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. 2016 Aug 16;186(1):86–95. doi: 10.1111/cei.12844

Inflammation, vitamin D and dendritic cell precursors in chronic kidney disease

K Paul 1,✉, S Franke 1, J Nadal 2, M Schmid 2, A Yilmaz 3, D Kretzschmar 4, B Bärthlein 5, S Titze 6, A Koettgen 7, G Wolf 1, M Busch 1; for the GCKD study group
PMCID: PMC5011369  PMID: 27414487

Summary

Decreased blood dendritic cell precursors (DCP) count is linked with atherosclerotic disease, while reduction of circulating DCP is also seen in patients with chronic kidney disease (CKD). As poor vitamin D status could be linked to a compromised innate immune response, we hypothesized that vitamin D status might be involved in the decrease in circulating DCP in CKD. Moreover, the potential role of inflammation was considered. Circulating myeloid (mDCP), plasmacytoid (pDCP) and total DCP (tDCP) were analysed using flow cytometry in 287 patients with CKD stage 3. Serum 25(OH)D and 1,25(OH)2D levels were measured using enzyme‐linked immunosorbent assays (ELISA), interleukin (IL)‐6, IL‐10 and tumour necrosis factor (TNF)‐α using cytometric bead array, C‐reactive protein (CRP) using a high‐sensitivity (hs) ELISA. Contrary to our hypothesis, there was no association between vitamin D levels and DCP, although their number was decreased significantly in CKD (P < 0·001). Instead, mDCP (r = −0·211) and tDCP (r = −0·188,) were associated slightly negatively with hsCRP but positively with the estimated glomerular filtration rate (eGFR, r = 0·314 for tDCP). According to multivariate linear regression, only higher hsCRP concentration and the presence of diabetes mellitus had a significant negative influence on DCP count (P < 0·03, respectively) but not vitamin D, age and eGFR. A significant impact of vitamin D on the reduction of circulating DCP in CKD 3 patients can be neglected. Instead, inflammation as a common phenomenon in CKD and diabetes mellitus had the main influence on the decrease in DCP. Thus, a potential role for DCP as a sensitive marker of inflammation and cardiovascular risk should be elucidated in future studies.

Keywords: cytokines, dendritic cells, inflammation

Introduction

Dendritic cells (DC) which are derived from bone marrow progenitor cells represent a heterogeneous population of professional antigen‐presenting cells 1. These cells can be found in an immature state in the blood as DC precursors (DCP) moving to peripheral tissues, forming a dense network 2, 3. Their main function is to monitor the internal environment to detect foreign and potentially harmful antigens 4. As a consequence of antigen uptake and by the presence of inflammatory stimuli, immature DC undergo terminal differentiation. By this process, DC are able to prime naive T cells and initiate an adaptive immune response by expressing different co‐stimulatory molecules 2, 5, 6. Thereby, DC play a major role in the initiation and maintenance of innate and adaptive immunity 2, 3.

In humans, there are at least two different DC subpopulations: myeloid DC (mDC) and plasmacytoid DC (pDC).

Uraemia is a chronic inflammatory state 7. Thus, elevated levels of inflammatory mediators are already detected in the early stages of chronic kidney disease (CKD) and increase further with the progression of kidney disease 8. Among others, vascular damage in CKD is initiated and perpetuated by the interaction of immune cells such as DC with cells of the vessel wall 8, 9. During atherogenesis, the amount of DC in the vessel wall and in atherosclerotic plaques increases 10, 11. By this mechanism, a decreasing amount of circulating DCP may serve as a biomarker indicating the presence of plaque burden and/or vascular inflammation. In fact, there was a significant decrease in circulating DCP in patients with different stages of coronary artery disease (CAD) and acute myocardial infarction 12, 13 or CKD stage 5 5. Moreover, as shown previously by our group, a significant decrease in both circulating myeloid DCP (mDCP) and plasmacytoid DCP (pDCP) is present in patients with CKD stage 3 compared to healthy controls as well as patients with ensured CAD 14. This finding might be a possible reflection of early vascular changes in CKD. Other potential reasons are unknown so far.

However, there is a high prevalence of vitamin D deficiency in CKD 15. Active vitamin D is a well‐known regulator of the immune system and influences innate immunity 4, 16. In vitamin D‐deficient subjects the function of primary or monocyte‐derived macrophages is impaired, leading to an increased susceptibility for tuberculosis and other infectious diseases 17, 18, 19. During the presence of 1,25(OH)2D in vitro, the differentiation and maturation of DC is inhibited, leading to the generation of tolerogenic rather than immunogenic DC 20, 21, 22.

In the present study, we aimed to clarify the hypothesis that both vitamin D status and vitamin D medication may influence DCP counts in CKD patients. Concerning this issue, the potential role of inflammation should be considered.

Methods

Patients

Patients were enrolled into the German Chronic Kidney Disease Study (GCKD), which is a German national cohort study 23. For GCKD, patients were screened to have an estimated glomerular filtration rate (eGFR) of between 30 and 60 ml/min/1·73 m2 calculated by the four‐variable modification of diet in renal disease (MDRD) formula and/or proteinuria > 0·5 g per day. The main criteria for exclusion from the GCKD study are described elsewhere 23.

Two hundred and eighty‐seven patients were included in this cross‐sectional substudy only, according to the stage 3 CKD inclusion criterion 23. These patients were recruited exclusively in the GCKD regional centre of Jena between January and August 2011.

The intake of any immunosuppressive drugs or suffering from diseases that could interfere with our analysis, e.g. any kind of infections, malignancies, autoimmune diseases and hyperthyroidism, were excluded.

Laboratory analysis

Blood samples were collected in 9‐ml ethylenediamine tetraacetic acid (EDTA) tubes and cooled immediately (4–10°C). Analysis for circulating DCP was conducted in the research laboratory of the University Hospital of Jena using flow cytometry within 8 h after the collection. Routine blood analyses were performed by standardized techniques in a central laboratory, as described previously 23. Circulating mDCP and pDCP were analysed in fresh blood samples by four‐colour staining and fluorescence activated cell sorter (FACS) analysis using the Blood Dendritic Cell Enumeration kitTM (BDCA kit; Miltenyi Biotec, Bergisch Gladbach, Germany). Briefly, circulating mDCP and pDCP were identified according to their expression of BDCA‐1, BDCA‐2 and the absence of other peripheral blood mononuclear cell markers. Thus, the cells were classified according to CD303, CD1c, CD14, CD19 and CD141. Finally, cells were analysed using a FACSCalibur flow cytometer with CellQuest software (Becton Dickinson, Heidelberg, Germany). Sample preparation and gating strategies were described in detail previously 14. The relative cell numbers of circulating DCP were determined as the percentage of white blood cells (WBC).

Plasma concentrations of cytokines were measured using cytometric bead array (CBA), an assay for quantitative measurement of various soluble proteins by FACS analysis. For the quantification of interleukin (IL)‐6, IL‐10 and tumour necrosis factor (TNF)‐α in plasma, the CBA human inflammation kit from BDTM (BD Biosciences Pharmingen, San Diego, CA, USA) was used. Three bead populations with distinct fluorescence intensities were coated with capture antibodies which bind to specific proteins of IL‐6, IL‐10 and TNF‐α. The capture beads, polyethylene‐conjugated detection antibodies, standards or test samples were incubated together to form sandwich complexes according to a multiplexed, particle‐based immunoassay. Using the flow cytometer, cytokines were quantified according to their fluorescence intensities. Acquisition of sample data was generated in graphical and tabular format using the specific BD CBA analysis software. Considering standard curves and limitations of specificity, plasma cytokine concentrations were calculated in pg/ml.

For quantitative measurement of 25OH‐vitamin D3 (25OHD) levels in human serum, the 25OHD‐Xpress ELISA kit from Immundiagnostik (Bensheim, Germany) was used, a competitive ELISA technique with a selected monoclonal antibody recognizing 25OHD. For reliable determination, it was necessary to release 25OHD from the 25OHD‐vitamin D binding protein‐complex. 1,25(OH)2‐vitamin D3 (1,25(OH)2D) was quantified using the 1,25(OH)2D ELISA kit from Immundiagnostik, a competitive enzyme immunoassay (EIA) technique with a selected monoclonal antibody.

Ethics

The study was carried out in accordance with the Declaration of Helsinki (2000) and was approved by the institutional ethics committees from the University of Jena. Written informed consent was obtained from all participants.

Statistical methods

Values are expressed as medians (minimum, maximum) for non‐normally distributed data and means (± standard deviation) for normally distributed data or percentages, as appropriate. A P‐value of < 0·05 was considered statistically significant. Student's t‐test (for normal distribution) and the Mann–Whitney rank sum test (for non‐normally distributed data) were used for the comparison between two independent groups. The Kruskal–Wallis test was used for comparison between three independent groups. All data were tested for normal distribution. Logarithmic transformation was used for some variables before linear regression analysis. For univariate and multivariate linear regression analysis tDCP count was used as outcome factor, whereas age, eGFR, diabetes mellitus, 25OHD and high‐sensitivity CRP (hsCRP) were used as covariates. Categorical clinical data (e.g. gender, presence of hypertension, smoking, diabetes mellitus) were compared using χ2 statistics. Correlation analyses were performed using Pearson's product–moment correlation for normally distributed data or Spearman's rank correlation for non‐normally distributed data. Data analysis was performed using SAS version 9.4 and r version 3.1.3.

Results

Clinical data

The clinical data of 287 patients with CKD stage 3 are shown in Table 1.

Table 1.

Clinical data of 287 patients with chronic kidney disease (CKD) stage 3

CKD 3 patients (n = 287)
Age (years) 63·45 ± 8·79
Male, number (%) 170 (59·2)
Diabetes mellitus, number (%) 114 (39·7)
Hypertension (%) 277 (96·5)
Smokers and ex‐smokers (%) 144 (50·4)
Leucocytes (Gpt/l) 6·7 ± 1·69
BMI (kg/m*) 29·90 ± 5·77
eGFR (ml/min/1·73 m*) 47·85 ± 15·25
Albumin (g/l) 38·69 ± 3·66
Creatinine (mg/dl) 1·44 (0·61, 3·64)
Cholesterol (mg/dl) 203·75 (90·79, 410·67)
HDL (mg/dl) 47·67 (20·81, 119·22)
LDL (mg/dl) 109·93 (20·91, 278·23)
Triglycerides (mg/dl) 183·82 (37·27, 805·97)
DCP count
mDCP rel. (% of WBC)† 0·17 (0·03, 0·44)
pDCP rel. (% of WBC)* 0·08 (0·02, 0·40)
tDCP rel. (% of WBC)‡ 0·26 (0·06, 0·58)
mDCP abs. (cells/µl)§ 10·64 (1·92, 35·64)
pDCP abs. (cells/µl) 5·13 (1·08, 16·74)
tDCP abs. (cells/µl)** 16·74 (3·84, 46·17)
Inflammatory markers
hsCRP (mg/l)†† 2·29 (0·18, 32·81)
IL‐6 (pg/ml)‡‡ 1·36 (0·00, 28·18)
IL‐10 (pg/ml)§§ 0·40 (0·00, 9·44)
TNF‐α (pg/ml)¶¶ 0·19 (0·00, 11·89)
Vitamin D
25OHD (ng/ml)***
≥ 30 (sufficient supply)††† 		18·05 (4·80, 48·09)
1,25(OH)2D (ng/l)***
16–70 (sufficient supply)††† 		33·61 (2·00, 150·40)
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Median (min., max.), mean ± standard deviation. †In 287 CKD stage 3 patients, 85 controls (median age 58 (21, 78), 58% male) had a median count of mDCP rel. of 022 (009, 058) % of WBC [14]. *In 287 CKD stage 3 patients, 85 controls had a median count of pDCP rel. of 0·13 (0·04, 0·30) % of white blood cells (WBC). ‡In 287 CKD stage 3 patients, 85 controls had a median count of tDCP rel. of 0·38 (0·15, 0·76) % of WBC. §In 287 CKD stage 3 patients, 85 controls had a median count of mDCP abs. of 15·3 (5·2, 34·4) cells/µl. In 287 CKD stage 3 patients, 85 controls had a median count of pDCP abs. of 8·9 (2·5, 16·5) cells/µl. **In 287 CKD stage 3 patients, 85 controls had a median count of tDCP abs. of 27·3 (9·6, 67·8) cells/µl. ††Cut‐off for intermediate cardiovascular risk according to the American Heart Association: 1·0–3·0 mg/l. ‡‡In 208 CKD stage 3 patients, 41 controls (mean age 61 (38, 78), 42% male) had a median interleukin (IL)‐6 of 0·82 (0·00–7·32) pg/ml. §§In 208 CKD stage 3 patients, 41 controls had a median IL‐10 of 0·20 (0·00–1·15) pg/ml. ¶¶In 208 CKD stage 3 patients, 41 controls had a median tumour necrosis factor (TNF)‐α of 0·10 (0·00–0·51) pg/ml. ***25OHD was measured in 272 CKD stage 3 patients, 1,25(OH)2D in 186 CKD stage 3 patients. †††According to the recommendations of the Institute of Medicine, 2010 24. BMI = body mass index; eGFR = estimated glomerular filtration rate; HDL = high‐density lipoprotein; LDL = low‐density lipoprotein; mDCP = myeloid dendritic cell precursors; pDCP = plasmacytoid DCP; tDCP = total DCP; rel. = relative; abs. = absolute.

DCP, CRP and cytokines

As shown previously in the same group of patients, absolute and relative numbers of circulating mDCP, pDCP and tDCP were decreased significantly compared with controls (P < 0·001, respectively 14, Table 1). Similarly, CKD 3 patients revealed significantly higher IL‐6 (P = 0·036), TNF‐α (P = 0·021) and IL‐10 (P < 0·001) concentrations (Table 1). There was no association between IL‐6, TNF‐α or IL‐10 with any DCP, either with absolute or relative numbers [P = not significant (n.s.)]. There was a slight but significant negative association between absolute numbers of mDCP (P < 0·001) and tDCP (P = 0·002) with hsCRP (Fig. 1). Absolute numbers of pDCP were not associated with hsCRP (P = 0·139).

Figure 1.

Figure 1

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Correlation of high‐sensitivity C‐reactive protein (hsCRP) with absolute numbers of myeloid dendritic cell precursors (mDCP), plasmacytoid (p)DCP and total (t)DCP in chronic kidney disease (CKD) stage 3 patients (n = 287).

Vitamin D and DCP count in patients with CKD

Vitamin D levels were measured in 272 (25OHD) and 186 (1,25(OH)2D) patients with CKD stage 3 (Table 1). The 25OHD levels of CKD 3 patients were consistent with vitamin D insufficiency (range 12–30 ng/ml, according to the Institute of Medicine, 2010) 24. The 1,25(OH)2D concentration of CKD 3 patients was sufficient (reference range of 16–70 ng/l 24). There were no gender‐related differences (P = n.s.). The absolute and relative numbers of mDCP, pDCP and tDCP did not correlate with either 25OHD or 1,25(OH)2D (Fig. 2). However, there was a slight but significant negative correlation of both 25OHD (r = −0·192, P = 0·001) and 1,25(OH)2D (r = −0·149, P = 0·041) with hsCRP and a positive association of 1,25(OH)2D (r = 0·197, P = 0·007), but not 25OHD (r = −0·033, P = 0·590) with eGFR (Fig. 3).

Figure 2.

Figure 2

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Correlation of 25OHD (n = 272) and 1,25(OH)2D (n = 186) with absolute numbers of myeloid dendritic cell precursors (mDCP), plasmacytoid (p)DCP and total (t)DCP in chronic kidney disease (CKD) stage 3 patients.

Figure 3.

Figure 3

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Correlation of estimated glomerular filtration rate (eGFR) and high‐sensitivity C‐reactive protein (hsCRP) with 25OHD (n = 272) and 1,25(OH)2D (n = 186) in chronic kidney disease (CKD) stage 3 patients.

For further investigation of an association between vitamin D and DCP, our group of CKD 3 patients was divided into three groups according to their vitamin D status 24. Thus, 57 CKD patients had 25OHD deficiency (21%), 182 patients 25OHD insufficiency (67%) and 37 patients (12%) had a sufficient vitamin D status. No significant difference of any DCP was found between these groups (data not shown). There was no difference of eGFR between these groups. However, hsCRP was significantly lower when 25OHD concentrations increased [hsCRP 2·62 (0·22, 18·15) mg/l in 25OHD deficiency, 2·38 (0·18–32·81) mg/l in 25OHD insufficiency and 1·61 (0·29, 11·17) mg/l in 25OHD sufficiency, P = 0·047].

DCP, inflammation and vitamin D

In order to identify the most potential factors influencing the count of tDCP in CKD, linear regression analysis was performed (Table 2).

Table 2.

Factors influencing dendritic cell precursors (DCP) in 287 patients with CKD stage 3 due to linear regression analysis

Univariate analysis for ln(tDCP) Multivariate analysis for ln(tDCP)
Estimate s.e. P‐value Estimate s.e. P‐value
Age (years) −0·0051 0·0025 0·0407 −0·0026 0·0025 0·3059
eGFR (ml/min/1·73 m2) 0·0019 0·0015 0·1980 0·0011 0·0015 0·4464
Diabetes mellitus (yes) −0·1244 0·0442 0·0053 −0·1007 0·0459 0·0294
Ln[25OHD (ng/ml)] 0·0296 0·0488 0·5453 0·0088 0·0489 0·8575
Ln[hsCRP (mg/l)] −0·0617 0·0206 0·0030 −0·0479 0·0216 0·0275
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CKD = chronic kidney disease; CRP = C‐reactive protein; eGFR = estimated glomerular filtration rate; tDCP = total DCP; s.e. = standard error.

In the univariate model, as well as in the multivariate model, there was a significant negative influence of hsCRP (P = 0·0030 for univariate analysis, P = 0·0275 for multivariate analysis) and of the presence of diabetes mellitus (P = 0·0053 for univariate analysis, P = 0·0294 for multivariate analysis) on tDCP count (Table 2).

DCP count and cytokines according to vitamin D medication

As expected, patients without vitamin D medication had a significantly higher eGFR compared to those taking such medication (49·0 ± 15·5 ml/min/1·73 m2 versus 43·2 ± 13·4 ml/min/1·73 m2; P = 0·0081). There was no significant difference in absolute DCP counts in patients with and without vitamin D medication (Fig. 4a–c). Patients with vitamin D supplementation (n = 61, 21%) revealed significant higher plasma levels of 25OHD (Fig. 4d, P < 0·001), but no difference in 1,25(OH)2D concentration (Fig. 4e, P = 0·821) compared to CKD 3 patients without any vitamin D supplementation (n = 226, 79%). The concentration of hsCRP [2·23 (0·29; 21·65) mg/l versus 2·30 (0·18; 32·81) mg/l, P = 0·498], as well as of IL‐6, IL‐10 and TNF‐α (data not shown), did not differ significantly between treated and untreated patients.

Figure 4.

Figure 4

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Comparison between absolute myeloid dendritic cell precursors (mDCP) (a), plasmacytoid (p)DCP (b) and total (t)DCP count (c), as well as plasma concentrations of 25OHD (d, n = 272) and 1,25(OH)2D (e, n = 186) in chronic kidney disease (CKD) stage 3 patients with (n = 61) and without (n = 226) vitamin D medication.

Discussion

Recently, our group showed that mDCP and pDCP in peripheral blood are reduced even in earlier stages of CKD 14. As vitamin D deficiency is common in CKD, we hypothesized that vitamin D status might be of relevance for reduced DCP counts in CKD.

In the present study in patients having stage 3 CKD, most of the patients suffered from insufficient 25OHD concentration according to the 2010 recommendations of the Institute of Medicine 24; 21% even had a deficient vitamin D status. CKD patients with stages 3–5 commonly have a high prevalence of a deficient supply of native vitamin D. Reasons for this could be the prescribed nutritional regimen and/or loss of appetite as a consequence of uraemia 15. Vitamin D is a well‐known regulator of the immune system, influencing the function of primary or monocyte‐derived macrophages 4, 16, 17, 18, 19. In particular, the differentiation and maturation of DC is influenced by vitamin D 20, 21, 22. Despite these known interactions of vitamin D with the immune system, our hypothesis of an association of mDCP, pDCP or tDCP count with the plasma concentrations of 25OHD or 1,25(OH)2D could not be confirmed in a cohort of CKD stage 3 patients, although their DCP counts were reduced significantly 14. Additionally, in univariate and multivariate regression models, vitamin D had no influence on DCP count. Even after dividing the CKD 3 patients according to their vitamin D status, no significant differences of DCP were found. However, a higher concentration of hsCRP was associated with lower DCP counts suggesting that inflammation had more influence on the decrease in DCP than vitamin D status. However, increased hsCRP but not DCP was accompanied with decreased 25OHD and 1,25(OH)2D concentrations. In particular, patients with vitamin D deficiency revealed higher CRP levels compared to patients with insufficient or sufficient vitamin D status, confirming previous findings 16. Clearly, vitamin D supplementation did not change this. However, patients without vitamin D medication had a significantly higher eGFR, presupposing an adequate vitamin D supply and a lower influence of CKD on vitamin D metabolism as well as on DCP counts 14. According to regression analysis, there was no significant influence of eGFR on tDCP count. The correlation of 1,25(OH)2D with eGFR seems to reflect the decreasing activity of 1α‐hydroxylase. The mean concentration of 1,25(OH)2D was within the normal range, due probably to preserved 1α‐hydroxylase activity in CKD stage 3 or to vitamin D treatment. Nevertheless, a sufficient 25OHD status was detected in only 12% of the investigated CKD patients, confirming previous findings 15. This may be of relevance, as a deficiency of 25OHD was shown to be a strong predictor for the progression of kidney disease and was related to all‐cause and cardiovascular death 25, 26. Moreover, vitamin D or its analogues exert positive effects on existing proteinuria, blood pressure and inflammation 27. In addition, vitamin D is a modulator of immune reactions 28, 29. Extrarenal 1‐α‐hydroxylase is regulated by various IFNs or ILs and depends upon the availability of vitamin D 30. In innate immune cells such as antigen‐presenting cells, T cells, B cells and monocytes, activation of the vitamin D receptor (VDR) by vitamin D induces the formation of anti‐microbial peptides 31. Hence, reduced vitamin D status was associated with higher incidence of infectious diseases 19. Vitamin D promotes the differentiation of progenitor cells into macrophages 32 but decreases proinflammatory cytokine expression 33. In the presence of calcitriol, naive T cells differentiate to regulatory T cells (and not to T effector cells) in order to prevent excessive tissue damage 34. The active form of vitamin D inhibits the differentiation and maturation of dendritic cells (DC), preserving immature DC, increases the secretion of IL‐10 and decreases the expression of co‐stimulatory molecules 22, 35.

As our CKD patients had increased inflammatory markers, CKD itself could be shown once more to be a state of inflammation 36. Univariate and multivariate regression models revealed a significant impact of increasing hsCRP concentration on decreasing DCP counts. This may allude to the fact that lower DCP counts in the peripheral blood of CKD 3 patients are mainly the result of latent inflammation, as expressed by higher levels of hsCRP. Thus, the decrease in DCP might be an extremely sensitive and early sign of inflammation. As CKD is an inflammatory disease along with a certain cardiovascular risk burden 37, inflammatory processes are involved in the initiation and progression of vascular disease but also vascular repair mechanisms 38. Taking this into account, such mild inflammation as reflected by decreasing DCP may reflect vascular disease 12, especially in CKD 14.

