Pub Date : 2024-05-24DOI: 10.1007/s11306-024-02107-0
Alexandre Boissais, Hélène Blasco, Patrick Emond, Antoine Lefèvre, Adrien Bigot, Yanis Ramdani, Nicole Ferreira Maldent, Denis Mulleman, Evangéline Pillebout, François Maillot, Alexandra Audemard-Verger
Introduction: IgA vasculitis diagnosis relies primarily on clinical features and is confirmed by pathological findings. To date, there is no reliable noninvasive diagnostic biomarker.
Objective: We aimed to explore the baseline serum metabolome of adult patients with IgA vasculitis to identify potential diagnostic biomarkers.
Methods: We performed a study comparing the serum metabolome of patients with IgA vasculitis to that of patients with inflammatory condition, namely spondyloarthritis. Serum analyses were performed by high-performance liquid chromatography-mass spectrometry.
Results: Fifty-five patients with IgA vasculitis and 77 controls with spondyloarthritis (age- and sex-matched) were included in this study. The median age of IgA vasculitis patients was 53 years. Two-thirds of patients were female (n = 32). At the time of vasculitis diagnosis, 100% of patients had skin involvement and 69% presented with glomerulonephritis (n = 38). Joint and digestive involvement were observed in 56% (n = 31) and 42% (n = 23) of patients. Four discriminative metabolites between the two groups were identified: 1-methyladenosine, L-glutamic acid, serotonin, and thymidine. The multivariate model built from the serum metabolomes of patients with IgA vasculitis and spondyloarthritis revealed an accuracy > 90%. As this model was significant according to the permutation test (p < 0.01), independent validation showed an excellent predictive value of the test set: sensitivity 98%; specificity 98%, positive predictive value 97% and negative predictive value 98%.
Conclusion: To our knowledge, this study is the first to use the metabolomic approach for diagnostic purposes in adult IgA vasculitis, highlighting a specific diagnostic metabolome signature.
导言:IgA 血管炎的诊断主要依赖于临床特征,并通过病理结果加以确认。迄今为止,尚无可靠的非侵入性诊断生物标志物:我们旨在探索成年 IgA 血管炎患者的血清代谢组基线,以确定潜在的诊断生物标志物:我们将 IgA 血管炎患者的血清代谢组与脊柱关节炎患者的血清代谢组进行了比较。血清分析采用高效液相色谱-质谱法进行:本研究共纳入 55 名 IgA 血管炎患者和 77 名脊柱关节炎对照组患者(年龄和性别匹配)。IgA 血管炎患者的中位年龄为 53 岁。三分之二的患者为女性(32 人)。在确诊为血管炎时,100%的患者皮肤受累,69%的患者出现肾小球肾炎(n = 38)。56%(31 人)和 42%(23 人)的患者出现关节和消化系统受累。两组患者的四种代谢物具有鉴别性:1-甲基腺苷、L-谷氨酸、血清素和胸腺嘧啶。根据 IgA 血管炎和脊柱关节炎患者的血清代谢组建立的多变量模型显示,准确率大于 90%。根据置换检验,该模型具有显著性(p 结论:"该模型的准确性高于90%":据我们所知,这项研究首次将代谢组学方法用于成人 IgA 血管炎的诊断,并突出了一个特定的诊断代谢组特征。
{"title":"A specific diagnostic metabolome signature in adult IgA vasculitis.","authors":"Alexandre Boissais, Hélène Blasco, Patrick Emond, Antoine Lefèvre, Adrien Bigot, Yanis Ramdani, Nicole Ferreira Maldent, Denis Mulleman, Evangéline Pillebout, François Maillot, Alexandra Audemard-Verger","doi":"10.1007/s11306-024-02107-0","DOIUrl":"10.1007/s11306-024-02107-0","url":null,"abstract":"<p><strong>Introduction: </strong>IgA vasculitis diagnosis relies primarily on clinical features and is confirmed by pathological findings. To date, there is no reliable noninvasive diagnostic biomarker.</p><p><strong>Objective: </strong>We aimed to explore the baseline serum metabolome of adult patients with IgA vasculitis to identify potential diagnostic biomarkers.</p><p><strong>Methods: </strong>We performed a study comparing the serum metabolome of patients with IgA vasculitis to that of patients with inflammatory condition, namely spondyloarthritis. Serum analyses were performed by high-performance liquid chromatography-mass spectrometry.</p><p><strong>Results: </strong>Fifty-five patients with IgA vasculitis and 77 controls with spondyloarthritis (age- and sex-matched) were included in this study. The median age of IgA vasculitis patients was 53 years. Two-thirds of patients were female (n = 32). At the time of vasculitis diagnosis, 100% of patients had skin involvement and 69% presented with glomerulonephritis (n = 38). Joint and digestive involvement were observed in 56% (n = 31) and 42% (n = 23) of patients. Four discriminative metabolites between the two groups were identified: 1-methyladenosine, L-glutamic acid, serotonin, and thymidine. The multivariate model built from the serum metabolomes of patients with IgA vasculitis and spondyloarthritis revealed an accuracy > 90%. As this model was significant according to the permutation test (p < 0.01), independent validation showed an excellent predictive value of the test set: sensitivity 98%; specificity 98%, positive predictive value 97% and negative predictive value 98%.</p><p><strong>Conclusion: </strong>To our knowledge, this study is the first to use the metabolomic approach for diagnostic purposes in adult IgA vasculitis, highlighting a specific diagnostic metabolome signature.</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":"20 3","pages":"61"},"PeriodicalIF":3.5,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11126474/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141088034","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-21DOI: 10.1007/s11306-024-02111-4
Krista A Zanetti, Lining Guo, Deeba Husain, Rachel S Kelly, Jessica Lasky-Su, David Broadhurst, Craig E Wheelock
Metabolomic epidemiology studies are complex and require a broad array of domain expertise. Although many metabolite-phenotype associations have been identified; to date, few findings have been translated to the clinic. Bridging this gap requires understanding of both the underlying biology of these associations and their potential clinical implications, necessitating an interdisciplinary team approach. To address this need in metabolomic epidemiology, a workshop was held at Metabolomics 2023 in Niagara Falls, Ontario, Canada that highlighted the domain expertise needed to effectively conduct these studies -- biochemistry, clinical science, epidemiology, and assay development for biomarker validation -- and emphasized the role of interdisciplinary teams to move findings towards clinical translation.
