The HIV RNA setpoint theory revisited

Abstract

Background: The evolution of plasma viral load after HIV infection has been described as reaching a setpoint, only to start rising again shortly before AIDS diagnosis. In contrast, CD4 T-cell count is considered to show a stable decrease. However, characteristics of marker evolution over time depend on the scale that is used to visualize trends. In reconsidering the setpoint theory for HIV RNA, we analyzed the evolution of CD4 T-cell count and HIV-1 RNA level from HIV seroconversion to AIDS diagnosis. Follow-up data were used from two cohort studies among homosexual men (N = 400), restricting to the period before highly active antiretroviral therapy became widely available (1984 until 1996). Individual trajectories of both markers were fitted and averaged, both from seroconversion onwards and in the four years preceding AIDS diagnosis, using a bivariate random effects model. Both markers were evaluated on a scale that is directly related to AIDS risk.

Results: Individuals with faster AIDS progression had higher HIV RNA level six months after seroconversion. For CD4 T-cell count, this ordering was less clearly present. However, HIV RNA level and CD4 T-cell count showed qualitatively similar evolution over time after seroconversion, also when stratified by rate of progression to AIDS. In the four years preceding AIDS diagnosis, a non-significant change in HIV RNA increase was seen, whereas a significant biphasic pattern was present for CD4 T-cell decline.

Conclusion: HIV RNA level has more setpoint behaviour than CD4 T-cell count as far as the level shortly after seroconversion is concerned. However, with respect to the, clinically more relevant, marker evolution over time after seroconversion, a setpoint theory holds as much for CD4 T-cell count as for HIV RNA level.

Figure 1

CD4 count patterns over time after seroconversion. Scatterplot of CD4 T-cell count values after seroconversion (grey points), together with the fitted least squares curve (i.e. average CD4 T-cell count values; thin grey line) and the fitted curve from the random effects model (i.e. average CD4 T-cell count patterns; thick black line, with 95% confidence intervals). Average patterns for the groups defined by progression times (in years) are shown as well (dashed grey lines).

Figure 2

HIV RNA patterns over time after seroconversion. Scatterplot of HIV RNA values after seroconversion (grey points), together with the fitted least squares curve (i.e. average HIV RNA values; thin grey line) and the fitted curve from the random effects model (i.e. average HIV RNA patterns; thick black line, with 95% confidence intervals). Average patterns for the groups defined by progression times (in years) are shown as well (dashed grey lines).

Figure 3

Marker evolution on the AIDS hazard scale. Average marker evolution after seroconversion, represented on AIDS risk scale (grey area: 95% CI). Left y-axis shows effects of fitted average marker patterns on AIDS hazard, relative to hazard at average values at seroconversion (656 cells/μL for CD4 T-cell count and 10^4.3 = 19952 copies/mL for HIV RNA level). Right y-axis shows corresponding fitted marker values over time after seroconversion. Dashed grey line shows CD4 effect standardized to HIV RNA value ten years after seroconversion (i.e. the CD4 curve is moved downward, such that it corresponds with the HIV RNA curve at ten years after seroconversion; the 95% CI is rescaled as well).