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Molecular phenotyping of a UK population: defining the human serum metabolome.

Dunn WB, Lin W, Broadhurst D, Begley P, Brown M, Zelena E, Vaughan AA, Halsall A, Harding N, Knowles JD, Francis-McIntyre S, Tseng A, Ellis DI, O'Hagan S, Aarons G, Benjamin B, Chew-Graham S, Moseley C, Potter P, Winder CL, Potts C, Thornton P, McWhirter C, Zubair M, Pan M, Burns A, Cruickshank JK, Jayson GC, Purandare N, Wu FC, Finn JD, Haselden JN, Nicholls AW, Wilson ID, Goodacre R, Kell DB - Metabolomics (2014)

Bottom Line: Overall, this is a large scale and non-targeted chromatographic MS-based metabolomics study, using samples from over 1,000 individuals, to provide a comprehensive measurement of their serum metabolomes.This work provides an important baseline or reference dataset for understanding the 'normal' relative concentrations and variation in the human serum metabolome.These may be related to our increasing knowledge of the human metabolic network map.

View Article: PubMed Central - PubMed

Affiliation: Faculty of Engineering and Physical Sciences, School of Chemistry, Manchester Institute of Biotechnology, The University of Manchester, Manchester, M1 7DN UK ; Faculty of Engineering & Physical Sciences, Manchester Centre for Integrative Systems Biology, Manchester Institute of Biotechnology, The University of Manchester, Manchester, M1 7DN UK ; Faculty of Medical and Human Sciences, Centre for Endocrinology and Diabetes, Institute of Human Development, The University of Manchester, Manchester, UK ; Centre for Advanced Discovery and Experimental Therapeutics (CADET), Central Manchester University Hospitals NHS Foundation Trust, Manchester Academic Health Sciences Centre, Manchester, M13 9WL UK ; School of Biosciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK.

ABSTRACT

Phenotyping of 1,200 'healthy' adults from the UK has been performed through the investigation of diverse classes of hydrophilic and lipophilic metabolites present in serum by applying a series of chromatography-mass spectrometry platforms. These data were made robust to instrumental drift by numerical correction; this was prerequisite to allow detection of subtle metabolic differences. The variation in observed metabolite relative concentrations between the 1,200 subjects ranged from less than 5 % to more than 200 %. Variations in metabolites could be related to differences in gender, age, BMI, blood pressure, and smoking. Investigations suggest that a sample size of 600 subjects is both necessary and sufficient for robust analysis of these data. Overall, this is a large scale and non-targeted chromatographic MS-based metabolomics study, using samples from over 1,000 individuals, to provide a comprehensive measurement of their serum metabolomes. This work provides an important baseline or reference dataset for understanding the 'normal' relative concentrations and variation in the human serum metabolome. These may be related to our increasing knowledge of the human metabolic network map. Information on the Husermet study is available at http://www.husermet.org/. Importantly, all of the data are made freely available at MetaboLights (http://www.ebi.ac.uk/metabolights/).

No MeSH data available.


A boxplot showing the distribution of methionine sulfoxide for males and females across different age categories. For each box, the central line is the median, the edges of the box are the upper and lower quartiles, the whiskers extend the box by a further ±1.5 × interquartile range (IQR), and outliers (>1.5 × IQR) are plotted as individual points. Data were analysed using 2-way ANOVA showing a significant difference between males and females, [F(1,901) = 20.3, p = 7.7 × 10−6]. There was no significant difference between age categories and no significant interaction between gender and age categories
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Fig4: A boxplot showing the distribution of methionine sulfoxide for males and females across different age categories. For each box, the central line is the median, the edges of the box are the upper and lower quartiles, the whiskers extend the box by a further ±1.5 × interquartile range (IQR), and outliers (>1.5 × IQR) are plotted as individual points. Data were analysed using 2-way ANOVA showing a significant difference between males and females, [F(1,901) = 20.3, p = 7.7 × 10−6]. There was no significant difference between age categories and no significant interaction between gender and age categories

