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Received for publication: 28.1.10; Accepted in revised form: 7.5.10
Nephrol Dial Transplant (2010) 25: 3924–3931
doi: 10.1093/ndt/gfq327
Advance Access publication 10 June 2010
Association of risk factors for cardiovascular disease and glomerular
filtration rate: a community-based study of 4925 adults in Beijing
Fan Wang, Ping Ye, Leiming Luo, Wenkai Xiao and Hongmei Wu
Department of Geriatric Cardiology, Chinese PLA General Hospital, Beijing, 100853, China
Correspondence and offprint requests to: Ping Ye; E-mail: [email protected]
Abstract
Background. Several large prospective studies have reported that a low estimated glomerular filtration rate
(eGFR) or chronic kidney disease (CKD) is independently associated with cardiovascular disease (CVD)
events and all-cause mortality in high-risk populations.
However, findings from community-based population
studies are scarce and inconsistent. We investigated the
level of eGFR and the relationship between CVD risk
factors and eGFR or CKD in the population of Beijing,
China.
Methods. This is a community-based observational survey
in residents from three communities in Beijing for a routine health status checkup. Out of 5100 individuals who
were eligible for inclusion, 4925 (96.57%) had complete
data and were investigated the level of eGFR and the associated factors of reduced renal function. 2085 individuals
with albuminuria values were included in the analyses on
the associated factors of CKD. A questionnaire was used
for risk factors of CVD. Anthropometry and blood pressure were measured. Serum creatinine, total cholesterol,
triglyceride (TG), low-density lipoprotein cholesterol,
high-density lipoprotein cholesterol and serum glucose
were detected. The urine albumin–creatinine ratio (ACR)
was used as an expression for albumin excretion. The oral
glucose tolerance test was performed for the participants
with no history of diabetes to diagnose diabetes. eGFR
was evaluated by the Chinese modified Modification of
Diet in Renal Disease equation. Reduced renal function
was defined2 as normal renal function: eGFR ≥90 mL/
min/1.73 m ; mild 2reduced renal function: eGFR 89–
reduced renal
60 mL/min/1.73 m ; moderate to severe
2
function: eGFR <60 mL/min/1.73 2m . CKD was diagnosed as eGFR <60 mL/min/1.73 m or albuminuria was
present.
Results. The prevalence of mild reduced renal function
(eGFR 89–60 mL/min/1.73 m2), moderate to severe reduced renal function (eGFR <60 mL/min/1.73 m2) and
CKD was 41.12% (2025/4925), 1.89% (93/4925) and
18.90% (394/2085) in the present study, respectively.
The proportion of risk factors was higher in the low level
of eGFR. Risk factors that exposed to reduced renal function were slightly different between male and female. The
results of multivariate logistic regression analysis showed
older age [increased by 10 years; odds ratios (OR)=1.22],
male gender (OR=1.38), diabetes (OR=1.67), hyperten-
© The Author 2010. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved.
For Permissions, please e-mail: [email protected]
Association of risk factors for CVD and eGFR: a community-based study in Beijing
sion (OR=1.84) and hypertriglyceridaemia (≥1.7 mmol/L;
OR=1.68) were independently associated with CKD.
Conclusions. In the general population in Beijing, China,
multiple CVD risk factors increased with a decline in
eGFR and older age, hypertension, diabetes and elevated
TG were independently associated with CKD.
Keywords: cardiovascular disease risk factors; chronic kidney disease;
cross-sectional study; glomerular filtration rate
Introduction
Cardiovascular disease (CVD) constitutes the leading cause
of mortality in chronic kidney disease (CKD) patients [1],
and a decreased level of kidney function is an independent
risk factor for CVD. Previous large-scale prospective clinical trials have shown that reduced glomerular filtration rate
(GFR) is an independent risk factor for all-cause mortality
as well as adverse CVD events, such as myocardial infarction and stroke [2,3], and populations with a low level of
GFR show increased exposure to CVD risk factors, such
as hypertension, diabetes and dyslipidaemia [4,5]. However,
these studies examined populations who were at high risk
for CKD or who had multiple risk factors for CVD. Clinical
trials examining the relationship between GFR and risk factors for CVD in the general population were limited [6]. In
addition, very few such data are available in Asia. In the
present study, we investigated the relationship between estimated GFR and CVD risk factors in a large sample of the
general population in Beijing during 2007 and 2008.
Materials and methods
Study population
This community-based cross-sectional survey was designed to establish
the association between GFR and the risk factors of CVD through a routine health status checkup in the population from three communities in
two districts of Beijing (an urban community of Shijingshan district, a
town community and a rural community of Daxing district). The communities were selected by convenience, representing distinct economic, civilizational and lifestyle profiles (village, town and city). A minimum of
90% of residents in each community entered into the study and the participants were ethnically homogeneous (100% Han people). All participants
were permanent residents, aged ≥18 years and were able to provide informed consent. Subjects with malignant tumours, bedridden status, mental disorders, severe heart and lung function failure or who were on
dialysis were excluded from the study. The study was approved by the
Ethics Committee of the PLA General Hospital and all participants signed
a written informed consent form. Data collection was carried out between
September 2007 and October 2008.
A total of 5100 individuals (2111 from the urban community, 1513
from the rural community and 1476 from the town community) were eligible for inclusion in this survey. In this study population, 2111 participants from the urban community were conveniently selected to measure
albuminuria. In the current paper, we excluded subjects with missing data
for essential variables (n=175). Of these, 69 had missing height and
weight measurements, 27 had missing systolic blood pressure (SBP)
and diastolic blood pressure (DBP) measurements, 135 had missing serum creatinine (Scr) values, 120 had missing blood glucose (Glu), total
cholesterol (TC), triglyceride (TG), low-density lipoprotein cholesterol
(LDL-C) or high-density lipoprotein cholesterol (HDL-C) values, resulting in a final sample size of 4925, and of these, a subpopulation sample
size of 2085 with albuminuria values.
3925
Measurements and definitions
Information on participants was collected by questionnaire, physical examination and laboratory measurement. A standard questionnaire that assessed risk factors for CVD was designed, and this was administered
using a face-to-face counselling method. The survey assessed common
risk factors for CVD, including age, gender, cigarette smoking, history
of hypertension and diabetes. Cigarette smoking was defined as having
one cigarette per day and keeping on smoking for at least 1 year [7].
The investigation was completed by physicians in the Department of Geriatric Cardiology of the People’s Liberation Army General Hospital who
were trained by the research team.
Physical examination included anthropometry and blood pressure measurement. Height was measured in centimetres using a wall-mounted measuring tape and weight was measured in kilogrammes using a digital scale
(Jingzhun, Armamentarium Limited Company, Tianjin, China). Body
mass index (BMI) was calculated as weight in kilogrammes divided by
the height in metres squared (kg/m2). Overweight was defined as BMI
≥24 kg/m2 according to the 2006 Guidelines on Preservation and Control
Overweight and Obesity in Chinese Adults classification [8].
Blood pressure was measured using calibrated desktop sphygmomanometers (Yuyue, Armamentarium Limited Company, Jiangsu, China) after
the participants were seated for at least 5 min, consistent with current recommendations [9]. Blood pressure was measured three times consecutively, with at least 1 min between measurements, and the reported blood
pressure was the average of these three measurements. A subject was considered to have hypertension if (i) SBP ≥140 mmHg, (ii) DBP ≥90 mmHg
and/or (iii) the subject was taking an antihypertensive drug [10].
Venous blood sample was collected by venipuncture after an overnight
fast of at least 12 h. Blood sample (10 mL) in two tubes were routinely
stored at 4°C and delivered to the Department of Biochemistry and the
laboratory of the Nephrology Department, People’s Liberation Army General Hospital on the same day. The levels of TC, TG, LDL-C, HDL-C and
Glu were measured by a qualified technician using a Roche autoanalyser
(Roche Diagnostics, Indianapolis, IN) in the Department of Biochemistry.
Scr was measured by enzymatic method on a Hitachi 7600 autoanalyser (Hitachi, Tokyo, Japan) in the laboratory of the Nephrology Department. In addition, Scr of 60 serum samples were analysed by both
enzymatic method and Jaffe's kinetic method on a Hitachi 7600 autoanalyser (Hitachi, Tokyo, Japan) in the laboratory of the Nephrology Department. A calibration equation was generated from the results (R2 =0.999)
[11]: Jaffe’s kinetic method Scr (mg/dL)=0.795×[enzymatic method Scr
(mg/dL)]+0.29.
Estimated glomerular filtration rate (eGFR) was calculated using
the Chinese modified Modification of Diet in Renal Disease (CMDRD) equation [12]: eGFR (mL/min/1.73 m2)=175×standard creatinine (mg/dL)−1.234 ×age (year)−0.179 ×(0.79 if female). eGFR ≥90 mL/
min/1.73 m2 was defined as the normal renal function; eGFR 89–
60 mL/min/1.73 m2 was defined as the mild reduced renal function;
eGFR <60 mL/min/1.73 m2 was defined as the moderate to severe
reduced renal function.
Morning random urine samples were collected from the participants of
one of the three communities (the urban community). Urine albumin was
measured by an immunoturbidimetric method (Dako A/S, Glostrup,
Denmark), and urine creatinine urine was measured by the Hitachi 7170
autoanalyser (Hitachi, Tokyo, Japan). The urine albumin–creatinine ratio
(UACR) was used as an expression for albumin excretion. The UACR was
calculated according to the following equation: UACR (mg/g)=urine albumin concentration (mg/L)/urine creatinine concentration (g/L). Albuminuria was diagnosed if the UACR was ≥30 mg/g [13]. Results
showed that 17.02% of those with eGFR ≥90 mL/min/1.73 m2, 19.62%
of those with eGFR of 89–60 mL/min/1.73 m2 and 44.4% of those with
eGFR <60 mL/min/1.73 m2 had albuminuria.
