(Stroke. 1996;27:1274-1282.)
© 1996 American Heart Association, Inc.
Articles |
Clinical Correlates of White Matter Findings on Cranial Magnetic Resonance Imaging of 3301 Elderly People
The Cardiovascular Health Study
the Departments of Neurology (W.T.L.), Epidemiology (W.T.L.), and Biostatistics (A.A.), University of Washington, Seattle; Division of Epidemiology and Clinical Applications, National Heart, Lung, and Blood Institute, Bethesda, Md (T.A.M.); Department of Public Health Sciences, Bowman Gray School of Medicine of Wake Forest University, Winston-Salem, NC (G.L.B.); Departments of Radiology (Neuroradiology Division) (N.B.) and Medicine (L.F.), Johns Hopkins University School of Medicine, Baltimore, Md; Departments of Radiology and Neurological Surgery, University of Pittsburgh (Pa) Medical Center (C.A.J.); Respiratory Sciences Center, University of Arizona, Tucson (P.L.E.); and Department of Radiology, New England Medical Center, Boston, Mass (D.O'L.).
Correspondence to W.T. Longstreth, Jr, MD, Department of Neurology, Box 359775, Harborview Medical Center, 325 Ninth Ave, Seattle, WA 98104-2499. E-mail wl@u.washington.edu.
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Abstract |
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 Top Abstract IntroductionSubjects and MethodsResultsDiscussionAppendix References |
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Background and Purpose Our aim was to identify potential risk factors for and clinical manifestations of white matter findings on cranial MRI in elderly people.
Methods Medicare eligibility lists were used to obtain a representative sample of 5888 community-dwelling people aged 65 years or older. Correlates of white matter findings were sought among 3301 participants who underwent MRI scanning and denied a history of stroke or transient ischemic attack. Participants underwent extensive standardized evaluations at baseline and on follow-up, including standard questionnaires, physical examination, multiple blood tests, electrocardiogram, pulmonary function tests, carotid sonography, and M-mode echocardiography. Neuroradiologists graded white matter findings from 0 (none) to 9 (maximal) without clinical information.
Results Many potential risk factors were related to the white matter grade, but in the multivariate model the factors significantly (all P<.01) and independently associated with increased grade were greater age, clinically silent stroke on MRI, higher systolic blood pressure, lower forced expiratory volume in 1 second (FEV1), and income less than $50 000 per year. If excluded, FEV1 was replaced in the model by female sex, history of smoking, and history of physician-diagnosed hypertension at the baseline examination. Many clinical features were correlated with the white matter grade, especially those indicating impaired cognitive and lower extremity function.
Conclusions White matter findings were significantly associated with age, silent stroke, hypertension, FEV1, and income. The white matter findings may not be considered benign because they are associated with impaired cognitive and lower extremity function.
Key Words: aged • cognition • hypertension • magnetic resonance imaging • white matter
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Introduction |
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TopAbstractIntroductionSubjects and MethodsResultsDiscussionAppendix References |
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Cranial MRI often reveals white matter findings in elderly people. As Pantoni and Garcia1 reported in their recent review, the risk factors for developing these findings and the clinical syndrome associated with them remain incompletely defined. Most commonly, the white matter findings have been attributed to aging and hypertension and are thought to result from disease in the long penetrating arteries of the brain. Such findings have at one extreme been recognized in otherwise normal elderly people and at the other in patients with cognitive and gait impairments. If the MRI findings are markers for early cognitive and gait impairments, identification and control of causal factors could reduce the risk of these common problems of the elderly.
Many previous studies have been limited by having small numbers of subjects, concentrating on patients with stroke or Alzheimer's disease, or having a variable quality of information on imaging findings, risk factors, and clinical manifestations.1 Recent population-based studies have demonstrated associations with a number of cardiovascular risk factors2 3 4 5 and with cognitive impairment.3 6 7 The largest of these studies included 120 subjects.5 6 The CHS is a population-based, longitudinal study of coronary heart disease and stroke in 5888 adults aged 65 years or older.8 9 As part of their comprehensive evaluation, more than 3000 of these elderly men and women have undergone cranial MRI. The results of these scans provide a unique opportunity to seek correlates of these white matter findings with respect to both cardiovascular risk factors and clinical manifestations.
