Original ArticlesEpidemiology and Outcomes

Obesity and Mortality Risk among Younger Dialysis Patients

Ellen K. Hoogeveen, Nynke Halbesma, Kenneth J. Rothman, Theo Stijnen, Sandra van Dijk, Friedo W. Dekker, Elisabeth W. Boeschoten, Renée de Mutsert and for the Netherlands Cooperative Study on the Adequacy of Dialysis-2 (NECOSAD) Study Group
CJASN February 2012, 7 (2) 280-288; DOI: https://doi.org/10.2215/CJN.05700611

Summary

Background and objectives Many studies show that obesity in dialysis patients is not strongly associated with mortality but not whether this modest association is constant over age. This study investigated the extent to which the relation of body mass index (BMI) and mortality differs between younger and older dialysis patients.

Design, setting, participants, & measurements Adult dialysis patients were prospectively followed from their first dialysis treatment for 7 years or until death or transplantation. Patients were stratified by age (<65 or ≥65 years) and baseline BMI (<20, 20–24 [reference], 25–29, and ≥30 kg/m2).

Results The study sample included 984 patients younger than 65 years and 765 patients 65 years or older; cumulative survival proportions at end of follow-up were 50% and 16%. Age-standardized mortality rate was 1.7 times higher in obese younger patients than those with normal BMI, corresponding to an excess rate of 5.2 deaths/100 patient-years. Mortality rates were almost equal between obese older patients and those with normal BMI. Excess rates of younger and older patients with low compared with normal BMI were 8.7 and 1.1 deaths/100 patient-years. After adjustment for age, sex, smoking, comorbidity, and treatment modality, hazard ratios by increasing BMI were 2.00, 1, 0.95, and 1.57 for younger patients and 1.07, 1, 0.88, and 0.91 for older patients, implying that obesity is a 1.7-fold (95% confidence interval, 1.1- to 2.9-fold) stronger risk factor in younger than older patients.

Conclusions In contrast to older dialysis patients, younger patients with low or very high BMI had a substantially elevated risk for death.

Introduction

Obesity has reached global epidemic proportions. Trends in obesity prevalence among dialysis patients mirror trends in the general population. Over the past decade the prevalence of obesity, measured as a body mass index (BMI, calculated as weight in kilograms divided by the square of height in meters) of 30 kg/m2 or greater, has increased among adults with ESRD to 10% in the Netherlands and to 30% in the United States (1,2). Obesity increases the risk for diabetes and hypertension, the primary causes of ESRD in the Netherlands and the United States (2,3). Most likely, obesity is also an independent risk factor for ESRD as a result of glomerular hyperfiltration and activation of the renin-angiotensin system (4). Finally, it has become evident that obesity changes adipose tissue to a pro-inflammatory state that could contribute to focal and segmental glomerulosclerosis (5).

Obesity is an established risk factor for cardiovascular disease and death in the general population (albeit a less important one in the elderly) and kidney transplant recipients (3,6–9). Several studies, but not all, have suggested that the effect of obesity in patients with ESRD undergoing maintenance dialysis is paradoxically in the opposite direction, showing that a high BMI is associated with improved survival (the so-called obesity paradox) (10–13).

Patients with ESRD who require maintenance dialysis have a high annual mortality rate of about 20%; about 40% of this figure reflects cardiovascular disease (10). Identification of potential modifiable cardiovascular risk factors is important for targeted prevention and improved life expectancy. Nevertheless, in the general population the association of such risk factors as hypercholesterolemia, hypertension, smoking, and diabetes with cardiovascular disease and mortality, when measured on a relative scale, decreases with age (14–18). Differences in mortality between younger and elderly obese dialysis patients have not been well studied, prompting us to investigate the extent to which the relation between obesity and mortality differs between younger (<65 years) and older (≥65 years) dialysis patients.

Materials and Methods

Study Design and Population

The Netherlands Cooperative Study on the Adequacy of Dialysis-2 (NECOSAD) is a prospective multicenter cohort study of patients with incident ESRD older than 18 years. Patients were followed starting with their first renal replacement therapy. Enrollment occurred between January 1997 and April 2004 throughout the Netherlands. All patients gave informed consent, and all local medical ethics committees approved the study. We followed patients at 3 months after the start of dialysis until death or censoring, which occurred because of a transfer to a nonparticipating dialysis center, withdrawal from the study, kidney transplantation, or the end of the follow-up period, which was a maximum of 7 years. All deaths during follow-up were immediately ascertained by a nephrologist. Causes of death were classified according to the coding system of the European Renal Association–European Dialysis and Transplantation Association as previously described elsewhere (3).

The cohort comprised 1957 patients who were at least 18 years of age, had not had previous renal replacement therapy, and survived at least 90 days after starting hemodialysis (HD) or peritoneal dialysis (PD). We began follow-up 90 days after the start of dialysis to avoid the problem that fluid overload at the start of dialysis would distort BMI measurements.

