Abstract
BACKGROUND AND OBJECTIVES
To describe the distribution of body mass index (BMI) and its relationship with clinical features, management, and in-hospital outcomes of patients admitted with acute coronary syndromes (ACS).
DESIGN AND SETTINGS
The Saudi Project for Assessment of Coronary Events is a prospective registry. ACS patients admitted to 17 hospitals from December 2005–2007 were included in this study.
METHODS
BMI was available for 3469 patients (68.6%) admitted with ACS and categorized into 4 groups: normal weight, overweight, obese, and morbidly obese.
RESULTS
Of patients admitted with ACS, 72% were either overweight or obese. A high prevalence of diabetes (57%), hypertension (56.6%), dyslipidemia (42%), and smoking (32.4%) was reported. Increasing BMI was significantly associated with diabetes, hypertension, and hyperlipidemia. Overweight and obese patients were significantly younger than the normal-weight group (P=.006). However, normal-weight patients were more likely to be smokers and had 3-vessel coronary artery disease, worse left ventricular dysfunction, and ST elevation myocardial infarction. Glycoprotein IIb-IIIa antagonists were used significantly more in overweight, obese, and morbidly obese ACS patients than in normal-weight patients (P≤.001). Coronary angiography and percutaneous intervention were reported more in overweight and obese patients than in normal-weight patients (P≤.001). In-hospital outcomes were not significantly different among the BMI categories.
CONCLUSION
High BMI is prevalent among Saudi patients with ACS. BMI was not an independent factor for in-hospital outcomes. In contrast with previous reports, high BMI was not associated with improved outcomes, indicating the absence of obesity paradox observed in other studies.
Obesity is increasing in prevalence worldwide and has reached epidemic proportions in many countries. Two-thirds of the US population is overweight or obese.1,2 According to the most recent National Health and Nutrition Examination Survey, 68% of adult men and women are overweight or obese.3,4 The EUROASPIRE III survey revealed that obesity (body mass index [BMI] >30) was highly prevalent in coronary heart disease patients across all 22 participating countries, reaching an overall prevalence of 35%.5 In the Saudi population, the prevalence of overweight and obesity was 72.5%, which is similar to that in the United States.6 Obesity is known to be associated with increased morbidity and mortality,7,8 and it has been predicted that if the current obesity trends continue, the negative effects of increasing BMI will overwhelm the positive effects of decline in smoking.9 The aim of our study was to evaluate the impact of BMI in unselected Saudi patients admitted with acute coronary syndrome (ACS) in relation to demographic data, clinical presentation, medication, use of angiography and revascularization procedures, and in-hospital outcomes.
METHODS
The Saudi Project for Assessment of Coronary Events (SPACE) registry was a prospective study conducted in 17 hospitals from 5 major regions in Saudi Arabia. The details of the study were outlined previously.10,11 In summary, between December 2005 and December 2007, 5056 patients admitted with ACS were enrolled. All patients with ACS had a final diagnosis of ST elevation myocardial infarction (STEMI), non–ST segment elevation myocardial infarction (NSTEMI), and unstable angina. Diagnosis of different types of ACS and definitions of data variables were based on the definitions of the American College of Cardiology.12 Data were collected for the following variables: patient demographics, medical history, provisional diagnosis on admission and final discharge diagnosis, investigations, medical therapy, use of cardiac procedures and interventions, in-hospital outcome, and mortality. BMI was calculated as weight in kilograms divided by the square of height in meters. Patients were categorized according to the World Health Organization classification 13 into 4 groups: underweight or normal weight, BMI <25; overweight, BMI 25–29.9; obese, BMI 30–39.9; and morbidly obese, BMI >40.
Statistical analysis
Categorical data were summarized with absolute numbers and percentages. Continuous data were summarized with means and standard deviations or median and interquartile range. Comparisons among different BMI groups were performed using chi-square test for categorical variables and analysis of variance or Kruskal–Wallis test for continuous variables. To study the relationship of mortality with BMI and other risk factors, a multiple logistic regression model was used. The odds ratio estimates and corresponding 95% confidence intervals were obtained using a multiple logistic regression model. All analyses were performed using SAS/STAT software, version 9.1.3 (SAS Institute, Cary, NC, USA).
RESULTS
Study population
In total, 5056 consecutive patients were enrolled in the SPACE registry. The data required for this study were available for 3469 ACS patients (68.6%), who were classified according to their BMI into 4 groups as shown in Table 1. Of the ACS patients included in the study, 72% were either overweight or obese.
Table 1.