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Subjects and Methods |
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Members of the CHS cohort were recruited from a random sample of the Health Care Financing Administration Medicare eligibility lists in four US communities: Forsyth County, North Carolina; Sacramento County, California; Washington County, Maryland; and Pittsburgh (Allegheny County), Pennsylvania. Details of the study design and characteristics of the original 5201 participants are published elsewhere.8 9 Briefly, potential participants had to be 65 years or older, able to give informed consent, and able to respond to questions without the aid of a surrogate respondent. They could not be institutionalized, wheelchair-bound in the home, or under treatment for cancer. Fifty-eight percent of eligible persons were enrolled in the study and underwent a baseline evaluation between 1989 and 1990 (year 2 of the study). Those who agreed to participate were more educated, younger, and more likely to be married than those who refused.8 9 To enhance the minority representation in the original cohort of 5201 subjects, 687 black subjects were recruited from the centers in North Carolina, California, and Pennsylvania between 1992 and 1993 (year 5 of the study), bringing the total size of the cohort to 5888 people.
Eligible and consenting participants underwent an extensive baseline evaluation including standard questionnaires, BP measurements in upper and lower extremities, anthropometric measurements, 12-lead resting electrocardiogram, fasting lipid analyses, coagulation studies, glucose and insulin measurements, pulmonary function tests, carotid sonography, and M-mode echocardiography. We assessed participants for the presence of orthostatic hypotension by recording BP with the patient supine and again after standing for 3 minutes. Participants were considered to have orthostatic hypotension if the systolic BP fell by 20 mm Hg or more, if the diastolic BP fell by 10 mm Hg or more, or if the subject complained of dizziness, lightheadedness, or faintness. More details on the methods used in these evaluations are supplied elsewhere.8 9
Questionnaires included two standard measures of cognitive function, a modification of the MMSE10 11 and the Digit-Symbol Substitution Test.12 The modification of the MMSE measures several areas of cognitive function in greater detail than the original measure and has scores ranging from 0 to 100.11 In the Digit-Symbol Substitution Test, the subject indicates the symbol associated with a particular digit using a code that is available throughout the test.12 The score is the number of items that the subject correctly codes in 90 seconds. The test assesses visual-motor speed and cognitive flexibility.
Other questionnaires included a standard measure of depression,13 activities of daily living, and instrumental activities of daily living.14 For these analyses, the two measures of activities of daily living were collapsed into dichotomous variables, indicating those without problems in any of the activities and those with a problem in one or more of the activities. Parts of the baseline evaluation have been repeated at various times since the initial examinations were conducted between June 1989 and May 1990 (year 2 of the study).
After an MRI pilot study was conducted, which included 303 members of the cohort during year 4 of the study,15 16 the remaining members of the cohort who had follow-up clinic visits scheduled were invited to have cranial MRI scans during years 5 and 6 of the study. Those without contraindication who consented underwent imaging in a standard fashion, as detailed in the pilot study.15 16 MRI was performed on General Electric or Picker 1.5-T scanners at three field centers and on a 0.35-T Toshiba instrument at the fourth. The scanning protocol included a series of axial spin density and T2-weighted scans angled parallel to the anterior-posterior commissure line from vertex to skull base with the following parameters: repetition time, 3000 milliseconds; echo time, 30 and 100 milliseconds; compensated flow; 5 mm thickness; 0 gap; 256x192 matrix; and 1/2 nex (1 nex on a 0.35-T scanner). A series of axial T1-weighted scans was performed, also angled parallel to the anterior-posterior commissure line from vertex to skull base with the following parameters: repetition time, 500 milliseconds; echo time, 20 milliseconds; 5 mm thickness; 0 gap; 256x192 matrix; and 1 nex (2 nex on a 0.35-T scanner).
