Abstract
Background:
In people with HIV (PWH), urine tubular biomarkers have been linked to kidney function decline, but urine concentration variability limits their clinical utility. Plasma biomarkers may offer more stable indicators of kidney tubular health.
Methods:
We conducted a case–cohort study of 440 PWH from the Multicenter AIDS Cohort Study (MACS)/Women’s Interagency HIV Study (WIHS) Combined Cohort Study (MWCCS). Cases developed rapid kidney function decline [RKFD: ≥30% estimated glomerular filtration rate (eGFR) reduction]. We measured plasma biomarkers of tubular injury [kidney injury molecule-1 (KIM-1)], inflammation [tumor necrosis factor receptor-1 (TNFr1) and tumor necrosis factor receptor-2 (TNFr2)], and synthetic function [uromodulin (UMOD) and epidermal growth factor (EGF)] at baseline and year 2. Associations with RKFD were assessed using multivariable risk regression, adjusting for chronic kidney disease (CKD) and HIV-related risk factors, eGFR, and albuminuria. In a random sub-cohort, linear mixed models evaluated associations with annualized eGFR change.
Results:
At baseline, median age was 49 years; 33% were women; 69% were virally suppressed; eGFR was similar in cases vs. noncases (93 vs. 94 ml/min/1.73 m2). Over a median of 4.5 years, 172 RKFD events occurred. Each 1-standard deviation higher baseline KIM-1, TNFr1, TNFr2, UMOD, and EGF level was associated with adjusted relative risks (RR) for RKFD of 1.26 [95% confidence interval (CI): 1.15–1.39], 1.39 (1.24–1.55), 1.40 (1.24–1.57), 0.84 (0.77–0.93), and 0.85 (0.78–0.92), respectively. Findings were similar at year 2 and for 2-year biomarker changes. In joint models, baseline KIM-1, TNFr2, and UMOD remained independently associated with RKFD [RR: 1.19 (1.08–1.31), 1.27 (1.12–1.43), and 0.86 (0.78–0.95)], respectively. No biomarker was associated with annualized eGFR change in the sub-cohort.
Conclusion:
In PWH, plasma biomarkers reflecting impaired kidney tubular health were independently associated with RKFD and may be useful prognosticators of adverse kidney outcomes.
Keywords: biomarkers, HIV, inflammation, kidney, tubular injury
Introduction
People with HIV (PWH) receiving antiretroviral therapy (ART) have life expectancies approaching those of the general population, yet they are disproportionately affected by age-related diseases, including chronic kidney disease (CKD) [1]. CKD is twice as prevalent in PWH relative to those without HIV of similar age [2,3], and portends higher risks for progression to kidney failure, cardiovascular disease events, and death [4,5]. The causes of CKD in PWH are diverse, encompassing cardiometabolic risk factors, coinfections, and medications that cause tubular injury [6,7]. Other processes related to HIV that may uniquely predispose PWH to loss of kidney function include HIV infection of glomerular and tubular cells, immune dysregulation, and inflammation [8–10].
While estimated glomerular filtration rate (eGFR) and urine albumin-to-creatinine ratio (UACR) are used in clinical practice to assess kidney health, these markers have limitations [11]. eGFR does not fall until significant kidney damage has already occurred, limiting its ability to detect early disease. Moreover, eGFR and UACR primarily reflect glomerular function and injury; therefore, they often fail to capture tubulointerstitial inflammation and fibrosis, which are common to all forms of CKD and strongly prognostic of progression to kidney failure [12]. Biomarkers, which reflect tubulointerstitial processes could enable detection of future declines in kidney health, leading to earlier initiation and tailoring of evidence-based treatments to patients at the highest risk of adverse kidney outcomes. Over the last decade, numerous studies in non-HIV cohorts have identified plasma biomarkers that capture tubular injury, inflammation, and fibrosis and are associated with adverse kidney outcomes [13–18]. Studies among PWH have focused on urine biomarkers [19–23], but their interpretation and precision may be limited by differences in urine concentration and inter-individual biomarker variability [24]. Conversely, to our knowledge, no studies in PWH have utilized plasma kidney health biomarkers, which may offer an advantage of yielding more precise measurements.
