ISSN : 2146-3123
E-ISSN : 2146-3131

Effect of the Obesity Paradox on Mortality in Patients with Acute Coronary Syndrome: A Comprehensive Meta-analysis of the Literature
Faysal Şaylık1, Tufan Çınar2, Mert İlker Hayıroğlu3
1Clinic of Cardiology, University of Health Sciences Turkey, Van Training and Research Hospital, Van, Turkey
2Clinic of Cardiology, University of Health Sciences Turkey, Sultan Abdulhamid Han Training and Research Hospital, İstanbul, Turkey
3Clinic of Cardiology, Dr. Siyami Ersek Thoracic and Cardiovascular Surgery Training and Research Hospital, İstanbul, Turkey
DOI : 10.4274/balkanmedj.galenos.2022.2022-11-56
Pages : 93-103

Abstract

Background: The protective effect of obesity in patients with acute coronary syndrome undergoing percutaneous coronary intervention or bypass surgery has been described as the obesity paradox in the literature.
Aims: In this comprehensive meta-analysis, we aimed to investigate the pooled effect of the obesity paradox on mortality in acute coronary syndrome patients.
Study Design: Systemic meta-analysis and metaregression.
Methods: We searched PubMed, Google Scholar, and the Cochrane Library for eligible studies that compared the mortality rates between body mass index cut-off points in acute coronary syndrome patients. This meta-analysis comprised 54 studies with 534,903 patients. Random- and fixed-effect models were used to calculate pooled effects sizes in the presence of moderately high and low heterogeneity between studies, respectively. A metaregression analysis was used to detect possible causes of heterogeneity. A dose-response meta-analysis was also conducted to detect the association between mortality risk and body mass index.
Results: Overweight patients had lower mortality risk for 30-day (RR =0.69; 0.62-0.76, p < 0.01) and long-term (RR =0.73; 0.70-0.77, p < 0.01) mortality than normal-weight patients. The 30-day mortality risk was higher in low-weight patients than in normal-weight patients (RR =1.74; 1.39-2.18, p < 0.01). Meta-regression could not explain the possible causes of between-study heterogeneity. Patients with body mass index <21.5 kg/m2 and >40 kg/m2 had a higher risk of mortality, which was lowest at approximately 30 kg/m2.
Conclusion: Low-weight and overweight acute coronary syndrome patients had higher mortality risk than normal-weight patients. A U-shaped nonlinear association was detected between body mass index and mortality risk.


INTRODUCTION

The prevalence of obesity has considerably increased worldwide and has become a major social and health issue. Obesity is associated with multiple cardiometabolic abnormalities, such as metabolic syndrome, diabetes mellitus, hypertension, and hyperlipidemia.1 Moreover, obesity is a major predictor of future cardiovascular and all-cause mortality, accounting for one in every five deaths globally.2 Although obesity is a predisposing factor for cardiovascular disease, when acute cardiovascular decompensation develops, such as in congestive heart failure, obese patients may have a survival benefit, a phenomenon known as the “obesity paradox”.3 Furthermore, it has been hypothesized that obese persons might have better outcomes following coronary artery bypass surgery.4 In the current literature, although some studies show favorable cardiovascular outcomes and mortality in obese patients with acute coronary syndrome (ACS), many other investigations reveal a negative cardiovascular impact of obesity. Therefore, in this meta-analysis, we intended to investigate the role of the obesity paradox in mortality in patients with ACS.

MATERIALS AND METHODS

Data Gathering

We conducted the meta-analysis in line with the recommendations of the Cochrane Collaboration. We searched PubMed, Google Scholar, and the Cochrane Library for relevant articles using the following keywords: obesity, obesity paradox, acute coronary syndrome, percutaneous coronary intervention, ST elevation myocardial infarction, non-ST elevation myocardial infarction, unstable angina, and body mass index (BMI). After deleting duplicate reports, 1,006 out of 1,348 reports were retained. After reviewing the summaries of these papers, we removed 956 of them and left 83 for full-text review. We eliminated 39 studies from the meta-analysis after reviewing the full texts because they had inappropriate research designs or non-ACS populations, utilized inaccurate data to evaluate effect magnitude, or were review articles. Thus, our meta-analysis ultimately included 54 studies (Figure 1).1-3,5-55 Our meta-analysis was registered in the PROSPERO database (CRD42022355750).

