Abstract
Background
Sarcopenia is increasingly recognized as a major age-related disease with important clinical and public health implications, leading to updated international consensus definitions. We evaluated temporal changes in dual energy X-ray absorptiometry (DXA)-derived low muscle mass and estimated the prevalence and associated comorbidities of sarcopenia using nationally representative data.
Methods
We analyzed adults aged ≥50 years from the Korea National Health and Nutrition Examination Survey (KNHANES) 2008-2011 and 2024. Appendicular lean mass was assessed using wholebody DXA. Low muscle mass was defined according to the Asian Working Group for Sarcopenia 2025 criteria. Sarcopenia, defined as the coexistence of low muscle mass and low handgrip strength, was assessed only in KNHANES 2024. Sampling weight was applied to all analyses.
Results
The age-standardized prevalence of low muscle mass increased from 27.2% in 2008-2011 to 47.1% in 2024, with similar trends observed in men and women. Male sex, greater height, lower body weight, lower total energy intake, a higher proportion of energy derived from fat, and diabetes mellitus were independently associated with a higher prevalence of low muscle mass. The between-period difference in prevalence remained statistically significant after multivariable adjustment. In 2024, the overall prevalence of sarcopenia was 6.7% and increased with age, reaching 21.5% among adults aged ≥80 years. Sarcopenia was independently associated with diabetes mellitus, anemia, malnutrition, poor self-rated health, and recent bedridden status.
Conclusions
Low muscle mass and sarcopenia are increasingly prevalent among older adults in Korea and are strongly associated with comorbidities.
Keywords: Epidemiology, Muscles, Prevalence, Sarcopenia
GRAPHICAL ABSTRACT
INTRODUCTION
As population aging accelerates worldwide, recognition of sarcopenia as a distinct major age-related disease has increased substantially, accompanied by rapid accumulation of epidemiologic, mechanistic, and clinical outcome data.[1,2] These advances have led to repeated updates of international diagnostic frameworks and consensus definitions, including those from the Asian Working Group for Sarcopenia (AWGS), most recently updated in 2025.[3] Along with this growing global consensus, updated national estimates are needed to inform clinical practice and public health policy, particularly in regions undergoing rapid population aging such as Korea.[4]
The Korea National Health and Nutrition Examination Survey (KNHANES) provides nationally representative health data for the Korean population and has served as the primary source of population-level estimates of body composition and musculoskeletal health.[5] In 2024, KNHANES resumed dual energy X-ray absorptiometry (DXA) assessments for the first time since 2011 and, importantly, incorporated handgrip strength (HGS) testing. This marks the first KNHANES cycle in which DXA-based appendicular lean mass (ALM) and muscle strength can be jointly evaluated, enabling operational definitions of sarcopenia consistent with consensus guidelines.[1,3] The availability of KNHANES 2024 data thus provides a unique opportunity to reassess secular changes in the prevalence of DXA-derived low muscle mass since 2008-2011, to estimate the current population prevalence of sarcopenia, and to examine its associations with comorbidities in a nationally representative setting.
In this study, we used data from KNHANES 2008–2011 and KNHANES 2024 to examine temporal changes in the prevalence of DXA-derived low muscle mass among Korean adults aged 50 years or older. Using KNHANES 2024, we further estimated the prevalence of sarcopenia based on combined measures of muscle mass and strength and evaluated its associations with demographic factors and major comorbid conditions. Through these analyses, we aimed to provide updated, population-based evidence on the prevalence and clinical implications of low muscle mass and sarcopenia in Korea.
METHODS
1. Study participants
Data of participants in KNHANES 2008-2011 and KNHANES 2024 were analyzed. The KNHANES, initiated in 1998, was designed to generate nationally representative data to inform the development and evaluation of health policies and programs by the Korea Disease Control and Prevention Agency. [6] For the present study, we analyzed data from KNHANES 2008–2011 (the 4th and 5th survey cycles) and KNHANES 2024 (the 9th survey cycle), as DXA measurements were available only during these periods (Fig. 1). KNHANES uses a stratified, multistage cluster sampling design to ensure national representativeness of the non-institutionalized Korean population. All KNHANES protocols were approved by the Institutional Review Board of the Korea Disease Control and Prevention Agency (2007–2014, 2018).
