The life-course approach to health has emerged as a central paradigm in contemporary public health and population health research. Rooted in interdisciplinary traditions spanning epidemiology, sociology, demography, and social policy, the life-course perspective emphasises that health outcomes are shaped by cumulative exposures and experiences across different stages of life rather than by isolated risk factors occurring at a single point in time [14, 15]. This perspective aligns closely with evolving international life-course frameworks that highlight the importance of promoting functional ability, resilience, and wellbeing across all ages, from early development to healthy ageing [3].

Within this framework, health outcomes are understood as being shaped by biological, behavioural, and social determinants operating across the lifespan. Early-life conditions, including family socioeconomic status, education, and nutrition, play a foundational role in shaping later health outcomes by influencing physical development, cognitive capacities, and health behaviours [16]. These early influences interact with subsequent exposures in adolescence and adulthood, including educational attainment, employment opportunities, and social environments, contributing to cumulative patterns of advantage and disadvantage in health. Three primary models are commonly used to conceptualise life-course influences on health: the critical period model, the accumulation of risk model, and the pathway model [14]. These models are not mutually exclusive and often operate simultaneously, underscoring the complexity of life-course health dynamics.

In the context of non-communicable diseases such as obesity, the life-course approach is particularly relevant. Obesity is influenced by long-term patterns of diet, physical activity, and socioeconomic conditions that evolve over time. Understanding how these factors interact across different life stages is essential for designing effective prevention strategies that extend beyond individual behaviour change to address structural determinants of health [17].

Socioeconomic status (SES) is widely recognised as a fundamental determinant of health. It encompasses multiple dimensions, including education, income, occupation, and wealth, which shape access to resources, opportunities, and social environments that influence health outcomes [6]. Extensive evidence documents strong and persistent associations between SES and morbidity, mortality, and self-rated health [18]. From a life-course perspective, socioeconomic inequalities in health arise through cumulative and pathway mechanisms. Individuals from disadvantaged backgrounds are more likely to experience adverse early-life conditions and limited opportunities for upward mobility, with effects that persist into adulthood and shape health behaviours and access to care [19].

Education plays a central role in shaping health outcomes across the life-course. Higher educational attainment is associated with greater health literacy, improved employment prospects, and higher income, all of which contribute to better health outcomes [20]. In contrast, lower education is often linked to precarious employment and increased exposure to health risks. Household income reflects the material resources available for maintaining healthy living conditions and accessing healthcare [21]. Employment status and working conditions also influence health across the life-course. Stable employment provides financial security and social integration, whereas unemployment or precarious employment is associated with financial stress and adverse health outcomes [22]. Marital status and household composition further shape socioeconomic and psychosocial environments, influencing health behaviours and social support networks [23]. Importantly, these socioeconomic dimensions interact over time, contributing to cumulative patterns of advantage and disadvantage that shape health outcomes throughout adult life. Persistent socioeconomic disadvantage across life stages increases the risk of adverse outcomes, including obesity and related chronic conditions.

Obesity has emerged as one of the most pressing public health challenges globally. It is associated with increased risk of cardiovascular disease, diabetes, certain cancers, and reduced quality of life [24]. While obesity has traditionally been more prevalent in high-income Western countries, its prevalence has risen rapidly in many Asian contexts, including South Korea, reflecting broader socioeconomic and lifestyle changes [25].

The determinants of obesity are complex and multifactorial, encompassing genetic, behavioural, environmental, and socioeconomic factors. From a life-course perspective, obesity is shaped by long-term patterns of energy balance influenced by diet, physical activity, and metabolic processes [26]. Early-life nutrition, childhood socioeconomic conditions, and educational attainment play important roles in shaping lifelong health behaviours and metabolic health. Socioeconomic inequalities in obesity have been widely documented, although their patterns vary across countries and population groups. In rapidly developing economies, the relationship between SES and obesity may shift as countries undergo economic and nutritional transitions [27].

