Recent evidence suggests that glycemic control is associated with cognitive function in older patients with type 2 diabetes who are carriers of the haptoglobin (Hp) 1-1 genotype compared with noncarriers. We assessed whether poor glycemic control in Hp 1-1 carriers is more strongly associated with smaller hippocampal volume than in noncarriers. Hippocampal volume was generated from high-resolution structural T1 MRI obtained for 224 participants (28 Hp 1-1 carriers [12.5%] and 196 noncarriers [87.5%]) from the Israel Diabetes and Cognitive Decline (IDCD) study, who had a mean (SD) number of years in the Maccabi Healthcare Services (MHS) registry of 8.35 (2.63) and a mean (SD) HbA1c level of 6.66 (0.73)% [49 mmol/mol]. A stronger negative association between right hippocampal volume and HbA1c was found in patients with the Hp 1-1 genotype, with a 0.032-mL decrease in right hippocampal volume per 14% increase in HbA1c (P = 0.0007) versus a 0.009-mL decrease in Hp 1-1 noncarriers (P = 0.047), after adjusting for total intracranial volume, age, sex, follow-up years in the registry, and cardiovascular factor (interaction, P = 0.025). This indicates that 29.66% of the total variance in right hippocampal volume is explained by HbA1c levels among Hp 1-1 carriers and that 3.22% is explained by HbA1c levels among Hp 1-1 noncarriers. Our results suggest that the hippocampus of Hp 1-1 carriers may be more vulnerable to the insults of poor glycemic control.

Type 2 diabetes (T2D) is associated with a lower level of cognitive function, a faster rate of cognitive decline, and an increased risk of dementia—both Alzheimer disease (AD) and vascular dementia (1). Serum HbA1c is the gold standard measure for glycemic control, reflects mean blood glucose levels, and has also been consistently associated with poorer cognitive outcomes (2). While the association of T2D with late-life cognitive impairment may be explained in part based on its association with cerebrovascular disease (3) and brain imaging abnormalities (4,5), the underlying mechanisms for the increased dementia risk associated with T2D remain to be elucidated.

Haptoglobin (Hp) is a hemoglobin-binding protein acting as an antioxidant, expressed by a genetic polymorphism as three major genotypes: Hp 1-1, Hp 2-1, and Hp 2-2 (6). Each has structural and functional differences (7). The Hp polymorphism has been shown to be associated with the prevalence and incidence of adverse health outcomes in T2D (6). Hp 2-2 genotype is associated with an increased risk of peripheral and coronary artery disease and increased incidence of micro- and macrovascular complications (7,8). Hp 1-1 genotype has been implicated with increased prevalence of small lacunar strokes (9) and white matter hyperintensities (10). We have recently reported that cognitively normal elderly individuals with T2D carrying the Hp 1-1 genotype have lower cognitive function than both Hp 1-2 and Hp 2-2 carriers (11). Moreover, the association of glycemic control with cognitive function was primarily observed for individuals with Hp 1-1 but not for Hp 1-2 and Hp 2-2 carriers. These findings suggest that individuals with the Hp 1-1 genotype may be more susceptible to the deleterious effects of poor glycemic control (12). We examined the interrelationship of Hp genotype, HbA1c levels, and regional volume of the hippocampus obtained from MRI of the brain in older individuals with T2D. In line with our findings on cognition, we hypothesized that the negative association of HbA1c with regional brain volume would be stronger among elderly T2D patients carrying the Hp 1-1 genotype compared with the other genotypes.

