Literature DB >> 31438869

BMI and its association with death and the initiation of renal replacement therapy (RRT) in a cohort of patients with chronic kidney disease (CKD).

Zaimin Wang1,2, Jianzhen Zhang3,4, Samuel Chan3,4,5, Anne Cameron3,4,5, Helen G Healy3,5, Sree K Venuthurupalli3,6, Ken-Soon Tan3,7, Wendy E Hoy3,4.   

Abstract

BACKGROUND: A survival advantage associated with obesity has often been described in dialysis patients. The association of higher body mass index (BMI) with mortality and renal replacement therapy (RRT) in preterminal chronic kidney disease (CKD) patients has not been established.
METHODS: Subjects were patients with pre-terminal CKD who were recruited to the CKD.QLD registry. BMI at time of consent was grouped as normal (BMI 18.5-24.9 kg/m2), overweight (BMI 25-29.9 kg/m2), mild obesity (BMI 30-34.9 kg/m2) and moderate obesity+ (BMI ≥ 35 kg/m2) as defined by WHO criteria. The associations of BMI categories with mortality and starting RRT were analysed.
RESULTS: The cohort consisted of 3344 CKD patients, of whom 1777 were males (53.1%). The percentages who had normal BMI, or were overweight, mildly obese and moderately obese+ were 18.9, 29.9, 25.1 and 26.1%, respectively. Using people with normal BMI as the reference group, and after adjusting for age, socio-economic status, CKD stage, primary renal diagnoses, comorbidities including cancer, diabetes, peripheral vascular disease (PVD), chronic lung disease, coronary artery disease (CAD), and all other cardiovascular disease (CVD), the hazard ratios (HRs, 95% CI) of males for death without RRT were 0.65 (0.45-0.92, p = 0.016), 0.60 (0.40-0.90, p = 0.013), and 0.77 (0.50-1.19, p = 0.239) for the overweight, mildly obese and moderately obese+. With the same adjustments the hazard ratios for death without RRT in females were 0.96 (0.62-1.50, p = 0.864), 0.94 (0.59-1.49, p = 0.792) and 0.96 (0.60-1.53, p = 0.865) respectively. In males, with normal BMI as the reference group, the adjusted HRs of starting RRT were 1.15 (0.71-1.86, p = 0.579), 0.99 (0.59-1.66, p = 0.970), and 0.95 (0.56-1.61, p = 0.858) for the overweight, mildly obese and moderately obese+ groups, respectively, and in females they were 0.88 (0.44-1.76, p = 0.727), 0.94 (0.47-1.88, p = 0.862) and 0.65 (0.33-1.29, p = 0.219) respectively.
CONCLUSIONS: More than 80% of these CKD patients were overweight or obese. Higher BMI seemed to be a significant "protective" factor against death without RRT in males but there was not a significant relationship in females. Higher BMI was not a risk factor for predicting RRT in either male or female patients with CKD.

Entities:  

Keywords:  BMI; Chronic kidney disease; Epidemiology; Mortality; Obesity; RRT

Mesh:

Year:  2019        PMID: 31438869      PMCID: PMC6704588          DOI: 10.1186/s12882-019-1513-9

Source DB:  PubMed          Journal:  BMC Nephrol        ISSN: 1471-2369            Impact factor:   2.388


Introduction

Obesity was one of the major themes of World Kidney Day in 2017 [1]. Over the past two decades, a multitude of observational studies have illustrated that obese patients on renal replacement therapy (RRT) have better survival than those with normal BMI. This concept of “reverse epidemiology” has also been observed in patients with heart failure, chronic obstructive pulmonary disease, liver cirrhosis and metastatic cancer, as well as in the geriatric population [2-7]. Recent studies have confirmed this “obesity paradox” in contemporary cohorts across different ethnicities and races, as well as different geographic regions on the world [2-7]. In a multicenter prospective observational cohort study of 2833 patients in Korea on maintenance hemodialysis, individuals with higher BMI had better survival over an average follow-up of 24.2 months [8]. Some groups have also reported that obesity in non-dialysis dependent CKD was associated with lower risk for all-cause mortality [9-13]. We recently showed that patients with chronic kidney disease (CKD) in three metropolitan public renal practices in an Australian setting had strikingly higher rates of obesity than the general population [14]. We described the cross-sectional associations of obesity with clinical characteristics [14], but we did not report the outcomes of RRT and death without RRT in the context of BMI. Here our specific objectives are to examine the association between BMI and death prior to RRT and starting RRT in this same cohort, followed for up to 5 years.

