Effect of high-fat diet on metabolic indices, cognition, and neuronal physiologyin aging F344 rats

Tristano Pancani a,1, Katie L. Anderson a, Lawrence D. Brewer a, Inga Kadish b, Chris DeMoll a,Philip W. Land!eld a, Eric M. Blalock a, Nada M. Porter a, Olivier Thibault a,*aDepartment of Molecular and Biomedical Pharmacology, University of Kentucky Medical Center, Lexington, KY, USAbDepartment of Cell Biology, University of Alabama, Birmingham, AL, USA

a r t i c l e i n f o

Article history:Received 24 September 2012Received in revised form 31 January 2013Accepted 22 February 2013Available online 29 March 2013

Keywords:AdiponectinAHPCalciumLearningAgingHippocampusMetabolism

a b s t r a c t

The prevalence of obesity and type 2 diabetes increases with age. Despite this, few studies haveexamined these conditions simultaneously in aged animals, and fewer studies have measured the impactof these conditions on brain function. Using an established animal model of brain aging (F344 rats), weinvestigated whether a high-fat diet (HFD) exacerbates cognitive decline and the hippocampal calcium-dependent afterhyperpolarization (a marker of age-dependent calcium dysregulation). Young and mid-aged animals were maintained on control or HFD for 4.5 months, and peripheral metabolic variables,cognitive function, and electrophysiological responses to insulin in the hippocampus were measured.HFD increased lipid accumulation in the periphery, although overt diabetes did not develop, nor werespatial learning and memory altered. Hippocampal adiponectin levels were reduced in aging animals butwere unaffected by HFD. For the !rst time, however, we show that the AHP is sensitive to insulin, andthat this sensitivity is reduced by HFD. Interestingly, although peripheral glucose regulation was rela-tively insensitive to HFD, the brain appeared to show greater sensitivity to HFD in F344 rats.

! 2013 Elsevier Inc. All rights reserved.

1. Introduction

Multiple components of metabolic syndrome, including obesityand diabetes correlate with, and even predict a higher incidence ofAlzheimer’s disease (Frisardi et al., 2010; Luchsinger et al., 2012;Whitmer et al., 2008). Importantly, considerable evidence alsoindicates that metabolic syndrome plays a critical role in cognitivedecline during normal aging (Biessels et al., 2008; Launer, 2005;McNay, 2005; Morley, 2004). Although metabolic dysregulationand cognitive decline appear to be distinct pathological processes,some common aspects of both conditions include brain insulinresistance, vascular disease, and/ or in"ammation (McNay andRecknagel, 2011; Talbot et al., 2012). Still, little is known abouthow or whether changes in peripheral glucose or lipid metabolismaffect neuronal function and brain aging.

Despite considerable attention focusing on the links betweenthe periphery and the brain regarding food intake and energy

metabolism, it is not yet clear how peripheral hormones/peptides(e.g., insulin, adiponectin) that regulate these processes changewith age. It is also unclear how or to what extent peripheralmetabolic dysregulation in"uences cognitive decline or neuronalvulnerability in disease states (Stranahan and Mattson, 2012).Interestingly, caloric restriction and exercise, two manipulationsthat slow aging and associated cognitive decline (Keenan et al.,1995), are also able to enhance adiponectin levels, which improveinsulin sensitivity (Fruebis et al., 2001; Gustafson, 2010).

Animal models have been used extensively for studies of dia-betes and obesity, but have some limitations for studies of brainaging and cognitive decline. Genetic models of diabetes (Zuckerdiabetic fatty rat and the db/db mouse) show decreased learningand altered synaptic plasticity (Li et al., 2002), although this is notalways the case (Belanger et al., 2004). Irrespective of the resultsseen with these and other genetic models, one critical limitation istheir short lifespan, which precludes studies of aging. Experimen-tally induced diabetes using streptozotocin (STZ) decreaseshippocampal-dependent learning in young animals (Dou et al.,2005; Popovic et al., 2001), but only a few studies have been con-ducted in aged animals using this model (Kamal et al., 2000).Furthermore, the STZ model, although valuable for studies of theimpact of type 1 diabetes on learning and memory, does not reca-pitulate the conditionmost commonly seen in the aging population,

* Corresponding author at: Department of Molecular and Biomedical Pharma-cology, University of Kentucky Medical Center, 800 Rose Street, Lexington, KY40536-0084. Tel.: !(859) 323-4863; fax: !(859) 323-1981.

E-mail address: othibau@uky.edu (O. Thibault).1 T.P. is currently at the Vanderbilt Center for Neuroscience Drug Discovery,

Vanderbilt University Medical Center, 1205 Light Hall, Nashville, TN 37232.

Contents lists available at SciVerse ScienceDirect

Neurobiology of Aging

journal homepage: www.elsevier .com/locate/neuaging

0197-4580/$ e see front matter ! 2013 Elsevier Inc. All rights reserved.http://dx.doi.org/10.1016/j.neurobiolaging.2013.02.019

Neurobiology of Aging 34 (2013) 1977e1987

including the clinically silent period of hyperinsulinemia thatprecedes type 2 diabetes (T2DM).

An alternative approach to study how peripheral metabolicdysregulation might in"uence cognitive decline in aging is to usea high-fat diet (HFD). Numerous rodent studies indicate that HFDdecreases insulin sensitivity while increasing cholesterol levels andbody weight (Buettner et al., 2007). HFD increases visceral fat massand circulating free fatty acids (FFA), resulting in widespreadin"ammation via cytokine/adipokine secretion (Xu et al., 2003). AnFFA-mediated reduction in insulin receptor signaling is a recog-nized pathway linking obesity to insulin resistance in liver, muscle,and fat. Indeed, several reports indicate that elevated plasma FFAsinduce insulin resistance through inhibition of glucose transport,mediated, in part, by a decrease in phosphotidyl inositol 3-kinase(PI3K) and its interaction with insulin receptor substrate 1 (IRS1)(Curtis et al., 2005; Furuhashi et al., 2007).

Among themost commonly used strains for aging research is theF344 rat. Although this model has been characterized and isroutinely used in studies of brain aging, the response to HFD has notbeen studied as extensively as in other strains. This is important,because the impact of dietary manipulations is clearly sensitive torat strain (Barzilai and Rossetti, 1995; Narimiya et al., 1984; Reavenet al., 1983). Furthermore, most studies of HFD in aging rodentsfocus solely on effects in the periphery and largely ignore the impactof the diet on the brain (Bracho-Romero and Reaven, 1977; Keenanet al., 1995; Mooradian et al., 1997). To address this gap, wecompared long-term HFD in young andmiddle-aged F344s. Middle-aged rats were used to parallel the age at which the initial rise inperipheral metabolic dysregulation is typically observed in thehuman population. We assessed effects of HFD on peripheralmetabolic variables and cognitive acuity in the Morris Water Maze(MWM). At the cellular level, we measured an electrophysiologicalmarker of age-related cognitive decline, the Ca2!-dependent after-hyperpolarization (AHP), in area CA1 pyramidal neurons (Blalocket al., 2010; Gant et al., 2006; Moyer Jr et al., 1996; Thibault andLand!eld, 1996). In the same brain area, we also measured insulinsensitivity, insulin signaling, and adiponectin. In both age groups,HFD was associated with robust dyslipidemia and mild obesity but,surprisingly, did not induce diabetes or alter spatial memory. This isunlike other models and perhaps is related to the comparativelyhigher peripheral levels of adiponectin that we observed in F344s.Electrophysiological measures in the hippocampus show, for the!rst time, that the AHP is sensitive to insulin, and that this sensi-tivity is reduced by HFD. Together, these results suggest theintriguing possibility that in F344 rats, the brain may be moresensitive to the effects of HFD than the periphery.

2. Methods

2.1. Subjects

All experiments presented here were conducted under anapproved Institutional Animal Care and Use Committee (IACUC)protocol granted by the University of Kentucky. A total of 66 maleF344/NIA rats were maintained single-housed on a control diet(CD) for 3 weeks, and baseline values for the glucose tolerance test(GTT), insulin tolerance test (ITT) and glycated hemoglobin(HbA1c) were obtained (see section 2.5). After 3 weeks on CD,animals were separated into 4 groups as follows: young-adult(2.5e4 months old) on CD (n " 13); young-adult on HFD (n "13); middle-aged (12.5e13.5 months old) on CD (n " 20); andmiddle-aged on HFD (n" 20). HFDwas initiated on the 4th week ofthe study. GTT, ITT, and HbA1c tests were repeated after 4.5months. Food consumption and body weight were monitored 2 to3 times per week for the duration of the study and were averaged

per week per animal. Four middle-aged animals on HFD, 3 middle-aged animals on CD, and 1 young-adult on CD were excludedbecause of poor health.

