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Topical Reviews |
1 Plasma Proteome Institute, Washington, DC, USA
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(Received 1 December 2004;
accepted after revision 16 December 2004;
first published online 20 December 2004)
Corresponding author L. Anderson: PO Box 53450, Washington, DC 20009-3450, USA. Email: leighanderson{at}plasmaproteome.org
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Introduction |
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Challenges of the plasma proteome
Plasma, which (together with its close cognate serum) is the primary biochemically useful clinical specimen, comprises the largest and deepest version of the human proteome. This makes it the most difficult sample to work with in proteomics, despite the relatively good behaviour (i.e. solubility) of its protein components. The daunting size of the plasma proteome is a reflection of the sheer number of different proteins to be detected. A rough calculation of this number can be made as follows. (1) Assume that 10% of the 
30 000 genes encode secreted proteins, that each of these is made in an average of three splice forms, that two cleaved versions of each exist, and that there are an average of five post-translational modifications for each protein (a low estimate given the extreme carbohydrate microheterogeneity of most major plasma proteins). Since all these events can occur independently of the others, we obtain 3000 x 3 x 2 x 5 = 90 000 different secreted molecules. (2) Assume that all the non-secreted human proteins and their various modified forms are released into plasma at some low level as a result of cell turnover in the tissues. Using levels of modification similar to the secreted proteins, we obtain a further 810 000 protein species present at low levels. (3) Finally, assume that there are 10 000 000 distinct clonal immunoglobulin sequences present in plasma reflecting the immune history of the individual. The sum of these admittedly rough estimates is > 106 different molecules representing products of all 30 000 genes: in other words, plasma is the largest version of the human proteome in one sample. Proteomics technologies can typically resolve 100 different species per dimension of separation, indicating that 3 or more perfectly independent separative dimensions would be required, or more probably 4–5 dimensions of realistically implementable technology.
The enormous ‘depth’ of the plasma proteome is a reflection of the dynamic range (difference between the highest and lowest concentration) over which proteins must be detected. Approximately half of the total protein mass in plasma is accounted for by one protein (albumin, present at 55 000 000 000 pg ml–1), while roughly 10 proteins together make up 90% of the total. At the other end of the concentration histogram are the cytokines, such as interleukin-6 (IL-6), which is normally present at 1–5 pg ml–1. The difference in concentration between albumin and IL-6 is thus 1010. This range, of course, covers the proteins we know and consider useful as markers today, and ignores potentially valuable markers to be found in the future at even lower concentrations. The fact that we know anything about the concentrations of these proteins, and hence have been able to use them as biomarkers, is due to the power of specific protein tests, typically immunoassays of one protein at a time, and not to proteomics as currently defined, where currently technology is limited to a dynamic range of 103–104 (see Fig. 2).
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Given the analytical challenges inherent in the plasma proteome, what practical strategies exist for finding and confirming protein biomarkers? The problem can be approached from two opposite directions: (1) complete analysis (to see all differences) and (2) targeted analysis (to measure one or more hypothesis-generated candidates). The advantages of complete analysis, if it is possible, are substantial. Complete analysis would allow the direct selection of optimal biomarker proteins at the outset, thus skipping over what is currently a very long and laborious iterative process. Not surprisingly, progress towards complete analysis has been the focus of most proteomics research for the past decade. The number of proteins detectable in plasma has risen from 40 in 1975 (Anderson & Anderson, 1977) to 300–1000 reported in various recent studies (Adkins et al. 2002; Pieper et al. 2003a; Tirumalai et al. 2003). The latter datasets have been combined (Anderson et al. 2004b) to generate a non-redundant set of 1173 proteins, which revealed surprisingly small commonality between the results of these three different proteomics platforms (respectively multidimensional chromatography of proteins followed by 2-D electrophoresis and mass spectrometry (MS) identification of resolved proteins; tryptic digestion and multidimensional chromatography of peptides followed by MS identification; and tryptic digestion and multidimensional chromatography of peptides from low-molecular weight plasma components followed by MS identification). When these datasets are searched for a group of candidate disease markers (the cardiovascular candidates described below) for which plasma concentration normal values exist, the result illustrates the limited sensitivity of the platforms as a means of complete plasma proteome analysis (Fig. 2). Most proteins in the top 3 logs of the concentration distribution are detected by two or three of the three platforms, a fair proportion of the proteins in the middle two logs are seen by at least one of the platforms, and very few of the proteins in the bottom 5 logs are detected by any of the three. Thus it appears that current proteomics technology is unlikely to be able to provide a complete analysis of the most relevant diagnostic samples (e.g. serum and plasma). An additional important feature of this plot is that the candidate proteins show a smooth distribution between 10 and 109 pg ml–1, demonstrating that presumed disease relationships appear to occur independently of a protein's plasma concentration. In particular there does not seem to be a bias towards either very low abundance proteins (e.g. cytokines) or high abundance molecules. Since plasma concentration was not a criterion in the selection of these proteins (just a relationship with cardiovascular disease or stroke), this observation is probably meaningful.
Targeted analysis, which emerged as a means of searching for disease marker associations in the 1950s (in the form of enzyme assays), has a longer history than proteomics (which emerged in the form of 2-D electrophoresis in the mid-1970s), and has produced most of the protein markers now in diagnostic use. Typically a researcher interested in a specific protein develops hypotheses regarding a specific disease, and arranges to apply a lab bench assay to sets of samples from patients and controls. The specific assays involved are usually immunoassays, which, because of the great specificity of antibodies, are often able to detect proteins in plasma at much lower concentrations than current proteomics platforms. While this approach adheres to the conventions of hypothesis-driven research (and is thus fundable through grants), it has a substantial weakness in the poor probability of success when one marker is tested in one disease at a time: there are, as indicated, at least tens of thousands of candidate protein forms, and at least hundreds of disease entities. Even if it were the case that there is one protein capable of serving as a robust marker of each disease state, this method will take a long time to find them, and unfortunately it will take as much effort to find the last such marker as it took to find the first. More discouraging yet is the fact that any disease state in which several markers need to be considered together to produce an accurate result (i.e. a multiplex panel) would represent an enormous combinatorial discovery problem: since the experimental assays are typically developed in separate laboratories, bringing them together for application as a prototype panel is an organizational challenge, compounded by the increased sample requirement of multiple separate assays.
