Alzheimer's disease is type 3 diabetes-evidence reviewed

Abstract

Alzheimer's disease (AD) has characteristic histopathological, molecular, and biochemical abnormalities, including cell loss; abundant neurofibrillary tangles; dystrophic neurites; amyloid precursor protein, amyloid-beta (APP-Abeta) deposits; increased activation of prodeath genes and signaling pathways; impaired energy metabolism; mitochondrial dysfunction; chronic oxidative stress; and DNA damage. Gaining a better understanding of AD pathogenesis will require a framework that mechanistically interlinks all these phenomena. Currently, there is a rapid growth in the literature pointing toward insulin deficiency and insulin resistance as mediators of AD-type neurodegeneration, but this surge of new information is riddled with conflicting and unresolved concepts regarding the potential contributions of type 2 diabetes mellitus (T2DM), metabolic syndrome, and obesity to AD pathogenesis. Herein, we review the evidence that (1) T2DM causes brain insulin resistance, oxidative stress, and cognitive impairment, but its aggregate effects fall far short of mimicking AD; (2) extensive disturbances in brain insulin and insulin-like growth factor (IGF) signaling mechanisms represent early and progressive abnormalities and could account for the majority of molecular, biochemical, and histopathological lesions in AD; (3) experimental brain diabetes produced by intracerebral administration of streptozotocin shares many features with AD, including cognitive impairment and disturbances in acetylcholine homeostasis; and (4) experimental brain diabetes is treatable with insulin sensitizer agents, i.e., drugs currently used to treat T2DM. We conclude that the term "type 3 diabetes" accurately reflects the fact that AD represents a form of diabetes that selectively involves the brain and has molecular and biochemical features that overlap with both type 1 diabetes mellitus and T2DM.

Keywords: Alzheimer's disease; central nervous system; diabetes; insulin gene expression; insulin signaling.

Figures

Figure 1.

Impaired insulin and IGF (A, C) receptor and (B, D) polypeptide gene expression in late/end-stage AD (A, B) temporal cortex and (C, D) hippocampus. Gene expression was measured by qRT-PCR using RNA isolated from the temporal cortex or hippocampus from postmortem histopathologically confirmed cases of severe AD or normal aging. We reverse transcribed mRNA, and the resulting cDNA was PCR amplified. The products were detected continuously with a BIO-RAD iCycler Multi-Color RealTime PCR Detection System. Gene expression was normalized to 18S rRNA measured in the same samples. Graphs depict the mean ± standard deviation of results obtained from 28 AD and 26 control cases. Data were analyzed using Student's t-tests. Significant p-values are indicated over the bars. Note that insulin gene expression was not detected in temporal cortex.

Figure 2.

Brain insulin and IGF deficiency and resistance increase with progression of AD. Postmortem histopathological studies categorized the brains as having normal aging (Braak 0–1), or mild (Braak 2–3), moderate to severe (Braak 4–5), or end-stage (Braak 6) AD. We used mRNA isolated from fresh frozen frontal lobe tissue to measure insulin, IGF-1, or IGF-2 (A) polypeptide or (B) receptor gene expression by qRT-PCR. Results were normalized to 18S rRNA measured in the same samples. (C) For the competitive equilibrium binding assays, frontal lobe membrane protein extracts were incubated with [125I]-labeled insulin, IGF-1, or IGF-2 in the presence or absence of excess cold ligand. Radioactivity present in membrane protein precipitates was measured in a gamma counter. Specific binding (fmol/mg) was calculated using the GraphPad Prism 4 software. All graphs depict the mean ± standard deviation of results obtained from 9–12 cases per group. Intergroup comparisons were made using analysis of variance (ANOVA) with post hoc Tukey–Kramer significance tests. Significant p-values are indicated over the bars. Note axis break in Panel B.

Figure 3.

Effects of intracerebral ic-STZ treatment on CNS expression of insulin and IGF (A) genes and (B) receptors and (C) ligand binding to the insulin, IGF-1, or IGF-2 receptors in temporal lobe tissue. Rat pups were given 50 mg/kg ic-STZ or vehicle and sacrificed 14 days later. Temporal lobe mRNA was used to measure gene expression by qRT-PCR, and results were normalized to 18S rRNA measured in the same samples. Note axis break in Panel A. (C) Competitive equilibrium binding assays were used to measure specific binding to the insulin, IGF-1, or IGF-2 receptors as described in Figure 2. Graphs depict the mean ± standard error of the mean of results. Data were analyzed statistically using Student's t-tests. Significant p-values are indicated over the bar graphs.

Figure 4.

Treatment with PPAR agonists restores brain insulin receptor binding in ic-STZ-treated rats. Long Evans rat pups were treated with 50 mg/kg ic-STZ or vehicle and sacrificed 30 days later to examine brains for insulin and IGF polypeptide and receptor gene expression and insulin and IGF receptor binding. Temporal lobe membrane protein extracts were used in competitive equilibrium binding assays to measure specific binding to the (A) insulin, (B) IGF-1, or (C) IGF-2 receptors as described in Figure 2. Graphs depict the mean ± standard error of the mean of results. Data were analyzed using ANOVA with the Tukey–Kramer post hoc significance test. Significant p-values are shown within each panel.

Figure 5.

Peroxisome proliferator-activated receptor-δ agonist treatment preserves visual-spatial learning and memory in ic-STZ-treated rats. Long Evans rat pups were treated with 50 mg/kg ic-STZ or vehicle, followed by a single intraperitoneal injection of a PPAR-α (GW7647; 25 µg/kg), PPAR-δ (L-160,043; 2 µg/kg), or PPAR-γ (F-L-Leu; 20 µg/kg) agonist (n = 8 rats per group). Four weeks later, the rats were subjected to Morris water maze testing, in which the latency required to locate the hidden platform was measured for 3 independent trials on 4 consecutive days. Area under the curve (AUC) was computed for the 3 daily trials. Graphs depict the mean AUC ± standard error of the mean for latency (seconds) in each group. Data were analyzed using ANOVA with the Tukey–Kramer post hoc significance test. Performance in the control and ic-STZ + PPAR-δ groups were similar, and on Days 2, 3, and 4, their mean latencies required to locate the hidden platform were significantly shorter than in the other 3 groups.