TNF receptors 1 and 2 (TNFR1 and TNFR2) and kidney injury molecule 1 (KIM-1) are strongly associated with CKD progression, even after accounting for patient characteristics including eGFR and proteinuria.1 2 – 3 biomarkers of inflammation and tubular injury. We hypothesize that biomarker levels may reflect, at least in part, the cumulative effect of prior episodes of AKI.
AKI is associated with the development and progression of CKD,4 5 6 7 8 – 9 10 11 , 12 13 biomarkers strongly predictive of CKD progression.
In this study, we test the hypothesis that among patients with CKD, an episode of hospitalized AKI (hitherto referred to as AKI) is associated with increases in TNFR1, TNFR2, and KIM-1 levels, evident months after the acute insult.
Methods
We studied participants in the Chronic Renal Insufficiency Cohort (CRIC) study, an ongoing multicenter prospective observational cohort study of adults with CKD. Initial recruitment into CRIC started in 2003 and study participants were invited to re-enroll in subsequent 5-year extensions. Additional study participants, who were relatively older and had more preserved eGFR, were enrolled starting in 2013.14 Figure 1 ). Hospitalizations were ascertained at annual study site visits and mid-year telephone calls to participants or their proxies, along with study personnel review of hospital discharge records collected by active surveillance.15 , 16 Supplemental Appendix 1 16
Figure 1.: Overview of study design. Selection of study visits and measurements of biomarkers before and after hospitalization .
This study population is drawn from CRIC study participants who were alive and active in the study after July 2013, because it was only after this date that there was systematic capture of inpatient serum creatinine readings available to define presence or absence of AKI. The last hospitalization in our cohort was in December 2019. We adapted the Kidney Disease: Improving Global Outcomes definition to define AKI hospitalizations as those with peak/nadir inpatient serum creatinine values ≥1.5.17 , 18 hospitalization , we only considered hospital admissions if there were CRIC study visits with both plasma and urine sample collection within 2 years before admission and within 1 year after discharge (Figure 1 ).
We identified 199 AKI hospitalizations and 1534 non-AKI hospitalizations fulfilling the inclusion/exclusion criteria. We matched each AKI hospitalization to a non-AKI hospitalization (patients could only contribute one hospitalization to the matching) using the following criteria assessed at the prehospitalization annual CRIC study visit in decreasing priority: eGFR from the CRIC specific equation19 2 ), urine protein-creatinine ratio (0–0.3, 0.3–1, or >1 g/g), duration between hospital discharge and next CRIC visit (by 0–30, 30–180, and >180 days), diabetes status (yes/no), age (by 20-year strata), sex (male/female), duration between hospital admission and prior CRIC visit (by 0–30, 30–180, and >180 days). In total, 147 matches met all seven matching criteria, then additional matches were added by sequentially eliminating the next lowest priority criterion. One patient with AKI could not be matched due to missing prehospitalization eGFR. Our final cohort thus included 198 patients with AKI and 198 patients who were non-AKI.
Plasma samples collected at study visits were frozen locally at either −20 or −80°C before being shipped to the central laboratory on dry ice, where they were stored at −80°C until they were thawed for measurement. Plasma biomarkers (TNFR1, TNFR2, and KIM-1) were measured using a multiplex U-PLEX assay on the Meso Scale Discovery platform (Meso Scale Discovery, Gaithersburg, MD) at the Brigham and Women’s Hospital.
Descriptive statistics were presented as proportions, means and standard deviations, or medians and interquartile ranges (IQR). McNemar’s tests for categorical variables and paired t tests and Wilcoxon signed-rank tests (for means and medians, respectively) of continuous variables were used to generate P values for descriptive statistics and for raw biomarker concentrations. TNFR1, KIM-1, and urine protein-creatinine ratio values were right skewed, so values were log-transformed for analysis.
Table 1. -
Prehospitalization characteristics
Characteristics
Patients with AKI (n =198)
Patients Without AKI (n =198)
P Value
Age, yr
66 (9)
66 (9)
0.36
Female
39%
36%
0.21
Race and ethnicity
0.67
Hispanic
10%
8%
Non-Hispanic Black
47%
49%
Non-Hispanic White
40%
40%
Other
3%
4%
Diabetes mellitus
68%
68%
1.00
Estimated GFR, ml/min per 1.73 m2
48 (17)
48 (17)
0.13
Urine protein-creatinine ratio, g/g
0.24 (0.10–0.99)
0.26 (0.08–0.90)
0.48
Systolic blood pressure, mm Hg
129 (22)
129 (20)
0.89
ACEi/ARB use
72%
72%
1.00
Number of antihypertensive medication classes
0.35
0
5%
3%
1
7%
14%
2
26%
25%
3
24%
25%
4+
38%
33%
Days between prehospitalization measurement and admission
221 (138–291)
220 (123–283)
0.06
Mean (SD) or median (IQR) for continuous variable or percentage for categorical variables. Missingness was nine patients for urine protein-creatinine ratio, two patients for systolic BP, and one patient for medication use. ACEi, angiotensin-converting enzyme inhibitor; ARB, angiotensin receptor blocker.