BMI

We used BMI measured at baseline as an index of adiposity. Weight was measured in HD patients after a dialysis session. In PD patients the volume of the intraperitoneal fluid, if present, was recorded and subtracted from the measured weight. According to the World Health Organization guidelines, obesity was defined as a BMI of 30 kg/m2 or greater, and overweight was defined as a BMI of 25–29 kg/m2. We defined normal weight as a BMI of 20–24 kg/m2, which is within the normal range according to the WHO, and we considered this a priori as the referent category (7). We defined underweight as a BMI less than 20 kg/m2.

Other Demographic and Clinical Data

Data on age, sex, primary kidney disease, diabetes, history of cardiovascular disease (myocardial infarction, ischemic stroke, or limb amputation due to peripheral arterial disease), history of malignancy, and chronic lung disease were collected at the start of dialysis treatment. Primary kidney disease was classified according to the codes of the European Renal Association–European Dialysis and Transplant Association (19). We grouped patients into four classes of primary kidney disease: GN, diabetes mellitus, renal vascular disease, and other kidney diseases. Diabetes was defined on the basis of diabetes mellitus registered as the primary kidney disease or as a comorbid condition. Patients were classified as current smokers of cigarettes (including those who had quit smoking within the past 6 months), former smokers, or never-smokers. Serum albumin, creatinine, and urea were determined from the blood samples. Urea and creatinine levels were also measured in the urine samples. Renal function, expressed as GFR, was calculated as the mean of creatinine and urea clearance corrected for body surface area (ml/min per 1.73 m2).

Statistical Analyses

Variables are presented as mean ± SD, median (interquartile range), or number (proportion) where appropriate. We assessed the relation between BMI and the primary endpoint (mortality observed over 7 years of follow-up) using several methods. In all analyses, survival was measured from 90 days after the start of dialysis. We used life tables to calculate cumulative proportions surviving during follow-up. Because obesity shows its detrimental effects after long-term exposure, we studied BMI as a fixed risk factor at baseline (20). “Elderly” is often defined as age 65 years or older; therefore, we chose that boundary point a priori for all our analyses. To compare the association of BMI and mortality between younger (<65 years) and elderly (≥65 years) dialysis patients, patients were divided into eight categories on the basis of their age (<65 or ≥65 years) and baseline BMI (<20, 20–24, 25–29, and ≥30 kg/m2).

First, absolute mortality rates were calculated within each age and BMI category. In addition, age-standardized mortality rates were calculated for each BMI category using 5-year age groups. For younger patients the age standard was the age distribution of all study dialysis patients younger than 65 years; for older patients it was the age distribution of study patients 65 years or older. Within each of the two broad age categories, age distribution differences across the four BMI categories were thus taken into account. We did not standardize for sex because we found that sex was not an important confounder. The age-standardized mortality rates can be compared only within each of the two broad age strata (younger or older).

Second, we conducted a proportional hazards regression analysis, obtaining hazard ratios (HRs) estimating the effect of various BMI and age categories. We performed the analyses within two age groups (<65 or ≥65 years) using a BMI of 20–24 kg/m2 in younger patients as the reference. Analyses were adjusted for potential confounders: sex, smoking, comorbidity (history of cardiovascular disease, chronic lung disease, and malignancy) and treatment modality (HD or PD). To avoid bias toward the null, any factor postulated to be in the causal pathway between elevated BMI and death, such as hypertension, hypercholesterolemia, and insulin resistance, should not be treated as a confounder (7). To remove the effect of age on the association between BMI and mortality, we performed an additional analysis using a BMI of 20–24 kg/m2 within each age group as the referent category and adjusted for all potential confounders, including age within the two broad age categories (using both linear and squared terms for age). Statistical interaction between age and BMI was modeled by including a product term for age and BMI in the proportional hazards model. In a separate analysis we also adjusted for primary kidney disease, including diabetes. GFR was missing for 274 (16%) patients. We used multiple imputation to impute missing values of GFR (21).

Third, we evaluated biologic, as opposed to statistical, interaction between age and obesity by assessing departures from additive effects, as described by Rothman and colleagues (22–24). Basically, this approach examines the extent to which the effect of both risk factors when present jointly departs from the sum of their effects when present singly. We quantified the amount of biologic interaction by assessing departures from additivity using the synergy index (S):Embedded Imagewhere HR designates hazard ratio and the subscripts represent presence or absence of each of the two risk factors (25). S is the ratio of the joint effect of the two risk factors to the sum of the effects of each factor in the absence of the other. With no biologic interaction, S = 1. S > 1 measures synergy, and S < 1 measures antagonism. Ninety-five percent confidence intervals (CIs) were calculated as described elsewhere (26).

