Skip to main content
NCBI home page
BMJ Open logoLink to BMJ Open
. 2026 Jun 22;16(6):e105488. doi: 10.1136/bmjopen-2025-105488

Korean Medicine for Aging Cohort (KoMAC) study—a prospective multicentre cohort in urban and rural Korea: cohort profile

Mi Mi Ko 1,0, Young-Eun Kim 2,0, Wonbae Ha 3, Seojae Jeon 4, Myunghwa Kim 4, So Young Jung 5, Kwang-Ho Choi 1, Geonhui Kang 1, Seungkwan Choi 3, Jungho Jo 6, Junghan Lee 4,7,*, Jeeyoun Jung 1,*
PMCID: PMC13288976  PMID: 42331573

Abstract

Purpose

The KoMAC (Korean Medicine for Aging Cohort) study investigates health characteristics related to ageing from both conventional and Korean medicine (KM) perspectives through a prospective, multicentre cohort study that also serves as a platform for future embedded intervention studies.

Participants

This study aimed to recruit 1000 adults aged 50–65 years from both urban (Iksan and surrounding areas, Jeonbuk Province) and rural (Jangheung and surrounding areas, Jeonnam Province) regions in Korea. Recruitment began in August 2023. The inclusion criteria were residence in the target region, literacy and written consent.

Findings to date

Initial data showed regional differences in health behaviours and conditions; rural participants showed higher rates of alcohol consumption, smoking and childbirth experience among women, while urban participants showed lower pulmonary function and less favourable hearing thresholds. Ageing was associated with a general decline in physical and psychosocial function, particularly in women. KM-based phenotypic analysis revealed an age-related increase in kidney deficiency patterns and blood stasis, particularly in women. Emotional assessments indicated a decline in joy with age. Laboratory findings showed age-related increases in blood urea nitrogen levels in women, consistent with changes observed in KM-based phenotypes.

Future plans

The KoMAC study will continue with annual follow-ups, and blood samples will be collected again at the final follow-up visit to identify ageing-related risk factors. Embedded randomised controlled trials will evaluate the effects of KM-based interventions on health outcomes.

Trial registration number

Clinical Research Information Service (KCT0008863).

Keywords: Aging, Health Surveys, COMPLEMENTARY MEDICINE, Follow-Up Studies, GERIATRIC MEDICINE, PREVENTIVE MEDICINE

STRENGTHS AND LIMITATIONS OF THIS STUDY.

  • The Korean Medicine for Aging Cohort (KoMAC) study includes community-dwelling middle-aged adults from both urban and rural regions, enabling direct comparisons of ageing-related factors across residential settings and providing valuable insights into the role of living environments in the ageing process.

  • A major strength of the KoMAC study lies in its multidimensional approach to healthy ageing, which incorporates biological, psychological, social and Korean medicine (KM)-based phenotypic factors, thereby offering a more comprehensive evaluation than conventional biomedical approaches alone.

  • A comprehensive research database and blood biobank are being established to support the long-term follow-up and development of KM-based healthy ageing models, with data available to qualified researchers for secondary analyses.

  • Participants were recruited from two regions without stratification by age or sex; therefore, the demographic composition of the cohort may not reflect the broader national population, which may limit the generalisability of the findings. In addition, participants were recruited through voluntary community-based participation, which may have introduced healthy volunteer bias.

  • Although the overall sample size was sufficient for many analyses, it may have been insufficient to detect associations in rare outcomes or small subgroups; furthermore, several variables rely on self-reported data, which may be subject to recall bias or misclassification.

Introduction

South Korea became an aged society in 2018 and is projected to transition to a super-aged society by 2025, with the proportion of the population aged 65 years and older expected to exceed 20% and reach 40.1% by 2050.1 This rapid demographic transition has implications for healthcare and welfare systems,2 as the rising prevalence of chronic diseases and functional decline continues to intensify the burden on individuals, families and healthcare providers.3,5

In response, there is growing interest in shifting from a disease-centred model towards more proactive, preventive and integrative approaches to healthy ageing. According to the WHO, healthy ageing is a comprehensive process that goes beyond the treatment of disease, emphasising the maintenance of functional ability and the promotion of well-being by supporting intrinsic capacity and creating enabling environments.6 To achieve this, the WHO emphasises the importance of integrated care approaches, community-level support structures and effective strategies for managing chronic conditions.7 8

Korean medicine (KM), with its emphasis on individualised care based on constitutional and functional patterns rather than disease diagnosis alone, is well positioned to support this agenda by focusing on disease prevention and health maintenance. In Korea, KM is widely used by older adults, highlighting its potential role in ageing-related healthcare and the need for further research on its effectiveness in this population,9 10 yet robust longitudinal evidence on its contribution to healthy ageing remains limited. Cohort studies are particularly valuable in this context, enabling the prospective examination of ageing trajectories across biological, psychological, social and KM-specific dimensions.11 12 Furthermore, ageing-related health behaviours and outcomes differ markedly between urban and rural communities in Korea,13 14 yet no existing cohort has examined these regional disparities through a KM lens.

To address these gaps, the Korean Medicine for Aging Cohort (KoMAC) study was established as a prospective, multicentre cohort of adults aged 50–65 years recruited from both urban (Iksan) and rural (Jangheung) regions of South Korea. The KoMAC study is designed with the following objectives: (1) to comprehensively evaluate biological, psychological, social and constitution-related phenotypic factors associated with healthy ageing from the perspective of KM; (2) to compare ageing-related health characteristics and identify geographic disparities among middle-aged and older adults living in urban and rural regions; (3) to establish a biobank of blood samples that will enable future biomarker research relevant to KM and ageing; (4) to establish a cohort platform capable of supporting future cohort multiple randomised controlled trials (cmRCTs) of KM-based interventions; and (5) to develop an evidence-based, KM-informed framework that supports personalised and integrative care strategies aimed at enhancing quality of life and health outcomes for older adults in Korea.

Cohort description

Study design and setting

The KoMAC study was a prospective multicentre cohort study designed to serve as a platform for future cmRCTs. The baseline data were collected between August 2023 and August 2024. This study was conducted across both urban and rural regions of South Korea, specifically at Wonkwang University Korean Medicine Hospital in Iksan (an urban site) and Jangheung Integrative Medical Hospital in Jangheung (a rural site; figure 1). Iksan is a medium-sized urbanised city in Jeonbuk Province, whereas Jangheung is a predominantly rural county in Jeonnam Province with lower population density and stronger agricultural characteristics. These sites were selected to enable comparisons between urban and rural populations and because the participating institutions had the infrastructure required to support standardised cohort recruitment and long-term follow-up. While these regions were selected to represent urban and rural settings, they were not intended to constitute a nationally representative sample of the Korean population.

Figure 1. Location of the regions involved in the Korean Medicine for Aging Cohort study.

Figure 1

Open in a new tab

Participants

Based on 2023 administrative data, the target population aged 50–65 years in the two study regions (Iksan and Jangheung) was approximately 82 498 individuals.15 16 To represent this population, a recruitment target of 1% (approximately 825 individuals) was established. Accounting for an anticipated attrition rate of 20%, the final goal was to enrol 1000 participants across both urban and rural centres. Participants were eligible if they (1) were aged 50–65 years, (2) resided in Iksan and surrounding areas or Jangheung and surrounding areas, (3) were able to read and complete the questionnaires and (4) provided written informed consent. Individuals with a history of major psychiatric disorders, cognitive or communication impairment or conditions precluding study participation were excluded. Recruitment was conducted through public health centre banners and community outreach. Prospective participants were contacted after expressing their interest and scheduled for an in-person screening. During the initial visit, the details of the study were explained, written informed consent was obtained and eligibility was confirmed. Of the 1000 enrolled participants, 445 (44.5%) and 555 (55.5%) were recruited in 2023 and 2024, respectively.

Cohort multiple randomised controlled trials

The KoMAC study also incorporates a cmRCT framework as a platform for future embedded intervention studies. At baseline, participants provided consent for longitudinal follow-up and for possible consideration in future intervention studies. Within this framework, eligible subgroups for specific interventions will be identified according to predefined criteria. Among eligible participants, a subset may be randomly selected and invited to receive the intervention, while the remaining eligible participants will continue in the observational cohort and may serve as controls. Importantly, only participants randomly selected and offered the intervention will be informed of their allocation, whereas those not selected will continue routine follow-up without additional contact, thereby reducing the risk of performance bias. At the time of this cohort profile, no intervention studies have yet been initiated within the cohort. This cmRCT framework is designed to minimise selection and attrition bias in future embedded trials by maintaining a stable cohort population and reducing differential dropout between intervention and control groups. Outcomes can be assessed using routinely collected longitudinal follow-up data, thereby avoiding additional data collection in non-selected participants and enhancing feasibility. This approach enables efficient integration of pragmatic randomised trials within the ongoing cohort follow-up. Accordingly, the present report focuses on the cohort design and baseline findings.

