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Download Author Version (PDF) RSC Advances This is an Accepted Manuscript, which has been through the Royal Society of Chemistry peer review process and has been accepted for publication. Accepted Manuscripts are published online shortly after acceptance, before technical editing, formatting and proof reading. Using this free service, authors can make their results available to the community, in citable form, before we publish the edited article. This Accepted Manuscript will be replaced by the edited, formatted and paginated article as soon as this is available. You can find more information about Accepted Manuscripts in the Information for Authors. Please note that technical editing may introduce minor changes to the text and/or graphics, which may alter content. The journal’s standard Terms & Conditions and the Ethical guidelines still apply. In no event shall the Royal Society of Chemistry be held responsible for any errors or omissions in this Accepted Manuscript or any consequences arising from the use of any information it contains. www.rsc.org/advances Page 1 of 34 RSC Advances Color graphic The figure displayed the flow chart of the study. Manuscript Accepted Text Eight breath biomarkers of T2DM were discovered by a newly SPME-GC-MS based metabolic profiling tool. Advances RSC RSC Advances Page 2 of 34 Discovery of potential biomarkers in exhaled breath for diagnosis of type 2 diabetes mellitus based on GC-MS with metabolomics Yanyue Yan 1, Qihui Wang 1, Wenwen Li 1, Zhongjun Zhao 2, Xin Yuan 2, Yanping Huang 2, and Yixiang Duan 3* 1Research Center of Analytical Instrumentation, Analytical & Testing Center, Sichuan University, Chengdu, P. R. China Manuscript 2Research Center of Analytical Instrumentation, College of Chemistry, Sichuan University, Chengdu, P. R. China 3Research Center of Analytical Instrumentation, College of Life Sciences, Sichuan Accepted University, Chengdu, P. R. China. Advances *Corresponding author contact information: Prof. Yixiang Duan, Research Center of Analytical Instrumentation, Sichuan University, 29 Wangjiang Road, Chengdu RSC 610064, China. E-mail: [email protected]; Fax: +86 028 85418180; Tel: +86 028 85418180 Page 3 of 34 RSC Advances Abstract The aim of the study was to apply the gas chromatography-mass spectrometry (GC-MS) combined with metabolomics approach to identify distinct metabolic signatures of type 2 diabetes mellitus (T2DM) and healthy controls from exhaled breath, which are characterized by a number of differentially expressed breath metabolites. In this study, breath samples of patients with type 2 diabetes mellitus (T2DM, n = 48) and healthy subjects (n = 39) were analyzed by GC-MS. Multivariate data analysis including principal component analysis (PCA) and orthogonal partial Manuscript least squares discriminant analysis (OPLS-DA) was successfully applied to discriminate the T2DM and healthy controls. Eight specific metabolites were identified and may be used as potential biomarkers for diagnosis of T2DM. Isopropanol and 2,3,4-trimethylhexane, 2,6,8-trimethyldecane, tridecane and Accepted undecane in combination might be the best biomarkers for the clinical diagnosis of T2DM with a sensitivity of 97.9% and a specificity of 100%. The study indicated that this breath metabolite profiling approach may be a promising non-invasive diagnostic tool for T2DM. Advances Keywords: Biomarkers, Breath analysis, Non-invasive diagnosis, Metabolomics, Type 2 diabetes mellitus RSC Abbreviations: T2DM, type 2 diabetes mellitus; GC-MS, gas chromatography-mass spectrometry; TIC, total ion chromatogram; NIST, National Institute of Standards and Technology; SPME, solid phase micro extraction; MVA, multivariate statistical RSC Advances Page 4 of 34 analysis; PCA, principal component analysis; PLS-DA, partial least square discriminant analysis; OPLS-DA, orthogonal partial least squares discriminant analysis; VIP, variable importance in the projection; ROC, receiver operator characteristic curves; AUC, areas under curve; VOCs, volatile organic compounds; NADH, nicotinamide adenine dinucleotide. Manuscript Accepted Advances RSC Page 5 of 34 RSC Advances 1. Introduction Non-invasive breath diagnosis with metabolomics and metabolic profiling add an important scientific value to established biomarkers 1, 2. Furthermore, it is known that volatile breath compounds have important relationships with metabolism and pathological states, such as acetone response to glucose and fat metabolism in uncontrolled diabetes3-6. Therefore, metabolic monitoring of volatile target compounds in exhaled breath is quite a considerable tool for the investigation of some diseases. Type 2 diabetes mellitus (T2DM) is a metabolic disorder that is characterized by high blood glucose in the context of insulin resistance and