Abstract
Background and objectives: The aim of our study was to examine serum markers of the TNF and Fas pathways for association with cystatin-C based estimated glomerular filtration rate (cC-GFR) in subjects with type 1 diabetes (T1DM) and no proteinuria.
Design, setting, participants, & measurements: The study group (the 2nd Joslin Kidney Study) comprised patients with T1DM and normoalbuminuria (NA) (n = 363) or microalbuminuria (MA) (n = 304). Impaired renal function (cC-GFR <90 ml/min) was present in only 10% of patients with NA and 36% of those with MA. We measured markers of the tumor necrosis factor α (TNFα) pathway [TNFα, soluble TNF receptor 1 (sTNFR1), and 2 (sTNFR2)], its downstream effectors [soluble intercellular and soluble vascular adhesion molecules (sICAM-1 and sVCAM-1), interleukin 8 (IL8/CXCL8), monocytes chemoattractant protein-1 (MCP1), and IFNγ inducible protein-10 (IP10/CXCL10)], the Fas pathway [soluble Fas (sFas) and Fas ligand (sFasL)], CRP, and IL6.
Results: Of these, TNFα, sTNFRs, sFas, sICAM-1, and sIP10 were associated with cC-GFR. However, only the TNF receptors and sFas were associated with cC-GFR in multivariate analysis. Variation in the concentration of the TNF receptors had a much stronger impact on GFR than clinical covariates such as age and albumin excretion.
Conclusions: Elevated concentrations of serum markers of the TNFα and Fas-pathways are strongly associated with decreased renal function in nonproteinuric type 1 diabetic patients. These effects are independent of those of urinary albumin excretion. Follow-up studies are needed to characterize the role of these markers in early progressive renal function decline.
The traditional model of the development of end-stage renal disease in type 1 diabetes mellitus (T1DM), in which microalbuminuria (MA) leads to proteinuria and then proteinuria is followed by renal function loss, has been challenged recently. Increase in urinary albumin excretion is an important determinant of diabetic nephropathy progression, but it does not entirely explain this phenomenon. For example, the loss of renal function commences earlier than previously recognized and precedes the onset of proteinuria (1). In our longitudinal study of T1DM (The First Joslin Study of Natural History of Microalbuminuria), renal function decline began with the onset of MA in about one-third of patients and progressed in a linear fashion from normal kidney function to renal insufficiency. Also, renal function decline occurred in a noticeable proportion of patients with T1DM and normal albumin excretion (1,2).
Low-grade chronic inflammation is thought to be involved in the pathogenesis of diabetic nephropathy (3,4). Tumor necrosis factor alpha (TNFα/TNF) is a key mediator of inflammation and plays a role in apoptosis. In animal models, its effects on kidneys include reduced glomerular filtration rate (GFR) and increased albumin permeability (3). It mediates its signal via two distinct receptors, TNF receptor 1 (TNFR1/TNFRSF1A) and TNF receptor 2 (TNFR2/TNFRSF1B), which are membrane-bound and also present in soluble form in serum (5). TNFα mediates its inflammatory effects by induction of a broad spectrum of chemokines, including interleukin 8 (IL8/CXCL8); monocyte chemotactic protein-1 (MCP-1/CCL2); IFN-γ inducible protein-10 (IP-10/CXCL10); and adhesion molecules such as intercellular adhesion molecule-1 (ICAM-1) and vascular adhesion molecule-1 (VCAM-1) (6,7).
The Fas pathway mediates apoptosis and may play a role in the progression of diabetic nephropathy (8–11). The binding of Fas ligand (FasL) to Fas, its membrane-bound receptor that is also present in serum in soluble form (sFasL, sFas), leads to an apoptotic response (12,13).
Most studies on serum markers of TNFα-mediated inflammation and apoptosis in diabetic nephropathy have explored their association with MA and proteinuria rather than with GFR (14).
The goal of this large cross-sectional study was to investigate whether serum concentrations of markers mediated by TNFα (sTNFR1, sTNFR2, sICAM-1, sVCAM-1, IL8, MCP-1, IP-10) or involved in Fas-related apoptosis (sFasL and sFas) are associated, independently from albuminuria, with variation in renal function in patients with T1DM who do not have proteinuria or advanced renal function impairment. This knowledge should facilitate the development of new diagnostic tools for identifying patients with early renal function decline and help the search for intervention protocols for high-risk patients that may be more effective if implemented 5 to 10 yr earlier in the disease course.
