Impaired Insulin Sensitivity as Indexedby the HOMA Score Is AssociatedWithDeficits in Verbal Fluency and TemporalLobe GrayMatter Volume in the ElderlyCHRISTIAN BENEDICT, PHD

1

SAMANTHA J. BROOKS, PHD1

JOEL KULLBERG, PHD2

JONATHAN BURGOS, MSC1

MATTHEW J. KEMPTON, PHD3

RICHARD NORDENSKJÖLD, MSC2

RUTA NYLANDER, MD2

LENA KILANDER, MD, PHD4

SUZANNE CRAFT, PHD5

ELNA-MARIE LARSSON, MD, PHD2

LARS JOHANSSON, PHD2

HÅKAN AHLSTRÖM, MD, PHD2

LARS LIND, MD, PHD6

HELGI B. SCHIÖTH, PHD1

OBJECTIVEdImpaired insulin sensitivity is linked to cognitive deficits and reduced brainsize. However, it is not yet known whether insulin sensitivity involves regional changes in graymatter volume. Against this background, we examined the association between insulin sensitiv-ity, cognitive performance, and regional gray matter volume in 285 cognitively healthy elderlymen and women aged 75 years from the Prospective Investigation of the Vasculature in UppsalaSeniors (PIVUS) study.

RESEARCH DESIGN ANDMETHODSdInsulin sensitivity was calculated from fastingserum insulin and plasma glucose determinations using the homeostasis model assessment ofinsulin resistance (HOMA-IR) method. Cognitive performance was examined by a categoricalverbal fluency. Participants also underwent a magnetic resonance imaging (MRI) brain scan.Multivariate analysis using linear regression was conducted, controlling for potential confound-ers (sex, education, serum LDL cholesterol, mean arterial blood pressure, and abdominal visceralfat volume).

RESULTSdThe HOMA-IR was negatively correlated with verbal fluency performance, brainsize, and temporal lobe graymatter volume in regions known to be involved in speech production(Brodmann areas 21 and 22, respectively). No such effects were observed when examining diabetic(n = 55) and cognitively impaired (n = 27) elderly subjects as separate analyses.

CONCLUSIONSdThese cross-sectional findings suggest that both pharmacologic and life-style interventions improving insulin signaling may promote brain health in late life but must beconfirmed in patient studies.

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Ahigh density of insulin receptorsoccurs in the temporal lobe, a brainregion associated with memory func-

tions (1). In addition to its essential role fordeclarative memory (i.e., the ability to recol-lect facts and events) (2), the temporal lobe

has also been linked to verbal fluency (VF)(3). Highlighting an important role of insu-lin for temporal lobe–dependent functions,previous studies show that intranasal in-sulin, providing a direct route to the brain(4), improves declarative and verbal

memory in both healthy and cognitivelyimpaired humans (5,6). Contrasting thesedirect effects of insulin on central nervoussystem (CNS) function, reduced periph-eral insulin sensitivity has been linked tosubtle cognitive deficits and reducedspontaneous cortical activity in otherwisecognitively healthy humans (7,8), sug-gesting that the ability of insulin to benefitmemory functions is attenuated when pe-ripheral insulin signaling is disrupted.

Against this background, in the pres-ent community-based study of elderly menand women, we aimed to examine, byusing linear regression models, whethercognitive functions are linked to fluctua-tions in fasting insulin sensitivity, as indexedby the homeostasis model assessment(HOMA) (9). To this aim, we administeredthe 7-minute screen (7MS) test, a briefcognitive instrument used for clinical as-sessment of cognitive ability (10). The7MS consists of four components involv-ing temporal lobes: Benton temporal ori-entation (BTO) (11), enhanced cued recall(ECR) (12), clock drawing (CD) (13),and a categorical VF test. Previous findingsshow that insulin has neuroprotectiveproperties in that it reduces the attachmentof amyloid-b–derived diffusible ligands atneural synapses (14). This process iswidelyaccepted to play a prominent role in trig-gering cognitive deficits and neurode-generation (15). Thus, in addition to thecognitivemeasures, we examinedmagneticresonance imaging (MRI) brain scans forlinks between alterations in insulin sensi-tivity and changes in total and regionalbrain volume. Our main question con-cerned how fluctuations in peripheralinsulin sensitivity link to changes in cogni-tion and brain structure in a cognitivelyhealthy elderly population. To this aim,cognitively impaired subjects were notconsidered eligible for the main analysis(i.e., those who have a mini–mental ex-amination score ,27) (16) and, thus,were tested separately from the main co-hort. Furthermore, since insulin sensitiv-ity in diabetic subjects treated withinsulin, or in those taking insulin

