, Volume 54, Issue 8, pp 2143–2151 | Cite as

Susceptibility to diet-induced obesity and glucose intolerance in the APP SWE/PSEN1 A246E mouse model of Alzheimer’s disease is associated with increased brain levels of protein tyrosine phosphatase 1B (PTP1B) and retinol-binding protein 4 (RBP4), and basal phosphorylation of S6 ribosomal protein

  • N. Mody
  • A. Agouni
  • G. D. Mcilroy
  • B. Platt
  • M. Delibegovic



Obesity is a major risk factor for development of insulin resistance, a proximal cause of type 2 diabetes and is also associated with an increased relative risk of Alzheimer’s disease. We therefore investigated the susceptibility of transgenic mice carrying human mutated transgenes for amyloid precursor protein (APP SWE) and presenilin 1 (PSEN1 A246E) (APP/PSEN1), or PSEN1 A246E alone, which are well-characterised animal models of Alzheimer’s disease, to develop obesity, glucose intolerance and insulin resistance, and whether this was age- and/or diet-dependent.


We analysed the effects of age and/or diet on body weight of wild-type, PSEN1 and APP/PSEN1 mice. We also analysed the effects of diet on glucose homeostasis and insulin signalling in these mice.


While there were no body weight differences between 16–17- and 20–21-month-old PSEN1 mice, APP/PSEN1 mice and their wild-type controls on standard, low-fat, chow diet, the APP/PSEN1 mice still exhibited impaired glucose homeostasis, as investigated by glucose tolerance tests. This was associated with increased brain protein tyrosine phosphatase 1B protein levels in APP/PSEN1 mice. Interestingly, short-term high-fat diet (HFD) feeding of wild-type, PSEN1 and APP/PSEN1 mice for a period of 8 weeks led to higher body weight gain in APP/PSEN1 than in PSEN1 mice and wild-type controls. In addition, HFD-feeding caused fasting hyperglycaemia and worsening of glucose maintenance in PSEN1 mice, the former being further exacerbated in APP/PSEN1 mice. The mechanism(s) behind this glucose intolerance in PSEN1 and APP/PSEN1 mice appeared to involve increased levels of brain retinol-binding protein 4 and basal phosphorylation of S6 ribosomal protein, and decreased insulin-stimulated phosphorylation of Akt/protein kinase B and extracellular signal-regulated kinase 1/2 in the brain.


Our results indicate that Alzheimer’s disease increases susceptibility to body weight gain induced by HFD, and to the associated glucose intolerance and insulin resistance.


Diet-induced obesity Insulin resistance Glucose intolerance Type 2 diabetes Alzheimer’s disease Human mutations Transgenes 



Amyloid precursor protein


Mice carrying human mutated transgenes for amyloid precursor protein (APP SWE) and presenilin 1 (PSEN1 A246E)


Extracellular signal-regulated kinase


Glucose tolerance test


High-fat diet


Akt/protein kinase B


Presenilin 1


Protein tyrosine phosphatase 1B


Retinol-binding protein 4


Alzheimer’s disease, the most common type of progressive dementia in the elderly [1], is characterised by neurofibrillary tangles and deposits of β-amyloid. Three genes have been identified in familial early-onset Alzheimer’s disease: (1) amyloid-β protein precursor (AβPP, also known as APP); (2) presenilin 1 (PSEN1); and (3) PSEN2 [2]. The generation of transgenic mice with β-amyloid plaques has improved our understanding of pathophysiological processes associated with Alzheimer’s disease [3, 4]. Thus, transgenic mice carrying human mutated transgenes for APP, PSEN1 or PSEN2/APP+PSEN1 mutated transgenes are a well-characterised animal model of Alzheimer’s disease.

Increasing evidence suggests that insulin resistance, i.e. the inability of tissues to respond to the circulating effects of insulin, is a risk factor associated with pathogenesis of Alzheimer’s disease [5]. Therefore, insulin-sensitising drugs such as the peroxisome proliferator-activated receptor γ agonist, rosiglitazone, have been found to improve cognitive function in transgenic APP mouse models of Alzheimer’s disease [6]. Studies using analogues for the gut hormone glucagon-like peptide-1 (GLP-1) such as liraglutide, which facilitate insulin release, have shown that even short-term treatment can improve learning and memory, in addition to improving glucose homeostasis in mice with diet-induced obesity [7]. Moreover, an increase of GLP-1 signalling in the brain has also been suggested as a promising strategy to ameliorate the degenerative processes observed in Alzheimer’s disease [8], while studies crossing APP mice to mouse models with alterations in brain insulin-signalling have revealed an important role for insulin signalling and glucose homeostasis in cognitive behaviour in these mice [9, 10, 11].

Currently, several potential mechanisms have been suggested that connect insulin resistance to Alzheimer’s disease. One is that insulin crosses the blood–brain barrier from the periphery to the central nervous system and competes with β-amyloid for insulin degrading enzyme in the brain [12]. Peripheral hyperinsulinaemia may also inhibit brain insulin production, which, in turn, results in impaired amyloid clearance and a higher risk of Alzheimer’s disease [13, 14]. Other mechanisms have also been suggested, such as advanced glycation end-products, which can be identified immunohistochemically in senile plaques and neurofibrillary tangles, the pathological hallmarks of Alzheimer’s disease [15]. In addition, associations between adipokine levels and Alzheimer’s disease have also been made [5]. Thus, obesity and the associated hyperinsulinaemia and hyperleptinaemia have been found to play an important role in early-onset learning deficits in APP mouse model of Alzheimer’s disease, as crossing these mice to genetically obese ob/ob mice results in profound learning and memory deficits in mice as young as 8 weeks of age [16].

