Differential Signature of Obesity in the Relationship with Acute Kidney Injury and Mortality after Coronary Artery Bypass Grafting

Hongran Moon; Yeonhee Lee; Sejoong Kim; Dong Ki Kim; Ho Jun Chin; Kwon Wook Joo; Yon Su Kim; Ki Young Na; Seung Seok Han

doi:10.3346/jkms.2018.33.e312

Journal List > J Korean Med Sci > v.33(48) > 1107984

Go to TopGo to Top Go to BottomGo to Bottom

TOOLS

Moon, Lee, Kim, Kim, Chin, Joo, Kim, Na, and Han: Differential Signature of Obesity in the Relationship with Acute Kidney Injury and Mortality after Coronary Artery Bypass Grafting

Original Article

Nephrology

Journal of Korean Medical Science 2018; 33(48): e312.

Published online: 9 November 2018

DOI: https://doi.org/10.3346/jkms.2018.33.e312

Differential Signature of Obesity in the Relationship with Acute Kidney Injury and Mortality after Coronary Artery Bypass Grafting

Hongran Moon¹

, Yeonhee Lee¹

, Sejoong Kim^1,²

, Dong Ki Kim¹

, Ho Jun Chin^1,²

, Kwon Wook Joo¹

, Yon Su Kim¹

, Ki Young Na^1,²

, Seung Seok Han¹

¹Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea.

²Department of Internal Medicine, Seoul National University Bundang Hospital, Seongnam, Korea.

Address for Correspondence: Seung Seok Han, MD, PhD. Department of Internal Medicine, Seoul National University College of Medicine, 103 Daehak-ro, Jongno-gu, Seoul 03080, Korea. hansway80@gmail.com

Address for Correspondence: Ki Young Na, MD, PhD. Department of Internal Medicine, Seoul National University Bundang Hospital, 82 Gumi-ro, 173 beon-gil, Bundang-gu, Seongnam 13620, Republic of Korea. kyna@snubh.org

Received 2 May 2018 Accepted 30 August 2018

The Korean Academy of Medical Sciences

(open-access):

This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (https://creativecommons.org/licenses/by-nc/4.0/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Background

Obesity is related to several comorbidities and mortality, but its relationship with acute kidney injury (AKI) and long-term mortality remain undetermined in patients undergoing coronary artery bypass grafting.

Methods

Data from 3,018 patients (age ≥ 18 years) who underwent coronary artery bypass graft surgery from two tertiary referral centers were retrospectively reviewed between 2004 and 2015. Obesity was defined using the body mass index, according to the World Health Organization's recommendation. The odds and hazard ratios in post-surgical, AKI, and all-cause mortality were calculated after adjustment for multiple covariates. Patients were followed for 90 ± 40.9 months (maximum: 13 years).

Results

Among the cohort, 37.4%, 2.4%, 21.1%, 35.1%, and 4.0% of patients were classified as normal weight, underweight, overweight-at-risk, obese I, and obese II, respectively. Post-surgical AKI developed in 799 patients (26.5%). Patients in the obese groups (overweight-at-risk to obese II) had a higher risk of AKI than did those in the normal-weight group. During the follow-up period, 787 patients (26.1%) died. Underweight patients had a higher risk of mortality than did normal-weight patients, whereas overweight-at-risk, obese I, and obese II patients showed better survival rates.

Conclusion

After coronary artery bypass graft surgery, obese patients encountered a high risk of AKI, and underweight patients exhibited a low chance of survival. Awareness of both obese and underweight statuses should be raised in these patients.

Graphical Abstract

Keywords: Acute Kidney Injury, Coronary Artery Bypass Grafting, Obesity, Underweight, Mortality

INTRODUCTION

Obesity has reached epidemic levels, not only in Caucasians but also in Asians.1 2 The prevalence of overweight people of Asian descent, as defined by the World Health Organization (WHO)'s criteria for Asians, has steadily increased from 31.7% in 2007 to 33.2% in 2015. This prevalence has especially increased in Korean men, from 36.2% to 39.7%.3 Obesity is strongly associated with the risk of diabetes mellitus, dyslipidemia, hypertension, and cardiovascular diseases.4 Furthermore, obesity can lead to atherosclerosis as a result of a disruption of endothelial function, as well as enhancing oxidative stress and pro-inflammatory status.5 Due to the increasing prevalence of obesity, cardiovascular disease and related mortality rates have also increased.6 Coronary artery bypass grafting (CABG) is one of the most common surgical procedures, and it was shown to improve the survival of patients with severe angina.7 An awareness of obesity should be raised in patients undergoing CABG because obesity may be related to poorer post-surgical outcomes.8

