Renal Sinus Fat and Poor Blood Pressure Control in Middle-Aged and Elderly Individuals at Risk for Cardiovascular Events
Fat in the renal sinus (RS), a region of the kidney in which low pressure venous and lymphatic vessels are present, may indirectly influence blood pressure. The purpose of this study was to assess the association between RS fat and control of blood pressure on receipt of antihypertensive medications. A total of 205 participants aged 55 to 85 years at risk for cardiovascular events underwent MRI assessments of abdominal and RS fat, measurement of blood pressure, and determination of the number of prescribed antihypertensive medications. Multivariable linear regression was used to determine associations among RS fat, blood pressure, and the number of prescribed antihypertensive medications. Abdominal fat averaged 416±160 cm3 (median and interquartile range of 396 cm3 and 308 to 518 cm3); intraperitoneal fat averaged 141±73 cm3 (median and interquartile range of 129 cm3 and 86 to 194 cm3); and RS fat averaged 4.6±3.2 cm3 (median and interquartile range of 4.2 cm3 and 2.2 to 6.6 cm3). After accounting for age, sex, height, body mass index, and intraperitoneal fat, RS fat correlated with the number of prescribed antihypertensive medications (P=0.010), stage II hypertension (P=0.02), and renal size (P≤0.001). In conclusion, after accounting for other body fat depots and risk factors for hypertension, RS fat volume is associated with the number of prescribed antihypertensive medications and stage II hypertension. These results indicate that further studies are warranted to determine whether fat accumulation in the RS promotes hypertension.
In the last 2 decades in the United States, the prevalence of overweight middle-aged and elderly adults has increased from 60% to 71%, and the prevalence of obesity has increased from 22% to 32%.1 The accumulation of intraperitoneal (IP) fat attributed to obesity is associated with adverse cardiovascular (CV) outcomes.2,3 Understanding mechanisms by which IP fat (namely, abdominal or visceral fat) promotes CV events would enable practitioners to target therapies to reduce CV events in individuals with high IP fat.
A potential mechanism by which obesity and IP fat could promote CV events is through accumulation of fat in the renal sinus (RS). The RS is a perirenal area bounded from the hilum of the kidney to the edge of the renal parenchyma.4,5 It is physically separated from the renal parenchyma by a reflection of the external capsule. The major branches of the renal artery and vein, along with the major and minor calices of the collecting system and ureters, are located within the RS. The remainder of the RS normally contains small amounts of adipose tissue and lymphatic channels.4,5
In animal models, excessive accumulation of fat within the RS displaces and compresses the low pressure renal lymphatics and veins, as well as the ureters.6,7 Compression of these structures increases renal hydrostatic pressure (providing a stimulus to increase renal size) and activates the renin-angiotensin-aldosterone system (RAAS).6,7 Activation of the RAAS promotes hypertension, insulin resistance, atherosclerosis, and other adverse physiological effects related to obesity.6,7 Thus, excessive adipose tissue in the RS could compress low pressure conduits and serve as a stimulus to medical conditions (eg, hypertension) that have been associated with CV events.
Despite this rationale, to date, no study has assessed the association between RS fat and hypertension in humans. We hypothesized that RS fat was associated with the severity of hypertension in middle aged and older adults at risk for CV events. To address this hypothesis, we measured the association between RS fat and both antihypertensive medication use and systolic blood pressure (SBP). In addition, we examined the strength of these associations after accounting for fat depots in other body compartments, as well as other factors associated with hypertension.
This study is performed in accordance with the National Institutes of Health grant R01HL076438 titled the “Pulmonary Edema and Stiffness of the Vascular System” (PREDICT). The purpose of PREDICT is to identify abnormalities of the CV system that forecast a first episode of congestive heart failure in middle aged and elderly individuals. To accomplish this, PREDICT investigators plan to recruit 560 middle-aged and elderly individuals (aged 55 to 85 years) with CV risk factors for a first episode of congestive heart failure. Participants receive MRI measures of body composition and then 4 years of longitudinal ascertainment for CV events. At present, PREDICT is in the early stages of enrollment, and longitudinal follow-up has yet to be performed. The present study uses data from the first 205 individuals consecutively enrolled in the first year into the PREDICT Study with images acceptable for analysis. The study is approved by the institutional review board of the Wake Forest University School of Medicine, is registered with clinicaltrials.gov (NCT00542503), and each participant provides witnessed informed consent.
