Markers of T-cell dysfunction and not inflammaging predict the waning of humoral responses to SARS-CoV-2 mRNA booster vaccination in people with HIV

Abstract

In this prospective longitudinal study, we evaluated the durability of humoral responses to SARS-CoV-2 mRNA booster vaccination in 93 people with HIV, exploring the possible role of T-cell dysfunction and inflammaging biomarkers in predicting antibody waning. We found that, despite a negligible influence of the inflammaging milieu, low CD4/CD8 ratio and CD4+CD127+ percentage as well as high CD8+CD38+CD45RO+ percentage are associated with faster antibody waning, in turn contributing to our understanding of the determinants of COVID-19 vaccine-elicited immune response in this population.

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DOI:10.1097/QAD.0000000000004008
Research Letters 1987
Research Letter
AIDS 2024, 38:1982–1990
Markers of T-cell dysfunction and not
inflammaging predict the waning of humoral
responses to SARS-CoV-2 mRNA booster
vaccination in people with HIV
Matteo Augello, Valeria Bono, Roberta Rovito,
Andrea Santoro, Camilla Tincati and Giulia Marchetti
In this prospective longitudinal study, we evaluated
the durability of humoral responses to SARS-CoV-2
mRNA booster vaccination in 93 people with HIV,
exploring the possible role of T-cell dysfunction and
inflammaging biomarkers in predicting antibody
waning. We found that, despite a negligible influ-
ence of the inflammaging milieu, low CD4/CD8
ratio and CD4
R
CD127
R
percentage as well as high
CD8
R
CD38
R
CD45RO
R
percentage are associated
with faster antibody waning, in turn contributing
to our understanding of the determinants of
COVID-19 vaccine-elicited immune response in
this population.
People with HIV (PWH) may suffer worse COVID-19
outcomes compared to the general population [1,2].
Preventing SARS-CoV-2 infection remains a relevant
goal in PWH, also in the endemic phase of COVID-19,
since it has been associated with a higher incidence of
long COVID-19 and subsequent major cardiovascular
events in this population independently of its severity
[3,4]. Indeed, the widespread use of SARS-CoV-2
vaccination considerably decreased the risk of infection
and severe disease by eliciting strong humoral and T-cell
immunity [1,5]. However, while vaccine-induced T-cell
responses are durable, thus ensuring long-lasting protec-
tion from severe disease, humoral immunity wanes
over time, in turn affecting the protection against SARS-
CoV-2 infection [6,7]. Furthermore, while factors
influencing SARS-CoV-2 vaccines immunogenicity
have been extensively characterized in PWH [1,5,8–
10], determinants of antibody waning are largely
unknown.
Persistent T-cell dysregulation and inflammaging des-
pite virologically-effective antiretroviral therapy (ART)
are hallmarks of HIV infection, and have been
associated with adverse immunological and clinical
outcomes [11], as well as poor vaccine immunogenicity
[12–15].
We therefore aimed to assess the durability of humoral
responses to a SARS-CoV-2 mRNA booster in PWH
and to explore the possible role of baseline HIV-related
T-cell dysfunction and inflammaging milieu in predicting
antibody waning.
In this prospective longitudinal study conducted at the
Clinic of Infectious Diseases and Tropical Medicine at San
Paolo Hospital in Milan, Italy, PWH on virologically-
effective ART who received a monovalent mRNA
booster (Moderna Spikevax) 6 months after the primary
cycle were consecutively enrolled, and followed-up from
baseline (T0) to 1 month (T1) and 6 months (T2) after the
booster administration.
Anti-spike (S) immunoglobulin G (IgG) antibodies
were quantitatively measured on serum samples at each
time point by the DiaSorin LIAISON SARS-CoV-2
TrimericS IgG assay. Serum anti-nucleocapsid (N) IgG
were semi-quantitatively determined by the EURO-
IMMUN Anti-SARS-CoV-2 NCP ELISA IgG to track
SARS-CoV-2 infection at baseline and throughout the
follow-up period.
To assess T-cell dysfunction, CD4/CD8 ratio, as well as
percentages of CD4
þ
CD127
þ
T-cells (functionally-
competent CD4
þ
T-cells) and CD8
þ
CD38
þ
CD45RO
þ
T-cells (primed activated CD8
þ
T-cells) were measured
on blood samples at baseline by flow cytometry (Figure
S1, Supplemental Digital Content, http://links.lww.
com/QAD/D313). Markers of inflammaging were
quantified in plasma at T0 by Luminex assay [tumor
necrosis factor alpha (TNF-a), IP-10, interleukin 2 (IL-2),
IL-4, IL-17A, IL-6, IL-8, sCD14, sCD163, GDF-15,
MMP-9, TIMP-1] or ELISA (thymosin-a1, Elabscience
Human Thymosin-a1 ELISA kit). A composite
‘‘inflammaging score’’ranging from 0 to 13 was calculated
as the number of biomarkers with an abnormal level (at or
above the 75th percentile for TNF-a, IP-10, IL-17A, IL-6,
IL-8, sCD14, sCD163, GDF-15, MMP-9, and TIMP-1;
at or below the 25th percentile for IL-2, IL-4, and
thymosin-a1), similar as previously described [16].
