Prevalence and risk factors of frailty in older adults with diabetes: A systematic review and meta-analysis

Abstract

Objective

This systematic review and meta-analysis aimed to evaluate the prevalence of frailty and pre-frailty in older adults with diabetes; and to identify the risk factors associated with frailty in this population.

Design

Systematic review and meta-analysis.

Participants

24,332 people aged 60 years and older with diabetes.

Methods

Six databases were searched (PubMed, Embase, the Cochrane Library, Web of Science, China Knowledge Resource Integrated Database, and Chinese Biomedical Database) up to 15 January 2024. Random effects models were used in instances of significant heterogeneity. Subgroup analysis and meta-regression were conducted to identify the potential source of heterogeneity. The Agency for Healthcare Research and Quality (AHRQ) and the Newcastle-Ottawa Scale (NOS) were applied to assess the quality of included studies.

Results

3,195 abstracts were screened, and 39 full-text studies were included. In 39 studies with 24,332 older people with diabetes, the pooled prevalence of frailty among older adults with diabetes was 30.0% (95% CI: 23.6%-36.7%). Among the twenty-one studies involving 7,922 older people with diabetes, the pooled prevalence of pre-frailty was 45.1% (95% CI: 38.5%-51.8%). The following risk factors were associated with frailty among older adults with diabetes: older age (OR = 1.08, 95% CI: 1.04–1.13, p<0.05), high HbA1c (OR = 2.14, 95% CI: 1.30–3.50, p<0.001), and less exercise (OR = 3.11, 95% CI: 1.36–7.12, p<0.001).

Conclusions

This suggests that clinical care providers should be vigilant in identifying frailty and risk factors of frailty while screening for and intervening in older adults with diabetes. However, there are not enough studies to identify comprehensive risk factors of frailty in older adults with diabetes.

Trial registration

PROSPERO registration number: CRD42023470933.

Introduction

Diabetes continues to be a significant public health challenge worldwide. According to the IDF Diabetes Atlas (10th edition), the estimated prevalence of diabetes among adults aged 75–79 will be 24.7% in 2045 [1]. With the world’s population aging, the proportion of people over 60 with diabetes will continue to rise [2]. Evidence shows that frailty is becoming the third type of complication of diabetes in addition to the traditional microvascular and macrovascular complications [3]. Frailty is characterized by a decline in functioning across multiple physiological systems and an elevated vulnerability to stressors [4]. Frailty is a significant risk factor for death and disability in older adults with diabetes [5]. Moreover, the rate of frailty is 3–5 times higher in older adults with diabetes than in non-diabetics [6]. However, current estimates of the prevalence of frailty in older adults with diabetes vary substantially. Indeed, the reported prevalence of frailty among older adults with diabetes varies across the studies from 6.3% to 84.4% [7,8]. Several factors contribute to the significant variation in the prevalence of frailty, including: 1) differences in the characteristics of older adults across studies, such as the severity and duration of diabetes mellitus; 2) discrepancies in the assessment criteria used for frailty; and 3) variations in the clinical characteristics of the participants.

It is now clear that diabetes and frailty are closely intertwined and can interact in a vicious circle. Frailty determines the prognosis for older adults with diabetes [9]; moreover, scientific evidence suggests that both pre-frailty and frailty increase healthcare utilization and lead to adverse health outcomes in people with diabetes mellitus [10]. Indeed, frailty is not simply a consequence of aging and is not always progressive [11]; instead, the functional deficits linked to frailty in older adults are reversible and dynamic [12]. Therefore, understanding the true magnitude of frailty among patients with diabetes is imperative for implementing possible early screening and suitable intervention strategies, subsequently reducing the incidence of complications and improving the prognosis.

To date, no systematic review or meta-analysis has assessed the overall prevalence of frailty and pre-frailty in older adults with diabetes. Evidence from such a meta-analysis will provide robust information on the epidemiology of frailty and pre-frailty among older adults with diabetes, which would be necessary to plan early and suitable intervention strategies for those population groups. Therefore, this article aims to systematically analyze published studies on the prevalence of frailty and pre-frailty in older adults with diabetes and identify the risk factors.

Methods

Protocol and registration

This review has been registered in PROSPERO (registration number CRD42023470933). Searches of the International Prospective Register of Systematic Reviews (PROSPERO) have confirmed that no protocols for a meta-analysis with an identical scope exist.

Design and selection criteria

We followed the methods recommended by the Cochrane Collaboration. We complied with the reporting standards of the 2020 Preferred Reporting Items for Systematic Review and Meta-analysis (PRISMA) guideline [13] (S1 Table). The PECOS (population, exposure, comparison, outcomes, study design) model was used to shape the clinical question and search strategy (S2 Table). The criteria for inclusion of the study: 1) people with diabetes aged ≥60 years; 2) exact diagnostic criteria for the frailty were available; 3) prevalence or risk factors of frailty were reported; 4) cross-sectional, case-control, or cohort study. The exclusion criteria: 1) incomplete data; 2) the language of the publication was other than English or Chinese; 3) the sample size was less than 100; 4) studies were assessed as high-risk of bias.

Search strategy

We conducted a comprehensive literature search in the online databases of PubMed, Embase, the Cochrane Library, Web of Science, China Knowledge Resource Integrated Database (CNKI), and Chinese Biomedical Database (CBM) up to 15 January 2024 (S3 Table). Reference lists of all included studies and reviews were checked for possible other studies. Initial keywords included “Aged”, “Elderly”, “Frail Elderly”, “old people”, “elder*”, “older people”, “Diabetes Mellitus”, “Diabetes Insipidus”, “Diabetes Insipidus”, “Glucose Intolerance”, “Diabetes”, “Glycuresis”, “Diabetic”, “type 2 diabetes”, “Frailty”, “Frailties”, “Frailness”, “frailty syndrome”, “Debility”, “Debilities”, “prevalence*”, “epidemiology”, “incidence*”, “morbidity”, “factor*”, “influencing factor*”, “relevant factor*”, “dangerous factor*”, “factor*”, “risk”, “cohort studies”, “case-control studies”, “comparative study”, “risk factors”, “cohort”, “compared”, “groups”, “case control”, “multivariate”. Each key search term’s Medical Subject Heading (MeSH) and combinations were explored in every database. Boolean operators such as “AND” and “OR” were used to search the relevant studies.

Study selection and data extraction

Upon execution of the searches, the complete list of records was stored and managed using the Rayyan platform (https://www.rayyan.ai) for screening [14]. The search, screening, and data extraction were performed independently by two reviewers (Liu and Zhang). Data extraction was accomplished through online document collaboration. Disagreements were resolved through discussion with the senior investigator (Li). The following information was recorded: demographic information (e.g., age, gender); methodological data included study design, sample size, data collection period, region, method(s) of diagnosis of frailty, and data collection method(s); outcome data included prevalence of frailty, prevalence of pre-frailty and risk factors reported. The missing 95% CI of OR will be calculated in logistic regression: .

Quality assessment of studies

Two investigators (Liu and Zhang) evaluated the study quality based on the study designs. Conflicts were resolved by another reviewer (Luo). The 11-item criteria recommended by the US Agency for Healthcare Quality and Research (AHRQ) was used to assess the risk of bias in cross-sectional studies. Studies scoring 0 to 3 are considered low quality, 4 to 7 indicate moderate quality, and 8 to 11 are regarded as high quality [15]. For cohort studies, the Newcastle-Ottawa Scale (NOS) was used. Three domains were evaluated with the following items: 1) selection, 2) comparability, and 3) exposure [16]. High quality was considered when the assigned score was≥8, moderate quality when the score was between four and seven, and low quality when the score was≤3. Studies with AHRQ and NOS scores of less than four were assessed as having a high risk of bias and were not included in the study.

Data analysis

We used STATA 17.0 (Stata Corporation, College Station, TX) to perform the meta-analysis. A p-value of <0.05 was considered statistically significant. I² statistics and forest plots assessed heterogeneity. I² value between 25%-50% was regarded as low heterogeneity, 50%-75% moderate heterogeneity, and I²>75% high heterogeneity [17]. A random effects model was used where moderate or high heterogeneity occurs; otherwise, the fixed effect model was used. Microsoft Excel was utilized to draw the forest plot based on the data. In general, there is a high degree of heterogeneity in prevalence studies; our analytic plan included an exploration of the causes of variation. We hypothesized that clinical and patient factors would be associated with the reported prevalence. Factors related to heterogeneity were identified via subgroup analyses in case of categorical factors (e.g., gender, regions, study design, study population, and tools of diagnosis of frailty) and meta-regression for continuous variables (e.g., year of publication, participants’ mean age, and percentage of females). In the meta-regression results, p<0.10 was considered statistically significant due to the low power of these tests [18]. To assess the risk factors of frailty, the odds ratios (ORs) and associated 95% confidence intervals (CIs) were extracted from included studies. A funnel plot was used to assess publication bias [19], and asymmetry was tested using Egger’s linear regression method (p<0.05 is considered significant) [20].

