Skip to main content

Main menu

  • Home
  • Content
    • Published Ahead of Print
    • Current Issue
    • Podcasts
    • Subject Collections
    • Archives
    • ASN Meeting Abstracts
    • Saved Searches
  • Authors
    • Submit a Manuscript
    • Author Resources
    • Reprint Information
  • Trainees
    • Peer Review Program
    • Prize Competition
  • About CJASN
    • About CJASN
    • Editorial Team
    • CJASN Impact
    • CJASN Recognitions
  • More
    • Alerts
    • Advertising
    • Reprint Information
    • Subscriptions
    • Feedback
  • ASN Kidney News
  • Other
    • JASN
    • Kidney360
    • Kidney News Online
    • American Society of Nephrology

User menu

  • Subscribe
  • My alerts
  • Log in
  • My Cart

Search

  • Advanced search
American Society of Nephrology
  • Other
    • JASN
    • Kidney360
    • Kidney News Online
    • American Society of Nephrology
  • Subscribe
  • My alerts
  • Log in
  • My Cart
Advertisement
American Society of Nephrology

Advanced Search

  • Home
  • Content
    • Published Ahead of Print
    • Current Issue
    • Podcasts
    • Subject Collections
    • Archives
    • ASN Meeting Abstracts
    • Saved Searches
  • Authors
    • Submit a Manuscript
    • Author Resources
    • Reprint Information
  • Trainees
    • Peer Review Program
    • Prize Competition
  • About CJASN
    • About CJASN
    • Editorial Team
    • CJASN Impact
    • CJASN Recognitions
  • More
    • Alerts
    • Advertising
    • Reprint Information
    • Subscriptions
    • Feedback
  • ASN Kidney News
  • Visit ASN on Facebook
  • Follow CJASN on Twitter
  • CJASN RSS
  • Community Forum
Original ArticlesEpidemiology and Outcomes
You have accessRestricted Access

Adiposity Patterns and the Risk for ESRD in Postmenopausal Women

Nora Franceschini, Natalia A. Gouskova, Alex P. Reiner, Andrew Bostom, Barbara V. Howard, Mary Pettinger, Jason G. Umans, M. Alan Brookhart, Wolfgang C. Winkelmayer, Charles B. Eaton, Gerardo Heiss and Jason P. Fine
CJASN February 2015, 10 (2) 241-250; DOI: https://doi.org/10.2215/CJN.02860314
Nora Franceschini
Departments of *Epidemiology and
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Natalia A. Gouskova
†Biostatistics, University of North Carolina, Chapel Hill, North Carolina;
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Alex P. Reiner
‡Department of Epidemiology, School of Public Health, University of Washington, Seattle, Washington;
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Andrew Bostom
§Department of Family Medicine and Epidemiology, Alpert Medical School of Brown University, Providence, Rhode Island;
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Barbara V. Howard
‖MedStar Health Research Institute, Hyattsville, Maryland;
¶Center for Clinical and Translational Sciences and Department of Medicine, Georgetown-Howard Universities, Washington, DC;
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Mary Pettinger
**Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, Washington; and
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Jason G. Umans
‖MedStar Health Research Institute, Hyattsville, Maryland;
¶Center for Clinical and Translational Sciences and Department of Medicine, Georgetown-Howard Universities, Washington, DC;
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
M. Alan Brookhart
Departments of *Epidemiology and
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Wolfgang C. Winkelmayer
††Section of Nephrology, Baylor College of Medicine, Houston, Texas
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Charles B. Eaton
§Department of Family Medicine and Epidemiology, Alpert Medical School of Brown University, Providence, Rhode Island;
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Gerardo Heiss
Departments of *Epidemiology and
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Jason P. Fine
†Biostatistics, University of North Carolina, Chapel Hill, North Carolina;
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • Article
  • Figures & Data Supps
  • Info & Metrics
  • View PDF
Loading

Abstract

Background and objectives Body mass index and waist circumference associate with adverse health outcomes, including CKD. Studies of the association of body mass index and ESRD have been inconsistent; these adiposity measures have not been previously assessed together for ESRD risk or among postmenopausal women.

Design, settings, participants, & measurements This was prospective cohort study of 20,117 postmenopausal women enrolled in the multiethnic cohort of the Women’s Health Initiative. Body mass index and waist circumference were obtained at baseline, incident ESRD was obtained from the US Renal Data System, and all-cause death was obtained from surveillance data. A competing-risk framework was used to account for the effect of mortality before ESRD while adjusting for significant predictors and baseline kidney function. Associations of adiposity with mortality were also studied.

Results Events included 212 patients with incident ESRD and 3104 deaths for a mean follow-up of 11.6 years. Increased waist circumference and body mass index were associated with 2.59- (95% confidence interval, 1.89 to 3.53) and 1.97-fold (95% confidence interval, 1.30 to 2.98) higher hazards of ESRD as well as 1.42- (95% confidence interval, 1.32 to 1.53) and 1.21-fold (95% confidence interval, 1.11 to 1.33) higher hazards of death, respectively, compared with the lower categories in adjusted analyses. The associations of waist circumference with ESRD varied by baseline renal function (P for interaction=0.01) and were significant only among women without baseline eGFR-defined CKD (hazard ratio, 1.93; 95% confidence interval, 1.23 to 3.03).

Conclusions Central obesity was associated with an increased risk of ESRD in postmenopausal women, even among women with normal body mass index but not among women with reduced baseline kidney function, and an increased risk of death. Body mass index was associated with ESRD, and the association is likely mediated through hypertension and diabetes.

  • chronic renal failure
  • clinical epidemiology
  • epidemiology and outcomes
  • ESRD
  • risk factors

Introduction

Obesity is associated with multiple adverse health outcomes, including CKD (1–3). CKD affects approximately 14.5% of the adult United States population, with a high burden occurring among aging individuals and racial/ethnic minorities (4), including blacks (5) and Hispanics (6). CKD is associated with premature cardiovascular disease (CVD) (7) and death (4), decreased quality of life (8), and progression to ESRD. Treatment of ESRD and its complications accounts for a large percentage of United States health care use and costs (9). Older individuals now have the highest rates of both early-stage CKD and new ESRD in the United States (10) along with increased CKD-related comorbidities. CKD remains understudied in aging populations. Postmenopausal women have increased prevalence of comorbidities, including obesity (11), that are associated with both hypertension and diabetes, the most common attributable causes of CKD. Menopause transition is associated with an increased intra-abdominal adiposity independent of age and total adiposity (11), and adiposity patterns may associate with CKD. An important clinical question relevant to the care of postmenopausal women is the role of obesity (central and overall) beyond other risk factors in development and progression of CKD, because increased adiposity is amenable to lifestyle interventions. In addition, assessment of adiposity before development of ESRD is needed to better understand the reverse epidemiology observed among individuals with chronic conditions including ESRD, in whom an increased body mass index (BMI) is associated with reduced mortality (12,13).

