Zika virus infection enhances future risk of severe dengue disease

Abstract

The Zika pandemic sparked intense interest in whether immune interactions among dengue virus serotypes 1 to 4 (DENV1 to -4) extend to the closely related Zika virus (ZIKV). We investigated prospective pediatric cohorts in Nicaragua that experienced sequential DENV1 to -3 (2004 to 2015), Zika (2016 to 2017), and DENV2 (2018 to 2020) epidemics. Risk of symptomatic DENV2 infection and severe disease was elevated by one prior ZIKV infection, one prior DENV infection, or one prior DENV infection followed by one ZIKV infection, compared with being flavivirus-naïve. By contrast, multiple prior DENV infections reduced dengue risk. Further, although high preexisting anti-DENV antibody titers protected against DENV1, DENV3, and ZIKV disease, intermediate titers induced by previous ZIKV or DENV infection enhanced future risk of DENV2 disease and severity, as well as DENV3 severity. The observation that prior ZIKV infection can modulate dengue disease severity like a DENV serotype poses challenges to development of dengue and Zika vaccines.

Fig. 1.. Dengue and Zika cases, DENV- and ZIKV-Ab titers, and infection histories in the Pediatric Dengue Cohort Study (2004–2020).

(A) Confirmed dengue and Zika cases by epidemic season and infecting virus. DENV iELISA titers (B), ZIKV iELISA titers (C), and DENV and ZIKV infection histories (D) for cohort participants, measured at the beginning of each epidemic season. Infection histories: flavivirus-naïve (Naive), entered cohort DENV-immune without subsequent infections (DENV immune), entered flavivirus-naïve with one DENV (DENV) or ZIKV (ZIKV) infection, one prior DENV infection followed by a DENV (DENV-DENV) or ZIKV (DENV-ZIKV) infection, ≥2 prior DENV infections without (2+DENV) or with (2+DENV-ZIKV) a subsequent ZIKV infection.

Fig. 2.. Probability of symptomatic and severe DENV2 infection by prior DENV and ZIKV infection histories and preexisting antibody titers, 2019–20.

Log-binomial generalized linear models (GLMs) were used to estimate the probability dengue disease in 2019–20 in the cohort study by DENV and ZIKV infection history (A,B,C), preexisting DENV iELISA titers (I,J,K), or preexisting ZIKV iELISA titers (L,M,N), with bootstrap resampling (n=10,000) to construct 95% confidence intervals. Continuous relationships between titers and disease were modeled with log-binomial generalized additive models (GAMs, black lines show probabilities, grey lines show 95% confidence intervals). (D) Infection histories for children in the cohort, cohort dengue cases, and hospital study dengue cases. Differences in the proportion with histories of prior ZIKV+/−DENV or DENV only were tested with a Kruskal-Wallis rank sum test. Probability of severe dengue disease among confirmed dengue cases in cohort (E) or cohort and hospital studies (F) by infection history, estimated using logistic regression. Bootstrap resampling (n=10,000) was used to construct 95% confidence intervals. DENV iELISA (G) and ZIKV iELISA (H) titer distributions for cohort participants in 2019–20 by DENV and ZIKV infection history (triangles show median values, vertical bars show +/−1 standard deviation). All models were adjusted for age and sex, and probabilities are shown for an average study participant (male, age 8). P-values (*, <0.05; **, <0.01; ***, <0.001) indicate significantly different probability estimates from the Naïve group.

Fig. 3.. Probability of disease caused by DENV2, DENV1, DENV3, and ZIKV infection by preexisting DENV and ZIKV iELISA titers, 2004–17.

Each disease outcome was modeled as a function of preexisting antibody titer on both a discrete (colored bars) and continuous (black lines) scale, shown with 95% confidence intervals. Continuous relationships between DENV and ZIKV iELISA titers and each disease outcome were modeled using log-binomial GAMs. Probabilities of Dengue (A,D,G,J), Dengue with Warning Signs/Severe Dengue (B,E,H,K), and Dengue Hemorrhagic Fever/Dengue Shock Syndrome (C,F,I,L) by discrete DENV iELISA titer bins was modeled separately for each infecting serotype (DENV2, A,B,C; DENV1, D,E,F; DENV3, G,H,I) and all serotypes (DENV, J,K,L) in the pre-Zika era (2004–15) using generalized estimating equation (GEE) log-binomial models. Probability of Zika (2016) by pre-infection DENV iELISA (M) and ZIKV iELISA (N) titer bins was modeled using log-binomial GLMs, with 95% confidence intervals constructed using bootstrap resampling (n=10,000). All models were adjusted for age and sex, and model estimates are shown for an average study participant (male, age 8). P-values (*, <0.05, **, <0.01, ***, <0.001) indicate iELISA titer bins significantly different from the Naive group.