RH patients being significantly reduced.
3. An application of a biomarker combination for RH patients in diagnosis of RH, the biomarker combination comprising L-Ascorbate and Citric acid, and original contents of the L-Ascorbate and the Citric acid in the RH patients being significantly reduced.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| LU505705A LU505705B1 (en) | 2023-12-06 | 2023-12-06 | Construction method for classification model of resistant hypertension group based on metabolic biomarkers and application thereof |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| LU505705A LU505705B1 (en) | 2023-12-06 | 2023-12-06 | Construction method for classification model of resistant hypertension group based on metabolic biomarkers and application thereof |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| LU505705B1 true LU505705B1 (en) | 2024-06-06 |
Family
ID=91432222
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| LU505705A LU505705B1 (en) | 2023-12-06 | 2023-12-06 | Construction method for classification model of resistant hypertension group based on metabolic biomarkers and application thereof |
Country Status (1)
| Country | Link |
|---|---|
| LU (1) | LU505705B1 (en) |
-
2023
- 2023-12-06 LU LU505705A patent/LU505705B1/en active
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111430027B (en) | Duplex affective disorder biomarker based on intestinal microorganisms and screening application thereof | |
| Yasar et al. | Is an elevated neutrophil-to-lymphocyte ratio a predictor of metabolic syndrome in patients with chronic obstructive pulmonary disease? | |
| Wang et al. | Phenotypes and subphenotypes of patients with COVID-19: a latent class modeling analysis | |
| CN110904213B (en) | Ulcerative colitis biomarker based on intestinal flora and application thereof | |
| EP3786305A1 (en) | Biomarker for depression and use thereof | |
| CN111279193B (en) | Behcet's disease diagnosis kit and method for detecting metabolite difference in urine | |
| CN107022605A (en) | A kind of biomarker of active tuberculosis | |
| Arun et al. | Indian diabetes risk score (IDRS), a strong predictor of diabetes mellitus: A cross sectional study among urban and rural population of Lucknow | |
| CN112768079A (en) | Liver disease cognitive model construction method and system based on machine learning | |
| Han et al. | Diagnostic prediction model development using data from dried blood spot proteomics and a digital mental health assessment to identify major depressive disorder among individuals presenting with low mood | |
| Song et al. | Clinical Significance of Procalcitonin, C‐Reactive Protein, and Interleukin‐6 in Helping Guide the Antibiotic Use for Patients with Acute Exacerbations of Chronic Obstructive Pulmonary Disease | |
| Kim et al. | Diagnostic utility of serum and urinary metabolite analysis in patients with interstitial cystitis/painful bladder syndrome | |
| LU505705B1 (en) | Construction method for classification model of resistant hypertension group based on metabolic biomarkers and application thereof | |
| CN113436673A (en) | Molecular marker for liver cancer prognosis prediction and application thereof | |
| CN113470813A (en) | Survival rate prognosis model for liver cancer patient | |
| CN113345589A (en) | Construction method and application method of liver cancer prognosis model and electronic equipment | |
| CN113030318B (en) | Application of plasma short-chain fatty acid as multi-system atrophy diagnosis marker | |
| CN115354074A (en) | Coronary heart disease diagnostic kit and diagnostic system for type 2 diabetes patients | |
| Montaser et al. | Predicting Immunological risk for stage 1 and stage 2 diabetes using a 1-week CGM home test, nocturnal glucose increments, and standardized liquid mixed meal breakfasts, with classification enhanced by machine learning | |
| CN113495137A (en) | Application of two metabolic markers in preparation of kit for diagnosing Crohn's disease independently or jointly | |
| CN114674969A (en) | Application of urine biomarker detection reagent in preparation of neocoronary pneumonia diagnostic kit | |
| CN111808941B (en) | Peripheral blood circRNA marker related to pulmonary fibrosis auxiliary diagnosis and application thereof | |
| Fu et al. | A cross-sectional study: a breathomics based pulmonary tuberculosis detection method | |
| CN108070656A (en) | Lung cancer marker and its application | |
| CN113241181A (en) | Prognosis risk assessment model and assessment device for liver cancer patient |