Thus both the complete and targeted analytical approaches have important limitations (sensitivity and mono-analyte focus, respectively) that diminish the output of novel disease marker proteins. This situation has led in recent years to consideration of hybrid approaches, in which a set of preselected proteins could be measured at high sensitivity. By focusing on a limited number of candidate biomarker proteins, assay technologies providing higher sensitivity and dynamic range than current proteomics could be used. By looking at multiple proteins, instead of one, the odds of finding useful disease associations, and effective panels, would be increased. The odds can be further improved through intelligent selection of candidate markers: here there is an opportunity to make use of information from many sources in addition to proteomics: expression microarray data suggesting tissue-specific or disease-altered synthesis of specific proteins, relationships of proteins to disease pathways, and classical biochemistry. Such a hybrid approach, combining the multiprotein view of proteomics and the advantages of targeted specific assays can be termed targeted proteomics.
Technology platforms for targeted proteomics of candidate markers
The central technical issue in targeted proteomics is how best to measure a limited set of proteins in complex samples such as plasma. Two broad strategies are developing: miniaturized, mulitplexed immunoassays and quantitative mass spectrometry. The former approach, which includes antibody arrays in both planar and particle suspension formats (recently reviewed by Joos (Joos et al. 2002) and a review in this series), has the advantage that immunoassays are well-understood, sensitive and specific. Antibody arrays are limited, however, by the availability of suitable antibodies, and this has proved to be a critical bottleneck for the development of immunoassays for new marker content. While a single research immunoassay costs less to assemble than the $2–4 million required for a commercial diagnostic test, each additional new marker assay costs the same again as the first, typically requiring development of two different high-affinity antibodies. It thus appears that substantial time will be required to generate large sets of new immunoassays to candidate markers, and that an alternative approach based on quantitative mass spectrometry may serve to evaluate candidate biomarkers prior to investment in immunoassays. Here I focus on the emerging MS methodologies for specific protein quantification.
Mass spectrometry is widely used for the quantitative measurement of specific small molecules (e.g. drugs (Streit et al. 2002, 2004), drug metabolites (Kostiainen et al. 2003), hormones (Tai et al. 2004), and pesticides (Sannino et al. 2004)), with excellent precision (Tai et al. 2004) and very high throughput (Bakhtiar et al. 2002; Deng et al. 2002). In these methods, a sample is typically subjected to some form of high-throughput prefractionation (e.g. solid phase extraction; SPE) followed by a rapid reversed-phase chromatography separation, and the resulting output stream is introduced through an ionizing spray interface into a triple-quadrupole MS (TQMS). Within the MS, the first mass analyser (MS1) is set to pass the parent molecule (the ‘analyte’), rejecting components of other mass-to-charge ratios (m/z). The analyte is then fragmented in a collision chamber and passed to a second mass analyser (MS2) set to pass a known specific fragment. This two-stage selection of parent and fragment ions (selected reaction monitoring: SRM) affords great specificity, with the result that the detected signal usually traces a peak in the chromatogram at the expected retention time corresponding to the selected analyte. Integrating this peak gives a measure of the quantity of the analyte. Figure 3 presents an example of this approach in which a specific tryptic peptide of the coagulation protease prothrombin is measured in a tryptic digest of plasma. This measurement, based on precise molecular characteristics of the peptide, is in fact more specific for prothrombin than a typical immunoassay (in which lack of perfect antibody specificity is usually overlooked). An internal standard is often spiked into the sample to provide a reference signal to which the analyte is compared for absolute quantification. Lower limits of quantification (LLOQ) of 5–25 ng ml–1 (20 nM) can be obtained for drug metabolites (Zhang et al. 2000a), and < 10 ng ml–1 for pesticides in vegetable samples (Sannino, 2004). In serum and plasma, methods based on two-stage mass spectrometry (MS/MS) quantify the drugs mycophenolic acid (Streit et al. 2004) (0.5 ng ml–1) and sirolimus (Streit et al. 2002) (0.25 ng ml–1), as well as hormones and metabolites such as thyroid hormone T3 (Tai et al. 2004) (a reference method with coefficient of variation (CV) < 3%), homocysteine (Magera et al. 1999; Arndt et al. 2004; Stabler & Allen, 2004), S-adenosylmethionine and S-adenosylhomocysteine (Struys et al. 2000) (LLOQs of 3 and 1 ng ml–1, respectively, with CV < 8%).
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However, the method as described above is not generally useful for proteins larger than about 10 kDa, whose higher mass is not as well resolved by current MS or LC systems as peptides, which do not fragment efficiently into a few discrete pieces, and for which labelled internal standards are significantly more expensive. MS analysis of whole proteins from plasma is typically restricted to non-quantitative applications in which an available high affinity antibody is used to capture the protein, which is then eluted and analysed by MS (Kiernan et al. 2003; Nepomuceno et al. 2004), or digested to peptides that can be subjected to MS/MS for structural analysis (Labugger et al. 2003; Nedelkov et al. 2004). Such methods are useful for detecting protein sequence variants and post-translational modifications (PTMs), and can be quantitative in the rare cases where a purified cross-reacting homologue from another species is available to serve as an internal standard (e.g. the assay of 7.6 kDa IGF1 (Nelson et al. 2004) at 100 pg ml–1).
Thus in order to effectively leverage the successful methods of LC–MS/MS quantification to proteins in a sample such as plasma, one must ‘disassemble’ each protein quantitatively into its constituent peptides by complete chemical or enzymatic cleavage. Within this digest one can select a monitor peptide to serve as a quantitative surrogate for the protein, and achieve accurate quantification by spiking with a stable isotope-labelled version of the same peptide as internal standard (Stemmann et al. 2001). Such ‘postdigest’ assays have been generated for some higher-abundance plasma proteins such as ApoA-I lipoprotein (Barr et al. 1996) (CV < 4%) and Hb A1C (Jeppsson et al. 2002) (an International Federation of Clinical Chemistry reference method in which a glycated peptide is measured with interlaboratory CVs of 1.4–2.3%). Attempts to assay the 26 kDa cancer marker prostate-specific antigen (PSA) (Barnidge et al. 2004) using a standard LC–MS/MS system yielded a detection limit of 4.5 mg ml–1 (0.17 mg ml–1 of the monitored peptide, a level 1000 times higher than the clinically relevant level), while measurement of CRP (after a molecular weight enrichment by SDS gel) yielded quantitative measurements at < 1 mg ml–1 (Kuhn et al. 2004).
While individual analytes within each class of molecule vary, the published data lead us to conclude that serum concentrations in the order of 1 ng ml–1 for drugs, 1–10 ng ml–1 for plasma peptides, and 100 ng ml–1 for peptides in a complex plasma digest can be measured by existing LC–MS/MS-based assay methods. On average, proteins in plasma are 34 times as large as the roughly 10 amino acid-long monitor peptides chosen to represent them, and thus the protein detection limit (measuring a peptide in a digest) would be expected to be roughly 3 mg ml–1.
