Gender-specific pathway differences in the human serum metabolome
Abstract The susceptibility for various diseases as well as the response to treatments differ considerably between men and women. As a basis for a gender-specific personalized healthcare, an extensive characterization of the molecular differences between the two genders is required. In the present s...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Krumsiek, Jan [verfasserIn] Mittelstrass, Kirstin [verfasserIn] Do, Kieu Trinh [verfasserIn] Stückler, Ferdinand [verfasserIn] Ried, Janina [verfasserIn] Adamski, Jerzy [verfasserIn] Peters, Annette [verfasserIn] Illig, Thomas [verfasserIn] Kronenberg, Florian [verfasserIn] Friedrich, Nele [verfasserIn] Nauck, Matthias [verfasserIn] Pietzner, Maik [verfasserIn] Mook-Kanamori, Dennis O. [verfasserIn] Suhre, Karsten [verfasserIn] Gieger, Christian [verfasserIn] Grallert, Harald [verfasserIn] Theis, Fabian J. [verfasserIn] Kastenmüller, Gabi [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2015 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Metabolomics - Berlin : Springer, 2005, 11(2015), 6 vom: 04. Aug., Seite 1815-1833 |
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| Übergeordnetes Werk: |
volume:11 ; year:2015 ; number:6 ; day:04 ; month:08 ; pages:1815-1833 |
| Links: |
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| DOI / URN: |
10.1007/s11306-015-0829-0 |
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| Katalog-ID: |
SPR018701248 |
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| 245 | 1 | 0 | |a Gender-specific pathway differences in the human serum metabolome |
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| 520 | |a Abstract The susceptibility for various diseases as well as the response to treatments differ considerably between men and women. As a basis for a gender-specific personalized healthcare, an extensive characterization of the molecular differences between the two genders is required. In the present study, we conducted a large-scale metabolomics analysis of 507 metabolic markers measured in serum of 1756 participants from the German KORA F4 study (903 females and 853 males). One-third of the metabolites show significant differences between males and females. A pathway analysis revealed strong differences in steroid metabolism, fatty acids and further lipids, a large fraction of amino acids, oxidative phosphorylation, purine metabolism and gamma-glutamyl dipeptides. We then extended this analysis by a network-based clustering approach. Metabolite interactions were estimated using Gaussian graphical models to get an unbiased, fully data-driven metabolic network representation. This approach is not limited to possibly arbitrary pathway boundaries and can even include poorly or uncharacterized metabolites. The network analysis revealed several strongly gender-regulated submodules across different pathways. Finally, a gender-stratified genome-wide association study was performed to determine whether the observed gender differences are caused by dimorphisms in the effects of genetic polymorphisms on the metabolome. With only a single genome-wide significant hit, our results suggest that this scenario is not the case. In summary, we report an extensive characterization and interpretation of gender-specific differences of the human serum metabolome, providing a broad basis for future analyses. | ||
| 650 | 4 | |a Epidemiology |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Metabolic networks |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Metabolomics |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Gender differences |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Systems biology |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Mittelstrass, Kirstin |e verfasserin |4 aut | |
| 700 | 1 | |a Do, Kieu Trinh |e verfasserin |4 aut | |
| 700 | 1 | |a Stückler, Ferdinand |e verfasserin |4 aut | |
| 700 | 1 | |a Ried, Janina |e verfasserin |4 aut | |
| 700 | 1 | |a Adamski, Jerzy |e verfasserin |4 aut | |
| 700 | 1 | |a Peters, Annette |e verfasserin |4 aut | |
| 700 | 1 | |a Illig, Thomas |e verfasserin |4 aut | |
| 700 | 1 | |a Kronenberg, Florian |e verfasserin |4 aut | |
| 700 | 1 | |a Friedrich, Nele |e verfasserin |4 aut | |
| 700 | 1 | |a Nauck, Matthias |e verfasserin |4 aut | |
| 700 | 1 | |a Pietzner, Maik |e verfasserin |4 aut | |
