Metabolite page

Showing information for HMDB0000122 ('glucose', 'D-glucose')

Metabolite information
HMDB ID	HMDB0000122
Synonyms	3-methylcrotonyl-coa carboxylase deficiency 3-methylcrotonylglycinuria type 1 3mcc deficiency Adipose Adult-onset diabetes Aerobic glycolysis Aml Anhydrous dextrose Atherosclerotic heart disease Beta-methylcrotonyl-coenzyme a carboxylase deficiency Bmcc deficiency Body fat Buccal cavity CPC Hydrate Cad Cerebrovascular accident Cerebrovascular insult Cerelose Cerelose 2001 Chronic adrenal insufficiency Chronic renal disease Ckd Clearsweet 95 Clintose L Coffee Coffee bean Corn sugar Csf Cucurbits Cva Cvi D Glucose D-GLC D-GLCP D[+]-Glucose Dextropur Dextrose Dextrose, anhydrous Dextrosol Digestion Dyslipidemia Er Extracellular region Faecal Faeces Fat tissue Fauna Fecal Flora GLC-OH Gdm Gestational diabetes mellitus Ghd Glucodin Glucolin Glucose Glucose monohydrate Glucose, [DL]-isomer Glucose, [L]-isomer Glucose, [alpha-D]-isomer Glucose, [beta-D]-isomer Goldsugar Golgi apparatus Golgi complex Golgi ribbon Gourds Gramineae Grape sugar Hyperlipidaemia Hyperlipoproteinemia Hypocorticism Hypocortisolism Hypoglykemia due to glucagon deficiency Kidneys L Glucose L-Glucose Legume Lungs Meritose Monohydrate, glucose Myelin Nephropathy Neuron Niddm Non-insulin-dependent diabetes mellitus Oral Oral cavity Pad Paod Papilionoideae Peripheral arterial disease Peripheral artery disease Peripheral artery occlusive disease Prostate gland Pvd Renal disease Roferose ST Soy Soya Soya bean Soybean Staleydex 111 Staleydex 95m Stool Tabfine 097[HS] Vadex [+]-Glucose
Chemical formula	C6H12O6
IUPAC name	(3R,4S,5S,6R)-6-(hydroxymethyl)oxane-2,3,4,5-tetrol
CAS registry number	50-99-7
Monisotopic molecular weight	180.063388116
Chemical taxonomy
Super class	Organic oxygen compounds
Class	Organooxygen compounds
Sub class	Carbohydrates and carbohydrate conjugates
Biological properties
Pahtways	Congenital disorder of glycosylation CDG-IId Fabry disease Fanconi-bickel syndrome Fructose-1,6-diphosphatase deficiency GLUT-1 deficiency syndrome Galactose Metabolism Galactosemia Gaucher Disease Glibenclamide Action Pathway Gliclazide Action Pathway Globoid Cell Leukodystrophy Gluconeogenesis Glucose-Alanine Cycle Glycogen Storage Disease Type 1A [GSD1A] or Von Gierke Disease Glycogenosis, Type IA. Von gierke disease Glycogenosis, Type IB Glycogenosis, Type IC Glycogenosis, Type VII. Tarui disease Glycolysis Insulin Signalling Krabbe disease Lactose Degradation Lactose Intolerance Lactose Synthesis Metachromatic Leukodystrophy [MLD] Nateglinide Action Pathway Pancreas Function Phosphoenolpyruvate carboxykinase deficiency 1 [PEPCK1] Repaglinide Action Pathway Sphingolipid Metabolism Transfer of Acetyl Groups into Mitochondria Triosephosphate isomerase Warburg Effect
Author-emphasized biomarker in the paper(s)	Wikoff et al. 2015 Callejon-Leblic et al. 2019

Lung cancer metabolomics studies that identify HMDB0000122 ('glucose', 'D-glucose')