According to the Kidney Disease Outcomes Quality Initiative (K/
DOQI) guidelines, CKD was defined as eGFR <60 mL/min/1.73 m2 or
the presence of albuminuria [14].
All participants with no history of diabetes mellitus were given a standard 75-g oral glucose tolerance test (OGTT). Results showed that 4559
participants were tested with the OGTT. Fasting venous blood was collected from participants with a history of diabetes to measure blood Glu. A
subject was considered to have diabetes if (i) fasting venous blood Glu
≥7.0 mmol/L, (ii) 2 h venous blood Glu ≥11.0 mmol/L or (iii) the subject
was taking a hypoglycaemic drug or insulin [15].
Dyslipidaemia was defined by the 2007 Guidelines for Prevention and
Treatment of Dyslipidaemia in Adults in China [16]. Thus, a subject was
3926
F. Wang et al.
2
Table 1. Clinical characteristics of participants by level of eGFR (in millilitres per minute per 1.73 m )
Variables
eGFR ≥90 (n=2807)
eGFR 89–60 (n=2025)
eGFR<60 (n=93)
P-value
Age (years)
Male (%)
Smoking (%)
Diabetes (%)
Hypertension (%)
BMI (kg/m2)
SBP (mmHg)
DBP (mmHg)
TC (mmol/L)
TG (mmol/L)
LDL-C (mmol/L)
HDL-C (mmol/L)
46.37±12.79
1370 (48.81)
797 (28.39)
283 (10.08)
793 (28.25)
25.54±3.85
125.60±17.95
77.14±10.86
4.83±0.94
1.60±0.68
2.61±0.76
1.42±0.35
57.42±10.90
971 (47.95)
647 (31.95)
267 (13.19)
896 (44.24)
25.89±3.58
131.27±18.92
78.17±8.92
5.16±0.96
1.78±0.46
2.90±0.68
1.36±0.36
70.11±9.34
42 (45.16)
39 (41.94)
21 (22.58)
62 (66.67)
25.58±2.92
137.49±16.22
76.25±7.23
5.06±0.99
1.91±0.97
3.14±0.78
1.25±0.64
<0.001
0.073
<0.001
<0.001
<0.001
0.157
<0.001
0.181
<0.001
<0.001
<0.001
<0.001
The values outside the parentheses are the number of people, and the values inside the parentheses are the percentages. Data are expressed as mean ±
SD. BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; TC, total cholesterol; TG, triglyceride; LDL-C, low-density
lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol. P<0.05 with statistical significance.
considered to have dyslipidaemia if (i) TC ≥5.18 mmol/L, (ii) TG
≥1.70 mmol/L, (iii) LDL-C ≥3.37 mmol/L, (iv) HDL-C <1.04 mmol/L,
or (v) the patient was undergoing treatment for any of these conditions.
Statistical analysis
All quantitative data are presented as the mean ± standard deviation (x̄± s),
and all categorical data are presented as percentages. The differences of
these variables among different groups (eGFR >90, 89–60 and <60 mL/
min/1.73 m2) were examined using chi-square statistics for categorical
variables and one-way analysis of variance for continuous values. The adjusted odds ratios (OR) between different eGFR stages (≥90, 89–60, and
<60 mL/min/1.73 m2; ≥90 mL/min/1.73 m2 as the reference) and the risk
factors of CVD were determined by multivariate logistic regression analysis. In subgroup analyses for the population with albuminuria values, the
differences of these variables were compared between subjects with CKD
and those without CKD by Student’s t-test for continuous variables and by
the chi-square test for categorical variables. The unadjusted OR between
the exposure variables and CKD were determined by univariate logistic
regression analysis. A multivariate logistic regression analysis was performed to evaluate the simultaneous effects of the various exposure variables, with adjustment for any confounding variables. The exposure
variables included age, gender, smoking, diabetes, hypertension, overweight, SBP, DBP, TC, TG, LDL-C and HDL-C. All data entry and management were performed on Excel spreadsheet and then were analysed by
the SPSS statistical package, version 15.0 (SPSS Inc., http://www.SPSS.
com). A P-value <0.05 was considered statistically significant.
(≥90, 89–60 and <60 mL/min/1.73 m 2), the levels of
age, SBP, TG and LDL-C were increased, the percentages
of hypertension, smoking and diabetes were higher and the
level of HDL-C was decreased. The percentage of males
and the levels of BMI and DBP were not significantly different among the three groups (P>0.05).
