- Email: [email protected]
The Brain-Derived Neurotrophic Factor VAL66MET Polymorphism and Cerebral White Matter Hyperintensities in Late-Life Depression
The Brain-Derived Neurotrophic Factor VAL66MET Polymorphism and Cerebral White Matter Hyperintensities in Late-Life Depression Warren D. Taylor, M.D., Stephan Zu ¨ chner, M.D., Douglas R. McQuoid, B.S., Martha E. Payne, Ph.D., James R. MacFall, Ph.D., David C. Steffens, M.D., M.H.S., Marcy C. Speer, Ph.D., K. Ranga R. Krishnan, M.D.
Objective: In animal models, brain-derived neurotrophic factor (BDNF) appears to protect against cerebral ischemia. The authors examined whether the BDNF Val66Met polymorphism, which affects BDNF distribution, was associated with greater volumes of hyperintense lesions as detected on magnetic resonance imaging in a cohort of depressed and nondepressed elders. Design: Subjects completed cross-sectional assessments, including clinical evaluation and a brain magnetic resonance imaging scan, and provided blood samples for Val66Met genotyping. Setting: The study was conducted at a universitybased academic hospital. Participants: Participants included 199 depressed and 113 nondepressed subjects aged 60 years or older. Measurement: Hyperintensity lesion volumes were measured using a semiautomated segmentation procedure. Statistical models examined the relationship between genotype and lesion volume while controlling for depression, presence of hypertension, age, and sex. Results: After controlling for covariates, Met66 allele carriers exhibited significantly greater white matter hyperintensity volumes (F1,311 ⫽ 4.09, p ⫽ 0.0442). This effect was independent of a diagnosis of depression or report of hypertension. Genotype was not significantly related to gray matter hyperintensity volume (F1,311 ⫽ 1.14, p ⫽ 0.2871). Conclusions: The BDNF Met66 allele is associated with greater white matter hyperintensity volumes in older individuals. Further work is needed to determine how this may be associated with other clinically relevant findings in late-life depression. (Am J Geriatr Psychiatry 2008; 16:263–271)
Key Words: genetic polymorphisms, magnetic resonance imaging, depression
H
yperintense lesions are bright areas in the brain parenchyma observed on magnetic resonance imaging (MRI). These lesions are generally consid-
ered to be ischemic in origin, although pathologic examination may also reveal perivascular dilation or demyelination, and ultimately the pathologic exam-
Received May 18, 2007; revised August 10, 2007; accepted August 15, 2007. From the Center for Human Genetics (SZ, MCS), the Neuropsychiatric Imaging Research Laboratory (WDT, MEP, JRM, KRRK), and the Departments of Medicine (MCS), Psychiatry (WDT, DRM, MEP, DCS, KRRK), and Radiology (JRM), Duke University Medical Center, Durham, NC; The Miami Institute of Human Genomics (SZ), University of Miami Miller School of Medicine, Miami, FL; and Duke–NUS Graduate Medical School (KRRK), Singapore. Send correspondence and reprint requests to Dr. Warren D. Taylor, Duke University Medical Center, DUMC 3903, Durham, NC 27710. e-mail: [email protected] © 2008 American Association for Geriatric Psychiatry
Am J Geriatr Psychiatry 16:4, April 2008
263
BNDF VAL66MET Polymorphism and Cerebral White Matter Hyperintensities ination correlates to what is observed on MRI, but may vary dependent on the brain region being examined.1–3 Hyperintensities are more severe and of greater volume in older depressed subjects than in comparison groups4 –7 and are more common with initial development of depression later in life.8 –12 This increased hyperintensity severity is associated with poorer antidepressant response13–16 and a more chronic course of depression.17,18 A number of clinical factors are associated with hyperintensity severity. Although age19,20 is perhaps the most strongly associated factor, medical comorbidity is also associated with hyperintensity severity, particularly vascular risk factors, including but not limited to hypertension, heart disease, and carotid artery stenosis.21–23 Recent work has moved beyond these associations, finding that hyperintensity severity may be associated with metabolic24,25 and dietary differences,26 and that differences in hyperintensity severity between depressed and nondepressed subjects may be even greater in those without significant vascular risk factors.27 There has also been interest in genetic polymorphisms that may increase the risk of developing hyperintensities, including polymorphisms related to blood pressure28,29 or homocysteine metabolism.24 Another potential genetic candidate may be the Val66Met polymorphism of the brain-derived neurotrophic factor (BDNF) gene. This polymorphism results in a valine (Val) to methionine (Met) substitution with the Met allele being associated with abnormal intracellular packaging and altered distribution of BDNF.30,31 Although the Met66 allele has been mostly associated with structural and functional alterations of the prefrontal cortex and the hippocampus,31–33 this work has primarily been done in younger adult populations. Meanwhile, animal studies of induced cerebral ischemia suggest that BDNF expression reduces lesion volume and improves functional recovery.34,35 Hypothetically, the Met66 polymorphism negatively affects BDNF secretion, which in turn would result in a reduced or slower BDNF response and less protection against ischemia. In this study, we examined the relationship between the BDNF Val66Met polymorphism and volumes of both white matter hyperintensities (WMHs) and gray matter hyperintensities (GMHs) in a cohort of older depressed and nondepressed subjects. We
264
hypothesized that carriers of the Met66 allele (Met66 allele homozygotes or heterozygotes) would exhibit larger hyperintensity volumes than would Val66 allele homozygotes.
METHODS Sample and Clinical Assessments Subjects were participants in the National Institute of Mental Health (NIMH) Conte Center for the Neuroscience of Depression in Late Life located at Duke University Medical Center. Eligibility was limited to patients aged 60 years or older. Depressed subject eligibility included a diagnosis of major depression based on NIMH Diagnostic Interview Schedule36 and clinical evaluation by a geriatric psychiatrist. Exclusion criteria included 1) another major psychiatric illness, although coexisting anxiety symptoms considered to be secondary to the depression diagnosis were allowed; 2) history of alcohol or drug abuse or dependence; 3) primary neurologic illness, including dementia; and 4) any contraindication to MRI. Subjects were recruited for the study primarily through clinical referrals to the study, but also through limited advertising at Duke University Medical Center and through self-referral. All depressed subjects had age of first depression onset assessed through self-report, and depression severity was measured using the Montgomery–Asberg Depression Rating Scale.37 Comparison subjects were community-dwelling and recruited from the Aging Center Subject Registry at Duke University. Eligible comparison subjects had a nonfocal neurologic examination, no self-report of neurologic or psychiatric illness, no evidence of a depression diagnosis based on the Diagnostic Interview Schedule, and no contraindication to MRI. The study protocol was approved by the Duke University Medical Center Institutional Review Board. All subjects provided written informed consent before beginning study procedures. This study included white subjects previously included in a study examining BDNF Val66Met allele frequency in geriatric depression.38 However, some of the depressed subjects from that study were not included in this study because they did not complete neuroimaging or their neuroimaging data could not
Am J Geriatr Psychiatry 16:4, April 2008
Taylor et al. be processed. The current study also includes additional nondepressed subjects recruited since the previous study. The majority of these subjects also had their neuroimaging results included in a previous study examining hyperintensity volume differences between depressed and nondepressed subjects.22 Subjects were excluded if they had a diagnosis of dementia or if the study geriatric psychiatrist suspected dementia at baseline. The majority of subjects had Mini-Mental State Examination39 scores above 24; some severely depressed individuals had scores below 25. These subjects were followed through an acute three-month treatment phase; if the scores remained below 25, they were not included in this study. Presence of comorbid hypertension, diabetes, and heart disease (coded as “heart trouble”) was assessed through a self-report questionnaire. The format was derived from questions included in NIMH Epidemiological Catchment Area Program.40 Only hypertension was included as a primary variable because we have previously demonstrated that unlike the other diagnoses, it is associated with cross-sectional hyperintensity lesion volumes22 and is more common in individuals with more severe hyperintensity lesion burden.41 Diabetes and heart disease were included for secondary analyses.
Genotyping Fresh blood samples were obtained from all participants and DNA was extracted and stored according to methods and quality checks previously reported.42 An aliquot of DNA was used for genotyping of the BDNF Val66Met polymorphism. DNA samples were placed in 96-well plates together with no-template controls and four sample duplicates in an asymmetric pattern to avoid unintended plateswitching. DNA was polymerase chain reaction-amplified applying a Taqman by-design assay (Applied Biosystems) that recognized the single nucleotide polymorphism, which defines the Val66Met polymorphism (rs6265). The samples were examined with an ABI7900 DNA analyzer (Applied Biosystems) and the genotypes determined with the SDS software package (Applied Biosystems). Greater than 95% genotyping efficiency was required before data were submitted for further analysis.
Am J Geriatr Psychiatry 16:4, April 2008
Magnetic Resonance Image Acquisition Subjects were imaged using a 1.5-Tesla wholebody MRI system (Signa; GE Medical Systems, Milwaukee, WI) using the standard head (volumetric) radiofrequency coil. 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 confirmed the alignment. A dual-echo fast spin-echo acquisition was obtained in the axial plane for morphometric analysis of lesion volumes. The pulse sequence parameters are relaxation time ⫽ 4000 msec, excitation time ⫽ 30, 135 msec, 32 kHz (⫾16 KHz) full imaging bandwidth, echo train length ⫽ 16, a 256 ⫻ 256 matrix, 3-mm section thickness, 1 excitation, and a 20-cm field of view. The images were acquired 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 with no gap.
