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Published in final edited form as: Genes Brain Behav. 2012 May 3;11(5):539–544. doi: 10.1111/j.1601-183X.2012.00789.x

A Multivariate Twin Study of Hippocampal Volume, Self-Esteem and Well-Being in Middle Aged Men

Thomas S Kubarych a,o, Elizabeth C Prom-Wormley a,l, Carol E Franz b,n, Matthew S Panizzon b, Anders M Dale c,d, Bruce Fischl e, Lisa T Eyler a,f, Christine Fennema-Notestine b,c, Michael D Grant g, Richard L Hauger b, Dirk H Hellhammer h, Amy J Jak b, Terry L Jernigan b, Sonia J Lupien i,j, Michael J Lyons g, Sally P Mendoza k, Michael C Neale a,l, Larry J Seidman m, Ming T Tsuang b,f,n, William S Kremen b,f,n
PMCID: PMC3389179  NIHMSID: NIHMS367889  PMID: 22471516

Abstract

Self-esteem and well-being are important for successful aging, and some evidence suggests that self-esteem and well-being are associated with hippocampal volume, cognition, and stress responsivity. Whereas most of this evidence is based on studies of older adults, we investigated self-esteem, well-being and hippocampal volume in 474 male middle-age twins. Self-esteem was significantly positively correlated with hippocampal volume (.09, p=.03 for left hippocampus, .10, p=.04 for right). Correlations for well-being were not significant (ps ≫.05). There were strong phenotypic correlations between self-esteem and well-being (.72, p<.001) and between left and right hippocampal volume (.72, p<.001). In multivariate genetic analyses, a 2-factor AE model with well-being and self-esteem on one factor and left and right hippocampal volumes on the other factor fit the data better than Cholesky, independent pathway or common pathway models. The correlation between the two genetic factors was .12 (p=.03); the correlation between the environmental factors was .09 (p>05). Our results indicate that largely different genetic and environmental factors underlie self-esteem and well-being on the one hand and hippocampal volume on the other.

Keywords: self-esteem, well-being, hippocampus, twins, heritability, aging

Reduced hippocampal volume has been associated with mild cognitive impairment, Alzheimer’s disease, and other disorders (Gilbertson et al. 2002;Nelson et al. 1998;Seab et al. 1988;Sheline et al. 1999;Shen et al. 2010). The hippocampus is a major target of cortisol released in the hypothalamic-pituitary-adrenal (HPA) axis, a major stress-response system (Hauger et al. 2006). HPA-axis dysregulation is inversely correlated with hippocampal volume in some, but not all, studies (Kremen et al. 2010a;Lupien et al. 1998;MacLullich et al. 2005;Pruessner et al. 2007;Wolf et al. 2002). HPA-axis responsivity to stress is also associated with self-esteem (Bushman et al. 2009;Ford and Collins 2010;Pruessner et al. 1997). Chronic stress might result in reduced self-esteem or high self-esteem might buffer responses to stress. Thus, self-esteem could be associated with hippocampal volume.

Pruessner et al. (2005) found that self-esteem was significantly correlated with left and right hippocampal volume, averaging r=.55 in young and r=.52 in older adults. A significant correlation was replicated in men, but not women (Pruessner et al. 2007b). In elderly adults, the same group found no significant correlation between self-esteem and hippocampal volume (Engert et al. 2010).

Given the sometimes strong but inconsistent relationship between self-esteem and hippocampal volume, we sought to extend the findings of Pruessner and colleagues in our large twin sample (n=474). With our focus on twins, we were also interested in the genetic underpinnings of this relationship. The heritability of hippocampal volume in our sample was .63 in the left and .64 in the right hemisphere (Kremen et al. 2010b). The median heritability estimate for self-esteem in adolescents and adults using the Rosenberg Self-Esteem Scale (Rosenberg, 1965) is .40 (range: .29–.62) (Roy et al., 1995; Kamakura et al., 2001, 2007;Kendler et al., 1998;Raevuori et al., 2007). Keyes et al. (2010) estimated the heritability of well-being at .52. In the present sample, the correlation between self-esteem and well-being is .72. Other studies have shown that self-esteem is stable across age except in terminal decline (Gerstorf, Ram, Röcke, Lindenberger and Smith, 2008). We therefore expected that well-being would correlate similarly with hippocampal volume as did self-esteem.

