Growth Differentiation Factor-15 and Incident Chronic Kidney Disease: A Population-Based Cohort Study

Growth Differentiation Factor-15 and Incident Chronic Kidney Disease: A Population-Based Cohort Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Growth Differentiation Factor-15 and Incident Chronic Kidney Disease: A Population-Based Cohort Study Xue Bao, Biao Xu, Yan Borné, Marju Orho-Melander, Olle Melander, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-394958/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Background The relationship between growth differentiation factor 15 (GDF-15) and the development of chronic kidney disease (CKD) is still unclear. We sought to examine whether plasma GDF-15 was related to incident CKD and kidney function decline using a large prospective cohort study. Methods 4,318 participants of the Malmö Diet and Cancer Study-Cardiovascular Cohort were examined in 1991–1994 and followed prospectively until 2013 for incidence of CKD, as detected from national registers. Estimated glomerular filtration rate (eGFR) was available for all participants at baseline, and was re-measured in a subgroup of 2,744 subjects after 16.6 ± 1.49 years. Incidence of CKD was examined in relation to GDF-15 using Cox regression analysis. Logistic regression was used to examine the association of GDF-15 with eGFR change and eGFR-based CKD. Models were carefully corrected for potential confounders including baseline eGFR, N-terminal pro-B-type natriuretic peptide, and competing risk from death. Results 165 patients developed CKD after 19.2 ± 4.04 years of follow-up. The adjusted hazard ratio (95% confidence interval, CI) for CKD in 4th versus 1st quartile of GDF-15 was 2.37 (1.33, 4.24) ( p for trend < 0.01). Each per 1 standard deviation increase in GDF-15 was associated with a decline in eGFR of -0.97 mL/min/1.73 m 2 (95% CI, -1.49~-0.45; p < 0.001). GDF-15 was also significantly associated eGFR-based CKD in 2,713 subjects with baseline eGFR ≥ 60 mL/min/1.73 m 2 . Conclusions GDF-15 predicted incidence of CKD and eGFR decline in the general population, independent of a wide range of potential risk factors and competing risk of death. Urology & Nephrology growth differentiation factor 15 chronic kidney disease estimated glomerular filtration rate competing risk cohort study. Figures Figure 1 Introduction As estimated recently, around 9.1% of the global population are suffering from chronic kidney disease (CKD), accounting for 35.8 million disability-adjusted life-years and 1.2 million deaths [ 1 ]. Treatment of CKD can be costly, in particular for patients with end-stage renal disease (ESRD), which poses a considerable financial burden to families and health systems [ 2 ]. Fortunately, CKD is preventable with early detection and timely intervention [ 3 , 4 ]. Efforts have thus been made to develop efficient screening strategies for CKD. In this regard, biomarkers have drawn increasing attention as they may not only help identify high-risk individuals but also provide insights into mechanism of kidney injury. CKD is a major risk factor for CVD [ 1 , 5 ], and vice versa, cardiac dysfunction could also lead to kidney injury [ 6 ]. Due to the interdependency of heart and kidney, several studies have explored kidney disease in relation to cardiovascular biomarkers, among which the predictive value of growth differentiation factor 15 (GDF-15) was recognized [ 7 – 13 ]. Most available evidence has focused on patients with existing kidney pathology [ 9 – 12 ]. For instance, GDF-15 was found to predict estimated glomerular filtration rate (eGFR) decline and mortality in type 1 diabetic patients with nephropathy [ 9 ], mortality in ESRD [ 10 ], eGFR decline and progression to ESRD in CKD [ 11 ], and progression to dialysis and mortality in light chain amyloidosis [ 12 ]. Cross-sectionally, GDF-15 was negatively associated with eGFR and was higher in the elderly with than without CKD [ 13 ]. However, community-based data regarding kidney function decline in relation to GDF-15 are scarce. To our knowledge, there were only two relevant studies. Participants in the study by Carlsson, et al. [ 8 ] were limited to elderly people. The authors demonstrated that GDF-15 did not predict decline of eGFR independently of baseline eGFR. In contrast, Ho, et al. [ 7 ] reported a positive association of GDF-15 with incident CKD, but some potential confounders (smoking, obesity, C-reactive protein, etc. [ 14 ]) were not adjusted for. In addition, incident CKD was identified by calculation of eGFR < 60 mL/min/1.73 m 2 , and no information about clinical diagnoses were included. Therefore, we aimed to investigate the association of GDF-15 with incident CKD, as obtained from national registers, as well as from eGFR calculation. Analyses were performed in a prospective study with a large general population sample and a long-term follow-up, while taking into account potential baseline confounders and competing risk from death. Methods The Malmö Diet and Cancer Cardiovascular (MDC-CV) cohort study The MDCS is a large prospective cohort study with participants recruited from Malmö, a city in southern Sweden [ 15 ]. During 1991-1994, a random sample of 6,103 participants was taken from MDCS to investigate the epidemiology of carotid artery atherosclerosis (MDC-CV cohort study) [ 14 ]. Among them, 5,540 donated fasting blood samples. We excluded participants with missing baseline data on eGFR, other covariates or GDF-15, or participants with previously diagnosed CKD or lost to follow-up. Therefore, 4,318 participants ( Figure 1 , mean aged 57.5 ± 5.95 years, male 39.4 %) remained for analyses of incident CKD, as detected by national registers of hospital inpatients and outpatients [ 16 ]. During 2007-2012, MDC-CV participants who were still alive and living in the Malmö area were invited to a re-examination. A total of 3,734 attended, which corresponds to 75.8% of the eligible population [ 17 ]. Among the 4318 individuals in this study, 2,827 attended re-examination and 2,744 had follow-up data available for eGFR. This sub-cohort study was then analyzed for decline in eGFR ( Figure 1 ). Incident CKD based on eGFR was further analyzed as the outcome in 2,713 participants with baseline eGFR ≥60 mL/min/1.73 m 2 ( Figure 1 ). Written informed consent was obtained from all included participants. The study conformed to the Declaration of Helsinki and was approved by the ethical committee at Lund University, Lund, Sweden (LU 51/90). GDF-15 measurement Fasting blood samples were collected from the cubital vein and stored at −80°C until assay. GDF-15 levels were measured by the SciLifeLab analysis service (Uppsala, Sweden) using Proseek® Multiplex CVD I 96×96 reagent kit where a Proximity Extension Assay technology was applied [ 14 , 18 ]. Briefly, the assay procedure consisted of three key steps: incubation, extension and detection. Raw Proseek data went through a pre-processing normalization procedure and were set relative to a fixed background level, after which Normalized Protein Expression (log2 scale) values were generated, measured in arbitrary units (AU). High AU values corresponded to a high protein concentration. GDF-15 levels in 987 subjects measured by Proseek assay closely correlated ( r =0.89 [L. Lind, unpublished data]) with the values by an electrochemiluminescence immunoassay (Roche Diagnostics, Mannheim, Germany) [ 14 ]. CKD based on the ICD codes from the national register Information on CKD diagnosis was obtained from the Swedish patient register with nation-wide coverage. Moreover, the Swedish renal registry was searched for any additional cases of CKD [ 19 ] . CKD was defined as codes 585-586 according to ICD-9, and N18 and N19 according to ICD-10. All participants without any previous diagnosis of CKD were followed from baseline until the occurrence of a diagnosis of CKD (registry-based CKD), emigration from Sweden, death or December 31st, 2013, whichever came first. The CKD diagnosis in the Swedish patient register has been previously described and validated [ 20 ]. Briefly, for validation, CKD diagnoses were evaluated by two experienced specialists in nephrology. Patient records and laboratory data were reviewed and CKD cases were defined following the 2012 KDIGO criteria [ 21 ]. Validation showed that 94% of patients had a correct diagnosis of CKD [ 20 ]. CKD based on eGFR, and eGFR decline from baseline to follow-up The eGFR at baseline and follow-up was determined from a combination of plasma creatinine and cystatin C using the CKD-Epidemiology Collaboration 2012 equation [ 22 ]. Single measurements of eGFR were assessed at each time point. A cut-off value of 60 mL/min/1.73 m 2 was used to identify participants with eGFR-based CKD [ 20 ]. The difference between these two measurements was defined as eGFR change. At baseline, creatinine and cystatin C were analyzed with the Jaffé method (Beckman Synchron LX20-4; Beckman-Coulter) and with a particle-enhanced immunonephelometry assay (N Latex Cystatin; Dade Behring, Deerfield, IL), respectively. Since the world calibrator was not introduced until 2010, cystatin C values were not standardized (reference value: 0.53~0.95 mg/L). During 2007-2012, creatinine was determined in follow-up samples using an enzymatic method (Cobas autoanalyzer; Roche Diagnostics) calibrated by isotope-dilution mass spectrometry-traceable (IDMS) creatinine [ 23 ], and cystatin C was analyzed using an automated particle-based immunoassay, adjusted to the international reference preparation ERM-DA 71/IFCC.38 T [ 24 ]. Therefore, values of creatinine and cystatin C could not be directly compared between baseline and follow-up. Other variables and definitions Baseline characteristics were obtained from self-administered questionnaire, physical examination, and blood measurements. Data on medication, smoking habits and alcohol consumption were collected by questionnaires. Participants were classified into current smokers, former smokers and never smokers. An average daily alcohol consumption >40 g for males or >30 g for females was considered as high alcohol consumption. Waist circumference was determined as being midpoint between the end of the 12 th rib and the iliac crest. Blood pressure was measured with a mercury-column sphygmomanometer after 10 min of rest while the subject was in a supine position. Participant with a history of coronary event or stroke was considered to have CVD at baseline. Glucose concentration was measured in fresh whole blood samples after an overnight fasting, following standard procedures at the Department of Clinical Chemistry, University Hospital Malmö. Diabetes was defined as self-reported physician diagnosis of diabetes, use of anti-diabetic drugs or fasting whole blood glucose ≥6.1 mmol/L (corresponding to plasma glucose ≥7.0 mmol/L). Low density lipoprotein (LDL) concentration was estimated using the Friedewald’s formula. Measurements of biomarkers were conducted later using frozen (−80°C) plasma samples. C-reactive protein (CRP) was measured with a Tina-quant® CRP latex assay (Roche Diagnostics, Basel, Switzerland). Methods to measure N-terminal pro-B-type natriuretic peptide (NT-proBNP) levels was the same way as that for GDF-15 [ 14 , 18 ]. Statistical analyses Baseline characteristics are presented for participants divided into quartiles (Q1-Q4) according to GDF-15 concentration, using sex-specific quartile limits. For skewed variables, log-transformation was performed to achieve a normal distribution. Differences across GDF-15 quartiles were examined using analysis of variance for continuous variables and logistic regression analysis for categorized variables. Cox proportional hazard regression was used to analyze the association between baseline GDF-15 and incident CKD discovered by the national register. Hazard ratios (HRs) and 95% confidential intervals (CIs) were obtained. GDF-15 was treated both as a continuous variable (per standard deviation (SD) change) and as a categorized variable (in quartiles). In multivariate-adjusted models, potential covariates