# Tobacco, alcohol, cannabis, and illicit drug use and their association with CD4/CD8 cell count ratio in people with controlled HIV: a cross-sectional study (ANRS CO3 AQUIVIH-NA-QuAliV)

Sophie Devos, François Bonnet, Mojgan Hessamfar, Didier Neau, Marc-Olivier Vareil, Olivier Leleux, Nicolas Rouanes, Pierre Duffau, François Dabis, Linda Wittkop, et al.

► To cite this version:

Sophie Devos, François Bonnet, Mojgan Hessamfar, Didier Neau, Marc-Olivier Vareil, et al. Tobacco, alcohol, cannabis, and illicit drug use and their association with CD4/CD8 cell count ratio in people with controlled HIV: a cross-sectional study (ANRS CO3 AQUIVIH-NA-QuAliV). BMC Infectious Diseases, 2023, 23 (1), 10.1186/s12879-022-07963-6. doi: 10.1186/s12879-022-07963-6

HAL Id: hal-04590453

https://hal.science/hal-04590453

Submitted on 28 May 2024

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
# Tobacco, alcohol, cannabis, and illicit drug use and their association with CD4/CD8 cell count ratio in people with controlled HIV: a cross-sectional study (ANRS CO3 AQUIVIH-NA-QuAliV)

Sophie Devos, François Bonnet, Mojgan Hessamfar, Didier Neau, Marc-Olivier Vareil, Olivier Leleux, Nicolas Rouanes, Pierre Duffau, François Dabis, Linda Wittkop, et al.

► To cite this version:

Sophie Devos, François Bonnet, Mojgan Hessamfar, Didier Neau, Marc-Olivier Vareil, et al. Tobacco, alcohol, cannabis, and illicit drug use and their association with CD4/CD8 cell count ratio in people with controlled HIV: a cross-sectional study (ANRS CO3 AQUIVIH-NA-QuAliV). BMC Infectious Diseases, 2023, 23 (1), 10.1186/s12879-022-07963-6. doi: 10.1186/s12879-022-07963-6

HAL Id: hal-04590453

https://hal.science/hal-04590453v1

Submitted on 28 May 2024

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
RESEARCH

Open Access

<figure><img src="image_2.png" /></figure>

Check for
updates

# Tobacco, alcohol, cannabis, and illicit drug use and their association with CD4/CD8 cell count ratio in people with controlled HIV: a cross-sectional study (ANRS CO3 AQUIVIH-NA-QuAliV)

Sophie Devos¹,², Fabrice Bonnet¹,³,⁴, Mojgan Hessamfar¹,²,³, Didier Neau⁵, Marc-Olivier Vareil⁶, Olivier Leleux⁴, Charles Cazanave⁵, Nicolas Rouanes⁷, Pierre Duffau³,⁸, Estibaliz Lazaro⁹, François Dabis¹,², Linda Wittkop¹,⁴,¹⁰,¹¹, Diana Barger¹* and ANRS CO3-AQUIVIH-NA-QuAliV

**Abstract**

**Background** To evaluate drug use (alcohol, tobacco, cannabis and other drugs) and its association with mean CD4/CD8 T cell count ratio, a marker of chronic inflammation, in virally suppressed people living with HIV-1 (PLWH) in Nou-velle Aquitaine, France.

**Methods** A multi-centric, cross-sectional analysis was conducted in 2018–19 in the QuAliV study—ANRS CO3 AQUIVIH-NA cohort. Tobacco, alcohol, cannabis, and other drug use (poppers, cocaine, amphetamines, synthetic cathinones, GHB/GBL) were self-reported. CD4 and CD8 T cell counts and viral load measures, ± 2 years of self-report, and other characteristics were abstracted from medical records. Univariable and multivariable linear regression models, adjusted for age, sex, HIV risk group, time since HIV diagnosis, and other drug use were fit for each drug and most recent CD4/CD8 ratio.

**Results** 660 PLWH, aged 54.7 ± 11.2, were included. 47.7% [315/660] had a CD4/CD8 ratio of < 1. Their mean CD4/CD8 ratio was 1.1 ± 0.6. 35% smoked; ~40% were considered to be hazardous drinkers or have alcohol use disorder; 19.9% used cannabis and 11.9% other drugs. Chemsex-associated drug users' CD4/CD8 ratio was on average 0.226 (95% confidence interval [95% CI] – 0.383, – 0.070) lower than that of non-users in univariable analysis (p = 0.005) and 0.165 lower [95% CI – 0.343, 0.012] in multivariable analysis (p = 0.068).

**Conclusions** Mean differences in CD4/CD8 ratio were not significantly different in tobacco, alcohol and cannabis users compared to non-users. However, Chemsex-associated drug users may represent a population at risk of chronic inflammation, the specific determinants of which merit further investigation.

Trial registration number: NCT03296202.

**Keywords** CD4/CD8 ratio, Chronic inflammation, Chemsex, Drug use, HIV

<footnote>*Correspondence:
Diana Barger
diana.barger@u-bordeaux.fr
Full list of author information is available at the end of the article</footnote>
RESEARCH

Open Access

<figure><img src="image_2.png" /></figure>

Check for
updates

# Tobacco, alcohol, cannabis, and illicit drug use and their association with CD4/CD8 cell count ratio in people with controlled HIV: a cross-sectional study (ANRS CO3 AQUIVIH-NA-QuAliV)

Sophie Devos¹,², Fabrice Bonnet¹,³,⁴, Mojgan Hessamfar¹,²,³, Didier Neau⁵, Marc-Olivier Vareil⁶, Olivier Leleux⁴, Charles Cazanave⁵, Nicolas Rouanes⁷, Pierre Duffau³,⁸, Estibaliz Lazaro⁹, François Dabis¹,², Linda Wittkop¹,⁴,¹⁰,¹¹, Diana Barger¹* and ANRS CO3-AQUIVIH-NA-QuAliV

**Abstract**

**Background** To evaluate drug use (alcohol, tobacco, cannabis and other drugs) and its association with mean CD4/CD8 T cell count ratio, a marker of chronic inflammation, in virally suppressed people living with HIV-1 (PLWH) in Nouvelle Aquitaine, France.

**Methods** A multi-centric, cross-sectional analysis was conducted in 2018–19 in the QuAliV study—ANRS CO3 AQUIVIH-NA cohort. Tobacco, alcohol, cannabis, and other drug use (poppers, cocaine, amphetamines, synthetic cathinones, GHB/GBL) were self-reported. CD4 and CD8 T cell counts and viral load measures, ± 2 years of self-report, and other characteristics were abstracted from medical records. Univariable and multivariable linear regression models, adjusted for age, sex, HIV risk group, time since HIV diagnosis, and other drug use were fit for each drug and most recent CD4/CD8 ratio.

**Results** 660 PLWH, aged 54.7 ± 11.2, were included. 47.7% [315/660] had a CD4/CD8 ratio of < 1. Their mean CD4/CD8 ratio was 1.1 ± 0.6. 35% smoked; ~40% were considered to be hazardous drinkers or have alcohol use disorder; 19.9% used cannabis and 11.9% other drugs. Chemsex-associated drug users' CD4/CD8 ratio was on average 0.226 (95% confidence interval [95% CI] − 0.383, − 0.070) lower than that of non-users in univariable analysis (p = 0.005) and 0.165 lower [95% CI − 0.343, 0.012] in multivariable analysis (p = 0.068).

**Conclusions** Mean differences in CD4/CD8 ratio were not significantly different in tobacco, alcohol and cannabis users compared to non-users. However, Chemsex-associated drug users may represent a population at risk of chronic inflammation, the specific determinants of which merit further investigation.

Trial registration number: NCT03296202.

**Keywords** CD4/CD8 ratio, Chronic inflammation, Chemsex, Drug use, HIV

<footnote>*Correspondence:
Diana Barger
diana.barger@u-bordeaux.fr
Full list of author information is available at the end of the article</footnote>
## Background

Residual immune activation and persistent inflammation have now become hallmarks of Human Immunodeficiency Virus (HIV) infection [1, 2]. A low or inverted (<1) CD4 to CD8 T cell count ratio (CD4/CD8 ratio) is a recognized indirect marker of this impaired immune function [3, 4] and only one-third of people living with HIV (PLWH) see a “normalization” of their CD4/CD8 ratio despite adhering to antiretroviral therapy (ART) and achieving sustained viral suppression [5, 6]. A low CD4/CD8 ratio has been associated with a higher risk of common co-morbidities (cardiovascular, renal and hepatic diseases as well as cancers) and mortality [7–9]. Gaining a better understanding of the mechanisms responsible for immune activation remains an ongoing priority.

Smoking, alcohol misuse and other drug use (i.e. cocaine and cannabis), which are common in PLWH, appear to affect the immune system, specifically its lymphocytes, already targeted by HIV infection [10–20]. Furthermore, “Chemsex”, the phenomenon of drug use to enhance sexual pleasure, has become increasingly common in men who have sex with men (MSM), a population also living with HIV, with Poppers, GHB or GBL, cocaine, amphetamines, and more recently synthetic cathinones being the drugs most frequently taken [21]. We hypothesized that a low CD4/CD8 ratio could be a mediator of the relationship between drug use and the occurrence of common co-morbidities. We aimed to assess the relationship between reported drug use (alcohol, tobacco, cannabis and other drugs) and the CD4/CD8 ratio in virally suppressed PLWH.

## Methods

### Setting, study design, population

The ANRS CO3 AQUIVIH-NA cohort is an open, prospective and multi-centric cohort of PLWH in hospital-based HIV care in 15 public hospitals in the Nouvelle Aquitaine region of France. Clinical and epidemiological data, reflecting routine care from patients' medical records are entered into an electronic Case Report Form by Clinical Research Associates. Participants' laboratory reports are transferred from the hospitals' laboratory information systems. The QuAliV study is a cross-sectional study, conducted within the ANRS CO3 AQUIVIH-NA cohort, assessing the multi-dimensional quality of life and other patient-reported outcomes (PROs) in PLWH via a self-administered assessment [22, 23]. Cohort participants were recruited to the QuAliV study during their routine HIV consultation and were invited to complete the self-assessment independently via patient-facing module of the cohort's information system designed for the collection of electronic PROs. We conducted an analytical cross-sectional study to assess the

association between drug use and the most recent CD4/CD8 ratio among virally-suppressed PLWH in care in one of six centres recruiting participants to the QuAliV study between 07/2018 and 12/2019. To be included in this analysis, participants had to have completed the “My lifestyle” module of PROs assessment which details tobacco, alcohol, cannabis and other drug use and have available CD4 and CD8 cell count measures within ±2 years of the self-assessment.

### Outcome

Clinical guidelines for PLWH on treatment with an undetectable viral load recommend routine blood monitoring, including CD4 and CD8 cell counts, at least every two years [13]. We therefore considered CD4 and CD8 T cell counts, measured on the same day, to calculate the CD4/CD8 ratio closest to the date of self-report. As there is no clear threshold for a “normal” CD4/CD8 ratio, we treated the CD4/CD8 ratio as a continuous variable.

