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Serial MR Imaging of Volumes of Hyperintense White Matter Lesions in Elderly Patients: Correlation with Vascular Risk Factors

¹ Department of Psychiatry, Duke University Medical Center, DUMC 3903, Durham, NC 27710.
² Department of Radiology, Duke University Medical Center, DUMC 3808, Durham, NC 27710.

Received December 11, 2002; accepted after revision February 5, 2003.

 
Supported by a National Alliance for Research in Schizophrenia and Affective Disorders Young Investigator Award and National Institute of Mental Health grants P50 MH60451 and R01 MH54846.

Address correspondence to W. D. Taylor (taylor066{at}mc.duke.edu).

Abstract

Top Abstract Introduction Subjects and Methods Results Discussion References

 
OBJECTIVE. The purpose of the study was to examine change in volume of hyperintense white matter lesions in a cohort of community-dwelling elderly subjects without neuropsychiatric disease.

SUBJECTS AND METHODS. One hundred seventeen volunteers underwent brain MR imaging on a 1.5-T scanner. Demographic data and the presence of specific medical illnesses were recorded at the time of the initial scanning. Hyperintense white matter lesion volume was measured using a supervised semiautomated technique that seeded lesions and then created a segmented lesion image. Subjects underwent repeated MR imaging at a mean of 25 months. Mean change in lesion volume and mean percentage of change were determined between the two time points. Logistic regression models were used to examine the differential effects of age, sex, race, and self-reported medical morbidity.

RESULTS. Mean baseline volume of cerebral hyperintense lesions was 4.91 cc, and at 2-year follow-up, it was 6.42 cm² (p < 0.0001), for a mean increase of 26.7%. Comparable results were seen in separate analyses of hemispheric hyperintense lesion volumes. Neither sex, race, nor baseline hyperintense lesion volume was significantly associated with an interval increase in lesion volume. Age (p = 0.0117) and presence of diabetes (p = 0.0215) were associated with greater change.

CONCLUSION. Elderly subjects exhibited approximately a 27% increase in hyperintense lesion volume over a 2-year period, a finding influenced by both age and medical comorbidity rates. Because hyperintense lesions can be associated with several neuropsychiatric conditions, further research is needed to determine if interventions designed to slow hyperintense lesion disease progression may improve neuropsychiatric outcomes.

Introduction

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Hyperintense white matter lesions are bright regions seen in the brain parenchyma on T2-weighted MR imaging. These hyperintense lesions are associated with increased age [1–6] and medical comorbidity [1, 7–10]; vascular risk factors may be particularly important [1, 7–11]. In younger populations, hyperintense lesions may be associated with specific genetic findings [12]. In the elderly, greater hyperintense lesion burden is associated with cognitive [1, 13], motor [14], and psychiatric morbidity [15–18]. Despite these associations, it is unclear to what degree hyperintense lesions change over time and what factors contribute to this change.

Because greater hyperintense lesion severity is associated with advanced age, one would expect that hyperintense lesion severity would increase over time. Several studies using visual rating scales have supported this theory. These studies indicate that hypertension significantly contributes to hyperintense lesion progression [19–21], and pharmacologic control of hypertension may reduce the risk of developing more severe hyperintense lesion disease [19]. These data are further supported by cross-sectional studies showing greater severity of hyperintensity in subjects with uncontrolled hypertension compared with subjects with controlled hypertension or normotensive subjects [22, 23].

The purpose of this study was to examine lesion volume over time using a semiautomated measuring technique. We evaluated change in deep white matter hyperintense lesion volume over a 2-year period in a group of elderly subjects without neurologic or psychiatric disease. We hypothesized that greater change would be associated with age, but not other demographic variables or initial lesion volume. We also hypothesized that risk factors for cerebrovascular disease, specifically the presence of diabetes, heart disease, and hypertension, would be associated with greater hyperintense lesion volumes and greater lesion progression.

Subjects and Methods

Top Abstract Introduction Subjects and Methods Results Discussion References

 
Sample
From the Aging Center Subject Registry at Duke University Medical Center, subjects were recruited from a listing of over 1900 community-dwelling elders who had expressed a willingness to participate in Duke Aging Center research. They were age- and sex-matched to serve as a control group for ongoing research in geriatric depression at Duke University Medical Center. Eligible subjects were 60 years old or older, with a nonfocal neurologic examination, no self-reported neurologic or depressive illness, and no evidence of a diagnosis of depression based on the Diagnostic Interview Schedule [24] portion of the Duke Depression Evaluation Schedule [25]. The study was approved by the Duke University institutional review board. The purpose and procedures of the study were explained to all subjects. Those who provided written informed consent were enrolled.