Results

Study process

The initial search yielded 4,209 results; after duplicates were removed, 3,195 records remained for screening. After the titles and abstract review, 96 articles were selected for full-text review to exclude those that were irrelevant or did not meet the pre-specified eligibility criteria. Finally, 39 studies (14 in Chinese and 25 in English) were utilized for the meta-analysis. The selection process is displayed within the PRISMA diagram (Fig 1). Of these articles, 39 studies reported frailty prevalence among older people with a confirmed diagnosis of diabetes, and 21 studies reported pre-frailty prevalence among older people with diabetes [21–41]. Additionally, we identified 13 studies on risk factors of frailty in older adults with diabetes [22,24,25,28–30,32,40,42–45], of which nine were included in the meta-analysis [22,24,25,28–30,32,40,43]. The reasons for excluding the 57 studies can be found in S4 Table.

[Figure omitted. See PDF.]

Study characteristics

The key characteristics of the included studies are presented in Table 1. These studies were conducted in Asia (n = 27, 69.2%) [21–35,38–40,42–50], Europe (n = 6, 15.4%) [7,36,37,51–53], North America (n = 5, 12.8%) [8,54–57] and South America (n = 1, 2.6%) [41]. Out of 39 articles, 29 were cross-sectional studies (9,371 patients) [8,21–35,37,38,40–43,45–47,49–52]; 10 were cohort studies [7,36,39,44,48,53–57] with 14,961 patients. Of the 39 included studies, 15 used the Frailty Phenotype to assess the frailty level of older people with diabetes [22,25,26,28,29,32,35,36,38–41,49,53,55]. Seven studies used the Frail scale [7,21,23,24,30,33,43], 5 used the Tilburg Frailty Indicator [42,45,46,50,51], 4 used the Clinical Frailty Scale [8,27,44,57], 4 used the Frailty Index [36,37,54,56]. One study used three different diagnostic criteria [36], and the rest used other diagnostic tools [31,34,47,48,52]. More detailed research data is available in S5 Table.

[Figure omitted. See PDF.]

(n = 39).

Risk of bias assessment of included studies

The assessment of the risk of bias in the included studies using AHRQ for cross-sectional studies (n = 29) (S6 Table and S1 Fig). The final scores ranged between 4 and 7, indicating moderate quality. The assessment of the risk of bias in the included studies using NOS for cohort studies. Among the 10 cohort studies, three studies (30%) were high-quality studies (scored 8), and the remaining were moderate-quality articles (scored between 4 and 7) (S7 Table and S2 Fig).

Pooled prevalence of frailty prevalence in older adults with diabetes

The meta-analysis of the pooled prevalence of frailty prevalence in older adults with diabetes was 30.0% (95% CI: 23.6%-36.7%, I² = 99.2%, p<0.001) in 39 studies with 24,332 patients (S3 Fig). A summary of frailty prevalence is shown in Fig 2.

[Figure omitted. See PDF.]

Pooled prevalence of pre-frailty prevalence in older adults with diabetes

The meta-analysis of the pooled prevalence of pre-frailty prevalence in older adults with diabetes was 45.1% (95% CI: 38.5%-51.8%, I² = 97.1%, p<0.001) in 21 studies with 7,922 patients (S4 Fig). A summary of pre-frailty prevalence is shown in Fig 3.

[Figure omitted. See PDF.]

Risk factors of frailty in older adults with diabetes

The pooled analysis identified three potential risk factors associated with frailty in older adults with diabetes (Fig 4): older age (OR = 1.08, 95% CI: 1.04–1.13, p<0.05), high HbA1c (OR = 2.14, 95% CI: 1.30–3.50, p<0.001), less exercise (OR = 3.11, 95% CI: 1.36–7.12, p<0.001). The association of frailty with depression did not reach statistical significance (OR = 1.52, 95% CI: 1.25–1.85, p = 0.16). The meta-analysis only included risk factors reported in 3 or more articles to ensure the scientific validity of the results. Meta-analyses of other factors for frailty and risk factors for pre-frailty were not conducted due to insufficient information.

[Figure omitted. See PDF.]

Subgroup analysis and meta-regression

Subgroup analysis was done to identify the potential source of heterogeneity (Figs 2 and 3). Regarding gender, the prevalence of frailty among females was higher at 22.8% (95% CI: 16.1%-30.2%) than males at 20.4% (95% CI: 13.2%-28.6%). According to the grouping of the study population, the pooled prevalence of frailty was 27.3% among community residents, 36.2% among hospitalized patients, and 27.3% among outpatients; by region, the pooled prevalence of frailty was lowest in Asia (26.7%, 95% CI: 20.8%-32.9%), and highest in North America (51.4%, 95% CI: 40.9%-61.9%). Subgroup analysis was not conducted for risk factors due to insufficient data.

Meta-regression showed an inverse association between the prevalence of frailty and gender (female%). However, this inverse association was not statistically significant (regression = -0.133, 95% CI: (-0.409)—(0.502), p>0.1). Also, an inverse association between the prevalence of Frailty and the year of publication was not statistically significant (regression = -0.007, 95% CI: (-0.038)—(0.025), p>0.1). But, there was a direct association between the prevalence of Frailty and mean age. This association was statistically significant (regression = 0.017, 95% CI: (-0.001)-(0.036), p<0.1). Moreover, gender (female%) (regression = 1.794, 95% CI: 1.106–2.483, p<0.001) were associated with prevalence of pre-frailty, whereas mean age (regression = -0.003, 95% CI: (-0.032)—(0.026), p>0.1) or year of publication (regression = 0.002, 95% CI: (-0.044)—(0.048), p>0.1) was not (S5 Fig).

Publication bias

The funnel plot for frailty prevalence and pre-frailty prevalence asymmetry both revealed evidence of publication bias between study heterogeneity. Egger’s test results further confirmed the funnel plots’ asymmetry (p<0.05, see S6 and S7 Figs). Publication bias was not assessed for risk factors studies due to insufficient studies.

Discussion

This study represents the first systematic review and meta-analysis examining the prevalence and risk factors of frailty among older adults with diabetes. Based on 39 studies included in the current meta-analysis, which involved a total of 24,332 older adults with diabetes, the pooled prevalence of frailty is 30.0%; the prevalence rate is significantly higher than that of the general elderly population in the United States (15.3%) [58], Japan (7.4%) [59] and China (9.9%) [60]. This may be attributed to patients with diabetes being in a hyperglycemic state, which impairs skeletal muscle regeneration, leading to reduced myocyte size and strength and thus accelerating the onset of frailty [61]. Frail older individuals are more susceptible to adverse events, including hypoglycemia and mortality [62]. Indeed, some defined models of frailty (e.g., Frailty Phenotype and Clinical Frailty Scale) have been used to set goals for diabetes medications in Europe and the United States [63,64]. Considering the high prevalence of frailty in older adults with diabetes, we suggest that clinicians should take frailty into more significant consideration when planning diabetes treatment for older adults. Our study also found that the prevalence of pre-frailty (45.1%) in older adults with diabetes was higher than the prevalence of frailty. This suggests that clinical care providers should be vigilant in identifying pre-frailty while screening for and intervening in frailty among older adults with diabetes [65].

Impact of risk factors

The assessment of potential risk factors associated with frailty indicated a statistically significant correlation with three factors: older age, high HbA1c, and less exercise. In a previous study, these factors have also been demonstrated to correlate significantly with frailty among patients with diabetes [66]. However, there is controversy regarding the idea that HbA1c influences the frailty condition of older adults with diabetes. Most studies suggest that a high level of HbA1c is a risk factor for frailty; still, a study conducted in Japan concluded that a low HbA1c level is an independent risk factor for frailty in older adults with type 2 diabetes mellitus [44]. One possible reason for this is the variation in the frailty scale used; among the studies that reported on HbA1c, three used the FP scale [22,29,32], one used the Frail scale [24], and the Japanese one used the Clinical Frail Scale [44]. Age is acknowledged as an independent risk factor for frailty, with its prevalence escalating as individuals grow older. This increase is attributed to age-related degeneration of organs and a reduction in reserve capacity [59]. The current study has demonstrated that older age is a risk factor for frailty in older adults with diabetes, consistent with previous findings [67].

