Open Access

Obesity and diabetes cause cognitive dysfunction in the absence of accelerated β-amyloid deposition in a novel murine model of mixed or vascular dementia

  • Dana M Niedowicz1,
  • Valerie L Reeves1, 2,
  • Thomas L Platt2,
  • Katharina Kohler1,
  • Tina L Beckett1,
  • David K Powell4,
  • Tiffany L Lee1, 3,
  • Travis R Sexton2,
  • Eun Suk Song2,
  • Lawrence D Brewer3,
  • Caitlin S Latimer3,
  • Susan D Kraner1,
  • Kara L Larson2,
  • Sabire Ozcan2,
  • Christopher M Norris1, 3,
  • Louis B Hersh2,
  • Nada M Porter3,
  • Donna M Wilcock1, 3 and
  • Michael Paul Murphy1, 2Email author
Acta Neuropathologica Communications20142:64

https://doi.org/10.1186/2051-5960-2-64

Received: 3 June 2014

Accepted: 4 June 2014

Published: 10 June 2014

Abstract

Mid-life obesity and type 2 diabetes mellitus (T2DM) confer a modest, increased risk for Alzheimer’s disease (AD), though the underlying mechanisms are unknown. We have created a novel mouse model that recapitulates features of T2DM and AD by crossing morbidly obese and diabetic db/db mice with APP ΔNL/ΔNL x PS1 P264L/P264L knock-in mice. These mice (db/AD) retain many features of the parental lines (e.g. extreme obesity, diabetes, and parenchymal deposition of β-amyloid (Aβ)). The combination of the two diseases led to additional pathologies-perhaps most striking of which was the presence of severe cerebrovascular pathology, including aneurysms and small strokes. Cortical Aβ deposition was not significantly increased in the diabetic mice, though overall expression of presenilin was elevated. Surprisingly, Aβ was not deposited in the vasculature or removed to the plasma, and there was no stimulation of activity or expression of major Aβ-clearing enzymes (neprilysin, insulin degrading enzyme, or endothelin-converting enzyme). The db/AD mice displayed marked cognitive impairment in the Morris Water Maze, compared to either db/db or APP ΔNL x PS1 P264L mice. We conclude that the diabetes and/or obesity in these mice leads to a destabilization of the vasculature, leading to strokes and that this, in turn, leads to a profound cognitive impairment and that this is unlikely to be directly dependent on Aβ deposition. This model of mixed or vascular dementia provides an exciting new avenue of research into the mechanisms underlying the obesity-related risk for age-related dementia, and will provide a useful tool for the future development of therapeutics.

Keywords

DementiaDiabetesObesityStrokeAlzheimer’s disease

Introduction

Alzheimer’s disease (AD) is a neurodegenerative disease affecting the elderly. There are two major neuropathologies associated with AD: extracellular plaques containing β-amyloid (Aβ) and intracellular neurofibrillary tangles composed of the microtubule-associated protein tau. The combined insults of Aβ and tau accumulation are thought to promote the progressive synaptic failure and neuronal loss, leading to memory loss and cognitive impairment [14]. While familial forms of AD exist, sporadic AD is far more common. Though the two forms of AD ultimately reflect similar pathologies, the underlying causes vary. Familial AD is linked to specific mutations in amyloid precursor protein (APP) or presenilin (PS1 or PS2), leading to accumulation of toxic β-amyloid species in the brain by mid-life. Sporadic AD manifests later in life, and the triggers are less clear and likely complex. Though there are genetic components associated with sporadic AD, environmental factors, such as lifestyle (e.g. diet and exercise), are also likely to impact disease onset and progression.

Obesity is a major worldwide public health problem, and is associated with the metabolic disorder type 2 diabetes mellitus (T2DM). Diabetes is associated with cognitive decline in both rodents and humans [57]. Due to improved treatments, T2DM patients are living longer, putting them at increased risk for age-related complications. Although simply living to an older age increases the risk of Alzheimer’s disease, there is a well-known (albeit poorly understood) link between obesity, T2DM and dementia [8]. The form of dementia afflicting these individuals combines elements of vascular pathology, small strokes and AD-related neuropathology. In fact, the amount of AD pathology is essentially unchanged in cases with a history of T2DM, while cerebrovascular pathology increases [9, 10]. Vascular dementia, or even cerebrovascular dysfunction as a general AD comorbidity, is a poorly understood condition with no viable treatment options. This is due to cerebrovascular dysfunction being understudied as a major cause of dementia and the lack of useful model systems in which to develop therapies or to study the disease process.

In this paper, we describe the creation of a novel mouse model combining the key features of obesity, diabetes, and AD. We crossed the obese and diabetic db/db mouse [1113] with the APPΔNL/ΔNL × PS1P264L/P264L knock-in model of AD [14, 15]. The resulting mice (which we have called db/AD) are morbidly obese, glucose intolerant, insulin resistant, and display parenchymal amyloid plaques, similar to the parental lines. In addition, although these mice had profound cognitive impairment and marked cerebrovascular abnormalities, this does not appear to be driven by Aβ deposition. The db/AD mice will be a useful tool with which to study the intersection of T2DM and dementia.

Materials and methods

Mouse breeding

In order to create a diabetic AD mouse model, we crossed the obese, diabetic Lepr db/db (db/db) mice [1113] with the APP ΔNL/ΔNL /PS1 P264L/P264L (APP/PS1) knock-in model of AD [14, 15]. Because the homozygous db/db mice are infertile, heterozygous (Lepr db/+ ) mice on a C57Bl/6 J background (Jackson Labs; Bar Harbor, ME) were bred with APP/PS1 mice on a CD-1/129 background (obtained from the breeding colony at the University of Kentucky). The resulting F1 mice heterozygous for all three alleles were then intercrossed to generate wild-type, heterozygous, and homozygous db mice that were either wild-type or homozygous for the AD knock-in genes. For most of the data presented here, we focused on four main genotypes: wild-type (WT; Lepr +/+ × APP +/+ /PS1 +/+ ), db (Lepr db/db × APP +/+ /PS1 +/+ ), AD (Lepr +/+ × APP ΔNL/ΔNL /PS1 P264L/P264L ), and db/AD (Lepr db/db x APP ΔNL/ΔNL /PS1 P264L/P264L ). Some analyses included Lepr db/+ APP +/+ /PS1 +/+ and Lepr db/+ × APP ΔNL/ΔNL /PS1 P264L/P264L mice (noted where appropriate). Mice were housed under a 12 hour light–dark cycle and fed standard rodent chow ad libitum. Mice were euthanized by CO2 asphyxiation, followed by decapitation. All animal work was conducted with prior University of Kentucky (UK) IACUC approval, and was performed in accordance with USDA and PHS guidelines.

Genotyping

Tail snips were collected prior to weaning. For some of the db and APP genotyping, tail snips were sent to Transnetyx (Cordova, TN) for purification and analysis. For those analyzed in our lab, as well as PS1 genotyping, genomic DNA was isolated and purified from tail snips using the Promega Wizard Genomic DNA kit (Promega; Madison, WI). db genotyping was performed using a single nucleotide polymorphism Taqman® genotyping kit (Applied Biosystems Life Technologies; Grand Island, NY). APP and PS1 genotyping were performed by PCR as described previously [16] using GoTaq® Flexi DNA Polymerase (Promega).

Mouse groups

The mice used for this study were broadly divided by age and will be referred to as young (1–4 months old: 3.0 ± 0.8 months), middle-aged (5–9 months old: 7.2 ± 1.6 months), and older (10–14 months old: 12.2 ± 1.0 months) based on the predicted lifespan of the db/AD mice (~15-16 months).

Glucose and insulin tolerance tests

Mice were fasted 3–6 hours prior to the start of the glucose tolerance test (GTT) or insulin tolerance test (ITT). All glucose measurements were obtained via tail bleed using a Bayer Breeze 2 glucometer and test strips (Bayer; Tarrytown, NY). For the GTT, a baseline measurement was obtained after which the GTT was initiated by intraperitoneal injection of dextrose (2 mg/g: Hospira; Lake Forrest, IL). Subsequent measurements were recorded at 15, 30, 60, and 120 minutes post-injection. For the ITT, a baseline glucose measurement was taken, after which insulin (0.75 U//kg: Eli Lilly; Indianapolis, IN) was injected intraperitoneally. Subsequent measurements were recorded at 15, 30, 60, and 120 minutes post-injection. Any glucometer reading of “HI” was set to 700 mg/dL for data analysis.

Blood pressure measurements

Blood pressure (BP) was measured using a Kent CODA 8 BP machine (Kent Scientific; Torrington, CT). Animals were allowed to acclimate to the tail blood pressure cuff for five minutes on a warming platform before recording BP measures. The BP measures consisted of 20 cycles of diastolic/systolic measures, with a 20 second rest period between cycles. After finishing the data collection, the mice were immediately released back into their home cages. The rodent restraints, cuffs, and warming platform were cleaned between animals; female animals were always run after male animals to avoid any possible irritation of the males. BP measures were performed at the same time each day to account for the possible influence of circadian rhythms.

Plasma measurements

Blood was collected upon decapitation in the presence of EDTA, centrifuged (1500 × g, 10 min.), and the plasma collected. Plasma leptin was measured by a commercially-available, species-specific ELISA (EMD Millipore; Billerica, MA), according to package instructions.

