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| 001 | 157074 | ||
| 005 | 20240229123128.0 | ||
| 024 | 7 | _ | |a 10.3390/metabo10050213 |2 doi |
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| 041 | _ | _ | |a eng |
| 082 | _ | _ | |a 540 |
| 100 | 1 | _ | |a Hardikar, Sheetal |b 0 |
| 245 | _ | _ | |a Impact of Pre-blood Collection Factors on Plasma Metabolomic Profiles. |
| 260 | _ | _ | |a Basel |c 2020 |b MDPI |
| 336 | 7 | _ | |a article |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Journal article |2 DataCite |
| 336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1594720649_24603 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
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| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 520 | _ | _ | |a Demographic, lifestyle and biospecimen-related factors at the time of blood collection can influence metabolite levels in epidemiological studies. Identifying the major influences on metabolite concentrations is critical to designing appropriate sample collection protocols and considering covariate adjustment in metabolomics analyses. We examined the association of age, sex, and other short-term pre-blood collection factors (time of day, season, fasting duration, physical activity, NSAID use, smoking and alcohol consumption in the days prior to collection) with 133 targeted plasma metabolites (acylcarnitines, amino acids, biogenic amines, sphingolipids, glycerophospholipids, and hexoses) among 108 individuals that reported exposures within 48 h before collection. The differences in mean metabolite concentrations were assessed between groups based on pre-collection factors using two-sided t-tests and ANOVA with FDR correction. Percent differences in metabolite concentrations were negligible across season, time of day of collection, fasting status or lifestyle behaviors at the time of collection, including physical activity or the use of tobacco, alcohol or NSAIDs. The metabolites differed in concentration between the age and sex categories for 21.8% and 14.3% metabolites, respectively. In conclusion, extrinsic factors in the short period prior to collection were not meaningfully associated with concentrations of selected endogenous metabolites in a cross-sectional sample, though metabolite concentrations differed by age and sex. Larger studies with more coverage of the human metabolome are warranted. |
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| 700 | 1 | _ | |a Albrechtsen, Richard D |b 1 |
| 700 | 1 | _ | |a Achaintre, David |b 2 |
| 700 | 1 | _ | |a Lin, Tengda |b 3 |
| 700 | 1 | _ | |a Pauleck, Svenja |b 4 |
| 700 | 1 | _ | |a Playdon, Mary |b 5 |
| 700 | 1 | _ | |a Holowatyj, Andreana N |b 6 |
| 700 | 1 | _ | |a Gigic, Biljana |b 7 |
| 700 | 1 | _ | |a Schrotz-King, Petra |0 P:(DE-He78)01ef71f71b01a3ec3b698653fd43fe86 |b 8 |u dkfz |
| 700 | 1 | _ | |a Boehm, Juergen |b 9 |
| 700 | 1 | _ | |a Habermann, Nina |b 10 |
| 700 | 1 | _ | |a Brezina, Stefanie |b 11 |
| 700 | 1 | _ | |a Gsur, Andrea |b 12 |
| 700 | 1 | _ | |a van Roekel, Eline H |b 13 |
| 700 | 1 | _ | |a Weijenberg, Matty P |b 14 |
| 700 | 1 | _ | |a Keski-Rahkonen, Pekka |b 15 |
| 700 | 1 | _ | |a Scalbert, Augustin |b 16 |
| 700 | 1 | _ | |a Ose, Jennifer |b 17 |
| 700 | 1 | _ | |a Ulrich, Cornelia M |b 18 |
| 773 | _ | _ | |a 10.3390/metabo10050213 |g Vol. 10, no. 5, p. 213 - |0 PERI:(DE-600)2662251-8 |n 5 |p 213 |t Metabolites |v 10 |y 2020 |x 2218-1989 |
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