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Nephrol Dial Transplant. Mar 2011; 26(3): 920–926.
Published online Aug 3, 2010. doi:  10.1093/ndt/gfq471
PMCID: PMC3108344
Inflammation, kidney function and albuminuria in the Framingham Offspring cohort
Ashish Upadhyay,1 Martin G. Larson,2,4 Chao-Yu Guo,2 Ramachandran S. Vasan,2,3 Izabella Lipinska,5 Christopher J. O’Donnell,2,6 Sekar Kathiresan,6 James B. Meigs,7 John F. Keaney, Jr.,8 Jian Rong,2,9 Emelia J. Benjamin,2,3,9 and Caroline S. Fox2,10
1Division of Nephrology, Tufts Medical Center and Tufts University School of Medicine, Boston, MA, USA
2Boston University and National Heart, Lung and Blood Institute’s Framingham Heart Study, Framingham, MA, USA
3Preventive Medicine and Cardiology Sections, and Whitaker Cardiovascular Institute, Boston University School of Medicine, Boston, MA, USA
4Department of Mathematics and Statistics, Boston University, Boston, MA, USA
5Cardiovascular Division, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
6Division of Cardiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
7Division of General Internal Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
8Division of Cardiovascular Medicine, UMass Memorial Health Care and University of Massachusetts Medical School, Worcester, MA, USA
9Department of Epidemiology, School of Public Health, Boston University, Boston, MA, USA
10Department of Endocrinology, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
Correspondence and offprint requests to: Ashish Upadhyay; E-mail: aupadhyay/at/tuftsmedicalcenter.org
Support: The Framingham Heart Study is supported by the National Heart, Lung and Blood Institute grant N01-HC-25195. Additional funding provided by the National Institute of Health’s grants R01-HL70139 and R01-Hl04334 for R.S.V., R01-HL-64753 and R01-HL-076784 AG028321 for E.J.B., and the American Diabetes Association research grant and the National Institute of Diabetes and Digestive and Kidney Diseases grant K24-DK080140 for J.B.M.
Received January 28, 2010; Revised July 9, 2010; Accepted July 12, 2010.
Background. Inflammation and chronic kidney disease (CKD) are both associated with cardiovascular disease (CVD). Whether inflammatory biomarkers are associated with kidney function and albuminuria after accounting for traditional CVD risk factors is not completely understood.
Methods. The sample comprised Framingham Offspring cohort participants (n = 3294, mean age 61, 53% women) who attended the seventh examination cycle (1998–2001). Inflammatory biomarkers [C-reactive protein (CRP), tumour necrosis factor (TNF)-alpha, interleukin-6, TNF receptor 2 (TNFR2), intercellular adhesion molecule-1 (ICAM-1), monocyte chemoattractant protein-1 (MCP-1), P-selectin, CD-40 ligand, osteoprotegerin, urinary isoprostanes, myeloperoxidase and fibrinogen] were measured on fasting blood samples. Serum creatinine-based estimated glomerular filtration rate (eGFR) and serum cystatin C concentration were used to assess kidney function. Urinary albumin-to-creatinine ratio (UACR) was used to assess albuminuria. Linear or logistic regression was used to test associations between biomarkers and kidney measures.
Results. Chronic kidney disease (CKD), defined as eGFR < 59/64 mL/min/1.73 m2 in women/men, was present in 8.8% (n = 291) of participants. TNF-alpha, interleukin-6, TNFR2, MCP-1, osteoprotegerin, myeloperoxidase and fibrinogen were higher among individuals with CKD; all biomarkers except for urinary isoprostanes were elevated in higher cystatin C quartiles; and TNF-alpha, interleukin-6, TNFR2, ICAM-1 and osteoprotegerin were elevated in higher UACR quartiles—all assessed after multivariable adjustment. Almost 6% and 17% of variability in TNFR2 were explained by CKD status and higher cystatin C quartiles, respectively.
Conclusions. Biomarkers of inflammation are associated with kidney function and albuminuria. In particular, substantial variability in soluble TNFR2 is explained by CKD and cystatin C.