In our studies, we asked whether the presence of Alzheimer’s disease genes was a risk factor for development of diet-induced obesity and glucose intolerance. Thus, we used the well-characterised double mutant mice carrying human mutated transgenes for amyloid precursor protein (APP SWE) and presenilin 1 (PSEN1 A246E) (APP/PSEN1) mice, as these mice develop numerous amyloid deposits much earlier (9 months onwards) than age-matched mice expressing APP SWE (positive at 18 months), PSEN A246E or wild-type mice [1]. In addition, behavioural and physiological deficits in APP/PSEN1 mice are obvious, starting at around 6 months of age, prior to the presence of amyloid deposits, [3, 17]. We set out to investigate if the presence of Alzheimer’s disease genes increases the risk of age- and diet-associated obesity and associated glucose intolerance and insulin resistance, by comparing aged APP/PSEN1, PSEN1 and wild-type mice on standard rodent chow or when challenged with a high-fat diet (HFD).


Transgenic animals

All animal studies were performed under a project licence approved by the Home Office under the Animals (Scientific Procedures) Act 1986. Mice were maintained on a 12 h light/dark cycle in a temperature-controlled barrier facility, with free access to water and food. Transgenic mice were purchased (Jackson Laboratories, Bar Harbor, ME, USA) and breeding colonies established at the University of Aberdeen, UK, as described previously [17].

For body weight analysis studies on chow diet, we used one cohort of male mice, which we weighed longitudinally (APP/PSEN1 n = 11, wild-type n = 8 and PSEN1 n = 3). For the effects of chow vs HFD feeding on body weight gain and glucose tolerance, we used another cohort of male mice, which we examined pre- (i.e. while on chow) and post-HFD feeding (APP/PSEN n = 18, PSEN1 n = 9 and wild-type n = 8).

Metabolic measurements

Tail blood glucose was determined using a Accu-Check glucometer (Roche Diagnotics, Mannheim, Germany). Glucose tolerance tests (GTT) were performed as described previously [18, 19, 20, 21]. Briefly, mice were fasted overnight (16 h) and experiments started at 09:00 hours the following day. Mice were separated into individual cages, and their body weight and fasting blood glucose determined, followed by i.p. injection of a bolus of glucose (2 g/kg body weight). Blood glucose levels were determined immediately before and at 15, 30, 60 and 120 min post-injection. For HFD studies, mice were placed on diet containing 55% kcal from fat, primarily hydrogenated vegetable shortening, TD93075 (Harlan Teklad, Madison, WI, USA) for a period of 8 weeks.

Biochemical analysis

Whole-cell lysates were prepared by extraction in RIPA buffer (10 mmol/l TRIS-HCl, pH 7.4; 150 mmol/l NaCl; 0.1% [wt/vol.] SDS; 1% [vol./vol.] Triton X-100; 1% [wt/vol.] sodium deoxycholate; 5 mmol/l EDTA; 1 mmol/l NaF; 1 mmol/l sodium orthovanadate; protease inhibitors) at 4°C, followed by clarification at 14,000 g, as described previously [21]. For western blots, proteins were resolved by SDS-PAGE using pre-cast 18-well 10% gels (BioRad Laboratories, Hemel Hempstead, UK) and transferred to nitrocellulose membranes. Immunoblots were performed with rabbit polyclonal antibodies against phospho-AKT, phospho-extracellular signal-regulated kinase (ERK)1/ERK2, phospho-S6, mouse monoclonal ERK2 (all from Cell Signaling/New England Biolabs, Hitchin UK), protein tyrosine phosphatase 1B (PTP1B) (Upstate/Millipore, Watford, UK) and retinol-binding protein 4 (RBP4, A0040; Dako, Ely UK), following the manufacturer’s directions. Proteins were visualised using enhanced chemiluminescence and quantified using a Fusion CCD imaging system and Bio1D software (Peqlab, Sarisbury Green, UK).

Statistical analysis

Results are expressed as mean ± SEM. Comparisons between groups were made by ANOVA (one- or two-way, with repeated measures, where appropriate), with values of p < 0.05 considered statistically significant.


