Trends, Social Context, and Transplant Implications of Obesity Among Incident Dialysis Patients in the United States : Transplantation

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Original Clinical Science—General

Trends, Social Context, and Transplant Implications of Obesity Among Incident Dialysis Patients in the United States

Lavenburg, Linda-Marie U. DO1; Kim, Yuna MS2; Weinhandl, Eric D. PhD3,4; Johansen, Kirsten L. MD5; Harhay, Meera N. MD MSCE2,6,7

Author Information
doi: 10.1097/TP.0000000000004243
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Abstract

INTRODUCTION

Nearly half of adults in the United States (US) are obese. Obesity is an independent risk factor for end-stage kidney disease (ESKD)1,2 and increases risks for diabetes mellitus and hypertension, the leading causes of ESKD in the US. People with ESKD and higher body mass index (BMI) might have a survival advantage on dialysis compared to those with lower BMI3,4; however, obesity may also impair mobility and worsen other comorbidities.5-8 Obesity trends also have important implications for access to kidney transplantation (KT) because many US KT programs consider obesity to be a relative or absolute contraindication to waitlist eligibility.9

Less than 20% of nonelderly individuals who initiate dialysis for ESKD are waitlisted or receive KT within 1 y of dialysis initiation.10 Improving waitlisting rates is a national priority,11 leading to recent policies to enhance financial incentives for dialysis providers with more waitlisted patients.12 Given the rise of obesity in the general population13 and the common use of BMI thresholds by transplant centers,9 policies and clinical guidelines that directly address obesity and its determinants might also help increase KT access. For example, obesity prevalence is high among individuals with job and food insecurities, those with lower access to quality foods, and those living in built environments that limit the convenience and practicality of physical activity.15 In the context of ESKD, more knowledge is needed on the populations with the highest obesity burdens and on the implications of obesity for KT access to inform the targeting of obesity-related guidance and interventions.

The objective of this study was to examine trends and KT-related outcomes of obesity among individuals initiating dialysis in the US over a 10-y period. We aimed to quantify differences in waitlisting rates by obesity status, accounting for the competing risk of death. We also examined whether the likelihood of early access to KT (ie, waitlisting within a year of dialysis initiation) among those with obesity differed by US region and in subgroups of patients with historically lower access to KT. Finally, we explored associations between neighborhood social deprivation, obesity, and KT access among individuals with ESKD.

MATERIALS AND METHODS

Study Population

We used the US Renal Data System (USRDS) standard analytic file to identify the study population, which included adults (age ≥18 y) who initiated dialysis for ESKD in the US between January 1, 2007‚ and December 31, 2016. We excluded those with missing information on BMI when they initiated dialysis, those with missing state of residence (n = 42) or residence in US territories, and those with a death date on or before the dialysis initiation date (Figure S1, SDC, https://links.lww.com/TP/C477, cohort inclusion diagram). This overall population (N = 1 084 816) was used to determine national trends in obesity among incident dialysis patients. In a secondary analysis, we used the neighborhood Social Deprivation Index (SDI)16 as a proxy measure for socioeconomic status. The SDI was developed by The Robert Graham Center and updated using the 2011 to 2015 American Community Survey 5-y estimates. The SDI is a composite measure based on 7 demographic characteristics from the American Community Survey data: percent living in poverty, percent with <12 y of education, percent of single-parent household, percent living in a rented housing unit, percent living in the overcrowded housing unit, percent of households without a car, and percent of nonemployed adults <65 y old.16,17 The SDI score is represented as percentiles (a range of 1–100), where a higher SDI score indicates more social deprivation. Given that the SDI captured US neighborhood characteristics between 2011 and 2015, for the secondary analysis, we included individuals who had nonmissing zip codes and initiated dialysis between 2011 and 2015 (N = 540 735). For all analyses of waitlisting for KT as the primary outcome with death as a competing risk, we excluded adults aged ≥75 y and those who were waitlisted before dialysis initiation. After also excluding observations with missing covariate information, the primary waitlisting analysis included 760 661 individuals, and the secondary waitlisting analysis included 381 317 individuals. The Drexel University Institutional Review Board reviewed the proposal and determined that it was not research involving human subjects as defined by US Department of Health and Human Services and Food and Drug Administration regulations. The clinical and research activities being reported are consistent with the Principles of the Declaration of Istanbul as outlined in the “Declaration of Istanbul on Organ Trafficking and Transplant Tourism.”

Exposure

The primary exposure was BMI, as calculated by USRDS based on height and weight information recorded in the ESRD Medical Evidence Report. We examined BMI as a continuous variable and also as categories (<18.5 kg/m2, ≥18.5 and <25 kg/m2, ≥25 and <30 kg/m2, ≥30 and <35 kg/m2, ≥35 and <40 kg/m2, and ≥40 kg/m2) corresponding to WHO classifications of underweight, normal weight, overweight, and obesity class 1, 2, and 3, respectively.18

Outcome

We assessed waitlisting incidence among those not waitlisted on or before dialysis initiation over 1 y of follow-up from dialysis onset with death as a competing risk. Waitlisting dates were ascertained from USRDS standard analytic files, which are administratively linked with United Network for Organ Sharing data. Transplant dates within the first year of dialysis were treated as the waitlisting date if there was no waitlisting date before the transplant date.

Covariates

We included the following demographic and baseline characteristics of the study cohort: age at dialysis onset (years), race/ethnicity (categorized as non-Hispanic White, non-Hispanic Black, Hispanic, Native American, Asian, and other/unknown), sex, kidney replacement therapy setting (center and home), health insurance coverage (Medicare, Medicaid, commercial insurance, and uninsured), comorbid conditions (diabetes, cardiovascular disease, cancer, cerebrovascular disease, needs assistance with daily activities, and institutionalized in nursing home), year of dialysis initiation (to account for potential time trends), ESRD network, and serum creatinine at dialysis initiation (mg/dL).

