CN117147751A - Metabolic marker combination for assessing cardiovascular disease risk in a subject and uses thereof - Google Patents
Metabolic marker combination for assessing cardiovascular disease risk in a subject and uses thereof Download PDFInfo
- Publication number
- CN117147751A CN117147751A CN202211178138.1A CN202211178138A CN117147751A CN 117147751 A CN117147751 A CN 117147751A CN 202211178138 A CN202211178138 A CN 202211178138A CN 117147751 A CN117147751 A CN 117147751A
- Authority
- CN
- China
- Prior art keywords
- subject
- sample
- cardiovascular
- risk
- metabolic
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 230000002503 metabolic effect Effects 0.000 title claims abstract description 67
- 239000003550 marker Substances 0.000 title claims abstract description 66
- 208000024172 Cardiovascular disease Diseases 0.000 title claims abstract description 58
- 230000002526 effect on cardiovascular system Effects 0.000 claims abstract description 54
- 208000026106 cerebrovascular disease Diseases 0.000 claims abstract description 53
- 238000001514 detection method Methods 0.000 claims abstract description 45
- YDNKGFDKKRUKPY-JHOUSYSJSA-N C16 ceramide Natural products CCCCCCCCCCCCCCCC(=O)N[C@@H](CO)[C@H](O)C=CCCCCCCCCCCCCC YDNKGFDKKRUKPY-JHOUSYSJSA-N 0.000 claims abstract description 44
- CRJGESKKUOMBCT-VQTJNVASSA-N N-acetylsphinganine Chemical compound CCCCCCCCCCCCCCC[C@@H](O)[C@H](CO)NC(C)=O CRJGESKKUOMBCT-VQTJNVASSA-N 0.000 claims abstract description 44
- 229940106189 ceramide Drugs 0.000 claims abstract description 44
- ZVEQCJWYRWKARO-UHFFFAOYSA-N ceramide Natural products CCCCCCCCCCCCCCC(O)C(=O)NC(CO)C(O)C=CCCC=C(C)CCCCCCCCC ZVEQCJWYRWKARO-UHFFFAOYSA-N 0.000 claims abstract description 44
- VVGIYYKRAMHVLU-UHFFFAOYSA-N newbouldiamide Natural products CCCCCCCCCCCCCCCCCCCC(O)C(O)C(O)C(CO)NC(=O)CCCCCCCCCCCCCCCCC VVGIYYKRAMHVLU-UHFFFAOYSA-N 0.000 claims abstract description 44
- 208000029078 coronary artery disease Diseases 0.000 claims abstract description 24
- GETQZCLCWQTVFV-UHFFFAOYSA-N trimethylamine Chemical compound CN(C)C GETQZCLCWQTVFV-UHFFFAOYSA-N 0.000 claims abstract description 22
- 206010019280 Heart failures Diseases 0.000 claims abstract description 19
- 201000001320 Atherosclerosis Diseases 0.000 claims abstract description 16
- 206010003658 Atrial Fibrillation Diseases 0.000 claims abstract description 15
- KWIUHFFTVRNATP-UHFFFAOYSA-N Betaine Natural products C[N+](C)(C)CC([O-])=O KWIUHFFTVRNATP-UHFFFAOYSA-N 0.000 claims abstract description 11
- JFLIEFSWGNOPJJ-JTQLQIEISA-N N(2)-phenylacetyl-L-glutamine Chemical compound NC(=O)CC[C@@H](C(O)=O)NC(=O)CC1=CC=CC=C1 JFLIEFSWGNOPJJ-JTQLQIEISA-N 0.000 claims abstract description 11
- KWIUHFFTVRNATP-UHFFFAOYSA-O N,N,N-trimethylglycinium Chemical compound C[N+](C)(C)CC(O)=O KWIUHFFTVRNATP-UHFFFAOYSA-O 0.000 claims abstract description 11
- 229960003237 betaine Drugs 0.000 claims abstract description 11
- OEYIOHPDSNJKLS-UHFFFAOYSA-N choline Chemical compound C[N+](C)(C)CCO OEYIOHPDSNJKLS-UHFFFAOYSA-N 0.000 claims abstract description 11
- 229960001231 choline Drugs 0.000 claims abstract description 11
- 239000000523 sample Substances 0.000 claims description 71
- OKKJLVBELUTLKV-UHFFFAOYSA-N Methanol Chemical compound OC OKKJLVBELUTLKV-UHFFFAOYSA-N 0.000 claims description 66
- KFZMGEQAYNKOFK-UHFFFAOYSA-N Isopropanol Chemical compound CC(C)O KFZMGEQAYNKOFK-UHFFFAOYSA-N 0.000 claims description 35
- OAKJQQAXSVQMHS-UHFFFAOYSA-N Hydrazine Chemical compound NN OAKJQQAXSVQMHS-UHFFFAOYSA-N 0.000 claims description 24
- 238000001819 mass spectrum Methods 0.000 claims description 19
- WEVYAHXRMPXWCK-UHFFFAOYSA-N Acetonitrile Chemical compound CC#N WEVYAHXRMPXWCK-UHFFFAOYSA-N 0.000 claims description 18
- 238000003745 diagnosis Methods 0.000 claims description 16
- 150000002500 ions Chemical class 0.000 claims description 16
- 239000012472 biological sample Substances 0.000 claims description 14
- 239000000126 substance Substances 0.000 claims description 14
- 238000004811 liquid chromatography Methods 0.000 claims description 13
- 238000004949 mass spectrometry Methods 0.000 claims description 13
- VZTDIZULWFCMLS-UHFFFAOYSA-N ammonium formate Chemical compound [NH4+].[O-]C=O VZTDIZULWFCMLS-UHFFFAOYSA-N 0.000 claims description 11
- DDRJAANPRJIHGJ-UHFFFAOYSA-N creatinine Chemical compound CN1CC(=O)NC1=N DDRJAANPRJIHGJ-UHFFFAOYSA-N 0.000 claims description 10
- LFQSCWFLJHTTHZ-UHFFFAOYSA-N Ethanol Chemical compound CCO LFQSCWFLJHTTHZ-UHFFFAOYSA-N 0.000 claims description 9
- DNIAPMSPPWPWGF-UHFFFAOYSA-N Propylene glycol Chemical compound CC(O)CO DNIAPMSPPWPWGF-UHFFFAOYSA-N 0.000 claims description 9
- KRKNYBCHXYNGOX-UHFFFAOYSA-N citric acid Chemical compound OC(=O)CC(O)(C(O)=O)CC(O)=O KRKNYBCHXYNGOX-UHFFFAOYSA-N 0.000 claims description 9
- 238000011156 evaluation Methods 0.000 claims description 9
- 238000012502 risk assessment Methods 0.000 claims description 9
- 239000000090 biomarker Substances 0.000 claims description 8
- 238000001294 liquid chromatography-tandem mass spectrometry Methods 0.000 claims description 8
- 238000005173 quadrupole mass spectroscopy Methods 0.000 claims description 8
- 239000000203 mixture Substances 0.000 claims description 7
- 239000000047 product Substances 0.000 claims description 7
- 238000001195 ultra high performance liquid chromatography Methods 0.000 claims description 7
- KPGXRSRHYNQIFN-UHFFFAOYSA-N 2-oxoglutaric acid Chemical compound OC(=O)CCC(=O)C(O)=O KPGXRSRHYNQIFN-UHFFFAOYSA-N 0.000 claims description 6
- DHMQDGOQFOQNFH-UHFFFAOYSA-N Glycine Chemical compound NCC(O)=O DHMQDGOQFOQNFH-UHFFFAOYSA-N 0.000 claims description 6
- 239000000654 additive Substances 0.000 claims description 6
- 230000000996 additive effect Effects 0.000 claims description 6
- 238000006243 chemical reaction Methods 0.000 claims description 6
- 238000012790 confirmation Methods 0.000 claims description 6
- 238000012937 correction Methods 0.000 claims description 6
- JYGXADMDTFJGBT-VWUMJDOOSA-N hydrocortisone Chemical compound O=C1CC[C@]2(C)[C@H]3[C@@H](O)C[C@](C)([C@@](CC4)(O)C(=O)CO)[C@@H]4[C@@H]3CCC2=C1 JYGXADMDTFJGBT-VWUMJDOOSA-N 0.000 claims description 6
- SEOVTRFCIGRIMH-UHFFFAOYSA-N indole-3-acetic acid Chemical compound C1=CC=C2C(CC(=O)O)=CNC2=C1 SEOVTRFCIGRIMH-UHFFFAOYSA-N 0.000 claims description 6
- 238000012544 monitoring process Methods 0.000 claims description 6
- 238000001269 time-of-flight mass spectrometry Methods 0.000 claims description 6
- UYPYRKYUKCHHIB-UHFFFAOYSA-N trimethylamine N-oxide Chemical compound C[N+](C)(C)[O-] UYPYRKYUKCHHIB-UHFFFAOYSA-N 0.000 claims description 6
- 229940109239 creatinine Drugs 0.000 claims description 5
- 201000010099 disease Diseases 0.000 claims description 5
- 208000037265 diseases, disorders, signs and symptoms Diseases 0.000 claims description 5
- HFQKBOPMAOTAIR-TZSVBWBLSA-N α-d-galactosyl-(1->4)-β-d-galactosyl-(1->4)-β-d-glucosylceramide Chemical compound O[C@@H]1[C@@H](O)[C@H](OC[C@@H]([C@H](O)/C=C/CCCCCCCCCCCCC)NC(C)=O)O[C@H](CO)[C@H]1O[C@H]1[C@H](O)[C@@H](O)[C@@H](O[C@@H]2[C@@H]([C@@H](O)[C@@H](O)[C@@H](CO)O2)O)[C@@H](CO)O1 HFQKBOPMAOTAIR-TZSVBWBLSA-N 0.000 claims description 5
- 238000000605 extraction Methods 0.000 claims description 4
- 238000004128 high performance liquid chromatography Methods 0.000 claims description 4
- 239000007788 liquid Substances 0.000 claims description 4
- 239000012046 mixed solvent Substances 0.000 claims description 4
- 239000003960 organic solvent Substances 0.000 claims description 4
- 210000002966 serum Anatomy 0.000 claims description 4
- 239000006228 supernatant Substances 0.000 claims description 4
- 238000004885 tandem mass spectrometry Methods 0.000 claims description 4
- USFZMSVCRYTOJT-UHFFFAOYSA-N Ammonium acetate Chemical compound N.CC(O)=O USFZMSVCRYTOJT-UHFFFAOYSA-N 0.000 claims description 3
