Abstract
Background/objectives: Abdominal fat deposition is represented by means of the conicity index (CI), an anthropometric estimate that models the relative accumulation of abdominal fat. We examined the influence of markers of cardiovascular disease in terms of inflammation and lipid profile and body fat distribution on the progression of renal disease in patients with stable chronic kidney disease (CKD) stages 3–5. Material and methods: We studied 104 pre-dialysis CKD patients (64 males, 62%; age 64.6 ± 14.7 years). Glomerular filtration rate (GFR) was estimated (44.62 ± 14.38 mL/min/1.73 m2) by modification of diet in renal disease formula. GFR values were estimated at baseline and at the end of the 12-month follow-up. Patients were stratified into three groups: group 1 had a loss of GFR ≥20%; group 2 had a loss of GFR 10–20%; and group 3 patients had stable renal functions or GFR change <10% at the end of 12 months. Body mass index (BMI), waist/hip ratio (WHR), and CI were subsequently computed. Renal resistive index (RRI) was measured using Doppler ultrasonography. Results: CI was strongly correlated with total cholesterol (r = 0.37, p < 0.01), low-density lipoprotein (LDL) (r = 0.53, p < 0.01), C-reactive protein (r = 0.21, p < 0.05), and serum potassium (r = 0.216, p < 0.02), whereas BMI and WHR were not associated with these parameters. The values of CI, serum cholesterol, LDL, alkaline phosphatase, alanine aminotransferase, lactate dehydrogenase activity, the degree of proteinuria and microalbuminuria, and RRI were significantly lower in group 3. In linear regression model, LDL (r2 = 0.17, p = 0.02), uric acid (r2 = 0.19, p < 0.01), and RRI (r2 = 0.64, p < 0.01) were independently associated with CI for all groups. Conclusion: CI is an independent predictor of systemic inflammation, cardiovascular risk, and GFR in patients during the pre-dialysis period.
INTRODUCTION
The impact of obesity on renal function has been investigated over the years and recent studies have suggested that in addition to obesity, fat distribution is a major factor in renal dysfunction.Citation1–3 Central obesity has been related to lower glomerular filtration rate (GFR) and a decline in GFR over time.Citation2,3 It is not clear whether central obesity alone or other competing risk factors with obesity are associated with renal function. It is becoming apparent that the fat distribution (abdominal fat vs. peripheral fat) has different metabolic implications. In both renal and nonrenal populations, abdominal fat is more closely associated with increased mortality.Citation4–8 In chronic kidney disease (CKD) patients, abdominal fat has been associated with inflammation, insulin resistance, proteinuria, and dyslipidemia, each of which may predict the development of atherosclerosis and the progression of renal disease.Citation9–13
The body mass index (BMI) is an indicator of obesity but it is not completely correlated with the distribution of body fat. Waist circumference (WC) and the waist/hip ratio (WHR) are the indicators most often used to gauge centralized distribution of adipose tissues. The differences in body composition between different age groups and races make them difficult to define universal cutoff points.Citation14
Abdominal fat deposition was represented by means of the conicity index (CI), an anthropometric estimate that models the relative accumulation of abdominal fat as the deviation of body shape from a cylindrical toward a double cone shape.Citation15–18 The CI estimates abdominal obesity by using weight, height, and WC and has an association with cardiovascular risk factors.Citation18 Therefore, the simplicity of application and the relative ease of interpretation have made CI more popular for assessing excess body fat than radiologic measurements.
In this study we examined the influence of markers of cardiovascular disease in terms of inflammation and lipid profile and body fat distribution on the progression of renal disease and renal hemodynamics in patients with stable CKD stages 3–4. We also compared the sensitivity of CI with other indices of fat distribution. In order to explore and compare the association between different obesity indices with the progression of renal disease, we chose three different anthropometric indices as obesity measures: BMI, WHR, and CI.
