Association of Body Mass Index with Estimated Glomerular

Filtration Rate and Incident Proteinuria

Seung Min Lee

, Minseon Park

and Hyung-Jin Yoon

1,*

Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, Korea

Department of Family Medicine, Seoul National University Hospital, Seoul, Korea

*corresponding author

Keywords: Glomerular Filtration Rate, Body Mass Index, Chronic Kidney Disease, Clinical Epidemiology, Incident

Proteinuria.

Abstract: Obesity has been one of the most important risk factors of chronic kidney disease (CKD). But the association

of body mass index (BMI) with estimated glomerular filtration rate (eGFR) and incident proteinuria has not

been studied well. The goal of this study was to elucidate the association of BMI with eGFR and proteinuria

using nationwide health examination data. These associations were investigated with data of Korean adults

who had undergone health screenings at least three times between 2009 and 2014. eGFR was calculated with

Chronic Kidney Disease Epidemiology collaboration equation based on serum creatinine level. The

association between BMI and eGFR was analysed with a generalized addictive model adjusting for possible

confounders. Similarly, the association between BMI and incident proteinuria was analysed with Cox hazard

model adjusting for possible confounders. As a result, a V-shape relationship between BMI and eGFR was

observed. The nadir was around 29 kg/m

. With subgroup analyses for the association between BMI and

eGFR, a V-shape association was observed in men and younger age group and an inverse association was

observed in women and older age group. A reverse J-shape association between BMI and the adjusted hazard

ratio of incident proteinuria was observed. The nadir was approximately estimated around 22 kg/m

1 INTRODUCTION

Obesity has been one of the most important risk

factors of Chronic Kidney Disease (Mahmoodnia,

2017). CKD is an emerging health issue because of its

high prevalence and incidence worldwide and its

association with cardiovascular morbidity and

mortality. The relationship between body mass index

(BMI), the most representative index of obesity, and

estimated glomerular filtration rate (eGFR) has not

been studied well. Several studies with small sample

size have reported the inverse linear association

between BMI and eGFR (Grubbs, 2014). Obesity has

been associated with abnormally increased eGFR and

decrease of eGFR or measured GFR after successful

weight reduction, such as Bariatric surgery is rather

well-known (Li, 2016). These observations are not

consistent to the observed inverse linear association

between BMI and eGFR. Similarly, only a few

prospective cohort studies observed an association

between overweight and obesity and increased risk of

incident proteinuria, and the nature of the relationship

between BMI and incident proteinuria has not been

reported yet. A cross-sectional study has observed the

U-shape association between BMI and prevalent

proteinuria (Sato, 2014). Although a sex-specific

association of incident proteinuria with underweight

and no association with overweight and obesity after

2 years’ follow-up has been reported, the follow-up

period of that study might not be long enough (Jang,

2014).

The goal of this study was to elucidate the

association of BMI with eGFR and incident

proteinuria using nationwide health examination data.

2 METHODS

Regular health screening at designated screening

hospital across the country is obligatory for adult

Koreans. The number of eligible subjects between

2009 and 2014 was between 15,036,607 and

16,456,214. The participation rate was between

66.0% and 74.8% (Cheol, 2016). The data of Korean

Lee, S., Park, M. and Yoon, H-J.

Association of Body Mass Index with Estimated Glomerular Filtration Rate and Incident Proteinuria.

DOI: 10.5220/0006716905870590

In Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018) - Volume 5: HEALTHINF, pages 587-590

ISBN: 978-989-758-281-3

587

adults (27,448,308 participants) who had undergone

health screenings between 2009 and 2014 were

analysed. Health screening tests performed on the

participants under 20 years old and foreign citizens

were excluded (728,569 participants). Subjects with

missing information on BMI, eGFR and other

confounders were also excluded. For more accurate

analysis, we set the minimum time interval between

the first health screening of each individual to the next

his/her health screening to 6 months. Furthermore,

persons who had undergone health screenings at least

three times between 2009 and 2014 were included in

this study (12,211,062 adults). BMI was calculated

from the measured height and weight. The eGFR

(mL/min/1.73 m

) was calculated by the Chronic

Kidney Disease Epidemiology Collaboration

equation based on serum creatinine level (Inker,

2012). Depending on smoking habit, persons were

categorized as non-smoker, ex-smoker, and smoker.

