Exploring the potential effect of paricalcitol on markers of inflammation in de novo renal transplant recipients.

Following a successful renal transplantation circulating markers of inflammation may remain elevated, and systemic inflammation is associated with worse clinical outcome in renal transplant recipients (RTRs). Vitamin D-receptor (VDR) activation is postulated to modulate inflammation and endothelial function. We aimed to explore if a synthetic vitamin D, paricalcitol, could influence systemic inflammation and immune activation in RTRs. Newly transplanted RTRs were included in an open-label randomized controlled trial on the effect of paricalcitol on top of standard care over the first post-transplant year. Fourteen pre-defined circulating biomarkers reflecting leukocyte activation, endothelial activation, fibrosis and general inflammatory burden were analyzed in 74 RTRs at 8 weeks (baseline) and 1 year post-engraftment. Mean changes in plasma biomarker concentrations were compared by t-test. The expression of genes coding for the same biomarkers were investigated in 1-year surveillance graft biopsies (n = 60). In patients treated with paricalcitol circulating osteoprotegerin levels increased by 0.19 ng/ml, compared with a 0.05 ng/ml increase in controls (p = 0.030). In graft tissue, a 21% higher median gene expression level of TNFRSF11B coding for osteoprotegerin was found in paricalcitol-treated patients compared with controls (p = 0.026). Paricalcitol treatment did not significantly affect the blood- or tissue levels of any other investigated inflammatory marker. In RTRs, paricalcitol treatment might increase both circulating and tissue levels of osteoprotegerin, a modulator of calcification, but potential anti-inflammatory treatment effects in RTRs are likely very modest. [NCT01694160 (2012/107D)]; [www.clinicaltrials.gov].

RCT Entities:   Population Interventions Outcomes

Introduction

Chronic kidney disease (CKD) is associated with systemic inflammation. In end-stage renal disease there is a strong association between inflammation marker C-reactive protein (CRP) and risk of death and cardiovascular events [1]. Increased oxidative stress, the accumulation of toxic metabolites (e.g. microbiota-dependent amine oxides) and chronic activation of various cell subsets of both the innate and adaptive immune system creates a pro-inflammatory milieu in CKD [2, 3]. Following a successful renal transplantation with appropriate immunosuppression obtained by a combination of drugs, both endothelial dysfunction and low-grade inflammation is ameliorated by reversal of the uremic state [4, 5], while the lipid oxidative state seems more refractory [6]. Some derangements in immune function will however persist [7, 8], especially if graft function is sub-optimal. Even among renal transplant recipients (RTRs) with low Framingham risk scores, implying a limited burden of comorbidity, there is evidence for enhanced systemic inflammation [9]. In stable RTRs, interleukin-6 (IL-6) and CRP, has been associated with risk of major cardiovascular events and all-cause mortality [10], as has neopterin [11, 12], a marker of interferon (IFN) γ-mediated activation of monocytes/macrophages.

Vitamin D is a fat-soluble vitamin central to the maintenance of bone- and mineral homeostasis. Poor vitamin D status has also been associated with increased risk of cancer [13], infections [14, 15], autoimmune diseases [16], cardiovascular events [17], obesity [18] and diabetes [19]. However, interventional studies on vitamin D supplementation report equivocal and inconsistent effects on non-skeletal clinical outcomes, and larger randomized controlled trials are ongoing [20-22]. Anti-inflammatory and immune-modulating effects of vitamin D -supplements could be of particular benefit for renal transplant patients, as they are prone to vitamin D-deficiency, while at the same time carrying an increased risk of all of the above mentioned chronic conditions [23, 24].

Paricalcitol (19-nor-1,25-dihydroxyvitamin D2), a synthetic selective third generation vitamin D-receptor agonist (VDRA), is used in patients with CKD to treat secondary hyperparathyroidism. Potential non-skeletal benefits of paricalcitol include anti-proteinuric effects in patients with diabetes [25] and in RTRs [26]. VDRAs seem also to have anti-inflammatory potential, and experimental studies have indicated dampened tumor necrosis factor (TNF) and interleukin-8 (IL-8) production [27] and reduced inflammation and fibrosis development [28].

An observational study in RTRs with secondary hyperparathyroidism found that 3 months of treatment with paricalcitol reduced serum IL-6 and TNF levels, with corresponding lowered mRNA expression in peripheral blood mononuclear cells [29]. In a clinical trial of 168 RTRs with proteinuria, paricalcitol treatment on top of RAAS-blockade caused significant reductions in circulating IL-6 and the pro-fibrotic mediator transforming growth factor beta (TGF-β) [30]. We were, however, not able to demonstrate a treatment effect of paricalcitol on high sensitity (hs) CRP in 77 newly transplanted RTRs [31], but it is unlikely that hsCRP reflects all inflammatory pathways that are activated in RTRs.

Cytokines and inflammatory molecules are operating in a complex network, and our aim in the current study on renal transplant recipients was to explore the potential effect of paricalcitol on a broader range of pre-defined circulating inflammatory markers, including markers reflecting leukocyte activation, endothelial activation, fibrosis and more general vascular inflammation.

