Objective: To examine the day-to-day and within-day variation in urinary iodine excretion and the day-to-day variation in iodine intake. Design: Collection of consecutive 24-h urine samples and casual urine samples over 24 h. Setting: The study population consisted of highly motivated subjects from our Institute. Subjects: Study 1: Ten healthy subjects (seven females and three males) aged 30 ± 46 y. Study 2: Twenty-two healthy subjects (9 males and 13 females) aged 30 ± 55 y. Methods: Study 1: 24-h urine samples were collected for four consecutive days. Study 2: Each urine voided over 24 h was collected into separate containers. In both studies dietary records were kept. Main outcome measures: Twenty-four-hour urinary iodine excretion, 24-h urinary iodine excretion estimated as IaCr*24 h Cr and as a concentration in casual urine samples. Results: Study 1: Both iodine excreted in 24-h urine and iodine intake varied from day-to-day. Iodine excretion correlated with iodine intake (r 0.46, P 0.01). Iodine intake (mean 89AE 6.5 mgad) was not signi®cantly different from iodine excretion (mean 95 AE 5.3 mgad). Study 2: Twenty-four hour iodine excretion estimated as IaCr*24 h Cr from the morning urine sample was signi®cantly lower than actual 24-h iodine excretion, whereas 24-h iodine excretion estimated as IaCr*24 h Cr from the ®rst sample after the morning sample and the last sample before the subjects went to bed was not signi®cantly different from actual 24-h iodine excretion. Twentyfour-hour urine excretion estimated as a concentration was lower than actual 24-h iodine excretion in casual urine taken at any time of the day. Conclusions: For determination of iodine status in an individual, more than one 24-h urine sample must be used. The use of the IaCr ratio in casual urine samples is a usable measure of iodine status if corrected for the age-and sex-adjusted 24-h creatinine excretion. Further, the study suggests that fasting morning urine samples would underestimate iodine status in this population.

Motivation: Tandem mass spectrometry (MS/MS) has the potential to substantially improve metabolomics by acquiring spectra of fragmented ions. These fragmentation spectra can be represented as a molecular network, by measuring cosine distances between them, thus identifying signals from the same or similar molecules. Metrics that enable comparison between pairs of samples based on their metabolite profiles are in great need. Taking inspiration from the successful phylogeny-aware beta-diversity measures used in microbiome research, integrating chemical similarity information about the features in addition to their abundances could lead to better insights when comparing metabolite profiles. Chemical Structural and Compositional Similarity (CSCS) is a recently published similarity metric comparing the full set of signals and their chemical similarity between two samples. Efficient, scalable and easily accessible implementations of this algorithm is currently lacking. Here, we present an easily accessible and scalable implementation of CSCS in both python and R, including a version not weighted by intensity information. Results: We provide a new implementation of the CSCS algorithm that is over 300 times faster than the published implementation in R, making the algorithm suitable for large-scale metabolomics applications. We also show that adding chemical information enriches existing methods. Furthermore, the R implementation includes functions for exporting molecular networks directly from the mass spectral molecular networking platform GNPS for ease of use for downstream applications.

No abstract