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1.
John M. Wentworth Naiara G. Bediaga Megan A. S. Penno Esther Bandala-Sanchez Komal N. Kanojia Konstantinos A. Kouremenos Jennifer J. Couper Leonard C. Harrison ENDIA Study Group 《Metabolomics : Official journal of the Metabolomic Society》2018,14(10):130
Background
Cord blood lipids are potential disease biomarkers. We aimed to determine if their concentrations were affected by delayed blood processing.Method
Refrigerated cord blood from six healthy newborns was centrifuged every 12 h for 4 days. Plasma lipids were analysed by liquid chromatography/mass spectroscopy.Results
Of 262 lipids identified, only eight varied significantly over time. These comprised three dihexosylceramides, two phosphatidylserines and two phosphatidylethanolamines whose relative concentrations increased and one sphingomyelin that decreased.Conclusion
Delay in separation of plasma from refrigerated cord blood has minimal effect overall on the plasma lipidome.2.
Alexandre Seyer Samia Boudah Simon Broudin Christophe Junot Benoit Colsch 《Metabolomics : Official journal of the Metabolomic Society》2016,12(5):91
Introduction
Due to its proximity with the brain, cerebrospinal fluid (CSF) could be a medium of choice for the discovery of biomarkers of neurological and psychiatric diseases using untargeted analytical approaches.Objectives
This study explored the CSF lipidome in order to generate a robust mass spectral database using an untargeted lipidomic approach.Methods
Cerebrospinal fluid samples from 45 individuals were analyzed by liquid chromatography coupled to high-resolution mass spectrometry method (LC-HRMS). A dedicated data processing workflow was implemented using XCMS software and adapted filters to select reliable features. In addition, an automatic annotation using an in silico lipid database and several MS/MS experiments were performed to identify CSF lipid species.Results
Using this complete workflow, 771 analytically relevant monoisotopic lipid species corresponding to 550 unique lipids which represent five major lipid families (i.e., free fatty acids, sphingolipids, glycerophospholipids, glycerolipids, and sterol lipids) were detected and annotated. In addition, MS/MS experiments enabled to improve the annotation of 304 lipid species. Thanks to LC-HRMS, it was possible to discriminate between isobaric and also isomeric lipid species; and interestingly, our study showed that isobaric ions represent about 50 % of the total annotated lipid species in the human CSF.Conclusion
This work provides an extensive LC/HRMS database of the human CSF lipidome which constitutes a relevant foundation for future studies aimed at finding biomarkers of neurological disorders.3.
Jamie V. de Seymour Stephanie Tu Xiaoling He Hua Zhang Ting-Li Han Philip N. Baker Karolina Sulek 《Metabolomics : Official journal of the Metabolomic Society》2018,14(6):79
Introduction
Intrahepatic cholestasis of pregnancy (ICP) is a common maternal liver disease; development can result in devastating consequences, including sudden fetal death and stillbirth. Currently, recognition of ICP only occurs following onset of clinical symptoms.Objective
Investigate the maternal hair metabolome for predictive biomarkers of ICP.Methods
The maternal hair metabolome (gestational age of sampling between 17 and 41 weeks) of 38 Chinese women with ICP and 46 pregnant controls was analysed using gas chromatography–mass spectrometry.Results
Of 105 metabolites detected in hair, none were significantly associated with ICP.Conclusion
Hair samples represent accumulative environmental exposure over time. Samples collected at the onset of ICP did not reveal any metabolic shifts, suggesting rapid development of the disease.4.