Subjects and methods

Paticipants and variables

This is a longitudinal cohort study. Subjects were drawn from a group of 3382 patients with CKD under renal specialty care in three major metropolitan public hospitals in Queensland, Australia. These CKD patients, who were not receiving RRT, had been enrolled, with written informed consent, in the CKD.QLD registry, which began in May 2011. However, we did not include the 38 people with BMI < 18.5, suspecting that some could have been underweight as a consequence of serious morbidities coexisting with their CKD, which might confound our interpretations of findings. Thus a total of 3344 subjects were included in the analyses.

Exposure variable and outcomes

The exposure variable was BMI at baseline, which was grouped as normal BMI (BMI 18.5–24.9 kg/m2), overweight (BMI 25–29.9 kg/m2), mild obesity (BMI 30–34.9 kg/m2) and moderate obesity+ (BMI ≥ 35 kg/m2), as defined by WHO criteria [15]. Outcomes were all-cause mortality without RRT and starting RRT. Participants were followed up from the date of their consent until death or starting RRT or censoring on 30 June, 2016.

Co-variables

Co-variables included as potential confounders in multivariate analyses, based on data availability and results of univariate analyses-based associations with outcomes, included Age at consent: artificially categorized as “higher” and “lower” by median values Gender: male and females CKD stages at baseline: according to the National Kidney Foundation criteria [16] ie Stage 1 if estimated glomerular filtration rate (eGFR) ≥90 ml/min; Stage 2 if eGFR≥60 & < 90; Stage 3A if eGFR≥45 & < 60; Stage 3B if eGFR≥30 & < 45; Stage 4 if eGFR≥15 & < 30 and Stage 5 if eGFR< 15. Primary renal diagnoses: the etiology of CKD patients was examined based on their clinical summary. Diagnoses were then categorized as glomerulonephritis (GN), genetic renal disease (GRD), diabetic nephropathy (DN), renovascular disease, others and uncertain groups. Major categories of comorbidities available in the cohort included: cancer (all types), diabetes, peripheral vascular disease (PVD), chronic lung disease, coronary artery disease (CAD) and all other cardiovascular disease (CVD). Socioeconomic status: the Socio-Economic Indexes for Areas (SEIFA) Index of Relative Socio-economic Disadvantage (IRSD) scores and deciles are produced by the Australian Bureau of Statistics to measure socio-economic status by postcodes [17]. In the analysis, IRSD scores were grouped into quintiles (i.e. highest, high, middle, low and lowest) where the highest quintile represents postcodes with the highest IRSD scores (the least disadvantaged areas) and the lowest quintile represents postcodes with the lowest IRSD scores (the most disadvantage areas).

Statistical analyses

Crude incidence rates of mortality without RRT and starting RRT were compared across gender and BMI categories. Cox regression modelling was performed to determine hazard ratios (HRs) for mortality and starting RRT by BMI categories and other co-variables. All analyses were undertaken using Stata 14.1 (Stata Corp. Stata Statistical Software: Release 14.1, College Station. TX: StatCorp LP, 2016). Statistical significance was defined as a p value < 0.05 (two-tail). For those who were excluded in the analyses due to missing information on some co-variables, their profiles of age and BMI were compared with those included in the model to examine if the potential selective bias may occur.

Ethical approval

Written informed consent to participate was obtained from patients. This study was approved by the Royal Brisbane and Women’s Hospital Ethics Committee of Queensland Health (HREC/15/QRBW/294) and the Ethics Committee of The University of Queensland (No.2011000029), Australia.

