Inflammation, Hemostasis, and the Risk of Kidney Function Decline in the Atherosclerosis Risk in Communities (ARIC) Study

Inflammation, Hemostasis, and the Risk of Kidney Function Decline in the Atherosclerosis Risk in Communities (ARIC) Study Lori D. Bash, MPH,1,2 Thomas P. Erlinger, MD, MPH,3 Josef Coresh, MD, PhD,1,2,4,5 Jane Marsh-Manzi, PhD,2 Aaron R. Folsom, MD, MPH,6 and Brad C. Astor, PhD, MPH1,2,4 Background: Inflammation and hemostasis may increase the risk of kidney function decline; however, data from prospective studies are sparse. Study Design: The Atherosclerosis Risk in Communities (ARIC) Study, a prospective observational cohort. Setting & Participants: We used data from 14,854 middle-aged adults from 4 different US communities. Predictor: Markers of inflammation and hemostasis were examined. Outcomes & Measurements: The risk of kidney function decrease associated with these markers was studied. Glomerular filtration rate (GFR) was calculated from serum creatinine levels using the 4-variable Modification of Diet in Renal Disease (MDRD) Study equation. Chronic kidney disease (CKD) was defined as: (1) a decrease in estimated GFR to less than 60 mL/min/1.73 m2 from greater than 60 mL/min/1.73 m2 at baseline, or (2) a hospitalization discharge or death coded for CKD. Serum creatinine was measured at baseline and the 3- and 9-year follow-up visits. Hazard ratios (HRs) of CKD associated with increased levels of inflammatory and hemostatic variables were estimated by using multivariate Cox proportional hazards regression. Results: 1,787 cases of CKD developed between 1987 and 2004. After adjusting for demographics, smoking, blood pressure, diabetes, lipid levels, prior myocardial infarction, antihypertensive use, alcohol use, year of marker measurement, and baseline renal function using estimated GFR, the risk of incident CKD increased with increasing quartiles of white blood cell count (HR quartile 4 versus quartile 1, 1.30; 95% confidence interval [CI], 1.12 to 1.50; P trend ⫽ 0.001), fibrinogen (HR, 1.25; 95% CI, 1.09 to 1.44; P ⬍ 0.001), von Willebrand factor (HR, 1.46; 95% CI, 1.26 to 1.68; P ⬍ 0.001), and factor VIIIc (HR, 1.39; 95% CI, 1.20 to 1.60; P ⬍ 0.001). A strong inverse association was found between serum albumin level and risk of CKD (HR, 0.63; 95% CI, 0.55 to 0.72; P ⬍ 0.001). No independent association was found with factor VIIc level. Limitations: Although we lacked a direct measure of kidney function, associations were robust to case definitions. Conclusions: Markers of inflammation and hemostasis are associated with greater risk of kidney function decrease. Findings suggest that inflammation and hemostasis are antecedent pathways for CKD. Am J Kidney Dis 53:596-605. © 2009 by the National Kidney Foundation, Inc. INDEX WORDS: Inflammation; hemostasis; kidney; chronic kidney disease; Atherosclerosis Risk in Communities (ARIC).

everal risk factors for atherosclerotic cardiovascular disease, including hypertension and diabetes, also have a role in the development of chronic kidney disease (CKD).1-4 Findings from recent studies suggest that markers of inflammation and hemostasis, many of which predict car-

diovascular disease, may also predict kidney function decrease5,6 and damage.7 The significant overlap between hemostatic and inflammatory processes suggests that these factors should be examined together. Increased white blood cell (WBC) count and low serum albumin level predicted progression of kidney disease in a nationally representative

From the 1Department of Epidemiology, The Johns Hopkins Bloomberg School of Public Health; 2Welch Center for Prevention, Epidemiology and Clinical Research, The Johns Hopkins University, Baltimore, MD; 3Seton Hospital, Austin, TX; 4Department of Medicine, Johns Hopkins University School of Medicine; 5Department of Biostatistics, The Johns Hopkins Bloomberg School of Public Health, Baltimore, MD; and 6Division of Epidemiology and Community Health, University of Minnesota School of Public Health, Minneapolis, MN. Received July 21, 2008. Accepted in revised form October 10, 2008. Originally published online as doi: 10.1053/j.ajkd.2008.10.044 on December 25, 2008.

Because the Editor-in-Chief recused himself from consideration of this manuscript, the Deputy Editor (Daniel E. Weiner, MD, MS) served as Acting Editor-in-Chief. Details of the journal’s procedures for potential editor conflicts are given in the Editorial Policies section of the AJKD website. Address correspondence to Lori D. Bash, MPH, Welch Center for Prevention, Epidemiology and Clinical Research, The Johns Hopkins Bloomberg School of Public Health, 2024 E Monument St, Ste 2-604, Baltimore, MD 21201. E-mail: [email protected] © 2009 by the National Kidney Foundation, Inc. 0272-6386/09/5304-0006$36.00/0 doi:10.1053/j.ajkd.2008.10.044

Editorial, p. 572

S

596

American Journal of Kidney Diseases, Vol 53, No 4 (April), 2009: pp 596-605

Inflammation, Hemostasis, and Kidney Dysfunction

597

sample of adults and in the elderly.5,6,8,9 However, data for other inflammatory markers and markers of hemostasis are conflicting and sparse. C-Reactive protein level was not associated with progression of CKD in the Modification of Diet in Renal Disease (MDRD) Study, but was associated with decreasing kidney function in the elderly.6,10 Plasma fibrinogen and factor VII levels were found to predict kidney function decrease in the elderly in 1 study, but these findings have not been observed in other populations.6 In the present study, we explore the association between circulating levels of several markers of inflammation and hemostasis and decreasing kidney function in a middle-aged population-based cohort, the Atherosclerosis Risk in Communities (ARIC) Study. We hypothesized that increased levels of markers of inflammation and hemostasis are associated with increased risk of kidney function decrease after adjusting for potential confounders and baseline kidney function.