APP/PSEN1 mice are more susceptible to diet-induced body weight gain

To investigate whether transgenic Alzheimer’s disease mice are more susceptible to body weight gain with age, we analysed longitudinally ageing (16–17 and 20–21 months old) APP/PSEN1 (n = 11), wild-type (n = 8) and PSEN1 (n = 3) mice for total body weight on a standard rodent, chow diet (Fig. 1). No differences in body weight were revealed between any of the genotypes; there was no indication of age-dependent changes (two-way repeated-measures ANOVA; F < 1; p > 0.05).
Fig. 1

Body weight of 16–17- and 20–21-month-old mice followed up longitudinally on standard chow diet. Wild-type, chequered bars, n = 8; PSEN1, white bars, n = 3; APP/PSEN1, black bars, n = 11. No significant differences were observed (p > 0.05 for all; F < 1 for all)

To investigate whether HFD feeding differentially affects body weight gain in APP/PSEN1, PSEN1 and age-matched wild-type control littermate mice, we placed another cohort of 13-month-old APP/PSEN1 (n = 18), PSEN1 (n = 9) and wild-type (n = 7) mice on HFD for a period of 8 weeks and recorded body weight gain during this time. There were no statistical differences in total body weights between the three mouse groups before and after the HFD (Fig. 2a). However, both Alzheimer’s disease genotypes gained weight significantly (PSEN1 p = 0.03; APP/PSEN1 p < 0.001), while weight gain in wild-type mice did not reach significance (p = 0.054). The stronger significance obtained for the APP expressing mouse line was a first indication that this line was overall more prone to gain weight, as confirmed in Fig. 2b (gain of 13.1 ± 1.1 g in APP/PSEN1 mice vs 8.7 ± 2.0 g in PSEN1 mice and 8.2 ± 3.7 g in wild-type mice, respectively), indicating that these mice are more susceptible to diet-induced body weight gain. In addition, overnight fasting resulted in greater body weight loss in wild-type mice (4.0 ± 0.4 g) than in PSEN1 (2.8 ± 0.4 g) or APP/PSEN1 mice (2.9 ± 0.2 g), with significant differences observed between wild-type and APP/PSEN1 mice (one-way ANOVA, F = 4.11, p = 0.02) (Fig. 2c).
Fig. 2

a Body weight on standard chow and post HFD feeding. Wild-type (WT) controls, chequered bars, n = 7; PSEN1, white bars, n = 9; APP/PSEN1, black bars, n = 18. There were no statistical differences in total body weight between the three mouse groups before and after HFD. However, while both Alzheimer’s disease genotypes gained weight significantly (PSEN1 p = 0.03; APP/PSEN1 p < 0.001), wild-type mice weight gain did not reach significance (p = 0.054). b Body weight gain in mouse groups described in a after 8 weeks of HFD feeding indicated a significant genotype difference; p < 0.05 for PSEN1 vs APP/PSEN1 by one-way ANOVA. c Fasting body weight loss in mouse groups described in a after 8 weeks of HFD feeding indicated a significant genotype difference; p < 0.05 for wild-type vs APP/PSEN1 by one-way ANOVA

Mouse models of Alzheimer’s disease are susceptible to diet-induced hyperglycaemia, while presence of APP genotype leads to glucose intolerance

To evaluate whether PSEN1 and APP/PSEN1 mice have impaired glucose homeostasis in comparison to their control wild-type mice, we first analysed their glucose homeostasis on a standard, chow diet. To investigate the effect of Alzheimer’s disease-like phenotype on fasting hyperglycaemia, we withdrew food for a period of 16 h and measured fasting glucose levels. There were no differences between the groups on normal, low-fat standard chow diet (Fig. 3a). In contrast, short-term HFD-feeding significantly increased blood glucose levels in APP/PSEN1 (p < 0.001) and PSEN1 (p < 0.05) mice, with no significant effect on wild-type fasting hyperglycaemia (p > 0.05) (Fig. 3a). Interestingly, blood glucose levels in overnight-fasted mice on HFD were significantly higher in APP/PSEN1 mice than in PSEN1 and wild-type controls, while PSEN1 mice had significantly higher fasting glucose levels than wild-type mice, thereby exhibiting an intermediate phenotype (two-way repeated-measures ANOVA, interaction F = 4.82; p = 0.01) (Fig. 3a).
Fig. 3

a Overnight fasted blood glucose levels on chow and HFD. Wild-type controls, chequered bars, n = 8; PSEN1, white bars, n = 9; APP/PSEN1, black bars, n = 18. While genotypes did not differ under chow conditions, HFD-feeding increased fasting blood glucose levels significantly in PSEN1 and APP/PSEN1 group only. However, this rise was more pronounced in the APP/PSEN1 group; *p < 0.05 and ***p < 0.001. In addition, PSEN1 mice had higher fasting glucose levels than wild-type controls (p < 0.05 by two-way ANOVA). APP/PSEN1 mice had dramatically higher glucose levels than wild-type and PSEN1 mice; p < 0.001 and p < 0.05, respectively, by two-way ANOVA. GTTs on chow (b) and (c) HFD. Wild-type controls, circles, n = 8; APP/PSEN1, triangles, n = 18; PSEN1, squares, n = 9. On chow diet, APP/PSEN1 mice were more glucose-intolerant than (1) wild-type controls, as evidenced by their slower rate of glucose clearance at 30 and 60 min post-GTT, and (2) PSEN1 mice, as evidenced by their slower rate of clearance at 60 min post-GTT; p = 0.01 by two-way repeated-measures ANOVA; interaction F = 2.61. Significant differences between genotypes were as follows: *p < 0.05 for wild-type vs PSEN1; p < 0.05 and †† p < 0.01 for wild-type vs APP/PSEN1; p < 0.05 for PSEN1 vs APP/PSEN1. d Area under curve for GTTs pre- and post-HFD feeding (8 weeks) in mice. APP/PSEN1 mice were more glucose-intolerant than wild-type controls after HFD-feeding; **p < 0.01 by two-way repeated-measures ANOVA

