Ondrej Sochor

The Concept of Normal Weight Obesity

Individuals with normal body weight by body mass index (BMI) and high body fat percentage show a high degree of metabolic dysregulation. This phenomenon, defined as normal weight obesity, is associated with a significantly higher risk of developing metabolic syndrome, cardiometabolic dysfunction and with higher mortality. Recently, we have also shown that coronary artery disease patients with normal BMI and central obesity have the highest mortality risk as compared to other adiposity patterns. Therefore, it is important to recognize these high-risk groups for better adiposity-based risk stratification. There is a need for an updated definition of obesity based on adiposity, not on body weight.

Diagnostic performance of body mass index to identify obesity as defined by body adiposity in children and adolescents: A systematic review and meta-analysis

The ideal means of identifying obesity in children and adolescents has not been determined although body mass index (BMI) is the most widely used screening tool.

Normal-Weight Central Obesity: Implications for Total and Cardiovascular Mortality

Context It is uncertain whether measures of central obesity, such as waist-to-hip ratio (WHR), provide additional information beyond body mass index (BMI) in defining mortality risks associated with obesity. Contribution This population-based cohort study found that normal BMI and central obesity (defined by WHR) were associated with the worst long-term survival compared with individuals with normal fat distribution regardless of BMI category. Caution Information on body fat distribution was based on anthropometric indicators alone. Implication Normal weight with central obesity may represent an important group for targeted lifestyle modifications and other preventive strategies. Obesity defined by body mass index (BMI) or measures of central obesity, such as waist-to-hip ratio (WHR) and waist circumference, is associated with increased total and cardiovascular mortality (13). However, a recent meta-analysis (4) showed that being overweight according to BMI was actually associated with lower total mortality, challenging the paradigm that BMI is linked to increased mortality. Further, whether measures of fat distribution provide any incremental risk information beyond BMI alone has been a major source of controversy (2, 3, 58). Indeed, the 2013 American Heart Association/American College of Cardiology/The Obesity Society guideline for the management of obesity (9) does not recommend measuring WHR and assumes that persons with normal BMI are not exposed to any obesity-related cardiovascular risk in view of the limited available data proving otherwise. A recent large study (6) showed that incorporating waist circumference information in prediction models did not increase the prognostic value already provided by BMI; however, for a given BMI category, subgroups of waist circumference or WHR were also associated with increased mortality risk. Other studies showed that measures of central obesity, such as WHR, waist-to-height ratio (10, 11), or waist circumference alone, may provide additional information beyond BMI on mortality risk among middle-aged adults, provided there is no adjustment for obesity-related cardiovascular risk factors (2, 3, 7). Further, a study (12) has shown that measures of central obesity are more strongly associated with total and cardiovascular disease mortality rates than BMI. Another study (13) showed that persons with normal body weight measured by BMI, but who had increased body fat measured by bioimpedance, have higher total and cardiovascular mortality rates and a higher prevalence of metabolic syndrome and its components than those with normal BMI and body fat content. In addition, a recent meta-analysis of individual-patient data in those with coronary artery disease (12) has shown that persons with normal BMI who are in the top tertile of central obesity measures had the highest total mortality rate. These results have been attributed to several factors. First, the diagnostic accuracy of BMI for obesity is not optimal, especially in persons with greater body fat percentages and normal or intermediate BMI (14). Second, those with normal body weight and higher body fat have less muscle mass, which is a factor associated with higher mortality risk and metabolic dysregulation (15, 16). Third, animal and human studies (17) have recently shown that adipose tissue in the legs and buttocks may have a favorable effect on glucose metabolism. Finally, persons with a central distribution of fat, particularly when measured with WHR, have less adipose tissue in the lower extremities (17). To our knowledge, no studies in the general U.S. population have specifically focused on assessing the mortality risk in persons with normal BMI and central obesity compared with those who are overweight or obese according to BMI. Thus, we hypothesized that persons with normal BMI and central obesity would have greater mortality risk than those who have any other combination of BMI and central obesity. We investigated the total and cardiovascular mortality risks associated with different patterns of body adiposity in a large cohort of participants in NHANES III (Third National Health and Nutrition Examination Survey) (1988 to 1994). Because hip circumference was not measured in the most recent National Health and Nutrition Examination Surveys (conducted yearly from 1999 to 2010) and WHR data to define central obesity were crucial to our