Education, Faculty of
Medicine, Faculty of
Educational and Counselling Psychology, and Special Education (ECPS), Department of
Medicine, Department of
Family Practice, Department of
DSpace
Viruses 15 (2): 385 (2023)
Centre for Health Evaluation & Outcome Sciences
Providence Health Care (Vancouver, B.C.)
Fonseca-Cuevas, Alejandra
Newsome, Patrick
Wang, Lu
Chen, Michelle Y.
Richardson, Chris G.
Hull, Mark
McLinden, Taylor
Guillemi, Silvia
Barrios, Rolando
Montaner, Julio S. G.
Lima, Viviane D.
The incidence of chronic kidney disease (CKD) is increasing among people living with HIV (PLWH). Routine monitoring of indicators such as CD4:CD8 ratio might improve the early detection of CKD. Our objective was to identify clinically relevant CD4:CD8 ratio trajectories indicative of CKD risk. Participants were ≥ 18 years old, initiated antiretroviral therapy between 2000 and 2016, and were followed for ≥6 months until 31 March 2017 or last contact date. Outcome was incidence of CKD. Growth mixture models (GMMs) and decay models were used to compare CD4:CD8 ratio trajectories. Following GMM, 4547 (93.5%) participants were classified in Class 1 with 5.4% developing CKD, and 316 (6.5%) participants were classified in Class 2 with 20.9% developing CKD. The final model suggested that participants in Class 2 had 8.72 times the incidence rate of developing CKD than those in Class 1. Exponential decay models indicated a significant CD4:CD8 ratio decline among Class 2 participants who developed CKD. Among those who developed CKD in Class 2, starting at 5.5 years of follow-up, the slope of their ratio trajectory curve changed significantly, and the rate of decline increased dramatically. Routine monitored CD4:CD8 ratios can be an effective strategy to identify early CKD risk among PLWH.
Citation: Fonseca-Cuevas, A.;Newsome, P.; Wang, L.; Chen, M.Y.;Richardson, C.G.; Hull, M.;McLinden, T.; Guillemi, S.; Barrios,R.; Montaner, J.S.G.; et al. IdentifyingLongitudinal CD4:CD8 RatioTrajectories Indicative of ChronicRenal Disease Risk among PeopleLiving with HIV: An Application ofGrowth Mixture Models. Viruses2023, 15, 385. https://doi.org/10.3390/v15020385Academic Editors: Angela Kaida,Hélène C. F. Côté, Melanie Murrayand Elizabeth KingReceived: 9 December 2022Revised: 21 January 2023Accepted: 26 January 2023Published: 29 January 2023Copyright: © 2023 by the authors.Licensee MDPI, Basel, Switzerland.This article is an open access articledistributed under the terms andconditions of the Creative CommonsAttribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).virusesArticleIdentifying Longitudinal CD4:CD8 Ratio Trajectories Indicativeof Chronic Renal Disease Risk among People Living with HIV:An Application of Growth Mixture ModelsAlejandra Fonseca-Cuevas 1, Patrick Newsome 2, Lu Wang 1, Michelle Y. Chen 3 , Chris G. Richardson 4,Mark Hull 1,2, Taylor McLinden 1 , Silvia Guillemi 5, Rolando Barrios 1, Julio S. G. Montaner 1,2and Viviane D. Lima 1,2,*1 British Columbia Centre for Excellence in HIV/AIDS, Vancouver, BC V6Z 1Y6, Canada2 Department of Medicine, Faculty of Medicine, University of British Columbia,Vancouver, BC V5Z 1M9, Canada3 Department of Educational & Counselling Psychology & Special Education, University of British Columbia,Vancouver, BC V6T 1Z4, Canada4 Centre for Health Evaluation and Outcome Sciences, Providence Health Care,Vancouver, BC V6Z 1Y6, Canada5 Department of Family Medicine, University of British Columbia, Vancouver, BC V6T 1Z3, Canada* Correspondence: vlima@bccfe.ca; Tel.: +1-604-806-8796Abstract: The incidence of chronic kidney disease (CKD) is increasing among people living with HIV(PLWH). Routine monitoring of indicators such as CD4:CD8 ratio might improve the early detectionof CKD. Our objective was to identify clinically relevant CD4:CD8 ratio trajectories indicative of CKDrisk. Participants were ≥ 18 years old, initiated antiretroviral therapy between 2000 and 2016, andwere followed for≥6 months until 31 March 2017 or last contact date. Outcome was incidence of CKD.Growth mixture models (GMMs) and decay models were used to compare CD4:CD8 ratio trajectories.Following GMM, 4547 (93.5%) participants were classified in Class 1 with 5.4% developing CKD,and 316 (6.5%) participants were classified in Class 2 with 20.9% developing CKD. The final modelsuggested that participants in Class 2 had 8.72 times the incidence rate of developing CKD than thosein Class 1. Exponential decay models indicated a significant CD4:CD8 ratio decline among Class 2participants who developed CKD. Among those who developed CKD in Class 2, starting at 5.5 yearsof follow-up, the slope of their ratio trajectory curve changed significantly, and the rate of declineincreased dramatically. Routine monitored CD4:CD8 ratios can be an effective strategy to identifyearly CKD risk among PLWH.Keywords: CD4:CD8 ratio; HIV; chronic kidney disease; growth mixture modeling; decay model;risk factors1. IntroductionSince the introduction of antiretroviral therapy (ART), the life expectancy of peopleliving with HIV (PLWH) has increased, which has led to higher incidence of age-associatedconditions such as non-AIDS-related malignancies, liver, cardiovascular, cerebrovascular,and renal diseases in this population [1–3]. The complex process of aging, while livingwith HIV, presents many challenges for patients and healthcare providers, due to differentpsychosocial (e.g., social isolation), structural (e.g., access to healthcare resources, housing,food, and medications), and behavioral factors (e.g., substance use), and the cumulativetoxicity of HIV/non-HIV medications increasing the complexity in the clinical managementof individuals with multimorbidity [4]. Studies have shown that the management ofindividuals who present with a large number of medical issues can be quite complex,increasing the risk of polypharmacy and drug–drug interactions [5–8]. Since services andprograms for PLWH with multimorbidity in different settings can be highly disconnected,Viruses 2023, 15, 385. https://doi.org/10.3390/v15020385 https://www.mdpi.com/journal/virusesViruses 2023, 15, 385 2 of 14they are inefficient in adequately