Reference	Country	Specimen	Marker function	Participants (Case)						Participants (Control)
Reference	Country	Specimen	Marker function	Cancer type	Stage	Number	Gender (M,F)	Age mean (range) (M/F)	Smoking status	Type	Number	Gender (M,F)	Age mean (range) (M/F)	Smoking status
Chen et al. 2015	–	serum	–	lung cancer	–	30	–	61.58 ± 10.67	–	healthy	30	–	60.35 ± 12.48	–
Chen et al. 2015	–	serum	–	lung cancer (postoperative)	–	30	–	61.58 ± 10.67	–	healthy	30	–	60.35 ± 12.48	–
Roś-Mazurczyk et al. 2017	–	serum	diagnosis	adenocarcinoma, squamous cell carcinoma	I, II, III	31	17, 14	52-72	–	healthy	92	52, 40	52-73	–
Mazzone et al. 2016	–	serum	–	adenocarcinoma, squamous cell carcinoma	I, II, III	94	55.3%, 44.7%	68.7	–	at-risk controls	190	50.5%, 49.5%	66.2	–
Miyamoto et al. 2015	–	blood	diagnosis	NSCLC, SCLC, mesothelioma, secondary metastasis to lung	I, II, III, IV	11	4, 7	67 (61-73) / 67 (47-76)	smoker, non-smoker	healthy	11	5, 6	69 (61-83) / 54 (44-61)	unknown
Miyamoto et al. 2015	–	blood	diagnosis	adenocarcinoma	unknown (mostly late stage)	18	10, 8	67 (50-85) / 62 (53-72)	former, current	healthy	20	8, 12	64 (49-80) / 66 (58-82)	former, current
Fahrmann et al. 2015	–	plasma	diagnosis	adenocarcinoma	I, II, III, IV	52	17, 35	65.9 ± 9.66	–	healthy	31	11, 20	64.1 ± 8.97	–
Fahrmann et al. 2015	–	serum	diagnosis	adenocarcinoma	I, II, III, IV	49	17, 32	65.9 ± 9.87	–	healthy	31	11, 20	64.1 ± 8.97	–
Hori et al. 2011	–	tissue	diagnosis	adenocarcinoma, squamous cell carcinoma, SCLC	–	7	6, 1	median: 61 (53-82)	smoker, non-smoker	tumor vs. adjacent normal tissue	7	6, 1	median: 61 (53-82)	smoker, non-smoker
Fahrmann et al. 2015	–	plasma	diagnosis	adenocarcinoma	I, II, III, IV	43	21, 22	67.3 ± 10.10	–	healthy	43	21, 22	65.9 ± 8.05	–
Fahrmann et al. 2015	–	serum	diagnosis	adenocarcinoma	I, II, III, IV	43	21, 22	67.3 ± 10.10	–	healthy	43	21, 22	65.9 ± 8.05	–
Musharraf et al. 2015	–	plasma	–	adenocarcinoma, squamous cell carcinoma, NSCLC, SCLC, lung cancer	I, II, III	96	–	–	–	healthy, COPD	384	–	–	–
Callejon-Leblic et al. 2019	–	serum	diagnosis	NSCLC, SCLC	–	32	22, 8	66 ± 12	former, current, non-smoker	healthy	29	18, 11	56 ± 13	former, non-smoker
Callejón-Leblic et al. 2019	–	blood	diagnosis	NSCLC, SCLC	II, III, IV	30	25, 5	67 ± 12	former, current, non-smoker	healthy	30	14, 16	56 ± 14	former, non-smoker
Moreno et al. 2018	–	tissue	therapy, diagnosis	adenocarcinoma	I, II, III	33	24, 9	62.11 ± 9.73	–	tumor vs. adjacent normal tissue	33	24, 9	62.11 ± 9.73	–
Moreno et al. 2018	–	tissue	therapy, diagnosis	squamous cell carcinoma	I, II, III	35	35, 0	68.71 ± 7.46	–	tumor vs. adjacent normal tissue	35	35, 0	68.71 ± 7.46	–
Wikoff et al. 2015b	–	tissue	diagnosis	adenocarcinoma	I	39	15, 24	72.33 ± 8.78	smoker, non-smoker	tumor vs. adjacent normal tissue	39	15, 24	72.33 ± 8.78	smoker, non-smoker

Reference	Chromatography	Ion source	Positive/Negative mode	Mass analyzer	Identification level
Chen et al. 2015	GC	EI	–	quadrupole	–
Chen et al. 2015	GC	EI	–	quadrupole	–
Roś-Mazurczyk et al. 2017	GC	–	–	TOF	In-source fragmentation
Mazzone et al. 2016	GC	EI	–	quadrupole	MS/MS
Miyamoto et al. 2015	GC	EI	–	TOF	MS/MS
Miyamoto et al. 2015	GC	EI	–	TOF	MS/MS
Fahrmann et al. 2015	GC	EI	–	TOF	–
Fahrmann et al. 2015	GC	EI	–	TOF	–
Hori et al. 2011	GC	–	–	–	–
Fahrmann et al. 2015	GC	EI	–	TOF	–
Fahrmann et al. 2015	GC	EI	–	TOF	–
Musharraf et al. 2015	GC	EI	–	triple quadrupole	–
Callejon-Leblic et al. 2019	GC	EI	–	ion trap	–
Callejón-Leblic et al. 2019	DI	ESI	positive	Q-TOF	MS/MS
Moreno et al. 2018	LC, GC	ESI, EI	positive, negative	LC: linear ion‐trap, GC: single‐quadrupole	LC: MS/MS
Moreno et al. 2018	LC, GC	ESI, EI	positive, negative	LC: linear ion‐trap, GC: single‐quadrupole	LC: MS/MS
Wikoff et al. 2015b	GC	EI	–	TOF	–

