Causal associations between diabetes and Alzheimer’s disease

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Meng et al. Cell & Bioscience (2022) 12:28
https://doi.org/10.1186/s13578-022-00768-9
RESEARCH
Causal association evaluation of diabetes
with Alzheimer’s disease and genetic analysis
of antidiabetic drugs against Alzheimer’s
disease
Lei Meng1,2, Zhe Wang1,2, Hong‑Fang Ji1,2* and Liang Shen1,2*
Abstract
Background: Despite accumulating epidemiological studies support that diabetes increases the risk of Alzheimer’s
disease (AD), the causal associations between diabetes and AD remain inconclusive. The present study aimed to
explore: i) whether diabetes is causally related to the increased risk of AD; ii) and if so, which diabetes-related physi‑
ological parameter is associated with AD; iii) why diabetes drugs can be used as candidates for the treatment of AD.
Two-sample Mendelian randomization (2SMR) was employed to perform the analysis.
Results: Firstly, the 2SMR analysis provided a suggestive association between genetically predicted type 1 diabetes
(T1D) and a slightly increased AD risk (OR=1.04, 95% CI=[1.01, 1.06]), and type 2 diabetes (T2D) showed a much
stronger association with AD risk (OR=1.34, 95% CI=[1.05, 1.70]). Secondly, further 2SMR analysis revealed that dia‑
betes-related physiological parameters like fasting blood glucose and total cholesterol levels might have a detrimen‑
tal role in the development of AD. Thirdly, we obtained 74 antidiabetic drugs and identifed SNPs to proxy the targets
of antidiabetic drugs. 2SMR analysis indicated the expression of three target genes, ETFDH, GANC, and MGAM, were
associated with the increased risk of AD, while CPE could be a protective factor for AD. Besides, further PPI network
found that GANC interacted with MGAM, and further interacted with CD33, a strong genetic locus related to AD.
Conclusions: In conclusion, the present study provides evidence of a causal association between diabetes and
increased risk of AD, and also useful genetic clues for drug development.
Keywords: Alzheimer’s disease, Diabetes, Causal association, Drug targets, Mendelian randomization
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Introduction
Alzheimer’s disease (AD) is known as the most common
progressive neurodegenerative disease with an increasing
prevalence worldwide. According to the World Alzheimer Report 2018 from Alzheimer’s Disease International,
over 50 million people worldwide are sufering from
dementia [1], and AD accounts for 60%-80% of all cases
of dementia. With the aggravation of the disease, AD
patients will show a series of clinical features, including
progressive memory loss, gradual impairment of cognitive functions, behavioural and personality changes.
Given the steadily increasing burdens on patients, families, and society, screening modifable risk factors has
been performed to reduce the risk of AD.
Diabetes, including type 1 diabetes (T1D), type 2 diabetes (T2D), and gestational diabetes, is a chronic metabolic disease with high blood glucose levels that can
damage blood vessels and nerves and cause multiple
serious complications. According to the International
Open Access
Cell & Bioscience
*Correspondence: [email protected]; [email protected]
1
Institute of Biomedical Research, Shandong University of Technology,
Zibo, Shandong, People’s Republic of China
Full list of author information is available at the end of the article
Meng et al. Cell & Bioscience (2022) 12:28 Page 2 of 16
Diabetes Federation, 1 in 11 adults had diabetes (425 million people), and 12% of the global health expenditure
was spent on diabetes in 2017 [2].
More recently, increasing attention has been paid to
the associations of AD with several chronic disorders,
among which diabetes has attracted much interest due
to a series of pathogenic associations. For instance, in the
past few decades, signifcant epidemiological evidence
indicated that diabetes patients had an increased risk of
developing AD by approximately 53% [3–5]. Besides, the
mechanisms associated with diabetes, such as dysfunctional IR/PI3K/Akt signaling, increased infammation,
oxidative stress, and others, might accelerate the development of pathological events in AD [6, 7]. Moreover,
a growing number of studies also supported the associations between AD and diabetes at the genetic level. A
previous study has identifed 395 SNPs to be shared the
same risk allele for AD and T2D, suggesting common
genetic aetiological risk factors between two disorders
[8]. Correspondingly, inspired by the close association
between two disorders, the studies of examining antidiabetic drugs against AD have increased tremendously.
Excitedly, preliminary studies have indicated that many
antidiabetic drugs, such as liraglutide, pioglitazone, lixisenatide, rosiglitazone, insulin, and exendin-4, exhibited
therapeutic efects on AD [9–14], suggesting that diabetes and AD may share genetic etiological risk factors,
especially provide a potential novel approach for AD
drug development.