Kidney injury molecule-1 (KIM-1) is a transmembrane glycoprotein that is mainly expressed in proximal tubular cells and increases with tubular injury [25]. Studies in non-HIV cohorts have observed associations of higher plasma KIM-1 concentrations with declines in kidney function [15,26]. Tumor necrosis factor receptor-1 and tumor necrosis factor receptor-2 (TNFr1 and TNFr2) are circulating receptors of the proinflammatory cytokine, TNF-alpha, and are associated with CKD development and progression in non-HIV populations [13,14]. Uromodulin (UMOD) and epidermal growth factor (EGF) are proteins produced by tubular cells of the thick ascending limb and early distal tubule, respectively, which are markers of tubular synthetic function [27]. Although lower urinary levels of UMOD and EGF have been associated with increased risk of CKD [28], limited data exist on the relationship of their plasma levels with CKD risk. Together, these plasma biomarkers may provide important insights into mechanisms that drive loss of kidney function among PWH.
In the present study, we evaluated associations of repeated measurements of plasma KIM-1, TNFr1, TNFr2, UMOD, and EGF levels with subsequent changes in kidney function in the MACS/WIHS Combined Cohort Study (MWCCS).
Methods
Study design and population
The Women’s Interagency HIV Study (WIHS) and Multicenter AIDS Cohort Study (MACS) are prospective multicenter cohorts of women and men, respectively, with and without HIV in the United States. Briefly, WIHS and MACS participants have study visits every 6 months, which include detailed physical examinations, laboratory measurements, and structured interviews. Further details of WIHS and MACS are described elsewhere [29,30], and data collection instruments are available at https://statepi.jhsph.edu/mwccs/data-collection-forms/.
We designed a nested case–cohort study and selected 440 WIHS and MACS participants with HIV who had: two stored plasma specimens collected 2 years apart between 2010 and 2014; at least two eGFR measurements after 2010; and at least 2 years of follow-up after the second plasma specimen. Cases included 172 participants who developed rapid kidney function decline (RKFD), defined as at least 30% eGFR decline starting after year 2. A random sub-cohort of 286 participants was selected from the total eligible cohort of WIHS and MACS participants to estimate annualized eGFR change. The random sub-cohort included noncases and future cases (Fig. 1). All eligible participants were included based on the specified criteria above and followed through August 2019.
Fig. 1. Nested case–cohort design.
Cases were defined as those who developed rapid kidney function decline starting after year 2. Within a sub-cohort, comprised of a random sample of the total number of eligible participants from theWomen’s Interagency HIV Study (WIHS) and Multicenter AIDS Cohort Study (MACS) cohorts, annualized eGFR change was assessed after year 2.
The parent cohorts received Institutional Review Board approval, and the present study received IRB approval from the University of California, San Francisco.
Plasma biomarkers
Plasma specimens were collected at WIHS and MACS study visits and stored centrally at −80 °C with no intervening freeze–thaw cycles until the biomarker measurements were performed. The exposures of interest comprised five plasma biomarkers that reflect three different dimensions of kidney health: tubular injury (KIM-1), inflammation (TNFr1 and TNFr2), and tubular synthetic function (UMOD and EGF). Biomarkers were measured at baseline and year 2 using a multiplex UPLEX assay (Meso Scale Discovery, Meso Scale Diagnostics, LLC. Rockville, Maryland, USA). Biomarker analytic ranges and coefficients of variation are reported in Table S1, http://links.lww.com/QAD/D673. For each participant, baseline and follow-up biomarkers were measured simultaneously and in duplicate with controls on the same plate to avoid assay variability. All measures were conducted within a single lot of reagents.
Longitudinal kidney function
Serum creatinine was measured every 6 months at the laboratories of each WIHS and MACS site using the modified Jaffe method, traceable to isotope dilution mass spectrometry. Estimated GFR based on creatinine was calculated using the 2021 CKD Epidemiology Collaboration equation [31]. The primary outcome of RKFD was ascertained after year 2 [32]. RKFD was selected as the primary outcome rather than incident CKD, as RFKD allows evaluation across the entire range of eGFR values, avoids having a cut-point at 60 ml/min/1.73 m2, and has been used as a surrogate endpoint for progression to kidney failure [32,33]. Within the sub-cohort, we evaluated annualized eGFR change after year 2. Both outcome definitions started after year 2 so that we could evaluate early biomarker exposures as predictors of future kidney function decline and to avoid reverse causality with the biomarker exposures.