South Korea represents an important case in this regard. Rapid economic development and urbanisation have transformed dietary patterns, physical activity levels, and occupational structures, contributing to changes in obesity prevalence. Previous studies in South Korea have documented significant socioeconomic inequalities in obesity, although findings have varied depending on the socioeconomic indicator examined [21]. Educational attainment has consistently emerged as one of the strongest predictors of obesity, particularly among women, while associations with household income and occupation have been less consistent [28]. Research using earlier waves of KNHANES has shown that lower educational attainment is associated with a higher prevalence of obesity and related metabolic risk factors, suggesting that socioeconomic influences on obesity extend beyond current material resources alone [29]. These findings highlight the importance of examining multiple dimensions of socioeconomic positioning simultaneously rather than relying on a single SES indicator. Understanding how socioeconomic factors across the life-course influence obesity in the Korean context is therefore essential for informing targeted public health interventions.

The growing emphasis on life-course approaches to health underscores the need for data systems that capture exposures and outcomes across different stages of life. Linked administrative datasets integrating information from health, education, employment, and social services provide powerful tools for examining life-course research by enabling researchers to track individuals across time and sectors. Despite their analytical value, such comprehensive linked data infrastructures remain unevenly developed across countries. However, linkage across institutional datasets in South Korea remains methodologically and administratively challenging due to fragmented governance structures, differing access procedures across agencies, privacy regulations, and concerns regarding secure data sharing and long-term data stewardship. In many settings, including South Korea, administrative data are collected by separate agencies and are not routinely linked for research purposes. Privacy regulations, institutional barriers, and technical challenges further limit access to integrated datasets, meaning researchers often rely on cross-sectional survey data to examine health inequalities and their determinants [30]. In the Korean context, future linkage between national health survey data and administrative datasets such as National Health Insurance Service records, employment histories, educational records, mortality data, and neighbourhood-level contextual data could substantially strengthen the ability to examine long-term socioeconomic influences on health. National health surveys such as KNHANES provide valuable resources for life-course research, even in the absence of longitudinal linkage. These surveys typically include detailed information on socioeconomic characteristics, health behaviours, and health outcomes that can be analysed within a life-course framework. While they do not track individuals over time, they capture snapshots of different age cohorts and socioeconomic groups, allowing for the examination of cumulative patterns of disadvantage and health risk [12]. Moreover, national surveys can serve as foundational components of future linked data systems. By identifying key variables and data gaps, survey-based research can inform efforts to integrate data across sectors and improve the availability of longitudinal information for life-course analysis. In this sense, leveraging existing survey data is not only a pragmatic approach to studying health inequalities but also a step toward developing more comprehensive linked data infrastructures.

Despite the growing recognition of the importance of life-course approaches to health, relatively few studies have examined socioeconomic positioning and obesity within a life-course framework using nationally representative data from South Korea. While previous Korean studies have examined individual SES indicators, fewer studies have examined multiple dimensions of socioeconomic positioning within an explicit life-course framework. Existing research has often focused on single indicators of SES or specific age groups, limiting understanding of how multiple socioeconomic factors interact to shape health outcomes. Furthermore, there is limited discussion of how national health survey data can be leveraged for life-course research and integrated into broader linked data systems [18].

This study addresses these gaps by using data from KNHANES 9^th wave (2022–2024) to examine the relationship between socioeconomic indicators and adult obesity within a life-course framework. By analysing multiple dimensions of SES simultaneously, the study provides a more comprehensive understanding of how socioeconomic dimensions are associated with obesity risk. In addition, it situates the analysis within the broader context of linked data and life-course research, highlighting opportunities and challenges for enhancing data integration and policy relevance.

Through this approach, the paper contributes to ongoing efforts to operationalise life-course frameworks in public health research and to leverage routinely collected data for improving health and wellbeing across populations. It also offers insights relevant to other countries seeking to strengthen data systems and address health inequalities through a life-course perspective.

This study uses data from the ninth wave of the Korea National Health and Nutrition Examination Survey (KNHANES 9th wave), covering the period 2022–2024. KNHANES is a nationally representative, cross-sectional survey conducted by the Korea Disease Control and Prevention Agency (KDCA) to monitor the health and nutritional status of the Korean population [31]. Since its inception in 1998, KNHANES has served as a key source of population health data for research and policy development in South Korea. KNHANES employs a stratified, multistage probability sampling design to ensure representativeness of the non-institutionalised civilian population [31]. Sampling units are selected based on geographic region, sex, and age distribution using census data. The survey consists of three primary components: a health interview, a health examination, and a nutrition survey. Together, these components provide comprehensive information on demographic characteristics, socioeconomic status, health behaviours, clinical indicators, and nutritional intake.