achieving those who need care, adding to the complexityin the clinical management of these individuals. One of the most effective strategies toimprove the overall health of patients is to change the way clinicians and other healthcareproviders deliver care to a population. Care should pivot from focusing on individualconditions to a holistic approach that considers multiple interrelated health conditions andhealth determinants that contribute to an excessive disease burden [6,9,10]. Therefore, dueto the complexity in treating these patients, there is an urgent need to organize care byintegrating multiple healthcare practitioners and services, which allows patients to receivethe care they need, when they need it, in a cost-effective way to reduce morbidity andpremature mortality in this population [11–13].One of the most commonly diagnosed age-associated conditions among PLWH ischronic kidney disease (CKD), being three times more likely to develop among PLWH thanthe general population [14–17]. In British Columbia (BC), CKD affects 13.5% of PLWHcompared to 1.9% of HIV-negative individuals [17]. Risk factors for CKD include lowCD4 cell counts, high HIV viral loads, and common comorbidities such as hypertensionand type II diabetes mellitus [18–24]. Some antiretrovirals may increase the risk of CKDdue to their nephrotoxicity (mostly older drugs such as tenofovir disoproxil fumarate,lopinavir/atazanavir/darunavir boosted with lopinavir, and to a lesser extent tenofoviralafenamide) and others are known to inhibit the excretion of serum creatinine by proximalrenal tubular cells by the blockade of specific transporters, which are not toxic to thecell but are linked to changes in serum creatinine levels (e.g., dolutegravir, rilpivirine,cobicistat) [23,25,26]. Since CKD may lead to end-stage renal disease and death, thereforeearly identification of CKD is necessary to prevent disease progression [14].Current guidelines for CKD screening rely on annual/biannual urine and blood teststo obtain glomerular filtration rates (eGFR) and albumin/creatinine ratios (ACR) [24,27–31].However, eGFR and ACR estimations depend on serum creatinine from muscle whichis influenced by muscle mass, nutritional status, age, race, the amount resorbed in thekidney, and by some ART drugs leading to possible limitations on the early recognitionof CKD among PLWH [27,28,32,33]. Other routinely monitored tests among PLWH aremarkers of chronic inflammation and immune senescence, and they may aid in identifyingearly CKD, such as the CD4:CD8 ratio [29,34–38]. Studies have shown that CD4:CD8 ratiosare linked to chronic infection resulting in persistent immune system activation whichpredisposes people to a higher risk of chronic comorbidities [39]. Persistent CD4:CD8ratios less than 0.3 have been shown to be independently associated with increased riskof non-AIDS-related events and mortality [1,37,40]. Although these ratios provide goodprognostic information on non-AIDS related events, limited research has been conductedto assess whether CD4:CD8 ratio are linked to any disease-specific burden [1,40–45].Traditional methods have used CD4:CD8 ratios as a prognostic factor for diseaseprogression and inflammation using different predetermined cut-off points [40,46–50].However, the use of different cut-offs for CD4:CD8 ratio classification may lead to inconsis-tencies, thus compromising generalizability and comparability of results across studies [51].Identifying CD4:CD8 ratio trajectories specifically linked to CKD patients could improveearly recognition of CKD among PLWH, and support the implementation of interventionsand strategies to closely monitor PLWH at higher risk of CKD [52]. Thus, the objective ofthis study was to identify clinically relevant CD4:CD8 ratio trajectories indicative of earlyCKD risk among PLWH. We also explored the effect of sex on the risk of CKD.2. Materials and Methods2.1. Study Setting and DataData were obtained from the BC Seek and Treat for Optimal Prevention of HIV/AIDS(STOP HIV/AIDS) population-based cohort with access to data on PLWH since their ear-liest date indicating HIV seropositivity [53]. This cohort is based on a longitudinal datalinkage of several provincial databases including the BC Center for Excellence in HIV/AIDSDrug Treatment registry, the laboratory database, and several provincial administrativeViruses 2023, 15, 385 3 of 14health databases as explained in the Supplementary Materials [53–62]. Data captured inthe STOP HIV/AIDS linkages include sociodemographic factors such as sex, age, and geo-graphic location, ART treatment information, healthcare use, and diagnostic and laboratorybiomarkers such as CD4 cell counts, CD4:CD8 ratio, and HIV viral load. The last dataupdate had data until 31 March 2017.2.2. Study DesignA population-based cohort study design with the following inclusion criteria: (i) age≥18 years, (ii) ART initiation between 1 January 2000 and 30 September 2016, (iii) noprevious supervised ART interruptions, (iv) no participation in a blinded ART-relatedclinical trial, and (v) no previous diagnosis of CKD. Additionally, in order to fit the models,we required (i) at least six months of follow-up, (ii) available HIV viral load and CD4measurements during the follow-up, and (iii) at least two CD4:CD8 ratio measurementsthroughout the study. Eligible participants were followed until 31 March 2017, the lastcontact date (i.e., the last filled ART prescription refill date, the last available laboratorytest date or the date of last encounter with the healthcare system), or the date of death(all-causes).2.3. Outcome and Study CovariatesThe outcome was incidence of CKD identified using a case finding algorithm publishedby the BC Ministry of Health based on physician claims and hospitalizations with adiagnostic code for CKD Supplementary Materials, Table S1 [60]. To estimate incidence, weapplied a washout period of five years before first ART date. For participants lacking fiveyears of information prior to their first ART date, a washout period of five years after theirearliest encounter with the healthcare system was used. CD4:CD8 ratios were measuredevery