Dataset Information

Combining Urinary Biomarker Data From Studies With Different Measures of Urinary Dilution.

ABSTRACT:

Background

Guidance is lacking for how to combine urinary biomarker data across studies that use different measures of urinary dilution, that is, creatinine or specific gravity.

Methods

Among 741 pregnant participants from four sites of The Infant Development and Environment Study (TIDES) cohort, we assessed the relation of maternal urinary di-2-ethylhexyl phthalate (DEHP) concentrations with preterm birth. We compared scenarios in which all sites measured either urinary creatinine or specific gravity, or where measure of dilution differed by site. In addition to a scenario with no dilution adjustment, we applied and compared three dilution-adjustment approaches: a standard regression-based approach for creatinine, a standard approach for specific gravity (Boeniger method), and a more recently developed approach that has been applied to both (covariate-adjusted standardization method). For each scenario and dilution-adjustment method, we estimated the association between a doubling in the molar sum of DEHP (∑DEHP) and odds of preterm birth using logistic regression.

Results

All dilution-adjustment approaches yielded comparable associations (odds ratio [OR]) that were larger in magnitude than when we did not perform dilution adjustment. A doubling of ∑DEHP was associated with 9% greater odds of preterm birth (OR = 1.09, 95% confidence interval [CI] = 0.91, 1.30) when applying no dilution-adjustment method, whereas dilution-adjusted point estimates were higher, and similar across all scenarios and methods: 1.13-1.20 (regression-based), 1.15-1.18 (Boeniger), and 1.14-1.21 (covariate-adjusted standardization).

Conclusions

In our applied example, we demonstrate that it is possible and straightforward to combine urinary biomarker data across studies when measures of dilution differ.

SUBMITTER: Kuiper JR

PROVIDER: S-EPMC9585883 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Combining Urinary Biomarker Data From Studies With Different Measures of Urinary Dilution.

Kuiper Jordan R JR O'Brien Katie M KM Welch Barrett M BM Barrett Emily S ES Nguyen Ruby H N RHN Sathyanarayana Sheela S Milne Ginger L GL Swan Shanna H SH Ferguson Kelly K KK Buckley Jessie P JP

Epidemiology (Cambridge, Mass.) 20220425 4

<h4>Background</h4>Guidance is lacking for how to combine urinary biomarker data across studies that use different measures of urinary dilution, that is, creatinine or specific gravity.<h4>Methods</h4>Among 741 pregnant participants from four sites of The Infant Development and Environment Study (TIDES) cohort, we assessed the relation of maternal urinary di-2-ethylhexyl phthalate (DEHP) concentrations with preterm birth. We compared scenarios in which all sites measured either urinary creatinin ...[more]

PMID: 35473917

Similar Datasets

Project description:BackgroundOne of the important challenges in microarray analysis is to take full advantage of previously accumulated data, both from one's own laboratory and from public repositories. Through a comparative analysis on a variety of datasets, a more comprehensive view of the underlying mechanism or structure can be obtained. However, as we discover in this work, continual changes in genomic sequence annotations and probe design criteria make it difficult to compare gene expression data even from different generations of the same microarray platform.ResultsWe first describe the extent of discordance between the results derived from two generations of Affymetrix oligonucleotide arrays, as revealed in cluster analysis and in identification of differentially expressed genes. We then propose a method for increasing comparability. The dataset we use consists of a set of 14 human muscle biopsy samples from patients with inflammatory myopathies that were hybridized on both HG-U95Av2 and HG-U133A human arrays. We find that the use of the probe set matching table for comparative analysis provided by Affymetrix produces better results than matching by UniGene or LocusLink identifiers but still remains inadequate. Rescaling of expression values for each gene across samples and data filtering by expression values enhance comparability but only for few specific analyses. As a generic method for improving comparability, we select a subset of probes with overlapping sequence segments in the two array types and recalculate expression values based only on the selected probes. We show that this filtering of probes significantly improves the comparability while retaining a sufficient number of probe sets for further analysis.ConclusionsCompatibility between high-density oligonucleotide arrays is significantly affected by probe-level sequence information. With a careful filtering of the probes based on their sequence overlaps, data from different generations of microarrays can be combined more effectively.

Project description:BackgroundAlthough it has long been appreciated that ovarian carcinoma subtypes (serous, clear cell, endometrioid, and mucinous) are associated with different natural histories, most ovarian carcinoma biomarker studies and current treatment protocols for women with this disease are not subtype specific. With the emergence of high-throughput molecular techniques, distinct pathogenetic pathways have been identified in these subtypes. We examined variation in biomarker expression rates between subtypes, and how this influences correlations between biomarker expression and stage at diagnosis or prognosis.Methods and findingsIn this retrospective study we assessed the protein expression of 21 candidate tissue-based biomarkers (CA125, CRABP-II, EpCam, ER, F-Spondin, HE4, IGF2, K-Cadherin, Ki-67, KISS1, Matriptase, Mesothelin, MIF, MMP7, p21, p53, PAX8, PR, SLPI, TROP2, WT1) in a population-based cohort of 500 ovarian carcinomas that was collected over the period from 1984 to 2000. The expression of 20 of the 21 biomarkers differs significantly between subtypes, but does not vary across stage within each subtype. Survival analyses show that nine of the 21 biomarkers are prognostic indicators in the entire cohort but when analyzed by subtype only three remain prognostic indicators in the high-grade serous and none in the clear cell subtype. For example, tumor proliferation, as assessed by Ki-67 staining, varies markedly between different subtypes and is an unfavourable prognostic marker in the entire cohort (risk ratio [RR] 1.7, 95% confidence interval [CI] 1.2%-2.4%) but is not of prognostic significance within any subtype. Prognostic associations can even show an inverse correlation within the entire cohort, when compared to a specific subtype. For example, WT1 is more frequently expressed in high-grade serous carcinomas, an aggressive subtype, and is an unfavourable prognostic marker within the entire cohort of ovarian carcinomas (RR 1.7, 95% CI 1.2%-2.3%), but is a favourable prognostic marker within the high-grade serous subtype (RR 0.5, 95% CI 0.3%-0.8%).ConclusionsThe association of biomarker expression with survival varies substantially between subtypes, and can easily be overlooked in whole cohort analyses. To avoid this effect, each subtype within a cohort should be analyzed discretely. Ovarian carcinoma subtypes are different diseases, and these differences should be reflected in clinical research study design and ultimately in the management of ovarian carcinoma.

Dataset Information

Combining Urinary Biomarker Data From Studies With Different Measures of Urinary Dilution.

Background

Methods

Results

Conclusions

Publications

Combining Urinary Biomarker Data From Studies With Different Measures of Urinary Dilution.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets