RESEARCH ARTICLE


Kidney Function and Estimated Vascular Risk in Patients with Primary Dyslipidemia



Konstantinos Tziomalos1, Emmanuel S Ganotakis2, Irene F Gazi3, Devaki R Nair4, Dimitri P Mikhailidis1, *
1 Department of Clinical Biochemistry (Vascular Prevention Clinic) and Department of Surgery, Royal Free Campus, University College London Medical School, University College London (UCL), London, UK
2 Department of Internal Medicine, University Hospital of Heraklion, University of Crete Medical School, Heraklion, Crete, Greece
3 Department of Internal Medicine, School of Medicine, University of Ioannina, Ioannina, Greece
4 Department of Clinical Biochemistry (Vascular Prevention Clinic), Royal Free Hospital, London, UK

Article Information


Identifiers and Pagination:

Year: 2009
Volume: 3
First Page: 57
Last Page: 68
Publisher ID: TOCMJ-3-57
DOI: 10.2174/1874192400903010057

Article History:

Received Date: 20/5/2009
Revision Received Date: 22/5/2009
Acceptance Date: 25/5/2009
Electronic publication date: 16/6/2009
Collection year: 2009

Article Metrics

CrossRef Citations:
4
Total Statistics:

Full-Text HTML Views: 2143
Abstract HTML Views: 635
PDF Downloads: 339
Total Views/Downloads: 3117
Unique Statistics:

Full-Text HTML Views: 1214
Abstract HTML Views: 393
PDF Downloads: 252
Total Views/Downloads: 1859



Creative Commons License
© Tziomalos et al.; Licensee Bentham Open.

open-access license: This is an open access article licensed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.

* Address correspondence to this author at the Academic Head, Department of Clinical Biochemistry, Royal Free Hospital, University College Medical School, University College London (UCL), Pond Street, London NW3 2QG, UK; Tel: +44 20 7830 2258; Fax: +44 20 7830 2235; E-mail: MIKHAILIDIS@aol.com


Abstract

Background:

Chronic kidney disease (CKD) is associated with increased vascular risk. Some studies suggested that considering markers of CKD might improve the predictive accuracy of the Framingham risk equation.

Aim:

To evaluate the links between kidney function and risk stratification in patients with primary dyslipidemia.

Methods:

Dyslipidemic patients (n = 156; 83 men) who were non-smokers, did not have diabetes mellitus or evident vascular disease and were not on lipid-lowering or antihypertensive agents were recruited. Creatinine clearance (CrCl) was estimated using the Cockcroft-Gault equation. Estimated glomerular filtration rate (eGFR) was calculated using the Modification of Diet in Renal Disease (MDRD) equation. We estimated vascular risk using the Framingham equation.

Results:

In both men and women, there was a significant negative correlation between estimated Framingham risk and both eGFR and CrCl (p < 0.001 for all correlations). When men were divided according to creatinine tertiles, there were no significant differences in any parameter between groups. When men were divided according to either eGFR or CrCl tertiles, all estimated Framingham risks significantly increased as renal function declined (p<0.001 for all trends). When women were divided according to creatinine tertiles, all estimated Framingham risks except for stroke significantly increased as creatinine levels increased. When women were divided according to either eGFR or CrCl tertiles, all estimated Framingham risks significantly increased as renal function declined.

Conclusions:

Estimated vascular risk increases as renal function declines. The possibility that incorporating kidney function in the Framingham equation will improve risk stratification requires further evaluation.

Key Words: Creatinine, estimated glomerular filtration rate, chronic kidney disease, vascular risk, Framingham risk score..

INTRODUCTION

Primary prevention of vascular disease should be guided by the assessment of global risk [1-3]. Patients with higher vascular risk should be managed more aggressively [1, 3, 4]. A number of risk estimation engines that consider different risk factors have been developed [5, 6]. The Framingham risk score for subjects without evident vascular disease is well established [5].

The Framingham calculation considers the following vascular risk factors: age, gender, total cholesterol (TC), high density lipoprotein cholesterol (HDL-C), smoking, systolic blood pressure (SBP), diastolic blood pressure (DBP), the presence of diabetes mellitus (DM) and left ventricular hypertrophy [5]. Limitations of the Framingham risk equation include the absence of family history (FaHist) of premature vascular disease and age limits [1, 7, 8]. Furthermore, triglyceride (TG) levels and potentially relevant emerging risk factors are not considered [1, 7, 8]. In some studies, the assessment of emerging risk factors, such as high sensitivity C-reactive protein (hsCRP), added to the prognostic accuracy of the Framingham risk equation [9,10]. Similarly, chronic kidney disease (CKD) is associated with increased vascular risk in the general population [11-14]. Some studies suggested that considering markers of CKD might improve the predictive accuracy of the Framingham equation [15-17].

The aim of the present study was to evaluate the links between kidney function and risk stratification (using the Framingham equation) in non-smokers with primary dyslipidemia and no evident vascular disease or DM.

METHODS

Patient Selection

The records of 645 consecutive patients referred to a specialist centre for dyslipidemia were assessed [18, 19]. Among these patients, we identified 234 patients (144 men) without overt vascular disease or DM. In order to create an even more homogeneous patient group, the following exclusion criteria were also applied [18, 19]:

  1. Treatment with any lipid lowering or antihypertensive agent during the previous 4 months.
  2. Those with fasting serum glucose concentration > 5.0 mmol/l required a normal oral glucose tolerance test in order to be included in the survey.
  3. Abnormal liver function tests: Reference ranges were: aspartate aminotransferase = 5 - 40 u/l; alanine aminotransferase = 5 - 40 u/l; gamma-glutamyl transferase = 10 - 48 u/l; alkaline phosphatase = 35 - 130 u/l; albumin = 35 - 55 g/l; bilirubin = 3 - 17 μmol/l (values up to 25 μmol/l were allowed provided all other liver function tests were normal).
  4. Abnormal renal function: Reference ranges were: urea = 3.0 - 6.5 mmol/l (values up to 7.5 mmol/l were allowed for those above the age of 70 years); creatinine = 60 - 120 μmol/l; sodium = 135 - 145 mmol/l; potassium = 3.5 - 5.0 mmol/l.
  5. Abnormal thyroid function tests: Reference ranges were: thyroid stimulating hormone = 0.5 - 4.7 mU/l; free thyroxine = 10 - 25 pmol/l.
  6. Declared or determined history of alcohol or drug abuse. For alcohol consumption, the limits were set at 21 and 14 units/week for men and women, respectively.
  7. Psychiatric conditions, whether involving medication or not.
  8. Chronic inflammatory disease (e.g. rheumatoid arthritis, Crohn’s disease, ulcerative colitis, collagen diseases) or cancer [since an acute phase response may influence several variables (e.g. HDL-C)] [20-23].
  9. Treatment with retinoic acid derivatives, tamoxifen, androgens, oestrogens (hormone replacement therapy or oral contraceptives), progestins, fish oils or ciclosporin since these drugs may exert effects on lipids [24-28].
  10. Current or recent (4-month) pregnancy.
  11. Current smokers or those who quit had quit for less than 6 months before sampling. A 6-month period was selected to allow time for reversal of measured variables within a practical time frame.

Clinical and Laboratory Investigations

Collection of samples: All samples were collected in the morning after fasting for a minimum of 12 h with water only allowed.

Lipid profile: Serum TC, HDL-C and TG levels were assayed by standard enzymatic methods (Boehringer Mannheim, Sussex, England) adapted for the Hitachi 911 analyser (HDL-C was measured after precipitating apolipoprotein B using a phosphotungstate procedure). Serum low density lipoprotein cholesterol (LDL-C) levels were calculated by the Friedewald formula. Patients with serum TG levels > 4.5 mmol/l, in whom LDL-C cannot be determined by the above formula, are not included in the analysis.