Immunoassays

Frozen brain tissue was serially extracted in either PBS or HEPES (20 mM HEPES, 2 mM EDTA, 2 mM EGTA, 0.32 M sucrose) followed by 2% SDS, and 70% formic acid as previously described [17, 18]. Buffers were supplemented with protease inhibitor cocktail (Amresco; Solon, OH) and phosphatase inhibitor cocktail (EMD Millipore). The tissue was homogenized using an AHS200 PowerMax (VWR; Radnor, PA) homogenizer, the insoluble material was removed by centrifugation (PBS/HEPES/SDS: 20,800 × g, 30 minutes; formic acid; 20,800 × g, 60 min) and the supernatants frozen until use. Human-specific Aβ was measured by two-site sandwich ELISA as previously described [17]. Oligomeric Aβ (mouse and human) was measured by single-site sandwich ELISA as previously described [19, 20]. Briefly, 384-well plates (Immulon 4HBX: Thermo Scientific; Waltham, MA) were coated with either 0.5 μg Ab42.5 (Aβtotal and Aβ1–40), Ab2.1.3 (Aβ1–42), or 4G8 (oligomers: Covance, Princeton, NJ)/well and blocked with Synblock (Serotec; Raleigh, NC) for two hours. PBS and SDS extracts were diluted in AC buffer (0.2 M sodium phosphate (pH7), 0.4 M NaCl, 2 mM EDTA, 0.4% Block Ace (Serotec), 0.4% BSA, 0.05% CHAPS, 0.05% NaN3) for analysis. Formic acid extracts were first neutralized with TP buffer (1 M Tris base, 0.5 M sodium phosphate: 20-fold dilution), then further diluted with AC buffer for analysis. Similarly, plasma was diluted in AC buffer for analysis. A standard curve was prepared from recombinant human Aβ1–42, Aβ1–40, or oligomeric Aβ diluted in AC buffer. Standards and samples were measured at least in duplicate. After incubation with the samples and standards, Aβ was detected with either biotinylated-4G8 (Aβtotal, Aβ1–42, and oligomers: Covance) or biotinylated-13.1.1 (Aβ1–40), followed by incubation with 0.1 μg/mL NeutrAvidin-HRP (Pierce Technologies; Rockford, IL). The plate was developed with 3′,3′,5′,5′-tetramethylbenzidine (Kirkeguard and Perry Laboratories; Gaithersburg, MD) and the reaction stopped with 6% o-phosphoric acid. The absorbance at 450 nm was measured with a BioTek (Winooski, VT) multiwell plate reader.

Protein levels of PS1, BACE1, BACE2, phosphorylated and total tau, endothelin-converting enzyme 1 (ECE1), and PSD95 were determined by Western or spot blot, using protein-specific antibodies (PS1 (EMD Millipore), BACE1 (Epitomics; Burlingame, CA), BACE2 (Abcam; Cambridge, MA), pTau (AT8: Sigma-Aldrich; St. Louis, MO), total tau (HT7: Pierce: [21, 22]), ECE1 (Acris Antibodies; San Diego, CA), PSD95 (D27E11; Cell Signaling; Danvers, MA)). Immunoreactive bands for PS1, BACE1, BACE2, tau, and ECE1 were visualized with Super Signal West Dura chemiluminescence HRP substrate (Pierce) after incubation with HRP-conjugated secondary antibodies and exposed to film. Densitometric analyses were performed using Image J software. Expression was standardized to β-actin (Sigma-Aldrich) or GAPDH (Abcam) expression in the same lane or spot, respectively. PSD95 and its GAPDH loading control (Abcam) were visualized with fluorescently-labeled secondary antibodies (LI-COR; Lincoln, NE) using an Odyssey Infrared Imager (LI-COR) for quantitation and analysis.

qPCR

Tissue was homogenized in Trizol™ (Invitrogen; Grand Island, NY) in order to isolate RNA, followed by phenol/chloroform extraction. When needed, RNA was further purified by RNeasy columns (Qiagen; Valencia, CA). Expression of ECE1 and ECE2 were determined by two-step qRT-PCR, using iScript (BioRad; Hercules, CA) reverse transcription, followed by qPCR with PerfeCTa FastMix™ (Quanta BioSciences; Gaithersburg, MD). The geometric mean of the CT values for RPL30, cyclophilin, and RNA polymerase IIJ was used as an internal control to calculate and compare relative expression (2-ΔΔC T). Gene specific primer sets were obtained from IDT (Coralville, IA).

Neprilysin and insulin degrading enzyme activity

Neprilysin (NEP) activity was measured as described [23]. Briefly, hemibrains were homogenized in ice-cold Tris buffer (50 mM Tris–HCl and 150 mM NaCl, pH 7.2; 100 mg/mL) supplemented with 1 mM PMSF (Sigma-Aldrich), and 10 μM E-64 (RPI; Mt. Prospect, IL). The homogenate was centrifuged (1000 × g, 20 min., 4°C), followed by a high-speed centrifugation of the supernatant (100,000 × g, 1 hour, 4°C). The supernatant was removed, and the pellet resuspended in Tris buffer for the enzyme assay. NEP activity was measured using glutaryl-Ala-Ala-Phe-4-methoxy-2-naphthylamide (Sigma-Aldrich) as a substrate. Reactions were initiated with the addition of the membrane fraction, then fluorescent product formation was monitored (340 nm excitation, 425 nm emission, 37°C). Phosphoramidon (50 μM) and thiorphan (10 μM) were used to inhibit NEP activity and determine background fluorescence for each sample.

Insulin degrading enzyme (IDE) activity was measured using a commercially-available kit (EMD Millipore) according to manufacturer’s instructions. Briefly, hemibrains were homogenized in Tris buffer (100 mg/mL) supplemented with PMSF and E-64, centrifuged (20,800 × g, 30 min., 4°C), and the supernatant used for the activity assay. Samples were compared against rat IDE. Fluorescence was measured at an excitation wavelength of 320 nm and an emission wavelength of 405 nm.

MRI

T2*-MRI was performed using a horizontal bore Bruker Clinscan (7.0 T, 30 cm, 300 MHz: Billerica, MA) imager equipped with a triple-axis gradient (630 mT/m and 6300 T/m/s) and a helium-cooled 14 K quadrature head cryo-coil, cooled to 20°K. T2*-weighted images were acquired with a 2D GRE sequence with at 34 μm × 34 μm × 400 μm resolution, 15 mm FOV, 25 degree flip angle, 10 averages, TR 165 ms, and TE 15.3 ms. Mice were imaged under constant isofluorane anesthesia and their body temperature and respiration were continuously monitored. At least ten equally-spaced images were taken of each mouse brain. Asymmetrically-occurring dark spots on the images were considered indicative of vascular events (confirmed histologically, see below), whereas symmetrically-occurring dark areas were considered to be blood vessels and were excluded.

Vascular corrosion casting

Vascular corrosion casting was performed as described [24]. Briefly, mice were anesthetized using pentobarbital (100 mg/kg), followed by transcardial perfusion with heparinized saline (0.9%). Following a brief perfusion with para-formaldehyde (4%), the brains were perfused with the polyurethane resin Pu4ii (4 mL/min: VasQtec; Switzerland). After allowing the resin to cure for at least two days, the brains were incubated in KOH (7.5%, 50°C, 48 h), followed by formic acid (5%, 50°C, 24 h). The tissue was subsequently frozen, then lyophilized to macerate the soft tissue. Finally, the casted brains were sputter-coated in palladium and viewed by scanning electron microscopy (Hitachi S-4300: Schaumburg, IL), using the middle cerebral artery as a landmark. Endothelial cell density was determined by endothelial cell nuclear imprints measured directly using Image J software. Aneurysm pathology was assessed on a 4 point scale based on clear data break points (0 = none; 1 = 1 possible; 2 = 1–3 definite; 3 = 4+ definite. Vascular density was determined by rank order of representative images using three blinded, independent reviewers. Images were scored from 1 (most dense) – 26 (least dense), and the ranks from the three reviewers averaged.

Histology

Tissue was harvested and fixed in PBS-buffered 10% formalin for at least 24 hrs. For Aβ immunohistochemistry, hemibrains were embedded in a matrix and sectioned (30 μm) by NeuroScience Associates (Knoxville, TN). For Prussian blue staining, hemibrains were embedded in paraffin and sectioned to 8 μm using a microtome. For free-floating sections, the hemibrains were incubated in sucrose (10%, 20%, 30% sequentially for 24 hours each) for cryoprotection, then sectioned on a sliding, freezing microtome to 25 μm.

Perl’s Prussian blue staining of hemosiderin was performed as described [25]. Immunohistochemistry detecting Aβ was performed using antibody 4G8 (Covance) as described [19]. Some Aβ immunohistochemistry was performed by NeuroScience Associates. Densitometry was performed on these sections using Image J software. Vascular Aβ was visualized by three different methods: 1) Congo red (0.2% in NaCl-saturated 80% ethanol), 2) Thioflavin S (1%: Sigma-Aldrich), and 3) resorufin (Sigma-Aldrich: [26]). Cerebral blood vessels were imaged in free-floating sections using a mouse anti-α-actin antibody (A5228: Sigma-Aldrich), followed by quantitation with Image J software. Triple labeling of free-floating sections was performed with the fluorescent Aβ-specific Amylo-Glo stain (Biosensis; Thebarton, Australia), rabbit anti-collagen IV (ab6586: Abcam), and rabbit anti-glial fibrillary acidic protein (G9269: Sigma-Aldrich).