Study Evaluation

All possible studies were systematically explored by two authors (S.F., C.T.) for their applicability and likelihood of bias. The studies were evaluated using the following criteria: i-) studies that assessed mortality based on BMI, ii-) studies that published the mortality data, and iii-) studies that included only patients with ACS. Finally, we removed publications where the effect size and standard error could not be estimated. There were no limitations on sample size, follow-up period, or language.

Quality Assessment and Data Extraction

Two independent authors reviewed published studies that fulfilled the eligibility criteria, while a third reviewer resolved any discrepancies between the two reviewers. The quality of the observational cohort studies included in this analysis was assessed using the Newcastle-Ottawa standard ratings system. Based on the research population, study consistency, and outcome of interest, studies scored up to 9 points on that scale. A score of 0-5 on the Newcastle-Ottawa scale indicates poor quality, whereas a score of 6-9 indicates excellent quality. The Robin-I risk of bias tools, as outlined in the Cochrane Handbook for Systematic Reviews, were used to assess the risk of bias of nonrandomized trials.

Clinical Endpoints

Thirty-day and long-term mortality were the main endpoints assessed in this meta-analysis.

Statistical Analysis

All statistics were calculated using R software v. 3.6.3 (R statistical software, Institute for Statistics and Mathematics, Vienna, Austria). The “metabin” function in the “meta” package was used to estimate pooled risk ratios with 95% confidence intervals between the compared groups. The between-study heterogeneity was assessed with the Higgins I2 and Cochran Q tests. A heterogeneity of <25% was accepted as low, 25%-75% as moderate, and >75% as high. In the presence of moderate to high heterogeneity (I2 > 25%), the pooled effect size was computed using the random-effect model, whereas in the context of low heterogeneity (I2 < 25%), the fixed-effect model was calculated. To investigate potential publication bias, Egger’s regression test was used and visualized with a funnel plot. When a potential publication bias was detected either with the regression test or funnel plot, a Duval and Tweedie Trim and Fill method was used to obtain a bias-adjusted estimation of the pooled effect size. To identify the likely source of between-study heterogeneity, outlier and influence analyses were conducted. A sensitivity analysis was conducted by excluding outliers and influential studies from the meta-analysis. Furthermore, a metaregression analysis with potential covariates was performed to explain the causes of heterogeneity between studies. Finally, a dose-response meta-analysis was used to evaluate the relationship between BMI and all-cause death. To evaluate statistical significance, a two-tailed p-value of 0.05 was utilized.

RESULTS

This meta-analysis consisted of 54 studies with 534,903 patients. The quality assessments of all studies were adequate (Table 1). All of the studies except one (Nikolsky et al.8) had a moderate risk of bias due to the selection of participants (Supplementary File 1). The overweight patients had lower 30-day (RR =0.69, 0.62-0.76, p < 0.01, I2 = 65%) and long-term mortality (RR =0.73, 0.70-0.77, p < 0.01, I2 = 47%) than patients with normal weight (Figure 2). The 30-day and long-term mortalities were lower in obese patients than in normal-weight patients (RR =0.61, 0.52-0.70, p < 0.01, I2 =81%, RR =0.68, 0.62-0.74, p < 0.01, I2 = 76%; respectively) (Figure 3). Patients with low weight had higher mortality rates than patients with normal weight for 30-day and long-term mortality (RR =1.74, 1.39-2.18, p < 0.01, I2 = 40%, RR =2.06, 1.61-2.65, p < 0.01, I2 = 92%; respectively) (Figure 4). Three studies (Nikolsky et al.,8 Nigam et al.,24 and Angeras et al.1) were detected as outliers, and Angeras et al.1 was an influential study in the comparison of long-term mortality between obese and normal-weight persons. In the sensitivity analysis, the pooled effect was still significant after removing these three studies, but with a lower heterogeneity (RR =0.67, 0.62-0.73, p < 0.001; I2 = 60.9%). There may have been publication bias in the pooled estimate of long-term mortality between obesity and normal weight, which had a small study effect (Supplementary file 2). To address the bias, a bias-adjusted estimation was recalculated using the Duval and Tweedie Trim and Fill method. A bias-adjusted estimate was recalculated by adding six studies for missing studies and the result did not change (RR =0.68, 0.63-0.74, p < 0.001, I2 = 59.5). Three studies (Cheng et al.,28 Mobeirek Abdulelah et al.,51 and Ratwatte et al.52) were detected as outliers, and Diercks et al.40 was an influential study for the comparison of 30-day mortality between obesity and normal weight. The effect estimate did not change after removing these studies, but with a lower heterogeneity (RR =0.66, 0.60-0.72, p < 0.001, I2 = 48.7%). The study reported by O Brien et al.27 in 2015 was detected as an outlier study, and Angeras et al.1 and Park et al.31 were influential studies for long-term mortality between low and normal-weight patients. Therefore, we recalculated the pooled effect size after removing these studies. There was still a significant higher risk of mortality in the long term between patients with low and normal weight (RR = 1.96, 1.45-2.65, p = 0.0009; I2 = 68%).

Metaregression

A metaregression analysis was used to evaluate the underlying cause of between-study heterogeneity. We used covariates such as age, hypertension, diabetes mellitus, hyperlipidemia, prior MI, study year, ethnicity, follow-up time, male sex, cigarette smoking, congestive heart failure, cancer, and chronic obstructive pulmonary disease for the metaregression analysis. However, there was high multicollinearity between variables except for age, study year, follow-up time, and ethnicity. Thus, metaregression was conducted with these variables and none of them was detected as an underlying cause of heterogeneity for 30-day mortality between obese and normal-weight patients. For long-term mortality, only follow-up time seemed to contribute to the heterogeneity between obese and normal-weight patients as it explained 33.4% of the heterogeneity (p = 0.013).

Dose-response Meta-analysis

A dose-response meta-analysis was conducted to detect the association between BMI and all-cause mortality as proposed by Orsini et al.56 and Greenland and Longnecker57 Due to a nonlinear relationship between BMI and all-cause mortality, a two-stage dose response meta-analysis with restricted cubic splines and three knots (0.35, 0.65, and 0.95) was used (Figure 5). There was a U-shaped nonlinear diagram, with higher mortality risk for BMI < 21.5 kg/m2 and >40 kg/m2, whereas the lowest mortality risk was detected at approximately 30 kg/m2.

DISCUSSION

This meta-analysis demonstrated that overweight patients with ACS had lower 30-day and long-term mortality rates compared to normal-weight patients with ACS. In addition, patients with low weight had a higher mortality risk compared to normal-weight patients following ACS during the 30-day follow-up. Despite the presence of high between-study heterogeneity for outcomes between obese and normal weight patients for 30-day and long-term mortality and between low weight and normal-weight patients for long-term mortality, which could not be explained by the metregression analysis, the results appear to have an important effect on routine clinical practice with the inclusion of >500,000 patients with ACS.

There may be several potential explanations for the protective effects of being obese or overweight compared to having a normal weight in both the short- and long-term follow-up periods for patients with ACS. Adipose tissue might have cardioprotective benefits because of the production of leptin and adiponectin, which have anti-inflammatory, antiapoptotic, and antihypertrophic properties.38 Additionally, elevated levels of cannabinoids in overweight patients play a cardioprotective role in reperfusion by causing vasodilatation in the heart and preventing arrhytmias.9 Obesity is associated with lower platelet levels compared to a normal weight, and this prominently influences the pathogenesis and outcomes in ACS patients.9,38 The distribution of fat may be more influential than overall adiposity, since visceral fat has been associated with negative outcomes.58,59 Visceral adipose tissue enhances systemic inflammation in a low-grade manner by causing the elevated syntheses of proinflammatory cytokines, proatherosclerotic adipokines, and cardiodepressant adipokines, which results in a higher cardiometabolic risk.60,61 Thus, waist circumference, not only BMI, should be evaluated for cardiovascular risk. However, only one study in this meta-analysis had information about the waist circumference of patients. Cardiovascular disease has an increased catabolic effect on overall metabolism, and obesity may play an important role in protecting the metabolic reserve.62 In addition, patients with ACS may be exposed to longer hospitalization and multiple high-risk coronary interventions, which could easily weaken the metabolic endurance in patients with low reserve.63,64 Lastly, elevated serum triglyceride levels were observed in obese patients and might play a protective role against sudden cardiac death.16