Liver and renal function profiles and serum glucose concentration were all determined by standard methods used in our department.

Creatinine clearance (CrCl) was estimated using the Cockcroft-Gault equation: CrCl (ml/min) = [140 – age (in years)] x [weight (in kg)] x 0.85 (if female) / [72 x serum creatinine (in mg/dl)] [29]. Estimated glomerular filtration rate (eGFR) was calculated using the Modification of Diet in Renal Disease (MDRD) equation: eGFR (ml/min/1.73 m2) = 186 x [serum creatinine (in mg/dl)](-1.154) x [age (in years)] (-0.203) x 0.742 (if female) x 1.210 (if black) [30].

The Department of Clinical Biochemistry, Royal Free Hospital participates in several quality assurance programs and has full Clinical Pathology Accreditation (CPA).

Calculation of Vascular Risk Using the Framingham Equation [www.bhsoc.org]

The Framingham risk engine can only be used to calculate vascular risk in the absence of cardiovascular disease (CVD). The following variables are considered: age, gender, SBP and DBP, serum TC and HDL-C levels, smoking status and the presence/absence of DM or left ventricular hypertrophy based on electrocardiographic criteria [5]. The equation estimates the 10-year risk for coronary heart disease (CHD), stroke and overall CVD based on either SBP (SBP-CHD, SBP-stroke and SBP-CVD, respectively) or DBP (DBP-CHD, DBP-stroke and DBP-CVD, respectively). We also estimated CVD risk taking a FaHist of premature vascular disease (any event before the age of 60 years) into consideration (termed SBP-CVD+FaHist and DBP-CVD+FaHist, respectively). A positive FaHist was considered to add 50% to the overall risk.

The Framingham equation has age limits (32 to 74 years). To increase the number of patients, men aged 24-31 years were entered as 32 years old and those aged 75-76 years were entered as 74 years old. Similarly, women aged 27-31 years were entered as 32 and those aged 75-78 years were entered as 74 years old.

Statistical Analysis

All data were analyzed using the statistical package SPSS (version 12.0; SPSS Inc., Chicago, IL). Continuous values are expressed as median and range. Correlations between variables were assessed using Spearman Rank correlation. The Kruskal-Wallis test was used to assess the trend of variables divided according to creatinine, eGFR or CrCl tertiles. The chi-square test was used to compare the agreement between eGFR and CrCl in classifying patients in tertiles of renal function. Because we assessed the correlation between indices of renal function (creatinine, eGFR and CrCl) and 22 other parameters, a 2-tailed p < 0.031 was considered significant [31]. In all other analyses, a 2-tailed p < 0.05 was considered significant.

RESULTS

The clinical characteristics of the 156 patients (83 men) enrolled in this survey are listed in Table 1. Estimated risk for CHD, stroke and CVD based on SBP and DBP are shown in Table 2.

Table 1.

Clinical Characteristics of the Study Population


Men (n = 83) Women (n = 73)
Age (years) 49 (24-76) 55 (27-78)
Weight (kg) 81.2 (61.1-119.0) 65.3 (45.7-96.0)
Systolic blood pressure (mmHg) 130 (85-170) 135 (100-185)
Diastolic blood pressure (mmHg) 80 (60-100) 80 (70-115)
Lipid profile
TC (mmol/l) 7.1 (4.5-12.2) 7.6 (4.5-11.7)
LDL-C (mmol/l) 5.0 (2.4-9.3) 5.4 (2.7-9.7)
HDL-C (mmol/l) (0.6-2.1) 1.4 (0.6-2.6)
TG (mmol/l) 2.2 (0.7-7.4) 1.6 (0.5-4.8)
TC/HDL-C 6.3 (3.6-14.2) 5.6 (2.7-12.3)
LDL-C/HDL-C 4.3 (2.4-10.2) 4.0 (1.4-8.9)
Lipoprotein a (g/l) 0.25 (0.05-2.10) 0.34 (0.05-1.54)
Fibrinogen (g/l) 3.05 (1.44-5.47) 3.51 (2.11-6.29)
Glucose (mmol/l) 4.8 (3.6-5.6) 4.7 (3.3-6.1)
Urate (mmol/l) 0.38 (0.21-0.81) 0.28 (0.17-0.51)
Renal function
Creatinine (µmol/l) 93 (72-112) 74 (51-120)
eGFR (MDRD) (ml/min/1.73 m2) 80 (62-115) 76 (42-120)
Creatinine clearance (CG) (ml/min) 101 (58-153) 82 (30-148)

TC, total cholesterol; LDL-C, low density lipoprotein cholesterol; HDL-C, high density lipoprotein cholesterol; TG, triglycerides; eGFR, estimated glomerular filtration rate; MDRD, modification of diet in renal disease equation. CG, cockcroft-gault equation.

Table 2.

Estimated Vascular Risk of the Study Population


Men (n = 83) Women (n = 73)
SBP-CHD 11.2 (0.7-41.4) 9.1 (0.0-22.1)
SBP-stroke 1.1 (0.1-8.5) 1.4 (0.1-9.7)
SBP-CVD 12.4 (0.8-49.3) 10.6 (0.1-31.8)
DBP-CHD 10.7 (0.5-40.0) 9.3 (0.0-23.1)
DBP-stroke 0.9 (0.0-8.7) 1.3 (0.0-8.3)
DBP-CVD 11.7 (0.5-45.4) 11.0 (0.0-31.4)
SBP-CVD + FaHist 14.6 (1.2-73.9) 12.0 (0.1-47.7)
DBP-CVD + FaHist 14.3 (0.7-68.1) 13.5 (0.0-47.1)

CHD, coronary heart disease; CVD, cardiovascular disease; SBP, systolic blood pressure; DBP, diastolic blood pressure; FaHist, family history; SBP-CHD, estimated CHD risk based on SBP; SBP-stroke, estimated stroke risk based on SBP; SBP-CVD, estimated CVD risk based on SBP; DBP-CHD, estimated CHD risk based on DBP; DBP-stroke, estimated stroke risk based on DBP; DBP-CVD, estimated CVD risk based on DBP; SBP-CVD + FaHist, estimated CVD risk based on SBP and the presence of FaHist; DBP-CVD + FaHist, estimated CVD risk based on DBP and the presence of FaHist.

Table 3.

Significant Correlations Between Markers of Renal Function and Other Parameters in Men (n = 83). DUE to Multiple Correlations, a p value < 0.031 is Considered Significant


Correlations between serum creatinine levels and other parameters
Parameter r P
TC 0.252 0.022
LDL-C 0.249 0.023
TC/HDL-C ratio 0.256 0.02
LDL-C/HDL-C ratio 0.291 0.008
SBP-CHD 0.289 0.008
SBP-CVD 0.259 0.018
DBP-CHD 0.292 0.007
DBP-CVD 0.268 0.014
Correlations between eGFR (MDRD) and other parameters
Parameter r P
Age -0.544 <0.001
SBP-CHD -0.605 <0.001
SBP-stroke -0.456 <0.001
SBP-CVD -0.588 <0.001
DBP-CHD -0.620 <0.001
DBP-stroke -0.520 <0.001
DBP-CVD -0.607 <0.001
SBP-CVD + FaHist -0.501 <0.001
DBP-CVD + FaHist -0.533 <0.001
Correlations between creatinine clearance (CG) and other parameters
Parameter r P
Age -0.807 <0.001
Weight 0.599 <0.001
HDL-C -0.283 0.010
SBP-CHD -0.697 <0.001
SBP-stroke -0.703 <0.001
SBP-CVD -0.708 <0.001
DBP-CHD -0.714 <0.001
DBP-stroke -0.763 <0.001
DBP-CVD -0.727 <0.001
SBP-CVD + FaHist -0.632 <0.001
DBP-CVD + FaHist -0.667 <0.001

For abbreviations, see Tables 1 and 2.