taken into consideration were age, sex, waist circumference, smoking, high alcohol consumption, systolic blood pressure, LDL, CRP, diabetes, CVD, anti-hypertensive drug medication, and baseline eGFR. Since GDF-15 has been frequently considered as a cardiovascular biomarker in recent years, NT-proBNP, a traditional cardiovascular marker was additionally adjusted for in a sensitivity analysis to explore whether the association of GDF-15 with CKD could be mediated by cardiac function. A restricted cubic spline function was incorporated into the Cox model to test for possible non-linearity, with knots placed at 20, 40, 60 and 80 percentages of GDF-15 concentration. Possible effect modifications were examined by introducing an interaction term between GDF-15 levels and risk factors into the multivariate model one by one. The competing risks of death was accounted for in a sensitivity analysis by the Fine and Gray proportional subdistribution hazards models method. In another sensitivity analysis, the association between GDF-15 and CKD was analyzed while participants with baseline eGFR <60 mL/min/1.73 m 2 were excluded. In addition, for participants with follow-up data available for eGFR, multiple linear regression was used to analyze the association between GDF-15 and eGFR change from baseline to the end of the follow-up. A multiple logistic regression analysis was conducted for the association between GDF-15 and eGFR-based CKD. All analyses were performed using the Statistical Analysis System version 9.3 for Windows (SAS Institute Inc., Cary, NC, USA). A 2-tailed p <0.05 was considered statistically significant. Results Baseline Characteristics The mean GDF-15 concentration in the cohort was 8.75 ± 0.56 AU and mean eGFR was 89.2 ± 0.56 mL/min/1.73 m 2 . The clinical and biochemical characteristics of the population across GDF-15 quartiles are shown in Table 1 . As compared to participants with relatively low GDF-15 concentration, those with higher GDF-15 concentration tended to have decreased eGFR at baseline. An increasing trend was observed for most of the other covariates, except for sex and high alcohol consumption. Table 1 Characteristics of individuals across quartiles (Q1-Q4) of growth differentiation factor-15 (GDF-15). GDF15 quartiles p for trend a Q1 (n = 1080) Q2 (n = 1079) Q3 (n = 1080) Q4 (n = 1079) GDF-15 (AU) 8.09 ± 0.26 8.56 ± 0.11 8.90 ± 0.14 9.47 ± 0.38 < 0.0001 Age (years) 54.6 ± 5.60 56.6 ± 5.70 58.6 ± 5.50 60.1 ± 5.60 < 0.0001 Sex (male, %) 426 (9.87) 425 (9.84) 426 (9.87) 425 (9.84) 0.99 Waist circumference (cm) 81.6 ± 11.6 82.3 ± 12.3 83.5 ± 12.9 85.3 ± 13.5 < 0.001 Fasting glucose (mmol/L) b 4.80 (4.60–5.20) 4.90 (4.60–5.20) 4.90 (4.60–5.30) 5.00 (4.70–5.40) < 0.0001 Systolic blood pressure (mmHg) 136.1 ± 16.7 140 ± 18.9 142.4 ± 19.3 144.7 ± 19.3 < 0.0001 Diastolic blood pressure (mmHg) 85.6 ± 8.60 86.6 ± 9.40 86.9 ± 9.50 87.5 ± 9.60 < 0.001 Low-density lipoprotein cholesterol 4.09 ± 0.94 4.05 ± 0.94 4.25 ± 0.99 4.24 ± 1.03 < 0.001 C-reactive protein (mg/L) b 1.00 (0.50–1.90) 1.10 (0.60–2.30) 1.40 (0.70–2.90) 1.90 (1.00-4.20) < 0.0001 eGFR (mL/min/1.73 m 2 ) 94.3 ± 12.5 90.5 ± 12.2 88.1 ± 12.5 83.8 ± 14.5 < 0.0001 N-terminal pro-B-type natriuretic peptide (AU) -0.31 ± 0.92 -0.06 ± 0.94 0.09 ± 0.97 0.26 ± 1.03 < 0.0001 High alcohol consumption (%) 42 (0.97) 37 (0.86) 34 (0.79) 34 (0.79) 0.31 Smoking n (%) Never 528 (12.23) 458 (10.61) 420 (9.73) 369 (8.55) < 0.0001 Former 451 (10.4) 428 (9.91) 410 (9.50) 340 (7.87) < 0.0001 Current 101 (2.34) 193 (4.47) 250 (5.79) 370 (8.57) < 0.0001 Anti-hypertensive medication (%) 121 (2.80) 150 (3.47) 186 (4.31) 235 (5.44) < 0.0001 Diabetes (%) 40 (0.93) 59 (1.37) 81 (1.88) 144 (3.33) < 0.0001 Cardiovascular disease (%) 9 (0.21) 22 (0.51) 28 (0.65) 43 (1.00) < 0.0001 AU, arbitrary units; eGFR, estimated glomerular filtration rate according to the combined Chronic Kidney Disease Epidemiology Collaboration creatinine and cystatin C equation. a Analysis of variance or logistic regression analysis. b Glucose and C-reactive protein are presented as median (interquartile range in brackets) due to skewed distributions. All the other continuous values are presented as means ± standard deviation, unless otherwise stated. Incidence Of Register-based Ckd In Relation To Gdf-15 During a mean of 19.2 ± 4.04 years of follow-up, a total of 165 subjects developed CKD. After multivariate adjustment (Table 2 , Model 3), an increased GDF-15 level was observed to be associated with a higher risk of developing CKD. The HR (highest vs. lowest quartiles of GDF-15) for incident CKD was 2.74 (95% CI, 1.53 ~ 4.89; p for trend < 0.001). This value was slightly attenuated after additionally adjusting for baseline eGFR (HR, 2.37; 95% CI, 1.33 ~ 4.24; p for trend < 0.01). The adjusted HR for each 1 SD increase in GDF-15 was 1.39 (95% CI, 1.16 ~ 1.65; p < 0.001). In the sensitivity analysis when NT-proBNP was additionally added into the model, the association between GDF-15 and CKD hardly changed (HR: 1.35, 95% CI, 1.13 ~ 1.62; p < 0.01, per 1 SD increment of GDF-15). However, no statistical significance was observed for NT-proBNP ( p = 0.10). Table 2 Incidence of register-based chronic kidney disease in relation to growth differentiation factor-15 (GDF-15). Q1 Q2 Q3 Q4 p for trend a Per 1 standard deviation change in GDF-15 p a GDF-15 range male (AU) 6.09–8.47 8.47–8.82 8.82–9.21 9.21–11.2 - GDF-15 range female (AU) 6.78–8.35 8.35–8.66 8.66–9.02 9.02–12.3 - No. of subjects 1,080 1,079 1,080 1,079 - - - Incidence b 16 31 43 75 - - - Incidence (per 1000 person-years) 0.74 1.45 2.08 3.93 - - - Model 1 c d Reference 2.08 (1.14, 3.81) 3.15 (1.77, 5.60) 6.58 (3.82, 11.3) < 0.0001 1.97 (1.75, 2.22) < 0.0001 Model 2 c e Reference 1.66 (0.90, 3.04) 1.98 (1.10, 3.56) 3.46 (1.97, 6.10) < 0.0001 1.81 (1.56, 2.10) < 0.0001 Model 3 c f Reference 1.56 (0.85, 2.86) 1.66 (0.91, 3.00) 2.74 (1.53, 4.89) < 0.001 1.51 (1.27, 1.80) < 0.0001 Model 4 c g Reference 1.57 (0.85, 2.88) 1.62 (0.90, 2.92) 2.37 (1.33, 4.24) < 0.01 1.39 (1.16, 1.65) < 0.001 AU, arbitrary units. a Analysis by Cox proportional hazards model. b Defined as 585–586 according to International Classification of Diseases 9, and N18 and N19 according to International Classification of Diseases 10. c Adjusted hazard ratios (95 % confidence interval). d Crude model. e Adjusted for age and sex. f Additionally adjusted for waist circumference, smoking, high alcohol consumption, systolic blood pressure, low-density lipoprotein cholesterol, C-reactive protein level, diabetes, cardiovascular disease, and anti-hypertensive drug medication. g Additionally adjusted for baseline estimated glomerular filtration rate. No obvious evidence of non-linearity in the association between GDF-15 and CKD was detected by restricted cubic spline function ( p for effect test < 0.0001, p for non-linearity test = 0.26). Meanwhile, no interaction between GDF-15 and covariates was found with respect to CKD. During the follow-up period, 952 individuals died from causes other than CKD. When competing risk of death was taken in consideration, the adjusted HR was 2.11 (95% CI, 1.19 ~ 3.76) for Q4 versus Q1 of GDF-15 ( p for trend = 0.01), and was 1.23 (95% CI, 1.03 ~ 1.48) for each 1 SD increase in GDF-15 (data not shown). Among 4,244 individuals with baseline eGFR ≥ 60 mL/min/1.73 m 2 , 145 developed CKD. The adjusted HR was 2.08 (95% CI, 1.14 ~ 3.78; p for trend = 0.02), and 1.22 (95% CI, 1.01 ~ 1.48) for Q4 versus Q1 and per 1 SD increase of GDF-15, respectively (data not shown). eGFR decline and incidence of eGFR-based CKD in relation to GDF-15 A total of 2,744 had repeated eGFR values after a mean follow-up of 16.6 ± 1.49 years. The mean eGFR values at baseline and follow-up were 90.2 ± 12.8 and 66.2 ± 15.1 mL/min/1.73 m 2 , respectively (N = 2,744). The association of eGFR change in relation to GDF-15, both in quartiles and per 1 SD increase, is presented in Table 3 . As compared to Q1 of GDF-15, Q4 was associated with a greater eGFR decline during follow-up (-2.42 mL/min/1.73 m 2 (95% CI, -3.91~-0.94); p for trend < 0.01) after multivariate adjustment including baseline eGFR. In addition, each 1 SD increase in GDF-15 was associated with a decline in eGFR of -0.97 mL/min/1.73 m 2 (95% CI, -1.49~-0.45; p < 0.001) over the follow-up period. Results were consistent in participants with baseline eGFR ≥ 60 mL/min/1.73 m 2 (N = 2,713; B=-0.92; 95% CI, -1.49~-0.36, p < 0.01) (data not shown). Table 3 Change in estimated glomerular filtration rate in relation to baseline growth differentiation factor-15 (GDF-15). No. of subjects B (95% CI) a p GDF-15 Q1 790 Reference - GDF-15 Q2 763 -1.04 (-2.30, 0.22) 0.10 GDF-15 Q3 646 -1.67 (-3.01, -0.32) 0.02 GDF-15 Q4 545 -2.42 (-3.91, -0.94) < 0.01 Per 1 standard deviation change in GDF-15 2,744 -0.97 (-1.49, -0.45) < 0.001 CI, confidence interval; eGFR, estimated glomerular filtration rate according to the combined Chronic Kidney Disease Epidemiology Collaboration creatinine and cystatin C equation. a Adjusted for waist circumference, smoking, high alcohol consumption, systolic blood pressure, low-density lipoprotein cholesterol, C-reactive protein level, diabetes, cardiovascular disease, anti-hypertensive drug medication, and baseline estimated glomerular filtration rate. Out of the 2,713 individuals with baseline eGFR ≥ 60 mL/min/1.73 m 2 , 862 developed CKD, as defined by eGFR < 60 mL/min/1.73 m 2 . After adjusting for covariates including baseline eGFR, the odds ratio (OR) comparing Q4 vs Q1 of GDF-15 was 1.41 (95% CI, 1.06 ~ 1.89; p for trend = 0.02) for developing eGFR-based CKD. The corresponding OR was 1.15 (95% CI, 1.04 ~ 1.28) per 1 SD increase in GDF-15 (data not shown). Discussion Our findings suggest that GDF-15 is associated with increased incidence of CKD and eGFR decline. The association was independent of baseline eGFR, smoking, waist circumference, CRP, etc. , and remained after controlling for competing risk of death. GDF-15, therefore, may be a useful marker of increased risk of CKD. GDF-15 is a distant member of the transforming growth factor-β (TGF-β) family [ 25 ]. It has consistently been associated with deterioration of kidney function or adverse outcomes in patients with existing kidney diseases [ 9 – 12 ]. GDF-15 has also been investigated in relation to the development of CKD [ 7 , 8 ], yet the association has remained uncertain. Carlsson, et al. used a discovery cohort (the PIVUS Study; N = 687, mean age = 70 y) and a replication cohort (the ULSAM study; N = 360, mean age = 78 y) to identify predictors of eGFR decline from 80 CVD biomarkers [ 8 ]. Whereas GDF-15 was initially observed to be associated with eGFR decline during a 5-year follow-up period, the association disappeared after adjusting for eGFR at baseline. The validity of this study may be affected by the relatively small sample size, the elderly population and potential survival bias. Results from the Framingham Offspring cohort study (N = 2,614, mean follow-up period = 9.5 y) [ 7 ] suggested a superior predictive value of GDF-15 for incident CKD (estimated by one eGFR measurement). The association of GDF-15 with CKD was independent of baseline eGFR. Even though the exact production rate and the kidney clearance rate is currently not available in our understanding of GDF-15, as a 24.5-kDa active circulating dimeric protein [ 26 ], levels of GDF-15 may largely depend upon renal excretory function for elimination. In support of this view, GDF-15 concentration negatively correlated with eGFR in the current and previous studies [ 8 , 13 ], and was decreased by kidney function improvement through kidney transplantation [ 27 ]. Nevertheless, a strong association of GDF-15 with incident CKD was observed in our study and the study by Ho, et al [ 7 ], even after adjustment for baseline eGFR, which may abolish some of the doubts about the kidney elimination dependence. Noteworthily, some potential confounders were not taken into account in the study by Ho, et al [ 7 ]. We confirmed previous findings [ 14 ], suggesting that smoking, obesity, hs-CRP, etc. are