Magnetic Resonance Image Analysis The segmentation protocol has been previously described43,44 and uses a modified version of MrX software created by GE Corporate Research and Development (Schenectady, NY) originally modified by Brigham and Women’s Hospital (Boston) for image segmentation.45 This semiautomated method uses the multiple MRI contrasts to identify different tissue classifications through a “seeding” process in which a trained analyst manually selected pixels in each tissue type to be identified (such as gray matter, white matter, cerebrospinal fluid, lesions, and background). Lesion areas were selected based on a set of explicit rules developed from neuroanatomic guidelines, consultation with a neuroradiologist, and knowledge of the neuropathology of lesions. Both periventricular and deep white matter lesions were combined to provide a measure of WMHs on the segmented image. GMHs were those occurring in subcortical gray matter structures. All technicians received extensive training by experienced volumetric analysts. Reliability was established by repeated measurements on 16 MRI scans by each rater before raters were approved to process
265
BNDF VAL66MET Polymorphism and Cerebral White Matter Hyperintensities study data. Intraclass correlation coefficients were: left cerebral gray matter lesions ⫽ 0.995, right cerebral gray matter lesions ⫽ 0.996, left cerebral white matter lesions ⫽ 0.988, and right cerebral white matter lesions ⫽ 0.994. Statistical Analysis Tests for deviations from Hardy-Weinberg equilibrium were conducted in unrelated cases and controls using the exact test from Genetic Data Analysis software.46,47 All other analyses were conducted using SAS 8.02 (Cary, NC). Because the allele frequency for Met66 is low, homozygotes are rare. Thus, we dichotomized subjects into those who had no copies of Met66 (Val66 homozygotes) and those who carried at least one Met66 allele. Univariate analyses examined for differences in demographic variables and hyperintensity volumes between depressed and nondepressed subjects, but also between Val66 allele homozygotes and Met66 allele carriers. These tests used chi-square models for categorical variables and two-tailed t tests for continuous variables. The Satterthwaite t test was used for continuous variables with unequal variances. These initial analyses were extended by developing general linear models, in which demographic measures or hyperintensity volumes were dependent variables, whereas depression diagnosis and Val66Met genotype were independent variables. General linear models were created next, examining first WMH volume and then GMH volume as the
TABLE 1.
dependent variable. Independent variables included Val66Met genotype, presence or absence of depression, age, sex, and presence or absence of hypertension. They also included a gene-by-diagnosis interaction term, which was removed if it did not reach statistical significance. Secondary analyses additionally included presence or absence of diabetes and heart disease; these were not included in primary analyses because we have not previously found a cross-sectional relationship between these measures and hyperintensity volume.22
RESULTS This study included 312 white subjects, 199 of whom had a diagnosis of depression, and 113 were nondepressed control subjects. Minority subjects were not included, because we have previously identified a difference in allele frequency between minority subjects and white subjects.38 There were no significant differences in sex representation or age between those with and without depression (Table 1); however, the nondepressed group overall was more educated. As we have presented previously, the depressed group exhibited greater volumes of WMH lesions and GMH lesions.22 The depressed group exhibited a mean age of depression onset of 45.2 years (standard deviation: 20.9 years; range: 4 – 86 years) and a mean Montgomery–Asberg Depression
Univariate Group Differences by Diagnosis and BDNF Val66Met Genotype Depressed (N ⴝ 199)
Age Sex (percent female) Education WMH GMH hyperintensities
70.0 (7.8) 65.3% (130/199) 13.7 (2.8) 7.0 (11.0) 0.28 (0.50)
Percent depressed Age Sex (percent female) Education WMH GMH
Val/Val (N ⴝ 203) 59.1% (120/203) 70.5 (7.1) 66.5% (135/203) 14.4 (2.6) 5.5 (6.4) 0.21 (0.39)
Nondepressed (N ⴝ 113)
69.9 (5.6) 72.6% (82/113) 15.6 (1.6) 4.8 (6.4) 0.16 (0.23) BDNF Genotype Met Carrier (N ⴝ 109) 72.5% (79/109) 68.5 (6.8) 70.6% (77/109) 14.3 (2.7) 7.3 (13.6) 0.26 (0.47)
df
Test Statistic
p Value
294 1 310 294 280
t ⫽ 0.06 2 ⫽ 1.73 t ⫽ 7.57 t ⫽ 2.12 t ⫽ 2.77
0.9533 0.1878 ⬍0.0001 0.0351 0.0059
df 1 310 1 310 130 184
Test Statistic 2 ⫽ 5.48 t ⫽ 1.82 2 ⫽ 0.56 t ⫽ 0.30 t ⫽ 1.32 t ⫽ 0.75
p Value 0.0192 0.0696 0.4550 0.7651 0.1902 0.4513
Note: Age and education are in years. BDNF: brain-derived neurotrophic factor; WMH: white matter hyperintensity volume; GMH: gray matter hyperintensity volume. Both of these volume measures are in milliliters.
266
Am J Geriatr Psychiatry 16:4, April 2008
Taylor et al. Rating Scale score of 26.7 (standard deviation: 8.1; range: 16 –54). Two hundred three subjects were Val66 allele homozygotes, 11 were Met66 allele homozygotes, and the remaining 98 were heterozygotes. Tests for Hardy-Weinberg equilibrium deviations were calculated in affected and unaffected individuals. We found no evidence of deviation from Hardy-Weinberg equilibrium for rs6265 (Val66Met). As previously reported, there were significantly more Met66 allele carriers in the depressed cohort (Table 1).38 To better understand the relationship between depression diagnosis and Val66Met genotype, we tested for differences between hyperintensity volumes and demographic variables using general linear models in which diagnosis and genotype were independent variables (Table 2). In the initial univariate analyses (Table 1), there were no significant differences in demographic measures between Val66 homozygotes and Met66 allele carriers, although age was significantly different between genotype groups when controlling for depression (Table 2). Initially, it appeared that Met66 allele carriers exhibited larger white matter and gray matter hyperintense lesion volumes, although these differences were not statistically significant (Table 1). When the relationship between hyperintensity volume and genotype was examined by depression diagnosis (Table 2), the Met66 allele appeared consistently related to WMH volume in both diagnostic groups, but not GMH. Given previous work associating advanced age with greater lesion vol-
TABLE 2.
Group Differences by Both BDNF Val66Met Genotype and Diagnosis Depressed
Age Sex (percent female) Education
umes,19,20,22,48 which could confound the results, we elected to proceed with more complete statistical models. We hypothesized the lower age in the Met66 allele group may have influenced the results of the univariate analyses of lesion volumes. The first model examined WMH volume as the dependent variable while including the independent variables Val66Met genotype, presence or absence of depression, and hypertension, age, and sex. The initial model included a depression-by-genotype interaction term; this term was not statistically significant, so the model was rerun without it. In the subsequent model, Met66 allele carriers exhibited significantly greater WMH volumes (F1,311 ⫽ 4.09, p ⫽ 0.0442). Age (F1,311 ⫽ 45.48, p ⬍0.0001) and the presence of hypertension (F1,311 ⫽ 8.91, p ⫽ 0.0031) were also significantly associated with WMH volume. Neither sex (F1,311 ⫽ 0.21, p ⫽ 0.6487) nor depression (F1,311 ⫽ 1.30, p ⫽ 0.2552) was significantly associated with WMH volume. The next model used a similar structure to examine GMH volume as the dependent variable. The depression-by-genotype interaction term again did not reach a level of statistical significance and so was removed from the model and the model was rerun. BDNF genotype was not significantly associated with GMH volume (F1,311 ⫽ 1.14, p⫽0.2871). Depressed subjects exhibited significantly higher GMH volumes (F1,311 ⫽ 4.33, p ⫽ 0.0384), and age was positively associated with GMH volume (F1,311 ⫽ 31.41, p⬍0.0001). Neither hypertension (F1,311 ⫽ 1.08, p ⫽ 0.2992) nor sex
Nondepressed
Val/Val (N ⴝ 120)
Met carrier (N ⴝ 79)
Val/Val (N ⴝ 83)
Met Carrier (N ⴝ 30)
Independent Variable
F Value
p Value
70.3 (7.7)
68.8 (6.8)
70.1 (5.8)
67.8 (5.3)
76/120 (63.3%)
54/79 (68.4%)
59/83 (71.1%)
23/30 (76.7%)
13.7 (2.7)
13.8 (2.8)
15.5 (1.7)
15.6 (2.0)
Diagnosis Genotype Diagnosis Genotype Diagnosis Genotype Diagnosis Genotype Diagnosis Genotype
0.07 4.60 3.14 0.64 30.88 0.00 3.44 2.58 5.45 0.65
0.7914 0.0327 0.0774 0.4258 ⬍0.0001 0.9628 0.0648 0.1095 0.0202 0.4209
WMH
6.1 (7.3)
8.6 (15.7)
4.4 (5.1)
5.4 (8.8)
GMH
0.25 (0.48)
0.32 (0.55)
0.16 (0.22)
0.14 (0.25)
Note: Continuous variables presented as mean (standard deviation). Age and education are in years. BDNF: brain-derived neurotrophic factor; WMH: white matter hyperintensity volume; GMH: gray matter hyperintensity volume. Both of these volume measures are in milliliters. Test results are of analyses in which diagnosis and genotype were included as independent variables of a general linear model examining demographics or hyperintensity volume as the dependent variable. Each independent variable had 1,311 degrees of freedom.
