Skip main navigation
×

Fatty Kidney, Hypertension, and Chronic Kidney Disease

The Framingham Heart Study
Originally publishedhttps://doi.org/10.1161/HYPERTENSIONAHA.111.175315Hypertension. 2011;58:784–790

Abstract

Ectopic fat depots may mediate local and systemic disease. Animal models of diet-induced obesity demonstrate increased fat accumulation in the renal sinus. The association of renal sinus fat with hypertension, chronic kidney disease, and other metabolic disorders has not been studied in a large, community-based sample. Participants from the Framingham Heart Study (n=2923; mean age: 54 years; 51% women) underwent quantification of renal sinus fat area using computed tomography. High renal sinus fat (“fatty kidney”) was defined using sex-specific 90th percentiles in a healthy referent subsample. Multivariable linear and logistic regression was used to model metabolic risk factors as a function of fatty kidney and log-transformed renal sinus fat. Multivariable models were adjusted for age, sex, and outcome-specific covariates and then additionally adjusted for body mass index or abdominal visceral adipose tissue. The prevalence of fatty kidney was 30.1% (n=879). Individuals with fatty kidney had a higher odds ratio (OR) of hypertension (OR: 2.12; P<0.0001), which persisted after adjustment for body mass index (OR: 1.49; P<0.0001) or visceral adipose tissue (OR: 1.24; P=0.049). Fatty kidney was also associated with an increased OR for chronic kidney disease (OR: 2.30; P=0.005), even after additionally adjusting for body mass index (OR: 1.86; P=0.04) or visceral adipose tissue (OR: 1.86; P=0.05). We observed no association between fatty kidney and diabetes mellitus after adjusting for visceral adipose tissue. In conclusion, fatty kidney is a common condition that is associated with an increased risk of hypertension and chronic kidney disease. Renal sinus fat may play a role in blood pressure regulation and chronic kidney disease.

Introduction

See Editorial Commentary, pp 756–757

Obesity continues to be an important global public health problem. Current estimates indicate that more than two thirds of American adults1 and 1.3 billion adults worldwide2 are either overweight or obese. The impact of obesity on cardiovascular and metabolic diseases, including diabetes mellitus, hypertension, dyslipidemia, cardiovascular disease, and cardiovascular mortality, is well established.3 In addition, obesity is now recognized as a risk factor for the development of renal dysfunction, with a growing body of evidence supporting the association of higher body mass index (BMI) with chronic kidney disease (CKD).46

BMI is a good measure of general adiposity, but it captures both lean and fat mass and does not distinguish between patterns of fat distribution. Abdominal adiposity, independent of generalized obesity, is associated with CKD.7,8 The association of regional fat deposition with CKD suggests a potential role for ectopic fat.9 This is particularly relevant for the kidneys, which are surrounded by abdominal visceral adipose tissue (VAT) and have the potential to accumulate ectopic fat in the renal sinus.

The accumulation of renal sinus fat is important because the renal vein and artery pass through the renal sinus and may be compressed by ectopic fat. Renal vein constriction has been shown to increase kidney volume and renal interstitial pressure and to decrease sodium excretion in animal models.1013 Human studies and animal models have demonstrated fat accumulation in the renal sinus9,14,15 and renal parenchyma.1619 Concomitant structural and functional changes in the kidney and renal vasculature have been observed in animal models.14,1719 However, whether these renal changes are associated with diseases of the kidney in humans is uncertain. The association of renal sinus fat with hypertension, CKD, and other metabolic traits has not been characterized previously in a large, community-based sample. Thus, the aim of this study was to evaluate the association of renal sinus fat accumulation, or “fatty kidney,” quantified using computed tomography with these cardiometabolic and renal traits in the Framingham Heart Study. We hypothesized that renal sinus fat would be independently associated with measures of blood pressure and renal function and not with other cardiometabolic traits, after accounting for abdominal VAT as a measure of abdominal adiposity.

Methods

Study Sample

Participants were drawn from the Framingham multidetector computed tomography (MDCT) cohort, which consists of 3529 participants from the Framingham offspring (n=1418) and third-generation (n=2111) cohorts who underwent MDCT between June 2002 and March 2005, as described previously.20 Eligible participants included 2923 participants who attended the eighth offspring or first third-generation examination with an interpretable abdominal MDCT scan for the renal sinus fat measurement protocol. A subsample consisted of 1210 offspring participants with serum cystatin-C measured during the seventh offspring examination. Participants provided written informed consent, and this study was approved by the Boston University Medical Center and Massachusetts General Hospital institutional review boards.

Renal Sinus Fat Quantification

Abdominal MDCT scans were captured using an 8-slice MDCT scanner (LightSpeed Ultra, General Electric, Milwaukee, WI), covering 125 mm in the abdomen with 25 5.0-mm slices above the S1 level (120 kVp, 400 mA, gantry rotation time 500 ms, table feed 3:1), and were interpreted using the Aquarius 3D Workstation (TeraRecon, Inc, San Mateo, CA). Renal sinus fat was quantified in a single MDCT slice within the right kidney. Briefly, renal sinus fat (in centimeters squared) was measured by 1 reader manually tracing the right kidney from the abdominal MDCT scan after applying a selection rule to a set of candidate slices selected based on visual inspection. Adipose tissue was identified using MDCT pixel density in Hounsfield units (HU) centered on −120 HU with a window width of −195 to −45 HU. The interclass correlation coefficients were 0.93 and 0.86 for intrareader and interreader reproducibility, respectively. Our complete protocol appears in the supplementary methods in the online Data Supplement (please see ).

