PMCCPMCCPMCC

Search tips
Search criteria 

Advanced

 
Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
Obesity (Silver Spring). Author manuscript; available in PMC 2011 June 1.
Published in final edited form as:
PMCID: PMC3096746
NIHMSID: NIHMS267359

Association of Subcutaneous and Visceral Adiposity with Albuminuria: The Framingham Heart Study

Abstract

Microalbuminuria is a common condition associated with increased incidence of cardiovascular events and mortality. Abdominal obesity is associated with microalbuminuria, but studies linking visceral adipose tissue (VAT) and microalbuminuria are limited. Our objective was to determine the associations of albuminuria with VAT and subcutaneous adipose tissue (SAT). We performed a cross-sectional study in the Framingham Multi-detector Computed Tomography cohort (n = 3099, 48.2% women, mean age 53 years). VAT and SAT volumes were measured using computed tomography. Urinary albumin-to-creatinine ratio (UACR) was calculated from spot urine samples. Microalbuminuria was defined as a UACR >25 mg/g in women or >17 mg/g in men. Overall, 7.9% (n = 244) of the sample had microalbuminuria. Among men, VAT (Odds ratio [OR] 1.48 per standard deviation [SD], p<0.0001) and SAT (OR 1.37 per SD, p=0.0002) were associated with microalbuminuria in minimally-adjusted models, which remained significant after multivariable adjustment (VAT OR 1.34 per SD, p=0.001; SAT OR 1.28 per SD, p=0.005). Additionally, when considered jointly, VAT (p=0.002) but not SAT (p=0.2) was associated with microalbuminuria. In women, VAT was associated with microalbuminuria after minimal adjustment (OR 1.28, p=0.01), but not after multivariable adjustment (OR 1.03, p=0.8). In multivariable models in women, SAT was associated with a decreased odds of having microalbuminuria (OR 0.75 per SD, p=0.03). In conclusion, VAT is associated with microalbuminuria in men but not women. Albuminuria may be a manifestation of visceral adiposity.

Keywords: Abdominal obesity, Microalbuminuria, Computed Tomography

Introduction

Microalbuminuria is a common condition in the United States, affecting an estimated 8.2% of the adult population (1), and is associated with an increased risk of cardiovascular events (2, 3), cardiovascular mortality (2, 4, 5) and all-cause mortality (35). Prior research suggests that abdominal obesity, as measured by regional anthropometry, may be independently associated with microalbuminuria (612). However, the anthropometric measurements used in previous studies, such as waist circumference and waist-to-hip ratio, do not differentiate between abdominal visceral (VAT) and subcutaneous adipose tissue (SAT) accumulation. This is important because different fat depots carry differential metabolic risk (13). In particular, VAT is a metabolically active fat depot that may play a role in the development of cardiovascular disease risk factors (1318). Abdominal VAT accumulation can be measured using computed tomography, and previous small studies (n = 49 to 208) using this technique suggest that VAT may be associated with microalbuminuria (1922). However, results have been inconsistent, which may be due to differences in study populations. Additionally, potential sex-based differences may not be fully appreciated in prior work, as these studies were either restricted to men or were of limited sample size. In the Framingham Heart Study, we have collected volumetric measures of abdominal VAT and SAT as well as data on albuminuria in over 3000 participants. Thus, the aim of the present analysis was to assess the relation of microalbuminuria with abdominal VAT and SAT in women and men in a large, community-based sample. Given the strong association of cardio-metabolic risk factors with microalbuminuria, we hypothesized that VAT is more highly correlated with microalbuminuria than SAT.