Imaging data were archived on magnetic tape and sent to a single reading center for interpretation by neuroradiologists trained in the CHS protocol and without knowledge of the subjects' age, sex, race, ethnicity, or other clinical information. A method was sought to quantify white matter findings in a reliable manner. Because of concerns that specific findings such as rims or halos would be difficult to identify and quantify reliably, the decision was made to consider the total volume of white matter change rather than trying to grade specific findings or to grade separately changes in the periventricular and subcortical regions. Accordingly, neuroradiologists at the reading center estimated the total volume of periventricular and subcortical white matter signal abnormalities on spin density-weighted axial images by comparing the findings on any particular scan with sets of complete scans that demonstrated successively increasing changes from barely detectable (grade 1) to extensive and confluent (grade 8). The neuroradiologists also scored each scan on whether white matter findings were most prominent in the periventricular region or the subcortical regions or equally prominent in both regions. Abnormalities interpreted as representing areas of large- or small-vessel infarction were excluded from the grading of the white matter. Infarction was defined as an area of abnormal signal intensity in a vascular distribution that lacked mass effect, as detailed elsewhere.15 16 A text description of the white matter grades was supplied to the neuroradiologists but was not used as much as matching visual patterns to the template images, such as that shown in Fig 1
. The white matter grading was reliable, with an interreader intraclass correlation coefficient for grade of .76 and an intrareader coefficient of .89.
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Coronary heart disease, myocardial infarction, congestive heart failure, stroke, TIA, claudication, hypertension, and diabetes were defined with the use of patients' reports, hospital and clinic records, and data collected as part of the CHS study. Coronary heart disease included myocardial infarction, angina, coronary bypass graft, or angioplasty. For the purposes of these analyses, a condition was said to be present at baseline if the subject reported having been told by a physician that he or she had that condition regardless of whether that report could be confirmed. More details on the criteria used to define these conditions are reported elsewhere.8 9 17
For the analyses concerning potential risk factors and clinical manifestations, we used information that was collected closest in time but before the MRI was performed. For example, some data, such as the results of the echocardiogram, were available only from the baseline evaluation, while other data, such as the modified MMSE11 and BP measurements,18 were available in the yearly follow-up examinations. Exceptions were the baseline pulmonary function tests for the black cohort, which were available only in year 6, and the carotid sonographic studies, which were taken at year 5. Of note, only two thirds of the original cohort and none of the black cohort had echocardiograms. For simplicity, results restricted to the baseline evaluations are not presented but did not differ substantively from those reported below. Because of the potential for age and sex to confound the relation between white matter grade and potential risk factors or clinical manifestations, we attempted to control for the effects of age and sex in all analyses.
To examine the association of white matter grade with potential risk factors, we performed multiple linear regression, with white matter grade as the dependent variable and with age, sex, and the potential risk factors as independent variables. All of the variables evaluated as risk factors are listed in a later table. To identify those factors independently associated with white matter grade, we constructed more complex multivariate models. All of the variables evaluated as risk factors, whether significant or not, were available to enter the initial regression model aimed at identifying the key independent predictors of white matter grade. A stepwise method was used, and the probability value to enter the models was set at .01. We then repeated the analysis but removed from the list of candidates available to enter the model all of the echocardiogram variables because none of these variables were significant in the model aimed at identifying independent predictors and many of these variables had missing values. Finally, to maximize the number of subjects, we repeated the analysis using only those variables identified as significant from the stepwise model.
To examine the association of white matter grade with potential clinical manifestations, we no longer considered white matter grade as the dependent variable. Instead, we performed multiple linear regression or logistic regression, with the clinical manifestation as the dependent variable and with age, sex, and white matter grade as the independent variables. Additional models were constructed for those variables whose partial correlation coefficients for white matter grade adjusted for age and sex were significant at P<.01. Other variables that might also affect the function being evaluated were made available to the model in a stepwise fashion. Depending on the particular model, these additional variables included education (number of years of school up to 16 for a college graduate, and those with more education coded as 17), history of smoking, number of alcoholic drinks per week, income greater than $50 000, report of physician-diagnosed hypertension at baseline, use of antihypertensive medication in the 2 weeks before the visit closest in time to when the MRI was performed, systolic and diastolic BP, and the number of times the participant had performed the test before the MRI was performed. Once the significant variables were identified through the stepwise method for a particular model, they alone were entered into another regression model to include the largest number of subjects possible.