Outcome Assessment

Systolic (SBP) and diastolic (DBP) blood pressures were measured by the examining clinic physician using the mean of 2 readings. Hypertension was defined as SBP ≥140 mm Hg, DBP ≥90 mm Hg, or current use of prescription hypertension medication. Imputed blood pressure values were calculated by adding 10 mm Hg to SBP and 5 mm Hg to DBP if a participant was currently using hypertension medication.21

Serum creatinine was measured using the modified Jaffe method (interassay coefficient of variation [CV]: 2.8%, intra-assay CV: 4.0%; Roche Hitachi 911, Roche Diagnostics, Indianapolis, IN) and indirectly calibrated to the Third National Health and Nutrition Examination Survey serum creatinine values as described previously.22 Cystatin C was measured using nephelometry on previously frozen serum samples (interassay CV: 3.3%, intra-assay CV: 2.4%; Dade Behring Diagnostic, Marburg, Germany). The estimated glomerular filtration rate (eGFR) was determined using the abbreviated Modification of Diet in Renal Disease Study Equation (eGFRcrea)23 and the cystatin C-only Chronic Kidney Disease Epidemiology Collaboration equation (eGFRcys).24 CKDcrea and CKDcys were defined as eGFRcrea or eGFRcys <60 mL/min/1.73 m2, respectively.

Urinary albumin and creatinine were determined using spot urine samples. Urinary albumin was quantified using a Tina-quant albumin immunoturbidimetric assay (interassay CV: 3.1%; intra-assay CV: 2.1%; Roche Diagnostics). Urinary creatinine was quantified using a modified Jaffe method (interassay CV: 1.9%; intra-assay CV: 1.0%; Roche Diagnostics). The urinary albumin:creatinine ratio was calculated by dividing the amount of urinary albumin (in milligrams) by the amount of urinary creatinine (in grams). Microalbuminuria was defined as a urinary albumin:creatinine ratio >25 mg/g in women or >17 mg/g in men.

Serum levels of fasting plasma glucose, total cholesterol, high-density lipoprotein (HDL) cholesterol, and triglycerides were determined using a fasting blood sample from the clinic examination. Diabetes mellitus was defined as a fasting plasma glucose ≥126 mg/dL or current use of oral hypoglycemic treatment or insulin. High triglycerides were defined as serum triglycerides ≥150 mg/dL or current use of lipid-lowering medication. Low HDL cholesterol was defined as HDL cholesterol <50 mg/dL in women and <40 mg/dL in men.

Covariate Assessment

Height and waist circumference at the umbilicus were recorded to the nearest quarter inch and weight to the nearest pound by trained clinic staff. BMI was defined as weight divided by height2 (in kilograms per meter squared). Abdominal VAT volume was assessed by MDCT.20 Current smoking was defined as smoking ≥1 cigarette per day in the past year. High alcohol intake was defined as >7 drinks per week among women and >14 drinks per week among men based on self-report. Physical activity was determined by calculating a physical activity index based on a structured questionnaire.

Statistical Methods

Fatty kidney was defined as the presence of high renal sinus fat based on sex-specific 90th percentiles in a healthy referent subsample, defined using the following exclusion criteria: (1) BMI ≥30 kg/m2; (2) hypertension, high triglycerides, low HDL cholesterol, impaired fasting plasma glucose, or diabetes mellitus; (3) CKDcrea or microalbuminuria; (4) current smoking; (5) BMI <18.5 kg/m2; and (6) missing covariates described in the previous exclusion steps and other model covariates. The healthy referent sample consisted of 400 women and 213 men, with 90th percentile renal sinus fat cut points of 0.445 cm2 in women and 0.710 cm2 in men.

Renal sinus fat measurements below the observed lower limit of detection (0.0048 cm2) were set to 0.0040 cm2 in statistical analyses. Age- and sex-adjusted partial Pearson correlation coefficients were used to assess the correlation of log-transformed renal sinus fat with continuous covariates. Renal sinus fat was modeled dichotomously as fatty kidney and continuously with a natural-log transformation, standardized to a sex-specific mean of 0 and SD of 1. Linear and logistic regression was used to model continuous and dichotomous outcomes as functions of renal sinus fat. Models were initially adjusted for age and sex and then underwent further multivariable adjustment. Multivariable models of hypertension, imputed SBP, and imputed DBP, were adjusted for age, sex, current smoking, high alcohol intake, and physical activity index. Multivariable models of eGFR and CKD were adjusted for age, sex, diabetes mellitus, hypertension medication use, SBP, current smoking, and HDL cholesterol. Multivariable models of HDL cholesterol were adjusted for age, sex, current smoking, high alcohol intake, and lipid lowering medication use. Multivariable models of triglycerides were adjusted for age, sex, and current smoking. Finally, multivariable models were separately adjusted for BMI and abdominal VAT. To help disentangle the potential association of renal sinus fat and abdominal VAT with blood pressure and eGFR, we examined trends across sex-specific renal sinus fat tertiles within sex-specific abdominal VAT tertiles. Statistical analyses were performed using SAS version 9.2 (SAS Institute, Cary, NC).

Secondary Analyses

As a secondary analysis, we recreated the healthy referent sample excluding individuals with BMI ≥25 kg/m2 instead of BMI ≥30 kg/m2 (“lean healthy referent”) and determined the sex-specific 90th percentile cut points. The lean healthy referent sample included 282 women and 100 men; the 90th percentile cut points were 0.420 cm2 in women and 0.455 cm2 in men.

