Disclosure: The authors declared no conflict of interest.
Article first published online: 16 APR 2013
Copyright © 2013 The Obesity Society
Volume 21, Issue 3, pages E314–E321, March 2013
How to Cite
Bechlioulis, A., Vakalis, K., Naka, K. K., Bourantas, C. V., Papamichael, N. D., Kotsia, A., Tzimas, T., Pappas, K., Katsouras, C. S. and Michalis, L. K. (2013), Paradoxical protective effect of central obesity in patients with suspected stable coronary artery disease. Obesity, 21: E314–E321. doi: 10.1002/oby.20074
Funding source: This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.
- Issue published online: 16 APR 2013
- Article first published online: 16 APR 2013
- Accepted manuscript online: 18 OCT 2012 01:00PM EST
- Manuscript Accepted: 23 AUG 2012
- Manuscript Received: 17 FEB 2012
Increased body mass index (BMI) has been paradoxically inversely associated with the presence of angiographic coronary artery disease (CAD). Central obesity measures, considered to be more appropriate for assessing obesity-related cardiovascular risk, have been little studied in relation to the presence of CAD. The aim was to investigate the association of central obesity with the presence of angiographic CAD as well as the prognostic significance of obesity measures in CAD prediction when added to other cardiovascular risk factors.
Design and Methods:
Patients with suspected stable CAD (n = 403, age 61 ± 10 years, 302 males) referred for diagnostic coronary angiography with documented anthropometric data were enrolled.
Significant angiographic CAD was found in 51% of patients. Both BMI (OR = 0.64 per 1 SD increase, P = 0.001) and waist circumference (WC) (OR = 0.54 per 1 SD increase, P < 0.001) were inversely associated with the presence of CAD even after adjustment for cardiovascular risk factors. In subgroup analysis, BMI and WC were significantly inversely associated with the presence of CAD in males, non diabetics, patients >60 years old and patients with Framingham risk score (FRS) >20% (P < 0.01 for all). The addition of BMI or WC in FRS-based regression models improved prediction of CAD (P = 0.03 and P < 0.001 for BMI and WC respectively) without a significant difference between the two models (P = 0.08).
Central and overall obesity were independently associated with a reduced prevalence of angiographic CAD, lending further credence to the existence of the ‘obesity paradox’. Obesity measures may further improve risk discrimination for the presence of CAD when added in an established risk score such as FRS.
Obesity is a growing problem of the public health worldwide. It has been reported that nearly 70% of adults in Western Europe and United States are classified as overweight or obese (1). It is currently recognized that obesity is a major risk factor for cardiovascular diseases (CVD). Although it has been suggested that the obesity-related cardiovascular risk could be attributed entirely to cardiovascular risk factors associated with obesity (2), such as dyslipidemia, hypertension, and diabetes mellitus, large longitudinal studies in subjects without known CVD have documented an independent positive association of general and central obesity with cardiovascular events (3-8).
On the other hand, a paradoxical protective effect of obesity as assessed by increased BMI, known as “obesity paradox,” has been previously reported in various groups of cardiovascular patients; an improved prognosis in obese compared to lean subjects has been shown in patients with known or suspected coronary artery disease (CAD) (9-14), heart failure (15-17), or following coronary angioplasty (18-20). In accordance with the obesity paradox, the presence and extent of coronary atherosclerosis in patients undergoing coronary angiography have been inversely associated with BMI (21-23), whereas a few studies have shown a neutral effect of BMI on angiographic CAD (24, 25). The potential mechanisms underlying the “obesity paradox” have been little studied. It has been suggested that these findings may be explained by the lack of discriminatory power of BMI to differentiate between body fat and lean mass.
In contrast to BMI, central obesity has been suggested to be more appropriate for the assessment of the obesity-related cardiovascular risk in patients with (11-13, 26) or without (2, 3, 7, 26) known CVD. The association of central obesity measures with angiographic coronary atherosclerosis in patients undergoing coronary angiography has been little studied (24).
The aim of the present study was to determine the association of central obesity with the presence of angiographic CAD in patients with suspected stable CAD. The prognostic significance of obesity measures in the prediction of CAD in addition to other cardiovascular risk factors was also assessed.