six months.Time-invariant covariates measured at baseline included: sex (female, male), age(continuous), ART naïve (yes, no), and a categorical indicator reflecting a cohort effect(years 2000–2004, 2005–2010, 2011–2016). Time-invariant covariates measured at the endof follow-up included: history of substance use disorder (SUD) (yes, no), duration ofhypertension, diabetes mellitus, and cardiovascular disease (CVD) (no disease, < medianduration, ≥ median duration), area under the HIV viral load curve [63], proportion oftime on protease inhibitors (PI), proportion of time on non-nucleoside reverse transcriptaseinhibitors (NNRTI), proportion of time on tenofovir disoproxil fumarate (TDF), and follow-up time (continuous). CD4 nadir (continuous) was measured one year before the end offollow-up. Case-finding algorithms used to identify participants with SUD, hypertension,diabetes mellitus and CVD are described in the Supplementary Materials Table S1 Pleasenote that, beside TDF, we did not investigate other specific antiretrovirals known to increasethe risk of CKD [20–22]. The variables included in this study were chosen a priori basedon the clinical literature and treatment guidelines describing factors known to influenceCKD risk.2.4. Statistical AnalysesWe conducted bivariable analyses between risk factors and CKD using Pearson’schi-square or Fisher’s exact test for categorical variables (depending on cell count), andWilcoxon’s Rank Sum test for continuous variables. This study consisted of a two-prongedanalysis:2.4.1. Growth Mixture ModelingTo investigate the patterns of CD4:CD8 ratio trajectories among participants withand without CKD, we applied growth mixture modeling (GMM) [52,64,65]. This typeof modeling allows the identification of unobserved subpopulations or classes based ondistinctive trajectories of CD4:CD8 ratios. First, we fit unconditional models with a differentnumber of latent classes, and with linear (in case the ratio changes linearly over time) andViruses 2023, 15, 385 4 of 14quadratic (in case there is a non-linear pattern over time) growth factors; these terms wereintroduced one at a time. Second, we focused on examining the relationships betweencovariates and latent classes by fitting the GMM including covariates. Adding covariatesto the model helps us understand the key characteristics of each group and how differentcovariates may affect the longitudinal trajectory within each class. To examine the fitnessof the models, we used the Akaike information criteria (AIC), Bayesian information criteria(BIC), sample size-adjusted BIC (SBIC), entropy (range 0 to 1), adjusted Lo–Mendell–Rubinlikelihood ratio test (LMR-LRT), and class size. A LMR-LRT with p-value of <0.05 wasdeemed significant while lower values of AIC, BIC and SBIC indicated better fit [66,67].Third, we compared the incidence of CKD using two versions of the selected model,one following an unconditional approach (without covariates) and the other includingcovariates. To analyze the association between the outcome (CKD incidence) and thelatent classes, we assigned participants to their most likely classes and then included thisvariable as an explanatory variable in the Poisson regression to model the incidence CKDrate per 1000 person-years between the distinct trajectories. Last, to understand the keycharacteristics of participants in each class and how different covariates may affect theCD4:CD8 ratio trajectories, we conducted bivariable analyses between covariates and thelatent classes stratified by CKD status. Analyses were conducted with SAS version 9.4 (SASInstitute Inc., Cary, NC, USA) and Mplus v.8.1 (Los Angeles, CA, USA).2.4.2. Decay ModelBased on the results from the GMM, we estimated CD4:CD8 ratio trajectories toassess whether there are early indications of participants being at a high risk of CKD andpotential cut-off time points of intervention. CD4:CD8 ratio measurements were obtainedlongitudinally from study baseline until the end of follow-up. We modeled and comparedthe CD4:CD8 ratio depletion trajectories among the identified latent classes and CKDstatus. The natural logarithm of CD4:CD8 ratios were modeled using a non-linear mixedeffects model [68,69]. The follow-up period was modeled using exponential decay function,assuming a Gaussian distribution (identity link), an AR(1) correlation structure, and arandom intercept. The exponential decay model is described as follows:ln(CD4 : CD8)i,t ∼ γe−R∗ti + C0 + b0i + εi,twhere t represents each of the six-month intervals, i represents each participant at the study,εi is the random error distributed as Normal (0, Di), where D is the covariance matrix andb0i as N (0,Viruses 2023, 15, x FOR PEER REVIEW 4 of 16 2.4. Statistical Analyses We conducted bivariable analyses between risk factors and CKD using Pearson’s chi-square or Fisher’s exact test for categorical variables (depending on cell count), and Wil-coxon’s Rank Sum test for continuous variables. This study consisted of a two-pronged analysis: 2.4.1. Growth Mixture Modeling To investigate the patterns of CD4:CD8 ratio trajectories among participants with and without CKD, we applied growth mixture modeling (GMM) [52,64,65]. This type of modeling allows the identification of unobserved subpopulations or classes based on dis-tinctive trajectories of CD4:CD8 ratios. First, we fit unconditional models with a different number of latent classes, and with linear (in case the ratio changes linearly over time) and quadratic (in case there is a non-linear pattern over time) growth factors; these terms were introduced one at a time. Second, we focused on examining the relationships between covariates and latent classes by fitting the GMM including covariates. Adding covariates to the model helps us understand the key characteristics of each group and how different covariates may affect the longitudinal trajectory within each class. To examine the fitness of the models, we used the Akaike information criteria (AIC), Bayesian information crite-ria (BIC), sample size-adjusted BIC (SBIC), entropy (range 0 to 1), adjusted Lo–Mendell–Rubin likelihood ratio test (LMR-LRT), and class size. A