, Volume 46, Issue 12, pp 1604–1610 | Cite as

Type 2 diabetes and atrophy of medial temporal lobe structures on brain MRI

  • T. den Heijer
  • S. E. Vermeer
  • E. J. van Dijk
  • N. D. Prins
  • P. J. Koudstaal
  • A. Hofman
  • M. M. B. BretelerEmail author



Type 2 diabetes increases the risk not only of vascular dementia but also of Alzheimer’s disease. The question remains whether diabetes increases the risk of Alzheimer’s disease by diabetic vasculopathy or whether diabetes influences directly the development of Alzheimer neuropathology. In vivo, hippocampal and amygdalar atrophy on brain MRI are good, early markers of the degree of Alzheimer neuropathology. We investigated the association between diabetes mellitus, insulin resistance and the degree of hippocampal and amygdalar atrophy on magnetic resonance imaging (MRI) accounting for vascular pathology.


Data was obtained in a population-based study of elderly subjects without dementia between 60 to 90 years of age. The presence of diabetes mellitus and, in non-diabetic subjects, insulin resistance was assessed for 506 participants in whom hippocampal and amygdalar volumes on MRI were measured. We assessed the degree of vascular morbidity by rating carotid atherosclerosis, and brain white matter lesions and infarcts on MRI.


Subjects with diabetes mellitus had more hippocampal and amygdalar atrophy on MRI compared to subjects without diabetes mellitus. Furthermore, increasing insulin resistance was associated with more amygdalar atrophy on MRI. The associations were not due to vascular morbidity being more pronounced in persons with diabetes mellitus.


Type 2 diabetes is associated with hippocampal and amygdalar atrophy, regardless of vascular pathology. This could suggest that Type 2 diabetes directly influences the development of Alzheimer neuropathology.


Type 2 diabetes brain dementia epidemiology hippocampus amygdala memory MRI insulin glucose 



magnetic resonance imaging


Apolipoprotein E

Type 2 diabetes increases the risk of stroke [1] and vascular dementia [2]. Recently, patients with Type 2 diabetes were found to have an increased risk of the most common form of dementia, Alzheimer’s disease [3, 4]. The pathophysiological mechanism of the relation between diabetes mellitus and Alzheimer’s disease is not clear. Diabetic vasculopathy can cause cerebrovascular brain damage, which is frequently found in patients with Alzheimer’s disease [5]. However, other, more direct effects of diabetes on the development of Alzheimer neuropathology can also be involved. Advanced glycation end products increase aggregation of proteins involved in Alzheimer’s disease [6]. Furthermore, dysfunction of insulin signalling in the brain has been implicated in the pathogenesis of Alzheimer’s disease [7]. The neuropathology of Alzheimer’s disease occurs with greatest severity and in an early stage of the disease in the hippocampus and amygdala, brain structures in the medial temporal lobe [8]. In vivo assessment of hippocampal volume on magnetic resonance imaging (MRI) of the brain provides a good estimate of the degree of Alzheimer neuropathology, even in elderly subjects without clinical symptoms of dementia [9]. Several studies show that patients with mild Alzheimer’s disease have smaller volumes of the hippocampus [10, 11, 12] and amygdala [13, 14] on MRI compared to healthy control subjects. We examined the association between diabetes mellitus, insulin resistance and hippocampal and amygdalar atrophy on MRI using these as early MRI markers of Alzheimer’s disease. We accounted for atherosclerosis and cerebrovascular disease to examine whether any association was caused by concomitant vascular disease.