Table 4.

Significant Correlations Between Markers of Renal Function and Other Parameters in Women (n = 73). Due to Multiple Correlations, a p value < 0.031 is Considered Significant


Correlations between serum creatinine levels and other parameters
Parameter r p
Age 0.330 0.004
Triglycerides 0.273 0.019
Glucose 0.258 0.029
Urate 0.408 0.001
SBP-CHD 0.361 0.002
SBP-stroke 0.317 0.006
SBP-CVD 0.349 0.002
DBP-CHD 0.348 0.003
DBP-stroke 0.344 0.003
DBP-CVD 0.348 0.003
SBP-CVD+ FaHist 0.360 0.002
DBP-CVD+ FaHist 0.370 0.001
Correlations between eGFR (MDRD) and other parameters
Parameter r p
Age -0.535 <0.001
Fibrinogen -0.298 0.011
Glucose -0.342 0.003
Triglycerides -0.308 0.008
Urate -0.435 <0.001
SBP-CHD -0.510 <0.001
SBP-stroke -0.502 <0.001
SBP-CVD -0.511 <0.001
DBP-CHD -0.499 <0.001
DBP-stroke -0.544 <0.001
DBP-CVD -0.515 <0.001
SBP-CVD+ FaHist -0.525 <0.001
DBP-CVD+ FaHist -0.538 <0.001
Correlations between creatinine clearance (CG) and other parameters
Parameter r p
Age -0.685 <0.001
Weight 0.318 0.006
Fibrinogen -0.275 0.019
Glucose -0.303 0.01
SBP-CHD -0.556 <0.001
SBP-stroke -0.628 <0.001
SBP-CVD -0.582 <0.001
DBP-CHD -0.531 <0.001
DBP-stroke -0.649 <0.001
DBP-CVD -0.569 <0.001
SBP-CVD+ FaHist -0.607 <0.001
DBP-CVD+ FaHist -0.612 <0.001

For abbreviations, see Table 2.

Table 5.

Significant Differences Between Groups when men where Divided According to Estimated Glomerular Filtration Rate Tertiles (Modification of Diet in Renal Disease Equation)


Estimated Glomerular Filtration Rate Tertiles (ml/min/1.73 m2) p for Trend
< 75 (n = 28) 75-86 (n = 28) > 86 (n = 27)
LDL-C (mmol/l) 5.2 (3.3-7.7) 4.5 (2.4-6.9) 4.9 (2.6-9.3) 0.028
TC/HDL-C 7.2 (4.6-10.9) 5.5 (3.6-9.0) 6.4 (4.6-14.2) 0.002
LDL-C/HDL-C 5.2 (3.1-7.7) 3.5 (2.4-6.5) 4.3 (2.8-10.2) <0.001
Age (years) 52.5 (43-76) 51.5 (25-67) 36 (24-70) <0.001
SBP-CHD 17.2 (6.9-41.4) 10.2 (1.3-22.8) 5.4 (0.7-25.9) <0.001
SBP-stroke 1.5 (0.2-8.5) 1.3 (0.1-4.4) 0.4 (0.1-7.7) <0.001
SBP-CVD 18.2 (7.1-49.3) 11.6 (1.4-26.0) 5.7 (0.8-32.5) <0.001
DBP-CHD 16.3 (7.7-40.0) 10.0 (0.7-22.8) 5.0 (0.5-24.6) <0.001
DBP-stroke 1.2 (0.4-8.7) 1.2 (0.0-3.9) 0.2 (0.0-4.7) <0.001
DBP-CVD 17.8 (8.5-45.4) 10.8 (0.7-25.8) 5.2 (0.5-29.3) <0.001
SBP-CVD+ FaHist 19.8 (7.1-73.9) 14.6 (2.1-35.5) 7.9 (1.2-32.5) <0.001
DBP-CVD+ FaHist 19.2 (8.8-68.1) 15.4 (1.0-32.8) 7.3 (0.7-29.3) <0.001

For abbreviations, see Tables 1 and 2.

Table 6.

Significant Differences Between Groups when men where Divided According to Creatinine Clearance (Cockcroft-Gault Equation)


Creatinine Clearance Tertiles (ml/min) P for Trend
< 90 (n = 28) 90-109 (n = 28) > 109 (n = 27)
TG (mmol/l) 1.6 (0.7-3.3) 2.5 (0.8-7.4) 2.5 (0.9-4.8) 0.02
HDL-C (mmol/l) 1.2 (0.7-2.1) 1.1 (0.8-1.9) 1.0 (0.6-1.4) 0.042
Weight (kg) 72.9 (61.1-83.0) 87.1 (62.0-96.6) 84.5 (72.2-119.0) <0.001
Age (years) 58.5 (44-76) 49 (31-64) 34 (24-51) <0.001
SBP-CHD 16.1 (7.9-41.4) 12.1 (1.6-24.8) 5.2 (0.7-13.2) <0.001
SBP-stroke 2.2 (0.5-8.5) 1.1 (0.2-7.7) 0.3 (0.1-1.5) <0.001
SBP-CVD 18.4 (8.4-49.3) 13.4 (2.8-32.5) 5.6 (0.8-14.6) <0.001
DBP-CHD 16.5 (7.2-40.0) 12.3 (1.2-21.2) 5.0 (0.5-12.9) <0.001
DBP-stroke 2.3 (0.5-8.7) 0.9 (0.1-4.2) 0.2 (0.0-1.2) <0.001
DBP-CVD 18.3 (8.5-45.4) 13.6 (1.3-25.2) 5.2 (0.5-13.8) <0.001
SBP-CVD+FaHist 19.4 (10.8-73.9) 16.7 (4.2-36.9) 7.6 (1.2-20.7) <0.001
DBP-CVD+FaHist 19.7 (8.5-68.1) 16.3 (1.9-33.3) 6.4 (0.7-17.8) <0.001

For abbreviations, see Tables 1 and 2.

Table 7.

Significant Differences Between Groups when Women where Divided According to Serum Creatinine Tertiles


Parameter Serum Creatinine Tertiles (μmol/l) P for Trend
≤ 67 (n = 24) 68-79 (n = 24) ≥ 80 (n = 25)
DBP (mmHg) 80 (70-115) 85 (70-110) 80 (70-105) 0.041
TC (mmol/l) 7.6 (4.9-10.1) 7.1 (4.5-11.7) 8.0 (6.0-10.1) 0.023
TG (mmol/l) 1.2 (0.5-3.6) 1.7 (0.6-4.8) 1.8 (0.6-3.7) 0.044
LDL-C (mmol/l) 5.7 (2.7-8.4) 4.6 (2.7-9.7) 5.9 (3.9-7.8) 0.028
Urate (mmol/l) 0.24 (0.17-0.33) 0.30 (0.21-0.50) 0.32 (0.19-0.51) 0.006
SBP-CHD 4.8 (0.2-22.1) 6.4 (0.0-18.3) 11.2 (1.0-20.8) 0.015
SBP-CVD 6.3 (0.3-31.8) 9.2 (0.1-24.8) 13.3 (1.4-24.1) 0.03
DBP-CHD 4.9 (0.1-23.1) 8.6 (0.0-22.4) 12.3 (0.9-22.8) 0.022
DBP-CVD 6.3 (0.1-31.4) 10.3 (0.0-24.2) 14.2 (1.2-25.8) 0.029
SBP-CVD+FaHist 8.1 (0.3-47.7) 9.6 (0.1-24.8) 18.3 (2.1-36.1) 0.013
DBP-CVD+FaHist 8.0 (0.1-47.1) 11.0 (0.0-26.4) 18.3 (1.8-27.0) 0.009

For abbreviations, see Tables 1 and 2.

