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      Waist Circumference, Not Body Mass Index, Is Associated with Renal Function Decline in Korean Population: Hallym Aging Study

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          Abstract

          Background

          Prospective investigation of obesity and renal function decline in Asia is sparse. We examined the associations of body mass index (BMI) and waist circumference (WC) with renal function decline in a prospective study of Korean population.

          Methods

          A total of 454 participants who had baseline estimated glomerular filtration rate (eGFR) levels of more than 60 mL/min/1.73 m 2 in Hallym Aging Study (HAS) were included and followed for 6 years. Renal function decline was defined as follows: (1) an eGFR decline ≥3 mL/min/1.73 m 2/year (n = 82 cases); (2) an eGFR decrease of 20% or greater (n = 87 cases) at follow-up; (3) an eGFR decrease of 20% greater at follow-up or eGFR decline ≥3 mL/min/1.73 m 2/year (n = 91 cases); and (4) an eGFR <60 mL/min/1.73 m 2 at follow-up (n = 54 cases). eGFR was determined based on the Modification of Diet in Renal Disease (MDRD) Study equation. Multivariate logistic regression model was used to determine the association between obesity and renal function decline.

          Results

          We found that central obesity was associated with faster renal function decline. Comparing WC of >95 cm in men or >90 cm in women with ≤90 cm in men or ≤85 cm in women, ORs (95% CIs) ranged from 2.31 (1.14–4.69) to 2.78 (1.19–6.50) for the 4 definitions of renal function decline (all p-values for trend <0.05). Waist-to-hip ratio (WHR) and waist-to-height ratio (WHtR) also was associated with renal function decline. There was no significant association of BMI with renal function decline.

          Conclusions

          Central obesity, but not BMI, is associated with faster renal function decline in Korean population. Our results provide important evidence that simple measurement of central fat deposition rather than BMI could predict decline in renal function in Korean population.

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          Most cited references37

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          Waist circumference and not body mass index explains obesity-related health risk.

          The addition of waist circumference (WC) to body mass index (BMI; in kg/m(2)) predicts a greater variance in health risk than does BMI alone; however, whether the reverse is true is not known. We evaluated whether BMI adds to the predictive power of WC in assessing obesity-related comorbidity. Subjects were 14 924 adult participants in the third National Health and Nutrition Examination Survey, grouped into categories of BMI and WC in accordance with the National Institutes of Health cutoffs. Odds ratios for hypertension, dyslipidemia, and the metabolic syndrome were compared for overweight and class I obese BMI categories and the normal-weight category before and after adjustment for WC. BMI and WC were also included in the same regression model as continuous variables for prediction of the metabolic disorders. With few exceptions, overweight and obese subjects were more likely to have hypertension, dyslipidemia, and the metabolic syndrome than were normal-weight subjects. After adjustment for WC category (normal or high), the odds of comorbidity, although attenuated, remained higher in overweight and obese subjects than in normal-weight subjects. However, after adjustment for WC as a continuous variable, the likelihood of hypertension, dyslipidemia, and the metabolic syndrome was similar in all groups. When WC and BMI were used as continuous variables in the same regression model, WC alone was a significant predictor of comorbidity. WC, and not BMI, explains obesity-related health risk. Thus, for a given WC value, overweight and obese persons and normal-weight persons have comparable health risks. However, when WC is dichotomized as normal or high, BMI remains a significant predictor of health risk.
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            Blood pressure and end-stage renal disease in men.

