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Original Research |

Metabolically Healthy Obesity and Development of Chronic Kidney Disease: A Cohort StudyObesity and CKD

Yoosoo Chang, MD, PhD; Seungho Ryu, MD, PhD; Yuni Choi, BS; Yiyi Zhang, PhD; Juhee Cho, PhD; Min-Jung Kwon, MD, PhD; Young Youl Hyun, MD, PhD; Kyu-Beck Lee, MD, PhD; Hyang Kim, MD, PhD; Hyun-Suk Jung, MD; Kyung Eun Yun, MD, PhD; Jiin Ahn, MSPH; Sanjay Rampal, MD, PhD; Di Zhao, PhD; Byung-Seong Suh, MD, PhD; Eun Cheol Chung, MD, PhD; Hocheol Shin, MD, PhD; Roberto Pastor-Barriuso, PhD; and Eliseo Guallar, MD, DrPH
[+] Article, Author, and Disclosure Information

This article was published at www.annals.org on 9 February 2016.


From Center for Cohort Studies, Total Healthcare Center, Kangbuk Samsung Hospital, Samsung Advanced Institute for Health Sciences and Technology, and Sungkyunkwan University School of Medicine, Sungkyunkwan University, Seoul, Republic of Korea; Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland; Julius Centre, University of Malaya, Kuala Lumpur, Malaysia; and National Center for Epidemiology, Carlos III Institute of Health, and Consortium for Biomedical Research in Epidemiology and Public Health, Madrid, Spain.

Disclosures: Authors have disclosed no conflicts of interest. Forms can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M15-1323.

Editors' Disclosures: Christine Laine, MD, MPH, Editor in Chief, reports that she has no financial relationships or interests to disclose. Darren B. Taichman, MD, PhD, Executive Deputy Editor, reports that he has no financial relationships or interests to disclose. Cynthia D. Mulrow, MD, MSc, Senior Deputy Editor, reports that she has no relationships or interests to disclose. Deborah Cotton, MD, MPH, Deputy Editor, reports that she has no financial relationships or interest to disclose. Jaya K. Rao, MD, MHS, Deputy Editor, reports that she has stock holdings/options in Eli Lilly and Pfizer. Sankey V. Williams, MD, Deputy Editor, reports that he has no financial relationships or interests to disclose. Catharine B. Stack, PhD, MS, Deputy Editor for Statistics, reports that she has stock holdings in Pfizer.

Reproducible Research Statement:Study protocol and data set: Not available. Statistical code: Available from Dr. Ryu (e-mail, sh703.yoo@gmail.com), Dr. Guallar (e-mail, eguallar@jhu.edu), or Dr. Pastor-Barriuso (e-mail, rpastor@isciii.es).

Requests for Single Reprints: Seungho Ryu, MD, PhD, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Samsung Main Building B2, 250, Taepyung-ro 2ga, Jung-gu, Seoul, Korea, 100-742 [e-mail, sh703.yoo@gmail.com]; or Eliseo Guallar, MD, DrPH, Welch Center for Prevention, Epidemiology and Clinical Research, Johns Hopkins University Bloomberg School of Public Health, 2024 East Monument Street, Room 2-645, Baltimore, MD 21205 [e-mail, eguallar@jhu.edu].

Current Author Addresses: Drs. Chang and Ryu: Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Samsung Main Building B2, 250, Taepyung-ro 2ga, Jung-gu, Seoul, Korea 100-742; Department of Health Sciences and Technology, Samsung Advanced Institute of Health Sciences and Technology, Sungkyunkwan University, 50, Irwon-Dong, Gangnam-gu, Seoul, Korea 135-710.

Ms. Choi, Dr. Jung, and Ms. Ahn: Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Samsung Main Building B2, 250, Taepyung-ro 2ga, Jung-gu, Seoul, Korea 100-742.

Drs. Zhang, Zhao, and Guallar: Welch Center for Prevention, Epidemiology and Clinical Research, Johns Hopkins University Bloomberg School of Public Health, 2024 East Monument Street, Room 2-635, Baltimore, MD 21205.

Dr. Cho: Department of Health Sciences and Technology, Samsung Advanced Institute of Health Sciences and Technology, Sungkyunkwan University, 50, Irwon-Dong, Gangnam-Gu, Seoul, Korea 135-710.

Dr. Kwon: Department of Laboratory Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, 108 Pyung dong, jongro-Ku, Seoul, Korea 110-746.