The proportion of obese patients undergoing CABG is rising.9 Currently, the incidence of post-surgical acute kidney injury (AKI) has significantly increased because patients undergoing CABG are now generally older at the time of surgery, they have more overall preoperative comorbidities, and a larger proportion of patients may be undergoing emergent surgery than before.10 In this regard, studies have suggested a relationship among obesity, AKI,11 12 or mortality13 14 15 16 patients with heart disease, although these results were not uniform possibly because of the heterogeneous patient characteristics.14 15 17 18 To our knowledge, no studies have elucidated a direct relationship among the three factors in patients after CABG. In addition, the reason why we focused on obesity specifically for Asian patients who underwent CABG is that information on risk factors for cardiac surgery-associated acute kidney injury (CSA-AKI) comes from many retrospective observational studies19 20 21 22 23 24 but there no studies have focused on the correlation between AKI and body mass index (BMI). Accordingly, the purpose of the present study was to address the relationship among obesity, AKI, and long-term mortality in a large cohort of patients who underwent CABG.

METHODS

Study population

Data on patients undergoing CABG were retrospectively obtained from the aforementioned tertiary referral centers. A total of 3,089 consecutive patients underwent CABG between January 2003 and December 2015. Patients who were administered renal replacement therapy before surgery (n = 62), those who underwent revision CABG (n = 8), and those who were younger than 18 years old (n = 1) were excluded. Ultimately, 3,018 patients were included in the present analysis.

Data collection and definitions

The clinical parameters investigated were age, sex, current smoking and drinking status, hypertension, diabetes mellitus, history of myocardial infarction (MI), stroke, and peripheral vascular disease (PVD), prescribed medications, such as angiotensin-converting enzyme inhibitors (ACEi) or angiotensin receptor blockers, β-blockers, diuretics, and statins, history of coronary angiography (CAG) within 1 week before surgery, intraoperative use of a cardiopulmonary bypass (CPB), and perioperative use of an intra-aortic balloon pump (IABP). The left ventricular ejection fractions were determined using Simpson's modified biplane method from apical two- and four-chamber views on preoperative echocardiography. The baseline blood test findings, such as creatinine, white blood cell count, hemoglobin, and albumin, were examined. The C-reactive protein levels, which have high sensitivity, were not available for all the patients, so these were additionally adjusted as part of a sensitivity analysis. BMI was calculated as the body weight (in kilograms) divided by the square of the body height (in meters). The WHO's classification of the weight status of Asian people25 was used to categorize the patients as follows: underweight, < 18.5 kg/m²; normal weight, 18.5–23.5 kg/m²; overweight-at-risk, 23.5–25 kg/m²; obese I, 25–30 kg/m²; and obese II, ≥ 30 kg/m². The creatinine levels were measured until 48 hours after surgery. The estimated glomerular filtration rates were calculated using the creatinine equation developed by the Chronic Kidney Disease Epidemiology Collaboration.26 As per the Kidney Disease Improving Global Outcomes guideline, AKI was determined in patients with either an increase in the serum creatinine level by ≥ 0.3 mg/dL or ≥ 1.5 times from the baseline. The additional patient outcome was all-cause mortality after surgery. All patients, except for those who were death-censored, were followed up until April 2017. The mortality data were obtained from the national database of the Statistics Korea.

Statistical analysis

All statistical analyses were performed using SPSS (version 23.0.; IBM Corp., Chicago, IL, USA) and STATA (version 12.0; Stata Corp LP, College Station, TX, USA). We described the baseline characteristics, after stratifying them by BMI. The means and their standard deviations were calculated for all continuous variables, and all categorical variables were reported as the percentage of participants in each category. The χ² test was used to compare categorical variables. Comparisons between continuous variables were performed using an analysis of variance or a post hoc analysis of least significant difference, based on the number of comparison groups. Non-linear relationships were explored with a restricted cubic spline analysis. Odds ratios (ORs) with 95% confidence intervals (CIs) were calculated using a logistic regression model, with and without adjustment of multiple covariates (age; sex; current smoking and drinking status; hypertension; diabetes mellitus; history of MI, stroke, and PVD; prescribed medications, such as ACEi or angiotensin receptor blockers, β-blockers, diuretics, and statins; history of CAG within 1 week before surgery; intraoperative use of CPB; and perioperative use of an IAPB; left ventricular ejection fraction; and blood findings, such as creatinine, white blood cell count, hemoglobin, and albumin). To assess the relationship between BMI and mortality, we created a Cox proportional hazard ratio (HR) model to consider covariates (AKI plus the aforementioned covariates). A P value less than 0.05 was considered significant.

Ethics statement

The study protocol complied with the ethical principles of the Declaration of Helsinki and received full approval from the Institutional Review Boards of Seoul National University Hospital (H-1702-050-831) and Seoul National University Bundang Hospital (B-1702/384-103).

RESULTS

Baseline characteristics

The baseline characteristics of the patients are shown in Table 1. The study included 3,018 patients (73.7% men), with a mean age of 66 ± 9.8 years. Of these patients, 37.4%, 2.4%, 21.1%, 35.1%, and 4.0%, were normal weight, underweight, overweight-at-risk, obese I, and obese II, respectively. At baseline, patients in the underweight group were older and had a higher prevalence of chronic kidney disease than did those in the normal-weight group. More patients were diagnosed with hypertension in the overweight group, including those who were overweight-at-risk, obese I, and obese II than those in the normal-weight group. Patients in the obese II group took more ACEi or angiotensin receptor blockers than did those in the normal-weight group. In addition, length of stay (i.e., hospitalization period) based on BMI, was as follows; normal, 19.3 ± 18.0 days; underweight, 26.0 ± 19.0 days; overweight-at-risk, 16.4 ± 17.5 days; obese I, 17.3 ± 23.4 days and obese II, 17.1 ± 13.8 days (underweight and overweight-at-risk, P < 0.05 compared with normal weight group). The length of hospital stay was longer in the underweight group than that in normal weight group.

Table 1

Baseline characteristics of the study participants

Variables		Total (n = 3,018)	Normal weight (n = 1,129)	Underweight (n = 71)	Overweight			P value
Variables		Total (n = 3,018)	Normal weight (n = 1,129)	Underweight (n = 71)	At risk (n = 637)	Obese I (n = 1,060)	Obese II (n = 121)	P value
Age, yr		65.5 ± 9.84	66.7 ± 9.88	70.8 ± 9.95^b	65.3 ± 9.35^a	64.7 ± 9.48^c	60.0 ± 11.60^c	0.039^a
Sex, male, %		73.7	74.0	62.0	77.4	72.5	67.8	0.013^a
Current smoking, %		31.8	32.3	39.4	30.5	31.9	28.1	0.024^a
Alcohol, %		29.8	27.5	26.8	31.9^*	31.3^a	27.3	< 0.001^c
Comorbidities, %
	Hypertension	57.5	53.4	53.5	57.5	60.4^b	72.7^c	< 0.001^c
	Diabetes mellitus	43.8	43.2	28.2	43.5	45.0	49.6	< 0.001^c
	History of MI	9.2	9.5	7.0	8.3	9.2	13.2	0.012^*
	History of stroke	19.6	19.8	28.2	18.7	19.2	22.3	0.007^b
	PVD	6.5	6.8	11.3	6.9	5.9	2.5	< 0.001^c
	CKD	29.5	29.5	42.3	29.2	29.5	23.1	< 0.001^c
Medications, %
	ACEi or ARB	35.9	34.2	29.6	35.3	37.4	46.3	< 0.001^c
	β-blocker	38.3	37.3	23.9	38.8	39.7	40.5	< 0.001^c
	Diuretics	16.5	18.4	18.3	14.0	15.6	18.2	< 0.001^c
	Statin	40.6	38.8	31.0	42.4	40.8	51.2	< 0.001
CAG within 1 week, %		45.7	47.0	52.1	48.0	43.1	39.7	< 0.001^c
CPB, %		16.4	15.4	23.9	15.7	17.2	18.2	0.003^b
IABP, %		9.1	10.6	15.5	8.8	7.9	3.3	< 0.001^c
LV ejection fraction, %		54.8 ± 12.26	52.8 ± 13.25	50.5 ± 14.56	55.7 ± 11.83^c	56.2 ± 11.11^c	57.1 ± 9.60^c	< 0.001^c
No. of grafted arteries		2.4 ± 0.81	2.4 ± 0.80	2.3 ± 0.69	2.4 ± 0.78	2.5 ± 0.82	2.4 ± 0.89	0.056
Laboratory findings
	White blood cells, µL	7,848 ± 3,128	7,915 ± 3,112	9,247 ± 4,613^b	7,792 ± 3,057	7,885 ± 3,042	8,243 ± 3,132	0.001^b
	Hemoglobin level, g/dL	12.5 ± 2.10	12.3 ± 2.08	11.9 ± 1.83	12.7 ± 2.10^b	12.6 ± 2.10^a	12.9 ± 2.07^a	0.432
	Albumin, g/dL	3.8 ± 0.64	3.7 ± 0.46	3.7 ± 0.60	3.8 ± 0.64^a	3.8 ± 0.65	3.9 ± 0.60	0.349
	Creatinine, mg/dL	1.09 ± 0.39	1.10 ± 0.41	1.12 ± 0.47	1.09 ± 0.40	1.08 ± 0.36	1.04 ± 0.26	0.003^b
	eGFR, mL/min/1.73 m²	70.7 ± 20.17	69.7 ± 20.74	66.0 ± 23.59	70.3 ± 19.58	70.2 ± 19.63	73.4 ± 20.06	0.030^b