Statistical Analysis

Categorical variables were summarized as frequencies and percentages. Continuous variables were summarized as medians and interquartiles ranges. To compare study cohort characteristics among groups, we used the Wilcoxon rank-sum test, Kruskal-Wallis test, and chi-square test, as appropriate. First, we examined national trends in BMI among all adults who initiated dialysis during the study period and among racial/ethnic subgroups. Next, we estimated age, race/ethnicity, and sex-adjusted BMI trends within ESRD networks, comparing individuals in the network who were waitlisted for KT within 1 y to the population that remained unlisted in each network. We estimated 1-y cumulative incidence of waitlisting and death without waitlisting by BMI. We then used Fine-Gray subdistribution hazard regression models19 to examine the association between the initial BMI category and waitlisting over 1-y follow-up with death as a competing risk. We confirmed the proportional subhazard assumption by visually inspecting Schoenfeld-type residuals against time.20 After assessing the crude association, we adjusted the model for age group, race/ethnicity, year of dialysis initiation, modality of renal replacement therapy, ESRD network,21 uninsured status, Medicaid coverage, needing assistance with daily activities, institutionalized in nursing home, and log-transformed serum creatinine as a proxy for muscle mass.22 We also adjusted for the following comorbidities if they were selected on the Medical Evidence Report: diabetes, cardiovascular disease, cancer, and cerebrovascular disease. We evaluated interactions of BMI category with age group (18 to <45, 45 to <65, or ≥65 y), sex, race/ethnicity, ESRD network, diabetes, and Medicaid insurance. Interaction terms were tested using Wald tests with an alpha level of 0.1. For the secondary analysis, we linked individuals to their SDI at the Zip Code Tabulation Area level. We estimated the predicted cumulative incidence of 1-y waitlisting by ESRD network and SDI score, adjusting for the other covariates at their means. Unless otherwise specified, all statistical significance tests were 2-sided with an alpha level of 0.05. All analyses were performed using R 3.6.2 (R Core Team, 2019) and Stata/SE 16.1 (Stata Statistical Software: Release 16; StataCorps LLC, College Station, TX).

Missing Data

Data were missing in <1% of the study cohort on race/ethnicity (<0.1%), treatment modality (0.5%), and serum creatinine (<0.1%) information. Comorbidity data were considered as missing if no comorbidities were selected on the Medical Evidence Report comorbid conditions section and “none” was not also selected on the form. All primary analyses were conducted on complete cases.

RESULTS

Study Population Characteristics

The median age was 65 (IQR, 54–75) y, 42.6% were women, 27.2% were non-Hispanic Black, and 91.3% initiated in-center hemodialysis (Table 1). Compared to individuals with lower BMIs, those who initiated dialysis with class 3 obesity were younger (59 versus 67 y), more likely to be female (56.7% versus 39.8%), to be non-Hispanic Black (31.7% versus 25.7%), and to have diabetes (76.3% versus 47.5%), and more likely to have Medicaid coverage (30.8% versus 26.3%) (P < 0.001 for all comparisons). The ESRD network with the highest overall prevalence of obesity (ie, class 1, 2, and 3) includes Iowa, Kansas, Missouri, and Nebraska (43.9%), whereas the lowest prevalence was in Southern California (30.7%) (Table 2).

TABLE 1. - Study cohort characteristics, comparing the overall population to those classified by the WHO as normal weight, class 1 obesity, class 2 obesity, and class 3 obesity
Overall Normal weight Class 1 obesity Class 2 obesity Class 3 obesity
(N = 1084816) (N = 316474) (N = 208443) (N = 114450) (N = 98724)
Age median (Q1,Q3) 65.0 (54.0,75.0) 67.0 (55.0,78.0) 64.0 (54.0,73.0) 62.0 (52.0,70.0) 59.0 (50.0,67.0)
Race/ethnicity a
 Non-Hispanic White 577155 (53.2%) 164318 (51.9%) 113228 (54.3%) 62643 (54.7%) 54093 (54.8%)
 Non-Hispanic Black 295091 (27.2%) 81291 (25.7%) 57567 (27.6%) 33745 (29.5%) 31278 (31.7%)
 Hispanic 149033 (13.7%) 44503 (14.1%) 28765 (13.8%) 13972 (12.2%) 10364 (10.5%)
 Native American 10371 (1.0%) 2458 (0.8%) 2272 (1.1%) 1291 (1.1%) 1097 (1.1%)
 Asian 40312 (3.7%) 19951 (6.3%) 4204 (2.0%) 1486 (1.3%) 791 (0.8%)
 Other/unknown 12841 (1.2%) 3948 (1.2%) 2405 (1.2%) 1312 (1.1%) 1101 (1.1%)
Female sex 461909 (42.6%) 125898 (39.8%) 88966 (42.7%) 56535 (49.4%) 55972 (56.7%)
Modality
 Center 990058 (91.3%) 290782 (91.9%) 188053 (90.2%) 104134 (91.0%) 92226 (93.4%)
 Home 89314 (8.2%) 23364 (7.4%) 19715 (9.5%) 10034 (8.8%) 6316 (6.4%)
 Missing 5444 (0.5%) 2328 (0.7%) 675 (0.3%) 282 (0.2%) 182 (0.2%)
Insurance coverage b
 Medicare 659131 (60.8%) 198705 (62.8%) 125066 (60.0%) 65736 (57.4%) 54431 (55.1%)
 Medicaid 280808 (25.9%) 83348 (26.3%) 51205 (24.6%) 30035 (26.2%) 30445 (30.8%)
 Commercial 233100 (21.5%) 59331 (18.7%) 48864 (23.4%) 28350 (24.8%) 24013 (24.3%)
 Uninsured 71136 (6.6%) 22014 (7.0%) 13035 (6.3%) 7195 (6.3%) 6242 (6.3%)
Diabetes 652365 (60.1%) 150335 (47.5%) 143781 (69.0%) 84683 (74.0%) 75342 (76.3%)
CAD 531928 (49.0%) 148661 (47.0%) 104864 (50.3%) 58501 (51.1%) 51448 (52.1%)
Cancer 80226 (7.4%) 27199 (8.6%) 14028 (6.7%) 6676 (5.8%) 4762 (4.8%)
CVD 97849 (9.0%) 30790 (9.7%) 18336 (8.8%) 9342 (8.2%) 6935 (7.0%)
Needs assistance with daily activities 135909 (12.5%) 42389 (13.4%) 23662 (11.4%) 13624 (11.9%) 13861 (14.0%)
Institutionalized-nursing home 79454 (7.3%) 24489 (7.7%) 13876 (6.7%) 8290 (7.2%) 8334 (8.4%)
Serum Creatinine (mg/dl)‚ median (Q1,Q3) a 5.6 (4.2,7.5) 5.6 (4.2,7.6) 5.6 (4.3,7.4) 5.50 (4.2,7.3) 5.3 (4.1,7.1)
The study cohort includes all adults (age ≥18 y) who initiated dialysis in the United States between 2007 and 2016.
Values presented as column % and median (Q1,Q3). All P values <0.001 for statistical comparisons between groups using Kruskal-Wallis test and chi-square test, as appropriate.
WHO BMI classifications: normal weight =BMI ≥18.5 kg/m2 and <25 kg/m2, class 1 obesity = BMI ≥30 kg/m2 and <35 kg/m2, class 2 obesity = BMI ≥35 kg/m2 and <40 kg/m2, and class 3 obesity = BMI ≥40 kg/m2
aMissing not shown.
bAmong those with insurance, coverage categories are not mutually exclusive.
BMI, body mass index; CAD, cardiovascular disease; CVD, cerebrovascular disease; Q, quartile; WHO, world health organization.