c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c c

From the 1Department of Neuroscience, Uppsala University, Uppsala, Sweden; the 2Department of Radiology,Uppsala University, Uppsala, Sweden; the 3Department of Neuroimaging, King’s College London, London,U.K.; the 4Department of Public Health and Caring Sciences/Geriatrics, Uppsala University, Uppsala,Sweden; the 5Veterans Affairs Puget SoundHealth Care System, Geriatric Research, Education, and ClinicalCenter, Department of Psychiatry and Behavioral Sciences, School of Medicine, University of Washington,Seattle, Washington; and the 6Department of Medical Sciences, Uppsala University, Uppsala, Sweden.

Corresponding author: Christian Benedict, christian.benedict@neuro.uu.se.Received 25 October 2011 and accepted 6 December 2011.DOI: 10.2337/dc11-2075C.B. and S.J.B. contributed equally to this study.© 2012 by the American Diabetes Association. Readers may use this article as long as the work is properly

cited, the use is educational and not for profit, and thework is not altered. See http://creativecommons.org/licenses/by-nc-nd/3.0/ for details.

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C l i n i c a l C a r e / E d u c a t i o n / N u t r i t i o n / P s y c h o s o c i a l R e s e a r c hO R I G I N A L A R T I C L E

secretagogues, might simply reflect themechanism of action of the drug (17),those who were diabetic patients werealso tested separately.

On the basis of thememory-improvingand neuroprotective properties of insu-lin (5,6,14), it was hypothesized thatHOMA scores would negatively corre-late with brain structure and cognitivefunction.

RESEARCH DESIGN ANDMETHODS

Setting and participantsData was derived from the project Pro-spective Investigation of the Vascula-ture in Uppsala Seniors (PIVUS) (18), apopulation-based prospective cohortstudy that initially included 1,016 (50%females) individuals aged 70 years livingin the community of Uppsala, Sweden.After their inclusion between 2001 and2003, subjects participated in a follow-upinvestigation 5 years later (i.e., at age 75).

To minimize potential confoundingof variables generally related to our mainmeasures (e.g., cognition, insulin sensi-tivity, and brain volume), the followingfactors were used to exclude subjects fromanalysis of themain cohort: type 1 and type2 diabetes (n = 55); stroke (n = 27); mini–mental state examination score,27, indi-cating cognitive impairment (n = 2); whitematter lesions, cortical infarcts, and tu-mors (n = 9); excessive body weight (.3SD from the population mean, n = 3); anddementia (n = 1). On the basis of theseexclusion criteria, 285 elderly participants(134 females) were considered eligible foranalysis (corresponding 70% of 409 sub-jects who agreed to undergo an MRI scanat age 75 years) (Fig. 1). Note that the par-ticipants in this subsample, as a result oftime and cost constraints, were randomlyassigned to an MRI session. Independent ttests and x2 tests yielded no significantphysical or cognitive characteristic differ-ences between the MRI subsample and thewhole cohort with similar exclusion crite-ria, indicating a representative subsample(Table 1). The study was approved by theethics committee of the Faculty of Medi-cine, Uppsala University. All participantsgave their written informed consent. Thestudy was conducted according to theDeclaration of Helsinki.