Comparisons were evaluated using the χ² test for categorical variables and analysis of variance for continuous variables (post hoc analysis of least significant difference between the two groups). The normal-weight group served as a reference for the comparison between the two groups.

MI = myocardial infarction, PVD = peripheral vascular disease, CKD = chronic kidney disease, ACEi = angiotensin-converting enzyme inhibitors, ARB = angiotensin II receptor blocker, CAG = coronary angiography, CPB = cardiopulmonary bypass, IABP = intra-aortic balloon pump, LV = left ventricle, eGFR = estimated glomerular filtration rate.

^aP < 0.05; ^bP < 0.01; ^cP < 0.001.

Relationship between BMI and post-surgical AKI

Post-surgical AKI developed in 799 patients (26.5%). The prevalence of AKI based on the BMI categories is presented in Fig. 1A. When a non-linear relationship was applied, the risk of AKI increased, depending on the increase in BMI (Fig. 1B). In the multivariate analysis, BMI ≥ 30 kg/m² was associated with a higher OR for AKI (2.28; CI, 1.481–3.511) than was normal weight (Table 2). Although the C-reactive protein, which has high-sensitivity, was additionally adjusted in the multivariate model as a sensitivity analysis (n = 1,550), the relationship between obesity and AKI remained significant. Patients in the obese II group had a higher risk of AKI than did those in the normal weight group, with the following ORs: obese II, 2.31 (CI, 1.498–3.551), P < 0.001.

Fig. 1

Prevalence of post-surgical AKI. (A) Proportions of post-surgical AKI according to the obesity categories. (B) Non-linear relationship between BMI and the risk of AKI.

AKI = acute kidney injury, BMI = body mass index.

^aP < 0.05.

Table 2

OR for post-surgical AKI by obesity classification

Groups	AKI rate (%)	Univariate		Multivariate
Groups	AKI rate (%)	OR (95% CI)	P value	OR (95% CI)	P value
Normal weight	25.6	1 (reference)		1 (reference)
Underweight	29.6	1.22 (0.721–2.068)	0.458	1.03 (0.585–1.806)	0.924
Overweight-at-risk	25.6	1.00 (0.800–1.249)	0.997	1.124 (0.886–1.427)	0.336
Overweight obese I	26.7	1.06 (0.875–1.281)	0.558	1.194 (0.971–1.468)	0.093
Overweight obese II	35.5	1.60 (1.079–2.380)	0.019	2.281 (1.481–3.511)	< 0.001

AKI = acute kidney injury, OR = odds ratio, CI = confidence interval.

Effect of obesity on all-cause mortality after CABG

The relationship trend for mortality and obesity was different from that of the aforementioned relationship in post-surgical AKI. During the mean follow-up period of 74 ± 43.8 months (maximum: 13 years), 787 patients (26.1%) died. The mortality rate was 53.76 deaths per 1,000 person-years. The 5-year mortality rates for normal weight, underweight, overweight-at-risk, obese I, and obese II patients were 38.3%, 62.0%, 35.3%, 33.9%, and 38.8%, respectively (Fig. 2A), and their mortality rates were as follows: 31.4%, 62.0%, 20.4%, 22.0%, and 20.7%, respectively. The non-linear relationship analysis showed a similar pattern to these results (Fig. 2B).

