Abstract
Objective(s):
To determine baseline prevalence of proteinuria and albuminuria among REPRIEVE participants and evaluate associated risk factors.
Design:
Cross sectional analysis of a baseline sample of participants from the REPRIEVE Trial.
Methods:
REPRIEVE is an international primary cardiovascular prevention RCT of pitavastatin calcium vs. placebo among PWH on antiretroviral therapy. A representative subset (2791 participants) had urine collected at study entry. Urine protein to creatinine ratios (uPCR) and albumin to creatinine ratios (uACR) were classified as normal, moderately increased and severely increased. These were dichotomized to Normal or Abnormal for log-binomial regression analysis. Demographic, cardiometabolic, and HIV-specific data were compared among those with normal versus abnormal results.
Results:
Overall, median age 49 years, 41% female sex, 47% black or African American race, 36% had eGFR <90 mL/min/1.73mm2. For uPCR, 27% had moderately or severely increased values. For uACR, 9% had moderately or severely increased values. In the fully adjusted model for proteinuria, female sex, older age, residence in sub-Saharan Africa or East Asia, lower BMI, lower CD4 cell count, and use of TDF were associated with abnormal values. In the fully adjusted model for albuminuria, a diagnosis of HTN was associated with abnormal values.
Conclusions:
Abnormal proteinuria and albuminuria remain common (27% and 9%) despite controlled HIV. Lower current CD4 count and TDF use were strongly associated with proteinuria. Certain modifiable comorbidities, including HTN and smoking, were associated with abnormal values. In PWH with preserved eGFR, urine measures identify subclinical kidney disease and afford the opportunity for intervention.
Keywords: Proteinuria, Albuminuria, Chronic Kidney Disease, HIV, REPRIEVE
Introduction
Despite advances in antiretroviral therapy (ART), HIV infection remains an established risk factor for the development of chronic kidney disease (CKD) (1,2). With the transition of HIV to a manageable chronic illness, age-related comorbidities, including atherosclerotic cardiovascular disease (ASCVD) and CKD, have become leading causes of morbidity and mortality for persons with HIV (PWH) (3–6). There are numerous factors that likely contribute to excess CKD prevalence among PWH, including HIV infection of the kidney tubule epithelial cells, vascular inflammation due to HIV itself, exposure to nephrotoxic drugs, co-infection with HCV and HBV, and comorbid conditions such as hypertension and diabetes (7). When compared to the general population, PWH are at greater risk to develop CKD and end stage kidney disease (6,8). This fact reinforces the need for screening for early markers of renal function impairment among PWH.
While serum creatinine is generally used to estimate kidney function and identify people with kidney disease, this test lacks sensitivity to detect early changes in kidney function that may precede reductions in estimated glomerular filtration rate (eGFR). The course of CKD can be highly variable depending on numerous underlying factors, including management of comorbid illnesses, socioeconomic factors, genetics and other factors. Since urine is generally readily available and requires no invasive procedure to collect, an assessment for excess urinary protein, an indicator of underlying kidney disease, and albumin excretion, a marker of glomerular disease, is recommended and can serve as important prognostic measures for CKD progression (1,9). Risk stratification for progression of kidney disease and complications of CKD is based upon an assessment of both eGFR and severity of albuminuria (10).
Healthy persons excrete less than 150 mg of protein daily in the urine, including approximately 20mg of albumin (11). Excess proteinuria and albuminuria serve as markers of kidney damage and are used to predict progression of kidney disease. Given the highly vascular nature of the kidney, glomerular dysfunction, characterized by degree of albuminuria, also serves as a surrogate for endothelial dysfunction, a mechanism that contributes to ASCVD disease progression (10). Microalbuminuria predicts progression of both CKD and incident ASCVD (12).The current guidelines for the evaluation and management of CKD focus on the importance of assessment of albuminuria to serve as a prognostic marker for CKD progression (13).
We previously reported that 38% of the participants enrolled in the Randomized Trial to Prevent Vascular Events in HIV (REPRIEVE) had an eGFR <90 mL/min/1.73 m2 (14). In this analysis, we characterized the prevalence of albuminuria and proteinuria in a subset of the REPRIEVE cohort at baseline and evaluated clinical factors that are associated with albuminuria and proteinuria. A secondary objective included exploring the relationship of albuminuria and proteinuria with biomarkers of inflammation.
