Accepted Manuscript Obesity Paradox Rethinking: Do Unequal Sample Sizes and Racial Differences Matter? Hua Chai , MD Yuan-Ning Xu , MD Yong Peng , MD Mao Chen , MD, PhD De-Jia Huang , MD PII:
S0002-9149(14)01087-X
DOI:
10.1016/j.amjcard.2014.05.002
Reference:
AJC 20450
To appear in:
The American Journal of Cardiology
Received Date: 5 May 2014 Accepted Date: 6 May 2014
Please cite this article as: Chai H, Xu Y-N, Peng Y, Chen M, Huang D-J, Obesity Paradox Rethinking: Do Unequal Sample Sizes and Racial Differences Matter?, The American Journal of Cardiology (2014), doi: 10.1016/j.amjcard.2014.05.002. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
ACCEPTED MANUSCRIPT Obesity Paradox Rethinking: Do Unequal Sample Sizes and Racial Differences Matter?
We recently read Dr. Herrmann and colleagues’ report about body mass index (BMI) and its association
RI PT
with patients’ prognosis after acute myocardial infarction (AMI) with great interest.1 According to our knowledge, in most similar previous studies, the study population was divided into four groups by standard categories of BMI: lean (BMI < 18.5 or 20 Kg/m2), normal weight (18.5 or 20 Kg/m2 ~ 25 Kg/m2),
SC
overweight (25 ~ 30 Kg/m2) and obese (BMI ≥ 30 Kg/m2). This grouping strategy did make sense but led to
M AN U
highly unequal sample sizes between groups. For example, in Dr. Yuze Li and colleagues’ report, among 1429 patients recruited, only 15 (0.98%) patients were lean and only 189 (13.4%) patients were obese.2 Similarly in Dr. Hidehiro Kaneko and colleagues’ report, there were only 92 (7.6%) lean patients and 56 (4.6%) obese patients among the study population of 1205 patients.3 The significantly smaller sample sizes
TE D
in lean and obese groups could probably cause selection bias and attenuation of power of test in statistical analyses which might lead to distortion in results. Dr. Herrmann et al tried to overcome this problem by adopting a stratifying method that divided the study population by BMI into quartiles (30.1 Kg/m2). This method made the four groups almost the same in
AC C
sample size (890, 899, 898 and 892 patients in each group). However, an obvious disadvantage is that it made the results difficult to interpret. For example, the first group covered an excessively wide range of BMI from underweight to normal weight, whereas a middle quartile (median) of 27.1 Kg/m2 did not mean anything when discussing about BMI. We believed that a better method for solving the problem should be propensity score matching.4,5 By calculating the propensity scores, one could perform one to many matching between obese and normal weight groups, or between lean and normal weight groups. This method ensured a meaningful grouping strategy as well as balanced baseline characteristics and
ACCEPTED MANUSCRIPT comparable sample sizes between groups. Consequently, more reliable results that revealing the truth of obesity paradox would likely be achieved. In addition, we also noticed that in Dr. Herrmann and colleagues’ report, the study population was
RI PT
“multinational”. The cohort was described in the previous published report of HORIZONS-AMI trial as from “123 centers in 11 countries”, but no further details were provided.6 We would like to indicate that racial differences might possibly play a role in obesity paradox phenomenon. For example, obesity
SC
paradox was frequently reported in eastern Asian population, such as in Japanese3 and Korean7. But in
M AN U
China, three previous studies declared that obesity paradox was not observed in patients with AMI or undergoing percutaneous coronary intervention (PCI).2,8,9 Therefore, a clear description of racial structure was necessary in a multinational cohort, and subgroup analysis between races might be conducted. Hua Chai, MD
TE D
Yuan-Ning Xu, MD Yong Peng, MD
De-Jia Huang, MD
3 May 2014
AC C
Chengdu, China PR
EP
Mao Chen, MD, PhD
ACCEPTED MANUSCRIPT Reference 1. Herrmann J, Gersh BJ, Goldfinger JZ, Witzenbichler B, Guagliumi G, Dudek D, Kornowski R, Brener SJ, Parise H, Fahy M, McAndrew TC, Stone GW, Mehran R. Body Mass Index and Acute and Long-term Outcomes After Acute Myocardial Infarction (from the Harmonizing Outcomes With Revascularization and Stents in
RI PT
Acute Myocardial Infarction [HORIZONS-AMI] Trial). Am J Cardiol 2014;doi: 10.1016/j.amjcard.2014.03.057. 2. Li Y, Wu C, Sun Y, Jiang D, Zhang B, Ren L, Gao Y, Yu H, Yang G, Guan Q, Tian W, Zhang H, Guo L, Qi G. Obesity paradox: clinical benefits not observed in obese patients with ST-segment elevation myocardial infarction: a multicenter, prospective, cohort study of the northern region of China. Int J Cardiol
SC
2013;168:2949-2950.
