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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 (
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.
In 2021, the International Diabetes Federation estimated that there were more than 500 million adults with prediabetes among those aged 20 years to 79 years worldwide [
Prediabetes is highly heterogeneous, impeding the application of a one-size-fits-all health management strategy. Recently, a simple health aging indicator—frailty—has been demonstrated to be able to predict the risk of adverse outcomes (eg, CVD and mortality) [
Therefore, we performed a prospective cohort study among 38,950 middle-aged adults with prediabetes from the UK Biobank (UKB). Using a widely validated frailty measurement—frailty phenotype (FP) [
The UKB is a large-scale health research study with a long-term follow-up that began in 2006 to 2010 with the recruitment of approximately 500,000 adults in the United Kingdom [
Flow chart of the sample for analyses.
The UKB was approved by the North West Multi-Centre Research Ethics Committee (11/NW/0382). Written informed consent from all participants was obtained. The data used in this study were anonymized and de-identified for privacy and confidentiality protection.
In this study, the outcomes included T2DM, diabetes-related microvascular disease (including retinopathy, neuropathy, and nephropathy), diabetes-related macrovascular disease (ie, CVD including ischemic heart disease and stroke), CKD, eye disease (including cataract and glaucoma), dementia, depression, and all-cause mortality.
We defined prevalent and incident T2DM using a UKB algorithm that combined self-reported medical history and medication information (for the ascertainment of prevalent cases only), as well as linked hospital admissions records (Table S1 in
We used FP, a widely used physical frailty measurement proposed by Fried et al [
The 5 criteria for the frailty phenotype in the UK Biobank.
Number | Criteria description | Categories |
1 | Unintentional weight loss: Participants were asked “Compared with one year ago, has your weight changed?” | 1: “Yes, loss weight”; 0: Others |
2 | Exhaustion: Participants were asked “Over the past 2 weeks, how often have you felt tired or had little energy?” | 1: “More than half the days or nearly every day”; 0: Others |
3 | Weakness: Weakness was measured using grip strength with a Jamar J00105 hydraulic hand dynamometer (Lafayette Instrument). Participants were asked to complete a grip assessment for both hands once. The maximal value of the right and left hands was used. | 1: (1) Men: ≤29 kg for BMI ≤24 kg/m2; ≤30 kg for BMI 24.1-26 kg/m2; ≤30 kg for BMI 26.1-28 kg/m2; or ≤32 kg for BMI >28 kg/m2; (2) Women: ≤17 kg for BMI ≤23 kg/m2; ≤17.3 kg for BMI 23.1-26 kg/m2; ≤18 kg for BMI 26.1-29 kg/m2; or ≤21 kg for BMI >29 kg/m2; 0: Others |
4 | Slow gait speed: Participants were asked “How would you describe your usual walking pace?” | 1: “Slow pace”; 0: Others |
5 | Low physical activity: Participants were asked “In the last 4 weeks, did you spend any time doing light DIYa activity, heavy DIY activity, or strenuous sports?” | 1: “None or light activity with a frequency of once per week or less”; 0: Others |
aDIY: do it yourself.
Baseline data on age, sex (female or male), ethnicity (White, mixed race, South Asian, Black, Chinese, or other background), educational level (high, intermediate, or low), occupational status (working, retired, or other), alcohol consumption (never or special occasions only, 1 to 3 times per month, 1 to 4 times per week, or daily or almost daily), smoking status (never, previous smoker, or current smoker), healthy diet (yes or no), and family history of disease (including diabetes, CVD, dementia, and depression) were collected through a questionnaire interview. The Townsend deprivation index (TDI) was calculated based on areas before participants were recruited in the UKB. BMI was calculated as measured weight/height2 (kg/m2).
Baseline characteristics of the complete analyzed sample and by frailty status are presented as median (IQRs) and number (percentage) for continuous variables and categorical variables, respectively. Kruskal-Wallis tests and chi-square tests were used to compare the differences in characteristics by frailty status.