Methods
REPRIEVE (NCT02344290) is a randomized ASCVD prevention trial of PWH between ages 40 and 75 on stable ART. The full inclusion and exclusion criteria and design have been previously described (15). This is the baseline analysis of proteinuria and albuminuria for the Kidney Ancillary Study of REPRIEVE. The analyses presented here examine the prevalence of proteinuria and albuminuria at baseline and how these values vary by clinical risk factors. All analyses are limited to Kidney Ancillary Study population and include participants with samples collected on or before the start of REPRIEVE study treatment (i.e. pitavatatin vs. placebo), who enrolled at 42 sites participating in the Kidney Ancillary Study. Region of enrollment/residence was grouped according to the World Health Organization (WHO) Global Burden of Disease (GBD) Super Region categories (16). Each clinical research site obtained institutional review board/ethics committee approval and any other applicable regulatory entity approvals. Participants were provided with study information, including discussion of risks and benefits and were asked to sign the approved declaration of informed consent.
Biomarker Assessments:
Urine specimens were collected at baseline and kept in continuous storage at −80 °C until biomarker measurement. Urinary protein, albumin and creatinine were measured using colorimetric spectrophotometry at Quest Diagnostics Laboratory. Proteinuria was classified into categories using the urine protein to creatinine ratio (uPCR) as follows: normal to mildly increased (<150mg/g), moderately increased (150–500mg/g), and severely increased (>500mg/g) (13). Albuminuria was classified into categories using the urine albumin to creatinine ratio (uACR) as follows: normal to mildly increased (<30mcg/mg), moderately increased (30–300mcg/mg), and severely increased (>300mcg/mg) (10). Methods for quantification of sCD14 and oxLDL were previously reported (17).
Statistical analysis:
Participant characteristics were summarized overall and by proteinuria and albuminuria categories. Single and multivariable log binomial regression was performed to assess risk factors for proteinuria and albuminuria, combining the moderately and severely increased categories for modeling. Covariates were chosen based on clinical significance, and all covariates were included in the multivariable model. Adjusted analyses by sex, race, and region of enrollment were performed to determine if there were differential effects based on these categories with formal interaction tests evaluated in the overall model as indicated. Relative risk and associated 95% confidence intervals summarize the direction and magnitude of associations between each binary outcome and potential risk factors. Type 3 Wald p values are provided for overall covariate effects. We evaluated whether sCD14 and oxLDL levels were associated with proteinuria and albuminuria and adjusted for potential confounding effects of risk factors shown to be associated with both outcomes were assessed via linear regression. Given the large sample size, inference is based on a conservative .005 significance level and clinically meaningful effect sizes. All statistical analyses were performed using SAS version 9.4.
Results
Among 2791 REPRIEVE participants in the Kidney Ancillary Study, the median age was 49 years (Q1, Q3: 44, 53), 41% were assigned female sex at birth, 47% were black or African American, 30% white, 18% Asian, 45% current or former smokers, and median BMI was 26.0 kg/m2 (22.9, 29.8). All participants were receiving ART (48% NNRTI-based, 26% INSTI-based, 17% PI-based, 3% NRTI-sparing, 6% other), median CD4 count was 623 cells/mm3 (463, 815), 98% with HIV VL < 400cp/mL, median eGFR 98 mL/min (82, 111), (See table 1 and supplemental table 1 for baseline characteristics by category of proteinuria and albuminuria, respectively).