Materials and methods

Study design

From Jan 2013 up until Jan 2014, 77 patients >18 years of age who had received a kidney transplant or a combined kidney-pancreas transplant were randomized to receive either treatment with paricalcitol 2 μg daily or standard post-transplant care. Study randomization and baseline laboratory measurements took place 7–8 weeks after transplantation. The last study visit was scheduled one year after date of engraftment and included repeated blood sampling and collection of renal transplant biopsy tissue for subsequent histopathology evaluation and RNA extraction. To be eligible for the study, patients should have an estimated glomerular filtration rate (eGFR) by the CKD-EPI formula of at least 30 ml/min, total serum calcium levels should range between 2.0 and 2.6 mmol/l, and the immunosuppressive regimen should include a calcineurin inhibitor (CNI). Patients already undergoing treatment with vitamin D, VDRA or calcimimetic drugs were excluded, as were patients with established osteoporosis in the axial skeleton, patient with a history of hypersensitivity towards paricalcitol or related drugs and recipients receiving organs from a donor older than 75 years. Results concerning the primary endpoint (potential anti-proteinuric effect) and main secondary endpoints are found elsewhere [31], as is the Consort diagram presenting the screening- and inclusion process. In short, both the baseline- and 1-year visits consisted of the following investigations: Blood samples for routine biochemistry and frozen storage, spot urine albumin/creatinine ratio, pulse wave velocity measurements, assessment of endothelial function by a noninvasive plethysmographic method, allograft protocol biopsy with collection of tissue for RNA extraction and measured glomerular filtration rate (mGFR) by iohexol clearance. All patients provided written informed consent before inclusion in the trial. The study conformed to the principles of the Declaration of Helsinki and the Declaration of Istanbul. The study protocol was approved by the Regional Ethics Committee, officially known as REK South East (study no 2012/107) and the hospital´s Research Administration (The Oslo University Hospital Data Protection Authority as well as the Radiology Research Administration, FU-ARN). It was also approved by the Norwegian Medicines Agency, SLV (Eudract no: 2012-000429-32). The Department of Organ Transplantation at Oslo University Hospital was responsible for the coordination and conduction of the trial.

Outcomes

Cytokines and inflammatory molecules are operating in a complex network, and our aim in the current study was to explore the potential effect of paricalcitol on a broader range of stable and readily measurable circulating inflammatory markers. We included biomarkers reflecting leukocyte activation (neopterin, soluble CD14 and soluble CD163 as markers of monocyte/macrophage activation; neutrophil gelatinase-associated lipocalin [NGAL] as a marker of neutrophil activation), endothelial activation (von Willebrand factor [vWf], and angiopoietin-2), fibrosis (endostatin, matrix metalloprotease-9 [MMP-9], galectin3, tissue inhibitor of metalloproteinase 1 [TIMP-1], activin A) and more general vascular inflammation (osteoprotegerin [OPG]), soluble tumor necrosis factor-receptor 1 [TNFR 1] and delta like canonical Notch ligand 1 [DLL1]). As a supplementary analysis the levels of mRNA reflecting expression of genes coding for the same biomarkers were investigated in renal graft tissue at study end.

Laboratory

After an overnight fast, blood samples were drawn in the morning at the baseline visit and at the last study visit one year after transplantation. After centrifugation at 2350 g for 10 minutes, plasma and serum were immediately frozen and samples stored at -72° C until analysis (April-May 2018). Plasma levels of inflammatory markers were measured in duplicate by enzyme immunoassay (EIA) using R&D Systems (Stillwater, Minneapolis, MN) antibody pairs: Angiopoietin-2 (DY623), sCD14 (DY383), sCD163 (DY1607), DLL1 (DY1818), Endostatin (DY1098), MMP9 (DY911), sTNFr1(DY225), Galectin-3 (DY1154), NGAL (DY1757), ActivinA (DY338), OPG (DY805), TIMP-1 (DY970). For von Willebrand factor (VWF) the EIA was performed with antibodies (A0082, P0226) from DakoCytomation (Glostrup, Denmark) and for Neopterin, a kit (RE59321) from IBL International GmbH (Hamburg, Germany. Assays were performed in a 384‐format using the combination of a SELMA (Jena, Germany) pipetting robot and a BioTek (Winooski, VT) dispenser/washer (EL406). Primary and secondary antibody concentrations were used according to manufacturer instructions (Coating 1–4 μg/ml; secondary 0.2–2 μg/ml). Assay volume was 20 μl and coating was performed in phosphate buffered saline. Subsequent assay buffer was with 1% bovine serum albumin in PBS while sample diluent was PBS with 25% heat inactivated fetal calf serum (Gibco, Thermo Fisher Scientific, Waltham, MA). Wash buffer was PBS with 0.05% tween‐20 and three wash cycles were included per step. Samples were incubated overnight at 4°C. Absorption was read at 450 nm with wavelength correction set to 540 nm using an EIA plate reader (Synergy H1 Hybrid, Biotek, Winooski, VT). Intra‐ and interassay coefficients of variation were <10% for all assays. The assays included a series of known concentrations to generate standard curves.

Gene expression analyses

The procedure of RNA extraction from retrieved transplant biopsy tissue stored in RNAlater® solution, together with RNA quality assessment, amplification- and labelling procedures are described elsewhere [31]. Sixty samples, equally divided between study groups, were found to have sufficient quality for microarray gene expression analyses. For this post-hoc investigation, we selected 15 gene products reflecting the expression of 13 biomarker proteins. (Neopterin is a degradation product for which the circulating level is unlikely to be directly reflected by the expression any gene).

Statistical methods

The intention-to-treat population consisted of any patient who was randomized and, if assigned to the treatment group, received at least one dose of study drug, irrespective of any study protocol violation. The per-protocol population consisted of participants actually fulfilling the protocol requirements for eligibility, intervention and outcome assessment.