Fourth, to study the continuous relation between BMI and mortality, we modeled BMI by a four-knot restricted cubic spline (27). The knots were chosen at the 5th, 35th, 65th, and 95th percentiles of the BMI distribution, corresponding to 19.0, 22.9, 25.6, and 32.6 kg/m2. To account for age, which is the strongest determinant of mortality and a potential confounder, we used a five-knot restricted cubic spline with knots at the following percentiles: 5th, 28th, 50th, 72nd, and 95th. To investigate the modifying effect of age we added product terms between age (<65 years or ≥65 years) and the BMI spline covariates. In an additional analysis, we created a product term for quartiles of age, effectively allowing the coefficients of BMI to vary freely for each age quartile. In the proportional hazards regression models, the proportionality assumption for each covariate was checked by adding a product term between that covariate and the logarithm of follow-up time. All analyses were done using SPSS, version 19.0 (SPSS, Inc., Chicago, IL).

Results

Weight or height was missing for 302 patients. We included in the main analyses the 1749 patients (89%) for whom there were sufficient data to compute baseline BMI.

The mean ± SD age of the study cohort (n=1749) was 60±15 years, 62% of all patients were men, 62% were undergoing HD, and 92% were white. The mean ± SD BMIs at baseline for younger and elderly patients were 24.8±4.4 and 24.7±3.9 kg/m2, respectively, with an approximately normal distribution. Table 1 presents the baseline characteristics of the younger (<65 years) and older (≥65 years) dialysis patients.

View this table:
Table 1.

Baseline characteristics of a cohort of incident dialysis patients at 3 months after start of dialysis

We used life-table methods to estimate the cumulative proportions of younger and older patients who had died at the end of the 7-year follow-up period: 50% and 84%, respectively. Approximately 46% of all deaths were due to cardiovascular causes. The median survival times were 6.0 years for younger patients and 2.9 years for older patients. Overall, the crude mortality rates were 8.2 (95% CI, 7.3–9.3) per 100 person-years in younger and 25.2 (95% CI, 23.4–27.2) per 100 patient-years in older dialysis patients.

Figure 1 shows the crude and age-standardized mortality rates per 100 patient-years according to the four BMI categories at baseline for younger and older dialysis patients (Table 2). Age standardization had only a small effect on the rates except for the younger patients with low BMI. As expected, at all levels of BMI, the absolute risk for death was higher in older than in younger dialysis patients. The crude mortality rate was 1.9 times higher in younger obese patients than in younger patients with a normal BMI. The age-standardized mortality rate was 1.7 times higher in younger obese patients than in younger patients with a normal BMI, which corresponded to an excess rate of 5.2 deaths/100 patient-years. In older dialysis patients, the mortality rates were almost equal between obese and patients with a normal BMI. Thus, obesity appears to be a risk factor for mortality among younger but not among older dialysis patients. The crude mortality rate was 1.4 times higher in younger patients with a low BMI than in patients with a normal BMI, and the age-standardized mortality rate was 2.2 times higher in younger patients with a low BMI than in patients with a normal BMI, which corresponds to excess rates of 3.0 and 8.7 deaths/100 patient-years, respectively. In older dialysis patients the mortality rates were almost equal between patients with a low BMI and patients with a normal BMI.

Figure 1.
Figure 1.

Crude and age-standardized mortality rates by body mass index (BMI) category. Crude and age-standardized mortality rates (per 100 person-years) with 95% confidence intervals in four categories of baseline BMI during 7 years of follow-up of dialysis patients younger than 65 years and older than 65 years. py, patient-years.

View this table:
Table 2.

Crude and age-standardized mortality rates per body mass index category in 7-year follow-up of patients <65 and ≥65 years of age

Stratification by treatment modality (HD or PD) showed no evidence of confounding by indication. Therefore, we present results for both treatment modalities combined. We conducted a proportional hazards regression analysis, obtaining HRs estimating the associations of various BMI categories for younger and older patients. After checking the proportional hazards assumption we found no sign of violation. After stratification by age (<65 years or ≥65 years) and adjustment for sex, smoking, comorbidity, and treatment modality, we observed a U-shaped relation between BMI and mortality among younger, but not among older, dialysis patients (Table 3). Additional adjustment for serum albumin and residual renal function slightly attenuated the relation between BMI and survival. Additional adjustment for primary kidney disease, including diabetic nephropathy, attenuated the relation between obesity and mortality, as expected. An analysis restricted to patients with diabetes showed a similar trend (data not shown).

View this table:
Table 3.

Mortality rate ratios during 7-year follow-up from proportional hazards regression for body mass index at baseline for younger (<65 years) and older (≥65 years) dialysis patients

After multivariable adjustment, the synergy index was 0.57 (95% CI, 0.33–0.98) (Table 3). This result indicates an estimated effect of joint exposure that is less than the sum of the separate effects, which corresponds to biologic antagonism. It implies that older age attenuates the association between obesity and mortality risk.

Table 4 shows the association between BMI and mortality after multivariable adjustment, including age, for younger and older dialysis patients. The ratio of the HRs of older and younger obese patients was 0.58 (95% CI, 0.35–0.95), implying that obesity among younger dialysis patients is a 1.7-fold (95% CI, 1.1- to 2.9-fold) stronger risk factor compared with older obese dialysis patients.

