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American Journal of Alzheimer's Disease and Other Dementias logoLink to American Journal of Alzheimer's Disease and Other Dementias
. 2016 Jul 9;31(5):450–456. doi: 10.1177/1533317516653827

Family History of Alzheimer’s Disease and Cortical Thickness in Patients With Dementia

Steffi Ganske 1, Robert Haussmann 1, Antonia Gruschwitz 1, Annett Werner 2, Antje Osterrath 1,3, Johanna Baumgaertel 1, Jan Lange 1, Katharina L Donix 1, Jennifer Linn 2, Markus Donix 1,3,✉
PMCID: PMC10852676  PMID: 27303063

Abstract

A first-degree family history of Alzheimer’s disease reflects genetic risks for the neurodegenerative disorder. Recent imaging data suggest localized effects of genetic risks on brain structure in healthy people. It is unknown whether this association can also be found in patients who already have dementia. Our aim was to investigate whether family history risk modulates regional medial temporal lobe cortical thickness in patients with Alzheimer’s disease. We performed high-resolution magnetic resonance imaging and cortical unfolding data analysis on 54 patients and 53 nondemented individuals. A first-degree family history of Alzheimer’s disease was associated with left hemispheric cortical thinning in the subiculum among patients and controls. The contribution of Alzheimer’s disease family history to regional brain anatomy changes independent of cognitive impairment may reflect genetic risks that modulate onset and clinical course of the disease.

Keywords: hippocampus, entorhinal cortex, subiculum, family history of Alzheimer’s disease, MRI, Alzheimer’s disease

Introduction

Alzheimer’s disease (AD) affects approximately 500 000 new patients every year in the United States alone, a number that is expected to double in 2050. 1 Aside from early-onset forms of the neurodegenerative disease due to fully penetrant mutations in the amyloid precursor protein or presenilin (PSEN1 and PSEN2) genes, the common late-onset (sporadic) variant has a complex genetic background and an estimated heritability of approximately 60% to 80%. 2 The ∊4 allele of the apolipoprotein E (APOE4) gene is the most important known genetic risk factor associated with an allele dose-dependent increased risk for developing AD and earlier disease onset. 3 Genome-wide association studies have confirmed a number of other susceptibility loci, for example, clusterin (CLU), complement receptor 1 (CR1), bridging integrator 1 (BIN1) or phosphatidylinositol-binding clathrin assembly protein (PICALM) with small effects on AD risk, and rare genetic variants, such as phospholipase D3 (PLD3) and Triggering receptor expressed on myeloid cells 2 (TREM2) with moderate to large effects. 4 However, there could be yet undiscovered risk genes of even larger effect size than APOE4. 5 A first-degree family history (FH) of AD can be conceptualized as a composite risk factor reflecting an individual risk pattern of known and unknown susceptibility genes, 6 associated with a greater risk for developing the disease. 7 Across various neuroimaging studies, family history risk has been shown to be associated with structural and functional brain changes that can be differentiated from the influence of known genetic risk (for review, see Ref. 6).

Using high-resolution magnetic resonance imaging (MRI), genetic risk factor-associated structural brain changes can already be found in children and adolescents. Shaw and colleagues showed a thinner left entorhinal cortex (ERC) in children and young adults carrying the APOE4 allele. 8 This suggests that AD genetic risk modulates brain structure in medial temporal lobe regions preferentially susceptible to neurodegeneration later in life. 9 Neuropathological AD staging shows that neurofibrillary tangles first occur in the transentorhinal region before they spread into the subiculum (SUB) and the hippocampus. 9 The AD genetic risk could also influence structural hemispheric asymmetry. 8,10 It may contribute to the more severe left hemispheric pathology that precedes right hemispheric changes by up to 2 years. 11 Thompson and colleagues revealed that patients with AD show an accelerated gray matter volume loss in the left hemisphere when compared with the right hemisphere. 11 Pronounced left hemispheric pathology in AD has been consistently demonstrated in neuropathological investigations, 12 metabolic (fluorodeoxyglucose-positron emission tomography), 13 and structural neuroimaging studies. 11,14 Hemispheric asymmetry can be found in healthy people as well, such as left < right hippocampal volume differences. 15 The asymmetric hemispheric development is mediated by differential gene expression in both hemispheres, 16 and risk genes may contribute to lateralization effects in neurodegeneration. 8,10 Functional hemispheric specialization, such as the right hippocampus being predominantly involved in spatial memory, and the critical role of the left hippocampus for context-dependent episodic memory illustrate that the course of the clinical symptoms in AD reflects the asymmetric progression of neurodegenerative changes within the medial temporal lobes of both hemispheres. 17

