Gain in Adiposity Across 15 Years is Associated With Reduced Gray Matter Volume in Healthy Women
Soreca, Isabella MD; Rosano, Caterina MD; Jennings, J Richard PhD; Sheu, Lei K. PhD; Kuller, Lewis H. PhD; Matthews, Karen A. PhD; Aizenstein, Howard J. MD; Gianaros, Peter J. PhD
From the Departments of Psychiatry (I.S., J.R.J., L.K.S., K.A.M., H.J.A., P.J.G.) and Epidemiology (C.R., L.H.K., K.A.M.), University of Pittsburgh, Pittsburgh, Pennsylvania.
Address correspondence and reprint requests to Isabella Soreca, Department of Psychiatry, University of Pittsburgh, 3811 O’Hara Street, Pittsburgh, PA 15213. E-mail: firstname.lastname@example.org
Received for publication October 11, 2008; revision received January 17, 2009.
This research was supported by the Pittsburgh Mind-Body Center (http://www.pghmbc.org); National Institutes of Heath Grants HL076852/076858 (K.A.M.) and HL028266 (K.A.M., L.H.K as P.I.); and National Institute of Mental Health Grants K01-070616-01 (P.J.G.).
Disclosure statement: Authors have no conflict of interest to disclose.
Ethical statement: All the procedures of this study involving human subjects have been reviewed and approved by the University of Pittsburgh Institutional Review Board.
Objective: To test whether current gray matter volume (GMV) covaried with previously obtained longitudinal measures of weight gain—as assessed by increases in body mass index (BMI)—among otherwise healthy postmenopausal women. Cross-sectional results indicate that reduced GMV may be associated with excess body weight.
Methods: Demographic, biometric, and behavioral measures were obtained from 48 women as part of the Pittsburgh Healthy Women Study, a longitudinal epidemiological investigation initiated between 1983 and 1984. In 2005 and 2006, these women took part in a brain imaging protocol.
Results: Premenopausal BMI and a priori chosen confounding variables, including the number of years post menopause, an aggregate measure of perceived life stress spanning a 20-year period, resting blood pressure, total cerebral volume, and severity of white matter hyperintensities (a suspected indicator of aging-related silent cerebrovascular disease), explained ∼22% of variance in total GMV. An additional 15% of the variance was uniquely explained by the change in BMI between pre- and postmenopausal longitudinal assessments, such that an increase in BMI predicted a greater reduction in GMV.
Conclusions: An increase in BMI during the menopausal transition and beyond is associated with reduced GMV among otherwise healthy women.
BMI = body mass index; FOV = field of view; FWHM = full width at half-maximum; NEX = number of excitations; TE = time to echo; TI = time to inversion; TR = time to repetition.
The accelerated increase in obesity over the past two decades (1) has drawn attention to the effect of body weight not only on cardiovascular disease risk (2) and other health-related outcomes specific to peripheral target organ systems but also on the brain (3–6). In point, decreased brain gray matter volume (GMV) has been associated with obesity among community samples (7) and patients with Alzheimer’s disease (8). Further, decreased hippocampal volume has been associated with a greater body mass index (BMI) and fat distribution among elderly individuals (9). Volumetric differences in the gray matter of other brain areas have also been associated with indicators of body weight and fat among otherwise healthy individuals who are lean and obese (10). Changes in white matter brain tissue are also associated with weight gain (11,12). For example, prior reports demonstrated that the integrity of white matter fiber tracts in the frontal lobes is reduced in obese individuals compared with their lean counterparts (11,13).
Metabolic factors may explain the associations between body weight and alterations in brain tissue, such as brain tissue volume. More precisely, the brain relies on constant metabolic substrate delivery and constant tissue oxygenation, rendering brain tissue especially vulnerable to changes in vascular function. In this regard, increased body weight and obesity may affect brain tissue and morphology by influencing cellular vascular function and substrate delivery and promoting abnormal lipid metabolism and fat accumulation (11). These vascular and substrate delivery changes may, in turn, lead to alterations in indicators of brain tissue volume (7,10,11) and tract integrity (12,13) in association with excess body weight. Hence, from a public health perspective, it is important to consider the possibility that lifestyle interventions that control weight could plausibly protect against structural (and possibly functional) declines at the level of the brain. In a corollary extension of this notion, there is clinical evidence to suggest that severe malnutrition, as observed among individuals with eating disorders, is associated with decreased brain tissue volumes (14) and that these structural brain alterations reverse after long-term disorder remission (15).