V45.1, or V56), or either hemodialysis (39.95) or peritoneal dialysis (54.98) without simultaneous acute renal failure (584, 586, 788.9, and 958.5). Corresponding ICD-10 codes were used for deaths after ICD-10 implementation. Serum creatinine was measured by using a modified kinetic Jaffé reaction.13 Serum creatinine concentration was corrected for interlaboratory differences and calibrated to the Cleveland Clinic laboratory measurements by subtraction of 0.24 mg/dL at visits 1 and 2 and addition of 0.18 mg/dL at visit 4. GFR was estimated by using the 4-variable MDRD Study equation developed at the Cleveland Clinic: estimated GFR ⫽ 186.3 ⫻ (serum creatinine [mg/dL]⫺1.154) ⫻ (age⫺0.203) ⫻ (0.742 if female) ⫻ (1.21 if African American).14 As a sensitivity analysis, models were run by using an alternative CKD progression definition defined as either: (1) an increase in creatinine level of 0.4 mg/dL or greater from baseline to the 3- or 9-year follow-up visit, representing twice the normal short-term variation in creatinine levels; or (2) a hospitalization or death with CKD.15

METHODS Study Population The ARIC Study is a prospective biracial observational cohort of 15,792 individuals between the ages of 45 and 64 years. Participants were drawn from a probability-based sample from 4 US communities (Forsyth County, NC; Jackson, MS; suburban Minneapolis, MN; and Washington County, MD). Participants took part in examinations starting with the baseline visit between 1987 and 1989. Individuals had follow-up examinations approximately every 3 years at community clinics, as well as annual follow-up telephone interviews. Hospitalized events were ascertained through December 31, 2004. Details of the ARIC cohort have been published elsewhere.11 All participants with prevalent CKD at baseline (baseline estimated glomerular filtration rate [GFR] ⬍ 60 mL/min/ 1.73 m2; n ⫽ 457) were excluded from these analyses.

Outcome Assessment: CKD Incident CKD was defined as either: (1) an estimated GFR decreasing to less than 60 mL/min/1.73 m2, representing stages 3 to 5 of CKD, at the 3- or 9-year follow-up visit; or (2) a death or hospitalization with CKD.12 Deaths and hospitalizations through 2004 were identified through annual participant interviews, local hospital discharge lists, and county death certificates and included all those coded (International Classification of Diseases, Ninth Revision [ICD-9]) for chronic renal disease (581 to 583.91 and 585 to 588.91), hypertensive renal disease (403 to 403.91), hypertensive heart and renal disease (404 to 404.93), unspecified disorder of kidney and ureter (593.9), diabetes with renal manifestations (250.40 to 250.43), kidney transplant, renal dialysis or adjustment/fitting of catheter (V42.0,

Other Measurements Clinic examinations included interviews conducted by trained interviewers collecting demographic and lifestyle characteristics and physiological information, including anthropometrics, blood pressure, and venipuncture. Hematologic measurements were made from blood drawn from the antecubital vein after an 8-hour fast. Processing of samples was conducted according to a standardized protocol, and plasma specimens were stored at ⫺70°C. Detailed methods for blood processing and measurement of hemostatic variables have been published previously.16,17 Fibrinogen was measured by using the thrombin-time titration method; von Willebrand factor (VWF) antigen, by means of enzyme-linked immunosorbent assay; and factor VII and VIII activity (VIIc and VIIIc, respectively), by means of clotting assays.18 Measurements repeated on a sample of participants over several weeks yielded reliability coefficients of 0.72 for fibrinogen, 0.68 for VWF, 0.78 for factor VIIc, and 0.86 for factor VIIIc.19 Prevalent diabetes mellitus was defined as a fasting glucose level of 126 mg/dL or greater, nonfasting glucose level of 200 mg/dL or greater, or history of or treatment for diabetes. Three seated blood pressure measurements were performed by certified technicians using a random-zero sphygmomanometer after 5 minutes of rest. The mean of the last 2 measurements was used for analysis. Self-reported antihypertensive medication use was verified by inspection of bottles. Prevalent myocardial infarction (MI) included a self-reported physician diagnosis or an MI revealed on the electrocardiogram conducted during the baseline visit. Enzymatic methods were used to obtain total plasma cholesterol and triglyceride levels. Low-density lipoprotein (LDL) cholesterol was calculated by using the Friedewald equation20 (exclusive of those with incalculable LDL because of triglyceride values ⬎ 400 mg/dL). Participants with severely increased triglyceride levels and thus missing LDL values were assigned the mean LDL value, and an indicator for the presence of calculated LDL was included in the models. Smoking status was determined by using self-reported cigarette smoking.

