Very preterm infants are at high risk for suboptimal nutrition in the first weeks of life leading to insufficient weight gain and complications arising from metabolic imbalances such as... Show moreVery preterm infants are at high risk for suboptimal nutrition in the first weeks of life leading to insufficient weight gain and complications arising from metabolic imbalances such as insufficient bone mineral accretion. We investigated the use of a novel set of standardized parenteral nutrition (PN; MUC PREPARE) solutions regarding improving nutritional intake, accelerating termination of parenteral feeding, and positively affecting growth in comparison to individually prescribed and compounded PN solutions. We studied the effect of MUC PREPARE on macro- and micronutrient intake, metabolism, and growth in 58 very preterm infants and compared results to a historic reference group of 58 very preterm infants matched for clinical characteristics. Infants receiving MUC PREPARE demonstrated improved macro- and micronutrient intake resulting in balanced electrolyte levels and stable metabolomic profiles. Subsequently, improved energy supply was associated with up to 1.5 weeks earlier termination of parenteral feeding, while simultaneously reaching up to 1.9 times higher weight gain at day 28 in extremely immature infants (<27 GA weeks) as well as overall improved growth at 2 years of age for all infants. The use of the new standardized PN solution MUC PREPARE improved nutritional supply and short- and long-term growth and reduced PN duration in very preterm infants and is considered a superior therapeutic strategy. Show less
OBJECTIVE: To demonstrate and validate the improvement of current risk stratification for bronchopulmonary dysplasia (BPD) early after birth by plasma protein markers (sialic acid-binding lg-like... Show moreOBJECTIVE: To demonstrate and validate the improvement of current risk stratification for bronchopulmonary dysplasia (BPD) early after birth by plasma protein markers (sialic acid-binding lg-like lectin 14 (SIGLEC-14), basal cell adhesion molecule (BCAM), angiopoietin-like 3 protein (ANGPTL-3)) in extremely premature infants.METHODS AND RESULTS: Proteome screening in first-week-of-life plasma samples of n- 52 preterm infants <32 weeks gestational age (GA) on two proteomic platforms (SomaLogic (R), Olink-Proteomics (R)) confirmed three biomarkers with significant predictive power: BCAM, SIGLEC-14, and ANGPTL-3. We demonstrate high sensitivity (0.92) and specificity (0.86) under consideration of GA, show the proteins' critical contribution to the predictive power of known clinical risk factors, e.g., birth weight and GA, and predicted the duration of mechanical ventilation, oxygen supplementation, as well as neonatal intensive care stay. We confirmed significant predictive power for BPD cases when switching to a clinically applicable method (enzyme-linked immunosorbent assay) in an independent sample set (n = 25, p < 0.001) and demonstrated disease specificity in different cohorts of neonatal and adult lung disease.CONCLUSION: While successfully addressing typical challenges of clinical biomarker studies, we demonstrated the potential of BCAM, SIGLEC-14, and ANGPTL-3 to inform future clinical decision making in the preterm infant at risk for BPD. Show less
Faquih, T.; Smeden, M. van; Luo, J.; Cessie, S. le; Kastenmuller, G.; Krumsiek, J.; ... ; Mook-Kanamori, D.O. 2020
Metabolomics studies have seen a steady growth due to the development and implementation of affordable and high-quality metabolomics platforms. In large metabolite panels, measurement values are... Show moreMetabolomics studies have seen a steady growth due to the development and implementation of affordable and high-quality metabolomics platforms. In large metabolite panels, measurement values are frequently missing and, if neglected or sub-optimally imputed, can cause biased study results. We provided a publicly available, user-friendly R script to streamline the imputation of missing endogenous, unannotated, and xenobiotic metabolites. We evaluated the multivariate imputation by chained equations (MICE) and k-nearest neighbors (kNN) analyses implemented in our script by simulations using measured metabolites data from the Netherlands Epidemiology of Obesity (NEO) study (n = 599). We simulated missing values in four unique metabolites from different pathways with different correlation structures in three sample sizes (599, 150, 50) with three missing percentages (15%, 30%, 60%), and using two missing mechanisms (completely at random and not at random). Based on the simulations, we found that for MICE, larger sample size was the primary factor decreasing bias and error. For kNN, the primary factor reducing bias and error was the metabolite correlation with its predictor metabolites. MICE provided consistently higher performance measures particularly for larger datasets (n > 50). In conclusion, we presented an imputation workflow in a publicly available R script to impute untargeted metabolomics data. Our simulations provided insight into the effects of sample size, percentage missing, and correlation structure on the accuracy of the two imputation methods. Show less