{"title":"Workshop report - interdisciplinary metabolomic epidemiology: the pathway to clinical translation.","authors":"Krista A Zanetti, Lining Guo, Deeba Husain, Rachel S Kelly, Jessica Lasky-Su, David Broadhurst, Craig E Wheelock","doi":"10.1007/s11306-024-02111-4","DOIUrl":"10.1007/s11306-024-02111-4","url":null,"abstract":"<p><p>Metabolomic epidemiology studies are complex and require a broad array of domain expertise. Although many metabolite-phenotype associations have been identified; to date, few findings have been translated to the clinic. Bridging this gap requires understanding of both the underlying biology of these associations and their potential clinical implications, necessitating an interdisciplinary team approach. To address this need in metabolomic epidemiology, a workshop was held at Metabolomics 2023 in Niagara Falls, Ontario, Canada that highlighted the domain expertise needed to effectively conduct these studies -- biochemistry, clinical science, epidemiology, and assay development for biomarker validation -- and emphasized the role of interdisciplinary teams to move findings towards clinical translation.</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":"20 3","pages":"60"},"PeriodicalIF":3.5,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11108898/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141076240","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Background: Despite the clear clinical diagnostic criteria for necrozoospermia in andrology, the fundamental mechanisms underlying it remain elusive. This study aims to profile the lipid composition in seminal plasma systematically and to ascertain the potential of lipid biomarkers in the accurate diagnosis of necrozoospermia. It also evaluates the efficacy of a lipidomics-based random forest algorithm model in identifying necrozoospermia.
Methods: Seminal plasma samples were collected from patients diagnosed with necrozoospermia (n = 28) and normozoospermia (n = 28). Liquid chromatography-mass spectrometry (LC-MS) was used to perform lipidomic analysis and identify the underlying biomarkers. A lipid functional enrichment analysis was conducted using the LION lipid ontology database. The top 100 differentially significant lipids were subjected to lipid biomarker examination through random forest machine learning model.
Results: Lipidomic analysis identified 46 lipid classes comprising 1267 lipid metabolites in seminal plasma. The top five enriched lipid functions as follows: fatty acid (FA) with ≤ 18 carbons, FA with 16-18 carbons, monounsaturated FA, FA with 18 carbons, and FA with 16 carbons. The top 100 differentially significant lipids were subjected to machine learning analysis and identified 20 feature lipids. The random forest model identified lipids with an area under the curve > 0.8, including LPE(20:4) and TG(4:0_14:1_16:0).
Conclusions: LPE(20:4) and TG(4:0_14:1_16:0), were identified as differential lipids for necrozoospermia. Seminal plasma lipidomic analysis could provide valuable biochemical information for the diagnosis of necrozoospermia, and its combination with conventional sperm analysis may improve the accuracy and reliability of the diagnosis.
