Association of high-sensitivity C-reactive protein with cardiometabolic risk factors and micronutrient deficiencies in adults of Ouagadougou, Burkina Faso
Bibliographic record
Abstract
Increasing evidence suggests that high-sensitivity C-reactive protein (hs-CRP) is associated with cardiometabolic risk factors (CMRF) while being also related to micronutrient deficiencies. As part of a project on the double burden of under- and overnutrition in sub-Saharan Africa, we assessed the relationship between hs-CRP and both CMRF and micronutrient deficiencies in a population-based cross-sectional study carried out in the Northern district of Ouagadougou, the capital city of Burkina Faso. We randomly selected 330 households stratified by income tertile. In each income stratum, 110 individuals aged 25-60 years and having lived in Ouagadougou for at least 6 months were randomly selected, and underwent anthropometric measurements and blood sample collection. The prevalence of high hs-CRP was 39.4 %, with no sex difference. Vitamin A-deficient subjects (12.7 %) exhibited significant risk of elevated hs-CRP (OR 2.5; P= 0.015). Serum ferritin was positively correlated with log hs-CRP (r 0.194; P= 0.002). The risk of elevated hs-CRP was significant in subjects with BMI ≥ 25 kg/m² (OR 6.9; 95 % CI 3.6, 13.3), abdominal obesity (OR 4.6; 95 % CI 2.2, 7.3) and high body fat (OR 10.2; 95 % CI 5.1, 20.3) (P< 0.001, respectively). Independent predictors of hs-CRP in linear regression models were waist circumference (β = 0.306; P= 0.018) and serum TAG (β = 0.158; P= 0.027). In this sub-Saharan population, hs-CRP was consistently associated with adiposity. Assuming that plasma hs-CRP reflects future risk of cardiovascular events, intervention which reduces CRP, or chronic and acute nutrition conditions associated with it, could be effective in preventing their occurrence particularly in sub-Saharan Africa.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".