- 239000005695 Ammonium acetate Substances 0.000 claims description 3
- WHUUTDBJXJRKMK-UHFFFAOYSA-N Glutamic acid Natural products OC(=O)C(N)CCC(O)=O WHUUTDBJXJRKMK-UHFFFAOYSA-N 0.000 claims description 3
- 239000004471 Glycine Substances 0.000 claims description 3
- CKLJMWTZIZZHCS-REOHCLBHSA-N L-aspartic acid Chemical compound OC(=O)[C@@H](N)CC(O)=O CKLJMWTZIZZHCS-REOHCLBHSA-N 0.000 claims description 3
- WHUUTDBJXJRKMK-VKHMYHEASA-N L-glutamic acid Chemical compound OC(=O)[C@@H](N)CCC(O)=O WHUUTDBJXJRKMK-VKHMYHEASA-N 0.000 claims description 3
- AGPKZVBTJJNPAG-WHFBIAKZSA-N L-isoleucine Chemical compound CC[C@H](C)[C@H](N)C(O)=O AGPKZVBTJJNPAG-WHFBIAKZSA-N 0.000 claims description 3
- ROHFNLRQFUQHCH-YFKPBYRVSA-N L-leucine Chemical compound CC(C)C[C@H](N)C(O)=O ROHFNLRQFUQHCH-YFKPBYRVSA-N 0.000 claims description 3
- KZSNJWFQEVHDMF-BYPYZUCNSA-N L-valine Chemical compound CC(C)[C@H](N)C(O)=O KZSNJWFQEVHDMF-BYPYZUCNSA-N 0.000 claims description 3
- ROHFNLRQFUQHCH-UHFFFAOYSA-N Leucine Natural products CC(C)CC(N)C(O)=O ROHFNLRQFUQHCH-UHFFFAOYSA-N 0.000 claims description 3
- GVRCKHXHWSYDEF-QMMMGPOBSA-N N-acetyl-1-methyl-L-histidine Chemical compound CC(=O)N[C@H](C(O)=O)CC1=CN(C)C=N1 GVRCKHXHWSYDEF-QMMMGPOBSA-N 0.000 claims description 3
- OTCCIMWXFLJLIA-UHFFFAOYSA-N N-acetyl-DL-aspartic acid Natural products CC(=O)NC(C(O)=O)CC(O)=O OTCCIMWXFLJLIA-UHFFFAOYSA-N 0.000 claims description 3
- KTHDTJVBEPMMGL-VKHMYHEASA-N N-acetyl-L-alanine Chemical compound OC(=O)[C@H](C)NC(C)=O KTHDTJVBEPMMGL-VKHMYHEASA-N 0.000 claims description 3
- OTCCIMWXFLJLIA-BYPYZUCNSA-N N-acetyl-L-aspartic acid Chemical compound CC(=O)N[C@H](C(O)=O)CC(O)=O OTCCIMWXFLJLIA-BYPYZUCNSA-N 0.000 claims description 3
- PEDXUVCGOLSNLQ-WUJLRWPWSA-N N-acetyl-L-threonine Chemical compound C[C@@H](O)[C@@H](C(O)=O)NC(C)=O PEDXUVCGOLSNLQ-WUJLRWPWSA-N 0.000 claims description 3
- CZPWVGJYEJSRLH-UHFFFAOYSA-N Pyrimidine Chemical compound C1=CN=CN=C1 CZPWVGJYEJSRLH-UHFFFAOYSA-N 0.000 claims description 3
- VYPSYNLAJGMNEJ-UHFFFAOYSA-N Silicium dioxide Chemical compound O=[Si]=O VYPSYNLAJGMNEJ-UHFFFAOYSA-N 0.000 claims description 3
- KDYFGRWQOYBRFD-UHFFFAOYSA-N Succinic acid Natural products OC(=O)CCC(O)=O KDYFGRWQOYBRFD-UHFFFAOYSA-N 0.000 claims description 3
- KZSNJWFQEVHDMF-UHFFFAOYSA-N Valine Natural products CC(C)C(N)C(O)=O KZSNJWFQEVHDMF-UHFFFAOYSA-N 0.000 claims description 3
- HWXBTNAVRSUOJR-UHFFFAOYSA-N alpha-hydroxyglutaric acid Natural products OC(=O)C(O)CCC(O)=O HWXBTNAVRSUOJR-UHFFFAOYSA-N 0.000 claims description 3
- 229940009533 alpha-ketoglutaric acid Drugs 0.000 claims description 3
- 229940043376 ammonium acetate Drugs 0.000 claims description 3
- 235000019257 ammonium acetate Nutrition 0.000 claims description 3
- 235000003704 aspartic acid Nutrition 0.000 claims description 3
- OQFSQFPPLPISGP-UHFFFAOYSA-N beta-carboxyaspartic acid Natural products OC(=O)C(N)C(C(O)=O)C(O)=O OQFSQFPPLPISGP-UHFFFAOYSA-N 0.000 claims description 3
- KDYFGRWQOYBRFD-NUQCWPJISA-N butanedioic acid Chemical compound O[14C](=O)CC[14C](O)=O KDYFGRWQOYBRFD-NUQCWPJISA-N 0.000 claims description 3
- 229960004106 citric acid Drugs 0.000 claims description 3
- 235000013922 glutamic acid Nutrition 0.000 claims description 3
- 239000004220 glutamic acid Substances 0.000 claims description 3
- 229960000890 hydrocortisone Drugs 0.000 claims description 3
- 239000003617 indole-3-acetic acid Substances 0.000 claims description 3
- AGPKZVBTJJNPAG-UHFFFAOYSA-N isoleucine Natural products CCC(C)C(N)C(O)=O AGPKZVBTJJNPAG-UHFFFAOYSA-N 0.000 claims description 3
- 229960000310 isoleucine Drugs 0.000 claims description 3
- 229960003136 leucine Drugs 0.000 claims description 3
- KHPXUQMNIQBQEV-UHFFFAOYSA-N oxaloacetic acid Chemical compound OC(=O)CC(=O)C(O)=O KHPXUQMNIQBQEV-UHFFFAOYSA-N 0.000 claims description 3
- 238000012856 packing Methods 0.000 claims description 3
- 230000006920 protein precipitation Effects 0.000 claims description 3
- 239000000741 silica gel Substances 0.000 claims description 3
- 229910002027 silica gel Inorganic materials 0.000 claims description 3
- DUYSYHSSBDVJSM-KRWOKUGFSA-N sphingosine 1-phosphate Chemical compound CCCCCCCCCCCCC\C=C\[C@@H](O)[C@@H](N)COP(O)(O)=O DUYSYHSSBDVJSM-KRWOKUGFSA-N 0.000 claims description 3
- YNJBWRMUSHSURL-UHFFFAOYSA-N trichloroacetic acid Chemical compound OC(=O)C(Cl)(Cl)Cl YNJBWRMUSHSURL-UHFFFAOYSA-N 0.000 claims description 3
- 239000004474 valine Substances 0.000 claims description 3
- 229960004295 valine Drugs 0.000 claims description 3
- 229960004203 carnitine Drugs 0.000 claims description 2
- 239000005515 coenzyme Substances 0.000 claims description 2
- 229940079593 drug Drugs 0.000 claims description 2
- 239000003814 drug Substances 0.000 claims description 2
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 2
- 239000012502 diagnostic product Substances 0.000 claims 2
- PHIQHXFUZVPYII-ZCFIWIBFSA-O (R)-carnitinium Chemical compound C[N+](C)(C)C[C@H](O)CC(O)=O PHIQHXFUZVPYII-ZCFIWIBFSA-O 0.000 claims 1
- 238000004519 manufacturing process Methods 0.000 claims 1
- 230000035945 sensitivity Effects 0.000 abstract description 14
- 230000004907 flux Effects 0.000 abstract description 3
- 238000011088 calibration curve Methods 0.000 description 12
- 239000002207 metabolite Substances 0.000 description 11
- 238000000034 method Methods 0.000 description 10
- 239000012224 working solution Substances 0.000 description 7
- 230000000694 effects Effects 0.000 description 6
- 238000003908 quality control method Methods 0.000 description 6
- 238000000926 separation method Methods 0.000 description 6
- 206010020772 Hypertension Diseases 0.000 description 5
- 210000004369 blood Anatomy 0.000 description 5
- 239000008280 blood Substances 0.000 description 5
- 239000000463 material Substances 0.000 description 5
- 239000000243 solution Substances 0.000 description 5
- PHIQHXFUZVPYII-ZCFIWIBFSA-N (R)-carnitine Chemical compound C[N+](C)(C)C[C@H](O)CC([O-])=O PHIQHXFUZVPYII-ZCFIWIBFSA-N 0.000 description 4
- 208000002330 Congenital Heart Defects Diseases 0.000 description 4
- 208000018262 Peripheral vascular disease Diseases 0.000 description 4
- 208000006011 Stroke Diseases 0.000 description 4
- 208000028831 congenital heart disease Diseases 0.000 description 4
- 208000004124 rheumatic heart disease Diseases 0.000 description 4
- 206010008190 Cerebrovascular accident Diseases 0.000 description 3
- 239000003795 chemical substances by application Substances 0.000 description 3
- 150000001875 compounds Chemical class 0.000 description 3
- 208000014471 histiocytoid cardiomyopathy Diseases 0.000 description 3
- 238000005040 ion trap Methods 0.000 description 3
- 238000012360 testing method Methods 0.000 description 3
- 238000012795 verification Methods 0.000 description 3
- 239000012530 fluid Substances 0.000 description 2
- 238000000589 high-performance liquid chromatography-mass spectrometry Methods 0.000 description 2
- 238000007477 logistic regression Methods 0.000 description 2
- 238000002360 preparation method Methods 0.000 description 2
- 230000004044 response Effects 0.000 description 2
- 239000002904 solvent Substances 0.000 description 2
- 239000011550 stock solution Substances 0.000 description 2
- 238000001196 time-of-flight mass spectrum Methods 0.000 description 2
- 238000012549 training Methods 0.000 description 2
- 238000010200 validation analysis Methods 0.000 description 2
- 108010088751 Albumins Proteins 0.000 description 1
- 102000009027 Albumins Human genes 0.000 description 1
- 108091003079 Bovine Serum Albumin Proteins 0.000 description 1
- 208000031229 Cardiomyopathies Diseases 0.000 description 1
- KCXVZYZYPLLWCC-UHFFFAOYSA-N EDTA Chemical compound OC(=O)CN(CC(O)=O)CCN(CC(O)=O)CC(O)=O KCXVZYZYPLLWCC-UHFFFAOYSA-N 0.000 description 1
- 208000031226 Hyperlipidaemia Diseases 0.000 description 1