In this study, the GFR was estimated by a cystatin C-based formula (cC-GFR), previously shown as an accurate way of evaluating renal function in patients with diabetes (15,16)
Materials and Methods
The Committee on Human Subjects of the Joslin Diabetes Center approved the protocol and informed consent procedures for this study.
The study group was selected from the population attending the Joslin Clinic, a major center for the treatment of patients of all ages with T1DM or type 2 diabetes mellitus (T2DM). The population is about 90% Caucasian, and most reside in eastern Massachusetts. Between January 1, 2003 and December 31, 2004, patients with T1DM attending the Joslin Clinic were recruited into the Second Joslin Study on the Natural History of Microalbuminuria. Detailed descriptions of the Joslin Clinic population and the recruitment protocol for this study have been published previously (17). Eligibility criteria included residence in New England, diabetes diagnosed before age 40 yr, treatment with insulin, current age 18 to 64 yr, diabetes duration 3 to 39 yr, and multiple measurements in the preceding 2-yr interval of hemoglobin A1c (HbA1c) and urinary albumin-to-creatinine ratio (ACR). For each patient, the measurements of HbA1c were summarized by the mean, and the measurements of ACR by the median. Exclusion criteria included proteinuria (median ACR ≥ 250 for men and ≥ 355 μg/min for women), end-stage renal disease, other serious illness, extreme obesity (body mass index > 40 kg/m2), or a median HbA1c less than 6.5% (near normoglycemia).
Enrollment and Examination
Trained recruiters administered a structured interview and brief examination to eligible patients at a routine visit to the clinic (i.e., the enrollment visit). The interview solicited the history of diabetes and its treatment, other health problems, and use of medications. The recruiter measured seated blood pressure twice (5 min apart) with an automatic monitor (Omron Healthcare, Inc), averaged them to reduce variability, and obtained samples of blood and urine. At of the end of 2004, this study group included 667 participants: 304 with MA and 363 with normoalbuminuria (NA).
Assessment of Exposure Variables
Current and past use of medications (particularly angiotensin converting enzyme inhibitors, angiotensin II receptor blockers, and other antihypertensive drugs) was recorded during the enrollment interview and supplemented by examination of clinic records to confirm prescription dates. We also extracted all archived clinical laboratory measurements of HbA1c, ACR, and serum cholesterol. Details of the assays used were described previously (18,19). ACR values were converted to albumin excretion rate (AER) according to a formula published previously (19). For characterizing patients’ recent exposures, repeated measures were summarized by their median (AER) or mean (HbA1c, cholesterol, lipids).
Sample Collection and Laboratory Measurements
Enrollment blood samples were drawn by venipuncture into sterile collection tubes (SST Plus BD Vacutainer, BD, New Jersey); centrifuged at 3600 rpm for 10 min at 6°C (Centrifuge 5810 R); and then aliquoted into 1.5-ml sterile, nontoxic, nonpyrogenic tubes cryogenic tubes (CryoTubes CryoLine System; NUNC TM Serving Life Science) and frozen at −80°C until further analysis. Length of storage, defined as the interval between the dates of sample collection and assay determination (range 2 to 5 yr), was included as a covariate in the analysis to estimate the extent of degradation of each analyte during storage.
cC-GFR
Serum cystatin C concentration (Dade Behring Diagnostics) was assayed on a BN Prospec System nephelometer (Dade Behring, Inc., Newark, Delaware). The range of detection is 0.30 to 7.50 mg/L, and the reported reference range for young, healthy persons is 0.53 to 0.95 mg/L. In our laboratory, the intraindividual coefficient of variation for subjects with diabetes is 3.8 and 3.0% in samples from the lowest and highest quartiles of the cystatin C distribution, respectively (1).
The estimated GFR (cC-GFR ml/min/1.73 m2) is the reciprocal of cystatin C (mg/L) multiplied by 86.7 and reduced by subtracting 4.2. MacIsaac et al. recently developed this formula as a reliable estimate of GFR in patients with diabetes. Our method for measuring cystatin-C was similar with respect to assay, equipment, and coefficient of variation as that reported by MacIsaac et al. (15).
