Editorial
For reprint orders, please contact [email protected]
Preanalytics: what can metabolomics learn from clinical chemistry? “
After blood drawing the metabolism of billions of red and white blood cells as well as of platelets is still active resulting in the uptake and release of metabolites, thereby altering the metabolite profile in the sample tube.
”
Keywords: biobank • blood • body fluids • metabolic profiling • metabolite profile • metabolomics • plasma • preanalytical • sample handling • serum
The analytical quality in metabolomics projects is of utmost importance as a guarantor for reliable results and accordingly for the success of the entire project. However, the highest analytical quality employing the most sophisticated instruments is in vain if the sample quality is poor. This means that in addition to the analytical performance the major key for the success of metabolomics projects is the phase before the analyses, the so called preanalytical phase. In biomedical metabolomics projects the drawing, handling and processing of whole blood are these important preanalytical steps. A general opinion of medical researchers is that blood collection, which is often embedded in the clinical workflow, is the easy part in complex clinical studies. Therefore more attention is paid to other steps of these studies. However, tremendous negative effects on the outcome of clinical studies may result from inaccuracies and errors occurring during specimen collection and handling. Poor sample quality caused by systematic as well as accidental preanalytical errors could also mean that a lot of money is spent over many years for the storage of these potentially useless samples in biobanks. The actual quality of samples used for biomedical metabolomics studies is an underestimated pitfall. How may experiences from clinical chemistry contribute to avoid misleading results in biomedical metabolomics studies? Since the mid of the 19th century simplified methods suitable for the clinical routine measurement of various parameters in body fluids including metabolites like glucose [1] and urate [2]
10.4155/BIO.15.23 © 2015 Future Science Ltd
have been established. At about the same time chemical laboratories were installed in university hospitals, first in Germany and Austria. In the following years in many countries a new discipline named clinical chemistry was established. Since that time countless observations and scientific investigations leading to profound expert knowledge about specimen collection, handling and measurement in complex matrices like body fluids and tissue extracts came to pass in clinical chemistry. However, it took until the 1970s for the preanalytical phase to be introduced as a separate discipline of research focusing on the investigation of effects on the sample quality and the analytical result by, for example, specimen handling, pretreatment procedures for body fluids, short- and long-term storage conditions, etc. It is worth noting, the preanalytic phase may account for 60–80% of laboratory testing errors in daily routine clinical diagnostics [3–5] .
Rainer Lehmann Department of Clinical Chemistry & Pathobiochemistry, University Hospital Tuebingen, Tuebingen, Germany and Department of Molecular Diabetology, Institute for Diabetes Research & Metabolic Diseases of the Helmholtz Centre Munich at the University of Tuebingen, Tuebingen, Germany and German Center for Diabetes Research (DZD), Tuebingen, Germany [email protected]
Factors affecting the metabolite profile in blood samples In general the concentration of analytes in the blood is tightly controlled, often in a very narrow range. But under various (patho) physiological conditions, like fasting/feeding, exercise, diabetes, cancer and many others, huge and/or very fast alterations can occur. Besides endogenous effectors, these changes may also be caused by other factors, like drugs. However, as a third possibility, such alterations can also occur ex vivo, in other words, based on conditions which are not related to any action inside the human
Bioanalysis (2015) 7(8), 927–930
part of
ISSN 1757-6180
927
Editorial Lehmann body but depend on processes occurring inside the blood collection tube. Exposure of whole blood samples to room temperature may have tremendous effects on the composition of the entire metabolome inside the sample tube [6–8] . After blood drawing the metabolism of billions of red and white blood cells as well as of platelets is still active resulting in the uptake and release of metabolites, thereby altering the metabolite profile in the sample tube. Thus a comparison of two groups of individuals or patients may lead to the detection of ‘biomarkers’ based on differences in the number of platelets, erythrocytes and/or leukocytes, as well as differences in the metabolic activity of these blood cells.
“For all kinds of metabolomics analysis, targeted and in particular untargeted approaches, the preanalytical phase is a very important step which can greatly bias the final results.
