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. 2020 Dec;10(6):e12402.
doi: 10.1111/cob.12402. Epub 2020 Aug 26.

Obesity is common in chronic kidney disease and associates with greater antihypertensive usage and proteinuria: evidence from a cross-sectional study in a tertiary nephrology centre

William P Martin  1   2 , Jessica Bauer  1 , John Coleman  2 , Ludmilla Dellatorre-Teixeira  1 , Janice L V Reeve  3 , Patrick J Twomey  3 , Neil G Docherty  1   4 , Aisling O'Riordan  2 , Alan J Watson  2 , Carel W le Roux  1   4   5 , John Holian  2
Affiliations

Obesity is common in chronic kidney disease and associates with greater antihypertensive usage and proteinuria: evidence from a cross-sectional study in a tertiary nephrology centre

William P Martin et al. Clin Obes. 2020 Dec.

Abstract

Obesity is a treatable risk factor for chronic kidney disease progression. We audited the reporting of body-mass index in nephrology outpatient clinics to establish the characteristics of individuals with obesity in nephrology practice. Body-mass index, clinical information and biochemical measures were recorded for patients attending clinics between 3rd August, 2018 and 18th January, 2019. Inferential statistics and Pearson correlations were used to investigate relationships between body-mass index, type 2 diabetes, hypertension and proteinuria. Mean ± SD BMI was 28.6 ± 5.8 kg/m2 (n = 374). Overweight and obesity class 1 were more common in males (P = .02). Amongst n = 123 individuals with obesity and chronic kidney disease, mean ± SD age, n (%) female and median[IQR] eGFR were 64.1 ± 14.2 years, 52 (42.3%) and 29.0[20.5] mL/min/BSA, respectively. A positive correlation between increasing body-mass index and proteinuria was observed in such patients (r = 0.21, P = .03), which was stronger in males and those with CKD stages 4 and 5. Mean body-mass index was 2.3 kg/m2 higher in those treated with 4-5 versus 0-1 antihypertensives (P = .03). Amongst n = 59 patients with obesity, chronic kidney disease and type 2 diabetes, 2 (3.5%) and 0 (0%) were prescribed a GLP-1 receptor analogue and SGLT2-inhibitor, respectively. Our data provides a strong rationale not only for measuring body-mass index but also for acting on the information in nephrology practice, although prospective studies are required to guide treatment decisions in people with obesity and chronic kidney disease.

Keywords: chronic kidney disease; diabetes mellitus; diabetic kidney disease; obesity; overweight.

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Conflict of interest statement

ClR discloses personal fees outside of the submitted work from Novo Nordisk, GI Dynamics, Eli Lilly, Johnson and Johnson, Sanofi, Aventis, Astra Zeneca, Janssen, Bristol‐Myers Squibb, and Boehringer‐Ingelheim. The authors declare that they have no competing interests.

Figures

FIGURE 1
FIGURE 1
BMI distribution of people attending outpatient nephrology clinics, stratified by gender. Panel A: Histograms of BMI distribution stratified by gender. Black vertical dashed lines on the x‐axis (BMI) demarcate cutoffs for defining WHO BMI categories at 18.5, 25, 30, 35 and 40 kg/m2. Panel B: Frequency of WHO BMI categories by gender. Definition of WHO BMI categories is as follows: underweight (<18.5 kg/m2), normal weight (18.5‐24.9 kg/m2), overweight (25‐29.9 kg/m2), obesity class 1 (30‐34.9 kg/m2), obesity class 2 (35‐39.9 kg/m2), and obesity class 3 (≥40 kg/m2). Females are shaded in red, males in blue
FIGURE 2
FIGURE 2
Cross‐sectional relationships between BMI, urine protein‐to‐creatinine ratio (uPCR) and glycated haemoglobin amongst people with obesity and CKD. Panel A: Scatterplot of BMI and uPCR reveals a modest positive correlation (r = 0.21, P = .03). Panel B: Scatterplot of BMI and uPCR stratified by gender. Panel C: Scatterplot of BMI and uPCR stratified by CKD stage. G3 = grade 3 (eGFR 30‐60 mL/min/BSA); G4/5 = grades 4 and 5 (eGFR <30 mL/min/BSA). Panel D: Scatterplot of BMI and uPCR stratified by type 2 diabetes mellitus status. Individuals with type 1 diabetes mellitus were removed from the non‐type 2 diabetes mellitus group in this plot. Panel E: Scatterplot of BMI and HbA1c reveals no relationship between the two variables (r = 0.003, P = .97). Panel F: Scatterplot of BMI and HbA1c stratified by type 2 diabetes mellitus status. Individuals with type 1 diabetes mellitus were removed from the non‐type 2 diabetes mellitus group in this plot. Reported r and P values are derived from Pearson correlations. P <.05 was considered statistically significant
FIGURE 3
FIGURE 3
Cross‐sectional relationships between BMI and antihypertensive usage amongst people with obesity and CKD. Panel A: Boxplots of BMI stratified by the number of antihypertensive medications. Comparisons between three groups are made by ANOVA; comparisons between two groups by independent samples t test. Panel B: Boxplots of eGFR stratified by the number of antihypertensive medications. Comparisons between three groups are made by the Kruskal‐Wallis test; comparisons between two groups by Wilcoxon rank‐sum test. Panel C: Boxplots of uPCR stratified by the number of antihypertensive medications. Comparisons between three groups are made by the Kruskal‐Wallis test; comparisons between two groups by Wilcoxon rank‐sum test. The number of antihypertensive medications was categorized into three groups as follows: 0 to 1, 2 to 3 and 4 to 5. The size of individual data points is scaled by BMI, emphasizing the cross‐sectional associations between increasing BMI with increasing uPCR and antihypertensive usage. P <.05 was considered statistically significant
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