3. Kaneko H, Yajima J, Oikawa Y, Tanaka S, Fukamachi D, Suzuki S, Sagara K, Otsuka T, Matsuno S, Funada R,
M AN U
Kano H, Uejima T, Koike A, Nagashima K, Kirigaya H, Sawada H, Aizawa T, Yamashita T. Obesity paradox in Japanese patients after percutaneous coronary intervention: an observation cohort study. J Cardiol 2013;62:18-24.
4. Rosenbaum PR, Rubin DB. The central role of the propensity score in observational studies for causal effects. Biometrika 1983;70:41-55.
TE D
5. Heinze G, Juni P. An overview of the objectives of and the approaches to propensity score analyses. Eur Heart J 2011;32:1704-1708.
6. Stone GW, Witzenbichler B, Guagliumi G, Peruga JZ, Brodie BR, Dudek D, Kornowski R, Hartmann F, Gersh
2008;358:2218-2230.
EP
BJ, Pocock SJ. Bivalirudin during primary PCI in acute myocardial infarction. New Eng Journal Med
AC C
7. Park D-W, Kim Y-H, Yun S-C, Ahn J-M, Lee J-Y, Kim W-J, Kang S-J, Lee S-W, Lee CW, Park S-W. Association of body mass index with major cardiovascular events and with mortality after percutaneous coronary intervention. Circulation: Cardiovascular Interventions 2013;6:146-153. 8. Wang ZJ, Zhou YJ, Liu YY, Yu M, Shi DM, Zhao YX, Guo YH, Cheng WJ, Nie B, Ge HL. Obesity and cardiovascular thrombotic events in patients undergoing percutaneous coronary intervention with drug-eluting stents. Heart 2009;95:1587-1592. 9. Sun Y, Jiang D, Zhang B, Yu H, Gao Y, Li Y, Qi G. Impact of obesity on the outcome of Chinese patients with ST-segment myocardial infarction undergoing urgent percutaneous coronary intervention. Acta cardiologica 2012;67:541-548.
S0002-9149(14)01087-X
DOI:
10.1016/j.amjcard.2014.05.002
Reference:
AJC 20450
To appear in:
The American Journal of Cardiology
Received Date: 5 May 2014 Accepted Date: 6 May 2014
Please cite this article as: Chai H, Xu Y-N, Peng Y, Chen M, Huang D-J, Obesity Paradox Rethinking: Do Unequal Sample Sizes and Racial Differences Matter?, The American Journal of Cardiology (2014), doi: 10.1016/j.amjcard.2014.05.002. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
ACCEPTED MANUSCRIPT Obesity Paradox Rethinking: Do Unequal Sample Sizes and Racial Differences Matter?
We recently read Dr. Herrmann and colleagues’ report about body mass index (BMI) and its association
RI PT
with patients’ prognosis after acute myocardial infarction (AMI) with great interest.1 According to our knowledge, in most similar previous studies, the study population was divided into four groups by standard categories of BMI: lean (BMI < 18.5 or 20 Kg/m2), normal weight (18.5 or 20 Kg/m2 ~ 25 Kg/m2),
SC
overweight (25 ~ 30 Kg/m2) and obese (BMI ≥ 30 Kg/m2). This grouping strategy did make sense but led to
M AN U
highly unequal sample sizes between groups. For example, in Dr. Yuze Li and colleagues’ report, among 1429 patients recruited, only 15 (0.98%) patients were lean and only 189 (13.4%) patients were obese.2 Similarly in Dr. Hidehiro Kaneko and colleagues’ report, there were only 92 (7.6%) lean patients and 56 (4.6%) obese patients among the study population of 1205 patients.3 The significantly smaller sample sizes
TE D
in lean and obese groups could probably cause selection bias and attenuation of power of test in statistical analyses which might lead to distortion in results. Dr. Herrmann et al tried to overcome this problem by adopting a stratifying method that divided the study population by BMI into quartiles (30.1 Kg/m2). This method made the four groups almost the same in
AC C
sample size (890, 899, 898 and 892 patients in each group). However, an obvious disadvantage is that it made the results difficult to interpret. For example, the first group covered an excessively wide range of BMI from underweight to normal weight, whereas a middle quartile (median) of 27.1 Kg/m2 did not mean anything when discussing about BMI. We believed that a better method for solving the problem should be propensity score matching.4,5 By calculating the propensity scores, one could perform one to many matching between obese and normal weight groups, or between lean and normal weight groups. This method ensured a meaningful grouping strategy as well as balanced baseline characteristics and