MATERIAL AND METHODS
Patients and Study Design
We studied 104 pre-dialysis CKD patients between February 2008 and January 2009 (64 males, mean age 64.6 ± 14.7 years). We have performed a retrospective study to examine the influence of markers of lipid profile and body fat distribution on the progression of renal disease and renal hemodynamics in patients with stable CKD stages 3–4. The various disease-causing CKD were hypertension in 25 (24%) patients; chronic glomerulonephritis in 19 patients (18%); autosomal polycystic kidney disease in 16 patients (15%); interstitial nephritis in 14 patients (13%); and other or unknown etiologies in 30 patients (30%). The study protocol was approved by the local scientific ethics committee and informed consent was obtained from each patient. Patients’ demographic, anthropometric, and clinical variables (age, sex, and the etiology of CKD) were recorded.
Inclusion criteria for this study were subjects diagnosed with CKD stage 3–4; age above 18 years; and the presence of the following information in the medical record: BMI or WC, blood pressure, fasting lipid profile, and plasma glucose levels were all similar at baseline. Patients were excluded if they had used cholesterol-lowering medications or if they had any acute infection, severe malnutrition (serum albumin <2.5 g/dL), malignancy, or other systemic illnesses (e.g., rheumatoid arthritis or systemic lupus erythematosus). To reduce the confounding effects of glucose and lipid metabolism, we also excluded diabetic patients.
Serum creatinine (SrCr) levels were determined automatically using the enzymatic method with the normal range between 0.5 and 1.4 mg/dL. Using a computer desktop calculator, the patient’s age, gender, and SrCr value were used to determine the GFR (44.62 ± 14.38 mL/min/1.73 m2) according to the modification of diet in renal disease formula. GFR values were estimated during the initial period and at the end of the 12-month follow-up and retrospectively recorded. Patients were stratified into three groups: group 1 had a loss of GFR ≥20%; group 2 had a loss of GFR 10–20%; and group 3 patients had stable renal functions or GFR change <10% at the end of 12 months.
In all groups, venous serum samples were taken after 12 h of fasting and serum concentrations of glucose, total cholesterol, low-density lipoprotein (LDL), high density lipoprotein (HDL)-cholesterol, triglycerides (TGs), C-reactive protein (CRP), intact parathyroid hormone, ferritin, iron, transferrin saturation, complete blood count and 24 h urine volume, proteinuria, microalbuminuria, and creatinine were measured. Renal resistive index (RRI) was measured using Doppler ultrasonography. The RI was calculated using electronic calipers, by the following formula: (Peak systolic frequency – End diastolic frequency)/Peak systolic frequency.
Nutritional Status and Anthropometric Evaluation
The Subjective Global Assessment (SGA) questionnaire was used to evaluate nutritional status.Citation19 SGA includes six different components: three subjective assessments that are performed by the patients (concerning the patient’s history of weight loss, incidence of anorexia, and incidence of vomiting) and three assessments performed by the evaluators (subjective grading of muscle wasting, the presence of edema, and the loss of subcutaneous fat). On the basis of these assessments, each patient received a nutritional status score: (1) normal nutritional status, (2) mild malnutrition, (3) moderate malnutrition, and (4) severe malnutrition. For the purposes of the current study, malnutrition was defined as an SGA score >1. We defined protein-energy wasting (PEW) by SGA, as it has been shown to be a reliable method for distinguishing malnutrition/wasting from normal nutritional status.
Weight was measured in light clothing without shoes to the nearest 100 g on a digital scale (Seca®; Vcgel & Halke, Hamburg, Germany). Height was measured in standard position using a portable stadiometer and recorded to the nearest millimeter. WC was measured midway between the lowest rib and the iliac crest, with no garments during measurement. Hip circumference was measured in undergarments at the place of largest circumference around the buttocks. The measurements of weight, height, waist, and hip circumferences were taken two times each and a third measurement was obtained if the difference between the two original measures surpassed preestablished limits. CI was determined through the measurements of weight, height, and WC using the following formula by Valdez et al.:Citation20 WC (m)/[0.109 × √weight (kg)/height (m)].
