INTRODUCTION

With increasing age, individuals experience physiological and psychosocial changes and a heightened risk of chronic disease [1]. However, not all individuals of the same chronological age undergo aging at the same rate or in the same manner [2]. Biological or physiological age may differ from chronological age [3], and prior reports suggest that frailty can serve as a proxy measure of biological aging [4].

Frailty is common among older adults and represents an age-related clinical state that reflects multiple physiological, social, and psychological factors. It is also a dynamic condition that can fluctuate over time [5]. Frailty is characterized by increased vulnerability to stressors and reduced resilience due to cumulative declines [6], and it may be considered a pre-disability state [7]. Frailty is associated with adverse health outcomes, including diminished quality of life and increased risks of disability, hospitalization, institutionalization, morbidity, and mortality across various settings [6,8,9].

Several tools have been developed to assess frailty in different contexts and to identify populations at risk [10,11]. The frailty index (FI) is one such measure, and previous studies have shown that it predicts premature death in various age groups and is associated with increased hospitalization and healthcare use [12,13].

Most prior studies exploring the association between frailty and adverse outcomes such as health status, disability, healthcare utilization, and premature death have been conducted with a long-term perspective (≥5 years) [13-16], or have focused on patients with specific conditions, such as lung transplantation or sepsis [17,18]. In long-term follow-up studies of older adults, the effect of age on outcomes like mortality and frailty cannot be excluded [19], and the association between frailty and health outcomes may be diluted. This study aimed to assess the risk of all-cause mortality at 2 years in a community-based population of older adults according to FI severity, and to examine the association between baseline frailty and subjective health status among those with complete 2-year follow-up.

METHODS

Participants

In 2011, we established a prospective cohort of adults aged 60 years or older who were recipients of the National Basic Livelihood Security System in Chungju, a small city in central Korea. At the time, Chungju had a population of 208 202, with 40 196 individuals (19.3%) aged 60 or older. Among these, 1535 individuals were identified as eligible beneficiaries. After excluding those residing in institutions or hospitals and those unable to communicate due to cognitive impairment, 1262 participants were enrolled. Follow-up assessments were conducted between July 2013 and October 2013, and 1033 participants (81.7%) completed the 2-year follow-up. Ultimately, 936 participants were included in the analysis after excluding dropouts (n=229) and those with missing FI components at baseline (n=98) (Figure 1). Additionally, 822 of the 936 participants completed the European Quality of Life Five-Dimension Three-Level (EQ-5D-3L) questionnaire at follow-up. This study adhered to the guidelines of the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement (Supplemental Material 1).

Measurements

Exposure variable: the FI

Frailty was assessed using the FI, which quantifies accumulated health deficits, such as symptoms, signs, diseases, disabilities, or laboratory and diagnostic abnormalities, based on the premise that individuals with more deficits are more likely to be frail. For reliable estimation, it is generally recommended that the FI include about 30-40 variables, as indices with fewer than 10 items are less stable and valid [20]. However, previous research has shown that FIs with 25 or more items are adequately reliable, with intraclass correlation coefficients (ICCs) of 0.63 or greater [21]. We constructed the FI following published guidelines, including a component if it met the following criteria [20]: it was associated with health status, increased with age, was not saturated at an early age, covered a broad range of organ systems rather than a single function, and had less than 5% missing values. Our final FI consisted of 28 variables, all representing health status deficits measured by self-reported questionnaires (Supplemental Materials 2 and 3). These FI components were mapped to 5 health domains assessed during screening: medical history, physical health, psychological health, cognitive function, and social support. Each deficit was scored from 0 to 1, with 0 indicating absence and 1 indicating maximal expression of the deficit (i.e., the most unhealthy status). The FI score for each individual was calculated as the sum of all component scores divided by the number of measured components, yielding a continuous score between 0 and 1, with higher scores indicating greater frailty.

FI= Sum of scores for each components Number of measured components

Based on previous research [22-24], we categorized the FI into 3 levels: non-frail (FI<0.25 points), mildly frail (FI=0.25-0.34 points), and moderately-severely frail (FI≥0.35 points). The FI has demonstrated acceptable predictive validity (area under the curve, 0.77) for 1-year all-cause mortality in a previous study [25].

Outcome Variable

Mortality

We confirmed survival by visiting each participant’s home and reviewing Medical Aid records, defining death as termination of Medical Aid benefits without a recorded cause. Participants were considered lost to follow-up if their status could not be determined.

