Sources of Variability in Metabolite Measurements from Urinary Samples

Xiao, Qian; Moore, Stephen; Boca, Simina M.; Matthews, Charles E.; Rothman, Nathaniel; Sinha, Rashmi; Cross, Amanda J.; Sampson, Joshua N.

doi:10.1371/journal.pone.0095749

Cited by 30 publications

(31 citation statements)

References 26 publications

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“…Nevertheless, its integration into population-based research remains in its early stages (2). Rigorous methods studies identifying sources of variability in metabolite measurements for various types of biological specimens is critical (18, 32, 33). Presently, relatively few human studies have used untargeted metabolomics platforms to analyze fecal samples.…”

Section: Discussionmentioning

confidence: 99%

Comparison of Collection Methods for Fecal Samples for Discovery Metabolomics in Epidemiologic Studies

Loftfield

Vogtmann

Sampson

et al. 2016

Cancer Epidemiology, Biomarkers &Amp; Prevention

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Background The gut metabolome may be associated with the incidence and progression of numerous diseases. The composition of the gut metabolome can be captured by measuring metabolite levels in the feces. However, there is little data describing the effect of fecal sample collection methods on metabolomic measures. Methods We collected fecal samples from eighteen volunteers using four methods: no solution, 95% ethanol, fecal occult blood test (FOBT) cards, and fecal immunochemical test (FIT). One set of samples was frozen after collection (day 0), and for 95% ethanol, FOBT, and FIT, a second set was frozen after 96 hours at room temperature. We evaluated 1) technical reproducibility within sample replicates, 2) stability after 96 hours at room temperature for 95% ethanol, FOBT, and FIT, and 3) concordance of metabolite measures with the putative “gold standard,” day 0 samples without solution. Results Intraclass correlation coefficients (ICC) estimating technical reproducibility were high for replicate samples for each collection method. ICCs estimating stability at room temperature were high for 95% ethanol and FOBT (median ICC>0.87) but not FIT (median ICC=0.52). Similarly, Spearman correlation coefficients (rs) estimating metabolite concordance with the “gold standard” were higher for 95% ethanol (median rs=0.82) and FOBT (median rs=0.70) than for FIT (median rs=0.40). Conclusions Metabolomic measurements appear reproducible and stable in fecal samples collected with 95% ethanol or FOBT. Concordance with the “gold standard” is highest with 95% ethanol and acceptable with FOBT. Impact Future epidemiologic studies should collect feces using 95% ethanol or FOBT if interested in studying fecal metabolomics.

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Section: Discussionmentioning

confidence: 99%

Comparison of Collection Methods for Fecal Samples for Discovery Metabolomics in Epidemiologic Studies

Loftfield

Vogtmann

Sampson

et al. 2016

Cancer Epidemiology, Biomarkers &Amp; Prevention

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“…plant-insect interactions | metabolomics | mass spectrometry | natural variation E lucidating the structure of metabolites underlying complex traits and the factors that maintain their variation in natural populations are important challenges in plant ecological studies (1). Many studies have notably shown that stress-responsive pathways that produce secondary metabolites are sporadically found across different plant taxa with extensive diversification (2).…”

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confidence: 99%

Navigating natural variation in herbivory-induced secondary metabolism in coyote tobacco populations using MS/MS structural analysis

Baldwin

Gaquerel

2015

Proc. Natl. Acad. Sci. U.S.A.

