Abstract
Background and objectives: Obesity is a risk factor for incident chronic kidney disease (CKD). Visceral (VAT) and subcutaneous adipose tissue (SAT) may confer differential metabolic risk profiles. The relations of VAT and SAT were analyzed with CKD as estimated by creatinine- and cystatin-based estimating equations.
Design, setting, participants, & measurements: Participants from the Framingham Offspring Study who underwent abdominal computed tomography for VAT and SAT quantification were included (n = 1299; 53% women; mean age 60 yr). CKD was defined as estimated GFR <60 ml/min per 1.73 m2, as estimated using creatinine (n = 89) in the Modification of Diet in Renal Disease (MDRD) formula or by cystatin C (n = 136). Regression models evaluated the cross-sectional relations between VAT and SAT with CKD and cystatin C, with age and gender adjustment and cardiovascular risk factor adjustment.
Results: Neither VAT nor SAT was associated with CKD as estimated by the MDRD equation. In contrast, both VAT and SAT were associated with CKD when defined using cystatin-based equations. The estimated decrease in estimated GFR by cystatin C per 1-SD increase of VAT was 1.9 ml/min per 1.73 m2 and for SAT was 2.6 ml/min per 1.73 m2 in a multivariable-adjusted model.
Conclusions: VAT and SAT were associated with CKD when defined using cystatin C estimating equations but not when using a creatinine-based estimating equation. Mechanisms linking adipose tissue to cystatin C warrant further research.
Chronic kidney disease (CKD) is common and affects 13.1% of adults in the United States and continues to increase (1). As a consequence of CKD, risk for all-cause and cardiovascular mortality is increased (2). Obesity is a risk factor for CKD, although the mechanisms are poorly understood (3,4). Given the current epidemic of obesity (5), it is important to understand potential mechanisms that link obesity to CKD.
Several studies have suggested that visceral adiposity, as measured by computed tomography (CT) scan, may be a better predictor of cardiovascular disease (CVD) risk factors as compared with traditional clinical anthropometric measures such as body mass index (BMI) and waist circumference (6–8). Whereas clinical anthropometric measures offer easily obtainable measures of central adiposity, they do not allow for the differentiation between subcutaneous and visceral fat. One previous study suggested that central obesity as estimated by waist-to-hip ratio was inversely associated with level of kidney function (9). Because of the inability of waist-to-hip ratio to detect subcutaneous as compared with visceral fat, it is uncertain whether there is a differential association between abdominal fat compartments and kidney function.
Given the previous literature suggesting strong associations between metabolic risk factors with both visceral fat and CKD, we hypothesized that community-dwelling individuals with more visceral abdominal adiposity would be more likely to have CKD, using creatinine-based estimating equations. We also sought to relate cystatin C, a novel marker of kidney function, and cystatin C–based estimation of GFR to visceral (VAT) and subcutaneous adipose tissue (SAT).
Materials and Methods
Study Sample
Participants were drawn from the Framingham Offspring Study, a community-based study of the children and their spouses of the original Framingham Heart Study participants. Framingham participants were 99.7% white (10). Multidetector CT was conducted on 1418 participants who participated in a CT substudy from 2002 through 2005. Inclusion criteria were men age at least 35 or nonpregnant women at least age 40 yr. All patients had to weigh <160 kg. Inclusion in this CT substudy was weighted toward participants from larger Framingham families and toward those who resided in the Northeast. Exclusion criteria from this analysis were missing VAT and/or SAT measurements (n = 41), missing creatinine at examination 7 (n = 43), missing cystatin C at examination 7 (1998 through 2001; n = 32), missing BMI data (n = 1), and BMI <18.5 (n = 2), resulting in 1299 participants who were part of this study.
The institutional review boards of Massachusetts General Hospital and Boston University Medical Center approved this study. Written consent was obtained from all participants. The authors had full access to and take full responsibility for the integrity of the data.