View this table:
Table 4.

Ratio of mortality during 7-year follow-up from proportional hazards regression for body mass index at baseline for younger (<65 years) and older (≥65 years) dialysis patients

Figure 2 shows the relation between BMI and mortality rates within age quartiles expressed by the HR ratio after multivariable adjustment. We found a U-shaped relation between BMI and mortality among patients younger than 62 years of age (second quartile), which became more pronounced among patients younger than 49 years (first quartile), with a nadir around a BMI of 25 kg/m2. The relation was weaker in the two highest age quartiles. Figure 3 depicts the continuous relation between BMI and mortality for younger and older patients, along with the 95% CIs, and clearly shows the different shape for each age category.

Figure 2.
Figure 2.

Relation between body mass index (BMI) and mortality rates within age quartiles. Hazard ratios for mortality depending on BMI were modeled by separate restricted cubic splines within age quartiles in a Cox regression model. Patients with extreme values of BMI (<18 kg/m2 [2%] and >40 kg/m2 [0.4%]) were excluded from the figure. The reference was a BMI of 25 kg/m2. The model was adjusted for age, sex, product term between age and BMI, smoking, comorbidity (history of cardiovascular disease, chronic lung disease, and malignancy), and treatment modality (hemodialysis or peritoneal dialysis).

Figure 3.
Figure 3.

Relation between body mass index (BMI) and mortality rates with 95% confidence intervals (dotted curves) for younger (<65 years) and older (≥65 years) dialysis patients. Hazard ratios for mortality depending on BMI were modeled by separate restricted cubic splines for younger (<65 years) and older (≥65 years) dialysis patients in a Cox regression model. Patients with extreme values of BMI (<18 kg/m2 [2%] and >40 kg/m2 [0.4%]) were excluded from the figure. The reference was a BMI of 25 kg/m2. The model was adjusted for age, sex, smoking, comorbidity (history of cardiovascular disease, chronic lung disease, and malignancy), treatment modality (hemodialysis or peritoneal dialysis), and product term between age and BMI.

Discussion

This cohort study shows that among younger (<65 years) incident dialysis patients, obesity (BMI ≥30 kg/m2) compared with normal weight is associated with an almost 2-fold increased mortality rate. Independent of important confounders, patients younger than 65 years at the start of dialysis with a BMI ≥30 kg/m2 have a 70% higher risk for death compared with patients with a normal BMI. The association between obesity and mortality was even more pronounced among patients younger than 50 years. After adjustment for diabetic nephropathy, the relation between obesity and mortality was slightly attenuated but persisted, indicating that obesity exerts its detrimental effects also via mechanisms independent of diabetes. A low BMI was also associated with increased mortality in this age group, but this may in part be due to reverse causation. Finally, among older (≥65 years) dialysis patients we found no association between obesity and mortality.

In the general population, the negative association of obesity with life expectancy is greater at a young age and less pronounced at age 70 years (28,29). We found a similar continuous age-dependent association between obesity and mortality among dialysis patients. A possible explanation for why obesity is not a risk factor for mortality among older dialysis patients might be that they have a high mortality rate and that short-term effects of competitive risk factors, such as underweight and infection, may preempt long-term effects of obesity.

Our finding is consistent with large studies (>1000 participants) among incident dialysis patients with a long-term follow-up (5–10 years) that showed a U-shaped association between BMI and mortality or an increased mortality risk among obese patients (30–32). Our results contrast with those of previous large studies, which demonstrated a neutral or reduced risk for death among obese maintenance dialysis patients with a relative short median follow-up of 1–5 years (10–13,32–39). Leavey et al. stratified maintenance dialysis patients (of whom about 25% were black) into three age groups (<45, 45–64, ≥65 years), showing a lower mortality risk with obesity in all three age groups (37).

Part of the disparity between the findings of these observational studies and our results may be explained by the following: (1) too-short duration of follow-up, (2) survivor bias due to inclusion of maintenance dialysis patients, (3) differences in statistical approach of BMI as a variable (for instance, including low BMI [<20 kg/m2] in the reference category might result in underestimation of the association between obesity and mortality), (4) combining results of both younger and older dialysis patients, (5) misclassification of obesity due to fluid overload by calculating BMI at the time of the initiation of dialysis, (6) inadequate control for confounding (such as smoking), and (7) not taking into account differences of body composition between races. For instance, blacks have a greater muscle mass at any BMI than do persons of other races (39).

Several aspects of our study set it apart from others. Because we included only incident dialysis patients, our study, unlike those that included patients on maintenance dialysis, is not susceptible to survivor bias. Survivor bias is a form of selection bias that occurs when the risk for an outcome is estimated from data collected at a given time point among survivors rather than on data gathered in a group of incident cases (40). As with other biases, an increased study size cannot compensate for survivor bias (41). Second, we have a relatively long follow-up period. Third, we adjusted for chronic disease and smoking. Reverse causation owing to inadequate control for smoking status and preexisting chronic disease can distort the true relation between BMI and risk for death because smoking and chronic illness are associated with both decreased BMI and increased risk for death.