EQ-5D-3L

The EQ-5D-3L is a self-reported instrument measuring subjective health across 5 domains (mobility, self-care, usual activities, pain/discomfort, and anxiety/depression), each rated on a 3-level scale (no problems, some/moderate problems, extreme problems) [26]. Based on responses, an European Quality of Life Five-Dimension (EQ-5D) utility index score provides an overall assessment of perceived health [27]. To account for cultural differences, we used a Korean-specific valuation set developed by the time trade-off protocol at the Korea Centers for Disease Control and Prevention [27]. The EQ-5D utility index score ranges from -0.171 (worse than death) to 1.0 (perfect health).

Confounding variables

Potential confounders collected included age, sex, education level, history of mental illness, current alcohol use, current smoking status, and baseline EQ-5D utility index score. Education was categorized as none, ≤6 years, or ≥7 years. History of mental illness referred to any psychiatric disorder at baseline. Baseline EQ-5D utility scores were derived from initial responses.

Statistical Analysis

Baseline characteristics by frailty level were compared using the chi-square test or t-test. The 2-year cumulative incidence of all-cause mortality was examined by frailty level. Relative risks (RRs) and 95% confidence intervals (CIs) for all-cause mortality were estimated using the GENMOD procedure, with non-frailty as the reference group. In the multivariable analysis, model 2 adjusted for age and sex, and model 3 additionally adjusted for education, mental illness, current alcohol use, and current smoking.

For sensitivity analyses, we assumed all individuals lost to follow-up either died (worst-case) or survived (best-case), recalculating RRs and 95% CIs to assess whether frailty predicted 2-year mortality under each scenario. We also conducted stratified analyses by age group (<75 vs. ≥75 years) to account for age and cohort effects.

In exploratory analyses, we first included only participants who completed the EQ-5D-3L at follow-up to assess quality of life. We compared the prevalence of extreme problems in each EQ-5D-3L domain by frailty level and calculated prevalence ratios (PRs) for each domain. Second, to explore associations between specific frailty components and outcomes, we conducted domain-specific analyses across the 5 conceptual FI domains. The association between each domain and 2-year mortality was estimated using RRs and 95% CIs. For quality of life, multivariable linear regression models were used to assess the relationship between each domain and the EQ-5D-3L index score.

All analyses were performed using SAS version 9.4 (SAS Institute Inc., Cary, NC, USA). Statistical tests were 2-tailed, with an alpha of 5%, and 95% CIs were reported for all risk estimates.

Ethics Statement

Written consent was acquired from all participants following explanation of the nature of the principles of research, including confidentiality and voluntary participation. This study protocol received full review and approval from the Institutional Review Board of The Catholic University of Korea (CUMC11U035).

All procedures were performed in accordance with the Declaration of Helsinki.

RESULTS

In 2011, 936 participants were recruited, and a 2-year follow-up was conducted from baseline. Of these, 111 (11.9%) were classified as non-frail, 230 (24.6%) as mildly frail, and 595 (63.6%) as moderately to severely frail. As frailty severity increased, the proportions of females, older individuals, and those with a history of mental illness also increased. Conversely, the proportion of individuals with ≥7 years of education decreased as frailty increased, and current drinking was least common in the moderately to severely frail group. Current smoking status did not significantly differ among the groups (Table 1).

Figure 2 presents the relative proportions of participants by frailty level, stratified by age. Both the prevalence of frailty and the frequency of moderate–severe frailty increased with advancing age. The prevalence of any degree of frailty rose from 82.2% (240 of 292) in those aged <70 years, to 89.3% (410 of 459) in those aged 70-79 years, and to 94.6% (175 of 185) in those aged ≥80 years (p for trend <0.001). Similarly, the prevalence of moderate–severe frailty increased with age: 58.9% (172 of 292) among those <70 years, 61.9% (284 of 459) among those 70-79 years, and 75.1% (139 of 185) among those ≥80 years.

At the 2-year follow-up, 91 participants had died, and the mortality rate was higher in the frailty groups compared to the non-frailty group. After adjustment for potential confounders, mild frailty was not significantly associated with 2-year all-cause mortality (adjusted RR [aRR], 3.00; 95% CI, 0.92 to 17.38) compared to non-frailty, whereas the moderate–severe frailty group had a more than 5-fold increased risk of 2-year all-cause mortality (aRR, 5.79; 95% CI, 1.39 to 24.07). Each 0.1-unit increase in the FI was associated with a 1.53-fold increase in the risk of death (aRR, 1.53; 95% CI, 1.30 to 1.79) (Table 2). These findings were robust to changes in frailty thresholds (Supplemental Material 4).

When including scenarios for lost-to-follow-up cases, the mortality risk remained significantly elevated in the moderately to severely frail group compared to the non-frail group. For each 0.1-unit increase in FI, the mortality risk increased by 1.32-fold under the worst-case scenario and 1.41-fold under the best-case scenario (Supplemental Materials 5 and 6).