Serum Markers of TNFα and Fas-Mediated Pathways
All markers were measured by immunoassay. Samples were thawed, vortexed, and centrifuged, and measurements were performed in the supernatant. We measured sTNFR1, sTNFR2, and IL6 by ELISA (DRT100, DRT200, and high-sensitive immunoassay HS600B, respectively; R&D, Minneapolis, Minnesota) according to the manufacturer's protocol. We measured IL6 in only a subset of the study group (156 individuals). We measured the serum concentrations of the other protein markers in a multiplex assay run on the Luminex platform. This is a multiplex particle-enhanced, sandwich type, liquid-phase immunoassay with laser-based detection system based on flow cytometry. We used adipokine-panel B (HADK2-61K-B; Linco-Milipore) to measure TNFα; human sepsis-apoptosis panel (HSEP-63K; Linco-Milipore) to measure sFas, sFasL, sICAM-1, and sVCAM-1; and Beadlyte human multi-cytokine detection (48-011; Upstate-Milipore) with protocol B to measure IL8, IP-10, MCP-1. Protocols provided by vendors were followed. Briefly, the method included use of 96-well filter plates (Milipore), the capture antibodies specific for each analyte bound covalently to fluorescently labeled microspheres, biotinylated detection antibodies, and streptavidin-phycoerythrin. Detection incorporates two lasers and a high-tech fluidics system (Luminex 100S, Austin, Texas). Values of median fluorescence intensity were fitted to a five-parameter logistic standard curve (20).
Assay sensitivities were: TNFα, 0.14 pg/ml; sTNFR1 and sTNFR2, 0.77 pg/ml; sFas, 7 pg/ml; sFasL, 6 pg/ml; sICAM-1, 30 pg/ml; sVCAM-1, 33 pg/ml; IL8, 0.7 pg/ml; IP-10, 1.2 pg/ml; MCP-1, 1.9 pg/ml; and IL6, 0.04 pg/ml. If required, samples were diluted (sTNFR1, sTNFR2, sFAS, sFASL, sICAM-1, and sVCAM-1). The number of freeze-thaw cycles was one for all measurements of TNFα, IL8, IP-10, MCP-1, and for most measurements of the other analytes. The number did not exceed two for any measurement.
Two internal serum controls were prepared in the same manner as study samples and were stored in a large number of aliquots at −80°C. Aliquots of the two controls were included in each assay (21) for estimating the interassay coefficient of variation (CV). For most assays, interassay CV was between 8.5 and 15.8% (15.8% TNFα, 13.0% sTNFR1, 12.7% sTNFR2, 8.5% sFas, 13.5% sFasL, 8.1% sVCAM-1, and 14.7% IP-10). It was higher for the remaining three (25.2% sICAM-1, 33.3% IL8, and 28.4% MCP-1). Immunoassay for TNFα, sFas, and sFasL detects the free form of the protein, whereas ELISA for sTNFR1 and sTNFR2 detects the total amount of protein, free and bound with their ligand TNFα, (information provided by manufacturer).
Statistical Analyses
Analyses were done in SAS (SAS Institute, Cary, North Carolina, version 9.1.3). For continuous variables and frequencies, t-tests and χ2 tests with alpha = 0.05 were used, respectively. Analyses in Tables 2 and 3 and Figure 1 were ANOVA for unbalanced design. Linear regression with cC-GFR as the dependent variable was used for multivariate analysis. AER and serum concentrations of the markers were transformed to their logarithms for analysis. Missing data for serum markers never decreased the study sample by more than 5% in any model, so no remedial action was taken.
Table 2.
Characteristics of the study group according to albuminuria status and group-specific median cC-GFRa
| Variable | Normoalbuminuria | Microalbuminuria | Group Contrast | |||
|---|---|---|---|---|---|---|
| Characteristic | cC-GFR > 115 | cC-GFR < 115 | cC-GFR > 101 | cC-GFR < 101 | AER | cC-GFR |
| N | 183 | 180 | 152 | 152 | Pb | Pc |
| AER (μg/min) | 13 (10-18) | 18 (12-23) | 56 (42-100) | 85 (51-161) | By Design | <0.0001 |
| Age (yr) | 37 ± 11 | 40 ± 13 | 36 ± 12 | 45 ± 11 | <0.05 | <0.0001d |
| Diabetes duration (yr) | 19 ± 9 | 21 ± 10 | 20 ± 9 | 26 ± 9 | <0.0001 | <0.0001d |
| HbA1c (%) | 8.3 ± 1.2 | 8.3 ± 1.2 | 8.7 ± 1.6 | 8.4 ± 1.4 | <0.005 | NS |
| Body mass index (kg/m2) | 25.6 ± 3.6 | 26.7 ± 4.3 | 27.2 ± 4.8 | 27.7 ± 5.2 | <0.0005 | <0.05 |
| Systolic blood pressure (mmHg) | 118 ± 12 | 120 ± 13 | 124 ± 12 | 125 ± 15 | <0.0001 | NS |
| ACEI or ARB Rx (%)d | 18% | 21% | 49% | 55% | <0.0001 | NS |
| Antihypertensive Rx (%) | 7% | 16% | 14% | 30% | <0.001 | <0.0001 |
| Serum cholesterol (mg/dl) | 183 ± 29 | 181 ± 29 | 190 ± 33 | 193 ± 30 | <0.0001 | NS |
| Lipid-lowering Rx (%) | 24% | 34% | 31% | 42% | <0.05 | <0.005 |
| Current smoker (%) | 9% | 12% | 19% | 18% | <0.005 | NS |
Data are mean ± SD, median (quartiles), or %.