Comparisons of baseline variables between study groups were done using t-test, Mann-Whitney U Test or Pearson χ2 as found appropriate. The potential correlation between change in osteoprotegerin and change in PTH was tested using Pearsons Correlation test.

The levels of biomarkers in the circulation, expressed as change from baseline to study end, showed only marginal deviations from a normal distribution, rendering the t-test for independent observations applicable. As a sensitivity analysis, ANCOVA was performed, analyzing group differences in levels of biomarkers at study end with adjustments for baseline levels.

For analyses of microarray data, gene expression levels were log transformed, and normalized intensities were converted to Z-scores, which were used to identify differentially expressed genes between the paricalcitol group and controls. For each gene, a relative ratio of the mean Z-scores between the two groups was computed, and the statistical significance of relative ratios (P-value) was estimated by the two-sample Kolmogorov-Smirnov goodness-of-fit hypothesis test (KS-test), which does not have a prior assumption for the distribution of gene expression [32].

Statistical analyses of circulating biomarker levels were performed in SPSS version 21 (IBM, New York, USA). Microarray gene expression data analysis were performed by either MATLAB statistics toolbox (MathWorks, Natick, USA) or in-house script files such as Python based on previously published works [33].

Results

Baseline characteristics

A total of 74 patients had available plasma samples for analyses of circulating biomarkers at baseline and study end. Baseline demographics, as well as relevant laboratory values and vital signs are presented for both study groups in Table 1. There were no statistically significant differences between groups, but there was a trend towards more males (Pearson χ2, p = 0.19) and a lower baseline vitamin 25-OH-D (t-test, p = 0.16) in the control group. Twenty-eight patients (38%), equally divided between study groups, had an immunological cause of end-stage renal disease, e.g. glomerulonephritis.

Table 1

Baseline characteristics of the study population.

Variables	Paricalcitol	Control
	(n = 35)	(n = 39)
Age, years	55.6 (13.3)	55.1 (12.6)
Male gender	26 (74%)	33 (85%)
Caucasian ethnicity	34 (97%)	37 (95%)
BMI, kg/m2	26.2 (3.3)	25.5 (3.9)
Current smoking	5 (14%)	5 (13%)
Living donor	10 (29%)	12 (31%)
Cold ischemia time, hours	10.4 (6.4)	10.1 (5.7)
Glomerulonephritis as cause of CKD	13 (37.1)	15 (38.5)
Predialytic	13 (37%)	13 (33%)
Hypertension	29 (83%)	36 (92%)
Chronic heart disease	11 (31%)	13 (33%)
Pre-tx diabetes mellitus	6 (17%)	6 (15%)
Systolic blood pressure, mmHg	145 (21)	143 (22)
Diastolic blood pressure, mmHg	83 (10)	84 (11)
Treatment with ACEi/ARB, %	9 (26%)	14 (36%)
Cholesterol, mmol/L	5.8 (1.1)	5.9 (0.9)
HDL cholesterol, mmol/L	1.6 (0.5)	1.6 (0.4)
LDL cholesterol, mmol/L	3.8 (1.0)	3.9 (0.9)
Triglycerides, mmol/L*	1.3 (1.0)	1.4 (0.5)
Creatinine, μmol/L	115 (25)	122 (30)
Hemoglobin, g/L	12.4 (1.2)	12.3 (1.2)
hsCRP, mg/L ¤*	0.85 (2.20)	1.00 (1.19)
Calcium total, mmol/L	2.38 (0.09)	2.34 (0.21)
Phosphate, mmol/L *	0.9 (0.3)	0.8 (0.4)
Albumin, g/L	42.3 (2.5)	41.5 (2.4)
PTH, pmol/L *	10.1 (9.2)	10.2 (5.4)
Alkaline phosphatase, U/L	60.7 (21.8)	69.4 (28.6)
Vitamin 25-OH-D, nmol/l	50.1 (18.0)	44.8 (17.2)
Urine Albumin/creatinine ratio, mg/mmol *	3.1 (7.4)	4.5 (8.7)

BMI, body mass index; CKD, chronic kidney disease; HDL, high density lipoprotein; hsCRP, high-sensitive C-reactive protein; LDL, low density lipoprotein; PTH, parathyroid hormone; ACEi, angiotensin converting enzyme inhibitor; ARB, angiotensin receptor blocker.

All laboratory measurements are performed in plasma.

aModified version of table from the original publication [31]. Continuous data expressed as mean (standard deviation) or * median (interquartile range).

Categorical data expressed as number (percentage frequency).

¤ Values <0.60mg/L (laboratory detection cut-off) are all given the value 0.30. Values >15mg/L are rounded down to this value.

Two patients were diagnosed with biopsy-proven antibody mediated rejection at 1-year follow up, while four patients were being treated for acute cellular rejection at time of study inclusion or earlier postoperatively. Two patients suffered a cellular rejection in the interval between study visits. Seven patients suffered one or more infections needing systemic antibiotic treatment over the duration of the study. None of these participants have been excluded from the primary efficacy analyses.