We previously demonstrated that among cognitively healthy people, carrying the APOE4 allele is associated with cortical thinning in the ERC and the SUB. 18,19 We also showed that family history risk explains a greater variance in medial temporal cortical thickness than APOE4 and that the pattern of cortical thinning associated with both risks could reflect partially different mechanisms contributing to cortical atrophy. 20 Patients with cognitive impairment also show volume loss in the hippocampus and the SUB, 21 and cortical thinning in the ERC and the SUB predicts the clinical course of these patients. 22 However, there are no data available on how genetic risks influence local medial temporal lobe structure in patients who already have cognitive impairments and dementia. Standard MRI data analysis may prevent the detection of subtle risk factor-associated changes in brain regions already affected by atrophy. It also remains a research focus whether genetic risks for AD modulate development and clinical course of the disease. Whereas APOE4 may be more important for disease onset rather than disease progression, 3,23 the potentially more complex genetic background reflected in a family history makes a similar characteristic for this risk factor less likely.

In this study, we used high-resolution MRI and an image analysis technique that unfolds medial temporal lobe subregions into a 2-dimensional map 24,25 to investigate the influence of AD family history risk on subregional medial temporal cortical thickness among patients with AD and nondemented controls. We hypothesized that a family history of AD would be associated with cortical thinning in the ERC and the SUB, brain regions affected early in AD development. We also expected that the risk factor-associated cortical thinning would be primarily left lateralized and independent of the participants’ cognitive abilities.

Methods

Participants

One hundred seven people participated in this study. They were recruited through advertisements and through our university hospital’s memory clinic. We selected the participants from a pool of 130 individuals who participated in neuropsychological examinations and MRI scanning aimed at investigating AD risk factors. Twenty-three participants of this sample could not be recruited for the current study because they did not receive MRI scans. Written informed consent was obtained; the university’s ethics committee approved the study. Among the study participants were 53 nondemented people (mean age 68.9 ± 7.0 years) and 54 patients with AD (mean age 72.3 ± 6.6 years). All participants underwent detailed neuropsychological examinations and MRI brain scans. We only recruited patients with AD having mild dementia, meeting standard clinical criteria, 26 who had the cognitive capacity to consent. All study participants did not have psychiatric or neurological disorders other than the cognitive impairment or any systemic disease possibly affecting brain function. Patients with AD were on stable (>6 months) medication with an acetylcholinesterase inhibitor. All patients were right handed and did not receive any other psychotropic medication. Positive family history risk was defined as having at least 1 first-degree relative who had been diagnosed with AD. 26 All participants with this risk factor had a parental family history only. APOE genotype information was available for all participants with AD.