Results

Sample size and characteristics of cohort

Of 3344 CKD patients with baseline BMI data, 46.9% (n = 1567) were females and 53.1% (n = 1777) were males. The age of subjects at consent ranged from 18 to 100 years old with median of 68 years (67 and 68 years old for females and males, respectively). Only 7.1 and 11.9% patients were at CKD stages 1 and 2 respectively, stage 3 (A + B) accounted for 50%, while stages 4 and 5 accounted for 24.5 and 6.6%, respectively. Renovascular diseases (30.4%) and diabetic nephropathy (23.9%) were the two major primary renal diagnoses. The prevalence of major comorbidities including diabetes, PVD, cancer (all types), chronic lung disease, CAD and all other CVD were 45.9, 10.5, 22.2, 22.5, 25 and 40%, respectively. The percentages in lowest IRSD quintile (i.e the most disadvantage areas), low, middle, high and highest IRSD quintile (i.e the least disadvantage areas) were 24.7, 21.4, 22.9, 21.2 and 9.8%, respectively.

Profiles of overweight and obesity

The proportions who were of normal BMI, overweight, mildly obese and moderately obese+ were 18.9, 29.9, 25.1 and 26.1%, respectively. The overall prevalence of overweight or obesity was 81.1% (females 79.1% vs. males 82.9%, p = 0.005). Mean and median BMI were 30.8 (kg/m2) vs. 31.4 (kg/m2) and 30 (kg/m2) vs. 30.3 (kg/m2) for males and females, respectively. Table 1 shows the baseline characteristics in this cohort across different weight groups. Moderately obese+ patients had a slightly younger mean age compared with those in other BMI categories. The different weight groups had significantly different characteristics of gender, CKD stage 2 to CKD stage 4, major PRDs, comorbidities such as diabetes, cancer (all types), chronic lung disease, CAD, all other CVD and low to middle IRSD quintiles (p < 0.001, Table 1).
Table 1

Baseline characteristics

Normal weightOverweightMild obesityModerate obesity +All
(n = 632)(n = 1001)(n = 840)(n = 871)p value(n = 3344)
Mean age, yr. (SD)64.4 (19.1)66.5 (15.9)65.3 (14.8)62.6 (13.0)p < 0.00168.0 (15.7)
Gender, n (%)
 Male304 (17.1)583 (32.8)486 (27.4)404 (22.7)P < 0.0011777 (100.0)
 Female328 (20.9)418 (26.7)354 (22.6)467 (29.8)P < 0.0011567 (100.0)
CKD stages, n (%)
 Stage 146 (19.5)68 (28.8)59 (25.0)63 (26.7)p = 0.2115236 (100.0)
 Stage 260 (15.1)120 (30.2)100 (25.2)117 (29.5)p < 0.001397 (100.0)
 Stage 3A107 (17.7)180 (29.8)168 (27.8)149 (24.7)p < 0.001604 (100.0)
 Stage 3B181 (17.0)321 (30.1)271 (25.4)295 (27.6)p < 0.0011068 (100.0)
 Stage 4181 (22.1)243 (30.0)190 (23.2)204 (24.9)p = 0.01818 (100.0)
 Stage 557 (25.8)69 (31.2)52 (23.5)43 (19.5)p = 0.094221 (100.0)
PRD, n (%)
 Glomerulonephritis82 (19.2)140 (32.7)110 (25.7)96 (22.4)p < 0.001428 (100.0)
 Genetic renal disease58 (29.3)75 (37.9)36 (18.2)29 (14.7)p < 0.001198 (100.0)
 Diabetic nephropathy82 (10.3)162 (20.3)224 (28.0)331 (41.4)p < 0.001799 (100.0)
 Renovascular disease202 (19.9)342 (33.7)255 (25.1)216 (21.3)p < 0.0011015 (100.0)
 Uncertain48 (27.6)54 (31.0)35 (20.1)37 (21.2)p < 0.001174 (100.0)
 Others160 (21.9)228 (31.2)180 (24.7)162 (22.2)p = 0.1309730 (100.0)
Diabetes, n (%)
 Yes167 (10.9)366 (23.9)428 (27.9)572 (37.3)p < 0.0011533 (100.0)
 No465 (25.7)634 (35.1)411 (22.7)299 (16.5)p < 0.0011809 (100.0)
Cancer, N (%)
 Yes144 (19.9)234 (32.3)177 (24.5)169 (23.3)p < 0.001724 (100.0)
 No478 (18.9)739 (29.2)634 (25.0)682 (26.9)p < 0.0012533 (100.0)
PVD, n (%)
 Yes70 (20.5)99 (29.0)83 (24.3)90 (26.3)p = 0.1543342 (100.0)
 No552 (18.9)874 (30.0)728 (25.0)761 (26.1)p < 0.0012915 (100.0)
Chronic lung disease
 Yes143 (19.1)221 (29.5)170 (22.7)216 (28.8)p < 0.001750 (100.0)
 No489 (18.9)778 (30.0)669 (25.8)655 (25.3)p < 0.0012591 (100.0)
CAD
 Yes150 (17.9)239 (28.6)221 (26.4)226 (27.0)p < 0.001836 (100.0)
 No482 (19.2)762 (30.4)619 (24.7)645 (25.7)p < 0.0012508 (100.0)
All other CVD
 Yes247 (18.5)389 (29.1)319 (23.8)383 (28.6)p < 0.0011338 (100.0)
 No385 (19.2)612 (30.5)521 (26.0)488 (24.3)p < 0.0012006 (100.0)
IRSD quintiles, n (%)
 Lowest109 (13.9)230 (29.3)205 (26.1)242 (30.8)p < 0.001786 (100.0)
 Low116 (17.1)215 (31.6)163 (24.0)186 (27.4)p < 0.001680 (100.0)
 Middle144 (19.8)209 (28.7)211 (29.0)164 (22.5)p < 0.001728 (100.0)
 High152 (22.6)197 (29.3)156 (23.2)168 (25.0)p = 0.061673 (100.0)
 Highest77 (24.8)97 (31.2)72 (23.2)65 (20.9)p = 0.063311 (100.0)