Lailai Yan Juntuo Zhou Dongfang Wang Dandan Si Yaqiong Liu Lijun Zhong Yuxin Yin 《Metabolomics : Official journal of the Metabolomic Society》2018,14(6):80
Introduction
Schizophrenia (SCH) is one of the most common psychiatric disorders, which involves impairments in motivation and cognition. The pathological mechanisms underlying SCH are still unknown, and no effective therapies can prevent or treat perfectly the cognitive impairments and deficit symptoms caused by SCH.Objectives
We aimed to find the lipid expression change in plasma that underlie SCH onset and antipsychotics treatment.Methods
We performed a data independent acquisition-based untargeted lipidomic approach on a quadrupole-time of flight liquid chromatography coupled to mass spectrometry platform. The plasma lipidomic profiles of SCH patients (n?=?20) pre- and post-antipsychotics treatment were acquired as well as healthy controls (n?=?29). Grouped or paired t-test were used to analyze the data.Results
Over 1000 features were detected by our lipidomic analysis, of which 445 lipids belonging to 17 lipid species were reliably identified by tandem mass spectrometry. After statistical analysis, 47 lipids belonging to 9 lipid species were found to be dysregulated between naive SCH patients and healthy controls, and 50 lipids belonging to 9 lipid species were found to be dysregulated after antipsychotics treatment. These findings include several new SCH-relevant lipid species such as sphingomyelin, acylcarnitine and ceramide. Four types of lipid expression regulative patterns can be concluded from the above mentioned findings, revealing information about mechanism, side-effect and potential target of antipsychotics.Conclusions
The work presented here have revealed several new lipid species which are significantly dysregulated in SCH disease development or antipsychotics treatment. These lipids provide new evidence for the pathological studies of SCH and new antipsychotics development, or can be considered as potentially candidate biomarkers for further validation.5.
Lai-Fang Zhou Bo-Wen Zhao Ning-Nan Guan Wei-Min Wang Ze-Xia Gao 《Metabolomics : Official journal of the Metabolomic Society》2017,13(4):40
Introduction
In some fish species, it is difficult to distinguish mature females from immature females or females that have already spawned via appearance or other convenient methods. Few studies have investigated plasma metabolite profiling for the prediction of fish maturation.Objectives
We investigated the comprehensive metabolic profiles of plasma among immature females and mature females ready to spawn, as well as already spawned breeders of blunt snout bream (Megalobrama amblycephala). The purpose of this study was to screen out potential biomarkers for sexually mature female M. amblycephala compared to immature female individuals and already spawned breeders.Methods
Three groups were set up in this study, which included 1-year-old immature females, 2-year-old sexually mature females ready to spawn and successfully spawned females of M. amblycephala. Plasma samples were collected to investigate comprehensive metabolic profiles through UPLC-MS/MS based on a metabolomics analysis method.Results
According to multivariate and univariate statistical analysis, plasma metabolite profiles of the three groups were clearly separated. The differential plasma metabolites from three hormone related pathways including the GnRH signaling pathway, steroid hormone biosynthesis and steroid biosynthesis, were analyzed. A total of 29 metabolites were identified as differential biomarkers associated with the female maturation status.Conclusion
The identified potential biomarkers could be useful in separating mature M. amblycephala from immature individuals or ovulation-induced female individuals, which would allow for more effective artificial breeding. The results may contribute to a better understanding of the maturation mechanisms of fish in the aspect of metabolomics.6.
Hong Zheng Minjiang Chen Siming Lu Liangcai Zhao Jiansong Ji Hongchang Gao 《Metabolomics : Official journal of the Metabolomic Society》2017,13(10):121
Introduction
Liver cirrhosis (LC) is an advanced liver disease that can develop into hepatocellular carcinoma. Hepatitis B virus (HBV) infection is one of the main causes of LC. Therefore, there is an urgent need for developing a new method to monitor the progression of HBV-related LC (HBV-LC).Objectives
In this study, we attempted to examine serum metabolic changes in healthy individuals as well as patients with HBV and HBV-LC. Furthermore, potential metabolite biomarkers were identified to evaluate patients progressed from health to HBV-LC.Methods
Metabolic profiles in the serum of healthy individuals as well as patients with HBV and HBV-LC were detected using an NMR-based metabolomic approach. Univariate and multivariate analyses were conducted to analyze serum metabolic changes during HBV-LC progression. Moreover, potential metabolite biomarkers were explored by receiver operating characteristic curve analysis.Results
Serum metabolic changes were closely associated with the progression of HBV-LC, mainly involving energy metabolism, protein metabolism, lipid metabolism and microbial metabolism. Serum histidine was identified as a potential biomarker for HBV patients. Acetate, formate, pyruvate and glutamine in the serum were identified as a potential biomarker panel for patients progressed from HBV to HBV-LC. In addition, phenylalanine, unsaturated lipid, n-acetylglycoprotein and acetone in the serum could be considered as a potential common biomarkers panel for these patients.Conclusion
NMR-based serum metabolomic approach could be a promising tool to monitor the progression of liver disease. Different metabolites may reflect different stages of liver disease.7.
