1 Department of Clinical Research, Pingtung Christian Hospital, Pingtung 900, 2 Graduate Institute of Dental Sciences, 3 Graduate Institute of Oral Health Sciences and 4 Graduate Institute of Medicine, Kaohsiung Medical University, Kaohsiung 807, Taiwan
Correspondence and offprint requests to: Dr Yau-Jiunn Lee, Department of Clinical Research, Pingtung Christian Hospital, No. 60, Da-Lien Rd, Pingtung, 90000, Taiwan. Email: t3275{at}ms25.hinet.net
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Abstract |
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Methods. This is a cross-sectional community-based study. The 1466 aboriginal subjects, 4095 years of age, are a stratified random subpopulation identified during an integrative health care programme. They were sampled for drinking patterns. The main outcome measurements were serum creatinine, estimated CCr and GFR.
Results. Subjects with alcohol consumption had significantly higher levels of serum triglycerides, high-density lipoprotein cholesterol, uric acid, estimated CCr and GFR values than non-drinkers. Their blood pressure was also significantly higher. They had lower total cholesterol and low-density lipoprotein cholesterol concentrations. Increasing alcohol consumption was independently and significantly associated with a higher level of estimated CCr and GFR when analysed as both a categorical and continuous variable.
Conclusions. The present study shows that chronic alcohol consumption has a negative effect on blood pressure and lipid profile and stimulates the estimated GFR.
Keywords: alcohol; blood pressure; creatinine clearance rate; glomerular filtration rate
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Introduction |
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The association between alcohol consumption and renal function, however, is poorly understood. There is evidence that chronic alcohol consumption may cause direct damage to the kidneys [3,4]. It may also indirectly alter renal function by elevating blood pressure [5], inducing electrolyte imbalances [4] and inducing hyperuricaemia [6]. In one study, alcohol intake had no affect on glomerular filtration rate (GFR) and renal plasma flow [7]; however, in another study with rats, chronic ingestion led to impairment of GFR associated with renal hypertrophy [8]. In 1984, Savdie and colleagues demonstrated that drinkers had significantly higher blood pressure that non-drinkers, and drinkers of two or fewer drinks per day were found to have higher serum creatinine than non-drinking control subjects. This changed, however, to decreased serum creatinine at the level of three or more drinks per day [9]. In a prospective study of 1658 nurses, Knight et al. [10] showed that moderate alcohol intake has no long- term adverse effect on renal function as assessed by calculated creatinine clearance rate (CCr) and GFR, and may in fact have a renoprotective effect in women with hypertension.
It is apparent that chronic alcohol consumption regulates renal function. To describe this relationship more fully, we examined the renal effects of alcohol consumption using calculated CCr and GFR, modified through the CockcroftGault calculator and Modification of Diet in Renal Disease (MDRD) in a southern Taiwan aboriginal community with a high prevalence of alcohol consumption.
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Subjects and methods |
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Under the supervision of trained personnel, participants completed a comprehensive questionnaire concerning socio-economic status, physical activity, smoking status and eating habits, including alcohol consumption. In addition, a physician conducted interviews for medical history and drug use. Questionnaires were checked for proper completion and plausibility. Field work quality and standardization were ascertained regularly. All study subjects lived in the same region at the time of the study and were of Pai-Wan ancestry without known ancestry from other ethnicities. This study was approved by the Human Research Ethics Committee of our hospital, and informed consent was obtained from each participant.
For the purposes of the present study, we excluded participants with elevated serum creatinine >1.5 mg/dl, proteinuria (urine protein 300 mg/dl), urolithiasis or other known renal disease, and/or other reasons, including age (<40 years old), residence outside aboriginal communities or those with missing information. The ex-users may include some subjects who had given up drinking because of adverse effects on their health [11]. Hence, ex-users (who had stopped drinking for >1 year before the interviews) were excluded from the present study. We also excluded subjects with diabetes mellitus, because valid GFR estimates cannot be obtained for diabetic patients with normal renal function by estimation of creatinine clearance, nor from serum creatinine concentration measurements and rate of change in GFR [12].
Venous blood samples were drawn for biochemical analysis after a fasting period of at least 8 h. All biochemical analyses were carried out within 2 h of blood sampling at the Pingtung Christian Hospital laboratory. Serum creatinine was analysed by means of the Kinetic Jaffé's method on a SYNCHRON CX System analyser (SYNCHRON, Los Angeles, CA) using reagents from Beckman (Beckman Coulter Diagnostic, Los Angeles, CA). Serum triglycerides, total cholesterol, low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol, uric acid, albumin, serum urea nitrogen and glucose were determined by standard commercial methods on a parallel-multichannel analyser (SYNCHRON, Los Angeles, CA). The inter-assay coefficients of variation were 2.1% for creatinine, 4.1% for triglycerides, 2.2% for total cholesterol, 5.5% for LDL cholesterol, 4.9% for HDL cholesterol, 1.7% for glucose, 3.1% for blood urea nitrogen and 3.2% for albumin. The blood biochemistry analyses were under internal and external quality control at the laboratory according to the College of American Pathologists surveys.