P-value for the albuminuria main effect in an ANOVA
P-value for the cC-GFR main effect in an ANOVA.
ACEI, angiotensin converting enzyme inhibitor; ARB, angiotensin receptor blocker; Rx, treatment.
Table 3.
Serum concentrations of markers of inflammation or apoptosis according to AER group and cC-GFR above or below mediana
| Variable | Normoalbuminuria
|
Microalbuminuria
|
Group Contrast
|
|||
|---|---|---|---|---|---|---|
| cC-GFR > 115
|
cC-GFR < 115
|
cC-GFR > 101
|
cC-GFR < 101
|
AER
|
cC-GFR
|
|
| (n = 182) | (n = 181) | (n = 152) | (n = 152) | Pb | Pc | |
| TNF-mediated pathway | ||||||
| TNFα ( pg/ml) | 3.6 (2.3, 4.8) | 3.9 (2.8, 5.8) | 4.0 (2.6, 5.4) | 4.8 (3.3, 6.4) | NS | <0.005 |
| sTNFR1 (pg/ml) | 1.2 (1.0, 1.4) | 1.4 (1.2, 1.7) | 1.4 (1.2, 1.6) | 2.0 (1.6, 2.5) | <0.0001 | <0.0001 |
| sTNFR2 (ng/ml) | 2.1 (1.7, 2.6) | 2.6 (2.1, 3.6) | 2.3 (1.9, 2.9) | 3.2 (2.5, 5.4) | <0.0001 | <0.0001 |
| Potential downstream effectors | ||||||
| Chemokines | ||||||
| IL8 (pg/ml) | 4.4 (2.4, 10.4) | 6.1 (3.4, 13.3) | 7.6 (3.8, 18.3) | 7.0 (4.0, 15.5) | <0.05 | NS |
| IP-10 (pg/ml) | 107 (79, 136) | 122 (88, 171) | 102 (75, 141) | 115 (80, 158) | NS | <0.001 |
| MCP-1 (pg/ml) | 124 (75, 184) | 120 (77, 184) | 113 (78, 191) | 105 (77, 174) | NS | NS |
| Adhesion molecule | ||||||
| sICAM-1 (ng/ml) | 133 (109, 152) | 137 (119, 169) | 149 (123, 173) | 152 (123, 191) | <0.0005 | <0.005 |
| sVCAM-1 (ng/ml) | 386 (301, 481) | 389 (303, 489) | 376 (295, 467) | 394 (330, 495) | NS | NS |
| Fas-mediated pathway | ||||||
| sFasL (pg/ml) | 0.00.12 (0.08, 0.19) | 0.13 (0.07, 0.20) | 0.12 (08, 0.18) | 0.11 (0.06, 0.16) | NS | NS |
| sFas (ng/ml) | 3.8 (3.0, 4.7) | 4.5 (3.7, 5.5) | 4.5 (3.6, 5.6) | 5.4 (3.7, 6.9) | <0.0001 | <0.0001 |
| Other inflammatory markers | ||||||
| CRP (μg/ml) | 1.2 (0.5, 3.2) | 1.1 (0.6, 2.7) | 1.4 (0.5, 3.9) | 1.6 (0.8, 3.2) | <0.05 | NS |
| IL6 (pg/ml) | 0.8 (0.6, 1.4) | 0.9 (0.7, 1.5) | 0.8 (0.4, 1.3) | 0.9 (0.6, 2.2) | NS | NS |
Data are medians (quartiles); analyses were done on concentrations transformed to their logarithms.
