Potassium-based algorithm allows correction for the hematocrit bias in quantitative analysis of caffeine and its major metabolite in dried blood spots

Kesel, Pieter M. M. De; Capiau, Sara; Stove, Veronique; Lambert, Willy E.; Stove, Christophe

doi:10.1007/s00216-014-8114-z

Cited by 57 publications

(35 citation statements)

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“…the sum of all feature peak integrals per subject, and hematocrit. Here, we measured K + concentrations of DBS extracts as proxies for hematocrit (Capiau et al 2013; De Kesel et al 2014). As can be seen in Fig.…”

Section: Resultsmentioning

confidence: 99%

“…As validation of our methodology, we performed an untargeted analysis to pinpoint metabolites that were statistically associated with population characteristics. Our untargeted workflow includes: 1) data filtering and normalization for potassium levels as surrogates for hematocrit (Capiau et al 2013; De Kesel et al 2014), 2) multivariate statistical analysis to identify discriminating features, 3) manual examination of significant features for peak and integration quality, and 4) annotation of significant features with MSMS.…”

Section: Introductionmentioning

confidence: 99%

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An untargeted metabolomics method for archived newborn dried blood spots in epidemiologic studies

et al. 2017

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Introduction For pediatric diseases like childhood leukemia, a short latency period points to in-utero exposures as potentially important risk factors. Untargeted metabolomics of small molecules in archived newborn dried blood spots (DBS) offers an avenue for discovering early-life exposures that contribute to disease risks. Objectives The purpose of this study was to develop a quantitative method for untargeted analysis of archived newborn DBS for use in an epidemiological study (California Childhood Leukemia Study, CCLS). Methods Using experimental DBS from the blood of an adult volunteer, we optimized extraction of small molecules and integrated measurement of potassium as a proxy for blood hematocrit. We then applied this extraction method to 4.7-mm punches from 106 control DBS samples from the CCLS. Sample extracts were analyzed with liquid chromatography high resolution mass spectrometry (LC-HRMS) and an untargeted workflow was used to screen for metabolites that discriminate population characteristics such as sex, ethnicity, and birth weight. Results Thousands of small molecules were measured in extracts of archived DBS. Normalizing for potassium levels removed variability related to varying hematocrit across DBS punches. Of the roughly 1,000 prevalent small molecules that were tested, multivariate linear regression detected significant associations with ethnicity (3 metabolites) and birth weight (15 metabolites) after adjusting for multiple testing. Conclusions This untargeted workflow can be used for analysis of small molecules in archived DBS to discover novel biomarkers, to provide insights into the initiation and progression of diseases, and to provide guidance for disease prevention.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An untargeted metabolomics method for archived newborn dried blood spots in epidemiologic studies

et al. 2017

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“…Because the biological factor of interest is breastfeeding, two fatty acids that are abundant in breast milk, i.e., palmitoleic acid and docosahexaenoic acid (DHA) (Chuang et al, 2013;Gardner et al, 2017), were used as positive controls, selected prior to any analysis. Known sources of technical variation in these samples include NBS age, potassium level (a measure of hematocrit), run-order, and batch (De Kesel et al, 2014;Pupillo et al, 2016).…”

Section: Metabolomics Of Neonatal Blood Spotsmentioning

confidence: 99%

Data-adaptive pipeline for filtering and normalizing metabolomics data

Schiffman

Petrick

Perttula

et al. 2018

Preprint

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IntroductionUntargeted metabolomics datasets contain large proportions of uninformative features and are affected by a variety of nuisance technical effects that can bias subsequent statistical analyses. Thus, there is a need for versatile and data-adaptive methods for filtering and normalizing data prior to investigating the underlying biological phenomena. ConclusionOur proposed data-adaptive pipeline is intuitive and effectively reduces technical noise from untargeted metabolomics datasets. It is particularly relevant for interrogation of biological phenomena in data derived from complex matrices associated with biospecimens.

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“…Capiau et al recently demonstrated use of the measurement of potassium (K) from DBS sub-punches to estimate HCT [5]. This procedure has been used successfully to correct for effects of HCT in DBS analysis [6,7].…”

Section: Introductionmentioning

confidence: 97%

Estimation of hematocrit in filter paper dried bloodspots by potassium measurement: advantage of use of perimeter ring samples over circular center sub-punch samples

Rufail

McCloskey

Stickle

2016

Clinical Chemistry and Laboratory Medicine (CCLM)

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Potassium-based algorithm allows correction for the hematocrit bias in quantitative analysis of caffeine and its major metabolite in dried blood spots

Abstract: Although dried blood spot (DBS) sampling is increasingly receiving interest as a potential alternative to traditional blood sampling, the impact of hematocrit (Hct) on DBS results is limiting its final breakthrough in routine bioanalysis. To predict the Hct of a given DBS,

Cited by 57 publications

References 9 publications

An untargeted metabolomics method for archived newborn dried blood spots in epidemiologic studies

An untargeted metabolomics method for archived newborn dried blood spots in epidemiologic studies

Data-adaptive pipeline for filtering and normalizing metabolomics data

Estimation of hematocrit in filter paper dried bloodspots by potassium measurement: advantage of use of perimeter ring samples over circular center sub-punch samples

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