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 1. nature
 2. nature aging
 3. articles
 4. article

  * Article
  * Published: 06 December 2021

Endophenotype-based in silico network medicine discovery combined
with insurance record data mining identifies sildenafil as a
candidate drug for Alzheimer's disease

  * Jiansong Fang^1^ na1,
  * Pengyue Zhang^2^ na1,
  * Yadi Zhou^1^ na1,
  * Chien-Wei Chiang^3^ na1,
  * Juan Tan^1,
  * Yuan Hou^1,
  * Shaun Stauffer^4,
  * Lang Li^3,
  * Andrew A. Pieper^5,6,7,
  * Jeffrey Cummings^8 &
  * ...
  * Feixiong Cheng  ORCID: orcid.org/0000-0002-1736-2847^1,9,10 

Show authors

Nature Aging volume 1, pages 1175-1188 (2021)Cite this article

  * 7805 Accesses

  * 29 Citations

  * 2664 Altmetric

  * Metrics details

Subjects

  * Ageing
  * Alzheimer's disease
  * Network models

Abstract

We developed an endophenotype disease module-based methodology for
Alzheimer's disease (AD) drug repurposing and identified sildenafil
as a potential disease risk modifier. Based on retrospective
case-control pharmacoepidemiologic analyses of insurance claims data
for 7.23 million individuals, we found that sildenafil usage was
significantly associated with a 69% reduced risk of AD (hazard ratio
0.31, 95% confidence interval 0.25-0.39, P < 1.0 x 10^-8). Propensity
score-stratified analyses confirmed that sildenafil is significantly
associated with a decreased risk of AD across all four drug cohorts
tested (diltiazem, glimepiride, losartan and metformin) after
adjusting for age, sex, race and disease comorbidities. We also found
that sildenafil increases neurite growth and decreases phospho-tau
expression in neuron models derived from induced pluripotent stem
cells from patients with AD, supporting mechanistically its potential
beneficial effect in AD. The association between sildenafil use and
decreased incidence of AD does not establish causality, which will
require a randomized controlled trial.

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Fig. 1: A diagram illustrating an endophenotype network-based drug
repurposing framework for AD.
[43587_2021_138_Fig1_HTML]
Fig. 2: Network-based in silico drug repurposing for AD.
[43587_2021_138_Fig2_HTML]
Fig. 3: Longitudinal analyses reveal that sildenafil usage is
significantly associated with reduced likelihood of AD in a
longitudinal patient database with 7.23 million individuals.
[43587_2021_138_Fig3_HTML]
Fig. 4: Subgroup analyses of five drug cohort studies to evaluate
confounding by disease comorbidities.
[43587_2021_138_Fig4_HTML]
Fig. 5: Sex-specific and age-specific subgroup analyses across five
drug cohort studies.
[43587_2021_138_Fig5_HTML]
Fig. 6: Experimental validation of sildenafil's likely mechanism of
action in AD.
[43587_2021_138_Fig6_HTML]

Data availability

Data supporting the findings of this study are available within
Supplementary Tables. The human protein-protein interactome and
drug-target network can be downloaded from https://github.com/
ChengF-Lab/endoAD. Tissue/brain-specific expression data were
downloaded from GTEx database (https://www.gtexportal.org/home/). The
expression datasets used in this study were downloaded from the Gene
Expression Omnibus database (https://www.ncbi.nlm.nih.gov/geo/) with
accession codes GSE65067, GSE74437, GSE74438, GSE64398, GSE53480,
GSE56772 and GSE57583. Health insurance claims data are available
from the MarketScan Medicare Claims database (2012 to 2017) based on
the Medicare Advantage and Fee for services from IBM MarketScan
Research Databases.

Code availability

Source codes for network proximity analysis and disease module
identification are available at https://github.com/ChengF-Lab/GPSnet.