598

Analysis Analyses were limited to 14,854 ARIC participants, excluding those missing baseline serum creatinine (n ⫽ 150), diabetes or hypertension (n ⫽ 118), or lipid values (n ⫽ 69); race other than African American or white (n ⫽ 47); African Americans at Minnesota and Washington County field centers (n ⫽ 54); those with prevalent CKD (n ⫽ 457); or those missing values for all hemostatic and inflammatory markers (n ⫽ 43). All analyses were conducted using Stata, version 9.2, software (StataCorp, College Station, TX). Demographic and health characteristics of CKD cases were compared with those of noncases by using ␹2 and t tests. Hemostatic and inflammatory markers were treated as continuous variables in SD units, as well as categorized into quartiles for some analyses. Kaplan-Meier survival estimates were plotted by quartiles of each marker, and differences in survival across quartiles were tested by using log-rank tests. Proportional hazards regression was used to estimate unadjusted and adjusted hazard ratios (HRs) and 95% confidence intervals (CIs). Proportionality of hazards over time was confirmed by using the Schoenfeld test for each factor under study (all P ⬎ 0.06). Multivariate adjusted models included age, race, sex, systolic blood pressure, diabetes status, hypertension medication use, prevalent MI, smoking, alcohol use, log triglycerides, high-density lipoprotein cholesterol level, LDL cholesterol level, year of marker measurement, and estimated baseline GFR. Fully adjusted models also were run separately for cases meeting each CKD outcome criterion. Linearity of associations with CKD was assessed by significance (P ⬍ 0.05) of quadratic terms and plots of residuals. Quadratic terms were significant for serum albumin and factor VIIc levels; however, linear estimates are reported because they were similar and the most easily interpretable. The continuous association between each marker and incident CKD was predicted from a Poisson regression model including a fifth-order polynomial for each marker adjusted to the incidence rate for a 60-year-old white man with a baseline estimated GFR of 90 mL/min/1.73 m2. Additionally, to assess whether the association of the markers with CKD risk was affected by measurement error in baseline estimated GFR, analyses were repeated by using Poisson regression models incorporating measurement error estimation using regression calibration.21-23 Results were similar to those from primary analyses and therefore are not reported.

RESULTS After a mean follow-up of almost 14½ years (for a total of 213,548 person-years), there were 1,787 incident cases of CKD in 14,854 persons. Characteristics of the study population are listed in Table 1. At baseline, persons who subsequently developed CKD were more likely to use hypertension medications, have diabetes, and have higher blood pressure, cholesterol, triglyceride, WBC, fibrinogen, VWF, factor VIIc, and factor VIIIc values. Persons who subsequently

Bash et al

developed CKD also had a higher baseline creatinine level, lower albumin level, and lower baseline estimated GFR compared with persons who did not develop CKD. Kaplan-Meier plots by quartile of each factor are shown in Fig 1. Progressively higher quartiles of WBCs, fibrinogen, factor VIIIc, and VWF and lower quartiles of albumin were associated with greater risk of CKD. Patterns across quartiles of factor VIIc were less consistent. In unadjusted analyses (Table 2), the HR of CKD was significantly greater for the highest compared with the lowest quartile of WBCs, fibrinogen, VWF, factor VIIc, and factor VIIIc. A strong inverse association was found for serum albumin for the highest versus the lowest quartile (HR, 0.68; 95% CI, 0.60 to 0.77). After adjustment for cardiovascular and kidney disease risk factors and baseline kidney function, the relative hazards associated with low albumin level were nearly unaltered (HR, 0.63; 95% CI, 0.55 to 0.72), whereas those associated with greater WBC, fibrinogen, VWF, and factor VIIIc values were somewhat attenuated (Table 2). Associations were generally robust to changes in case definitions. However, defining cases by hospitalization ICD-9 codes (n ⫽ 959) resulted in somewhat stronger associations than defining cases by decreased GFR (n ⫽ 1,085; n ⫽ 257 identified as both types of cases). Lower albumin (P trend ⬍ 0.001) and greater VWF levels (P trend ⫽ 0.04) were significantly related to the risk of decreased GFR. Lower albumin (P ⬍ 0.001) and higher VWF (P trend ⬍ 0.001), fibrinogen (P trend ⫽ 0.001), and factor VIIIc levels (P trend ⫽ 0.02) also were significantly associated with greater risk of a serum creatinine level increase of 0.4 mg/dL or greater. Incidence rates of CKD by concentration of each hemostatic and inflammatory marker that was estimated by using minimally adjusted (for age, race, sex, and estimated GFR) fifth-order polynomial models for each of the markers (Fig 2) show graded associations across the entire range of each marker. These plots were consistent with quadratic terms that were found to be statistically significant for albumin and factor VIIc levels and showed mostly linear relationships with other markers. Assessments for interactions with any factor by sex, race, diabetes, or prior MI are listed in

Inflammation, Hemostasis, and Kidney Dysfunction

599

Table 1. Baseline Characteristics of the Atherosclerosis Risk in Communities Cohort by Incident CKD Status Characteristic

No CKD (n ⫽ 13,067)

CKD (n ⫽ 1,787)

P

Age (y) African American race (%) Men (%) Systolic blood pressure (mm Hg) Diastolic blood pressure (mm Hg) Hypertension medications (%) Total cholesterol (mg/dL) High-density lipoprotein cholesterol (mg/dL) Low-density lipoprotein cholesterol (mg/dL) Triglycerides (mg/dL) Estimated GFR (mL/min/1.73 m2) Creatinine (mg/dL) Diabetes (%) Myocardial infarction history (%) Smoker (%) Never Former Current Current alcohol use (%) Inflammatory and hemostatic markers* Serum albumin (g/dL) Total white blood cells (103/␮L) Fibrinogen (mg/dL) von Willebrand factor (%) Factor VIIc (%) Factor VIIIc (%)