Methods and Procedures
Study population and study design
This prospective study enrolled 403 consecutive subjects with suspected stable CAD scheduled to undergo coronary angiography. The study was conducted in a single institution (Department of Cardiology, University Hospital of Ioannina) during a 6 months period. Patients were referred due to symptoms or a positive stress test. Patients with suspected or documented acute coronary syndrome, history of CAD, cerebrovascular and peripheral vascular disease, valvular heart disease, prosthetic valves, congenital heart disease, hypertrophic obstructive cardiomyopathy, as well as those on hemodialysis were excluded from the study.
In all subjects, a full medical history was taken, and a complete physical examination was performed. Cardiovascular risk factors and medications were recorded in detail, and all patients underwent coronary angiography. Blood samples were drawn from all patients early in the morning after an overnight fast and just before coronary angiography.
The study protocol was approved by the local Ethics Committee. The study complied with the Declaration of Helsinki, and all participants provided written informed consent.
Cardiovascular risk factor assessment
The cardiovascular risk factors assessed in the present study were age, gender, family history of CAD, smoking habits, hypertension, hypercholesterolemia, and diabetes mellitus. A positive family history of CAD was determined by the presence of a first degree relative with a cardiovascular event at an age <65 years for women and <55 years for men. Smokers were defined as those who were smoking at the time of enrollment or had stopped for <12 months. Hypertension was defined as systolic blood pressure (SBP) >140 mmHg and/or diastolic blood pressure (DBP) >90 mmHg during the initial examination or administration of anti-hypertensive medications. Hypercholesterolemia was defined as low-density lipoprotein cholesterol (LDL-c) >160 mg/dl or total cholesterol >240 mg/dl or administration of anti-cholesterolemic medications (statins or fibrates). Diabetes was defined as a fasting blood glucose concentration ≥126mg/dl or administration of anti-hyperglycemic medications. Creatinine clearance was calculated using the MDRD formula (27). Fasting plasma glucose, serum lipids, and creatinine were measured using standard methodology.
Framingham risk score (FRS) is a multivariate risk function that predicts 10-year risk of developing coronary events; a risk score of <10%, 10-20%, and >20% indicates low, intermediate, and high risk, respectively. The risk factors included in FRS are age, gender, smoking, blood pressure, total and high-density lipoprotein (HDL) cholesterol, and diabetes.
The anthropometric data measured were weight, height, and waist circumference (WC). The minimum WC between the pelvic brim and the costal margin was measured. Several WC measurement protocols have been used based on either bony (i.e., last rib, pelvic brim, and midpoint) or external (minimal waist, largest abdominal circumference, umbilicus, etc.) landmarks. Measuring the minimum circumference between the lowest rib and pelvic brim is a common protocol for determining WC in many published studies (28) while there seems to be no effect of the protocol used for WC measurement on the association of WC with various endpoints (28). Patients were classified in three groups according to WC measurement: group 1—normal waist (based on ATP III 2006 criteria for central obesity), that is, waist circumference <102 cm in men and <88 cm in women; group 2—moderate central obesity, that is, waist circumference ≥102 and <110 cm in men and ≥88 and <105 cm in women; and group 3—severe central obesity, that is, waist circumference ≥110 cm in men and ≥105 cm in women. Body mass index (BMI) was calculated as weight (kg) divided by the square of height (m2). Patients were characterized based on their BMI as lean (BMI<25 kg/m2), overweight (BMI ≥ 25 and BMI<30 kg/m2), and obese (BMI ≥ 30 kg/m2).
Coronary angiography was performed according to the standard Judkins technique. All coronary angiograms were visually assessed by two experienced operators, and a consensus was reached. Significant CAD was defined as ≥50% stenosis in the internal diameter of at least one coronary artery (≥30% for the left main coronary artery). To describe the extent of CAD, patients with CAD were distinguished in those with single-vessel versus multivessel disease, defined as the presence of significant CAD in ≥2 epicardial coronary arteries or left main disease.
Continuous data are presented as mean ± SD. The Kolmogorov-Smirnov Z-test was used to identify continuous variables that were not normally distributed: age, creatinine, glucose, HDL-c, triglycerides, and FRS. Unpaired student's t-test and χ2 test were used to compare continuous and categorical variables, respectively, between patients with or without angiographic CAD. The comparison of not normally distributed continuous variables between the two groups was made with the Mann-Whitney U test.
