A Systematic Review on Dry Eye Syndrome in Patients with Diabetes Mellitus: Prevalence, Etiology, and Clinical Characteristics
Bibliographic record
Abstract
Background: Dry eye syndrome (DES), or keratoconjunctivitis sicca, is a common ocular surface disorder characterized by tear film instability, inflammation, and ocular discomfort. Diabetes mellitus (DM) is increasingly recognized as a major risk factor for DES due to metabolic, neuropathic, and vascular complications. However, the prevalence and clinical spectrum of DES in diabetic populations remain heterogeneous across studies. Objective: This systematic review aims to evaluate the prevalence, etiological mechanisms, and clinical characteristics of dry eye syndrome among patients with diabetes mellitus. Methods: A systematic literature search was conducted in PubMed, Scopus, Web of Science, Cochrane Library, and Google Scholar for studies published between January 2000 and July 2025. Observational studies, clinical trials, and meta-analyses reporting on prevalence, pathophysiology, and clinical features of DES in diabetic populations were included. Data on prevalence, diagnostic tools (Schirmer test, tear break-up time [TBUT], ocular surface disease index [OSDI]), and odds ratios (OR) were extracted. The quality of studies was assessed using the Newcastle-Ottawa Scale. Results: A total of 45 studies comprising over 10,000 diabetic patients were included. The prevalence of DES varied from 17.5% in community-based studies (China) to 54.3% in hospital-based settings (India), with a pooled prevalence of approximately 38–40%. Diabetic patients had a significantly higher risk of DES compared to non-diabetics (OR: 1.82; 95% CI: 1.23–2.67). Etiological factors included lacrimal gland dysfunction, Meibomian gland disease, corneal nerve damage, and hyperglycemia-induced inflammation. Clinical signs such as reduced Schirmer values (<5 mm/5 min) and decreased TBUT (<10 s) were common, often exceeding patient-reported symptoms due to diabetic neuropathy. Conclusion: Dry eye syndrome is a frequent and underrecognized complication of diabetes mellitus, strongly associated with poor glycemic control and disease duration. Routine screening and early management of DES should be integrated into diabetic eye care. Future research should focus on standardized diagnostic protocols, longitudinal studies, and tailored interventions to improve ocular and systemic outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".