Showing information for HMDB0000008 ('2-hydroxybutyric acid', 'α-hydroxybutyric acid', '2-hydroxybutanoic acid', '2-hydroxybutyrate', 'butanoic acid, 2-hydroxy')

Metabolite information
HMDB ID HMDB0000008
Synonyms
2-Hydroxy-DL-butyrate
2-Hydroxy-DL-butyric acid
2-Hydroxy-N-butyrate
2-Hydroxy-N-butyric acid
2-Hydroxy-butanoate
2-Hydroxy-butanoic acid
2-Hydroxybutanoate
2-Hydroxybutanoic acid
2-Hydroxybutyrate
2-Hydroxybutyric acid, [+-]-isomer
2-Hydroxybutyric acid, [R]-isomer
2-Hydroxybutyric acid, monosodium salt
2-Hydroxybutyric acid, monosodium salt, [+-]-isomer
Csf
Cytoplasma
DL-2-Hydroxybutanoate
DL-2-Hydroxybutanoic acid
DL-a-Hydroxybutyrate
DL-a-Hydroxybutyric acid
DL-alpha-Hydroxybutyrate
DL-alpha-Hydroxybutyric acid
Extracellular region
Faecal
Faeces
Fecal
Pdh
Prostate gland
Stool
[RS]-2-Hydroxybutyrate
[RS]-2-Hydroxybutyric acid
a-Hydroxy-N-butyrate
a-Hydroxy-N-butyric acid
a-Hydroxybutanoate
a-Hydroxybutanoic acid
a-Hydroxybutyrate
a-Hydroxybutyric acid
alpha-Hydroxy-N-butyrate
alpha-Hydroxy-N-butyric acid
alpha-Hydroxybutanoate
alpha-Hydroxybutanoic acid
alpha-Hydroxybutyrate
alpha-Hydroxybutyric acid
α-hydroxybutanoate
α-hydroxybutanoic acid
α-hydroxybutyrate
α-hydroxybutyric acid
Chemical formula C4H8O3
IUPAC name
2-hydroxybutanoic acid
CAS registry number 600-15-7
Monisotopic molecular weight 104.047344122

Chemical taxonomy
Super class Organic acids and derivatives
Class Hydroxy acids and derivatives
Sub class Alpha hydroxy acids and derivatives

Biological properties
Pahtways
Malonic Aciduria
Malonyl-coa decarboxylase deficiency
Methylmalonic Aciduria Due to Cobalamin-Related Disorders
Propanoate Metabolism
Author-emphasized biomarker in the paper(s)

Lung cancer metabolomics studies that identify HMDB0000008 ('2-hydroxybutyric acid', 'α-hydroxybutyric acid', '2-hydroxybutanoic acid', '2-hydroxybutyrate', 'butanoic acid, 2-hydroxy')