Methods and Procedures

Study sample

The original Framingham Heart Study cohort was established in 1948 and included 5209 individuals from Framingham, Massachusetts. In 1971, the Framingham Offspring cohort was established and included 5124 children of original cohort members and their spouses. The Third Generation cohort was established in 2002 and is comprised of 4095 children of participants in the Offspring cohort. For the present analysis, the study sample was drawn from the Framingham Multi-Detector Computed Tomography (MDCT) study. Between June 2002 and March 2005, 3529 participants (n = 1418 from the Offspring cohort and n = 2111 from the Third Generation cohort) underwent MDCT scanning; men who were ≥35 years old and women who were ≥40 years old and non-pregnant were eligible for this sub-cohort. The MDCT protocol has been described previously (23). After excluding participants with missing data on urinary albumin-to-creatinine ratio (UACR), VAT and SAT, and model covariates, 3099 (1122 Offspring and 1977 Third Generation) participants were eligible for the present analysis. This study was approved by the institutional review boards at Boston University Medical Center and Massachusetts General Hospital, and all participants provided written informed consent.

Assessment of VAT and SAT volumes

Measurement of VAT and SAT volume based on MDCT (LightSpeed Ultra, General Electric, Milwaukee, WI) was described in detail previously (23). Briefly, VAT and SAT volumes (cm3) were measured using 25 5-mm thick slices in the abdomen above S1 using an Aquarius 3D workstation (TeraRecon, Inc., San Mateo, CA). Manual tracing was used to separate VAT and SAT based on the abdominal muscular wall. Adipose tissue on computed tomography scans was identified using a window width of −195 to −45 Hounsfield units (HU) and a window center of −120 HU. The inter-reader intra class correlation coefficients for VAT and SAT were both 0.99 (23).

Assessment of the UACR

Urinary albumin and creatinine were determined using spot urine samples collected during the clinic examination. Urinary albumin was quantified using a Tina-quant albumin immunoturbidometric assay (Inter-assay coefficient of variation [CV]=3.1%, intra-assay CV=2.1%; Roche Diagnostics, Indianapolis, IN). Urinary creatinine was quantified using a modified Jaffe method (Inter-assay CV=1.9%, intra-assay CV=1.0%; Roche Diagnostics, Indianapolis, IN). The urinary albumin assay has a limit of detection of 3 mg/L. Participants in the Third Generation (n=957) and Offspring (n=486) cohorts with a urinary albumin under the limit of detection were coded as 2.9 mg/L and 3.0 mg/L, respectively, when determining UACR. UACR was calculated by dividing the amount of urinary albumin (mg) by the amount of urinary creatinine (g). Microalbuminuria was defined as a UACR >25 mg/g in women or >17 mg/g in men (24). Sex-specific cut points for microalbuminuria were implemented in order to address the impact of sex-based differences in creatinine excretion (25).

Covariate assessment

Covariate data was collected during the Offspring eighth examination cycle and Third Generation first examination cycle. Height was recorded to the nearest quarter-inch and weight was recorded to the nearest pound. Waist circumference was measured at the level of the umbilicus to the nearest quarter-inch. Body mass index (BMI) was calculated by dividing an individual’s weight in kilograms by height in meters squared. Obesity was defined as a BMI ≥30 kg/m2. Fasting blood glucose was determined using an overnight fasting blood sample. Systolic and diastolic blood pressure was measured as the mean of two readings by a physician during the examination. Diabetes was defined as fasting blood glucose ≥126 mg/dL or the use of oral hypoglycemic treatment or insulin for diabetes. Hypertension was defined as a systolic blood pressure ≥140 mmHg, a diastolic blood pressure ≥90 mmHg, or current use of hypertension medication. The presence of cardiovascular disease was defined as recognized myocardial infarction, angina pectoris, coronary insufficiency, cerebral embolism, intracerebral hemorrhage, subarachnoid hemorrhage, intermittent claudication, or congestive heart failure. Cardiovascular events were adjudicated by a three investigator panel. Current smoking status was defined as smoking at least one cigarette a day in the last year based on self-report. Alcohol use was measured as the average number of drinks per week based on self-report. A woman was categorized as menopausal if her period had stopped for at least one year.