Notes: Normal weight: BMI 18.5–24.9 kg/m2; Overweight: BMI 25.0–29.9; Obesity: 30–34.9; Moderate obesity +: BMI > =35. P-value: ANOVA test for continuous variables and Goodness-of-fit tests for categorical variables on multinomial data

Baseline characteristics Notes: Normal weight: BMI 18.5–24.9 kg/m2; Overweight: BMI 25.0–29.9; Obesity: 30–34.9; Moderate obesity +: BMI > =35. P-value: ANOVA test for continuous variables and Goodness-of-fit tests for categorical variables on multinomial data

Association of overweight or obesity with mortality and starting RRT

The outcomes of this CKD cohort were followed up until June 2016. The total follow up was 12,087 person years for the 3344 patients, with individual ranges from 0 to 5.5 years. The mean and median follow up years were 3.6 and 3.9 years, respectively. A total of 413 people died without RRT (234 males and 179 females), and 219 patients started RRT (143 males and 76 females). Among those who died without RRT, primary cause of death was reported as cardiovascular events in 61%, cancer (all types) in 17.1%, and end stage kidney failure (ESKF) in 22%. There was no significant difference in the crude incidence rate of death without RRT (per 100 person years) between males and females (rate, 95% CI: 3.66 (3.22–4.16) vs. 3.15 (2.72–3.64), p = 0.246). However the crude incidence rate of starting RRT was significantly higher in males than females (2.34 (1.90–2.64) vs.1.34 (1.07–1.67), p = 0.001). As shown in Fig. 1a, the crude incidence rate of death without RRT was significantly higher in male patients who were normal weight, (5.43 (4.22–6.97)) compared to those who were overweight (3.48 (2.77–4.37), p = 0.022), mildly obese (3.01 (2.29–3.95), p = 0.007) and moderately obese+ (3.31 (2.49–4.41), p = 0.033). There were similar trends in females but the differences were not statistically significant (Fig. 1a). Analyses also show that the primary cause of CVD, cancer (all types) and ESKF death was not significantly different among BMI categories (data not included). Figure 1b shows that the crude incidence rate of starting RRT per 100 person years was higher in patients with normal weight compared to those who were overweight, mildly obese and moderately obese+ in both males and females, but the differences were not statistically significant (Fig. 1b).
Fig. 1

a Crude incidence rate of death without RRT (per 100 person years, 95% CI) by BMI categories and gender. b Crude incidence rate of starting RRT (per 100 person years, 95% CI) by BMI categories and gender