Results of the regression models described above are summarized with the partial correlation coefficients and probability values for the variables of interest. The greater the absolute value of the partial correlation coefficient, the stronger the association. For the more complex multivariate models, the regression coefficients are also presented. The regression coefficient indicates the average change in the dependent variable for a change of one unit in the independent variable, when the other independent variables are fixed.
For these analyses, we assume that grade is a continuous variable but cannot ensure that the steps between each grade are equal. In these analyses, results were similar whether we used the raw data for white matter grade or transformations that normalized the data. For simplicity, we present only the results based on the raw scores. We considered collapsing the white matter grades to a smaller number of categories, but concerned that important information might be lost, we persisted in using the original 10 categories. Finally, all of these analyses were based on the updated CHS database, which incorporates minor corrections through January 30, 1995.
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Results |
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TopAbstractIntroductionSubjects and MethodsResultsDiscussionAppendix References |
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Of the 5888 members of the CHS cohort, 3658 (62%) underwent MRI scanning. Of the remaining 2230 people (38%), 842 were not recruited for scanning because they died (n=411) or lacked an appropriately timed follow-up visit (n=244) or for unknown reasons (n=187). Of the 5046 subjects available for MRI, 277 had contraindications, 596 refused, and 467 were unable to be scanned, typically because of a complaint of claustrophobia. Finally, data were not available in 48 other subjects for technical reasons. Some characteristics of the subgroups who did and did not undergo MRI are shown in Table 1
. Those who underwent MRI scanning were significantly younger, more educated, more likely to have never smoked, and healthier than those who did not undergo MRI. Recruitment of patients for MRI was similar from the four study sites (data not shown).
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After we adjusted for age and sex, white matter grade was significantly related to baseline disease status for stroke, TIA, myocardial infarction, and claudication (all P<.05) but not for congestive heart failure or coronary heart disease (all P>.05). The strongest association was for stroke. Of the 3658 participants who had an MRI, 327 (8.9%) reported a history of stroke or TIA before MRI was performed. Results were similar whether or not those with a history of stroke or TIA were included. For simplicity and ease of interpretation, these participants were excluded from further analyses. Thus, after these participants (n=327) and those missing the white matter grade (n=30) were excluded, 3301 remained for subsequent analyses. Despite these exclusions, we found that the scans of approximately one third of the remaining people had findings consistent with an ischemic or hemorrhagic stroke (1107 of 3301, 33.5%). Given the potential for silent stroke to confound the relation of white matter grade with risk factors and clinical manifestations, we included a variable for clinically silent stroke in the multivariate models.
Of all 3301 people without a reported stroke or TIA, only 4.4% of their scans were assigned a white matter grade of 0, which indicated that they were free of any abnormal signal in the white matter (Fig 2). Mean white matter grade increased with age and was greater in women than in men (Fig 3). Neuroradiologists judged the white matter findings, when present, to be more prominent in the periventricular than subcortical region in 73.9%, with the reverse occurring in only 4.7% and the remaining 21.4% equally distributed in both regions. They also judged the right and left sides of the brain to be similarly involved in 96.3% of scans and the brain stem to be involved in only 18.2%.
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Many potential risk factors, including age, sex, and baseline disease status, were evaluated, and Table 2 lists the partial correlation coefficients and their statistical significance. All models contained age and sex. The strongest associations were for older age, lower income, report of physician-diagnosed hypertension at baseline evaluation, higher systolic BP, higher diastolic BP, FEV1, and clinically silent stroke on MRI. Fasting blood glucose, fasting insulin, or report of physician-diagnosed diabetes were not associated with white matter grade. The strong association between white matter grade and systolic BP was similar for both men and women and is illustrated in Fig 4. We adjusted the results for age using a mean age of 75 years.
