Global aging is accelerating, with an estimated 21% of the population projected to be over 65 by 2050 [1]. The prevalence of cardiovascular disease (CVD) increases with age, particularly for inpatients, resulting in high medical costs and significant healthcare burdens [2, 3]. Given the complexity and heterogeneity of health issues in older adults [4], it is essential to find a tool that can be widely applied to detect high-risk CVD patients. Mobility function is strongly linked to the overall health status of older adults [5, 6]. Furthermore, previous studies have shown that mobility impairment among CVD patients predicts an increased risk of mortality [7]. The Timed Up and Go Test (TUGT) is a simple, quick and cost-effective tool for quantitatively assessing mobility function. It is widely utilized for evaluating the mobility and balance of the older adults, patients, and individuals in rehabilitation [8]. Several studies have suggested that TUGT could be a useful tool to predict the risk for all-cause mortality in older adults [9, 10, 11]. However, there is still uncertainty about whether TUGT can predict outcomes specifically for older patients with CVD. This study aims to evaluate the performance of TUGT in predicting 5-year mortality among older inpatients with CVD in China.

In this prospective cohort of older patients with CVD, a prolonged TUGT $>$15 s was found to be a significant independent predictor of increased all-cause mortality. After adjusting for age, sex, comorbidity and lab tests, the observed associations remained robust, suggesting that TUGT can be utilized as a predictive tool for 5-year clinical outcomes in hospitalized CVD patients.

Previous research (Table 4, Ref. [9, 13, 14]) has indicated that abnormal TUGT results are linked to an increased risk of all-cause mortality in community-dwelling older adults across diverse populations [9, 10, 11, 13]. A Peruvian study by Ascencio et al. [9] (2022) followed 501 adults aged 60 and older. The average follow-up period was 46.5 months. TUGT over 10 or 15 seconds was associated with lower survival rates. The strongest decline occurred when times exceeded 15 seconds. The study identified TUGT as an independent predictor of all-cause mortality, with each additional second increasing the risk of death by 5% (HR 1.05, 95% CI: 1.02–1.09).

A study was undertaken in Poland by Agnieszka Batko-Szwaczka and colleagues (2020) [13]. The study included 160 persons aged 60 to 74 residing in the community. TUGT $\ge$9 s was associated with increased chances of negative health outcomes within one year. The outcomes encompassed falls, hospitalizations, institutionalizations, or mortality. The TUGT outperformed the frailty phenotype in forecasting these outcomes. This benefit likely arises from its simplicity. The exam requires approximately 2.5 minutes for completion.

Meanwhile, Chun et al. [14] studied 1,084,875 community-dwelling 66-year-olds in Korea. They grouped TUGT as 10 s, 10–19 s, and $\ge$20 s. TUGT $\ge$20 s was linked to higher risks of myocardial infarction (MI), congestive heart failure (CHF), and all-cause mortality. Adjusted hazard ratios were 1.40 for MI, 1.59 for CHF, and 1.93 for mortality. The 10–19 seconds group had moderately increased risks. Our findings align with those of previous studies. Moreover, TUGT showed better performance in the Cox regression model 5 for predicting 5-year all-cause mortality, compared to single diseases or clinical parameters such as hypertension (HTN), AF, coronary artery disease (CAD), and LVEF, confirming that the predictive value of TUGT for all-cause mortality is applicable to patients with CVD.

CVD is one of the leading causes of death worldwide, contributing to a significant global health burden [15]. However, due to heterogeneity of CVD and the concurrent occurrence of multiple conditions in patients, a widely applicable and comprehensive tool or indicator for assessing the prognosis of CVD is lacking. The TUGT is a widely used assessment tool designed to evaluate functional mobility, balance, and risk of falls across various populations [8]. TUGT primarily reflects patients’overall physical performance and is not restricted to specific disease types, making it a promising tool for broad and comprehensive application in the prognostic assessment of CVD patients. Due to the simplicity, time efficiency, cost-effectiveness, and strong reproducibility of TUGT, our previous experience with this tool suggests that its application in CVD patients does not significantly increase the workload for healthcare providers. Patient safety is maintained during the testing process. Furthermore, TUGT can offer valuable insights for prioritizing targeted clinical follow-ups in high-risk CVD patients after discharge. In summary, our findings suggest that TUGT has significant potential risk stratification among older CVD patients.