primary hypothesis, we have used data from NHANES III instead. Methods Study Design and Participants NHANES III is a cross-sectional survey that produces generalizable health estimates for the U.S. population using a stratified, multistage, probability sampling design. From a sample of 39695 persons, 33994 were interviewed and 30818 were examined at mobile examination centers. The examination consisted of extensive anthropometric, physiologic, and laboratory testing. Waist and hip circumferences were measured by a trained examiner with a measuring tape positioned at the high point of the iliac crest for the waist and at the greatest circumference of the buttocks. The design and methods for the survey are available elsewhere (18). In our study, 16124 adults aged 18 years or older had WHRs available. Because extremely thin persons and those with a history of nonskin cancer have a higher mortality risk, we further restricted our analysis to persons with a BMI greater than 18.5 kg/m2 and those without a history of nonskin cancer. The resulting sample was 15184 participants (7249 men and 7935 women). Total and Cardiovascular Mortality Assessment Identifier data were matched to the National Death Index to determine mortality status, with mortality follow-up from the date of the NHANES survey through 31 December 2006. A complete description of the methodology to link baseline NHANES III data to the National Death Index can be found elsewhere (19). International Classification of Diseases, Ninth Revision (ICD-9), codes from 1986 to 1998 and International Classification of Diseases, 10th Revision (ICD-10), codes from 1999 to 2000 were used to ascertain the underlying cause of death. Cardiovascular deaths were defined as those with ICD-9 codes 390 to 398, 402, and 404 to 429 and ICD-10 codes I00 to I09, I11, I13, and I20 to I51 (NHANES III codes 53 to 75). Statistical Analysis The overarching analytic goal was to estimate the influence of various magnitudes of central obesity and BMI on total mortality. To do this, we conducted weighted survival modeling that enabled the estimation of the relative risk for mortality, quantified by the hazard ratio (HR), and the expected survival for NHANES III participants. The NHANES III survey design and sampling weights were incorporated into the statistical analysis to calculate weighted means, SEs for continuous variables, and weighted percentages for categorical variables. To determine the total mortality associated with the different patterns of adiposity, we created multivariable Cox proportional hazards models and adjusted for potential confounders previously shown to be associated with obesity and mortality (4). These variables were age at examination, sex, education level, and smoking history. Although we considered adjusting for obesity-related cardiovascular risk factors in the models, we did not adjust our final estimates for these factors. Epidemiologic obesity research has shown that it might be inappropriate to control for factors in the causal pathway between obesity and death, such as diabetes, atherogenic dyslipidemia, and hypertension. In testing for differences in mortality risk for combinations of central obesity (defined by WHR) and BMI, we considered higher-order interactions of WHR with BMI and other covariates. The association pattern of WHR and BMI was found to be different for men versus women (that is, statistically significant higher-order interaction terms), so the final modeling was conducted using sex-stratified data to more clearly present the findings. The interaction terms allowed the potential quadratic risks (U-shaped risks) of values for these variables. The estimated model contains polynomial functions of BMI and WHR (for example, BMI2 and BMI2WHR2). These terms allow for relationships that are more sensitive to change in risk for mortality based on unique combinations (profiles) of WHR and BMI. The estimated risk function, when other covariates in the model are controlled for, will resemble a saddle with high- and low-risk areas. When possible, covariates were the grand mean centered to lessen the collinearity induced from quadratic effects. Likelihood ratio tests were used to determine whether these higher-order model terms could be removed. The proportional hazards assumption for all variables was assessed and satisfied for the final models. After these models were established, we estimated HRs with estimated model variables for different combinations of WHR and BMI stratified by sex. For these comparisons, we chose a BMI of 22 kg/m2 to represent persons with normal BMI, 27.5 kg/m2 to represent overweight persons, and 33 kg/m2 to represent obese persons. For WHR, we chose 0.89 and 1.00 for men and 0.80 and 0.92 for women as a measure of central obesity. Each of these sets of values were chosen either to reflect the approximate midpoint of standard clinical interpretationsto avoid issues with values at common thresholds (for example, BMI values at 30 kg/m2)or to be clinical targets we sought to better understand. Wald-based (or large sample-based) HR estimates and their SEs were assessed to provide significance tests among these representative patient profiles (20). Once the fitted Cox model was deemed satisfactory, we sought to estimate measures of absolute risk by means-adjusted 5- and 10-year survival estimates (21). In this analysis, we replicated observations to standardize (reweight) them and ensure balance across sex, age, WHR, and BMI categories. The expected survival was computed as a weighted estimate