This is an open access article distributed under the Creative
Commons Attribution License 4.0 (CCBY), which permits
unrestricted use, distribution, and reproduction in any
medium, provided the original work is properly cited.
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1988 AIDS 2024, Vol 38 No 14
Demographic and clinical characteristics of the study
population were also recorded at baseline.
Repeated measures ANOVA, Pearson correlation test,
and multivariable linear regression were performed using
GraphPad Prism v10.
Ninety-three PWH were recruited and followed-up
between September 2021 and July 2022. Median age was
53 [interquartile range (IQR): 46–59] years, and 76
(81.7%) were males. Median CD4
þ
T-cell nadir was 226
(IQR: 53–353) cells/ml; current CD4
þ
T-cell count was
728 (IQR: 511–920) cells/ml, with a median CD4/CD8
ratio of 0.76 (IQR: 0.60–1.06). All participants have
been on ART for a median of 114 (IQR: 67 – 186)
months, and were virologically-suppressed (HIV viremia
<20 copies/ml). Study participants characteristics are
detailed in Table S1, Supplemental Digital Content,
http://links.lww.com/QAD/D313.
Four PWH had positive anti-N IgG at T0, four developed
anti-N IgG positivity between T0 and T1, and four more
between T1 and T2.
Anti-S IgG antibodies were significantly increased
1 month after the booster [median: 3.99 (IQR: 3.71 –
4.27) vs. 2.94 (2.62–3.17) log
10
(BAU/ml), P<0.0001],
with a subsequent 6-month decay at levels that were
still above baseline [3.36 (3.15–3.78) log
10
(BAU/ml),
P<0.0001] (Fig. 1a).
Anti-S IgG waning, measured as the difference between
T1 and T2 levels (D
T1– T2
), was negatively correlated with
CD4/CD8 ratio (r¼–0.2535, P¼0.0178) and
CD4
þ
CD127
þ
percentage (r¼–0.2421, P¼0.0239),
while positively with CD8
þ
CD38
þ
CD45RO
þ
percent-
age (r¼0.2244, P¼0.0367) (Fig. 1b–d). Noteworthy,
when controlling for potential confounders/predictors
(i.e., age, sex, Charlson Comorbidity Index, obesity,
smoke, anti-N IgG positivity at baseline and/or during
follow-up), anti-S IgG waning was confirmed negatively
associated with CD4/CD8 ratio [b¼–0.3336 (95% CI:
–0.5594, –0.1079), P¼0.0043] and CD4
þ
CD127
þ
percentage [b¼–0.0175 (95% CI: –0.0297, – 0.0054),
P¼0.0052], while positively with CD8
þ
CD38
þ
CD45RO
þ
percentage [b¼0.1262 (95% CI: 0.0165,
0.2360), P¼0.0247] (Fig. 1f). Similar results were
obtained when performing a sensitivity analysis after
excluding PWH with positive anti-N IgG antibodies at
any time point (Table S2, Supplemental Digital Content,
http://links.lww.com/QAD/D313).
Low CD4/CD8 ratio and CD4
þ
CD127
þ
T-cell per-
centage as well as high CD8
þ
CD38
þ
CD45RO
þ
percentage have been associated with unfavorable
immunological and clinical outcomes in HIV infection
0.5 1.0 1.5 2.0 2.5
2
3
4
5
CD4/CD8 ratio
anti-S IgG
[log10(BAU/mL)]
P = 0.0178
20 30 40 50
2
3
4
5
CD4+127+ %
anti-S IgG
[log10(BAU/mL)]
P = 0.0239
0246
2
3
4
5
CD8+CD38+CD45RO+ %
anti-S IgG
[log10(BAU/mL)]
r = 0.2244
P = 0.0367
012345678910
2
3
4
5
Inflammaging score
anti-S IgG
[log10(BAU/mL)]
r = 0.0423
P = 0.6872
-0.6 -0.4 -0.2 0.0 0.2 0.4 0.6
Inflammaging score
CD8+CD38+CD45RO+ %
CD4+CD127+ %
CD4/CD8 ratio P = 0.0043
P = 0.0052
= 0.1262
P = 0.0247
P = 0.9916
coefficient (95% CI)
for anti-S IgG [log10(BAU/mL)]
1
2
3
4
5
6
7
Anti-S IgG [log10(BAU/mL)]
T1 T2T0
P < 0.0001
P < 0.0001 P < 0.0001
(a
)
(
b
)
(
c
)
(
d
)
(
e
)
(
f
)
Fig. 1. Anti-S IgG antibodies concentrations and associations with markers of T-cell dysfunction and inflammaging. (a)
Trajectory of anti-S IgG antibodies concentration following the booster. T0: baseline (day of booster administration); T1: 1 month
after the booster; T2: 6 months after the booster; circles: individual values; black bars: median values; boxes: interquartile ranges;
statistical analysis: repeated measures ANOVA. (b–e) Correlations between waning of anti-S IgG antibodies, measured as the
difference between T1 and T2 levels (D
T1– T2
), and CD4/CD8 ratio, CD4
þ
CD127
þ
and CD8
þ
CD38
þ
CD45RO
þ
percentages, or
inflammaging score. Circles: individual values; solid black line: simple linear regression line; shadowed error bands within dashed
lines: interquartile ranges; statistical analysis: Pearson correlation test. (f) Forest plot showing associations between waning of anti-S
IgG antibodies (D
T1– T2
) and CD4/CD8 ratio, CD4
þ
CD127
þ
percentage, CD8
þ
CD38
þ
CD45RO
þ
percentage, or inflammaging
score. Circles:bcoefficients; error bars: 95% confidence intervals (95% CI); statistical analysis: multivariable linear regression
(adjusted for age, sex, Charlson Comorbidity Index, obesity, smoke, anti-N IgG status at baseline and during follow-up).