Reference	Data processing software	Database search
Chen et al. 2015	ChemStation	NIST
Chen et al. 2015	ChemStation	NIST
Roś-Mazurczyk et al. 2017	Leco ChromaTOF-GC	Replib, Mainlib and Fiehn libraries
Mazzone et al. 2016	Metabolon LIMS system	Metabolon LIMS system
Miyamoto et al. 2015	ChromaTOF software (Leco)	UC Davis Metabolomics BinBase database
Miyamoto et al. 2015	ChromaTOF software (Leco)	UC Davis Metabolomics BinBase database
Fahrmann et al. 2015	–	UC Davis Metabolomics BinBase database
Fahrmann et al. 2015	–	UC Davis Metabolomics BinBase database
Hori et al. 2011	Shimadzu GCMSsolution software	commercially available GC/MS Metabolite Mass Spectral Database (Shimadzu Co.), NIST Mass Spectral Library (NIST 08)
Fahrmann et al. 2015	–	UC Davis Metabolomics BinBase database
Fahrmann et al. 2015	–	UC Davis Metabolomics BinBase database
Musharraf et al. 2015	Agilent Mass Hunter Qualitative Analysis, Mass Hunter	Wiley registry NIST 11, Fiehn RTL libraries
Callejon-Leblic et al. 2019	XCMS	NIST Mass Spectral Library
Callejón-Leblic et al. 2019	–	HMDB, Metlin
Moreno et al. 2018	–	KEGG, HMDB
Moreno et al. 2018	–	KEGG, HMDB
Wikoff et al. 2015b	BinBase	NIST11, BinBase

Differential analysis
Classification analysis

Reference	Difference method	Mean concentration (case)	Mean concentration (control)	Fold change (case/control)	P-value	FDR	VIP
Chen et al. 2015	PCA, PLS-DA, independent t test	–	–	1.31039340385836	<0.001	–	1.14
Chen et al. 2015	PCA, PLS-DA, independent t test	–	–	1.53687518128801	<0.001	–	1.61
Roś-Mazurczyk et al. 2017	two-sample T test, U Mann-Whitney test, Benjamini-Hochberg approach	400.5 ± 198.36	413.3 ± 220.6	0.969029760464554	0.74647	0.8487	–
Mazzone et al. 2016	two- sample independent t test	1.021545± 0.1987323	1.007043± 0.2346031	1.01440057673803	0.6069514	0.642010476	–
Miyamoto et al. 2015	Analysis of Covariance	610872.636363636	630890.090909091	0.968271090584716	0.523434850323184	–	–
Miyamoto et al. 2015	Analysis of Covariance	650817.777777778	593938.65	1.09576599835316	0.422387074170335	–	–
Fahrmann et al. 2015	regress (by the covariates: age, gender and smoking history [packs per year]), permutation test	155035 ± 97076	194159 ± 137046	0.8	0.152	0.529	–
Fahrmann et al. 2015	regress (by the covariates: age, gender and smoking history [packs per year]), permutation test	167237 ± 121193	199282 ± 136738	0.84	0.122	0.457	–
Hori et al. 2011	student’s t-test, PLS-DA	–	–	2.23	0.062	–	–
Fahrmann et al. 2015	regress (by the covariates: age, gender and smoking history [packs per year]), permutation test	54600 ± 24166	60898 ± 22497	0.9	0.062	0.222	–
Fahrmann et al. 2015	regress (by the covariates: age, gender and smoking history [packs per year]), permutation test	80945 ± 29796	91479 ± 31954	0.885	0.022	0.162	–
Musharraf et al. 2015	one way ANOVA, Turkey’s honest Significance Difference	–	–	111939.524579634	0.001	–	–
Callejon-Leblic et al. 2019	PLS-LDA, one-way ANOVA	–	–	2.29	0.001	–	2.74
Callejón-Leblic et al. 2019	PCA, PLS-DA, one-way ANOVA	–	–	1.62	0.0005	–	1.73
Moreno et al. 2018	paired two‐sample t‐test, PLS-DA	–	–	0.6439387177875	0.0000582827335532788	0.000215969611547968	–
Moreno et al. 2018	paired two‐sample t‐test, PLS-DA	–	–	0.686209597952584	0.0000015526591806776	0.00000381237546517146	–
Wikoff et al. 2015b	OPLS-DA	–	–	2	–	0.00037	–

Reference	Classification method	Cutoff value	AUROC 95%CI	Sensitivity (%)	Specificity (%)	Accuracy (%)
Chen et al. 2015	–	–	–	–	–	–
Chen et al. 2015	–	–	–	–	–	–
Roś-Mazurczyk et al. 2017	ROC curve	–	–	–	–	–
Mazzone et al. 2016	–	–	–	–	–	–
Miyamoto et al. 2015	–	–	–	–	–	–
Miyamoto et al. 2015	–	–	–	–	–	–
Fahrmann et al. 2015	random forest	–	–	–	–	–
Fahrmann et al. 2015	random forest	–	–	–	–	–
Hori et al. 2011	–	–	–	–	–	–
Fahrmann et al. 2015	random forest	–	–	–	–	–
Fahrmann et al. 2015	random forest	–	–	–	–	–
Musharraf et al. 2015	Hierarchical clustering, Partial Least Square Discrimination	–	–	96.2	92	93.1
Callejon-Leblic et al. 2019	ROC curve analysis	–	0.82	–	–	–
Callejón-Leblic et al. 2019	ROC curve	–	0.65	–	–	–
Moreno et al. 2018	–	–	–	–	–	–
Moreno et al. 2018	–	–	–	–	–	–
Wikoff et al. 2015b	–	–	–	–	–	–