Skip to content

Advertisement

You're viewing the new version of our site. Please leave us feedback.

Learn more

BMC Research Notes

Open Access

Gender difference in relationship between body mass index and development of chronic kidney disease

  • Hiroshi Komura1,
  • Ikuo Nomura1,
  • Kazuo Kitamura1,
  • Kenji Kuwasako2 and
  • Johji Kato2Email author
BMC Research Notes20136:463

https://doi.org/10.1186/1756-0500-6-463

Received: 25 July 2013

Accepted: 11 November 2013

Published: 13 November 2013

Abstract

Background

An epidemiological approach to preventing the development or progression of chronic kidney disease (CKD) is necessary, while few effective preventive measures are currently available. We conducted a community-based, cohort study to identify the factors associated with the development of CKD in the general population.

Methods

We examined 1876 local residents of a Japanese community who had an annual health check-up and, of those, 1506 residents judged not to have CKD (473 men and 1033 women) were followed for the development of CKD over 10 years.

Results

The numbers of male and female residents who developed CKD during the follow-up period were 167 (35.3%) and 299 (28.9%), respectively. As compared to those without CKD development, the residents who developed CKD were older, and had a higher body mass index (BMI), systolic blood pressure, and creatinine in both genders. The rate of CKD development in obese female residents was higher than in non-obese women, but such a difference was not noted in male residents. In addition to age and serum creatinine, we identified BMI as an independently significant factor for the development of CKD in women, but not in men.

Conclusions

Increased BMI is a significant risk factor for the development of CKD in women, and there seems to be a gender difference in the association between increased BMI and the development of CKD in the general population.

Keywords

Chronic kidney diseaseBody mass indexObesityGeneral population

Background

Chronic kidney disease (CKD) is defined as either structural and/or functional abnormality of the kidney or a reduced glomerular filtration rate (GFR) to <60 ml/min/1.73 m2[1, 2]. CKD has recently been recognized as a challenging public health issue not only because the number of patients with end-stage renal disease (ESRD) is steadily increasing in developed countries including Japan, but also because epidemiological studies have shown that CKD is a significant risk factor for cardiovascular disease [35]. For example, the number of ESRD patients in Japan is currently some 300,000, and this has become an economic burden for the health care system [6]. On the other hand, it was shown in a Japanese community by a prospective study that ischemic heart disease and cerebral infarction are more frequent in male and female residents with CKD, respectively, than in those without [5]. In order to prevent or reduce the development of CKD at a community level, it is essential to identify or specify the causes of CKD. Possible factors shown to be involved in the development of CKD are age, hypertension, impaired glucose tolerance or diabetes mellitus, dyslipidemia and obesity [716]; however, the detailed mechanisms remains to be clarified, and we currently have few effective countermeasures to prevent the development or progression of CKD at a community level. According to our previous cross-sectional study, body mass index (BMI) was found to be an independently, significant factor for CKD, in addition to age, in male and female residents of the Kiyotake area, a Japanese community [17]. In the present study, we followed the local residents of that area, who had been judged not to have CKD, for 10 years to identify the factors associated with future development of CKD in a general population.

Methods

In this study, we examined local residents in the Kiyotake area, Miyazaki, Japan, who had an annual health check-up in October 1999, responding to the recommendations made by the municipal government for those of 40 years or older. Upon visiting the community center of Kiyotake town, blood pressure was measured with an oscillometric automatic device (BP-103iII; Colin, Tokyo, Japan) in a sitting position by experienced nurses. Then, urine was collected and blood was drawn from an antecubital vein. Serum lipid and glucose levels were measured by an automatic analyzer (AU2700; OLYMPUS, Tokyo, Japan) with serum creatinine levels determined by an enzymatic method. GFRs were calculated with the formula of the Japanese Society of \Nephrology: 194 × serum creatinine- 1.094 × age-0.287 ml/min/1.73 m2, further multiplied by 0.739 for women [18]. Residents were judged to have CKD when the estimated GFR was <60 ml/min/1.73 m2 or spot urine protein determined by dipstick measurement was ≥ +1 (30 mg/dl). Obesity was defined as BMI ≥25 kg/m2, according to the criteria of the Japan Society for the Study of Obesity [19]. In this health check-up program, 1876 residents (615 men and 1261 women; 58.7 ± 11.6 years; mean ± S.D.) visited during the time period mentioned above. Of those receiving the check-up in 1999, 1506 residents, who were judged not to have CKD (473 men and 1033 women; 58.2 ± 11.0 years), were followed up for the development of CKD over 10 years at the annual health check-ups held by the municipal government. The residents receiving the annual health check-ups had been notified of the results by mail every year.