{"title":"Lipidomics random forest algorithm of seminal plasma is a promising method for enhancing the diagnosis of necrozoospermia.","authors":"Tianqin Deng, Wanxue Wang, Zhihong Fu, Yuli Xie, Yonghong Zhou, Jiangbo Pu, Kexin Chen, Bing Yao, Xuemei Li, Jilong Yao","doi":"10.1007/s11306-024-02118-x","DOIUrl":"10.1007/s11306-024-02118-x","url":null,"abstract":"<p><strong>Background: </strong>Despite the clear clinical diagnostic criteria for necrozoospermia in andrology, the fundamental mechanisms underlying it remain elusive. This study aims to profile the lipid composition in seminal plasma systematically and to ascertain the potential of lipid biomarkers in the accurate diagnosis of necrozoospermia. It also evaluates the efficacy of a lipidomics-based random forest algorithm model in identifying necrozoospermia.</p><p><strong>Methods: </strong>Seminal plasma samples were collected from patients diagnosed with necrozoospermia (n = 28) and normozoospermia (n = 28). Liquid chromatography-mass spectrometry (LC-MS) was used to perform lipidomic analysis and identify the underlying biomarkers. A lipid functional enrichment analysis was conducted using the LION lipid ontology database. The top 100 differentially significant lipids were subjected to lipid biomarker examination through random forest machine learning model.</p><p><strong>Results: </strong>Lipidomic analysis identified 46 lipid classes comprising 1267 lipid metabolites in seminal plasma. The top five enriched lipid functions as follows: fatty acid (FA) with ≤ 18 carbons, FA with 16-18 carbons, monounsaturated FA, FA with 18 carbons, and FA with 16 carbons. The top 100 differentially significant lipids were subjected to machine learning analysis and identified 20 feature lipids. The random forest model identified lipids with an area under the curve > 0.8, including LPE(20:4) and TG(4:0_14:1_16:0).</p><p><strong>Conclusions: </strong>LPE(20:4) and TG(4:0_14:1_16:0), were identified as differential lipids for necrozoospermia. Seminal plasma lipidomic analysis could provide valuable biochemical information for the diagnosis of necrozoospermia, and its combination with conventional sperm analysis may improve the accuracy and reliability of the diagnosis.</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":"20 3","pages":"57"},"PeriodicalIF":3.5,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11108888/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141076236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-21DOI: 10.1007/s11306-024-02131-0
Kamar Hamade, Ophelie Fliniaux, Jean-Xavier Fontaine, Roland Molinié, Laurent Petit, David Mathiron, Vivien Sarazin, Francois Mesnard
Introduction: Bio stimulants are substances and/or microorganisms that are used to improve plant growth and crop yields by modulating physiological processes and metabolism of plants. While research has primarily focused on the broad effects of bio stimulants in crops, understanding their cellular and molecular influences in plants, using metabolomic analysis, could elucidate their effectiveness and offer possibilities for fine-tuning their application. One such bio stimulant containing galacturonic acid as elicitor is used in agriculture to improve wheat vigor and strengthen resistance to lodging.
Objective: However, whether a metabolic response is evolved by plants treated with this bio stimulant and the manner in which the latter might regulate plant metabolism have not been studied.
Method: Therefore, the present study used 1H-NMR and LC-MS to assess changes in primary and secondary metabolites in the roots, stems, and leaves of wheat (Triticum aestivum) treated with the bio stimulant. Orthogonal partial least squares discriminant analysis effectively distinguished between treated and control samples, confirming a metabolic response to treatment in the roots, stems, and leaves of wheat.
Results: Fold-change analysis indicated that treatment with the bio stimulation solution appeared to increase the levels of hydroxycinnamic acid amides, lignin, and flavonoid metabolism in different plant parts, potentially promoting root growth, implantation, and developmental cell wall maturation and lignification.
Conclusion: These results demonstrate how non-targeted metabolomic approaches can be utilized to investigate and monitor the effects of new agroecological solutions based on systemic responses.
{"title":"NMR and LC-MS-based metabolomics to investigate the efficacy of a commercial bio stimulant for the treatment of wheat (Triticum aestivum).","authors":"Kamar Hamade, Ophelie Fliniaux, Jean-Xavier Fontaine, Roland Molinié, Laurent Petit, David Mathiron, Vivien Sarazin, Francois Mesnard","doi":"10.1007/s11306-024-02131-0","DOIUrl":"10.1007/s11306-024-02131-0","url":null,"abstract":"<p><strong>Introduction: </strong>Bio stimulants are substances and/or microorganisms that are used to improve plant growth and crop yields by modulating physiological processes and metabolism of plants. While research has primarily focused on the broad effects of bio stimulants in crops, understanding their cellular and molecular influences in plants, using metabolomic analysis, could elucidate their effectiveness and offer possibilities for fine-tuning their application. One such bio stimulant containing galacturonic acid as elicitor is used in agriculture to improve wheat vigor and strengthen resistance to lodging.</p><p><strong>Objective: </strong>However, whether a metabolic response is evolved by plants treated with this bio stimulant and the manner in which the latter might regulate plant metabolism have not been studied.</p><p><strong>Method: </strong>Therefore, the present study used <sup>1</sup>H-NMR and LC-MS to assess changes in primary and secondary metabolites in the roots, stems, and leaves of wheat (Triticum aestivum) treated with the bio stimulant. Orthogonal partial least squares discriminant analysis effectively distinguished between treated and control samples, confirming a metabolic response to treatment in the roots, stems, and leaves of wheat.</p><p><strong>Results: </strong>Fold-change analysis indicated that treatment with the bio stimulation solution appeared to increase the levels of hydroxycinnamic acid amides, lignin, and flavonoid metabolism in different plant parts, potentially promoting root growth, implantation, and developmental cell wall maturation and lignification.</p><p><strong>Conclusion: </strong>These results demonstrate how non-targeted metabolomic approaches can be utilized to investigate and monitor the effects of new agroecological solutions based on systemic responses.</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":"20 3","pages":"58"},"PeriodicalIF":3.5,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11108958/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141076238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Introduction: Thyroid cancer incidence rate has increased substantially worldwide in recent years. Fine needle aspiration biopsy (FNAB) is currently the golden standard of thyroid cancer diagnosis, which however, is invasive and costly. In contrast, breath analysis is a non-invasive, safe and simple sampling method combined with a promising metabolomics approach, which is suitable for early cancer diagnosis in high volume population.