TABLE 2. - Proportion of United States adult (age >18 y) incident dialysis patients between 2007 and 2016 with WHO class 1, 2, or 3 obesity by ESRD network
Total % with obesity Class 1 obesity Class 2 obesity Class 3 obesity
All ESRD Networks 38.9 208443 (19.2%) 114450 (10.6%) 98724 (9.1%)
IA, KS, MO, NE 43.9 8363 (20.6%) 4944 (12.2%) 4506 (11.1%)
IN, KY, OH 43.8 17094 (20.2%) 10062 (11.9%) 9868 (11.7%)
MI, MN, ND, SD, WI 42.1 13880 (20.1%) 8081 (11.7%) 7181 (10.4%)
AR, LA, OK 42.1 9402 (20.5%) 5170 (11.3%) 4781 (10.4%)
AL, MS, TN 42.0 12091 (19.9%) 6987 (11.5%) 6405 (10.6%)
GA, NC, SC 41.8 18832 (19.9%) 10975 (11.6%) 9744 (10.3%)
DC, MD, VA, WV 40.5 12100 (19.3%) 7029 (11.2%) 6189 (9.9%)
IL 40.5 9474 (19.6%) 5333 (11.0%) 4812 (9.9%)
DE, PA 40.3 9518 (19.1%) 5553 (11.1%) 5010 (10.1%)
AK, ID, MT, OR, WA 40.1 6239 (19.7%) 3473 (11.0%) 2998 (9.5%)
TX 39.7 18791 (19.8%) 10139 (10.7%) 8700 (9.2%)
AZ, CO, NV, NM, UT, WY 37.5 9983 (19.4%) 5189 (10.1%) 4119 (8.0%)
FL 36.6 13787 (19.0%) 7005 (9.7%) 5699 (7.9%)
CT, ME, MA, NH, RI, VT 35.9 6637 (18.5%) 3402 (9.5%) 2839 (7.9%)
NJ 35.3 6482 (18.4%) 3436 (9.8%) 2490 (7.1%)
HI, Northern CA 33.7 9128 (17.6%) 4715 (9.1%) 3610 (7.0%)
NY 33.1 11982 (17.5%) 5970 (8.7%) 4698 (6.9%)
Southern CA 30.7 14660 (16.8%) 6987 (8.0%) 5075 (5.8%)
WHO BMI classifications: class 1 obesity = BMI ≥30 kg/m2 and <35 kg/m2, class 2 obesity = BMI ≥35 kg/m2 and <40 kg/m2, and class 3 obesity = BMI ≥40 kg/m2.
AL, Alabama; AK, Alaska; AR, Arkansas; AZ, Arizona; CA, California; CO, Colorado; CT, Connecticut; DE, Delaware; DC‚ Distric of Columbia; FL, Florida; GA, Georgia; HI, Hawaii; ID, Idaho; IL, Illinois; IN, Indiana; IA, Iowa; KS, Kansas; KY, Kentucky; LA, Louisiana; ME, Maine; MD, Maryland; MA, Massachusetts; MI, Michigan; MN, Minnesota; MS, Mississippi; MO, Missouri; MT, Montana; NE, Nebraska; NV, Nevada; NH, New Hampshire; NJ, New Jersey; NM, New Mexico; NY, New York; NC, North Carolina; ND, North Dakota; OH, Ohio; OK, Oklahoma; OR, Oregon; PA, Pennsylvania; RI, Rhode Island; SC, South Carolina; SD, South Dakota; TN, Tennessee; TX, Texas; UT, Utah; VT, Vermont; VA, Virginia; WA, Washington; WHO, World Health Organization; WV, West Virginia; WI, Wisconsin; WY, Wyoming.

BMI Trends Among Adult Incident Dialysis Patients in the US

The age-, sex-, and race/ethnicity-adjusted mean BMI of the US incident dialysis population increased from 28.1 kg/m2 (95% CI, 28.0-28.2 kg/m2) in 2007 to 29.2 kg/m2 (95% CI 29.1-29.3 kg/m2) in 2016. The age-, sex-, and race/ethnicity-adjusted prevalence of obesity (BMI ≥30 kg/m2) among incident dialysis patients increased from 31.9% (95% CI, 31.1-32.7%) to 38.2% (95% CI, 37.5-39.0%), whereas class 3 obesity prevalence increased from 6.2% (95% CI, 5.8-6.6%) to 7.8% (95% CI, 7.4-8.2%) between 2007 and 2016. Age- and sex-adjusted mean BMI increased in all race/ethnicity subgroups over time and was highest among non-Hispanic White patients (Figure 1).

F1
FIGURE 1.:
BMI trends of adult incident dialysis patients in the United States by race/ethnicity group (2007–2016). Figure depicts age- and sex-adjusted mean BMI with 95% confidence intervals by race/ethnicity. The 4 most prevalent racial/ethnic subgroups are shown in blue (non-Hispanic White), yellow (non-Hispanic Black), pink (Hispanic), and orange (Asian). BMI, body mass index.