Normal-weight central obesity

Context It is uncertain whether measures of central obesity, such as waist-to-hip ratio (WHR), provide additional information beyond body mass index (BMI) in defining mortality risks associated with obesity. Contribution This population-based cohort study found that normal BMI and central obesity (defined by WHR) were associated with the worst long-term survival compared with individuals with normal fat distribution regardless of BMI category. Caution Information on body fat distribution was based on anthropometric indicators alone. Implication Normal weight with central obesity may represent an important group for targeted lifestyle modifications and other preventive strategies. Obesity defined by body mass index (BMI) or measures of central obesity, such as waist-to-hip ratio (WHR) and waist circumference, is associated with increased total and cardiovascular mortality (13). However, a recent meta-analysis (4) showed that being overweight according to BMI was actually associated with lower total mortality, challenging the paradigm that BMI is linked to increased mortality. Further, whether measures of fat distribution provide any incremental risk information beyond BMI alone has been a major source of controversy (2, 3, 58). Indeed, the 2013 American Heart Association/American College of Cardiology/The Obesity Society guideline for the management of obesity (9) does not recommend measuring WHR and assumes that persons with normal BMI are not exposed to any obesity-related cardiovascular risk in view of the limited available data proving otherwise. A recent large study (6) showed that incorporating waist circumference information in prediction models did not increase the prognostic value already provided by BMI; however, for a given BMI category, subgroups of waist circumference or WHR were also associated with increased mortality risk. Other studies showed that measures of central obesity, such as WHR, waist-to-height ratio (10, 11), or waist circumference alone, may provide additional information beyond BMI on mortality risk among middle-aged adults, provided there is no adjustment for obesity-related cardiovascular risk factors (2, 3, 7). Further, a study (12) has shown that measures of central obesity are more strongly associated with total and cardiovascular disease mortality rates than BMI. Another study (13) showed that persons with normal body weight measured by BMI, but who had increased body fat measured by bioimpedance, have higher total and cardiovascular mortality rates and a higher prevalence of metabolic syndrome and its components than those with normal BMI and body fat content. In addition, a recent meta-analysis of individual-patient data in those with coronary artery disease (12) has shown that persons with normal BMI who are in the top tertile of central obesity measures had the highest total mortality rate. These results have been attributed to several factors. First, the diagnostic accuracy of BMI for obesity is not optimal, especially in persons with greater body fat percentages and normal or intermediate BMI (14). Second, those with normal body weight and higher body fat have less muscle mass, which is a factor associated with higher mortality risk and metabolic dysregulation (15, 16). Third, animal and human studies (17) have recently shown that adipose tissue in the legs and buttocks may have a favorable effect on glucose metabolism. Finally, persons with a central distribution of fat, particularly when measured with WHR, have less adipose tissue in the lower extremities (17). To our knowledge, no studies in the general U.S. population have specifically focused on assessing the mortality risk in persons with normal BMI and central obesity compared with those who are overweight or obese according to BMI. Thus, we hypothesized that persons with normal BMI and central obesity would have greater mortality risk than those who have any other combination of BMI and central obesity. We investigated the total and cardiovascular mortality risks associated with different patterns of body adiposity in a large cohort of participants in NHANES III (Third National Health and Nutrition Examination Survey) (1988 to 1994). Because hip circumference was not measured in the most recent National Health and Nutrition Examination Surveys (conducted yearly from 1999 to 2010) and WHR data to define central obesity were crucial to our primary hypothesis, we have used data from NHANES III instead. Methods Study Design and Participants NHANES III is a cross-sectional survey that produces generalizable health estimates for the U.S. population using a stratified, multistage, probability sampling design. From a sample of 39695 persons, 33994 were interviewed and 30818 were examined at mobile examination centers. The examination consisted of extensive anthropometric, physiologic, and laboratory testing. Waist and hip circumferences were measured by a trained examiner with a measuring tape positioned at the high point of the iliac crest for the waist and at the greatest circumference of the buttocks. The design and methods for the survey are available elsewhere (18). In our study, 16124 adults aged 18 years or older had WHRs available. Because extremely thin persons and those with a history of nonskin cancer have a higher mortality risk, we further restricted our analysis to persons with a BMI greater than 18.5 kg/m2 and those without a history of nonskin cancer. The resulting sample was 15184 participants (7249 men and 7935 women). Total and Cardiovascular Mortality Assessment Identifier data were matched to the National Death Index to determine mortality status, with mortality follow-up from the date of the NHANES survey through 31 December 2006. A complete description of the methodology to link baseline NHANES III data to the National Death Index can be found elsewhere (19). International Classification of Diseases, Ninth Revision (ICD-9), codes from 1986 to 1998 and International Classification of Diseases, 10th Revision (ICD-10), codes from 1999 to 2000 were used to ascertain the underlying cause of death. Cardiovascular deaths were defined as those with ICD-9 codes 390 to 398, 402, and 404 to 429 and ICD-10 codes I00 to I09, I11, I13, and I20 to I51 (NHANES III codes 53 to 75). Statistical Analysis The overarching analytic goal was to estimate the influence of various magnitudes of central obesity and BMI on total mortality. To do this, we conducted weighted survival modeling that enabled the estimation of the relative risk for mortality, quantified by the hazard ratio (HR), and the expected survival for NHANES III participants. The NHANES III survey design and sampling weights were incorporated into the statistical analysis to calculate weighted means, SEs for continuous variables, and weighted percentages for categorical variables. To determine the total mortality associated with the different patterns of adiposity, we created multivariable Cox proportional hazards models and adjusted for potential confounders previously shown to be associated with obesity and mortality (4). These variables were age at examination, sex, education level, and smoking history. Although we considered adjusting for obesity-related cardiovascular risk factors in the models, we did not adjust our final estimates for these factors. Epidemiologic obesity research has shown that it might be inappropriate to control for factors in the causal pathway between obesity and death, such as diabetes, atherogenic dyslipidemia, and hypertension. In testing for differences in mortality risk for combinations of central obesity (defined by WHR) and BMI, we considered higher-order interactions of WHR with BMI and other covariates. The association pattern of WHR and BMI was found to be different for men versus women (that is, statistically significant higher-order interaction terms), so the final modeling was conducted using sex-stratified data to more clearly present the findings. The interaction terms allowed the potential quadratic risks (U-shaped risks) of values for these variables. The estimated model contains polynomial functions of BMI and WHR (for example, BMI2 and BMI2WHR2). These terms allow for relationships that are more sensitive to change in risk for mortality based on unique combinations (profiles) of WHR and BMI. The estimated risk function, when other covariates in the model are controlled for, will resemble a saddle with high- and low-risk areas. When possible, covariates were the grand mean centered to lessen the collinearity induced from quadratic effects. Likelihood ratio tests were used to determine whether these higher-order model terms could be removed. The proportional hazards assumption for all variables was assessed and satisfied for the final models. After these models were established, we estimated HRs with estimated model variables for different combinations of WHR and BMI stratified by sex. For these comparisons, we chose a BMI of 22 kg/m2 to represent persons with normal BMI, 27.5 kg/m2 to represent overweight persons, and 33 kg/m2 to represent obese persons. For WHR, we chose 0.89 and 1.00 for men and 0.80 and 0.92 for women as a measure of central obesity. Each of these sets of values were chosen either to reflect the approximate midpoint of standard clinical interpretationsto avoid issues with values at common thresholds (for example, BMI values at 30 kg/m2)or to be clinical targets we sought to better understand. Wald-based (or large sample-based) HR estimates and their SEs were assessed to provide significance tests among these representative patient profiles (20). Once the fitted Cox model was deemed satisfactory, we sought to estimate measures of absolute risk by means-adjusted 5- and 10-year survival estimates (21). In this analysis, we replicated observations to standardize (reweight) them and ensure balance across sex, age, WHR, and BMI categories. The expected survival was computed as a weighted estimate

