Abstract
Background
Frailty in older adults is a multidimensional condition that extends beyond physical decline to include emotional and psychological vulnerability. However, evidence on how food security and overall diet quality vary according to frailty severity among older Korean adults is limited. This study aimed to systematically examine the associations between food security and diet quality, and frailty severity in this population.
Methods
A cross-sectional study was conducted using data from the Korean National Health and Nutrition Examination Survey from 2013 to 2020. Among the 62,686 participants, 9,263 adults aged 65 years and older were included after those with missing frailty index variables, no nutritional records, or extreme energy intake values were excluded. Frailty was assessed using a 41-item Frailty Index (FI), categorizing participants into nonfrail (FI ≤ 0.15), prefrail (0.15 < FI ≤ 0.25), and frail (FI > 0.25) groups. Diet quality was evaluated using the Korean Healthy Eating Index (KHEI), and food security status was assessed through a standardized questionnaire.
Results
The incidence of frailty was 14.1% in men and 23.8% in women, and it was associated with lower education, lower income, and living alone. KHEI scores decreased significantly with increasing frailty severity in both men and women (P for trend < 0.0001). Severe food insecurity was significantly associated with frailty risk with an OR = 3.62 (95% CI: 1.23–10.66) for men and an OR = 9.03 (95% CI: 2.92–27.97) for women. In the case of KHEI scores, only frail women had a significant trend for the odds of frailty (P for trend = 0.0202), whereas no significant association was observed in men.
Conclusions
These findings emphasize the importance of sex-specific nutritional interventions and stable food supply systems for preventing frailty in aging populations.
Supplementary Information
The online version contains supplementary material available at 10.1186/s12877-026-07818-8.
Keywords: Frailty, Diet, Food security
Introduction
The proportion of older adults in Korea has increased rapidly in recent decades. As of 2024, 19.2% of the total population is aged 65 years or older, and this proportion is expected to increase steadily in the coming years [1]. According to the World Health Organization (WHO), a society is classified as an “aging society” when the proportion of adults aged ≥ 65 years exceeds 7% and as an “aged society” when it exceeds 14% [2]. On the basis of this classification, Korea has already entered the stage of an aged society. The demographic shift toward population aging has broad socioeconomic implications, particularly with respect to increasing healthcare expenditures and medical service utilization associated with worsening frailty status among older adults [3, 4].
Frailty is a multidimensional geriatric syndrome characterized by decreases in muscle strength, endurance, and physical activity, which reduce physiological reserve and increase vulnerability to stressors [5]. These deficits can trigger a cycle of negative energy balance, sarcopenia, and reduced mobility, ultimately increasing the risk of adverse health outcomes such as falls, hospitalization, disability, and mortality. Given its complex etiology and clinical consequences, there is growing recognition of the need for early, comprehensive management of the factors associated with frailty. A wide range of determinants—including socioeconomic characteristics (sex, age, education, and income), lifestyle behaviors (alcohol consumption and smoking), health behaviors (dietary intake and physical activity), and psychological conditions (stress, depression, and anxiety)—have been implicated in the development and progression of frailty [6]. Among these determinants, nutritional status plays a particularly critical role. Adequate and balanced dietary intake may help prevent or attenuate frailty, whereas food insecurity can restrict access to essential nutrients and increase the likelihood of nutritional imbalance, thereby contributing to an increased risk of frailty [7, 8].
Dietary quality is a comprehensive indicator reflecting the extent to which an individual’s overall diet aligns with nutritional recommendations, taking into consideration nutrient adequacy, dietary diversity, moderation, and balance [9]. The Healthy Eating Index (HEI) is widely used in the United States to assess dietary quality, and the Korean Healthy Eating Index (KHEI) was developed on the basis of the HEI using data from the Korea National Health and Nutrition Examination Survey (KNHANES) to evaluate dietary patterns in the Korean population [10]. The KHEI consists of eight components representing adequacy, three components assessing moderation, and three components assessing balance, with total scores ranging from 0 to 100 [10]. A growing body of evidence suggests that lower dietary quality is associated with greater frailty risk in older adults, as nutritional imbalance may contribute to declines in physical function and reduced activity levels [10]. Maintaining a high level of dietary quality through diverse intake across food groups has therefore been proposed as a key strategy for preventing frailty in older adults.
Food insecurity reflects not only inadequate access to food but also broader socioeconomic disadvantage, including financial constraints, social isolation, and limited access to health-promoting resources [11–13]. These structural conditions disproportionately affect vulnerable populations, such as low-income households, older adults living alone, and individuals with limited social support [12, 13]. In Korea, a substantial proportion of older adults experience food insecurity, and inadequate protein intake among these individuals may accelerate the development of sarcopenia, one of the core mechanisms underlying frailty [14]. Accordingly, food insecurity should not be interpreted solely as a dietary issue but rather as a multidimensional factor associated with broader health inequalities [11].