Table 8.

Significant Differences Between Groups when Women where Divided According to Estimated Glomerular Filtration Rate Tertiles (Modification of Diet in Renal Disease Equation)


Estimated Glomerular Filtration Rate Tertiles (ml/min/1.73 m2) p for Trend
< 69 (n = 24) 69-83 (n = 25) > 83 (n = 24)
DBP (mmHg) 82 (70-105) 85 (70-115) 80 (70-90) 0.005
TG (mmol/l) 1.9 (0.9-3.7) 1.7 (0.6-4.8) 1.2 (0.5-3.6) 0.012
Fibrinogen (g/l) 3.64 (2.54-5.76) 3.58 (2.32-5.16) 3.15 (2.11-6.29) 0.028
Glucose(mmol/l) 4.8 (4.2-5.6) 4.7 (3.7-6.1) 4.4 (3.3-6.0) 0.032
Urate (mmol/l) 0.32 (0.19-0.51) 0.30 (0.21-0.50) 0.25 (0.17-0.33) 0.001
Age (years) 61 (40-72) 55 (38-78) 48 (27-67) <0.001
SBP-CHD 12.1 (1.0-20.8) 8.6 (1.1-22.1) 3.6 (0.0-18.5) <0.001
SBP-stroke 2.2 (0.4-7.8) 1.5 (0.2-9.7) 0.5 (0.1-6.3) 0.001
SBP-CVD 14.7 (1.4-24.1) 10.0 (1.3-31.8) 4.6 (0.1-19.5) <0.001
DBP-CHD 12.5 (0.9-22.8) 9.8 (1.2-23.1) 3.4 (0.0-19.2) <0.001
DBP-stroke 2.0 (0.3-5.1) 1.6 (0.2-8.3) 0.6 (0.0-2.9) <0.001
DBP-CVD 14.8 (1.2-25.8) 11.4 (1.4-31.4) 4.1 (0.0-20.0) <0.001
SBP-CVD+FaHist 20.2 (2.1-36.1) 10.8 (1.9-47.7) 5.2 (0.1-25.6) <0.001
DBP-CVD+FaHist 18.4 (1.8-27.0) 12.8 (1.9-47.1) 4.1 (0.0-24.0) <0.001

For abbreviations, see Tables 1 and 2.

Table 9.

Significant Differences Between Groups when Women where Divided According to Creatinine Clearance Tertiles (Cockcroft-Gault Equation)


Creatinine Clearance Tertiles (ml/min) p for Trend
< 72 (n = 24) 72-89 (n = 25) > 90 (n = 24)
Fibrinogen (g/l) 395 (234-576) 348 (227-629) 335 (211-446) 0.039
Glucose (mmol/l) 4.7 (3.9-6.1) 4.8 (3.7-6.0) 4.4 (3.3-5.6) 0.024
Weight (kg) 61.9 (45.7-79.6) 66.9 (46.3-84.8) 68.7 (53.6-96.0) 0.027
Age (years) 65 (40-78) 54 (38-68) 43 (27-71) <0.001
SBP-CHD 11.8 (1.0-20.8) 9.2 (1.1-22.1) 3.8 (0.0-18.5) <0.001
SBP-stroke 2.5 (0.4-8.7) 1.2 (0.2-9.7) 0.4 (0.1-3.5) 0.001
SBP-CVD 15.1 (1.4-24.8) 10.6 (1.3-31.8) 4.6 (0.1-19.5) <0.001
DBP-CHD 12.2 (0.9-22.8) 9.4 (1.2-23.1) 3.8 (0.0-19.2) 0.001
DBP-stroke 2.9 (0.3-6.1) 1.2 (0.2-8.3) 0.4 (0.0-7.2) <0.001
DBP-CVD 16.7 (1.2-25.8) 11.4 (1.4-31.4) 4.4 (0.0-20.0) <0.001
SBP-CVD+ FaHist 19.1 (2.1-36.1) 12.9 (1.9-47.7) 4.9 (0.1-25.6) <0.001
DBP-CVD+ FaHist 18.4 (1.8-27.0) 14.3 (1.9-47.1) 4.5 (0.0-24.0) <0.001

For abbreviations, see Tables 1 and 2.

Significant correlations between the indices of renal function (creatinine, eGFR and CrCl) and other parameters are shown in Tables 3 and 4. In men, there was a significant positive correlation between creatinine levels and SBP-CHD, SBP-CVD, DBP-CHD and DBP-CVD (Table 3). In women, creatinine levels correlated significantly with all estimated risks (Table 4). In both men and women, there was a significant negative correlation between all estimated risks and both eGFR and CrCl (p < 0.001 for all correlations; Tables 3 and 4).

When men were divided according to creatinine tertiles, there were no differences in any parameter between groups. When men were divided according to either eGFR or CrCl tertiles, all estimated risks increased significantly as renal function declined (p<0.001 for all trends; Tables 5 and 6). It should be noted that there was significant disagreement in the classification of men in tertiles according to eGFR or CrCl. Thus, among men in the lowest, middle and higher eGFR tertile, only 46, 29 and 70%, respectively, were also in the lowest, middle and higher CrCl tertile, respectively (p < 0.001).

When women were divided according to creatinine tertiles, all estimated risks except for stroke significantly increased as creatinine levels increased (Table 7). When women were divided according to either eGFR or CrCl tertiles, all estimated risks significantly increased as renal function declined (Tables 8 and 9). There was significant disagreement in the classification of women in tertiles according to eGFR or CrCl. Thus, among women in the lowest, middle and higher eGFR tertile, only 71, 44 and 67%, respectively, were also in the lowest, middle and higher CrCl tertile, respectively (p < 0.001).

DISCUSSION

CKD is defined as the presence of either eGFR < 60 ml/min/1.73m2 or persistent albuminuria [30]. The prevalence of CKD is rising due to the progressive aging of the population and the increasing number of patients with type 2 DM [32-36]. It was reported that approximately 13.1% of the US adult population has CKD [37]. The prevalence of CKD in the UK ranges between 5.8 and 12.0% [38,39]. In both countries, CKD is more frequent in women than in men [37, 39].

Several studies showed that impaired renal function is associated with increased vascular mortality in the general population [11-14], in patients with stable CHD [40-42], acute coronary syndromes (ACS) [43, 44], stroke [45] or peripheral arterial disease (PAD) [46]. CKD is also a risk factor for stroke in the general population [47] and in patients with CHD [48] although others reported an association only with hemorrhagic stroke [49]. CKD is associated with increased risk for PAD [50] and renal artery stenosis in the general population [51] and correlates with ankle-brachial index (ABI) in patients with PAD [52]. Both established and emerging risk factors are implicated in the increased vascular morbidity and mortality in CKD [53].

In our study, estimated vascular risk significantly increased as kidney function deteriorated. In previous reports, the Framingham risk score was higher in patients with CKD than in those with normal kidney function [54]. In addition, the Framingham model appears to underestimate vascular risk in patients with CKD [55]. In contrast, an analysis of the Atherosclerosis Risk in the Communities (ARIC) study showed that accounting for CKD did not improve discrimination of the Framingham equation for vascular events [17]. In the same study, considering renal function improved discrimination for total mortality in white men but not in white women [17].

