Biological variability dominates and influences analytical variance in HPLC-ECD studies of the human plasma metabolome

被引:23
作者
Shurubor, Yevgeniya [1 ,2 ]
Matson, Wayne [3 ,4 ]
Willett, Walter [3 ,4 ,5 ]
Hankinson, Susan [3 ,4 ,6 ]
Kristal, Bruce [1 ,2 ,7 ]
机构
[1] Brigham & Womens Hosp, Dept Neurosurg, 221 Longwood Ave,LM322B, Boston, MA 02115 USA
[2] Burke Med Res Inst, White Plains, NY 10605 USA
[3] ESA Inc, Chelmsford, MA 01824 USA
[4] Harvard Med Sch, Brigham & Womens Hosp, Dept Med, Channing Lab, Boston, MA 02115 USA
[5] Harvard Sch Publ Hlth, Dept Nutr, Boston, MA 02115 USA
[6] Harvard Sch Publ Hlth, Dept Epidemiol, Boston, MA 02115 USA
[7] Cornell Univ, Med Coll, Dept Neurosci, New York, NY 10021 USA
来源
BMC CLINICAL PATHOLOGY | 2007年 / 7卷
关键词
D O I
10.1186/1472-6890-7-9
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Background: Biomarker-based assessments of biological samples are widespread in clinical, preclinical, and epidemiological investigations. We previously developed serum metabolomic profiles assessed by HPLC-separations coupled with coulometric array detection that can accurately identify ad libitum fed and caloric-restricted rats. These profiles are being adapted for human epidemiology studies, given the importance of energy balance in human disease. Methods: Human plasma samples were biochemically analyzed using HPLC separations coupled with coulometric electrode array detection. Results: We identified these markers/metabolites in human plasma, and then used them to determine which human samples represent blinded duplicates with 100% accuracy (N = 30 of 30). At least 47 of 61 metabolites tested were sufficiently stable for use even after 48 hours of exposure to shipping conditions. Stability of some metabolites differed between individuals (N = 10 at 0, 24, and 48 hours), suggesting the influence of some biological factors on parameters normally considered as analytical. Conclusion: Overall analytical precision (mean median CV, similar to 9%) and total between-person variation (median CV, similar to 50-70%) appear well suited to enable use of metabolomics markers in human clinical trials and epidemiological studies, including studies of the effect of caloric intake and balance on long-term cancer risk.
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页数:14
相关论文
共 67 条
  • [11] Statistical methods for estimating usual intake of nutrients and foods: A review of the theory
    Dodd, Kevin W.
    Guenther, Patricia M.
    Freedman, Laurence S.
    Subar, Amy F.
    Kipnis, Victor
    Midthune, Douglas
    Tooze, Janet A.
    Krebs-Smith, Susan M.
    [J]. JOURNAL OF THE AMERICAN DIETETIC ASSOCIATION, 2006, 106 (10) : 1640 - 1650
  • [12] Metabolite profiling for plant functional genomics
    Fiehn, O
    Kopka, J
    Dörmann, P
    Altmann, T
    Trethewey, RN
    Willmitzer, L
    [J]. NATURE BIOTECHNOLOGY, 2000, 18 (11) : 1157 - 1161
  • [13] Metabolomics - the link between genotypes and phenotypes
    Fiehn, O
    [J]. PLANT MOLECULAR BIOLOGY, 2002, 48 (1-2) : 155 - 171
  • [14] Test result variation and the quality of evidence-based clinical guidelines
    Fraser, CG
    [J]. CLINICA CHIMICA ACTA, 2004, 346 (01) : 19 - 24
  • [15] Genomics and metabolomics as markers for the interaction of diet and health: Lessons from lipids
    German, JB
    Roberts, MA
    Watkins, SM
    [J]. JOURNAL OF NUTRITION, 2003, 133 (06) : 2078S - 2083S
  • [16] Metabolomics and individual metabolic assessment: The next great challenge for nutrition
    German, JB
    Roberts, MA
    Fay, L
    Watkins, SM
    [J]. JOURNAL OF NUTRITION, 2002, 132 (09) : 2486 - 2487
  • [17] Metabolomics by numbers: acquiring and understanding global metabolite data
    Goodacre, R
    Vaidyanathan, S
    Dunn, WB
    Harrigan, GG
    Kell, DB
    [J]. TRENDS IN BIOTECHNOLOGY, 2004, 22 (05) : 245 - 252
  • [18] HANKINSON SE, 1989, CLIN CHEM, V35, P2313
  • [19] Harrigan G.G., 2003, METABOLIC PROFILING
  • [20] Key T, 1996, CANCER EPIDEM BIOMAR, V5, P811