Taken together, cumulative evidence has thus revealed a cross-sectional association between indicators of brain morphology, particularly brain tissue volume, and current body weight with plausible vascular-metabolic substrates. It remains uncertain, however, whether weight gain per se may have an independent contribution to normative variation in brain tissue volume. Such provisional evidence would further suggest that dysregulation of weight could influence brain tissue volume. BMI is widely accepted as a general indicator of adiposity (16). Using BMI as a proxy for adiposity, we thus explored whether the change in BMI between midadult and later life would predict total volumes of gray and white matter in later life. To answer this question, we used volumetric brain imaging data obtained from 48 healthy women who have been studied over a 20-year period as part of the epidemiological Pittsburgh Healthy Women Study (HWS).
This study included 48 women studied as part of the Pittsburgh HWS (17). Details regarding sampling, exclusion criteria, and demographics for the cohort of 541 women who began the study in 1983 to 1984 are reported elsewhere (17). Participants were recruited in 1983 and 1984 and were eligible if they were between 42 and 50 years, not menopausal (had menstruated in the previous 3 months), were not receiving hormone replacement therapy, were not hypertensive (or on blood pressure-lowering medication), were not receiving psychotropic medication, antidiabetic or antilipemic medication, and were not receiving thyroid or replacement hormone therapy. In 2005 and 2006, a subsample of 50 eligible women were invited to participate in an ancillary brain imaging protocol. Women were ineligible for the brain imaging protocol if they had: (a) a history of cardiovascular or cerebrovascular disease; (b) a prior stroke or cerebrovascular incident; (c) claustrophobia; (d) Type I or II diabetes; (e) cancer; (f) a current or prior diagnosis of a neuropsychiatric disorder (including a mood disorder, dementia, or suspected Alzheimer’s disease); (g) used psychotropic, hypertensive, or glucoregulatory medications currently or in the past; and (h) a metallic implant. Of the 464 active HWS participants in September 2005, 209 met one or more exclusion criteria; 71 were not interested in participating; 134 could not be contacted or conveniently tested. Results reported herein are for 48 participants who did not differ in age or educational attainment from nonparticipants. Data were lost from 2 of the 50 women because they declined to participate in the full brain imaging protocol. The University of Pittsburgh Institutional Review Board granted study approval. We have previously reported associations between indicators of life stress and brain morphology and stress-related functional neural activation and blood pressure reactivity among this sample population (18,19). Participants provided their informed consent after receiving a study description. See Tables 1 and 2 for participant characteristics.
Study Measures and Assessments
Premenopausal women who entered the HWS received a cardiovascular risk factor and psychosocial evaluation and were reassessed approximately every 2 to 3 years, depending on the timing of their last menstrual period and use of hormone therapy. The measures that were collected at each time point included height and weight for BMI calculation. At each visit, height was measured barefoot, and weight was measured in light clothing with a calibrated scale. Potential confounding variables such as years since menopause at the time of the brain scanning protocol, resting systolic blood pressure, chronic perceived psychosocial stress, and white matter hyperintensities collected at the time of the brain imaging protocol were also included in the present analyses.