To evaluate the associations between frailty status (nonfrail, prefrail, and frail) and adverse outcomes, Cox proportional hazards regression models were performed. The Schoenfeld residuals test was used to verify the proportional hazard assumption, and no significant violation was found. We calculated hazard ratios (HRs) and corresponding 95% CIs using 2 models. Model 1 was adjusted for age and sex. Model 2 was further adjusted for ethnicity, educational level, occupational status, TDI, alcohol consumption, smoking status, healthy diet, BMI, and family history of disease based on Model 1. Additionally, we calculated HRs (95% CIs) for adverse outcomes per 1-point increase in FP score.
Several sensitivity analyses were conducted to confirm the robustness of the results. First, we compared the characteristics of included and excluded study participants. Second, to minimize the influence of reverse causality, we repeated the main analyses after excluding those without 2 years of follow-up. Third, to reduce the influence of poor health on frailty status, we repeated the main analyses after excluding participants with poor self-rated health status at baseline. Fourth, to account for the influence of missing data on results, we performed multiple imputations by chained equations [
We used SAS version 9.4 (SAS Institute) and R version 3.6.3 (2020-02-29) to conduct all statistical analyses. To account for multiple testing, we used Bonferroni correction in all analyses (
Among the 38,950 participants with prediabetes, the median age was 58.6 (IQR 53.1-62.0) years, and the majority were women (21,155/38,950, 54.3%) and White (34,705/38,950, 89.1%;
Baseline characteristics of study participants with prediabetes by frailty status.
Variables | Total (n=38,950) | Nonfrail (n=17,539) | Prefrail (n=19,122) | Frail (n=2289) | ||
Age (years), median (IQR) | 58.6 (53.1 to 62.0) | 59.0 (53.7 to 62.1) | 58.3 (52.7 to 61.8) | 58.3 (52.9 to 61.7) | <.001 | |
|
<.001 | |||||
|
Female | 21,155 (54.3) | 8928 (50.9) | 10,771 (56.3) | 1456 (63.6) |
|
|
Male | 17,795 (45.7) | 8611 (49.1) | 8351 (43.7) | 833 (36.4) |
|
|
<.001 | |||||
|
White | 34,705 (89.1) | 16,075 (91.7) | 16,719 (87.4) | 1911 (83.5) |
|
|
Mixed | 339 (0.9) | 137 (0.8) | 180 (0.9) | 22 (1.0) |
|
|
South Asian | 1558 (4.0) | 449 (2.6) | 943 (4.9) | 166 (7.3) |
|
|
Black | 1474 (3.8) | 563 (3.2) | 796 (4.2) | 115 (5.0) |
|
|
Chinese | 261 (0.7) | 104 (0.6) | 139 (0.7) | 18 (0.8) |
|
|
Other background | 613 (1.6) | 211 (1.2) | 345 (1.8) | 57 (2.5) |
|
|
<.001 | |||||
|
High | 11,198 (28.7) | 5647 (32.2) | 5156 (27.0) | 395 (17.3) |
|
|
Intermediate | 12,464 (32.0) | 5728 (32.7) | 6165 (32.2) | 571 (24.9) |
|
|
Low | 15,288 (39.3) | 6164 (35.1) | 7801 (40.8) | 1323 (57.8) |
|
|
<.001 | |||||
|
Working | 23,793 (61.1) | 11,059 (63.1) | 11,892 (62.2) | 842 (36.8) |
|
|
