Impaired Insulin Sensitivity as Indexed by the HOMA Score Is Associated With Deficits in Verbal Fluency and Temporal Lobe Gray Matter Volume in the Elderly

Setting and participants

Data was derived from the project Prospective Investigation of the Vasculature in Uppsala Seniors (PIVUS) (18), a population-based prospective cohort study that initially included 1,016 (50% females) individuals aged 70 years living in the community of Uppsala, Sweden. After their inclusion between 2001 and 2003, subjects participated in a follow-up investigation 5 years later (i.e., at age 75).

To minimize potential confounding of variables generally related to our main measures (e.g., cognition, insulin sensitivity, and brain volume), the following factors were used to exclude subjects from analysis of the main cohort: type 1 and type 2 diabetes (n = 55); stroke (n = 27); mini–mental state examination score <27, indicating cognitive impairment (n = 2); white matter lesions, cortical infarcts, and tumors (n = 9); excessive body weight (>3 SD from the population mean, n = 3); and dementia (n = 1). On the basis of these exclusion criteria, 285 elderly participants (134 females) were considered eligible for analysis (corresponding 70% of 409 subjects who agreed to undergo an MRI scan at age 75 years) (Fig. 1). Note that the participants in this subsample, as a result of time and cost constraints, were randomly assigned to an MRI session. Independent t tests and χ² tests yielded no significant physical or cognitive characteristic differences between the MRI subsample and the whole cohort with similar exclusion criteria, indicating a representative subsample (Table 1). The study was approved by the ethics committee of the Faculty of Medicine, Uppsala University. All participants gave their written informed consent. The study was conducted according to the Declaration of Helsinki.

Clinical and biochemical investigation

At age 75 years, blood samples were collected in the morning after an overnight fast. No medication or smoking was allowed after midnight. The instrument used for biochemical analysis was an Architect (Abbott, Abbott Park, IL), unless otherwise stated. After recording height and weight, allowing the calculation of BMI (kg/m²), the subjects were supine in a quiet room maintained at a constant temperature, and blood pressure (BP) was measured by a calibrated mercury sphygmomanometer in the noncannulated arm to the nearest mmHg. The systolic and diastolic BP of this recording were used to calculate the mean arterial BP in the current study. LDL cholesterol was calculated using Friedewald formula. The abdominal visceral fat volume was measured by abdominal MRI and was quantified by an automated postprocessing approach, as previously described (19). The educational level (i.e., university level vs. no university level education) for each subject was assessed by means of a standardized questionnaire.

7MS test

At age 75 years, a Swedish translation of the 7MS test was administered to the participants by trained study nurses. This test is clinically used to screen for dementia and cognitive decline and has been described previously (10). The 7MS consists of four brief cognitive tests:

• BTO (11): In this test, the orientation in time is measured and quantified in degree of error. The maximum score is 113 (10 error points for 1 year, 5 points for 1 month, 1 point for the date and the day of a week, and 1 point for each 30-min deviation in time).

• ECR (12): This test requires the subject to recall 16 figures. The score is the total number of items recalled in both free and cued recall (range 0–16).

• CD (13): In this test, the subject has to draw the face of a clock and place the hands of the clock at a given time. The maximum score is 7 points.

• Categorical VF: This test requires the subject to name as many different animals as possible in 1 min. There is a maximum score of 45.

The raw scores of the four subtests of the 7MS were summed with the logistic regression formula described previously (11):

where P is the probability of having dementia. Solomon et al. (10) estimated the formula by using the scores of the four tests from the screening battery as predictor variables. The ln of P/(1 − P) is equal to the total 7MS score of the above logistic regression formula. In the current study, total scores obtained by the formula are presented as absolute numbers (i.e., the lower the obtained value, the higher the probability of having dementia). According to Solomon et al. (10), an absolute total score of >4.6 equals a probability of having dementia <1%.

MRI acquisition and processing

At age 75 years, regional measures of brain volume were acquired with MRI. A high-resolution three-dimensional T1-weighted volumetric turbo field echo scan was acquired using a Philips 1.5 Tesla scanner (Gyroscan NT; Philips Medical Systems, Best, the Netherlands). The three-dimensional gradient echo sequence was used with scan parameters TR 8.6 ms, TE 4.0 ms, and flip angle 8°. Sagittal slices with a field of view of 240 × 240 mm, a slice thickness of 1.2 mm, and an in-slice resolution of 0.94 mm² were reconstructed.