Fig. 2

Relationship between obesity and all-cause mortality. (A) Five-year survival rates based on the obesity categories. (B) Non-linear relationship between BMI and the 5-year survival rates.

BMI = body mass index.

^aP < 0.05.

Patients in the underweight group had a higher HR for mortality (2.85; CI, 2.085–3.905) than did those in the normal-weight group, whereas patients who were classified as overweight-at-risk, obese I, and obese II had lower HRs than did those in the normal-weight group, as follows: 0.60 (CI, 0.491–0.734), 0.61 (CI, 0.512–0.720), and 0.65 (CI, 0.436–0.981), respectively. In the multivariate analysis, patients in the underweight group tended to have poor survival, and those in the overweight and obese groups had better survival, remaining consistent (P for all, < 0.05) (Fig. 3 and Table 3). Additionally, the high-sensitivity C-reactive protein levels were adjusted in the multivariate model. The relationship between being underweight and high mortality was significant, with an HR of 2.15 (CI, 1.555–2.972) compared with that of normal-weight patients.

Fig. 3

Kaplan-Meier survival curves according to the obesity categories.

Table 3

HRs for all-cause mortality after surgery

Groups	Univariate		Multivariate
Groups	HR (95% CI)	P value	HR (95% CI)	P value
Normal weight	1 (reference)		1 (reference)
Underweight	2.85 (2.085–3.905)	< 0.001	2.22 (1.604–3.076)	< 0.001
Overweight-at-risk	0.60 (0.491–0.734)	< 0.001	0.74 (0.603–0.906)	0.004
Overweight obese I	0.65 (0.546–0.760)	< 0.001	0.83 (0.701–0.987)	0.035
Overweight obese II	0.65 (0.436–0.981)	0.040	1.25 (0.826–1.900)	0.290

HR = hazard ratio, CI = confidence interval.

AKI has been related with several morbidities as well as mortality in several pathologic conditions. Therefore, we analyzed the relationship between AKI and mortality, and obtained the statistically significant results with the following HRs; 1.51 (CI, 1.298–1.756). As a result, AKI was significantly associated with mortality.

DISCUSSION

The obesity epidemic has extended worldwide. Obesity is not only a risk factor for cardiovascular disease but also for several disease conditions, such as diabetes, fatty liver, and cancer, all of which can further contribute to the development of cardiovascular diseases.27 Recently, obesity was reported to be associated with various kidney diseases.28 Nevertheless, systematic attempts to understand the relationship between obesity and an acute worsening of kidney function have not been thoroughly performed in patients undergoing CABG. In the present cohort of Korean patients who underwent CABG, patients in the overweight groups (BMI > 23.5 kg/m²) showed a higher risk of post-surgical AKI, but these patients had a more favorable prognosis in terms of overall mortality. Although several mechanisms possibly underlie the observations, the relationship of obesity with AKI or mortality might depend on its clinical or subclinical features, such as inflammation or nutritional support.

The effects of obesity on post-surgical AKI have been explored in diverse clinical settings. According to a study that was based on patients with acute respiratory distress, obesity was highly associated with the development of AKI, but these patients had decreased mortality in the intensive care unit and more ventilator-free days.29 In another study of 14,986 intensive care unit patients, each 5 kg/m² increment in BMI was associated with a 10% higher risk of AKI. With respect to in-hospital deaths, an increased BMI was associated with a decreasing trend of the OR.27 In elderly patients undergoing hip fracture surgery, obese patients were at an increased risk of AKI, but overweight and obesity were not observed to be associated with an increased risk of mortality compared to normal-weight patients.30 Notably, the relationships of BMI with AKI or mortality were similar to the present findings.

Some possible mechanisms of CSA-AKI include renal ischemia, reperfusion injury, oxidative stress, and inflammation.2 Obesity increases the levels of oxidative stress, endothelial dysfunction, and inflammation, although the effect of obesity in the setting of cardiac surgery has not been directly addressed.31 The current observations could be also explained by the effects of adiponectin. In addition to the fact that adiponectin regulates glucose and lipid metabolism, it was reported to confer protection against AKI in a renal ischemia reperfusion injury model.32 Additionally, it reduces renal inflammation and fibrosis by activating adenosine monophosphate protein kinase pathways, thus preventing albuminuria.2 33 However, the blood adiponectin level is lower in obese subjects than in their counterpart subjects,33 which may increase the risk of AKI in obese patients after CABG.