Two additional elements are required to enable quantitative MS/MS for targeted proteomics: the capability to assay many proteins at a time and a means to extend sensitivity downwards to the level of low abundance biomarkers such as cytokines (10 pg ml–1).
Multi-analyte methods are implemented in TQMS by rapidly switching between pairs of MS/MS parameters during the LC run. Published methods have measured up to 29 pesticides in one run (Barr et al. 2002) and prototype studies of up to 200 multiple-reaction monitoring (MRM) analytes performed. Sensitivity of MS assays can be increased by additional stages of fractionation prior to LC–MS/MS. Two such methods of particular promise involve the subtraction of specific high-abundance plasma proteins (e.g. albumin, transferrin, Igs, haptoglobin, etc.) using specific antibody columns (Pieper et al. 2003b), and the specific enrichment of selected monitor peptides through binding and release from antipeptide antibody columns (Anderson et al. 2004a). The former method provides a 10-fold improvement in sensitivity (by subtracting 90% of the mass of protein in plasma), while the latter method yields an additional 100-fold average improvement using relatively crude rabbit polyclonal antibodies. These extensions provide a reasonable basis for the expectation that panels of 20–50 protein analytes taken from the top 6 or 7 (of 10) orders of magnitude plasma concentration should be accessible for routine MS/MS measurement.
Candidate markers of cardiovascular disease
Given a technology platform for measuring a limited number of identified proteins, intelligent candidate selection is a high priority. As an example of a set of candidates to start with, I present here a table of proteins reported to have some connection with cardiovascular disease (here considered in a broad sense, and including heart disease, stroke, vascular disease, hyper- and hypo-coagulation) from literature and other sources (Table 1).
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40% of all deaths), and a major economic burden ($227 billion in direct medical costs this year) (2003). In 2001, there were more than 4 million visits to emergency departments with a primary diagnosis of CVD, and more than 6 million inpatient cardiovascular operations and procedures were performed (American Heart Association, 2003). Cardiovascular disease includes a range of phenomena differing markedly in timescale, physical size, and relative effects of genes and environment. It includes slow processes such as atherosclerosis, which can evolve over decades, and very rapid events such as myocardial infarction, which can be lethal in a matter of minutes. It involves subtle changes at the molecular level, as coagulation enzymes are activated at the site of a ruptured arterial plaque, and large-scale physical consequences, when a blood clot physically plugs a major coronary artery. Genetic factors (e.g. familial hypercholesterolaemia or levels of lipoprotein (a) (Lp(a)) are strongly involved, as are environmental and lifestyle factors, the most obvious of which are lipid intake and smoking. Largely on account of this breadth of causes and effects, and the diversity of treatment strategies that this makes possible, major progress has been made in the development of life-saving interventions. Damaged hearts can be repaired physically, by coronary artery bypass grafting (CABG) or percutaneous coronary intervention (PCI), or enzymatically, by administering recombinant human tissue plasminogen activator (tPA) to digest a clot; elevated blood pressure can be controlled by several different classes of drugs, and coagulation can be enhanced (in treatment of haemophilia by replacement of missing clotting factor proteins) or diminished (with aspirin, heparins, and platelet GP IIb/IIIA receptor antagonists).
A major challenge in medicine is thus deciding when, and upon whom, these effective interventions should be carried out. A patient presenting with chest pain may have an acute myocardial infarction (MI) requiring immediate PCI or tPA treatment, stable angina requiring nitroglycerine, oesophageal spasm with no cardiovascular consequences, etc. Given the urgency of this issue, the cardiology community has promulgated detailed guidelines concerning triage of chest-pain patients (Ryan et al. 1996; Braunwald et al. 2000). Perhaps most importantly, there is a window of opportunity, while conditions such as atherosclerosis and hypertension gradually worsen, in which the ability to anticipate an imminent acute event (e.g. MI or stroke) can have immense benefit. Where causal molecules or telltale molecular fingerprints can be identified, objective and reproducible laboratory tests can be created, helping to implement best medical practices at institutions large and small. Such tests are typically inexpensive in relation to drug treatment or surgical intervention, providing a major health economic benefit. And they can be fast, providing critical results in < 15 min when implemented in automated instruments near the patient.
History of protein markers in CVD. Cardiovascular disease is the most likely area in the spectrum of human disease to yield protein markers in plasma. Most pathologies of the cardiovascular system involve plasma proteins directly (e.g. the coagulation cascade with its positive and negative modulators (> 29 proteins), or proteins of lipid transport involved in atherosclerosis (> 16 proteins)), or proteins that interact with vessel walls, platelets, or both. In addition to these, numerous inflammatory modulators transported in the blood have direct and indirect relationships to cardiovascular disease, while release of proteins from the heart itself provides evidence of cardiac damage.
Consistent with this expectation, a number of very successful protein diagnostics have emerged in cardiovascular medicine. The most definitive of these is cardiac troponin I (TnI, or the alternative TnT, both muscle contractile proteins) as a primary indicator of myocardial infarction (Jaffe, 2001), often in combination with the cardiac isozyme of creatine kinase (CK-MB) and myoglobin. In this case, the diagnosis of MI typically includes a finding of elevated cardiac marker (e.g. TnI > 1 ng ml–1), leading to initiation of reperfusion treatment based on the knowledge that the marker signals destruction of cardiac muscle tissue surrounding an infarct. Brain-type natriuretic peptide (Maeda et al. 1998) (BNP or NTproBNP), a molecule produced in and released by the left ventricle, has recently been adopted as an effective test for congestive heart failure. Because of the clinical importance of these tests, they are performed in very large numbers: 85 million troponin assays and 10 million BNP assays are performed each year. Similarly the levels of inflammation markers like C-reactive protein (Ridker et al. 1998) (CRP), lipoprotein(a) (Agewall & Fagerberg, 2002), fibrinogen (Kannel et al. 1992), and the apportionment of cholesterol between high- and low-density lipoproteins (Luria et al. 1991) (usually distinguished in assays by their protein components) all serve as valuable measures of cardiovascular risk.
