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Abstract Background Long-term survival after premature birth is significantly determined by development of morbidities, primarily affecting the cardio-respiratory or central nervous system. Existing studies are limited to pairwise morbidity associations, thereby lacking a holistic understanding of morbidity co-occurrence and respective risk profiles. Methods Our study, for the first time, aimed at delineating and characterizing morbidity profiles at near-term age and investigated the most prevalent morbidities in preterm infants: bronchopulmonary dysplasia (BPD), pulmonary hypertension (PH), mild cardiac defects, perinatal brain pathology and retinopathy of prematurity (ROP). For analysis, we employed two independent, prospective cohorts, comprising a total of 530 very preterm infants: AIRR (“Attention to Infants at Respiratory Risks”) and NEuroSIS (“Neonatal European Study of Inhaled Steroids”). Using a data-driven strategy, we successfully characterized morbidity profiles of preterm infants in a stepwise approach and (1) quantified pairwise morbidity correlations, (2) assessed the discriminatory power of BPD (complemented by imaging-based structural and functional lung phenotyping) in relation to these morbidities, (3) investigated collective co-occurrence patterns, and (4) identified infant subgroups who share similar morbidity profiles using machine learning techniques. Results First, we showed that, in line with pathophysiologic understanding, BPD and ROP have the highest pairwise correlation, followed by BPD and PH as well as BPD and mild cardiac defects. Second, we revealed that BPD exhibits only limited capacity in discriminating morbidity occurrence, despite its prevalence and clinical indication as a driver of comorbidities. Further, we demonstrated that structural and functional lung phenotyping did not exhibit higher association with morbidity severity than BPD. Lastly, we identified patient clusters that share similar morbidity patterns using machine learning in AIRR (n=6 clusters) and NEuroSIS (n=8 clusters). Conclusions By capturing correlations as well as more complex morbidity relations, we provided a comprehensive characterization of morbidity profiles at discharge, linked to shared disease pathophysiology. Future studies could benefit from identifying risk profiles to thereby develop personalized monitoring strategies. Trial registration AIRR: DRKS.de, DRKS00004600, 28/01/2013. NEuroSIS: ClinicalTrials.gov, NCT01035190, 18/12/2009.

Newborns admitted to the neonatal intensive care unit (NICU) regularly undergo painful procedures and may face various painful conditions such as postoperative pain. Optimal management of pain in these vulnerable preterm and term born neonates is crucial to ensure their comfort and prevent negative consequences of neonatal pain. This entails accurate and timely identification of pain, non-pharmacological pain treatment and if needed administration of analgesic therapy, evaluation of treatment effectiveness, and monitoring of adverse effects. Despite the widely recognized importance of pain management, pain assessment in neonates has thus far proven to be a challenge. As self-report, the gold standard for pain assessment, is not possible in neonates, other methods are needed. Several observational pain scales have been developed, but these often rely on snapshot and largely subjective observations and may fail to capture pain in certain conditions. Incorporation of biomarkers alongside observational pain scores holds promise in enhancing pain assessment and, by extension, optimizing pain treatment and neonatal outcomes. This review explores the possibilities of integrating biomarkers in pain assessment in the NICU.

The severity of COVID-19 is linked to an imbalanced immune response. The dysregulated metabolism of small molecules and bioactive lipids has also been associated with disease severity. To promote understanding of the disease biochemistry and provide targets for intervention, we applied a range of LC-MS platforms to analyze over 100 plasma samples from patients with varying COVID-19 severity and with detailed clinical information on inflammatory responses (>30 immune markers). This is the third publication in a series, and it reports the results of comprehensive lipidome profiling using targeted LC-MS/MS. We identified 1076 lipid features across 25 subclasses, including glycerophospholipids, sterols, glycerolipids, and sphingolipids, among which 531 lipid features were dramatically changed in the plasma of intensive care unit (ICU) patients compared to patients in the ward. Patients in the ICU showed 1.3–57-fold increases in ceramides, (lyso-)glycerophospholipids, diglycerides, triglycerides, and plasmagen phosphoethanolamines, and 1.3–2-fold lower levels of a cyclic lysophosphatidic acid, sphingosine-1-phosphates, sphingomyelins, arachidonic acid-containing phospholipids, lactosylceramide, and cholesterol esters compared to patients in the ward. Specifically, phosphatidylinositols (PIs) showed strong fatty acid saturation-dependent behavior, with saturated fatty acid (SFA)- and monosaturated fatty acid (MUFA)-derived PI decreasing and polystaturated (PUFA)-derived PI increasing. We also found ~4000 significant Spearman correlations between lipids and multiple clinical markers of immune response with |R| ≥ 0.35 and FDR corrected Q < 0.05. Except for lysophosphatidic acid, lysophospholipids were positively associated with the CD4 fraction of T cells, and the cytokines IL-8 and IL-18. In contrast, sphingosine-1-phosphates were negatively correlated with innate immune markers such as CRP and IL-6. Further indications of metabolic changes in moderate COVID-19 disease were demonstrated in recovering ward patients compared to those at the start of hospitalization, where 99 lipid species were altered (6 increased by 30–62%; 93 decreased by 1.3–2.8-fold). Overall, these findings support and expand on early reports that dysregulated lipid metabolism is involved in COVID-19.

