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Wei, Justin, Li Chin Wong, and Sebastian Boland. "Lipids as Emerging Biomarkers in Neurodegenerative Diseases." International Journal of Molecular Sciences 25, no. 1 (December 21, 2023): 131. http://dx.doi.org/10.3390/ijms25010131.
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Biomarkers are molecules that can be used to observe changes in an individual’s biochemical or medical status and provide information to aid diagnosis or treatment decisions. Dysregulation in lipid metabolism in the brain is a major risk factor for many neurodegenerative disorders, including frontotemporal dementia, Alzheimer’s disease, Parkinson’s disease, and amyotrophic lateral sclerosis. Thus, there is a growing interest in using lipids as biomarkers in neurodegenerative diseases, with the anionic phospholipid bis(monoacylglycerol)phosphate and (glyco-)sphingolipids being the most promising lipid classes thus far. In this review, we provide a general overview of lipid biology, provide examples of abnormal lysosomal lipid metabolism in neurodegenerative diseases, and discuss how these insights might offer novel and promising opportunities in biomarker development and therapeutic discovery. Finally, we discuss the challenges and opportunities of lipid biomarkers and biomarker panels in diagnosis, prognosis, and/or treatment response in the clinic.
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Zarrouk, Amira, Meryam Debbabi, Maryem Bezine, El Mostafa Karym, Asmaa Badreddine, Olivier Rouaud, Thibault Moreau, et al. "Lipid Biomarkers in Alzheimer's Disease." Current Alzheimer Research 15, no. 4 (February 22, 2018): 303–12. http://dx.doi.org/10.2174/1567205014666170505101426.
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Background: There are now significant evidences that lipid metabolism is affected in numerous neurodegenerative diseases including Alzheimer’s disease. These dysfunctions lead to abnormal levels of certain lipids in the brain, cerebrospinal fluid and plasma. It is consequently of interest to establish lipid profiles in neurodegenerative diseases. This approach, which can contribute to identify lipid biomarkers of Alzheimers' disease, can also permit to identify new therapeutic targets. It was therefore of interest to focus on central and peripheral biomarkers in Alzheimer's disease. Methods: A review of the literature on 148 papers was conducted. Based on this literature, the involvement of lipids (cholesterol and oxysterols, fatty acids, phospholipids) in Alzheimer's disease has been proposed. Results: Of the 148 references cited for lipid biomarkers for Alzheimer's disease, 65 refer to cholesterol and oxysterols, 35 to fatty acids and 40 to phospholipids. Among these lipids, some of them such as 24S-hydroxyckolesterol, open up new therapeutic perspectives in gene therapy, in particular. The results on the very long-chain fatty acids suggest the potential of peroxisomal dysfunctions in Alzheimer's disease. As for the phospholipids, they could constitute interesting biomarkers for detecting the disease at the prodromal stage. Conclusion: There are now several lines of evidence that lipids play fundamental roles in the pathogenesis of AD and that some of them have a prognostic and diagnosis value. This may pave the way for the identification of new therapeutic targets, new effective drugs and / or new treatments.
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Hu, Chanchan, Luyang Chen, Yi Fan, Zhifeng Lin, Xuwei Tang, Yuan Xu, Yiming Zeng, and Zhijian Hu. "The Landscape of Lipid Metabolism in Lung Cancer: The Role of Structural Profiling." Journal of Clinical Medicine 12, no. 5 (February 21, 2023): 1736. http://dx.doi.org/10.3390/jcm12051736.
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The aim of this study was to explore the relationship between lipids with different structural features and lung cancer (LC) risk and identify prospective biomarkers of LC. Univariate and multivariate analysis methods were used to screen for differential lipids, and two machine learning methods were used to define combined lipid biomarkers. A lipid score (LS) based on lipid biomarkers was calculated, and a mediation analysis was performed. A total of 605 lipid species spanning 20 individual lipid classes were identified in the plasma lipidome. Higher carbon atoms with dihydroceramide (DCER), phosphatidylethanolamine (PE), and phosphoinositols (PI) presented a significant negative correlation with LC. Point estimates revealed the inverse associated with LC for the n-3 PUFA score. Ten lipids were identified as markers with an area under the curve (AUC) value of 0.947 (95%, CI: 0.879–0.989). In this study, we summarized the potential relationship between lipid molecules with different structural features and LC risk, identified a panel of LC biomarkers, and demonstrated that the n-3 PUFA of the acyl chain of lipids was a protective factor for LC.
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Zorkina, Yana, Valeria Ushakova, Aleksandra Ochneva, Anna Tsurina, Olga Abramova, Valeria Savenkova, Anna Goncharova, et al. "Lipids in Psychiatric Disorders: Functional and Potential Diagnostic Role as Blood Biomarkers." Metabolites 14, no. 2 (January 23, 2024): 80. http://dx.doi.org/10.3390/metabo14020080.
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Lipids are a crucial component of the human brain, serving important structural and functional roles. They are involved in cell function, myelination of neuronal projections, neurotransmission, neural plasticity, energy metabolism, and neuroinflammation. Despite their significance, the role of lipids in the development of mental disorders has not been well understood. This review focused on the potential use of lipids as blood biomarkers for common mental illnesses, such as major depressive disorder, anxiety disorders, bipolar disorder, and schizophrenia. This review also discussed the impact of commonly used psychiatric medications, such as neuroleptics and antidepressants, on lipid metabolism. The obtained data suggested that lipid biomarkers could be useful for diagnosing psychiatric diseases, but further research is needed to better understand the associations between blood lipids and mental disorders and to identify specific biomarker combinations for each disease.
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SERAFIM, Patricia Valeria Pereira, Adiel Goes de FIGUEIREDO JR, Aledson Vitor FELIPE, Edson Guimaraes Lo TURCO, Ismael Dale Cotrim Guerreiro da SILVA, and Nora Manoukian FORONES. "STUDY OF LIPID BIOMARKERS OF PATIENTS WITH POLYPS AND COLORECTAL CÂNCER." Arquivos de Gastroenterologia 56, no. 4 (October 2019): 399–404. http://dx.doi.org/10.1590/s0004-2803.201900000-80.
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ABSTRACT BACKGROUND: Colorectal cancer (CRC) is one of the leading causes of cancer worldwide. Early diagnostic methods using serum biomarkers are required. The study of omics, most recently lipidomics, has the purpose of analyzing lipids for a better understanding of human lipidoma. The evolution of mass spectrometry methods, such as MALDI-MS technology, has enabled the detection and identification of a wide variety of lipids with great potential to open new avenues for predictive and preventive medicine. OBJECTIVE: To determine the lipid profile of patients with colorectal cancer and polyps. METHODS: Patients with stage I-III CRC, adenomatous polyps and individuals with normal colonoscopy were selected. All patients underwent peripheral blood collection for lipid extraction. The samples were analyzed by MALDI-MS technique for lipid identification. STATISTICAL ANALYSIS: Univariate and multivariate (principal component analysis [PCA] and discriminant analysis by partial least squares [PLS-DA]) analyses workflows were applied to the dataset, using MetaboAnalyst 3.0 software. The ions were identified according to the class of lipids using the online database Lipid Maps (http://www.lipidmaps.org). RESULTS: We included 88 individuals, 40 with CRC, 12 with polyps and 32 controls. Boxplot analysis showed eight VIP ions in the three groups. Differences were observed between the cancer and control groups, as well as between cancer and polyp, but not between polyps and control. The polyketide (810.1) was the lipid represented in cancer and overrepresented in polyp and control. Among the patients with CRC we observed differences between lipids with lymph node invasion (N1-2) compared to those without lymph node invasion (N). CONCLUSION: Possible lipid biomarkers were identified among cancer patients compared to control and polyp groups. The polyketide lipid (810.1) was the best biomarker to differentiate the cancer group from control and polyp. We found no difference between the biomarkers in the polyp group in relation to the control.
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Gibson, Larry R., and Paul W. Bohn. "Non-aqueous microchip electrophoresis for characterization of lipid biomarkers." Interface Focus 3, no. 3 (June 6, 2013): 20120096. http://dx.doi.org/10.1098/rsfs.2012.0096.
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In vivo measurements of lipid biomarkers are hampered by their low solubility in aqueous solution, which limits the choices for molecular separations. Here, we introduce non-aqueous microchip electrophoretic separations of lipid mixtures performed in three-dimensional hybrid nanofluidic/microfluidic polymeric devices. Electrokinetic injection is used to reproducibly introduce discrete femtolitre to picolitre volumes of charged lipids into a separation microchannel containing low (100 μM–10 mM) concentration tetraalkylammonium tetraphenylborate background electrolyte (BGE) in N -methylformamide, supporting rapid electro-osmotic fluid flow in polydimethylsiloxane microchannels. The quality of the resulting electrophoretic separations depends on the voltage and timing of the injection pulse, the BGE concentration and the electric field strength. Injected volumes increase with longer injection pulse widths and higher injection pulse amplitudes. Separation efficiency, as measured by total plate number, N , increases with increasing electric field and with decreasing BGE concentration. Electrophoretic separations of binary and ternary lipid mixtures were achieved with high resolution ( R s ∼ 5) and quality ( N > 7.7 × 10 6 plates m −1 ). Rapid in vivo monitoring of lipid biomarkers requires high-quality separation and detection of lipids downstream of microdialysis sample collection, and the multilayered non-aqueous microfluidic devices studied here offer one possible avenue to swiftly process complex lipid samples. The resulting capability may make it possible to correlate oxidative stress with in vivo lipid biomarker levels.
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Toft-Bertelsen, Trine L., Søren Norge Andreassen, Nina Rostgaard, Markus Harboe Olsen, Nicolas H. Norager, Tenna Capion, Marianne Juhler, and Nanna MacAulay. "Distinct Cerebrospinal Fluid Lipid Signature in Patients with Subarachnoid Hemorrhage-Induced Hydrocephalus." Biomedicines 11, no. 9 (August 23, 2023): 2360. http://dx.doi.org/10.3390/biomedicines11092360.