Tese studies imply that diabetes is closely associated
with the risk of AD, and antidiabetic drugs also attracted
much attention in the treatment of AD; however, it is
unclear whether diabetes has causal associations with
AD, and the impact of antidiabetic drug targets against
AD remains to be further estimated. Mendelian randomization uses genetic variants as proxies for modifable
risk factors to test whether the risk factor is causally relevant to an outcome of interest, which could minimize
the impact of confounding factors [15]. Tus, the present
study performed a two-sample Mendelian randomization
(2SMR) analysis to assess: i) whether diabetes is causally
related to the increased risk of AD; ii) and if so, which
diabetes-related physiological parameters, like blood glucose, insulin, and others, is associated with AD; iii) how
diabetes drugs can be used as a candidate for the treatment of AD.
Methods
Based on existing data sources of the MR-base platform,
we selected genetic variants associated with the exposure
measure as an instrument to estimate causal efects. Candidate genetic variants of outcome (AD) were obtained
from the International Genomics of Alzheimer’s Project
(IGAP) [16]. As for exposures, we searched the EBIGWAS database by the MR-base platform with the following terms: “type 1 diabetes” and “type 2 diabetes”. And
10 T1D-related SNPs were extracted from a European
ancestry-specifc joint GWA study to estimate the association between T1D and AD [17], while a total of 37 SNPs
provided by the summary statistics of 48,286 cases and
250,671 controls were included to test the causal efect
of T2D on AD [18]. Further, to investigate how diabetes
afects the risk of AD, we also analyzed AD and diabetes-related parameters, including fasting blood glucose,
total cholesterol levels, and insulin levels [19–21]. Data
extraction and 2SMR analyses were automatically conducted using the software R and TwoSample MR package
0.5.0, and genome-wide signifcant (p-value<5× 10−8
)
was chosen for computational analysis [15]. We selected
inverse variance weighting (IVW) as the main analytical method, and various 2SMR methods, including
weighted median, weighted mode, and MR-Egger, were
employed to improve the reliability of the causal inference. P-value<0.05 was chosen as the discriminant criterion for the statistical signifcance of the 2SMR study.
Besides, to ensure the robustness of results, leave-oneout sensitivity analysis was used to test whether there is
an SNP that has an excessive impact on MR estimates.
Heterogeneity and pleiotropy tests were implemented
based on the code contained in the TwoSample MR package. Cochran’s Q statistics were used to explore the size
of heterogeneity, and whether there is pleiotropy was
decided by the intercept term of MR-Egger method.
Besides, inspired by the benefts of antidiabetic drugs
for AD, we then performed a further 2SMR analysis for
the causal associations between antidiabetic drug targets
and AD risk to assess the therapeutic efects. Firstly, we
searched the DrugBank database (http://www.drugbank.
ca/) with the term “diabetes” to retrieve antidiabetic drugs
and target genes [22]. Drugs or compounds that have
been approved or were being developed for the treatment
of diabetes were collected as available antidiabetic drugs.
Te information was extracted from each drug, including the name of antidiabetic drug, DrugBank ID, target
gene, and target type. Secondly, using the TwoSample
MR package, we identifed target-related SNPs based on
the GTEx eQTL catalog [23]. By using SNPs associated
with antidiabetic drug target genes and without any linkage disequilibrium, we calculated MR estimates and did
not defne tissue types. Since the number of SNPs contained in each drug target was relatively small, a more
liberal P-value threshold (p-value<5× 10−5
) was used to
flter available instrumental variables. In addition to the
above four methods, we also added another MR method,
wald ratio, which used a single instrumental variable to
estimate the causal association.
Meng et al. Cell & Bioscience (2022) 12:28 Page 3 of 16
Furthermore, based on the IGAP database, the threshold of p-value<1× 10−5
was used to screen susceptibility-associated SNPs of AD. Te identifed signifcant
SNPs were mapped into related susceptibility genes
according to the location of the SNPs on human chromosomes. We constructed network-based analyses by
the Search Tool for the Retrieval of Interacting Genes
(STRING) databases to investigate the protein–protein
interaction (PPI) information between the identifed targets and susceptibility genes [24], and the fnal network
was visualized by Cytoscape software (Version 3.7.1) [25].