Nevertheless our study has several limitations. We combined patients treated with HD or PD. Differences in outcome between these two patient groups have been suggested, although the determinants of dialysis modality are largely nonmedical. Two large studies among HD and PD patients showed little difference in risk for death during 2–5 years of follow-up (42,43). Second, we used BMI as a proxy for obesity, but BMI only indirectly reflects the risk stemming from the metabolic effects of an increased fat mass (44). Moreover, BMI does not fully reflect some age-related changes, namely that the proportion of body fat increases with age while muscle mass decreases. Third, in the Netherlands wait-listing for kidney transplantation is suspended for patients with a BMI of 30 kg/m2 or greater, resulting in differential selection of healthy patients with a BMI less than 30 kg/m2 for transplantation. This differential selection may have resulted in bias to the extent that we could not completely control for covariates related to the selection for kidney transplantation. Finally, failure to control for unintentional weight loss or cigarette smoking could result in residual confounding (7). Loss of body mass, even in obese dialysis patients, might portend greater mortality (12). We had no information about the number of pack-years smoked by patients, and therefore we cannot rule out residual confounding due to insufficient adjustment for smoking. Such residual confounding would tend to underestimate the effect of obesity.

The clinical implications of this study are as follows: first, that potential kidney recipients should be encouraged to lose weight if obese. Given the suggestion that obesity might be advantageous, some argue for caution in recommending that patients strive for a BMI less than 30 kg/m2 (32). Second, for patients younger than 65 years who are not eligible for kidney transplantation, identification of potential modifiable cardiovascular risk factors, such as obesity, is important to improve life expectancy.

In conclusion, obesity was not materially associated with an increased mortality in older dialysis patients, whereas among younger dialysis patients, having a BMI that was low or very high was associated with an almost twofold increased mortality rate.

Disclosures

None.

Acknowledgments

We thank the investigators and study nurses of the participating dialysis centers and the data managers of The Netherlands Cooperative Study on the Adequacy of Dialysis (NECOSAD) for collection and management of data. The members of the NECOSAD Study Group include A.J. Apperloo, J.A. Bijlsma, M. Boekhout, W.H. Boer, P.J.M. van der Boog, H.R. Büller, M. van Buren, F.Th. de Charro, C.J. Doorenbos, M.A. van den Dorpel, A. van Es, W.J. Fagel, G.W. Feith, C.W.H. de Fijter, L.A.M. Frenken, W. Grave, J.A.C.A. van Geelen, P.G.G. Gerlag, J.P.M.C. Gorgels, R.M. Huisman, K.J. Jager, K. Jie, W.A.H. Koning-Mulder, M.I. Koolen, T.K. Kremer Hovinga, A.T.J. Lavrijssen, A.J. Luik, J. van der Meulen, K.J. Parlevliet, M.H.M. Raasveld, F.M. van der Sande, M.J.M. Schonck, M.M.J. Schuurmans, C.E.H. Siegert, C.A. Stegeman, P. Stevens, J.G.P. Thijssen, R.M. Valentijn, G.H. Vastenburg, C.A. Verburgh, H.H. Vincent, and P.F. Vos. We thank the nursing staff of the participating dialysis centers and the staff of the NECOSAD trial office for their invaluable assistance in the collection and management of data for this study. We are indebted to Dr. Eric Melse for graphic art work.

This work was supported in part by unrestricted grants from the Dutch Kidney Foundation. The funding source was not involved in the collection, interpretation, or the analysis of the data or in the decision to write or submit this report for publication.

Footnotes

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

  • Received June 11, 2011.
  • Accepted November 20, 2011.

