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During the transition from a healthy state to cardiometabolic disease, patients become heavily medicated, which leads to an increasingly aberrant gut microbiome and serum metabolome, and complicates biomarker discovery.sup.1-5. Here, through integrated multi-omics analyses of 2,173 European residents from the MetaCardis cohort, we show that the explanatory power of drugs for the variability in both host and gut microbiome features exceeds that of disease. We quantify inferred effects of single medications, their combinations as well as additive effects, and show that the latter shift the metabolome and microbiome towards a healthier state, exemplified in synergistic reduction in serum atherogenic lipoproteins by statins combined with aspirin, or enrichment of intestinal Roseburia by diuretic agents combined with beta-blockers. Several antibiotics exhibit a quantitative relationship between the number of courses prescribed and progression towards a microbiome state that is associated with the severity of cardiometabolic disease. We also report a relationship between cardiometabolic drug dosage, improvement in clinical markers and microbiome composition, supporting direct drug effects. Taken together, our computational framework and resulting resources enable the disentanglement of the effects of drugs and disease on host and microbiome features in multimedicated individuals. Furthermore, the robust signatures identified using our framework provide new hypotheses for drug-host-microbiome interactions in cardiometabolic disease. An analysis of 2,173 individuals from the MetaCardis cohort quantifies the individual and combinatorial effects of a range of drugs on host health, metabolome and gut microbiome in cardiometabolic disease., Author(s): Sofia K. Forslund [sup.1] [sup.2] [sup.3] [sup.4] [sup.5] [sup.6] , Rima Chakaroun [sup.7] , Maria Zimmermann-Kogadeeva [sup.1] , Lajos Markó [sup.2] [sup.4] [sup.5] , Judith Aron-Wisnewsky [sup.8] [sup.9] , [...]

Aims: Heart failure with preserved ejection fraction (HFpEF) is a multifactorial, multisystemic syndrome that involves alterations in lipid metabolism. This study aimed to test whether distinct plasma lipid profiles or lipid entities or both are associated with clinical and functional echocardiographic parameters in HFpEF. Methods and results: We examined the human plasma lipidome in HFpEF patients (n = 18) with left ventricular ejection fraction ≥50% and N‐terminal pro‐brain natriuretic peptide (NT‐proBNP) >125 pg/mL and control subjects (n = 12) using mass spectrometry‐based shotgun lipidomics. The cohort included 8 women and 22 men with average age of 67.8 ± 8.6 SD. The control and disease groups were not significantly different with respect to age, body mass index, systolic and diastolic blood pressure, and waist‐to‐hip ratio. The disease group experienced more fatigue (P < 0.001), had more often coronary artery disease (P = 0.04), and received more medications (beta‐blockers, P < 0.001). The disease group had significantly different levels of HFpEF‐relevant parameters, including NT‐proBNP (P < 0.001), left ventricular mass index (P = 0.005), left atrial volume index (P = 0.001), and left ventricular filling index (P < 0.001), and lower left ventricular end‐diastolic diameter (P = 0.014), with no difference in left ventricular ejection fraction. Significant differences in lipid profiles between HFpEF patients and controls could not be detected, including no significant differences in abundance of circulating lipids binned by carbon chain length or by double bonds, nor at the level of individual lipid species. However, there was a striking correlation between selected lipids with smoking status that was independent of disease status, as well as between specific lipids and hyperlipidaemia [with corresponding significance of either false discovery rate (FDR) <0.1 or FDR < 0.01]. In an exploratory network analysis of correlations, we observed significantly stronger correlations within the HFpEF group between individual lipids from the cholesterol ester and phosphatidylcholine (PC) classes and clinical/echocardiographic parameters such as left atrial volume index, left ventricular end‐diastolic diameters, and heart rate (FDR < 0.1). In contrast, the control group showed significantly stronger negative correlations (FDR < 0.1) between individual species from the PC and sphingomyelin classes and left ventricular mass index or systolic blood pressure. Conclusions: We did not find significant direct associations between plasma lipidomic parameters and HFpEF and therefore could not conclude that any specific lipids are biomarkers of HFpEF. The validation in larger cohort is needed to confidently conclude the absence of first‐order associations. [ABSTRACT FROM AUTHOR]

Aims Recent technical developments have allowed the study of the human microbiome to accelerate at an unprecedented pace. Methodological differences may have considerable impact on the results obtained. Thus, we investigated how different storage, isolation, and DNA extraction methods can influence the characterization of the intestinal microbiome, compared to the impact of true biological signals such as intraindividual variability, nutrition, health, and demographics. Methods and results An observative cohort study in 27 healthy subjects was performed. Participants were instructed to collect stool samples twice spaced by a week, using six different methods (naive and Zymo DNA/RNA Shield on dry ice, OMNIgene GUT, RNALater, 95% ethanol, Zymo DNA/RNA Shield at room temperature). DNA extraction from all samples was performed comparatively using QIAamp Power Fecal and ZymoBIOMICS DNA Kits. 16S rRNA sequencing of the gut microbiota as well as qPCRs were performed on the isolated DNA. Metrics included alpha diversity as well as multivariate and univariate comparisons of samples, controlling for covariate patterns computationally. Interindividual differences explained 7.4% of overall microbiome variability, whereas the choice of DNA extraction method explained a further 5.7%. At phylum level, the tested kits differed in their recovery of Gram-positive bacteria, which is reflected in a significantly skewed enterotype distribution. Conclusion DNA extraction methods had the highest impact on observed microbiome variability, and were comparable to interindividual differences, thus may spuriously mimic the microbiome signatures of various health and nutrition factors. Conversely, collection methods had a relatively small influence on microbiome composition. The present study provides necessary insight into the technical variables which can lead to divergent results from seemingly similar study designs. We anticipate that these results will contribute to future efforts towards standardization of microbiome quantification procedures in clinical research. [ABSTRACT FROM AUTHOR]

Background: Schizophrenia (SCZ) is a heterogeneous neuropsychiatric disorder for which current treatment has insufficient efficacy and severe adverse effects. The modifiable gut microbiome might be a potential target for intervention to improve neurobiological functions through the gut-microbiome-brain axis., Methods: In this case-control study, gut microbiota of 132 patients with SCZ and increased waist circumference were compared with gut microbiota of two age- and sex-matched control groups, composed of 132 healthy individuals and 132 individuals with metabolic syndrome. Shotgun sequencing was used to characterize fecal samples at the taxonomic and functional levels. Cognition of the patients with SCZ was evaluated using the Brief Assessment of Cognition instrument., Results: SCZ gut microbiota differed significantly from those of healthy control subjects and individuals with metabolic syndrome in terms of richness and global composition. SCZ gut microbiota were notably enriched in Flavonifractor plautii , Collinsella aerofaciens , Bilophila wadsworthia , and Sellimonas intestinalis , while depleted in Faecalibacterium prausnitzii , Ruminococcus lactaris , Ruminococcus bicirculans , and Veillonella rogosae . Functional potential of the gut microbiota accounted for 11% of cognition variability. In particular, the bacterial functional module for synthesizing tyrosine, a precursor for dopamine, was in SCZ cases positively associated with cognitive score (ρ = 0.34, q ≤ .1)., Conclusions: Overall, this study shows that the gut microbiome of patients with SCZ differs greatly from that of healthy control subjects or individuals with metabolic syndrome. Cognitive function of patients with SCZ is associated with the potential for gut bacterial biosynthesis of tyrosine, a precursor for dopamine, suggesting that gut microbiota might be an intervention target for alleviation of cognitive dysfunction in SCZ., (© 2022 The Authors.)