| 700 | 1 | |a Mook-Kanamori, Dennis O. |e verfasserin |4 aut | |
| 700 | 1 | |a Suhre, Karsten |e verfasserin |4 aut | |
| 700 | 1 | |a Gieger, Christian |e verfasserin |4 aut | |
| 700 | 1 | |a Grallert, Harald |e verfasserin |4 aut | |
| 700 | 1 | |a Theis, Fabian J. |e verfasserin |4 aut | |
| 700 | 1 | |a Kastenmüller, Gabi |e verfasserin |4 aut | |
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10.1007/s11306-015-0829-0 doi (DE-627)SPR018701248 (SPR)s11306-015-0829-0-e DE-627 ger DE-627 rakwb eng 540 ASE 42.15 bkl Krumsiek, Jan verfasserin aut Gender-specific pathway differences in the human serum metabolome 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The susceptibility for various diseases as well as the response to treatments differ considerably between men and women. As a basis for a gender-specific personalized healthcare, an extensive characterization of the molecular differences between the two genders is required. In the present study, we conducted a large-scale metabolomics analysis of 507 metabolic markers measured in serum of 1756 participants from the German KORA F4 study (903 females and 853 males). One-third of the metabolites show significant differences between males and females. A pathway analysis revealed strong differences in steroid metabolism, fatty acids and further lipids, a large fraction of amino acids, oxidative phosphorylation, purine metabolism and gamma-glutamyl dipeptides. We then extended this analysis by a network-based clustering approach. Metabolite interactions were estimated using Gaussian graphical models to get an unbiased, fully data-driven metabolic network representation. This approach is not limited to possibly arbitrary pathway boundaries and can even include poorly or uncharacterized metabolites. The network analysis revealed several strongly gender-regulated submodules across different pathways. Finally, a gender-stratified genome-wide association study was performed to determine whether the observed gender differences are caused by dimorphisms in the effects of genetic polymorphisms on the metabolome. With only a single genome-wide significant hit, our results suggest that this scenario is not the case. In summary, we report an extensive characterization and interpretation of gender-specific differences of the human serum metabolome, providing a broad basis for future analyses. Epidemiology (dpeaa)DE-He213 Metabolic networks (dpeaa)DE-He213 Metabolomics (dpeaa)DE-He213 Gender differences (dpeaa)DE-He213 Systems biology (dpeaa)DE-He213 Mittelstrass, Kirstin verfasserin aut Do, Kieu Trinh verfasserin aut Stückler, Ferdinand verfasserin aut Ried, Janina verfasserin aut Adamski, Jerzy verfasserin aut Peters, Annette verfasserin aut Illig, Thomas verfasserin aut Kronenberg, Florian verfasserin aut Friedrich, Nele verfasserin aut Nauck, Matthias verfasserin aut Pietzner, Maik verfasserin aut Mook-Kanamori, Dennis O. verfasserin aut Suhre, Karsten verfasserin aut Gieger, Christian verfasserin aut Grallert, Harald verfasserin aut Theis, Fabian J. verfasserin aut Kastenmüller, Gabi verfasserin aut Enthalten in Metabolomics Berlin : Springer, 2005 11(2015), 6 vom: 04. Aug., Seite 1815-1833 (DE-627)482838671 (DE-600)2182289-X 1573-3890 nnns volume:11 year:2015 number:6 day:04 month:08 pages:1815-1833 https://dx.doi.org/10.1007/s11306-015-0829-0 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 42.15 ASE AR 11 2015 6 04 08 1815-1833 |
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10.1007/s11306-015-0829-0 doi (DE-627)SPR018701248 (SPR)s11306-015-0829-0-e DE-627 ger DE-627 rakwb eng 540 ASE 42.15 bkl Krumsiek, Jan verfasserin aut Gender-specific pathway differences in the human serum metabolome 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The susceptibility for various diseases as well as the response to treatments differ considerably between men and women. As a basis for a gender-specific personalized healthcare, an extensive characterization of the molecular differences between the two genders is required. In the present study, we conducted a large-scale metabolomics analysis of 507 metabolic markers measured in serum of 1756 participants from the German KORA F4 study (903 females and 853 males). One-third of the metabolites show significant differences between males and females. A pathway analysis revealed strong differences