strongly correlated with GDF-15 levels, and these are factors that also have been associated with CKD [ 28 – 30 ]. Our finding provides added evidence that GDF-15 was significantly associated with incidence of CKD and eGFR decline even after adjustments for baseline eGFR as well as other potential risk factors. Dysfunction of the heart or kidney could potentially induce dysfunction of the other organ [ 1 , 5 , 6 ], known as a pathologic condition termed the cardiorenal syndrome [ 31 ]. In our sensitivity analysis, GDF-15 and NT-proBNP were mutually adjusted for each other in the multivariate model. GDF-15 but not NT-proBNP remained significantly associated with CKD and eGFR decline, suggesting that GDF-15 might be more specific to kidney outcomes than other CVD biomarkers. Moreover, the competing risk of death is usually high in studies on geriatric populations or with long-term follow-ups, and may largely bias the findings [ 32 , 33 ]. We demonstrated for the first time that the fully-adjusted association of GDF-15 with eGFR decline and CKD remained after controlling for death as a competing risk. The kidney can be an important source of GDF-15. In kidney tissue from adult rats, GDF-15 mRNA expression was mainly detected in the S3 segment of the nephron and the collecting ducts by in situ hybridization [ 34 ]. In response to stimuli such as surgery, toxin, ischemia, and hyperoxia, GDF-15 can be immediately induced in kidney, possibly through TNF- and p53-dependent and -independent pathways, and acts as a regulator of inflammation, cell survival, proliferation, and apoptosis [ 35 ]. Duong et al., observed that when stimulated by metabolic acidosis, GDF-15 expression was strongly induced in mouse kidney outer medullary collecting duct [ 36 ]. The increased GDF-15 played a key role in collecting duct lengthening by triggering compensatory proliferation of acid-secreting intercalated cells [ 36 ]. In contrast, genetic deletion of GDF-15 aggravated tubular and interstitial injury, which resulted in glycosuria and polyuria in mice with diabetes [ 37 ]. Meanwhile, epidemiological evidence showed that circulating levels of GDF-15 were closely correlated with mRNA expression of GDF-15 in renal tubulointerstitium, and significantly predicted risk of disease progression in patients with CKD [ 11 ]. Similarly, urinary GDF-15 was tightly linked with proximal tubule damage and kidney function decline in diabetic patients [ 38 ]. Based on the above-mentioned evidence, it is speculated that the observed association between plasma GDF-15 and incident CKD might at least partly be due to enhanced GDF-15 expression in kidney as a protective response against early-stage renal damage. Strengths of this study included a prospective study design and a long-term follow-up. In addition, endpoints were retrieved from hospital registers with national coverage and a high case validity of the CKD diagnosis, as well as based on eGFR determined from a combination of plasma creatinine and cystatin C. There are several limitations that need to be mentioned. Our findings were descriptive in nature and provided limited information on mechanisms. CKD was diagnosed on one eGFR measurement at baseline and follow-up, respectively. Data on albuminuria was not available, but this did not affect the diagnosis of CKD based on eGFR < 60 mL/min/1.73 m 2 . CKD cases identified among hospital registers may not include less severe cases which were treated in primary care. However, kidney function was also measured based on eGFR, and negative associations of GDF-15 with eGFR decline and eGFR-based CKD were consistent. Another important limitation is that the calculation of eGFR change may be influenced since the methods of measuring cystatin C and creatinine at baseline were different from those at the follow-up. Nevertheless, we expect that any bias introduced by these different measurements should be non-differential in relation to GDF-15 levels, and a greater decline in eGFR from baseline to follow-up could still reflect deteriorated kidney function. Therefore, we consider that the association between GDF-15 and eGFR decline was valid. Conclusions In conclusion, this study indicated that elevated GDF-15 levels predicted incidence of CKD and eGFR decline in the general population, independent of a wide range of potential risk factors and competing risk of death. Abbreviations AU: arbitrary units; CI: confidence interval; CKD: chronic kidney disease; CRP: C-reactive protein; eGFR: estimated glomerular filtration rate; ESRD: end-stage renal disease; GDF-15: growth differentiation factor 15; HR hazard ratio; IDMS: isotope-dilution mass spectrometry-traceable; LDL: Low density lipoprotein; MDC-CV: Malmö Diet and Cancer Cardiovascular; NT-proBNP N-terminal pro-B-type natriuretic peptide; OR: odds ratio; SD: standard deviation; TGF-β: transforming growth factor-β. Declarations Ethics approval and consent to participate: All procedures performed in this study were approved by the ethics committee at Lund University Lund, Sweden (LU 51/90). Written informed consent was obtained from all individual participants included in the study. Consent for publication: Not applicable. Availability of data and materials : The data that support the findings of this study are available from Lund University, but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of Lund University. Competing interests: The authors declare that they have no competing interests. Funding: This work was supported by the Swedish Heart–Lung Foundation (2016-0315, 2017-0626); Lund University Infrastructure grant “Malmö population-based cohorts” (STYR 2019/2046); the Natural Science Foundation of Jiangsu Province (BK20200128); and the Fundamental Research Funds for the Central Universities (0214-14380474). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Author’s Contributions: XB, BX and GE contributed to the conception or design of the work. XB and YB contributed to the acquisition, analysis, or interpretation of data for the work. XB drafted the manuscript. BX, YB, MOM, OM, JN, AC and GE critically revised the manuscript. All authors reviewed and approved the final manuscript. Acknowledgments: We thank all the participants in this study and the staff of the department of Clinical Chemistry at University Hospital, Malmö. We also would like to acknowledge the Swedish Patient Registry and the Swedish Renal Registry for providing information about renal outcomes and the Clinical biomarker facility at SciLifeLab Sweden for providing assistance in protein analyses. References Collaboration GCKD. Global, regional, and national burden of chronic kidney disease, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2020; 395:709-33. Himmelfarb J, Ikizler TA. Hemodialysis. N Engl J Med. 2010; 363:1833-45. Stevens PE, Levin A, Members KDIGOCKDGDWG. Evaluation and management of chronic kidney disease: synopsis of the kidney disease: improving global outcomes 2012 clinical practice guideline. Ann Intern Med. 2013; 158:825-30. Berns JS. Nutritional Management of Chronic Kidney Disease. N Engl J Med. 2018; 378:584. Sarnak MJ, Levey AS, Schoolwerth AC, Coresh J, Culleton B, Hamm LL, McCullough PA, Kasiske BL, Kelepouris E, Klag MJ et al . Kidney disease as a risk factor for development of cardiovascular disease: a statement from the American Heart Association Councils on Kidney in Cardiovascular Disease, High Blood Pressure Research, Clinical Cardiology, and Epidemiology and Prevention. Circulation. 2003; 108:2154-69. Damman K, Testani JM. The kidney in heart failure: an update. Eur Heart J. 2015; 36:1437-44. Ho JE, Hwang SJ, Wollert KC, Larson MG, Cheng S, Kempf T, Vasan RS, Januzzi JL, Wang TJ, Fox CS. Biomarkers of cardiovascular stress and incident chronic kidney disease. Clin Chem. 2013; 59:1613-20. Carlsson AC, Ingelsson E, Sundstrom J, Carrero JJ, Gustafsson S, Feldreich T, Stenemo M, Larsson A, Lind L, Arnlov J. Use of Proteomics To Investigate Kidney Function Decline over 5 Years. Clin J Am Soc Nephrol. 2017; 12:1226-35. Lajer M, Jorsal A, Tarnow L, Parving HH, Rossing P. Plasma growth differentiation factor-15 independently predicts all-cause and cardiovascular mortality as well as deterioration of kidney function in type 1 diabetic patients with nephropathy. Diabetes Care. 2010; 33:1567-72. Breit SN, Carrero JJ, Tsai VW, Yagoutifam N, Luo W, Kuffner T, Bauskin AR, Wu L, Jiang L, Barany P et al . Macrophage inhibitory cytokine-1 (MIC-1/GDF15) and mortality in end-stage renal disease. Nephrol Dial Transplant. 2012; 27:70-5. Nair V, Robinson-Cohen C, Smith MR, Bellovich KA, Bhat ZY, Bobadilla M, Brosius F, de Boer IH, Essioux L, Formentini I et al . Growth Differentiation Factor-15 and Risk of CKD Progression. J Am Soc Nephrol. 2017; 28:2233-40. Kastritis E, Papassotiriou I, Merlini G, Milani P, Terpos E, Basset M, Akalestos A, Russo F, Psimenou E, Apostolakou F et al . Growth differentiation factor-15 is a new biomarker for survival and renal outcomes in light chain amyloidosis. Blood. 2018; 131:1568-75. Kim JS, Kim S, Won CW, Jeong KH. Association between Plasma Levels of Growth Differentiation Factor-15 and Renal Function in the Elderly: Korean Frailty and Aging Cohort Study. Kidney Blood Press Res. 2019; 44:405-14. Bao X, Borne Y, Muhammad IF, Nilsson J, Lind L, Melander O, Niu K, Orho-Melander M, Engstrom G. Growth differentiation factor 15 is positively associated with incidence of diabetes mellitus: the Malmo Diet and Cancer-Cardiovascular Cohort. Diabetologia. 2019; 62:78-86. Berglund G, Elmstahl S, Janzon L, Larsson SA. The Malmo Diet and Cancer Study. Design and feasibility. J Intern Med. 1993; 233:45-51. Ludvigsson JF, Andersson E, Ekbom A, Feychting M, Kim JL, Reuterwall C, Heurgren M, Olausson PO. External review and validation of the Swedish national inpatient register. BMC Public Health. 2011; 11:450. Rosvall M, Persson M, Ostling G, Nilsson PM, Melander O, Hedblad B, Engstrom G. Risk factors for the progression of carotid intima-media thickness over a 16-year follow-up period: the Malmo Diet and Cancer Study. Atherosclerosis. 2015; 239:615-21. Lundberg M, Eriksson A, Tran B, Assarsson E, Fredriksson S. Homogeneous antibody-based proximity extension assays provide sensitive and specific detection of low-abundant proteins in human blood. Nucleic Acids Res. 2011; 39:e102. Qureshi AR, Evans M, Stendahl M, Prutz KG, Elinder CG. The increase in renal replacement therapy (RRT) incidence has come to an end in Sweden-analysis of variations by region over the period 1991-2010. Clin Kidney J. 2013; 6:352-7. Harari F, Sallsten G, Christensson A, Petkovic M, Hedblad B, Forsgard N, Melander O, Nilsson PM, Borne Y, Engstrom G et al . Blood Lead Levels and Decreased Kidney Function in a Population-Based Cohort. Am J Kidney Dis. 2018; 72:381-9. Kidney Disease: Improving Global Outcomes (KDIGO) CKD Work Group. KDIGO 2012 clinical practice guideline for the evaluation and management of chronic kidney disease. Kidney Int Suppl 2013; 3: 1-150. Inker LA, Schmid CH, Tighiouart H, Eckfeldt JH, Feldman HI, Greene T, Kusek JW, Manzi J, Van Lente F, Zhang YL et al . Estimating glomerular filtration rate from serum creatinine and cystatin C. N Engl J Med. 2012; 367:20-9. Nyman U, Grubb A, Larsson A, Hansson LO, Flodin M, Nordin G, Lindstrom V, Bjork J. The revised Lund-Malmo GFR estimating equation outperforms MDRD and CKD-EPI across GFR, age and BMI intervals in a large Swedish population. Clin Chem Lab Med. 2014; 52:815-24. Grubb A, Horio M, Hansson LO, Bjork J, Nyman U, Flodin M, Larsson A, Bokenkamp A, Yasuda Y, Blufpand H et al . Generation of a new cystatin C-based estimating equation for glomerular filtration rate by use of 7 assays standardized to the international calibrator. Clin Chem. 2014; 60:974-86. Bootcov MR, Bauskin AR, Valenzuela SM, Moore AG, Bansal M, He XY, Zhang HP, Donnellan M, Mahler S, Pryor K et al . MIC-1, a novel macrophage inhibitory cytokine, is a divergent member of the TGF-beta superfamily. Proc Natl Acad Sci U S A. 1997; 94:11514-9. Desmedt S, Desmedt V, De Vos L, Delanghe JR, Speeckaert R, Speeckaert MM. Growth differentiation factor 15: A novel biomarker with high clinical potential. Crit Rev Clin Lab Sci. 2019; 56:333-50. Connelly PW, Yan AT, Nash MM, Lok CE, Gunaratnam L, Prasad GVR. Growth differentiation factor 15 is decreased by kidney transplantation. Clin Biochem. 