Procedures

Using a GE Signa HDxt 3-Tesla scanner (General Electric Health Care, Waukesha, Wisconsin), we obtained high-resolution oblique coronal T2-weighted fast-spin echo scans (repetition time: 5200 milliseconds; echo time: 105 milliseconds; slice thickness: 3 mm; spacing: 0 mm; 19 slices; in-plane voxel size: 0.39 × 0.39 mm; field of view: 200 mm). Data analysis was performed with a cortical unfolding procedure, 24,25 which improves the visibility of the convoluted medial temporal lobe cortex by flattening the gray matter volume into 2-dimensional space (Figure 1). We first manually masked white matter and cerebrospinal fluid on the original MRI sequence, and then connected layers of gray matter were grown out using a region-expansion algorithm. The gray matter volume contains cornu ammonis fields 1 (CA1), CA2,3 and the dentate gyrus (CA23DG), SUB, ERC, perirhinal cortex, parahippocampal cortex, and the fusiform gyrus. Computational unfolding of the gray matter volume is based on metric multidimensional scaling. Boundaries between subregions were delineated on the original MRI data using histological and MRI atlases 27,28 and mathematically projected to their flat map space coordinates. To measure cortical thickness, for each gray matter voxel, the distance to the closest nongray matter voxel is computed. In 2-dimensional space, for each voxel, the maximum distance value of the corresponding 3-dimensional voxels across all layers is taken and multiplied by 2. Thickness in each subregion is calculated by averaging the thickness of all voxels (for details on cortical unfolding procedures, see Refs. 24,29). In line with our previous studies and MRI data analysis strategies for cortical thickness in contrast to volumetric measures, we report raw data. 30 Investigators performing MRI scanning and cortical unfolding procedures were unaware of the patients’ clinical and demographic information.

Figure 1.

Figure 1.

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Cortical unfolding acquired on oblique coronal MRI scans (A) perpendicular to the long axis of the hippocampus (B). After manual segmentation of white matter and CSF, the gray matter volume is computationally unfolded and flattened based on metric multidimensional scaling (C, right hemispheric flat map). Boundaries between the subregions are applied to the high-resolution MRI sequence (B) and mathematically projected to the 2-dimensional space. CA23DG indicates cornu ammonis fields 2,3 and dentate gyrus (the anterior part of the cornu ammonis fields and dentate gyrus [AntCADG] is part of the CA23DG region); CA1, CA field 1; CSF, cerebrospinal fluid; ERC, entorhinal cortex; FUS, fusiform gyrus (fusiform boundary depicts the medial fusiform vertex, dotted line); MRI, magnetic resonance imaging; PHC, parahippocampal cortex; PRC, perirhinal cortex; SUB, subiculum.

In order to determine the influence of a first-degree family history of AD on the left and right hemispheric entorhinal and subicular cortical thickness, we estimated mixed general linear models with subregions as the dependent variables, cognitive status (nondemented/demented) and family history risk (yes/no) as between-group factors, and age and Mini Mental State Examination (MMSE) score as covariates. We also investigated a possible interaction between cognitive status and family history risk on cortical thickness. After we established significance with the multivariate F tests, we conducted post hoc univariate tests to determine the influence of family history risk on regional cortical thickness. Gender and family history distribution were compared with χ2 tests. Statistical analyses used a significance level of P < .05.

Results

Nondemented individuals and patients with AD did not significantly differ in educational status or gender distribution. Patients with dementia were significantly older; therefore, we modeled age as a covariate in all analyses. We also used the MMSE score as a covariate in all analyses in order to account for possible MMSE-related effects given the difference in MMSE distribution between patients and controls. However, the family history risk groups did not significantly differ in MMSE scores within patients and controls (controls FH−: 28.9, FH+: 28.8, patients FH−: 22.7, FH+: 22.9).

Although the groups did not significantly differ in this characteristic, there was an expected trend for a higher frequency of family history risk among patients with AD when compared with controls (Table 1), resulting in an unbalanced risk factor distribution. Therefore, we further investigated a possible factor interaction in the general linear model by determining the type of interaction. We found an ordinal interaction type that enables us to interpret the main effects in the way presented here.

Table 1.

Demographic Characteristics and Neuropsychological Scores.