Statistical Methods

All analyses were a priori sex-stratified due to our prior findings, which have demonstrated significant sex interactions between VAT, SAT and cardiovascular disease risk factors (13). Age-adjusted, sex-specific Pearson correlation coefficients were used to assess the correlation of natural log-transformed UACR with the measures of adiposity (VAT, SAT, BMI, and waist circumference).

Logistic regression was used to model microalbuminuria status as functions of VAT, SAT, BMI, or waist circumference separately in women and men. Minimal models in men were adjusted for age, alcohol use, and smoking status. Minimal models in women were adjusted for age, alcohol use, smoking status, hormone replacement therapy, and menopausal status. Multivariable models additionally included systolic blood pressure, hypertension treatment, and diabetes status. Linear regression models of log-transformed UACR as a function of each adiposity measure were also performed using the minimal and multivariable model covariates described above. Measures of adiposity were standardized separately among women and men to allow for direct comparison of the magnitude of the associations between the various fat depots and albuminuria. We assessed effect modification by sex on the multiplicative scale by including an interaction term between sex and each standardized adiposity measure (VAT, SAT, BMI, and waist circumference) in the minimally adjusted linear model.

In a sensitivity analysis, we varied the value (0.5, 1.0, 1.5 mg/L) assigned to urinary albumin measurements below the assay limit of detection before calculating UACR and determining MA status. In a secondary analysis, the logistic and linear models described above were also performed in a study sample restricted to those without diabetes. Regression diagnostics were performed in order to ensure that our linear and logistic models were properly specified, including the linear model assumptions of normality, constant variance, and independence and correctly specified model covariates. SAS version 9.1.3 for Windows (SAS Institute, Cary, NC) was used to perform statistical analyses.

Results

Study sample characteristics

Study sample characteristics of women and men are presented in Table 1. Women were a mean of 54 years of age and 26.6% (n = 397) were obese; men were a mean of 51 years of age and 29.5% (n = 473) were obese. The median UACR was 5.8 mg/g in women and 3.6 mg/g in men; microalbuminuria was present in 6.6% (n = 98) of women and 9.1% (n = 146) of men. Of the 196 participants with diabetes in our sample, 60 (30.6%) had microalbuminuria.

Table 1
Basic characteristics of study sample by sex

Interactions of sex with adiposity measures

Significant interactions of sex with each adiposity measure were observed in minimally adjusted linear models of log-transformed UACR (all p≤0.0008). These interaction terms indicated that parameter estimates associated with each adiposity measure were higher in men as compared to women.

Pearson correlations between measures of adiposity and log-transformed UACR

Age-adjusted correlation coefficients are presented in Table 2. In men, all measures of adiposity were correlated with UACR (all p≤0.002), whereas only SAT (p=0.05), but not VAT, BMI, or waist circumference (all p>0.3), was correlated with UACR in women.

Table 2
Sex-specific age-adjusted correlations of natural log-transformed urinary albumin-to-creatinine ratio and adiposity measures

Association of adipose measures and microalbuminuria

Among men, both VAT (odds ratio [OR] 1.48 per standard deviation [SD], p<0.0001) and SAT (OR 1.37 per SD, p=0.0001) were associated with an increased odds of having microalbuminuria in minimally-adjusted models (Table 3). After additional adjustment for systolic blood pressure, hypertension medication use, and diabetes status, associations were somewhat attenuated but remained statistically significant (all p≤0.005, Table 3). Similar results were observed for BMI and waist circumference (Table 3). When VAT and SAT were considered in the minimally adjusted model together, VAT (p=0.002) but not SAT (p=0.2) remained associated with microalbuminuria.

Table 3
Sex-specific odds ratios and 95% confidence intervals of microalbuminuriaa with adiposity measures in the overall sample

Among women, VAT (OR 1.28 per SD, p=0.01) but not SAT (OR 0.89 per SD, p=0.3) was associated with microalbuminuria after minimal adjustment (Table 3). After additional adjustment for systolic blood pressure, hypertension medication use, and diabetes status, VAT was not associated with microalbuminuria (p=0.8) and SAT was inversely associated with microalbuminuria (OR 0.75, p=0.03, Table 3).