Clinical characteristics of ACS patients according to BMI.
| Variable | BMI <25 (N=969, 28%) | BMI 25–29 (N=1419, 41%) | BMI 30–39.9 (N=762, 22%) | BMI ≥40 (N=319, 9%) | P |
|---|---|---|---|---|---|
|
| |||||
| Age mean (SD) (y) | 59.1 (14.2) | 57.6 (12.4) | 57.6 (12.4) | 56 (12.1) | .006 |
| Male (%) | 82.9 | 81.7 | 72.4 | 55.8 | <.001 |
| Saudi (%) | 77.1 | 82.3 | 84.0 | 86.2 | <.001 |
| Diabetes (%) | 52.2 | 58.2 | 60.0 | 62.7 | <.001 |
| Hypertension (%) | 50 | 52.8 | 65.2 | 73.4 | <.001 |
| Dyslipidemia (%) | 34.2 | 40.7 | 48.2 | 58.9 | <.001 |
| Current smoking (%) | 37.9 | 32.5 | 27.8 | 26.5 | <.001 |
| Prior CAD (%) | 38.7 | 45.6 | 49.4 | 49.5 | <.001 |
| Prior CABG (%) | 5.2 | 6.2 | 4.9 | 6.0 | .706 |
| Prior CHF (%) | 20.8 | 21.5 | 22.8 | 22.9 | .850 |
| Prior PAD (%) | 9.9 | 8.8 | 7.5 | 8.1 | .359 |
| Prior CVA (%) | 8.4 | 5.5 | 5.38 | 5.3 | .047 |
| HR >100/min (%) | 14.2 | 13 | 13.14 | 9.1 | .134 |
| SBP <90 mm Hg | 3.31 | 2.83 | 2.50 | 1.0 | .156 |
| Atypical chest pain (%) | 4 | 2.7 | 2.49 | 5.3 | .003 |
| Killip class >1 (%) | 20.8 | 21.5 | 22.2 | 22.9 | .85 |
| STEMI ACS | 46.75 | 42.28 | 36.75 | 29.78 | <.001 |
| NSTEMI ACS | 53.2 | 57.72 | 63.25 | 70.22 | <.001 |
| Cholesterol median (IQR) | 4.4 | 4.4 | 4.4 | 4.2 | .656 |
| LDL median (IQR) | 2.8 | 2.7 | 2.7 | 2.6 | .196 |
| Triglyceride median (IQR) | 1.4 | 1.5 | 1.6 | 1.6 | <.001 |
| Fasting glucose median (IQR) | 6.4 | 6.6 | 6.8 | 7 | .003 |
| LV ejection fraction <35% (%) | 40.1 | 34.6 | 32.7 | 23.6 | <.001 |
BMI: Body mass index, ACS: acute coronary syndromes, CAD: coronary artery disease, CABG: coronary bypass surgery, CHF: congestive heart failure, PAD: peripheral vascular disease, CVA: cerebrovascular disease, SBP: systolic blood pressure, STEMI: ST elevation myocardial infarction, NSETMI: non–ST elevation myocardial infarction, LV: left ventricle, LDL: low-density lipoprotein, IQR: interquartile range.
Demographics and clinical characteristics
Table 1 depicts the baseline clinical characteristics of our cohort. Overweight, obese, and morbidly obese patients were more likely to be younger, have diabetes, hypertension, hyperlipidemia, and a history of ischemic heart disease and were less likely to be smokers compared to the normal-weight BMI group (P<.05). The median cholesterol and low-density lipoprotein cholesterol values showed no relationship with BMI, while the median fasting glucose levels and triglyceride levels were significantly associated with higher BMI.
ACS presentation and management
The normal-weight patients were more likely to present with atypical chest pain than the higher-BMI patients (P=.003). STEMI patients represented 41.2% of the study population. The increased BMI patients were more likely to present with NSTEMI, while the normal BMI group tended to present with STEMI (P≤.001). A steady increase in the prevalence of NSTEMI was observed with increasing weight, and a steady increase in STEMI was observed with decreasing weight. No differences were recorded in heart rate, systolic blood pressure, and Killip class among the groups. In STEMI patients, BMI did not appear to influence the likelihood of receiving reperfusion therapy. BMI showed no relationship with either door-to-needle time or door-to-balloon time.
Medications
Table 2 depicts the in-hospital medications that were administered to the study participants. Similar proportions in each group were given aspirin, clopidogrel, beta-blockers, angiotensin-converting enzyme inhibitors (ACEI), and thrombolytics. The high-weight groups were more likely to be treated with angiotensin receptor blockers and glycoprotein IIb-IIIa inhibitors than those in the normal-weight group (P<.001).
