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Review

Gut dysbiosis, insulin resistance and Alzheimer’s disease: review of a novel approach to neurodegeneration

Evelyn Lazar1,*,Ayesha Sherzai1,Jennifer Adeghate2,Dean Sherzai1
1
Department of Neurology, Loma Linda University Health, Loma Linda, CA 92350, USA
2
Department of Ophthalmology, University of Pittsburgh, Pittsburgh, PA 15213, USA
DOI: 10.52586/S550 Volume 13 Issue 1, pp.17-29
Submited: 15 July 2020 Accepted: 29 January 2021 Published: 30 June 2021
*Corresponding Author(s):  
Evelyn Lazar
E-mail:  
evelynlazar.md@gmail.com
Copyright: © 2021 The author(s). Published by BRI. This is an open access article under the CC BY 4.0 license (http://creativecommons.org/licenses/by/4.0/).
Abstract

Alzheimer’s disease (AD) and type 2 diabetes mellitus (T2DM) share many common features including inflammation, oxidative stress and neuronal degeneration. Insulin resistance (IR) appears to be a common path in these pathological processes. IR is an early pathogenic event in AD, which leads to augmentation of hyperphosphorylated tau and Amyloid beta (Aβ).

The reviewed studies related to AD have revealed a positive association between T2DM and AD. This association was maintained in peripheral hyperinsulinemia cases without the presence of T2DM, which might be due to decreased insulin transport to the brain or the inadequate cerebral insulin production. Gut dysbiosis induces inflammation and consequently provokes both peripheral and cerebral IR and can amplify processes promoting AD.

Additionally, the risk of increased progression of AD was revealed due to pre­-diabetes, T2DM and gut dysbiosis. The pro-­inflammatory changes might affect progression of AD pathology by inhibition of the autophagolysosomal pathway and cerebral insulin signaling pathway. This review elaborates the role that cerebral IR might play in the underlying pathological events in AD.

Key words

Alzheimer’s disease; Type 2 diabetes mellitus; Gut dysbiosis; Brain insulin resistance; Peripheral insulin resistance

2. Introduction

Alzheimer’s disease (AD) is a neurodegenerative brain disorder and the most common cause of dementia [1].

According to an estimation of the 2010 US Census Bureau and the Chicago Health and Aging Project (CHAP) 5.8 million Americans age 65 and older are living with Alzheimer’s disease in 2020. The population with AD increases with age, and the estimated growth of the population of age 65 and older is from 56 million in 2020 to 88 million by 2050. If there are no preventative measures instituted, the number of those suffering from AD is expected to grow to 13.8 million by 2050 [2, 3].

In 2011, the National Institute on Aging (NIA) and the Alzheimer’s Association revised the diagnostic guidelines of AD, determining the stages of the disease based on clinical symptoms and biomarkers [4, 5]. Their recent studies have examined the brain processes underlying cognitive impairment by using post-mortem samples and in vivo biomarkers. In AD, slowly progressive cognitive decline is associated with characteristic pathological changes such as accumulation of beta-amyloid plaques outside neurons, and tau-protein tangles inside neurons [4, 5]. Inflammatory processes and enhanced amyloid aggregation consequently increase tau-protein accumulation, which exacerbate the progression of cognitive decline [6, 7].

Alzheimer’s disease is a multifactorial disease associated with both genetic and modifiable factors. Early-onset AD has been linked with genetic mutations of pre-senilin 1 and 2, as well as of the encoding gene of APP (amyloid precursor protein) [8]. Late-onset AD has been associated with factors such as older age, especially above 75 years, family history of AD, and being a carrier of the APOE (apo-lipoprotein E) ε4 gene [9, 10, 11, 12].

Multidomain lifestyle prevention trials have shown a significant effect on maintaining cognitive decline or improving cognitive performance among elderly who had increased risk of dementia. The interventions included dietary counseling, exercise, cognitive training, and management of vascular and metabolic risk factors [13, 14, 15]. The latest World Health Organization report highlighted diabetes, obesity, smoking and hypertension as leading risk factors contributing to increased risk of dementia and cognitive decline [16].

T2DM predisposes to the development of dementia in the elderly population and increases the risk of AD by two-to three-fold compared with subjects without T2DM [17, 18, 19]. Type 2 diabetes (T2DM) and AD share common pathological features including inflammation, oxidative stress, which contribute to insulin resistance and neuronal degeneration in both disorders [20, 21, 22, 23, 24]. Moreover, metabolic disturbances such as peripheral hyperglycemia and hyperinsulinemia before the T2DM stage have a negative impact on the pathophysiological processes and progression of AD [25, 26, 27]. A positive correlation was revealed between brain insulin signaling desensitization, brain insulin resistance and AD progression during the early stage of the disease regardless of the presence of T2DM [28, 29]. Dysbiosis due to the increased level of pro-inflammatory bacteria of the gut caused by a long-term high-fat diet can lead to systemic oxidative stress, inflammation and thus metabolic disturbance [30, 31]. This systemic inflammatory state might explain the increased risk of development of T2DM and AD with a high-fat diet [32, 33, 34].

Overall, prevention strategies that focus on improvement of metabolic impairment, such as lifestyle modification, may have a protective effect against cognitive decline in AD [13, 35, 36]. Our review aims to discuss the role that insulin resistance plays in Alzheimer’s disease as well as the effect of Type 2 diabetes and gut dysbiosis in the progression of cognitive decline in AD.

3. Methods

We searched PubMed for articles, clinical studies and human experimental studies published during 2015–2020, with search terms including Alzheimer’s disease, peripheral and brain insulin resistance, type 2 diabetes mellitus, gut microbiome and gut dysbiosis. The search yielded publications which covered human cell culture and brain tissue experiments, clinical trials, and population based studies, and excluded all animal related studies.

4. Impact of peripheral insulin resistance and presence of brain insulin resistance in Alzheimer’s disease

4.1 Population studies regarding the association between AD and T2DM

T2DM-associated decreased in cognitive function, memory impairment, and increased risk of AD have previously been shown by preliminary epidemiological studies [18, 37, 38]. Subsequent studies discussed below have focused on the correlation between AD progression and the level of peripheral insulin as well as the role that insulin plays in the brain.

Among numerous clinical trials which have demonstrated an association between DM and cognitive decline was a prospective cohort study, which showed a 19% greater cognitive decline over 20 years-in participants with diabetes than in participants without diabetes [39]. Decreased cognitive performance was found in the pre-diabetic group (HbA1c 5.7–6.4%), the poorly controlled diabetic group (HbA1c 7.0%), and in the group of participants who had longer standing diabetes. Moreover, a higher baseline insulin resistance, calculated using the homeostatic model assessment (HOMA), was related with a greater impairment of overall cognition, especially of memory. This association is independent of other vascular risk factors and hyperglycemic status [40, 41]. According to another prospective population-based study with an average 10-year follow up, insulin resistance and a higher level of plasma insulin increased the risk of AD within a short period [42]. However, the risk of AD was no longer evident after 3 years, which might indicate that insulin level is more an accelerator of neuropathological changes in AD rather than the causative factor (Table 1).

Table 1: Relationship between Alzheimer’s disease, cognitive performance, and insulin resistance.
Study design and objectives Sample Size Results Conclusions References
Statistical Analysis P-value
• Prospective study (1993–2004). To determine relation between insulin resistance and the risk of AD • N = 3139 Insulin resistance and AD: 1.39 (95% CI 1.04, 1.86) < 0.05 • Higher plasma insulin level and insulin resistance were associated a higher short-term risk of AD with an increase in risk of approximately 40%. [43]
• Prospective study (1987–2013). To determine if diabetes in mid-life is associated with a 20-year cognitive decline • N = 13351 20 years decline, No diabetes: -0.78 (95% CI: -0.80, -0.75) 0.071 • Diabetes in midlife was associated with significantly greater cognitive decline over 20 years. [40]
20 years decline, Diabetes: -0.92 (95% CI: -1.00, -0.85) Subjects with poorly controlled diabetes (HbA1c ≥ 7.0%) had a larger decline compared to persons whose diabetes was controlled (HbA1c < 7.0%).
Difference: -0.15 (95% CI: -0.22, -0.08)
• Prospective study (1990–2013). To determine the association • N = 1232 • Higher baseline HOMA-IR levels were associated with [41]
between HOMA- IR and cognitive performance in individuals 1. follow-up between 2004–2009 (N = 489) 1/a. β = -3.66 ± 1.24 1/a. 0.003 poorer cognitive performance after 15 years. The observed re-
with cardiovascular disease, with and without diabetes 1/b. exclusion of DM cases: β = -4.45 ± 1.54 1/b. 0.004 lationships were independent of vascular risk factors and dia-
2. follow-up between 2011–2013 (N = 347) 2/a. β = -0.16 ± 0.06 2/a. 0.006 betic status.
2/b. exclusion of DM cases: β = -0.17 ± 0.06 2/b. 0.008
• Cross-sectional study (2014). To determine the association • N = 444 • Hyperglycemia was associated with cognitive dysfunction, [42]
between HOMA-IR and cognitive performance 1. With diabetes (N = 61) 1. MMSE score: β = -0.105 HOMA-IR 1. 0.022 mainly in the executive function domain. IR was associated
2. Without diabetes (N = 383) 2. Logical memory II score: β = -0.091 2. 0.047 with memory impairment.
AD: Alzheimer’s disease; HOMA-IR: Homeostatic Model Assessment of Insulin Resistance; DM: diabetes mellitus; IR: insulin resistance.