Fig. 1.
Study flow. KNHANES, Korea National Health and Nutrition Examination Survey; DXA, dual energy X-ray absorptiometry; ALM, appendicular lean mass; HGS, handgrip strength.
2. DXA measurements
Standing height and body weight were measured by trained staff using standardized protocols, with participants wearing light clothing and no shoes. Body mass index (BMI) was calculated as weight divided by height squared (kg/m²). Whole-body composition, including lean mass and fat mass, was evaluated using DXA in mobile examination units (2008–2011, Discovery-W; 2024, Horizon Wi; Hologic Inc., Waltham, MA, USA). DXA whole body scans were conducted in participants aged ≥19 years from July 2008 to June 2009 and in those aged ≥10 years from July 2009 to May 2011, whereas in the 2024 survey cycle, measurements were performed in adults aged ≥40 years. Because whole-body DXA data from KNHANES 2008–2011 were originally generated using classic calibration, lean mass values from this period were recalibrated to align with the NHANES calibration standard applied in KNHANES 2024 by multiplying lean mass by a coefficient of 0.946.[7] Fat mass and body fat percent (head excluded) was subsequently recalculated accordingly to maintain internal consistency of whole-body composition measures. All DXA acquisitions and analyses were performed in accordance with the current International Society for Clinical Densitometry Official Positions.[8]
3. Sarcopenia definition
According to AWGS 2025 consensus definition, low muscle mass was defined using ALM index (ALM/height [m]2, kg/m2; in men and women; age 50-64 years, <7.2 kg/m2 and <5.5 kg/m2; age 65 years or older, <7.0 kg/m2 and <5.4 kg/m2).[3] For sensitivity analysis, low muscle mass was also defined using ALM/BMI (in men and women; age 50- 64 years, <0.80 and <0.55; age 65 years or older, <0.73 and <0.52). HGS was assessed using a digital hand dynamometer (T.K.K. 5401; Takei Scientific Instruments, Niigata, Japan) only in KNHANES 2024. Measurements were obtained twice for each hand, and the highest value recorded from either hand was used as the representative HGS. Low muscle strength was defined using representative HGS (in men and women; age 50-64, <34 kg and <20 kg; age 65 years or older, <28 kg and <18 kg). Sarcopenia was defined as the presence of both low muscle mass and low muscle strength, using ALM index-based low muscle mass as main analysis and ALM/BMI-based low muscle mass as a sensitivity analysis. As HGS was assessed only in KNHANES 2024, sarcopenia could be defined only for participants in the 2024 survey.
4. Covariates
Alcohol consumption was classified into three categories based on self-reported drinking frequency and typical amount consumed over the preceding year. Participants were categorized as ‘less than monthly’ drinkers (drinking less than once per month or abstaining during the past year), ‘at least monthly’ (drinking at least once per month during the past year), or heavy drinkers, defined as consuming ≥7 standard drinks per occasion for men or ≥5 standard drinks per occasion for women on two or more days per week. Current smoking status was defined as having smoked at least 100 cigarettes (≥5 packs) over the lifetime and reporting current cigarette use at the time of the survey. Self-reported regular walking activity was defined as engaging in walking for at least 30 min per session on five or more days during the previous week. Self-reported regular resistance exercise was defined as engaging in muscle-strengthening activities on two or more days in the past week. Dietary intake was assessed using a single 24-hr dietary recall conducted by trained interviewers as part of the KNHANES nutrition survey. Daily intake of total energy, carbohydrates, protein, and fat was calculated using the Korean food composition database. Estimated energy requirement (EER) was calculated using the Institute of Medicine predictive equations based on age, sex, height, and weight, with the physical activity level fixed at sedentary.[9] Low energy intake was defined as a total energy intake less than 75% of the EER.[9]
5. Comorbidities
Diabetes mellitus was defined using a composite definition, including a self-reported physician diagnosis of diabetes, current use of any antidiabetic medication, or a measured hemoglobin A1c level ≥6.5%. Hypertension was defined as a self-reported physician diagnosis of hypertension, current use of antihypertensive medication, systolic blood pressure ≥140 mmHg, or diastolic blood pressure ≥90 mmHg. Chronic kidney disease (CKD) was defined as a self-reported physician diagnosis of chronic kidney disease or an estimated glomerular filtration rate (eGFR) <45 mL/min/1.73 m² (CKD stage G3b or worse), calculated using the CKD-EPI 2021 race-free creatinine-based equation. [10] Anemia was defined as a hemoglobin concentration <13 g/dL in men and <12 g/dL in women. Malnutrition was defined as BMI <18.5 kg/m2 or low energy intake. Poor self-rated health was defined as a self-reported response of “poor” or “very poor” on a general health status question. Bedridden status was defined as being confined to bed for any reasons during the month preceding the survey, based on interviewer-administered questionnaires.