Frailty is characterized by a disruption in the harmonious interplay among various domains, referred to as dimensions, which encompass biological, psychological, nutritional, and socioeconomic aspects [4]. Each of these dimensions plays a pivotal role in the complex etiology of frailty, and their interdependencies contribute to the overall decline in an individual’s resilience and capacity to withstand stressors. Biologically, frailty may manifest as a decline in physiological reserve and homeostatic mechanisms, leading to a decreased ability to respond to and recover from stressors. This can include sarcopenia, the age-related loss of muscle mass and strength [68], as well as alterations in immune function and hormonal balance [69], which collectively impair the body’s ability to maintain stability and resist disease. Psychologically, frailty is often accompanied by symptoms of depression, anxiety, and a general sense of helplessness. A recent bidirectional Mendelian randomization study demonstrated a bidirectional causal relationship between frailty and depression [70]. Additionally, frailty is linked to nutritional imbalances and deficiencies that can affect overall health and function. Protein-energy malnutrition, for example, is a common issue in frail individuals, leading to muscle wasting and reduced strength [71].

Moreover, frailty is relevant to socioeconomic factors such as lower income, educational level, and social support, which can limit access to healthcare resources and contribute to poor health status [24,25,45]. The lack of social support networks can lead to social isolation, a known risk factor for the development and progression of frailty [72]. Furthermore, detrimental lifestyle choices, such as smoking, excessive alcohol consumption, and inadequate personal hygiene, contribute to the heightened risk of developing frailty [73,74]. Given the multifaceted nature of frailty, investigations into its risk factors among elderly individuals with diabetes should encompass an examination of these diverse domains. However, due to the paucity of empirical data specifically about older adults with diabetes, the inclusion of these potential risk factors in the current study was not feasible.

Subgroup analysis and meta-regression findings

The gender-stratified analysis showed that the prevalence of frailty in female patients was higher than in male patients (Fig 2), and it was in line with a previous report [75]. Furthermore, the results of meta-regression analyses also suggest that females are associated with a higher prevalence of pre-frailty (regression = 1.794, 95% CI: 1.106–2.483, p<0.001, see S5 Fig). Current academic research indicates that women are susceptible to frailty, likely due to the rapid loss of estrogen in postmenopausal women. This hormonal change negatively affects muscular strength, neuromuscular function, and postural stability, consequently increasing the incidence of frailty in older women [76]. Additionally, some scholars have observed that men are more inclined to neglect and underreport their health issues compared to women. This tendency also contributes to a gender disparity in the prevalence of frailty [77].

The analysis of regional subgroups indicated that the prevalence of frailty is higher in North America than in Asia and Europe. This observation aligns with the findings of a previous meta-analysis [65]. We discovered that in one of the North American studies, the participants had an average age of 80.6 years, and the prevalence of frailty was 84.4% [8]. To mitigate potential biases, we temporarily removed the paper with the high prevalence. However, even without that paper, the meta-analysis still indicated that the pooled prevalence of frailty in North America was 43.4% (95% CI: 38.4%-48.4%, p<0.001), which remained higher than that in Asia (26.7%) and Europe (26.9%). Further investigation into environmental influences and ethnic diversity’s role in frailty may elucidate the causes of this regional variance.

The meta-analysis encompassed studies that used 11 different frailty assessment tools. The prevalence of frailty reported using the various assessment tools varied widely, ranging from 18.7% (Frailty Scale) to 49.4% (Clinical Frailty Scale). This variation is likely due to differences in patient populations and the sensitivity and specificity of these scales (Fig 2). One possible explanation for the higher prevalence found with the Clinical Frailty Scale is its primary use in assessing frailty in hospitalized patients. This group typically has a higher prevalence of frailty (36.2%) due to generally poorer health compared to community-dwelling individuals and outpatients. Given the worldwide variation in frailty assessment tools for older adults with diabetes, adopting a multi-disciplinary approach to standardize criteria is essential. Such standardization would contribute to improving clinical practice and ensuring more consistent assessment of frailty across different healthcare settings.

Strengths and limitations of the study

The study presented multiple strengths. Firstly, it used a rigorous review methodology and delivered a comprehensive report. Additionally, it incorporated a large global sample that quantitatively synthesized the prevalence and risk factors of frailty among older adults with diabetes. Furthermore, subgroup analysis and meta-regression investigated the heterogeneous factors contributing to this demographic’s estimated prevalence of frailty. This review offered the inaugural synthesis of evidence on the global prevalence of frailty in older individuals with diabetes, providing significant findings to clinicians, researchers, and policymakers. The information provided could assist clinicians in optimizing care for older individuals with diabetes by customizing treatment objectives and protocols according to their frailty status. This personalized approach was crucial for enhancing patient outcomes and quality of life.

The meta-analysis exhibited limitations, notably the high heterogeneity (I² = 41.9%–99.6%) among the included studies. Despite subgroup analyses, this heterogeneity remained pronounced, likely due to the extensive scope of the systematic review and the substantial volume of data involved. Our analysis was based on data from published studies. Due to this limitation, we could not discount other potential risk factors at that time, including the impact of poor lifestyles like smoking and alcohol abuse. Most studies included in our analysis did not specify the diabetes typology. Consequently, our study did not distinguish between the different types of diabetes in people. In addition, the study’s scope was constrained by the inclusion of only English and Chinese language studies, which may have affected its comprehensiveness.

Conclusion

In conclusion, this systematic review revealed a pooled frailty prevalence of 30.0% and a pre-frailty prevalence of 45.1% among older individuals with diabetes. These high rates underscore the urgency of increasing public awareness about frailty and pre-frailty. While current literature may not suffice to draw comprehensive conclusions beyond pinpointing older age, high HbA1c, and less exercise as risk factors, this review summarizes the available evidence and catalyzes further high-quality research in this field.

Supporting information

S1 Fig. Critical appraisal of the 27 cross-sectional studies.

https://doi.org/10.1371/journal.pone.0309837.s001

S2 Fig. Critical appraisal of the 11 cohort studies.

https://doi.org/10.1371/journal.pone.0309837.s002

S3 Fig. Pooled prevalence of frailty.

https://doi.org/10.1371/journal.pone.0309837.s003

S4 Fig. Pooled prevalence of pre-frailty.

https://doi.org/10.1371/journal.pone.0309837.s004

S5 Fig. Meta regression analysis.

https://doi.org/10.1371/journal.pone.0309837.s005

S6 Fig. Publication bias assessment of frailty prevalence.

https://doi.org/10.1371/journal.pone.0309837.s006

S7 Fig. Publication bias assessment of pre-frailty prevalence.

https://doi.org/10.1371/journal.pone.0309837.s007

S1 Table. PRISMA 2020 checklist.

https://doi.org/10.1371/journal.pone.0309837.s008

S2 Table. PECOS model.

https://doi.org/10.1371/journal.pone.0309837.s009

(DOCX)

S3 Table. The search and screening strategy.

https://doi.org/10.1371/journal.pone.0309837.s010

(DOCX)

S4 Table. Excluded studies and reason (N = 57).

https://doi.org/10.1371/journal.pone.0309837.s011

(DOCX)

S5 Table. Detailed data of studies included.

https://doi.org/10.1371/journal.pone.0309837.s012

(XLSX)

S6 Table. Assessment of the quality of cross-sectional studies based on the criteria recommended by the US Agency for Healthcare Quality and Research (AHRQ).

https://doi.org/10.1371/journal.pone.0309837.s013

(DOCX)

S7 Table. Quality of cohort studies based on the Newcastle–Ottawa scale.

https://doi.org/10.1371/journal.pone.0309837.s014

(DOCX)

References

1. 1. Magliano DJ, Boyko EJ, Committee IDA 10th edition scientific. Global picture. IDF DIABETES ATLAS [Internet] 10th edition. International Diabetes Federation; 2021. Available: https://www.ncbi.nlm.nih.gov/books/NBK581940/.

