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Note: Intro -- Metabolomics for Biomedical Research -- Copyright -- Contents -- Contributors -- Preface -- Chapter 1: Introduction to metabolomics -- 1. What is metabolomics? -- 2. Flow chart of metabolomic research -- 2.1. Scientific question -- 2.2. Study design: General aspects -- 2.3. Study design: Pilot study and power calculation -- 2.4. Study design: Sample identity -- 2.5. Study design: Preanalytics -- 2.6. Study design: Sample matrix -- 2.7. Study design: Confounders -- 2.8. Study design: Time schedule -- 2.9. Study design: Randomization -- 2.10. Study design: Budgeting and resources -- 2.11. Study design: Contingency plan -- 2.12. Study design: Legal aspects -- 2.13. Study design: Governance -- 2.14. Study design: Replication -- 2.15. Analytics -- 2.16. Data validation -- 2.17. Data release -- 3. Future trends -- 3.1. Standardization -- 3.2. Coverage and speed -- 3.3. Accessibility -- 3.4. Mechanisms of disease -- 3.5. Biomarkers -- Acknowledgments -- References -- Chapter 2: Confounders in metabolomics -- 1. Introduction to the confounder concept -- 2. Important confounders in metabolomics -- 2.1. Genetic background and ethnicity -- 2.2. Sex -- 2.3. Age -- 2.4. Nutrition -- 3. Body mass index -- 3.1. Physical activity -- 3.2. Alcohol -- 3.3. Smoking -- 3.4. Stress -- 3.5. Circadian rhythm -- 3.6. Hormonal status -- 3.7. Medication -- 3.8. Lifestyle -- 4. Disease -- 5. Consequences for study design -- 6. Future trends -- References -- Chapter 3: Pre-analytics in biomedical metabolomics -- 1. Introduction -- 2. Principles to be considered before the collection of body fluids for biomedical metabolomics -- 2.1. Patients instructions and sophisticated questionaries can avoid outliers -- Points to be considered before sample collection -- Suggestions of choice for additional points in a study questionnaire. , 2.1.1. Control groups: Undiagnosed, asymptomatic diseases of apparently healthy subjects can bias metabolomics findings -- 2.2. Sample collectors for blood and urine in biomedical metabolomics studies -- 2.2.1. Blood collection tubes are potential sources of chemical noise disturbing metabolomics analysis -- 2.2.2. Additives in plasma blood collection tubes which can be used and which should be avoided -- 2.2.3. Additives in serum blood collection tubes may lead to chemical noise -- 2.2.4. Dried blood spot metabolomics: Stability of the detected mixture of metabolites from erythrocytes, leukocytes, thr ... -- 2.2.5. Urine cups and containers: Pretests are imperative to avoid analytical problems -- 2.3. Animal studies: Some pre-analytical characteristics are different from human biomedical metabolomics -- 3. Collection, handling, and storage of body fluids for metabolomics investigations -- 3.1. Blood -- 3.1.1. Handling of whole blood can greatly affect the sample quality and the metabolome of plasma and serum -- Plasma: Time and temperature until plasma separation is crucial -- Serum: The coagulation process must be tightly controlled -- Quality check of serum and plasma: Deviations from a pre-analytical protocol or SOP can be revealed by the quantification o ... -- 3.1.2. The metabolome of plasma and serum is different -- 3.1.3. The applied centrifugation force affects the metabolome in plasma -- 3.1.4. Effects of common pre-analytical errors on plasma and serum metabolomes -- Hemolysis alters the metabolome -- Effects of sample storage, and freeze and thaw cycles on the metabolome -- Storage at -20C for short term is acceptable but not preferable to lower temperatures -- Storage at -80C or lower is recommended -- Repetitive thawing and refreezing of sample aliquots should be avoided, in particular if lipids are part of the metabolite. , 3.2. Urine -- 3.2.1. Different types of urine specimen reflect different physiological states and metabolic active cells in pathologica ... -- 3.2.2. Storage of urine samples during collection at 4C minimize pre-analytical alterations -- 3.2.3. Long-term storage of urine samples at -80C or lower is recommended -- 3.3. Cerebrospinal fluid: Cooling at once after collection is recommended -- 3.4. Feces: Cooling, homogenizing, and sample preparation as soon as possible are pre-analytical essentials for fecal met ... -- 4. Key points for pre-analytical SOPs for blood and urine metabolomics -- Examples of potential limitations hindering the generation of perfect pre-analytical SOPs -- References -- Chapter 4: Mass spectrometry-based metabolite analytics -- 1. Mass spectrometric scan modes -- 1.1. Full-scan mass spectrometry -- 1.2. Selected-ion monitoring -- 1.3. Tandem mass spectrometry (MS/MS) -- 1.4. Product-ion scanning -- 