In our study, patients with lower weights were found to have a higher 30-day and long-term mortality risk compared to normal-weight patients. It has been reported that low-weight patients have a higher prevalence of comorbidities compared to normal-weight patients. Cancer, chronic inflammatory disease, and diastolic and systolic heart failure may be the underlying reason for the higher mortality risk in low-weight patients compared to normal-weight ones. The confounding factors in the studies included in this meta-analysis may have influenced the results. Thus, overweight and obese patients should target healthier lifestyles with a combination of exercise and diet instead of losing lean mass while reducing weight. The cutoff values of BMI classes varied minimally between studies. However, a dose-response meta-analysis was conducted to overcome the effect of these differences and presented the association between BMI and mortality on a continuous scale. A similar dose-response meta-analysis was conducted by Mei et al.65 with 15 studies including patients who underwent percutaneous coronary intervention.65 The results were in accordance with those of the current study; the mortality risk was higher in low-weight patients with a nadir of risk between 27 and 32 kg/m2, and it showed an upward trend after 32 kg/m2.

The most recent meta-analysis regarding the effect of BMI in patients with ACS was presented by Lamelas et al.,66 whose report included an investigation published before 2014 with 18 studies including 137,975 patients. Unlike the previous meta-analysis, in the current analysis, not only obese and overweight patients with ACS but also low-weight patients with ACS were compared with normal-weight patients. This appears to be one of the notable strengths of the current study. Moreover, such an extensive comparison allowed us to perform a dose-response meta-analysis. After including 38 investigations in the dose-response meta-analysis, it became evident that a BMI higher than 40 kg/m2 and lower than 21.5 kg/m2 might be associated with higher mortality risks, and the lowest mortality risk might be near a BMI of 30 kg/m2. Moreover, a pooled analysis using either hazard ratio or odds ratio for the calculation of pooled effect size as risk ratio can cause serious errors, in which hazard ratio cannot be converted to risk ratio and is also not pooled with odds ratio. This mistake was unfortunately made by Lamelas et al.;66 however, it was noted and accounted for in the current meta-analysis. Patients with low-weight and obese patients, especially those with a BMI over 40 kg/m2, should be followed up closely. Instead of gaining or losing weight in these patient groups, a healthier diet and physical activity are more important for the secondary prevention of cardiovascular disease and mortality.

There were several limitations in our meta-analysis. First, relatively few studies compared 30-day and long-term mortality according to BMI in patients with ACS. However, all studies were included in this meta-analysis in order to obtain more precise results. Second, there was high heterogeneity in the analysis of the studies due to the methodological sampling, BMI stratification, missing data, and factors considered for adjustment. Third, because few studies reported adjusted relative risks (odds and hazard ratios), we could not calculate pooled effect sizes using these parameters. Fourth, due to the presence of high heterogeneity for 30-day and long-term mortality between obese and normal-weight patients, and for long-term mortality between low- and normal-weight patients, which could not be explained by the metaregression analysis, we could not present precise conclusions for these outcomes in this meta-analysis. Fifth, the cutoff BMI values slightly differed in some studies, which might have led to between-study heterogeneity for effect sizes. Sixth, the lack of information regarding the waist circumferences of patients was another limitation. Seventh, because it has been reported that BMI was not strongly associated with mortality in physical active patients, the lack of information about physical activity or fitness status of patients in this meta-analysis was the last limitation.67 However, we were able to overcome this limitation by performing a dose-response meta-analysis. Overweight ACS patients had lower 30-day and long-term mortality, and low-weight ACS patients had higher 30-day mortality risk than normal-weight patients. Moreover, the mortality risk was higher with BMI lower than 21.5 kg/m2 and higher than 40 kg/m2, and was lowest at approximately 30 kg/m2 based on the dose-response meta-analysis.

