The prevalence of frailty and associated factors in patients with type 2 diabetes mellitus and coronary heart disease: A cross‐sectional study

ABSTRACT

Background

Frailty is increasingly recognized as an important clinical issue in patients with chronic diseases. However, evidence on the prevalence of frailty and its associated factors in patients with type 2 diabetes mellitus (T2DM) complicated by coronary heart disease (CHD) remains limited. This study aimed to investigate the prevalence of frailty and identify factors associated with frailty in patients with T2DM and CHD.

Methods

A cross‐sectional study was conducted among patients with T2DM and CHD recruited from the Departments of Endocrinology and Cardiology of a tertiary hospital in Shanghai, China, between September and December 2025. Frailty was assessed using the Tilburg Frailty Indicator (TFI). Data on demographic, clinical, lifestyle, and psychosocial characteristics were collected using a general information questionnaire and medical records. Univariate analyses were performed to compare differences between frail and non‐frail participants, and binary logistic regression analysis was conducted to identify factors associated with frailty.

Results

Among 217 participants, 43.8% were frail. Significant between‐group differences were observed in age, blood glucose, glycated hemoglobin, BNP, D‐dimer, NLR, SIIRI, grip strength, NYHA functional class, anxiety, nutritional risk, history of PCI, number of comorbidities, and sleep duration (all P < 0.05). Binary logistic regression analysis showed that blood glucose, BNP, anxiety, sleep duration, nutritional risk, history of PCI, and age were independently associated with frailty in patients with T2DM and CHD (all P < 0.05).

Conclusions

Frailty was common among patients with T2DM and CHD. Metabolic and cardiovascular factors, psychological and behavioral factors, and nutritional vulnerability were all associated with frailty, underscoring its multidimensional nature in this comorbid population. Early frailty screening and multidomain assessment may be important for improving the management of patients with T2DM and CHD.

Among 217 participants, 43.8% were frail. Frailty was common among patients with T2DM and CHD. Blood glucose, BNP, anxiety, sleep duration, nutritional risk, history of PCI, and age were independently associated with frailty in patients with T2DM and CHD. Early frailty screening and multidomain assessment may be important for improving the management of patients with T2DM and CHD.

INTRODUCTION

Type 2 diabetes mellitus (T2DM) and coronary heart disease (CHD) are major chronic diseases that impose a substantial burden on patients and health‐care systems ¹ . Diabetes itself, together with its common coexisting conditions, is a clear risk factor for atherosclerotic cardiovascular disease, and contemporary cardiovascular guidelines specifically emphasize cardiovascular risk stratification, screening, diagnosis, and treatment in patients with diabetes ² . As a result, the coexistence of T2DM and CHD is common in clinical practice and usually indicates a more complex clinical state characterized by persistent metabolic disturbance, vascular injury, reduced exercise tolerance, polypharmacy, and repeated health‐care use ³ , ⁴ , ⁵ .

Frailty is a multidimensional clinical syndrome characterized by decreased physiological reserve and increased vulnerability to stressors ⁶ . It has become increasingly recognized as an important issue in people with cardiometabolic disease because it is associated with a higher risk of adverse outcomes, including mortality, hospitalization, disability, and cardiovascular events. Recent evidence shows that frailty is highly prevalent in older adults with diabetes, with a pooled prevalence of 30.0% ⁷ , and is also common in older patients with CHD, with a pooled prevalence of 36% ⁸ . In addition, frailty in patients with diabetes has been linked to increased mortality, hospitalization, and diabetes‐related complications, underscoring its clinical relevance beyond chronological aging alone ⁹ .

The potential importance of frailty may be even greater in patients with coexisting T2DM and CHD ¹⁰ . These patients are exposed simultaneously to chronic hyperglycemia, insulin resistance, oxidative stress, endothelial dysfunction, low‐grade inflammation, and impaired cardiovascular reserve, all of which may accelerate multisystem decline and reduce resilience ¹¹ . Recent reviews ⁸ have suggested that frailty and cardiovascular disease share several biological pathways, including inflammation, sarcopenia, autonomic dysregulation, and impaired physiological reserve. Likewise, frailty in diabetes has been associated with higher glycated hemoglobin levels and less exercise, and population‐based cohort data indicate that frailty may interact with poor cardiovascular risk factor control to further increase cardiovascular risk in people with diabetes ⁹ .

