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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 tyrosine and tryptophan 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 across age categories (<50 years vs. >64 years) for tryptophan [F(1,778) = 11.7, p = 0.0007] and tyrosine [F(1,788) = 39.1, p = 6.8 × 10−10]. There was a significant difference across gender categories for tryptophan [F(1,788) = 55.4, p = 2.6 × 10−13]. There was no significant interaction between gender and age categories for tryptophan or tyrosine
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Fig6: A boxplot showing the distribution of tyrosine and tryptophan 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 across age categories (<50 years vs. >64 years) for tryptophan [F(1,778) = 11.7, p = 0.0007] and tyrosine [F(1,788) = 39.1, p = 6.8 × 10−10]. There was a significant difference across gender categories for tryptophan [F(1,788) = 55.4, p = 2.6 × 10−13]. There was no significant interaction between gender and age categories for tryptophan or tyrosine

Mentions: Age-related changes in amino acids were also observed. These changes included tryptophan [F(1,778) = 11.7, p = 0.0007]; also showed a significant difference between gender categories [F(1,778) = 55.4, p = 2.6 × 10−13] which decreases with age and tyrosine [F(1,788) = 39.1, p = 6.8 × 10−10] which increases with age (as shown in Fig. 6), threonine and serine which both decreased with age and methionine and cysteine [F(1,785) = 16.0, p = 7.1 × 10−5] which also both decreased with age. Cysteine also showed a significant difference between gender categories [F(1,785) = 12.9, p = 0.0003] and showed a significant interaction between gender and age categories [F(1,785) = 4.8, p = 0.03]. Vitamin D metabolites also show decreases with age in both males and females, and have been related to the onset of the metabolic syndrome [e.g. Lee et al. (2009), Lu et al. (2009)] and this observation might argue for the benefits of vitamin supplementation in older people. For example, 24-Hydroxygeminivitamin D3 showed a difference between age categories [F(1,703) = 52.2, p = 1.3 × 10−12], gender categories [F(1,703) = 36.8, p = 2.2 × 10−9] and a significant interaction between age and gender categories [F(1,703) = 5.7, p = 0.02]. Different fatty acids showed either increases or decreases with age (e.g. octadecadienoic acid increased with age [F(1,763) = 8.6, p = 0.003]), but no correlation between age and carbon number, nor degree of saturation, was observed for fatty acids. Erythritol and/or threitol showed an increase (as shown above) with age as did inositol [F(1,779) = 151.8, p = 5.5 × 10−32], which also showed a significant interaction between age and gender categories [F(1,779) = 11.3, p = 0.0008]. These two changes are consistent with the age-dependent increases in classes of carbohydrates that underpin diabetic complications (Brownlee 2001).Fig. 6


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 tyrosine and tryptophan 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 across age categories (<50 years vs. >64 years) for tryptophan [F(1,778) = 11.7, p = 0.0007] and tyrosine [F(1,788) = 39.1, p = 6.8 × 10−10]. There was a significant difference across gender categories for tryptophan [F(1,788) = 55.4, p = 2.6 × 10−13]. There was no significant interaction between gender and age categories for tryptophan or tyrosine
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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Fig6: A boxplot showing the distribution of tyrosine and tryptophan 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 across age categories (<50 years vs. >64 years) for tryptophan [F(1,778) = 11.7, p = 0.0007] and tyrosine [F(1,788) = 39.1, p = 6.8 × 10−10]. There was a significant difference across gender categories for tryptophan [F(1,788) = 55.4, p = 2.6 × 10−13]. There was no significant interaction between gender and age categories for tryptophan or tyrosine
Mentions: Age-related changes in amino acids were also observed. These changes included tryptophan [F(1,778) = 11.7, p = 0.0007]; also showed a significant difference between gender categories [F(1,778) = 55.4, p = 2.6 × 10−13] which decreases with age and tyrosine [F(1,788) = 39.1, p = 6.8 × 10−10] which increases with age (as shown in Fig. 6), threonine and serine which both decreased with age and methionine and cysteine [F(1,785) = 16.0, p = 7.1 × 10−5] which also both decreased with age. Cysteine also showed a significant difference between gender categories [F(1,785) = 12.9, p = 0.0003] and showed a significant interaction between gender and age categories [F(1,785) = 4.8, p = 0.03]. Vitamin D metabolites also show decreases with age in both males and females, and have been related to the onset of the metabolic syndrome [e.g. Lee et al. (2009), Lu et al. (2009)] and this observation might argue for the benefits of vitamin supplementation in older people. For example, 24-Hydroxygeminivitamin D3 showed a difference between age categories [F(1,703) = 52.2, p = 1.3 × 10−12], gender categories [F(1,703) = 36.8, p = 2.2 × 10−9] and a significant interaction between age and gender categories [F(1,703) = 5.7, p = 0.02]. Different fatty acids showed either increases or decreases with age (e.g. octadecadienoic acid increased with age [F(1,763) = 8.6, p = 0.003]), but no correlation between age and carbon number, nor degree of saturation, was observed for fatty acids. Erythritol and/or threitol showed an increase (as shown above) with age as did inositol [F(1,779) = 151.8, p = 5.5 × 10−32], which also showed a significant interaction between age and gender categories [F(1,779) = 11.3, p = 0.0008]. These two changes are consistent with the age-dependent increases in classes of carbohydrates that underpin diabetic complications (Brownlee 2001).Fig. 6

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.