Kidney Transplant Fibrosis Occurs Independent of Peritubular Capillary Loss
S Osasan1,2, D de Freitas 2, J Chang2, J Sellares2, Y Ozluk1, L Hidalgo1,2,
M Mengel1,2, P Halloran2, B Sis1,2
1 Department
of Laboratory Medicine and Pathology, 2Alberta Transplant Applied
Genomics Centre, University of Alberta, Edmonton, AB, Canada.
Background: It has been suggested that progressive loss of peritubular capillaries
(PTC) contributes to kidney fibrosis.
Objective: We hypothesized that kidney transplant fibrosis occurs independent of
microcirculation loss.
Method: We related PTC density to histopathology lesions, diagnoses, and posttransplant time in 100 kidney transplant biopsies for cause (BFC) and compared to 40
normal implantation biopsies. We performed CD31 immunostaining to identify the PTCs
and counted total number of CD31+ PTCs in the entire cortical area of the biopsies. The
mean PTC density was calculated by dividing the total number of PTCs by the number
of ocular grid areas (0.2mm2).
Results: The PTC density was lower in BFC when compared to controls, but did not
differ among diagnoses in BFC (figure 1). In early (<1 year) BFC, PTC density was
lower in biopsies with increased edema and tubulointerstitial inflammation. In late (>1
year) BFC, only edema correlated with decreased PTC density (Table). However,
increased interstitial fibrosis, tubular atrophy, arterial fibrous intimal thickening,
transplant glomerulopathy, peritubular capillary basement membrane multilayering, and
time post-transplant did not relate to reduced PTC density. In multivariate regression
analysis, edema was the only determinant of reduced PTC density in both early and late
BFC (Table).
Conclusion: Our results indicate that edema causes expansion of peritubular
interstitium, giving a false impression of reduced PTC density in allograft biopsies.
However, we did not observe reduced PTC density in late kidney transplant biopsies
with advanced scarring. Therefore, we propose that kidney transplant fibrosis and
nephron loss occur independent of microcirculation loss.
Table . Relationship of peritubular capillary density with histopathological lesions and post transplant time.
Peritubular Capillary Density
Spearman’s Correlation a
All BFC
(n=100)
Lesions
Early BFC
(n=42)
Late BFC
(n=58)
r
p-value
r
p-value
r
p-value
Edema
-0.317
0.001
-0.341
0.027
-0.292
0.026
Interstitial inflammation
-0.300
0.002
-0.414
0.006
-0.176
0.187
Tubulitis
-0.294
0.003
-0.406
0.008
-0.204
0.125
Intimal arteritis
-0.186
0.068
-0.210
0.194
-0.189
0.159
Glomerulitis
-0.042
0.680
-0.189
0.230
-0.014
0.919
Peritubular capillaritis
-0.120
0.236
-0.068
0.667
-0.212
0.110
C4d staining
0.041
0.686
0.203
0.197
-0.022
0.869
Transplant glomerulopathy
-0.003
0.978
-0.213
0.176
-0.044
0.745
Mesangial matrix increase
0.053
0.602
0.164
0.298
0.206
0.121
Peritubular capillary basement membrane
multilayering
-0.024
0.838
-0.205
0.337
-0.182
0.206
Fibrous intimal thickening
-0.151
0.140
-0.223
0.166
-0.081
0.550
Tubular atrophy
-0.046
0.649
-0.173
0.274
-0.041
0.757
Arterial interstitial fibrosis
-0.032
0.751
-0.085
0.592
-0.004
0.974
Arteriolar hyalinosis
0.097
0.337
0.067
0.674
0.085
0.527
Post transplantation time
0.015
0.886
-0.260
0.096
0.063
0.637
R2
p-value
R2
p-value
R2
p-value
0.180
<0.001
0.260
0.015
0.101
0.024
Stepwise multivariate linear regression
analysis a,b
Edema
a Transplant
Glomerulopathy was analyzed as binary variable (cg - 0,1 vs 2,3) and others were analyzed as ordinal variables (0,1,2,3).
Of the highly correlated variables (r≥0.50 ), only one of them was arbitrarily selected and entered into the multivariate model (i.e ci, i, cg and g were entered, but ct, t, mm and ptc were not). Otherwise, all other lesions
and time were entered into the multivariate model.
b