The ninth wave of KNHANES represents the most recent fully available dataset at the time of analysis [32]. As with previous waves, data collection includes measured anthropometric indicators such as height and weight, enabling accurate calculation of body mass index (BMI). The survey also collects detailed information on education, household income, employment, marital status, and residential characteristics, which are essential for examining socioeconomic determinants of health within a life-course framework.

The analytical sample for this study consists of adults aged 19 years and older who participated in KNHANES 9^th wave (2022–2024). Individuals with missing data on key variables, including BMI or socioeconomic indicators, were excluded from the final analysis. Both men and women were included to capture gender differences in socioeconomic patterns and obesity prevalence. The use of a nationally representative adult sample allows for examination of socioeconomic gradients in obesity across the broader Korean population. Sampling weights provided by KNHANES were applied in all analyses to account for the complex survey design and to produce estimates representative of the national population.

The primary outcome variable in this study is obesity status. Obesity was defined using body mass index (BMI), calculated as weight in kilograms divided by height in metres squared (kg/m²). Height and weight measurements in KNHANES are obtained through standardised health examinations conducted by trained personnel, ensuring high reliability and validity. Consistent with Korean and international public health guidelines, obesity was defined as BMI ≥25 kg/m² for Asian populations, reflecting evidence that obesity-related health risks occur at lower BMI thresholds than in Western populations [33]. Individuals with BMI below 25 kg/m² were classified as non-obese for the purposes of analysis. A binary variable indicating obesity status was constructed to facilitate logistic regression modelling.

To examine socioeconomic inequalities from a life-course perspective, this study incorporates multiple indicators of socioeconomic status (SES), including educational attainment, household income, employment status, marital status, and region of residence. Together, these variables capture key dimensions of current and cumulative socioeconomic positioning that influence health over time. The construction and life-course interpretation of these indicators are summarised in Table 1.

Educational attainment and household income were included as core indicators of socioeconomic positioning. Educational attainment was categorised into four groups (elementary school or less, middle school, high school, and college or higher) and serves as a proxy for earlier-life and cumulative socioeconomic conditions that may influence health behaviours and opportunities throughout adulthood. Household income was measured using equivalised household income quartiles (lowest, lower-middle, upper-middle, and highest), reflecting current material resources and living conditions.

Employment status, marital status, and region of residence were included to capture additional dimensions of adult socioeconomic and social context. Employment status was classified as employed or not employed and reflects economic stability and social integration. Marital status was categorised as married, never married, or previously married (divorced, separated, or widowed), capturing differences in household structure and social support. Region of residence was classified as urban or rural and serves as a contextual indicator of environmental conditions, healthcare access, and health-related resources.

The analytical sample consisted of adults aged 19 years and older drawn from the ninth wave of the Korea National Health and Nutrition Examination Survey (KNHANES 2022–2024). After excluding respondents with missing data on body mass index or key socioeconomic variables, the final weighted sample was nationally representative of the non-institutionalised adult population of South Korea [35]. After appending the 2022–2024 datasets and applying inclusion/exclusion criteria (age ≥19; non-missing BMI, SES variables; income quartiles 1–4), the final analytic sample included n = 12,075 respondents (unweighted). Using KNHANES sampling weights, the sample represents the national adult population of South Korea. The weighted obesity prevalence (BMI ≥25 kg/m²) was 38.0%.

Table 2 presents the descriptive characteristics of the study population. The sample included both men and women across diverse age groups and socioeconomic backgrounds. A substantial proportion of respondents had completed tertiary education, reflecting South Korea’s high educational attainment levels. However, notable variation remained across age cohorts, with lower educational attainment more common among older adults. Household income was distributed across quartiles, with representation across all socioeconomic strata. Most working-age adults were employed, although employment rates varied by gender and age. The majority of respondents were married, while a smaller proportion were never married or previously married. Consistent with national demographic patterns, most respondents resided in urban areas. Overall obesity prevalence (BMI ≥25 kg/m²) indicated that a considerable proportion of Korean adults were classified as obese, highlighting obesity as a significant public health concern.