Data collection and variables

The variables and follow-up schedule used in the KoMAC study are described in detail in our published protocol.17 At baseline, participants underwent physical (eg, blood pressure, pulse, temperature, body composition, physical performance, muscle strength, pulmonary function, hearing and vision), psychological (eg, sleep quality, depressive symptoms, cognitive function, quality of life, fear of falling and general perceived wellness), social assessments (education level, social and family relationships, reproductive history and lifestyle components) and completed the KM-based phenotype questionnaires (kidney deficiency, five-organ, blood stasis and emotional status based on the core seven emotions). Biological samples, including blood and urine, were also collected. Participants are followed up annually, with the first follow-up currently in progress. The same physical, psychological, social and KM-based phenotypic assessments are conducted at each follow-up visit. Biological samples were collected at the final follow-up visit (figure 2).

Figure 2. Overview of the KoMAC baseline cohort. The KoMAC baseline cohort comprised 1000 participants aged 50–65 years, recruited from Iksan and Jangheung, Republic of Korea, between August 2023 and August 2024 using community-based strategies. Eligibility screening was conducted concurrently with recruitment according to predefined inclusion and exclusion criteria. Participants who met the criteria were enrolled and underwent comprehensive baseline assessments, including physical, psychological, Korean medicine phenotype and laboratory evaluations. Ongoing follow-up assessments have been conducted since August 2024, comprising annual questionnaires and biennial laboratory testing. KoMAC, Korean Medicine for Aging Cohort.

Figure 2

Open in a new tab

KM phenotype assessment

Four KM-based phenotype instruments were administered at baseline. (1) The Kidney Deficiency Pattern (KDP) questionnaire18 assesses the KM concept of kidney deficiency—a pattern characterising ageing-related functional decline across musculoskeletal, urogenital, auditory and cognitive domains. The KDP comprises 12 core items plus Yin- and Yang-subtype items, each rated on a 4-point scale (0–3); participants scoring ≥3 on six or more items are classified as having kidney deficiency. (2) The Five-Organ Pattern questionnaire19 identifies which of the five KM organ-systems—liver, heart, spleen, lung and kidney—is most prominently involved in a participant’s current symptoms, based on the functional domains and characteristic symptom patterns associated with each organ-system, rather than assessing deficiency severity as in the KDP. (3) The Blood Stasis (BS) questionnaire20 quantifies symptoms of blood stasis—a KM pattern reflecting impaired blood circulation, clinically associated with fixed pain, bruising tendency and cardiovascular dysfunction. In this study, blood stasis was assessed using the Blood Stasis Questionnaire for Metabolic Syndrome (BSQ-MS), which was developed and validated for metabolic disease-related blood stasis assessment in Korean populations. The BSQ-MS score ranges from 0 to 32, and a cut-off value of ≥9 has been suggested for blood stasis classification. (4) The Core Seven Emotions Inventory-Short Form (CSEI-s)21 assesses emotional patterns across seven domains—joy, anger, anxiety, pensiveness, grief, fear and fright—reflecting the KM framework in which emotional states directly influence organ-system physiology, a construct with parallels to psychoneuroimmunology. The CSEI-s has demonstrated established reliability and validity in Korean samples.

Field work methods

As part of the baseline survey, all participants visited their assigned research centres to undergo in-person interviews, clinical examinations and laboratory testing.

Patient and public involvement

Participants were not involved in the design, conduct, reporting or dissemination plans of this study. However, individual results from health assessments, including blood and urine analyses, bone density testing, brain wave and pulsometry assessments, KM-based phenotype questionnaires and cognitive function tests, were communicated to participants after baseline evaluation. To enhance participant and public engagement, we plan to provide printed summaries of the main study findings and to develop publicly accessible guidelines for the prevention and management of physical frailty based on the results of the KoMAC study.

Data quality assurance

At each study site, two clinical research investigators were responsible for implementing the study procedures, and participant screening was conducted by clinical research coordinators (CRCs). 16 investigators participated in the study. All the investigators received standard operating procedure (SOP) training from the Korea Institute of Oriental Medicine (KIOM), which covered the inclusion and exclusion criteria, recruitment procedures and all aspects of the study protocol. SOP training was provided to study personnel on a quarterly basis, and all newly assigned researchers were required to complete this training before their participation in study-related activities.

All data collected during the cohort study were recorded in electronic case report forms and managed using a web-based platform (iCReaT; Korea National Institute of Health, Cheongju, Republic of Korea). The CRCs received prior training on the platform and entered data according to standardised guidelines. Data management was coordinated by the KIOM. To ensure data quality and protocol compliance, monitoring agents were dispatched weekly to each site during the first month of the study, followed by monthly visits throughout the remainder of the study. Data completeness was reviewed routinely during data entry and monitoring.

Data analysis

Quantitative variables were summarised using means and SD or frequencies and percentages, as appropriate. Descriptive analyses were performed to summarise baseline characteristics. For the baseline descriptive analyses presented in this cohort profile, available-case analyses were used for each variable, and the extent of missing data was assessed for each variable. Missing data proportions for major baseline variables are summarised in online supplemental table S1. Continuous variables were compared using Student’s t-test, analysis of variance or non-parametric methods where appropriate, and categorical variables were compared using the χ2 test or Fisher’s exact test. Correlation analyses were performed using Pearson or Spearman correlation coefficients as appropriate. Correlation coefficients are presented with corresponding 95% CIs. Analyses were primarily exploratory and unadjusted; where appropriate, selected analyses were adjusted for sex. Subgroup analyses were conducted according to sex and residential area (urban vs rural). All statistical analyses were performed using SAS V.9.4 (SAS Institute, Cary, North Carolina, USA). A two-sided p value <0.05 was considered statistically significant. In future longitudinal analyses, appropriate methods for handling missing data will be applied depending on the analytic context and mechanism of missingness.

Findings to date

Baseline characteristics and health profiles

The KoMAC baseline dataset included a total of 1000 community-dwelling adults aged 50–65 years—an age range representing the transitional period into older adulthood—from both urban and rural areas. The mean age of the participants was 57.4 years (SD=4.2), with the majority being female (n=763, 76.3%). The age distribution was as follows: 28.7%, 35.9% and 35.4% were aged 50–54 years, 55–59 years and 60–65 years, respectively. Regarding residential status, 54.3% and 45.7% of the participants resided in urban and rural areas, respectively.

The baseline characteristics are summarised in table 1. With ageing, both sexes showed trends toward reduced skeletal muscle mass and increased adiposity, particularly among women. As previously reported,22,24 age-related declines were observed in hearing, pulmonary function, bone density and these changes were more pronounced in women in the present study. Regional differences in health-related characteristics were observed between urban and rural participants; rural residents exhibited higher rates of alcohol consumption and smoking, greater parity among women and better pulmonary function and hearing, whereas urban participants showed a higher prevalence of sarcopenia. This counterintuitive finding differs from several previous studies reporting higher sarcopenia prevalence in rural populations.25 26 One possible explanation is the substantially different sex composition between the urban and rural groups in the present cohort, which may have influenced prevalence estimates. In addition, because the current analyses were exploratory and cross-sectional, further longitudinal analyses with multivariable adjustment are required. Given the descriptive nature of these baseline comparisons, observed urban–rural differences should be interpreted with caution, as they may be influenced by confounding factors such as age and sex. Full details are provided in online supplemental tables S2–S4.

Table 1. General characteristics of participants by sex and age group in Korean Medicine for Aging Cohort Study baseline survey, 2023–2024.