LMR-LRT with p-value of <0.05 was deemed significant while lower values of AIC, BIC and SBIC indicated better fit [66,67]. Third, we compared the incidence of CKD using two versions of the selected model, one follo ing an unconditio al approach (without covariat s) a d the other in-cluding covariates. To an lyze the associ tion between the tcome (CKD incidence) and the latent lasses, we assig ed par icipants to their most likely classes and then luded this variable as an explanatory variable in the Poisson regression to model the incidence CKD rate pe 1000 person-years between th distinct trajectories. Last, un rstand the key characteristics of participants in each class an how different covaria es may affect the CD4:CD8 tio traject ries, we co ducted bivariable analyses between covariates and the latent classes stratif ed by CKD tatus. Analys s were conducted with SAS version 9.4 (SAS Institu In ., Cary, NC, USA) and Mpl v.8.1 (Lo Angeles, CA, USA). 2.4.2. Decay Model Based on the results from the GMM, we estimated CD4:CD8 ratio trajectories to as-sess whether there are early indications of participants being at a high risk of CKD and potential cut-off time points of intervention. CD4:CD8 ratio measurements were obtained longitudinally from study baseline until the end of follow-up. We modeled and compared the CD4:CD8 ratio depletion trajectories among the identified latent classes and CKD sta-tus. The natural logarithm of CD4:CD8 ratios were modeled using a non-linear mixed ef-fects model [68,69]. The follow-up period was modeled using exponential decay function, assuming a Gaussian distribution (identity link), an AR(1) correlation structure, and a random intercept. Th ex onential decay mo el is described as follows: 𝑙𝑛(𝐶𝐷4: 𝐶𝐷8)௜,௧~ 𝛾𝑒ିோ∗௧೔ + 𝐶଴ + 𝑏଴௜ ε௜,௧ where t represents each of the six-month intervals, i represents each participant at the study, ε௜ is the random error distributed as Normal (0, Di), where D is the covari nce ma-trix and 𝑏଴௜ as ( , 𝜖2 ). The exponential decay function was modeled through 𝛾𝑒ିோ∗௧, where 𝛾 and R are coefficients of this function. Loss rate was modeled using the following equations. ). The exponential decay function was modeled through γe−R∗t, where γ andR are co fficients of this function. Loss rate was modeled using the foll wing equations.y = ln(r)dydt=d ln(r)dtdydt=1r× drdtwhere r is the CD4 : CD8 ratio, drdt is the CD4 : CD8 rate anddydt is the ln(CD4 : CD8) rate.These analyses were conducted in R© version 4.0.5.3. ResultsThe STOP HIV/AIDS cohort includes 15,599 participants, of which 8286 were excludedsince they were not eligible for the study. Thus, out of the remaining 7313 patients, 2450were eliminated since they did not fulfill the data requirements for the modeling. Therefore,4863 participants were included in our study Supplementary Materials, Figure S1. Thoseexcluded (i.e., 2450) were more likely to be female, with a history of SUD, not naïve to ART,enrolled in the cohort between 2011 and 2016 and of an older age Supplementary Materials,Viruses 2023, 15, 385 5 of 14Table S2. Most of the included participants were male (82.2%), ART naïve (89.6%), withouta history of SUD (57.3%), hypertension (87.2%), diabetes mellitus (92.0%), and CVD (93.1%).The median follow-up time was 6.45 (25th, 75th percentiles: 3.57, 9.89) years, and themedian number of CD4:CD8 measurements was 8 (25th, 75th percentiles: 4, 15). A total of310 (6.4%) participants developed CKD throughout the study. Bivariable analyses revealedthat participants who developed CKD and those who did not differ across all measuredcharacteristics, except age at the end of follow-up, and the proportion of time on NNRTITable 1. Participants who developed CKD were more likely to be female, naïve to ART,enrolled in the cohort between 2000 and 2004, with a history of SUD, hypertension, diabetesand CVD, older at study entry, higher baseline HIV viral load, shorter exposure to TDF,and longer exposure to PI.Table 1. Study population characteristics of the final analytical sample of eligible participants(n = 4863), stratified by chronic kidney disease status.OverallN = 4863Did NotDevelop CKDN = 4553DevelopedCKDN = 310Risk factors N (%) N (row %) N (row %) p-valueSex0.0010Female 867 (17.8) 789 (91.0) 78 (9.0)Male 3996 (82.2) 3764 (94.2) 232 (5.8)SUD<0.0001No 2784 (57.3) 2674 (96.0) 110 (4.0)Yes 1685 (34.7) 1495 (88.7) 190 (11.3)Unknown 394 (8.1) 384 (97.5) 10 (2.5)ART naïve0.0009Yes 4359 (89.6) 4064 (93.2) 295 (6.8)No 504 (10.4) 489 (97.0) 15 (3.0)Year of baseline date<0.00012000–2004 1026 (21.1) 876 (85.4) 150 (14.6)2005–2010 2012 (41.4) 1884 (93.6) 128 (6.4)2011–2016 1825 (37.5) 1793 (98.2) 32 (1.8)Hypertension during follow-up<0.0001No 4241 (87.2) 3996 (94.2) 245 (5.8)Yes 622 (12.8) 557 (89.5) 65 (10.5)Diabetes during follow-up<0.0001No 4476 (92.0) 4209 (94.0) 267 (6.0)Yes 387 (8.0) 344 (88.9) 43 (11.1)CVD during follow-up<0.0001No 4528 (93.1) 4271 (94.3) 257 (5.7)Yes 335 (6.9) 282 (84.2) 53 (15.8)Hypertension duration<0.0001No disease 4241 (87.2) 3996 (94.2) 245 (5.8)<median duration (4.8 years) 311 (6.4) 271 (87.1) 40 (12.9)≥median duration (4.8 years) 311 (6.4) 286 (92.0) 25 (8.0)Diabetes duration<0.0001No disease 4476 (92.0) 4209 (94.0) 267 (6.0)<median duration (4.2 years) 193 (4.0) 170 (88.1) 23 (11.9)≥median duration (4.2 years) 194 (4.0) 174 (89.7) 20 (10.3)CVD duration<0.0001No disease 4528 (93.1) 4271 (94.3) 257 (5.7)<median duration (3.2 years) 167 (3.4) 133 (79.6) 34 (20.4)≥median duration (3.2 years) 168 (3.5) 149 (88.7) 19 (11.3)Viruses 2023, 15, 385 6 of 14Table 1. Cont.OverallN = 4863Did notdevelop CKDN = 4553DevelopedCKDN = 310Median(Q1, Q3)Median(Q1, Q3)Median(Q1, Q3) p-valueAge at baseline (year) 41 (34, 49) 41 (34, 49) 43 (36, 52) 0.0010Age at the end of follow-up (year) 49 (41, 56) 49 (41, 56) 49 (41, 57) 0.6241CD4 Nadir (cells/µL) 250 (110, 410) 260 (120, 420) 110 (40, 200) <0.0001CD4 Nadir within one year prior tothe end (cells/µL) 480 (300, 670) 500 (320, 680) 210 (80, 387) <0.0001Baseline viral load (log10copies/mL) 4.7 (3.7, 5.0) 4.6 (3.6, 5.0) 5.0 (4.4, 5.0) <0.0001AUVLC (log10 copy-years/mL) 12.5(6.9, 19.5) 12.6 (7.0, 19.6)11.9 (5.6,17.5) 