”
Ex vivo changes in the metabolite profile can almost be prevented if whole blood is placed immediately in ice water after drawing [6–8] , which, unfortunately, is not possible for serum sample tubes (see below). After separation of plasma or serum from blood cells the metabolite profile is much more stable [9,10] . Therefore specimen processing (centrifugation, aliquotation, freezing) should be performed as soon as possible after blood drawing. This recommendation may stand in marked contrast to preanalytical standard operating procedures (SOPs) developed and optimized for clinical trails, especially in multi-center studies when compromises for the SOP have been made so that every participating hospital can fulfil the preanalytical SOP. For example, recommendations for specimen collection and handling developed for international breast cancer clinical trials discuss the exposure of whole blood for up to 8 h to room temperature before the separation of plasma or serum from blood cells, because most hospitals can manage this requirement well [11] . Therefore already existing preanalytical SOPs of clinical cooperation partners should be carefully checked by analytical (bio)chemists involved in the planning phase of clinical studies. It is always challenging to balance the needs for perfect sample quality for -omics approaches against the feasibility of preanalytical recommendations by the clinical cooperation partner. Specific preanalytical recommendations for biomedical metabolomics studies investigating blood, including a draft for a SOP, were recently described [6] . Hemolysis is another common factor affecting the metabolome in blood. In clinical studies it is one of the major pitfalls during blood drawing, which can
928
Bioanalysis (2015) 7(8)
lead to pronounced changes of around 18% of all detected metabolite ion signals, for example, lysophosphatidyl choline species, N-acetylornithine, sphingosine-1-phosphate, carnitine and tryptophane, in a nontargeted approach [6] . Serum or plasma, which material is preferable? For historical reasons serum is the dominating biospecimen collected in clinical studies and stored in biobanks. However, blood drawn for the generation of serum needs to be exposed to room temperature for a defined time, even when coagulation enhancers like kaolin are included in the sample collection tube. Until recently the recommended time span for a proper coagulation process was 30–60 min exposure to room temperature, with or without coagulation enhancers respectively. In 2014, new serum collection tubes containing thrombin as coagulation enhancer came on the market reducing this process to 5 min. This rapid coagulation at room temperature may reduce the risk of accidentally delayed sample processing caused by distractions during the daily clinical working routine. Although serum is the most widely used material plasma is the preferable specimen for metabolomics studies for two reasons. The first reason is that plasma blood collection tubes do not have to be exposed to room temperature but can be placed at once in ice water after blood drawing. The second reason is that platelets stay in the quiescent state in plasma blood collection tubes. In contrast to that, activated platelets, which are essentially needed for the coagulation process during the generation of serum, produce, release and modify a considerable number of metabolites (including lipids) and release proteases and other enzymes into the serum sample tube. Consequently, the metabolite profiles of plasma and serum are different per se [12]. In a targeted metabolomics approach the concentration of 104 out of 121 metabolites in serum were around 10% higher and the concentration of nine metabolites were 20–50% higher [13] . Based on relevant effects of platelets on the metabolite pattern in serum samples it is important to consider also the platelet number, which can vary around 50-fold between patients. Furthermore, prolonged clotting time affects the metabolome [14] . The preanalytical phase prior to sample collection Many clinical chemical routine parameters are affected by improper preparation of the individuals before sample collection. Recommendation for the preparation of the individuals and patients before specimen collection for the measurement of clinical chemical routine parameters [4,15] can directly be transferred
future science group
Preanalytics: what can metabolomics learn from clinical chemistry?