Kai Yang Fan Zhang Peng Han Zhuo-zhong Wang Kui Deng Yuan-yuan Zhang Wei-wei Zhao Wei Song Yu-qing Cai Kang Li Bin-bin Cui Zheng-Jiang Zhu 《Metabolomics : Official journal of the Metabolomic Society》2018,14(9):110
Introduction
Colorectal cancer (CRC) is a clinically heterogeneous disease, which necessitates a variety of treatments and leads to different outcomes. Only some CRC patients will benefit from neoadjuvant chemotherapy (NACT).Objectives
An accurate prediction of response to NACT in CRC patients would greatly facilitate optimal personalized management, which could improve their long-term survival and clinical outcomes.Methods
In this study, plasma metabolite profiling was performed to identify potential biomarker candidates that can predict response to NACT for CRC. Metabolic profiles of plasma from non-response (n?=?30) and response (n?=?27) patients to NACT were studied using UHPLC–quadruple time-of-flight)/mass spectrometry analyses and statistical analysis methods.Results
The concentrations of nine metabolites were significantly different when comparing response to NACT. The area under the receiver operating characteristic curve value of the potential biomarkers was up to 0.83 discriminating the non-response and response group to NACT, superior to the clinical parameters (carcinoembryonic antigen and carbohydrate antigen 199).Conclusion
These results show promise for larger studies that could result in more personalized treatment protocols for CRC patients.8.
Ferran Casbas Pinto Srinivarao Ravipati David A. Barrett T. Charles Hodgman 《Metabolomics : Official journal of the Metabolomic Society》2017,13(7):81
Introduction
It is difficult to elucidate the metabolic and regulatory factors causing lipidome perturbations.Objectives
This work simplifies this process.Methods
A method has been developed to query an online holistic lipid metabolic network (of 7923 metabolites) to extract the pathways that connect the input list of lipids.Results
The output enables pathway visualisation and the querying of other databases to identify potential regulators. When used to a study a plasma lipidome dataset of polycystic ovary syndrome, 14 enzymes were identified, of which 3 are linked to ELAVL1—an mRNA stabiliser.Conclusion
This method provides a simplified approach to identifying potential regulators causing lipid-profile perturbations.9.
Applications of metabolomics in the study and management of preeclampsia: a review of the literature
Rachel S. Kelly Rachel T. Giorgio Bo L. Chawes Natalia I. Palacios Kathryn J. Gray Hooman Mirzakhani Ann Wu Kevin Blighe Scott T. Weiss Jessica Lasky-Su 《Metabolomics : Official journal of the Metabolomic Society》2017,13(7):86
Introduction
Preeclampsia represents a major public health burden worldwide, but predictive and diagnostic biomarkers are lacking. Metabolomics is emerging as a valuable approach to generating novel biomarkers whilst increasing the mechanistic understanding of this complex condition.Objectives
To summarize the published literature on the use of metabolomics as a tool to study preeclampsia.Methods
PubMed and Web of Science were searched for articles that performed metabolomic profiling of human biosamples using either Mass-spectrometry or Nuclear Magnetic Resonance based approaches and which included preeclampsia as a primary endpoint.Results
Twenty-eight studies investigating the metabolome of preeclampsia in a variety of biospecimens were identified. Individual metabolite and metabolite profiles were reported to have discriminatory ability to distinguish preeclamptic from normal pregnancies, both prior to and post diagnosis. Lipids and carnitines were among the most commonly reported metabolites. Further work and validation studies are required to demonstrate the utility of such metabolites as preeclampsia biomarkers.Conclusion
Metabolomic-based biomarkers of preeclampsia have yet to be integrated into routine clinical practice. However, metabolomic profiling is becoming increasingly popular in the study of preeclampsia and is likely to be a valuable tool to better understand the pathophysiology of this disorder and to better classify its subtypes, particularly when integrated with other omic data.10.
