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The Effect of Pre-Analytical Conditions on Blood Metabolomics in Epidemiological Studies.
Santos Ferreira, Diana L; Maple, Hannah J; Goodwin, Matt; Brand, Judith S; Yip, Vikki; Min, Josine L; Groom, Alix; Lawlor, Debbie A; Ring, Susan.
Affiliation
  • Santos Ferreira DL; Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol BS8 2BN, UK. diana.santosferreira@bristol.ac.uk.
  • Maple HJ; Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 2PS, UK. diana.santosferreira@bristol.ac.uk.
  • Goodwin M; Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol BS8 2BN, UK. hannah.maple@bio-techne.com.
  • Brand JS; Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 2PS, UK. hannah.maple@bio-techne.com.
  • Yip V; Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol BS8 2BN, UK. matt.goodwin@bristol.ac.uk.
  • Min JL; Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 2PS, UK. matt.goodwin@bristol.ac.uk.
  • Groom A; Medical Research Council Integrative Epidemiology Unit at the University of Bristol, Bristol BS8 2BN, UK. judith.brand@bristol.ac.uk.
  • Lawlor DA; Population Health Sciences, Bristol Medical School, University of Bristol, Bristol BS8 2PS, UK. judith.brand@bristol.ac.uk.
  • Ring S; Clinical Epidemiology and Biostatistics, School of Medical Sciences, Örebro University, 701 85 Örebro, Sweden. judith.brand@bristol.ac.uk.
Metabolites ; 9(4)2019 Apr 03.
Article in En | MEDLINE | ID: mdl-30987180
Serum and plasma are commonly used in metabolomic-epidemiology studies. Their metabolome is susceptible to differences in pre-analytical conditions and the impact of this is unclear. Participant-matched EDTA-plasma and serum samples were collected from 37 non-fasting volunteers and profiled using a targeted nuclear magnetic resonance (NMR) metabolomics platform (n = 151 traits). Correlations and differences in mean of metabolite concentrations were compared between reference (pre-storage: 4 °C, 1.5 h; post-storage: no buffer addition delay or NMR analysis delay) and four pre-storage blood processing conditions, where samples were incubated at (i) 4 °C, 24 h; (ii) 4 °C, 48 h; (iii) 21 °C, 24 h; and (iv) 21 °C, 48 h, before centrifugation; and two post-storage sample processing conditions in which samples thawed overnight (i) then left for 24 h before addition of sodium buffer followed by immediate NMR analysis; and (ii) addition of sodium buffer, then left for 24 h before NMR profiling. We used multilevel linear regression models and Spearman's rank correlation coefficients to analyse the data. Most metabolic traits had high rank correlation and minimal differences in mean concentrations between samples subjected to reference and the different conditions tested, that may commonly occur in studies. However, glycolysis metabolites, histidine, acetate and diacylglycerol concentrations may be compromised and this could bias results in association/causal analyses.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Metabolites Year: 2019 Document type: Article Country of publication: Switzerland

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Metabolites Year: 2019 Document type: Article Country of publication: Switzerland