In fact, many proteins in plasma show changes associated with cardiovascular disease states. Thus the strategy of seeking single-protein tests (each with a defined reference interval, or normal range, outside of which a patient value is clearly diseased) has been vigorously pursued. Unfortunately, in most cases these changes are not sufficiently specific to provide a test of useful predictive value: the change may be real but too small in relation to genetic and environmental ‘noise’, or it occurs with other diseases as well. Where useful biomarkers have emerged, the discovery and development of each test was the result of efforts over a number of years. The appearance of cardiac troponin in plasma in MI was reported in 1987 (Cummins et al. 1987), the test was introduced commercially in 1995, and it emerged as the core parameter for MI diagnosis in 2000 (Alpert et al. 2000; Braunwald et al. 2000). BNP, probably the most rapidly adopted new diagnostic test in CVD, was shown to be diagnostic for congestive heart failure (CHF) in 1996 (Yamamoto et al. 1996) and introduced as a commercial test in 2002. However, most markers have been under investigation for many years: myoglobin since 1977 (Rosano et al. 1977), cardiac fatty acid-binding protein (FABP) since 1992 (Kleine et al. 1992) and cardiac myosin light chain 1 since 1994 (Uchino et al. 1994). On average, there appears to be a delay of approximately 10 years between discovery of a CVD marker and its commercial implementation in a form that can benefit clinical medicine (assuming it is specific and sensitive). Reducing this time lag while maintaining the rigor of clinical validation is a high priority.
Collection of candidate CVD markers. Table 1 presents a set of proteins that are confirmed or potential plasma markers of some aspect of cardiovascular disease (in the heart, vessels or brain). To my knowledge, no comparable list of proteins associated with a specific disease area has been assembled and published. Results from several sources were pooled to generate this list. A large set (> 2000) of papers was selected through keyword searches on cardiovascular disease and stroke, and these were classified and clustered using the RefViz program where titles and abstracts were scanned for protein names. A table of these proteins was constructed in an Excel spreadsheet, to which was added additional ‘pathway’-derived potential markers derived from a literature survey of the protein components of coagulation and thrombolysis pathways, as well as acute phase reactants and known inflammatory markers. The resulting list comprised 177 protein targets, some of which were composed of multiple subunits, and some of which were different fragments of a single protein. Where possible, the normal plasma concentration was extracted from the literature references, or, in the case of existing clinical markers, from the normal range values used in test interpretation. These values are of critical importance in developing strategies for measurement: the 50 most abundant candidates are likely to be measurable by MS/MS (as in Fig. 3) without additional enrichment steps, while the others may require more elaborate sample preparation or fractionation prior to quantification.
While almost all of these candidates have been evaluated in some form of CVD or stroke, none has been surveyed across all forms of these diseases, and very few have been investigated jointly in the same sample sets. Thus these candidates include many proteins that have disease relationships that are significant (though not definitive enough to provide a specific single protein test): precisely the kinds of candidates from which multiplex panels of great specificity might be drawn.
Table 2 presents 28 additional known or candidate biomarkers of CVD that are not individual proteins. These include specific protein complexes, protein modifications, antibodies against specific proteins and smaller molecules (typically metabolites). While these markers are not directly accessible to the MS-based approach outlined here, they can be measured by immunoassay or by alternative MS-based methodologies.
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This paper makes an argument for a candidate-based approach to protein biomarker development, supplementing the methods of classical proteomics that seek a complete analysis of a target proteome. Specific features of the plasma proteome, including its complexity and dynamic range, make it resistant to complete analysis in the near future. A targeted proteomics approach, aimed at selected candidates, can provide greater sensitivity and thus greater coverage of markers across the 10 orders of magnitude spanning known markers.
The fact that a non-exhaustive search for candidates related to CVD and stroke produced 177 different proteins (and protein forms) is revealing. A great deal of exploratory work has already been done, providing a targeted approach with an excellent starting point. The fact that most of these proteins have not yet become stand-alone clinical markers does not prevent them from providing incremental statistical improvement to multiprotein panels yielding improved specificity.
Two other factors also motivate a targeted approach. In the limiting case, the number of human genes is relatively small (25 000), and it might be reasonable to design specific MS-based assays (and ultimately antibodies for immunoassays) for all of these. Quantifying a major form of each human protein as a candidate disease marker is an attractive goal, though obviously far less comprehensive than the complete analysis goal (all forms of all proteins) implicit in the aims commonly expressed in proteomics.
A second and more practical factor favouring targeted assays is quantification itself. Most of the methods currently employed in proteomics can detect many proteins, but generally with poor quantitative accuracy. In particular when aiming for greatest sensitivity, proteome surveys of plasma detect quite variable subsets of proteins, even in repeat runs on the same sample. This makes it very difficult to assemble a coherent analytical dataset, since proteins are typically detected in one run but not the next: the dataset is filled with holes. This is acceptable when one is looking for hints as to the involvement of individual proteins in specific processes, but it is a major disadvantage when trying to develop a statistical case associating a protein with a disease in the human population. In this case accurate determinations of a protein in each sample are needed, as one obtains from specific assays.
By fusing the approaches taken by proteomics, analytical chemistry and clinical chemistry, hybrid methods should emerge capable of rapidly expanding the range of biomarkers for the study of disease, ageing and physiology.
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References |
|---|
|
TopAbstractIntroductionReferences |
|---|
Adkins
JN, Varnum
SM, Auberry
KJ, Moore
RJ, Angell
NH, Smith
RD, Springer
DL
&
Pounds
JG (2002). Toward a human blood serum proteome: analysis by multidimensional separation coupled with mass spectrometry. Mol Cell Proteomics
1, 947–955.
Agewall S & Fagerberg B (2002). Lipoprotein(a) was an independent predictor for major coronary events in treated hypertensive men. Clin Cardiol 25, 287–290.[Medline]
American Heart Association (2003). Heart Disease and Stroke Statistics – 2004 Update. American Heart Association, Dallas, TX, USA.
Akenzua GI, Ihongbe JC & Asemota HN (1992). Alpha-hydroxybutyrate dehydrogenase and the diagnosis of painful crisis in sickle cell anaemia. Afr J Med Med Sci 21, 13–17.[Medline]
Alpert
JS, Thygesen
K, Antman
E
&
Bassand
JP (2000). Myocardial infarction redefined – a consensus document of The Joint European Society of Cardiology/American College of Cardiology Committee for the redefinition of myocardial infarction. J Am Coll Cardiol
36, 959–969.
Anderson
L
&
Anderson
NG (1977). High resolution two-dimensional electrophoresis of human plasma proteins. Proc Natl Acad Sci U S A
74, 5421–5425.
Anderson
NL
&
Anderson
NG (2002). The human plasma proteome: history, character, and diagnostic prospects. Mol Cell Proteomics
1, 845–867.