Results
Diabetes and AD
Te 2SMR analysis provided a suggestive association
between genetically predicted T1D and higher risks of
AD (IVW, OR=1.04, 95% CI=[1.01, 1.06], p=2.90E-03,
Table 1, Fig. 1). Cochran’s Q statistics showed little evidence of heterogeneity between T1D and AD, and the
MR-Egger intercept suggested that there was no pleiotropy in the SNPs included in this study. Te further
leave-one-out analysis also found that there were no SNP
had an excessive impact on the results (all lines are on the
right side of 0). However, compared with other SNPs, the
independent SNP rs9272346 exerted a relatively signifcant efect on the association between T1D and AD risk.
According to the NCBI database, rs9272346 was located
at HLA-DQA1, and the protein encoded by which plays a
central role in the immune system by presenting peptides
derived from extracellular proteins.
Compared with T1D, T2D seemed to show a much
stronger association with an increased risk of AD
(IVW, OR=1.34, 95% CI=[1.05, 1.70], p=0.02, Fig. 2).
Cochran’s Q statistics showed little evidence of heterogeneity between T2D and AD. Te MR-Egger intercept suggested that there was no pleiotropy in the SNPs included
in this study. Moreover, the leave-one-out method did
not fnd that a certain SNP would have an excessive
impact on the MR results, which also supported that the
MR results were robust.
Diabetes‑related parameters and AD
In addition, we also conducted further analysis to investigate the causal association between diabetes-related
physiological parameters and the risk of AD. In view of
the fact that blood glucose and dyslipidemia are widely
recognized as physiological changes in diabetes, we conducted a 2SMR analysis to evaluate their causal association with AD. By performing a 2SMR analysis of the
diabetes-related physiological parameters and AD, we
found that fasting blood glucose and total cholesterol levels may have a causative role in the development of AD
as shown in Fig. 3. Fasting blood glucose was associated
with a 57% increase in the risk of AD (IVW, OR=1.57,
95% CI=[1.14, 2.17], p=6.33E-03), total cholesterol levels also showed a strong causal association with the risk
of AD (IVW, OR=1.62, 95% CI=[1.21, 2.18], p=1.23E03). Besides, as one of the typical characteristics of diabetes, the causal association between insulin level and
AD was also included in this study. However, based on
the currently available data, the 2SMR analysis results did
not support the causal efect of insulin levels on AD risk
(data not shown).
Antidiabetic drugs and AD
Based on the DrugBank database, we obtained 74 antidiabetic drugs up to July 2021, covering 96 target and
enzyme genes extracted from the involved drugs. Te
details of these drugs, including drug names, DrugBank
ID, target genes, and enzyme genes, are displayed in
Table 2.
By using SNPs associated with antidiabetic drug target genes (p-value<5× 10−5
) as instrumental variables,
we conducted a 2SMR analysis for the causal associations between antidiabetic drug targets and AD risk
(Table 3). Preliminary results showed that four targets,
Table 1 2SMR estimates of the causality between diabetes and AD
Study Method Number of
SNPs
b se P-value OR 95% CI Cochran’s Q
statistic (P-value)
MR-egger
intercept
(P-value)
T1D Inverse variance weighted 4 0.04 0.01 2.90E−03 1.04 1.01–1.06 3.66 (0.30)
MR Egger 4 0.06 0.02 0.08 1.06 1.03–1.10 0.94 (0.63) − 0.02 (0.24)
Weighted median 4 0.04 0.01 8.83E−04 1.04 1.02–1.07
Weighted mode 4 0.04 0.01 0.04 1.04 1.02–1.07
T2D Inverse variance weighted 11 0.29 0.12 0.02 1.34 1.05–1.70 9.43 (0.49)
MR Egger 11 0.70 0.40 0.11 2.01 0.93–4.36 8.28 (0.51) − 0.02 (0.31)
Weighted median 11 0.18 0.17 0.28 1.20 0.86–1.68
Weighted mode 11 0.04 0.23 0.88 1.04 0.65–1.64
Meng et al. Cell & Bioscience (2022) 12:28 Page 4 of 16
including carboxypeptidase E (CPE), electron transfer favoprotein-ubiquinone oxidoreductase (ETFDH),
neutral alpha-glucosidase C (GANC), and maltaseglucoamylase (MGAM), were identifed to be causally associated with AD. Among them, genetically
predicted the CPE gene could be a protective factor in
AD (IVW, OR=0.94, 95%CI=[0.89, 1.00], p=0.05,
Fig. 4), while the expressions of ETFDH (IVW, OR=1.08,
95%CI=[1.01,1.16], p=0.03, Fig. 5), GANC (IVW,
OR=1.09, 95%CI=[1.02,1.18], p=0.02, Fig. 6), and
MGAM (Wald ratio, OR=1.04, 95%CI=[1.00,1.09],
p=0.04) were causally associated with the increased
risk of AD. Notably, the present study showed high
expressions of ETFDH, GANC, and MGAM have causal
efects on the increased risk of AD, in other words, inhibiting the expression of three target genes is benefcial to
the treatment of AD to a certain extent. Interestingly,
based on the pharmacological actions obtained from the
DrugBank database, three targets related to antidiabetic
drugs, including metformin, miglitol, acarbose, voglibose, were the corresponding inhibitors of the above targets, suggesting that identifed targets might provide
useful genetic clues to understand the anti-AD efects of
selected antidiabetic drugs.