Covariates
Demographic and behavioral variables included study site, age, race, ethnicity, smoking, alcohol, and illicit drug use. CKD risk factors included BMI, SBP, DBP, hypertension, hemoglobin a1c (HbA1c), diabetes, and hepatitis C virus (HCV) infection (based on antibody and viral RNA testing) and HCV treatment. Hypertension was defined as a SBP at least 140 mmHg or DBP at least 90 mmHg, self-report of hypertension diagnosis, or use of antihypertensive medicines. Diabetes was defined by having self-reported diagnosis of diabetes after confirmation by antidiabetic medication use, fasting glucose at least 126 mg/dl, or HbA1C at least 6.5% [34]. Use of an angiotensin-converting enzyme inhibitor (ACEi) or angiotensin receptor blocker (ARB) was included as a covariate as they can have acute and chronic effects on eGFR. HIV-related factors included viral load, CD4+ cell count, history of AIDS defined by prior opportunistic infection, ART use, and potentially nephrotoxic ART, namely tenofovir disoproxil fumarate (TDF) or atazanavir. Medications that can alter tubular creatinine secretion were grouped together as a binary variable and included integrase inhibitors, ritonavir, cobicistat, rilpivirine, feno-fibrate, and trimethoprim.
Statistical analysis
We summarized baseline and year 2 characteristics by case status and biomarker distributions at baseline, year 2, and the 2-year change. Spearman correlation coefficients evaluated relationships between the biomarkers, eGFR, and UACR. Plasma biomarkers were log-transformed to correct for rightward skew and standardized to the same scale [mean 0, standard deviation (SD) 1] to facilitate comparisons in the magnitude of associations.
Before constructing models, we calculated sampling weights to account for oversampling of participants with RKFD in the case–cohort. Weights were calculated as the inverse probability of selection within strata by case status, sex, and age to ensure that analyses reflected full cohort characteristics. While rates of missing data for individual covariates were low (<11%), a complete case analysis would have excluded 26% of participants. We, therefore, used multiple imputation with the Markov chain Monte Carlo method for arbitrarily missing multivariate normal data to impute missing covariates with 10 imputations.
We modeled associations of each biomarker with RKFD and annualized change in eGFR using the: baseline value; year 2 value; and 2-year change with simultaneous adjustment for the baseline value. For each approach, we calculated relative risks (RR) with 95% confidence intervals (CI). Models were adjusted for sociodemographics, comorbidities, HIV-related factors, eGFR, and UACR. Baseline biomarker models adjusted for baseline covariates whereas models using year 2 and 2-year changes adjusted for year 2 covariates.
We evaluated associations of each biomarker with RKFD using Poisson relative risk regression with a robust variance estimator while incorporating sampling weights as described above. This method has fewer convergence problems than the log-binomial model and gives an unbiased estimate of the relative risk when the response variable is binary. We used linear mixed effects model with random intercepts and slopes and biomarker-by-time interactions to estimate each biomarker’s association with annualized eGFR change. The linear mixed effects model adjusted for the same covariates as above as well as interactions of each covariate with time.
Finally, using adaptive least absolute shrinkage and selection operator (LASSO) penalized regression, we modeled the biomarkers in combination to determine whether any biomarkers were independently associated with RKFD or annualized eGFR change. This method is able to shrink regression coefficients to zero and selects predictors by imposing a penalty on their size. Biomarkers were simultaneously included as candidate covariates, while demographic and clinical characteristics were treated as unpenalized covariates and retained in all models. Cross-validation determined the number of included biomarkers and degree of coefficient shrinkage to avoid over-fitting. This approach was used internally to guide fitting rather than for evaluating predictive performance on external data.
Statistical significance was defined as P<0.05. Adaptive LASSO regression was performed using the ncvreg package for R. All other analyses were conducted using SAS, version 9.4 (SAS Institute, Inc, Cary, North Carolina, USA).
Results
Participant characteristics
Among 440 participants, a total of 172 (39%) incident cases of RKFD occurred over a median of 4.5 years [interquartile range (IQR) 3.1–5.6 years]. Although cases and noncases had similar baseline eGFR (93 vs. 94 ml/min/1.73 m2) and albuminuria (9.9 vs. 7.4 mg/g), their longitudinal changes in eGFR differed substantially (Fig. 2, Table S2, http://links.lww.com/QAD/D673). A higher proportion of cases had CKD risk factors including smoking, illicit drug use, diabetes, and hypertension (Table 1). With respect to HIV-related factors, cases were more likely to have a history of HCV and AIDS and were less likely to have an undetectable viral load or to report TDF use. Over 40% of the case–cohort was receiving a medication that can reduce tubular creatinine secretion (most common: ritonavir). Differences between cases and noncases at year 2 were similar to those at the baseline visit. (Table S3, http://links.lww.com/QAD/D673).