4.2 Association between CSF insulin level and insulin resistance and AD

It is known that peripheral insulin levels correspond with insulin levels in the cerebrospinal fluid (CSF), as human studies have shown an increase in CSF insulin after injection of insulin peripherally in normal individuals [43]. Recent studies showed that individuals with peripheral insulin resistance have reduced CSF insulin levels. A study by Heni et al. showed a positive correlation between CSF insulin level and serum insulin level in insulin sensitive individuals, and a negative correlation between the two factors in insulin-resistant participants [44]. Another study by Kern et al., obese human subjects showed, independently from other variables, that insulin resistance negatively correlated with the CSF: plasma insulin ratio [45]. Reduced CSF insulin levels might be a consequence of impaired insulin transport through the blood-brain barrier (BBB) by receptor mediated transcytosis [46, 47]. According to an experimental model, reduced insulin receptor density on microvascular endothelial cell cultures of T2DM subjects can support this theory [48].

Reduced insulin levels were found in the CSF of participants with mild cognitive impairment (MCI) and early stage AD without the presence of an increased level in peripheral insulin [49]. On one hand, this could potentially be explained by the reduced brain insulin production in AD [29]. The insulin gene and insulin receptor expression was found to be at a higher distribution in the hypothalamus and the hippocampus in postmortem brain tissue and its reduction corresponded with the progression of AD [50, 51]. On the other hand, transcytosis of insulin may also be affected in AD and have an impact on the CSF insulin level. An experimental model of the BBB consisting of human cerebral microvascular endothelial cells (hCMEC/D3) showed decreased insulin transcytosis in the presence of Aβ40 and Aβ42 [52].

4.3 Association between CSF biomarkers, insulin resistance and AD

Although an inverse correlation was found between peripheral insulin levels and CSF insulin levels in AD, but a positive association was found between peripheral insulin levels and levels of AD biomarkers. This is supported by the study performed by Westwood et al., whereby a significant association was found between plasma insulin levels and CSF Aβ/tau ratio and tau levels [53]. In this study, the CSF and serum levels of molecules involved in the pathogenesis of AD and insulin resistance were also measured. One of the highlighted proteins was FCN2 (Ficolin-2), previously associated with brain atrophy, which is reduced in insulin resistance and its level showed a negative correlation with CSF Aβ levels in the insulin-resistant group [54, 55]. This can be support the idea that AD and insulin resistance, and thus T2DM, share common pathological pathways. Another study showed that even in cognitively asymptomatic individuals, the higher the Homeostatic Model Assessment of Insulin Resistance (HOMA-IR) value, the higher the level of soluble beta-amyloid precursor protein (sAPP-β) and Aβ42 markers in the CSF and the worse the memory performance [55]. sAPP-β is a product of cleavage of amyloid precursor protein (APP) by the enzyme β-secretase (BACE1), which is part of the amyloidogenic pathway and contributes to the formation of amyloid plaques [56]. An experiment model showed decreased cleavage at the β-secretase sites of APP in the presence of insulin [57]. The influence of insulin resistance on the accumulation of amyloid plaques examined with Pittsburgh compound B (PiB) PET scan and increased HOMA-IR value was associated with a higher amyloid burden in the frontal and temporal lobes in cognitively normal individuals [58]. Further investigation via follow-up of individuals with higher HOMA-IR should be performed in order to observe changes in cognitive function and amyloid deposition (Table 2).

Table 2: Relationship between Insulin Resistance and Alzheimer’s disease biomarkers and pathology.
Study designs and objectives Sample Size Results Conclusions References
Statistical Analysis P-value
• Cross-sectional study. To evaluate whether a higher HOMA- • N = 186 1. HOMA-IR frontal: F (1, 135) = 5.429 1. 0.021 • Normoglycemia with higher insulin resistance corresponded [58]
IR may predict greater amyloid burden using [C-11]-Pittsburgh 2. HOMA-IR temporal: F (1,135) = 4.751 2. 0.031 to higher PiB uptake in frontal and temporal areas, reflecting
compound (PiB) and PET scanning in asymptomatic, late 3. PiB uptake frontal: R2 = 0.071 3. < 0.05 increased amyloid deposition.
middle-aged adults. 4. PiB uptake temporal: R2 = 0.036 4. < 0.05
• Cross-sectional study. To evaluate whether higher HOMA- • N = 70 middle-aged 1. CSF sAPP-β (HOMA-IR): F (1 ,63) = 4.21 1. 0.044 • Higher HOMA-IR was associated with higher sAPP-β and [55]
IR and APOE-ε4 levels would be associated with greater AD cognitively asymptomatic 2. Aβ42 (HOMA-IR): F (1, 63) = 4.26 2. 0.043 Aβ42 levels. APOE-ε4 carriers had significantly higher lev-
pathology in the CSF and worse memory performance. adults with a parenteral 3. CSF sAPP-α (APOE ε4): F (1, 63) = 8.65 3. 0.005 els of sAPP-α, sAPP-β and P-tau181/ Aβ42 ratios compared
history of AD 4. sAPP-β (APOE ε4): F(1,63) = 7.74 4. 0.007 to noncarriers. Higher HOMA-IR and greater P-tau181/ Aβ42
5. P-tau181/Aβ42: F (1,63) = 5.21 5. 0.026 ratios predicted lower memory performance.
6. memory performance: F (1,60) = 6.14 6. 0.016
• Cross-sectional study. To examine the influence of IR on AD using plasma and CSF biomarkers related to IR and AD in cog- • N = 58 cognitively asymptomatic men • Significant correlation between plasma insulin and CSF Aβ/tau ratio. CSF and serum proteins significantly correlated [53]
nitively healthy men (age and APOE-ε4- matched). 1. IR (N = 28) 1. P-insulin and CSF T-tau: r = 0.310 1. 0.018 with CSF AD biomarkers (Aβ, T-tau and P-tau).
2. non-IR (N = 30) 2. P-insulin and CSF T-tau: r = 0.299 2. 0.023
3. compare IR (N = 28) and non-IR (N = 30) 3. FCN2 β = -0.57 3. 0.014
Aβ, amyloid beta; AD, Alzheimer’s disease; APOE, apolipoprotein E; CSF, cerebrospinal fluid; FCN2, Ficolin-2; HOMA-IR, Homeostatic Model Assessment of Insulin Resistance; IR, insulin resistance; PET, Positron emission tomography; P-insulin, peripheral insulin; sAPP-α, soluble amyloid beta precursor protein alfa; sAPP-β, soluble amyloid beta precursor protein beta.

4.4 Association between brain glucose metabolism, insulin resistance and AD

Cerebral glucose uptake through the BBB and metabolism in the brain are mainly insulin-independent and peripheral hyperinsulinemia does not have a strong effect on this process [59, 60]. However, some studies have found insulin resistance to alter brain glucose metabolism.

Insulin-independent glucose transporters are glucose transporter (GLUT) 1 in the astrocytes, GLUT3 in the neurons, and GLUT5 in the microglia [61, 62, 63]. The insulin-dependent, GLUT4 has limited expression in the brain and is found mainly in astrocytes [64]. A study examining the effects of insulin on human SH-SY5Y neuroblastoma cells revealed increased GLUT4 transporter translocation to the plasma membrane, as well as increased glucose uptake in the presence of insulin [65]. However, the role of insulin is mainly of regulatory nature within the brain. Insulin plays an important role in memory and learning processes, which was demonstrated in the medial-temporal lobe where it enhanced neuronal activity [66].