Adipocytes have been shown to be metabolically active and are associated with inflammation, oxidative stress, and endothelial dysfunction (14). Suppression of cell autophagy has also been recently proposed as a mechanism for obesity-induced kidney dysfunction (15). Evidence from observational studies suggests that both overall and central obesity, assessed by waist circumference or waist-to-hip ratio, are associated with incident CKD in populations (16–22). However, the association between obesity and ESRD has been inconsistent (2,3), and assessment of central adiposity measures for ESRD risk has yet to be studied, particularly among postmenopausal women. A large study using health care data found positive associations between overweight and obesity with incident ESRD (2). Similar results were described in a large Chinese study (3) but not among United States veterans (23). Mechanisms related to the development of CKD and progression to ESRD are still poorly understood, especially the racial/ethnic differences in incident ESRD.

The main goal of this study was to examine the association of adiposity with ESRD in postmenopausal women. We used data from the Women’s Health Initiative (WHI) study, an ethnically diverse cohort of 161,808 postmenopausal women with extensive data on physiologic, behavioral, and clinical risk factors in addition to validated cardiovascular and renal outcomes. We assessed the risks of ESRD and death by categories of BMI and waist circumference in a sample of 20,117 African-American, white, and Hispanic WHI participants with available kidney function at baseline.

Materials and Methods

Study Sample and Population

The WHI is a prospective population-based cohort study investigating postmenopausal women’s health in the United States (24). In total, 161,808 women ages 50–79 years old were recruited from 40 United States clinical centers between 1993 and 1998 to participate in the observational study and several clinical trials: postmenopausal hormone therapy (estrogen alone or estrogen plus progestin), a calcium and vitamin D supplement trial, and a dietary modification trial of reduced total fat intake to 20% of calories and increased intake of vegetables/fruits (24). Recruitment was done through mass mailing to age-eligible women obtained from voter registration, driver’s license, and Health Care Financing Administration or other insurance lists, with emphasis on recruitment of minorities and older women (24). Exclusions included participation in other randomized trials, predicted survival <3 years, alcoholism, drug dependency, mental illness, and dementia. For the clinical trials, women were ineligible if they had systolic BP >200 mmHg or diastolic BP >105 mmHg, a history of hypertriglyceridemia, or endometrial cancer.

Demographic characteristics, socioeconomic data (education), lifestyle (smoking and alcohol consumption), medical history (history of hypertension, diabetes, CVD, and kidney dialysis treatment), and self-reported medications were collected using standardized questionnaires at the screening visit. Body height, weight, waist circumference, and BP were measured at a clinical visit as described previously (25). Fasting blood samples were obtained at the baseline clinic visit. A subset of women had serum creatinine measured using an enzymatic method that was traceable to an isotope dilution mass spectrometry reference creatinine standard (coefficient of variation=3.7%), enabling eGFR using the CKD Epidemiology Collaboration (CKD-EPI) equation. Results are reported in milligrams per deciliter. Study protocols and consents were approved by the institutional review boards at all participating institutions, and the research was conducted in adherence to the Declaration of Helsinki.

A subset of 20,117 women with serum creatinine measurements at baseline was included in analyses. Women were selected for biomarker characterization if they were participants in the Single Nucleotide Polymorphism (SNP) Health Association Resource project, a randomly selected subsample of 8515 (70.1%) black and 3642 (66.6%) Hispanic women, or participants in the hormone therapy clinical trials in a subsample that reflected the age distribution of the entire white women in these trials. Within the biomarker sample, we excluded women with BMI<18.5 kg/m2 and those with missing covariate data.

Exposures.

Waist measurement was obtained in the standing position to the nearest 0.5 cm over nonbinding undergarments at the level of the umbilicus. BMI was estimated using height and weight (kilograms per meter2) in women using light clothes. Categories of BMI and waist circumference were defined using National Heart, Lung, and Blood Institute intervals (25). We also examined the risk across quintiles of the exposure distribution.

Covariates.

Diabetes mellitus was defined by self-report of physician diagnosis or use of oral hypoglycemic medications or insulin. Hypertension was defined on the basis of BP≥140/90 mmHg or the use of antihypertensive drugs (26). Prevalent CVD was on the basis of an affirmative answer to the question of have you been hospitalized for a heart attack (myocardial infarction), coronary angioplasty or stent, coronary artery bypass graft surgery, or angina. We estimated GFR using the equation developed by Levey and Stevens (27) (CKD-EPI) on the basis of age, sex, race, and serum creatinine.

Outcome measures—Incident ESRD and Mortality.

Patients with ESRD were drawn from the US Renal Data System (USRDS), a national registry of all patients with ESRD in the United States (28). USRDS files were linked to the WHI cohort data using personal identifiers. Events as of June 30, 2010, are included in these analyses. Annual (observation study) and semiannual (clinical trials) follow-up identified events, including deaths, which were classified by an expert panel of physicians on the basis of review of hospital records, death certificates, and interviews with next of kin (29).

Statistical Analyses

For descriptive analyses of baseline data, means, SDs, and frequencies were measured. We assessed differences in baseline BMI categories or waist circumference on subsequent risk of ESRD and mortality before ESRD. Because an increase in mortality in older patients may indirectly lead to an apparent reduction in ESRD, we used a competing-risk framework (30). The primary end point was the cumulative incidence of ESRD and death before ESRD, which quantifies the risks of the events over time analogously to the Kaplan–Meier estimator for data without competing risks. Person-time at risk was defined as the time between baseline visit and the ESRD event, death, or the last date of follow-up up (June 30, 2010). Nonparametric estimates were obtained within BMI categories (normal [<25 kg/m2], overweight [25–29 kg/m2], and obese [30 kg/m2 or more]) and waist circumference groups (≤88 cm versus >88 cm). Nonparametric tests for differences in these cumulative incidences were assessed using Gray’s test (31) to account for competing causes. The Fine and Gray (32) proportional subdistribution hazards model for the cumulative incidence function was fitted to obtain hazard ratios (HRs), which summarize differences in these cumulative incidence functions. A secondary end point was the cause-specific hazard of the competing–risk end points (33), which provides complementary information into the patterns of failure in the cumulative incidence functions. Log-rank tests were performed for the statistical significance of differences across BMI and waist circumference groups. Regression modeling for the cause-specific hazards was performed using the standard Cox model with joint modeling of BMI and waist circumference considered as well as the effect of each as a continuous variable. HRs from the subdistribution hazard and the cause-specific hazard models were then compared.