2. 2. Sun H, Saeedi P, Karuranga S, Pinkepank M, Ogurtsova K, Duncan BB, et al. IDF Diabetes Atlas: Global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045. Diabetes Res Clin Pract. 2022;183: 109119. pmid:34879977

* View Article

* PubMed/NCBI

* Google Scholar

3. 3. Ulley J, Abdelhafiz AH. Frailty predicts adverse outcomes in older people with diabetes. Practitioner. 2017;261: 17–20. pmid:29023081

* View Article

* PubMed/NCBI

* Google Scholar

4. 4. Pilotto A, Custodero C, Maggi S, Polidori MC, Veronese N, Ferrucci L. A multidimensional approach to frailty in older people. Ageing Res Rev. 2020;60: 101047. pmid:32171786

* View Article

* PubMed/NCBI

* Google Scholar

5. 5. Castro-Rodríguez M, Carnicero JA, Garcia-Garcia FJ, Walter S, Morley JE, Rodríguez-Artalejo F, et al. Frailty as a Major Factor in the Increased Risk of Death and Disability in Older People With Diabetes. J Am Med Dir Assoc. 2016;17: 949–955. pmid:27600194

* View Article

* PubMed/NCBI

* Google Scholar

6. 6. Yanase T, Yanagita I, Muta K, Nawata H. Frailty in elderly diabetes patients. Endocr J. 2018;65: 1–11. pmid:29238004

* View Article

* PubMed/NCBI

* Google Scholar

7. 7. Lopez-Garcia E, Hagan K, Fung T, Hu F, Rodríguez-Artalejo F. Mediterranean diet and risk of frailty syndrome among women with type 2 diabetes. The American journal of clinical nutrition. 2018;107: 763–771. pmid:29722845

* View Article

* PubMed/NCBI

* Google Scholar

8. 8. MacKenzie HT, Tugwell B, Rockwood K, Theou O. Frailty and Diabetes in Older Hospitalized Adults: The Case for Routine Frailty Assessment. Canadian Journal of Diabetes. 2020;44: 241–245.e1. pmid:31466827

* View Article

* PubMed/NCBI

* Google Scholar

9. 9. Strain WD, Down S, Brown P, Puttanna A, Sinclair A. Diabetes and Frailty: An Expert Consensus Statement on the Management of Older Adults with Type 2 Diabetes. Diabetes Ther. 2021;12: 1227–1247. pmid:33830409

* View Article

* PubMed/NCBI

* Google Scholar

10. 10. Chao C-T, Wang J, Chien K-L, COhort of GEriatric Nephrology in NTUH (COGENT) study group. Both pre-frailty and frailty increase healthcare utilization and adverse health outcomes in patients with type 2 diabetes mellitus. Cardiovasc Diabetol. 2018;17: 130. pmid:30261879

* View Article

* PubMed/NCBI

* Google Scholar

11. 11. Rodriguez-Mañas L, Laosa O, Vellas B, Paolisso G, Topinkova E, Oliva-Moreno J, et al. Effectiveness of a multimodal intervention in functionally impaired older people with type 2 diabetes mellitus. J Cachexia Sarcopenia Muscle. 2019;10: 721–733. pmid:31016897

* View Article

* PubMed/NCBI

* Google Scholar

12. 12. Gill TM, Gahbauer EA, Allore HG, Han L. Transitions between frailty states among community-living older persons. Arch Intern Med. 2006;166: 418–423. pmid:16505261

* View Article

* PubMed/NCBI

* Google Scholar

13. 13. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. PLoS Med. 2021;18: e1003583. pmid:33780438

* View Article

* PubMed/NCBI

* Google Scholar

14. 14. Ouzzani M, Hammady H, Fedorowicz Z, Elmagarmid A. Rayyan-a web and mobile app for systematic reviews. Syst Rev. 2016;5: 210. pmid:27919275

* View Article

* PubMed/NCBI

* Google Scholar

15. 15. Huang T, Li C, Chen F, Xie D, Yang C, Chen Y, et al. Prevalence and risk factors of osteosarcopenia: a systematic review and meta-analysis. BMC Geriatr. 2023;23: 369. pmid:37322416

* View Article

* PubMed/NCBI

* Google Scholar

16. 16. Zeng X, Zhang Y, Kwong JSW, Zhang C, Li S, Sun F, et al. The methodological quality assessment tools for preclinical and clinical studies, systematic review and meta-analysis, and clinical practice guideline: a systematic review. J Evid Based Med. 2015;8: 2–10. pmid:25594108

* View Article

* PubMed/NCBI

* Google Scholar

17. 17. Barendregt JJ, Doi SA, Lee YY, Norman RE, Vos T. Meta-analysis of prevalence. J Epidemiol Community Health. 2013;67: 974–978. pmid:23963506

* View Article

* PubMed/NCBI

* Google Scholar

18. 18. Deeks JJ, Higgins JP, Altman DG, on behalf of the Cochrane Statistical Methods Group. Analysing data and undertaking meta‐analyses. 1st ed. In: Higgins JPT, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, et al., editors. Cochrane Handbook for Systematic Reviews of Interventions. 1st ed. Wiley; 2019. pp. 241–284. https://doi.org/10.1002/9781119536604.ch10

19. 19. Hunter JP, Saratzis A, Sutton AJ, Boucher RH, Sayers RD, Bown MJ. In meta-analyses of proportion studies, funnel plots were found to be an inaccurate method of assessing publication bias. J Clin Epidemiol. 2014;67: 897–903. pmid:24794697

* View Article

* PubMed/NCBI

* Google Scholar

20. 20. Egger M, Smith GD, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. BMJ. 1997;315: 629–634. pmid:9310563

* View Article

* PubMed/NCBI

* Google Scholar

21. 21. Guo X, Gao J, Xiang Y, Lv J, Liu J, Yu J, et al. Correlation study on chronic complications of and frailty in elderly patients with type 2 diabetes. Chinese Journal of Convalescent Medicine. 2019;28: 225–229.

* View Article

* Google Scholar

22. 22. Guo X, Gao J, Xiang Y, Hou X, Wu C, Jiang X, et al. Status and influencing factors of frailty syndrome in elderly patients with type 2 diabetes mellitus. China Journal of Modern Medicine. 2018;28: 90–96.

* View Article

* Google Scholar

23. 23. Cheng Z, Xing Q, Xu H, Bao D, Mu C. Analysis on status and influential factors of frailty in elderly patients with diabetes m ellitus. Tianjin Journal of Nursing. 2020;28: 293–298.

* View Article

* Google Scholar

24. 24. Chen X, Yang S, Wang Y, Liu S. Influencing Factors of Frailty in Elderly Patients with Diabetes and Intervention Strategies. Chinese General Practice. 2019;22: 1772–1777.

* View Article

* Google Scholar

25. 25. Wu P, Gao J, Hu Y, Luo Q, Liao L. Current Status and Influencing Factors of Elderly Diabetic Patients in Community with Frailty. Journal of Medical Information. 2021;34: 153–157.

* View Article

* Google Scholar

26. 26. Ge Q, Yu W, Xu K, Wu K, Li S, Feng H. Study on relationship between blood glucose levels and frailty. Chinese Nursing Research. 2020;34: 1092–1094.

* View Article

* Google Scholar

27. 27. Li X, Zhang X, Liang X, Cao B, Wang H, Zhang W, et al. Analysis of the correlation between alexithymia and frailty in elderly patients with type 2 diabetes mellitus in elderly care facilities. Chinese Journal of Prevention and Control of Chronic Diseases. 2022;30: 19–23.

* View Article

* Google Scholar

28. 28. Deng Q, Hu Y, Li L, Li P, Zhou Y, Liao L. Mediating effect of depressive symptoms on diabetes-related distress and frailty in elderly patients with diabetes in the community. Medical Science Journal of Central South China. 2020;48: 229–232,237.

* View Article

* Google Scholar

29. 29. Zhao H, Zheng Y, Zhang G. Relationship between motor function and frailty in hospitalised elderly patients with diabetes mellitus. Chinese Journal of Gerontology. 2023;43: 6122–6125.

* View Article

* Google Scholar

30. 30. Wang Y, Li R, Yuan L, Yang X, Lv J, Ye Z, et al. Association between diabetes complicated with comorbidities and frailty in older adults: A cross-sectional study. Journal of clinical nursing. 2023;32: 894–900. pmid:35934867

* View Article

* PubMed/NCBI

* Google Scholar

31. 31. Hayakawa M, Motokawa K, Mikami Y, Yamamoto K, Shirobe M, A E, et al. Low dietary variety and diabetes mellitus are associated with frailty among community-dwelling older japanese adults: A cross-sectional study. Nutrients. 2021;13. pmid:33669388

* View Article

* PubMed/NCBI

* Google Scholar

32. 32. Kong L, Zhao H, Fan J, Wang Q, Li J, Bai J, et al. Predictors of frailty among Chinese community-dwelling older adults with type 2 diabetes: a cross-sectional survey. BMJ open. 2021;11: e041578. pmid:33664069

* View Article

* PubMed/NCBI

* Google Scholar

33. 33. Wang Q, Wang J, Dai G. Prevalence, characteristics, and impact on health outcomes of frailty in elderly outpatients with diabetes: A cross-sectional study. Medicine. 2023;102: E36187. pmid:38013336

* View Article

* PubMed/NCBI

* Google Scholar

34. 34. Nishimura A, Harashima S, Hosoda K, Arai H, Inagaki N, Nishimura A, et al. Sex-related differences in frailty factors in older persons with type 2 diabetes: a cross-sectional study. THERAPEUTIC ADVANCES IN ENDOCRINOLOGY AND METABOLISM. 2019;10. pmid:30858966

