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Comparability of serum, plasma, and urinary estrogen and estrogen metabolite measurements by sex and menopausal status.

ABSTRACT: PURPOSE:The comparability between serum, plasma, and urinary measurements of estrogen metabolites via liquid chromatography-tandem mass spectrometry (LC-MS/MS) has not been largely explored, and it is unclear if urinary LC-MS/MS measurements are suitable surrogates of circulating levels. METHODS:Serum, plasma (EDTA and heparin), and urinary estrogen/estrogen metabolite levels were measured via LC-MS/MS in paired samples from 64 healthy volunteers (18 men, 20 premenopausal women, 26 postmenopausal women). Geometric means and Spearman correlation coefficients were used to compare individual and combined pathway levels of estrogens/estrogen metabolites across biologic matrices by sex/menopausal status. RESULTS:Measured concentrations of estrogens/estrogen metabolites across blood matrices were almost identical (percent differences?

SUBMITTER: Coburn SB

PROVIDER: S-EPMC6447065 | biostudies-literature | 2019 Jan

REPOSITORIES: biostudies-literature

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Comparability of serum, plasma, and urinary estrogen and estrogen metabolite measurements by sex and menopausal status.

Coburn Sally B SB Stanczyk Frank Z FZ Falk Roni T RT McGlynn Katherine A KA Brinton Louise A LA Sampson Joshua J Bradwin Gary G Xu Xia X Trabert Britton B

Cancer causes & control : CCC 20181201 1

<h4>Purpose</h4>The comparability between serum, plasma, and urinary measurements of estrogen metabolites via liquid chromatography-tandem mass spectrometry (LC-MS/MS) has not been largely explored, and it is unclear if urinary LC-MS/MS measurements are suitable surrogates of circulating levels.<h4>Methods</h4>Serum, plasma (EDTA and heparin), and urinary estrogen/estrogen metabolite levels were measured via LC-MS/MS in paired samples from 64 healthy volunteers (18 men, 20 premenopausal women, 2 ...[more]

PMID: 30506492

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Project description:BackgroundHuman plasma and serum are widely used matrices in clinical and biological studies. However, different collecting procedures and the coagulation cascade influence concentrations of both proteins and metabolites in these matrices. The effects on metabolite concentration profiles have not been fully characterized.Methodology/principal findingsWe analyzed the concentrations of 163 metabolites in plasma and serum samples collected simultaneously from 377 fasting individuals. To ensure data quality, 41 metabolites with low measurement stability were excluded from further analysis. In addition, plasma and corresponding serum samples from 83 individuals were re-measured in the same plates and mean correlation coefficients (r) of all metabolites between the duplicates were 0.83 and 0.80 in plasma and serum, respectively, indicating significantly better stability of plasma compared to serum (p?=?0.01). Metabolite profiles from plasma and serum were clearly distinct with 104 metabolites showing significantly higher concentrations in serum. In particular, 9 metabolites showed relative concentration differences larger than 20%. Despite differences in absolute concentration between the two matrices, for most metabolites the overall correlation was high (mean r?=?0.81±0.10), which reflects a proportional change in concentration. Furthermore, when two groups of individuals with different phenotypes were compared with each other using both matrices, more metabolites with significantly different concentrations could be identified in serum than in plasma. For example, when 51 type 2 diabetes (T2D) patients were compared with 326 non-T2D individuals, 15 more significantly different metabolites were found in serum, in addition to the 25 common to both matrices.Conclusions/significanceOur study shows that reproducibility was good in both plasma and serum, and better in plasma. Furthermore, as long as the same blood preparation procedure is used, either matrix should generate similar results in clinical and biological studies. The higher metabolite concentrations in serum, however, make it possible to provide more sensitive results in biomarker detection.

Project description:BackgroundDiabetic kidney disease (DKD) is a common and potentially fatal consequence of diabetes. Chronic renal failure or end-stage renal disease may result over time. Numerous studies have demonstrated the function of the microbiota in health and disease. The use of advanced urine culture techniques revealed the presence of resident microbiota in the urinary tract, undermining the idea of urine sterility. Studies have demonstrated that the urine microbiota is related with urological illnesses; nevertheless, the fundamental mechanisms by which the urinary microbiota influences the incidence and progression of DKD remain unclear. The purpose of this research was to describe key characteristics of the patients with DKD urinary microbiota in order to facilitate the development of diagnostic and therapeutic for DKD.MethodsWe evaluated the structure and composition of the microbiota extracted from urine samples taken from DKD patients (n = 19) and matched healthy controls (n = 15) using 16S rRNA gene sequencing. Meanwhile, serum metabolite profiles were compared using liquid chromatography-tandem mass spectrometry (LC-MS/MS). Associations between clinical characteristics, urine microbiota, and serum metabolites were also examined. Finally, the interaction between urine microbiota and serum metabolites was clarified based on differential metabolite abundance analysis.ResultsThe findings indicated that the DKD had a distinct urinary microbiota from the healthy controls (HC). Taxonomic investigations indicated that the DKD microbiome had less alpha diversity than a control group. Proteobacteria and Acidobacteria phyla increased in the DKD, while Firmicutes and Bacteroidetes decreased significantly (P < 0.05). Acidobacteria was the most prevalent microbiota in the DKD, as determined by the Linear discriminant analysis Effect Size (LEfSe) plot. Changes in the urinary microbiota of DKD also had an effect on the makeup of metabolites. Short-chain fatty acids (SCFAs) and protein-bound uremic toxins (PBUTs) were shown to be specific. Then we discovered that arginine and proline metabolism was the primary mechanism involved in the regulation of diabetic kidney disease.ConclusionsThis study placed the urinary microbiota and serum metabolite of DKD patients into a functional framework and identified the most abundant microbiota in DKD (Proteobacteria and Acidobacteria). Arginine metabolites may have a major effect on DKD patients, which correlated with the progression of DKD.

Dataset Information

Comparability of serum, plasma, and urinary estrogen and estrogen metabolite measurements by sex and menopausal status.

Publications

Comparability of serum, plasma, and urinary estrogen and estrogen metabolite measurements by sex and menopausal status.

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