The Rotterdam Study is a large population-based cohort study in the Netherlands that investigates the prevalence, incidence and determinants of chronic diseases in the elderly [15]. Baseline examinations were done in 1990 to 1993. In 1995 to 1996, we randomly selected 965 living members (60–90 years of age) of the cohort in strata of sex and age (5 years) to participate in the Rotterdam Scan Study, designed to investigate age-related brain abnormalities on MRI [16]. After excluding individuals who were demented (n=17) [17] or had MRI contraindications (n=116), 832 people were eligible and invited. Among these, 563 participants gave their written informed consent and underwent MRI scanning of the brain (participation rate: 68%). Participants were in general healthier than non-participants [18]. The study protocol was approved by the medical ethics committee of the Erasmus Medical Center, Rotterdam, The Netherlands.

Assessment of diabetes mellitus and insulin resistance

Presence of diabetes mellitus was assessed at the baseline of the Rotterdam Study (1990–1993) and at time of MRI (1995–1996). Participants where considered to have diabetes mellitus if they reported use of oral anti-diabetic treatment or of insulin, or if they had a random serum glucose concentration greater than or equal to 11.1 mmol/l. In addition, if a post-load glucose concentration (2 h after a glucose drink of 75 g in 200 ml water) at baseline was greater than or equal to 11.1 mmol/l the participant was also considered to have diabetes mellitus. Glucose concentrations were measured by the glucose hexokinase method. Insulin resistance in non-diabetic subjects at baseline was assessed by the ratio of the post-load insulin concentration (Medgenix, Brussels, Belgium) over post-load glucose concentration.

MRI procedures

In 1995 to 1996, standard T1, T2 and proton-density weighted MR sequences of the brain were made using a 1.5 Tesla MR unit (VISION MR, Siemens, Erlangen, Germany) [19]. After these sequences were finished, an additional custom-made three-dimensional MRI sequence of the whole brain was acquired (named half-Fourier acquisition single-shot turbo spin echo [20]). This three-dimensional MRI sequence was used for later volumetric assessments of the hippocampus and amygdala. A total of 52 participants developed claustrophobia during the MRI scanning period, leaving 511 participants with a completed three-dimensional MRI sequence.

Hippocampal and amygdalar volumes on MRI

For the 511 participants with a three-dimensional MRI sequence, we reformatted a series of coronal brain slices (contiguous 1.5-mm slices) aligned to be perpendicular to the long axis of the hippocampus and the middle sagittal slice. The procedure of segmenting the hippocampus and amygdala has been described in detail [20]. Briefly, we manually traced the boundaries of the hippocampi and amygdalae using a mouse-driven pointer (Fig. 1), which yielded outlined areas (mm2). We proceeded from posterior to anterior, starting on the slice where the crux of the fornices of the hippocampus was in full profile. We multiplied the summed areas with slice thickness (1.5-mm) to calculate estimates of the left and right hippocampal and amygdalar volume (ml). Total hippocampal and amygdalar volumes were calculated by summing the left and right hippocampal volume and the left and right amygdalar volume. As a proxy for head size, we measured on the middle sagittal MRI slice the intracranial cross-sectional area [20, 21]. We corrected for head size difference across the subjects as follows [13, 21]. First, each subject’s hippocampal or amygdalar volumes were divided by their measured head size area. Next, to obtain head size corrected, normalised volumes the ratios for each subject were multiplied by the average head size area (men and women separately).
Fig. 1