Variable Total Male (n=237) Female (n=763)
50–54 55–59 60–65 50–54 55–59 60–65
Total baseline study participants, n (%) 1000 (100) 54 (5.4) 93 (9.3) 90 (9.0) 233 (23.3) 266 (26.6) 264 (26.4)
 2023, n (%) 445 (44.5) 35 (3.5) 49 (4.9) 49 (4.9) 117 (11.7) 91 (9.1) 105 (10.5)
 2024, n (%) 555 (55.5) 19 (19.0) 44 (4.4) 41 (4.1) 116 (11.6) 175 (17.5) 159 (15.9)
Demographics, n (%)
 Marital status
  Single 23 (2.3) 3 (5.6) 5 (5.4) 1 (1.1) 8 (3.4) 4 (1.5) 2 (0.8)
  Married 862 (86.2) 48 (88.9) 83 (89.2) 84 (93.3) 206 (88.4) 223 (83.8) 218 (28.6)
  Divorced/widowed 114 (11.4) 3 (5.6) 5 (5.4) 5 (5.6) 19 (8.2) 38 (14.3) 44 (16.7)
 Residence area
  Urban 543 (54.3) 26 (48.1) 35 (37.6) 27 (30.0) 150 (64.4) 159 (59.8) 146 (55.3)
  Rural 457 (45.7) 28 (51.9) 58 (62.4) 63 (70.0) 83 (35.6) 107 (40.2) 118 (44.7)
 Education
  <Middle school 47 (4.7) 0 (0.0) 2 (2.2) 2 (2.2) 3 (1.3) 11 (4.1) 29 (11.0)
  Middle and high school 531 (53.1) 22 (40.7) 43 (46.2) 45 (50.0) 104 (44.6) 158 (59.4) 159 (60.2)
  ≥College 421 (42.1) 32 (59.3) 48 (51.6) 43 (47.8) 125 (53.6) 97 (36.5) 76 (28.8)
Lifestyle characteristics
 Current drinker, n (%) 336 (33.6) 40 (74.1) 58 (62.4) 45 (50.0) 83 (35.6) 71 (26.7) 39 (14.8)
 Current smoker, n (%) 100 (10.0) 28 (51.9) 30 (32.3) 25 (27.8) 11 (4.7) 5 (1.9) 1 (0.4)
Body composition, mean (SD)
 BMI (kg/m²) 24.7 (3.4) 26.3 (3.0) 25.5 (2.9) 25.5 (3.2) 23.9 (3.7) 24.5 (3.4) 24.7 (3.4)
 Waist circumference (cm) 83.6 (9.8) 90.9 (8.1) 89.8 (8.0) 90.8 (7.8) 80 (9.5) 82.2 (8.9) 82.2 (9.6)
 Fat mass (kg) 20.5 (6.3) 19.3 (5.6) 18.4 (5.7) 19.4 (6.3) 20.4 (6.9) 21.3 (6.3) 21.0 (6.0)
 Body fat percentage (%) 32.1 (7.0) 25.0 (5.0) 24.6 (5.3) 25.7 (5.2) 33 (6.5) 34.7 (6.1) 34.8 (5.5)
 Skeletal muscle mass (kg) 23.5 (5.1) 32.0 (3.5) 31.0 (3.8) 30.4 (3.7) 21.6 (2.7) 21.1 (2.6) 20.7 (2.4)
 Skeletal muscle index (SMI, kg/m²) 6.8 (1.0) 8.3 (0.6) 8.1 (0.6) 8.0 (0.7) 6.4 (0.7) 6.4 (0.6) 6.3 (0.6)
 Sarcopenia*, n (%) 79 (7.9) 1 (1.9) 1 (1.1) 5 (5.6) 20 (8.6) 23 (8.7) 29 (11.0)
CCI score, mean (SD) 1.6 (0.8) 1.2 (0.5) 1.3 (0.6) 2.2 (0.6) 1.3 (0.8) 1.2 (0.6) 2.2 (0.6)
Visual acuity (decimal)
 Left eye 1.1 (0.7) 1.1 (0.3) 1.1 (0.6) 0.9 (0.3) 1.1 (0.7) 1.1 (0.6) 1.0 (0.8)
 Right eye 1.0 (0.4) 1.1 (0.3) 1.1 (0.3) 1.1 (1) 1.1 (0.3) 1.0 (0.3) 0.9 (0.3)
Hearing thresholds (dB HL)
 Threshold_1000Hz_Left 24.6 (11.5) 24.8 (11.8) 24.6 (14.8) 22.4 (11.4) 24.9 (10.3) 23.7 (10.6) 25.9 (11.8)
 Threshold_1000Hz_Right 24.2 (11.4) 24.5 (10.1) 22.5 (11.5) 23.3 (14.3) 24.1 (10.1) 24.0 (11.9) 25.3 (11.2)
 Threshold_4000Hz_Left 25.8 (16.3) 33.2 (22.2) 36.3 (20.7) 41.1 (18.7) 18.5 (11.2) 21.9 (12.8) 25.9 (13.0)
 Threshold_4000Hz_Right 25.4 (16.5) 33.4 (19.3) 35.3 (19.8) 40.8 (20.7) 18.6 (10.8) 21.6 (14.4) 24.8 (13.3)
Pulmonary function test
 FVC (L) 2.5 (0.8) 3.4 (0.9) 3.4 (0.9) 3.2 (0.8) 2.3 (0.6) 2.3 (0.7) 2.2 (0.6)
 FVC predicted (%) 85.5 (26.1) 91.8 (31.7) 88.2 (24.3) 85.5 (21.7) 80.4 (23.4) 86.4 (26.5) 87 (28.2)
 FEV1 (L) 2.2 (0.8) 2.9 (0.8) 2.9 (0.7) 2.8 (0.8) 2 (0.6) 2.0 (0.7) 1.8 (0.6)
 FEV1 predicted (%) 88.9 (28.4) 94.9 (29.9) 96.2 (25.6) 93.9 (24.4) 83.3 (26) 87.3 (28.8) 90.1 (30.8)
 FEV1/FVC predicted (%) 88.7 (15.6) 89.6 (17.9) 89.6 (11) 88.1 (12.5) 90.3 (17.5) 87.1 (16.1) 88.5 (15.0)
Bone mineral density measurements
 Lumbar spine (L1–L4) T-score −0.26 (1.6) −0.02 (1.2) 0.67 (1.8) 0.31 (1.9) 0.22 (1.5) −0.58 (1.4) −0.91 (1.3)
 Femoral neck T-score −0.23 (3.4) 0.34 (1.0) 0.39 (1.0) 0.19 (1.4) −0.12 (1.2) −0.21 (6.3) −0.85 (1.0)
 Total femur T-score 0.05 (3.4) 0.8 (1.0) 0.97 (1.3) 0.85 (1.3) 0.0 (1.1) −0.06 (6.3) −0.56 (1.0)
Open in a new tab

Values are presented by age group (50–54, 55–59, 60–65 years) within each sex.

*

Sarcopenia was defined according to the AWGS 2019 criteria as an SMI<7.0 kg/m² for men and <5.7 kg/m² for women.

BMI, body mass index; CCI index, Charlson Comorbidity Index; FEV1, forced expiratory volume in 1 second; FVC, forced vital capacity.

In the functional assessments, the Korean Instrumental Activities of Daily Living score,27 which assesses independence in complex daily tasks, increased significantly with age in women. Fall-related self-efficacy, as measured using the Korean version of Falls Efficacy Scale-International,28 increased significantly with age in both men and women. The total Short Physical Performance Battery29 score declined with age in women, including significant decreases in the chair stand test and walking speed. The hand grip strength showed a decreasing trend with age in men. The Korean version of the Montreal Cognitive Assessment30 score declined with age in women, with a corresponding increase in the proportion classified as having mild cognitive impairment. Among nutritional indicators, the mini nutritional assessment-short form31 score increased with age in women, whereas the simplified nutritional appetite questionnaire32 score increased with age in men (table 2). Differences by residential area are summarised in online supplemental tables S5–S7.

Table 2. Health habits, conditions, physical and cognitive function by sex and age group in Korean Medicine for Aging Cohort Study baseline survey, 2023–2024.