* View Article

* PubMed/NCBI

* Google Scholar

35. 35. Kang S, Oh TJ, Cho BL, Park YS, Roh E, Kim HJ, et al. Sex differences in sarcopenia and frailty among community‐dwelling Korean older adults with diabetes: The Korean Frailty and Aging Cohort Study. J of Diabetes Invest. 2021;12: 155–164. pmid:32627923

* View Article

* PubMed/NCBI

* Google Scholar

36. 36. Aguayo G, Hulman A, Vaillant M, Donneau A, Schritz A, S S, et al. Prospective Association Among Diabetes Diagnosis, HbA1c, Glycemia, and Frailty Trajectories in an Elderly Population. Diabetes care. 2019;42: 1903–1911. pmid:31451533

* View Article

* PubMed/NCBI

* Google Scholar

37. 37. Muszalik M, Stępień H, Puto G, Cybulski M, Kurpas D. Implications of the metabolic control of diabetes in patients with frailty syndrome. International journal of environmental research and public health. 2022;19. pmid:36011979

* View Article

* PubMed/NCBI

* Google Scholar

38. 38. Xiu S, Mu Z, Zhao L, Sun L. Low free triiodothyronine levels are associated with risk of frailty in older adults with type 2 diabetes mellitus. Experimental gerontology. 2020;138: 111013. pmid:32590129

* View Article

* PubMed/NCBI

* Google Scholar

39. 39. Kitamura A, Taniguchi Y, Seino S, Yokoyama Y, Amano H, Fujiwara Y, et al. Combined effect of diabetes and frailty on mortality and incident disability in older Japanese adults. Geriatrics & Gerontology International. 2019;19: 423–428. pmid:30788903

* View Article

* PubMed/NCBI

* Google Scholar

40. 40. Sun L, Xiu S, Mu Z, Zhao L, Han Q. Correlation between anemia and frailty in elderly patients with type 2 diabetes mellitus. Journal of Chinese Practical Diagnosis and Therapy. 2020;34: 936–939.

* View Article

* Google Scholar

41. 41. Lima Filho BFD, Gama AGD, Dias VDN, Silva EMTD, Cavalcanti FADC, Gazzola JM. The frailty syndrome in older adults with type 2 diabetes mellitus and associated factors. Rev bras geriatr gerontol. 2020;23: e190196.

* View Article

* Google Scholar

42. 42. Lin C, Yu N, Wu H, Liu Y. Risk factors associated with frailty in older adults with type 2 diabetes: A cross-sectional study. Journal of clinical nursing. 2022;31: 967–974. pmid:34250666

* View Article

* PubMed/NCBI

* Google Scholar

43. 43. Liu T, Meng J, Zhu H, Chen Y, Qi P, Pi H. Current status of frailty in elderly diabetes patients and its influencing factors. Chinese Journal of Multiple Organ Diseases in the Elderly. 2023;22: 881–885.

* View Article

* Google Scholar

44. 44. Yanagita I, Fujihara Y, Eda T, Tajima M, Yonemura K. Low glycated hemoglobin level is associated with severity of frailty in Japanese elderly diabetes patients. Journal of diabetes investigation. 2018;9: 419–425. pmid:28556518

* View Article

* PubMed/NCBI

* Google Scholar

45. 45. Jia W, Zhao H, Dai F, Zhang H, Wang L, Chen R. The current situation and influential factors of frailty in elderly patients with diabetes. Chinese Journal of Nursing. 2019;54: 188–193.

* View Article

* Google Scholar

46. 46. Sun M, Sun X, Wang H, Yu X, Li M. Status quo and influencing factors of frailty in elderly diabetic patients. Chinese Nursing Research. 2024;38: 280–286.

* View Article

* Google Scholar

47. 47. Ge X, Li M, Gao L. Current situation and influencing factors of frailty in elderly diabetes patients. Journal of Nursing Science. 2020;35: 25–29.

* View Article

* Google Scholar

48. 48. Sable‐Morita S, Tanikawa T, Satake S, Okura M, Tokuda H, Arai H. Microvascular complications and frailty can predict adverse outcomes in older patients with diabetes. Geriatrics Gerontology Int. 2021;21: 359–363. pmid:33576140

* View Article

* PubMed/NCBI

* Google Scholar

49. 49. Nguyen AT, Pham HQ, Nguyen TX, Nguyen TTH, Nguyen HTT, Nguyen TN, et al. Knowledge, Attitude and Practice of Elderly Outpatients with Type 2 Diabetes Mellitus in National Geriatric Hospital, Vietnam. DMSO. 2020;13: 3909–3917. pmid:33116737

* View Article

* PubMed/NCBI

* Google Scholar

50. 50. Sun X, Wu Z, Cui Q, Ye X, Chen C, Xu J, et al. Clinical study of risk factors of frailty in elderly t2dm patients in the physical examination center. ACTA MEDICA MEDITERRANEA. 2021;37: 621–625.

* View Article

* Google Scholar

51. 51. Bąk E, Młynarska A, Marcisz C, Bobiński R, Sternal D, Młynarski R. The influence of frailty syndrome on quality of life in elderly patients with type 2 diabetes. Quality of life research: an international journal of quality of life aspects of treatment, care and rehabilitation. 2021;30: 2487–2495. pmid:33834352

* View Article

* PubMed/NCBI

* Google Scholar

52. 52. Cacciatore F, Testa G, Galizia G, Della-Morte D, Mazzella F, Langellotto A, et al. Clinical frailty and long-term mortality in elderly subjects with diabetes. Acta Diabetol. 2013;50: 251–260. pmid:22732903

* View Article

* PubMed/NCBI

* Google Scholar

53. 53. García-Esquinas E, Graciani A, Guallar-Castillón P, López-García E, Rodríguez-Mañas L, Rodríguez-Artalejo F. Diabetes and Risk of Frailty and Its Potential Mechanisms: A Prospective Cohort Study of Older Adults. Journal of the American Medical Directors Association. 2015;16: 748–754. pmid:25986874

* View Article

* PubMed/NCBI

* Google Scholar

54. 54. Qin Y, Zheng X. Association of frailty index with congestive heart failure, all-cause and cardiovascular mortality among individuals with type 2 diabetes: a study from National Health and Nutrition Examination Surveys (NHANES), 1999–2018. Diabetology & metabolic syndrome. 2023;15. pmid:37875981

* View Article

* PubMed/NCBI

* Google Scholar

55. 55. Zaslavsky O, Walker R, Crane P, Gray S, Larson E, Zaslavsky O, et al. Glucose levels and risk of frailty. JOURNALS OF GERONTOLOGY SERIES A-BIOLOGICAL SCIENCES AND MEDICAL SCIENCES. 2016;71: 1223–1229. pmid:26933160

* View Article

* PubMed/NCBI

* Google Scholar

56. 56. Ferri-Guerra J, Aparicio-Ugarriza R, Salguero D, Baskaran D, Mohammed Y, Florez H, et al. The association of frailty with hospitalizations and mortality among community dwelling older adults with diabetes. The Journal of frailty & aging. 2020;9: 94–100. pmid:32259183

* View Article

* PubMed/NCBI

* Google Scholar

57. 57. Hubbard RE, Andrew MK, Fallah N, Rockwood K. Comparison of the prognostic importance of diagnosed diabetes, co-morbidity and frailty in older people. Diabetic Medicine. 2010;27: 603–606. pmid:20536960

* View Article

* PubMed/NCBI

* Google Scholar

58. 58. Bandeen-Roche K, Seplaki CL, Huang J, Buta B, Kalyani RR, Varadhan R, et al. Frailty in Older Adults: A Nationally Representative Profile in the United States. J Gerontol A Biol Sci Med Sci. 2015;70: 1427–1434. pmid:26297656

* View Article

* PubMed/NCBI

* Google Scholar

59. 59. Kojima G, Iliffe S, Taniguchi Y, Shimada H, Rakugi H, Walters K. Prevalence of frailty in Japan: A systematic review and meta-analysis. J Epidemiol. 2017;27: 347–353. pmid:28142044

* View Article

* PubMed/NCBI

* Google Scholar

60. 60. Ma L, Tang Z, Zhang L, Sun F, Li Y, Chan P. Prevalence of Frailty and Associated Factors in the Community-Dwelling Population of China. J Am Geriatr Soc. 2018;66: 559–564. pmid:29168883

* View Article

* PubMed/NCBI

* Google Scholar

61. 61. Assar ME, Laosa O, Rodríguez Mañas L. Diabetes and frailty. Curr Opin Clin Nutr Metab Care. 2019;22: 52–57. pmid:30394893

* View Article

* PubMed/NCBI

* Google Scholar

62. 62. Wood SJ, Bell JS, Magliano DJ, Fanning L, Cesari M, Keen CS, et al. Impact of Age, Frailty, and Dementia on Prescribing for Type 2 Diabetes at Hospital Discharge 2012–2016. J Frailty Aging. 2021; 1–7. pmid:34549249

* View Article

* PubMed/NCBI

* Google Scholar

63. 63. Kirkman MS, Briscoe VJ, Clark N, Florez H, Haas LB, Halter JB, et al. Diabetes in older adults: a consensus report. J Am Geriatr Soc. 2012;60: 2342–2356. pmid:23106132

* View Article

* PubMed/NCBI

* Google Scholar

64. 64. Pilotto A, Noale M, Maggi S, Addante F, Tiengo A, Perin PC, et al. Hypoglycemia is independently associated with multidimensional impairment in elderly diabetic patients. Biomed Res Int. 2014;2014: 906103. pmid:24689062

* View Article

* PubMed/NCBI

* Google Scholar

65. 65. Gao Q, Mei F, Zhao L, Chen F, Hu K, Shang Y, et al. Prevalence of frailty in older patients with diabetes: a systematic review. Chin J Nurs. 2021;56: 686–693.