The MDRD equation is the proposed method for eGFR assessment in clinical practice [30]. It is currently recommended that serum creatinine levels should not be used as the sole means to assess kidney function [33]. However, the MDRD equation was developed in patients with CKD and appears to be less accurate in patients with normal kidney function or moderately reduced eGFR [30,56,57]. The Cockcroft-Gault equation also misclassified approximately 30% of subjects in population studies [58]. Significant differences in classification regarding renal function comparing MDRD and Cockcroft-Gault equations were also seen in the present study. Other indices of kidney function might also be useful. Cystatin C levels might reflect GFR more accurately than creatinine [59]. Elevated cystatin C levels were associated with vascular events in elderly subjects [60] and in patients with established CHD [61]. However, cystatin C levels also show variations depending on age, gender, body weight, smoking and presence of inflammation [62].

We estimated vascular risk using the Framingham risk equation. Some studies performed in the UK showed that the Framingham engine accurately predicts vascular events [63] although others reported an overestimation of CHD risk with this model [64-66]. A meta-analysis showed a considerable variation in the predictive value of the Framingham risk score in different populations [67]. It appears to overestimate risk in low risk populations and to underestimate risk in high risk populations [67]. The Prospective Cardiovascular Munster (PROCAM) score is also used to estimate vascular risk [6]. This score considers all risk factors of the Framingham equation but replaces LDL-C for TC levels and includes TG levels and FaHist of CHD [6]. Elevated TG levels appear to be associated with increased vascular risk [68]. Studies in the UK showed that PROCAM and Framingham models have similar predictive values [65, 66]. In contrast, we reported that, in dyslipidemic patients without established vascular disease, the Framingham risk score predicted higher risk than PROCAM [69]. We did not use the PROCAM calculation in this study due to its narrow age limits [6].

It is of interest that statins appear to prevent the decline of renal function in high risk patients without established vascular disease and in patients with CVD [40, 41, 70-78]. A number of small studies also reported that statins might reduce albuminuria [79, 80]. Other lipid profile modifying agents, including ezetimibe and omega-3 fatty acids, might also “protect” kidney function [81-86]. In contrast, fibrates appear to raise serum creatinine levels [87-94]. It was suggested that the fibrates-induced rise in creatinine level is due to increased production of creatinine and does not reflect a true decline of kidney function [91, 93, 95]. In addition, fibrates appear to reduce microalbuminuria in diabetic patients [96, 97].

Subgroup analyses of randomized controlled trials in high risk patients without established vascular disease and in patients with CHD showed that statins reduce vascular risk in patients with CKD [42, 98-101]. The ongoing Study of Heart and Renal Protection (SHARP) will assess the effects of the simvastatin plus ezetimibe combination treatment in patients with CKD but without established CHD [102]. In the Veterans’ Affairs High-Density Lipoprotein Intervention Trial (VA-HIT), gemfibrozil reduced vascular events in patients with CKD and established CHD [103]. However, there was an increased risk of sustained increase in creatinine levels in the gemfibrozil group [103].

Uric acid levels have not been consistent predictors of vascular risk [40,104,105]. Some evidence identified a relationship only in women [105]. Therefore it is of interest that in the present study uric acid levels showed a significant trend in relation to serum creatinine levels and eGFR only in women.

A limitation of our study is that we did not evaluate the presence of albuminuria, another diagnostic criterion for CKD [30] that is also associated with increased vascular risk [106-108]. Evaluation of both eGFR and albuminuria is currently recommended for the detection of CKD [30] since patients with CKD might only have a low eGFR or isolated albuminuria [109-111]. In addition, albuminuria and eGFR appear to predict vascular disease independently of each other [110]. Albuminuria predicted vascular events in hypertensive patients independently of the Framingham risk score [112]. It also appears that considering albuminuria might improve the predictive accuracy of the Framingham risk equation [15,16]. However, the Framingham Heart study reported that only reduced eGFR predicted all cause mortality whereas microalbuminuria did not [111]. A “statistical” disadvantage is that many of the variables that differed between the tertiles of renal function are actually included (directly or indirectly) in the Framingham equation.

An advantage of our study is the homogeneous nature of the population. None of the participants had DM or overt CVD, none were smokers and they were not taking any lipid lowering or antihypertensive drugs.

In conclusion, estimated vascular risk (using the Framingham equation) progressively increases as renal function declines. The possibility that incorporating kidney function in the Framingham predictive equation will improve risk stratification requires further work.

ABBREVIATIONS
ABI = Ankle-brachial index
ACS = Acute coronary syndromes
ARIC = Atherosclerosis risk in the communities
CHD = Coronary heart disease
CKD = Chronic kidney disease
CrCl = Creatinine clearance
CVD = Cardiovascular disease
DBP = Diastolic blood pressure
DM = Diabetes mellitus
eGFR = Estimated glomerular filtration rate
FaHist = Family history
HDL-C = High density lipoprotein cholesterol
hsCRP = High sensitivity C-reactive protein
LDL-C = Low density lipoprotein cholesterol
MDRD = Modification of diet in renal disease
PAD = Peripheral arterial disease
PROCAM = Prospective cardiovascular munster
SBP = Systolic blood pressure
SHARP = Study of heart and renal protection
TC = Total cholesterol
VA-HIT = Veterans’ affairs high-density lipoprotein intervention trial

DECLARATION OF INTEREST

This study was performed independently; no company or institution supported it financially. Some of the authors have attended conferences, given lectures and participated in advisory boards or trials sponsored by various pharmaceutical companies. Konstantinos Tziomalos is supported by a grant from the Hellenic Atherosclerosis Society.