Here we address two issues: 1) Whether hippocampal volume is associated with self-esteem and well-being; and 2) The extent to which associations between self-esteem, well-being and hippocampal volume are accounted for by common genetic influences. The sample of late middle-aged twins we use here is nearly 15 years younger on average than the older groups in previous studies.

Materials and Methods

Participants

Details of the sample can be found in Kremen et al. (2006, 2010a). Participants were 474 individuals with analyzable MRI data who are part of a sample of 1237 twins who participated in wave 1 of the Vietnam Era Twin Study of Aging (VETSA); there were 404 paired twins (110 monozygotic [MZ] and 92 dizygotic [DZ] pairs) and 70 unpaired twins. All participants are male-male twins who both served in the United States military sometime between 1965 and 1975. Sixty-three percent of the twins with MRI data were not exposed to combat. All participants gave informed consent as approved by the Institutional Review Boards of participating institutions.

Mean age of the participants was 55.8 (2.6) years (range: 51–59). Mean years of education was 13.9 (SD=2.1), and 85.2% were right-handed. Most participants were employed full-time (74.9%), 4.2% were employed part-time, and 11.2% were retired. There were 88.3% non-Hispanic white, 5.3% African-American, 3.4% Hispanic, and 3.0% “other” participants. Self-reported overall health status was as follows: excellent (14.8%); very good (36.5%); good (37.4%); fair (10.4%); and poor (0.9%). These demographic characteristics did not differ from the entire VETSA sample, nor were there significant differences between MZ and DZ twins. Basic demographic and health characteristics of the VETSA sample are comparable to U.S. census data for similarly aged men (National Health and Nutrition Examination Survey (NHANES III), 1999–2004; Kremen et al, 2006).

Measures and Procedures

MRI images were acquired on Siemens 1.5 Tesla scanners (241 at University of California, San Diego; 233 at Massachusetts General Hospital). Sagittal transverse relaxation time (T1)-weighted Magnetization Prepared RApid Gradient Echo (MPRAGE) sequences were employed with a T1=1000ms, echo time (TE)=3.31ms, repetition time (TR)=2730ms, flip angle=7 degrees, slice thickness=1.33mm, voxel size 1.3×1.0×1.3mm. Raw Digital Imaging and Communication in Medicine (DICOM) MRI scans (including two T1-weighted volumes per case) were downloaded to the MGH site. Images were automatically corrected for spatial distortion caused by gradient nonlinearity and B1 field inhomogeneity. The two T1-weighted images were registered and averaged to improve signal-to-noise.

Volumetric segmentation (Fischl et al., 2002; Fischl, Salat et al., 2004) and cortical surface reconstruction (Dale, Fischl, & Sereno, 1999; Dale & Sereno, 1993; Fischl et al., 2002; Fischl, Salat et al., 2004; Fischl, Sereno, & Dale, 1999; Fischl, van der Kouwe et al., 2004) methods were based on the publicly available FreeSurfer software package version 3.0.1b. The semi-automated, fully 3D whole-brain segmentation procedure uses a probabilistic atlas and applies a Bayesian classification rule to assign a neuroanatomical label to each voxel (Fischl et al., 2002; Fischl, Salat et al., 2004). A widely used training atlas has been shown to be comparable to that of expert manual labeling (Fischl et al., 2002; Fischl, Salat et al., 2004), but we created a VETSA-specific atlas that further increased accuracy compared to expert manual labeling (Kremen et al, 2010b).

Self –esteem was assessed using the 10-item Rosenberg Self-Esteem Scale. The Rosenberg is a reliable and valid measure of self esteem (Schimmack and Diener, 2003). Global well-being was assessed using 18 items developed by Ryff to measure psychological well-being (Ryff, 1989; Ryff and Keyes, 1995).

As a check on other factors that could affect the results, we also included the Center for Epidemiologic Studies Depression Scale (CES-D; Radloff, 1977), Combat Exposure Index (Janes et al., 1991), and report of doctor diagnosis of psychiatric and medical conditions.

