Characteristics of Framingham Offspring Participants With Long-lived Parents

The Framingham Heart Study has contributed importantly to understanding of the causes of coronary heart disease (CHD), stroke, and other cardiovascular diseases. Framingham research has helped define the quantitative and additive nature of these causes or, as they are now called, “cardiovascular risk factors.”1 The National Cholesterol Education Program (NCEP)2 3 has made extensive use of Framingham data in developing its strategy for preventing CHD by controlling high cholesterol levels. The NCEP guidelines2 3 adjust the intensity of cholesterol-lowering therapy with absolute risk as determined by summation of risk factors. The National High Blood Pressure Education Program (NHBPEP) has set forth a parallel approach for blood pressure control. In contrast to the NCEP,2 however, earlier NHBPEP reports issued through the Joint National Committee4 did not match the intensity of therapy to absolute risk for CHD. “Normalization” of blood pressure is the essential goal of therapy regardless of risk status. Blood pressure–lowering therapy is carried out as much for prevention of stroke and other cardiovascular complications as for reduction of CHD risk. Nonetheless, risk assessment could be important for making decisions about type and intensity of therapy for hypertension. Thus, the most recent Joint National Committee report5 gives more attention to risk stratification for adjustment of therapy for hypertension. Although Framingham data have already been influential in the development of national guidelines for risk factor management, the opportunity may exist for both cholesterol and blood pressure programs to draw more extensively from Framingham results when formulating improved risk assessment guidelines and recommending more specific strategies for risk factor modification. The American Heart Association has previously used Framingham risk factor data to prepare charts for estimating CHD risk. Framingham investigators of the National Heart, Lung, and Blood Institute prepared the original charts and have now revised …

At least 25% of coronary patients have sudden death or nonfatal myocardial infarction without prior symptoms.1 Therefore, the search for coronary patients with subclinical disease who could potentially benefit from intensive primary prevention efforts is critically important. The American Heart Association’s (AHA) Prevention V Conference, “Beyond Secondary Prevention: Identifying the High Risk Patient for Primary Prevention,” addressed ways to identify more patients who are asymptomatic and clinically free of coronary heart disease (CHD) but at sufficiently high risk for a future coronary event to justify more intensive risk reduction efforts.2 In this report, we amplify on key findings and recommendations of the AHA Prevention V conference, highlight new research since the conference, and propose an approach to the use of office-based testing and additional noninvasive procedures in selected patients to better define their coronary event risk. The recommendations are concordant with the recently released approach to risk assessment and management from the third report of the Adult Treatment Panel of the National Cholesterol Education Program (ATP-III).3 Enthusiasm for primary prevention and risk assessment in asymptomatic people has been spurred by recent advances in prevention research. Lipid-lowering trials demonstrated that primary prevention of coronary events is feasible, evidenced by the West of Scotland Coronary Primary Prevention Study (WOSCOPS) trial4 of hypercholesterolemic men and by the Air Force/Texas Coronary Atherosclerosis Prevention Study (AFCAPS/TexCAPS) trial5 in average or typical risk men and women with only moderate lipid abnormalities. Aspirin6 or ACE inhibitors7 can also reduce risk in selected asymptomatic, high-risk people. Emerging coronary risk factors have been described including inflammatory, infectious, and thrombotic markers,8 and there has been a steady flow of reports that focus attention on potential new ways of predicting coronary risk.9 In addition, noninvasive tests for subclinical atherosclerotic disease are available …