Am J Geriatr Psychiatry 16:4, April 2008
267
BNDF VAL66MET Polymorphism and Cerebral White Matter Hyperintensities (F1,311 ⫽ 0.07, p ⫽ 0.7974) was significantly associated with GMH volume. Secondary models were run including presence of diabetes and heart disease as dependent variables. Neither measure was associated with either WMH or GMH volume. Inclusion of these variables did not substantially change our findings.
DISCUSSION This report demonstrates an association between the BDNF Val66Met polymorphism and hyperintense lesion volume in older individuals, expanding our understanding of the influence of this genetic locus on brain structure beyond what has been observed in younger adult populations.31–33 Our finding adds to the broader literature of other genetic risk factors for hyperintensities; other polymorphisms associated with hyperintensity severity or ischemic disease include polymorphisms affecting homocysteine metabolism24 and the renin–angiotensin–aldosterone system.28,29,49 Although we have found that the Met66 allele is more common in depressed older subjects, and we now associate the Met66 allele with greater WMH volume, we did not find a gene by diagnosis interaction. Thus, the Met66 allele is associated with greater hyperintensity severity in both depressed and nondepressed elders. Studies reporting an association between the Met66 allele and alterations in structure or function of the prefrontal cortex and hippocampus have observed that these regions show abundant BDNF expression.32,50 They propose that the relationship they observed may be secondary to fixed changes of synaptic and cellular plasticity, presumably related to altered BDNF secretion seen with Met66 alleles, which in turn could affect cortical morphology.31 In general, studies examining hippocampal neurons support a role for BDNF in neuronal growth and survival.51,52 The mechanism for how BDNF is related to hyperintensity lesion volume may hinge on its role in neuronal survival. Although hyperintensities may represent various pathologic findings, cerebral ischemia is thought to be a common contributor to their development.1,2 BDNF may be protective against ischemia, because animal models have shown that
268
BDNF infusion will reduce cerebral damage caused by transient ischemia as well as improve functional outcomes.34,35,53 Thus, the Met66 allele, which is associated with altered BDNF secretion, may be related to the severity of cerebral damage seen with ischemia. This relationship may become particularly important in individuals with comorbid medical conditions that increase the risk of developing hyperintense lesions or cerebral ischemia. Hypertension is associated with hyperintensity lesion severity, whereas diabetes is associated with greater progression of lesion severity.22,54 Additionally, hypertension and elevated glucose levels appear to downregulate BDNF expression.55,56 Thus, the presence of comorbid vascular risk factors, in conjunction with the Met66 allele, may predispose individuals to a situation in which they cannot adequately upregulate BDNF secretion in response to ischemia, resulting in more severe injury to the brain. Clearly, this hypothesis needs further investigation. Our previous work38 supports that the BDNF Met66 allele occurs more frequently in depressed elders; we now report that the Met66 allele is also associated with greater WMH severity but independent of diagnosis. In multivariate models, the difference in WMH between depressed and nondepressed subjects was not statistically significant after controlling for BDNF genotype; this polymorphism apparently accounts for some of the differences in WMH volume observed between the diagnostic groups. However, other genetic polymorphism affecting the renin–angiotensin system or homocysteine metabolism also affect WMH severity28,29 and have been associated with depression.57,58 Thus, individuals who have more severe WMH disease, which is associated with depression, may have more of these polymorphisms, although anyone regardless of a depression diagnosis with one or more of these polymorphisms may have greater WMH severity than someone without any. Thus, gene effects are not necessarily specific to depression, but someone whose depression may be related to WMH disease may have more of these polymorphisms. It is possible that the BDNF Met66 allele may increase the risk for depression independently of its relationship with WMH severity. Presumably by affecting BDNF’s role in neurogenesis, the Met66 allele has been associated with the structure and cognitive function of the hippocampus33,59 and dorsolateral
Am J Geriatr Psychiatry 16:4, April 2008
Taylor et al. prefrontal cortex,32 two regions critically involved in mood regulation. These changes in turn could predispose individuals to depressive responses to adversity as recent studies have associated BDNF with social stress adaptation60 and how individuals respond to aversive social experiences.61,62 We demonstrate an association between this polymorphism and greater severity of hyperintense lesions. However, given that this study is only crosssectional, it limits our ability to make conclusions on the mechanism behind this relationship. The study has other limitations, including use of self-report for the evaluation of hypertension, heart disease, and diabetes. Although these measures have been used in other studies, there is the possibility of false-negative reports in which medical illness is present but not recognized. In addition, other risk factors for cerebrovascular disorders were not included in the study such as hyperlipidemia or tobacco use; moreover, clinical measures of vital signs may be preferable to obtaining only a clinical history. A particular strength of the study is the use of measures of hyperintensity volume rather than visual rating scales as seen in other studies examining this issue in late-life depression. It should be noted that there is a mean difference of 1.8 mL of WMH volume between those with and without the Met66 allele, in which Met66 allele carriers exhibit a 32.7% greater mean WMH volume. Although 1.8 mL is not a large amount when total brain volume in this population is approximately
1000 mL,7 an increase of one third of the lesion volume may be clinically relevant when considered that increased hyperintense lesion volumes are associated not only with psychiatric comorbidity, but also cognitive deficits,20,63 gait instability,64 and incontinence.65 It is not clear how the Val66Met polymorphism is related to other critical issues of late-life depression such as cognitive deficits, treatment outcomes, or mortality. Future studies examining these issues should test for potential interactions between this polymorphism and other genetic polymorphisms associated with hyperintense lesions as well as examine for a relationship with severity of comorbid medical conditions such as hypertension and diabetes. Identification of genetic polymorphisms related to WMH may have clinical implications, because such individuals may require more aggressive control of comorbid conditions to prevent or slow WMH development and improve clinical outcomes. This study was supported by NIMH grants K23 MH65939, R01 MH54846, and P50 MH60451 and National Institute of Environmental Health Sciences grant ES11961. Preliminary data were presented at the 2007 Annual Meeting of the American Association for Geriatric Psychiatry, New Orleans, LA, March 2– 4, 2007.
References 1. Thomas AJ, O’Brien JT, Davis S, et al: Ischemic basis for deep white matter hyperintensities in major depression. Arch Gen Psychiatry 2002; 59:785–792 2. Thomas AJ, Perry R, Barber R, et al: Pathologies and pathological mechanisms for white matter hyperintensities in depression. Ann NY Acad Sci 2002; 977:333–339 3. Tomimoto H, Lin J, Ihara M, et al: Subinsular vascular lesions: An analysis of 119 consecutive autopsied brains. Eur J Neurol 2007; 14:95–101 4. Fujikawa T, Yamawaki S, Touhouda Y: Incidence of silent cerebral infarction in patients with major depression. Stroke 1993; 24:1631–1634 5. Krishnan KRR, Hays JC, Blazer DG: MRI-defined vascular depression. Am J Psychiatry 1997; 154:497–501 6. Krishnan KRR, McDonald WM, Doraiswamy PM, et al: Neuroanatomical substrates of depression in the elderly. Eur Arch Psychiatry Clin Neurosci 1993; 243:41–46 7. Kumar A, Bilker W, Jin Z, et al: Atrophy and high intensity lesions: complementary neurobiological mechanisms in latelife depression. Neuropsychopharmacology 2000; 22:264 –274
Am J Geriatr Psychiatry 16:4, April 2008
8. de Groot JC, de Leeuw F, Oudkerk M, et al: Cerebral white matter lesions and depressive symptoms in elderly adults. Arch Gen Psychiatry 2000; 57:1071–1076 9. Figiel GS, Krishnan KRR, Doraiswamy PM, et al: Subcortical hyperintensities on brain magnetic resonance imaging: a comparison between late age onset and early onset elderly depressed subjects. Neurobiol Aging 1991; 12:245–247 10. Lavretsky H, Lesser IM, Wohl M, et al: Relationship of age, age at onset, and sex to depression in older adults. Am J Geriatr Psychiatry 1998; 6:248 –256 11. Salloway S, Malloy P, Kohn R, et al: MRI and neuropsychological differences in early- and late-life-onset geriatric depression. Neurology 1996; 46:1567–1574 12. Lesser IM, Hill-Gutierrez E, Miller BL, et al: Late-onset depression with white matter lesions. Psychosomatics 1993; 34:364 –367 13. Hickie I, Scott E, Mitchell P, et al: Subcortical hyperintensities on magnetic resonance imaging: Clinical correlates and prognostic significance in patients with severe depression. Biol Psychiatry 1995; 37:151–160 14. O’Brien J, Ames D, Chiu E, et al: Severe deep white matter lesions
269