Participants were recruited from the Israel Diabetes and Cognitive Decline (IDCD) study (Fig. 1), a collaboration of the Icahn School of Medicine at Mount Sinai, NY, Sheba Medical Center, Israel, and Maccabi Healthcare Services (MHS), Israel. All three institutions provided institutional review board approval, and each participant provided informed consent. The IDCD study design has been previously described in detail (13). Briefly, community-dwelling elderly Israeli individuals with T2D (≥65 years old) were recruited from the MHS diabetes registry. Criteria for enrollment into the IDCD study were the following: 1) having T2D; 2) normal cognition at entry to the IDCD study; 3) being free of any neurological (e.g., Parkinson disease, stroke), psychiatric (e.g., schizophrenia), or other diseases (e.g., alcohol or drug abuse) that might affect cognition; 4) having an informant; 5) fluency in Hebrew; and 6) living in the area of Tel Aviv. Serum was extracted from blood samples, and Hp genotypes were determined by polyacrylamide gel electrophoresis from 10 μL of plasma using established methods (14) and dichotomized as Hp 1-1 carriers versus Hp 2-1 or Hp 2-2 carriers. We chose to use the dichotomy of Hp 1-1 carriers versus others because we found no difference between the Hp 1-2 and Hp 2-2 carriers in our prior studies assessing the association of Hp with cognition (11,12). Demographic details and T2D-related characteristics including longitudinal glycemic control based on repeated HbA1c levels, systolic and diastolic blood pressure, LDL, HDL, total cholesterol, and creatinine were available for all participants. To obtain a measure of glycemic control, we used a multilevel mixed effects linear model with random intercepts to estimate the subject-specific mean HbA1c, adjusting for the number of years in the IDCD registry. Using this type of model enabled us to take into account the degree of correlation between the repeated measurements made within a patient as well as both the number of assessments (median [Q1, Q3] 14 [9, 20], range 3–57) and duration in the registry (mean [SD] 8.35 [2.63] years, range 1.46–13.47).

Figure 1

Flowchart of the study cohort.

Figure 1

Flowchart of the study cohort.

A subsample of participants from the IDCD cohort underwent an MRI scan. MRI scans were performed in the diagnostic imaging department at Sheba Medical Center with a 3T scanner (GE, Signa HDxt, v16VO2). High-resolution (1 mm3) images were acquired using a 3-dimensional spoiled gradient-recalled echo (SPGR) T1-weighted sequence (TR/TE = 7.3/2.7 s, 20° flip angle, TI = 450 ms).

T1-weighted anatomical images for each subject were processed using the voxel-based morphometry (VBM [15]) toolbox developed by Gaser (http://www.fil.ion.ucl.ac.uk/spm/ext/#VBMtools) and implemented in Statistical Parametric Mapping (SPM8) software. This procedure included automated iterative skull stripping; segmentation of the images into gray matter (GM), white matter, and cerebrospinal fluid probability images; and spatial normalization of the GM images to a customized GM template in standard MNI (Montreal Neurological Institute) space. In order to optimize signal to noise, the GM maps were smoothed using an 8-mm Gaussian kernel. GM probability maps were thresholded at 0.1 to minimize inclusion of incorrect tissue types. Total intracranial volume (TICV) was calculated using the segmented and thresholded images (TICV = GM + white matter + cerebrospinal fluid). Based on our a priori hypothesis, we used a region of interest approach centered on the left and right hippocampus, identified by using the human Automated Anatomical Labeling (AAL) atlas (16) within the Wake Forest University PickAtlas (17) and extracted using the MarsBaR region of interest toolbox (18) as implemented in SPM8.

Statistical Analysis

The skewedness and kurtosis for the adjusted HbA1c mean levels were 1.28 and 2.72, respectively, suggesting the adjusted HbA1c mean variable was not distributed normally. Therefore, we applied a natural log transformation to the HbA1c mean variable, which improved skewedness and kurtosis to 0.87 and 1.53, respectively; we then used the natural log–transformed adjusted HbA1c variable in all further analyses.

We assessed the relationship of mean levels of HbA1c with left and right hippocampal volumes using a multiple linear regression analysis with backward selection. The selection criterion of P < 0.1 was used for elimination of a variable. The linear regression analysis allowed for control of TICV, age, sex, number of follow-up years in the registry (a surrogate of duration of T2D), and a cardiovascular risk score created from the first principal component of a factor analyses consisting of systolic and diastolic blood pressure, LDL and HDL, total cholesterol, creatinine, and albumin. The interaction of Hp genotype with HbA1c was assessed.