Variable Total Male (n=237) Female (n=763)
50–54 55–59 60–65 50–54 55–59 60–65
Physical function, mean (SD)
 PSQI-K score 7.6 (3.3) 7.6 (3.0) 6.7 (2.9) 6.8 (3.1) 7.4 (3.0) 8.1 (3.4) 8 (3.6)
 IPAQ-K (MET-min/week)
  Vigorous activity 1132.3 (2670.2) 1425.2 (1790.3) 1524.3 (3026.6) 1457.3 (3072.5) 1045.8 (3070.6) 932.5 (2291.2) 1101.3 (2505.1)
  Moderate activity 751.8 (1637.4) 975.6 (1679.1) 991.2 (1791.6) 746.9 (1447.1) 669.4 (1731.9) 620.5 (1381.1) 828.3 (1776.6)
  Walking 1604.2 (2181.8) 1430.9 (2305.0) 1637.4 (2647.0) 1548.8 (2012.5) 1515.3 (2181.2) 1618.4 (2030.2) 1711 (2193.8)
  Total 12 673.3 (79151.2) 12 631.7 (50343.6) 18 587.7 (80770.4) 4188.6
(6224.3)		 5838.6 (36755.7) 14 749.9 (104728.9) 17 430.8 (92964.7)
 K-IADL score 10.4 (1.4) 10.5 (1.7) 11.1 (2.6) 10.9 (2) 10.1 (0.4) 10.2 (0.9) 10.3 (1.5)
 SPPB score (range: 0–12) 11.4 (0.8) 11.5 (0.7) 11.5 (0.7) 11.2 (0.9) 11.6 (0.7) 11.4 (0.9) 11.3 (0.9)
  Standing balance 4.0 (0.1) 4.0 (0.1) 4.0 (0.0) 4 (0.1) 4.0 (0.1) 4.0 (0.1) 4 (0.2)
  Walking speed 3.8 (0.5) 3.9 (0.3) 3.9 (0.3) 3.8 (0.4) 3.8 (0.4) 3.7 (0.6) 3.6 (0.6)
  Chair-stand 3.7 (0.6) 3.6 (0.6) 3.6 (0.6) 3.5 (0.7) 3.8 (0.5) 3.7 (0.6) 3.7 (0.6)
 Grip strength* (left) (kg) 27.0 (8.6) 40.6 (7.9) 39.8 (6.4) 38.6 (6.2) 23.3 (4.7) 23.2 (4.1) 22.9 (4.2)
 Grip strength (right) (kg) 28.5 (8.7) 42.9 (6.3) 41.5 (6.0) 40.1 (6.7) 24.9 (4.7) 24.6 (4.4) 24.2 (4.6)
 KFES-I (range: 16–64) 17.1 (2.8) 16.4 (1.8) 16.5 (1.4) 17.3 (2.6) 16.7 (2.2) 17.2 (3.4) 17.6 (3)
 Physical resilience scale 4.5 (2.6) 4.6 (2.6) 4.5 (2.5) 4.5 (2.6) 4.0 (2.0) 4.6 (2.6) 4.8 (2.9)
Psychological function, mean (SD)
 EQ-5D score (range: 0–1) 0.9 (0.1) 0.9 (0.1) 0.9 (0.1) 0.9 (0.1) 0.9 (0.1) 0.9 (0.1) 0.9 (0.1)
  Mobility 1.3 (0.6) 1.1 (0.4) 1.2 (0.4) 1.4 (0.8) 1.1 (0.4) 1.3 (0.6) 1.3 (0.6)
  Self-care 1.1 (0.3) 1.0 (0.1) 1.0 (0.1) 1.1 (0.4) 1.0 (0.2) 1.1 (0.3) 1.1 (0.4)
  Usual activities 1.2 (0.5) 1.1 (0.4) 1.1 (0.2) 1.1 (0.4) 1.1 (0.4) 1.2 (0.5) 1.2 (0.5)
  Pain/discomfort 1.7 (0.7) 1.5 (0.7) 1.5 (0.6) 1.6 (0.8) 1.7 (0.6) 1.8 (0.8) 1.7 (0.7)
  Anxiety/depression 1.5 (0.7) 1.4 (0.6) 1.2 (0.5) 1.4 (0.6) 1.5 (0.6) 1.6 (0.8) 1.5 (0.6)
 EQ-5D VAS (range: 0–100) 75.1 (15.0) 77.9 (14.6) 77.6 (13.4) 77.6 (17.1) 75.7 (14.5) 74.5 (14.8) 72.9 (15.3)
 GDS-SF-K (range: 0–15) 2.2 (2.9) 1.8 (2.4) 1.7 (2.5) 1.9 (3.1) 2.0 (2.6) 2.5 (3.3) 2.4 (3)
 MOS-SSS (range: 19–95) 77.7 (14.2) 80.4 (11.2) 79.5 (12.8) 79.3 (13.3) 79.5 (13.1) 76.1 (15.2) 75.9 (15.2)
Cognitive function, mean (SD)
 MoCA-K (range: 0–30) 25.8 (2.9) 26.3 (2.3) 25.7 (3.1) 25.6 (3) 26.7 (2.4) 25.9 (2.7) 25.1 (3.2)
  Normal (≥23), n (%) 872 (87.2) 51 (94.4) 79 (84.9) 79 (87.8) 220 (94.4) 232 (87.2) 211 (79.9)
  MCI (<23), n (%) 128 (1.28) 3 (5.6) 14 (15.1) 11 (12.2) 13 (5.6) 34 (12.8) 53 (20.1)
Nutritional status, mean (SD)
 MNA-SF score (range: 0–14) 12.7 (1.5) 13.2 (1) 13.0 (1.5) 12.9 (1.6) 12.4 (1.6) 12.5 (1.5) 12.8 (1.5)
 SNAQ score (range: 0–20) 14.5 (1.9) 14.3 (1.8) 15 (1.5) 15.1 (1.8) 14.6 (2.1) 14.2 (1.9) 14.3 (1.9)
Dietary supplement use, n (%) 593 (59.3) 26 (48.1) 49 (52.7) 51 (56.7) 144 (61.8) 159 (59.8) 164 (62.1)
 Mean number of supplements per person 2.7 (1.8) 2.3 (1.2) 2.3 (1.4) 2.4 (2) 2.6 (1.7) 2.7 (1.9) 2.9 (2)
Constipation (CAS≥4), n (%) 189 (18.9) 8 (14.8) 12 (12.9) 10 (11.1) 42 (18.0) 59 (22.2) 58 (22.0)
PAVING Wheel Questionnaire (range: 1–25)
Physical activity 16.0 (4.5) 15.7 (4.4) 15.6 (4.4) 16.7 (4.3) 15.6 (4.6) 15.5 (4.6) 16.9 (4.3)
Stress management 16.7 (3.4) 15.9 (3.3) 16.3 (3.3) 15.7 (3.5) 17.1 (3.3) 16.7 (3.4) 17.1 (3.3)
Attitude 18.7 (3.0) 18.0 (2.7) 18.5 (2.7) 18.3 (3.3) 18.7 (3.1) 18.7 (3) 19.1 (2.9)
Time-out 17.2 (3.5) 17.5 (2.7) 17.2 (3.3) 17.1 (3.8) 17.6 (3.3) 17.1 (3.5) 17 (3.7)
Variety 15.2 (3.5) 15.0 (3.0) 15.2 (3.5) 15.1 (3.5) 14.9 (3.5) 14.9 (3.6) 15.8 (3.6)
Energy appraisal 18.6 (3.2) 17.7 (2.7) 18.3 (3.0) 17.8 (3.5) 19 (3) 18.7 (3.1) 18.9 (3.4)
Investigation 17.0 (3.2) 15.9 (2.2) 15.9 (3.3) 16.6 (3.7) 17.2 (3) 16.9 (3.2) 17.6 (3.4)
Purpose identification 18.3 (3.7) 18.9 (2.6) 18.5 (3.6) 17.8 (4.1) 18.5 (3.4) 18.1 (3.8) 18.2 (3.9)
Nutrition 14.1 (3.6) 13.4 (2.7) 13.7 (3.4) 13.9 (3.5) 14 (3.4) 13.5 (3.6) 15 (4)
Sleep 14.4 (3.3) 13.9 (2.4) 13.7 (3.1) 14.8 (3) 14.3 (3.1) 14.1 (3.4) 15.2 (3.6)
Goal 14.3 (4.4) 14.4 (3.5) 14.8 (4.2) 14.4 (4.5) 14.7 (4.4) 13.8 (4.5) 14.3 (4.5)
Social support 18.2 (3.6) 17.5 (3.2) 17.4 (3.3) 17.3 (3.6) 18.6 (3.6) 18.5 (3.6) 18.4 (3.7)
Open in a new tab

Values are presented by age group (50–54, 55–59, 60–65 years) within each sex.

*

Grip strength was assessed twice for each hand, and the maximum value among the two trials was used in the analysis.

CAS, Constipation Assessment Scale; EQ-5D, EuroQol-5 Dimension; GDS-SF-K, Korean version of the Geriatric Depression Scale-Short Form; IPAQ-K, Korean version of the International Physical Activity Questionnaire; KFES-I, Korean version of the Falls Efficacy Scale-International; K-IADL, Korean Instrumental Activities of Daily Living; MCI, mild cognitive impairment; MET, metabolic equivalent of task; MNA-SF, Mini Nutritional Assessment-Short Form; MoCA-K, Korean version of the Montreal Cognitive Assessment; MOS-SSS, Medical Outcomes Study-Social Support Survey; PSQI-K, Korean version of the Pittsburgh Sleep Quality Index; SNAQ, Simplified Nutritional Appetite Questionnaire; SPPB, Short Physical Performance Battery; VAS, visual analogue scale.