Objectives: This study aims to achieve a more comprehensive and definitive exhaled breath metabolism profile in papillary thyroid cancer patients (PTCs).
Methods: We studied both end-tidal and mixed expiratory breath, solid-phase microextraction gas chromatography coupled with high resolution mass spectrometry (SPME-GC-HRMS) was used to analyze the breath samples. Multivariate combined univariate analysis was applied to identify potential breath biomarkers.
Results: The biomarkers identified in end-tidal and mixed expiratory breath mainly included alkanes, olefins, enols, enones, esters, aromatic compounds, and fluorine and chlorine containing organic compounds. The area under the curve (AUC) values of combined biomarkers were 0.974 (sensitivity: 96.1%, specificity: 90.2%) and 0.909 (sensitivity: 98.0%, specificity: 74.5%), respectively, for the end-tidal and mixed expiratory breath, indicating of reliability of the sampling and analysis method CONCLUSION: This work not only successfully established a standard metabolomic approach for early diagnosis of PTC, but also revealed the necessity of using both the two breath types for comprehensive analysis of the biomarkers.
{"title":"Identifying potential breath biomarkers for early diagnosis of papillary thyroid cancer based on solid-phase microextraction gas chromatography-high resolution mass spectrometry with metabolomics.","authors":"Lan Li, Xinxin Wen, Xian Li, Yaqi Yan, Jiayu Wang, Xuyang Zhao, Yonghui Tian, Rui Ling, Yixiang Duan","doi":"10.1007/s11306-024-02119-w","DOIUrl":"10.1007/s11306-024-02119-w","url":null,"abstract":"<p><strong>Introduction: </strong>Thyroid cancer incidence rate has increased substantially worldwide in recent years. Fine needle aspiration biopsy (FNAB) is currently the golden standard of thyroid cancer diagnosis, which however, is invasive and costly. In contrast, breath analysis is a non-invasive, safe and simple sampling method combined with a promising metabolomics approach, which is suitable for early cancer diagnosis in high volume population.</p><p><strong>Objectives: </strong>This study aims to achieve a more comprehensive and definitive exhaled breath metabolism profile in papillary thyroid cancer patients (PTCs).</p><p><strong>Methods: </strong>We studied both end-tidal and mixed expiratory breath, solid-phase microextraction gas chromatography coupled with high resolution mass spectrometry (SPME-GC-HRMS) was used to analyze the breath samples. Multivariate combined univariate analysis was applied to identify potential breath biomarkers.</p><p><strong>Results: </strong>The biomarkers identified in end-tidal and mixed expiratory breath mainly included alkanes, olefins, enols, enones, esters, aromatic compounds, and fluorine and chlorine containing organic compounds. The area under the curve (AUC) values of combined biomarkers were 0.974 (sensitivity: 96.1%, specificity: 90.2%) and 0.909 (sensitivity: 98.0%, specificity: 74.5%), respectively, for the end-tidal and mixed expiratory breath, indicating of reliability of the sampling and analysis method CONCLUSION: This work not only successfully established a standard metabolomic approach for early diagnosis of PTC, but also revealed the necessity of using both the two breath types for comprehensive analysis of the biomarkers.</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":"20 3","pages":"59"},"PeriodicalIF":3.5,"publicationDate":"2024-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141076233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Introduction: The world is experiencing exponential growth in communication, especially wireless communication. Wireless connectivity has recently become a part of everyone's daily life. Recent developments in low-cost, low-power, and miniature devices contribute to a significant rise in radiofrequency-electromagnetic field (RF-EM) radiation exposure in our environment, raising concern over its effect on biological systems. The inconsistent and conflicting research results make it difficult to draw definite conclusions about how RF-EM radiation affects living things.
Objectives: This study identified two micro-environments based on their level of exposure to cellular RF-EM radiation, one with significantly less exposure and another with very high exposure to RF-EM radiation. Emphasis is given to studying the metabolites in the urine samples of humans naturally exposed to these two different microenvironments to understand short-term metabolic dysregulations.
Methods: Untargeted 1H NMR spectroscopy was employed for metabolomics analyses to identify dysregulated metabolites. A total of 60 subjects were recruited with 5 ml urine samples each. These subjects were divided into two groups: one highly exposed to RF-EM (n = 30) and the other consisting of low-exposure populations (n = 30).
Results: The study found that the twenty-nine metabolites were dysregulated. Among them, 19 were downregulated, and 10 were upregulated. In particular, Glyoxylate and dicarboxylate and the TCA cycle metabolism pathway have been perturbed. The dysregulated metabolites were validated using the ROC curve analysis.
Conclusion: Untargeted urine metabolomics was conducted to identify dysregulated metabolites linked to RF-EM radiation exposure. Preliminary findings suggest a connection between oxidative stress and gut microbiota imbalance. However, further research is needed to validate these biomarkers and understand the effects of RF-EM radiation on human health. Further research is needed with a diverse population.
