Urine metabolic profiles in paediatric asthma
ABSTRACT Background and objective Asthma is a global problem and complex disease suited for metabolomic profiling. This study explored the candidate biomarkers specific to paediatric asthma and provided insights into asthmatic pathophysiology. Methods Children (aged 6–11 years) meeting the criteria...
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| Veröffentlicht in: | Respirology (Carlton, Vic.) Vic.), 2019-06, Vol.24 (6), p.572-581 |
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| creator | Tao, Jia‐Lei Chen, Yan‐Zhen Dai, Qi‐Gang Tian, Man Wang, Shou‐Chuan Shan, Jin‐Jun Ji, Jian‐Jian Lin, Li‐Li Li, Wei‐Wei Yuan, Bin |
| description | ABSTRACT
Background and objective
Asthma is a global problem and complex disease suited for metabolomic profiling. This study explored the candidate biomarkers specific to paediatric asthma and provided insights into asthmatic pathophysiology.
Methods
Children (aged 6–11 years) meeting the criteria for healthy control (n = 29), uncontrolled asthma (n = 37) or controlled asthma (n = 43) were enrolled. Gas chromatography–mass spectrometry was performed on urine samples of the patients to explore the different types of metabolite profile in paediatric asthma. Additionally, we employed a comprehensive strategy to elucidate the relationship between significant metabolites and asthma‐related genes.
Results
We identified 51 differential metabolites mainly related to dysfunctional amino acid, carbohydrate and purine metabolism. A combination of eight candidate metabolites, including uric acid, stearic acid, threitol, acetylgalactosamine, heptadecanoic acid, aspartic acid, xanthosine and hypoxanthine (adjusted P 1.5 or 0.97 across groups. Enrichment analysis based on these targets revealed that the Fc receptor, intracellular steroid hormone receptor signalling pathway, DNA damage and fibroblast proliferation were involved in inflammation, immunity and stress‐related biological progression of paediatric asthma.
Conclusion
Metabolomic analysis of patient urine combined with network‐biology approaches allowed discrimination of asthma profiles and subtypes according to the metabolic patterns. The results provided insight into the potential mechanism of paediatric asthma.
We investigated metabolic profiles of paediatric asthma patients to identify asthma‐specific biomarkers in urine. A combination of eight metabolites showed excellent discrimination across groups. Enrichment analysis identified complex biological processes associated with immunity, inflammation, oxidative stress and DNA damage. These approaches enabled discrimination between asthma stages and elucidate its mechanisms.
See related Editorial |
| doi_str_mv | 10.1111/resp.13479 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2229107282</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2226335966</sourcerecordid><originalsourceid>FETCH-LOGICAL-c3939-a65e336a67b34f319307dd93d221114bf3001f4bc64efdf0b8e2ff3fd5343a93</originalsourceid><addsrcrecordid>eNp9kF1LwzAUhoMobk5v_AFS8EaEziQnTZtLGfMDBorO65A2CXb0y6RF9u_N7PTCC3NzwuHhPS8PQucEz0l4N874bk6ApeIATQljOCYZg8PwBwpxmgoxQSfebzDGkODkGE0ApxxExqYofnNlY6La9Cpvq7KIOtfasjI-KpuoU0aXqndhrXz_XqtTdGRV5c3Zfs7Q-m65XjzEq6f7x8XtKi5AgIgVTwwAVzzNgVkgItzTWoCmNPRluQWMiWV5wZmx2uI8M9RasDoBBkrADF2NsaHMx2B8L-vSF6aqVGPawUtKqSA4pRkN6OUfdNMOrgnldhQHSATngboeqcK13jtjZefKWrmtJFjuHMqdQ_ntMMAX-8ghr43-RX-kBYCMwGcQtf0nSr4sX5_H0C-avXo_</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2226335966</pqid></control><display><type>article</type><title>Urine metabolic profiles in paediatric asthma</title><source>MEDLINE</source><source>Wiley Online Library Journals Frontfile Complete</source><creator>Tao, Jia‐Lei ; Chen, Yan‐Zhen ; Dai, Qi‐Gang ; Tian, Man ; Wang, Shou‐Chuan ; Shan, Jin‐Jun ; Ji, Jian‐Jian ; Lin, Li‐Li ; Li, Wei‐Wei ; Yuan, Bin</creator><creatorcontrib>Tao, Jia‐Lei ; Chen, Yan‐Zhen ; Dai, Qi‐Gang ; Tian, Man ; Wang, Shou‐Chuan ; Shan, Jin‐Jun ; Ji, Jian‐Jian ; Lin, Li‐Li ; Li, Wei‐Wei ; Yuan, Bin</creatorcontrib><description>ABSTRACT
Background and objective
Asthma is a global problem and complex disease suited for metabolomic profiling. This study explored the candidate biomarkers specific to paediatric asthma and provided insights into asthmatic pathophysiology.