2.2. Diets

Puri!ed diets were provided ad libitum (Harlan Teklad, Madison,WI). The CD consisted of the following (in % Kcal): 19.2 protein, 67.9carbohydrate, and 13 fat [TD. 08485]. The HFD, a “Western-style”diet, consisted of the following (in % Kcal): 15.2 protein, 42.7carbohydrate, and 42 fat [TD. 88137].

2.3. Glucose and insulin tolerance tests

To test for differences between weight-adjusted and standardsingle-dose glucose or insulin tolerance tests (GTT, ITT, respec-tively), we monitored blood glucose levels in a series of pilotanimals (n " 5e6 per group; 2 months and 13 months old). Theseanimals were exposed to a weight-adjusted glucose (2 g/Kg bodyweight of 60% glucose), a weight-adjusted insulin dose (2 mU/gbody weight of Humalog, Eli Lilly, Indianapolis, IN, USA), anda standard glucose/insulin dose (670 mg glucose per animal, or 450mU Humalog per animal). The data are presented in supplementalFigure 1 and supplemental Table 1, and show that in the weight-adjusted condition, glucose levels were signi!cantly increased inolder animals (F1,26 " 21.6, p < 0.0001), an effect mediated mostlyby greater glucose levels seen at the 30-minute time point (posthoc). This result was likely due to the greater mean group weightseen in older animals, and disappeared entirely under standardsingle-dose glucose injection conditions (supplemental Fig. 1B1).No signi!cant differences were found in measures of area-under-the-curve (AUC) for both glucose and insulin tolerance tests usingeither approach (supplemental Table 1). Following a 6-hour fast,glucose levels were monitored (mg/dL using a FreeStyle Lite gluc-ometer; Abott Diabetes Care Inc, Alameda, CA) from tail prick bloodsamples taken at 30-minute intervals over a 2-hour period inresponse to an intraperitoneal glucose or insulin injection (Blalocket al., 2010). For some analyses, glucose responses over time wereintegrated to determine the area-under-the-curve (AUC) andnormalized to the initial time-point value. Tests of glucose orinsulin sensitivity were separated by one week to limit stress andcarry-over effects. Animals that did not show at least a 9% change inglucose levels at the 30-minute time point after glucose or insulininjection were removed from the analysis.

2.4. Tissue collection

Twenty-four animals were anesthetized with pentobarbital(80 mg/Kg body weight), and blood was collected from the rightventricle immediately before perfusion for 10minutes with ice-coldsaline solution (0.9%). Brains were post-!xed in 4% parafor-maldehyde overnight and cryoprotected in 30% sucrose solutionat #20 $C until sectioning for immunohistochemistry. Heart, liver,and epididymal and retroperitoneal fat pads were collected andweighed (Table 1).

2.5. Blood serum analyses

Serum was isolated from whole blood and frozen for chemicalanalyses (Comparative Pathology Laboratory, University ofCalifornia, Davis, CA, USA). Adiponectin levels were determinedby the Vanderbilt Hormone Assay Core Lab (Vanderbilt University,Nashville, TN, USA). Because glycated hemoglobin levels areconsidered to be the “gold standard” for clinical monitoring ofdiabetes, before blood separation, a drop was used for HbA1c

T. Pancani et al. / Neurobiology of Aging 34 (2013) 1977e19871978

measurement using a DCA Vantage Analyzer (Siemens HealthcareDiagnostics, Tarrytown, NY, USA). Serum insulin levels weredetermined using Millipore rat/mouse insulin enzyme-linkedimmunosorbent assay (ELISA) kit (Billerica, MA, USA) according tothe manufacturer’s instructions.

2.6. Western blotting

Hippocampal dorsal and ventral quarters were combined andused to quantify protein levels. Insulin receptor (IR) and insulinreceptor substrate 1 (IRS-1) were quanti!ed with a standardWestern blot protocol. Subcellular fractions (membrane and cyto-solic) were obtained from homogenates following a protocoladapted from Dou et al. (2005), and quanti!ed for proteins usinga Bradford assay. Samples were run on 10% Mini-PROTEAN TGXgels (Bio-Rad, Hercules, CA, USA). After transfer to nitrocellulosemembranes, samples were immunostained overnight at 4 $C(antieIRS-1 antibody, Santa Cruz Biotechnology, sc-81466, 1:800;anti-IR b antibody, Santa Cruz Biotechnology, sc-560, 1:600; andanti-b-actin antibody, Sigma-Aldrich, A1978, 1:2000). After sec-ondary antibody exposure, membranes were developed with ECLplus (GE Healthcare, Pittsburgh, PA, USA) and visualized usinga phosphoimager (Storm 840; Molecular Dynamics, Sunnyvale, CA,USA). ImageJ was used for signal intensity measures (NationalInstitutes of Health, Bethesda, MD, USA; http://rsb.info.nih.gov/ij/),and each sample was normalized to b-actin. All experiments wererun in duplicate.

2.7. Morris Water Maze (MWM)

As in prior work (Blalock et al., 2010), temperature was main-tained at 25$ to 26 $C, and 1 quadrant contained a 15-cm diameterescape platform. Videomex-V (Columbus Instrument, Columbus,OH, USA) was used to record animal movements. For all trainingdays, 3 trials were run (each with a different start location). Animalswere placed in the pool and given 60 seconds to !nd the platform,otherwise, they were guided to the platform. All animals wereallowed to stay on the platform for 30 seconds. After this 30-secondperiod, animals were taken outside the MWM enclosure for 2minutes and returned to the MWM for a second and then a thirdtrial. On day 1, 3 cue trials were run with a hanging white cuppositioned over the partially visible platform. The next 3 days oftraining/learning (days 4, 5, and 6) animals continued to receive 3trials per day (without cue and a submerged platform). Twenty-fourhours after the last training day, a 1- minute probe test with theplatform removed was conducted. The next day, animals weretrained to learn a new platform location (reversal trainingdoppositequadrant from previous position) also using 3 trials per day. Next, 72hours later a reversal probe was conducted. Performance was eval-uated using measures of path length to platform and a cumulative

search error index. The index is representative of how far eachanimal deviated from an ideal path to goal (Blalock et al., 2010;Gallagher et al., 1993).

2.8. Immunohistochemistry

Coronal sections (30 mm) cut on a freezing-sliding microtomewere exposed to the following primary antibodies: adiponectin(rabbit, 1:500; Abbiotec, San Diego, CA, USA), insulin receptor-aantibody (rabbit IR-a, 1:200, Abbiotec, San Diego, CA, USA), andinsulin receptor subtype-1 antibody (rabbit IRS-1, 1:200, LSBio,Seattle, WA, USA). For adiponectin and IRS-1 staining, sections werepretreated 30 minutes at 85 $C in sodium citrate (pH " 6.5). Alltissues were pretreated 30 minutes in 2% H202. Following incu-bation with primary antibody in TBS-T overnight at room tem-perature, tissues were rinsed 3 times and incubated with theappropriate biotinylated secondary antibody for 2 hours at roomtemperature. Sections were rinsed 3 times, incubated 2 hours withthe tertiary antibody Extra-Avidinperoxidase, rinsed 3 times, andexposed to nickel-enhanced DAB staining for visualization. Slideswere air dried, dehydrated in xylene, and coverslipped with DPX.For each animal, 2 sections were measured using an area drawnaround the pyramidal layer of area CA1 (NIH Scion Image program)and averaged. Images of hippocampal areas were acquired with anOlympus DP70 digital camera (Olympus, Tokyo, Japan). For eachantibody, all sections were processed in parallel.