Medical history, physical examination, laboratory, and MRI data were collected on participant enrollment into the study. Anthropometric measurements, including weight and height, were performed in loose clothing without shoes. Blood pressure and heart rate were measured by a trained nurse between 8:00 am and 10:00 am, with the patient in a sitting position for a period of 10 minutes. Brachial blood pressure was determined manually. Laboratory assessments, including fasting serum electrolytes, creatinine, glucose, lipids, and C-reactive protein, were acquired according to previously published techniques in the fasting state.8–10 Information regarding the use of antihypertensive therapy was derived from personal face-to-face interviews with study participants. Questions related to these therapies were reconciled after review of the participant medical charts. Antihypertensive agents were classified in the following categories: β-adrenergic blockers, angiotensin-converting enzyme inhibitors, angiotensin receptor blockers, diuretics, calcium channel blockers, and others. Patients were classified as receiving 1, 2, or 3 antihypertensive agents. Afterward, each participant underwent a cardiac magnetic resonance examination at a field strength of 1.5T (Siemens Medical Solutions). During the MRI examination, images were acquired for the purpose of determining abdominal fat according to techniques published previously (Figure 1).11–13
For the purpose of this study, hypertension was defined according to the Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure as a SBP of ≥140 mm Hg, a diastolic blood pressure (DBP) ≥90 mm Hg, or the concurrent use of antihypertensive medications.14 Furthermore, for those with hypertension, we used these same Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure criteria to define stage I hypertension as an SBP of 140 to 159 mm Hg or a DBP of 90 to 99 mm Hg and stage II hypertension as an SBP of ≥160 mm Hg or a DBP of ≥100 mm Hg. We also defined coronary artery disease in accordance with American College of Cardiology/American Heart Association guidelines.15 Each component of the data that was acquired was accomplished by personnel blinded to other components of the study. For example, those acquiring and documenting medical history, physical examination, and laboratory data were blinded to the results of MRI; those analyzing abdominal fat were blinded to all of the other test results from the participants.
Measurement of Abdominal and RS Fat
According to techniques published previously, MRI measurements of total abdominal fat were accomplished in a single axial section positioned at the level of the second lumbar vertebra12; abdominal fat was segmented into subcutaneous (SC), retroperitoneal (RP), and IP compartments, as shown in Figure 1.11–13 RS fat was collected from this same image section. Previous studies have shown an excellent correlation between the fat in a single abdominal section and total fat in the abdomen.11,12
The cross-sectional area of both kidneys (centimeters squared) was measured in the same section used to determine IP and RS fat depots. Both kidneys were measured, and RS fat from each kidney was summed and averaged to produce a single value. Patients with hydronephrosis or congenital renal anomalies were excluded. The Slice-O-Matic 4.2 Rev-10 (TomoVision) software analysis program was used for all of the measurements.16 Regions of interest were drawn on each section, and their areas were determined by multiplying the number of pixels within the regions of interest by the size of each pixel.17 To obtain the volume of fat for each territory (SC, IP, RP, RS) in cubic centimeters, the area was multiplied by section thickness. To determine the interobserver variability of the measurements of fat within each compartment, images from 20 randomly selected participants were redrawn by a different observer blinded to all of the other study results.
Because of skewness in distribution of many of the variables, the distributions were summarized by the median and interquartile range. Statistical tests to determine whether the continually increasing levels of RS fat were associated with increasing or decreasing levels of each variable were based on Spearman rank correlation coefficients. Multiple tests of correlation coefficients were deemed significant at the 5% level of significance controlling for an overall 5% false discovery rate. Groups were separated by quartiles of RS fat only to illustrate trends for tables and not for hypothesis testing. The primary aim of the study was to see whether RS fat was associated with measures of blood pressure (SBP and DBP), the number of prescribed hypertensive medications, and renal function (renal size and serum creatinine); therefore, tests of association between RS fat with these 5 primary outcome measures were adjusted for 5 multiple comparisons using the Bonferroni technique. Partial rank correlation was used to adjust the association between fat subtypes and CV risk factors after accounting for other potentially confounding variables, such as age, sex, body mass index (BMI), and height. To compare dichotomous variables with amounts of RS fat, Wilcoxon rank-sum tests were used. Data were analyzed using SPSS and SAS. Unless stated otherwise, all of the data were presented as mean±SD; a 2-sided P<0.05 was considered significant. The authors had full access to the data and take responsibility for the integrity.