Although frailty has been widely studied in older adults and in patients with single chronic diseases, evidence focusing specifically on patients with both T2DM and CHD remains limited. A recent observational study in Vietnam examined frailty in older people with T2DM and CHD ¹² , but its primary focus was the prescription of secondary prevention medications rather than a broader multidimensional assessment of factors associated with frailty. Moreover, currently available evidence in diabetes and CHD has largely come from separate disease‐specific studies, leaving an important gap in understanding frailty in this comorbid population. Therefore, this study aimed to investigate the prevalence of frailty and identify factors associated with frailty among patients with T2DM and CHD, in order to provide evidence for early identification and targeted multidomain intervention in clinical practice.

METHODS

Study design

This was a cross‐sectional study conducted to investigate the prevalence of frailty and its associated factors among patients with T2DM and CHD.

Population

Participants were recruited using convenience sampling from the Departments of Endocrinology and Cardiology of a tertiary hospital in Shanghai, China, between September and December 2025. Eligible participants were adults diagnosed with T2DM complicated by CHD.

The inclusion criteria were as follows: (1) aged 45 years or older; (2) met the diagnostic criteria for T2DM according to the Chinese Guidelines for the Prevention and Treatment of Type 2 Diabetes (2020 edition) ¹³ , and had a clinical diagnosis of CHD confirmed by coronary angiography; (3) were in a stable condition, with clear consciousness and adequate communication and reading abilities; and (4) provided written informed consent.

The exclusion criteria were as follows: (1) severe cardiac, pulmonary, hepatic, or renal insufficiency; (2) malignancy, end‐stage disease, or an expected survival time of <1 year; (3) severe functional impairment due to other identifiable causes, such as neurological or musculoskeletal disorders; and (4) pregnancy or lactation.

The sample size was estimated based on the requirement for logistic regression analysis, which recommends a sample size of at least 5–10 times the number of independent variables. A total of 27 candidate variables were included in this study. After allowing for 10% invalid questionnaires or missing data, the required sample size was estimated to be 150–300 participants.

This study was approved by the Medical Ethics Committee of the study hospital (2025‐144‐45). Written informed consent was obtained from all participants prior to data collection.

Measurements

General questionnaire

Based on the study objectives, literature review, and a clinical presurvey, the research team developed a general information questionnaire to collect potential factors associated with frailty. The questionnaire included the following domains:

1. Demographic characteristics: age, sex, body mass index (BMI), waist circumference, marital status, and educational level.

2. Disease‐related variables: high‐density lipoprotein (HDL), blood glucose, glycated hemoglobin (HbA1c), albumin, triglycerides, low‐density lipoprotein (LDL), D‐dimer, brain natriuretic peptide (BNP), grip strength, New York Heart Association (NYHA) cardiac function classification, and history of percutaneous coronary intervention (PCI).

Inflammation‐related indices were also collected, including the systemic inflammation immune response index (SIIRI), platelet‐to‐lymphocyte‐based inflammatory index (PIV), and neutrophil‐to‐lymphocyte ratio (NLR). SIIRI was calculated as platelet count × neutrophil count × monocyte count/lymphocyte count ⁴ , ⁵ , and PIV was calculated as neutrophils × monocytes × platelets/lymphocytes. All clinical and laboratory data were extracted from medical records using the most recent results available on the day of physical assessment.

• 3Lifestyle factors: smoking status, alcohol consumption, sleep quality, and history of chronic diseases.
• 4Psychosocial factors: family support, social support, anxiety, and depression.

Tilburg frailty indicator (TFI)

Frailty was assessed using the TFI, which was developed by Gobbens et al. based on the Integral Conceptual Model of Frailty ¹⁴ . TFI includes physical (8 items), psychological (4 items), and social dimensions (3 items), with a total of 15 items. This scale adopts a dichotomous scoring method. Total scores range from 0 to 15, with higher scores indicating greater frailty. A total score of 5 or higher was used to define frailty. The Cronbach's alpha coefficient of the total scale was 0.846.

Data collection procedures

Before data collection, three investigators received standardized training on the study procedures and questionnaire administration. Data were collected through face‐to‐face interviews using a one‐to‐one approach. When participants had difficulty understanding individual items, the investigators provided neutral clarification without leading their responses and assisted in recording answers when necessary. Anthropometric measurements were obtained using standardized procedures. The same brand and model of measuring tape were used throughout the study. After questionnaire completion, all data were checked for completeness and accuracy before being entered into the database.

Statistical analysis

Data were entered into a database using EpiData version 3.1 and analyzed using SPSS version 24.0 and Python version 3.13. Missing data were first examined and verified. Cases with substantial missing data were excluded.

Continuous variables with a normal distribution were described as mean ± standard deviation and compared using the independent‐samples t‐test. Continuous variables with a non‐normal distribution were described as median (P25, P75) and compared using nonparametric tests. Categorical variables were presented as frequencies and percentages and compared using the chi‐square test. Ordinal variables were analyzed using the rank‐sum test.

Variables with statistical significance in the univariate analyses were entered into the multivariable logistic regression model to identify factors independently associated with frailty. A two‐sided P‐value <0.05 was considered statistically significant.

RESULTS

Demographic and clinical characteristics of the study participants

A total of 230 questionnaires were distributed. After excluding 13 with excessive missing data, 217 valid questionnaires were recovered, yielding an effective response rate of 94.35%. Among the participants, 63.59% were aged <65 years, and the proportion of men and women was similar (49.31% vs. 50.69%). Most participants were married (82.95%), had a BMI ≥24 kg/m² (57.60%), and had a waist circumference ≥90 cm (56.68%). In addition, 94.93% had blood glucose levels ≥6.1 mmol/L, 80.65% had HbA1c levels ≥6.5%, and 44.70% had a disease duration of 10–20 years. Most participants were classified as NYHA class I–II (88.94%), while 14.75% had a history of PCI. Regarding lifestyle and psychosocial characteristics, 61.75% had grip strength of 20–30 kg, 51.61% reported anxiety, 61.80% slept less than 6 h per day, and 75.58% had a nutritional score ≥3. Detailed characteristics are presented in Table 1.

Table 1.

Basic characteristics of the study participants

Variables	Category	n	%
Age, years	<65	138	63.59
	65–80	74	34.10
	≥80	5	2.30
Sex	Male	107	49.31
	Female	110	50.69
Education	Illiterate	31	14.29
	Middle School	52	23.96
	High school	76	35.02
	Junior college	27	12.44
	Bachelor's degree or above	31	14.20
Marital status	Married	180	82.95
	Single	15	6.91
	Divorced	22	10.14
BMI, kg/m2	<18.5	6	2.76
	18.5–23.9	86	39.63
	≥24	125	57.60
Waist circumference, cm	<90	94	43.32
	≥90	123	56.68
Blood glucose, mmol/L	3.9–6.1	11	5.07
	≥6.1	206	94.93
Duration of illness, years	<10	86	39.63
	10–20	97	44.70
	≥20	34	15.67
Glycated hemoglobin, %	<6.5	42	19.35
	≥6.5	175	80.65
BNP, pg/mL	<100	165	76.04
	≥100	52	24.0
D‐dimer, mg/L	<0.5	164	75.58
	≥0.5	53	24.42
Platelet count, 109/L	100–200	66	30.41
	≥200	151	69.59
Neutrophil count, 109/L	<4	163	75.12
	4–10	54	24.88
Monocyte count, 109/L	0.1–0.6	165	76.04
	≥0.6	52	23.96
Lymphocyte, 109/L	1.1–3.2	185	85.25
	<1.1	19	8.76
	≥3.2	13	5.99
NLR	<1	26	11.98
	1–3	161	74.19
	≥3	30	13.82
SIIRI	<300	146	67.28
	300–600	53	24.42
	≥600	18	8.29
PIV	<200	81	37.33
	200–400	42	19.35
	≥400	94	43.32
Albumin, g/L	<35	14	6.45
	≥35	203	93.55
Triglycerides, mmol/L	<1.7	152	70.05
	1.7–2.3	32	14.75
	≥2.3	33	15.21
HDL‐C, mmol/L	<1.3	105	48.39
	≥1.3	112	51.61
LDL‐C, mmol/L	<3.3	202	93.09
	≥3.3	15	6.91
C‐reactive protein, mg/L	<0.5	196	90.32
	≥0.5	21	9.68
Grip strength, kg	<20	22	10.14
	20–30	134	61.75
	≥30	61	28.11
NYHA functional class	Class I–II	193	88.94
	Class III–IV	24	11.06
Anxiety	No anxiety	105	48.39
	Anxiety	112	51.61
Nutritional risk screening	<3	53	24.42
	≥ 3	164	75.58
History of PCI	No	185	85.25
	Yes	32	14.75
Number of comorbidities	<3	18	8.29
	≥3	199	91.71
Sleep duration, h	<6	134	61.8
	≥6	83	38.2
Smoking status	Never	122	56.22
	Current smoker	72	33.18
	Former smoker	23	10.60
Drinking status	Never	118	54.4
	Current drinker	78	37.33
	Former drinker	21	9.68

