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      Impact of Age and Sex on CD4+ Cell Count Trajectories following Treatment Initiation: An Analysis of the Tanzanian HIV Treatment Database

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          Abstract

          Objective

          New guidelines recommend that all HIV-infected individuals initiate antiretroviral treatment (ART) immediately following diagnosis. This study describes how immune reconstitution varies by gender and age to help identify poorly reconstituting subgroups and inform targeted testing initiatives.

          Design

          Longitudinal data from the outpatient monitoring system of the National AIDS Control Program in Tanzania.

          Methods

          An asymptotic nonlinear mixed effects model was fit to post-treatment CD4+ cell count trajectories, allowing for fixed effects of age and sex, and an age by sex interaction.

          Results

          Across 220,544 clinic visits from 32,069 HIV-infected patients, age- and sex-specific average CD4+ cell count at ART initiation ranged from 83–136 cells/mm 3, long term asymptotic CD4+ cell count ranged from 301–389 cells/mm 3, and time to half of maximal CD4+ reconstitution ranged from 3.57–5.68 months. CD4+ cell count at ART initiation and asymptotic CD4+ cell count were 1.28 (95% CI: 1.18–1.40) and 1.25 (95% CI: 1.20–1.31) times higher, respectively, for females compared to males in the youngest age group (19–29 years). Older patients started treatment at higher CD4+ counts but experienced slower CD4+ recovery than younger adults. Treatment initiation at greater CD4+ cell counts was correlated with greater asymptotic CD4+ cell counts within all sex and age groups.

          Conclusion

          Older adults should initiate care early in disease progression because total immune reconstitution potential and rate of reconstitution appears to decrease with age. Targeted HIV testing and care linkage remains crucial for patient populations who tend to initiate treatment at lower CD4+ cell counts, including males and younger adults.

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          Most cited references26

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          CD4+ cell count 6 years after commencement of highly active antiretroviral therapy in persons with sustained virologic suppression.

          Sustained suppression of the human immunodeficiency virus (HIV) type 1 RNA load with the use of highly active antiretroviral therapy (HAART) results in immunologic improvement, but it is not clear whether the CD4(+) cell count increases to normal levels or whether it reaches a less-than-normal plateau. We characterized the increase in the CD4(+) cell count in patients in clinical practice who maintained sustained viral suppression for up to 6 years. All patients were from the Johns Hopkins HIV Clinical Cohort, a longitudinal observational study of patients receiving primary HIV care in Baltimore, Maryland, who were observed for >1 year while receiving HAART and who had sustained suppression of the HIV RNA load at 350 cells/microL, and we assessed the development of clinical events (death and new acquired immunodeficiency syndrome-defining illness) by Kaplan-Meier analysis. A total of 655 patients were observed for a median of 46 months (range, 13-72 months). The median change from baseline to most recent CD4(+) cell count was +274 cells/microL, with 92% of patients having an increase in CD4(+) cell count. By 6 years, the median CD4(+) cell count was 493 cells/microL among patients with baseline CD4(+) cell counts 350 cells/microL. In addition to baseline CD4(+) cell count, injection drug use and older age were associated with a lesser CD4(+) cell count response, and duration of therapy was associated with a greater CD4(+) cell count response. Only patients with baseline CD4(+) cell counts >350 cells/microL returned to nearly normal CD4(+) cell counts after 6 years of follow-up. Significant increases were observed in all CD4(+) cell count strata during the first year, but there was a lower plateau CD4(+) cell count at lower baseline CD4(+) cell strata. These data suggest that waiting to start HAART at lower CD4(+) cell counts will result in the CD4(+) cell count not returning to normal levels.
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            When Aging Reaches CD4+ T-Cells: Phenotypic and Functional Changes

            Beyond midlife, the immune system shows aging features and its defensive capability becomes impaired, by a process known as immunosenescence that involves many changes in the innate and adaptive responses. Innate immunity seems to be better preserved globally, while the adaptive immune response exhibits profound age-dependent modifications. Elderly people display a decline in numbers of naïve T-cells in peripheral blood and lymphoid tissues, while, in contrast, their proportion of highly differentiated effector and memory T-cells, such as the CD28null T-cells, increases markedly. Naïve and memory CD4+ T-cells constitute a highly dynamic system with constant homeostatic and antigen-driven proliferation, influx, and loss of T-cells. Thymic activity dwindles with age and essentially ceases in the later decades of life, severely constraining the generation of new T-cells. Homeostatic control mechanisms are very effective at maintaining a large and diverse subset of naïve CD4+ T-cells throughout life, but although later than in CD8 + T-cell compartment, these mechanisms ultimately fail with age.
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              Normalisation of CD4 counts in patients with HIV-1 infection and maximum virological suppression who are taking combination antiretroviral therapy: an observational cohort study.