References

  1. ↵
    1. Drechsler C,
    2. de Mutsert R,
    3. Grootendorst DC,
    4. Boeschoten EW,
    5. Krediet RT,
    6. le Cessie S,
    7. Wanner C,
    8. Dekker FW
    ; NECOSAD Study Group: Association of body mass index with decline in residual kidney function after initiation of dialysis. Am J Kidney Dis 53: 1014–1023, 2009pmid:19217702
    OpenUrlCrossRefPubMed
  2. ↵
    1. Adams KF,
    2. Schatzkin A,
    3. Harris TB,
    4. Kipnis V,
    5. Mouw T,
    6. Ballard-Barbash R,
    7. Hollenbeck A,
    8. Leitzmann MF
    : Overweight, obesity, and mortality in a large prospective cohort of persons 50 to 71 years old. N Engl J Med 355: 763–778, 2006pmid:16926275
    OpenUrlCrossRefPubMed
  3. ↵
    1. de Mutsert R,
    2. Snijder MB,
    3. van der Sman-de Beer F,
    4. Seidell JC,
    5. Boeschoten EW,
    6. Krediet RT,
    7. Dekker JM,
    8. Vandenbroucke JP,
    9. Dekker FW
    : Association between body mass index and mortality is similar in the hemodialysis population and the general population at high age and equal duration of follow-up. J Am Soc Nephrol 18: 967–974, 2007pmid:17267739
    OpenUrlAbstract/FREE Full Text
  4. ↵
    1. Praga M
    : Obesity—a neglected culprit in renal disease. Nephrol Dial Transplant 17: 1157–1159, 2002pmid:12105233
    OpenUrlCrossRefPubMed
  5. ↵
    1. Stienstra R,
    2. Joosten LA,
    3. Koenen T,
    4. van Tits B,
    5. van Diepen JA,
    6. van den Berg SA,
    7. Rensen PC,
    8. Voshol PJ,
    9. Fantuzzi G,
    10. Hijmans A,
    11. Kersten S,
    12. Müller M,
    13. van den Berg WB,
    14. van Rooijen N,
    15. Wabitsch M,
    16. Kullberg BJ,
    17. van der Meer JW,
    18. Kanneganti T,
    19. Tack CJ,
    20. Netea MG
    : The inflammasome-mediated caspase-1 activation controls adipocyte differentiation and insulin sensitivity. Cell Metab 12: 593–605, 2010pmid:21109192
    OpenUrlCrossRefPubMed
  6. ↵
    1. Coresh J,
    2. Selvin E,
    3. Stevens LA,
    4. Manzi J,
    5. Kusek JW,
    6. Eggers P,
    7. Van Lente F,
    8. Levey AS
    : Prevalence of chronic kidney disease in the United States. JAMA 298: 2038–2047, 2007pmid:17986697
    OpenUrlCrossRefPubMed
  7. ↵
    World Health Organization: Obesity: Preventing and Managing the Global Epidemic. Report of a WHO Consultation. World Health Organ Tech Rep Ser 894: 39–68, 2000
    OpenUrl
    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
  9. ↵
    1. Hoogeveen EK,
    2. Aalten J,
    3. Rothman KJ,
    4. Roodnat JI,
    5. Mallat MJ,
    6. Borm G,
    7. Weimar W,
    8. Hoitsma AJ,
    9. de Fijter JW
    : Effect of obesity on the outcome of kidney transplantation: a 20-year follow-up. Transplantation 91: 869–874, 2011pmid:21326138
    OpenUrlCrossRefPubMed
  10. ↵
    1. Kalantar-Zadeh K,
    2. Streja E,
    3. Kovesdy CP,
    4. Oreopoulos A,
    5. Noori N,
    6. Jing J,
    7. Nissenson AR,
    8. Krishnan M,
    9. Kopple JD,
    10. Mehrotra R,
    11. Anker SD
    : The obesity paradox and mortality associated with surrogates of body size and muscle mass in patients receiving hemodialysis. Mayo Clin Proc 85: 991–1001, 2010pmid:21037042
    OpenUrlCrossRefPubMed
    1. Kalantar-Zadeh K,
    2. Block G,
    3. Humphreys MH,
    4. Kopple JD
    : Reverse epidemiology of cardiovascular risk factors in maintenance dialysis patients. Kidney Int 63: 793–808, 2003pmid:12631061
    OpenUrlCrossRefPubMed
  12. ↵
    1. Salahudeen AK
    : Obesity and survival on dialysis. Am J Kidney Dis 41: 925–932, 2003pmid:12722026
    OpenUrlCrossRefPubMed
  13. ↵
    1. Wong JS,
    2. Port FK,
    3. Hulbert-Shearon TE,
    4. Carroll CE,
    5. Wolfe RA,
    6. Agodoa LY,
    7. Daugirdas JT
    : Survival advantage in Asian American end-stage renal disease patients. Kidney Int 55: 2515–2523, 1999pmid:10354301
    OpenUrlCrossRefPubMed
  14. ↵
    1. Weverling-Rijnsburger AW,
    2. Blauw GJ,
    3. Lagaay AM,
    4. Knook DL,
    5. Meinders AE,
    6. Westendorp RG
    : Total cholesterol and risk of mortality in the oldest old. Lancet 350: 1119–1123, 1997pmid:9343498
    OpenUrlCrossRefPubMed
    1. Goodwin JS
    : Embracing complexity: A consideration of hypertension in the very old. J Gerontol A Biol Sci Med Sci 58: 653–658, 2003pmid:12865483