Sociodemographic and medical characteristics of the two Hp genotype groups were compared using Student t test, Pearson χ2 test, and nonparametric Mann-Whitney U test. A P value of 0.05 (two sided) was used to determine statistical significance level. For analysis, we used IBM SPSS Statistics for Windows (version 23.0; IBM, Armonk, NY).

Participants (n = 224; 28 Hp 1-1 carriers [12.5%] and 196 noncarriers [87.5%]) had a mean (SD) age of 71.30 (4.26) years. The mean (SD) number of years in the MHS registry was 8.35 (2.63), ranging from 1.46 to 13.47. Mean (SD) HbA1c level was 6.66 (0.73)% [49 mmol/mol]. The majority of participants were males (61%). Hp 1-1 carriers did not differ significantly from noncarriers on any of the demographic, medical, or brain volume characteristics (Table 1).

Table 1

Demographics and clinical and brain measures of T2D patients by Hp genotypes

T2D patientsHp 1-1 carriers (N = 28)Hp 1-1 noncarriers (N = 196)P value
Male, n (%) 16 (57) 121 (62) 0.681 
Age (years) 71.24 (3.38) 71.31 (4.38) 0.922 
HbA1c (%) 6.73 (0.85) 6.65 (0.71) 0.658 
Years in the registry 8.17 (2.96) 8.37 (2.59) 0.758 
Number of HbA1c measurements  14 [8, 19] 14 [9, 20] 0.479 
Cardiovascular factor −0.09 [−0.58, 0.94] −0.01 [−0.67, 0.58] 0.416 
TICV (mL) 1,316.18 (126.94) 1,337.66 (137.76) 0.413 
GM volume (mL) 510.93 (43.17) 514.94 (51.67) 0.657 
Hippocampal volume (ccs) 0.457 (0.04) 0.448 (0.04) 0.329 
T2D patientsHp 1-1 carriers (N = 28)Hp 1-1 noncarriers (N = 196)P value
Male, n (%) 16 (57) 121 (62) 0.681 
Age (years) 71.24 (3.38) 71.31 (4.38) 0.922 
HbA1c (%) 6.73 (0.85) 6.65 (0.71) 0.658 
Years in the registry 8.17 (2.96) 8.37 (2.59) 0.758 
Number of HbA1c measurements  14 [8, 19] 14 [9, 20] 0.479 
Cardiovascular factor −0.09 [−0.58, 0.94] −0.01 [−0.67, 0.58] 0.416 
TICV (mL) 1,316.18 (126.94) 1,337.66 (137.76) 0.413 
GM volume (mL) 510.93 (43.17) 514.94 (51.67) 0.657 
Hippocampal volume (ccs) 0.457 (0.04) 0.448 (0.04) 0.329 

Data are mean (SD) or median [Q1, Q3] unless otherwise indicated. ccs, cubic centimeters.

In multivariable regression modeling of right hippocampal volume, there was a significant interaction observed between Hp genotype and HbA1c level (P = 0.025) after adjusting for TICV, age, sex, follow-up years in the registry, and cardiovascular factor. Results indicate a stronger negative association between right hippocampal volume and HbA1c in Hp 1-1 carriers, with a 0.032-mL decrease in right hippocampal volume per 14% increase in HbA1c (P = 0.0007), than in Hp 1-1 noncarriers, who experienced a 0.009-mL decrease per 14% increase in HbA1c (P = 0.047) (Table 2 and Fig. 2). For the Hp 1-1 carriers, the squared semipartial correlation coefficient for HbA1c was 0.2969, and for the Hp 1-1 noncarriers, it was 0.0322; this indicates that 29.69% of the total variance in right hippocampal volume is explained by HbA1c levels among the Hp 1-1 carriers and, in contrast, 3.22% of the right hippocampal volume total variance is explained by HbA1c levels among the Hp 1-1 noncarriers. In multivariable regression modeling of the left hippocampal volume, we did not find a significant interaction between Hp genotype and HbA1c level (P = 0.717) (Table 3) after adjusting for all covariates.

