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HIV-1 superinfection is associated with an accelerated viral load increase but has a limited impact on disease progression

Ronen, Kesheta; Richardson, Barbra A.b,c,d,e; Graham, Susan M.d,f,g,h; Jaoko, Walteri; Mandaliya, Kishorj; McClelland, R. Scottd,f,g,h; Overbaugh, Juliea,b

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doi: 10.1097/QAD.0000000000000422


HIV-1 superinfection is defined as reinfection with a different viral variant at a later time. It has been shown to occur at an appreciable rate in several cohorts [1–3], but whether superinfection accelerates disease course remains unclear. Such an effect has been hypothesized on the basis of two main observations. First, the association of early viral diversity with accelerated disease progression [4] suggests that viral diversity resulting from superinfection might have the same effect. Second, initial case reports of superinfection described increases in viral load at the time of superinfection detection [5,6], though whether this effect is generalizable or sustained is unknown.

Early studies investigated clinical progression in dual infection, including both coinfection (simultaneous acquisition of two variants) and superinfection (sequential acquisition). One study reported much faster development of AIDS in five dually infected than 57 singly infected individuals [7], raising concern than superinfection might have similarly severe consequences. Two small cohort studies have investigated the clinical impact of superinfection alone, rather than in aggregate with coinfection. One, analysing six superinfection cases and 27 single infections, reported faster CD4+ T-cell decline in superinfected individuals [8]; the other, analysing seven cases and 18 single infections, reported faster viral load increase in superinfected individuals [9]. These findings are consistent with the idea that superinfection accelerates progression, but are limited by small sample sizes. Both studies focused on MSM infected with subtype B virus and superinfected early in infection, so their relevance to other populations, viral subtypes and superinfection timing is unknown. Moreover, it remains unclear whether the reported differences between superinfected and singly infected individuals are a consequence of superinfection acquisition, or established prior to superinfection.

In a recent screen of 146 women from a well characterized prospective cohort in Mombasa, Kenya, we identified and specified the timing of 21 cases of superinfection, the largest cohort of superinfected individuals published to date [1,10–12]. The incidence of superinfection in this cohort was approximately half that of initial infection. Women were infected with viruses of subtypes A, C and D. Both intrasubtype and intersubtype superinfections were detected, with timing ranging from 63 to 1895 days postinfection. In the present study, we took advantage of frequent monitoring of this large cohort to investigate the impact of superinfection on measures of clinical progression in women infected with diverse viral subtypes. We present analyses of longitudinal change in viral load and CD4+ cell count, and time to clinical events during single infection and superinfection.

Materials and methods

Study participants and clinical data

This study received ethical approval from the University of Nairobi, the University of Washington and Fred Hutchinson Cancer Research Center. All participants provided written informed consent. HIV-negative female sex workers in Mombasa, Kenya, enrolled in a prospective cohort and attended monthly visits, at which interviews and clinical examinations were conducted. Plasma viral load and CD4+ cell counts were quantified quarterly. HIV-1 infection timing was estimated on the basis of a combination of serology and viral load testing [13]. Superinfection timing was defined as the midpoint of the interval between the last singly infected and first superinfected timepoints [1,10–12]. HLA-B typing was performed using the LABType SSO medium resolution typing test (One Lambda); cases of HLA-B*35 were further classified by sequencing of exons 2–3 of HLA-B.

Statistical analysis

Statistical analysis was performed using R. Linear mixed effect (LME) models were run using the package nlme. We included all viral load and CD4+ cell count data from women with at least one measurement at least 6 months postinfection and within the period in which they had been screened for superinfection. To exclude nonlinear changes in viral load and CD4+ cell count during acute infection, data from the first 6 months after initial infection were censored; this cutoff was determined following visual inspection of longitudinal viral load data from 50 representative women to identify the time at which viral load slopes flattened. Log-transformed viral load and square root transformed CD4+ cell count data were entered into LME models to estimate the log10VL and √CD4+ cell count intercepts and their rates of change over time.

Time to disease progression was evaluated by Cox proportional hazards regression, with superinfection as a time-dependent covariate. Disease progression was defined as the first of CD4+ cell count less than 200 cells/μl, antiretroviral therapy (ART) initiation or death. Follow-up time was censored at the last visit for which superinfection screening was performed.