Coronal MRI slice on which the hippocampus (H) and amygdala (A) are depicted

Other measurements

We interviewed and gave physical examinations to the participants to obtain information on their: educational level (according to UNESCO [22]), BMI (weight divided by the square of the height), pack-years of cigarette smoking, blood pressure and serum total cholesterol [23]. Memory function was evaluated with a 15-word learning test which consisted of three immediate learning trials and a delayed recall trial [19]. For each participant, we calculated z-scores (individual test score minus mean test score divided by the standard deviation). We constructed a compound score for memory performance by averaging the z-score of the three immediate recall trials and the delayed recall trial [19]. Apolipoprotein E (APOE) genotyping yielded the following alleles: ε2, ε3, and ε4 [24]. We classified participants into those with and without a ε4 allele because the ε4 allele is a strong risk factor of Alzheimer’s disease [25]. Because the presence of the ε2 allele can reduce the risk of Alzheimer’s disease [25], we excluded persons with genotype ε2ε4 (n=9) in the analyses considering APOE genotype. To assess carotid atherosclerosis, participants underwent ultrasonography of the carotid arteries [26]. Presence of atherosclerotic plaques was determined at the common carotid artery, the carotid bifurcation, and the internal carotid artery at the left and right side and summed (range 0–6). The intima-media thickness was measured by longitudinal two-dimensional ultrasound of the anterior and posterior wall of both common carotid arteries. We calculated the mean of these four locations. Cerebral white matter lesions were assessed on proton density weighted axial MR images and were scored in the periventricular regions (range 0–9) and the subcortical regions (approximated volume) [19]. Brain infarcts were defined as focal hyperintensities on T2 weighted images, and, if present in the white matter, with corresponding prominent hypointensity on T1 [23].

Data analysis

We missed information on the presence of diabetes mellitus in five participants, leaving a total of 506 participants for the analyses. We used multiple linear regression modelling to quantify the relation between diabetes, insulin resistance in non-diabetic subjects, and MRI volumes. Adjustments were made for age and sex. Additional adjustments included BMI, pack-years of cigarette smoking, blood pressure and serum cholesterol as co-variates. To investigate whether vascular disease was mediating any association between diabetes, insulin resistance and MRI volumes, we adjusted for carotid atherosclerosis, white matter lesions and brain infarcts on MRI. We repeated all analyses excluding subjects with infarcts on MRI. Finally, because the effect of diabetes on the risk of dementia might differ across APOE genotypes [4], we studied possible effect modification by APOE genotype through stratified analyses (non-carrier of the ε4 allele versus carrier of the ε4 allele). Assumptions of the model were verified by residual diagnostics. A p value of less than 0.05 was considered to be statistically significant.


Selected characteristics of the study cohort according to the presence of diabetes mellitus are given in Table 1. In total, 41 participants (8.1%) had diabetes mellitus. Twenty-six of them were treated with anti-diabetic medication at time of MRI. Their median age when they were diagnosed with diabetes mellitus was 64 years (range 43–84), suggesting that they all had Type 2 diabetes. Of note, although all participants were clinically free from dementia, persons with diabetes mellitus performed worse on memory tests (Table 1). Persons with diabetes mellitus had more atherosclerotic plaques in the carotid arteries (Table 1). They also had more cerebral white matter lesions on MRI, but this was not statistically significant after accounting for age and sex differences. Brain infarcts were 1.7 times (95% CI 0.8 to 3.3) more frequent in subjects with diabetes mellitus compared to those without diabetes mellitus, after adjusting for age and sex.
Table 1

Characteristics of participants with and without diabetes mellitus


No diabetes mellitus

Diabetes mellitus

Adjusted differencea



Age, years



4 (1; 6)

Women, %



−17 (−33; −0.6)

Education, primary only %



7 (−8; 20)

Memory performance, Z-score



−0.33 (−0.60; −0.06)

BMI, kg/m2



0.7 (−0.5; 1.8)

Pack-years of cigarette smoking



2 (−5; 10)

Diastolic blood pressure, mmHg



−1 (−5; 3)

Systolic blood pressure, mmHg



−1 (−8; 5)

Total cholesterol, mmol/l



0.2 (−0.2; 0.5)

Carotid plaques, total number



0.7 (0.2; 1.3)

Intima-media thickness, mm



0.04 (−0.01; 0.08)

Periventricular white matter lesions, grade


3.6 (2.2)

0.4 (−0.2; 1.1)

Subcortical white matter lesions, ml


2.3 (3.6)

0.01 (−1.0; 1.0)

Brain infarcts, %



11 (−3; 25)

APOE, ε4 carriers, %



5 (−11; 21)