P-value for the albuminuria main effect in an ANOVA.
P-value for the cC-GFR main effect in an ANOVA.
sTNFR1, soluble TNF receptor 1; sTNFR2, soluble TNF receptor 2; IP-10, IFN-γ inducible protein-10; MCP-1, monocyte chemotactic protein-1; sICAM-1, soluble intracellular adhesion molecule-1; sVCAM-1, soluble vascular adhesion molecule-1; sFasL, soluble Fas ligand; sFas, soluble Fas; CRP, C-reactive protein.
Figure 1.
Mean serum cystatin C GFR (cC-GFR) in the study population of individuals with type 1 diabetes according to albuminuria status (NA, normoalbuminuria; MA, microalbuminuria) and tertile (T1, T2, T3) of an inflammatory marker: (A) sTNFR1; (B) sTNFR2; (C) TNFα; (D) sFas; (E) sICAM-1; and (F) IP-10. P value for trend across the tertiles in NA and MA, respectively.
Results
Characteristics of the Study Population
This project included 667 patients with T1DM from the Second Joslin Study on the Natural History of Microalbuminuria who were recruited according to their urinary AER during the 2-yr interval preceding enrollment: 363 with NA and 304 with MA. Selected characteristics at their enrollment are summarized in Table 1 according to AER group. In the NA group, the 25th, 50th, and 75th percentiles of the AER distribution (11, 15, and 21 μg/min) were centered in the NA range (<30 μg/min), but in the MA group these AER percentiles (45, 69, 131 μg/min) were entirely in the lower half of the MA range (30 to 300 μg/min). In comparison with the NA group, the MA group had an older age, higher proportion of men, longer duration of diabetes, higher HbA1c, and significantly lower cC-GFR. The difference in cC-GFR between the two study groups was clearer when renal function was grouped into categories, the latter two of the four corresponding to mild and moderate renal function impairment, present in 36% of the MA group but only in 10% of the NA group.
Table 1.
Characteristics of the study group according to albuminuria statusa
| Characteristics | Normoalbuminuria (n = 363) | Microalbuminuria (n = 304) | P |
|---|---|---|---|
| AERb (μg/min) | 15 (11 to 21) | 69 (45 to 131) | By design |
| Age (yr) | 39 ± 12 | 41 ± 12 | <0.05 |
| Male (%) | 44% | 61% | <0.0001 |
| Diabetes duration (yr) | 20 ± 9 | 23 ± 10 | <0.0001 |
| HbA1cc (%) | 8.3 ± 1.2 | 8.6 ± 1.5 | <0.01 |
| cC-GFRd (ml/min/1.73 m2) | 118 ± 24 | 99 ± 27 | <0.0001 |
| cC-GFR categories: | |||
| >130 ml/min | 30% | 10% | |
| 90 to 130 | 61% | 54% | |
| 60 to 89 | 9% | 28% | |
| <60 | 1% | 8% |
Data are mean ± SD, median (quartiles), or percent.
AER: median albumin excretion rate during the preceding 2-yr window
HbA1c: mean hemoglobin A1c during the preceding 2-yr window
cC-GFR: estimated GFR based on serum cystatin-C
To distinguish the relative contributions of AER and various clinical characteristics to the large variation in renal function within the study group, we divided the NA and MA groups at the group-specific median cC-GFR (115 and 101 ml/min, respectively) into groups with higher and lower cC-GFR (Table 2). The median (25th, 75th percentiles) of the resulting distributions of cC-GFR in the NA groups was 136 (125,148) and 102 (92,109) ml/min, and in the MA groups was 115 (108,124) and 82 (64,91) ml/min. All of the characteristics in Table 2 were significantly different between NA and MA groups, but many were not significantly different between the groups with higher and lower cC-GFR (two-way ANOVA). For example, the expected associations of higher HbA1c, systolic blood pressure, and serum cholesterol with MA were present, as were the associations of cigarette smoking and treatment with an angiotensin converting enzyme inhibitor or angiotensin II receptor blocker. However, none of these characteristics were associated with lower cC-GFR. In contrast, older age and longer diabetes duration were significantly associated with both MA and lower cC-GFR, as was evidenced by medical attention represented by treatment with antihypertensive or lipid lowering agents.
