To Örebro University

In patients with schizophrenia, cannabis use exacerbates symptoms and can lead to a relapse of psychosis. Some experimental studies in healthy volunteers suggest that pre-treatment with cannabidiol (CBD) may reduce these effects, but others do not. Here, we investigated whether pre-treatment with CBD ameliorates the acute adverse effects of cannabis in patients with schizophrenia. Participants (n = 30) had schizophrenia or schizoaffective disorder plus a comorbid cannabis use disorder. In a double-blind, randomised, placebo-controlled, crossover trial, participants received oral CBD 1000 mg or placebo three hours before inhaling vaporised cannabis (containing Delta 9-tetrahydrocannabinol (THC) 20-60 mg). The primary outcome was delayed verbal recall measured with the Hopkins Verbal Learning Test-Revised. We also measured psychotic symptoms with the Positive and Negative Syndrome Scale (PANSS) - positive subscale. Delayed verbal recall after cannabis administration was 3.5 words (95% confidence interval [CI]: 2.5-4.5) following pre-treatment with CBD, compared to 4.8 words (95% CI: 3.9 to 5.8) following pre-treatment with placebo (mean difference [MD] = -1.3 [95% CI: -2.0 to -0.6]; p = 0.001). After CBD pre-treatment, inhalation of cannabis was associated with an increase in PANSS-P score of 5.0 (95% CI: 3.6 to 6.5), compared to 2.9 (95% CI: 1.5 to 4.3) following pre-treatment with placebo (MD = 2.2 [95% CI: 0.6 to 3.7]; p = 0.01). Administration of CBD did not have a significant effect on plasma concentration of THC or its active metabolite, 11-hydroxy-THC. In patients with schizophrenia and a comorbid cannabis use disorder, pre-treatment with CBD did not attenuate the acute effects of cannabis on memory impairment or psychotic symptoms, but appeared to exacerbate them. The study was registered on Clinicaltrials.gov (NCT04605393).

Background and aims: Phosphatidylcholines (PCs) are a major hepatic reservoir of polyunsaturated fatty acids (PUFAs). In murine models of metabolic dysfunction-associated steatotic liver disease (MASLD), liver injury is induced by PUFA-PC depletion. The lipid droplet enzyme HSD17B13 metabolizes PUFA derivatives, and its loss-of-function variant rs72613567:TA protects against steatohepatitis (MASH) and fibrosis, although the mechanism underlying this protection remains unknown. We investigated whether hepatic PUFA-PC metabolism is influenced by MASLD or the protective HSD17B13 variant.

Method: Obese patients with a liver biopsy (n = 135) underwent genotyping for HSD17B13 rs72613567:TA and analysis of the hepatic lipidome by UPLC-MS. A state-of-the-art deep learning image analysis method (Aiforia Technologies) quantified steatosis via the hepatic parenchymal fat fraction. Using a recruit-by-genotype approach, we studied homozygous rs72613567:TA carriers (n = 13) and non-carriers (n = 13) to determine whether the variant affects incorporation of 13C-labeled PUFAs linoleic acid (LA) and alphalinolenic acid (ALA) into triglycerides (TGs) and phospholipids (PLs) secreted by the liver in very-low-density lipoprotein (VLDL). We tested whether targeting HSD17B13 with a small molecule inhibitor, INI-822, affects PC metabolism using two models: in vitro, a primary human liver-on-a-chip system challenged with high-fat media for 20 days; and in vivo, Zucker obese rats fed either an atherogenic diet or a choline-deficient, L-amino acid-defined high-fat diet for 21 days.

Results: Human MASLD livers were characterized by a marked depletion of hepatic PUFA-PCs containing 5–8 double bonds. Steatosis had a particularly accentuated effect to lower PUFA-PCs in individuals without the HSD17B13 rs72613567:TA variant. However, this effect was abolished in variant carriers due to markedly increased concentrations of hepatic PUFA-PCs compared to non-carriers. Homozygous carriers had significantly decreased incorporation of [U-13 C]LA into VLDL-TG (P < 0.001) and of [U-13 C] LA and[U-13 C]ALA into VLDL-PL (P = 0.01 and 0.05), consistent with retention of these PUFAs in the liver. In vitro, inhibition of HSD17B13 by INI-822 in the human liver-on-a-chip system lowered fibrotic markers while stabilizing choline utilization and increasing PC concentrations. In vivo, inhibition of HSD17B13 in Zucker obese rats fed MASH-inducing diets reduced liver enzymes and dose-dependently increased hepatic PC concentrations.