Health hazards of obesity have been recognized for centuries, appearing, for example, in writings attributed to Hippocrates. From the later decades of the 20th century through the present, there have been numerous epidemiological studies of the relationship between excess weight and the total, or all-cause, mortality rate,1 a critical cumulative measure of the public health impact of any health condition. Using body mass index (BMI), an indicator of relative weight for height (weight [kg]/height [m]2) and a frequently used surrogate for assessment of excess body fat, these studies have found linear, U-shaped, or J-shaped relationships between total mortality and BMI. That is, in some studies, both the thin and the obese were more likely to die than those in between. There is, however, always a point at which increasing BMI is associated with increasing mortality risk, but the BMI at which this occurs varies across studies and populations.2 Currently,3 overweight in adults is defined as a BMI of 25.0 to <30.0 kg/m2 and obesity as a BMI of ≥30.0 kg/m2 (Table 1). A number of studies have found no significant relationship between BMI in the overweight range and mortality rate4 and have shown the nadir of mortality risk to be in the overweight range. In particular, commentaries in both the lay press5–7 and scientific literature2,8,9 subsequent to recent reports from National Health and Nutrition Examination Surveys (NHANES)10,11 have highlighted the confusion and controversy regarding this issue. Some have interpreted the recent data to mean that overweight is not detrimental to health and is not in itself a public health concern and that drawing attention to the need for weight loss in this range will have negative effects on the health and well-being of the general population.8 Others have argued …

Stroke ranks as the third leading cause of death in the United States. It is now estimated that there are more than 700 000 incident strokes annually and 4.4 million stroke survivors.1 2 The economic burden of stroke was estimated by the American Heart Association to be $51 billion (direct and indirect costs) in 1999.3 Despite the advent of treatment of selected patients with acute ischemic stroke with tissue plasminogen activator and the promise of other experimental therapies, the best approach to reducing the burden of stroke remains prevention.4 5 High-risk or stroke-prone individuals can be identified and targeted for specific interventions.6 This is important because epidemiological data suggest a substantial leveling off of prior declines in stroke-related mortality and a possible increase in stroke incidence.7 8 The Stroke Council of the American Heart Association formed an ad hoc writing group to provide a clear and concise overview of the evidence regarding various established and potential stroke risk factors. The writing group was chosen based on expertise in specific subject areas, and it used literature review, reference to previously published guidelines, and expert opinion to summarize existing evidence and formulate recommendations (Table 1⇓). View this table: Table 1. Levels of Evidence and Grading of Recommendations As given in Tables 2 through 4⇓⇓⇓, risk factors or risk markers for a first stroke were classified according to potential for modification (nonmodifiable, modifiable, or potentially modifiable) and strength of evidence (well documented, less well documented).5 The tables give the estimated prevalence, population attributable risk, relative risk, and risk reduction with treatment for each factor when known. Population attributable risk reflects the proportion of ischemic strokes in the population that can be attributed to a particular risk factor and is given by the formula 100×[prevalence(relative risk−1)/prevalence(relative risk−1)+1]). …

ObjectiveThat treatment with second-generation antipsychotics (SGAs) causes metabolic side effects and atherosclerosis in patients with schizophrenia and bipolar disorder (BD) is well-known. Increased arterial stiffness is an important marker of arteriosclerosis and has been identified as an independent risk factor for cardiovascular diseases. We measured pulse wave velocity (PWV) as a marker of arteriosclerosis in patients with schizophrenia and BD who use SGAs.MethodsPatients and controls were collected from our psychiatry outpatient clinics or family medicine. Mental illness was diagnosed according to the Diagnostic and Statistical Manual of Mental Disorders, 4th edition. Mean age, gender, systolic and diastolic blood pressure, body mass index, Framingham risk score (FRS), etc. were determined. Simultaneous electrocardiography and pulse wave were recorded with an electromyography device. The photo-plethysmographic method was used to record the pulse wave. Inclusion criteria included use of SGAs for at least the last six months. Patients with diseases that are known to cause stiffness and the use of typical antipsychotics were excluded.ResultsNinety-six subject (56 patients, 40 controls) were included in our study. There were 49 females, 47 males. Patients had schizophrenia (n=17) and BD (n=39). Their treatments were quetiapine (n=15), risperidone (n=13), olanzapine (n=15), and aripiprazole (n=13). Although differences in mean age, gender, and FRS in the patient and control groups were not statistically significant (p=1), PWV was greater in patients in the antipsychotic group (p=0.048).ConclusionThis study supported the liability to stiffness in patients with schizophrenia and BD. Using SGAs may contribute to arterial stiffness in these patients.