Insulin has been shown to directly stimulate neurite outgrowth by regulation of tau phosphorylation, which likely contributes to neuronal cytoskeleton dynamics and neural plasticity [67, 68]. Additionally, insulin enhances the proliferation and glycogen storage of astrocytes [69], which is supported by the fact that abundant insulin-dependent glucose transporters (GLUT4) can be found in astrocytes [64]. Thus, astrocytes can contribute to the metabolic changes in the brain during disease processes by effect on the metabolic demand of neurons [24].

Older adults with prediabetes or diabetes were shown to have greater insulin resistance associated with decreased cerebral glucose metabolism observed on fluorodeoxyglucose (FDG)-positron emission tomography (PET) [70]. The brain regions with reduced glucose metabolism were found in the posterior cingulate cortex, the precuneus region, the parietal cortices (Brodmann areas (BA) 7 and 40, the temporal/angular gyri (BA 39)), and the anterior and inferior prefrontal cortices, which are all affected in AD as well. Although the participants were not diagnosed with MCI, a reduced ability to recall words was recorded during an activation scan compared to the healthy adult group of similar age and level of education [70].

Other studies have found similar results, whereby a higher HOMA-IR value and decreased glucose metabolism in the medial temporal lobe were associated with worse immediate and delayed memory performance on neuropsychological testing [71]. Further examinations of the medial temporal lobe and hippocampus of individuals with MCI and higher HOMA-IR values showed hypo- or hypermetabolism in these areas, depending on the rate of progression of the disease [72]. Namely, active progression of the MCI stage is related with hypermetabolism, and AD with hypometabolism, as detected by FDG-PET scanning [72]. This phenomenon might be explained by metabolic compensation against the incremental amount of the amyloid deposition [73]. In addition to using FDG-PET scanning to map cognitive performance, Dowling et al. also measured CSF biomarkers in subjects during a 24-month period [74]. This study found that towards the later stages of AD, there is an inverse correlation between baseline CSF biomarkers of intra-neuronal neurofibrillary degeneration, t-Tau and p-Tau181p, and the progression of hypometabolism and cognitive decline [74]. Tau hyperphosphorylation can be stimulated by amyloid beta oligomers and it was revealed that insulin is able to inhibit Aβ-induced neuronal cell death and prevent Aβ fibrillarization in AD [75, 76].

Reduced brain insulin signaling, and thus brain insulin resistance, which was observed in AD and T2DM cases, can promote neurodegeneration by decreasing brain glucose metabolism and hyperphosphorylation of tau [76]. These pathological changes and the previously mentioned regulatory role of insulin points toward the direction that insulin resistance has an indirect effect on metabolic disturbance of the brain by contribution to neuronal cell death (Table 3) (Fig. 1).

.

Figure 1: Connection between neuronal insulin resistance and progression of Alzheimer’s Disease.
Table 3: Relationship between Alzheimer’s disease, insulin resistance, and cerebral glucose metabolism.
Study designs and objectives Sample Size Results Conclusions References
Statistical Analysis P-value
• Cross-sectional study. To examine cognitively normal individual with higher HOMA-IR value and diagnosed prediabetes • N = 23 with pre-diabetes or diabetes 1. Right frontal glucose metabolic rate uptake (HOMA-IR): r = -0.63 1. < 0.05 • Higher HOMA-IR associated with reduced glucose metabolic rate at areas affected by AD, including posterior cingulate [25]
and diabetes were associated with reduced cerebral glucose metabolic rate in AD related brain areas. 2. Posterior cingulate cortex glucose metabolic rate uptake (HOMA-IR): r = -0.58 2. < 0.05 cortex, the precuneus region, parietal cortices, the temporal/angular gyri, and the anterior and inferior prefrontal cortices.
• Cross-sectional study. To determine the association between IR, deficits in brain glucose metabolism, and cognitive perfor- • N = 150 middle-aged adults with normal cogni- 1. global glucose metabolism (HOMA-IR): β = -0.29 1. < 0.01 • Insulin resistance is associated with significantly lower regional cerebral glucose metabolism, especially the medial tem- [71]
mance in those at risk for AD. tion and parental history of AD 2. medial temporal lobe glucose metabolism (HOMA-IR): R2 = 0.178 2. < 0.001 poral lobe, which in turn may predict poorer memory performance.
3. immediate memory (Lower glucose metabolism): β = 0.317 3. < 0.001
4. delayed memory (Lower glucose metabolism): β = 0.305 4. < 0.001
• N = 280
• Cross-sectional study. To determine the association between 1. Control (N = 26) • Higher HOMA-IR predicted lower FDG metabolism in the [72]
FDG metabolism and HOMA-IR in MCI and AD. 2. MCI (N = 194) medial temporal lobe and hippocampus among participants
3. Stable (N = 148) with AD, and higher FDG for MCI participants who progressed
4. MCI progressed to AD (N = 39) FDG metabolism in hippocampus (MCI progressed): F = 0.098 ± 0.029 4. /a < 0.01 to AD by 24 months.
FDG metabolism in medial temporal lobe (MCI progressed): F = 0.099 ± 0.020 R2 = 0.211 4. /b < 0.001
5. AD (N = 60) 5. /a FDG metabolism in hippocampus (AD): F= -0.076 ± 0.032 5. /a < 0.05
5. /b FDG metabolism in medial temporal lobe: F = -0.074 ± 0.034 R2 = 0.096 5. /b < 0.05
• Prospective study (24-month follow-up). To evaluate rela- • N = 412 t-Tau (FDG-PET 24 months): r = -0.17 < 0.00047 • Higher baseline concentrations of t-Tau, and p-Tau181p were [74]
tionships between cerebrospinal fluid (CSF) analyses include p-Tau181p (FDG-PET 24 months): r = -0.27 associated with a decline in cerebral glucose metabolism.
hyperphosphorylated tau (p-Tau181p), β-amyloid 1-42 (Aβ1-42) and total tau (t-Tau). To evaluate change in cognitive func- p-Tau181p/Aβ1-42 (FDG-PET 24 months): r = 0.25 FDG-PET changes appeared to mediate t-Tau or t-Tau/Aβ1-42-associated cognitive change across all brain regions. Sig-
tion. To assess change in FDG uptake using PET scanning. t-Tau/Aβ1-42 ADAS-Cog (24 months): r = 0.37 nificant direct effects of alterations in Aβ1-42 levels on hypometabolism were observed in a single brain region: mid-
t-Tau (ADAS-Cog 24 months): r = 0.28 dle/inferior temporal gyrus.
FDG-PET M24 (ADAS-Cog 24 months): r = -0.66
1. Normal cognition (N = 82)
2. MCI (N = 241)
3. AD (N = 89) 3. FDG-PET M24 (ADAS-Cog 24 months): r = -0. 40 3. < 0.00047
Aβ, amyloid beta; AD, Alzheimer’s disease; ADAS-Cog, Alzheimer’s disease assessment scale-cognitive subscale-13 items; FDG, [18F]-fluorodeoxyglucose; HOMA-IR, Homeostatic Model Assessment of Insulin Resistance; MCI, mild cognitive impairment; PET, Positron emission tomography.

5. Brain insulin pathway and mechanism

The presence of insulin resistance and the detailed steps of the insulin pathway were examined on post-mortem brain tissues from non-diabetic subjects with AD and MCI and control subjects [77, 78]. The examined areas were the hippocampus, the dentate gyrus and subiculum, the prefrontal cortex and the cerebellar cortex. The insulin biological pathway under normal conditions is Insulin Insulin receptor (IR) insulin receptor substrate-1 (IRS1) phosphoinositide 3-kinase (PI3K) Akt, which inhibits several intracellular regulatory molecules, including apoptosis- inducing molecules such as, glycogen synthase kinase 3 (GSK-3) and the mammalian target of rapamycin (mTOR) complex. The central molecule in insulin signaling is IRS1, which is inhibited by serine kinases such as GSK-3 and mTOR via feedback inhibition, and extracellular signal-regulated kinase 2 (ERK2), inhibitor of kappa B kinase (IKK), and c-Jun N-terminal kinase (JNK) via feedforward inhibition. Phosphorylation of the serine residue instead of the tyrosine residue on IRS1 leads to the disruption of the insulin signal, and therefore towards insulin resistance.