Among those who completed follow-up, the prevalence of extreme problems increased with frailty across all EQ-5D-3L domains except mobility (Figure 3). After adjusting for baseline EQ-5D utility index score, the prevalence of problems rose in a dose–response pattern with increasing frailty severity in all domains (Supplemental Material 7). The moderately to severely frail group also had significantly greater 2-year prevalence ratios for problems in 4 domains (mobility, activities of daily living, pain/discomfort, and anxiety/depression) compared to the non-frailty group.

To distinguish between age and cohort effects, participants were categorized into 2 groups: those aged 75 years and over, and those under 75 years. A dose-response trend was evident, with increased mortality risk associated with increasing frailty; however, no statistically significant associations were observed within each age group (Supplemental Material 8).

To further examine the contribution of specific frailty domains to mortality risk, we calculated domain-specific frailty scores (Supplemental Material 9). Participants classified as frail in the psychological health domain had more than double the risk of death compared to their non-frail counterparts (RR, 2.03; 95% CI, 1.15 to 3.57). Similarly, frailty in the cognitive function domain was associated with increased mortality risk (RR, 2.07; 95% CI, 1.16 to 3.71). Regarding quality of life, all 5 frailty domains were significantly associated with the EQ-5D-3L index score (Supplemental Material 10).

DISCUSSION

In this study, frail older adults—particularly those with moderate to severe frailty—were at increased risk for mortality. Compared to the non-frailty group, the 2-year mortality risk was increased by approximately 5.7-fold in the moderately to severely frail group, and each 0.1-unit increase in FI was associated with a 52% higher risk of death. Among those who completed follow-up, higher frailty severity was also linked to a greater frequency of extreme problems in subjective health status. Sensitivity analyses, in which we assumed all subjects lost to follow-up were either deceased or alive at 2 years, confirmed the robustness of our findings, with consistent results in both scenarios. Our study demonstrates that frailty—especially moderate to severe frailty—predicts not only objective health outcomes, but also individuals’ perceived health problems over the short term. However, the magnitude of the effect differed from previous studies. Long-term studies have reported that frailty increases the risk of mortality by 2 times to 3 times compared to non-frailty [13,28]. The higher effect estimates observed in our study may be attributable to differences in follow-up period or to potential age effects, which could have resulted in weaker estimates over a longer timeframe.

In this study, the proportion of non-frail people decreased as age increased, which is consistent with a previous report [13]. We observed a dose–response relationship between frailty and 2-year mortality risk, with a unidirectional increase in mortality risk as frailty severity increased, in line with prior studies [12,29]. This dose–response pattern persisted even after adjusting frailty thresholds, indicating that the association between frailty and mortality was unlikely to be due to observational bias. Thus, moderate to severe frailty in older adults can be a significant predictor of death within 2 years.

The adverse health outcomes of frailty, including mortality, can be explained by the underlying pathophysiology of frailty. Frailty represents dysregulation across multiple regulatory systems, leading to compromised homeostasis and resilience [5]. While physiological reserves generally decline gradually with age, frailty accelerates this process, and homeostatic mechanisms may begin to fail [6]. When a threshold number of regulatory systems become dysregulated, frailty develops, elevating the risk of mortality and disability [5].

While earlier research has shown that pre-frailty and mild frailty increase mortality risk by approximately 1.3 times and 1.5 times, respectively [30,31], we observed a significant association between frailty and mortality only in the moderate-severe frailty group. No significant association was found between mild frailty and 2-year mortality. One possible reason for this finding is the study’s follow-up duration. Although mortality is typically a long-term outcome, our study had a relatively short follow-up period of 2 years, which may not be sufficient to capture the association between less severe frailty and mortality. It is likely that a meaningful association between death and frailty could only be observed among those with moderate to severe frailty. These findings emphasize the importance of careful frailty screening, particularly for individuals who are moderately to severely frail, as they face an elevated risk of serious health outcomes, including death, even over a short timeframe.

In an exploratory analysis among those who completed follow-up, the most extreme problems across all 5 quality-of-life domains were most prevalent in moderately to severely frail individuals. Problem prevalence ratios demonstrated a dose–response relationship, increasing with frailty severity and showing a significant association with anxiety and depression. The higher prevalence of mental health problems may be partly attributable to the inclusion of depressive symptoms and hopelessness as FI components. Nonetheless, the association remained significant even after adjusting for baseline EQ-5D utility index score. As previous studies have shown that frailty is significantly associated with poorer mental health–related quality of life at 2 years [8], individuals with high frailty may be particularly vulnerable to subsequent mental health problems, underscoring the need for preventive interventions.