Table 2.
In-patient management of ACS patients according to BMI.
| Variable | BMI <25 (N=969, 28%) | BMI 25–29 (N=1,419, 41%) | BMI 30–39.9 (N=762, 22%) | BMI ≥40 (N=319, 9%) | P |
|---|---|---|---|---|---|
|
| |||||
| Inpatient medications of the study population (%) | |||||
| Aspirin | 98. | 98.4 | 98.7 | 98.7 | .852 |
| Clopidogrel | 87.5 | 87.0 | 89.6 | 88.0 | .349 |
| Beta-blockers | 84.1 | 86.2 | 86.9 | 85.2 | .361 |
| ACEI | 71.7 | 72.8 | 70.8 | 71.4 | .781 |
| ARB | 4.4 | 5.0 | 9.5 | 8.2 | <.001 |
| Statin | 97 | 96.8 | 96.4 | 93.7 | .037 |
| Heparin | 83.01 | 84.21 | 84.3 | 81.8 | .688 |
| GPIIb–IIIa inhibitors | 26.9 | 31.6 | 37.0 | 30.4 | <.001 |
| STEMI management | |||||
| Thrombolytics (%) | 50.4 | 52.8 | 53.1 | 45.4 | .488 |
| Door-to-needle time <30 min (%) | 23.8 | 25.5 | 26.0 | 25.0 | .977 |
| Door-to-balloon time <90 min (%) | 25.9 | 30.5 | 35.1 | 63.6 | .101 |
| Discharge medications of the study population (%) | |||||
| Aspirin | 97.1 | 97.5 | 98.1 | 97.7 | .64 |
| Clopidogrel | 70.8 | 77.0 | 74.6 | 70.4 | .004 |
| Beta-blockers | 88.8 | 80.7 | 89.7 | 86.0 | .084 |
| ACEI | 72.3 | 74.9 | 70.3 | 71.1 | .113 |
| ARB | 6.5 | 6.5 | 11.4 | 10.7 | <.001 |
| Statin | 95.6 | 97.0 | 96.1 | 96.4 | .397 |
| Coronary angiography (%) | |||||
| Total coronary angiography | 61.7 | 67.5 | 74.2 | 71.8 | <.001 |
| LMS | 7.0 | 4.7 | 4.8 | 3.5 | .104 |
| SVD | 27.0 | 30.9 | 32.6 | 35.4 | .218 |
| DVD | 20.5 | 23.5 | 23.4 | 15.7 | .046 |
| TVD | 38.2 | 32.7 | 29.6 | 24.9 | <.001 |
| PCI | 29.3 | 35.3 | 38.8 | 36.7 | .002 |
| CABG | 9.1 | 7.9 | 9.9 | 6.6 | .155 |
ACE: Angiotensin converting enzyme inhibitors, ACS: acute coronary syndromes, ARB: angiotensin receptor blockers, GPIIa–IIb inhibitors: glycoprotein IIb–IIIa inhibitors, STEMI: ST elevation myocardial infarction, LMS: left main stem disease, SVD: single-vessel disease, DVD: double-vessel disease, TVD: triple-vessel disease, PCI: percutaneous intervention, CABG: coronary bypass surgery, CHF: congestive heart failure.
Procedures
Patients in the normal-weight group were more likely to have moderate-to-severe left ventricular (LV) systolic dysfunction (P<.001). Patients in the overweight, obese, and morbidly obese categories underwent significantly more cardiac catheterization procedures compared to the normal-weight group (P≤.001). Three-vessel coronary artery disease (CAD) was less prevalent in the overweight and obese groups compared to the normal-weight group (P≤.001). Patients in the overweight and obese groups undergoing percutaneous coronary intervention (PCI) (P≤.001) and coronary stenting (P≤.002) were more in number than in the normal-weight group (Table 2).
In-hospital outcomes
In-hospital outcomes were similar among the 4 BMI groups (Table 3). Patients who were overweight or obese had similar in-hospital adverse events and mortality compared to normal-weight patients.
Table 3.
