PMID- 37200070 OWN - NLM STAT- MEDLINE DCOM- 20230522 LR - 20240418 IS - 2369-2960 (Electronic) IS - 2369-2960 (Linking) VI - 9 DP - 2023 May 18 TI - Associations Between Frailty and the Increased Risk of Adverse Outcomes Among 38,950 UK Biobank Participants With Prediabetes: Prospective Cohort Study. PG - e45502 LID - 10.2196/45502 [doi] LID - e45502 AB - 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/=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. CI - (c)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. FAU - Cao, Xingqi AU - Cao X AUID- ORCID: 0000-0002-4726-7170 AD - Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China. AD - Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, China. AD - The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, China. FAU - Li, Xueqin AU - Li X AUID- ORCID: 0000-0001-5270-4825 AD - Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China. AD - Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, China. AD - The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, China. FAU - Zhang, Jingyun AU - Zhang J AUID- ORCID: 0000-0002-8049-2230 AD - Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China. AD - Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, China. AD - The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, China. FAU - Sun, Xiaoyi AU - Sun X AUID- ORCID: 0009-0003-3036-2731 AD - Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China. AD - Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, China. AD - The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, China. FAU - Yang, Gan AU - Yang G AUID- ORCID: 0000-0002-9792-540X AD - Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China. AD - Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, China. AD - The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, China. FAU - Zhao, Yining AU - Zhao Y AUID- ORCID: 0009-0006-6157-6048 AD - Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China. AD - Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, China. AD - The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, China. FAU - Li, Shujuan AU - Li S AUID- ORCID: 0000-0003-4740-9615 AD - Department of Neurology, Fuwai Hospital, National Center for Cardiovascular Diseases, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China. FAU - Hoogendijk, Emiel O AU - Hoogendijk EO AUID- ORCID: 0000-0001-9660-5108 AD - Department of Epidemiology & Data Science, Amsterdam Public Health Research Institute, Amsterdam University Medical Center, Amsterdam, Netherlands. FAU - Wang, Xiaofeng AU - Wang X AUID- ORCID: 0000-0001-7333-8676 AD - National Clinical Research Center for Aging and Medicine, Huashan Hospital, Shanghai, China. AD - Human Phenome Institute, Fudan University, Shanghai, China. FAU - Zhu, Yimin AU - Zhu Y AUID- ORCID: 0000-0001-8409-7636 AD - Department of Epidemiology & Biostatistics, School of Public Health, Zhejiang University School of Medicine, Hangzhou, China. FAU - Allore, Heather AU - Allore H AUID- ORCID: 0000-0001-7685-8175 AD - Department of Internal Medicine, Yale School of Medicine, New Haven, CT, United States. FAU - Gill, Thomas M AU - Gill TM AUID- ORCID: 0000-0002-6450-0368 AD - Department of Internal Medicine, Yale School of Medicine, New Haven, CT, United States. FAU - Liu, Zuyun AU - Liu Z AUID- ORCID: 0000-0001-6120-5913 AD - Center for Clinical Big Data and Analytics of the Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China. AD - Department of Big Data in Health Science School of Public Health, Zhejiang University School of Medicine, Hangzhou, China. AD - The Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province, Zhejiang University School of Medicine, Hangzhou, China. LA - eng GR - P30 AG021342/AG/NIA NIH HHS/United States GR - UL1 TR001863/TR/NCATS NIH HHS/United States PT - Journal Article PT - Research Support, N.I.H., Extramural PT - Research Support, Non-U.S. Gov't DEP - 20230518 PL - Canada TA - JMIR Public Health Surveill JT - JMIR public health and surveillance JID - 101669345 SB - IM MH - Humans MH - Aged MH - *Frailty/complications/epidemiology MH - *Prediabetic State/complications/epidemiology MH - *Diabetes Mellitus, Type 2/complications/epidemiology MH - Prospective Studies MH - Biological Specimen Banks MH - Geriatric Assessment MH - *Cardiovascular Diseases/epidemiology MH - United Kingdom/epidemiology MH - *Dementia PMC - PMC10236284 OTO - NOTNLM OT - adverse outcomes OT - diabetes OT - frailty OT - prediabetes OT - prospective study COIS- Conflicts of Interest: None declared. EDAT- 2023/05/18 13:09 MHDA- 2023/05/22 06:41 PMCR- 2023/05/18 CRDT- 2023/05/18 11:53 PHST- 2023/01/05 00:00 [received] PHST- 2023/03/23 00:00 [accepted] PHST- 2023/03/17 00:00 [revised] PHST- 2023/05/22 06:41 [medline] PHST- 2023/05/18 13:09 [pubmed] PHST- 2023/05/18 11:53 [entrez] PHST- 2023/05/18 00:00 [pmc-release] AID - v9i1e45502 [pii] AID - 10.2196/45502 [doi] PST - epublish SO - JMIR Public Health Surveill. 2023 May 18;9:e45502. doi: 10.2196/45502.