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Table 1 Summary of studies on the application of metabolomics as a tool for predicting hypertension

From: Metabolomics profiling in hypertension and blood pressure regulation: a review

S/N Samples Sample size Methodology Results References
1. Spontaneously hypertensive rats (SHR) and normotensive controls, Wistar Kyoto rats (WKY) 6 SHR and WKY each (total n = 12) 1H-NMR-based urine metabolomics There was clear separation in the principal components between the 2 samples. This was attributed to comparatively low levels of citrate, α-ketoglutarate, and hippurate, and high levels of creatine, creatinine, and some other metabolites in the urines of SHR than WKY. [7]
2. Spontaneously hypertensive rats (SHR) and their aged-matched Wistar Kyoto rats (WKY) 6 SHR and WKY each (total n = 12) 1H-NMR-based 24 h urine metabolomics Major metabolic changes were observed for the rats including differences in citrate, a-ketoglutarate, succinate, hippurate, phenylacetylglycine, p-cresol glucuronide, creatine, taurine, and medium chain dicarboxylates [23]
3. Spontaneously hypertensive rats (SHR) and their aged-matched Wistar Kyoto rats (WKY) 29 SHR and 18 WKY LC-QTOF-MS of urine and plasma metabolomics Partial least squares of several metabolites found in the urine and plasma samples of rats (symmetric dimethylarginine, N2-acetyl-L-ornithine, buthionine sulfoximine, uric acid, α-tocopherol succinate, L-isoleucine, creatinine, and phospholipids) were significantly correlated with hypertension. [26]
4. Patients with low/normal systolic blood pressure (SBP ≤130 mmHg), borderline SBP (131–149 mmHg) and high SBP (≤150 mmHg; n = 17) SBP ≤130 mmHg, n = 28;
SBP (131–149 mmHg, n = 19;
SBP (≤150 mm; Hg, n = 17; Total n = 64
1H-NMR of serum metabolites The PCA showed clear differences between serum profiles of patients with low/normal SBP and that of the patients with borderline or high SBP but not between samples of borderline and high SBP. These differences were attributed to differences in serum lipid moieties between the groups. [21]
5. Uppsala Longitudinal Study of Adult Men Cohort 504 individuals GC-MS of plasma Plasma levels of ceramide, triacylglycerol, total glycerolipids and oleic acid directly correlated with longitudinal changes diastolic BP while cholesterylester levels on the other hand inversely correlated with longitudinal
diastolic BP change. However, two glycerolipids validated in an independent cohort. These results suggest a pathophysiological
pathophysiological pathways of hypertension
[35]
6. Chinese Municipal Cohort Study (CMCS) 460 GC-MS of serum 241 metabolites identified, 26 were significantly different between hypertension and control at baseline and 16 out of these were associated with hypertension after adjusting for BMI, smoking and drinking. [14]
7. Sympathetic activity and Ambulatory Blood Pressure in Africans (SABPA) 25 GC-MS, LC-MS of urine 38 metabolites differed greatly between the two blood pressure groups with clear PCA separation. Pathway analysis showed changes in ethanol metabolism. [36]
8. Hypertensive and normotensive Uygur patients 250 1H-NMR of plasma metabolites OPLS-DA showed clear separation indicating differences between the two groups. Twelve different metabolites were identified suggesting biomarkers of hypertension. [37]
9. Case controlled study of 64 essential hypertension and 59 healthy controls 123 NMR of filtered serum Several metabolites were identified but six of these (alanine, pyruvate, methionine, arginine, adenine, and uracil) were discovered to significantly differentiate EH from HC cohorts. Arginine was up-regulated and alanine, pyruvate, methionine, adenine, and uracil were down-regulated in EH compared to HC. [18]
10. European Prospective Investigation Into Cancer and Nutrition (EPIC)–Potsdam study 1116 (135 cases and 981 non cases) of incident hypertension AbsoluteIDQ p150 Kits (Biocrates Life Scienes AG, Innsbruck, Austria) based on flow injection analysis tandem mass spectrometry technique 127 metabolites were validated to predict hypertension. Six of these were identified as the most predictive biomarkers of incident hypertension. Up-regulation of serine, glycine, and acyl-alkyl-phosphatidylcholines (C42:4 and C44:3) were associated with higher and down-regulation of diacyl-phosphatidylcholines C38:4 and C38:3 with lower predicted 10-year hypertension free survival. [3]
11. Black normotensives from The Atherosclerosis Risk in Communities Study 896 black normotensives including 565 women aged 45–64 years Untargeted GC-MS and LC-MS based quantification of serum samples metabolites. 204 metabolites were measured during a 4–6 weeks period. 38% of the baseline normotensives measured developed hypertension during a 10 years follow-up period. Up-regulation of 4-hydroxyhippurate, a product of gut microbial fermentation was found to significantly increase the risk of hypertension by 17%. PCA yielded sex steroids, α - amino acids, and branch-chain amino acids with only the sex hormone revealed to be significantly associated with hypertension. [17]
12. Participants in The Atherosclerosis in Risk Communities Study 9104 ARIC participants without hypertension at baseline   Serum uric acid was positively correlated with incident hypertension. The association is more pronounced with Blacks than Whites. [38]
13. San Antonio Family Heart Study 1192 individuals drawn from 42 families HPLC combined with tandem MS plasma lipidomics of 319 lipid species Diacylglycerols, especially DG 16:0/22:5 and DG 16:0/22:6 were significantly associated with incident hypertension during follow-up. Four lipid species, including the DG 16:0/22:5 and DG 16:0/22:6 species were also genetically correlated with hypertension. [39]
14. Patients with essential hypertension and its Chinese subtypes a(YDYHS and YYDS) Controls (n = 22);
YDYHS (n = 31); YYDS (n = 29);
Total n = 82
1H-NMR and GC-MS of plasma metabolites The PCA and PLS-DA showed clear separation between the groups with a little overlap between YDYHS and healthy volunteers. Glucose was found to be markedly increased in both YDYHS and YYDS groups, suggesting abnormality in glucose metabolism. [16]
  1. aYDYHS and YYDS - “Yin-deficiency and Yang-hyperactivity syndrome” and “Yin-Yang deficiency syndrome” respectively