Keywords: albuminuria, chronic kidney disease, C-reactive protein, inflammation
Chronic kidney disease (CKD) is an independent risk factor for cardiovascular diseases (CVD) [1,2], and most people with mild-to-moderate CKD die from CVD before needing kidney replacement therapies [3]. Systemic inflammation has been proposed as one of the non-traditional mechanisms contributing to higher CVD burden in individuals with CKD [4].
Multiple studies have demonstrated associations between measures of inflammation, including C-reactive protein (CRP) [5–11], interleukin-6 [7,9], tumour necrosis factor receptor 2 (TNFR2) [11,12] and fibrinogen [5,8,9,12], and lower kidney function. Furthermore, markers of inflammation have also been shown to be associated with adverse cardiovascular outcomes and mortality among patients with CKD [13–16]. These observations suggest an important role for inflammation in the cardiovascular health of CKD patients.
Although prior studies have examined associations between one or a small panel of inflammatory markers and selected measures of kidney disease [5,6,8–12], there is a dearth of studies examining relations between a comprehensive panel of markers representing multiple potential inflammatory pathways and kidney measures in a community-based setting. We hypothesized that a broad array of inflammatory biomarkers is associated with lower kidney function and albuminuria in the community. To test this hypothesis, we examined the relations between 13 inflammatory markers and CKD, defined using Modification of Diet in Renal Disease (MDRD)-estimated glomerular filtration rate (eGFR), cystatin C and urinary albumin-to-creatinine ratio (UACR) in the Framingham Offspring cohort.
Study sample
Study participants were comprised of members from the Framingham Offspring Study who attended the seventh examination cycle (1998–2001). The design of the Framingham Offspring Study has been described previously [17]. Out of 3539 eligible participants, we excluded participants with missing creatinine (n = 232), missing covariates (n = 13), missing UACR (n = 633; excluded only for albuminuria analyses) and missing cystatin C (n = 65; excluded only for cystatin C analyses), resulting in 3294 participants for CKD analyses, 3229 participants for cystatin C analyses and 2661 participants for albuminuria analyses. The study was approved by the Boston University Medical Center Institutional Review Board, and all participants provided written informed consent.
Assessment of inflammatory markers
Inflammatory markers were measured from fasting blood samples obtained during the seventh examination cycle (1998–2001) that were kept frozen at − 80°C. CRP was quantified using a high-sensitivity assay [Dade Behring BN100 nephelometer, Dade Behring Diagnostic, Marburg, Germany; intra-assay coefficient of variation (CV) =3.2% on 139 phantom replicates], and fibrinogen was quantified from citrated plasma by the Clauss method (Diagnostica Stago Reagents, Diagnostica Stago Incorporate, Parsippany, NJ, USA; intra-assay CV = 2.1%). Additional markers were quantified using enzyme-linked immunosorbent assay (ELISA) kits [R&D Systems Incorporate, Minneapolis, MN, USA for interleukin-6, TNFR2, intercellular adhesion molecule-1 (ICAM-1), P-selectin, monocyte chemoattractant protein-1 (MCP-1); Bender MedSystems, Vienna, Austria for CD40 ligand; Cayman Chemical, Ann Arbor, MI, USA for isoprostanes; OXIS International Incorporate, Beverly Hills, CA, USA for myeloperoxidase; and BioMedica Incorporate, San Diego, CA, USA for osteoprotegerin] and run-in duplicates. Mean intra-assay CVs from serum specimens were 3.7% for ICAM-1, 3.1% for interleukin-6, 3.8% for MCP-1, 3.0% for myeloperoxidase and 3.7% for osteoprotegerin, and from plasma specimens were 4.4% for CD40 ligand, 1.1% for fibrinogen, 3.7% for osteoprotegerin, 3.0% for P-selectin 3.0% and 2.2% for TNFR2. Urinary isoprostanes were quantified on morning urine specimens using ELISA (Cayman Chemical, Ann Arbor, MI, USA; intra-assay CV = 9.6 ± 6.8%) and indexed to urinary creatinine concentration (Abbot Spectrum CCX, Columbus, OH, USA; intra-assay CV = 2–4%).