Importantly, food insecurity may act as an upstream determinant of dietary behaviors, influencing both the quantity and quality of food intake [11]. In this context, dietary quality may partially capture the nutritional consequences of food insecurity. Limited access to diverse foods has also been linked to higher risks of obesity and chronic diseases while complicating the management of existing conditions and reducing resilience to health stressors [11]. Therefore, examining food insecurity in addition to dietary quality provides a more integrated framework for understanding the nutritional and socioeconomic pathways contributing to frailty risk in older adults [15, 16].
Despite the growing interest in the nutritional determinants of frailty, few studies in Korea have focused on the associations among dietary quality, food insecurity, and frailty using nationally representative data. In particular, to our knowledge, no study has simultaneously examined food insecurity, KHEI scores, and frailty among older Korean adults using KNHANES data. Therefore, this study aimed to assess the associations between food insecurity and dietary quality and frailty among adults aged 65 years and older using data from the 2013–2020 KNHANES.
Materials and methods
Study design and population
This study used a cross-sectional design based on raw data from the 6th (2013–2015), 7th (2016–2018), and the first and second years of the 8th (2019–2020) KNHANES. Among the 62,686 participants in the 2013–2020 KNHANES, 12,912 individuals were aged ≥ 65 years. The following exclusion criteria were applied: (1) missing data exceeding 20% of the variables required to calculate the Frailty Index (FI; >8 missing items, n = 2,571); (2) the absence of 24-hour dietary survey data (n = 925); and (3) extreme total energy intake (< 500 kcal or > 5,000 kcal/day; n = 127). After applying these criteria, 9,263 participants (4,083 men and 5,180 women) were included in the final analysis (Fig. 1).
Fig. 1.
Flowchart of the study participants. KNHANES, Korea National Health and Nutrition Examination Survey; FI, Frailty index
Frailty index
Frailty was assessed using the Frailty Index (FI), which is based on the deficit accumulation model and is widely used to reflect heterogeneity in health status and vulnerability to adverse outcomes among older adults. The FI is calculated as a continuous score ranging from 0 to 1, with higher values indicating greater frailty severity [17, 18]. Variables were selected from items available in the KNHANES, referencing prior studies that developed similar FI measures using national health survey data [18–22]. To comprehensively represent accumulated health deficits, the FI included variables across multiple domains, including physician-diagnosed conditions and medical history (13 items), current treatment status (2 items), anthropometric and laboratory indicators (13 items), subjective health status and functional limitations (3 items), symptoms and lifestyle factors (3 items), quality of life and functional impairment indicators (5 items), physical activity levels (1 item), and hospitalization (1 item). In total, 41 variables were included.
The participants were categorized into three groups on the basis of FI scores: nonfrail (FI ≤ 0.15), prefrail (0.15 < FI ≤ 0.25), and frail (FI > 0.25). For example, an individual with deficits in 15 of 41 variables would have an FI score of 0.37 and would be classified as frail [20, 22].
Dietary quality
Dietary quality was assessed using the KHEI, which was calculated based on the basis of the 24-hour dietary recall survey. The KHEI evaluates overall dietary patterns, with higher scores indicating better dietary quality. The KHEI includes 14 components across three domains: adequacy (8 items), moderation (3 items), and balance (3 items). The adequacy components include breakfast consumption, whole grain intake, total fruit, fresh fruit, total vegetables, vegetables excluding kimchi and pickled items, meat/fish/egg/legume intake, and milk/dairy intake. The moderation components include the energy ratio of saturated fat, sodium intake, and the energy ratio of sugars and beverages. The balance components consist of the energy ratios of carbohydrates and fat, and appropriate total energy intake.
All the components were scored using either a 5- or 10-point scale based on the Dietary Reference Intakes for Koreans (KDRIs, 2015), with a total possible score of 100 points. Scoring criteria for some moderation items were adopted from the HEI used in the United States [23, 24]. Higher total scores reflect greater adherence to dietary guidelines and better nutritional adequacy.
Food security
Food security status was assessed using a single-item questionnaire collected in the KNHANES. The participants were asked “Which of the following best describes your household’s food situation during the past year?” The responses were categorized into four levels:
Food secure: “Our family was able to eat enough and a variety of foods.”
Mildly food insecure: “Our family ate enough food, but lacked dietary variety.”
Moderately food insecure: “Our family occasionally lacked food due to economic hardship.”
Severely food insecure: “Our family frequently lacked food due to economic hardship.”
These categories reflect both quantitative and qualitative dimensions of food access.
Sociodemographic and lifestyle factors
Sociodemographic and lifestyle characteristics were assessed using KNHANES questionnaires. Sociodemographic variables included age, education level, marital status, household income, and household type. Education level was categorized as middle school or below, high school graduate, or college degree or above. Marital status was classified as married/cohabiting, separated/divorced, widowed, or single. Household income was divided into quartiles: lowest (Q1), lower-middle (Q2), upper-middle (Q3), and highest (Q4). Household type was categorized as single-person household vs. multiperson household.