Association Between BMI and 1-Y Kidney Transplant Waitlisting

In unadjusted analyses using BMI as a continuous variable, the probability of waitlisting within 1 y of dialysis initiation declined steeply among patients with a BMI of ≥35 kg/m2, whereas the incidence of death without waitlisting did not increase substantially among those with high BMI (Figure 2). The unadjusted cumulative incidence of waitlisting within 1 y was highest among individuals with a BMI in the normal weight, overweight, and class 1 obesity categories, whereas the cumulative incidence of death without waitlisting was highest in the underweight and normal weight BMI groups, respectively (Figure S2, SDC, https://links.lww.com/TP/C477). In unadjusted Fine-Gray regression models, relative to normal BMI, the incidence of waitlisting was lower among those with class 2 obesity (subhazard ratio [SHR], 0.92; 95% CI, 0.90-0.95; P < 0.001) and class 3 obesity (SHR, 0.41; CI, 0.40-0.43; P < 0.001). In the multivariable model for waitlisting (Figure 3), there was effect modification on the association between BMI category and KT waitlisting by age, race/ethnicity, sex, region, and diabetes (Pinteraction < 0.001 for all) but not by Medicaid benefits status (Pinteraction = 0.38). Differences in waitlisting rates between those with normal BMI and class 2 and class 3 obesity were larger in younger (versus older) individuals and in women (versus men). For example, among individuals aged ≥65 y, those with a BMI ≥40 kg/m2 had a 2-fold lower rate of waitlisting (adjusted SHR [aSHR], 0.53; 95% CI, 0.48-0.59) than those with normal BMI. Among those aged 18 to 44 y, those with a BMI ≥40 kg/m2 had a nearly 3-fold lower rate of waitlisting (aSHR, 0.38; 95% CI, 0.36-0.40) than their counterparts with normal BMI. Differences in waitlisting rates between normal BMI and class 2 and 3 obesity were smallest among non-Hispanic Blacks (class 3 obesity aSHR, 0.57; 95% CI, 0.54-0.61) and largest among Asians and other race/ethnicity individuals (class 3 obesity aSHR among Asians, 0.35; 95% CI, 0.27-0.45).

F2
FIGURE 2.:
Observed probability of 1 y waitlisting and death without waitlisting by initial BMI among adult incident dialysis patients in the United States with LOESS smoothing lines and 95% confidence intervals. Individuals were included if they were not previously waitlisted before initiating dialysis and were adults (age ≥18 and <75 y). Each dot represents an approximately equal number of incident dialysis patients. Dots are shown according to average starting BMI of the group (x-axis) and the proportion of each group that was waitlisted for kidney transplantation (triangle) or death (circle) within 1 y of starting dialysis (y-axis). BMI, body mass index.
F3
FIGURE 3.:
Variations in the waitlisting rates of individuals with class 2 and 3 obesity, relative to normal weight individuals, in age, sex, race/ethnicity, and diabetes subgroups. The figure shows adjusted subhazard ratios (with 95% confidence intervals) for waitlisting over 1 y of follow-up within subgroups, relative to normal BMI. (A) displays estimates for class 2 obesity, whereas (B) displays estimates for class 3 obesity. The reference category for each row is individuals with the same characteristics and normal BMI. For example, the first row in (A) is the relative subhazard ratio for individuals age 18 to 44 y with class 2 obesity, relative to individuals age 18 to 44 y with normal BMI. Estimates were derived from individual Fine-Gray subdistribution hazard regression models adjusting for age group, race/ethnicity, year of dialysis initiation, modality of renal replacement therapy, ESRD network, uninsured status, Medicaid coverage, diabetes, cardiovascular disease, cancer, cerebrovascular disease, need for assistance with daily activities, institutionalized in nursing home, and log-transformed serum creatinine and including an interaction term between BMI and effect modifier of interest. BMI, body mass index.

Regional Trends in BMI and Early Waitlisting

The mean BMI of individuals who were waitlisted within 1 y of dialysis initiation was relatively stable in most ESRD networks during the study period despite the rise in BMI in the overall incident dialysis population (Figure 4). In the fully adjusted model including an interaction term for the ESRD network, there was regional variation in the differences between waitlisting rates of people with normal BMI versus those with class 2 and 3 obesity, respectively (Table 3). Relative to normal BMI, class 2 obesity was associated with lower waitlisting only in California and the US Southwest. Lower waitlisting rates were observed for people with class 3 obesity in all US ESRD networks, but the difference in waitlisting rates between those with class 3 obesity and normal BMI was smallest in the ESRD networks including Maryland, Virginia, District of Columbia, West Virginia. and Illinois (adjusted waitlisting incidence difference‚ 2.5%).