Clinical Assessment Procedures
Demographic data were recorded at baseline. Subjects completed a self-report questionnaire that asked about the presence or absence of several medical conditions, including diabetes, heart trouble, and hypertension. These data were self-reported only and were developed from questions included in the National Institute of Mental Health Epidemiological Catchment Area program [26]. The term "heart trouble" represents signs and symptoms of cardiac disease, typically heart failure or coronary artery disease. MR imaging was obtained at baseline and 2 years later. The mean (± SD) time between scans was 745 ± 31.8 days; minimal time, 680 days; maximal time, 892 days.

MR Imaging
MR imaging parameters.—Subjects were imaged with a 1.5-T whole-body research-dedicated MR system (Signa, General Electric Medical Systems, Milwaukee, WI) using a standard head (volumetric) radiofrequency coil. Padding was used to immobilize the head without causing discomfort. The scanner alignment light was used to adjust the head tilt and rotation so that the axial plane lights passed across the canthomeatal line and the sagittal lights were aligned with the center of the nose. A rapid sagittal localizer scan was acquired to confirm the alignment.

A dual-echo fast spin-echo acquisition was obtained in the axial plane for morphometry. The pulse sequence parameters were TR/first-echo TE, second-echo TE, 4000/30,135; 32 KHz (± 16 KHz); full imaging bandwidth; echo-train length, 16; matrix size, 256 x 256; section thickness, 3 mm; number of excitations, 1; and field of view, 20 cm. The images were obtained in two separate acquisitions with a 3-mm gap between sections for each acquisition. The second acquisition was offset by 3 mm from the first so that the resulting data set consisted of contiguous sections.

MR image processing.—Images were archived on magnetic optical disks in the scanning facility and transferred to the Duke Neuropsychiatric Imaging Research Laboratory for processing on SunOS workstations. Volume measurements were performed using a modified version of MrX software (General Electric Corporate Research and Development, Schenectady, NY), which included previous adjustments for image segmentation (Brigham and Women's Hospital, Boston, MA).

The basic semiautomated segmentation protocol was modified from a version developed by Kikinis et al. [27] and has been previously described [28, 29]. Changes to these basic procedures were required for segmenting scans of elderly subjects, particularly for identifying hyperintense lesions; this method has also been previously described [28, 29]. Figure 1A, 1B, 1C, 1D shows serial proton density–weighted cranial MR images and the corresponding segmented images.

View larger version (149K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 1A. —73-year-old woman with normal cognition who reported diagnosis of diabetes. Proton density–weighted axial MR image (TR/TE, 4000/30) shows multiple hyperintense foci scattered throughout centrum semiovale.

View larger version (92K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 1B. —73-year-old woman with normal cognition who reported diagnosis of diabetes. Volumetric segmentation image obtained from A shows hyperintense foci (red), cerebrospinal fluid (blue), white matter (yellow), and gray matter (gray). Cerebral hyperintense white matter lesion volume is 7.00 cm2.

View larger version (148K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 1C. —73-year-old woman with normal cognition who reported diagnosis of diabetes. Proton density–weighted image obtained approximately 23 months after A shows substantial change in number of hyperintense lesions. No change in clinical status was noted.

View larger version (104K): [in this window] [in a new window] [as a PowerPoint slide]   Fig. 1D. —73-year-old woman with normal cognition who reported diagnosis of diabetes. Volumetric segmentation image obtained from C shows cerebral hyperintense white matter lesions with volume of 11.60 cm2 or volume increase of 65.7%.

Hyperintense lesion areas were selected on the basis of a set of explicit rules that allowed lesions to be classified as periventricular or deep white matter in location. These rules were developed from neuroanatomic guidelines, after consultation with a neuroradiologist, and on the basis of knowledge of the neuropathology of lesions [28, 29]. Deep white matter lesions were located in the white matter tracts and may or may not have adjoined periventricular lesions. Periventricular lesions were defined as regions that were contiguous with the lateral ventricle and did not extend into the white matter tracts. Both these lesion types were classified as white matter lesions on the segmented image. Subcortical gray matter lesions were not included in this study.