Ethics Committee Approval: Ethics committee approval was not needed since this was a meta-analysis of the literature.

Data Sharing Statement: The data that support the findings of this study are available from the corresponding author upon reasonable request.

Author Contributions: Concept- F.Ş., T.Ç., M.İ.H.; Design- F.Ş., T.Ç., M.İ.H.; Analysis or Interpretation- F.Ş., T.Ç., M.İ.H.; Writing- F.Ş., T.Ç., M.İ.H.

Conflict of Interest: No conflict of interest was declared by the authors.

Funding: The authors declared that this study received no financial support.

Supplementary: http://balkanmedicaljournal.org/uploads/pdf/2022-11-56-supplementarymaterials.pdf

REFERENCES

  1. Angerås O, Albertsson P, Karason K, et al. Evidence for obesity paradox in patients with acute coronary syndromes: a report from the Swedish Coronary Angiography and Angioplasty Registry. Eur Heart J. 2013;34:345-353.
  2. Calabrò P, Moscarella E, Gragnano F, et al. Effect of Body Mass Index on Ischemic and Bleeding Events in Patients Presenting With Acute Coronary Syndromes (from the START-ANTIPLATELET Registry). Am J Cardiol. 2019;124:1662-1668.
  3. Fukuoka S, Kurita T, Dohi K, et al. Untangling the obesity paradox in patients with acute myocardial infarction after primary percutaneous coronary intervention (detail analysis by age). Int J Cardiol. 2019;289:12-18.
  4. Le-Bert G, Santana O, Pineda AM, et al. The obesity paradox in elderly obese patients undergoing coronary artery bypass surgery. Interact Cardiovasc Thorac Surg. 2011;13:124-127.
  5. Lopez-Jimenez F, Jacobsen SJ, Reeder GS, et al. Prevalence and Secular Trends of Excess Body Weight and Impact on Outcomes After Myocardial Infarction in the Community. Chest. 2004;125:1205-1212.
  6. Rana JS, Mukamal KJ, Morgan JP, et al. Obesity and the risk of death after acute myocardial infarction. American Heart Journal. 2004;147:841-846.
  7. Eisenstein EL, McGuire DK, Bhapkar MV, et al. Elevated body mass index and intermediate-term clinical outcomes after acute coronary syndromes. Am J Med. 2005; 118:981-990.
  8. Nikolsky E, Stone GW, Grines CL, et al. Impact of body mass index on outcomes after primary angioplasty in acute myocardial infarction. Am Heart J. 2006; 151:168-175.
  9. Buettner HJ, Mueller C, Gick M, et al. The impact of obesity on mortality in UA/non-ST-segment elevation myocardial infarction. Eur Heart J. 2007;28:1694-1701.
  10. Mehta L, Devlin W, McCullough PA, et al. Impact of Body Mass Index on Outcomes After Percutaneous Coronary Intervention in Patients With Acute Myocardial Infarction. Am J Cardiol. 2007;99:906-910.
  11. Lopez-Jimenez F, Wu CO, Tian X, et al. Weight Change after Myocardial Infarction—the Enhancing Recovery in Coronary Heart Disease patients (ENRICHD) Experience. Am Heart J. 2008;155:478-484.
  12. Wienbergen H, Gitt AK, Juenger C, et al. Impact of the body mass index on occurrence and outcome of acute ST-elevation myocardial infarction. Clin Res Cardiol. 2008;97:83-88.
  13. Aronson D, Nassar M, Goldberg T, Kapeliovich M, Hammerman H, Azzam ZS. The impact of body mass index on clinical outcomes after acute myocardial infarction. Int J Cardiol. 2010;145:476-480.
  14. Shechter M, Hammerman H, Boyko V, Hod H, Behar S, Matetzky S. The obesity paradox in hospitalized acute coronary syndrome patients in Israel: A national survey. CVD Prevention and Control. 2010;5:81-87.
  15. Timóteo AT, Ramos R, Toste A, et al. Impact of body mass index in the results after primary angioplasty in patients with ST segment elevation acute myocardial infarction. Acute Card Care. 2011;13:123-128.