Conclusion: In humans, HSD17B13 rs72613567:TA prevents MASLD-induced hepatic PUFA-PC depletion by retaining PUFAs within the liver. Pharmacological inhibition of HSD17B13 recapitulates the human phenotype in vitro and in vivo. These findings suggest tha thepatic PC enrichment is central to the protective effects associated with HSD17B13 loss-of-function.

Lipids play essential roles in cellular functions such as membrane structure, signaling, and energy storage. Lipidomics-global study of lipids in cells and tissues-seeks to identify biomarkers and uncover underlying metabolic pathways. Human lipid concentrations in cells and biofluids can fluctuate at multiple temporal scales, reflecting normal biochemical and physiological variation, or responses to various internal and external stimuli. Understanding both intra- and inter-individual lipid variability is crucial for accurate lipidomics study design and interpretation. This knowledge also supports personalized healthcare, moving beyond static reference ranges. Here, we review key drivers of intra-individual variation, including diet, circadian rhythms, sleep, environmental exposures, and physiological states. We also examine inter-individual factors such as genetics, age, sex, microbiome composition, and medications. Finally, we discuss how these variables interact and influence lipidomics outcomes, aiming to enhance reproducibility and guide future research.

Mass spectrometry-based methods have become fundamental to exposome research, providing the capability to explore a broad spectrum of chemical exposures. Liquid and gas chromatography coupled with low/high-resolution mass spectrometry (MS) are among the most frequently employed platforms due to their sensitivity and accuracy. However, these approaches present challenges, such as the inherent complexity of MS data and the expertise of biologists, chemists, clinicians, and data analysts to integrate and interpret MS data with other datasets effectively. The "omics" era advances rapidly, driven by developments of AI-based algorithms and an increase in accessible data; nevertheless, further efforts are necessary to ensure that exposomics outputs are comparable and reproducible, thus enhancing research findings. This review outlines the principles of MS-based methods for the exposome analytical pipeline, from sample collection to data analysis. We summarize and review both standard and cutting-edge strategies in exposome research, covering sample preparation, focusing on MS-based platforms, data acquisition strategies, and data annotation. The ultimate goal of this review is to highlight applications that enable the simultaneous analysis of endogenous metabolites and xenobiotics, which can help enhance our understanding of the impact of human exposure on health and disease and support personalized healthcare.

The metabolome is an intermediate phenotype, summarizing the profile of all small molecules (<1.5 kDa) in biospecimens. The metabolome provides a readout for the net influence of the chemical exposome, diet, gut microbiome, and genome on human health. Metabolic changes observed in exposome studies may thus provide clues about adverse outcome pathways related to cancer, diabetes, heart disease, cognitive impairment and other neurological conditions such as Alzheimer's disease (AD). Whilst the number of human cohort studies including both metabolomic and exposomic profiles is increasing, they are particularly limited in the domain of neurological conditions. Environmental exposures and chemical toxicants are known to have significant effects on the brain, gut microbiome, and gut-brain axis. Environmental chemicals of greatest interest include bisphenols, phthalates, persistent organic pollutants such as polychlorinated biphenyls (PCBs) and per- and poly-fluoroalkyl substances (PFAS), heavy metals, chemicals from household products and pesticides/herbicides; all of which may increase the risk of AD as they impact relevant biochemical mechanisms, especially with chronic exposure. In this review we describe how the chemical exposome can be assessed, including the approach our consortium is taking in the context of AD. Further, we review the current evidence about the impact of the chemical exposome on cognition as well as its influence on the risk and pathogenesis of AD. Finally, we highlight our approach to study the exposome in AD as part of large national and international collaborative efforts on the topic.