Adipose Tissue Volumetric Imaging and Measuring
Participants had an abdominal multislice CT scan covering 125 mm above the level of S1 using a slice collimation of 8.0 × 2.5 mm (120 KVp, 300 mA, rotation time 500 ms) for quantification of VAT and SAT. VAT and SAT were quantified using pixel measurement (Aquarius 3D Workstation; TeraRecon Inc., San Mateo, CA). The image display window width of −195 to −45, with a center window of −120 Hounsfield units identified fat-containing pixels. The abdominal wall separating VAT and SAT was traced manually. Two independent readers assessed interreader reliability on 100 random participants. VAT interclass correlation was 0.992, and SAT interclass correlation was 0.997 (11). CT scans were done on average 4.5 yr after biochemical data were obtained.
Kidney Measurements
CKD was defined as estimated GFR (eGFR) <60 ml/min per 1.73 m2. Serum creatinine was measured by colorimetric Jaffe reaction (Roche Hitachi 911; Roche Diagnostics Corporation, Indianapolis, IN). Intra-assay coefficient of variation (CV) was 4.0%, and interassay CV was 2.8%. Serum creatinine was statistically indirectly calibrated to Third National Health and Nutrition Examination Survey (NHANES III) data (12,13). Creatinine-based GFR (eGFRcr) was estimated by the four-variable Modification of Diet in Renal Disease (MDRD) equation: eGFRcr = 186 × Pcr−1.154× age−0.203× 1.212 (if black) × 0.742 (if female) (14). Serum cystatin C was measured by nephelometry on previously frozen samples, which were stored at −80°C (Dade Behring Diagnostic, Marburg, Germany). The CV intra-assay was 2.4%, and interassay was 3.3%. The detection range was 0.29 to 7.22 mg/L. Cystatin C–based GFR (eGFRcys) was estimated using a recently derived, validated equation (eGFRcys = 76.7 × Scys−1.18) (15).
Covariates
Covariates were selected on the basis of our previous work in examining risk factors for the development of CKD over time (12). Fasting morning laboratory measurements were assessed at the examination cycle. BMI was measured as the weight in kilograms divided by the height squared in meters. BP was obtained by a physician using a sphygmomanometer, with participants seated. The average of two BP readings was used. Hypertension was defined as the average systolic BP ≥140 mmHg, average diastolic BP ≥90 mmHg, or antihypertensive medication use. Diabetes was defined as fasting blood glucose level ≥126 mg/dl, taking oral hypoglycemics, or using insulin. Current tobacco use was defined as at least one cigarette daily for the past year. Postmenopausal status was defined as >1 yr without a menses. High alcohol consumption was defined as ≥14 drinks weekly for men and ≥7 drinks weekly for women (16).
Statistical Analyses
SAT and VAT were standardized to a mean of 0 and SD of 1 to facilitate direct comparison of regression coefficients. The association between VAT or SAT and CKD was evaluated with logistic regression, using eGFR <60 ml/min per 1.73 m2, estimated by both the MDRD creatinine-based equation and the cystatin C–based estimating equation. For assessment of relations between VAT or SAT and cystatin C concentration, eGFRcys was modeled as a continuous dependent variable using linear regression.
All models were first age and gender adjusted, and then multivariable adjusted (age, gender, systolic BP, hypertension treatment [yes/no], HDL cholesterol, lipid treatment [yes/no], diabetes [yes/no], prevalent CVD [yes/no], tobacco use [yes/no], menopausal status [yes/no], hormone replacement therapy [yes/no], and excess alcohol use [yes/no]). In a secondary analysis, we tested for effect modification with gender. A two sided P < 0.05 was considered statistically significant for all results.
Results
The mean age of the sample was 60 yr, and 53% were women. The prevalence of CKD on the basis of the MDRD estimation was 6.9% (n = 89). The prevalence of CKD on the basis of eGFRcys was 10.5% (n = 136); overall, 54 people were defined as having CKD by both measures. Study participant characteristics, creatinine, and cystatin C data are presented in Table 1. The age- and gender-adjusted correlation between VAT and SAT was 0.58. Mean BMI was 28.3 kg/m2, and 30% of participants were obese. The correlation of VAT with BMI and waist circumference was 0.65 and 0.67, respectively (P < 0.001); the correlation of SAT with BMI and waist circumference was 0.78 and 0.81, respectively (P < 0.001) (17).