Table 2. -
Biomarker levels
Result
AKI Hospitalization (n =198)
Non-AKI Hospitalization (n =198)
P Value
TNFR1, pg/ml
Prehospitalization
1373 (843–2416)
1371 (822–2274)
0.64
Posthospitalization
1621 (985–2870)
1460 (860–2554)
0.17
Raw change (and % change)
247 (−104–815) (23%)
79 (−182–477) (10%)
<0.01
TNFR2, pg/ml
Prehospitalization
47,141 (33,333–67,269)
46,135 (32,623–65,936)
0.43
Posthospitalization
53,718 (38,077–78,547)
47,559 (33,044–72,837)
<0.01
Raw change (and % change)
4697 (−1790–13,494) (10%)
1235 (−3479–8222) (3%)
<0.01
KIM-1, pg/ml
Prehospitalization
857 (437–1698)
719 (443–1672)
0.57
Posthospitalization
927 (518–2062)
720 (463–1475)
<0.01
Raw change (and % change)
59 (−141–481) (13%)
−13 (−200–161) (−2%)
<0.01
Values given in median (IQR).
Table 3. -
Hospitalization and posthospitalization characteristics
Characteristics
Patients with AKI (n =198)
Patients without AKI (n =198)
P Value
AKI stage
Stage 1
66%
N/A
N/A
Stage 2
28%
N/A
N/A
Stage 3
7%
N/A
N/A
Cardiovascular hospitalization
25%
27%
0.73
Infection-related hospitalization
14%
8%
0.12
Post-hospitalization annual CRIC visit characteristics
Estimated GFR, ml/min per 1.73 m2
44 (17)
47 (19)
<0.01
Urine protein to creatinine ratio, g/g
0.31 (0.11–1.06)
0.27 (0.10–0.91)
0.55
Systolic blood pressure, mm Hg
129 (23)
127 (17)
0.40
ACEi/ARB use
54%
62%
0.19
Number of antihypertensive medication classes
0.82
0
5%
5%
1
12%
11%
2
22%
25%
3
20%
24%
4+
41%
36%
Days between discharge and posthospitalization measurement
161 (93–229)
160 (93–247)
0.20
Days between prehospitalization and post-hospitalization measurements
369 (348–413)
365 (349–390)
<0.01
Mean (SD) or Median (IQR) for continuous variable or percentage for categorical variables. Missingness was five patients for eGFR, 23 patients for urine protein-creatinine ratio, four patients for systolic BP, and three patients for medication use. ACEi, angiotensin-converting enzyme inhibitor; ARB, angiotensin receptor blocker.
To account for possible correlations among matched pairs of patients, the primary analysis comparing changes in biomarkers between AKI and non-AKI groups used a linear mixed-effects model, including the fixed effects of AKI, change between the pre/posthospitalization visits, and their interaction (AKI with change between visits), and random effects of match ID and participant ID: Y = TNFR2 concentration/natural log TNFR1 and KIM-1 concentration = β 0 + β 1 (AKI) + β 2 (posthospitalization) + β 3 (AKI*posthospitalization) + random intercept for participant ID + random intercept for matched pair, where β 0 is the mean (or mean of the log) of the non-AKI prehospitalization biomarker concentration, and AKI and posthospitalization are binary variables, indicating whether the measurement was measured in a patient with AKI and at the posthospitalization visit. In the primary analysis, no statistical adjustment for confounders was performed because patients had already been matched on important confounders during cohort assembly. However, because some pairs did not meet all seven matching criteria, two sensitivity analyses were performed: (1 ) repeating the analysis in the subset that did meet all seven matching criteria (n =147 pairs) and (2 ) further adjustment for matching variables such as age in the entire study cohort (n =198 pairs), because there may be residual differences in, say, age among matched patients with AKI and non-AKI within each 20-year stratum.