Mentions: Two-way ANOVA was performed using Gender (male, female) and Age (four grouped categories: <40, 40–49, 50–64 and >64 years) as the main effects. Many differences in the serum metabolome were observed when comparing the metabolic profiles of males and females. A number of these had been observed previously highlighting the robustness of our study; these included 4-hydroxyphenyllactic acid [F(1,1123) = 245.1, p = 3.9 × 10−50], creatinine, citrate, urate [F(1,1092) = 512.3, p = 2.6 × 10−93], glycerol [F(1,1081) = 93.7, p = 2.6 × 10−21], hexadecenoic acid [F(1,1097) = 62.8, p = 5.5 × 10−15] and tyrosine (Kochhar et al. 2006; Lawton et al. 2008; Slupsky et al. 2007). For glycerol, there was also a significant difference between age categories [F(3,1081) = 3.1, p = 1.1 × 10−12]. Tukey post hoc test showed that, independent of gender, comparisons of age categories <40 vs. 40–49 (p = 0.0005), <40 vs. 50–64 (p = 9.1 × 10−12), <40 vs. 65–81 (p = 1.8 × 10−8), 40–49 vs. 50–64 (p = 0.004) and 40–49 vs. 65–81 (p = 0.03) were significant using a critical p value of 0.05. There was also a significant interaction between gender and age categories for urate [F(3,1092) = 4.8, p = 0.002], glycerol [F(3,1081) = 2.8, p = 0.039] and hexadecenoic acid [F(3,1097) = 4.7, p = 0.003]. In our study, 4-hydroxyphenyllactic acid was found to be higher and tyrosine lower in males. Both of these metabolites are structurally related and these differences may reflect differences in gut microfloral co-metabolism, or the effects of alcohol consumption (Liebich and Pickert 1985). However, we observed a multitude of other robust changes related to gender also. Eight diacylglycerides were observed to be higher in relative concentration in the serum of women compared to men including DG(44:6) [F(1,808) = 276.5, p = 1.3 × 10−53] and DG(46:2) [F(1,848) = 206.1, p = 5.3 × 10−42]). For DG(46:2) there was also a significant difference between age categories [F(3,848) = 5.8, p = 0.0006] and a significant interaction between gender and age categories [F(3,848) = 7.5, p = 6.0 × 10−5]. Tukey post hoc test showed that, independent of gender, comparisons of age categories <40 vs. 65–81 (p = 0.002) and 50–64 vs. 65–81 (p = 0.0009) were significant using a critical p-value of 0.05. Four fatty acids (for example, hexadecenoic acid as shown above) and thirteen glycerophospholipids (for example, PC(36:2) [F(1,1103) = 224.8, p = 2.2 × 10−46]) showed the same trend as diacylglycerides. PC(36:2) also showed a significant difference between age categories [F(3,1103) = 3.4, p = 0.02] and a significant interaction between gender and age categories [F(3,1103) = 4.5, p = 0.004]. Tukey post hoc test showed that, independent of gender, comparisons of age categories <40 vs. 40–49 (p = 0.02) was significant using a critical p-value of 0.05. Serum creatinine relative concentrations were observed to be higher in females than males and, when integrated with higher phosphate levels, might suggest greater breakdown of creatine phosphate in muscles in females. Caffeine relative concentrations were higher in women [F(1,847) = 38.3, p = 9.6 × 10−10] perhaps reflecting coffee/tea/chocolate consumption, as was 2-aminomalonic acid [F(1,1048) = 87.6, p = 4.8 × 10−20] which has been associated with atherosclerotic plaques (Rupérez et al. 2012) and renal failure (Mao et al. 2008). For caffeine [F(3,847) = 9.3, p = 5.0 × 10−6] and 2-aminomalonic acid [F(3,1048) = 3.6, p = 0.01] there was also a significant difference between age categories and a significant interaction between gender and age categories for caffeine [F(3,847) = 6.3, p = 0.0003] and 2-aminomalonic acid [F(3,1048) = 24.3, p = 3.5 × 10−15]. Tukey post hoc test for caffeine showed that, independent of gender, comparisons of age categories <40 vs. 40–49 (p = 8.2 × 10−5), <40 vs. 50–64 (p = 0.0002) and <40 vs. 65–81 (p = 1.4 × 10−5) were significant using a critical p-value of 0.05. Tukey post hoc test for 2-aminomalonic acid showed that, independent of gender, comparisons of age categories <40 vs. 50–64 (p = 0.03) and 40–49 vs. 50–64 (p = 0.03) were significant using a critical p-value of 0.05. Three glycerol-like metabolites (glyceric acid [F(1,1107) = 9.1, p = 0.003], glycerol [F(1,1081) = 93.7, p = 2.6 × 10−21] and glycerol-3-phosphate [F(1,1127) = 11.8, p = 0.0006]) were present in greater amounts in the serum of women compared to men, suggesting differences in glycerol metabolism and potentially related to differences in the rate of glycerolipid and glycerophospholipid synthesis. For glycerol [F(3,1081) = 20.1, p = 1.1 × 10−12] and glyceric acid [F(3,1107) = 6.8, p = 0.0001] there was also a significant difference between age categories. There was also a significant interaction between gender and age categories for glycerol [F(3,1081) = 2.8, p = 0.04] and glycerol-3-phosphate [F(3,1127) = 8.7, p = 1.1 × 10−5]. Tukey post hoc tests showed that, independent of gender, comparisons of age categories for glycerol [<40 vs. 40–49 (p = 0.0005), <40 vs. 50–64 (p = 9.1 × 10−12), <40 vs. 65–81 (p = 1.8 × 10−8), 40–49 vs. 50–64 (p = 0.004) and 40–49 vs. 65–81 (p = 0.03)], glycerol-3-phosphate [<40 vs. 50–64 (p = 0.04)] and glyceric acid [<40 vs. 40–49 (p = 0.006), <40 vs. 50–64 (p = 0.0002), <40 vs. 65–81 (p = 0.005)] were significant using a critical p-value of 0.05. Methionine sulfoxide, also present in greater amounts in the serum of women [F(1,901) = 20.3, p = 7.7 × 10−6], is an oxidation product of methionine and is considered to be a marker of oxidative stress (Bachi et al. 2013) (Fig. 4). Other gender-specific changes in the metabolome as a function of age, BMI and BP were also observed and are discussed below.Fig. 4


