Greater central adiposity resulting from increased market integration is associated with elevated C-reactive protein levels in older women from the Republic of Vanuatu
Bibliographic record
Abstract
Objective: We characterized the relationship between circulating C-reactive protein (CRP) levels and nine anthropometric measures of body fat to identify the best anthropometric predictors of CRP in Ni-Vanuatu women. Sample and Methods: Anthropometric data and blood spot samples were collected from sixty-four Ni-Vanuatu female participants (age 35-78 years) on five islands with varying degrees of market integration, cultural change, and obesity. CRP concentration was determined with a high-sensitivity ELISA (hsCRP) assay and then compared to nine different anthropometric measurements. Results: BMI was significantly correlated with CRP (p=0.047). Among the eight additional anthropometrics, the suprailiac skinfold (p=0.003) and waist-circumference (p=0.009) were better predictors of CRP than BMI. Moreover, our stepwise selection model indicated that the suprailiac skinfold explained ~14% of CRP level variance. Conclusions: The BMI-CRP correlation coefficient for Ni-Vanuatu women falls within the range of previously reported values for East Asian populations with whom they share genetic ancestry. However, the best anthropometric predictors of CRP levels were waist circumference and suprailiac skinfold thickness. These measures capture central adiposity and are more closely associated with elevated CRP level and cardiovascular disease risk than fat distributed elsewhere on the body. Ni-Vanuatu in urban settings with high market integration are at greater risk for obesity, which is associated with elevated CRP levels. However, because nearly all Ni-Vanuatu still retain horticultural knowledge and land ownership, consumption of processed, imported foods is largely determined by degree of market integration and personal choice. Therefore, health interventions focusing on sustainable traditional food practices are feasible.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".