### Exposures

Tobacco, alcohol, cannabis, and drug use (poppers, cocaine, amphetamines/ecstasy/MDMA/methamphetamine, opiates, synthetic cathinone, GHB/GBL or synthetic cannabinoids) were self-reported, collected through valid, widely-used, instruments (Fagerström Test for Nicotine Dependence, Alcohol Use Disorders Identification Test (AUDIT-C)) [22]. Participants reported the frequency and location of use (e.g. private home, clubs, pubs, restaurant, street, parks, etc.). We created a variable for current tobacco use based on responses to a single question about smoking and responses to the Fagerström Test for Nicotine Dependence, a 6-item instrument designed to assess the intensity of physical addiction to nicotine which generates a score of 0–10. We classified participants as non-smoking, non-daily smoking or very low to low addiction to nicotine (score 0–4), medium addiction to nicotine (score 5–6) and strong to very strong addiction to nicotine (score 7–10). We created a variable for alcohol use using data generated from the AUDIT-C instrument, a 3-item questionnaire designed to screen for hazardous drinking or those who have active alcohol use disorders. We classified participants as non-drinkers, no misuse, or hazardous drinkers/alcohol use disorder. Cannabis use in the past 12 months was dichotomized as non-user versus user. Other drug use in the past 12 months was collected via a single question on use of “other drugs to feel better or get high”. We created a composite variable identifying those who used drugs commonly associated with Chemsex by aggregating reported use of poppers and/or cocaine and/or amphetamines and/or GHB/GBL and/or synthetic cathinones at home or someone else’s home. Two categorical variables
## Background

Residual immune activation and persistent inflammation have now become hallmarks of Human Immunodeficiency Virus (HIV) infection [1, 2]. A low or inverted (<1) CD4 to CD8 T cell count ratio (CD4/CD8 ratio) is a recognized indirect marker of this impaired immune function [3, 4] and only one-third of people living with HIV (PLWH) see a “normalization” of their CD4/CD8 ratio despite adhering to antiretroviral therapy (ART) and achieving sustained viral suppression [5, 6]. A low CD4/CD8 ratio has been associated with a higher risk of common co-morbidities (cardiovascular, renal and hepatic diseases as well as cancers) and mortality [7–9]. Gaining a better understanding of the mechanisms responsible for immune activation remains an ongoing priority.

Smoking, alcohol misuse and other drug use (i.e. cocaine and cannabis), which are common in PLWH, appear to affect the immune system, specifically its lymphocytes, already targeted by HIV infection [10–20]. Furthermore, “Chemsex”, the phenomenon of drug use to enhance sexual pleasure, has become increasingly common in men who have sex with men (MSM), a population also living with HIV, with Poppers, GHB or GBL, cocaine, amphetamines, and more recently synthetic cathinones being the drugs most frequently taken [21]. We hypothesized that a low CD4/CD8 ratio could be a mediator of the relationship between drug use and the occurrence of common co-morbidities. We aimed to assess the relationship between reported drug use (alcohol, tobacco, cannabis and other drugs) and the CD4/CD8 ratio in virally suppressed PLWH.

## Methods

### Setting, study design, population

The ANRS CO3 AQUIVIH-NA cohort is an open, prospective and multi-centric cohort of PLWH in hospital-based HIV care in 15 public hospitals in the Nouvelle Aquitaine region of France. Clinical and epidemiological data, reflecting routine care from patients' medical records are entered into an electronic Case Report Form by Clinical Research Associates. Participants' laboratory reports are transferred from the hospitals' laboratory information systems. The QuAliV study is a cross-sectional study, conducted within the ANRS CO3 AQUIVIH-NA cohort, assessing the multi-dimensional quality of life and other patient-reported outcomes (PROs) in PLWH via a self-administered assessment [22, 23]. Cohort participants were recruited to the QuAliV study during their routine HIV consultation and were invited to complete the self-assessment independently via patient-facing module of the cohort's information system designed for the collection of electronic PROs. We conducted an analytical cross-sectional study to assess the

association between drug use and the most recent CD4/CD8 ratio among virally-suppressed PLWH in care in one of six centres recruiting participants to the QuAliV study between 07/2018 and 12/2019. To be included in this analysis, participants had to have completed the “My lifestyle” module of PROs assessment which details tobacco, alcohol, cannabis and other drug use and have available CD4 and CD8 cell count measures within ±2 years of the self-assessment.

### Outcome

Clinical guidelines for PLWH on treatment with an undetectable viral load recommend routine blood monitoring, including CD4 and CD8 cell counts, at least every two years [13]. We therefore considered CD4 and CD8 T cell counts, measured on the same day, to calculate the CD4/CD8 ratio closest to the date of self-report. As there is no clear threshold for a “normal” CD4/CD8 ratio, we treated the CD4/CD8 ratio as a continuous variable.

### Exposures

Tobacco, alcohol, cannabis, and drug use (poppers, cocaine, amphetamines/ecstasy/MDMA/methamphetamine, opiates, synthetic cathinone, GHB/GBL or synthetic cannabinoids) were self-reported, collected through valid, widely-used, instruments (Fagerström Test for Nicotine Dependence, Alcohol Use Disorders Identification Test (AUDIT-C)) [22]. Participants reported the frequency and location of use (e.g. private home, clubs, pubs, restaurant, street, parks, etc.). We created a variable for current tobacco use based on responses to a single question about smoking and responses to the Fagerström Test for Nicotine Dependence, a 6-item instrument designed to assess the intensity of physical addiction to nicotine which generates a score of 0–10. We classified participants as non-smoking, non-daily smoking or very low to low addiction to nicotine (score 0–4), medium addiction to nicotine (score 5–6) and strong to very strong addiction to nicotine (score 7–10). We created a variable for alcohol use using data generated from the AUDIT-C instrument, a 3-item questionnaire designed to screen for hazardous drinking or those who have active alcohol use disorders. We classified participants as non-drinkers, no misuse, or hazardous drinkers/alcohol use disorder. Cannabis use in the past 12 months was dichotomized as non-user versus user. Other drug use in the past 12 months was collected via a single question on use of “other drugs to feel better or get high”. We created a composite variable identifying those who used drugs commonly associated with Chemsex by aggregating reported use of poppers and/or cocaine and/or amphetamines and/or GHB/GBL and/or synthetic cathinones at home or someone else’s home. Two categorical variables
were then created for both general drug use and Chem-
sex, coded as non-other drug user, users of a specific
drug (e.g. Poppers), and non-user of said drug but user of
another drug to “feel better or get high”.

Other variables

Demographic and epidemiological variables included
participants' age, sex (male or female), HIV transmis-
sion risk group (MSM, heterosexual contact, IV drug
use or other), years since diagnosis (0–10, 11–20;
21–30, > 30 years), and the viral load measure closest to
the QuAliV assessment (± 2 years). We defined viral sup-
pression as a measure of < 50 copies/mL.

Statistical analysis

We used univariable and multivariable linear regres-
sion models to study the mean difference in CD4/CD8
ratios between users of each drug and non-users. We
adjusted all analyses for age, sex, HIV risk group, time
since diagnosis and other drug use, which were con-
sidered as potential confounders a priori. We equated
a p-value <0.05 with statistically significance. Missing
values for drug use were encountered and reasons for
missingness investigated. Study participants who did not
answer all items differed from those who did. They were
older or more likely to be female depending on the drug.
Since these groups were more likely to be unfamiliar with
the drugs in question, we considered that missing data
likely depended on the intensity of drug use and were
therefore “Missing Not At Random.” We therefore per-
formed a complete case analysis. All statistical analyses
were performed in R version 3.6.1 and R studio version
1.0.153.

Results

Of the 929 participants who had completed the self-
assessment prior to 31 December 2019, 867 (93.3%) had
completed the "My lifestyle" module. One-hundred and
thirty-four participants who lacked CD4 and/or CD8
cell count measure, 44 participants who were not virally-
suppressed (last viral load ≥ 50 copies/mL) and 29 who
lacked a recent last viral load measure and were excluded.
We ultimately considered 660 participants.

As described in Table 1, participants’ mean age was
54.7 (standard deviation [s.d.] 11.2) years old and
72.4% [487/660] were male of whom 65.3% were MSM
[318/487]. Twenty-two percent [147/660] had been
diagnosed with HIV for 0–10 years, 25% [170/660] for
11–20 years, 36% [243/660] for 21–30 years and 15%
[100/660] for more than 30 years. Their mean CD4/CD8
ratio was 1.1 (s.d. 0.6) and 47.7% [315/660] had a CD4/
CD8 ratio of <1. Thirty-five percent [225/660] were

current smokers and 34.5% [225/660] had a history of
smoking. Eighty-three percent [539/660] reported being
current drinkers, among whom hazardous drinking and
alcohol use disorder were detected in 36.8% [185/539]
and 2.2% [11/539] respectively. Approximately twenty
percent [129/660] used cannabis and nearly twelve per-
cent [76/660] used other drugs, the most common of
which were poppers (82.9%, n=63), synthetic cathinones
(50.0%, n=38), and cocaine (34.2%, n=26). We consid-
ered 90.8% [69/76] of those who reported other drug use
to be using drugs commonly associated with Chemsex
based on the aforementioned criteria.

We present the results of univariable and multivariable
analyses conducted for each drug in Table 2. Mean CD4/
CD8 ratios of participants using tobacco, alcohol and
cannabis was not found to be significantly different com-
pared to non-users in neither univariable nor multivari-
able analyses. The CD4/CD8 ratio of participants using at
least one Chemsex-associated drug was on average 0.226
(95% confidence interval [CI] – 0.383, – 0.070) lower
than that of non-users in univariable analysis (p=0.005)
and 0.165 lower [95% C.I – 0.343, – 0.012] in multivari-
able analysis, however, the observed difference was only
of borderline statistical significance (p=0.068). In multi-
variable analyses, considering each drug individually, we
failed to find statistically significant mean differences in
CD4/CD8 ratios in users compared to non-users.

Discussion

Although we did not find a statistically significant mean
difference in CD4/CD8 ratio between users and non-
users of a specific drug, our analysis suggests that drug
use in the context of Chemsex may be associated with
a lower CD4/CD8 ratio. Indeed, univariable analyses
showed a lower CD4/CD8 ratio among users of Pop-
pers and synthetic cathinones, drugs which are very
commonly used for Chemsex as well as among users of
at least one Chemsex associated drug (poppers and/or
cocaine and/or amphetamines and/or GHB/GBL and/or
synthetic cathinones) at home or someone else’s home.
These associations were statistically significant or close
to significant. Multivariable analyses followed the same
trend, suggesting a lack of statistical power in our analy-
ses. These results therefore do not seem to incriminate a
particular drug but rather the context of use and thus a
group of at-risk users in whom certain characteristics or
lifestyle factors appear to hinder immune restoration in
spite of viral suppression. A longitudinal study conducted
in the United States found no association between poly-
drug use and lymphocytes in PLWH, however, this study
was conducted in pre HAART era, before the emergence
of drug use trends like Chemsex, making comparisons
futile [19].
were then created for both general drug use and Chem-
sex, coded as non-other drug user, users of a specific
drug (e.g. Poppers), and non-user of said drug but user of
another drug to “feel better or get high”.