Drs. Hyun, Lee, and Kim: Department of Internal Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, 108 Pyung dong, jongro-Ku, Seoul, Korea 110-746.

Dr. Rampal: Julius Centre for Clinical Epidemiology and Evidence-Based Medicine, Department of Social and Preventive Medicine, Faculty of Medicine, University of Malaya, 50603 Kuala Lumpur, Malaysia.

Dr. Suh: Department of Occupational and Environmental Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, 108 Pyung dong, jongro-Ku, Seoul, Korea 110-746.

Dr. Chung: Department of Radiology, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, 108 Pyung dong, jongro-Ku, Seoul, Korea 110-746.

Dr. Shin: Department of Family Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, 108 Pyung dong, jongro-Ku, Seoul, Korea 110-746.

Dr. Pastor-Barriuso: National Center for Epidemiology, Carlos III Institute of Health, Av. Monforte de Lemos 5, 28029, Madrid, Spain.

Author Contributions: Conception and design: Y. Chang, S. Ryu, J. Cho, H. Shin, E. Guallar.

Analysis and interpretation of the data: Y. Chang, S. Ryu, Y. Choi, J. Cho, M.J. Kwon, Y.Y. Hyun, K.B. Lee, H. Kim, H.S. Jung, S. Rampal, D. Zhao, B.S. Suh, E.C. Chung, R. Pastor-Barriuso, E. Guallar.

Drafting of the article: Y. Chang, S. Ryu, Y. Choi, J. Cho, S. Rampal, H. Shin, R. Pastor-Barriuso, E. Guallar.

Critical revision of the article for important intellectual content: Y. Chang, S. Ryu, Y. Zhang, J. Cho, Y.Y. Hyun, H.S. Jung, K.E. Yun, R. Pastor-Barriuso, E. Guallar.

Final approval of the article: Y. Chang, S. Ryu, Y. Choi, Y. Zhang, J. Cho, M.J. Kwon, Y.Y. Hyun, K.B. Lee, H. Kim, H.S. Jung, K.E. Yun, J. Ahn, S. Rampal, D. Zhao, B.S. Suh, E.C. Chung, H. Shin, R. Pastor-Barriuso, E. Guallar.

Provision of study materials or patients: Y. Chang, S. Ryu, H. Kim, E.C. Chung, H. Shin.

Statistical expertise: S. Ryu, Y. Zhang, J. Cho, R. Pastor-Barriuso, E. Guallar.

Administrative, technical, or logistic support: S. Ryu, Y. Choi, Y. Zhang, J. Cho, S. Rampal, E. Guallar.

Collection and assembly of data: Y. Chang, S. Ryu, Y. Choi, Y. Zhang, J. Cho, J. Ahn, S. Rampal, B.S. Suh, H. Shin, E. Guallar.


Ann Intern Med. 2016;164(5):305-312. doi:10.7326/M15-1323
© 2016 American College of Physicians
Text Size: A A A

Background: The risk for chronic kidney disease (CKD) among obese persons without obesity-related metabolic abnormalities, called metabolically healthy obesity, is largely unexplored.

Objective: To investigate the risk for incident CKD across categories of body mass index in a large cohort of metabolically healthy men and women.

Design: Prospective cohort study.

Setting: Kangbuk Samsung Health Study, Kangbuk Samsung Hospital, Seoul, South Korea.

Participants: 62 249 metabolically healthy, young and middle-aged men and women without CKD or proteinuria at baseline.

Measurements: Metabolic health was defined as a homeostasis model assessment of insulin resistance less than 2.5 and absence of any component of the metabolic syndrome. Underweight, normal weight, overweight, and obesity were defined as a body mass index less than 18.5 kg/m2, 18.5 to 22.9 kg/m2, 23 to 24.9 kg/m2, and 25 kg/m2 or greater, respectively. The outcome was incident CKD, defined as an estimated glomerular filtration rate less than 60 mL/min/1.73 m2.

Results: During 369 088 person-years of follow-up, 906 incident CKD cases were identified. The multivariable-adjusted differences in 5-year cumulative incidence of CKD in underweight, overweight, and obese participants compared with normal-weight participants were −4.0 (95% CI, −7.8 to −0.3), 3.5 (CI, 0.9 to 6.1), and 6.7 (CI, 3.0 to 10.4) cases per 1000 persons, respectively. These associations were consistently seen in all clinically relevant subgroups.