Behavioral testing

Testing was performed by the UK Rodent Behavioral Core (http://www.rodentbehaviorcore.uky.edu/default.aspx/0_UK_Rodent_Behavior_Core). Mice were tested using the Morris Water Maze paradigm. The maze consisted of a circular pool (134.5 cm diameter) filled with 25°C water. A circular platform (11 cm diameter) was placed in the northeast quadrant 1 cm below the surface of the water so that it was not visible. Nontoxic tempura paint was used to create opaque water, thus obscuring the platform. The pool was placed behind dark curtains holding external maze cues. The cues were rotated each day. There were five consecutive training/acquisition days. On each-training day the animals swam four trials (rotating initial placement each time), lasting one minute each, with a five minute interval between trials. After a 30 minute rest upon the conclusion of training on the fifth day, we performed a probe trial where the platform was removed from the pool. The animal’s location in the pool was recorded for one minute and used to calculate the time spent in the target quadrant and the number of times crossing the platform area. After the completion of training, mice were tested for visual acuity during which the external cues were provided along with a visibly-raised platform. The mice were tested for visual acuity in four trials during one day. Water Maze data (e.g. swim speed, distance, latency to platform. etc.) were collected and analyzed using EthoVision XT software (Noldus Information Technology; Leesburg, VA).

Passive immunization

A small number of db/AD mice (N = 7; 9–12 month old; 3 M/4 F) were injected intraperitoneally with Ab42.5 (300 μg in sterile saline) every two weeks for two months. Mice were imaged by T2* MRI prior to starting the treatment (baseline) and prior to death (endpoint). The majority of the brains (N = 6; 2 M/4 F) were extracted in RIPA buffer (50 mM Tris–HCl, 150 mM NaCl, 1% Triton X-100, 0.5% deoxycholate, 0.1% SDS; pH = 8.0) with protease inhibitor cocktail (Amresco) for Aβtotal ELISA measurement as described above. Brains from untreated, age-matched db/AD mice (N = 6; 2 M / 4 F) were also extracted in RIPA and used as controls. Endpoint MRI scans were compared against untreated, age-matched (11–14 months old) db/AD mice.

Statistics

Weight data were analyzed by student’s t-test at each age using Microsoft Excel, and the probability adjusted using the Holm-Bonferroni method [27]. All other data were analyzed with SPSS (Hewlett Packard; Palo Alto, CA) using the general linear model (GLM) module for ANOVA with the independent variables gender, db genotype, and AD genotype (for an explanation of this model, see http://pic.dhe.ibm.com/infocenter/spssstat/v21r0m0/index.jsp?topic=%2Fcom.ibm.spss.statistics.help%2Fidh_glm_multivariate.htm). Post-hoc multiple comparisons were conducted using Tukey’s test, Dunnett’s test, or similar. Chi-square analyses were performed on the visual acuity measurements for the Morris Water Maze. We performed correlation analyses using either Pearson’s r or Spearman’s ρ (parametric and nonparametric values, respectively), and adjusted probability using the Holm-Bonferroni method. For nonparametric comparisons, we used a Kruskal-Wallis ANOVA, or Mann–Whitney U test, where appropriate. For most presented statistics, we note an overall effect of genotype (db or AD) across the data set. In some cases, we also present a direct comparison between two different genotypes.

Results

Creation and characterization of the db/AD mouse model

In order to explore the mechanisms underlying the increased risk for AD in T2DM patients, we created a unique mouse model that recapitulates features of both diseases. The obese, leptin-resistant, and diabetic db/db (Lepr db/db ) mouse was crossed with the APP ΔNL/ΔNL x PS1 P264L/P264L knock-in model of AD. Since APP and PS1 are under the control of their endogenous promoters, expression follows normal murine levels and patterns. The resulting mice homozygous for the Lepr db mutation were morbidly obese (Figure 1a). In addition, young Lepr db/db mice displayed elevated fasting glucose and impaired glucose tolerance (Figure 1b: F[2, 30] = 38.2, p < 0.0001). On average, Lepr db/db mice were 2-fold heavier than Lepr +/+ and Lepr db/+ mice (Figure 1c-d: p ≤ 0.001 for all ages). The AD genotype had no effect on weight (p ≥ 0.3 for all ages). Consistent with their increased weight, older Lepr db/db mice had significantly more plasma leptin (168 ± 15 ng/mL; n = 9 F/17 M) than Lepr +/+ (54 ± 15 ng/mL; n = 6 F/20 M) or Lepr db/+ (49 ± 15; n = 11 F/15 M; p ≤ 0.0001: not shown) animals. The AD genotype had no effect on circulating leptin (APP +/+ × PS1 +/+ 81 ± 12 ng/mL, n = 12 F/26 M vs. APP ΔNL/ΔNL × PS1 P264L/P264L 99 ± 12 ng/mL, n = 15 F/23 M; p = 0.3). The Lepr db/db × APP ΔNL/ΔNL /PS1 p264L/P264L mice exhibited decreased survival compared with all other genotypes, particularly in the males, which had a ~50% attrition rate by 10 months (Figure 1e-f). Since there is little apparent difference in the tested metabolic parameters between Lepr +/+ and Lepr db/+ mice, we chose to focus on the Lepr +/+ and Lepr db/db genotypes for subsequent experiments. For simplicity, we will refer to the four main genotypes as WT (Lepr +/+ × APP +/+ /PS1 +/+ ), db (Lepr db/db × APP +/+ /PS1 +/+ ), AD (Lepr +/+ × APP ΔNL/ΔNL /PS1 P264L/P264L ), and db/AD (.Lepr db/db x APP ΔNL/ΔNL /PS1 P264L/P264L ). Middle-aged db and db/AD mice also had impaired insulin sensitivity (Figure 1g: F[15,177] = 4.64, p ≤ 0.0001). Collectively, these data indicated that the db homozygous mice had metabolic dysfunction. Blood pressure was not different in middle-aged diabetic mice compared with nondiabetics, although there was a modest tendency (Figure 1h: p ≤ 0.1) for it to be slightly lower overall.
Figure 1

db/AD characteristics. The diabetic Lepr db/db mouse was crossed to the APP/PS1 knock-in model of AD to create the db/AD line. (a) Mice homozygous for the db gene were obese (female, 9 months old; left: Lepr db/db mouse; right: Lepr db/+ mouse). (b) Lepr db/db mice showed an impaired response to glucose (F[2,30] = 38.2, p ≤ 0.0001 for the db genotype overall, n = 13 Lepr +/+ , n = 5 Lepr db/+ , n = 18 Lepr db/db ; ** = p ≤ 0.01, * = p ≤ 0.05, Tukey’s LSD). The AD genotype did not affect the GTT (not shown). (c - d) Lepr db/db mice showed substantial weight gain from an early age. Weight was relatively stable after ~7 months, and there was no effect of the AD genotype (* = p ≤ 0.01, t-test; Holm-Bonferroni [27] correction; n = ~14 mice / genotype). Female (e) and male (f) Lepr db/db x APP ΔNL/ΔNL /PS1 p264L/P264L mice had reduced survivability compared with other genotypes, though the males had a particularly high attrition rate. (N = 367 F/359 M: Lepr db/db x APP ΔNL/ΔNL /PS1 p264L/P264L , n = 56 F/53 M; Lepr db/db x APP +/+ / PS1 +/+ , n = 73 F/60 M; Lepr +/+ x APP ΔNL/ΔNL /PS1 p264L/P264L , n = 54 F/44 M; Lepr +/+ x APP +/+ / PS1 +/+ , n = 56 F/61 M; Lepr db/+ × APP ΔNL/ΔNL /PS1 P264L/P264L , n = 69 F/61 M; Lepr db/+ × APP +/+ / PS1 +/+ , n = 59 F/80 M) In subsequent experiments, we focused on the four main genotypes. For simplicity we have named them WT, db, AD, and db/AD. (g) Lepr db/db (db and db/AD) mice were insulin resistant (F[15,177] = 4.64, p ≤ 0.0001 for the db genotype overall; WT, n = 14; db, n = 13; AD, n = 20; db/AD, n = 18; ~6 months of age; * = p ≤ 0.05, ** = p ≤ 0.01; Dunnett’s test, relative to Lepr +/+ (WT and AD)). (h) Blood pressure was not different, although there was a modest tendency (F[2,15] = 3.24, p ≤ 0.1) for it to be slightly lower overall in Lepr db/db mice regardless of the AD genotype, at least at this age (n = 5–7 mice / genotype, ~7-8 months of age).

