Methods

The CRIC study population is a racially and ethnically diverse cohort of men and women aged 21 to 74 years with mild-to-moderate renal disease, approximately half of which have diabetes. The CRIC participants were recruited from May 2003 to August 2008 from 7 clinical centers in the United States. The identification of subjects was facilitated through searches of laboratory databases, medical records, and referrals from health care providers. Subjects with cirrhosis, HIV infection, polycystic kidney disease, or renal cell carcinoma and those on dialysis or recipients of a kidney transplant or those taking immunosuppressant drugs were excluded from study participation. An eGFR entry criterion (20 to 70 ml/min/1.73 m ² ) was used as an enrollment criterion to limit the proportion of older subjects who were recruited with age-related diminutions of GFR but otherwise nonprogressive CKD. A total of 3,939 CRIC participants were screened for this analysis. Of those, 2,068 had baseline noncontrast CT scans with AVC quantification and 1,964 had CT scans between their baseline and year 3 visit. These 1,964 patients were included in the unadjusted analysis, and a subset of those with nonmissing eGFR calculated by the CRIC equation was used for the adjusted analyses (n = 1,923). Data used in the analyses were taken from first noncontrast CT scan visit with the exception of the following variables which were taken from the baseline study visit: total metabolic equivalent (MET sum, MET h/wk), hemoglobin A1c (percentage), 24-hour urine albumin (gram per 24 hours), high-sensitivity CRP, uric acid (milligrams per deciliter), total plasma homocysteine (micromoles per liter), phosphate (milligrams per deciliter), total parathyroid hormone (PTH, picogram per milliliter), and Lp(a) (milligrams per deciliter).

This study was approved by the institutional review boards from each of the participating clinical centers and the scientific and data co-ordinating center. A written informed consent was obtained from all participants. This study also conformed to the Health Insurance Portability and Accountability Act guidelines.

Estimated glomerular filtration rate (eGFR) was computed using the Modification of Diet in Renal Disease Study equation. CKD was defined as an eGFR <60 ml/min/1.73 m ² based on the National Kidney Foundation’s Kidney disease Outcome Quality Initiative guidelines.

All CRIC participants included in this analysis underwent baseline noncontrast CT scans, which were analyzed for both coronary artery and AVC. Spatial resolution for each system was 1.15 mm ³ for multidetector detector row CT (0.68 × 0.68 × 2.50 mm) and 1.38 mm ³ for electron beam CT (0.68 × 0.68 × 3.00 mm). Full details concerning the equipment, scanning methods, and CT quality control in CRIC, including results of coronary artery calcium associations with GFR, have been reported previously.

All scans were sent to a central CRIC CT reading center (Harbor-UCLA Research and Education Institute, Los Angeles, California). Calcium strongly attenuates x-rays, appears bright on CT scans, and is readily differentiated from surrounding tissue. All scans were analyzed with a commercially available software package (Neo Imagery Technologies, City of Industry, California). An attenuation threshold of 130 Hounsfield units (Hu) and a minimum of 3 contiguous pixels were used for identification of a calcific lesion. Each focus exceeding the minimum criteria was scored using the algorithm developed by Agatston et al, calculated by multiplying the lesion area by a density factor derived from the maximal Hu within this area. The density factor was assigned in the following manner: 1 for lesions with peak attenuation of 130 to 199 Hu, 2 for lesions with peak attenuation of 200 to 299 Hu, 3 for lesions with peak attenuation of 300 to 399 Hu, and 4 for lesions with peak attenuation >400 Hu. Consistent with previous methodology, lesion was classified as total AVC if it resided within the aortic valve leaflets or aortic annulus, aortic sinuses, and aortic wall at the level of the aortic valve and if there were 3 contiguous pixels of at least 130 Hu in brightness. Single-lesion measurements were then summed to give a total Agatston score. If no lesions reached threshold values, the Agatston score was recorded as zero.

The study population was divided into 3 groups based on the presence and severity of AVC, where total AVC of 0 is no disease, AVC = 0 to 100 represents mild-to-moderate disease, and ≥100 represents severe disease. Demographic and baseline characteristics were described using mean (SD) and/or median (interquartile range) for continuous variables and frequency (percent) for categorical variable. Analysis of variance or Wilcoxon rank-sum test was used to compare the distribution of continuous variables across the 3 AVC categories. The chi-square test was used for categorical variables. The mean and median AVC scores and the percent of participants in each AVC category were reported for different CKD stages. Multinomial logistic regression models were fit to adjust for traditional AVC risk factors (model 1), novel AVC risk factors (including CRP, Lp(a), uric acid, and homocysteine [model 2]), and bone metabolism risk factors (model 3) in a sequential fashion. Subgroup analyses by race (black vs nonblack) were done in the final model (model 3) in which all risk factors were included. A statistical significance threshold of α = 0.05 was used.