Furthermore, a total of 2746 SNPs of AD were discovered from the IGAP database using a genome-wide
Fig. 1. 2SMR analysis of the causal association between T1D and the risk of AD. a Scatter plot. The slope of the line corresponds to a causal estimate
using each of the four diferent methods. b Funnel plot. The vertical line shows a causal estimate using all SNPs combined into a single instrument
for each of two diferent methods. c Forest plot. Each black dot represents the MR estimate of each SNP using the wald ratio, and the horizontal line
represents the 95% CI. The red points show a combined causal estimate using all SNPs in a single instrument, including the 2SMR estimates of IVW
and MR-Egger. d Leave-one-out sensitivity analysis. Each black dot represents the result of MR-IVW excluding that particular SNP, and the red dot
depicts the IVW estimate using all SNPs
Meng et al. Cell & Bioscience (2022) 12:28 Page 5 of 16
significance threshold (p-value < 1 × 10−5
). By mapping the significant SNPs to genes on the basis of the
NCBI database, 152 AD susceptibility genes were
identified and included in this study. A PPI network
that followed was constructed by identified targets
(CPE, ETFDH, GANC, MGAM) and AD susceptibility
genes (Fig. 7). It was found that CPE and ETFDH were
not interacted with any degree in the network, while
GANC was related to MGAM, and further interacted
with CD33 (Fig. 8), which was a strong genetic locus
associated with AD.
Discussion
Trough performing a 2SMR analysis of the available
data, we found that diabetes had a causal efect on AD
risk, which is in line with previous epidemiological studies. Tere should be multiple mechanisms underlying the
association between diabetes and AD. First, insulin signaling dysregulation may be a critical pathological change
in AD, and it has been reported that insulin signaling is
impaired in postmortem brain tissue from AD patients
[26, 27]. Te insulin signaling pathway contributes to
the control of neuronal excitability and metabolism, and
Fig. 2. 2SMR analysis of the causal association between T2D and the risk of AD. a Scatter plot. The slope of the line corresponds to a causal estimate
using each of the four diferent methods. b Funnel plot. The vertical line shows a causal estimate using all SNPs combined into a single instrument
for each of two diferent methods. c Forest plot. Each black dot represents the MR estimate of each SNP using the wald ratio, and the horizontal line
represents the 95% CI. The red points show a combined causal estimate using all SNPs in a single instrument, including the 2SMR estimates of IVW
and MR-Egger. d Leave-one-out sensitivity analysis. Each black dot represents the result of MR-IVW excluding that particular SNP, and the red dot
depicts the IVW estimate using all SNPs
Meng et al. Cell & Bioscience (2022) 12:28 Page 6 of 16
cerebrovascular changes, such as infammation and alterations in brain insulin signaling, might play a pivotal role
in AD development [28, 29]. Second, as a mechanistic
linker between AD and diabetes, infammation can accelerate the development of diabetes by infuencing islet
function and peripheral insulin sensitivity. Moreover, as
a starting point of AD pathological progression, the normal synaptic function will be disrupted by cerebrovascular and central infammation, along with the increased
accumulation of Aβ [30].
Further 2SMR analysis revealed that some diabetesrelated physiological parameters, such as fasting blood
glucose and total cholesterol levels, were causally associated with the risk of AD. Previous studies have demonstrated that metabolic dysfunction of diabetes, especially
glucose-related dysfunction, may play a causative role
in the development of AD. For example, a large-scale
genome-wide cross-trait analysis identifed 4 loci that
were associated with AD and fasting glucose [31]. Also,
as the most cholesterol-rich organ, the cholesterol homeostasis in the human brain may be closely related to the
occurrence and development of AD [32]. Recent studies
have indicated that lipid metabolism-related genes, such
as APOC1 and APOE, might be major risk factors for AD
due to the involvement in the maintenance of brain lipid
homeostasis [33, 34]. Furthermore, our previous study
also identifed a total of six SNPs shared between T2D
and AD and found that lipid metabolism-related pathways were common between the two disorders by functional enrichment analysis [35].
