Central obesity and coronary heart disease in men.
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Biomedical subjects
Publications and source records attributed to R D Abbott.
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The relation between central body fat distribution, determined by measurement of subscapular skinfold thickness (SSF), and the development of definite coronary heart disease (CHD) was examined after 12 years of follow-up in a cohort of 7692 men who participated in the Honolulu Heart Program. The risk of incident coronary events was directly related to SSF. Compared with men in the lowest tertile of SSF, those in the middle tertile experienced a 70% excess of definite CHD. For those in the highest tertile, the excess more than doubled. For a given level of body mass index (BMI), SSF remains a significant and independent predictor of definite CHD, even after adjustment for age, total cholesterol, glucose, triglycerides, hypertensive status, and cigarette smoking. In contrast, the independent effect of BMI was not significant after adjustment for SSF. Thus centrally obese individuals are at increased risk of CHD, independent of BMI.
From 1965 to 1968, the Honolulu Heart Program began following up a cohort of men in a prospective study of cardiovascular disease. For this report, we examined the 12-year risk of stroke in 690 diabetic and 6908 nondiabetic subjects free of coronary heart disease and a history of stroke at study entry. In 12 years of follow-up, 62.3 per 1000 diabetic men and 32.7 per 1000 nondiabetic men experienced a stroke. The relative risk of thromboembolic stroke for those with diabetes compared with those without diabetes was 2.0 (95% confidence limits, 1.4 to 3.0). Although diabetes was usually associated with an atherogenic risk profile, control of hypertension, complicating myocardial infarction, and other risk factors failed to diminish the effect of diabetes on stroke. Among those without diabetes, the relative risk of thromboembolic stroke for those at the 80th percentile of serum glucose level compared with those at the 20th percentile (199 vs 115 mg/dL [11.0 vs 6.4 mmol/L]) was 1.4 (95% confidence limits, 1.1 to 1.8). In the nondiabetic sample, the relative risk of thromboembolic stroke for those with glucosuria compared with those without glucosuria was 2.7 (95% confidence limits, 1.6 to 4.5). There was no association between diabetes, or measures of glucose intolerance, and hemorrhagic stroke. We conclude that diabetes, even in a possibly undiagnosed subset of hyperglycemic individuals, imparts an additional independent risk of stroke unexplained by clinically measured risk factors.
In 1976-1978, a battery of eight neuropsychologic tests were administered to 2,123 participants in the Framingham Study who were aged 55-89 years. Performance on each test was examined in relation to concurrently measured systolic and diastolic blood pressure while controlling for age, sex, education, antihypertensive medication, alcohol consumption, and smoking. Those with a diagnosis of stroke were excluded from the analysis. In the remaining sample of 2,032, neither blood pressure nor antihypertensive treatment was significantly associated with cognitive performance. Even after excluding persons on antihypertensive medication, blood pressure was still unrelated to cognitive performance. In contrast to other studies, the authors found no consistent relation between blood pressure and cognitive performance.
Since 1965, the Honolulu Heart Program has followed 8006 men of Japanese ancestry, aged 45-70 yr at study entry, for the development of cardiovascular disease. To investigate the role of glucose concentration 1 h after a 50-g challenge on the risk of fatal coronary heart disease (CHD) and nonfatal myocardial infarction (MI), 6394 nondiabetic men were followed for 12 yr for the first development of CHD. The rate of fatal CHD increased linearly with amount of glucose. Men in the fourth quintile of postchallenge glucose (157-189 mg/dl) had twice the age-adjusted risk of fatal CHD of those in the lowest quintile (P less than .05). Relative risk increased to threefold among those in the top quintile and remained statistically significant after adjustment for other risk factors including body mass, total cholesterol, hypertension, left ventricular hypertrophy, and hematocrit (P less than .001). When glucose was considered as a linear term in the proportional hazards model, a highly significant relation was noted with fatal CHD alone and when combined with nonfatal MI (P less than .001). We conclude that a continuously increasing risk gradient exists between postchallenge glucose and subsequent CHD that is independent of other known risk factors.