Urine samples were collected from a total of 2111
subjects and ACR were calculated. Complete data on the
questionnaire, physical examination and laboratory measurement were available for 98.77% (n=2085) of the participants examined. Of these, 394 subjects were diagnosed
with CKD (18.90%) based on eGFR <60 mL/min/
1.73 m2 or the presence of albuminuria. The clinical characteristics of the population with CKD and without CKD
are listed in Table 2. Subjects with CKD were older, had
higher prevalence of diabetes (14.97%) and hypertension
(57.11%) and had higher SBP, TC, TG and LDL-C compared with those without CKD (P<0.05). There was no
Table 2. Clinical characteristics of the subpopulation with albuminuria
values
Results
Variables
No CKD (n=1691)
CKD (n=394)
P-value
Characteristics of participants
Age (years)
Male (%)
Smoking (%)
Diabetes (%)
Hypertension (%)
BMI (kg/m2)
SBP (mmHg)
DBP (mmHg)
TC (mmol/L)
TG (mmol/L)
LDL-C (mmol/L)
HDL-C (mmol/L)
50.01±12.77
676 (39.98)
610 (36.07)
118 (6.98)
627 (37.08)
26.25±3.92
128.98±19.22
81.30±10.21
5.04±0.98
1.182±0.27
2.88±0.78
1.471±0.34
54.73±13.78
134 (34.01)
123 (31.22)
59 (14.97)
225 (57.11)
27.35±4.26
138.51±23.54
82.63±11.99
5.22±1.05
1.239±0.32
2.97±1.79
1.46±0.35
<0.001
<0.001
<0.001
<0.001
<0.001
0.094
<0.001
0.081
0.002
<0.001
0.024
0.103
A total of 4925 subjects were included in the current analysis. There were 2383 males (48.39%) and 2542 females
(51.61%). The age range was 18–96 years, with an average
of 51.30 ± 11.98 years. Of these, there were 1483 smoker
(30.11%), 571 diabetic (11.59%) and 1751 hypertensive
subjects (35.55%). Out of the 4559 participants tested with
the OGTT, 145 subjects were newly diagnosed with diabetes (2.94%); 317 subjects were newly diagnosed with hypertension (6.44%). The prevalence of moderate to severe
reduced renal function was 1.89% (n=93).
Participants were divided into three groups based on
eGFR (≥90, 89–60 and <60 mL/min/1.73 m2). Table 1
shows the clinical characteristics of the population by level
of eGFR. For subjects with decreasing levels of eGFR
The values outside the parentheses are the number of people, and the values
inside the parentheses are the exposure rates. BMI, body mass index; TC,
total cholesterol; TG, triglyceride; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol. P<0.05 with statistical
significance. CKD was defined as eGFR <60 mL/min/1.73 m2 or the presence of albuminuria.
Association of risk factors for CVD and eGFR: a community-based study in Beijing
3927
2
Table 3. Proportion of risk factors with eGFR levels (in millilitres per minute per 1.73 m ) in males and females
Male [eGFR (mL/min/1.73 m2)]
Female [eGFR (mL/min/1.73 m2)]
Variables
≥90 (n=1370)
89–60 (n=971)
<60 (n=42)
≥90 (n=1437)
89–60 (n=1054)
<60 (n=51)
Smoking
Diabetes
Hypertension
BMI ≥24 (kg/m2)
TC ≥5.18 (mmol/L)
TG ≥1.70 (mmol/L)
LDL-C ≥3.37 (mmol/L)
HDL-C <1.04 (mmol/L)
745
130
343
900
429
489
224
226
511
120
400
656
387
386
176
182
23
9
29
28
16
20
12
14
96
153
450
912
507
399
297
84
110
147
496
714
553
384
335
82
14
12
33
29
28
28
16
8
(54.38)
(9.49)
(25.04)
(65.55)
(31.31)
(35.62)
(16.35)
(16.50)
(52.63)
(12.36)
(41.19)
(67.56)
(39.86)**
(39.75)
(18.13)
(18.74)
(54.76)
(21.43)*
(69.04)*
(66.67)
(38.10)**
(47.62)*
(20.00)*
(33.33)*
(6.68)
(10.65)
(31.32)
(63.46)
(35.28)
(27.77)
(20.67)
(5.85)
(10.44)
(13.95)
(47.06)
(67.74)
(52.47)**
(36.43)
(31.78)**
(7.78)
(27.45)*
(23.53)*
(64.71)*
(56.86)*
(54.90)**
(54.90)*
(31.37)**
(15.69)*
*
P<0.05, the three groups were compared.
P<0.05, compared with the group of eGFR ≥90 mL/min/1.73 m2.
The values outside the parentheses are the number of people, and the values inside the parentheses are the exposure rates. BMI, body mass index; TC,
total cholesterol; TG, triglyceride; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol. P<0.05 with statistical
significance.
**
significant difference in the levels of BMI, DBP and
HDL-C between subjects with or without CKD (P>0.05).
Proportion of risk factors in the three eGFR groups
Table 3 presents the proportion of risk factors in the three
eGFR groups. Among males, the proportion of diabetes,
hypertension, elevated TG and LDL-C and decreased
HDL-C was significantly different in the three eGFR
groups (P < 0.05) and the proportion of risk factors was
the highest in the moderate to severe reduced renal function
group. The proportion of smoking and overweight was not
significantly different in the three eGFR groups (P>0.05).
