A systematic review on type 3 diabetes: bridging the gap between metabolic dysfunction and Alzheimer’s disease

Abstract

Background

Alzheimer’s disease (AD) is increasingly associated with metabolic dysfunction, particularly insulin resistance, which impairs neuronal signaling and energy metabolism. Disruption of brain insulin pathways contributes to amyloid-beta accumulation, tau pathology, and neuroinflammation. These shared features have led to the concept of “Type 3 Diabetes” (T3D). This review aims to investigate the molecular links between insulin resistance and AD and to highlight emerging therapeutic strategies.

Methods

A systematic review was conducted in accordance with PRISMA guidelines using PubMed, Scopus, Web of Science, and the Cochrane Library to identify studies published between January 2010 and July 2025. Search terms included “Diabetes Mellitus”, “Insulin Resistance”, “Alzheimer Disease”, “Nerve Degeneration”, “Cognitive Dysfunction”, and other related molecular and clinical keywords. After removing duplicates and applying predefined inclusion and exclusion criteria, a total of 213 peer-reviewed articles were included in the final analysis.

Results

Insulin resistance was consistently identified as a key pathological driver, impairing brain glucose uptake, amyloid-beta clearance, and tau phosphorylation. Disruption of insulin signaling pathways, especially PI3K/Akt and GLUT4 translocation, was associated with neuroinflammation, oxidative stress, and cognitive decline. Additionally, transcriptomic data highlighted the role of non-coding RNAs, including MEG3 and MALAT1, in modulating insulin sensitivity and glucose homeostasis, linking metabolic imbalance to neuronal dysfunction.

Conclusion

Insulin resistance and disrupted glucose metabolism play a central role in the development and progression of AD, supporting the concept of T3D. Targeting these pathways shows promising neuroprotective potential. Future studies should focus on validating these interventions in large-scale clinical trials.

Scheme 1.

A schematic illustration of multiple pathological mechanisms contributing to Alzheimer's disease progression is shown in Scheme 1 (as a graphical abstract). Multiple pathological mechanisms contribute to Alzheimer’s disease progression. The figure illustrates the major pathological processes involved in AD, including microglial activation, synaptic dysfunction, mitochondrial dysfunction, insulin resistance, neurofibrillary tangle (NFTs) formation, neuronal degeneration, inflammation, and amyloid-β (Aβ) deposition

Introduction

Diabetes mellitus (DM) is an endocrine disease, that affects more than 400 million people worldwide [1, 2]. In recent years, researchers have uncovered a compelling link between metabolic dysfunction and neurodegenerative diseases, leading to the emergence of the term “Type 3 Diabetes” (T3D) [3]. This term highlights a potential connection between insulin resistance, impaired glucose metabolism, and the development of Alzheimer’s disease (AD) [4]. Exploring this relationship is crucial, as it may open doors to new approaches for diagnosing, preventing, and treating both conditions.

The human brain relies heavily on glucose as its primary source of energy, and insulin plays a key role in regulating this process [5, 6]. When insulin signaling in the brain is disrupted, it can lead to changes associated with Alzheimer’s, such as the formation of amyloid-beta (Aβ) plaques, the formation of neurofibrillary tangles, and chronic inflammation [7]. At the same time, the metabolic imbalances seen in diabetes, such as high blood sugar, oxidative stress, and the production of harmful advanced glycation end-products (AGEs), which parallel the mechanisms that contribute to brain dysfunction [8, 9]. These overlapping pathways suggest that T3D could be more than just a metaphor; it may represent a specific form of Alzheimer’s driven by metabolic disturbances.

Population studies have consistently shown that people with diabetes are at greater risk of developing AD, and those with AD often show signs of disrupted glucose metabolism [4, 10]. This two-way relationship points to a deeper, shared biology between the two conditions. However, the underlying mechanisms remain poorly understood. It is still unclear whether diabetes directly contributes to AD pathogenesis or whether both disorders arise from common molecular and metabolic pathways. Although previous research has made progress in identifying potential links, findings are often fragmented, and no comprehensive synthesis exists that integrates molecular, clinical, and transcriptomic evidence across diverse studies. This highlights a critical gap in the literature. Therefore, a systematic review is warranted to consolidate current knowledge, clarify mechanistic overlaps, and evaluate emerging therapeutic strategies targeting this intersection.

This systematic review aims to address the concept of T3D by synthesizing current evidence on the molecular and metabolic links between insulin resistance and AD. Specifically, it examines how impaired insulin signaling, disrupted glucose metabolism, and regulatory non-coding RNAs contribute to AD pathogenesis. Furthermore, it highlights the role of neuroinflammation, oxidative stress, and neuronal energy deficits in this process. This review also evaluates the therapeutic potential of agents in targeting these shared mechanisms. Ultimately, this article aims to provide an integrated understanding of T3D as a bridge between metabolic dysfunction and neurodegeneration, and to guide future research directions and clinical interventions.

Methods

Literature search strategy

This systematic review was conducted in adherence to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines (https://www.prisma-statement.org/). A comprehensive literature search was carried out using multiple databases, including PubMed, Scopus, Web of Science, and the Cochrane library, covering studies published from January 2010 to July 2025. The primary search terms used in the review included “Diabetes Mellitus”, AND “Insulin Resistance”, AND “Metabolic Diseases “, AND “Alzheimer Disease”, AND “Amyloid beta-Peptides”, AND “Tau Proteins”, AND “Nerve Degeneration”, AND “Cognitive Dysfunction”, AND “Neuroinflammatory Diseases”. Complementary keywords were categorized to ensure a thorough exploration of related mechanisms, such as cellular processes (“Mitochondrial Diseases”, AND “Oxidative Stress”, AND “Autophagy”, AND “Apoptosis”, AND “Transcriptome”, AND “MicroRNAs”, AND “Non-coding RNA”, AND “Long Non-coding RNA”), inflammatory markers (“Cytokines”, AND “Reactive Oxygen Species”, AND “Antioxidants”), metabolic parameters (“Lipid Metabolism”, AND “Glucose Metabolism Disorders”, AND “Ketone Bodies”, AND “Non-coding RNA”), and clinical aspects (“Therapeutics”, AND “Disease Progression”, AND “Treatment Outcome”).