BMI, body mass index; BNP, brain natriuretic peptide; HDL, high‐density lipoprotein; LDL, low‐density lipoprotein; NLR, neutrophil‐to‐lymphocyte ratio; NYHA, New York Heart Association; PCI, percutaneous coronary intervention; PIV, platelet‐to‐lymphocyte‐based inflammatory index; SIIRI, systemic inflammation immune response index.

Univariate analysis of factors associated with frailty in patients with type 2 diabetes mellitus and coronary heart disease

Of the 217 patients included in the study, 122 (56.2%) were non‐frail and 95 (43.8%) were frail. Univariate analyses showed that, compared with the non‐frail group, the frail group was older and had higher levels of blood glucose, HbA1c, BNP, D‐dimer, NLR, and SIIRI, but lower grip strength. Significant differences were also observed between the two groups in NYHA functional class, anxiety status, nutritional risk, history of PCI, number of comorbidities, and sleep duration (all P < 0.05). No statistically significant differences were found in sex, education level, marital status, BMI, waist circumference, disease duration, platelet count, neutrophil count, monocyte count, lymphocyte count, PIV, albumin, triglycerides, HDL, LDL, C‐reactive protein, smoking status, or alcohol consumption (all P > 0.05) (Table 2).

Table 2.

Univariate analysis of factors associated with frailty in patients with type 2 diabetes mellitus and coronary heart disease