In-hospital outcomes (%).
| Variable | BMI <25 (N=969, 2%) | BMI 25–29 (N=1,419, 41%) | BMI 30–39.9 (N=762, 22%) | BMI ≥40 (N=319, 9%) | P |
|---|---|---|---|---|---|
|
| |||||
| Major bleeding | 1.34 | 1.41 | 1.05 | 1.57 | .863 |
| Stroke | 1.4 | 0.85 | 0.79 | 1.88 | .336 |
| Re-infarction | 1.34 | 1.06 | 0.79 | 2.19 | .242 |
| CHF | 9.9 | 9.59 | 10.10 | 10.66 | .861 |
| Cardiogenic shock | 4.75 | 4.79 | 3.67 | 3.45 | .53 |
| Death | 2.48 | 2.89 | 2.62 | 3.76 | .644 |
CHF: Congestive heart failure
Multiple logistic regression analysis
Multiple logistic regression analysis was carried out for the predictors of in-hospital mortality (Table 4). The independent variables significantly associated with mortality were age, beta-blockers, ACEI, statin, heart failure, and Killip class. BMI was not an independent predictor of in-hospital mortality.
Table 4.
Multiple logistic regression analysis for predictors of mortality in patients with ACS.
| Factor | OR | 95% CI | P |
|---|---|---|---|
|
| |||
| Male gender | 0.752 | (0.44–1.28) | .296 |
| Age | 1.049 | (1.02–1.07) | <.001 |
| Diabetes mellitus | 1.24 | (0.60–2.52) | .514 |
| Hypertension | 1.965 | (0.95–4.07) | .069 |
| Dyslipidemia | 0.965 | (0.49–1.87) | .917 |
| Smoking | 1.179 | (0.60–2.31) | .6317 |
| BMI <25 (normal/underweight) reference | |||
| BMI 25–29.9 (overweight) | 1.399 | (0.768–2.55) | .272 |
| BMI 30–39.9 (obese) | 1.252 | (0.616–2.54) | .534 |
| BMI ≥40 (very obese) | 1.307 | (0.547–3.11) | .546 |
| Atypical chest pain | 0.854 | (0.11–6.62) | .879 |
| Coronary angiography | 0.896 | (0.47–1.68) | .653 |
| Clopidogrel | 0.655 | (0.31–1.35) | .2536 |
| Beta-blockers | 0.56 | (0.32–0.96) | .0354 |
| ACEI | 0.57 | (0.34–0.94) | .0274 |
| Statin | 0.34 | (0.15–0.76) | .0088 |
| CHF | 4.463 | (2.53–7.84) | <.001 |
| Killip class >1 | 0.354 | (0.20–0.63) | <.001 |
BMI: Body mass index, ACEI: angiotensin-converting enzyme inhibitors, CHF: congestive heart failure, CI: confidence interval, ACS: acute coronary syndromes.
DISCUSSION
Obesity is associated with numerous adverse cardiovascular effects. These include increased risk of hypertension, type 2 diabetes mellitus, LV hypertrophy, dyslipidemia, heart failure, atrial fibrillation, stroke, sudden cardiac death, endothelial dysfunction,14 and premature occurrence of myocardial infarction.15
A meta-analysis of 21 long-term studies that followed more than 300 000 patients identified a 45% increase in coronary heart disease in patients who were overweight or obese, even after adjustments for blood pressure and cholesterol levels.16 It has also been shown that obesity confers an elevated risk of ACS; each unit increase in BMI was associated with a 5% and 7% higher risk of ACS among women and men, respectively.17 In patients with angiographically confirmed CAD, BMI is independently associated with unstable angina and myocardial infarction even after adjusting for other risk factors.18
BMI is an important health parameter and has a role in patient management, such as lifestyle modification; however, it was not available for 31% of patients with ACS in the SPACE registry. A high prevalence of overweight and obesity has been reported in the Saudi population. Al-Nozha et al reported a prevalence of 72.5% of overweight and obesity among the Saudi population. 6 A high prevalence was also reported in another recent study from the eastern province in Saudi Arabia, where 195 874 people were screened and the overall prevalence of overweight and obesity was 78.9%.19 The prevalence of overweight and obesity in Saudi patients presenting with ACS has not been studied previously. Our study revealed a high prevalence of both conditions among the studied population; 72% of patients admitted with ACS for whom BMI was available were found to be either overweight or obese. This finding is in agreement with the results from the CRUSADE initiative and from the national cardiovascular registry (NCDR) in the United States. In the CRUSADE initiative involving 80 845 patients with NSTEMI, 70.5% of patients were classified as overweight or obese.20 In the recently reported NCDR involving 50 149 patients with STEMI, three-fourths of the patients were overweight or obese.21 Additionally, patients admitted with ACS who were overweight or obese tended to be younger than normal-weight patients. This observation was similar to the results of other ACS registries.20–27 This is due to the fact that obesity appears to accelerate established CAD, leading to premature occurrence of myocardial infarction.15
Our study showed that normal-weight ACS patients, in addition to being older, were more likely to have a history of smoking and 3-vessel CAD (revealed by angiography) and more significant LV systolic dysfunction. It is well known that older age and smoking are independent risk factors for ACS regardless of the weight status. In patients admitted with ACS, an increasing trend was observed with increasing prevalence of risk factors (hypertension, diabetes, and hyperlipidemia) because the BMI increases across all the BMI categories. The prevalence of diabetes was extremely high in the studied population (57%), with a higher prevalence in overweight, obese, and very obese patients (58%, 60%, and 63%, respectively) compared to normal-weight patients (52%). The prevalence of diabetes in our study is the highest among the reported studies. 15,20–26 This finding might be due to the high prevalence of diabetes among the Saudi population.28 A history of CAD was also strongly associated with rising BMI. The same relationship, but to a lesser extent, was found with cerebrovascular disease. The fasting glucose and triglyceride levels were more elevated in patients in the higher BMI categories, reflecting the relationship of obesity with impaired glucose tolerance, metabolic syndrome, diabetes, and dyslipidemia.