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Patients with subarachnoid hemorrhage (SAH) may develop posthemorrhagic hydrocephalus (PHH), which is treated with surgical cerebrospinal fluid (CSF) diversion. This diversion is associated with risk of infection and shunt failure. Biomarkers for PHH etiology, CSF dynamics disturbances, and potentially subsequent shunt dependency are therefore in demand. With the recent demonstration of lipid-mediated CSF hypersecretion contributing to PHH, exploration of the CSF lipid signature in relation to brain pathology is of interest. Despite being a relatively new addition to the omic’s landscape, lipidomics are increasingly recognized as a tool for biomarker identification, as they provide a comprehensive overview of lipid profiles in biological systems. We here employ an untargeted mass spectroscopy-based platform and reveal the complete lipid profile of cisternal CSF from healthy control subjects and demonstrate its bimodal fluctuation with age. Various classes of lipids, in addition to select individual lipids, were elevated in the ventricular CSF obtained from patients with SAH during placement of an external ventricular drain. The lipidomic signature of the CSF in the patients with SAH suggests dysregulation of the lipids in the CSF in this patient group. Our data thereby reveal possible biomarkers present in a brain pathology with a hemorrhagic event, some of which could be potential future biomarkers for hypersecretion contributing to ventriculomegaly and thus pharmacological targets for pathologies involving disturbed CSF dynamics.
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McGranaghan, Peter, Jennifer A. Kirwan, Mariel A. Garcia-Rivera, Burkert Pieske, Frank Edelmann, Florian Blaschke, Sandeep Appunni, et al. "Lipid Metabolite Biomarkers in Cardiovascular Disease: Discovery and Biomechanism Translation from Human Studies." Metabolites 11, no. 9 (September 14, 2021): 621. http://dx.doi.org/10.3390/metabo11090621.
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Lipids represent a valuable target for metabolomic studies since altered lipid metabolism is known to drive the pathological changes in cardiovascular disease (CVD). Metabolomic technologies give us the ability to measure thousands of metabolites providing us with a metabolic fingerprint of individual patients. Metabolomic studies in humans have supported previous findings into the pathomechanisms of CVD, namely atherosclerosis, apoptosis, inflammation, oxidative stress, and insulin resistance. The most widely studied classes of lipid metabolite biomarkers in CVD are phospholipids, sphingolipids/ceramides, glycolipids, cholesterol esters, fatty acids, and acylcarnitines. Technological advancements have enabled novel strategies to discover individual biomarkers or panels that may aid in the diagnosis and prognosis of CVD, with sphingolipids/ceramides as the most promising class of biomarkers thus far. In this review, application of metabolomic profiling for biomarker discovery to aid in the diagnosis and prognosis of CVD as well as metabolic abnormalities in CVD will be discussed with particular emphasis on lipid metabolites.
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Franco, Jackeline, Bartek Rajwa, Paulo Gomes, and Harm HogenEsch. "Local and Systemic Changes in Lipid Profile as Potential Biomarkers for Canine Atopic Dermatitis." Metabolites 11, no. 10 (September 30, 2021): 670. http://dx.doi.org/10.3390/metabo11100670.
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Lipids play a critical role in the skin as components of the epidermal barrier and as signaling and antimicrobial molecules. Atopic dermatitis in dogs is associated with changes in the lipid composition of the skin, but whether these precede or follow the onset of dermatitis is unclear. We applied rapid lipid-profiling mass spectrometry to skin and blood of 30 control and 30 atopic dogs. Marked differences in lipid profiles were observed between control, nonlesional, and lesional skin. The lipid composition of blood from control and atopic dogs was different, indicating systemic changes in lipid metabolism. Female and male dogs differed in the degree of changes in the skin and blood lipid profiles. Treatment with oclacitinib or lokivetmab ameliorated the skin condition and caused changes in skin and blood lipids. A set of lipid features of the skin was selected as a biomarker that classified samples as control or atopic dermatitis with 95% accuracy, whereas blood lipids discriminated between control and atopic dogs with 90% accuracy. These data suggest that canine atopic dermatitis is a systemic disease and support the use of rapid lipid profiling to identify novel biomarkers.
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Sagini, Krizia, Lorena Urbanelli, Sandra Buratta, Carla Emiliani, and Alicia Llorente. "Lipid Biomarkers in Liquid Biopsies: Novel Opportunities for Cancer Diagnosis." Pharmaceutics 15, no. 2 (January 28, 2023): 437. http://dx.doi.org/10.3390/pharmaceutics15020437.
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Altered cellular metabolism is a well-established hallmark of cancer. Although most studies have focused on the metabolism of glucose and glutamine, the upregulation of lipid metabolism is also frequent in cells undergoing oncogenic transformation. In fact, cancer cells need to meet the enhanced demand of plasma membrane synthesis and energy production to support their proliferation. Moreover, lipids are precursors of signaling molecules, termed lipid mediators, which play a role in shaping the tumor microenvironment. Recent methodological advances in lipid analysis have prompted studies aimed at investigating the whole lipid content of a sample (lipidome) to unravel the complexity of lipid changes in cancer patient biofluids. This review focuses on the application of mass spectrometry-based lipidomics for the discovery of cancer biomarkers. Here, we have summarized the main lipid alteration in cancer patients’ biofluids and uncovered their potential use for the early detection of the disease and treatment selection. We also discuss the advantages of using biofluid-derived extracellular vesicles as a platform for lipid biomarker discovery. These vesicles have a molecular signature that is a fingerprint of their originating cells. Hence, the analysis of their molecular cargo has emerged as a promising strategy for the identification of sensitive and specific biomarkers compared to the analysis of the unprocessed biofluid.
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Carrillo-Larco, Rodrigo M., Leonardo Albitres-Flores, Noël C. Barengo, and Antonio Bernabe-Ortiz. "The association between serum lipids and risk of premature mortality in Latin America: a systematic review of population-based prospective cohort studies." PeerJ 7 (October 4, 2019): e7856. http://dx.doi.org/10.7717/peerj.7856.
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Objective To synthetize the scientific evidence on the association between serum lipids and premature mortality in Latin America (LA). Methods Five data bases were searched from inception without language restrictions: Embase, Medline, Global Health, Scopus and LILACS. Population-based studies following random sampling methods were identified. The exposure variable was lipid biomarkers (e.g., total, LDL- or HDL- cholesterol). The outcome was all-cause and cause-specific mortality. The risk of bias was assessed following the Newcastle-Ottawa criteria. Results were summarized qualitatively. Results The initial search resulted in 264 abstracts, five (N = 27,903) were included for the synthesis. Three papers reported on the same study from Puerto Rico (baseline in 1965), one was from Brazil (1996) and one from Peru (2007). All reports analysed different exposure variables and used different risk estimates (relative risks, hazard ratios or odds ratios). None of the reviewed reports showed strong association between individual lipid biomarkers and all-cause or cardiovascular mortality. Conclusion The available evidence is outdated, inconsistently reported on several lipid biomarker definitions and used different methods to study the long-term mortality risk. These findings strongly support the need to better ascertain the mortality risk associated with lipid biomarkers in LA.
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Zhang, Wen, Lili Gong, Song Yang, Yali Lv, Feifei Han, He Liu, and Lihong Liu. "Lipidomics Profile Changes of Type 2 Diabetes Mellitus with Acute Myocardial Infarction." Disease Markers 2019 (November 16, 2019): 1–7. http://dx.doi.org/10.1155/2019/7614715.
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The morbidity and mortality of cardiovascular disease (CVD)/acute myocardial infarction (AMI) of type 2 diabetes mellitus (T2DM) patients are extremely higher than those without T2DM. Biomarkers can be used to predict the occurrence of acute myocardial infarction, thus effectively reducing the incidence of CVD events, particularly in T2DM patients. Lipids have been shown to be biomarkers and potential therapeutic targets for human diseases. The aim of our study was to investigate the prognostic value of lipid biomarkers for predicting AMI in T2DM patients. A total of 420 subjects were recruited in this research. Liquid Chromatography-Electrospray Ionization-Quadrupole Time of Flight-Mass Spectrometer- (LC-ESI-QTOF-MS-) and Liquid Chromatography/Mass Spectrometer- (LC/MS-) based metabolomic methods were applied to characterize metabolic profiles in each plasma sample. In the first untargeted set, 40 T2DM patients with AMI, 40 T2DM patients without AMI, and 40 control subjects were gender- and age-matched. Eight lipid metabolites showed a significant difference among three groups. Then, in the second set, targeted metabolic profiling assays for these 8 lipid biomarker concentrations in plasma were performed; another 100 T2DM patients with AMI, 100 T2DM patients without AMI, and 100 control subjects were selected independently. Receiver operating characteristic (ROC) curves were constructed, and the area under the ROC curves (AUC) was calculated to determine the potential biomarkers. ROC curve analysis showed that the AUC value of lysophosphatidylcholine (LysoPC) 18:0 is more than 0.7, indicating that LysoPC 18:0 may be a potential sensitive and specific biomarker for T2DM with AMI. The changed plasma concentrations of lipids were significantly associated with T2DM with AMI, which showed great value to be biomarkers, though it requires a prospective cohort study for further validation.
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Colak, Cemil, Fatma Hilal Yagin, Abdulmohsen Algarni, Ali Algarni, Fahaid Al-Hashem, and Luca Paolo Ardigò. "Untargeted Lipidomic Biomarkers for Liver Cancer Diagnosis: A Tree-Based Machine Learning Model Enhanced by Explainable Artificial Intelligence." Medicina 61, no. 3 (February 26, 2025): 405. https://doi.org/10.3390/medicina61030405.