Clinical and biochemicalinvestigationAt age 75 years, blood samples were col-lected in the morning after an overnight

fast. No medication or smoking was al-lowed after midnight. The instrument usedfor biochemical analysis was an Architect(Abbott, Abbott Park, IL), unless otherwisestated. After recording height and weight,allowing the calculation of BMI (kg/m2),the subjects were supine in a quiet roommaintained at a constant temperature, andblood pressure (BP) was measured by acalibrated mercury sphygmomanometerin the noncannulated arm to the nearestmmHg. The systolic and diastolic BP ofthis recording were used to calculate themean arterial BP in the current study. LDLcholesterol was calculated using Friedewaldformula. The abdominal visceral fat volumewas measured by abdominal MRI and wasquantified by an automated postprocessingapproach, as previously described (19). Theeducational level (i.e., university level vs. nouniversity level education) for each subjectwas assessed by means of a standardizedquestionnaire.

7MS testAt age 75 years, a Swedish translation ofthe 7MS test was administered to the par-ticipants by trained study nurses. This test isclinically used to screen for dementia andcognitive decline and has been describedpreviously (10). The 7MS consists of fourbrief cognitive tests:c BTO (11): In this test, the orientation intime is measured and quantified indegree of error. The maximum score is113 (10 error points for 1 year, 5 pointsfor 1 month, 1 point for the date andthe day of a week, and 1 point for each30-min deviation in time).

c ECR (12): This test requires the subjectto recall 16 figures. The score is thetotal number of items recalled in bothfree and cued recall (range 0–16).

c CD (13): In this test, the subject has todraw the face of a clock and place thehands of the clock at a given time. Themaximum score is 7 points.

c Categorical VF: This test requires thesubject to name as many different ani-mals as possible in 1 min. There is amaximum score of 45.

The raw scores of the four subtests of the7MS were summed with the logistic regres-sion formula described previously (11):

ln½P=ð12PÞ� ¼ 35:5921:3033ðECR21:378Þ3ðVFþ 3:298Þ3ðBTO20:838Þ3CD

where P is the probability of havingdementia. Solomon et al. (10) estimated

the formula by using the scores of the fourtests from the screening battery as pre-dictor variables. The ln of P/(1 2 P) isequal to the total 7MS score of the abovelogistic regression formula. In the currentstudy, total scores obtained by the formulaare presented as absolute numbers (i.e.,the lower the obtained value, the higher theprobability of having dementia). Accordingto Solomon et al. (10), an absolute totalscore of.4.6 equals a probability of havingdementia ,1%.

MRI acquisition and processingAt age 75 years, regional measures of brainvolume were acquired with MRI. A high-resolution three-dimensional T1-weightedvolumetric turbo field echo scan was ac-quired using a Philips 1.5 Tesla scanner(GyroscanNT;PhilipsMedical Systems, Best,the Netherlands). The three-dimensionalgradient echo sequence was used with scanparameters TR 8.6 ms, TE 4.0 ms, and flipangle 88. Sagittal slices with a field of viewof 240 3 240 mm, a slice thickness of 1.2mm, and an in-slice resolution of 0.94mm2

were reconstructed.Images were processed using voxel-

based morphometry (VBM), an MRI-basedtechnique that uses statistical parametricmapping to determine local concentrationsof gray matter volume on a voxel-by-voxelbasis (20). Morphological changes in graymatter were calculated by segmenting graymatter from white matter and cerebrospi-nal fluid using the unified segmentationapproach (21). After this segmentationprocedure, probability maps of gray mat-ter were “modulated” to account for theeffect of spatial normalization bymultiply-ing the probability value of each voxel byits relative volume in native space beforeand after warping. Gray matter imagesbased on probability maps at each voxelwere normalized into Montreal Neurolog-ical Institute (MNI) standard space with avoxel size of 2 mm 3 2 mm 3 2 mm.Modulated images were smoothed withan 8-mm full-width half-maximumGaussian kernel, in line with other recentVBM studies (22). This smoothing kernelwas applied prior to statistical analysis toreduce signal noise and to correct for im-age variability. VBM analyses were carriedoutusingSPM8 (UniversityCollegeLondon,London, U.K.) (20).

