Impact of Metabolic Syndrome on Development of Contrast‐Induced Nephropathy After Elective Percutaneous Coronary Intervention Among Nondiabetic Patients

ABSTRACT

Background

Identifying patients who are vulnerable to development of contrast‐induced nephropathy (CIN) is essential because of its association with prolonged hospitalization, increased cost, and increased in‐hospital and long‐term mortality rates.

Hypothesis

Individual components of metabolic syndrome (MetS) are well‐established risk factors for kidney injury. Nondiabetic patients diagnosed with MetS might be at an increased risk of developing CIN after elective percutaneous coronary intervention (PCI).

Methods

A total of 599 nondiabetic patients were enrolled, of whom 313 met the MetS criteria and 286 were included in the control group. Patients were evaluated for development of CIN after elective PCI.

Results

Contrast‐induced nephropathy occurred in 9.3% (29 of 313) of the MetS group and 4.9% (14 of 286) of the control group (P = 0.04). The multivariable regression model revealed that baseline glomerular filtration rate < 30 mL/min, multivessel intervention, and MetS increased and use of statin decreased the probability of CIN independent from confounding factors (odds ratio [OR]: 7.84, 95% confidence interval [CI]: 3.46‐24.36, P < 0.01 for baseline glomerular filtration rate < 30 mL/min; OR: 0.82, 95% CI: 0.42‐0.96, P = 0.02 for statin use; OR: 2.64, 95% CI: 1.46‐6.56, P < 0.01 for multivessel intervention; and OR: 1.66, 95% CI: 1.12‐2.61, P = 0.03 for MetS).

Conclusions

Metabolic syndrome is a risk factor for CIN in patients with stable coronary artery disease who undergo elective PCI. We suggest that clinicians recognize the patients with MetS before elective coronary interventions.

Introduction

Contrast‐induced nephropathy (CIN) causes 10% of hospital‐acquired renal failure.1, 2 Contrast‐induced nephropathy is diagnosed as an absolute ≥0.5 mg/dL or a relative ≥25% increase in serum creatinine (SCr) above the baseline within 24 to 72 hours of administration of intravascular contrast media with no other explainable cause of renal injury.3, 4 The reported incidence of CIN varies widely across the literature (2%–50%) due to different diagnostic criteria used and various risk profiles of the population studied.4, 5 Identifying patients who are vulnerable to development of CIN is essential because of its association with prolonged hospitalization, increased cost, and increased in‐hospital and long‐term mortality rates.5, 6

The metabolic syndrome (MetS) is a combination of risk factors including hypertension, insulin resistance, central obesity, impaired glucose tolerance, and dyslipidemia, which are highly associated with increased mortality and morbidity of cardiovascular diseases.7 Metabolic syndrome predicts reduced renal function and chronic kidney disease.8 The components of MetS, such as abdominal obesity, insulin resistance, dyslipidemia, and hypertension, have been shown to contribute the development of renal injury.8, 9, 10 On the basis of these data, we hypothesized that patients diagnosed with MetS might be at an increased risk of developing CIN. Therefore, we designed a prospective cohort study to investigate the impact of MetS on the development of CIN in nondiabetic patients who underwent nonurgent percutaneous coronary intervention (PCI).

Methods

An observational prospective cohort study was designed to determine whether MetS predicts the development of CIN after elective PCI. All patients scheduled for elective PCI were screened for eligibility from February 2014 until July 2014. Metabolic syndrome was defined, according to the Third Report of the National Cholesterol Education Program Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (NCEP‐ATP III), as the presence of ≥3 of these components: high fasting glucose (fasting serum glucose ≥100 mg/dL), abdominal obesity (given as waist circumference >102 cm in men and >88 cm in women), high blood pressure (≥130/≥85 mm Hg or drug treatment for hypertension), hypertriglyceridemia (serum triglycerides ≥150 mg/dL), low high‐density lipoprotein cholesterol (HDL‐C; < 40 mg/dL in men and < 50 mg/dL in women).3 Exclusion criteria included diabetes mellitus (DM), acute coronary events, low left ventricular ejection fraction, known acute renal failure, end‐stage renal failure requiring hemodialysis, contrast allergy, exposure to nephrotoxic agents (nonsteroidal anti‐inflammatory drugs, aminoglycosides, tacrolimus, amphotericin B, cisplatin, and cyclosporine) within 1 week before PCI, and exposure to contrast agents within 1 week before the procedure. Acute renal failure was identified as an increase in SCr by ≥1.5× baseline, which is known to or presumed to have occurred within the prior 7 days. A total of 599 nondiabetic patients were enrolled, of whom 313 met the MetS criteria and 286 were included in the control group (Figure 1). All patients provided written informed consent. This study complied with the Declaration of Helsinki and was approved by the institutional committee on human research and registered at ClinicalTrials.gov (identifier NCT02192372).