Background and aims: Per- and polyfluoroalkyl substances (PFAS) are emerging environmental pollutants linked to metabolic disorders, including metabolic dysfunction-associated steatotic liver disease (MASLD). This study investigates the associations between PFAS exposure and MASLD progression, focusing on lipidomic, metabolomic, and transcriptomic biomarkers, functional pathways, and gender-specific responses.

Method: Metabolomic and lipidomic analyses were performed to examine changes in metabolite and lipid composition across steatosis grades (0–3) and between MASH-positive and MASH-negative samples. Transcriptomic data were analyzed using weighted gene co-expression network analysis (WGCNA), with KEGG pathway enrichment for functional insights. Mediation analysis explored whether specific metabolites mediated the association between PFAS exposure and steatosis.

Results: Lipidomic analysis revealed significant shifts in lipidcomposition with steatosis severity, including increased TG-SFA and TG-MUFA levels in MASH-positive samples and decreased phosphatidylinositol and phosphatidylcholine levels. Gender-specific transcriptomic analysis using WGCNA identified significant modules in both females and males. These modules were significantly negatively correlated with pathways, including arginine biosynthesis, amino acid metabolism, and other related processes in both genders. Metabolomics analysis supported these findings, identifying metabolites negatively correlated with MASLD progression enriched in similar pathways, implicating a disruption in amino acid metabolism with disease progression. Positively correlated transcriptomic modules in females were linked to cell cycle, steroid biosynthesis, and fatty acid metabolism. Furthermore, significant positive correlations were observed between PFAS compounds (e.g., PFUnDA, PFDoDA) and lipids such as phosphatidylinositol, while acylcarnitines showed negative correlations. Mediation analysis showed that specific metabolites partially mediated the relationship between PFAS exposure and steatosis. Phosphatidylinositol mediated the effect of PFHxA ( p = 0.002), TG-SFA mediated PFHpA’s effect ( p = 0.028), and lactosylceramide (Laccer) mediated PFHpA’s effect ( p = 0.004).

Conclusion: This study provides insights into the molecular mechanisms linking PFAS exposure to MASLD progression, high-lighting disrupted amino acid and lipid metabolism and gender-specific responses. Identifying specific metabolites as mediators highlights new targets for addressing PFAS-related liver diseases.

Food residues that bypass human digestion are further digested by gut microbes, leading to the production of diverse metabolites, including lipids. To investigate how lipids are affected during this transition, we used a colon simulator with four distinct vessels that mimics the proximal to distal part of the human colon. We observed dynamic shifts in a diverse array of microbially derived lipid molecules in the simulated intestinal chyme, including bile acids and N-acyl amides with short and odd-chain lipids. Histamine-linked N-acyl lipids increased from the proximal to the distal colon vessels (pH 5.5 - 7.0), whereas putrescine-linked, initially abundant in the media, decreased across the colon vessels. We uncovered dynamic associations between in vitro-derived short-chain N-acyl lipids and major lipid species such as cholesterol esters, phosphatidylethanolamines, ceramides, and sphingomyelins. To determine the broader relevance of these findings, we applied a reverse metabolomics approach and examined lipid profiles in human small intestine and fecal samples from public datasets. This validated the colon simulator as a model for studying diet-derived and microbially transformed metabolites with relevance to human and animal health and could perhaps be used as a strategy to discover microbial metabolites.

BACKGROUND: Traumatic brain injury (TBI) can come with long term consequences for functional outcome that can complicate return to work. OBJECTIVES: This study aims to make accurate patient-specific predictions on one-year return to work after TBI using machine learning algorithms. Within this process, specific research questions were defined: 1 How can we make accurate predictions on employment outcome, and does this require follow-up data beyond hospitalization? 2 Which predictors are required to make accurate predictions? 3 Are predictions accurate enough for use in clinical practice?

METHODS: This study used the core CENTER-TBI observational cohort dataset, collected across 18 European countries between 2014 and 2017. Hospitalized patients with sufficient follow-up data were selected for the current analysis (N = 586). Data regarding hospital stay and follow-up until three months post-injury were used to predict return to work after one year. Three distinct algorithms were used to predict employment outcomes: elastic net logistic regression, random forest and gradient boosting. Finally, a reduced model and corresponding ROC-curve was created.