Study selection process

An initial search of the database identified 867 potentially relevant studies. After removing 425 duplicate articles, the remaining 442 titles and abstracts were reviewed according to predefined eligibility criteria. The studies were selected based on their examination of the molecular mechanisms linking insulin resistance and metabolic dysfunction to Alzheimer’s disease (referred to as type 3 diabetes), with a focus on cellular interactions, molecular pathways, and their neurobiological implications. Only original research, reviews, meta-analyses, cohort, cross-sectional, and clinical trial articles published in peer-reviewed journals, written in English, with clear methodological details and conducted on humans or animal models, were included. We excluded case reports, conference abstracts, non-peer-reviewed articles, non-English studies, those focused solely on clinical outcomes without mechanistic exploration, and articles that had insufficient data. After applying these criteria, a total of 213 studies were included in our study (Fig. 1).

Fig. 1.

PRISMA flow diagram for the selection of included studies

Data extraction and synthesis

Data extraction was independently performed by two authors (M.M. and T.N.) who screened the titles and abstracts of identified studies to exclude irrelevant articles, followed by a thorough review of the full texts of potentially relevant studies for final inclusion. Disagreements during the selection process were resolved by consulting the corresponding author (F.A.). Both M.M. and T.N. independently collected and verified data using a pre-defined standardized form, with any disputed studies or ambiguous data points referred to the corresponding author for final adjudication. Extracted data included authors, publication year, study design, diagnostic criteria for type 3 diabetes and Alzheimer’s disease, and key findings, with a primary focus on the molecular, clinical, and epidemiological relationships between diabetes and Alzheimer’s disease.

Quality assessment

According to the type of this study, no tests, scales, or biostatistical analyses were performed.

Ethical statement

As this is a systematic review, registration with the Research Ethics Committee (REC) was not required. Furthermore, all data supporting the findings of this review are fully available within the article.

Results

Prevalence of type 3 diabetes in global populations

Epidemiological evidence for type 3 diabetes

A recent meta-analysis revealed that individuals with Type 2 diabetes (T2D) face a 59% higher risk of developing dementia compared to those without diabetes [10]. In a cross-sectional descriptive study of 332 diabetic patients at Holy Family Hospital, Pakistan, 81 patients (24.4%) exhibited cognitive impairment [11]. Similarly, Mexican patients with type 2 diabetes had twice the risk of developing dementia compared to those without diabetes [12]. In Lebanon, approximately 68% of the diabetic patients showed signs of possible cognitive impairment [13] (Table 1).

Table 1.

Prevalence of type 3 diabetes in global populations

Region	Sample Size	Percentage with Cognitive Impairment	Study Duration	Ref.
United States	14,988 (4,192 diabetic patients)	19.9%	22 years	[14]
Sacramento Area Latino	1,617 patients	9.8% (159)	10 years	[15]
Mexico	1,193 patients	Twice the risk compared to non-diabetics	3 years	[16]
Chile	358 patients	2.8 times higher risk in older T2DM	Oct. 2017 to Sep. 2019	[17]
Pakistan	332 patients	24.4% (81 patients)	6 months	[11]
Lebanon	318 patients	68.2% (217 patients)	5 months	[13]
South India	108 patients	41.70% (45 patients)	1 month	[18]

T2DM: Type 2 diabetes mellitus

Age and gender disparities

Age is a key contributor to the development of dementia, and diabetes notably worsens nerve damage, thus increasing the risk of dementia [10]. The cognitive impairment is more common in women with diabetes than in men with the same condition [19]. However, diabetic women showed a significantly lower prevalence of cognitive impairment compared to men and performed better on cognitive function tests, particularly those assessing memory [20]. In terms of age, Dementia affects up to 16% of individuals with diabetes over the age of 65 and 24% of those over 75 [21].

Ethnic and genetic influences on prevalence

In an ADVANCE trial of 11,140 individuals with T2D from 20 countries, Participants of Asian ethnicity had higher odds of developing cognitive decline or dementia compared to non-Asians [22]. Similarly, in diabetic persons of Chinese ethnicity, ethnic-specific R192H variation in PAX4 has been linked to attention-specific cognitive impairment [23].

A summary of molecular mechanisms and pathophysiology of type 3 diabetes is shown in Table 2.

Table 2.

Molecular mechanisms and pathophysiology of type 3 diabetes

Author	Molecular Mechanism/Pathway	Key Findings	Ref.
Pan et al.	PI3K/Akt Signaling Pathway	Impaired PI3K/Akt pathway leads to decreased neuronal survival, tau hyperphosphorylation, and amyloid plaques	[24]
Albaik et al.	GLUT4 Dysfunction	Impaired GLUT4 translocation leads to decreased glucose uptake, disrupting synaptic transmission	[25]
Pivovarova et al.	IDE Dysfunction	Insulin resistance lower IDE expression, reducing Aβ clearance and promoting plaque formation	[26]
Jitendra Joshi, and Raja Sekhar Reddy	Tau Hyperphosphorylation	GSK-3β hyperactivity results in tau hyperphosphorylation and aggregation into NFTs	[27]
Dash et al.	Oxidative Stress and ROS Production	Elevated ROS levels, due to impaired mitochondrial function, contribute to oxidative stress and neurodegeneration	[28]
Marttinen et al.	Aβ Aggregation and Toxicity	Aβ aggregation induced by oxidative stress leads to synaptic dysfunction and neuroinflammation	[29]
Trigo et al.	Mitochondrial Dysfunction and Impaired Energy Homeostasis	Reduced mitochondrial biogenesis and increased oxidative stress hinder neuronal energy supply	[30]
Zhang et al.	Neuroinflammation (Microglial Activation)	Chronic inflammation and microglial activation elevate pro-inflammatory cytokines (e.g., IL-1β, TNF-α)	[31]
Green et al.	Astrocytic Dysfunction and Glutamate Toxicity	Reduced EAAT2 expression in astrocytes leads to glutamate accumulation, overstimulation of NMDA receptors	[32]
Davanzo et al.	Insulin Resistance and BBB Dysfunction	Insulin resistance leads to BBB breakdown, allowing pro-inflammatory monocyte infiltration	[33]

GLUT4: Glucose transporter type 4, IDE: Insulin-degrading enzyme, Aβ: Amyloid beta, GSK-3β: Glycogen synthase kinase-3 beta, NFTs: Neurofibrillary tangles, ROS: Reactive oxygen species, IL-1β: Interleukin-1β, TNF-α: Tumor Necrosis Factor-α, EAAT2: Excitatory amino acid transporter 2, BBB: Blood brain barrier

Insulin resistance and brain glucose metabolism

Impaired PI3K/Akt signaling and neuronal survival

In insulin-resistant states, reduced insulin receptor (IR) sensitivity results in decreased PI3K activation and impaired Akt phosphorylation [34]. This dysfunction leads to dephosphorylation and activation of glycogen synthase kinase-3 beta (GSK-3β), promoting tau hyperphosphorylation and neurofibrillary tangles (NFTs) [35, 36]. Akt dysfunction can also prevent the inhibition of (forkhead box O1) FOXO1, resulting in a reduction in cell survival and increased apoptosis [37] (Fig. 2).