54 ⫾ 5.7 26 45 120 ⫾ 18 73 ⫾ 11 27 214 ⫾ 41 52 ⫾ 17 137 ⫾ 39 107 (77-152) 96 ⫾ 19 0.83 ⫾ 0.17 9.4 3.3

57 ⫾ 5.5 27 47 128 ⫾ 21 75 ⫾ 12 46 221 ⫾ 46 48 ⫾ 17 143 ⫾ 41 126 (90-182) 84.2 ⫾ 20 0.93 ⫾ 0.19 25.8 8.7

⬍0.001 0.09 0.2 ⬍0.001 ⬍0.001 ⬍0.001 ⬍0.001 ⬍0.001 ⬍0.001 ⬍0.001 ⬍0.001 ⬍0.001 ⬍0.001 ⬍0.001 0.1

41.5 32.0 26.5 57.6

41.2 34.1 24.8 48.4

⬍0.001

3.9 ⫾ 0.26 6.1 ⫾ 1.9 300 ⫾ 63 116 ⫾ 47 118 ⫾ 29 129 ⫾ 37

3.8 ⫾ 0.28 6.4 ⫾ 2.1 318 ⫾ 74 131 ⫾ 53 125 ⫾ 33 142 ⫾ 44

⬍0.001 ⬍0.001 ⬍0.001 ⬍0.001 ⬍0.001 ⬍0.001

Note: Values expressed as percent, mean ⫾ SD, or median (interquartile range). Conversion factors for units: total, high-density lipoprotein, or low-density lipoprotein cholesterol in mg/dL to mmol/L, ⫻0.02586; triglycerides in mg/dL to mmol/L, ⫻0.01129; estimated GFR in mL/min/1.73 m2 to mL/s/1.73 m2, ⫻0.01667; serum creatinine in mg/dL to ␮mol/L, ⫻88.4; serum albumin in g/dL to g/L, ⫻10; fibrinogen in mg/dL to umol/L, ⫻0.0256. White blood cells in 103/␮L and 109/L are equivalent. Abbreviations: CKD, chronic kidney disease; GFR, glomerular filtration rate. *Numbers of missing values for markers were fibrinogen, 67; albumin, 0; total white blood cell count, 60; von Willebrand factor, 64; factor VIIc, 350; and factor VIIIc, 72.

Table 3. There was evidence for a stronger association of factor VIIc level with risk of CKD in African Americans compared with whites (P interaction ⫽ 0.03). Albumin, fibrinogen, and factor VIIIc levels were all more strongly associated with CKD in persons with diabetes compared with persons without diabetes (P interactions ⬍ 0.01). There was borderline evidence of a stronger association of WBC and fibrinogen values with risk of CKD in African Americans compared with whites (P ⫽ 0.08 and P ⫽ 0.09, respectively). Analyses using race-specific quartiles resulted in similar HRs for which WBC quartiles were still more strongly associated with incident CKD in African Americans than whites (P interaction ⫽ 0.004). WBC and VWF values were more strongly associated with CKD risk in indi-

viduals with compared with those without diabetes (P ⫽ 0.06 for both), whereas risk in men was more strongly associated with fibrinogen level than risk in women (P ⫽ 0.07). WBC, fibrinogen, VWF, and factor VIIIc values were positively associated with risk of CKD in all subgroups examined. It is not surprising that VWF and factor VIIIc levels have similar associations with risk of CKD because these 2 factors are highly correlated (because of plasma binding), as listed in Table 4.

DISCUSSION Cardiovascular disease and CKD share several common antecedents, including increased blood pressure, dyslipidemia, and diabetes.1,3,4 Our results suggest that markers of inflammation and hemostasis also are associated with risk

600

Bash et al

Figure 1. Chronic kidney disease (CKD)–free time for inflammatory and hemostatic markers by quartiles (Q), Atherosclerosis Risk in Communities Study 1987-2004. Serum albumin level is reported in g/dL; total white blood cell count, 103/␮L; and fibrinogen, mg/dL. Log-rank P ⬍ 0.001 for all. Abbreviation: VWF, von Willebrand factor.

of kidney function decrease in middle-aged persons. In this study, increased WBC, fibrinogen, factor VIIIc, and VWF levels and decreased serum albumin levels were associated with greater occurrences of CKD. Among the strengths of the present study are the large population-based sample and the relatively long follow-up (mean follow-up, ⬎14 years). In addition, the ARIC cohort is very well characterized and has a large number of African Americans, a group at particularly high risk of CKD. This study assessed multiple inflammatory and hemostatic markers and included extensive data for potential confounders. Because measures for albuminuria were available at only visit 4, we could exclude only prevalent cases of CKD stage 3 or higher; therefore, our sample may include individuals with prevalent CKD stage 1 or 2 at baseline. Also among the limitations of the present study is the lack of a direct measure of kidney function. Direct measurement of kidney function is impractical in large cohorts, and this limitation is common in such studies. However, we found associations to be robust to case definitions because they persisted when defining cases based solely on coded hospitalizations and deaths. The stronger association observed when limiting the case definition to hospitalizations and deaths likely is caused by the greater specificity of this definition