The levels of all the aforementioned kinases were elevated in brain tissue samples of subjects with AD, and elements of the amyloid plaques (Aβ oligomers) were shown to activate some of these kinases as well [77, 78]. Gradually increased levels of serine phosphorylated IRS1 (pSer-IRS1), from MCI to AD, were measured in post mortem brain tissue without diabetes and independently of APOE ϵ4 status, which found that elevated levels of serine kinases correlated with an increased accumulation of amyloid plaques [79]. Another complex study further provided evidence of the role of IRS1 in AD, which focused on the association between the neuronal phosphorylated IRS1 and brain atrophy in AD [80]. In this study, brain volume was positively associated with p-panTyr-IRS-1 (insulin signaling pathway) and negatively associated with pSer312-IRS-1 (insulin inhibition pathway) in the parietal-occipital junction and middle temporal gyrus bilaterally [80]. The volumetric variations were spatially correlated with IRS1 expression in normal brains [81]. Briefly, a likely cause of atrophy could be the impaired inhibitory effect of the insulin signal against apoptosis and oxidative stress [80]. All of these steps are directly affected by Aβ oligomers and lead to insulin signal inhibition [82].

Insulin has a role in impeding amyloid beta accumulation by promoting APP cleavage into the non-amyloidogenic, soluble sAPPalfa and stimulating the degradation of Aβ and proper functioning of the autophago-lysosomal pathway [56, 57, 77, 83]. Insulin enhances the transcription of α-secretase (ADAM10), which cleaves the APP in normal conditions [56, 57]. However, APP cleaved by beta-secretase (BACE1) and y-secretase lead to the amyloidogenic sAPP-β and AICG production [56, 57]. GSK3 phosphorylates APP intracellular domain (AICD), which is then able to translocate into the nucleus and form a complex with nuclear proteins, thereby activating transcription of amyloid production proteins such BACE1, APP, GSK3. Insulin inhibits AICD translocation by inhibition of GSK3 activity [83, 84]. Insulin also stimulates insulin-degrading enzyme (IDE) transcription, which promote Aβ degradation. Astrocytes are the main source of IDE and Aβ can be degraded by stimulating IDE secretion of astrocytes via the autophagy-based secretory pathway in AD [85]. Increased IDE activity was found in postmortem brain tissue in AD and its reduced activity towards the later stage of the disease is explained by increased neurodegeneration [86].

Aβ and tau are removed by autophago-lysosomal pathway and mTOR is one of the regulatory molecules of autophagy induction. However, the increasing level of Aβ leads to mTOR hyperactivity, which in turn inhibits autophagosome and lysosome fusion in neurons [87]. The increased level of Aβ further activates mTOR, which as a vicious cycle leads to a higher level of Aβ. Insulin can contribute the proper autophagy function by induction of mTOR through the PI3K/akt pathway [77, 83].

The Aβ monomer, in contrast to the Aβ oligomer, is the physiologic form of Aβ. It activates the PI3K/Akt pathway, leading to the phosphorylation of cAMP response element binding (CREB) protein, which binds to the cAMP response element (CRE) and mediates brain-derived neurotrophic factor (BDNF) transcription [88]. Aβ oligomers inhibit BDNF transcription by decreasing the level of the phosphorylated active form of CREB [88]. BDNF has a crucial role in human hippocampal synaptic plasticity via increasing the expression of synaptic proteins involved in the learning and memory processes, and the absence of it can therefore lead to neurodegeneration [89, 90].

6. Gut dysbiosis- inflammatory pathway—neurodegeneration

The microglia is the main phagocyte in the central nervous system (CNS), and provides a surveillance mechanism against pathogens via toll-like receptors (TLR), antigen presentation and cytotoxicity activity, such as the production of reactive oxygen species and cytokines [91]. Microglia shows pro-inflammatory hyperactivation in AD, which is induced by interferon gamma (INFγ), tumor necrosis factor alpha (TNFα), interleukins (IL) 4 and 13 and TLR ligand. The cytokines produced by microglia are TNFα, IL1-β and α, and IL6 [92, 93]. One of the several effects of the pro-inflammatory signal is insulin signal inhibition via augmentation of JNK activity, which in turn leads to the inhibition of IRS1 as was detected in post-mortem AD brain tissues [94].

Furthermore, the transcription of certain microRNAs (miRNA), such as miRNA-125b, is upregulated in AD, and has a higher concentration in the CSF [95]. This is thought to be a consequence of activated nuclear factor kappa B (NF-κB) via the inflammatory pathway. The upregulated miRNA-125b has been shown to downregulate several essential brain genes that have a critical role in neuroprotection via neuroprotectin D1, anti-inflammation via NF-kB regulation, and immuno-regulation via vitamin D3 receptor (VDR) [96]. The overexpression of miRNA-125b was also associated with tau hyperphosphorylation due to the downregulation of phosphatases and the neuroprotective Bcl-2-like protein 2 (Bcl2L2, Bcl-w) [97].

Peripheral proinflammatory cytokines may be able to activate the microglia, as the integrity of the BBB decreases with age. BBB degradation, which begins in the hippocampus, was observed to be more prominent in MCI and in early-onset AD than in normal aging brains [98, 99]. Moreover, hyperglycemia and hyperinsulinemia, as seen in T2DM, synergistically impair the permeability of the BBB [100]. Additionally, T2DM enhances the pro-inflammatory signals due to the increase the oxidative stress and NF-κB - mediated inflammation [101].

The enhanced pro-inflammatory signals in the brain due to the increased cytokines and reactive oxygen species (ROS) leads to initiation of autophagy. Accumulation of Aβ increases the production of ROS and blocks the aforementioned lysosomal degradation. A study by Lipinski et al. revealed that besides the ROS- dependent activation, autophagy is up-regulated at the transcriptional level as well in AD. Although increased autophagic activity can be preventive in normal aging brain it is counterproductive in AD due to the failure of autophagolysosome formation [102, 103].

Production of pro-inflammatory cytokines (IL-1, IL-6, TNFα) can be triggered by bacterial lipopolysaccharides (LPS), and as the integrity of the human intestinal barrier decreases with age, the cytokines and LPS can further cause systemic and cerebral inflammation [104, 105]. Furthermore, these pathological connections correspond to the detected LPS and Escherichia coli fragments in amyloid plaques of post-mortem AD brain tissues [105].

Several bacteria have the ability to cause inflammation, such as Bacteriodes, Alistipes, Gemella, and Blautia, which are more abundantly found in AD cases, whereas the anti-inflammatory bacteria, including Firmicutes, Actinobacteria, Dialister, and Bifidobacterium are less abundant in AD [106]. An increased number of AD-related bacteria was associated with a greater level of CSF AD biomarkers (p-tau and p-tau/Aβ42), while presence of the less abundant AD-related bacteria were associated with a lower level of AD biomarkers in the CSF [106].

An investigation by Cattaneo et al. showed a greater number of pro-inflammatory bacteria, such as Escherichia and /Shigella species, and a lower number of anti-inflammatory, Eubacterium rectale species in the gut of cognitively impaired individuals [107]. Furthermore, an increased level of pro-inflammatory bacteria was found in cognitively impaired subjects with detectable amyloid plaques by PET scan, but not in subjects with undetectable amyloid plaques [107].

The abundant pro-inflammatory bacteria already predominate in the pre-diabetic state, and the increased ratio of Bacteroidetes to Firmicutes is accompanied by reduced glucose tolerance in diabetes [108, 109]. This indicates that systemic inflammation, influenced by the composition of the gut microbiome, may have a significant role in the progression of pre-diabetes and that of AD.

Dietary habits influence the composition of the microbiome; an animal-based diet, including meat, eggs, and cheeses increased the abundance of Bacteriodes and decreased the abundance of Firmicutes [110]. In contrast, the plant-based diet, rich in grains, legumes, fruits, and vegetables increased the abundance of fiber-fermenting Firmicutes, such as Eubacteria and Roseburia, which leads to an increased level of short-chain fatty acids [110].

SCFAs, particularly butyrate, have several protective features such as maintenance of the integrity of the intestinal wall, tight junction amplification and maintaining the balance of the inflammatory pathways [111]. Butyrate suppresses production of the bacterial LPS-induced pro-inflammatory cytokines, IL-1, IL2, IL6, IL8, IL12, and TNFα by blocking the NF-κB transcription factor [112]. Moreover, it can promote the differentiation of anti-inflammatory IL-10-producing type 1 regulatory T cell by inhibiting histone deacetylases [113]. In addition to the immunomodulatory features, SCFAs have been shown to regulate the protein-protein interactions between Aβ1-40 and Aβ1-42 peptides, thus impeding the assembly of neurotoxic Aβ aggregates [114] (Fig. 2).