In our sample, 11 participants (4 women, 7 men) exceeded the sex-specific range for microalbuminuria (UACR > 355mg/g in women and >250 mg/g in men) (26). Results from microalbuminuria models remained essentially unchanged when these participants were excluded.

Association of adipose measures and log-transformed UACR

Among men, results from linear models of log-transformed UACR were consistent with results observed in the microalbuminuria models (Table 4), although the association of SAT with UACR was no longer significant after multivariable adjustment. Among women, similar trends were observed in linear models of log-transformed UACR (Table 4) as for microalbuminuria models.

Table 4
Sex-specific parameter estimates of natural log-transformed UACR modeled as a function of adiposity measuresa

Secondary analyses

In a sub-sample of men free of diabetes (n = 1494), results from microalbuminuria models were essentially unchanged. Among women without diabetes (n = 1409), VAT was not associated with microalbuminuria in either the minimally adjusted (OR 1.09 95% 0.84–1.41, p=0.5) or the multivariable adjusted model (OR 0.96 95% CI 0.71–1.30, p=0.8). In our sensitivity analysis, results from the multivariable MA and UACR models did not change appreciably when values assigned to urinary albumin measurements below the assay limit of detection were varied (data not shown).

Discussion

Both VAT and SAT were associated with microalbuminuria among men. However, when considered jointly, only VAT was associated with microalbuminuria, suggesting that unique characteristics of VAT may contribute to the presence of microalbuminuria. Among women, no significant associations were observed for VAT and microalbuminuria or log-transformed UACR after multivariable adjustment.

The relation of abdominal adiposity and UACR has been previously explored (612). In diabetes-free individuals in the PREVEND study, obese individuals with abdominal adiposity (based on a waist-to-hip ratio ≥0.9 [men] or ≥0.8 [women]) had a higher odds of developing microalbuminuria when compared to lean individuals without abdominal adiposity (8). Similar results were observed in the MONICA Augsburg study (6) and the EDIC study (9). Taken together, these studies support the concept that abdominal adiposity is associated with albuminuria. However, these studies cannot address the associations of specific components of abdominal adiposity with albuminuria, as the primary limitation of clinical anthropometric measures of abdominal fat is the inability to distinguish subcutaneous from visceral abdominal fat.

In addition to the present analysis, a few studies have reported on the association of computed tomography-based VAT and SAT measurements with albuminuria (1922), demonstrating conflicting results. In a small study of women and men free of hypertension and diabetes (n=49), urinary albumin excretion was not significantly correlated with VAT or SAT (19), whereas in 64 men with type 1 diabetes from the EDIC study, only VAT, measured on single slice computed tomography scan, was associated with microalbuminuria (21). Among Japanese adults with type 2 diabetes, VAT was associated with microalbuminuria (22) and macroalbuminuria in men (20). Differences in the prior literature may be due to different study sample characteristics, such as the use of healthy volunteers (19) versus patients with type 1(21) or type 2 (20, 22) diabetes. The findings in the present study extend the current literature with its large sample size, inclusion of participants unselected for obesity or diabetes-related conditions, adjustment for several covariates including blood pressure and diabetes, and by the use of volumetric assessment of VAT and SAT from computed tomography scans, as compared to the area assessment used in the prior analyses.