Lifestyle variables included smoking status, alcohol consumption, and family meal participation. Smoking status was categorized as nonsmoker, former smoker, or current smoker. Monthly drinking status was defined as consuming alcohol ≥ 1 time/month. Family meal participation was defined as eating breakfast, lunch, or dinner with family at least once during the past year.
Statistical analysis
All the statistical analyses accounted for the complex, multistage, probability sampling design of the KNHANES by applying sampling weights, stratification variables, and primary sampling units to derive nationally representative estimates. When data from multiple survey cycles were pooled (2013–2020), the sampling weights were recalculated by dividing the original survey weights by the number of survey years included, following the KNHANES analytic guidelines. Categorical variables are presented as weighted percentages with standard errors (SEs), and group differences were tested using the Rao–Scott chi-square test. Continuous variables are presented as weighted means with SEs, and group differences were evaluated using analysis of variance (ANOVA).
To examine the association between KHEI scores and frailty status, participants were categorized into quartiles (Q1–Q4) on the basis of KHEI scores. Multinomial logistic regression analysis was used to estimate the associations of food security and dietary quality with frailty status (nonfrail, prefrail, and frail). The results are presented as odds ratios (ORs) with 95% confidence intervals (CIs). Linear trends across quartiles were assessed using the median value for each quartile in a weighted multinomial regression model, and the P for trend was derived. The interaction between sex and frailty status (P for interaction) was evaluated by including an interaction term in weighted regression models. All analyses were stratified by sex.
The following variables were included as covariates in all linear and multinomial regression models: age (continuous), education level (three categories), marital status (four categories), household income (quartiles), household type (single vs. multiperson), smoking status (three categories), monthly alcohol consumption (yes/no), total energy intake (kcal/day) and survey year. Furthermore, KHEI scores and food security were mutually adjusted in the same model. All analyses were conducted using SAS version 9.4 (SAS Institute, Cary, NC, USA). Statistical significance was defined as a two-sided P value < 0.05.
Results
A total of 9,263 older adults were analyzed (4,083 men and 5,180 women). On the basis of the Frailty Index, 35.9% of the participants were nonfrail, 44.8% were prefrail, and 19.3% were frail. The prevalence of frailty was higher in women than in men (23.8% vs. 14.1%) (Fig. 2).
Fig. 2.
Distribution of frailty status in all participants, men, and women (Nonfrail, Prefrail, and Frail)
The mean age increased with increasing frailty severity in both sexes, while total energy intake was highest among nonfrail participants (men: 2,030.5 kcal; women: 1,614.3 kcal) and lowest among frail participants (men: 1,681.4 kcal; women: 1,324.7 kcal) (Table 1). Low education and low-income status were most prevalent in the frail group, particularly among women (92.3% and 61.6%, respectively). The proportion of single-person households increased with increasing frailty severity. Former smoking was most common among frail men (63.3%), whereas 91.3% of frail women were nonsmokers. The proportion of nondrinkers increased with increasing frailty severity (57.2% in frail men and 87.0% in frail women).
Table 1.
Characteristics of study participants according to frailty groups
| Characteristics | Men (n = 4083) | Women (n = 5180) | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Nonfrail (n = 1817) |
Prefrail (n = 1660) |
Frail (n = 606) |
Nonfrail (n = 1405) |
Prefrail (n = 2501) |
Frail (n = 1274) |
|||||||
| % (s.e) or mean ± s.e | ||||||||||||
| Age (years) | 71.1 ± 0.1 | 73.0 ± 0.1 | 74.1 ± 0.2 | 70.3 ± 0.1 | 72.5 ± 0.1 | 74.5 ± 0.1 | ||||||