Obesity prevalence varied significantly across socioeconomic groups (Table 3). Clear gradients were observed by education and household income. Individuals with lower educational attainment exhibited higher obesity prevalence (41.2%) compared with those holding tertiary education (37.4%). Differences by income quartile were modest in unadjusted comparisons. Employed adults had higher obesity prevalence (40.1%) than those coded as not employed (34.0%).

Married individuals, particularly men, showed higher prevalence compared with never married individuals. Rural residents exhibited slightly higher obesity prevalence than urban residents, suggesting potential contextual influences related to environment and access to health-promoting resources. These descriptive findings indicate the presence of socioeconomic inequalities in obesity within the Korean adult population.

To examine the independent association between socioeconomic status and obesity, logistic regression models were estimated (Table 4). All models accounted for complex survey design and applied sampling weights. In the fully adjusted model, women had substantially lower odds of obesity than men. Compared to adults with college+ education, those with elementary education or less had significantly higher odds of obesity (OR 1.55). Income gradients were small and not statistically strong after adjusting for other SES measures. Several occupation categories showed higher odds compared to the reference group (occupation=7: unemployed/housewives/students), suggesting heterogeneity by work type and social positioning. Rural residence was not strongly associated after full adjustment.

Educational attainment emerged as the most consistent socioeconomic predictor of obesity. Compared with individuals with college or higher education, those with elementary education or less had significantly higher odds of obesity in both partially and fully adjusted models, while those with middle school education also showed elevated but smaller effects. These findings indicate that lower educational attainment is associated with increased obesity risk even after adjusting for income, employment, marital status, region, age, and sex. By contrast, household income showed weaker and less consistent associations with obesity. Although lower-income groups had slightly higher odds than the highest income group, differences were modest and not statistically strong in fully adjusted models, suggesting that educational attainment may be a more robust indicator of life-course socioeconomic disadvantage than current incomealone.

Employment status and occupational category demonstrat­ed heterogeneous associations with obesity. Compared with the non-employed reference group, several occupational categories, including office workers and service and sales workers, showed higher odds of obesity. These patterns suggest that work-related factors, including sedentary work environments and occupational stress, may contribute to obesity risk among employed adults. Marital status showed limited association with obesity after adjustment for other socioeconomic factors, while rural residence was not strongly associated with obesity after full adjustment. Together, these findings suggest that socioeconomic and demographic factors may play a more substantial role in obesity risk than household structure or geographic location alone.

Overall, the findings highlight that multiple dimensions of socioeconomic status contribute to obesity risk among Korean adults, with educational attainment emerging as the most consistent predictor. The results support a life-course perspective in which cumulative socioeconomic conditions shape health outcomes [36].

The observed prevalence underscores obesity as a population health issue rather than a niche clinical concern. This matters for the special issue theme because life-course approaches emphasise optimising health and wellbeing across ages, not merely managing disease at a single stage. When obesity is prevalent across broad segments of the population, even modest socioeconomic gradients translate into large absolute numbers of affected individuals, reinforcing the importance of structural prevention strategies (e.g., workplace and community design, food environments, prevention-oriented primary care) rather than relying exclusively on individual behaviour change. At the same time, a life-course view implies that obesity prevention should be staged: upstream interventions targeting early-life socioeconomic conditions (education, early adult transitions into labour markets) and midstream interventions that target working-age constraints (workplace sedentary exposure, time poverty, occupational stress) are both relevant [38]. Sex differences were pronounced in the multivariable models, with women showing substantially lower odds of obesity than men under the BMI ≥25 definition. This gap likely reflects a combination of behavioural, occupational, social, and cultural factors that differ by sex in South Korea. From a life-course standpoint, these differences can be understood as the result of gendered patterns in education-to-work transitions, differential exposure to workplace environments, and differences in health behaviour norms and weight-related social pressures [39]. Crucially, this large sex difference also signals that one-size-fits-all interventions may be inefficient. Policies and programmes aiming to improve obesity-related health outcomes may need sex-sensitive design, particularly in occupational settings, if the exposure patterns and constraints differ across men and women. Future research may benefit from sex-stratified analyses to examine whether socioeconomic influences on obesity operate differently among men and women across the life-course.