Fig. 2.
Comparison of estimated glomerular filtration rate trajectories (mean±SD) between cases and non-cases.
Table 1.
Summary demographic and clinical characteristics at the baseline visit (n = 440).
| WIHS/MACS cases (n = 172) | WIHS/MACS non-cases (n = 268) | |
|---|---|---|
| Calendar year | ||
| 2010 | 160 (93) | 255 (95) |
| 2011–2014 | 12 (7) | 13 (5) |
| Sociodemographic | ||
| Age (years) | 50 (44–57) | 48 (43–54) |
| Sex | ||
| Male | 108 (63) | 198 (74) |
| Female | 64 (37) | 70 (26) |
| Race | ||
| Black/African American | 79 (46) | 96 (36) |
| White/Caucasian | 69 (40) | 118 (44) |
| Other | 15 (9) | 32 (12) |
| Multi-racial | 9 (5) | 22 (8) |
| Hispanic ethnicity | 18 (10) | 61 (23) |
| Smoking | ||
| Never | 36 (21) | 83 (31) |
| Current | 64 (37) | 62 (23) |
| Former | 54 (31) | 108 (40) |
| Illicit drug use | 22 (13) | 25 (9) |
| Comorbidities, clinical, and laboratory parameters | ||
| Diabetes | 39 (23) | 44 (16) |
| BMI (kg/m2) | 27 (23–31) | 26 (24–30) |
| HDL (mg/dl) | 46 (38–58) | 47 (38–55) |
| LDL (mg/dl) | 96 (76–120) | 97 (81–119) |
| SBP (mmHg) | 126 (115–136) | 120 (111–132) |
| Diastolic BP, mmHg | 77 (71–82) | 74 (69–82) |
| Hypertension | 70 (41) | 92 (34) |
| ACEi or ARB use | 36 (21) | 54 (20) |
| eGFRcr (ml/min/1.73m2) | 93 (82–105) | 94 (78–107) |
| Urine albumin-creatinine ratio (mg/g) | 9.9 (5.6–29.8) | 7.4 (4.7–16.3) |
| HIV-related characteristics | ||
| HIV RNA (copies/ml) | 50 (48–760) | 50 (47–50) |
| HIV RNA undetectablea | 104 (60) | 201 (75) |
| CD4+ cell count (cells/mm3) | 523 (361–729) | 566 (400–738) |
| History of AIDS | 32 (19) | 43 (16) |
| On ART | 137 (80) | 236 (88) |
| History of HCV infection | 36 (21) | 35 (13) |
| Drugs that reduce tubular creatinine secretionb | 74 (43) | 125 (47) |
| Nephrotoxic ART | ||
| Tenofovir disoproxil fumarate | 95 (55) | 172 (64) |
| Atazanavir | 40 (23) | 65 (24) |
Continuous variables are expressed as median (IQR) and categorical variables are expressed as numbers (%). ACEi, angiotensin-converting enzyme inhibitor; ARB, angiotensin receptor blocker; ART, antiretroviral therapy; eGFRcr, creatinine-based estimated glomerular filtration rate; HCV, hepatitis C virus; HDL, high-density lipoprotein; LDL, low-density lipoprotein.
HIV RNA undetectable varied by cohort and study visit with a lower limit of detection defined as <20 copies/ml (32%), <48 copies/ml (24%), <50 copies/ml (43%), <400 copies/ml (<1%).
Drugs that reduce tubular creatinine secretion include trimethoprim-sulfamethoxazole, fenofibrate, and integrase inhibitor, rilpivirine, cobicistat, or ritonavir containing ART regimens.
Biomarker correlations and biomarker levels
Plasma levels of TNFr1 and TNFr2 were strongly and directly correlated with each other (r=0.72) and modestly correlated with KIM-1 (r range 0.23–0.38) at baseline (Figure S1, http://links.lww.com/QAD/D673). In contrast, UMOD was weakly to modestly and inversely correlated with KIM-1 (r= −0.13), TNFr1 (r= −0.25), and TNFr2 (r = −0.24), and EGF was weakly and inversely correlated only with TNFr2 (r= −0.14). KIM-1, TNFr1, and TNFr2 inversely correlated with eGFR (r range −0.22 to −0.40) and directly correlated with UACR (r range 0.19–0.39). Conversely, UMOD directly correlated with eGFR (r = 0.25) and inversely correlated with UACR (r= −0.23). Similar patterns were observed at year 2 with slightly stronger biomarker correlations (Figure S1, http://links.lww.com/QAD/D673).