and adapted to metabolomics studies. Factors which affect the metabolome are the nutritional state [16] , the time of day of sample collection [17] , the physical activity before specimen collection [18] , the composition of the diet [19] , individual characteristics (BMI, age, sex, stress etc.), and life style factors (e.g., smoking). Two very common factors leading to heterogeneous sample sets with a very high variability in metabolite levels are differences in the nutritional state and the time of day when the samples were collected. Usually samples are collected in the morning between 7 and 10 am after a 12-h period of fasting (drink: only water). Of note, in huge epidemiological studies like national cohort studies the samples are not only collected in the morning, therefore sample selection from the biobanks of those cohort studies is challenging. Recently it has been demonstrated that around 20% of metabolites, including lysophosphatidyl cholines, LP-ethanolamines, carnitines, amino acids, cortisol and bilirubin showed significant time-of-day differences [17] . Drugs are of course other very common factors affecting the metabolism and subsequently the results of metabolomics and lipidomics investigations. Less considered, but very common in use, are xenobiotics like dietary supplements (vitamins, fish oil capsules, amino acid and protein shakes, etc.). They may exert strong effects on the metabolite profiles and are often not reported in patients questionnaires. The possible effects of dietary supplements and drugs should always be considered and intake should be reported to the analytical (bio)chemists by the clinical cooperation partner. Conclusion For all kinds of metabolomics analysis, targeted and in particular untargeted approaches, the preanalytical phase is a very important step which can greatly bias the final results. Therefore, scientists performing biomedical metabolomics studies should be aware that in diagnostic and functional biomarker search References 1
Heller JF. Höchst einfache und sichere Methode zur Diagnose des Zuckers in thierischen Flüssigkeiten; Unterscheidung des Harn-, Milch- und Rohrzuckers. Physiologischen Und Pathologischen Chemie Und Mikroskopie 1, 292–298 (1844).
2
Heller JF. Über die Erkennung von Albumin, der Urate, der Knochenerde und einer eigentümlichen Proteinverbindung im Harn. Physiologischen Und Pathologischen Chemie Und Mikroskopie 5, 161–171 (1852).
3
Carraro P, Zago T, Plebani M. Exploring the initial steps of the testing process: frequency and nature of pre-preanalytic errors. Clin. Chem. 58, 638–642 (2012).
future science group
Editorial
significant differences in the levels of metabolites may not only depend on the investigated (patho)physiological condition or disease state but many other in vivo and in vitro factors may also affect the metabolite levels and composition of the investigated body fluids. These ex vivo factors may be preanalytical or analytical and attention should be paid to the fact that variabilities due to preanalytical factors are often greater than the influence of analytical factors. Even prior to specimen collection a proper preparation of the study subject or patient is mandatory. It has been demonstrated by clinical chemists that most errors affecting the final result occur in the preanalytical phase [20] . In this context it is important to note that the quality of current biobank samples is not defined and may therefore be overestimated. No biobank currently has real proof of the actual sample quality before storage, since only protocols and no biomarker measurements document how the samples were handled after blood drawing. This should be taken into account before starting with a biomedical metabolomics project using samples from a clinical or epidemiological study stored in a biobank. To conclude, accidental or systematic incorrect processing in the preanalytical phase leads to poor, possibly misleading findings and conclusions in metabolomics studies. All efforts from the specialist in the analytical phase cannot compensate for preanalytical errors. Responsible research needs to consider the preanalytical aspects. Financial & competing interests disclosure The author has no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending or royalties. No writing assistance was utilized in the production of this manuscript.
4
Lippi G, Guidi GC, Mattiuzzi C et al. Preanalytical variability: the dark side of the moon in laboratory testing. Clin. Chem. Lab Med. 44, 358–365 (2006).
5
Szecsi PB, Odum L. Error tracking in a clinical biochemistry laboratory. Clin. Chem. Lab. Med. 47, 1253–1257 (2009).
6
Yin P, Peter A, Franken H et al. Preanalytical aspects and sample quality assessment in metabolomics studies of human blood. Clin. Chem. 59, 833–845 (2013).
7
Bernini P, Bertini I, Luchinat C et al. Standard operating procedures for pre-analytical handling of blood and urine for metabolomic studies and biobanks. J. Biomol. NMR 49, 231–243 (2011).
www.future-science.com
929
Editorial Lehmann 8
Kamlage B, Maldonado SG, Bethan B et al. Quality markers addressing preanalytical variations of blood and plasma processing identified by broad and targeted metabolite profiling. Clin. Chem. 60, 399–412 (2014).
9
Dunn WB, Broadhurst D, Ellis DI et al. A GC-TOF-MS study of the stability of serum and urine metabolomes during the UK Biobank sample collection and preparation protocols. Int. J. Epidemiol. 37(Suppl. 1), i23–i30 (2008).