a Crude incidence rate of death without RRT (per 100 person years, 95% CI) by BMI categories and gender. b Crude incidence rate of starting RRT (per 100 person years, 95% CI) by BMI categories and gender Kaplan-Meier survival curves with associated risk tables (Fig. 2a and b) show the higher rates of death without RRT in older subjects (here expressed as ≥ 68 years of age at consent, the group median age), and amongst older males, with a significant difference by BMI categories (log rank test P = 0.009) (Fig. 2a). The probability estimates of Kaplan-Meier survival curves for death without RRT at 5- year follow up were 0.51 for normal weight and 0.66 for both overweight and mildly obese males, and 0.70 for moderately obese+ males aged ≥ 68 years. There were no significant differences in female survivals by BMI category (Fig. 2b). Kaplan-Meier survival curves for RRT show higher rates for younger than older people, and no significant differences by BMI category in males or females (Fig. 3a, b).
Fig. 2

a Kaplan_Meier estimates for death without RRT by BMI categories (Males). b Kaplan_Meier estimates for death without RRT by BMI categories (Females)

Fig. 3

a Kaplan Meier estimates for starting RRT by BMI categories (Males). b Kaplan Meier estimates for starting RRT by BMI categories (Females)

a Kaplan_Meier estimates for death without RRT by BMI categories (Males). b Kaplan_Meier estimates for death without RRT by BMI categories (Females) a Kaplan Meier estimates for starting RRT by BMI categories (Males). b Kaplan Meier estimates for starting RRT by BMI categories (Females) Of 3344 CKD patients with baseline BMI data, 50 males (2.8% out of 1777 males) and 37 females (2.4% out of 1567 females) were excluded in the multivariate models due to the missing data for some co-variables. As the means of age and BMI were not significantly different between those missing patients and those patients in the models, the bias due to missing data in this study were ignored; thus a total of 3257 CKD patients were included in the final models. Those co-variables that were significantly associated with the outcomes in the univariate analysis were included in the final multivariate models. Using people of normal BMI as the reference group, and after adjusting for age, CKD stage at consent, primary renal diagnoses, comorbidities of cancer, diabetes, PVD, chronic lung disease, CAD and all other CVD, and for IRSD quintiles, the hazard ratios (HRs, 95% CI) for death without RRT in males were 0.65 (0.45–0.92, p = 0.016), 0.60 (0.40–0.90, p = 0.013), and 0.77 (0.50–1.19, p = 0.239) for the overweight, mildly obese and moderately obese+ respectively (Table 2), and for females were 0.96 (0.62–1.50, p = 0.864), 0.94 (0.59–1.49, p = 0.792) and 0.96 (0.60–1.53, p = 0.865) respectively (Table 3). In males, the adjusted HRs for starting RRT were 1.15 (0.71–1.86, p = 0.579), 0.99 (0.59–1.66, p = 0.970), and 0.95 (0.56–1.61, p = 0.858) for the overweight, mildly obese and moderately obese+ groups, respectively (Table 4), and in females they were 0.88 (0.44–1.76, p = 0.727), 0.94 (0.47–1.88, p = 0.862) and 0.65 (0.33–1.29, p = 0.219) respectively (Table 5).
Table 2

Prediction of mortality without RRT (Males)

VariablesHRs95% CI p
BMI categories
 Normal BMIReference group
 Overweight0.650.450.920.016
 Mild obesity0.600.400.900.013
 Moderate obesity+0.770.501.190.239
 Age > =68 years2.331.643.32< 0.001
CKD stage
 Stage 1Reference group
 Stage 22.170.2617.900.471
 Stage 3A3.220.4324.390.257
 Stage 3B3.220.4423.770.252
 Stage 45.430.7439.880.096
 Stage 53.960.5329.630.181
Primary renal diagnoses
 GlomerulonephritisReference group
 Genetic renal disease2.650.749.530.136
 Diabetic nephropathy4.151.729.990.002
 Renovascular disease3.901.679.100.002
 Cancer1.341.011.780.041
 Diabetes1.210.851.700.288
 PVD1.481.072.040.017
 Chronic lung disease1.140.861.520.374
 CAD1.781.322.40< 0.001
 All other CVD1.240.891.710.203
IRSD quintiles
 LowestReference group
 Low0.930.631.370.718
 Middle0.600.400.900.014
 High0.600.400.910.015
 Highest0.520.320.860.011
Table 3

Prediction of mortality without RRT (Females)