Limitation: Chronic kidney disease was identified by a single measurement at each visit.

Conclusion: Overweight and obesity are associated with an increased incidence of CKD in metabolically healthy young and middle-aged participants. These findings show that metabolically healthy obesity is not a harmless condition and that the obese phenotype, regardless of metabolic abnormalities, can adversely affect renal function.

Primary Funding Source: None.

Figures

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Figure 1.

Study flow diagram.

CKD = chronic kidney disease; HDL = high-density lipoprotein.

* Participants in the screening program could have >1 criterion that made them ineligible for the study.

† Eligible participants could have missing data in >1 study variable.

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Figure 2.

Adjusted cumulative incidence of CKD, by BMI category at baseline, among metabolically healthy participants in the Kangbuk Samsung Health Study, 2002–2009 to 2013.

Parametric cumulative incidence curves (smooth lines) were estimated from a spline-based parametric survival model and nonparametric cumulative incidence curves (step functions) from Kaplan–Meier methods, both weighted by stabilized inverse probability weights and stratified by BMI category. Stabilized weights were used to standardize cumulative incidence curves in each category to the empirical distribution of baseline confounders in the overall study sample, including age (<30, 30-34, 35-39, 40-44, 45-49, or ≥50 y), sex (female or male), study center (Seoul or Suwon), year of screening examination (2002–2003, 2004–2005, 2006–2007, or 2008–2009), smoking status (never, former, or current), alcohol intake (0, <20, or ≥20 g/d), and regular exercise (<3 or ≥3 times/wk). BMI = body mass index; CKD = chronic kidney disease.

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Appendix Figure.

Adjusted differences in 5-y cumulative incidence of chronic kidney disease per 1000 persons comparing categories of body mass index at baseline with the normal-weight category in prespecified subgroups of metabolically healthy participants in the Kangbuk Samsung Health Study, 2002–2009 to 2013.

Subgroup-specific risk differences (squares with area inversely proportional to the variance) and their 95% CIs (horizontal lines) were obtained from spline-based parametric survival models weighted by stabilized inverse probability weights and stratified by category of body mass index and covariate subgroup. Subgroup-specific weights were used to standardize cumulative incidences in each category of body mass index and covariate subgroup to the empirical distribution of baseline confounders in the overall covariate subgroup, including age (<30, 30-34, 35-39, 40-44, 45-49, or ≥50 y), sex (female or male), study center (Seoul or Suwon), year of screening examination (2002–2003, 2004–2005, 2006–2007, or 2008–2009), smoking status (never, former, or current), alcohol intake (0, <20, or ≥20 g/d), and regular exercise (<3 or ≥3 times/wk).

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References

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Increased weight may not reflect adiposity
Posted on February 29, 2016
Henry S Kahn, MD, FACP, Meda E Pavkov, MD, PhD
Centers for Disease Control and Prevention
Conflict of Interest: None Declared
TO THE EDITOR: In their longitudinal study of metabolically healthy workers, Chang and colleagues demonstrated that higher categories of the body mass index (BMI, weight/height2) were associated with an increased incidence of chronic kidney disease (1). The authors interpreted this finding as an adverse consequence of adiposity. However, their description of participant subgroups suggests to us that their reported association with elevated BMI was driven by relationships found primarily among older adults, men, and persons who exercised frequently. These are subgroups in which BMI status might reflect variations in the preservation or formation of muscle mass rather than the accumulation of adipose tissue. Greater muscle mass leads to an increased production of creatinine, which, in turn, is associated with a reduced estimated glomerular filtration rate (eGFR) (2) (consistent with the bottom line of the article’s Table 1). A reduction of eGFR to <60 mL/min/1.73 m2 was the quantitative threshold for identifying incident events of chronic kidney disease.
Our curiosity leads to testable hypotheses. We suggest that Chang et al. might use their Sangbuk Samsung Health Study to assess whether the decline in eGFR might be associated more strongly with baseline lean mass rather than adipose tissue. As recently reported by these same authors (3), their healthy-worker cohort also provided measurements of bioelectric impedance and the waist circumference. Thus, baseline values can be calculated for percent fat mass, percent fat-free mass, and the waist/height ratio.
Controversies exist about how to interpret the BMI (4, 5). To clarify how BMI contributes to the decline in eGFR, the authors could consider which tissue components or anatomical distributions of body mass best predict the described outcome.

The authors declare no real or potential conflicts of interest.