10
11
12
13
930
14
Teahan O, Gamble S, Holmes E et al. Impact of analytical bias in metabonomic studies of human blood serum and plasma. Anal. Chem. 78, 4307–4318 (2006).
15
Guder WG. Preanalytical factors and their influence on analytical quality specifications. Scand. J. Clin. Lab. Invest. 59, 545–549 (1999).
16
Brauer R, Leichtle A, Fiedler G et al. Preanalytical standardization of amino acids and acylcarnitine metabolite profiling in human blood using tandem mass spectrometry. Metabolomics 7, 344–352 (2011).
17
Leyland-Jones BR, Ambrosone CB, Bartlett J et al. Recommendations for collection and handling of specimens from group breast cancer clinical trials. J. Clin. Oncol. 26, 5638–5644 (2008).
Minami Y, Kasukawa T, Kakazu Y et al. Measurement of internal body time by blood metabolomics. Proc. Natl Acad. Sci. USA 106, 9890–9895 (2009).
18
Denery JR, Nunes AA, Dickerson TJ. Characterization of differences between blood sample matrices in untargeted metabolomics. Anal. Chem. 83, 1040–1047 (2011).
Weigert C, Lehmann R, Hartwig S et al. The secretome of the working human skeletal muscle-a promising opportunity to combat the metabolic disaster? Proteomics. Clin. Appl. 8, 5–18 (2014).
19
Yu Z, Kastenmuller G, He Y et al. Differences between human plasma and serum metabolite profiles. PLoS One 6, e21230 (2011).
Gibney MJ, Walsh M, Brennan L et al. Metabolomics in human nutrition: opportunities and challenges. Am. J. Clin. Nutr. 82, 497–503 (2005).
20
Bonini P, Plebani M, Ceriotti F et al. Errors in laboratory medicine. Clin. Chem. 48, 691–698 (2002).
Dunn WB, Broadhurst D, Begley P et al. Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry. Nat. Protoc. 6, 1060–1083 (2011).
Bioanalysis (2015) 7(8)
future science group
For reprint orders, please contact [email protected]
Preanalytics: what can metabolomics learn from clinical chemistry? “
After blood drawing the metabolism of billions of red and white blood cells as well as of platelets is still active resulting in the uptake and release of metabolites, thereby altering the metabolite profile in the sample tube.
”
Keywords: biobank • blood • body fluids • metabolic profiling • metabolite profile • metabolomics • plasma • preanalytical • sample handling • serum
The analytical quality in metabolomics projects is of utmost importance as a guarantor for reliable results and accordingly for the success of the entire project. However, the highest analytical quality employing the most sophisticated instruments is in vain if the sample quality is poor. This means that in addition to the analytical performance the major key for the success of metabolomics projects is the phase before the analyses, the so called preanalytical phase. In biomedical metabolomics projects the drawing, handling and processing of whole blood are these important preanalytical steps. A general opinion of medical researchers is that blood collection, which is often embedded in the clinical workflow, is the easy part in complex clinical studies. Therefore more attention is paid to other steps of these studies. However, tremendous negative effects on the outcome of clinical studies may result from inaccuracies and errors occurring during specimen collection and handling. Poor sample quality caused by systematic as well as accidental preanalytical errors could also mean that a lot of money is spent over many years for the storage of these potentially useless samples in biobanks. The actual quality of samples used for biomedical metabolomics studies is an underestimated pitfall. How may experiences from clinical chemistry contribute to avoid misleading results in biomedical metabolomics studies? Since the mid of the 19th century simplified methods suitable for the clinical routine measurement of various parameters in body fluids including metabolites like glucose [1] and urate [2]
10.4155/BIO.15.23 © 2015 Future Science Ltd
have been established. At about the same time chemical laboratories were installed in university hospitals, first in Germany and Austria. In the following years in many countries a new discipline named clinical chemistry was established. Since that time countless observations and scientific investigations leading to profound expert knowledge about specimen collection, handling and measurement in complex matrices like body fluids and tissue extracts came to pass in clinical chemistry. However, it took until the 1970s for the preanalytical phase to be introduced as a separate discipline of research focusing on the investigation of effects on the sample quality and the analytical result by, for example, specimen handling, pretreatment procedures for body fluids, short- and long-term storage conditions, etc. It is worth noting, the preanalytic phase may account for 60–80% of laboratory testing errors in daily routine clinical diagnostics [3–5] .
