Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2021 Jan 14:10:e42390.
doi: 10.7554/eLife.42390.

A mechanistic model for long-term immunological outcomes in South African HIV-infected children and adults receiving ART

Eva Liliane Ujeneza  1   2 , Wilfred Ndifon  2 , Shobna Sawry  3 , Geoffrey Fatti  4   5 , Julien Riou  6 , Mary-Ann Davies  7 , Martin Nieuwoudt  1   8 ; IeDEA-Southern Africa collaboration
Affiliations

A mechanistic model for long-term immunological outcomes in South African HIV-infected children and adults receiving ART

Eva Liliane Ujeneza et al. Elife. .

Abstract

Long-term effects of the growing population of HIV-treated people in Southern Africa on individuals and the public health sector at large are not yet understood. This study proposes a novel 'ratio' model that relates CD4+ T-cell counts of HIV-infected individuals to the CD4+ count reference values from healthy populations. We use mixed-effects regression to fit the model to data from 1616 children (median age 4.3 years at ART initiation) and 14,542 adults (median age 36 years at ART initiation). We found that the scaled carrying capacity, maximum CD4+ count relative to an HIV-negative individual of similar age, and baseline scaled CD4+ counts were closer to healthy values in children than in adults. Post-ART initiation, CD4+ growth rate was inversely correlated with baseline CD4+ T-cell counts, and consequently higher in adults than children. Our results highlight the impacts of age on dynamics of the immune system of healthy and HIV-infected individuals.

Keywords: CD4+ T-cells; hiv-1; human; immune system; infectious disease; microbiology; mixed model; modeling.

Plain language summary

The human immunodeficiency virus (HIV) remains an ongoing global pandemic. There is currently no cure for HIV, but antiretroviral therapies can keep the virus in check and allow individuals with HIV to live longer, healthier lives. These drugs work in two ways. They block the ability of the virus to multiply and they allow numbers of an important type of infection-fighting cell called CD4+ T cells to rebound. As more patients with HIV survive and transition from one life stage to the next, it is critical to understand how long-term antiretroviral therapies will affect normal age-related changes in their immune systems. The health of an immune system can be evaluated by looking at the number of CD4+ T cells an individual has, though this will vary by age and location. Clinicians use the same metrics to assess the immune health of individuals with HIV, however, as they age, it becomes a challenge to identify if a patient’s immune system recovers normally or insufficiently. Thus, learning more about age-related differences in CD4+ T cells in people living with HIV may help improve their care. Using data from 1,616 children and 14,542 adults from South Africa, Ujeneza et al. created a simple mathematical model that can compare the immune system of person with HIV with the immune system of a similarly aged healthy individual. The model shows that among individuals with HIV receiving antiretroviral therapies, children have CD4+ T-cell numbers that are closest to the numbers seen in healthy individuals of the same age. This suggests that children may be more able to recover immune system function than adults after beginning treatment. Children also start antiretroviral therapies before their immune system has been severely damaged, while adults tend to start treatment much later when they have fewer CD4+ T cells left. Ujeneza et al. show that the fewer CD4+ T cells a person has when they start treatment, the faster the number of these cells grows after starting treatment. This suggests that the more damaged the immune system is, the harder it works to recover. This reinforces the need to identify people infected with HIV as soon as possible through testing and to begin treatment promptly. The new model may help clinicians and policy makers develop screening and treatment protocols tailored to the specific needs of children and adults living with HIV.