Results

Table 1 summarizes demographics and baseline characteristics from the CRIC database participants who underwent baseline CT with calcium scoring of their aortic valves (n = 1,964). The participants were grouped based on the presence and severity of AVC. Increasing age, body mass index, waist circumference, blood pressure, cholesterol, hemoglobin A1c, and decreased physical activity and a history of diabetes, hypertension, or cardiovascular disease were all independently associated with increased AVC in this unadjusted analysis. Novel cardiovascular risk factors such as high-sensitivity CRP, uric acid, total plasma homocysteine, and Lp(a) were also associated with increased AVC. The average eGFR of the cohort was 44.6 ml/min/1.73 m ² , and decreased eGFR was associated with increased AVC (p <0.0001). Figure 1 illustrates this inverse relation between eGFR and aortic valve calcification across the study population.

Mean aortic valve calcification by eGFR illustrates the inverse relation between aortic valve calcification and eGFR. Dashed lines represent 95% confidence intervals.

Table 2 summarizes the graded relation between stage of CKD and increased AVC (p = 0.0082). Patients with more significant renal impairment had greater prevalence of AVC and more severe AVC with a higher burden of calcium. A clear difference in prevalence and severity of calcium was noted between patients with stage 3A and stage 3B CKD.

Multinomial regression models were used to examine the relation of AVC and CKD with traditional and novel cardiovascular risk factors and markers of bone metabolism in a sequential manner ( Table 3 ). Model 1 examined eGFR and traditional cardiovascular risk factors, where eGFR was independently associated with severity of AVC. It also identified age, race, history of cardiovascular disease, diabetes, systolic blood pressure, high cholesterol, and low high-density lipoprotein as independent factors associated with more severe AVC. Of these risk factors, age and hyperlipidemia had the strongest association with AVC. A second model added novel cardiovascular risk factors to model 1. When adjusting for these novel risk factors, eGFR was no longer independently associated with AVC burden. Furthermore, CRP and plasma homocysteine were additional risk factors that were independently associated with AVC. In this analysis, Lp(a) and uric acid were not independently associated with AVC. From model 2, a third model incorporated markers of bone metabolism identified in the unadjusted analysis, including calcium, phosphate, and PTH. eGFR and these markers were not independently associated with AVC.

Table 3

Odds ratios from the adjusted multinomial regression models including traditional (model 1), novel (model 2), and bone metabolism (model 3) risk factors of AVC. For the following continuous variables, the odds ratio is per standard deviation of the value: eGFR, age, waist circumference, total METs at baseline, LDL, HDL, and phosphate. The natural log of one plus the following continuous variables was used: hs-CRP, lipoprotein a, total PTH, 24 hours urine protein. The following variables were from baseline measurements: total MET, hsCRP, uric acid, homocysteine, lipoprotein a, and total PTH