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Background and Objectives: Liver cancer ranks among the leading causes of cancer-related mortality, necessitating the development of novel diagnostic methods. Deregulated lipid metabolism, a hallmark of hepatocarcinogenesis, offers compelling prospects for biomarker identification. This study aims to employ explainable artificial intelligence (XAI) to identify lipidomic biomarkers for liver cancer and to develop a robust predictive model for early diagnosis. Materials and Methods: This study included 219 patients diagnosed with liver cancer and 219 healthy controls. Serum samples underwent untargeted lipidomic analysis with LC-QTOF-MS. Lipidomic data underwent univariate and multivariate analyses, including fold change (FC), t-tests, PLS-DA, and Elastic Network feature selection, to identify significant biomarker candidate lipids. Machine learning models (AdaBoost, Random Forest, Gradient Boosting) were developed and evaluated utilizing these biomarkers to differentiate liver cancer. The AUC metric was employed to identify the optimal predictive model, whereas SHAP was utilized to achieve interpretability of the model’s predictive decisions. Results: Notable alterations in lipid profiles were observed: decreased sphingomyelins (SM d39:2, SM d41:2) and increased fatty acids (FA 14:1, FA 22:2) and phosphatidylcholines (PC 34:1, PC 32:1). AdaBoost exhibited a superior classification performance, achieving an AUC of 0.875. SHAP identified PC 40:4 as the most efficacious lipid for model predictions. The SM d41:2 and SM d36:3 lipids were specifically associated with an increased risk of low-onset cancer and elevated levels of the PC 40:4 lipid. Conclusions: This study demonstrates that untargeted lipidomics, in conjunction with explainable artificial intelligence (XAI) and machine learning, may effectively identify biomarkers for the early detection of liver cancer. The results suggest that alterations in lipid metabolism are crucial to the progression of liver cancer and provide valuable insights for incorporating lipidomics into precision oncology.
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Larrouy-Maumus, Gerald. "Lipids as Biomarkers of Cancer and Bacterial Infections." Current Medicinal Chemistry 26, no. 11 (June 28, 2019): 1924–32. http://dx.doi.org/10.2174/0929867325666180904120029.
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Lipids are ubiquitous molecules, known to play important roles in various cellular processes. Alterations to the lipidome can therefore be used as a read-out of the signs of disease, highlighting the importance to consider lipids as biomarkers in addition of nucleic acid and proteins. Lipids are among the primary structural and functional constituents of biological tissues, especially cell membranes. Along with membrane formation, lipids play also a crucial role in cell signalling, inflammation and energy storage. It was shown recently that lipid metabolism disorders play an important role in carcinogenesis and development. As well, the role of lipids in disease is particularly relevant for bacterial infections, during which several lipid bacterial virulence factors are recognized by the human innate immune response, such as lipopolysaccharide in Gram-negative bacteria, lipoteichoic acid in Gram-positive bacteria, and lipoglycans in mycobacteria. Compared to nucleic acids and proteins, a complete analysis of the lipidome, which is the comprehensive characterization of different lipid families, is usually very challenging due to the heterogeneity of lipid classes and their intrinsic physicoproperties caused by variations in the constituents of each class. Understanding the chemical diversity of lipids is therefore crucial to understanding their biological relevance and, as a consequence, their use as potential biomarkers for non-infectious and infectious diseases. This mini-review exposes the current knowledge and limitations of the use of lipids as biomarkers of the top global killers which are cancer and bacterial infections.
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Kim, Boyun, Gaeun Kim, Hyun Pyo Jeon, and Jewon Jung. "Lipidomics Analysis Unravels Aberrant Lipid Species and Pathways Induced by Zinc Oxide Nanoparticles in Kidney Cells." International Journal of Molecular Sciences 25, no. 8 (April 12, 2024): 4285. http://dx.doi.org/10.3390/ijms25084285.
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Zinc oxide nanoparticles (ZnO NPs) are widely used in versatile applications, from high technology to household products. While numerous studies have examined the toxic gene profile of ZnO NPs across various tissues, the specific lipid species associated with adverse effects and potential biomarkers remain elusive. In this study, we conducted a liquid chromatography-mass spectrometry based lipidomics analysis to uncover potential lipid biomarkers in human kidney cells following treatment with ZnO NPs. Furthermore, we employed lipid pathway enrichment analysis (LIPEA) to elucidate altered lipid-related signaling pathways. Our results demonstrate that ZnO NPs induce cytotoxicity in renal epithelial cells and modulate lipid species; we identified 64 lipids with a fold change (FC) > 2 and p < 0.01 with corrected p < 0.05 in HK2 cells post-treatment with ZnO NPs. Notably, the altered lipids between control HK2 cells and those treated with ZnO NPs were associated with the sphingolipid, autophagy, and glycerophospholipid pathways. This study unveils novel potential lipid biomarkers of ZnO NP nanotoxicity, representing the first lipidomic profiling of ZnO NPs in human renal epithelial cells.
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Gao, Bei, Suling Zeng, Luca Maccioni, Xiaochun Shi, Aaron Armando, Oswald Quehenberger, Xinlian Zhang, Peter Stärkel, and Bernd Schnabl. "Lipidomics for the Prediction of Progressive Liver Disease in Patients with Alcohol Use Disorder." Metabolites 12, no. 5 (May 11, 2022): 433. http://dx.doi.org/10.3390/metabo12050433.
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Alcohol-related liver disease is a public health care burden globally. Only 10–20% of patients with alcohol use disorder have progressive liver disease. This study aimed to identify lipid biomarkers for the early identification of progressive alcohol-related liver disease, which is a key step for early intervention. We performed untargeted lipidomics analysis in serum and fecal samples for a cohort of 49 subjects, including 17 non-alcoholic controls, 16 patients with non-progressive alcohol-related liver disease, and 16 patients with progressive alcohol-related liver disease. The serum and fecal lipidome profiles in the two patient groups were different from that in the controls. Nine lipid biomarkers were identified that were significantly different between patients with progressive liver disease and patients with non-progressive liver disease in both serum and fecal samples. We further built a random forest model to predict progressive alcohol-related liver disease using nine lipid biomarkers. Fecal lipids performed better (Area Under the Curve, AUC = 0.90) than serum lipids (AUC = 0.79). The lipid biomarkers identified are promising candidates for the early identification of progressive alcohol-related liver disease.
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Nguyen, Allen, Kent Wong, Ari Chakrabarti, Jenny Jiang, Sheila Ulufatu, Shannon Liu, Nicole Valle, et al. "Plasma surfactant proteins and lipid mediators in the airway associate with different stages of viral infection and resolution in a sub-lethal mouse Influenza A model." Journal of Immunology 200, no. 1_Supplement (May 1, 2018): 60.16. http://dx.doi.org/10.4049/jimmunol.200.supp.60.16.
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Abstract Viral quantification serves as a valid biomarker for detecting Influenza A infection however viral burden is not closely associated with disease severity or clinical outcomes. Biomarkers reflecting the status of the host immune response are needed to monitor and/or predict disease outcomes in a hospitalized setting. Here, we utilized a sub-lethal mouse model of Influenza A infection to explore plasma and airway cytokines, plasma surfactant proteins (SP-D, SP-A and CC16), and airway bioactive lipid mediators as potential biomarkers of lung leakage and host response, respectively. This study aimed to assess the pharmacodynamic response of these biomarkers after treatment with an efficacious dose of an anti-HA stalk-binding antibody and to assess association of these biomarkers with disease severity metrics. Plasma SP-D and IP-10 correlated with lung viral titers while SP-A and CC16 tracked more closely with changes in bronchial albumin levels and body weight. Bioactive lipid precursors Arachidonic Acid (AA), Docosahexaenoicacid (DHA) and Eicosapentaenoic acid (EPA), reported to have both pro- and anti-inflammatory properties, increase in the lung with infection. 17-HDHA, a DHA metabolite reported to be pro-resolving, was induced later during disease resolution. With anti-HA antibody treatment, all plasma surfactant proteins and precursor lipids in the airway were reduced and 17-HDHA production was accelerated. In summary, plasma SP-D may serve as an early biomarker of host response and/or potentially indicate a decline in lung epithelium integrity. Plasma SP-A, CC16, and pro-inflammatory lipid mediators such as AA may reflect the ongoing host response and may therefore be more closely associated with disease outcomes.
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Briganti, Stefania, Mauro Truglio, Antonella Angiolillo, Salvatore Lombardo, Deborah Leccese, Emanuela Camera, Mauro Picardo, and Alfonso Di Costanzo. "Application of Sebum Lipidomics to Biomarkers Discovery in Neurodegenerative Diseases." Metabolites 11, no. 12 (November 29, 2021): 819. http://dx.doi.org/10.3390/metabo11120819.
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Lipidomics is strategic in the discovery of biomarkers of neurodegenerative diseases (NDDs). The skin surface lipidome bears the potential to provide biomarker candidates in the detection of pathological processes occurring in distal organs. We investigated the sebum composition to search diagnostic and, possibly, prognostic, biomarkers of Alzheimer’s disease (AD) and Parkinson’s disease (PD). The observational study included 64 subjects: 20 characterized as “probable AD with documented decline”, 20 as “clinically established PD”, and 24 healthy subjects (HS) of comparable age. The analysis of sebum by GCMS and TLC retrieved the amounts (µg) of 41 free fatty acids (FFAs), 7 fatty alcohols (FOHs), vitamin E, cholesterol, squalene, and total triglycerides (TGs) and wax esters (WEs). Distributions of sebum lipids in NDDs and healthy conditions were investigated with multivariate ANOVA-simultaneous component analysis (ASCA). The deranged sebum composition associated with the PD group showed incretion of most composing lipids compared to HS, whereas only two lipid species (vitamin E and FOH14:0) were discriminant of AD samples and presented lower levels than HS sebum. Thus, sebum lipid biosynthetic pathways are differently affected in PD and AD. The characteristic sebum bio-signatures detected support the value of sebum lipidomics in the biomarkers search in NDDs.