Table 2

Slope estimates from multivariable linear regression model of right hippocampal volume regressed on natural log–transformed adjusted HbA1c by Hp genotype carrier status

T2D patientsUnit increaseSlope95% CIP valueP value
HbA1c in Hp 1-1 carriers 14%** −0.032 −0.050, −0.014 0.0007* 0.0249* 
HbA1c in Hp 2-1/Hp 2-2 carriers 14%** −0.009 −0.017, −0.001 0.0467* 
Age 5 years −0.014 −0.021, −0.007 <0.0001* 
TICV 150 mL −0.013 −0.021, −0.005 0.0012* 
Cardiovascular factor 1 unit 0.008 0.001, 0.014 0.0155* 
Male  0.007 −0.008, 0.023 0.3422 
Years in the registry 5 years −0.0017 −0.013, 0.010 0.7725 
T2D patientsUnit increaseSlope95% CIP valueP value
HbA1c in Hp 1-1 carriers 14%** −0.032 −0.050, −0.014 0.0007* 0.0249* 
HbA1c in Hp 2-1/Hp 2-2 carriers 14%** −0.009 −0.017, −0.001 0.0467* 
Age 5 years −0.014 −0.021, −0.007 <0.0001* 
TICV 150 mL −0.013 −0.021, −0.005 0.0012* 
Cardiovascular factor 1 unit 0.008 0.001, 0.014 0.0155* 
Male  0.007 −0.008, 0.023 0.3422 
Years in the registry 5 years −0.0017 −0.013, 0.010 0.7725 

HbA1c was adjusted for number of years in registry.

*P value <0.05.

P value testing the slope = 0.

P value testing for interaction between Hp genotype and natural log–transformed adjusted HbA1c.

**14% increase in HbA1c, i.e., increasing from 7 to 8.

Figure 2

A negative association between right hippocampal volume and natural log–transformed adjusted HbA1c in Hp 1-1 carriers (P = 0.0007) and in Hp 1-1 noncarriers (P = 0.0467) is shown; interaction between Hp genotype and HbA1c level (P = 0.025) after adjusting for TICV, age, sex, follow-up years in the registry, and cardiovascular factor. ccs, cubic centimeters.

Figure 2

A negative association between right hippocampal volume and natural log–transformed adjusted HbA1c in Hp 1-1 carriers (P = 0.0007) and in Hp 1-1 noncarriers (P = 0.0467) is shown; interaction between Hp genotype and HbA1c level (P = 0.025) after adjusting for TICV, age, sex, follow-up years in the registry, and cardiovascular factor. ccs, cubic centimeters.

Table 3

Slope estimates from multivariable linear regression model of left hippocampal volume regressed on natural log–transformed adjusted HbA1c by Hp genotype carrier status

T2D patientsUnit increaseSlope95% CIP valueP value
HbA1c in Hp 1-1 carriers 14%** −0.014 −0.033, 0.005 0.1517 0.5814 
HbA1c in Hp 2-1/Hp 2-2 carriers 14% −0.008 −0.017, 0.001 0.0806 
Age 5 years −0.016 −0.023, −0.009 <0.0001 
TICV  150 mL −0.019 −0.027, −0.011 <0.0001 
Cardiovascular factor 1 unit 0.006 −0.0008, 0.012 0.0834 
Male  −0.005 −0.021, 0.010 0.5024 
Years in the registry 5 years 0.0006 −0.012, 0.013 0.9218 
T2D patientsUnit increaseSlope95% CIP valueP value
HbA1c in Hp 1-1 carriers 14%** −0.014 −0.033, 0.005 0.1517 0.5814 
HbA1c in Hp 2-1/Hp 2-2 carriers 14% −0.008 −0.017, 0.001 0.0806 
Age 5 years −0.016 −0.023, −0.009 <0.0001 
TICV  150 mL −0.019 −0.027, −0.011 <0.0001 
Cardiovascular factor 1 unit 0.006 −0.0008, 0.012 0.0834 
Male  −0.005 −0.021, 0.010 0.5024 
Years in the registry 5 years 0.0006 −0.012, 0.013 0.9218 

HbA1c was adjusted for number of years in registry.