Since measuring circulating blood glucose levels is not the most comprehensive way to determine subtle differences in glucose homeostasis, we also performed GTTs in the mouse groups. On chow diet, no differences in 5 h (data not shown) or 16 h fasted circulating blood glucose levels (Fig. 3a) were observed, whereas GTTs revealed that APP/PSEN1 mice were more glucose-intolerant than their wild-type controls, as evidenced by their slower rate of glucose clearance at 30 and 60 min post-GTT, and more inkolerant than PSEN1 mice at 60 min (two-way repeated-measures ANOVA, interaction F = 2.60, p = 0.01) (Fig. 3b). In addition, PSEN1 mice exhibited an intermediate phenotype with worsening of glucose clearance significantly different from that in wild-type controls at 30 min post-GTT (Fig. 3b). Even though HFD-feeding induced glucose intolerance in all groups of mice, APP/PSEN1 mice had much less efficient glucose clearance than their wild-type controls (Fig. 3c), with significance reached for glucose clearance after 30 min (two-way repeated-measures ANOVA). Again, PSEN1 mice exhibited an intermediate phenotype, suggesting that presence of APP genotype is necessary for diet-induced glucose intolerance to be detectable. Analysis of the AUC (Fig. 3d) during a GTT further confirmed that APP/PSEN1 mice are more glucose-intolerant than their wild-type control littermates with short-term HFD-feeding (two-way repeated-measures ANOVA, interaction F = 4.07, p = 0.02) (Fig. 3d).

Increased brain PTP1B and RBP4 protein levels and nutrient-driven basal S6 phosphorylation in mouse models of Alzheimer’s disease are associated with glucose intolerance in these mice

To investigate some of the potential mechanisms behind the increased glucose intolerance in PSEN1 and APP/PSEN1 mice, we determined insulin signalling components in mice fed chow (Fig. 4a) and HFD (Fig. 4b). Mice were fasted overnight and the following morning injected with a bolus of saline or insulin (10 mU/g body weight) for 10 min, followed by cervical dislocation and tissue extraction. Brains were homogenised in RIPA buffer as previously described [21], and brain insulin-signalling pathway analysed using phospho-specific antibodies.
Fig. 4

Whole-cell lysates from brain (protein loading at 30 μg/lane) of wild-type (WT), PSEN1 and APP/PSEN1 mice fed (a) chow or (b) HFD, fasted overnight and injected with saline or insulin (10 min i.p., 10 mU/g) as indicated (insulin, +INS). Immunoblots were probed with pAkt/PKB (S473 site), total Akt/PKB, pERK1/2, total ERK2 (mouse monoclonal), pS6, PTP1B and RBP4 antibodies and visualised by enhanced chemiluminescence. Quantification of western blots, as indicated in Methods, from whole-cell lysates for (c) chow-fed and (d) HFD-fed brain pPKB/Akt (S473) normalised to total PKB/Akt. Quantification of blots for (e) chow-fed and (f) HFD-fed pS6 normalised to total PKB/Akt, and for (g) chow-fed and (h) HFD-fed pERK1/2 normalised to total ERK2. Quantification of blots for (i) PTP1B and (j) RBP4 protein levels. All statistics were analysed using two-way ANOVA; *p < 0.05 and **p < 0.01. c–j Chequered bars, wild-type; white bars, PSEN1; black bars, APP/PSEN1

Total protein levels of brain PTP1B, which is the major insulin receptor phosphatase and negative regulator of insulin and leptin signalling, were increased by Alzheimer’s disease genotype on chow diet (Fig. 4a, i), while HFD-feeding increased brain PTP1B levels in all groups of mice (Fig. 4b, i), consistent with previous findings [22].

Serum RBP4 is an adipokine found to be elevated in insulin-resistant humans and in many mouse models of obesity and insulin resistance, including HFD [23], although not all studies show this effect [24]. According to many studies, the level of elevation correlates highly with the degree of insulin resistance in mice and in humans, and serum RBP4 levels were highly predictive of metabolic syndrome risk in a large population-based study [25]. We analysed the levels of brain RBP4 in our mice on chow and HFD. Our data suggest that chow-fed APP/PSEN1 mice tend to have increased levels of brain RBP4 in comparison to wild-type and PSEN1 mice (Fig. 4a, j), while HFD-feeding significantly increases brain RBP4 levels in PSEN1 and APP/PSEN1 mice, relative to wild-type control littermates (two-way ANOVA) (Fig. 4b, j).

We also examined nutrient- and insulin-regulated S6 ribosomal protein phosphorylation, finding that in mice on HFD, basal S6 phosphorylation was dramatically higher in brains from PSEN1 and APP/PSEN1 mice (Fig. 4b, f) and was as high as wild-type insulin-stimulated S6 phosphorylation (two-way ANOVA). This was not the case on standard low-fat chow diet (Fig. 4a, e).