BNDF VAL66MET Polymorphism and Cerebral White Matter Hyperintensities and outcome in elderly patients with major depressive disorder: follow up study. BMJ 1998; 317:982–984 15. Simpson S, Talbot PR, Snowden JS, et al: Subcortical vascular disease in elderly patients with treatment resistant depression. J Neurol Neurosurg Psychiatry 1997; 62:196 –197 16. Iosifescu DV, Renshaw PF, Lyoo IK, et al: Brain white-matter hyperintensities and treatment outcome in major depressive disorder. Br J Psychiatry 2006; 188:180 –185 17. Lavretsky H, Lesser IM, Wohl M, et al: Clinical and neuroradiologic features associated with chronicity in late-life depression. Am J Geriatr Psychiatry 1999; 7:309 –316 18. Taylor WD, Steffens DC, MacFall JR, et al: White matter hyperintensity progression and late-life depression outcomes. Arch Gen Psychiatry 2003; 60:1090 –1096 19. Awad IA, Spetzler RF, Hodak JA, et al: Incidental subcortical lesions identified on magnetic resonance imaging in the elderly. I. Correlation with age and cerebrovascular risk factors. Stroke 1986; 17:1084 –1089 20. Longstreth WTJ, Manolio TA, Arnold A, et al: Clinical correlates of white matter findings on cranial magnetic resonance imaging of 3301 elderly people: The Cardiovascular Health Study. Stroke 1996; 27:1274 –1282 21. Fazekas F, Niederkor K, Schmidt R, et al: White matter signal abnormalities in normal individuals: correlation with carotid ultrasonography, cerebral blood flow measurements, and cerebrovascular risk factors. Stroke 1988; 19:1285–1288 22. Taylor WD, MacFall JR, Payne ME, et al: Greater MRI lesion volumes in elderly depressed subjects than in control subjects. Psychiatry Res 2005; 139:1–7 23. Patankar T, Widjaja E, Chant H, et al: Relationship of deep white matter hyperintensities and cerebral blood flow in severe carotid artery stenosis. Eur J Neurol 2006; 13:10 –16 24. Hassan A, Hunt BJ, O’Sullivan M, et al: Homocysteine is a risk factor for cerebral small vessel disease, acting via endothelial dysfunction. Brain 2004; 127:212–219 25. Iosifescu DV, Papakostas GI, Lyoo IK, et al: Brain MRI hyperintensities and one-carbon cycle metabolism in non-geriatric outpatients with major depressive disorder (part I). Psychiatry Res 2005; 140:291–299 26. Payne ME, Haines PS, Chambless LE, et al: Food group intake and brain lesions in late-life vascular depression. Int Psychogeriatr 2007; 19:295–305 27. Firbank MJ, Lloyd AJ, Ferrier N, et al: A volumetric study of MRI signal hyperintensities in late-life depression. Am J Geriatr Psychiatry 2004; 12:606 –612 28. Takami S, Imai Y, Katsuya T, et al: Gene polymorphism of the renin–angiotensin system associates with risk for lacunar infarction. The Ohasama Study. Am J Hypertens 2000; 13:121–127 29. Schmidt H, Fazekas F, Kostner GM, et al: Angiotensin gene promoter haplotype and microangiopathy-related cerebral damage. Results of the Austrian Stroke Prevention Study. Stroke 2001; 32:405–412 30. Chen ZY, Patel PD, Sant G, et al: Variant brain-derived neurotrophic factor (BDNF) (Met66) alters the intracellular trafficking and activity-dependent secretion of wild-type BDNF in neurosecretory cells and cortical neurons. J Neurosci 2004; 24:4401–4411 31. Egan MF, Kojima M, Callicott JH, et al: The BDNF val66met polymorphism affects activity-dependent secretion of BDNF and human memory and hippocampal function. Cell 2003; 112:257– 269 32. Pezawas L, Verchinski BA, Mattay VS, et al: The brain-derived neurotrophic factor val66met polymorphism and variation in human cortical morphology. J Neurosci 2004; 24:10099 –10102
270
33. Szesko PR, Lipsky R, Mentschel C, et al: Brain-derived neurotrophic factor val66met polymorphism and volume of the hippocampal formation. Mol Psychiatry 2005; 10:631–636 34. Nomura T, Honmou OHK, Houkin K, et al: I.V. infusion of brain-derived neurotrophic factor gene-modified human mesenchymal stem cells protects against injury in a cerebral ischemia model in adult rat. Neuroscience 2005; 136:161–169 35. Yanamoto H, Xue JH, Miyamoto S, et al: Spreading depression induces long-lasting brain protection against infarcted lesion development via BDNF gene-dependent mechanism. Brain Res 2004; 1019:178 –188 36. Robins LN, Helzer JE, Croughan J, et al: National Institute of Mental Health Diagnostic Interview Schedule. Its history, characteristics, and validity. Arch Gen Psychiatry 1981; 38:381–389 37. Montgomery SA, Asberg M: A new depression scale designed to be sensitive to change. Br J Psychiatry 1979; 134:382–389 38. Taylor WD, Zuchner S, McQuoid DR, et al: Allelic differences in the BDNF val66met polymorphism in late-life depression. Am J Geriatr Psychiatry 2007; 15:850 –857 39. Folstein MF, Folstein SE, McHugh PR: ‘Mini-mental state’: a practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 1975; 12:189 –198 40. Regier DA, Myers JK, Kramer M, et al: The NIMH Epidemiologic Catchment Area program: historical context, major objectives, and study population characteristics. Arch Gen Psychiatry 1984; 41:934 –941 41. Taylor WD, McQuoid DR, Krishnan KR: Medical comorbidity in late-life depression. Int J Geriatr Psychiatry 2004; 19:935–943 42. Rimmler J, McDowell JG, Slotterback BD, et al: Development of a data coordinating center (DCC): data quality control for complex disease studies. Am J Hum Genet 1998; SupplementSuppl 63: A240 43. Byrum CE, MacFall JR, Charles HC, et al: Accuracy and reproducibility of brain and tissue volumes using a magnetic resonance segmentation method. Psychiatry Res 1996; 67:215–234 44. Payne ME, Fetzer DL, MacFall JR, et al: Development of a semiautomated method for quantification of MRI gray and white matter lesions in geriatric subjects. Psychiatry Res 2002; 115: 63–77 45. Kikinis R, Shenton ME, Gerig G, et al: Routine quantitative analysis of brain and cerebrospinal fluid spaces with MR imaging. J Magn Reson Imaging 1992; 2:619 –629 46. Lewis PO, Zaykin D: Genetic Data Analysis 2000. Available at: http://hydrodictyon.eeb.uconn.edu/people/plewis/software.php 47. Weir BS: Genetic Data Analysis II: Methods for Discrete Population Genetic Data. Sunderland, MA, Sinauer Associates, 1996 48. Kumar A, Bilker W, Jin Z, et al: Age of onset of depression and quantitative neuroanatomic measures: absence of specific correlates. Psychiatry Res 1999; 91:101–110 49. Verpillat P, Alperovitch A, Cambien F, et al: Aldosterone synthase (CYP11B2) gene polymorphism and cerebral white matter hyperintensities. Neurology 2001; 56:673–675 50. Webster MJ, Weickert CS, Herman MM, et al: BDNF mRNA expression during postnatal development, maturation and aging of the human prefrontal cortex. Brain Res Dev Brain Res 2002; 139:139 –150 51. Shetty AK, Turner DA: In vitro survival and differentiation of neurons derived from epidermal growth factor-responsive postnatal hippocampal stem cells: inducing effects of brain-derived neurotrophic factor. J Neurobiol 1998; 35:395–425 52. Lowenstein DH, Arsenault L: The effects of growth factors on the survival and differentiation of cultured dentate gyrus neurons. J Neurosci 1996; 16:759 –769
Am J Geriatr Psychiatry 16:4, April 2008
Taylor et al. 53. Schwartz ED, Shumsky JS, Wehrli S, et al: Ex vivo MR determined apparent diffusion coefficients correlate with motor recovery mediated by intraspinal transplants of fibroblasts genetically modified to express BDNF. Exp Neurol 2003; 182:49 –63 54. Taylor WD, MacFall JR, Provenzale JM, et al: Serial MR imaging of hyperintense white matter lesion volumes in elderly subjects: correlation with vascular risk factors. AJR Am J Roentgenol 2003; 181:571–576 55. Krabbe KS, Nielsen AR, Krogh-Madsen R, et al: Brain-derived neurotrophic factor (BDNF) and type 2 diabetes. Diabetologia 2007; 50:431–438 56. Lee TH, Yang JT, Kato H, et al: Hypertension downregulates the expression of brain-derived neurotrophic factor in the ischemiavulnerable hippocampal CA1 and cortical areas after carotid artery occlusion. Brain Res 2006; 1116:31–38 57. Baghai TC, Binder EB, Schule C, et al: Polymorphisms in the angiotensin-converting enzyme gene are associated with unipolar depression, ACE activity and hypercortisolism. Mol Psychiatry 2006; 11:1003–1015 58. Bjelland I, Tell GS, Vollset SE, et al: Folate, vitamin B12, homocysteine, and the MTHFR 677C-⬎T polymorphism in anxiety and depression: The Hordaland Homocysteine Study. Arch Gen Psychiatry 2003; 60:618 –626 59. Hariri AR, Goldberg TE, Mattay VS, et al: Brain-derived neurotro-
Am J Geriatr Psychiatry 16:4, April 2008
phic factor val66met polymorphism affects human memory-related hippocampal activity and predicts memory performance. J Neurosci 2003; 23:6690 –6694 60. Surtees PG, Wainwright NW, Willis-Owen SA, et al: The brainderived neurotrophic factor Val66Met polymorphism is associated with a sense of coherence in a non-clinical community sample of 7335 adults. J Psychiatr Res 2006; Epub July 25 61. Berton O, McClung CA, DiLeone RJ, et al: Essential role of BDNF in the mesolimbic dopamine pathway in social defeat stress. Science 2006; 311:864 –868 62. Kaufman J, Yang BZ, Douglas-Palumberi H, et al: Brain-derived neurotrophic factor-5-HTTLPR interactions and environmental modifiers of depression in children. Biol Psychiatry 2006; 59: 673–680 63. O’Brien JT, Wiseman R, Burton EJ, et al: Cognitive associations of subcortical white matter lesions in older people. Ann N Y Acad Sci 2002; 977:436 –444 64. Whitman GT: A prospective study of cerebral white matter abnormalities in older people with gait dysfunction. Neurology 2001; 57:990 –994 65. Sakakibara R, Hattori T, Uchiyama T, et al: Urinary function in elderly people with and without leukoaraiosis: relation to cognitive and gait function. J Neurol Neurosurg Psychiatry 1999; 67: 658 –660
271