Univariate generalized linear model analysis was used to assess the associations of WC and BMI with other studied parameters in our population. Logistic regression analysis was used to assess WC- and BMI-related crude and multiple-adjusted risk for the presence of significant CAD. An intermediate adjusted model included as covariates non-metabolic parameters associated with CAD, that is, age, gender, and smoking. A fully adjusted model included as covariates all the parameters that showed associations with CAD at the level of P < 0.2 (age, gender, smoking, diabetes mellitus, DBP, glucose, creatinine, triglycerides, HDL-c, LDL-c, and statins), whereas the WC-related risk was further adjusted also for BMI. Not normally distributed continuous variables were logarithmically transformed to enter regression analysis. Logistic regression analysis was also used to assess WC- and BMI-related crude risk for the presence of multivessel disease in patients with significant CAD in the entire population as well as the presence of CAD in specific subgroups of our population (men/women, diabetics/non-diabetics, >60/≤60 years old, high risk/moderate/low risk). The area under the curve of regression models including FRS alone, FRS, and WC or BMI or their combination were calculated, and their predictive accuracy was compared using the methodology described by Hannley and McNeil (c-statistic) (29).
P values were always two-sided, and a value of P < 0.05 was considered significant. The SPSS statistical software package (version 15.0 for Windows, SPSS, Chicago, IL) was used.
Characteristics of patients with or without CAD are shown in Table 1. Significant angiographic CAD was found in 207 (51%) patients. Patients with CAD were older and proportionally more males, smokers and diabetics (P<0.05 for all) compared to patients without CAD. Framingham risk score and fasting glucose were higher, whereas HDL-c, BMI, and WC were lower in patients with CAD compared to patients without CAD (P<0.05) (Table 1).
|Patients with CAD N=207||Patients without CAD N=196||P|
|Male gender, n (%)||178 (86)||124 (63)||<0.001|
|Family history CAD, n (%)||57 (28)||52 (27)||0.73|
|Smoking, n (%)||150 (72)||102 (52)||<0.001|
|Hypertension, n (%)||170 (82)||150 (76)||0.16|
|Antihypertensive medications, n (%)|
|RAAS inhibitors||75 (36)||74 (38)||0.75|
|Calcium blockers||64 (31)||50 (26)||0.27|
|Beta blockers||123 (59)||92 (47)||0.015|
|Diuretics||27 (13)||38 (19)||0.08|
|Hypercholesterolemia, n (%)||139 (67)||143 (73)||0.24|
|Statins, n (%)||107 (51)||73 (37)||0.004|
|Diabetes mellitus, n (%)||81 (39)||55 (28)||0.023|
|Body mass index, kg/m2||27.77±3.29||29.04±3.79||<0.001|
|Waist circumference, cm||101±9||104±11||0.003|
|Systolic BP, mmHg||141±18||140±19||0.53|
|Diastolic BP, mmHg||81±10||83±11||0.19|
|GFR, ml/min/1.73 m2||78.47±14.14||77.03±12.12||0.28|
|Total cholesterol, mg/dl||211±43||219±45||0.06|
|Framingham score, %||44±23||31±21||<0.001|
Obesity measures and the presence of angiographic CAD
The characteristics of the patients in the BMI and WC subgroups are shown in Table 2. The univariate associations of WC and BMI with other studied parameters are presented in Table 3. The crude and adjusted for various risk factors (Model 1) associations of obesity measures with the presence of angiographic CAD are shown in detail in Table 4. Waist circumference was inversely associated with the presence of CAD, even after adjustment for cardiovascular risk factors [OR=0.54 per 1 SD increase (95% CIs 0.41-0.70)]. Patients in the severe central obesity (group 3) presented a lower risk for CAD compared to patients with moderate central obesity (group 2) [OR=0.47 (95% CI 0.26-0.84)] and patients with normal WC (group 1) [OR=0.28 (95% CI 0.16-0.50)]. A non-significant trend for lower risk for CAD in patients with moderate central obesity compared to patients with normal WC was found [OR=0.59 (95% CI 0.33-1.04), P=0.069]. Further adjusting for BMI did not change the independent inverse association of WC with CAD (Table 4). BMI was also inversely associated with the presence of CAD, an association that did not change essentially after adjustment for cardiovascular risk factors [OR=0.64 per 1 SD increase (95% CIs 0.50-0.81)]. In the adjusted model, obese patients had a lower risk for CAD compared to overweight [OR=0.55 (95% CIs 0.32-0.92)] and lean patients [OR=0.32 (95% CIs 0.16-0.65)], while there was not a significant difference between overweight and lean patients [OR=0.59 (95% CIs 0.31-1.13), P=0.114].