Assessment of kidney function
Kidney function was assessed using eGFR and cystatin C concentration. The four-variable MDRD study equation was used to calculate eGFR [18,19]. The CKD definition was based on the previously described slight modification of the National Kidney Foundation Kidney Disease Outcome Quality Initiative working group’s definition of CKD [18,20]. CKD was defined as eGFR < 59 mL/min/1.73 m2 in women and < 64 mL/min/1.73 m2 in men [20]. Serum creatinine was measured using the modified Jaffe method (inter-assay CV = 2.8% and intra-assay CV = 4.0%), and calibrated for the MDRD study equation using a previously described two-step process to minimize any potential inter-laboratory variability [20]. Serum cystatin C was measured using particle-enhanced immunonephelometry (Dade Behring BN 100 nephelometer; Dade Behring Diagnostic, Marburg, Germany; intra-assay CV = 2.4% and inter-assay CV = 3.3%).
Assessment of albuminuria
Albuminuria was assessed using UACR measurements. UACR is a validated and reliable indicator of the 24-h urinary albumin excretion [21]. Urinary albumin concentration was measured by immunoturbimetry (Tina-quant albumin assay; Roche Diagnostics, Indianapolis, IN, USA; intra-assay CV = 7.2%). Urinary creatinine concentration was measured using a modified Jaffe method (intra-assay CV = 2.3%). Urinary albumin and creatinine measurements were performed on average 2.9 years before the seventh examination cycle in the Offspring sixth examination cycle (1995–98).
Risk factor assessment
Details regarding the methods of risk factor measurement and laboratory analysis have been described [22]. Each examination included a CVD assessment and blood testing. Diabetes was defined as having a fasting glucose level ≥ 126 mg/dL (7.0 mmol/L) or being treated by insulin and/or oral hypoglycaemic medications. Hypertension was defined as having systolic blood pressure ≥ 140 mmHg or diastolic blood pressure ≥ 90 mmHg (average of two readings taken by the examining physician) or being treated by antihypertensive medications. Total and high-density lipoprotein (HDL) cholesterol concentrations were measured from fasting blood samples (Table 1). Smoking status was defined as smoking one or more cigarettes per day in the year preceding the examination. Body mass index was defined as weight (kilograms) divided by height (metres) squared.
Table 1
Table 1
Study sample characteristics
Statistical analysis
Inflammatory biomarkers were defined as dependent variables, and kidney disease measures were defined as regressor variables. Inflammatory biomarkers were log-transformed and then standardized to a mean of 0 and a standard deviation of 1 to facilitate direct comparisons among the markers. Mean concentrations of standardized inflammatory biomarkers were compared among individuals with and without CKD, in age- and sex-adjusted models (Figure 1) and in multivariable-adjusted linear models (Tables 2–4) adjusting for age, sex, systolic blood pressure, hypertension treatment, smoking, body mass index, waist circumference, triglycerides, total/HDL cholesterol, lipid treatment, hormone replacement therapy, diabetes, aspirin use at least three times per week, and prevalent cardiovascular disease. Estimated GFR was also included in multivariable models for UACR analyses. We also examined inflammatory marker concentration by sex-specific quartiles of cystatin C and UACR. Secondary analyses were performed excluding individuals with either diabetes or CVD for CKD, cystatin C and UACR analyses, and individuals with CKD by eGFR for cystatin C analysis. To determine the variability in marker concentrations explained by measures of kidney function, the type III sum of squares derived by multivariable models was divided by the corrected total sum of squares.
Fig. 1
Fig. 1
(A) Age- and sex-adjusted mean standardized inflammatory marker levels by chronic kidney disease status. Bars represent standard errors. CRP, C-reactive protein; TNF, tumour necrosis factor; IL-6, interleukin-6; TNFR2, TNF receptor 2; ICAM1, intercellular (more ...)
Table 2
Table 2
Multivariable-adjusted least-squares means and standard errors of inflammatory markers (dependent variables) by CKD status
Table 3
Table 3
Multivariable-adjusted least-square means and standard errors of inflammatory marker (dependent variables) by cystatin C quartile
Table 4
Table 4
Multivariable-adjusted least-squares means and standard errors of inflammatory markers (dependent variables) by UACR quartile
In addition, we also performed multivariable-adjusted step-wise logistic regression analyses with CKD and albuminuria as dependent binary variables and inflammatory biomarkers as predictors.