There is a paradoxical decrease in morbidity and mortality with an increase in BMI.13 A previous study on patients after cardiac surgery reported a protective effect of obesity in terms of hospital survival,15 although this trend was reduced in critically ill patients with AKI.27 Other studies have rebutted this paradox based on the fact that BMI does not completely reflect fat mass,34 35 and obese people might be more aggressively treated with disease-modifying agents than their normal-weight counterparts.14 36 Theoretically, the favorable survival of obese patients could be attributed to their high metabolic reserves.13 Because obese patients have a potential reservoir to secrete amino acids and adipokines, particularly in stressful conditions, this milieu would decrease the levels of B-type natriuretic peptide, oxidative stress, and inflammation.13 Obese patients receive sufficient nutritional support37 38 after surgery, which may overwhelm the deleterious effects of obesity. The obese patients in this study were generally younger and had higher hemoglobin and albumin levels, which might reflect the ample nutritional status compared with the underweight patients. Nevertheless, obesity should be considered potentially harmful if patients maintain a nutritionally balanced diet.

Being underweight is directly and indirectly associated with high mortality.39 40 The reason for the high mortality rate in the underweight group could be explained by a lower metabolic reserve and nutritional support in stressful situations. Even underweight patients had higher mortality than their obese counterparts, partly because they were older and had greater severity of certain illnesses.29

This study has some limitations. Firstly, because this study was based on Korean (i.e., Asian) patients, generalizing the study findings to Caucasian patients may be limited.12 Secondly, imbalanced baseline characteristics between groups could affect the statistical results (e.g., the longer hospitalization period and older age in underweight group could reflect the severity of medical status). Thirdly, as the study was retrospective, we could only obtain the information regarding whether or not the patients died or not, but the causes of mortality could not be determined. Furthermore, obesity was measured using BMI alone, and body compositions were not considered.

Obesity was related to a high risk of AKI, but not high mortality, whereas being underweight was associated with poor survival after CABG. These results suggest that obesity or BMI can be used to predict the outcomes of CABG and will form the basis of future studies regarding the underlying relationships between obesity and heart and kidney function.

Notes

^Funding This study was supported by a grant of the Health Fellowship Foundation (2016).

^Disclosure The authors have no potential conflicts of interest to disclose.

^{Author Contributions}

Conceptualization: Moon H.
Data curation: Moon H, Lee Y, Kim S, Kim DK, Chin HJ, Joo KW.
Formal analysis: Moon H, Han SS.
Investigation: Moon H, Chin HJ, Joo KW.
Methodology: Moon H, Han SS.
Software: Moon H, Chin HJ, Joo KW.
Validation: Moon H, Kim YS, Han SS.
Writing - original draft: Moon H.
Writing - review & editing: Moon H, Han SS, Na KY.

References

1. Yoon KH, Lee JH, Kim JW, Cho JH, Choi YH, Ko SH, et al. Epidemic obesity and type 2 diabetes in Asia. Lancet. 2006; 368(9548):1681–1688.

2. O'Neal JB, Shaw AD, Billings FT 4th. Acute kidney injury following cardiac surgery: current understanding and future directions. Crit Care. 2016; 20(1):187.

3. Park YW, Choi KB, Kim SK, Lee DG, Lee JH. Obesity in Korean men: results from the Fourth through Sixth Korean National Health and Nutrition Examination Surveys (2007~2014). World J Mens Health. 2016; 34(2):129–136.

4. Alexander CM, Landsman PB, Teutsch SM, Haffner SM. Third National Health and Nutrition Examination Survey (NHANES III). National Cholesterol Education Program (NCEP). NCEP-defined metabolic syndrome, diabetes, and prevalence of coronary heart disease among NHANES III participants age 50 years and older. Diabetes. 2003; 52(5):1210–1214.

5. Bays HE, González-Campoy JM, Bray GA, Kitabchi AE, Bergman DA, Schorr AB, et al. Pathogenic potential of adipose tissue and metabolic consequences of adipocyte hypertrophy and increased visceral adiposity. Expert Rev Cardiovasc Ther. 2008; 6(3):343–368.

6. Van Gaal LF, Mertens IL, De Block CE. Mechanisms linking obesity with cardiovascular disease. Nature. 2006; 444(7121):875–880.