Variables	Frailty group (n = 95)	Non‐frailty group (n = 122)	Test statistic	P‐value
Age, years	64.8 ± 11.2	60.1 ± 10.3	t = 3.21	0.002
Sex			X 2 = 0.862	0.389
Male	50 (52.6%)	57 (46.7%)
Female	45 (47.4%)	65 (53.3%)
Education			X 2 = 1.425	0.154
Illiterate	8 (8.4%)	23 (18.9%)
Middle school	24 (25.3%)	28 (23.0%)
High school	35 (36.8%)	41 (33.6%)
Junior college	20 (21.1%)	7 (5.7%)
Bachelor's degree or above	8 (8.4%)	23 (18.8%)
Marital status			X 2 = 0.656	0.512
Married	76 (80.0%)	104 (85.2%)
Unattached	7 (7.4%)	8 (6.6%)
Divorced	12 (12.6%)	10 (8.2%)
NYHA functional class			X 2 = 11.82	<0.001
Class I–II	92 (96.8%)	101 (82.8%)
Class III–IV	3 (3.2%)	21 (17.2%)
Anxiety			X 2 = 4.926	0.026
No anxiety	64 (67.4%)	41 (33.6%)
Anxiety	31 (32.6%)	81 (66.4%)
Nutritional risk screening			X 2 = 56.72	<0.001
<3	48 (50.5%)	5 (4.1%)
≥3	47 (49.5%)	117 (95.9%)
History of PCI
No	92 (96.8%)	93 (76.2%)	X 2 = 17.97	<0.001
Yes	3 (3.2%)	29 (23.8%)
Number of comorbidities			X 2 = 5.31	0.021
<3	12 (12.6%)	6 (4.9%)
≥3	83 (87.4%)	116 (95.1%)
Sleep duration, h			X 2 = 25.34	<0.001
<6 h	40 (42.1%)	94 (77.0%)
≥6 h	55 (57.9%)	28 (23.0%)
Smoking status			X 2 = 1.512	0.131
Never	49 (51.6%)	73 (59.8%)
Current smoker	32 (33.7%)	40 (32.8%)
Former smoker	14 (14.7%)	9 (7.4%)
Drinking status
Never	54 (56.8%)	64 (52.4%)	X 2 = 0.617	0.537
Current drinker	34 (35.8%)	44 (36.1%)
Former drinker	7 (7.4%)	14 (11.5%)
BMI, kg/m2	24.4 (22.3, 28.2)	24.3 (22.5, 28)	Z = 0.583	0.560
Waist circumference, cm	90 (87, 96)	94 (89, 98)	Z = 1.155	0.246
Blood glucose, mmol/L	9 (7.7, 11)	9.1 (7.7, 11.7)	Z = 2.517	0.012
Duration of illness, years	12 (9, 20)	12 (9, 20)	Z = 0.865	0.387
Glycated hemoglobin, %	7.2 (6.5, 9.1)	8.1 (7.1, 9.1)	Z = 2.343	0.019
BNP, pg/mL	31.34 (22, 62)	77.4 (42.8, 132.5)	Z = 6.051	<0.001
D‐dimer, mg/L	0.19 (0.13, 0.50)	0.26 (0.15, 0.53)	Z = 1.714	0.042
Platelet count, 109/L	221 (190, 234)	225 (194, 255)	Z = 1.435	0.151
Neutrophil count, 109/L	3.3 (2.7, 4)	3.4 (2.7, 4.2)	Z = 1.616	0.106
Lymphocyte, 109/L	1.7 (1.4, 2.2)	1.8 (1.3, 2.1)	Z = 0.282	0.778
NLR	1.78 (1.5, 2.2)	1.97 (1.6, 2.5)	Z = 2.003	0.043
SIIRI	165.73 (114, 304.6)	235.60 (127.9, 361)	Z = 1.971	0.049
PIV	306.9 (202, 537.6)	366.3 (226.4, 644)	Z = 1.739	0.082
Albumin, g/L	38.8 (36.8, 42.1)	38.9 (36.8, 43.1)	Z = 0.184	0.854
Triglycerides, mmol/L	1.22 (0.87, 1.78)	1.35 (0.73, 1.74)	Z = 0.255	0.822
HDL‐C, mmol/L	1.18 (0.86, 1.54)	1.27 (0.89, 1.53)	Z = 0.682	0.496
LDL‐C, mmol/L	2.51 (1.62, 2.80)	2.23 (1.61, 2.67)	Z = 1.021	0.307
Grip strength, kg	24.7 (22.1, 28.3)	28.0 (23, 37.1)	Z = 2.943	0.003

Continuous variables are presented as mean ± standard deviation or median (P25, P75), as appropriate. Categorical variables are presented as n (%). Group comparisons were performed using the independent‐samples t‐test, Mann–Whitney U‐test, chi‐square test, or Fisher's exact test, as appropriate. BMI, body mass index; BNP, brain natriuretic peptide; HDL, high‐density lipoprotein; LDL, low‐density lipoprotein; NLR, neutrophil‐to‐lymphocyte ratio; NYHA, New York Heart Association; PCI, percutaneous coronary intervention; PIV, platelet‐to‐lymphocyte‐based inflammatory index; SIIRI, systemic inflammation immune response index.

Binary logistic regression analysis of factors influencing frailty in patients with type 2 diabetes and coronary heart disease

A binary logistic regression analysis was performed to identify factors independently associated with frailty score in patients with T2DM and CHD. Variables that were statistically significant in the univariate analysis were entered into the regression model after assignment (Table 3). The overall regression model was statistically significant (F = 7.847, P‐value <0.001) and explained 56.3% of the variance in frailty score. The results showed that blood glucose, BNP, D‐dimer, anxiety, sleep duration, nutritional risk, history of PCI, and age were independently associated with frailty score (all P‐value <0.05). Specifically, higher blood glucose, BNP, anxiety, nutritional risk, history of PCI, and older age were positively associated with frailty score, whereas longer sleep duration and higher D‐dimer coding were negatively associated with frailty score. Glycated hemoglobin, NLR, SIIRI, and heart function classification were not significantly associated with frailty score in the multivariable model (Table 4).