In the past decades, theoretical and experimental
investigations of novel drugs for AD have attracted much
attention. It is noteworthy that drug repositioning based
on the approved drugs may represent an important
source for AD drug discovery, a case of this is antidiabetic
drug repositioning. By the 2SMR analysis, four targets,
including CPE, ETFDH, GANC, and MGAM, were identifed to be causally associated with AD in this paper. In
particular, in combination with the present 2SMR results
and pharmacological actions obtained from the DrugBank database, ETFDH-, GANC-, and MGAM-related
antidiabetic drugs, including metformin, miglitol, acarbose, voglibose, were precisely the corresponding inhibitors of the above targets, indicating potential therapeutic
efects on AD. Notably, among those, miglitol, acarbose,
and voglibose are currently used in the management of
glycemic control by inhibiting α-glucosidase, which is an
important biological target/enzyme that can catalyze the
degradation of dietary polysaccharides into monosaccharides. Te preliminary data in this paper proposed
that the targets of α-glucosidase inhibitors, for example,
GANC and MGAM, were causally associated with the
increased risk of AD, suggesting the therapeutic implications of α-glucosidase on AD. However, at present, the
antidiabetic drugs for the treatment of AD mainly focus
on GLP-1R agonists (liraglutide, exenatide), thiazolidinediones (pioglitazone, rosiglitazone), DPP-4 inhibitors
(sitagliptin, vildagliptin), and so on, while there are limited studies of α-glucosidase inhibitors in the treatment
of AD, and these fndings remain to be further estimated.
Fig. 3. 2SMR estimates of the causality between fasting blood glucose and total cholesterol levels and AD. a) the causal efects of fasting blood
glucose and AD. b) the causal efects of total cholesterol levels and AD
Meng et al. Cell & Bioscience (2022) 12:28 Page 7 of 16
Table 2 Main characteristics of the antidiabetic drugs included in the PPI network
Name Drugbank ID Target genes Target type
Ebselen DB12610 EPHX2 Target
INCB13739 DB05064 HSD11B1 Target
PSN357 DB05044 PYGL Target
Bisegliptin DB06127 DPP4 Target
NOX-700 DB05464 NFKB2 Target
NFKB1 Target
CLX-0921 DB05854 PPARG Target
Reglitazar DB04971 PPARA Target
PPARG Target
ISIS 113715 DB05506 PTPN1 Target
AT1391 DB05120 INSR Target
NN344 DB05115 INSR Target
CYP1A2 Enzyme
APD668 DB05166 GPR119 Target
Dutogliptin DB11723 DPP4 Target
MB-07803 DB05053 FBP1 Target
PSN9301 DB05001 DPP4 Target
Gliquidone DB01251 ABCC8 Target
KCNJ8 Target
CYP2C9 Enzyme
Albiglutide DB09043 GLP1R Target
Pramlintide DB01278 CALCR Target
RAMP1 Target
RAMP2 Target
RAMP3 Target
Voglibose DB04878 MGAM Target
Dapaglifozin DB06292 SLC5A2 Target
CYP1A1 Enzyme
CYP1A2 Enzyme
CYP2A6 Enzyme
CYP2C9 Enzyme
CYP2D6 Enzyme
CYP3A4 Enzyme
UGT1A9 Enzyme
UGT2B4 Enzyme
UGT2B7 Enzyme
Miglitol DB00491 MGAM Target
GAA Target
GANAB Target
GANC Target
AMY2A Enzyme
Vildagliptin DB04876 DPP4 Target
Dulaglutide DB09045 GLP1R Target
Phenformin DB00914 PRKAA1 Target
KCNJ8 Target
CYP2D6 Enzyme
AMG-131 DB05490 PPARG Target
Acarbose DB00284 MGAM Target
GAA Target
Meng et al. Cell & Bioscience (2022) 12:28 Page 8 of 16
Table 2 (continued)
Name Drugbank ID Target genes Target type
SI Target
AMY2A Target
Sitagliptin DB01261 DPP4 Target
CYP3A4 Enzyme
CYP2C8 Enzyme
Acetohexamide DB00414 KCNJ1 Target
CBR1 Enzyme
CYP2C9 Enzyme
Canaglifozin DB08907 SLC5A2 Target
UGT1A9 Enzyme