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Research Letters 1989
[17–19]. Additionally, CD4/CD8 ratio has been reported
to negatively correlate with SARS-CoV-2 vaccines
immunogenicity in PWH [5,8,20]. Our findings argue
that such peculiar HIV-related anomalies are predictive of
faster antibody waning following SARS-CoV-2 booster
vaccination, thus potentially expanding the utility of these
markers in this setting.
By contrast, when assessing inflammaging score, we
found that it was not associated with anti-S IgG waning,
either at univariable (r¼0.0423, P¼0.6872) (Fig. 1e) or
multivariable analysis [b¼–0.0002 (95% CI: – 0.0492,
0.0487), P¼0.9916] (Fig. 1f). A lack of association
between anti-S IgG waning and inflammaging was also
found when analyzing inflammaging markers individually
(Figure S2, Supplemental Digital Content. http://links.
lww.com/QAD/D313).
This observation was somehow unexpected, given
previous reports showing an association of reduced
vaccine-elicited serological memory with markers of
inflammation [21,22] and thymic dysfunction [23].
However, these studies assessed immune responses to
different vaccines or in different populations/models, and
thus cannot be directly translated to our setting.
Some limitations need to be acknowledged in this study.
Firstly, the sample size was relatively small, thus
potentially hindering the generalizability of such findings.
Furthermore, the follow-up was limited to 6 months,
hence longer periods should be included in future studies
to ascertain the actual long-term durability of vaccine-
induced humoral immunity and thus the optimal timing
for boosters administration. Besides, while the present
study was specifically designed to explore whether
markers of T-cell dysfunction and inflammaging mea-
sured at baseline are able to predict waning of vaccine-
elicited humoral responses later on, it would be
interesting for future studies to evaluate whether
administration of vaccine boosters may have an impact
on T-cell phenotypes and cytokine milieu over time.
In summary, our study points to low CD4/CD8 ratio
and CD4
þ
CD127
þ
T-cells as well as high
CD8
þ
CD38
þ
CD45RO
þ
T-cells as factors negatively
affecting the durability of humoral responses to SARS-
CoV-2 mRNA booster in PWH, in all suggesting the
potential for T-cell dysfunction biomarkers in the
assessment of individuals to be prioritized for future
SARS-CoV-2 boosters.
Acknowledgements
We are grateful to all the individuals enrolled in this study
who agreed to participate to this research. Our special
thanks also go to all the physicians and nurses at the Clinic
of Infectious Diseases and Tropical Medicine at San Paolo
Hospital in Milan who helped in patients’ care and
enrollment. We are also thankful to Alessandro Cozzi-Lepri
for his precious suggestions for statistical analyses design.
Authors contributions: M.A. conceived and designed the
study, collected clinical and laboratory data, analyzed and
interpreted the data, designed the figures, and wrote the
manuscript. V.B., R.R., and A.S. contributed to data
collection. C.T. contributed to the critical revision of the
manuscript. G.M. conceived and supervised the study,
interpreted the data, and wrote the manuscript.
Funding: This study was supported by funding from the
European Union‘s Horizon 2020 Research and Innova-
tion Program under grant agreement no. 101016167
within the ORCHESTRA project (Connecting Euro-
pean Cohorts to Increase Common and Effective
Response to SARS-CoV-2 Pandemic), and grant
agreement no. 101046016 within the EuCARE project
(European Cohorts of Patients and Schools to Advance
Response to Epidemics).
Conflicts of interest
Authors have no conflicts of interest related to this work
to disclose.
Clinic of Infectious Diseases and Tropical Medicine,
San Paolo Hospital, ASST Santi Paolo e Carlo,
Department of Health Sciences, University of Milan,
Milan, Italy.
Correspondence to Giulia Marchetti, MD, PhD, Clinic
of Infectious Diseases and Tropical Medicine, San
Paolo Hospital, ASST Santi Paolo e Carlo, Department
of Health Sciences, University of Milan, via A. di
Rudinı`8, 20142 Milan, Italy. Tel: +39 0281843064;
fax: +39 0281843054;
e-mail: giulia.marchetti@unimi.it
Received: 10 July 2024; revised: 22 August 2024;
accepted: 3 September 2024.
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