Methods
Children (aged 6–11 years) meeting the criteria for healthy control (n = 29), uncontrolled asthma (n = 37) or controlled asthma (n = 43) were enrolled. Gas chromatography–mass spectrometry was performed on urine samples of the patients to explore the different types of metabolite profile in paediatric asthma. Additionally, we employed a comprehensive strategy to elucidate the relationship between significant metabolites and asthma‐related genes.
Results
We identified 51 differential metabolites mainly related to dysfunctional amino acid, carbohydrate and purine metabolism. A combination of eight candidate metabolites, including uric acid, stearic acid, threitol, acetylgalactosamine, heptadecanoic acid, aspartic acid, xanthosine and hypoxanthine (adjusted P < 0.05 and fold‐change >1.5 or <0.67), showed excellent discriminatory performance for the presence of asthma and the differentiation of poor‐controlled or well‐controlled asthma, and area under the curve values were >0.97 across groups. Enrichment analysis based on these targets revealed that the Fc receptor, intracellular steroid hormone receptor signalling pathway, DNA damage and fibroblast proliferation were involved in inflammation, immunity and stress‐related biological progression of paediatric asthma.
Conclusion
Metabolomic analysis of patient urine combined with network‐biology approaches allowed discrimination of asthma profiles and subtypes according to the metabolic patterns. The results provided insight into the potential mechanism of paediatric asthma.
We investigated metabolic profiles of paediatric asthma patients to identify asthma‐specific biomarkers in urine. A combination of eight metabolites showed excellent discrimination across groups. Enrichment analysis identified complex biological processes associated with immunity, inflammation, oxidative stress and DNA damage. These approaches enabled discrimination between asthma stages and elucidate its mechanisms.
See related Editorial</description><identifier>ISSN: 1323-7799</identifier><identifier>EISSN: 1440-1843</identifier><identifier>DOI: 10.1111/resp.13479</identifier><identifier>PMID: 30763984</identifier><language>eng</language><publisher>Chichester, UK: John Wiley & Sons, Ltd</publisher><subject>Amino acids ; Aspartic acid ; Asthma ; Asthma - complications ; Asthma - physiopathology ; Asthma - urine ; Biomarkers - urine ; Carbohydrate metabolism ; Case-Control Studies ; Child ; childhood ; DNA damage ; Fc receptors ; Female ; Gas chromatography ; gas chromatography–mass spectrometry ; Humans ; Hypoxanthine ; Inflammation ; Intracellular signalling ; Male ; Mass spectroscopy ; Metabolism ; Metabolites ; Metabolome ; Metabolomics ; network‐biology ; Respiratory diseases ; Signal transduction ; Stearic acid ; Uric acid ; Urine</subject><ispartof>Respirology (Carlton, Vic.), 2019-06, Vol.24 (6), p.572-581</ispartof><rights>2019 Asian Pacific Society of Respirology</rights><rights>2019 Asian Pacific Society of Respirology.