  16. Bucholz EM, Rathore SS, Reid KJ, et al. Body Mass Index and Mortality in Acute Myocardial Infarction Patients. Am J Med. 2012;125:796-803.
  17. Lazzeri C, Valente S, Chiostri M, et al. Impact of age on the prognostic value of body mass index in ST-Elevation myocardial infarction. Nutr Metab Cardiovasc Dis. 2013;23:205-211.
  18. Herrmann J, Gersh BJ, Goldfinger JZ, et al. Body Mass Index and Acute and Long-Term Outcomes After Acute Myocardial Infarction (from the Harmonizing Outcomes With Revascularization and Stents in Acute Myocardial Infarction Trial). Am J Cardiol. 2014;114:9-16.
  19. Colombo MG, Meisinger C, Amann U, et al. Association of obesity and long-term mortality in patients with acute myocardial infarction with and without diabetes mellitus: results from the MONICA/KORA myocardial infarction registry. Cardiovasc Diabetol. 2015;14:24.
  20. Kang WY, Jeong MH, Ahn YK, et al. Obesity paradox in Korean patients undergoing primary percutaneous coronary intervention in ST-segment elevation myocardial infarction. J Cardiol. 2010;55:84-91.
  21. Moscarella E, Spitaleri G, Brugaletta S, et al. Impact of Body Mass Index on 5-Year Clinical Outcomes in Patients With ST–Segment Elevation Myocardial Infarction After Everolimus-Eluting or Bare-Metal Stent Implantation. Am J Cardiol. 2017;120:1460-1466.
  22. Ndrepepa G, Keta D, Byrne RA, et al. Impact of body mass index on clinical outcome in patients with acute coronary syndromes treated with percutaneous coronary intervention. Heart Vessels. 2010; 25:27-34.
  23. Zeller M, Steg PG, Ravisy J, et al. Relation Between Body Mass Index, Waist Circumference, and Death After Acute Myocardial Infarction. Circulation. 2008;118:482-490.
  24. Nigam A, Wright RS, Allison TG, et al. Excess weight at time of presentation of myocardial infarction is associated with lower initial mortality risks but higher long-term risks including recurrent re-infarction and cardiac death. Int J Cardiol. 2006; 110:153-159.
  25. Hoit BD, Gilpin EA, Maisel AA, Henning H, Carlisle J, Ross J Jr. Influence of obesity on morbidity and mortality after acute myocardial infarction. Am Heart J. 1987; 114:1334-1341.
  26. Kennedy LM, Dickstein K, Anker SD, et al. The prognostic importance of body mass index after complicated myocardial infarction. J Am Coll Cardiol. 2005;45:156-158.
  27. O’Brien EC, Fosbol EL, Peng SA, et al. Association of Body Mass Index and Long-Term Outcomes in Older Patients With Non–ST-Segment–Elevation Myocardial Infarction. Circ Cardiovasc Qual Outcomes. 2014;7:102-109.
  28. Cheng CC, Huang WC, Chiou KR, et al. Body Mass Index and Outcome of Acute Myocardial Infarction - Is There an Obesity Paradox? Acta Cardiol Sin. 2013;29:413-420.
  29. Bucholz EM, Beckman AL, Krumholz HA, et al. Excess weight and life expectancy after acute myocardial infarction: The obesity paradox reexamined. Am Heart J. 2016; 172:173-81.
  30. Samanta R, Pouliopoulos J, Kumar S, et al. Influence of BMI on inducible ventricular tachycardia and mortality in patients with myocardial infarction and left ventricular dysfunction: The obesity paradox. Int J Cardiol. 2018;265:148-154.
  31. Park S-J, Ha KH, Kim DJ. Body mass index and cardiovascular outcomes in patients with acute coronary syndrome by diabetes status: the obesity paradox in a Korean national cohort study. Cardiovasc Diabetol. 2020;19:191.
