Long-Term Risk of Colorectal Cancer in Patients With Prediabetes: A Comprehensive Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND AND AIMS: Prediabetes is often underdiagnosed and underreported due to its asymptomatic state in over 80% of individuals. Considering its role in promoting cancer incidence and limited evidence linking prediabetes and colorectal cancer (CRC), we conducted a systematic review and meta-analysis to evaluate the incidence of colorectal cancer in people with prediabetes. METHODS: A comprehensive search through PubMed/Medline, Embase, Scopus, and Google Scholar was performed until June 1, 2022, to screen for studies reporting CRC incidence/risk in prediabetics. Binary random-effects models were used to perform meta-analysis and subgroup analyses. Sensitivity analysis was done using leave-one-out method. The quality of the studies was assessed by the Newcastle Ottawa Scale for observational studies. RESULTS: Seven prospective and one retrospective study comprising 15 cohorts and a pooled number of 854,876 cases and 219,0511 controls were included in the analysis (2 Japan, 2 Korea, 1 Sweden, 1 UK, 1 China, and 1 USA). After combining all the studies, the forest plots for adjusted analysis shows a statistically significant increase in odds of having CRC with prediabetes (OR=1.16; 1.08-1.25, p< 0.01; I2=56.06%) and unadjusted analysis also shows a statistically significant increase in odds of having CRC with prediabetes (OR=1.62; 1.35-1.95, p< 0.01; I2=85.72% ). Sensitivity analysis using the Leave-one-out method did confirm equivalent results. Subgroup analysis based on type of study, the odds of developing CRC was higher in prospective studies (OR=1.175; 1.065-1.298) (p=0.001) than retrospective studies (OR=1.162; 1.033- 1.306) (p=0.012). The odds of developing CRC were not significantly higher in ages >60 (OR=1.446; 0.887-2.356) (p=0.139) compared to less than 60 years. The strongest association b/w prediabetes and CRC was found on a median 5-10 years (aOR=1.257; 1.029-1.534) (p=0.025) follow-up compared to < 5 years and 10 years and higher. CONCLUSIONS: This study showed that the odds of developing CRC is 16% higher in patients with prediabetes than those with normal blood glucose. Lifestyle modifications such as weight loss, proper diet, and exercise are essential to control prediabetes. This study further warrants a specific prediabetes screening for patients already at high risk of colorectal cancer with other risk factors.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.035 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".