At baseline, mean levels of KIM-1, TNFr1, and TNFr2 were 24, 17, and 19% higher, respectively, in cases compared to noncases, while UMOD levels were 10% lower. Similar differences were observed at year 2 (Table S4, http://links.lww.com/QAD/D673).
Biomarkers associations with rapid kidney function decline
In unadjusted analyses, higher baseline KIM-1, TNFr1, and TNFr2 levels were each associated with increased risk of RKFD (Table 2). In fully adjusted models, accounting for baseline eGFR and UACR, each 1-SD higher baseline level of KIM-1, TNFr1, and TNFr2 was independently associated with 26% (95% CI: 15–39%), 39% (24–55%), and 40% (24–57%) higher risk of RKFD, whereas each 1-SD higher baseline level of UMOD and EGFR were associated with 16% (7–23%) and 15% (8–22%) lower risk, respectively (Fig. 3).
Table 2.
Plasma biomarker associations with rapid kidney function decline: 30% estimated glomerular filtration rate decline between years 2 and 7(n = 440).
| Baseline |
Year 2 |
2-year changeb |
||||
|---|---|---|---|---|---|---|
| Unadjusted RR (95% CI) |
Adjusteda RR (95% CI) |
Unadjusted RR (95% CI) |
Adjusteda RR (95% CI) |
Unadjusted RR (95% CI) | Adjusteda RR (95% CI) |
|
| Individual biomarkers (per SD) | ||||||
| EGF | 0.95 (0.89–1.02) | 0.85*** (0.78– 0.92) | 1.00 (0.94– 1.07) | 1.00 (0.92– 1.09) | 1.03 (0.96– 1.11) | 1.01 (0.92– 1.11) |
| UMOD | 0.78*** (0.72– 0.84) | 0.84** (0.77– 0.93) | 0.77*** (0.72– 0.83) | 0.91* (0.83– 1.00) | 0.89** (0.83– 0.95) | 0.93* (0.86– 1.00) |
| TNFr1 | 1.38*** (1.29– 1.47) | 1.39*** (1.24– 1.55) | 1.56*** (1.46– 1.67) | 1.31*** (1.18– 1.46) | 1.37*** (1.29– 1.45) | 1.22*** (1.13– 1.32) |
| TNFr2 | 1.36*** (1.27– 1.45) | 1.40*** (1.24– 1.57) | 1.40*** (1.31– 1.48) | 1.23** (1.09– 1.38) | 1.21*** (1.13– 1.29) | 1.16* (1.05– 1.27) |
| KIM-1 | 1.39*** (1.31– 1.49) | 1.26*** (1.15– 1.39) | 1.46*** (1.37– 1.56) | 1.22** (1.10– 1.34) | 1.22*** (1.14– 1.30) | 1.07 (0.98– 1.17) |
| Combined biomarkers (per SD) | ||||||
| UMOD | 0.86* (0.78– 0.95) | – | – | |||
| TNFr1 | – | 1.31*** (1.18– 1.46) | 1.22*** (1.13– 1.32) | |||
| TNFr2 | 1.27** (1.12– 1.43) | – | – | – | ||
| KIM-1 | 1.19** (1.08– 1.31) | – | – | – | ||
Covariates are taken from the baseline visit (for baseline biomarker models) or year 2 visit (for year 2 biomarker and change from baseline biomarker models). Bolded values are those that are statistically significant with
P< 0.05,
P< 0.001,
P< 0.0001.
CI, confidence interval; EGF, epidermal growth factor; KIM-1, kidney injury molecule-1; RR, relative risk; TNFr1, tumor necrosis factor receptor-1; TNFr2, tumor necrosis factor receptor-2; UMOD, uromodulin.
Final multivariable model adjusted for study site, age, gender, race/ethnicity, smoking, illicit drug use, diabetes, SBP, DBP, hypertension, antihypertensive medication use, ACEi/ARB use, HDL, LDL, CD4+ cell count, HIV viral load, history of AIDS, history of HCV infection, HCV meds, ART use, TDF duration, ATV duration, time-updated drugs that affect tubular secretion of creatinine, eGFR and UACR.
Models of 2-year change in biomarker exposure were adjusted for the baseline value.
Fig. 3. Multivariable associations of baseline, year 2, and 2-year change in each biomarker with risk of rapid kidney function decline.
