Association of pre-diabetes with the risks of adverse health outcomes and complex multimorbidity: evidence from population-based studies in the NIS and UK Biobank

Introduction This study aimed to examine the risk of common diseases among people with pre-diabetes and explored the relationship between pre-diabetes and multimorbidity (in this case, two or more comorbid diseases).Methods An observational multicohort study using data from the UK Biobank database a...

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Main Authors: Yue Zhang, Jiajun Zhao, Zhongshang Yuan, Hang Dong, Mingzhuo Li, Yingzhou Shi, Yafei Wu, Xiude Fan, Zinuo Yuan, Junming Han, Yiping Cheng, Xiaoshan Feng, Zhixiang Wang, Ruirui Xuan, Yingchun Dong, Yang Tian, Qingling Guo, Yongfeng Song
Format: Article
Language:English
Published: BMJ Publishing Group 2025-04-01
Series:BMJ Public Health
Online Access:https://bmjpublichealth.bmj.com/content/3/1/e001539.full
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author Yue Zhang
Jiajun Zhao
Zhongshang Yuan
Hang Dong
Mingzhuo Li
Yingzhou Shi
Yafei Wu
Xiude Fan
Zinuo Yuan
Junming Han
Yiping Cheng
Xiaoshan Feng
Zhixiang Wang
Ruirui Xuan
Yingchun Dong
Yang Tian
Qingling Guo
Yongfeng Song
author_facet Yue Zhang
Jiajun Zhao
Zhongshang Yuan
Hang Dong
Mingzhuo Li
Yingzhou Shi
Yafei Wu
Xiude Fan
Zinuo Yuan
Junming Han
Yiping Cheng
Xiaoshan Feng
Zhixiang Wang
Ruirui Xuan
Yingchun Dong
Yang Tian
Qingling Guo
Yongfeng Song
author_sort Yue Zhang
collection DOAJ
description Introduction This study aimed to examine the risk of common diseases among people with pre-diabetes and explored the relationship between pre-diabetes and multimorbidity (in this case, two or more comorbid diseases).Methods An observational multicohort study using data from the UK Biobank database and the National Inpatient Sample (NIS) database (2016–2018) was conducted. We analysed 461 535 participants and 17 548 442 patients aged 18 years or older from both databases, of whom 14.0% and 0.7% were diagnosed with pre-diabetes, respectively. A total of 76 common diseases of various body systems were selected as adverse health outcomes for analysis.Results Among 64 523 individuals with pre-diabetes in the UK Biobank, the mean age was 60 years, 35 304 (54.7%) were female. There were 24 non-overlapping diseases associated with pre-diabetes with significant multiple test results in both databases, and most of them are circulatory system diseases. Compared with normoglycaemia, the confounder-adjusted HR in the UK Biobank for pre-diabetes was 1.46 (95% CI 1.43 to 1.49) for accompanying complex multimorbidity (ie, four or more pre-diabetes-related diseases), the corresponding confounder-adjusted OR in the NIS study was 10.03 (95% CI 9.66 to 10.40).Conclusion Pre-diabetes was associated with a significantly higher risk of multimorbidity. Pre-diabetes, thus, might represent an important target for multimorbidity prevention, and stronger emphasis on its management seems necessary to reduce the risk of the development of multiple comorbidities, especially before the onset of overt diabetes.