7. Locker C, Schaff HV, Daly RC, Dearani JA, Bell MR, Frye RL, et al. Multiple arterial grafts improve survival with coronary artery bypass graft surgery versus conventional coronary artery bypass grafting compared with percutaneous coronary interventions. J Thorac Cardiovasc Surg. 2016; 152(2):369–379.e4.

8. Bamgbade OA, Rutter TW, Nafiu OO, Dorje P. Postoperative complications in obese and nonobese patients. World J Surg. 2007; 31(3):556–560.

9. Kuduvalli M, Grayson AD, Oo AY, Fabri BM, Rashid A. The effect of obesity on mid-term survival following coronary artery bypass surgery. Eur J Cardiothorac Surg. 2003; 23(3):368–373.

10. Thorsteinsson K, Fonager K, Mérie C, Gislason G, Køber L, Torp-Pedersen C, et al. Age-dependent trends in postoperative mortality and preoperative comorbidity in isolated coronary artery bypass surgery: a nationwide study. Eur J Cardiothorac Surg. 2016; 49(2):391–397.

11. Kumar AB, Bridget Zimmerman M, Suneja M. Obesity and post-cardiopulmonary bypass-associated acute kidney injury: a single-center retrospective analysis. J Cardiothorac Vasc Anesth. 2014; 28(3):551–556.

12. Wulan SN, Westerterp KR, Plasqui G. Ethnic differences in body composition and the associated metabolic profile: a comparative study between Asians and Caucasians. Maturitas. 2010; 65(4):315–319.

13. Dixon JB, Lambert GW. The obesity paradox--a reality that requires explanation and clinical interpretation. Atherosclerosis. 2013; 226(1):47–48.

14. Hällberg V, Kataja M, Lahtela J, Tarkka M, Inamaa T, Palomäki A, et al. Obesity paradox disappears in coronary artery bypass graft patients during 20-year follow-up. Eur Heart J Acute Cardiovasc Care. 2017; 6(8):771–777.

15. Johnson AP, Parlow JL, Whitehead M, Xu J, Rohland S, Milne B. Body mass index, outcomes, and mortality following cardiac surgery in Ontario, Canada. J Am Heart Assoc. 2015; 4(7):e002140.

16. Peng Y, Chen F, Huang FY, Xia TL, Huang BT, Chai H, et al. Body composition and mortality in coronary artery disease with mild renal insufficiency in Chinese patients. J Ren Nutr. 2017; 27(3):187–193.

17. Wang TK, Ramanathan T, Stewart R, Gamble G, White H. Lack of relationship between obesity and mortality or morbidity after coronary artery bypass grafting. N Z Med J. 2013; 126(1386):56–65.

18. Stamou SC, Nussbaum M, Stiegel RM, Reames MK, Skipper ER, Robicsek F, et al. Effect of body mass index on outcomes after cardiac surgery: is there an obesity paradox? Ann Thorac Surg. 2011; 91(1):42–47.

19. Wang Y, Bellomo R. Cardiac surgery-associated acute kidney injury: risk factors, pathophysiology and treatment. Nat Rev Nephrol. 2017; 13(11):697–711.

20. Thiele RH, Isbell JM, Rosner MH. AKI associated with cardiac surgery. Clin J Am Soc Nephrol. 2015; 10(3):500–514.

21. Haase M, Bellomo R, Story D, Letis A, Klemz K, Matalanis G, et al. Effect of mean arterial pressure, haemoglobin and blood transfusion during cardiopulmonary bypass on post-operative acute kidney injury. Nephrol Dial Transplant. 2012; 27(1):153–160.

22. Cheungpasitporn W, Thongprayoon C, Kittanamongkolchai W, Srivali N, O'Corragain OA, Edmonds PJ, et al. Comparison of renal outcomes in off-pump versus on-pump coronary artery bypass grafting: a systematic review and meta-analysis of randomized controlled trials. Nephrology (Carlton). 2015; 20(10):727–735.

23. Lamy A, Devereaux PJ, Prabhakaran D, Taggart DP, Hu S, Paolasso E, et al. Effects of off-pump and on-pump coronary-artery bypass grafting at 1 year. N Engl J Med. 2013; 368(13):1179–1188.

