Advertisement

Diabetologia

, 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
Article

Abstract

Aim/hypothesis

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.

Methods

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.

Results

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.

Conclusions/interpretation

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.

Keywords

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

Abbreviations

MRI

magnetic resonance imaging

APOE

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.

Methods

Participants

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.

Results

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

Variable

No diabetes mellitus

Diabetes mellitus

Adjusted differencea

n=465

n=41

Age, years

73±8

77±8

4 (1; 6)

Women, %

50

34

−17 (−33; −0.6)

Education, primary only %

30

38

7 (−8; 20)

Memory performance, Z-score

0.03±0.92

−0.51±0.80

−0.33 (−0.60; −0.06)

BMI, kg/m2

26.2±3.7

26.8±2.9

0.7 (−0.5; 1.8)

Pack-years of cigarette smoking

20±25

24±24

2 (−5; 10)

Diastolic blood pressure, mmHg

77±11

75±13

−1 (−5; 3)

Systolic blood pressure, mmHg

146±20

146±21

−1 (−8; 5)

Total cholesterol, mmol/l

5.8±1.0

5.9±1.3

0.2 (−0.2; 0.5)

Carotid plaques, total number

1.6±1.6

2.6±1.6

0.7 (0.2; 1.3)

Intima-media thickness, mm

0.86±0.14

0.93±0.13

0.04 (−0.01; 0.08)

Periventricular white matter lesions, grade

2.7±2.2

3.6 (2.2)

0.4 (−0.2; 1.1)

Subcortical white matter lesions, ml

1.7±3.4

2.3 (3.6)

0.01 (−1.0; 1.0)

Brain infarcts, %

27

44

11 (−3; 25)

APOE, ε4 carriers, %

27

31

5 (−11; 21)

Postload insulin, pmol/lb

422.0±312.6

Insulin resistance, pmol/mmolb

65.1±40.1

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

Hippocampusa

p

Amygdalaa

p

Age and sex

−0.28 (−0.55 to −0.01)

0.042

−0.33 (−0.55 to −0.11)

0.004

Age, sex, and carotid atherosclerosis

−0.27 (−0.55 to 0.00)

0.053

−0.32 (−0.54 to −0.10)

0.005

Age, sex, white matter lesions and infarcts on MRI

−0.27 (−0.54 to 0.00)

0.053

−0.33 (−0.56 to −0.11)

0.003

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)

Hippocampusa

p

Amygdalaa

p

Post-load insulin (per SD)

Adjusted for

  Age and sex

−0.02 (−0.11 to 0.06)

0.57

−0.08 (−0.14 to −0.01)

0.020

  Age, sex and carotid atherosclerosis

−0.03 (−0.11 to 0.05)

0.51

−0.08 (−0.14 to −0.01)

0.018

  Age, sex, white matter lesions and infarcts on MRI

−0.02 (−0.11 to 0.06)

0.56

−0.08 (−0.15 to −0.02)

0.013

Insulin resistance (per SD)

Adjusted for

  Age and sex

−0.00 (−0.09 to 0.08)

0.93

−0.08 (−0.15 to −0.02)

0.012

  Age, sex and carotid atherosclerosis

−0.01 (−0.10 to 0.07)

0.75

−0.09 (−0.15 to −0.02)

0.008

  Age, sex, white matter lesions and infarcts on MRI

−0.00 (−0.08 to 0.08)

0.95

−0.09 (−0.15 to −0.02)

0.008

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

Discussion

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.

Notes

Acknowledgements

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.