Statistical Analysis

Models were fitted to the raw data using full information maximum likelihood (FIML) using OpenMx (Boker et al., 2011). Descriptive statistics for self-esteem, well-being, and left and right hippocampal volumes are presented in Table 1. We tested four multivariate models of self-esteem, well-being, and left and right hippocampal volume: Cholesky, independent pathway, common pathway and 2-factor models.

Table 1.

Descriptive Statistics for Self-esteem, Well-Being, Left and Right Hippocampal Volume

Mean Std. Dev. Minimum Maximum
Self-Esteem 3.43 0.47 1 4
Well-Being 4.74 0.63 2 6
Left Hippocampus (mm3) 3991.75 390.98 2794.00 5359.00
Right Hippocampus 4225.29 431.40 2846.00 5771.00
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The Cholesky model is illustrated in Figure 1. The first latent variable causes variation in all four observed variables. The second factor is uncorrelated with the first, and causes variation in all except the first variable. The remaining factors are similarly configured, such that factor i influences only variables i to 4. This model is a simple way to estimate all variances and covariances subject to the constraint that the covariance matrix is positive definite. This patterning of factor loadings is specified for the additive genetic (A), common environmental (C) and unique environmental (E) sources of variance. The Cholesky model thus estimates all A, C and E variances covariances, and therefore yields the best fit to the data using these variance components. The estimates from the Cholesky model can be standardized to yield genetic correlations, a measure of the extent to which genetic influences of one variable overlap with another variable. While convenient for keeping estimation matrices positive definite, the parameter estimates from a Cholesky model are not always easy to interpret. However, its fit to the data is very useful as a baseline model against which other models may be compared.

Figure 1.

Figure 1

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Path Diagram of Cholesky model. Variables in circles represent latent variables or factors (shown only for genetic factor). Variables in boxes represent observed variables. Triangles represent means. Diagram is shown only for twin 1. Paths between variables represent estimated genetic contributions to phenotypic variance of observed variables.

The independent pathway model, sometimes called the biometric model (Neale and Cardon, 1992; McArdle and Goldsmith, 1990), is depicted in Figure 2. In this model, the A, C and E components are made from a common factor component which influences all four variables, and unique components specific to each variable. Thus the common factors generate variance within and covariance between the variables, while the unique factors generate only variance within each variable. Note that the A and C common factors generate both cross-twin within variable and cross-twin cross-variable covariances, while the A and C unique factors contribute only to cross-twin within-variable covariances.

Figure 2.

Figure 2

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Path Diagram of Independent pathway model. Variables in circles represent latent variables or factors. Variables in boxes represent observed variables. Diagram is shown only for twin 1. Paths between variables represent estimated additive genetic, common environmental and unique environmental contributions to phenotypic variance of observed variables.

The common pathway model, sometimes called the psychometric model, is depicted in Figure 3. This model is more restrictive than the Cholesky or independent pathway models. The common pathway model specifies that the covariation between variables is caused by a single underlying phenotypic variable, which in turn is caused by genetic and environmental factors. In other words, this model tests the hypothesis that covariance between well-being, self-esteem and hippocampal volume all come from a single latent variable. Like the independent pathway model, this model has variable-specific genetic and environmental source of variance. This model can be extended to include multiple intermediate latent variables, although in this article we use only a single factor.

Figure 3.

Figure 3

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Path Diagram of Common pathway model. Variables in circles represent latent variables or factors (shown only for genetic factor). Variables in boxes represent observed variables. Paths between variables represent estimated additive genetic, common environmental and unique environmental contributions to phenotypic variance of observed variables.

Finally, we also fitted a 2-factor model, depicted in Figure 4. Well-being and self-esteem were constrained to load on one genetic factor, left and right hippocampus on the second factor. These two factors were allowed to correlate. This same two-factor structure was used for the C and E common factors.

Figure 4.

Figure 4

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Path Diagram of Two-factor model. Variables in circles represent latent variables or factors (shown only for genetic factor). Variables in boxes represent observed variables. Triangles represent means. Diagram is shown only for twin 1. Paths between variables represent estimated genetic contributions to phenotypic variance of observed variables.