Effects of paricalcitol on serum levels of inflammatory markers

Paricalcitol did not significantly reduce the levels of measured inflammatory markers as compared with no treatment. Table 2 presents intention-to-treat analyses, showing the mean (or median) levels of inflammatory biomarkers in each group at 8 weeks and 1 year post transplant, together with the changes from baseline to study end given in percent of baseline mean (or median). T-test for likelihood of observing the reported results given no true group difference is presented with corresponding p-values and confidence interval for the absolute group difference in change. MMP-9 levels decreased in the paricalcitol group, while it increased in the control group, but the difference was not statistically significant (t-test for difference in change [ng/ml], p = 0.058; CI -56.3–0.95). Paricalcitol treatment was, however, associated with increased mean OPG levels, as opposed to nearly no change in the control group (t-test for difference in change [ng/ml], p = 0.030; CI 0.01–0.26). A few extreme values were responsible for at least some of this difference (Fig 1). For all other parameters, p-values for differences in changes were >0.1 and potential effect sizes were small (Table 2).

Table 2

Plasma levels of biomarkers at baseline and study end, by treatment group.

Biomarker(plasma levels)	Paricalcitol (n = 35)			Control (n = 39)			t-test	ANCOVA
	Baseline Mean (SD)	1-year Mean (SD)	Change (%)	Baseline Mean (SD)	1 year Mean (SD)	Change (%)	p-values (CI ng/ml) for group differences in change	p-values (CI ng/ml)
Angiopoietin-2 (ng/ml)*	0.74 (0.49)	0.69 (0.41)	-6.8	0.67 (0.46)	0.72 (0.55)	+7.5	0.478 (-0.57–0.27)	0.561 (-0.52–0.29)
sCD14 (ng/ml)	1.57 (0.21)	1.57 (0.30)	+0.0	1.58 (0.27)	1.52 (0.29)	-3.8	0.383 (-0.08–0.21)	0.375 (-0.07–0.19)
sCD163 (ng/ml)	441 (230)	547 (316)	+24.0	467 (238)	512 (190)	+9.6	0.241 (-42.3–166)	0.287 (-45.0–150)
DLL1 (ng/ml)	8.88 (2.07)	9.73 (2.83)	+9.6	9.62 (2.40)	9.85 (2.84)	+2.4	0.212 (-0.37–1.62)	0.312 (-0.49–1.51)
Endostatin (ng/ml)	99.1 (21.8)	93.4 (21.6)	-5.8	108.7 (32.2)	103.0 (32.1)	-5.2	0.908 (-9.9–11.2)	0.593 (-12.5–7.2)
MMP9 (ng/ml)	99.8 (67.7)	74.4 (43.7)	-25.4	77.4 (35.9)	79.7 (78.8)	+2.3	0.058 (-56.3–0.95)	0.357 (-31.6–11.5)
sTNFr1(ng/ml)	1.95 (0.67)	2.01 (0.91)	+3.1	2.22 (0.78)	2.16 (0.79)	-2.7	0.464 (-0.21–0.46)	0.809 (-0.29–0.37)
Galectin-3 (ng/ml)	1.41 (0.56)	1.17 (0.55)	-17.1	1.48 (0.57)	1.26 (0.44)	-14.9	0.835 (-0.25–0.20)	0.543 (-0.24–0.13)
NGAL (ng/ml)	282 (122)	312 (144)	+10.6	287 (145)	305 (133)	+6.5	0.735 (-52.0–73.3)	0.681 (-48.6–73.9)
vWF in % of ref.plasma*	82.5 (89.5)	57.1(37.0)	-30.8	102.8 (118.3)	57.8 (58.6)	-43.8	0.228 (-15.1–62.3)	0.215 (-8.6–37.4)
ActivinA (ng/ml)*	344 (739)	323 (1075)	-6.1	578 (910)	681 (873)	+17.8	0.696 (-206–306)	0.778 (-227–302)
OPG (ng/ml)	0.91 (0.37)	1.10 (0.44)	+20.9	1.08 (0.53)	1.13 (0.52)	+4.6	0.030 (0.01–0.26)	0.062 (-0.01–0.24)
TIMP-1 (ng/ml)	110 (23)	112 (26)	+1.8	124 (32)	122 (38)	-1.6	0.461 (-7.2–15.6)	0.808 (-10.2–13.0)
Neopterin (nmol/L)*	21.4 (18.5)	21.1 (13.8)	+1.4	22.1 (13.7)	20.4 (11.4)	+7.7	0.787 (-12.5–16.3)	0.169 (-2.2–12.04)

Intention-to-treat population. T-test for difference in change and supplementary ANCOVA: p-values presented with corresponding confidence intervals (CI).

DLL1, delta like canonical Notch ligand 1; MMP9, matrix metalloprotease-9; sTNFR1, soluble tumor necrosis factor receptor-1; NGAL, neutrophil gelatinase-associated lipocalin; vWF, von Willebrand factor; OPG, osteoprotegerin; TIMP-1, Tissue inhibitor of metalloproteinase 1.

Data expressed as mean (standard deviation) or * median (interquartile range).

Continuous data expressed as mean (standard deviation) or * median (interquartile range).

Fig 1

Changes in levels of osteoprotegerin across the study period.

Osteoprotegerin change (ng/nl) in patients treated with paricalcitol vs patients receiving no extra treatment; median (horizontal line), interquartile range (blue box), outlier (°).

No significant correlation was found between change in osteoprotegerin and change in PTH (Pearson’s Correlation test, p = 0.74). Sensitivity analyses using ANCOVA did not materially change results: p = 0.062 for difference between groups in OPG at 1-year adjusted for baseline OPG level (rightmost column Table 2).