Training and reliability.—All technicians received extensive training by experienced volumetric analysts. Reliability was established by repeated measurements on multiple MR images before raters were approved to process study data. In addition, an ongoing reliability study was conducted to ensure that the quality of volumetric analyses was maintained throughout the study. Intraclass correlation coefficients were 0.988 for left cerebral white matter lesions and 0.994 for right cerebral white matter lesions.

Analytic Strategy
Summary statistics were derived for demographic and clinical variables, including MR imaging results. Differences between baseline and follow-up MR imaging were reported as mean differences or mean percentage of change for the lesion volumes, calculated by subtracting the baseline hyperintense lesion volumes from the follow-up hyperintense lesion volumes. The levels of significance between the two groups were determined using t tests. Similar methods were used to report MR imaging results when the subject pool was dichotomized on the basis of self-reported medical illness. Linear regression models helped clarify the relationship between mean percentage of change in hyperintense lesion volumes, demographic variables, and self-reported medical comorbidity.

Results

Top Abstract Introduction Subjects and Methods Results Discussion References

 
Sample Characteristics
One hundred seventeen subjects were included in this study; all had two cranial MR imaging examinations spaced 2 years apart. One hundred thirteen subjects completed medical illness self-reported data forms. Most subjects were white (84.6%) women (70.9%), with a mean age of 69.1 years (± 6.20). Nonwhite subjects comprised mostly African Americans.

Volumetric Measurements
Volume measurements and differences over time are displayed in Table 1. Total cerebral left and right hemispheric mean hyperintense lesion volumes all showed significant increases over the study period, a mean increase of approximately 27%. No statistically significant differences were found between hyperintense lesion volumes within hemispheres on either the initial or follow-up imaging.

Correlation of Changes in Lesion Volume with Demographic Data
We hypothesized that age, but not other demographic data, would be associated with greater change in hyperintense lesion volume over time. We tested this hypothesis using a linear regression model. In this model, age (estimate, 0.0144; SE, 0.0049; t test, 2.91; p = 0.0043), but not sex (estimate, 0.0066; SE, 0.0333; t test, 0.20; p = 0.8421) or race (estimate, 0.0566; SE, 0.0423; t test, 1.34; p = 0.1833) was associated with change in hyperintense lesion volume.

Correlation of Changes in Lesion Volume with Initial Lesion Volume
To determine if baseline hyperintense lesion volume affected lesion volume change, we developed two linear regression models (Table 2). These models covaried for age, sex, race, and initial hyperintense lesion volume. For the first model, the independent variable was the mean difference in hyperintense lesion volumes between the initial and follow-up scans. For the second model, the independent variable was the mean percentage of change in hyperintense lesion volume between the two MR imaging scans. Greater initial hyperintense lesion volume was associated with greater mean volume differences over the study period, but not greater percentage of changes—that is, the rate of hyperintense lesion volume progression (percentage of change) was not influenced by initial lesion volume. However, those subjects with greater initial lesion volumes had greater mean changes in lesion volumes.

Correlation of Changes in Lesion Volumes with Reported Medical Illnesses
Finally, we examined if self-reported medical illness was associated with more severe hyperintense lesion disease or greater change in lesion volume over time. Of the 113 subjects who completed this questionnaire, eight subjects (7%) each had diabetes or heart disease, and 24 subjects (21%) had hypertension. For these self-reported categories, we tested for differences between hyperintense lesion volumes and change in lesion volume (Table 3). Only hypertension was associated with greater hyperintense lesion volume at all time points, although diabetes was also significantly associated with total cerebral and right hemisphere lesion volumes on the follow-up scanning. Subjects reporting diabetes and hypertension exhibited significantly greater change in lesion volume over time. The presence of signs or symptoms of cardiac disease was not associated with a statistically significant difference in hyperintense lesion volume change. In a separate analysis, there did not appear to be an association with advanced age and report of more medical illnesses (data not shown).

Our final model tested the effect of these medical illnesses on percentage of change in total cerebral hyperintense lesion volume while also controlling for age, sex, and race (Table 4). Advanced age continued to be associated with greater change, whereas sex and race were not. A self-report of diabetes was associated with significantly greater percentage change over the study period, whereas signs or symptoms of cardiac disease were significantly associated with less change. In this model, hypertension was not significantly associated with lesion volume change.