2019; 73:57-61. Jones-Burton C, Seliger SL, Scherer RW, Mishra SI, Vessal G, Brown J, Weir MR, Fink JC. Cigarette smoking and incident chronic kidney disease: a systematic review. Am J Nephrol. 2007; 27:342-51. Garofalo C, Borrelli S, Minutolo R, Chiodini P, De Nicola L, Conte G. A systematic review and meta-analysis suggests obesity predicts onset of chronic kidney disease in the general population. Kidney Int. 2017; 91:1224-35. Cachofeiro V, Goicochea M, de Vinuesa SG, Oubina P, Lahera V, Luno J. Oxidative stress and inflammation, a link between chronic kidney disease and cardiovascular disease. Kidney Int Suppl. 2008:S4-9. Wang J, Zhang W, Wu L, Mei Y, Cui S, Feng Z, Chen X. New insights into the pathophysiological mechanisms underlying cardiorenal syndrome. Aging (Albany NY). 2020; 12:12422-31. Berry SD, Ngo L, Samelson EJ, Kiel DP. Competing risk of death: an important consideration in studies of older adults. J Am Geriatr Soc. 2010; 58:783-7. Ensrud KE, Harrison SL, Cauley JA, Langsetmo L, Schousboe JT, Kado DM, Gourlay ML, Lyons JG, Fredman L, Napoli N et al . Impact of Competing Risk of Mortality on Association of Weight Loss With Risk of Central Body Fractures in Older Men: A Prospective Cohort Study. J Bone Miner Res. 2017; 32:624-32. Unsicker K, Spittau B, Krieglstein K. The multiple facets of the TGF-beta family cytokine growth/differentiation factor-15/macrophage inhibitory cytokine-1. Cytokine Growth Factor Rev. 2013; 24:373-84. Zimmers TA, Jin X, Hsiao EC, McGrath SA, Esquela AF, Koniaris LG. Growth differentiation factor-15/macrophage inhibitory cytokine-1 induction after kidney and lung injury. Shock. 2005; 23:543-8. Duong Van Huyen JP, Cheval L, Bloch-Faure M, Belair MF, Heudes D, Bruneval P, Doucet A. GDF15 triggers homeostatic proliferation of acid-secreting collecting duct cells. J Am Soc Nephrol. 2008; 19:1965-74. Mazagova M, Buikema H, van Buiten A, Duin M, Goris M, Sandovici M, Henning RH, Deelman LE. Genetic deletion of growth differentiation factor 15 augments renal damage in both type 1 and type 2 models of diabetes. Am J Physiol Renal Physiol. 2013; 305:F1249-64. Simonson MS, Tiktin M, Debanne SM, Rahman M, Berger B, Hricik D, Ismail-Beigi F. The renal transcriptome of db/db mice identifies putative urinary biomarker proteins in patients with type 2 diabetes: a pilot study. Am J Physiol Renal Physiol. 2012; 302:F820-9. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Major revision 07 Jun, 2021 Reviews received at journal 20 Apr, 2021 Reviewers agreed at journal 20 Apr, 2021 Reviewers invited by journal 15 Apr, 2021 Editor assigned by journal 13 Apr, 2021 Editor invited by journal 07 Apr, 2021 Submission checks completed at journal 07 Apr, 2021 First submitted to journal 05 Apr, 2021 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-394958","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":20220951,"identity":"cb351be4-5dc5-4314-941d-0e9ace748e90","order_by":0,"name":"Xue Bao","email":"","orcid":"","institution":"Drum Tower Hospital, Medical School of Nanjing University","correspondingAuthor":false,"prefix":"","firstName":"Xue","middleName":"","lastName":"Bao","suffix":""},{"id":20220952,"identity":"3c0cf390-ac3f-4e82-acce-d17cfef24060","order_by":1,"name":"Biao Xu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3klEQVRIie3RsQrCMBCA4ROhXSKuKajPECgUoX2YBKEubecOgpkyCa59DMEXSA3oUveCzk4iurkotk5OIW6C+beD+7jhAGy2nwyBBIhGfQDaTF1jEvse/4Y0bdhKvicDQuq0lPdcdtYHfsKQh4y7e6klXpHRclEdu8FRxhiqKeMoo1rSxwmRPXFygprGuCMU4xgRLXEaUj6EQn7RkqcBaa+onlCY4JZwA+ItLlQNqpjgmk7GdDv1BUr0hOxSdTvn0XxZJKy+zsLh0q305CNE3890TPebXPnFss1ms/1TL2yZSBhQ6uTCAAAAAElFTkSuQmCC","orcid":"","institution":"Drum Tower Hospital, Medical School of Nanjing University","correspondingAuthor":true,"prefix":"","firstName":"Biao","middleName":"","lastName":"Xu","suffix":""},{"id":20220953,"identity":"80a9d8a7-68b3-4582-b63d-ed16a4761984","order_by":2,"name":"Yan Borné","email":"","orcid":"","institution":"Lund University","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Borné","suffix":""},{"id":20220954,"identity":"e479fc31-cdd5-48c6-96da-1a025544e870","order_by":3,"name":"Marju Orho-Melander","email":"","orcid":"","institution":"Lund University","correspondingAuthor":false,"prefix":"","firstName":"Marju","middleName":"","lastName":"Orho-Melander","suffix":""},{"id":20220959,"identity":"bf0b1747-5823-4ede-b38f-87e85edfa26c","order_by":4,"name":"Olle Melander","email":"","orcid":"","institution":"Lund University","correspondingAuthor":false,"prefix":"","firstName":"Olle","middleName":"","lastName":"Melander","suffix":""},{"id":20220960,"identity":"57512dd6-5b22-4747-8081-cfdf8c41453a","order_by":5,"name":"Jan Nilsson","email":"","orcid":"","institution":"Lund University","correspondingAuthor":false,"prefix":"","firstName":"Jan","middleName":"","lastName":"Nilsson","suffix":""},{"id":20220964,"identity":"a1473260-8f43-4364-b5b4-949c650b2601","order_by":6,"name":"Anders Christensson","email":"","orcid":"","institution":"Lund University","correspondingAuthor":false,"prefix":"","firstName":"Anders","middleName":"","lastName":"Christensson","suffix":""},{"id":20220965,"identity":"1ec4db3a-41d1-46c3-a673-1c8ca8a19ad9","order_by":7,"name":"Gunnar Engström","email":"","orcid":"","institution":"Lund University","correspondingAuthor":false,"prefix":"","firstName":"Gunnar","middleName":"","lastName":"Engström","suffix":""}],"badges":[],"createdAt":"2021-04-05 08:29:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-394958/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-394958/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":7795293,"identity":"17f34dfc-9064-4fa9-a8dd-9546523a50a6","added_by":"auto","created_at":"2021-04-08 14:35:11","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":82896,"visible":true,"origin":"","legend":"Population flow chart.","description":"","filename":"fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-394958/v1/bce735977fda145a3c6a4741.jpg"},{"id":13684457,"identity":"4067c783-46b0-475e-b990-b83640c8cc77","added_by":"auto","created_at":"2021-09-17 12:08:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":505895,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-394958/v1/9d5223b3-f54a-43e5-823f-0bf2e553ddce.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Growth Differentiation Factor-15 and Incident Chronic Kidney Disease: A Population-Based Cohort Study","fulltext":[{"header":"Introduction","content":" \u003cp\u003eAs estimated recently, around 9.1% of the global population are suffering from chronic kidney disease (CKD), accounting for 35.8\u0026nbsp;million disability-adjusted life-years and 1.2\u0026nbsp;million deaths [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Treatment of CKD can be costly, in particular for patients with end-stage renal disease (ESRD), which poses a considerable financial burden to families and health systems [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Fortunately, CKD is preventable with early detection and timely intervention [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Efforts have thus been made to develop efficient screening strategies for CKD. In this regard, biomarkers have drawn increasing attention as they may not only help identify high-risk individuals but also provide insights into mechanism of kidney injury.\u003c/p\u003e \u003cp\u003eCKD is a major risk factor for CVD [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], and vice versa, cardiac dysfunction could also lead to kidney injury [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Due to the interdependency of heart and kidney, several studies have explored kidney disease in relation to cardiovascular biomarkers, among which the predictive value of growth differentiation factor 15 (GDF-15) was recognized [\u003cspan additionalcitationids=\"CR8 CR9 CR10 CR11 CR12\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Most available evidence has focused on patients with existing kidney pathology [\u003cspan additionalcitationids=\"CR10 CR11\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. For instance, GDF-15 was found to predict estimated glomerular filtration rate (eGFR) decline and mortality in type 1 diabetic patients with nephropathy [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], mortality in ESRD [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], eGFR decline and progression to ESRD in CKD [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], and progression to dialysis and mortality in light chain amyloidosis [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Cross-sectionally, GDF-15 was negatively associated with eGFR and was higher in the elderly with than without CKD [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. However, community-based data regarding kidney function decline in relation to GDF-15 are scarce. To our knowledge, there were only two relevant studies. Participants in the study by Carlsson, et al. [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] were limited to elderly people. The authors demonstrated that GDF-15 did not predict decline of eGFR independently of baseline eGFR. In contrast, Ho, et al. [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] reported a positive association of GDF-15 with incident CKD, but some potential confounders (smoking, obesity, C-reactive protein, \u003cem\u003eetc.\u003c/em\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]) were not adjusted for. In addition, incident CKD was identified by calculation of eGFR\u0026thinsp;\u0026lt;\u0026thinsp;60 mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e, and no information about clinical diagnoses were included.\u003c/p\u003e \u003cp\u003eTherefore, we aimed to investigate the association of GDF-15 with incident CKD, as obtained from national registers, as well as from eGFR calculation. Analyses were performed in a prospective study with a large general population sample and a long-term follow-up, while taking into account potential baseline confounders and competing risk from death.\u003c/p\u003e "},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eThe Malm\u0026ouml; Diet and Cancer Cardiovascular (MDC-CV) cohort study\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe MDCS is a large prospective cohort study with participants recruited from Malm\u0026ouml;, a city in southern Sweden [\u003ca href=\"#_ENREF_15\"\u003e15\u003c/a\u003e]. During 1991-1994, a random sample of 6,103 participants was taken from MDCS to investigate the epidemiology of carotid artery atherosclerosis (MDC-CV cohort study) [\u003ca href=\"#_ENREF_14\"\u003e14\u003c/a\u003e]. Among them, 5,540 donated fasting blood samples.\u003c/p\u003e\n\u003cp\u003eWe excluded participants with missing baseline data on eGFR, other covariates or GDF-15, or participants with previously diagnosed CKD or lost to follow-up. Therefore, 4,318 participants (\u003cstrong\u003eFigure 1\u003c/strong\u003e, mean aged 57.5 \u0026plusmn; 5.95 years, male 39.4 %) remained for analyses of incident CKD, as detected by national registers of hospital inpatients and outpatients [\u003ca href=\"#_ENREF_16\"\u003e16\u003c/a\u003e]. During 2007-2012, MDC-CV participants who were still alive and living in the Malm\u0026ouml; area were invited to a re-examination. A total of 3,734 attended, which corresponds to 75.8% of the eligible population [\u003ca href=\"#_ENREF_17\"\u003e17\u003c/a\u003e]. Among the 4318 individuals in this study, 2,827 attended re-examination and 2,744 had follow-up data available for eGFR. This sub-cohort study was then analyzed for decline in eGFR (\u003cstrong\u003eFigure 1\u003c/strong\u003e). Incident CKD based on eGFR was further analyzed as the outcome in 2,713 participants with baseline eGFR \u0026ge;60 mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e (\u003cstrong\u003eFigure 1\u003c/strong\u003e). Written informed consent was obtained from all included participants. The study conformed to the Declaration of Helsinki and was approved by the ethical committee at Lund University, Lund, Sweden (LU 51/90).