0.0054Follow-up time (year) 6.4 (3.6, 9.9) 6.6 (3.7, 10.0) 4.5 (2.4, 8.1) <0.0001Proportion of time on TDF 0.7 (0.1, 0.9) 0.7 (0.2, 1.0) 0.3 (0, 0.8) <0.0001Proportion of time on PI 0.2 (0, 0.8) 0.2 (0, 0.8) 0.5 (0, 0.9) <0.0001Proportion of time on NNRTI 0.0 (0, 0.8) 0.0 (0, 0.8) 0.0 (0, 0.6) 0.2729Note: We present row percentages for easier interpretation of factors associated with those who developed or notchronic kidney disease, Q1, Q3: 25th and 75th percentiles, CKD: chronic kidney disease, SUD: history of substanceuse disorder, ART: antiretroviral therapy, CVD: cardiovascular disease, AUVLC: area under the plasma viral loadcurve, TDF: tenofovir disoproxil fumarate, PI: protease inhibitors, NNRTI: non-nucleoside reverse transcriptaseinhibitors.3.1. CD4:CD8 Ratio Trajectories and CKD StatusWe built different GMMs to model the CD4:CD8 ratio measurements over time. Basedon the goodness-of-fit statistics found in the Supplementary Materials Table S3, the finalmodel included two classes (linear) with time-invariant covariates Supplementary MaterialsFigure S2. Having two classes means that there are two groups in our population, and theydiffer in the CD4:CD8 ratio trajectory in a “particular way”, which we try to understandbased on the following analyses. We also compared the rate of renal disease of the two-class(linear) model with and without time-invariant covariates Table 2. Our results showedthat in both models, participants in Class 2 had a higher rate of CKD than those in Class1. For the final model, i.e., two-class (linear) model with time-invariant, those assignedClass 2 had 8.72 times the rate (95% CI 6.64–11.44) of developing incident CKD than thosein Class 1.Table 2. Comparison of chronic kidney disease rates of participants assigned Class 1 and 2 using thetwo-class linear model with and without time-invariant covariates.Number ofParticipantsNumber ofParticipantsWho Did NotDeveloped CKDNumber ofParticipantsWhoDeveloped CKDCKD Rate, 95% CI(per 1000Person-Years)Rate Ratio(95% CI)Two-class linear model (unconditional)Class 1 4725 4442 283 8.49 (7.55–9.53) ReferenceClass 2 138 111 27 32.70 (22.43–47.69) 3.85 (2.60–5.72)Two-class linear model (time-invariant covariates)Class 1 4547 4303 244 7.36 (6.49–8.34) ReferenceClass 2 316 250 66 64.16 (50.41–81.67) 8.72(6.64–11.44)Note: 95% CI: 95% confidence interval, CKD: chronic kidney disease.Viruses 2023, 15, 385 7 of 14Based on the final model, we plotted the CD4:CD8 ratio trajectories for each classSupplementary Materials Figure S3 and performed bivariable analyses Table 3. A total of4547 (93.5%) participants were classified in Class 1 with 244 (5.4%) of them developingincident CKD, and 316 (6.5%) were classified in Class 2 with 66 (20.9%) developing incidentCKD. Our bivariable analyses suggested that among Class 2 participants, those whodevelop CKD were more likely to have history of SUD, be ART naïve, enrolled in the cohortbetween 2000 and 2004, younger at baseline, with a lower CD4 nadir closer to the end offollow-up, and a shorter time on TDF (p-values <0.05).Table 3. Descriptive statistics of the study population by CD4:CD8 class identified by two-class linearmodel with time-invariant covariates on chronic kidney disease (CKD) status.Class 1 Class 2CKD No(n = 4303)CKD Yes(n = 244)CKD No(n = 250)CKD Yes(n = 66)Risk factors N (row %) N (row %) p-Value N (row %) N (row %) p-valueSex0.0090 0.2790Female 726 (92.7) 57 (7.3) 63 (75.0) 21 (25.0)Male 3577 (95.0) 187 (5.0) 187 (80.6) 45 (19.4)SUD<0.0001 0.0084No 2567 (96.5) 94 (3.5) 107 (87.0) 16 (13.0)Yes 1362 (90.6) 141 (9.4) 133 (73.1) 46–49 *Unknown 374 (97.7) 9 (2.3) 10 (90.9) <5 **ARV naïve0.0138 0.0129Yes 3843 (94.4) 230 (5.6) 221 (77.3) 62–64 *No 460 (97.0) 14 (3.0) 29 (96.7) <5 **Year of baseline date<0.0001 <0.00012000–2004 808 (87.5) 115 (12.5) 68 (66.0) 35 (34.0)2005–2010 1814 (94.5) 106 (5.5) 70 (76.1) 22 (23.9)2011–2016 1681 (98.7) 23 (1.3) 112 (92.6) 9 (7.4)Hypertension duration<0.0001 0.9990No disease 3764 (95.3) 184 (4.7) 232 (79.2) 61 (20.8)<median duration (4.8 years) 256 (87.7) 36 (12.3) 15 (78.9) <5 **≥median duration (4.8 years) 283 (92.2) 24 (7.8) <5 (75.0) <5 **Diabetes duration<0.0001 0.3850No disease 3970 (95.1) 205 (4.9) 239 (79.4) 62 (20.6)<median duration (4.2 years) 160 (88.9) 20 (11.1) 10 (76.9) <5 **≥median duration (4.2 years) 173 (90.1) 19 (9.9) 1 (50.0) <5 **CVD duration<0.0001 0.5507No disease 4031 (95.4) 195 (4.6) 240 (79.5) 62 (20.5)<median duration (3.2 years) 126 (80.3) 31 (19.7) 6–9 <5 **≥median duration (3.2 years) 146 (89.0) 18 (11.0) <5 ** <5 **Median(Q1, Q3)Median(Q1, Q3) p-ValueMedian(Q1, Q3)Median(Q1, Q3) p-valueAge at baseline 41 (34–49) 44 (37–53) <0.0001 43 (34–49) 38 (31–45) 0.0048CD4 Nadir within one year prior to theend (cells/µL) 520 (360–690) 270 (150–440) <0.0001 70 (20–130) 30 (10–60) <0.0001AUVLC (log10 copy-years/mL) 12.7 (7.4–19.8) 12.2 (6.0–17.9) 0.0587 8.9 (3.6–16.0) 8.7 (3.8–15.4) 0.9469Follow-up time (year) 6.9 (4.0–10.2) 5.8 (2.9–8.5) <0.0001 2.5 (1.4–4.9) 2.5 (1.1–3.9) 0.3473Proportion of time on TDF 0.7 (0.2–1.0) 0.4 (0–0.9) <0.0001 0.2 (0–0.6) 0.2 (0–0.4) 0.0286Proportion of time on PI 0.2 (0–0.8) 0.6 (0–0.9) <0.0001 0.2 (0–0.5) 0.2 (0–0.5) 0.6681Proportion of time on NNRTI 0.1 (0–0.8) 0.04 (0–0.8) 0.5783 0 (0–0.2) 0 (0–0.2) 0.1088Note: We present row percentages for easier interpretation of factors associated with those who have or notchronic kidney disease, Q1, Q3: 25th and 75th percentiles, CKD: chronic kidney disease, SUD: history of substanceuse disorder, ART: antiretroviral therapy, CVD: cardiovascular disease, AUVLC: Area under the plasma viral loadcurve, TDF: tenofovir disoproxil fumarate, PI: protease inhibitors, NNRTI: non-nucleoside reverse transcriptaseinhibitors. * Due to data privacy, we provided a range due the cells with <5 participants, ** Due to data privacy,we replaced the exact numbers if the cell contained <5 participants.Viruses 2023, 15, 385 8 of 143.2. CD4:CD8 Ratio Decay among Classes and CKD StatusWe fit an exponential decay function model to estimate the CD4:CD8 ratio trajectoriesby GMM class and CKD status. A total of 60,869 CD4:CD8 measurements were includedwith 