High Seroprevalence of <i>Helicobacter pylori</i> and CagA/VacA Virulence Factors in Northern Central America
Bibliographic record
Abstract
Background: Northern Central America is unique in the Western Hemisphere, with a high incidence of gastric cancer, low/middle-income country (LMIC) status, and a substantial emigration to the United States. The two primary Helicobacter pylori (H. pylori) virulence factors related to carcinogenesis are cytotoxin-associated gene A (CagA) and vacuolating cytotoxin A (VacA). The prevalence of these factors may help delineate gastric cancer risk in the region. We aimed to characterize the H. pylori seroprevalence and virulence factors in two Central American Countries (Honduras and Guatemala). Methods: Healthy volunteers from Western Honduras and Central-Western Guatemala were recruited and tested for antibodies against 13 H. pylori antigens using a novel multiplex serology assay. H. pylori seropositivity was defined as positivity for ≥ 4 antigens, and active infection was defined as positivity for a combination of 2/4 antigens: VacA, GroEl, HcpC, and HP1564, based upon the literature. Multivariate logistic regression models were used to estimate the odds ratios for the association between H. pylori and CagA positivity. Results: A total of 1,143 healthy adults were tested using the H. pylori multiplex serology assay (444 in Guatemala and 699 in Honduras). Mean age was 54.2 ± 14.5 years, 46.2% were male, 60% were from rural settings, and 56% lived > 1,000 meters above sea level. H. pylori prevalence was 87%, and 83% with active infection. The CagA and VacA seropositivity rates were 82% and 75%, respectively. No significant differences were noted according to country, age group, sex, or rural/urban location. None of the socioeconomic variables were significantly associated with the presence of H. pylori or CagA. Conclusions: A high prevalence of H. pylori, CagA, and VacA is observed in Honduras and Guatemala, with implications for Northern Central America and immigrants from the region. Innovative and resource-appropriate primary and secondary prevention programs are needed.
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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 | 0.001 |
| 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".