Among females, the proportion of smoking, diabetes, hypertension, overweight, elevated TG and decreased HDLC was significantly different in the three eGFR groups (P
<0.05). The proportion of risk factors increased with the
decreasing of the level of eGFR except BMI.
Associated risk factors of reduced renal function
We performed a multivariate analysis of the relationship
between eGFR and risk factors for CVD, reduced renal
function (eGFR 89–60 mL/min/1.73 m 2 or eGFR
<60 mL/min/1.73 m2) as the dependent variable and
factors that may affect eGFR (age, sex, smoking, hypertension, diabetes, BMI, SBP, DBP, TC, TG, LDL-C and
HDL-C) as the independent variables. Table 4 shows the
multivariable association between level of eGFR and
risk factors of CVD. Older age (increased by 10 years;
OR = 1.06), male gender (OR = 1.35), smoking (OR =
1.32), diabetes (OR=1.44), hypertension (OR=2.28) and
hypertriglyceridaemia (>1.7 mmol/L; OR=1.23) were independently associated with mild reduced renal function
(eGFR 89–60 mL/min/1.73 m2 vs eGFR ≥90 mL/min/
1.73 m2). Older age (increased by 10 years; OR=1.16),
diabetes (OR=2.38), hypertension (OR=2.45), hypertriglyceridaemia (≥1.7 mmol/L; OR = 1.39) and higher
Table 4. Multivariable association between level of eGFR and risk factors of CVD
eGFR 89–60 to GFR ≥90 (mL/min/1.73 m2)
eGFR <60 to GFR ≥90 (mL/min/1.73 m2)
Variables
β
P-value
OR (95% CI)a
β
P-value
OR (95% CI)a
Age
Male
Smoking
Diabetes
Hypertension
BMI
SBP
DBP
TC
TG
LDL-C
HDL-C
0.69
0.298
0.274
0.365
0.826
0.061
0.04
0.094
0.240
0.207
0.179
−0.007
<0.001
<0.001
<0.001
<0.001
<0.001
0.405
0.701
0.418
0.070
0.003
0.583
0.927
1.06
1.35
1.32
1.44
2.28
0.94
1.04
1.10
1.27
1.23
1.20
0.99
0.146
0.104
0.435
0.382
0.897
0.414
0.343
0.143
0.319
0.292
0.288
−0.684
<0.001
0.739
0.152
0.002
<0.001
0.162
0.110
0.784
0.385
<0.001
0.007
0.052
1.16 (1.124–1.191)
0.90 (0.488–1.664)
0.65 (0.356–1.174)
2.38 (1.129–3.049)
2.45 (1.337–4.495)
1.51 (0.847–2.701)
1.409 (0.926–2.145)
1.15 (0.417–3.188)
0.73 (0.354–1.491)
1.39 (1.072–1.673)
1.33 (1.003–1.617)
1.98 (0.995–3.946)
(1.056–1.067)
(1.159–1.565)
(1.121–1.542)
(1.180–1.758)
(1.225–4.259)
(0.816–1.086)
(0.847–1.280)
(0.875–1.378)
(0.983–1.643)
(1.099–1.508)
(0.631–2.268)
(0.856–1.152)
BMI, body mass index; SBP, systolic blood pressure; DBP diastolic blood pressure; TC, total cholesterol; TG, triglyceride; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol. P<0.05 with statistical significance.
a
OR and 95% confidence interval (95% CI) estimated from multivariable logistic regression model adjusting for age (10 years), gender (female, male),
smoking (yes, no), diabetes mellitus (present, absent), hypertension (present, absent), BMI (≥24 or <24 kg/m2), SBP (≥140 or <140 mmHg), DBP (≥90
or <90 mmHg), TC (≥5.18 or <5.18 mmol/L), TG (≥1.70 or <1.70 mmol/L), LDL-C (≥3.37 or <3.37 mmol/L), HDL-C (<1.04 or ≥1.04 mmol/L).