relative insulin deficiency 7. Rates of T2DM have increased Manuscript markedly since 1960 in parallel with obesity: As of 2010 there are approximately 285 million people with the disease compared to around 30 million in 1985 8, 9. Traditional diagnosis and management of T2DM hinges on blood tests (for plasma glucose and glycated hemoglobin), which may be expensive, unpractical, and even painful. Frequent blood testing is especially necessary for patients undergoing Accepted insulin treatment. Therefore large resources have been invested worldwide in developing non-invasive devices for diabetes diagnosis and management. Breath analysis, as is one of the most promising approaches for clinical routine, is increasingly being exploited for clinical diagnosis 10 . Breath testing is non-invasive offering an attractive, inexpensive, and patient-friendly evaluation. Furthermore, sample Advances collection is easy and can even be obtained from unconscious patients. Diabetes and its related dysmetabolic states could clearly greatly benefit from the introduction of similar non-invasive tests for RSC diagnostic, preventive and monitoring purposes too. This study investigated breath analysis as a diagnostic tool for T2DM and identified the biomarkers in the breath volatile organic compounds (VOCs). Due to its higher consistency, robustness, and sensitivity, gas chromatography coupled with mass RSC Advances Page 6 of 34 spectrometry (GC-MS) is one of the most frequently used analytical techniques for profiling primary metabolites 11 . Combined with public databases, the application of GC-MS for compound identification makes it of great value for metabolomics 12 . It has been widely applied in disease biomarker discovery. As a metabolite profiling technique, GC-MS has been used to detect and discriminate various diseases, such as diabetic kidney disease 13 , colon cancer 14 , type 2 diabetes mellitus 15 , cirrhosis and hepatic encephalopathy 2. For the GC-MS experiments, we performed a solid phase micro extraction (SPME) coupled with GC-MS in metabolic profiling for analysis of T2DM. SPME is a simple, rapid and solvent- free sample preparation technique that can be directly coupled to GC-MS 16 . It has several advantages including, faster extraction and desorption, direct compatibility with the GC inlet, and less Manuscript required sample. The breath compounds were identified as many as possible by the use of SPME. Metabolomics is the scientific study of chemical processes involving metabolites. The metabolome represents the collection of all metabolites in a biological cell, tissue, organ or organism, which are the Accepted end products of cellular processes 17 . The idea that biological fluids and tissues have important relationships with the health of an individual has existed for a long time 18 . Even in ancient times, physicians recognized that certain breath odors were associated with specific pathological states, such as a ‘fishy’ smell in response to renal failure and a ‘fruity’ smell will associate with diabetes 19 . Ancient Chinese doctors used ants to detect whether the urine of patients contained high levels of glucose, and Advances hence detect diabetes 20 . Recently, many scientists have studied metabolic diseases using metabolomics RSC as a main technique. For example, the Wang-Sattler group have used statistical and bioinformatical methods for analyzing metabolite concentration profiles for the identification of candidate biomarkers of T2DM 21, 22 . It was demonstrated that GC-MS could be used to measure compounds present in human urine and tissue extracts by Horning et al. in 197123 . Followed, the Horning group, along with that of Page 7 of 34 RSC Advances Linus Pauling and Arthur B Robinson led the development of GC-MS methods to monitor the metabolites present in urine through the 1970s 24 . The role of metabolomics and metabolic profiling coupled with GC-MS is a rapidly growing field in disease diagnosis 15, 25 , therapeutics 26, 27 , functional genomic 28, 29 and toxicology studies 30, 31 . In this study, the GC-MS data would then be coupled with chemometric methods to develop models to identify the biomarkers of T2DM. Data obtained from metabolomics studies are complex and diverse 32 . Therefore, the common statistical approach used in metabolomics data analysis is based on multivariate statistical analysis (MVA) for biomarker discovery including principal component analysis (PCA) 33, 34 , partial least square discriminant analysis (PLS-DA) 35, 36 and orthogonal partial least squares discriminant analysis (OPLS-DA) 37 . The method is Manuscript the simplest of the true eigenvector-based multivariate analyses, which is mostly used as a tool in exploratory data analysis and for making predictive models. PCA can supply the
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