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spelling doaj-art-6dedba1b55e84ec69f9ca53d689fa7a32025-08-20T03:31:01ZengBMJ Publishing GroupBMJ Public Health2753-42942025-04-013110.1136/bmjph-2024-001539Association of pre-diabetes with the risks of adverse health outcomes and complex multimorbidity: evidence from population-based studies in the NIS and UK BiobankYue Zhang0Jiajun Zhao1Zhongshang Yuan2Hang Dong3Mingzhuo Li4Yingzhou Shi5Yafei Wu6Xiude Fan7Zinuo Yuan8Junming Han9Yiping Cheng10Xiaoshan Feng11Zhixiang Wang12Ruirui Xuan13Yingchun Dong14Yang Tian15Qingling Guo16Yongfeng Song17Key Laboratory of Endocrine Glucose & Lipids Metabolism and Brain Aging, Ministry of Education, Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, ChinaShandong Institute of Endocrine and Metabolic Diseases, Jinan, Shandong, ChinaDepartment of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, ChinaKey Laboratory of Endocrine Glucose & Lipids Metabolism and Brain Aging, Ministry of Education, Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, ChinaCenter for Big Data Research in Health and Medicine, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, Shandong, ChinaKey Laboratory of Endocrine Glucose & Lipids Metabolism and Brain Aging, Ministry of Education, Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, ChinaKey Laboratory of Endocrine Glucose & Lipids Metabolism and Brain Aging, Ministry of Education, Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, ChinaKey Laboratory of Endocrine Glucose & Lipids Metabolism and Brain Aging, Ministry of Education, Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, ChinaKey Laboratory of Endocrine Glucose & Lipids Metabolism and Brain Aging, Ministry of Education, Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, ChinaKey Laboratory of Endocrine Glucose & Lipids Metabolism and Brain Aging, Ministry of Education, Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, ChinaKey Laboratory of Endocrine Glucose & Lipids Metabolism and Brain Aging, Ministry of Education, Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, ChinaKey Laboratory of Endocrine Glucose & Lipids Metabolism and Brain Aging, Ministry of Education, Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, ChinaKey Laboratory of Endocrine Glucose & Lipids Metabolism and Brain Aging, Ministry of Education, Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, ChinaKey Laboratory of Endocrine Glucose & Lipids Metabolism and Brain Aging, Ministry of Education, Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, ChinaShandong Clinical Research Center of Diabetes and Metabolic Diseases, Jinan, Shandong, ChinaDepartment of Neurology, Guangzhou Women and Children’s Medical Center, Guangzhou 510623, Guangdong, ChinaKey Laboratory of Endocrine Glucose & Lipids Metabolism and Brain Aging, Ministry of Education, Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, ChinaDepartment of Endocrinology, Central Hospital Affiliated to Shandong First Medical University, Jinan, Shandong, ChinaIntroduction This study aimed to examine the risk of common diseases among people with pre-diabetes and explored the relationship between pre-diabetes and multimorbidity (in this case, two or more comorbid diseases).Methods An observational multicohort study using data from the UK Biobank database and the National Inpatient Sample (NIS) database (2016–2018) was conducted. We analysed 461 535 participants and 17 548 442 patients aged 18 years or older from both databases, of whom 14.0% and 0.7% were diagnosed with pre-diabetes, respectively. A total of 76 common diseases of various body systems were selected as adverse health outcomes for analysis.Results Among 64 523 individuals with pre-diabetes in the UK Biobank, the mean age was 60 years, 35 304 (54.7%) were female. There were 24 non-overlapping diseases associated with pre-diabetes with significant multiple test results in both databases, and most of them are circulatory system diseases. Compared with normoglycaemia, the confounder-adjusted HR in the UK Biobank for pre-diabetes was 1.46 (95% CI 1.43 to 1.49) for accompanying complex multimorbidity (ie, four or more pre-diabetes-related diseases), the corresponding confounder-adjusted OR in the NIS study was 10.03 (95% CI 9.66 to 10.40).Conclusion Pre-diabetes was associated with a significantly higher risk of multimorbidity. Pre-diabetes, thus, might represent an important target for multimorbidity prevention, and stronger emphasis on its management seems necessary to reduce the risk of the development of multiple comorbidities, especially before the onset of overt diabetes.https://bmjpublichealth.bmj.com/content/3/1/e001539.full
spellingShingle Yue Zhang
Jiajun Zhao
Zhongshang Yuan
Hang Dong
Mingzhuo Li
Yingzhou Shi
Yafei Wu
Xiude Fan
Zinuo Yuan
Junming Han
Yiping Cheng
Xiaoshan Feng
Zhixiang Wang
Ruirui Xuan
Yingchun Dong
Yang Tian
Qingling Guo
Yongfeng Song
Association of pre-diabetes with the risks of adverse health outcomes and complex multimorbidity: evidence from population-based studies in the NIS and UK Biobank
BMJ Public Health
title Association of pre-diabetes with the risks of adverse health outcomes and complex multimorbidity: evidence from population-based studies in the NIS and UK Biobank
title_full Association of pre-diabetes with the risks of adverse health outcomes and complex multimorbidity: evidence from population-based studies in the NIS and UK Biobank
title_fullStr Association of pre-diabetes with the risks of adverse health outcomes and complex multimorbidity: evidence from population-based studies in the NIS and UK Biobank
title_full_unstemmed Association of pre-diabetes with the risks of adverse health outcomes and complex multimorbidity: evidence from population-based studies in the NIS and UK Biobank
title_short Association of pre-diabetes with the risks of adverse health outcomes and complex multimorbidity: evidence from population-based studies in the NIS and UK Biobank
title_sort association of pre diabetes with the risks of adverse health outcomes and complex multimorbidity evidence from population based studies in the nis and uk biobank
url https://bmjpublichealth.bmj.com/content/3/1/e001539.full
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