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

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

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Matters Arising to this article was published on 22 May 2023

An Author Correction to this article was published on 20 March 2023

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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 × 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.
Fig. 2: Network-based in silico drug repurposing for AD.
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.
Fig. 4: Subgroup analyses of five drug cohort studies to evaluate confounding by disease comorbidities.
Fig. 5: Sex-specific and age-specific subgroup analyses across five drug cohort studies.
Fig. 6: Experimental validation of sildenafil’s likely mechanism of action in AD.

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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

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  1. Jiansong Fang
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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.

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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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