KM-based phenotype profiles

Significant age-related differences in KM-based phenotype scores were observed, predominantly among female participants (table 3). KDP scores increased with age in women across multiple functional domains, including musculoskeletal symptoms (leg weakness, lumbar and knee soreness and persistent joint pain), urogenital function (nocturia, urinary urgency), sensory function (tinnitus and hearing impairment) and cognitive function (forgetfulness). Both the KDP-Yin subtype (characterised by heat sensation and dry mouth) and KDP-Yang subtype (characterised by lower limb oedema) showed significant age-related increments in women. Consistent with these item-level patterns, the proportion of women classified as having kidney deficiency (defined as scoring ≥3 on six or more KDP items) increased with age, and a similar increasing trend was also observed in men.

Table 3. Traditional Korean medicine questionnaire by sex and age group in Korean Medicine for Aging Cohort Study baseline survey, 2023–2024.

Variable Total Male (n=237) Female (n=763)
50–54 55–59 60–65 50–54 55–59 60–65
KM questionnaire, mean (SD)
 Kidney Deficiency Pattern (KDP)* 5.1 (3.1) 3.6 (3.1) 4.0 (2.7) 4.5 (3.1) 4.6 (2.8) 5.6 (2.9) 5.9 (3.2)
  KDP 1 general weakness, fatigue 2.8 (1.0) 2.4 (0.9) 2.4 (1.0) 2.4 (1.0) 2.8 (0.9) 2.9 (1.0) 2.8 (1.0)
  KDP 2 leg weakness, difficulty walking 1.8 (0.9) 1.5 (0.6) 1.8 (0.8) 1.8 (0.9) 1.8 (0.8) 1.9 (0.9) 1.9 (0.9)
  KDP 3 numbness/soreness (lumbar/knee) 2.5 (1.1) 2.2 (1.0) 2.2 (1.0) 2.2 (1.0) 2.3 (1.1) 2.7 (1.0) 2.7 (1.1)
  KDP 4 persistent pain (lumbar/knee) 2.4 (1.0) 2.1 (1.0) 2.2 (1.0) 2.3 (1.0) 2.3 (1.0) 2.6 (1.0) 2.6 (1.1)
  KDP 5 dull pain, stiffness (lumbar/knee) 2.4 (1.0) 2.1 (1.0) 2.1 (0.9) 2.3 (1.0) 2.2 (0.9) 2.5 (1.0) 2.6 (1.1)
  KDP 6 reduced libido/reproductive issues 2.7 (1.2) 2.1 (1.0) 2.4 (1.1) 2.5 (1.2) 2.7 (1.1) 2.9 (1.2) 2.9 (1.3)
  KDP 7 tinnitus or hearing impairment 2.0 (1.0) 2.0 (1.2) 1.8 (0.9) 2.0 (1.0) 1.8 (0.9) 2.1 (1.0) 2.1 (1.1)
  KDP 8 urinary urgency/incontinence 2.0 (0.9) 1.7 (1.0) 1.8 (0.8) 2.0 (0.9) 1.9 (0.9) 2.1 (1.0) 2.2 (1.0)
  KDP 9 nocturia (≥3 times/night) 2.2 (1.1) 1.9 (1.0) 1.9 (0.9) 2.2 (1.1) 2.1 (1.0) 2.3 (1.0) 2.5 (1.1)
  KDP 10 hair thinning/hair loss 2.9 (1.1) 2.5 (1.2) 2.7 (1.1) 2.8 (1.3) 2.9 (1.0) 3.0 (1.1) 2.9 (1.1)
  KDP 11 memory decline 2.5 (0.9) 2.1 (0.9) 2.3 (0.9) 2.2 (0.9) 2.4 (0.8) 2.7 (0.9) 2.7 (0.9)
  KDP 12 loose teeth/weak gums 2.2 (0.9) 2.3 (1.2) 2.3 (1.0) 2.2 (0.9) 2.0 (0.9) 2.2 (0.9) 2.2 (0.9)
  KDP-Yin 1 heat in palms/soles 1.9 (0.9) 1.8 (0.8) 1.9 (0.9) 1.7 (0.7) 1.9 (0.9) 2.1 (1.0) 1.9 (0.9)
  KDP-Yin 2 afternoon heat, chest discomfort 1.9 (0.8) 1.6 (0.7) 1.7 (0.8) 1.7 (0.7) 1.8 (0.8) 2.0 (0.9) 1.9 (0.8)
  KDP-Yin 3 night sweats 2.0 (0.9) 1.8 (0.8) 1.8 (0.8) 1.9 (0.9) 1.9 (0.9) 2.0 (1.0) 2.0 (1.0)
  KDP-Yin 4 thirst, dry mouth 2.3 (1.0) 2.2 (1.0) 2.2 (1.0) 2.2 (0.9) 2.2 (0.9) 2.3 (1.0) 2.4 (1.1)
  KDP-Yang 1 cold intolerance 2.9 (1.2) 2.1 (1.2) 2.5 (1.1) 2.4 (1.1) 3.0 (1.2) 3.0 (1.2) 3.0 (1.2)
  KDP-Yang 2 oedema (legs/ankles) 2.3 (1.0) 1.8 (0.7) 1.8 (0.8) 1.7 (0.8) 2.6 (1.1) 2.5 (1.1) 2.3 (1.0)
  KDP-Yang 3 loose stools 2.1 (0.9) 2.1 (1.1) 2.2 (0.9) 2.1 (0.9) 2.1 (0.9) 2.1 (0.9) 2.0 (0.9)
  KDP-Yang 4 dawn diarrhoea/abdominal pain 1.5 (0.6) 1.6 (0.7) 1.6 (0.7) 1.5 (0.6) 1.5 (0.6) 1.5 (0.6) 1.5 (0.6)
Kidney deficiency†, n (%)
 Kidney deficiency 435 (43.5) 15 (27.8) 24 (25.8) 36 (40.0) 85 (36.5) 131 (49.2) 144 (54.5)
 Non-kidney deficiency 565 (56.5) 39 (72.2) 69 (74.2) 54 (60.0) 148 (63.5) 135 (50.8) 120 (45.2)
Five organ deficiency
 Liver 42.1 (14.6) 37.2 (13.7) 38.5 (12.8) 37.6 (12.7) 43.3 (13.9) 44.2 (15.2) 42.6 (15.4)
 Heart 39.5 (14.3) 35.7 (13.5) 36.6 (12.8) 35.9 (11.4) 39.2 (13.9) 41.8 (14.8) 40.6 (15.2)
 Spleen 36.2 (13.1) 34.4 (12.8) 33.8 (12.0) 33.9 (11.8) 36 (12.4) 37.8 (13.6) 36.8 (13.8)
 Lung 39.2 (14.3) 38.3 (13.9) 38.7 (13.8) 38.2 (12.7) 37.7 (14.0) 40.6 (15.0) 39.9 (14.8)
 Kidney 40.9 (12.8) 38.1 (14.7) 39.5 (12.3) 40.3 (11.6) 39.9 (11.6) 42.3 (13.1) 41.6 (13.4)
Blood stasis status (range: 0–32) 4.0 (4.1) 3.8 (4.8) 4.3 (4.5) 4.7 (4.7) 3.5 (3.8) 4.4 (4.2) 4.0 (3.9)
 Blood stasis‡, n (%)
  Blood stasis 130 (13.0) 7 (13.0) 15 (16.1) 17 (18.9) 22 (9.4) 41 (15.4) 28 (10.6)
  Non-blood stasis 870 (87.0) 47 (87.0) 78 (83.9) 73 (81.1) 211 (90.6) 225 (84.6) 236 (89.4)
CSEI-s score (each range: 0–20)
 Joy (Hui) 11.0 (3.0) 11.0 (2.6) 11.6 (2.9) 10.4 (2.9) 11.4 (2.9) 10.9 (3.0) 10.7 (3.0)
 Anger (No) 6.9 (2.7) 7.0 (2.8) 6.5 (2.2) 6.8 (2.7) 7.0 (2.5) 7.1 (2.9) 6.7 (2.8)
 Thought (Sa) 9.5 (3.4) 9.5 (3.4) 8.5 (2.6) 8.4 (2.8) 9.7 (3.2) 10.1 (3.8) 9.5 (3.6)
 Depression (U) 6.1 (2.6) 5.7 (2.5) 5.4 (1.7) 5.9 (2.3) 6.2 (2.4) 6.5 (2.9) 6 (2.7)
 Sorrow (Bi) 7.2 (3.2) 6.6 (3.0) 6.1 (2.0) 6.7 (2.7) 7.1 (2.8) 7.8 (3.6) 7.5 (3.6)
 Fear (Gong) 7.5 (2.9) 6.4 (2.5) 6.3 (2.5) 6.4 (2.4) 7.9 (2.8) 7.9 (2.9) 7.8 (3.3)
 Fright (Kyeong) 6.8 (3.2) 5.6 (2.5) 5.3 (2.1) 5.2 (2.0) 7.1 (3.0) 7.5 (3.6) 7.2 (3.4)
Open in a new tab

Values are presented by age group (50–54, 55–59, 60–65 years) within each sex.