            End-stage renal disease in the United States creates a large burden for both individuals and society as a whole. Efforts to prevent the condition require an understanding of modifiable risk factors. We assessed the development of end-stage renal disease through 1990 in 332,544 men, 35 to 57 years of age, who were screened between 1973 and 1975 for entry into the Multiple Risk Factor Intervention Trial (MRFIT). We used data from the national registry for treated end-stage renal disease of the Health Care Financing Administration and from records on death from renal disease from the National Death Index and the Social Security Administration. During an average of 16 years of follow-up, 814 subjects either died of end-stage renal disease or were treated for that condition (15.6 cases per 100,000 person-years of observation). A strong, graded relation between both systolic and diastolic blood pressure and end-stage renal disease was identified, independent of associations between the disease and age, race, income, use of medication for diabetes mellitus, history of myocardial infarction, serum cholesterol concentration, and cigarette smoking. As compared with men with an optimal level of blood pressure (systolic pressure or = 210 mm Hg or diastolic pressure > or = 120 mm Hg) was 22.1 (P < 0.001). These relations were not due to end-stage renal disease that occurred soon after screening and, in the 12,866 screened men who entered the MRFIT study, were not changed by taking into account the base-line serum creatinine concentration and urinary protein excretion. The estimated risk of end-stage renal disease associated with elevations of systolic pressure was greater than that linked with elevations of diastolic pressure when both variables were considered together. Elevations of blood pressure are a strong independent risk factor for end-stage renal disease; interventions to prevent the disease need to emphasize the prevention and control of both high-normal and high blood pressure.
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              Body fat distribution and risk of non-insulin-dependent diabetes mellitus in women. The Nurses' Health Study.

              Obesity is an established risk factor for non-insulin-dependent diabetes mellitus (NIDDM). Anthropometric measures of overall and central obesity as predictors of NIDDM risk have not been as well studied, especially in women. Among 43,581 women enrolled in the Nurses' Health Study who in 1986 provided waist, hip, and weight information and who were initially free from diabetes and other major chronic diseases, NIDDM incidence was followed from 1986 to 1994. After adjustment for age, family history of diabetes, smoking, exercise, and several dietary factors, the relative risk of NIDDM for the 90th percentile of body mass index (BMI) (weight (kg)/height (m)2) (BMI = 29.9) versus the 10th percentile (BMI = 20.1) was 11.2 (95% confidence interval (CI) 7.9-15.9). Controlling for BMI and other potentially confounding factors, the relative risk for the 90th percentile of waist: hip ratio (WHR) (WHR = 0.86) versus the 10th percentile (WHR = 0.70) was 3.1 (95% CI 2.3-4.1), and the relative risk for the 90th percentile of waist circumference (36.2 inches or 92 cm) versus the 10th percentile (26.2 inches or 67 cm) was 5.1 (95% CI 2.9-8.9). BMI, WHR, and waist circumference are powerful independent predictors of NIDDM in US women. Measurement of BMI and waist circumference (with or without hip circumference) are potentially useful tools for clinicians in counseling patients regarding NIDDM risk and risk reduction.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2013
                25 March 2013
                : 8
                : 3
                : e59071
                Affiliations
                [1 ]Department of Food and Nutrition, Sookmyung Women's University, Seoul, Korea
                [2 ]Department of Social and Preventive Medicine, Hallym University, Chuncheon, Korea
                [3 ]Korea Health Promotion Foundation, Seoul, Korea
                [4 ]Department of Nursing, Chung-Ang University, Seoul, Korea
                [5 ]Hallym Research Institute of Clinical Epidemiology, Chuncheon, Korea
                University of Groningen, The Netherlands
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Interpretation of the results: HO JEL D-HK. Conceived and designed the experiments: HO JEL D-HK. Performed the experiments: SAQ J-YJ S-NJ D-HK. Analyzed the data: HO. Contributed reagents/materials/analysis tools: SAQ J-YJ S-NJ D-HK. Wrote the paper: HO JEL.

                Article
                PONE-D-12-30982
                10.1371/journal.pone.0059071
                3607569
                23536858
                4d71cf40-143c-467a-b64c-2a90d9a70f24
                Copyright @ 2013

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 9 October 2012
                : 11 February 2013
                Page count
                Pages: 8
                Funding
                This study was supported by Hallym University Research Fund, 2012 (HRF-S-2012-5). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology
                Anatomy and Physiology
                Physiological Processes
                Aging
                Medicine
                Anatomy and Physiology
                Renal System
                Clinical Research Design
                Cohort Studies
                Epidemiology
                Longitudinal Studies
                Epidemiology
                Epidemiology of Aging
                Nephrology
                Chronic Kidney Disease
                Geriatric Nephrology
                Non-Clinical Medicine
                Health Care Policy
                Health Risk Analysis
                Nutrition
                Obesity

                Uncategorized
                Uncategorized

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