This study was approved by the Review Committee of Cooperative and Commissioned Research and the Ethics Committee of the University of Miyazaki Faculty of Medicine. The check-up programs are carried out by the municipal government according to Japanese law. In the present study, we used the database of the municipal government, from which it was impossible for us to identify individual residents. Therefore, written informed consent was not obtained individually, but instead, the local residents were informed of this study via a local newspaper issued by the municipal government, as required by the Ethics Committee.

All the data were analyzed with IBM SPSS software version 20.0 (IBM, Armonk, NY, USA). Two groups were compared by the unpaired t-test or chi-squared test. The Kaplan-Meier method and log rank test were used to see differences in the development of CKD between obese and non-obese residents. The relationships between the development of CKD and the other parameters were tested by Cox proportional hazard models. All data are expressed as the means ± S.D. and P <0.05 was considered to be significant.

Results

Table 1 shows the basal profiles of the male and female residents examined in this study. As shown, 167 male and 299 female residents were found to have developed CKD during 1999 to 2009, the diagnosis of which was made by estimated GFR <60 ml/min/1.73 m2 in 268 residents, by dipstick proteinuria ≥ +1 in 188 or by both in 10. The development of CKD in male residents (35.3%) was more frequent (P <0.05) than in female residents (28.9%). The residents who had developed CKD were older, showing higher systolic blood pressure and serum creatinine than those without CKD development, in both genders. Additionally, fasting blood glucose in men and aspartate aminotransferase (AST) in women were higher in those with CKD development. BMI in male and female residents who developed CKD was higher, and obesity was significantly more prevalent in women than in those who did not develop CKD. In the Kaplan-Meier analysis (Figure 1), no significant difference in CKD development was noted between obese and non-obese male (A), but CKD occurred more frequently in obese than in non-obese women (B). Comparable with this, there was no difference in newly developed proteinuria between obese and non-obese male residents (16.8 vs. 16.7%), while development of proteinuria during the follow-up period was more frequent in obese than in non-obese women (20.2 vs. 11.7%, P < 0.01).
Table 1

Basal profiles of male and female residents with or without development of CKD

 

Men

Women

Development of CKD

(−)

(+)

(−)

(+)

n

306

167

734

299

Age (years)

58.0 ±11.5

64.6 ± 8.9**

56.2 ± 10.6

59.6 ± 11.1**

Body mass index (kg/m2)

22.8 ± 2.9

23.4 ± 3.0*

22.1 ± 3.0

23.0 ± 3.2**

Obesity (%)

21.6

28.1

16.5

24.1**

Systolic blood pressure (mmHg)

124 ± 18

132 ± 18**

122 ± 17

125 ± 18**

Diastolic blood pressure (mmHg)

76 ± 11

78 ± 10*

73 ± 10

74 ± 10

Total cholesterol (mg/dL)

195 ± 32

194 ± 34

205 ± 33

207 ± 34

HDL-cholesterol (mg/dL)

57 ± 15

56 ± 15

64 ± 15

62 ± 15

Triglyceride (mg/dL)

106 ± 73

103 ± 52

88 ± 45

94 ± 56

Fasting blood glucose (mg/dL)

93 ± 15

97 ± 17*

90 ± 12

90 ± 10

AST (IU/L)

26 ± 8

27 ± 11

22 ± 7

24 ± 9*

ALT (IU/L)

24 ± 13

26 ± 22

18 ± 10

20 ± 17

γ-GTP (IU/L)

43 ± 50

58 ± 96

21 ± 22

22 ± 21

Creatinine (mg/dl)

0.71 ± 0.09

0.78 ± 0.12**

0.51 ± 0.08

0.59 ± 0.09**

AST, aspartate aminotransferase; ALT, alanine aminotransferase; GTP, glutamyl transpeptidase; means ± S.D.; *P < 0.05, **P < 0.01, vs. without development of CKD.