Given that recent data have shown that crossing APP mice to ob/ob mice resulted in complete absence of phosphorylation of Akt/protein kinase B (PKB) at S473 site in muscle and liver, we also investigated phosphorylation status of this site in brains from our mice. While insulin was able to stimulate phosphorylation of Akt/PKB on S473 site in wild-type mice, which was somewhat diminished with short-term HFD feeding (Fig. 4b–d), insulin was not able to cause any phosphorylation of Akt/PKB in APP/PSEN1 or PSEN1 mice on chow diet (Fig. 4a, c), or on HFD (two-way ANOVA) (Fig. 4b, c). Phosphorylation of ERK1/2 was strikingly higher in insulin-stimulated brains from wild-type mice fed chow diet (Fig. 4a, g) and HFD (Fig. 4b, h) than in PSEN1 and APP/PSEN1 mice on the same diets.


Obesity is a major risk factor for the development of several disorders such as type 2 diabetes, metabolic syndrome, cardiovascular disease, dementia and cancer. In addition, due to improvements in medicine, housing, economic factors and general well-being in the developed countries, people are living much longer. Ageing is also associated with the induction of most of these diseases, with associated costs to healthcare systems in the developed world also rapidly raising. Alzheimer’s disease is the most common form of dementia, characterised by loss of recent memory as one of the first symptoms and a progressive decline in all cognitive skills later on in the disease [3, 26]. Considerable evidence suggests that insulin resistance and obesity may directly contribute to the pathogenesis of Alzheimer’s disease [27, 28, 29, 30, 31]. However, not much emphasis has been put on the role of Alzheimer’s disease in obesity and type 2 diabetes development, as characterised by insulin resistance. In this study, we set out to investigate, in a well-established APP/PSEN1 and PSEN1 mouse model, whether genotypes relevant to Alzheimer’s disease make mice more susceptible to obesity development than their wild-type control littermates with increasing age and under different dietary conditions (low-fat standard chow diet vs HFD). We found that aged PSEN1 and APP/PSEN1 mice weighed exactly the same as their wild-type control mice, so ageing on its own did not seem to make Alzheimer’s disease mice more susceptible to increased body weight gain in comparison to their controls. However, even a short-term dietary treatment with HFD, consisting of 55% fat, made APP/PSEN1 mice significantly more susceptible to body weight gain than PSEN1 littermates or wild-type controls. Thus, APP genotype is a risk factor for diet-induced body weight gain, with potential implications with regard to other secondary co-morbidities that may develop with an increase in body weight e.g. type 2 diabetes. Indeed, our HFD study is in agreement with a similar study using sucrose-rich diet (10% sucrose) as a dietary supplement, which also led to an increase in body weight gain in APP/PSEN1 mice [27]; however, that study only used non-supplemented diet vs supplemented diet in APP/PSEN1 mice. No wild-type or transgene alone mice were included for a comparison. Our aim, however, was to determine whether the APP gene was the risk factor for increase in body weight gain or just diet alone.

Thus, we next set out to investigate whether an Alzheimer’s disease-like phenotype would make mice glucose-intolerant and insulin-resistant, and whether this was controlled by dietary conditions. Interestingly, even though we observed no differences in body weight between APP/PSEN1, PSEN1 and wild-type mice on chow diet, APP/PSEN1 mice were significantly more glucose-intolerant than PSEN1 transgene mice and/or their wild-type controls. However, at this point, APP/PSEN1 mice were still normoglycaemic, suggesting that we detected early changes in glucose maintenance. However, glucose intolerance induced by diet-induced obesity was strikingly evident in APP/PSEN1 mice, as they also developed diet-induced fasting hyperglycaemia after 8 weeks of HFD feeding. This was accompanied by worsening in glucose maintenance as measured by GTTs. In addition, PSEN1 transgene alone mice exhibited an intermediate phenotype, with fasting hyperglycaemia present under HFD feeding conditions. They were, however, significantly more glucose-tolerant than their APP/PSEN1 control littermates. Thus, presence of APP genotype presents a risk factor for development of glucose intolerance under normal dietary conditions as well as under HFD-feeding.

To investigate the potential mechanism(s) associated with this glucose intolerance, we examined the levels of PTP1B, a well-known negative regulator of insulin and leptin receptor signalling [32] whose levels are elevated in obesity in humans [33, 34] and rodents, and under inflammatory conditions [22, 35, 36]. Mice lacking PTP1B globally or specifically in the brain [35, 37, 38] are lean, while those lacking muscle- or liver-PTP1B are protected against HFD-induced insulin resistance and glucose intolerance [18, 19]. Interestingly, we found brain PTP1B protein levels to be increased in APP/PSEN1 mice on chow diet in comparison to wild-type controls, with PSEN1 mice having an intermediate phenotype. As expected, HFD feeding increased PTP1B levels in the brain in all groups of mice, with no statistical differences between groups. However, brain RBP4 protein levels tended to be higher in APP/PSEN1 mice on chow diet and significantly higher in PSEN1 and APP/PSEN1 mice on HFD, suggesting that increased RBP4 levels may be another mechanism by which Alzheimer’s disease genotype predisposes to diet-induced glucose intolerance. RBP4 is an adipokine that was recently found to be elevated in insulin-resistant states as well as in obesity in mice [23, 39, 40] and humans [41, 42, 43, 44], although not all studies have reported this [24, 45]. These data suggest that there are early markers of insulin resistance already present with the APP genotype, making these mice more susceptible to the effects of HFD feeding and associated insulin resistance. They also raise an interesting therapeutic possibility that reduction PTP1B and/or RBP4 levels by pharmacological means [39] may in turn protect against body weight gain and the associated glucose intolerance in Alzheimer’s disease mouse models. This could be an interesting avenue for further studies.