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Natural variation can be extremely useful in unraveling the determinants of phenotypic trait evolution but has rarely been analyzed with unbiased metabolic profiling to understand how its effects are organized at the level of biochemical pathways. Native populations of Nicotiana attenuata, a wild tobacco species, have been shown to be highly genetically diverse for traits important for their interactions with insects. To resolve the chemodiversity existing in these populations, we developed a metabolomics and computational pipeline to annotate leaf metabolic responses to Manduca sexta herbivory. We selected seeds from 43 accessions of different populations from the southwestern United Statesincluding the well-characterized Utah 30th generation inbred accession-and grew 183 plants in the glasshouse for standardized herbivory elicitation. Metabolic profiles were generated from elicited leaves of each plant using a high-throughput ultra HPLC (UHPLC)-quadrupole TOFMS (qTOFMS) method, processed to systematically infer covariation patterns among biochemically related metabolites, as well as unknown ones, and finally assembled to map natural variation. Navigating this map revealed metabolic branch-specific variations that surprisingly only partly overlapped with jasmonate accumulation polymorphisms and deviated from canonical jasmonate signaling. Fragmentation analysis via indiscriminant tandem mass spectrometry (idMS/MS) was conducted with 10 accessions that spanned a large proportion of the variance found in the complete accession dataset, and compound spectra were computationally assembled into spectral similarity networks. The biological information captured by this networking approach facilitates the mining of the mass spectral data of unknowns with high natural variation, as demonstrated by the annotation of a strongly herbivory-inducible phenolic derivative, and can guide pathway analysis.plant-insect interactions | metabolomics | mass spectrometry | natural variation E lucidating the structure of metabolites underlying complex traits and the factors that maintain their variation in natural populations are important challenges in plant ecological studies (1). Many studies have notably shown that stress-responsive pathways that produce secondary metabolites are sporadically found across different plant taxa with extensive diversification (2). This important diversification suggests that particular metabolic systems have been recruited through natural selection when the set of compounds that they produce address specific ecological needs. Interactions with insects are important selection pressures that have sculpted plant metabolism, and many plant metabolites protect against herbivore attack and physical damage (3-5). The timely production of particular secondary metabolites in response to insect attack benefits plants by decreasing the costs of constitutive metabolite production. Trade-offs between defense metabolite productions and the intrinsic growth-related functions of central metabolic pathways likely prov...

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“…The following metabolites show higher concentrations in men than in women (also higher in high REE or LBM, respectively): creatinine, leucine, 4-deoxythreonic acid, and uric acid. These metabolites are either involved in muscle energy metabolism [13,16,18,47,48] and known to be determined by the larger muscle mass in men [49], or their function is yet unknown [19,50]. It is likely that sex differences cause a differential production of muscle-specific metabolites.…”

Section: Discussionmentioning

confidence: 99%

Metabolite profiles evaluated, according to sex, do not predict resting energy expenditure and lean body mass in healthy non-obese subjects

et al. 2018

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Purpose Differences in resting energy expenditure (REE) between men and women mainly result from sex-related differences in lean body mass (LBM). So far, a little is known about whether REE and LBM are reflected by a distinct human metabolite profile. Therefore, we aimed to identify plasma and urine metabolite patterns that are associated with REE and LBM of healthy subjects. Methods We investigated 301 healthy male and female subjects (18-80 years) under standardized conditions in the crosssectional KarMeN (Karlsruhe Metabolomics and Nutrition) study. REE was determined by indirect calorimetry and LBM by dual X-ray absorptiometry. Fasting blood and 24 h urine samples were analyzed by targeted and non-targeted metabolomics methods using GC × GC-MS, GC-MS, LC-MS, and NMR. Data were evaluated by predictive modeling of combined data using different machine learning algorithms, namely SVM, glmnet, and PLS. Results When evaluating data of men and women combined, we were able to predict REE and LBM with high accuracy (> 90%). This, however, was a clear effect of sex, which is supported by the high degree of overlap in identified important metabolites for LBM, REE, and sex, respectively. The applied machine learning algorithms did not reveal a metabolite pattern predictive of REE or LBM, when analyzing data for men and women, separately. Conclusions We could not identify a sex independent predictive metabolite pattern for REE or LBM. REE and LBM have no impact on plasma and urine metabolite profiles in the KarMeN Study participants. Studies applying metabolomics in healthy humans need to consider sex specific data evaluation.

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Sources of Variability in Metabolite Measurements from Urinary Samples

Cited by 30 publications

References 26 publications

Comparison of Collection Methods for Fecal Samples for Discovery Metabolomics in Epidemiologic Studies

Comparison of Collection Methods for Fecal Samples for Discovery Metabolomics in Epidemiologic Studies

Navigating natural variation in herbivory-induced secondary metabolism in coyote tobacco populations using MS/MS structural analysis

Metabolite profiles evaluated, according to sex, do not predict resting energy expenditure and lean body mass in healthy non-obese subjects

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