The contribution of physical fitness to individual and ethnic differences in risk markers for type 2 diabetes in children: The Child Heart and Health Study in England (CHASE)
Bibliographic record
Abstract
Background The relationship between physical fitness and risk markers for type 2 diabetes (T2D) in children and the contribution to ethnic differences in these risk markers have been little studied. We examined associations between physical fitness and early risk markers for T2D and cardiovascular disease in 9- to 10-year-old UK children. Methods Cross-sectional study of 1445 9- to 10-year-old UK children of South Asian, black African-Caribbean and white European origin. A fasting blood sample was used for measurement of insulin, glucose (from which homeostasis model assessment [HOMA]-insulin resistance [IR] was derived), glycated hemoglobin (HbA1c), urate, C-reactive protein (CRP), and lipids. Measurements of blood pressure (BP) and fat mass index (FMI) were made; physical activity was measured by accelerometry. Estimated VO2 max was derived from a submaximal fitness step test. Associations were estimated using multilevel linear regression. Results Higher VO2 max was associated with lower FMI, insulin, HOMA-IR, HbA1c, glucose, urate, CRP, triglycerides, LDL-cholesterol, BP and higher HDL-cholesterol. Associations were reduced by adjustment for FMI, but those for insulin, HOMA-IR, glucose, urate, CRP, triglycerides and BP remained statistically significant. Higher levels of insulin and HOMA-IR in South Asian children were partially explained by lower levels of VO2max compared to white Europeans, accounting for 11% of the difference. Conclusions Physical fitness is associated with risk markers for T2D and CVD in children, which persist after adjustment for adiposity. Higher levels of IR in South Asians are partially explained by lower physical fitness levels compared to white Europeans. Improving physical fitness may provide scope for reducing risks of T2D.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".