Overweight and obese patients were more likely to have NSTEMI, while normal-weight patients were more likely to present with STEMI. These findings were in contrast to what was observed in a Portuguese ACS registry of 14 391 patients in which there were no differences in the prevalence of STEMI and NSTEMI in the high- and low-BMI groups.26
Most patients received medications according to ACS guidelines.29,30 Saudi Arabia is developing in terms of its heath care system, and the drugs needed for ACS are available in most of the centers. Our study reflected the proper utilization of drugs for most cases. This could explain the observed low mortality among patients with ACS in our study. Interestingly, there was no significant relationship between the use of most medications and BMI, which may indicate that our health care professionals are treating ACS cases equally, irrespective of their weight. Glycoprotein IIb-IIIa antagonists use was significantly greater in overweight, obese, and morbidly obese patients. This could be explained by the fact that there is a higher prevalence of diabetes and higher rates of PCI among these patients, for whom the evidence for anti-platelet use is stronger.31
Cardiac catheterization is not available in all hospitals in Saudi Arabia, yet a significant fraction of the study population underwent this procedure. This is because the study participants were recruited mostly from tertiary cardiac centers. Three-vessel coronary disease and LV systolic dysfunction were more prevalent in the normal/underweight BMI category compared to other BMI categories. However, more patients in the overweight and obese groups underwent coronary angiography and PCI compared to the underweight and normal-weight groups, similar to previously reported studies from the United States and Europe.20,22,24
According to the “obesity paradox,” overweight and obese patients with established cardiovascular disease seem to have a more favorable prognosis than leaner patients despite clustering of risk factors in the former. 32,33 This observation has been reported in previous ACS registries from the United States, Europe, Japan, Portugal, and Israel.20,22,25–27 In a meta-analysis of 40 studies with a mean follow-up of 3.8 years that included 250 152 patients with established CAD, outcomes for cardiovascular and total mortality were better for overweight and mildly obese groups compared to normal-weight patients. Adjustment for confounding factors did not change the findings.34 The reasons behind this surprising paradox are unclear. Possible explanations include presentation at a younger age for obese patients with cardiovascular disease; more metabolic reserves for obese patients, who can thus better tolerate the catabolic stress of myocardial ischemia, lower levels of circulating atrial natriuretic peptides, and attenuated sympathetic nervous system and renin–angiotensin responses; and greater toleration of higher doses of cardioprotective medications by obese individuals. 14,33 The more aggressive strategy of treatment in overweight and obese patients, including increased use of medications, coronary angiography, and PCI, has been proposed to explain the improved outcome in patients with ACS.20,22,24 Conversely, weight loss due to chronic disease may serve as a confounding variable, worsening outcomes in lower-weight patients. Other investigators have reported a U-shaped relationship between BMI and mortality in ischemic heart disease, with higher mortality in underweight and extremely obese patients and lower mortality in overweight and moderately obese patients.21,27 In the recently reported NCDR from the United States, there was higher adjusted in-hospital mortality in the extremely obese patients compared to other weight categories. In contrast, and despite the more aggressive treatment of the high-BMI group, our study did not show evidence of an obesity paradox; no significant differences were reported in the outcomes regardless of the BMI, which is in agreement with a previous observation from the Middle East.27 This could be due to ethnicity differences between the Middle Eastern and Western societies, and might also be explained by the younger age group in our study population (across all BMI categories) and the narrower age gap between normal-weight and overweight and obese patients in our study population compared to other reported studies, in which the age difference was larger.19,24–26,28–31 In addition, this might also be due to the high prevalence of diabetes and other risk factors among all BMI groups, which would decrease the differences in the outcomes between the normal-weight and high-BMI groups. Based on multiple logistic regression analysis, BMI was not an independent predictor of mortality in this study; this is in agreement with an earlier study from the Middle East.23
Several limitations exist in our study. First, the use of BMI is limited by the fact that it cannot discriminate between fat mass and lean mass; the cutoff values may be different for specific ethnic populations, and BMI does not reflect body fat distribution, such as abdominal fat. Therefore, BMI may not be the best measure of cardiovascular risk.34 The combination of BMI and waist circumference may offer a better method for assessing obesity than BMI alone. Second, our study was limited to short-term in-hospital outcome. Third, as with most other registries, hospital enrollment was voluntary; thus, the study results may not be a representative of clinical practice in all hospitals in the country. In addition, hospitals that participated in the registry could be more enthusiastic about adherence to guidelines and quality improvement initiatives. However, the wide geographic distribution of several hospitals from different health care sectors in our study provides a reasonable overall representation of ACS care. Fourth, there was an inherent selection bias because of the observational nature of the study design. Finally, the sample size in the extremely obese BMI group was small.