. The gut microbiome plays an important role in insulin resistance in Alzheimer’s Disease and in Type 2 Diabetes.

Figure 2: Dietary effect on the gut microbiome. The gut microbiome plays an important role in insulin resistance in Alzheimer’s Disease and in Type 2 Diabetes.

7.Conclusions

The pathological processes of AD and T2DM share many common features such as inflammation. Moreover, the peripheral insulin resistance without the development of T2DM can further exacerbate the pathological processes in the progression of AD. The pathological changes of AD may also be involved in causing insulin resistance in neurons. It is important to note that the function of the insulin in the central nervous system is primarily neuroregulatory, and has less of a role in the metabolism of glucose in the brain, in contrast to its function in peripheral organ systems.

Numerous factors play a role in the development of AD, and each enhancing the other can cause a vicious cycle in its progression. The discussed experimental models, clinical trials and population-based studies indicate that brain insulin resistance can be present independently of peripheral insulin resistance, which itself leads to amyloid plaque and tau neurofibrillary tangle formation and consequently neuronal cell death.

The presented studies indicate that gut dysbiosis might be one of the causative factors of brain insulin resistance, independently of peripheral insulin resistance. This theory is supported by the previously revealed pathological inflammatory pathways stimulated by gut dysbiosis. Peripheral insulin resistance can also develop or become further accelerated by the stimulated pro-inflammatory pathways in gut dysbiosis. Therefore, evidence suggests that gut dysbiosis may have a crucial role in the progression of AD by promoting insulin resistance in the periphery and in the brain. There is a negative association between a reduced anti-inflammatory bacterial load and AD pathology. On the other hand, an abundantly anti-inflammatory gut microbiome presumably decreases the risk and progression of AD by production of protective factors, such as SCFA. Consequently, lifestyle modification, which a properly composed healthy diet is a pivotal part of, has proved its efficient protective role in deceleration of cognitive decline in AD.

Both AD and T2DM are considered chronic diseases, which constantly develop from the asymptomatic to the symptomatic forms. The progression of AD can be mitigating by alleviation of aggravating factors, such as systemic inflammation and diabetes. The presence of brain IR in AD, elaborated in this review, needs further clarification by possible postmortem brain tissue evaluation and clinical trials. Increasing evidence shows the determinative role of inflammation in the progression of AD that might attain its effect through the brain insulin pathway and the defective autophagic function. Moreover, it was revealed that insulin can affect autophagy via regulatory molecules. These associations point towards new therapeutic targets.

In conclusion, we propose the importance of implementing adequate lifestyle changes and initiating timely treatment of chronic inflammatory conditions and metabolic dysfunction in order to decrease the risk of and prevent progression of AD. Further research is warranted in the investigation of these associations.

8. Author contributions

EL conceived and designed the research project and interpreted the data. She was a major contributor in writing the manuscript. AS, DS and JA contributed writing and editing the manuscript. AS and DS revised manuscript. All authors read and approved the final manuscript.

9. Ethics approval and consent to participate

Not applicable.

10. Acknowledgment

Not applicable.

11. Funding

Not applicable.

12. Conflict of interest

The author declares no conflict of interest.