| Energy intake (kcal) | 2030.5 ± 19.2 | 1851.2 ± 20.1 | 1681.4 ± 26.8 | 1614.3 ± 18.6 | 1461.6 ± 15.1 | 1324.7 ± 16.7 | ||||||
| Education level | ||||||||||||
| Middle school or below | 48.9 | (1.5) | 63.8 | (1.3) | 66.2 | (2.3) | 71.0 | (1.6) | 83.8 | (1.0) | 92.3 | (0.9) |
| High school | 28.3 | (1.2) | 24.6 | (1.1) | 22.9 | (2.0) | 18.6 | (1.3) | 12.5 | (0.9) | 5.9 | (0.8) |
| College or above | 22.9 | (1.3) | 11.6 | (0.9) | 10.9 | (1.5) | 10.4 | (1.1) | 3.7 | (0.5) | 1.8 | (0.5) |
| Marital status | ||||||||||||
| Married/living with partner | 90.5 | (0.8) | 87.4 | (0.9) | 83.3 | (1.8) | 61.3 | (1.6) | 51.6 | (1.2) | 38.5 | (1.7) |
| Separated / divorced | 4.1 | (0.6) | 4.6 | (0.5) | 6.6 | (1.1) | 4.5 | (0.7) | 4.5 | (0.5) | 5.4 | (0.7) |
| Widowed | 5.0 | (0.6) | 7.4 | (0.7) | 9.3 | (1.4) | 33.6 | (1.5) | 43.1 | (1.2) | 55.4 | (1.7) |
| Never married | 0.4 | (0.1) | 0.7 | (0.2) | 0.7 | (0.4) | 0.5 | (0.2) | 0.7 | (0.2) | 0.7 | (0.2) |
| Household incomea | ||||||||||||
| Low | 30.3 | (1.3) | 42.8 | (1.3) | 54.7 | (2.3) | 39.3 | (1.6) | 48.6 | (1.3) | 61.6 | (1.7) |
| Middle-low | 28.6 | (1.2) | 32.8 | (1.3) | 25.1 | (2.0) | 28.5 | (1.4) | 25.4 | (1.0) | 21.1 | (1.3) |
| Middle-high | 22.4 | (1.1) | 16.1 | (1.1) | 12.7 | (1.6) | 18.8 | (1.3) | 16.4 | (0.9) | 10.8 | (1.1) |
| High | 18.7 | (1.2) | 8.3 | (0.8) | 7.5 | (1.3) | 13.4 | (1.1) | 9.7 | (0.8) | 6.5 | (0.9) |
| Household type | ||||||||||||
| Single-person | 9.1 | (0.7) | 11.8 | (0.8) | 12.5 | (1.4) | 19.3 | (1.1) | 24.5 | (1.0) | 32.4 | (1.5) |
| Multi-person | 90.9 | (0.7) | 88.2 | (0.8) | 87.5 | (1.4) | 80.7 | (1.1) | 75.5 | (1.0) | 67.6 | (1.5) |
| Smoking status | ||||||||||||
| Never smokers | 22.7 | (1.2) | 21.2 | (1.1) | 16.5 | (1.7) | 96.0 | (0.7) | 94.9 | (0.5) | 91.3 | (1.0) |
| Former-smokers | 60.5 | (1.3) | 58.9 | (1.4) | 63.3 | (2.3) | 1.9 | (0.5) | 2.8 | (0.4) | 4.6 | (0.6) |
| Current smokers | 16.8 | (1.0) | 20.0 | (1.2) | 20.2 | (1.8) | 2.1 | (0.5) | 2.3 | (0.4) | 4.1 | (0.8) |
| Prevalence of alcohol useb | ||||||||||||
| No | 35.4 | (1.3) | 45.7 | (1.4) | 57.2 | (2.4) | 78.1 | (1.3) | 81.5 | (0.8) | 87.0 | (1.1) |
| Yes | 64.6 | (1.3) | 54.3 | (1.4) | 42.8 | (2.4) | 21.9 | (1.3) | 18.5 | (0.8) | 13.0 | (1.1) |
| Meals without familyc | ||||||||||||
| Breakfastd | 27.3 | (1.2) | 28.9 | (1.3) | 30.0 | (2.2) | 36.5 | (1.5) | 42.7 | (1.2) | 51.5 | (1.6) |
| Lunch | 30.7 | (1.3) | 35.4 | (1.4) | 32.7 | (2.2) | 44.6 | (1.5) | 45.6 | (1.2) | 52.9 | (1.6) |
| Dinner | 19.2 | (1.1) | 24.3 | (1.2) | 26.6 | (2.2) | 32.8 | (1.4) | 39.8 | (1.2) | 51.0 | (1.7) |
Continuous variables are expressed as mean ± standard error (mean ± s.e.), and categorical variables are presented as percentages with their standard errors (%, s.e.)
aHousehold income was categorized into quartiles based on equalized household income: low (lowest 25%), middle-low (26%-50%), middle-high (51%-75%), and high (highest 25%)
bDefined 'Yes' as consuming alcohol at least once per month (≥ 1/month) over the past year
CDefined as not eating breakfast, lunch, or dinner with family during the past year
dIn men, the proportion of not eating breakfast with family did not differ across frailty levels (P=0.4764)
P-values for continuous variables were calculated using F tests, and P-values for categorical variables were calculated using the Rao-Scott Chi-Square Test
Total KHEI scores were significantly lower among frail participants than among nonfrail participants (men: 64.04 vs. 67.50; women: 64.84 vs. 70.42), with a significant decreasing trend across frailty levels in both sexes (P for trend < 0.0001) (Table 2). Intake scores for total fruit, fresh fruit, total vegetables, vegetables excluding kimchi/pickled items, meat/fish/eggs/legumes, and milk/dairy decreased consistently across frailty categories (all P for trend < 0.0001). Sodium scores were higher in the frail group (men: 7.41 vs. 6.35; women: 8.53 vs. 7.91; both P < 0.0001). The carbohydrate energy ratio and appropriate total energy intake scores decreased with increasing frailty severity, but the fat energy ratio decreased significantly only among women (P < 0.0001).