In conclusion, this study revealed a high prevalence of obesity among patients with ACS. Moreover, we did not observe the obesity paradox in our study population in Saudi Arabia. Primary prevention programs for obesity are sorely needed at the national level. More studies with longer follow-up periods that incorporate both BMI and waist circumference or BMI and waist-to-hip ratios are needed.35,36
REFERENCES
- 1.Debora Boardley, Rebecca Pobocik. Obesity on the rise. Obesity Management. Prim Care Clin Office Pract. 2009;36:243–255. [Google Scholar]
- 2.Yanovski Susan Z, Yanovski Jack A. Obesity Prevalence in the United States – Up, Down, or Sideways. NEJM. 2011;364(11):987–989. doi: 10.1056/NEJMp1009229. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Flegal KM, Carrol MD, Ogden CL, Curtin LR. Prevalence and trends in obesity among US adults, 1999–2008. JAMA. 2010;303:235–241. doi: 10.1001/jama.2009.2014. [DOI] [PubMed] [Google Scholar]
- 4.Wang Y, Beydoun MA. The obesity epidemic in the United States-gender, age, socioeconomic, racial/ethnic, and geographic characteristics: a systematic review and meta-regression analysis. Epidemiol Rev. 2007;29:6–28. doi: 10.1093/epirev/mxm007. [DOI] [PubMed] [Google Scholar]
- 5.De Bacquer D, Dallongeville J, Heidrich J, Kosteva K, Reiner Z, Gaita D, Prugger C, et al. Management of overweight and obese patients with coronary heart disease across Europe. European journal of Cardiovascular Prevention and Rehabilitation. 2010;17:447–454. doi: 10.1097/HJR.0b013e328336a05f. [DOI] [PubMed] [Google Scholar]
- 6.Al-Nozha MM, Al-Mazrou YY, Al-Maatouq MA, Arafah MR, Khalil M, Khan N, et al. Obesity in Saudi Arabia. Saudi Med J. 2005;26(5):824–829. [PubMed] [Google Scholar]
- 7.de Gonzalez Amy Berrington, Patricia Hartge, Cerhan James R, Flint Alan J, Hannan Li, MacInnis Robert J, et al. Body-Mass Index and Mortality among 1.46 Million White Adults. NEJM. 2010;363(23):2211–2219. doi: 10.1056/NEJMoa1000367. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Whitlock G, Lewington S, Sherliker P, Clarke R, Emberson J, Halsey J, et al. Prospective Studies Collaboration. Body-mass index and cause-specific mortality in 900 000 adults: collaborative analyses of 57 prospective studies. Lancet. 2009;373:1083–1096. doi: 10.1016/S0140-6736(09)60318-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Stewart Susan T, Cutler David M, Rosen Allison B. Forecasting the Effects of Obesity and Smoking on U.S. Life Expectancy. NEJM. 2009;361(23):2252–2260. doi: 10.1056/NEJMsa0900459. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.AlHabib KF, Hersi A, AlFaleh H, Kurdi M, Arafah M, Youssef M, et al. The Saudi Project for Assessment of Coronary Events (SPACE) registry: design and results of a phase 1 pilot study. Can J Cardiol. 2009;25(7):e255–e258. doi: 10.1016/s0828-282x(09)70513-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.AlHbib Khalid F, Ahmed Hersi, Hussam Alfaleh, Khalid Alnemer, Shukri Alsaif, Amir Taraben, et al. Baseline characteristics, management practices, and in-hospital outcomes of patients with acute coronary syndromes: Results of the Saudi project for assessment of coronary events (SPACE) registry. Journal of the Saudi Heart Association. 2011;23:233–239. doi: 10.1016/j.jsha.2011.05.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Thygesen K, Alpert JS, White HD on behalf of the Joint ESC/ACCF/AHA/WHF Task Force for the Redefinition of Myocardial Infarction. Universal definition of Myocardial Infarction. J Am Coll Cardiol. 2007;50:2173–2195. doi: 10.1016/j.jacc.2007.09.011. [DOI] [PubMed] [Google Scholar]