Both our findings and previous research indicate that frailty predicts premature death and other adverse health outcomes [6,8,12], suggesting an urgent need to identify and manage frailty in older adults in the community. Such adverse outcomes lead to increased healthcare utilization and costs, with individuals with frailty reportedly spending about twice as much on healthcare as those without frailty [32,33]. Frailty can be identified in primary care settings [34] and addressed through community-level interventions. Previous studies have demonstrated that physical activity or nutrition interventions can prevent frailty [35], or improve physical function [36]. Additionally, clinical goals for moderately to severely frail older adults may include medication optimization, comprehensive geriatric assessment, or disease-specific interventions. Because frailty is a potentially preventable and modifiable condition, efforts to prevent and slow its progression are essential [37].

This study has several limitations. First, although guidelines exist for its construction, the FI is not a fixed tool, and its composition may vary across studies. Searle et al. [20]’s guideline recommends including at least 30 components; however, we assessed FI using only 28, which is a limitation. Nonetheless, ICCs improve as the number of components increases, and FIs with 25 or more components have been reported to achieve coefficients of ≥0.63, indicating moderate reliability [21].

Second, since the FI has no universally accepted clinical thresholds [20], we used 0.25 as the cutoff, consistent with previous studies. This approach may introduce validity issues and potential misclassification. Additionally, although trained surveyors collected data to minimize error, misclassification in the baseline FI cannot be entirely ruled out, as its components were self-reported rather than confirmed by diagnostic criteria. Nevertheless, the association with 2-year mortality risk remained consistent when the frailty threshold was varied and with each 0.1-unit increase in FI (Supplemental Material 3).

Third, the prevalence of frailty exceeded 50% in each age group ≥60 years, which is greater than the pooled prevalence of frailty among those aged ≥50 years in a previous meta-analysis of 240 studies (24%; 95% CI, 22 to 26) [38]. There are 2 main reasons for this: first, our participants were all aged ≥60 years, and second, our participants were recruited from Medical Aid beneficiaries with low socioeconomic status. Frailty has been shown to be more prevalent among individuals of low socioeconomic status compared to those of higher socioeconomic status [39,40].

Fourth, although we collected and adjusted for potential confounders, we could not account for unmeasured confounders, such as healthcare utilization or chronic diseases (e.g., heart disease, metabolic syndrome), which may have influenced our results. Consequently, we cannot fully exclude the effects of unmeasured confounding. Furthermore, while stratified analyses were conducted to account for potential cohort and age effects, some residual influence may have persisted.

Nonetheless, our study has several important strengths. We conducted a prospective cohort study with a 2-year follow-up, allowing us to examine the temporal relationships between frailty, mortality, and quality of life. The relatively short follow-up enabled us to assess both subjective health outcomes and the objective outcome of mortality in a community-based sample of older adults with minimal age-related confounding. At the 2-year follow-up, we evaluated quality of life among those who completed the follow-up by examining the prevalence of problems in each EQ-5D-3L domain, thereby identifying domain-specific difficulties as perceived by the participants. As our study focused on a beneficiary population, it provides valuable insights into a medically vulnerable group. Additionally, although dropout bias cannot be completely eliminated in cohort studies, the follow-up rate was high at 81.7%.

Frail older adults face an increased risk of mortality and a rapid decline in physical and mental health over a short period. Our findings are valuable in that they comprehensively address both mortality as an objective outcome and quality of life as a subjective outcome—two key health indicators in older adults. Therefore, healthcare providers should consider implementing and managing continuous, community-based support for moderately to severely frail older adults to prevent adverse health outcomes.

Supplementary Materials

Notes

Conflict of Interest

The authors have no conflicts of interest associated with the material presented in this paper.

Funding

None.

Acknowledgements

We would like to thank the personnel of Chungju Public Health Center for recruiting the participants and providing technical assistance for this study.

Author Contributions

Conceptualization: Ko W, Jeong H. Data curation: Ko W, Jeong H. Formal analysis: Ko W. Funding acquisition: None. Methodology: Ko W, Jeong H, Yim HW. Project administration: Ko W, Jeong H. Visualization: Ko W, Jeong H. Writing – original draft: Ko W. Writing – review & editing: Ko W, Jeong H, Yim HW.

Figure. 1.

Flow diagram of the process for selecting the final study participants.

Figure. 2.

Frailty prevalence by age.

Figure. 3.

Prevalence of self-reported extreme problems in each domain of the European Quality of Life Five-Dimension Three-Level questionnaire at the 2-year follow-up (n=822).

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