Other variables

Demographic and epidemiological variables included
participants' age, sex (male or female), HIV transmis-
sion risk group (MSM, heterosexual contact, IV drug
use or other), years since diagnosis (0–10, 11–20;
21–30, > 30 years), and the viral load measure closest to
the QuAliV assessment (± 2 years). We defined viral sup-
pression as a measure of < 50 copies/mL.

Statistical analysis

We used univariable and multivariable linear regres-
sion models to study the mean difference in CD4/CD8
ratios between users of each drug and non-users. We
adjusted all analyses for age, sex, HIV risk group, time
since diagnosis and other drug use, which were con-
sidered as potential confounders a priori. We equated
a p-value <0.05 with statistically significance. Missing
values for drug use were encountered and reasons for
missingness investigated. Study participants who did not
answer all items differed from those who did. They were
older or more likely to be female depending on the drug.
Since these groups were more likely to be unfamiliar with
the drugs in question, we considered that missing data
likely depended on the intensity of drug use and were
therefore “Missing Not At Random.” We therefore per-
formed a complete case analysis. All statistical analyses
were performed in R version 3.6.1 and R studio version
1.0.153.

Results

Of the 929 participants who had completed the self-
assessment prior to 31 December 2019, 867 (93.3%) had
completed the "My lifestyle" module. One-hundred and
thirty-four participants who lacked CD4 and/or CD8
cell count measure, 44 participants who were not virally-
suppressed (last viral load ≥ 50 copies/mL) and 29 who
lacked a recent last viral load measure and were excluded.
We ultimately considered 660 participants.

As described in Table 1, participants' mean age was
54.7 (standard deviation [s.d.] 11.2) years old and
72.4% [487/660] were male of whom 65.3% were MSM
[318/487]. Twenty-two percent [147/660] had been
diagnosed with HIV for 0–10 years, 25% [170/660] for
11–20 years, 36% [243/660] for 21–30 years and 15%
[100/660] for more than 30 years. Their mean CD4/CD8
ratio was 1.1 (s.d. 0.6) and 47.7% [315/660] had a CD4/
CD8 ratio of <1. Thirty-five percent [225/660] were

current smokers and 34.5% [225/660] had a history of
smoking. Eighty-three percent [539/660] reported being
current drinkers, among whom hazardous drinking and
alcohol use disorder were detected in 36.8% [185/539]
and 2.2% [11/539] respectively. Approximately twenty
percent [129/660] used cannabis and nearly twelve per-
cent [76/660] used other drugs, the most common of
which were poppers (82.9%, n=63), synthetic cathinones
(50.0%, n=38), and cocaine (34.2%, n=26). We consid-
ered 90.8% [69/76] of those who reported other drug use
to be using drugs commonly associated with Chemsex
based on the aforementioned criteria.

We present the results of univariable and multivariable
analyses conducted for each drug in Table 2. Mean CD4/
CD8 ratios of participants using tobacco, alcohol and
cannabis was not found to be significantly different com-
pared to non-users in neither univariable nor multivari-
able analyses. The CD4/CD8 ratio of participants using at
least one Chemsex-associated drug was on average 0.226
(95% confidence interval [CI] – 0.383, – 0.070) lower
than that of non-users in univariable analysis (p=0.005)
and 0.165 lower [95% C.I – 0.343, – 0.012] in multivari-
able analysis, however, the observed difference was only
of borderline statistical significance (p=0.068). In multi-
variable analyses, considering each drug individually, we
failed to find statistically significant mean differences in
CD4/CD8 ratios in users compared to non-users.

Discussion

Although we did not find a statistically significant mean
difference in CD4/CD8 ratio between users and non-
users of a specific drug, our analysis suggests that drug
use in the context of Chemsex may be associated with
a lower CD4/CD8 ratio. Indeed, univariable analyses
showed a lower CD4/CD8 ratio among users of Pop-
pers and synthetic cathinones, drugs which are very
commonly used for Chemsex as well as among users of
at least one Chemsex associated drug (poppers and/or
cocaine and/or amphetamines and/or GHB/GBL and/or
synthetic cathinones) at home or someone else's home.
These associations were statistically significant or close
to significant. Multivariable analyses followed the same
trend, suggesting a lack of statistical power in our analy-
ses. These results therefore do not seem to incriminate a
particular drug but rather the context of use and thus a
group of at-risk users in whom certain characteristics or
lifestyle factors appear to hinder immune restoration in
spite of viral suppression. A longitudinal study conducted
in the United States found no association between poly-
drug use and lymphocytes in PLWH, however, this study
was conducted in pre HAART era, before the emergence
of drug use trends like Chemsex, making comparisons
futile [19].
<table><caption>**Table 1** Demographic, clinical and substance use in virally suppressed people living with HIV, QuAliV -ANRS CO3 AQUIVIH-NA Cohort, 2018–2019, Nouvelle-Aquitaine, France (N = 660)</caption><thead><tr><th></th><th colspan="2">Statistics*</th></tr></thead><tbody><tr><td>Demographics</td><td></td><td></td></tr><tr><td>Age (mean, s.d.)</td><td>54.7 ± 11.2</td><td></td></tr><tr><td>Sex (% male)</td><td>487 (72.4)</td><td></td></tr><tr><td>Country of birth (% born in France)</td><td>561 (85.0)</td><td></td></tr><tr><td>HIV infection</td><td></td><td></td></tr><tr><td>Time since diagnosis (years) (mean, s.d.)</td><td>20.0 ± 9.7</td><td></td></tr><tr><td>HIV transmission risk group</td><td></td><td></td></tr><tr><td>Men who have sex with men</td><td>318 (48.2)</td><td></td></tr><tr><td>IV drug use</td><td>70 (10.6)</td><td></td></tr><tr><td>Heterosexual contact</td><td>229 (34.7)</td><td></td></tr><tr><td>Other</td><td>43 (6.5)</td><td></td></tr><tr><td>Last CD4 count (cells/mm³)</td><td>736.4 ± 333.8</td><td></td></tr><tr><td>Last CD8 count (cells/mm³)</td><td>775.1 ± 387.5</td><td></td></tr><tr><td>Last CD4/CD8 ratio</td><td>1.1 ± 0.6</td><td></td></tr><tr><td></td><td>1.0 [0.0–5.7]</td><td></td></tr><tr><td>&lt; 1</td><td>315 (47.7)</td><td></td></tr><tr><td>≥ 1</td><td>345 (52.3)</td><td></td></tr><tr><td>Drug use</td><td></td><td></td></tr><tr><td>Tobacco</td><td></td><td></td></tr><tr><td>Current smoker</td><td>225 (34.5)</td><td>Very low to low nicotine addiction<fnref n="1" /> 52 (31.3)<br>Medium nicotine addiction<fnref n="1" /> 55 (33.1)<br>Strong nicotine addiction<fnref n="1" /> 37 (22.3)<br>Very strong nicotine addiction<fnref n="1" /> 22 (13.3)</td></tr><tr><td>Former smoker</td><td>225 (34.5)</td><td></td></tr><tr><td>Never smoker</td><td>202 (31.0)</td><td></td></tr><tr><td>Alcohol</td><td></td><td></td></tr><tr><td>Current drinker</td><td>539 (82.8)</td><td>No misuse² 307 (61.0)<br>Hazardous drinking² 185 (36.8)<br>Alcohol use disorder² 11 (2.2)</td></tr><tr><td>Non-drinker</td><td>112 (17.2)</td><td></td></tr><tr><td>Cannabis</td><td></td><td></td></tr><tr><td>Current user (last 12 months)</td><td>129 (19.9)</td><td></td></tr><tr><td>Non-user</td><td>519 (80.1)</td><td></td></tr><tr><td>Other drugs</td><td></td><td></td></tr><tr><td>Current user (last 12 months)</td><td>76 (11.9)</td><td></td></tr><tr><td>Non-user</td><td>563 (88.1)</td><td>Use in a private home</td></tr><tr><td>Poppers</td><td>63 (82.9)</td><td>53 (84.1)</td></tr><tr><td>Cocaine</td><td>26 (34.2)</td><td>24 (92.3)</td></tr><tr><td>Amphetamines/ecstasy/MDMA/methamphetamines</td><td>21 (27.6)</td><td>9 (42.9)</td></tr><tr><td>Synthetic cathinones</td><td>38 (50.0)</td><td>37 (97.4)</td></tr><tr><td>GHB GBL</td><td>19 (25.0)</td><td>18 (94.7)</td></tr><tr><td>Opiates</td><td>3 (3.9)</td><td>3 (100.0)</td></tr><tr><td>Synthetic cannabinoids</td><td>0 (0.0)</td><td>-</td></tr><tr><td>"Chemsex drug user"**</td><td>69 (90.8)</td><td></td></tr></tbody></table>

<footnote>*Data are presented as n (%), mean ± SD, or median [min–max]</footnote>

<footnote n="1">¹ based on the Fagerström test score, ²based on the Audit-C test score</footnote>

