The Association Between Diabetes Mellitus and Perianal Abscess: A Meta-Analysis
Bibliographic record
Abstract
Introduction: Perianal abscess (PA), a common anorectal emergency, affects approximately 2– 3% of the global population. Increasing evidence indicates a relationship between PA and metabolic disorders. Considering the anticipated rise in diabetes mellitus (DM) cases to 1 billion worldwide by 2045, clarifying the epidemiological association and clinical characteristics linking glucose dysregulation with PA holds considerable clinical importance. Purpose: This study aimed to elucidate the bidirectional epidemiological relationship between DM and PA and to define their respective clinical profiles, thereby contributing to risk stratification and optimization of management strategies. Methods: Observational studies examining the association between DM and PA were systematically retrieved from PubMed, EMBASE, Cochrane Library, CNKI, Wanfang, and VIP databases (from inception to November 2024). Two independent reviewers conducted study selection, data extraction, and quality assessment using the Newcastle-Ottawa Scale. Random-effects models were applied to calculate pooled prevalence, odds ratios (OR), relative risks (RR), and 95% confidence intervals (CI). Heterogeneity, sensitivity, and publication bias were evaluated using Cochran’s Q test, I 2 statistics, subgroup analyses, and Egger’s test. Results: The meta-analysis demonstrated a strong bidirectional association between DM and PA, with 30.7% of PA cases occurring in patients with DM and 22.0% of individuals with DM developing PA. Male sex conferred a significantly higher risk (OR=15.00, 95% CI: 7.87– 28.59). DM was linked to altered microbial profiles—lower prevalence of Escherichia coli (OR=0.57, 95% CI: 0.39– 0.84) and higher detection of Klebsiella pneumoniae (OR=4.73, 95% CI: 1.30– 17.19)—as well as increased occurrence of high abscesses (OR=1.72, 95% CI: 1.00– 2.94), older age at onset (+5.53 years), and higher recurrence rates compared with non-DM patients. Conclusion: The interrelationship between DM and PA is characterized by mutual risk amplification and distinct clinical manifestations, including sex-related vulnerability, microbial alterations, and poorer prognostic outcomes. These findings indicate the necessity for individualized antimicrobial regimens and enhanced postoperative monitoring in patients with DM. Keywords: hyperglycemia, anorectal infection, comorbidity, evidence-based medicine
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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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.047 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".