Results

Overall Study Sample Characteristics

Renal sinus fat ranged from the lower limit of detection in 133 participants to 4.89 cm2 with a median value of 0.31 cm2. The prevalence of fatty kidney (renal sinus fat ≥0.445 cm2 in women and ≥0.710 cm2 in men) was 30.9% in the overall sample (n=879). Individuals with fatty kidney were older, had a higher BMI, and had a more adverse metabolic risk factor profile when compared with individuals without fatty kidney. The prevalence of hypertension, CKDcys, CKDcrea, and microalbuminuria was also higher among those with as compared with those without fatty kidney (Table 1). Age- and sex-adjusted correlations of renal sinus fat with adiposity measures and continuous covariates are presented in Table 2. Renal sinus fat was correlated with all of the covariates examined (P≤0.03) except urinary albumin:creatinine ratio (P=0.18), with the strongest correlations observed for other adiposity measures and age.

Table 1. Study Sample Characteristics by Fatty Kidney Status

VariableFatty Kidney (n=879)*No Fatty Kidney (n=2044)Age- and Sex- Adjusted P
Age, y61±1351±12<0.0001
Women, %42.7 (375)54.6 (1117)<0.0001
Renal sinus fat, cm20.97 (0.73, 1.34)0.18 (0.06, 0.33)<0.0001
Body mass index, kg/m230.3±5.726.6±4.8<0.0001
Visceral adipose tissue, cm32557±10401456±842<0.0001
Waist circumference, cm106±1494±14<0.0001
HDL cholesterol, mg/dL52±1557±18<0.0001
Triglycerides, mg/dL117 (82, 165)91 (66, 134)<0.0001
Systolic blood pressure, mm Hg129±17120±15<0.0001
Diastolic blood pressure, mm Hg75±1175±9<0.0001
Hypertension, %57.8 (508)26.6 (543)<0.0001
Use of antihypertensive medication, %46.2 (406)18.6 (381)<0.0001
Diabetes mellitus, %14.6 (128)4.2 (86)<0.0001
Current smoking status, %10.5 (92)12.6 (257)0.17
High alcohol intake, %9.2 (123)11.2 (228)0.003
Physical activity index36±737±70.002
eGFRcrea, mL/min/1.73 m284.9±19.890.9±17.80.40
CKDcrea, %9.2 (81)2.8 (57)0.04
eGFRcys, mL/min/1.73 m2§81±1789±160.0005
CKDcys, %§9.1 (53)2.8 (18)0.0005
Urinary albumin:creatinine ratio, mg/g5.4 (3.1, 11.1)4.5 (2.8, 8.8)0.005
Microalbuminuria, %13.4 (118)6.0 (123)0.01

All of the participants in the study sample are white. Continuous variables are presented as mean±SD and dichotomous variables presented as % (N), unless otherwise specified.

CKDcrea indicates chronic kidney disease, defined as eGFRcrea <60 mL/min/1.73 m2; CKDcys, chronic kidney disease, defined as eGFRcys <60 mL/min/1.73 m2; eGFRcrea, estimated glomerular filtration rate using the modified Modification of Diet in Renal Disease Study equation; eGFRcys, estimated glomerular filtration rate using the cystatin C only Chronic Kidney Disease Epidemiology Collaboration equation; HDL, high-density lipoprotein.

*Sex-specific cut points for fatty kidney are ≥0.710 cm2 in men; ≥0.445 cm2 in women.

Data are presented as median (25th, 75th percentiles).

Data show >7 drinks per wk in women, >14 drinks per wk in men.

§Cystatin C levels were collected during the offspring seventh examination cycle (n=1210; fatty kidney: n=561; no fatty kidney: n=649).

Table 2. Age- and Sex-Adjusted Partial Pearson Correlations (r) With Log-Transformed Renal Sinus Fat

VariablerP
Age*0.40<0.0001
Visceral adipose tissue0.48<0.0001
Subcutaneous adipose tissue0.34<0.0001
Body mass index0.38<0.0001
Waist circumference0.38<0.0001
Systolic blood pressure0.14<0.0001
Diastolic blood pressure0.13<0.0001
High density lipoprotein cholesterol−0.14<0.0001
Log(triglycerides)0.26<0.0001
Fasting plasma glucose0.13<0.0001
log(UACR)0.020.18
eGFRcrea0.040.03
eGFRcys−0.100.0004

eGFRcrea indicates estimated glomerular filtration rate using the modified Modification of Diet in Renal Disease Study equation; eGFRcys, estimated glomerular filtration rate using the cystatin C only Chronic Kidney Disease Epidemiology Collaboration equation; UACR, urine albumin:creatinine ratio.

*Data show the sex-adjusted partial Pearson correlation.

Renal Sinus Fat and Hypertension

Individuals with fatty kidney had a higher odds ratio (OR) for hypertension (Table 3; OR: 2.12; P<0.0001), which persisted after adjustment for BMI (OR: 1.49; P<0.0001) or VAT (OR: 1.24; P=0.049). Individuals with fatty kidney also had higher imputed SBP (4.8 mm Hg) and DBP (2.3 mm Hg) compared with those without fatty kidney (Table 3; both P<0.0001). In models with continuous renal sinus fat as the exposure, estimates were similar (Table S1, please see the online Data Supplement).