Postload insulin, pmol/lb


Insulin resistance, pmol/mmolb


Data are given as means ± standard deviation or percentages

aAge and sex adjusted difference (95% CI) in variable between participants with and without diabetes mellitus

bPresent for 405 participants without diabetes

Subjects with diabetes mellitus had smaller hippocampal and amygdalar volumes on MRI (Fig. 2). Diabetes mellitus had a similar effect on the left and the right-sided brain volumes separately. Additional adjustments for BMI, pack-years of cigarette smoking, blood pressure and cholesterol, did not change the results. Although subjects with diabetes mellitus had more vascular disease, accounting for markers of vascular disease did not change the association between diabetes and hippocampal or amygdalar volumes (Table 2). Exclusion of participants with infarcts (n=142) did not change the results either. There was no difference in association between diabetes and MRI volumes according to APOE strata. In non-carriers of the ε4 allele, the age and sex adjusted difference in hippocampal volume between persons with and without diabetes mellitus was −0.30 (95% CI −0.66 to 0.06). In carriers of the ε4 allele the difference in hippocampal volume was −0.31 (95% CI −0.84 to 0.21). In non-carriers of the ε4 allele, the age and sex adjusted difference in amygdalar volume between persons with and without diabetes mellitus was −0.28 (95% CI −0.58 to 0.01). In carriers of the ε4 allele the difference in amygdalar volume was −0.41 (95% CI −0.86 to 0.04).
Fig. 2

Hippocampal volumes and amygdalar volumes (+standard error) on brain MRI in participants with diabetes (n=41) and without diabetes (n=465). Volumes are adjusted for age and sex and normalised to average head size

Table 2

Hippocampal and amygdalar volume on MRI in participants with and without diabetes mellitus accounting for markers of vascular disease

Volume difference between participants with and without diabetes mellitus, ml (95% CI)

Difference adjusted for





Age and sex

−0.28 (−0.55 to −0.01)


−0.33 (−0.55 to −0.11)


Age, sex, and carotid atherosclerosis

−0.27 (−0.55 to 0.00)


−0.32 (−0.54 to −0.10)


Age, sex, white matter lesions and infarcts on MRI

−0.27 (−0.54 to 0.00)


−0.33 (−0.56 to −0.11)


Data are given as adjusted differences (95% CI and p value) in MRI volumes (ml) between participants without diabetes mellitus (n=465) and participants with diabetes mellitus (n=41)

aVolumes are normalised to average head size

In non-diabetic participants (n=465), post-load insulin concentrations and insulin resistance were present for 405 participants. Persons with higher post-load insulin concentrations or insulin resistance had smaller amygdalar volumes, but not smaller hippocampal volumes on MRI (Table 3). Additional adjusting for BMI, pack-years of cigarette smoking, blood pressure, cholesterol, carotid atherosclerosis, white matter lesions and infarcts did not change the associations, nor did excluding participants with infarcts. The association between insulin resistance and amygdalar volumes on MRI was similar in APOE strata, although statistically significant only in non-carriers of the ε4 allele [non-carriers: adjusted difference in amygdalar volume per standard deviation increase in insulin resistance −0.11 (95% CI −0.19 to −0.04); carriers: −0.02 (95% CI −0.18 to 0.15)].
Table 3

Insulin resistance in participants without diabetes (n=405) in relation to hippocampal and amygdalar volumes on MRI

Volume difference per SD increase in insulin concentrations and insulin resistance, ml (95% CI)





Post-load insulin (per SD)

Adjusted for

  Age and sex

−0.02 (−0.11 to 0.06)


−0.08 (−0.14 to −0.01)


  Age, sex and carotid atherosclerosis

−0.03 (−0.11 to 0.05)


−0.08 (−0.14 to −0.01)


  Age, sex, white matter lesions and infarcts on MRI

−0.02 (−0.11 to 0.06)


−0.08 (−0.15 to −0.02)