3928
F. Wang et al.
Table 5. Association between CKD and risk factors for CVD
Univariate estimates
Age
Male
Smoking
Diabetes
Hypertension
BMI
SBP
DBP
TC
TG
LDL-C
HDL-C
Multivariate estimates
β
P-value
OR (95% CI)
β
P-value
OR (95% CI)b
0.264
0.256
0.218
0.853
0.815
0.389
0.726
0.771
0.212
0.437
0.206
−0.384
<0.001
0.029
0.069
<0.001
<0.001
0.003
<0.001
<0.001
0.059
<0.001
0.010
0.043
1.30
1.29
0.80
2.35
2.26
1.48
2.07
2.16
1.24
1.55
1.23
1.47
0.200
0.319
0.121
0.510
0.609
0.049
0.093
0.312
−0.028
0.518
0.084
−0.342
<0.001
0.043
0.890
0.002
<0.001
0.733
0.574
0.063
0.858
0.001
0.621
0.104
1.22
1.38
0.89
1.67
1.84
1.05
1.10
1.37
0.97
1.68
0.92
1.41
a
(1.196–1.418)
(1.027–1.626)
(0.636–1.017)
(1.681–3.279)
(1.808–2.823)
(1.141–1.907)
(1.651–2.588)
(1.651–2.829)
(0.992–1.539)
(1.235–1.939)
(0.962–1.571)
(1.012–2.128)
(1.108–1.348)
(1.009–1.873)
(0.649–1.210)
(1.200–2.312)
(1.290–2.622)
(0.794–1.389)
(0.793–1.518)
(0.984–1.898)
(0.716–1.321)
(1.226–2.301)
(0.660–1.282)
(0.932–2.127)
BMI, body mass index; SBP, systolic blood pressure; DBP diastolic blood pressure; TC, total cholesterol; TG, triglyceride; LDL-C, low-density lipoprotein cholesterol; HDL-C, high-density lipoprotein cholesterol. P<0.05 with statistical significance. CKD was defined as eGFR <60 mL/min/1.73 m2
or the presence of albuminuria.
a
OR and 95% CI estimated from univariable logistic regression model unadjusted variables.
b
OR and 95% CI estimated from multivariable logistic regression model adjusting for age (years), gender (female, male), smoking (yes, no), diabetes
mellitus (present, absent), hypertension (present, absent), BMI (≥24 or <24 kg/m2), SBP (≥140 or <140 mmHg), DBP (≥90 or <90 mmHg), TC (≥5.18
or <5.18 mmol/L), TG (≥1.70 or <1.70 mmol/L), LDL-C (≥3.37 or <3.37 mmol/L), HDL-C (<1.04 or ≥1.04 mmol/L).
LDL-C (≥3.37 mmol/L; OR=1.33) were independently
associated with moderate to severe reduced renal function
(eGFR <60 mL/min/1.73 m2 vs eGFR ≥90 mL/min/
1.73 m2).
Associated risk factors of CKD
The results of the univariate and multivariate logistic regression analyses are listed in Table 5. Results showed that
older age (increased by 10 years; OR=1.22), male gender
(OR=1.38), diabetes (OR=1.67), hypertension (OR=1.84)
and hypertriglyceridaemia (≥1.7 mmol/L; OR=1.68) were
independently associated with CKD.
Discussion
In this large population-based cross-sectional study, we investigated exposure of CVD risk factors in different levels
of eGFR by gender and related CVD risk factors for reduced kidney function and CKD. This finding showed
that, in the general population of Beijing, China, multiple
CVD risk factors were increased with a decline in eGFR
and older age, hypertension, diabetes and elevated TG
were independently associated with CKD.
In the present study, GFR was estimated by the modified
MDRD equation based on data from the Chinese population. It offered significant advantages in different CKD
stages. Especially, underestimation of GFR in CKD Stages
1–2 was significantly improved, resulting in a lower overestimation of the prevalence of reduced renal function if
used in the Chinese population [12]. By using this equation, the prevalence of the moderate to severe reduced renal function in our study was estimated to be 1.89%, which
was similar to the result from another community-based
study in China [17]. In this study, the prevalence of decreased renal function was 1.7%. The prevalence of
CKD in our study was 18.90%, which was higher than
the report from a study in Beijing in which the prevalence
of CKD was 11.3% [11].
CVD shares many similar risk factors with CKD and
hypertension, diabetes, dyslipidaemia and proteinuria are
important risk factors for CVD and CKD. Recently, nontraditional risk factors for CKD, including inflammation,
oxidative stress and metabolism of minerals, have generated more attention [18]. However, most prospective studies
showed that modification of non-traditional factors does
not significantly improve the survival rate of patients with
CKD [19]. The control of traditional risk factors (hypertension, diabetes and dyslipidaemia) has a greater impact on
the progress of CKD [20,21]. In the present study, we investigated the presence of traditional CVD risk factors in
males and females from the general population who were
stratified into three eGFR groups (normal, mild reduced
and moderate to severe reduced renal function). Diabetes,
hypertension, higher levels of TG, LDL-C or lower level of
HDL-C were more common in males with reduced renal
function (P<0.05). Furthermore, smoking, diabetes, hypertension, overweight, elevated TG and decreased HDL-C
were more common in females with reduced renal function
(P<0.05). Therefore, we suggest that control of CVD risk
factors, including blood Glu, hypertension and dyslipidaemia, be improved in CKD patients.
Previous epidemiological studies showed that different
factors (blood pressure, blood Glu and blood lipids) had
slightly different effects on risk for CKD in different populations. In particular, a retrospective cohort study in Japan
investigated the effects of blood pressure, blood lipids,
blood Glu, smoking habit and BMI on the progression
of CKD in 2012 cases [22]. Their multivariate analysis
showed that smoking habit, high blood pressure and low
levels of HDL-C were independent risk factors for CKD.