ACCEPTED MANUSCRIPT comparable sample sizes between groups. Consequently, more reliable results that revealing the truth of obesity paradox would likely be achieved. In addition, we also noticed that in Dr. Herrmann and colleagues’ report, the study population was
RI PT
“multinational”. The cohort was described in the previous published report of HORIZONS-AMI trial as from “123 centers in 11 countries”, but no further details were provided.6 We would like to indicate that racial differences might possibly play a role in obesity paradox phenomenon. For example, obesity
SC
paradox was frequently reported in eastern Asian population, such as in Japanese3 and Korean7. But in
M AN U
China, three previous studies declared that obesity paradox was not observed in patients with AMI or undergoing percutaneous coronary intervention (PCI).2,8,9 Therefore, a clear description of racial structure was necessary in a multinational cohort, and subgroup analysis between races might be conducted. Hua Chai, MD
TE D
Yuan-Ning Xu, MD Yong Peng, MD
De-Jia Huang, MD
3 May 2014
AC C
Chengdu, China PR
EP
Mao Chen, MD, PhD
ACCEPTED MANUSCRIPT Reference 1. Herrmann J, Gersh BJ, Goldfinger JZ, Witzenbichler B, Guagliumi G, Dudek D, Kornowski R, Brener SJ, Parise H, Fahy M, McAndrew TC, Stone GW, Mehran R. Body Mass Index and Acute and Long-term Outcomes After Acute Myocardial Infarction (from the Harmonizing Outcomes With Revascularization and Stents in
RI PT
Acute Myocardial Infarction [HORIZONS-AMI] Trial). Am J Cardiol 2014;doi: 10.1016/j.amjcard.2014.03.057. 2. Li Y, Wu C, Sun Y, Jiang D, Zhang B, Ren L, Gao Y, Yu H, Yang G, Guan Q, Tian W, Zhang H, Guo L, Qi G. Obesity paradox: clinical benefits not observed in obese patients with ST-segment elevation myocardial infarction: a multicenter, prospective, cohort study of the northern region of China. Int J Cardiol
SC
2013;168:2949-2950.
3. Kaneko H, Yajima J, Oikawa Y, Tanaka S, Fukamachi D, Suzuki S, Sagara K, Otsuka T, Matsuno S, Funada R,
M AN U
Kano H, Uejima T, Koike A, Nagashima K, Kirigaya H, Sawada H, Aizawa T, Yamashita T. Obesity paradox in Japanese patients after percutaneous coronary intervention: an observation cohort study. J Cardiol 2013;62:18-24.
4. Rosenbaum PR, Rubin DB. The central role of the propensity score in observational studies for causal effects. Biometrika 1983;70:41-55.
TE D
5. Heinze G, Juni P. An overview of the objectives of and the approaches to propensity score analyses. Eur Heart J 2011;32:1704-1708.
6. Stone GW, Witzenbichler B, Guagliumi G, Peruga JZ, Brodie BR, Dudek D, Kornowski R, Hartmann F, Gersh
2008;358:2218-2230.
EP
BJ, Pocock SJ. Bivalirudin during primary PCI in acute myocardial infarction. New Eng Journal Med
AC C
7. Park D-W, Kim Y-H, Yun S-C, Ahn J-M, Lee J-Y, Kim W-J, Kang S-J, Lee S-W, Lee CW, Park S-W. Association of body mass index with major cardiovascular events and with mortality after percutaneous coronary intervention. Circulation: Cardiovascular Interventions 2013;6:146-153. 8. Wang ZJ, Zhou YJ, Liu YY, Yu M, Shi DM, Zhao YX, Guo YH, Cheng WJ, Nie B, Ge HL. Obesity and cardiovascular thrombotic events in patients undergoing percutaneous coronary intervention with drug-eluting stents. Heart 2009;95:1587-1592. 9. Sun Y, Jiang D, Zhang B, Yu H, Gao Y, Li Y, Qi G. Impact of obesity on the outcome of Chinese patients with ST-segment myocardial infarction undergoing urgent percutaneous coronary intervention. Acta cardiologica 2012;67:541-548.
