Associations Between Frailty and the Increased Risk of Adverse Outcomes Among 38,950 UK Biobank Participants With Prediabetes: Prospective Cohort Study.


Journal

JMIR public health and surveillance
ISSN: 2369-2960
Titre abrégé: JMIR Public Health Surveill
Pays: Canada
ID NLM: 101669345

Informations de publication

Date de publication:
18 05 2023
Historique:
received: 05 01 2023
accepted: 23 03 2023
revised: 17 03 2023
medline: 22 5 2023
pubmed: 18 5 2023
entrez: 18 5 2023
Statut: epublish

Résumé

Compared with adults with normal glucose metabolism, those with prediabetes tend to be frail. However, it remains poorly understood whether frailty could identify adults who are most at risk of adverse outcomes related to prediabetes. We aimed to systematically evaluate the associations between frailty, a simple health indicator, and risks of multiple adverse outcomes including incident type 2 diabetes mellitus (T2DM), diabetes-related microvascular disease, cardiovascular disease (CVD), chronic kidney disease (CKD), eye disease, dementia, depression, and all-cause mortality in late life among middle-aged adults with prediabetes. We evaluated 38,950 adults aged 40 years to 64 years with prediabetes using the baseline survey from the UK Biobank. Frailty was assessed using the frailty phenotype (FP; range 0-5), and participants were grouped into nonfrail (FP=0), prefrail (1≤FP≤2), and frail (FP≥3). Multiple adverse outcomes (ie, T2DM, diabetes-related microvascular disease, CVD, CKD, eye disease, dementia, depression, and all-cause mortality) were ascertained during a median follow-up of 12 years. Cox proportional hazards regression models were used to estimate the associations. Several sensitivity analyses were performed to test the robustness of the results. At baseline, 49.1% (19,122/38,950) and 5.9% (2289/38,950) of adults with prediabetes were identified as prefrail and frail, respectively. Both prefrailty and frailty were associated with higher risks of multiple adverse outcomes in adults with prediabetes (P for trend <.001). For instance, compared with their nonfrail counterparts, frail participants with prediabetes had a significantly higher risk (P<.001) of T2DM (hazard ratio [HR]=1.73, 95% CI 1.55-1.92), diabetes-related microvascular disease (HR=1.89, 95% CI 1.64-2.18), CVD (HR=1.66, 95% CI 1.44-1.91), CKD (HR=1.76, 95% CI 1.45-2.13), eye disease (HR=1.31, 95% CI 1.14-1.51), dementia (HR=2.03, 95% CI 1.33-3.09), depression (HR=3.01, 95% CI 2.47-3.67), and all-cause mortality (HR=1.81, 95% CI 1.51-2.16) in the multivariable-adjusted models. Furthermore, with each 1-point increase in FP score, the risk of these adverse outcomes increased by 10% to 42%. Robust results were generally observed in sensitivity analyses. In UK Biobank participants with prediabetes, both prefrailty and frailty are significantly associated with higher risks of multiple adverse outcomes, including T2DM, diabetes-related diseases, and all-cause mortality. Our findings suggest that frailty assessment should be incorporated into routine care for middle-aged adults with prediabetes, to improve the allocation of health care resources and reduce diabetes-related burden.