We included viral subtype based on env sequences [1,10–12], genital ulcer disease at the time of infection [13] and presence of HLA-B alleles that influence HIV-1 disease progression (HLA-B*57, B*27, B*35-Px) [14] as adjustments in the LME and Cox models.


Between 1993 and 2008, 309 women in the Mombasa cohort acquired HIV-1 infection and 146 of them were screened for superinfection [1,10–12]. Of these, 144 women were selected for inclusion in the present analysis, based on having initial infection timing defined to a window of within 1 year. Twenty-one women acquired superinfection. All had one or more viral load measurements after acute infection (≥6 months postinfection) and prior to ART, with a median of 10 measurements each. One-hundred and thirty-three women (18 superinfection cases) had one or more CD4+ cell count measurements at least 6 months postinfection and prior to ART, with a median of 10 measurements each. Baseline characteristics were similar in singly infected and superinfected women (Supplementary Table S1,

The viral load intercept at 6 months postinfection estimated by LME across all women was 4.45 [95% confidence interval (CI) 4.32–4.57] log10 copies/ml, and the rate of viral load change was an increase of 0.008 (95% CI 0.006–0.010) log10 copies/ml/month. The estimated √CD4+ cell count intercept was 23.4 (95% CI 22.4–24.3) √CD4+ cells/μl, and the rate of change was a decrease of 0.085 (95% CI –0.102 to –0.069) √CD4+ cells/μl/month.

Results of analyses comparing viral load and CD4+ cell count in women who remained singly infected throughout follow-up and women who ultimately acquired superinfection are summarized in Table 1 and Fig. 1a,b. Superinfection cases were found to have 0.009 log10 HIV copies/ml/month faster viral load increase (P = 0.0008) and showed a trend for faster CD4+ cell count decline than singly infected women, by 0.047 √CD4+ cells/μl/month (P = 0.06). Inclusion in the model of factors that influence disease progression – presence of genital ulcer disease at HIV-1 acquisition, initial viral subtype and relevant HLA-B alleles – had a negligible effect on model parameters (Table 1).

Table 1
Table 1:
Linear mixed effects model parameters.
Fig. 1
Fig. 1:
Effect of superinfection on viral load, CD4+ cell count and clinical events.(a,b) Longitudinal log-transformed viral load (a) and square root transformed CD4+ cell counts (b) in women who remained singly infected (solid blue) and women who acquired superinfection (dashed red). Raw data are represented by points, linear mixed model fits are represented by lines. (c) Kaplan–Meier curve showing clinical progression events over time. Event-free survival is plotted against months of infection for women during single infection (solid blue) or after superinfection (dashed red).

In order to determine whether superinfection cases differed from singly infected women prior to superinfection acquisition, we fit LME models to viral load and CD4+ cell count, including initial infection data only (Table 1). Data from all 123 singly infected women and 12 superinfection cases with one or more presuperinfection data points were included in the viral load analysis. The model showed a borderline association between ultimate superinfection acquisition and lower presuperinfection viral load intercept (–0.44 log10 copies/ml, P = 0.06); this association reached significance in the adjusted analysis (–0.45 log10 copies/ml, P = 0.05). No significant differences in presuperinfection CD4+ cell count intercept or rate of change were found between noncases (n = 115) and ultimate cases (n = 5), although data were limited for this analysis (Supplementary Figure S1,

In order to investigate whether viral load and CD4+ cell count trajectories changed upon acquisition of superinfection, data from superinfection cases only were analysed, comparing data pre versus postsuperinfection (Table 1). This analysis included 12 superinfection cases with viral load data both before and after superinfection. A borderline association was detected for higher viral load intercept following superinfection (+0.21 log10 HIV copies/ml, P = 0.09). No significant differences were observed in presuperinfection (n = 5) and postsuperinfection (n = 18) CD4+ cell count, although data were limited for this analysis (Supplementary Figure S1,

We next examined the impact of superinfection on time to clinical progression events. During the course of follow-up, 91 of 144 women experienced a clinical event (summarized in Fig. 1c). No statistically significant effect of superinfection on time to clinical events was detected by Cox regression (hazard ratio 1.07, 95% CI 0.60–1.89, P = 0.76). Adjustment for setpoint viral load, initial virus subtype and relevant HLA alleles had a negligible effect on model parameters (data not shown).