Figure 1.

Flow diagram of patients. The diagram includes detailed information on the excluded patients. Abbreviations: CIN, contrast‐induced nephropathy; MetS, metabolic syndrome.

Blood samples were collected to measure serum levels of creatinine, glucose, hemoglobin, and lipids following a fasting period of 8 hours. Serum creatinine levels were measured at 24 and 48 hours after the procedure. Contrast‐induced nephropathy was defined as an increase in SCr of ≥25% or ≥0.5 mg/dL above the baseline value 24 or 48 hours after angiography. Glomerular filtration rate (GFR) was estimated using the Cockcroft‐Gault formula: (140 − age) × weight (kg) / SCr (mg/dL) × 72 (×0.85 for females).

The coronary angiography was performed using iso‐osmolar, nonionic contrast medium (iodixanol, Visipaque 320; Opakim, Turkey). All of the recruited patients had ≥1 coronary intervention. Total volume of the contrast agent used during the procedure was recorded for each patient. All patients received standard protocol of hydration with 100 mL/h of saline for 12 hours before and after the procedure. No other medication was administered routinely unless CIN was seen.

Sample size was calculated with assumed event rates 14% and 3.6%, respectively, in MetS patients and the control group, which is in agreement with a previous prospective study.11 The total sample size of 380 was necessary for a 2‐sided test with 0.95 statistical power and α level of 0.05. We recruited 313 patients with MetS and 286 age‐ and sex‐adjusted controls. The endpoint of this trial was the development of CIN. All analyses were performed using SPSS software version 20 for Windows (IBM Corp., Armonk, NY). Data are expressed as frequencies for discrete variables and as mean ± SD for continuous variables. The Shapiro‐Wilk test assessed distribution of data. Continuous variables were compared by the Student unpaired t test or Mann‐Whitney U test according to the distribution of data. The χ² analysis or Fisher exact test was used to assess the significance of differences between dichotomous variables. The following were assessed in univariate logistic regression analysis: age, sex, body mass index, presentation of hypertension and MetS, smoking status, history of myocardial infarction, presentation with chronic renal failure; plasma levels of total cholesterol, low‐density lipoprotein cholesterol (LDL‐C), HDL‐C, and triglycerides before the procedure; use of statins, β‐blockers, ACEI/ARB, or calcium channel blockers; and presence of a multivessel intervention. Univariate correlates of periprocedural infarction with a P value < 0.1 were included in the multiple logistic regression analyses. Receiver operating characteristic curve analyses of baseline GFR for the prediction of CIN and determination of the cutoff point for baseline GFR were performed. All tests were 2‐sided, and the results with a P value < 0.05 were considered significant.

Results

Seven hundred and seventy‐four consecutive patients scheduled for elective PCI were screened. A total of 175 patients were excluded for various reasons (Figure 1). Therefore, this study included 599 patients who were allocated to MetS and control groups (Figure 1). Comparisons of different demographic, clinical, and angiographic characteristics are shown in Table 1. Most of the patients were male (70%), with a mean age of 61.5 years. Baseline characteristics were significantly different between patients with MetS and those without MetS. Compared with control subjects, patients in the MetS group more often had a history of hypertension (P < 0.01) and had higher fasting blood glucose (P < 0.01) and triglyceride (P < 0.01) levels, and also had lower serum HDL cholesterol (P = 0.02) levels compared with control subjects. Patients in both groups received a comparable mean volume of contrast agent (P = 0.36). Baseline SCr and GFR were similar between groups. The differences of SCr levels after PCI are shown in Table 2. Serum creatinine was significantly higher (P = 0.04) among patients with MetS after the procedure. The overall rate of the endpoint of this trial, development of CIN, was 7.2% (43/599). A significantly higher rate of CIN was observed in the patients with MetS than in those without MetS (9.3% vs 4.9%, respectively; P = 0.04). The amounts of the contrast volume were similar in patients with no impairment of renal function and those who developed CIN (162.7 ± 84 mL vs 169.5 ± 78 mL, respectively; P = 0.28). The influence of MetS on CIN was examined by evaluating the predictive values of various factors described previously. Univariate analysis identified baseline GFR < 30 mL/min, statin treatment, multivessel intervention, and MetS as significant factors for the development of CIN. The multivariable regression model revealed that baseline GFR < 30 mL/min, multivessel intervention, and MetS increased and use of statin decreased the probability of CIN independent from confounding factors (odds ratio [OR]: 7.84, 95% confidence interval [CI]: 3.46‐24.36, P < 0.01 for baseline GFR < 30 mL/min; OR: 0.82, 95% CI: 0.42‐0.96, P = 0.02 for statin use; OR: 2.64, 95% CI: 1.46‐6.56, P < 0.01 for multivessel intervention; OR: 1.66, 95% CI: 1.12‐2.61, P = 0.03 for metabolic syndrome; Table 3). Receiver operating characteristic curve analysis of GFR determined the best cutoff value for GFR as 67.3 mL/min for patients with MetS, with a sensitivity value of 59% and a specificity value of 73% (area under curve: 0.687, 95% CI: 0.609‐0.766, P = 0.036). A lower cutoff value for GFR was detected among patients without MetS (cutoff value: 59.1 mL/min, sensitivity 54%, specificity 72%, area under curve: 0.564, 95% CI: 0.483‐0.646, P = 0.07; Figure 2).