BMI was calculated as the weight (kg) divided by the square of the height (m2) and WHR was calculated by dividing the WC by the hip circumference.
Statistical Analyses
Statistical analyses were performed mostly using the SPSS for Windows statistical software package version 13.0 (SPSS Inc., Chicago, IL, USA). Descriptive statistics for obesity indices were calculated for all groups. Differences in continuous variables between groups were tested using Student’s t-test, including age, systolic blood pressure, diastolic blood pressure, WC, BMI, WHR, and CI. Differences in binomial categorized variables between men and women were analyzed using Pearson χ2-test, including biochemical parameters. Correlation coefficients between WC, BMI, WHR, and CI were calculated by Pearson correlation analyses. Because anthropometric indices (continuous) are highly correlated with each other, they were tested separately in multiple backward stepwise logistic regression models after adjusting for all parameters. GFR indices were then categorized as trinity variables using their quartiles as cutoff points and tested separately again in multiple backward stepwise logistic regression models using the first trio as reference category.
RESULTS
The demographic, clinical, and biochemical characteristics of study patients are detailed in . The mean age was 64.6 ± 14.7 years, 64 patients (62%) were male, and 58 patients were (56%) with CKD stage 3. Patients were divided into groups according to tertiles of CI distribution. These groups are compared in . Not unexpectedly, patients with an increased CI tended to be older (p < 0.01). Across CI tertiles, low CI patients had significantly higher creatinine (p = 0.02), phosphorus (p = 0.03), and GFR (p < 0.01). Low CI patients had significantly lower uric acid (p < 0.01), total cholesterol (p < 0.01), LDL (p < 0.01), CRP (p < 0.01), and proteinuria (p < 0.01). However, high CI patients tended to be more often malnourished (SGA > 1) (p-values <0.05).
Table 1. Demographic, clinical, and biochemical characteristics of the study patients.
Table 2. Clinical characteristics of 104 chronic kidney disease (CKD) patients according to tertiles of abdominal fat deposition evaluated by the conicity index (CI).
Linear regression model was performed to find independent factors affecting CI. Variables including LDL (r2 = 0.17, p = 0.02), uric acid (r2 = 0.19, p < 0.01), and RI (r2 = 0.64, p < 0.01) were independently associated with CI for all groups.
Patients were stratified into three groups: group 1 had a loss of GFR ≥20%; group 2 had a loss of GFR 10–20%; and group 3 patients had stable renal functions or GFR change <10% at the end of 12 months. Considering the GFR groups, group 1 patients who exhibited ≥20% of GFR loss had significantly higher CI (p < 0.01), total cholesterol (p < 0.01), LDL (p < 0.01), proteinuria (p < 0.01), microalbuminuria (p = 0.02), RRI (p < 0.01), and lower hemoglobin (p < 0.01) than group 3 patients. According to the GFR groups there are no significant relationships between WHR and BMI measurements. Comparison of variable factors in patients with low and high GFR groups is shown in .
Table 3. Comparison between groups.
In regression analyses, variables including CI (r2 = 0.17, p = 0.02) and CRP (r2 = 0.19, p = 0.00) were independently associated with GFR for all groups.
DISCUSSION
Identifying and treating risk factors may be the best strategy to prevent renal impairment. The impact of obesity on renal function has been investigated over the years. Obesity is a risk factor for CKD, although the mechanisms are poorly understood.Citation21 Recent studies have suggested that in addition to obesity, fat distribution is a major factor in renal dysfunction.Citation20–23 Inflammation and malnutrition were also recently mentioned as a possible contributing factor in CKD progression.24–Citation26 Therefore, we investigated the relationship between GFR levels and visceral adiposity measured by CI and compared GFR levels between fat distribution subtypes defined by other anthropometric measurements. Studies focusing on relationships between central obesity and abnormal kidney function in a end-stage renal disease (ESRD) population are limited. This is the first study attempted to comparatively evaluate WC, BMI, and CI as predictors of cardiovascular and renal risk factors.