2.9. Electrophysiology

Electrophysiological data were collected from 31 animals at least1 week after the last day of MWM to limit the impact of learning andarousal on transient hippocampal excitability changes (Moyer Jret al., 1996). Hippocampal slices were obtained according to previ-ously published protocols (Blalock et al., 2010). Animals were brie"yanesthetized in a CO2 chamber and decapitated. Hippocampi wereremoved, and transverse slices were prepared (350 mm) in ice-cold,low-calcium, arti!cial cerebrospinal "uid (ACSF) composed of thefollowing (in mmol/L): 128 NaCl, 1.25 KH2PO4, 10 glucose,26 NaHCO3, 3 KCl, 0.1 CaCl2, 2 MgCl2, using a Vibratome (TPI, SaintLouis, MO, USA). Slices were transferred to a heated (32 $C)interface-type chamber and maintained in oxygenated (95% O2, 5%CO2) normal-calcium ACSF containing 2 mmol/L CaCl2 and 2mmol/LMgCl2 for at least 2 hours before recording. Each hippocampal slicewas placed in a recording chamber (RC22C, Warner Instruments,Hamden, CT, USA) and maintained in a continuous "ow of oxygen-ated normal-calcium ACSF pre-heated at 32 $C using a TC2Bip/HPRE2 in-line heating system (Cell Micro Controls, Norfolk, VA,USA). The recording chamber was mounted on the stage of a NikonE600FN inverted microscope (Nikon, Tokyo, Japan). Cells wereimpaled with sharp microelectrodes !lled with 2mol/L KmeSO4 and10 mmol/L HEPES, pH 7.4, pulled from borosilicate glass capillaries(World Precision Instruments, Sarasota, FL, USA).

To generate an afterhyperpolarization (AHP), cells were heldat #65 mV (baseline) and depolarized with a 100-millisecondintracellular current injection suf!cient to generate 3 Na! actionpotentials. AHPs were elicited every 30 seconds, and between 2 and10 sweeps were averaged for each cell. The medium AHP (mAHP)was measured as the peak hyperpolarization immediately after theoffset of the depolarizing current injection; the slow AHP (sAHP)was measured 800 milliseconds after the end of the current injec-tion. AHP duration was measured from the end of the depolarizingstep until return to baseline. Neurons with input resistance greaterthan 35 MU, holding current less than 250 pA, and overshootingaction potentials were included in this analysis. Action potentialheight, input resistance, current injection to elicit the AHPs, and

Table 1Normalized tissue weights in young and middle-aged F344 rats fed CD or HFD

Tissue Young CD Young HFD Middle-agedCD

Middle-agedHFD

Heartb 0.289 % 0.008 0.274 % 0.008 0.284 % 0.003 0.274 % 0.005Livera,b 3.42 % 0.10 3.80 % 0.06 3.30 % 0.10 3.50 % 0.07Epididymal fatb 3.79 % 0.12 4.43 % 0.10 3.94 % 0.08 4.16 % 0.06Retroperitoneal

fata,b3.89 % 0.11 5.02 % 0.17 4.65 % 0.20 5.92 % 0.19

Data are mean % SEM. Tissue weights at time of sacri!ce were normalized toindividual body weight and multiplied by 100.Key: CD, control diet, HFD, high-fat diet.

a Indicates signi!cance by age.b Indicates signi!cance by diet.

T. Pancani et al. / Neurobiology of Aging 34 (2013) 1977e1987 1979

recording electrode resistance were quanti!ed (Table 3). Data wereacquired using pClamp 8.0 (Molecular Devices) software througha Digidata 1320A A/D converter and an Axoclamp2B (MolecularDevices).

Some hippocampal slices were exposed to acute insulin todetermine its effect on the AHP. Humalog was perfused into the"ow of oxygenated recording ACSF at a concentration of 4 mmol/L(diluted in ACSF from the 600 mmol/L stock) and subjected to an 8-fold dilution, resulting in a !nal concentration at 500 nmol/Linsulin. Another set of cells were recorded (n " 5) to control for theimpact of time and vehicle application. In these cells, a 15-minuteperfusion with ACSF did not alter the AHP.

2.10. Statistical analysis

Body weight and food consumption were analyzed from weeks4 to 23 using a 2-way repeated-measures (RM) analysis of variance(ANOVA; for time and diet effects) in young and middle-agedanimals separately. A 2-way ANOVA across diet and age was alsoused at week 23. Two-way RM ANOVAs were used for all othermeasures with a primary focus on diet and age effects. Post hocanalyses used the Bonferroni test. For all variables measured,outliers (>2 SD from group mean) were removed from the analysis.

The peripheral lipid index (PLI) for each animal in the study wasconstructed from measures of retroperitoneal fat, the HDL-to-LDLratio, and cholesterol. Each measure obtained from each animalwas ranked. The average of each animal’s rank from these 3measures was de!ned as that subject’s PLI value (higher valuesrepresent poorer lipid pro!les). A similar procedure was used toobtain a spatial learning and memory index (SLMI) using measures

of cumulative search errors during learning, and during the probeand reversal probe (high values represent poorer spatial memory).Correlations report the Pearson r values.

3. Results

3.1. Body weights, organ weights, and food consumption

Bodyweights increased signi!cantly fromweeks 4 to 23 in young(F19,23 " 213.1; p < 0.0001) and middle-aged animals (F19,31 " 28.0;p < 0.0001) on a control diet (CD). As shown in Fig. 1A, HFDincreased body weights more robustly in young animals (F1,23 " 9.2;p < 0.01) compared to middle-aged animals (F1,31 " 2.0; p " 0.17).By the end of the study (week 23) signi!cant aging (F1,54" 44.0; p<0.0001) and diet effects (F1,54 " 11.9; p " 0.001) were still present,with a greater impact of the diet seen onmeasures of bodyweight inyounger animals. Table 1 provides analyses of peripheral tissueweights normalized to bodyweight. Epididymal and retroperitonealfat pad weights were increased by HFD (epididymal F1,54 " 23.8;p < 0.0001, retroperitoneal F1,54 " 42.0; p < 0.0001), but onlyretroperitoneal was also increased by age (F1,54 " 20.0; p < 0.0001).Liver weights were increased by diet (F1,54 " 10.9; p < 0.005) anddecreased by age (F1,54" 5.5; p< 0.05), whereas heart weights weresigni!cantly decreased by diet (F1,54" 4.2; p< 0.05). The decrease innormalized liver and heart weights is likely due to the increase inbody weights in the middle-aged and HFD groups (analysis onnoneweight-corrected data did not reveal decreases in absolutetissue weights). As shown in Fig. 1B, across weeks 4 to 23, HFDcaused a signi!cant reduction in food consumption, but onlyin older animals (F1,31 " 29.9; p " 0.0001). Analysis of food con-sumption by the end of the study, on week 23, shows signi!cantaging (F1,54"17.6; p< 0.0001) and diet effects (F1,54" 6.8; p< 0.05).The greater decrease in food consumption seen in the older animalsis consistent with their lower weight gain in comparison to theyounger animals.

3.2. Blood measures

Consistent with prior reports in aging rats (Barzilai and Rossetti,1995; Kalant et al., 1988; Li et al., 1996), and based on blood levels ofglucose and insulin, our animals did not show signs of hypergly-cemia either initially, or after 4.5 months of HFD or CD (Table 2). Wecomplemented these static measures with assessment of dynamicglucose responses to glucose or insulin injections (measured asAUC, Table 2). Because the conventional clinical approach for theGTT uses a single dose irrespective of weight, and because of theresults from our pilot study (see Methods), we report data usingthe standard single-dose method. Animals were subjected to the

Table 2In vivo and in vitro serum lipid- and glucose-related markers in young or middle-aged F344 rats fed CD or HFD

Young CD Young HFD Middle-AgedCD

Middle-AgedHFD

Glucose(initial, mg/dL)

80.7 % 1.7 83.7 % 1.0

Glucose(post, mg/dL)

78.4 % 1.3 76.8 % 1.1 80.6 % 1.8 81.9 % 2.2

Insulin(post, ng/mL)

6.05 % 0.71 4.51 % 0.38 5.84 % 1.05 4.41 % 0.76

AUC GTT (initial) 3230 % 293 3175 % 215AUC GTT (post)a 2735 % 352 4030 % 408 2816 % 384 3503 % 407AUC ITT (initial) #2693 % 120 #3063 % 158AUC ITT (post)b #1819 % 319 #2257 % 168 #2545 % 185 #3107 % 305HbA1c (initial) 3.87 % 0.03 3.63 % 0.02HbA1c (post)a,b 3.67 % 0.04 3.92 % 0.06 3.29 % 0.06 3.59 % 0.05Cholesterol

(mg/dL)b147.4 % 3.9 142.1 % 4.7 237.4 % 14.4 223.5 % 8.9

HDL (mg/dL)a,b 94.4 % 2.1 47.7 % 4.4 180.4 % 16.1 82.6 % 4.8LDL (mg/dL)b 17.7 % 0.7 14.0 % 1.4 47.0 % 6.6 42.2 % 3.6HDL: LDLa,b 6.02 % 0.30 3.79 % 0.28 4.50 % 0.61 2.23 % 0.27NEFA (mEq/L) 0.90 % 0.07 0.96 % 0.04 0.84 % 0.06 0.91 % 0.06Triglycerides

(mg/dL)a,b117.7 % 9.5 213.3 % 16.4 50.0 % 4.5 125.0 % 13.0

Glucagon(pg/mL)

101.7 % 10.5 87.6 % 4.8 111.4 % 9.0 101.4 % 7.0

Adiponectin(mg/mL)

19.5 % 1.1 18.0 % 0.5 19.7 % 1.5 20.4 % 0.7

Data are mean % SEM. Some data were obtained before the initiation of the dietchange (initial) as well as toward the end of the study after 4.5 months on the diets(post). If not speci!ed, numbers were obtained at the end of the study.Key: AUC, area-under-the-curve; CD, control diet; GTT, glucose tolerance test;HbA1c, percent glycated hemoglobin; HDL, high-density lipoprotein; HFD, high-fatdiet; ITT. insulin tolerance test; LDL. low-density lipoprotein; NEFA, non-esteri!edfatty acids.

a Signi!cance by diet.b Signi!cance by age.