Background: Proteomics is expected to provide novel insights in the underlying pathophysiology of type 2 diabetes mellitus. In the present study, we aimed to identify and biochemically characterize... Show moreBackground: Proteomics is expected to provide novel insights in the underlying pathophysiology of type 2 diabetes mellitus. In the present study, we aimed to identify and biochemically characterize proteins associated with diabetes mellitus in a Qatari population.Methods: In a diabetes case-control study (175 cases, 164 controls; Arab, South Asian and Philippine ethnicities), we conducted a discovery study to screen 1141 blood protein levels for associations with diabetes mellitus. Additional analyses were done in controls in relation to Hb1Ac, and biochemical characterization of the main findings was performed with metabolomics (501 metabolites). We performed two-sample Mendelian Randomization to provide evidence of potential causality using data from European descent of the DIAGRAM consortium (74,124 cases of diabetes mellitus and 824,006 controls) for the identified proteins for T2D and Hb1Ac.Results: After accounting for multiple testing, 30 protein levels were different (p-values < 8.6e(-5)) between cases and controls. Of these, a higher Hb1Ac in controls was associated with a lower IGFBP-2 level (p-value=4.1e(-6)). IGFBP-2 protein level was found lower among cases compared with controls across all ethnicities. In controls, IGFBP-2 was associated with 21 metabolite levels, but specifically connected to the metabolite citrulline in network analyses. We observed no evidence, however, that the association between IGFBP-2 and diabetes mellitus was causal.Conclusions: We specifically identified IGFBP-2 to be associated with diabetes mellitus, although with no evidence for causality, which was specifically connected to citrulline metabolism. Show less
Benedetti, E.; Gerstner, N.; Pucic-Bakovic, M.; Keser, T.; Reiding, K.R.; Ruhaak, L.R.; ... ; Krumsiek, J. 2020
Glycomics measurements, like all other high-throughput technologies, are subject to technical variation due to fluctuations in the experimental conditions. The removal of this non-biological signal... Show moreGlycomics measurements, like all other high-throughput technologies, are subject to technical variation due to fluctuations in the experimental conditions. The removal of this non-biological signal from the data is referred to as normalization. Contrary to other omics data types, a systematic evaluation of normalization options for glycomics data has not been published so far. In this paper, we assess the quality of different normalization strategies for glycomics data with an innovative approach. It has been shown previously that Gaussian Graphical Models (GGMs) inferred from glycomics data are able to identify enzymatic steps in the glycan synthesis pathways in a data-driven fashion. Based on this finding, here, we quantify the quality of a given normalization method according to how well a GGM inferred from the respective normalized data reconstructs known synthesis reactions in the glycosylation pathway. The method therefore exploits a biological measure of goodness. We analyzed 23 different normalization combinations applied to six large-scale glycomics cohorts across three experimental platforms: Liquid Chromatography-ElectroSpray Ionization-Mass Spectrometry (LC-ESI-MS), Ultra High Performance Liquid Chromatography with Fluorescence Detection (UHPLC-FLD), and Matrix Assisted Laser Desorption Ionization-Furier Transform Ion Cyclotron Resonance-Mass Spectrometry (MALDI-FTICR-MS). Based on our results, we recommend normalizing glycan data using the 'Probabilistic Quotient' method followed by log-transformation, irrespective of the measurement platform. This recommendation is further supported by an additional analysis, where we ranked normalization methods based on their statistical associations with age, a factor known to associate with glycomics measurements. Show less
Quell, J.D.; Romisch-Margl, W.; Haid, M.; Krumsiek, J.; Skurk, T.; Halama, A.; ... ; Kastenmuller, G. 2019
Kit-based assays, such as AbsoluteIDQ(TM) p150, are widely used in large cohort studies and provide a standardized method to quantify blood concentrations of phosphatidylcholines (PCs). Many... Show moreKit-based assays, such as AbsoluteIDQ(TM) p150, are widely used in large cohort studies and provide a standardized method to quantify blood concentrations of phosphatidylcholines (PCs). Many disease-relevant associations of PCs were reported using this method. However, their interpretation is hampered by lack of functionally-relevant information on the detailed fatty acid side-chain compositions as only the total number of carbon atoms and double bonds is identified by the kit. To enable more substantiated interpretations, we characterized these PC sums using the side-chain resolving Lipidyzer(TM) platform, analyzing 223 samples in parallel to the AbsoluteIDQ(TM). Combining these datasets, we estimated the quantitative composition of PC sums and subsequently tested their replication in an independent cohort. We identified major constituents of 28 PC sums, revealing also various unexpected compositions. As an example, PC 16:0_22:5 accounted for more than 50% of the PC sum with in total 38 carbon atoms and 5 double bonds (PC aa 38:5). For 13 PC sums, we found relatively high abundances of odd-chain fatty acids. In conclusion, our study provides insights in PC compositions in human plasma, facilitating interpretation of existing epidemiological data sets and potentially enabling imputation of PC compositions for future meta-analyses of lipidomics data. Show less
Wahl, A.; Akker, E. van den; Klaric, L.; Stambuk, J.; Benedetti, E.; Plomp, R.; ... ; Gieger, C. 2018