              Combination antiretroviral therapy (cART) has been shown to reduce mortality and morbidity in patients with HIV. As viral replication falls, the CD4 count increases, but whether the CD4 count returns to the level seen in HIV-negative people is unknown. We aimed to assess whether the CD4 count for patients with maximum virological suppression (viral load <50 copies per mL) continues to increase with long-term cART to reach levels seen in HIV-negative populations. We compared increases in CD4 counts in 1835 antiretroviral-naive patients who started cART from EuroSIDA, a pan-European observational cohort study. Rate of increase in CD4 count (per year) occurring between pairs of consecutive viral loads below 50 copies per mL was estimated using generalised linear models, accounting for multiple measurements for individual patients. The median CD4 count at starting cART was 204 cells per microL (IQR 85-330). The greatest mean yearly increase in CD4 count of 100 cells per microL was seen in the year after starting cART. Significant, but lower, yearly increases in CD4 count, around 50 cells per microL, were seen even at 5 years after starting cART in patients whose current CD4 count was less than 500 cells per microL. The only groups without significant increases in CD4 count were those where cART had been taken for more than 5 years with a current CD4 count of more than 500 cells per microL, (current mean CD4 count 774 cells per microL; 95% CI 764-783). Patients starting cART with low CD4 counts (<200 cells per microL) had significant rises in CD4 counts even after 5 years of cART. Normalisation of CD4 counts in HIV-infected patients for all infected individuals might be achievable if viral suppression with cART can be maintained for a sufficiently long period of time.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                7 October 2016
                2016
                : 11
                : 10
                : e0164148
                Affiliations
                [1 ]Department of Global Health, University of Washington, Seattle, Washington, United States of America
                [2 ]Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, United States of America
                [3 ]The South African Department of Science and Technology, National Research Foundation Centre of Excellence in Epidemiological Modelling and Analysis (SACEMA), University of Stellenbosch, Stellenbosch, South Africa
                [4 ]Department of Mathematics and Statistics, School of Health and Applied Sciences, Polytechnic of Namibia, Windhoek, Namibia
                [5 ]National AIDS Control Program, Ministry of Health and Social Welfare (MOHSW), Dar es Salaam, The United Republic of Tanzania
                [6 ]Department of Epidemiology and Biostatistics, College of Public Health, University of Georgia, Athens, Georgia, United States of America
                University of Medicine and Dentistry of New Jersey - New Jersey Medical School, UNITED STATES
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                • Conceptualization: ARM KAR ELU IM JN SEB.

                • Data curation: JN SEB.

                • Formal analysis: SEB ARM KAR ELU.

                • Funding acquisition: SEB.

                • Methodology: ARM KAR ELU IM JN SEB.

                • Software: SEB.

                • Supervision: SEB.

                • Validation: SEB.

                • Visualization: SEB.

                • Writing – original draft: ARM KAR ELU SEB.

                • Writing – review & editing: ARM KAR ELU IM JN SEB.

                ‡ Indicates alphabetically ordered joint first authorship.

                Article
                PONE-D-16-12588
                10.1371/journal.pone.0164148
                5055355
                27716818
                4c15a21a-0952-4129-af65-b0ad60a269e5
                © 2016 Means et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 14 April 2016
                : 20 September 2016
                Page count
                Figures: 3, Tables: 1, Pages: 13
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/100000057, National Institute of General Medical Sciences;
                Award ID: R25GM102149
                Funded by: funder-id http://dx.doi.org/10.13039/100000002, National Institutes of Health;
                Award ID: T32AI102623
                Funded by: funder-id http://dx.doi.org/10.13039/100002322, Schlumberger Foundation;
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100000057, National Institute of General Medical Sciences;
                Award ID: U01GM087719
                All authors gratefully acknowledge support from the Clinic on the Meaningful Modeling of Epidemiological Data (MMED) where this work was initiated; MMED is an NIH-funded joint initiative under the University of Florida, the South African Centre for Epidemiological Modelling and Analysis (SACEMA), and the African Institute for Mathematical Sciences (AIMS) (NIH NIGMS R25GM102149 to Juliet RC Pulliam and Alex Welte). KR has been supported by the National Institutes of Health (T32AI102623). ELU received funding from the Schlumberger Foundation. SEB was supported by a National Institute of General Medical Sciences MIDAS grant to Lauren Ancel Meyers and Alison P Galvani (U01GM087719).
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                Data are from the National AIDS Control Program of the Tanzanian Ministry of Health an Social Welfare. We recommend that interested readers contact Dr. Gissenge Lija ( j.lija@ 123456hotmail.com ) to establish a data transfer agreement.

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