REFERENCES

[1] Grundy SM, Pasternak R, Greenland P, Smith S Jr, Fuster V. Assessment of cardiovascular risk by use of multiple-risk-factor assessment equations: a statement for healthcare professionals from the American Heart Association and the American College of Cardiology Circulation 1999; 100: 1481-92.
[2] Pearson TA, Blair SN, Daniels SR, et al. AHA guidelines for primary prevention of cardiovascular disease and stroke: 2002 update: consensus panel guide to comprehensive risk reduction for adult patients without coronary or other atherosclerotic vascular diseases. american heart association science advisory and coordinating committee Circulation 2002; 106: 388-91.
[3] Graham I, Atar D, Borch-Johnsen K, et al. European guidelines on cardiovascular disease prevention in clinical practice: executive summary Eur Heart J 2007; 28: 2375-414.
[4] Grundy SM. Primary prevention of coronary heart disease: integrating risk assessment with intervention Circulation 1999; 100: 988-.
[5] Anderson KM, Wilson PW, Odell PM, Kannel WB. An updated coronary risk profile. A statement for health professionals Circulation 1991; 83: 356-62.
[6] Assmann G, Cullen P, Schulte H. Simple scoring scheme for calculating the risk of acute coronary events based on the 10-year follow-up of the prospective cardiovascular Munster [PROCAM] study Circulation 2002; 105: 310-5.
[7] Grundy SM, Balady GJ, Criqui MH, et al. Primary prevention of coronary heart disease: guidance from Framingham: a statement for healthcare professionals from the AHA Task Force on Risk Reduction. American Heart Association Circulation 1998; 97: 1876-87.
[8] Jurgensen JS. The value of risk scores Heart 2006; 92: 1713-4.
[9] Koenig W, Lowel H, Baumert J, Meisinger C. C-reactive protein modulates risk prediction based on the Framingham Score: implications for future risk assessment: results from a large cohort study in southern Germany Circulation 2004; 109: 1349-53.
[10] Ridker PM, Rifai N, Rose L, Buring JE, Cook NR. Comparison of C-reactive protein and low-density lipoprotein cholesterol levels in the prediction of first cardiovascular events N Engl J Med 2002; 347: 1557-65.
[11] Astor BC, Hallan SI, Miller ER III, Yeung E, Coresh J. Glomerular filtration rate, albuminuria, and risk of cardiovascular and all-cause mortality in the US population Am J Epidemiol 2008; 167: 1226-34.
[12] Go AS, Chertow GM, Fan D, McCulloch CE, Hsu CY. Chronic kidney disease and the risks of death, cardiovascular events, and hospitalization N Engl J Med 2004; 351: 1296-305.
[13] Meisinger C, Doring A, Lowel H. Chronic kidney disease and risk of incident myocardial infarction and all-cause and cardiovascular disease mortality in middle-aged men and women from the general population Eur Heart J 2006; 27: 1245-50.
[14] Van Biesen W, De Bacquer D, Verbeke F, Delanghe J, Lameire N, Vanholder R. The glomerular filtration rate in an apparently healthy population and its relation with cardiovascular mortality during 10 years Eur Heart J 2007; 28: 478-83.
[15] Ajani UA, Ford ES, McGuire LC. Distribution of lifestyle and emerging risk factors by 10-year risk for coronary heart disease Eur J Cardiovasc Prev Rehabil 2006; 13: 745-52.
[16] Cao JJ, Biggs ML, Barzilay J, et al. Cardiovascular and mortality risk prediction and stratification using urinary albumin excretion in older adults ages 68-102: the Cardiovascular Health Study Atherosclerosis 2008; 197: 806-13.
[17] Weiner DE, Tighiouart H, Griffith JL, et al. Kidney disease, Framingham risk scores, and cardiac and mortality outcomes Am J Med 2007; 120: 552-8.
[18] Ganotakis ES, Gazi IF, Papadakis JA, Jagroop IA, Nair DR, Mikhailidis DP. The relationship between circulating fibrinogen and lipoprotein [a] levels in patients with primary dyslipidemia Clin Appl Thromb Hemost 2007; 13: 35-42.
[19] Ganotakis ES, Vrentzos GE, Gazi IF, et al. Fibrinogen, lipoprotein [a], albumin and bilirubin [F-L-A-B] levels and cardiovascular risk calculated using the Framingham equation In Vivo 2007; 21: 685-94.
[20] Koenig W, Rosenson RS. Acute-phase reactants and coronary heart disease Semin Vasc Med 2002; 2: 417-28.
[21] Khovidhunkit W, Kim MS, Memon RA, et al. Effects of infection and inflammation on lipid and lipoprotein metabolism: mechanisms and consequences to the host J Lipid Res 2004; 45: 1169-96.
[22] Athyros VG, Kakafika AI, Karagiannis A, Mikhailidis DP. Do we need to consider inflammatory markers when we treat atherosclerotic disease? Atherosclerosis 2008; 200: 1-12.
[23] Kakafika AI, Liberopoulos EN, Mikhailidis DP. Fibrinogen: a predictor of vascular disease Curr Pharm Des 2007; 13: 1647-59.
[24] Stone NJ. Secondary causes of hyperlipidemia Med Clin North Am 1994; 78: 117-41.
[25] Athyros VG, Tziomalos K, Mikhailidis DP, et al. Do we need a statin-nicotinic acid-aspirin mini-polypill to treat combined hyperlipidaemia? Expert Opin Pharmacother 2007; 8: 2267-77.
[26] Mikhailidis DP, Spyropoulos KA. The effect of tamoxifen on lipid and haemostatic predictors of ischaemic heart disease J Drug Dev Clin Pract 1996; 8: 19-24.
[27] Tziomalos K, Athyros VG, Mikhailidis DP. Fish oils and vascular disease prevention: an update Curr Med Chem 2007; 14: 2622-8.
[28] Filippatos TD, Liberopoulos EN, Pavlidis N, Elisaf MS, Mikhailidis DP. Effects of hormonal treatment on lipids in patients with cancer Cancer Treat Rev 2009; 35: 175-84.
[29] Cockcroft DW, Gault MH. Prediction of creatinine clearance from serum creatinine Nephron 1976; 16: 31-41.
[30] Brosius FC III, Hostetter TH, Kelepouris E, et al. Detection of chronic kidney disease in patients with or at increased risk of cardiovascular disease: a science advisory from the American Heart Association Kidney And Cardiovascular Disease Council; the Councils on High Blood Pressure Research, Cardiovascular Disease in the Young, and Epidemiology and Prevention; and the Quality of Care and Outcomes Research Interdisciplinary Working Group: developed in collaboration with the National Kidney Foundation Circulation 2006; 114: 1083-7.
[31] Kusuoka H, Hoffman JI. Advice on statistical analysis for Circulation Research Circ Res 2002; 91: 662-71.
[32] Meguid EN, Bello AK. Chronic kidney disease: the global challenge Lancet 2005; 365: 331-40.
[33] Levey AS, Coresh J, Balk E, et al. National Kidney Foundation practice guidelines for chronic kidney disease: evaluation, classification, and stratification Ann Intern Med 2003; 139: 137-47.
[34] Fox CS, Larson MG, Leip EP, Culleton B, Wilson PW, Levy D. Predictors of new-onset kidney disease in a community-based population JAMA 2004; 291: 844-50.
[35] Obermayr RP, Temml C, Knechtelsdorfer M, et al. Predictors of new-onset decline in kidney function in a general middle-european population Nephrol Dial Transplant 2008; 23: 1265-73.
[36] Brancati FL, Whelton PK, Randall BL, Neaton JD, Stamler J, Klag MJ. Risk of end-stage renal disease in diabetes mellitus: a prospective cohort study of men screened for MRFIT. Multiple Risk Factor Intervention Trial JAMA 1997; 278: 2069-74.
[37] Coresh J, Selvin E, Stevens LA, et al. Prevalence of chronic kidney disease in the United States JAMA 2007; 298: 2038-47.
[38] Raymond NT, Zehnder D, Smith SC, Stinson JA, Lehnert H, Higgins RM. Elevated relative mortality risk with mild-to-moderate chronic kidney disease decreases with age Nephrol Dial Transplant 2007; 22: 3214-20.