Table 3Electrophysiological properties in young and middle-aged F344 rats fed CD or HFD

Young CD Young HFD Middle-agedCD

Middle-agedHFD

Electroderesistance (MU)

128.3 % 10.3 121.6 % 3.56 114.6 % 8.06 113.5 % 5.61

Input resistance (MU) 47.67 % 3.81 48.00 % 3.55 48.88 % 2.84 44.42 % 1.67Action potential

height (mV)12.58 % 2.41 15.94 % 2.62 11.92 % 1.64 14.69 % 1.25

Current injection (pA) 251.7 % 18.7 233.3 % 14.2 231.2 % 16.7 296.7 % 28.4

Data are mean % SEM.No signi!cant differences (2-way analysis of variance, p> 0.05) were found in any ofthe following electrophysiological properties: electrode resistance, input resistance,action potential height (measured at peak), and intracellular current injectionrequired to elicit 3 action potentials.Key: CD, control diet; HFD, high-fat diet.

T. Pancani et al. / Neurobiology of Aging 34 (2013) 1977e19871980

GTT and ITT experiments initially, and after 4.5 months on eitherdiet (Fig. 2). A slightly greater response to glucose injection overtime was seen in animals on HFD when compared to CD (Fig. 2B1),as determined by a greater AUC (F1,51 " 6.15; p < 0.05; Table 2).Surprisingly, an enhanced insulin response was seen (decreasedglucose levels) in older animals after 4.5 months on either diet(Table 2 and Fig. 2B2), again as determined by a greater AUC(F1,41 " 8.7; p " 0.005), which suggests greater insulin sensitivity inolder animals. Although these data are normalized to baselineglucose levels, analyses of non-normalized data at the 30-minutetime-point also yielded signi!cant age-dependent decreases inglucose levels (F1,41"13.3; p" 0.0008). To complement the analysisof glucoregulation, we measured glycated hemoglobin levels(HbA1c). HbA1c were signi!cantly reduced in middle-aged animals(F1,53 " 40.2; p < 0.0001), and HFD caused a small (6e9%), yetsigni!cant, increase in glycated hemoglobin levels in both agegroups (F1,53 " 24.3; p < 0.0001; Table 2). The diet-mediated in-crease in HbA1c is consistent with the greater AUC seen in responseto the GTT (Table 2), and may indicate longer postprandial glu-cose excursions in the HFD group. Nevertheless, together thesedata indicate that neither animal group showed signs of frankdiabetes.

In contrast to the small HFD-induced responses in glucor-egulation, HFD had a robust impact on lipid homeostasis (Table 2).Total cholesterol was signi!cantly increased in middle-agedanimals (F1,52 " 57.3; p < 0.0001), but levels were not altered by

the diet. High-density lipoprotein (HDL) levels were signi!cantlyincreased with age (F1,52 " 32.6; p < 0.0001) but decreased by HFD(F1,52" 46.6; p< 0.0001). Low-density lipoprotein (LDL) levels weresigni!cantly elevated in middle-aged animals (F1,52 " 42.6; p <0.0001), but were not sensitive to the HFD. The HDL-to-LDL ratio,a relevant clinical index, was computed and found to be signi!-cantly reduced by age (F1,54 " 12.8; p " 0.0007) and HFD (F1,54 "27.3; p< 0.0001). Triglyceride levels were signi!cantly increased byHFD (F1,52 " 53.2; p < 0.0001), but middle-aged animals had lowerlevels compared to younger animals (F1,52" 44.6; p< 0.0001). Non-esteri!ed fatty acids (NEFA) and adiponectin levels were notchanged by either diet or age (Table 2).

To summarize these data, we compiled a peripheral lipid index(PLI) for each animal using measures of retroperitoneal fat, HDL-to-LDL ratio and cholesterol. This approach revealed signi!cantdifferences by both age (F1,54 " 53.7; p < 0.0001) and diet (F1,54 "29.3; p < 0.0001), effectively separating the 4 animal groups(Fig. 3A). The value of each animal using the PLI was then correlatedwith a learning and memory index (described below).

3.3. Behavior

We monitored learning and memory performance using theMWM spatial task. As previously reported (Frick et al., 1995; Roweet al., 2007), middle-aged animals on CD showed a signi!cantdecrease in performance (Fig. 4). This was evidenced by age-dependent differences in time to reach the platform during thetraining/ learning phase (F1,54 " 7.2; p < 0.05; data not shown).Performance was also analyzed using a cumulative search errorindex for both the learning phase (Fig. 4B; F1,54" 9.24; p< 0.01) andthememory phase of the task (probe, Fig. 4C). During the probe triala signi!cant age-dependent decrease in memory performance wasseen (F1,54" 4.1; p< 0.05). During the reversal probe, 72 hours afterreversal training (new platform location), similar results wereobserved (F1,54 " 5.8; p < 0.05; data not shown).

In comparison to the effects of aging on learning and memory,the HFD had no impact on maze performance (Fig. 4B). Neverthe-less, we tested for the presence of an association between theperipheral lipid index (PLI) and learning/memory. For each animal,individual values obtained from the cumulative search errorsduring learning, the probe and the reversal probe, were ranked andaveraged to yield a single index. This provided us with a spatiallearning and memory index (SLMI) for each animal. We correlatedPLI data with SLMI data for all 58 animals and obtained a weak,albeit signi!cant, correlation (r " 0.3; p < 0.05; Fig. 3B). We inter-pret this as supporting evidence for a periphery-brain link;however, the association appears to be mediated mostly by strongchanges in peripheral variables, as opposed to behavioral variables(HFD had no signi!cant impact on learning or memory).

3.4. Physiology

We also evaluated the potential effects of HFD on hippocampalphysiology in area CA1 neurons ex vivo. We focused on the after-hyperpolarization (AHP) because of its sensitivity to the agingprocess and learning (Song et al., 2012; Thibault and Land!eld,1996). Electrophysiological cell properties and electrode charac-teristics were not affected by age or diet (Table 3). As previouslyreported, the AHP was larger in middle-aged animals (Fig. 5C)(Disterhoft and Oh, 2007; Gant et al., 2006). The medium AHP(mAHP) was signi!cantly increased with age (F1,31"6.81; p< 0.05),and trends were seen for increases in the slow AHP amplitude(F1,31 " 2.9; p < 0.1) and duration (F1,31 " 3.0; p < 0.1). HFD,however, was unable to signi!cantly alter the AHP, albeit a strongtrend for an increase in the mAHP was seen in young animals

Fig. 1. Body weight and food consumption across 23 weeks of HFD. (A) Mean bodyweights across weeks of treatment. (B) Amounts of food consumed (averaged ona weekly basis). Data analysis includes the period from 4 to 23 weeks during which theHFD was in effect. Mean % SEM are shown. Asterisks indicate signi!cant difference atthe level of p < 0.05.

T. Pancani et al. / Neurobiology of Aging 34 (2013) 1977e1987 1981

(F1,31 " 3.8; p < 0.1; Fig. 5C). Given the relationship between theAHP and learning, the lack of a diet effect on the AHP is consistentwith the lack of a diet effect on spatial learning and memory.