Objective: In animal models, brain-derived neurotrophic factor (BDNF) appears to protect against cerebral ischemia. The authors examined whether the BDNF Val66Met polymorphism, which affects BDNF distribution, was associated with greater volumes of hyperintense lesions as detected on magnetic resonance imaging in a cohort of depressed and nondepressed elders. Design: Subjects completed cross-sectional assessments, including clinical evaluation and a brain magnetic resonance imaging scan, and provided blood samples for Val66Met genotyping. Setting: The study was conducted at a universitybased academic hospital. Participants: Participants included 199 depressed and 113 nondepressed subjects aged 60 years or older. Measurement: Hyperintensity lesion volumes were measured using a semiautomated segmentation procedure. Statistical models examined the relationship between genotype and lesion volume while controlling for depression, presence of hypertension, age, and sex. Results: After controlling for covariates, Met66 allele carriers exhibited significantly greater white matter hyperintensity volumes (F1,311 ⫽ 4.09, p ⫽ 0.0442). This effect was independent of a diagnosis of depression or report of hypertension. Genotype was not significantly related to gray matter hyperintensity volume (F1,311 ⫽ 1.14, p ⫽ 0.2871). Conclusions: The BDNF Met66 allele is associated with greater white matter hyperintensity volumes in older individuals. Further work is needed to determine how this may be associated with other clinically relevant findings in late-life depression. (Am J Geriatr Psychiatry 2008; 16:263–271)
Key Words: genetic polymorphisms, magnetic resonance imaging, depression
H
yperintense lesions are bright areas in the brain parenchyma observed on magnetic resonance imaging (MRI). These lesions are generally consid-
ered to be ischemic in origin, although pathologic examination may also reveal perivascular dilation or demyelination, and ultimately the pathologic exam-
Received May 18, 2007; revised August 10, 2007; accepted August 15, 2007. From the Center for Human Genetics (SZ, MCS), the Neuropsychiatric Imaging Research Laboratory (WDT, MEP, JRM, KRRK), and the Departments of Medicine (MCS), Psychiatry (WDT, DRM, MEP, DCS, KRRK), and Radiology (JRM), Duke University Medical Center, Durham, NC; The Miami Institute of Human Genomics (SZ), University of Miami Miller School of Medicine, Miami, FL; and Duke–NUS Graduate Medical School (KRRK), Singapore. Send correspondence and reprint requests to Dr. Warren D. Taylor, Duke University Medical Center, DUMC 3903, Durham, NC 27710. e-mail: [email protected] © 2008 American Association for Geriatric Psychiatry
Am J Geriatr Psychiatry 16:4, April 2008
263
BNDF VAL66MET Polymorphism and Cerebral White Matter Hyperintensities ination correlates to what is observed on MRI, but may vary dependent on the brain region being examined.1–3 Hyperintensities are more severe and of greater volume in older depressed subjects than in comparison groups4 –7 and are more common with initial development of depression later in life.8 –12 This increased hyperintensity severity is associated with poorer antidepressant response13–16 and a more chronic course of depression.17,18 A number of clinical factors are associated with hyperintensity severity. Although age19,20 is perhaps the most strongly associated factor, medical comorbidity is also associated with hyperintensity severity, particularly vascular risk factors, including but not limited to hypertension, heart disease, and carotid artery stenosis.21–23 Recent work has moved beyond these associations, finding that hyperintensity severity may be associated with metabolic24,25 and dietary differences,26 and that differences in hyperintensity severity between depressed and nondepressed subjects may be even greater in those without significant vascular risk factors.27 There has also been interest in genetic polymorphisms that may increase the risk of developing hyperintensities, including polymorphisms related to blood pressure28,29 or homocysteine metabolism.24 Another potential genetic candidate may be the Val66Met polymorphism of the brain-derived neurotrophic factor (BDNF) gene. This polymorphism results in a valine (Val) to methionine (Met) substitution with the Met allele being associated with abnormal intracellular packaging and altered distribution of BDNF.30,31 Although the Met66 allele has been mostly associated with structural and functional alterations of the prefrontal cortex and the hippocampus,31–33 this work has primarily been done in younger adult populations. Meanwhile, animal studies of induced cerebral ischemia suggest that BDNF expression reduces lesion volume and improves functional recovery.34,35 Hypothetically, the Met66 polymorphism negatively affects BDNF secretion, which in turn would result in a reduced or slower BDNF response and less protection against ischemia. In this study, we examined the relationship between the BDNF Val66Met polymorphism and volumes of both white matter hyperintensities (WMHs) and gray matter hyperintensities (GMHs) in a cohort of older depressed and nondepressed subjects. We
264
hypothesized that carriers of the Met66 allele (Met66 allele homozygotes or heterozygotes) would exhibit larger hyperintensity volumes than would Val66 allele homozygotes.
METHODS Sample and Clinical Assessments Subjects were participants in the National Institute of Mental Health (NIMH) Conte Center for the Neuroscience of Depression in Late Life located at Duke University Medical Center. Eligibility was limited to patients aged 60 years or older. Depressed subject eligibility included a diagnosis of major depression based on NIMH Diagnostic Interview Schedule36 and clinical evaluation by a geriatric psychiatrist. Exclusion criteria included 1) another major psychiatric illness, although coexisting anxiety symptoms considered to be secondary to the depression diagnosis were allowed; 2) history of alcohol or drug abuse or dependence; 3) primary neurologic illness, including dementia; and 4) any contraindication to MRI. Subjects were recruited for the study primarily through clinical referrals to the study, but also through limited advertising at Duke University Medical Center and through self-referral. All depressed subjects had age of first depression onset assessed through self-report, and depression severity was measured using the Montgomery–Asberg Depression Rating Scale.37 Comparison subjects were community-dwelling and recruited from the Aging Center Subject Registry at Duke University. Eligible comparison subjects had a nonfocal neurologic examination, no self-report of neurologic or psychiatric illness, no evidence of a depression diagnosis based on the Diagnostic Interview Schedule, and no contraindication to MRI. The study protocol was approved by the Duke University Medical Center Institutional Review Board. All subjects provided written informed consent before beginning study procedures. This study included white subjects previously included in a study examining BDNF Val66Met allele frequency in geriatric depression.38 However, some of the depressed subjects from that study were not included in this study because they did not complete neuroimaging or their neuroimaging data could not
Am J Geriatr Psychiatry 16:4, April 2008
Taylor et al. be processed. The current study also includes additional nondepressed subjects recruited since the previous study. The majority of these subjects also had their neuroimaging results included in a previous study examining hyperintensity volume differences between depressed and nondepressed subjects.22 Subjects were excluded if they had a diagnosis of dementia or if the study geriatric psychiatrist suspected dementia at baseline. The majority of subjects had Mini-Mental State Examination39 scores above 24; some severely depressed individuals had scores below 25. These subjects were followed through an acute three-month treatment phase; if the scores remained below 25, they were not included in this study. Presence of comorbid hypertension, diabetes, and heart disease (coded as “heart trouble”) was assessed through a self-report questionnaire. The format was derived from questions included in NIMH Epidemiological Catchment Area Program.40 Only hypertension was included as a primary variable because we have previously demonstrated that unlike the other diagnoses, it is associated with cross-sectional hyperintensity lesion volumes22 and is more common in individuals with more severe hyperintensity lesion burden.41 Diabetes and heart disease were included for secondary analyses.
Genotyping Fresh blood samples were obtained from all participants and DNA was extracted and stored according to methods and quality checks previously reported.42 An aliquot of DNA was used for genotyping of the BDNF Val66Met polymorphism. DNA samples were placed in 96-well plates together with no-template controls and four sample duplicates in an asymmetric pattern to avoid unintended plateswitching. DNA was polymerase chain reaction-amplified applying a Taqman by-design assay (Applied Biosystems) that recognized the single nucleotide polymorphism, which defines the Val66Met polymorphism (rs6265). The samples were examined with an ABI7900 DNA analyzer (Applied Biosystems) and the genotypes determined with the SDS software package (Applied Biosystems). Greater than 95% genotyping efficiency was required before data were submitted for further analysis.
Am J Geriatr Psychiatry 16:4, April 2008
Magnetic Resonance Image Acquisition Subjects were imaged using a 1.5-Tesla wholebody MRI system (Signa; GE Medical Systems, Milwaukee, WI) using the standard head (volumetric) radiofrequency coil. 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 confirmed the alignment. A dual-echo fast spin-echo acquisition was obtained in the axial plane for morphometric analysis of lesion volumes. The pulse sequence parameters are relaxation time ⫽ 4000 msec, excitation time ⫽ 30, 135 msec, 32 kHz (⫾16 KHz) full imaging bandwidth, echo train length ⫽ 16, a 256 ⫻ 256 matrix, 3-mm section thickness, 1 excitation, and a 20-cm field of view. The images were acquired 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 with no gap.