<footnote>**Poppers and/or cocaine and/or amphetamines and/or synthetic cathinones and/or GHB/GBL used in a private home</footnote>
<table><caption>**Table 1** Demographic, clinical and substance use in virally suppressed people living with HIV, QuAliV -ANRS CO3 AQUIVIH-NA Cohort, 2018–2019, Nouvelle-Aquitaine, France (N = 660)</caption><thead><tr><th></th><th colspan="2">Statistics*</th></tr></thead><tbody><tr><td>Demographics</td><td></td><td></td></tr><tr><td>Age (mean, s.d.)</td><td>54.7 ± 11.2</td><td></td></tr><tr><td>Sex (% male)</td><td>487 (72.4)</td><td></td></tr><tr><td>Country of birth (% born in France)</td><td>561 (85.0)</td><td></td></tr><tr><td>HIV infection</td><td></td><td></td></tr><tr><td>Time since diagnosis (years) (mean, s.d.)</td><td>20.0 ± 9.7</td><td></td></tr><tr><td>HIV transmission risk group</td><td></td><td></td></tr><tr><td>Men who have sex with men</td><td>318 (48.2)</td><td></td></tr><tr><td>IV drug use</td><td>70 (10.6)</td><td></td></tr><tr><td>Heterosexual contact</td><td>229 (34.7)</td><td></td></tr><tr><td>Other</td><td>43 (6.5)</td><td></td></tr><tr><td>Last CD4 count (cells/mm³)</td><td>736.4 ± 333.8</td><td></td></tr><tr><td>Last CD8 count (cells/mm³)</td><td>775.1 ± 387.5</td><td></td></tr><tr><td>Last CD4/CD8 ratio</td><td>1.1 ± 0.6</td><td></td></tr><tr><td></td><td>1.0 [0.0–5.7]</td><td></td></tr><tr><td>&lt; 1</td><td>315 (47.7)</td><td></td></tr><tr><td>≥ 1</td><td>345 (52.3)</td><td></td></tr><tr><td>Drug use</td><td></td><td></td></tr><tr><td>Tobacco</td><td></td><td></td></tr><tr><td>Current smoker</td><td>225 (34.5)</td><td>Very low to low nicotine addiction<fnref n="1" /> 52 (31.3)<br>Medium nicotine addiction<fnref n="1" /> 55 (33.1)<br>Strong nicotine addiction<fnref n="1" /> 37 (22.3)<br>Very strong nicotine addiction<fnref n="1" /> 22 (13.3)</td></tr><tr><td>Former smoker</td><td>225 (34.5)</td><td></td></tr><tr><td>Never smoker</td><td>202 (31.0)</td><td></td></tr><tr><td>Alcohol</td><td></td><td></td></tr><tr><td>Current drinker</td><td>539 (82.8)</td><td>No misuse² 307 (61.0)<br>Hazardous drinking² 185 (36.8)<br>Alcohol use disorder² 11 (2.2)</td></tr><tr><td>Non-drinker</td><td>112 (17.2)</td><td></td></tr><tr><td>Cannabis</td><td></td><td></td></tr><tr><td>Current user (last 12 months)</td><td>129 (19.9)</td><td></td></tr><tr><td>Non-user</td><td>519 (80.1)</td><td></td></tr><tr><td>Other drugs</td><td></td><td></td></tr><tr><td>Current user (last 12 months)</td><td>76 (11.9)</td><td></td></tr><tr><td>Non-user</td><td>563 (88.1)</td><td>Use in a private home</td></tr><tr><td>Poppers</td><td>63 (82.9)</td><td>53 (84.1)</td></tr><tr><td>Cocaine</td><td>26 (34.2)</td><td>24 (92.3)</td></tr><tr><td>Amphetamines/ecstasy/MDMA/methamphetamines</td><td>21 (27.6)</td><td>9 (42.9)</td></tr><tr><td>Synthetic cathinones</td><td>38 (50.0)</td><td>37 (97.4)</td></tr><tr><td>GHB GBL</td><td>19 (25.0)</td><td>18 (94.7)</td></tr><tr><td>Opiates</td><td>3 (3.9)</td><td>3 (100.0)</td></tr><tr><td>Synthetic cannabinoids</td><td>0 (0.0)</td><td>-</td></tr><tr><td>"Chemsex drug user"**</td><td>69 (90.8)</td><td></td></tr></tbody></table>

<footnote>*Data are presented as n (%), mean ± SD, or median [min–max]</footnote>

<footnote n="1">¹ based on the Fagerström test score, ²based on the Audit-C test score</footnote>

<footnote>**Poppers and/or cocaine and/or amphetamines and/or synthetic cathinones and/or GHB/GBL used in a private home</footnote>
<table><caption>**Table 2** Univariable and multivariable analyses of smoking, alcohol, cannabis, and drug use compared to non-use in PLWH, QuAliV-ANRS CO3 AQUIVIH-NA Cohort, 2018–2019, Nouvelle-Aquitaine, France (N = 660)</caption><thead><tr><th rowspan="2"></th><th colspan="4">Univariate analysis</th><th colspan="4">Multivariate analysis*</th></tr><tr><th>Non-user CD4/CD8 ratio (β0)</th><th>Mean CD4/CD8 ratio difference</th><th>95% interval confidence</th><th>p-value<sup>#</sup></th><th>Mean CD4/CD8 ratio difference</th><th>95% interval confidence</th><th>p-value<sup>#</sup></th></tr></thead><tbody><tr><td>Tobacco<sup>1</sup></td><td>1.12</td><td></td><td></td><td></td><td></td><td></td><td><strong>0.391</strong></td></tr><tr><td>Non-daily smoking or very low to low dependence vs. non-smoking<sup>a</sup></td><td></td><td>− 0.013</td><td>− 0.152; 0.127</td><td>0.857</td><td>0.014</td><td>− 0.136; 0.165</td><td>0.851</td></tr><tr><td>Medium dependence vs. non-smoking<sup>b</sup></td><td></td><td>0.087</td><td>− 0.090; 0.264</td><td>0.333</td><td>0.161</td><td>− 0.030; 0.351</td><td>0.098</td></tr><tr><td>High to very high dependence vs. non-smoking</td><td></td><td>− 0.068</td><td>− 0.239; 0.104</td><td>0.439</td><td>− 0.016</td><td>− 0.199; 0.167</td><td>0.863</td></tr><tr><td>Alcohol<sup>2</sup></td><td>1.10</td><td></td><td></td><td></td><td></td><td></td><td><strong>0.371</strong></td></tr><tr><td>No misuse versus non-drinking</td><td></td><td>0.001</td><td>− 0.135; 0.138</td><td>0.986</td><td>0.054</td><td>− 0.089; 0.197</td><td>0.460</td></tr><tr><td>Misuse or dependence versus non-drinking</td><td></td><td>0.062</td><td>− 0.085; 0.209</td><td>0.406</td><td>0.108</td><td>− 0.045; 0.260</td><td>0.167</td></tr><tr><td>Cannabis</td><td>1.13</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Current user vs. non-user</td><td></td><td>− 0.014</td><td>− 0.135; 0.107</td><td>0.819</td><td>0.027</td><td>− 0.124; 0.179</td><td>0.723</td></tr><tr><td>Poppers</td><td>1.15</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Current user vs. non-user</td><td></td><td>− 0.222</td><td>− 0.385; − 0.059</td><td>0.008</td><td>− 0.159</td><td>− 0.341; 0.023</td><td>0.086</td></tr><tr><td>Cocaine</td><td>1.15</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Current user vs. non-user</td><td></td><td>− 0.142</td><td>− 0.388; 0.105</td><td>0.259</td><td>− 0.086</td><td>− 0.371; 0.200</td><td>0.557</td></tr><tr><td>Amphetamines/ ecstasy/MDMA/meth- amphetamines</td><td>1.15</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Current user vs. non-user</td><td></td><td>− 0.010</td><td>− 0.283; 0.262</td><td>0.941</td><td>0.092</td><td>− 0.223; 0.406</td><td>0.567</td></tr><tr><td>Synthetic cathinones</td><td>1.15</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Current user vs non-user</td><td></td><td>− 0.216</td><td>− 0.422; − 0.010</td><td>0.039</td><td>− 0.167</td><td>− 0.400; 0.065</td><td>0.157</td></tr><tr><td>GHB/GBL</td><td>1.15</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Current user vs. non-user</td><td></td><td>− 0.229</td><td>− 0.515; 0.057</td><td>0.118</td><td>− 0.180</td><td>− 0.483; 0.122</td><td>0.242</td></tr><tr><td>« Chemsex drug user»<sup>3</sup></td><td>1.15</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Current user vs non-user</td><td></td><td>− 0.226</td><td>− 0.383; − 0.070</td><td>0.005</td><td>− 0.165</td><td>− 0.343; 0.012</td><td>0.068</td></tr></tbody></table>

Since Chemsex has increased in the last ten years, the lower CD4/CD8 ratio that we found in Chemsex-associated drug user compared to non-users could have been

due to a recent diagnosis and initiation of ART. However, those considered as using Chemsex-associated drugs had been in care and receiving ART for more than 10 years in

<footnote>*Adjustment factors: age, sex, HIV transmission risk group, time since diagnosis, other substance use</footnote>

<footnote><sup>#</sup> T-test or Fisher test (bold)</footnote>

<footnote><sup>a</sup> Based on the Fagerström test score, <sup>b</sup>Based on the Audit-C test score, <sup>c</sup>Poppers and/or cocaine and/or amphetamines and/or synthetic cathinones and/or GHB/GBL use in a private home</footnote>

<footnote><sup>a</sup> "The average CD4/CD8 ratio is 0.013 lower in non-daily smokers or smokers with a very low to low nicotine addiction compared to non-smokers.</footnote>

<footnote><sup>b</sup> "The average CD4/CD8 ratio is 0.087 higher in smokers with a medium nicotine addiction compared to non-smokers.</footnote>
<table><caption>**Table 2** Univariable and multivariable analyses of smoking, alcohol, cannabis, and drug use compared to non-use in PLWH, QuAliV-ANRS CO3 AQUIVIH-NA Cohort, 2018–2019, Nouvelle-Aquitaine, France (N = 660)</caption><thead><tr><th rowspan="2"></th><th colspan="4">Univariate analysis</th><th colspan="4">Multivariate analysis*</th></tr><tr><th>Non-user CD4/CD8 ratio (β0)</th><th>Mean CD4/CD8 ratio difference</th><th>95% interval confidence</th><th>p-value<sup>#</sup></th><th>Mean CD4/CD8 ratio difference</th><th>95% interval confidence</th><th>p-value<sup>#</sup></th></tr></thead><tbody><tr><td>Tobacco<sup>1</sup></td><td>1.12</td><td></td><td></td><td></td><td></td><td></td><td><strong>0.391</strong></td></tr><tr><td>Non-daily smoking or very low to low dependence vs. non-smoking<sup>a</sup></td><td></td><td>− 0.013</td><td>− 0.152; 0.127</td><td>0.857</td><td>0.014</td><td>− 0.136; 0.165</td><td>0.851</td></tr><tr><td>Medium dependence vs. non-smoking<sup>b</sup></td><td></td><td>0.087</td><td>− 0.090; 0.264</td><td>0.333</td><td>0.161</td><td>− 0.030; 0.351</td><td>0.098</td></tr><tr><td>High to very high dependence vs. non-smoking</td><td></td><td>− 0.068</td><td>− 0.239; 0.104</td><td>0.439</td><td>− 0.016</td><td>− 0.199; 0.167</td><td>0.863</td></tr><tr><td>Alcohol<sup>2</sup></td><td>1.10</td><td></td><td></td><td></td><td></td><td></td><td><strong>0.371</strong></td></tr><tr><td>No misuse versus non-drinking</td><td></td><td>0.001</td><td>− 0.135; 0.138</td><td>0.986</td><td>0.054</td><td>− 0.089; 0.197</td><td>0.460</td></tr><tr><td>Misuse or dependence versus non-drinking</td><td></td><td>0.062</td><td>− 0.085; 0.209</td><td>0.406</td><td>0.108</td><td>− 0.045; 0.260</td><td>0.167</td></tr><tr><td>Cannabis</td><td>1.13</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Current user vs. non-user</td><td></td><td>− 0.014</td><td>− 0.135; 0.107</td><td>0.819</td><td>0.027</td><td>− 0.124; 0.179</td><td>0.723</td></tr><tr><td>Poppers</td><td>1.15</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Current user vs. non-user</td><td></td><td>− 0.222</td><td>− 0.385; − 0.059</td><td>0.008</td><td>− 0.159</td><td>− 0.341; 0.023</td><td>0.086</td></tr><tr><td>Cocaine</td><td>1.15</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Current user vs. non-user</td><td></td><td>− 0.142</td><td>− 0.388; 0.105</td><td>0.259</td><td>− 0.086</td><td>− 0.371; 0.200</td><td>0.557</td></tr><tr><td>Amphetamines/ecstasy/MDMA/meth-amphetamines</td><td>1.15</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Current user vs. non-user</td><td></td><td>− 0.010</td><td>− 0.283; 0.262</td><td>0.941</td><td>0.092</td><td>− 0.223; 0.406</td><td>0.567</td></tr><tr><td>Synthetic cathinones</td><td>1.15</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Current user vs non-user</td><td></td><td>− 0.216</td><td>− 0.422; − 0.010</td><td>0.039</td><td>− 0.167</td><td>− 0.400; 0.065</td><td>0.157</td></tr><tr><td>GHB/GBL</td><td>1.15</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Current user vs. non-user</td><td></td><td>− 0.229</td><td>− 0.515; 0.057</td><td>0.118</td><td>− 0.180</td><td>− 0.483; 0.122</td><td>0.242</td></tr><tr><td>« Chemsex drug user»<sup>3</sup></td><td>1.15</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Current user vs non-user</td><td></td><td>− 0.226</td><td>− 0.383; − 0.070</td><td>0.005</td><td>− 0.165</td><td>− 0.343; 0.012</td><td>0.068</td></tr></tbody></table>