Fig. 2.

Disrupted insulin signaling cascade in neurodegenerative diseases. Insulin binds to its receptor, activating insulin receptor substrates (IRS1/2) and the PI3K-Akt pathway. Dysregulation leads to downstream effects such as inhibition of mTORC1 and activation of GSK-3β, contributing to tau hyperphosphorylation and neurofibrillary tangles (NFTs) formation. FOXO1 dysregulation induces apoptosis and blood-brain barrier (BBB) disruption, while PGC1-α impairment affects mitochondrial biogenesis through reduced NRF1/2 and TFAM activity. Furthermore, inflammatory cytokines (TNF-α, IL-1β), Aβ oligomers, and JNK activation exacerbate neuronal damage, illustrating a multifaceted mechanism of neurodegeneration. Black arrows represent activation pathways, while red lines indicate inhibition

GLUT4 dysfunction and cognitive decline

In insulin resistance, glucose transporter type 4 (GLUT4) translocation is impaired due to disrupted PI3K/Akt signaling [38]. This reduction in glucose uptake causes an energy crisis in neurons, leading to decreased production of ATP and subsequent failure in maintaining ion gradients and neurotransmitter release [39, 40] (Fig. 3).

Fig. 3.

Insulin-mediated glucose uptake pathway and its impairment in insulin resistance. Insulin binds to its receptor, triggering the translocation of GLUT4 transporters from vesicles to the plasma membrane, which facilitates glucose uptake into cells. Insulin resistance inhibits this process, resulting in reduced GLUT4 membrane translocation and decreased glycolysis, which in turn leads to reduced pyruvate and ATP production. The downstream effects include impaired synthesis of neurotransmitters such as glutamate and GABA, contributing to cellular energy deficits and dysfunction. Black arrows denote activation, while red lines represent inhibition. For a detailed discussion of its implications in AD pathology, see the Discussion section

Energy deficits: alternative energy substrates

In response to glucose deficits, neurons adapt by utilizing alternative energy substrates such as ketone bodies and lactate [41]. Ketone bodies are metabolized more efficiently by neurons, astrocytes, and oligodendrocytes, ensuring a more optimal energy supply for brain cells [42]. Additionally, lactate, supplied by astrocytes via the astrocyte-neuron lactate shuttle (ANLS), becomes a primary energy source [43]. However, in insulin resistance, astrocytic dysfunction reduces lactate availability, further compromising neuronal energy homeostasis [44]. Decreased lactate transport also affects synaptic plasticity, as lactate is critical for memory consolidation [45].

Amyloid-beta pathology in metabolic dysfunction

Aβ generation: role of insulin-degrading enzyme (IDE)

IDE plays a dual role in degrading both insulin and Aβ [46]. In insulin-resistant states, IDE is sequestered to degrade excess insulin, reducing its availability for Aβ clearance [47, 48]. This results in Aβ accumulation, forming extracellular plaques [49] (Fig. 4). Benedict et al. reported that when rats were treated with high doses of insulin, the clearance of Aβ was significantly reduced. This suggests that the IR is saturated by excess insulin, making it less effective at promoting Aβ degradation [50].

Fig. 4.

Schematic representation of the molecular interactions. Molecular interactions between insulin signaling, amyloid-beta (Aβ) aggregation, and the role of insulin-degrading enzyme (IDE) in Alzheimer’s disease (AD) pathogenesis. Insulin binds to IDE, competing with Aβ for its degradation site, leading to increased Aβ accumulation. This imbalance contributes to insulin resistance and the formation of toxic Aβ oligomers, a hallmark of AD. Pioglitazone, a PPARγ agonist, modulates IDE expression through nuclear transcriptional regulation involving co-regulators and RXR, promoting Aβ clearance and ameliorating insulin resistance

The PI3K/Akt pathway also influences Aβ generation by modulating β-secretase (BACE1) activity [51]. Insulin resistance disrupts Akt signaling, leading to increased phosphorylation of BACE1 and enhanced cleavage of Amyloid precursor protein (APP) into Aβ [52] (Fig. 5). BACE1 has been widely studied for its role in brain amyloidogenesis and is directly implicated in Aβ production, as demonstrated by findings from various knockout mouse models [53].

Fig. 5.

Metformin-mediated AMPK activation pathway in neuroprotection. The figure depicts how metformin activates AMPK, leading to multiple neuroprotective mechanisms. These include inhibition of neuroinflammation through NF-κB pathway suppression, reduction of Aβ aggregation and toxicity, restoration of mitochondrial dynamics through DRP1 and MFN2 phosphorylation, and activation of antioxidant responses via NRF2. The pathway also shows metformin’s role in inhibiting mTOR signaling and the subsequent caspase cascade, ultimately preventing neuronal apoptosis. Black arrows indicate activation, while red lines indicate inhibition

Aβ aggregation and toxicity

In addition to increased production, insulin resistance promotes Aβ aggregation through oxidative stress and metal ion dysregulation [54, 55]. Oxidative stress leads to Aβ oxidation, increasing their tendency to aggregate [56]. This process is further exacerbated in insulin-resistant brains due to iron and copper dysregulation, as these metals act as catalysts for Aβ oxidation [57–60].

Tau hyperphosphorylation and NFT formation

Role of GSK-3β in Tau pathology

Tau, a microtubule-associated protein, becomes hyperphosphorylated in AD, leading to the formation of NFTs [61]. Tau has also been implicated in insulin resistance, as it is an important regulator of insulin signaling [62]. GSK-3β is a key kinase responsible for tau phosphorylation, and its activity is tightly regulated by insulin signaling [63] (Fig. 2).

Aggregation of hyperphosphorylated Tau

Hyperphosphorylated tau aggregates into paired helical filaments (PHFs), which further assemble into NFTs [64]. These aggregates disrupt neuronal function by sequestering normal tau and other microtubule-associated proteins [65] (Fig. 2). Additionally, tau aggregates propagate between neurons in a prion-like manner, spreading pathology across the brain [66].