Persons who performed moderate or high intensity

exercise more than three times a week were classified

as ‘yes’ in the category of regular exercise. Alcohol

consumption were categorized as non-drinker, 1~2

times a week, 3~4 times a week and heavy drinker

(almost daily).

To observe any differential association between

BMI and eGFR according to age or sex, the

participants were divided into subgroups by sex and

sex-specific median age. The relationship of BMI with

eGFR was analysed with a generalized additive model

adjusting for possible confounding variables at

baseline such as age, sex, systolic blood pressure,

fasting serum glucose, serum triglycerides, serum

high-density lipoprotein- cholesterol, smoking status,

regular alcohol consumption, regular exercise, and

known history of medication for diabetes and hypertension.

To investigate the association between BMI and

incident proteinuria with national health screening

data, we analysed the

Table 1: Characteristics according to body mass index categories.

Total

(n=12,211,062)

BMI

< 18.5 kg/m

(n=415,403)

18.5 kg/m

≤ BMI < 25 kg/m

(n=7,841,447)

BMI ≥ 25 kg/m

(n=3,954,212)

P-value

Age (years)

46.9 ± 13.6

39.3 ± 14.9

46.4 ± 13.6

48.56 ± 13.0

<.001

Sex

Male

Female

5,541,961 (45.4%)

6,669,101 (54.6%)

280,406 (67.5%)

134,997 (32.5%)

3,783,941 (48.3%)

4,057,506 (51.7%)

1,477,614 (37.4%)

2,476,598 (62.6%)

<.001

SBP

(mmHg)

122.2 ± 14.8

113.0 ± 13.5

120.3 ± 14.3

127.1 ± 14.4

<.001

FSG

(mg/dL)

96.9 ± 22.4

90.2 ± 17.9

95.2 ± 20.9

101.0 ± 24.9

<.001

(mg/dL)

132.6 ± 91.9

82.1 ± 49.1

119.2 ± 80.4

164.7 ± 106.6

<.001

HDL

(mg/dL)

55.0 ± 13.6

63.0 ± 14.3

56.4 ± 13.7

51.3 ± 12.4

<.001

Smoking

Never

Formal

Current

7,366,297 (60.3%)

1,821,408 (14.9%)

3,023,357 (24.8%)

297,972 (71.7%)

27,479 (6.6%)

89,952 (21.7%)

4,892,862 (62.4%)

1,073,589 (13.7%)

1,874,996 (23.9%)

2,175,463 (55%)

720,340 (18.2%)

1,058,409 (26.8%)

<.001

Regular exercise

Yes

2,926,581 (24%)

9,284,481 (76%)

60,088 (14.5%)

355,315 (85.5%)

1,867,823 (23.8%)

5,973,624 (76.2%)

998,670 (25.3%)

2,955,542 (74.7%)

<.001

Alcohol consumption

None

1 ~ 2

3 ~ 4

> 4

6,234,122 (51.1%)

4,383,086 (35.9%)

1,155,360 (9.5%)

438,494 (3.6%)

234,699 (56.5%)

143,920 (34.6%)

25,504 (6.1%)

11,280 (2.7%)

4,078,439 (52%)

2,793,094 (35.6%)

693,639 (8.8%)

276,275 (3.5%)

1,920,984 (48.6%)

1,446,072 (36.6%)

436,217 (11%)

150,939 (3.8%)

<.001

Anti-HT

Yes

1,679,739 (13.8%)

10,531,323 (86.2%)

13,916 (3.3%)

401,487 (96.7%)

829,837 (10.6%)

7,011,610 (89.4%)

835,986 (21.1%)

3,118,226 (78.9%)

<.001

Anti-Diabetic

Yes

556,411 (4.6%)

11,654,651 (95.4%)

5,543 (1.3%)

409,860 (98.7%)

291,845 (3.7%)

7,549,602 (96.3%)

259,023 (6.6%)

3,695,189 (93.4%)

<.001

Body mass index;

Systolic blood pressure;

Fasting serum glucose;

Serum triglycerides;

Serum high-density lipoprotein-cholesterol;

Regular

exercise: moderate or high intensity exercise, more than three times per week;

Number of alcohol consumption per week;

History of anti-

hypertensive medication;

History of anti-diabetic medication

Note: Number (percentage %) for categorical variables; Mean ± standard deviation for continuous variables.