Analyses of the per-protocol population (n = 67) did not change results; p = 0.041 for increase in OPG and p = 0.076 for reductions in MMP-9 with paricalcitol treatment (t-test, S1 Table). Results of sensitivity analyses excluding patients diagnosed with rejection at any time point during follow up (n = 67) were also comparable to the main analysis; p = 0.048 for increase in OPG and p = 0.086 for reductions in MMP-9 (t-test, S2 Table). Exclusion of patients with glomerulonephritis as cause of ESRD did not affect results; n = 46, OPG: p = 0.054 and MMP-9: p = 0.018 (t-test, S2 Table).

Gene expression in renal graft tissue in response to paricalcitol

In renal graft tissue of patients treated with paricalcitol, there was a 21% higher expression of TNFRSF11B, the gene coding for osteoprotegerin, compared with the control group (median gene expression 0.808 vs 0.668; p = 0.026 by KS test). We detected no other significant differences in the expression of biomarker genes between patients treated with paricalcitol and controls, as illustrated by the heat map (Fig 2). All microarray data are available at the Gene Expression Omnibus (GEO) database; http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE83486.

Fig 2

Heat map of inflammatory marker gene expression levels in graft tissue.

The expression of 15 genes coding for 13 inflammatory biomarkers in 30 treated patients (to the left) vs 30 patients in the control group (to the right). Darker color indicates higher expression levels. Z-scores of duplicated genes in the array are averaged. Genes coding for proteins with different nomenclature: ACVR1/ACVR1B/ ACVR1C, activin A receptor subunits; ANGPT2, angiopoietin-2; COL18A1, endostatin; LCN2, neutrophil gelatinase-associated lipocalin (NGAL); LGALS3, galectin-3; TNFRSF11B, osteoprotegerin; TNFRSF1A, soluble tumor necrosis factor receptor-1 (sTNFr1).

Discussion

We explored the potential effects of paricalcitol treatment during the first year after kidney transplantation on a wide range of biomarkers reflecting several aspects of inflammatory responses, but were unable to confirm clinically or statistically significant effects ameliorating inflammation. Paricalcitol treatment did, however, increase circulating OPG levels. Importantly, this was accompanied by a corresponding increase in TNFRSF11b, the gene coding for OPG, in biopsies from renal allografts, supporting a link between paricalcitol and OPG.

The proposed anti-inflammatory effects of paricalcitol are ascribed to a regulatory role of vitamin D receptor activation in several subsets of immune cells, such as macrophages, dendritic cells and T-cells [34, 35]. However, in one clinical study in patients with vascular inflammation, paricalcitol appeared to exert its effect rather selectively on T-helper cells by interfering with calcineurin-mediated responses [36]. Modulation of adaptive immune responses in CKD-patients has been demonstrated, reflected by reductions in several inflammatory markers of Th1- Th2 and Th17-responses [37]. Oblak et al. [30] demonstrated treatment effects in a quite large interventional study of RTRs. However, most clinical trials suggesting anti-inflammatory properties of paricalcitol in CKD-patients [38-41] and RTRs [42] has been hampered by a limited sample size. The adequately powered VITAL-study (n = 281) failed to show significant effects of paricalcitol on inflammatory biomarkers (CRP, fibrinogen, interleukin 6, TNF)in type 2 diabetes mellitus [25]. Taken together, the results of interventional studies on vitamin D agonist treatment seem inconclusive.

In the present study we found no evidence of paricalcitol influencing markers reflecting monocyte/macrophage (i.e. sCD163, sCD14, neopterin) or neutrophil (i.e. NGAL) activation, which signals no major clinically beneficial effect of paricalcitol on the activation and interplay of immune cells in the context of kidney transplantation. However, in light of recent findings suggesting that paricalcitol modulates inflammatory responses by influencing the calcineurin-axis [36], is it possible that anti-inflammatory effects could be masked by calcineurin inhibitor treatment, the corner stone in the immunosuppressive regimen for all our study participants.

Paricalcitol appears also to reduce development of renal interstitial fibrosis in obstructive nephropathy [43] and RTRs [44]. Metalloproteases, including MMP-9, are major regulators of ECM protein metabolism [45]. One might be tempted to interpret the trend towards reduced MMP-9 in paricalcitol-treated patients as a potential effect of VDRA on tissue extracellular matrix (ECM) remodeling, but it remains a speculation. A major source of MMP-9 in the circulation is neutrophils [46], hence plasma levels might also to some extent reflect neutrophil activation status.

We found that patients randomized to paricalcitol experienced an increase in circulating levels of OPG not seen in the control group. Correspondingly, the expression of the gene TNFRSF11B coding for OPG was also higher in renal graft tissue of patients in the intervention group. The result is consistent with experimental data on the immunomodulatory effects of 1,25-hydroxyvitamin D3 [47] and a similar clinical trial in hemodialysis patients [48]. Hansen et al. [48] found the rise in OPG in patients treated with paricalcitol to be correlated with the degree of suppression of PTH, partly explaining their results. Such a correlation was not clear in our cohort, despite a significant PTH-lowering effect of paricalcitol [31]. OPG protects the skeleton from excessive bone resorption by attaching to receptor activator of nuclear factor kappa-Β ligand (RANKL) and preventing it from binding to its receptor on osteoclasts, RANK [49]. Plasma OPG has been suggested as a stable marker of the general activity in the RANKL/RANK system, a system that is linked to fibrogenesis and regulation of extracellular matrix. It is debated whether OPG itself is cardioprotective or a reactive proinflammatory molecule [50, 51]but modulatory roles in vascular injury and calcification, systemic inflammation and atherosclerosis, as well as in fibrosis pathways have been suggested [52, 53]. Thus, together with the potential downregulation of MMP-9 the effect seen on OPG may suggest that paricalcitol could have some effect on fibrogenesis in RTRs.