Oxidative stress and Tau pathology

Oxidative stress exacerbates tau pathology by enhancing tau phosphorylation and aggregation [67]. Reactive oxygen species (ROS) modify tau via oxidative post-translational modifications (PTMs), such as carbonylation, which promote its aggregation [67]. Phosphorylation and acetylation are also considered key PTMs associated with AD [68]. According to the report of Kelly et al., both phosphorylation and acetylation can reduce tau affinity for microtubules, leading to tau aggregation [69].

Mitochondrial dysfunction and oxidative stress: the energetic collapse

Mitochondrial energy deficits in insulin resistance

Mitochondria are essential for neuronal survival, providing energy through oxidative phosphorylation (OXPHOS) [70]. Insulin resistance leads to a reduction in mitochondrial number.

Oxidative stress and ROS overproduction

Oxidative stress results from an imbalance between ROS generation and elimination [71]. Mitochondrial superoxide is a key ROS that accumulates in insulin-resistant states [72]. Manganese superoxide dismutase (SOD2) is a mitochondrial antioxidant enzyme, which is downregulated in diabetes, allowing superoxide to react with nitric oxide (NO) to form peroxynitrite [73–75]. Olufunmilayo et al. reported that lipoproteins derived from AD patients can promote peroxynitrite generation in astrocytes [76]. This highlights the involvement of central nervous system (CNS) cellular components in the complex interaction between oxidative stress and the progression of AD pathophysiology [76].

Impaired mitophagy and neurodegeneration

Mitophagy is regulated by the PINK1/Parkin pathway, ensuring the removal of damaged mitochondria [77]. Inhibition of Parkin- and PTEN-induced putative kinase 1 (PINK1)-mediated mitophagy has been shown to exacerbate hepatic lipogenesis, inflammation, and insulin resistance [78]. Upon mitochondrial depolarization, PINK1 accumulates on damaged mitochondria, undergoes autophosphorylation, and activates Parkin by phosphorylating both Parkin itself and ubiquitin, which together release Parkin from its autoinhibited state, allowing it to translocate to mitochondria, associate with substrates like VDAC1 and MFN2 (mitofusin 2), and mediate polyubiquitination of outer mitochondrial membrane proteins to recruit the autophagic machinery and facilitate mitophagy [79–81].

Neuroinflammation dysfunction in insulin resistance

Astrocytic dysfunction and glutamate toxicity

Astrocytes are critical for maintaining glutamate homeostasis via glutamate uptake through excitatory amino acid transporter 2 (EAAT2) [82]. In insulin resistance, EAAT2 expression is reduced due to impaired Akt signaling, leading to extracellular glutamate accumulation [83, 84]. High glutamate levels overstimulate NMDA receptors, causing excitotoxicity and calcium overload in neurons, triggering calpain-mediated proteolysis and apoptosis [85, 86]. Astrocytic insulin resistance also disrupts lactate shuttling to neurons. Lactate, produced by astrocytic glycolysis, serves as an energy substrate for neurons under metabolic stress [87]. Reduced lactate availability impairs neuronal energy supply and synaptic function, further accelerating neurodegeneration [88] (Fig. 6).

Fig. 6.

Comparison of normal and Alzheimer’s disease (AD) synaptic metabolism. The figure illustrates the glucose-lactate shuttle between astrocytes and neurons in both normal and AD conditions. In the normal state (right), glucose enters through GLUT1/3 transporters and undergoes glycolysis to produce lactate, which is transported via MCTs to neurons for energy production. In AD (left), there is decreased metabolic activity and EAAT2 dysfunction, leading to glutamate (Glu) accumulation. This triggers calcium dysregulation through NMDA receptors (NMDAR), ultimately resulting in calpain-mediated proteolysis and neuronal apoptosis. EAAT2: Excitatory Amino Acid Transporter 2

Peripheral-central immune crosstalk

Peripheral inflammation drives neuroinflammation by facilitating monocyte infiltration into the brain [89]. Insulin resistance weakens BBB integrity through reduced claudin-5 and occludin expression, allowing pro-inflammatory monocytes to cross into the CNS [90, 91]. These infiltrating cells differentiate into macrophages, intensifying microglial activation and inflammatory response [92]. Chronic inflammatory state promotes oxidative stress, accelerates Aβ aggregation, and triggers tau hyperphosphorylation, ultimately contributing to neurodegeneration and cognitive decline [93] (Fig. 7).

Fig. 7.

Blood-Brain Barrier (BBB) alterations in normal and AD conditions. The illustration shows the structural components of the BBB, including endothelial cells, pericytes, and astrocytes. In normal conditions (left), tight junction proteins (Claudin-5 and Occludin) maintain BBB integrity. In AD (right), there is disruption of these tight junctions, increased inflammatory signaling (IL-6), monocyte infiltration, and transformation into macrophages. This leads to microglial activation and increased neuroinflammation

Transcriptomic insights into type 3 diabetes

Regulatory RNAs in insulin resistance and glucose metabolism

Regulatory RNAs, including microRNAs (miRNAs) and long non-coding RNAs (lncRNAs), have emerged as crucial players in the development of insulin resistance and impaired glucose metabolism [94]. Numerous studies have shown that lncRNAs significantly contribute to the progression of type 2 diabetes mellitus (T2DM) through mechanisms such as insulin resistance and dysregulated glucose homeostasis [95–97]. For example, maternally expressed gene 3 (MEG3), a well-characterized lncRNA, has been shown to play a pivotal role in glucose metabolism in both type 1 diabetes (T1D) and T2D mouse models [98, 99].Interestingly, a single nucleotide polymorphism (SNP), rs941576, located on an intron of the MEG3 gene, was linked to T1D in a diabetic patient cohort, further supporting the role of MEG3 in diabetes pathogenesis [100]. Further studies have demonstrated that other lncRNAs, like H19, also contribute to insulin resistance [101–103]. In human diabetic subjects and insulin-resistant mice, H19 expression was decreased, and its function as a molecular sponge for let-7 miRNAs was found to regulate glucose homeostasis [104]. PVT1, another lncRNA, stimulated by glucose, has been associated with both T1D and T2D [105]. Additionally, MIAT, a kind of lncRNA upregulated in vascular endothelial cells of diabetic rats, was found to be overexpressed under high-glucose conditions, further linking lncRNA dysregulation to diabetes [106]. A key lncRNA involved in insulin signaling is steroid receptor RNA activator (SRA). This RNA is a part of the ribonucleoprotein complex that modulates gene expression by interacting with chromatin-remodeling complexes [107]. SRA has been shown to enhance insulin signaling by increasing the expression of the IR, blocking phosphorylation of c-Jun and p38 MAPK [108]. In SRA knockout mice, reduced insulin sensitivity was observed, marked by decreased phosphorylation of IRS1 and impaired glucose levels in the brain, particularly in high-fat diet-induced obesity models [108]. Moreover, the lncRNA MALAT1, which is known to be upregulated in the livers of obese mice, has been implicated in hepatic insulin resistance [109] (Table 3).