Cardiovascular mortality in Hispanics compared to non-Hispanic whites: A systematic review and meta-analysis of the Hispanic paradox

BACKGROUND Hispanics, the largest minority in the U.S., have a higher prevalence of several cardiovascular (CV) risk factors than non-Hispanic whites (NHW). However, some studies have shown a paradoxical lower rate of CV events among Hispanics than NHW. OBJECTIVE To perform a systematic review and a meta-analysis of cohort studies comparing CV mortality and all-cause mortality between Hispanic and NHW populations in the U.S. METHODS We searched EMBASE, MEDLINE, Web of Science, and Scopus databases from 1950 through May 2013, using terms related to Hispanic ethnicity, CV diseases and cohort studies. We pooled risk estimates using the least and most adjusted models of each publication. RESULTS We found 341 publications of which 17 fulfilled the inclusion criteria; data represent 22,340,554 Hispanics and 88,824,618 NHW, collected from 1950 to 2009. Twelve of the studies stratified the analysis by gender, and one study stratified people by place of birth (e.g. U.S.-born, Mexican-born, and Central/South American-born). There was a statistically significant association between Hispanic ethnicity and lower CV mortality (OR 0.67; 95% CI, 0.57-0.78; p<0.001), and lower all-cause mortality (0.72; 95% CI, 0.63-0.82; p<0.001). A subanalysis including only studies that reported prevalence of CV risk factors found similar results. OR for CV mortality among Hispanics was 0.49; 95% CI 0.30-0.80; p-value <0.01; and OR for all-cause mortality was 0.66; 95% CI 0.43-1.02; p-value 0.06. CONCLUSION These results confirm the existence of a Hispanic paradox regarding CV mortality. Further studies are needed to identify the mechanisms mediating this protective CV effect in Hispanics.