β-amyloid

We have shown previously that leptin downregulates expression of the γ-secretase components, particularly presenilin [28]. We reasoned that leptin resistance, as seen in obesity and diabetes, would likely increase PS1 expression in the brain and, as a consequence, β-amyloid deposition in the db/AD mice. In order to visualize the AD pathology, brains from middle-aged mice were sectioned and stained for Aβ (Ab4G8 – specific for Aβ17–24). As expected, only mice containing the AD-related mutations in APP and PS1 were positive for Aβ-containing plaques (Figure 2a-d). Quantitation of plaque burden indicated that the number of plaques present in db/ AD mice decreased relative to AD mice (0.104 ± 0.012 vs. 0.168 ± 0.013 A.U.; p < 0.001; n = 9 AD, 10 db/AD; not shown). Contrary to the effect on plaques, the db mutation increased expression of PS1 protein (Figure 2e; p < 0.002) Expression of BACE1 (Lepr +/+ 0.89 ± 0.06; Lepr db/+ 0.87 ± 0.03; Lepr db/db 0.80 ± 0.05 A.U.) and BACE2 (Lepr +/+ 0.83 ± 0.05; Lepr db/+ 0.85 ± 0.03; Lepr db/db 0.84 ± 0.04 A.U.), the β-secretase proteins, was unaffected by the db genotype (n = 8 F/7 M Lepr +/+ ; 13 F/11 M Lepr db/db ; 22 F/28 M Lepr db/+ : p ≥ 0.41 for both proteins: not shown). The db mutation significantly increased tau phosphorylation (Figure 2f: p < 0.003), although no neurofibrillary tangle pathology was observed (not shown). On the other hand, Aβ oligomers were significantly elevated in diabetic mice (Figure 2g: p < 0.0005). Young (3 months) AD and db/AD mice had approximately equal amounts of total Aβ (Figure 2h: p < 0.3). At 6 and 12 months, there was still no overall effect of the db genotype on Aβ levels (Figure 2i-j: p < 0.23 for Aβtotal: p < 0.07 for Aβ1–40) though there was a modest, but significant, reduction in Aβ1–42 (Figure 2k: p < 0.01).
Figure 2

Amyloid pathology in db/AD mice. (a - d) Aβ deposition (4G8 IHC) in mouse neocortex (~8 months old: magnification: 4×). db/AD (a) and AD (c) mice displayed amyloid-containing plaques, while db (b) and WT (d) mice did not. The db genotype significantly upregulated both PS1 expression (p < 0.002 for db overall: e) and tau phosphorylation (p < 0.003 standardized to total tau: f) in the brain (middle-aged: n = 10 F/12 M db wild-type; 13 F/11 M db homozygotes; 22 F/28 M heterozygotes). The results are similar for phosphor-tau when it is not standardized to total tau (not shown) (g) Aβ oligomers were significantly increased in diabetic (db and db/AD) mice (p < 0.001 for db overall, WT, n = 8; db, n = 8; AD, n = 8; db/AD, n = 7) compared to non-diabetic mice. Additionally, oligomers were significantly higher in db/AD compared to AD mice ( # = p < 0.0005). (h) Young (3 month old) db/AD mice did not have significantly more Aβ than AD mice (n = 5 F/1 M WT; 5 F/1 M db; 3 F/3 M AD; 3 F/3 M db/AD: p < 0.29). (i) Total Aβ (Ab42.5/4G8), (j)1–40 (Ab42.5 / 13.1.1), and (k)1–42 (Ab2.1.3 / 4G8) all increased with age (p ≤ 0.0001); the db genotype had no overall effect, although there was a modest reduction in Aβ1–42 (p ≤ 0.01). * = p ≤ 0.05, relative to db WT. N = 77 [genotype, age (6–7 mo./12 mo.): WT, n = 13/8; db, n = 13/8; AD, n = 9 / 8; db/AD, n = 11/7)].

Cerebral amyloid angiopathy (CAA), in which Aβ is deposited in the vasculature, is a co-morbidity in many AD patients [29]. We therefore hypothesized that excess Aβ was created, but was deposited in blood vessels rather than the brain parenchyma in the db/AD mice. To investigate this possibility, we pursued a variety of histopathological staining techniques. There was no appreciable Congo Red, Thioflavin S, or resorufin staining in any of the aged db/AD mice tested, nor did we detect any Aβ immunoreactivity in large or small blood vessels (not shown). Additionally, there was no co-labeling of amyloid (by Amylo-Glo) and anti-collagen IV-labeled vessels (Figure 3a). These data demonstrate a lack of vascular deposition and indicate that CAA is not a primary pathology in these mice. Additionally, we hypothesized that excess Aβ could have been produced, but cleared from the brain at an increased rate in the db/AD mice. We therefore measured plasma Aβ levels. Neither Aβ1–40 (Figure 3b: p < 0.18) nor Aβ1–42 (Figure 3c: p ≤ 0.06) were significantly increased in the plasma of older db/AD mice, compared to AD mice. We also reasoned that increased activity of clearance enzymes could also account for a loss of Aβ. However, neither NEP (Figure 3d) nor IDE (Figure 3e) activities were increased by the db genotype. In fact, the diabetic mice displayed significantly reduced activities of these major clearance enzymes (NEP; IDE; p ≤ 0.01 for both), compared to non-diabetic mice. Expression of endothelin-converting enzyme (ECE1) protein was unaffected by the db genotype (Figure 3f: p < 0.65). Similarly, mRNA expression of ECE1 (Figure 3g) and ECE2 (not shown) was unaffected by genotype (n = 1 F/4 M WT; 2 F/4 M db; 1 F/3 M AD; 1 F/3 M db/AD: p ≥ 0.13 for both genes).
Figure 3

Aβ is not deposited in the vasculature. Large numbers of activated astrocytes could be seen both around amyloid cores and around some larger blood vessels (arrowheads) in older db/AD mice (a) There was no significant co-staining of amyloid and collagen IV, indicating that Aβ was not deposited in blood vessels. Red: GFAP, Green: Collagen IV, Blue: Amylo-Glo. db/AD mice did not have significantly more Aβ40 (b) or Aβ42 (c) in plasma, compared with AD mice (n = 18 F WT; 18 F db; 14 F AD; 13 F db/AD: 7–12 months old: p > 0.06), though mice containing the AD mutations had significantly more than those without (p ≤ 0.0001 for AD overall). Though the activities were significantly different in mice homozygous for the db mutation, neprilysin (d; p < 0.02) and insulin degrading enzyme (IDE: e; p < 0.003) activities were not increased relative to db WT mice (1–4 months old: n = 5 F/7 M Lepr +/+ ; 8 F/4 M Lepr db/db ; 6 F/6 M Lepr db/+ ). Endothelin converting enzyme (ECE1: f) protein expression was unaffected by the db (p < 0.65) and AD (p > 0.1) genotypes (7–12 months old: n = 14 F/7 M WT; 16 F/5 M db; 12 F/5 M AD; 12 F/6 M db/AD). Similarly ECE1 (g) and ECE2 (not shown) mRNA expression was unchanged in diabetic mice (p > 0.38 for the db genotype overall), though ECE1 expression was reduced in AD mice relative to WT (* = p < 0.03: n = 1 F/4 M WT; 2 F/4 M db; 1 F/3 M AD; 1 F/3 M db/AD).

Cognitive deficit

Since the diabetes phenotype did not significantly impact amyloid accumulation, we next determined if there was an effect on cognition in these mice. We tested older mice using a standard Morris Water Maze paradigm. While there was no difference in swim distance on the first day of training (Figure 4a: p < 0.38), the db/AD mice had significantly longer swim paths on each of the subsequent days (p ≤ 0.04 on each of days 2–5), indicating a learning and/or memory impairment. Neither db nor AD mice showed any significant impairment at this age (p ≥ 0.13 for each genotype). Swim speeds were not significantly different on the first training day (WT 374 ± 18; AD 356 ± 21; db 368 ± 18; db/AD 352 ± 18 mm/s: p ≥ 0.4 for all comparisons: not shown) demonstrating that db/AD mice are capable of swimming as well as other genotypes. After the fifth day of acquisition trials, we performed a probe trial during which the platform was removed. In this trial, db/AD mice spent significantly less time in the target quadrant (Figure 4b: p < 0.04) and less time in proximity to the platform location (p < 0.01). Since diabetics are prone to retinopathy and blindness, we wanted to exclude a profound visual impairment that would affect performance on the Morris Water Maze. We, therefore, performed a visual acuity test in which the platform location was visible and cued. All animals, with the exception of one db/AD mouse, were able to locate the cued platform (χ2 = 3.22: p < 0.36). We also analyzed the visual acuity data using an ANOVA: the db genotype had no effect on either swim distance (WT 2610 ± 2059; AD 3576 ± 2511; db 5805 ± 1757; db/AD 9700 ± 3173 mm: p < 0.07: not shown) or latency to platform (WT 10.6 ± 3.4; AD 11.7 ± 3.6; db 13.45 ± 2.4; db/AD 21.6 ± 4.5 seconds: p < 0.12: not shown), indicating that visual impairment could not account for the deficit. These data indicate that intersection of the both diabetes and AD is necessary for cognitive impairment.
Figure 4

db/AD mice display impaired cognition. db/AD mice showed a significant acquisition deficit in the Morris Water Maze (a): ANOVA p-value by day: 0.4, 02, 0.05, 0.001, 0.03) using a standard paradigm of 4 trials/day (mean swim distance is shown for each block: (WT, n = 4 F/4 M; AD, n = 4 F/2 M; db, n = 6 F/5 M; db/AD, n = 7 F/1 M). (b) db/AD mice did not learn the location of the hidden platform, as shown by probe trial (ANOVA, p < 0.03). The db/AD mice spent less time in the target quadrant, and less time in proximity to the platform location (* = p ≤ 0.05, ** = p ≤ 0.01. Tukey’s test, relative to WT). All groups performed similarly on the cued version of the task (both distance (p < 0.07) and latency (p < 0.12), indicating that the db and db/AD mice did not have a profound visual impairment (not shown).

Synapse loss

Because synaptic dysfunction and subsequent loss have been implicated in AD- associated memory impairment [24], we next measured the amount of the synaptic marker PSD95 in older mice. Neither diabetes nor the AD genotype significantly affected the level of PSD95 in the brain (p > 0.1: Figure 5), indicating that the number of synapses is not substantially reduced in the db/AD mice at the age at which we have observed learning and/or memory deficiencies.
Figure 5

Synapse Loss in db/AD mice. (a) A representative immunoblot of PSD95 expression in brains from older db/AD mice. The immunoblot was visualized with an Odyssey Infrared Imager (LI-COR). Red = PSD95, Green = GAPDH. (b) Analysis of PSD95 expression in the four main genotypes. PSD95 expression was standardized to that of GAPDH in the same lane. PSD95 expression was unaffected by either the db (p > 0.3) or the AD (p > 0.09) genotype.