Table 3.

Regulatory RNAs in insulin resistance and glucose metabolism

Regulatory RNA	Mechanism	Study Highlights	Ref
MEG3	Modulates insulin synthesis and secretion	MEG3 knockdown restored insulin function and improved glucose tolerance in T1D and T2D mouse models. SNP rs941576 was linked to diabetes progression.	[98–100, 110]
H19	Functions as a molecular sponge for let-7 miRNAs to regulate glucose homeostasis	Reduced H19 expression observed in diabetic subjects and insulin-resistant mice.	[101–104]
PVT1	Stimulated by glucose, contributing to diabetic complications	Associated with T1D and T2D in diabetic cohorts.	[105]
SRA	Enhances insulin signaling via chromatin-remodeling complexes	Knockout studies showed reduced insulin sensitivity, decreased IRS1 phosphorylation, and impaired glucose levels in the brain.	[107, 108]
MALAT1	Promotes stability of SREBP-1 C, leading to hepatic insulin resistance	Found in the livers of obese mice, upregulated in high-glucose conditions, and linked to diabetes-related microvascular diseases and neuronal dysfunctions.	[109, 111–114]

MEG3: Maternally expressed gene 3, T1D: Type 1 diabetes, T2D: Type 2 diabetes, SNP: Single nucleotide polymorphism, miRNAs: microRNAs, SRA: Steroid receptor RNA activator, IRS1: Insulin receptor substrate 1

Influence of non-coding RNAs on neuroinflammation

In T3D, a link between metabolic dysfunction and AD is supported by the role of non-coding RNAs in neuroinflammation. The lncRNA LincRNA-Cox2 is involved in activating immune-related genes in macrophages and mediating inflammation through the NF-κB pathway [115]. This leads to the production of inflammatory cytokines, such as IL-6, which are known to exacerbate neuroinflammation and cognitive decline [116]. Similarly, NEAT1, a well-characterized lncRNA, has been shown to regulate the formation of nuclear paraspeckle bodies, and in doing so, it promotes the expression of IL-8, a cytokine associated with cognitive dysfunction in AD [110, 117, 118] (Fig. 8).

Fig. 8.

Schematic representation of lncRNA-mediated pathways in Alzheimer’s disease pathogenesis. Long non-coding RNAs (lncRNAs) influence various molecular mechanisms involved in amyloid-beta (Aβ) deposition and neuroinflammation. lncRNA 51 A alters SORL1 splicing, reducing its function and facilitating amyloid precursor protein (APP) cleavage into Aβ. lncRNA BACE1-AS upregulates BACE1 expression, enhancing APP cleavage and Aβ production. lncRNA NDM29 enhances APP cleavage, contributing to increased Aβ deposition. lncRNA-Cox2 activates the NF-κB signaling pathway, leading to the synthesis of pro-inflammatory cytokines, including interleukin 6 (IL-6). Similarly, lncRNA NEAT1 regulates paraspeckle body formation and promotes the synthesis of interleukin 8 (IL-8), which exacerbates neuroinflammatory responses. Black arrows indicate activation, while red lines indicate inhibition

Regulatory RNAs in amyloid-beta pathology

The accumulation of Aβ plaques is a hallmark of AD, and several regulatory RNAs contribute to this process [119]. The lncRNA 51 A, which overlaps with the sortilin-related receptor 1 (SORL1) gene, has been found to influence Aβ formation and is upregulated in AD [120, 121]. Similarly, the lncRNA BACE1-AS, which is transcribed from the antisense strand of the BACE1 gene, has been shown to promote Aβ production [122, 123]. Its overexpression increases BACE1 levels, an enzyme that cleaves APP to produce Aβ peptides, thereby accelerating Aβ formation in AD [122, 124, 125]. Another significant player is the lncRNA NDM29, which interacts with the processing machinery of APP, leading to increased Aβ accumulation and further supporting its role in Alzheimer’s pathogenesis [126] [127–129]. (Fig. 8).

Regulatory RNAs in synaptic dysfunction and plasticity

Synaptic dysfunction and the loss of synaptic plasticity are key features in the progression of AD, and several lncRNAs have been implicated in these processes [130]. BC1/BC200 lncRNA plays a critical role in synaptic plasticity and is downregulated in the brains of Alzheimer’s patients, leading to impaired dendritic mRNA transport [110, 126, 131, 132]. Similarly, the lncRNA GOMAFU, which is expressed throughout the brain, regulates the splicing of ERRB4, a gene associated with synaptic function [133–135] (Table 4).

Table 4.

Regulatory RNAs in neurodegenerative processes

Regulatory RNA	Focus Area	Mechanism	Ref
LincRNA-Cox2	Neuroinflammation	Activates immune-related genes and NF-κB signaling	[115, 116]
NEAT1	Neuroinflammation	Regulates paraspeckle bodies and promotes IL-8 expression	[110, 117, 118]
H19	Neuroinflammation	Polarizes microglial cells to the pro-inflammatory M1 phenotype	[136, 137]
51 A	Amyloid-Beta Pathology	Alters SORL1 splicing pattern, reducing protective SORL1 protein levels	[120, 121, 138]
BACE1-AS	Amyloid-Beta Pathology	Promotes BACE1 expression, enhancing APP cleavage and Aβ formation	[122–125]
NDM29	Amyloid-Beta Pathology	Regulates APP cleavage	[126–129]
BC1/BC200	Synaptic Dysfunction	Downregulation disrupts dendritic delivery	[110, 131, 132]
GOMAFU	Synaptic Dysfunction	Regulates splicing of ERRB4	[133–135]

NF-κB: Nuclear factor kappa B, IL-8: Interleukin 8, SORL1: Sortilin related receptor 1, BACE1: Beta-site amyloid precursor protein cleaving enzyme 1, Aβ: Amyloid-Beta, APP: Amyloid precursor protein

Therapeutic implications: targeting molecular pathways

Intranasal insulin and restoring IR function

Intranasal insulin directly activates brain IRs, bypassing peripheral insulin resistance [139]. Studies by Erichsen et al. showed that intranasal insulin administration has the potential to enhance IR and IRS1 activation, thereby enhancing PI3K/Akt activity and restoring insulin signaling in the brain [140].