VariablesHRs95% CI p
BMI categories
 Normal BMIReference group
 Overweight0.960.621.500.864
 Mild obesity0.940.591.490.792
 Moderate obesity+0.960.601.530.865
 Age > =68 years2.001.372.93< 0.001
CKD stage
 Stage 1Reference group
 Stage 23.890.4831.630.204
 Stage 3A3.850.4830.860.205
 Stage 3B5.100.6838.170.113
 Stage 48.451.1462.720.037
 Stage 59.931.3175.450.027
Primary renal diagnoses
 GlomerulonephritisReference group
 Genetic renal disease0.350.081.590.175
 Diabetic nephropathy0.900.431.860.769
 Renovascular disease0.670.341.340.263
 Cancer1.761.252.460.001
 Diabetes1.531.022.290.042
 PVD1.460.942.260.093
 Chronic lung disease1.350.951.920.096
 CAD1.400.982.000.068
 All other CVD1.541.092.180.015
IRSD quintiles
 LowestReference group
 Low0.860.521.400.534
 Middle0.810.521.250.338
 High0.880.561.370.572
 Highest0.760.421.370.369
Table 4

Prediction of starting RRT (Males)

VariablesHRs95% CI p
BMI categories
 Normal BMIReference group
 Overweight1.150.711.860.579
 Mild obesity0.990.591.660.970
 Moderate obesity+0.950.561.610.858
 Age > =68 years0.260.170.39< 0.001
CKD stage
 Stage 1 + 2Reference group
 Stage 3A + 3B7.951.0560.260.045
 Stage 4 + 581.7611.32590.52< 0.001
Primary renal diagnoses
 GlomerulonephritisReference group
 Genetic renal disease1.520.693.340.299
 Diabetic nephropathy1.890.943.840.076
 Renovascular disease1.270.652.470.478
 Cancer0.730.431.230.236
 Diabetes0.950.561.610.842
 PVD1.090.671.760.737
 Chronic lung disease0.920.581.450.721
 CAD0.670.431.030.069
 All other CVD0.980.651.470.913
IRSD quintiles
 LowestReference group
 Low1.020.611.710.949
 Middle0.820.511.330.419
 High0.880.501.530.648
 Highest0.770.421.420.403
Table 5

Prediction of starting RRT (Females)

VariablesHRs95% CI p
BMI categories
 Normal BMIReference group
 Overweight0.880.441.760.727
 Mild obesity0.940.471.880.862
 Moderate obesity+0.650.331.290.219
 Age > =68 years0.190.100.37< 0.001
CKD stage
 Stage 1 + 2Reference group
 Stage 3A + 3B3.350.3730.420.283
 Stage 4 + 562.278.44459.66< 0.001
Primary renal diagnoses
 GlomerulonephritisReference group
 Genetic renal disease1.280.483.370.622
 Diabetic nephropathy2.470.867.090.092
 Renovascular disease0.960.362.590.938
 Cancer0.980.472.040.953
 Diabetes0.520.241.140.102
 PVD0.810.331.950.635
 Chronic lung disease1.300.702.400.412
 CAD0.650.291.430.280
 All other CVD0.960.531.730.890
IRSD quintiles
 LowestReference group
 Low0.870.401.900.733
 Middle0.720.361.440.351
 High1.010.502.050.969
 Highest1.270.483.410.630
Prediction of mortality without RRT (Males) Prediction of mortality without RRT (Females) Prediction of starting RRT (Males) Prediction of starting RRT (Females)

