IDDF2019-ABS-0143 Association between bariatric surgery and macrovascular disease outcomes in patients with type 2 diabetes and severe obesity: a meta-analysis of cohort studies
Bibliographic record
Abstract
Background Severely obese Type 2 Diabetes Mellitus (T2DM) patients are on increased risk of mortality, morbidity and macrovascular complications. Real-world evidence suggested a reduced rate of macrovascular complications following bariatric surgery. So, we undertook this meta-analysis to understand the impact of bariatric surgery in macrovascular disease outcomes in severely obese T2DM patients. Methods A comprehensive search was performed in PubMed, and Embase database from inception to October 2018. The inclusion criteria were as follows: (a) obese T2DM patients (BMI >35 kg./m2) who underwent bariatric surgery (b) provided hazard ratio (HR) or relative risk (RR). Study quality was assessed using the Newcastle-Ottawa Scale. The primary outcome was to assess the impact of bariatric surgery and macrovascular complications risk. Statistical analysis was performed using Review Manager software. Results This meta-analysis comprised of five studies with a total of 49211 participants (75% female) of which 14434 underwent bariatric surgery and 34777 underwent usual care. The participants in the bariatric surgery group had a mean age of 48 ± 8.98 years, mean BMI of 44.67 ± 6.3 kg/m2 and mean diabetes duration and a follow-up period of 5.48 ± 5.11 years and 10.96 years, respectively. Included studies were of high quality. Participants who underwent bariatric surgery group had significantly lower risk of macrovascular complications as compared to participants who underwent nonsurgery group with a RR of 0.50 (95% CI: 0.35 - 0.73), p = 0.0003) (figure 1). Subgroup analysis revealed studies conducted in US showed higher reduction [RR 0.41 (95% CI: 0.32 - 0.53, p = <0.00001)] in incident macrovascular complications as compared to those conducted in other parts of the world [RR 0.71 (95% CI: 0.56 - 0.89), p = 0.003]. The risk of all-cause mortality was also significantly lower in bariatric surgery group (RR of 0.39 [95% CI: 0.30 - 0.50], p = <0.00001). Conclusions Our meta-analysis supports the benefit of bariatric surgery in reducing macrovascular complications in morbidly obese T2DM patients. However, the observational design of included studies might have precluded the inference despite adjustment of confounding factors. Hence, these findings need to be confirmed in well-designed randomized trials.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.018 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.002 |
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".