Table 1:
Baseline Characteristics of PWH by Categories of Proteinuria
| Proteinuria Categories based on Urine Protein to Creatinine Ratio (PCR) | ||||
|---|---|---|---|---|
| Characteristic | normal to mildly increased (N=1963) | moderately increased (N=656) | severely increased (N=74) | |
| All participants | 1,963 (73%) | 656 (24%) | 74 (3%) | |
| Natal sex | Female | 711 (36%) | 347 (53%) | 45 (61%) |
| Male | 1,252 (64%) | 309 (47%) | 29 (39%) | |
| Age (years) | Median (Q1,Q3) | 48 (44,53) | 49 (45,54) | 48 (44,53) |
| GBD Super Region | High Income | 1,243 (63%) | 296 (45%) | 33 (45%) |
| Latin America and Caribbean | 110 (6%) | 32 (5%) | 2 (3%) | |
| S.East/East Asia | 272 (14%) | 149 (23%) | 14 (19%) | |
| Sub-Saharan Africa | 338 (17%) | 179 (27%) | 25 (34%) | |
| Race | Black or African American | 894 (46%) | 308 (47%) | 38 (51%) |
| White | 641 (33%) | 167 (25%) | 18 (24%) | |
| Asian | 288 (15%) | 150 (23%) | 14 (19%) | |
| Other | 140 (7%) | 31 (5%) | 4 (5%) | |
| ASCVD risk score (%) | Median (Q1,Q3) | 3.4 (1.4,6.1) | 2.9 (1.2,5.4) | 2.7 (1.2,5.3) |
| Hypertension | 580 (30%) | 212 (32%) | 19 (26%) | |
| History of diabetes mellitus | 10 (1%) | 3 (0%) | 0 (0%) | |
| Smoking status | Current | 389 (20%) | 139 (21%) | 10 (14%) |
| Former | 495 (25%) | 145 (22%) | 16 (22%) | |
| Never | 1,074 (55%) | 371 (57%) | 48 (65%) | |
| BMI (kg/m2) | <30 | 1,455 (74%) | 528 (80%) | 61 (82%) |
| 30+ | 507 (26%) | 128 (20%) | 13 (18%) | |
| ART regimen class | NRTI + INSTI | 576 (29%) | 119 (18%) | 13 (18%) |
| NRTI + NNRTI | 897 (46%) | 377 (57%) | 34 (46%) | |
| NRTI + PI | 310 (16%) | 118 (18%) | 16 (22%) | |
| NRTI-sparing | 57 (3%) | 7 (1%) | 5 (7%) | |
| Other NRTI-containing | 123 (6%) | 35 (5%) | 5 (7%) | |
| Current NRTI Exposure | ABC | 213 (11%) | 46 (7%) | 3 (4%) |
| Any TAF | 310 (16%) | 46 (7%) | 4 (5%) | |
| Any TDF | 1,236 (63%) | 526 (80%) | 60 (81%) | |
| No NRTI | 73 (4%) | 10 (2%) | 6 (8%) | |
| Thymidine Analogue | 131 (7%) | 28 (4%) | 1 (1%) | |
| CD4 count (cells/mm3) | Median (Q1,Q3) | 629 (468,826) | 595 (437,775) | 609 (443,778) |
| HIV-1 RNA (copies/mL) | <LLQ | 1,474 (88%) | 442 (88%) | 52 (91%) |
| LLQ -< 400 | 164 (10%) | 53 (11%) | 4 (7%) | |
| 400+ | 29 (2%) | 9 (2%) | 1 (2%) | |
| eGFR by CKD-EPI (mL/min per 1.73 mm2) | Median (Q1-Q3) | 98 (82–110) | 99 (83–113) | 91 (72–110) |
| LDL-C, calculated (mg/dL) | Median (Q1-Q3) | 107 (87–127) | 106 (84–129) | 110 (94–127) |
| HDL-C (mg/dL) | Median (Q1-Q3) | 50 (41–61) | 52 (43–63) | 52 (41–62) |
| Oxidized LDL (U/L) | Median (Q1-Q3) | 55 (43–72) | 53 (41–68) | 63 (44–73) |
| Soluble Cd14 (ng/mL) | Median (Q1-Q3) | 1,734 (1,456–2,076) | 1,919 (1,602–2,283) | 1,982 (1,607–2,473) |
Continuous variables are described as Median (Q1, Q3). Other variables reported as count and percentage. Age, Diabetes, Hypertension, Obesity, CD4, and HIV viral load are time updated as of January 2020; remaining variables are as reported at study entry.
For HIV VL measures, there were missing data for 465 participants; one participant is missing data for eGFR; 492 participants are missing oxLDL; 493 participants are missing sCD14.
The baseline prevalence of proteinuria and albuminuria was 27% and 9%, respectively (Table 2, Supplemental Figure 1). Proteinuria and albuminuria are presented by eGFR categories in Table 2: 47% of participants with severely increased proteinuria and 38% with severely increased albuminuria had reduced eGFR (<90 mL/min/1.73 m2). For participants with severely increased proteinuria, median eGFR was lower than for other groups (91 vs 98 mL/min/1.73 m2). No difference in median eGFR was observed among participants with severely increased albuminuria.