  32. Migaj J, Prokop E, Straburzyńska-Migaj E, Lesiak M, Grajek S, Mitkowski P. Does the influence of obesity on prognosis differ in men and women? A study of obesity paradox in patients with acute coronary syndrome. Kardiol Pol. 2015;73:761-767.
  33. Shehab A, Al-Dabbagh B, AlHabib K, et al. The Obesity Paradox in Patients With Acute Coronary Syndrome: Results From the Gulf RACE-2 Study. Angiology. 2013;65:585-9.
  34. Kouvari M, Chrysohoou C, Tsiamis E, et al. The “overweight paradox” in the prognosis of acute coronary syndrome for patients with heart failure-A truth for all? A 10-year follow-up study. Maturitas. 2017;102:6-12.
  35. Akin I, Schneider H, Nienaber CA, et al. Lack of “obesity paradox” in patients presenting with ST-segment elevation myocardial infarction including cardiogenic shock: a multicenter German network registry analysis. BMC Cardiovasc Disord. 2015;15:67.
  36. Li Y, Wu C, Sun Y, et al. 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. 2013;168:2949-2950.
  37. Karrowni W, Kennedy K, Jones P, et al. OBESITY PARADOX AMONG SURVIVORS OF ACUTE MYOCARDIAL INFARCTION AND ITS INTERACTION WITH TIME. JACC Journals. 2015;65:A31.
  38. Kanic V, Vollrath M, Frank B, et al. An obesity paradox in patients with myocardial infarction undergoing percutaneous intervention. Nutr Metab Cardiovasc Dis. 2021;31:127-136.
  39. Neeland IJ, Das SR, Simon DN, et al. The obesity paradox, extreme obesity, and long-term outcomes in older adults with ST-segment elevation myocardial infarction: results from the NCDR. Eur Heart J Qual Care Clin Outcomes. 2017;3:183-191.
  40. Diercks DB, Roe MT, Mulgund J, et al. The obesity paradox in non–ST-segment elevation acute coronary syndromes: Results from the Can Rapid risk stratification of Unstable angina patients Suppress ADverse outcomes with Early implementation of the American College of Cardiology/American Heart Association Guidelines Quality Improvement Initiative. Am Heart J. 2006;152:140-148.
  41. Goldberg RJ, Cui J, Olendzki B, et al. Excess body weight, clinical profile, management practices, and hospital prognosis in men and women after acute myocardial infarction. Am Heart J. 2006;151:1297-1304.
  42. Iakobishvili Z, Danicek V, Porter A, Assali AR, Battler A, Hasdai D. Is increased body mass index associated with a cardioprotective effect after ST-segment-elevation myocardial infarction? Acute Cardiac Care. 2006;8:95-98.
  43. Wells B, Gentry M, Ruiz-Arango A, et al. Relation Between Body Mass Index and Clinical Outcome in Acute Myocardial Infarction. Am J Cardiol. 2006;98:474-477.
  44. Mehta RH, Gitt AK, JÜNger C, et al. Body Mass Index and Effectiveness of Reperfusion Strategies: Implications for the Management of Patients with ST-Elevation Myocardial Infarction. J Interv Cardiol. 2008;21:8-14.
  45. Hadi HAR, Zubaid M, Al Mahmeed W, et al. The Prevalence and Outcome of Excess Body Weight Among Middle Eastern Patients Presenting With Acute Coronary Syndrome. Angiology. 2009;61:456-64.
  46. Mahaffey KW, Tonev ST, Spinler SA, et al. Obesity in patients with non-ST-segment elevation acute coronary syndromes: Results from the SYNERGY trial. Int J Cardiol. 2010;139:123-133.
  47. Das Sandeep R, Alexander Karen P, Chen Anita Y, et al. Impact of Body Weight and Extreme Obesity on the Presentation, Treatment, and In-Hospital Outcomes of 50,149 Patients With ST-Segment Elevation Myocardial Infarction. J Am Coll Cardiol. 2011;58:2642-2650.
  48. Camprubi M, Cabrera S, Sans J, et al. Body Mass Index and Hospital Mortality in Patients with Acute Coronary Syndrome Receiving Care in a University Hospital. J Obes. 2012;2012:287939.