Interpretation of findings

Since inflammatory markers typically have a wide distribution and relatively large SD’s, the power to detect group differences in a moderate-sized study, such as ours, could be lower than anticipated. As an example, MMP-9-levels were reduced by mean 25 ng/ml in the intervention group, while the controls had a 2 ng/ml rise during the study period. However, a 45 ng/ml difference in treatment effect would be needed to claim statistical significance (p <0.05). This is a high threshold for a biomarker whose reference range in healthy males is approximately 20–100 (M ±2SD) ng/ml [54]. Effect sizes may be more relevant that any p-value in itself [55]. Thus, although significant p-values are lacking in the current study, our results should not be interpreted as firm evidence against a potential anti-inflammatory effect of paricalcitol. Instead our results signal possible small-to-moderate effects within the limits of the reported confidence intervals. Taken together, evidence nevertheless seems too inconsistent to motivate the routine use of VDRA to reduce inflammation or improve vascular health in the transplant population. However, the interaction between paricalcitol and OPG as seen both at protein and transcript levels should be further explored as a potential important target for VDRAs. Also, though considered beyond the scope of the current investigation, the potential effect of paricalcitol on markers of oxidative stress (e.g. lipid peroxidation metabolites), as well as inflammatory metabolites (e.g. colonic microbiota-derived uraemic retention solutes) would be an interesting focus for future studies.

Strengths and limitations

There was a high level of adherence to treatment in the paricalcitol group and no patient-initiated withdrawals [31]. The study population has been well characterized. Both circulating biomarker levels and tissue biomarker gene expression were evaluated, thus increasing the robustness of the results. However, sample size was calculated for the primary trial endpoint, not for the detection of potential treatment effects on levels of inflammatory markers. Notably, this study is of an explorative nature, testing many biomarkers at the same time. The suggested association between VDRA and OPG must be interpreted with caution, due to the possibility of making type 1-errors when performing multiple statistical tests. If strict Bonferroni correction for multiple testing was to be applied in this study, a p-value of <0,003 (0,05/15) would be needed to demonstrate statistical significance. Conclusions drawn from this study might only be valid for a white European population of RTRs with a reasonably good allograft function (i.e. eGFR>30 ml/min). Results are not necessarily applicable for recipients with vitamin D deficiency. We acknowledge that investigational bias might be a problem in open label trials, but for administrative reasons placebo drugs were unfortunately not available.

Conclusions

In newly transplanted RTRs with adequate graft function, we were not able to demonstrate convincing reductions in levels of circulating biomarkers of inflammation and endothelial function after ten months of paricalcitol treatment. If present, a modulating effect of VDRA-treatment on systemic inflammation in this patient group is likely to be modest. We found that VDRA-treatment might increase levels of OPG, both in the circulation and in renal tissue, but this result needs to be replicated and validated.

Results for the per-protocol population.

(DOCX)

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Results for patients with no rejection during the study period.

Results for patients with non-inflammatory cause of end-stage kidney disease.

(DOCX)

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Project protocol.

(DOC)

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Consort checklist for main trial.

(DOC)

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Consort diagram.

(BMP)

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7 Sep 2020

PONE-D-20-18424

Exploring the potential effect of paricalcitol on markers of inflammation in de novo renal transplant recipients

PLOS ONE

Dear Dr. Pihlstrøm,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

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ACADEMIC EDITOR:

Generally well-executed study albeit with quite a few limitations (acknowledged by authors), but seems to have merit if reviewers' concerns can be adequately addressed by authors.

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Please also clarify which tests were run at the one year visit. If a questionnaire was used during the visit, ensure that you have provided sufficient details that others could replicate the analyses. For instance, if you developed a questionnaire as part of this study and it is not under a copyright more restrictive than CC-BY, please include a copy, in both the original language and English, as Supporting Information.

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Reviewer #1: Yes

Reviewer #2: Yes

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: No

**********

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Reviewer #1: Yes

Reviewer #2: Yes

**********

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Reviewer #1: The authors examined markers of inflammation in patients with a recent renal transplant in a RCT involving the use of paricalcitol to see if paricalcitol would aid in the reduction of inflammation.

The study was designed to investigate a different primary outcome (which the authors have published separately). Here, they analysis the results of cytokine analysis as well as microarray analysis.

All statistical analyses seem appropriately performed. Table 1 & 2 are nicely put together, with easily identifiable information provided in the table description/legend. It is clear what data is normally distributed and what was not. It owuld be nice when the authors present a result in the text with a p-value to also include which type of statistical test was run to obtain this p-value. This will make the manuscript even more reproducible.

Figure 2 in the version that printed for this reviewer does not contain the gene name information. The powerpoint linked in the online version, does. It should be made certain that the gene information appears in the final version, as this is important information to convey to the reader.

The authors did a nice job of describing study limitations.

Reviewer #2: Generally well written and adequate interpretation of a study with many limitations as the authors have pointed out.

I note the following major points -

1. The authors are measuring inflammatory markers post transplantation and they could have looked at the effect of paricalcitol on inflammatory metabolites such as metabolites eg TMAO, p-cresyl sulphate (PCS), p-cresyl glucuronide (PCG), indoxyl sulphate (IS).