Chronic atrial fibrillation without valvular disease has been associated with increased stroke incidence. The impact of atrial fibrillation on the risk of stroke with increasing age was examined in 5184 men and women in the Framingham Heart Study. After 30 years of follow-up, chronic atrial fibrillation appeared in 303 persons. Age-specific incidence rates steadily increased from 0.2 per 1000 for ages 30 to 39 years to 39.0 per 1000 for ages 80 to 89 years. The proportion of strokes associated with this arrhythmia was 14.7%, 68 of the total 462 initial strokes, increasing steadily with age from 6.7% for ages 50 to 59 years to 36.2% for ages 80 to 89 years. In contrast to the impact of cardiac failure, coronary heart disease, and hypertension, which declined with age, atrial fibrillation was a significant contributor to stroke at all ages.
The first report from the Framingham Study that demonstrated an inverse relationship between high-density lipoprotein cholesterol (HDL-C) and the incidence of coronary heart disease (CHD) was based on four years of surveillance. These participants, aged 49 to 82 years, have now been followed up for 12 years, and this report shows that the relationship between the fasting HDL-C level and subsequent incidence of CHD does not diminish appreciably with time. Since a second measurement of HDL-C is available eight years after the initial determination, the relationship of HDL-C measurements on the same subjects at two points in time is examined. This second HDL-C measurement is also used in a multivariate model that includes cigarette smoking, relative weight, alcohol consumption, casual blood glucose, total cholesterol, and blood pressure. It is concluded that even after these adjustments, nonfasting HDL-C and total cholesterol levels are related to development of CHD in both men and women aged 49 years and older. Study participants at the 80th percentile of HDL-C were found to have half the risk of CHD developing when compared with subjects at the 20th percentile of HDL-C.
From 1965 to 1968, the Honolulu Heart Program began following 8006 men of Japanese ancestry in a prospective study of cardiovascular disease. Of the subjects who had not had a stroke by the time of study entry, 3435 were cigarette smokers and 4437 were nonsmokers. In 12 years of follow-up, 171 smokers and 117 nonsmokers had a stroke. As compared with nonsmokers, cigarette smokers had two to three times the risk of thromboembolic or hemorrhagic stroke, after control for age, diastolic blood pressure, coronary heart disease, and other risk factors (P less than 0.001). Subjects who continued to smoke in the course of follow-up had the highest risk of stroke. When these subjects were compared with those who never smoked, their risk of hemorrhagic events was increased four- to six-fold (P less than 0.001). Subjects who were smokers at study entry but stopped smoking in the course of follow-up had a slight excess risk of stroke. When these subjects were compared with those who continued to smoke, however, their risk was reduced by more than half after adjustment for risk factors (P less than 0.05), indicating that stopping smoking had significant benefits.
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Since 1965, the Honolulu Heart Program has followed up 8,006 men in a prospective study of cardiovascular disease. Of those subjects free of stroke at the time of study entry, 2,916 were classified as nondrinkers of alcohol and 4,962 as drinkers. In 12 years of follow-up, 197 drinkers and 93 nondrinkers experienced a stroke. No significant relationships were noted between alcohol and thromboembolic stroke. When compared with nondrinkers, however, the risk of hemorrhagic stroke more than doubled for light drinkers and nearly tripled for those considered to be heavy drinkers. These findings are statistically significant and independent of hypertensive status and other risk factors. Results further indicate that alcohol has a greater effect on hemorrhagic strokes that are subarachnoid in origin, conferring a threefold to fourfold increased risk for moderate and heavy drinkers compared with nondrinkers.