In addition, we investigated brain insulin-signalling events in all groups of mice. Mice were fasted overnight and injected with a bolus of saline or insulin. Basal and insulin-stimulated phosphorylation events were then examined under chow and HFD feeding conditions. Since HFD is known to promote insulin resistance through nutrient overload-associated increase in S6K1 activity, we used phosphorylation of S6 as a read-out of S6K1 activity [46]. We found that under HFD feeding conditions, basal S6 phosphorylation was significantly higher in brains from PSEN1 and APP/PSEN1 mice and was as high as the insulin-stimulated S6 phosphorylation observed in wild-type mice, consistent with the insulin resistance observed in Alzheimer’s disease mice. Since a decrease in insulin-stimulated Akt/PKB phosphorylation was observed in other mouse models of Alzheimer’s disease in specific regions of brain or peripheral tissues [16, 28], we examined Akt/PKB phosphorylation from whole-brain lysates, finding, as expected from their impaired glucose tolerance, that insulin-stimulated Akt/PKB phosphorylation was decreased in PSEN1 and APP/PSEN1 mice under chow as well as HFD feeding conditions. In addition, insulin-stimulated phosphorylation of ERK1 and -2 was downregulated in PSEN1 and APP/PSEN1 mice in comparison to wild-type controls, both on chow and HFD. Thus it appears that in our Alzheimer’s disease mice, insulin-stimulated Akt/PKB phosphorylation was inhibited, while nutrient-driven S6K signalling became augmented.

In conclusion, we report here that transgenic Alzheimer’s disease mice are susceptible to diet-induced body weight gain, and the associated glucose intolerance and fasting hyperglycaemia, which are associated with increased brain PTP1B and RBP4 levels, and nutrient-driven basal S6 phosphorylation, as well as with decreased insulin-stimulated Akt/PKB and ERK1/2 phosphorylation. Our data suggest that Alzheimer’s disease is a risk factor for diet-induced body weight gain and associated glucose intolerance, and that Alzheimer’s disease patients should be advised to avoid foods high in fat, in order to prevent development of obesity-associated co-morbidities.



This work was funded by the College of Life Sciences and Medicine, University of Aberdeen, start-up funds to M. Delibegovic. M. Delibegovic is also funded by an RCUK Fellowship, Diabetes UK, British Heart Foundation, and the Royal Society. N. Mody is funded by an intermediate basic research fellowship from the British Heart Foundation. G. D. Mcilroy is funded by a BBSRC postdoctoral training studentship.

Duality of interest

The authors declare that there is no duality of interest associated with this manuscript.


