Juan P. Rodriguez-Escudero

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

Normal weight obesity and mortality in United States subjects ≥60 years of age (from the third national health and nutrition examination survey)

Current body mass index (BMI) strata likely misrepresent the accuracy of true adiposity in older adults. Subjects with normal BMI with elevated body fat may metabolically have higher cardiovascular and overall mortality than previously suspected. We identified 4,489 subjects aged ≥60 years (BMI = 18.5 to 25 kg/m(2)) with anthropometric and bioelectrical impedance measurements from the National Health and Nutrition Examination Surveys III (1988 to 1994) and mortality data linked to the National Death Index. Normal weight obesity (NWO) was classified in 2 ways: creation of tertiles with highest percentage of body fat and body fat percent cutoffs (men >25% and women >35%). We compared overall and cardiovascular mortality rates, models adjusted for age, gender, smoking, race, diabetes, and BMI. The final sample included 1,528 subjects, mean age was 70 years, median (interquartile range) follow-up was 12.9 years (range 7.5 to 15.3) with 902 deaths (46.5% cardiovascular). Prevalence of NWO was 27.9% and 21.4% in men and 20.4% and 31.3% in women using tertiles and cutoffs, respectively. Subjects with NWO had higher rates of abnormal cardiovascular risk factors. Lean mass decreased, whereas leptin increased with increasing tertile. There were no gender-specific differences in overall mortality. Short-term mortality (<140 person-months) was higher in women, whereas long-term mortality (>140 person-months) was higher in men. We highlight the importance of considering body fat in gender-specific risk stratification in older adults with normal weight. In conclusion, NWO in older adults is associated with cardiometabolic dysregulation and is a risk for cardiovascular mortality independent of BMI and central fat distribution.

Availability and Characteristics of Cardiovascular Rehabilitation Programs in South America.

PURPOSE: Cardiac rehabilitation (CR) programs decrease morbidity and mortality rates in patients with coronary artery disease, the leading cause of death in Latin America. This study was carried out to assess the characteristics and current level of CR program implementation in South America. METHODS: We carried out a survey of CR programs that were identified using the directory of the South American Society of Cardiology and through an exhaustive search by the investigators. RESULTS: We identified 160 CR programs in 9 of the 10 countries represented in the South American Society of Cardiology and 116 of those responded to our survey. On the basis of survey results from the responding programs, we estimate that the availability of CR programs in South America is extremely low, approximately 1 CR program for every 2 319 312 inhabitants. These CR programs provided services to a median of 180 patients per year (interquartile range, 60–400) and were most commonly led by cardiologists (84%) and physical therapists (72%). Phases I, II, III, and IV CR were offered in 49%, 91%, 89%, and 56% of the centers, respectively. The most commonly perceived barrier to participation in a CR program was lack of referral from the cardiologist or primary care physician, as reported by 70% of the CR program directors. CONCLUSIONS: The number of CR programs in South America appears to be insufficient for a population with a high and growing burden of cardiovascular disease. In addition, there appears to be a significant need for standardization of CR program components and services in the region.

The current and potential capacity for cardiac rehabilitation utilization in the United States.

PURPOSE:Prior studies suggest that program capacity restraints may be an important reason for outpatient cardiac rehabilitation (CR) underutilization. We sought to measure current CR capacity and growth potential. METHODS:We surveyed all CR program directors listed in the American Association of Cardiovascular and Pulmonary Rehabilitation (AACVPR) database in November 2012. Respondents reported current enrollment levels, program capacity, expansion potential, and obstacles to growth. RESULTS:Of the 812 program directors in the AACVPR database, 290 (36%) completed the full survey. Respondents represented somewhat larger programs than nonrespondents but were otherwise representative of all registered AACVPR programs. Current enrollment, estimated capacity, and estimated expansion capacity were reported at a median (interquartile range) of 140 (75, 232), 192 (100, 300), and 240 (141, 380) patients annually, respectively. Using these data, we estimated that, in the year 2012, national CR utilization was 28% (min, max: 20, 38) of eligible patients. Even with modest expansion of all existing programs operating at capacity, a maximum of 47% (min, max: 32, 67) of qualifying patients in the United States could be serviced by existing CR programs. Obstacles to increasing patient participation were primarily controllable system-related problems such as facility restraints and staffing needs. CONCLUSIONS:Even with substantial expansion of all existing CR programs, there is currently insufficient capacity to meet national service needs. This limit probably contributes to CR underutilization and has important policy implications. Solutions to this problem will likely include the creation of new CR programs, improved CR reimbursement strategies, and new models of CR delivery.

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.