Table 1.
Study sample characteristicsa
| Characteristic | Value (n = 1299) |
|---|---|
| Age (yr; mean ± SD) | 60 ± 9 |
| BMI (kg/m2; mean ± SD) | 28.3 ± 5.1 |
| Obese, BMI >30 (% [n]) | 30.4 (395) |
| VAT (cm3 [SD]) | |
| women | 1656 (870) |
| men | 2660 (1105) |
| SAT (cm3 [SD]) | |
| women | 3310 (1407) |
| men | 2701 (1122) |
| Systolic BP (mmHg; mean ± SD) | 126 ± 18 |
| Hypertension (% [n]) | 41.7 (541) |
| Hypertension treatment (% [n]) | 30.1 (399) |
| HDL cholesterol (mg/dl; mean ± SD) | 53 ± 12 |
| Lipid treatment (% [n]) | 19.9 (259) |
| Diabetes (% [n]) | 8.6 (111) |
| Existing CVD (% [n]) | 13.7 (264) |
| Current smoker (% [n]) | 9.9 (128) |
| Menopausal status (% [n]) | 84.5 (578) |
| Hormone replacement therapy (% [n]) | 33.9 (232) |
| Excess alcohol, yes/no (% [n])b | 20.3 (259) |
| eGFR MDRD (ml/min per 1.73 m2; mean ± SD) | 80 ± 18 |
| eGFRcys (ml/min per 1.73 m2; mean ± SD) | 79 ± 16 |
| Creatinine (mg/dl; mean ± SD) | 0.87 ± 0.21 |
| Creatinine (mg/dl; range) | 0.40 to 2.98 |
| Cystatin C (mg/L; mean ± SD) | 0.95 ± 0.18 |
| CKD based on eGFRcr (% [n])c | 6.8 (89) |
| CKD based on eGFRcys (% [n])d | 10.5 (136) |
BMI, body mass index; CKD, chronic kidney disease; CVD, cardiovascular disease; eGFR, estimated GFR; MDRD, Modification of Diet in Renal Disease; SAT, subcutaneous adipose tissue; VAT, visceral adipose tissue.
Defined as ≥14 drinks weekly for men and ≥7 for women.
Defined as GFRcr <60 ml/min per 1.73 m2 estimated using four-variable MDRD equation.
Defined as eGFRcys <60 ml/min per 1.73 m2.
Associations between VAT, SAT, and CKD
Neither VAT nor SAT was associated with CKD when using the MDRD equation. In age- and gender-adjusted analyses, the odds ratio of CKD per 1 SD of VAT was 1.05 (95% confidence interval [CI] 0.82 to 1.34; P = 0.72). Results were similar after multivariable adjustment (OR 0.90; 95% CI 0.68 to 1.18; P = 0.45). Similar results were observed for SAT in age- and gender- and multivariable-adjusted models (Table 2).
Table 2.
Association of VAT and SAT with the binary outcome of prevalent CKD in all participantsa
| Exposure | Model Adjustment | OR per 1 SD VAT or SAT | 95% CI | P |
|---|---|---|---|---|
| CKD by eGFRcr | ||||
| VAT | Age and gender | 1.05 | 0.82 to 1.34 | 0.7200 |
| Multivariableb | 0.90 | 0.68 to 1.18 | 0.4500 | |
| SAT | Age and gender | 0.88 | 0.68 to 1.12 | 0.2900 |
| Multivariableb | 0.80 | 0.61 to 1.04 | 0.1000 | |
| CKD by eGFRcys | ||||
| VAT | Age and gender | 1.48 | 1.24 to 1.78 | <0.0001 |
| Multivariableb | 1.35 | 1.10 to 1.65 | 0.0060 | |
| SAT | Age and gender | 1.45 | 1.19 to 1.76 | <0.0010 |
| Multivariableb | 1.39 | 1.34 to 1.70 | 0.0020 |
CI, confidence interval; OR, odds ratio.