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Fernandis, Aaron Zefrin, and Markus Rene Wenk. "Lipid-based biomarkers for cancer." Journal of Chromatography B 877, no. 26 (September 2009): 2830–35. http://dx.doi.org/10.1016/j.jchromb.2009.06.015.
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Mohanty, Bimal Prasanna, Soma Bhattacharjee, Prasenjit Paria, Arabinda Mahanty, and Anil Prakash Sharma. "Lipid Biomarkers of Lens Aging." Applied Biochemistry and Biotechnology 169, no. 1 (November 21, 2012): 192–200. http://dx.doi.org/10.1007/s12010-012-9963-6.
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Nicholls, Stephen J., and Leonard Kritharides. "Lipid Biomarkers and Cardiovascular Risk." Journal of the American College of Cardiology 65, no. 13 (April 2015): 1296–97. http://dx.doi.org/10.1016/j.jacc.2015.02.015.
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Khoury, Spiro, Jenny Colas, Véronique Breuil, Eva Kosek, Aisha S. Ahmed, Camilla I. Svensson, Fabien Marchand, Emmanuel Deval, and Thierry Ferreira. "Identification of Lipid Biomarkers for Chronic Joint Pain Associated with Different Joint Diseases." Biomolecules 13, no. 2 (February 9, 2023): 342. http://dx.doi.org/10.3390/biom13020342.
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Lipids, especially lysophosphatidylcholine LPC16:0, have been shown to be involved in chronic joint pain through the activation of acid-sensing ion channels (ASIC3). The aim of the present study was to investigate the lipid contents of the synovial fluids from controls and patients suffering from chronic joint pain in order to identify characteristic lipid signatures associated with specific joint diseases. For this purpose, lipids were extracted from the synovial fluids and analyzed by mass spectrometry. Lipidomic analyses identified certain choline-containing lipid classes and molecular species as biomarkers of chronic joint pain, regardless of the pathology, with significantly higher levels detected in the patient samples. Moreover, correlations were observed between certain lipid levels and the type of joint pathologies. Interestingly, LPC16:0 levels appeared to correlate with the metabolic status of patients while other choline-containing lipids were more specifically associated with the inflammatory state. Overall, these data point at selective lipid species in synovial fluid as being strong predictors of specific joint pathologies which could help in the selection of the most adapted treatment.
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Chen, Xi, Xiang Guo, and Xing Lv. "Influence of widely targeted quantitative lipidomics on plasma lipid predictors and pathway dysregulation for nasopharyngeal carcinoma." Journal of Clinical Oncology 41, no. 16_suppl (June 1, 2023): 6030. http://dx.doi.org/10.1200/jco.2023.41.16_suppl.6030.
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6030 Background: Dysregulation of lipid metabolism is closely associated with cancer progression. We aimed to establish a prognostic model to predict distant metastasis-free survival (DMFS) in patients with locoregionally advanced nasopharyngeal carcinoma (NPC), based on widely targeted quantitative lipidomics. Methods: We measured and quantified the plasma lipid profiles of 179 patients with NPC using widely targeted quantitative lipidomics. Then, patients were randomly split into the training (125 patients, 69.8%) and validation (54 patients, 30.2%) sets. To identify distant metastasis-associated lipids, univariate Cox regression was applied to the training set ( p< 0.05). We employed a deep survival method called DeepSurv to develop our proposed model based on significant lipid species ( p< 0.01) and clinical biomarkers to predict DMFS. Concordance index and receiver operating curve analyses were performed to assess model effectiveness. We also explored the potential role of lipid alterations in the prognosis of NPC. Results: A total of 665 plasma endogenous lipid species consisting of 27 lipid classes and subclasses from 179 patients with NPC were annotated in lipidomics analysis. Forty lipids were recognized as distant metastasis-associated ( p< 0.05) by univariate Cox regression. The concordance indices of our proposed model were 0.764 (95% confidence interval (CI), 0.682–0.846) and 0.760 (95% CI, 0.649–0.871) in the training and validation sets, respectively. We also calculated the C-index values of the baseline survival model based only on clinical biomarkers, with 0.718 (95% CI, 0.591–0.845) and 0.672 (95% CI, 0.511–0.833) being detected in the training and validation sets, respectively, indicating the outstanding predictive performance of lipid biomarkers. High-risk patients had poorer 5-year DMFS compared with low-risk patients (Hazard ratio, 26.18; 95% CI, 3.52–194.80; p< 0.0001). Moreover, the six lipids were significantly correlated with immunity- and inflammation-associated biomarkers and were mainly enriched in metabolic pathways. Conclusions: Widely targeted quantitative lipidomics reveals plasma lipid predictors and pathway dysregulation for locoregionally advanced NPC, the prognostic model based on that demonstrated superior performance in predicting metastasis in NPC patients.
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Agatonovic-Kustrin, Snezana, David William Morton, Valeriy Smirnov, Alexey Petukhov, Vladimir Gegechkori, Vera Kuzina, Natalya Gorpinchenko, and Galina Ramenskaya. "Analytical Strategies in Lipidomics for Discovery of Functional Biomarkers from Human Saliva." Disease Markers 2019 (December 4, 2019): 1–11. http://dx.doi.org/10.1155/2019/6741518.
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Human saliva is increasingly being used and validated as a biofluid for diagnosing, monitoring systemic disease status, and predicting disease progression. The discovery of biomarkers in saliva biofluid offers unique opportunities to bypass the invasive procedure of blood sampling by using oral fluids to evaluate the health condition of a patient. Saliva biofluid is clinically relevant since its components can be found in plasma. As salivary lipids are among the most essential cellular components of human saliva, there is great potential for their use as biomarkers. Lipid composition in cells and tissues change in response to physiological changes and normal tissues have a different lipid composition than tissues affected by diseases. Lipid imbalance is closely associated with a number of human lifestyle-related diseases, such as atherosclerosis, diabetes, metabolic syndromes, systemic cancers, neurodegenerative diseases, and infectious diseases. Thus, identification of lipidomic biomarkers or key lipids in different diseases can be used to diagnose diseases and disease state and evaluate response to treatments. However, further research is needed to determine if saliva can be used as a surrogate to serum lipid profiles, given that highly sensitive methods with low limits of detection are needed to discover salivary biomarkers in order to develop reliable diagnostic and disease monitoring salivary tests. Lipidomic methods have greatly advanced in recent years with a constant advance in mass spectrometry (MS) and development of MS detectors with high accuracy and high resolution that are able to determine the elemental composition of many lipids.
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Humaloja, Jaana, Maximo Vento, Julia Kuligowski, Sture Andersson, José David Piñeiro-Ramos, Ángel Sánchez-Illana, Erik Litonius, et al. "High Oxygen Does Not Increase Reperfusion Injury Assessed with Lipid Peroxidation Biomarkers after Cardiac Arrest: A Post Hoc Analysis of the COMACARE Trial." Journal of Clinical Medicine 10, no. 18 (September 17, 2021): 4226. http://dx.doi.org/10.3390/jcm10184226.
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The products of polyunsaturated fatty acid peroxidation are considered reliable biomarkers of oxidative injury in vivo. We investigated ischemia-reperfusion-related oxidative injury by determining the levels of lipid peroxidation biomarkers (isoprostane, isofuran, neuroprostane, and neurofuran) after cardiac arrest and tested the associations between the biomarkers and different arterial oxygen tensions (PaO2). We utilized blood samples collected during the COMACARE trial (NCT02698917). In the trial, 123 patients resuscitated from out-of-hospital cardiac arrest were treated with a 10–15 kPa or 20–25 kPa PaO2 target during the initial 36 h in the intensive care unit. We measured the biomarker levels at admission, and 24, 48, and 72 h thereafter. We compared biomarker levels in the intervention groups and in groups that differed in oxygen exposure prior to randomization. Blood samples for biomarker determination were available for 112 patients. All four biomarker levels peaked at 24 h; the increase appeared greater in younger patients and in patients without bystander-initiated life support. No association between the lipid peroxidation biomarkers and oxygen exposure either before or after randomization was found. Increases in the biomarker levels during the first 24 h in intensive care suggest continuing oxidative stress, but the clinical relevance of this remains unresolved.
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Ke, Zunli, Chaowen Fan, Jun Li, La Wang, Haiyang Li, Weiyi Tian, and Qi Yu. "Nobiletin Intake Attenuates Hepatic Lipid Profiling and Oxidative Stress in HFD-Induced Nonalcoholic-Fatty-Liver-Disease Mice." Molecules 28, no. 6 (March 12, 2023): 2570. http://dx.doi.org/10.3390/molecules28062570.
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Nobiletin (NOB) is a naturally occurring compound, commonly found in citrus peel, that shows hepatoprotective and lipid-reducing effects. However, the lipid biomarkers and the potential improvement mechanisms have not been adequately explored. Therefore, we investigated the ameliorative effect and the molecular mechanism of NOB on NAFLD induced by a high-fat diet in mice. The results showed that supplementation with NOB over 12 weeks markedly improved glucose tolerance, serum lipid profiles, inflammatory factors, hepatic steatosis, and oxidative stress. These beneficial effects were mainly related to reduced levels of potential lipid biomarkers including free fatty acids, diacylglycerols, triacylglycerols, and cholesteryl esters according to hepatic lipidomic analysis. Twenty lipids, including DGs and phosphatidylcholines, were identified as potential lipid biomarkers. Furthermore, RT-qPCR and Western blot analysis indicated that NOB inhibited the expression of lipogenesis-related factors such as SREBP-1c, SCD-1, and FAS, and upregulated the expression of lipid oxidation (PPARα) and cholesterol conversion (LXRα, CYP7A1, and CYP27A1) genes as well as antioxidation-related factors (Nucl-Nrf2, NQO1, HO-1, and GCLC), indicating that NOB intake may reduce lipid biosynthesis and increase lipid consumption to improve hepatic steatosis and oxidative stress. This study is beneficial for understanding the ameliorative effects of NOB on NAFLD.