Figure 1

Kaplan-Meier analyses for the development of CKD in male (A) and female (B) residents.

Next, we analyzed the development of CKD by Cox proportional hazard models with single or multiple covariates, as shown in Tables 2 and 3. In univariate analysis, age, systolic and diastolic blood pressure, aspartate aminotransferase (AST), and creatinine levels were found to be significant covariates for CKD development in both genders. Also shown to be significant were fasting blood glucose levels in male residents and triglyceride in female residents. BMI was judged to be a significant factor in female residents but not in male residents. We then conducted multivariate analyses, where clinical parameters were used as independent covariates if they were significantly different between residents with and without CKD development (Table 1) or judged to be a significant factor by univariate analyses. As shown in the right columns of Tables 2 and 3, age and serum creatinine were significant in both genders, but similar to univariate analysis, BMI was extracted as a significant factor only in female residents.
Table 2

Identification of factors associated with development of CKD in male subjects

Male

Univariate

Multivariate

 

RR (95% CI)

RR (95% CI)

Age (years)

1.058 (1.041-1.075)**

1.051 (1.032-1.070)**

Body mass index (kg/m2)

1.038 (0.987-1.092)

1.035 (0.978-1.096)

Systolic blood pressure (mmHg)

1.020 (1.012-1.029)**

1.008 (0.996-1.021)

Diastolic blood pressure (mmHg)

1.019 (1.004-1.034)*

1.002 (0.981-1.023)

Total cholesterol (mg/dL)

0.999 (0.995-1.004)

 

HDL-cholesterol (mg/dL)

0.996 (0.986-1.006)

 

Triglyceride (mg/dL)

0.999 (0.997-1.002)

 

Fasting blood glucose (mg/dL)

1.012 (1.004-1.019)**

1.009 (0.999-1.018)

AST (IU/L)

1.016 (1.001-1.031)*

1.001 (0.979-1.023)

ALT (IU/L)

1.005 (0.996-1.013)

 

γ-GTP (IU/L)

1.002 (1.000-1.003)*

1.002 (1.000-1.004)

Creatinine (mg/dl)

160 (33–785)**

101 (20–522)**

RR, risk ratio; AST, aspartate aminotransferase; ALT, alanine aminotransferase; GTP, glutamyl transpeptidase; *P < 0.05, **P < 0.01.

Table 3

Identification of factors associated with development of CKD in female subjects

Female

Univariate

Multivariate

 

RR (95% CI)

RR (95% CI)

Age (years)

1.034 (1.022-1.046)**

1.030 (1.017-1.044)**

Body mass index (kg/m2)

1.080 (1.042-1.119)**

1.048 (1.008-1.089)*

Systolic blood pressure (mmHg)

1.012 (1.005-1.018)**

1.008 (0.998-1.019)

Diastolic blood pressure (mmHg)

1.012 (1.000-1.023)*

0.995 (0.978-1.012)

Total cholesterol (mg/dL)

1.002 (0.999-1.005)

 

HDL-cholesterol (mg/dL)

0.993 (0.985-1.000)

 

Triglyceride (mg/dL)

1.002 (1.000-1.004)*

0.999 (0.997-1.002)

Fasting blood glucose (mg/dL)

1.005 (0.996-1.014)

 

AST (IU/L)

1.015 (1.002-1.028)*

1.003 (0.988-1.018)

ALT (IU/L)

1.006 (0.999-1.014)

 

γ-GTP (IU/L)

1.001 (0.997-1.006)

 

Creatinine (mg/dl)

6290 (1472–26882)**

6870 (1549–30470)**

RR, risk ratio; AST, aspartate aminotransferase; ALT, alanine aminotransferase; GTP, glutamyl transpeptidase; *P < 0.05, **P < 0.01.

Discussion

Chronic kidney disease (CKD) has recently been recognized not only as a risk factor of ESRD, but also of cardiovascular disease, which is a leading cause of death in developed countries [35]. An interventional approach is necessary to prevent the development or progression of CKD, while a number of factors have been reported to be involved in the development of CKD and currently there are few specific countermeasures against CKD at community levels. Obesity has been shown to be an important factor associated with CKD [10, 14, 16, 20, 21], and indeed, according to our previous, community-based, cross-sectional study [17], BMI was found to be significant for CKD in the Kiyotake area, a Japanese community, in both genders. We therefore conducted this cohort, 10-year follow-up study with local residents without CKD in the same area, and found that an increase in BMI was independently and significantly associated with the development of CKD only in women.