Since Chemsex has increased in the last ten years, the lower CD4/CD8 ratio that we found in Chemsex-associated drug user compared to non-users could have been

due to a recent diagnosis and initiation of ART. However, those considered as using Chemsex-associated drugs had been in care and receiving ART for more than 10 years in

<footnote>*Adjustment factors: age, sex, HIV transmission risk group, time since diagnosis, other substance use</footnote>

<footnote><sup>#</sup> T-test or Fisher test (bold)</footnote>

<footnote><sup>a</sup> Based on the Fagerström test score, <sup>b</sup>Based on the Audit-C test score, <sup>c</sup>Poppers and/or cocaine and/or amphetamines and/or synthetic cathinones and/or GHB/GBL use in a private home</footnote>

<footnote><sup>a</sup>"The average CD4/CD8 ratio is 0.013 lower in non-daily smokers or smokers with a very low to low nicotine addiction compared to non-smokers.</footnote>

<footnote><sup>b</sup>"The average CD4/CD8 ratio is 0.087 higher in smokers with a medium nicotine addiction compared to non-smokers</footnote>
addition to being virally suppressed. Furthermore, compared to the overall study population, these participants had higher mean CD4 and CD8 counts, which supports the chronic inflammation hypothesis. A recent study comparing MSM and heterosexual men, all of whom were HIV-negative, found MSM and MSM reporting multiple recent partners more specifically to have lower CD4/CD8 ratios on average. Cytomegalovirus (CMV) infection was hypothesized to contribute to observed differences, but drug use did not appear to be associated [24]. Unfortunately, we were not able to account for CMV infection in our analysis due to data reliability concerns.

We found no association between tobacco use and a low CD4/CD8 ratio. However, smoking has been associated with disturbances in lymphocyte function, specifically an increase in lymphocyte activation and an increase in helper CD4 T-lymphocytes and thus an increase in the CD4/CD8 ratio [14, 25, 26]. Our results also show no association between alcohol use and the CD4/CD8 ratio. Several studies have shown that PLWH who are heavy drinkers tend to have lower CD4 cell counts than more moderate drinkers, which seems to contradict our findings [27, 28], yet other studies have not shown alcohol consumption to be associated with CD4 cell count levels in PLWH [29]. A recent longitudinal study found that exclusive cannabis use had no effect on PLWH's lymphocytes but that other drug use was associated with increased CD8 cell activation [20]. These results are consistent with ours.

While our study provides quality data on PLWH's recent drug use patterns and raises questions for future studies, we are also aware of its limitations. It was conducted in a non-random sample of those in care in Nouvelle Aquitaine and therefore might not be generalizable to all of those in care in our region, namely non-users who might be less likely to participate. Nevertheless, MSM, who are the most likely to engage in Chemsex, appear to be well-represented. Furthermore, there is strong evidence that data on sensitive topics like drugs use can be collected reliably using anonymous validated instruments as these methods are less subject to social desirability bias. Our study is cross-sectional and therefore does not account for drug use and CD4/CD8 ratio trajectories over the course of HIV infection and its treatment. We assessed current drug use within the past 12 months, which may or not reflect participants' drug use history. It is therefore possible those in the non-user group previously engaged in drug use. This type of misclassification would have resulted in a dilution of effect. This might be one explanation of non-significant findings, namely those which are discordant with previous research. Finally, we relied on CD4 and CD8 cell count measures recorded within 2 years of the self-reported

assessment. While this is an ostensibly large window in which to draw on participants' available laboratory data, we feel that it is acceptable given our study sample's characteristics, the exclusion of those who were not completely virally-suppressed, and CD4 and CD8 cell counts trajectories over the course of HIV treatment. Finally, the relatively small number of drug users in our sample may have resulted in low statistical power.

## Conclusions

While we did not find an association between tobacco, alcohol and cannabis use and the CD4/CD8 ratio, our results suggest that those who use Chemsex-associated drugs could represent a population at risk of chronic inflammation. The specific determinants of which merit further investigation.

### Abbreviations

<table><tr><td>ART</td><td>Antiretroviral therapy</td></tr><tr><td>CD4/CD8 ratio</td><td>CD4 to CD8 T cell count ratio</td></tr><tr><td>CI</td><td>Confidence interval</td></tr><tr><td>CMV</td><td>Cytomegalovirus</td></tr><tr><td>HIV</td><td>Human Immunodeficiency Virus</td></tr><tr><td>MSM</td><td>Men who have sex with men</td></tr><tr><td>PRO</td><td>Patient-reported outcomes</td></tr><tr><td>PLWH</td><td>People living with HIV</td></tr><tr><td>s.d.</td><td>Standard deviation</td></tr></table>

### Acknowledgements

We would like to acknowledge Alain Volny-Anne (European AIDS Treatment Group) who reviewed the content of the battery of PRO measures and colleagues at AIDES who are part of the QuAliV Steering Committee. We also thank Eugenie Destandau who provided input on the QuAliV study's communication strategy. We acknowledge the contribution of members of the IT department who developed the APREGE® 2.0 module for the collection of electronic PROs. We would like to thank the COREVIH Nouvelle Aquitaine's President and Coordinators (François Dabis, Julie Lamant, Maya Paliteau) and its Clinical Research Associates (Marie-Jose Blaizeau, Fatou Diarra, Madeleine Decoin, Sandrine Delveaux, Corinne Hanappier, Anne Pougetoux, Bellancille Uwamaliya, Kateryna Zara) and investigators, cited below, for their unstinting support in successfully implementing this initial phase of the QuAliV study.
The ANRS CO3-AQUIVIH-NA cohort study group
Scientific committee : P. Bellecave, P. Blanco, F. Bonnet (Chair), S. Bouchet, D. Breilh, C. Cazanave, S. Desjardin, V. Gaborieau, A. Gimbert, M. Hessamfar, L. Lacaze-Buzy, D. Lacoste, ME Lafon, S. Lawson-Ayayi, E. Lazaro, O. Leleux, F. Le Marec, G. Le Moal, D. Malvy, L. Marchand, P. Mercié, D. Neau, I. Pellegrin, A. Perrier, V. Petrov-Sanchez, M.O. Vareil, L. Wittkop (Methodologist)
Participating centers : Hôpital Saint André, CHU de Bordeaux, Médecine Interne et Maladies Infectieuses, (N. Bernard, F. Bonnet, D. Bronnimann H. Chaussade, D. Dondia, P. Duffau, I. Faure, M. Hessamfar, P. Mercié, P. Morlat, E. Mériglier, F. Paccalin, E. Riebero, C. Rivoisy, MA Vandenhende) ; Hôpital Pellegrin, CHU de Bordeaux, Maladies Infectieuses et Tropicales, (L. Barthod, C. Cazanave, FA. Dauchy, A. Desclaux, M. Ducours, H. Dutronc, A. Duvignaud, J. Leitao, M. Lescure, D. Neau, D. Nguyen, D. Malvy, T. Pistone, M. Puges, G. Wirth) ; Hôpital Haut-Lévêque, CHU de Bordeaux, Médecine Interne et Maladies Infectieuses, (C. Courtault, F. Camou, C. Greib, E. Lazaro, JL. Pellegrin, E. Rivière, JF. Viallard) ; Hôpital d'Agen, Médecine Interne (Y. Imbert, M. Thierry-Mieg, P. Rispal) ; Hôpital de Libourne, Médecine Interne (O. Caubet, H. Ferrand, S. Tchamgoué) ; Hôpital de Bayonne, Maladies Infectieuses (S. Farbos, MO. Vareil, H. Wille) ; Hôpital de Dax, Médecine Interne et Maladies Infectieuses, (K. Andre, L. Caunegre, Y. Gerard, F. Osorio-Perez) ; Hôpital Saint-Cyril/Villeneuve-sur-Lot, Maladies Infectieuses, (I. Chossat) ; Hôpital de Mont de Marsan, Médecine Interne et Maladies Infectieuses, (G. Iles, Y. Gerard, M. Labasse-Depis, F. Lacassin) ; Hôpital d'Arcachon, Médecine Interne, (A. Barret, C. Courault) ; Hôpital
addition to being virally suppressed. Furthermore, compared to the overall study population, these participants had higher mean CD4 and CD8 counts, which supports the chronic inflammation hypothesis. A recent study comparing MSM and heterosexual men, all of whom were HIV-negative, found MSM and MSM reporting multiple recent partners more specifically to have lower CD4/CD8 ratios on average. Cytomegalovirus (CMV) infection was hypothesized to contribute to observed differences, but drug use did not appear to be associated [24]. Unfortunately, we were not able to account for CMV infection in our analysis due to data reliability concerns.

We found no association between tobacco use and a low CD4/CD8 ratio. However, smoking has been associated with disturbances in lymphocyte function, specifically an increase in lymphocyte activation and an increase in helper CD4 T-lymphocytes and thus an increase in the CD4/CD8 ratio [14, 25, 26]. Our results also show no association between alcohol use and the CD4/CD8 ratio. Several studies have shown that PLWH who are heavy drinkers tend to have lower CD4 cell counts than more moderate drinkers, which seems to contradict our findings [27, 28], yet other studies have not shown alcohol consumption to be associated with CD4 cell count levels in PLWH [29]. A recent longitudinal study found that exclusive cannabis use had no effect on PLWH's lymphocytes but that other drug use was associated with increased CD8 cell activation [20]. These results are consistent with ours.