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Černiauskas, Linas, Asta Mažeikienė, Eglė Mazgelytė, Eglė Petrylaitė, Aušra Linkevičiūtė-Dumčė, Neringa Burokienė, and Dovilė Karčiauskaitė. "Malondialdehyde, Antioxidant Defense System Components and Their Relationship with Anthropometric Measures and Lipid Metabolism Biomarkers in Apparently Healthy Women." Biomedicines 11, no. 9 (September 3, 2023): 2450. http://dx.doi.org/10.3390/biomedicines11092450.
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Cardiovascular diseases are the leading cause of mortality worldwide. Since atherosclerosis, an inflammatory, lipid-driven disease, is an underlying basis for the development of cardiovascular disease, it is important to understand its relationship with confounding factors, such as oxidative lipid degradation. In contrast, circulating antioxidants prevent oxidative lipid damage, and therefore, may be associated with reduced development of atherosclerosis. We aimed to assess oxidative lipid degradation biomarker malondialdehyde (MDA) and antioxidant defense system components, total antioxidant capacity (TAC) and superoxide dismutase (SOD) inhibition rate levels, in healthy women and evaluate their relationships with age, anthropometric measures, and lipid metabolism biomarkers. The study included 86 healthy middle-aged women. MDA in human serum samples was evaluated by HPLC, and the TAC and SOD inhibition rates were measured by photometric methods. MDA was found to be associated with age, total cholesterol, non-HDL cholesterol, apolipoprotein B and triacylglycerols. TAC was shown to be associated with age, BMI, and waist circumference, as well as lipid metabolism biomarkers apolipoprotein B and triacylglycerol, while SOD inhibition rate was only associated with total cholesterol, apolipoprotein B and triacylglycerols. In conclusion, the association of oxidative status indices, MDA, TAC and SOD, with cardiovascular risk factors suggests that they could be additional useful biomarkers in the research of aging, obesity, and atherosclerosis pathogenesis.
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Varga, Tibor V., Jinxi Liu, Ronald B. Goldberg, Guannan Chen, Samuel Dagogo-Jack, Carlos Lorenzo, Kieren J. Mather, Xavier Pi-Sunyer, Søren Brunak, and Marinella Temprosa. "Predictive utilities of lipid traits, lipoprotein subfractions and other risk factors for incident diabetes: a machine learning approach in the Diabetes Prevention Program." BMJ Open Diabetes Research & Care 9, no. 1 (March 2021): e001953. http://dx.doi.org/10.1136/bmjdrc-2020-001953.
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IntroductionAlthough various lipid and non-lipid analytes measured by nuclear magnetic resonance (NMR) spectroscopy have been associated with type 2 diabetes, a structured comparison of the ability of NMR-derived biomarkers and standard lipids to predict individual diabetes risk has not been undertaken in larger studies nor among individuals at high risk of diabetes.Research design and methodsCumulative discriminative utilities of various groups of biomarkers including NMR lipoproteins, related non-lipid biomarkers, standard lipids, and demographic and glycemic traits were compared for short-term (3.2 years) and long-term (15 years) diabetes development in the Diabetes Prevention Program, a multiethnic, placebo-controlled, randomized controlled trial of individuals with pre-diabetes in the USA (N=2590). Logistic regression, Cox proportional hazards model and six different hyperparameter-tuned machine learning algorithms were compared. The Matthews Correlation Coefficient (MCC) was used as the primary measure of discriminative utility.ResultsModels with baseline NMR analytes and their changes did not improve the discriminative utility of simpler models including standard lipids or demographic and glycemic traits. Across all algorithms, models with baseline 2-hour glucose performed the best (max MCC=0.36). Sophisticated machine learning algorithms performed similarly to logistic regression in this study.ConclusionsNMR lipoproteins and related non-lipid biomarkers were associated but did not augment discrimination of diabetes risk beyond traditional diabetes risk factors except for 2-hour glucose. Machine learning algorithms provided no meaningful improvement for discrimination compared with logistic regression, which suggests a lack of influential latent interactions among the analytes assessed in this study.Trial registration numberDiabetes Prevention Program: NCT00004992; Diabetes Prevention Program Outcomes Study: NCT00038727.
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Quintana, Francisco J., Ada Yeste, Howard L. Weiner, and Ruxandra Covacu. "Lipids and lipid-reactive antibodies as biomarkers for multiple sclerosis." Journal of Neuroimmunology 248, no. 1-2 (July 2012): 53–57. http://dx.doi.org/10.1016/j.jneuroim.2012.01.002.
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Basu, Arpita. "Role of Berry Bioactive Compounds on Lipids and Lipoproteins in Diabetes and Metabolic Syndrome." Nutrients 11, no. 9 (August 22, 2019): 1983. http://dx.doi.org/10.3390/nu11091983.
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Blood lipids are an important biomarker of cardiovascular health and disease. Among the lipid biomarkers that have been widely used to monitor and predict cardiovascular diseases (CVD), elevated LDL and low HDL cholesterol (C), as well as elevated triglyceride-rich lipoproteins, deserve special attention in their predictive abilities, and thus have been the targets of several therapeutic and dietary approaches to improving lipid profiles. Among natural foods and nutraceuticals, dietary berries are a rich source of nutrients, fiber, and various types of phytochemicals. Berries as whole fruits, juices, and purified extracts have been shown to lower total and LDL-C, and increase HDL-C in clinical studies in participants with elevated blood lipids, type 2 diabetes or metabolic syndrome. This short review aimed to further discuss the mechanisms and magnitude of the lipid-lowering effects of dietary berries, with emphasis on reported clinical studies. Based on the emerging evidence, colorful berry fruits may thus be included in a healthy diet for the prevention and management of CVD.
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Taubner, Ruth-Sophie, Lydia M. F. Baumann, Thorsten Bauersachs, Elisabeth L. Clifford, Barbara Mähnert, Barbara Reischl, Richard Seifert, Jörn Peckmann, Simon K. M. R. Rittmann, and Daniel Birgel. "Membrane Lipid Composition and Amino Acid Excretion Patterns of Methanothermococcus okinawensis Grown in the Presence of Inhibitors Detected in the Enceladian Plume." Life 9, no. 4 (November 14, 2019): 85. http://dx.doi.org/10.3390/life9040085.
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Lipids and amino acids are regarded as important biomarkers for the search for extraterrestrial life in the Solar System. Such biomarkers may be used to trace methanogenic life on other planets or moons in the Solar System, such as Saturn’s icy moon Enceladus. However, little is known about the environmental conditions shaping the synthesis of lipids and amino acids. Here, we present the lipid production and amino acid excretion patterns of the methanogenic archaeon Methanothermococcus okinawensis after exposing it to different multivariate concentrations of the inhibitors ammonium, formaldehyde, and methanol present in the Enceladian plume. M. okinawensis shows different patterns of lipid and amino acids excretion, depending on the amount of these inhibitors in the growth medium. While methanol did not show a significant impact on growth, lipid or amino acid production rates, ammonium and formaldehyde strongly affected these parameters. These findings are important for understanding the eco-physiology of methanogens on Earth and have implications for the use of biomarkers as possible signs of extraterrestrial life for future space missions in the Solar System.
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Chen, Ji-ying, Wu-jie Chen, Zhi-ying Zhu, Shi Xu, Li-lan Huang, Wen-qing Tan, Yong-gang Zhang, and Yan-li Zhao. "Screening of serum biomarkers in patients with PCOS through lipid omics and ensemble machine learning." PLOS ONE 20, no. 1 (January 7, 2025): e0313494. https://doi.org/10.1371/journal.pone.0313494.
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Polycystic ovary syndrome (PCOS) is a primary endocrine disorder affecting premenopausal women involving metabolic dysregulation. We aimed to screen serum biomarkers in PCOS patients using untargeted lipidomics and ensemble machine learning. Serum from PCOS patients and non-PCOS subjects were collected for untargeted lipidomics analysis. Through analyzing the classification of differential lipid metabolites and the association between differential lipid metabolites and clinical indexes, ensemble machine learning, data preprocessing, statistical test pre-screening, ensemble learning method secondary screening, biomarkers verification and evaluation, and diagnostic panel model construction and verification were performed on the data of untargeted lipidomics. Results indicated that different lipid metabolites not only differ between groups but also have close effects on different corresponding clinical indexes. PI (18:0/20:3)-H and PE (18:1p/22:6)-H were identified as candidate biomarkers. Three machine learning models, logistic regression, random forest, and support vector machine, showed that screened biomarkers had better classification ability and effect. In addition, the correlation of candidate biomarkers was low, indicating that the overlap between the selected biomarkers was low, and the combination of panels was more optimized. When the AUC value of the test set of the constructed diagnostic panel model was 0.815, the model’s accuracy in the test set was 0.74, specificity was 0.88, and sensitivity was 0.7. This study demonstrated the applicability and robustness of machine learning algorithms to analyze lipid metabolism data for efficient and reliable biomarker screening. PI (18:0/20:3)-H and PE (18:1p/22:6)-H showed great potential in diagnosing PCOS.
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Villanueva, Laura, and Marco J. L. Coolen. "Contributions of Genomics to Lipid Biomarker Research: From Paleoclimatology to Evolution." Elements 18, no. 2 (April 1, 2022): 87–92. http://dx.doi.org/10.2138/gselements.18.2.87.