{"title":"Analysis of the metabolic profile of humans naturally exposed to RF-EM radiation.","authors":"Neel Mani Rangesh, Arun Kumar Malaisamy, Nitesh Kumar, Sanjay Kumar","doi":"10.1007/s11306-024-02121-2","DOIUrl":"10.1007/s11306-024-02121-2","url":null,"abstract":"<p><strong>Introduction: </strong>The world is experiencing exponential growth in communication, especially wireless communication. Wireless connectivity has recently become a part of everyone's daily life. Recent developments in low-cost, low-power, and miniature devices contribute to a significant rise in radiofrequency-electromagnetic field (RF-EM) radiation exposure in our environment, raising concern over its effect on biological systems. The inconsistent and conflicting research results make it difficult to draw definite conclusions about how RF-EM radiation affects living things.</p><p><strong>Objectives: </strong>This study identified two micro-environments based on their level of exposure to cellular RF-EM radiation, one with significantly less exposure and another with very high exposure to RF-EM radiation. Emphasis is given to studying the metabolites in the urine samples of humans naturally exposed to these two different microenvironments to understand short-term metabolic dysregulations.</p><p><strong>Methods: </strong>Untargeted <sup>1</sup>H NMR spectroscopy was employed for metabolomics analyses to identify dysregulated metabolites. A total of 60 subjects were recruited with 5 ml urine samples each. These subjects were divided into two groups: one highly exposed to RF-EM (n = 30) and the other consisting of low-exposure populations (n = 30).</p><p><strong>Results: </strong>The study found that the twenty-nine metabolites were dysregulated. Among them, 19 were downregulated, and 10 were upregulated. In particular, Glyoxylate and dicarboxylate and the TCA cycle metabolism pathway have been perturbed. The dysregulated metabolites were validated using the ROC curve analysis.</p><p><strong>Conclusion: </strong>Untargeted urine metabolomics was conducted to identify dysregulated metabolites linked to RF-EM radiation exposure. Preliminary findings suggest a connection between oxidative stress and gut microbiota imbalance. However, further research is needed to validate these biomarkers and understand the effects of RF-EM radiation on human health. Further research is needed with a diverse population.</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":"20 3","pages":"55"},"PeriodicalIF":3.5,"publicationDate":"2024-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140958001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-18DOI: 10.1007/s11306-024-02123-0
Jay Idler, Onur Turkoglu, Ali Yilmaz, Nadia Ashrafi, Marta Szymanska, Ilyas Ustun, Kara Patek, Amy Whitten, Stewart F Graham, Ray O Bahado-Singh
Introduction: Preeclampsia (PreE) remains a major source of maternal and newborn complications. Prenatal prediction of these complications could significantly improve pregnancy management.
Objectives: Using metabolomic analysis we investigated the prenatal prediction of maternal and newborn complications in early and late PreE and investigated the pathogenesis of such complications.
Methods: Serum samples from 76 cases of PreE (36 early-onset and 40 late-onset), and 40 unaffected controls were collected. Direct Injection Liquid Chromatography-Mass Spectrometry combined with Nuclear Magnetic Resonance (NMR) spectroscopy was performed. Logistic regression analysis was used to generate models for prediction of adverse maternal and neonatal outcomes in patients with PreE. Metabolite set enrichment analysis (MSEA) was used to identify the most dysregulated metabolites and pathways in PreE.
Results: Forty-three metabolites were significantly altered (p < 0.05) in PreE cases with maternal complications and 162 metabolites were altered in PreE cases with newborn adverse outcomes. The top metabolite prediction model achieved an area under the receiver operating characteristic curve (AUC) = 0.806 (0.660-0.952) for predicting adverse maternal outcomes in early-onset PreE, while the AUC for late-onset PreE was 0.843 (0.712-0.974). For the prediction of adverse newborn outcomes, regression models achieved an AUC = 0.828 (0.674-0.982) in early-onset PreE and 0.911 (0.828-0.994) in late-onset PreE. Profound alterations of lipid metabolism were associated with adverse outcomes.
Conclusion: Prenatal metabolomic markers achieved robust prediction, superior to conventional markers for the prediction of adverse maternal and newborn outcomes in patients with PreE. We report for the first-time the prediction and metabolomic basis of adverse maternal and newborn outcomes in patients with PreE.