Sections du résumé

BACKGROUND
Compared with adults with normal glucose metabolism, those with prediabetes tend to be frail. However, it remains poorly understood whether frailty could identify adults who are most at risk of adverse outcomes related to prediabetes.
OBJECTIVE
We aimed to systematically evaluate the associations between frailty, a simple health indicator, and risks of multiple adverse outcomes including incident type 2 diabetes mellitus (T2DM), diabetes-related microvascular disease, cardiovascular disease (CVD), chronic kidney disease (CKD), eye disease, dementia, depression, and all-cause mortality in late life among middle-aged adults with prediabetes.
METHODS
We evaluated 38,950 adults aged 40 years to 64 years with prediabetes using the baseline survey from the UK Biobank. Frailty was assessed using the frailty phenotype (FP; range 0-5), and participants were grouped into nonfrail (FP=0), prefrail (1≤FP≤2), and frail (FP≥3). Multiple adverse outcomes (ie, T2DM, diabetes-related microvascular disease, CVD, CKD, eye disease, dementia, depression, and all-cause mortality) were ascertained during a median follow-up of 12 years. Cox proportional hazards regression models were used to estimate the associations. Several sensitivity analyses were performed to test the robustness of the results.
RESULTS
At baseline, 49.1% (19,122/38,950) and 5.9% (2289/38,950) of adults with prediabetes were identified as prefrail and frail, respectively. Both prefrailty and frailty were associated with higher risks of multiple adverse outcomes in adults with prediabetes (P for trend <.001). For instance, compared with their nonfrail counterparts, frail participants with prediabetes had a significantly higher risk (P<.001) of T2DM (hazard ratio [HR]=1.73, 95% CI 1.55-1.92), diabetes-related microvascular disease (HR=1.89, 95% CI 1.64-2.18), CVD (HR=1.66, 95% CI 1.44-1.91), CKD (HR=1.76, 95% CI 1.45-2.13), eye disease (HR=1.31, 95% CI 1.14-1.51), dementia (HR=2.03, 95% CI 1.33-3.09), depression (HR=3.01, 95% CI 2.47-3.67), and all-cause mortality (HR=1.81, 95% CI 1.51-2.16) in the multivariable-adjusted models. Furthermore, with each 1-point increase in FP score, the risk of these adverse outcomes increased by 10% to 42%. Robust results were generally observed in sensitivity analyses.
CONCLUSIONS
In UK Biobank participants with prediabetes, both prefrailty and frailty are significantly associated with higher risks of multiple adverse outcomes, including T2DM, diabetes-related diseases, and all-cause mortality. Our findings suggest that frailty assessment should be incorporated into routine care for middle-aged adults with prediabetes, to improve the allocation of health care resources and reduce diabetes-related burden.

Identifiants

pubmed: 37200070
pii: v9i1e45502
doi: 10.2196/45502
pmc: PMC10236284
doi:

Types de publication

Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

e45502

Subventions

Organisme : NIA NIH HHS
ID : P30 AG021342
Pays : United States
Organisme : NCATS NIH HHS
ID : UL1 TR001863
Pays : United States

Informations de copyright

©Xingqi Cao, Xueqin Li, Jingyun Zhang, Xiaoyi Sun, Gan Yang, Yining Zhao, Shujuan Li, Emiel O Hoogendijk, Xiaofeng Wang, Yimin Zhu, Heather Allore, Thomas M Gill, Zuyun Liu. Originally published in JMIR Public Health and Surveillance (https://publichealth.jmir.org), 18.05.2023.

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Auteurs

Xingqi Cao (X)

Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, China.
The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, China.

Xueqin Li (X)

Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, China.
The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, China.

Jingyun Zhang (J)

Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, China.
The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, China.

Xiaoyi Sun (X)

Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, China.
The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, China.

Gan Yang (G)

Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, China.
The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, China.

Yining Zhao (Y)

Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, China.
The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, China.

Shujuan Li (S)

Department of Neurology, Fuwai Hospital, National Center for Cardiovascular Diseases, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China.

Emiel O Hoogendijk (EO)

Department of Epidemiology & Data Science, Amsterdam Public Health Research Institute, Amsterdam University Medical Center, Amsterdam, Netherlands.

Xiaofeng Wang (X)

National Clinical Research Center for Aging and Medicine, Huashan Hospital, Shanghai, China.
Human Phenome Institute, Fudan University, Shanghai, China.

Yimin Zhu (Y)

Department of Epidemiology & Biostatistics, School of Public Health, Zhejiang University School of Medicine, Hangzhou, China.

Heather Allore (H)

Department of Internal Medicine, Yale School of Medicine, New Haven, CT, United States.

Thomas M Gill (TM)

Department of Internal Medicine, Yale School of Medicine, New Haven, CT, United States.

Zuyun Liu (Z)

Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, China.
The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, China.

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