Cerebrovascular abnormalities

Since the learning and memory deficit in db/AD mice was not obviously attributable to accumulation of Aβ or synapse loss, we next focused on changes in the cerebrovasculature. We examined the brain vasculature in older mice using vascular corrosion casting followed by scanning electron microscopy. WT and db mice had normal appearing vasculature (Figure 6a). By contrast, AD and db/AD mice displayed a marked pattern of cerebrovascular pathologies. We observed evidence of widespread saccular aneurysms, often occurring at the vessel branch points (Figure 6b). Some of the mice tested also presented with extensive clusters of apparent aneurysms along the arteries and arterioles (Figure 6c) as well as arterial blebbing that may represent weakened areas of the vessel wall (Figure 6d). We next scored the aneurysm pathology on a four-point scale (0 = none; 1 = 1 possible; 2 = 1–3 definite; 3 = 4+ definite) and found that aneurysms were significantly more numerous in mice with the AD genotype (AD and db/AD mice 1.7 ± 0.23 vs. WT and db mice 0.45 ± 0.24: F[1, 15] = 14.14, p < 0.002 for the AD genotype overall; N = 4-5 F/ genotype: not shown). The presence of the diabetes genotype did not significantly increase the number of aneurysms (p < 0.2 for the db genotype overall).
Figure 6

Cerebrovascular pathology in db/AD mice. SEM images of vascular casts from brains of 4 mice (a-d). (a) Relatively normal appearing cerebrovasculature of a WT mouse (large vessel is a small artery; note the clear endothelial cell nuclear imprints and their elongated shapes). (b) Aneurysm (arrow) in the brain of an AD mouse near a large vein. Some of the AD mice exhibited more severe cerebrovascular pathology, possibly representing clusters of saccular aneurysms (c) or arterial blebbing (d). In comparison, WT and db mice had minimal pathology at this age. (e - f) Prussian blue (with neutral red counterstain) staining showing microhemorrhages in two different cortical areas in older db/AD mice.

Because aneurysms are unstable and prone to rupture, we next looked for evidence of hemorrhage in the AD and db/AD mice using Prussian blue staining for hemosiderin. Prussian blue staining showed a significant incidence of microhemorrhages in older db/AD mice (n = 7; χ2 = 4.75, p < 0.03); we did not find microhemorrhages in genotypes other than the db/AD (n = 9: Figure 6e-f), including AD mice, which also displayed significant aneurysm pathology.

We scanned a separate cohort of older mice using small animal magnetic resonance imaging (MRI) in order to visualize areas of hemorrhage and infarcts. Indeed, the majority of the db/AD mice tested (11/15) showed evidence of multiple vascular events by MRI (Figure 7a-b, h-j). Histological staining of brains from the scanned mice was negative for Prussian blue staining (indicating the lack of hemorrhage). Moreover, micrographs from the same neuroanatomical level as the largest event detected by MRI showed obvious necrosis in the surrounding tissue and an obvious lack of Prussian blue positive staining (Figure 7c-d). The histological data suggest that the vascular events are likely ischemic strokes. By contrast, no WT (0/9: Figure 7e) or db (0/10: Figure 7f) and only a small number of AD (2/9: Figure 7g) mice presented with strokes, suggesting that the presence of both the db and AD genotypes is required to promote these events (χ2 = 21.769; p ≤ 0.0001). In addition, there were multiple events present in the db/AD mice (Figure 7h-j), whereas only one or two were present in the AD mice positive for strokes.
Figure 7

Stroke pathology in db/AD mice. Sequential (a posterior to b) T2*-MRI coronal images from an older db/AD mouse showing a neocortical event (arrow) near the corpus callosum; images are separated by ~300 μm. (c - d) The brain was sectioned transversely to obtain confirmation of stroke extent. Prussian blue staining with neutral red counterstain showed no evidence of hemorrhage, indicating an ischemic stroke event. P – A: posterior / anterior, for orientation (N.B.: section is perpendicular to scanning axis). (e- j) ~70% of db/AD mice (n = 11/15) had strokes (arrowheads); these were rare in AD mice (g: n = 2/9), and not found at all in WT (e: n = 9) or db mice (f: n = 10). All mice imaged were 12–14 months old. The db/AD mice (h - j) often have multiple incidents as opposed to the two AD mice, which only displayed one or two small strokes. Representative cases are shown (all scans are at about the same neuroanatomical level).

Passive immunization

Our data suggest that the intersection of the db and AD genotypes is necessary to induce strokes in these mice. In light of this data and the absence of diabetes-induced amyloid accumulation, we believe that the stroke pathology is unlikely to be due to Aβ accumulation. In order to test this hypothesis, we next performed a pilot study in which we immunized older db/AD mice with an Aβ antibody (Ab42.5) for two months. Parenchymal Aβ was significantly reduced via immunization compared to age-matched, untreated db/AD mice (~17% decrease; n = 6 / group; p < 0.006: not shown). Though Aβ was significantly reduced in the brain, there was no evidence that the stroke phenotype was rescued. Most of the treated mice imaged by MRI showed evidence of stroke (4/6 treated vs. 11/15 untreated; p < 0.67: not shown).

Vascular density

γ-Secretase has been implicated in the regulation of VEGF-dependent angiogenesis [3032]. Since we observed an upregulation of PS1 expression in the db/AD mice in the absence of Aβ accumulation, we hypothesized that PS1 might contribute to the observed vascular pathology through the regulation of angiogenesis. We therefore measured the amount of blood vessels present in the brains of older db/AD mice. Staining for smooth muscle α-actin indicated that the brains of db/AD mice were significantly more vascularized than those of WT mice (1.35 ± 0.52 vs. 0.31 ± 0.52: n = 4/ genotype: p < 0.04: Figure 8a-b). SEM images showed a similar increase in the density of the cerebrovasculature in the brains subjected to vascular corrosion casting (Figure 8c-d). Indeed, median vascular density scores of the SEM images from three blinded, independent raters indicated that the db genotype significantly increased vascular density (Figure 8e: N = 19: p < 0.02, Mann–Whitney U-test). The AD genotype had a marginal effect, (p ≤ 0.05). Similarly, db/AD mice had a greater number of endothelial cells than the other genotypes (p ≤ 0.05; Kruskal-Wallis ANOVA), as measured by the endothelial cell nuclear imprints. Endothelial cell density was correlated with vascular density (Figure 8f: p < 0.03). Direct measurement of cell size (68 ± 27 cells / animal) from the middle cerebral artery indicated that endothelial cells were smaller in diabetic mice (F[3, 8] = 17.9, p < 0.01), and size was inversely correlated with density (R 2  = 0.41, p < 0.02). Collectively, these data indicate that db/AD mice had an increase in the number of cerebral blood vessels, supportive of increased angiogenesis or arteriogenesis.
Figure 8

Vascular and endothelial density increase in db/AD mice. (a - b) Immunohistochemical staining for smooth muscle α-actin indicates that the brains of db/AD mice are more vascularized. (c - d) SEM images of the brains subjected to vascular corrosion casting show a similar increase in the density of the cerebrovasculature. (e) Median vascular density scores of SEM images from three blinded, independent raters (* = p ≤ 0.05, ** = p ≤ 0.01 compared to WT, Mann–Whitney U-test). Endothelial cells were also directly counted on five randomly-selected arteries / animal (* = p ≤ 0.05 compared to WT; Kruskal-Wallis ANOVA). Direct measurement of cell size (68 ± 27 cells / animal) from the middle cerebral artery indicated that db/AD endothelial cells were smaller (F[3,8] = 7.8, p < 0.01) than those from WT mice, and as expected size was inversely correlated with density (R 2  = 0.41, p < 0.02; not shown). (f) Endothelial cell density was also correlated with vascular density (p = 0.03).

Discussion

The db/AD model

We have created a unique mouse model that encapsulates features of both T2DM and AD- the db/AD mouse. These mice are morbidly obese and glucose intolerant at a young age (Figure 1a-d), and have a profound cognitive impairment by 12 months (Figure 4). The db/AD mice display decreased survival (Figure 1e-f), the cause of which is currently unknown. Male db/AD mice appear to be more susceptible to premature death, though sexual dimorphism has been noted in many AD models [3335]. While their lifespan is shortened relative to control genotypes, we were able to routinely age the db/AD mice beyond 12 months, allowing significant Aβ accumulation, plaque formation, stroke pathology, and cognitive impairment.

Contrary to our expectations, the db/AD mice did not exhibit increased parenchymal Aβ accumulation compared with the normoglycemic AD mice (Figure 2h-k), in spite of the observed increase in PS1 expression (Figure 2e). Aβ oligomers were modestly elevated in both db and db/AD mice (Figure 2g), though the potential impact of this increase is unknown at this point. It is possible that the detected oligomers are formed from murine Aβ and, thus, are not toxic. The reason for the relative dearth of excess Aβ in db/AD mice is unclear, though it does not appear to be due to stimulation of clearance mechanisms. While we cannot rule out clearance by other enzymes, the major enzyme activities that proteolyze Aβ (neprilysin, IDE, and ECE) were not increased in db/AD mice (Figure 3d-g), nor was there an increase in peripheral Aβ in the plasma (Figure 3b-c). In addition, we found no evidence that Aβ is deposited in the vasculature (Figure 3a), despite using multiple different staining techniques. Based on this data, it is likely that excess Aβ is simply not made in db/AD mice. In addition, there is no evidence of a significant reduction in the number of synapses in older db/AD mice (Figure 5). These findings indicate that neither CAA nor synaptic loss causes the cognitive decline observed in our mouse model.