Table 2:
Distribution of Proteinuria and Albuminuria Categories by Estimated Glomerular Filtration Rate
| Proteinuria Categories* based on Urine Protein to Creatinine Ratio (uPCR) | ||||||
|---|---|---|---|---|---|---|
| normal to mildly increased | moderately increased | severely increased | ||||
|
| ||||||
|
eGFR by CKD-EPI (mL/min per 1.73 mm2)
|
1243 | (63.4%) | 417 | (63.6%) | 39 | (52.7%) |
|
≥ 90
| ||||||
|
60 - <90
|
678 | (34.6%) | 220 | (33.5%) | 29 | (39.2%) |
| <60 | 41 | (2.1%) | 19 | (2.9%) | 6 | (8.1%) |
|
| ||||||
| Albuminuria Categories* based on Urine Albumin to Creatinine Ratio (uACR) | ||||||
| normal to mildly increased | moderately increased | severely increased | ||||
|
| ||||||
|
eGFR by CKD-EPI (mL/min per 1.73 mm2)
|
1604 | (63.3%) | 138 | (60.3%) | 16 | (61.5%) |
|
≥ 90
| ||||||
|
60 - <90
|
877 | (34.6%) | 79 | (34.5%) | 8 | (30.8%) |
| <60 | 54 | (2.1%) | 12 | (5.2%) | 2 | (7.7%) |
Proteinuria categories: normal to mildly increased (< 150mg/g); moderately increased (150–500mg/g); severely increased (>500mg/g). Albuminuria categories: normal to mildly increased (< 30mg/g); moderately increased (30–300mg/g); severely increased (>300mg/g). Column percentages are presented.
In the unadjusted analysis, a higher prevalence of proteinuria was identified for females compared to males, residence in sub-Saharan Africa and East Asia compared to High income, CD4 <500 c/mm3, and current tenofovir disoproxil fumarate (TDF) exposure (regardless of duration), while a lower risk of proteinuria was apparent with higher BMI. Associations with female sex, older age, enrollment from Sub-Saharan Africa and East Asian sites, BMI, exposure to tenofovir disoproxil fumarate persisted in adjusted analysis (see figure 1).
Figure 1: Relative Risk of Proteinuria (moderately or severely increased versus normal to mildly increased), unadjusted and adjusted.
To assess risk factors for proteinuria, combining the moderately and severely increased categories for modeling, Single and multivariable log binomial regression was performed with unadjusted relative risk presented in panel A and adjusted relative risk in panel B.
Because we identified inconsistencies for the relationship between region and race effects in relation to proteinuria, we performed adjusted analyses by region and race (See supplemental figures 2 and 3). Female sex and TDF exposure demonstrated a similar association with a higher prevalence of proteinuria across all regions, with the TDF effect appearing strongest in the Sub-Saharan Africa region and female sex having strongest risk in high income regions. Older age was associated with higher risk of proteinuria in Sub-Saharan Africa and, with a similar trend in East Asia and High Income Regions. In adjusted analyses performed for each race, regional and age differences in the risk of proteinuria are apparent only among blacks. Notably, Sub-Saharan Africa blacks had a higher risk of proteinuria compared to blacks in High Income regions.
Similarly, we dichotomized albuminuria to normal (<30mcg/mg) versus moderately or severely increased albuminuria (≥30mcg/mg) for log binomial regression analyses. In the unadjusted analysis, a higher risk of albuminuria was identified for females compared to males, residence in sub-Saharan Africa and East Asia compared to High income, and a diagnosis of hypertension. In the adjusted analysis, a diagnosis of hypertension remained associated with a higher risk of albuminuria (see figure 2). In models adjusted for sex, region and race, the effect of hypertension remained apparent with no differences observed by race, sex or region.
Figure 2: Relative Risk of Albuminuria (moderately or severely increased versus normal to mildly increased), unadjusted and adjusted.
To assess risk factors for albuminuria, combining the moderately and severely increased categories for modeling, Single and multivariable log binomial regression was performed with unadjusted relative risk presented in panel A and adjusted relative risk in panel B.
To assess whether proteinuria and albuminuria were linked with inflammation and/or early atherosclerotic changes, we assessed whether soluble CD14 and oxidized LDL were associated with these parameters. Median sCD14 values were higher for both moderately increased and severely increased proteinuria categories compared to normal values with overlap of the range of values. Median oxidized LDL was only higher for the severely increased proteinuria category (table 1). For the albuminuria categories, the values of these biomarkers overlapped without notable differences (supplemental table 1).