2. There is quite a bit of vague terminology

e.g. in the 1st paragraph - "inflammatory imbalance in the immune system". What does this actually mean?

"interleukin-6 (IL-6) and its major product, CRP" - CRP is not a product of Il-6, instead Il-6 can control hepatic CRP generation.

3. Is the statistical analysis for data in table 2 performed with ANCOVA? This would be more appropriate than multiple t-tets. How did the authors control for multiple statistical testing; could they have applied a Bonferroni correction?

4. Soluble CD25 is not a well validated marker for T cell activation. I would remove this data unless they can demonstrate CD25 expression on isolated T cells by FACS.

5. In discussion, "evidence nevertheless seems too inconsistent to motivate the routine use of VDRA to reduce inflammation or improve vascular health in the transplant population". How could the current study be adapted to answer this?

**********

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Reviewer #1: No

Reviewer #2: No

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Submitted filename: PONE-D-20-18424_reviewer.pdf

Click here for additional data file.

28 Oct 2020

A: Journal Requirements:

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming.

ANSWER: We have gone through the requirements once more and hopefully corrected any examples of lacking/incorrect information.

2. PLOS ONE requires that methods are described in enough detail to allow suitably skilled investigators to fully replicate and evaluate the study. Please provide more details on the methods and sample types used to quantify the inflammatory markers in your study (i.re, RNA quantification and ELISA experiments) and all sources and catalog numbers of reagents and ELISA kits used to measure them.

ANSWER: Thank you for pointing out this requirement! We have now included a more elaborate description of the enzyme immunoassay in the methods section of the manuscript As for the gene expression analysis, an elaborate method description is to be found in the main study publication (ref 29)., which we have cited in the current manuscript. Do you still want it repeated explicitly in the text?

Please also clarify which tests were run at the one year visit. If a questionnaire was used during the visit, ensure that you have provided sufficient details that others could replicate the analyses. For instance, if you developed a questionnaire as part of this study and it is not under a copyright more restrictive than CC-BY, please include a copy, in both the original language and English, as Supporting Information.

ANSWER: The original publication listed as ref 29 (Pihlstrom et al, Transplant International 2017, PMID: 28436117) describes in detail what tests were performed at the 1-year visit. On your request we have included a sentence briefly summing up these investigations in the “study design” chapter. For the current analyses, the only relevant “tests” are the biomarker analyses, which were performed en bloc at our Research Laboratory as described above. No questionnaire was part of the data collection process.

3. We note that you have indicated that data from this study are available upon request. PLOS only allows data to be available upon request if there are legal or ethical restrictions on sharing data publicly. For information on unacceptable data access restrictions, please see http://journals.plos.org/plosone/s/data-availability#loc-unacceptable-data-access-restrictions. In your revised cover letter, please address the following prompts:

a) If there are ethical or legal restrictions on sharing a de-identified data set, please explain them in detail (e.g., data contain potentially identifying or sensitive patient information) and who has imposed them (e.g., an ethics committee). Please also provide contact information for a data access committee, ethics committee, or other institutional body to which data requests may be sent.

ANSWER: Unfortunately there are ethical and legal restrictions preventing us from uploading data to public repositories or including the full dataset as Supplementary Material. Norwegian kidney transplant recipients receiving their transplant in the time-frame of the current study belong to a relatively small group, and very little personal data would be needed in order to indirectly identify individual study participants. We have been in dialogue with the Data Protection Authority of Oslo University Hospital in this matter, and here is their response:

"CONCERNING SHARING OF RESEARCH DATA

Pursuant to Regulation (EU) No. 2016/679, General Data Protection Regulation (GDPR) article 37, the designated Data Protection Officer at Oslo University Hospital (OUS) is appointed. The controller and the processor shall ensure that the data protection officer is involved, properly and in a timely manner, in all issues which relate to the protection of personal data, cf. GDPR article 38.

According to GDPR article 9, the processing of genetic data or health data shall be prohibited unless the data subject has given an explicit consent to the processing of those personal data for one or more specified purposes. Personal data is defined as any information relating to an identified or identifiable natural person; an identifiable natural person is one who can be identified, directly or indirectly.

Consequently, depositing de-identified data in a public, community-supported repository in order to submit an article is not considered compliant with EU and Norwegian law in this matter. In order to comply with the relevant legislations, data would need to be fully anonymized.

If required, provisions can be made for the inspection of the data as long as the data is under the hospital's control, hence the Data Controller-responsibility".

The implication of the above described regulations is that any reader interested in inspecting the data may prompt this request to the OUS Data Protection Officer, Tor Åsmund Martinsen (personvern@oslo-universitetssykehus.no). The corresponding author, Hege Kampen Pihlstrøm (hegphi@ous-hf.no) should also be contacted. A de-identified study data file may then be made available.

4. We note that you have included the phrase “data not shown” in your manuscript. Unfortunately, this does not meet our data sharing requirements. PLOS does not permit references to inaccessible data. We require that authors provide all relevant data within the paper, Supporting Information files, or in an acceptable, public repository. Please add a citation to support this phrase or upload the data that corresponds with these findings to a stable repository (such as Figshare or Dryad) and provide and URLs, DOIs, or accession numbers that may be used to access these data. Or, if the data are not a core part of the research being presented in your study, we ask that you remove the phrase that refers to these data.