GLP-1 receptor agonists and neuroprotection

Glucagon-Like Peptide-1 (GLP-1) agonists, like liraglutide, reduce oxidative stress by activating NRF2, a transcription factor regulating antioxidant response elements (AREs) [141]. Liraglutide is also capable of crossing the BBB and promoting neurogenesis in the dentate gyrus in female wild-type mice [142].

Metformin and AMPK activation in neuroprotection

Metformin, a widely used antidiabetic drug, exerts its neuroprotective effects primarily through AMPK activation [143]. AMPK regulates energy homeostasis and enhances autophagy, which is crucial for clearing aggregated Aβ and tau protein [144, 145]. Several studies have shown that metformin treatment in insulin-resistant neurons restored mitochondrial dynamics by increasing the activation of dynamin-related protein 1 (DRP1) and MFN2, proteins critical for mitochondrial fission and fusion, respectively [146–149].

AMPK activation also modulates transcription of antioxidant genes through NRF2 [150]. This pathway reduces ROS levels by enhancing the expression of superoxide dismutase (SOD), glutathione peroxidase (GPx), and catalase [151]. Furthermore, metformin suppresses neuroinflammation by inhibiting NF-κB signaling in microglia, thus reducing pro-inflammatory cytokines production, such as IL-1β and TNF-α [152] (Fig. 5).

Discussion

Age and diabetes synergistically increase the risk of dementia, with prevalence reaching 24% in diabetic individuals over 75 [10, 21]. Although cognitive impairment is generally more common in diabetic women, some data suggest better memory performance and lower impairment rates compared to men. However, discrepancies in gender and ethnic differences in cognitive decline persist [19, 20]. This variability underscores the need for more detailed demographic analyses to understand these disparities.

In healthy neuronal cells, insulin binds to the IR, triggering a cascade of intracellular signaling pathways [153]. The most critical of these is the PI3K/Akt pathway, which governs glucose uptake, energy homeostasis, and neuronal survival [154, 155]. Upon insulin binding, PI3K is activated, leading to the phosphorylation of Akt [156]. Akt phosphorylates downstream targets such as GSK-3β, inhibiting its pro-apoptotic activity, and promotes cell survival [157, 158]. In insulin-resistant states, reduced IR sensitivity impairs PI3K/Akt signaling, leading to GSK-3β activation and tau hyperphosphorylation, which contribute to neurofibrillary tangle formation. Additionally, Akt dysfunction fails to inhibit FOXO1, thereby reducing cell survival and promoting neuronal apoptosis (Fig. 2).

The mammalian target of rapamycin (mTOR) signaling pathway is a protein kinase that controls cellular metabolism, catabolism, immune responses, and autophagy [159]. Activation of mTOR complex 1 (mTORC1) results in feedback inhibition of IRS1/2, which occurs through the direct phosphorylation of IRS1/2 by mTOR (Fig. 2). PI3K/Akt/mTOR pathway alterations are linked to disruption of autophagy [160]. Impaired autophagy prevents the clearance of damaged organelles and misfolded proteins, including Aβ aggregates, which exacerbate neurodegeneration [161, 162].

Upon insulin binding, IR autophosphorylation activates PI3K/Akt and MAPK pathways. Notably, the PI3K/Akt cascade regulates neuronal glucose uptake by promoting the translocation of insulin-sensitive glucose transporters, including GLUT4 and GLUT8 [163]. GLUT4 is the key insulin-responsive glucose transporter in the brain, predominantly located in neuronal cell bodies and dendrites within the hippocampus, hypothalamus, amygdala, and cerebellum. It is intracellularly stored in transport vesicles, the Golgi, and the rough Endoplasmic Reticulum. Insulin stimulates GLUT4 translocation to the neuronal membrane via the PI3K/Akt pathway, a mechanism first described in muscle and adipose tissue. This process was also confirmed in neuroblastoma cells and in vivo, where insulin administration increased hippocampal GLUT4 membrane insertion and Akt phosphorylation, effects blocked by PI3K inhibition. These findings suggest that insulin enhances glucose availability in active neurons during cognitive tasks through GLUT4-mediated uptake [164].

In normal conditions, insulin signaling triggers the translocation of GLUT4-containing vesicles to the plasma membrane via the PI3K/Akt pathway, leading to glucose uptake [165]. Neurons rely on this process for energy-intensive activities such as synaptic transmission and plasticity [166, 167]. A significant observation from this review is the impact of insulin resistance on glucose transport in the brain, particularly through GLUT4 dysfunction [168]. In insulin resistance, impaired PI3K/Akt signaling hinders GLUT4 translocation, reducing neuronal glucose uptake and ATP production. This energy deficit disrupts ion homeostasis and neurotransmitter release [39, 40]. Chronic energy deficits also impair synaptic plasticity, as glucose-derived metabolites are critical for the synthesis of neurotransmitters like glutamate and gamma-aminobutyric acid (GABA) [169, 170] (Fig. 3). The impaired glucose uptake, coupled with reduced synaptic plasticity, establishes an energy crisis in neurons, potentially contributing to cognitive dysfunction [40, 169]. Moreover, the shift to alternative energy substrates, like ketone bodies and lactate, while adaptive in some conditions, may not sufficiently support neuronal demands during prolonged insulin resistance [41, 42].

T3D represents a critical area of research, linking metabolic dysfunction with neurodegeneration [171]. The designation of AD as T3D has sparked considerable debate within the scientific community. Proponents argue that brain insulin resistance and impaired glucose metabolism are central features linking AD pathogenesis with metabolic dysfunction, suggesting a shared pathophysiological basis with diabetes. Several studies, including a meta-analysis, have shown that individuals with T2D are at a higher risk of developing dementia [10]. This aligns with other studies, emphasizing the role [21, 172]. The current evidence reviewed demonstrates how insulin resistance, particularly in T2D, exacerbates cognitive decline through mechanisms such as disrupted brain glucose metabolism, Aβ accumulation, tau hyperphosphorylation, and mitochondrial dysfunction [173, 174].