In 30 years of follow-up in the Framingham study, routine biennial ECG examinations revealed 315 subjects with ECG-LVH and 164 with unrecognized ECG-MI without previous cardiac explanation. Among subjects initially free of clinically evident coronary heart disease and both ECG abnormalities, the incidence of ECG-LVH was about double that of ECG-MI. Both events exhibited a male predominance and hypertensive subjects were more vulnerable to each. In subjects with asymptomatic ECG-LVH and ECG-MI, the 10-year, age-adjusted incidence of clinical coronary heart disease was greater than the rate experienced by the general Framingham sample. Rates for ECG-LVH were almost as large as those for ECG-MI. Cardiac failure and stroke also occurred more frequently among subjects with either ECG abnormality, and rates for ECG-LVH exceeded those for ECG-MI. Death from coronary heart disease, and sudden death in particular, was also increased two- to fourfold with similar risks for ECG-LVH and ECG-MI. ECG-LVH carried a significantly greater risk than ECG-MI for cardiovascular deaths in women. These findings suggest that ECG-LVH and ECG-MI are similar subclinical events with respect to predisposing characteristics and prognosis for subsequent overt cardiovascular disease including clinical manifestations of coronary heart disease.
Survival models are important tools for the analysis of data when a disease event occurs in time and subjects are lost to follow-up. Many models, however, can also be adapted for use when an event is characterized by transitions through intermediate states of disease with increasing severity. In this presentation, such adaptations will be demonstrated for a class of conditional regression models for the analysis of transient state events occurring among grouped event times. The type of conditioning that will be described is useful in providing comparisons of specific disease states and an assessment of transition-dependent risk factor effects. An example will be given based on the Framingham Heart Study.
In 30 years of follow-up of 5,209 participants in the Framingham Study, 193 men and 183 women developed atrial fibrillation (AF). Among this group, "lone" AF occurred in 32 men and 11 women free of coronary heart disease, congestive heart failure, rheumatic heart disease, and hypertensive cardiovascular disease. To determine the characteristics and prognosis of lone AF, each case was matched to controls in the remaining Framingham sample. Comparisons indicated that levels of several risk factors associated with coronary heart disease were similar between the two groups. Atrial fibrillation cases, however, had significantly higher rates of preexisting nonspecific T- or ST-wave abnormalities and intraventricular block as determined by electrocardiograms. Follow-up for new cardiovascular events indicated similar rates of coronary heart disease and congestive heart failure, but the rate of strokes was significantly greater in the lone AF group. Findings suggest that subjects with lone AF, despite similar cardiovascular risk profiles to normal controls, have a distinct preponderance of preexisting electrocardiographic abnormalities. Furthermore, contrary to general belief, lone AF is not a benign condition; it has a serious prognosis, indicating a greater need for detection and treatment.
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Logistic regression has been applied to numerous investigations that examine the relationship between risk factors and various disease events. Recently, the ability to consider the time element of event occurrences by proportional hazards models has meant that logistic regression has played a less important role in the analysis of survival data. This paper, however, shows that when event times are grouped into intervals, logistic regression can be adapted to the analysis of such data by modeling the interval when an event occurs. Furthermore, it is shown that results from such an adaptation will often lead to parameter estimates close to those obtained by the proportional hazards model in the grouped event time setting. An illustration of the application of logistic regression to survival analysis is based on data from the Framingham Heart Study.
Of 708 myocardial infarctions among 5127 participants in the Framingham Study, more than 25 per cent were discovered only through the appearance of new diagnostic evidence during routine biennial electrocardiographic examinations. Of these unrecognized infarctions almost half were "silent," and the others caused atypical symptoms. The proportion of all infarcts that were unrecognized was higher in women and in older men. Such infarcts were uncommon in persons with angina. Unrecognized infarctions were as likely as recognized ones to cause death, heart failure, or strokes. Recurrent infarctions were more common in women with recognized than with unrecognized infarcts, but this difference was not present in men. Recurrent infarctions were more likely to be recognized than were first infarctions. We conclude that unrecognized infarctions are common and have as serious a prognosis as recognized infarctions.
Multiple logistic models are frequently used in observational studies to assess the contribution of a risk factor to disease while controlling for one or more covariates. Often, the covariates are correlated with the risk factor, resulting in multiple logistic coefficients that are difficult to interpret. This paper highlights the problem of assessing the magnitude of a multiple logistic coefficient and proposes a supplemental procedure to the usual logistic analysis for describing the relationship between a risk factor and disease. An example is given, along with results that are not apparent when the multiple logistic coefficient is considered alone. Conclusions that are presented are important in biologic studies if describing the effect of a risk factor is influenced by correlation with a covariate.
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