- QIVBCDIJIAJPQS-VIFPVBQESA-N L-tryptophane Chemical class C1=CC=C2C(C[C@H](N)C(O)=O)=CNC2=C1 QIVBCDIJIAJPQS-VIFPVBQESA-N 0.000 description 1
- 238000000585 Mann–Whitney U test Methods 0.000 description 1
- 229910019142 PO4 Inorganic materials 0.000 description 1
- XAGFODPZIPBFFR-UHFFFAOYSA-N aluminium Chemical compound [Al] XAGFODPZIPBFFR-UHFFFAOYSA-N 0.000 description 1
- 229910052782 aluminium Inorganic materials 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 230000010100 anticoagulation Effects 0.000 description 1
- 229960005261 aspartic acid Drugs 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000036772 blood pressure Effects 0.000 description 1
- 229940098773 bovine serum albumin Drugs 0.000 description 1
- 210000004556 brain Anatomy 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 239000003153 chemical reaction reagent Substances 0.000 description 1
- 238000003759 clinical diagnosis Methods 0.000 description 1
- 239000013068 control sample Substances 0.000 description 1
- 238000010828 elution Methods 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 238000004817 gas chromatography Methods 0.000 description 1
- 208000031169 hemorrhagic disease Diseases 0.000 description 1
- 208000023589 ischemic disease Diseases 0.000 description 1
- 230000000302 ischemic effect Effects 0.000 description 1
- 238000002156 mixing Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000002552 multiple reaction monitoring Methods 0.000 description 1
- 208000010125 myocardial infarction Diseases 0.000 description 1
- 230000008506 pathogenesis Effects 0.000 description 1
- 239000010452 phosphate Substances 0.000 description 1
- 238000007781 pre-processing Methods 0.000 description 1
- 230000001376 precipitating effect Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000011084 recovery Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000007789 sealing Methods 0.000 description 1
- 239000010421 standard material Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 230000009885 systemic effect Effects 0.000 description 1
- 230000008685 targeting Effects 0.000 description 1
- 238000004809 thin layer chromatography Methods 0.000 description 1
- 239000003643 water by type Substances 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
- G01N30/02—Column chromatography
- G01N30/88—Integrated analysis systems specially adapted therefor, not covered by a single one of the groups G01N30/04 - G01N30/86
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
- G01N30/02—Column chromatography
- G01N30/88—Integrated analysis systems specially adapted therefor, not covered by a single one of the groups G01N30/04 - G01N30/86
- G01N2030/8809—Integrated analysis systems specially adapted therefor, not covered by a single one of the groups G01N30/04 - G01N30/86 analysis specially adapted for the sample
- G01N2030/8813—Integrated analysis systems specially adapted therefor, not covered by a single one of the groups G01N30/04 - G01N30/86 analysis specially adapted for the sample biological materials
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N30/00—Investigating or analysing materials by separation into components using adsorption, absorption or similar phenomena or using ion-exchange, e.g. chromatography or field flow fractionation
- G01N30/02—Column chromatography
- G01N30/88—Integrated analysis systems specially adapted therefor, not covered by a single one of the groups G01N30/04 - G01N30/86
- G01N2030/8809—Integrated analysis systems specially adapted therefor, not covered by a single one of the groups G01N30/04 - G01N30/86 analysis specially adapted for the sample
- G01N2030/884—Integrated analysis systems specially adapted therefor, not covered by a single one of the groups G01N30/04 - G01N30/86 analysis specially adapted for the sample organic compounds
Landscapes
- Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Biochemistry (AREA)
- General Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Other Investigation Or Analysis Of Materials By Electrical Means (AREA)
Abstract
The invention discloses a metabolic marker combination for assessing the risk of cardiovascular and cerebrovascular diseases of a subject, comprising ceramide Cer d18:1/16: 0. ceramide Cer d18:1/18: 0. ceramide Cer d18:1/24: 0. phenylacetylglutamine, trimethylamine, betaine, and choline; the cardiovascular and cerebrovascular diseases are selected from coronary heart disease, atherosclerosis, atrial fibrillation and heart failure. The metabolic marker combination has the advantages of high sensitivity, good specificity, high quantitative and detection flux and the like in the aspect of evaluating or predicting the risk of cardiovascular and cerebrovascular diseases.
Description
Technical Field
The invention relates to the field of cardiovascular and cerebrovascular diseases, in particular to a metabolic marker for evaluating the risk of cardiovascular and cerebrovascular diseases of a subject and application thereof.
Background
Cardiovascular and cerebrovascular diseases are the collective term for cardiovascular and cerebrovascular diseases, and refer broadly to ischemic or hemorrhagic diseases of heart, brain and systemic tissues caused by hyperlipidemia, blood viscosity, atherosclerosis, hypertension, etc., including hypertension (elevated blood pressure), coronary heart disease (heart attack), cerebrovascular disease (stroke), peripheral vascular disease, heart failure, rheumatic heart disease, congenital heart disease, cardiomyopathy, etc.
The incidence rate and death rate of cardiovascular and cerebrovascular diseases are high, and the pathogenesis is complex. However, most of the markers used for diagnosing cardiovascular and cerebrovascular diseases at present cannot provide more reference value for clinicians due to lack of sensitivity and specificity. Therefore, finding efficient, sensitive and accurate biomarkers for diagnosing cardiovascular and cerebrovascular diseases and/or predicting the risk of the diseases becomes a problem to be solved in clinical diagnosis and treatment.
Meanwhile, the traditional biomarker detection methods at present comprise thin layer chromatography, liquid chromatography, immunochemistry method, gas chromatography and the like, and the traditional methods are difficult to meet the high standards of the medical industry, especially the accurate medical industry on the specificity, the accuracy, the application range and the dynamic range. The existing research and detection method for cardiovascular and cerebrovascular diseases based on mass spectrometry mainly carries out disease diagnosis or evaluation detection in a non-targeted relative quantitative or semi-quantitative mode or in an absolute quantitative mode of a single type of compound, so that the accuracy and the comprehensiveness of detection results have certain limitations and a certain distance from the truly effective clinical mass spectrometry detection and risk evaluation.
Therefore, on the basis of newly developed biomarkers, the method and the system for detecting the high-standard biomarkers, which meet medical treatment, particularly accurate medical treatment, are established, and the method and the system have important significance.
Disclosure of Invention
The invention provides a metabolic marker combination for evaluating the risk of cardiovascular and cerebrovascular diseases of a subject, and application of the metabolic marker combination in preparing a product for diagnosing cardiovascular and cerebrovascular diseases or evaluating cardiovascular and cerebrovascular diseases, a kit, a quantitative detection method and a computer system, and the metabolic marker combination has the advantages of high sensitivity, good specificity, high quantitative and/or detection flux and the like in evaluating or predicting the risk of cardiovascular and cerebrovascular diseases.