Our results suggest that both VAT and SAT are associated with microalbuminuria, although VAT may be differentially associated with microalbuminuria. Several mechanisms are proposed in the relation of obesity and renal disease, including renin angiotensin system and sympathetic nervous system activation, glucose intolerance, and compression-related changes in renal interstitial pressure (27). VAT as compared to SAT has been more closely associated with markers of inflammation and oxidative stress (28) and cardio-metabolic risk factors (1318) including hypertension and diabetes, which are major correlates of microalbuminuria (15, 17). The differential association of VAT and microalbuminuria may also be related to the role of adiponectin in renal dysfunction. Increased VAT is associated with lower levels of adiponectin (2931), and previous studies report an inverse association of serum adiponectin and albuminuria (3234). Glomerular podocyte dysfunction has also been described in adiponectin-deficient mouse models (34), suggesting that lower adiponectin levels may promote glomerular damage. Finally, localized effects of adipose tissue depots remote from but highly correlated with VAT also have the potential to influence the development of microalbuminuria. For example, proposed vasocrine signaling mechanisms of perivascular fat could lead to microalbuminuria via inflammation-induced vascular damage due to adipocytokines released by VAT surrounding the renal arteries (35).

We observed sex differences, as VAT was associated with microalbuminuria in men but not women. This is in contrast to our prior work (13), where we have consistently observed more adverse risk factor profiles with respect to VAT among women as compared to among men. The reasons for these associations are uncertain, but may be due to potential sex-based differences in fat distribution patterns and renal outcomes related to differential steroid hormone levels (3638). Estrogen plays a role in fat distribution patterns, with higher SAT and lower VAT accumulation observed in women than in men (36) and evidence from animal and human studies suggest that estrogens or hormone therapy treatment may have a beneficial effect on proteinuria (37, 39, 40).

There are several strengths associated with this study. The Framingham MDCT cohort represents a community-based sample, reducing the potential for selection bias. VAT and SAT were precisely quantified volumetrically, and these measurements were highly reproducible (23).

There are limitations associated with this study. The present analysis is cross-sectional and observational in design; therefore, temporality and causality cannot be determined. A single spot urine sample was used to determine the UACR, which may not capture the true level of urinary albumin excretion. However, this potential outcome misclassification is likely non-differential with respect to our adiposity measures and other covariates. In the Offspring cohort, VAT and SAT measurements were obtained approximately 2.5 years prior to albuminuria and covariate assessment. While this temporal difference in assessment may introduce measurement error, this is likely non-differential with respect to exposure assessment. Finally, our predominately white study sample limits the generalizability of our results to other ethnic groups.

In summary, VAT is correlated with microalbuminuria in men but not in women. Albuminuria may be a manifestation of visceral adiposity.

Acknowledgments

The Framingham Heart Study is supported by the National Heart, Lung and Blood Institute (N01-HC-25195).

Footnotes

Disclosure statement

The authors have no conflicts of interest to declare.