While our study provides quality data on PLWH's recent drug use patterns and raises questions for future studies, we are also aware of its limitations. It was conducted in a non-random sample of those in care in Nouvelle Aquitaine and therefore might not be generalizable to all of those in care in our region, namely non-users who might be less likely to participate. Nevertheless, MSM, who are the most likely to engage in Chemsex, appear to be well-represented. Furthermore, there is strong evidence that data on sensitive topics like drugs use can be collected reliably using anonymous validated instruments as these methods are less subject to social desirability bias. Our study is cross-sectional and therefore does not account for drug use and CD4/CD8 ratio trajectories over the course of HIV infection and its treatment. We assessed current drug use within the past 12 months, which may or not reflect participants' drug use history. It is therefore possible those in the non-user group previously engaged in drug use. This type of misclassification would have resulted in a dilution of effect. This might be one explanation of non-significant findings, namely those which are discordant with previous research. Finally, we relied on CD4 and CD8 cell count measures recorded within 2 years of the self-reported

assessment. While this is an ostensibly large window in which to draw on participants' available laboratory data, we feel that it is acceptable given our study sample's characteristics, the exclusion of those who were not completely virally-suppressed, and CD4 and CD8 cell counts trajectories over the course of HIV treatment. Finally, the relatively small number of drug users in our sample may have resulted in low statistical power.

## Conclusions

While we did not find an association between tobacco, alcohol and cannabis use and the CD4/CD8 ratio, our results suggest that those who use Chemsex-associated drugs could represent a population at risk of chronic inflammation. The specific determinants of which merit further investigation.

### Abbreviations

<table><tr><td>ART</td><td>Antiretroviral therapy</td></tr><tr><td>CD4/CD8 ratio</td><td>CD4 to CD8 T cell count ratio</td></tr><tr><td>CI</td><td>Confidence interval</td></tr><tr><td>CMV</td><td>Cytomegalovirus</td></tr><tr><td>HIV</td><td>Human Immunodeficiency Virus</td></tr><tr><td>MSM</td><td>Men who have sex with men</td></tr><tr><td>PRO</td><td>Patient-reported outcomes</td></tr><tr><td>PLWH</td><td>People living with HIV</td></tr><tr><td>s.d.</td><td>Standard deviation</td></tr></table>

### Acknowledgements

We would like to acknowledge Alain Volny-Anne (European AIDS Treatment Group) who reviewed the content of the battery of PRO measures and colleagues at AIDES who are part of the QuAliV Steering Committee. We also thank Eugenie Destandau who provided input on the QuAliV study's communication strategy. We acknowledge the contribution of members of the IT department who developed the APREGE® 2.0 module for the collection of electronic PROs. We would like to thank the COREVIH Nouvelle Aquitaine's President and Coordinators (François Dabis, Julie Lamant, Maya Paliteau) and its Clinical Research Associates (Marie-Jose Blaizeau, Fatou Diarra, Madeleine Decoin, Sandrine Delveaux, Corinne Hanappier, Anne Pougetoux, Bellancille Uwamaliya, Kateryna Zara) and investigators, cited below, for their unstinting support in successfully implementing this initial phase of the QuAliV study.

The ANRS CO3-AQUIVIH-NA cohort study group

Scientific committee : P. Bellecave, P. Blanco, F. Bonnet (Chair), S. Bouchet, D. Breilh, C. Cazanave, S. Desjardin, V. Gaborieau, A. Gimbert, M. Hessamfar, L. Lacaze-Buzy, D. Lacoste, ME Lafon, S. Lawson-Ayayi, E. Lazaro, O. Leleux, F. Le Marec, G. Le Moal, D. Malvy, L. Marchand, P. Mercié, D. Neau, I. Pellegrin, A. Perrier, V. Petrov-Sanchez, M.O. Vareil, L. Wittkop (Methodologist)

Participating centers : Hôpital Saint André, CHU de Bordeaux, Médecine Interne et Maladies Infectieuses, (N. Bernard, F. Bonnet, D. Bronnimann H. Chaussade, D. Dondia, P. Duffau, I. Faure, M. Hessamfar, P. Mercié, P. Morlat, E. Mériglier, F. Paccalin, E. Riebero, C. Rivoisy, MA Vandenhende) ; Hôpital Pellegrin, CHU de Bordeaux, Maladies Infectieuses et Tropicales, (L. Barthod, C. Cazanave, FA. Dauchy, A. Desclaux, M. Ducours, H. Dutronc, A. Duvignaud, J. Leitao, M. Lescure, D. Neau, D. Nguyen, D. Malvy, T. Pistone, M. Puges, G. Wirth) ; Hôpital Haut-Lévêque, CHU de Bordeaux, Médecine Interne et Maladies Infectieuses, (C. Courtault, F. Camou, C. Greib, E. Lazaro, JL. Pellegrin, E. Rivière, JF. Viallard) ; Hôpital d'Agen, Médecine Interne (Y. Imbert, M. Thierry-Mieg, P. Rispal) ; Hôpital de Libourne, Médecine Interne (O. Caubet, H. Ferrand, S. Tchamgoué) ; Hôpital de Bayonne, Maladies Infectieuses (S. Farbos, MO. Vareil, H. Wille) ; Hôpital de Dax, Médecine Interne et Maladies Infectieuses, (K. Andre, L. Caunegre, Y. Gerard, F. Osorio-Perez) ; Hôpital Saint-Cyril/Villeneuve-sur-Lot, Maladies Infectieuses, (I. Chossat) ; Hôpital de Mont de Marsan, Médecine Interne et Maladies Infectieuses, (G. Iles, Y. Gerard, M. Labasse-Depis, F. Lacassin) ; Hôpital d'Arcachon, Médecine Interne, (A. Barret, C. Courtault) ; Hôpital
de Périgueux, Médecine Interne et Maladies Infectieuses, (B Castan, J. Koffi, N. Rouanes, A. Saunier, JB Zabbe); Hôpital de Pau, Médecine Interne et Maladies Infectieuses, (G. Dumondin, V. Gaborieau); Hôpital d'Orthez, Médecine Interne, (Y. Gerard); CHU de Poitiers, Médecine Interne et Maladies Infectieuses, (G. Beraud, G. Le Moal, M. Catroux, M. Garcia, V. Giraud, JP. Martellosio, F. Roblot); Hôpital de Saintes, Médecine Interne, (T. Pasdeloup); Hôpital d'Angoulême, Médecine Interne, (A. Riché, M. Grosset, S. Males, C. Ngo Bell); Hôpital de Jonzac, Maladies Infectieuses, (T. Pasdeloup), Hôpital de Saint jean d'Angely, Maladies Infectieuses, (T. Pasdeloup)

Other departements

Immunology: P. Blanco, I. Pellegrin; CRB-BBS: C. Carpentier, I. Pellegrin; Virol-ogy: P. Bellecave, ME. Lafon, C. Tumiotto; Pharmacology: S. Bouchet, D. Breilh, G. Miremont-Salamé; Data collection: D. Arma, G. Arnou, MJ. Blaizeau, P. Camps, M. Decoin, S. Delveaux, F. Diarra, L. Gabrea, W-H. Lai, S. Lawson-Ayayi, E. Lenaud, D. Plainchamps, A. Pougetoux, B. Uwamaliya, K. Zara; IT departement: V. Conte, M. Gapillout; Project Team: O. Leleux (Project Leader), F. Le Marec (Statistitien), A. Perrier (Data Manager)

## Author contributions

SD, FB and DB developed research question. SD conducted analyses. FB, LW, DB critically contributed to analyses. FB, FD, DB designed the QuAliV study and secured funding. OL, MH, DN, MOV, NR, EL, PD, CC, FB contributed to the study's conduct. All authors read, critically reviewed, and approved the final manuscript.

## Funding

This work was supported by seed funding and a grant from the ANRS Maladies infectieuses émergentes [Grant Number ECTZ100440 2019-2]. Diana Barger received a "Young Researcher Fellowship" from the French NGO Sidaction (2016-19) and a post-doctoral fellowship from the ANRS Maladies infectieuses émergentes [grant number ECTZ117276 2020-1]. The ANRS CO3 AQUIVIM-NA cohort is sponsored by the Bordeaux University Hospital and has received funding from the Bordeaux University Hospital and ANRS Maladies infectieuses émergentes. The cohort is coordinated from within the Inserm UMR 1219 Bordeaux Population Health Research Centre.

## Availability of data and materials

The anonymized individual data and the data dictionary of the study will be made available to other researchers by the coordinating investigator, Professor Fabrice Bonnet (fabrice.bonnet@chu-bordeaux.fr) after approval of a methodologically sound proposal by the study's steering committee and the signature of a data access agreement.

## Declarations

### Ethics approval and consent to participate

The ANRS CO3 Aquitaine-AQUIVIM-NA cohort study was approved by an Institutional Review Board on May 27, 2016 and the QuAliV study was granted ethical approval in August 2017. The National Commission on Informatics and Liberty (CNIL), the French regulatory agency charged with enforcing data privacy laws, reviewed and approved QuAliV study-specific amendments to authorizations on March 12, 2018. Written and informed consent is required of all cohort participants. All methods were carried out in accordance with relevant guidelines and regulations.

### Consent for publication

Not applicable.

### Competing interests

SD, MH, DN, NR, EL, PD, CC, OL, FD, LW have no relevant financial or non-financial competing interests to disclose. MOV reports financial support from Gilead Sciences, VIIv Healthcare and MSD outside the submitted work (conference participation). FB reports non-financial support from Gilead Sciences, Janssen, VIIv Healthcare, and MSD, personal fees from Gilead Sciences, Janssen, VIIv Healthcare and MSD outside the submitted work. DB has received speaking fees from Gilead Sciences, VIIv Healthcare, and MSD and been compensated as an expert on a Gilead Sciences advisory board outside the submitted work.

## Author details

¹Univ. Bordeaux, INSERM, BPH, U1219, 146, rue Léo Saignat-CS61292, 33076 Bordeaux Cedex, France. ²CHU de Bordeaux, COREVIH Nouvelle Aquitaine, INSERM, U1219, 1 Rue Jean Burguet, 33000 Bordeaux, France. ³CHU de Bordeaux, Service de Médecine Interne et Maladies Infectieuses, INSERM, U1219, 1 Rue Jean Burguet, 33000 Bordeaux, France. ⁴Univ. Bordeaux, INSERM, Institut Bergonié, BPH, U1219, CIC-P 1401, 146, rue Léo Saignat-CS61292, 33076 Bordeaux Cedex, France. ⁵CHU de Bordeaux, Service des Maladies Infectieuses et Tropicales, INSERM, U1219, Pl. Amélie Raba Léon, U121933000 Bordeaux, France. ⁶Centre Hospitalier de la Côte Basque, Service de Maladies Infectieuses, 13 Avenue de l'interne Jacques Loëb, BP 8, 64109 Bayonne Cedex, France. ⁷Centre Hospitalier de Périgueux, Service de Médecine Polyvalente, 80 Av. Georges Pompidou, 22400 Périgueux, France. ⁸Univ. Bordeaux, Department of Immunology, CNRS, ImmunoConcEpT, UMR 5164, 33000 Bordeaux, France. ⁹CHU de Bordeaux, Service de Médecine Interne, 1 Avenue de Magellen, 33600 Pessac, France. ¹⁰Univ. Bordeaux, INSERM, INRIA, BPH, U1219, 146, rue Léo Saignat-CS61292, 33076 Bordeaux Cedex, France. ¹¹CHU de Bordeaux, Service d'information médicale, INSERM, U1219, 146, rue Léo Saignat-CS61292, 33076 Bordeaux Cedex, France.