We attempted to explain the mechanisms by which TUGT can predict clinical outcomes in older CVD patients. According to the European Working Group on Sarcopenia in Older People, 2nd Consensus (EWGSOP2), TUGT is utilized to categorize the severity of sarcopenia [16]. Sarcopenia, which is the progressive loss of muscle mass, strength and function, is a significant concern for older adults, particularly those with CVD [17, 18]. Sarcopenia is linked to an increased risk of several negative health outcomes, including death, falls, disability, hospitalization, and loss of independence [19]. There is a reciprocal relationship between CVD and sarcopenia [20]. Sarcopenia contributes to metabolic disturbances such as increased adiposity (fat), chronic inflammation and insulin resistance, which in turn increases the risk of CVD events [21]. Conversely, patients with CVD are often in a chronic inflammatory state, and conditions such as malnutrition and reduced physical activity can contribute to a catabolic state (where the body breaks down muscle and tissue). This accelerates muscle loss and worsens sarcopenia [22]. Aging itself brings about changes in body composition, including a decline in muscle and bone mass, an increase in body fat, and fat infiltration into muscles, bones, and organs like the liver. These changes lead to conditions such as myosteatosis (fat accumulation within muscle tissue) and sarcopenic obesity, where there is an increase in both fat and muscle loss [23]. Studies have shown that low lean muscle mass and sarcopenia are associated with higher arterial stiffness and arteriolosclerosis, which results in the thickening and hardening of small blood vessels [24, 25]. Inflammation, which is common in both sarcopenia and CVD, has also been linked to an increased risk of chronic diseases such as atherosclerotic CVD, HF, and adverse health outcomes [26]. Given the closely related mechanisms underlying sarcopenia and CVD, TUGT, as a measure of sarcopenia severity, has the potential to predict the outcomes of CVD disease in patients.

Our study indicates that TUGT can be an effective tool for early identification and screening of the mortality risk in older CVD patients. A systematic review of interventions targeting sarcopenia, such as exercise and nutritional strategies, found that most interventions improved sarcopenia-related measures (e.g., muscle mass and strength) [27]. Therefore, TUGT performance may also serve as a prognostic metric for patients undergoing nutritional and exercise interventions. Initiating TUGT assessments in middle-aged individuals may facilitate earlier detection of functional impairment and enable more timely intervention.

Our research especially focuses on high-risk older cardiovascular inpatients. Prognostic prediction in this group facilitates improved clinical decision-making, enhancing the applicability of the results in clinical practice. And based on our findings, we present a straightforward and user-friendly tool (TUGT) for forecasting long-term mortality risk in older cardiovascular patients, particularly those with heart failure and stroke/TIA. This offers crucial clinical utility for patient management. Moreover, our study utilized a prospective hospital-based design, ensuring detailed data and controlled conditions. The 5-year all-cause mortality endpoint was selected to capture long-term outcomes relevant to elderly CVD patients, where mortality rates are expected to be significant over this period.

Also, our study has some limitations. First, since this research was conducted at just one tertiary hospital with a relatively small sample size, the findings may not be generalizable to other populations or settings, which could limit their broader applicability. Second, the cohort was not specifically designed to investigate the relationship between TUGT performance and mortality risk. While a multivariate Cox regression model was used to account for potential confounding variables, there may still be residual or unknown confounders that could influence the results, potentially affecting the conclusions drawn from the data. Third, we utilized all-cause death instead of cardiovascular mortality as the outcome, we recognize the presence of competing risks in this population. This is an expansive term, and the etiology of certain fatalities may be unrelated to cardiovascular disorders or movement functions. Moreover, other confounding variables that could influence the correlation between physical performance and mortality were not considered in this study (e.g., sarcopenia, malnutrition, duration of hospital stay, drugs, etc.). Certain research indicate that polypharmacy may influence the mobility of older patients; nevertheless, our article did not address the patients’ medication usage [28].

TUGT demonstrated significant independent predictive value for 5-year all-cause mortality in older adults with CVD. A TUGT $>$15 seconds is associated with an elevated risk of mortality. TUGT offers advantages such as simplicity, non-invasiveness, and low cost, making it a useful tool for quick clinical assessment. These findings indicate that TUGT can be integrated into routine clinical practice to help healthcare providers identify patients who may benefit from more intensive monitoring and intervention.

Data are available upon reasonable request with the corresponding authors.

Conceptualization: HW, JFY, NS, WH and WZL; Data curation: YHW,and WZL; Formal analysis: YHW, KC, YDL and MZ; Funding acquisition: HW and JFY; Investigation: WH, YDL, NS, KC and MZ; Methodology: JS; Project administration: YDL; Resources: HW and JFY; Software: YHW and WZL; Supervision: HW; Validation: HW; Visualization: WZL; Writing—original draft: YHW, MZ, HW and WZL; Writing—review & editing: HW, YDL, KC, NS and JFY. All authors contributed to editorial changes in the manuscript. All authors read and approved the final manuscript. All authors have participated sufficiently in the work and agreed to be accountable for all aspects of the work.

This study adheres to the ethical guidelines of the Declaration of Helsinki. The study received approval from the Ethics Committee of Beijing Hospital (No. 2018BJYYEC-121-02). All of the participants provided signed informed consent.

This study was supported by grants from the Beijing Municipal Science and Technology Commission, China (no. D181100000218003); the Capital’s Funds for Health Improvement and Research (2022-1-4052); the National High Level Hospital Clinical Research Funding (BJYY-2023-070); the National Natural Science Foundation of China (no. 82170396); and the CAMS Innovation Fund for Medical Sciences (2021-I2M-1-050).

The authors declare no conflict of interest.

Supplementary material associated with this article can be found, in the online version, at https://doi.org/10.31083/RCM37636.