  49. Witassek F, Schwenkglenks M, Erne P, et al. Impact of Body Mass Index on mortality in Swiss hospital patients with ST-elevation myocardial infarction: does an obesity paradox exist? Swiss Med Wkly. 2014;144:w13986.
  50. Kosuge M, Kimura K, Kojima S, et al. Impact of Body Mass Index on In-Hospital Outcomes After Percutaneous Coronary Intervention for ST Segment Elevation Acute Myocardial Infarction. Circ J. 2007;72:521-525.
  51. Mobeirek Abdulelah F, Al-Habib K, Al-Faleh H, et al. Absence of obesity paradox in Saudi patients admitted with acute coronary syndromes: insights from SPACE registry. Ann Saudi Med. 2014;34:38-45.
  52. Ratwatte S, Hyun K, D’Souza M, et al. Relation of Body Mass Index to Outcomes in Acute Coronary Syndrome. Am J Cardiol. 2021;138:11-19.
  53. Kim H-T, Jung S-Y, Nam J-H, et al. Clinical Implication of ‘Obesity Paradox’ in Elderly Patients With Acute Myocardial Infarction. Heart Lung Circ. 2021;30:481-488.
  54. Kim D-W, Her S-H, Park HW, et al. Association between body mass index and 1-year outcome after acute myocardial infarction. PLOS ONE. 2019;14:e0217525.
  55. Yokoyama H, Higuma T, Endo T, et al. Prognostic impact of body mass index and culprit lesion calcification in patients with acute myocardial infarction. Heart Vessels. 2019; 34:1909-16.
  56. Orsini N, Li R, Wolk A, et al. Meta-Analysis for Linear and Nonlinear Dose-Response Relations: Examples, an Evaluation of Approximations, and Software. Am J Epidemiol. 2012;175:66-73.
  57. Greenland S, Longnecker MP. Methods for Trend Estimation from Summarized Dose-Response Data, with Applications to Meta-Analysis. Am J Epidemiol. 1992;135:1301-9.
  58. Lavie CJ, Arena R, Alpert MA, Milani RV, Ventura HO. Management of cardiovascular diseases in patients with obesity. Nat Rev Cardiol. 2018;15:45-56.
  59. Kim SH, Després J-P, Koh KK. Obesity and cardiovascular disease: friend or foe? Eur Heart J. 2016;37:3560-8.
  60. Carbone S, Del Buono MG, Ozemek C, et al. Obesity, risk of diabetes and role of physical activity, exercise training and cardiorespiratory fitness. Prog Cardiovasc Dis. 2019; 62:327-33.
  61. Çınar T, Çağdaş M, Rencüzoğulları İ, et al. Prognostic efficacy of C-reactive protein/albumin ratio in ST elevation myocardial infarction. Scand Cardiovasc J. 2019;53:83-90.
  62. Dallongeville J, Bhatt DL, Steg PG, et al. Relation between body mass index, waist circumference, and cardiovascular outcomes in 19,579 diabetic patients with established vascular disease: the REACH Registry. Eur J Prev Cardiol. 2012;19:241-9.
  63. Akinnusi ME, Pineda LA, El Solh AA. Effect of obesity on intensive care morbidity and mortality: A meta-analysis*. Crit Care Med. 2008;36:151-8.
  64. Karabağ Y, Çınar T, Çağdaş M, Rencüzoğulları İ, Tanık VO. In-hospital and long-term prognoses of patients with a mid-range ejection fraction after an ST-segment myocardial infarction. Acta Cardiol 2019;74:351-8.
  65. Mei X, Hu S, Mi L, Zhou Y, Chen T. Body mass index and all-cause mortality in patients with percutaneous coronary intervention: A dose–response meta-analysis of obesity paradox. Obes Rev. 2021;22:e13107.
  66. Lamelas PM, Maheer K, Schwalm J-D. Body mass index and mortality after acute coronary syndromes: a systematic review and meta-analysis. Acta Cardiol. 2017; 72:655-61.
  67. Moholdt T, Lavie CJ, Nauman J. Interaction of Physical Activity and Body Mass Index on Mortality in Coronary Heart Disease: Data from the Nord-Trøndelag Health Study. Am J Med. 2017;130:949-57.

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