Discussion

Top Abstract Introduction Subjects and Methods Results Discussion References

 
To our knowledge, this is the first longitudinal study to measure white matter hyperintense lesion volumes over time using a semiautomated measurement technique. We found that the subject group exhibited a mean 27% increase in hyperintense lesion volume over 2 years. Although individuals with greater lesion volume at baseline exhibited greater mean changes, initial lesion volume did not appear to be associated with a greater percentage of change. In the final analytic model, age, diabetes, and absence of signs or symptoms of cardiac disease were associated with greater progression of lesion volumes. Hypertension was associated with greater lesion volumes at each time point and with greater change when comparing subjects with and without hypertension. However, hypertension was not associated with greater hyperintense lesion volume change in the final regression model.

This study is concordant with previous studies that found increases in the severity of hyperintensities over time [19–21, 30]. It builds on these previous studies by using a semiautomated method of measuring hyperintense lesion volumes rather than by relying on a visual rating scale or volume estimates [14]. It also supports previous research associating age with greater hyperintense lesion severity [5, 6].

Our results differ somewhat from those of previous studies regarding the association between medical illnesses and change in hyperintense lesion severity. Previous studies found an association between hypertension and both greater hyperintense lesion severity [10, 11, 13, 22, 23] and greater hyperintense lesion disease progression [19–21]. Although we replicated the findings associating hypertension with greater hyperintense lesion volumes, our final analytic model did not find an association between hypertension and greater lesion progression. In contrast, diabetes has been previously associated with hyperintense lesions [9], and we built on this work by identifying diabetes as a potentially important contributor to lesion volume progression.

What could explain these findings? Diabetes is known to be associated with vascular disease; if hyperintense lesions in the elderly are caused by vascular disease, this association would be logical. However, heart disease and hypertension are also vascular risk factors, so why are they not associated with greater lesion progression? Because our sample was a group of relatively healthy community volunteers, we may have inadvertently selected a more health-conscious sample that would more likely be compliant with medications, which would then result in less change. This theory is supported by previous research showing that controlled hypertension results in less hyperintense lesion progression than uncontrolled hypertension [19]. A similar explanation may be plausible for the negative association between signs or symptoms of cardiac disease and lesion volume change. Specific medications used to treat heart disease may slow lesion progression, but this finding could also be caused by a small sample size or the heterogeneity of patients with signs or symptoms of cardiac disease.

This study is important because it shows that hyperintensity volume may be measured and compared over time. This method should provide a more objective means of measuring the severity of hyperintensity than the visual rating scales. It also further reveals the differential effect of various vascular risk factors on lesion development.

There are limitations to the study. Although our cohort lacked neurologic or psychiatric disease, they were not well controlled for other medical morbidities. Screening for medical morbidities was limited to patient self-reports and did not reflect the severity of the disease or the effectiveness of treatment, nor did it capture unrecognized disease. Moreover, only a small number of subjects reported diabetes and cardiac disease, which limits our enthusiasm for our findings associating these disease states with changes in lesion volume over the study period. Further, those subjects who reported a history of cardiac disease may in fact be reporting several different disease states. More accurate diagnoses of disease states are critical for future studies. No medication data were available; this information may be crucial if medications have differential effects on lesion severity [13].

Our semiautomated method of creating a segmented image of hyperintense lesions may not include the entire region that is distinct from the surrounding white matter; our method selected regions only if the intensity was that of partial voluming of gray matter and cerebrospinal fluid. If a region was the same intensity as either gray matter or cerebrospinal fluid, it was not considered a potential hyperintense lesion. This methodological limitation may have affected our findings.

Future studies should account for these deficits. Longitudinal studies may better define the role that specific medical illnesses play in the pathogenesis of hyperintense lesions, while also better tracking medical morbidity rates and monitoring for the confounding effect of medications, such as antihypertensive agents. These issues are critical because hyperintense lesions are associated with neuropsychiatric morbidity rates. Further research is needed to determine which interventions are capable of slowing the progression of hyperintense lesions and if these interventions reduce the morbidity rates.

Acknowledgments
 
We thank Denise Fetzer for her assistance in MR image processing.

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