Aggregated Aβ induces synaptic dysfunction by disrupting synaptic vesicle recycling and inhibiting NMDA receptor signaling [175]. Aβ oligomers also activate the receptor for advanced glycation end-products (RAGE) pathway, amplifying oxidative stress and cytokine release, thereby establishing a connection between Aβ toxicity and neuroinflammation [176, 177]. Aβ clearance is mediated by multiple pathways, including phagocytosis by microglia, enzymatic degradation, and transport across the BBB via low-density lipoprotein receptor-related protein 1 (LRP1) [178, 179]. Insulin resistance reduces LRP1 expression in endothelial cells, which impairs Aβ efflux from the brain [180].

Microglia dynamically shift between resting and activated states in response to environmental cues [181]. Systemic inflammation marked by elevated levels of TNF-α and IL-6 primes microglia, driving them toward a pro-inflammatory M1 phenotype [182]. In this state, microglia upregulate NLRP3 inflammasomes, which process pro-IL-1β into its active form, IL-1β [183]. The release of IL-1β amplifies inflammation by stimulating nearby astrocytes to produce additional cytokines [184]. Another critical pathway involves RAGE [185]. Hyperglycemia-induced accumulation of AGEs activates RAGE in microglia, triggering NF-κB signaling that promotes cytokine release and ROS production [184]. Notably, Wang et al. demonstrated that targeted inhibition of RAGE lowers Aβ levels in brain of db/db mice [186]. In Insulin resistance, microglial dysfunction leads to reduced phagocytic capacity, exacerbating plaque accumulation [187]. Additionally, H19, another lncRNA, plays a role in the polarization of microglial cells to the pro-inflammatory M1 phenotype, which has been implicated in the progression of neurodegenerative diseases such as Alzheimer’s [136, 137].

In insulin resistance, impaired PI3K/Akt signaling results in GSK-3β hyperactivation, promoting pathological tau modifications [51, 188]. Hyperphosphorylated tau loses its affinity for microtubules, leading to their destabilization and impaired axonal transport [189]. Hyperphosphorylated tau loses its affinity for microtubules, leading to their destabilization and impaired axonal transport [189].

In insulin resistance, reduced activation of the PI3K/Akt pathway leads to reduced expression of Peroxisome proliferator-activated receptor gamma coactivator-1 alpha (PGC-1α), a master regulator of mitochondrial biogenesis [190, 191]. PGC-1α activates nuclear respiratory factor 1 and 2 (NRF1/2) and mitochondrial transcription factor A (TFAM), which binds to mitochondrial DNA (mtDNA), and activates transcription and replication [192, 193] (Fig. 2).

However, many researchers caution that this terminology may oversimplify the multifactorial nature of AD, which involves complex genetic, inflammatory, and protein aggregation processes beyond metabolic dysregulation. Critics also point out that the current evidence, especially from human studies, is insufficient to definitively categorize AD as a form of diabetes. Moreover, the lack of standardized diagnostic criteria for T3D and inconsistent biomarker findings further challenge its acceptance. Therefore, while the concept of T3D provides a compelling framework to explore metabolic contributions to neurodegeneration, it remains a hypothesis requiring more robust validation before being widely adopted as a diagnostic label. The term “type 3 diabetes” is not formally recognized as a clinical diagnosis by leading health organizations such as the World Health Organization (WHO) or the American Diabetes Association (ADA), nor is it incorporated into clinical classification systems. Instead, it serves as a conceptual framework proposed by some researchers to describe the potential link between the pathological processes of AD and T2DM. This hypothesis has gained traction because epidemiological studies have observed an increased incidence of dementia, particularly AD, among individuals with diabetes [4].

The relationship between Aβ pathology and insulin resistance was also highlighted in several studies, particularly through the modulation of IDE and BACE1 [46, 51]. The dysregulation of IDE in insulin-resistant states impairs Aβ clearance, contributing to plaque accumulation [47] (Fig. 4). The role of oxidative stress in amplifying Aβ aggregation and tau pathology was another key point of discussion, with several studies indicating that oxidative stress accelerates both processes, creating a vicious cycle of neurodegeneration [54, 55, 67].

In addition to amyloid and tau pathology, mitochondrial dysfunction and impaired mitophagy in insulin-resistant brains are significant contributors to neuronal degeneration [77]. Previous studies showed that mitochondrial deficits lead to reduced ATP production, exacerbating neurodegeneration [194]. Moreover, impaired mitophagy, through the PINK1/Parkin pathway, has been linked to the accumulation of dysfunctional mitochondria, which further contributes to oxidative stress and neuronal damage [78, 79].

Recent studies also highlight the critical role of regulatory RNAs, including miRNAs and lncRNAs, in the metabolic and neurodegenerative mechanisms underlying T3D. Key lncRNAs, such as MEG3 and MALAT1, have been implicated in insulin resistance and glucose dysregulation, with MALAT1 also contributing to neuronal dysfunctions and diabetes-related vascular complications [195, 196]. Interference with MEG3 reduced triglyceride levels and attenuated glucose intolerance in a high-fat diet-induced obesity mouse model [98]. According to the study of Liu et al., MEG3 was found to promote insulin resistance in both type 1 diabetes (T1D) and T2D mouse models by modulating insulin synthesis and secretion [99]. Knockdown of MEG3 restored insulin function and improved glucose tolerance [110]. The lncRNA MALAT1 promotes the stability of SREBP-1 C, a transcription factor critical for lipid biosynthesis, thereby contributing to hepatic insulin resistance [109]. MALAT1 also plays a role in diabetes-related microvascular diseases, where its knockdown suppresses the viability of endothelial cells and reduces the expression of inflammatory markers, further linking it to the pathogenesis of diabetes complications [112–114]. Interestingly, MALAT1 also has neuronal implications, where it has been shown to regulate processes in neuronal function, suggesting a potential dual role in both metabolic and neural dysfunctions associated with T3D [111] Additionally, BACE1-AS and 51 A have been shown to enhance Aβ accumulation, linking non-coding RNAs to hallmark Alzheimer’s pathology in T3D [197, 198]. In a study by Rybak-Wolf and Plass, it has been reported that the overexpression of 51 A in the brains of Alzheimer’s patients enhances Aβ accumulation by altering the splicing pattern of SORL1 transcripts, leading to decreased levels of this protective protein [138]. These findings emphasize the potential of regulatory RNAs as therapeutic targets, warranting further exploration into their roles in T3D and related neurodegenerative processes.