*

Mean score of six or more items among KDP1–KDP12.

†

Kidney deficiency defined as participants with scores ≥5 on six or more items among KDP1–KDP12.

‡

Blood stasis defined as participants with scores ≥9 in Blood Stasis questionnaire.

BS, Blood Stasis; CSEI-s, Core Seven Emotions Inventory-Short Form; KM, Korean medicine.

Among the five-organ pattern scores, only the kidney domain showed an age-related increase in women, whereas no such changes were observed in the liver, heart, lung or spleen domains in either sex. KM-based phenotypic analysis showed an increase in the prevalence of Kidney deficiency patterns with age. Unlike other organ domains, this pattern was consistently reflected in the five-organ scoring system, suggesting that kidney-related KM phenotypes may reflect broader functional changes associated with ageing.33 Blood stasis scores increased significantly with age in women, but not in men. Of the seven emotional domains assessed by the CSEI-s, only the joy domain showed a significant age-related decline in both sexes; no other emotion domain differed significantly by age group. Detailed results are presented in table 3; urban–rural comparisons are provided in online supplemental tables S8–S10.

Laboratory, biochemical and KM-phenotype correlation profiles

Significant age-related changes in laboratory parameters were more consistently observed in women than in men (online supplemental table S11). Metabolic and bone markers, including alkaline phosphatase, fasting glucose, glycated haemoglobin and estimated average glucose, increased significantly with age in women. Reproductive hormones showed pronounced age-related shifts in women (increased follicle-stimulating hormone and luteinising hormone; decreased estradiol and dehydroepiandrosterone sulfate (DHEA-S)), while DHEA-S also declined in men. Urban–rural comparisons are provided in online supplemental tables S12–S14.

As a preliminary exploratory analysis, KDP scores were correlated with key physiological and biochemical parameters (table 4). KDP scores showed negative correlations with physical function indicators, including grip strength, chair stand performance and gait speed. They were also associated with body composition, characterised by higher body fat percentage and lower skeletal muscle mass. In addition, pulmonary function indicators (forced vital capacity and forced expiratory volume in 1 s) and several biochemical markers, including 25-hydroxyvitamin D, vitamin B12, DHEA-S and cortisol, were correlated with KDP scores. Associations with body composition, walking speed and chair rise time remained after adjustment for sex. In KM theory, the kidney is considered to govern a broad range of physiological functions, including ageing, bone metabolism, endocrine regulation and functional capacity, rather than representing a single organ system. The observed associations between KDP scores and multiple functional and biochemical indicators are consistent with this conceptual framework.33 34 These findings should be interpreted cautiously, as the observed effect sizes were small and the analyses were based on cross-sectional baseline data. In addition, biomarkers such as vitamin D and vitamin B12 may be influenced by external factors, including dietary intake, supplementation and lifestyle behaviours, which were not fully controlled in the present analysis. Full versions of tables 1–4 are provided in online supplemental tables S15–S18, including detailed stratification by sex and age group.

Table 4. Association analysis of Kidney Deficiency Pattern score and parameters.

Crude Sex-adjusted
r (95% CI) P value r (95% CI) P value
Age (years) 0.141 (0.080 to 0.201) <0.001 0.157 (0.096 to 0.217) <0.001
Body composition, mean (SD)
 BMI (kg/m²) 0.012 (−0.050 to 0.074) 0.703 0.042 (−0.020 to 0.104) 0.180
 Waist circumference (cm) −0.009 (−0.071 to 0.053) 0.786 0.068 (0.006 to 0.129) 0.032
 Fat mass (kg) 0.077 (0.015 to 0.138) 0.015 0.054 (−0.008 to 0.116) 0.086
 Body fat percentage (%) 0.153 (0.092 to 0.213) <0.001 0.065 (0.003 to 0.126) 0.041
 Skeletal muscle mass (kg) −0.149 (−0.209 to −0.088) <0.001 0.002 (−0.060 to 0.064) 0.954
 Skeletal muscle index (SMI, kg/m²) −0.132 (−0.192 to −0.071) <0.001 0.009 (−0.053 to 0.071) 0.774
Hearing thresholds (dB HL)
 Threshold_1000Hz_Left −0.003 (−0.065 to 0.059) 0.933 −0.010 (−0.072 to 0.052) 0.759
 Threshold_1000Hz_Right 0.033 (−0.029 to 0.095) 0.299 0.025 (−0.037 to 0.087) 0.422
 Threshold_4000Hz_Left 0.012 (−0.050 to 0.074) 0.706 0.093 (0.031 to 0.154) 0.003
 Threshold_4000Hz_Right −0.040 (−0.102 to 0.022) 0.207 0.035 (−0.027 to 0.097) 0.269
Pulmonary function test
 FVC (L) −0.083 (−0.145 to −0.022) 0.008 0.019 (−0.043 to 0.081) 0.547
 FVC predicted (%) 0.061 (−0.001 to 0.123) 0.052 0.072 (0.010 to 0.134) 0.022
 FEV1 (L) −0.066 (−0.127 to −0.004) 0.038 0.034 (−0.028 to 0.096) 0.284
 FEV1 predicted (%) 0.076 (0.014 to 0.137) 0.017 0.100 (0.038 to 0.161) 0.002
 FEV1/FVC predicted (%) 0.023 (−0.039 to 0.085) 0.549 0.026 (−0.036 to 0.088) 0.410
SPPB score
 Total −0.160 (−0.220 to −0.099) <0.001 −0.166 (−0.226 to −0.105) <0.001
 Standing balance −0.045 (−0.107 to 0.017) 0.151 −0.046 (−0.108 to 0.016) 0.147
 Walking speed −0.108 (−0.169 to −0.047) 0.001 −0.091 (−0.152 to −0.029) 0.004
 Chair-stand −0.123 (−0.184 to −0.062) <0.001 −0.147 (−0.207 to −0.085) <0.001
Grip strength (left) (kg) −0.177 (−0.236 to −0.116) <0.001 −0.050 (−0.112 to 0.012) 0.111
Grip strength (right) (kg) −0.176 (−0.236 to −0.115) <0.001 −0.049 (−0.110 to 0.014) 0.125
Bone mineral density measurements
 Lumbar spine (L1–L4) T-score −0.089 (−0.150 to −0.027) 0.005 −0.051 (−0.113 to 0.011) 0.108
 Femoral neck T-score 0.038 (−0.024 to 0.100) 0.224 0.056 (−0.006 to 0.117) 0.079
 Total femur T-score 0.032 (−0.030 to 0.094) 0.311 0.059 (−0.003 to 0.120) 0.064
Bone and mineral metabolism
 Osteocalcin (ng/mL) 0.062 (−0.000 to 0.123) 0.050 0.020 (−0.042 to 0.082) 0.519
 ALP (U/L) 0.007 (−0.056 to 0.068) 0.837 −0.009 (−0.071 to 0.053) 0.775
 Calcium (mg/dL) −0.016 (−0.078 to 0.046) 0.608 −0.016 (−0.078 to 0.046) 0.613
 Phosphate (mg/dL) 0.125 (0.063 to 0.185) <0.001 0.055 (−0.007 to 0.116) 0.084
 25-OH vitamin D (ng/mL) 0.080 (0.018 to 0.142) 0.011 0.073 (0.011 to 0.134) 0.022
 Iron (μg/dL) −0.079 (−0.140 to −0.017) 0.013 −0.047 (−0.109 to 0.015) 0.136
 TIBC (μg/dL) 0.050 (−0.012 to 0.112) 0.114 0.041 (−0.021 to 0.102) 0.199
 Vitamin B12 (pg/mL) 0.119 (0.057 to 0.179) <0.001 0.096 (0.034 to 0.157) 0.002
Glucose and metabolic
 Glucose (mg/dL) 0.006 (−0.056 to 0.068) 0.853 0.031 (−0.031 to 0.093) 0.327
 HbA1c (NGSP) (%) 0.028 (−0.034 to 0.090) 0.372 0.039 (−0.023 to 0.101) 0.221
 HbA1c (IFCC) (mmol/mol) 0.030 (−0.032 to 0.092) 0.346 0.042 (−0.021 to 0.103) 0.190
 eAG (mg/dL) 0.029 (−0.033 to 0.091) 0.363 0.040 (−0.022 to 0.102) 0.206
 Insulin (μIU/mL) 0.020 (−0.042 to 0.082) 0.534 0.020 (−0.042 to 0.082) 0.524
 Cortisol (μg/dL) −0.081 (−0.142 to −0.019) 0.010 −0.063 (−0.124 to −0.001) 0.048
Reproductive hormones
 FSH* (mIU/mL) 0.037 (−0.034 to 0.108) 0.309 0.033 (−0.038 to 0.104) 0.362
 LH* (mIU/mL) 0.009 (−0.062 to 0.080) 0.795 0.006 (−0.065 to 0.077) 0.874
 Testosterone† (ng/mL) −0.021 (−0.147 to 0.106) 0.750 −0.022 (−0.149 to 0.106) 0.736
 Estradiol* (pg/mL) −0.075 (−0.145 to −0.004) 0.039 −0.075 (−0.145 to −0.004) 0.039
 DHEA-S (μg/dL) −0.159 (−0.219 to −0.098) <0.001 −0.094 (−0.156 to −0.033) 0.003
Open in a new tab