Fan Zhang Yuanyuan Zhang Chaofu Ke Ang Li Wenjie Wang Kai Yang Huijuan Liu Hongyu Xie Kui Deng Weiwei Zhao Chunyan Yang Ge Lou Yan Hou Kang Li 《Metabolomics : Official journal of the Metabolomic Society》2018,14(5):65
Background
Previous metabolomic studies have revealed that plasma metabolic signatures may predict epithelial ovarian cancer (EOC) recurrence. However, few studies have performed metabolic profiling of pre- and post-operative specimens to investigate EOC prognostic biomarkers.Objective
The aims of our study were to compare the predictive performance of pre- and post-operative specimens and to create a better model for recurrence by combining biomarkers from both metabolic signatures.Methods
Thirty-five paired plasma samples were collected from 35 EOC patients before and after surgery. The patients were followed-up until December, 2016 to obtain recurrence information. Metabolomics using rapid resolution liquid chromatography–mass spectrometry was performed to identify metabolic signatures related to EOC recurrence. The support vector machine model was employed to predict EOC recurrence using identified biomarkers.Results
Global metabolomic profiles distinguished recurrent from non-recurrent EOC using both pre- and post-operative plasma. Ten common significant biomarkers, hydroxyphenyllactic acid, uric acid, creatinine, lysine, 3-(3,5-diiodo-4-hydroxyphenyl) lactate, phosphohydroxypyruvic acid, carnitine, coproporphyrinogen, l-beta-aspartyl-l-glutamic acid and 24,25-hydroxyvitamin D3, were identified as predictive biomarkers for EOC recurrence. The area under the receiver operating characteristic (AUC) values in pre- and post-operative plasma were 0.815 and 0.909, respectively; the AUC value after combining the two sets reached 0.964.Conclusion
Plasma metabolomic analysis could be used to predict EOC recurrence. While post-operative biomarkers have a predictive advantage over pre-operative biomarkers, combining pre- and post-operative biomarkers showed the best predictive performance and has great potential for predicting recurrent EOC.11.
Objectives
To characterize biomarkers that underlie osteosarcoma (OS) metastasis based on an ego-network.Results
From the microarray data, we obtained 13,326 genes. By combining PPI data and microarray data, 10,520 shared genes were found and constructed into ego-networks. 17 significant ego-networks were identified with p < 0.05. In the pathway enrichment analysis, seven ego-networks were identified with the most significant pathway.Conclusions
These significant ego-modules were potential biomarkers that reveal the potential mechanisms in OS metastasis, which may contribute to understanding cancer prognoses and providing new perspectives in the treatment of cancer.12.
Nicholas J. Bond Albert Koulman Julian L. Griffin Zoe Hall 《Metabolomics : Official journal of the Metabolomic Society》2017,13(11):128
Introduction
Mass spectrometry imaging (MSI) experiments result in complex multi-dimensional datasets, which require specialist data analysis tools.Objectives
We have developed massPix—an R package for analysing and interpreting data from MSI of lipids in tissue.Methods
massPix produces single ion images, performs multivariate statistics and provides putative lipid annotations based on accurate mass matching against generated lipid libraries.Results
Classification of tissue regions with high spectral similarly can be carried out by principal components analysis (PCA) or k-means clustering.Conclusion
massPix is an open-source tool for the analysis and statistical interpretation of MSI data, and is particularly useful for lipidomics applications.13.
Miriam Banas Sindy Neumann Johannes Eiglsperger Eric Schiffer Franz Josef Putz Simone Reichelt-Wurm Bernhard Karl Krämer Philipp Pagel Bernhard Banas 《Metabolomics : Official journal of the Metabolomic Society》2018,14(9):116
Introduction
Allograft rejection is still an important complication after kidney transplantation. Currently, monitoring of these patients mostly relies on the measurement of serum creatinine and clinical evaluation. The gold standard for diagnosing allograft rejection, i.e. performing a renal biopsy is invasive and expensive. So far no adequate biomarkers are available for routine use.Objectives
We aimed to develop a urine metabolite constellation that is characteristic for acute renal allograft rejection.Methods
NMR-Spectroscopy was applied to a training cohort of transplant recipients with and without acute rejection.Results
We obtained a metabolite constellation of four metabolites that shows promising performance to detect renal allograft rejection in the cohorts used (AUC of 0.72 and 0.74, respectively).Conclusion
A metabolite constellation was defined with the potential for further development of an in-vitro diagnostic test that can support physicians in their clinical assessment of a kidney transplant patient.14.
