Association between lipid profiles and Graves orbitopathy: A systematic review and meta analysis
Bibliographic record
Abstract
Graves orbitopathy (GO) is an autoimmune disorder affecting the tissues around the eyes, seen in 25–50 % of individuals with Graves disease (GD). Thyroid receptor antibodies (TRAb) target the TSH receptor, which can provoke an inflammatory response and promote fat cell formation by activating these receptors. Given this mechanism, statins that are commonly used for managing hyperlipidemia could be a potential treatment for GO. This review explores the connection between Graves orbitopathy and lipid profiles. This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. Articles were sourced from databases such as MEDLINE, ScienceDirect, EBSCO, ProQuest, Cochrane, and Google Scholar. The inclusion criteria covered both published and unpublished studies examining the relationship between GO and lipid profiles. Meta-analysis was conducted using Review Manager 5.4, and the risk of bias was evaluated using the Newcastle-Ottawa Scale (NOS). This review included a total of four cross-sectional studies. All studies found a significant association between LDL cholesterol levels and Graves orbitopathy. However, the relationships with triglycerides (TG), HDL levels, and total cholesterol (TC) were not consistently statistically significant. The meta-analysis revealed significant differences in TC, LDL cholesterol, and TG levels across all groups (SMD = 0.48, CI = 0.17–0.80, p = 0.002, I 2 = 67 %; SMD = 0.42, CI = 0.18–0.67, p = 0.0008, I 2 = 47 %; SMD = 0.24, CI = 0.07–0.41, p = 0.005, I 2 = 0 %, respectively), while HDL levels did not show significant differences among the groups (SMD = 0.16, CI = −0.02-0.34, p = 0.08, I 2 = 10 %). Significant differences in serum lipid profiles, including TC, LDL, and TG except HDL, were found between patients with GO and those without. Further research is needed to confirm these findings. • The pathophysiology of GO involves oxidative stress, autoimmune processes, a hypermetabolic state, and nutritional imbalances. • This study underlines the significance higher level of TC, LDL, and TG in GO group compared to no GO group. • The mechanism linking GO to cholesterol may involve the inflammatory response associated with hypercholesterolemia. • The elevated lipid levels are associated with a higher risk of developing GO in patients with Graves disease (GD).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".