References

  1. 1.
    Goldstein LB, Adams R, Becker K et al. (2001) Primary prevention of ischemic stroke: a statement for healthcare professionals from the Stroke Council of the American Heart Association. Circulation 103:163–182PubMedGoogle Scholar
  2. 2.
    Hébert R, Lindsay J, Verreault R et al. (2000) Vascular dementia: incidence and risk factors in the Canadian study of health and aging. Stroke 31:1487–1493PubMedGoogle Scholar
  3. 3.
    Ott A, Stolk RP, van Harskamp F et al. (1999) Diabetes mellitus and the risk of dementia: the Rotterdam Study. Neurology 53:1937–1942PubMedGoogle Scholar
  4. 4.
    Peila R, Rodriguez BL, Launer LJ (2002) Type 2 diabetes, APOE gene, and the risk for dementia and related pathologies: the Honolulu-Asia Aging Study. Diabetes 51:1256–1262PubMedGoogle Scholar
  5. 5.
    Snowdon DA, Greiner LH, Mortimer JA et al. (1997) Brain infarction and the clinical expression of Alzheimer disease. The Nun Study. JAMA 277:813–817PubMedGoogle Scholar
  6. 6.
    Vitek MP, Bhattacharya K, Glendening JM et al. (1994) Advanced glycation end products contribute to amyloidosis in Alzheimer disease. Proc Natl Acad Sci USA 91:4766–4770PubMedGoogle Scholar
  7. 7.
    Gasparini L, Netzer WJ, Greengard P et al. (2002) Does insulin dysfunction play a role in Alzheimer’s disease? Trends Pharmacol Sci 23:288–293PubMedGoogle Scholar
  8. 8.
    Braak H, Braak E (1991) Neuropathological stageing of Alzheimer-related changes. Acta Neuropathol 82:239–259PubMedGoogle Scholar
  9. 9.
    Gosche KM, Mortimer JA, Smith CD et al. (2002) Hippocampal volume as an index of Alzheimer neuropathology: findings from the Nun Study. Neurology 58:1476–1482PubMedGoogle Scholar
  10. 10.
    Convit A, De Leon MJ, Tarshish C et al. (1997) Specific hippocampal volume reductions in individuals at risk for Alzheimer’s disease. Neurobiol Aging 18:131–138PubMedGoogle Scholar
  11. 11.
    Jack CR Jr, Petersen RC, Xu YC et al. (1999) Prediction of AD with MRI-based hippocampal volume in mild cognitive impairment. Neurology 52:1397–1403PubMedGoogle Scholar
  12. 12.
    Schott JM, Fox NC, Frost C et al. (2003) Assessing the onset of structural change in familial Alzheimer’s disease. Ann Neurol 53:181–188CrossRefPubMedGoogle Scholar
  13. 13.
    Callen DJ, Black SE, Gao F et al. (2001) Beyond the hippocampus: MRI volumetry confirms widespread limbic atrophy in AD. Neurology 57:1669–1674PubMedGoogle Scholar
  14. 14.
    Krasuski JS, Alexander GE, Horwitz B et al. (1998) Volumes of medial temporal lobe structures in patients with Alzheimer’s disease and mild cognitive impairment (and in healthy controls). Biol Psychiatry 43:60–68Google Scholar
  15. 15.
    Hofman A, Grobbee DE, de Jong PTVM et al. (1991) Determinants of disease and disability in the elderly: the Rotterdam Elderly Study. Eur J Epidemiol 7:403–422PubMedGoogle Scholar
  16. 16.
    Breteler MMB (2000) Vascular involvement in cognitive decline and dementia. Epidemiologic evidence from the Rotterdam Study and the Rotterdam Scan Study. Ann NY Acad Sci 903:457–465PubMedGoogle Scholar
  17. 17.
    Ott A, Stolk RP, Hofman A et al. (1996) Association of diabetes mellitus and dementia: the Rotterdam Study. Diabetologia 39:1392–1397PubMedGoogle Scholar
  18. 18.
    de Leeuw FE, de Groot JC, Oudkerk M et al. (1999) A follow-up study of blood pressure and cerebral white matter lesions. Ann Neurol 46:827–833PubMedGoogle Scholar