- 13.World Health Organization. Report of a WHO consultation. Geneva (Switzerland): World Health Organization; 2000. Obesity: preventing and managing the global epidemic. (Technical report series no. 894). [PubMed] [Google Scholar]
- 14.Lavie CJ, Milani RV, Ventura HO. Obesity and Cardiovascular Disease: Risk factor, Paradox, and Impact of Weight Loss. J Am Coll Cardiol. 2009;53:1925–1932. doi: 10.1016/j.jacc.2008.12.068. [DOI] [PubMed] [Google Scholar]
- 15.Madala MC, Franklin BA, Chen AY, Berman AD, Roe MT, Peterson ED, et al. the CRUSADE Investigators. Obesity and the age of first non-ST-segment elevation myocardial infarction. J Am Coll Cardiol. 2008;52(12):979–985. doi: 10.1016/j.jacc.2008.04.067. [DOI] [PubMed] [Google Scholar]
- 16.Bogers RP, Bemelmans WJ, Hoogenveen RT, Boshuizen HC, Woodward M, Knekt P, et al. Association of overweight with increased risk of coronary heart disease partly independent of blood pressure and cholesterol levels. Arch Intern Med. 2007;167(16):1720–1728. doi: 10.1001/archinte.167.16.1720. [DOI] [PubMed] [Google Scholar]
- 17.Jensen MK, Chiuve S, Rimm EB, Dethlefsen C, Tjønneland A, Joensen AM, et al. Obesity, Behavioral Life style Factors, and Risk of Acute Coronary Events. Circulation. 2008;117:3062–3069. doi: 10.1161/CIRCULATIONAHA.107.759951. [DOI] [PubMed] [Google Scholar]
- 18.Robert Wolk, Peter Berger, Lennon Rayan J, Brilakis Emmanouil S, Somers Virend K. Body Mass Index A Risk Factor for Unstable Angina and Myocardial Infarction in Patients With Angiographically Confirmed Coronary Artery Disease. Circulation. 2003;108:2206–2211. doi: 10.1161/01.CIR.0000095270.85646.E8. [DOI] [PubMed] [Google Scholar]
- 19.Al-Baghli Nadira A, Al-Ghamdi Aqeel J, Al-Turki Khalid A, El-Zubair Ahmed G, Al-Ameer Mahmood M, Al-Baghli Fadel A. Overweight and obesity in the eastern province of Saudi Arabia. Saudi Med J. 2008;29(9):1319–1325. [PubMed] [Google Scholar]
- 20.Diercks Deborah B, Roe Matthew T, Jyostsna Mulgund, Pollack Charles V, Jr, Douglas Kirk J, Brian Gibler W, 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 ACC/AHA Guidelines Quality improvement Initiative. Am Heart J. 2006;152:140–148. doi: 10.1016/j.ahj.2005.09.024. [DOI] [PubMed] [Google Scholar]
- 21.Das SR, Alexander KP, Chen AY, Powell-Wiley TM, Diercks DB, Peterson ED, 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: results from the NCDR (national Cardiovascular Data Registry) J Am Coll Cardiol. 2011;58:2642–2650. doi: 10.1016/j.jacc.2011.09.030. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Heinz Buettner, Christain Mueller, Michael Gick, Mark Ferenc, Juergen Allgeier, Thomas Coberg, et al. The impact of obesity on mortality in UA/non-ST-segment elevation myocardial infarction. Eur Heart J. 2007;28:1694–1701. doi: 10.1093/eurheartj/ehm220. [DOI] [PubMed] [Google Scholar]
- 23.Hadi AR Hadi, Zubaid Mohammad, Al Mahmeed Wael, El-Menyar Ayman A, Alsheikh-Ali Alawi A, Singh Rajivir, et al. The prevalence and outcome of excess body weight among Middle Eastern patients presenting with acute coronary syndrome. Angiology. 2010;6(5):456–464. doi: 10.1177/0003319709355801. [DOI] [PubMed] [Google Scholar]