Brain Imaging Protocol
Brain magnetic resonance imaging (MRI) measures included quantitative volumetric measurements of total brain tissue volumes and visual ratings of white matter hyperintensities, which are taken as indicators of small cerebrovascular disease that are detectable even in the absence of clinical neurological signs (20). MRI images used to derive these measures were acquired with a 3T Signa scanner (GE Medical Systems, Milwaukee, Wisconsin). Brain tissue volumes were derived from coronal images acquired with a T1-weighted 3D spoiled gradient recalled (SPGR) acquisition sequence (time to echo (TE) = 5 ms; time to repetition (TR) = 25 ms; flip angle = 40°; number of excitations (NEX) = 1; 124 slices 1.5 mm thick; 0-mm spacing between slices; matrix size = 256 × 192 pixels; field of view (FOV) = 24 × 18 cm). White matter hyperintensities were assessed from axial images obtained with a T2-weighted fast spin-echo inversion recovery (FSEIR) sequence (effective TE = 160 ms; TR = 10004 ms; time to inversion (TI) = 2250 ms; NEX = 2). FSEIR images were acquired in the plane of the anterior and posterior commissures (5-mm slice thickness; 1-mm spacing between slices; matrix size = 256 × 192 pixels; FOV = 20 × 20 cm).
Quantitative Brain Volume Measures
Total volumes of gray matter, white matter, and cerebrospinal fluid (CSF) were determined using a previously validated procedure (21–23), termed the “Automated Labeling Pathway” (ALP). This procedure involves the application of a nonlinear registration algorithm (24) to transform a template brain (Montreal Neurological Institute Colin27 template) into the native anatomical space of each individual’s brain. Specifically, after skull and scalp stripping (25), the images are segmented into gray matter, white matter, and CSF. The brain images are then intensity normalized to match the image intensity distributions of the template image. A fully deformable automatic algorithm (26) is then used to register the template image to the individual subjects’ brain images. Total GMV, white matter volume, and CSF volume (in mm3) were estimated by summing all voxels classified as these tissue types, using Insight Segmentation and Registration Toolkit (ITK) (available at www.itk.org).
Assessment of White Matter Hyperintensities
MRI indicators of white matter hyperintensities in the periventricular and subcortical white matter areas are taken to reflect the presence of age-related ischemic lesions and they have been correlated with lower regional GMV in older adults (27). To estimate the severity of white matter hyperintensities, two readers graded films of the T2-weighted FSEIR images by the protocol of the Cardiovascular Health Study (28). Both readers were blind to participant characteristics and the study purpose. Using an atlas of predefined visual standards, each reader graded the T2 images for white matter with a 9-point scale anchored by 0 = minimal and 8 = extensive. As determined by intraclass correlation coefficients (ICCs), the readers showed high interrater agreement for periventricular white matter grades (ICC = 0.94) and subcortical white matter grades (ICC = 0.92). White matter grades were thus averaged across the two readers. Also, because rater-averaged periventricular and subcortical white matter grades were highly correlated (r = .92), these two grades were averaged to compute a composite indicator of white matter severity. Compared with population-based norms for older individuals (29), the present sample had minimal white matter grades (Table 1). Further, none of the participants showed signs of gross brain pathology or an undiagnosed prior stroke, as determined by consensus evaluations between our readers and a neuroradiologist.
To test whether changes between pre- and postmenopausal BMI predicted GMV or white matter volume, we executed two-step hierarchical regression analyses (modeling GMV and white matter volume separately as dependent variables). In the first step, we entered all potential confounders selected on the basis of previous research, including the premenopausal BMI. In the second step, we added the postmenopausal BMI and examined the R2 change. With this method, the residuals calculated by entering postmenopausal BMI adjusted for premenopausal BMI reflected the change in BMI. The set of step 1 confounding variables selected for the models were chosen on the basis of previous research and clinical reasoning. These included years since menopause measured at the time of the scan (30), resting systolic blood pressure at the time of the scan (31,32), a measure of chronic perceived psychosocial stress summed across assessments from the premenopausal evaluation to the most recent postmenopausal evaluation used in our prior report on these women (18), grade of white matter hyperintensities, total brain tissue volumes, and premenopausal BMI. Age was not included as a primary covariate because of its restricted variance in our sample (standard deviation (SD) = 1.32). Years postmenopause was included because of the overall purpose of the HWS was to study the effects of the menopausal transition on health. Moreover, studies have shown that estrogen therapy, if initiated in the early menopausal period, is protective against the age-related neuronal loss in postmenopausal women (33,34). Systolic blood pressure was selected because it is more reliable to assess than diastolic blood pressure and because it is a better predictor of cardiovascular morbidity in the elderly. We did not include smoking because only two subjects were current smokers. At the time of the scan, women also completed the Center for Epidemiologic Studies Depression Scale (CES-D) (35): none of the subjects exceeded the threshold of 16, which indicates suspected clinical depression. Further by study design (17), none of the women reported being treated for psychiatric syndromes or using psychotropic medications at study entry. The mean ± SD CES-D score at the time of the scan was 4.35 ± 3.02, and was not significantly correlated with whole brain GMV or white matter volume; CES-D score was unrelated to the percentage of variance explained by BMI change in the regression models. Therefore, depression was omitted from analyses.