[39] Stevens PE, O'Donoghue DJ, de Lusignan S, et al. Chronic kidney disease management in the United Kingdom: NEOERICA project results Kidney Int 2007; 72: 92-.
[40] Athyros VG, Mikhailidis DP, Liberopoulos EN, et al. Effect of statin treatment on renal function and serum uric acid levels and their relation to vascular events in patients with coronary heart disease and metabolic syndrome: a subgroup analysis of the GREek Atorvastatin and Coronary heart disease Evaluation [GREACE] Study Nephrol Dial Transplant 2007; 22: 118-27.
[41] Athyros VG, Mikhailidis DP, Papageorgiou AA, et al. The effect of statins versus untreated dyslipidaemia on renal function in patients with coronary heart disease. A subgroup analysis of the Greek atorvastatin and coronary heart disease evaluation [GREACE] study J Clin Pathol 2004; 57: 728-34.
[42] Tonelli M, Isles C, Curhan GC, et al. Effect of pravastatin on cardiovascular events in people with chronic kidney disease Circulation 2004; 110: 1557-63.
[43] Anavekar NS, McMurray JJ, Velazquez EJ, et al. Relation between renal dysfunction and cardiovascular outcomes after myocardial infarction N Engl J Med 2004; 351: 1285-95.
[44] Shlipak MG, Heidenreich PA, Noguchi H, Chertow GM, Browner WS, McClellan MB. Association of renal insufficiency with treatment and outcomes after myocardial infarction in elderly patients Ann Intern Med 2002; 137: 555-62.
[45] Perkovic V, Ninomiya T, Arima H, et al. Chronic kidney disease, cardiovascular events, and the effects of perindopril-based blood pressure lowering: data from the PROGRESS study J Am Soc Nephrol 2007; 18: 2766-72.
[46] Pasqualini L, Schillaci G, Pirro M, et al. Renal dysfunction predicts long-term mortality in patients with lower extremity arterial disease J Intern Med 2007; 262: 668-77.
[47] Wannamethee SG, Shaper AG, Perry IJ. Serum creatinine concentration and risk of cardiovascular disease: a possible marker for increased risk of stroke Stroke 1997; 28: 557-63.
[48] Koren-Morag N, Goldbourt U, Tanne D. Renal dysfunction and risk of ischemic stroke or TIA in patients with cardiovascular disease Neurology 2006; 67: 224-8.
[49] Bos MJ, Koudstaal PJ, Hofman A, Breteler MM. Decreased glomerular filtration rate is a risk factor for hemorrhagic but not for ischemic stroke: the Rotterdam Study Stroke 2007; 38: 3127-2.
[50] de Vinuesa SG, Ortega M, Martinez P, Goicoechea M, Campdera FG, Luno J. Subclinical peripheral arterial disease in patients with chronic kidney disease: prevalence and related risk factors Kidney Int Suppl 2005; S44-7.
[51] Paraskevas KI, Hamilton G, Cross JM, Mikhailidis DP. Atherosclerotic renal artery stenosis: association with emerging vascular risk factors Nephron Clin Pract 2008; 108: c56-66.
[52] Daskalopoulou SS, Pathmarajah M, Kakkos SK, et al. Association between ankle-brachial index and risk factor profile in patients newly diagnosed with intermittent claudication Circ J 2008; 72: 441-8.
[53] Efstratiadis G, Tziomalos K, Mikhailidis DP, Athyros VG, Hatzitolios A. Atherogenesis in renal patients: a model of vascular disease? Curr Vasc Pharmacol 2008; 6: 93-107.
[54] Parikh NI, Hwang SJ, Larson MG, Meigs JB, Levy D, Fox CS. Cardiovascular disease risk factors in chronic kidney disease: overall burden and rates of treatment and control Arch Intern Med 2006; 166: 1884-91.
[55] Weiner DE, Tighiouart H, Elsayed EF, et al. The Framingham predictive instrument in chronic kidney disease J Am Coll Cardiol 2007; 50: 217-4.
[56] Cirillo M, Anastasio P, De Santo NG. Relationship of gender, age, and body mass index to errors in predicted kidney function Nephrol Dial Transplant 2005; 20: 1791-8.
[57] Rule AD, Larson TS, Bergstralh EJ, Slezak JM, Jacobsen SJ, Cosio FG. Using serum creatinine to estimate glomerular filtration rate: accuracy in good health and in chronic kidney disease Ann Intern Med 2004; 141: 929-37.
[58] Froissart M, Rossert J, Jacquot C, Paillard M, Houillier P. Predictive performance of the modification of diet in renal disease and Cockcroft-Gault equations for estimating renal function J Am Soc Nephrol 2005; 16: 763-3.
[59] Dharnidharka VR, Kwon C, Stevens G. Serum cystatin C is superior to serum creatinine as a marker of kidney function: a meta-analysis Am J Kidney Dis 2002; 40: 221-6.
[60] Shlipak MG, Sarnak MJ, Katz R, et al. Cystatin C and the risk of death and cardiovascular events among elderly persons N Engl J Med 2005; 352: 2049-60.
[61] Ix JH, Shlipak MG, Chertow GM, Whooley MA. Association of cystatin C with mortality, cardiovascular events, and incident heart failure among persons with coronary heart disease: data from the Heart and Soul Study Circulation 2007; 115: 173-9.
[62] Knight EL, Verhave JC, Spiegelman D, et al. Factors influencing serum cystatin C levels other than renal function and the impact on renal function measurement Kidney Int 2004; 65: 1416-21.
[63] Influence of pravastatin and plasma lipids on clinical events in the West of Scotland Coronary Prevention Study [WOSCOPS] Circulation 1998; 97: 1440-5.
[64] Brindle P, Emberson J, Lampe F, et al. Predictive accuracy of the Framingham coronary risk score in British men: prospective cohort study BMJ 2003; 327: 1267-0.
[65] Cooper JA, Miller GJ, Humphries SE. A comparison of the PROCAM and Framingham point-scoring systems for estimation of individual risk of coronary heart disease in the Second Northwick Park Heart Study Atherosclerosis 2005; 181: 93-100.
[66] Empana JP, Ducimetiere P, Arveiler D, et al. Are the Framingham and PROCAM coronary heart disease risk functions applicable to different European populations? The PRIME Study. Eur Heart J 2003; 24: 1903-1.
[67] Brindle P, Beswick A, Fahey T, Ebrahim S. Accuracy and impact of risk assessment in the primary prevention of cardiovascular disease: a systematic review Heart 2006; 92: 1752-9.
[68] Sarwar N, Danesh J, Eiriksdottir G, et al. Triglycerides and the risk of coronary heart disease: 10,158 incident cases among 262,525 participants in 29 Western prospective studies Circulation 2007; 115: 450-8.
[69] Vrentzos GE, Papadakis JA, Ganotakis ES, et al. Predicting coronary heart disease risk using the Framingham and PROCAM equations in dyslipidaemic patients without overt vascular disease Int J Clin Pract 2007; 61: 1643-53.
[70] Shepherd J, Kastelein JJ, Bittner V, et al. Effect of intensive lipid lowering with atorvastatin on renal function in patients with coronary heart disease: the Treating to New Targets [TNT] study Clin J Am Soc Nephrol 2007; 2: 1131-9.
[71] Tonelli M, Isles C, Craven T, et al. Effect of pravastatin on rate of kidney function loss in people with or at risk for coronary disease Circulation 2005; 112: 171-8.
[72] Collins R, Armitage J, Parish S, Sleigh P, Peto R. MRC/BHF Heart Protection Study of cholesterol-lowering with simvastatin in 5963 people with diabetes: a randomised placebo-controlled trial Lancet 2003; 361: 2005-16.
[73] Athyros VG, Papageorgiou AA, Elisaf M, Mikhailidis DP. Statins and renal function in patients with diabetes mellitus Curr Med Res Opin 2003; 19: 615-7.
[74] Youssef F, Gupta P, Mikhailidis DP, Hamilton G. Risk modification in patients with peripheral arterial disease: a retrospective survey Angiology 2005; 56: 279-87.
[75] Youssef F, Gupta P, Seifalian AM, Myint F, Mikhailidis DP, Hamilton G. The effect of short-term treatment with simvastatin on renal function in patients with peripheral arterial disease Angiology 2004; 55: 53-62.
