Evidence map›Paper›PMID 40476595›Full record

ArticleClinical pharmacology and therapeutics2025

Repurposing Acebutolol for Osteoporosis Treatment: Insights From Multi-Omics and Multi-Modal Data Analysis.

Dan-Yang Liu, Hui Shen, Jonathan Greenbaum, Qiao-Rong Yi, Shuang Liang, Yue Zhang, Jia-Chen Liu, Chuan Qiu, Lan-Juan Zhao, Qing Tian and 12 more

Abstract read
In one paragraph

Article in Clinical pharmacology and therapeutics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

22 authors.

Dan-Yang LiuLaboratory of Molecular and Statistical Genetics, College of Life Sciences, Hunan Normal University, Changsha, Hunan, China.ORCID 0000-0001-8015-4103
Hui ShenTulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University School of Medicine, New Orleans, Louisiana, USA.ORCID 0000-0003-0335-6064
Jonathan GreenbaumTulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University School of Medicine, New Orleans, Louisiana, USA.ORCID 0000-0002-7817-5981
Qiao-Rong YiLaboratory of Molecular and Statistical Genetics, College of Life Sciences, Hunan Normal University, Changsha, Hunan, China.ORCID 0000-0002-7103-5919
Shuang LiangCenter for System Biology, Data Sciences, and Reproductive Health, School of Basic Medical Science, Central South University, Changsha, Hunan, China.
Yue ZhangLaboratory of Molecular and Statistical Genetics, College of Life Sciences, Hunan Normal University, Changsha, Hunan, China.
Jia-Chen LiuCenter for System Biology, Data Sciences, and Reproductive Health, School of Basic Medical Science, Central South University, Changsha, Hunan, China.ORCID 0000-0002-1084-1714
Chuan QiuTulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University School of Medicine, New Orleans, Louisiana, USA.ORCID 0000-0001-6202-9229
Lan-Juan ZhaoTulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University School of Medicine, New Orleans, Louisiana, USA.ORCID 0000-0001-6342-2495
Qing TianTulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University School of Medicine, New Orleans, Louisiana, USA.
Kuan-Jui SuTulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University School of Medicine, New Orleans, Louisiana, USA.ORCID 0000-0002-5163-9774
Zhe LuoTulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University School of Medicine, New Orleans, Louisiana, USA.ORCID 0000-0001-6495-408X
Li WuTulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University School of Medicine, New Orleans, Louisiana, USA.
Xiang-He MengCenter for System Biology, Data Sciences, and Reproductive Health, School of Basic Medical Science, Central South University, Changsha, Hunan, China.ORCID 0000-0001-8731-2899
Hong-Mei XiaoCenter for System Biology, Data Sciences, and Reproductive Health, School of Basic Medical Science, Central South University, Changsha, Hunan, China.ORCID 0000-0002-8121-9498
Yun DengZebrafish Genetics Laboratory, College of Life Sciences, Hunan Normal University, Changsha, China.
Yang LiDivision in Cellular and Molecular Medicine, Department of Pathology and Laboratory Medicine, Tulane University School of Medicine, New Orleans, Louisiana, USA.
Dragana LovreSection of Endocrinology, Tulane University Health Sciences Center, New Orleans, Louisiana, USA.ORCID 0000-0002-2607-8768
Vivian FonsecaSection of Endocrinology, Tulane University Health Sciences Center, New Orleans, Louisiana, USA.ORCID 0000-0002-3381-7151
Fernando L SanchezDepartment of Orthopaedic Surgery, Tulane University Medical School, New Orleans, Louisiana, USA.
Li-Jun TanLaboratory of Molecular and Statistical Genetics, College of Life Sciences, Hunan Normal University, Changsha, Hunan, China.ORCID 0000-0003-1405-6008
Hong-Wen DengTulane Center of Biomedical Informatics and Genomics, Deming Department of Medicine, Tulane University School of Medicine, New Orleans, Louisiana, USA.ORCID 0000-0002-0387-8818