Multivariable models adjusted for age, sex, systolic BP, hypertension treatment, HDL cholesterol, lipid treatment, diabetes, prevalent CVD, tobacco, menopausal status, hormone replacement therapy, and alcohol use.
VAT and SAT were associated with CKD when determined by eGFRcys. An increase in 1 SD of VAT was associated with a 48% increase in the odds of CKD (OR 1.48; 95% CI 1.24 to 1.78; P < 0.001) in an age- and gender-adjusted model. In the multivariable-adjusted model, 1 SD of VAT was associated with a 35% increase risk for CKD (OR 1.35; 95% CI 1.10 to 1.65; P = 0.006). Similar results were observed for SAT. Upon adjustment for BMI and waist circumference, findings were attenuated (P > 0.55).
Associations between VAT, SAT, and eGFRcys
VAT and SAT were associated with eGFRcys also when evaluated as a continuous analysis (Table 3). The estimated decrease in eGFRcys per 1-SD increase of VAT was 2.6 ml/min per 1.73 m2 (95% CI 1.9 to 3.4; P < 0.001) in an age- and gender-adjusted model and 1.9 (95% CI 1.0 to 2.8; P < 0.001) in a multivariable-adjusted model. Findings were similar for SAT (Table 3). In secondary analyses, we tested for effect modification by gender with CKD as defined by creatinine and cystatin; no significant interactions were observed (all P > 0.21).
Table 3.
Association of VAT and SAT with eGFRcys in all participants, using continuous outcomesa
| Exposure | Model | Decrease per 1 SD VAT or SAT | 95% CI | P |
|---|---|---|---|---|
| VAT | Age and gender | 2.6 | 1.8 to 3.4 | <0.001 |
| Multivariableb | 1.9 | 1.0 to 2.8 | <0.001 | |
| SAT | Age and gender | 2.9 | 2.2 to 3.7 | <0.001 |
| Multivariableb | 2.6 | 1.8 to 3.3 | <0.001 |
β Coefficients are expressed in ml/min per 1.73 m2.
Multivariable models adjusted covariates listed in Table 2 legend.
Discussion
Principal Findings
In our cross-sectional analysis, neither VAT nor SAT was associated with an increased odds ratio for CKD as determined by the MDRD estimating equation; however, both VAT and SAT were associated with CKD using eGFRcys <60 ml/min per 1.73 m2. Furthermore, both higher VAT and SAT were associated with lower eGFRcys.
Two primary explanations or components of both may account for our seemingly disparate findings between the MDRD and cystatin C–based estimating equations. Kidney function may be associated with adipose tissue, but the use of a creatinine-based formula to estimate GFR in ambulatory middle-aged and older individuals may not be sensitive. Because creatinine is a by-product of muscle mass, it decreases in both the infirm and the elderly; therefore, the MDRD eGFR may overestimate GFR in older individuals, especially those with seemingly preserved kidney function (18). Cystatin C estimation of GFR may be a better marker of kidney function than eGFRcr in older adults (19) or may simply be a more sensitive measure of renal dysfunction. Our finding of the relationship between eGFRcys and adiposity in the entire cohort also supports this explanation.