{"title":"Metabolomic prediction of severe maternal and newborn complications in preeclampsia.","authors":"Jay Idler, Onur Turkoglu, Ali Yilmaz, Nadia Ashrafi, Marta Szymanska, Ilyas Ustun, Kara Patek, Amy Whitten, Stewart F Graham, Ray O Bahado-Singh","doi":"10.1007/s11306-024-02123-0","DOIUrl":"10.1007/s11306-024-02123-0","url":null,"abstract":"<p><strong>Introduction: </strong>Preeclampsia (PreE) remains a major source of maternal and newborn complications. Prenatal prediction of these complications could significantly improve pregnancy management.</p><p><strong>Objectives: </strong>Using metabolomic analysis we investigated the prenatal prediction of maternal and newborn complications in early and late PreE and investigated the pathogenesis of such complications.</p><p><strong>Methods: </strong>Serum samples from 76 cases of PreE (36 early-onset and 40 late-onset), and 40 unaffected controls were collected. Direct Injection Liquid Chromatography-Mass Spectrometry combined with Nuclear Magnetic Resonance (NMR) spectroscopy was performed. Logistic regression analysis was used to generate models for prediction of adverse maternal and neonatal outcomes in patients with PreE. Metabolite set enrichment analysis (MSEA) was used to identify the most dysregulated metabolites and pathways in PreE.</p><p><strong>Results: </strong>Forty-three metabolites were significantly altered (p < 0.05) in PreE cases with maternal complications and 162 metabolites were altered in PreE cases with newborn adverse outcomes. The top metabolite prediction model achieved an area under the receiver operating characteristic curve (AUC) = 0.806 (0.660-0.952) for predicting adverse maternal outcomes in early-onset PreE, while the AUC for late-onset PreE was 0.843 (0.712-0.974). For the prediction of adverse newborn outcomes, regression models achieved an AUC = 0.828 (0.674-0.982) in early-onset PreE and 0.911 (0.828-0.994) in late-onset PreE. Profound alterations of lipid metabolism were associated with adverse outcomes.</p><p><strong>Conclusion: </strong>Prenatal metabolomic markers achieved robust prediction, superior to conventional markers for the prediction of adverse maternal and newborn outcomes in patients with PreE. We report for the first-time the prediction and metabolomic basis of adverse maternal and newborn outcomes in patients with PreE.</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":"20 3","pages":"56"},"PeriodicalIF":3.5,"publicationDate":"2024-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11102370/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140958008","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-11DOI: 10.1007/s11306-024-02127-w
Isabella J Theron, Shayne Mason, Mari van Reenen, Zinandré Stander, Léanie Kleynhans, Katharina Ronacher, Du Toit Loots
Introduction: The prevalence of type 2 diabetes has surged to epidemic proportions and despite treatment administration/adherence, some individuals experience poorly controlled diabetes. While existing literature explores metabolic changes in type 2 diabetes, understanding metabolic derangement in poorly controlled cases remains limited.
Objective: This investigation aimed to characterize the urine metabolome of poorly controlled type 2 diabetes in a South African cohort.
Method: Using an untargeted proton nuclear magnetic resonance metabolomics approach, urine samples from 15 poorly controlled type 2 diabetes patients and 25 healthy controls were analyzed and statistically compared to identify differentiating metabolites.
Results: The poorly controlled type 2 diabetes patients were characterized by elevated concentrations of various metabolites associated with changes to the macro-fuel pathways (including carbohydrate metabolism, ketogenesis, proteolysis, and the tricarboxylic acid cycle), autophagy and/or apoptosis, an uncontrolled diet, and kidney and liver damage.
Conclusion: These results indicate that inhibited cellular glucose uptake in poorly controlled type 2 diabetes significantly affects energy-producing pathways, leading to apoptosis and/or autophagy, ultimately contributing to kidney and mild liver damage. The study also suggests poor dietary compliance as a cause of the patient's uncontrolled glycemic state. Collectively these findings offer a first-time comprehensive overview of urine metabolic changes in poorly controlled type 2 diabetes and its association with secondary diseases, offering potential insights for more targeted treatment strategies to prevent disease progression, treatment efficacy, and diet/treatment compliance.
{"title":"Characterizing poorly controlled type 2 diabetes using <sup>1</sup>H-NMR metabolomics.","authors":"Isabella J Theron, Shayne Mason, Mari van Reenen, Zinandré Stander, Léanie Kleynhans, Katharina Ronacher, Du Toit Loots","doi":"10.1007/s11306-024-02127-w","DOIUrl":"10.1007/s11306-024-02127-w","url":null,"abstract":"<p><strong>Introduction: </strong>The prevalence of type 2 diabetes has surged to epidemic proportions and despite treatment administration/adherence, some individuals experience poorly controlled diabetes. While existing literature explores metabolic changes in type 2 diabetes, understanding metabolic derangement in poorly controlled cases remains limited.</p><p><strong>Objective: </strong>This investigation aimed to characterize the urine metabolome of poorly controlled type 2 diabetes in a South African cohort.</p><p><strong>Method: </strong>Using an untargeted proton nuclear magnetic resonance metabolomics approach, urine samples from 15 poorly controlled type 2 diabetes patients and 25 healthy controls were analyzed and statistically compared to identify differentiating metabolites.</p><p><strong>Results: </strong>The poorly controlled type 2 diabetes patients were characterized by elevated concentrations of various metabolites associated with changes to the macro-fuel pathways (including carbohydrate metabolism, ketogenesis, proteolysis, and the tricarboxylic acid cycle), autophagy and/or apoptosis, an uncontrolled diet, and kidney and liver damage.