ANSWER: We appreciate the policy that the findings referred to in the manuscript should be available in more detail than what we have provided. Hence we have chosen to include supplementary tables (1-2) for sensitivity analyses and added them to the submission as Supporting Information Files.

Reviewer #1: The authors examined markers of inflammation in patients with a recent renal transplant in a RCT involving the use of paricalcitol to see if paricalcitol would aid in the reduction of inflammation.

The study was designed to investigate a different primary outcome (which the authors have published separately). Here, they analysis the results of cytokine analysis as well as microarray analysis.

All statistical analyses seem appropriately performed. Table 1 & 2 are nicely put together, with easily identifiable information provided in the table description/legend. It is clear what data is normally distributed and what was not. It would be nice when the authors present a result in the text with a p-value to also include which type of statistical test was run to obtain this p-value. This will make the manuscript even more reproducible.

ANSWER: Reference to the tests used has been added in the text!

Figure 2 in the version that printed for this reviewer does not contain the gene name information. The powerpoint linked in the online version, does. It should be made certain that the gene information appears in the final version, as this is important information to convey to the reader.

ANSWER: We will ensure the final figure 2 contains the gene information! Thank you for pointing out this lapse in the version you received. The figure has also been revised (removal of genes related to CD25 as recommended by reviewer 2), and we have included an annotation in the figure legend for genes in the heat map which are known by different names than the proteins they encode.

The authors did a nice job of describing study limitations.

Reviewer #2: Generally well written and adequate interpretation of a study with many limitations as the authors have pointed out.

I note the following major points -

1. The authors are measuring inflammatory markers post transplantation and they could have looked at the effect of paricalcitol on inflammatory metabolites such as metabolites eg TMAO, p-cresyl sulphate (PCS), p-cresyl glucuronide (PCG), indoxyl sulphate (IS).

ANSWER: Thank you for the valuable comment! It would indeed have been interesting to investigate inflammatory metabolites as well as the selected circulating biomarkers. However, we are already close to the limit of parameters possible to investigate in this dataset due to its limited size. Hence we feel the analyses of colonic microbiota-derived uraemic retention solutes would be a great idea for a future study, but unfortunately out of the scope of the current project. A comment has been included in the discussion section, suggesting these inflammatory metabolites as focus for upcoming studies.

2. There is quite a bit of vague terminology

e.g. in the 1st paragraph - "inflammatory imbalance in the immune system". What does this actually mean?

"interleukin-6 (IL-6) and its major product, CRP" - CRP is not a product of Il-6, instead Il-6 can control hepatic CRP generation.

ANSWER: Thank you for pointing out imprecise terminology in the manuscript. We have rephrased some sentences in the introduction section according to your comments. Hopefully they will serve as clarification.

3. Is the statistical analysis for data in table 2 performed with ANCOVA? This would be more appropriate than multiple t-tets. How did the authors control for multiple statistical testing; could they have applied a Bonferroni correction?

ANSWER: Table 2 presented results of t-tests. As described in the methods section, ANCOVA was performed as a sensitivity analysis. Editors have made it clear that all data which is part of the study should be presented/available for the reader, hence we have included the ANCOVA analyses in a separate column in table 2. Results are comparable to the t-test results.

With reference to the two- tailed P-value of ≤0.05 commonly used as cut-off for statistical significance, a p-value of ≤0,003 (0,05/15) would be needed to demonstrate statistical significance after Bonferroni correction for multiple testing in this study. We believe that we have already made it a focus point in the discussion that the study lacks power to conclude firmly on any anti-inflammatory treatment effects, and that the suggested influence of paricalcitol treatment on OPG should be explored in larger studies. We have now added a sentence acknowledging the issue of multiple testing in the limitations section.

4. Soluble CD25 is not a well validated marker for T cell activation. I would remove this data unless they can demonstrate CD25 expression on isolated T cells by FACS.

ANSWER: We have removed the data on CD25 as you suggested.

5. In discussion, "evidence nevertheless seems too inconsistent to motivate the routine use of VDRA to reduce inflammation or improve vascular health in the transplant population". How could the current study be adapted to answer this?

ANSWER: To be adequately powered to help us conclude on the potential treatment effect of paricalcitol on inflammation in renal transplant recipients, the study should have been larger, with a longer follow-up time. Being a post-hoc analysis of a study designed to investigate a different primary endpoint, we do not have the option of extending the duration of the study or include more patients. We have tried to underscore these limitations at the end of the discussion section.

FINAL REMARKS: One of the coauthors, My H S Svensson, did not feel that she had contributed enough to the study and the manuscript to deserve authorship, and consequently we have removed her from the list of authors.

Submitted filename: RESPONSE TO REVIEWERS 031020.docx

Click here for additional data file.

30 Nov 2020

Exploring the potential effect of paricalcitol on markers of inflammation in de novo renal transplant recipients

PONE-D-20-18424R1

Dear Dr. Pihlstrøm,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

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Kind regards,

Frank JMF Dor, M.D., Ph.D., FEBS, FRCS

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: (No Response)

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: (No Response)

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: (No Response)

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: (No Response)

Reviewer #2: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: (No Response)

Reviewer #2: All my comments have been addressed and no further comments to add from my side. I accept the revised submission

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

3 Dec 2020

PONE-D-20-18424R1

Exploring the potential effect of paricalcitol on markers of inflammation in de novo renal transplant recipients

Dear Dr. Pihlstrøm:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

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Thank you for submitting your work to PLOS ONE and supporting open access.

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on behalf of

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