Although it may be that cystatin C is associated with kidney function, the second possibility is that cystatin C is associated with adipose tissue independent of kidney function. Cystatin C has been independently associated with obesity and waist circumference in multivariable regression models (20–22). Adipocytes have been shown to secrete cystatin C (23). The Health ABC study found that cystatin C was associated with both BMI and lean body mass, suggesting that cystatin C may be associated with underlying metabolism (24). Age, gender, height, weight, tobacco use, and even C-reactive protein have been associated with cystatin C, despite adjustment for creatinine clearance (22). Also, cystatin C was increased by administering corticosteroids, supporting the concept that cystatin C may not be purely a marker of kidney function (25); therefore, our findings may be due to extrarenal sources of cystatin C and may not solely represent a pathologic association between adipose tissue and cystatin C as a marker of kidney function. Despite this, most studies have shown that iothalamate GFR is the primary determinant of cystatin C (18,19); however, if fat mass is associated with cystatin C, then cystatin C–based GFR estimating equations may be confounded by increasing adiposity. It is likely that these two explanations are not mutually exclusive, and both may be partly true.
Context within the Current Literature
Obesity, as measured by BMI, has been associated with kidney disease, independent from other comorbidities. A large cohort study of Kaiser Permanente patients found that higher BMI was associated with increasing odds for developing ESRD (4). The Physicians’ Health Study found that participants with higher baseline BMI were more likely to have CKD after 14 yr of follow-up (3). The cross-sectional Prevention of Renal and Vascular End-stage Disease (PREVEND) study of 7676 participants without diabetes found that a central pattern of fat distribution, as determined by waist-to-hip ratio, was associated with increased risk for decreased eGFR, in obese people as well as normal-weight people (9). Our findings that VAT and SAT are associated with higher levels of cystatin C may support the concept that central adiposity, including both abdominal visceral adiposity and subcutaneous fat in the abdominal area, is associated with CKD.
Potential Mechanisms
Inflammation, insulin resistance, endothelial dysfunction, and activation of the renin-angiotensin system, all of which may be interrelated, may explain the link between adipose tissue and cystatin C (26–30). Visceral adiposity is a key regulator of numerous adipokines and cytokines, including leptin, adiponectin, and resistin (31), and has also been associated with insulin resistance, metabolic syndrome, and diabetes (32–34), all physiologic processes that have been connected with incident CKD. Despite the literature linking VAT with metabolic processes, recent work also suggested that SAT is an important correlate of metabolic risk factors (17,35), as well as markers of inflammation and oxidative stress (36). These associations may help to explain our findings that both VAT and SAT are similarly associated with cystatin C.
Limitations
The limitations of the study include the cross-sectional design and lack of longitudinal data; therefore, causality cannot be inferred. Our sample was nearly all white, and it is uncertain whether these findings would be generalizable to other ethnic groups. Biochemical tests and weight were collected 4.5 yr before the CT scan assessment; however, this would not explain the differential association between VAT, SAT, and cystatin C as compared with CKD. Because cystatin C and eGFR are measured at the same time, this should not affect our detection of differences in the relationship between these variables and adipose tissue. Creatinine may be a better measure of kidney function, although it likely underestimates glomerular filtration. Cystatin C estimating equations overestimate glomerular filtration (15), which would not explain the results of our study. Another limitation was that we had relatively few participants with CKD and had only modest power to detect an association. Also, although the differences in eGFR are statistically significant, in absolute terms, they are small. The severity of CKD in our sample was moderate; therefore, it is unclear whether our findings are generalizable to samples with more severe CKD. However, our results are generalizable to stage 3 CKD, which is the largest group of patients with kidney disease in the United States (13).
Implications for Further Research
Given the epidemic of obesity and type 2 diabetes (5) and the increasing burden of CKD in the American population (1,37), understanding the cause of obesity-related CKD is of critical importance. Previous literature suggested that VAT and SAT convey differential risk (31). Given the complex nature of this relationship, mechanistic studies, as well as prospective studies, are needed to elucidate further the complex interplay of risk factors. Interventions may therefore be targeted to the appropriate population and risk factors. In addition, the potential impact of adipose tissue on cystatin C production needs to be further elucidated, given the implications for cystatin C–based GFR equations.
Conclusions
Both VAT and SAT were associated with CKD but only when defined as eGFR <60 ml/min per 1.73 m2 as estimated by cystatin C, not creatinine. Higher levels of VAT or SAT were associated with higher cystatin C concentrations. Mechanisms linking adipose tissue to cystatin C warrant further research.