Characteristics and Measures CTL SD AD SD Significance (P Value)a
N 54 53
Age (years) 68.9 ±7.0 72.3 ±6.6 .01
Female sex (%) 45.3 50.0 .63
Education (years) 14.3 ±2.6 13.4 ±2.5 .06
FH+ (%) 24.5 40.7 .07
MMSE (score range 0-30) 28.8 ±1.2 22.8 ±5.1 <.001
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Abbreviations: AD, Alzheimer’s disease; CTL, nondemented control participants; FH+, first-degree family history of Alzheimer’s disease; MMSE, Mini Mental State Examination; SD, standard deviation.

aχ2tests for gender and family history risk distribution.

When considering right and left (average) cortical thickness, the mixed general linear model yielded significant effects for cognitive status (demented/nondemented; F = 16.4, df = 2.98, P < .001) and family history risk (yes/no) (F = 3.81, df = 2.98, P = .026). There was no significant interaction between cognitive status and family history risk. Patients with AD had a significantly thinner ERC than controls (AD: 2.23 ± 0.13 mm, controls: 2.55 ± 0.22 mm, P < .001) and a thinner SUB (AD: 1.90 ± 0.15 mm, controls: 2.08 ± 0.14 mm, P = .001). Patients with a positive first-degree family history of AD showed a significantly thinner SUB than participants without this risk factor (FH+: 1.92 ± 0.13 mm, controls: 2.03 ± 0.18 mm, P = .008), whereas entorhinal cortical thickness was not significantly different.

When investigating both hemispheres separately (Figure 2), dementia was associated with a thinner ERC and SUB in both hemispheres (right hemisphere: F = 25.08, df = 2.98, P < .001, left hemisphere: F = 7.97, df = 2,98, P = .001). Positive AD family history was associated with a thinner SUB only in the left hemisphere (F = 4.4, df = 2,98, P = .015). Entorhinal cortical thickness did not vary due to family history status in both hemispheres.

Figure 2.

Figure 2.

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Right and left hemispheric cortical thickness is shown for the subiculum (SUB) and entorhinal cortex (ERC). As expected, patients with Alzheimer’s disease (DAT) showed a thinner cortex in both regions compared with controls (CTL) in the right and left hemisphere. A positive first-degree family history of Alzheimer’s disease (FH+) was associated with a thinner subiculum in the left hemisphere independent of the participants’ cognitive abilities.

We additionally investigated whether the APOE genotype would influence cortical thickness in the entorhinal region and the SUB among patients with AD. Although we found a trend in the left hemisphere toward entorhinal cortical thinning, there was no significant association of APOE genotype and cortical thickness in patients with dementia. Cortical thinning in the SUB was not explained by the APOE genotype.

Discussion

In this study, we show that a positive first-degree family history of AD is associated with cortical thinning in the left hemispheric SUB. The effect was independent of the patients’ cognitive impairments and could be detected in individuals with AD as well as in control participants. Entorhinal cortical thickness did not vary due to the presence of the family history risk factor.

We previously demonstrated how genetic risks modulate entorhinal and subicular cortical thickness in cognitively healthy people. 18,19 An additive effect for family history risk and the APOE4 allele was detectable in the SUB in contrast to the ERC, where both risks did not influence cortical thickness more than either risk factor alone. 20 In this study, we additionally investigated a possible influence of APOE4 genetic risk on entorhinal and subicular cortical thickness in patients with AD. Although we found a trend for entorhinal rather than subicular cortical thinning due to the risk allele, this association was not significant. This finding is in line with the hypothesis that APOE4 may preferentially influence the onset rather than the clinical course of AD 3,23 ; however, the impact of APOE4 allele dose on disease progression may become more obvious using nonlinear statistical models. 31 We show that family history-associated effects are still detectable among patients with AD. The possibly more complex genetic background that contributes to family history risk could involve risk genes that modulate brain structure and function through many different mechanisms across preclinical and clinical stages of AD development and progression.

