Does waist circumference uncorrelated with BMI add valuable information?
Bibliographic record
Abstract
BACKGROUND: Estimation of relative contribution of Body Mass Index (BMI) and waist circumference (WC) on health outcomes requires a regression model that includes both obesity metrics. But, multicollinearity could yield biased estimates. METHODS: To address the multicollinearity issue between BMI and WC, we used the residual model approach. The standard WC (Y-axis) was regressed on the BMI (X-axis) to obtain residual WC. Data from two adult population surveys (Nunavik Inuit and James Bay Cree) were analysed to evaluate relative effect of BMI and WC on four cardiometabolic risk factors: insulin, triglycerides, systolic blood pressure and high-density lipoprotein levels. RESULTS: In multivariate models, standard WC and BMI were significantly associated with cardiometabolic outcomes. Residual WC was not linked with any outcomes. The BMI effect was weakened by including standard WC in the model, but its effect remained unchanged if residual WC was considered. CONCLUSIONS: The strong correlation between standard WC and BMI does not allow assessment of their relative contributions to health in the same model without a risk of making erroneous estimations. By contrast with BMI, fat distribution (residual WC) does not add valuable information to a model that already contains overall adiposity (BMI) in Inuit and Cree.
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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 | 0.019 | 0.091 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".