and limiting to more severe cases (cases in individuals admitted to the hospital). Our finding of an inverse relationship between serum albumin level and risk of decreasing kidney function could reflect inadequate nutrition and not inflammation. As evidence against this hypothesis, the ARIC cohort comprised middleaged free-living individuals in whom significant malnutrition is unlikely. Findings with respect to serum albumin level as a predictor of kidney function decrease are consistent with findings from 2 other large cohorts and consistent with levels of serum albumin shown to predict coronary heart disease.5,6,24 This study also lacks measurement of urinary albumin, requiring CKD to be defined solely on hospitalizations and estimated GFR (there was no estimate of the latter after 1999). Last, as in any observational study, there is potential for reverse causation. However, we also observed the relative risk of CKD to be stronger with increasing follow-up in several markers (results not shown), which helps rule out these concerns. Our findings are consistent with those of the Cardiovascular Health Study (CHS), which found several markers of inflammation and hemostasis, including increased C-reactive protein, WBC, fibrinogen, and factor VIIc levels and decreased

Inflammation, Hemostasis, and Kidney Dysfunction

601

Table 2. Risk of Chronic Kidney Disease by Quartile of Hemostatic or Inflammatory Factor, Atherosclerosis Risk in Communities 1987-2004 Factor

Serum albumin Mean (range) Events Incidence/1,000 person-years Unadjusted HR (95% CI) Adjusted* HR (95% CI) Total white blood cell count Mean (range) Events Incidence/1,000 person-years Unadjusted HR (95% CI) Adjusted* HR (95% CI) Fibrinogen Mean (range) Events Incidence/1,000 person-years Unadjusted HR (95% CI) Adjusted* HR (95% CI) von Willebrand factor Mean (range) Events Incidence/1,000 person-years Unadjusted HR (95% CI) Adjusted* HR (95% CI) Factor VIIc Mean (range) Events Incidence/1,000 person-years Unadjusted HR (95% CI) Adjusted* HR (95% CI) Factor VIIIc Mean (range) Events Incidence/1,000 person-years Unadjusted HR (95% CI) Adjusted* HR (95% CI)

Quartile 1

Quartile 2

Quartile 3

Quartile 4

P Trend

3.6 (2.3-3.7) 640 10.0 1.00 1.00

3.9 (3.8-3.9) 536 8.1 0.81 (0.72-0.90) 0.79 (0.70-0.89)

4.0 (4.0-4.0) 235 8.0 0.79 (0.68-0.92) 0.75 (0.64-0.88)

4.2 (4.1-5.1) 376 6.8 0.68 (0.60-0.77) 0.63 (0.55-0.72)

⬍0.001 ⬍0.001 ⬍0.001

4.1 (1.2-4.8) 401 7.0 1.00 1.00

5.3 (4.9-5.8) 398 7.3 1.03 (0.90-1.18) 1.00 (0.86-1.15)

6.4 (5.9-7.1) 442 8.3 1.18 (1.03-1.35) 0.99 (0.86-1.14)

8.8 (7.2-42) 539 11.1 1.58 (1.39-1.80) 1.30 (1.13-1.50)

⬍0.001 ⬍0.001 0.001

233 (97-260) 375 6.6 1.00 1.00

277 (261-294) 373 7.0 1.05 (0.91-1.22) 0.91 (0.79-1.06)

314 (295-336) 427 8.1 1.22 (1.06-1.40) 0.92 (0.80-1.06)

388 (337-858) 603 12.0 1.84 (1.61-2.09) 1.25 (1.09-1.44)

⬍0.001 ⬍0.001 0.001

68 (22-83) 298 5.3 1.00 1.00

96 (84-109) 415 7.6 1.42 (1.22-1.65) 1.18 (1.02-1.37)

124 (110-142) 449 8.4 1.59 (1.37-1.84) 1.19 (1.02-1.38)

183 (143-764) 616 12.4 2.35 (2.05-2.70) 1.46 (1.26-1.68)

⬍0.001 ⬍0.001 ⬍0.001

86 (17-99) 373 6.9 1.00 1.00

108 (100-115) 341 6.6 0.94 (0.82-1.09) 0.90 (0.77-1.04)

125 (116-135) 454 8.4 1.21 (1.06-1.39) 0.99 (0.86-1.14)

157 (136-616) 558 11.2 1.62 (1.42-1.85) 1.11 (0.96-1.28)

⬍0.001 ⬍0.001 0.06

90 (18-105) 322 5.6 1.00 1.00

116 (106-126) 384 7.1 1.28 (1.10-1.48) 1.07 (0.92-1.25)

138 (127-150) 452 8.7 1.57 (1.36-1.81) 1.16 (1.01-1.35)

182 (151-488) 618 12.5 2.28 (1.99-2.61) 1.39 (1.20-1.60)

⬍0.001 ⬍0.001 ⬍0.001

Abbreviations: CI, confidence interval; HR, hazard ratio. *Adjusted for age, race, sex, smoking, blood pressure, diabetes, log triglycerides, high-density lipoprotein cholesterol level, low-density lipoprotein cholesterol level, prior myocardial infarction, antihypertensive use, alcohol use, year of marker measurement, and baseline estimated glomerular filtration rate.

albumin and hemoglobin levels to be associated with increased risk of reduced kidney function.6,8 They are also consistent with findings from follow-up of the Second National Health and Nutrition Survey (NHANES II), in which greater WBC and lower serum albumin values were associated with increased risk of kidney failure or death related to kidney disease.5 In addition, increased WBC count has been associated with more rapid kidney disease progression in patients with type 1 diabetes.9