Because there is controversy on whether white and gray volumes may depend on total brain size, we ran an exploratory hierarchical regression including a measure of total cerebral volume derived from the sum of total gray, total white, and total CSF volumes. As we did not find any significant association between BMI change and total white matter volume in initial analyses, we only used total gray volume as a dependent variable for the exploratory analysis including total cerebral volume as an additional step 1 covariate.
Sample characteristics are shown in Tables 1 and 2. Only one woman showed a decrease in BMI between longitudinal assessments (−0.3 BMI points), whereas the remainder increased in BMI (mean change = 3.79; range = −0.3 to 12.89).
In the first set of analyses, wherein total gray volume was treated as the dependent variable, premenopausal BMI, years since menopause, resting systolic blood pressure, chronic perceived psychosocial stress, and white matter hyperintensities explained 22% of the variance (step 1 R2 = .222, ΔF p = .078). In the second step, BMI change—defined as the residuals calculated by entering postmenopausal BMI adjusted for premenopausal BMI—explained an additional 15% of the variance (step 2 ΔR2 = .155, ΔF p = .004; β = −0.592) (Table 3). Similar results (step 2 ΔR2 = .089, ΔF p = .045; β = −0.509) were obtained using baseline weight and weight at the time of the scan (see Table 1 for weight changes description). Moreover, this association could not be explained by normative variation in total cerebral volume. Hence, in exploratory analyses, the association between BMI change and total gray volume remained significant after the inclusion of total cerebral volume to the set of step 1 covariates in the regression model (step 1 R2 = .515; step 2 ΔR2 = .063, F = 5.309, p = .027; β = −0.392) (Table 4).
When total white matter was treated as a dependent variable, the association with all step 1 covariates was not significant (R2 = .117, ΔF p = .426). In step 2, BMI change defined as the residuals calculated by entering postmenopausal BMI adjusted for premenopausal BMI did not account for a significant change in the percentage of variance explained by the model (ΔR2 = .048, ΔF p = .828; β = −0.051) (Table 5).
The present results demonstrate a relationship between GMV and the change in BMI between the pre- and postmenopausal years, spanning an approximate 20-year period in the present sample. As such, these results provide novel evidence that an increase in BMI during an important life transition (menopause) among women is uniquely associated with reduced gray matter, apart from current BMI and other potential confounders. We emphasize that the current results do not permit causal inferences. Hence, it may be that preexisting variation in GMV may contribute to weight gain and a corresponding increase in BMI over time. Although we cannot definitively exclude this possibility, we would expect that behavioral changes or changes in metabolic regulation leading to increased BMI over time would more likely be the consequence of structural (and hence, functional) abnormalities in specific brain regions involved in the regulation of body weight homeostasis. The result of reduced gray volume associated with increased BMI is particularly noteworthy considering that our subjects were healthy older women, with no history of cardiovascular or psychiatric disease and no current or past pharmacological treatment for a chronic medical condition or psychiatric disorder. Further, none of the participants met the threshold for obesity (BMI ≥30) at their midlife evaluation and the postmenopausal mean BMI was 27.1. Finally, all of these women underwent a natural (nonsurgical) menopause.