Magnetic Resonance Image Analysis The segmentation protocol has been previously described43,44 and uses a modified version of MrX software created by GE Corporate Research and Development (Schenectady, NY) originally modified by Brigham and Women’s Hospital (Boston) for image segmentation.45 This semiautomated method uses the multiple MRI contrasts to identify different tissue classifications through a “seeding” process in which a trained analyst manually selected pixels in each tissue type to be identified (such as gray matter, white matter, cerebrospinal fluid, lesions, and background). Lesion areas were selected based on a set of explicit rules developed from neuroanatomic guidelines, consultation with a neuroradiologist, and knowledge of the neuropathology of lesions. Both periventricular and deep white matter lesions were combined to provide a measure of WMHs on the segmented image. GMHs were those occurring in subcortical gray matter structures. All technicians received extensive training by experienced volumetric analysts. Reliability was established by repeated measurements on 16 MRI scans by each rater before raters were approved to process
265
BNDF VAL66MET Polymorphism and Cerebral White Matter Hyperintensities study data. Intraclass correlation coefficients were: left cerebral gray matter lesions ⫽ 0.995, right cerebral gray matter lesions ⫽ 0.996, left cerebral white matter lesions ⫽ 0.988, and right cerebral white matter lesions ⫽ 0.994. Statistical Analysis Tests for deviations from Hardy-Weinberg equilibrium were conducted in unrelated cases and controls using the exact test from Genetic Data Analysis software.46,47 All other analyses were conducted using SAS 8.02 (Cary, NC). Because the allele frequency for Met66 is low, homozygotes are rare. Thus, we dichotomized subjects into those who had no copies of Met66 (Val66 homozygotes) and those who carried at least one Met66 allele. Univariate analyses examined for differences in demographic variables and hyperintensity volumes between depressed and nondepressed subjects, but also between Val66 allele homozygotes and Met66 allele carriers. These tests used chi-square models for categorical variables and two-tailed t tests for continuous variables. The Satterthwaite t test was used for continuous variables with unequal variances. These initial analyses were extended by developing general linear models, in which demographic measures or hyperintensity volumes were dependent variables, whereas depression diagnosis and Val66Met genotype were independent variables. General linear models were created next, examining first WMH volume and then GMH volume as the
TABLE 1.
dependent variable. Independent variables included Val66Met genotype, presence or absence of depression, age, sex, and presence or absence of hypertension. They also included a gene-by-diagnosis interaction term, which was removed if it did not reach statistical significance. Secondary analyses additionally included presence or absence of diabetes and heart disease; these were not included in primary analyses because we have not previously found a cross-sectional relationship between these measures and hyperintensity volume.22
RESULTS This study included 312 white subjects, 199 of whom had a diagnosis of depression, and 113 were nondepressed control subjects. Minority subjects were not included, because we have previously identified a difference in allele frequency between minority subjects and white subjects.38 There were no significant differences in sex representation or age between those with and without depression (Table 1); however, the nondepressed group overall was more educated. As we have presented previously, the depressed group exhibited greater volumes of WMH lesions and GMH lesions.22 The depressed group exhibited a mean age of depression onset of 45.2 years (standard deviation: 20.9 years; range: 4 – 86 years) and a mean Montgomery–Asberg Depression
Univariate Group Differences by Diagnosis and BDNF Val66Met Genotype Depressed (N ⴝ 199)
Age Sex (percent female) Education WMH GMH hyperintensities
70.0 (7.8) 65.3% (130/199) 13.7 (2.8) 7.0 (11.0) 0.28 (0.50)
Percent depressed Age Sex (percent female) Education WMH GMH
Val/Val (N ⴝ 203) 59.1% (120/203) 70.5 (7.1) 66.5% (135/203) 14.4 (2.6) 5.5 (6.4) 0.21 (0.39)
Nondepressed (N ⴝ 113)
69.9 (5.6) 72.6% (82/113) 15.6 (1.6) 4.8 (6.4) 0.16 (0.23) BDNF Genotype Met Carrier (N ⴝ 109) 72.5% (79/109) 68.5 (6.8) 70.6% (77/109) 14.3 (2.7) 7.3 (13.6) 0.26 (0.47)
df
Test Statistic
p Value
294 1 310 294 280
t ⫽ 0.06 2 ⫽ 1.73 t ⫽ 7.57 t ⫽ 2.12 t ⫽ 2.77
0.9533 0.1878 ⬍0.0001 0.0351 0.0059
df 1 310 1 310 130 184
Test Statistic 2 ⫽ 5.48 t ⫽ 1.82 2 ⫽ 0.56 t ⫽ 0.30 t ⫽ 1.32 t ⫽ 0.75
p Value 0.0192 0.0696 0.4550 0.7651 0.1902 0.4513
Note: Age and education are in years. BDNF: brain-derived neurotrophic factor; WMH: white matter hyperintensity volume; GMH: gray matter hyperintensity volume. Both of these volume measures are in milliliters.
266
Am J Geriatr Psychiatry 16:4, April 2008
Taylor et al. Rating Scale score of 26.7 (standard deviation: 8.1; range: 16 –54). Two hundred three subjects were Val66 allele homozygotes, 11 were Met66 allele homozygotes, and the remaining 98 were heterozygotes. Tests for Hardy-Weinberg equilibrium deviations were calculated in affected and unaffected individuals. We found no evidence of deviation from Hardy-Weinberg equilibrium for rs6265 (Val66Met). As previously reported, there were significantly more Met66 allele carriers in the depressed cohort (Table 1).38 To better understand the relationship between depression diagnosis and Val66Met genotype, we tested for differences between hyperintensity volumes and demographic variables using general linear models in which diagnosis and genotype were independent variables (Table 2). In the initial univariate analyses (Table 1), there were no significant differences in demographic measures between Val66 homozygotes and Met66 allele carriers, although age was significantly different between genotype groups when controlling for depression (Table 2). Initially, it appeared that Met66 allele carriers exhibited larger white matter and gray matter hyperintense lesion volumes, although these differences were not statistically significant (Table 1). When the relationship between hyperintensity volume and genotype was examined by depression diagnosis (Table 2), the Met66 allele appeared consistently related to WMH volume in both diagnostic groups, but not GMH. Given previous work associating advanced age with greater lesion vol-
TABLE 2.
Group Differences by Both BDNF Val66Met Genotype and Diagnosis Depressed
Age Sex (percent female) Education
umes,19,20,22,48 which could confound the results, we elected to proceed with more complete statistical models. We hypothesized the lower age in the Met66 allele group may have influenced the results of the univariate analyses of lesion volumes. The first model examined WMH volume as the dependent variable while including the independent variables Val66Met genotype, presence or absence of depression, and hypertension, age, and sex. The initial model included a depression-by-genotype interaction term; this term was not statistically significant, so the model was rerun without it. In the subsequent model, Met66 allele carriers exhibited significantly greater WMH volumes (F1,311 ⫽ 4.09, p ⫽ 0.0442). Age (F1,311 ⫽ 45.48, p ⬍0.0001) and the presence of hypertension (F1,311 ⫽ 8.91, p ⫽ 0.0031) were also significantly associated with WMH volume. Neither sex (F1,311 ⫽ 0.21, p ⫽ 0.6487) nor depression (F1,311 ⫽ 1.30, p ⫽ 0.2552) was significantly associated with WMH volume. The next model used a similar structure to examine GMH volume as the dependent variable. The depression-by-genotype interaction term again did not reach a level of statistical significance and so was removed from the model and the model was rerun. BDNF genotype was not significantly associated with GMH volume (F1,311 ⫽ 1.14, p⫽0.2871). Depressed subjects exhibited significantly higher GMH volumes (F1,311 ⫽ 4.33, p ⫽ 0.0384), and age was positively associated with GMH volume (F1,311 ⫽ 31.41, p⬍0.0001). Neither hypertension (F1,311 ⫽ 1.08, p ⫽ 0.2992) nor sex
Nondepressed
Val/Val (N ⴝ 120)
Met carrier (N ⴝ 79)
Val/Val (N ⴝ 83)
Met Carrier (N ⴝ 30)
Independent Variable
F Value
p Value
70.3 (7.7)
68.8 (6.8)
70.1 (5.8)
67.8 (5.3)
76/120 (63.3%)
54/79 (68.4%)
59/83 (71.1%)
23/30 (76.7%)
13.7 (2.7)
13.8 (2.8)
15.5 (1.7)
15.6 (2.0)
Diagnosis Genotype Diagnosis Genotype Diagnosis Genotype Diagnosis Genotype Diagnosis Genotype
0.07 4.60 3.14 0.64 30.88 0.00 3.44 2.58 5.45 0.65
0.7914 0.0327 0.0774 0.4258 ⬍0.0001 0.9628 0.0648 0.1095 0.0202 0.4209
WMH
6.1 (7.3)
8.6 (15.7)
4.4 (5.1)
5.4 (8.8)
GMH
0.25 (0.48)
0.32 (0.55)
0.16 (0.22)
0.14 (0.25)
Note: Continuous variables presented as mean (standard deviation). Age and education are in years. BDNF: brain-derived neurotrophic factor; WMH: white matter hyperintensity volume; GMH: gray matter hyperintensity volume. Both of these volume measures are in milliliters. Test results are of analyses in which diagnosis and genotype were included as independent variables of a general linear model examining demographics or hyperintensity volume as the dependent variable. Each independent variable had 1,311 degrees of freedom.
Am J Geriatr Psychiatry 16:4, April 2008
267
BNDF VAL66MET Polymorphism and Cerebral White Matter Hyperintensities (F1,311 ⫽ 0.07, p ⫽ 0.7974) was significantly associated with GMH volume. Secondary models were run including presence of diabetes and heart disease as dependent variables. Neither measure was associated with either WMH or GMH volume. Inclusion of these variables did not substantially change our findings.