We used three primary estimates of renal function. These were serum creatinine, estimated CCr calculated by the modification of the CockcroftGault formula [13] and estimated GFR calculated with the MDRD-extended version. These two formulae are freely available from the Internet (http://www.kidney.org/professionals/kdoqi/index.cfm).
The habitual alcohol consumption of study subjects was reviewed with a structured questionnaire. Subjects who had consumed one serving of an alcoholic beverage (including beer, liquor and wine) or more per month for at least 1 year were defined as ever drinkers. Among them, current drinkers were those with these habits in the past year, and ex-users were those who had stopped such behaviour for at least 1 year before the interviews. The ever drinkers who had stopped drinking <1 year before the interviews and current drinkers were defined as current alcohol users in the present study. For all of the ever drinkers, a detailed history of their drinking habits was recorded, including daily consumption, primary alcohol type, frequency, amount per consumption, age of commencement, duration of practice, whether additional beverage types were consumed (yes/no) and if (yes) what beverages and the amount consumed. Average alcohol consumption (in g per month) was calculated by multiplying the frequency and amount of alcohol intake, using a standard ethanol content of 20 vol% in Taiwan michiu, 4.5 vol% in Taiwan beer, 8 vol% in Taiwan whisbih, 8 vol% in Taiwan paulid-B and 57 vol% in Kinmen kao liang liquor. If the questionnaire was answered with a range of values, the midpoint of the range of frequency or amount was used. Valid questionnaires were examined further.
In order to assess the relationship between dosage and research variables, the subjects were categorized into four groups: non-drinkers, light drinkers (150 g alcohol/month), moderate drinkers (1511500 g alcohol/month) and heavy drinkers (>1500 g alcohol/month). A socio-economic status index (low, middle and high) was created from education level, occupation and household income. Smokers were subjects who had smoked one cigarette or more per day for at least 1 year. Similar to our classification of drinkers, current smokers were those who had practised these habits within the past year, and ex-users were those who had stopped the practice for at least 1 year before the interviews. Those who had smoked <100 cigarettes in their entire lives were categorized as non-smokers. In this study, a smoker who had stopped the practice <1 year before the interviews was defined as a current smoker.
Blood pressure was measured after the subject had rested for 5 min in a sitting position. In this study, hypertension was defined as a systolic blood pressure (SBP) 140 mmHg, a diastolic blood pressure (DBP)
90 mmHg, or under antihypertensive treatment. Diabetes mellitus was defined according to the 1997 World Health Organization criteria, or having received treatment for diabetes mellitus. Height, body weight, waist and hip circumferences were measured, and body mass index (BMI) and waist to hip ratio (WHR) were calculated.
Statistical analysis
Descriptive data were examined for all variables. For continuous variables, results are presented as the mean±SD. Statistical differences in variables were compared using one-way analysis of variance (ANOVA) and unpaired Student's t-test for normally distributed variables. Before statistical testing, serum triglycerides were logarithmically transformed to achieve a normal distribution. Categorical variables were recorded as frequency counts, and intergroup comparisons were analysed by a 2 test. General linear modelling (GLM) function analysis was used to control for potential confounders other than age (e.g. gender, BMI and smoking status).
Associations between estimated CCr and GFR and other parameters were first analysed by simple linear regression and then by multiple linear stepwise regression. Variables used in this analysis were age, gender, BMI, smoking, drinking status, SBP, DBP, fasting sugar, total cholesterol, triglycerides, HDL cholesterol, LDL cholesterol and uric acid levels, but old age, sex and BMI for estimated CCr, and old age and sex for estimated GFR in multiple linear stepwise regression analysis.
Multiple-adjusted least-square means of estimated CCr and GFR, lipid profiles, blood pressure and WHR as well as the SEM within the four alcohol consumption groups were tested for trends using information on maximum monthly alcohol amount, compared with non-drinkers as a reference and calculated using analysis of variance with proc GLM using the Statistical Analysis System (SAS). The pairwise correlations method was used to calculate the relationship among estimated CCr, GFR and amount of alcohol consumption. Statistical significance was accepted if P<0.05. All statistical analysis was performed using SAS statistical software, version 8.0 (SAS Institute Inc., Cary, NC).