The mechanisms linking decreased whole brain gray volume to weight gain during the transition into menopause may involve numerous biological and even behavioral or lifestyle factors. For example, vascular factors or metabolic abnormalities may affect substrate delivery to the brain. Both the transition to menopause and weight gain are independently associated with increased incidence of cardiovascular risk factors and cardiovascular diseases in women (17,36). In addition, metabolic disturbances associated with weight gain, such as Type 2 diabetes and altered peripheral insulin sensitivity, have been associated to brain volume changes (37–41). The subjects in this study were healthy and free of cardiovascular, cerebrovascular diseases, or diabetes; hence, any contribution of the pathophysiology of these diseases must occur at subclinical levels among the current women. It is possible that subtle metabolic alteration may take place even in the absence of threshold hypertension, diabetes, or dyslipidemia.
In addition, it is plausible that circulating inflammatory cytokines may mediate the effect of an increase in BMI related to the menopause on brain tissue volume. In this regard, it is noteworthy that adipocytes produce inflammatory cytokines, such as interleukin-6 (IL-6) and tumor necrosis factor-α (42,43), and that obesity and overweight are associated with increased inflammatory cytokine levels (44). Further, there is recent evidence that higher circulating levels of the inflammatory cytokine, IL-6, are associated with reduced hippocampal volume among otherwise healthy men and women (45). The transition to menopause and aging in general are also associated to an increase of circulating cytokines (46). Increased circulating cytokines are linked to a variety of negative health outcomes in men and women across different age spans, including greater brain atrophy than expected for age (47) and poorer performance on cognitive tests of attention/working memory and executive function in healthy volunteers (48). Although the women in our study did not have clinical cardiovascular disease, it is possible that, in this otherwise healthy sample, weight gain during the menopausal transition promoted subtle modifications in glucose metabolism and cytokine production, which, in turn, affected brain morphology via peripheral to central pathways (49).
Behavioral and lifestyle factors associated with the transition into menopause may also play an important role in determining the increase in BMI. Menopause can presage important lifestyle changes, such as retirement from work, changes in family structure (children moving away), and changes in dietary and activity habits, which could lead to weight gain. Although we could not control for dietary changes or changes in physical activity, the measures of physical activity at the time of the scan were not associated with total gray volume (data not shown). We also had information about marital status at the time of the scan. We found no difference in current BMI or BMI change among women who were married or living with a partner, never married, divorced, or widowed (data omitted for brevity, available on request). Further, whereas the loss of a spouse among older women may trigger abrupt and considerable lifestyle changes leading to weight gain, only three women in our sample were widowed at the time of the scan—suggesting that this factor did not account for our findings. We have previously reported associations between indicators of chronic life stress and region-specific brain morphology in post menopause (18); higher level of chronic perceived stress were found to be associated with smaller right hippocampal volume, independent from total gray volume. Importantly, the effect of change in BMI was independent from chronic perceived stress in this study, suggesting that stress-related factors may not directly account for our current observations. In aggregate, the present study expands the knowledge about the correlates of brain morphology in healthy individuals by highlighting the association between prospectively measured weight changes and total GMV.
Our results suggest that weight gain is not associated with white matter volume. Although other studies (11) reported a relationship between increased BMI and increased regional white matter volume, to our knowledge, the extent to which BMI affects total white matter volume has not been sufficiently explored. It is possible that only specific brain regions morphology (as opposed to total white volume) is affected by BMI changes or that changes in white matter volume become apparent as an individual approaches more marked changes in BMI compared with the women in this study.
Limitations that may affect the interpretation of these results need to be acknowledged. First, although we have longitudinal assessments of BMI, we only have one postmenopausal measurement of brain volumes. Therefore, conclusions about actual changes in total gray volume cannot be drawn; it is possible that those subjects who experience a greater increase in BMI had lower total GMV throughout their life. Second, despite the fact that we observed a wide range of weight changes across the years, none of the subjects were obese at the baseline assessment; therefore, our results cannot be generalized to those women who are obese throughout their life. The inclusion of only women may also limit the generalizability of our results: we do not know whether weight gain across the decades of life would show similar association with total GMVs in men. The narrow age range may also limit the generalizability of the results, in that it was not possible to compare brain volumes in younger versus older women.