Reference Country Specimen Marker function Participants (Case) Participants (Control)
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
Hori et al. 2011 – serum diagnosis adenocarcinoma, squamous cell carcinoma, SCLC I, II 11 – – – healthy 29 23, 6 median: 64 (34-78) smoker, non-smoker, unknown
Hori et al. 2011 – serum diagnosis adenocarcinoma, squamous cell carcinoma, SCLC I, II, III, IV 33 26, 7 median: 65 (55-81) smoker, non-smoker, unknown healthy 29 23, 6 median: 64 (34-78) smoker, non-smoker, unknown
Hori et al. 2011 – serum diagnosis adenocarcinoma, squamous cell carcinoma, SCLC III, IV 22 – – – healthy 29 23, 6 median: 64 (34-78) smoker, non-smoker, unknown
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 –
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 – serum diagnosis adenocarcinoma I, II, III, IV 49 17, 32 65.9 ± 9.87 – healthy 31 11, 20 64.1 ± 8.97 –
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 –
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 –
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
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 –
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 – serum diagnosis adenocarcinoma I, II, III, IV 43 21, 22 67.3 ± 10.10 – healthy 43 21, 22 65.9 ± 8.05 –
Klupczynska et al. 2016b – serum diagnosis adenocarcinoma, squamous cell carcinoma I, II, III 90 58, 32 64 ± 6.9 smoker, non-smoker, unknown healthy 62 40, 22 62 ± 8.8 smoker, non-smoker, unknown
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 –
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 –
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
Hao et al. 2016 – serum diagnosis lung cancer I, II, III, IV 25 15, 10 64 (42–77) smoker, non-smoker before vs. after treatment (radiation treatment) – – – smoker, non-smoker
Reference Chromatography Ion source Positive/Negative mode Mass analyzer Identification level
Hori et al. 2011 GC – – – –
Hori et al. 2011 GC – – – –
Hori et al. 2011 GC – – – –
Roś-Mazurczyk et al. 2017 GC – – TOF In-source fragmentation
Miyamoto et al. 2015 GC EI – TOF MS/MS
Fahrmann et al. 2015 GC EI – TOF –
Moreno et al. 2018 LC, GC ESI, EI positive, negative LC: linear ion‐trap, GC: single‐quadrupole LC: MS/MS
Fahrmann et al. 2015 GC EI – TOF –
Miyamoto et al. 2015 GC EI – TOF MS/MS
Fahrmann et al. 2015 GC EI – TOF –
Hori et al. 2011 GC – – – –
Fahrmann et al. 2015 GC EI – TOF –
Klupczynska et al. 2016b LC ESI negative triple quadrupole MS/MS
Moreno et al. 2018 LC, GC ESI, EI positive, negative LC: linear ion‐trap, GC: single‐quadrupole LC: MS/MS
Mazzone et al. 2016 GC EI – quadrupole MS/MS
Wikoff et al. 2015b GC EI – TOF –
Hao et al. 2016 GC – – TOF –
Reference Data processing software Database search
Hori et al. 2011 Shimadzu GCMSsolution software commercially available GC/MS Metabolite Mass Spectral Database (Shimadzu Co.), NIST Mass Spectral Library (NIST 08)
Hori et al. 2011 Shimadzu GCMSsolution software commercially available GC/MS Metabolite Mass Spectral Database (Shimadzu Co.), NIST Mass Spectral Library (NIST 08)
Hori et al. 2011 Shimadzu GCMSsolution software commercially available GC/MS Metabolite Mass Spectral Database (Shimadzu Co.), NIST Mass Spectral Library (NIST 08)
Roś-Mazurczyk et al. 2017 Leco ChromaTOF-GC Replib, Mainlib and Fiehn libraries
Miyamoto et al. 2015 ChromaTOF software (Leco) UC Davis Metabolomics BinBase database
Fahrmann et al. 2015 – UC Davis Metabolomics BinBase database
Moreno et al. 2018 – KEGG, HMDB
Fahrmann et al. 2015 – 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
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
Klupczynska et al. 2016b Analyst software –
Moreno et al. 2018 – KEGG, HMDB
Mazzone et al. 2016 Metabolon LIMS system Metabolon LIMS system
Wikoff et al. 2015b BinBase NIST11, BinBase
Hao et al. 2016 Chenomx NMR Suite 7.1, Metabolite Detector HMDB
Reference Difference method Mean concentration (case) Mean concentration (control) Fold change (case/control) P-value FDR VIP
Hori et al. 2011 student’s t-test, PLS-DA – – 1 0.909 – –
Hori et al. 2011 student’s t-test, PLS-DA – – 0.96 0.609 – –
Hori et al. 2011 student’s t-test, PLS-DA – – 0.95 0.534 – –
Roś-Mazurczyk et al. 2017 two-sample T test, U Mann-Whitney test, Benjamini-Hochberg approach 5.4432 ± 1.9203 8.5115 ± 8.1791 0.63951124948599 0.39668 0.67727 –
Miyamoto et al. 2015 Analysis of Covariance 46231.1666666667 42560.5 1.08624585394125 0.389464076909449 – –
Fahrmann et al. 2015 regress (by the covariates: age, gender and smoking history [packs per year]), permutation test 16636 ± 8787 14667 ± 9574 1.13 0.295 0.621 –
Moreno et al. 2018 paired two‐sample t‐test, PLS-DA – – 1.06330388847615 0.289784077133675 0.354359916049258 –
Fahrmann et al. 2015 regress (by the covariates: age, gender and smoking history [packs per year]), permutation test 12996 ± 7017 11486 ± 8094 1.13 0.217 0.589 –
Miyamoto et al. 2015 Analysis of Covariance 48482.7272727273 40115.8181818182 1.20856882571826 0.192237621657549 – –
Fahrmann et al. 2015 regress (by the covariates: age, gender and smoking history [packs per year]), permutation test 8718 ± 4691 7190 ± 4294 1.21 0.079 0.258 –
Hori et al. 2011 student’s t-test, PLS-DA – – 1.64 0.022 – –
Fahrmann et al. 2015 regress (by the covariates: age, gender and smoking history [packs per year]), permutation test 16545 ± 8851 12608 ± 6812 1.312 0.006 0.067 –
Klupczynska et al. 2016b Mann-Whitney U test 72.43 ± 33.38 μmol/l 58.99 ± 33.39 μmol/l 1.22783522630954 0.00283 – –
Moreno et al. 2018 paired two‐sample t‐test, PLS-DA – – 1.22806369249725 0.0000160399689746499 0.0000335955255689518 –
Mazzone et al. 2016 two- sample independent t test 1.403545± 0.7636425 0.99769± 0.5246037 1.40679469574718 0.0000003 0.015006832 –
Wikoff et al. 2015b OPLS-DA – – 1.1 – 0.178 –
Hao et al. 2016 OPLS-DA, CV-ANOVA – – – – – –
Reference Classification method Cutoff value AUROC 95%CI Sensitivity (%) Specificity (%) Accuracy (%)
Hori et al. 2011 – – – – – –
Hori et al. 2011 – – – – – –
Hori et al. 2011 – – – – – –
Roś-Mazurczyk et al. 2017 ROC curve – – – – –
Miyamoto et al. 2015 – – – – – –
Fahrmann et al. 2015 random forest – – – – –
Moreno et al. 2018 – – – – – –
Fahrmann et al. 2015 random forest – – – – –
Miyamoto et al. 2015 – – – – – –
Fahrmann et al. 2015 random forest – – – – –
Hori et al. 2011 – – – – – –
Fahrmann et al. 2015 random forest – – – – –
Klupczynska et al. 2016b ROC curve analysis – 0.643 – – –
Moreno et al. 2018 – – – – – –
Mazzone et al. 2016 – – – – – –
Wikoff et al. 2015b – – – – – –
Hao et al. 2016 – – – – – –