Disclosures
None.
Acknowledgments
This study was supported by the National Heart, Lung, and Blood Institute's Framingham Heart Study (N01-HC-25195). R.S.V. is supported in part by 2K24HL04334 (National Heart, Lung, and Blood Institute/National Institutes of Health).
This article was previously presented as an abstract at the annual meeting of the American Society of Nephrology; October 31 through November 5, 2007.
Published online ahead of print. Publication date available at www.cjasn.org.
References
- 1.Coresh J, Selvin E, Stevens LA, Manzi J, Kusek JW, Eggers P, Van Lente F, Levey AS: Prevalence of chronic kidney disease in the United States. JAMA 298: 2038–2047, 2007 [DOI] [PubMed] [Google Scholar]
- 2.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 351: 1296–1305, 2004 [DOI] [PubMed] [Google Scholar]
- 3.Gelber RP, Kurth T, Kausz AT, Manson JE, Buring JE, Levey AS, Gaziano JM: Association between body mass index and CKD in apparently healthy men. Am J Kidney Dis 46: 871–880, 2005 [DOI] [PubMed] [Google Scholar]
- 4.Hsu CY, McCulloch CE, Iribarren C, Darbinian J, Go AS: Body mass index and risk for end-stage renal disease. Ann Intern Med 144: 21–28, 2006 [DOI] [PubMed] [Google Scholar]
- 5.Curbing the obesity epidemic. Lancet 367: 1549, 2006 [DOI] [PubMed] [Google Scholar]
- 6.Hayashi T, Boyko EJ, Leonetti DL, McNeely MJ, Newell-Morris L, Kahn SE, Fujimoto WY: Visceral adiposity and the risk of impaired glucose tolerance: A prospective study among Japanese Americans. Diabetes Care 26: 650–655, 2003 [DOI] [PubMed] [Google Scholar]
- 7.Fujimoto WY, Bergstrom RW, Boyko EJ, Chen KW, Leonetti DL, Newell-Morris L, Shofer JB, Wahl PW: Visceral adiposity and incident coronary heart disease in Japanese-American men: The 10-year follow-up results of the Seattle Japanese-American Community Diabetes Study. Diabetes Care 22: 1808–1812, 1999 [DOI] [PubMed] [Google Scholar]
- 8.Harris TB, Visser M, Everhart J, Cauley J, Tylavsky F, Fuerst T, Zamboni M, Taaffe D, Resnick HE, Scherzinger A, Nevitt M: Waist circumference and sagittal diameter reflect total body fat better than visceral fat in older men and women. The Health, Aging and Body Composition Study. Ann N Y Acad Sci 904: 462–473, 2000 [DOI] [PubMed] [Google Scholar]
- 9.Pinto-Sietsma SJ, Navis G, Janssen WM, de Zeeuw D, Gans RO, de Jong PE: A central body fat distribution is related to renal function impairment, even in lean subjects. Am J Kidney Dis 41: 733–741, 2003 [DOI] [PubMed] [Google Scholar]
- 10.Splansky GL, Corey D, Yang Q, Atwood LD, Cupples LA, Benjamin EJ, D’Agostino RB Sr, Fox CS, Larson MG, Murabito JM, O’Donnell CJ, Vasan RS, Wolf PA, Levy D: The Third Generation Cohort of the National Heart, Lung, and Blood Institute's Framingham Heart Study: Design, recruitment, and initial examination. Am J Epidemiol 165: 1328–1335, 2007 [DOI] [PubMed] [Google Scholar]
- 11.Maurovich-Horvat P, Massaro J, Fox CS, Moselewski F, O’Donnell CJ, Hoffmann U: Comparison of anthropometric, area- and volume-based assessment of abdominal subcutaneous and visceral adipose tissue volumes using multi-detector computed tomography. Int J Obes (Lond) 31: 500–506, 2007 [DOI] [PubMed] [Google Scholar]