In summary, our results suggest a cumulative increase of “risk” for weight-related volumetric brain tissue changes over the course of the menopausal transition in women. This cumulative burden of additional weight on brain volume was observed among otherwise healthy women. Weight gain and BMI are highly modifiable risk factors that may be targeted to prevent or slow the progression of potentially adverse age-related changes in brain morphology. Future research will need to investigate the regional specificity of the current findings. Women may be particularly motivated to maintain a healthy weight in the postmenopausal years, should it be confirmed that weight gain causes alteration in brain function that is important to quality of life.
We thank Leslie Mitrik for testing participants and Joelle Scanlon and Ellyn White for reading the structural MRI films.
1. Mokdad AH, Bowman BA, Ford ES, Vinicor F, Marks JS, Koplan JP. The continuing epidemics of obesity and diabetes in the United States. JAMA 2001;286:1195–200.
2. Grundy SM. Metabolic syndrome: part II. Endocrinol Metab Clin North Am 2004;33:xi–xiii.
3. Carey VJ, Walters EE, Colditz GA, Solomon CG, Willett WC, Rosner BA, Speizer FE, Manson JE. Body fat distribution and risk of non-insulin-dependent diabetes mellitus in women. The nurses’ health study. Am J Epidemiol 1997;145:614–9.
4. Dagenais GR, Yi Q, Mann JF, Bosch J, Pogue J, Yusuf S. Prognostic impact of body weight and abdominal obesity in women and men with cardiovascular disease. Am Heart J 2005;149:54–60.
5. Renehan AG, Tyson M, Egger M, Heller RF, Zwahlen M. Body-mass index and incidence of cancer: a systematic review and meta-analysis of prospective observational studies. Lancet 2008;371:569–78.
6. Teichtahl AJ, Wang Y, Wluka AE, Cicuttini FM. Obesity and knee osteoarthritis: new insights provided by body composition studies. Obesity (Silver Spring) 2008;16:232–40.
7. Taki Y, Kinomura S, Sato K, Inoue K, Goto R, Okada K, Uchida S, Kawashima R, Fukuda H. Relationship between body mass index and gray matter volume in 1,428 healthy individuals. Obesity (Silver Spring) 2008;16:119–24.
8. Gustafson D, Rothenberg E, Blennow K, Steen B, Skoog I. An 18-year follow-up of overweight and risk of Alzheimer disease. Arch Intern Med 2003;163:1524–8.
9. Jagust W, Harvey D, Mungas D, Haan M. Central obesity and the aging brain. Arch Neurol 2005;62:1545–8.
10. Pannacciulli N, Del Parigi A, Chen K, Le DS, Reiman EM, Tataranni PA. Brain abnormalities in human obesity: a voxel-based morphometric study. Neuroimage 2006;31:1419–25.
11. Haltia LT, Viljanen A, Parkkola R, Kemppainen N, Rinne JO, Nuutila P, Kaasinen V. Brain white matter expansion in human obesity and the recovering effect of dieting. J Clin Endocrinol Metab 2007;92:3278–84.
12. Gouw AA, van der Flier WM, Fazekas F, van Straaten EC, Pantoni L, Poggesi A, Inzitari D, Erkinjuntti T, Wahlund LO, Waldemar G, Schmidt R, Scheltens P, Barkhof F, LADIS Study Group. Progression of white matter hyperintensities and incidence of new lacunes over a 3-year period: the leukoaraiosis and disability study. Stroke 2008;39:1414–20.
13. Gazdzinski S, Kornak J, Weiner MW, Meyerhoff DJ. Body mass index and magnetic resonance markers of brain integrity in adults. Ann Neurol 2008;63:652–7.
14. Frank GK, Bailer UF, Henry S, Wagner A, Kaye WH. Neuroimaging studies in eating disorders. CNS Spectr 2004;9:539–48.
15. Wagner A, Greer P, Bailer UF, Frank GK, Henry SE, Putnam K, Meltzer CC, Ziolko SK, Hoge J, McConaha C, Kaye WH. Normal brain tissue volumes after long-term recovery in anorexia and bulimia nervosa. Biol Psychiatry 2006;59:291–3.