Insulin resistance (per SD)

Adjusted for

  Age and sex

−0.00 (−0.09 to 0.08)


−0.08 (−0.15 to −0.02)


  Age, sex and carotid atherosclerosis

−0.01 (−0.10 to 0.07)


−0.09 (−0.15 to −0.02)


  Age, sex, white matter lesions and infarcts on MRI

−0.00 (−0.08 to 0.08)


−0.09 (−0.15 to −0.02)


Data are given as adjusted differences in hippocampal or amygdalar volume on MRI (ml, 95% CI) per standard deviation (SD) increase in post-load insulin concentration and insulin resistance

aVolumes are normalised to average head size


We observed that people with Type 2 diabetes had more hippocampal and amygdalar atrophy on MRI than people without diabetes. Moreover, in persons without diabetes mellitus, insulin resistance was associated to amygdalar atrophy on MRI. The presence of atherosclerosis or cerebrovascular disease did not explain the associations.

The strengths of our study are its population-based design and the large sample with volumetric MRI. The prevalence of diabetes mellitus in our study was comparable to another Dutch population study [27], leading to a moderate number of people with diabetes mellitus studied in the sample. However, the associations were robust and statistically significant suggesting that we had sufficient power. A limitation of our study was the indirect assessment of insulin resistance through calculating the ratio of post-load insulin concentrations with glucose concentrations. This ratio however in non-diabetic subjects correlates well with the degree of insulin resistance assessed with precise clamping techniques [28].

Several studies have found an increased risk of Alzheimer’s disease in people with diabetes mellitus [3, 4, 17, 29, 30, 31]. Other studies did not find this association or merely an association between diabetes mellitus and vascular dementia [32, 33, 34, 35]. Difficulties in diagnosing Alzheimer’s disease in life and distinguishing it from vascular dementia could have resulted in different findings across studies. Alzheimer’s disease is generally characterised by slow progression in clinical symptoms which is thought to reflect gradual development of the specific Alzheimer pathology over time [8]. The pathological hallmarks of Alzheimer’ s disease, neurofibrillary tangles and amyloid plaques, occur in the most early stage of the disease in the hippocampus and amygdala [8] causing neuronal loss and atrophy that can be visualised on MRI [36]. At this stage, dysfunction of the hippocampus could cause memory impairment, a well known early neuropsychological sign of Alzheimer’s disease [37]. In our study, we used hippocampal and amygdalar atrophy on MRI in elderly subjects who were clinically free of dementia as markers of pre-clinical Alzheimer’s disease. Several studies including our own [20] show that people with hippocampal atrophy on MRI have lower verbal memory performance [38]. Moreover, besides memory impairment, people with hippocampal atrophy on MRI frequently develop other symptoms of Alzheimer’s disease later in life [11, 12]. To our knowledge, no study has prospectively examined the specific role of amygdalar atrophy in Alzheimer’s disease. However, patients with very mild Alzheimer’s disease have equal volume losses in the hippocampus and amygdala on MRI compared to control subjects [13, 14] suggesting that atrophy of both hippocampal and amygdalar are early MRI markers of incipient Alzheimer’s disease.