in steroid metabolism, fatty acids and further lipids, a large fraction of amino acids, oxidative phosphorylation, purine metabolism and gamma-glutamyl dipeptides. We then extended this analysis by a network-based clustering approach. Metabolite interactions were estimated using Gaussian graphical models to get an unbiased, fully data-driven metabolic network representation. This approach is not limited to possibly arbitrary pathway boundaries and can even include poorly or uncharacterized metabolites. The network analysis revealed several strongly gender-regulated submodules across different pathways. Finally, a gender-stratified genome-wide association study was performed to determine whether the observed gender differences are caused by dimorphisms in the effects of genetic polymorphisms on the metabolome. With only a single genome-wide significant hit, our results suggest that this scenario is not the case. In summary, we report an extensive characterization and interpretation of gender-specific differences of the human serum metabolome, providing a broad basis for future analyses. Epidemiology (dpeaa)DE-He213 Metabolic networks (dpeaa)DE-He213 Metabolomics (dpeaa)DE-He213 Gender differences (dpeaa)DE-He213 Systems biology (dpeaa)DE-He213 Mittelstrass, Kirstin verfasserin aut Do, Kieu Trinh verfasserin aut Stückler, Ferdinand verfasserin aut Ried, Janina verfasserin aut Adamski, Jerzy verfasserin aut Peters, Annette verfasserin aut Illig, Thomas verfasserin aut Kronenberg, Florian verfasserin aut Friedrich, Nele verfasserin aut Nauck, Matthias verfasserin aut Pietzner, Maik verfasserin aut Mook-Kanamori, Dennis O. verfasserin aut Suhre, Karsten verfasserin aut Gieger, Christian verfasserin aut Grallert, Harald verfasserin aut Theis, Fabian J. verfasserin aut Kastenmüller, Gabi verfasserin aut Enthalten in Metabolomics Berlin : Springer, 2005 11(2015), 6 vom: 04. Aug., Seite 1815-1833 (DE-627)482838671 (DE-600)2182289-X 1573-3890 nnns volume:11 year:2015 number:6 day:04 month:08 pages:1815-1833 https://dx.doi.org/10.1007/s11306-015-0829-0 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 42.15 ASE AR 11 2015 6 04 08 1815-1833 |
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10.1007/s11306-015-0829-0 doi (DE-627)SPR018701248 (SPR)s11306-015-0829-0-e DE-627 ger DE-627 rakwb eng 540 ASE 42.15 bkl Krumsiek, Jan verfasserin aut Gender-specific pathway differences in the human serum metabolome 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The susceptibility for various diseases as well as the response to treatments differ considerably between men and women. As a basis for a gender-specific personalized healthcare, an extensive characterization of the molecular differences between the two genders is required. In the present study, we conducted a large-scale metabolomics analysis of 507 metabolic markers measured in serum of 1756 participants from the German KORA F4 study (903 females and 853 males). One-third of the metabolites show significant differences between males and females. A pathway analysis revealed strong differences in steroid metabolism, fatty acids and further lipids, a large fraction of amino acids, oxidative phosphorylation, purine metabolism and gamma-glutamyl dipeptides. We then extended this analysis by a network-based clustering approach. Metabolite interactions were estimated using Gaussian graphical models to get an unbiased, fully data-driven metabolic network representation. This approach is not limited to possibly arbitrary pathway boundaries and can even include poorly or uncharacterized metabolites. The network analysis revealed several strongly gender-regulated submodules across different pathways. Finally, a gender-stratified genome-wide association study was performed to determine whether the observed gender differences are caused by dimorphisms in the effects of genetic polymorphisms on the metabolome. With only a single genome-wide significant hit, our results suggest that this scenario is not the case. In summary, we report an extensive characterization and interpretation of gender-specific differences of the human serum metabolome, providing a broad basis for future analyses. Epidemiology (dpeaa)DE-He213 Metabolic networks (dpeaa)DE-He213 Metabolomics (dpeaa)DE-He213 Gender differences (dpeaa)DE-He213 Systems biology (dpeaa)DE-He213 Mittelstrass, Kirstin verfasserin