Stefan Roeder

Validation of a multi-analyte HPLC-DAD method for determination of uric acid, creatinine, homovanillic acid, niacinamide, hippuric acid, indole-3-acetic acid and 2-methylhippuric acid in human urine

During the last decades exposure sciences and epidemiological studies attracts more attention to unravel the mechanisms for the development of chronic diseases. According to this an existing HPLC-DAD method for determination of creatinine in urine samples was expended for seven analytes and validated. Creatinine, uric acid, homovanillic acid, niacinamide, hippuric acid, indole-3-acetic acid, and 2-methylhippuric acid were separated by gradient elution (formate buffer/methanol) using an Eclipse Plus C18 Rapid Resolution column (4.6mm×100mm). No interfering signals were detected in mobile phase. After injection of blank urine samples signals for the endogenous compounds but no interferences were detected. All analytes were linear in the selected calibration range and a non weighted calibration model was chosen. Bias, intra-day and inter-day precision for all analytes were below 20% for quality control (QC) low and below 10% for QC medium and high. The limits of quantification in mobile phase were in line with reported reference values but had to be adjusted in urine for homovanillic acid (45mg/L), niacinamide 58.5(mg/L), and indole-3-acetic acid (63mg/L). Comparison of creatinine data obtained by the existing method with those of the developed method showing differences from -120mg/L to +110mg/L with a mean of differences of 29.0mg/L for 50 authentic urine samples. Analyzing 50 authentic urine samples, uric acid, creatinine, hippuric acid, and 2-methylhippuric acid were detected in (nearly) all samples. However, homovanillic acid was detected in 40%, niacinamide in 4% and indole-3-acetic acid was never detected within the selected samples.

Evaluation of Population and Individual Variances of Urinary PhthalateMetabolites in terms of Epidemiological Studies

In the context of an epidemiological study, urinary concentrations of nine phthalic diester metabolites (monoethyl-, mono-(3-carboxypropyl)-, mono-n-butyl-, monoisobutyl-, monobenzyl-, mono-(2-ethylhexyl)-, mono-(5-hydroxy-2- ethylhexyl)-, mono-(5-oxo-2-ethylhexyl)- and mono-(5-carboxy-2-ethylpentyl)-phthalate) were quantified via LC-MS/ MS. As in the majority of epidemiological studies only single spot samples were available for urine analysis, the implicit assumption in this case is, that exposure data obtained from single spot samples are representative for a longer exposure period. To validate the relevance of single spot analyses we quantified the respective intra-individual variances of urine samples collected from ten volunteers once daily over a period of 30 days. Using the values for the daily variances, approximate values for the underlying population variances in the cohort samples representing the differences between the average individual metabolite levels were calculated. For most of the volunteers, daily metabolites variations were lower, than the variations observed in the epidemiological setup. The results showed that by accounting for the contribution of daily variance, the standard deviations of the log-transformed phthalate values of the cohort samples are reduced (14% to 28%) but still larger (3% to 66%) than daily standard deviation values, with the exception of MCPrP concentrations.

Assessment of maternal drug intake by urinary bio monitoring during pregnancy and postpartally until the third perinatal year.

Although sales of prescribed and over‐the‐counter (OTC) medication are rising, little is known about individual drug intake. This study was aimed to obtain complementary information about drug intake.

Visualization of depending patterns in metabonomics

This paper describes an approach for visualization of patterns in large data sets. The data sets are combined from external exposure and internal stress factors on human health. For deduction of modes of action on human health, external and internal stress factors have to be combined and classified. The approach shown in this paper is based upon clustering algorithms. Relationships between cases ban be obtained by visual inspection of clustering results.

Biomonitoring of prenatal analgesic intake and correlation with infantile anti-aeroallergens IgE.

An association between prenatal acetaminophen or ibuprofen intake and an increased risk of asthma and increased IgE level in children is discussed in various epidemiological studies. Although the molecular mechanistic link is still unknown, the question whether or not acetaminophen and/or ibuprofen are safe pain medications during pregnancy arose. In this study, we associate maternal acetaminophen and ibuprofen intake during pregnancy and breastfeeding to infantile asthma phenotypes and elevated IgE level. Therefore, we analysed questionnaires from a local mother–child cohort and monitored drug intake by LC‐MS biomonitoring in urine. No association was found between drug intake and any analysed health outcome using questionnaire data. For the information obtained from biomonitoring, no association was found for ibuprofen and acetaminophen intakes during breastfeeding. However, an association between prenatal acetaminophen intake and increased infantile IgEs related to aeroallergens was statistically detected, but not for asthma phenotypes.

Application of mnSOM on Linking External Exposure to Internal Load

The internal load in humans caused by an external exposure is different in each person and mainly depends on metabolism. Using the recently proposed method of mnSOM we are able to describe the human metabolism using a functional module (linear or nonlinear) for each individual.mnSOM enables us to subdivide individuals into classes based on the functional description of each individuals metabolism. Furthermore the shown approach is able to show dependencies between external exposure and internal load in humans. In environmental epidemiology this will be used to establish links between external exposure and internal load patterns to gather clinical relevant information for practitioners.

Model Screening: How to Choose the Best Fitting Regression Model?

The problem space in epidemiological research is characterized by large datasets with many variables as candidates for logistic regression model building. Out of these variables the variable combinations which form a sufficient logistic regression model have to be selected. Usually methods like stepwise logistic regres`sion apply. These methods deliver suboptimal results in most cases, because they cannot screen the entire problem space which is formed by different variable combinations with their resulting case set. Screening the entire problem space causes an enormous effort in computing power. Furthermore the resulting models have to be judged. This paper describes an approach for calculating the complete problem space using a computer grid as well as quality indicators for judgement of every particular model in order to find the best fitting models. We are using this system for epidemiological studies addressing specific problems in human epidemiology.