Molecular phenotyping of a UK population: defining the human serum metabolome.

Dunn WB, Lin W, Broadhurst D, Begley P, Brown M, Zelena E, Vaughan AA, Halsall A, Harding N, Knowles JD, Francis-McIntyre S, Tseng A, Ellis DI, O'Hagan S, Aarons G, Benjamin B, Chew-Graham S, Moseley C, Potter P, Winder CL, Potts C, Thornton P, McWhirter C, Zubair M, Pan M, Burns A, Cruickshank JK, Jayson GC, Purandare N, Wu FC, Finn JD, Haselden JN, Nicholls AW, Wilson ID, Goodacre R, Kell DB - Metabolomics (2014)

A boxplot showing the distribution of methionine sulfoxide for males and females across different age categories. For each box, the central line is the median, the edges of the box are the upper and lower quartiles, the whiskers extend the box by a further ±1.5 × interquartile range (IQR), and outliers (>1.5 × IQR) are plotted as individual points. Data were analysed using 2-way ANOVA showing a significant difference between males and females, [F(1,901) = 20.3, p = 7.7 × 10−6]. There was no significant difference between age categories and no significant interaction between gender and age categories
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

Show All Figures
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Fig4: A boxplot showing the distribution of methionine sulfoxide for males and females across different age categories. For each box, the central line is the median, the edges of the box are the upper and lower quartiles, the whiskers extend the box by a further ±1.5 × interquartile range (IQR), and outliers (>1.5 × IQR) are plotted as individual points. Data were analysed using 2-way ANOVA showing a significant difference between males and females, [F(1,901) = 20.3, p = 7.7 × 10−6]. There was no significant difference between age categories and no significant interaction between gender and age categories
Mentions: Two-way ANOVA was performed using Gender (male, female) and Age (four grouped categories: <40, 40–49, 50–64 and >64 years) as the main effects. Many differences in the serum metabolome were observed when comparing the metabolic profiles of males and females. A number of these had been observed previously highlighting the robustness of our study; these included 4-hydroxyphenyllactic acid [F(1,1123) = 245.1, p = 3.9 × 10−50], creatinine, citrate, urate [F(1,1092) = 512.3, p = 2.6 × 10−93], glycerol [F(1,1081) = 93.7, p = 2.6 × 10−21], hexadecenoic acid [F(1,1097) = 62.8, p = 5.5 × 10−15] and tyrosine (Kochhar et al. 2006; Lawton et al. 2008; Slupsky et al. 2007). For glycerol, there was also a significant difference between age categories [F(3,1081) = 3.1, p = 1.1 × 10−12]. Tukey post hoc test showed that, independent of gender, comparisons of age categories <40 vs. 40–49 (p = 0.0005), <40 vs. 50–64 (p = 9.1 × 10−12), <40 vs. 65–81 (p = 1.8 × 10−8), 40–49 vs. 50–64 (p = 0.004) and 40–49 vs. 65–81 (p = 0.03) were significant using a critical p value of 0.05. There was also a significant interaction between gender and age categories for urate [F(3,1092) = 4.8, p = 0.002], glycerol [F(3,1081) = 2.8, p = 0.039] and hexadecenoic acid [F(3,1097) = 4.7, p = 0.003]. In our study, 4-hydroxyphenyllactic acid was found to be higher and tyrosine lower in males. Both of these metabolites are structurally related and these differences may reflect differences in gut microfloral co-metabolism, or the effects of alcohol consumption (Liebich and Pickert 1985). However, we observed a multitude of other robust changes related to gender also. Eight diacylglycerides were observed to be higher in relative concentration in the serum of women compared to men including DG(44:6) [F(1,808) = 276.5, p = 1.3 × 10−53] and DG(46:2) [F(1,848) = 206.1, p = 5.3 × 10−42]). For DG(46:2) there was also a significant difference between age categories [F(3,848) = 5.8, p = 0.0006] and a significant interaction between gender and age categories [F(3,848) = 7.5, p = 6.0 × 10−5]. Tukey post hoc test showed that, independent of gender, comparisons of age categories <40 vs. 65–81 (p = 0.002) and 50–64 vs. 65–81 (p = 0.0009) were significant using a critical p-value of 0.05. Four fatty acids (for example, hexadecenoic acid as shown above) and thirteen glycerophospholipids (for example, PC(36:2) [F(1,1103) = 224.8, p = 2.2 × 10−46]) showed the same trend as diacylglycerides. PC(36:2) also showed a significant difference between age categories [F(3,1103) = 3.4, p = 0.02] and a significant interaction between gender and age categories [F(3,1103) = 4.5, p = 0.004]. Tukey post hoc test showed that, independent of gender, comparisons of age categories <40 vs. 40–49 (p = 0.02) was significant using a critical p-value of 0.05. Serum creatinine relative concentrations were observed to be higher in females than males and, when integrated with higher phosphate levels, might suggest greater breakdown of creatine phosphate in muscles in females. Caffeine relative concentrations were higher in women [F(1,847) = 38.3, p = 9.6 × 10−10] perhaps reflecting coffee/tea/chocolate consumption, as was 2-aminomalonic acid [F(1,1048) = 87.6, p = 4.8 × 10−20] which has been associated with atherosclerotic plaques (Rupérez et al. 2012) and renal failure (Mao et al. 2008). For caffeine [F(3,847) = 9.3, p = 5.0 × 10−6] and 2-aminomalonic acid [F(3,1048) = 3.6, p = 0.01] there was also a significant difference between age categories and a significant interaction between gender and age categories for caffeine [F(3,847) = 6.3, p = 0.0003] and 2-aminomalonic acid [F(3,1048) = 24.3, p = 3.5 × 10−15]. Tukey post hoc test for caffeine showed that, independent of gender, comparisons of age categories <40 vs. 40–49 (p = 8.2 × 10−5), <40 vs. 50–64 (p = 0.0002) and <40 vs. 65–81 (p = 1.4 × 10−5) were significant using a critical p-value of 0.05. Tukey post hoc test for 2-aminomalonic acid showed that, independent of gender, comparisons of age categories <40 vs. 50–64 (p = 0.03) and 40–49 vs. 50–64 (p = 0.03) were significant using a critical p-value of 0.05. Three glycerol-like metabolites (glyceric acid [F(1,1107) = 9.1, p = 0.003], glycerol [F(1,1081) = 93.7, p = 2.6 × 10−21] and glycerol-3-phosphate [F(1,1127) = 11.8, p = 0.0006]) were present in greater amounts in the serum of women compared to men, suggesting differences in glycerol metabolism and potentially related to differences in the rate of glycerolipid and glycerophospholipid synthesis. For glycerol [F(3,1081) = 20.1, p = 1.1 × 10−12] and glyceric acid [F(3,1107) = 6.8, p = 0.0001] there was also a significant difference between age categories. There was also a significant interaction between gender and age categories for glycerol [F(3,1081) = 2.8, p = 0.04] and glycerol-3-phosphate [F(3,1127) = 8.7, p = 1.1 × 10−5]. Tukey post hoc tests showed that, independent of gender, comparisons of age categories for glycerol [<40 vs. 40–49 (p = 0.0005), <40 vs. 50–64 (p = 9.1 × 10−12), <40 vs. 65–81 (p = 1.8 × 10−8), 40–49 vs. 50–64 (p = 0.004) and 40–49 vs. 65–81 (p = 0.03)], glycerol-3-phosphate [<40 vs. 50–64 (p = 0.04)] and glyceric acid [<40 vs. 40–49 (p = 0.006), <40 vs. 50–64 (p = 0.0002), <40 vs. 65–81 (p = 0.005)] were significant using a critical p-value of 0.05. Methionine sulfoxide, also present in greater amounts in the serum of women [F(1,901) = 20.3, p = 7.7 × 10−6], is an oxidation product of methionine and is considered to be a marker of oxidative stress (Bachi et al. 2013) (Fig. 4). Other gender-specific changes in the metabolome as a function of age, BMI and BP were also observed and are discussed below.Fig. 4