Markers of Inflammation or Apoptosis and Impaired Renal Function—Univariate Analyses
Serum concentrations of markers of inflammation or apoptosis were examined in the same manner as the characteristics shown in Table 2. Four markers (sTNFR1, sTNRF2, sFas, and sICAM-1) were significantly associated both with AER and with cC-GFR (Table 3). TNFα and IP-10 were significantly associated only with the cC-GFR group and two (IL8 and C-reactive protein) were significantly associated only with the AER group.
For the six markers significantly associated with cC-GFR in Table 3, the patterns of association are illustrated in Figures 1A through 1F. Separately for the NA and MA groups, patients were grouped according to the tertiles of the distribution of each marker, and the mean cC-GFR for each subgroup was depicted as a vertical bar. In both AER groups, the decrease in cC-GFR with increasing marker concentration was steepest for sTNFR1 and sTNFR2. The pattern was similar for TNFα, but the differences among subgroups were smaller. For all three markers, the decrease appeared steeper in the MA group than in the NA group. For the remaining three markers (sICAM-1, IP-10 and sFas), a pattern of differences among subgroups was less obvious
We studied these markers further by examining their correlations with each other, and with the two nephropathy measures, cC-GFR and AER (Table 4). The negative correlations between the six markers and cC-GFR recapitulate the negative associations shown in Table 3 and Figure 1. All pairs of markers are significantly correlated, but the coefficients are generally modest. Only the correlation of the two receptors (sTNFR1 and sTNFR2) with cC-GFR and with each other exceeded 0.50. Note the poor (although significant) correlations between TNFα and its receptors (r = 0.11 for TNFα/sTNFR1 and r = 0.20 for TNFα/sTNFR2).
Table 4.
Spearman correlation coefficients between cC-GFR, AER, and serum markers of inflammation and apoptosis in the study group
| AER | TNFα | sTNFR1 | sTNFR2 | sFas | IP-10 | sICAM | |
|---|---|---|---|---|---|---|---|
| cC-GFR | −0.31 | −0.15 | −0.57 | −0.56 | −0.27 | −0.13a | −0.17 |
| AER | 1.00 | 0.11 | 0.41 | 0.28 | 0.04c | −0.12b | 0.20 |
| TNFa | 1.00 | 0.11a | 0.20 | 0.34 | 0.19 | 0.17 | |
| sTNFR1 | 1.00 | 0.81 | 0.26 | 0.20 | 0.21 | ||
| sTNFR2 | 1.00 | 0.32 | 0.26 | 0.27 | |||
| sFas | 1.00 | 0.14a | 0.12a | ||||
| IP-10 | 1.00 | 0.14a | |||||
| sICAM | 1.00 |
P < 0.01,
P < 0.05,
P = NS, otherwise all other P < 0.0001.
The independence of the associations of these six markers of inflammation or apoptosis with cC-GFR was examined in multiple regression models. Only sTNFR1, sTNFR2, and sFAS remained significant when all were included in the model. Although sTNFR2 was statistically significant in this model, its contribution was small because of its high collinearity with sTNFR1, so it was not retained in subsequent modeling. Most notable about this model was that the serum markers alone (sTNFR1 and Fas) explained 41% of the variation in cC-GFR (adjusted r2), and addition of age and AER to the model only increased the adjusted r2 to 45% (Table 5). Addition of the other clinical covariates from Table 2 did not improve the adjusted r2 (data not shown). The relative influence of these covariates on cC-GFR is summarized in Table 5 by the cC-GFR estimated at the 25th, 50th, and 75th percentiles of each covariate, with and without adjustment for other covariates. The effect on cC-GFR is the most pronounced for sTNFR1, and it is hardly changed by multivariate adjustment. Adjustment for the other potentially relevant clinical covariates—such as gender, HbA1c, body mass index, renoprotective and other antihypertensive treatment and lipid-lowering treatment, and duration of storage of serum specimens—did not modify the association of sTNFR1 and Fas with cC-GFR. When the analysis was repeated using sTNFR2 instead of sTNFR1, the result was similar, indicating that measurement of either receptor yields roughly the same information.
Table 5.