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More detailed multivariate models seeking independent predictors of white matter grade were generated with the use of all of the variables listed in Table 2
. The regression coefficients and partial correlation coefficients for the variables entering these more complex models are shown in Table 3. In the first model, higher white matter grade was independently associated with greater age, evidence for a clinically silent stroke on MRI, higher systolic BP, lower FEV1, and income less than $50 000 per year. Interactions with sex and MRI evidence of a clinically silent stroke were examined in this model, and none were significant. Forcing height into this model had little effect (results not shown). On the other hand, if sex and history of smoking were forced into the model, FEV1 did not enter the model. The results of this second model are shown in the middle portion of Table 3. This model had the greatest R2 value of all the models examined, at .17. In addition to the associations already noted, higher white matter grade was independently associated with female sex, history of smoking, and report of physician-diagnosed hypertension at baseline evaluation. Finally, to better understand the associations with MRI evidence of a clinically silent stroke, we performed the same analysis as in the second model except that we excluded silent stroke from the list of candidate variables for the model. The results of this third model are shown in the bottom portion of Table 3. In addition to the associations already mentioned, higher white matter grade was independently associated with a higher diastolic BP and the presence of orthostatic hypotension. Despite a strong association between orthostatic hypotension and age (P=.006), orthostatic hypotension remained significantly associated with white matter grade even after we controlled for age and the other factors in the model. When information regarding whether white matter findings were most prominent in the periventricular region or the subcortical region or equally prominent in both regions was added to the risk factor models presented in Table 3, none of the relations between potential risk factors and white matter grade were substantially altered.
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Table 4 shows the partial correlation coefficients between white matter grade and a variety of potential clinical manifestations, adjusted for age, sex, and MRI evidence of a clinically silent stroke. Higher grades were associated with impaired cognitive function, lower extremity dysfunction, and some upper extremity dysfunction. Results of some of the more detailed models are presented in Table 5. For the models of cognitive function, which contain the modified MMSE and the Digit-Symbol Substitution Test scores, the depression score was also available to enter the model. The models demonstrate that the relations between the white matter grade and the clinical manifestations listed in Table 5 remained strong even after we controlled for the effects of several potential confounding variables. The only potential clinical manifestations in Table 4 in which the probability values were less than .01 but then rose above this cutoff with these more complex models were as follows: depression score, ability to walk one half mile, frequent falls, and ability to perform a tandem stand. Fig 5 illustrates for men and women the relation between white matter grade and the score on the modified MMSE, adjusted for age with a mean age of 75 years. Performance on the tests of cognitive function declined with increasing grade, particularly in men, although the clinical significance of changes of this magnitude is uncertain.
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Discussion |
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TopAbstractIntroductionSubjects and MethodsResultsDiscussionAppendix References |
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We examined the correlates of white matter findings on cranial MRI of 3301 participants from the CHS. Despite the exclusion of those who reported a history of previous strokes or TIAs, one third of the group had findings on their MRI interpreted by CHS neuroradiologists as consistent with a previous stroke. As in previous studies of elderly people,1 white matter findings were common among those without a history of stroke, with only 4.4% free of any findings. Most of the findings were mild, with approximately 80% of those scanned falling into grades 1, 2, or 3. The white matter findings were most commonly symmetrical, supratentorial, and periventricular.
Many clinical factors were associated with the white matter findings, but only five remained independently related to white matter grade in more detailed multivariate models. Higher grade was associated with greater age, evidence for a clinically silent stroke on MRI, higher systolic BP, lower FEV1, and income less than $50 000 per year. The FEV1 may be summarizing information from other variables associated with a higher grade, such as female sex, history of smoking, and reported physician-diagnosed hypertension at the baseline examination. Finally, if the variable for a clinically silent stroke on MRI was excluded from the multivariate model, in addition to the factors above, a higher grade was associated with higher diastolic BP and orthostatic hypotension. In all the models examined, age had the strongest association with white matter grade. The other variables examined could not explain this association, suggesting that white matter changes may in part reflect some change associated with the aging process itself. However, even the best multivariate model left most of the variability in white matter grade unexplained, suggesting that other as yet unidentified risk factors exist or that these models may be inappropriate.