16. Jackson AS, Stanforth PR, Gagnon J, Rankinen T, Leon AS, Rao DC, Skinner JS, Bouchard C, Wilmore JH. The effect of sex, age and race on estimating percentage body fat from body mass index: the heritage family study. Int J Obes Relat Metab Disord 2002;26:789–96.
17. Matthews KA, Meilahn E, Kuller LH, Kelsey SF, Caggiula AW, Wing RR. Menopause and risk factors for coronary heart disease. N Engl J Med 1989;321:641–6.
18. Gianaros PJ, Jennings JR, Sheu LK, Derbyshire SW, Matthews KA. Heightened functional neural activation to psychological stress covaries with exaggerated blood pressure reactivity. Hypertension 2007;49:134–40.
19. Gianaros PJ, Jennings JR, Sheu LK, Greer PJ, Kuller LH, Matthews KA. Prospective reports of chronic life stress predict decreased gray matter volume in the hippocampus. Neuroimage 2007;35:795–803.
20. Rosano C, Kuller L, Arnold AM, Chung H, Longstreth W Jr, Newman AB. MRI study of brain lesions as predictors of physical functional decline in older adults. J Am Geriatr Soc 2005;53:649–54.
21. Rosano C, Becker J, Lopez O, Lopez-Garcia P, Carter CS, Newman A, Kuller L, Aizenstein H. Morphometric analysis of gray matter volume in demented older adults: exploratory analysis of the cardiovascular health study brain MRI database. Neuroepidemiology 2005;24:221–9.
22. Tzourio-Mazoyer N, Landeau B, Papathanassiou D, Crivello F, Etard O, Delcroix N, Mazoyer B, Joliot M. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 2002;15:273–89.
23. Zhang Y, Brady M, Smith S. Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Trans Med Imaging 2001;20:45–57.
24. Woods RP, Grafton ST, Watson JD, Sicotte NL, Mazziotta JC. Automated image registration: II. Intersubject validation of linear and nonlinear models. J Comput Assist Tomogr 1998;22:153–65.
25. Smith SM. Fast robust automated brain extraction. Hum Brain Mapp 2002;17:143–55.
26. Thirion JP. Image matching as a diffusion process: an analogy with Maxwell’s demons. Med Image Anal 1998;2:243–60.
27. Wu M, Carmichael O, Carter CS, Figurski JL, Lopez-Garcia P, Aizenstein HJ. Quantitative comparison of neuroimage registration for fMRI analyses by AIR, SPM, and a fully deformable model. 13th International Society for Magnetic Resonance in Medicine. Miami, FL, 2005.
28. Wen W, Sachdev PS, Chen X, Anstey K. Gray matter reduction is correlated with white matter hyperintensity volume: a voxel-based morphometric study in a large epidemiological sample. Neuroimage 2006;29:1031–9.
29. Longstreth WT Jr, Manolio TA, Arnold A, Burke GL, Bryan N, Jungreis CA, Enright PL, O’Leary D, Fried L. Clinical correlates of white matter findings on cranial magnetic resonance imaging of 3301 elderly people. The cardiovascular health study. Stroke 1996;27:1274–82.
30. Sherwin BB, Henry JF. Brain aging modulates the neuroprotective effects of estrogen on selective aspects of cognition in women: a critical review. Front Neuroendocrinol 2008;29:88–113.
31. Firbank MJ, Wiseman RM, Burton EJ, Saxby BK, O’Brien JT, Ford GA. Brain atrophy and white matter hyperintensity change in older adults and relationship to blood pressure. Brain atrophy, WMH change and blood pressure. J Neurol 2007;254:713–21.
32. Gianaros PJ, Greer PJ, Ryan CM, Jennings JR. Higher blood pressure predicts lower regional gray matter volume: consequences on short-term information processing. Neuroimage 2006;31:754–65.
33. Boccardi M, Ghidoni R, Govoni S, Testa C, Benussi L, Bonetti M, Binetti G, Frisoni GB. Effects of hormone therapy on brain morphology of healthy postmenopausal women: a Voxel-based morphometry study. Menopause 2006;13:584–91.