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Results |
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Table 1 shows the 1256 subjects grouped to probe for differences in clinical and laboratory characteristics in non-alcohol users, alcohol users and ex-users. The values of age, gender, BMI, smoking, hypertension, diabetes mellitus rates, blood pressure, fasting sugar, total cholesterol, HDL cholesterol, LDL cholesterol and uric acid levels differ among the three types of alcohol consumption. Interestingly, serum creatinine levels are not statistically different among non-alcohol users, alcohol users and ex-users, while the estimated CCr and GFR are significantly higher in ex-users and alcohol users than in non-alcohol users. Table 1 also presents the clinical and biochemical information of 146 diabetic subjects, showing that those with an alcohol habit had a significantly higher estimated CCr and GFR. As stated in Subjects and methods, those with diabetes and ex-users were excluded from further study.
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Discussion |
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In the present study, we found that the mean serum creatinine level in subjects with alcohol consumption was not statistically different from that of non-drinkers. Serum creatinine concentration is widely used as an index of renal function, but creatinine concentration measurement may be affected by factors other than GFR. GFR measurement has been well known as the gold standard of renal function assessment [15] and probably the best variable for diagnosing and monitoring kidney disease. However, GFR measurement is expensive and labour-intensive and therefore not widely used in routine measurement of renal function in large-scale epidemiological studies. Cockcroft and Gault [13] and Levey [15] proposed formulae for CCr and GFR calculation, which were found reasonably to estimate renal function. However, possible factors influencing the results should be mentioned to avoid bias [16]. In our work, the study design and laboratory analysis were arranged in order to avoid this possible inconsistency. Serum creatinine measurement was rigorously supervised for quality and centralized in one laboratory. Jaffé's method was used to reduce possible bias by multipoint calibration against authenticated serum-based standards to give the serum creatinine through true creatinine values. Study populations were also ethnically consistent.
Alcohol may have both detrimental and salutatory effects on renal function. The adverse effects may include glomerular and tubulo-interstitial damage, especially in subjects with pre-existing renal disease or with alcohol overconsumption. In micropuncture experiments, high concentrations of alcohol do not interfere with tubular cell function [17], while in vitro studies show that ethanol interferes with the Na+K+-ATPase of rabbit renal brush border membrane vesicles [18]. In the present study, the estimated CCr and GFR were calculated, although it is possible that alcohol consumption had an effect on creatinine tubular secretion. The mechanism relating alcohol consumption to renal function regulation is not clearly known, but most plausible is the connection of alcohol-related effects on blood pressure, lipid profile and vasoactive peptides regulation.
Ethanol intake may induce both hypertensive and antihypertensive effects. Parekh and Klag recently considered the main relationship between alcohol consumption and renal function regulation to be increased blood pressure with alcohol consumption [19]. In general, it is accepted that ethanol alters the activity of a number of neurotransmitters and hormonal systems that in turn mediate a number of its effects. Ethanol intake may inhibit the activity of substances having a suppressor effect and/or stimulate the activity of substances having antihypertensive effects [20]. Thus, alcohol consumption may affect regulation of vasoactive substances, consequently affecting renal haemodynamics and GFR.
There are several limitations in the present study. As in all studies that rely on participants report, recall bias and social desirability bias may have produced a spurious association. Furthermore, our definition of non-drinkers (i.e. who never consumed >12 drinks per year in their lives) most probably includes some people who were infrequent drinkers. The inclusion of infrequent drinkers in the non-drinkers category will cause the odds ratios (ORs) for moderate alcohol intake to be underestimated. Subjects with alcohol abuse may not have responded to the invitation to participate in the present study and may have had severe interference of renal function not included in the present study. However, the effects of alcohol abuse on renal function are another issue for study. We studied only aborigines of Taiwan, so caution is needed in generalizing our conditions in other populations. The MDRD formula is not tested for accuracy in renal function evaluation of the present study population, and thus may be used for relative measurements of GFR and comparisons between alcohol and non-alcohol users.
In summary, the present data indicate that alcohol consumption may have an effect on GFR and CCr, and have diverse systemic effects on blood pressure and lipid profiles. However, the present analysis should not be construed as endorsing heavy alcohol consumption for longevity. Instead, our goal is to evaluate potential pathways for the observed effects between alcohol and renal function in alcohol intake using epidemiological data. Moderate alcohol intake consistent with public health recommendations should be encouraged as part of a global health strategy. The results of our study raise an important public health issue and need to be confirmed by large-scale and cohort studies in other populations.
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Acknowledgments |
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Conflict of interest statement. None declared.
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References |
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