Data are presented as relative risks with 95% CI. Final multivariable model adjusted for study site, age, gender, race/ethnicity, smoking, illicit drug use, diabetes, SBP, DBP, hypertension, antihypertensive medication use, ACEi/ARB use, HDL, LDL, CD4+ cell count, HIV viral load, history of AIDS, history of HCV infection, HCV meds, ARTuse, TDF duration, ATV duration, time-updated drugs that affect tubular secretion of creatinine, eGFR and UACR. ACEi, angiotensin-converting enzyme inhibitor; ARB, angiotensin receptor blocker; ART, antiretroviral therapy; eGFR, estimated glomerular filtration rate; HCV, hepatitis C virus; HDL, high-density lipoprotein; LDL, low-density lipoprotein.
Analyses evaluating the year 2 biomarkers yielded similar results, except for EGF, which was not associated with RKFD (Table 2). After adjustment for baseline biomarker values, the 2-year changes in the biomarkers had similar associations with RKFD. In fully adjusted models, each 1-SD increase in TNFr1 and TNFr2 over 2 years was associated with 22% (13–32%), and 16% (5–27%) greater risks whereas each 1-SD increase in UMOD over 2 years was associated with a 7% (0–14%) lower risk of RKFD.
When LASSO was used to evaluate the biomarkers in combination, it selected baseline KIM-1, TNFr2, and UMOD as independently associated with RKFD. Modeled in combination, the point estimates for these biomarkers were only modestly attenuated compared to when they were modeled individually (Table 2). For year 2 associations and 2-year changes controlling for baseline, only TNFr1 was selected by LASSO.
Biomarker associations with annualized estimated glomerular filtration range change
Among 286 participants in the sub-cohort, the median follow-up was 6.7 years (IQR: 6.5–6.9) and median annualized eGFR change was −0.83 ml/min/1.73 m2 (IQR: −1.05 to −0.60). None of the biomarkers at baseline, year 2, or the 2-year change adjusted for the baseline value was associated with annualized eGFR change (Table S5, http://links.lww.com/QAD/D673). Similarly, when the biomarkers were modeled simultaneously, no biomarker combination was selected by LASSO as independently associated with annualized eGFR change.
Discussion
In this diverse cohort of women and men with HIV and relatively preserved kidney function, we observed that repeated measures of plasma biomarkers reflecting tubular injury, inflammation, and synthetic function were associated with RKFD, independent of eGFR and albuminuria. In addition, baseline TNFr2, KIM-1, and UMOD were jointly associated with RKFD, suggesting this panel of biomarkers together may inform future CKD risk in PWH. To our knowledge, this is the first study to examine associations of these plasma biomarkers with longitudinal changes in kidney function in PWH. Together, our findings suggest that these plasma biomarkers may quantify important aspects of kidney tubular health that may be useful for identifying PWH at risk for clinically important declines in kidney function. These findings also reinforce the importance of tubular health as an underappreciated determinant of adverse kidney outcomes.
The need for biomarkers that evaluate kidney tubular health is important in PWH, as HIV can directly infect kidney tubular cells, leading to upregulation of inflammatory cytokines and injury [8]. Advances in HIV treatment over the last several decades have modified the patterns of kidney injury in PWH. Two studies conducted in the ART era found that tubulointerstitial disease was the dominant site of injury among ~25% of PWH who underwent a clinically indicated kidney biopsy [35,36]. Prior WIHS and MACS studies align with these findings wherein PWH with normal kidney function were shown to have higher urinary biomarkers of proximal tubular dysfunction and reduced tubular secretory clearance compared to people without HIV, indicating the presence of subclinical tubular damage [19,22,37]. Tubule-toxic ART (e.g. TDF), age-related physiologic changes, comorbidities such as hypertension, and worse HIV control have all been associated with a greater extent of tubular damage among PWH [8].
In the present study, higher plasma KIM-1 levels at baseline and 2 years were associated with RKFD. KIM-1 is upregulated in response to tubular injury. Persistent expression of KIM-1 promotes scar tissue formation, leading to kidney fibrosis [38]. Higher KIM-1 concentrations in the blood and urine have been associated with acute kidney injury, CKD development, and its progression [13,14,26,39–42]. In a histopathology study of 524 individuals with CKD, higher plasma KIM-1 correlated with more severe tubular injury and tubulointerstitial inflammation on biopsy, demonstrating its ability to noninvasively capture tubular damage [43]. Cross-sectionally, HIV seropositivity has been shown to be independently associated with higher urinary KIM-1 concentrations [19]. Prior cohort studies of PWH have identified associations of higher urinary KIM-1 with acute kidney injury development and with faster eGFR decline after starting TDF [44,45]. Our findings extend those observations and suggest that plasma KIM-1 may predict adverse kidney events in PWH.