Reference List

1. Coresh J, Selvin E, Stevens LA, et al. Prevalence of chronic kidney disease in the United States. JAMA. 2007;298(17):2038–47. [PubMed]
2. Gerstein HC, Mann JF, Yi Q, et al. Albuminuria and risk of cardiovascular events, death, and heart failure in diabetic and nondiabetic individuals. JAMA. 2001;286(4):421–6. [PubMed]
3. Romundstad S, Holmen J, Kvenild K, Hallan H, Ellekjaer H. Microalbuminuria and all-cause mortality in 2,089 apparently healthy individuals: a 4.4-year follow-up study. The Nord-Trondelag Health Study (HUNT), Norway. Am J Kidney Dis. 2003;42(3):466–73. [PubMed]
4. Hillege HL, Fidler V, Diercks GF, et al. Urinary albumin excretion predicts cardiovascular and noncardiovascular mortality in general population. Circulation. 2002;106(14):1777–82. [PubMed]
5. Yuyun MF, Khaw KT, Luben R, et al. Microalbuminuria independently predicts all-cause and cardiovascular mortality in a British population: The European Prospective Investigation into Cancer in Norfolk (EPIC-Norfolk) population study. Int J Epidemiol. 2004;33(1):189–98. [PubMed]
6. Liese AD, Hense HW, Doring A, Stieber J, Keil U. Microalbuminuria, central adiposity and hypertension in the non-diabetic urban population of the MONICA Augsburg survey 1994/95. J Hum Hypertens. 2001;15(11):799–804. [PubMed]
7. Mulyadi L, Stevens C, Munro S, Lingard J, Bermingham M. Body fat distribution and total body fat as risk factors for microalbuminuria in the obese. Ann Nutr Metab. 2001;45(2):67–71. [PubMed]
8. 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. 2003;41(4):733–41. [PubMed]
9. de Boer IH, Sibley SD, Kestenbaum B, et al. Central obesity, incident microalbuminuria, and change in creatinine clearance in the epidemiology of diabetes interventions and complications study. J Am Soc Nephrol. 2007;18(1):235–43. [PMC free article] [PubMed]
10. Chandie Shaw PK, Berger SP, Mallat M, Frolich M, Dekker FW, Rabelink TJ. Central obesity is an independent risk factor for albuminuria in nondiabetic South Asian subjects. Diabetes Care. 2007;30(7):1840–4. [PubMed]
11. Lin CC, Liu CS, Li TC, Chen CC, Li CI, Lin WY. Microalbuminuria and the metabolic syndrome and its components in the Chinese population. Eur J Clin Invest. 2007;37(10):783–90. [PubMed]
12. Kramer H, Reboussin D, Bertoni AG, et al. Obesity and albuminuria among adults with type 2 diabetes: the Look AHEAD (Action for Health in Diabetes) Study. Diabetes Care. 2009;32(5):851–3. [PMC free article] [PubMed]
13. Fox CS, Massaro JM, Hoffmann U, et al. Abdominal visceral and subcutaneous adipose tissue compartments: association with metabolic risk factors in the Framingham Heart Study. Circulation. 2007;116(1):39–48. [PubMed]
14. Pascot A, Lemieux S, Lemieux I, et al. Age-related increase in visceral adipose tissue and body fat and the metabolic risk profile of premenopausal women. Diabetes Care. 1999;22(9):1471–8. [PubMed]
15. Goodpaster BH, Krishnaswami S, Resnick H, et al. Association between regional adipose tissue distribution and both type 2 diabetes and impaired glucose tolerance in elderly men and women. Diabetes Care. 2003;26(2):372–9. [PubMed]
16. Nicklas BJ, Penninx BW, Ryan AS, Berman DM, Lynch NA, Dennis KE. Visceral adipose tissue cutoffs associated with metabolic risk factors for coronary heart disease in women. Diabetes Care. 2003;26(5):1413–20. [PubMed]
17. Hayashi T, Boyko EJ, Leonetti DL, et al. Visceral adiposity is an independent predictor of incident hypertension in Japanese Americans. Ann Intern Med. 2004;140(12):992–1000. [PubMed]
18. Goodpaster BH, Krishnaswami S, Harris TB, et al. Obesity, regional body fat distribution, and the metabolic syndrome in older men and women. Arch Intern Med. 2005;165(7):777–83. [PubMed]
19. Nielsen S, Jensen MD. Relationship between urinary albumin excretion, body composition, and hyperinsulinemia in normotensive glucose-tolerant adults. Diabetes Care. 1999;22(10):1728–33. [PubMed]
