Overweight, obesity and risk of multimorbidity: A systematic review and meta‐analysis of longitudinal studies
Bibliographic record
Abstract
Summary This study aimed to review and quantify the association between overweight and obesity in the risk of multimorbidity among the general population. We conducted a systematic review and meta‐analysis in the databases of Pubmed, Lilacs, Web of Science, Scopus, and Embase. We included cohort studies that assessed the association between overweight and/or obesity with the risk of multimorbidity. The Newcastle‐Ottawa assessed the studies' individual quality. A random‐effect model meta‐analysis was performed to evaluate the association between overweight and obesity with the relative risk (RR) of multimorbidity; the I2test evaluated heterogeneity. After excluding duplicates, we found 1.655 manuscripts, of which eight met the inclusion criteria. Of these, seven (87.5%) evidenced an increased risk of multimorbidity among subjects with overweight and/or obesity. Overall, we observed an increased risk of multimorbidity among subjects with overweight (RR: 1.26; CI95%: 1.12; 1.40, I2 = 98%) and obesity (RR: 1.99; CI95%: 1.45;2.72, I2 = 99%) compared to normal weight. According to the I2test, the heterogeneities of the meta‐analyses were high. The Newcastle‐Ottawa scale showed that all studies were classified as high quality. Further longitudinal studies are needed, including different populations and stratifications by sex, age, and other variables.
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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 | 0.027 | 0.056 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.031 |
| Bibliometrics | 0.010 | 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".