Table 2.
KHEI component scores according to Frailty groups in men and women
| Component of KHEI | Men (n = 4083) | Women (n = 5180) | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Nonfrail (n = 1817) |
Prefrail (n = 1660) |
Frail (n = 606) |
P-value | P for trend | Nonfrail (n = 1405) |
Prefrail (n = 2501) |
Frail (n = 1274) |
P-value | P for trend | |
| KHEI score (0–100) | 67.50 ± 0.33 | 65.09 ± 0.32 | 64.04 ± 0.57 | < 0.0001 | < 0.0001 | 70.42 ± 0.41 | 67.99 ± 0.31 | 64.84 ± 0.41 | < 0.0001 | < 0.0001 |
| Breakfast (0–10) | 9.61 ± 0.05 | 9.53 ± 0.06 | 9.59 ± 0.08 | 0.6245 | 0.5860 | 9.34 ± 0.07 | 9.45 ± 0.05 | 9.20 ± 0.08 | 0.0164 | 0.2421 |
| Whole grains (0–5) | 2.82 ± 0.06 | 2.71 ± 0.07 | 2.49 ± 0.10 | 0.0183 | 0.0056 | 2.63 ± 0.07 | 2.62 ± 0.05 | 2.45 ± 0.08 | 0.1214 | 0.0841 |
| Total fruit, including juice (0–5) | 2.59 ± 0.06 | 2.29 ± 0.07 | 2.14 ± 0.10 | < 0.0001 | < 0.0001 | 3.38 ± 0.07 | 3.03 ± 0.06 | 2.58 ± 0.08 | < 0.0001 | < 0.0001 |
| Fruit, excluding juice (0–5) | 2.72 ± 0.07 | 2.48 ± 0.07 | 2.35 ± 0.12 | 0.0055 | 0.0017 | 3.35 ± 0.08 | 3.10 ± 0.06 | 2.64 ± 0.08 | < 0.0001 | < 0.0001 |
| Total vegetables, including Kimchi and pickles (0–5) | 3.95 ± 0.03 | 3.69 ± 0.04 | 3.44 ± 0.07 | < 0.0001 | < 0.0001 | 3.91 ± 0.04 | 3.64 ± 0.03 | 3.37 ± 0.05 | < 0.0001 | < 0.0001 |
| Vegetable, excluding Kimchi and pickles (0–5) | 3.46 ± 0.04 | 3.08 ± 0.05 | 2.91 ± 0.08 | < 0.0001 | < 0.0001 | 3.85 ± 0.05 | 3.57 ± 0.04 | 3.27 ± 0.06 | < 0.0001 | < 0.0001 |
| Meat, fish, eggs, and beans (0–10) | 7.27 ± 0.09 | 6.46 ± 0.09 | 6.03 ± 0.15 | < 0.0001 | < 0.0001 | 7.51 ± 0.10 | 6.55 ± 0.08 | 5.97 ± 0.12 | < 0.0001 | < 0.0001 |
| Milk and dairy (0–10) | 2.55 ± 0.11 | 2.12 ± 0.11 | 2.25 ± 0.19 | 0.0185 | 0.0265 | 3.14 ± 0.14 | 2.94 ± 0.11 | 2.57 ± 0.14 | 0.0158 | 0.0050 |
| Saturated fatty acid (0–10) | 8.97 ± 0.07 | 9.08 ± 0.07 | 9.05 ± 0.12 | 0.5153 | 0.3931 | 8.93 ± 0.08 | 9.02 ± 0.06 | 9.16 ± 0.08 | 0.1558 | 0.0589 |
| Sodium (0–10) | 6.35 ± 0.09 | 6.90 ± 0.09 | 7.41 ± 0.13 | < 0.0001 | < 0.0001 | 7.91 ± 0.08 | 8.31 ± 0.06 | 8.53 ± 0.08 | < 0.0001 | < 0.0001 |
| Empty calorie foods (0–10) | 8.87 ± 0.07 | 8.66 ± 0.08 | 8.88 ± 0.12 | 0.0988 | 0.4443 | 8.54 ± 0.10 | 8.67 ± 0.07 | 8.59 ± 0.10 | 0.5488 | 0.6893 |
| Carbohydrate (0–5) | 2.10 ± 0.06 | 1.97 ± 0.06 | 1.85 ± 0.09 | 0.0418 | 0.0118 | 1.88 ± 0.07 | 1.55 ± 0.05 | 1.46 ± 0.07 | < 0.0001 | < 0.0001 |
| Fat (0–5) | 2.91 ± 0.06 | 2.80 ± 0.06 | 2.68 ± 0.10 | 0.1248 | 0.0419 | 2.76 ± 0.07 | 2.42 ± 0.05 | 2.21 ± 0.07 | < 0.0001 | < 0.0001 |
| Total energy (0–5) | 3.33 ± 0.06 | 3.32 ± 0.06 | 2.97 ± 0.11 | 0.0082 | 0.0150 | 3.28 ± 0.07 | 3.11 ± 0.05 | 2.85 ± 0.08 | 0.0002 | < 0.0001 |
KHEI Korean healthy eating index. P-values for continuous variables were calculated using F tests, and P for trend values were calculated using multivariable linear regression models to evaluate linear trends across ordered categories of frailty groups
Across KHEI quartiles, the prevalence of frailty was highest in Q1 and lowest in Q4 (men: 18.4% to 12.2%; women: 32.9% to 16.7%) (Fig. 3). The proportion of nonfrail participants increased from Q1 to Q4 for both sexes. The prevalence of frailty increased as food insecurity worsened (secure vs. severe: men 12.0% vs. 33.9%; women 18.0% vs. 39.7%). In the severe food insecurity group, the proportion of nonfrail participants was lower among women than among men (5.4% vs. 25.3%) (Fig. 4).