The most striking feature of this mouse model is the severe vascular abnormalities that are present, apparently in the absence of a corresponding increase in Aβ deposition. Older AD and db/AD mice exhibited profound aneurysm pathology (Figure 6b-d) and db/AD mice had small strokes (Figure 7). Though we did observe a few areas of hemosiderin-positive staining in those animals with the largest number of vascular events (Figure 6e-f), we did not see substantial numbers of hemorrhages in the db/AD animals. Indeed, it is possible that the more extensive pathologies observed by SEM are representative of ischemic stroke, but take on this appearance during the vascular corrosion casting process. In addition, the largest event observed by MRI (Figure 7a-b) did not stain positive for hemosiderin (Figure 7c-d) and was likely ischemic in nature. We feel that infarction is the likely cause of these events, but further characterization will be needed. This is broadly consistent with the type of cerebrovascular disease observed in human diabetics [36, 37]. Given that the db/AD mice were the only genotype to exhibit both stroke pathology and cognitive impairment, we believe that it is these strokes that are responsible for the observed cognitive decline.

Mechanism of vascular pathology

Based on our data, it is likely that the aneurysm and stroke pathologies are separable events. Aneurysms were prevalent in AD animals, regardless of diabetic phenotype and were not exacerbated by diabetes. This suggests that the aneurysms may be caused by some feature of the AD genotype. While aneurysms are not typically associated with AD in humans, increased blood vessel tortuosity, which is associated with aneurysms in other diseases, has been observed [38, 39]. In addition, mutations in the presenilin substrate Notch are associated with thoracic aneurysms, likely through crosstalk with TGFβ signaling [40, 41]. The mutation in PS1 present in the AD mice may also affect this Notch signaling pathway, resulting in the aneurysm pathology.

On the other hand, the intersection of the db and AD genotypes was necessary to induce strokes in these mice (Figure 7). In light of this data and the absence of diabetes-induced amyloid accumulation, we believe that the stroke pathology is unlikely to be due to Aβ accumulation. This hypothesis was supported by preliminary data from our passive immunization study, which showed that stroke incidence was not reduced in db/AD mice treated with an anti-Aβ antibody, though brain Aβ levels did decrease. While interesting, a more extensive study will be needed for a more definitive conclusion.

Diabetes itself has profound effects on the vasculature. Obesity and diabetes are associated with hypertension and atherosclerosis [42]. In addition, diabetic rodents, including db/db mice, have increased neovascularization such as angiogenesis and arteriogenesis [4345]. This neovascularization consists of immature, unstable blood vessels that display increased permeability of the blood–brain barrier. Similar pathologic angiogenesis occurs in diabetic retinopathy and is thought to involve presenilin and γ-secretase regulation of VEGF signaling [30, 46]. We have evidence that PS1 expression increased in diabetic mice (Figure 2e) regardless of the AD mutations present- as expected with the use of “knocked-in” genes under endogenous promoters. Consistent with PS1 upregulation, the db/AD mice have a significantly higher density of blood vessels in the brain than any of the other genotypes tested (Figure 8). Further studies will be needed to determine if neovascularization may indeed play a role in the strokes and/or cognitive impairment, or if some other diabetes-related phenomenon underlies these pathologies. We have shown previously that leptin downregulates PS1 expression in both in vitro and in vivo models [28]. It will be interesting to determine if leptin resistance in the db/AD mice contributes to neovascularization via regulation of the γ-secretase complex.

A unique model of mixed dementia

The form of dementia afflicting diabetic individuals combines elements of vascular pathology, small strokes and AD-related neuropathology. In fact, the amount of AD pathology is essentially unchanged in cases with a history of T2DM, while cerebrovascular pathology increases [9, 10]. The db/AD mice share these features. One way of looking at this seemingly paradoxical observation is that cerebrovascular pathology lowers the threshold for incipient AD pathology to become unmasked as a clinical dementia as has been suggested elsewhere [10].

A small number of studies have examined the linkage between obesity, diabetes and dementia in rodent models [47, 48]. The majority of these are focused on two paradigms: treatment with streptozotocin (STZ) and feeding a high fat, or typical Western, diet (TWD). STZ, a pancreatic islet toxin, is primarily used to model type I diabetes; thus it does not address the issue of obesity. Although TWD feeding induces obesity, and has some short-term effects on AD-related neuropathology in these models [4951], these studies have failed to provide any detailed mechanistic insights into how obesity might influence the development of age-related neurologic disease. Further, TWD feeding does not have strong long-term effects on AD and vascular dementia-related neuropathology [52]. Studies utilizing genetic models of diabetes have been more limited. When Tg2576 mice, which overexpress APP ΔNL , are crossed with Irs2 −/− insulin resistant mice, the resulting animals show reduced amyloid pathology [53]. In addition, a recent study examined the outcome of a cross between leptin-resistant ob/ob mice and APP23 mice [54]. These animals showed a very early Morris Water Maze deficit (2–3 months old) unrelated to amyloid load, as the animals had no plaques and no differences in Aβ levels compared with non-diabetic controls. Even at the oldest age examined (12 months old), plaque pathology in these mice was virtually nonexistent, although there was some vascular amyloid in a very small number of animals (n = 3). The choice of parental mouse lines has a profound effect on the viability of the resulting mice as well: a cross between the ob/ ob and Tg2576 lines yielded animals with significantly reduced viability [55]. While our data are in broadly supportive of these other studies, the db/AD mice are unique in that they have Aβ plaques, very little vascular-associated Aβ, and profound underlying vascular abnormalities, even in the absence of a high-fat diet.

In summary, the db/AD mouse is a unique model of mixed dementia, possessing both AD-related and vascular pathologies. Older mice present with extensive stroke pathology, arising from a combination of the diabetic and AD phenotypes, thus leading to significant cognitive impairment. While these data suggest that Aβ is not a primary factor in the observed cognitive impairment, we cannot exclude the possibility that a soluble form of Aβ, such as oligomers, may play a role in the cognitive decline. Future studies will focus on the mechanisms behind the vascular abnormalities, at both the cellular and tissue levels. Finally, the db/AD mouse is a novel model in which to test possible therapeutic and preventative strategies to treat cognitive decline from mixed dementia.

Declarations

Acknowledgements

This work was funded by the NIH (NS058382, NS083692, GM103486, DK020579), the Coins for Alzheimer’s Research Trust (CART), the American Heart Association (13IRG14330016) and the Alzheimer’s Association (IIRG-10-172905). Special thanks to Dr. Shaun Carlson for assistance with confocal pictures Alexandra Sutphin for assistance with histological quantitation, and Dr. Pete Nelson for neuropathological diagnoses. The authors declare no competing financial interests.

Authors’ Affiliations

(1)
Sanders Brown Center on Aging, University of Kentucky
(2)
Department of Molecular and Cellular Biochemistry, University of Kentucky
(3)
Department of Molecular and Biomedical Pharmacology, University of Kentucky
(4)
Magnetic Resonance Imaging and Spectroscopy Center, University of Kentucky