Statistical analysisData were analyzed using multivariatelinear regression models. Normal distri-bution of all variables was confirmed byKolmogorov-Smirnov testing. Because of a

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skewed distribution, the HOMA of in-sulin resistance (HOMA-IR) variable wasincluded in the multivariate linear re-gression models as ln-transformed index:ln [100 3 (x 2 min)/(max 2 min)](Kolmogorov-Smirnov P. 0.49). Unlessotherwise specified, HOMA shall refer tothe transformed index score for HOMA-IR.The primary analysis (model A) was a mul-tivariate linear regression adjusting for sexto examine the association betweenHOMA

and cognitive functions and gray mattervolume, respectively. To assess the robust-ness of the association between thesevariables, in secondary analyses, we ad-ditionally adjusted for factors that havebeen previously described to be associ-ated with cognition and brain structurein the general population (model B): ed-ucation (23), serum concentration of LDLcholesterol (24), mean arterial BP (25),and abdominal visceral fat volume (26).

To control for individual differences inhead size, the VBM regression models de-scribed above were both additionallycontrolled for the total intracranial vol-ume (TIV; defined as the sum of graymat-ter, white matter, and cerebrospinal fluid).All clusters and peak voxels of gray mattert statistic brain maps reported werethresholded at P , 0.05 by using family-wise error (FWE). To avoid possible edgeeffects between different tissue types, weexcluded from our analyses all voxelswith gray matter probabilities of,0.1 (ab-solute threshold masking). For cognitivemeasurements, the level of significancewas set to P , 0.05.

RESULTSdDescriptive characteristicsof participants, subject inclusionary cri-teria, and sample sizes are shown in Table1 and Fig. 1, respectively.

7MS score and VF performanceRegression analyses in the main cohortrevealed a negative association betweenthe HOMA and the score that was ob-tained on the 7MS (model A: b =20.154,SE = 0.69, P = 0.01; model B: b =20.188,SE = 0.77, P = 0.005). Using model A (i.e.,covarying for sex), further regressionanalysis revealed that the negative associ-ation between the HOMA and the 7MStest score was associated with a variancein the VF subtest score (b =20.165, SE =0.75, P = 0.005). This effect also remainedin cumulative regression model B (b =20.187, SE = 0.012, P = 0.005).

Brain volumesSignificant peak voxel statistics and MNIcoordinates are presented in Table 2. TheVBM regression analysis showed that theHOMA was negatively correlated withtwomajor clusters inmodelA (i.e., covaryingfor sex and TIV) comprising Brodmannareas (BAs) 22, 42, and 21 of the righttemporal lobe (cluster size: 3,715 vox-els, PFWE = 0.0001) and BAs 21 and 22of the left temporal lobe (cluster size:2,321 voxels, PFWE = 0.0001) (Fig. 2A).Using cumulative model B (i.e., covary-ing for sex, education, serum concentra-tion of LDL, mean arterial BP, abdominalvisceral fat volume, and TIV), both clus-ters survived FWE correction (clustersize: right temporal lobe, 2,231 voxels,PFWE = 0.0001; left temporal lobe, 948voxels, PFWE = 0.0001) (Fig. 2B). TheVBM regression analysis did not producepositive associations between the HOMAand gray matter volume.

Figure 1dSubject inclusionary criteria and sample sizes. Initially, 1,016 individuals (509females) were examined at age 70 years. From these individuals, 409 underwent an MRIwhole-brain scan at age 75. At the same age, the participant’s ability to generate sponta-neously words of a certain category was tested by means of a VF task. To ensure a cognitivelyhealthy population, those participants with a history of dementia or stroke, a diagnosis oftype 1 or type 2 diabetes, a BMI.3 SD, pathological lesions on MRI (including arachnoidalcyst, bilateral hygroma, meningioma, and schwannoma), and who scored,27 on the mini–mental state examination (MMSE) to control for mild cognitive impairment were excluded.Furthermore, we examined each individual’s MRI scan and excluded one that was affected bymovement artifacts. Thus, 285 elderly men and women were considered eligible to analyzethe association between the HOMA-IR, cognitive functions, and gray matter volume at age75 years.