UGT2B4 Enzyme
CYP3A4 Enzyme
Pioglitazone DB01132 PPARG Target
MAOB Target
CYP2C8 Enzyme
CYP3A4 Enzyme
CYP1A1 Enzyme
Glisoxepide DB01289 KCNJ8 Target
CYP2C9 Enzyme
Glipizide DB01067 ABCC8 Target
PPARG Target
CYP2C9 Enzyme
UGT1A1 Enzyme
Insulin Glargine DB00047 INSR Target
IGF1R Target
CYP1A2 Enzyme
Insulin Degludec DB09564 INSR Target
IGF1R Target
CYP1A2 Enzyme
Chlorpropamide DB00672 ABCC8 Target
CYP2C9 Enzyme
CYP2C19 Enzyme
PTGS1 Enzyme
Linagliptin DB08882 DPP4 Target
CYP3A4 Enzyme
Repaglinide DB00912 ABCC8 Target
PPARG Target
CYP2C8 Enzyme
CYP3A4 Enzyme
Insulin Pork DB00071 INSR Target
IGF1R Target
IDE Enzyme
CYP1A2 Enzyme
Nateglinide DB00731 ABCC8 Target
PPARG Target
CYP2C9 Enzyme
CYP3A4 Enzyme
CYP3A5 Enzyme
CYP3A7 Enzyme
Meng et al. Cell & Bioscience (2022) 12:28 Page 9 of 16
Table 2 (continued)
Name Drugbank ID Target genes Target type
PTGS1 Enzyme
UGT1A9 Enzyme
CYP2D6 Enzyme
Insulin Aspart DB01306 INSR Target
IGF1R Target
CYP1A2 Enzyme
Insulin Detemir DB01307 INSR Target
IGF1R Target
CYP1A2 Enzyme
Saxagliptin DB06335 DPP4 Target
CYP3A4 Enzyme
CYP3A5 Enzyme
Insulin Glulisine DB01309 INSR Target
IGF1R Target
CYP1A2 Enzyme
Tolbutamide DB01124 ABCC8 Target
KCNJ1 Target
CYP2C9 Enzyme
CYP2C8 Enzyme
CYP2C19 Enzyme
CYP2C18 Enzyme
Rosiglitazone DB00412 PPARG Target
ACSL4 Target
PPARA Target
PPARD Target
RXRA Target
RXRB Target
RXRG Target
CYP2C8 Enzyme
CYP2C9 Enzyme
PTGS1 Enzyme
CYP1A2 Enzyme
CYP3A4 Enzyme
CYP2B6 Enzyme
CYP2D6 Enzyme
CYP2E1 Enzyme
Mitiglinide DB01252 ABCC8 Target
PPARG Target
UGT1A3 Enzyme
UGT2B7 Enzyme
Insulin Human DB00030 INSR Target
IGF1R Target
CPE Target
NOV Target
LRP2 Target
IGFBP7 Target
IDE Enzyme
PCSK2 Enzyme
PCSK1 Enzyme
Meng et al. Cell & Bioscience (2022) 12:28 Page 10 of 16
Table 2 (continued)
Name Drugbank ID Target genes Target type
CYP1A2 Enzyme
Insulin Lispro DB00046 INSR Target
IGF1R Target
CYP1A2 Enzyme
IDE Enzyme
Lixisenatide DB09265 GLP1R Target
Metformin DB00331 PRKAB1 Target
ETFDH Target
GPD1 Target
Lobeglitazone DB09198 PPARG Target
CYP1A2 Enzyme
CYP2C9 Enzyme
CYP2C19 Enzyme
CYP3A4 Enzyme
Managlinat dialanetil DB05518 FBP1 Target
Levoketoconazole DB05667 CYP11B1 Target
CYP51A1 Target
CYP3A4 Enzyme
CYP3A5 Enzyme
CYP51A1 Enzyme
CYP17A1 Enzyme
CYP21A2 Enzyme
CYP11B1 Enzyme
Tesaglitazar DB06536 PPARA Target
PPARG Target
Ertiprotafb DB06521 PTPN1 Target
IKBKB Target
PPARA Target
PPARG Target
Glycodiazine DB01382 KCNJ1 Target
ABCC8 Target
Muraglitazar DB06510 PPARA Target
PPARG Target
CYP1A2 Enzyme
UGT1A3 Enzyme
UGT1A1 Enzyme
CYP2C8 Enzyme
Troglitazone DB00197 PPARG Target
ACSL4 Target
SERPINE1 Target
SLC29A1 Target
ESRRG Target
ESRRA Target
PPARD Target
PPARA Target
GSTP1 Target
CYP3A4 Enzyme
CYP2C19 Enzyme
UGT1A1 Enzyme
Meng et al. Cell & Bioscience (2022) 12:28 Page 11 of 16
Table 2 (continued)
Name Drugbank ID Target genes Target type
CYP2C8 Enzyme
CYP19A1 Enzyme
CYP1A1 Enzyme
CYP2B6 Enzyme
CYP2C9 Enzyme
CYP3A5 Enzyme
CYP3A7 Enzyme
UGT1A3 Enzyme
UGT1A4 Enzyme
UGT1A6 Enzyme
UGT1A7 Enzyme
UGT1A8 Enzyme
UGT1A9 Enzyme
UGT1A10 Enzyme
UGT2B7 Enzyme
UGT2B15 Enzyme
Ertuglifozin DB11827 SLC5A2 Target
UGT1A9 Enzyme
UGT2B7 Enzyme
UGT1A1 Enzyme
UGT1A4 Enzyme
Exenatide DB01276 GLP1R Target
DPP4 Enzyme
Naveglitazar DB12662 PPARG Target
Alogliptin DB06203 DPP4 Target
CYP3A4 Enzyme
CYP2D6 Enzyme
Liraglutide DB06655 GLP1R Target
DPP4 Enzyme
MME Enzyme
Semaglutide DB13928 GLP1R Target
DPP4 Enzyme
MME Enzyme
LPL Enzyme
AMY1A Enzyme
Glimepiride DB00222 KCNJ11 Target
KCNJ1 Target