Fig. 3.
Prevalence of frailty groups across men (A) and women (B) aged 65 and above stratified into quartiles on the basis of their Korean healthy eating index scores
Fig. 4.
Percentage of all participants, men (A), and women (B), aged 65 and above with food security stratified by frailty severity
In women, higher dietary quality was associated with lower odds of frailty (Table 3). Compared with those in the lowest quartile of KHEI scores, women in the highest quartile had lower odds of being classified as frail (OR = 0.72, 95% CI: 0.52–1.01; P for trend = 0.0202), although the association did not reach statistical significance at the individual comparison level. No significant association between KHEI scores and frailty was observed in men.
Table 3.
Odds ratios for frailty by KHEI score and food security in men and women
| Variable | Men | Women | P for interaction | ||
|---|---|---|---|---|---|
| Prefrail OR (95% CI) |
Frail OR (95% CI) |
Prefrail OR (95% CI) |
Frail OR (95% CI) |
||
| KHEI scores | 0.0005 | ||||
| Q1 | 1 | 1 | 1 | 1 | |
| Q2 | 0.96 (0.72–1.27) | 0.97 (0.66–1.43) | 1.24 (0.93–1.64) | 1.01 (0.72–1.40) | |
| Q3 | 0.93 (0.70–1.23) | 0.99 (0.67–1.48) | 0.97 (0.73–1.28) | 0.92 (0.67–1.27) | |
| Q4 | 0.89 (0.68–1.16) | 1.05 (0.70–1.57) | 0.95 (0.73–1.24) | 0.72 (0.52–1.01) | |
| P for trend | 0.3388 | 0.7619 | 0.1403 | 0.0202 | |
| Food security | 0.0078 | ||||
| Secure | 1 | 1 | 1 | 1 | |
| Mild | 1.10 (0.94–1.30) | 1.24 (0.98–1.56) | 1.20 (1.01–1.42) | 1.72 (1.39–2.12) | |
| Moderate | 1.60 (1.02–2.49) | 2.39 (1.42–4.03) | 2.01 (1.27–3.18) | 3.61 (2.23–5.84) | |
| Severe | 1.65 (0.68–4.02) | 3.62 (1.23–10.66) | 6.40 (2.16–18.98) | 9.03 (2.92–27.97) | |
| P for trend | 0.0328 | 0.0006 | < 0.0001 | < 0.0001 | |
Models were adjusted for age(year), education(middle school or below, high school, college or above), marital status(married/living with partner, separated/divorced, widowed, never married), household income(low, middle-low, middle-high, high), smoking status(non, former, current), household type(single, multi), energy intake(kcal) and survey year. Furthermore, KHEI scores and food security were mutually adjusted in the same model
A multinomial logistic regression analysis was conducted to determine the association between KHEI scores, food security, and frailty groups, and P for trend was used to evaluate the linear trend across frailty groups
OR Odds ratio, CI Confidence interval, KHEI Korean Healthy Eating Index
P for interaction was obtained from the sex × frailty category interaction term
Food insecurity was associated with an increased risk of frailty in both sexes. Compared with the food-secure group, men with moderate and severe food insecurity had higher odds of frailty (OR = 2.39, 95% CI: 1.42–4.03 and OR = 3.62, 95% CI: 1.23–10.66, respectively). In women, severe food insecurity was associated with substantially higher odds of both prefrailty and frailty (OR = 6.40, 95% CI: 2.16–18.98 and OR = 9.03, 95% CI: 2.92–27.97, respectively). A dose–response relationship was observed in both sexes (P for trend = 0.0006 for men and < 0.0001 for women), and sex interactions were statistically significant (P for interaction = 0.0078).