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In line with recent observations (27),both model A (i.e., covarying for sex andTIV) and model B (i.e., covarying addi-tionally for education, serum concentration

of LDL, mean arterial BP, and abdominalvisceral fat volume) revealed that theHOMAwas inversely linked to the total brain vol-ume (i.e., graymatter volume +whitematter

volume) (b = 20.096, SE = 5.928, P =0.008 and b = 20.102, SE = 6.797, P =0.013, respectively).

For all analyses presented, the inter-action term of the HOMA variable by sexdid not reach significance. Regarding thecognitively impaired (n = 27) and diabeticsubgroups (n = 55), separate regressionanalyses (using model A and B) revealedno significant association between theHOMA score on one hand and cognitivefunctions and brain structure on the otherhand.

CONCLUSIONSdInsulin sensitivityas indexed by the HOMA value in ourcommunity-based data shows strong linksto deficits in speech production. We arethe first to demonstrate this in a negativeassociation between the HOMA valueand VF scores in cognitively healthy,nondiabetic elderly men and women.Furthermore, we found that HOMA isnegatively coupled with gray mattervolume in the temporal lobe (specifi-cally BAs 21 and 22) brain regions thatsupport language ability (3,28,29). Cor-roborating previous findings (27), weshow that the HOMA value is negativelycorrelated with total brain size as well.In contrast, separate analyses in the cog-nitively impaired and diabetic sub-groups did not produce significantassociations. These results provide evi-dence that fluctuations in insulin sensi-tivity in cognitively healthy, nondiabeticelderly men and women (independent ofsex effects) are linked to changes in cogni-tive performance and brain volume.

Both impaired glucose tolerance andincreased HOMA values have been pre-viously associated with subtle impair-ments in cognitive functions, includingVF (30,31). We found a similar patternin that the HOMA value was inverselyassociated with the VF test score. TheHOMA value was also negatively linkedto a reduction in gray matter volume,specifically to BA 22 and BA 21 of theright and left temporal lobes, respec-tively. These brain areas play an impor-tant role in language processing andspeech production (3,28,29). This is in-triguing inasmuch as disturbed speechproduction is a common trait of Alz-heimer disease (32), a disease that hasbeen previously linked to CNS insulinresistance (33). However, a larger set ofcognitive tests that assess brain func-tions involving language should be ex-amined in the future to determine ifthey also show similar associations

Table 1dDescriptive characteristics of study participants aged 75 years

Present studyMinimum,maximum PIVUS1

Subjects (n) 285 N/A 578Male/female (n) 142/143 N/A 273/305University education/no universityeducation (n) 77/208 N/A 406/172

BMI (kg/m2) 26.3 6 0.2 17.0, 39.0 26.4 6 0.2Abdominal visceral fat volume (L) 1.99 6 0.05 0.30, 4.90 N/APlasma glucose concentration (mmol/L; A) 4.88 6 0.03 3.20, 6.10 4.80 6 0.02Serum insulin concentration (mg/dL; B) 6.78 6 0.24 0.66, 22.76 7.08 6 0.18HOMA-IR (A 3 B/22.5) 1.50 6 0.06 0.13, 5.87 1.54 6 0.04Serum LDL cholesterol (mmol/L) 3.48 6 0.05 1.40, 6.10 3.49 6 0.04Systolic BP (mmHg; C) 147 6 1 102, 216 148 6 1Diastolic BP (mmHg; D) 76 6 1 50, 100 76 6 0.4Mean arterial BP (1/3 3 C + 2/3 3 D) 99 6 1 75, 135 100 6 0.5MMSE (maximum score 30) 29 6 0.01 27, 30 29 6 0.047MS (absolute value) 18 6 0.45 0.37, 36.60 19 6 0.45VF score (60 s, maximum score 45 words) 21 6 0.32 8, 39 21 6 0.24BTO (maximum error score 113) 0.6 6 0.12 0, 11 0.4 6 0.07ECR (maximum score 16) 15.8 6 0.04 11, 16 15.8 6 0.02CD (maximum score 7) 6 6 0.04 2, 7 6 6 0.05TIV (mL) 1,744 6 9 1,358, 2,196 N/ABrain size (mL) 1,018 6 6 712, 1,322 N/AGray matter (mL) 572 6 4 408, 723 N/AWhite matter (mL) 446 6 3 291, 608 N/ACerebrospinal fluid (mL) 726 6 6 502, 1,041 N/AData are mean6 SEM unless otherwise indicated. All bloodmeasurements were done in the morning after anovernight fast. MMSE, mini–mental state examination. 1To illustrate that subjects assessed in the currentstudy were representative of the cognitively healthy, nondiabetic participants of the PIVUS cohort, MRI-independent exclusion criteria (i.e., stroke, diabetes, dementia, cognitive impairment, and BMI) were appliedand descriptive statistics of the result are presented. A, B, C, and D provide information showing how theHOMA values and the mean BP were calculated.