A WHO-aligned life-course approach emphasises maintaining and enhancing functional ability and wellbeing across all ages through person-centred and multisectoral strategies [38]. This perspective is closely aligned with the WHO Framework for Implementing a Life-Course Approach in Practice, which emphasises integrated, multisectoral, and prevention-oriented approaches to health across the lifespan. The framework highlights the importance of addressing social and structural determinants of health throughout different stages of life while strengthening data systems to support longitudinal research and evidence-based life-course policy [42]. The findings of this study align closely with this framework and demonstrate how nationally representative health survey data can inform life-course health analysis even in the absence of fully linked longitudinal datasets.

This study supports a life-course approach in several important ways. First, the persistent association between educational attainment and obesity highlights the importance of life-course interpretation [14, 37]. Educational attainment, typically established early in adulthood and remaining relatively stable thereafter, shapes long-term socioeconomic positioning, employment opportunities, and health behaviours. The strong and consistent relationship observed between lower education and higher obesity risk is consistent with the possibility that earlier social positioning contributes to later socioeconomic and health-related exposures associated with obesity risk. These findings are broadly consistent with life-course perspectives emphasising pathway mechanisms through which earlier socioeconomic conditions shape later opportunities, constraints, and health-related exposures associated with obesity risk. Second, the heterogeneity observed across occupational categories underscores the importance of multisectoral leverage points for health policy. Differences in obesity risk across occupational groups suggest that workplace environments, labour conditions, and time constraints play meaningful roles in shaping health behaviours and metabolic outcomes. These findings point to the workplace and labour policy domain as key intervention spaces extending beyond traditional health services. From a life-course perspective, occupational environments represent sustained exposures that may influence adult health outcomes over time. Third, the results support the need for prevention across life stages rather than focusing solely on late-stage clinical management of obesity. The persistence of socioeconomic gradients is consistent with the possibility that structural determinants operating across education, employment, and social environments shape obesity risk long before clinical intervention occurs. Upstream and midstream interventions targeting educational inequalities, workplace conditions, and broader social determinants of health are therefore essential components of a comprehensive life-course strategy.

Beyond its substantive findings, this study highlights the relevance of linked data for advancing life-course research. Although KNHANES is cross-sectional, it can support analyses within a life-course framework through relatively stable socioeconomic indicators such as education with adult structural measures including occupation and income. Expanding linked data infrastructures would further strengthen life-course research.

KNHANES can be positioned as a central platform within an emerging linked data ecosystem in several ways. First, it can function as a hub dataset for enrichment [43]. KNHANES provides high-quality measured health outcomes and detailed socioeconomic information. Linking it with administrative and sectoral datasets would enable more comprehensive analyses of weight patterns, chronic disease onset, healthcare utilisation, employment histories, and environmental exposures across the life-course. Second, KNHANES can serve as a governance and methodological testbed for developing privacy-preserving linkage models that balance research utility with confidentiality. At the same time, several governance and methodological challenges continue to limit broader linked data development in South Korea. Administrative and health-related datasets are often managed by separate institutions with differing access procedures, governance structures, and privacy regulations, which may complicate inter-agency linkage and data harmonisation. In addition, concerns regarding confidentiality, secure data sharing, and long-term governance frameworks may limit broader research accessibility to integrated datasets. These challenges highlight the importance of developing secure and transparent linkage infrastructures capable of balancing data protection with research utility in future life-course research initiatives. Third, the Korean experience offers a transferable model for countries without mature linked data systems, demonstrating how life-course analyses can begin with existing national surveys while progressively expanding linkage capacity. In this sense, KNHANES illustrates how routinely collected survey data can support life-course research and inform policy even where fully integrated longitudinal systems remain under development. To synthesise these implications, Table 5 summarises how the empirical findings of this study relate to life-course interpretation and to future data linkage opportunities.