We fitted three models. Model 1 adjusted for age and baseline eGFR. Model 2 adjusted additionally for race/ethnicity, education (<12 years versus ≥12 years), ever smoking, and indicator variables for participation in the observational versus clinical trials and geographic region. Model 3 adjusted additionally for history of diabetes mellitus, systolic and diastolic BPs, and use of antihypertensive drugs (including angiotensin-converting enzyme (ACE) inhibitors and/or angiotensin receptor blockers). We tested for interactions between adiposity measures and race/ethnicity and between adiposity measures and CKD, which was defined by an eGFR<60 ml/min per 1.73 m2. We also tested interactions between BMI and waist circumference on the risk of ESRD or death. Subdistribution, cause-specific HRs, and 95% confidence intervals (95% CIs) are reported. We used an α=0.05 in a two-sided test. All analyses were performed using STATA 11.0 (Stata Corp LP, College Station, TX).

Results

The mean age was comparable among the biomarker sample and the overall WHI cohort for women of the same ethnic/racial background (Table 1). The prevalence of African-American and Hispanic women was higher in the subsample compared with the WHI cohort. In addition, women in the biomarker sample had higher mean BMI and waist circumference and higher prevalence of diabetes, hypertension, and treated hypertension compared with those in the WHI cohort. None of the participants in the WHI reported being on dialysis at enrollment.

View this table:
  • View inline
  • View popup
Table 1.

Baseline characteristic of participants in the biomarker sample and the whole cohort: Women’s Health Initiative, 1993–1998

Six percent of women met criteria for (stages 3–5) CKD on the basis of eGFR (Table 1). We identified 212 ESRD events and 3104 deaths over a mean follow-up of 11.6 years (SD=3.0). Traditional ESRD predictors, including diabetes, hypertension, and baseline eGFR, were significantly associated with incident ESRD (P<0.05). Obese women as well as those with an increased waist circumference had higher cumulative incident ESRD compared with women in the lower categories (Figure 1). Obesity and increased waist circumference were associated with 2- and 2.6-fold increases in ESRD, respectively, when adjusting for age and baseline eGFR (model 1, Table 2). When accounting for other risk factors, estimates for BMI association with ESRD were attenuated but remained significant, except when adjusting for diabetes and hypertension-related variables (models 2 and 3, Table 2). Waist circumference was associated with ESRD in all models (Table 2). For comparison, we also show the estimates for incident ESRD without accounting for competing risks in Table 2 (cause-specific HR obtained from the conventional Cox proportional hazard models). The increased risk of ESRD was also observed across quintiles of the waist circumference distribution starting near the clinically defined threshold and vary by baseline eGFR (Figure 2). BMI and waist circumference were highly correlated in our study (coefficient=0.81; P<0.001). However, continuous waist circumference was independently and significantly associated with ESRD after additional adjustment for BMI in model 2 (HR, 1.02; 95% CI, 1.02 to 1.03).

Figure 1.
  • Download figure
  • Open in new tab
  • Download powerpoint
Figure 1.

Cumulative incidence function estimates and 95% confidence limits for ESRD by categories of (A) body mass index (BMI) and (B) waist circumference. The test by Gray (31) for equality of cumulative incidence curves among categories of BMI (kilograms per meter2) or waist circumference (centimeters) was significant (P<0.001 for both).

View this table:
  • View inline
  • View popup
Table 2.

Association of body mass index and waist circumference categories with ESRD and mortality

Figure 2.
  • Download figure
  • Open in new tab
  • Download powerpoint
Figure 2.

Age-adjusted hazard ratio and 95% confidence intervals of associations with ESRD by quintiles of waist circumference for (A) the biomarker sample, (B) women without eGFR-defined CKD, and (C) women with CKD. Numbers on the x axis are the quintiles and below the mean values for each quintile. 95% CI, 95% confidence interval.

We then tested interactions of race/ethnicity with BMI or waist circumference on the risk of ESRD, which were not significant (P=0.21 and P=0.51, respectively). There were also no significant interactions among BMI and waist circumference categories on the risk of ESRD (P=0.55). Because decreased eGFR is a strong predictor of ESRD, we also tested its interactions with BMI and waist circumference on the risk of ESRD; this was significant only for waist circumference (P=0.01). In a stratified analysis, waist circumference higher was significantly associated with incident ESRD among women with an eGFR≥60 ml/min per 1.73 m2 (HR, 1.93; 95% CI, 1.23 to 3.03; n=18,963; P<0.01) but not among women with reduced eGFR, for whom the point estimate was close to 1 (HR, 0.96; 95% CI, 0.57 to 1.60; n=1154; P=0.87).

In analysis of the mortality outcome, women with increased waist circumference had a 42% higher risk of death compared with those in the reference category (model 2, Table 2). Obesity was associated with mortality in models adjusted for age, baseline eGFR, and other risk factors but not models adjusted for diabetes and hypertension (Table 2). Because mortality can vary by race/ethnicity, we also tested the interactions of BMI or waist circumference with race, which were of borderline significance only for waist circumference (P<0.10). Stratified analysis by race/ethnicity showed a stronger risk of death for an increased waist circumference among Hispanic women followed by African Americans and whites (Table 3). Although the interaction by race was not significant, obesity was significantly associated with mortality in Hispanic women only in fully adjusted models.

View this table:
  • View inline
  • View popup
Table 3.

Hazard ratios and 95% confidence intervals of mortality by race/ethnicity subgroups and adiposity categories

Discussion

Obesity has reached epidemic proportions and is associated with metabolic syndrome, diabetes, CVD complications, and short life expectancy. Our study showed strong associations of waist circumference in addition to BMI categories with ESRD when adjusting for multiple confounders, baseline kidney function, and competing risk of death. Notably, we have shown a 2.6-fold higher hazard of incident ESRD and 42% higher hazard of death for women with increased compared with normal waist circumference. ESRD risk increased in women with waist circumference above the clinically defined threshold; this increased risk was independent of BMI, and it was stronger among women with preserved eGFR and not significant in women with low eGFR.