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Acknowledgements

This work was primarily supported by the National Institute of Aging
(NIA) of the National Institutes of Health (NIH) under Award Number
R01AG066707 to F.C. This work was supported in part by U01AG073323,
3R01AG066707-01S1, 3R01AG066707-02S1, and 1R56AG074001-01 to F.C.
This work was supported in part by the Translational Therapeutics
Core of the Cleveland Alzheimer's Disease Research Center (NIH/NIA:
P30AG072959) to F.C., A.A.P. and J.C. This work was supported in part
by the Brockman Foundation, Project 19PABH134580006-AHA/Allen
Initiative in Brain Health and Cognitive Impairment, the Elizabeth
Ring Mather & William Gwinn Mather Fund, S. Livingston Samuel Mather
Trust, and the Louis Stokes VA Medical Center resources and
facilities to A.A.P. This work was supported in part by the NIA grant
R35AG071476 and the Alzheimer's Disease Drug Discovery Foundation
(ADDF) to J.C.

Author information

Author notes

 1. These authors contributed equally: Jiansong Fang, Pengyue Zhang,
    Yadi Zhou, Chien-Wei Chiang.

Authors and Affiliations

 1. Genomic Medicine Institute, Lerner Research Institute, Cleveland
    Clinic, Cleveland, OH, USA

    Jiansong Fang, Yadi Zhou, Juan Tan, Yuan Hou & Feixiong Cheng

 2. Department of Biostatistics and Health Data Science, School of
    Medicine, Indiana University, Indianapolis, IN, USA

    Pengyue Zhang

 3. Department of Biomedical Informatics, College of Medicine, Ohio
    State University, Columbus, OH, USA

    Chien-Wei Chiang & Lang Li

 4. Center for Therapeutics Discovery, Lerner Research Institute,
    Cleveland Clinic, Cleveland, OH, USA

    Shaun Stauffer

 5. Harrington Discovery Institute, University Hospitals Case Medical
    Center, Cleveland, OH, USA

    Andrew A. Pieper

 6. Department of Psychiatry, Case Western Reserve University,
    Cleveland, OH, USA

    Andrew A. Pieper

 7. Geriatric Research Education and Clinical Centers, Louis Stokes
    Cleveland VAMC, Cleveland, OH, USA

    Andrew A. Pieper

 8. Chambers-Grundy Center for Transformative Neuroscience,
    Department of Brain Health, School of Integrated Health Sciences,
    University of Nevada Las Vegas, Las Vegas, NV, USA

    Jeffrey Cummings

 9. Department of Molecular Medicine, Cleveland Clinic Lerner College
    of Medicine, Case Western Reserve University, Cleveland, OH, USA

    Feixiong Cheng

10. Case Comprehensive Cancer Center, Case Western Reserve University
    School of Medicine, Cleveland, OH, USA

    Feixiong Cheng

Authors

 1. Jiansong Fang
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 2. Pengyue Zhang
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 3. Yadi Zhou
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 4. Chien-Wei Chiang
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 5. Juan Tan
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 6. Yuan Hou
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 7. Shaun Stauffer
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 8. Lang Li
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 9. Andrew A. Pieper
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10. Jeffrey Cummings
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11. Feixiong Cheng
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Contributions

F.C. conceived the study. J.F. and Y.Z. performed all multi-omics and
network proximity experiments and analysis. P.Z. and C.W.C. performed
all patient data analysis. J.T. performed human microglia and iPSC
experiments and data analysis. Y.H., S.S., A.A.P., L.L. and J.C.
interpreted the data analysis. F.C., J.F., P.Z., A.A.P. and J.C.
drafted the manuscript. All authors critically revised and approved
the manuscript.

Corresponding author

Correspondence to Feixiong Cheng.

Ethics declarations

Competing interests

J.C. has provided consultation to Acadia, Actinogen, Alkahest,
Alzheon, Annovis, Avanir, Axsome, Biogen, BioXcel, Cassava, Cerecin,
Cerevel, Cortexyme, Cytox, EIP Pharma, Eisai, Foresight, GemVax,
Genentech, Green Valley, Grifols, Karuna, Merck, Novo Nordisk,
Otsuka, Resverlogix, Roche, Samumed, Samus, Signant Health, Suven,
Third Rock and United Neuroscience pharmaceutical and assessment
companies. J.C. has stock options in ADAMAS, AnnovisBio, MedAvante
and BiOasis. The other authors declare no competing interests.

