Detection and Correlation Analysis of Serum Uric Acid in Patients with Thyroid-Associated Ophthalmopathy

Causality of blood metabolites and metabolic pathways on Graves' disease and Graves' ophthalmopathy: a two-sample Mendelian randomization study

OBJECTIVES

There is ample that metabolic dysregulation is involved in Graves' disease (GD) and Graves' ophthalmopathy (GO). Recent studies have identified numerous metabolites associated with GD and GO. However, the causal impact of metabolites on GD and GO remains to be investigated.

METHODS

This two-sample Mendelian randomization (MR) analysis investigated the causal relationships between 486 blood metabolites and GD and GO. Sensitivity analysis was also performed to examine heterogeneity and pleiotropy.

RESULTS

MR analysis showed that 9 and 13 metabolites were associated with GD and GO, respectively, each meeting the nominal significance criteria (inverse variance weighted, p < 0.05). Additionally, four metabolic pathways were identified for each condition using network-based MetaboAnalyst 5.0.

CONCLUSIONS

The metabolites and pathways discovered in this study could serve as circulating metabolic biomarkers for clinical screening and prevention of GD and GO. They can be also used for further studies on the mechanisms and drug targets in GD and GO.

Retracted: Detection and Correlation Analysis of Serum Uric Acid in Patients with Thyroid-Associated Ophthalmopathy

[This retracts the article DOI: 10.1155/2022/8406834.].

Predictive markers for anti-inflammatory treatment response in thyroid eye disease

Anti-inflammatory treatment is the primary and vital therapeutic approach for active, moderate-to-severe thyroid eye disease (TED). Accurate pretreatment prediction of treatment response is of paramount importance for the prognosis of patients. However, relying solely on the clinical activity score asa determinant of activity has led to unsatisfactory treatment outcomes. In recent years, significant advancements have been made in identifying predictive markers for anti-inflammatory treatment response in TED, clinical markers, body fluid biomarkers and imaging biomarkers. Several clinical studies have developed prediction models based on these markers. However, there is still a lack of comprehensive elucidation or comparison between the different markers. Therefore, this review aims to provide a detailed analysis of the definition, characteristics, and application of predictive markers for anti-inflammatory treatment response in TED. Through detailed literature search, 26 articles applying anti-inflammatory treatment effect prediction with a total of 1948 TED patients were used for analysis and discussion. By gaining a better understanding of the current research on predictive markers, we can accelerate and guide the exploration of treatment prediction strategies, leading us towards an era of precise therapy for TED.