The baseline and clinical characteristics of the study participants are shown in Table 1. The mean age of the participants was 69±7 years (median and interquartile range of 69 and 63 to 75 years); 51% were men. RS fat averaged 4.6±3.2 cm3 (range: 0.0 to 16.6 cm3; median and interquartile range of 4.2 and 2.2 to 6.6 cm3); IP fat averaged 141±73 cm3 (range: 12 to 368 cm3; median and interquartile range of 129 and 86 to 194 cm3); RP fat averaged 58±30 cm3 (range: 11 to 168 cm3; median and interquartile range of 55 and 35 to 78 cm3); and SC fat averaged 216±114 cm3 (range: 23 to 716 cm3; median and interquartile range of 195 and 137 to 274 cm3). Fifty-six percent, 45%, 42%, 27%, and 17%, respectively, received diuretics, β-adrenergic blockers, angiotensin-converting enzyme inhibitors, calcium channel blockers, or angiotensin receptor blockers; 43% of patients received ≥2 antihypertensive medications. Associations of RS fat with participant demographics are shown in Table 2.
RS fat volume correlated with both RP and IP fat volume (r=0.47, P≤0.001; and r=0.41, P≤0.001, respectively) but not with SC fat volume (r=−0.08; P=0.24; Table 2). The interobserver reliability was assessed by the intraclass correlation coefficients, which were 0.976, 0.944, 0.996, and 0.825 for IP, RP, SC, and RS depots, respectively.
RS fat correlated with the number of prescribed antihypertensive medications (P=0.01). The volume of RS fat increased as the number of antihypertensive medications increased (3.9, 4.9, and 6.3 cm3, for 1, 2, and 3 prescribed antihypertensive medications, respectively; P=0.006). RS fat was associated with serum creatinine (r=0.28; P<0.001), but the correlation of RS fat with creatinine clearance, performed using Cockroft-Gault formula,18 did not reach statistical significance (P=0.25).
There was a trend for an association between higher pulse pressure and RS fat (r=0.13; P=0.07). The amount of RS fat was higher in those with stage I hypertension; however, this association was lost after accounting for the number of prescribed hypertensive medications. The amount of RS fat was significantly higher in those with stage II hypertension (6.0 versus 4.4 cm3, P=0.021, respectively), both before (P=0.02) and after (P=0.03) accounting for the number of prescribed antihypertensive medications (Figure 2). After accounting for multiple comparisons, RS fat remained associated with renal size, serum creatinine, and the number of prescribed antihypertensive medications (Table 3).
Patients with and without diabetes mellitus had similar amounts of RS fat (4.6 versus 4.5 cm3; P=0.85). RS fat was not associated with or fasting glucose levels (P=0.24). Those with (5.6 cm3) versus without (4.0 cm3) coronary artery disease exhibited more RS fat (P=0.001). Partial correlation was performed for the association of variables with RS fat after adjusting for age, sex, BMI, and height. RS fat remained independently associated with the number of antihypertensive medications, IP fat volume, RP fat volume, and renal size after accounting for all of these variables (Table 4).
We also performed a multivariable linear regression using the number of prescribed antihypertensive medications as a dependant variable and SC, IP, and RS fat as covariates. The significant association of IP fat with the number of prescribed antihypertensive medications was lost (β=0.026; P=0.75), but, importantly, there remained a persistent association between the number of antihypertensive medications used by study participants and the volume of RS fat (β=0.193; P=0.01) present in these same participants (Table 5).
The results of this study indicate that RS fat is associated with the following: (1) IP and RP fat; (2) an increase in the number of antihypertensive medications used to treat blood pressure (Tables 3 and 4⇑); and (3) metrics of “control” of hypertension, classified as Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure VII stage II hypertension (a brachial cuff pressure ≥160/100 mm Hg versus <160/100 mm Hg; Figure 2). All of these associations remain after accounting for age, sex, BMI, and height (Table 4). In addition, RS fat volume is associated with the number of antihypertensive medications used by study participants after accounting for age, sex, BMI, and height in addition to accounting for IP fat (Table 5). To the best of our knowledge, this is the first study in human subjects to address the association of RS fat with hypertension after accounting for patient demographics.
Many classifications have been proposed for the subdivision of body fat to different compartments; most commonly, abdominal fat depots are classified into IP (or visceral) and SC fat compartments.13 Previous studies have described differences in the metabolic and endocrine profiles of individuals with larger amounts of IP versus SC fat.13 Unlike SC fat, IP fat is associated with adverse CV events, including myocardial infarction and stroke.19 Also, IP fat is associated with insulin resistance20–22 and higher incidences of obesity-related hypertension.23
In this study, we sought to address the association between RS fat and hypertension for the following reasons: (1) in general, those with higher amounts of IP fat also exhibit more RS fat; (2) mild elevation of compressive forces within the RS could constrict several relatively low pressure conduits (renal veins and ureters) that could adversely impact blood pressure; and (3) in animal models, increased amounts of RS fat have been associated with larger kidney size and reduced kidney function.