Discussion
This study examined the associations of dietary quality and food security with frailty among older Korean adults using nationally representative data from the KNHANES. Frailty severity was consistently associated with lower KHEI scores, including lower scores for fruit, vegetables, protein-rich foods, and dairy products. Higher KHEI quartiles were associated with lower odds of frailty, with a significant trend observed in women. In addition, increasing levels of food insecurity were associated with higher odds of frailty in both sexes.
Compared with men, women had a higher prevalence of frailty (14.1% vs. 28.0%). This sex difference is consistent with findings from previous studies in Korea and elsewhere [10, 25–28]. ]. A large cohort study in China reported a higher prevalence of frailty among older women than among men [25], and a systematic review across 19 countries also revealed higher mean frailty scores among women [27]. The higher burden of frailty among older women has been linked to greater socioeconomic vulnerability, and prior research has indicated that lower socioeconomic status is more strongly associated with frailty risk in women than in men [29]. Our findings support this evidence by showing pronounced differences in educational attainment, income level, household type, and widowhood between frail men and women.
Frailty progression was associated with a significant decline in overall dietary quality. These findings are consistent with those of previous meta-analyses and cohort studies reporting inverse associations between healthy dietary patterns—such as the KHEI, HEI, Mediterranean diet, and Dietary Approaches to Stop Hypertension (DASH)—and frailty risk [7, 10, 30, 31]. Diets rich in fruits, vegetables, and whole grains and low in saturated fats are known to provide antioxidants and bioactive compounds that mitigate oxidative stress and chronic inflammation, key pathological processes contributing to sarcopenia and functional decline in aging populations [7, 30, 31].
Protein intake plays a critical role in maintaining skeletal muscle integrity by stimulating muscle protein synthesis and attenuating sarcopenia, which is a core pathway underlying frailty [32]. However, anabolic resistance increases with aging, indicating a reduced response of muscle protein synthesis to dietary protein intake [32, 33]. Accordingly, older adults are recommended to consume more protein to maintain muscle mass. In our study, scores for “meat, fish, eggs, and legumes”—the primary source of dietary protein—declined significantly with increasing frailty severity (P for trend < 0.001), suggesting that protein intake may be insufficient to meet physiological requirements among frail individuals. A systematic review reported mixed findings regarding total protein intake and frailty, but compared with nonfrail adults, frail adults consistently reported lower intake of animal-based protein [34].
Unlike other KHEI adequacy components, sodium scores increased with increasing frailty severity in both sexes. In the KHEI scoring system, a higher sodium score reflects lower sodium intake than the recommended intake threshold in the Dietary Reference Intakes for Koreans (KDRIs, 2015) [23]. Thus, higher sodium scores in frail individuals are unlikely to reflect intentional adherence to a low-sodium diet but rather the result of reduced overall dietary intake due to aging-related appetite loss and gastrointestinal symptoms [10]. Although reducing sodium intake remains an important public health objective in Korea, strict reduction of sodium intake among frail older adults with low body weight, anorexia, and multiple chronic conditions may inadvertently contribute to reduced energy and protein intake, potentially worsening frailty outcomes [35].
The association between dietary quality and frailty risk differed significantly by sex (P for interaction = 0.0005). An inverse trend between KHEI score and frailty was observed in women (P for trend = 0.0202), whereas no significant association was observed in men. This finding is consistent with a study conducted in Australian older adults, which reported that healthy dietary patterns were associated with lower frailty risk primarily in women [36]. Similar associations have been reported in the Nurses’ Health Study and other large female cohorts [37, 38]. However, Japanese and Singaporean cohort studies have reported significant associations between dietary quality and frailty risk in both sexes without sex-specific interactions [39, 40]. These discrepancies may reflect cultural differences in dietary habits or the choice of dietary assessment tools.
Food insecurity was significantly associated with higher risks of both prefrailty and frailty among men and women after sociodemographic factors and total energy intake were adjusted. Importantly, these findings should be interpreted within a broader public health framework, as food insecurity represents a structural social determinant of health that may operate upstream of dietary behaviors.
In this context, food insecurity may constrain food choices and dietary quality, thereby contributing to nutritional inadequacy and increased vulnerability to frailty.
These findings are consistent with those of prior studies in Mexico, India, and the United States, which reported significant associations between food insecurity and frailty or prefrailty risk among older adults [11, 41, 42]. The mechanisms linking food insecurity to frailty may involve multiple pathways, including nutritional deficiencies, limited access to health services, and psychological stress. Food insecurity is a key social determinant of health, reflecting structural vulnerabilities related to income, education, housing, and social resources [42]. A systematic review revealed that older adults who experience food insecurity have lower dietary quantity and quality and higher risks of undernutrition, micronutrient deficiency, and weight loss [43].
