The KNHANES is conducted annually to assess the health and health-related behaviors of the Korean population based on Article 16 of the National Health Promotion Act. It collects comprehensive data on the entire Korean population’s sociodemographic characteristics, health behaviors, health status, biochemical profiles, disease history, and nutrient intake [4,5]. A stratified, multistage probability sampling method was used. Participants included Korean family members aged 1 year and older in the selected households, corresponding to 9682 individuals. The health interview and health examination surveys were conducted in mobile examination centers, while the nutrition survey was performed by visiting households [6]. Of the 9682 eligible participants, 7090 (response rate=73.2%) ultimately participated in the survey. Of these survey respondents, we finally selected 1806 individuals aged 65 years and older.

The 2021 KNHANES was approved by the institutional review board (IRB) of the Korea Centers for Disease Control and Prevention (2018-01-03-5C-A). This study used publicly available secondary, deidentified data. The IRB review exemption was approved by the IRB of the Catholic University of Korea.

Table 1 presents the estimated probabilities of belonging to the 3 classes. Class 1 patients had the lowest probability of having any of the 9 chronic diseases. Class 2 patients had a high probability of having hypertension, dyslipidemia, diabetes mellitus, myocardial infarction, and stroke. Class 3 patients had a high probability of having arthritis, osteoarthritis, allergic rhinitis, or asthma. Figure 1 shows the distribution of the three latent classes according to the 9 chronic diseases.

Table 2 presents the sociodemographic characteristics, chronic disease patterns, and HRQoL according to the latent classes. Of this study’s sample of 1806 Korean older adults, 42.7% (771/1806) were male participants. The mean age was 73.36 (SD 5.18) years. The prevalence of multimorbidity in the sample was 54.8%. The mean HRQoL measured by the HINT-8 was 14.07 (SD 3.89).

Among the 3 latent classes, class 1 (741/1806, 41.03%) had the lowest prevalence of current smoking and drinking. The mean number of chronic diseases was 0.57 (SD 0.59). The class 2 (636/1806, 35.22%) group with cardiometabolic conditions had a mean 2.65 (SD 1.06) chronic diseases per individual. Among them, 71.9% (447/622) experienced pain, 67.2% (418/622) lacked vitality, 44.8% (278/620) had depression, and 74% (459/620) felt unhappy. Of the class 3 group (429/1806, 23.75%) that included individuals with arthritis, allergies, or asthma, 79.8% (340/426) of the participants reported difficulty in climbing stairs, 81.2% (346/426) reported pain, 55.2% (342/426) reported depression, and 64.6% (275/426) reported sleep problems. The mean number of chronic diseases in class 3 was 2.50 (SD 1.13). Compared with class 1 participants, those participants classified into classes 2 and 3 were older, more likely to be female participants, and had lower levels of education.

Table 3 presents the associations among the 3 latent classes and the total scores of the HINT-8. Class 2 patients had higher HRQoL scores by 1.14 points than class 1. Class 3 patients had higher HRQoL scores by 2.30 points than class 1 patients, which was significant.

Table 4 presents the adjusted ORs and shows the associations between chronic disease patterns and HRQoL based on the HINT-8 items. The class 2 group had a higher OR of difficulty in climbing stairs (OR 1.49, 95% CI 1.08-2.06), pain (OR 1.58, 95% CI 1.13-2.23), and unhappiness (OR 1.28, 95% CI 1.03-1.69) than the class 1 group. The class 3 group had a higher OR of difficulty in climbing stairs (OR 1.92, 95% CI 1.35-2.71), pain (OR 1.88, 95% CI 1.32-2.67), lack of vitality (OR 1.35, 95% CI 1.02-1.79), depression (OR 2.51, 95% CI 1.09-4.98), sleep problems (OR 1.46, 95% CI 1.01-2.10), and unhappiness (OR 1.54, 95% CI 1.13-2.26) than the class 1 group. Generally, the class 3 group was more likely to have problems in all 8 items of the HINT-8, even in the absence of a statistically significant result for the lack of vitality and memory difficulties.

This study investigated the prevalence of multimorbidity, chronic disease patterns, and the association between latent classes and quality of life among Korean older adults using LCA. Of the sample of 1806 Korean older adults, 54.8% had multimorbidities, with a mean number of chronic conditions of 1.82 (SD 1.35). This prevalence of multimorbidity was lower than that found by previous studies in other countries. At least two-thirds of those older than 65 years have multimorbidities in the United Kingdom and the United States [22,23], while approximately 80% of Australians aged 65 years and older have 3 or more chronic conditions [8]. The standardized national prevalences of multimorbidity in Japan are approximately 50% for ages between 60 and 69 years and 60% for ages between 70 and 74 years [24].