aut Do, Kieu Trinh verfasserin aut Stückler, Ferdinand verfasserin aut Ried, Janina verfasserin aut Adamski, Jerzy verfasserin aut Peters, Annette verfasserin aut Illig, Thomas verfasserin aut Kronenberg, Florian verfasserin aut Friedrich, Nele verfasserin aut Nauck, Matthias verfasserin aut Pietzner, Maik verfasserin aut Mook-Kanamori, Dennis O. verfasserin aut Suhre, Karsten verfasserin aut Gieger, Christian verfasserin aut Grallert, Harald verfasserin aut Theis, Fabian J. verfasserin aut Kastenmüller, Gabi verfasserin aut Enthalten in Metabolomics Berlin : Springer, 2005 11(2015), 6 vom: 04. Aug., Seite 1815-1833 (DE-627)482838671 (DE-600)2182289-X 1573-3890 nnns volume:11 year:2015 number:6 day:04 month:08 pages:1815-1833 https://dx.doi.org/10.1007/s11306-015-0829-0 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 42.15 ASE AR 11 2015 6 04 08 1815-1833 |
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10.1007/s11306-015-0829-0 doi (DE-627)SPR018701248 (SPR)s11306-015-0829-0-e DE-627 ger DE-627 rakwb eng 540 ASE 42.15 bkl Krumsiek, Jan verfasserin aut Gender-specific pathway differences in the human serum metabolome 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The susceptibility for various diseases as well as the response to treatments differ considerably between men and women. As a basis for a gender-specific personalized healthcare, an extensive characterization of the molecular differences between the two genders is required. In the present study, we conducted a large-scale metabolomics analysis of 507 metabolic markers measured in serum of 1756 participants from the German KORA F4 study (903 females and 853 males). One-third of the metabolites show significant differences between males and females. A pathway analysis revealed strong differences in steroid metabolism, fatty acids and further lipids, a large fraction of amino acids, oxidative phosphorylation, purine metabolism and gamma-glutamyl dipeptides. We then extended this analysis by a network-based clustering approach. Metabolite interactions were estimated using Gaussian graphical models to get an unbiased, fully data-driven metabolic network representation. This approach is not limited to possibly arbitrary pathway boundaries and can even include poorly or uncharacterized metabolites. The network analysis revealed several strongly gender-regulated submodules across different pathways. Finally, a gender-stratified genome-wide association study was performed to determine whether the observed gender differences are caused by dimorphisms in the effects of genetic polymorphisms on the metabolome. With only a single genome-wide significant hit, our results suggest that this scenario is not the case. In summary, we report an extensive characterization and interpretation of gender-specific differences of the human serum metabolome, providing a broad basis for future analyses. Epidemiology (dpeaa)DE-He213 Metabolic networks (dpeaa)DE-He213 Metabolomics (dpeaa)DE-He213 Gender differences (dpeaa)DE-He213 Systems biology (dpeaa)DE-He213 Mittelstrass, Kirstin verfasserin aut Do, Kieu Trinh verfasserin aut Stückler, Ferdinand verfasserin aut Ried, Janina verfasserin aut Adamski, Jerzy verfasserin aut Peters, Annette verfasserin aut Illig, Thomas verfasserin aut Kronenberg, Florian verfasserin aut Friedrich, Nele verfasserin aut Nauck, Matthias verfasserin aut Pietzner, Maik verfasserin aut Mook-Kanamori, Dennis O. verfasserin aut Suhre, Karsten verfasserin aut Gieger, Christian verfasserin aut Grallert, Harald verfasserin aut Theis, Fabian J. verfasserin aut Kastenmüller, Gabi verfasserin aut Enthalten in Metabolomics Berlin : Springer, 2005 11(2015), 6 vom: 04. Aug., Seite 1815-1833 (DE-627)482838671 (DE-600)2182289-X 1573-3890 nnns volume:11 year:2015 number:6 day:04 month:08 pages:1815-1833 https://dx.doi.org/10.1007/s11306-015-0829-0 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 42.15 ASE AR 11 2015 6 04 08 1815-1833 |