* View Article

* Google Scholar

66. 66. Niu X, Zhang J, Guo J, Ruan H, Chi J, Tao H, et al. Meta-analysis of Risk Factors for Frailty in Diabetic Patients. Journal of Nursing (China). 2021;28: 1–6.

* View Article

* Google Scholar

67. 67. Nishimura A, Harashima S-I, Ikeda K, Mano F, Higashi K, Ueda N, et al. Specific factors associated with frailty in elderly type 2 diabetes patients: A cross-sectional study. Diabetes. 2016;65: A362.

* View Article

* Google Scholar

68. 68. Liccini AP, Malmstrom TK. Frailty and Sarcopenia as Predictors of Adverse Health Outcomes in Persons With Diabetes Mellitus. Journal of the American Medical Directors Association. 2016;17: 846–851. pmid:27569712

* View Article

* PubMed/NCBI

* Google Scholar

69. 69. Lee E, Lim S-T, Kim W-N. Aquatic exercise for improving immune function and mental stress in pre-frailty elderly women. J Women Aging. 2021;33: 611–619. pmid:32130088

* View Article

* PubMed/NCBI

* Google Scholar

70. 70. Deng M-G, Liu F, Liang Y, Wang K, Nie J-Q, Liu J. Association between frailty and depression: A bidirectional Mendelian randomization study. Sci Adv. 2023;9: eadi3902. pmid:37729413

* View Article

* PubMed/NCBI

* Google Scholar

71. 71. Park Y, Choi J-E, Hwang H-S. Protein supplementation improves muscle mass and physical performance in undernourished prefrail and frail elderly subjects: a randomized, double-blind, placebo-controlled trial. Am J Clin Nutr. 2018;108: 1026–1033. pmid:30475969

* View Article

* PubMed/NCBI

* Google Scholar

72. 72. Cesari M, Prince M, Thiyagarajan JA, De Carvalho IA, Bernabei R, Chan P, et al. Frailty: An Emerging Public Health Priority. Journal of the American Medical Directors Association. 2016;17: 188–192. pmid:26805753

* View Article

* PubMed/NCBI

* Google Scholar

73. 73. Shin J, Kim KJ, Choi J. Smoking, alcohol intake, and frailty in older Korean adult men: cross-sectional study with nationwide data. Eur Geriatr Med. 2020;11: 269–277. pmid:32297188

* View Article

* PubMed/NCBI

* Google Scholar

74. 74. Amiri S, Behnezhad S. Systematic review and meta-analysis of the association between smoking and the incidence of frailty. Neuropsychiatr. 2019;33: 198–206. pmid:31300971

* View Article

* PubMed/NCBI

* Google Scholar

75. 75. Chhetri JK, Zheng Z, Xu X, Ma C, Chan P. The prevalence and incidence of frailty in Pre-diabetic and diabetic community-dwelling older population: results from Beijing longitudinal study of aging II (BLSA-II). BMC Geriatr. 2017;17: 47. pmid:28178934

* View Article

* PubMed/NCBI

* Google Scholar

76. 76. Wong CH, Weiss D, Sourial N, Karunananthan S, Quail JM, Wolfson C, et al. Frailty and its association with disability and comorbidity in a community-dwelling sample of seniors in Montreal: a cross-sectional study. Aging Clin Exp Res. 2010;22: 54–62. pmid:19940555

* View Article

* PubMed/NCBI

* Google Scholar

77. 77. Bao H, Tan P, Yu P, Liu J, Ding G, Tong Q, et al. Analysis of influencing factors of frailty based on geriatric comprehensive assessment in the elderly patients with type 2 diabetes mellitus. Pract Geriatr. 2022;36: 1264–1268.

* View Article

* Google Scholar

Citation: Liu Y, Zhang L, Li X, Luo A, Guo S, Liu X, et al. (2024) Prevalence and risk factors of frailty in older adults with diabetes: A systematic review and meta-analysis. PLoS ONE 19(10): e0309837. https://doi.org/10.1371/journal.pone.0309837

About the Authors:

Yaqing Liu

Contributed equally to this work with: Yaqing Liu, Longhan Zhang, Xiaoyun Li

Roles: Data curation, Formal analysis, Resources, Writing – original draft, Writing – review & editing

Affiliation: School of Nursing, University of South China, Hengyang, Hunan, China

Longhan Zhang

Contributed equally to this work with: Yaqing Liu, Longhan Zhang, Xiaoyun Li

Roles: Formal analysis, Methodology, Software, Supervision, Writing – original draft, Writing – review & editing

Affiliation: School of Nursing, University of South China, Hengyang, Hunan, China

ORICD: https://orcid.org/0009-0007-4791-2516

Xiaoyun Li

Contributed equally to this work with: Yaqing Liu, Longhan Zhang, Xiaoyun Li

Roles: Software, Supervision, Writing – review & editing

Affiliation: School of Computer, University of South China, Hengyang, Hunan, China

An Luo

Roles: Data curation, Methodology, Writing – review & editing

Affiliation: School of Nursing, University of South China, Hengyang, Hunan, China

Sixuan Guo

Roles: Visualization, Writing – review & editing

Affiliation: School of Nursing, University of South China, Hengyang, Hunan, China

Xun Liu

Roles: Resources, Software, Supervision

Affiliation: School of Nursing, University of South China, Hengyang, Hunan, China

Xingyu Wei

Roles: Methodology, Writing – original draft, Writing – review & editing

Affiliation: Clinical Medical college of Acupuncture Moxibustion and Rehabilitation, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China

Yuanhong Sun

Roles: Formal analysis, Software

Affiliation: Hengyang Medical School, University of South China, Hengyang, Hunan, China

ORICD: https://orcid.org/0009-0001-4174-8897

Manyi Wang

Roles: Supervision, Writing – review & editing

Affiliation: School of Nursing, University of South China, Hengyang, Hunan, China

Li Liao

Roles: Funding acquisition, Project administration, Resources, Supervision, Writing – review & editing

E-mail: [email protected]

Affiliation: School of Nursing, University of South China, Hengyang, Hunan, China

ORICD: https://orcid.org/0009-0008-6143-5143

[/RAW_REF_TEXT]

[/RAW_REF_TEXT]

References

1. Magliano DJ, Boyko EJ, Committee IDA 10th edition scientific. Global picture. IDF DIABETES ATLAS [Internet] 10th edition. International Diabetes Federation; 2021. Available: https://www.ncbi.nlm.nih.gov/books/NBK581940/.

2. Sun H, Saeedi P, Karuranga S, Pinkepank M, Ogurtsova K, Duncan BB, et al. IDF Diabetes Atlas: Global, regional and country-level diabetes prevalence estimates for 2021 and projections for 2045. Diabetes Res Clin Pract. 2022;183: 109119. pmid:34879977

3. Ulley J, Abdelhafiz AH. Frailty predicts adverse outcomes in older people with diabetes. Practitioner. 2017;261: 17–20. pmid:29023081

4. Pilotto A, Custodero C, Maggi S, Polidori MC, Veronese N, Ferrucci L. A multidimensional approach to frailty in older people. Ageing Res Rev. 2020;60: 101047. pmid:32171786

5. Castro-Rodríguez M, Carnicero JA, Garcia-Garcia FJ, Walter S, Morley JE, Rodríguez-Artalejo F, et al. Frailty as a Major Factor in the Increased Risk of Death and Disability in Older People With Diabetes. J Am Med Dir Assoc. 2016;17: 949–955. pmid:27600194

6. Yanase T, Yanagita I, Muta K, Nawata H. Frailty in elderly diabetes patients. Endocr J. 2018;65: 1–11. pmid:29238004

7. Lopez-Garcia E, Hagan K, Fung T, Hu F, Rodríguez-Artalejo F. Mediterranean diet and risk of frailty syndrome among women with type 2 diabetes. The American journal of clinical nutrition. 2018;107: 763–771. pmid:29722845