Retired | 10,407 (26.7) | 5095 (29.0) | 4710 (24.6) | 602 (26.3) |
|
|
Other | 4750 (12.2) | 1385 (7.9) | 2520 (13.2) | 845 (36.9) |
|
Townsend deprivation index, median (IQR) | –1.7 (–3.5 to 1.2) | –2.2 (–3.7 to 0.3) | –1.4 (–3.2 to 1.6) | 0.5 (–2.3 to 3.6) | <.001 | |
BMI (kg/m2), median (IQR) | 28.5 (25.4 to 32.1) | 27.5 (24.8 to 30.8) | 29.2 (25.9 to 32.9) | 31.6 (27.8 to 36.4) | <.001 | |
|
<.001 | |||||
|
Never | 19,301 (49.6) | 8963 (51.1) | 9366 (49.0) | 972 (42.5) |
|
|
Previous | 12,788 (32.8) | 5929 (33.8) | 6137 (32.1) | 722 (31.5) |
|
|
Current | 6861 (17.6) | 2647 (15.1) | 3619 (18.9) | 595 (26.0) |
|
|
<.001 | |||||
|
Never or special occasions only | 9939 (25.5) | 3308 (18.9) | 5551 (29.0) | 1080 (47.2) |
|
|
1 to 3 times per month | 4919 (12.6) | 2045 (11.7) | 2587 (13.5) | 287 (12.5) |
|
|
1 to 4 times per week | 17,545 (45.0) | 8674 (49.5) | 8176 (42.8) | 695 (30.4) |
|
|
Daily or almost daily | 6547 (16.8) | 3512 (20.0) | 2808 (14.7) | 227 (9.9) |
|
|
<.001 | |||||
|
No | 9146 (23.5) | 3444 (19.6) | 4930 (25.8) | 772 (33.7) |
|
|
Yes | 29,804 (76.5) | 14,095 (80.4) | 14,192 (74.2) | 1517 (66.3) |
|
Glycated hemoglobin (mmol/mol), median (IQR) | 40.4 (39.6 to 42.0) | 40.3 (39.5 to 41.6) | 40.5 (39.6 to 42.1) | 40.9 (39.8 to 42.6) | <.001 | |
|
||||||
|
Cardiovascular disease | 3477 (8.9) | 1157 (6.6) | 1835 (9.6) | 485 (21.2) | <.001 |
|
Chronic kidney disease | 180 (0.5) | 55 (0.3) | 88 (0.5) | 37 (1.6) | <.001 |
|
Eye disease | 1303 (3.3) | 520 (3.0) | 646 (3.4) | 137 (6.0) | <.001 |
|
Dementia | 14 (0.0) | 4 (0.0) | 7 (0.0) | 3 (0.1) | .37 |
|
Depression | 2954 (7.6) | 812 (4.6) | 1636 (8.6) | 506 (22.1) | <.001 |
|
||||||
|
Diabetes mellitus | 11,197 (28.7) | 4716 (26.9) | 5720 (29.9) | 761 (33.2) | <.001 |
|
Cardiovascular disease | 23,633 (60.7) | 10,448 (59.6) | 11,711 (61.2) | 1474 (64.4) | <.001 |
|
Dementia | 4733 (12.2) | 2164 (12.3) | 2283 (11.9) | 286 (12.5) | .44 |
|
Depression | 5146 (13.2) | 2087 (11.9) | 2637 (13.8) | 422 (18.4) | <.001 |
aGenerated using chi-square and Kruskal-Wallis tests for categorical and continuous variables, respectively.
bEducational level was classified as high (college or university degree), intermediate (A/AS levels or equivalent, O levels/General Certificate of Secondary Education levels or equivalent), and low (none of the above).
During a median follow-up of 12 years, there were 5289 incident T2DM cases, 2657 incident diabetes-related microvascular disease cases, 3234 incident CVD cases, 1439 incident CKD cases, 3525 incident eye disease cases, 325 incident dementia cases, 1265 incident depression cases, and 2016 deaths. We found that frail participants developed more adverse outcomes than did their prefrail and nonfrail counterparts over the 12-year follow-up (
Age-adjusted incidence of adverse outcomes among UKB participants with prediabetes during 12 years of follow-up. UKB: UK Biobank.