Values are correlation coefficients with 95% CIs. Sex-adjusted coefficients were calculated as partial correlations adjusted for sex.

Bold values indicate statistical significance at p < 0.05.

*

Measured in females only.

†

Measured in males only.

ALP, alkaline phosphatase; BMI, body mass index; DHEA-S, dehydroepiandrosterone sulfate; eAG, estimated average glucose; FEV1, forced expiratory volume in 1 s; FSH, follicle-stimulating hormone; FVC, forced vital capacity; HbA1c, glycated haemoglobin; IFCC, International Federation of Clinical Chemistry and Laboratory Medicine; LH, luteinising hormone; NGSP, National Glycohaemoglobin Standardisation Programme; SMI, skeletal muscle index; SPPB, Short Physical Performance Battery; TIBC, total iron-binding capacity.

Brief analysis plans

Using longitudinal KoMAC data, we aim to examine both cross-sectional and longitudinal associations between age-related physical and psychological decline and various blood biomarkers, including multi-omics data, to better understand health deterioration with ageing. In addition, we will analyse KM-based phenotypes to identify phenotype-specific multi-omics markers, ageing patterns and related diseases.

Furthermore, we will conduct multiple randomised controlled trials applying tailored interventions—such as community-based programmes—targeted to well-defined KM phenotypes, with a particular focus on the KD pattern, to assess the effectiveness of KM in promoting healthy ageing. Ultimately, this research will support the development of an integrative, KM-based health management model.

Strengths and limitations

The KoMAC study has several notable strengths. The selected age range of 50–65 years captures individuals in the transitional stage into older adulthood, during which early functional decline and age-related health changes may begin to emerge.35 36 This age range precedes the typical onset of advanced frailty and multimorbidity, allowing the investigation of early ageing-related changes and potential preventive strategies.37 The study includes community-dwelling middle-aged adults from both urban and rural areas, enabling comparative analyses of ageing-related health profiles across diverse environments and providing insights into the influence of residential context on ageing. It adopts a multidimensional framework for assessing healthy ageing by integrating biological, psychological, social and KM-based phenotypic factors, thereby offering a comprehensive view of ageing mechanisms. In addition, robust research database and blood biobank to support long-term follow-up and aid in developing KM-based predictive models for healthy ageing. Subgroup-specific clinical studies are planned to allow targeted investigation of specific ageing-related conditions and interventions. Furthermore, the dataset is openly accessible to qualified researchers, promoting collaboration and enabling a wide range of secondary analyses.

Despite its strengths, this study has several limitations. Because participants were recruited from only two regions in South Korea, the cohort may not fully represent the national population, which may limit the generalisability of the findings. In addition, participants were recruited through voluntary community-based participation without stratification by age or sex, which may have introduced healthy volunteer bias and contributed to the predominance of women in the cohort. This pattern may reflect differential participation commonly observed in community-based health studies and should therefore be considered when interpreting sex-specific findings. Restricting the cohort to the age range of 50–65 years may limit the generalisability of the findings to younger or older populations. As an observational cohort study, it cannot establish causal relationships between exposures and outcomes. Although the overall sample size is sufficient for many analyses, statistical power may be limited for detecting associations involving rare outcomes or small subgroups. In addition, some variables—such as medical history, lifestyle habits and supplement use—were self-reported and may be subject to recall bias or misclassification. To mitigate this risk, standardised data collection protocols were consistently applied across all study sites. Furthermore, the KM-based phenotype instruments used in this study were primarily developed and validated in Korean populations; therefore, caution is warranted when interpreting the findings in non-Korean populations or broader international contexts. Given the exploratory nature of the analyses and the large number of comparisons performed, the findings should be interpreted cautiously because of the increased risk of type I error.

Collaboration

The KoMAC study supports collaborative research related to ageing-related health characteristics and KM-based approaches to healthy ageing. Individual-level data and biospecimens collected through KoMAC, including KM-based phenotype data and conventional health assessments, may be made available to qualified researchers on reasonable request and subject to applicable ethical and data-sharing regulations. All collaboration proposals are reviewed by the KoMAC Data Access Committee to ensure scientific validity, ethical compliance and participant confidentiality. Further information regarding data access and collaboration can be obtained from the corresponding authors.

Supplementary material

online supplemental file 1
bmjopen-16-6-s001.pdf (2.3MB, pdf)
DOI: 10.1136/bmjopen-2025-105488

Acknowledgements

The authors extend their appreciation to the participants and research staff of the Korean Medicine for Aging Cohort (KoMAC) Study for their dedicated involvement and collaboration.

Footnotes

Funding: This study was supported by the Korea Institute of Oriental Medicine, Republic of Korea (grant number KSN2312021). The funders had no role in the study design, data collection and analysis, decision to publish or manuscript preparation.

Prepublication history and additional supplemental material for this paper are available online. To view these files, please visit the journal online (https://doi.org/10.1136/bmjopen-2025-105488).

Provenance and peer review: Not commissioned; externally peer reviewed.

Patient consent for publication: Not applicable.

Ethics approval: This study involves human participants. This study was approved by the Institutional Review Board (IRB) of Wonkwang University Korean Medicine Hospital, Republic of Korea (IRB No. WKUIOMH-IRB-2023-05, approved on 16 August 2023) and by the Institutional Review Board of Jangheung Integrative Medical Hospital, Republic of Korea (IRB No. WKUJIM-202307-001, approved on 21 August 2023). Participants gave informed consent to participate in the study before taking part.

Data availability free text: All data relevant to this study are included in the article or uploaded as supplementary materials. Additional data are available upon reasonable request.

Map disclaimer: The depiction of boundaries on this map does not imply the expression of any opinion whatsoever on the part of BMJ (or any member of its group) concerning the legal status of any country, territory, jurisdiction or area or of its authorities. This map is provided without any warranty of any kind, either express or implied.

Patient and public involvement: Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

Data availability statement

Data are available upon reasonable request. All data relevant to the study are included in the article or uploaded as supplementary information.