BMI is imprecise as a measure of body composition, but it does not distinguish between muscle mass and fat mass. The CI estimates abdominal obesity by using simple anthropometric measurements (WC, height, and weight).Citation11 Thus, patients with an elevated CI are those with an abnormal fat deposition in the abdominal region with respect to their height and weight. We report that an abnormal abdominal fat mass deposition in ESRD patients is concurrently associated with a higher prevalence of malnutrition and inflammation, as well as an increased renal function. This study expands the concept of “obese sarcopenia” in CKD, whereby low muscle mass can occur despite fat accumulation.Citation27 We show that abdominal fat deposition is associated with a more wasted, sarcopenic, and proinflammatory phenotype and that this leads to increased renal function. Our findings are similar to the study by Honda et al.,Citation27 who observed that overweight patients with PEW were characterized by increased fat body mass and inflammation. The current analysis is in agreement with the result of this previous study in context by linking the abnormal accumulation of fat in the abdominal area to both inflammation and PEW.
Several cardiovascular risk factors are linked metabolically to body composition. Total cholesterol and TG levels are increased in CKD patients by factors such as increased levels of lipoprotein lipase inhibitors such as apolipoprotein CIII (apo CIII). These pathologic features are linked to insulin resistance and increased body adiposity that may not be linked to body composition.Citation28 We found that visceral fat measured by CI is associated with higher levels of uric acid, dyslipidemia, CRP, and proteinuria. Visceral adiposity is a key regulator of numerous adipokines and cytokines, including leptin, adiponectin, and resistin and has also been associated with insulin resistance, metabolic syndrome, and diabetes, all physiologic processes that have been connected with incident CKD.Citation20 Inflammation, as assessed by plasma CRP concentrations, is also an important cardiovascular risk factor, both in ESRD patients and in subjects with normal kidney function.Citation29 CRP levels are thought to be associated with adiposity in subjects with normal kidney function as a result of inflammation within or caused by increased visceral adipose mass.Citation30
We observed a strong relationship between GFR and visceral adiposity measured by CI. When dividing the subjects into three groups according to visceral fat, the visceral fat dominant group presented a significantly lower GFR. We found that CI is more sensitive than the other anthropometric measurements such as BMI and WHR to determine the renal function loss. Visceral obesity has been associated with kidney disease independent from other comorbidities. The cross-sectional Prevention of Renal and Vascular End-stage Disease study of 7676 participants without diabetes found that a central pattern of fat distribution was associated with increased risk for decreased estimated glomerular filtration rate in obese people as well as normal-weight people.Citation20 The mechanisms of renal damage by visceral adiposity are intriguing and poorly understood. Cardiovascular risk factors such as malnutrition, inflammation, high uric acid levels, and dyslipidemia are associated with both central obesity and GFR and may play a significant mediating role.Citation21 Therefore, it was not clear whether visceral adiposity alone or the presence of accompanying risk factors were associated with decreased renal function.
Our study shows that visceral adiposity is involved in mild GFR reduction and this relationship is independent of other cardiovascular risk factors, especially dyslipidemia, inflammation, and malnutrition. There might be other possible factors that contribute to this relationship. Visceral adipose tissue is a source of proinflammatory cytokines and circulating hormones such as tumor necrosis factor-α, interleukin-6, adiponectin, retinol-binding protein, and angiotensinogen.Citation11 Substances from visceral fat can alter glomerular function and may play an important role in pathogenesis.Citation31
Further studies on causality between visceral adiposity and renal injury are warranted. In summary, our study shows that even in apparently ESRD patients those with central fat distribution have a greater risk of diminished renal filtration. CI, which shows visceral adiposity, should be looked at more closely on behalf of renal damage in addition to conventional cardiovascular risk factors.
Declaration of interest: The authors report no conflicts of interest. The authors alone are responsible for the content and writing of the paper.
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