In current biomedical and complex trait research, increasing numbers of large molecular profiling (omics) data sets are being generated. At the same time, many studies fail to be reproduced (Baker 2016, Kim 2018). In order to improve study reproducibility and data reuse, including integration of data sets of different types and origins, it is imperative to work with omics data that is findable, accessible, interoperable, and reusable (FAIR, Wilkinson 2016) at the source. The data analysis, integration and stewardship pillar of the Netherlands X-omics Initiative aims to facilitate multi-omics research by providing tools to create, analyze and integrate FAIR omics data. We here report a joint activity of X-omics and the Netherlands Twin Register demonstrating the FAIRification of a multi-omics data set and the development of a FAIR multi-omics data analysis workflow.The implementation of FAIR principles (Wilkinson 2016) can improve scientific transparency and facilitate data reuse. However, Kim (2018) showed in a case study that the availability of data and code are required but not sufficient to reproduce data analyses. They highlighted the importance of interoperable and open formats, and structured metadata. In order to increase research reproducibility on the data analysis level, additional practices such as version-control, code licensing, and documentation have been proposed. These include recommendations for FAIR software by the Netherlands eScience Center and the Dutch Data Archiving and Networked Services (DANS), and FAIR principles for research software proposed by the Research Data Alliance (Chue Hong 2022). Data analysis in biomedical research usually comprises multiple steps often resulting in complex data analysis workflows and requiring additional practices, such as containerization, to ensure transparency and reproducibility (Goble 2020, Stoudt 2021).We apply these practices to a multi-omics data set that comprises genome-wide DNA methylation profiles, targeted metabolomics, and behavioral data of two cohorts that participated in the ACTION Biomarker Study (ACTION, Aggression in Children: Unraveling gene-environment interplay to inform Treatment and InterventiON strategies, see consortium members in Suppl. material 1) (Boomsma 2015, Bartels 2018, Hagenbeek 2020, van Dongen 2021, Hagenbeek 2022). The ACTION-NTR cohort consists of twins that are either longitudinally concordant or discordant for childhood aggression. The ACTION-Curium-LUMC cohort consists of children referred to the Dutch LUMC Curium academic center for child and youth psychiatry. With the joint analysis of multi-omics data and behavioral data, we aim to identify substructures in the ACTION-NTR cohort and link them to aggressive behavior. First, the individuals are clustered using Similarity Network Fusion (SNF, Wang 2014), and latent feature dimensions are uncovered using different unsupervised methods including Multi-Omics Factor Analysis (MOFA) (Argelaguet 2018) and Multiple Correspondence Analysis (MCA, Lê 2008, Husson 2017). In a second step, we determine correlations between -omics and phenotype dimensions, and use them to explain the subgroups of individuals from the ACTION-NTR cohort. In order to validate the results, we project data of the ACTION-Curium-LUMC cohort onto the latent dimensions and determine if correlations between omics and phenotype data can be reproduced.Integration of data across cohorts and across data types, requires interoperability. We applied different practices to make the data FAIR, including conversion of files to community-standard formats, and capturing experimental metadata using the ISA (Investigation, Study, Assay) metadata framework (Johnson 2021) and ontology-based annotations. All data analysis steps including pre-processing of different omics data types were implemented in either R or Python and combined in a modular Nextflow (Di Tommaso 2017) workflow, where the environment for each step is provided as a Singularity (Kurtzer 2017) container. The analysis workflow is packaged in a Research Object Crate (RO-Crate) (Soiland-Reyes 2022). The RO-Crate is a FAIR digital object that contains the Nextflow workflow including ontology-based annotations of each analysis step. Since omics data is considered to be potentially personally identifiable, the packaged workflow contains a minimal synthetic data set resembling the original data structure. Finally, the code is made available on GitHub and the workflow is registered at Workflowhub (Goble 2021). Since our Nextflow workflow is set up in a modular manner, the individual analysis steps can be reused in other workflows. We demonstrate this replicability by applying different sub-workflows to data from two different cohorts.