ABCC8 Target
CYP2C9 Enzyme
Sarpogrelate DB12163 HTR2C Target
HTR2A Target
Glyburide DB01016 ABCC9 Target
ABCB11 Target
ABCA1 Target
CFTR Target
CPT1A Target
TRPM4 Target
CYP2C9 Enzyme
CYP2C19 Enzyme
Meng et al. Cell & Bioscience (2022) 12:28 Page 12 of 16
Several limitations of the present analysis need to be
noted. In the 2SMR analysis, we avoided the infuence of
diferent ethnicities to the greatest extent by screening for
European ancestry in the involved studies. However, there
are also a few studies that have mixed populations with a
small proportion outside Europe. At the same time, the
limitation of European ancestry also indicates that our
fndings may not be applicable to other ethnicities. In addition, the small number of variants for each exposure is the
limitation of these analyses. Tese factors may interfere
with the stability of the conclusion.
Conclusions
Te present 2SMR analysis based on extensive data
uncovered causal associations between diabetes and AD.
It is interesting to note that T2D seemed to show a more
signifcant association with AD risk than T1D. Further
analysis identifed several diabetes-related physiological
Table 2 (continued)
Name Drugbank ID Target genes Target type
CYP3A4 Enzyme
CYP3A7 Enzyme
CYP3A5 Enzyme
Gliclazide DB01120 ABCC8 Target
VEGFA Target
CYP2C9 Enzyme
CYP2C19 Enzyme
Empaglifozin DB09038 SLC5A2 Target
UGT2B7 Enzyme
UGT1A3 Enzyme
UGT1A8 Enzyme
UGT1A9 Enzyme
Glymidine DB01382 KCNJ1 Target
ABCC8 Target
Balaglitazone DB12781 CYP3A4 Enzyme
CYP2C8 Enzyme
Glibornuride DB08962 CYP2C9 Enzyme
Rivoglitazone DB09200 CYP3A4 Enzyme
CYP2C8 Enzyme
AB192 DB06111 MPO Enzyme
Lisofylline DB12406 CYP1A2 Enzyme
Table 3 2SMR estimates of the causality between antidiabetic targets and AD
Target gene Drugs Action Method Numbers of
SNPs
OR 95% CI P-value
CPE Insulin Human Modulator (Unknown) MR Egger 3 0.98 0.82–1.17 0.86
Inverse variance weighted 3 0.94 0.89–1.00 0.05
Weighted median 3 0.95 0.89–1.01 0.08
Weighted mode 3 0.95 0.89–1.01 0.24
ETFDH Metformin Inhibitor Inverse variance weighted 2 1.08 1.01–1.16 0.03
GANC Miglitol Antagonist Inverse variance weighted 2 1.09 1.02–1.18 0.02
MGAM Voglibose Inhibitor Wald ratio 1 1.04 1.00–1.09 0.04
Acarbose Inhibitor
Miglitol Antagonist,
inhibitor
Meng et al. Cell & Bioscience (2022) 12:28 Page 13 of 16
Fig. 4. 2SMR estimates of the causality between CPE target and AD. a Scatter plot. The slope of the line corresponds to a causal estimate using
each of the four diferent methods. b Funnel plot. The vertical line shows a causal estimate using all SNPs combined into a single instrument for
each of two diferent methods. c Forest plot. Each black dot represents the MR estimate of each SNP using the wald ratio, and the horizontal line
represents the 95% CI. The red points show a combined causal estimate using all SNPs in a single instrument, including the 2SMR estimates of IVW
and MR-Egger. d Leave-one-out sensitivity analysis. Each black dot represents the result of MR-IVW excluding that particular SNP, and the red dot
depicts the IVW estimate using all SNPs
Meng et al. Cell & Bioscience (2022) 12:28 Page 14 of 16
parameters that may have a causative role in the development of AD. Besides, four targets from antidiabetic
drugs were identifed to be causally associated with AD,
indicating potential therapeutic efects on AD and might
provide implications for drug development. In summary,
our study indicates that diabetes and antidiabetic drugs
