Evaluating the Effect of Data Merging and Postacquisition Normalization on Statistical Analysis of Untargeted High-Resolution Mass Spectrometry Based Urinary Metabolomics Data.

Urine is one of the most widely used biofluids in metabolomic studies because it can be collected noninvasively and is available in large quantities. However, it shows large heterogeneity in sample concentration and consequently requires normalization to reduce unwanted variation and extract meaningful biological information. Biological samples like urine are commonly measured with electrospray ionization (ESI) coupled to a mass spectrometer, producing data sets for positive and negative modes. Combining these gives a more complete picture of the total metabolites present in a sample. However, the effect of this data merging on subsequent data analysis, especially in combination with normalization, has not yet been analyzed. To address this issue, we conducted a neutral comparison study to evaluate the performance of eight postacquisition normalization methods under different data merging procedures using 1029 urine samples from the Food Chain plus (FoCus) cohort. Samples were measured with a Fourier transform ion cyclotron resonance mass spectrometer (FT-ICR-MS). Normalization methods were evaluated by five criteria capturing the ability to remove sample concentration variation and preserve relevant biological information. Merging data after normalization was generally favorable for quality control (QC) sample similarity, sample classification, and feature selection for most of the tested normalization methods. Merging data after normalization and the usage of probabilistic quotient normalization (PQN) in a similar setting are generally recommended. Relying on a single analyte to capture sample concentration differences, like with postacquisition creatinine normalization, seems to be a less preferable approach, especially when data merging is applied.

Cohort profile: the Food Chain Plus (FoCus) cohort.

The Food Chain Plus (FoCus) cohort was launched in 2011 for population-based research related to metabolic inflammation. To characterize this novel pathology in a comprehensive manner, data collection included multiple omics layers such as phenomics, microbiomics, metabolomics, genomics, and metagenomics as well as nutrition profiling, taste perception phenotyping and social network analysis. The cohort was set-up to represent a Northern German population of the Kiel region. Two-step recruitment included the randomised enrolment of participants via residents' registration offices and via the Obesity Outpatient Centre of the University Medical Center Schleswig-Holstein (UKSH). Hence, both a population- and metabolic inflammation- based cohort was created. In total, 1795 individuals were analysed at baseline. Baseline data collection took place between 2011 and 2014, including 63% females and 37% males with an age range of 18-83 years. The median age of all participants was 52.0 years [IQR: 42.5; 63.0 years] and the median baseline BMI in the study population was 27.7 kg/m2 [IQR: 23.7; 35.9 kg/m2]. In the baseline cohort, 14.1% of participants had type 2 diabetes mellitus, which was more prevalent in the subjects of the metabolic inflammation group (MIG; 31.8%). Follow-up for the assessment of disease progression, as well as the onset of new diseases with changes in subject's phenotype, diet or lifestyle factors is planned every 5 years. The first follow-up period was finished in 2020 and included 820 subjects.

Precision Nutrition in Chronic Inflammation.

The molecular foundation of chronic inflammatory diseases (CIDs) can differ markedly between individuals. As our understanding of the biochemical mechanisms underlying individual disease manifestations and progressions expands, new strategies to adjust treatments to the patient's characteristics will continue to profoundly transform clinical practice. Nutrition has long been recognized as an important determinant of inflammatory disease phenotypes and treatment response. Yet empirical work demonstrating the therapeutic effectiveness of patient-tailored nutrition remains scarce. This is mainly due to the challenges presented by long-term effects of nutrition, variations in inter-individual gastrointestinal microbiota, the multiplicity of human metabolic pathways potentially affected by food ingredients, nutrition behavior, and the complexity of food composition. Historically, these challenges have been addressed in both human studies and experimental model laboratory studies primarily by using individual nutrition data collection in tandem with large-scale biomolecular data acquisition (e.g. genomics, metabolomics, etc.). This review highlights recent findings in the field of precision nutrition and their potential implications for the development of personalized treatment strategies for CIDs. It emphasizes the importance of computational approaches to integrate nutritional information into multi-omics data analysis and to predict which molecular mechanisms may explain how nutrients intersect with disease pathways. We conclude that recent findings point towards the unexhausted potential of nutrition as part of personalized medicine in chronic inflammation.