Association Between Body Size and Colorectal Adenoma Recurrence

Introduction. Early atherosclerosis is characterized by vessel-wall thickening with preserved lumen size. Contrast-enhanced coronary artery computed tomography (CTA) has been validated for stenosis detection and characterization of atherosclerotic plaques but vessel wall thickening in patients with high Framingham risk score (FRS) has not been shown. We hypothesized that increased mean percent vessel wall area (PVWA) and remodeling index (RI) reflect early vessel wall thickening in subjects with higher FRS, with preservation of luminal size. Methods. We assessed 25 patients with coronary CTA using a validated, highly standardized approach on a commercial post processing workstation (Vitrea; SurePlaque 4.0). FRS was calculated by standard criteria. We evaluated 153 segments in patients with low FRS (&lt;10%) and 35 segments in patients with intermediate-high FRS (&gt;10%). Mean minimal lumen diameter (MLD), minimal lumen area (MLA), remodeling index (RI), and PVWA were compared with unpaired t-test in patients with low versus &gt;10% FRS. Mean PVWA was calculated as follows: (total vessel area-lumen area)/total vessel area. p-value &lt;0.05 was considered significant. Results. In all patients, mean FRS was 7%; 20 patients had FRS&lt;10% and 5 had FRS&gt;10%. PVWA and RI were significantly higher in the intermediate-high FRS group compared to low FRS (0.46±0.07 vs 0.42±0.07; p=0.04 and 0.97±0.15 vs 0.92±0.13; p=0.004, respectively), while MLD/MLA were similar (Figure 1 ). Conclusions. This is the first demonstration that coronary CTA can detect vessel wall thickening with preserved lumen size in patients with FRS&gt;10%. Figure 1. Cross sectional image demonstrating normal wall thickness (A). Cross sectional image demonstrating increased wall thickness (B). Bar graph demonstrating significant difference in PVWA between low and intermediate-high Framingham risk groups (C). Bar graph demonstrating significant difference in RI between low and intermediate-high Framingham risk groups (D).

Background: Research on the health impact of the built environment has focused on health behaviors such as diet and exercise, and conditions such as obesity and diabetes. Few studies have examined its influence on downstream outcomes such as cardiovascular disease. We investigated the proportional variance in the 10-year and 30-year Framingham risk score (FRS) attributable to neighborhoods in the Framingham Heart Study. Methods: Offspring- and Generation 3 cohort members’ homes at the time of exam 7 (Offspring, 1998-2001) or exam 1 (Generation 3, 2002-2005) were geocoded to 2000 Census block groups. We evaluated Framingham Offspring and Generation 3 cohort participants inhabiting private residences in block groups within Massachusetts containing the residences of 5 or more participants. Analyses of the 10-year FRS were further restricted to participants aged 30-74 at the time of the relevant exam and those of the 30-year FRS to participants 20-59 years old. Cardiovascular risk was determined on the bases of sex, age, systolic blood pressure, anti-hypertensive medication, smoking, diabetes, total cholesterol, and HDL. The outcomes were the standardized residuals of log-transformed FRS regressed on age and sex. We analyzed the percentage of variance of FRS explained at the block-group level and 95% confidence intervals using multilevel linear regression. An empty model was first used to estimate the total variance and the following factors were then added singly to evaluate their influence on the group-level FRS variance explained by education, body mass index, waist circumference, physical activity score, and depression (CES-D score ≥16). Analyses were repeated stratified by sex. Results: The analysis of 10-year FRS included a total of 2,882 participants in 188 census block groups. The block-group-level variance explained for this outcome was 1.77% (95% CI=0.69, 4.44). Upon the addition of BMI to the model, the variance explained dropped to 1.11% (95% CI=0.28%, 4.30%). None of the other covariates had a substantial impact. Among 1,363 women in 117 block groups, the block level group explained a total of 2.03% of the FRS variance at the block level group (95% CI=0.61, 6.56), which dropped to 0.64% (95% CI=0.03, 13.82) when BMI was added to the model. Results were somewhat stronger in analysis of the 30-year FRS. The group-level FRS variance explained by census blocks was 3.56% (95% CI=1.75, 7.12) among 2096 participants in 156 neighborhoods. Similar to the 10-year CVD risk score, the variance explained among women (959 in 97 block groups) was higher (6.06%, 95% CI=2.75, 12.83), but null among males. Conclusions: In this relatively homogenous suburban white population, census block groups explained a small percentage of the variance in CVD risk. The explained variance was higher among women (19% non-working vs. 5% of males), and largely explained by the clustering of obesity.