TABLE 3. - Waitlisting rates among individuals with WHO class 2 and 3 obesity compared with those with normal BMI by United States ESRD region
Reference: BMI ≥ 18.5 kg/m2 and < 25 kg/m2 BMI ≥35 kg/m2 and < 40 kg/m2 BMI ≥ 40 kg/m2
1-y cumulative incidence of waitlisting Adjusted SHR a (95% CI) 1-y cumulative incidence of waitlisting Difference a Adjusted SHR a (95% CI) 1-y cumulative incidence of waitlisting Difference a
CT, ME, MA, NH, RI, VT 9.8 1.01 (0.90-1.14) 9.9 −0.1 0.46 (0.39-0.54) 4.6 5.2
NY 8.8 1.09 (0.99-1.19) 9.5 −0.7 0.68 (0.61-0.77) 6.1 2.7
NJ 9.1 1.21 (1.07-1.37) 10.9 −1.8 0.59 (0.50-0.71) 5.5 3.6
DE, PA 8.6 0.98 (0.88-1.09) 8.4 0.2 0.52 (0.45-0.59) 4.5 4.0
MD, VA, DC, WV 6.1 1.15 (1.04-1.26) 7.0 −0.9 0.58 (0.51-0.65) 3.6 2.5
GA, NC, SC 4.3 1.06 (0.97-1.15) 4.5 −0.2 0.38 (0.34-0.44) 1.7 2.6
FL 4.0 0.89 (0.78-1.01) 3.5 0.4 0.31 (0.25-0.38) 1.2 2.7
AL, MI, TN 5.0 1.08 (0.97-1.19) 5.4 −0.4 0.41 (0.35-0.47) 2.1 2.9
IN, KY, OH 5.2 1.00 (0.91-1.11) 5.2 0.0 0.35 (0.30-0.40) 1.8 3.4
IL 7.0 1.08 (0.96-1.20) 7.5 −0.5 0.64 (0.56-0.73) 4.6 2.5
MI, MN, ND, SD, WI 8.5 0.93 (0.85-1.02) 8.0 0.5 0.44 (0.39-0.50) 3.8 4.7
IA, KS, MS, NE 6.2 1.01 (0.89-1.14) 6.3 −0.1 0.39 (0.33-0.46) 2.5 3.7
AR, LA, OK 4.2 0.89 (0.78-1.03) 3.8 0.4 0.36 (0.30-0.45) 1.5 2.6
TX 5.8 0.94 (0.87-1.02) 5.5 0.3 0.45 (0.40-0.50) 2.7 3.2
AZ, CO, NV, NM, UT, WY 6.1 0.80 (0.71-0.9) 4.9 1.2 0.29 (0.24-0.35) 1.8 4.3
AK, ID, MT, OR, WA 5.1 0.99 (0.85-1.15) 5.0 0.1 0.29 (0.23-0.38) 1.5 3.6
HI, Northern CA 10.8 0.92 (0.84-1.01) 10.0 0.8 0.39 (0.34-0.44) 4.3 6.5
Southern CA 4.6 0.85 (0.76-0.94) 3.9 0.7 0.35 (0.29-0.42) 1.6 3.0
Estimates are from Fine-Gray subdistribution hazard regression models to examine the association between initial BMI category and waitlisting over 1 y follow-up with death as a competing risk. The models were adjusted for patient characteristics and comorbidities among adult individuals who initiated dialysis without prior waitlisting
aReference group is BMI 18.5 kg/m2 & < 25 kg/m2.
AL, Alabama; AK, Alaska; AR, Arkansas; AZ, Arizona; BMI, body mass index; CA, California; CO, Colorado; CT, Connecticut; DE, Delaware; DC‚ Distric of Columbia; FL, Florida; GA‚ Georgia; HI, Hawaii; ID, Idaho; IL, Illinois; IN, Indiana; IA, Iowa; KS, Kansas; KY, Kentucky; LA, Louisiana; ME, Maine; MD, Maryland; MA, Massachusetts; MI, Michigan; MN, Minnesota; MS, Mississippi; MO, Missouri; MT, Montana; NE, Nebraska; NV, Nevada; NH, New Hampshire; NJ, New Jersey; NM, New Mexico; NY, New York; NC, North Carolina; ND, North Dakota; OH, Ohio; OK, Oklahoma; OR, Oregon; PA, Pennsylvania; RI, Rhode Island; SC, South Carolina; SD, South Dakota; SHR, subhazard ratio; TN, Tennessee; TX, Texas; UT, Utah; VT, Vermont; VA, Virginia; WA, Washington; WHO, World Health Organization; WI, Wisconsin; WV, West Virginia; WY, Wyoming.

F4
FIGURE 4.:
BMI trends among adult incident dialysis patients in the United States by ESRD network and waitlisting status. Figure depicts age-, sex-, and race/ethnicity-adjusted mean BMI (kg/m2), with 95% confidence intervals, of adults, age <75 y, who initiated dialysis in the United States between 2006 and 2017 and were not previously waitlisted for kidney transplant. Green lines depict individuals who were alive and not waitlisted within 1 y of initiating dialysis, whereas orange lines depict individuals who were waitlisted during 1 y follow-up of starting dialysis. BMI, body mass index.

Secondary Analysis

Among all individuals who initiated dialysis between 2011 and 2015 (N = 540 735), those who resided in the highest SDI quartile (ie, most social deprivation) were more likely to be racial and ethnic minorities and to have class 3 obesity (9.4% versus 8.2%; P < 0.001) than those who resided in the lowest SDI quartile (Table 4). In the subgroup aged <75 y that was not waitlisted before dialysis (N = 383 562), higher SDI (ie, more social deprivation) was associated with a lower waitlisting probability and lower rates of death (Figure S3, SDC, https://links.lww.com/TP/C477). In a fully adjusted model, SDI modified the association between BMI and waitlisting over 1 y of follow-up (Pinteraction = 0.0004) (Table S1, SDC, https://links.lww.com/TP/C477). Although class 3 obesity was associated with low waitlisting across all SDI scores, the difference in waitlisting rates between those with class 3 obesity and those with normal BMI was largest among people residing in neighorhoods with the least social deprivation (Figure S4, SDC, https://links.lww.com/TP/C477).

TABLE 4. - Characteristics of United States adults (age ≥18 y) initiating dialysis between 2011 and 2015, stratified by neighborhood SDI quartile (higher score = more social deprivation)
Characteristic SDI Quartiles (higher score = more social deprivation)
(1,25) (25,50) (50,75) (75,100)
(N = 88653) (N = 113897) (N = 141618) (N = 196567)
Age median (Q1,Q3) 69.0 (58.0,78.0) 66.0 (56.0,76.0) 64.0 (54.0,74.0) 62.0 (51.0,72.0)
Race/ethnicity*
 Non-Hispanic White 70410 (79.4%) 80217 (70.4%) 82172 (58.0%) 54501 (27.7%)
 Non-Hispanic Black 9541 (10.8%) 17561 (15.4%) 34968 (24.7%) 83243 (42.3%)
 Hispanic 4248 (4.8%) 8924 (7.8%) 16555 (11.7%) 46179 (23.5%)
 Native American 260 (0.3%) 688 (0.6%) 1222 (0.9%) 2835 (1.4%)
 Asian 3495 (3.9%) 4932 (4.3%) 4951 (3.5%) 7357 (3.7%)
 Other/unknown 698 (0.8%) 1573 (1.4%) 1746 (1.2%) 2450 (1.2%)
Female sex 33826 (38.2%) 46124 (40.5%) 60087 (42.4%) 87984 (44.8%)
Treatment location
 Center 78490 (88.5%) 101340 (89.0%) 127638 (90.1%) 181473 (92.3%)
 Home 10060 (11.3%) 12364 (10.9%) 13654 (9.6%) 14129 (7.2%)
 Missing 103 (0.1%) 193 (0.2%) 326 (0.2%) 965 (0.5%)
Categorical BMI
 BMI <18.5 kg/m2 2851 (3.2%) 3583 (3.1%) 4441 (3.1%) 6696 (3.4%)
 18.5≤ BMI <25 kg/m2 26096 (29.4%) 31797 (27.9%) 38784 (27.4%) 56970 (29.0%)
 25≤ BMI <30 kg/m2 26097 (29.4%) 32166 (28.2%) 40193 (28.4%) 55629 (28.3%)
 30≤ BMI <35 kg/m2 17153 (19.3%) 22950 (20.1%) 28149 (19.9%) 37950 (19.3%)
 35≤ BMI <40 kg/m2 9169 (10.3%) 12747 (11.2%) 15966 (11.3%) 20757 (10.6%)
 BMI ≥40 kg/m2 7287 (8.2%) 10654 (9.4%) 14085 (9.9%) 18565 (9.4%)
Medicaid 11176 (12.6%) 21269 (18.7%) 35486 (25.1%) 73887 (37.6%)
Commercial insurance 23394 (26.4%) 25500 (22.4%) 27952 (19.7%) 31473 (16.0%)
Uninsured 2987 (3.4%) 5590 (4.9%) 8934 (6.3%) 15856 (8.1%)
Diabetes 49038 (55.3%) 67630 (59.4%) 87746 (62.0%) 125553 (63.9%)
Cardiovascular disease 45658 (51.5%) 57597 (50.6%) 69776 (49.3%) 87095 (44.3%)
Cancer 9112 (10.3%) 9562 (8.4%) 10428 (7.4%) 10378 (5.3%)
Cerebrovascular disease 7377 (8.3%) 9902 (8.7%) 12676 (9.0%) 17177 (8.7%)
Needs assistance with daily activities 10391 (11.7%) 14281 (12.5%) 18814 (13.3%) 26029 (13.2%)
Institutionalized-nursing home 6414 (7.2%) 8499 (7.5%) 10946 (7.7%) 14149 (7.2%)
Serum creatinineN1 (mg/dl)‚ median (Q1, Q3) 5.30 (4.10,7.00) 5.40 (4.10,7.10) 5.50 (4.20,7.40) 5.90 (4.50,8.10)
aMissing not shown.
bAll P values <0.001 for statistical comparisons between groups using Kruskal-Wallis test and chi-square test, as appropriate.
BMI, body mass index; SDI, social deprivation index; Q, quartile.

