The influence of stunting on obesity in adulthood: results from the EPIPorto cohort
Bibliographic record
Abstract
OBJECTIVE: To retrospectively investigate the association between short stature and increased sitting height ratio (SHR) - indicators of stunting - and obesity markers in adults. DESIGN: Cross-sectional evaluation of the EPIPorto cohort. Weight, height, sitting height and waist circumference were measured. Obesity was assessed for men and women through BMI and waist-to-height ratio (WHtR). Short stature (women, <152 cm; men, <164 cm) and high SHR (women, ≥54·05 %; men, ≥53·25 %) were taken as stunting measures. OR with 95 % CI were computed using logistic regression models. SETTING: Representative sample of adults from EPIPorto, an adult cohort study from Porto, Portugal. SUBJECTS: A sample of 1682 adults, aged 18-86 years, was analysed. RESULTS: Higher obesity prevalence was found among women (BMI≥30·0 kg/m2: 25·5 v. 13·3 %, P<0·001) and a higher proportion of men presented abdominal obesity (WHtR≥0·5: 80·1 v. 71·1 %, P<0·001). A positive association was found between short stature and obesity measures for women (multivariate-adjusted OR; 95 % CI: 1·75; 1·17, 2·62 for BMI≥30·0 kg/m2; 1·89; 1·24, 2·87 for WHtR≥0·5). Increased SHR was associated with higher likelihood of having BMI≥30·0 kg/m2 in both sexes (multivariate-adjusted OR; 95 % CI: 2·10; 1·40, 3·16 for women; 1·92; 1·07, 3·43 for men) but not with WHtR≥0·5. CONCLUSIONS: Different growth markers are associated with obesity in adults. However, this association depends on the population and anthropometric measures used: short stature is associated with a higher risk of presenting excessive weight in women but not in men; SHR is more sensitive to detect this effect in both sexes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".