Table 3. Imputed Blood Pressure and Renal Function Outcomes Modeled as Functions of Fatty Kidney Status

Model Outcome of InterestAge and SexMultivariable*Multivariable+BMIMultivariable+VAT
Continuous outcomes
    Systolic blood pressure, mm Hg4.9 (0.7) P<0.00014.8 (0.7) P<0.00012.2 (0.7) P=0.0021.2 (0.7) P=0.11
    Diastolic blood pressure, mm Hg2.4 (0.4) P<0.00012.3 (0.4) P<0.00010.8 (0.4) P=0.070.5 (0.5) P=0.31
    eGFRcys, mL/min/1.73 m2−3.27 (0.89) P=0.0002−2.20 (0.89) P=0.01−0.64 (0.90) P=0.48−0.90 (0.96) P=0.35
    eGFRcrea, mL/min/1.73 m20.61 (0.72) P=0.400.57 (0.72) P=0.430.05 (0.75) P=0.95−0.27 (0.78) P=0.72
Dichotomous outcomes
    Hypertension2.13 (1.77–2.57) P<0.00012.12 (1.75–2.56) P<0.00011.49 (1.22–1.83) P<0.00011.24 (1.00–1.53) P=0.049
    CKDcys2.68 (1.51–4.75) P=0.00072.30 (1.28–4.14) P=0.0051.86 (1.02–3.42) P=0.041.86 (1.00–3.46) P=0.05
    CKDcrea1.49 (1.01–2.20) P=0.041.14 (0.76–1.72) P=0.531.16 (0.76–1.78) P=0.491.20 (0.77–1.86) P=0.43

BMI indicates body mass index; eGFRcrea, estimated glomerular filtration rate using the modified Modification of Diet in Renal Disease Study equation; eGFRcys, estimated glomerular filtration rate using the cystatin C only Chronic Kidney Disease Epidemiology Collaboration equation; VAT, abdominal visceral adipose tissue volume; CKDcrea, chronic kidney disease status based on eGFRcrea; CKDcys, chronic kidney disease status based on eGFRcys. Sex-specific cut points for fatty kidney are ≥0.710 cm2 in men and ≥0.445 cm2 in women. Increments in the outcome when fatty kidney is present (SE shown in parentheses) are presented for continuous outcomes. Odds ratios (95% CIs) comparing those with fatty kidney to those without fatty kidney are presented for dichotomous outcomes.

*Multivariable models are adjusted for age and sex, as well as covariates listed below by outcome: eGFRcys, eGFRcrea, CKDcys, CKDcrea: diabetes mellitus status, current hypertension medication use, systolic blood pressure, current smoking status, and high-density lipoprotein cholesterol level. Imputed systolic blood pressure, diastolic blood pressure, hypertension: current smoking status, high alcohol intake, and physical activity index.

Renal Sinus Fat and Renal Function

Among participants with cystatin C measurements (n=1210), 5.9% (n=71) had CKDcys. Fatty kidney was associated with an increased OR for CKDcys (Table 3; OR: 2.30; P=0.005), which persisted after adjustment for BMI (OR: 1.86; P=0.04) or VAT (OR: 1.86; P=0.05). Fatty kidney was associated with an increased OR for CKDcrea after age and sex adjustment (OR: 1.49; P=0.04) but not after multivariable adjustment (Table 3; OR: 1.14; P=0.53).

The prevalence of microalbuminuria was 13.4% among individuals with fatty kidney and 6.0% among those without fatty kidney (Table 1). Fatty kidney was associated with an increased OR of microalbuminuria in age- and sex-adjusted models (OR: 1.45 [95% CI: 1.08–1.94]; P=0.01), which was attenuated and no longer statistically significant after multivariable adjustment (OR: 1.17 [95% CI: 0.86–1.59]; P=0.31).

Renal Sinus Fat and Additional Metabolic Risk Factors

Fatty kidney was associated with an increased OR for diabetes mellitus after age and sex adjustment (OR: 2.26; P<0.0001; Table 4), which was attenuated after adjustment for VAT (OR: 1.09; P=0.62); similar results were observed for fasting plasma glucose (Table 4). Similarly, fatty kidney was associated with an increased OR for high triglycerides (Table 4; OR: 1.88; P<0.0001) but not after adjustment for VAT (OR: 1.04; P=0.69).

Table 4. Lipid and Glucose Outcomes as a Function of Fatty Kidney Status*

Model Outcomes of InterestAge and SexMultivariable*Multivariable+BMIMultivariable+VAT
Continuous outcomes
    HDL cholesterol, mg/dL−4.16 (0.67) P<0.0001−4.14 (0.66) P<0.0001−1.10 (0.67) P=0.101.07 (0.68) P=0.12
    Triglycerides, log transformed, mg/dL0.20 (0.02) P<0.00010.20 (0.02) P<0.00010.10 (0.02) P<0.00010.01 (0.02) P=0.71
    Fasting plasma glucose, mg/dL5.66 (0.90) P<0.00011.99 (0.92) P=0.03−0.07 (0.96) P=0.94
Dichotomous outcomes
    Low HDL cholesterol1.39 (1.15–1.69) P=0.00091.41 (1.15–1.72) P=0.00080.99 (0.80–1.23) P=0.930.74 (0.59–0.93) P=0.009
    High triglycerides1.89 (1.58–2.26) P<0.00011.88 (1.58–2.25) P<0.00011.38 (1.14–1.66) P=0.0011.04 (0.85–1.28) P=0.69
    Diabetes mellitus2.26 (1.66–3.09) P<0.00011.42 (1.02–1.97) P=0.041.09 (0.77–1.55) P=0.62

BMI indicates body mass index; HDL, high-density lipoprotein; VAT, abdominal visceral adipose tissue volume. Increments in each outcome associated with the presence of fatty kidney (SEs shown in parentheses) are presented for continuous outcomes. Odds ratios (95% CIs) comparing those with fatty kidney to those without fatty kidney are presented for dichotomous outcomes. Sex-specific cut points for fatty kidney are ≥0.710 cm2 in men and ≥0.445 cm2 in women.