Discussion

A larger proportion of the non-dialysis CKD population in this Australian study is overweight or obese. Fifty one point two (51.2%) percent of subjects were obese, compared with 28.4% of a contemporaneous National Health Survey (NHS) sample of the general Australian population [18]. Even patients without diabetes in our CKD study population were notably more often overweight or obese than the NHS sample population [14, 18]. Although obesity is widely considered a health risk [19], when followed for 5 years, overweight or mildly obese patients in this cohort of patients with CKD, particularly those males who were above the group median age of 68 years at enrolment, had a significantly lower risk for death without RRT than males with a normal BMI. There were no significant associations between higher BMI and starting RRT in either males or females. The phenomenon of lower mortality in older males with CKD who were overweight or obese is consistent with recent studies in which dialysis patients of higher BMI had lower rates of death than those of “normal” BMI, termed the “obesity paradox” [8, 20]. The explanation for this phenomenon remains elusive. Does it represent exacerbated mortality risk in “normal” BMI patients and “protective” factors in overweight and obese subjects, or contributions from both [21, 22]? The phenomenon persists with adjustment for age, so that lower BMIs in older patients are not the major explanation. There were no clear differences in “cause” of death by BMI category, with about 60% in each BMI category dying with cardiovascular conditions, comparable percentages dying with cancer (about 16%) and marginally fewer people of normal BMI (17.0%) dying with ESKF than those of higher BMIs (23%). The association persisted after adjustment for all the common co-morbidities that were independently associated with mortality risk. Some have speculated that overweight or obese CKD patients may have better and more frequent care than others, and have adopted a healthier lifestyle. However, the standard settings and policies in these major public specialty CKD practices make it unlikely that normal weight CKD patients are relatively neglected in these domains of health care. Similarly, better nutritional status in those CKD patients with higher BMI may conceivably be a protective factor for mortality. However, no data of nutritional status were collected in this cohort. Others propose that CKD patients of higher BMI may better tolerate the side effects of some interventions [23, 24]. We note, too, that the patients’ BMIs in our report were ascertained more than 3.5 years prior to death on average, and might have changed substantially since first measured. Weight loss is common with progression of kidney diseases and BMIs close to death might have been substantially lower than documented on recruitment into the CKD.QLD cohort [23]. It has also been argued that, because CKD patients are more likely to develop fluid retention, BMI might not be an ideal measure for examining the risk in CKD [23], and using both BMI and waist circumference (WC), which reflects visceral adiposity, would better represent the associations between muscle mass, adipose tissue and various causes of death in CKD [21, 25–29]. Some of these factors seem unlikely to be major contributors to this phenomenon. There are undoubtedly also unrevealed confounders affecting this association. Given that this “reverse epidemiology” applies in many settings, groups of patients and conditions, CKD-specific explanations can have only limited applicability. An intuitive explanation might be that, in a population where most adults are overweight or obese, the presence of lower or “normal BMIs” can flag significant health disadvantage or frank illness for a substantial proportion of people. Our exclusion of patients with BMIs < 18.5 in this analysis would not entirely mitigate against this influence. Major strengths of this study include the large cohort of well-studied CKD patients, followed up for 5 years, the availability of data on all-cause mortality without RRT and on starting RRT, and ability to adjust for some important comorbidities including cancer, diabetes, PVD, chronic lung disease, CAD and all other CVD, which are potentially related to the risk of mortality. Their continued follow up will expose more findings over time, as numbers of death and RRT starts increase. However, it should be emphasised that these CKD patients do not represent the general community-based CKD population, as this cohort is selected for severity by being already referred to renal specialty practices. In addition some other confounders that may affect the associations were not included in our analyses.

Conclusions

In summary, more than 80% of these CKD patients were overweight or obese. Higher BMI seemed to be a significant “protective” factor against death without RRT in males, but there was not a significant relationship in females. Higher BMI was not a risk factor for predicting RRT in either males or female CKD patients.
  26 in total

1.  K/DOQI clinical practice guidelines for chronic kidney disease: evaluation, classification, and stratification.

Authors: 
Journal:  Am J Kidney Dis       Date:  2002-02       Impact factor: 8.860

2.  Diagnosing obesity by body mass index in chronic kidney disease: an explanation for the "obesity paradox?".

Authors:  Rajiv Agarwal; Jennifer E Bills; Robert P Light
Journal:  Hypertension       Date:  2010-09-27       Impact factor: 10.190

3.  Obesity: preventing and managing the global epidemic. Report of a WHO consultation.

Authors: 
Journal:  World Health Organ Tech Rep Ser       Date:  2000

4.  Overweight, obesity, and mortality from cancer in a prospectively studied cohort of U.S. adults.

Authors:  Eugenia E Calle; Carmen Rodriguez; Kimberly Walker-Thurmond; Michael J Thun
Journal:  N Engl J Med       Date:  2003-04-24       Impact factor: 91.245

Review 5.  Survival advantages of obesity in dialysis patients.