Cross-sectional and longitudinal studies have shown associations of obesity measures (principally BMI) with CKD and ESRD (2,3,20,22,34,35). Few population studies, however, have tested the association of central obesity with incident ESRD, and none have focused in women. A prospective population-based study from Iran reported an association of waist circumference rather than BMI with the development of CKD, which was defined by an eGFR<60 ml/min per 1.73 m2 (20). A study using data from white and African-American participants of the Atherosclerosis Risk in Communities and Cardiovascular Health Study also showed significant associations of waist circumference but not BMI with CKD outcomes (defined as an increase of 0.4 mg serum creatinine or decrease in eGFR of at least 15 ml/min per 1.73 m2 to a final value below 60 ml/min per 1.73m2) (36). This study did not examine sex-specific associations. A case-control study of moderately severe CKD, which used serial BMI calculated using self-reported weight at ages 20, 40, and 60 years old, showed significant associations of obesity and overweight at earlier ages with subsequent CKD but no association of current BMI with CKD (37). Our findings were similar to these findings but provide important new data supporting increased waist circumference as a risk factor for progression to ESRD. Our study showed strong associations of waist circumference in addition to BMI categories with ESRD when adjusting for multiple confounders, baseline kidney function, and competing risk of death. Our findings expand the current knowledge on adiposity risk factors for ESRD among postmenopausal women, who have increased intra-abdominal obesity compared with premenopausal women (11), and across race/ethnicity by studying a multiethnic cohort of white, African-American, and Hispanic individuals. They suggest that adiposity distribution is an important clinical measure to be assessed in healthy postmenopausal women and that it should be targeted in prevention and lifestyle interventions.

Waist circumference is a commonly used surrogate of central adiposity in epidemiologic studies, and its assessment is currently recommended only for adults with a BMI of 25.0–34.9 kg/m2 by clinical expert panels (38). Our study did not find significant interactions of waist circumference and BMI, suggesting similar effects among women with normal and increased BMI. Waist circumference is better correlated with visceral fat and simpler to interpret than other measures of central adiposity, such as waist-to-hip ratio (39–41). Prior studies have shown a role of central obesity in cardiovascular and metabolic diseases, including type 2 diabetes, coronary heart disease, and stroke (42–47). Interestingly, we did not see differences in the association of adiposity measures with ESRD by race/ethnicity, although there are known differences in obesity prevalence, fat distribution, and cardiometabolic risk among some racial/ethnic subgroups.

Waist circumference has also been associated with mobility disabilities and death (48,49). In the European Prospective Investigation into Cancer and Nutrition, both BMI and waist circumference were associated with increased risk of death (48). A recent analysis of postmenopausal WHI participants also showed an increase in mortality before 85 years among women with waist circumference >88 cm (49). A BMI of ≥30 kg/m2 was also associated with mortality in our data, even when accounting for effects on potential mediators, but only among obese Hispanic women. We noted a nonsignificant protective effect among overweight women on mortality across race/ethnicity. This reverse epidemiology has been reported previously for BMI and mortality among individuals with chronic conditions, such as CKD (12) and heart failure (13), and its mechanisms are still not well understood. Our study, however, is composed of postmenopausal women with higher socioeconomic status compared with the general population, and the WHI study has excluded women with severe hypertension, hyperlipidemia, cancer, drug addiction, and expected poor survival.

The prevalence of CKD on the basis of eGFR was low in our study (<6%) among women for whom serum creatinine was available, a sample that overrepresented racial/ethnic minorities, and women with comorbidities compared with the overall WHI sample. After adjustments for BMI, comorbidities, and other risk factors, waist circumference was still significantly associated with both ESRD and mortality, although the estimates were attenuated by these adjustments. These findings suggest that some risk factors (e.g., diabetes and hypertension) may mediate these associations. Potential mediation may also explain the lack of association of BMI with incident ESRD in the fully adjusted models.

A recent analysis of the Reasons for Geographic and Racial Differences in Stroke (REGARDS) study also showed an attenuation of the association of metabolic risk factors, including BMI, with CKD when adjusting for baseline eGFR and albuminuria (50). The REGARDS study was restricted to individuals with an eGFR<60 ml/min per 1.73 m2, which is in contrast with the low prevalence of eGFR-defined CKD in our study. We identified a significant interaction of waist circumference and eGFR. The increased ESRD risk attributable to a large waist circumference was significant only among women without reduced kidney function. We could not account for albuminuria in our analysis, because the WHI study lacks these data. Universal screening for CKD (albuminuria and kidney function) in postmenopausal women is currently not recommended by clinical guidelines, except for individuals with hypertension and diabetes. However, BMI is routinely evaluated at primary care visits.

Our study suggests that assessment of central obesity among postmenopausal women with normal kidney function may contribute additional important clinical information to estimation of ESRD risk. The absolute risk of ESRD among women with low prevalence of eGFR-defined CKD was 1.5% for women with an increased waist circumference compared with 0.6% for women with normal waist circumference. Waist circumference is a simple, easy to measure, low-cost, and feasible measure to implement in clinical care with potential added prognostic value for ESRD risk. Although interventions to reduce central adiposity are difficult to implement in clinical practice, increased awareness and prevention of central obesity in postmenopausal women could be important public health targets to reduce ESRD risk in aging populations.

Strengths of our study are the large sample size of postmenopausal women, the multiethnic cohort, the standardized measures of adiposity and other risk factors, the prospective data, and the large number of ESRD events. Some of the limitations of our study are that the waist circumference cannot distinguish between subcutaneous and intra-abdominal fat and that we relied on a single measure of adiposity obtained at screening visit; future research accounting for temporal changes of BMI and waist circumference is needed. Our findings cannot be generalized to men or premenopausal women who were not included in the WHI cohort. Power to detect interactions may be limited in our study. In addition, women with low BMI were excluded because of the small number of events and sample size.

In summary, our study has shown an important association of waist circumference, a measure of fat distribution, with the risk of ESRD among postmenopausal women, which was stronger among those women with preserved baseline eGFR and racial/ethnic minorities. Increased waist circumference was also associated with death, particularly among Hispanic women, suggesting that central obesity measures may have a clinical role in efforts to prevent and treat obesity-related CKD and its complications. Our overall findings suggest that accounting for waist circumference will be important in future studies of adiposity and CVD outcomes and mortality.