  1. 1.
    Borchelt DR, Ratovitski T, van Lare J et al (1997) Accelerated amyloid deposition in the brains of transgenic mice coexpressing mutant presenilin 1 and amyloid precursor proteins. Neuron 19:939–945PubMedCrossRefGoogle Scholar
  2. 2.
    Selkoe DJ (1997) Alzheimer’s disease: genotypes, phenotypes, and treatments. Science 275:630–631PubMedCrossRefGoogle Scholar
  3. 3.
    Filali M, Lalonde R, Rivest S (2009) Cognitive and non-cognitive behaviors in an APP SWE/PS1 bigenic model of Alzheimer’s disease. Genes Brain Behav 8:143–148PubMedCrossRefGoogle Scholar
  4. 4.
    Filali M, Lalonde R (2009) Age-related cognitive decline and nesting behavior in an APP SWE/PS1 bigenic model of Alzheimer’s disease. Brain Res 1292:93–99PubMedCrossRefGoogle Scholar
  5. 5.
    Luchsinger JA, Gustafson DR (2009) Adiposity, type 2 diabetes, and Alzheimer’s disease. J Alzheimers Dis 16:693–704PubMedGoogle Scholar
  6. 6.
    Pedersen WA, McMillan PJ, Kulstad JJ, Leverenz JB, Craft S, Haynatzki GR (2006) Rosiglitazone attenuates learning and memory deficits in Tg2576 Alzheimer mice. Exp Neurol 199:265–273PubMedCrossRefGoogle Scholar
  7. 7.
    Porter DW, Kerr BD, Flatt PR, Holscher C, Gault VA (2010) Four weeks administration of Liraglutide improves memory and learning as well as glycaemic control in mice with high fat dietary-induced obesity and insulin resistance. Diabetes Obes Metab 12:891–899PubMedCrossRefGoogle Scholar
  8. 8.
    Wang XH, Li L, Holscher C, Pan YF, Chen XR, Qi JS (2010) Val8-glucagon-like peptide-1 protects against Abeta1-40-induced impairment of hippocampal late-phase long-term potentiation and spatial learning in rats. Neuroscience 170:1239–1248PubMedCrossRefGoogle Scholar
  9. 9.
    Killick R, Scales G, Leroy K et al (2009) Deletion of Irs2 reduces amyloid deposition and rescues behavioural deficits in APP transgenic mice. Biochem Biophys Res Commun 386:257–262PubMedCrossRefGoogle Scholar
  10. 10.
    Freude S, Hettich MM, Schumann C et al (2009) Neuronal IGF-1 resistance reduces Abeta accumulation and protects against premature death in a model of Alzheimer’s disease. FASEB J 23:3315–3324PubMedCrossRefGoogle Scholar
  11. 11.
    Ma T, Hoeffer CA, Capetillo-Zarate E et al (2010) Dysregulation of the mTOR pathway mediates impairment of synaptic plasticity in a mouse model of Alzheimer’s disease. PLoS ONE 5:e12845PubMedCrossRefGoogle Scholar
  12. 12.
    Risner ME, Saunders AM, Altman JF et al (2006) Efficacy of rosiglitazone in a genetically defined population with mild-to-moderate Alzheimer’s disease. Pharmacogenomics J 6:246–254PubMedGoogle Scholar
  13. 13.
    Reger MA, Watson GS, Frey WH 2nd et al (2006) Effects of intranasal insulin on cognition in memory-impaired older adults: modulation by APOE genotype. Neurobiol Aging 27:451–458PubMedCrossRefGoogle Scholar
  14. 14.
    Reger MA, Craft S (2006) Intranasal insulin administration: a method for dissociating central and peripheral effects of insulin. Drugs Today (Barc) 42:729–739CrossRefGoogle Scholar
  15. 15.
    Cummings JL (2004) Alzheimer’s disease. N Engl J Med 351:56–67PubMedCrossRefGoogle Scholar
  16. 16.
    Takeda S, Sato N, Uchio-Yamada K et al (2010) Diabetes-accelerated memory dysfunction via cerebrovascular inflammation and Abeta deposition in an Alzheimer mouse model with diabetes. Proc Natl Acad Sci USA 107:7036–7041PubMedCrossRefGoogle Scholar
  17. 17.
    Jyoti A, Plano A, Riedel G, Platt B (2010) EEG, activity, and sleep architecture in a transgenic AbetaPPSWE/PSEN1A246E Alzheimer’s disease mouse. J Alzheimers Dis 22:873–887PubMedGoogle Scholar
  18. 18.
    Delibegovic M, Bence KK, Mody N et al (2007) Improved glucose homeostasis in mice with muscle-specific deletion of protein-tyrosine phosphatase 1B. Mol Cell Biol 27:7727–7734PubMedCrossRefGoogle Scholar
  19. 19.
    Delibegovic M, Zimmer D, Kauffman C et al (2009) Liver-specific deletion of protein-tyrosine phosphatase 1B (PTP1B) improves metabolic syndrome and attenuates diet-induced endoplasmic reticulum stress. Diabetes 58:590–599PubMedCrossRefGoogle Scholar
  20. 20.
    Matsuo K, Delibegovic M, Matsuo I et al (2010) Altered glucose homeostasis in mice with liver-specific deletion of Src homology phosphatase 2. J Biol Chem 285:39750–39758PubMedCrossRefGoogle Scholar
  21. 21.
    Agouni A, Owen C, Czopek A, Mody N, Delibegovic M (2010) In vivo differential effects of fasting, re-feeding, insulin and insulin stimulation time course on insulin signaling pathway components in peripheral tissues. Biochem Biophys Res Commun 401:104–111PubMedCrossRefGoogle Scholar
  22. 22.
    Zabolotny JM, Kim YB, Welsh LA, Kershaw EE, Neel BG, Kahn BB (2008) Protein-tyrosine phosphatase 1B expression is induced by inflammation in vivo. J Biol Chem 283:14230–14241PubMedCrossRefGoogle Scholar
  23. 23.
    Yang Q, Graham TE, Mody N et al (2005) Serum retinol binding protein 4 contributes to insulin resistance in obesity and type 2 diabetes. Nature 436:356–362PubMedCrossRefGoogle Scholar
  24. 24.
    McTernan PG, Kumar S (2007) Editorial: Retinol binding protein 4 and pathogenesis of diabetes. J Clin Endocrinol Metab 92:2430–2432PubMedCrossRefGoogle Scholar
  25. 25.