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10.1007/s11306-015-0829-0 doi (DE-627)SPR018701248 (SPR)s11306-015-0829-0-e DE-627 ger DE-627 rakwb eng 540 ASE 42.15 bkl Krumsiek, Jan verfasserin aut Gender-specific pathway differences in the human serum metabolome 2015 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The susceptibility for various diseases as well as the response to treatments differ considerably between men and women. As a basis for a gender-specific personalized healthcare, an extensive characterization of the molecular differences between the two genders is required. In the present study, we conducted a large-scale metabolomics analysis of 507 metabolic markers measured in serum of 1756 participants from the German KORA F4 study (903 females and 853 males). One-third of the metabolites show significant differences between males and females. A pathway analysis revealed strong differences in steroid metabolism, fatty acids and further lipids, a large fraction of amino acids, oxidative phosphorylation, purine metabolism and gamma-glutamyl dipeptides. We then extended this analysis by a network-based clustering approach. Metabolite interactions were estimated using Gaussian graphical models to get an unbiased, fully data-driven metabolic network representation. This approach is not limited to possibly arbitrary pathway boundaries and can even include poorly or uncharacterized metabolites. The network analysis revealed several strongly gender-regulated submodules across different pathways. Finally, a gender-stratified genome-wide association study was performed to determine whether the observed gender differences are caused by dimorphisms in the effects of genetic polymorphisms on the metabolome. With only a single genome-wide significant hit, our results suggest that this scenario is not the case. In summary, we report an extensive characterization and interpretation of gender-specific differences of the human serum metabolome, providing a broad basis for future analyses. Epidemiology (dpeaa)DE-He213 Metabolic networks (dpeaa)DE-He213 Metabolomics (dpeaa)DE-He213 Gender differences (dpeaa)DE-He213 Systems biology (dpeaa)DE-He213 Mittelstrass, Kirstin verfasserin aut Do, Kieu Trinh verfasserin aut Stückler, Ferdinand verfasserin aut Ried, Janina verfasserin aut Adamski, Jerzy verfasserin aut Peters, Annette verfasserin aut Illig, Thomas verfasserin aut Kronenberg, Florian verfasserin aut Friedrich, Nele verfasserin aut Nauck, Matthias verfasserin aut Pietzner, Maik verfasserin aut Mook-Kanamori, Dennis O. verfasserin aut Suhre, Karsten verfasserin aut Gieger, Christian verfasserin aut Grallert, Harald verfasserin aut Theis, Fabian J. verfasserin aut Kastenmüller, Gabi verfasserin aut Enthalten in Metabolomics Berlin : Springer, 2005 11(2015), 6 vom: 04. Aug., Seite 1815-1833 (DE-627)482838671 (DE-600)2182289-X 1573-3890 nnns volume:11 year:2015 number:6 day:04 month:08 pages:1815-1833 https://dx.doi.org/10.1007/s11306-015-0829-0 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 42.15 ASE AR 11 2015 6 04 08 1815-1833 |
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Enthalten in Metabolomics 11(2015), 6 vom: 04. Aug., Seite 1815-1833 volume:11 year:2015 number:6 day:04 month:08 pages:1815-1833 |
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Enthalten in Metabolomics 11(2015), 6 vom: 04. Aug., Seite 1815-1833 volume:11 year:2015 number:6 day:04 month:08 pages:1815-1833 |
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Epidemiology Metabolic networks Metabolomics Gender differences Systems biology |
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Krumsiek, Jan @@aut@@ Mittelstrass, Kirstin @@aut@@ Do, Kieu Trinh @@aut@@ Stückler, Ferdinand @@aut@@ Ried, Janina @@aut@@ Adamski, Jerzy @@aut@@ Peters, Annette @@aut@@ Illig, Thomas @@aut@@ Kronenberg, Florian @@aut@@ Friedrich, Nele @@aut@@ Nauck, Matthias @@aut@@ Pietzner, Maik @@aut@@ Mook-Kanamori, Dennis O. @@aut@@ Suhre, Karsten @@aut@@ Gieger, Christian @@aut@@ Grallert, Harald @@aut@@ Theis, Fabian J. @@aut@@ Kastenmüller, Gabi @@aut@@ |
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2015-08-04T00:00:00Z |
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482838671 |
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3540 |
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SPR018701248 |
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Krumsiek, Jan |
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Krumsiek, Jan ddc 540 bkl 42.15 misc Epidemiology misc Metabolic networks misc Metabolomics misc Gender differences misc Systems biology Gender-specific pathway differences in the human serum metabolome |