- 12.Fox CS, Larson MG, Leip EP, Culleton B, Wilson PW, Levy D: Predictors of new-onset kidney disease in a community-based population. JAMA 291: 844–850, 2004 [DOI] [PubMed] [Google Scholar]
- 13.Coresh J, Astor BC, Greene T, Eknoyan G, Levey AS: Prevalence of chronic kidney disease and decreased kidney function in the adult US population: Third National Health and Nutrition Examination Survey. Am J Kidney Dis 41: 1–12, 2003 [DOI] [PubMed] [Google Scholar]
- 14.K/DOQI clinical practice guidelines for chronic kidney disease: Evaluation, classification, and stratification. Am J Kidney Dis 39: S1–S266, 2002 [PubMed] [Google Scholar]
- 15.Stevens LA, Coresh J, Schmid CH, Feldman HI, Froissart M, Kusek J, Rossert J, Van Lente F, Bruce RD 3rd, Zhang YL, Greene T, Levey AS: Estimating GFR using serum cystatin C alone and in combination with serum creatinine: A pooled analysis of 3,418 individuals with CKD. Am J Kidney Dis 51: 395–406, 2008 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Walsh CR, Larson MG, Evans JC, Djousse L, Ellison RC, Vasan RS, Levy D: Alcohol consumption and risk for congestive heart failure in the Framingham Heart Study. Ann Intern Med 136: 181–191, 2002 [DOI] [PubMed] [Google Scholar]
- 17.Fox CS, Massaro JM, Hoffmann U, Pou KM, Maurovich-Horvat P, Liu CY, Vasan RS, Murabito JM, Meigs JB, Cupples LA, D’Agostino RB Sr, O’Donnell CJ: Abdominal visceral and subcutaneous adipose tissue compartments: Association with metabolic risk factors in the Framingham Heart Study. Circulation 116: 39–48, 2007 [DOI] [PubMed] [Google Scholar]
- 18.Stevens LA, Coresh J, Greene T, Levey AS: Assessing kidney function: Measured and estimated glomerular filtration rate. N Engl J Med 354: 2473–2483, 2006 [DOI] [PubMed] [Google Scholar]
- 19.Menon V, Shlipak MG, Wang X, Coresh J, Greene T, Stevens L, Kusek JW, Beck GJ, Collins AJ, Levey AS, Sarnak MJ: Cystatin C as a risk factor for outcomes in chronic kidney disease. Ann Intern Med 147: 19–27, 2007 [DOI] [PubMed] [Google Scholar]
- 20.Muntner P, Winston J, Uribarri J, Mann D, Fox CS: Overweight, obesity, and elevated serum cystatin C levels in adults in the United States. Am J Med 121: 341–348, 2008 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Retnakaran R, Connelly PW, Harris SB, Zinman B, Hanley AJ: Cystatin C is associated with cardiovascular risk factors and metabolic syndrome in Aboriginal youth. Pediatr Nephrol 22: 1007–1013, 2007 [DOI] [PubMed] [Google Scholar]
- 22.Knight EL, Verhave JC, Spiegelman D, Hillege HL, de Zeeuw D, Curhan GC, de Jong PE: Factors influencing serum cystatin C levels other than renal function and the impact on renal function measurement. Kidney Int 65: 1416–1421, 2004 [DOI] [PubMed] [Google Scholar]
- 23.Taleb S, Cancello R, Clement K, Lacasa D: Cathepsin s promotes human preadipocyte differentiation: Possible involvement of fibronectin degradation. Endocrinology 147: 4950–4959, 2006 [DOI] [PubMed] [Google Scholar]
- 24.Fried LF, Lee JS, Shlipak M, Chertow GM, Green C, Ding J, Harris T, Newman AB: Chronic kidney disease and functional limitation in older people: Health, Aging and Body Composition Study. J Am Geriatr Soc 54: 750–756, 2006 [DOI] [PubMed] [Google Scholar]