REFERENCES
1. Chang Y, Ryu S, Choi Y, et al. Metabolically healthy obesity and development of chronic kidney disease: A cohort study. Ann Intern Med. 2016; ePub ahead [PMID 26857595].
2. Levey AS, Becker C, Inker LA. Glomerular filtration rate and albuminuria for detection and staging of acute and chronic kidney disease in adults: A systematic review. JAMA. 2015;313(8):837-846 [PMID 25710660].
3. Zhao D, Kim MH, Pastor-Barriuso R, et al. A longitudinal study of association between adiposity markers and intraocular pressure: The Kangbuk Samsung Health Study. PLoS ONE. Vol. 11: Public Library of Science; 2016:e0146057 [PMID not available].
4. Bastien M, Poirier P, Lemieux I, Despres JP. Overview of epidemiology and contribution of obesity to cardiovascular disease. Prog Cardiovasc Dis. 2014;56(4):369-81 [PMID 24438728].
5. Tomiyama AJ, Hunger JM, Nguyen-Cuu J, Wells C. Misclassification of cardiometabolic health when using body mass index categories in NHANES 2005-2012. Int J Obes (Lond). 2016;ePub ahead([accepted article preview 4 February 2016; doi: 10.1038/ijo.2016.17 [PMID 26841729]).

Comment
Posted on March 15, 2016
Dustin J. Little, Wondaye T. Deressa, Maura A. Watson and Christina M. Yuan
Walter Reed National Military Medical Center
Conflict of Interest: Disclaimer: The views expressed in this paper are those of the authors and do not reflect the official policy of the US Departments of the Army, Navy, or Defense of the US government.
Chang and colleagues investigate rates of chronic kidney disease (CKD) in metabolically healthy obese (MHO) compared to normal weight Korean adults (1). MHO subjects were significantly more likely to develop estimated glomerular filtration rate (eGFR) calculated by the 4-variable Modification of Diet in Renal Disease (MDRD) study equation to be < 60 mL/min/1.73m2, but significant methodological limitations call into question the authors’ conclusions that MHO associates with CKD and adversely affects renal function.
The absence of albuminuria data is a major weakness of this analysis and precludes a basic assessment of subjects' CKD status and risk of adverse health outcomes such as cardiovascular mortality and end-stage renal disease (2). SCr concentrations correlate significantly with body weight, so decreased eGFR in obese subjects may have resulted from increased SCr due to increased creatinine production, rather than decreased GFR (3). Serum concentrations of cystatin C (SCysC) do not correlate with body weight (3). GFR estimation using both SCr and SCysC (eGFRcr-cys) improves accuracy and precision of eGFR, and decreases false positive CKD diagnosis (4). In one study, 38% of subjects with eGFRcr of 45-59 mL/min/1.73m2 had measured GFR of ≥ 60 mL/min/1.73m2 (4). In 44% of these subjects, eGFRcr-cys testing enabled appropriate reclassification to eGFR ≥ 60 mL/min/1.73m2 (4).
We recently reported our experience implementing CKD guidelines suggesting eGFRcr-cys for subjects with eGFRcr of 45-59 mL/min/1.73m2 and no other markers of kidney damage (5). eGFRcr-cys was higher than eGFRcr in all 50 subjects, and eGFRcr-cys was ≥ 60 mL/min/1.73m2 in 88% of subjects. Rates of overweight and obesity were 32% and 56%, respectively, suggesting confounding of eGFRcr due to increased body weight.
In summary, we contend that conclusions regarding MHO and CKD cannot be made without further study using albuminuria and eGFRcr-cys.