Neuropsychiatric symptoms, APOE ε4, and the risk of incident dementia: A population-based study

Objective:To investigate the population-based interaction between a biological variable (APOE &egr;4), neuropsychiatric symptoms, and the risk of incident dementia among subjects with prevalent mild cognitive impairment (MCI). Methods:We prospectively followed 332 participants with prevalent MCI (aged 70 years and older) enrolled in the Mayo Clinic Study of Aging for a median of 3 years. The diagnoses of MCI and dementia were made by an expert consensus panel based on published criteria, after reviewing neurologic, cognitive, and other pertinent data. Neuropsychiatric symptoms were determined at baseline using the Neuropsychiatric Inventory Questionnaire. We used Cox proportional hazards models, with age as a time scale, to calculate hazard ratios (HRs) and 95% confidence intervals (CIs). Models were adjusted for sex, education, and medical comorbidity. Results:Baseline agitation, nighttime behaviors, depression, and apathy significantly increased the risk of incident dementia. We observed additive interactions between APOE &egr;4 and depression (joint effect HR = 2.21; 95% CI = 1.24–3.91; test for additive interaction, p < 0.001); and between APOE &egr;4 and apathy (joint effect HR = 1.93; 95% CI = 0.93–3.98; test for additive interaction, p = 0.031). Anxiety, irritability, and appetite/eating were not associated with increased risk of incident dementia. Conclusions:Among prevalent MCI cases, baseline agitation, nighttime behaviors, depression, and apathy elevated the risk of incident dementia. There was a synergistic interaction between depression or apathy and APOE &egr;4 in further elevating the risk of incident dementia.Objective: To investigate the population-based interaction between a biological variable (APOE ε4), neuropsychiatric symptoms, and the risk of incident dementia among subjects with prevalent mild cognitive impairment (MCI). Methods: We prospectively followed 332 participants with prevalent MCI (aged 70 years and older) enrolled in the Mayo Clinic Study of Aging for a median of 3 years. The diagnoses of MCI and dementia were made by an expert consensus panel based on published criteria, after reviewing neurologic, cognitive, and other pertinent data. Neuropsychiatric symptoms were determined at baseline using the Neuropsychiatric Inventory Questionnaire. We used Cox proportional hazards models, with age as a time scale, to calculate hazard ratios (HRs) and 95% confidence intervals (CIs). Models were adjusted for sex, education, and medical comorbidity. Results: Baseline agitation, nighttime behaviors, depression, and apathy significantly increased the risk of incident dementia. We observed additive interactions between APOE ε4 and depression (joint effect HR = 2.21; 95% CI = 1.24–3.91; test for additive interaction, p < 0.001); and between APOE ε4 and apathy (joint effect HR = 1.93; 95% CI = 0.93–3.98; test for additive interaction, p = 0.031). Anxiety, irritability, and appetite/eating were not associated with increased risk of incident dementia. Conclusions: Among prevalent MCI cases, baseline agitation, nighttime behaviors, depression, and apathy elevated the risk of incident dementia. There was a synergistic interaction between depression or apathy and APOE ε4 in further elevating the risk of incident dementia.

Increased Cardio-ankle Vascular Index in Hyperlipidemic Patients without Diabetes or Hypertension