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Lipid biomarkers can be preserved over long geological timescales. They are widely used as taxonomic markers of past and present microbial communities and as parts of organic paleoclimate proxies. However, questions remain regarding the precise biological sources and evolution of the acquisition of specific lipids, and why and how they are synthesized. In the last two decades, the use of DNA-based approaches has proven to be key in unraveling some of these questions. As methodological approaches improve, (paleo) genomics increasingly supports lipid biomarker research. Here, we provide an overview of the usefulness of DNA-based approaches over the years, including ancient sedimentary DNA research and phylogenomics, and a perspective on the upcoming challenges of this field.
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Abdul Rashid, Khairunnisa, Kamariah Ibrahim, Jeannie Hsiu Ding Wong, and Norlisah Mohd Ramli. "Lipid Alterations in Glioma: A Systematic Review." Metabolites 12, no. 12 (December 16, 2022): 1280. http://dx.doi.org/10.3390/metabo12121280.
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Gliomas are highly lethal tumours characterised by heterogeneous molecular features, producing various metabolic phenotypes leading to therapeutic resistance. Lipid metabolism reprogramming is predominant and has contributed to the metabolic plasticity in glioma. This systematic review aims to discover lipids alteration and their biological roles in glioma and the identification of potential lipids biomarker. This systematic review was conducted using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. Extensive research articles search for the last 10 years, from 2011 to 2021, were conducted using four electronic databases, including PubMed, Web of Science, CINAHL and ScienceDirect. A total of 158 research articles were included in this study. All studies reported significant lipid alteration between glioma and control groups, impacting glioma cell growth, proliferation, drug resistance, patients’ survival and metastasis. Different lipids demonstrated different biological roles, either beneficial or detrimental effects on glioma. Notably, prostaglandin (PGE2), triacylglycerol (TG), phosphatidylcholine (PC), and sphingosine-1-phosphate play significant roles in glioma development. Conversely, the most prominent anti-carcinogenic lipids include docosahexaenoic acid (DHA), eicosapentaenoic acid (EPA), and vitamin D3 have been reported to have detrimental effects on glioma cells. Furthermore, high lipid signals were detected at 0.9 and 1.3 ppm in high-grade glioma relative to low-grade glioma. This evidence shows that lipid metabolisms were significantly dysregulated in glioma. Concurrent with this knowledge, the discovery of specific lipid classes altered in glioma will accelerate the development of potential lipid biomarkers and enhance future glioma therapeutics.
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Isom, Madeline, Eden P. Go, and Heather Desaire. "Groomed Fingerprint Sebum Sampling: Reproducibility and Variability According to Anatomical Collection Region and Biological Sex." Molecules 30, no. 3 (February 6, 2025): 726. https://doi.org/10.3390/molecules30030726.
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Sebum lipids, accessible via groomed latent fingerprints, may be a valuable, underappreciated sample source for future biomarker research. Sampling sebum lipids from the skin is painless for patients, efficient for researchers, and has already demonstrated the potential to contain disease biomarkers. However, before sebum sampling can be implemented in routine studies, more information is needed regarding sampling reproducibility and variability. This information will enable researchers to choose the best practices for sebum-based studies. Herein, we use our recently established workflow for the collection and analysis of groomed fingerprints to assess the reproducibility of lipid profiles obtained via mass spectrometry. Using 180 fingerprint samples collected from 30 participants, we also assess lipid changes according to biological sex and anatomical grooming region (cheek, neck, and forehead) via supervised and unsupervised classification. The results demonstrate that this sampling protocol achieves satisfactory reproducibility, and negligible differences exist between male and female groomed fingerprint lipids. Moreover, the anatomical grooming region can impact the fingerprint lipid profile: cheek- and forehead-groomed fingerprints are more similar to one another than either collection site is to neck-groomed fingerprints. This information will inform future sebum-based biomarker investigations, enabling researchers to collect meaningful lipidomic datasets from groomed fingerprint samples.
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Klekowski, Jakub, Mariusz Chabowski, Małgorzata Krzystek-Korpacka, and Mariusz Fleszar. "The Utility of Lipidomic Analysis in Colorectal Cancer Diagnosis and Prognosis—A Systematic Review of Recent Literature." International Journal of Molecular Sciences 25, no. 14 (July 14, 2024): 7722. http://dx.doi.org/10.3390/ijms25147722.
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Colorectal cancer (CRC) is among the most prevalent and lethal malignancies. Lipidomic investigations have revealed numerous disruptions in lipid profiles across various cancers. Studies on CRC exhibit potential for identifying novel diagnostic or prognostic indicators through lipidomic signatures. This review examines recent literature regarding lipidomic markers for CRC. PubMed database was searched for eligible articles concerning lipidomic biomarkers of CRC. After selection, 36 articles were included in the review. Several studies endeavor to establish sets of lipid biomarkers that demonstrate promising potential to diagnose CRC based on blood samples. Phosphatidylcholine, phosphatidylethanolamine, ceramides, and triacylglycerols (TAGs) appear to offer the highest diagnostic accuracy. In tissues, lysophospholipids, ceramides, and TAGs were among the most altered lipids, while unsaturated fatty acids also emerged as potential biomarkers. In-depth analysis requires both cell culture and animal studies. CRC involves multiple lipid metabolism alterations. Although numerous lipid species have been suggested as potential diagnostic markers, the establishment of standardized methods and the conduct of large-scale studies are necessary to facilitate their clinical application.
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Li, Zhengjun, Wanjian Gu, Yingzhuo Wang, Bin Qin, Wei Ji, Zhongqiu Wang, and Shijia Liu. "Untargeted Lipidomics Reveals Characteristic Biomarkers in Patients with Ankylosing Spondylitis Disease." Biomedicines 11, no. 1 (December 25, 2022): 47. http://dx.doi.org/10.3390/biomedicines11010047.
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Objective. Ankylosing spondylitis (AS) is a chronic inflammatory disease of the axial skeleton. Early and accurate diagnosis is necessary for the timely and effective treatment of this disease and its common complications. Lipid metabolites form various kinds of bioactive molecules that regulate the initiation and progression of inflammation. However, there are currently few studies that investigate the alteration of serum lipid in AS patients. Methods. Blood samples were collected from 115 AS patients and 108 healthy controls (HCs). Serum-untargeted lipidomics were performed using ultrahigh-performance liquid chromatography coupled with Q-Exactive spectrometry, and the data were determined by multivariate statistical methods to explore potential lipid biomarkers. Results. Lipid phenotypes associated with disease activity were detected in the serum of patients with AS. Of all 586 identified lipids, there are 297 differential lipid metabolites between the AS and HC groups, of which 15 lipid metabolites are significant. In the AS groups, the levels of triacylglycerol (TAG) (18:0/18:1/20:0) were increased, and the levels of phosphatidylcholine (PC) (16:0e/26:4) and PC (18:1/22:6) were decreased. The areas under the receiver operating characteristic curve (AUC) of TAG (18:0/18:1/20:0), PC (16:0e/26:4), and PC (18:1/22:6) were 0.919, 0.843, and 0.907, respectively. Conclusion. Our findings uncovered that lipid deregulation is a crucial hallmark of AS, thereby providing new insights into the early diagnosis of AS.
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Kozakova, Michaela, Carmela Morizzo, Giuli Jamagidze, Daniele Della Latta, Sara Chiappino, Dante Chiappino, and Carlo Palombo. "Association between Low-Density Lipoprotein Cholesterol and Vascular Biomarkers in Primary Prevention." Biomedicines 11, no. 6 (June 18, 2023): 1753. http://dx.doi.org/10.3390/biomedicines11061753.
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Several noninvasive vascular biomarkers have been proposed to improve risk stratification for atherothrombotic events. To identify biomarkers suitable for detecting intermediate-risk individuals who might benefit from lipid-lowering treatment in primary prevention, the present study tested the association of plasma LDL-cholesterol with coronary artery calcification (CAC) Agatston score, high carotid and femoral intima-media thickness (IMT), low carotid distensibility and high carotid-femoral pulse-wave velocity in 260 asymptomatic individuals at intermediate cardiovascular risk and without diabetes and lipid-lowering treatment. High or low vascular biomarkers were considered when their value was above the 95th or below the 5th percentile, respectively, of the distribution in the healthy or in the study population. LDL-cholesterol was independently associated with the CAC score = 0 (OR 0.67; 95%CI 0.48–0.92, p = 0.01), CAC score > 100 (1.59; 1.08–2.39, p = 0.01) and high common femoral artery (CFA) IMT (1.89; 1.19–3.06, p < 0.01), but not with other biomarkers. Our data confirm that in individuals at intermediate risk, lipid-lowering treatment can be avoided in the presence of a CAC score = 0, while it should be used with a CAC score > 100. CFA IMT could represent a useful biomarker for decisions regarding lipid-lowering treatment. However, sex- and age-specific reference values should be established in a large healthy population.
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Eglinton, G., R. J. Parkes, and M. Zhao. "Lipid biomarkers in biogeochemistry: Future roles?" Marine Geology 113, no. 1-2 (July 1993): 141–45. http://dx.doi.org/10.1016/0025-3227(93)90155-o.
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Mika, Adriana, Tomasz Sledzinski, and Piotr Stepnowski. "Current Progress of Lipid Analysis in Metabolic Diseases by Mass Spectrometry Methods." Current Medicinal Chemistry 26, no. 1 (March 14, 2019): 60–103. http://dx.doi.org/10.2174/0929867324666171003121127.
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Background: Obesity, insulin resistance, diabetes, and metabolic syndrome are associated with lipid alterations, and they affect the risk of long-term cardiovascular disease. A reliable analytical instrument to detect changes in the composition or structures of lipids and the tools allowing to connect changes in a specific group of lipids with a specific disease and its progress, is constantly lacking. Lipidomics is a new field of medicine based on the research and identification of lipids and lipid metabolites present in human organism. The primary aim of lipidomics is to search for new biomarkers of different diseases, mainly civilization diseases. Objective: We aimed to review studies reporting the application of mass spectrometry for lipid analysis in metabolic diseases. Method: Following an extensive search of peer-reviewed articles on the mass spectrometry analysis of lipids the literature has been discussed in this review article. Results: The lipid group contains around 1.7 million species; they are totally different, in terms of the length of aliphatic chain, amount of rings, additional functional groups. Some of them are so complex that their complex analyses are a challenge for analysts. Their qualitative and quantitative analysis of is based mainly on mass spectrometry. Conclusion: Mass spectrometry techniques are excellent tools for lipid profiling in complex biological samples and the combination with multivariate statistical analysis enables the identification of potential diagnostic biomarkers.