PubMed Disclaimer

Conflict of interest statement

EU, WN, SS, GF, JR, MD, MN No competing interests declared

Figures

Figure 1.
Figure 1.. Data chart explaining the exclusion and inclusion criteria.
Figure 2.
Figure 2.. Plot of the logistic growth model.
The dotted red line represents that carrying capacity k, while the dotted blue line is at the inflexion point k/2.
Figure 3.
Figure 3.. Children population-level CD4 trajectory, as estimated by the unadjusted ratio and asymptotic models, and the adjusted ratio model.
Simulation of population-level CD4+ count trajectory for children, from unadjusted fixed estimates of the asymptotic model (AM) in blue and the ratio model (RM) in green. The red line represents simulation from the adjusted population-level RM estimates. Parameters used for the AM are presented in Supplementary file 3 – Table 1, scenario 1. Those used for the RM are estimated fixed effect for the null model (not shown in the paper): K = 3.4, Q = 0.9, r = 0.35, s = 0.017, z0 = 0.18. Fixed effect presented in Table 3 (scenario 1) are used for the adjusted ratio model (Adj RM), for baseline covariates z-score BMI, age, log viral load; and sex and suppression of viral load within 12 months of starting therapy. Convergence plots for the Adj RM are given in Figure 3—figure supplement 1, and simulation of individual fits in Figure 3—figure supplement 2.
Figure 3—figure supplement 1.
Figure 3—figure supplement 1.. Convergence plots for children adjusted ratio model.
The x-axis represents the number of iterations, while the y-axis is the parameter value. The red vertical line indicates the end of the 300 iterations, where the algorithm explores freely the parameter space. The second phase has 150 iterations, where the step size is gradually decreased in order to ensure convergence.
Figure 3—figure supplement 2.
Figure 3—figure supplement 2.. Sample of individual children plots for the adjusted ratio model.
TOFU on the x-axis stands for the time of follow-up since ART initiation. The points are individual scaled CD4+ counts. The dashed line and the solid line represent the population-level and individual-level fits respectively.
Figure 4.
Figure 4.. Adults population-level CD4 trajectory, as estimated by the unadjusted ratio and asymptotic models, and the adjusted ratio model.
Simulation of population-level CD4+ count trajectory for adults, from unadjusted fixed estimates of the asymptotic model (AM) in blue, and the ratio model (RM) in green. The red line represents simulation from the adjusted population-level RM estimates. Parameters used for the AM are presented in Supplementary file 3 – Table 1, scenario 1. Those used for the RM are estimated fixed effect for the null model (not shown in the paper): K = 2.54, Q = 0.38, r = 1.23, s = 0.01, z0 = 0.13. Fixed effect presented in Table 3 (scenario 1) are used for the adjusted ratio model (Adj RM), for baseline covariates sex, age, log viral load, and suppression of viral load within 12 months of starting therapy. Convergence plots for the Adj RM are given in Figure 4—figure supplement 1, and simulation of individual fits in Figure 4—figure supplement 2.
Figure 4—figure supplement 1.
Figure 4—figure supplement 1.. Convergence plots for adults adjusted ratio model.
The x-axis represents the number of iterations, while the y-axis is the parameter value. The red vertical line indicates the end of the 300 iterations, where the algorithm explores freely the parameter space. The second phase has 150 iterations, where the step size is gradually decreased in order to ensure convergence.
Figure 4—figure supplement 2.
Figure 4—figure supplement 2.. Sample of individual adult plots for the adjusted ratio model.
TOFU on the x-axis stands for the time of follow-up since ART initiation. The points are an individual scaled CD4+ counts. The dashed line and the solid line represent the population-level and individual-level fits, respectively. The table below indicates the clinical details about the patients represented above, in their respective index location.
Appendix 1—figure 1.
Appendix 1—figure 1.. Plot of the simulated reference values for children.
The dots represent the cross-sectional data for healthy children. The fitted red line shows the age-dependent reference values used in the scaling of CD4+ counts of HIV-infected children.
Appendix 1—figure 2.
Appendix 1—figure 2.. Plot of the simulated reference values for adults.
The points represent the published median values. The red line shows the CD4+ count for women, blue line is for men. CD4+ reference values were simulated yearly, for ages ranging between 17 and 95.
See this image and copyright information in PMC

Similar articles

See all similar articles

Cited by

References

    1. Abuye C, Tsegaye A, West CE, Versloot P, Sanders EJ, Wolday D, Hamann D, De Wit TF, Fontanet AL. Determinants of CD4 counts among HIV-negative ethiopians: role of body mass index, gender, cigarette smoking, khat (Catha edulis) chewing, and possibly altitude? Journal of Clinical Immunology. 2005;25:127–133. doi: 10.1007/s10875-005-2818-y. - DOI - PubMed
    1. ANRS 1215/90 Study Group. De Beaudrap P, Etard JF, Diouf A, Ndiaye I, Guèye NF, Guèye PM, Sow PS, Mboup S, Ndoye I, Ecochard R, Eric D. Modeling CD4+ cell count increase over a six-year period in HIV-1-infected patients on highly active antiretroviral therapy in Senegal. The American Journal of Tropical Medicine and Hygiene. 2009;80:1047–1053. doi: 10.4269/ajtmh.2009.80.1047. - DOI - PubMed
    1. ARROW Trial Team. Picat MQ, Lewis J, Musiime V, Prendergast A, Nathoo K, Kekitiinwa A, Nahirya Ntege P, Gibb DM, Thiebaut R, Walker AS, Klein N, Callard R. Predicting patterns of long-term CD4 reconstitution in HIV-infected children starting antiretroviral therapy in sub-Saharan africa: a cohort-based modelling study. PLOS Medicine. 2013;10:e1001542. doi: 10.1371/journal.pmed.1001542. - DOI - PMC - PubMed
    1. ART-LINC Collaboration of IeDEA. Nash D, Katyal M, Brinkhof MW, Keiser O, May M, Hughes R, Dabis F, Wood R, Sprinz E, Schechter M, Egger M. Long-term immunologic response to antiretroviral therapy in low-income countries: a collaborative analysis of prospective studies. Aids. 2008;22:2291–2302. doi: 10.1097/QAD.0b013e3283121ca9. - DOI - PMC - PubMed
    1. Autran B, Carcelain G, Li TS, Blanc C, Mathez D, Tubiana R, Katlama C, Debré P, Leibowitch J. Positive effects of combined antiretroviral therapy on CD4+ T cell homeostasis and function in advanced HIV disease. Science. 1997;277:112–116. doi: 10.1126/science.277.5322.112. - DOI - PubMed

Publication types

Substances