References

  1. Niedowicz DM, Nelson PT, Murphy MP: Alzheimer’s disease: pathological mechanisms and recent insights. Curr Neuropharmacol 2011, 9(4):674–684. doi:10.2174/157015911798376181 10.2174/157015911798376181View ArticlePubMedPubMed CentralGoogle Scholar
  2. Ferreira ST, Klein WL: The Abeta oligomer hypothesis for synapse failure and memory loss in Alzheimer’s disease. Neurobiol Learn Mem 2011, 96(4):529–543. doi:10.1016/j.nlm.2011.08.003 10.1016/j.nlm.2011.08.003View ArticlePubMedPubMed CentralGoogle Scholar
  3. LaFerla FM, Oddo S: Alzheimer’s disease: Abeta, tau and synaptic dysfunction. Trends Mol Med 2005, 11(4):170–176. doi:10.1016/j.molmed.2005.02.009 10.1016/j.molmed.2005.02.009View ArticlePubMedGoogle Scholar
  4. Selkoe DJ: Alzheimer’s disease is a synaptic failure. Science 2002, 298(5594):789–791. doi:10.1126/science.1074069 10.1126/science.1074069View ArticlePubMedGoogle Scholar
  5. Biessels GJ, Kamal A, Urban IJ, Spruijt BM, Erkelens DW, Gispen WH: Water maze learning and hippocampal synaptic plasticity in streptozotocin-diabetic rats: effects of insulin treatment. Brain Res 1998, 800(1):125–135. 10.1016/S0006-8993(98)00510-1View ArticlePubMedGoogle Scholar
  6. Li XL, Aou S, Oomura Y, Hori N, Fukunaga K, Hori T: Impairment of long-term potentiation and spatial memory in leptin receptor-deficient rodents. Neuroscience 2002, 113(3):607–615. 10.1016/S0306-4522(02)00162-8View ArticlePubMedGoogle Scholar
  7. Messier C: Impact of impaired glucose tolerance and type 2 diabetes on cognitive aging. Neurobiol Aging 2005, 26(Suppl 1):26–30. doi:10.1016/j.neurobiolaging.2005.09.014View ArticlePubMedGoogle Scholar
  8. Luchsinger JA, Gustafson DR: Adiposity, type 2 diabetes, and Alzheimer’s disease. J Alzheimers Dis 2009, 16(4):693–704. doi:10.3233/JAD-2009–1022PubMedPubMed CentralGoogle Scholar
  9. Ahtiluoto S, Polvikoski T, Peltonen M, Solomon A, Tuomilehto J, Winblad B, Sulkava R, Kivipelto M: Diabetes, Alzheimer disease, and vascular dementia: a population-based neuropathologic study. Neurology 2010, 75(13):1195–1202. doi:10.1212/WNL.0b013e3181f4d7f8 10.1212/WNL.0b013e3181f4d7f8View ArticlePubMedGoogle Scholar
  10. Nelson PT, Head E, Schmitt FA, Davis PR, Neltner JH, Jicha GA, Abner EL, Smith CD, Van Eldik LJ, Kryscio RJ, Scheff SW: Alzheimer’s disease is not “brain aging”: neuropathological, genetic, and epidemiological human studies. Acta Neuropathol 2011, 121(5):571–587. doi:10.1007/s00401–011–0826-y 10.1007/s00401-011-0826-yView ArticlePubMedPubMed CentralGoogle Scholar
  11. Chen H, Charlat O, Tartaglia LA, Woolf EA, Weng X, Ellis SJ, Lakey ND, Culpepper J, Moore KJ, Breitbart RE, Duyk GM, Tepper RI, Morgenstern JP: Evidence that the diabetes gene encodes the leptin receptor: identification of a mutation in the leptin receptor gene in db/db mice. Cell 1996, 84(3):491–495. 10.1016/S0092-8674(00)81294-5View ArticlePubMedGoogle Scholar
  12. Coleman DL: Obese and diabetes: two mutant genes causing diabetes-obesity syndromes in mice. Diabetologia 1978, 14(3):141–148. 10.1007/BF00429772View ArticlePubMedGoogle Scholar
  13. Srinivasan K, Ramarao P: Animal models in type 2 diabetes research: an overview. Indian J Med Res 2007, 125(3):451–472.PubMedGoogle Scholar
  14. Reaume AG, Howland DS, Trusko SP, Savage MJ, Lang DM, Greenberg BD, Siman R, Scott RW: Enhanced amyloidogenic processing of the beta-amyloid precursor protein in gene-targeted mice bearing the Swedish familial Alzheimer’s disease mutations and a “humanized” Abeta sequence. J Biol Chem 1996, 271(38):23380–23388. 10.1074/jbc.271.38.23380View ArticlePubMedGoogle Scholar
  15. Siman R, Reaume AG, Savage MJ, Trusko S, Lin YG, Scott RW, Flood DG: Presenilin-1 P264L knock-in mutation: differential effects on abeta production, amyloid deposition, and neuronal vulnerability. J Neurosci 2000, 20(23):8717–8726.PubMedGoogle Scholar
  16. Anantharaman M, Tangpong J, Keller JN, Murphy MP, Markesbery WR, Kiningham KK, St Clair DK: Beta-amyloid mediated nitration of manganese superoxide dismutase: implication for oxidative stress in a APPNLH/NLH X PS-1P264L/P264L double knock-in mouse model of Alzheimer’s disease. Am J Pathol 2006, 168(5):1608–1618. 10.2353/ajpath.2006.051223View ArticlePubMedPubMed CentralGoogle Scholar
  17. Murphy MP, Beckett TL, Ding Q, Patel E, Markesbery WR, St Clair DK, LeVine H 3rd, Keller JN: Abeta solubility and deposition during AD progression and in APPxPS-1 knock-in mice. Neurobiol Dis 2007, 27(3):301–311. doi:10.1016/j.nbd.2007.06.002 10.1016/j.nbd.2007.06.002View ArticlePubMedGoogle Scholar
  18. Niedowicz DM, Beckett TL, Matveev S, Weidner AM, Baig I, Kryscio RJ, Mendiondo MS, LeVine H 3rd, Keller JN, Murphy MP: Pittsburgh compound B and the postmortem diagnosis of Alzheimer disease. Ann Neurol 2012, 72(4):564–570. doi:10.1002/ana.23633 10.1002/ana.23633View ArticlePubMedPubMed CentralGoogle Scholar
  19. Beckett TL, Niedowicz DM, Studzinski CM, Weidner AM, Webb RL, Holler CJ, Ahmed RR, LeVine H 3rd, Murphy MP: Effects of nonsteroidal anti-inflammatory drugs on amyloid-beta pathology in mouse skeletal muscle. Neurobiol Dis 2010, 39(3):449–456. doi:10.1016/j.nbd.2010.05.018 10.1016/j.nbd.2010.05.018View ArticlePubMedPubMed CentralGoogle Scholar
  20. LeVine H 3rd: Alzheimer’s beta-peptide oligomer formation at physiologic concentrations. Anal Biochem 2004, 335(1):81–90. doi:10.1016/j.ab.2004.08.014 10.1016/j.ab.2004.08.014View ArticlePubMedGoogle Scholar
  21. Resende R, Ferreiro E, Pereira C, Oliveira CR: ER stress is involved in Abeta-induced GSK-3beta activation and tau phosphorylation. J Neurosci Res 2008, 86(9):2091–2099. doi:10.1002/jnr.21648 10.1002/jnr.21648View ArticlePubMedGoogle Scholar
  22. Vale C, Alonso E, Rubiolo JA, Vieytes MR, LaFerla FM, Gimenez-Llort L, Botana LM: Profile for amyloid-beta and tau expression in primary cortical cultures from 3xTg-AD mice. Cell Mol Neurobiol 2010, 30(4):577–590. doi:10.1007/s10571–009–9482–3 10.1007/s10571-009-9482-3View ArticlePubMedGoogle Scholar
  23. Guan H, Liu Y, Daily A, Police S, Kim MH, Oddo S, LaFerla FM, Pauly JR, Murphy MP, Hersh LB: Peripherally expressed neprilysin reduces brain amyloid burden: a novel approach for treating Alzheimer’s disease. J Neurosci Res 2009, 87(6):1462–1473. doi:10.1002/jnr.21944 10.1002/jnr.21944View ArticlePubMedPubMed CentralGoogle Scholar
  24. Latimer CS, Searcy JL, Bridges MT, Brewer LD, Popovic J, Blalock EM, Landfield PW, Thibault O, Porter NM: Reversal of glial and neurovascular markers of unhealthy brain aging by exercise in middle-aged female mice. PLoS ONE 2011, 6(10):e26812. doi:10.1371/journal.pone.0026812 10.1371/journal.pone.0026812View ArticlePubMedPubMed CentralGoogle Scholar
  25. Wilcock DM, Rojiani A, Rosenthal A, Subbarao S, Freeman MJ, Gordon MN, Morgan D: Passive immunotherapy against Abeta in aged APP-transgenic mice reverses cognitive deficits and depletes parenchymal amyloid deposits in spite of increased vascular amyloid and microhemorrhage. J Neuroinflammation 2004, 1(1):24. doi:10.1186/1742–2094–1-24 10.1186/1742-2094-1-24View ArticlePubMedPubMed CentralGoogle Scholar
  26. Han BH, Zhou ML, Vellimana AK, Milner E, Kim DH, Greenberg JK, Chu W, Mach RH, Zipfel GJ: Resorufin analogs preferentially bind cerebrovascular amyloid: potential use as imaging ligands for cerebral amyloid angiopathy. Mol Neurodegener 2011, 6: 86. doi:10.1186/1750–1326–6-86 10.1186/1750-1326-6-86View ArticlePubMedPubMed CentralGoogle Scholar
  27. Holm S: A simple sequentially rejective multiple test procedure. Scand J Statist 1979, 6: 65–70.Google Scholar
  28. Niedowicz DM, Studzinski CM, Weidner AM, Platt TL, Kingry KN, Beckett TL, Bruce-Keller AJ, Keller JN, Murphy MP: Leptin regulates amyloid beta production via the gamma-secretase complex. Biochim Biophys Acta 2013, 1832(3):439–444. doi:10.1016/j.bbadis.2012.12.009 10.1016/j.bbadis.2012.12.009View ArticlePubMedGoogle Scholar
  29. Thal DR, Ghebremedhin E, Orantes M, Wiestler OD: Vascular pathology in Alzheimer disease: correlation of cerebral amyloid angiopathy and arteriosclerosis/lipohyalinosis with cognitive decline. J Neuropathol Exp Neurol 2003, 62(12):1287–1301.View ArticlePubMedGoogle Scholar