In order to achieve the above purpose, the present invention provides the following technical solutions:
in a first aspect, the invention discloses a metabolic marker combination for assessing the risk of cardiovascular and cerebrovascular disease in a subject, the metabolic marker combination comprising ceramide Cer d18:1/16: 0. ceramide Cer d18:1/18: 0. ceramide Cer d18:1/24: 0. phenylacetylglutamine, trimethylamine, betaine, and choline; the cardiovascular and cerebrovascular diseases are selected from hypertension, coronary heart disease, atherosclerosis, atrial fibrillation, heart failure, apoplexy, peripheral vascular disease, heart failure, rheumatic heart disease, congenital heart disease or cardiomyopathy.
In some specific embodiments, the metabolic marker combination further comprises one or more of the following metabolic markers: ceramide Cer d18:1/24: 1. ceramide GlcCer d18:1/12: 0. trihexosylceramide d18:1/24: 0. sphingosine-1-phosphate, trimethylamine oxide, carnitine or creatinine.
In some specific embodiments, the metabolic marker combination further comprises one or more of the following metabolic markers: aspartic acid, N-acetylalanine, glycine, N-acetylaspartic acid, N-acetylthreonine, N-acetyl-1-methylhistidine, indoleacetic acid, cortisol, citric acid, leucine, isoleucine, valine, pyrimidine, succinic acid, acetyl-coenzyme, glutamic acid, oxaloacetic acid or alpha-ketoglutaric acid.
In some specific embodiments, the subject is a human.
In some specific embodiments, the metabolic marker is obtained by detecting a biological sample of a subject, the biological sample selected from plasma or serum.
In a second aspect, the invention also discloses the use of the aforementioned metabolic marker combination for the preparation of a product for diagnosing cardiovascular and cerebrovascular diseases or evaluating cardiovascular and cerebrovascular drugs, the product taking the expression level of the aforementioned metabolic marker combination as an evaluation index.
In some specific embodiments, the product is selected from the group consisting of a kit, a diagnostic device, and a computer system.
In a third aspect, the invention also discloses a kit for detecting the metabolic marker combination, the kit comprises a standard product of the metabolic marker and a metabolic marker extracting agent, wherein the metabolic marker extracting agent is a mixture of an organic solvent and water, and the organic solvent is one or more of isopropanol, methanol and acetonitrile.
In a fourth aspect, the present invention also discloses a method for quantitatively detecting the combination of the metabolic markers, wherein the method comprises the steps of treating a biological sample of a subject, and quantitatively detecting the combination of the metabolic markers in the biological sample by using a liquid chromatography tandem mass spectrometry (LC-MS/MS) method.
In some specific embodiments, the liquid chromatography comprises High Performance Liquid Chromatography (HPLC), ultra high performance liquid chromatography (UPLC), and Nano-liter liquid chromatography (Nano-LC), and the tandem mass spectrometry comprises Quadrupole mass spectrometry (Q), time of Flight mass spectrometry (Time of Flight, TOF), ion hydrazine mass spectrometry (Ion Trap), and high resolution orbital hydrazine mass spectrometry (Orbitrap).
In some specific embodiments, the separation conditions of the liquid chromatograph include: the mobile phase A is a methanol solution containing an additive, the additive is selected from any one of ammonium formate, ammonium acetate and trichloroacetic acid, and the mobile phase B is selected from one or a combination of more of isopropanol, methanol, acetonitrile, ethanol and propylene glycol; the chromatographic column is selected from C8 and C18 silica gel packing columns, the column temperature is set to 25-45 ℃, and the flow rate is 0.2-0.6 ml/min; the detection conditions of the mass spectrum include: and (3) carrying out data acquisition by adopting a triple quadrupole mass spectrometry multi-reaction monitoring (MRM) mode, selecting characteristic ion pair information of the metabolic markers, carrying out information confirmation and detection method establishment by adopting a standard substance, and carrying out quantitative correction by adopting an internal standard substance to obtain accurate concentration values and related proportion values of the metabolic markers in the biological sample.
As an exemplary specific embodiment, the separation conditions of the liquid chromatography may include: 5 microliters of sample were introduced, mobile phase A was 60% methanol+10 millimoles ammonium formate, mobile phase B was 90% isopropyl alcohol+10% methanol+10 millimoles ammonium formate, column temperature was set to 40℃for a 100 millimoles C18 column, flow rate was 0.3 mL/min, 0-0.5 min was maintained at 50% B,0.5-1.8 min was linearly varied from 50% B to 75% B,1.8-3.0 min was linearly varied from 75% B to 80% B,3.0-3.4 min was linearly varied from 80% B to 98% B,3.4-4.3 min was maintained at 98% B,4.3-4.5 min was linearly varied from 98% B to 50% B,4.5-6 min was maintained at 50% B;
in some specific embodiments, the mass spectrum is selected from the group consisting of a quadrupole mass spectrum, a time-of-flight mass spectrum, an ionic hydrazine mass spectrum, and a high resolution orbital hydrazine mass spectrum; the conditions of the mass spectrum and the setting of the mass spectrum qualitative and quantitative detection mode comprise: selecting an electrospray ion source (ESI), and selecting an ion scanning mode according to the response of the detection target compound; and (3) carrying out data acquisition by adopting a triple quadrupole mass spectrometry multi-reaction monitoring (MRM) mode, selecting characteristic ion pair information of the metabolic markers, carrying out information confirmation and detection method establishment by adopting a standard substance, and carrying out quantitative correction by adopting an internal standard substance to obtain accurate concentration values and related proportion values of the metabolic markers in the biological sample.
In some embodiments, the processing of the biological sample comprises adding the biological sample to a precipitating agent selected from the group consisting of a mixed solvent of isopropanol and methanol; in some embodiments, the precipitant is a mixed solvent of isopropanol and methanol; the volume ratio can be 1:1-1:10, and the volume ratio can be 1:3-1:5 as a preferable mode.
In a fifth aspect, the present invention also discloses a computer system for assessing a subject's risk of cardiovascular and cerebrovascular diseases, the system comprising an information acquisition module and a cardiovascular and cerebrovascular disease risk assessment module;
the information acquisition module is at least used for executing the following operations: obtaining metabolic marker combination detection information in a subject sample, the metabolic marker combination selected from the metabolic marker combinations described previously;
the cardiovascular and cerebrovascular disease risk assessment module is at least used for executing the following operations: evaluating whether the subject suffers from cardiovascular and cerebrovascular diseases or has a disease risk of cardiovascular and cerebrovascular diseases according to the metabolic marker set level acquired by the information acquisition module; the cardiovascular and cerebrovascular diseases are selected from hypertension, coronary heart disease, atherosclerosis, atrial fibrillation, heart failure, apoplexy, peripheral vascular disease, heart failure, rheumatic heart disease, congenital heart disease or cardiomyopathy.
In some specific embodiments, the cardiovascular and cerebrovascular disease risk assessment module is configured to perform at least the following: inputting the level of the metabolic marker group acquired by the information acquisition module into a diagnosis model, and evaluating whether the subject has cardiovascular and cerebrovascular diseases or has the risk of cardiovascular and cerebrovascular diseases according to the diagnosis model.
In some specific embodiments, the diagnostic model is as follows:
P＝C1*0.9414+C2*5.6311+C3*0.5817+C4*0.09151-C5*0.03620+C6*0.05425+C7*0.02096+2.2141；
wherein, C1 is ceramide Cer d18 in the sample: 1/16:0 in μM concentration unit, and C2 is ceramide Cer d18 in the sample: 1/18:0 in μM concentration unit, and C3 is ceramide Cer d18 in the sample: 1/24:0 in the concentration unit of mu M, C4 is the concentration value of phenylacetylglutamine in the sample in the concentration unit of mu M, C5 is the concentration value of trimethylamine in the sample in the concentration unit of mu M, C6 is the concentration value of betaine in the sample in the concentration unit of mu M, and C7 is the concentration value of choline in the sample in the concentration unit of mu M;
assessing that the subject is suffering from or at risk of suffering from the cardiovascular disease if the P-value of the subject is within a threshold range, wherein
The threshold is selected from 0.4 to 0.5, preferably 0.4243, when the subject is predicted to be at risk for coronary heart disease;
the threshold is selected from 1.0 to 1.2, preferably 1.019, when the subject is predicted to have a risk of heart failure;
the threshold is selected from 1.4 to 1.42, preferably 1.412, when the subject is predicted to have a risk of atrial fibrillation;
the threshold is selected from 0.3 to 0.31, preferably 0.303, when the subject is predicted to be at risk of atherosclerosis.
In some embodiments, the system further comprises a sample detection module for performing at least the operation of detecting the level of the marker in the sample.
In some specific embodiments, the system is at least for performing a liquid chromatography tandem mass spectrometry (LC-MS/MS) operation for detecting a biomarker; the liquid chromatography may be selected from High Performance Liquid Chromatography (HPLC), ultra high performance liquid chromatography (UPLC), and Nano-liter liquid chromatography (Nano-LC), and the tandem mass spectrometry may be selected from quaternary rod mass spectrometry (Q), time of Flight mass spectrometry (TOF), ion hydrazine mass spectrometry (Ion Trap), and high resolution orbital hydrazine mass spectrometry (Orbitrap).
