Metabolomics for Prediction of Relapse in Graves' Disease: Observational Pilot Study

Genetically Determined Metabolites in Graves Disease: Insight From a Mendelian Randomization Study

Context

Graves disease (GD) is a prevalent autoimmune disorder with a complex etiology. The association between serum metabolites and GD remains partially understood.

Objective

This study aimed to elucidate the causal connections between serum metabolites and predisposition to GD, examining potential genetic interplay.

Methods

A 1-sample Mendelian randomization (MR) study was conducted on the GD analysis that included 2836 cases and 374 441 controls. We utilized genome-wide association study summary data from the FinnGen project, analyzing the causal impact of 486 serum metabolites on GD. Approaches used were the inverse variance weighted methodology, Cochran's Q test, MR-Egger regression, MR-PRESSO, Steiger test, and linkage disequilibrium score regression analyses to assess genetic influence on metabolites and GD.

Results

19 metabolites were identified as having a pronounced association with GD risk, of which 10 maintained noteworthy correlations after stringent sensitivity assessments. Three metabolites exhibited significant heritability: kynurenine (OR 3.851, P = 6.09 × 10-4), a risk factor; glycerol 2-phosphate (OR 0.549, P = 3.58 × 10-2) and 4-androsten-3beta,17beta-diol disulfate 2 (OR 0.461, P = 1.34 × 10-2) were recognized as protective factors against GD. Crucially, all 3 exhibited no shared genetic interrelation with GD, further substantiating their potential causal significance in the disease.

Conclusion

This study unveils pivotal insights into the intricate relationships between serum metabolites and GD risk. By identifying specific risk and protective factors, it opens avenues for more precise disease understanding and management. The findings underline the importance of integrating genomics with metabolomics to fathom the multifaceted nature of GD.

Changes in the gut microbiota of patients with Graves' orbitopathy according to severity grade

BACKGROUND

To explore the changes of gut microbiota in Graves' orbitopathy (GO) patients of different severity grades and to identify the pathogenic bacteria of GO and the associated mechanism.

METHODS

A total of 18 healthy controls and 62 GO patients were recruited. The baseline information and faecal samples of all subjects were collected for gut microbiota analysis and metabolic function prediction analysis. 16SrDNA sequencing was used for microbial diversity detection. The operational taxonomic unit (OTU) was divided using the Mothur software, and the dominant microbiota was analysed. OTU number, Chao1 index, ACE index, and Shannon index of microbiota in faecal samples were analysed using the QIIME1.9.0 software. The relative abundance of microbiota in faecal samples was analysed through principal component analysis (PCA) using the Canoco Software 5.0. The metabolic function of microbiota in faecal samples was predicted using PICRUSt 2.0.

RESULTS

There was no remarkable difference in gut microbiota diversity between groups; however, the gut microbial community and dominant microbiota significantly differed among groups. Klebsiella_pneumoniae was deemed the potentially pathogenic bacteria of GO, and its abundance was positively correlated with disease severity. The metabolic prediction results revealed that inorganic nutrition metabolism, fatty acid and lipid degradation, electron transfer, aromatic compound degradation, and alcohol degradation were notably different between groups with high and low abundance of Klebsiella_pneumoniae and among groups with different GO severity grades, thereby showing a positive correlation with GO clinical risks.

CONCLUSIONS

Klebsiella_pneumoniae was a potential GO-related pathogen, which may regulate the metabolic pathways to affect GO progression.

Gut Microbiome and the Role of Metabolites in the Study of Graves’ Disease

Graves’ disease (GD) is an autoimmune thyroid disease (AITD), which is one of the most common organ-specific autoimmune disorders with an increasing prevalence worldwide. But the etiology of GD is still unclear. A growing number of studies show correlations between gut microbiota and GD. The dysbiosis of gut microbiota may be the reason for the development of GD by modulating the immune system. Metabolites act as mediators or modulators between gut microbiota and thyroid. The purpose of this review is to summarize the correlations between gut microbiota, microbial metabolites and GD. Challenges in the future study are also discussed. The combination of microbiome and metabolome may provide new insight for the study and put forward the diagnosis, treatment, prevention of GD in the future.

Screening for New Markers to Assess Thyroid Hormone Action by OMICs Analysis of Human Samples

Determination of the levels of thyroid-stimulating hormone (TSH) and free thyroid hormones (fTHs) is crucial for assessing thyroid function. However, as a result of inter-individual genetic variability and different environmental factors individual set points exist for TSH and fTHs and display considerable variation. Furthermore, under specific pathophysiological conditions like central hypothyroidism, TSH secreting pituitary tumors, or thyroid hormone resistance the established markers TSH and fTH fail to reliably predict thyroid function and adequate supply of TH to peripheral organs. Even in case of overt hyper- and hypothyroidism circulating fTH concentrations do not correlate with clinical symptoms. Therefore, there is a clear need for novel, more specific biomarkers to diagnose and monitor thyroid function. OMICs screening approaches allow parallel profiling of hundreds to thousands of molecules and thus comprehensive monitoring of molecular alterations in tissues and body fluids that might be associated with changes in thyroid function. These techniques thus constitute promising tools for the identification of urgently needed novel biomarkers. This mini review summarizes the findings of OMICs studies in thyroid research with a particular focus on population-based and patient studies as well as interventional approaches investigating the effects of thyroid hormone administration.

Integration of epidemiologic, pharmacologic, genetic and gut microbiome data in a drug-metabolite atlas

Progress in high-throughput metabolic profiling provides unprecedented opportunities to obtain insights into the effects of drugs on human metabolism. The Biobanking BioMolecular Research Infrastructure of the Netherlands has constructed an atlas of drug-metabolite associations for 87 commonly prescribed drugs and 150 clinically relevant plasma-based metabolites assessed by proton nuclear magnetic resonance. The atlas includes a meta-analysis of ten cohorts (18,873 persons) and uncovers 1,071 drug-metabolite associations after evaluation of confounders including co-treatment. We show that the effect estimates of statins on metabolites from the cross-sectional study are comparable to those from intervention and genetic observational studies. Further data integration links proton pump inhibitors to circulating metabolites, liver function, hepatic steatosis and the gut microbiome. Our atlas provides a tool for targeted experimental pharmaceutical research and clinical trials to improve drug efficacy, safety and repurposing. We provide a web-based resource for visualization of the atlas (http://bbmri.researchlumc.nl/atlas/).