Prevalence of underweight, overweight, and obesity in children and adolescents with type 1 diabetes: Data from the international SWEET registry
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: To assess the prevalence of underweight (UW), overweight (OW), and obesity in children and adolescents with type 1 diabetes (T1D). METHODS: An international cross-sectional study including 23 026 T1D children (2-18 years, duration of diabetes ≥1 year) participating in the SWEET prospective, multicenter diabetes registry. Body mass index SD score (BMI-SDS) was calculated using the World Health Organization BMI charts. Children were categorized as UW (BMI-SDS < -2SD), OW (+1SD < BMI-SDS ≤ +2SD), and obese (OB) (BMI-SDS > +2SD). Hierarchic regression models were applied with adjustment for sex, age, and duration of diabetes. RESULTS: The prevalence of UW, OW, and obesity was: 1.4%, 22.3%, and 7.3% in males and 0.6%, 27.2%, and 6.8% in females. Adjusted BMI-SDS was significantly higher in females than in males (mean ± SEM: 0.54 ± 0.05 vs 0.40 ± 0.05, P < 0.0001). In males, BMI-SDS significantly decreased by age (P < 0.0001) in the first three age categories 0.61 ± 0.06 (2 to <10 years), 0.47 ± 0.06 (10 to <13 years), 0.34 ± 0.05 (13 to <16 years). In females, BMI-SDS showed a U-shaped distribution by age (P < 0.0001): 0.54 ± 0.04 (2 to <10 years), 0.39 ± 0.04 (10 to <13 years), 0.55 ± 0.04 (13 to <16 years). BMI-SDS increased by diabetes duration (<2 years: 0.38 ± 0.05, 2 to <5 years: 0.44 ± 0.05, and ≥5 years: 0.50 ± 0.05, P < 0.0001). Treatment modality did not affect BMI-SDS. Adjusted HbA1c was significantly higher in females than in males (8.20% ± 0.10% vs 8.06% ± 0.10%, P < 0.0001). In both genders, the association between HbA1c and BMI-SDS was U-shaped with the highest HbA1c in the UW and obesity groups. CONCLUSIONS: The high rate of OW and obesity (31.8%) emphasize the need for developing further strategies to prevent and treat excess fat accumulation in T1D.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it