1.5. Precursor-ion scanning -- 1.6. Neutral loss scanning -- 2. Biological sample preparation -- 2.1. Chromatographic separation -- 2.2. Chemical derivatization -- 2.2.1. Silylation -- 2.2.2. Acylation -- 2.2.3. Alkylation -- 2.2.4. Dansylation -- 2.2.5. Sequential derivatization -- 2.3. Solid-phase extraction -- 3. Method validation for newly developed analytics -- 3.1. Analytical sensitivity and specificity -- 3.2. Reproducibility -- 3.3. Stability and robustness -- 3.4. Isotope-dilution mass spectrometry in exact quantification -- References -- Chapter 5: Computational analysis of metabolic data -- 1. Why bioinformatics is needed to analyze pathological alterations of metabolism -- 2. Data processing methods to achieve meaningful biological comparisons -- 2.1. Missing values -- 2.2. Sample normalization-Reduce sample to sample variation -- 2.3. Data scaling -- 2.4. Data transformation. , 3. Univariate methods to select important metabolites from metabolomics data sets -- 4. Machine learning methods -- 4.1. Dimensionality reduction methods -- 4.2. Classification models -- 5. How to extract functional information -- 5.1. Correlation networks -- 5.2. Statistical enrichment analysis -- 5.3. Pathway analysis for untargeted metabolomics -- 5.4. Limitations of pathway analysis -- References -- Chapter 6: Genetic influence on the metabolome -- 1. Introduction -- 2. GWAS: Basic approaches -- 3. Metabolomics GWAS -- 3.1. Proximity from gene to phenotype -- 3.2. Additional analytical considerations in metabolomics GWAS -- 3.3. Genetic architecture of the metabolome -- 4. The microbiome and the metabolome -- 5. Integrating genomics and metabolomics: Potential applications -- 6. Future directions -- References -- Chapter 7: The use of animal models in metabolomics -- 1. Introduction -- 2. What makes a good animal model for metabolomics studies? -- 3. The use of animal models to study tissue-specific metabolic changes -- 4. The use of animal models in whole organism physiology -- 5. Pitfalls and complications of animal models -- 6. Conclusions and future trends -- References -- Chapter 8: Metabolomics applied to cultured human and animal cells -- 1. Introduction -- 2. Advantages and disadvantages of cultured cell metabolomics -- 3. Requirements for cell culture metabolomics -- 3.1. Study design -- 3.2. Ensuring the identity of used cell lines -- 3.3. Growth conditions -- 3.4. Metabolism quenching and sample collection -- 3.5. Normalization -- 4. Two-dimensional cell culture or three-dimensional cell culture-Benefits and challenges -- 5. Applications of metabolomics in cell culture -- 5.1. Pharmacology -- 5.2. Toxicology -- 5.3. Stem cells/induced pluripotent stem cell research -- 5.4. Human diseases -- 5.5. Multiomics and systems biology. , References -- Chapter 9: Drug development -- 1. Introduction -- 2. Metabolomics in support of target identification and validation -- 2.1. Diseases have a metabolic footprint -- 2.2. Metabolomics supports the understanding of systems regulations -- 2.3. Metabolomics in phenotypic screens -- 3. Metabolites as biomarkers-Proximal and distal biomarkers -- 4. Metabolomics in drug disposition -- 5. Metabolites as markers of toxicity -- 6. Pharmacometabolomics and personalized medicine -- 7. Drug resistance -- 8. Perspectives -- References -- Chapter 10: Biomarker discovery -- 1. Introduction -- 2. Why developing new biomarkers? -- 3. Metabolomics and biomarker discovery -- 3.1. Study design -- 3.2. Biospecimens -- 3.3. Analytical techniques -- 3.3.1. Nuclear magnetic resonance spectroscopy vs mass spectrometry -- 3.3.2. Targeted metabolomics -- 3.3.3. Untargeted metabolomics -- 3.3.4. Metabolite annotation in untargeted metabolomics -- 3.3.5. Semiquantitative analyses in untargeted metabolomics -- 3.4. Statistical approaches -- 4. Biomarker discovery in epidemiological studies -- 4.1. Discovery of biomarkers of exposure to disease risk factors -- 4.2. Biomarkers of disease risk -- 5. Biomarker validation -- 6. Conclusions -- References -- Chapter 11: The role of metabolomics in personalized medicine -- 1. Introduction to personalized medicine -- 2. The dynamics of human metabolome is key to precision medicine efforts -- 3. Metabolomics is a goldmine of research data for personalized outcomes -- 4. Metabolomics in a healthy state is a valuable baseline reference -- 5. Development of a pathological state leads to a deviation from baseline metabolome -- 6. The journey of metabolomics: From research laboratories to clinical applications -- 7. What defines a successful clinical translation of a metabolomics research?. , 8. What are the barriers to entry for successful clinical applications of metabolomics?.