Findings from this study are similar to those from other population-based studies, indicating that age, BP, and silent stroke are related to white matter findings.1 3 4 5 Some associations suggested by other studies, such as for diabetes,5 were not confirmed by this study. Other associations, such as intimal-medial wall thickness of the common carotid artery,2 ratio of systolic BP in the ankle to that in the arm,2 or fibrinogen,3 were present when adjustments were made only for age and sex but were not found to be independently related to white matter grade in the more complex multivariate models. FEV1 had not been evaluated previously. How female sex, income of less than $50 000 a year, and orthostatic hypotension could affect the risk is less clear. Like FEV1, income may be a marker for other factors, such as access to medical care, long-term BP control, long-term smoking status, and other factors. Orthostatic hypotension could cause injury to white matter, which is supplied by the terminal vessels of the major cerebral arteries.
Despite the exclusion of all subjects who reported a history of stroke, one third of those scanned had evidence of a clinically silent stroke on MRI, all of which were read in a standard fashion. White matter findings and silent stroke were strongly associated. This association between silent stroke and white matter grade suggests that the pathophysiology of the two may be similar. Most of the factors increasing the risk of the white matter findings could do so by damaging long arteries that penetrate into the subcortical and periventricular white matter, as has been suggested by pathological studies.1 19 20 Such a mechanism could also underlie the risk for silent strokes, particularly those in the deep central nuclei. These structures are also supplied by terminal arteries.
White matter grade was associated with many symptoms and signs of neurological impairment, particularly those affecting cognitive and lower extremity function, as described in previous studies.1 These relations could not be explained by the participants' history of a clinically recognized stroke because these patients were excluded from the analysis. Those with silent stroke were not excluded, but this variable was controlled for in the analysis along with age, sex, and other potential confounding variables. Although the statistical significance was great for many of these comparisons, their clinical importance is less certain. The gait impairment observed in this study with higher white matter grades and reported in previous studies1 20 21 22 could result from periventricular involvement of descending and ascending pathways serving the lower extremities.21 22 Although some functions of the upper extremity were impaired, they were mostly performance tasks (such as time to put on and button a shirt, time to open a lock, and ability to dial a phone), the impairment of which could as likely reflect a problem with cognition as with the upper extremity.
The strengths of the present study lie in the large sample size, the fact that participants were drawn from the general population rather than a clinic, and the wealth of prospectively measured data on risk factors and clinical manifestations. MRI scans were read in a standard fashion by neuroradiologists who were not provided any clinical information, not even age and sex. However, the subjects of these analyses represent a somewhat select group who are healthier than the entire population of people aged 65 years or older because of the CHS eligibility criteria, selective participation in CHS, and its MRI component.8 9 If anything, this selection should lead to an underestimate of the prevalence of these white matter findings. In addition, this study included a limited neuropsychological evaluation. It is reassuring that more extensive neuropsychological testing performed in another population-based study yielded similar results.6 7 Also, the manner in which the white matter was evaluated, in which total volume of white matter findings was graded, does not allow us to investigate whether the correlates may differ for periventricular and subcortical findings. The questions raised concerning the causes and clinical manifestations of the white matter findings could best be addressed with a prospective longitudinal study, and thus the results of this cross-sectional analysis should be interpreted with caution.
White matter findings on MRI scans of elderly people likely result from injury to the long penetrating arteries of the brain. They correlate with several clinical factors, including age, silent stroke, hypertension, spirometry, and income. The white matter findings may not be considered benign because the higher the white matter grade, the more likely a subject will have impaired cognitive function and gait, even after one adjusts for other factors that could affect these functions. However, most of this population had lower grades with predominantly mild periventricular findings, which may have little clinical significance. If white matter findings are markers for these problems, as suggested by this and other studies,1 the search for causal risk factors is important because modification of them might reduce the risk of these dysfunctions in the elderly. Longitudinal studies will be needed to determine whether the white matter findings are themselves predictors of future diseases such as stroke and dementia.