References
  • [1] Barker WW, Luis CA, Kashuba A, Luis M, Harwood DG, Loewenstein D, et al. Relative frequencies of Alzheimer disease, lewy body, vascular and frontotemporal dementia, and hippocampal sclerosis in the state of Florida Brain Bank. Alzheimer Disease & Associated Disorders. 2002; 16: 203–212.
  • [2] Bienias JL, Beckett LA, Bennett DA, Wilson RS, Evans DA. Design of the Chicago Health and Aging Project (CHAP). Journal of Alzheimer’s Disease. 2004; 5: 349–355.
  • [3] Hebert LE, Weuve J, Scherr PA, Evans DA. Alzheimer disease in the United States (2010–2050) estimated using the 2010 census. Neurology. 2013; 80: 1778–1783.
  • [4] Jack CR, Albert MS, Knopman DS, McKhann GM, Sperling RA, Carrillo MC, et al. Introduction to the recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimer’s & Dementia. 2011; 7: 257–262.
  • [5] Jack CR, Bennett DA, Blennow K, Carrillo MC, Dunn B, Haeberlein SB, et al. NIA-AA Research Framework: toward a biological definition of Alzheimer’s disease. Alzheimer’s & Dementia. 2018; 14: 535–562.
  • [6] Hanseeuw BJ, Betensky RA, Jacobs HIL, Schultz AP, Sepulcre J, Becker JA, et al. Association of amyloid and Tau with cognition in preclinical Alzheimer disease. JAMA Neurology. 2019; 76: 915–924.
  • [7] Sato C, Barthélemy NR, Mawuenyega KG, Patterson BW, Gordon BA, Jockel-Balsarotti J, et al. Tau kinetics in neurons and the human central nervous system. Neuron. 2018; 97: 1284–1298.e7.
  • [8] Lautenschlager NT, Cupples LA, Rao VS, Auerbach SA, Becker R, Burke J, et al. Risk of dementia among relatives of Alzheimer’s disease patients in the MIRAGE study: what is in store for the oldest old? Neurology. 1996; 46: 641–650.
  • [9] Fratiglioni L, Ahlbom A, Viitanen M, Winblad B. Risk factors for late-onset Alzheimer’s disease: a population-based, case-control study. Annals of Neurology. 1993; 33: 258–266.
  • [10] Mayeux R, Sano M, Chen J, Tatemichi T, Stern Y. Risk of dementia in first-degree relatives of patients with Alzheimer’s disease and related disorders. Archives of Neurology. 1991; 48: 269–273.
  • [11] Saunders AM, Strittmatter WJ, Schmechel D, St. George-Hyslop PH, Pericak-Vance MA, Joo SH, et al. Association of apolipoprotein E allele ϵ4 with late-onset familial and sporadic Alzheimer’s disease. Neurology. 1993; 43: 1467–1467.
  • [12] Kandimalla RJ, Prabhakar S, Binukumar BK, Wani WY, Gupta N, Sharma DR, et al. Apo-Eε4 allele in conjunction with Aβ42 and tau in CSF: biomarker for Alzheimer’s disease. Current Alzheimer Research. 2011; 8: 187–196.
  • [13] Ngandu T, Lehtisalo J, Solomon A, Levälahti E, Ahtiluoto S, Antikainen R, et al. A 2 year multidomain intervention of diet, exercise, cognitive training, and vascular risk monitoring versus control to prevent cognitive decline in at-risk elderly people (FINGER): a randomised controlled trial. Lancet. 2015; 385: 2255–2263.
  • [14] Andrieu S, Guyonnet S, Coley N, Cantet C, Bonnefoy M, Bordes S, et al. Effect of long-term omega 3 polyunsaturated fatty acid supplementation with or without multidomain intervention on cognitive function in elderly adults with memory complaints (MAPT): a randomised, placebo-controlled trial. Lancet Neurology. 2017; 16: 377–389.
  • [15] van Charante EPM, Richard E, Eurelings LS, van Dalen J, Ligthart SA, van Bussel EF, et al. Effectiveness of a 6-year multidomain vascular care intervention to prevent dementia (preDIVA): a cluster-randomised controlled trial. Lancet. 2016; 388: 797–805.
  • [16] W. H. Organization. Risk reduction of cognitive decline and dementia: WHO guidelines. 2019.
  • [17] Crane PK, Walker R, Hubbard RA, Li G, Nathan DM, Zheng H, et al. Glucose levels and risk of dementia. New England Journal of Medicine. 2013; 369: 540–548.
  • [18] Ott A, Stolk RP, van Harskamp F, Pols HA, Hofman A, Breteler MM. Diabetes mellitus and the risk of dementia: the Rotterdam Study. Neurology. 1999; 53: 1937–1942.
  • [19] Stolk RP, Breteler MM, Ott A, Pols HA, Lamberts SW, Grobbee DE, et al. Insulin and cognitive function in an elderly population. The Rotterdam Study. Diabetes Care. 1997; 20: 792–795.
  • [20] Shoelson SE, Lee J, Goldfine AB. Inflammation and insulin resistance. Journal of Clinical Investigation. 2006; 116: 1793–1801.
  • [21] Marques-Vidal P, Schmid R, Bochud M, Bastardot F, von Känel R, Paccaud F, et al. Adipocytokines, hepatic and inflammatory biomarkers and incidence of type 2 diabetes. The CoLaus study. PLoS ONE. 2012; 7: e51768.
  • [22] Moran C, Beare R, Phan TG, Bruce DG, Callisaya ML, Srikanth V. Type 2 diabetes mellitus and biomarkers of neurodegeneration. Neurology. 2015; 85: 1123–1130.
  • [23] de la Monte SM, Re E, Longato L, Tong M. Dysfunctional pro-ceramide, ER stress, and insulin/IGF signaling networks with progression of Alzheimer’s disease. Journal of Alzheimer’s Disease. 2012; 30: S217–S229.
  • [24] Lee S, Tong M, Hang S, Deochand C, de la Monte S. CSF and brain indices of insulin resistance, oxidative stress and neuro-inflammation in early versus late Alzheimer’s disease. Journal of Alzheimer Disease & Parkinsonism. 2013; 3: 128.
  • [25] Baker LD, Cross DJ, Minoshima S, Belongia D, Watson GS, Craft S. Insulin resistance and Alzheimer-like reductions in regional cerebral glucose metabolism for cognitively normal adults with prediabetes or early type 2 diabetes. Archives of Neurology. 2011; 68: 51–57.
  • [26] Matsuzaki T, Sasaki K, Tanizaki Y, Hata J, Fujimi K, Matsui Y, et al. Insulin resistance is associated with the pathology of Alzheimer disease: the Hisayama Study. Neurology. 2010; 75: 764–770.
  • [27] Kerti L, Witte AV, Winkler A, Grittner U, Rujescu D, Flöel A. Higher glucose levels associated with lower memory and reduced hippocampal microstructure. Neurology. 2013; 81: 1746–1752.
  • [28] Frölich L, Blum-Degen D, Bernstein HG, Engelsberger S, Humrich J, Laufer S, et al. Brain insulin and insulin receptors in aging and sporadic Alzheimer’s disease. Journal of Neural Transmission. 1998; 105: 423–438.
  • [29] Rivera EJ, Goldin A, Fulmer N, Tavares R, Wands JR, de la Monte SM. Insulin and insulin-like growth factor expression and function deteriorate with progression of Alzheimer’s disease: link to brain reductions in acetylcholine. Journal of Alzheimer’s Disease. 2005; 8: 247–268.
  • [30] Wu GD, Chen J, Hoffmann C, Bittinger K, Chen Y, Keilbaugh SA, et al. Linking long-term dietary patterns with gut microbial enterotypes. Science. 2011; 334: 105–108.
  • [31] Simões CD, Maukonen J, Kaprio J, Rissanen A, Pietiläinen KH, Saarela M. Habitual dietary intake is associated with stool microbiota composition in monozygotic twins. Journal of Nutrition. 2013; 143: 417–423.
  • [32] Anderson EJ, Lustig ME, Boyle KE, Woodlief TL, Kane DA, Lin C, et al. Mitochondrial H2O2 emission and cellular redox state link excess fat intake to insulin resistance in both rodents and humans. Journal of Clinical Investigation. 2009; 119: 573–581.
  • [33] Eskelinen MH, Ngandu T, Helkala E, Tuomilehto J, Nissinen A, Soininen H, et al. Fat intake at midlife and cognitive impairment later in life: a population-based CAIDE study. International Journal of Geriatric Psychiatry. 2008; 23: 741–747.
  • [34] Kalmijn S, Launer LJ, Ott A, Witteman JC, Hofman A, Breteler MM. Dietary fat intake and the risk of incident dementia in the Rotterdam Study. Annals of Neurology. 1997; 42: 776–782.
  • [35] Craft S. Insulin resistance and Alzheimer’s disease pathogenesis: potential mechanisms and implications for treatment. Current Alzheimer Research. 2007; 4: 147–152.
  • [36] Bayer-Carter JL, Green PS, Montine TJ, VanFossen B, Baker LD, Watson GS, et al. Diet intervention and cerebrospinal fluid biomarkers in amnestic mild cognitive impairment. Archives of Neurology. 2011; 68: 743–752.
  • [37] Leibson CL, Rocca WA, Hanson VA, Cha R, Kokmen E, O’Brien PC, et al. Risk of dementia among persons with diabetes mellitus: a population-based cohort study. American Journal of Epidemiology. 1997; 145: 301–308.
  • [38] Perlmuter LC, Hakami MK, Hodgson-Harrington C, Ginsberg J, Katz J, Singer DE, et al. Decreased cognitive function in aging non-insulin-dependent diabetic patients. American Journal of Medicine. 1984; 77: 1043–1048.
  • [39] Rawlings AM, Sharrett AR, Schneider ALC, Coresh J, Albert M, Couper D, et al. Diabetes in midlife and cognitive change over 20 years: a cohort study. Annals of Internal Medicine. 2014; 161: 785–793.