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGDF-15 measurement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFasting blood samples were collected from the cubital vein and stored at \u0026minus;80\u0026deg;C until assay. GDF-15 levels were measured by the SciLifeLab analysis service (Uppsala, Sweden) using Proseek\u0026reg; Multiplex CVD I\u003csup\u003e96\u0026times;96\u003c/sup\u003e reagent kit where a Proximity Extension Assay technology was applied [\u003ca href=\"#_ENREF_14\"\u003e14\u003c/a\u003e, \u003ca href=\"#_ENREF_18\"\u003e18\u003c/a\u003e]. Briefly, the assay procedure consisted of three key steps: incubation, extension and detection. Raw Proseek data went through a pre-processing normalization procedure and were set relative to a fixed background level, after which Normalized Protein Expression (log2 scale) values were generated, measured in arbitrary units (AU). High AU values corresponded to a high protein concentration. GDF-15 levels in 987 subjects measured by Proseek assay closely correlated (\u003cem\u003er \u003c/em\u003e=0.89 [L. Lind, unpublished data]) with the values by an electrochemiluminescence immunoassay (Roche Diagnostics, Mannheim, Germany) [\u003ca href=\"#_ENREF_14\"\u003e14\u003c/a\u003e].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCKD based on the ICD codes from the national register\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformation on CKD diagnosis was obtained from the Swedish patient register with nation-wide coverage. Moreover, the Swedish renal registry was searched for any additional cases of CKD \u003csup\u003e[\u003c/sup\u003e\u003ca href=\"#_ENREF_19\"\u003e\u003csup\u003e19\u003c/sup\u003e\u003c/a\u003e\u003csup\u003e]\u003c/sup\u003e. CKD was defined as codes 585-586 according to ICD-9, and N18 and N19 according to ICD-10. All participants without any previous diagnosis of CKD were followed from baseline until the occurrence of a diagnosis of CKD (registry-based CKD), emigration from Sweden, death or December 31st, 2013, whichever came first.\u003c/p\u003e\n\u003cp\u003eThe CKD diagnosis in the Swedish patient register has been previously described and validated [\u003ca href=\"#_ENREF_20\"\u003e20\u003c/a\u003e]. Briefly, for validation, CKD diagnoses were evaluated by two experienced specialists in nephrology. Patient records and laboratory data were reviewed and CKD cases were defined following the 2012 KDIGO criteria [\u003ca href=\"#_ENREF_21\"\u003e21\u003c/a\u003e]. Validation showed that 94% of patients had a correct diagnosis of CKD [\u003ca href=\"#_ENREF_20\"\u003e20\u003c/a\u003e].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCKD based on eGFR, and eGFR decline from baseline to follow-up\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe eGFR at baseline and follow-up was determined from a combination of plasma creatinine and cystatin C using the CKD-Epidemiology Collaboration 2012 equation [\u003ca href=\"#_ENREF_22\"\u003e22\u003c/a\u003e]. Single measurements of eGFR were assessed at each time point. A cut-off value of 60 mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e was used to identify participants with eGFR-based CKD [\u003ca href=\"#_ENREF_20\"\u003e20\u003c/a\u003e]. The difference between these two measurements was defined as eGFR change.\u003c/p\u003e\n\u003cp\u003eAt baseline, creatinine and cystatin C were analyzed with the Jaff\u0026eacute; method (Beckman Synchron LX20-4; Beckman-Coulter) and with a particle-enhanced immunonephelometry assay (N Latex Cystatin; Dade Behring, Deerfield, IL), respectively. Since the world calibrator was not introduced until 2010, cystatin C values were not standardized (reference value: 0.53~0.95 mg/L). During 2007-2012, creatinine was determined in follow-up samples using an enzymatic method (Cobas autoanalyzer; Roche Diagnostics) calibrated by isotope-dilution mass spectrometry-traceable (IDMS) creatinine [\u003ca href=\"#_ENREF_23\"\u003e23\u003c/a\u003e], and cystatin C was analyzed using an automated particle-based immunoassay, adjusted to the international reference preparation ERM-DA 71/IFCC.38 T [\u003ca href=\"#_ENREF_24\"\u003e24\u003c/a\u003e]. Therefore, values of creatinine and cystatin C could not be directly compared between baseline and follow-up.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOther variables and definitions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBaseline characteristics were obtained from self-administered questionnaire, physical examination, and blood measurements. Data on medication, smoking habits and alcohol consumption were collected by questionnaires. Participants were classified into current smokers, former smokers and never smokers. An average daily alcohol consumption \u0026gt;40 g for males or \u0026gt;30 g for females was considered as high alcohol consumption. Waist circumference was determined as being midpoint between the end of the 12\u003csup\u003eth\u003c/sup\u003e rib and the iliac crest. Blood pressure was measured with a mercury-column sphygmomanometer after 10 min of rest while the subject was in a supine position. Participant with a history of coronary event or stroke was considered to have CVD at baseline.\u003c/p\u003e\n\u003cp\u003eGlucose concentration was measured in fresh whole blood samples after an overnight fasting, following standard procedures at the Department of Clinical Chemistry, University Hospital Malm\u0026ouml;. Diabetes was defined as self-reported physician diagnosis of diabetes, use of anti-diabetic drugs or fasting whole blood glucose \u0026ge;6.1 mmol/L (corresponding to plasma glucose \u0026ge;7.0 mmol/L). Low density lipoprotein (LDL) concentration was estimated using the Friedewald\u0026rsquo;s formula. Measurements of biomarkers were conducted later using frozen (\u0026minus;80\u0026deg;C) plasma samples. C-reactive protein (CRP) was measured with a Tina-quant\u0026reg; CRP latex assay (Roche Diagnostics, Basel, Switzerland). Methods to measure N-terminal pro-B-type natriuretic peptide (NT-proBNP) levels was the same way as that for GDF-15 [\u003ca href=\"#_ENREF_14\"\u003e14\u003c/a\u003e, \u003ca href=\"#_ENREF_18\"\u003e18\u003c/a\u003e].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBaseline characteristics are presented for participants divided into quartiles (Q1-Q4) according to GDF-15 concentration, using sex-specific quartile limits. For skewed variables, log-transformation was performed to achieve a normal distribution. Differences across GDF-15 quartiles were examined using analysis of variance for continuous variables and logistic regression analysis for categorized variables.\u003c/p\u003e\n\u003cp\u003eCox proportional hazard regression was used to analyze the association between baseline GDF-15 and incident CKD discovered by the national register. Hazard ratios (HRs) and 95% confidential intervals (CIs) were obtained. GDF-15 was treated both as a continuous variable (per standard deviation (SD) change) and as a categorized variable (in quartiles). In multivariate-adjusted models, potential covariates taken into consideration were age, sex, waist circumference, smoking, high alcohol consumption, systolic blood pressure, LDL, CRP, diabetes, CVD, anti-hypertensive drug medication, and baseline eGFR. Since GDF-15 has been frequently considered as a cardiovascular biomarker in recent years, NT-proBNP, a traditional cardiovascular marker was additionally adjusted for in a sensitivity analysis to explore whether the association of GDF-15 with CKD could be mediated by cardiac function. A restricted cubic spline function was incorporated into the Cox model to test for possible non-linearity, with knots placed at 20, 40, 60 and 80 percentages of GDF-15 concentration. Possible effect modifications were examined by introducing an interaction term between GDF-15 levels and risk factors into the multivariate model one by one. The competing risks of death was accounted for in a sensitivity analysis by the Fine and Gray proportional subdistribution hazards models method. In another sensitivity analysis, the association between GDF-15 and CKD was analyzed while participants with baseline eGFR \u0026lt;60 mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e were excluded. In addition, for participants with follow-up data available for eGFR, multiple linear regression was used to analyze the association between GDF-15 and eGFR change from baseline to the end of the follow-up. A multiple logistic regression analysis was conducted for the association between GDF-15 and eGFR-based CKD.\u003c/p\u003e\n\u003cp\u003eAll analyses were performed using the Statistical Analysis System version 9.3 for Windows (SAS Institute Inc., Cary, NC, USA). A 2-tailed \u003cem\u003ep \u003c/em\u003e\u0026lt;0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":" \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eBaseline Characteristics\u003c/h2\u003e \u003cp\u003eThe mean GDF-15 concentration in the cohort was 8.75\u0026thinsp;\u0026plusmn;\u0026thinsp;0.56 AU and mean eGFR was 89.2\u0026thinsp;\u0026plusmn;\u0026thinsp;0.56 mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e. The clinical and biochemical characteristics of the population across GDF-15 quartiles are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. As compared to participants with relatively low GDF-15 concentration, those with higher GDF-15 concentration tended to have decreased eGFR at baseline. An increasing trend was observed for most of the other covariates, except for sex and high alcohol consumption.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of individuals across quartiles (Q1-Q4) of growth differentiation factor-15 (GDF-15).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eGDF15 quartiles\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e for trend \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ1 (n\u0026thinsp;=\u0026thinsp;1080)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ2 (n\u0026thinsp;=\u0026thinsp;1079)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ3 (n\u0026thinsp;=\u0026thinsp;1080)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ4 (n\u0026thinsp;=\u0026thinsp;1079)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDF-15 (AU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.90\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.47\u0026thinsp;\u0026plusmn;\u0026thinsp;0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54.6\u0026thinsp;\u0026plusmn;\u0026thinsp;5.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56.6\u0026thinsp;\u0026plusmn;\u0026thinsp;5.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58.6\u0026thinsp;\u0026plusmn;\u0026thinsp;5.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e60.1\u0026thinsp;\u0026plusmn;\u0026thinsp;5.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (male, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e426 (9.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e425 (9.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e426 (9.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e425 (9.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaist circumference (cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e81.6\u0026thinsp;\u0026plusmn;\u0026thinsp;11.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e82.3\u0026thinsp;\u0026plusmn;\u0026thinsp;12.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e83.5\u0026thinsp;\u0026plusmn;\u0026thinsp;12.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e85.3\u0026thinsp;\u0026plusmn;\u0026thinsp;13.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFasting glucose (mmol/L) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.80 (4.60\u0026ndash;5.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.90 (4.60\u0026ndash;5.