Anderson NL, Anderson NG, Haines LR, Hardie DB, Olafson RW & Pearson TW (2004a). Mass spectrometric quantitation of peptides and proteins using Stable Isotope Standards and Capture by Anti-Peptide Antibodies (SISCAPA). J Proteome Res 3, 235–244.[CrossRef][Medline]
Anderson
NL, Polanski
M, Pieper
R, Gatlin
T, Tirumalai
RS, Conrads
TP, Veenstra
TD, Adkins
JN, Pounds
JG, Fagan
R
&
Lobley
A (2004b). The human plasma proteome: A non-redundant list developed by combination of four separate sources. Mol Cell Proteomics
3, 311–326.
Arndt
T, Guessregen
B, Hohl
A
&
Heicke
B (2004). Total plasma homocysteine measured by liquid chromatography-tandem mass spectrometry with use of 96-well plates. Clin Chem
50, 755–757.
Artieda
M, Cenarro
A, Ganan
A, Jerico
I, Gonzalvo
C, Casado
JM, Vitoria
I, Puzo
J, Pocovi
M
&
Civeira
F (2003). Serum chitotriosidase activity is increased in subjects with atherosclerosis disease. Arterioscler Thromb Vasc Biol
23, 1645–1652.
Asaka M, Kimura T, Nishikawa S, Saitoh M, Miyazaki T, Takatori T & Alpert E (1990). Serum aldolase isozyme levels in patients with cerebrovascular diseases. Am J Med Sci 300, 291–295.[CrossRef][Medline]
Atalar E, Ozmen F, Haznedaroglu I, Acil T, Ozer N, Ovunc K, Aksoyek S & Kes S (2002). Effects of short-term atorvastatin treatment on global fibrinolytic capacity, and sL-selectin and sFas levels in hyperlipidemic patients with coronary artery disease. Int J Cardiol 84, 227–231.[CrossRef][Medline]
Aukrust P, Ueland T, Lien E, Bendtzen K, Muller F, Andreassen AK, Nordoy I, Aass H, Espevik T, Simonsen S, Froland SS & Gullestad L (1999). Cytokine network in congestive heart failure secondary to ischemic or idiopathic dilated cardiomyopathy. Am J Cardiol 83, 376–382.[CrossRef][Medline]
Ay
H, Arsava
EM
&
Saribas
O (2002). Creatine kinase-MB elevation after stroke is not cardiac in origin: comparison with troponin T levels. Stroke
33, 286–289.
Bakhtiar R, Lohne J, Ramos L, Khemani L, Hayes M & Tse F (2002). High-throughput quantification of the anti-leukemia drug STI571 (Gleevec) and its main metabolite (CGP 74588) in human plasma using liquid chromatography-tandem mass spectrometry. J Chromatogr B Anal Technol Biomed Life Sci 768, 325–340.[CrossRef][Medline]
Baldus
S, Heeschen
C, Meinertz
T, Zeiher
AM, Eiserich
JP, Munzel
T, Simoons
ML
&
Hamm
CW (2003). Myeloperoxidase serum levels predict risk in patients with acute coronary syndromes. Circulation
108, 1440–1445.
Barnidge DR, Goodmanson MK, Klee GG & Muddiman DC (2004). Absolute quantification of the model biomarker prostate-specific antigen in serum by LC-Ms/MS using protein cleavage and isotope dilution mass spectrometry. J Proteome Res 3, 644–652.[CrossRef][Medline]
Barr DB, Barr JR, Maggio VL, Whitehead RD Jr, Sadowski MA, Whyatt RM & Needham LL (2002). A multi-analyte method for the quantification of contemporary pesticides in human serum and plasma using high-resolution mass spectrometry. J Chromatogr B Anal Technol Biomed Life Sci 778, 99–111.[CrossRef][Medline]
Barr
JR, Maggio
VL, Patterson
DG
Jr, Cooper
GR, Henderson
LO, Turner
WE, Smith
SJ, Hannon
WH, Needham
LL
&
Sampson
EJ (1996). Isotope dilution – mass spectrometric quantification of specific proteins: model application with apolipoprotein A-I. Clin Chem
42, 1676–1682.
Bayes-Genis
A, Conover
CA, Overgaard
MT, Bailey
KR, Christiansen
M, Holmes
DR
Jr, Virmani
R, Oxvig
C
&
Schwartz
RS (2001). Pregnancy-associated plasma protein A as a marker of acute coronary syndromes. N Engl J Med
345, 1022–1029.
Beaudeux JL, Giral P, Bruckert E, Bernard M, Foglietti MJ & Chapman MJ (2003). Serum matrix metalloproteinase-3 and tissue inhibitor of metalloproteinases-1 as potential markers of carotid atherosclerosis in infraclinical hyperlipidemia. Atherosclerosis 169, 139–146.[CrossRef][Medline]
Belboul
A, Roberts
D, Borjesson
R
&
Johnsson
J (2001). Oxygen free radical generation in healthy blood donors and cardiac patients: the protective effect of allopurinol. Perfusion
16, 59–65.
Birnie
DH, Holme
ER, McKay
IC, Hood
S, McColl
KE
&
Hillis
WS (1998). Association between antibodies to heat shock protein 65 and coronary atherosclerosis. Possible mechanism of action of Helicobacter pylori and other bacterial infections in increasing cardiovascular risk. Eur Heart J
19, 387–394.
Bitsch A, Horn C, Kemmling Y, Seipelt M, Hellenbrand U, Stiefel M, Ciesielczyk B, Cepek L, Bahn E, Ratzka P, Prange H & Otto M (2002). Serum tau protein level as a marker of axonal damage in acute ischemic stroke. Eur Neurol 47, 45–51.[CrossRef][Medline]
Blankenberg
S, Rupprecht
HJ, Bickel
C, Jiang
XC, Poirier
O, Lackner
KJ, Meyer
J, Cambien
F
&
Tiret
L (2003). Common genetic variation of the cholesteryl ester transfer protein gene strongly predicts future cardiovascular death in patients with coronary artery disease. J Am Coll Cardiol
41, 1983–1989.
Blankenberg
S, Tiret
L, Bickel
C, Peetz
D, Cambien
F, Meyer
J
&
Rupprecht
HJ (2002). Interleukin-18 is a strong predictor of cardiovascular death in stable and unstable angina. Circulation
106, 24–30.
