| 1. |
- Brunius, Carl, 1974, et al.
(författare)
-
Prediction and modeling of pre-analytical sampling errors as a strategy to improve plasma NMR metabolomics data
- 2017
-
Ingår i: Bioinformatics. - : Oxford University Press (OUP). - 1367-4803 .- 1460-2059 .- 1367-4811. ; 33:22, s. 3567-3574
-
Tidskriftsartikel (refereegranskat)abstract
- Biobanks are important infrastructures for life science research. Optimal sample handling regarding e.g. collection and processing of biological samples is highly complex, with many variables that could alter sample integrity and even more complex when considering multiple study centers or using legacy samples with limited documentation on sample management. Novel means to understand and take into account such variability would enable high-quality research on archived samples. This study investigated whether pre-analytical sample variability could be predicted and reduced by modeling alterations in the plasma metabolome, measured by NMR, as a function of pre-centrifugation conditions (1-36 h pre-centrifugation delay time at 4 A degrees C and 22 A degrees C) in 16 individuals. Pre-centrifugation temperature and delay times were predicted using random forest modeling and performance was validated on independent samples. Alterations in the metabolome were modeled at each temperature using a cluster-based approach, revealing reproducible effects of delay time on energy metabolism intermediates at both temperatures, but more pronounced at 22 A degrees C. Moreover, pre-centrifugation delay at 4 A degrees C resulted in large, specific variability at 3 h, predominantly of lipids. Pre-analytical sample handling error correction resulted in significant improvement of data quality, particularly at 22 A degrees C. This approach offers the possibility to predict pre-centrifugation delay temperature and time in biobanked samples before use in costly downstream applications. Moreover, the results suggest potential to decrease the impact of undesired, delay-induced variability. However, these findings need to be validated in multiple, large sample sets and with analytical techniques covering a wider range of the metabolome, such as LC-MS.
|
|
| 2. |
- Brunius, Carl, 1974, et al.
(författare)
-
Targeted and untargeted metabolomics for specific food intake assessment whole grains as an example
- 2017
-
Ingår i: Advances in the Assessment of Dietary Intake. - Boca Raton : CRC Press, 2017. : CRC Press. - 9781498749329 ; , s. 315-336
-
Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- This chapter provides an overview of the current available knowledge about alternative approaches to address food intakes by using dietary biomarkers. Biomarkers of whole grain intake identified by targeted and untargeted metabolomics approaches will be used as illustrative examples. The chapter addresses discovery, validation, and implementation of biomarkers of specific foods using targeted, and with particular emphasis, untargeted metabolomics approaches with whole grain foods as an example of a food category important to human health. The choice of experimental design for biomarker discovery will have a large impact on the choice of statistical techniques, what can be found in the data, and also on workload of study participants and researchers. The gold standard for statistical modeling validation is to use external validation, i.e. that separate datasets are used for model construction and validation. The chapter outlines a suggested approach to discover urinary biomarkers of specific food consumption using whole grain rye consumption in a healthy, free-living population as an example.
|
|