1381 LOW SERUM TESTOSTERONE IS ASSOCIATED WITH OBSTRUCTIVE SLEEP APNEA IN MIDDLE AGED MEN

Applying Framingham Risk Score 2008 to Predict the 10-Year Risk of Cardiovascular Disease in a Group of Office Workers in Hanoi, Vietnam

CD34+ Circulating Progenitor Cell Frequency Is Highly Heritable

ObjectivesTo assess the completeness of cardiovascular disease (CVD) risk factor recording and levels of risk factors in patients eligible for the NHS Health Check.DesignCross-sectional study.SettingTwenty-eight general practices located in Hammersmith and Fulham, London, UK.Participants42,306 patients aged 40 to 74 years without existing cardiovascular disease or diabetes.Main Outcome MeasuresMeasurement and level of CVD risk factors: blood pressure, cholesterol, body mass index (BMI), blood glucose and smoking status.ResultsThere was a high recording of smoking status (86.1%) and blood pressure (82.5%); whilst BMI, cholesterol and glucose recording was lower. There was large variation in BMI, cholesterol, glucose recording between practices (29.7–91.5% for BMI). Women had significantly better risk factor recording than men (AOR = 1.70 [1.61–1.80] for blood pressure). All risk factors were better recorded in the least deprived patient group (AOR = 0.79 [0.73–0.85] for blood pressure) and patients with diagnosed hypertension (AOR = 7.24 [6.67–7.86] for cholesterol). Risk factor recording varied considerably between practices but was more strongly associated with patient than practice level characteristics. Age-adjusted levels of cholesterol and BMI were not significantly different between men and women. More men had raised blood glucose, blood pressure and BMI than women (29.7% [29.1–30.4] compared to 19.8% [19.3–20.3] for blood pressure).ConclusionsBefore the NHS Health Check, CVD risk factor recording varied considerably by practice and patient characteristics. We identified significant elevated levels of raised CVD risk factors in the population eligible for a Health Check, which will require considerable work to manage.

Coronary artery disease (CAD) is a leading cause of morbidity and mortality worldwide. Easy-to-perform and reliable parameters are needed to predict the presence and severity of CAD and to implement efficient diagnostic and therapeutic modalities. We aimed to examine whether the Framingham risk scoring system can be used for this purpose. A total of 222patients (96women, 126men; mean age, 59.1 ± 11.9years) who underwent coronary angiography were enrolled in the study. Presence of > %50 stenosis in a coronary artery was assessed as critical CAD. The Framingham risk score (FRS) was calculated for each patient. CAD severity was assessed by the Gensini score. The relationship between the FRS and the Gensini score was analyzed by correlation and regression analyses. The mean Gensini score was 18.9 ± 25.8, the median Gensini score was 7.5 (0-172), the mean FRS was 7.7 ± 4.2, and the median FRS was 7 (0-21). Correlation analysis revealed a significant relationship between FRS and Gensini score (r = 0.432, p < 0.0001). This relationship was confirmed by linear regression analysis (β = 0.341, p < 0.0001). A cut-off level of 7.5 for FRS predicted severe CAD with a sensitivity of 68 % and a specificity of 73.6 % (ROC area under curve: 0.776, 95 % CI: 0.706-0.845, PPV: 78.1 %, NPV: 62.3 %, p < 0.0001). Our work suggests that the FRS system is a simple and feasible method that can be used for prediction of CAD severity. As the sample size was small in our study, further large-scale studies are needed on this subject to draw solid conclusions.