  • [40] Lutski M, Weinstein G, Goldbourt U, Tanne D. Insulin resistance and future cognitive performance and cognitive decline in elderly patients with cardiovascular disease. Journal of Alzheimer’s Disease. 2017; 57: 633–643.
  • [41] Umegaki H, Makino T, Uemura K, Shimada H, Hayashi T, Cheng XW, et al. The associations among insulin resistance, hyperglycemia, physical performance, diabetes mellitus, and cognitive function in relatively healthy older adults with subtle cognitive dysfunction. Frontiers in Aging Neuroscience. 2017; 9: 72.
  • [42] Schrijvers EMC, Witteman JCM, Sijbrands EJG, Hofman A, Koudstaal PJ, Breteler MMB. Insulin metabolism and the risk of Alzheimer disease: the Rotterdam Study. Neurology. 2010; 75: 1982–1987.
  • [43] Grizzanti J, Corrigan R, Casadesus G. Neuroprotective effects of amylin analogues on Alzheimer’s disease pathogenesis and cognition. Journal of Alzheimer’s Disease. 2018; 66: 11–23.
  • [44] Heni M, Schöpfer P, Peter A, Sartorius T, Fritsche A, Synofzik M, et al. Evidence for altered transport of insulin across the blood-brain barrier in insulin-resistant humans. Acta Diabetologica. 2014; 51: 679–681.
  • [45] Kern W, Benedict C, Schultes B, Plohr F, Moser A, Born J, et al. Low cerebrospinal fluid insulin levels in obese humans. Diabetologia. 2006; 49: 2790–2792.
  • [46] Gray SM, Aylor KW, Barrett EJ. Unravelling the regulation of insulin transport across the brain endothelial cell. Diabetologia. 2017; 60: 1512–1521.
  • [47] Ohtsuki S, Ikeda C, Uchida Y, Sakamoto Y, Miller F, Glacial F, et al. Quantitative targeted absolute proteomic analysis of transporters, receptors and junction proteins for validation of human cerebral microvascular endothelial cell line hCMEC/D3 as a human blood-brain barrier model. Molecular Pharmaceutics. 2013; 10: 289–296.
  • [48] Gogg S, Smith U, Jansson P. Increased MAPK activation and impaired insulin signaling in subcutaneous microvascular endothelial cells in type 2 diabetes: the role of endothelin-1. Diabetes. 2009; 58: 2238–2245.
  • [49] de la Monte SM, Tong M, Daiello LA, Ott BR. Early-stage Alzheimer’s disease is associated with simultaneous systemic and central nervous system dysregulation of insulin-linked metabolic pathways. Journal of Alzheimer’s Disease. 2019; 68: 657–668.
  • [50] Steen E, Terry BM, J. Rivera E, Cannon JL, Neely TR, Tavares R, et al. Impaired insulin and insulin-like growth factor expression and signaling mechanisms in Alzheimer’s disease - is this type 3 diabetes? Journal of Alzheimer’s Disease. 2005; 7: 63–80.
  • [51] Hopkins DF, Williams G. Insulin receptors are widely distributed in human brain and bind human and porcine insulin with equal affinity. Diabetic Medicine. 1997; 14: 1044–1050.
  • [52] Sarma V. Insulin trafficking perturbations at the blood-brain barrier in alzheimer’s disease models [dissertation]. Retrieved from the University of Minnesota Digital Conservancy. 2017.
  • [53] Westwood S, Liu B, Baird AL, Anand S, Nevado-Holgado AJ, Newby D, et al. The influence of insulin resistance on cerebrospinal fluid and plasma biomarkers of Alzheimer’s pathology. Alzheimer’s Research & Therapy. 2017; 9: 1–11.
  • [54] Sattlecker M, Kiddle SJ, Newhouse S, Proitsi P, Nelson S, Williams S, et al. Alzheimer’s disease biomarker discovery using SOMAscan multiplexed protein technology. Alzheimer’s & Dementia. 2014; 10: 724–734.
  • [55] Hoscheidt SM, Starks EJ, Oh JM, Zetterberg H, Blennow K, Krause RA, et al. Insulin resistance is associated with increased levels of cerebrospinal fluid biomarkers of Alzheimer’s disease and reduced memory function in at-risk healthy middle-aged adults. Journal of Alzheimer’s Disease. 2016; 52: 1373–1383.
  • [56] Sinha S, Anderson JP, Barbour R, Basi GS, Caccavello R, Davis D, et al. Purification and cloning of amyloid precursor protein β-secretase from human brain. Nature. 1999; 402: 537–540.
  • [57] Wang X, Yu S, Gao SJ, Hu JP, Wang Y, Liu HX. Insulin inhibits Abeta production through modulation of APP processing in a cellular model of Alzheimer’s disease. Neuro Enocrinology Letters. 2014; 35: 224–229.
  • [58] Willette AA, Johnson SC, Birdsill AC, Sager MA, Christian B, Baker LD, et al. Insulin resistance predicts brain amyloid deposition in late middle-aged adults. Alzheimer’s & Dementia. 2015; 11: 504–510.e1.
  • [59] Seaquist ER, Damberg GS, Tkac I, Gruetter R. The effect of insulin on in vivo cerebral glucose concentrations and rates of glucose transport/metabolism in humans. Diabetes. 2001; 50: 2203–2209.
  • [60] Hasselbalch SG, Knudsen GM, Videbaek C, Pinborg LH, Schmidt JF, Holm S, et al. No effect of insulin on glucose blood-brain barrier transport and cerebral metabolism in humans. Diabetes. 1999; 48: 1915–1921.
  • [61] Sasaki A, Horikoshi Y, Yokoo H, Nakazato Y, Yamaguchi H. Antiserum against human glucose transporter 5 is highly specific for microglia among cells of the mononuclear phagocyte system. Neuroscience Letters. 2003; 338: 17–20.
  • [62] Maher F, Vannucci S, Takeda J, Simpson IA. Expression of mouse-GLUT3 and human-GLUT3 glucose transporter proteins in brain. Biochemical and Biophysical Research Communications. 1992; 182: 703–711.
  • [63] Morgello S, Uson RR, Schwartz EJ, Haber RS. The human blood-brain barrier glucose transporter (GLUT1) is a glucose transporter of gray matter astrocytes. Glia. 1995; 14: 43–54.
  • [64] Nijland PG, Michailidou I, Witte ME, Mizee MR, van der Pol SMA, van Het Hof B, et al. Cellular distribution of glucose and monocarboxylate transporters in human brain white matter and multiple sclerosis lesions. Glia. 2015; 62: 1125–1141.
  • [65] Benomar Y, Naour N, Aubourg A, Bailleux V, Gertler A, Djiane J, et al. Insulin and leptin induce Glut4 plasma membrane translocation and glucose uptake in a human neuronal cell line by a phosphatidylinositol 3-kinase- dependent mechanism. Endocrinology. 2006; 147: 2550–2556.
  • [66] Rotte M, Baerecke C, Pottag G, Klose S, Kanneberg E, Heinze H, et al. Insulin affects the neuronal response in the medial temporal lobe in humans. Neuroendocrinology. 2005; 81: 49–55.
  • [67] Recio-Pinto E, Lang FF, Ishii DN. Insulin and insulin-like growth factor II permit nerve growth factor binding and the neurite formation response in cultured human neuroblastoma cells. Proceedings of the National Academy of Sciences of the United States of America. 1984; 81: 2562–2566.
  • [68] Lesort M, Jope RS, Johnson GVW. Insulin transiently increases tau phosphorylation: Involvement of glycogen synthase kinase-3β and Fyn tyrosine kinase. Journal of Neurochemistry. 1999; 72: 576–584.
  • [69] Heni M, Hennige AM, Peter A, Siegel-Axel D, Ordelheide A, Krebs N, et al. Insulin promotes glycogen storage and cell proliferation in primary human astrocytes. PLoS ONE. 2011; 6: e21594.
  • [70] Kasischke KA, Vishwasrao HD, Fisher PJ, Zipfel WR, Webb WW. Neural activity triggers neuronal oxidative metabolism followed by astrocytic glycolysis. Science. 2004; 305: 99–103.
  • [71] Willette AA, Bendlin BB, Starks EJ, Birdsill AC, Johnson SC, Christian BT, et al. Association of insulin resistance with cerebral glucose uptake in late middle-aged adults at risk for Alzheimer disease. JAMA Neurology. 2015; 72: 1013.
  • [72] Willette AA, Modanlo N, Kapogiannis D. Insulin resistance predicts medial temporal hypermetabolism in mild cognitive impairment conversion to Alzheimer disease. Diabetes. 2015; 64: 1933–1940.
  • [73] Mormino EC, Smiljic A, Hayenga AO, H. Onami S, Greicius MD, Rabinovici GD, et al. Relationships between beta-amyloid and functional connectivity in different components of the default mode network in aging. Cerebral Cortex. 2011; 21: 2399–2407.
  • [74] Dowling NM, Johnson SC, Gleason CE, Jagust WJ. The mediational effects of FDG hypometabolism on the association between cerebrospinal fluid biomarkers and neurocognitive function. NeuroImage. 2015; 105: 357–368.
  • [75] Rensink AAM, Otte-Höller I, de Boer R, Bosch RR, ten Donkelaar HJ, de Waal RMW, et al. Insulin inhibits amyloid β-induced cell death in cultured human brain pericytes. Neurobiology of Aging. 2004; 25: 93–103.
  • [76] De Felice FG, Wu D, Lambert MP, Fernandez SJ, Velasco PT, Lacor PN, et al. Alzheimer’s disease-type neuronal tau hyperphosphorylation induced by aβ oligomers. Neurobiology of Aging. 2008; 29: 1334–1347.
  • [77] Liu Y, Liu F, Grundke-Iqbal I, Iqbal K, Gong C. Deficient brain insulin signalling pathway in Alzheimer’s disease and diabetes. Journal of Pathology. 2011; 225: 54–62.