P value testing the slope = 0.

P value testing for interaction between Hp genotype and natural log–transformed adjusted HbA1c.

**14% increase in HbA1c, i.e., increasing from 7 to 8.

In secondary analyses of GM volume, the interaction between Hp genotype and HbA1c level was not statistically significant after adjusting for TICV, age, sex, follow-up years in the registry, and the cardiovascular factor (P = 0.223), although the association of HbA1c (per 14% increase) with GM volume was nominally stronger in the Hp 1-1 carriers (slope = −14.3; P = 0.04) than in noncarriers (slope = −4.84; P = 0.15).

We found a significant interaction of Hp genotype with HbA1c in right hippocampal volume, indicating that in elderly patients with T2D, higher HbA1c is associated with smaller right hippocampal volume in Hp 1-1 carriers but not in noncarriers, which may suggest that Hp 1-1 carriers may be more vulnerable to the harmful effects of high levels of HbA1c on the hippocampus.

T2D patients exhibit brain volume loss (4) at an accelerated rate (more than the normal rate of age-related atrophy ([19]) and accelerated expansion of the ventricles (20). Previous studies have examined the association between T2D and hippocampal volume, revealing conflicting results. T2D has been associated with GM loss mainly in middle temporal gyrus (21,22), parahippocampal gyrus, cingulate cortex, precuneus, and insula (23), as well as in medial-frontal regions (21,23). Other studies found that T2D patients showed reduced total cortical volume in the right hemisphere (24) and in the hippocampal region (5,24). In contrast, a recent pooled analysis of three studies showed that T2D patients had greater brain atrophy but not greater hippocampal atrophy compared with control subjects (4). The associations found between HbA1c levels and brain volume in patients with T2D have also been inconsistent. Higher levels of HbA1c have been shown to be associated with cortical atrophy in T2D patients (25). However, other studies have not found such associations (4,26). With regard to the hippocampus, HbA1c was found to be a significant predictor of hippocampal volume (5), even when controlling for other variables commonly associated with T2D (27). However, a study examining the relationship between HbA1c and GM volume of the right temporal lobe showed no significant correlation in older adults with T2D (22). Our results imply that these discrepancies might be, at least in part, explained by a potential moderating role of the Hp genotype in the relationship between glycemic control and hippocampal atrophy.

Hp has two polypeptide chains: β and α. Hp 1-1 expresses only α-1 chains, Hp 2-1 expresses α-1 and α-2 chains, and Hp 2-2 expresses only α-2 chains (6). Patients with AD and vascular dementia have greater levels of α-1 Hp chains in their cerebrospinal fluid (28), suggesting a possible role of Hp 1-1 in dementia. The hippocampus is robustly involved in AD and dementia (29), and this region is vulnerable to the accumulation of abnormally phosphorylated tau protein in the earliest stages of AD, decades before appearance of clinical symptoms (29). It is also one of the brain regions most susceptible to damage by several mechanisms of insults, including hypoglycemia and hypoxia (30). Participants of this study were cognitively normal, suggesting perhaps that the strong association between smaller right hippocampal volume and poorer glycemic control in Hp 1-1 carriers may reflect a preclinical signal of vulnerability to dementia in this subgroup of elderly individuals with diabetes. Unilateral volume changes in the right hippocampus were previously described in older adults at risk for cognitive decline; individuals with diabetes and low diastolic blood pressure had significantly smaller right hippocampal volumes (31). Consistent with this finding in cognitively intact middle-aged women at risk for AD, more insulin resistance was associated with smaller right hippocampal volume (32). Finally, in a study examining the reliability of hippocampal volumetry in the early diagnosis of AD, right hippocampal atrophy was the most strongly correlated with AD and mild cognitive impairment (33).