Bottom Line: Overall, this is a large scale and non-targeted chromatographic MS-based metabolomics study, using samples from over 1,000 individuals, to provide a comprehensive measurement of their serum metabolomes.This work provides an important baseline or reference dataset for understanding the 'normal' relative concentrations and variation in the human serum metabolome.These may be related to our increasing knowledge of the human metabolic network map.

View Article: PubMed Central - PubMed

Affiliation: Faculty of Engineering and Physical Sciences, School of Chemistry, Manchester Institute of Biotechnology, The University of Manchester, Manchester, M1 7DN UK ; Faculty of Engineering & Physical Sciences, Manchester Centre for Integrative Systems Biology, Manchester Institute of Biotechnology, The University of Manchester, Manchester, M1 7DN UK ; Faculty of Medical and Human Sciences, Centre for Endocrinology and Diabetes, Institute of Human Development, The University of Manchester, Manchester, UK ; Centre for Advanced Discovery and Experimental Therapeutics (CADET), Central Manchester University Hospitals NHS Foundation Trust, Manchester Academic Health Sciences Centre, Manchester, M13 9WL UK ; School of Biosciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, UK.

ABSTRACT

Phenotyping of 1,200 'healthy' adults from the UK has been performed through the investigation of diverse classes of hydrophilic and lipophilic metabolites present in serum by applying a series of chromatography-mass spectrometry platforms. These data were made robust to instrumental drift by numerical correction; this was prerequisite to allow detection of subtle metabolic differences. The variation in observed metabolite relative concentrations between the 1,200 subjects ranged from less than 5 % to more than 200 %. Variations in metabolites could be related to differences in gender, age, BMI, blood pressure, and smoking. Investigations suggest that a sample size of 600 subjects is both necessary and sufficient for robust analysis of these data. Overall, this is a large scale and non-targeted chromatographic MS-based metabolomics study, using samples from over 1,000 individuals, to provide a comprehensive measurement of their serum metabolomes. This work provides an important baseline or reference dataset for understanding the 'normal' relative concentrations and variation in the human serum metabolome. These may be related to our increasing knowledge of the human metabolic network map. Information on the Husermet study is available at http://www.husermet.org/. Importantly, all of the data are made freely available at MetaboLights (http://www.ebi.ac.uk/metabolights/).

No MeSH data available.