- 25.Bokenkamp A, Domanetzki M, Zinck R, Schumann G, Byrd D, Brodehl J: Cystatin C serum concentrations underestimate glomerular filtration rate in renal transplant recipients. Clin Chem 45: 1866–1868, 1999 [PubMed] [Google Scholar]
- 26.Sokol JP, Schiemann WP: Cystatin C antagonizes transforming growth factor beta signaling in normal and cancer cells. Mol Cancer Res 2: 183–195, 2004 [PubMed] [Google Scholar]
- 27.Shlipak MG, Katz R, Cushman M, Sarnak MJ, Stehman-Breen C, Psaty BM, Siscovick D, Tracy RP, Newman A, Fried L: Cystatin-C and inflammatory markers in the ambulatory elderly. Am J Med 118: 1416, 2005 [DOI] [PubMed] [Google Scholar]
- 28.Cheng KH, Chu CS, Lee KT, Lin TH, Hsieh CC, Chiu CC, Voon WC, Sheu SH, Lai WT: Adipocytokines and proinflammatory mediators from abdominal and epicardial adipose tissue in patients with coronary artery disease. Int J Obes (Lond) 32: 268–274, 2008 [DOI] [PubMed] [Google Scholar]
- 29.Marcus Y, Stern N: Fat cell-derived modulators of vascular cell pathophysiology: The list keeps growing. J Cardiometab Syndr 1: 121–124, 2006 [DOI] [PubMed] [Google Scholar]
- 30.Kim S, Soltani-Bejnood M, Quignard-Boulange A, Massiera F, Teboul M, Ailhaud G, Kim JH, Moustaid-Moussa N, Voy BH: The adipose renin-angiotensin system modulates systemic markers of insulin sensitivity and activates the intrarenal renin-angiotensin system. J Biomed Biotechnol 2006: 27012, 2006 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Wajchenberg BL: Subcutaneous and visceral adipose tissue: Their relation to the metabolic syndrome. Endocr Rev 21: 697–738, 2000 [DOI] [PubMed] [Google Scholar]
- 32.Goodpaster BH, Krishnaswami S, Harris TB, Katsiaras A, Kritchevsky SB, Simonsick EM, Nevitt M, Holvoet P, Newman AB: Obesity, regional body fat distribution, and the metabolic syndrome in older men and women. Arch Intern Med 165: 777–783, 2005 [DOI] [PubMed] [Google Scholar]
- 33.Goodpaster BH, Krishnaswami S, Resnick H, Kelley DE, Haggerty C, Harris TB, Schwartz AV, Kritchevsky S, Newman AB: Association between regional adipose tissue distribution and both type 2 diabetes and impaired glucose tolerance in elderly men and women. Diabetes Care 26: 372–379, 2003 [DOI] [PubMed] [Google Scholar]
- 34.Marasinghe JP, Amarasinghe AA: Visceral fat mass as a marker of insulin resistance and metabolic disturbance in women with polycystic ovary syndrome. BJOG 114: 235, author reply 236, 2007 [DOI] [PubMed] [Google Scholar]
- 35.Vega GL, Adams-Huet B, Peshock R, Willett D, Shah B, Grundy SM: Influence of body fat content and distribution on variation in metabolic risk. J Clin Endocrinol Metab 91: 4459–4466, 2006 [DOI] [PubMed] [Google Scholar]
- 36.Pou KM, Massaro JM, Hoffmann U, Vasan RS, Maurovich-Horvat P, Larson MG, Keaney JF Jr, Meigs JB, Lipinska I, Kathiresan S, Murabito JM, O’Donnell CJ, Benjamin EJ, Fox CS: Visceral and Subcutaneous adipose tissue volumes are cross-sectionally related to markers of inflammation and oxidative stress: The Framingham Heart Study. Circulation 116: 1234–1241, 2007 [DOI] [PubMed] [Google Scholar]
- 37.Kiberd B: The chronic kidney disease epidemic: Stepping back and looking forward. J Am Soc Nephrol 17: 2967–2973, 2006 [DOI] [PubMed] [Google Scholar]