|BMI classification||Waist circumference classification|
|Lean (n=72)||Overweight (n=213)||Obese (n=118)||Group 1 (n=145)||Group 2 (n=131)||Group 3 (n=127)|
|Male gender, n (%)||56 (78)||177 (83)||70 (59)||135 (93)||85 (65)||83 (65)|
|Family history CAD, n (%)||21 (29)||59 (28)||29 (24)||36 (25)||37 (28)||36 (28)|
|Smoking, n (%)||46 (64)||138 (65)||67 (57)||106 (73)||71 (54)||74 (58)|
|Hypertension, n (%)||56 (77)||173 (81)||91 (77)||114 (78)||102 (78)||104 (82)|
|Antihypertensive medications, n (%)|
|RAAS||22 (31)||80 (38)||47 (40)||45 (31)||40 (30)||64 (50)|
|Ca blockers||25 (34)||51 (24)||38 (32)||42 (29)||35 (27)||37 (29)|
|Diuretics||8 (11)||36 (17)||21 (17)||17 (12)||15 (12)||33 (26)|
|b-blockers||44 (61)||109 (51)||63 (53)||76 (52)||74 (56)||66 (52)|
|Hypercholesterolemia, n (%)||50 (70)||155 (73)||80 (68)||99 (68)||94 (72)||92 (73)|
|Statins, n (%)||34 (47)||95 (45)||50 (43)||67 (46)||54 (41)||58 (46)|
|Diabetes mellitus, n (%)||25 (34)||69 (32)||43 (37)||51 (35)||32 (24)||53 (42)|
|Body mass index, kg/m2||23.6±1.2||27.6±1.4||32.8±2.3||25.6±2.2||28.2±2.5||31.7±3.1|
|(range 20.3-24.9)||(range 25.0-29.9)||(range 30.0-40.3)||(range 20.3-30.1)||(range 22.4-35.8)||(range 23.8-40.3)|
|Waist circumference, cm||92±8||102±7||112±8||93±6||102±5||114±6|
|♂ range (74-112)||♂ range (86-117)||♂ range (96-130)||♂ range (74-101)||♂ range (102-109)||♂ range (110-130)|
|♀ range (78-110)||♀ range (75-113)||♀ range (90-137)||♀ range (75-87)||♀ range (88-104)||♀ range (105-137)|
|Systolic blood pressure, mmHg||142±19||140±18||139±18||141±19||138±19||142±18|
|Diastolic blood pressure, mmHg||81±9||82±10||83±11||80±9||82±11||84±10|
|Clearance creatinine, ml/min||74.6±20.8||85.0±21.9||98.9±25.6||78.6±17.9||85.8±22.5||98.4±28.0|
|Total cholesterol, mg/dl||218±45||214±43||214±47||211±40||219±42||215±51|
|CAD, n (%)||40 (56)||121 (57)||46 (39)||92 (63)||66 (50)||50 (40)|
|Body mass index||Waist circumference|
|Age, years*||−0.93 (−3.03, 1.18), P=0.387||−1.10 (−7.05, 4.84), P=0.716|
|Male gender||−2.03 (−2.83, −1.23), P<0.001||−0.41 (−2.74, 1.91), P=0.726|
|Family history CAD||0.18 (−0.62, 0.99), P=0.654||0.66 (−1.61, 2.92), P=0.570|
|Smoking||−0.71 (−1.44, 0.03), P=0.058||0.63 (−1.45, 2.70), P=0.554|
|Hypertension||0.14 (−0.74, 1.03), P=0.749||2.40 (−0.08, 4.87), P=0.058|
|Hypercholesterolemia||−0.19 (−0.97, 0.59), P=0.633||0.43 (−1.77, 2.62), P=0.701|
|Diabetes Mellitus||−0.06 (−0.81, 0.70), P=0.886||1.40 (−0.72, 3.52), P=0.196|
|Systolic BP, mmHg||−0.003 (−0.02, 0.02), P=0.777||0.02 (−0.03, 0.08), P=0.459|