Rainer Lehmann Department of Clinical Chemistry & Pathobiochemistry, University Hospital Tuebingen, Tuebingen, Germany and Department of Molecular Diabetology, Institute for Diabetes Research & Metabolic Diseases of the Helmholtz Centre Munich at the University of Tuebingen, Tuebingen, Germany and German Center for Diabetes Research (DZD), Tuebingen, Germany [email protected]
Factors affecting the metabolite profile in blood samples In general the concentration of analytes in the blood is tightly controlled, often in a very narrow range. But under various (patho) physiological conditions, like fasting/feeding, exercise, diabetes, cancer and many others, huge and/or very fast alterations can occur. Besides endogenous effectors, these changes may also be caused by other factors, like drugs. However, as a third possibility, such alterations can also occur ex vivo, in other words, based on conditions which are not related to any action inside the human
Bioanalysis (2015) 7(8), 927–930
part of
ISSN 1757-6180
927
Editorial Lehmann body but depend on processes occurring inside the blood collection tube. Exposure of whole blood samples to room temperature may have tremendous effects on the composition of the entire metabolome inside the sample tube [6–8] . After blood drawing the metabolism of billions of red and white blood cells as well as of platelets is still active resulting in the uptake and release of metabolites, thereby altering the metabolite profile in the sample tube. Thus a comparison of two groups of individuals or patients may lead to the detection of ‘biomarkers’ based on differences in the number of platelets, erythrocytes and/or leukocytes, as well as differences in the metabolic activity of these blood cells.
“For all kinds of metabolomics analysis, targeted and in particular untargeted approaches, the preanalytical phase is a very important step which can greatly bias the final results.
”
Ex vivo changes in the metabolite profile can almost be prevented if whole blood is placed immediately in ice water after drawing [6–8] , which, unfortunately, is not possible for serum sample tubes (see below). After separation of plasma or serum from blood cells the metabolite profile is much more stable [9,10] . Therefore specimen processing (centrifugation, aliquotation, freezing) should be performed as soon as possible after blood drawing. This recommendation may stand in marked contrast to preanalytical standard operating procedures (SOPs) developed and optimized for clinical trails, especially in multi-center studies when compromises for the SOP have been made so that every participating hospital can fulfil the preanalytical SOP. For example, recommendations for specimen collection and handling developed for international breast cancer clinical trials discuss the exposure of whole blood for up to 8 h to room temperature before the separation of plasma or serum from blood cells, because most hospitals can manage this requirement well [11] . Therefore already existing preanalytical SOPs of clinical cooperation partners should be carefully checked by analytical (bio)chemists involved in the planning phase of clinical studies. It is always challenging to balance the needs for perfect sample quality for -omics approaches against the feasibility of preanalytical recommendations by the clinical cooperation partner. Specific preanalytical recommendations for biomedical metabolomics studies investigating blood, including a draft for a SOP, were recently described [6] . Hemolysis is another common factor affecting the metabolome in blood. In clinical studies it is one of the major pitfalls during blood drawing, which can
928
Bioanalysis (2015) 7(8)
lead to pronounced changes of around 18% of all detected metabolite ion signals, for example, lysophosphatidyl choline species, N-acetylornithine, sphingosine-1-phosphate, carnitine and tryptophane, in a nontargeted approach [6] . Serum or plasma, which material is preferable? For historical reasons serum is the dominating biospecimen collected in clinical studies and stored in biobanks. However, blood drawn for the generation of serum needs to be exposed to room temperature for a defined time, even when coagulation enhancers like kaolin are included in the sample collection tube. Until recently the recommended time span for a proper coagulation process was 30–60 min exposure to room temperature, with or without coagulation enhancers respectively. In 2014, new serum collection tubes