DISCUSSION

In this national study, we found that average BMI increased among US adults initiating dialysis over a 10-y period across all race/ethnicity groups. Class 2 and 3 obesity were associated with lower 1-y waitlisting rates and similar 1-y mortality rates when compared to normal BMI. We observed substantial heterogeneity in waitlisting rates by obesity status in age, race/ethnicity, sex, and other subgroups. We also found associations between obesity among incident dialysis patients and low socioeconomic status, as defined by Medicaid benefits or by neighborhood social deprivation, respectively. These findings underscore the importance of addressing obesity in ESKD care processes and raise potential health equity questions surrounding BMI thresholds for KT enlistment.

Between 1995 and 2002, the average BMI among adult incident dialysis patients increased from 25.7 kg/m2 to 27.5 kg/m2.23 Our results show that the trend continued between 2007 and 2016, when the age, race/ethnicity, and sex-adjusted average BMI of adult incident dialysis patients was 29.2 kg/m2, and nearly 10% of the population had class 3 obesity. Trends in obesity suggest that BMI thresholds for KT are likely to impact far more patients today than when Holley et al24 reported BMI ≥35 kg/m2 as the third-most common reason for exclusion from KT in 1998. Although observational studies have linked higher BMI to survival on dialysis,25,26 obesity among KT recipients is associated with higher relative risks of delayed graft function, wound infection and dehiscence, incisional hernias, prolonged hospitalization, acute rejection, allograft loss, de novo posttransplant diabetes mellitus, and cardiovascular disease.27-29 Therefore, the findings of our study on ESKD population trends of obesity have important implications for both KT access and outcomes.30

We observed associations between proxies for low income and class 3 obesity among incident dialysis patients, consistent with research in the general population.31 For example, we found that incident dialysis patients with class 3 obesity were more likely to be Medicaid beneficiaries than those with normal BMI. Independent of socioeconomic status, weight-based stigma might prevent patients with obesity from accessing or benefiting from needed health care.32 Individuals with obesity and low income are likely to have additional barriers because of costs of health care, healthy foods, and weight management programs.33 Community-level data are also important when characterizing social disadvantage and designing health interventions.34,35 In our secondary analyses, we found that neighborhoods with higher social deprivation were more likely to include incident ESKD patients with class 2 and 3 obesity than neighborhoods with the least social deprivation; however, although differences in waitlisting rates between those with class 3 obesity and normal BMI were highest in the least socially disadvantaged neighborhoods, we observed that class 3 obesity was associated with the lowest waitlisting rates in low, medium, and high deprivation neighborhoods. Overall, our results suggest that interventions are needed to address obesity in ESKD populations across the spectrum of socioeconomic and social circumstances.

The regional variability we observed in waitlisting of patients with obesity might reflect heterogeneity in the application of BMI thresholds and other eligibility requirements among US transplant programs. For example, whereas some programs may not evaluate candidates who are above a BMI threshold, others might determine eligibility for those with obesity based on factors such as fat distribution and pelvis depth. Indeed, the use of BMI criteria alone for transplant candidacy has become increasingly controversial given that body composition and fat distribution may be more predictive of KT outcomes than BMI.36,37 These findings might also reflect differences in transplant program use of bariatric and robotic surgery to serve patients with obesity.38,39 Program differences in BMI acceptance thresholds have important implications for patients with limited means or long distances to travel to be assessed by other KT programs.40 Relatedly, we also found that many of the US regions with the highest obesity prevalence also had the lowest likelihood of waitlisting dialysis patients with obesity. Knowledge is needed on which weight management interventions and other care processes might improve waitlisting rates for ESKD patients with obesity, particularly in regions with high or increasing obesity prevalence.