Blann AD, Amiral J & McCollum CN (1997). Prognostic value of increased soluble thrombomodulin and increased soluble E-selectin in ischaemic heart disease. Eur J Haematol 59, 115–120.[Medline]
Bloem LJ, Manatunga AK, Tewksbury DA & Pratt JH (1995). The serum angiotensinogen concentration and variants of the angiotensinogen gene in white and black children. J Clin Invest 95, 948–953.[Medline]
Bossowska A, Kiersnowska-Rogowska B, Bossowski A, Galar B & Sowinski P (2003). Cytokines in patients with ischaemic heart disease or myocardial infarction. Kardiol Pol 59, 105–114.[Medline]
Braeckman L, De Bacquer D, Delanghe J, Claeys L & De Backer G (1999). Associations between haptoglobin polymorphism, lipids, lipoproteins and inflammatory variables. Atherosclerosis 143, 383–388.[CrossRef][Medline]
Braunwald
E, Antman
EM, Beasley
JW, Califf
RM, Cheitlin
MD, Hochman
JS, Jones
RH, Kereiakes
D, Kupersmith
J, Levin
TN, Pepine
CJ, Schaeffer
JW, Smith
EE
3rd, Steward
DE, Theroux
P, Gibbons
RJ, Alpert
JS, Eagle
KA, Faxon
DP, Fuster
V, Gardner
TJ, Gregoratos
G, Russell
RO
&
Smith
SC
Jr (2000). ACC/AHA guidelines for the management of patients with unstable angina and non-ST-segment elevation myocardial infarction: executive summary and recommendations. A report of the American College of Cardiology/American Heart Association task force on practice guidelines (committee on the management of patients with unstable angina). Circulation
102, 1193–1209.
Brodin E, Borvik T, Sandset PM, Bonaa KH, Nordoy A & Hansen JB (2004). Coagulation activation in young survivors of myocardial infarction (MI) – a population-based case-control study. Thromb Haemost 92, 178–184.[Medline]
Browner
WS, Lui
LY
&
Cummings
SR (2001). Associations of serum osteoprotegerin levels with diabetes, stroke, bone density, fractures, and mortality in elderly women. J Clin Endocrinol Metab
86, 631–637.
Brscic E, Bergerone S, Gagnor A, Colajanni E, Matullo G, Scaglione L, Cassader M, Gaschino G, Di Leo M, Brusca A, Pagano GF, Piazza A & Trevi GP (2000). Acute myocardial infarction in young adults: prognostic role of angiotensin-converting enzyme, angiotensin II type I receptor, apolipoprotein E, endothelial constitutive nitric oxide synthase, and glycoprotein IIIa genetic polymorphisms at medium-term follow-up. Am Heart J 139, 979–984.[CrossRef][Medline]
Buerke
M, Murohara
T
&
Lefer
AM (1995). Cardioprotective effects of a C1 esterase inhibitor in myocardial ischemia and reperfusion. Circulation
91, 393–402.
Burtis CA & Ashwood ER (1999). Tietz Textbook of Clinical Chemistry. W. B. Saunders Company, Philadelphia.
Bury J, Michiels G & Rosseneu M (1986). Human apolipoprotein C-II quantitation by sandwich enzyme-linked immunosorbent assay. J Clin Chem Clin Biochem 24, 457–463.[Medline]
Carson CW, Beall LD, Hunder GG, Johnson CM & Newman W (1993). Serum ELAM-1 is increased in vasculitis, scleroderma, and systemic lupus erythematosus. J Rheumatol 20, 809–814.[Medline]
Carter AM, Anagnostopoulou K, Mansfield MW & Grant PJ (2003). Soluble P-selectin levels, P-selectin polymorphisms and cardiovascular disease. J Thromb Haemost 1, 1718–1723.[CrossRef][Medline]
Castellanos
M, Leira
R, Serena
J, Blanco
M, Pedraza
S, Castillo
J
&
Davalos
A (2004). Plasma cellular-fibronectin concentration predicts hemorrhagic transformation after thrombolytic therapy in acute ischemic stroke. Stroke
35, 1671–1676.
Castellanos
M, Leira
R, Serena
J, Pumar
JM, Lizasoain
I, Castillo
J
&
Davalos
A (2003). Plasma metalloproteinase-9 concentration predicts hemorrhagic transformation in acute ischemic stroke. Stroke
34, 40–46.
Catto
A, Carter
AM, Barrett
JH, Stickland
M, Bamford
J, Davies
JA
&
Grant
PJ (1996). Angiotensin-converting enzyme insertion/deletion polymorphism and cerebrovascular disease. Stroke
27, 435–440.
Ceconi
C, Ferrari
R, Bachetti
T, Opasich
C, Volterrani
M, Colombo
B, Parrinello
G
&
Corti
A (2002). Chromogranin A in heart failure; a novel neurohumoral factor and a predictor for mortality. Eur Heart J
23, 967–974.
Cella G, Colby SI, Taylor AD, McCracken L, Parisi AF & Sasahara AA (1983). Platelet factor 4 (PF4) and heparin-released platelet factor 4 (HR-PF4) in patients with cardiovascular disorders. Thromb Res 29, 499–509.[CrossRef][Medline]
Chen CH, Tsai ST & Chou P (1999). Correlation of fasting serum C-peptide and insulin with markers of metabolic syndrome-X in a homogenous Chinese population with normal glucose tolerance. Int J Cardiol 68, 179–186.[CrossRef][Medline]
Chen
J, Tung
CH, Mahmood
U, Ntziachristos
V, Gyurko
R, Fishman
MC, Huang
PL
&
Weissleder
R (2002). In vivo imaging of proteolytic activity in atherosclerosis. Circulation
105, 2766–2771.
Cimminiello C, Arpaia G, Aloisio M, Uberti T, Rossi F, Pozzi F & Bonfardeci G (1994). Platelet-derived growth factor (PDGF) in patients with different degrees of chronic arterial obstructive disease. Angiology 45, 289–293.[Medline]
Cronlund M, Hardin J, Burton J, Lee L, Haber E & Bloch KJ (1976). Fibrinopeptide A in plasma of normal subjects and patients with disseminated intravascular coagulation and systemic lupus erythematosus. J Clin Invest 58, 142–151.[Medline]
Cummins B, Auckland ML & Cummins P (1987). Cardiac-specific troponin-I radioimmunoassay in the diagnosis of acute myocardial infarction. Am Heart J 113, 1333–1344.[CrossRef][Medline]
Cummins P, McGurk B & Littler WA (1981). Radioimmunoassay of human cardiac tropomyosin in acute myocardial infarction. Clin Sci (Lond) 60, 251–259.[Medline]
Dada N, Kim NW & Wolfert RL (2002). Lp-PLA2: an emerging biomarker of coronary heart disease. Expert Rev Mol Diagn 2, 17–22.[CrossRef][Medline]
Dahlback B (2003). The discovery of activated protein C resistance. J Thromb Haemost 1, 3–9.[CrossRef][Medline]
Dahlback B (2004). Progress in the understanding of the protein C anticoagulant pathway. Int J Hematol 79, 109–116.[Medline]
Dangas G, Konstadoulakis MM, Epstein SE, Stefanadis CI, Kymionis GD, Toutouza MG, Liakos C, Sadaniantz A, Cohen AM, Chesebro JH & Toutouzas PK (2000). Prevalence of autoantibodies against contractile proteins in coronary artery disease and their clinical implications. Am J Cardiol 85, 870–872.[CrossRef][Medline]
Dass C, Fridland GH, Tinsley PW, Killmar JT & Desiderio DM (1989). Characterization of beta-endorphin in human pituitary by fast atom bombardment mass spectrometry of trypsin-generated fragments. Int J Pept Protein Res 34, 81–87.[Medline]
de Groot
MJ, Muijtjens
AM, Simoons
ML, Hermens
WT
&
Glatz
JF (2001). Assessment of coronary reperfusion in patients with myocardial infarction using fatty acid binding protein concentrations in plasma. Heart
85, 278–285.