</p><p><strong>Conclusion: </strong>These results indicate that inhibited cellular glucose uptake in poorly controlled type 2 diabetes significantly affects energy-producing pathways, leading to apoptosis and/or autophagy, ultimately contributing to kidney and mild liver damage. The study also suggests poor dietary compliance as a cause of the patient's uncontrolled glycemic state. Collectively these findings offer a first-time comprehensive overview of urine metabolic changes in poorly controlled type 2 diabetes and its association with secondary diseases, offering potential insights for more targeted treatment strategies to prevent disease progression, treatment efficacy, and diet/treatment compliance.</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":"20 3","pages":"54"},"PeriodicalIF":3.5,"publicationDate":"2024-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11088559/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140909493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-09DOI: 10.1007/s11306-024-02113-2
Grace Scheidemantle, Likun Duan, Mareca Lodge, Magdalina J Cummings, Dalton Hilovsky, Eva Pham, Xiaoqiu Wang, Arion Kennedy, Xiaojing Liu
<p><strong>Introduction: </strong>Despite the well-recognized health benefits, the mechanisms and site of action of metformin remains elusive. Metformin-induced global lipidomic changes in plasma of animal models and human subjects have been reported. However, there is a lack of systemic evaluation of metformin-induced lipidomic changes in different tissues. Metformin uptake requires active transporters such as organic cation transporters (OCTs), and hence, it is anticipated that metformin actions are tissue-dependent. In this study, we aim to characterize metformin effects in non-diabetic male mice with a special focus on lipidomics analysis. The findings from this study will help us to better understand the cell-autonomous (direct actions in target cells) or non-cell-autonomous (indirect actions in target cells) mechanisms of metformin and provide insights into the development of more potent yet safe drugs targeting a particular organ instead of systemic metabolism for metabolic regulations without major side effects.</p><p><strong>Objectives: </strong>To characterize metformin-induced lipidomic alterations in different tissues of non-diabetic male mice and further identify lipids affected by metformin through cell-autonomous or systemic mechanisms based on the correlation between lipid alterations in tissues and the corresponding in-tissue metformin concentrations.</p><p><strong>Methods: </strong>A dual extraction method involving 80% methanol followed by MTBE (methyl tert-butyl ether) extraction enables the analysis of free fatty acids, polar metabolites, and lipids. Extracts from tissues and plasma of male mice treated with or without metformin in drinking water for 12 days were analyzed using HILIC chromatography coupled to Q Exactive Plus mass spectrometer or reversed-phase liquid chromatography coupled to MS/MS scan workflow (hybrid mode) on LC-Orbitrap Exploris 480 mass spectrometer using biologically relevant lipids-containing inclusion list for data-independent acquisition (DIA), named as BRI-DIA workflow followed by data-dependent acquisition (DDA), to maximum the coverage of lipids and minimize the negative effect of stochasticity of precursor selection on experimental consistency and reproducibility.</p><p><strong>Results: </strong>Lipidomics analysis of 6 mouse tissues and plasma allowed a systemic evaluation of lipidomic changes induced by metformin in different tissues. We observed that (1) the degrees of lipidomic changes induced by metformin treatment overly correlated with tissue concentrations of metformin; (2) the impact on lysophosphatidylcholine (lysoPC) and cardiolipins was positively correlated with tissue concentrations of metformin, while neutral lipids such as triglycerides did not correlate with the corresponding tissue metformin concentrations; (3) increase of intestinal tricarboxylic acid (TCA) cycle intermediates after metformin treatment.</p><p><strong>Conclusion: </strong>The data collected in this study from no
{"title":"Data-dependent and -independent acquisition lipidomics analysis reveals the tissue-dependent effect of metformin on lipid metabolism.","authors":"Grace Scheidemantle, Likun Duan, Mareca Lodge, Magdalina J Cummings, Dalton Hilovsky, Eva Pham, Xiaoqiu Wang, Arion Kennedy, Xiaojing Liu","doi":"10.1007/s11306-024-02113-2","DOIUrl":"10.1007/s11306-024-02113-2","url":null,"abstract":"<p><strong>Introduction: </strong>Despite the well-recognized health benefits, the mechanisms and site of action of metformin remains elusive. Metformin-induced global lipidomic changes in plasma of animal models and human subjects have been reported. However, there is a lack of systemic evaluation of metformin-induced lipidomic changes in different tissues. Metformin uptake requires active transporters such as organic cation transporters (OCTs), and hence, it is anticipated that metformin actions are tissue-dependent. In this study, we aim to characterize metformin effects in non-diabetic male mice with a special focus on lipidomics analysis. The findings from this study will help us to better understand the cell-autonomous (direct actions in target cells) or non-cell-autonomous (indirect actions in target cells) mechanisms of metformin and provide insights into the development of more potent yet safe drugs targeting a particular organ instead of systemic metabolism for metabolic regulations without major side effects.</p><p><strong>Objectives: </strong>To characterize metformin-induced lipidomic alterations in different tissues of non-diabetic male mice and further identify lipids affected by metformin through cell-autonomous or systemic mechanisms based on the correlation between lipid alterations in tissues and the corresponding in-tissue metformin concentrations.