Chikungunya outbreak (2015) in the Colombian Caribbean: Latent classes and gender differences in virus infection

Abstract

Chikungunya virus (CHIKV), a mosquito-borne alphavirus of the Togaviridae family, is part of a group of emergent diseases, including arbovirus, constituting an increasing public health problem in tropical areas worldwide. CHIKV causes a severe and debilitating disease with high morbidity. The first Colombian autochthonous case was reported in the Colombian Caribbean region in September 2014. Within the next two to three months, the CHIKV outbreak reached its peak. Although the CHIKV pattern of clinical symptomatology has been documented in different epidemiological studies, understanding of the relationship between clinical symptomatology and variation in phenotypic response to CHIKV infection in humans remains limited. We performed a cross sectional study following 1160 individuals clinically diagnosed with CHIKV at the peak of the Chikungunya outbreak in the Colombian Caribbean region. We examined the relationship between symptomatology and diverse phenotypic responses. Latent Class Cluster Analysis (LCCA) models were used to characterize patients’ symptomatology and further identify subgroups of individuals with differential phenotypic response. We found that most individuals presented fever (94.4%), headache (73.28%) and general discomfort (59.4%), which are distinct clinical symptoms of a viral infection. Furthermore, 11/26 (43.2%) of the categorized symptoms were more frequent in women than in men. LCCA disclosed seven distinctive phenotypic response profiles in this population of CHIKV infected individuals. Interestingly, 282 (24.3%) individuals exhibited a lower symptomatic “extreme” phenotype and 74 (6.4%) patients were within the severe complex “extreme” phenotype. Although clinical symptomatology may be diverse, there are distinct symptoms or group of symptoms that can be correlated with differential phenotypic response and perhaps susceptibility to CHIKV infection, especially in the female population. This suggests that, comparatively to men, women are a CHIKV at-risk population. Further study is needed to validate these results and determine whether the distinct LCCA profiles are a result of the immune response or a mixture of genetic, lifestyle and environmental factors. Our findings could contribute to the development of machine learning and artificial intelligence approaches to characterizing CHIKV infection in other populations. Preliminary results show that the accuracy reached of some approaches reaches up to 92% overall, with substantial sensitivity, specificity and accuracy values per LCCA-derived cluster. The Chikungunya virus (CHIKV) infection is a mosquito-borne virus of the Togaviridae family, part of the arbovirus group of mosquito-transmitted pathogens. CHIKV causes a severe and debilitating disease with high morbidity. In this study, we comprehensively analysed clinical data from 1160 individuals from the Colombian Caribbean, who were diagnosed with CHIKV infection during the 2014 epidemic peak and before the Zika epidemic (registered back in 2015). Further, the presence of latent classes and predictors of CHIKV susceptibility and severity of the CHIKV infection were analysed. Although it is well known that people respond differently to infection, our results showed that these differences are not arbitrary and may come from the specific orchestration of our immune response and specific genetic makeup. For example, we identified that females infected with CHIKV exhibited significant and heterogeneous phenotypic response patterns compared to men. Overall, these results inform about potential predictors and outlining strategies to study the natural history of CHIKV infection. Future studies assessing the contribution of demographic, immunological and genetic factors to symptom co-occurrence could shed some light on the severity of the clinical symptomatology and, ultimately, lead to more accurate, more efficient and differential diagnosis. These results could contribute to the development of machine learning approaches to characterizing CHIKV infection in other populations and provide more accurate and differential diagnosis.