The metabolic syndrome is a construct that has come into common usage over the last few decades. In 1956, Jean Vague1 first demonstrated that upper-body obesity determined predisposition to diabetes mellitus, atherosclerosis, gout, and renal calculi; however, it was the presentation of this concept by Gerald Reaven in his Banting Lecture in 19882 that initiated the current focus on this entity as a clinical and pathological construct. Dr Reaven suggested that 3 major conditions—non–insulin-dependent diabetes mellitus (type 2 diabetes mellitus), hypertension, and coronary artery disease—had an causal commonality (resistance to insulin-stimulated glucose uptake and hyperinsulinemia) and referred to this constellation of abnormalities as “syndrome X.” He did not include abdominal obesity in the original description, nor did he comment significantly on a potential clinical role for his observations. In fact, his concluding sentences stated, “What remains to be seen is the magnitude of the role that resistance to insulin-stimulated glucose uptake plays in the etiology of human disease. I can only hope that this presentation has outlined the possibilities for future efforts to answer this question.” Response by Kahn p 1818 Since that time, this construct has evolved conceptually and functionally into an entity that many refer to as a “syndrome.” More recently, there have been many questions about the legitimacy of this designation, summarized by Kahn et al3 in a September 2005 position statement for the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD). Their comments called into question many of the assumptions that underlay the designation of “syndrome,” and they posed the following key questions: How clear is the existing definition of the metabolic syndrome for diagnostic purposes? How useful is the syndrome definition in predicting cardiovascular disease (CVD) risk? Do the individual components of the syndrome convey “risk” …

Type 2 diabetes mellitus (T2DM) and obesity are closely interlinked conditions, contributing significantly to health complications. Metabolic surgery (MS) could help to improve metabolic conditions, including diabetes, hypertension, and hyperlipidemia, which are significant risk factors for cardiovascular disease. This study aimed to evaluate the short-term and long-term effects of MS on obesity outcomes and the Framingham Risk Score (FRS) in Iranian diabetic patients. This multicenter, retrospective study included T2DM patients with body mass index (BMI) ≥ 30 kg/m² who underwent MS in Ahvaz, Iran, from 2019 to 2023. Of the 151 patients, 105 with a follow-up of at least one year were included. Among them, 29 patients (27.6%) had a long-term follow-up (four to five years). Anthropometric and biochemical parameters, comorbidities, and FRS were evaluated at baseline and at one-year and long-term follow-ups. One year postoperatively, there were significant reductions in weight, BMI, systolic and diastolic blood pressure, FRS, triglycerides, low-density lipoprotein (LDL), liver enzymes, fasting blood glucose, and hemoglobin A1c (HbA1c), with an increase in high-density lipoprotein (HDL) levels. The percentage of total weight loss (%TWL) and excess weight loss (%EWL) were 29.13 ± 8.42 and 71.03 ± 20.88, respectively. The median FRS decreased from 11.70 pre-surgery to 4.50 and 6.30 at one-year and long-term follow-ups (P<0.0001). Complete remission of T2DM, hypertension, and hyperlipidemia was achieved in 75.2%, 48.2%, and 35.1% of patients, respectively, and improvements were sustained throughout the follow-up period. The type of MS had no significant impact on changes in weight or BMI (P>0.05). Surgery-related side effects were observed in four cases (3.80%). MS is a safe and effective procedure for reducing obesity-related complications, including T2DM, hypertension, hyperlipidemia, and cardiovascular risk in both the short and long term. These findings highlight the promising potential of MS in managing diabetes.