were causally relevant to AD and certainly warrants
more well-designed studies clinical verifcations in the
future. At the same time, these fndings also inspire us
that preventing or delaying the risk factors of AD, such
as diabetes, are likely to be more achievable goals in the
foreseeable future.
Fig. 5. 2SMR estimates of the causality between ETFDH target and AD. a Scatter plot. The slope of the line corresponds to a causal estimate using
each of the four diferent methods. b Funnel plot. The vertical line shows a causal estimate using all SNPs combined into a single instrument for
each of two diferent methods. c Forest plot. Each black dot represents the MR estimate of each SNP using the wald ratio, and the horizontal line
represents the 95% CI. The red points show a combined causal estimate using all SNPs in a single instrument, including the 2SMR estimates of IVW
and MR-Egger
Fig. 6. 2SMR estimates of the causality between GANC target and AD. a Scatter plot. The slope of the line corresponds to a causal estimate using
each of the four diferent methods. b Funnel plot. The vertical line shows a causal estimate using all SNPs combined into a single instrument for
each of two diferent methods. c Forest plot. Each black dot represents the MR estimate of each SNP using the wald ratio, and the horizontal line
represents the 95% CI. The red points show a combined causal estimate using all SNPs in a single instrument, including the 2SMR estimates of IVW
and MR-Egger
Meng et al. Cell & Bioscience (2022) 12:28 Page 15 of 16
Abbreviations
AD: Alzheimer’s disease; T1D: Type 1 diabetes; T2D: Type 2 diabetes; 2SMR:
Two-sample Mendelian randomization; IGAP: International genomics of
Alzheimer’s project; SNPs: Single nucleotide polymorphisms.
Acknowledgements
This work was supported by the University Youth Innovation Team of Shan‑
dong Province (Grant No. 2019KJK017), Shandong Provincial Natural Science
Foundation (Grant No. ZR2019MH020), Talent Program of Zibo and School-City
integration program of Zhangdian district.
Authors’ contributions
LS and HFJ conceived and designed the study; LM and ZW collected data; LM
and ZW performed calculations; LM, ZW, HFJ and LS analyzed the data; LM, LS
and HFJ wrote and revised the paper. All authors read and approved the fnal
manuscript.
Funding
The funding body had no roles in the design of the study and collection,
analysis, and interpretation of data and in writing the manuscript should be
declared.
Availability of data and materials
The data is available from the corresponding author upon request.
Declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Author details
1
Institute of Biomedical Research, Shandong University of Technology, Zibo,
Shandong, People’s Republic of China. 2
Shandong Provincial Research Center
Fig. 7 A network-based analysis based on identifed targets (CPE, ETFDH, GANC, MGAM) and AD susceptibility genes. The combined score is
mapped to the edge size (low values to small sizes and bright color), and the node degree is mapped to the node size and node color (low values
to small sizes and bright color)
Fig. 8 The sub-network analysis based on identifed targets and frst
neighbors of AD susceptibility genes
Meng et al. Cell & Bioscience (2022) 12:28 Page 16 of 16
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Received: 2 December 2021 Accepted: 26 February 2022
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