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Selected Abbreviations and Acronyms |
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Acknowledgments |
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This study was supported by contracts N01-HC-85079, N01-HC-85080, N01-HC-85081, N01-HC-85082, N01-HC-85083, N01-HC-85084, N01-HC-85085, N01-HC-85086, and N01-HC-15103 from the National Heart, Lung, and Blood Institute, Bethesda, Md.
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Footnotes |
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Participating institutions and principal staff are listed in the "Appendix."
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Appendix |
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TopAbstractIntroductionSubjects and MethodsResultsDiscussionAppendix References |
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Participating Institutions and Principal Staff
Forsyth County, North Carolina-Bowman Gray School of Medicine of Wake Forest University: Gregory L. Burke, Alan Elster, Walter H. Ettinger, Curt D. Furberg, Edward Haponik, Gerardo Heiss, Dalane Kitzman, H. Sidney Klopfenstein, Margie Lamb, David S. Lefkowitz, Mary F. Lyles, Maurice B. Mittelmark, Cathy Nunn, Ward Riley, Grethe S. Tell, James F. Toole, Beverly Tucker; Forsyth County, North Carolina-Bowman Gray School of Medicine of Wake Forest University, Electrocardiography Reading Center: Kris Calhoun, Harry Calhoun, Farida Rautaharju, Pentti Rautaharju, Loralee Robertson; Sacramento County, California-University of California, Davis: William Bommer, Charles Bernick, Andrew Duxbury, Mary Haan, Calvin Hirsch, Paul Kellerman, Lawrence Laslett, Marshall Lee, Virginia Poirier, John Robbins, Marc Schenker, Nemat Borhani; Washington County, Maryland-The Johns Hopkins University: M. Jan Busby-Whitehead, Joyce Chabot, George W. Comstock, Linda P. Fried, Joel G. Hill, Steven J. Kittner, Shiriki Kumanyika, David Levine, Joao A. Lima, Neil R. Powe, Thomas R. Price, Jeff Williamson, Moyses Szklo Melvyn Tockman; Washington County, Maryland-The Johns Hopkins University, Magnetic Resonance Imaging Reading Center: R. Nick Bryan, Carolyn C. Meltzer, Douglas Fellows, Melanie Hawkins, Patrice Holtz, Michael Kraut, Grace Lee, Larry Schertz, Earl P. Steinberg, Scott Wells, Linda Wilkins, Nancy C. Yue; Allegheny County, Pennsylvania-University of Pittsburgh: Diane G. Ives, Charles A. Jungreis, Laurie Knepper, Lewis H. Kuller, Elaine Meilahn, Peg Meyer, Reberta Moyer, Anne Newman, Richard Schulz, Vivienne E. Smith, Sidney K. Wolfson; Echocardiography Reading Center (Baseline)-University of California, Irvine: Hoda Anton-Culver, Julius M. Gardin, Margaret Knoll, Tom Kurosaki, Nathan Wong; Echocardiography Reading Center (Follow-up)-Georgetown Medical Center: John Gottdiener, Eva Hausner, Stephen Kraus, Judy Gay, Sue Livengood, Mary Ann Yohe, Retha Webb; Ultrasound Reading Center-Geisinger Medical Center: Daniel H. O'Leary, Joseph F. Polak, Laurie Funk; Central Blood Analysis Laboratory-University of Vermont: Edwin Bovill, Elaine Cornell, Mary Cushman, Russell P. Tracy; Respiratory Sciences-University of Arizona, Tucson: Paul Enright; Coordinating Center-University of Washington, Seattle: Alice Arnold, Annette L. Fitzpatrick, Bonnie K. Lind, Richard A. Kronmal, Bruce M. Psaty, David S. Siscovick, Lynn Shemanski, Lloyd Fisher, Will Longstreth, Patricia W. Wahl, David Yanez, Paula Diehr, Maryann McBurnie; National Heart, Lung, and Blood Institute Project Office: Diane E. Bild, Teri A. Manolio, Peter J. Savage, Patricia Smith.
Received October 25, 1995; revision received March 28, 1996; accepted April 2, 1996.
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References |
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