Images were processed using voxel-based morphometry (VBM), an MRI-based technique that uses statistical parametric mapping to determine local concentrations of gray matter volume on a voxel-by-voxel basis (20). Morphological changes in gray matter were calculated by segmenting gray matter from white matter and cerebrospinal fluid using the unified segmentation approach (21). After this segmentation procedure, probability maps of gray matter were “modulated” to account for the effect of spatial normalization by multiplying the probability value of each voxel by its relative volume in native space before and after warping. Gray matter images based on probability maps at each voxel were normalized into Montreal Neurological Institute (MNI) standard space with a voxel size of 2 mm × 2 mm × 2 mm. Modulated images were smoothed with an 8-mm full-width half-maximum Gaussian kernel, in line with other recent VBM studies (22). This smoothing kernel was applied prior to statistical analysis to reduce signal noise and to correct for image variability. VBM analyses were carried out using SPM8 (University College London, London, U.K.) (20).

Statistical analysis

Data were analyzed using multivariate linear regression models. Normal distribution of all variables was confirmed by Kolmogorov-Smirnov testing. Because of a skewed distribution, the HOMA of insulin resistance (HOMA-IR) variable was included in the multivariate linear regression models as ln-transformed index: ln [100 × (x − min)/(max − min)] (Kolmogorov-Smirnov P > 0.49). Unless otherwise specified, HOMA shall refer to the transformed index score for HOMA-IR. The primary analysis (model A) was a multivariate linear regression adjusting for sex to examine the association between HOMA and cognitive functions and gray matter volume, respectively. To assess the robustness of the association between these variables, in secondary analyses, we additionally adjusted for factors that have been previously described to be associated with cognition and brain structure in the general population (model B): education (23), serum concentration of LDL cholesterol (24), mean arterial BP (25), and abdominal visceral fat volume (26). To control for individual differences in head size, the VBM regression models described above were both additionally controlled for the total intracranial volume (TIV; defined as the sum of gray matter, white matter, and cerebrospinal fluid). All clusters and peak voxels of gray matter t statistic brain maps reported were thresholded at P < 0.05 by using family-wise error (FWE). To avoid possible edge effects between different tissue types, we excluded from our analyses all voxels with gray matter probabilities of <0.1 (absolute threshold masking). For cognitive measurements, the level of significance was set to P < 0.05.

7MS score and VF performance

Regression analyses in the main cohort revealed a negative association between the HOMA and the score that was obtained on the 7MS (model A: β = −0.154, SE = 0.69, P = 0.01; model B: β = −0.188, SE = 0.77, P = 0.005). Using model A (i.e., covarying for sex), further regression analysis revealed that the negative association between the HOMA and the 7MS test score was associated with a variance in the VF subtest score (β = −0.165, SE = 0.75, P = 0.005). This effect also remained in cumulative regression model B (β = −0.187, SE = 0.012, P = 0.005).

Brain volumes

Significant peak voxel statistics and MNI coordinates are presented in Table 2. The VBM regression analysis showed that the HOMA was negatively correlated with two major clusters in model A (i.e., covarying for sex and TIV) comprising Brodmann areas (BAs) 22, 42, and 21 of the right temporal lobe (cluster size: 3,715 voxels, P_FWE = 0.0001) and BAs 21 and 22 of the left temporal lobe (cluster size: 2,321 voxels, P_FWE = 0.0001) (Fig. 2A). Using cumulative model B (i.e., covarying for sex, education, serum concentration of LDL, mean arterial BP, abdominal visceral fat volume, and TIV), both clusters survived FWE correction (cluster size: right temporal lobe, 2,231 voxels, P_FWE = 0.0001; left temporal lobe, 948 voxels, P_FWE = 0.0001) (Fig. 2B). The VBM regression analysis did not produce positive associations between the HOMA and gray matter volume.

In line with recent observations (27), both model A (i.e., covarying for sex and TIV) and model B (i.e., covarying additionally for education, serum concentration of LDL, mean arterial BP, and abdominal visceral fat volume) revealed that the HOMA was inversely linked to the total brain volume (i.e., gray matter volume + white matter volume) (β = −0.096, SE = 5.928, P = 0.008 and β = −0.102, SE = 6.797, P = 0.013, respectively).

For all analyses presented, the interaction term of the HOMA variable by sex did not reach significance. Regarding the cognitively impaired (n = 27) and diabetic subgroups (n = 55), separate regression analyses (using model A and B) revealed no significant association between the HOMA score on one hand and cognitive functions and brain structure on the other hand.