20. Asakawa H, Tokunaga K, Kawakami F. Relationship of abdominal fat with metabolic disorders in diabetes mellitus patients. Diabetes Res Clin Pract. 2002;55(2):139–49. [PubMed]
21. Sibley SD, de Boer I, Steffes MW, Brunzell JD. Intra-abdominal fat and elevated urine albumin excretion in men with type 1 diabetes. Diabetes Care. 2007;30(7):1898–900. [PMC free article] [PubMed]
22. Hanai K, Babazono T, Nyumura I, et al. Involvement of visceral fat in the pathogenesis of albuminuria in patients with type 2 diabetes with early stage of nephropathy. Clin Exp Nephrol. 2009 [PubMed]
23. 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) 2007;31(3):500–6. [PubMed]
24. Warram JH, Gearin G, Laffel L, Krolewski AS. Effect of duration of type I diabetes on the prevalence of stages of diabetic nephropathy defined by urinary albumin/creatinine ratio. J Am Soc Nephrol. 1996;7(6):930–7. [PubMed]
25. Epstein M. Aging and the kidney. J Am Soc Nephrol. 1996;7(8):1106–22. [PubMed]
26. K/DOQI clinical practice guidelines for chronic kidney disease: evaluation, classification, and stratification. Am J Kidney Dis. 2002;39(2 Suppl 1):S1–266. [PubMed]
27. Hall JE, Henegar JR, Dwyer TM, et al. Is obesity a major cause of chronic kidney disease? Adv Ren Replace Ther. 2004;11(1):41–54. [PubMed]
28. Pou KM, Massaro JM, Hoffmann U, et al. Visceral and subcutaneous adipose tissue volumes are cross-sectionally related to markers of inflammation and oxidative stress: the Framingham Heart Study. Circulation. 2007;116(11):1234–41. [PubMed]
29. Cnop M, Havel PJ, Utzschneider KM, et al. Relationship of adiponectin to body fat distribution, insulin sensitivity and plasma lipoproteins: evidence for independent roles of age and sex. Diabetologia. 2003;46(4):459–69. [PubMed]
30. Yatagai T, Nagasaka S, Taniguchi A, et al. Hypoadiponectinemia is associated with visceral fat accumulation and insulin resistance in Japanese men with type 2 diabetes mellitus. Metabolism. 2003;52(10):1274–8. [PubMed]
31. Hanley AJ, Bowden D, Wagenknecht LE, et al. Associations of adiponectin with body fat distribution and insulin sensitivity in nondiabetic Hispanics and African-Americans. J Clin Endocrinol Metab. 2007;92(7):2665–71. [PubMed]
32. Tsioufis C, Dimitriadis K, Chatzis D, et al. Relation of microalbuminuria to adiponectin and augmented C-reactive protein levels in men with essential hypertension. Am J Cardiol. 2005;96(7):946–51. [PubMed]
33. Yano Y, Hoshide S, Ishikawa J, et al. Differential impacts of adiponectin on low-grade albuminuria between obese and nonobese persons without diabetes. J Clin Hypertens (Greenwich) 2007;9(10):775–82. [PubMed]
34. Sharma K, Ramachandrarao S, Qiu G, et al. Adiponectin regulates albuminuria and podocyte function in mice. J Clin Invest. 2008;118(5):1645–56. [PMC free article] [PubMed]
35. Yudkin JS, Eringa E, Stehouwer CD. “Vasocrine” signalling from perivascular fat: a mechanism linking insulin resistance to vascular disease. Lancet. 2005;365(9473):1817–20. [PubMed]
36. Lovejoy JC, Sainsbury A. Sex differences in obesity and the regulation of energy homeostasis. Obes Rev. 2009;10(2):154–67. [PubMed]
37. Sandberg K. Mechanisms underlying sex differences in progressive renal disease. Gend Med. 2008;5(1):10–23. [PubMed]
38. Maric C. Sex, diabetes and the kidney. Am J Physiol Renal Physiol. 2009;296(4):F680–F688. [PubMed]
39. Agarwal M, Selvan V, Freedman BI, Liu Y, Wagenknecht LE. The relationship between albuminuria and hormone therapy in postmenopausal women. Am J Kidney Dis. 2005;45(6):1019–25. [PubMed]
40. Zhang X, Jia Y, Jackson EK, Tofovic SP. 2-Methoxyestradiol and 2-ethoxyestradiol retard the progression of renal disease in aged, obese, diabetic ZSF1 rats. J Cardiovasc Pharmacol. 2007;49(1):56–63. [PubMed]