Because prior clinical evidence shows that aging and diabetescan downregulate insulin transporters at the bloodebrain barrier(Duelli et al., 2000; McNay, 2005) and may be responsible fordecreased insulin sensitivity in the aged brain (Gasparini et al.,2002; Steen et al., 2005), we hypothesized that aging and/or HFDmay alter insulin sensitivity in the hippocampus. In a subset of cells(Fig. 5D and E), we quanti!ed insulin sensitivity by measuring theAHP before and after a 15-minute insulin perfusion (500 nmol/LHumalog). Irrespective of age (Fig. 5D), the sAHP from animals onCD showed a signi!cant reduction in response to acute insulin(F1,10 " 7.1; p < 0.05). Although there was no signi!cant interactionterm in the 2-way RM ANOVA (p " 0.14), the degree of insulin-mediated reduction in the sAHP appears to be larger in aged,compared to younger, animals (Fig. 5D). Importantly, in HFDanimals, this reductionwas eliminated (Fig. 5E), suggesting the HFDcaused a decrease in insulin sensitivity.

3.5. Western blot and immunohistochemistry analysis fromhippocampal tissue

As an additional measure of insulin signaling in the brain,and because insulin signaling is dependent on IRS-1 in its earlystage (Talbot et al., 2012), we quanti!ed protein levels for IR andIRS-1 using Western and immunohistochemical techniques. Priorstudies have reported decreases in brain IR signaling in agingand AD, as well as in response to dietary manipulations and AD(Pratchayasakul et al., 2011; Talbot et al., 2012; Zhao et al., 2004).However, these effects may be dependent on animal strain, treat-ment duration (Mielke et al., 2006; Ross et al., 2012), and cognitivestatus (Dou et al., 2005; Zhao et al., 2004). Western blot analysisshowed no signi!cant age or diet effects on the upstream portionsof the insulin pathway (IR and IRS-1 levels; Fig. 6A); however, IRS-1

staining showed a small, yet signi!cant, age-dependent reduc-tion in area CA1 immunoreactivity (F1,20 " 11.55; p < 0.005; Fig. 6Bmiddle panel). A robust age-dependent reduction in adiponectinstaining was seen in the pyramidal layer of area CA1 (F1,20 " 165.1;p < 0.0001; Fig. 6B and C). Given that this reduction was seen inthe same animals that displayed decreases in cognitive function(Fig. 4C), this suggested that CNS adiponectin may modulatecognition with age. To test this possible association, we correlatedthe SLMI index for each animal to ranked area CA1 adiponectinstaining and found that a signi!cant associationwas indeed present(Fig. 3C; r " 0.5, p < 0.05).

4. Discussion

In recent years, increasing attention has focused on deter-mining whether peripheral metabolic dysregulation and associ-ated weight gain and diabetes pose a risk for accelerated brainaging. The F344 rat has been used extensively to study aging;however, few studies have examined the impact of peripheraldysregulation in this animal model. Here, we treated aging F344rats with a “Western-style” high fat diet (HFD) to induce periph-eral dysregulation and to test whether brain markers of aging (e.g.,impaired learning/memory and larger Ca2!-dependent AHPs)were exacerbated. Prior results with F344s are inconsistent, withsome studies showing these animals to be resistant to obesity(Levin et al., 1983) or to the impact of a high-fructose diet withaging (Mooradian and Albert, 1999). Other studies, however,report signs of insulin resistance as early as 2 to 3months of age ona regular diet (Levy et al., 2002) or with aging (Barzilai andRossetti, 1995; Bracho-Romero and Reaven, 1977; Kalant et al.,1988; Li et al., 1996). Our results showed that a chronic HFD inboth young and aged F344s caused signi!cant lipid dysregulation;but, surprisingly, these animals appeared to maintain normalperipheral glucose regulation. Although HFD did not exacerbatespatial learning and memory, it did reduce insulin sensitivity in

Fig. 2. Glucose tolerance test (GTT) and insulin tolerance test (ITT) over a 2-hour period using a single-dose approach. (A) Mean glucose levels during both GTT (A1) and ITT (A2)before initiation of the high-fat diet (HFD). Similar results during GTT (B1) and ITT (B2) taken after 19 weeks on the HFD are shown in B. Pound sign (#) shows a signi!cant effect ofdiet on area-under-the-curve (AUC) data (see Table 2). A main effect of age was noted at 30 minutes after insulin injection (B2; *), indicating greater insulin sensitivity with age.Mean % SEM are shown. Signi!cant differences between age and diet groups was tested at the at the level of p < 0.05.

T. Pancani et al. / Neurobiology of Aging 34 (2013) 1977e19871982

the brain, as shown by electrophysiological analyses of theAHP. Importantly, these results provide the !rst demonstrationof the sensitivity of the AHP to insulin, and suggest that the

consequences of HFD can be observed in the brain even in theabsence of overt peripheral metabolic dysregulation.

4.1. Peripheral effects

The HFD had some of the intended effects in the periphery, atleast on measures of lipid homeostasis, increasing triglycerides and

Fig. 3. Correlations across peripheral lipid index (PLI), spatial learning and memoryindex (SLMI), and adiponectin levels. (A) For each animal, an index was created fromcholesterol levels, HDL:LDL ratio, and retroperitoneal fat weight (higher number isassociated with greater dyslipidemia). Lower (better) scores on the PLI are seen inyounger animals on control diet (CD). Main effects of both diet and age were seen andclearly separate the animal groups. Asterisks represent signi!cant differences betweentreatment groups at p < 0.05 level. (B) Correlation between PLI and SLMI for allanimals in the study. Ninety-!ve percent con!dence intervals are shown along withtrendline and results of the analysis. (C) Similar analysis between ranked adiponectinstaining levels and the SLMI for 24 animals from which both data sets were available.

Fig. 4. Morris water maze learning and memory task. (A) Day 1 was cue day. Days 4, 5,and 6 were training days, followed by a probe on day 7. Day 8 was reversal training,followed 72 hours later by a reversal probe on day 11. (B) Based on the cumulativesearch index, a signi!cant age-dependent decrease in the learning phase of the task(training days 1e6) was seen in control diet (CD) animals. (C) The cumulative index ofthe probe trial is shown. As shown in A and C, HFD had no observable impact onlearning and memory performance. Means % SEM are shown. *Signi!cant agingdifferences at p < 0.05.

T. Pancani et al. / Neurobiology of Aging 34 (2013) 1977e1987 1983

lowering the HDL/LDL ratio (Table 1). This was supported by otherperipheral variables including increased fat pads and liver weight(Fig.1 and Table 1). Interestingly, the diet-induced reduction in foodconsumption (Fig. 1B) was larger in older animals and mirrors thereduced weight gain seen in this group (Fig. 1A), suggesting greaterage-dependent accommodation to the higher caloric density of theHFD. Nevertheless, the effects of the HFD on peripheral lipids aswell as in 3 key metabolic hormones (insulin, glucagon, and adi-ponectin), were similar in both young and aging rats (Table 2).

Sustained blood glucose levels during a 2-hour glucose toler-ance test (GTT) are re"ective of poor glucose uptake into cells (i.e.,insulin resistance) and, together with high insulin levels betweenmeals, are indicative of a prediabetic/diabetic condition. Eventhough we show evidence for a small diet-mediated increase in thearea-under-curve of the glucose response during the GTT (Table 2),it does not appear that F344s on HFD were prediabetic, becauseinsulin levels were not signi!cantly changed and even tended todecrease with HFD. Furthermore, analyses of insulin sensitivityusing the insulin tolerance test (ITT) showed lower glucose levels at30 min in middle-aged animals (Fig. 2B2). Although this suggeststhat older animals are more sensitive to insulin, reductions inglucagon levels and glycogenolysis (altered counter regulation)could also have accounted for lower glucose levels. Serum glucagonlevels, however, were not signi!cantly different by either age or diet(Table 2). Finally, even though HFD increased HbA1c slightly, it islikely that such small changes have little physiological impact,

given the absence of elevated fasting glucose levels (Fig. 2A1 andB1). Together, these results highlight a clear effect of HFD onperipheral lipids, supportive of the adipogenic nature of this diet.However, in the F344, neither frank diabetes not prediabetesappear to develop. In fact, we present some evidence for an increasein insulin sensitivity in older animals (Fig. 2B2).