All analyses were performed using SAS version 8.2 (SAS Institute, Cary, NC, USA). A two-sided P-value < 0.05 was considered statistically significant.
Overall, the 291 participants with CKD (n = 291, 8.8%) were nearly a decade older than individuals without CKD and had a more adverse risk factor profile (Table 1).
Inflammatory markers by CKD status
Age- and sex-adjusted standardized mean marker levels by CKD status, cystatin C quartiles and UACR quartiles are presented in Figure 1A–C, respectively. Nearly all markers, with the exception of urinary isoprostanes, were higher among individuals with CKD as compared with those without CKD.
In multivariable-adjusted models (Table 2), higher concentrations of TNF-alpha, interleukin-6, TNFR2, MCP-1, osteoprotegerin, myeloperoxidase and fibrinogen were positively associated with CKD status. Overall, CKD status accounted for 5.7% of variability in TNFR2 concentrations. Analyses repeated after exclusion of participants with diabetes or prevalent CVD did not substantively change the results (data not shown).
Additional multivariable-adjusted step-wise logistic regression analysis with CKD as the dependent variable showed significant associations with TNFR2, ICAM-1, MCP-1 and urinary isoprostanes (Supplementary Table 1).
Inflammatory markers by quartile of cystatin C
In multivariable-adjusted models, all inflammatory markers with the exception of urinary isoprostanes were positively associated with higher quartiles of cystatin C (Table 3). Results were not materially different when participants with CVD or diabetes were excluded from analyses (data not shown), or when analyses were limited to individuals without CKD defined by eGFR (Supplementary Table 2). Nearly 17% of variability in TNFR2 concentrations was explained by higher cystatin C quartile.
Inflammatory markers by quartile of UACR
Differences in concentrations of each inflammatory marker were examined by quartiles of UACR. In multivariable-adjusted models, higher TNF-alpha, interleukin-6, TNFR2, ICAM-1 and osteoprotegerin concentrations showed statistically significant associations with higher UACR quartiles (Table 4). Quartiles of UACR explained 0.6% of variability in TNFR2 concentrations.
Additional multivariable-adjusted step-wise logistic regression analysis with the highest UACR quartile as the dependent variable showed significant associations with TNFR2, P-selectin, osteoprotegerin and urinary isoprostanes (Supplementary Table 3).
In our community-based sample not selected for kidney-related outcomes, a broad array of inflammatory markers was associated with CKD status, cystatin C concentration and urinary albumin excretion. Even among individuals without CKD by creatinine-based eGFR, markers of inflammation were strongly associated with higher cystatin C quartiles. Most notably, a significant proportion of variability in TNFR2 concentration was explained by CKD status and higher cystatin C quartiles.
Our data show that higher concentrations of TNF-alpha, interleukin-6, TNFR2, osteoprotegerin and fibrinogen are associated with CKD status, higher cystatin C quartiles and higher UACR quartiles. In addition, higher concentrations of MCP-1 and myeloperoxidase correlate with CKD status; higher levels of CRP, ICAM-1, P-selectin, MCP-1 and myeloperoxidase correlate with higher cystatin C quartiles; and higher concentration of ICAM-1 correlates with higher UACR quartiles.
Inflammation, oxidative stress and a pro-coagulant state are thought to be important mechanisms for CVD in patients with CKD [4]. CRP is a well-established marker of inflammation, and TNF-alpha along with interleukin-6 is a key cytokine that mediates both acute and chronic inflammation [23]. TNFR2, a soluble TNF-alpha receptor, likely plays an important role in downstream signalling of inflammatory and proliferative mediators [24]. ICAM-1 and P-selectin facilitate leucocyte recruitment and vascular transmigration, and MCP-1 recruits monocytes during the inflammatory process [23]. CD40 ligand plays a role in inducing cytokine production, fibroblast proliferation, lymphocyte proliferation and endothelial cell activation [25]. Osteoprotegerin facilitates monocyte adhesion and sensitizes endothelial cells to TNF-alpha [26]. Myeloperoxidase and urinary isoprostanes are markers of oxidative stress [27], and fibrinogen is an indicator of a pro-coagulant state [28].