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540 ASE 42.15 bkl Gender-specific pathway differences in the human serum metabolome Epidemiology (dpeaa)DE-He213 Metabolic networks (dpeaa)DE-He213 Metabolomics (dpeaa)DE-He213 Gender differences (dpeaa)DE-He213 Systems biology (dpeaa)DE-He213 |
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Krumsiek, Jan Mittelstrass, Kirstin Do, Kieu Trinh Stückler, Ferdinand Ried, Janina Adamski, Jerzy Peters, Annette Illig, Thomas Kronenberg, Florian Friedrich, Nele Nauck, Matthias Pietzner, Maik Mook-Kanamori, Dennis O. Suhre, Karsten Gieger, Christian Grallert, Harald Theis, Fabian J. Kastenmüller, Gabi |
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gender-specific pathway differences in the human serum metabolome |
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Gender-specific pathway differences in the human serum metabolome |
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Abstract The susceptibility for various diseases as well as the response to treatments differ considerably between men and women. As a basis for a gender-specific personalized healthcare, an extensive characterization of the molecular differences between the two genders is required. In the present study, we conducted a large-scale metabolomics analysis of 507 metabolic markers measured in serum of 1756 participants from the German KORA F4 study (903 females and 853 males). One-third of the metabolites show significant differences between males and females. A pathway analysis revealed strong differences in steroid metabolism, fatty acids and further lipids, a large fraction of amino acids, oxidative phosphorylation, purine metabolism and gamma-glutamyl dipeptides. We then extended this analysis by a network-based clustering approach. Metabolite interactions were estimated using Gaussian graphical models to get an unbiased, fully data-driven metabolic network representation. This approach is not limited to possibly arbitrary pathway boundaries and can even include poorly or uncharacterized metabolites. The network analysis revealed several strongly gender-regulated submodules across different pathways. Finally, a gender-stratified genome-wide association study was performed to determine whether the observed gender differences are caused by dimorphisms in the effects of genetic polymorphisms on the metabolome. With only a single genome-wide significant hit, our results suggest that this scenario is not the case. In summary, we report an extensive characterization and interpretation of gender-specific differences of the human serum metabolome, providing a broad basis for future analyses. |
| abstractGer |
Abstract The susceptibility for various diseases as well as the response to treatments differ considerably between men and women. As a basis for a gender-specific personalized healthcare, an extensive characterization of the molecular differences between the two genders is required. In the present study, we conducted a large-scale metabolomics analysis of 507 metabolic markers measured in serum of 1756 participants from the German KORA F4 study (903 females and 853 males). One-third of the metabolites show significant differences between males and females. A pathway analysis revealed strong differences in steroid metabolism, fatty acids and further lipids, a large fraction of amino acids, oxidative phosphorylation, purine metabolism and gamma-glutamyl dipeptides. We then extended this analysis by a network-based clustering approach. Metabolite interactions were estimated using Gaussian graphical models to get an unbiased, fully data-driven metabolic network representation. This approach is not limited to possibly arbitrary pathway boundaries and can even include poorly or uncharacterized metabolites. The network analysis revealed several strongly gender-regulated submodules across different pathways. Finally, a gender-stratified genome-wide association study was performed to determine whether the observed gender differences are caused by dimorphisms in the effects of genetic polymorphisms on the metabolome. With only a single genome-wide significant hit, our results suggest that this scenario is not the case. In summary, we report an extensive characterization and interpretation of gender-specific differences of the human serum metabolome, providing a broad basis for future analyses. |
| abstract_unstemmed |