containing thrombin as coagulation enhancer came on the market reducing this process to 5 min. This rapid coagulation at room temperature may reduce the risk of accidentally delayed sample processing caused by distractions during the daily clinical working routine. Although serum is the most widely used material plasma is the preferable specimen for metabolomics studies for two reasons. The first reason is that plasma blood collection tubes do not have to be exposed to room temperature but can be placed at once in ice water after blood drawing. The second reason is that platelets stay in the quiescent state in plasma blood collection tubes. In contrast to that, activated platelets, which are essentially needed for the coagulation process during the generation of serum, produce, release and modify a considerable number of metabolites (including lipids) and release proteases and other enzymes into the serum sample tube. Consequently, the metabolite profiles of plasma and serum are different per se [12]. In a targeted metabolomics approach the concentration of 104 out of 121 metabolites in serum were around 10% higher and the concentration of nine metabolites were 20–50% higher [13] . Based on relevant effects of platelets on the metabolite pattern in serum samples it is important to consider also the platelet number, which can vary around 50-fold between patients. Furthermore, prolonged clotting time affects the metabolome [14] . The preanalytical phase prior to sample collection Many clinical chemical routine parameters are affected by improper preparation of the individuals before sample collection. Recommendation for the preparation of the individuals and patients before specimen collection for the measurement of clinical chemical routine parameters [4,15] can directly be transferred
future science group
Preanalytics: what can metabolomics learn from clinical chemistry?
and adapted to metabolomics studies. Factors which affect the metabolome are the nutritional state [16] , the time of day of sample collection [17] , the physical activity before specimen collection [18] , the composition of the diet [19] , individual characteristics (BMI, age, sex, stress etc.), and life style factors (e.g., smoking). Two very common factors leading to heterogeneous sample sets with a very high variability in metabolite levels are differences in the nutritional state and the time of day when the samples were collected. Usually samples are collected in the morning between 7 and 10 am after a 12-h period of fasting (drink: only water). Of note, in huge epidemiological studies like national cohort studies the samples are not only collected in the morning, therefore sample selection from the biobanks of those cohort studies is challenging. Recently it has been demonstrated that around 20% of metabolites, including lysophosphatidyl cholines, LP-ethanolamines, carnitines, amino acids, cortisol and bilirubin showed significant time-of-day differences [17] . Drugs are of course other very common factors affecting the metabolism and subsequently the results of metabolomics and lipidomics investigations. Less considered, but very common in use, are xenobiotics like dietary supplements (vitamins, fish oil capsules, amino acid and protein shakes, etc.). They may exert strong effects on the metabolite profiles and are often not reported in patients questionnaires. The possible effects of dietary supplements and drugs should always be considered and intake should be reported to the analytical (bio)chemists by the clinical cooperation partner. Conclusion For all kinds of metabolomics analysis, targeted and in particular untargeted approaches, the preanalytical phase is a very important step which can greatly bias the final results. Therefore, scientists performing biomedical metabolomics studies should be aware that in diagnostic and functional biomarker search References 1
Heller JF. Höchst einfache und sichere Methode zur Diagnose des Zuckers in thierischen Flüssigkeiten; Unterscheidung des Harn-, Milch- und Rohrzuckers. Physiologischen Und Pathologischen Chemie Und Mikroskopie 1, 292–298 (1844).
2
Heller JF. Über die Erkennung von Albumin, der Urate, der Knochenerde und einer eigentümlichen Proteinverbindung im Harn. Physiologischen Und Pathologischen Chemie Und Mikroskopie 5, 161–171 (1852).
3
Carraro P, Zago T, Plebani M. Exploring the initial steps of the testing process: frequency and nature of pre-preanalytic errors. Clin. Chem. 58, 638–642 (2012).