*Multivariable models are adjusted for age and sex, as well as covariates as listed below: HDL cholesterol, log(triglycerides), low HDL cholesterol: current smoking status, high alcohol intake, current use of lipid lowering medication; and high triglycerides: current smoking status.

Models of fasting plasma glucose and diabetes mellitus were not adjusted beyond age and sex before further adjustment for BMI or VAT.

Abdominal Fat Distribution Patterns and CKD

The prevalence of CKDcrea and CKDcys by fat distribution pattern category is presented in the Figure. The highest prevalence of both conditions was observed among individuals with fatty kidney and high VAT. The prevalence of CKDcys (P=0.002), but not CKDcrea (P=0.20), varied across all 4 of the distribution categories. Among those with high VAT, the prevalence of CKDcys and CKDcrea were higher among those with fatty kidney when compared with those without fatty kidney (P=0.01 and 0.04, respectively).

Figure.

Figure. Prevalence of chronic kidney disease (CKD) by fatty kidney and abdominal visceral adipose tissue (VAT) distribution Patterns. CKDcys is defined as estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m2, based on the cystatin C only Chronic Kidney Disease Epidemiology Collaboration equation. CKDcrea is defined as eGFR <60 mL/min/1.73 m2, based on the modified Modification of Diet in Renal Disease (MDRD) Study equation. , Not fatty kidney and normal VAT (group 1); , fatty kidney and normal VAT (Group 2); , not fatty kidney and high VAT (group 3); ▪, fatty kidney and high VAT (group 4). For comparison, CKDcys and CKDcrea status at the seventh offspring examination cycle are presented. P values are adjusted for age and sex. CKDcrea was available in 2943 participants (group 1: N=1437; group 2: N=236; group 3: N=605; group 4: N=665). CKDcys was available in 1206 participants with cystatin C measures (group 1: N=375; group 2: N=122; group 3: N=273; group 4: N=436).

Mean imputed SBP and eGFRcys within renal sinus fat and VAT tertiles are presented in Figure S1A and S1B, respectively. When examining trends across renal sinus fat tertiles within VAT tertiles, we observed that mean SBP is higher and eGFRcys is lower as renal sinus fat increases within each VAT tertile, highlighting the association between renal sinus fat and SBP and eGFRcys.

Secondary Analyses

Using lean healthy referent cut points, the prevalence of fatty kidney was 38.9% (26.9% of women and 51.4% of men). Among participants not currently using hypertension medication (n=2129), results from models of SBP and DBP as functions of fatty kidney were essentially unchanged (data not shown). Results from blood pressure, renal function, and lipid models remained consistent in diabetes mellitus–free subsamples from the overall (n=2702) and cystatin C subgroups (n=1103).

Discussion

Fatty kidney is a common condition, present in nearly one third of our community-based sample. Fatty kidney is associated with both hypertension and CKD based on cystatin C. These associations persisted after accounting for measures of generalized or abdominal adiposity, suggesting that renal sinus fat may have an independent association with renal function. In contrast, the observed associations of renal sinus fat with other cardiometabolic traits were generally attenuated after accounting for overall VAT, which is consistent with the hypothesized localized impact of renal sinus fat and provides further support for the potential unique role of this fat depot in hypertension and renal dysfunction.

The association of obesity with the development of CKD46 and hypertension25 is well established, although the pathogenic mechanisms are not fully understood. Ectopic fat accumulation is one of several mechanisms proposed to explain these associations. Renal sinus fat accumulation has been observed in small imaging studies of children (n=15)26 and adults (n=6)27 with normal renal function. The association of renal sinus fat with hypertension and creatinine clearance was studied recently in 205 participants from the Pulmonary Edema and Stiffness of the Vascular System Study, a study designed to assess predictors of congestive heart failure in older individuals.15 In this study, patients with SBP ≥160 mm Hg or DBP ≥100 mm Hg had higher levels of renal sinus fat, quantified by MRI, than in those with blood pressure <160/100 mm Hg, although associations with SBP, DBP, and renal function were not observed,15 perhaps because of the small sample of highly selected individuals.

Animal models of diet-induced obesity provide additional insight into potential mechanisms involved in the pathogenesis of renal sinus fat. Rabbits with diet-induced obesity undergo a 61% increase in renal sinus mass, primarily driven by increases in fat within the renal sinus.14 This increase in renal sinus mass is observed concomitantly with increases in blood pressure.14 It has been hypothesized that renal sinus fat deposition leads to increases in renal interstitial pressure through the compression of vessels exiting the kidney, including the renal vein and lymph vessels.9 This potential mechanism is supported by studies in dog and rat models in which renal vein compression leads to increased renal interstitial pressure, kidney volume, and, in the presence of volume expansion, increased sodium reabsorption in the loop of Henle and decreased sodium excretion.1013 Increased tubular reabsorption and retention of sodium is also observed in the dog model of obesity-related hypertension,28 a model for the development of obesity-related hypertension in humans.29