Hippocampal shape changes are most pronounced in CA1 and the SUB among patients with AD. 32 Scher and colleagues highlight localized hippocampal atrophy in the lateral portion of the left hippocampus that includes the SUB in AD when compared with vascular dementia. 33 Recent data from an ultrahigh-field MRI postmortem study suggest microscopic iron and activated microglia specifically in the SUB of patients with AD, consistent with spreading neurodegeneration along entorhinal projections to the hippocampus. 34,35 It shows that innovative MRI techniques contribute to our understanding of AD-associated pathology and its subregional effects on medial temporal lobe integrity. Recent animal data show that the SUB is the earliest hippocampal region showing severe neuronal loss in a mice model of AD 36 and that subicular neuron damage could be related to AD through neuroinflammatory pathways. 37

In AD, the neurofibrillary tangle load correlates with cognitive impairment. 38 Neuropathological data suggest that neurofibrillary tangle formation could be accelerated in the SUB in contrast to the entorhinal region, 39 an effect that was not associated with the APOE4 allele. 39 To date, the possible association of AD susceptibility genes other than APOE4 with brain structure has been rarely investigated. Bralten and colleagues did not find genotype-related local gray matter volume differences in healthy people for CLU and PICALM but showed smaller ERC volumes among CR1 risk allele carriers. 40 Others found an association of PICALM with hippocampal volume. 41 A rare and dysfunctional variant of the TREM2 gene, encoding a protein with anti-inflammatory characteristics that is highly expressed in the hippocampus, increases the risk for developing late-onset AD to a magnitude possibly comparable with APOE4 40 ; however, brain imaging data are not yet available.

It is likely that many risk alleles ultimately influence neuronal damage and atrophy in AD; however, measuring global cortical or medial temporal lobe atrophy 42 may not be sufficient to reveal the regional effects of genetic risks that could be additive with or independent of APOE4-associated structural brain changes. 20 Furthermore, in MRI studies, subregional analyses may be necessary to detect the differences in risk-related brain structure changes across risk factors. 6,20

Investigating family history risk-associated changes in radiological brain anatomy cannot be used to differentiate the individual effects of risk genes that could vary among individuals. Other study limitations include that nongenetic risk factors that are passed on through generations could also be part of the family history risk factor. 6,43 In our participant sample, APOE information was only available for patients with dementia, so we cannot make direct inferences about the differences or similarities of APOE- and family history risk-associated effects among control participants. However, we previously demonstrated the influence of both risks on brain structure in healthy individuals. 18 –20 Finally, healthy relatives of study participants could develop AD in the future, and it cannot be determined clinically whether APOE4 and/or other risk genes contributed to the family history among individual participants with this risk factor.

In summary, we show an association of a first-degree family history of AD with subicular cortical thinning in the left hemisphere among patients with AD and nondemented individuals. Our data contribute to the idea that genetic risks for AD modulate local brain structure in key regions susceptible to neuropathological changes. Localization and lateralization patterns of such effects reflect the characteristic clinical impairments and their time course. Future research will be necessary to determine the influence and interplay of individual risk genes on brain anatomy and function. Until then, it is important to model the family history factor in MRI investigations of people at risk for cognitive decline and also in patients who already have AD.

Acknowledgments

The authors would like to thank Susan Bookheimer, Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, for support and assistance with cortical unfolding. The authors also thank Cathrin Sauer for her suggestions on the statistical models.

Authors’ Note: Steffi Ganske and Robert Haussmann contributed equally to the manuscript.

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding: The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: Linn has received research support from B. Braun Stiftung, Friedrich-Baur-Stiftung, and the Förderprogram für Forschung und Lehre and received travel expenses and/or honoria for educational lectures from Bayer Healthcare, Phillips Healthcare, and Bracco. Donix has received research support from the Roland Ernst Stiftung and a consulting fee from Trommsdorff. The study was funded by a Young Investigator Grant (MeDDrive 60.338) of the TU Dresden to Markus Donix.

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