It has been suggested that a number of factors are associated with the development of CKD: these are male gender, age, hypertension, impaired glucose tolerance or diabetes mellitus, dyslipidemia, obesity, and smoking [716]. Accordant with those reports, the development of CKD was more frequent in male residents than in female residents, and age was determined to be a significant factor of CKD development in both genders in the present study. Obesity is closely associated with hypertension, diabetes mellitus, and dyslipidemia, all of which are detrimental factors to renal function [712]. Indeed, systolic and diastolic blood pressure levels were significant for the development of CKD in both genders in univariate analyses in the present study. Also found to be significant were the levels of fasting blood glucose in men and those of triglycerides in women. Meanwhile, obesity has recently been shown to be a risk factor for the development or progression of CKD, which is independent of the other risks mentioned above [13, 16, 22]. Possible mechanisms of this obesity-induced renal damage are fat tissue-derived factors detrimental to renal function, such as tumor necrosis factor-α, interleukin-6 and plasminogen activator inhibitor-1 [22].

An interesting finding of the present study was a gender difference in the relationship between the development of CKD and obesity or BMI. As shown in the basal profiles of the study subjects (Table 1), BMI in the residents with CKD development was higher than in those without in both genders; however, significant associations between CKD development and BMI or obesity were found only in women by Kaplan-Meier and univariate or multivariate analyses. A similar finding was observed by Yamagata et al. [15], who reported that obesity was a significant factor for CKD development only in women in a Japanese community, although no further data, including BMI, were available in their paper. We examined male and female obese residents with basal or metabolic parameters possibly involved in the development of CKD, but failed to find any clear difference between these two groups (data not shown); therefore, there seem to be no clear explanations for the gender difference in CKD development observed in the present study. This important point is discussed below in comparison with the results from cohort studies of subjects without CKD and from cross-sectional studies.

We previously conducted a cross-sectional study of local residents of the same community as examined in the present study, and found a significant relationship between CKD and BMI, which was independent of age and other cardiovascular risk factors in both genders [17]. Meanwhile, Ishizaka et al. and Shankar et al. performed similar cross-sectional community-based studies, showing significant relationships between CKD and BMI in men but not in women, suggesting the susceptibility of obese men to CKD in comparison with obese women [20, 21]. The findings of these studies appear to contradict those of the present study, where obese women were prone to CKD compared with non-obese women, but such a difference was not noted in men. Possible explanations for this discrepancy are as follows. First, the present study is a cohort study where subjects without CKD were followed up over 10 years, hence, both the study design and the study subjects were essentially different. Second, the age of the subjects may be a factor: the mean age of the subjects in those studies was around 60 years, while in the present study, residents of a similar age range were followed over 10 years. A relationship between CKD and obesity might have been found in men if younger subjects had been followed for a certain period of time. In an attempt to prove this, we analyzed the data of male residents younger than 60 years, but the number of subjects was relatively small (n = 189) and no significant difference was noted in CKD development between obese and non-obese men; therefore, this hypothesis needs to be tested by future studies.

Lastly, we need to mention the limitations of the present study. First, there might have been a selection bias because the local residents examined in this study were those who received a health check-up responding to the recommendations made by the municipal government. For example, because the present subjects had relatively lower BMI with smaller standard deviations than in other studies [12, 21], the remainder of the general population might have different risk factors for CKD development. Additionally, a type 2 statistical error cannot completely be excluded from the gender difference: the number of male residents examined was smaller than that of female residents, while the Kaplan-Meier curves of CKD development differed between male and female residents with substantially different p values (Figure 1). Second, the subjects examined in this study were ethnically Japanese; therefore, caution should be taken when the present findings are discussed in comparison with other races or ethnic groups. Third, no data were available regarding whether the study subjects had hypertension or diabetes mellitus, important factors in CKD development, at enrollment in this study; instead, the data were adjusted by blood pressure and fasting blood glucose levels.