Predictive fit indexes assess model fit in a hypothetical replication of the data from the same population and of the same size as a researcher’s sample. The best known predictive fit index under maximum likelihood estimation is Akaike’s Information Criterion (AIC). AIC is a parsimony-adjusted statistic used to select among competing models. The model with the smallest AIC is chosen as most likely to replicate. More complex models are less likely to replicate (Kline, 2005).

Results

Phenotypic Analyses

Phenotypic correlations of self-esteem and well-being with age and hippocampus volumes are displayed in Table 2. Self-esteem was significantly positively correlated with left (.09, p=.03) and right (.10, p=.04) hippocampal volume. The phenotypic correlation between self-esteem and well-being was .72 (p<.0001). Correlations between well-being and hippocampus volumes did not reach significance but showed the same general pattern. Age was not significantly correlated with either self-esteem or well-being. In order to be more comparable to the sample of Pruessner et al. (2005), we re-ran the analyses excluding participants with depression (based on scores above 16 on the CES-D), history of any psychiatric illness, history of head trauma and other medical conditions. The corresponding correlations in this subsample (N=168) were −.08 between self-esteem and left hippocampal volume (p > .30) and −.04 between self-esteem and right hippocampal volume (p > .58). Adjusting for the possible stress of prior combat exposure had little impact on the results; correlations with self-esteem were .11 (p=.03) for left hippocampal volume and .15 (p=.001) for right hippocampal volume.

Table 2.

Phenotypic correlations of self-esteem and well-being with and hippocampus volumes

Self-Esteem p Well-Being p
Age .05 .21 .08 .08
Left Hippocampus* .09 .04 .05 .23
Right Hippocampus* .10 .03 .01 .77
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*

N=474. After restricting sample to subjects in good health (N=168), these correlations were no longer significant. If intracranial volume (ICV) is not controlled for, these correlations become .13 for both left and right hippocampus

Twin Analyses

The model fitting results are summarized in Table 3. Comparison of ACE and AE models revealed no deterioration of fit after dropping C from the model, so results are listed only for AE models. Neither the independent nor common pathway models provided a good fit to the data. Only the 2-factor model had acceptable fit based on its nonsignificant χ2 value. We, therefore, selected the 2-factor model as the best-fitting model. The heritabilities for well-being, self-esteem, left and right hippocampal volumes based on the AE 2-factor model were estimated at .47, .44, .74 and .74 (Table 4). The remainder of the variance was attributable to environmental influences not shared by the twins. The correlation between the two genetic factors was .12 (p=.03). The correlation between the two environmental factors was .09 (p>.05).

Table 3.

Model fitting results for multivariate analysis of self-esteem, well-being, and left and right hippocampal volume.

Model −2LL parameters df χ2 Δdf p-value AIC
Cholesky 14566.86 24 1868 - - - 10830.86
Independent pathway 14611.91 20 1872 45.05 4 <.001 10867.91
Common pathway 15018.64 22 1871 451.78 2 <.001 11276.64
2-factor 14568.65 22 1870 1.79 2 0.41 10828.65
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−2LL=minus twice the log likelihood

Δdf= change in degrees of freedom

AIC=Akaike’s Information Criterion

Best fitting model in bold

Table 4.

Estimates of Additive Genetic (A) and Unique Environmental (E) Variance for Well-being, Self-esteem and Left and Right Hippocampal Volume Computed from 2-factor Model.

2-factor model
A (95%CI) E (95%CI)
Well-Being .47 (.32, .59) .53(.41,.68
Self-esteem .44 (.29,.57) .56(.43, .71)
Left Hippocampus .74(.66, .80) .26(.20, .34)
Right Hippocampus .74(.66, .80) .26(.20, .34)
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Correlation between genetic factors = .12

Correlation between environmental factors =.09

Discussion

While the phenotypic correlations between self-esteem and left and right hippocampal volumes in this study were significant, these correlations were small and substantially lower than those reported by Pruessner et al (2005) for 16 healthy individuals aged 20–24 and 23 individuals aged 60 to 84, including both males and females. Multivariate analyses in our large, genetically-informative study revealed that both the genetic and environmental factors underlying self-esteem/well-being and hippocampal volume are mostly distinct, the correlations being .12 for the genetic factors and .09 for the environmental factors.