Inflammation plays an important role in CKD development and progression, which is especially concerning for PWH, a group with ongoing inflammation despite viral suppression [46]. HIV activates the TNF-/TNF-receptor pathway, resulting in production of inflammatory mediators [47]. Several studies have reported higher circulating levels of TNFr1 and TNFr2 in PWH compared to those without HIV [48,49]. Elevated plasma TNFr1 and TNFr2 have been associated with incident and progressive CKD and kidney failure across populations with diabetes or hypertension and community-based cohorts [13,39,50–52]. Our study extends the relevance of these biomarkers for kidney prognosis to PWH for the first time, demonstrating that they are consistently associated with an increased risk of RKFD, independent of conventional markers of kidney health [11]. The consistency of our findings with non-HIV cohorts and across broad ranges of kidney function support inflammation’s role in the pathogenesis of CKD.
UMOD is the most abundant protein in the urine and is found in the blood at far lower concentrations, potentially through its release from the basolateral membranes of tubular cells. Urinary UMOD plays critical roles in maintenance of sodium homeostasis and prevention of kidney stones and urinary tract infections, and hereditary defects in UMOD production are known to cause kidney failure at young ages [27,53,54]. Beyond these roles, circulating UMOD has distinct immunomodulating functions. Experimental models have shown that plasma UMOD exerts beneficial effects on the kidney through modulation of innate immune responses and reduction of systemic inflammation and oxidative stress [55,56]. In our study, higher plasma UMOD levels at baseline and increases over 2 years were associated with reduced risk of RKFD. These findings align with a recent analysis of 500 hypertensive African American adults with CKD, which found that concentrations of circulating UMOD and their changes over time were inversely associated with risk of kidney failure [18].
EGF plays roles in cellular proliferation, kidney tubular repair, and maintenance of kidney function. Prior WIHS studies of women with HIV found that lower urinary EGF and their reductions over time were independent risk factors for CKD, suggesting that lower levels are detrimental to kidney health [19,20]. Although a recent proteomic study found that downregulation of plasma EGF receptor was associated with CKD progression [57], we did not observe an association of plasma EGF with declines in kidney function. As there was poor correlation between EGF and the other biomarkers, we surmise that plasma EGF may be less relevant than urinary EGF to kidney pathophysiology.
Given their ability to prognosticate risk of clinically significant changes in kidney function among PWH, plasma KIM-1, TNFr1, TNFr2, and UMOD have several potential roles, as we enter the biomarker era in nephrology. First, there is a lack of clinical trials of interventions to prevent and treat CKD in PWH, and these biomarkers could guide targeted selection of high-risk PWH for future trials. Second, they may inform personalized risk prediction and guide earlier initiation of kidney-protective therapies to the PWH who would have the greatest benefit. Lastly, as biomarker changes were prognostic of RKFD, they may assist in evaluating responses to therapies that slow CKD progression. The roles of KIM-1, TNFr1, and TNFr2 as pharmacodynamic biomarkers were evaluated in a post hoc analysis of the CANVAS trial of more than 3500 diabetic participants [58]. In this analysis, canagliflozin reduced plasma levels of all three proteins compared to placebo. Moreover, early reductions in TNFr1 and TNFr2 were associated with lower risk of CKD progression. Studies are needed to test whether interventions can improve kidney tubular health in PWH, and whether that impact will be associated with better kidney outcomes.
In our study, none of the biomarkers were associated with annualized eGFR change. Possible explanations for these null findings are reduced power due to the relatively small size of the sub-cohort and ‘healthy cohort effect’, as only 18 participants in the sub-cohort developed RKFD, and the average rate of eGFR decline in noncases was very mild.
Notable strengths of our study include two contemporary and diverse cohorts of women and men with HIV, repeated measurements of a panel of biomarkers reflecting different processes that may impact kidney health, adjustment for multiple traditional and HIV-related CKD risk factors, and an outcome that allowed us to assess clinically meaningful changes in kidney function across a broad range of eGFR values.