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.90 (4.60\u0026ndash;5.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.00 (4.70\u0026ndash;5.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystolic blood pressure (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e136.1\u0026thinsp;\u0026plusmn;\u0026thinsp;16.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e140\u0026thinsp;\u0026plusmn;\u0026thinsp;18.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e142.4\u0026thinsp;\u0026plusmn;\u0026thinsp;19.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e144.7\u0026thinsp;\u0026plusmn;\u0026thinsp;19.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiastolic blood pressure (mmHg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e85.6\u0026thinsp;\u0026plusmn;\u0026thinsp;8.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e86.6\u0026thinsp;\u0026plusmn;\u0026thinsp;9.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e86.9\u0026thinsp;\u0026plusmn;\u0026thinsp;9.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e87.5\u0026thinsp;\u0026plusmn;\u0026thinsp;9.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow-density lipoprotein cholesterol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.05\u0026thinsp;\u0026plusmn;\u0026thinsp;0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.24\u0026thinsp;\u0026plusmn;\u0026thinsp;1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eC-reactive protein (mg/L) \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.00 (0.50\u0026ndash;1.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.10 (0.60\u0026ndash;2.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.40 (0.70\u0026ndash;2.90)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.90 (1.00-4.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR (mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e94.3\u0026thinsp;\u0026plusmn;\u0026thinsp;12.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e90.5\u0026thinsp;\u0026plusmn;\u0026thinsp;12.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e88.1\u0026thinsp;\u0026plusmn;\u0026thinsp;12.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e83.8\u0026thinsp;\u0026plusmn;\u0026thinsp;14.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN-terminal pro-B-type natriuretic peptide (AU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.31\u0026thinsp;\u0026plusmn;\u0026thinsp;0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.06\u0026thinsp;\u0026plusmn;\u0026thinsp;0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.26\u0026thinsp;\u0026plusmn;\u0026thinsp;1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh alcohol consumption (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42 (0.97)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37 (0.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34 (0.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34 (0.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking n (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e528 (12.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e458 (10.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e420 (9.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e369 (8.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e451 (10.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e428 (9.91)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e410 (9.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e340 (7.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e101 (2.34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e193 (4.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e250 (5.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e370 (8.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnti-hypertensive medication (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e121 (2.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e150 (3.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e186 (4.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e235 (5.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40 (0.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e59 (1.37)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e81 (1.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e144 (3.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiovascular disease (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9 (0.21)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22 (0.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28 (0.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43 (1.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eAU, arbitrary units; eGFR, estimated glomerular filtration rate according to the combined Chronic Kidney Disease Epidemiology Collaboration creatinine and cystatin C equation.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003ea\u003c/sup\u003e Analysis of variance or logistic regression analysis.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003eb\u003c/sup\u003e Glucose and C-reactive protein are presented as median (interquartile range in brackets) due to skewed distributions. All the other continuous values are presented as means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, unless otherwise stated.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \n\u003ch2\u003eIncidence Of Register-based Ckd In Relation To Gdf-15\u003c/h2\u003e\n \u003cp\u003eDuring a mean of 19.2\u0026thinsp;\u0026plusmn;\u0026thinsp;4.04 years of follow-up, a total of 165 subjects developed CKD. After multivariate adjustment (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Model 3), an increased GDF-15 level was observed to be associated with a higher risk of developing CKD. The HR (highest vs. lowest quartiles of GDF-15) for incident CKD was 2.74 (95% CI, 1.53\u0026thinsp;~\u0026thinsp;4.89; \u003cem\u003ep\u003c/em\u003e for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.001). This value was slightly attenuated after additionally adjusting for baseline eGFR (HR, 2.37; 95% CI, 1.33\u0026thinsp;~\u0026thinsp;4.24; \u003cem\u003ep\u003c/em\u003e for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.01). The adjusted HR for each 1 SD increase in GDF-15 was 1.39 (95% CI, 1.16\u0026thinsp;~\u0026thinsp;1.65; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In the sensitivity analysis when NT-proBNP was additionally added into the model, the association between GDF-15 and CKD hardly changed (HR: 1.35, 95% CI, 1.13\u0026thinsp;~\u0026thinsp;1.62; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01, per 1 SD increment of GDF-15). However, no statistical significance was observed for NT-proBNP (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.10).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIncidence of register-based chronic kidney disease in relation to growth differentiation factor-15 (GDF-15).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQ1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQ2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQ3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eQ4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e for trend \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePer 1 standard deviation change in GDF-15\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDF-15 range male (AU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.09\u0026ndash;8.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.47\u0026ndash;8.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.82\u0026ndash;9.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.21\u0026ndash;11.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDF-15 range female (AU)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.78\u0026ndash;8.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.35\u0026ndash;8.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.66\u0026ndash;9.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.02\u0026ndash;12.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo. of subjects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,080\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncidence \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncidence (per 1000 person-years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1 \u003csup\u003ec d\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.08 (1.14, 3.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.15 (1.77, 5.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.58 (3.82, 11.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.97 (1.75, 2.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2 \u003csup\u003ec e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.66 (0.90, 3.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.98 (1.10, 3.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.46 (1.97, 6.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.81 (1.56, 2.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 3 \u003csup\u003ec f\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.56 (0.85, 2.86)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.66 (0.91, 3.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.74 (1.53, 4.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.51 (1.27, 1.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 4 \u003csup\u003ec g\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.57 (0.85, 2.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.62 (0.90, 2.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.37 (1.33, 4.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.39 (1.16, 1.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003eAU, arbitrary units.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003ea\u003c/sup\u003e Analysis by Cox proportional hazards model.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003eb\u003c/sup\u003e Defined as 585\u0026ndash;586 according to International Classification of Diseases 9, and N18 and N19 according to International Classification of Diseases 10.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003ec\u003c/sup\u003e Adjusted hazard ratios (95 % confidence interval).\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003ed\u003c/sup\u003e Crude model.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003ee\u003c/sup\u003e Adjusted for age and sex.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003ef\u003c/sup\u003e Additionally adjusted for waist circumference, smoking, high alcohol consumption, systolic blood pressure, low-density lipoprotein cholesterol, C-reactive protein level, diabetes, cardiovascular disease, and anti-hypertensive drug medication.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"9\" nameend=\"c9\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003eg\u003c/sup\u003e Additionally adjusted for baseline estimated glomerular filtration rate.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNo obvious evidence of non-linearity in the association between GDF-15 and CKD was detected by restricted cubic spline function (\u003cem\u003ep\u003c/em\u003e for effect test\u0026thinsp;\u0026lt;\u0026thinsp;0.0001, \u003cem\u003ep\u003c/em\u003e for non-linearity test\u0026thinsp;=\u0026thinsp;0.26). Meanwhile, no interaction between GDF-15 and covariates was found with respect to CKD. During the follow-up period, 952 individuals died from causes other than CKD. When competing risk of death was taken in consideration, the adjusted HR was 2.11 (95% CI, 1.19\u0026thinsp;~\u0026thinsp;3.76) for Q4 versus Q1 of GDF-15 (\u003cem\u003ep\u003c/em\u003e for trend\u0026thinsp;=\u0026thinsp;0.01), and was 1.23 (95% CI, 1.03\u0026thinsp;~\u0026thinsp;1.48) for each 1 SD increase in GDF-15 (data not shown). Among 4,244 individuals with baseline eGFR\u0026thinsp;\u0026ge;\u0026thinsp;60 mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e, 145 developed CKD. The adjusted HR was 2.08 (95% CI, 1.14\u0026thinsp;~\u0026thinsp;3.78; \u003cem\u003ep\u003c/em\u003e for trend\u0026thinsp;=\u0026thinsp;0.02), and 1.22 (95% CI, 1.01\u0026thinsp;~\u0026thinsp;1.48) for Q4 versus Q1 and per 1 SD increase of GDF-15, respectively (data not shown).