</p><p><strong>Methods: </strong>A dual extraction method involving 80% methanol followed by MTBE (methyl tert-butyl ether) extraction enables the analysis of free fatty acids, polar metabolites, and lipids. Extracts from tissues and plasma of male mice treated with or without metformin in drinking water for 12 days were analyzed using HILIC chromatography coupled to Q Exactive Plus mass spectrometer or reversed-phase liquid chromatography coupled to MS/MS scan workflow (hybrid mode) on LC-Orbitrap Exploris 480 mass spectrometer using biologically relevant lipids-containing inclusion list for data-independent acquisition (DIA), named as BRI-DIA workflow followed by data-dependent acquisition (DDA), to maximum the coverage of lipids and minimize the negative effect of stochasticity of precursor selection on experimental consistency and reproducibility.</p><p><strong>Results: </strong>Lipidomics analysis of 6 mouse tissues and plasma allowed a systemic evaluation of lipidomic changes induced by metformin in different tissues. We observed that (1) the degrees of lipidomic changes induced by metformin treatment overly correlated with tissue concentrations of metformin; (2) the impact on lysophosphatidylcholine (lysoPC) and cardiolipins was positively correlated with tissue concentrations of metformin, while neutral lipids such as triglycerides did not correlate with the corresponding tissue metformin concentrations; (3) increase of intestinal tricarboxylic acid (TCA) cycle intermediates after metformin treatment.</p><p><strong>Conclusion: </strong>The data collected in this study from no","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":"20 3","pages":"53"},"PeriodicalIF":3.5,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11145978/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140898923","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-05-09DOI: 10.1007/s11306-024-02110-5
Mingyu Zhang, Wei Perng, Sheryl L Rifas-Shiman, Izzuddin M Aris, Emily Oken, Marie-France Hivert
Introduction: Metabolite signatures for blood pressure (BP) may reveal biomarkers, elucidate pathogenesis, and provide prevention targets for high BP. Knowledge regarding metabolites associated with BP in adolescence remains limited.
Objectives: Investigate the associations between metabolites and adolescent BP, both cross-sectionally (in early and late adolescence) and prospectively (from early to late adolescence).
Methods: Participants are from the Project Viva prospective cohort. During the early (median: 12.8 years; N = 556) and late (median: 17.4 years; N = 501) adolescence visits, we conducted untargeted plasma metabolomic profiling and measured systolic (SBP) and diastolic BP (DBP). We used linear regression to identify metabolites cross-sectionally associated with BP at each time point, and to assess prospective associations of changes in metabolite levels from early to late adolescence with late adolescence BP. We used Weighted Gene Correlation Network Analysis and Spearman's partial correlation to identify metabolite clusters associated with BP at each time point.
Results: In the linear models, higher androgenic steroid levels were consistently associated with higher SBP and DBP in early and late adolescence. A cluster of 59 metabolites, mainly composed of androgenic steroids, correlated with higher SBP and DBP in early adolescence. A cluster primarily composed of fatty acid lipids was marginally associated with higher SBP in females in late adolescence. Multiple metabolites, including those in the creatine and purine metabolism sub-pathways, were associated with higher SBP and DBP both cross-sectionally and prospectively.
Conclusion: Our results shed light on the potential metabolic processes and pathophysiology underlying high BP in adolescents.
{"title":"Metabolomic signatures for blood pressure from early to late adolescence: findings from a U.S. cohort.","authors":"Mingyu Zhang, Wei Perng, Sheryl L Rifas-Shiman, Izzuddin M Aris, Emily Oken, Marie-France Hivert","doi":"10.1007/s11306-024-02110-5","DOIUrl":"10.1007/s11306-024-02110-5","url":null,"abstract":"<p><strong>Introduction: </strong>Metabolite signatures for blood pressure (BP) may reveal biomarkers, elucidate pathogenesis, and provide prevention targets for high BP. Knowledge regarding metabolites associated with BP in adolescence remains limited.</p><p><strong>Objectives: </strong>Investigate the associations between metabolites and adolescent BP, both cross-sectionally (in early and late adolescence) and prospectively (from early to late adolescence).</p><p><strong>Methods: </strong>Participants are from the Project Viva prospective cohort. During the early (median: 12.8 years; N = 556) and late (median: 17.4 years; N = 501) adolescence visits, we conducted untargeted plasma metabolomic profiling and measured systolic (SBP) and diastolic BP (DBP). We used linear regression to identify metabolites cross-sectionally associated with BP at each time point, and to assess prospective associations of changes in metabolite levels from early to late adolescence with late adolescence BP. We used Weighted Gene Correlation Network Analysis and Spearman's partial correlation to identify metabolite clusters associated with BP at each time point.</p><p><strong>Results: </strong>In the linear models, higher androgenic steroid levels were consistently associated with higher SBP and DBP in early and late adolescence. A cluster of 59 metabolites, mainly composed of androgenic steroids, correlated with higher SBP and DBP in early adolescence. A cluster primarily composed of fatty acid lipids was marginally associated with higher SBP in females in late adolescence. Multiple metabolites, including those in the creatine and purine metabolism sub-pathways, were associated with higher SBP and DBP both cross-sectionally and prospectively.</p><p><strong>Conclusion: </strong>Our results shed light on the potential metabolic processes and pathophysiology underlying high BP in adolescents.</p>","PeriodicalId":18506,"journal":{"name":"Metabolomics","volume":"20 3","pages":"52"},"PeriodicalIF":3.5,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11195684/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140898925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}