Disclosures

None.

Acknowledgments

The Women’s Health Initiative program is funded by National Heart, Lung, and Blood Institute (NHLBI), National Institutes of Health (NIH), US Department of Health and Human Services Contracts HHSN268201100046C, HHSN268201100001C, HHSN268201100002C, HHSN268201100003C, HHSN268201100004C, and HHSN271201100004C. Funding for the linkage to the US Renal Data System was provided by NIH Contract HHSN268201100004C NHLBI Control Number N01-WH-04354 (to N.F.). N.F. is supported by R21HL123677-01.

Footnotes

  • Published online ahead of print. Publication date available at www.cjasn.org.

  • Received March 19, 2014.
  • Accepted October 20, 2014.
  • Copyright © 2015 by the American Society of Nephrology

References

  1. ↵
    1. Tanner RM,
    2. Brown TM,
    3. Muntner P
    : Epidemiology of obesity, the metabolic syndrome, and chronic kidney disease. Curr Hypertens Rep 14: 152–159, 2012pmid:22318504
    OpenUrlCrossRefPubMed
  2. ↵
    1. Hsu CY,
    2. McCulloch CE,
    3. Iribarren C,
    4. Darbinian J,
    5. Go AS
    : Body mass index and risk for end-stage renal disease. Ann Intern Med 144: 21–28, 2006pmid:16389251
    OpenUrlCrossRefPubMed
  3. ↵
    1. Reynolds K,
    2. Gu D,
    3. Muntner P,
    4. Chen J,
    5. Wu X,
    6. Yau CL,
    7. Duan X,
    8. Chen CS,
    9. Hamm LL,
    10. He J
    : Body mass index and risk of ESRD in China. Am J Kidney Dis 50: 754–764, 2007pmid:17954288
    OpenUrlCrossRefPubMed
  4. ↵
    1. Everson S,
    2. Eggers P,
    3. Agodoa L
    1. Collins AJ,
    2. Foley RN,
    3. Chavers B,
    4. Gilbertson D,
    5. Herzog C,
    6. Johansen K,
    7. Kasiske B,
    8. Kutner N,
    9. Liu J,
    10. St Peter W,
    11. Guo H,
    12. Gustafson S,
    13. Heubner B,
    14. Lamb K,
    15. Li S,
    16. Peng Y,
    17. Qiu Y,
    18. Roberts T,
    19. Skeans M,
    20. Snyder J,
    21. Solid C,
    22. Thompson B,
    23. Wang C,
    24. Weinhandl E,
    25. Zaun D,
    26. Arko C,
    27. Chen SC,
    28. Daniels F,
    29. Ebben J,
    30. Frazier E,
    31. Hanzlik C,
    32. Johnson R,
    33. Sheets D,
    34. Wang X,
    35. Forrest B,
    36. Constantini E
    ,Everson S, Eggers P, Agodoa L: United States Renal Data System 2011 Annual Data Report: Atlas of chronic kidney disease & end-stage renal disease in the United States. Am J Kidney Dis 59[1 Suppl 1]: e1–e420, 2012
    OpenUrlCrossRefPubMed
  5. ↵
    1. Xue JL,
    2. Eggers PW,
    3. Agodoa LY,
    4. Foley RN,
    5. Collins AJ
    : Longitudinal study of racial and ethnic differences in developing end-stage renal disease among aged medicare beneficiaries. J Am Soc Nephrol 18: 1299–1306, 2007pmid:17329578
    OpenUrlAbstract/FREE Full Text
  6. ↵
    1. Peralta CA,
    2. Shlipak MG,
    3. Fan D,
    4. Ordoñez J,
    5. Lash JP,
    6. Chertow GM,
    7. Go AS
    : Risks for end-stage renal disease, cardiovascular events, and death in Hispanic versus non-Hispanic white adults with chronic kidney disease. J Am Soc Nephrol 17: 2892–2899, 2006pmid:16959827
    OpenUrlAbstract/FREE Full Text
  7. ↵
    1. Sarnak MJ
    : Cardiovascular complications in chronic kidney disease. Am J Kidney Dis 41[Suppl]: 11–17, 2003pmid:12776309
    OpenUrlCrossRefPubMed
  8. ↵
    1. Shidler NR,
    2. Peterson RA,
    3. Kimmel PL
    : Quality of life and psychosocial relationships in patients with chronic renal insufficiency. Am J Kidney Dis 32: 557–566, 1998pmid:9774115
    OpenUrlCrossRefPubMed
  9. ↵
    1. Hunsicker LG
    : The consequences and costs of chronic kidney disease before ESRD. J Am Soc Nephrol 15: 1363–1364, 2004pmid:15100382
    OpenUrlFREE Full Text
  10. ↵
    1. Kurella M,
    2. Covinsky KE,
    3. Collins AJ,
    4. Chertow GM
    : Octogenarians and nonagenarians starting dialysis in the United States. Ann Intern Med 146: 177–183, 2007pmid:17283348
    OpenUrlCrossRefPubMed
  11. ↵
    1. Toth MJ,
    2. Tchernof A,
    3. Sites CK,
    4. Poehlman ET
    : Menopause-related changes in body fat distribution. Ann N Y Acad Sci 904: 502–506, 2000pmid:10865795
    OpenUrlPubMed
  12. ↵
    1. Port FK,
    2. Ashby VB,
    3. Dhingra RK,
    4. Roys EC,
    5. Wolfe RA
    : Dialysis dose and body mass index are strongly associated with survival in hemodialysis patients. J Am Soc Nephrol 13: 1061–1066, 2002pmid:11912267
    OpenUrlAbstract/FREE Full Text
  13. ↵
    1. Curtis JP,
    2. Selter JG,
    3. Wang Y,
    4. Rathore SS,
    5. Jovin IS,
    6. Jadbabaie F,
    7. Kosiborod M,
    8. Portnay EL,
    9. Sokol SI,
    10. Bader F,
    11. Krumholz HM
    : The obesity paradox: Body mass index and outcomes in patients with heart failure. Arch Intern Med 165: 55–61, 2005pmid:15642875
    OpenUrlCrossRefPubMed
  14. ↵
    1. Rüster C,
    2. Wolf G
    : Adipokines promote chronic kidney disease. Nephrol Dial Transplant 28[Suppl 4]: iv8–iv14, 2013pmid:24179016
    OpenUrlCrossRefPubMed
  15. ↵
    1. Yamahara K,
    2. Kume S,
    3. Koya D,
    4. Tanaka Y,
    5. Morita Y,
    6. Chin-Kanasaki M,
    7. Araki H,
    8. Isshiki K,
    9. Araki S,
    10. Haneda M,
    11. Matsusaka T,
    12. Kashiwagi A,
    13. Maegawa H,
    14. Uzu T
    : Obesity-mediated autophagy insufficiency exacerbates proteinuria-induced tubulointerstitial lesions. J Am Soc Nephrol 24: 1769–1781, 2013pmid:24092929
    OpenUrlAbstract/FREE Full Text
  16. ↵
    1. Fox CS,
    2. Larson MG,
    3. Leip EP,
    4. Culleton B,
    5. Wilson PW,
    6. Levy D
    : Predictors of new-onset kidney disease in a community-based population. JAMA 291: 844–850, 2004pmid:14970063
    OpenUrlCrossRefPubMed
    1. Munkhaugen J,
    2. Lydersen S,
    3. Widerøe TE,
