Cross-sectional Associations between Anthropometric Measures and Cardiometabolic Risk Factors Among Young Children
Bibliographic record
Abstract
Purpose: Associations between anthropometry and child cardiometabolic risk (CMR) are understudied. We investigated cross-sectional associations between body mass index (BMI) z-score, waist circumference (WC), waist-to-height ratio (WHtR), blood pressure (BP), and serum biomarkers (triglycerides, total cholesterol, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), total cholesterol:HDL-cholesterol, C-reactive protein, glucose, insulin, and glycated hemoglobin (HbA1c)) among young children in the Guelph Family Health Study. Methods: This study included 210 children (1.5–6.2 years, from 171 families). Blood samples were provided by 46 of the participating children. BP and anthropometry were measured during a health assessment visit. Generalized estimating equations applied to regression models were used to determine associations between anthropometric measures and CMR factors, while controlling for relevant covariates. Results: WHtR was positively associated with diastolic BP ([Formula: see text] = 5.73; 95% confidence interval (CI) (1.12, 10.3)) and glucose ([Formula: see text] = 0.50; 95% CI (0.21, 0.79)), total cholesterol ([Formula: see text] = 0.87; 95% CI (0.02, 1.73)) and negatively associated with insulin ([Formula: see text] = −0.01; 95% CI (−18.6, 2.54)). BMI z-score was negatively associated with HbA1c ([Formula: see text] = −0.13; 95% CI (−0.23, −0.04)), and WC was positively associated with HDL-C ([Formula: see text] = 0.04; 95% CI (0.00, 0.08)). No other associations were significant. Conclusions: WHtR, more so than BMI z-score or WC, may show promise as a noninvasive screening tool for lipid- and/or BP-related risk in early childhood.
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 | 0.001 | 0.003 |
| 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".