Moreover, other studies showed that high blood pressure,
high levels of TG and LDL-C were independent risk fac-
Association of risk factors for CVD and eGFR: a community-based study in Beijing
tors for CKD [23,24]. In the present study, our multivariate
analysis showed that older age (OR=1.22, P<0.001), hypertension (OR=1.84, P<0.001), diabetes (OR=1.67, P=
0.002) and higher levels of TG (OR=1.68, P=0.001) were
independent risk factors for CKD. Among the risk factors
that we identified, hypertension had the greatest impact on
CKD. We suggest that control of blood pressure may reduce the prevalence of CKD.
The relative risks associated with the major components
of blood lipids (TC, LDL-C, TG and HDL-C) for CKD remains controversial. A previous epidemiological study indicated that CKD patients typically had elevated TG and
reduced HDL-C, but no significant alterations in TC and
LDL-C [25]. However, a recent study found that, although
there were no significant changes in total LDL-C in CKD
patients, these patients tended to have elevated LDL-C subtypes (VLDL and oxidized LDL) [26]. In our study, elevated
TG was shown as an independent risk factor with CKD and
reduced renal function. Therefore, lipid-lowering therapy
appears to be an important part of CKD management. We
suggest that the objectives and strategies of lipid-lowering
therapy for CKD patients in the current K/DOQI need to
be further verified and revised based on the results of
large-scale prospective clinical trials [27,28].
Previous studies have shown an independent association
between obesity and ESRD [31]; studies have also indicated that BMI levels below the obesity range, including overweight BMI range, are related to ESRD [32]. Recent
epidemiological studies have shown that, compared to normal BMI, overweight and obese BMI categories are independently related to stages of kidney disease even earlier in
CKD [33]. Recently, a population-based study from Singapore showed that higher BMI levels were positively associated with CKD among men but not women [19]. But in
the present study BM of ≥24 kg/m2 had no relationship
with reduced eGFR. Results of univariate logistic regression analysis showed that overweight or obesity had a association with CKD. However, there was no relationship
between overweight or obesity and CKD after adjusting related variables.
BMI is a well-established risk factor for CVD. Recently, several prospective studies have reported that obesity
was associated with an increased risk for CKD or
ESRD. The data from the Framingham Offspring Study
showed that higher BMI was a risk factor for the development of new-onset kidney disease after a mean follow-up of 18.5 years [29]. Similarly, studies have
demonstrated a significant positive relationship between
BMI and ESRD risk [30,31]. However, no independent
association between BMI and CKD was found in our
study. Several possibilities may underlie this lack of relation. In a cross-sectional analysis of data from the National Health and Nutrition Examination Survey, only
morbid obesity (defined as BMI ≥35 kg/m2) was related
to CKD [32], whereas only 41 individuals had a BMI
of ≥35 kg/m2 in our study. Another possibility is that
BMI may not be a suitable predictor for reflecting the
relationship between overweight or obesity and CKD.
Noori et al. [33] suggested that the waist circumference
was a better predictor of CKD than waist–hip ratio and
BMI.
3929
Smoking has long been established as a risk factor for
CVD in the general population. However, smoking is a
controversial risk factor for CKD. Several studies have
identified smoking as a potential risk factor for CKD
among those with diabetes [34,35]. However, several studies failed to detect the association between smoking and
CKD [36]. Other studies found that only a high cumulative
smoking dose increased the risk for CKD [37,38]. In two
studies from the Chinese population, the association between smoking and CKD was not present, similar to our
study [17,39].
Albuminuria, a sign of kidney disease in people with diabetes, is associated with an increased prevalence of CVD
risk factors, including dyslipidaemia, hypertension and
poor Glu control [20]. A pooled analysis of patients with
Type 2 diabetes in 11 cohort studies showed that albuminuria was associated with increased cardiovascular morbidity and mortality [40]. Similar findings have been reported
in people without diabetes. One cross-sectional cohort
study of 40 856 people in the Netherlands found that
6.6% had albuminuria but not diabetes or hypertension; albuminuria was independently associated with several CVD
risk factors [41]. According to the K/DOQI guidelines, the
presence of albuminuria is a prerequisite for diagnosing
CKD if eGFR > 60 mL/min/1.73 m 2 . In our study,
17.02% of participants with eGFR ≥90 mL/min/1.73 m2,
19.62% with eGFR of 89–60 mL/min/1.73 m2 and 44.4%
with eGFR <60 mL/min/1.73 m2 had albuminuria.
Clinical trials have demonstrated the benefits of attenuation of progression of CKD with the angiotensin-converting
enzyme inhibitors (ACEI) or the angiotensin receptor blockers agents [42]. A meta-analysis suggests that a mortality
benefit can be seen even in ESRD patients who receive ACEI
[43]. However, the use rate of ACEI was only 1.06% (22/
2068) for the treatment of hypertension in the present study.
Thus, we did not analyse the association of ACEI as an independent variable with CKD.