Table 1.

Baseline Demographics and Clinical and Angiographic Characteristics of Patients

Characteristics	Patients With MetS (n = 313)	Patients Without MetS (n = 286)	P Value
Age, y	61.6 ± 9.2	62.1 ± 10.2	0.38
Male sex	222 (71)	197 (69)	0.84
Hypertension	260 (83)	155 (54)	<0.01
Current smoker	47 (15)	51 (18)	0.48
FBG, mg/dL	112.7 ± 63.0	90.5 ± 58.8	<0.01
BMI, kg/m2	29.8 ± 5.9	26.4 ± 3.4	<0.01
Total cholesterol, mg/dL	180.7 ± 54.0	176.3 ± 43.6	0.27
LDL‐C, mg/dL	106.6 ± 40.9	105.6 ± 39.9	0.77
HDL‐C, mg/dL	40.1 ± 12.8	44.7 ± 11.1	0.02
Triglyceride, mg/dL	194.5 ± 103.4	127.7 ± 68.7	<0.01
Hg, g/dL	13.3 ± 3.9	13.6 ± 3.3	0.78
HCT, %	40.1 ± 5.7	40.8 ± 6.1	0.64
Previous MI	107 (34)	94 (33)	0.69
Previous CABG	45 (14)	27 (9)	0.06
LVEF, %	61 ± 11	62 ± 11	0.58
Baseline medications
β‐Blocker	226 (72)	196 (69)	0.23
Statin	270 (86)	235 (82)	0.12
ACEI/ARB	217 (69)	189 (66)	0.40
CCB	20 (6)	24 (8)	0.44
Procedural characteristics
Multivessel intervention	93 (30)	83 (29)	0.91
No. of stents per patient	1.6 ± 0.8	1.6 ± 0.9	0.75
Volume of contrast agent, mL	168.2 ± 91.3	157.7 ± 77.0	0.36

Abbreviations: ACEI, angiotensin‐converting enzyme inhibitor; ARB, angiotensin receptor blocker; BMI, body mass index; CABG, coronary artery bypass graft; CCB, calcium channel blocker; FBG, fasting blood glucose; HCT, hematocrit; HDL‐C, high‐density lipoprotein cholesterol; Hg, hemoglobin; LDL‐C, low‐density lipoprotein cholesterol; LVEF, left ventricular ejection fraction; MI, myocardial infarction; SD, standard deviation.

Data are presented as mean ± SD or n (%).

Table 2.

GFR and SCr Levels in the Study Patients

Parameter	Patients With MetS (n = 313)	Patients Without MetS (n = 286)	P Value
Baseline SCr, mg/dL	0.96 ± 0.46	0.98 ± 0.27	0.42
Postprocedural SCr, mg/dL	1.15 ± 0.65	1.05 ± 0.50	0.04
Baseline GFR, mL/min	81.1 ± 29.9	81.9 ± 27.6	0.82
CIN	29 (9.3)	14 (4.9)	0.04
Baseline SCr of patients with CIN, mg/dL	0.95 ± 0.18	0.96 ± 0.34	0.64
Postprocedural SCr of patients with CIN, mg/dL	2.52 ± 1.1	2.44 ± 0.82	0.18

Abbreviations: CIN, contrast‐induced nephropathy; GFR, glomerular filtration rate; MetS, metabolic syndrome; SCr, serum creatinine; SD, standard deviation.