Conclusions

Increased BMI was significantly associated with the development of CKD in female residents in the general population, while such a relationship was unclear in male residents, suggesting a possible difference in susceptibility to CKD associated with obesity. Interventional studies or approaches are warranted to see the effect of body weight reduction on the development of CKD at community levels.

Abbreviations

CKD: 

Chronic kidney disease

BMI: 

Body mass index

ESRD: 

End-stage renal disease

GFR: 

Glomerular filtration rate

AST: 

Aspartate aminotransferase

ALT: 

Alanine aminotransferase

GTP: 

Glutamyl transpeptidase.

Declarations

Acknowledgements

We are grateful to Mr. Tadashi Tojiki, the Mayor of Miyazaki City, and the staff of Miyazaki City Health Center for their assistance. This study was supported by Grants-in-Aids from the Japanese Society for the Promotion of Science and by research grants from the Japan Health Foundation and Daiwa Securities Health Foundation.

Authors’ Affiliations

(1)
Department of Internal Medicine, Circulatory and Body Fluid Regulation, Faculty of Medicine
(2)
Frontier Science Research Center, University of Miyazaki

References

  1. National Kidney Foundation: K/DOQI clinical practice guidelines for chronic kidney disease: evaluation, classification, and stratification. Am J Kidney Dis. 2002, 39 (2 Suppl 1): S1-S266.Google Scholar
  2. Levey AS, Coresh J, Balk E, Kausz AT, Levin A, Steffes MW, Hogg RJ, Perrone RD, Lau J, Eknoyan G: National Kidney Foundation practice guidelines for chronic kidney disease: evaluation, classification, and stratification. Ann Intern Med. 2003, 139: 137-147. 10.7326/0003-4819-139-2-200307150-00013.PubMedView ArticleGoogle Scholar
  3. Sarnak MJ, Levey AS, Schoolwerth AC, Coresh J, Culleton B, Hamm LL, McCullough PA, Kasiske BL, Kelepouris E, Klag MJ, Parfrey P, Pfeffer M, Raij L, Spinosa DJ, Wilson PW: Kidney disease as a risk factor for development of cardiovascular disease: a statement from the American Heart Association Councils on kidney in cardiovascular disease, high blood pressure research, clinical cardiology, and epidemiology and prevention. Hypertension. 2003, 42: 1050-1065. 10.1161/01.HYP.0000102971.85504.7c.PubMedView ArticleGoogle Scholar
  4. Go AS, Chertow GM, Fan D, McCulloch CE, Hsu CY: Chronic kidney disease and the risks of death, cardiovascular events, and hospitalization. N Engl J Med. 2004, 351: 1296-1305. 10.1056/NEJMoa041031.PubMedView ArticleGoogle Scholar
  5. Ninomiya T, Kiyohara Y, Kubo M, Tanizaki Y, Doi Y, Okubo K, Wakugawa Y, Hata J, Oishi Y, Shikata K, Yonemoto K, Hirakata H, Iida M: Chronic kidney disease and cardiovascular disease in a general Japanese population: the Hisayama study. Kidney Int. 2005, 68: 228-236. 10.1111/j.1523-1755.2005.00397.x.PubMedView ArticleGoogle Scholar
  6. The Japanese Society of Nephrology: Clinical practice guidebook for diagnosis and treatment of chronic kidney disease 2012. Jpn J Nephrol. 2012, 54: 1031-1189.Google Scholar
  7. Klag MJ, Whelton PK, Randall BL, Neaton JD, Brancati FL, Ford CE, Shulman NB, Stamler J: Blood pressure and end-stage renal disease in men. N Engl J Med. 1996, 334: 13-18. 10.1056/NEJM199601043340103.PubMedView ArticleGoogle Scholar
  8. Iseki K, Iseki C, Ikemiya Y, Kinjo K, Takishita S: Risk of developing low glomerular filtration rate or elevated serum creatinine in a screened cohort in Okinawa, Japan. Hypertens Res. 2007, 30: 167-174. 10.1291/hypres.30.167.PubMedView ArticleGoogle Scholar