24. Shroyer AL, Grover FL, Hattler B, Collins JF, McDonald GO, Kozora E, et al. On-pump versus off-pump coronary-artery bypass surgery. N Engl J Med. 2009; 361(19):1827–1837.

25. World Health Organization. Regional Office for the Western Pacific. The Asia-Pacific Perspective: Redefining Obesity and Its Treatment. Sydney: Health Communications Australia;2000.

26. Levey AS, Stevens LA, Schmid CH, Zhang YL, Castro AF 3rd, Feldman HI, et al. A new equation to estimate glomerular filtration rate. Ann Intern Med. 2009; 150(9):604–612.

27. Danziger J, Chen KP, Lee J, Feng M, Mark RG, Celi LA, et al. Obesity, acute kidney injury, and mortality in critical illness. Crit Care Med. 2016; 44(2):328–334.

28. Wickman C, Kramer H. Obesity and kidney disease: potential mechanisms. Semin Nephrol. 2013; 33(1):14–22.

29. Soto GJ, Frank AJ, Christiani DC, Gong MN. Body mass index and acute kidney injury in the acute respiratory distress syndrome. Crit Care Med. 2012; 40(9):2601–2608.

30. Pedersen AB, Gammelager H, Kahlert J, Sørensen HT, Christiansen CF. Impact of body mass index on risk of acute kidney injury and mortality in elderly patients undergoing hip fracture surgery. Osteoporos Int. 2017; 28(3):1087–1097.

31. Billings FT 4th, Pretorius M, Schildcrout JS, Mercaldo ND, Byrne JG, Ikizler TA, et al. Obesity and oxidative stress predict AKI after cardiac surgery. J Am Soc Nephrol. 2012; 23(7):1221–1228.

32. Cheng CF, Lian WS, Chen SH, Lai PF, Li HF, Lan YF, et al. Protective effects of adiponectin against renal ischemia-reperfusion injury via prostacyclin-PPARα-heme oxygenase-1 signaling pathway. J Cell Physiol. 2012; 227(1):239–249.

33. Christou GA, Kiortsis DN. The role of adiponectin in renal physiology and development of albuminuria. J Endocrinol. 2014; 221(2):R49–R61.

34. Romero-Corral A, Somers VK, Sierra-Johnson J, Thomas RJ, Collazo-Clavell ML, Korinek J, et al. Accuracy of body mass index in diagnosing obesity in the adult general population. Int J Obes. 2008; 32(6):959–966.

35. Han SS, Kim KW, Kim KI, Na KY, Chae DW, Kim S, et al. Lean mass index: a better predictor of mortality than body mass index in elderly Asians. J Am Geriatr Soc. 2010; 58(2):312–317.

36. Benedetto U, Danese C, Codispoti M. Obesity paradox in coronary artery bypass grafting: myth or reality? J Thorac Cardiovasc Surg. 2014; 147(5):1517–1523.

37. Casas-Vara A, Santolaria F, Fernández-Bereciartúa A, González-Reimers E, García-Ochoa A, Martínez-Riera A. The obesity paradox in elderly patients with heart failure: analysis of nutritional status. Nutrition. 2012; 28(6):616–622.

38. Obi Y, Qader H, Kovesdy CP, Kalantar-Zadeh K. Latest consensus and update on protein-energy wasting in chronic kidney disease. Curr Opin Clin Nutr Metab Care. 2015; 18(3):254–262.

39. Jee SH, Sull JW, Park J, Lee SY, Ohrr H, Guallar E, et al. Body-mass index and mortality in Korean men and women. N Engl J Med. 2006; 355(8):779–787.

40. Flegal KM, Graubard BI, Williamson DF, Gail MH. Excess deaths associated with underweight, overweight, and obesity. JAMA. 2005; 293(15):1861–1867.

TOOLS

ORCID iDs

Hongran Moon
https://orcid.org/0000-0002-0076-9413

Yeonhee Lee
https://orcid.org/0000-0002-9216-420X

Sejoong Kim
https://orcid.org/0000-0002-7238-9962

Dong Ki Kim
https://orcid.org/0000-0002-5195-7852

Ho Jun Chin
https://orcid.org/0000-0003-1185-2631

Kwon Wook Joo
https://orcid.org/0000-0001-9941-7858

Yon Su Kim
https://orcid.org/0000-0003-3091-2388

Ki Young Na
https://orcid.org/0000-0002-8872-8236

Seung Seok Han
https://orcid.org/0000-0003-0137-5261

Similar articles