Data are presented as mean ± SD or n (%).

Table 3.

Risk Factors for Development of CIN

	Univariate			Multivariable
Variable	OR	95% CI	P Value	OR	95% CI	P Value
Age	0.98	0.95‐1.02	0.25
Male sex	0.78	0.35‐1.73	0.55
BMI	0.94	0.85‐1.04	0.23
Hypertension	1.61	0.66‐3.95	0.29
Current smoker	0.96	0.44‐2.12	0.93
History of MI	1.89	0.88‐4.05	0.10	1.84	0.70‐3.98	0.24
GFR < 30 mL/min	8.42	3.96‐18.24	<0.01	7.84	3.46‐24.36	<0.01
Total cholesterol	0.99	0.98‐1.01	0.81
HDL‐C	1.01	0.98‐1.02	0.91
LDL‐C	0.99	0.98‐1.01	0.65
TG	0.99	0.99‐1.01	0.67
Statin	0.65	0.35‐0.92	0.01	0.82	0.42‐0.96	0.02
β‐Blocker	1.21	0.48‐3.11	0.68
ACEI/ARB	3.75	0.89‐15.67	0.07	1.96	0.61‐5.59	0.09
CCB	0.85	0.35‐2.05	0.71
Multivessel intervention	2.89	1.64‐7.68	<0.01	2.64	1.46‐6.56	<0.01
MetS	1.94	1.18‐2.88	0.02	1.66	1.12‐2.61	0.03

Abbreviations: ACEI, angiotensin‐converting enzyme inhibitor; ARB, angiotensin receptor blocker; BMI, body mass index; CCB, calcium channel blocker; CI, confidence interval; CIN, contrast‐induced nephropathy; GFR, glomerular filtration rate; HDL‐C, high‐density lipoprotein cholesterol; LDL‐C, low‐density lipoprotein cholesterol; MetS, metabolic syndrome; MI, myocardial infarction; OR, odds ratio; TG, triglycerides.

Figure 2.

ROC curve analysis of GFRs for the prediction of CIN. (A) Patients with MetS. © = Cutoff value for GFR: 67.3 mL/min. Sensitivity 59%, specificity 73%. AUC: 0.687, 95% CI: 0.609‐0.766, P = 0.036. (B) Patients without MetS. © = Cutoff value for GFR: 59.1 mL/min. Sensitivity 54%, specificity 72%. AUC: 0.564, 95% CI: 0.483‐0.646, P = 0.07. Abbreviations: AUC, area under the curve; CI, confidence interval; CIN, contrast‐induced nephropathy; GFR, glomerular filtration rate; MetS, metabolic syndrome; ROC, receiver operating characteristic.

Discussion

The current prospective cohort study demonstrated an increased risk of CIN among nondiabetic patients with MetS who underwent nonurgent PCI (OR: 1.66, 95% CI: 1.12‐2.61, P = 0.03). The incidence of CIN was 9.3% in patients with MetS. Postprocedural creatinine levels significantly increased in the MetS group compared with control subjects (P = 0.04). Lower values of baseline GFR are highly sensitive to determine the development of CIN, at the cost of decreased specificity. A higher level for baseline GFR was found sufficient to develop CIN if MetS is present (best cutoff for GFR to develop CIN is 67.3 mL/min vs 59.1 mL/min in patients with and without MetS, respectively). The sensitivity and the specificity rates of these cutoff points were not high enough to utilize in clinical practice. However, these data about baseline GFR may provide awareness to clinicians to initiate preventive measures, especially if the baseline GFR values were not normal.

Patients who developed CIN after PCI have a worse prognosis than patients without renal insult.6, 12 They are subjected to electrolyte imbalance, bleeding tendency, stroke, pulmonary embolus, sepsis, and respiratory failure.4, 5, 13 Treatment options for developed CIN are limited to supportive measures and hemodialysis. Precautionary maneuvers, such as adequate hydration, cessation of nephrotoxic agents, selection of nonionic iso‐osmolar contrast agents, and limitation of the contrast volume, are effective and should be applied to all patients with renal failure and risk factors for CIN. Thus, identification of patients who are at risk of CIN deserves much importance. Individual components of MetS, such as abdominal obesity, insulin resistance, dyslipidemia, and hypertension, are well‐established risk factors for kidney injury. Therefore, we sought to investigate whether diagnosis of MetS increases the risk of CIN in stable coronary artery disease (CAD).