Compared to other rodent models, the level of circulatingadiponectin in the F344 appears to be elevated. Reported totaladiponectin levels typically range between 10 ng/mL andapproximately 7 mg/mL (Garekani et al., 2011; Malloy et al., 2006;Zhang et al., 2010; Zhu et al., 2004). Here we found adiponectinlevels that are higher and reaching approximately 20 mg/mL(Table 2). These levels are comparable to those induced by 12weeks of medium-intensity exercise seen in young Wistar rats(Garekani et al., 2011). Increased adiponectin levels have beenshown to protect from metabolic dysregulation associated withHFD (Asterholm and Scherer, 2010) and are inversely correlatedwith insulin resistance (Gustafson, 2010). Thus, it seems reason-able to propose that the increased levels of adiponectin in theF344 may have protected against some of the impact of HFD in theperiphery, thereby maintaining glucoregulation. Consistent withthis possibility, one of the strongest predictors of insulin sensi-tivity is the circulating adiponectin level, with insulin-sensitiveobese subjects showing greater adiponectin levels whencompared to insulin-resistant and diabetic obese patients (Klotinget al., 2010).

Fig. 5. The acute effects of insulin on the afterhyperpolarization (AHP). (A) Representative AHP recorded in a young animal on the control diet (CD) before (black) and after (red)insulin administration. Action potentials are truncated to emphasize the AHP. (B) Representative AHP from a middle-aged CD animal before (black) and after (red) insulinadministration. (C) Average medium AHP (mAHP) and reveals a signi!cant aging effect, but no diet effect. (D and E) Degree of insulin-mediated AHP reduction, and hence, insulinsensitivity in CD and HFD animals, respectively. Although insulin reduced the sAHP in both age groups, the degree of inhibition appeared to be greater in older animals on CD (D).HFD was able to reduce insulin sensitivity in older animals (E). Asterisks indicate signi!cant difference at the level of p < 0.05.

T. Pancani et al. / Neurobiology of Aging 34 (2013) 1977e19871984

4.2. Brain effects

As previously reported (Blalock et al., 2010), we observedimpaired learning andmemory performance in aging F344s (Fig. 4).However, the peripheral lipid dysregulation caused by the HFD wasonly weakly correlated with memory performance in young or agedanimals (Fig. 3B). There is considerable inconsistency in the litera-ture regarding the impact of HFD on cognition. In young Long-Evansrats, increased fat intake has been shown to impair spatial learningand memory (Greenwood and Winocur, 1990), particularly whena lard-type diet is used (Jurdak et al., 2008). Although a high-fat/high-sucrose diet causes insulin resistance and reduction inlearning and memory in Sprague-Dawley rats (Stranahan et al.,2008) and CD1 mice (Farr et al., 2008), this is not the case forC57BL/6 mice (Mielke et al., 2006; Pistell et al., 2010). In our study,HFD did not impair learning and memory in young rats or exacer-bate decrements in performance in the older rats. Perhaps earlier orlonger HFD exposuremay be required to negatively alter learning ormemory in this model. Alternatively, the age-dependent decline incognition may have been too extensive and therefore less likely tobe affected by HFD. Importantly, the F344s appears to be resistantto the potential negative consequences of HFD on cognition, and

could well serve as a valid model to identify factors that conferprotection.

At the cellular level, and to complement data fromWestern blotsand immunohistochemistry, we used electrophysiological analysesand monitored brain insulin response and sensitivity directly. Thisallowed us to determine the impact of HFD on a key biomarker ofbrain aging associated with impaired cognition, namely the Ca2!-dependent AHP, which is larger with aging. Importantly, reductionsin the AHP have been shown to improve memory (Moyer Jr et al.,1996; Song et al., 2012). We show for the !rst time that the AHPwas signi!cantly reduced by acute insulin exposure to hippocampalslices. Furthermore, the insulin-mediated inhibition appeared to begreater in older compared to younger animals on the control diet(Fig. 5D), providing some support for increased insulin sensitivitywith age in this animal model. Notably, the sensitivity of the AHP toinsulin was lost by exposure to HFD in both age groups (Fig. 5E).These results may have relevance for clinical studies examining thepotential bene!ts of intranasal insulin for cognitive decline in AD(Schioth et al., 2012). Thus, in addition to other bene!cial effects ofinsulin, including those on metabolism, vascularization, neuro-transmission, and Ab clearance (Kim and Feldman, 2012; McNayand Recknagel, 2011), the reduction in the AHP may represent

Fig. 6. Insulin receptor and insulin receptor substratee1 expression and adiponectin immunostaining. (A) Results of Western blot analyses on hippocampal insulin receptorsubstratee1 (IRS-1; 175 kDa) and insulin receptor b subunit (IR-b; 95 kDa) separated into membrane and cytosolic fractions. Data represent mean % SEM. (B) Semiquantitative meandensity measures of immunostained brain sections for IR-a, IRS-1, and adiponectin in stratum pyramidale of area CA1. Asterisks indicate signi!cant difference at the level of p < 0.05.(C) Representative photomicrographs of 2 adiponectin-immunostained sections with different magni!cations of the hippocampus area, obtained from young-adult (left panel) andmiddle-aged animals (right panel). Although no diet effect was seen, note the signi!cant age-dependent reduction in immunostained neurons in stratum pyramidale.

T. Pancani et al. / Neurobiology of Aging 34 (2013) 1977e1987 1985

a novel neuroprotective mechanism. Our results also demonstratethat neurons can respond to acute insulin relatively quickly. Itremains to be determined whether the impact of insulin in thebrain represents acute or chronic effects of the hormone (Kamalet al., 2012). Finally, it is interesting to note that hormones thatoppose insulin actions such as glucocorticoids, increase the AHP(Kerr et al., 1989), and drugs that restore insulin sensitivity (e.g.,thiazolidinediones) reduce the AHP (Blalock et al., 2010). Thus, thehippocampal AHP appears to be an insulin-sensitive target, themodulation of which could well represent a mechanism by whichinsulin improves cognition in aging and AD.

Adiponectin staining in the brain revealed a robust age-dependent decrease in the hippocampus that did not show sensi-tivity to HFD (Fig. 6B and C). Given that peripheral adiponectinlevels did not change with age, this result suggests reduced adi-ponectin transport into the brain with aging. There is clearevidence for adiponectin transport into the CNS (Qi et al., 2004;Une et al., 2011), where it is able to increase peripheral energymetabolism (Qi et al., 2004) and to stimulate neurogenesis (Zhanget al., 2011). Because a decrease in adiponectin levels is associatedwith mild cognitive impairment (Teixeira et al., 2013), this led us toexamine the relationship between hippocampal adiponectinstaining and the spatial learning andmemory index (SLMI) for eachanimal. The correlation was signi!cant (Fig. 3C), and providesfurther support for the potential role of CNS adiponectin in mod-ulating learning and memory processes. Future studies directlymanipulating adiponectin levels in the aged brain should clarifythis relationship.

4.3. Conclusion

Human studies are implicating the peripheral metabolic dys-regulation that occurs with type 2 diabetes mellitus as a riskfactor for accelerated aging-related cognitive decline; yet, little isknown regarding possible underlying mechanisms, as the corre-sponding studies in aging animal models have not been per-formed. We treated young and aging F344s with a HFD and,surprisingly, found that they did not display peripheral signs ofglucose/insulin dysregulation or overt diabetes, unlike what isseen in other animal models. However, the effects in the brainappeared to differ from those in the periphery, and we found that:(1) in rats fed the control diet, the AHP, which determines theextent of neuronal excitability, was modulated by acute insulin;and (2) the response to insulin was lost in F344s on the HFD. Aswe show, for the !rst time, that the AHP is sensitive to insulin, ourresults appear to have important implications for neuronal func-tion, irrespective of age, and show that a common Western-stylediet can have an impact on the brain even in the absence of overtdiabetes-like changes in the periphery. Because of the ratherresilient nature of the F344 to diabetes, our studies also suggestthat the F344 may represent a unique model to better understandmechanisms that may help to prevent diabetes and associatedcomplications.

Disclosure statement

None of the authors on themanuscript has an actual or potentialcon"ict of interest to declare.

Acknowledgments

This work was supported by National Institutes of Health (NIH)/National Institute on Aging (NIA) grant AG033649. The authorsthank Drs. Hadley and Piascik for critical reading and reviewing ofthe manuscript.

Appendix A. Supplementary data

Supplementary data associated with this article can be found, inthe online version, at http://dx.doi.org/10.1016/j.neurobiolaging.2013.02.019.

References

Asterholm, I.W., Scherer, P.E., 2010. Enhanced metabolic "exibility associated withelevated adiponectin levels. Am. J. Pathol. 176, 1364e1376.