future science group
Editorial
significant differences in the levels of metabolites may not only depend on the investigated (patho)physiological condition or disease state but many other in vivo and in vitro factors may also affect the metabolite levels and composition of the investigated body fluids. These ex vivo factors may be preanalytical or analytical and attention should be paid to the fact that variabilities due to preanalytical factors are often greater than the influence of analytical factors. Even prior to specimen collection a proper preparation of the study subject or patient is mandatory. It has been demonstrated by clinical chemists that most errors affecting the final result occur in the preanalytical phase [20] . In this context it is important to note that the quality of current biobank samples is not defined and may therefore be overestimated. No biobank currently has real proof of the actual sample quality before storage, since only protocols and no biomarker measurements document how the samples were handled after blood drawing. This should be taken into account before starting with a biomedical metabolomics project using samples from a clinical or epidemiological study stored in a biobank. To conclude, accidental or systematic incorrect processing in the preanalytical phase leads to poor, possibly misleading findings and conclusions in metabolomics studies. All efforts from the specialist in the analytical phase cannot compensate for preanalytical errors. Responsible research needs to consider the preanalytical aspects. Financial & competing interests disclosure The author has no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending or royalties. No writing assistance was utilized in the production of this manuscript.
4
Lippi G, Guidi GC, Mattiuzzi C et al. Preanalytical variability: the dark side of the moon in laboratory testing. Clin. Chem. Lab Med. 44, 358–365 (2006).
5
Szecsi PB, Odum L. Error tracking in a clinical biochemistry laboratory. Clin. Chem. Lab. Med. 47, 1253–1257 (2009).
6
Yin P, Peter A, Franken H et al. Preanalytical aspects and sample quality assessment in metabolomics studies of human blood. Clin. Chem. 59, 833–845 (2013).
7
Bernini P, Bertini I, Luchinat C et al. Standard operating procedures for pre-analytical handling of blood and urine for metabolomic studies and biobanks. J. Biomol. NMR 49, 231–243 (2011).
www.future-science.com
929
Editorial Lehmann 8
Kamlage B, Maldonado SG, Bethan B et al. Quality markers addressing preanalytical variations of blood and plasma processing identified by broad and targeted metabolite profiling. Clin. Chem. 60, 399–412 (2014).
9
Dunn WB, Broadhurst D, Ellis DI et al. A GC-TOF-MS study of the stability of serum and urine metabolomes during the UK Biobank sample collection and preparation protocols. Int. J. Epidemiol. 37(Suppl. 1), i23–i30 (2008).
10
11
12
13
930
14
Teahan O, Gamble S, Holmes E et al. Impact of analytical bias in metabonomic studies of human blood serum and plasma. Anal. Chem. 78, 4307–4318 (2006).
15
Guder WG. Preanalytical factors and their influence on analytical quality specifications. Scand. J. Clin. Lab. Invest. 59, 545–549 (1999).
16
Brauer R, Leichtle A, Fiedler G et al. Preanalytical standardization of amino acids and acylcarnitine metabolite profiling in human blood using tandem mass spectrometry. Metabolomics 7, 344–352 (2011).
17
Leyland-Jones BR, Ambrosone CB, Bartlett J et al. Recommendations for collection and handling of specimens from group breast cancer clinical trials. J. Clin. Oncol. 26, 5638–5644 (2008).
Minami Y, Kasukawa T, Kakazu Y et al. Measurement of internal body time by blood metabolomics. Proc. Natl Acad. Sci. USA 106, 9890–9895 (2009).
18
Denery JR, Nunes AA, Dickerson TJ. Characterization of differences between blood sample matrices in untargeted metabolomics. Anal. Chem. 83, 1040–1047 (2011).
Weigert C, Lehmann R, Hartwig S et al. The secretome of the working human skeletal muscle-a promising opportunity to combat the metabolic disaster? Proteomics. Clin. Appl. 8, 5–18 (2014).
19
Yu Z, Kastenmuller G, He Y et al. Differences between human plasma and serum metabolite profiles. PLoS One 6, e21230 (2011).
Gibney MJ, Walsh M, Brennan L et al. Metabolomics in human nutrition: opportunities and challenges. Am. J. Clin. Nutr. 82, 497–503 (2005).
20
Bonini P, Plebani M, Ceriotti F et al. Errors in laboratory medicine. Clin. Chem. 48, 691–698 (2002).
Dunn WB, Broadhurst D, Begley P et al. Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry. Nat. Protoc. 6, 1060–1083 (2011).
Bioanalysis (2015) 7(8)
future science group