34. Ghidoni R, Boccardi M, Benussi L, Testa C, Villa A, Pievani M, Gigola L, Sabattoli F, Barbiero L, Frisoni GB, Binetti G. Effects of estrogens on cognition and brain morphology: involvement of the cerebellum. Maturitas 2006;54:222–8.
35. Radloff L. The CES-D scale: a self report depression scale for research in the general population. Applied Psychological Measurement 1977;1:6.
36. Rosano GM, Vitale C, Marazzi G, Volterrani M. Menopause and cardiovascular disease: the evidence. Climacteric 2007;10(Suppl 1):19–24.
37. Enzinger C, Fazekas F, Matthews PM, Ropele S, Schmidt H, Smith S, Schmidt R. Risk factors for progression of brain atrophy in aging: six-year follow-up of normal subjects. Neurology 2005;64:1704–11.
38. Gold SM, Dziobek I, Sweat V, Tirsi A, Rogers K, Bruehl H, Tsui W, Richardson S, Javier E, Convit A. Hippocampal damage and memory impairments as possible early brain complications of type 2 diabetes. Diabetologia 2007;50:711–9.
39. Jongen C, van der Grond J, Kappelle LJ, Biessels GJ, Viergever MA, Pluim JP. Automated measurement of brain and white matter lesion volume in type 2 diabetes mellitus. Diabetologia 2007;50:1509–16.
40. Kumar A, Haroon E, Darwin C, Pham D, Ajilore O, Rodriguez G, Mintz J. Gray matter prefrontal changes in type 2 diabetes detected using MRI. J Magn Reson Imaging 2008;27:14–9.
41. Novak V, Last D, Alsop DC, Abduljalil AM, Hu K, Lepicovsky L, Cavallerano J, Lipsitz LA. Cerebral blood flow velocity and periventricular white matter hyperintensities in type 2 diabetes. Diabetes Care 2006;29:1529–34.
42. Bahceci M, Gokalp D, Bahceci S, Tuzcu A, Atmaca, S, Arikan S. The correlation between adiposity and adiponectin, tumor necrosis factor alpha, interleukin-6 and high sensitivity C-reactive protein levels. Is adipocyte size associated with inflammation in adults? J Endocrinol Invest 2007;30:210–4.
43. Tilg H, Moschen AR. Adipocytokines: mediators linking adipose tissue, inflammation and immunity. Nat Rev Immunol 2006;6:772–83.
44. Cartier A, Lemieux I, Alméras N, Tremblay A, Bergeron J, Després JP. Visceral obesity and plasma glucose-insulin homeostasis: contributions of IL-6 and tumor necrosis factor-alpha in men. J Clin Endocrinol Metab 2008;93:1931–8.
45. Marsaland A, GP, Brown SM, Manuck SB, Hariri AR. Interleukin-6 covaries inversely with hippocampal gray matter volume in middle aged adults. Biol Psychiatry 2008;64:484–90.
46. Giuliani N, Sansoni P, Girasole G, Vescovini R, Passeri G, Passeri M, Pedrazzoni M. Serum interleukin-6, soluble interleukin-6 receptor and soluble gp130 exhibit different patterns of age- and menopause-related changes. Exp Gerontol 2001;36:547–57.
47. Jefferson AL, Massaro JM, Wolf PA, Seshadri S, Au R, Vasan RS, Larson MG, Meigs JB, Keaney JF Jr, Lipinska I, Kathiresan S, Benjamin EJ, DeCarli C. Inflammatory biomarkers are associated with total brain volume: the Framingham heart study. Neurology 2007;68:1032–8.
48. Marsland AL, Petersen KL, Sathanoori R, Muldoon MF, Neumann SA, Ryan C, Flory JD, Manuck SB. Interleukin-6 covaries inversely with cognitive performance among middle-aged community volunteers. Psychosom Med 2006;68:895–903.
49. Wrona D. Neural-immune interactions: an integrative view of the bidirectional relationship between the brain and immune systems. J Neuroimmunol 2006;172:38–58.
body mass index; gray matter volume; menopause; weight gain
Copyright © 2009 by American Psychosomatic Society
Highlight selected keywords in the article text.