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3939-a65e336a67b34f319307dd93d221114bf3001f4bc64efdf0b8e2ff3fd5343a93</citedby><cites>FETCH-LOGICAL-c3939-a65e336a67b34f319307dd93d221114bf3001f4bc64efdf0b8e2ff3fd5343a93</cites><orcidid>0000-0002-6071-8925</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1111%2Fresp.13479$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1111%2Fresp.13479$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,776,780,1411,27901,27902,45550,45551</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/30763984$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Tao, Jia‐Lei</creatorcontrib><creatorcontrib>Chen, Yan‐Zhen</creatorcontrib><creatorcontrib>Dai, Qi‐Gang</creatorcontrib><creatorcontrib>Tian, Man</creatorcontrib><creatorcontrib>Wang, Shou‐Chuan</creatorcontrib><creatorcontrib>Shan, Jin‐Jun</creatorcontrib><creatorcontrib>Ji, Jian‐Jian</creatorcontrib><creatorcontrib>Lin, Li‐Li</creatorcontrib><creatorcontrib>Li, Wei‐Wei</creatorcontrib><creatorcontrib>Yuan, Bin</creatorcontrib><title>Urine metabolic profiles in paediatric asthma</title><title>Respirology (Carlton, Vic.)</title><addtitle>Respirology</addtitle><description>ABSTRACT
Background and objective
Asthma is a global problem and complex disease suited for metabolomic profiling. This study explored the candidate biomarkers specific to paediatric asthma and provided insights into asthmatic pathophysiology.
Methods
Children (aged 6–11 years) meeting the criteria for healthy control (n = 29), uncontrolled asthma (n = 37) or controlled asthma (n = 43) were enrolled. Gas chromatography–mass spectrometry was performed on urine samples of the patients to explore the different types of metabolite profile in paediatric asthma. Additionally, we employed a comprehensive strategy to elucidate the relationship between significant metabolites and asthma‐related genes.
Results
We identified 51 differential metabolites mainly related to dysfunctional amino acid, carbohydrate and purine metabolism. A combination of eight candidate metabolites, including uric acid, stearic acid, threitol, acetylgalactosamine, heptadecanoic acid, aspartic acid, xanthosine and hypoxanthine (adjusted P < 0.05 and fold‐change >1.5 or <0.67), showed excellent discriminatory performance for the presence of asthma and the differentiation of poor‐controlled or well‐controlled asthma, and area under the curve values were >0.97 across groups. Enrichment analysis based on these targets revealed that the Fc receptor, intracellular steroid hormone receptor signalling pathway, DNA damage and fibroblast proliferation were involved in inflammation, immunity and stress‐related biological progression of paediatric asthma.
Conclusion
Metabolomic analysis of patient urine combined with network‐biology approaches allowed discrimination of asthma profiles and subtypes according to the metabolic patterns. The results provided insight into the potential mechanism of paediatric asthma.
We investigated metabolic profiles of paediatric asthma patients to identify asthma‐specific biomarkers in urine. A combination of eight metabolites showed excellent discrimination across groups. Enrichment analysis identified complex biological processes associated with immunity, inflammation, oxidative stress and DNA damage. These approaches enabled discrimination between asthma stages and elucidate its mechanisms.