Received: 30 May 2022 Accepted: 21 December 2022

Published online: 09 January 2023

## References

1. Lederman MM, Funderburg NT, Sekaly RP, Klatt NR, Hunt PW. Residual immune dysregulation syndrome in treated HIV infection. Adv Immunol. 2013;119:51-83.

2. Deeks SG, Tracy R, Douek DC. Systemic effects of inflammation on health during chronic HIV infection. Immunity. 2013;39(4):633-45.

3. McBride JA, Striker R. Imbalance in the game of T cells: What can the CD4/CD8 T-cell ratio tell us about HIV and health? PLoS Pathog. 2017;13(11): e1006624.

4. Serrano-Villar S, Sainz T, Lee SA, Hunt PW, Sinclair E, Shacklett BL, et al. HIV-infected individuals with low CD4/CD8 ratio despite effective antiretroviral therapy exhibit altered T cell subsets, heightened CD8+ T cell activation, and increased risk of non-AIDS morbidity and mortality. PLoS Pathog. 2014;10(5): e1004078.

5. Caby F, Guihot A, Lambert-Niclot S, Guiguet M, Boutolleau D, Agher R, et al. Determinants of a low CD4/CD8 ratio in HIV-1-infected individuals despite long-term viral suppression. Clin Infect Dis. 2016;62(10):1297-303.

6. Caby F. CD4+/CD8+ ratio restoration in long-term treated HIV-1-infected individuals. AIDS. 2017;31(12):1685-95.

7. Serrano-Villar S, Pérez-Elías MJ, Dronda F, Casado JL, Moreno A, Royuela A, et al. Increased risk of serious non-AIDS-related events in HIV-infected subjects on antiretroviral therapy associated with a low CD4/CD8 ratio. PLoS ONE. 2014;9(1): e85798.

8. Hema MN, Ferry T, Dupon M, Cuzin L, Verdon R, Thiébaut R, et al. Low CD4/CD8 ratio is associated with non AIDS-defining cancers in patients on antiretroviral therapy: ANRS CO8 (Aproco/Copilote) prospective cohort study. PLoS ONE. 2016;11(8): e0161594.

9. Mussini C, Lorenzini P, Cozzi-Lepri A, Lapadula G, Marchetti G, Nicastri E, et al. CD4/CD8 ratio normalisation and non-AIDS-related events in individuals with HIV who achieve viral load suppression with antiretroviral therapy: an observational cohort study. Lancet HIV. 2015;2(3):e98-106.

10. Michel L, Carrieri MP, Fugon L, Roux P, Aubin HJ, Lert F, et al. Harmful alcohol consumption and patterns of substance use in HIV-infected patients receiving antiretrovirals (ANRS-EN12-VESPA Study): relevance for clinical management and intervention. AIDS Care. 2010;22(9):1136-45.

11. Garin N, Velasco C, De Pourcq JT, Lopez B, Gutierrez Mdel M, Haro JM, et al. Recreational drug use among individuals living with HIV in Europe: review of the prevalence, comparison with the general population and HIV guidelines recommendations. Front Microbiol. 2015;6:690.

12. Observatoire Français des Drogues et des Toxicomanelies et Fédération Addiction, Drogues et addictions, données essentielles. 2019 https://www.ofdt.fr/BDD/publications/docs/DADE2019.pdf. Accessed 25 May 2020.

13. Ministère de la Santé et des Solidarités. Prise en charge médicale des personnes vivant avec le VIH : Recommendations du groupe d'experts. Paris: France; 2018.
de Périgueux, Médecine Interne et Maladies Infectieuses, (B Castan, J. Koffi, N. Rouanes, A. Saunier, JB Zabbe); Hôpital de Pau, Médecine Interne et Maladies Infectieuses, (G. Dumondin, V. Gaborieau); Hôpital d'Orthez, Médecine Interne, (Y. Gerard); CHU de Poitiers, Médecine Interne et Maladies Infectieuses, (G. Beraud, G. Le Moal, M. Catroux, M. Garcia, V. Giraud, JP. Martellosio, F. Roblot); Hôpital de Saintes, Médecine Interne, (T. Pasdeloup); Hôpital d'Angoulême, Médecine Interne, (A. Riché, M. Grosset, S. Males, C. Ngo Bell); Hôpital de Jonzac, Maladies Infectieuses, (T. Pasdeloup), Hôpital de Saint jean d'Angely, Maladies Infectieuses, (T. Pasdeloup)

Other departements

Immunology: P. Blanco, I. Pellegrin; CRB-BBS: C. Carpentier, I. Pellegrin; Virol-ogy: P. Bellecave, ME. Lafon, C. Tumiotto; Pharmacology: S. Bouchet, D. Breilh, G. Miremont-Salamé; Data collection: D. Arma, G. Arnou, MJ. Blaizeau, P. Camps, M. Decoin, S. Delveaux, F. Diarra, L. Gabrea, W-H. Lai, S. Lawson-Ayayi, E. Lenaud, D. Plainchamps, A. Pougetoux, B. Uwamaliya, K. Zara; IT departement: V. Conte, M. Gapillout; Project Team: O. Leleux (Project Leader), F. Le Marec (Statistitien), A. Perrier (Data Manager)

## Author contributions

SD, FB and DB developed research question. SD conducted analyses. FB, LW, DB critically contributed to analyses. FB, FD, DB designed the QuAliV study and secured funding. OL, MH, DN, MOV, NR, EL, PD, CC, FB contributed to the study's conduct. All authors read, critically reviewed, and approved the final manuscript.

## Funding

This work was supported by seed funding and a grant from the ANRS Maladies infectieuses émergentes [Grant Number ECTZ100440 2019-2]. Diana Barger received a "Young Researcher Fellowship" from the French NGO Sidaction (2016-19) and a post-doctoral fellowship from the ANRS Maladies infectieuses émergentes [grant number ECTZ117276 2020-1]. The ANRS CO3 AQUIVIM-NA cohort is sponsored by the Bordeaux University Hospital and has received funding from the Bordeaux University Hospital and ANRS Maladies infectieuses émergentes. The cohort is coordinated from within the Inserm UMR 1219 Bordeaux Population Health Research Centre.

## Availability of data and materials

The anonymized individual data and the data dictionary of the study will be made available to other researchers by the coordinating investigator, Professor Fabrice Bonnet (fabrice.bonnet@chu-bordeaux.fr) after approval of a methodologically sound proposal by the study's steering committee and the signature of a data access agreement.

## Declarations

### Ethics approval and consent to participate

The ANRS CO3 Aquitaine-AQUIVIM-NA cohort study was approved by an Institutional Review Board on May 27, 2016 and the QuAliV study was granted ethical approval in August 2017. The National Commission on Informatics and Liberty (CNIL), the French regulatory agency charged with enforcing data privacy laws, reviewed and approved QuAliV study-specific amendments to authorizations on March 12, 2018. Written and informed consent is required of all cohort participants. All methods were carried out in accordance with relevant guidelines and regulations.

### Consent for publication

Not applicable.

### Competing interests

SD, MH, DN, NR, EL, PD, CC, OL, FD, LW have no relevant financial or non-financial competing interests to disclose. MOV reports financial support from Gilead Sciences, VIIv Healthcare and MSD outside the submitted work (conference participation). FB reports non-financial support from Gilead Sciences, Janssen, VIIv Healthcare, and MSD, personal fees from Gilead Sciences, Janssen, VIIv Healthcare and MSD outside the submitted work. DB has received speaking fees from Gilead Sciences, VIIv Healthcare, and MSD and been compensated as an expert on a Gilead Sciences advisory board outside the submitted work.

## Author details

¹Univ. Bordeaux, INSERM, BPH, U1219, 146, rue Léo Saignat-CS61292, 33076 Bordeaux Cedex, France. ²CHU de Bordeaux, COREVIH Nouvelle Aquitaine, INSERM, U1219, 1 Rue Jean Burguet, 33000 Bordeaux, France. ³CHU de Bordeaux, Service de Médecine Interne et Maladies Infectieuses, INSERM, U1219, 1 Rue Jean Burguet, 33000 Bordeaux, France. ⁴Univ. Bordeaux, INSERM, Institut Bergonié, BPH, U1219, CIC-P 1401, 146, rue Léo Saignat-CS61292, 33076 Bordeaux Cedex, France. ⁵CHU de Bordeaux, Service des Maladies Infectieuses et Tropicales, INSERM, U1219, Pl. Amélie Raba Léon, U121933000 Bordeaux, France. ⁶Centre Hospitalier de la Côte Basque, Service de Maladies Infectieuses, 13 Avenue de l'interne Jacques Loëb, BP 8, 64109 Bayonne Cedex, France. ⁷Centre Hospitalier de Périgueux, Service de Médecine Polyvalente, 80 Av. Georges Pompidou, 22400 Périgueux, France. ⁸Univ. Bordeaux, Department of Immunology, CNRS, ImmunoConcEpT, UMR 5164, 33000 Bordeaux, France. ⁹CHU de Bordeaux, Service de Médecine Interne, 1 Avenue de Magellen, 33600 Pessac, France. ¹⁰Univ. Bordeaux, INSERM, INRIA, BPH, U1219, 146, rue Léo Saignat-CS61292, 33076 Bordeaux Cedex, France. ¹¹CHU de Bordeaux, Service d'information médicale, INSERM, U1219, 146, rue Léo Saignat-CS61292, 33076 Bordeaux Cedex, France.

Received: 30 May 2022 Accepted: 21 December 2022

Published online: 09 January 2023

## References

1. Lederman MM, Funderburg NT, Sekaly RP, Klatt NR, Hunt PW. Residual immune dysregulation syndrome in treated HIV infection. Adv Immunol. 2013;119:51-83.

2. Deeks SG, Tracy R, Douek DC. Systemic effects of inflammation on health during chronic HIV infection. Immunity. 2013;39(4):633-45.

3. McBride JA, Striker R. Imbalance in the game of T cells: What can the CD4/CD8 T-cell ratio tell us about HIV and health? PLoS Pathog. 2017;13(11):e1006624.

4. Serrano-Villar S, Sainz T, Lee SA, Hunt PW, Sinclair E, Shacklett BL, et al. HIV-infected individuals with low CD4/CD8 ratio despite effective antiretroviral therapy exhibit altered T cell subsets, heightened CD8+ T cell activation, and increased risk of non-AIDS morbidity and mortality. PLoS Pathog. 2014;10(5): e1004078.

5. Caby F, Guihot A, Lambert-Niclot S, Guiguet M, Boutolleau D, Agher R, et al. Determinants of a low CD4/CD8 ratio in HIV-1-infected individuals despite long-term viral suppression. Clin Infect Dis. 2016;62(10):1297-303.

6. Caby F. CD4+/CD8+ ratio restoration in long-term treated HIV-1-infected individuals. AIDS. 2017;31(12):1685-95.

7. Serrano-Villar S, Pérez-Elías MJ, Dronda F, Casado JL, Moreno A, Royuela A, et al. Increased risk of serious non-AIDS-related events in HIV-infected subjects on antiretroviral therapy associated with a low CD4/CD8 ratio. PLoS ONE. 2014;9(1): e85798.

