Diabetes mellitus and latent tuberculosis infection: an updated meta-analysis and systematic review
Bibliographic record
Abstract
BACKGROUND: Previous studies have demonstrated an association between diabetes mellitus (DM) and latent tuberculosis infection (LTBI). This study was conducted to update the current understanding of the association between DM and LTBI. By conducting a systematic review and meta-analysis using adjusted odds ratios (aOR) or risk ratios (aRR), we aimed to further explore the association between DM and LTBI and provide essential reference for future research. METHODS: We conducted comprehensive searches in Embase, Cochrane Library, and PubMed without imposing any start date or language restrictions, up to July 19, 2022. Our study selection encompassed observational research that compared from LTBI positive rates in both DM and non-DM groups and reported aRR or aOR results. The quality of the included studies was assessed utilizing the Newcastle-Ottawa Scale. Pooled effect estimates were calculated using random-effects models, along with their associated 95% confidence intervals (CI). RESULTS: We included 22 studies involving 68,256 subjects. Three cohort studies were eligible, with a pooled aRR of 1.26 (95% CI: 0.71-2.23). Nineteen cross-sectional studies were eligible, with a pooled aOR of 1.21 (95% CI: 1.14-1.29). The crude RR (cRR) pooled estimate for three cohort studies was 1.62 (95% CI: 1.03-2.57). Among the cross-sectional studies we included, sixteen studies provided crude ORs, and the crude OR (cOR) pooled estimate was 1.64 (95% CI: 1.36-1.97). In the diagnosis of diabetes, the pooled aOR of the HbA1c group was higher than that of self-reported group (pooled aOR: 1.56, 95% CI: 1.24-1.96 vs. 1.17, 95% CI: 1.06-1.28). CONCLUSION: Our systematic review and meta-analysis suggest a positive association between DM and LTBI. Individuals with DM may have a higher risk of LTBI compared to those without DM. These findings provide important insights for future research and public health interventions in managing LTBI in diabetic populations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.004 |
| 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".