Additional information

Peer review information Nature Aging thanks Heather Allore, Emre
Guney and the other, anonymous, reviewer(s) for their contribution to
the peer review of this work.

Publisher's note Springer Nature remains neutral with regard to
jurisdictional claims in published maps and institutional
affiliations.

Extended data

Extended Data Fig. 1 Proof-of-concept of network module of
Alzheimer's disease (AD).

A subnetwork highlighting disease module (AD seed module)
characterized by both amyloidosis and tauopathy under the human
protein-protein interactome model. The AD seed module includes 227
protein-protein interactions (PPIs) (edges or links) connecting 102
unique proteins (nodes).

Extended Data Fig. 2 Heatmap illustrates the network proximity
between molecular targets of 21 ongoing repurposable AD drugs and 37
AD disease modules.

These drugs could target amyloid, tau, or both amyloid and tau
pathologies or their related pathways in in vitro or in vivo mouse
models or in patients with AD. We built three AD network modules by
assembling experimentally validated (seed) genes in amyloidosis
(amyloid), tauopathy (tau) and AD (characterized by both amyloid and
tau). In addition, we also built disease modules from 34
differentially expressed gene (DEG) sets derived from transcriptomics
data (including microarray and bulk RNA-sequencing) from AD genetic
mouse.

Extended Data Fig. 3 The efficacy of endophenotype-based repurposable
drugs in Alzheimer's disease (AD).

Molecular targets of 21 ongoing repurposable AD drugs that target
both Amyloid and Tau have significantly closer network distance with
AD network modules built from transcriptomics (a) and proteomics (b)
data from AD genetic mouse, in comparison to drugs targeting amyloid
or tau alone. In a, we built AD modules from 34 differentially
expressed gene sets derived from transcriptomics data (including
microarray, and bulk RNA-sequencing) from AD genetic mouse. In b, we
built AD modules for 10 differentially expressed protein sets from
proteomics data in AD genetic mouse. P values were calculated by
Wilcoxon test (one-side) in a and b.

Extended Data Fig. 4 Network-based in silico drug repurposing for
Alzheimer's disease (AD).

13 AD disease modules, including 3 AD seed genes and 10
differentially expressed protein (DEP) sets from AD mouse proteomics
data, were used to screen FDA-approved drugs for AD. A sankey plot
illustrates a global view of 66 drug candidates identified by network
proximity. Drugs are grouped by their first-level Anatomical
Therapeutic Chemical Classification (ATC) codes.

Extended Data Fig. 5 Longitudinal analyses reveal that sildenafil
usage is significantly associated with reduced likelihood of AD in
individuals with coronary artery disease (CAD).

Five comparator analyses were conducted for coronary artery disease
patients including: (a) sildenafil vs. matched non- sildenafil, (b)
sildenafil vs. diltiazem (an anti-hypertensive drug), (c) sildenafil
vs. losartan (an anti-hypertensive drug candidate in an AD clinical
trial [ClinicalTrials.gov Identifier: NCT02913664]), (d) sildenafil
vs. glimepiride (an anti-diabetic drug), and (e) sildenafil vs.
metformin (an anti-diabetic drug in an AD clinical trial
[ClinicalTrials.gov Identifier: NCT00620191]). For each comparator,
we estimated the propensity score by using the variables described in
Table 1. Then, we estimated the un-stratified Kaplan-Meier curves,
conducted propensity score-stratified (n strata = 10) log-rank test
and Cox model. All p-values in the Kaplan Meier plots are based on
two-sided log-rank test. Sildenafil n = 19,093; diltiazem n = 51,771;
losartan n = 111,592; glimepiride n = 30,083; metformin n = 91,705.

Extended Data Fig. 6 Longitudinal analyses reveal that sildenafil
usage is significantly associated with reduced likelihood of AD in
individuals with hypertension (HT).