Baseline renal dysfunction, DM, nephrotoxic drugs, hemodynamic instability, volume depletion, anemia, hypoalbuminemia, and congestive heart failure are well‐defined risk factors for the development of CIN.4, 14 A risk score was developed by Mehran et al for prediction of CIN after PCI.15 Patients with a higher Mehran risk score are more likely to develop persistent renal dysfunction after CIN.16 Metabolic syndrome has increased the risk of CIN among nondiabetic elderly patients with mild to moderate kidney insufficiency, in a previous study.11 This prospective cohort study demonstrated the association of MetS with CIN in larger study population with stable CAD.

The most important risk factor for CIN is baseline renal dysfunction.14 Patients with low‐baseline GFR also had the greatest risk of CIN in the present study. Several mechanisms have been suggested as etiologic factors for CIN. The main pathophysiologic process is reduction of renal blood flow due to acute vasoconstriction, which is induced by release of endothelin and adenosine triggered by the contrast agent. Also, concentration of the contrast agent in the renal tubules and collecting ducts causes direct cellular injury to the renal tubular cells.4 Hypoperfusion of the kidneys caused by sustained hypotension, bleeding complications, or intra‐aortic balloon counterpulsation also facilitates the development of CIN.

Components of MetS were suggested to be associated with kidney injury or CIN. Abdominal obesity, an important feature of MetS, may lead to focal and segmental glomerulosclerosis, tubulointerstitial fibrosis, and tubular cell injury via inducing multiple mediators. It may also indirectly contribute to kidney insult via triggering DM, hypertension, and atherosclerosis.8, 9 Activation of the renin‐angiotensin‐aldosterone system also affects the kidney function via hypertension, oxidative stress, and inflammatory cytokines in obese MetS patients.17 Insulin resistance, a key pathophysiologic feature of MetS, creates a pro‐inflammatory state,18, 19 and increased inflammation is strongly associated with obesity in patients with kidney failure.20 Diabetes mellitus and hypertension are the leading causes of chronic kidney disease.21 Diabetes mellitus is also known to be an independent and strong predictor of CIN.4, 15 However, the association between CIN and prediabetic or prehypertensive state, which are the characteristics of MetS, remains debatable.4, 22 Dyslipidemia is known to be independently associated with decreased kidney function.23 Cellular lipid overload related to MetS and obesity is suggested to contribute to kidney insult.10

The present study demonstrated the presence of baseline GFR ≤30 mL/min and multivessel intervention as other risk predictors for the development of CIN, as expected. On the other hand, use of statins decreased the risk of CIN by 18%. Clinical trials have supported the favorable effects of periprocedural statin use for CIN.24, 25 Renoprotective effects of statins are thought to be due to their positive effects on endothelial function at the level of glomerulus and systemic anti‐inflammatory properties.4

The lowest risk of CIN was shown with the use of iodixanol, a nonionic iso‐osmolar contrast agent.26 We routinely used iodixanol and intravenous hydration for all study patients during PCI. Although statins, ascorbic acid, and N‐acetyl cysteine seem to be protective among patients with renal dysfunction,4 there is no established pharmacologic measure to prevent CIN except volume expansion and to reverse dehydration.27 Future studies are warranted to test the potential effect of the above‐mentioned drugs in patients with MetS.

Study Limitations

Novel markers like neutrophil gelatinase‐associated lipocalin and cystatin C to detect renal dysfunction were not used in this study. Rather, SCr and GFR, which have been available in clinical practice and widely used in clinical studies, were preferred to detect the impact of MetS on CIN.

Our data showed a protective effect of statin for CIN among patients with MetS. The International Diabetes Federation emphasized that small LDL‐C particles may be part of the definition for MetS.28 They also suggest the use of statins to reduce all apolipoprotein B‐containing lipoproteins. We did not design the study to identify MetS according to LDL‐C levels or statin use. The main indication of statin therapy in this trial was established CAD. It would be difficult to distinguish the definition of MetS among patients with CAD, most of whom were already under statin treatment. So this trial is not able to distinguish the favorable effect of statins on CIN development from MetS definition.

Conclusion

MetS is a significant predictor for CIN in nondiabetic patients with stable CAD. We suggest that clinicians recognize those patients with MetS before elective coronary interventions. This may lead to taking preventive measures before using contrast agents and may decrease the incidence of CIN.