Similar mixed-effect models were used to compare changes in eGFR and proteinuria (instead of biomarker concentrations as the outcome).
All analyses were performed using R 4.0.2 (R Core Team [2020] R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria; https://www.R-project.org/
Results
Our final study population included 198 patients with AKI during hospitalization and 198 matched patients without AKI during hospitalization . Patients were well matched on all prehospitalization characteristics, including eGFR (mean 48 ml/min per 1.73 m2 ), proteinuria, and blood pressure (Table 1 ), ascertained at the most recent annual CRIC study visit before hospitalization . Prehospitalization plasma concentrations of TNFR1, TNFR2, and KIM-1, measured from banked samples after the cohort was assembled, were also very similar (Table 2 , Supplemental Figures 1 2 3
The duration between the prehospitalization biomarker measurement and hospital admission was about 7 months in both groups (median 221 days, IQR, 138–291 in the AKI group versus 220 days, IQR, 123–283, in the non-AKI group; P =0.06). The period between hospital discharge and the posthospitalization biomarker measurement was also similar at about 5 months (median 161 days, IQR, 93–229, in the AKI group versus 160 days, IQR, 93–247, in the non-AKI group; P =0.20; Table 3 ). Roughly one quarter of hospitalizations were due to cardiovascular causes in both the AKI and non-AKI groups. A higher percentage of AKI hospitalizations were infection-related (14% in the AKI group versus 8% in the non-AKI group; P =0.12). Two thirds of patients had mild AKI (stage 1). There were no patients with AKI who required dialysis.
Comparing pre- and posthospitalization measurements, AKI was associated with significantly greater increases in the median concentrations of all three plasma biomarkers (Table 2 ). TNFR1 increased by a median 23% in the AKI group and by 10% in the non-AKI group (P <0.01). TNFR2 increased by a median 10% in the AKI group and by 3% in the non-AKI group (P <0.01). KIM-1 increased by a median 13% in the AKI group, but decreased by 2% in the non-AKI group (P <0.01) (Table 2 ). In the mixed-effects model accounting for matched pairs, all differences remained statistically significant (P =0.01, P =0.003, P =0.002 for TNFR1, TNFR2, and KIM-1, respectively). Results were substantively unchanged in sensitivity analyses accounting for imperfect matching (Supplemental Table 1
eGFR decreased from prehospitalization to posthospitalization in the AKI group, from a mean of 48 ml/min per 1.73 m2 to 44 ml/min per 1.73 m2 compared with 48 ml/min per 1.73 m2 to 47 ml/min per 1.73 m2 in the non-AKI group (P =0.008 for difference in the mixed-effects model accounting for matched pairs). The mean urine PCR went from 0.24 g/g to 0.31 g/g in the AKI group versus 0.26 g/g to 0.27 g/g in the non-AKI group (P =0.29).
Discussion
We found that among patients with pre-existing CKD, AKI was associated with long-term increases in plasma TNFR1, TNFR2, and KIM-1, despite similar prehospitalization levels. These elevations were found on post-AKI measurements a median of 5 months after hospital discharge (median 1 year between the prehospitalization and posthospitalization measurements), which demonstrates that AKI-associated changes in plasma biomarkers are not limited to the perihospitalization period. These long-term changes in biomarkers related to tubular injury and inflammation may help provide insight into the pathophysiology connecting AKI and subsequent further loss of kidney function.