Mean cC-GFR at the 25th, 50th, and 75th percentiles of each significant covariate and the corresponding estimates adjusted for the other covariates
| Covariate | Percentile | Univariate Analysis
|
Multivariate Analysisa
|
||
|---|---|---|---|---|---|
| cC-GFR (ml/min/1.73m2) | P | cC-GFR (ml/min/1.73m2) | P | ||
| Age (yr) | <0.0001 | <0.002 | |||
| 31 | 25th | 115 | 114 | ||
| 40 | 50th | 109 | 112 | ||
| 48 | 75th | 104 | 110 | ||
| AER (μg/min) | <0.0001 | <0.0001 | |||
| 22 | 25th | 119 | 115 | ||
| 39 | 50th | 111 | 112 | ||
| 79 | 75th | 102 | 108 | ||
| sTNFR1 (pg/ml) | <0.0001 | <0.0001 | |||
| 1216 | 25th | 121 | 120 | ||
| 1442 | 50th | 112 | 112 | ||
| 1764 | 75th | 101 | 103 | ||
| sFas (pg/ml) | <0.0001 | <0.008 | |||
| 3.63 | 25th | 112 | 113 | ||
| 4.50 | 50th | 110 | 112 | ||
| 5.72 | 75th | 107 | 111 | ||
Adjusted r2 for the multivariate model was 0.45, whereas it was 0.41 after adjustment for sTNFR1 and sFas only. Adjustments for gender, HbA1c, body mass index, antihypertensive and lipid-lowering treatments, and duration of storage samples did not significantly modify the associations.
Discussion
In this large cross-sectional study we examined twelve serum markers of inflammation and apoptosis. We sought to discover a profile of markers that is associated with renal function impairment in patients with T1DM and either NA or MA. To our knowledge, the novelty of this study is its primary focus on cC-GFR (not albuminuria) as an outcome in early diabetic nephropathy and its attempt to differentiate the observed effect of markers on GFR from their potential associations with AER. We attempted this approach in both uni- and multivariate analyses. In univariate analyses, six markers were unrelated to renal function (C-reactive protein, IL6, IL8, MCP-1, sVCAM-1, and sFasL) and six were significantly associated with variation in cC-GFR (TNFα, sTNFR1, sTNFR2, sFas, sICAM-1, and IP-10). Among the six, the associations of TNF receptors with decreased cC-GFR were the strongest.
Of the six markers, only the concentrations of sTNFR1, sTNFR2, and sFas contributed independently to cC-GFR. The effect of TNF receptors on cC-GFR was much more pronounced than the effects of clinical covariates such as age and AER (Table 5). Furthermore, serum concentrations of sTNFR1 and sTNFR2 are highly correlated (Spearman r = 0.81) and show roughly the same associations with cC-GFR. Therefore, further studies are required to determine whether measurement of both rather than just one is worthwhile. Our study provides evidence for the first time that markers of TNFα- and Fas-mediated pathways are strongly associated with variation in cC-GFR in patients with T1DM and early diabetic nephropathy. This association is independent of the association of these markers with AER. Our findings support the hypothesis that inflammation and apoptosis are involved in early renal function decline in T1DM.
Other cross-sectional studies in T1DM reported that serum concentrations of TNFα-related markers were elevated in comparison with healthy subjects and that the higher concentrations of these markers were associated with elevated urinary albumin excretion (14,22). Cross-sectional association between serum concentrations of sTNFR and variation in GFR has been shown in type 2 diabetes mellitus (23), as well as in nondiabetic individuals (24,25). In the prospective CARE study, high serum concentrations of sTNFR2 were found to be associated with faster progression of renal function loss (26); however, all subjects in that study had chronic kidney disease (GFR < 60ml/min/1.73m2) at baseline.
Whatever mechanisms could underlie a causal relationship between renal function decline and elevated serum concentrations of sTNFR1 or TNFR2 (and whether those mechanisms include an activated TNFα pathway) remain to be discovered. Soluble receptors bind TNFα and may serve as a slow-release reservoir of TNFα in a diabetic (and possibly low-grade inflammatory) state (27). There is also some experimental evidence for activation of TNFα-pathway in diabetes (3,28). Possible factors that could influence serum concentrations of TNF receptors include their upstream regulators in serum, such as TNFα or IL1, and intramembrane activity of ADAM17 (TNF receptor sheddase) (29). Concentrations of interleukin 1 below our detection limits prevented us from measuring it reliably (data not shown), and measurement of ADAM17 was not possible with the methods we used in our study.