de Lemos
JA, Morrow
DA, Sabatine
MS, Murphy
SA, Gibson
CM, Antman
EM, McCabe
CH, Cannon
CP
&
Braunwald
E (2003). Association between plasma levels of monocyte chemoattractant protein-1 and long-term clinical outcomes in patients with acute coronary syndromes. Circulation
107, 690–695.
Deng Y, Wu JT, Lloyd TL, Chi CL, Olah TV & Unger SE (2002). High-speed gradient parallel liquid chromatography/tandem mass spectrometry with fully automated sample preparation for bioanalysis: 30 seconds per sample from plasma. Rapid Commun Mass Spectrom 16, 1116–1123.[CrossRef][Medline]
Desiderio DM & Kai M (1983). Preparation of stable isotope-incorporated peptide internal standards for field desorption mass spectrometry quantification of peptides in biologic tissue. Biomed Mass Spectrom 10, 471–479.[CrossRef][Medline]
Diamantopoulos EJ, Andreadis EA, Vassilopoulos CV, Theodorides TG, Giannakopoulos NS, Chatzis NA & Christopoulou-Kokkinou VD (2003). Increased plasma plasminogen activator inhibitor-1 levels: a possible marker of hypertensive target organ damage. Clin Exp Hypertens 25, 1–9.[CrossRef][Medline]
Disthabanchong S, Gonzalez EA & Martin KJ (2002). Soluble IL-6 receptor levels in patients on chronic hemodialysis. Clin Nephrol 58, 289–295.[Medline]
Doherty NS, Littman BH, Reilly K, Swindell AC, Buss JM & Anderson NL (1998). Analysis of changes in acute-phase plasma proteins in an acute inflammatory response and in rheumatoid arthritis using two-dimensional gel electrophoresis. Electrophoresis 19, 355–363.[CrossRef][Medline]
Donatelli M, Scarpinato A, Bucalo ML, Russo V, Iraci T & Vassallo G (1991). Stepwise increase in plasma insulin and C-peptide concentrations in obese, in obese hypertensive, and in obese hypertensive diabetic subjects. Diabetes Res 17, 125–129.[Medline]
Dugi KA, Schmidt N, Brandauer K, Ramacher D, Fiehn W & Kreuzer J (2002). Activity and concentration of lipoprotein lipase in post-heparin plasma and the extent of coronary artery disease. Atherosclerosis 163, 127–134.[CrossRef][Medline]
Dziedzic
T, Bartus
S, Klimkowicz
A, Motyl
M, Slowik
A
&
Szczudlik
A (2002). Intracerebral hemorrhage triggers interleukin-6 and interleukin-10 release in blood. Stroke
33, 2334–2335.
Ebeling F, Petaja J, Alanko S, Hirvasniemi A, Holm T, Lahde M, Nuutila A, Pesonen H, Vahtera E & Rasi V (2003). Infant stroke and beta-2-glycoprotein 1 antibodies: six cases. Eur J Pediatr 162, 678–681.[CrossRef][Medline]
Ehrenreich H, Hasselblatt M, Dembowski C, Cepek L, Lewczuk P, Stiefel M, Rustenbeck HH, Breiter N, Jacob S, Knerlich F, Bohn M, Poser W, Ruther E, Kochen M, Gefeller O, Gleiter C, Wessel TC, De Ryck M, Itri L, Prange H, Cerami A, Brines M & Siren AL (2002). Erythropoietin therapy for acute stroke is both safe and beneficial. Mol Med 8, 495–505.[Medline]
Eldar-Geva
T, Spitz
IM, Groome
NP, Margalioth
EJ
&
Homburg
R (2001). Follistatin and activin A serum concentrations in obese and non-obese patients with polycystic ovary syndrome. Hum Reprod
16, 2552–2556.
Elneihoum
AM, Falke
P, Axelsson
L, Lundberg
E, Lindgarde
F
&
Ohlsson
K (1996). Leukocyte activation detected by increased plasma levels of inflammatory mediators in patients with ischemic cerebrovascular diseases. Stroke
27, 1734–1738.
Elneihoum AM, Falke P, Hedblad B, Lindgarde F & Ohlsson K (1997). Leukocyte activation in atherosclerosis: correlation with risk factors. Atherosclerosis 131, 79–84.[CrossRef][Medline]
Erbas T, Erbas B, Kabakci G, Aksoyek S, Koray Z & Gedik O (2000). Plasma big-endothelin levels, cardiac autonomic neuropathy, and cardiac functions in patients with insulin-dependent diabetes mellitus. Clin Cardiol 23, 259–263.[Medline]
Esmon CT (2003). Coagulation and inflammation. J Endotoxin Res 9, 192–198.[Medline]
Facer CA & Theodoridou A (1994). Elevated plasma levels of P-selectin (GMP-140/CD62P) in patients with Plasmodium falciparum malaria. Microbiol Immunol 38, 727–731.[Medline]
Falke P, Elneihoum AM & Ohlsson K (2000). Leukocyte activation: relation to cardiovascular mortality after cerebrovascular ischemia. Cerebrovasc Dis 10, 97–101.[Medline]
Fareed
J, Hoppensteadt
DA, Leya
F, Iqbal
O, Wolf
H
&
Bick
R (1998). Useful laboratory tests for studying thrombogenesis in acute cardiac syndromes. Clin Chem
44, 1845–1853.
