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A longitudinal dataset of hypertensive osteoporotic fracture patients: treatments and long-term outcomes
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  • Published: 13 March 2026

A longitudinal dataset of hypertensive osteoporotic fracture patients: treatments and long-term outcomes

  • Chong Li  ORCID: orcid.org/0000-0002-1526-221X1,2,
  • Ke Lu  ORCID: orcid.org/0000-0002-0029-78741,2,
  • Li-wen Su  ORCID: orcid.org/0000-0003-1332-455X2,3,
  • Peng Zhou  ORCID: orcid.org/0009-0006-7726-91141,2,4,
  • Guo-ji Lin  ORCID: orcid.org/0009-0003-0422-06921,2,
  • Jia-qi Liang  ORCID: orcid.org/0009-0001-2223-67731,2,
  • Ya-qin Gong  ORCID: orcid.org/0000-0001-8695-40482,5,
  • Jian Jin  ORCID: orcid.org/0009-0009-4300-884X6 &
  • …
  • Wen-rong Xu  ORCID: orcid.org/0000-0003-0903-19737 

Scientific Data , Article number:  (2026) Cite this article

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We are providing an unedited version of this manuscript to give early access to its findings. Before final publication, the manuscript will undergo further editing. Please note there may be errors present which affect the content, and all legal disclaimers apply.

Subjects

  • Hypertension
  • Metabolic bone disease
  • Risk factors

Abstract

Osteoporotic fractures (OPF) and hypertension frequently co-occur in older adults, yet comprehensive datasets integrating clinical, pharmacological, and longitudinal outcome data remain scarce. We describe a longitudinal dataset derived from the Osteoporotic Fracture Registration System at Kunshan Hospital, Jiangsu University, including patients aged ≥ 50 years hospitalized for OPF between 2017 and 2024. A total of 4,782 patients were initially registered. After applying predefined eligibility criteria, 4,325 patients were included in the final analytical cohort. The dataset integrates demographic, clinical, and pharmacologic variables with long-term outcomes on mortality and refracture through deterministic linkage with regional health and mortality registries. Longitudinal antihypertensive prescription records (n = 42,367) were linked via the Kunshan Municipal Health Data Integration Platform, enabling detailed characterization of medication exposure patterns over time. Technical validation, including survival analysis, propensity score methods, and risk prediction modeling, was conducted to assess internal consistency and illustrate potential applications. This structured and de-identified dataset provides a quality-checked resource to support future research in osteoporosis, cardiovascular comorbidity, multimorbidity, and real-world comparative effectiveness studies.

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Data availability

The dataset described in this study is openly available on Figshare at https://doi.org/10.6084/m9.figshare.30971476.

Code availability

All custom R scripts used for data curation, preprocessing, statistical analysis, technical validation, and figure generation are publicly available in a GitHub repository at https://github.com/LiwenSu0625/hypertensive-osteoporotic-fracture.

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Acknowledgements

The study was supported by National Natural Science Foundation of China (82172441), Suzhou MunicipalScience and Technology Development Plan (Construction of Key Municipal Laboratories) (SZS2024018), SuzhouCity Major Disease Multicenter Clinical Research Project (DZXYJ202312), Special Funding for Jiangsu ProvinceScience and Technology Plan (Key Research and Development Program for Social Development) (BE2023738)and Gusu Health Talent Plan Scientific Research Project (GSWS2022105).

Author information

Authors and Affiliations

  1. Department of Orthopedics, Affiliated Kunshan Hospital of Jiangsu University, Suzhou, Jiangsu, 215300, China

    Chong Li, Ke Lu, Peng Zhou, Guo-ji Lin & Jia-qi Liang

  2. Kunshan Biomedical Big Data Innovation Application Laboratory, Suzhou, Jiangsu, 215300, China

    Chong Li, Ke Lu, Li-wen Su, Peng Zhou, Guo-ji Lin, Jia-qi Liang & Ya-qin Gong

  3. Department of Statistics, Jiangsu University of Technology, Changzhou, Jiangsu, 213001, China

    Li-wen Su

  4. Department of Orthopedics, Gusu School, Nanjing Medical University, The First People’s Hospital of Kunshan, Suzhou, Jiangsu, 215300, China

    Peng Zhou

  5. Information Department, Affiliated Kunshan Hospital of Jiangsu University, Suzhou, Jiangsu, 215300, China

    Ya-qin Gong

  6. Kunshan municipal Health and Family Planning Information Center, Suzhou, Jiangsu, 215300, China

    Jian Jin

  7. Zhenjiang Key Laboratory of High Technology Research on sEVs Foundation and Transformation Application, School of Medicine, Jiangsu University, 301 Xuefu Road, Zhenjiang, 212013, Jiangsu, China

    Wen-rong Xu

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Contributions

Chong Li has been involved in conceptualization, data curation, formal analysis, investigation, methodology, visualization, writing, and editing; Li-wen Su and Peng Zhou have contributed to data curation, analysis, methodology, and reviewing; Guo-ji Lin and Jia-qi Liang have been part of data collection, analysis, and manuscript reviewing; Ya-qin Gong has contributed to the methodology, data validation, and reviewing; Jian Jin has provided technical support and manuscript editing; Ke Lu and Wen-rong Xu, as the corresponding authors, have been responsible for project supervision, funding acquisition, manuscript reviewing, and final approval. All authors have read and approved the final manuscript.

Corresponding authors

Correspondence to Ke Lu or Wen-rong Xu.

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The authors declare no competing interests.

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Supplementary information

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Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

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Cite this article

Li, C., Lu, K., Su, Lw. et al. A longitudinal dataset of hypertensive osteoporotic fracture patients: treatments and long-term outcomes. Sci Data (2026). https://doi.org/10.1038/s41597-026-07031-7

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  • Received: 23 July 2025

  • Accepted: 05 March 2026

  • Published: 13 March 2026

  • DOI: https://doi.org/10.1038/s41597-026-07031-7

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