Background
Early
initiation of anti-retroviral treatment (ART) decreases mortality as compared
to deferred treatment, but whether it preserves immune cells from early loss or
promotes their recovery remains undefined. Determination of complex
immunological endpoints in infants is often marred by missing data due to
missed visits and/or inadequate sampling. Specialized methods are required to
address missingness and facilitate data analysis.
Methods
We
characterized the changes in cellular and humoral immune parameters over the
first year of life in 66 HIV-infected infants (0–1 year of age) enrolled in the
CHER study starting therapy within 12 weeks of birth (n = 42) or upon disease
progression (n = 24). A convenience cohort of 23 uninfected infants aged 0–6
months born to mothers with HIV-1 infection was used as controls. Flow
cytometry and ELISA were used to evaluate changes in natural killer (NK) cells,
plasmacytoid dendritic cells (pDC), and CD4+ or CD8+ T-cell frequencies. Data missingness was
assessed using Little's test. Complete datasets for analysis were created using
Multiple Imputation (MI) or Bayesian modeling and multivariate analysis was
conducted on the imputed datasets.
Results
HIV-1-infected
infants had greater frequency of CD4+ T cells with naïve
phenotype, as well as higher serum IL-7 levels than HIV exposed/uninfected
infants. The elevated data missingness was completely at random, allowing the
use of both MI and Bayesian modeling. Both methods indicate that early ART
initiation results in higher CD4+ T cell frequency, lower expression of CD95
in CD8+ T cell, and preservation of naïve T cell
subsets. In contrast, innate immune effectors appeared to be similar
independently of the timing of ART initiation.
Conclusions
Early ART initiation in infants with perinatal HIV
infection reduces immune activation and preserves an early expansion of naïve
T-cells with undiminished innate cell numbers, giving greater immune
reconstitution than achieved with deferred ART. Both statistical approaches
concurred in this finding.
Below: Statistical analysis summary.
The tree represents the statistical analyses applied in the
order in which they were performed. Distribution assumptions are indicated.
Statistical packages are listed; where not specified, tests were conducted using
R.
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By:
Livio Azzoni, Emmanouil Papasavvas, Luis J. Montaner
The Wistar Institute,
Philadelphia, Pennsylvania, United States of America
Russell Barbour
Biostatistics Department, Yale
School of Public Health, New Haven, Connecticut, United States of America
Deborah K. Glencross, Wendy S. Stevens
Department of Molecular Medicine
and Hematology, University of the Witwatersrand and National Health Laboratory
Service, Johannesburg, South Africa
Mark F. Cotton
Children’s Infectious Diseases
Clinical Research Unit, Department of Paediatrics and Child Health,
Stellenbosch University, Cape Town, South Africa
Avy Violari
Perinatal HIV Research Unit,
University of the Witwatersrand, Johannesburg, South Africa
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