Using LCA, this study identified 3 chronic disease groups: a relatively healthy group without multimorbidities, a cardiometabolic condition group, and an arthritis, allergy, or asthma group. The cardiometabolic condition group had a high probability of having hypertension, dyslipidemia, diabetes mellitus, myocardial infarction, and stroke, while the other group had a high probability of experiencing arthritis, osteoarthritis, allergic rhinitis, or asthma. Similar disease patterns to those found in this study have been noted previously [3,25-28]. Cardiometabolic diseases are one of the most reproducible multimorbidity patterns [29]. The association between dietary patterns and multimorbidity within the cardiometabolic domain suggests the importance of dietary interventions in preventing and managing multimorbidity [30].

Our findings were also similar to the results of another study among Koreans aged 50 years and older that classified hypertension, hyperlipidemia, diabetes, and stroke as a single latent class of multimorbidity using data from the KNHANES conducted in 2013 and 2014 [3]. Another study using a network analysis approach indicated that hypertension had stronger connections with hyperlipidemia and diabetes than other pairs of morbidities in older adults, using data from the KNHANES conducted between 2010 and 2018. The substantial overlap between hypertension, diabetes, and hyperlipidemia in etiology and disease mechanisms might have resulted in this phenomenon [2]. The further replicability of results between studies that use different methodologies is an important step toward moving from exploratory to confirmatory approaches.

This study indicated that the association between chronic disease patterns and HRQoL differed by multimorbidity group. The cardiometabolic condition and arthritis, allergy, or asthma groups had lower HRQoL than the relatively healthy group [31-34]. Moreover, the total scores of the HINT-8 were higher in the arthritis, allergy, or asthma group than in the cardiometabolic condition group, indicating a lower quality of life [35].

Compared with older adults with a single chronic disease, those with multiple chronic conditions face mental health-related social needs and social isolation [36,37]. Their low quality of life and combination of chronic diseases may diminish their overall health [28]. Thus, these chronic disease patterns can be used to support better screening, targeted management, and integrated health services for older adults with chronic diseases [32,38,39]. Further research is required to investigate whether multimorbidity associations are similar to biological pathways or the result of interactions.

Current health care models are disease-oriented, meaning that the management of chronic conditions applies to a single condition and may not be relevant to those with multimorbidities [40]. Individuals with multimorbidities typically receive services from multiple health care specialists who only focus on a single health condition and do not view them in their entirety. A key challenge faced by individuals with multimorbidities, and health care professionals is thus fragmented clinical care, which creates miscommunication; this may result in inconsistent health care messages and could lead to an increase in polypharmacy [28]. Consequently, further research is needed to develop guidelines and approaches for the management of multimorbidity and identify chronic disease groups.

This study had some limitations. First, it focused on a limited range of chronic diseases, specifically those with a prevalence of at least 3% as reported by the KNHANES; hence, the list of chronic conditions may not have been exhaustive. If more diseases are considered, a higher prevalence could be estimated [41]. However, while we performed an additional LCA including all the chronic diseases registered in the KNHANES data set, the samples for certain combinations of multimorbidities were insufficient to provide reliable results despite our relatively large sample overall [9].

Second, the findings could differ if a broader scope of chronic conditions is considered along with the different aspects of the conditions. A long illness negatively impacts the quality of life of chronic patients [42]. Moreover, information on disease severity may have helped distinguish between patterns; unfortunately, these data were unavailable.

Third, the participants self-reported their chronic diseases, increasing the risk of reporting bias. However, self-reported data are the most appropriate alternative in the absence of objective diagnoses and have been shown to provide solid estimates [9,43].

Finally, the selected class name in LCA may not always accurately represent class membership [20]. However, any issues arising from this so-called “naming fallacy” may be mediated by presenting the distribution of the selected indicators for each class (Table 1) to allow an individual interpretation. Despite these limitations, the chronic disease patterns among older adults were examined using nationally representative data.

This study identified 3 distinct chronic disease groups among Korean older adults: a relatively healthy group without multimorbidities, a cardiometabolic condition group, and an arthritis, allergy, or asthma group. Those in the latter group had a lower quality of life than those in the cardiometabolic condition group. Understanding these combinations of long-term conditions may help target disease prevention and improve care integration efforts. The further replicability of results between studies that use different methodologies is an important step toward moving from exploratory to confirmatory approaches. Future research on the effectiveness of interventions aimed at preventing and improving the management of multimorbidities is needed.