Five comparator analyses were conducted for hypertension patients
including: (a) sildenafil vs. matched non- sildenafil, (b) sildenafil
vs. diltiazem (an anti-hypertensive drug), (c) sildenafil vs.
losartan (an anti-hypertensive drug candidate in an AD clinical trial
[ClinicalTrials.gov Identifier: NCT02913664]), (d) sildenafil vs.
glimepiride (an anti-diabetic drug), and (e) sildenafil vs. metformin
(an anti-diabetic drug in an AD clinical trial [ClinicalTrials.gov
Identifier: NCT00620191]). For each comparator, we estimated the
propensity score by using the variables described in Table 1. Then,
we estimated the un-stratified Kaplan-Meier curves, conducted
propensity score-stratified (n strata = 10) log-rank test and Cox
model. All p-values in the Kaplan Meier plots are based on two-sided
log-rank test. Sildenafil n = 49,541; diltiazem n = 119,097; losartan
n = 339,940; glimepiride n = 74,018; metformin n = 275,328.

Extended Data Fig. 7 Longitudinal analyses reveal that sildenafil
usage is significantly associated with reduced likelihood of AD in
individuals with type-2 diabetes (T2D).

Five comparator analyses were conducted for type-2 diabetes patients
including: (a) sildenafil vs. matched non- sildenafil, (b) sildenafil
vs. diltiazem (an anti-hypertensive drug), (c) sildenafil vs.
losartan (an anti-hypertensive drug candidate in an AD clinical trial
[ClinicalTrials.gov Identifier: NCT02913664]), (d) sildenafil vs.
glimepiride (an anti-diabetic drug), and (e) sildenafil vs. metformin
(an anti-diabetic drug in an AD clinical trial [ClinicalTrials.gov
Identifier: NCT00620191]). For each comparator, we estimated the
propensity score by using the variables described in Table 1. Then,
we estimated the un-stratified Kaplan-Meier curves, conducted
propensity score stratified (n strata = 10) log-rank test and Cox
model. All p-values in the Kaplan Meier plots are based on two-sided
log-rank test. Sildenafil n = 21,978; diltiazem n = 51,300; losartan
n = 156,308; glimepiride n = 100,298; metformin n = 367,754.

Supplementary information

Supplementary Information

Supplementary Materials and Methods, Results and Discussion, Figs.
1-9 and Tables 1-15.

Reporting Summary.

Supplementary Tables

Supplementary Table 1. Functional enrichment analysis result for the
AD seed gene set (n = 144). Supplementary Table 2. Detailed
information of 21 existing drugs in AD clinical trials to test the
clinical efficacy of endophenotype hypothesis. Supplementary Table 3.
Anti-AD clinical, in vivo and BBB properties from publicly available
databases and in silico predictions for the 66 potential AD drugs.
Supplementary Table 4. Prioritizing 100 candidate drugs with the
lowest z score by AD seed module using the PxEA approach and
obtaining 56 candidate drugs. Supplementary Table 5. 120 FDA-approved
drugs with reported clinical or preclinical (in vivo) evidence in AD
from the AlzGPS database. Supplementary Table 6. Network proximity
scores between molecular targets of 12 ongoing repurposable AD drugs
(dually targeting amyloid and tau) and AD seed module. Supplementary
Table 8. Age-stratified analysis for all comparisons of sildenafil.
Supplementary Table 9. Sex-based subgroup analyses for sildenafil.
Supplementary Table 13. List of experimentally validated (seed) genes
in amyloidosis (amyloid), tauopathy (tau) and AD. Supplementary Table
14. Transcriptomic and proteomic datasets from transgenic AD mouse
models.

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Fang, J., Zhang, P., Zhou, Y. et al. Endophenotype-based in silico
network medicine discovery combined with insurance record data mining
identifies sildenafil as a candidate drug for Alzheimer's disease.
Nat Aging 1, 1175-1188 (2021). https://doi.org/10.1038/
s43587-021-00138-z

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  * Received: 28 August 2020

  * Accepted: 15 October 2021

  * Published: 06 December 2021

  * Issue Date: December 2021

  * DOI: https://doi.org/10.1038/s43587-021-00138-z

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