Farsak B, Gunaydin S, Yorgancioglu C & Zorlutuna Y (2003). Elevated levels of s-100beta correlate with neurocognitive outcome after cardiac surgery. J Cardiovasc Surg (Torino) 44, 31–35.[Medline]
Fassbender
K, Mossner
R, Motsch
L, Kischka
U, Grau
A
&
Hennerici
M (1995). Circulating selectin- and immunoglobulin-type adhesion molecules in acute ischemic stroke. Stroke
26, 1361–1364.
Fierens
C, Stockl
D, Baetens
D, De Leenheer
AP
&
Thienpont
LM (2003). Standardization of C-peptide measurements in urine by method comparison with isotope-dilution mass spectrometry. Clin Chem
49, 992–994.
Fridman AI, Matveev SA, Agalakova NI, Fedorova OV, Lakatta EG & Bagrov AY (2002). Marinobufagenin, an endogenous ligand of alpha-1 sodium pump, is a marker of congestive heart failure severity. J Hypertens 20, 1189–1194.[CrossRef][Medline]
Frijns
CJ, Kappelle
LJ, van Gijn
J, Nieuwenhuis
HK, Sixma
JJ
&
Fijnheer
R (1997). Soluble adhesion molecules reflect endothelial cell activation in ischemic stroke and in carotid atherosclerosis. Stroke
28, 2214–2218.
Fu ML, Herlitz H, Schulze W, Wallukat G, Micke P, Eftekhari P, Sjogren KG, Hjalmarson A, Muller-Esterl W & Hoebeke J (2000). Autoantibodies against the angiotensin receptor (AT1) in patients with hypertension. J Hypertens 18, 945–953.[CrossRef][Medline]
Fujinami A, Obayashi H, Ohta K, Ichimura T, Nishimura M, Matsui H, Kawahara Y, Yamazaki M, Ogata M, Hasegawa G, Nakamura N, Yoshikawa T, Nakano K & Ohta M (2004). Enzyme-linked immunosorbent assay for circulating human resistin: resistin concentrations in normal subjects and patients with type 2 diabetes. Clin Chim Acta 339, 57–63.[CrossRef][Medline]
Galvani M, Ferrini D, Ottani F, Nanni C, Ramberti A, Amboni P, Iamele L, Vernocchi A & Nicolini FA (2000). Soluble E-selectin is not a marker of unstable coronary plaque in serum of patients with ischemic heart disease. J Thromb Thrombolysis 9, 53–60.[Medline]
Gechtman Z & Shaltiel S (1997). Phosphorylation of vitronectin on Ser362 by protein kinase C attenuates its cleavage by plasmin. Eur J Biochem 243, 493–501.[Medline]
Getz GS & Reardon CA (2004). Paraoxonase, a cardioprotective enzyme: continuing issues. Curr Opin Lipidol 15, 261–267.[CrossRef][Medline]
Glatz JF, van der Vusse GJ, Simoons ML, Kragten JA, van Dieijen-Visser MP & Hermens WT (1998). Fatty acid-binding protein and the early detection of acute myocardial infarction. Clin Chim Acta 272, 87–92.[CrossRef][Medline]
Glowinska B, Urban M, Koput A & Galar M (2003). [Selected new atherosclerosis risk factors and markers of fibrinolysis in children and adolescents with obesity, hypertension and diabetes]. Przegl Lek 60, 12–17.[Medline]
Goetze JP, Videbaek R, Boesgaard S, Aldershvile J, Rehfeld JF & Carlsen J (2004). Pro-brain natriuretic peptide as marker of cardiovascular or pulmonary causes of dyspnea in patients with terminal parenchymal lung disease. J Heart Lung Transplant 23, 80–87.[CrossRef][Medline]
Goto T, Takase H, Toriyama T, Sugiura T, Kurita Y, Tsuru N, Masuda H, Hayashi K, Ueda R & Dohi Y (2002). Increased circulating levels of natriuretic peptides predict future cardiac event in patients with chronic hemodialysis. Nephron 92, 610–615.[CrossRef][Medline]
Green
G, Dyce
D, Gimovsky
A
&
Lo
DH (1976). Automated approach to radioimmunoassays of somatotropin (human growth hormone) and insulin. Clin Chem
22, 1510–1515.
Grote L (2004). [Influence of circadian rhythms on cardiovascular function]. Internist (Berl) 45, 994–1005.[Medline]
Guerin J, Smith O, White B, Sweetman G, Feighery C & Jackson J (1998). Antibodies to prothrombin in antiphospholipid syndrome and inflammatory disorders. Br J Haematol 102, 896–902.[CrossRef][Medline]
Hambsch J, Osmancik P, Bocsi J, Schneider P & Tarnok A (2002). Neutrophil adhesion molecule expression and serum concentration of soluble adhesion molecules during and after pediatric cardiovascular surgery with or without cardiopulmonary bypass. Anesthesiology 96, 1078–1085.[CrossRef][Medline]
Hayashi T & Matuo Y (2001). A new stroke marker as detected by serum phosphoglycerate mutase B-type isozyme. Biochem Biophys Res Commun 287, 843–845.[CrossRef][Medline]
Hayden
K, Tetlow
L, Byrne
G
&
Bundred
N (2000). Radioimmunoassay for the measurement of thrombospondin in plasma and breast cyst fluid: validation and clinical application. Ann Clin Biochem
37, 319–325.
He M, Wen Z, He X, Xiong S, Liu F, Xu J, Li J, Xie Q, Jian Z, Chen F, Xiao B, Pu X & He S (2002). Observation on tissue factor pathway and some other coagulation parameters during the onset of acute cerebrocardiac thrombotic diseases. Thromb Res 107, 223–228.[CrossRef][Medline]
Heald AH, Cruickshank JK, Riste LK, Cade JE, Anderson S, Greenhalgh A, Sampayo J, Taylor W, Fraser W, White A & Gibson JM (2001). Close relation of fasting insulin-like growth factor binding protein-1 (IGFBP-1) with glucose tolerance and cardiovascular risk in two populations. Diabetologia 44, 333–339.[CrossRef][Medline]
Herrmann
M, Vos
P, Wunderlich
MT, de Bruijn
CH
&
Lamers
KJ (2000). Release of glial tissue-specific proteins after acute stroke: a comparative analysis of serum concentrations of protein S-100B and glial fibrillary acidic protein. Stroke
31, 2670–2677.
Hirayama A, Arita M, Takagaki Y, Tsuji A, Kodama K & Inoue M (1990). Clinical assessment of specific enzyme immunoassay for the human cardiac myosin light chain II (MLC II) with use of monoclonal antibodies. Clin Biochem 23, 515–522.[CrossRef]