In our study, we found that differences in waitlisting between those with severe obesity and normal BMI differed in certain subgroups of individuals with incident ESKD. For example, waitlisting differences between class 3 obesity and normal BMI were higher among the youngest adults than among older adults, among diabetics, and among women than among men. In a study of incident dialysis patients from 1995 to 2007, Gill et al41 also observed sex differences in access to the KT waiting list, and KT rates from living and deceased donors were also substantially lower for women with class 2 and 3 obesity relative to men with a similar BMI. It is possible that sex-related differences in fat distribution might explain some of these observed differences in KT access. For example, on average, women have higher percent body fat than men,42 and central obesity prevalence is substantially higher among US women than men.43 On the other hand, our finding that the difference in waitlisting between normal BMI and class 3 obesity was larger among younger adults than among older adults was somewhat surprising because one might expect that transplant survival benefits would be higher and comorbidity burdens lower among younger adults with obesity than older adults with obesity. Our results engender questions about whether younger patients with class 2 or 3 obesity are less likely to be referred for KT or more likely required to lose weight before waitlisting than older counterparts with obesity. Furthermore, although the average BMI of incident dialysis patients increased across all race/ethnicity subgroups, the differences in waitlisting rates between class 3 obesity and normal BMI were higher among Asians than among non-Hispanic Whites, non-Hispanic Black, and Hispanic subgroups. These findings are especially relevant given that the age-adjusted prevalence of obesity among Asians in the US is rising and was >30% in 2018.44 Prior research suggests that over half of US Asian adults have central obesity,44 with similar sex differences compared to those observed in the overall US population.43,45 Although lower BMI cutoffs for overweight (23.0–27.5 kg/m2) and obesity (≥ 27.5 kg/m2) are also recommended for Asians because of higher cardiovascular and diabetes risks with higher BMI,46 it is not known if these alternative thresholds are routinely used to evaluate Asians for KT candidacy. KT programs might differ in both candidate population demographic compositions and in selection criteria for obesity, potentially explaining some of the variability we observed in waitlisting rates between race/ethnicity and other subgroups.

Our study has several strengths, including its large size, incorporating almost the entire US incident dialysis population during the study period; however, there are several limitations of the study that should be considered when interpreting our findings. We did not have information on referral patterns or decline decisions that may clarify barriers to waitlisting. We lacked access to individual-level income but incorporated proxies for socioeconomic status‚ including Medicaid benefits and area-level social deprivation. Although we adjusted for functional status correlates such as serum creatinine, requirements for assistance with activities of daily living, and nursing home residence, we did not have information on measured frailty, which is associated with lower KT waitlisting and adverse post-KT outcomes.47 We also lacked information on the severity of comorbid conditions such as vascular disease and on measures of body composition or fat distribution (eg, waist circumference and pelvis depth) that are prognostically informative and might be used by some transplant centers to inform obesity management care and transplant candidacy assessments. We also did not focus on individual body weight trajectories, which are associated with transplant access48 and outcomes.49,50

In conclusion, the results of this national study demonstrate that the obesity epidemic has worsened among incident adult dialysis patients. Waitlisting of those with obesity is highly variable across ESRD networks, and differences in waitlisting rates between those with normal BMI and class 2 and class 3 obesity are more pronounced among younger patients and certain racial/ethnic subgroups. Proxies for low socioeconomic status were associated with higher obesity prevalence among incident ESKD patients, and waitlisting rates among those with class 3 obesity were low among individuals living in a neighborhoods across the range of social deprivation. Strategies to increase KT waitlisting should address obesity and its determinants among people with ESKD.

ACKNOWLEDGMENTS

The data reported here have been supplied by the USRDS under a data use agreement. The interpretation and reporting of these data are the responsibility of the authors and in no way should be seen as an official policy or interpretation of the US government.