How elevated concentrations of soluble TNF receptors may lead to renal injury is not known. If they represent an activated TNFα-pathway, several mechanisms may be involved. The TNFα-pathway has a broad range of inflammatory and apoptotic properties. Dysregulation of these processes may contribute to injury of the diabetic kidney. In addition, the TNFα-pathway directly increases glomerular vasoconstriction and albumin permeability. Exposure of the kidney to TNFα increases mRNA expression of TNF receptors in renal tubulointerstitium and triggers cell death. Also, an apoptotic response follows exposure of human kidney cells to sTNFR as well. This effect is more pronounced after exposure to sTNFR1 than to sTNFR2 (3,30,31). Therefore, our findings may support a hypothesis that elevated serum concentrations of soluble TNF receptors, by themselves or as markers of activation of TNFα pathway, contribute to early renal function decline.
One may argue that the association of TNFα receptors and cC-GFR simply reflects impaired renal handling of these proteins. Indeed, these receptors are cleared mainly by the kidneys as shown by tracer studies of radiolabeled sTNFR2 in animals (32). Also, serum concentrations of soluble TNF receptors increase in advanced renal failure, as demonstrated in binephrectomized mice (32) and in human studies (33). However, most patients in our study had normal renal function, and even the renal function loss resulting from uninephrectomy does not raise serum sTNF receptor concentrations in animals (32). Moreover, serum concentrations of sFasL, which has a molecular mass similar to soluble TNF receptors, is not associated with cC-GFR, whereas the receptors are strongly associated with variation in cC-GFR. On the basis of those data, potentially decreased clearance of those molecules has to be mentioned here, but it does not stand for the most likely explanation of our findings.
Adhesion molecules and chemokines are potential downstream effectors of the TNF–sTNFRs inflammatory pathway (6). Expression of IL8, MCP-1, and IP-10 mRNA is induced in TNFα-activated peripheral blood mononuclear cells taken from individuals with diabetes, but not from healthy ones (7). Expression and serum concentrations of chemokines and adhesion molecules, VCAM-1 and ICAM-1, increase as diabetic nephropathy develops (7,34). In our univariate analysis, serum concentrations of IP-10 and sICAM-1 were associated with variation in cC-GFR and they correlated with their potential upstream regulators. Nevertheless, the observed effects were weak, and disappeared in multivariate analyses, as one would expect if their effect were not independent of the TNF receptors or sFas.
Analysis of the Fas-mediated pathway revealed an independent effect of the serum concentration of sFas on variation in cC-GFR and a lack of an effect of the serum concentration of sFasL. A similar pattern of disparate effects of sFas and sFasL was previously demonstrated in individuals with advanced kidney disease (11). Also, in a few individuals with T1DM and without proteinuria, sFas was reported to correlate with both ACR and GFR (35).
The mechanism of action of soluble Fas receptor has not been well known but may be similar to that of TNF receptors in that it leads to an enhanced Fas-mediated response in the kidney. The Fas-related system is involved mainly in regulation of apoptosis (10), whereas the TNF-system regulates apoptotic and inflammatory responses. Consistent with this is the tubulointerstitial apoptosis seen in strepotozocin-induced diabetic rats (8) and in human diabetic kidneys (9). Some evidence also suggests that TNFα may induce Fas-mediated apoptosis (36,37). In our study serum concentrations of TNFα and sFas were markedly correlated.
The main limitation of our study is its cross-sectional study design; therefore inferences about the causality of the associations remain tentative. Furthermore, high interassay CV of some of the measured markers would have weakened or obscured true associations with cC-GFR. Furthermore, the assay for TNFα only measures the free form of TNFα. In low-grade chronic inflammation (which we expect to be the case in this condition), most circulating TNFα is bound to its receptors and undetected by the assay used. This fact may account for the noticeably poor correlations between TNFα and its receptors as well as its association with cC-GFR being weaker than that of its receptors.
In conclusion, this study provides the first clinical evidence that markers of the TNF- and Fas-mediated pathways are strongly associated with GFR in patients with T1DM and NA or MA. sTNFR1, sTNFR2, and sFas are the markers most strongly representing these associations. These findings support the hypothesis that inflammation and apoptosis are involved in renal function decline in patients with T1DM and early diabetic nephropathy.
Disclosures
None.
Acknowledgments
This study was supported by grant DK-41526 from the National Institutes of Health.
M.A. Niewczas was supported by American Diabetes Association mentor-based fellowship # 7-03-MN-28. The authors thank Harry Spaulding for his assistance in the preparation of this manuscript.
Published online ahead of print. Publication date available at www.cjasn.org.
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