See related Editorial</description><subject>Amino acids</subject><subject>Aspartic acid</subject><subject>Asthma</subject><subject>Asthma - complications</subject><subject>Asthma - physiopathology</subject><subject>Asthma - urine</subject><subject>Biomarkers - urine</subject><subject>Carbohydrate metabolism</subject><subject>Case-Control Studies</subject><subject>Child</subject><subject>childhood</subject><subject>DNA damage</subject><subject>Fc receptors</subject><subject>Female</subject><subject>Gas chromatography</subject><subject>gas chromatography–mass spectrometry</subject><subject>Humans</subject><subject>Hypoxanthine</subject><subject>Inflammation</subject><subject>Intracellular signalling</subject><subject>Male</subject><subject>Mass spectroscopy</subject><subject>Metabolism</subject><subject>Metabolites</subject><subject>Metabolome</subject><subject>Metabolomics</subject><subject>network‐biology</subject><subject>Respiratory diseases</subject><subject>Signal transduction</subject><subject>Stearic acid</subject><subject>Uric acid</subject><subject>Urine</subject><issn>1323-7799</issn><issn>1440-1843</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9kF1LwzAUhoMobk5v_AFS8EaEziQnTZtLGfMDBorO65A2CXb0y6RF9u_N7PTCC3NzwuHhPS8PQucEz0l4N874bk6ApeIATQljOCYZg8PwBwpxmgoxQSfebzDGkODkGE0ApxxExqYofnNlY6La9Cpvq7KIOtfasjI-KpuoU0aXqndhrXz_XqtTdGRV5c3Zfs7Q-m65XjzEq6f7x8XtKi5AgIgVTwwAVzzNgVkgItzTWoCmNPRluQWMiWV5wZmx2uI8M9RasDoBBkrADF2NsaHMx2B8L-vSF6aqVGPawUtKqSA4pRkN6OUfdNMOrgnldhQHSATngboeqcK13jtjZefKWrmtJFjuHMqdQ_ntMMAX-8ghr43-RX-kBYCMwGcQtf0nSr4sX5_H0C-avXo_</recordid><startdate>201906</startdate><enddate>201906</enddate><creator>Tao, Jia‐Lei</creator><creator>Chen, Yan‐Zhen</creator><creator>Dai, Qi‐Gang</creator><creator>Tian, Man</creator><creator>Wang, Shou‐Chuan</creator><creator>Shan, Jin‐Jun</creator><creator>Ji, Jian‐Jian</creator><creator>Lin, Li‐Li</creator><creator>Li, Wei‐Wei</creator><creator>Yuan, Bin</creator><general>John Wiley & Sons, Ltd</general><general>Wiley Subscription Services, Inc</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7T5</scope><scope>H94</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0002-6071-8925</orcidid></search><sort><creationdate>201906</creationdate><title>Urine metabolic profiles in paediatric asthma</title><author>Tao, Jia‐Lei ; Chen, Yan‐Zhen ; Dai, Qi‐Gang ; Tian, Man ; Wang, Shou‐Chuan ; Shan, Jin‐Jun ; Ji, Jian‐Jian ; Lin, Li‐Li ; Li, Wei‐Wei ; Yuan, Bin</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3939-a65e336a67b34f319307dd93d221114bf3001f4bc64efdf0b8e2ff3fd5343a93</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Amino acids</topic><topic>Aspartic acid</topic><topic>Asthma</topic><topic>Asthma - complications</topic><topic>Asthma - physiopathology</topic><topic>Asthma - urine</topic><topic>Biomarkers - urine</topic><topic>Carbohydrate metabolism</topic><topic>Case-Control Studies</topic><topic>Child</topic><topic>childhood</topic><topic>DNA damage</topic><topic>Fc receptors</topic><topic>Female</topic><topic>Gas chromatography</topic><topic>gas chromatography–mass spectrometry</topic><topic>Humans</topic><topic>Hypoxanthine</topic><topic>Inflammation</topic><topic>Intracellular signalling</topic><topic>Male</topic><topic>Mass spectroscopy</topic><topic>Metabolism</topic><topic>Metabolites</topic><topic>Metabolome</topic><topic>Metabolomics</topic><topic>network‐biology</topic><topic>Respiratory diseases</topic><topic>Signal transduction</topic><topic>Stearic acid</topic><topic>Uric acid</topic><topic>Urine</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Tao, Jia‐Lei</creatorcontrib><creatorcontrib>Chen, Yan‐Zhen</creatorcontrib><creatorcontrib>Dai, Qi‐Gang</creatorcontrib><creatorcontrib>Tian, Man</creatorcontrib><creatorcontrib>Wang, Shou‐Chuan</creatorcontrib><creatorcontrib>Shan, Jin‐Jun</creatorcontrib><creatorcontrib>Ji, Jian‐Jian</creatorcontrib><creatorcontrib>Lin, Li‐Li</creatorcontrib><creatorcontrib>Li, Wei‐Wei</creatorcontrib><creatorcontrib>Yuan, Bin</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Immunology Abstracts</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Respirology (Carlton, Vic.)