In some specific embodiments, the separation conditions of the liquid chromatograph include: the mobile phase A is a methanol solution containing an additive, the additive is selected from any one of ammonium formate, ammonium acetate and trichloroacetic acid, and the mobile phase B is selected from one or a combination of more of isopropanol, methanol, acetonitrile, ethanol and propylene glycol; the chromatographic column is selected from C8 and C18 silica gel packing columns, the column temperature is set to 25-45 ℃, and the flow rate is 0.2-0.6 ml/min; the detection conditions of the mass spectrum include: and (3) carrying out data acquisition by adopting a triple quadrupole mass spectrometry multi-reaction monitoring (MRM) mode, selecting characteristic ion pair information of the metabolic markers, carrying out information confirmation and detection method establishment by adopting a standard substance, and carrying out quantitative correction by adopting an internal standard substance to obtain accurate concentration values and related proportion values of the metabolic markers in the biological sample.
As an exemplary specific embodiment, the separation conditions of the liquid chromatography may include: 5 microliters of sample were introduced, mobile phase A was 60% methanol+10 millimoles ammonium formate, mobile phase B was 90% isopropyl alcohol+10% methanol+10 millimoles ammonium formate, column temperature was set to 40℃for a 100 millimoles C18 column, flow rate was 0.3 mL/min, 0-0.5 min was maintained at 50% B,0.5-1.8 min was linearly varied from 50% B to 75% B,1.8-3.0 min was linearly varied from 75% B to 80% B,3.0-3.4 min was linearly varied from 80% B to 98% B,3.4-4.3 min was maintained at 98% B,4.3-4.5 min was linearly varied from 98% B to 50% B,4.5-6 min was maintained at 50% B;
in some specific embodiments, the mass spectrum is selected from the group consisting of a quadrupole mass spectrum, a time-of-flight mass spectrum, an ionic hydrazine mass spectrum, and a high resolution orbital hydrazine mass spectrum; the conditions of the mass spectrum and the setting of the mass spectrum qualitative and quantitative detection mode comprise: selecting an electrospray ion source (ESI), and selecting an ion scanning mode according to the response of the detection target compound; and (3) carrying out data acquisition by adopting a triple quadrupole mass spectrometry multi-reaction monitoring (MRM) mode, selecting characteristic ion pair information of the metabolic markers, carrying out information confirmation and detection method establishment by adopting a standard substance, and carrying out quantitative correction by adopting an internal standard substance to obtain accurate concentration values and related proportion values of the metabolic markers in the biological sample.
In some embodiments, the system further comprises a sample pretreatment module for performing at least the operations of protein precipitation and marker set extraction; the operation comprises extracting the subject sample with a mixed solvent of isopropanol and methanol, centrifuging, and collecting supernatant for detection, wherein the volume ratio can be 1:1-1:10, and preferably can be 1:3-1:5.
Advantageous effects
(1) The invention discovers and verifies 32 differential metabolites in a cardiovascular and cerebrovascular disease subject sample for the first time relative to a normal subject, wherein 7 differential metabolites are particularly important. Based on the findings, the invention provides a brand-new metabolic marker combination and application thereof, and establishes a corresponding quantitative detection method and a computer evaluation system for the metabolic marker combination, which are beneficial to improving the sensitivity and specificity of cardiovascular and cerebrovascular disease diagnosis.
(2) The invention establishes a prediction model based on 7 particularly important cardiovascular and cerebrovascular disease differential metabolites, and when the model is used for distinguishing 147 coronary heart disease patients, the AUC=0.897, the sensitivity is 85.0%, the specificity is 89.4%, and the accuracy is 86.7%. The computer system provided by the invention has the advantages of high sensitivity and specificity and good accuracy when the model is used for evaluating the cardiovascular and cerebrovascular disease risk of a subject. The differential metabolites and the predictive model discovered by the invention can also be used for risk assessment of cardiovascular diseases such as heart failure, atrial fibrillation, atherosclerosis and the like of a subject.
(3) In the preferred scheme, the method adopts a high performance liquid chromatography-tandem mass spectrometry combined mode to detect the marker level in the sample, further optimizes the detection conditions, realizes simultaneous targeting quantitative detection of multiple biomarkers, solves the problems of detection specificity, accuracy and diversity of the cardiovascular and cerebrovascular disease markers, and has the advantages of small sample detection quantity and high flux.
Drawings
FIG. 1 is a liquid chromatograph mass spectrum representative ion chromatogram of the metabolic marker of the present invention in blood;
ceramide molecule Cerd18 in 5% bsa in fig. 2: 1/16: a calibration curve of 0;
FIG. 3 is ceramide molecule Cer d18 in 5% BSA: 1/18: a calibration curve of 0;
FIG. 4 is a calibration curve for phenylacetylglutamine in 5% BSA; FIG. 5 is ceramide molecule Cer d18 in 5% BSA: 1/24: a calibration curve of 0;
FIG. 6 is ceramide molecule Cer d18 in 5% BSA: 1/24: 1;
FIG. 7 is a calibration curve for L-carnitine in 5% BSA;
FIG. 8 is a calibration curve for trihexosylceramide in 5% BSA;
FIG. 9 is a calibration curve for trimethylamine oxide in 5% BSA;
FIG. 10 is a calibration curve for creatinine in 5% BSA;
FIG. 11 is a calibration curve for trimethylamine in 5% BSA;
FIG. 12 is a calibration curve for betaine in 5% BSA;
FIG. 13 is a calibration curve for choline in 5% BSA;
FIG. 14 is a graph of the operating characteristics (ROC) of coronary heart disease patients and healthy group subjects;
Detailed Description
Embodiments of the present invention will be described in detail below with reference to examples, but it will be understood by those skilled in the art that the following examples are only for illustrating the present invention and should not be construed as limiting the scope of the present invention. The specific conditions are not noted in the examples and are carried out according to conventional conditions or conditions recommended by the manufacturer. The reagents or apparatus used were conventional products commercially available without the manufacturer's attention.
Example one, blood sample collection
Adopts clinical EDTA anticoagulation vacuum blood collection tube to collect 3-5ml of fasting venous blood from coronary heart disease patient and healthy control normal person. Plasma was separated at 1500g×15 min within 1 hour after collection. Equal amount (150 microliters) is packaged in a 200 microliter centrifuge tube, and the centrifuge tube is quickly placed at-80 ℃ for standby and information registration is performed. The method comprises the following steps: (1) training set: clinical plasma samples 183 of patients with coronary heart disease and control plasma samples 94 of healthy people; (2) validation set: clinical plasma samples for coronary heart disease patients are 147 cases, and healthy human control plasma samples are 94 cases.
Example two, preparation of calibration Curve working fluid, inner calibration Material and quality control Material
The marker standard to be tested is dissolved into a stock solution of 0.1mM by using a solvent of isopropanol to acetonitrile of 9:1 respectively. The mixture was then further diluted with 50mg/mL bovine serum albumin (BSA, albumin) solution to form a mixed calibrator (standard) curve working solution.
Illustratively, the ceramide Cer 18 may be added to the mixed calibrator working solution: 1/16: 0. ceramide Cer d18:1/18: 0. ceramide GlcCer d18:1/12: sphingosine-0 and 1-phosphate at concentration points of 2. Mu.M, 1. Mu.M, 0.4. Mu.M, 0.2. Mu.M, 0.08. Mu.M and 0.04. Mu.M, respectively; the mixed calibrator working solution can be added with the ceramide Cer d18:1/24: 0. ceramide Cerd18:1/24: 1. trihexosylceramide d18:1/24: ) And phenylacetylglutamine at respective concentration points of 10. Mu.M, 5. Mu.M, 2. Mu.M, 1. Mu.M, 0.4. Mu.M and 0.2. Mu.M; trimethylamine oxide, trimethylamine, choline, L-carnitine, betaine and creatinine can be added into the mixed calibrator working solution, wherein the concentration points are 100 mu M, 50 mu M, 20 mu M, 10 mu M, 4 mu M and 2 mu M. Note that this example is merely illustrative of metabolic markers in a mixed calibration sample working fluid, and is not exhaustive.
Inner calibrator ceramide molecule Cerd18:1/17: 0. deuterated trimethylamine oxide and deuterated tryptophan were dissolved in 0.1mM stock solution, respectively, and then diluted with isopropanol/methanol 8:2 solvent to 300nM of mixed internal standard working solution.
According to the concentration range of each marking composition in the sample, three quality control samples with high, medium and low relative concentrations are prepared.
Third embodiment, pretreatment of plasma sample and extraction of diagnostic marker composition
The total of 518 plasma samples, calibration material (standard material) curve working solution and quality control material are respectively taken into a V-bottom 96-well plate with 10 microliters, then 190 microliters of internal calibration material working solution is added, an aluminum sealing film is attached, and vibration and mixing are carried out at 650rpm for 20 minutes. Then, the mixture was centrifuged at 4000 Xg for 20 minutes, and 100. Mu.l of the supernatant was collected for detection by high performance liquid chromatography-mass spectrometry.