  30. Boulton ME, Cai J, Grant MB: gamma-Secretase: a multifaceted regulator of angiogenesis. J Cell Mol Med 2008, 12(3):781–795. doi:10.1111/j.1582–4934.2008.00274.x 10.1111/j.1582-4934.2008.00274.xView ArticlePubMedPubMed CentralGoogle Scholar
  31. Cai J, Chen Z, Ruan Q, Han S, Liu L, Qi X, Boye SL, Hauswirth WW, Grant MB, Boulton ME: gamma-Secretase and presenilin mediate cleavage and phosphorylation of vascular endothelial growth factor receptor-1. J Biol Chem 2011, 286(49):42514–42523. doi:10.1074/jbc.M111.296590 10.1074/jbc.M111.296590View ArticlePubMedPubMed CentralGoogle Scholar
  32. Vartanian A, Gatsina G, Grigorieva I, Solomko E, Dombrovsky V, Baryshnikov A, Stepanova E: The involvement of Notch signaling in melanoma vasculogenic mimicry. Clin Exp Med 2012. doi:10.1007/s10238–012–0190–9Google Scholar
  33. Clinton LK, Billings LM, Green KN, Caccamo A, Ngo J, Oddo S, McGaugh JL, LaFerla FM: Age-dependent sexual dimorphism in cognition and stress response in the 3xTg-AD mice. Neurobiol Dis 2007, 28(1):76–82. doi:10.1016/j.nbd.2007.06.013 10.1016/j.nbd.2007.06.013View ArticlePubMedPubMed CentralGoogle Scholar
  34. Melnikova T, Savonenko A, Wang Q, Liang X, Hand T, Wu L, Kaufmann WE, Vehmas A, Andreasson KI: Cycloxygenase-2 activity promotes cognitive deficits but not increased amyloid burden in a model of Alzheimer’s disease in a sex-dimorphic pattern. Neuroscience 2006, 141(3):1149–1162. doi:10.1016/j.neuroscience.2006.05.001 10.1016/j.neuroscience.2006.05.001View ArticlePubMedGoogle Scholar
  35. Oddo S, Caccamo A, Shepherd JD, Murphy MP, Golde TE, Kayed R, Metherate R, Mattson MP, Akbari Y, LaFerla FM: Triple-transgenic model of Alzheimer’s disease with plaques and tangles: intracellular Abeta and synaptic dysfunction. Neuron 2003, 39(3):409–421. 10.1016/S0896-6273(03)00434-3View ArticlePubMedGoogle Scholar
  36. Sweetnam D, Holmes A, Tennant KA, Zamani A, Walle M, Jones P, Wong C, Brown CE: Diabetes impairs cortical plasticity and functional recovery following ischemic stroke. J Neurosci 2012, 32(15):5132–5143. doi:10.1523/JNEUROSCI.5075–11.2012 10.1523/JNEUROSCI.5075-11.2012View ArticlePubMedGoogle Scholar
  37. Wang CY, Kim HH, Hiroi Y, Sawada N, Salomone S, Benjamin LE, Walsh K, Moskowitz MA, Liao JK: Obesity increases vascular senescence and susceptibility to ischemic injury through chronic activation of Akt and mTOR. Sci Signal 2009, 2(62):ra11. doi:10.1126/scisignal.2000143View ArticlePubMedPubMed CentralGoogle Scholar
  38. Buee L, Hof PR, Bouras C, Delacourte A, Perl DP, Morrison JH, Fillit HM: Pathological alterations of the cerebral microvasculature in Alzheimer’s disease and related dementing disorders. Acta Neuropathol 1994, 87(5):469–480. 10.1007/BF00294173View ArticlePubMedGoogle Scholar
  39. de la Torre JC: Hemodynamic consequences of deformed microvessels in the brain in Alzheimer’s disease. Ann N Y Acad Sci 1997, 826: 75–91.View ArticlePubMedGoogle Scholar
  40. Loeys BL, Schwarze U, Holm T, Callewaert BL, Thomas GH, Pannu H, De Backer JF, Oswald GL, Symoens S, Manouvrier S, Roberts AE, Faravelli F, Greco MA, Pyeritz RE, Milewicz DM, Coucke PJ, Cameron DE, Braverman AC, Byers PH, De Paepe AM, Dietz HC: Aneurysm syndromes caused by mutations in the TGF-beta receptor. N Engl J Med 2006, 355(8):788–798. doi:10.1056/NEJMoa055695 10.1056/NEJMoa055695View ArticlePubMedGoogle Scholar
  41. Moltzer E, Essers J, van Esch JH, Roos-Hesselink JW, Danser AH: The role of the renin-angiotensin system in thoracic aortic aneurysms: clinical implications. Pharmacol Ther 2011, 131(1):50–60. doi:10.1016/j.pharmthera.2011.04.002 10.1016/j.pharmthera.2011.04.002View ArticlePubMedGoogle Scholar
  42. Ergul A: Endothelin-1 and diabetic complications: focus on the vasculature. Pharmacol Res 2011, 63(6):477–482. doi:10.1016/j.phrs.2011.01.012 10.1016/j.phrs.2011.01.012View ArticlePubMedGoogle Scholar
  43. Li W, Prakash R, Kelly-Cobbs AI, Ogbi S, Kozak A, El-Remessy AB, Schreihofer DA, Fagan SC, Ergul A: Adaptive cerebral neovascularization in a model of type 2 diabetes: relevance to focal cerebral ischemia. Diabetes 2010, 59(1):228–235. doi:10.2337/db09–0902 10.2337/db09-0902View ArticlePubMedGoogle Scholar
  44. Prakash R, Johnson M, Fagan SC, Ergul A: Cerebral neovascularization and remodeling patterns in two different models of type 2 diabetes. PLoS ONE 2013, 8(2):e56264. doi:10.1371/journal.pone.0056264 10.1371/journal.pone.0056264View ArticlePubMedPubMed CentralGoogle Scholar
  45. Prakash R, Somanath PR, El-Remessy AB, Kelly-Cobbs A, Stern JE, Dore-Duffy P, Johnson M, Fagan SC, Ergul A: Enhanced cerebral but not peripheral angiogenesis in the Goto-Kakizaki model of type 2 diabetes involves VEGF and peroxynitrite signaling. Diabetes 2012, 61(6):1533–1542. doi:10.2337/db11–1528 10.2337/db11-1528View ArticlePubMedPubMed CentralGoogle Scholar
  46. Cai J, Wu L, Qi X, Li Calzi S, Caballero S, Shaw L, Ruan Q, Grant MB, Boulton ME: PEDF regulates vascular permeability by a gamma-secretase-mediated pathway. PLoS ONE 2011, 6(6):e21164. doi:10.1371/journal.pone.0021164 10.1371/journal.pone.0021164View ArticlePubMedPubMed CentralGoogle Scholar
  47. Park SA: A common pathogenic mechanism linking type-2 diabetes and Alzheimer’s disease: evidence from animal models. J Clin Neurol 2011, 7(1):10–18. doi:10.3988/jcn.2011.7.1.10 10.3988/jcn.2011.7.1.10View ArticlePubMedPubMed CentralGoogle Scholar
  48. Thibault O, Anderson KL, DeMoll C, Brewer LD, Landfield PW, Porter NM: Hippocampal calcium dysregulation at the nexus of diabetes and brain aging. Eur J Pharmacol 2013, 719(1–3):34–43. doi:10.1016/j.ejphar.2013.07.024View ArticlePubMedGoogle Scholar
  49. Studzinski CM, Li F, Bruce-Keller AJ, Fernandez-Kim OS, Le Z, Weidner AM, Markesbery WR, Paul Murphy M, Keller JN: Effects of short term western diet on cerebral oxidative stress and diabetes related factors in APP x PS-1 knock-in mice. J Neurochem 2008. doi:10.1111/j.1471–4159.2008.05798.xGoogle Scholar
  50. Kohjima M, Sun Y, Chan L: Increased food intake leads to obesity and insulin resistance in the tg2576 Alzheimer’s disease mouse model. Endocrinology 2010, 151(4):1532–1540. doi:10.1210/en.2009–1196 10.1210/en.2009-1196View ArticlePubMedPubMed CentralGoogle Scholar
  51. Julien C, Tremblay C, Phivilay A, Berthiaume L, Emond V, Julien P, Calon F: High-fat diet aggravates amyloid-beta and tau pathologies in the 3xTg-AD mouse model. Neurobiol Aging 2010, 31(9):1516–1531. doi:10.1016/j.neurobiolaging.2008.08.022 10.1016/j.neurobiolaging.2008.08.022View ArticlePubMedGoogle Scholar
  52. Zhang L, Dasuri K, Fernandez-Kim SO, Bruce-Keller AJ, Freeman LR, Pepping JK, Beckett C, Murphy MP, Keller JN: Prolonged diet induced obesity has minimal effects towards brain pathology in mouse model of cerebral amyloid angiopathy: implications for studying obesity-brain interactions in mice. Biochim Biophys Acta 2013, 1832: 1456–1462. doi:10.1016/j.bbadis.2013.01.002 10.1016/j.bbadis.2013.01.002View ArticlePubMedPubMed CentralGoogle Scholar
  53. Killick R, Scales G, Leroy K, Causevic M, Hooper C, Irvine EE, Choudhury AI, Drinkwater L, Kerr F, Al-Qassab H, Stephenson J, Yilmaz Z, Giese KP, Brion JP, Withers DJ, Lovestone S: Deletion of Irs2 reduces amyloid deposition and rescues behavioural deficits in APP transgenic mice. Biochem Biophys Res Commun 2009, 386(1):257–262. doi:10.1016/j.bbrc.2009.06.032 10.1016/j.bbrc.2009.06.032View ArticlePubMedPubMed CentralGoogle Scholar
  54. Takeda S, Sato N, Uchio-Yamada K, Sawada K, Kunieda T, Takeuchi D, Kurinami H, Shinohara M, Rakugi H, Morishita R: Diabetes-accelerated memory dysfunction via cerebrovascular inflammation and Abeta deposition in an Alzheimer mouse model with diabetes. Proc Natl Acad Sci U S A 2010, 107(15):7036–7041. doi:10.1073/pnas.1000645107 10.1073/pnas.1000645107View ArticlePubMedPubMed CentralGoogle Scholar
  55. Lubitz I, Haroutunian V, Katsel P, Leroith D, Landa N, Castel D, Shaish A, Shnerb R, Schnaider-Beeri M: Non-viability of crossing the Alzheimer’s mouse model Tg2576 with the type 2 diabetes mouse model ob/ob. Neurobiol Aging 2014, 35(7):e19-e20. 10.1016/j.neurobiolaging.2014.01.138View ArticlePubMedGoogle Scholar

Copyright

© Niedowicz et al.; licensee BioMed Central Ltd. 2014

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Advertisement