|Diastolic BP, mmHg||0.05 (0.01, 0.08), P=0.006||0.18 (0.08, 0.28), P<0.001|
|Glucose, mg/dl*||0.40 (−0.87, 1.67), P=0.539||2.86 (−0.67, 6.38), P=0.112|
|Creatinine, mg/dl*||−1.77 (−3.78, 0.24), P=0.083||1.66 (−4.02, 7.34), P=0.566|
|GFR, ml/min/1.73 m2||−0.02 (−0.05, 0.01), P=0.125||−0.03 (−0.11, 0.04), P=0.403|
|Total cholesterol, mg/dl||0.00 (−0.01, 0.01), P=0.935||−0.005 (−0.03, 0.02), P=0.685|
|Triglycerides, mg/dl*||0.72 (−0.07, 1.51), P=0.075||2.66 (0.45, 4.88), P=0.018|
|HDL-c, mg/dl*||−0.32 (−1.66, 1.02), P=0.637||−4.09 (−7.82, −0.37), P=0.032|
|LDL-c, mg/dl||−0.002 (−0.01, 0.01), P=0.651||−0.006 (−0.03, 0.02), P=0.638|
|Framingham score, %*||−0.53 (−1.05, −0.02), P=0.041||1.37 (−0.07, 2.82), P=0.063|
|RAAS inhibitors||0.91 (0.18, 1.64), P=0.015||3.39 (1.33, 5.44), P=0.001|
|Calcium blockers||0.21 (−0.59, 1.00), P=0.607||0.77 (−0.20, 1.74), P=0.121|
|Beta blockers||−0.58 (−1.29, 0.13), P=0.111||−1.27 (−3.28, 0.75), P=0.217|
|Diuretics||0.77 (−1.46, 3.01), P=0.497||3.25 (0.53, 5.97), P=0.019|
|Statins||−0.47 (−1.19, 0.24), P=0.195||−0.52 (−2.54, 1.50), P=0.614|
|Waist circumference associated risk|
|Group 2 vs. 1||Group 3 vs.1||Group 3 vs. 2||P||Per 1 SD increase||P|
|Crude||0.58 (0.36, 0.95)||0.38 (0.23, 0.63)||0.65 (0.40, 1.08)||0.001||0.73 (0.60, 0.90)||0.003|
|model 1 Intermediate||0.78 (0.46, 1.34)||0.46 (0.27, 0.79)||0.59 (0.34, 1.00)||0.016||0.69 (0.55, 0.87)||0.002|
|model 2 Full||0.59 (0.33, 1.04)||0.28 (0.16, 0.50)||0.47 (0.26, 0.84)||<0.001||0.54 (0.41, 0.70)||<0.001|
|model 2 + BMI||0.61 (0.34, 1.09)||0.27 (0.15, 0.48)||0.44 (0.25, 0.71)||<0.001||0.53 (0.41, 0.70)||<0.001|
|Body mass index associated risk|
|Overweight vs lean||Obese vs lean||Obese vs overweight||P||per 1 SD increase||P|
|Crude||1.04 (0.60, 1.80)||0.51 (0.28, 0.93)||0.49 (0.31, 0.78)||0.008||0.69 (0.56, 0.85)||0.001|
|model 1 Intermediate||0.98 (0.54, 1.75)||0.64 (0.34, 1.24)||0.66 (0.40, 1.10)||0.235||0.78 (0.62, 0.98)||0.033|
|model 2 Full||0.59 (0.31, 1.13)||0.32 (0.16, 0.65)||0.55 (0.32, 0.92)||0.005||0.64 (0.50, 0.81)||<0.001|
In the intermediated adjusted model, a significant inverse association of CAD with WC was shown [OR=0.69 per 1 SD increase (95% CIs 0.55-0.87)]; this association was a lot weaker with BMI [OR=0.78 per 1 SD increase (95% CIs 0.62-0.98)].
In patients with CAD, neither BMI [OR per 1 SD 1.14 (95% CI 0.83-1.55), P = 0.418] nor WC [OR per 1 SD increase 1.16 (95% CI 0.85-1.58), P = 0.347] were significantly associated with multivessel CAD.