Funding

Tulane COBRE in Cardiometabolic Diseases Clinical Research CoreP20GM109036 · NIGMS · TULANE UNIVERSITY OF LOUISIANA · PI Tanika Nicole Kelly · 2016 to 2026
$25.3M
Trans-omics Integration of Multi-omics Studies for OsteoporosisU19AG055373 · NIA · TULANE UNIVERSITY OF LOUISIANA · PI HONG-WEN DENG · 2017 to 2026
$24.3M
Pilot Projects ProgramP30GM145498 · NIGMS · TULANE UNIVERSITY OF LOUISIANA · PI S MICHAL JAZWINSKI · 2022 to 2026
$8.6M
Inhibiting Periodontitis by Targeting Cathepsin K and Attenuating TLR SignalingR01DE023813 · NIDCR · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI LI, YI-PING · 2014 to 2023
$3.9M
Intensive Lifestyle Intervention, Metabolomics, and Risk of Frailty Fracture in Overweight or Obese Patients with Type 2 DiabetesR01AG068232 · NIA · UNIVERSITY OF TENNESSEE HEALTH SCI CTR · PI JOHNSON, KAREN C, ZHAO, QI · 2021 to 2025
$3.1M
Identification of Metabolomic Profiles for Sarcopenia Traits in Older Whites and BlacksR01AG061917 · NIA · UNIVERSITY OF TENNESSEE HEALTH SCI CTR · PI SHEN, HUI, ZHAO, QI · 2019 to 2023
$3.0M
Decoding Methylation Mediated Epigenomic Contributions to Male OsteoporosisR01AR069055 · NIAMS · TULANE UNIVERSITY OF LOUISIANA · PI DENG, HONG-WEN · 2017 to 2021
$2.9M
Mechanistic basis of the role of Cbx3 in negatively regulating osteoclast differentiation through epigenetic modificationR01AR075735 · NIAMS · UNIVERSITY OF ALABAMA AT BIRMINGHAM · PI LI, YI-PING · 2019 to 2024
$2.4M
CSRD VA IK2 CX002225Natural Science Foundation of China 31772548Natural Science Foundation of China 31970504Natural Science Foundation of China 81570807NIAMS NIH HHS R01 AR069055NIAMS NIH HHS R01 AR075735NIA NIH HHS R01 AG061917NIA NIH HHS R01 AG068232NIA NIH HHS U19 AG055373NIDCR NIH HHS R01 DE023813NIGMS NIH HHS P20 GM109036NIGMS NIH HHS P30 GM145498NIH HHS R01AR069055NIH HHS U19AG05537301
6 · The paper itself

Abstract

Osteoporosis is a common metabolic bone disease with aging, characterized by low bone mineral density (BMD) and higher fragility fracture risk. Although current pharmacological interventions provide therapeutic benefits, long-term use is limited by side effects and comorbidities. In this study, we employed driver signaling network identification (DSNI) and drug functional networks (DFN) to identify repurposable drugs from the Library of Integrated Network-Based Cellular Signatures. We constructed osteoporosis driver signaling networks (ODSN) using multi-omics data and developed DFN based on drug similarity. By integrating ODSN and DFN with drug-induced transcriptional responses, we screened 10,158 compounds and identified several drugs with strong targeting effects on ODSN. Mendelian randomization assessed potential causal links between cis-eQTLs of drug targets and BMD using genome-wide association study data. Our findings indicate four drugs, including Ruxolitinib, Alfacalcidol, and Doxercalciferol, may exert anti-osteoporosis effects. Notably, Acebutolol, a β-blocker for hypertension, has not previously been implicated in osteoporosis therapy. For validation, zebrafish osteoporosis models were established using Dexamethasone-induced bone loss, followed by treatment with Acebutolol hydrochloride and Alfacalcidol. Both compounds demonstrated significant protective effects against osteoporosis-related bone deterioration. Furthermore, a population-based data set, utilizing propensity score matching and analyzed via a generalized linear model, revealed that individuals taking β-blocker drugs exhibited significantly higher BMD than users of other cardiovascular medications. In summary, this study integrates multi-omics approaches, experimental validation, and real-world population data to propose acebutolol as a novel candidate for osteoporosis treatment. These findings warrant further mechanistic studies and clinical trials to evaluate its efficacy in osteoporosis management.

Indexed as

Bone Density Conservation AgentsDrug RepositioningOsteoporosisAnimalsBone DensityDisease Models, AnimalGenome-Wide Association StudyHumansMendelian Randomization AnalysisMultiomicsSignal TransductionZebrafishBone Density Conservation Agents

Identifiers

PMID40476595
PMCPMC12254003

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.