Associations between frailty and adverse health outcomes among middle-aged adults with prediabetes.
Outcomes | Frailty status | Hazard ratio (HR) per 1-point increase | ||||
|
Nonfrail | Prefrail | Frail | |||
|
||||||
|
Number of events/person-years | 1724/207,929 | 2965/218,522 | 600/23,890 | —b | — |
|
Model 1c, HR (95% CI) | Reference | 1.70 (1.61-1.81) | 3.37 (3.07-3.71) | <.001 | 1.45 (1.42-1.49) |
|
Model 2d, HR (95% CI) | Reference | 1.35 (1.27-1.43) | 1.73 (1.55-1.92) | <.001 | 1.19 (1.16-1.23) |
|
||||||
|
Number of events/person-years | 926/212,240 | 1417/226,956 | 314/25,445 | — | — |
|
Model 1c, HR (95% CI) | Reference | 1.54 (1.42-1.67) | 3.23 (2.84-3.68) | <.001 | 1.45 (1.40-1.50) |
|
Model 2d, HR (95% CI) | Reference | 1.29 (1.18-1.40) | 1.89 (1.64-2.18) | <.001 | 1.24 (1.19-1.29) |
|
||||||
|
Number of events/person-years | 1314/195,009 | 1651/201,855 | 269/20,099 | — | — |
|
Model 1c, HR (95% CI) | Reference | 1.31 (1.22-1.41) | 2.39 (2.09-2.72) | <.001 | 1.29 (1.25-1.34) |
|
Model 2d, HR (95% CI) | Reference | 1.17 (1.08-1.26) | 1.66 (1.44-1.91) | <.001 | 1.16 (1.12-1.21) |
|
||||||
|
Number of events/person-years | 513/213,304 | 758/228,894 | 168/25,929 | — | — |
|
Model 1c, HR (95% CI) | Reference | 1.47 (1.31-1.64) | 3.01 (2.53-3.58) | <.001 | 1.43 (1.36-1.50) |
|
Model 2d, HR (95% CI) | Reference | 1.22 (1.09-1.37) | 1.76 (1.45-2.13) | <.001 | 1.23 (1.16-1.30) |
|
||||||
|
Number of events/person-years | 1470/202,556 | 1792/216,687 | 263/24,132 | — | — |
|
Model 1c, HR (95% CI) | Reference | 1.20 (1.12-1.29) | 1.62 (1.42-1.85) | <.001 | 1.17 (1.13-1.21) |
|
Model 2d, HR (95% CI) | Reference | 1.12 (1.04-1.20) | 1.31 (1.14-1.51) | <.001 | 1.10 (1.06-1.14) |
|
||||||
|
Number of events/person-years | 111/215,549 | 181/232,270 | 33/26,881 | — | — |
|
Model 1c, HR (95% CI) | Reference | 1.69 (1.34-2.15) | 2.87 (1.94-4.23) | <.001 | 1.41 (1.28-1.56) |
|
Model 2d, HR (95% CI) | Reference | 1.57 (1.23-2.01) | 2.03 (1.33-3.09) | <.001 | 1.29 (1.16-1.44) |
|
||||||
|
Number of events/person-years | 387/204,125 | 687/209,605 | 191/19,994 | — | — |
|
Model 1c, HR (95% CI) | Reference | 1.71 (1.51-1.94) | 4.97 (4.18-5.92) | <.001 | 1.63 (1.55-1.71) |
|
Model 2d, HR (95% CI) | Reference | 1.48 (1.30-1.68) | 3.01 (2.47-3.67) | <.001 | 1.42 (1.34-1.50) |
|
||||||
|
Number of events/person-years | 783/195,777 | 1047/204,710 | 186/20,940 | — | — |
|
Model 1c, HR (95% CI) | Reference | 1.39 (1.27-1.53) | 2.65 (2.26-3.12) | <.001 | 1.35 (1.29-1.41) |
|
Model 2d, HR (95% CI) | Reference | 1.25 (1.14-1.38) | 1.81 (1.51-2.16) | <.001 | 1.21 (1.16-1.27) |
aCalculated to test linear trend using frailty status (3 categories) as a continuous variable.
bNot applicable.
cModel 1 was adjusted for age and sex.
dModel 2 was further adjusted for ethnicity, educational level, occupational status, Townsend deprivation index, alcohol consumption, smoking status, healthy diet, BMI, and family history of disease based on Model 1.