In univariate analysis, BMI and WC were significantly inversely associated with the presence of CAD only in patients >60 years old, males, non-diabetics, and in patients with high FRS (> 20%) (Table 5).
|Age >60 years old (N=221)||Age ≤60 years old (N=182)|
|WC per 1 SD||OR 0.60 (0.44, 0.82), P<0.001||OR 0.87 (0.65, 1.16), P=0.333|
|BMI per 1 SD||OR 0.57 (0.42, 0.77), P<0.001||OR 0.84 (0.63, 1.12), P=0.233|
|Males (N=302)||Females (N=101)|
|WC per 1 SD||OR 0.65 (0.50, 0.84), P=0.001||OR 0.91 (0.62, 1.34), P=0.639|
|BMI per 1 SD||OR 0.65 (0.49, 0.85), P=0.002||OR 1.06 (0.73, 1.53), P=0.777|
|Non Diabetics (N=267)||Diabetics (N=136)|
|WC per 1 SD||OR 0.68 (0.53, 0.89), P=0.004||OR 0.78 (0.55, 1.10), P=0.158|
|BMI per 1 SD||OR 0.67 (0.51, 0.87), P=0.002||OR 0.74 (0.52, 1.04), P=0.09|
|High risk (FRS >20 %) (N=305)||Low/Intermediate risk (N=98)|
|WC per 1 SD||OR 0.60 (0.46, 0.78), P<0.001||OR 0.96 (0.64, 1.43), P=0.824|
|BMI per 1 SD||OR 0.62 (0.48, 0.81), P<0.001||OR 1.06 (0.71, 1.57), P=0.791|
Prediction of significant angiographic CAD
In ROC analysis for prediction of significant angiographic CAD, the AUC for FRS was 0.674 (standard error 0.027, P < 0.001). Logistic regression models that included FRS + BMI (AUC 0.698, standard error 0.026, P < 0.001) and FRS + WC (AUC 0.713, standard error 0.026, P < 0.001) significantly improved the predictive accuracy of the FRS model (P = 0.03 and P = 0.006 for BMI and WC models, respectively). The AUC for the FRS + BMI and FRS + WC models did not differ significantly (P = 0.08). A model including FRS and both BMI and WC yielded the same AUC as for the model including FRS + WC (AUC 0.713, standard error 0.026, P < 0.001).
The findings of this study indicated that increased WC and BMI may be related to a lower incidence of significant angiographic CAD in patients with suspected stable CAD. A significant decrease in the rates of angiographic CAD in the group of patients with the greatest WC values compared to normal or moderately elevated WC was found. These associations were observed despite the fact that patients with increased WC had higher DBP and triglycerides and lower HDL-c and remained significant after adjustment for these risk factors in multivariate analysis. Furthermore, patients with BMI ≥30 kg/m2 were less likely to have significant CAD compared to lean or overweight patients. The fact that patients with increased BMI were proportionally more females and less smokers did not account for the inverse association of BMI with CAD in multivariate analysis. In addition, we found that the paradoxical “protective” effect of obesity on angiographic coronary atherosclerosis was apparent only in specific subgroups of patients with suspected stable CAD. We also showed that there was no significant relation between obesity measures and the extent of angiographic CAD (single vs. 2 or 3 vessel disease).
Large epidemiological studies of men and women without known CVD have consistently related obesity with an increased risk for cardiovascular events (3-8). Nevertheless, among patients with known CAD (9-14), heart failure (15-17) or multiple risk factors (30), and post coronary angioplasty (18-20), a paradoxically improved prognosis has been described in obese compared to lean patients (obesity paradox). Previous studies have reported conflicting results regarding the association of BMI with the occurrence and severity of angiographic CAD. In patients referred for angiography with suspected CAD or acute coronary syndromes, BMI was inversely associated with the presence and severity of CAD (21-23); in one study, this association was lost after adjustment for risk factors (22). On the other hand, a few studies have reported that there was no significant association between BMI and the presence or extent of angiographic CAD in patients at risk for CAD (24, 25).
Central obesity measures have been suggested to be more appropriate for the assessment of obesity-related cardiovascular risk (2, 3, 7, 11-13, 26) since visceral adipose tissue is considered to play a major role in insulin resistance and dyslipidemia and in the induction of prothrombotic and chronic inflammatory states that contribute to atherosclerotic progression (31). Currently, we showed that central obesity, as assessed by increased WC, was a negative predictor of angiographic CAD. Furthermore, this association appeared to be “independent” from the association observed between BMI and CAD. This is the first study to report a paradoxical protective role of increased WC (central obesity paradox) for the presence of significant angiographic CAD; in a recent study, increased WC (compared to normal waist) was found to be an independent predictor of improved outcome in patients with advanced heart failure (32). A small study has previously reported a lack of independent association of central obesity, assessed by increased waist to hip ratio, with the presence and extent of CAD (24) in patients undergoing coronary angiography. Several studies have previously associated central obesity measures, such as visceral adipose tissue area or WC with the presence and extent of non-calcified coronary atherosclerotic plaques in CT angiography (33-35). The conflicting results probably underlie the impact of different methods used to assess central obesity as well as coronary atherosclerosis on the association observed between central obesity measures and CAD prevalence.