Here, we present the largest study to date of the effect of HIV-1 superinfection on disease progression, comparing 123 singly infected and 21 superinfected women. We found that superinfected individuals showed significantly accelerated viral load increase over time and a trend for accelerated CD4+ cell count decline. Our findings suggest that superinfection, similar to dual infection, has some detrimental effects on laboratory markers of disease progression. However, in contrast to results reported in an early study of dual infection [7], we did not detect a significant effect of superinfection on time to clinical events. This difference may indicate that the consequences of superinfection are modest compared with those of coinfection, or that they differ between men (who made up four of the five dually infected individuals described in [7]) and women.

Our findings of accelerated viral load increase and CD4+ cell count decline in women with intersubtype and intrasubtype superinfections occurring up to 5 years postinfection are supported by previous smaller studies of men infected with subtype B virus and superinfected within the first year of infection [8,9]. Moreover, the larger sample size, detailed covariate data and varied superinfection timing in this cohort enabled us to perform analyses of different measures of disease progression, including analyses restricted to before or after superinfection. Interestingly, these analyses showed effects of superinfection on viral load both before and after superinfection acquisition. Prior to superinfection, we observed lower viral load among individuals who ultimately acquired superinfection than in those who did not. One interpretation of this finding is that lower replication by the initial virus may predispose to superinfection, possibly due to limited immune stimulation, or because a less fit virus can be more readily out-competed by a superinfecting variant. After superinfection, we observed a borderline association with increased viral load intercept, suggesting that acquisition of superinfection may raise viral load. Although our power to detect significant differences may have been limited by sample size in some analyses, in aggregate, these findings suggest that superinfection is associated with increased viral replication. Furthermore, they raise the possibility that this may be mediated by the combination of lower starting viral load in individuals susceptible to superinfection, and viral load increase at superinfection acquisition.

Despite observing accelerated viral load increase and a borderline association with accelerated CD4+ cell count decline in superinfected women, we found no significant effect of superinfection on time to clinical events (CD4+ cell count <200 cells/μl, ART initiation or death). This may indicate that the magnitudes of the differences in viral load and CD4+ cell count – our LME models predict that a superinfected individual would have viral load 0.23 log10 copies/ml higher and CD4+ cell count 27 cells/μl lower after 5 years of infection – are insufficient to cause detectable clinical changes. Our study of 144 women with 91 clinical events had 80% power to detect a hazard ratio of 1.82 by Cox regression; it remains possible that superinfection has a subtle effect on disease progression that we were underpowered to detect.

Overall, our findings suggest that superinfection is associated with a modest increase in viral load, but no large difference in clinical outcome. Furthermore, our finding that individuals who ultimately acquire superinfection show lower baseline viral load suggests that there may be host or viral determinants of susceptibility to superinfection. Elucidation of these factors may shed light on early events in HIV-1 acquisition and potential avenues to its prevention.


We gratefully acknowledge the participants of the Mombasa cohort and staff at the study clinic. We thank Connor McCoy, Julie Weis and Jim Hughes for helpful discussions, and Christopher Cottrell for assistance with HLA typing.

K.R. participated in study design, data collection, analysis and interpretation, and manuscript preparation. B.A.R. participated in study design, data analysis and interpretation, and manuscript preparation. S.M.G. participated in study design, data interpretation and manuscript preparation. W.J. participated in data collection and manuscript preparation. K.M. participated in data collection and manuscript preparation. R.S.M. participated in data collection, study design, data interpretation and manuscript preparation. J.O. participated in study design, data interpretation and manuscript preparation.

This work has been presented in part at the Conference on Retroviruses and Opportunistic Infections in March 2014 in Boston, Massachusetts, USA.

Conflicts of interest

The authors have no conflicts of interest to declare.


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HIV-1; pathogenesis; progression; superinfection; women

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