RESULTS: Full models without follow-up achieved an area under the curve (AUC) of about 81 %, which increased up to 88 % with follow-up data. A reduced model with five predictors achieved similar results with an AUC of 90 %.

CONCLUSION: The addition of three-month follow-up data causes a notable increase in model performance. The reduced model - containing Glasgow Outcome Scale Extended, pre-injury job class, pre-injury employment status, length of stay and age - matched the predictive performance of the full models. Accurate predictions on post-TBI vocational outcomes contribute to realistic prognosis and goal setting, targeting the right interventions to the right patients.

BACKGROUND: Perfluoroalkyl and polyfluoroalkyl substances (PFAS) are ubiquitous persistent organic pollutants associated with adverse health outcomes in humans. Although they are associated with autism spectrum disorder and behavioural outcomes, whether PFAS affect brain development is unclear. We aimed to characterise the relationship between maternal PFAS and brain structure and function in typically developing children.

METHODS: This study was set within the FinnBrain Birth Cohort Study, a prospective observational study that enrolled mothers from three clinics in Turku, Finland, during their first trimester of pregnancy. Maternal serum samples at gestational week 24 were analysed for PFAS by mass spectrometry and, at age 5 years, children were assessed through structural, diffusion-weighted, and functional MRI. Whole-brain voxel-level and vertex-level maps of grey matter volume, white matter fractional anisotropy and mean diffusivity, and cortical thickness and surface area were combined to compute ten independent components. Data were analysed by correlation network, elastic net regression, and multivariate linear regression with multiple testing correction.

FINDINGS: Pregnant mothers were enrolled into the birth cohort study between Dec 1, 2011, and April 30, 2015, and study visits at age 5 years took place between Oct 1, 2017, and March 31, 2020. This analysis involved 51 mother-child dyads for whom maternal PFAS concentrations and structural MRI data for the child were available. PFAS concentrations in maternal serum samples were mostly 0-1 ng/mL. Maternal perfluorononanoic acid (PFNA; R2=0·13, β=0·39 [95% CI 0·09-0·69], padj=0·024) and linear perfluorooctanoic acid (PFOA; 0·13, 0·36 [0·09-0·63], padj=0·022) linearly predicted a multimodal component dominated by corpus callosal integrity, whereas branched perfluorohexanesulphonic acid (PFHxS; R2=0·12, β=0·31, padj=0·036) and branched PFOA (R2=0·14, β=0·36, padj=0·016) predicted a component comprising mainly occipital cortex volume and surface area. Branched perfluorooctanesulphonic acid predicted hypothalamic microstructure (R2=0·10, β=0·29, p=0·026). PFNA, linear PFOA, and branched PFOA are associated with increased functional connectivity in the right precentral gyrus, whereas branched PFHxS predicts decreased connectivity in the intracalcerine cortices. Associations were not influenced by sex assigned at birth, but were related to PFAS chemical structure.

INTERPRETATION: We show an association between prenatal PFAS exposure and brain developmental outcomes in children. These findings are pertinent given the ubiquitous circulation of PFAS in humans and the extreme environmental persistence of these substances. 

Recent studies reveal that gut microbes produce diverse bile acid conjugates, termed microbially conjugated bile acids (MCBAs). However, their regulation and health effects remain unclear. Here, we analyzed early-life MCBA patterns and their link to islet autoimmunity. We quantified 110 MCBAs in 303 stool samples collected longitudinally (3-36 months) from children who developed one or more islet autoantibodies and controls who remained autoantibody-negative. Stool MCBAs showed distinct age-dependent trajectories and correlated with gut microbiome composition. Altered levels of ursodeoxycholic and deoxycholic acid conjugates were linked to islet autoimmunity as well as modulated monocyte activation in response to immunostimulatory lipopolysaccharide and Th17/Treg cell balance. These findings suggest MCBAs influence immune development and type 1 diabetes risk.