Table 2dPeak voxel statistics for the negative association between insulin resistanceand regional gray matter

Regressionmodel

Brainhemisphere BA TD label

MNI PFWE

valuet

valuex y z

A Right 22 Superior temporal gyrus 54 8 22 0.002 5.38Right 42 Superior temporal gyrus 66 214 8 0.011 4.98Right 21 Middle temporal gyrus 46 26 28 0.014 4.93Left 21 Middle temporal gyrus 244 24 28 0.016 4.88Left 22 Superior temporal gyrus 262 210 6 0.024 4.79Left 22 Superior temporal gyrus 254 6 24 0.040 4.66

B Right 22 Superior temporal gyrus 54 8 22 0.006 5.13Left 21 Middle temporal gyrus 244 26 28 0.037 4.59

Multiple voxel comparisons were done with random permutation testing, and respective PFWE values areadjusted for multiple comparisons. The coordinates of the peak voxels (i.e., the voxels with the maximumt value) are displayed in the stereotactic space of theMNI. Basic regressionmodel A controlled for sex and TIV,whereas complex regression model B further controlled for education, serum LDL cholesterol, mean arterialBP, and abdominal visceral fat volume as covariates of no interest. TD, Talairach Daemon. P , 0.05 wasconsidered significant.

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with insulin resistance and temporallobe gray matter reduction.

The cross-sectional nature of our studyprecludes any assumption about the causeand effect relationship between variables.However, there is convincing evidence thatCNS insulin signaling enhances synapticlong-term potentiation (34), modulates theaction of neurotransmitters involved inmemory processing (e.g., norepinephrineand acetylcholine) (35), and attenuates cor-tisol secretion (36). Moreover, insulin hasbeen shown to mitigate pathological bind-ing of amyloid-b–derived diffusible ligands(potent CNS neurotoxins) to synapsesof neurons (14). Thus, a condition

of peripheral insulin resistance mayrepresent a metabolic state in that theability of insulin to exert its neuropro-tective and neuromodulatory effects inthe CNS is disrupted. This may there-fore accelerate the process of neurode-generation in brain regions that aresensitive to changes in CNS insulin sig-naling, such as the temporal lobes,which express high densities of insulinreceptors (1). Supporting this assump-tion, peripheral insulin resistance hasbeen associated with reduced insulintransport across the blood-brain barrier(37); reduced cerebral glucose meta-bolic rate in frontal, parietotemporal,

and cingulate brain regions (7); and re-duced cortical excitability in responseto intravenous insulin infusion (8).Furthermore, a chronically elevatedblood glucose concentration, the hallmarkmetabolic abnormality associated with so-matic insulin resistance, has been shownto accelerate formations of advanced gly-cation end products, which are hypothe-sized to promote the development ofAlzheimer disease (38). However, alterna-tive mechanismsmay also play a role in theobserved associations in elderly people. Forinstance, since vascular damage is linked todeficits in cognitive function and brainvolume (39), the negative associationsbetween the HOMA score and both cog-nitive function and brain structure mightbe secondary to the detrimental effect ofimpaired insulin sensitivity on vascularhealth (40).