    4. Hallan S
    : Prehypertension, obesity, and risk of kidney disease: 20-year follow-up of the HUNT I study in Norway. Am J Kidney Dis 54: 638–646, 2009pmid:19515474
    OpenUrlCrossRefPubMed
    1. Muntner P,
    2. Winston J,
    3. Uribarri J,
    4. Mann D,
    5. Fox CS
    : Overweight, obesity, and elevated serum cystatin C levels in adults in the United States. Am J Med 121: 341–348, 2008pmid:18374694
    OpenUrlCrossRefPubMed
    1. Knight EL,
    2. Verhave JC,
    3. Spiegelman D,
    4. Hillege HL,
    5. de Zeeuw D,
    6. Curhan GC,
    7. de Jong PE
    : Factors influencing serum cystatin C levels other than renal function and the impact on renal function measurement. Kidney Int 65: 1416–1421, 2004pmid:15086483
    OpenUrlCrossRefPubMed
  17. ↵
    1. Noori N,
    2. Hosseinpanah F,
    3. Nasiri AA,
    4. Azizi F
    : Comparison of overall obesity and abdominal adiposity in predicting chronic kidney disease incidence among adults. J Ren Nutr 19: 228–237, 2009pmid:19261489
    OpenUrlCrossRefPubMed
    1. de Boer IH,
    2. Sibley SD,
    3. Kestenbaum B,
    4. Sampson JN,
    5. Young B,
    6. Cleary PA,
    7. Steffes MW,
    8. Weiss NS,
    9. Brunzell JD,
    10. Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications Study Research Group
    : Central obesity, incident microalbuminuria, and change in creatinine clearance in the epidemiology of diabetes interventions and complications study. J Am Soc Nephrol 18: 235–243, 2007pmid:17151331
    OpenUrlAbstract/FREE Full Text
  18. ↵
    1. Thomas G,
    2. Sehgal AR,
    3. Kashyap SR,
    4. Srinivas TR,
    5. Kirwan JP,
    6. Navaneethan SD
    : Metabolic syndrome and kidney disease: A systematic review and meta-analysis. Clin J Am Soc Nephrol 6: 2364–2373, 2011pmid:21852664
    OpenUrlAbstract/FREE Full Text
  19. ↵
    1. Perry HM Jr..,
    2. Miller JP,
    3. Fornoff JR,
    4. Baty JD,
    5. Sambhi MP,
    6. Rutan G,
    7. Moskowitz DW,
    8. Carmody SE
    : Early predictors of 15-year end-stage renal disease in hypertensive patients. Hypertension 25: 587–594, 1995pmid:7721402
    OpenUrlAbstract/FREE Full Text
  20. ↵
    1. The Women’s Health Initiative Study Group
    : Design of the Women’s Health Initiative clinical trial and observational study. Control Clin Trials 19: 61–109, 1998pmid:9492970
    OpenUrlCrossRefPubMed
  21. ↵
    National Institutes of Health: Clinical Guidelines on the Identification, Evaluation and Treatment of Overweight and Obesity in Adults: The Evidence Report, NIH Publication No. 98-4083, Bethesda, MD, National Institutes of Health, 1998
  22. ↵
    1. Chobanian AV,
    2. Bakris GL,
    3. Black HR,
    4. Cushman WC,
    5. Green LA,
    6. Izzo JL Jr..,
    7. Jones DW,
    8. Materson BJ,
    9. Oparil S,
    10. Wright JT Jr..,
    11. Roccella EJ,
    12. Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure. National Heart, Lung, and Blood Institute,
    13. National High Blood Pressure Education Program Coordinating Committee
    : Seventh report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure. Hypertension 42: 1206–1252, 2003pmid:14656957
    OpenUrlAbstract/FREE Full Text
  23. ↵
    1. Levey AS,
    2. Stevens LA
    : Estimating GFR using the CKD Epidemiology Collaboration (CKD-EPI) creatinine equation: More accurate GFR estimates, lower CKD prevalence estimates, and better risk predictions. Am J Kidney Dis 55: 622–627, 2010pmid:20338463
    OpenUrlCrossRefPubMed
  24. ↵
    1. USRDS
    : The United States Renal Data System. Am J Kidney Dis 42[Suppl 5]: 1–230, 2003pmid:14655174
    OpenUrlPubMed
  25. ↵
    1. Curb JD,
    2. McTiernan A,
    3. Heckbert SR,
    4. Kooperberg C,
    5. Stanford J,
    6. Nevitt M,
    7. Johnson KC,
    8. Proulx-Burns L,
    9. Pastore L,
    10. Criqui M,
    11. Daugherty S,
    12. WHI Morbidity and Mortality Committee
    : Outcomes ascertainment and adjudication methods in the Women’s Health Initiative. Ann Epidemiol 13[Suppl]: S122–S128, 2003pmid:14575944
    OpenUrlCrossRefPubMed
  26. ↵
    1. Pepe MS,
    2. Mori M
    : Kaplan-Meier, marginal or conditional probability curves in summarizing competing risks failure time data? Stat Med 12: 737–751, 1993pmid:8516591
    OpenUrlCrossRefPubMed
  27. ↵
    1. Gray RJ
    : A class of K-sample tests for comparing the cumulative incidence of a competing risks. Ann Stat 16: 1141–1154, 1988
    OpenUrlCrossRef
  28. ↵
    1. Fine JP,
    2. Gray RJ
    : A proportional hazards model for the subdistribution of a competing risk. J Am Stat Assoc 446: 496–509, 1999
    OpenUrl
  29. ↵
    1. Prentice RL,
    2. Kalbfleisch JD,
    3. Peterson AV Jr..,
    4. Flournoy N,
    5. Farewell VT,
    6. Breslow NE
    : The analysis of failure times in the presence of competing risks. Biometrics 34: 541–554, 1978pmid:373811
    OpenUrlCrossRefPubMed
  30. ↵
    1. Bash LD,
    2. Astor BC,
    3. Coresh J
    : Risk of incident ESRD: A comprehensive look at cardiovascular risk factors and 17 years of follow-up in the Atherosclerosis Risk in Communities (ARIC) Study. Am J Kidney Dis 55: 31–41, 2010pmid:19932544
    OpenUrlCrossRefPubMed
  31. ↵
    1. Hsu CY,
    2. Iribarren C,
    3. McCulloch CE,
    4. Darbinian J,
    5. Go AS
    : Risk factors for end-stage renal disease: 25-year follow-up. Arch Intern Med 169: 342–350, 2009pmid:19237717
    OpenUrlCrossRefPubMed
  32. ↵
    1. Elsayed EF,
    2. Sarnak MJ,
    3. Tighiouart H,
    4. Griffith JL,
    5. Kurth T,
    6. Salem DN,
    7. Levey AS,
    8. Weiner DE
    : Waist-to-hip ratio, body mass index, and subsequent kidney disease and death. Am J Kidney Dis 52: 29–38, 2008pmid:18511168