  • [78] Talbot K, Wang H. The nature, significance, and glucagon-like peptide-1 analog treatment of brain insulin resistance in Alzheimer’s disease. Alzheimer’s & Dementia. 2014; 10: S12–S25.
  • [79] Talbot K, Wang H, Kazi H, Han L, Bakshi KP, Stucky A, et al. Demonstrated brain insulin resistance in Alzheimer’s disease patients is associated with IGF-1 resistance, IRS-1 dysregulation, and cognitive decline. Journal of Clinical Investigation. 2012; 122: 1316–1338.
  • [80] Mullins RJ, Mustapic M, Goetzl EJ, Kapogiannis D. Exosomal biomarkers of brain insulin resistance associated with regional atrophy in Alzheimer’s disease. Human Brain Mapping. 2017; 38: 1933–1940.
  • [81] Hawrylycz MJ, Lein ES, Guillozet-Bongaarts AL, Shen EH, Ng L, Miller JA, et al. An anatomically comprehensive atlas of the adult human brain transcriptome. Nature. 2012; 489: 391–399.
  • [82] Najem D, Bamji-Mirza M, Yang Z, Zhang W. Aβ-induced insulin resistance and the effects of insulin on the cholesterol synthesis pathway and Aβ secretion in neural cells. Neuroscience Bulletin. 2016; 32: 227–238.
  • [83] Pandini G, Pace V, Copani A, Squatrito S, Milardi D, Vigneri R. Insulin has multiple antiamyloidogenic effects on human neuronal cells. Endocrinology. 2013; 154: 375–387.
  • [84] Liao M, Muratore CR, Gierahn TM, Sullivan SE, Srikanth P, De Jager PL, et al. Single-cell detection of secreted aβ and sappα from human ipsc-derived neurons and astrocytes. Journal of Neuroscience. 2016; 36: 1730–1746.
  • [85] Son SM, Cha M, Choi H, Kang S, Choi H, Lee M, et al. Insulin-degrading enzyme secretion from astrocytes is mediated by an autophagy-based unconventional secretory pathway in Alzheimer disease. Autophagy. 2016; 12: 784–800.
  • [86] Miners JS, Baig S, Tayler H, Kehoe PG, Love S. Neprilysin and insulin-degrading enzyme levels are increased in Alzheimer disease in relation to disease severity. Journal of Neuropathology & Experimental Neurology. 2009; 68: 902–914.
  • [87] Oddo S. The role of mTOR signaling in Alzheimer disease. Frontiers in Bioscience (Scholar Edition). 2013; 4: 941–952.
  • [88] Zimbone S, Monaco I, Gianì F, Pandini G, Copani AG, Giuffrida ML, et al. Amyloid Beta monomers regulate cyclic adenosine monophosphate response element binding protein functions by activating type-1 insulin-like growth factor receptors in neuronal cells. Aging Cell. 2018; 17: e12684.
  • [89] Mariga A, Mitre M, Chao MV. Consequences of brain-derived neurotrophic factor withdrawal in CNS neurons and implications in disease. Neurobiology of Disease. 2017; 97: 73–79.
  • [90] Hariri AR, Goldberg TE, Mattay VS, Kolachana BS, Callicott JH, Egan MF, et al. Brain-derived neurotrophic factor val66met polymorphism affects human memory-related hippocampal activity and predicts memory performance. Journal of Neuroscience. 2003; 23: 6690–6694.
  • [91] Boche D, Perry VH, Nicoll JA. Review: activation patterns of microglia and their identification in the human brain. Neuropathology and Applied Neurobiology. 2013; 39: 3–18.
  • [92] Walker DG, Lue L. Immune phenotypes of microglia in human neurodegenerative disease: challenges to detecting microglial polarization in human brains. Alzheimer’s Research & Therapy. 2015; 7: 1–9.
  • [93] Suh H, Zhao M, Derico L, Choi N, Lee SC. Insulin-like growth factor 1 and 2 (IGF1, IGF2) expression in human microglia: differential regulation by inflammatory mediators. Journal of Neuroinflammation. 2013; 10: 805.
  • [94] Taga M, Minett T, Classey J, Matthews FE, Brayne C, Ince PG, et al. Metaflammasome components in the human brain: a role in dementia with Alzheimer’s pathology? Brain Pathology. 2017; 27: 266–275.
  • [95] Alexandrov PN, Dua P, Hill JM, Bhattacharjee S, Zhao Y, Lukiw WJ. microRNA (miRNA) speciation in Alzheimer’s disease (AD) cerebrospinal fluid (CSF) and extracellular fluid (ECF). International Journal of Biochemistry and Molecular Biology. 2012; 3: 365–373.
  • [96] Zhao Y, Bhattacharjee S, Jones BM, Hill J, Dua P, Lukiw WJ. Regulation of neurotropic signaling by the inducible, NF-kB-sensitive miRNA-125b in Alzheimer’s disease (AD) and in primary human neuronal-glial (HNG) cells. Molecular Neurobiology. 2014; 50: 97–106.
  • [97] Banzhaf-Strathmann J, Benito E, May S, Arzberger T, Tahirovic S, Kretzschmar H, et al. MicroRNA-125b induces tau hyperphosphorylation and cognitive deficits in Alzheimer’s disease. The EMBO Journal. 2014; 33: 1667–1680.
  • [98] van de Haar HJ, Jansen JFA, van Osch MJP, van Buchem MA, Muller M, Wong SM, et al. Neurovascular unit impairment in early Alzheimer’s disease measured with magnetic resonance imaging. Neurobiology of Aging. 2016; 45: 190–196.
  • [99] Montagne A, Barnes SR, Sweeney MD, Halliday MR, Sagare AP, Zhao Z, et al. Blood-brain barrier breakdown in the aging human hippocampus. Neuron. 2015; 85: 296–302.
  • [100] Ito S, Yanai M, Yamaguchi S, Couraud P, Ohtsuki S. Regulation of tight-junction integrity by insulin in an in vitro model of human blood-brain barrier. Journal of Pharmaceutical Sciences. 2017; 106: 2599–2605.
  • [101] Muriach M, Flores-Bellver M, Romero FJ, Barcia JM. Diabetes and the brain: oxidative stress, inflammation, and autophagy. Oxidative Medicine and Cellular Longevity. 2014; 2014: 102158.
  • [102] Lipinski MM, Zheng B, Lu T, Yan Z, Py BF, Ng A, et al. Genome-wide analysis reveals mechanisms modulating autophagy in normal brain aging and in Alzheimer’s disease. Proceedings of the National Academy of Sciences of the United States of America. 2010; 107: 14164–14169.
  • [103] François A, Julian A, Ragot S, Dugast E, Blanchard L, Brishoual S, et al. Inflammatory stress on autophagy in peripheral blood mononuclear cells from patients with Alzheimer’s disease during 24 months of follow-up. PLoS ONE. 2015; 10: e0138326.
  • [104] Man AL, Bertelli E, Rentini S, Regoli M, Briars G, Marini M, et al. Age-associated modifications of intestinal permeability and innate immunity in human small intestine. Clinical Science. 2015; 129: 515–527.
  • [105] Zhan X, Stamova B, Jin L, DeCarli C, Phinney B, Sharp FR. Gram-negative bacterial molecules associate with Alzheimer disease pathology. Neurology. 2016; 87: 2324–2332.
  • [106] Vogt NM, Kerby RL, Dill-McFarland KA, Harding SJ, Merluzzi AP, Johnson SC, et al. Gut microbiome alterations in Alzheimer’s disease. Scientific Reports. 2017; 7: 1–11.
  • [107] Cattaneo A, Cattane N, Galluzzi S, Provasi S, Lopizzo N, Festari C, et al. Association of brain amyloidosis with pro-inflammatory gut bacterial taxa and peripheral inflammation markers in cognitively impaired elderly. Neurobiology of Aging. 2017; 49: 60–68.
  • [108] Larsen N, Vogensen FK, van den Berg FWJ, Nielsen DS, Andreasen AS, Pedersen BK, et al. Gut microbiota in human adults with type 2 diabetes differs from non-diabetic adults. PLoS ONE. 2010; 5: e9085.
  • [109] Lambeth SM, Carson T, Lowe J, Ramaraj T, Leff JW, Luo L, et al. Composition, diversity and abundance of gut microbiome in prediabetes and type 2 diabetes. Journal of Diabetes and Obesity. 2015; 2: 1.
  • [110] David LA, Maurice CF, Carmody RN, Gootenberg DB, Button JE, Wolfe BE, et al. Diet rapidly and reproducibly alters the human gut microbiome. Nature. 2014; 505: 559–563.
  • [111] Van den Abbeele P, Taminiau B, Pinheiro I, Duysburgh C, Jacobs H, Pijls L, et al. Arabinoxylo-oligosaccharides and inulin impact inter-individual variation on microbial metabolism and composition, which immunomodulates human cells. Journal of Agricultural and Food Chemistry. 2018; 66: 1121–1130.
  • [112] Säemann MD, Parolini O, Böhmig GA, Kelemen P, Krieger PM, Neumüller J, et al. Bacterial metabolite interference with maturation of human monocyte-derived dendritic cells. Journal of Leukocyte Biology. 2002; 71: 238–246.
  • [113] Kaisar MMM, Pelgrom LR, van der Ham AJ, Yazdanbakhsh M, Everts B. Butyrate conditions human dendritic cells to prime type 1 regulatory T cells via both histone deacetylase inhibition and G protein-coupled receptor 109A signaling. Frontiers in Immunology. 2019; 8: 1429.
  • [114] Ho L, Ono K, Tsuji M, Mazzola P, Singh R, Pasinetti GM. Protective roles of intestinal microbiota derived short chain fatty acids in Alzheimer’s disease-type beta-amyloid neuropathological mechanisms. Expert Review of Neurotherapeutics. 2018; 18: 83–90.
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Evelyn Lazar, Ayesha Sherzai, Jennifer Adeghate, Dean Sherzai. Gut dysbiosis, insulin resistance and Alzheimer’s disease: review of a novel approach to neurodegeneration. Frontiers in Bioscience-Scholar. 2021. 13(1); 17-29.