Fibrinogen was inversely associated with estimated GFR in patients with CKD in the Heart and Soul Study, but was not associated in those with normal kidney function.25 These discrepancies could be explained by significant differences in the populations studied, with more polycystic kidney disease or primary glomerular disease and no person with diabetic kidney disease in the MDRD Study. Our study population is similar to the CHS study population, although younger, which may explain why our findings generally are consistent with those

602

Bash et al

Figure 2. Adjusted incidence rates and 95% confidence intervals (shaded area) of chronic kidney disease by each hemostatic and inflammatory factor, Atherosclerosis Risk in Communities Study 1987-2004. The curve represents minimally adjusted incidence rates based on a Poisson regression model including a fifth-order polynomial for (A) albumin, (B) white blood cell (WBC), (C) fibrinogen, (D) von Willebrand factor, (E) factor VIIc, or (F) factor VIIIc values adjusted to the incidence rate for a 60-year-old white man with a baseline estimated glomerular filtration rate of 90 mL/min/1.73 m2. The histogram represents the frequency distribution of A to E in the (5th to 95th percentile) of the study sample. Serum albumin level is reported in g/dL; total WBC count, 103/␮L; and fibrinogen, mg/dL.

of the CHS. However, there are important differences between findings in the CHS and those presented here. Factor VIIc level was associated with decreased risk of GFR decrease in the CHS,

but factor VIIc level was not associated with decreased estimated GFR in our study. These results suggest that factor VIIc is not an important hemostatic predictor of decreasing kidney

Table 3. Multivariate Adjusted Interactions of Race, Sex, Diabetes, and Prevalent Cardiovascular Disease by Hemostatic and Inflammatory Markers, Atherosclerosis Risk in Communities 1987-2004 Hazard Ratio* (95% confidence interval)

Characteristic (cases/total)

Albumin

White Blood Cell Count

Fibrinogen

von Willebrand Factor

Factor VIIc

Factor VIIIc

Men (835/6,742) Women (957/8,181) P interaction African American (494/3,885) White (1,298/11,038) P interaction Diabetes (462/1,693) No diabetes (1,330/13,230) P interaction Prevalent myocardial infarction (1,615/14,140) No prevalent myocardial infarction (154/581) P interaction

0.80 (0.74-0.87) 0.83 (0.78-0.89) 0.3 0.74 (0.68-0.81) 0.84 (0.79-0.89) 0.2 0.71 (0.65-0.78) 0.86 (0.81-0.91) ⬍0.001

1.12 (1.04-1.21) 1.09 (1.03-1.16) 0.6 1.14 (1.07-1.23) 1.10 (1.04-1.17) 0.08 1.11 (1.01-1.22) 1.12 (1.06-1.18) 0.06

1.18 (1.10-1.26) 1.10 (1.03-1.17) 0.07 1.24 (1.14-1.34) 1.10 (1.04-1.17) 0.09 1.31 (1.21-1.42) 1.09 (1.03-1.15) 0.008

1.14 (1.07-1.22) 1.12 (1.05-1.18) 0.3 1.15 (1.07-1.23) 1.12 (1.06-1.19) 0.7 1.20 (1.11-1.29) 1.09 (1.04-1.15) 0.06

1.09 (1.00-1.18) 1.04 (0.97-1.10) 0.8 1.11 (1.03-1.20) 1.03 (0.97-1.09) 0.03 1.05 (0.97-1.14) 1.05 (0.99-1.11) 0.9

1.19 (1.11-1.27) 1.12 (1.06-1.19) 0.08 1.20 (1.12-1.29) 1.12 (1.05-1.19) 0.3 1.27 (1.18-1.37) 1.07 (1.01-1.14) ⬍0.001

0.90 (0.75-1.08)

1.19 (1.01-1.41)

1.15 (0.97-1.35)

1.13 (0.99-1.30)

0.99 (0.84-1.17)

1.27 (1.09-1.47)

0.80 (0.76-0.84) 0.1

1.13 (1.08-1.19) 0.3

1.14 (1.08-1.19) 0.7

1.13 (1.08-1.19) 0.5

1.06 (1.01-1.12) 0.2

1.14 (1.09-1.20) 0.8

Note: Adjusted for age, race, sex, smoking, blood pressure, diabetes, log triglycerides, high-density lipoprotein cholesterol level, low-density lipoprotein cholesterol level, prior myocardial infarction, antihypertensive use, alcohol use, year of marker measurement, and baseline estimated glomerular filtration rate. *Hazard ratios represent the risk difference for a 1-SD increment in marker.