Nan Jiang Guofen Zhang Lijie Pan Chengping Yan Liwei Zhang Yan Weng Wenjun Wang Xianyang Chen Guoshan Yang 《Biotechnology letters》2017,39(11):1657-1666
Objective
To find new biomarkers for early diagnosis of breast cancer.Results
847 lipid species were identified from 78 plasma samples (37 breast cancer samples and 41 healthy controls) by ultra HPLC coupled with quadrupole time-of-flight tandem mass spectrometry. These include 321 glycerophospholipids (GPs), 265 glycerolipids (GLs), 91 sphingolipids (SPs), 77 fatty acyls (FAs), 68 sterol lipids (STs), 18 prenol lipids (PRs), 6 polyketides (PKs), and 1 saccharolipid (SL). Separation was observed from an orthogonal signal correction Partial Least Square Discrimination Analysis model. Based on this analysis, six differentiating lipids were identified: PC (20:2/20:5), PC (22:0/24:1), TG (12:0/14:1), and DG (18:1/18:2) had high levels, whereas PE (15:0/19:1) and N-palmitoyl proline had low levels in the breast cancer samples compared with the healthy controls. Furthermore, significant differences in metabolites were found among some clinical characteristics.Conclusions
Our results reveal that six specific lipids could serve as potential biomarkers for early diagnosis of breast cancer.15.
Takeo Moriya Yoshinori Satomi Hiroyuki Kobayashi 《Metabolomics : Official journal of the Metabolomic Society》2016,12(12):179
Introduction
Human plasma metabolomics offer powerful tools for understanding disease mechanisms and identifying clinical biomarkers for diagnosis, efficacy prediction and patient stratification. Although storage conditions can affect the reliability of data from metabolites, strict control of these conditions remains challenging, particularly when clinical samples are included from multiple centers. Therefore, it is necessary to consider stability profiles of each analyte.Objectives
The purpose of this study was to extract unstable metabolites from vast metabolome data and identify factors that cause instability.Method
Plasma samples were obtained from five healthy volunteers, were stored under ten different conditions of time and temperature and were quantified using leading-edge metabolomics. Instability was evaluated by comparing quantitation values under each storage condition with those obtained after ?80 °C storage.Result
Stability profiling of the 992 metabolites showed time- and temperature-dependent increases in numbers of significantly changed metabolites. This large volume of data enabled comparisons of unstable metabolites with their related molecules and allowed identification of causative factors, including compound-specific enzymatic activity in plasma and chemical reactivity. Furthermore, these analyses indicated extreme instability of 1-docosahexaenoylglycerol, 1-arachidonoylglycerophosphate, cystine, cysteine and N6-methyladenosine.Conclusion
A large volume of data regarding storage stability was obtained. These data are a contribution to the discovery of biomarker candidates without misselection based on unreliable values and to the establishment of suitable handling procedures for targeted biomarker quantification.16.
Emily G. Armitage Andrew D. Southam 《Metabolomics : Official journal of the Metabolomic Society》2016,12(9):146
Introduction
Cellular metabolism is altered during cancer initiation and progression, which allows cancer cells to increase anabolic synthesis, avoid apoptosis and adapt to low nutrient and oxygen availability. The metabolic nature of cancer enables patient cancer status to be monitored by metabolomics and lipidomics. Additionally, monitoring metabolic status of patients or biological models can be used to greater understand the action of anticancer therapeutics.Objectives
Discuss how metabolomics and lipidomics can be used to (i) identify metabolic biomarkers of cancer and (ii) understand the mechanism-of-action of anticancer therapies. Discuss considerations that can maximize the clinical value of metabolic cancer biomarkers including case–control, prognostic and longitudinal study designs.Methods
A literature search of the current relevant primary research was performed.Results
Metabolomics and lipidomics can identify metabolic signatures that associate with cancer diagnosis, prognosis and disease progression. Discriminatory metabolites were most commonly linked to lipid or energy metabolism. Case–control studies outnumbered prognostic and longitudinal approaches. Prognostic studies were able to correlate metabolic features with future cancer risk, whereas longitudinal studies were most effective for studying cancer progression. Metabolomics and lipidomics can help to understand the mechanism-of-action of anticancer therapeutics and mechanisms of drug resistance.Conclusion
Metabolomics and lipidomics can be used to identify biomarkers associated with cancer and to better understand anticancer therapies.17.