Discussion
In this global cohort of people with HIV with well controlled virus, low to moderate risk for ASCVD, and normal estimates of kidney function, proteinuria and albuminuria were identified in 27% and 9% of the cohort, respectively, indicating that a substantial proportion of this population has preclinical renal impairment. Several factors related to excess proteinuria were identified, including older age, diagnosis of hypertension, and use of TDF. In addition, enrollment at sites on Sub-Saharan Africa and East Asia was associated with proteinuria while being overweight or obese was associated with a lower risk of proteinuria. The prevalence of urinary abnormalities in this otherwise healthy cohort of PWH reinforce the importance of assessing for risk factors associated with CKD and to screen for preclinical kidney disease using urine biomarkers. Identifying these derangements early will allow for measures to prevent the progression to overt CKD.
Proteinuria and albuminuria are early manifestations of kidney disease, and routine monitoring of proteinuria is indicated for all persons with CKD as its presence identifies persons who are at greater risk for disease progression (13). However, data from the general population highlight that this simple assessment of spot uPCR is underutilized, even in diabetics who account for 20% of all cases of CKD and the most common cause of ESRD (18). Previous reports have demonstrated the utility of uPCR as a screening test for predicting nephropathy in persons with hypertension in both high income and low and middle income countries (19–21). Given the high prevalence of CKD among PWH, uPCR and uACR should be assessed more frequently among PWH. Early identification of these derangements can facilitate early treatment, such as blockade of the renin-angiotensin system or use of certain calcium channel blockers, to reduce albuminuria and limit progressive CKD. Similarly, recent reviews of CKD and HIV strongly encourage the examination for urine proteinuria as part of routine management to identify PWH for early intervention (22).
The association of proteinuria with female sex is provocative but not unique. Interestingly, the relationship between female sex and proteinuria was identified in all regions (see supplemental figure 3). While much of the published data regarding proteinuria in women focuses on pregnancy, Szczech and colleagues previously reported a similar high prevalence of proteinuria (32%) among a cohort of 2057 women with HIV (23). In their cohort, advanced HIV disease (lower CD4 cell counts and detectable HIV viremia) as well as HCV co-infection and black race were associated with proteinuria. We have previously demonstrated sex differences in inflammation markers in the REPRIEVE cohort (17). The relationship between proteinuria and higher sCD14 levels is a potential indicator that excess inflammation is playing a role in early kidney injury in the setting of HIV. This possibly explains the greater prevalence for proteinuria among women in out cohort. A strong relationship with proteinuria and albuminuria and both local inflammation and alterations in the adaptive immune system is well described in diabetic kidney disease literature and is worthy of further evaluation in longitudinal analyses (24).
Enrollment from Sub-Saharan Africa and Asian sites was also associated with excess proteinuria. These findings require additional analysis and an assessment of why these differences were noted. Potential explanations include exposure to additional factors causing proteinuria, lack of access to prevention services at the sites in low and middle income countries. The availability of services for diagnosis, management, and monitoring of CKD are lower in LMIC compared to high income countries (25). For instance, uPCR is available at only 15% of low income countries in a recent analysis (26). In addition, there are significant disparities in the distribution of the global nephrology workforce with many countries in Sub-Saharan Africa having < 5 nephrologists per 1 million population (27). Resource limited countries with a high prevalence of HIV may have limited services to meet the demand for management of chronic disease manifestations that encompass HIV care.
In published literature, the relationship between TDF and proteinuria has been widely reported and these data are confirmatory (28,29). In a secondary analysis evaluating factors limited to persons with severe proteinuria, current use of TDF was strongly associated [odds ratio of 5.02 (95%CI: 1.22, 20.57)]. This finding reinforces the importance of monitoring for proteinuria when PWH are in receipt of TDF. Notably, current receipt of tenofovir alafenamide (TAF) was not associated with excess proteinuria in this cohort. While not novel, this finding confirms this well characterized different between these two agents (30). The relationship between NRTI-sparing regimens and proteinuria likely reflects a survivor bias with only 3% of the cohort being on this type of regimen and warrants additional analysis longitudinally. Furthermore, the longitudinal data from REPRIEVE will further characterize the effects of these and other antiretrovirals on renal function.