Our study also has limitations. First, there were no kidney biopsy data available to correlate the biomarkers to specific histological lesions. As a result, it remains unclear whether the observed associations of biomarkers with RKFD reflect HIV-related kidney disease, comorbidity-related factors, or both. Second, we were not powered to determine whether risk factors known to cause tubular injury (e.g. HIV viremia and TDF) modify biomarker associations with RKFD. While a lower proportion of cases had an undetectable viral load, this rate was representative of the US HIV population during the study period, and viral load was adjusted for in our analyses [59]. Third, although changes in biomarker levels were associated with RKFD, biomarkers were only measured at two time points, limiting our ability to evaluate the impact of long-term changes in kidney tubule health on outcomes.
In conclusion, plasma KIM-1, TNFr1, TNFr2, and UMOD were associated with RKFD in PWH, independent of eGFR and albuminuria. These findings extend their role as prognostic biomarkers of kidney function decline to PWH and highlight the importance of kidney tubular health on kidney outcomes.
Supplementary Material
Acknowledgements
The authors gratefully acknowledge the contributions of the study participants and dedication of the staff at the MWCCS sites.
Funding support:
the contents of this publication are solely the responsibility of the authors and do not represent the official views of the National Institutes of Health (NIH). This project was supported by NIH/NIA grant 5R01AG034853. M.C.F.’s research is support by National Center for Advancing Translational Sciences of the National Institutes of Health, K12TR004411. MWCCS (Principal Investigators): Atlanta CRS (Ighovwerha Ofotokun, Anandi Sheth, and Gina Wingood), 01-HL146241; Baltimore CRS (Todd Brown and Joseph Margolick), U01-HL146201; Bronx CRS (Kathryn nastos, David Hanna, and Anjali Sharma), U01-HL146204; Brooklyn CRS (Deborah Gustafson and Tracey Wilson), U01-HL146202; Data Analysis and Coordination Center (Gypsyamber D’Souza, Stephen Gange and Elizabeth Topper), U01-HL146193; Chicago-Cook County CRS (Mardge Cohen, Audrey French, and Ryan Ross), U01-HL146245; Chicago-Northwestern CRS (Steven Wolinsky, Frank Palella, and Valentina Stosor), U01-HL146240; Northern California CRS (Bradley Aouizerat, Jennifer Price, and Phyllis Tien), U01-HL146242; Los Angeles CRS (Roger Detels and Matthew Mimiaga), U01-HL146333; Metropolitan Washington CRS (Seble Kassaye and Daniel Merenstein), U01-HL146205; Miami CRS (Maria Alcaide, Margaret Fischl, and Deborah Jones), U01-HL146203; Pittsburgh CRS (Jeremy Martinson and Charles Rinaldo), U01-HL146208; UAB-MS CRS (Mirjam-Colette Kempf, James B. Brock, Emily Levitan, and Deborah Konkle-Parker), U01-HL146192; UNC CRS (M. Bradley Drummond and Michelle Floris-Moore), U01-HL146194. The MWCCS is funded primarily by the National Heart, Lung, and Blood Institute (NHLBI), with additional co-funding from the Eunice Kennedy Shriver National Institute of Child Health & Human Development (NICHD), National Institute on Aging (NIA), National Institute of Dental & Craniofacial Research (NIDCR), National Institute of Allergy and Infectious Diseases (NIAID), National Institute of Neurological Disorders and Stroke (NINDS), National Institute of Mental Health (NIMH), National Institute on Drug Abuse (NIDA), National Institute of Nursing Research (NINR), National Cancer Institute (NCI), National Institute on Alcohol Abuse and Alcoholism (NIAAA), National Institute on Deafness and Other Communication Disorders (NIDCD), National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK), National Institute on Minority Health and Health Disparities (NIMHD), and in coordination and alignment with the research priorities of the National Institutes of Health, Office of AIDS Research (OAR). MWCCS data collection is also supported by UL1-TR000004 (UCSF CTSA), UL1-TR003098 (JHU ICTR), UL1-TR001881 (UCLA CTSI), P30-AI-050409 (Atlanta CFAR), P30-AI-073961 (Miami CFAR), P30-AI-050410 (UNC CFAR), P30-AI-027767 (UAB CFAR), P30-AI-124414 (ERC-CFAR), P30-MH-116867 (Miami CHARM), UL1-TR001409 (DC CTSA), KL2-TR001432 (DC CTSA), and TL1-TR001431 (DC CTSA).
Footnotes
Conflicts of interest
M.M.E. has received honoraria from Boehringer-Ingelheim, Inc. and AstraZeneca, Inc. and has a research collaborative agreement with Bayer, Inc. There are no conflicts of interest for the remaining authors.
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