Associations of body mass index and waist circumference with risk of Guillain-Barré syndrome in women and men: A prospective analysis of three cohort studies
Bibliographic record
Abstract
BACKGROUND: The association of body mass index (BMI) and waist circumference (WC) with risk of Guillain-Barré syndrome (GBS) has been inconsistent in previous studies. METHODS: We examined the associations of BMI and WC in relation to risk of GBS among 252,980 participants from the Nurses' Health Study (NHS), NHS-II, and the Health Professional Follow-up Study (HPFS). BMI and WC were assessed by self-reported questionnaire, and GBS cases were self-reported. RESULTS: We documented 328 incident GBS cases during a total of 5,422,788 person years of follow-up. Compared to participants with BMI<25kg/m2, the multivariate pooled hazard ratio (HR) of GBS was 1.34 (95% CI: 1.04, 1.73) for overweight participants (25kg/m2≤BMI<30 kg/m2), and 1.68 (95% CI: 1.21, 2.35) for obese participants (BMI≥30 kg/m2) (P for trend = 0.001). Compared to participants with normal WC (<35 inches for women and <40 inches for men), the HR of GBS was 1.55 (95% CI: 1.10, 2.18) for participants with high WC (≥35 inches for women, and ≥40 inches for men). The positive associations of BMI and WC with risk of GBS were attenuated to null after mutually adjusting for BMI and WC. Joint analysis showed that the HR was 1.84 (95% CI: 1.27, 2.66) for participants with high WC and BMI≥25 kg/m2 in comparison to participants with normal WC and BMI<25kg/m2. CONCLUSION: These data from large cohorts showed that higher BMI and WC jointly were associated with higher risk of GBS. Our study highlighted the importance of maintaining a normal body weight and waist circumference in prevention of GBS.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".