Association between Leptin, Adiponectin Levels, and Nutritional Status in Children with Down Syndrome
Bibliographic record
Abstract
Background: Children with Down Syndrome (DS) have been associated with obesity. Leptin and adiponectin were also significant predictors of obesity and its comorbidity in DS. However, there was limited data regarding leptin and adiponectin in children with DS, particularly who were undernutrition. This study aimed to seek the role of leptin levels, adiponectin levels, and nutritional status in children with DS. Methods: This cross-sectional study was conducted on 40 children with DS aged 1 - 5 years. Height and weight were measured, and then the growth was interpreted using a DS growth chart. The Weight for Height Z-Score (WHZ) and Height for Age Z-Score (HAZ) were determined, and Mid-Upper Arm Circumference (MUAC) was measured. Leptin and adiponectin serum were analyzed using the enzyme-linked immunosorbent assay (ELISA) method. Mann-Whitney test was done to compare leptin and adiponectin levels in normal and wasted groups, while Spearman’s analysis was carried out to correlate laboratory results and anthropometric parameters. Results: Forty children participated (23 males, 17 females) with a median age was 25.5 months. Ten out of 40 children with DS (25%) were wasted and leptin was significantly lower in wasted compared to normal children. In addition, leptin was significantly correlated with WHZ (r = 0.415; p = 0.008), and MUAC (r = 0.427; p = 0.006), while adiponectin did not significantly correlate with those anthropometric variables in both wasted or non-wasted groups. Conclusion: Leptin is associated with WHZ and MUAC, and it decreases in wasted children with DS.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".