DISCUSSION This report demonstrates an association between the BDNF Val66Met polymorphism and hyperintense lesion volume in older individuals, expanding our understanding of the influence of this genetic locus on brain structure beyond what has been observed in younger adult populations.31–33 Our finding adds to the broader literature of other genetic risk factors for hyperintensities; other polymorphisms associated with hyperintensity severity or ischemic disease include polymorphisms affecting homocysteine metabolism24 and the renin–angiotensin–aldosterone system.28,29,49 Although we have found that the Met66 allele is more common in depressed older subjects, and we now associate the Met66 allele with greater WMH volume, we did not find a gene by diagnosis interaction. Thus, the Met66 allele is associated with greater hyperintensity severity in both depressed and nondepressed elders. Studies reporting an association between the Met66 allele and alterations in structure or function of the prefrontal cortex and hippocampus have observed that these regions show abundant BDNF expression.32,50 They propose that the relationship they observed may be secondary to fixed changes of synaptic and cellular plasticity, presumably related to altered BDNF secretion seen with Met66 alleles, which in turn could affect cortical morphology.31 In general, studies examining hippocampal neurons support a role for BDNF in neuronal growth and survival.51,52 The mechanism for how BDNF is related to hyperintensity lesion volume may hinge on its role in neuronal survival. Although hyperintensities may represent various pathologic findings, cerebral ischemia is thought to be a common contributor to their development.1,2 BDNF may be protective against ischemia, because animal models have shown that
268
BDNF infusion will reduce cerebral damage caused by transient ischemia as well as improve functional outcomes.34,35,53 Thus, the Met66 allele, which is associated with altered BDNF secretion, may be related to the severity of cerebral damage seen with ischemia. This relationship may become particularly important in individuals with comorbid medical conditions that increase the risk of developing hyperintense lesions or cerebral ischemia. Hypertension is associated with hyperintensity lesion severity, whereas diabetes is associated with greater progression of lesion severity.22,54 Additionally, hypertension and elevated glucose levels appear to downregulate BDNF expression.55,56 Thus, the presence of comorbid vascular risk factors, in conjunction with the Met66 allele, may predispose individuals to a situation in which they cannot adequately upregulate BDNF secretion in response to ischemia, resulting in more severe injury to the brain. Clearly, this hypothesis needs further investigation. Our previous work38 supports that the BDNF Met66 allele occurs more frequently in depressed elders; we now report that the Met66 allele is also associated with greater WMH severity but independent of diagnosis. In multivariate models, the difference in WMH between depressed and nondepressed subjects was not statistically significant after controlling for BDNF genotype; this polymorphism apparently accounts for some of the differences in WMH volume observed between the diagnostic groups. However, other genetic polymorphism affecting the renin–angiotensin system or homocysteine metabolism also affect WMH severity28,29 and have been associated with depression.57,58 Thus, individuals who have more severe WMH disease, which is associated with depression, may have more of these polymorphisms, although anyone regardless of a depression diagnosis with one or more of these polymorphisms may have greater WMH severity than someone without any. Thus, gene effects are not necessarily specific to depression, but someone whose depression may be related to WMH disease may have more of these polymorphisms. It is possible that the BDNF Met66 allele may increase the risk for depression independently of its relationship with WMH severity. Presumably by affecting BDNF’s role in neurogenesis, the Met66 allele has been associated with the structure and cognitive function of the hippocampus33,59 and dorsolateral
Am J Geriatr Psychiatry 16:4, April 2008
Taylor et al. prefrontal cortex,32 two regions critically involved in mood regulation. These changes in turn could predispose individuals to depressive responses to adversity as recent studies have associated BDNF with social stress adaptation60 and how individuals respond to aversive social experiences.61,62 We demonstrate an association between this polymorphism and greater severity of hyperintense lesions. However, given that this study is only crosssectional, it limits our ability to make conclusions on the mechanism behind this relationship. The study has other limitations, including use of self-report for the evaluation of hypertension, heart disease, and diabetes. Although these measures have been used in other studies, there is the possibility of false-negative reports in which medical illness is present but not recognized. In addition, other risk factors for cerebrovascular disorders were not included in the study such as hyperlipidemia or tobacco use; moreover, clinical measures of vital signs may be preferable to obtaining only a clinical history. A particular strength of the study is the use of measures of hyperintensity volume rather than visual rating scales as seen in other studies examining this issue in late-life depression. It should be noted that there is a mean difference of 1.8 mL of WMH volume between those with and without the Met66 allele, in which Met66 allele carriers exhibit a 32.7% greater mean WMH volume. Although 1.8 mL is not a large amount when total brain volume in this population is approximately
1000 mL,7 an increase of one third of the lesion volume may be clinically relevant when considered that increased hyperintense lesion volumes are associated not only with psychiatric comorbidity, but also cognitive deficits,20,63 gait instability,64 and incontinence.65 It is not clear how the Val66Met polymorphism is related to other critical issues of late-life depression such as cognitive deficits, treatment outcomes, or mortality. Future studies examining these issues should test for potential interactions between this polymorphism and other genetic polymorphisms associated with hyperintense lesions as well as examine for a relationship with severity of comorbid medical conditions such as hypertension and diabetes. Identification of genetic polymorphisms related to WMH may have clinical implications, because such individuals may require more aggressive control of comorbid conditions to prevent or slow WMH development and improve clinical outcomes. This study was supported by NIMH grants K23 MH65939, R01 MH54846, and P50 MH60451 and National Institute of Environmental Health Sciences grant ES11961. Preliminary data were presented at the 2007 Annual Meeting of the American Association for Geriatric Psychiatry, New Orleans, LA, March 2– 4, 2007.
References 1. Thomas AJ, O’Brien JT, Davis S, et al: Ischemic basis for deep white matter hyperintensities in major depression. Arch Gen Psychiatry 2002; 59:785–792 2. Thomas AJ, Perry R, Barber R, et al: Pathologies and pathological mechanisms for white matter hyperintensities in depression. Ann NY Acad Sci 2002; 977:333–339 3. Tomimoto H, Lin J, Ihara M, et al: Subinsular vascular lesions: An analysis of 119 consecutive autopsied brains. Eur J Neurol 2007; 14:95–101 4. Fujikawa T, Yamawaki S, Touhouda Y: Incidence of silent cerebral infarction in patients with major depression. Stroke 1993; 24:1631–1634 5. Krishnan KRR, Hays JC, Blazer DG: MRI-defined vascular depression. Am J Psychiatry 1997; 154:497–501 6. Krishnan KRR, McDonald WM, Doraiswamy PM, et al: Neuroanatomical substrates of depression in the elderly. Eur Arch Psychiatry Clin Neurosci 1993; 243:41–46 7. Kumar A, Bilker W, Jin Z, et al: Atrophy and high intensity lesions: complementary neurobiological mechanisms in latelife depression. Neuropsychopharmacology 2000; 22:264 –274
Am J Geriatr Psychiatry 16:4, April 2008
8. de Groot JC, de Leeuw F, Oudkerk M, et al: Cerebral white matter lesions and depressive symptoms in elderly adults. Arch Gen Psychiatry 2000; 57:1071–1076 9. Figiel GS, Krishnan KRR, Doraiswamy PM, et al: Subcortical hyperintensities on brain magnetic resonance imaging: a comparison between late age onset and early onset elderly depressed subjects. Neurobiol Aging 1991; 12:245–247 10. Lavretsky H, Lesser IM, Wohl M, et al: Relationship of age, age at onset, and sex to depression in older adults. Am J Geriatr Psychiatry 1998; 6:248 –256 11. Salloway S, Malloy P, Kohn R, et al: MRI and neuropsychological differences in early- and late-life-onset geriatric depression. Neurology 1996; 46:1567–1574 12. Lesser IM, Hill-Gutierrez E, Miller BL, et al: Late-onset depression with white matter lesions. Psychosomatics 1993; 34:364 –367 13. Hickie I, Scott E, Mitchell P, et al: Subcortical hyperintensities on magnetic resonance imaging: Clinical correlates and prognostic significance in patients with severe depression. Biol Psychiatry 1995; 37:151–160 14. O’Brien J, Ames D, Chiu E, et al: Severe deep white matter lesions
269