59,191 belonging to Class 1 and 1678 to Class 2. The models and goodness-of-fit assess-ments can be found in the Supplementary Materials Table S3, Figure S4. Figure 1a,b presentthe ln(CD4 : CD8) trajectory, and Figure 1c,d present the ln(CD4 : CD8) loss rate over time,for every 6-month period. We observed that the estimated trajectory of ln(CD4 : CD8)ratio of participants who developed CKD differed significantly between Classes 1 and 2.Those in Class 1 experienced a rapid increase and after 7.5 years, the ratio reached a plateauFigure 1a. Please note that although the rate of increase between those with and withoutCKD was very similar, those with CKD in Class 1 had significantly lower ratios. For thosein Class 2, we observed a decline in ln(CD4 : CD8) among those with and without CKD.However, among those who developed CKD, starting around 5.5–6.5 years of follow-upFigure 1b,d, the slope of their ratio trajectory curve changed significantly, and the rate ofdecline increased dramatically. Data for Figure 1 can be found in Tables S4 and S5, as wellas the original values for the ratio in this analysis.Viruses 2023, 15, x FOR PEER REVIEW 9 of 16 assessments can be found in the Supplementary Materials Table S3, Figure S4. Figure 1a,b present the 𝑙𝑛(𝐶𝐷4: 𝐶𝐷8) trajectory, and Figure 1c,d present the 𝑙𝑛(𝐶𝐷4: 𝐶𝐷8) loss rate over time, for every 6-month period. We observed that the estimated trajectory of 𝑙𝑛(𝐶𝐷4: 𝐶𝐷8) ratio of participants who developed CKD differed significantly between Classes 1 and 2. Those in Class 1 experienced a rapid increase and after 7.5 years, the ratio reached a plateau Figure 1a. Please note that although the rate of increase between those with and without CKD was very similar, those with CKD in Class 1 had significantly lower ratios. For those in Class 2, we observed a decline in 𝑙𝑛(𝐶𝐷4: 𝐶𝐷8) among those with and without CKD. However, among those who developed CKD, starting around 5.5–6.5 years of follow-up Figures 1b,d, the slope of their ratio trajectory curve changed sig-nificantly, and the rate of decline increased dramatically. Data for Figure 1 can be found in Tables S4 and S5, as well as the original values for the ratio in this analysis. Figure 1. 𝑙𝑛(𝐶𝐷4: 𝐶𝐷8) trajectory and loss rate plots per six months by chronic kidney disease (CKD) status. (A) Estimated trajectory in Class 1. (B) Estimated trajectory in Class 2. (C) Estimated loss rate for Class 1. (D) Estimated loss rate for Class 2. Red line CKD and Green line no CKD. 3.3. Sex-Based Analysis We conducted a sensitivity analysis by sex at birth. Please note that to run GMM or any other statistical model, we need power and since we have very few females in our study, we could only run descriptive analyses and fit the decay model for Class 2 only including sex as a covariate. We noticed that the rate of CKD is 53% higher in females (Table 4) and the rate of decline in 𝑙𝑛(𝐶𝐷4: 𝐶𝐷8) among Class 2 with CKD was also more accelerated among females (Figure 2). Table 4. Comparison of chronic kidney disease (CKD) rates of by sex. Sex Number of<break/> Participants Number of Participants Who Developed CKS CKD Rate, 95% CI <break/> (per 1000 Person-Years) Rate Ratio (95% CI) Male 3996 232 8.28 (7.28–9.41) Ref Female 867 78 12.68 (10.16–15.83) 1.53 (1.19–1.98) Note: 95% CI: 95% confidence interval, CKD: chronic kidney disease. Figure 1. ln(CD4 CD8) trajectory and loss r te t b chronic kidney disease(CKD) status. (A) Estimated trajectory in Class 1. (B) Estimated trajectory in Class 2. (C) Estimatedloss rate for Class 1. (D) Estimated loss rate for Class 2. Red line CKD and Green line no CKD.3.3. Sex-Based AnalysisWe conducted a sensitivity analysis by sex at birth. Please note that to run G orany other statistical odel, e need po er and since e have very few females in ourstudy, we could only run descriptive analyses and fit the decay odel for Class 2 onlyincluding sex as a covariate. e noticed that the rate of CKD is 53% higher in females(Table 4) and the rate of decline in ln(CD4 : CD8) among Class 2 with CKD was also moreaccelerated among females (Figure 2).Table 4. Comparison of chronic kidney disease (CKD) rates of by sex.Sex Number ofParticipantsNumber ofParticipantsWho DevelopedCKSCKD Rate, 95% CI(per 1000Person-Years)Rate Ratio (95%CI)Male 3996 232 8.28 (7.28–9.41) RefFemale 867 78 12.68 (10.16–15.83) 1.53 (1.19–1.98)Note: 95% CI: 95% confidence interval, CKD: chronic kidney disease.Viruses 2023, 15, 385 9 of 14Viruses 2023, 15, x FOR PEER REVIEW 10 of 16 Figure 2. 𝑙𝑛(𝐶𝐷4: 𝐶𝐷8) trajectory plot per six months among participants who developed chronic kidney disease (CKD) in Class 2 by sex. 4. Discussion Using routinely monitored tests such as the CD4:CD8 ratio, this study identified clin-ically relevant subpopulations of PLWH who are at increased risk of incident CKD. Our novel use of GMM allowed us identify higher CKD rates among participants classified into the Class 2 trajectories. Furthermore, our subsequent decay functions and CD4:CD8 ratio loss rates also suggested that, among Class 2 participants, there was a significant CD4:CD8 ratio decline among those who developed CKD starting at 5.5–6.5 years of fol-low-up (a CD4:CD8 ratio <0.10); or a decline of 45% from baseline values. This finding is interesting and puzzling. The usual pattern for patients is to improve their CD4:CD8 ratio once they start ART [70]. For those with advanced HIV prior to the start of ART (e.g., low CD4 nadir, prior opportunistic infection), the improvement usually seen in the CD4:CD8 ratio is minor [2,70]. In this study, CKD incidence was higher among participants with the poorest ratio response in both classes, especially those in Class 2. We also observed that in our study, females had a higher CKD incidence, and the rate of decline in CD4:CD8 was more accelerated than in males. Some studies have shown that females, especially done of child-bearing age may experience higher rates of CKD due to biological factors as well as inequities in accessing care for early diagnosis of kidney disease.