Clinical implications and diagnostic challenges of type 3 diabetes

The clinical implications of defining AD as T3D are both promising and challenging. Currently, no standardized diagnostic criteria exist for T3D, limiting its application in routine clinical practice. Nevertheless, research efforts focus on identifying specific biomarkers reflecting brain insulin resistance and metabolic dysfunction, including Altered insulin-mediated signaling and brain glucose hypometabolism and reduced glucose uptake detected via fluorodeoxyglucose positron emission tomography (FDG-PET) [199, 200]. These diagnostic tools may help distinguish T3D-related neurodegeneration from other AD subtypes. For clinicians, recognizing the metabolic component of AD underscores the potential for targeted therapeutic strategies, such as insulin sensitizers or intranasal insulin, aimed at improving brain insulin signaling [201]. However, due to variability in biomarker findings and lack of consensus on diagnostic thresholds, the translation of T3D concepts into clinical settings remains limited. Continued research is essential to establish reliable diagnostic markers and evaluate the efficacy of metabolic interventions in AD patients.

SGLT2 inhibitors: emerging therapeutics

SGLT2 inhibitors, primarily used for glucose control, have shown potential neuroprotective effects by reducing systemic inflammation and oxidative stress [202]. These drugs improve glycemic control, lower circulating levels of pro-inflammatory cytokines, and indirectly enhance insulin sensitivity [203]. Their effects on brain energy metabolism are linked to ketone body production, which serves as an alternative energy source for neurons in insulin-resistant states [42]. It has been shown that SGLT2 inhibitors like empagliflozin has beneficial effect on cognitive function, which may be attributed to their ability to improve glycemic control, reduce inflammation, and enhance cerebral energy metabolism [42, 204].

Therapeutic implications: targeting molecular pathways

Therapeutically, several agents, including intranasal insulin, Glucagon-like peptide-1 (GLP-1) agonists, metformin, and pioglitazone, have been proposed as potential treatments for mitigating the cognitive decline associated with T3D [205]. As demonstrated by Erichsen et al., Intranasal insulin directly targets brain insulin receptors (IRs) [139], . enhancing IR and IRS1 activation and subsequently stimulating the PI3K/Akt pathway to restore central insulin signaling [140]. GLP-1 agonists, and liraglutide, activate NRF2, and regulate antioxidant response elements (AREs) [142]. Metformin exerts its neuroprotective effects primarily through AMPK activation [143] which was explained in detail in the results section. But other drugs are also used in this field. Pioglitazone, which is a PPAR-γ agonist, has shown promise in improving insulin sensitivity and reducing AD pathology [206]. PPAR-γ activation enhances lipid metabolism in neurons, preventing lipid accumulation and subsequent oxidative stress [152]. Furthermore, it promotes anti-inflammatory effects by shifting microglial activation from the M1 to the M2 phenotype [207]. A study conducted by Zhao et al. demonstrated that after treating chronic mild stress (CMS) mice with pioglitazone, the expression of pro-inflammatory cytokines was reduced and the levels of anti-inflammatory cytokines were increased [208]. Pioglitazone also increases IDE expression, enhancing Aβ degradation and clearance [209] (Fig. 4). However, long-term pioglitazone use is associated with adverse effects such as weight gain, which limits its utility as a standalone therapy [210]. The findings of several studies support the neuroprotective potential of these compounds, particularly through their ability to restore insulin signaling, reduce oxidative stress, and modulate neuroinflammation [211–213]. However, while promising, these therapies must be rigorously tested in clinical trials to establish their safety and efficacy in preventing or reversing cognitive decline in individuals with T2D.

Conclusion

This systematic review investigates T3D as a potential pathophysiological link between insulin resistance and AD. Central insulin resistance impairs cerebral glucose uptake, promotes Aβ accumulation, tau hyperphosphorylation, mitochondrial dysfunction, and neuroinflammation, key drivers of cognitive decline. Disruption of PI3K/Akt signaling, GLUT4 translocation, and oxidative stress contributes to synaptic and neuronal dysfunction. Peripheral insulin resistance worsens central inflammation by compromising the BBB and activating microglia. A novel aspect of this review is the integration of transcriptomic data, highlighting the regulatory roles of non-coding RNAs (e.g., MEG3, MALAT1, BACE1-AS, 51 A) in modulating insulin sensitivity and amyloid pathology. These findings suggest RNA-based biomarkers and therapeutics as emerging opportunities. Promising interventions, such as intranasal insulin, GLP-1 receptor agonists, metformin, pioglitazone, and SGLT2 inhibitors, demonstrate neuroprotective effects by restoring insulin signaling and reducing oxidative stress. However, challenges remain, including the lack of standardized diagnostic criteria, variable biomarkers, and limited clinical translation. Future studies should focus on biomarker development, RNA-based mechanisms, and large-scale validation. Recognizing T3D as a distinct clinical entity could transform the diagnosis and treatment of AD.

Author contributions

Supervision, F.A.; Conceptualization and Study Design, F.A.; Methodology, M.M, T.N., M.B., N.H.; Search Strategy, M.M, and T.N.; Validation, F.A.; Investigation, M.M, T.N., M.B., N.H.; Literature Review, M.M, T.N., M.B., N.H.; Visualization, Image, and Table Designation, M.M., M.B., and N.H.; Data Acquisition, M.M, and T.N.; Academic, Scientific, and Grammatical Revision, F.A., M.M., and M.H.; Definition of Intellectual Content, F.A., and M.H.; Investigation of the Clinical and Experimental Studies, F.A., and M.H.; Formal Analysis, F.A., and M.H.; Preparation of the first draft of the manuscript, M.M, T.N., M.B., N.H., F.A., and M.H.; Preparation of the last draft of the manuscript, F.A., M.M., T.N., and M.H. All of the authors listed on the title page have read and approved the final version of the manuscript, and they have received an electronic copy of the manuscript. The requirements for authorship have been met, and each author believes that the manuscript represents honest work. Also, they attest to the validity and legitimacy of the data, and agreed to submit this manuscript to this journal. The corresponding author F.A., oversaw the final scientific revision, editing, and submission process, including all communications with the journal.

Funding

No grant is received from any financial organizations or funding agencies in the public, commercial, or not-for-profit sectors.

Data availability

No datasets were generated or analysed during the current study.

Declarations

Ethics approval and consent to participate

Not required.

Competing interests

The authors declare no competing interests.