The main strengths of the current study are its community-based population in China and large sample size,
which provide important ethnic data for the prevalence
and related risk factors of CKD. The main study limitation is that UCAR as indicator of kidney damage was defined based on a single measurement. The measurements
of kidney damage were made just once. In the K/DOQI
guidelines [14], the definition of CKD needs persistence
of kidney damage for at least 3 months, which means
that, in order to define CKD, indicators of kidney damage should be re-evaluated at least once after 3 months.
The single measurement of indicators of CKD in the
present study might overestimate the prevalence of
CKD. It may be one of the reasons that the prevalence
of CKD in our study was higher than another report from
the same area of China. Another limitation is that we did
not have data on non-traditional risk factors of CKD such as
markers of inflammation to examine their role in CKD.
Conclusion
In conclusion, the present study showed that exposure rates
of CVD risk factors increased with the level of eGFR de-
3930
creasing in the general population. Age, hypertension, diabetes and hypertriglyceridaemia were independent risk
factors for CKD. Our results provide an important basis
for future study on the prevention of CKD.
Acknowledgements. The authors would like to thank all the numerous
physicians and study nurses who have participated in collecting the data.
Prof. Qingli Cheng and Prof. Yao He provided some consultation on nephrology and statistics, respectively.
Conflict of interest statement. None declared.
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Received for publication: 29.11.09; Accepted in revised form: 20.5.10
Nephrol Dial Transplant (2010) 25: 3931–3934
doi: 10.1093/ndt/gfq303
Advance Access publication 2 June 2010
The acetyl-coenzyme A carboxylase beta (ACACB) gene is associated
with nephropathy in Chinese patients with type 2 diabetes
Sydney C.W. Tang1, Violet T.M. Leung2, Loretta Y.Y. Chan1, Sunny S.H. Wong3, Daniel W.S. Chu2,
Joseph C.K. Leung1, Yiu Wing Ho3, Kar Neng Lai1, Lijun Ma4, Steven C. Elbein4,
Donald W. Bowden4,5,8,9, Pamela J. Hicks4, Mary E. Comeau6, Carl D. Langefeld6
and Barry I. Freedman7
1
Department of Medicine, The University of Hong Kong, Queen Mary Hospital, Hong Kong, 2Department of Family Medicine and
Primary Healthcare, Hong Kong East Cluster and Hong Kong West Cluster, Hong Kong, 3Department of Medicine and Geriatrics,
United Christian Hospital, Hong Kong, 4Departments of Internal Medicine—Endocrinology, 5Biochemistry, 6Biostatistical Sciences,
7
Internal Medicine—Nephrology, 8Centers for Diabetes Research and 9Human Genomics, Wake Forest University School of
Medicine, Winston-Salem, NC, USA
Correspondence and offprint requests to: Sydney C.W. Tang; E-mail: [email protected]
Abstract
Background. A single-nucleotide polymorphism (SNP),
rs2268388, in the acetyl-coenzyme A carboxylase beta
(ACACB) gene is associated with susceptibility to type 2
diabetic nephropathy (T2DN) in Japanese and EuropeanAmerican populations. Whether this association also
exists in Chinese patients is unclear. Attempts at replication in small Singaporean and Korean samples were not
significant.
Methods. Eight ACACB SNPs were genotyped in 595 subjects with type 2 diabetes mellitus born in Hong Kong or
southern China, 295 with advanced T2DN and 300 with
long-standing diabetes lacking nephropathy. Association
analyses were focused primarily on SNP rs2268388 and
secondarily on flanking SNPs and haplotypes.
Results. Adjusting for age, gender and diabetes duration,
ACACB SNP rs2268388 was significantly associated
with advanced T2DN (odds ratio=2.39; recessive model;
P=0.0129).
Conclusion. These results in the Chinese replicate the association between T2DN and rs2268388, as seen in Japanese and European Americans. The ACACB gene and
attendant alterations in fatty acid oxidation may play important roles in susceptibility to T2DN. Targeting this
pathway may provide novel treatment options for the prevention of diabetic nephropathy.
Keywords: ACACB; Chinese; diabetic nephropathy; kidney; type 2
diabetes mellitus
Introduction
Diabetic nephropathy (DN) is the leading cause of endstage renal disease (ESRD) in developed countries where
type 2 diabetes mellitus (T2DM) has reached epidemic
proportions. Although the exact pathogenesis of type 2 diabetic nephropathy (T2DN) is not fully understood and is
likely diverse in nature, there are convincing data that genetic susceptibility plays an important role. Maeda et al.
[1–3] performed a genome-wide association study
(GWAS) using gene-based single-nucleotide polymorphisms (SNPs) in Japanese T2DN subjects and identified
genes encoding solute carrier family 12 (sodium/chloride)
member 3 (SLC12A3), engulfment and cell motility 1
(ELMO1) and neurocalcin δ (NCALD) as associated with
T2DN. The ELMO1 association has since been replicated
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