Our findings add to the existing literature on inflammatory biomarkers and kidney disease. Multiple reports have suggested associations between kidney measures and one or small panel of inflammatory markers [5,6,8–12]. More recently, a report from the Multi-Ethnic Study of Atherosclerosis (MESA) cohort showed significant associations between a panel of six inflammatory markers that included CRP, interleukin-6, TNF-alpha receptor-1 (TNFR1), ICAM-1, fibrinogen, and factor VIII and both CKD and cystatin C [7]. The strongest association was observed with TNFR1 for both CKD and cystatin C. The MESA Study, however, did not test the associations with urinary albumin excretion, and their panel did not include markers of oxidative stress and osteoprotegerin pathways. Our study is unique for using a wide-breadth of biomarkers involved in multiple pathways of inflammatory response as well as for robust multivariable adjustments.
We found TNFR2 to be the marker most associated with measures of kidney function and albuminuria. Our results are in agreement with the Health Professionals’ Follow-up Study’s findings which showed that men with TNFR2 concentrations in the highest as compared with the lowest quartile have eight times more likelihood of having CKD [12]. Similarly, the post hoc analysis of the Cholesterol and Recurrent Events trial also showed that participants with the highest tertile compared with the lowest tertile of TNFR2 have the higher rate of loss of kidney function [11]. TNF-alpha receptors are cleared by kidneys [29], and elevated levels seen with lower kidney function may be a reflection of decreased clearance. Our findings with TNFR2, the MESA Study’s findings with TNFR1, and the strong associations we observed between TNF-alpha and kidney function suggest that TNF-alpha pathway may potentially be a key player in the mediation of inflammation in kidney disease.
CRP, in our study, was not related to CKD after accounting for CVD risk factors. This finding is consistent with the results from post hoc analysis of the MDRD Study which showed that CRP did not correlate with worsening kidney function over a 2.2-year follow-up period [30]. Our finding, however, is in contrast with the observations in the MESA Study, the Cardiovascular Health Study, and the report from the National Health and Nutrition Examination Survey III dataset that showed higher CRP in individuals with CKD [7–9]. Difference in the definition of CKD and the use of more limited covariate models in other reports may account for the inconsistent results. It is, therefore, uncertain if CRP is associated with mild-to-moderate CKD in the community after accounting for multiple CVD risk factors.
Our results for markers of oxidative stress were variable. Unlike a prior report of higher plasma F2 isoprostane concentration in individuals on haemodialysis [31], we observed lower urinary isoprostane levels in participants with CKD. We also did not observe significant relations between urinary isoprostane concentration and higher cystatin C quartiles. Higher myeloperoxidase levels, on the other hand, were associated with both CKD and higher cystatin C quartiles. Future studies are needed to better delineate roles of specific oxidative stress markers in kidney disease.
Our study has a number of strengths. We examined a broad panel of biomarkers representing multiple pathways involved in the inflammatory process. Our assessment of kidney disease was comprehensive with the use of serum creatinine, cystatin C and UACR. Our multivariable adjustments were robust, and our sample size provided adequate power to detect modest associations.
The most important limitation of our study is our inability to infer causality from the observed associations. This limitation is inherent in cross-sectional and observational studies and is particularly important in our study as some inflammatory biomarkers may have shown associations with kidney disease simply as a result of lower renal clearance. Higher biomarker concentration in these cases may be the result of rather than the cause of lower kidney function. This may be an important consideration for markers like TNF-alpha receptors which are cleared by kidneys but not for biomarkers such as CRP and interleukin-6 which are non-renally cleared. [29]
Our study has some other important limitations. Our samples were composed entirely of individuals of European ancestry, and it is uncertain how our findings might apply to other racial and ethnic groups or to a group selected for kidney pathology. We defined CKD using a one-time measurement of serum creatinine, and this could have led to misclassifications as the MDRD equation underestimates kidney function in individuals without CKD [32]. Some individuals who were classified as having CKD may have had reversible causes for lower kidney function as well. It is also possible that albuminuria and reduced kidney function may just be markers of systemic vascular disease, and the associations between inflammatory biomarkers and kidney measures may have reflected associations between inflammation and vascular diseases. Our primary findings, however, did not change when individuals with prevalent CVD or diabetes were excluded from the analysis. Finally, not accounting for the presence of chronic inflammatory conditions in our participants may have potentially confounded our analyses as well.