REFERENCES

1. McCullough KP, Morgenstern H, Saran R, et al. Projecting ESRD incidence and prevalence in the United States through 2030. J Am Soc Nephrol. 2019;30:127–135.
2. Yu Z, Grams ME, Ndumele CE, et al. Association between midlife obesity and kidney function trajectories: the atherosclerosis risk in communities (ARIC) study. Am J Kidney Dis. 2021;77:376–385.
3. Kalantar-Zadeh K, Streja E, Kovesdy CP, et al. The obesity paradox and mortality associated with surrogates of body size and muscle mass in patients receiving hemodialysis. Mayo Clin Proc. 2010;85:991–1001.
4. Doshi M, Streja E, Rhee CM, et al. Examining the robustness of the obesity paradox in maintenance hemodialysis patients: a marginal structural model analysis. Nephrol Dial Transplant. 2016;31:1310–1319.
5. Guh DP, Zhang W, Bansback N, et al. The incidence of co-morbidities related to obesity and overweight: a systematic review and meta-analysis. BMC Public Health. 2009;9:88.
6. Duclos M. Osteoarthritis, obesity and type 2 diabetes: the weight of waist circumference. Ann Phys Rehabil Med. 2016;59:157–160.
7. Isoyama N, Qureshi AR, Avesani CM, et al. Comparative associations of muscle mass and muscle strength with mortality in dialysis patients. Clin J Am Soc Nephrol. 2014;9:1720–1728.
8. O’Hare AM, Tawney K, Bacchetti P, et al. Decreased survival among sedentary patients undergoing dialysis: results from the dialysis morbidity and mortality study wave 2. Am J Kidney Dis. 2003;41:447–454.
9. Segev DL, Simpkins CE, Thompson RE, et al. Obesity impacts access to kidney transplantation. J Am Soc Nephrol. 2008;19:349–355.
10. United States Renal Data System. 2020 annual data report. Available at https://adr.usrds.org/2020/. Accessed May 1, 2022.
11. Office of Disease Prevention and Health Promotion. Healthy people 2020 topics & objectives: chronic kidney disease. Available at https://www.healthypeople.gov/2020/topics-objectives/topic/chronic-kidney-disease/objectives. Accessed May 1, 2022.
12. Centers for Medicare & Medicaid Services. ESRD treatment choices (ETC) model. Available at https://innovation.cms.gov/innovation-models/esrd-treatment-choices-model. 2022. Accessed May 9, 2022.
13. Ogden CL, Fryar CD, Martin CB, et al. Trends in obesity prevalence by race and hispanic origin-1999-2000 to 2017-2018. JAMA. 2020;324:1208–1210.
14. Hales CM, Carroll MD, Fryar CD, et al. Prevalence of obesity and severe obesity among adults: United States, 2017-2018. NCHS Data Brief. 2020; 360:1–8.
15. Petersen R, Pan L, Blanck HM. Racial and ethnic disparities in adult obesity in the United States: CDC’s tracking to inform state and local action. Prev Chronic Dis. 2019;16:E46.
16. Robert Graham Center. Social Deprivation Index (SDI). American academy of family physicians. Available at https://www.graham-center.org/maps-data-tools/social-deprivation-index.html. Accessed May 2, 2022.
17. Butler DC, Petterson S, Phillips RL, et al. Measures of social deprivation that predict health care access and need within a rational area of primary care service delivery. Health Serv Res. 2013;48:539–559.
18. World Health Organization. Global strategy on diet, physical activity and health: what is overweight and obesity? Available at https://www.who.int/dietphysicalactivity/childhood_what/en/. Accessed November 11, 2019.
19. Austin PC, Fine JP. Practical recommendations for reporting Fine-Gray model analyses for competing risk data. Stat Med. 2017;36:4391–4400.
20. Zhou B, Fine J, Laird G. Goodness-of-fit test for proportional subdistribution hazards model. Stat Med. 2013;32:3804–3811.
21. Centers for Medicare & Medicaid Services. ESRD network organizations. Available at https://www.cms.gov/Medicare/End-Stage-Renal-Disease/ESRDNetworkOrganizations. Accessed April 22, 2022.
22. Molnar MZ, Streja E, Kovesdy CP, et al. Associations of body mass index and weight loss with mortality in transplant-waitlisted maintenance hemodialysis patients. Am J Transplant. 2011;11:725–736.
23. Kramer HJ, Saranathan A, Luke A, et al. Increasing body mass index and obesity in the incident ESRD population. J Am Soc Nephrol. 2006;17:1453–1459.
24. Holley JL, Monaghan J, Byer B, et al. An examination of the renal transplant evaluation process focusing on cost and the reasons for patient exclusion. Am J Kidney Dis. 1998;32:567–574.
25. Gill JS, Lan J, Dong J, et al. The survival benefit of kidney transplantation in obese patients. Am J Transplant. 2013;13:2083–2090.
26. Kaballo MA, Canney M, O’Kelly P, et al. A comparative analysis of survival of patients on dialysis and after kidney transplantation. Clin Kidney J. 2018;11:389–393.
27. Lafranca JA, IJermans JN, Betjes MG, et al. Body mass index and outcome in renal transplant recipients: a systematic review and meta-analysis. BMC Med. 2015;13:111.
28. Malik RF, Jia Y, Mansour SG, et al. Post-transplant diabetes mellitus in kidney transplant recipients: a multicenter study. Kidney360. 2021;2:1296–1307.
29. Bardonnaud N, Pillot P, Lillaz J, et al. Outcomes of renal transplantation in obese recipients. Transplant Proc. 2012;44:2787–2791.
30. Johansen KL. Obesity and body composition for transplant wait-list candidacy–challenging or maintaining the BMI limits? J Ren Nutr. 2013;23:207–209.
31. Ogden CL, Fakhouri TH, Carroll MD, et al. Prevalence of obesity among adults, by household income and education - United States, 2011-2014. MMWR Morb Mortal Wkly Rep. 2017;66:1369–1373.
32. Phelan SM, Burgess DJ, Yeazel MW, et al. Impact of weight bias and stigma on quality of care and outcomes for patients with obesity. Obes Rev. 2015;16:319–326.
33. Suresh A, Robinson L, Milliron BJ, et al. Approaches to obesity management in dialysis settings: renal dietitian perspectives. J Ren Nutr. 2020;30:561–566.
34. Cottrell EK, Hendricks M, Dambrun K, et al. Comparison of community-level and patient-level social risk data in a network of community health centers. JAMA Netw Open. 2020;3:e2016852.
35. Gottlieb LM, Francis DE, Beck AF. Uses and misuses of patient- and neighborhood-level social determinants of health data. Perm J. 2018;22:18–078.
36. Streja E, Molnar MZ, Kovesdy CP, et al. Associations of pretransplant weight and muscle mass with mortality in renal transplant recipients. Clin J Am Soc Nephrol. 2011;6:1463–1473.
37. Marcelli D, Usvyat LA, Kotanko P, et al.; MONitoring Dialysis Outcomes (MONDO) Consortium. Body composition and survival in dialysis patients: results from an international cohort study. Clin J Am Soc Nephrol. 2015;10:1192–1200.
38. Modanlou KA, Muthyala U, Xiao H, et al. Bariatric surgery among kidney transplant candidates and recipients: analysis of the United States renal data system and literature review. Transplantation. 2009;87:1167–1173.
39. Soliman BG, Tariq N, Law YY, et al. Effectiveness of bariatric surgery in increasing kidney transplant eligibility in patients with kidney failure requiring dialysis. Obes Surg. 2021;31:3436–3443.
40. Axelrod DA, Dzebisashvili N, Schnitzler MA, et al. The interplay of socioeconomic status, distance to center, and interdonor service area travel on kidney transplant access and outcomes. Clin J Am Soc Nephrol. 2010;5:2276–2288.
41. Gill JS, Hendren E, Dong J, et al. Differential association of body mass index with access to kidney transplantation in men and women. Clin J Am Soc Nephrol. 2014;9:951–959.
42. Karastergiou K, Smith SR, Greenberg AS, et al. Sex differences in human adipose tissues - the biology of pear shape. Biol Sex Differ. 2012;3:13.
43. Beltrán-Sánchez H, Harhay MO, Harhay MM, et al. Prevalence and trends of metabolic syndrome in the adult U.S. population, 1999-2010. J Am Coll Cardiol. 2013;62:697–703.
44. Liu B, Du Y, Wu Y, et al. Trends in obesity and adiposity measures by race or ethnicity among adults in the United States 2011-18: population based study. BMJ. 2021;372:n365.
45. Ford ES, Maynard LM, Li C. Trends in mean waist circumference and abdominal obesity among US adults, 1999-2012. JAMA. 2014;312:1151–1153.
46. WHO Expert Consultation. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet. 2004;363:157–163.
47. Haugen CE, Chu NM, Ying H, et al. Frailty and access to kidney transplantation. C. 2019.
48. Brilleman SL, Moreno-Betancur M, Polkinghorne KR, et al. Changes in body mass index and rates of death and transplant in hemodialysis patients: a latent class joint modeling approach. Epidemiology. 2019;30:38–47.
49. Harhay MN, Ranganna K, Boyle SM, et al. Association between weight loss before deceased donor kidney transplantation and posttransplantation outcomes. Am J Kidney Dis. 2019;74:361–372.
50. Harhay MN, Chen X, Chu NM, et al. Pre-kidney transplant unintentional weight loss leads to worse post-kidney transplant outcomes. Nephrol Dial Transplant. 2021;36:1927–1936.

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