Association between nickel exposure and diabetes risk: an updated meta-analysis of observational studies
Bibliographic record
Abstract
Objective The results of epidemiological studies on the association between nickel exposure and diabetes remain controversial. Therefore, an update meta-analysis was conducted to examine the association between urinary nickel levels and diabetes risk, and to focus on whether there is an association between blood nickel levels and diabetes risk. Methods Relevant studies were comprehensively searched from PubMed, Web of Science, and Wanfang databases from their inception to July 2024. The random-effects model was utilized to determine pooled Standard Mean Difference (SMD) and 95% confidence intervals (CIs), with stratified and sensitivity analyses also performed. Heterogeneity between studies was assessed using I2 statistic, while publication bias was evaluated using Egger's and Begg's tests. The quality of the included studies was assessed using the Newcastle-Ottawa Scale. Results A total of 19 studies involving 46,071 participants were included in this meta-analysis. The random-effects model indicated that the pooled SMD for nickel exposure levels in diabetic patients and non-diabetic controls were 0.16 (95% CI 0.07–0.2) for urine and 0.03 (95% CI −0.20 to 0.27) for blood, respectively. Conclusion It was discovered that diabetes risk was positively correlated with urinary nickel levels, whereas there was no significant correlation with blood nickel levels. Furthermore, it appeared that the association between nickel exposure and diabetes risk differ in individuals with diabetes compared to those with pre-diabetes, and that the direction of the correlation may even be reversed. In conclusion, more high-quality prospective studies are needed in order to validate these findings in future research endeavors. Systematic Review Registration https://www.crd.york.ac.uk/PROSPERO , registration number: CRD42024534139.
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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 | 0.022 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.055 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".