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Hassan, Syed Adeel, Courtney Perry, Justin Thomas, Mohamed Eltaher, Mahmoud Elkammar, Nabila Dawoud, Lesley Wempe, et al. "PERIPHERAL BLOOD EXTRACELLULAR VESICLE LIPIDS AS BIOMARKERS FOR HUMAN INFLAMMATORY BOWEL DISEASE." Inflammatory Bowel Diseases 29, Supplement_1 (January 26, 2023): S13. http://dx.doi.org/10.1093/ibd/izac247.027.
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Abstract BACKGROUND The current paradigm in inflammatory bowel disease (IBD) diagnostics is based on peripheral biomarkers such as C-reactive protein and fecal calprotectin that achieve low sensitivity and specificity for intestinal inflammation. Extracellular vesicles (EVs) are lipid-enveloped particles involved in inter tissue and cell-cell interactions. They also have unique properties reflecting the metabolic and phenotypic nature of the producer cells. Recent data revealed that surface proteins on intestinal epithelial cells-derived EVs can be detected in the peripheral blood. We posit that lipid profiling of circulating EVs (PBEs) can be used to discriminate active IBD from healthy subjects and further classify different stages of IBD (PCT Patent Pending: 13177N/2194P5). METHODS Patients diagnosed with Ulcerative Colitis (UC, n=50) or normal controls (n=50) were recruited at the UK IBD clinic or colonoscopy suite. During their colonoscopy or regular outpatient labs, 2 tubes of blood (20-30 mL) were drawn into K2-EDTA tubes. The blood samples were immediately stored on ice and then centrifuged at 1,500 g for 15 minutes at 4 °C within 30 minutes. The clarified plasma was immediately aliquoted and frozen in liquid N2. PBEs were isolated from plasma using size-exclusion microcolumns. Isolated exosomal preparations were lysed in cold acetonitrile, followed by extraction using a modified Folch method. The lipid fraction was carefully aspirated and dried in a Vacufuge before reconstitution in a chloroform/methanol mixture containing butylated hydroxytoluene. The extracted lipids from active UC and healthy control patient plasma were analyzed using direct infusion ultrahigh resolution Orbitrap mass spectrometry. We used statistical tools LASSO and Random Forest to select informative lipid features and build classification models. The performance of the classifiers was quantified by the receiver operating characteristic (ROC) curve and area under the ROC curve (AUC) based on 10-fold cross-validation. RESULTS Three PBE lipids identified by LASSO were also identified using the Random Forest method along with 7 additional lipids. Discriminating lipid classifiers between active UC and normal patients identified by both LASSO and Random Forest included phosphatidylcholines, plasmalogens, and sphingolipids. An AUC of 0.86 discriminated active UC from normal patients using the Random Forest method and 0.80 using the LASSO method. CONCLUSION These results are the first-ever depiction of harnessing the diagnostic utility of PBEs through lipid profiling. Differences in PBE lipid composition accurately discriminated active UC patients from normal patients paving the way for a diagnostic liquid biopsy for patients with IBD.
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Chen, Zhangjian, Jiaqi Shi, Yi Zhang, Jiahe Zhang, Shuqiang Li, Li Guan, and Guang Jia. "Lipidomics Profiles and Lipid Metabolite Biomarkers in Serum of Coal Workers’ Pneumoconiosis." Toxics 10, no. 9 (August 26, 2022): 496. http://dx.doi.org/10.3390/toxics10090496.
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As a serious occupational pulmonary fibrosis disease, pneumoconiosis still lacks effective biomarkers. Previous studies suggest that pneumoconiosis may affect the body’s lipid metabolism. The purpose of this study was to explore lipidomics profiles and lipid metabolite biomarkers in the serum of coal workers’ pneumoconiosis (CWP) by a population case-control study. A total of 150 CWP cases and 120 healthy controls from Beijing, China were included. Blood lipids were detected in serum biochemistry. Lipidomics was performed in serum samples for high-throughput detection of lipophilic metabolites. Serum high density lipoprotein cholesterol (HDL-C) decreased significantly in CWP cases. Lipidomics data found 131 differential lipid metabolites between the CWP case and control groups. Further, the top eight most important differential lipid metabolites were screened. They all belonged to differential metabolites of CWP at different stages. However, adjusting for potential confounding factors, only three of them were significantly related to CWP, including acylhexosylceramide (AHEXCER 43:5), diacylglycerol (DG 34:8) and dimethyl-phosphatidylethanolamine (DMPE 36:0|DMPE 18:0_18:0), of which good sensitivity and specificity were proven. The present study demonstrated that lipidomics profiles could change significantly in the serum of CWP patients and that the lipid metabolites represented by AHEXCER, DG and DMPE may be good biomarkers of CWP.
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Liu, Ding, Maureen Meister, Shiying Zhang, Chi-In Vong, Shuaishuai Wang, Ruixie Fang, Lei Li, Peng George Wang, Pierre Massion, and Xiangming Ji. "Identification of lipid biomarker from serum in patients with chronic obstructive pulmonary disease." Respiratory Research 21, no. 1 (September 21, 2020). http://dx.doi.org/10.1186/s12931-020-01507-9.
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Abstract Background Chronic obstructive pulmonary disease (COPD) is the third leading cause of death in the United States with no effective treatment. The current diagnostic method, spirometry, does not accurately reflect the severity of COPD disease status. Therefore, there is a pressing unmet medical need to develop noninvasive methods and reliable biomarkers to detect early stages of COPD. Lipids are the fundamental components of cell membranes, and dysregulation of lipids was proven to be associated with COPD. Lipidomics is a comprehensive approach to all the pathways and networks of cellular lipids in biological systems. It is widely used for disease diagnosis, biomarker identification, and pathology disorders detection relating to lipid metabolism. Methods In the current study, a total of 25 serum samples were collected from 5 normal control subjects and 20 patients with different stages of COPD according to the global initiative for chronic obstructive lung disease (GOLD) (GOLD stages I ~ IV, 5 patients per group). After metabolite extraction, lipidomic analysis was performed using electrospray ionization mass spectrometry (ESI-MS) to detect the serum lipid species. Later, the comparisons of individual lipids were performed between controls and patients with COPD. Orthogonal projections to latent structures discriminant analysis (OPLS-DA) and receiver operating characteristic (ROC) analysis were utilized to test the potential biomarkers. Finally, correlations between the validated lipidomic biomarkers and disease stages, age, FEV1% pack years and BMI were evaluated. Results Our results indicate that a panel of 50 lipid metabolites including phospholipids, sphingolipids, glycerolipids, and cholesterol esters can be used to differentiate the presence of COPD. Among them, 10 individual lipid species showed significance (p < 0.05) with a two-fold change. In addition, lipid ratios between every two lipid species were also evaluated as potential biomarkers. Further multivariate data analysis and receiver operating characteristic (ROC: 0.83 ~ 0.99) analysis suggest that four lipid species (AUC:0.86 ~ 0.95) and ten lipid ratios could be potential biomarkers for COPD (AUC:0.94 ~ 1) with higher sensitivity and specificity. Further correlation analyses indicate these potential biomarkers were not affected age, BMI, stages and FEV1%, but were associated with smoking pack years. Conclusion Using lipidomics and statistical methods, we identified unique lipid signatures as potential biomarkers for diagnosis of COPD. Further validation studies of these potential biomarkers with large population may elucidate their roles in the development of COPD.
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Lu, Jiawei, Yunke Guo, Yan Lu, Wei Ji, Lili Lin, Wenjuan Qian, Wenjun Chen, et al. "Untargeted lipidomics reveals specific lipid abnormalities in Sjögren’s syndrome." Rheumatology, September 10, 2020. http://dx.doi.org/10.1093/rheumatology/keaa456.
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Abstract Objective The relationship between serum lipid variations in SS and healthy controls was investigated to identify potential predictive lipid biomarkers. Methods Serum samples from 230 SS patients and 240 healthy controls were collected. The samples were analysed by ultrahigh-performance liquid chromatography coupled with Q Exactive™ spectrometry. Potential lipid biomarkers were screened through orthogonal projection to latent structures discriminant analysis and further evaluated by receiver operating characteristic analysis. Results A panel of three metabolites [phosphatidylcholine (18:0/22:5), triglyceride (16:0/18:0/18:1) and acylcarnitine (12:0)] was identified as a specific biomarker of SS. The receiver operating characteristic analysis showed that the panel had a sensitivity of 84.3% with a specificity of 74.8% in discriminating patients with SS from healthy controls. Conclusion Our approach successfully identified serum biomarkers associated with SS patients. The potential lipid biomarkers indicated that SS metabolic disturbance might be associated with oxidized lipids, fatty acid oxidation and energy metabolism.
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Gao, Jiawei, Shulan Xu, Rong Bi, Yaoyao Wang, Yang Ding, Hong Che, Jing Zhang, Peng Yao, Jie Shi, and Meixun Zhao. "Vertical distributions of lipid biomarkers in spring and summer in coastal regions of the East China Sea." Frontiers in Marine Science 11 (May 14, 2024). http://dx.doi.org/10.3389/fmars.2024.1384334.