Abstract The susceptibility for various diseases as well as the response to treatments differ considerably between men and women. As a basis for a gender-specific personalized healthcare, an extensive characterization of the molecular differences between the two genders is required. In the present study, we conducted a large-scale metabolomics analysis of 507 metabolic markers measured in serum of 1756 participants from the German KORA F4 study (903 females and 853 males). One-third of the metabolites show significant differences between males and females. A pathway analysis revealed strong differences in steroid metabolism, fatty acids and further lipids, a large fraction of amino acids, oxidative phosphorylation, purine metabolism and gamma-glutamyl dipeptides. We then extended this analysis by a network-based clustering approach. Metabolite interactions were estimated using Gaussian graphical models to get an unbiased, fully data-driven metabolic network representation. This approach is not limited to possibly arbitrary pathway boundaries and can even include poorly or uncharacterized metabolites. The network analysis revealed several strongly gender-regulated submodules across different pathways. Finally, a gender-stratified genome-wide association study was performed to determine whether the observed gender differences are caused by dimorphisms in the effects of genetic polymorphisms on the metabolome. With only a single genome-wide significant hit, our results suggest that this scenario is not the case. In summary, we report an extensive characterization and interpretation of gender-specific differences of the human serum metabolome, providing a broad basis for future analyses. |
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Mittelstrass, Kirstin Do, Kieu Trinh Stückler, Ferdinand Ried, Janina Adamski, Jerzy Peters, Annette Illig, Thomas Kronenberg, Florian Friedrich, Nele Nauck, Matthias Pietzner, Maik Mook-Kanamori, Dennis O. Suhre, Karsten Gieger, Christian Grallert, Harald Theis, Fabian J. Kastenmüller, Gabi |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR018701248</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230519191341.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201006s2015 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11306-015-0829-0</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR018701248</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s11306-015-0829-0-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">540</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">42.15</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Krumsiek, Jan</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Gender-specific pathway differences in the human serum metabolome</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2015</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract The susceptibility for various diseases as well as the response to treatments differ considerably between men and women. As a basis for a gender-specific personalized healthcare, an extensive characterization of the molecular differences between the two genders is required. In the present study, we conducted a large-scale metabolomics analysis of 507 metabolic markers measured in serum of 1756 participants from the German KORA F4 study (903 females and 853 males). One-third of the metabolites show significant differences between males and females. A pathway analysis revealed strong differences in steroid metabolism, fatty acids and further lipids, a large fraction of amino acids, oxidative phosphorylation, purine metabolism and gamma-glutamyl dipeptides. We then extended this analysis by a network-based clustering approach. Metabolite interactions were estimated using Gaussian graphical models to get an unbiased, fully data-driven metabolic network representation. This approach is not limited to possibly arbitrary pathway boundaries and can even include poorly or uncharacterized metabolites. The network analysis revealed several strongly gender-regulated submodules across different pathways. Finally, a gender-stratified genome-wide association study was performed to determine whether the observed gender differences are caused by dimorphisms in the effects of genetic polymorphisms on the metabolome. With only a single genome-wide significant hit, our results suggest that this scenario is not the case. 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