AIM The cardio-ankle vascular index (CAVI) is a sensitive non-invasive marker of arterial stiffness and atherosclerosis. The aim of this work was to compare the CAVI values in patients with dyslipidemia (without diabetes mellitus and hypertension) and healthy controls. METHODS A Total 248 subjects with dyslipidemia (104 men, 144 women), 55.0 (95% CI 30-70) years of age with combined hyperlipidemia or primary hypercholesterolemia and 537 healthy controls (244 men, 293 women) 40.0 (95% CI 26-62) years of age were included in this study. Fasting blood samples were collected to measure the serum total cholesterol, triglyceride, HDL-cholesterol and apolipoprotein A1 and B levels. The LDL cholesterol level was also calculated, and the CAVI was measured using the VaSera(®) 1500 system. RESULTS The CAVI values were significantly higher in the dyslipidemic patients (8.08, 95% CI 6.00-10.05) than in the controls (7.11, 95% CI 5.77-9.05; p < 0.01). In addition, the CAVI values were elevated in both subgroups of patients with hypercholesterolemia (7.95, 95% CI 5.85-6.90; p < 0.01) and combined hyperlipidemia (8.30, 95% CI 6.60-10.15; p < 0.01) in comparison with those observed in the controls. After adopting the propensity score method in order to balance the confounding factors (age, gender, body mass index) and adjust the analysis for diastolic blood pressure, the CAVI values in the dyslipidemic patients remained significantly high (7.78, 95% CI 5.80-9.69) compared to that observed in the controls (7.31, 95% CI 5.44-9.35; p < 0.001). However, the CAVI values did not differ significantly between the controls and both subgroups of dyslipidemic patients(primary hypercholesterolemia, combined hyperlipidemia). CONCLUSIONS The present findings demonstrated that dyslipidemia increases the CAVI values in comparison to that seen in healthy subjects.

Normal-Weight Obesity: Implications for Cardiovascular Health

We sought to review the epidemiological features and clinical implications of normal-weight obesity. The concept of normal-weight obesity has been recently reported as an important risk factor for cardiovascular disease, metabolic dysregulation, and poor functional outcomes. However, in clinical practice, normal-weight obesity is not commonly recognized. In this review, we examine the clinical significance and important epidemiological outcomes of normal-weight obesity and describe other variants of adiposity and adiposity-related metabolic status. The incorporation of measures of body fat content and distribution in the clinical setting could allow more accurate identification of adiposity-related long-term risk. This could in turn lead to early lifestyle changes and behavioral modifications that are essential to the treatment of obesity.

Mechanisms of Adverse Cardiometabolic Consequences of Obesity

Obesity is an epidemic that threatens the health of millions of people worldwide and is a major risk factor for cardiovascular diseases, hypertension, diabetes, and dyslipidemia. There are multiple and complex mechanisms to explain how obesity can cause cardiovascular disease. In recent years, studies have shown some limitations in the way we currently define obesity and assess adiposity. This review focuses on the mechanisms involved in the cardiometabolic consequences of obesity and on the relationship between obesity and cardiovascular comorbidities, and provides a brief review of the latest studies focused on normal weight obesity and the obesity paradox.

Trends and Predictors of Smoking Cessation After Percutaneous Coronary Intervention (from Olmsted County, Minnesota, 1999 to 2010)

Smoke-free ordinance implementation and advances in smoking cessation (SC) treatment have occurred in the past decade; however, little is known about their impact on SC in patients with coronary artery disease. We conducted a retrospective cohort study of 2,306 consecutive patients from Olmsted County, Minnesota, who underwent their first percutaneous coronary intervention (PCI) from 1999 to 2009, and assessed the trends and predictors of SC after PCI. Smoking status was ascertained by structured telephone survey 6 and 12 months after PCI (ending in 2010). The prevalence of smoking in patients who underwent PCI increased nonsignificantly from 20% in 1999 to 2001 to 24% in 2007 to 2009 (p = 0.14), whereas SC at 6 months after PCI decreased nonsignificantly from 50% (1999 to 2001) to 49% (2007 to 2009), p = 0.82. The 12-month quit rate did not change significantly (48% in 1999 to 2001 vs 56% in 2007 to 2009, p = 0.38), even during the time periods after the enactment of smoke-free policies. The strongest predictor of SC at 6 months after PCI was participation in cardiac rehabilitation (odds ratio [OR] 3.17, 95% confidence interval [CI] 2.05 to 4.91, p <0.001), older age (OR 1.42 per decade, 95% CI 1.16 to 1.73, p <0.001), and concurrent myocardial infarction at the time of PCI (OR 1.77, 95% CI 1.18 to 2.65, p = 0.006). One-year mortality was lower in the group of smokers compared with never smokers (3% vs 7%, p <0.001). In conclusion, SC rates have not improved after PCI over the past decade in our cohort, despite the presence of smoke-free ordinances and improved treatment strategies. Improvements in delivery of systematic services aimed at promoting SC (such as cardiac rehabilitation) should be part of future efforts to improve SC rates after PCI.