  19. 19.
    de Groot JC, de Leeuw FE, Oudkerk M et al. (2000) Cerebral white matter lesions and cognitive function: the Rotterdam Scan Study. Ann Neurol 47:145–151CrossRefPubMedGoogle Scholar
  20. 20.
    Hackert VH, den Heijer T, Oudkerk M et al. (2002) Hippocampal head size associated with verbal memory performance in nondemented elderly. Neuroimage 17:1365–1372PubMedGoogle Scholar
  21. 21.
    Maunoury C, Michot JL, Caillet H et al. (1996) Specificity of temporal amygdala atrophy in Alzheimer’s disease: quantitative assessment with magnetic resonance imaging. Dementia 7:10–14PubMedGoogle Scholar
  22. 22.
    UNESCO: International Standard Classification of Education (ISCED). Document 19C/3. Paris, 1976Google Scholar
  23. 23.
    Vermeer SE, den Heijer T, Koudstaal PJ et al. (2003) Incidence and risk factors of silent brain infarcts in the population-based Rotterdam Scan Study. Stroke 34:392–396PubMedGoogle Scholar
  24. 24.
    Slooter AJC, Cruts M, Kalmijn S et al. (1998) Risk estimates of dementia by apolipoprotein E genotypes from a population-based incidence study: the Rotterdam Study. Arch Neurol 55:964–968PubMedGoogle Scholar
  25. 25.
    Farrer LA, Cupples LA, Haines JL et al. (1997) Effects of age, sex, and ethnicity on the association between apolipoprotein E genotype and Alzheimer disease. A meta-analysis. APOE and Alzheimer Disease Meta Analysis Consortium. JAMA 278:1349–1356PubMedGoogle Scholar
  26. 26.
    Bots ML, Hoes AW, Koudstaal PJ et al. (1997) Common carotid intima-media thickness and risk of stroke and myocardial infarction: the Rotterdam Study. Circulation 96:1432–1437PubMedGoogle Scholar
  27. 27.
    Mooy JM, Grootenhuis PA, de Vries H et al. (1995) Prevalence and determinants of glucose intolerance in a Dutch caucasian population. The Hoorn Study. Diabetes Care 18:1270–1273PubMedGoogle Scholar
  28. 28.
    Laakso M (1993) How good a marker is insulin level for insulin resistance? Am J Epidemiol 137:959–965PubMedGoogle Scholar
  29. 29.
    Leibson CL, Rocca WA, Hanson VA et al. (1997) The risk of dementia among persons with diabetes mellitus: a population-based cohort study. Ann NY Acad Sci 826:422–427PubMedGoogle Scholar
  30. 30.
    Brayne C, Gill C, Huppert FA et al. (1998) Vascular risks and incident dementia: results from a cohort study of the very old. Dement Geriatr Cogn Disord 9:175–180PubMedGoogle Scholar
  31. 31.
    Yoshitake T, Kiyohara Y, Kato I et al. (1995) Incidence and risk factors of vascular dementia and Alzheimer’s disease in a defined elderly Japanese population: the Hisayama Study. Neurology 45:1161–1168PubMedGoogle Scholar
  32. 32.
    Luchsinger JA, Tang MX, Stern Y et al. (2001) Diabetes mellitus and risk of Alzheimer’s disease and dementia with stroke in a multiethnic cohort. Am J Epidemiol 154:635–641PubMedGoogle Scholar
  33. 33.
    MacKnight C, Rockwood K, Awalt E et al. (2002) Diabetes mellitus and the risk of dementia, Alzheimer’s disease and vascular cognitive impairment in the Canadian Study of Health and Aging. Dement Geriatr Cogn Disord 14:77–83PubMedGoogle Scholar
  34. 34.
    Curb JD, Rodriguez BL, Abbott RD et al. (1999) Longitudinal association of vascular and Alzheimer’s dementias, diabetes, and glucose tolerance. Neurology 52:971–975PubMedGoogle Scholar
  35. 35.
    Tariot PN, Ogden MA, Cox C et al. (1999) Diabetes and dementia in long-term care. J Am Geriatr Soc 47:423–429PubMedGoogle Scholar