Barzilai, N., Rossetti, L., 1995. Relationship between changes in body compositionand insulin responsiveness in models of the aging rat. Am. J. Physiol. 269,E591eE597.

Belanger, A., Lavoie, N., Trudeau, F., Massicotte, G., Gagnon, S., 2004. Preserved LTPand water maze learning in hyperglycaemic-hyperinsulinemic ZDF rats. Physiol.Behav. 83, 483e494.

Biessels, G.J., Deary, I.J., Ryan, C.M., 2008. Cognition and diabetes: a lifespanperspective. Lancet Neurol. 7, 184e190.

Blalock, E.M., Phelps, J.T., Pancani, T., Searcy, J.L., Anderson, K.L., Gant, J.C., Popovic, J.,Avdiushko, M.G., Cohen, D.A., Chen, K.C., Porter, N.M., Thibault, O., 2010. Effectsof long-term pioglitazone treatment on peripheral and central markers of aging.PLoS ONE 5, e10405.

Bracho-Romero, E., Reaven, G.M., 1977. Effect of age and weight on plasma glucoseand insulin responses in the rat. J. Am. Geriatr. Soc. 25, 299e302.

Buettner, R., Scholmerich, J., Bollheimer, L.C., 2007. High-fat diets: modeling themetabolic disorders of human obesity in rodents. Obesity (Silver Spring) 15,798e808.

Curtis, R., Geesaman, B.J., DiStefano, P.S., 2005. Ageing and metabolism: drugdiscovery opportunities. Nat. Rev. Drug Discov. 4, 569e580.

Disterhoft, J.F., Oh, M.M., 2007. Alterations in intrinsic neuronal excitability duringnormal aging. Aging Cell 6, 327e336.

Dou, J.T., Chen, M., Dufour, F., Alkon, D.L., Zhao, W.Q., 2005. Insulin receptorsignaling in long-term memory consolidation following spatial learning. Learn.Mem. 12, 646e655.

Duelli, R., Maurer, M.H., Staudt, R., Heiland, S., Duembgen, L., Kuschinsky, W., 2000.Increased cerebral glucose utilization and decreased glucose transporter Glut1during chronic hyperglycemia in rat brain. Brain Res. 858, 338e347.

Farr, S.A., Yamada, K.A., Butter!eld, D.A., Abdul, H.M., Xu, L., Miller, N.E., Banks, W.A.,Morley, J.E., 2008. Obesity and hypertriglyceridemia produce cognitive impair-ment. Endocrinology 149, 2628e2636.

Frick, K.M., Baxter, M.G., Markowska, A.L., Olton, D.S., Price, D.L., 1995. Age-relatedspatial reference and working memory de!cits assessed in the water maze.Neurobiol. Aging 16, 149e160.

Frisardi, V., Solfrizzi, V., Seripa, D., Capurso, C., Santamato, A., Sancarlo, D.,Vendemiale, G., Pilotto, A., Panza, F., 2010. Metabolic-cognitive syndrome:a cross-talk between metabolic syndrome and Alzheimer’s disease. Ageing Res.Rev. 9, 399e417.

Fruebis, J., Tsao, T.S., Javorschi, S., Ebbets-Reed, D., Erickson, M.R., Yen, F.T.,Bihain, B.E., Lodish, H.F., 2001. Proteolytic cleavage product of 30-kDa adipocytecomplement-related protein increases fatty acid oxidation in muscle and causesweight loss in mice. Proc. Natl. Acad. Sci. U. S. A. 98, 2005e2010.

Furuhashi, M., Tuncman, G., Gorgun, C.Z., Makowski, L., Atsumi, G., Vaillancourt, E.,Kono, K., Babaev, V.R., Fazio, S., Linton, M.F., Sulsky, R., Robl, J.A., Parker, R.A.,Hotamisligil, G.S., 2007. Treatment of diabetes and atherosclerosis by inhibitingfatty-acid-binding protein aP2. Nature 447, 959e965.

Gallagher, M., Burwell, R., Burchinal, M., 1993. Severity of spatial learning impair-ment in aging: development of a learning index for performance in the MorrisWater Maze. Behav. Neurosci. 107, 618e626.

Gant, J.C., Sama, M.M., Land!eld, P.W., Thibault, O., 2006. Early and simultaneousemergence of multiple hippocampal biomarkers of aging is mediated by Ca2!-induced Ca2! release. J. Neurosci. 26, 3482e3490.

Garekani, E.T., Mohebbi, H., Kraemer, R.R., Fathi, R., 2011. Exercise training intensity/volume affects plasma and tissue adiponectin concentrations in the male rat.Peptides 32, 1008e1012.

Gasparini, L., Netzer, W.J., Greengard, P., Xu, H., 2002. Does insulin dysfunction playa role in Alzheimer’s disease? Trends Pharmacol. Sci. 23, 288e293.

Greenwood, C.E., Winocur, G., 1990. Learning and memory impairment in rats feda high saturated fat diet. Behav. Neural Biol. 53, 74e87.

Gustafson, D.R., 2010. Adiposity hormones and dementia. J. Neurol. Sci. 299, 30e34.Jurdak, N., Lichtenstein, A.H., Kanarek, R.B., 2008. Diet-induced obesity and spatial

cognition in young male rats. Nutr. Neurosci. 11, 48e54.Kalant, N., Stewart, J., Kaplan, R., 1988. Effect of diet restriction on glucose metab-

olism and insulin responsiveness in aging rats. Mech. Ageing Dev. 46, 89e104.Kamal, A., Biessels, G.J., Duis, S.E., Gispen, W.H., 2000. Learning and hippocampal

synaptic plasticity in streptozotocin-diabetic rats: interaction of diabetes andageing. Diabetologia 43, 500e506.

Kamal, A., Ramakers, G.M., Gispen, W.H., Biessels, G.J., 2012. Effect of chronicintracerebroventricular insulin administration in rats on the peripheral glucosemetabolism and synaptic plasticity of CA1 hippocampal neurons. Brain Res.1435, 99e104.

Keenan, K.P., Soper, K.A., Hertzog, P.R., Gumprecht, L.A., Smith, P.F., Mattson, B.A.,Ballam, G.C., Clark, R.L., 1995. Diet, overfeeding, and moderate dietary

T. Pancani et al. / Neurobiology of Aging 34 (2013) 1977e19871986

restriction in control Sprague-Dawley rats: II. Effects on age-related prolifera-tive and degenerative lesions. Toxicol. Pathol. 23, 287e302.

Kerr, D.S., Campbell, L.W., Hao, S.Y., Land!eld, P.W., 1989. Corticosteroid modulationof hippocampal potentials: increased effect with aging. Science 245, 1505e1509.

Kim, B., Feldman, E.L., 2012. Insulin resistance in the nervous system. TrendsEndocrinol. Metab. 23, 133e141.

Kloting, N., Fasshauer, M., Dietrich, A., Kovacs, P., Schon, M.R., Kern, M., Stumvoll, M.,Bluher, M., 2010. Insulin-sensitive obesity. Am. J. Physiol. Endocrinol. Metab.299, E506eE515.

Launer, L.J., 2005. Diabetes and brain aging: epidemiologic evidence. Curr. Diab. Rep.5, 59e63.

Levin, B.E., Triscari, J., Sullivan, A.C., 1983. Relationship between sympathetic activityand diet-induced obesity in two rat strains. Am. J. Physiol. 245, R364eR371.

Levy, J.R., Davenport, B., Clore, J.N., Stevens, W., 2002. Lipid metabolism and resistingene expression in insulin-resistant Fischer 344 rats. Am. J. Physiol. Endocrinol.Metab. 282, E626eE633.

Li, Y.M., Steffes, M., Donnelly, T., Liu, C., Fuh, H., Basgen, J., Bucala, R., Vlassara, H.,1996.Prevention of cardiovascular and renal pathology of aging by the advanced gly-cation inhibitor aminoguanidine. Proc. Natl. Acad. Sci. U. S. A. 93, 3902e3907.

Li, X.L., Aou, S., Oomura, Y., Hori, N., Fukunaga, K., Hori, T., 2002. Impairment of long-term potentiation and spatial memory in leptin receptor-de!cient rodents.Neuroscience 113, 607e615.

Luchsinger, J.A., Cheng, D., Tang, M.X., Schupf, N., Mayeux, R., 2012. Central obesityin the elderly is related to late-onset alzheimer disease. Alzheimer Dis.Assoc. Disord. 26, 101e105.