This study had several strengths, including a relatively large neuroimaging sample size of participants from the IDCD study. Our cohort is representative of elderly individuals with T2D in Israel, and we had access to numerous measurements of T2D characteristics for this cohort, including long-term data on HbA1c, which allowed an average of 15 measurements, indicating a long-term mean level rather than a single observation.

One main limitation of the study is the cross-sectional design. Another limitation is that although our results on right hippocampal volume are significant, they are based on a relatively small sample size of Hp 1-1 carriers, hence necessitating further replication using a larger Hp 1-1 carrier group of T2D patients. Also, as Hp 2 carriers have a higher risk for myocardial infraction and mortality, our results may reflect a survival bias toward Hp 1-1 carriers.

The current study shows for the first time that the relationship of HbA1c levels with hippocampal volume in T2D patients may depend on the Hp genotype. This may imply a common underlying mechanism for glycemic control (34) and Hp genotype (35) in hippocampal atrophy, such as endothelial dysfunction, leading to differential hippocampal vulnerability of T2D patients to the deleterious effects of poor glycemic control. Future studies should investigate whether glucose-lowering or insulin treatments in T2D patients may be more beneficial for Hp 1-1 carriers, thus reducing hippocampal atrophy, possibly contributing to reduction in risk for cognitive impairment.

Funding. This research was conducted while I.C. was a recipient of a New Investigator Award in Alzheimer’s Disease from the American Federation for Aging Research. This research was also supported by National Institutes of Health grants R01-AG-034087 and R21-AG-043878 to M.S.B. and P50-AG-05138 to M.S., as well as Bader Philanthropies, the Leroy Schecter Foundation, and the Irma T. Hirschl Scholar Award to M.S.B.

Duality of Interest. No potential conflicts of interest relevant to this article were reported.

Author Contributions. A.Li. researched data and wrote the manuscript. R.R.-S. and M.S.B. researched data, contributed to the discussion, and reviewed the manuscript. A.H., E.M., and A.Le. researched data and reviewed the manuscript. R.P., G.T., L.R., R.M., and N.T. researched data. T.K., I.C., L.G., J.S., M.S., B.B.B., and A.S.B. contributed to the discussion and reviewed the manuscript. I.C. and M.S.B. are the guarantors of this work and, as such, had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