[71,72] One ques-tion that comes to mind is whether HIV-related factors drove the poor response seen in the CD4:CD8 ratio. In our study, the AUCVL in all groups did not indicate that poor vi-rologic control influenced our results. Additionally, the effect may have been driven by a loss of memory CD4 from living with HIV for a longer time, as indicated in our results. Third, we controlled for the effect of CD4 nadir and antiretrovirals known to be risk fac-tors for CKD, and the our CD4:CD8 GMM model was still able to distinguish those more likely to develop CKD. Last, suppose that non-HIV-related factors are involved in the ob-served trends in the CD4:CD8 ratio, especially in Class 2. In this case, the question that remains is, as CKD progresses, is there a biological mechanism that predicts immunologic non-response among PLWH? The use of eGFR and ACR has been identified as gold standard to identify and mon-itor CKD [73]. However, these measurements can yield low sensitivity in detecting early CKD, especially among PLWH, participants with certain comorbidities (e.g., chronic liver disease, obesity) [30,74,75], and external factors such as muscle mass, nutritional status, age, and race. Among PLWH, routinely monitored CD4:CD8 ratio measurements have been identified as emerging biomarkers due to its low values being associated with in-creased risk of morbidity and mortality [39,50,76–78]. Previous studies have focused on using the CD4:CD8 ratio as a marker of greater risk of cardiovascular events, non-AIDS defining cancers, diabetes, and hypertriglyceridemia [37,40,42,46]. These studies have also Figure 2. ln CD4 : CD8) trajectory plot per six months among participants who developed chronickidney disease ( ) i lass 2 se .4. DiscussionUsing routinely monitored tests such as the CD4:CD8 ratio, this study identifiedclinically relevant subpopulations of PLWH who are at increased risk of incident CKD. Ournovel use of GMM allowed us identify higher CKD rates among participants classified intothe Class 2 trajectories. Furthermore, our subsequent decay functions and CD4:CD8 ratioloss rates also suggested that, among Class 2 participants, there was a significant CD4:CD8ratio decline among those who developed CKD starting at 5.5–6.5 years of follow-up (aCD4:CD8 ratio < 0.10); or a decline of 45% from baseline values. This finding is interestingand puzzling. The usual pattern for patients is to improve their CD4:CD8 ratio once theystart ART [70]. For those with advanced HIV prior to the start of ART (e.g., low CD4nadir, prior opportunistic infection), the improvement usually seen in the CD4:CD8 ratiois minor [2,70]. In this study, CKD incidence was higher among participants with thepoorest ratio response in both classes, especially those in Class 2. We also observed that inour study, females had a higher CKD incidence, and the rate of decline in CD4:CD8 wasmore accelerated than in males. Some studies have shown that females, especially done ofchild-bearing age may experience higher rates of CKD due to biological factors as well asinequities in accessing care for early diagnosis of kidney disease [71,72]. One question thatcomes to mind is whether HIV-related factors drove the poor response seen in the CD4:CD8ratio. In our study, the AUCVL in all groups did not indicate that poor virologic controlinfluenced our results. Additionally, the effect may have been driven by a loss of memoryCD4 from living with HIV for a longer time, as indicated in our results. Third, we controlledfor the effect of CD4 nadir and antiretrovirals known to be risk factors for CKD, and the ourCD4:CD8 GMM model was still able to distinguish those more likely to develop CKD. Last,suppose that non-HIV-related factors are involved in the observed trends in the CD4:CD8ratio, especially in Class 2. In this case, the question that remains is, as CKD progresses, isthere a biological mechanism th t predicts immunologic no -response among PLWH?The use of eGFR and ACR has be n identified as gold standard to identify and monitorCKD [73]. However, these measurements can yield low sensitivity in detecting earlyCKD, especially among PLWH, participants with certain comorbidities (e.g., chronic liverdisease, obesity) [30,74,75], and external factors such as muscle mass, nutritional status, age,and race. Among PLWH, routinely moni ored CD4:CD8 ratio measurements have be nidentified a emerging biomark rs due to its low values being associated with increasedrisk of morbidity and mortality [39,50,76–78]. Previous studies have focu ed on using theCD4:CD8 ratio a marker of greater risk of cardiovascu ar ev nts, non-AIDS defin gcanc rs, diabetes, an hypertriglyceridemia [37,40,42,46]. These studies have also usedprede ermined CD4:CD8 ratio cut-offs to ass ss the risk of morbidity and mortality [79].However, given our PLWH population and their v riability in demographic, clinical, andbehavioral characteristics, it is key to use an approach that accounts for the heterogeneity ofparticipants, such as GMM [52]. Our GMM allowed us to examine the dynamic changes inViruses 2023, 15, 385 10 of 14CD4:CD8 ratio during treatment and identify the diversity in trajectories while accountingfor traditional risk factors such as sex, history of SUD, ART regimen and presence ofcomorbidities. This model aided in the identification of clinically relevant subpopulationsof PLWH with higher CKD risk [46], through the different trajectories in CD4:CD8 ratio.Please note that one of the factors that we explored in this study was time exposed todifferent classes of ART and TDF. As expected, longer time exposed to PIs was associatedwith a higher risk of CKD. Please note that, during the study, a large proportion of PLWHreceived ritonavir-boosted atazanavir followed by ritonavir-boosted lopinavir. We alsofound that those who developed CKD received TDF for a shorter time. This finding isrelated to confounding by indication, i.e., for some participants, TDF is rapidly discontinuedlikely due to abnormal renal test results.Our study has some important strengths. First, the use of linked datasets comprisedof different administrative health databases allowed us to obtain clinical informationnecessary to account for population characteristics, using GMM, and estimate