Hyperlipidemia and lipid-lowering therapy in diabetic retinopathy (DR): A bibliometric study and visualization analysis in 1993–2023
Bibliographic record
Abstract
Background Diabetic retinopathy (DR) is a common complication in diabetic patients. DR is also a neurodegenerative disease. Patients with hyperglycemia, hyperlipidemia, and hypertension are vulnerable to retinopathy development. While the roles of blood glucose and blood pressure in the development of retinopathy have been extensively studied, the relationship between body fat and DR pathogenesis and the impact of lipid-reducing drugs on DR has just emerged as a research hotspot in DR study. We aim to visualize the contributions and cooperation of reporters, organizations, and nations, in addition to the research hotspots and trends in DR-related lipid research from 1993 to 2023, by bibliometric analysis. Methods We extracted all publications about DR-related lipid research from 1993 to 2023 from the Web of Science Core Collection, and bibliometric features were studied using VOSviewer and the CiteSpace program. Results 1402 documents were retrieved. The number of studies has risen consistently for three decades, from an average of 16.8/year in the 1990s to 28.8/year in the 2000s, 64.5/year in 2010s, and reached 112/year in 2020–2022, confirming they are hot research topic in the field. These reports were from 93 nations/regions, with the USA, China, Japan, Australia, and England taking the leading positions. Diabetes Research and Clinical Practice was the journal that published the most studies, and Diabetes Care was the most quoted. We identified 6979 authors, with Wong TY having the most papers and being the most commonly co-cited. The most popular keyword, according to our research, is diabetic retinopathy. Oxidative stress, diabetic macular edema (DME), lipid peroxidation, and other topics have often been investigated. Conclusion DR-related lipid research is conducted mainly in North America, Asia, Oceania, and Europe. Much study has centered on the relationship between lipid-lowering therapy and DR pathogenesis. These studies strongly support using lipid-reducing medications (fenofibrate, statins, and omega-3 PUFAs), combined with hyperglycemia and hypertension therapy, to prevent and treat DR. However, the impact of fenofibrate or statin on retinopathy is not correlated with their action on blood lipid profiles. Thus, more randomized clinical trials with primary endpoints related to DR in T1D or T2D are merited. In addition, the lipid biomarker for DR (lipid aldehydes, ALEs, and cholesterol crystals), the action of lipid-reducing medicines on retinopathy, the mechanism of lipid-lowering medications preventing or curing DR, and ocular delivery of lipid-lowering drugs to diabetic patients are predicted as the research focus in the future in the DR-related lipid research field.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.018 | 0.050 |
| 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; both teacher heads agree on what is shown here.
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".