TNFR1, TNFR2, and KIM-1 are biomarkers strongly associated with kidney disease progression.1 2 – 3 , 20 plasma TNFR1, TNFR2, and KIM-1 independently predicted progression of diabetic kidney disease.21 plasma biomarkers found that >75% of the predictive information provided by the panel came from KIM-1 and CD27 (another marker of inflammation in the TNF receptor superfamily).22 Plasma KIM-1 and TNFR1 have both been shown to be associated with progressive kidney disease in type 1 diabetes despite adjustments for eGFR, blood pressure, A1c, and urinary albumin.23 3 21 , 24
Some investigators have suggested these biomarkers are not only indicators of risk, but actually mechanistically important in the pathophysiological pathway.25 α that are shed into the plasma after cleavage by a metalloproteinase.26 renal injury . Both reflect activation of the proinflammatory TNF pathway. KIM-1, a transmembrane protein involved in immune regulation and the inflammatory response, is upregulated in the proximal tubule after acute tubular injury.25 , 27 10 , 13 28 29 30 biomarkers may be worthwhile targets for therapeutics aimed at reducing CKD progression.31 , 32
Among patients with CKD, there is large between-person variability in levels of TNFR1, TNFR2, and KIM-1, which is not readily explained by observed variability in concurrent levels of eGFR, albuminuria, or other patient-level characteristics. With the greater appreciation in the past 10–15 years that AKI and CKD have a closer relationship than previously realized, and the natural history of CKD is punctuated by episodes of AKI, we hypothesized that prior episodes of AKI may contribute to differences in plasma levels of TNFR1, TNFR2, and KIM-1 in patients with CKD. Apart from the results of this study, little is known about changes in TNFR1 and TNFR2 levels after AKI. Higher levels of TNFR1 and TNFR2 measured at a single timepoint in patients who are critically ill have been associated with AKI and mortality,33 , 34 plasma TNFR1 and TNFR2 levels before and after AKI, although increases have been reported in murine models of AKI from cisplatin nephrotoxicity and ischemic-reperfusion injury.26 , 29 , 35 Plasma levels of KIM-1 are known to rise after AKI, but most studies have measured biomarkers only in the short term (days after AKI). Plasma KIM-1 levels rose after postcardiac surgery AKI in two studies with pre- and postsurgery samples,3 , 36 37
Although caution must be exercised in the interpretation of absolute biomarker levels (because these may vary by assay platform), the absolute rise in KIM-1 we observed in the AKI group (59 pg/ml) is similar in magnitude to the difference between patients who developed early diabetic kidney disease and controls in ACCORD (75 pg/ml).21 plasma KIM-1 was small (31 pg/ml), but was still associated with the development of CKD.23 24 21 21
We found eGFR was lower at the posthospitalization visit (median 161 days after discharge) in patients with AKI. Thus, another possible explanation for the biomarker increases we observed is that decreased GFR results in elevated concentrations of these molecules because of reduced clearance. We believe this is less likely (although impossible to rule out) because these molecules are rather large (90 kDa soluble portion for KIM-1,27 38 39 plasma biomarker concentrations predict future CKD progression despite adjustment for eGFR level21 22 23 – 24 28 , 29 biomarkers may directly cause GFR to fall (rather than a falling GFR causing biomarkers to rise).
Strengths of our study include the use of pre-AKI biomarker levels, which are often not available, and the long-term follow-up of patients with repeat biomarker measurements several months after hospital discharge. CRIC is an ideal data source for this type of analysis because it has regular annual study visits with plasma collection and detailed serum creatinine information from intervening hospitalizations. Another strength of these data is the use of strict definitions for both AKI and non-AKI on the basis of laboratory information. With these definitions, patients who were borderline, and were not clearly AKI or non-AKI, were excluded to avoid misclassification. Our data were not restricted to a particular type of hospital admission or cause of AKI (e.g. , postcardiac surgery), as has been the case for other studies looking at plasma AKI biomarkers ,3 , 36
Our study also has some limitations. We cannot exclude the possibility that some confounding factor associated with AKI but not accounted for here (e.g ., critical illness requiring intensive care unit admission) may be the cause of the elevated biomarker levels. We also do not have information on the AKI etiology (although there is no accepted method for adjudicating AKI etiology40 e.g ., ischemic versus nephrotoxic). Numerous patients had additional hospitalizations in between the two biomarker measurements (median 1 hospitalization with IQR, 0–2 hospitalizations in both the AKI and non-AKI groups). But only 7% of additional hospitalizations included an AKI episode in the AKI group, compared with 3% in the non-AKI group, so misclassification appears unlikely to be an important source of bias. The definition of AKI we used relies entirely on changes in creatinine and does not capture changes in urinary volume and proteomics. It may therefore be nonspecific for intrinsic kidney damage (versus other causes of creatinine rise such as volume depletion).41
In summary, hospitalized AKI was associated with long-term increases in plasma TNFR1, TNFR2, and KIM-1 concentrations several months after hospitalization . Additional studies to better characterize long-term trends in biomarkers of tubular injury and inflammation after AKI, coupled with studies to explore the direct effects of elevations in these biomarkers , may provide further insight into the pathophysiology by which AKI leads to progressive CKD.