In our study, after introducing RS fat into a multivariable regression model, the association between IP fat and the number of prescribed antihypertensive medications was lost, but the association of RS fat with these metrics persisted (Table 5). This finding suggests that some of the relationship between the number of medications used to treat BP elevations depends on RS fat and is independent of IP fat. Also, these data suggest that further studies are warranted to determine whether increased RS fat causes or contributes to poor control of hypertension in humans.
RS fat may exert influence on hypertension and CV risk through one of several mechanisms. Compression of blood vessels, lymph vessels, and ureters in the RS (sinus lipomatosis)7 may obstruct the renal outflow tract and increase intrarenal hydrostatic pressure.24 As a result, kidney size may increase. This observation has been shown previously in animal studies,25 in which obese rabbits exhibited larger kidneys (30%) with larger fat deposits within the RS. This occurred because of renal lymphatic compression, despite an absence of detectable fat accumulation within the renal parenchyma.25 Our study results demonstrated an association between RS fat and kidney size after accounting for age, sex, and body size (Tables 3 and 4⇑).
Although the results of the study did demonstrate an association of RS fat with serum creatinine (Table 3), they did not demonstrate an association of RS fat with estimated creatinine clearance. This latter observation may be related to the fact that we did not perform power analyses to determine a sample size necessary to identify differences in creatinine clearance in this study population. Importantly however, the results of this study, along with those from previous studies in animals, suggest that further studies are warranted to determine mechanisms by which RS fat could promote hypertension. Our study has limitations. First, our results are associative and observational; therefore, we cannot establish causal relationships between RS fat and hypertension. In addition, our association may be related to unrecognized potentially confounding variables that are present in our study population. To account for this possibility, we performed multivariable regression analyses that accounted for factors known to influence hypertension, as well as CV risk (eg, age, sex, BMI, and height). Our association between RS fat and both CV risk factors and the number of medications used to treat high blood pressure persisted. Second, our study population was composed primarily of whites. Larger studies are needed to determine whether the observations in this study are present in individuals of various races and ethnicities. Third, many of our variables exhibited a skewed distribution. Accordingly, we provided the median and interquartile range (Table 1) and both parametric and nonparametric statistical tests where appropriate. Fourth, many of our study participants received antihypertensive medications, and we did not stop these medications to determine blood pressure after an extended washout period. Although measures of medications reflect absolute blood pressure, this was not possible to accomplish in our cohort, because many participants had coronary artery disease and were at risk for CV events. However, we were able to demonstrate important associations between RS fat and the number of medications needed to control blood pressure, as well as direct measures of BP on receipt of antihypertensive therapy (Figure 2). Fifth, we used a single abdominal section at the level of second lumbar vertebra to measure various compartments of fat. Although previous studies have correlated fat volumes in a single section with total abdominal fat,11,12 the studies measuring total fat in the abdomen may provide more detailed assessments of different subgroups of fat, including RS fat. Sixth, we did not directly assess activity of RAAS or measure laboratory values associated with insulin resistance. Future studies should be directed toward direct assessment of the functionality of the RAAS and its association with RS fat.
In conclusion, our results demonstrate an association between RS fat accumulation and metrics of hypertension control, including the number of medications needed to treat hypertension and blood pressure after receipt of these medications. These associations are independent of body size, age, sex, and, importantly, IP or visceral fat. These observations, combined with data from previous animal studies, suggest that further studies are warranted to investigate whether a causal relationship exists between RS fat accumulation and hypertension in middle-aged and elderly individuals at risk for CV events.
This article demonstrates an association between fat accumulation in the RS and the number of medications necessary to treat hypertension, as well as SBP, after receipt of antihypertensive therapy. In animals, RS fat plays an important role in the pathophysiology of resistant hypertension. By compressing low-pressure structures in the renal hilum (veins and lymphatics), RS fat increases intracapsular pressure, which leads to enhanced activation of the renin-angiotensin system that can promote resistant hypertension despite receipt of multiple antihypertensive medications. The results of this and other studies in animals indicate that research is needed to determine whether RS fat promotes resistant hypertension.
We appreciate the assistance of Deanna Carr and Dr Paul Biggers, who helped with the preparation of this article.
Sources of Funding
Research was supported in part by the following grants from the National Institutes of Health: RO1HL076438, R33CA1219601, P30AG21332, and MO1-RR07122.
- Received June 1, 2010.
- Revision received June 24, 2010.
- Accepted August 18, 2010.
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