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Tao, Jia‐Lei</au><au>Chen, Yan‐Zhen</au><au>Dai, Qi‐Gang</au><au>Tian, Man</au><au>Wang, Shou‐Chuan</au><au>Shan, Jin‐Jun</au><au>Ji, Jian‐Jian</au><au>Lin, Li‐Li</au><au>Li, Wei‐Wei</au><au>Yuan, Bin</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Urine metabolic profiles in paediatric asthma</atitle><jtitle>Respirology (Carlton, Vic.)</jtitle><addtitle>Respirology</addtitle><date>2019-06</date><risdate>2019</risdate><volume>24</volume><issue>6</issue><spage>572</spage><epage>581</epage><pages>572-581</pages><issn>1323-7799</issn><eissn>1440-1843</eissn><abstract>ABSTRACT
Background and objective
Asthma is a global problem and complex disease suited for metabolomic profiling. This study explored the candidate biomarkers specific to paediatric asthma and provided insights into asthmatic pathophysiology.
Methods
Children (aged 6–11 years) meeting the criteria for healthy control (n = 29), uncontrolled asthma (n = 37) or controlled asthma (n = 43) were enrolled. Gas chromatography–mass spectrometry was performed on urine samples of the patients to explore the different types of metabolite profile in paediatric asthma. Additionally, we employed a comprehensive strategy to elucidate the relationship between significant metabolites and asthma‐related genes.
Results
We identified 51 differential metabolites mainly related to dysfunctional amino acid, carbohydrate and purine metabolism. A combination of eight candidate metabolites, including uric acid, stearic acid, threitol, acetylgalactosamine, heptadecanoic acid, aspartic acid, xanthosine and hypoxanthine (adjusted P < 0.05 and fold‐change >1.5 or <0.67), showed excellent discriminatory performance for the presence of asthma and the differentiation of poor‐controlled or well‐controlled asthma, and area under the curve values were >0.97 across groups. Enrichment analysis based on these targets revealed that the Fc receptor, intracellular steroid hormone receptor signalling pathway, DNA damage and fibroblast proliferation were involved in inflammation, immunity and stress‐related biological progression of paediatric asthma.
Conclusion
Metabolomic analysis of patient urine combined with network‐biology approaches allowed discrimination of asthma profiles and subtypes according to the metabolic patterns. The results provided insight into the potential mechanism of paediatric asthma.
We investigated metabolic profiles of paediatric asthma patients to identify asthma‐specific biomarkers in urine. A combination of eight metabolites showed excellent discrimination across groups. Enrichment analysis identified complex biological processes associated with immunity, inflammation, oxidative stress and DNA damage. These approaches enabled discrimination between asthma stages and elucidate its mechanisms.
See related Editorial</abstract><cop>Chichester, UK</cop><pub>John Wiley & Sons, Ltd</pub><pmid>30763984</pmid><doi>10.1111/resp.13479</doi><tpages>10</tpages><orcidid>https://orcid.org/0000-0002-6071-8925</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Amino acids Aspartic acid Asthma Asthma - complications Asthma - physiopathology Asthma - urine Biomarkers - urine Carbohydrate metabolism Case-Control Studies Child childhood DNA damage Fc receptors Female Gas chromatography gas chromatography–mass spectrometry Humans Hypoxanthine Inflammation Intracellular signalling Male Mass spectroscopy Metabolism Metabolites Metabolome Metabolomics network‐biology Respiratory diseases Signal transduction Stearic acid Uric acid Urine |
| title | Urine metabolic profiles in paediatric asthma |
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