Inflammation, Hemostasis, and Kidney Dysfunction

603

Table 4. Pairwise Correlation Matrix of Each Hemostatic and Inflammatory Factor

Albumin P value White blood cell count P value Fibrinogen P value von Willebrand factor P value Factor VIIc P value Factor VIIIc P value

Albumin

White Blood Cell Count

Fibrinogen

von Willebrand Factor

Factor VIIc

Factor VIIIc

1 — ⫺0.0869 ⬍0.001 ⫺0.2242 ⬍0.001 ⫺0.1672 ⬍0.001 ⫺0.0245 0.003 ⫺0.1989 ⬍0.001

— — 1 — 0.2846 ⬍0.001 0.0595 ⬍0.001 0.0351 ⬍0.001 0.0373 ⬍0.001

— — — — 1 — 0.2495 ⬍0.001 0.1163 ⬍0.001 0.2796 ⬍0.001

— — — — — — 1 — 0.0385 ⬍0.001 0.7078 ⬍0.001

— — — — — — — — 1 — 0.1351 ⬍0.001

— — — — — — — — — — 1 —

function. Conversely, strong associations were seen for factor VIIIc and VWF levels with decreasing kidney function in this study. These factors were not measured in the CHS. Unlike the CHS, we found similar associations for all markers in both African Americans and whites. We found that associations were stronger in individuals with diabetes than in those without diabetes for albumin, WBC, fibrinogen, VWF, and factor VIIIc values. The reasons for these findings are speculative. Advanced glycation end products (AGEs; glycated proteins) are driven forward by hyperglycemia, and increased AGE levels are observed even in persons with uncomplicated diabetes.26 AGEs, which stimulate inflammatory cytokine production, also have been implicated in the progression to diabetic nephropathy27,28 and involved in inflammatory disorders.27 It is possible that increased inflammatory factor levels in individuals with diabetes represent increased AGE levels, in addition to a measure of inflammation from other causes.29 Because individuals with diabetes more often are overweight and obese, it also is possible that adipose tissue serves as a source of inflammation.30 Additional adjustment for body mass index or waist circumference did not meaningfully change the results. These findings could have important implications for the progression of diabetes-related nephropathy. Despite stronger associations in diabetic individuals for several risk factors studied, increased levels of inflammatory and hemostatic factors were also associated with incident CKD in persons without diabetes. The analytes studied also may be markers or mediators of underlying disease, such as endothe-

lial damage. Albuminuria, a potent predictor of kidney disease progression,31 is believed to represent systemic endothelial cell dysfunction.32 Unfortunately, we did not have measures of urinary albumin excretion available with which to test this hypothesis. Increased levels of these analytes also are indicative of a prothrombotic state, which may directly or indirectly impact on progression of kidney disease. For example, hypertensive patients have been shown to experience changes in platelet physiological characteristics; specific changes differed by the presence or absence of target-organ damage and may be implicative of underlying cardiovascular disease pathological states, such as that related to abnormal platelet activation and a procoagulant state.33 There also has been other evidence of an activated coagulation system in hypertensive patients with mild to moderate kidney dysfunction.34 These results add to a growing body of evidence suggesting that inflammation and hemostasis are important pathways in the progression of kidney disease that are independent of major traditional risk factors. Although a specific mechanism that can be targeted for intervention has not been identified, these findings may help focus investigations into pathological mechanisms for CKD and have implications for the development of prevention and treatment strategies designed to reduce the impact of kidney disease in the population.

ACKNOWLEDGEMENTS We thank the staff and participants of the ARIC Study for their important contributions.

604

Bash et al

Support: The ARIC Study is carried out as a collaborative study supported by National Heart, Lung, and Blood Institute contracts N01-HC-55015, N01-HC-55016, N01-HC55018, N01-HC-55019, N01-HC-55020, N01-HC-55021, and N01-HC-55022. The work described in this article was supported in part by Grants 5R01-DK-076770-02, 5T32-HL007024-33, and 5T32-RR-023253-02 from the National Institutes of Health. Financial Disclosure: None.

REFERENCES 1. Coresh J, Astor BC, Greene T, Eknoyan G, Levey AS: Prevalence of chronic kidney disease and decreased kidney function in the adult US population: Third National Health And Nutrition Examination Survey. Am J Kidney Dis 41:112, 2003 2. Collins AJ, Kasiske B, Herzog C, et al, for the US Renal Data System Excerpts from the United States Renal Data System: 2004 Annual Data Report: Atlas of end-stage renal disease in the United States. Am J Kidney Dis 45:A5A7, 2005 3. Haroun MK, Jaar BG, Hoffman SC, Comstock GW, Klag MJ, Coresh J: Risk factors for chronic kidney disease: A prospective study of 23,534 men and women in Washington County, Maryland. J Am Soc Nephrol 14:2934-2941, 2003 4. Klag MJ, Whelton PK, Randall BL, et al: Blood pressure and end-stage renal disease in men. N Engl J Med 334:13-18, 1996 5. Erlinger TP, Tarver-Carr ME, Powe NR, et al: Leukocytosis, hypoalbuminemia, and the risk for chronic kidney disease in US adults. Am J Kidney Dis 42:256-263, 2003 6. 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 15:31843191, 2004 7. Kshirsagar AV, Bomback AS, Bang H, et al: Association of C-reactive protein and microalbuminuria (from the National Health And Nutrition Examination Surveys, 1999 to 2004). Am J Cardiol 101:401-406, 2008 8. Bleyer AJ, Shemanski LR, Burke GL, Hansen KJ, Appel RG: Tobacco, hypertension, and vascular disease: Risk factors for renal functional decline in an older population. Kidney Int 57:2072-2079, 2000 9. Orchard TJ, Chang YF, Ferrell RE, Petro N, Ellis DE: Nephropathy in type 1 diabetes: A manifestation of insulin resistance and multiple genetic susceptibilities? Further evidence from the Pittsburgh Epidemiology of Diabetes Complication Study. Kidney Int 62:963-970, 2002 10. 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 62:2208-2215, 2002 11. ARIC Investigators: The Atherosclerosis Risk In Communities (ARIC) Study: Design and objectives. Am J Epidemiol 129:687-702, 1989 12. Shoham DA, Vupputuri S, Diez Roux AV, et al: Kidney disease in life-course socioeconomic context: The Atherosclerosis Risk in Communities (ARIC) Study. Am J Kidney Dis 49:217-226, 2007