Alternative animal models of obesity reported lipid accumulation within the renal parenchyma, supporting proposed mechanisms of obesity leading to kidney damage and hypertension through lipotoxicity, oxidative stress, inflammation, and fibrosis.30,31 Obese mice fed a high-fat diet developed lipid accumulation in the glomeruli and proximal tubules in addition to albuminuria, increased SBP and oxidative stress, and a larger glomerular tuft area and mesangial matrix when compared with mice fed a low-fat diet.18 Zucker diabetic fatty rats exhibit greater lipid accumulation in the renal cortex when compared with pair-fed lean controls.17 Lipid accumulation within the renal parenchyma has also been described in humans.16

Overall, evidence from animal models supports the presence of obesity-related increases in renal lipid accumulation with concomitant structural and functional changes in the kidney and vasculature. However, it is uncertain whether these changes are specifically because of renal fat accumulation as compared with generalized weight gain and adiposity. Higher levels of BMI are among the strongest correlates of many ectopic fat depots.20,32,33 We have attempted to dissect the specific role of renal sinus fat as compared with generalized markers of adiposity and ectopic fat through our modeling structure and serial adjustment for BMI and VAT. Although our findings are attenuated after accounting for each of these adiposity-related variables, the residual statistical significance suggests a potential independent association of renal sinus fat with hypertension and CKD. We have also addressed this issue by evaluating the trends in SBP and eGFRcys with increasing renal sinus fat within narrower ranges of abdominal VAT; these findings further support an independent association with renal sinus fat. Finally, we did not observe an association with the presence of diabetes mellitus after accounting for abdominal VAT, which is in contrast to our previous work demonstrating consistent associations with VAT,20 liver fat,34 and upper body subcutaneous fat.35 This suggests that potential mechanisms for the association of renal sinus fat with hypertension and CKD are unlikely because of high correlations among different fat depots and provides support for a unique and specific association between renal sinus fat and hypertension and CKD.

Similar to our previous findings investigating abdominal VAT and CKD,36 we observed in the present analysis that fatty kidney is associated with CKD when using cystatin C–based eGFR but not creatinine-based eGFR. One potential explanation is that cystatin C may be a more sensitive marker for assessing renal function as compared with serum creatinine in older populations,37 given that serum creatinine is predominately derived from muscle tissue and that overall muscle mass is lower in older individuals.23,38 However, our results may also reflect potential confounding because of the independent association of cystatin C with nonrenal factors,39,40 including BMI.39 Cystatin C is secreted by adipose tissue,41 and the prevalence of cystatin C–based CKD may be overestimated in overweight and obese individuals when compared with the prevalence based on the Modification of Diet in Renal Disease Study equation.42 Although it is important to consider the role of adiposity in cystatin C production, if our association observed for fatty kidney and cystatin C–based CKD was solely attributed to confounding by overall adiposity, the association would have been completely attenuated after adjusting for BMI or abdominal VAT. Conversely, we observed a significant residual association on further adjustment. Therefore, secretion of cystatin C by adipocytes is unlikely to fully explain our observations.

The Framingham MDCT cohort is a large, well-characterized, community-based sample with multiple measures of adiposity, allowing for adjustment of several important confounders and further adjustment for generalized and central adiposity. Using computed tomography, we were able to develop a noninvasive, reproducible method to quantify renal sinus fat accumulation in a community-based setting. Some limitations warrant mention. This is an observational study, which limits our ability to assess the causality of our findings. Given the cross-sectional design, we were also unable to assess the temporality of our observed associations. Our study sample is composed of white participants. Based on this, our findings may not be generalizable to populations consisting of other racial or ethnic groups.

Perspectives

Fatty kidney is a common condition associated with an increased risk of hypertension and CKD. Our results suggest that renal sinus fat may be associated with blood pressure regulation and CKD in humans and provides additional insight into the pathophysiologic role of adiposity in renal dysfunction. Further research is necessary to evaluate the longitudinal associations of renal sinus fat with markers of renal function and metabolic risk factors.

Sources of Funding

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

Disclosures

None.

Footnotes

Continuing medical education (CME) credit is available for this article. Go to http://cme.ahajournals.org to take the quiz.

This article was presented in part as an abstract at the American Heart Association 50th Annual Epidemiology and Prevention/Nutrition, Physical Activity and Metabolism Joint Conference 2010; March 2–5, 2010.

Correspondence to Caroline S. Fox,
73 Mt Wayte Ave, Suite #2, Framingham, MA 01702
. E-mail

