Development and Application of Children's Sex‐ and Age‐Specific Fat‐Mass and Muscle‐Mass Reference Curves From Dual‐Energy X‐Ray Absorptiometry Data for Predicting Cardiometabolic Risk
Bibliographic record
Abstract
BACKGROUND: A dual-energy x-ray absorptiometry (DXA)-derived phenotype classification based on fat mass and muscle mass has been developed for adults. We extended this to a paediatric population. METHODS: Children's (≤ 17 years) DXA data in NHANES (n = 6120) were used to generate sex- and age-specific deciles of appendicular skeletal muscle mass index and fat mass index with the Lambda Mu Sigma method. Four phenotypes (high [H] or low [L], adiposity [A] and muscle mass [M]: HA-HM, HA-LM, LA-HM, LA-LM) were identified based on being above/below the median compared to same-sex and same-age peers. These reference curves were applied to the QUALITY cohort (n = 630, 8-10 years of age in 2005) to assess whether the phenotypes correctly identified cardiometabolic risk at baseline, follow-up (2008-2010), and their longitudinal changes. Multiple linear regression models were adjusted for age, sex, and Tanner's stage. RESULTS: Compared to the LA-HM reference group, the HA-HM phenotype was associated with less favourable HDL, triglycerides, and HOMA-IR at baseline and first follow-up, but not in their changes. The HA-LM phenotype was associated with less favourable HOMA-IR at baseline and first follow-up. CONCLUSIONS: Results suggest that phenotypes based on fat and muscle mass may have clinical utility in children and should be further investigated.
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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".