    OpenUrlCrossRefPubMed
    1. Hakala SM,
    2. Tilvis RS,
    3. Strandberg TE
    : Blood pressure and mortality in an older population. A 5-year follow-up of the Helsinki Ageing Study. Eur Heart J 18: 1019–1023, 1997pmid:9183596
    OpenUrlCrossRefPubMed
    1. Danaei G,
    2. Ding EL,
    3. Mozaffarian D,
    4. Taylor B,
    5. Rehm J,
    6. Murray CJL,
    7. Ezzati M
    : The preventable causes of death in the United States: comparative risk assessment of dietary, lifestyle, and metabolic risk factors. PLoS Med 6: e1000058, 2009pmid:19399161
    OpenUrlCrossRefPubMed
  18. ↵
    1. Mulnier HE,
    2. Seaman HE,
    3. Raleigh VS,
    4. Soedamah-Muthu SS,
    5. Colhoun HM,
    6. Lawrenson RA,
    7. De Vries CS
    : Risk of stroke in people with type 2 diabetes in the UK: A study using the General Practice Research Database. Diabetologia 49: 2859–2865, 2006pmid:17072582
    OpenUrlCrossRefPubMed
  19. ↵
    1. van Dijk PC,
    2. Jager KJ,
    3. de Charro F,
    4. Collart F,
    5. Cornet R,
    6. Dekker FW,
    7. Grönhagen-Riska C,
    8. Kramar R,
    9. Leivestad T,
    10. Simpson K,
    11. Briggs JD
    ; ERA-EDTA registry: Renal replacement therapy in Europe: The results of a collaborative effort by the ERA-EDTA registry and six national or regional registries. Nephrol Dial Transplant 16: 1120–1129, 2001pmid:11390709
    OpenUrlCrossRefPubMed
  20. ↵
    1. Dekker FW,
    2. de Mutsert R,
    3. van Dijk PC,
    4. Zoccali C,
    5. Jager KJ
    : Survival analysis: Time-dependent effects and time-varying risk factors. Kidney Int 74: 994–997, 2008pmid:18633346
    OpenUrlCrossRefPubMed
  21. ↵
    1. Moons KG,
    2. Donders RA,
    3. Stijnen T,
    4. Harrell FE Jr.
    : Using the outcome for imputation of missing predictor values was preferred. J Clin Epidemiol 59: 1092–1101, 2006pmid:16980150
    OpenUrlCrossRefPubMed
  22. ↵
    1. Rothman KJ,
    2. Greenland S
    : Concepts of interaction. In: Modern Epidemiology, edited by Rothman KJ, Greenland S, Philadelphia, Lippincott-Raven Publishers, 1998, pp 329–342
    1. Rothman KJ
    : Measuring interactions. In: Epidemiology: An Introduction, New York, Oxford University Press, 2002, pp 168–180
  24. ↵
    1. Rothman KJ,
    2. Greenland S,
    3. Walker AM
    : Concepts of interaction. Am J Epidemiol 112: 467–470, 1980pmid:7424895
    OpenUrlPubMed
  25. ↵
    1. Rothman KJ
    : Synergy and antagonism in cause-effect relationships. Am J Epidemiol 99: 385–388, 1974pmid:4841816
    OpenUrlPubMed
  26. ↵
    1. Hosmer DW,
    2. Lemeshow S
    : Confidence interval estimation of interaction. Epidemiology 3: 452–456, 1992pmid:1391139
    OpenUrlCrossRefPubMed
  27. ↵
    1. Greenland S
    : Dose-response and trend analysis in epidemiology: Alternatives to categorical analysis. Epidemiology 6: 356–365, 1995pmid:7548341
    OpenUrlCrossRefPubMed
  28. ↵
    1. van Dis I,
    2. Kromhout D,
    3. Geleijnse JM,
    4. Boer JM,
    5. Verschuren WM
    : Body mass index and waist circumference predict both 10-year nonfatal and fatal cardiovascular disease risk: Study conducted in 20,000 Dutch men and women aged 20-65 years. Eur J Cardiovasc Prev Rehabil 16: 729–734, 2009pmid:19809330
    OpenUrlCrossRefPubMed
  29. ↵
    1. Reynolds SL,
    2. Saito Y,
    3. Crimmins EM
    : The impact of obesity on active life expectancy in older American men and women. Gerontologist 45: 438–444, 2005pmid:16051906
    OpenUrlCrossRefPubMed
  30. ↵
    1. McDonald SP,
    2. Collins JF,
    3. Johnson DW
    : Obesity is associated with worse peritoneal dialysis outcomes in the Australia and New Zealand patient populations. J Am Soc Nephrol 14: 2894–2901, 2003pmid:14569099
    OpenUrlAbstract/FREE Full Text
    1. Beddhu S,
    2. Pappas LM,
    3. Ramkumar N,
    4. Samore M
    : Effects of body size and body composition on survival in hemodialysis patients. J Am Soc Nephrol 14: 2366–2372, 2003pmid:12937315
    OpenUrlAbstract/FREE Full Text
  32. ↵
    1. Stack AG,
    2. Murthy BV,
    3. Molony DA
    : Survival differences between peritoneal dialysis and hemodialysis among “large” ESRD patients in the United States. Kidney Int 65: 2398–2408, 2004pmid:15149353