References

  • 1. Flegal KM, Carroll MD, Ogden CL, Curtin LR. Prevalence and trends in obesity among US adults, 1999–2008. JAMA. 2010; 303:235–241.CrossrefMedlineGoogle Scholar
  • 2. Kelly T, Yang W, Chen CS, Reynolds K, He J. Global burden of obesity in 2005 and projections to 2030. Int J Obes (Lond).. 2008; 32:1431–1437.CrossrefMedlineGoogle Scholar
  • 3. Malnick SD, Knobler H. The medical complications of obesity. QJM. 2006; 99:565–579.CrossrefMedlineGoogle Scholar
  • 4. Fox CS, Larson MG, Leip EP, Culleton B, Wilson PW, Levy D. Predictors of new-onset kidney disease in a community-based population. JAMA. 2004; 291:844–850.CrossrefMedlineGoogle Scholar
  • 5. 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. 2005; 46:871–880.CrossrefMedlineGoogle Scholar
  • 6. Foster MC, Hwang SJ, Larson MG, Lichtman JH, Parikh NI, Vasan RS, Levy D, Fox CS. Overweight, obesity, and the development of stage 3 CKD: the Framingham Heart Study. Am J Kidney Dis. 2008; 52:39–48.CrossrefMedlineGoogle Scholar
  • 7. Kurella M, Lo JC, Chertow GM. Metabolic syndrome and the risk for chronic kidney disease among nondiabetic adults. J Am Soc Nephrol. 2005; 16:2134–2140.CrossrefMedlineGoogle Scholar
  • 8. Lee JE, Choi SY, Huh W, Kim YG, Kim DJ, Oh HY. Metabolic syndrome, C-reactive protein, and chronic kidney disease in nondiabetic, nonhypertensive adults. Am J Hypertens. 2007; 20:1189–1194.MedlineGoogle Scholar
  • 9. Montani JP, Carroll JF, Dwyer TM, Antic V, Yang Z, Dulloo AG. Ectopic fat storage in heart, blood vessels and kidneys in the pathogenesis of cardiovascular diseases. Int J Obes Relat Metab Disord. 2004; 28(suppl 4):S58–S65.CrossrefMedlineGoogle Scholar
  • 10. Ott CE, Navar LG, Guyton AC. Pressures in static and dynamic states from capsules implanted in the kidney. Am J Physiol. 1971; 221:394–400.CrossrefMedlineGoogle Scholar
  • 11. Stolarczyk J, Carone FA. Effects of renal lymphatic occlusion and venous constriction on renal function. Am J Pathol. 1975; 78:285–296.MedlineGoogle Scholar
  • 12. Burnett JC, Knox FG. Renal interstitial pressure and sodium excretion during renal vein constriction. Am J Physiol. 1980; 238:F279–F282.MedlineGoogle Scholar
  • 13. Burnett JC, Haas JA, Knox FG. Segmental analysis of sodium reabsorption during renal vein constriction. Am J Physiol. 1982; 243:F19–F22.MedlineGoogle Scholar
  • 14. Dwyer TM, Mizelle HL, Cockrell K, Buhner P. Renal sinus lipomatosis and body composition in hypertensive, obese rabbits. Int J Obes Relat Metab Disord. 1995; 19:869–874.MedlineGoogle Scholar
  • 15. Chughtai HL, Morgan TM, Rocco M, Stacey B, Brinkley TE, Ding J, Nicklas B, Hamilton C, Hundley WG. Renal sinus fat and poor blood pressure control in middle-aged and elderly individuals at risk for cardiovascular events. Hypertension. 2010; 56:901–906.LinkGoogle Scholar
  • 16. Lee HS, Lee JS, Koh HI, Ko KW. Intraglomerular lipid deposition in routine biopsies. Clin Nephrol. 1991; 36:67–75.MedlineGoogle Scholar
  • 17. Bobulescu IA, Dubree M, Zhang J, McLeroy P, Moe OW. Effect of renal lipid accumulation on proximal tubule Na+/H+ exchange and ammonium secretion. Am J Physiol Renal Physiol. 2008; 294:F1315–F1322.CrossrefMedlineGoogle Scholar
  • 18. Deji N, Kume S, Araki S, Soumura M, Sugimoto T, Isshiki K, Chin-Kanasaki M, Sakaguchi M, Koya D, Haneda M, Kashiwagi A, Uzu T. Structural and functional changes in the kidneys of high-fat diet-induced obese mice. Am J Physiol Renal Physiol. 2009; 296:F118–F126.CrossrefMedlineGoogle Scholar
  • 19. do Carmo JM, Tallam LS, Roberts JV, Brandon EL, Biglane J, da Silva AA, Hall JE. Impact of obesity on renal structure and function in the presence and absence of hypertension: evidence from melanocortin-4 receptor-deficient mice. Am J Physiol Regul Integr Comp Physiol. 2009; 297:R803–R812.CrossrefMedlineGoogle Scholar
  • 20. Fox CS, Massaro JM, Hoffmann U, Pou KM, Maurovich-Horvat P, Liu CY, Vasan RS, Murabito JM, Meigs JB, Cupples LA, D'Agostino RB, O'Donnell CJ. Abdominal visceral and subcutaneous adipose tissue compartments: association with metabolic risk factors in the Framingham Heart Study. Circulation. 2007; 116:39–48.LinkGoogle Scholar
  • 21. Levy D, Ehret GB, Rice K, Verwoert GC, Launer LJ, Dehghan A, Glazer NL, Morrison AC, Johnson AD, Aspelund T, Aulchenko Y, Lumley T, Kottgen A, Vasan RS, Rivadeneira F, Eiriksdottir G, Guo X, Arking DE, Mitchell GF, Mattace-Raso FU, Smith AV, Taylor K, Scharpf RB, Hwang SJ, Sijbrands EJ, Bis J, Harris TB, Ganesh SK, O'Donnell CJ, Hofman A, Rotter JI, Coresh J, Benjamin EJ, Uitterlinden AG, Heiss G, Fox CS, Witteman JC, Boerwinkle E, Wang TJ, Gudnason V, Larson MG, Chakravarti A, Psaty BM, van Duijn CM. Genome-wide association study of blood pressure and hypertension. Nat Genet. 2009; 41:677–687.CrossrefMedlineGoogle Scholar