Our study has important implications for future research. Our findings show that inflammation is significantly associated with mild-to-moderate CKD in community-dwelling individuals. Our results also suggest that numerous pathways are likely involved in mediating inflammation in CKD. The strong results for TNF-alpha and TNFR2 indicate the particular importance of the TNF-alpha pathway in kidney disease. Future research from well-powered longitudinal studies is needed to better characterize the potential role of various inflammatory markers in assessing for adverse CVD outcomes in individuals with mild-to-moderate CKD.
Supplementary data
Supplementary data is available online at http://ndt.oxfordjournals.org.
Supplementary Data
Acknowledgments
Conflict of interest statement. None declared.
1. Go AS, Chertow GM, Fan D, et al. Chronic kidney disease and the risks of death, cardiovascular events, and hospitalization. N Engl J Med. 2004;351:1296–1305. [PubMed]
2. Manjunath G, Tighiouart H, Ibrahim H, et al. Level of kidney function as a risk factor for atherosclerotic cardiovascular outcomes in the community. J Am Coll Cardiol. 2003;41:47–55. [PubMed]
3. Keith DS, Nichols GA, Gullion CM, et al. Longitudinal follow-up and outcomes among a population with chronic kidney disease in a large managed care organization. Arch Intern Med. 2004;164:659–663. [PubMed]
4. Cachofeiro V, Goicochea M, de Vinuesa SG, et al. Oxidative stress and inflammation, a link between chronic kidney disease and cardiovascular disease. Kidney Int Suppl. 2008;74:S4–S9. [PubMed]
5. Fried L, Solomon C, Shlipak M, et al. Inflammatory and prothrombotic markers and the progression of renal disease in elderly individuals. J Am Soc Nephrol. 2004;15:3184–3191. [PubMed]
6. Garg AX, Blake PG, Clark WF, et al. Association between renal insufficiency and malnutrition in older adults: results from the NHANES III. Kidney Int. 2001;60:1867–1874. [PubMed]
7. Keller C, Katz R, Cushman M, et al. Association of kidney function with inflammatory and procoagulant markers in a diverse cohort: a cross-sectional analysis from the Multi-Ethnic Study of Atherosclerosis (MESA) BMC Nephrol. 2008;9:9. [PMC free article] [PubMed]
8. Muntner P, Hamm LL, Kusek JW, et al. The prevalence of nontraditional risk factors for coronary heart disease in patients with chronic kidney disease. Ann Intern Med. 2004;140:9–17. [PubMed]
9. Shlipak MG, Fried LF, Crump C, et al. Elevations of inflammatory and procoagulant biomarkers in elderly persons with renal insufficiency. Circulation. 2003;107:87–92. [PubMed]
10. Stuveling EM, Hillege HL, Bakker SJ, et al. C-reactive protein is associated with renal function abnormalities in a non-diabetic population. Kidney Int. 2003;63:654–661. [PubMed]
11. Tonelli M, Sacks F, Pfeffer M, et al. Biomarkers of inflammation and progression of chronic kidney disease. Kidney Int. 2005;68:237–245. [PubMed]
12. Lin J, Hu FB, Rimm EB, et al. The association of serum lipids and inflammatory biomarkers with renal function in men with type II diabetes mellitus. Kidney Int. 2006;69:336–342. [PMC free article] [PubMed]
13. Mallamaci F, Tripepi G, Cutrupi S, et al. Prognostic value of combined use of biomarkers of inflammation, endothelial dysfunction, and myocardiopathy in patients with ESRD. Kidney Int. 2005;67:2330–2337. [PubMed]
14. Menon V, Greene T, Wang X, et al. C-reactive protein and albumin as predictors of all-cause and cardiovascular mortality in chronic kidney disease. Kidney Int. 2005;68:766–772. [PubMed]