Alzheimer’s disease (AD) is an aging-related neurodegenerative disease, leading to the progressive loss of memory and other cognitive functions. As there is still no cure for AD, the growth in the number of susceptible individuals represents a major emerging threat to public health. Currently, the pathogenesis and etiology of AD remain poorly understood, while no efficient treatments are available to slow down the degenerative effects of AD. Metabolomics allows the study of biochemical alterations in pathological processes which may be involved in AD progression and to discover new therapeutic targets. In this review, we summarized and analyzed the results from studies on metabolomics analysis performed in biological samples of AD subjects and AD animal models. Then this information was analyzed by using MetaboAnalyst to find the disturbed pathways among different sample types in human and animal models at different disease stages. We discuss the underlying biochemical mechanisms involved, and the extent to which they could impact the specific hallmarks of AD. Then we identify gaps and challenges and provide recommendations for future metabolomics approaches to better understand AD pathogenesis.

Immunometabolism, which concerns the interplay between metabolism and the immune system, is increasingly recognized as a potential source of novel drug targets and biomarkers. In this context, the use of metabolomics to identify metabolic characteristics associated with specific functional immune response processes is of value. Currently, there is a lack of tools to determine known associations between metabolites and immune processes. Consequently, interpretation of metabolites in metabolomics studies in terms of their role in the immune system, or selection of the most relevant metabolite classes to include in metabolomics studies, is challenging. Here, we describe the Immunometabolic Atlas (IMA), a public web application and library of R functions to infer immune processes associated with specific metabolites and vice versa. The IMA derives metabolite-immune process associations utilizing a protein-metabolite network analysis algorithm that associates immune system-associated annotated proteins in Gene Ontology to metabolites. We evaluated IMA inferred metabolite-immune system associations using a text mining strategy, identifying substantial overlap, but also demonstrating a significant chemical space of immune system-associated metabolites that should be confirmed experimentally. Overall, the IMA facilitates the interpretation and design of immunometabolomics studies by the association of metabolites to specific immune processes.

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 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 (

COVID-19 is characterised by a dysregulated immune response, that involves signalling lipids acting as mediators of the inflammatory process along the innate and adaptive phases. To promote understanding of the disease biochemistry and provide targets for intervention, we applied a range of LC-MS platforms to analyse over 100 plasma samples from patients with varying COVID-19 severity and with detailed clinical information on inflammatory responses (>30 immune markers). The second publication in a series reports the results of quantitative LC-MS/MS profiling of 63 small lipids including oxylipins, free fatty acids, and endocannabinoids. Compared to samples taken from ward patients, intensive care unit (ICU) patients had 2–4-fold lower levels of arachidonic acid (AA) and its cyclooxygenase-derived prostanoids, as well as lipoxygenase derivatives, exhibiting negative correlations with inflammation markers. The same derivatives showed 2–5-fold increases in recovering ward patients, in paired comparison to early hospitalisation. In contrast, ICU patients showed elevated levels of oxylipins derived from poly-unsaturated fatty acids (PUFA) by non-enzymatic peroxidation or activity of soluble epoxide hydrolase (sEH), and these oxylipins positively correlated with markers of macrophage activation. The deficiency in AA enzymatic products and the lack of elevated intermediates of pro-resolving mediating lipids may result from the preference of alternative metabolic conversions rather than diminished stores of PUFA precursors. Supporting this, ICU patients showed 2-to-11-fold higher levels of linoleic acid (LA) and the corresponding fatty acyl glycerols of AA and LA, all strongly correlated with multiple markers of excessive immune response. Our results suggest that the altered oxylipin metabolism disrupts the expected shift from innate immune response to resolution of inflammation.

The COVID-19 pandemic raised a need to characterise the biochemical response to SARS-CoV-2 infection and find biological markers to identify therapeutic targets. In support of these aims, we applied a range of LC-MS platforms to analyse over 100 plasma samples from patients with varying COVID-19 severity and with detailed clinical information on inflammatory responses (>30 immune markers). The first publication in a series reports the results of quantitative LC-MS/MS profiling of 56 amino acids and derivatives. A comparison between samples taken from ICU and ward patients revealed a notable increase in ten post-translationally modified amino acids that correlated with markers indicative of an excessive immune response: TNF-alpha, neutrophils, markers for macrophage, and leukocyte activation. Severe patients also had increased kynurenine, positively correlated with CRP and cytokines that induce its production. ICU and ward patients with high IL-6 showed decreased levels of 22 immune-supporting and anti-oxidative amino acids and derivatives (e.g., glutathione, GABA). These negatively correlated with CRP and IL-6 and positively correlated with markers indicative of adaptive immune activation. Including corresponding alterations in convalescing ward patients, the overall metabolic picture of severe COVID-19 reflected enhanced metabolic demands to maintain cell proliferation and redox balance, alongside increased inflammation and oxidative stress.

The role of the gut microbiota in hepatic lipid metabolism is controversial and incompletely understood. Here the authors perform multi-omics analyses of altered lipid metabolic processes in germ-free and specific pathogen-free mice, revealing how the gut microbiota affects hepatic fatty acid desaturation and elongation.