Aim
Human hookworm disease caused by Ancylostoma duodenale and Necator americanus is a serious public health problem. Hookworm infection activates eosinophil-mediated tissue inflammatory responses, involving the production of the eosinophil-specific chemokine (eotaxin), recruitment of eosinophils, secretion of the cationic protein, and production of antiparasite immunoglobulin E (IgE). We investigated eosinophil-mediated immune response as markers (CCL11, eosinophil cationic protein [ECP], and IgE) for detecting hookworm infection.
Conclusion
ECP best predicts eosinophil-mediated immune response for detecting hookworm infection, while CCL11 and eosinophil count better predict the intensity of hookworm. Moreover, the ECP level is a good indicator of hookworm infection and intensity and may require additional investigations to augment current hookworm diagnostic techniques.
Methods
This case-control study was carried out in hookworm endemic areas within the Kintampo North Municipality.Forty hookworm-positive subjects and 36 apparently healthy individuals were recruited as cases and controls, respectively. Stool samples were collected for hookworm detection by the Kato-Katz technique and speciation by polymerase chain reaction. Approximately, 5 ml of intravenous blood was used to obtain plasma for the immunological assays.
Results
Of eosinophil-mediated immune response markers studied, ECP and CCL11 were significantly higher among hookworm patients compared to controls. Increasing CCL11 (β = -0.81, p = 0.015) was associated with a significant decrease hookworm intensity. However, increasing eosinophil count (β = 0.62, p = 0.027) was associated with significant increase in hookworm intensity. In receiver operator characteristics analysis, ECP could significantly detect hookworm infection with a very high area under the curve (AUC) (AUC = 0.97, p < 0.0001). At a cutoff of 39.05, ECP was the best eosinophil-mediated immune response marker for detecting hookworm infection with a sensitivity of 97.2%, specificity of 87.8%, a positive predictive value of 89.7%, and a negative predictive value of 96.6%.