Authors:  Kamyar Kalantar-Zadeh; Kevin C Abbott; Abdulla K Salahudeen; Ryan D Kilpatrick; Tamara B Horwich
Journal:  Am J Clin Nutr       Date:  2005-03       Impact factor: 7.045

6.  Paradoxical association between body mass index and mortality in men with CKD not yet on dialysis.

Authors:  Csaba P Kovesdy; John E Anderson; Kamyar Kalantar-Zadeh
Journal:  Am J Kidney Dis       Date:  2007-05       Impact factor: 8.860

7.  Prevalence and trends in obesity among US adults, 1999-2008.

Authors:  Katherine M Flegal; Margaret D Carroll; Cynthia L Ogden; Lester R Curtin
Journal:  JAMA       Date:  2010-01-13       Impact factor: 56.272

Review 8.  Reverse epidemiology of conventional cardiovascular risk factors in patients with chronic heart failure.

Authors:  Kamyar Kalantar-Zadeh; Gladys Block; Tamara Horwich; Gregg C Fonarow
Journal:  J Am Coll Cardiol       Date:  2004-04-21       Impact factor: 24.094

9.  Waist-to-hip ratio and body mass index as risk factors for cardiovascular events in CKD.

Authors:  Essam F Elsayed; Hocine Tighiouart; Daniel E Weiner; John Griffith; Deeb Salem; Andrew S Levey; Mark J Sarnak
Journal:  Am J Kidney Dis       Date:  2008-06-02       Impact factor: 8.860

Review 10.  Reverse epidemiology of cardiovascular risk factors in maintenance dialysis patients.

Authors:  Kamyar Kalantar-Zadeh; Gladys Block; Michael H Humphreys; Joel D Kopple
Journal:  Kidney Int       Date:  2003-03       Impact factor: 10.612

View more
  4 in total

1.  Prevalence and factors associated with multimorbidity among primary care patients with decreased renal function.

Authors:  Jennifer A Hirst; José M Ordóñez Mena; Chris A O'Callaghan; Emma Ogburn; Clare J Taylor; Yaling Yang; F D Richard Hobbs
Journal:  PLoS One       Date:  2021-01-15       Impact factor: 3.240

2.  Effectiveness of Chinese herbal medicine combined with Western medicine on deferring dialysis initiation for nondialysis chronic kidney disease stage 5 patients: a multicenter prospective nonrandomized controlled study.

Authors:  Yifan Wu; Chuang Li; Lei Zhang; Chuan Zou; Peng Xu; Zehuai Wen; Wenwei Ouyang; Nizhi Yang; Min Zhang; Qizhan Lin; Fuhua Lu; Lixin Wang; Kun Bao; Daixin Zhao; Lizhe Fu; Xinfeng Guo; Lihong Yang; Aihua Ou; Zehui He; Heng Weng; Jianmin Li; Wei Shi; Xiaoqin Wang; Liqun Song; Yongli Zhan; Wei Sun; Lianbo Wei; Niansong Wang; Dingkun Gui; Jihong Zhan; Ying Lu; Hongyu Chen; Yuning Liu; Hongtao Yang; Ming Chen; Yiping Wang; Peiqing Zhang; Yueyi Deng; Lanfen Meng; Xiaohong Cheng; Feng Li; Dajun Yu; Damin Xu; Jing'ai Fang; Hongyan Li; Junzhou Fu; Yuansheng Xie; Wenge Li; Jinghong Zhao; Yuanhang Huang; Zhaoyu Lu; Guobin Su; Xindong Qin; Yuan Xu; Yu Peng; Haijing Hou; Lili Deng; Hui Liu; Xina Jie; Lichang Liu; Fang Tang; Hongfei Pei; Ping Li; Wei Mao; Xusheng Liu
Journal:  Ann Transl Med       Date:  2021-03

3.  Obesity, Weight Loss, Lifestyle Interventions, and Autosomal Dominant Polycystic Kidney Disease.

Authors:  Cortney Steele; Kristen Nowak
Journal:  Kidney Dial       Date:  2022-03-04

Review 4.  The potential of GLP-1 receptor agonists in type 2 diabetes and chronic kidney disease: from randomised trials to clinical practice.

Authors:  Bernt Johan von Scholten; Frederik Flindt Kreiner; Søren Rasmussen; Peter Rossing; Thomas Idorn
Journal:  Ther Adv Endocrinol Metab       Date:  2022-07-19       Impact factor: 4.435

  4 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.