The differences in characteristics between included and excluded participants were observed. Those who were excluded were more likely to be older, women, non-White, and frail (Table S2 in
In a large sample of UKB participants with prediabetes, we, for the first time, demonstrated that both prefrailty and frailty were associated with higher risks of multiple adverse outcomes, including T2DM, diabetes-related microvascular disease, CVD, CKD, eye disease, dementia, depression, and all-cause mortality. Our findings support the heterogeneity of prediabetes in middle-aged adulthood and suggest that assessing frailty status among middle-aged adults with prediabetes may help to identify those who were most at risk of subsequent adverse outcomes.
We observed a nearly twice higher prevalence of frailty among middle-aged adults with prediabetes (ie, 5.9%) in this study than that in general adults (ie, 3.3%) from the UKB as well [
To the best of our knowledge, this study provided new evidence on the associations between frailty and higher risks of a series of adverse outcomes in middle-aged adults with prediabetes. A few studies on the relationship between frailty and adverse outcomes included middle-aged adults with diabetes as part of the study sample [
This study draws attention to the accelerated aging process in adults with prediabetes, which may lead to rapid diabetes progression and contribute to the development of diabetes-related complications [
The major strengths of this study were the large sample of middle-aged adults with prediabetes, the long follow-up time, rich phenotype data, and linked hospital admissions records, enabling us to systematically evaluate the prospective associations between frailty and multiple adverse outcomes. There were several potential limitations. First, the UKB was not representative of the sampling population, and the majority of included adults were White. Also, there were differences in baseline characteristics between included and excluded participants. Thus, selection bias existed in this study, and the results may not be generalizable to populations from other countries. Second, transitions in frailty status may occur over time [
In this prospective cohort study of middle-aged UKB participants with prediabetes, both prefrailty and frailty were significantly associated with increased risks of multiple adverse outcomes, including T2DM, diabetes-related microvascular disease, CVD, CKD, eye disease, dementia, depression, and all-cause mortality. The findings underscore the importance of frailty assessment in routine care for middle-aged adults with prediabetes. Detecting frailty at an early stage (ie, accelerated aging) and implementing timely targeted interventions may help to improve the allocation of health care resources and to reduce diabetes-related burden.
Supplementary tables.
American Diabetes Association
chronic kidney disease
cardiovascular disease
frailty phenotype
glycated hemoglobin
hazard ratio
International Statistical Classification of Diseases and Related Health Problems, 9th version
the International Statistical Classification of Diseases and Related Health Problems, 10th version
type 2 diabetes mellitus
Townsend deprivation index
UK Biobank
This research was conducted using the UK Biobank resource under application number 61856. We wish to acknowledge the UK Biobank participants who formed the sample that made the data available. This study was supported by a grant from the National Natural Science Foundation of China (82171584); the Fundamental Research Funds for the Central Universities, Key Laboratory of Intelligent Preventive Medicine of Zhejiang Province (2020E10004); and Zhejiang University Global Partnership Fund (188170-11103). TMG is supported by the Claude D. Pepper Older Americans Independence Center at Yale School of Medicine from the National Institute on Aging (P30AG021342) and the National Center for Advancing Translational Sciences (UL1TR001863). The funders had no role in the study design; data collection, analysis, or interpretation; in the writing of the report; or in the decision to submit the article for publication.
The data sets analyzed during this study are available at [
None declared.