Combination of Multiple Low-Risk Lifestyle Behaviors and Incident Type 2 Diabetes: A Systematic Review and Dose-Response Meta-analysis of Prospective Cohort Studies
Bibliographic record
Abstract
OBJECTIVE: Combined low-risk lifestyle behaviors (LRLBs) have been associated with a reduction in type 2 diabetes risk. This relationship has not been systematically quantified. RESEARCH DESIGN AND METHODS: A systematic review and meta-analysis was conducted to assess the association of combined LRLBs with type 2 diabetes. Databases were searched up to September 2022. Prospective cohort studies reporting the association between a minimum of three combined LRLBs (including healthy diet) with incident type 2 diabetes were included. Independent reviewers extracted data and assessed study quality. Risk estimates of extreme comparisons were pooled using a random-effects model. Global dose-response meta-analysis (DRM) for maximum adherence was estimated using a one-stage linear mixed model. The certainty of the evidence was assessed using GRADE (Grading of Recommendations, Assessment, Development and Evaluations). RESULTS: Thirty cohort comparisons (n = 1,693,753) involving 75,669 incident type 2 diabetes cases were included. LRLBs, with author-defined ranges, were healthy body weight, healthy diet, regular exercise, smoking abstinence or cessation, and light alcohol consumption. LRLBs were associated with 80% lower risk of type 2 diabetes (relative risk [RR] 0.20; 95% CI 0.17-0.23), comparing the highest with lowest adherence. Global DRM for maximum adherence to all five LRLBs reached 85% protection (RR 0.15; 95% CI 0.12-0.18). The overall certainty of the evidence was graded as high. CONCLUSIONS: There is a very good indication that a combination of LRLBs that includes maintaining a healthy bodyweight, healthy diet, regular exercise, smoking abstinence or cessation, and light alcohol consumption is associated with a lower risk of incident type 2 diabetes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.048 |
| Bibliometrics | 0.006 | 0.007 |
| 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) | 0.003 | 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".