8. Hema MN, Ferry T, Dupon M, Cuzin L, Verdon R, Thiébaut R, et al. Low CD4/CD8 ratio is associated with non AIDS-defining cancers in patients on antiretroviral therapy: ANRS CO8 (Aproco/Copilote) prospective cohort study. PLoS ONE. 2016;11(8): e0161594.

9. Mussini C, Lorenzini P, Cozzi-Lepri A, Lapadula G, Marchetti G, Nicastri E, et al. CD4/CD8 ratio normalisation and non-AIDS-related events in individuals with HIV who achieve viral load suppression with antiretroviral therapy: an observational cohort study. Lancet HIV. 2015;2(3):e98-106.

10. Michel L, Carrieri MP, Fugon L, Roux P, Aubin HJ, Lert F, et al. Harmful alcohol consumption and patterns of substance use in HIV-infected patients receiving antiretrovirals (ANRS-EN12-VESPA Study): relevance for clinical management and intervention. AIDS Care. 2010;22(9):1136-45.

11. Garin N, Velasco C, De Pourcq JT, Lopez B, Gutierrez Mdel M, Haro JM, et al. Recreational drug use among individuals living with HIV in Europe: review of the prevalence, comparison with the general population and HIV guidelines recommendations. Front Microbiol. 2015;6:690.

12. Observatoire Français des Drogues et des Toxicomanelies et Fédération Addiction, Drogues et addictions, données essentielles. 2019 https://www.ofdt.fr/BDD/publications/docs/DADE2019.pdf. Accessed 25 May 2020.

13. Ministère de la Santé et des Solidarités. Prise en charge médicale des personnes vivant avec le VIH : Recommendations du groupe d'experts. Paris: France; 2018.
14. Valiathan R, Miguez MJ, Patel B, Arheart KL, Asthana D. Tobacco smoking increases immune activation and impairs T-cell function in HIV infected patients on antiretrovirals: a cross-sectional pilot study. PLoS ONE. 2014;9(5): e97698.

15. Pasala S, Barr T, Messaoudi I. Impact of alcohol abuse on the adaptive immune system. Alcohol Res Curr Rev. 2015;37(2):185-97.

16. Bagby GJ, Amedee AM, Siggins RW, Molina PE, Nelson S, Veazey RS. Alcohol and HIV effects on the immune system. Alcohol Res Curr Rev. 2015;37(2):287-97.

17. Zaparte A, Schuch JB, Viola TW, Baptista TAS, Beidacki AS, do Prado CH, et al. Cocaine use disorder is associated with changes in Th1/Th2/Th17 cytokines and lymphocytes subsets. Front Immunol. 2019;10:2435.

18. Rafie C, Campa A, Smith S, Huffman F, Newman F, Baum MK. Cocaine reduces thymic endocrine function: another mechanism for accelerated HIV disease progression. AIDS Res Hum Retroviruses. 2011;27(8):815-22.

19. Chao C, Jacobson LP, Tashkin D, Martínez-Maza O, Roth MD, Margolick JB, et al. Recreational drug use and T lymphocyte subpopulations in HIV-uninfected and HIV-infected men. Drug Alcohol Depend. 2008;94(1-3):165-71.

20. Chaillon A, Nakazawa M, Anderson C, Christensen-Quick A, Ellis RJ, Franklin D, et al. Effect of cannabis use on human immunodeficiency virus DNA during suppressive antiretroviral therapy. Clin Infect Dis. 2020;70(1):140-3.

21. Milhet M, Néfau T. Observatoire Français des Drogues et des Toxicomanies et Fédération Addiction, Chemsex, Slam. Renouvellement des usages de drogues en contextes sexuels parmi les HSH 2017. https://www.ofdt.fr/BDD/publications/docs/epfxmmx7.pdf. Accessed 25 May 2020.

22. Barger D, Leleux O, Conte V, Sapparrart V, Gapillout M, Crespel I, et al. Integrating electronic patient-reported outcome measures into routine HIV care and the ANRS CO3 aquitaine cohort's data capture and visualization system (QuAliV): protocol for a formative research study. JMIR Res Protocols. 2018;7(6): e147.

23. Barger D, Leleux O, Conte V, Sapparrart V, Gapillout M, Crespel I, et al. Web-based module for the collection of electronic patient-reported outcomes in people living with HIV in nouvelle aquitaine, france: usability evaluation. JMIR Form Res. 2019;3(4): e15013.

24. Verboeke SO, Wit FW, Verheij E, van Zoest RA, Kootstra NA, van der Valk M, et al. HIV-negative Men Who Have Sex with Men have higher CD8+ T-cell Counts and Lower CD4+/CD8+ T-cell ratios compared to HIV-negative Heterosexual Men. J Infect Dis. 2020. https://doi.org/10.1093/infdis/jiaa048.

25. Tollerud DJ, Clark JW, Brown LM, Neuland CY, Mann DL, Pankiw-Trost LK, et al. The effects of cigarette smoking on T cell subsets. A population-based survey of healthy caucasians. Am Rev Respir Dis. 1989;139(6):1446-51.

26. Zeidel A, Beilin B, Yardeni I, Mayburd E, Smirnov G, Bessler H. Immune response in asymptomatic smokers. Acta Anaesthesiol Scand. 2002;46(8):959-64.

27. Baum MK, Rafie C, Lai S, Sales S, Page JB, Campa A. Alcohol use accelerates HIV disease progression. AIDS Res Hum Retroviruses. 2010;26(5):511-8.

28. Samet JH, Cheng DM, Libman H, Nunes DP, Alperen JK, Saitz R. Alcohol consumption and HIV disease progression. J Acquir Immune Defic Syndr. 2007;46(2):194-9.

29. Conen A, Wang Q, Glass TR, Fux CA, Thurnheer MC, Orasch C, et al. Association of alcohol consumption and HIV surrogate markers in participants of the swiss HIV cohort study. J Acquir Immune Defic Syndr. 2013;64(5):472-8.

## Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Ready to submit your research? Choose BMC and benefit from:

* fast, convenient online submission

* thorough peer review by experienced researchers in your field

* rapid publication on acceptance

* support for research data, including large and complex data types

* gold Open Access which fosters wider collaboration and increased citations

* maximum visibility for your research: over 100M website views per year

At BMC, research is always in progress.

Learn more biomedcentral.com/submissions

<figure><img src="image_3.png" /></figure>
14. Valiathan R, Miguez MJ, Patel B, Arheart KL, Asthana D. Tobacco smoking increases immune activation and impairs T-cell function in HIV infected patients on antiretrovirals: a cross-sectional pilot study. PLoS ONE. 2014;9(5): e97698.

15. Pasala S, Barr T, Messaoudi I. Impact of alcohol abuse on the adaptive immune system. Alcohol Res Curr Rev. 2015;37(2):185-97.

16. Bagby GJ, Amedee AM, Siggins RW, Molina PE, Nelson S, Veazey RS. Alcohol and HIV effects on the immune system. Alcohol Res Curr Rev. 2015;37(2):287-97.

17. Zaparte A, Schuch JB, Viola TW, Baptista TAS, Beidacki AS, do Prado CH, et al. Cocaine use disorder is associated with changes in Th1/Th2/Th17 cytokines and lymphocytes subsets. Front Immunol. 2019;10:2435.

18. Rafie C, Campa A, Smith S, Huffman F, Newman F, Baum MK. Cocaine reduces thymic endocrine function: another mechanism for accelerated HIV disease progression. AIDS Res Hum Retroviruses. 2011;27(8):815-22.

19. Chao C, Jacobson LP, Tashkin D, Martínez-Maza O, Roth MD, Margolick JB, et al. Recreational drug use and T lymphocyte subpopulations in HIV-uninfected and HIV-infected men. Drug Alcohol Depend. 2008;94(1-3):165-71.

20. Chaillon A, Nakazawa M, Anderson C, Christensen-Quick A, Ellis RJ, Franklin D, et al. Effect of cannabis use on human immunodeficiency virus DNA during suppressive antiretroviral therapy. Clin Infect Dis. 2020;70(1):140-3.

21. Milhet M, Néfau T. Observatoire Français des Drogues et des Toxicomanies et Fédération Addiction, Chemsex, Slam. Renouvellement des usages de drogues en contextes sexuels parmi les HSH 2017. https://www.ofdt.fr/BDD/publications/docs/epfxmmx7.pdf. Accessed 25 May 2020.

22. Barger D, Leleux O, Conte V, Sapparrart V, Gapillout M, Crespel I, et al. Integrating electronic patient-reported outcome measures into routine HIV care and the ANRS CO3 aquitaine cohort's data capture and visualization system (QuAliV): protocol for a formative research study. JMIR Res Protocols. 2018;7(6): e147.

23. Barger D, Leleux O, Conte V, Sapparrart V, Gapillout M, Crespel I, et al. Web-based module for the collection of electronic patient-reported outcomes in people living with HIV in nouvelle aquitaine, france: usability evaluation. JMIR Form Res. 2019;3(4): e15013.

24. Verboeke SO, Wit FW, Verheij E, van Zoest RA, Kootstra NA, van der Valk M, et al. HIV-negative Men Who Have Sex with Men have higher CD8+ T-cell Counts and Lower CD4+/CD8+ T-cell ratios compared to HIV-negative Heterosexual Men. J Infect Dis. 2020. https://doi.org/10.1093/infdis/jiaa048.

25. Tollerud DJ, Clark JW, Brown LM, Neuland CY, Mann DL, Pankiw-Trost LK, et al. The effects of cigarette smoking on T cell subsets. A population-based survey of healthy caucasians. Am Rev Respir Dis. 1989;139(6):1446-51.

26. Zeidel A, Beilin B, Yardeni I, Mayburd E, Smirnov G, Bessler H. Immune response in asymptomatic smokers. Acta Anaesthesiol Scand. 2002;46(8):959-64.

27. Baum MK, Rafie C, Lai S, Sales S, Page JB, Campa A. Alcohol use accelerates HIV disease progression. AIDS Res Hum Retroviruses. 2010;26(5):511-8.

28. Samet JH, Cheng DM, Libman H, Nunes DP, Alperen JK, Saitz R. Alcohol consumption and HIV disease progression. J Acquir Immune Defic Syndr. 2007;46(2):194-9.

29. Conen A, Wang Q, Glass TR, Fux CA, Thurnheer MC, Orasch C, et al. Association of alcohol consumption and HIV surrogate markers in participants of the swiss HIV cohort study. J Acquir Immune Defic Syndr. 2013;64(5):472-8.

**Publisher's Note**

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Ready to submit your research? Choose BMC and benefit from:

* fast, convenient online submission

* thorough peer review by experienced researchers in your field

* rapid publication on acceptance

* support for research data, including large and complex data types

* gold Open Access which fosters wider collaboration and increased citations

* maximum visibility for your research: over 100M website views per year

At BMC, research is always in progress.

Learn more biomedcentral.com/submissions

<figure><img src="image_3.png" /></figure>