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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 citric acid 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 are plotted as individual points (>1.5 × IQR). Data were analysed using 2-way ANOVA. There was a significant difference between males and females (F(1,779) = 79.8, p = 3.1 × 10−18). There was no significant difference between age categories and no significant interaction between gender and age categories
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Fig5: A boxplot showing the distribution of citric acid 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 are plotted as individual points (>1.5 × IQR). Data were analysed using 2-way ANOVA. There was a significant difference between males and females (F(1,779) = 79.8, p = 3.1 × 10−18). There was no significant difference between age categories and no significant interaction between gender and age categories

Mentions: We assessed age-related changes through the comparison of all subjects below the age of 50 years with all subjects older than 64 years. Two-way ANOVA was performed using Gender and Age (two categories: <50 years, and >64 years) as the main effects. Different classes of metabolites showed changes related to age, with some changes not being gender-related and others being specific to one gender. For example, citric acid showed a general increase with age for both males and females [F(1,779) = 79.8, p = 3.1 × 10−18] and therefore is probably not thus a biomarker for pancreatic cancer (Bathe et al. 2011); visually the rate of increase was greater in females than in males (Fig. 5). Citrate has previously been shown to be related to age, along with other metabolites also observed in our study. These include serine [F(1,755) = 6.5, p = 0.011], phosphate, aspartate, erythritol/threitol [F(1,743) = 171.0, p = 2.6 × 10−35], caffeine [F(1,565) = 8.8, p = 0.0032], hexadecenoic acid, glycerol-3-phosphate, histidine, tryptophan [F(1,778) = 39.1, p = 0.0007], tyrosine [F(1,788) = 39.1, p = 6.8 × 10−10] and threonine [F(1,778) = 3.9, p = 0.05] (Lawton et al. 2008; Menni et al. 2013). There was a significant difference between gender categories for serine [F(1,755) = 7.4, p = 0.007], erythritol/threitol [F(1,743) = 10.5, p = 0.001], caffeine [F(1,565) = 24.3, p = 1.1 × 10−6] and tryptophan [F(1,778) = 55.4, p = 2.6 × 10−13]. There was also a significant interaction between gender and age categories for caffeine [F(1,565) = 17.6, p = 3.2 × 10−5].Fig. 5


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 citric acid 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 are plotted as individual points (>1.5 × IQR). Data were analysed using 2-way ANOVA. There was a significant difference between males and females (F(1,779) = 79.8, p = 3.1 × 10−18). There was no significant difference between age categories and no significant interaction between gender and age categories
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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Fig5: A boxplot showing the distribution of citric acid 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 are plotted as individual points (>1.5 × IQR). Data were analysed using 2-way ANOVA. There was a significant difference between males and females (F(1,779) = 79.8, p = 3.1 × 10−18). There was no significant difference between age categories and no significant interaction between gender and age categories
Mentions: We assessed age-related changes through the comparison of all subjects below the age of 50 years with all subjects older than 64 years. Two-way ANOVA was performed using Gender and Age (two categories: <50 years, and >64 years) as the main effects. Different classes of metabolites showed changes related to age, with some changes not being gender-related and others being specific to one gender. For example, citric acid showed a general increase with age for both males and females [F(1,779) = 79.8, p = 3.1 × 10−18] and therefore is probably not thus a biomarker for pancreatic cancer (Bathe et al. 2011); visually the rate of increase was greater in females than in males (Fig. 5). Citrate has previously been shown to be related to age, along with other metabolites also observed in our study. These include serine [F(1,755) = 6.5, p = 0.011], phosphate, aspartate, erythritol/threitol [F(1,743) = 171.0, p = 2.6 × 10−35], caffeine [F(1,565) = 8.8, p = 0.0032], hexadecenoic acid, glycerol-3-phosphate, histidine, tryptophan [F(1,778) = 39.1, p = 0.0007], tyrosine [F(1,788) = 39.1, p = 6.8 × 10−10] and threonine [F(1,778) = 3.9, p = 0.05] (Lawton et al. 2008; Menni et al. 2013). There was a significant difference between gender categories for serine [F(1,755) = 7.4, p = 0.007], erythritol/threitol [F(1,743) = 10.5, p = 0.001], caffeine [F(1,565) = 24.3, p = 1.1 × 10−6] and tryptophan [F(1,778) = 55.4, p = 2.6 × 10−13]. There was also a significant interaction between gender and age categories for caffeine [F(1,565) = 17.6, p = 3.2 × 10−5].Fig. 5

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.