Owing to economic and physical constraints, food-insecure older adults may have limited access to fresh fruits and vegetables, dairy products, and protein-rich foods and may instead adopt diets high in refined grains and processed foods [13, 43]. This dietary pattern can lead to reduced intake of energy, protein, vitamins, and minerals, which contributes to weight loss, reduced lean mass, and impaired physical function, increasing the risk of frailty and sarcopenia [44, 45]. Given the diminished physiological reserve among older adults, even short periods of insufficient energy and protein intake can accelerate muscle loss and functional decline [33, 44], highlighting the importance of food security as a modifiable risk factor for frailty.
The association between food insecurity and frailty appeared more pronounced in women than in men, with higher odds observed among women with severe food insecurity (women’s OR: 9.03 vs. men’s OR: 3.62), and a significant interaction was identified (P for interaction = 0.0078). This findings align with prior research reporting a closer link between food insecurity and health outcomes in older women than in men [46, 47]. A global analysis of data from 146 countries revealed that women consistently reported higher levels of food insecurity than men did and that this gap was largely attributable to socioeconomic differences in income, education, employment, marital status, and social support [48]. Prior evidence has identified low education and income as key determinants of health-related inequality among older women, who report higher rates of functional limitations, chronic disease, and poor self-rated health [49, 50]. In our study, compared with frail men, frail women had markedly lower education levels (92.3% vs. 66.2%) and income levels and higher proportions of single-person households and widowhood, suggesting that the stronger association observed in women may reflect overlapping nutritional and socioeconomic vulnerability.
This study has several strengths. First, it used nationally representative data from the KNHANES to analyze a large sample of Korean adults aged ≥ 65 years, increasing the generalizability of the findings. Second, both dietary quality and food security were evaluated together, allowing for a more comprehensive understanding of the nutritional and socioeconomic determinants of frailty. Third, sex-stratified analyses and interaction testing provided insight into sex-specific vulnerability.
However, this study has several limitations. First, owing to the crosssectional design, causal relationships between dietary quality, food security, and frailty risk cannot be established. Longitudinal studies are needed to clarify temporal associations. Second, dietary intake was assessed using a 24-hour recall, which may be subject to recall bias. Third, although multiple confounders were adjusted for, residual confounding from unmeasured factors such as genetic predisposition or psychological status cannot be excluded.
In conclusion, this study provides evidence that lower dietary quality and greater food insecurity are associated with greater risk of frailty among older Korean adults, particularly among women. These findings underscore the importance of integrating nutritional and socioeconomic strategies into frailty prevention in aging societies. Future longitudinal and intervention studies are needed to clarify causal pathways and to evaluate the effectiveness of sex-specific nutritional interventions and food security policies for older adults.
Conclusion
This study revealed that lower dietary quality and higher food insecurity are associated with greater risk of frailty among older Korean adults. Although food security reduced the risk of frailty in both sexes, higher dietary quality was protective only in women, who exhibited a sharper increase in frailty risk under severe food insecurity, indicating overlapping nutritional and socioeconomic vulnerability. Frailty prevention should incorporate sex-specific strategies that prioritize dietary quality, particularly adequate protein and micronutrient intake. Strengthening food access for socioeconomically disadvantaged groups, especially older women, is essential. Integrating nutritional interventions with structural support may reduce inequities and delay the occurrence of frailty in aging populations.
Supplementary Information
Acknowledgements
NA.
Authors’ contributions
Conceptualization: Kim SB, Kang MJ. Data curation: Kim SB. Formal analysis: Kim SB. Funding acquisition: None. Methodology: Kim SB, Kang MJ. Project administration: Kang MJ. Visualization: Kim SB. Writing–original draft: Kim SB, Kim SJ, Kim EH, Park NY, Kwak DW, Choi ES. Writing–review & editing: Kim SB, Kim SJ, Kim EH, Kang MJ.
Funding
This work was supported by the National Research Foundation of Korea grant funded by the Korean government (Ministry of Science and ICT; grant number RS-2026-25495997). The funding body played no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.
Data availability
The data that support the findings of this study is publicly available and can be accessed from the Korea Disease Control and Prevention Agency ( https://www.kdca.go.kr/yhs ).
Declarations
Ethics approval and consent to participate
The KNHANES protocols were approved by the Institutional Review Board of the Korea Disease Control and Prevention Agency (approval numbers: 2013-07CON-03–4 C, 2013-12EXP-03–5 C, 2018-01-03-P-A, 2018-01-03-C-A, 2018-01-03–2 C-A, and 2018-01-03–5 C-A). Written informed consent was obtained from all participants before data collection.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Footnotes
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Subeen Kim and Seon-Jip Kim contributed equally to this work.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Data Availability Statement
The data that support the findings of this study is publicly available and can be accessed from the Korea Disease Control and Prevention Agency ( https://www.kdca.go.kr/yhs ).