[76] Youssef F, Seifalian AM, Jagroop IA, et al. The early effect of lipid-lowering treatment on carotid and femoral intima media thickness [IMT] Eur J Vasc Endovasc Surg 2002; 23: 358-64.
[77] Alnaeb ME, Youssef F, Mikhailidis DP, Hamilton G. Short-term lipid-lowering treatment with atorvastatin improves renal function but not renal blood flow indices in patients with peripheral arterial disease Angiology 2006; 57: 65-71.
[78] Kakafika A, Liamis G, Elisaf M, Mikhailidis D. Effect of atorvastatin on serum creatinine levels Curr Med Res Opin 2001; 17: 230-1.
[79] Douglas K, O'Malley PG, Jackson JL. Meta-analysis: the effect of statins on albuminuria Ann Intern Med 2006; 145: 117-24.
[80] Tonolo G, Melis MG, Formato M, et al. Additive effects of Simvastatin beyond its effects on LDL cholesterol in hypertensive type 2 diabetic patients Eur J Clin Invest 2000; 30: 980-7.
[81] Athyros VG, Tziomalos K, Kakafika AI, Koumaras H, Karagiannis A, Mikhailidis DP. Effectiveness of ezetimibe alone or in combination with twice a week Atorvastatin [10 mg] for statin intolerant high-risk patients Am J Cardiol 2008; 101: 483-5.
[82] Gazi IF, Daskalopoulou SS, Nair DR, Mikhailidis DP. Effect of ezetimibe in patients who cannot tolerate statins or cannot get to the low density lipoprotein cholesterol target despite taking a statin Curr Med Res Opin 2007; 23: 2183-92.
[83] Sweny P, Wheeler DC, Lui SF, et al. Dietary fish oil supplements preserve renal function in renal transplant recipients with chronic vascular rejection Nephrol Dial Transplant 1989; 4: 1070-5.
[84] Lauretani F, Semba RD, Bandinelli S, et al. Plasma polyunsaturated fatty acids and the decline of renal function Clin Chem 2008; 54: 475-81.
[85] Nakamura T, Sato E, Fujiwara N, et al. Ezetimibe decreases serum levels of asymmetric dimethylarginine [ADMA] and ameliorates renal injury in non-diabetic chronic kidney disease patients in a cholesterol-independent manner Pharmacol Res 2009.
[86] Fras Z, Mikhailidis DP. Statin plus ezetimibe treatment in clinical practice: the SI-SPECT [Slovenia [SI] Statin Plus Ezetimibe in Cholesterol Treatment] monitoring of clinical practice study Curr Med Res Opin 2008; 24: 2467-76.
[87] Broeders N, Knoop C, Antoine M, Tielemans C, Abramowicz D. Fibrate-induced increase in blood urea and creatinine: is gemfibrozil the only innocuous agent? Nephrol Dial Transplant 2000; 15: 1993-9.
[88] Rizos E, Kostoula A, Elisaf M, Mikhailidis DP. Effect of ciprofibrate on C-reactive protein and fibrinogen levels Angiology 2002; 53: 273-7.
[89] Rizos E, Bairaktari E, Ganotakis E, Tsimihodimos V, Mikhailidis DP, Elisaf M. Effect of ciprofibrate on lipoproteins, fibrinogen, renal function, and hepatic enzymes J Cardiovasc Pharmacol Ther 2002; 7: 219-6.
[90] Tziomalos K, Athyros VG. Fenofibrate: a novel formulation [Triglide] in the treatment of lipid disorders: a review Int J Nanomedicine 2006; 1: 129-47.
[91] Tsimihodimos V, Miltiadous G, Daskalopoulou SS, Mikhailidis DP, Elisaf MS. Fenofibrate: metabolic and pleiotropic effects Curr Vasc Pharmacol 2005; 3: 87-98.
[92] Tsimihodimos V, Kakafika A, Elisaf M. Fibrate treatment can increase serum creatinine levels Nephrol Dial Transplant 2001; 16: 1301.
[93] Tsimihodimos V, Miltiadous G, Bairaktari E, Elisaf M. Possible mechanisms of the fibrate-induced increase in serum creatinine Clin Nephrol 2002; 57: 407-8.
[94] Filippatos TD, Kiortsis DN, Liberopoulos EN, Georgoula M, Mikhailidis DP, Elisaf MS. Effect of orlistat, micronised fenofibrate and their combination on metabolic parameters in overweight and obese patients with the metabolic syndrome: the FenOrli study Curr Med Res Opin 2005; 21: 1997-2006.
[95] Hottelart C, El Esper N, Rose F, Achard JM, Fournier A. Fenofibrate increases creatininemia by increasing metabolic production of creatinine Nephron 2002; 92: 536-41.
[96] Keech A, Simes RJ, Barter P, et al. Effects of long-term fenofibrate therapy on cardiovascular events in 9795 people with type 2 diabetes mellitus [the FIELD study]: randomised controlled trial Lancet 2005; 366: 1849-61.
[97] Ansquer JC, Foucher C, Rattier S, Taskinen MR, Steiner G. Fenofibrate reduces progression to microalbuminuria over 3 years in a placebo-controlled study in type 2 diabetes: results from the Diabetes Atherosclerosis Intervention Study [DAIS] Am J Kidney Dis 2005; 45: 485-93.
[98] Shepherd J, Kastelein JJ, Bittner V, et al. Intensive lipid lowering with atorvastatin in patients with coronary heart disease and chronic kidney disease: the TNT [Treating to New Targets] study J Am Coll Cardiol 2008; 51: 1448-54.
[99] Chonchol M, Cook T, Kjekshus J, Pedersen TR, Lindenfeld J. Simvastatin for secondary prevention of all-cause mortality and major coronary events in patients with mild chronic renal insufficiency Am J Kidney Dis 2007; 49: 373-82.
[100] MRC/BHF Heart Protection Study of cholesterol lowering with simvastatin in 20,536 high-risk individuals: a randomised placebo-controlled trial Lancet 2002; 360: 7-22.
[101] Sever PS, Dahlof B, Poulter NR, et al. Prevention of coronary and stroke events with atorvastatin in hypertensive patients who have average or lower-than-average cholesterol concentrations, in the Anglo-Scandinavian Cardiac Outcomes Trial--Lipid Lowering Arm [ASCOT-LLA]: a multicentre randomised controlled trial Lancet 2003; 361: 1149-58.
[102] Baigent C, Landry M. Study of Heart and Renal Protection [SHARP] Kidney Int Suppl 2003; S207-10.
[103] Tonelli M, Collins D, Robins S, Bloomfield H, Curhan GC. Gemfibrozil for secondary prevention of cardiovascular events in mild to moderate chronic renal insufficiency Kidney Int 2004; 66: 1123-30.
[104] Athyros VG, Elisaf M, Papageorgiou AA, et al. Effect of statins versus untreated dyslipidemia on serum uric acid levels in patients with coronary heart disease: a subgroup analysis of the GREek Atorvastatin and Coronary-heart-disease Evaluation [GREACE] study Am J Kidney Dis 2004; 43: 589-99.
[105] Feig DI, Kang DH, Johnson RJ. Uric acid and cardiovascular risk N Engl J Med 2008; 359: 1811-21.
[106] Gerstein HC, Mann JF, Yi Q, et al. Albuminuria and risk of cardiovascular events, death, and heart failure in diabetic and nondiabetic individuals JAMA 2001; 286: 421-6.
[107] Arnlov J, Evans JC, Meigs JB, et al. Low-grade albuminuria and incidence of cardiovascular disease events in nonhypertensive and nondiabetic individuals: the Framingham Heart Study Circulation 2005; 112: 969-75.
[108] Karagiannis A, Mikhailidis DP, Tziomalos K, Kakafika AI, Athyros VG. Has the time come for a new definition of microalbuminuria? Curr Vasc Pharmacol 2008; 6: 81-3.
[109] Garg AX, Kiberd BA, Clark WF, Haynes RB, Clase CM. Albuminuria and renal insufficiency prevalence guides population screening: results from the NHANES III Kidney Int 2002; 61: 2165-75.
[110] Cirillo M, Lanti MP, Menotti A, et al. Definition of kidney dysfunction as a cardiovascular risk factor: use of urinary albumin excretion and estimated glomerular filtration rate Arch Intern Med 2008; 168: 617-24.
[111] Foster MC, Hwang SJ, Larson MG, et al. Cross-classification of microalbuminuria and reduced glomerular filtration rate: associations between cardiovascular disease risk factors and clinical outcomes Arch Intern Med 2007; 167: 1386-92.
[112] Olsen MH, Wachtell K, Ibsen H, et al. Reductions in albuminuria and in electrocardiographic left ventricular hypertrophy independently improve prognosis in hypertension: the LIFE study J Hypertens 2006; 24: 775-81.