Stojan Maleschlijski Adam Autry Llewellyn Jalbert Marram P. Olson Tracy McKnight Tracy Luks Sarah Nelson 《Metabolomics : Official journal of the Metabolomic Society》2017,13(12):149
Introduction
Infiltrating gliomas are primary brain tumors that express significant biological and clinical heterogeneity in adults, which complicates their treatment and prognosis. Characterization of tumor subtypes using spectroscopic analysis may assist in predicting malignant transformation and quantification of response to therapy.Study objective
To implement an automated algorithm for classification of metabolomic profiles for the classification of glioma pathological grades and the prediction of malignant progression using spectra obtained by high-resolution magic angle spinning (HR-MAS) spectroscopy of patient-derived tissue samples.Methods
237 image-guided tissue samples were obtained from 152 patients who underwent surgery for newly diagnosed or recurrent glioma and analyzed via HR-MAS spectroscopy. Orthogonal projection to latent structures discriminant analysis was used as a classifier and the variable-influence-on-projection values were evaluated to identify signature spectral regions.Results
The accuracy of classifiers developed for discriminating glioma subtypes was 68% for newly diagnosed grade II versus III samples; 86 and 92% for new and recurrent grade III versus IV, respectively; 95% for newly diagnosed grade II versus IV; and 88% for recurrent grade II versus IV lesions. Classifiers distinguished between samples from newly diagnosed vs. recurrent lesions with an accuracy of 78% for grade III and 99% for grade IV glioma.Conclusion
Classifying metabolomic profiles for new and recurrent glioma without prior assumptions regarding spectral components identified candidate in vivo biomarkers for use in assessing changes that are likely to impact treatment decisions.18.
Zhongwei Xu Kaimin Xu Shijia Ding Jiao Luo Tingmei Chen Aiguo Zhou Zhenxing Wen Jian Zhang 《Metabolomics : Official journal of the Metabolomic Society》2017,13(6):73
Introduction
Non-traumatic osteonecrosis of the femoral head (NTONFH) is a progressive disease, always leading to hip dysfunction if no early intervention was applied. The difficulty for early diagnosis of NTONFH is due to the slight symptoms at early stages as well as the high cost for screening patients by using magnetic resonance imaging.Objective
The aim was to detect biomarkers of early-stage NTONFH, which was beneficial to the exploration of a cost-effective approach for the early diagnose of the disease.Methods
Metabolomic approaches were employed in this study to detect biomarkers of early-stage NTONFH (22 patients, 23 controls), based on the platform of ultra-performance liquid chromatography tandem quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS) and the uses of multivariate statistic analysis, putative metabolite identification, metabolic pathway analysis and biomarker analysis.Results
In total, 33 serum metabolites were found altered between NTONFH group and control group. In addition, glycerophospholipid metabolism and pyruvate metabolism were highly associated with the disease.Conclusion
The combination of LysoPC (18:3), l-tyrosine and l-leucine proved to have a high diagnostic value for early-stage NTONFH. Our findings may contribute to the protocol for early diagnosis of NTONFH and further elucidate the underlying mechanisms of the disease.19.
Jia Tu Yandong Yin Meimei Xu Ruohong Wang Zheng-Jiang Zhu 《Metabolomics : Official journal of the Metabolomic Society》2018,14(1):5
Introduction
The absolute quantitation of lipids at the lipidome-wide scale is a challenge but plays an important role in the comprehensive study of lipid metabolism.Objectives
We aim to develop a high-throughput quantitative lipidomics approach to enable the simultaneous identification and absolute quantification of hundreds of lipids in a single experiment. Then, we will systematically characterize lipidome-wide changes in the aging mouse brain and provide a link between aging and disordered lipid homeostasis.Methods
We created an in-house lipid spectral library, containing 76,361 lipids and 181,300 MS/MS spectra in total, to support accurate lipid identification. Then, we developed a response factor-based approach for the large-scale absolute quantifications of lipids.Results
Using the lipidomics approach, we absolutely quantified 1212 and 864 lipids in human cells and mouse brains, respectively. The quantification accuracy was validated using the traditional approach with a median relative error of 12.6%. We further characterized the lipidome-wide changes in aging mouse brains, and dramatic changes were observed in both glycerophospholipids and sphingolipids. Sphingolipids with longer acyl chains tend to accumulate in aging brains. Membrane-esterified fatty acids demonstrated diverse changes with aging, while most polyunsaturated fatty acids consistently decreased.Conclusion
We developed a high-throughput quantitative lipidomics approach and systematically characterized the lipidome-wide changes in aging mouse brains. The results proved a link between aging and disordered lipid homeostasis.20.
Fernanda Bertuccez Cordeiro Thais Regiani Cataldi Lívia do Vale Teixeira da Costa Beatriz Zappellini de Souza Daniela Antunes Montani Renato Fraietta Carlos Alberto Labate Agnaldo Pereira Cedenho Edson Guimarães Lo Turco 《Metabolomics : Official journal of the Metabolomic Society》2017,13(10):120