Table 3.

Coding of independent variables included in the binary logistic regression model

Variables	Assignment scenarios
Blood glucose	Original value
Heart function classification	III–IV = 0; I–II = 1
Anxiety	Yes = 0; No = 1
Number of comorbidities	<3 = 0; ≥3 = 1
Grip strength	<20 = 0; 20–30 = 1; ≥30 = 2
Nutrition risk screening	<3 = 0; ≥ 3 = 1
PCI	No = 0; Yes = 1
Sleep duration, h	<6 = 0; ≥6 = 1
BNP	Original value
SIIRI	Original value
NLR	Original value
D‐dimer	Original value
Age, years	Original value

BNP, brain natriuretic peptide; NLR, neutrophil‐to‐lymphocyte ratio; PCI, percutaneous coronary intervention; SIIRI, systemic inflammation immune response index.

Table 4.

Binary logistic regression analysis of factors associated with frailty in patients with type 2 diabetes mellitus and coronary heart disease

Variable	B	Standard error	Wald X 2	P‐value	OR	95%CI
Age	0.054	0.025	4.790	0.029	1.056	1.006, 1.108
Blood glucose (mmol/l)	0.217	0.154	1.976	0.160	1.242	0.918, 1.681
BNP	0.012	0.004	7.883	0.005	1.012	1.004, 1.020
Anxiety	1.221	0.455	7.214	0.007	3.391	1.391, 8.268
Sleep duration	1.041	0.465	5.017	0.025	2.831	1.139, 7.039
Nutrition risk score	5.151	0.994	26.859	<0.001	172.582	24.603, 1210.59
PCI	1.718	0.778	4.879	0.027	5.572	1.214, 25.584
SIIRI	0.002	0.001	1.467	0.226	1.002	0.999, 1.004
D‐dimer	−0.871	0.591	2.177	0.140	0.418	0.131, 1.331

Hosmer–Lemeshow test X ² = 6.362, P = 0.607, Nagelkerke R ² = 0.674.

Binary logistic regression analysis of factors associated with frailty in patients with type 2 diabetes mellitus and coronary heart disease

Frailty was the dependent variable. Variables with a P‐value <0.05 in the univariate analysis were included in the multivariate logistic regression. To reduce multicollinearity, strongly correlated variables were screened before inclusion. Only variables with a variance inflation factor (VIF) <5 were included. To prevent overfitting and meet logistic regression requirements for independent variables, only a subset of variables was included in the multivariate analysis. The results showed that age, BNP, anxiety scores, sleep duration of <6 h per night, nutritional status, and a history of PCI were independent risk factors for frailty in patients with T2DM and CHD.

DISCUSSION

This cross‐sectional study investigated the prevalence of frailty and its associated factors among patients with type 2 diabetes mellitus and coronary heart disease. The results showed that 43.8% of the participants were frail. In the univariate analysis, significant differences were observed between the frail and non‐frail groups in age, blood glucose, glycated hemoglobin, BNP, D‐dimer, NLR, SIIRI, grip strength, NYHA functional class, anxiety, nutritional risk, history of PCI, number of comorbidities, and sleep duration. Further multivariable logistic regression analysis showed that blood glucose, BNP, D‐dimer, anxiety, sleep duration, nutritional risk score, history of PCI, and age were independently associated with frailty. Overall, these results indicate that frailty is prevalent in this population and is associated with a combination of metabolic, cardiovascular, psychological, and lifestyle factors.

