FreshRSS

🔒
❌ Acerca de FreshRSS
Hay nuevos artículos disponibles. Pincha para refrescar la página.
AnteayerTus fuentes RSS

Systemic inflammation and malaria co-infection in irritable bowel syndrome: a cross-sectional study of 142 Yemeni patients

Por: Ali · N. T. · Mehdi · M. A. H. · Abdullah · R. S. · Gubran · A. N. M. · Al-Abd · N. M. · Ali · G. S. · Mohsen Ali · H.
Objective

To determine the prevalence of malaria co-infection among patients with irritable bowel syndrome (IBS) in Yemen and to evaluate the association of systemic inflammatory biomarkers (neutrophil-to-lymphocyte ratio (NLR), mean platelet volume (MPV) and platelet-to-lymphocyte ratio (PLR)) with this co-infection.

Design

Multicentre, cross-sectional observational study conducted between April and December 2024.

Setting

Primary and secondary healthcare facilities across 21 governorates in Yemen.

Participants

142 consecutive adult patients (aged 18–70 years) diagnosed with IBS according to the Rome IV criteria.

Outcome measures

The primary outcome was the prevalence of malaria infection, confirmed by a rapid diagnostic test . Secondary outcomes included differences in NLR, MPV and PLR between groups, assessed using independent t-tests, and the diagnostic performance of these biomarkers evaluated by receiver operating characteristic curve analysis with AUC calculation. Multivariate binary logistic regression was used to identify independent predictors of malaria co-infection, adjusting for potential confounders.

Results

The mean age was 42.3 years (SD 11.7) with an equal gender distribution. The prevalence of malaria co-infection was 45.1% (64/142). Patients with malaria positivity had significantly higher NLR (mean difference 0.56, 95% CI 0.40 to 0.72; p

Conclusions

Nearly half of the Yemeni patients with IBS in this study had malaria co-infection, with the highest burden in diarrhoea-predominant and mixed subtypes. Elevated NLR and PLR were strongly associated with co-infection, suggesting these readily available biomarkers could aid targeted screening in resource-limited, endemic settings.

❌