\u003c/p\u003e \u003cp\u003e \u003cb\u003eeGFR decline and incidence of eGFR-based CKD in relation to GDF-15\u003c/b\u003e \u003c/p\u003e \u003cp\u003eA total of 2,744 had repeated eGFR values after a mean follow-up of 16.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.49 years. The mean eGFR values at baseline and follow-up were 90.2\u0026thinsp;\u0026plusmn;\u0026thinsp;12.8 and 66.2\u0026thinsp;\u0026plusmn;\u0026thinsp;15.1 mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e, respectively (N\u0026thinsp;=\u0026thinsp;2,744). The association of eGFR change in relation to GDF-15, both in quartiles and per 1 SD increase, is presented \u003cb\u003ein\u003c/b\u003e Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. As compared to Q1 of GDF-15, Q4 was associated with a greater eGFR decline during follow-up (-2.42 mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e (95% CI, -3.91~-0.94); \u003cem\u003ep\u003c/em\u003e for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.01) after multivariate adjustment including baseline eGFR. In addition, each 1 SD increase in GDF-15 was associated with a decline in eGFR of -0.97 mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e (95% CI, -1.49~-0.45; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) over the follow-up period. Results were consistent in participants with baseline eGFR\u0026thinsp;\u0026ge;\u0026thinsp;60 mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e (N\u0026thinsp;=\u0026thinsp;2,713; B=-0.92; 95% CI, -1.49~-0.36, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) (data not shown).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eChange in estimated glomerular filtration rate in relation to baseline growth differentiation factor-15 (GDF-15).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo. of subjects\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eB (95% CI) \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDF-15 Q1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e790\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDF-15 Q2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e763\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.04 (-2.30, 0.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDF-15 Q3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e646\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.67 (-3.01, -0.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGDF-15 Q4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2.42 (-3.91, -0.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePer 1 standard deviation change in GDF-15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,744\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.97 (-1.49, -0.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003eCI, confidence interval; eGFR, estimated glomerular filtration rate according to the combined Chronic Kidney Disease Epidemiology Collaboration creatinine and cystatin C equation.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e \u003cp\u003e\u003csup\u003ea\u003c/sup\u003e Adjusted for waist circumference, smoking, high alcohol consumption, systolic blood pressure, low-density lipoprotein cholesterol, C-reactive protein level, diabetes, cardiovascular disease, anti-hypertensive drug medication, and baseline estimated glomerular filtration rate.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eOut of the 2,713 individuals with baseline eGFR\u0026thinsp;\u0026ge;\u0026thinsp;60 mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e, 862 developed CKD, as defined by eGFR\u0026thinsp;\u0026lt;\u0026thinsp;60 mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e. After adjusting for covariates including baseline eGFR, the odds ratio (OR) comparing Q4 vs Q1 of GDF-15 was 1.41 (95% CI, 1.06\u0026thinsp;~\u0026thinsp;1.89; \u003cem\u003ep\u003c/em\u003e for trend\u0026thinsp;=\u0026thinsp;0.02) for developing eGFR-based CKD. The corresponding OR was 1.15 (95% CI, 1.04\u0026thinsp;~\u0026thinsp;1.28) per 1 SD increase in GDF-15 (data not shown).\u003c/p\u003e "},{"header":"Discussion","content":" \u003cp\u003eOur findings suggest that GDF-15 is associated with increased incidence of CKD and eGFR decline. The association was independent of baseline eGFR, smoking, waist circumference, CRP, \u003cem\u003eetc.\u003c/em\u003e, and remained after controlling for competing risk of death. GDF-15, therefore, may be a useful marker of increased risk of CKD.\u003c/p\u003e \u003cp\u003eGDF-15 is a distant member of the transforming growth factor-β (TGF-β) family [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. It has consistently been associated with deterioration of kidney function or adverse outcomes in patients with existing kidney diseases [\u003cspan additionalcitationids=\"CR10 CR11\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. GDF-15 has also been investigated in relation to the development of CKD [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], yet the association has remained uncertain. Carlsson, et al. used a discovery cohort (the PIVUS Study; N\u0026thinsp;=\u0026thinsp;687, mean age\u0026thinsp;=\u0026thinsp;70 y) and a replication cohort (the ULSAM study; N\u0026thinsp;=\u0026thinsp;360, mean age\u0026thinsp;=\u0026thinsp;78 y) to identify predictors of eGFR decline from 80 CVD biomarkers [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Whereas GDF-15 was initially observed to be associated with eGFR decline during a 5-year follow-up period, the association disappeared after adjusting for eGFR at baseline. The validity of this study may be affected by the relatively small sample size, the elderly population and potential survival bias. Results from the Framingham Offspring cohort study (N\u0026thinsp;=\u0026thinsp;2,614, mean follow-up period\u0026thinsp;=\u0026thinsp;9.5 y) [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] suggested a superior predictive value of GDF-15 for incident CKD (estimated by one eGFR measurement). The association of GDF-15 with CKD was independent of baseline eGFR.\u003c/p\u003e \u003cp\u003eEven though the exact production rate and the kidney clearance rate is currently not available in our understanding of GDF-15, as a 24.5-kDa active circulating dimeric protein [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], levels of GDF-15 may largely depend upon renal excretory function for elimination. In support of this view, GDF-15 concentration negatively correlated with eGFR in the current and previous studies [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], and was decreased by kidney function improvement through kidney transplantation [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Nevertheless, a strong association of GDF-15 with incident CKD was observed in our study and the study by Ho, et al [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], even after adjustment for baseline eGFR, which may abolish some of the doubts about the kidney elimination dependence. Noteworthily, some potential confounders were not taken into account in the study by Ho, et al [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. We confirmed previous findings [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], suggesting that smoking, obesity, hs-CRP, \u003cem\u003eetc.\u003c/em\u003e are strongly correlated with GDF-15 levels, and these are factors that also have been associated with CKD [\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Our finding provides added evidence that GDF-15 was significantly associated with incidence of CKD and eGFR decline even after adjustments for baseline eGFR as well as other potential risk factors.\u003c/p\u003e \u003cp\u003eDysfunction of the heart or kidney could potentially induce dysfunction of the other organ [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], known as a pathologic condition termed the cardiorenal syndrome [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. In our sensitivity analysis, GDF-15 and NT-proBNP were mutually adjusted for each other in the multivariate model. GDF-15 but not NT-proBNP remained significantly associated with CKD and eGFR decline, suggesting that GDF-15 might be more specific to kidney outcomes than other CVD biomarkers. Moreover, the competing risk of death is usually high in studies on geriatric populations or with long-term follow-ups, and may largely bias the findings [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. We demonstrated for the first time that the fully-adjusted association of GDF-15 with eGFR decline and CKD remained after controlling for death as a competing risk.\u003c/p\u003e \u003cp\u003eThe kidney can be an important source of GDF-15. In kidney tissue from adult rats, GDF-15 mRNA expression was mainly detected in the S3 segment of the nephron and the collecting ducts by in situ hybridization [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. In response to stimuli such as surgery, toxin, ischemia, and hyperoxia, GDF-15 can be immediately induced in kidney, possibly through TNF- and p53-dependent and -independent pathways, and acts as a regulator of inflammation, cell survival, proliferation, and apoptosis [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Duong et al., observed that when stimulated by metabolic acidosis, GDF-15 expression was strongly induced in mouse kidney outer medullary collecting duct [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. The increased GDF-15 played a key role in collecting duct lengthening by triggering compensatory proliferation of acid-secreting intercalated cells [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. In contrast, genetic deletion of GDF-15 aggravated tubular and interstitial injury, which resulted in glycosuria and polyuria in mice with diabetes [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Meanwhile, epidemiological evidence showed that circulating levels of GDF-15 were closely correlated with mRNA expression of GDF-15 in renal tubulointerstitium, and significantly predicted risk of disease progression in patients with CKD [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Similarly, urinary GDF-15 was tightly linked with proximal tubule damage and kidney function decline in diabetic patients [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Based on the above-mentioned evidence, it is speculated that the observed association between plasma GDF-15 and incident CKD might at least partly be due to enhanced GDF-15 expression in kidney as a protective response against early-stage renal damage.\u003c/p\u003e \u003cp\u003eStrengths of this study included a prospective study design and a long-term follow-up. In addition, endpoints were retrieved from hospital registers with national coverage and a high case validity of the CKD diagnosis, as well as based on eGFR determined from a combination of plasma creatinine and cystatin C. There are several limitations that need to be mentioned. Our findings were descriptive in nature and provided limited information on mechanisms. CKD was diagnosed on one eGFR measurement at baseline and follow-up, respectively. Data on albuminuria was not available, but this did not affect the diagnosis of CKD based on eGFR\u0026thinsp;\u0026lt;\u0026thinsp;60 mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e. CKD cases identified among hospital registers may not include less severe cases which were treated in primary care. However, kidney function was also measured based on eGFR, and negative associations of GDF-15 with eGFR decline and eGFR-based CKD were consistent. Another important limitation is that the calculation of eGFR change may be influenced since the methods of measuring cystatin C and creatinine at baseline were different from those at the follow-up. Nevertheless, we expect that any bias introduced by these different measurements should be non-differential in relation to GDF-15 levels, and a greater decline in eGFR from baseline to follow-up could still reflect deteriorated kidney function. Therefore, we consider that the association between GDF-15 and eGFR decline was valid.