Example IV, high Performance liquid chromatography-Mass Spectrometry detection of diagnostic marker compositions
The liquid chromatography separation conditions and parameters are mainly as follows: 5 microliters of sample was introduced, mobile phase A was 60% methanol+10 mM ammonium formate, mobile phase B was 90% isopropanol+10% methanol+10 mM ammonium formate, the column was 100mM C18 reverse phase column (Waters Acquity BEH C), the column temperature was set at 40℃and the flow rate was 0.3 mL/min. Elution gradient: maintaining 50% B for 0-0.5 min, linearly changing from 50% B to 75% B for 0.5-1.8 min, linearly changing from 75% B to 80% B for 1.8-3.0 min, linearly changing from 80% B to 98% B for 3.0-3.4 min, maintaining 98% B for 3.4-4.3 min, and maintaining 50% B for 4.3-4.5 min from 98% B to 50% B for 4.5-6 min.
The mass spectrum detection parameters are mainly as follows: data acquisition was performed using triple quadrupole mass spectrometry Multiple Reaction Monitoring (MRM) mode, with an ion profile as shown in fig. 1.
Example five, standard Curve
Calibration standard curves for each of the metabolic markers are established, as illustrated by way of example in fig. 2-13, wherein the abscissa represents the concentration (μm) of the corresponding detection substance and the ordinate represents the mass spectrum signal peak area. The calibration standard curve obtained in this embodiment may be used to query the concentration of the corresponding substance to be tested in the serum sample of the subject.
Example six, quality control
The coronary heart disease sample and the healthy control sample of the test item are taken and mixed in equal amounts to form a QC sample, then the QC sample and the standard sample are detected together, quantitative recovery rate tests of standard curve linearity, quantitative LOQ, standard addition and non-standard addition comparison are carried out, meanwhile, the non-standard addition QC sample is inserted into the item sample to carry out quantitative repeatability tests at intervals, the detection of the sample is ensured to accord with quality control, and the relevant results of partial metabolites are shown in a table 1 in an exemplary manner.
TABLE 1 quality control test results
Seventh embodiment, marker diagnostic model establishment
The selection criteria for the variable weights VIP value (VIP > 1) and the P value (P < 0.05) provided by the Mann-Whitney U test provided by the multidimensional OPLS-DA model, resulted in 32 differential metabolites from the training set samples for distinguishing coronary heart disease from normal controls, comprising: ceramide Cer d18:1/16: 0. ceramide Cer d18:1/18: 0. ceramide Cer d18:1/24: 0. ceramide Cer d18:1/24: 1. ceramide GlcCer d18:1/12: 0. trihexosylceramide d18:1/24: 0. sphingosine-1-phosphate, phenylacetylglutamine, trimethylamine oxide, trimethylamine, choline, L-carnitine, betaine, creatinine, aspartic acid, N-acetylalanine, glycine, N-acetylaspartic acid, N-acetylthreonine, N-acetyl-1-methylhistidine, indoleacetic acid, cortisol, citric acid, leucine, isoleucine, valine, pyrimidine, succinic acid, acetyl-co-enzyme, glutamic acid, oxaloacetic acid and alpha-ketoglutaric acid.
The experiment is carried out by using a logistic regression model, and the effect of 7 metabolites (namely, ceramide molecules Cer18:1/16:0, ceramide molecules Cer18:1/18:0, ceramide molecules Cer18:1/24:0, phenylacetylglutamine, trimethylamine, betaine and choline) serving as coronary heart disease markers is particularly important. The markers were then evaluated in coronary heart disease plasma/serum samples using a clinical diagnostic performance curve (ROC curve).
According to the comprehensive factors such as the difference multiple size, the significance size, the concentration value size range and the like of each diagnostic marker between the coronary heart disease and the normal control group, a scoring diagnostic model superior to a single marker index is established through a logistic regression model:
model score p=c1×0.9414+c2×5.6311+c3×0.5817+c4×0.09151-c5×0.03620+c6×0.05425+c7×0.02096+2.2141.
Wherein, C1 is ceramide Cer d18 in the sample: 1/16:0 is a concentration value expressed in units of concentration of μm; if the concentration value is expressed in mM, it is converted to μM and then taken as a model, for example, if ceramide Cer d18:1/16:0, if the detection concentration is 0.02mM, it is converted into 20. Mu.M, 20 is taken as a C1 value to be taken into a model for calculation, and all of the following C2 to C7 are treated in the same manner;
the C2 is ceramide Cer d18 in the sample: 1/18:0 is a concentration value expressed in units of concentration of μm; the C3 is ceramide Cer d18 in the sample: 1/24:0 is a concentration value expressed in units of concentration of μm; the C4 is a concentration value taken when the phenylacetylglutamine in the sample is expressed in the concentration unit of mu M; c5 is a concentration value taken when trimethylamine in the sample is expressed in mu M concentration units; the C6 is a concentration value taken when betaine in the sample is expressed in the concentration unit of mu M; the C7 is the concentration value of choline in the sample expressed in mu M concentration units.
The optimal diagnostic threshold 0.4243 is obtained by analysis of a subject operating characteristic curve (ROC) curve (see fig. 14 of the specification). The concentration value of each sample is then detected by the diagnostic markers of the sample, and a score value is calculated according to the diagnostic model, and whether the subject is ill or at risk of suffering from the disease is assessed by comparison with a diagnostic threshold.
Example eight verification of marker diagnostic model
The established marker diagnosis model is applied to 147 other coronary heart disease patients and 94 control samples, the score value of each sample is calculated, and then the diagnosis specificity, the sensitivity and the accuracy are obtained through statistics, and the result shows that the prediction model has a good coronary heart disease prediction result as shown in the table 2 below. The 7 metabolite groups were used to distinguish 147 coronary heart disease patients with auc= 0.871, sensitivity of 85.0%, specificity of 89.4% and accuracy of 86.7% (table 2).
TABLE 2 evaluation of the diagnostic coronary Effect of the predictive model described in example 7
Example nine verification of marker diagnostic model
The established marker diagnosis model is applied to 121 heart failure patients and 89 control samples, the score value of each sample is calculated, and then the diagnosis specificity, the sensitivity and the accuracy are obtained through statistics, so that the result shows that the prediction model has a good heart failure prediction result. The 7 metabolite groups were used to distinguish 121 heart failure patients with auc=0.905, sensitivity 88.4%, specificity 83.1%, accuracy 86.2%, threshold 1.019, see table 3.
TABLE 3 evaluation of diagnostic heart failure Effect of the predictive model described in example 7
Example verification of a ten marker diagnostic model
The established marker diagnosis model is applied to 134 atrial fibrillation patients and 111 control samples, the score value of each sample is calculated, and then the diagnosis specificity, the sensitivity and the accuracy are obtained through statistics, so that the result shows that the prediction model has a good atrial fibrillation prediction result. The 7 metabolite groups were used to distinguish 134 patients with atrial fibrillation with auc=0.836, sensitivity of 82.8%, specificity of 82.9%, accuracy of 82.9, threshold of 1.412, see table 4.
TABLE 4 evaluation of diagnostic atrial fibrillation effects of the predictive model described in example 7
Example eleven marker diagnostic model validation
The established marker diagnosis model is applied to 123 atherosclerosis patients and 98 control samples, the score value of each sample is calculated, and then the diagnosis specificity, the sensitivity and the accuracy are obtained through statistics, so that the result shows that the prediction model has a good atherosclerosis prediction result. The 7 metabolite groups were used to distinguish 123 atherosclerosis patients with auc=0.838, sensitivity of 81.3%, specificity of 79.6%, accuracy of 80.5%, and threshold of 0.303, see table 5.
TABLE 5 evaluation of diagnostic atherosclerosis Effect of the predictive model described in example 7
Embodiment twelve establishment of computer System for detecting cardiovascular and cerebrovascular diseases
According to embodiments 1-11, the present embodiment establishes a computer system for assessing a subject's risk of cardiovascular and cerebrovascular diseases, comprising an information acquisition module, a cardiovascular and cerebrovascular disease risk assessment module, a sample detection module, and a sample pre-processing module.
The information acquisition module is at least used for executing the following operations: obtaining metabolic marker combination detection information in a subject sample, the metabolic marker combination selected from the metabolic marker combinations described previously.
The cardiovascular and cerebrovascular disease risk assessment module is at least used for executing the following operations: according to the metabolic marker group level acquired by the information acquisition module, evaluating whether the subject is faithful to or at risk of suffering from cardiovascular and cerebrovascular diseases; the cardiovascular and cerebrovascular diseases are selected from hypertension, coronary heart disease, atherosclerosis, atrial fibrillation, heart failure, apoplexy, peripheral vascular diseases, heart failure, rheumatic heart disease, congenital heart disease or cardiomyopathy; the method specifically comprises the steps of inputting the level of the metabolic marker group acquired by the information acquisition module into a diagnosis model, and evaluating whether the subject suffers from cardiovascular and cerebrovascular diseases or has the risk of suffering from cardiovascular and cerebrovascular diseases according to the diagnosis model; the diagnostic model is as follows:
P＝C1*0.9414+C2*5.6311+C3*0.5817+C4*0.09151-C5*0.03620+C6*0.05425+C7*0.02096+2.2141；
wherein, C1 is ceramide Cer d18 in the sample: 1/16:0 in μM concentration unit, and C2 is ceramide Cer d18 in the sample: 1/18:0 in μM concentration unit, and C3 is ceramide Cer d18 in the sample: 1/24:0 in the concentration unit of mu M, C4 is the concentration value of phenylacetylglutamine in the sample in the concentration unit of mu M, C5 is the concentration value of trimethylamine in the sample in the concentration unit of mu M, C6 is the concentration value of betaine in the sample in the concentration unit of mu M, and C7 is the concentration value of choline in the sample in the concentration unit of mu M;
if the P-value of the subject corresponds to a different threshold range, assessing that the subject is suffering from or at risk of suffering from the cardiovascular disease, wherein:
the threshold is selected from 0.4 to 0.5, preferably 0.4243, when the subject is predicted to be at risk for coronary heart disease;
the threshold is selected from 1.0 to 1.2, preferably 1.019, when the subject is predicted to have a risk of heart failure;
the threshold is selected from 1.4 to 1.42, preferably 1.412, when the subject is predicted to have a risk of atrial fibrillation;
the threshold is selected from 0.3 to 0.31, preferably 0.303, when the subject is predicted to be at risk of atherosclerosis.