Excess albuminuria was present in 9% of the cohort with hypertension being independently associated with albuminuria. While the absolute prevalence of albuminuria was low, this proportion remains a significant minority, given previous work linking albuminuria with ASCVD events and all-cause mortality among PWH (31,32). In previous studies of PWH, the prevalence of albuminuria ranged from 14–20%; this higher prevalence reflects the inclusion of PWH with although including PWH with eGFR < 60ml/min and greater comorbidity burden. As the REPRIEVE cohort excludes persons with elevated ASCVD risk and eGFR < 60ml/min, our estimate likely underestimates the burden of subclinical renal function impairment among PWH. However, given the relationship between microalbuminuria and progressive CKD and ASCVD, our data reinforce the importance of identifying interventions to reduce albuminuria and prevent these detrimental outcomes. We will determine the effects of statin therapy on these measures in future analyses of the REPRIEVE cohort.
The present analysis has certain limitations. REPRIEVE excluded PWH with advanced CKD (eGFR < 60mL/min) and thus have preferentially selected a cohort with less proteinuria than the general PWH population. The high median CD4 count, well controlled viremia, and low ASCVD risk score of the cohort may limit the generalizability of these results although the management of non-AIDS-related comorbidities are a significant priority for many HIV care providers (33). However, these relatively healthier characteristics of this population may shed light on the initial development of CKD at an earlier time point in the disease progression. The data included are cross sectional in nature and lack multiple measures of these urine biomarkers. Future analyses will evaluate longitudinal changes in kidney function markers in this cohort and determine whether the REPRIEVE trial intervention can prevent the progression of CKD among PWH.
In summary, we report that a substantial proportion of the REPRIEVE cohort have proteinuria and albuminuria in the setting of well controlled HIV and preserved eGFR. Because early manifestations of CKD, i.e. proteinuria and albuminuria, are often asymptomatic, the utilization of laboratory-based assessments of urinary biomarkers play a crucial role in identification of early CKD (34). Early detection facilitates appropriate interventions to reduce the long term complications of CKD. Given the low cost of spot urine assessment and the ease of urine collection, uPCR and uACR are screening tools that should be promoted for routine HIV care regardless of the clinical setting. Future analyses will assess the longitudinal data regarding kidney function in this cohort.
Supplementary Material
Acknowledgements
These data were presented as a poster presentation at CROI 2022; Feb 13–16; Virtually in Denver, CO. The study investigators thank the study participants, site staff, and study-associated personnel for their ongoing participation in the trial. In addition, we thank the following: the ACTG for clinical site support; ACTG Clinical Trials Specialists (Laura Moran, MPH, and Jhoanna Roa, MD) for protocol development and implementation support; the data management center, Frontier Science Foundation, for data support; the Center for Biostatistics in AIDS Research for statistical support; and the Community Advisory Board for input for the community. The views expressed in this manuscript are those of the authors and do not necessarily represent the views of the National Institute of Diabetes and Digestive and Kidney Diseases; the National Heart, Lung, and Blood Institute; the National Institute of Allergy and Infectious Diseases; the National Institutes of Health; or the U.S. Department of Health and Human Services.
Funding:
This work is supported through NIH grants U01HL123336 and 1UG3HL164285, to the Clinical Coordinating Center, and U01HL123339 and 1U24HL164284, to the Data Coordinating Center, as well as funding from Kowa Pharmaceuticals America, Inc., Gilead Sciences, and ViiV Healthcare. The NIDDK supported this work through grant R01 DK108438. The NIAID supported this study through grants UM1 AI068636, which supports the ACTG Leadership and Operations Center; and UM1 AI106701, which supports the ACTG Laboratory Center. This work was also supported by the Nutrition Obesity Research Center at Harvard (P30DK040561 to SKG).
Footnotes
Disclosures: ETO is currently employed by ViiV Healthcare Medical Affairs; the work presented here was performed prior to his current employment. CF has received research support to his institution from Abbvie, Gilead Sciences, Merck, and ViiV Healthcare. GAM has served as a consultant for Janssen Pharmaceuticals, Gilead Sciences, ViiV Healthcare, Merck, and Theratechnologies. CM has served as a consultant for Gilead Sciences, Pfizer, and ViiV Healthcare. ML has received research support to his institution from AstraZeneca AB, Ionis, Johnson and Johnson, Kowa, MedImmune, and National Academy of Medicine. SG has served as a consultant for Theratechnologies and Marathon Asset Management and received research support to his institution from Kowa, Gilead Sciences, and ViiV Healthcare. HR has received research support to her institution from Gilead Sciences and Kowa. All the other authors declared no competing interests.
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