	Unadjusted Model (n=1923)			Model 1: Traditional CV risk factors (n=1691)			Model 2: Novel CV risk factors (n=1470)			Model 3: Markers of bone metabolism (n=1442)
	Total AVC			Total AVC			Total AVC			Total AVC
	>0 – 100	100+	p	>0 – 100	100+	p	>0 – 100	100+	p	>0 – 100	100+	p
eGFR using CRIC equation	0.84 (0.76, 0.94)	0.60 (0.53, 0.69)	<.001	1.08 (0.92, 1.27)	0.79 (0.65, 0.97)	0.015	1.14 (0.94, 1.40)	0.97 (0.76, 1.24)	0.297	1.12 (0.90, 1.39)	0.95 (0.72, 1.24)	0.41
Participant Age				2.87 (2.37, 3.48)	6.04 (4.64, 7.85)	<.001	3.03 (2.45, 3.74)	6.33 (4.72, 8.50)	<.001	3.15 (2.53, 3.93)	6.85 (5.04, 9.30)	<.001
Black				0.90 (0.66, 1.22)	0.56 (0.39, 0.80)	0.005	0.65 (0.46, 0.94)	0.36 (0.24, 0.55)	<.001	0.66 (0.45, 0.95)	0.38 (0.25, 0.59)	<.001
Female				1.21 (0.90, 1.65)	0.95 (0.67, 1.36)	0.3	1.20 (0.85, 1.69)	1.08 (0.72, 1.62)	0.59	1.14 (0.79, 1.63)	1.02 (0.67, 1.57)	0.775
Cardio-Vascular Disease				1.41 (1.04, 1.93)	2.17 (1.56, 3.03)	<.001	1.26 (0.89, 1.77)	2.00 (1.39, 2.90)	<.001	1.18 (0.84, 1.67)	1.92 (1.32, 2.79)	0.002
Current Smoker				0.99 (0.63, 1.56)	1.33 (0.80, 2.21)	0.485	0.91 (0.55, 1.50)	0.94 (0.51, 1.72)	0.927	0.91 (0.55, 1.52)	0.89 (0.48, 1.66)	0.911
Body Mass Index (kg/mˆ2)				1.01 (0.97, 1.06)	1.01 (0.96, 1.06)	0.807	1.01 (0.97, 1.06)	1.00 (0.95, 1.05)	0.813	1.01 (0.97, 1.06)	1.00 (0.95, 1.05)	0.779
Waist Circumference (cm)				0.98 (0.74, 1.29)	1.08 (0.79, 1.48)	0.801	0.93 (0.69, 1.25)	1.01 (0.72, 1.43)	0.85	0.92 (0.68, 1.25)	1.01 (0.71, 1.43)	0.822
Total MET sum (METhrs/week)				0.99 (0.86, 1.14)	0.96 (0.80, 1.15)	0.883	1.03 (0.89, 1.19)	0.96 (0.79, 1.17)	0.793	1.04 (0.89, 1.20)	0.95 (0.78, 1.17)	0.72
Diabetes				1.90 (1.42, 2.55)	1.86 (1.34, 2.60)	<.001	2.01 (1.46, 2.77)	1.82 (1.26, 2.64)	<.001	1.89 (1.36, 2.63)	1.87 (1.27, 2.74)	<.001
Systolic BP (mmHg)				1.00 (1.00, 1.01)	1.01 (1.00, 1.02)	0.009	1.01 (1.00, 1.02)	1.02 (1.01, 1.03)	0.001	1.01 (1.00, 1.02)	1.02 (1.01, 1.03)	0.001
High Cholesterol				1.66 (1.11, 2.49)	2.44 (1.41, 4.23)	0.002	1.98 (1.26, 3.09)	2.54 (1.41, 4.59)	<.001	2.04 (1.29, 3.22)	2.55 (1.39, 4.66)	<.001
Low-density Lipoprotein (mg/dL)				0.99 (0.85, 1.14)	1.16 (0.99, 1.37)	0.11	0.96 (0.82, 1.13)	1.16 (0.96, 1.39)	0.127	0.96 (0.82, 1.13)	1.13 (0.93, 1.36)	0.242
High-density Lipoprotein (mg/dL)				1.13 (0.98, 1.30)	0.92 (0.77, 1.10)	0.051	1.11 (0.95, 1.30)	0.93 (0.76, 1.13)	0.148	1.12 (0.95, 1.31)	0.94 (0.77, 1.15)	0.185
24-hour urine protein				1.05 (0.79, 1.40)	0.92 (0.66, 1.27)	0.704	1.01 (0.73, 1.40)	0.93 (0.63, 1.35)	0.886	0.96 (0.68, 1.34)	0.98 (0.66, 1.45)	0.966
High Sensitivity CRP							1.17 (0.97, 1.42)	1.36 (1.10, 1.69)	0.019	1.17 (0.97, 1.42)	1.34 (1.08, 1.67)	0.03
Uric Acid (mg/dL)							1.01 (0.93, 1.10)	1.06 (0.96, 1.18)	0.444	1.01 (0.92, 1.10)	1.08 (0.97, 1.20)	0.329
Total Plasma Homocysteine							1.01 (0.98, 1.04)	1.08 (1.04, 1.12)	<.001	1.01 (0.98, 1.05)	1.08 (1.04, 1.12)	<.001
Lipoprotein(a)(mg/dl)							1.12 (1.00, 1.27)	1.10 (0.96, 1.27)	0.139	1.13 (0.99, 1.27)	1.10 (0.95, 1.27)	0.15
Calcium (mg/dL)										0.77 (0.56, 1.06)	1.02 (0.71, 1.48)	0.186
Phosphate (mg/dL)										1.20 (1.01, 1.42)	1.06 (0.86, 1.30)	0.104
Total Parathyroid Hormone (pg/ml)										0.82 (0.63, 1.06)	0.83 (0.61, 1.11)	0.262

Only gold members can continue reading. Log In or Register to continue

Share this:

• Click to share on Twitter (Opens in new window)
• Click to share on Facebook (Opens in new window)

Related

Related posts:

1. Comparison Among Patients ≥75 Years Having Percutaneous Coronary Angioplasty Using Drug-Eluting Stents Versus Bare Metal Stents
2. Usefulness of Regional Distribution of Coronary Artery Calcium to Improve the Prediction of All-Cause Mortality
3. Prognostic Value of Serial B-Type Natriuretic Peptide Measurement in Transcatheter Aortic Valve Replacement (from the PARTNER Trial)
4. Erratum for Nazare et al. “Usefulness of Measuring Both Body Mass Index and Waist Circumference for the Estimation of Visceral Adiposity and Related Cardiometabolic Risk Profile (from the INSPIRE ME IAA Study).” Am J Cardiol 2015;115:307–315

Stay updated, free articles. Join our Telegram channel

Join