Normal weight obesity and functional outcomes in older adults

BACKGROUND Obesity defined by body mass index (BMI) is associated with higher levels of functional impairment. However, BMI strata misrepresent true adiposity, particularly in those with a normal BMI but elevated body fat (BF%) (normal weight obesity [NWO]) whom are at higher metabolic and mortality risk. Whether this subset of patients is associated with worsening functional outcomes is unclear. METHODS Subjects aged ≥60 years with a BMI ≥18.5 kg/m(2) from NHANES III (1988-1994) were included. We created sex-specific tertiles of BF%. Data on physical limitations (PL), instrumental (IADL) and basic activities of daily living (BADL) were obtained. The analysis focused on the association between NWO and these outcomes. Comparative rates among each tertile using logistic regression (referent=lowest tertile) were assessed, incrementally adding co-variates. RESULTS Of the 4484 subjects aged ≥60 years, 1528 had a normal BMI, and the range of the mean age of tertiles was 69.9-71.2 years. Lean mass was lowest in the elevated BF% group than in the middle or low tertiles (42.6 vs 44.9 vs 45.8; p<0.001). Those with NWO had higher PL risk than the referent in females only in our adjusted model (males OR 1.18 [0.63-2.21]; females OR 1.90 [1.04-3.48]) but not after incorporating lean mass (males OR 1.11[0.56-2.20]; females (1.73 [0.92-3.25]). Neither sex with high BF% had higher IADL risk than the corresponding tertiles (males OR 0.67 [0.35-1.33]; females OR 1.20 [0.74-1.93]). NWO was protective in males only (OR 0.28 [0.10-0.83]) but not in females (OR 0.64 [0.40-1.03]). CONCLUSIONS NWO is associated with increased physical impairment in older adults in females only, highlighting the importance of recognizing the association of obesity with disability in elders.

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.

Participation Rates, Process Monitoring, and Quality Improvement Among Cardiac Rehabilitation Programs in the United States: A NATIONAL SURVEY.

PURPOSE: Although strategies exist for improving cardiac rehabilitation (CR) participation rates, it is unclear how frequently these strategies are used and what efforts are being made by CR programs to improve participation rates. METHODS: We surveyed all CR program directors in the American Association of Cardiovascular and Pulmonary Rehabilitations database. Data collection included program characteristics, the use of specific referral and recruitment strategies, and self-reported program participation rates. RESULTS: Between 2007 and 2012, 49% of programs measured referral of inpatients from the hospital, 21% measured outpatient referral from office/clinic, 71% measured program enrollment, and 74% measured program completion rates. Program-reported participation rates (interquartile range) were 68% (32-90) for hospital referral, 35% (15-60) for office/clinic referral, 70% (46-80) for enrollment, and 75% (62-82) for program completion. The majority of programs utilized a hospital-based systematic referral, liaison-facilitated referral, or inpatient CR program referral (64%, 68%, and 60% of the time, respectively). Early appointments (<2 weeks) were utilized by 35%, and consistent phone call appointment reminders were utilized by 50% of programs. Quality improvement (QI) projects were performed by about half of CR programs. Measurement of participation rates was highly correlated with performing QI projects (P < .0001.) CONCLUSIONS: Although programs are aware of participation rate gaps, the monitoring of participation rates is suboptimal, QI initiatives are infrequent, and proven strategies for increasing patient participation are inconsistently utilized. These issues likely contribute to the national CR participation gap and may prove to be useful targets for national QI initiatives.

Effect of a lifestyle therapy program using cardiac rehabilitation resources on metabolic syndrome components.

PURPOSE: To assess the effect of a lifestyle therapy program using cardiac rehabilitation (CR) resources for patients at risk for metabolic syndrome (MetS). Methods: We designed a cardiometabolic program (CMP) using CR facilities and resources. We compared MetS components of 240 patients classified as either obese (body mass index, ≥30 kg/m2) or hyperglycemic (fasting glucose, >100 mg/dL): 58 enrolled and completed the CMP, 59 signed up for the CMP but never attended or dropped out early (control 1) but had followup data, and 123 did not sign up for the CMP (control 2). Results: The CMP group showed a significant improvement at 6 weeks in waist circumference, body weight, diastolic blood pressure, and total cholesterol. At 6 months, fasting glucose also improved. In contrast, improvements in control 1 and control 2 were modest at best. Comparing the 6-month changes in the CMP group versus control 1 group, those in the CMP had pronounced weight loss (−4.5 ± 5 kg vs −0.14 ± 6 kg; P < .001), decreased systolic blood pressure (−1.1 ± 17 mm Hg vs +9.6 ± 20 mm Hg; P = .004), and decreased diastolic blood pressure (−4.6 ± 11 mm Hg vs +3.4 ± 15 mm Hg; P = .002). Similarly, comparing CMP group versus control 2 group, body weight (−4.5 ± 5 kg vs −0.9 ± 3 kg; P < .001) and diastolic blood pressure (−4.6 ± 11 mm Hg vs −0.7 ± 9 mm Hg; P = .02) declined in the CMP group. Conclusion: A lifestyle therapy program using resources of a CR program is effective for individuals who have or are at risk for MetS, although enrollment and completion rates are low.