References
1. Chang Y, Ryu S, Choi Y, Zhang Y, Cho J, Kwon MJ, et al. Metabolically healthy obesity and development of chronic kidney disease: A cohort study. Ann Intern Med. 2016;164:305-312. [PMID: 26857595] doi:10.7326/M15-1323
2. Kidney Disease: Improving Global Outcomes (KDIGO) CKD Work Group. KDIGO clinical practice guideline for the evaluation and management of chronic kidney disease. Kidney Int Suppl. 2013;3:1-150.
3. Baxmann AC, Ahmed MS, Marques NC, Menon VB, Pereira AB, Kirsztajn GM, et al. Influence of muscle mass and physical activity on serum and urinary creatinine and serum cystatin C. Clin J Am Soc Nephrol. 2008;3:358-354. [PMID 18235143] doi: 10.2215/CJN.02870707
4. Inker LA, Schmid CH, Tighiouart H, Eckfeldt JH, Feldman HI, Greene T, et al. Estimating glomerular filtration rate from serum creatinine and cystatin C. N Engl J Med 2012;367:20-29. [PMID: 22762315] doi:10.1056/NEJMoa1114248
5. Little DJ, Mascio HM, Altenburg RJ, Moon DS, Wong S, Yuan CM. Implementing GFR estimation guidelines using cystatin C: A quality improvement project. Am J Kidney Dis 2015 23Nov [Epub ahead of print]. [PMID 26616337] doi: 10.1053/ajkd.2015.10.014
Exploring the Obesity Paradox, Diverse Obesity Phenotypes, and Severe Obesity in CKD
Posted on March 21, 2016
Fatima Cody Stanford, MD, MPH, MPA, and W. Scott Butsch, MD, MSc
Massachusetts General Hospital and Harvard Medical School
Conflict of Interest: None Declared
In their article, Metabolically Healthy Obesity and Development of Chronic Kidney Disease(1), Chang and colleagues report that overweight and obesity are associated with an increased incidence of CKD in metabolically healthy young and middle-aged adults. As global obesity rates continue to climb (2), it is important to evaluate the significant role that obesity plays in the development of co-morbidities such as CKD. Obesity is a complex multi-factorial chronic disease, and there are several points the authors should consider before we are able to extrapolate their results and potential impact to the greater population of individuals with obesity. First, previous studies have demonstrated that persons who are overweight (BMI 25-29.9 kg/m2) or have class 1 (BMI 30-34.9 kg/m2) or class 2 (BMI 35-39.9 kg/m2) obesity have a lower risk for cardiovascular-related, malignancy-related, and noncardiovascular/ nonmalignancy-related deaths.(3) The authors should consider the “obesity paradox” in patients with chronic kidney disease. Several studies have demonstrated that while obesity is often associated with poor outcomes in many co-morbid conditions, obesity is related to improved survival in chronic kidney disease.(4) In addition, while previous studies have shown that the incidence of chronic kidney disease increases in persons with obesity regardless of whether or not there is co-existing metabolic syndrome, it is still unclear whether CKD portends greater morbidity in this population.(5) Second, the authors suggest that there is a linear relationship with BMI and CKD in their metabolically healthy cohort, however one must recognize obesity is a heterogeneous chronic disease that clearly presents in many different phenotypes. There are less clearly understood factors that account for the differences in patients with obesity and the risk of developing CKD. In addition, while the authors did account for the ethnicity by including Asian cutoffs for BMI, the homogeneous cohort BMI only ranged up to 35 kg/m2, which excludes persons with more severe forms of obesity. The higher BMI category in this study included those persons with a BMI >25 kg/m2, which was primarily made up of older aged men who may have had higher all-cause morbidity .We would have liked to see the authors include data at higher BMI categories.

References
1. Chang Y, Ryu S, Choi Y, Zhang Y, Cho J, Kwon MJ, et al. Metabolically Healthy Obesity and Development of Chronic Kidney Disease: A Cohort Study. Ann Intern Med. 2016;164(5):305-12. doi: 10.7326/M15-1323. Epub 2016 Feb 9.
2. Ng M, Fleming T, Robinson M, Thomson B, Graetz N, Margono C, et al. Global, regional, and national prevalence of overweight and obesity in children and adults during 1980-2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet. 2014;384(9945):766-81. doi: 10.1016/S0140-6736(14)60460-8. Epub 2014 May 29.
3. Navaneethan SD, Schold JD, Arrigain S, Kirwan JP, Nally JV, Jr. Body mass index and causes of death in chronic kidney disease. Kidney Int. 2016;89(3):675-82. doi: 10.1016/j.kint.2015.12.002. Epub 6 Jan 12.
4. Agarwal R, Bills JE, Light RP. Diagnosing obesity by body mass index in chronic kidney disease: an explanation for the "obesity paradox?". Hypertension. 2010;56(5):893-900. doi: 10.1161/HYPERTENSIONAHA.110.160747. Epub 2010 Sep 27.
5. Cao X, Zhou J, Yuan H, Wu L, Chen Z. Chronic kidney disease among overweight and obesity with and without metabolic syndrome in an urban Chinese cohort. BMC Nephrol. 2015;16:85.(doi):10.1186/s12882-015-0083-8.
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