P-value was calculated by ANOVA-test for continuous variables and Pearson’s chi-squared test for categorical variables. P-values < 0.05 denote

statistical significance.

HEALTHINF 2018 - 11th International Conference on Health Informatics

588

data of participants who had eGFR 60 mL/min/1.73

or above and normo-proteinuria at baseline

(11,559,520 adults). Incident proteinuria was defined

as the occurred event when the result of urine dipstick

test showed a protein 1+ or higher during the follow-

up period in each individual who had negative result

in urine dipstick test at the first health screening.

Figure 1: The relationship between body mass index and

estimated glomerular filtration rate was evaluated with a

general additive model with adjustment for age, sex,

smoking status, regular exercise, regular alcohol

consumption, known history of diabetes or hypertension

medication, systolic blood pressure, fasting serum glucose,

serum triglycerides, and serum high-density lipoprotein-

cholesterol at baseline. Estimated glomerular filtration rate

was calculated with Chronic Kidney Disease Epidemiology

Collaboration equation based on serum creatinine. The

shaded area represents 95% confidence interval.

The Cox hazard model was used to analyze the

association between BMI and incident proteinuria

with the adjustment of possible confounding

variables at baseline, such as age, sex, fasting serum

glucose, serum triglycerides, serum high-density

lipoprotein-cholesterol, systolic blood pressure,

known history of diabetes or hypertension medication,

smoking status, regular alcohol consumption, and

regular exercise. To visualize the association of BMI

with eGFR and incident proteinuria, penalized splines

as the smoothing were implemented by the R function

pspline in package survival (degree of freedom set as

default).

3 RESULTS

As shown in figure 1, the V-shape association between

BMI and eGFR was observed in the analysis of total

population. The nadir was around 29 kg/m

. With

subgroup analyses, the V-shape association was

observed only in men and younger age group (sex-

specific median age; 44 years old in men, 49 years old

in women). In women and older age group, the

inverse association between BMI and eGFR was

observed.

Figure 2: The association between body mass index and

incident proteinuria in total population. A reverse J-shape

association between body mass index and adjusted hazard

ratio of incident proteinuria was observed with adjustment

for age, sex, smoking status, regular exercise, regular

alcohol consumption, known history of diabetes or

hypertension medication, systolic blood pressure, fasting

serum glucose, serum triglycerides, and serum high-density

lipoprotein-cholesterol at baseline. The shaded area

represents 95% confidence interval.

Association of Body Mass Index with Estimated Glomerular Filtration Rate and Incident Proteinuria

589

The reverse J-shape association between BMI and the

adjusted hazard ratio of incident proteinuria was

observed. The nadir was approximately estimated

around 22 kg/m

as shown in figure 2. With subgroup

analyses, there was no difference according to sex and

age (sex-specific median age; 44 years old in men, 49

years old in women).

Figure 3: The association between body mass index and

incident proteinuria in subgroups; A reverse J-shape

association between body mass index and the adjusted hazard

ratio of incident proteinuria was not differential according to

sex and age with adjustment for age, sex, smoking status,

regular exercise, regular alcohol consumption, known history

of diabetes or hypertension medication, systolic blood

pressure, fasting serum glucose, serum triglycerides, and serum

high-density lipoprotein-cholesterol at baseline. The shaded

area represents 95% confidence interval.

4 CONCLUSIONS

The association of BMI and eGFR was not linear and

differential according to sex and age. Between BMI

and incident proteinuria, the reverse J-shape

association was observed and its nadir between 22

and 23 kg/m

. It is necessary to consider non-linear

association of BMI and eGFR and incident

proteinuria when the association of obesity with renal

function or incident proteinuria is evaluated. Clinical

implications of these observations need to be studied

with future studies.

ACKNOWLEDGEMENTS

This research was supported by Next-Generation

Information Computing Development Program

through the National Research Foundation of Korea

(NRF) funded by the Ministry of Science, ICT (NRF-

2017M3C4A7083412). The National Health

Information Database made by the National Health

Insurance Service (NHIS) of Korea was used.

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