The sample detection module is at least used for executing the operation of detecting the marker level in the sample; specifically including at least liquid chromatography tandem mass spectrometry (LC-MS/MS) operations for performing detection of biomarkers; the liquid chromatography may be selected from High Performance Liquid Chromatography (HPLC), ultra high performance liquid chromatography (UPLC), and Nano-liter liquid chromatography (Nano-LC), and the tandem mass spectrometry may be selected from quaternary rod mass spectrometry (Q), time of Flight mass spectrometry (TOF), ion hydrazine mass spectrometry (Ion Trap), and high resolution orbital hydrazine mass spectrometry (Orbitrap);
the separation conditions of the liquid chromatography include: 5 microliters of sample were introduced, mobile phase A was 60% methanol+10 millimoles ammonium formate, mobile phase B was 90% isopropyl alcohol+10% methanol+10 millimoles ammonium formate, column temperature was set to 40℃for a 100 millimoles C18 column, flow rate was 0.3 mL/min, 0-0.5 min was maintained at 50% B,0.5-1.8 min was linearly varied from 50% B to 75% B,1.8-3.0 min was linearly varied from 75% B to 80% B,3.0-3.4 min was linearly varied from 80% B to 98% B,3.4-4.3 min was maintained at 98% B,4.3-4.5 min was linearly varied from 98% B to 50% B,4.5-6 min was maintained at 50% B;
the detection conditions of the mass spectrum include: and (3) carrying out data acquisition by adopting a triple quadrupole mass spectrometry multi-reaction monitoring (MRM) mode, selecting characteristic ion pair information of the metabolic markers, carrying out information confirmation and detection method establishment by adopting a standard substance, and carrying out quantitative correction by adopting an internal standard substance to obtain accurate concentration values and related proportion values of the metabolic markers in the biological sample. The sample pretreatment module is at least used for performing operations of protein precipitation and marker group extraction; the operations include administering isopropanol to the subject sample: extracting with methanol, centrifuging, and collecting supernatant for detection.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention.
Claims (10)
1. A metabolic marker combination for assessing the risk of cardiovascular and cerebrovascular disease in a subject, wherein the metabolic marker combination comprises ceramide Cer d18:1/16: 0. ceramide Cer d18:1/18: 0. ceramide Cer d18:1/24: 0. phenylacetylglutamine, trimethylamine, betaine, and choline; the cardiovascular and cerebrovascular diseases are selected from coronary heart disease, atherosclerosis, atrial fibrillation and heart failure; the metabolic marker combination may also optionally include one or more of the following metabolic markers: ceramide Cer d18:1/24: 1. ceramide Glc Cer d18:1/12: 0. trihexosylceramide d18:1/24: 0. sphingosine-1-phosphate, trimethylamine oxide, carnitine or creatinine; the foregoing metabolic marker combinations may also optionally include one or more of the following metabolic markers: aspartic acid, N-acetylalanine, glycine, N-acetylaspartic acid, N-acetylthreonine, N-acetyl-1-methylhistidine, indoleacetic acid, cortisol, citric acid, leucine, isoleucine, valine, pyrimidine, succinic acid, acetyl-coenzyme, glutamic acid, oxaloacetic acid or alpha-ketoglutaric acid; the subject is a human; the metabolic marker is obtained by detecting a biological sample of a subject, the biological sample being selected from plasma or serum.
2. Use of the metabolic marker combination according to claim 1 for the manufacture of a diagnostic product for diagnosing cardiovascular and cerebrovascular diseases or for evaluating cardiovascular and cerebrovascular drugs, said product taking the expression level of the metabolic marker combination according to claim 1 as an evaluation index.
3. The use according to claim 1, wherein the diagnostic product is selected from the group consisting of a kit, a diagnostic device and a computer system.
4. A kit for detecting a combination of metabolic markers according to claim 1, characterized in that the kit comprises a standard for the metabolic markers and a metabolic marker extractant selected from a mixture of an organic solvent and water, the organic solvent being selected from one or more of isopropanol, methanol, acetonitrile.
5. A computer system for assessing a subject's risk of cardiovascular and cerebrovascular disease, the system comprising an information acquisition module and a cardiovascular and cerebrovascular disease risk assessment module;
the information acquisition module is at least used for executing the following operations: obtaining metabolic marker combination detection information in a sample of a subject, the metabolic marker combination selected from the metabolic marker combinations of claim 1;
the cardiovascular and cerebrovascular disease risk assessment module is at least used for executing the following operations: evaluating whether the subject suffers from cardiovascular and cerebrovascular diseases or has a disease risk of cardiovascular and cerebrovascular diseases according to the metabolic marker set level acquired by the information acquisition module; the cardiovascular and cerebrovascular diseases are selected from coronary heart disease, atherosclerosis, atrial fibrillation and heart failure.
6. The system of claim 5, wherein the cardiovascular and cerebrovascular disease risk assessment module is configured to perform at least the following: inputting the level of the metabolic marker group acquired by the information acquisition module into a diagnosis model, and evaluating whether the subject has cardiovascular and cerebrovascular diseases or has the risk of cardiovascular and cerebrovascular diseases according to the diagnosis model.
7. The system of claim 6, wherein the diagnostic model is as follows:
P＝C1*0.9414+C2*5.6311+C3*0.5817+C4*0.09151-C5*0.03620+C6*0.05425+C7*0.02096+2.2141；
wherein, C1 is ceramide Cer d18 in the sample: 1/16:0 in μM concentration unit, and C2 is ceramide Cer d18 in the sample: 1/18:0 in μM concentration unit, and C3 is ceramide Cer d18 in the sample: 1/24:0 in the concentration unit of mu M, C4 is the concentration value of phenylacetylglutamine in the sample in the concentration unit of mu M, C5 is the concentration value of trimethylamine in the sample in the concentration unit of mu M, C6 is the concentration value of betaine in the sample in the concentration unit of mu M, and C7 is the concentration value of choline in the sample in the concentration unit of mu M; assessing that the subject is suffering from or at risk of suffering from the cardiovascular disease if the P-value of the subject corresponds to a different threshold range, wherein: the threshold is selected from 0.4-0.5, and the subject is predicted to have coronary heart disease risk; the threshold is selected from 1.0-1.2, and the subject is predicted to have heart failure risk; the threshold is selected from 1.4-1.42, and the subject is predicted to have a risk of atrial fibrillation; the threshold is selected from 0.3 to 0.31, and the subject is predicted to be at risk of atherosclerosis.
8. The system of claim 7, wherein the threshold is selected from 0.4243, and wherein the subject is predicted to have a risk of coronary heart disease; when the threshold is selected from 1.019, predicting that the subject is at risk for heart failure; when the threshold is selected from 1.412, predicting that the subject is at risk of atrial fibrillation; the threshold is selected from 0.303, the subject is predicted to be at risk of atherosclerosis.
9. The system of claim 5, further comprising a sample detection module for performing at least the operation of detecting the level of the marker in the sample; the sample detection module is at least used for executing liquid chromatography tandem mass spectrometry combined operation for detecting the biomarker; the liquid chromatography may be selected from high performance liquid chromatography, ultra high performance liquid chromatography, and nano liter liquid chromatography, and the tandem mass spectrometry may be selected from quaternary rod mass spectrometry, time of flight mass spectrometry, ion hydrazine mass spectrometry, and high resolution orbital hydrazine mass spectrometry; the mobile phase A of the liquid chromatograph is a methanol solution containing an additive, the additive is selected from any one of ammonium formate, ammonium acetate and trichloroacetic acid, and the mobile phase B is selected from one or a combination of more of isopropanol, methanol, acetonitrile, ethanol and propylene glycol; the chromatographic column is selected from C8 and C18 silica gel packing columns, the column temperature is set to 25-45 ℃, and the flow rate is 0.2-0.6 ml/min; the detection conditions of the mass spectrum include: and (3) adopting a triple quadrupole mass spectrometry multi-reaction monitoring mode to acquire data, selecting characteristic ion pair information of the metabolic markers, adopting a standard substance to carry out information confirmation and detection method establishment, and adopting an internal standard substance to carry out quantitative correction at the same time so as to obtain accurate concentration values and related proportion values of the metabolic markers in the biological sample.