Waist circumference has been reported to be closely related both to visceral and subcutaneous adipose tissue (34, 36), suggesting thus that the paradoxical protective effect of central obesity on CAD prevalence observed in our study could be linked to subcutaneous abdominal fat and not to visceral fat based on WC measurement only. The results of our study demonstrated that the independent association of WC with CAD after adjustment for metabolic factors related to visceral obesity (fully adjusted model) remained significant but weaker after adjustment only for non-metabolic factors (intermediate model). This finding may indicate that the subcutaneous fat component is probably mainly responsible for the paradoxical protective effect of central obesity observed, whereas the visceral fat component may have an opposing effect and be related to a small increase in risk for angiographic CAD.
The paradoxical effect of obesity in our study was mainly evident in patients at high cardiovascular risk (as indicated by FRS >20%) and males; in women and patients at intermediate/low risk, there was no association of obesity measures with CAD (ORs close to 1.00). It should be noted that BMI has been previously inversely associated with mortality in patients with high CAD risk (30). In older and non-diabetic patients, obesity measures showed an inverse association with the presence of CAD, similar to the association of the entire population, while in younger patients (<60 years old) and patients with diabetes, no association was found. Further larger studies are needed to elucidate the different pattern of association between obesity measures and angiographic CAD in various subgroups of patients.
The reason for the paradoxical relation of obesity with angiographic CAD is not well understood. This has been previously attributed to the fact that among patients referred for angiography, obesity was reported to be associated with younger age, indicating that obese patients were probably referred for angiography at an earlier stage of the atherosclerotic disease (22). The present study showed no significant association between obesity measures and the age of patients. Furthermore, it has been suggested that obese patients are more likely to be referred for angiography more liberally by their physicians probably due to the greater risk factor burden or more pronounced symptoms and clinical disability reported by obese patients (21, 22). In our study, although an “early referral” bias cannot be reliably excluded as there were no relevant available data, this does not seem likely as our population appears to have many similarities to the population referred for coronary angiography due to suspected stable CAD in large registries (37). Finally, a positive correlation between obesity and coronary atherosclerosis, as assessed by intravascular coronary ultrasound, has been demonstrated in the absence of significant vascular stenoses in angiography. This finding which was attributed to greater positive remodeling of coronary arteries resulting in larger vessel diameter in obese patients (38) was not investigated in our study.
In the present study, we showed that WC and BMI were both negative independent predictors of the presence of angiographic CAD in patients with suspected stable CAD and, when added to the information provided by the FRS, could improve the prediction of angiographic CAD. The logistic regression prediction model that included WC had a slightly better predictive accuracy, although not significant, compared to the model that included BMI as an independent variable. A model including both WC and BMI on top of FRS did not seem to offer a greater benefit in prediction of angiographic CAD than using WC alone in addition to FRS. Although FRS is used for the prediction of 10-year risk of having a coronary event, it has been previously shown that it can also predict the presence of angiographic CAD with accuracy similar to our findings (39, 40).
This was an observational study that could reveal risk associations but could not establish causal relationships or explore potential mechanisms. The findings of the study cannot exclude the hypothesis that the inverse association of central obesity assessed by WC may be primarily due to subcutaneous and not to visceral fat. Further studies using other indices of visceral adiposity should be performed to clarify this. Moreover, this was a single center study; however, consecutive patients were studied in order to minimize patient selection bias. Other confounding parameters, such as socioeconomic factors, physical activity as well as factors related to the decision made by the physicians who referred patients for angiography, might have affected our results and warrant further investigation in larger studies.
In conclusion, both central and overall obesity, as assessed by WC and BMI, were related to a reduced incidence of significant angiographic CAD in patients with suspected stable CAD even after adjustment for multiple cardiovascular risk factors lending further credence to the existence of “obesity paradox”. The paradoxical protective role of obesity was prominent in specific subgroups of patients: >60 years old, males, non-diabetics, and patients with high FRS. Both measures of obesity (WC and BMI) improved risk discrimination for angiographic CAD when added to an established risk score, such as FRS. Further larger studies are needed to confirm and expand these findings and investigate the underlying mechanisms.