    Qi Q, Yu Z, Ye X et al (2007) Elevated retinol-binding protein 4 levels are associated with metabolic syndrome in Chinese people. J Clin Endocrinol Metab 92:4827–4834PubMedCrossRefGoogle Scholar
  26. 26.
    Puolivali J, Wang J, Heikkinen T et al (2002) Hippocampal A beta 42 levels correlate with spatial memory deficit in APP and PS1 double transgenic mice. Neurobiol Dis 9:339–347PubMedCrossRefGoogle Scholar
  27. 27.
    Cao D, Lu H, Lewis TL, Li L (2007) Intake of sucrose-sweetened water induces insulin resistance and exacerbates memory deficits and amyloidosis in a transgenic mouse model of Alzheimer disease. J Biol Chem 282:36275–36282PubMedCrossRefGoogle Scholar
  28. 28.
    Ho L, Qin W, Pompl PN et al (2004) Diet-induced insulin resistance promotes amyloidosis in a transgenic mouse model of Alzheimer’s disease. FASEB J 18:902–904PubMedGoogle Scholar
  29. 29.
    Refolo LM, Malester B, LaFrancois J et al (2000) Hypercholesterolemia accelerates the Alzheimer’s amyloid pathology in a transgenic mouse model. Neurobiol Dis 7:321–331PubMedCrossRefGoogle Scholar
  30. 30.
    Shie FS, Jin LW, Cook DG, Leverenz JB, LeBoeuf RC (2002) Diet-induced hypercholesterolemia enhances brain A beta accumulation in transgenic mice. Neuroreport 13:455–459PubMedCrossRefGoogle Scholar
  31. 31.
    Levin-Allerhand JA, Lominska CE, Smith JD (2002) Increased amyloid-levels in APP SWE transgenic mice treated chronically with a physiological high-fat high-cholesterol diet. J Nutr Health Aging 6:315–319PubMedGoogle Scholar
  32. 32.
    Delibegovic M, Mody N (2009) Protein tyrosine phosphatase 1B (PTP1B) in obesity and type 2 diabetes. Acta Med Salin 38:2–7Google Scholar
  33. 33.
    Ahmad F, Considine RV, Bauer TL, Ohannesian JP, Marco CC, Goldstein BJ (1997) Improved sensitivity to insulin in obese subjects following weight loss is accompanied by reduced protein-tyrosine phosphatases in adipose tissue. Metabolism 46:1140–1145PubMedCrossRefGoogle Scholar
  34. 34.
    Ahmad F, Azevedo JL, Cortright R, Dohm GL, Goldstein BJ (1997) Alterations in skeletal muscle protein-tyrosine phosphatase activity and expression in insulin-resistant human obesity and diabetes. J Clin Invest 100:449–458PubMedCrossRefGoogle Scholar
  35. 35.
    Klaman LD, Boss O, Peroni OD et al (2000) Increased energy expenditure, decreased adiposity, and tissue-specific insulin sensitivity in protein-tyrosine phosphatase 1B-deficient mice. Mol Cell Biol 20:5479–5489PubMedCrossRefGoogle Scholar
  36. 36.
    Zabolotny JM, Bence-Hanulec KK, Stricker-Krongrad A et al (2002) PTP1B regulates leptin signal transduction in vivo. Dev Cell 2:489–495PubMedCrossRefGoogle Scholar
  37. 37.
    Bence KK, Delibegovic M, Xue B et al (2006) Neuronal PTP1B regulates body weight, adiposity and leptin action. Nat Med 12:917–924PubMedCrossRefGoogle Scholar
  38. 38.
    Elchebly M, Payette P, Michaliszyn E et al (1999) Increased insulin sensitivity and obesity resistance in mice lacking the protein tyrosine phosphatase-1B gene. Science 283:1544–1548PubMedCrossRefGoogle Scholar
  39. 39.
    Preitner F, Mody N, Graham TE, Peroni OD, Kahn BB (2009) Long-term Fenretinide treatment prevents high-fat diet-induced obesity, insulin resistance, and hepatic steatosis. Am J Physiol Endocrinol Metab 297:E1420–E1429PubMedCrossRefGoogle Scholar
  40. 40.
    Mody N, Graham TE, Tsuji Y, Yang Q, Kahn BB (2008) Decreased clearance of serum retinol-binding protein and elevated levels of transthyretin in insulin-resistant ob/ob mice. Am J Physiol Endocrinol Metab 294:E785–E793PubMedCrossRefGoogle Scholar
  41. 41.
    Goodman E, Graham TE, Dolan LM, Daniels SR, Goodman ER, Kahn BB (2009) The relationship of retinol binding protein 4 to changes in insulin resistance and cardiometabolic risk in overweight black adolescents. J Pediatr 154:67–73.e1PubMedCrossRefGoogle Scholar
  42. 42.
    Hammarstedt A, Pihlajamaki J, Graham TE et al (2008) High circulating levels of RBP4 and mRNA levels of aP2, PGC-1alpha and UCP-2 predict improvement in insulin sensitivity following pioglitazone treatment of drug-naive type 2 diabetic subjects. J Intern Med 263:440–449PubMedCrossRefGoogle Scholar
  43. 43.
    Balagopal P, Graham TE, Kahn BB, Altomare A, Funanage V, George D (2007) Reduction of elevated serum retinol binding protein in obese children by lifestyle intervention: association with subclinical inflammation. J Clin Endocrinol Metab 92:1971–1974PubMedCrossRefGoogle Scholar
  44. 44.
    Kloting N, Graham TE, Berndt J et al (2007) Serum retinol-binding protein is more highly expressed in visceral than in subcutaneous adipose tissue and is a marker of intra-abdominal fat mass. Cell Metab 6:79–87PubMedCrossRefGoogle Scholar
  45. 45.
    Janke J, Engeli S, Boschmann M et al (2006) Retinol-binding protein 4 in human obesity. Diabetes 55:2805–2810PubMedCrossRefGoogle Scholar
  46. 46.
    Um SH, D’Alessio D, Thomas G (2006) Nutrient overload, insulin resistance, and ribosomal protein S6 kinase 1, S6K1. Cell Metab 3:393–402PubMedCrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  1. 1.Institute of Biological & Environmental SciencesUniversity of AberdeenAberdeenUK
  2. 2.Institute of Medical SciencesUniversity of AberdeenAberdeenUK

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