    OpenUrlCrossRefPubMed
    1. Johansen KL,
    2. Young B,
    3. Kaysen GA,
    4. Chertow GM
    : Association of body size with outcomes among patients beginning dialysis. Am J Clin Nutr 80: 324–332, 2004pmid:15277152
    OpenUrlAbstract/FREE Full Text
    1. Chazot C,
    2. Gassia JP,
    3. Di Benedetto A,
    4. Cesare S,
    5. Ponce P,
    6. Marcelli D
    : Is there any survival advantage of obesity in Southern European haemodialysis patients? Nephrol Dial Transplant 24: 2871–2876, 2009pmid:19369686
    OpenUrlCrossRefPubMed
    1. Kopple JD,
    2. Zhu X,
    3. Lew NL,
    4. Lowrie EG
    : Body weight-for-height relationships predict mortality in maintenance hemodialysis patients. Kidney Int 56: 1136–1148, 1999pmid:10469384
    OpenUrlCrossRefPubMed
    1. Lowrie EG,
    2. Li Z,
    3. Ofsthun N,
    4. Lazarus JM
    : Body size, dialysis dose and death risk relationships among hemodialysis patients. Kidney Int 62: 1891–1897, 2002pmid:12371994
    OpenUrlCrossRefPubMed
  37. ↵
    1. Leavey SF,
    2. McCullough K,
    3. Hecking E,
    4. Goodkin D,
    5. Port FK,
    6. Young EW
    : Body mass index and mortality in ‘healthier’ as compared with ‘sicker’ haemodialysis patients: results from the Dialysis Outcomes and Practice Patterns Study (DOPPS). Nephrol Dial Transplant 16: 2386–2394, 2001pmid:11733631
    OpenUrlCrossRefPubMed
    1. Abbott KC,
    2. Glanton CW,
    3. Trespalacios FC,
    4. Oliver DK,
    5. Ortiz MI,
    6. Agodoa LY,
    7. Cruess DF,
    8. Kimmel PL
    : Body mass index, dialysis modality, and survival: Analysis of the United States Renal Data System Dialysis Morbidity and Mortality Wave II Study. Kidney Int 65: 597–605, 2004pmid:14717930
    OpenUrlCrossRefPubMed
  39. ↵
    1. Ricks J,
    2. Molnar MZ,
    3. Kovesdy CP,
    4. Kopple JD,
    5. Norris KC,
    6. Mehrotra R,
    7. Nissenson AR,
    8. Arah OA,
    9. Greenland S,
    10. Kalantar-Zadeh K
    : Racial and ethnic differences in the association of body mass index and survival in maintenance hemodialysis patients. Am J Kidney Dis 58: 574–582, 2011pmid:21658829
    OpenUrlCrossRefPubMed
  40. ↵
    1. Jager KJ,
    2. Stel VS,
    3. Zoccali C,
    4. Wanner C,
    5. Dekker FW
    : The issue of studying the effect of interventions in renal replacement therapy — to what extent may we be deceived by selection and competing risk? Nephrol Dial Transplant 25: 3836–3839, 2010pmid:20833688
    OpenUrlCrossRefPubMed
  41. ↵
    1. Rothman KJ
    : Biases in study design. In: Epidemiology: An introduction, New York, Oxford University Press, 2002, pp 94–95
  42. ↵
    1. Mehrotra R,
    2. Chiu YW,
    3. Kalantar-Zadeh K,
    4. Bargman J,
    5. Vonesh E
    : Similar outcomes with hemodialysis and peritoneal dialysis in patients with end-stage renal disease. Arch Intern Med 171: 110–118, 2011pmid:20876398
    OpenUrlCrossRefPubMed
  43. ↵
    1. Termorshuizen F,
    2. Korevaar JC,
    3. Dekker FW,
    4. Van Manen JG,
    5. Boeschoten EW,
    6. Krediet RT
    ; Netherlands Cooperative Study on the Adequacy of Dialysis Study Group: Hemodialysis and peritoneal dialysis: comparison of adjusted mortality rates according to the duration of dialysis: Analysis of The Netherlands Cooperative Study on the Adequacy of Dialysis 2. J Am Soc Nephrol 14: 2851–2860, 2003pmid:14569095
    OpenUrlAbstract/FREE Full Text
  44. ↵
    1. Rothman KJ
    : BMI-related errors in the measurement of obesity. Int J Obes (Lond) 32(Suppl 3): S56–S59, 2008pmid:18695655
    OpenUrlCrossRefPubMed
Back to top

In this issue

View Selected Citations (0)
Print
Download PDF
Email Article
Citation Tools
Request Permissions
Obesity and Mortality Risk among Younger Dialysis Patients
Ellen K. Hoogeveen, Nynke Halbesma, Kenneth J. Rothman, Theo Stijnen, Sandra van Dijk, Friedo W. Dekker, Elisabeth W. Boeschoten, Renée de Mutsert, for the Netherlands Cooperative Study on the Adequacy of Dialysis-2 (NECOSAD) Study Group
CJASN Feb 2012, 7 (2) 280-288; DOI: 10.2215/CJN.05700611
del.icio.us logo Digg logo Reddit logo Twitter logo CiteULike logo Facebook logo Google logo Mendeley logo

Jump to section

More in this TOC Section

Cited By...

Similar Articles

Related Articles

  • No related articles found.