15. Tripepi G, Mallamaci F, Zoccali C. Inflammation markers, adhesion molecules, and all-cause and cardiovascular mortality in patients with ESRD: searching for the best risk marker by multivariate modeling. J Am Soc Nephrol. 2005;16:S83–S88. [PubMed]
16. Wang AY, Wang M, Woo J, et al. Inflammation, residual kidney function, and cardiac hypertrophy are interrelated and combine adversely to enhance mortality and cardiovascular death risk of peritoneal dialysis patients. J Am Soc Nephrol. 2004;15:2186–2194. [PubMed]
17. Kannel WB, Feinleib M, McNamara PM, et al. An investigation of coronary heart disease in families. The Framingham Offspring Study. Am J Epidemiol. 1979;110:281–290. [PubMed]
18. K/DOQI clinical practice guidelines for chronic kidney disease: evaluation, classification, and stratification. Am J Kidney Dis. 2002;39:S1–S266. [PubMed]
19. Dong LQ, Zhang RB, Langlais P, et al. Primary structure, tissue distribution, and expression of mouse phosphoinositide-dependent protein kinase-1, a protein kinase that phosphorylates and activates protein kinase Czeta. J Biol Chem. 1999;274:8117–8122. [PubMed]
20. Fox CS, Larson MG, Leip EP, et al. Predictors of new-onset kidney disease in a community-based population. JAMA. 2004;291:844–850. [PubMed]
21. Nathan DM, Rosenbaum C, Protasowicki VD. Single-void urine samples can be used to estimate quantitative microalbuminuria. Diab Care. 1987;10:414–418. [PubMed]
22. Cupples LA, D’Agostino RB. Some risk factors related to the annual incidence of cardiovascular disease and death using pooled repeated biennial measurements: Framingham Study, 30-year follow-up. In: Kannel WB, Wolf PA, Garrison RJ, editors. The Framingham Heart Study: An Epidemiological Investigation of Cardiovascular Disease. Washington, DC: Government Printing Office. NIH Publication; 1987. pp. 87–203. Section 34.
23. Feghali CA, Wright TM. Cytokines in acute and chronic inflammation. Front Biosci. 1997;2:d12–d26. [PubMed]
24. MacEwan DJ. TNF receptor subtype signalling: differences and cellular consequences. Cell Signal. 2002;14:477–492. [PubMed]
25. Yellin MJ, Brett J, Baum D, et al. Functional interactions of T cells with endothelial cells: the role of CD40L-CD40-mediated signals. J Exp Med. 1995;182:1857–1864. [PMC free article] [PubMed]
26. Mangan SH, van Campenhout A, Rush C, et al. Osteoprotegerin upregulates endothelial cell adhesion molecule response to tumor necrosis factor-alpha associated with induction of angiopoietin-2. Cardiovasc Res. 2007;76:494–505. [PMC free article] [PubMed]
27. Griendling KK, FitzGerald GA. Oxidative stress and cardiovascular injury: Part I: basic mechanisms and in vivo monitoring of ROS. Circulation. 2003;108:1912–1916. [PubMed]
28. Reinhart WH. Fibrinogen—marker or mediator of vascular disease? Vasc Med. 2003;8:211–216. [PubMed]
29. Bemelmans MH, Gouma DJ, Buurman WA. Tissue distribution and clearance of soluble murine TNF receptors in mice. Cytokine. 1994;6:608–615. [PubMed]
30. Sarnak MJ, Poindexter A, Wang SR, et al. Serum C-reactive protein and leptin as predictors of kidney disease progression in the Modification of Diet in Renal Disease Study. Kidney Int. 2002;62:2208–2215. [PubMed]
31. Ikizler TA, Morrow JD, Roberts LJ, et al. Plasma F2-isoprostane levels are elevated in chronic hemodialysis patients. Clin Nephrol. 2002;58:190–197. [PubMed]
32. Rule AD, Larson TS, Bergstralh EJ, et al. Using serum creatinine to estimate glomerular filtration rate: accuracy in good health and in chronic kidney disease. Ann Intern Med. 2004;141:929–937. [PubMed]
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