  36. 36.
    Bobinski M, de Leon MJ, Wegiel J et al. (2000) The histological validation of post mortem magnetic resonance imaging-determined hippocampal volume in Alzheimer’s disease. Neuroscience 95:721–725PubMedGoogle Scholar
  37. 37.
    Petersen RC, Smith GE, Ivnik RJ et al. (1994) Memory function in very early Alzheimer’s disease. Neurology 44:867–872PubMedGoogle Scholar
  38. 38.
    Petersen RC, Jack CR Jr, Xu YC et al. (2000) Memory and MRI-based hippocampal volumes in aging and AD. Neurology 54:581–587PubMedGoogle Scholar
  39. 39.
    Convit A, Wolf OT, Tarshish C et al. (2003) Reduced glucose tolerance is associated with poor memory performance and hippocampal atrophy among normal elderly. Proc Natl Acad Sci USA 100:2019–2022PubMedGoogle Scholar
  40. 40.
    Heitner J, Dickson D (1997) Diabetics do not have increased Alzheimer-type pathology compared with age-matched control subjects. A retrospective postmortem immunocytochemical and histofluorescent study. Neurology 49:1306–1311PubMedGoogle Scholar
  41. 41.
    Münch G, Schinzel R, Loske C et al. (1998) Alzheimer’s disease—synergistic effects of glucose deficit, oxidative stress and advanced glycation endproducts. J Neural Transm 105:439–461PubMedGoogle Scholar
  42. 42.
    Ledesma MD, Bonay P, Colaco C et al. (1994) Analysis of microtubule-associated protein tau glycation in paired helical filaments. J Biol Chem 269:21614–21619PubMedGoogle Scholar
  43. 43.
    Kuusisto J, Koivisto K, Mykkänen L et al. (1997) Association between features of the insulin resistance syndrome and Alzheimer’s disease independently of apolipoprotein e4 phenotype: cross sectional population based study. BMJ 315:1045–1049PubMedGoogle Scholar
  44. 44.
    Craft S, Peskind E, Schwartz MW et al. (1998) Cerebrospinal fluid and plasma insulin levels in Alzheimer’s disease: relationship to severity of dementia and apolipoprotein E genotype. Neurology 50:164–168PubMedGoogle Scholar
  45. 45.
    Frölich L, Blum-Degen D, Bernstein HG et al. (1998) Brain insulin and insulin receptors in aging and sporadic Alzheimer’s disease. J Neural Transm 105:423–438PubMedGoogle Scholar
  46. 46.
    Liolitsa D, Powell J, Lovestone S (2002) Genetic variability in the insulin signalling pathway may contribute to the risk of late onset Alzheimer’s disease. J Neurol Neurosurg Psychiatry 73:261–266PubMedGoogle Scholar
  47. 47.
    Zhao WQ, Alkon DL (2001) Role of insulin and insulin receptor in learning and memory. Mol Cell Endocrinol 177:125–134PubMedGoogle Scholar
  48. 48.
    Kurochkin IV (2001) Insulin-degrading enzyme: embarking on amyloid destruction. Trends Biochem Sci 26:421–425PubMedGoogle Scholar
  49. 49.
    Farris W, Mansourian S, Chang Y et al. (2003) Insulin-degrading enzyme regulates the levels of insulin, amyloid β-protein, and the β-amyloid precursor protein intracellular domain in vivo. Proc Natl Acad Sci USA 100:4162–4167PubMedGoogle Scholar
  50. 50.
    Frölich L, Blum-Degen D, Riederer P et al. (1999) A disturbance in the neuronal insulin receptor signal transduction in sporadic Alzheimer’s disease. Ann NY Acad Sci 893:290–293PubMedGoogle Scholar
  51. 51.
    Schulingkamp RJ, Pagano TC, Hung D et al. (2000) Insulin receptors and insulin action in the brain: review and clinical implications. Neurosci Biobehav Rev 24:855–872PubMedGoogle Scholar

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

Personalised recommendations