CD4:CD8ratio trajectories. Second, we used a relatively long washout period (i.e., five years) toestimate CKD incidence and minimize the risk of capturing prevalent cases. Third, wewere able to evaluate cumulative exposure to ART known to increase the risk of CKD.However, there are also some limitations to consider. First, our estimates relied on atleast two measurements of CD4:CD8 ratio per participant. Thus, participants with onlyone measurement were omitted from the study. Second, we were unable to adjust forcovariates in our CD4:CD8 ratio decay model due to sample size limitations. However, weconducted goodness-of-fit assessments to choose our models, and the model showed goodperformance with the standardized residuals and the fitted values being correlated with theobserved values. Third, we used AUCVL to summarize the overall viral load trajectoriesof each participant in our study. The measure as calculated may be subjected to selectionbias since participants have different measures of viral load over time [80]. However, weshowed in the table comparing the characteristics of those belonging to each of the classes,that the AUCVL was not statistically significantly different between the comparison groups,and therefore, we believe that this bias did not play a significant role in our results. Last,although our models yield good estimates, we did not have access to other factors thatmay explain the risk of CKD (e.g., race/ethnicity, hepatitis C co-infection, smoking) in ourpopulation [30].5. ConclusionsThis study demonstrated the potential use of the CD4:CD8 ratio as a complementarymeasure to eGFR and ACR to aid the early identification of PLWH who may be at elevatedrisk of developing CKD, and could therefore benefit from closer longitudinal monitoring oftheir renal function.Supplementary Materials: The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/v15020385/s1.Author Contributions: V.D.L. conceptualized and designed the study. J.S.G.M., R.B., V.D.L. curatedthe data and acquired funding. V.D.L., M.Y.C., L.W. performed statistical analyses. A.F.-C., P.N.,V.D.L. wrote the original draft. A.F.-C., P.N., L.W., M.Y.C., C.G.R., M.H., T.M., S.G., R.B., J.S.G.M. andV.D.L. provided critical feedback regarding the results and interpretation. A.F.-C., P.N., L.W., M.Y.C.,C.G.R., M.H., T.M., S.G., R.B., J.S.G.M. and V.D.L. reviewed all manuscript drafts. All authors haveread and agreed to the published version of the manuscript.Funding: This research was funded by a grant from the Canadian Institutes of Health Research(PJT-148595), and the Canadian Foundation for AIDS Research (CANFAR Innovation Grant—30-101).Institutional Review Board Statement: This study was approved by the Research Ethics Board,Providence Health Care, the University of British Columbia ethics certificate number H16-02036. TheSTOP HIV/AIDS Linked Database does not independently generate or accumulate any data; its datacome only from other existing databases. All relevant documentation is in place to allow for the dataViruses 2023, 15, 385 11 of 14linkage, including Privacy Impact Assessment and Data sharing agreements with all parties involved.The research is minimal risk only.Informed Consent Statement: Patient consent was waived since this study was conducted usingstrictly anonymized laboratory and administrative databases, and thus informed consent was notrequired. This study complies with BC’s Freedom of Information and Protection of Privacy Act.Data Availability Statement: The British Columbia Center for Excellence in HIV/AIDS is prohibitedfrom making individual-level data available publicly due to provisions in our service contracts, insti-tutional policy, and ethical requirements. The British Columbia Center for Excellence in HIV/AIDSdata are not available externally due to prohibitions in service contracts with our funders or dataproviders. Institutional policies stipulate that all external data requests require collaboration with aBC Center for Excellence in HIV/AIDS. For more information regarding data access, please contactMr. Mark Helberg, the Senior Director, Internal and External Relations & Strategic Development ofthe British Columbia Center for Excellence in HIV/AIDS, at mhelberg@bccfe.ca. In his position, heoversees some of the daily operations from the Center and any data access request that may arise.Acknowledgments: The authors thank all the participants included in STOP HIV/AIDS, as wellas the British Columbia Center for Excellence in HIV/AIDS, the BC Ministry of Health and theinstitutional data stewards for granting access to the data.Conflicts of Interest: JSGM’s Treatment as Prevention research, paid to his institution, has receivedsupport from the BC Ministry of Health, Health Canada, Public Health Agency of Canada, VancouverCoastal Health and VGH Foundation. Institutional grants have been provided by Gilead, Merck andViiV Healthcare. MH has received honoraria for advisory boards and speaking engagements fromGilead Lifesciences and Merck. MH has received research funding for an investigator-initiated studyfrom Gilead. The other authors have no conflict of interest to declare. The sponsors had no role inthe design, data collection, data analysis, data interpretation or writing of the report. All inferences,opinions, and conclusions drawn in this manuscript are those of the authors, and do not reflect theopinions or policies of the data stewards.References1. Mussini, C.; Lorenzini, P.; Cozzi-Lepri, A.; Lapadula, G.; Marchetti, G.; Nicastri, E.; Cingolani, A.; Lichtner, M.; Antinori, A.; Gori,A.; et al. CD4/CD8 ratio normalisation and non-AIDS-related events in individuals with HIV who achieve viral load suppressionwith antiretroviral therapy: An observational cohort study. Lancet HIV 2015, 2, e98–e106. [CrossRef]2. 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CD4:CD8 ratio
HIV
chronic kidney disease
growth mixture modeling
decay model
risk factors
Identifying Longitudinal CD4:CD8 Ratio Trajectories Indicative of Chronic Renal Disease Risk among People Living with HIV : An Application of Growth Mixture Models
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