In summary, our results provide apotential rationale for hypothesizing thatinterventions improving insulin sensi-tivity, such as exercise, are attractivestrategies to deter cognitive ageing incognitively healthy, nondiabetic elderlymen and women. However, we did notfind any cross-sectional association be-tween the HOMA score, cognitive func-tion, and brain structure in the cognitivelyimpaired and diabetic subgroups. Thesenegative findings aremost probably due tothe relatively low sample size available inthese subpopulations, but they also mightbe caused by the biasing influence ofantidiabetes and psychopharmacologictreatments. Thus, when interpreting ourmain results (i.e., that fluctuations in in-sulin sensitivity are associated with speechproduction and temporal lobe gray mat-ter volume in elderly men and women),caution is warranted. The reader must becareful in making assumptions about theinfluence of insulin sensitivity on cognitiveperformance and brain structure in cogni-tively impaired and diabetic patients.

AcknowledgmentsdThis study was fundedby the Swedish Research Council, the Brain Re-search Foundation (Sweden), the Olle EngkvistByggmästare Foundation (Sweden), the ToreNilsons Foundation (Sweden), the Gunvor ochJosef Anérs Foundation (Sweden), the ÅkeWibergs Foundation (Sweden), the ÅhlensFoundation (Sweden), and Uppsala University(Sweden).Funding for the study also was provided by

Novo Nordisk (Denmark) and AstraZeneca(Sweden). The funding sources had no rolein the design and conduct of the study; collec-tion, management, analysis, and interpretation

Figure 2dInsulin resistance is linked to deficits in temporal lobe gray matter volume in elderlymen and women. VBM in 285 elderly men and women aged 75 years illustrates a significantinverse association between insulin resistance as indexed by the HOMA-IR and two majorclusters situated in the left and right temporal lobe of the brain, as indicated by colored areas.A: Basic regression model rendering controlled for sex and TIV. B: Complex regression modelfurther controlled for education, serum LDL concentration, mean arterial BP, and abdominalvisceral fat volume as covariates of no interest. Neurologic sections were focused at the signif-icant peak voxels (MNI coordinates shown in brackets), and the colored bar indicates thet values. All clusters illustrated were thresholded at P , 0.05 by using FWE. R, right brainhemisphere; L, left brain hemisphere. (A high-quality digital representation of this figure isavailable in the online issue.)

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of data; and preparation, review, or approval ofthe manuscript. No other potential conflicts ofinterest relevant to this article were reported.C.B. and S.J.B. contributed to study concept

and design, analyzed and interpreted data,contributed to statistical analysis, and wrotethe manuscript. J.K. contributed to studyconcept and design; acquired, analyzed, andinterpreted data; contributed to statisticalanalysis; and critically revised the manuscriptfor important intellectual content. J.B. con-tributed to study concept and design, analyzedand interpreted data, contributed to statisticalanalysis, and wrote and critically revised themanuscript for important intellectual content.M.J.K. and R.No. analyzed and interpreteddata, contributed to statistical analysis, andcritically revised the manuscript for importantintellectual content. R.Ny. contributed to studyconcept and design, acquired data, and criti-cally revised the manuscript for important in-tellectual content. L.K. acquired data andcritically revised the manuscript for importantintellectual content. S.C. analyzed and in-terpreted data and critically revised the man-uscript for important intellectual content.E.-M.L., L.J., and H.A. contributed to studyconcept and design; acquired data; obtainedfunding; provided administrative, technical, ormaterial support; critically revised the manu-script for important intellectual content; andsupervised the study. L.L. contributed to studyconcept and design, acquired data, analyzedand interpreted data, obtained funding, criti-cally revised the manuscript for importantintellectual content, and supervised the study.H.B.S. contributed to study concept and de-sign, analyzed and interpreted data, obtainedfunding, critically revised the manuscript forimportant intellectual content, and supervisedthe study.All authors had full access to all data and

take responsibility for the integrity and accu-racy of the data analysis. C.B. is the guarantorof this work.

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