13. National Heart, Lung, and Blood Institute, Atherosclerosis Risk in Communities (ARIC) Study: Operations manual no. 10: clinical chemistry determinations. Version 1.0. Chapel Hill, NC, ARIC Coordinating Center, School of Public Health, University of North Carolina, 1987 14. Levey AS, Bosch JP, Lewis JB, Greene T, Rogers N, Roth D: A more accurate method to estimate glomerular filtration rate from serum creatinine: A new prediction equation. Modification of Diet in Renal Disease Study Group. Ann Intern Med 130:461-470, 1999 15. Eckfeldt JH, Chambless LE, Shen YL: Short-term, within-person variability in clinical chemistry test results. Experience from the Atherosclerosis Risk In Communities study. Arch Pathol Lab Med 118:496-500, 1994 16. Papp AC, Hatzakis H, Bracey A, Wu KK: ARIC hemostasis study—I. Development of a blood collection and processing system suitable for multicenter hemostatic studies. Thromb Haemost 61:15-19, 1989 17. National Heart, Lung, and Blood Institute, Atherosclerosis Risk in Communities (ARIC) Study: Operation manual no. 9: hemostasis determinations. Version 1.0. Chapel Hill, NC, ARIC Coordinating Center, School of Public Health, University of North Carolina, 1987 18. Folsom AR, Rosamond WD, Shahar E, et al: Prospective study of markers of hemostatic function with risk of ischemic stroke. The Atherosclerosis Risk in Communities (ARIC) Study Investigators. Circulation 100:736742, 1999 19. Chambless LE, McMahon R, Wu K, Folsom A, Finch A, Shen YL: Short-term intraindividual variability in hemostasis factors. The ARIC Study. Atherosclerosis Risk in Communities intraindividual variability study. Ann Epidemiol 2:723-733, 1992 20. Friedewald WT, Levy RI, Fredrickson DS: Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clin Chem 18:499-502, 1972 21. Carroll R, Stefanski L: Approximate quasilikelihood estimation in models with surrogate predictors. J Am Stat Assoc 85:652-663, 1990 22. Gleser LJ: Improvements of the naïve approach to estimation in nonlinear errors-in-variables regression models, in Brown PJ, Fuller WA (eds): Statistical Analysis of Measurement Error Models and Applications. Providence, RI, American Mathematical Society, 1990, pp 99-114 23. Hardin J, Schmiediche H, Carroll R: The regression calibration method for fitting generalized linear models with additive measurement error. Stata J 3:361-372, 2003 24. Danesh J, Collins R, Appleby P, Peto R: Association of fibrinogen, C-reactive protein, albumin, or leukocyte count with coronary heart disease: Meta-analyses of prospective studies. JAMA 279:1477-1482, 1998 25. Singh D, Whooley MA, Ix JH, Ali S, Shlipak MG: Association of cystatin C and estimated GFR with inflammatory biomarkers: The Heart and Soul Study. Nephrol Dial Transplant 22:1087-1092, 2007 26. Huebschmann AG, Regensteiner JG, Vlassara H, Reusch JE: Diabetes and advanced glycoxidation end products. Diabetes Care 29:1420-1432, 2006

Inflammation, Hemostasis, and Kidney Dysfunction

605

27. Cooper ME: Importance of advanced glycation end products in diabetes-associated cardiovascular and renal disease. Am J Hypertens 17 (12 pt 2):31S-38S, 2004 28. Forbes JM, Thallas-Bonke V, Cooper ME, Thomas MC: Advanced glycation: How are we progressing to combat this web of sugar anomalies in diabetic nephropathy. Curr Pharm Des 10:3361-3372, 2004 29. Forbes JM, Fukami K, Cooper ME: Diabetic nephropathy: Where hemodynamics meets metabolism. Exp Clin Endocrinol Diabetes 115:69-84, 2007 30. Pou KM, Massaro JM, Hoffmann U, et al: Visceral and subcutaneous adipose tissue volumes are cross-sectionally related to markers of inflammation and oxidative stress: The Framingham Heart Study. Circulation 116:1234-1241, 2007 31. Sika M, Lewis J, Douglas J, et al, for the AASK Group: Baseline characteristics of participants in the African

American Study of Kidney disease and Hypertension (AASK) clinical trial and cohort study. Am J Kidney Dis 50:78-89, 89.e1, 2007 32. Stehouwer CD, Nauta JJ, Zeldenrust GC, Hackeng WH, Donker AJ, den Ottolander GJ: Urinary albumin excretion, cardiovascular disease, and endothelial dysfunction in non-insulin-dependent diabetes mellitus. Lancet 340:319323, 1992 33. Nadar SK, Blann AD, Kamath S, Beevers DG, Lip GY: Platelet indexes in relation to target organ damage in high-risk hypertensive patients: A substudy of the AngloScandinavian cardiac outcomes trial (ASCOT). J Am Coll Cardiol 44:415-422, 2004 34. Catena C, Zingaro L, Casaccio D, Sechi LA: Abnormalities of coagulation in hypertensive patients with reduced creatinine clearance. Am J Med 109:556-561, 2000