  • 22. Coresh J, Astor BC, McQuillan G, Kusek J, Greene T, Van LF, Levey AS. Calibration and random variation of the serum creatinine assay as critical elements of using equations to estimate glomerular filtration rate. Am J Kidney Dis. 2002; 39:920–929.CrossrefMedlineGoogle Scholar
  • 23. Levey AS, Coresh J, Greene T, Stevens LA, Zhang YL, Hendriksen S, Kusek JW, Van LF. Using standardized serum creatinine values in the modification of diet in renal disease study equation for estimating glomerular filtration rate. Ann Intern Med. 2006; 145:247–254.CrossrefMedlineGoogle Scholar
  • 24. Stevens LA, Coresh J, Schmid CH, Feldman HI, Froissart M, Kusek J, Rossert J, Van LF, Bruce RD, 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. 2008; 51:395–406.CrossrefMedlineGoogle Scholar
  • 25. Lavie CJ, Milani RV, Ventura HO. Obesity and cardiovascular disease: risk factor, paradox, and impact of weight loss. J Am Coll Cardiol. 2009; 53:1925–1932.CrossrefMedlineGoogle Scholar
  • 26. Dietrich RB, Kangarloo H. Kidneys in infants and children: evaluation with MR. Radiology. 1986; 159:215–221.CrossrefMedlineGoogle Scholar
  • 27. Hricak H, Crooks L, Sheldon P, Kaufman L. Nuclear magnetic resonance imaging of the kidney. Radiology. 1983; 146:425–432.CrossrefMedlineGoogle Scholar
  • 28. Hall JE, Brands MW, Dixon WN, Smith MJ. Obesity-induced hypertension: renal function and systemic hemodynamics. Hypertension. 1993; 22:292–299.LinkGoogle Scholar
  • 29. Rocchini AP, Moorehead C, Wentz E, Deremer S. Obesity-induced hypertension in the dog. Hypertension. 1987; 9:III64–III68.LinkGoogle Scholar
  • 30. Hall JE, Brands MW, Henegar JR, Shek EW. Abnormal kidney function as a cause and a consequence of obesity hypertension. Clin Exp Pharmacol Physiol. 1998; 25:58–64.CrossrefMedlineGoogle Scholar
  • 31. Reisin E, Jack AV. Obesity and hypertension: mechanisms, cardio-renal consequences, and therapeutic approaches. Med Clin North Am. 2009; 93:733–751.CrossrefMedlineGoogle Scholar
  • 32. Rosito GA, Massaro JM, Hoffmann U, Ruberg FL, Mahabadi AA, Vasan RS, O'Donnell CJ, Fox CS. Pericardial fat, visceral abdominal fat, cardiovascular disease risk factors, and vascular calcification in a community-based sample: the Framingham Heart Study. Circulation. 2008; 117:605–613.LinkGoogle Scholar
  • 33. Schlett CL, Massaro JM, Lehman SJ, Bamberg F, O'Donnell CJ, Fox CS, Hoffmann U. Novel measurements of periaortic adipose tissue in comparison to anthropometric measures of obesity, and abdominal adipose tissue. Int J Obes (Lond). 2009; 33:226–232.CrossrefMedlineGoogle Scholar
  • 34. Speliotes EK, Massaro JM, Hoffmann U, Vasan RS, Meigs JB, Sahani DV, Hirschhorn JN, O'Donnell CJ, Fox CS. Fatty liver is associated with dyslipidemia and dysglycemia independent of visceral fat: the Framingham Heart Study. Hepatology. 2010; 51:1979–1987.CrossrefMedlineGoogle Scholar
  • 35. Preis SR, Massaro JM, Hoffmann U, D'Agostino RB, Levy D, Robins SJ, Meigs JB, Vasan RS, O'Donnell CJ, Fox CS. Neck circumference as a novel measure of cardiometabolic risk: the Framingham Heart Study. J Clin Endocrinol Metab. 2010; 95:3701–3710.CrossrefMedlineGoogle Scholar
  • 36. Young JA, Hwang SJ, Sarnak MJ, Hoffmann U, Massaro JM, Levy D, Benjamin EJ, Larson MG, Vasan RS, O'Donnell CJ, Fox CS. Association of visceral and subcutaneous adiposity with kidney function. Clin J Am Soc Nephrol. 2008; 3:1786–1791.CrossrefMedlineGoogle Scholar
  • 37. 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. 2007; 147:19–27.CrossrefMedlineGoogle Scholar
  • 38. Levey AS. Measurement of renal function in chronic renal disease. Kidney Int. 1990; 38:167–184.CrossrefMedlineGoogle Scholar
  • 39. Stevens LA, Schmid CH, Greene T, Li L, Beck GJ, Joffe MM, Froissart M, Kusek JW, Zhang YL, Coresh J, Levey AS. Factors other than glomerular filtration rate affect serum cystatin C levels. Kidney Int. 2009; 75:652–660.CrossrefMedlineGoogle Scholar
  • 40. Knight EL, Verhave JC, Spiegelman D, Hillege HL, de ZD, Curhan GC, de Jong PE. Factors influencing serum cystatin C levels other than renal function and the impact on renal function measurement. Kidney Int. 2004; 65:1416–1421.CrossrefMedlineGoogle Scholar
  • 41. Taleb S, Cancello R, Clement K, Lacasa D. Cathepsin s promotes human preadipocyte differentiation: possible involvement of fibronectin degradation. Endocrinology. 2006; 147:4950–4959.CrossrefMedlineGoogle Scholar
  • 42. Vupputuri S, Fox CS, Coresh J, Woodward M, Muntner P. Differential estimation of CKD using creatinine- versus cystatin C-based estimating equations by category of body mass index. Am J Kidney Dis. 2009; 53:993–1001.CrossrefMedlineGoogle Scholar