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Lipid biomarkers are amongst the most widely used proxies in studies of the structure and functioning of marine food webs. However, vertical distributions of lipid biomarkers and their controlling mechanisms remain unclear, especially in highly dynamic coastal ecosystems. Here we tested vertical profiles of key lipid biomarkers (sterols and fatty acids) in suspended particles and their correlations with water masses in spring of 2017 and summer of 2018 in the Zhejiang coasts of the East China Sea. The Changjiang Diluted Water, the Taiwan Strait Water and the Kuroshio Subsurface Water showed strong contributions in the surface layer in spring, the surface layer in summer, and the deep layer in both seasons, respectively. Accordingly, lipid biomarker composition also varied between different water layers. Overall, lipid biomarker concentrations in the surface layer were around 2 ~ 7 times higher than those in the deep layer, indicating high phytoplankton biomass in the surface layer. The ratio of docosahexaenoic acid to eicosapentaenoic acid was also higher in the surface layer, especially in the south of our study region, suggesting high nutritional quality of particulate organic matters in the surface layer. Significant correlations between the depth profiles of lipid biomarkers and water masses suggested the control of water masses on lipid biomarker production. The distribution patterns of lipid biomarkers in our study are consistent with previous findings on zooplankton grazing and fish larvae, highlighting the significance of lipid biomarkers as trophic markers to study food web structure and functioning in highly dynamic coasts.
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Lyu, Lingna, Hongyan Jia, Qiuyue Liu, Wenxia Ma, Zihui Li, Liping Pan, and Xiuli Zhang. "Individualized lipid profile in urine-derived extracellular vesicles from clinical patients with Mycobacterium tuberculosis infections." Frontiers in Microbiology 15 (May 30, 2024). http://dx.doi.org/10.3389/fmicb.2024.1409552.
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BackgroundLipids are a key nutrient source for the growth and reproduction of Mycobacterium tuberculosis (Mtb). Urine-derived extracellular vesicles (EVs), because of its non-invasive sampling, lipid enrichment, and specific sorting character, have been recognized as a promising research target for biomarker discovery and pathogenesis elucidation in tuberculosis (TB). We aim to profile lipidome of Mtb-infected individuals, offer novel lipid signatures for the development of urine-based TB testing, and provide new insights into the lipid metabolism after Mtb infection.MethodsUrine-derived extracellular vesicles from 41 participants (including healthy, pulmonary tuberculosis, latent tuberculosis patients, and other lung disease groups) were isolated and individually detected using targeted lipidomics and proteomics technology platforms. Biomarkers were screened by multivariate and univariate statistical analysis and evaluated by SPSS software. Correlation analyses were performed on lipids and proteins using the R Hmisc package.ResultsOverall, we identified 226 lipids belonging to 14 classes. Of these, 7 potential lipid biomarkers for TB and 6 for latent TB infection (LTBI) were identified, all of which were classified into diacylglycerol (DAG), monoacylglycerol (MAG), free fatty acid (FFA), and cholesteryl ester (CE). Among them, FFA (20:1) was the most promising biomarker target in diagnosing TB/LTBI from other compared groups and also have great diagnostic performance in distinguishing TB from LTBI with AUC of 0.952. In addition, enhanced lipolysis happened as early as individuals got latent Mtb infection, and ratio of raft lipids was gradually elevated along TB progression.ConclusionThis study demonstrated individualized lipid profile of urinary EVs in patients with Mtb infection, revealed novel potential lipid biomarkers for TB/LTBI diagnosis, and explored mechanisms by which EV lipid raft-dependent bio-processes might affect pathogenesis. It lays a solid foundation for the subsequent diagnosis and therapeutic intervention of TB.
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Zhu, Qingfu, Hengrui Li, Zheng Ao, Hao Xu, Jiaxin luo, Connor Kaurich, Rui Yang, et al. "Lipidomic identification of urinary extracellular vesicles for non-alcoholic steatohepatitis diagnosis." Journal of Nanobiotechnology 20, no. 1 (July 27, 2022). http://dx.doi.org/10.1186/s12951-022-01540-4.
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Abstract Background and Aims Non-alcoholic fatty liver disease (NAFLD) is a usual chronic liver disease and lacks non-invasive biomarkers for the clinical diagnosis and prognosis. Extracellular vesicles (EVs), a group of heterogeneous small membrane-bound vesicles, carry proteins and nucleic acids as promising biomarkers for clinical applications, but it has not been well explored on their lipid compositions related to NAFLD studies. Here, we investigate the lipid molecular function of urinary EVs and their potential as biomarkers for non-alcoholic steatohepatitis (NASH) detection. Methods This work includes 43 patients with non-alcoholic fatty liver (NAFL) and 40 patients with NASH. The EVs of urine were isolated and purified using the EXODUS method. The EV lipidomics was performed by LC-MS/MS. We then systematically compare the EV lipidomic profiles of NAFL and NASH patients and reveal the lipid signatures of NASH with the assistance of machine learning. Results By lipidomic profiling of urinary EVs, we identify 422 lipids mainly including sterol lipids, fatty acyl lipids, glycerides, glycerophospholipids, and sphingolipids. Via the machine learning and random forest modeling, we obtain a biomarker panel composed of 4 lipid molecules including FFA (18:0), LPC (22:6/0:0), FFA (18:1), and PI (16:0/18:1), that can distinguish NASH with an AUC of 92.3%. These lipid molecules are closely associated with the occurrence and development of NASH. Conclusion The lack of non-invasive means for diagnosing NASH causes increasing morbidity. We investigate the NAFLD biomarkers from the insights of urinary EVs, and systematically compare the EV lipidomic profiles of NAFL and NASH, which holds the promise to expand the current knowledge of disease pathogenesis and evaluate their role as non-invasive biomarkers for NASH diagnosis and progression.
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Deng, Tianqin, Wanxue Wang, Zhihong Fu, Yuli Xie, Yonghong Zhou, Jiangbo Pu, Kexin Chen, Bing Yao, Xuemei Li, and Jilong Yao. "Lipidomics random forest algorithm of seminal plasma is a promising method for enhancing the diagnosis of necrozoospermia." Metabolomics 20, no. 3 (May 21, 2024). http://dx.doi.org/10.1007/s11306-024-02118-x.
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Abstract 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.
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Chen, Xiaoli, Yong Zhu, Mayumi Jijiwa, Masaki Nasu, Junmei Ai, Shengming Dai, Bin Jiang, Jicai Zhang, Gang Huang, and Youping Deng. "Identification of plasma lipid species as promising diagnostic markers for prostate cancer." BMC Medical Informatics and Decision Making 20, S9 (September 2020). http://dx.doi.org/10.1186/s12911-020-01242-7.
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Abstract Background Prostate cancer is a very common and highly fatal in men. Current non-invasive detection methods like serum biomarker are unsatisfactory. Biomarkers with high accuracy for diagnostic of prostate cancer are urgently needed. Many lipid species have been found related to various cancers. The purpose of our study is to explore the diagnostic value of lipids for prostate cancer. Results Using triple quadruple liquid chromatography electrospray ionization tandem mass spectrometry, we performed lipidomics profiling of 367 lipids on a total 114 plasma samples from 30 patients with prostate cancer, 38 patients with benign prostatic hyperplasia (BPH), and 46 male healthy controls to evaluate the lipids as potential biomarkers in the diagnosis of prostate cancer. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database was used to construct the potential mechanism pathway. After statistical analysis, five lipids were identified as a panel of potential biomarkers for the detection of prostate cancer between prostate cancer group and the BPH group; the sensitivity, specificity, and area under curve (AUC) of the combination of these five lipids were 73.3, 81.6%, and 0.800, respectively. We also identified another panel of five lipids in distinguishing between prostate cancer group and the control group with predictive values of sensitivity at 76.7%, specificity at 80.4%, and AUC at 0.836, respectively. The glycerophospholipid metabolism pathway of the selected lipids was considered as the target pathway. Conclusions Our study indicated that the identified plasma lipid biomarkers have potential in the diagnosis of prostate cancer.
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Long, Nguyen Phuoc, Nguyen Ky Anh, Nguyen Thi Hai Yen, Nguyen Ky Phat, Seongoh Park, Vo Thuy Anh Thu, Yong-Soon Cho, Jae-Gook Shin, Jee Youn Oh, and Dong Hyun Kim. "Comprehensive lipid and lipid-related gene investigations of host immune responses to characterize metabolism-centric biomarkers for pulmonary tuberculosis." Scientific Reports 12, no. 1 (August 4, 2022). http://dx.doi.org/10.1038/s41598-022-17521-4.
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AbstractDespite remarkable success in the prevention and treatment of tuberculosis (TB), it remains one of the most devastating infectious diseases worldwide. Management of TB requires an efficient and timely diagnostic strategy. In this study, we comprehensively characterized the plasma lipidome of TB patients, then selected candidate lipid and lipid-related gene biomarkers using a data-driven, knowledge-based framework. Among 93 lipids that were identified as potential biomarker candidates, ether-linked phosphatidylcholine (PC O–) and phosphatidylcholine (PC) were generally upregulated, while free fatty acids and triglycerides with longer fatty acyl chains were downregulated in the TB group. Lipid-related gene enrichment analysis revealed significantly altered metabolic pathways (e.g., ether lipid, linolenic acid, and cholesterol) and immune response signaling pathways. Based on these potential biomarkers, TB patients could be differentiated from controls in the internal validation (random forest model, area under the curve [AUC] 0.936, 95% confidence interval [CI] 0.865–0.992). PC(O-40:4), PC(O-42:5), PC(36:0), and PC(34:4) were robust biomarkers able to distinguish TB patients from individuals with latent infection and healthy controls, as shown in the external validation. Small changes in expression were identified for 162 significant lipid-related genes in the comparison of TB patients vs. controls; in the random forest model, their utilities were demonstrated by AUCs that ranged from 0.829 to 0.956 in three cohorts. In conclusion, this study introduced a potential framework that can be used to identify and validate metabolism-centric biomarkers.