Studies by Pruessner and colleagues have found a significant relationship between self-esteem and hippocampal volume (Pruessner et al., 2005), a significant relationship for men only (Pruessner et al., 2007b), and a nonsignificant relationship (Engert et al., 2010). Although we found a small, but significant relationship, our results are probably most consistent with the previous negative findings in much smaller samples. Sampling differences (e.g., susceptibility of small samples to stochastic processes, age or sex differences) could be one factor underlying inconsistent results. Differences in imaging methods might also be a factor, but high correlations (≈.85) for hippocampal volumes between FreeSurfer and manual tracing make it unlikely the size of the observed inconsistencies would be accounted for by these methodological differences (Tae et. al., 2008). The results of Engert et al. suggest another possibility, namely, that main effects may be obscured by mediating or moderating effects of self-esteem or well-being.

Not surprisingly, self-esteem and well-being were highly phenotypically correlated (r=.72), indicating that these constructs are very similar. We found moderate heritabilities of .47 for scores on the Ryff Psychological Well-Being Scale and .44 for scores on the Rosenberg Self-Esteem scale. The remaining variance in each was accounted for entirely by unique environmental influences. These results are very similar to previous heritability estimates for psychological well-being in adults (Keyes et al. 2010).

There is a need for mediational studies that could be tested longitudinally as further waves of the VETSA data become available. Such models are consistent with the notion that self-esteem and well-being are important in successful aging, and that well-being is both a predictor and a consequence of successful aging (Lyubomirsky, King and Diener, 2005). Cognitive ability, socioeconomic status, family environments, stressful life events, personality variables other than self-esteem such as resilience, and other brain regions might modulate the relationship between self-esteem, cortisol response, and hippocampal volume. It may be that as the hippocampus atrophies with age, the stress-response system becomes less efficient, but there are characteristics (e.g., higher cognitive ability, greater self-esteem) and conditions (good health, not smoking) that make this decline in efficiency less acute.

In conclusion, we found a small but significant association between self-esteem and hippocampal volumes in a large sample of middle-aged men. One earlier study found a fairly strong association between hippocampal volume and self-esteem (Pruessner et al., 2005), but another did not (Engert et al, 2010). As suggested by the study of Engert et al., inconsistencies may be due to the fact that self-esteem is a mediator rather than a characteristic with a direct relationship to hippocampal volume. We do not know if our results would generalize to women; however, Pruessner et al. (2007b) replicated the association in men but not in women. It is also possible that the results may not generalize to individuals who did not serve in the military. On the other hand, we have noted that our sample is in many ways representative of similarly aged American men. Moreover, there are many published studies based on twins from this registry, and these have been largely consistent with those from other samples. In addition, adjusting for prior combat exposure had little impact on the results. Given the importance of these psychological (self-esteem) and biological (hippocampus, HPA axis function) processes, it will be important for future studies to continue to examine influences on brain structure and function in healthy and pathological aging.

Acknowledgements

The VETSA is supported by grants from NIH/NIA (R01 AG018386, R01 AG018384, R01 AG022381, and R01 AG022982). The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIA or the NIH. The Cooperative Studies Program of the U.S. Department of Veterans Affairs has provided financial support for the development and maintenance of the Vietnam Era Twin (VET) Registry. Numerous organizations have provided invaluable assistance in the conduct of this study, including: Department of Defense; National Personnel Records Center, National Archives and Records Administration; the Internal Revenue Service; National Opinion Research Center; National Research Council, National Academy of Sciences; the Institute for Survey Research, Temple University. This material was, in part, the result of work supported with resources of the Center of Excellence for Stress and Mental Health, VA San Diego Healthcare System. Most importantly, the authors gratefully acknowledge the continued cooperation and participation of the members of the VET Registry and their families. Without their contribution this research would not have been possible. We also appreciate the time and energy of many staff and students on the VETSA projects.

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