- 24.Steinberg Benjamin A, Cannon Christropher P, Hernandes Adrian F, Pan Winqin, Peterson Eric D, Fonarow Gregg C for the GWTG Scientific Advisory Committee and Investigators. Medical Therapies and Invasive Treatments for Coronary Artery Disease by Body Mass: The “Obesity Paradox” in the Get With The Guidelines Database. Am J Cardiol. 2007;100:1333–1335. doi: 10.1016/j.amjcard.2007.06.019. [DOI] [PubMed] [Google Scholar]
- 25.Kosuge M, Kimura K, Koiima S, Sakamoto T, Masahiro S, Asada Y, et al. for the Japanese Acute Coronary Syndrome Study (JACSS) Investigators. Impact of body mass index on in-hospital outcomes after percutaneous coronary intervention for ST segment elevation acute myocardial infarction. Circ J. 2008;72(4):521–525. doi: 10.1253/circj.72.521. [DOI] [PubMed] [Google Scholar]
- 26.Durao Luis Dourado. Acute coronary syndromes and body mass index. On behalf of Portuguese registry of acute coronary syndromes. Eur Heart J. 2008;29(Abstract Supplement):101. [Google Scholar]
- 27.Michael Shechter, Haim Hammerman, Valentina Boyko, Hanoch Hod, Solomon Behar, Shlomi Matezky. The obesity paradox in hospitalized acute coronary syndromes in Israel: A national survey. CVD Prevention and Control. 2010;5(3):81–87. [Google Scholar]
- 28.Al-Nozha MM, Al-maatouq MA, AL-Mazrou YY, Al-Harthi Saad S, Mohammed Arafah, Khalil Mohamed Z, et al. Diabetes mellitus in Saudi Arabia. Saudi Med J. 2004;25(11):1603–1610. [PubMed] [Google Scholar]
- 29.ACC/AHA 2007 Guidelines for the Management of Patients with Unstable Angina/Non ST Elevation Myocardial Infarction Executive Summary: A Report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol. 2007;50:652–726. [Google Scholar]
- 30.2009 Focused Update: ACC/AHA Guidelines for the Management of Patients With ST-Elevation Myocardial Infarction. J Am Coll Cardio. 2009;54:2205–2241. doi: 10.1016/j.jacc.2009.10.015. [DOI] [PubMed] [Google Scholar]
- 31.Roffi M, Chew DP, Mukherjee D, Bahatt DL, White JA, Heeschen C, Hamm CW, Molitemo DJ, Califf RM, White HD, Klieman NS, Théroux P, Topol EJ. Platelet Glycoprotein IIb/IIIa inhibitors reduce mortality in patients with non-ST-segment elevation acute coronary syndromes. Circulation. 2001;104:2767–2771. doi: 10.1161/hc4801.100029. [DOI] [PubMed] [Google Scholar]
- 32.Lavie CJ, Milani RV. Obesity and cardiovascular disease: the Hippocrates paradox? J Am Coll Cadiol. 2003;42:667–679. doi: 10.1016/s0735-1097(03)00784-8. [DOI] [PubMed] [Google Scholar]
- 33.Lavie CJ, Milani RV, Ventura HO. Impact of Obesity on Outcomes in Myocardial Infarction: Combating the Obesity Paradox. J Am Coll Cardiol. 2011;58:2651–2653. doi: 10.1016/j.jacc.2011.09.032. [DOI] [PubMed] [Google Scholar]
- 34.Romero-Corral A, Montori VM, Somers VK, Korinek J, Thomas RJ, Thomas GA, et al. Association of bodyweight with total mortality and with cardiovascular events in coronary artery disease: a systematic review of cohort studies. Lancet. 2006;368:666–678. doi: 10.1016/S0140-6736(06)69251-9. [DOI] [PubMed] [Google Scholar]
- 35.Sang-Hee Lee, Jong-Seon Park, Woong Kim, Dong-Gu Shin, Young-Jo Kim, Dong-Su Kim, et al. Impact of Body Mass Index and Waist-to-Hip Ratio on Clinical Outcomes in Patients With ST-Segment Elevation Myocardial Infarction (from the Korean Acute Myocardial Infarction Registry) AM J Cardiol. 2008;102:957–965. doi: 10.1016/j.amjcard.2008.06.022. [DOI] [PubMed] [Google Scholar]
- 36.Marianne Zeller, Gabriel Steg Philippe, Jack Ravisy, Luc Lorgis, Yves Laurent, Pierre Sicard, et al. Relation between body mass index, waist circumference, and death after acute myocardial infarction. Circulation. 2008;118:482–490. doi: 10.1161/CIRCULATIONAHA.107.753483. [DOI] [PubMed] [Google Scholar]