Obstructive sleep apnea (OSA) and obesity have been linked to systolic and diastolic dysfunction of the left ventricle. Right ventricular function is poorly understood in the 2 clinical conditions. ...Data from this study show that otherwise healthy obese patients with OSA had increased an left atrial volume index compared with similarly obese patients without OSA (16.3 ± 1.2 ml/m in obese patients without OSA vs 20.2 ± 1.0 ml/m in those with OSA, p = 0.02) and altered diastolic function reflected by changes in mitral annular late diastolic velocity (−5.7 ± 0.7 cm/s in obese patients without OSA vs −7.3 ± 0.7 cm/s in those with OSA, p = 0.007), mitral annular early diastolic velocity (−7.9 ± 0.6 cm/s in obese patients without OSA vs −6.4 ± 0.3 cm/s in those with OSA, p = 0.05), and early to late diastolic annular ratio >1 (82% of obese patients without OSA vs 26% of those with OSA, p = 0.001), which may be signs of early subclinical impairment of cardiac function. Importantly, healthy obese subjects had similarly increased left ventricular mass compared with obese patients with OSA but normal diastolic function and left atrial size. There was a trend toward abnormal right ventricular filling in patients with OSA, measured by altered superior vena cava diastolic velocity during expiration (−15 ± 2 cm/s in obese patients without OSA vs −10 ± 3 cm/s in those with OSA, p = 0.2) and a tendency toward diastolic dysfunction reflected by decreased lateral tricuspid annular early diastolic velocity (−7.2 ± 0.5 cm/s in obese patients without OSA vs −6.1 ± 0.5 cm/s in those with OSA, p = 0.1) beyond that seen in obesity alone. In conclusion, OSA independent of obesity may induce cardiac changes that could predispose to atrial fibrillation and heart failure.
Background High cardiorespiratory fitness and body mass index (BMI) are associated with decreased mortality in patients with coronary artery disease. Our objective was to determine the joint impact ...of fitness and adiposity measures on all-cause mortality in this subgroup. Methods Coronary artery disease patients (n = 855) enrolled in the Mayo Clinic cardiac rehabilitation program from 1993 to 2007 were included. Fitness levels were determined by cardiopulmonary exercise testing. Patients were divided into low and high fitness by sex-specific median values of peak oxygen consumption and total treadmill time. Adiposity was measured through BMI and waist-to-hip ratio (WHR). Results There were 159 deaths during 9.7 ± 3.6 years of mean follow-up. After adjusting for potential confounding factors, low fitness, shorter treadmill time, low BMI, and high WHR were significantly associated with increased mortality. Using low WHR–high fitness group as reference, significantly increased mortality was noted in low WHR–low fitness (hazard ratio 4.2, 95% CI, 1.8-9.8), centrally obese–high fitness (2.3, 1.0-5.4), and centrally obese–low fitness (6.1, 2.7-13.6) groups. Overweight–high fitness (2.2, 0.63-7.4), obese–high fitness (3.2, 0.88-11.4), and obese–low fitness (3.3, 0.96-11.4) subjects did not have a significantly different mortality as compared with the reference group of normal weight–high fitness subjects, whereas normal weight–low fitness (9.6, 2.9-31.8) and overweight–low fitness (6.8, 2.1-22.2) groups had significantly increased mortality. Conclusions Low fitness and central obesity were independently and cumulatively associated with increased mortality in coronary artery disease patients attending cardiac rehabilitation. The association of BMI with mortality is complex and altered by fitness levels.
To investigate the effect of diabetes mellitus on exercise heart rate and the role of impaired heart rate in excess mortality in diabetes.
Patients without cardiovascular disease who underwent ...exercise testing from September 1, 1993, through December 31, 2010, were included. Mortality was determined from Mayo Clinic records and the Minnesota Death Index. Multivariate linear regression was used to compare heart rate responses in patients with vs without diabetes. Cox regression was used to determine the effect of abnormal heart rate recovery and abnormal chronotropic index on survival.
A total of 21,396 patients (65.4% men) with a mean ± SD age of 51±11 years, including 1200 patients with diabetes (5.4%), were included. Patients with diabetes had a higher resting heart rate (81±14 vs 77±13 beats/min), lower peak heart rate (154±20 vs 165±19 beats/min), heart rate reserve (73±19 vs 88±19 beats/min), chronotropic index (0.86±0.22 vs 0.99±0.20), and heart rate recovery (15±8 vs 19±9 beats/min) vs patients without diabetes. There were 1362 deaths (6.4%) during a mean ± SD follow-up of 11.9±4.9 years. Adjusting for age, sex, and heart rate-lowering drug use, a chronotropic index less than 0.8 contributed significantly to risk in patients with diabetes (hazard ratio HR, 2.21; 95% CI, 1.62-3.00; P<.001) and patients without diabetes (HR, 1.94; 95% CI, 1.71-2.20; P<.001), as did abnormal heart rate recovery (patients with diabetes: HR, 2.21; 95% CI, 1.60-5.05; P<.001; patients without diabetes: HR, 1.75; 95% CI, 1.55-1.97).