8. MacKenzie HT, Tugwell B, Rockwood K, Theou O. Frailty and Diabetes in Older Hospitalized Adults: The Case for Routine Frailty Assessment. Canadian Journal of Diabetes. 2020;44: 241–245.e1. pmid:31466827

9. Strain WD, Down S, Brown P, Puttanna A, Sinclair A. Diabetes and Frailty: An Expert Consensus Statement on the Management of Older Adults with Type 2 Diabetes. Diabetes Ther. 2021;12: 1227–1247. pmid:33830409

10. Chao C-T, Wang J, Chien K-L, COhort of GEriatric Nephrology in NTUH (COGENT) study group. Both pre-frailty and frailty increase healthcare utilization and adverse health outcomes in patients with type 2 diabetes mellitus. Cardiovasc Diabetol. 2018;17: 130. pmid:30261879

11. Rodriguez-Mañas L, Laosa O, Vellas B, Paolisso G, Topinkova E, Oliva-Moreno J, et al. Effectiveness of a multimodal intervention in functionally impaired older people with type 2 diabetes mellitus. J Cachexia Sarcopenia Muscle. 2019;10: 721–733. pmid:31016897

12. Gill TM, Gahbauer EA, Allore HG, Han L. Transitions between frailty states among community-living older persons. Arch Intern Med. 2006;166: 418–423. pmid:16505261

13. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. PLoS Med. 2021;18: e1003583. pmid:33780438

14. Ouzzani M, Hammady H, Fedorowicz Z, Elmagarmid A. Rayyan-a web and mobile app for systematic reviews. Syst Rev. 2016;5: 210. pmid:27919275

15. Huang T, Li C, Chen F, Xie D, Yang C, Chen Y, et al. Prevalence and risk factors of osteosarcopenia: a systematic review and meta-analysis. BMC Geriatr. 2023;23: 369. pmid:37322416

16. Zeng X, Zhang Y, Kwong JSW, Zhang C, Li S, Sun F, et al. The methodological quality assessment tools for preclinical and clinical studies, systematic review and meta-analysis, and clinical practice guideline: a systematic review. J Evid Based Med. 2015;8: 2–10. pmid:25594108

17. Barendregt JJ, Doi SA, Lee YY, Norman RE, Vos T. Meta-analysis of prevalence. J Epidemiol Community Health. 2013;67: 974–978. pmid:23963506

18. Deeks JJ, Higgins JP, Altman DG, on behalf of the Cochrane Statistical Methods Group. Analysing data and undertaking meta‐analyses. 1st ed. In: Higgins JPT, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, et al., editors. Cochrane Handbook for Systematic Reviews of Interventions. 1st ed. Wiley; 2019. pp. 241–284. https://doi.org/10.1002/9781119536604.ch10

19. Hunter JP, Saratzis A, Sutton AJ, Boucher RH, Sayers RD, Bown MJ. In meta-analyses of proportion studies, funnel plots were found to be an inaccurate method of assessing publication bias. J Clin Epidemiol. 2014;67: 897–903. pmid:24794697

20. Egger M, Smith GD, Schneider M, Minder C. Bias in meta-analysis detected by a simple, graphical test. BMJ. 1997;315: 629–634. pmid:9310563

21. Guo X, Gao J, Xiang Y, Lv J, Liu J, Yu J, et al. Correlation study on chronic complications of and frailty in elderly patients with type 2 diabetes. Chinese Journal of Convalescent Medicine. 2019;28: 225–229.

22. Guo X, Gao J, Xiang Y, Hou X, Wu C, Jiang X, et al. Status and influencing factors of frailty syndrome in elderly patients with type 2 diabetes mellitus. China Journal of Modern Medicine. 2018;28: 90–96.

23. Cheng Z, Xing Q, Xu H, Bao D, Mu C. Analysis on status and influential factors of frailty in elderly patients with diabetes m ellitus. Tianjin Journal of Nursing. 2020;28: 293–298.

24. Chen X, Yang S, Wang Y, Liu S. Influencing Factors of Frailty in Elderly Patients with Diabetes and Intervention Strategies. Chinese General Practice. 2019;22: 1772–1777.

25. Wu P, Gao J, Hu Y, Luo Q, Liao L. Current Status and Influencing Factors of Elderly Diabetic Patients in Community with Frailty. Journal of Medical Information. 2021;34: 153–157.

26. Ge Q, Yu W, Xu K, Wu K, Li S, Feng H. Study on relationship between blood glucose levels and frailty. Chinese Nursing Research. 2020;34: 1092–1094.

27. Li X, Zhang X, Liang X, Cao B, Wang H, Zhang W, et al. Analysis of the correlation between alexithymia and frailty in elderly patients with type 2 diabetes mellitus in elderly care facilities. Chinese Journal of Prevention and Control of Chronic Diseases. 2022;30: 19–23.

28. Deng Q, Hu Y, Li L, Li P, Zhou Y, Liao L. Mediating effect of depressive symptoms on diabetes-related distress and frailty in elderly patients with diabetes in the community. Medical Science Journal of Central South China. 2020;48: 229–232,237.

29. Zhao H, Zheng Y, Zhang G. Relationship between motor function and frailty in hospitalised elderly patients with diabetes mellitus. Chinese Journal of Gerontology. 2023;43: 6122–6125.

30. Wang Y, Li R, Yuan L, Yang X, Lv J, Ye Z, et al. Association between diabetes complicated with comorbidities and frailty in older adults: A cross-sectional study. Journal of clinical nursing. 2023;32: 894–900. pmid:35934867

31. Hayakawa M, Motokawa K, Mikami Y, Yamamoto K, Shirobe M, A E, et al. Low dietary variety and diabetes mellitus are associated with frailty among community-dwelling older japanese adults: A cross-sectional study. Nutrients. 2021;13. pmid:33669388

32. Kong L, Zhao H, Fan J, Wang Q, Li J, Bai J, et al. Predictors of frailty among Chinese community-dwelling older adults with type 2 diabetes: a cross-sectional survey. BMJ open. 2021;11: e041578. pmid:33664069

33. Wang Q, Wang J, Dai G. Prevalence, characteristics, and impact on health outcomes of frailty in elderly outpatients with diabetes: A cross-sectional study. Medicine. 2023;102: E36187. pmid:38013336

34. Nishimura A, Harashima S, Hosoda K, Arai H, Inagaki N, Nishimura A, et al. Sex-related differences in frailty factors in older persons with type 2 diabetes: a cross-sectional study. THERAPEUTIC ADVANCES IN ENDOCRINOLOGY AND METABOLISM. 2019;10. pmid:30858966

35. Kang S, Oh TJ, Cho BL, Park YS, Roh E, Kim HJ, et al. Sex differences in sarcopenia and frailty among community‐dwelling Korean older adults with diabetes: The Korean Frailty and Aging Cohort Study. J of Diabetes Invest. 2021;12: 155–164. pmid:32627923

36. Aguayo G, Hulman A, Vaillant M, Donneau A, Schritz A, S S, et al. Prospective Association Among Diabetes Diagnosis, HbA1c, Glycemia, and Frailty Trajectories in an Elderly Population. Diabetes care. 2019;42: 1903–1911. pmid:31451533

37. Muszalik M, Stępień H, Puto G, Cybulski M, Kurpas D. Implications of the metabolic control of diabetes in patients with frailty syndrome. International journal of environmental research and public health. 2022;19. pmid:36011979

38. Xiu S, Mu Z, Zhao L, Sun L. Low free triiodothyronine levels are associated with risk of frailty in older adults with type 2 diabetes mellitus. Experimental gerontology. 2020;138: 111013. pmid:32590129

39. Kitamura A, Taniguchi Y, Seino S, Yokoyama Y, Amano H, Fujiwara Y, et al. Combined effect of diabetes and frailty on mortality and incident disability in older Japanese adults. Geriatrics & Gerontology International. 2019;19: 423–428. pmid:30788903

40. Sun L, Xiu S, Mu Z, Zhao L, Han Q. Correlation between anemia and frailty in elderly patients with type 2 diabetes mellitus. Journal of Chinese Practical Diagnosis and Therapy. 2020;34: 936–939.

41. Lima Filho BFD, Gama AGD, Dias VDN, Silva EMTD, Cavalcanti FADC, Gazzola JM. The frailty syndrome in older adults with type 2 diabetes mellitus and associated factors. Rev bras geriatr gerontol. 2020;23: e190196.

42. Lin C, Yu N, Wu H, Liu Y. Risk factors associated with frailty in older adults with type 2 diabetes: A cross-sectional study. Journal of clinical nursing. 2022;31: 967–974. pmid:34250666

43. Liu T, Meng J, Zhu H, Chen Y, Qi P, Pi H. Current status of frailty in elderly diabetes patients and its influencing factors. Chinese Journal of Multiple Organ Diseases in the Elderly. 2023;22: 881–885.

