Prevalence of underweight, overweight, and obesity in children and adolescents with type 1 diabetes: Data from the international SWEET registry
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.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".