Patients with diabetes exhibit abnormal heart rate responses to exercise, which are independently predictive of reduced long-term survival in patients with diabetes as in patients without diabetes.
Objectives The aim of this study was to examine the association of central (waist circumference WC and waist-hip ratio WHR) and total obesity (body mass index BMI) measures with mortality in coronary ...artery disease (CAD) patients. Background The question of which measure of obesity better predicts survival in patients with CAD is controversial. Methods We searched OVID/Medline, EMBASE, CENTRAL, and Web of Science from 1980 to 2008 and asked experts in the field for unpublished data meeting inclusion criteria, in which all subjects had: 1) CAD at baseline; 2) measures of WC or WHR; 3) mortality data; and 4) a minimum follow-up of 6 months. Results From 2,188 studies found, 6 met inclusion criteria. We obtained individual subject data from 4, adding unpublished data from a cardiac rehabilitation cohort. A variable called “central obesity” was created on the basis of tertiles of WHR or WC. Cox-proportional hazards were adjusted for age, sex, and confounders. The final sample consisted of 15,923 subjects. There were 5,696 deaths after a median follow-up of 2.3 (interquartile range 0.5 to 7.4) years. Central obesity was associated with mortality (hazard ratio HR: 1.70, 95% confidence interval CI: 1.58 to 1.83), whereas BMI was inversely associated with mortality (HR: 0.64, 95% CI: 0.59 to 0.69). Central obesity was also associated with higher mortality in the subset of subjects with normal BMI (HR: 1.70, 95% CI: 1.52 to 1.89) and BMI ≥30 kg/m2 (HR: 1.93, 95% CI: 1.61 to 2.32). Conclusions In subjects with CAD, including those with normal and high BMI, central obesity but not BMI is directly associated with mortality.
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.
To develop and validate a machine learning model that predicts the most successful antihypertensive for an individual.
The causal, deep neural network-based model was trained on data from 16,917 ...newly diagnosed hypertensive patients attending Mayo Clinic’s primary care practices from January 1, 2005 to December 31, 2021. Eligibility criteria included a diagnosis of primary hypertension, blood pressure and creatinine measurements prior to antihypertensive treatment, treatment within nine months of diagnosis, and at least one year of follow-up. Primary outcome was model performance in predicting likelihood of antihypertensive treatment success one year from start of treatment. Treatment success was defined as achieving blood pressure control with no moderate or severe side effects. Model validation and guideline agreement was assessed on 1,000 patients.
In the training set of 16,917 participants (60.8 14.7 years; 8,344 49.3% women), 33.8% achieved blood pressure control without moderate or severe side effects for at least a year with initial treatment. Most common treatment was ACE inhibitor (39.1% average success) and most successful was ACE inhibitor-thiazide combination (44.4% average success). Our custom-built causal, deep neural network-based model had the highest accuracy in predicting individualized treatment success with a precision of 51.7%, recall of 44.4%, and F1 score of 47.8%. Compared to actual physician practice on the validation set (77.9% agreement), the algorithm aligned with the JNC 8 hypertension guidelines 95.7% of the time.
A machine learning algorithm can accurately predict the likelihood of antihypertensive treatment success and help personalize hypertension management.
The Obesity Paradox and Survivors of Ischemic Stroke Wohlfahrt, Peter, MD, PhD; Lopez-Jimenez, Francisco, MD, MSc; Krajcoviechova, Alena, MD ...
Journal of stroke and cerebrovascular diseases,
06/2015, Letnik:
24, Številka:
6
Journal Article
Recenzirano
Background Although obesity is a risk factor for stroke and achieving normal weight is advocated to decrease stroke risk, the risk associated with obesity and weight loss after stroke has not been ...well established. The aim of this study was to assess the association of obesity at the time of stroke admission and weight loss after stroke with total mortality. Methods We analyzed 736 consecutive patients (mean age, 66 ± 11 years; 58% men) hospitalized for their first ischemic stroke. Body weight at hospital admission and at the outpatient visit during follow-up was used in the analysis. Results After multivariate adjustment, obesity at admission was associated with lower mortality risk as compared with normal weight (hazard ratio HR, .50, P = .03). At the outpatient visit, with a median follow-up time of 16 months, 21% of patients had lost more than 3 kg of weight. Stroke severity, heart failure, transient ischemic attack, and depression after stroke were independently associated with significant weight loss. Weight loss of more than 3 kg was associated with increased mortality risk (HR, 5.87; P = .001) independently of other factors. Similar results were seen when weight loss was defined as losing more than 3% of baseline weight (HR, 4.97; P = .004). Weight gain of more than 5% of the baseline weight tended to be associated with better survival when compared with no weight change (log-rank test, P = .07). Conclusions Normal weight at hospital admission and weight loss after ischemic stroke are independently associated with increased mortality. Overweight and obesity at baseline do not decrease the risk associated with weight loss.
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