44. Yanagita I, Fujihara Y, Eda T, Tajima M, Yonemura K. Low glycated hemoglobin level is associated with severity of frailty in Japanese elderly diabetes patients. Journal of diabetes investigation. 2018;9: 419–425. pmid:28556518

45. Jia W, Zhao H, Dai F, Zhang H, Wang L, Chen R. The current situation and influential factors of frailty in elderly patients with diabetes. Chinese Journal of Nursing. 2019;54: 188–193.

46. Sun M, Sun X, Wang H, Yu X, Li M. Status quo and influencing factors of frailty in elderly diabetic patients. Chinese Nursing Research. 2024;38: 280–286.

47. Ge X, Li M, Gao L. Current situation and influencing factors of frailty in elderly diabetes patients. Journal of Nursing Science. 2020;35: 25–29.

48. Sable‐Morita S, Tanikawa T, Satake S, Okura M, Tokuda H, Arai H. Microvascular complications and frailty can predict adverse outcomes in older patients with diabetes. Geriatrics Gerontology Int. 2021;21: 359–363. pmid:33576140

49. Nguyen AT, Pham HQ, Nguyen TX, Nguyen TTH, Nguyen HTT, Nguyen TN, et al. Knowledge, Attitude and Practice of Elderly Outpatients with Type 2 Diabetes Mellitus in National Geriatric Hospital, Vietnam. DMSO. 2020;13: 3909–3917. pmid:33116737

50. Sun X, Wu Z, Cui Q, Ye X, Chen C, Xu J, et al. Clinical study of risk factors of frailty in elderly t2dm patients in the physical examination center. ACTA MEDICA MEDITERRANEA. 2021;37: 621–625.

51. Bąk E, Młynarska A, Marcisz C, Bobiński R, Sternal D, Młynarski R. The influence of frailty syndrome on quality of life in elderly patients with type 2 diabetes. Quality of life research: an international journal of quality of life aspects of treatment, care and rehabilitation. 2021;30: 2487–2495. pmid:33834352

52. Cacciatore F, Testa G, Galizia G, Della-Morte D, Mazzella F, Langellotto A, et al. Clinical frailty and long-term mortality in elderly subjects with diabetes. Acta Diabetol. 2013;50: 251–260. pmid:22732903

53. García-Esquinas E, Graciani A, Guallar-Castillón P, López-García E, Rodríguez-Mañas L, Rodríguez-Artalejo F. Diabetes and Risk of Frailty and Its Potential Mechanisms: A Prospective Cohort Study of Older Adults. Journal of the American Medical Directors Association. 2015;16: 748–754. pmid:25986874

54. Qin Y, Zheng X. Association of frailty index with congestive heart failure, all-cause and cardiovascular mortality among individuals with type 2 diabetes: a study from National Health and Nutrition Examination Surveys (NHANES), 1999–2018. Diabetology & metabolic syndrome. 2023;15. pmid:37875981

55. Zaslavsky O, Walker R, Crane P, Gray S, Larson E, Zaslavsky O, et al. Glucose levels and risk of frailty. JOURNALS OF GERONTOLOGY SERIES A-BIOLOGICAL SCIENCES AND MEDICAL SCIENCES. 2016;71: 1223–1229. pmid:26933160

56. Ferri-Guerra J, Aparicio-Ugarriza R, Salguero D, Baskaran D, Mohammed Y, Florez H, et al. The association of frailty with hospitalizations and mortality among community dwelling older adults with diabetes. The Journal of frailty & aging. 2020;9: 94–100. pmid:32259183

57. Hubbard RE, Andrew MK, Fallah N, Rockwood K. Comparison of the prognostic importance of diagnosed diabetes, co-morbidity and frailty in older people. Diabetic Medicine. 2010;27: 603–606. pmid:20536960

58. Bandeen-Roche K, Seplaki CL, Huang J, Buta B, Kalyani RR, Varadhan R, et al. Frailty in Older Adults: A Nationally Representative Profile in the United States. J Gerontol A Biol Sci Med Sci. 2015;70: 1427–1434. pmid:26297656

59. Kojima G, Iliffe S, Taniguchi Y, Shimada H, Rakugi H, Walters K. Prevalence of frailty in Japan: A systematic review and meta-analysis. J Epidemiol. 2017;27: 347–353. pmid:28142044

60. Ma L, Tang Z, Zhang L, Sun F, Li Y, Chan P. Prevalence of Frailty and Associated Factors in the Community-Dwelling Population of China. J Am Geriatr Soc. 2018;66: 559–564. pmid:29168883

61. Assar ME, Laosa O, Rodríguez Mañas L. Diabetes and frailty. Curr Opin Clin Nutr Metab Care. 2019;22: 52–57. pmid:30394893

62. Wood SJ, Bell JS, Magliano DJ, Fanning L, Cesari M, Keen CS, et al. Impact of Age, Frailty, and Dementia on Prescribing for Type 2 Diabetes at Hospital Discharge 2012–2016. J Frailty Aging. 2021; 1–7. pmid:34549249

63. Kirkman MS, Briscoe VJ, Clark N, Florez H, Haas LB, Halter JB, et al. Diabetes in older adults: a consensus report. J Am Geriatr Soc. 2012;60: 2342–2356. pmid:23106132

64. Pilotto A, Noale M, Maggi S, Addante F, Tiengo A, Perin PC, et al. Hypoglycemia is independently associated with multidimensional impairment in elderly diabetic patients. Biomed Res Int. 2014;2014: 906103. pmid:24689062

65. Gao Q, Mei F, Zhao L, Chen F, Hu K, Shang Y, et al. Prevalence of frailty in older patients with diabetes: a systematic review. Chin J Nurs. 2021;56: 686–693.

66. Niu X, Zhang J, Guo J, Ruan H, Chi J, Tao H, et al. Meta-analysis of Risk Factors for Frailty in Diabetic Patients. Journal of Nursing (China). 2021;28: 1–6.

67. Nishimura A, Harashima S-I, Ikeda K, Mano F, Higashi K, Ueda N, et al. Specific factors associated with frailty in elderly type 2 diabetes patients: A cross-sectional study. Diabetes. 2016;65: A362.

68. Liccini AP, Malmstrom TK. Frailty and Sarcopenia as Predictors of Adverse Health Outcomes in Persons With Diabetes Mellitus. Journal of the American Medical Directors Association. 2016;17: 846–851. pmid:27569712

69. Lee E, Lim S-T, Kim W-N. Aquatic exercise for improving immune function and mental stress in pre-frailty elderly women. J Women Aging. 2021;33: 611–619. pmid:32130088

70. Deng M-G, Liu F, Liang Y, Wang K, Nie J-Q, Liu J. Association between frailty and depression: A bidirectional Mendelian randomization study. Sci Adv. 2023;9: eadi3902. pmid:37729413

71. Park Y, Choi J-E, Hwang H-S. Protein supplementation improves muscle mass and physical performance in undernourished prefrail and frail elderly subjects: a randomized, double-blind, placebo-controlled trial. Am J Clin Nutr. 2018;108: 1026–1033. pmid:30475969

72. Cesari M, Prince M, Thiyagarajan JA, De Carvalho IA, Bernabei R, Chan P, et al. Frailty: An Emerging Public Health Priority. Journal of the American Medical Directors Association. 2016;17: 188–192. pmid:26805753

73. Shin J, Kim KJ, Choi J. Smoking, alcohol intake, and frailty in older Korean adult men: cross-sectional study with nationwide data. Eur Geriatr Med. 2020;11: 269–277. pmid:32297188

74. Amiri S, Behnezhad S. Systematic review and meta-analysis of the association between smoking and the incidence of frailty. Neuropsychiatr. 2019;33: 198–206. pmid:31300971

75. Chhetri JK, Zheng Z, Xu X, Ma C, Chan P. The prevalence and incidence of frailty in Pre-diabetic and diabetic community-dwelling older population: results from Beijing longitudinal study of aging II (BLSA-II). BMC Geriatr. 2017;17: 47. pmid:28178934

76. Wong CH, Weiss D, Sourial N, Karunananthan S, Quail JM, Wolfson C, et al. Frailty and its association with disability and comorbidity in a community-dwelling sample of seniors in Montreal: a cross-sectional study. Aging Clin Exp Res. 2010;22: 54–62. pmid:19940555

77. Bao H, Tan P, Yu P, Liu J, Ding G, Tong Q, et al. Analysis of influencing factors of frailty based on geriatric comprehensive assessment in the elderly patients with type 2 diabetes mellitus. Pract Geriatr. 2022;36: 1264–1268.