Malloy, V.L., Krajcik, R.A., Bailey, S.J., Hristopoulos, G., Plummer, J.D., Orentreich, N.,2006. Methionine restriction decreases visceral fat mass and preserves insulinaction in aging male Fischer 344 rats independent of energy restriction. AgingCell 5, 305e314.

McNay, E.C., Recknagel, A.K., 2011. Brain insulin signaling: a key component ofcognitive processes and a potential basis for cognitive impairment in type 2diabetes. Neurobiol. Learn. Mem. 96, 432e442.

McNay, E.C., 2005. The impact of recurrent hypoglycemia on cognitive function inaging. Neurobiol. Aging 26 (Suppl 1), 76e79.

Mielke, J.G., Nicolitch, K., Avellaneda, V., Earlam, K., Ahuja, T., Mealing, G.,Messier, C., 2006. Longitudinal study of the effects of a high-fat diet on glucoseregulation, hippocampal function, and cerebral insulin sensitivity in C57BL/6mice. Behav. Brain. Res. 175, 374e382.

Mooradian, A.D., Albert, S.G., 1999. The age-related changes in lipogenic enzymes:the role of dietary factors and thyroid hormone responsiveness. Mech. AgeingDev. 108, 139e149.

Mooradian, A.D., Wong, N.C., Shah, G.N., 1997. Apolipoprotein A1 expression inyoung and aged rats is modulated by dietary carbohydrates. Metabolism 46,1132e1136.

Morley, J.E., 2004. The metabolic syndrome and aging. J. Gerontol. A Biol. Sci. Med.Sci. 59, 139e142.

Moyer Jr., J.R., Thompson, L.T., Disterhoft, J.F., 1996. Trace eyeblink conditioningincreases CA1 excitability in a transient and learning-speci!c manner. J. Neu-rosci. 16, 5536e5546.

Narimiya,M., Azhar, S., Dolkas, C.B.,Mondon, C.E., Sims, C.,Wright, D.W., Reaven,G.M.,1984. Insulin resistance in older rats. Am J Physiol. 246, E397eE404.

Pistell, P.J., Morrison, C.D., Gupta, S., Knight, A.G., Keller, J.N., Ingram, D.K.,Bruce-Keller, A.J., 2010. Cognitive impairment following high fat diet con-sumption is associated with brain in"ammation. J. Neuroimmunol. 219, 25e32.

Popovic, M., Biessels, G.J., Isaacson, R.L., Gispen, W.H., 2001. Learning and memory instreptozotocin-induced diabetic rats in a novel spatial/object discriminationtask. Behav. Brain Res. 122, 201e207.

Pratchayasakul, W., Kerdphoo, S., Petsophonsakul, P., Pongchaidecha, A.,Chattipakorn, N., Chattipakorn, S.C., 2011. Effects of high-fat diet on insulinreceptor function in rat hippocampus and the level of neuronal corticosterone.Life Sci. 88, 619e627.

Qi, Y., Takahashi, N., Hileman, S.M., Patel, H.R., Berg, A.H., Pajvani, U.B., Scherer, P.E.,Ahima, R.S., 2004. Adiponectin acts in the brain to decrease body weight. Nat.Med. 10, 524e529.

Reaven, E., Wright, D., Mondon, C.E., Solomon, R., Ho, H., Reaven, G.M., 1983. Effectof age and diet on insulin secretion and insulin action in the rat. Diabetes 32,175e180.

Ross, A.P., Bruggeman, E.C., Kasumu, A.W., Mielke, J.G., Parent, M.B., 2012. Non-alcoholic fatty liver disease impairs hippocampal-dependent memory in malerats. Physiol. Behav. 106, 133e141.

Rowe, W.B., Blalock, E.M., Chen, K.C., Kadish, I., Wang, D., Barrett, J.E., Thibault, O.,Porter, N.M., Rose, G.M., Land!eld, P.W., 2007. Hippocampal expression analysesreveal selective association of immediate-early, neuroenergetic, and myelino-genic pathways with cognitive impairment in aged rats. J. Neurosci. 27,3098e3110.

Schioth, H.B., Craft, S., Brooks, S.J., Frey 2nd, W.H., Benedict, C., 2012. Brain insulinsignaling and Alzheimer’s disease: current evidence and future directions. MolNeurobiol. 46, 4e10.

Song, C., Detert, J.A., Sehgal, M., Moyer Jr., J.R., 2012. Trace fear conditioningenhances synaptic and intrinsic plasticity in rat hippocampus. J. Neurophysiol.107, 3397e3408.

Steen, E., Terry, B.M., Rivera, E.J., Cannon, J.L., Neely, T.R., Tavares, R., Xu, X.J.,Wands, J.R., de la Monte, S.M., 2005. Impaired insulin and insulin-like growthfactor expression and signaling mechanisms in Alzheimer’s diseasedis this type3 diabetes? J. Alzheimers Dis. 7, 63e80.

Stranahan, A.M., Mattson, M.P., 2012. Metabolic reserve as a determinant ofcognitive aging. J. Alzheimers Dis 30 (Suppl 2), 5e13.

Stranahan, A.M., Norman, E.D., Lee, K., Cutler, R.G., Telljohann, R.S., Egan, J.M.,Mattson, M.P., 2008. Diet-induced insulin resistance impairs hippocampalsynaptic plasticity and cognition in middle-aged rats. Hippocampus 18,1085e1088.

Talbot, K., Wang, H.Y., Kazi, H., Han, L.Y., Bakshi, K.P., Stucky, A., Fuino, R.L.,Kawaguchi, K.R., Samoyedny, A.J., Wilson, R.S., Arvanitakis, Z., Schneider, J.A.,Wolf, B.A., Bennett, D.A., Trojanowski, J.Q., Arnold, S.E., 2012. Demonstratedbrain insulin resistance in Alzheimer’s disease patients is associated with IGF-1resistance, IRS-1 dysregulation, and cognitive decline. J. Clin. Invest. 122,1316e1338.

Teixeira, A.L., Diniz, B.S., Campos, A.C., Miranda, A.S., Rocha, N.P., Talib, L.L.,Gattaz, W.F., Forlenza, O.V., 2013. Decreased levels of circulating adiponectin inmild cognitive impairment and Alzheimer’s disease. Neuromol. Med 15,115e121.

Thibault, O., Land!eld, P.W., 1996. Increase in single L-type calcium channels inhippocampal neurons during aging. Science 272, 1017e1020.

Une, K., Takei, Y.A., Tomita, N., Asamura, T., Ohrui, T., Furukawa, K., Arai, H., 2011.Adiponectin in plasma and cerebrospinal "uid in MCI and Alzheimer’s disease.Eur. J. Neurol. 18, 1006e1009.

Whitmer, R.A., Gustafson, D.R., Barrett-Connor, E., Haan, M.N., Gunderson, E.P.,Yaffe, K., 2008. Central obesity and increased risk of dementia more than threedecades later. Neurology 71, 1057e1064.

Xu, H., Barnes, G.T., Yang, Q., Tan, G., Yang, D., Chou, C.J., Sole, J., Nichols, A., Ross, J.S.,Tartaglia, L.A., Chen, H., 2003. Chronic in"ammation in fat plays a crucial role inthe development of obesity-related insulin resistance. J Clin. Investig. 112,1821e1830.

Zhang, W., Liu, C.Q., Wang, P.W., Sun, S.Y., Su, W.J., Zhang, H.J., Li, X.J., Yang, S.Y., 2010.Puerarin improves insulin resistance and modulates adipokine expression inrats fed a high-fat diet. Eur. J. Pharmacol. 649, 398e402.

Zhang, D., Guo, M., Zhang, W., Lu, X.Y., 2011. Adiponectin stimulates proliferationof adult hippocampal neural stem/progenitor cells through activation ofp38 mitogen-activated protein kinase (p38MAPK)/glycogen synthase kinase3beta (GSK-3beta)/beta-catenin signaling cascade. J. Biol. Chem. 286,44913e44920.

Zhao, W.Q., Chen, H., Quon, M.J., Alkon, D.L., 2004. Insulin and the insulin receptorin experimental models of learning and memory. Eur. J. Pharmacol. 490,71e81.

Zhu, M., Miura, J., Lu, L.X., Bernier, M., DeCabo, R., Lane, M.A., Roth, G.S.,Ingram, D.K., 2004. Circulating adiponectin levels increase in rats on calo-ric restriction: the potential for insulin sensitization. Exp. Gerontol. 39,1049e1059.

T. Pancani et al. / Neurobiology of Aging 34 (2013) 1977e1987 1987