\u003c/p\u003e "},{"header":"Conclusions","content":" \u003cp\u003eIn conclusion, this study indicated that elevated GDF-15 levels predicted incidence of CKD and eGFR decline in the general population, independent of a wide range of potential risk factors and competing risk of death.\u003c/p\u003e"},{"header":"Abbreviations","content":" \u003cp\u003eAU: arbitrary units; CI: confidence interval; CKD: chronic kidney disease; CRP: C-reactive protein; eGFR: estimated glomerular filtration rate; ESRD: end-stage renal disease; GDF-15: growth differentiation factor 15; HR hazard ratio; IDMS: isotope-dilution mass spectrometry-traceable; LDL: Low density lipoprotein; MDC-CV: Malm\u0026ouml; Diet and Cancer Cardiovascular; NT-proBNP N-terminal pro-B-type natriuretic peptide; OR: odds ratio; SD: standard deviation; TGF-β: transforming growth factor-β.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate: \u003c/strong\u003eAll procedures performed in this study were approved by the ethics committee at Lund University Lund, Sweden (LU 51/90). Written informed consent was obtained from all individual participants included in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication: \u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003cstrong\u003e: \u003c/strong\u003eThe data that support the findings of this study are available from Lund University, but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of Lund University.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests: \u003c/strong\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This work was supported by the Swedish Heart\u0026ndash;Lung Foundation (2016-0315, 2017-0626); Lund University Infrastructure grant \u0026ldquo;Malm\u0026ouml; population-based cohorts\u0026rdquo; (STYR 2019/2046); the Natural Science Foundation of Jiangsu Province (BK20200128); and the Fundamental Research Funds for the Central Universities (0214-14380474). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026rsquo;s Contributions: \u003c/strong\u003eXB, BX and GE contributed to the conception or design of the work. XB and YB contributed to the acquisition, analysis, or interpretation of data for the work. XB drafted the manuscript. BX, YB, MOM, OM, JN, AC and GE critically revised the manuscript. All authors reviewed and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u003c/strong\u003e We thank all the participants in this study and the staff of the department of Clinical Chemistry at University Hospital, Malm\u0026ouml;. We also would like to acknowledge the Swedish Patient Registry and the Swedish Renal Registry for providing information about renal outcomes and the Clinical biomarker facility at SciLifeLab Sweden for providing assistance in protein analyses.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCollaboration GCKD. Global, regional, and national burden of chronic kidney disease, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. 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Am J Kidney Dis. 2018; 72:381-9.\u003c/li\u003e\n\u003cli\u003eKidney Disease: Improving Global Outcomes (KDIGO) CKD Work Group. KDIGO 2012 clinical practice guideline for the evaluation and management of chronic kidney disease. Kidney Int Suppl 2013; 3: 1-150.\u003c/li\u003e\n\u003cli\u003eInker LA, Schmid CH, Tighiouart H, Eckfeldt JH, Feldman HI, Greene T, Kusek JW, Manzi J, Van Lente F, Zhang YL\u003cem\u003e et al\u003c/em\u003e. Estimating glomerular filtration rate from serum creatinine and cystatin C. N Engl J Med. 2012; 367:20-9.\u003c/li\u003e\n\u003cli\u003eNyman U, Grubb A, Larsson A, Hansson LO, Flodin M, Nordin G, Lindstrom V, Bjork J. The revised Lund-Malmo GFR estimating equation outperforms MDRD and CKD-EPI across GFR, age and BMI intervals in a large Swedish population. Clin Chem Lab Med. 2014; 52:815-24.\u003c/li\u003e\n\u003cli\u003eGrubb A, Horio M, Hansson LO, Bjork J, Nyman U, Flodin M, Larsson A, Bokenkamp A, Yasuda Y, Blufpand H\u003cem\u003e et al\u003c/em\u003e. Generation of a new cystatin C-based estimating equation for glomerular filtration rate by use of 7 assays standardized to the international calibrator. Clin Chem. 2014; 60:974-86.\u003c/li\u003e\n\u003cli\u003eBootcov MR, Bauskin AR, Valenzuela SM, Moore AG, Bansal M, He XY, Zhang HP, Donnellan M, Mahler S, Pryor K\u003cem\u003e et al\u003c/em\u003e. MIC-1, a novel macrophage inhibitory cytokine, is a divergent member of the TGF-beta superfamily. Proc Natl Acad Sci U S A. 1997; 94:11514-9.\u003c/li\u003e\n\u003cli\u003eDesmedt S, Desmedt V, De Vos L, Delanghe JR, Speeckaert R, Speeckaert MM. Growth differentiation factor 15: A novel biomarker with high clinical potential. Crit Rev Clin Lab Sci. 2019; 56:333-50.\u003c/li\u003e\n\u003cli\u003eConnelly PW, Yan AT, Nash MM, Lok CE, Gunaratnam L, Prasad GVR. Growth differentiation factor 15 is decreased by kidney transplantation. Clin Biochem. 2019; 73:57-61.\u003c/li\u003e\n\u003cli\u003eJones-Burton C, Seliger SL, Scherer RW, Mishra SI, Vessal G, Brown J, Weir MR, Fink JC. Cigarette smoking and incident chronic kidney disease: a systematic review. Am J Nephrol. 2007; 27:342-51.\u003c/li\u003e\n\u003cli\u003eGarofalo C, Borrelli S, Minutolo R, Chiodini P, De Nicola L, Conte G. A systematic review and meta-analysis suggests obesity predicts onset of chronic kidney disease in the general population. Kidney Int. 2017; 91:1224-35.\u003c/li\u003e\n\u003cli\u003eCachofeiro V, Goicochea M, de Vinuesa SG, Oubina P, Lahera V, Luno J. Oxidative stress and inflammation, a link between chronic kidney disease and cardiovascular disease. Kidney Int Suppl. 2008:S4-9.\u003c/li\u003e\n\u003cli\u003eWang J, Zhang W, Wu L, Mei Y, Cui S, Feng Z, Chen X. New insights into the pathophysiological mechanisms underlying cardiorenal syndrome. Aging (Albany NY). 2020; 12:12422-31.\u003c/li\u003e\n\u003cli\u003eBerry SD, Ngo L, Samelson EJ, Kiel DP. Competing risk of death: an important consideration in studies of older adults. J Am Geriatr Soc. 2010; 58:783-7.\u003c/li\u003e\n\u003cli\u003eEnsrud KE, Harrison SL, Cauley JA, Langsetmo L, Schousboe JT, Kado DM, Gourlay ML, Lyons JG, Fredman L, Napoli N\u003cem\u003e et al\u003c/em\u003e. Impact of Competing Risk of Mortality on Association of Weight Loss With Risk of Central Body Fractures in Older Men: A Prospective Cohort Study. J Bone Miner Res. 2017; 32:624-32.\u003c/li\u003e\n\u003cli\u003eUnsicker K, Spittau B, Krieglstein K. The multiple facets of the TGF-beta family cytokine growth/differentiation factor-15/macrophage inhibitory cytokine-1. Cytokine Growth Factor Rev. 2013; 24:373-84.\u003c/li\u003e\n\u003cli\u003eZimmers TA, Jin X, Hsiao EC, McGrath SA, Esquela AF, Koniaris LG. Growth differentiation factor-15/macrophage inhibitory cytokine-1 induction after kidney and lung injury. Shock. 2005; 23:543-8.\u003c/li\u003e\n\u003cli\u003eDuong Van Huyen JP, Cheval L, Bloch-Faure M, Belair MF, Heudes D, Bruneval P, Doucet A. GDF15 triggers homeostatic proliferation of acid-secreting collecting duct cells. J Am Soc Nephrol. 2008; 19:1965-74.\u003c/li\u003e\n\u003cli\u003eMazagova M, Buikema H, van Buiten A, Duin M, Goris M, Sandovici M, Henning RH, Deelman LE. Genetic deletion of growth differentiation factor 15 augments renal damage in both type 1 and type 2 models of diabetes. Am J Physiol Renal Physiol. 2013; 305:F1249-64.\u003c/li\u003e\n\u003cli\u003eSimonson MS, Tiktin M, Debanne SM, Rahman M, Berger B, Hricik D, Ismail-Beigi F. The renal transcriptome of db/db mice identifies putative urinary biomarker proteins in patients with type 2 diabetes: a pilot study. Am J Physiol Renal Physiol. 2012; 302:F820-9.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-nephrology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bnep","sideBox":"Learn more about [BMC Nephrology](http://bmcnephrol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bnep/default.aspx","title":"BMC Nephrology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"growth differentiation factor 15, chronic kidney disease, estimated glomerular filtration rate, competing risk, cohort study.","lastPublishedDoi":"10.21203/rs.3.rs-394958/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-394958/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe relationship between growth differentiation factor 15 (GDF-15) and the development of chronic kidney disease (CKD) is still unclear. We sought to examine whether plasma GDF-15 was related to incident CKD and kidney function decline using a large prospective cohort study.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003e4,318 participants of the Malm\u0026ouml; Diet and Cancer Study-Cardiovascular Cohort were examined in 1991\u0026ndash;1994 and followed prospectively until 2013 for incidence of CKD, as detected from national registers. Estimated glomerular filtration rate (eGFR) was available for all participants at baseline, and was re-measured in a subgroup of 2,744 subjects after 16.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.49 years. Incidence of CKD was examined in relation to GDF-15 using Cox regression analysis. Logistic regression was used to examine the association of GDF-15 with eGFR change and eGFR-based CKD. Models were carefully corrected for potential confounders including baseline eGFR, N-terminal pro-B-type natriuretic peptide, and competing risk from death.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003e165 patients developed CKD after 19.2\u0026thinsp;\u0026plusmn;\u0026thinsp;4.04 years of follow-up. The adjusted hazard ratio (95% confidence interval, CI) for CKD in 4th versus 1st quartile of GDF-15 was 2.37 (1.33, 4.24) (\u003cem\u003ep\u003c/em\u003e for trend\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Each per 1 standard deviation increase in GDF-15 was associated with a decline in eGFR of -0.97 mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e (95% CI, -1.49~-0.45; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). GDF-15 was also significantly associated eGFR-based CKD in 2,713 subjects with baseline eGFR\u0026thinsp;\u0026ge;\u0026thinsp;60 mL/min/1.73 m\u003csup\u003e2\u003c/sup\u003e.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eGDF-15 predicted incidence of CKD and eGFR decline in the general population, independent of a wide range of potential risk factors and competing risk of death.\u003c/p\u003e","manuscriptTitle":"Growth Differentiation Factor-15 and Incident Chronic Kidney Disease: A Population-Based Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-04-08 14:26:25","doi":"10.21203/rs.3.rs-394958/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2021-06-07T07:54:41+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2021-04-20T09:48:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"c7957c0d-d728-4d10-b22f-0e2391e39ea2","date":"2021-04-20T07:51:37+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2021-04-15T06:29:42+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2021-04-13T11:31:23+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2021-04-07T10:17:58+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2021-04-07T06:24:50+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Nephrology","date":"2021-04-05T08:15:00+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-nephrology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bnep","sideBox":"Learn more about [BMC Nephrology](http://bmcnephrol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bnep/default.aspx","title":"BMC Nephrology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"557eecba-a49e-436b-b2b5-6cd9950994ff","owner":[],"postedDate":"April 8th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":3493844,"name":"Urology \u0026 Nephrology"}],"tags":[],"updatedAt":"2021-09-28T09:59:13+00:00","versionOfRecord":[],"versionCreatedAt":"2021-04-08 14:26:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-394958","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-394958","identity":"rs-394958","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}