10. The system of claim 5, further comprising a sample pretreatment module for performing at least the operations of protein precipitation and marker set extraction; the operation comprises extracting the subject sample with a mixed solvent of isopropanol and methanol, centrifuging, and taking supernatant for detection.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211178138.1A CN117147751A (en) | 2021-01-04 | 2021-01-04 | Metabolic marker combination for assessing cardiovascular disease risk in a subject and uses thereof |
Applications Claiming Priority (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110000655.9A CN112505228B (en) | 2021-01-04 | 2021-01-04 | Metabolic marker combination for assessing cardiovascular disease risk of subject and application thereof |
| CN202211178138.1A CN117147751A (en) | 2021-01-04 | 2021-01-04 | Metabolic marker combination for assessing cardiovascular disease risk in a subject and uses thereof |
Related Parent Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110000655.9A Division CN112505228B (en) | 2021-01-04 | 2021-01-04 | Metabolic marker combination for assessing cardiovascular disease risk of subject and application thereof |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117147751A true CN117147751A (en) | 2023-12-01 |
Family
ID=74952123
Family Applications (2)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211178138.1A Pending CN117147751A (en) | 2021-01-04 | 2021-01-04 | Metabolic marker combination for assessing cardiovascular disease risk in a subject and uses thereof |
| CN202110000655.9A Active CN112505228B (en) | 2021-01-04 | 2021-01-04 | Metabolic marker combination for assessing cardiovascular disease risk of subject and application thereof |
Family Applications After (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110000655.9A Active CN112505228B (en) | 2021-01-04 | 2021-01-04 | Metabolic marker combination for assessing cardiovascular disease risk of subject and application thereof |
Country Status (2)
| Country | Link |
|---|---|
| CN (2) | CN117147751A (en) |
| WO (1) | WO2022144028A1 (en) |
Families Citing this family (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117147751A (en) * | 2021-01-04 | 2023-12-01 | 深圳市绘云生物科技有限公司 | Metabolic marker combination for assessing cardiovascular disease risk in a subject and uses thereof |
| CN115112776B (en) * | 2021-03-18 | 2023-07-04 | 中国科学院大连化学物理研究所 | Combined marker, application thereof in diagnosing atrial fibrillation and diagnostic reagent or kit |
| CN113176364A (en) * | 2021-04-19 | 2021-07-27 | 中国医学科学院北京协和医院 | Method for simultaneously detecting trimethylamine oxide and phenylacetylglutamine, detection kit and application thereof |
| CN113533754A (en) * | 2021-07-12 | 2021-10-22 | 北京市心肺血管疾病研究所 | Application of ceramide in preparation of kit for evaluating risk of adverse event of hypertensive patient |
| CN116068063A (en) * | 2021-10-29 | 2023-05-05 | 上海市第一人民医院 | Serum lipid marker related to diabetic retinopathy fundus hard exudation and application thereof |
| CN116087488A (en) * | 2021-11-08 | 2023-05-09 | 中国医学科学院阜外医院 | Biomarkers for early prediction of cardiovascular disease (CVD) in diabetics and uses thereof |
| CN114813986B (en) * | 2022-01-26 | 2024-04-16 | 北京市心肺血管疾病研究所 | Lipid metabolite group for predicting high-risk dilated cardiomyopathy |
| CN115047087A (en) * | 2022-03-30 | 2022-09-13 | 武汉迈特维尔生物科技有限公司 | Method for simultaneously detecting trimethylamine oxide substances in serum sample |
| CN115267214A (en) * | 2022-08-10 | 2022-11-01 | 南方医科大学珠江医院 | Lipid metabolism marker, screening method thereof and application of lipid metabolism marker in intracranial aneurysm |
| CN115181799B (en) * | 2022-09-13 | 2023-02-03 | 中国中医科学院医学实验中心 | Use of markers for predicting, detecting or diagnosing heart failure |
| CN115901987A (en) * | 2022-10-18 | 2023-04-04 | 华中科技大学同济医学院附属同济医院 | Protein precipitant, kit and method for detecting heart failure and coronary heart disease markers |
| CN115389756B (en) * | 2022-10-24 | 2023-02-17 | 首都医科大学附属北京安贞医院 | Detection kit for predicting risk of atrial fibrillation and application thereof |
| CN116228205B (en) * | 2023-03-29 | 2023-11-21 | 东莞先知大数据有限公司 | Charging pile outage risk diagnosis method, device and storage medium |
| CN117233367B (en) * | 2023-11-16 | 2024-02-09 | 哈尔滨脉图精准技术有限公司 | Metabolic marker for pregnancy hypertension risk assessment |
| CN118150738A (en) * | 2024-05-09 | 2024-06-07 | 中国医学科学院阜外医院 | Biomarker of chronic unpredictable stress model and application thereof |
Family Cites Families (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20160209428A1 (en) * | 2013-08-21 | 2016-07-21 | The Regents Of The University Of California | Diagnostic and predictive metabolite patterns for disorders affecting the brain and nervous system |
| CA2958735A1 (en) * | 2014-09-01 | 2016-03-10 | Metanomics Gmbh | Means and methods for diagnosing heart failure in a subject |
| CN105628809B (en) * | 2015-12-25 | 2018-04-03 | 中国药科大学 | The metabolic markers of coronary atherosclerosis and coronary heart disease are distinguished for diagnosing |
| CN105486773A (en) * | 2015-12-25 | 2016-04-13 | 齐炼文 | Metabolic marker for diagnosing coronary heart disease |
| WO2019195638A1 (en) * | 2018-04-04 | 2019-10-10 | Human Longevity, Inc. | Systems and methods for measuring obesity using metabolome analysis |
| CN111830142A (en) * | 2019-04-23 | 2020-10-27 | 北京市心肺血管疾病研究所 | Use of ceramides for the production of a kit for assessing the risk of an adverse event in a heart failure patient |
| CN117147751A (en) * | 2021-01-04 | 2023-12-01 | 深圳市绘云生物科技有限公司 | Metabolic marker combination for assessing cardiovascular disease risk in a subject and uses thereof |
-
2021
- 2021-01-04 CN CN202211178138.1A patent/CN117147751A/en active Pending
- 2021-01-04 CN CN202110000655.9A patent/CN112505228B/en active Active
-
2022
- 2022-01-04 WO PCT/CN2022/070155 patent/WO2022144028A1/en active Application Filing
Also Published As
| Publication number | Publication date |
|---|---|
| WO2022144028A1 (en) | 2022-07-07 |
| CN112505228A (en) | 2021-03-16 |
| CN112505228B (en) | 2022-10-21 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112505228B (en) | Metabolic marker combination for assessing cardiovascular disease risk of subject and application thereof | |
| Ramautar et al. | CE‐MS in metabolomics | |
| de Jong et al. | Current status and future developments of LC-MS/MS in clinical chemistry for quantification of biogenic amines | |
| US11181530B2 (en) | Mass spectrometry method for detection and quantitation of metabolites | |
| Rodriguez-Morato et al. | Targeting human urinary metabolome by LC–MS/MS: a review | |
| French | Advances in clinical mass spectrometry | |
| CN112630311B (en) | Metabolic markers and kits for detecting affective disorders and methods of use | |
| Kanemitsu et al. | Simultaneous quantitative analysis of uremic toxins by LC–MS/MS with a reversed-phase/cation-exchange/anion-exchange tri-modal mixed-mode column | |
| CN111562338B (en) | Application of transparent renal cell carcinoma metabolic marker in renal cell carcinoma early screening and diagnosis product | |
| US8758992B2 (en) | Method for the differential diagnosis of non-alcoholic fatty liver disease based on a metabolomic profile | |
| CN109709220B (en) | Combined marker for diagnosing bladder cancer, kit and application | |
| WO2022166006A1 (en) | Integrated biomarker system for assessing risk of impaired fasting glucose and type 2 diabetes mellitus | |
| WO2015179952A1 (en) | A metabolite panel for improved screening and diagnostic testing of cystic fibrosis | |
| CN112305121B (en) | Application of metabolic marker in atherosclerotic cerebral infarction | |
| CN112136043A (en) | Mass spectrometry method for detecting and quantifying liver function metabolites | |
| CN114460188A (en) | Kit for detecting human plasma catecholamine intermediate metabolite and testing method | |
| Mirnaghi et al. | Challenges of analyzing different classes of metabolites by a single analytical method | |
| Chen et al. | Targeting amine-and phenol-containing metabolites in urine by dansylation isotope labeling and liquid chromatography mass spectrometry for evaluation of bladder cancer biomarkers | |
| Shanmuganathan et al. | New Advances for newborn screening of inborn errors of metabolism by capillary electrophoresis-mass spectrometry (CE-MS) | |
| Kim et al. | Urine proteomics and biomarkers in renal disease | |
| Dong et al. | Slug-flow microextraction mass spectrometry for enhanced detection of analytes in human tear fluids using noninvasive microsampling and nanoelectrospray ionization via a capillary | |
| CN114689754B (en) | Serum metabolism marker related to phthisis and application thereof | |
| Yu et al. | Nanoconfined liquid phase nanoextraction combined with in-fiber derivatization for simultaneous quantification of seventy amino-containing metabolites in plasma by LC-MS/MS: Exploration of lung cancer screening model | |
| CN116754772A (en) | Peripheral blood protein marker for early diagnosis of senile dementia, application and auxiliary diagnosis system | |
| Zhang et al. | Mass Spectrometry Analysis for Clinical Applications: A Review |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |