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Respiratory Research

, 20:151 | Cite as

Consequences of chronic kidney disease in chronic obstructive pulmonary disease

  • Franziska C. Trudzinski
  • Mohamad Alqudrah
  • Albert Omlor
  • Stephen Zewinger
  • Danilo Fliser
  • Timotheus Speer
  • Frederik Seiler
  • Frank Biertz
  • Armin Koch
  • Claus Vogelmeier
  • Tobias Welte
  • Henrik Watz
  • Benjamin Waschki
  • Sebastian Fähndrich
  • Rudolf Jörres
  • Robert BalsEmail author
  • on behalf of the German COSYCONET consortium
Open Access
Research

Abstract

Background

The combination of chronic obstructive pulmonary disease (COPD) and chronic kidney disease (CKD) is associated with a higher prevalence of comorbidities and increased mortality. The impact of kidney function on patient-centered outcomes in COPD has not been evaluated.

Methods

Patients from the German COPD and Systemic Consequences - Comorbidities Network (COSYCONET) cohort COPD were analysed. CKD was diagnosed if the estimated glomerular filtration rate (eGFR) measurements were < 60 mL/min/1.73m2 at study inclusion and six month later. The effect of CKD, on comorbidities, symptoms [modified British Medical Research Council dyspnoea scale], physical capacity [six-minute walk test, and timed up and go] and St George’s Respiratory Questionnaire were analysed. Restricted cubic spline models were used to evaluate a nonlinear relationship between eGFR with patient-centered outcomes, cox survival analysis was applied to evaluate mortality.

Results

2274 patients were analysed, with CKD diagnosed in 161 (7.1%). Spline models adjusted for age, gender, BMI, FEV1 and cardiovascular comorbidities revealed independent associations between eGFR with modified British Medical Research Council dyspnoea scale, St George’s Respiratory Questionnaire, (p < 0.001 and p = 0.011), six-minute walk test (p = 0.015) and timed up and go (p < 0.001). CKD was associated with increased mortality, independently from for other cardiovascular comorbidities (hazard ratio 2.3; p < 0.001).

Conclusion

These data show that CKD is a relevant comorbidity in COPD patients which impacts on patient-centered outcomes and mortality.

Trial registration

NCT01245933

Keywords

Chronic obstructive pulmonary disease Chronic kidney disease Patient-centered outcomes Cohort study 

Abbreviations

6MWT

Six-minute walk test

BMI

Body mass index

CAD

Coronary artery disease

CAT

COPD assessment test

CKD

Chronic kidney disease

CKD-EPI

Chronic kidney disease epidemiology collaboration

COPD

Chronic obstructive pulmonary disease

CRP

C-reactive protein

CVI

Cardiovascular index

DM

Diabetes mellitus

EC

Exercise capacity

eGFR

Estimated glomerular filtration rate

EQ-5D

EuroQol- 5 dimension

FEV1

Forced expiratory volume in 1 s

FS

Functional status

HbA1c

Glycosylated haemoglobin

HDL

High density lipoprotein

ITGV

Intrathoracic gas volume

KDOQI

National kidney foundation–kidney disease outcomes quality Initiative

LDL

Low density lipoprotein

MCI

Main comorbidity index

MI

Myocardial infarction

mMRC

Modified british medical research council dyspnoea scale

PAD

Peripheral artery disease

PY

Pack-years

QoL

Quality of life

RV

Residual volume

SGRQ

St George’s Respiratory Questionnaire

TLC

Total lung capacity

TLCO

Transfer factor for carbon monoxide.

TuG

Timed up and go

Introduction

Chronic obstructive pulmonary disease (COPD) and chronic kidney disease (CKD) affect a large number of patients. The World Health Organization estimates COPD to become the 3rd leading cause of mortality worldwide in 2030 [1]. CKD, defined by abnormalities of kidney structure or function for more than 3 months [2], affected 14.8% of the U.S. adult general population in 2011–2014 [3]. Cigarette smoking and increasing age are risk factors for the development of both COPD and CKD [4, 5, 6], with systemic inflammation as an extrapulmonary manifestation of COPD potentially increasing the risk of comorbid CKD [7]. This combination of COPD and CKD is independently associated with a higher prevalence of other comorbidities (especially cardiovascular) and increased mortality [8, 9].

The presence of a number of comorbidities has been shown to correlate with limitations of exercise capacity in COPD patients. Cardiovascular dysfunction is a well-known predictor of a limited functional capacity and health status [10]. Whether CKD and kidney function have a role for functional limitations independent of established cardiovascular disease is currently unknown. The German COPD and Systemic Consequences - Comorbidities Network (COSYCONET) is a multicentre prospective cohort study investigating the interaction of COPD, comorbidities and systemic inflammation [11]. The present study aimed to analyse the relationship between COPD, CKD and estimated glomerular filtration rate (eGFR), focusing on patient-centered outcomes and mortality.

Methods

Study population

COSYCONET recruited patients age ≥ 40 years and with a diagnosis of COPD or symptoms of chronic bronchitis who were available to attend repeated study visits up to 18 months. The characteristics of the cohort have been described previously [11]. A total of 2741 participants were recruited from September 2010 to December 2013 in 31 study centres throughout Germany. The present study analysed data from the baseline visit and the first follow-up at 6 months. Mortality was assessed until November 2017.

Definition and staging of chronic kidney disease

CKD was diagnosed by the estimated glomerular filtration rate, based on the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) creatinine equation [12]. Patients with an eGFR < 60 mL/min/1.73 m2 at study inclusion and at the six month visit were considered as having CKD, as per the Kidney Disease Outcome Quality Initiative (KDOQI) guidelines [2]. CKD category 1 and 2 (eGFR ≥90 and 60–89 mL/min/1.73 m2, respectively), reflecting normal or mildly decreased kidney function, were combined into the category ‘no CKD’. CKD categories were defined as follows: CKD category 3a (eGFR 45–59 mL/min/1.73 m2), category 3b (eGFR 30–44 mL/min/1.73 m2), category 4 (eGFR 15–29 mL/min/1.73 m2) and category 5 (eGFR < 15 mL/min/1.73 m2). Patients with missing laboratory values at one or both time points were excluded from the first part of the present analyses.

Pulmonary function, GOLD classification

All pulmonary function tests (i.e. forced spirometry, body plethysmography and diffusion capacity) were performed 45 min after inhalation of 400 μg salbutamol and 80 μg ipratropium bromide according to current recommendations [13, 14, 15, 16, 17].

Due to the above mentioned inclusion criteria there were also some patients with a FEV1/FVC ratio above 70% at baseline. These patients were described as GOLD Stage 0. This group was defined as having a FEV1/FVC ratio > 70% and (i) having a doctor’s diagnosis of chronic bronchitis and/or (ii) indicating a severity of cough of at least 3 in the respective COPD Assessment Test (CAT) item and/or (iii) indicating a severity of phlegm of at least 3 in the respective CAT item [11].

Comorbidities

All participants underwent structured interviews to identify other comorbidities. The overall comorbid burden was summarised in a main comorbidity index (MCI). The MCI depicts a non-weighted summary score of the 34 following conditions: allergic diseases, arrhythmia, asthma, cancer, cirrhosis of the liver, coronary artery disease, chronic bronchitis, epilepsy, gastritis, gastroesophageal reflux, gout, heart failure, hepatitis, hypertension, hypothyroidism, hyperthyroidism or hyperparathyroidism, mental disorders, insulin-dependent diabetes mellitus, multiple sclerosis, myocardial infarction, non-insulin-dependent diabetes mellitus, osteoarthrosis, osteoarthritis, osteoporosis, peptic ulcer, parkinson disease, peripheral artery disease, peripheral neuropathy, pulmonary fibrosis, renal colic or renal calculi, sarcoidosis, sleep apnea, stroke and venous thrombosis. The MCI was calculated by counting each item with 1 point. A summarised assessment of cardiovascular comorbidity was performed in a similar manner using the cardiovascular index (CVI), which includes the five cardiovascular items hypertension, coronary artery disease, myocardial infarction, arrhythmia and stroke. Patients with a CVI of ≥1 point were considered as having cardiovascular comorbidities.

Measurements of symptoms, functional status, exercise capacity and health status

Severity of dyspnoea was assessed using the modified British Medical Research Council dyspnoea scale (mMRC) [18]. The COPD related symptom load was assessed by the COPD Assessment Test (CAT) [19]. Functional status and exercise capacity were assessed by the ‘timed up and go’ (TuG) and the six-minute walk test (6MWT). The ‘timed up and go’ measures the time taken for the patient to rise from a chair, walk 3 m, turn, walk back, and sit down again [20]. The six-minute walk test was performed as described in the former American Thoracic Society (ATS) guidelines [21]. COPD specific health status was measured by the St George’s Respiratory Questionnaire (SGRQ) [22]. Quality of life was measured by the EuroQoL 5-dimension (EQ-5D) Questionnaire.

Statistical analysis

The association of CKD with functional, laboratory values and other comorbidities were analyzed using group comparisons. We described categorical data using frequencies and percentages. For continuous data we used means (standard deviations), those values which were markedly different from normal distribution are presented as median (interquartile range). Comparisons between the “CKD and the ‘no CKD’ group were performed by Fisher’s exact test or X2 test, as appropriate in case of categorical variables, t-tests or Wilcoxon test were used for continuous variables as appropriate. Multivariate regression models with included established risk factors (e.g. age, sex, BMI, FEV1% pred.) were used for analysis of the impact of CKD for different numeric variables. Analysis was performed in SAS 9.3 and results were considered statistically significant for P values less than 0.05. Because of the non-linear association between mMRC, TuG, 6MWT, SGRQ, FEV1, BMI and eGFR, we analyzed non-linear associations between the aforementioned parameters and eGFR from the first visit by using restricted cubic splines of eGFR with three knots. Knots were placed at 59.6 ml/min, 84.8 ml/min, and 100.8 ml/min which corresponds to the 10th, 50th, and 90th percentile of the eGFR values. Analyses were adjusted for age, sex, BMI, FEV1 (% pred.) and CVI score, where appropriate. Analyses were performed using STATA IC 15. Multivariate adjusted restricted cubic spline analyses were performed using the STATA package ‘postrcspline’. Cox analysis was used to characterize the impact of CKD on mortality with additional independent variables: BMI, sex, CVI, and FEV1% pred. Analysis was performed using SPSS version 24 (IBM, Armonk NY, USA).

Results

Study subjects and prevalence of CKD

After screening of all 2741 patients from the COSYCONET study cohort, 2274 were eligible for analysis of CKD. 467 patients with missing laboratory values at one or both of the two defined time points were excluded from the CKD part of the analysis. CKD was diagnosed in 161 of 2274 patients (7.1%). The majority of all patients (60.6%) were male, and the mean ± SD age was 65.0 ± 8.4 years. Among the 161 patients with CKD, 114 (70.8%) were category 3A, 43 (26.7%) were category 3B, and 4 (2.5%) were category 4. There were no patients with an eGRF < 15 mL/min/1.73 m2 or on Dialysis. The distribution of chronic kidney disease categories in the study population is presented in Table 1.
Table 1

Distribution of chronic kidney disease categories in the study population

Kidney function

CKD categories

eGFR (mL)

No. of Patients (%)

normal to mild reduced

1–2

> 60

2113 (92.9)

moderate reduced

3A

45–59

114 (5.0)

 

3B

20–44

43 (1.9)

severely reduced

4

15–29

4 (0.2)

kidney failure

5

<  15 or on Dialysis

0 (0)

CKD categories were defined in accordance with the National Kidney Foundation–Kidney Disease Outcomes Quality Initiative (KDOQI) guideline Abbreviations: CKD Chronic Kidney Disease; eGFR estimated Glomerular Filtration Rate

Patients characteristics

Patients with CKD were significantly older and had a significantly higher BMI than those with normal or mildly reduced kidney function (i.e. the ‘no CKD’ group) (Table 2). Compared with the ‘no CKD’ group, patients with CKD showed less residual volume, and were more likely to be classified to be in the lower GOLD stages (0.0073). There were no differences between the two groups in terms of other spirometric parameters, diffusion capacity, or oxygenation. The characteristics of the study population are presented in Table 2.
Table 2

Patient characteristics

 

N

All

No CKD

CKD

p value

Age (years)

2274

65.0 ± 8.4

64.5 ± 8.3

72.2 ± 6.6

< 0.0001

Male

2274

1378 (60.6%)

1280 (60.6%)

98 (60.9%)

0.9471

BMI (kg/m2)

2272

27.2 ± 5.2

27.00 ± 5.2

28.7 ± 5.3

< 0.0001

Smoking history (PY) a

2192

40.0 [16.5–63.8]

39.0 [16.5–63.0]

51.3 ± 43.3

0.0793

Lung function

FVC (L)

2256

3.0 ± 1.0

3.0 ± 1.0

2.9 ± 0.9

0.1660

FVC (%pred)

2256

78.6 ± 18.9

78.7 ± 18.9

77.4 ± 18.4

0.3875

FEV1 (L) a

2260

16 [1.1–2.1]

1.6 [1.2–2.1]

1.5 [1.1–2.0]

0.3162

FEV1 (%pred)

2260

57.0 ± 21.0

57.0 ± 20.9

56.6 ± 22.4

0.7416

ITGV (L)

2205

4.7 ± 1.3

4.7 ± 1.3

4.7 ± 1.3

0.4425

ITGV (%pred)

2205

143.5 ± 37.7

143.2 ± 37.5

147.1 ± 39.6

0.2098

RV (L)

2194

3.8 ± 1.23

3.8 ± 1.2

3.9 ± 1.3

0.1667

RV (%pred)

2134

167.42 ± 58.6

168.2 ± 58.8

156.1 ± 54.5

0.0143

TLC (L)

2189

7.1 ± 1.5

7.1 ± 1.5

7.1 ± 1.4

0.7341

TLC (%pred)

2189

115.5 ± 20.3

115.3 ± 20.2

117.9 ± 21.3

0.1474

TLCO (%)

2146

55.7 ± 21.8

55.9 ± 21.7

55.4 ± 23.1

0.7861

GOLD Classification

Stage 0

2260

363 (16.1)

333 (15.8)

30 (18.6)

0.0073

Stage I

2260

182 (8.1)

166 (7.9)

16 (9.9)

 

Stage II

2260

831 (36.7)

760 (36.2)

71 (44.10)

 

Stage III

2260

706 (31.2)

666 (31.8)

40 (24.8)

 

Stage IV

2260

178 (7.9)

174 (8.3)

4 (2.5)

 

Blood gas analysis

pH value

2213

7.4 ± 0.1

7.4 ± 0.1

7.4 ± 0.0

0.3847

PaO2 (mmHg)

2212

67.4 ± 9.2

67.3 ± 9.1

68.5 ± 10.8

0.1792

PacO2 (mmHg)

2212

37.7 ± 4.9

37.7 ± 4.9

37.6 ± 4.6

0.7490

HCO3 (mmol/L)

2211

24.3 ± 2.9

24.2 ± 2.9

24.4 ± 2.8

0.6371

Values are presented as mean ± standard deviations or number (%). Those values which were markedly different from normal distribution (a) are presented as median [interquartile range]. p ≤ 0.05 was considered statistically significant (bold)

Abbreviations: BMI body mass index, PY pack-years, FEV1 forced expiratory volume in 1 s, RV residual volume, TLC total lung capacity, ITGV intrathoracic gas volume, TLCO transfer factor for carbon monoxide

Comorbidity burden

Self-reported comorbidities were more frequent in the CKD group, in particular cardio- and cerebrovascular disease, peripheral artery disease (PAD), diabetes, gout and malignancies (Table 3). Furthermore, compared to the ‘no CKD’ group, patients with CKD were more likely to have higher CVI and MCI scores.
Table 3

Selected self-reported comorbidities

comorbidities

N

All

No CKD

CKD

p value

Hypertension

2206

1227 (55.7)

1112 (54.32)

115 (74.2)

< 0.0001

CAD

2200

343 (15.7)

306 (15.0)

39 (25.2)

0.0008

MI

2202

182 (8.3)

162 (7.9)

20 (12.9)

0.0315

Arrhythmia

1183

196 (16.7)

168 (15.2)

28 (36.8)

< 0.0001

Heart failure

1182

118 (10)

101 (9.1)

17 (22.4)

0.0003

Stroke

2202

92 (4.2)

80 (3.9)

12 (7.7)

0.0242

PAD

2202

255 (11.6)

226 (11.0)

29 (18.7)

0.0046

DM

2202

111 (5.0)

92 (4.5)

19 (12.3)

< 0.0001

Gout

2202

380 (17.2)

320 (15.6)

60 (38.7)

< 0.0001

Malignant tumour

2202

256 (11.6)

226 (11.0)

31 (20.0)

0.0009

Osteoporosis

2201

320 (14.5)

290 (14.1)

30 (19.4)

0.0787

Pathologic fracture

2202

100 (4.5)

93 (4.5)

7 (4.5)

0.9876

CVI (≥1)

2274

1398 (63.4)

1315 (62.2)

129 (80.1)

< 0.0001

MCI (≥5)

2274

1045 (47.4)

961 (45.9)

109 (67.4)

< 0.0001

Abbreviations: CAD coronary artery disease, MI myocardial infarction, PAD peripheral artery disease, DM Type I and Type II diabetes mellitus using insulin, CVI cardiovascular index, MCI main comorbidity index. Values are presented number (%). p ≤ 0.05 was considered statistically significant (bold)

Laboratory testing

Haemoglobin was significant lower in patients with CKD compared with the ‘no CKD’ group. CKD patients presented significantly elevated blood glucose and glycosylated haemoglobin compared to the‘no CKD’ group. There were no differences between the two groups in term of leucocytes, C-reactive protein or cholesterol. Laboratory findings are summarized in the Additional file 1: Table S1.

Measurements of symptoms, functional status, exercise capacity and health status

Patients with CKD had a significant higher mMRC values as compared to the ‘no CKD’ group. The COPD related symptom load as measured by the CAT showed no differences between the two groups (Table 4). Functional status and exercise capacity were reduced in CKD patients as they took significantly longer to complete the TuG as compared to the ‘no CKD’ group and the distance walked in 6 min was significantly shorter. COPD specific health status and quality of life showed no differences between the two groups (Table 4). Multivariate regression models with included established risk factors (e.g. age, sex, BMI, FEV1%pred) were used for analysis of the impact of CKD for different numeric variables (dyspnoea, functional status, exercise capacity and QOL). The effect of CKD on the distance walked in 6 min was independent from the effect of age, gender, BMI, FEV1 and CVI (point estimate, 17.6 m; 95% confidence interval, 0.8–34.4,p < .0001).
Table 4

Measurement of dyspnoea, COPD specific health status, quality of life, exercise capacity and physical activity

 

N

All

No CKD

CKD

p value

mMRC

2260

   

< 0.0001

0

 

207 (9.2)

196 (9.3)

12 (7.5)

 

1

 

1067 (47.2)

1014 (48.3)

53 (32.9)

 

2

 

614 (27.2)

557 (26.5)

57 (35.4)

 

3

 

353 (15.6)

317 (15.1)

36 (22.4)

 

4

 

19 (0.9)

16 (0.8)

3 (1.9)

 

CAT

2263

17.8 ± 7.2

17.8 ± 7.2

17.4 ± 7.0

0.5117

SGRQ

2259

41.7 ± 19.6

41.5 ± 19.5

44.0 ± 19.9

0.1196

EQ 5D

2266

0.8 ± 0.2

0.8 ± 0.2

0.8 ± 0.2

0.6427

6MWD

2225

424.7 ± 105.2

427.6 ± 104.3

385.9 ± 110.1

< 0.0001

TuG (sec.)

2224

6.9 ± 2.2

6.9 ± 2.2

7.5 ± 2.4

0.0004

Abbreviations: mMRC Modified British Medical Research Council dyspnoea scale, CAT COPD Assessment Test, 6-MWD Six minute walk distance, SGRQ St George’s Respiratory Questionnaire, EQ-5D EuroQol- 5 dimension. Values are presented as N (%) or mean ± SD. p ≤ 0.05 was considered statistically significant (bold)

Restricted cubic spline models

Spline models adjusted for age, sex, BMI, FEV1 (% pred.) and cardiovascular comorbidity (CVI score) were performed to analyse the non-linear association of eGFR with dyspnea, functional status (FS), exercise capacity (EC) and quality of life (QoL). These models revealed independent relationships of eGFR with mMRC, TuG, 6MWT, and SGRQ. Figure 1a and d show eGFR as an independent predictor of mMRC (p < 0.001) and SGRQ (p = 0.011) with j-shaped associations. Figure 1c shows an u-shaped relationship of eGFR with 6MWT (p < 0.001), while the association of eGFR with the timed up and go is reverse j-shaped (p = 0.015, Fig. 1b). Figure 1e and f show spline plots for the association of eGFR with FEV1 (% pred.) and BMI adjusted for age, sex, cardiovascular comorbidity (CVI score) and either BMI or FEV1 (% pred.). These models reveal an association of lower FEV1% pred. and BMI with higher eGFR values (P = 0.003 and 0.001 respectively)
Fig. 1

Restricted cubic spline plots of the association of eGFR with (a) Modified British Medical Research Council dyspnoea scale; mMRC, (b) timed up and go TuG in seconds, (c) six-minute walk test in meters, (d) St George’s Respiratory Questionnaire; SGRQ, (e) forced expiratory volume in 1 s; FEV1 in % predicted and (f) body mass index; BMI. The red line indicates the estimated change of mMRC, TuG, 6MWT, SGRQ, FEV1 (%pred.) with the respective 95% confidence interval (gray area). a-d are adjusted for age, gender, BMI, FEV1 (% pred.) and cardiovascular comorbidity (CVI 1–5). e and f for age, sex, cardiovascular comorbidity (CVI score) and either BMI or FEV1 (% pred.)

Impact of CKD mortality

To investigate whether COPD patients with comorbid CKD have an increased risk of dying, we performed Cox regression analysis with age, BMI, sex, packyears, CVI, and FEV1% pred. as cofounders and found that CKD is significantly associated with increased mortality (Fig. 2). This association was stable also from models that included the individual comorbidities or risk factors (data not shown). The hazard ratios (confidence intervals, p value) were: CKD, 2.35 (1.52–3.63, p = < 0.001); sex (male) 1.49 (1.03–2.14, p = 0.032), FEV1% pred. 0.96 (0.95–0.97, p = 0.000); age 1.09 (1.06–1.11, p = 0.000). No significance was found for CVI and BMI.
Fig. 2

Cox analysis with BMI, sex, packyears, CVI, and FEV1% pred. as cofounders showed that CKD is significantly associated with mortality

Discussion

The present study characterized patients with comorbid COPD and CKD from the German COSYCONET study cohort. This is to our knowledge the first study analysing the effects of comorbid CKD on patient-centered outcomes in COPD. COPD Patients with CKD were more likely to have additional comorbidities, reported increased dyspnea, and had a significantly reduced exercise capacity compared with the ‘no CKD’ group. Spline models adjusted for age, gender, BMI, FEV1 (% pred.) and cardiovascular comorbidity revealed independent nonlinear associations of eGFR with dyspnoea, functional status, exercise capacity and health status. CKD was furthermore a predictor for mortality independently from other cardiovascular comorbidities.

There are several studies focusing on the prevalence of CKD in patients with COPD, conducted in a range of populations [23, 24, 25, 26, 27, 28, 29]. Most of these studies are single-center studys with a small sample size One recent meta-analysis by Gaddam and colleagues showed an increased prevalence of CKD in patients with COPD even after adjustment for co-variates including age, gender, BMI and smoking status, thus suggesting an independent association of CKD with COPD [30]. The overall CKD prevalence in our study population was 7.1%. This finding is consistent with that in other COPD populations reporting a CKD prevalence of 4–8% [23, 25, 29]. Systemic inflammation might be one linking element between these two conditions [7].

In the present study, mMRC scores were higher in patients with CKD and spline interpolations revealed an independent inverse association of eGFR with mMRC. Increased mMRC values in turn are linked with reduced physical activity levels in patients with COPD [31]. The higher level of dyspnoea in patients with comorbid CKD and COPD was also associated with reduced exercise capacity as measured by the six minute walk test. Spline interpolations for the association of eGFR with 6MWD distance showed a linear independent association if eGFR values were below 60 ml/min/1,73m2. This relationship was also shown for eGFR and COPD specific health status measured by the SGRQ if kidney function were reduced. However those patients with normal kidney function showed mixed outcomes for mMRC, timed up and go, six minute walk test and SGRQ. Especially those patients with high eGFR (> 90 ml/min/1.73m2) values presented more symptoms and inferior performance. The combination of high eGFR values and unfavourable outcomes in apparently healthy subjects was described as renal hyperfiltratration (RH). The pathogenesis of RH is still poorly understood, but there are associations with hypertension, diabetes, obesity and smoking [32]. Renal hyperfiltratration was shown as an independent predictor of chronic cardiopulmonary diseases and all-cause mortality [33]. This is commonly regarded as an overestimation of GFR because of muscle wasting in a high risk group. Our data support this theory as low FEV1 and BMI were independently associated with high eGFR values in our cohort. COPD related inactivity and sarcopenia might be on explanation for these findings, however the BMI values were still in the normal range and the BMI includes no information on body composition.

We also assessed whether CKD is associated with increased mortality. CKD is closely associated with cardiovascular diseases and an independent risk factor for death [34, 35]. This finding is in agreement with earlier studies that used health care system data [8, 36] and showed that COPD increased risk of death in CKD patients. CKD also increases the mortality risk in patients with acute exacerbations of COPD [37]. Our study suggests that these negative outcomes might be mediated by an impact of CKD on symptoms, functional status and exercise capacity. The effects of CKD on exercise capacity cannot simply be explained by the higher frequency of these comorbidities, but suggest that CKD per se has a negative effect on exercise capacity. The underlying mechanisms for this finding are likely complex and include increased systemic inflammation, (patho-)physiological interaction between lung and kidney, or network effects between several comorbidities including cardiovascular diseases. CKD contributes substantially to other common systemic manifestations of COPD such as malnutrition, muscle wasting, anaemia [38], osteoporosis and cardiovascular disease [38, 39], which in total negatively affect exercise capacity [21] and therefore, might explain the results of our study.

The present study has some limitations: The presence of comorbidities was based on patients’ reports. Other limitations are mainly related to the limited sample size in the CKD categories. The majority of patients within this category had only moderate kidney impairment, probably because these are the ones that are more willing to participate into cohorts, which might lead to a selection bias.

Our results from eGFR spline interpolations suggest that there is an increasing impact on dyspnoea, exercise capacity and health status with increasing kidney impairment. We therefore speculate that a higher proportion of CKD category 4 and 5 patients would have led to more pronounced differences between the two categories.

Conclusion

CKD is a frequent finding in COPD patients and possibly an important contributor to the comorbidome of the disease as well as to many important disease outcomes, including mortality. Spline models showed a nonlinear association of eGFR on different patient-centered outcomes, CKD but also high eGFR values might be predictors for inactivity and progressive deconditioning in COPD. Interventions that increase physical activity levels might play a key role to improve outcomes in these special groups of patients. CKD is therefore a relevant COPD comorbidity, and there is an urgent need for more information to improve outcome in this high risk group of patients.