References

  • 1.Statistics Korea Populatation projection for Korea. Daejeon: statistics Korea 2024, 2022–72 year (based on the 2022 population census) https://kosis.kr/statHtml/statHtml.do?orgId=101&tblId=DT_1BPA002&conn_path=I2 n.d. Available.
  • 2.Kim CY, Chung SH. Demographic transition in South Korea: implications of falling birth rates. Clin Exp Pediatr . 2024;67:498–509. doi: 10.3345/cep.2023.01599. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.GBD 2019 Ageing Collaborators Global, regional, and national burden of diseases and injuries for adults 70 years and older: systematic analysis for the Global Burden of Disease 2019 Study. BMJ. 2019;376:e068208. doi: 10.1136/bmj-2021-068208. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Hou X, Liu L, Cain J. Can higher spending on primary healthcare mitigate the impact of ageing and non-communicable diseases on health expenditure. BMJ Glob Health. 2022;7:e010513. doi: 10.1136/bmjgh-2022-010513. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Saito M, Aida J, Kondo N, et al. Reduced long-term care cost by social participation among older Japanese adults: a prospective follow-up study in JAGES. BMJ Open. 2019;9:e024439. doi: 10.1136/bmjopen-2018-024439. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 6.World Health Organization . WHO: Geneva; 2015. World report on ageing and health.https://www.who.int/publications/i/item/9789241565042 Available. [Google Scholar]
  • 7.Beard JR, Officer A, de Carvalho IA, et al. The World report on ageing and health: a policy framework for healthy ageing. The Lancet. 2016;387:2145–54. doi: 10.1016/S0140-6736(15)00516-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Cesari M, Araujo de Carvalho I, Amuthavalli Thiyagarajan J, et al. Evidence for the Domains Supporting the Construct of Intrinsic Capacity. J Gerontol A Biol Sci Med Sci. 2018;73:1653–60. doi: 10.1093/gerona/gly011. [DOI] [PubMed] [Google Scholar]
  • 9.Sung AD-M, Baik Y-S, Jang S, et al. Utilization of Traditional Korean Medicine Services by the Older Population: A Cross-Sectional Study. Health Care (Don Mills) 10:1444. doi: 10.3390/healthcare10081444. n.d. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Hong S, Choi D, Jeong J. The Effect of Korean Medicine on Medical Expenses and Quality of Life in Elderly People: A Retrospective Study of Korean Health Panel Survey Data. J Int Korean Med . 2025;46:25–42. doi: 10.22246/jikm.2025.46.1.25. [DOI] [Google Scholar]
  • 11.Khalatbari-Soltani S, Si Y, Dominguez M, et al. Worldwide cohort studies to support healthy ageing research: data availabilities and gaps. Ageing Res Rev. 2024;96:102277. doi: 10.1016/j.arr.2024.102277. [DOI] [PubMed] [Google Scholar]
  • 12.Lee RZY, Yu J, Rawtaer I, et al. CHI study: protocol for an observational cohort study on ageing and mental health in community-dwelling older adults. BMJ Open. 2020;10:e035003. doi: 10.1136/bmjopen-2019-035003. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Sok SR, Cheon BK, Gu MK, et al. Comparisons of Health Promoting Behavior, Depression, and Life Satisfaction Between Older Adults in Rural Areas in South Korea Living in Group Homes and at Home. J Nurs Res. 2019;27:e21. doi: 10.1097/JNR.0000000000000290. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Kim S, Cho S, Morgan MR. Neighborhood and Depressive Symptoms in Older Adults Living in Rural and Urban Regions in South Korea. Health Care (Don Mills) 2023;11:476. doi: 10.3390/healthcare11040476. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 15.The official website of Iksan City Population status of Iksan city, Iksan 2023. [8-Jun-2023]. https://www.iksan.go.kr/board/list.iksan?boardId=BBS_IKSAN_HUMN&menuCd=DOM_000002005005004000&contentsSid=1215&cpath= Available. Accessed.
  • 16.The official website of Jangheung country. Population status of Jangheung-gun. [8-Jun-2023]. https://www.jangheung.go.kr/www/jeongnamjin/general_status/population Available. Accessed.
  • 17.Ko MM, Jeon S, Ha W, et al. The Korean medicine for aging cohort (KoMAC) study: A protocol for a prospective, multicenter cohort study on healthy aging in the population entering old age in South Korea. PLoS One. 2025;20:e0316986. doi: 10.1371/journal.pone.0316986. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Cho N, Jun H, Ha W-B, et al. Sharing Experiences in Selecting Clinical Outcome and Approving Validated Questionnaires : Insights from an Elderly Registry Study. J Korean Med . 2024;45:17–43. doi: 10.13048/jkm.24002. [DOI] [Google Scholar]
  • 19.Jang E, Yun YG, Yoo HR, et al. Preliminary Study to Develop the Instrument of Korean Medical Pattern Identification and Functional Evaluation for Five Organ. kjopp . 2017;31:126–37. doi: 10.15188/kjopp.2017.04.31.2.126. [DOI] [Google Scholar]
  • 20.Kang B-K, Jang S, Ko MM, et al. A Study on the Development of a Korean Metabolic Syndrome Questionnaire Using Blood Stasis Clinical Data. Evid Based Complement Alternat Med. 2019;2019:8761417. doi: 10.1155/2019/8761417. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Cheong MJ, Lee G-E, Lee Y, et al. Validation of the Core Seven-Emotions Inventory - short form. Integr Med Res. 2019;8:116–9. doi: 10.1016/j.imr.2019.04.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Brotto D, Benvegnù F, Colombo A, et al. Age-related changes in auditory perception. Hearing loss in the elderly: aging ear or aging brain? Aging Clin Exp Res. 2023;35:2349–54. doi: 10.1007/s40520-023-02570-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Smit AE, Meijer OC, Winter EM. The multi-faceted nature of age-associated osteoporosis. Bone Rep. 2024;20:101750. doi: 10.1016/j.bonr.2024.101750. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.Brito DVC, Esteves F, Rajado AT, et al. Assessing cognitive decline in the aging brain: lessons from rodent and human studies. NPJ Aging . 2023;9:23. doi: 10.1038/s41514-023-00120-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Li X, Wang R, Hou Z, et al. Urban-rural differences in the prevalence and associated factors of sarcopenia: A systematic review and meta-analysis. Arch Gerontol Geriatr. 2024;122:105390. doi: 10.1016/j.archger.2024.105390. [DOI] [PubMed] [Google Scholar]
  • 26.Moon SW, Kim KJ, Lee HS, et al. Low muscle mass, low muscle function, and sarcopenia in the urban and rural elderly. Sci Rep. 2022;12:14314. doi: 10.1038/s41598-022-18167-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Won CW, Yang KY, Rho YG, et al. The development of Korean activities of daily living (K-ADL) and Korean instrumental activities of daily living (K-IADL) scale. Ann Geriatr Med Res. 2002;6:107–20. [Google Scholar]
  • 28.Giburm P, Belong C, Soon KI, et al. Reliability and validity of Korean version of falls efficacy scale-international (KFES-I) Ann Rehabil Med. 2010;34:554–9. [Google Scholar]
  • 29.Welch SA, Ward RE, Beauchamp MK, et al. The Short Physical Performance Battery (SPPB): A Quick and Useful Tool for Fall Risk Stratification Among Older Primary Care Patients. J Am Med Dir Assoc. 2021;22:1646–51. doi: 10.1016/j.jamda.2020.09.038. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Song CS, Lee HS, Chun BY. Comparison of Montreal Cognitive Assessment in Korean Version for Predicting Mild Cognitive Assessment in 65-Year and Over Individuals. Occup Ther Int. 2022;2022:4108434. doi: 10.1155/2022/4108434. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Kokkinakis S, Venianaki M, Petra G, et al. A Comparison of the Malnutrition Universal Screening Tool (MUST) and the Mini Nutritional Assessment-Short Form (MNA-SF) Tool for Older Patients Undergoing General Surgery. J Clin Med. 2021;10:5860. doi: 10.3390/jcm10245860. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Lau S, Pek K, Chew J, et al. The Simplified Nutritional Appetite Questionnaire (SNAQ) as a Screening Tool for Risk of Malnutrition: Optimal Cutoff, Factor Structure, and Validation in Healthy Community-Dwelling Older Adults. Nutrients. 2020;12:2885. doi: 10.3390/nu1209288524. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Zhang Y, Liu Y, Li X, et al. Age-related changes in prevalence and symptom characteristics in kidney deficiency syndrome with varied health status: a cross-sectional observational study. J Tradit Chin Med Sci. 2014;1:20–7. doi: 10.1016/j.jtcms.2014.11.009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.Shu B, Shi Q, Wang Y. Shen (Kidney)-tonifying principle for primary osteoporosis: to treat both the disease and the Chinese medicine syndrome. Chin J Integr Med. 2015;21:656–61. doi: 10.1007/s11655-015-2306-z. [DOI] [PubMed] [Google Scholar]
  • 35.Singh-Manoux A, Kivimaki M, Glymour MM, et al. Timing of onset of cognitive decline: results from Whitehall II prospective cohort study. BMJ. 2012;344:d7622. doi: 10.1136/bmj.d7622. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Lachman ME. Development in midlife. Annu Rev Psychol. 2004;55:305–31. doi: 10.1146/annurev.psych.55.090902.141521. [DOI] [PubMed] [Google Scholar]
  • 37.World Health Organization . World report on ageing and health. Geneva: World Health Organization; 2015. [Google Scholar]

Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    online supplemental file 1
    bmjopen-16-6-s001.pdf (2.3MB, pdf)
    DOI: 10.1136/bmjopen-2025-105488

    Data Availability Statement

    Data are available upon reasonable request. All data relevant to the study are included in the article or uploaded as supplementary information.

    Articles from BMJ Open are provided here courtesy of BMJ Publishing Group

    RESOURCES