1.
Ravona-Springer
R
,
Luo
X
,
Schmeidler
J
, et al
.
Diabetes is associated with increased rate of cognitive decline in questionably demented elderly
.
Dement Geriatr Cogn Disord
2010
;
29
:
68
74
[PubMed]
2.
Ravona-Springer
R
,
Moshier
E
,
Schmeidler
J
, et al
.
Changes in glycemic control are associated with changes in cognition in non-diabetic elderly
.
J Alzheimers Dis
2012
;
30
:
299
309
[PubMed]
3.
UK Prospective Diabetes Study (UKPDS) Group
.
Intensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes (UKPDS 33)
.
Lancet
1998
;
352
:
837
853
[PubMed]
4.
Wisse
LE
,
de Bresser
J
,
Geerlings
MI
, et al.;
Utrecht Diabetic Encephalopathy Study Group
;
SMART-MR Study Group
.
Global brain atrophy but not hippocampal atrophy is related to type 2 diabetes
.
J Neurol Sci
2014
;
344
:
32
36
[PubMed]
5.
Zhang
YW
,
Zhang
JQ
,
Liu
C
, et al
.
Memory dysfunction in type 2 diabetes mellitus correlates with reduced hippocampal CA1 and subiculum volumes
.
Chin Med J (Engl)
2015
;
128
:
465
471
[PubMed]
6.
Langlois
MR
,
Delanghe
JR
.
Biological and clinical significance of haptoglobin polymorphism in humans
.
Clin Chem
1996
;
42
:
1589
1600
[PubMed]
7.
Carter
K
,
Worwood
M
.
Haptoglobin: a review of the major allele frequencies worldwide and their association with diseases
.
Int J Lab Hematol
2007
;
29
:
92
110
[PubMed]
8.
Asleh
R
,
Levy
AP
.
In vivo and in vitro studies establishing haptoglobin as a major susceptibility gene for diabetic vascular disease
.
Vasc Health Risk Manag
2005
;
1
:
19
28
[PubMed]
9.
Staals
J
,
Pieters
BM
,
Knottnerus
IL
, et al
.
Haptoglobin polymorphism and lacunar stroke
.
Curr Neurovasc Res
2008
;
5
:
153
158
[PubMed]
10.
Costacou
T
,
Rosano
C
,
Aizenstein
H
, et al
.
The haptoglobin 1 allele correlates with white matter hyperintensities in middle-aged adults with type 1 diabetes
.
Diabetes
2015
;
64
:
654
659
[PubMed]
11.
Ravona-Springer
R
,
Heymann
A
,
Schmeidler
J
, et al
.
Haptoglobin 1-1 genotype is associated with poorer cognitive functioning in the elderly with type 2 diabetes
.
Diabetes Care
2013
;
36
:
3139
3145
[PubMed]
12.
Guerrero-Berroa
E
,
Ravona-Springer
R
,
Heymann
A
, et al
.
Haptoglobin genotype modulates the relationships of glycaemic control with cognitive function in elderly individuals with type 2 diabetes
.
Diabetologia
2015
;
58
:
736
744
[PubMed]
13.
Beeri
MS
,
Ravona-Springer
R
,
Moshier
E
, et al
.
The Israel Diabetes and Cognitive Decline (IDCD) study: design and baseline characteristics
.
Alzheimers Dement
2014
;
10
:
769
778
[PubMed]
14.
Hochberg
I
,
Roguin
A
,
Nikolsky
E
,
Chanderashekhar
PV
,
Cohen
S
,
Levy
AP
.
Haptoglobin phenotype and coronary artery collaterals in diabetic patients
.
Atherosclerosis
2002
;
161
:
441
446
[PubMed]
15.
Ashburner
J
,
Friston
KJ
.
Voxel-based morphometry--the methods
.
Neuroimage
2000
;
11
:
805
821
[PubMed]
16.
Tzourio-Mazoyer
N
,
Landeau
B
,
Papathanassiou
D
, et al
.
Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain
.
Neuroimage
2002
;
15
:
273
289
[PubMed]
17.
Maldjian JA, Laurienti PJ, Kraft RA, Burdette JH. An automated method for neuroanatomic and cytoarchitectonic atlas-based interrogation of fMRI data sets. Neuroimage 2003;19:1233–1239
18.
Brett M, Anton J-L, Valabregue R, Poline J-B. Region of interest analysis using an SPM toolbox (Abstract). Presented at the 8th International Conference on Functional Mapping of the Human Brain, 2–6 June 2002, Sendai, Japan. Available on CD-ROM in NeuroImage, Vol. 16, No. 2, abstract 497