Notes

Acknowledgments

The authors thank David Young of Young Medical Communications and Consulting Ltd. for his critical review of the manuscript and all investigators and study centers who contributed in patient recruitment and data collection as listed on http://www.asconet.net/html/cosyconet/studzent.

Authors’ contributions

FCT and RB, FB, MA and TS contributed to conception of the study, to data analysis and interpretation and drafted the manuscript AK, CV, TW, HW, BW, SF and RJ and RB contributed to data collection, data interpretation and revised the manuscript critically for important intellectual content. AO, SZ, and DF and FS contributed to data interpretation and revised the manuscript critically for important intellectual content. All authors approved the final version of the manuscript.

Funding

This work was supported by the Competence Network Asthma and COPD (ASCONET). The COSYCONET COPD Cohort is funded by the German Federal Ministry of Education and Research (BMBF) with grant numbers 01GI0881 and 01GI0882, as well as by unrestricted grants from AstraZeneca GmbH, Bayer Schering Pharma AG, Boehringer Ingelheim Pharma GmbH & Co. KG, Chiesi GmbH, GlaxoSmithKline, Grifols Deutschland GmbH, MSD Sharp & Dohme GmbH, Mundipharma GmbH, Novartis Deutschland GmbH, Pfizer Pharma GmbH, Takeda Pharma Vertrieb GmbH & Co. KG for patient investigations and laboratory measurements.

Ethics approval and consent to participate

The COSYCONET study has been approved by the ethics committee of the medical faculty of the Philipps-Universität Marburg, the local ethics committees of the participating centers (a list of all participating study centers can be found here: http://www.asconet.net/html/cosyconet/studzent) and by the concerned data security authority (data security agency of the federal states of Hessen, Baden-Württemberg, Lower-Saxony, and Saarland).

Consent for publication

All cohort participants gave their written informed consent and agreed to the scientific evaluation and publication of the collected data.

Competing interests

The authors declare that they have no competing interests.

Supplementary material

12931_2019_1107_MOESM1_ESM.docx (19 kb)
Additional file 1: Table S1. Laboratory values. (DOCX 18 kb)

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Copyright information

© The Author(s). 2019

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors and Affiliations

  • Franziska C. Trudzinski
    • 1
  • Mohamad Alqudrah
    • 1
  • Albert Omlor
    • 1
  • Stephen Zewinger
    • 2
  • Danilo Fliser
    • 2
  • Timotheus Speer
    • 2
  • Frederik Seiler
    • 1
  • Frank Biertz
    • 3
  • Armin Koch
    • 3
  • Claus Vogelmeier
    • 4
  • Tobias Welte
    • 5
  • Henrik Watz
    • 6
  • Benjamin Waschki
    • 6
  • Sebastian Fähndrich
    • 1
  • Rudolf Jörres
    • 7
  • Robert Bals
    • 1
    Email author
  • on behalf of the German COSYCONET consortium
  1. 1.Department of Internal Medicine V - Pulmonology, Allergology Critical Care Care MedicineSaarland University HospitalHomburgGermany
  2. 2.Department of Internal Medicine IV – NephrologySaarland University HospitalHomburgGermany
  3. 3.Institute for BiostatisticsHannover Medical SchoolHannoverGermany
  4. 4.Department of Medicine, Pulmonary and Critical Care MedicineUniversity Medical Center Giessen and Marburg, Philipps-University Marburg, Member of the German Center for Lung Research (DZL)MarburgGermany
  5. 5.Clinic for Pneumology Hannover Medical SchoolMember of the German Center for Lung ResearchHannoverGermany
  6. 6.Pulmonary Research Institute at LungenClinic Grosshansdorf Airway Research Center NorthMember of the German Center for Lung ResearchGrosshansdorfGermany
  7. 7.Institute and Outpatient Clinic for OccupationalSocial and Environmental MedicineMunichGermany

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