Disclosures
A. Go reports employment with Kaiser Permanente Northern California; and reports receiving research funding from Amarin Pharmaceuticals, Bristol Meyers-Squibb, CSL Behring, Esperion Pharmaceuticals, iRhythm Technologies, Janssen Research and Development, and Novartis. A. Srivastava reports being a consultant for CVS Caremark, and Tate & Latham (Medicolegal consulting); reports receiving honoraria from AstraZeneca, Bayer, and Horizon Therapeutics; and reports receiving speakers bureau from AstraZeneca. C. Hsu reports consulting for legal cases involving acute or CKD, and consults on an ad hoc basis for companies regarding kidney disease; reports receiving research funding from Satellite Healthcare; and reports receiving honoraria from Satellite Healthcare and royalties from UpToDate. C. Parikh reports being a consultant for Genfit Biopharmaceutical Company; reports having an ownership interest in Renaltix AI; reports receiving research funding from the National Heart, Lung, and Blood Institute and the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK); and reports having an advisory or leadership role with Genfit Biopharmaceutical Company and Renalytix. J. Bonventre reports consultancy agreements with Aditum, Caladrius, Citrine, Janssen, MediBeacon, Praxis, and Sarepta; reports having an ownership interest in Autonomous Medical Devices, Coegin, Goldfinch, Goldilocks, Medibeacon, Pacific Biosciences, Theravance, Sentien, and Verinano; reports having patents for Kim-1 patents and kidney organoid patents assigned to Mass General Brigham; and reports having an advisory or leadership role with Advisory Board of Northwest Kidney Center, Angion, CPath AKI Biomarker Initiative, Editor Seminars in Nephrology, Kidney Health Initiative AKI Biomarker Initiative, and Wearable Artificial Organs. D. Cohen reports being a consultant for Medtronic, Metavention, Novartis, and Recor; reports having an ownership interest with Incyte and Kura (spouse); reports receiving research funding from CRIC study and Medtronic; reports receiving honoraria from Medtronic, Metavention, Novartis, Recor; reports having patents or royalties Incyte (spouse); and reports having an advisory or leadership role with Kura Oncology (spouse). I. Mccoy reports receiving research funding from Satellite Healthcare Inc. (nonprofit). J. Hsu reports having an advisory or leadership role with the American Journal of Kidney Diseases (AJKD), Statistics/Epidemiology Editor, PLOS ONE, Statistical Advisory Board. K. Liu reports being a consultant for AM Pharma, Biomerieux, BOA Medical, and Seastar Medical; reports having an ownership interest Amgen (hold stock only); reports having an advisory or leadership role with American Journal of Respiratory and Critical Care Medicine, AJKD, and CJASN Editorial Boards, the American Thoracic Society, and the National Kidney Foundation Scientific Advisory Board; and other reports interests or relationships with UpToDate. P. Rao reports receiving honoraria from AstraZeneca; and reports having an advisory or leadership role with AstraZeneca Nephrology Fellowship Advisory Board, GSK scientific advisory board, and the Renal Research Institute. All remaining authors have nothing to disclose.
Funding
This work was supported by the National Institutes of Health NIDDK University of Pennsylvania National Center for Advancing Translational Sciences (NCATS) NCATS
Acknowledgments
A portion of this work was presented at the American Society of Nephrology Annual Meeting in San Diego, CA, November 4, 2021 (PO0417).
Author Contributions
Dr. Chi-yuan Hsu, Dr. Ian E. McCoy, Dr. Jesse Y. Hsu, Dr. Joseph V. Bonventre, Dr. Chirag R. Parikh, Dr. Alan S. Go, Dr. Jiang He, and Dr. Panduranga Rao designed the study; Dr. Chi-yuan Hsu, Dr. Ian E. McCoy, Dr. Jesse Y. Hsu, Dr. Joseph V. Bonventre, Dr. Chirag R. Parikh, Dr. Alan S. Go, Dr. Kathleen D. Liu, Dr. Ana Ricardo, Dr. Debbie L. Cohen, Dr. Jiang He, Dr. Jing Chen, and Dr. Panduranga Rao acquired the data; Dr. Joseph V. Bonventre carried out assays; Dr. Jesse Y. Hsu performed the statistical analysis; Dr. Ian E. McCoy and Dr. Chi-yuan Hsu drafted the paper; and all authors analyzed and interpreted the data, revised the paper, and approved the final version of the manuscript.
Data Sharing Statement
Observational Data is available in the NIDDK Repository (https://repository.niddk.nih.gov/home/
Supplemental Material
This article contains the following supplemental material online at http://jasn.asnjournals.org/lookup/suppl/doi:10.1681/ASN.2021111453/-/DCSupplemental
Supplemental Appendix 1
Supplemental Table 1
Supplemental Figure 1 plasma TNFR1 concentrations.
Supplemental Figure 2 plasma TNFR2 concentrations.
Supplemental Figure 3 plasma KIM-1 concentrations.
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