Current status of frailty in patients with type 2 diabetes mellitus and coronary heart disease

In the present study, the prevalence of frailty among patients with T2DM and CHD was 43.8%, indicating that frailty is common in this comorbid population. This prevalence appears to be higher than that reported in older adults with diabetes alone and coronary heart disease alone, suggesting that the coexistence of T2DM and CHD may confer an additional burden that increases vulnerability to frailty ⁷ , ⁸ . Frailty in patients with T2DM and CHD should therefore not be viewed merely as a consequence of aging, but rather as a manifestation of cumulative physiological vulnerability under the combined burden of chronic metabolic and cardiovascular disease. Compared with individuals with a single chronic condition, patients with both T2DM and CHD are more likely to experience long‐term hyperglycemia, vascular endothelial injury, impaired cardiac reserve, reduced exercise tolerance, polypharmacy, and repeated healthcare utilization. These factors may jointly accelerate the decline in physiological reserve and functional capacity, thereby increasing susceptibility to frailty ¹¹ , ¹⁵ . Clinically, this finding highlights the need to incorporate frailty screening into the routine management of patients with T2DM and CHD, particularly in those with prolonged disease burden or reduced functional status. Future studies should further clarify whether frailty trajectories differ between patients with isolated T2DM, isolated CHD, and T2DM‐CHD comorbidity, in order to better define high‐risk populations and the optimal timing of intervention.

Factors associated with frailty in patients with type 2 diabetes mellitus and coronary heart disease

The present study found that metabolic and cardiovascular factors were closely associated with frailty, including blood glucose, BNP, D‐dimer, history of PCI, and age. These findings suggest that frailty in patients with T2DM and CHD may be closely linked to the combined effects of metabolic dysregulation, cardiovascular stress, thrombotic or circulatory disturbance, and previous invasive cardiovascular events. Persistent hyperglycemia may impair endothelial function, promote oxidative stress, and aggravate chronic low‐grade inflammation, all of which can contribute to microvascular damage, skeletal muscle catabolism, and reduced physical performance ¹¹ , ¹⁶ . Elevated BNP reflects increased cardiac wall stress and reduced cardiac reserve, which may result in fatigue, decreased exercise tolerance, and impaired tissue perfusion. More broadly, frailty and cardiovascular disease share several biological mechanisms, including chronic inflammation, autonomic dysregulation, sarcopenia, and progressive loss of reserve ¹⁵ , ¹⁷ . The association with D‐dimer further suggests that coagulation activation and vascular dysfunction may also be involved in the frailty process, as these pathways have been proposed as shared biological mechanisms linking cardiovascular disease and frailty; however, the specific role of D‐dimer in frailty development still requires further validation in longitudinal studies ¹⁸ . In addition, a history of PCI may reflect more advanced coronary disease and greater cumulative cardiovascular burden rather than simply a procedural history, indicating that frailty may be more common in patients with more severe underlying disease. Similar studies in patients with CHD have also shown that frailty is closely related to inflammatory burden and cardiovascular dysfunction ⁸ , ¹⁹ . Taken together, these findings support the view that frailty in this population is not an isolated geriatric syndrome, but a clinical expression of multisystem reserve depletion under sustained cardiometabolic stress. From a clinical perspective, frailty assessment should be integrated with routine metabolic and cardiovascular evaluation, rather than considered separately. Future research should examine whether improving glycemic control, optimizing cardiac function, and reducing vascular complications can delay or reverse frailty progression in this comorbid population.

Frailty in patients with T2DM and CHD is influenced not only by biomedical abnormalities but also by emotional burden and daily behavioral regulation ²⁰ , ²¹ . Anxiety may increase frailty risk through several pathways, including chronic activation of the hypothalamic–pituitary–adrenal axis, sympathetic overactivity, inflammatory activation, and reduced motivation for self‐management ²² , ²³ . At the same time, patients with anxiety are more likely to experience poor treatment adherence, physical inactivity, and heightened symptom perception, which may further worsen metabolic and cardiovascular control ²¹ . Sleep disturbance may exert an additional negative effect by disrupting circadian rhythm, impairing energy restoration, reducing daytime activity, and exacerbating endocrine and inflammatory dysregulation; recent evidence also supports an association between short or poor sleep and frailty in older adults ²⁰ , ²⁴ . For patients with T2DM and CHD, who already face complex treatment regimens and high symptom burden, the coexistence of anxiety and poor sleep may create a vicious cycle in which psychological distress, behavioral disruption, and physical decline reinforce one another, thereby accelerating frailty development. These findings indicate that frailty management in this population should go beyond disease‐centered treatment alone and include psychological screening and sleep assessment as part of comprehensive care. Future studies may explore whether interventions targeting anxiety reduction, sleep improvement, and self‐management support can produce measurable benefits in frailty outcomes.