Three biological explanations support an association between diabetes mellitus and hippocampal and amygdalar atrophy on MRI. First, diabetes mellitus leads to vasculopathy and changes in lipid metabolism, which in turn could be associated to hippocampal and amygdalar atrophy. Although we found a clear association between diabetes mellitus and carotid atherosclerosis, the relation between diabetes and cerebrovascular disease, as noted by the degree of white matter lesions and brain infarcts on MRI, was not very strong. Moreover, when we accounted for vascular disease the relation between diabetes mellitus and atrophy on MRI remained the same. This is in line with a recent study in people free of cerebrovascular disease that showed impaired glucose tolerance to relate to hippocampal atrophy on MRI [39]. Thus, although diabetes mellitus is a vascular risk factor, other non-vascular pathways seem to play a role in the findings. A second biological explanation is that hyperglycaemia in diabetic patients is directly associated to hippocampal and amygdalar atrophy. A prospective study found that subjects with diabetes mellitus have increased amyloid plaques and neurofibrillary tangles in the hippocampus at autopsy [4], though another post-mortem study did not [40]. In diabetic subjects, accelerated formation of advanced glycation endproducts can cross-link amyloid proteins leading to aggregation into the amyloid plaques [6, 41]. In addition, glycation of the microtubule associated protein-tau could lead to formation of neurofibrillary tangles [42]. Pointing to a third biological explanation was the finding that peripheral insulin resistance was associated to amygdalar atrophy on MRI. Insulin resistance is characterised by high plasma insulin concentrations and relatively normal glucose concentrations. Patients with Alzheimer’s disease have higher plasma insulin concentrations compared with control subjects [43, 44] but lower cerebrospinal-fluid insulin concentrations [44]. This suggests that insulin transport from plasma to the brain is diminished in Alzheimer patients [44]. Other investigations report that dysfunction of insulin signal transduction is involved in Alzheimer’s disease [7, 45]. Genetic variability in genes encoding for components of the insulin-signalling pathway is associated to Alzheimer’s disease [46]. Insulin regulates metabolism of amyloid proteins, prevents tau phosphorylation [7] and promotes neuronal survival [47], all actions that in case of dysfunction of the insulin pathway can lead to Alzheimer’s disease. Furthermore, the insulin-degrading enzyme is, in addition to its role in degrading insulin, important in cleaving amyloid protein in the brain [48]. Mice with hypo-function of this enzyme have increased accumulation of amyloid in the brain and increased plasma insulin concentrations, further supporting a connection between insulin and Alzheimer’s disease [49]. It is postulated that patients with Alzheimer’s disease try to compensate for impaired insulin signalling by increasing the amount of insulin receptors [50]. Interestingly, we found a restricted relation between insulin resistance and amygdalar atrophy on MRI. Higher plasma insulin concentrations or insulin resistance were not related to hippocampal atrophy on MRI, in agreement with others [39]. The hippocampus and amygdalar differ according to the amount of insulin receptors, the hippocampus having a higher density [51]. A speculative explanation for the absence of a relation between insulin concentrations and hippocampal volumes is that the high density of insulin receptors in combination with high plasma insulin concentrations compensates for dysfunctions in the insulin-signalling pathway. However, the differential effects of insulin on the hippocampus and amygdala have yet to be confirmed.

In summary, in this community sample we found that people with Type 2 diabetes have smaller hippocampal and amygdalar volumes on MRI, supporting the view that diabetes is a risk factor for Alzheimer’s disease. Since atherosclerosis or cerebrovascular disease were not explaining the associations, it is likely that direct metabolic effects of diabetes mellitus are involved. Our finding that insulin resistance was associated to amygdalar atrophy on MRI is in line with suggestions that dysfunction of insulin signalling is involved in the pathogenesis of Alzheimer’s disease.



This research was financially supported by the Netherlands Organisation for Scientific Research (NWO) and the Health Research and Development Council (ZON). We thank F. Hoebeek and Dr E. Achten for their help in collecting the data.


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Copyright information

© Springer-Verlag 2003

Authors and Affiliations

  • T. den Heijer
    • 1
    • 2
  • S. E. Vermeer
    • 1
    • 2
  • E. J. van Dijk
    • 1
    • 2
  • N. D. Prins
    • 1
    • 2
  • P. J. Koudstaal
    • 1
    • 2
  • A. Hofman
    • 1
  • M. M. B. Breteler
    • 1
    Email author
  1. 1.Department of Epidemiology and BiostatisticsErasmus Medical CenterRotterdamThe Netherlands
  2. 2.Department of NeurologyErasmus Medical CenterRotterdamThe Netherlands

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