    OpenUrlCrossRefPubMed
  33. ↵
    1. Ejerblad E,
    2. Fored CM,
    3. Lindblad P,
    4. Fryzek J,
    5. McLaughlin JK,
    6. Nyrén O
    : Obesity and risk for chronic renal failure. J Am Soc Nephrol 17: 1695–1702, 2006pmid:16641153
    OpenUrlAbstract/FREE Full Text
  34. ↵
    1. Expert Panel on the Identification, Evaluation, and Treatment of Overweight in Adults
    : Clinical guidelines on the identification, evaluation, and treatment of overweight and obesity in adults: Executive summary. Expert Panel on the Identification, Evaluation, and Treatment of Overweight in Adults. Am J Clin Nutr 68: 899–917, 1998pmid:9771869
    OpenUrlCrossRefPubMed
  35. ↵
    1. Molarius A,
    2. Seidell JC
    : Selection of anthropometric indicators for classification of abdominal fatness—a critical review. Int J Obes Relat Metab Disord 22: 719–727, 1998pmid:9725630
    OpenUrlCrossRefPubMed
    1. Pouliot MC,
    2. Després JP,
    3. Lemieux S,
    4. Moorjani S,
    5. Bouchard C,
    6. Tremblay A,
    7. Nadeau A,
    8. Lupien PJ
    : Waist circumference and abdominal sagittal diameter: Best simple anthropometric indexes of abdominal visceral adipose tissue accumulation and related cardiovascular risk in men and women. Am J Cardiol 73: 460–468, 1994pmid:8141087
    OpenUrlCrossRefPubMed
  36. ↵
    1. Després JP,
    2. Prud’homme D,
    3. Pouliot MC,
    4. Tremblay A,
    5. Bouchard C
    : Estimation of deep abdominal adipose-tissue accumulation from simple anthropometric measurements in men. Am J Clin Nutr 54: 471–477, 1991pmid:1877502
    OpenUrlAbstract/FREE Full Text
  37. ↵
    1. Nicklas BJ,
    2. Cesari M,
    3. Penninx BW,
    4. Kritchevsky SB,
    5. Ding J,
    6. Newman A,
    7. Kitzman DW,
    8. Kanaya AM,
    9. Pahor M,
    10. Harris TB
    : Abdominal obesity is an independent risk factor for chronic heart failure in older people. J Am Geriatr Soc 54: 413–420, 2006pmid:16551307
    OpenUrlCrossRefPubMed
    1. Loehr LR,
    2. Rosamond WD,
    3. Poole C,
    4. McNeill AM,
    5. Chang PP,
    6. Folsom AR,
    7. Chambless LE,
    8. Heiss G
    : Association of multiple anthropometrics of overweight and obesity with incident heart failure: The Atherosclerosis Risk in Communities study. Circ Heart Fail 2: 18–24, 2009pmid:19808311
    OpenUrlAbstract/FREE Full Text
    1. Taylor AE,
    2. Ebrahim S,
    3. Ben-Shlomo Y,
    4. Martin RM,
    5. Whincup PH,
    6. Yarnell JW,
    7. Wannamethee SG,
    8. Lawlor DA
    : Comparison of the associations of body mass index and measures of central adiposity and fat mass with coronary heart disease, diabetes, and all-cause mortality: A study using data from 4 UK cohorts. Am J Clin Nutr 91: 547–556, 2010pmid:20089729
    OpenUrlAbstract/FREE Full Text
    1. Vazquez G,
    2. Duval S,
    3. Jacobs DR Jr..,
    4. Silventoinen K
    : Comparison of body mass index, waist circumference, and waist/hip ratio in predicting incident diabetes: A meta-analysis. Epidemiol Rev 29: 115–128, 2007pmid:17494056
    OpenUrlCrossRefPubMed
    1. Canoy D,
    2. Boekholdt SM,
    3. Wareham N,
    4. Luben R,
    5. Welch A,
    6. Bingham S,
    7. Buchan I,
    8. Day N,
    9. Khaw KT
    : Body fat distribution and risk of coronary heart disease in men and women in the European Prospective Investigation Into Cancer and Nutrition in Norfolk cohort: A population-based prospective study. Circulation 116: 2933–2943, 2007pmid:18071080
    OpenUrlAbstract/FREE Full Text
  38. ↵
    1. Folsom AR,
    2. Kushi LH,
    3. Anderson KE,
    4. Mink PJ,
    5. Olson JE,
    6. Hong CP,
    7. Sellers TA,
    8. Lazovich D,
    9. Prineas RJ
    : Associations of general and abdominal obesity with multiple health outcomes in older women: The Iowa Women’s Health Study. Arch Intern Med 160: 2117–2128, 2000pmid:10904454
    OpenUrlCrossRefPubMed
  39. ↵
    1. Pischon T,
    2. Boeing H,
    3. Hoffmann K,
    4. Bergmann M,
    5. Schulze MB,
    6. Overvad K,
    7. van der Schouw YT,
    8. Spencer E,
    9. Moons KG,
    10. Tjønneland A,
    11. Halkjaer J,
    12. Jensen MK,
    13. Stegger J,
    14. Clavel-Chapelon F,
    15. Boutron-Ruault MC,
    16. Chajes V,
    17. Linseisen J,
    18. Kaaks R,
    19. Trichopoulou A,
    20. Trichopoulos D,
    21. Bamia C,
    22. Sieri S,
    23. Palli D,
    24. Tumino R,
    25. Vineis P,
    26. Panico S,
    27. Peeters PH,
    28. May AM,
    29. Bueno-de-Mesquita HB,
    30. van Duijnhoven FJ,
    31. Hallmans G,
    32. Weinehall L,
    33. Manjer J,
    34. Hedblad B,
    35. Lund E,
    36. Agudo A,
    37. Arriola L,
    38. Barricarte A,
    39. Navarro C,
    40. Martinez C,
    41. Quirós JR,
    42. Key T,
    43. Bingham S,
    44. Khaw KT,
    45. Boffetta P,
    46. Jenab M,
    47. Ferrari P,
    48. Riboli E
    : General and abdominal adiposity and risk of death in Europe. N Engl J Med 359: 2105–2120, 2008pmid:19005195
    OpenUrlCrossRefPubMed
  40. ↵
    1. Rillamas-Sun E,
    2. LaCroix AZ,
    3. Waring ME,
    4. Kroenke CH,
    5. LaMonte MJ,
    6. Vitolins MZ,
    7. Seguin R,
    8. Bell CL,
    9. Gass M,
    10. Manini TM,
    11. Masaki KH,
    12. Wallace RB
    : Obesity and late-age survival without major disease or disability in older women. JAMA Intern Med 174: 98–106, 2014pmid:24217806
    OpenUrlCrossRefPubMed
  41. ↵
    1. Muntner P,
    2. Judd SE,
    3. Gao L,
    4. Gutiérrez OM,
    5. Rizk DV,
    6. McClellan W,
    7. Cushman M,
    8. Warnock DG
    : Cardiovascular risk factors in CKD associate with both ESRD and mortality. J Am Soc Nephrol 24: 1159–1165, 2013pmid:23704285
    OpenUrlAbstract/FREE Full Text
PreviousNext
Back to top