Nutritional risk was another important factor associated with frailty, and this finding can be understood in the broader context of nutritional vulnerability and reduced physical reserve. In patients with T2DM and CHD, inadequate nutritional intake may arise from multiple causes, such as poor appetite, dietary restriction, gastrointestinal discomfort, reduced activity, or fear of worsening blood glucose and cardiovascular symptoms. Over time, insufficient nutrient intake may lead to loss of muscle mass, impaired protein synthesis, reduced grip strength, and declining physical resilience, thereby making patients more likely to enter a frail state. Evidence from recent studies suggests that nutritional status is closely linked to physical performance, and that protein intake and combined exercise–nutrition interventions may help improve grip strength and physical function in vulnerable older adults ²⁵ , ²⁶ . Although grip strength did not remain in the final multivariable model, it's between‐group difference in the univariate analysis still suggests that frailty in this population has an important functional component related to muscular performance. Therefore, nutritional impairment and physical weakness should be viewed as interconnected processes rather than separate problems. In practice, routine care for patients with T2DM and CHD should include nutritional screening and simple functional assessment, such as grip strength or mobility evaluation, in order to identify early decline before irreversible disability occurs. Future research should further investigate whether combined nutritional intervention and resistance or rehabilitation training may be more effective than single‐component approaches in improving frailty among patients with cardiometabolic comorbidity ²⁶ , ²⁷ .

With the accelerating aging of the population and changes in lifestyle, type 2 diabetes and coronary heart disease (CHD) have become major chronic conditions with steadily rising prevalence worldwide. Clinically, these two conditions often coexist, forming a large high‐risk patient population ¹ . Studies show that approximately 33.9% of patients with type 2 diabetes (T2DM) in China also have CHD ²⁸ . Furthermore, diabetes can increase a patient's risk of developing CHD by two to four times ²⁹ . The comorbidity of T2DM and CHD complicates disease management, reduces patients' quality of life, increases medical resource consumption, and adds to the social burden ³⁰ . Diabetic patients face a significantly higher risk of CHD than non‐diabetic individuals, and this comorbidity leads to poorer outcomes and higher mortality after cardiovascular events ³¹ . In addition, managing patients with both diabetes and CHD requires greater medical resources and long‐term chronic disease management. According to a World Health Organization report, the costs of treating diabetes and cardiovascular diseases account for nearly 10% of global health expenditures ³² . Therefore, implementing effective early risk identification for this high‐risk population with T2DM and CHD has significant public health implications for improving patient outcomes and optimizing resource allocation. Integrating frailty risk assessments into routine nursing care helps nursing staff identify high‐risk individuals before a significant decline in physical function occurs. This enables earlier nursing interventions. Early interventions—such as developing personalized exercise and nutrition plans, strengthening psychological support, and optimizing medication regimens—may delay or even reverse the progression of frailty.

Limitations

This study has several limitations. First, participants were recruited from two hospitals within the same medical consortium in a single region, which may limit the representativeness of the sample and reduce the generalizability of the findings. Second, because of the cross‐sectional design, the associations identified in this study should be interpreted with caution, and causal relationships between frailty and its associated factors cannot be established. In particular, the observed associations between frailty and inflammatory markers require further confirmation in prospective studies. Therefore, future multicenter studies with larger sample sizes and longitudinal follow‐up are needed to validate the present findings and to further clarify the mechanisms underlying frailty in patients with T2DM and CHD.

CONCLUSION

This cross‐sectional study showed that frailty was common among patients with T2DM and CHD. Metabolic and cardiovascular factors, psychological and behavioral factors, and nutritional vulnerability were all associated with frailty, highlighting its multidimensional nature in this comorbid population. However, the findings should be interpreted with caution because the cross‐sectional design and relatively limited regional sample restrict causal inference and generalizability. Future multicenter longitudinal studies are needed to validate these findings, clarify the underlying mechanisms, and inform the development of targeted multidomain interventions for frailty prevention and management in patients with T2DM and CHD.

DISCLOSURE

The authors declare that they have no competing interests. This research received no specific grant from any funding agency in the public, commercial, or not‐for‐profit sectors.

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.