Nearly 40% of our cohort had diabetes mellitus (DM). The presence of DM could be identified as the second factor affecting DCP. This supports the concept that DCP count might serve as a cardiovascular risk marker 12, 13, as DM at least doubles cardiovascular risk 39 and is also associated with inflammation. In people with early type 2 diabetes, arterial stiffness was associated positively with 18F‐fluorodeoxyglucose positron emission tomography‐assessed subclinical vascular inflammation 40. Studies performed recently showed that CKD is associated with an increase of DC in the intima of the abdominal aorta, pointing to an increased consumption of DC and thus DCP during vascular disease 41. This process might be considered as vascular inflammation, suggesting a role of DC in the pathogenesis of atherosclerotic vascular disease, especially in CKD 14, 42. In this regard, inflammation markers such as CRP were already shown to be predictive of cardiovascular events, especially in CKD patients 38, 42. The influence of CRP on circulating DCP could already be demonstrated by in‐vitro investigations in which CRP induced DC activation, leading to a reduction of circulating DCP 43. Such activated DC induced a T cell response followed by augmented cytokine production 43. The lack of a significant association between pDCP and hsCRP might be related to the fact that pDCP predominantly play a role in immune response to viral infection 2, whereas mDCP are generated as a consequence of inflammation 44. CKD is associated with elevated concentrations of CRP 45. Higher CRP concentrations could also be linked to an increase in total and cardiovascular mortality in patients with CKD stages 3 and 4 46. Increased CRP levels are probably explained by the chronic inflammatory state in CKD. But increased CRP itself promotes endothelial dysfunction and glomerular damage which induces a further decline in renal function 47. In turn, the progression of kidney disease causes increased concentrations of inflammatory markers 48. Thus, anti‐inflammatory therapy with pentoxifylline showed positive effects on the preservation of kidney function 48. Disturbed immune response and permanent immune stimulation, as found in CKD, results in altered cytokine balance. IL‐10, IL‐6 and TNF‐α are involved in the development of type 1/type 2 helper cell imbalance and are therefore important for the pathogenesis of cardiovascular disease in the uraemic milieu 49. A significant increase of IL‐10, IL‐6 and TNF‐α in CKD was confirmed by our findings. However, in contrast to hsCRP, there was no association between DCP and the concentration of any of these cytokines. As renal failure is one of the most important factors for an increase of TNF‐α activity 50, 51, significantly higher TNF‐α concentrations could be detected in the observed CKD 3 patients.

In conclusion, vitamin D status was not a significant effector on the count of circulating DCP in peripheral blood of CKD stage 3 patients. Instead, a significant negative influence of hsCRP and of diabetes mellitus on circulating DCP could be demonstrated. A potential role for DCP as a sensitive marker of inflammation and cardiovascular risk should be elucidated in future studies.

Disclosures

The authors declare that they have no disclosures.

Current GCKD investigators and collaborators with the GCKD study

University of Erlangen‐Nürnberg: Kai‐Uwe Eckardt, Stephanie Titze, Heike Meiselbach, Markus Schneider, Tom Dienemann, Hans‐Ulrich Prokosch, Barbara Bärthlein, André Reis, Arif B. Ekici, Olaf Gefeller, Karl F. Hilgers, Silvia Hübner, Susanne Avendaño, Dinah Becker‐Grosspitsch, Birgit Hausknecht, Rita Zitzmann, Anke Weigel, Andreas Beck, Thomas Ganslandt, Sabine Knispel and Thomas Dressel; University of Freiburg: Anna Köttgen, Ulla Schultheiß, Simone Meder, Erna Mitsch, Ursula Reinhard and Gerd Walz; RWTH Aachen University: Jürgen Floege, Georg Schlieper, Turgay Saritas, Sabine Ernst and Stefan Lipski; Charité, University Medicine Berlin: Elke Schaeffner, Seema Baid‐Agrawal and Kerstin Petzold; Hannover Medical School: Jan T. Kielstein, Hermann Haller, Johan Lorenzen and Petra Otto; University of Heidelberg: Claudia Sommerer, Claudia Föllinger and Martin Zeier; University of Jena: Martin Busch, Gunter Wolf, Katharina Paul and Rainer Fuß; Ludwig‐Maximilians University of München: Robert Hilge, Thomas Sitter and Claudia Blank; University of Würzburg: Christoph Wanner, Vera Krane, Sebastian Toncar, Daniel Schmiedeke, Daniela Cavitt, Karina Schönowsky and Antje Börner‐Klein; Medical University of Innsbruck, Division of Genetic Epidemiology: Florian Kronenberg, Julia Raschenberger, Barbara Kollerits, Lukas Forer, Sebastian Schönherr and Hansi Weissensteiner; University of Regensburg, Institute of Functional Genomics: Peter Oefner, Wolfram Gronwald and Helena Zacharias; Department of Medical Biometry, Informatics and Epidemiology (IMBIE), University of Bonn: Matthias Schmid and Jennifer Nadal.

Acknowledgements

We are very grateful for the willingness and time of all study participants of the GCKD study. The enormous effort of the study personnel at the regional centres is highly appreciated. We also thank the large number of nephrologists for their support of the GCKD study (list of nephrologists currently collaborating with the GCKD study is available at http://www.gckd.org).

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