  9. Chen J, Muntner P, Hamm LL, Jones DW, Batuman V, Fonseca V, Whelton PK, He J: The metabolic syndrome and chronic kidney disease in U.S. adults. Ann Intern Med. 2004, 140: 167-174. 10.7326/0003-4819-140-3-200402030-00007.PubMedView ArticleGoogle Scholar
  10. Hallan S, De Mutsert R, Carlsen S, Dekker FW, Aasarød K, Holmen J: Obesity, smoking, and physical inactivity as risk factors for CKD: are men more vulnerable?. Am J Kidney Dis. 2006, 47: 396-405. 10.1053/j.ajkd.2005.11.027.PubMedView ArticleGoogle Scholar
  11. Fox CS, Larson MG, Leip EP, Meigs JB, Wilson PWF, Levy D: Glycemic status and development of kidney disease. The Framingham heart study. Diabetes Care. 2005, 28: 2436-2440. 10.2337/diacare.28.10.2436.PubMedView ArticleGoogle Scholar
  12. Tanaka H, Shiohira Y, Uezu Y, Higa A, Iseki K: Metabolic syndrome and chronic kidney disease in Okinawa, Japan. Kidney Int. 2006, 69: 369-374. 10.1038/sj.ki.5000050.PubMedView ArticleGoogle Scholar
  13. Nguyen S, Hsu CY: Excess weight as a risk factor for kidney failure. Curr Opin Nephrol Hypertens. 2007, 16: 71-76. 10.1097/MNH.0b013e32802ef4b6.PubMedView ArticleGoogle Scholar
  14. Kramer H, Luke A, Bidani A, Cao G, Cooper R, McGee D: Obesity and prevalent and incident CKD: the hypertension detection and follow-Up program. Am J Kidney Dis. 2005, 46: 587-594. 10.1053/j.ajkd.2005.06.007.PubMedView ArticleGoogle Scholar
  15. Yamagata K, Ishida K, Sairenchi T, Takahashi H, Ohba S, Shiigai T, Narita M, Koyama A: Risk factors for chronic kidney disease in a community-based population: a 10-year follow-up study. Kidney Int. 2007, 71: 159-166. 10.1038/sj.ki.5002017.PubMedView ArticleGoogle Scholar
  16. Wang Y, Chen X, Song Y, Caballero B, Cheskin LJ: Association between obesity and kidney disease: a systematic review and meta-analysis. Kidney Int. 2008, 73: 19-33. 10.1038/sj.ki.5002586.PubMedView ArticleGoogle Scholar
  17. Nomura I, Kato J, Kitamura K: Association between body mass index and chronic kidney disease: a population-based, cross-sectional study of a Japanese community. Vasc Health Risk Manag. 2009, 5: 315-320.PubMedPubMed CentralGoogle Scholar
  18. Matsuo S, Imai E, Horio M, Yasuda Y, Tomita K, Nitta K, Yamagata K, Tomino Y, Yokoyama H, Hishida A: Revised equations for estimated GFR from serum creatinine in Japan. Am J Kidney Dis. 2009, 53: 982-992. 10.1053/j.ajkd.2008.12.034.PubMedView ArticleGoogle Scholar
  19. Examination Committee of Criteria for ‘Obesity Disease’ in Japan, Japan Society for the Study of Obesity: New criteria for ‘obesity disease’ in Japan. Circ J. 2002, 66: 987-992. 10.1253/circj.66.987.View ArticleGoogle Scholar
  20. Ishizaka N, Ishizaka Y, Toda E, Koike K, Seki G, Nagai R, Yamakado M: Association between obesity and chronic kidney disease in Japanese: differences in gender and hypertensive status?. Hypertens Res. 2007, 30: 1059-1064. 10.1291/hypres.30.1059.PubMedView ArticleGoogle Scholar
  21. Shankar A, Leng C, Chia KS, Koh D, Tai ES, Saw SM, Lim SC, Wong TY: Association between body mass index and chronic kidney disease in men and women: population-based study of Malay adults in Singapore. Nephrol Dial Transplant. 2008, 23: 1910-1918. 10.1093/ndt/gfm878.PubMedView ArticleGoogle Scholar
  22. Hunley TE, Ma LJ, Kon V: Scope and mechanisms of obesity-related renal disease. Curr Opin Nephrol Hypertens. 2010, 19: 227-234. 10.1097/MNH.0b013e3283374c09.PubMedPubMed CentralView ArticleGoogle Scholar

Copyright

© Komura et al.; licensee BioMed Central Ltd. 2013

This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Advertisement