19.
van Elderen
SG
,
de Roos
A
,
de Craen
AJ
, et al
.
Progression of brain atrophy and cognitive decline in diabetes mellitus: a 3-year follow-up
.
Neurology
2010
;
75
:
997
1002
[PubMed]
20.
Lee
JH
,
Yoon
S
,
Renshaw
PF
, et al
.
Morphometric changes in lateral ventricles of patients with recent-onset type 2 diabetes mellitus
.
PLoS One
2013
;
8
:
e60515
[PubMed]
21.
Zhang
X
,
Yao
S
,
Zhu
X
,
Wang
X
,
Zhu
X
,
Zhong
M
.
Gray matter volume abnormalities in individuals with cognitive vulnerability to depression: a voxel-based morphometry study
.
J Affect Disord
2012
;
136
:
443
452
[PubMed]
22.
Chen
Z
,
Li
L
,
Sun
J
,
Ma
L
.
Mapping the brain in type II diabetes: voxel-based morphometry using DARTEL
.
Eur J Radiol
2012
;
81
:
1870
1876
[PubMed]
23.
Moran
C
,
Phan
TG
,
Chen
J
, et al
.
Brain atrophy in type 2 diabetes: regional distribution and influence on cognition
.
Diabetes Care
2013
;
36
:
4036
4042
[PubMed]
24.
Brundel
M
,
van den Heuvel
M
,
de Bresser
J
,
Kappelle
LJ
,
Biessels
GJ
;
Utrecht Diabetic Encephalopathy Study Group
.
Cerebral cortical thickness in patients with type 2 diabetes
.
J Neurol Sci
2010
;
299
:
126
130
[PubMed]
25.
den Heijer
T
,
Vermeer
SE
,
van Dijk
EJ
, et al
.
Type 2 diabetes and atrophy of medial temporal lobe structures on brain MRI
.
Diabetologia
2003
;
46
:
1604
1610
[PubMed]
26.
Kerti
L
,
Witte
AV
,
Winkler
A
,
Grittner
U
,
Rujescu
D
,
Flöel
A
.
Higher glucose levels associated with lower memory and reduced hippocampal microstructure
.
Neurology
2013
;
81
:
1746
1752
[PubMed]
27.
Gold
SM
,
Dziobek
I
,
Sweat
V
, et al
.
Hippocampal damage and memory impairments as possible early brain complications of type 2 diabetes
.
Diabetologia
2007
;
50
:
711
719
[PubMed]
28.
Mattila
KM
,
Pirttilä
T
,
Blennow
K
,
Wallin
A
,
Viitanen
M
,
Frey
H
.
Altered blood-brain-barrier function in Alzheimer’s disease?
Acta Neurol Scand
1994
;
89
:
192
198
[PubMed]
29.
Braak
H
,
Braak
E
.
Evolution of the neuropathology of Alzheimer’s disease
.
Acta Neurol Scand Suppl
1996
;
165
:
3
12
[PubMed]
30.
Bruehl
H
,
Sweat
V
,
Tirsi
A
,
Shah
B
,
Convit
A
.
Obese adolescents with type 2 diabetes mellitus have hippocampal and frontal lobe volume reductions
.
Neurosci Med
2011
;
2
:
34
42
[PubMed]
31.
Elcombe
EL
,
Lagopoulos
J
,
Duffy
SL
, et al
.
Hippocampal volume in older adults at risk of cognitive decline: the role of sleep, vascular risk, and depression
.
J Alzheimers Dis
2015
;
44
:
1279
1290
[PubMed]
32.
Rasgon
NL
,
Kenna
HA
,
Wroolie
TE
, et al
.
Insulin resistance and hippocampal volume in women at risk for Alzheimer’s disease
.
Neurobiol Aging
2011
;
32
:
1942
1948
[PubMed]
33.
Yavuz
BB
,
Ariogul
S
,
Cankurtaran
M
, et al
.
Hippocampal atrophy correlates with the severity of cognitive decline
.
Int Psychogeriatr
2007
;
19
:
767
777
[PubMed]
34.
Ceriello
A
,
Esposito
K
,
Ihnat
M
,
Thorpe
J
,
Giugliano
D
.
Long-term glycemic control influences the long-lasting effect of hyperglycemia on endothelial function in type 1 diabetes
.
J Clin Endocrinol Metab
2009
;
94
:
2751
2756
[PubMed]
35.
Rouhl
RP
,
van Oostenbrugge
RJ
,
Damoiseaux
JG
, et al
.
Haptoglobin phenotype may alter endothelial progenitor cell cluster formation in cerebral small vessel disease
.
Curr Neurovasc Res
2009
;
6
:
32
41
[PubMed]
Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered. More information is available at http://www.diabetesjournals.org/content/license.