BNDF VAL66MET Polymorphism and Cerebral White Matter Hyperintensities and outcome in elderly patients with major depressive disorder: follow up study. BMJ 1998; 317:982–984 15. Simpson S, Talbot PR, Snowden JS, et al: Subcortical vascular disease in elderly patients with treatment resistant depression. J Neurol Neurosurg Psychiatry 1997; 62:196 –197 16. Iosifescu DV, Renshaw PF, Lyoo IK, et al: Brain white-matter hyperintensities and treatment outcome in major depressive disorder. Br J Psychiatry 2006; 188:180 –185 17. Lavretsky H, Lesser IM, Wohl M, et al: Clinical and neuroradiologic features associated with chronicity in late-life depression. Am J Geriatr Psychiatry 1999; 7:309 –316 18. Taylor WD, Steffens DC, MacFall JR, et al: White matter hyperintensity progression and late-life depression outcomes. Arch Gen Psychiatry 2003; 60:1090 –1096 19. Awad IA, Spetzler RF, Hodak JA, et al: Incidental subcortical lesions identified on magnetic resonance imaging in the elderly. I. Correlation with age and cerebrovascular risk factors. Stroke 1986; 17:1084 –1089 20. Longstreth WTJ, Manolio TA, Arnold A, et al: Clinical correlates of white matter findings on cranial magnetic resonance imaging of 3301 elderly people: The Cardiovascular Health Study. Stroke 1996; 27:1274 –1282 21. Fazekas F, Niederkor K, Schmidt R, et al: White matter signal abnormalities in normal individuals: correlation with carotid ultrasonography, cerebral blood flow measurements, and cerebrovascular risk factors. Stroke 1988; 19:1285–1288 22. Taylor WD, MacFall JR, Payne ME, et al: Greater MRI lesion volumes in elderly depressed subjects than in control subjects. Psychiatry Res 2005; 139:1–7 23. Patankar T, Widjaja E, Chant H, et al: Relationship of deep white matter hyperintensities and cerebral blood flow in severe carotid artery stenosis. Eur J Neurol 2006; 13:10 –16 24. Hassan A, Hunt BJ, O’Sullivan M, et al: Homocysteine is a risk factor for cerebral small vessel disease, acting via endothelial dysfunction. Brain 2004; 127:212–219 25. Iosifescu DV, Papakostas GI, Lyoo IK, et al: Brain MRI hyperintensities and one-carbon cycle metabolism in non-geriatric outpatients with major depressive disorder (part I). Psychiatry Res 2005; 140:291–299 26. Payne ME, Haines PS, Chambless LE, et al: Food group intake and brain lesions in late-life vascular depression. Int Psychogeriatr 2007; 19:295–305 27. Firbank MJ, Lloyd AJ, Ferrier N, et al: A volumetric study of MRI signal hyperintensities in late-life depression. Am J Geriatr Psychiatry 2004; 12:606 –612 28. Takami S, Imai Y, Katsuya T, et al: Gene polymorphism of the renin–angiotensin system associates with risk for lacunar infarction. The Ohasama Study. Am J Hypertens 2000; 13:121–127 29. Schmidt H, Fazekas F, Kostner GM, et al: Angiotensin gene promoter haplotype and microangiopathy-related cerebral damage. Results of the Austrian Stroke Prevention Study. Stroke 2001; 32:405–412 30. Chen ZY, Patel PD, Sant G, et al: Variant brain-derived neurotrophic factor (BDNF) (Met66) alters the intracellular trafficking and activity-dependent secretion of wild-type BDNF in neurosecretory cells and cortical neurons. J Neurosci 2004; 24:4401–4411 31. Egan MF, Kojima M, Callicott JH, et al: The BDNF val66met polymorphism affects activity-dependent secretion of BDNF and human memory and hippocampal function. Cell 2003; 112:257– 269 32. Pezawas L, Verchinski BA, Mattay VS, et al: The brain-derived neurotrophic factor val66met polymorphism and variation in human cortical morphology. J Neurosci 2004; 24:10099 –10102
270
33. Szesko PR, Lipsky R, Mentschel C, et al: Brain-derived neurotrophic factor val66met polymorphism and volume of the hippocampal formation. Mol Psychiatry 2005; 10:631–636 34. Nomura T, Honmou OHK, Houkin K, et al: I.V. infusion of brain-derived neurotrophic factor gene-modified human mesenchymal stem cells protects against injury in a cerebral ischemia model in adult rat. Neuroscience 2005; 136:161–169 35. Yanamoto H, Xue JH, Miyamoto S, et al: Spreading depression induces long-lasting brain protection against infarcted lesion development via BDNF gene-dependent mechanism. Brain Res 2004; 1019:178 –188 36. Robins LN, Helzer JE, Croughan J, et al: National Institute of Mental Health Diagnostic Interview Schedule. Its history, characteristics, and validity. Arch Gen Psychiatry 1981; 38:381–389 37. Montgomery SA, Asberg M: A new depression scale designed to be sensitive to change. Br J Psychiatry 1979; 134:382–389 38. Taylor WD, Zuchner S, McQuoid DR, et al: Allelic differences in the BDNF val66met polymorphism in late-life depression. Am J Geriatr Psychiatry 2007; 15:850 –857 39. Folstein MF, Folstein SE, McHugh PR: ‘Mini-mental state’: a practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 1975; 12:189 –198 40. Regier DA, Myers JK, Kramer M, et al: The NIMH Epidemiologic Catchment Area program: historical context, major objectives, and study population characteristics. Arch Gen Psychiatry 1984; 41:934 –941 41. Taylor WD, McQuoid DR, Krishnan KR: Medical comorbidity in late-life depression. Int J Geriatr Psychiatry 2004; 19:935–943 42. Rimmler J, McDowell JG, Slotterback BD, et al: Development of a data coordinating center (DCC): data quality control for complex disease studies. Am J Hum Genet 1998; SupplementSuppl 63: A240 43. Byrum CE, MacFall JR, Charles HC, et al: Accuracy and reproducibility of brain and tissue volumes using a magnetic resonance segmentation method. Psychiatry Res 1996; 67:215–234 44. Payne ME, Fetzer DL, MacFall JR, et al: Development of a semiautomated method for quantification of MRI gray and white matter lesions in geriatric subjects. Psychiatry Res 2002; 115: 63–77 45. Kikinis R, Shenton ME, Gerig G, et al: Routine quantitative analysis of brain and cerebrospinal fluid spaces with MR imaging. J Magn Reson Imaging 1992; 2:619 –629 46. Lewis PO, Zaykin D: Genetic Data Analysis 2000. Available at: http://hydrodictyon.eeb.uconn.edu/people/plewis/software.php 47. Weir BS: Genetic Data Analysis II: Methods for Discrete Population Genetic Data. Sunderland, MA, Sinauer Associates, 1996 48. Kumar A, Bilker W, Jin Z, et al: Age of onset of depression and quantitative neuroanatomic measures: absence of specific correlates. Psychiatry Res 1999; 91:101–110 49. Verpillat P, Alperovitch A, Cambien F, et al: Aldosterone synthase (CYP11B2) gene polymorphism and cerebral white matter hyperintensities. Neurology 2001; 56:673–675 50. Webster MJ, Weickert CS, Herman MM, et al: BDNF mRNA expression during postnatal development, maturation and aging of the human prefrontal cortex. Brain Res Dev Brain Res 2002; 139:139 –150 51. Shetty AK, Turner DA: In vitro survival and differentiation of neurons derived from epidermal growth factor-responsive postnatal hippocampal stem cells: inducing effects of brain-derived neurotrophic factor. J Neurobiol 1998; 35:395–425 52. Lowenstein DH, Arsenault L: The effects of growth factors on the survival and differentiation of cultured dentate gyrus neurons. J Neurosci 1996; 16:759 –769
Am J Geriatr Psychiatry 16:4, April 2008
Taylor et al. 53. Schwartz ED, Shumsky JS, Wehrli S, et al: Ex vivo MR determined apparent diffusion coefficients correlate with motor recovery mediated by intraspinal transplants of fibroblasts genetically modified to express BDNF. Exp Neurol 2003; 182:49 –63 54. Taylor WD, MacFall JR, Provenzale JM, et al: Serial MR imaging of hyperintense white matter lesion volumes in elderly subjects: correlation with vascular risk factors. AJR Am J Roentgenol 2003; 181:571–576 55. Krabbe KS, Nielsen AR, Krogh-Madsen R, et al: Brain-derived neurotrophic factor (BDNF) and type 2 diabetes. Diabetologia 2007; 50:431–438 56. Lee TH, Yang JT, Kato H, et al: Hypertension downregulates the expression of brain-derived neurotrophic factor in the ischemiavulnerable hippocampal CA1 and cortical areas after carotid artery occlusion. Brain Res 2006; 1116:31–38 57. Baghai TC, Binder EB, Schule C, et al: Polymorphisms in the angiotensin-converting enzyme gene are associated with unipolar depression, ACE activity and hypercortisolism. Mol Psychiatry 2006; 11:1003–1015 58. Bjelland I, Tell GS, Vollset SE, et al: Folate, vitamin B12, homocysteine, and the MTHFR 677C-⬎T polymorphism in anxiety and depression: The Hordaland Homocysteine Study. Arch Gen Psychiatry 2003; 60:618 –626 59. Hariri AR, Goldberg TE, Mattay VS, et al: Brain-derived neurotro-
Am J Geriatr Psychiatry 16:4, April 2008
phic factor val66met polymorphism affects human memory-related hippocampal activity and predicts memory performance. J Neurosci 2003; 23:6690 –6694 60. Surtees PG, Wainwright NW, Willis-Owen SA, et al: The brainderived neurotrophic factor Val66Met polymorphism is associated with a sense of coherence in a non-clinical community sample of 7335 adults. J Psychiatr Res 2006; Epub July 25 61. Berton O, McClung CA, DiLeone RJ, et al: Essential role of BDNF in the mesolimbic dopamine pathway in social defeat stress. Science 2006; 311:864 –868 62. Kaufman J, Yang BZ, Douglas-Palumberi H, et al: Brain-derived neurotrophic factor-5-HTTLPR interactions and environmental modifiers of depression in children. Biol Psychiatry 2006; 59: 673–680 63. O’Brien JT, Wiseman R, Burton EJ, et al: Cognitive associations of subcortical white matter lesions in older people. Ann N Y Acad Sci 2002; 977:436 –444 64. Whitman GT: A prospective study of cerebral white matter abnormalities in older people with gait dysfunction. Neurology 2001; 57:990 –994 65. Sakakibara R, Hattori T, Uchiyama T, et al: Urinary function in elderly people with and without leukoaraiosis: relation to cognitive and gait function. J Neurol Neurosurg Psychiatry 1999; 67: 658 –660
271