In this issue

Clinical Journal of the American Society of Nephrology: 10 (2)
Clinical Journal of the American Society of Nephrology
Vol. 10, Issue 2
February 06, 2015
  • Table of Contents
  • Table of Contents (PDF)
  • Index by author
View Selected Citations (0)
Print
Download PDF
Sign up for Alerts
Email Article
Thank you for your help in sharing the high-quality science in CJASN.
Enter multiple addresses on separate lines or separate them with commas.
Adiposity Patterns and the Risk for ESRD in Postmenopausal Women
(Your Name) has sent you a message from American Society of Nephrology
(Your Name) thought you would like to see the American Society of Nephrology web site.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Citation Tools
Adiposity Patterns and the Risk for ESRD in Postmenopausal Women
Nora Franceschini, Natalia A. Gouskova, Alex P. Reiner, Andrew Bostom, Barbara V. Howard, Mary Pettinger, Jason G. Umans, M. Alan Brookhart, Wolfgang C. Winkelmayer, Charles B. Eaton, Gerardo Heiss, Jason P. Fine
CJASN Feb 2015, 10 (2) 241-250; DOI: 10.2215/CJN.02860314

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
Request Permissions
Share
Adiposity Patterns and the Risk for ESRD in Postmenopausal Women
Nora Franceschini, Natalia A. Gouskova, Alex P. Reiner, Andrew Bostom, Barbara V. Howard, Mary Pettinger, Jason G. Umans, M. Alan Brookhart, Wolfgang C. Winkelmayer, Charles B. Eaton, Gerardo Heiss, Jason P. Fine
CJASN Feb 2015, 10 (2) 241-250; DOI: 10.2215/CJN.02860314
del.icio.us logo Digg logo Reddit logo Twitter logo CiteULike logo Facebook logo Google logo Mendeley logo
  • Tweet Widget
  • Facebook Like

Jump to section

  • Article
    • Abstract
    • Introduction
    • Materials and Methods
    • Results
    • Discussion
    • Disclosures
    • Acknowledgments
    • Footnotes
    • References
  • Figures & Data Supps
  • Info & Metrics
  • View PDF

More in this TOC Section

Original Articles

  • Acute Kidney Injury among Black Patients with Sickle Cell Trait and Sickle Cell Disease
  • Acute Kidney Injury, Microvascular Rarefaction, and Estimated Glomerular Filtration Rate in Kidney Transplant Recipients
  • The Association of Time to Organ Procurement on Short- and Long-Term Outcomes in Kidney Transplantation
Show more Original Articles

Epidemiology and Outcomes

  • Urine Kidney Injury Biomarkers and Risks of Cardiovascular Disease Events and All-Cause Death: The CRIC Study
  • Association between Monocyte Count and Risk of Incident CKD and Progression to ESRD
  • Association of TNF Receptor 2 and CRP with GFR Decline in the General Nondiabetic Population
Show more Epidemiology and Outcomes

Cited By...

  • Comparison between Different Measures of Body Fat with Kidney Function Decline and Incident CKD
  • Google Scholar

Similar Articles

Related Articles

  • No related articles found.
  • PubMed
  • Google Scholar

Keywords

  • chronic renal failure
  • clinical epidemiology
  • epidemiology and outcomes
  • ESRD
  • risk factors

Articles

  • Current Issue
  • Early Access
  • Subject Collections
  • Article Archive
  • ASN Meeting Abstracts

Information for Authors

  • Submit a Manuscript
  • Trainee of the Year
  • Author Resources
  • ASN Journal Policies
  • Reuse/Reprint Policy

About

  • CJASN
  • ASN
  • ASN Journals
  • ASN Kidney News

Journal Information

  • About CJASN
  • CJASN Email Alerts
  • CJASN Key Impact Information
  • CJASN Podcasts
  • CJASN RSS Feeds
  • Editorial Board

More Information

  • Advertise
  • ASN Podcasts
  • ASN Publications
  • Become an ASN Member
  • Feedback
  • Follow on Twitter
  • Password/Email Address Changes
  • Subscribe

© 2021 American Society of Nephrology

Print ISSN - 1555-9041 Online ISSN - 1555-905X

Powered by HighWire