6. Statistical analysis
All analyses accounted for the complex sampling design of the KNHANES by applying survey weights, stratification, and clustering variables. For the KNHANES 2008-2011, integrated survey weights were used, whereas cycle-specific weights were applied for the 2024 survey. Participant characteristics were summarized using survey-weighted estimates, and differences between survey cycles were evaluated using Wald tests. Sex-specific age-standardized prevalence of low muscle mass was calculated using internal direct standardization, with the age distribution of the analytic sample within each sex serving as the standard population. Adjusted prevalence ratios (PRs) for low muscle mass were estimated using multivariable Poisson regression models with robust variance estimation. To assess the impact of potential confounders, we examined whether PRs comparing the two survey periods were attenuated after adjustment for age, height, body weight, current smoking status, alcohol consumption, physical activity, energy intake, and comorbid conditions. In analyses restricted to KNHANES 2024, sex-specific prevalence estimates of low muscle mass and sarcopenia were derived across 10-year age strata starting at 50 years of age. Associations between sarcopenia and comorbidities were examined using multivariable logistic regression models. All statistical analyses were conducted using STATA 18.0 (Stata Corp., College Station, TX, USA), and statistical significance was defined as a two-sided P value of less than 0.05.
RESULTS
1. Characteristics of study participants
Compared with participants in KNHANES 2008–2011, those in KNHANES 2024 were slightly older (mean age, 62.1 vs. 62.9 years) and had greater height, body weight, and BMI (Table 1, Fig. 1). Despite the increase in body size and EER, total energy intake did not increase over time, resulting in a higher prevalence of low energy intake in KNHANES 2024 (20.1% vs. 29.0%). In addition, the prevalence of diabetes mellitus, hypertension, and chronic kidney disease was higher in KNHANES 2024 than in KNHANES 2008–2011.
Table 1.
Sampling-weighted characteristics of study participants
| Variables | KNHANES 2008–2011 (N=9,219) | KNHANES 2024 (N=2,481) | P-value |
|---|---|---|---|
| Age (yr) | 62.1 (61.8–62.4) | 62.9 (62.4–63.5) | 0.007 |
| Women (%) | 53.9 [53.0–54.8] | 51.9 [50.2–53.5] | 0.035 |
| Height (cm) | 159.6 (159.4–159.9) | 162.7 (162.2–163.1) | <0.001 |
| Weight (kg) | 61.4 (61.1–61.7) | 64.3 (63.7–64.9) | <0.001 |
| BMI (kg/m2) | 24.0 (23.9–24.1) | 24.2 (24.1–24.4) | 0.024 |
| Alcohol drinking (%) | |||
| Less than monthly | 54.5 [53.2–55.8] | 51.8 [49.2–54.4] | 0.198 |
| At least monthly | 36.2 [34.9–37.5] | 38.2 [35.9–40.5] | |
| Heavy | 9.3 [8.5–10.2] | 10.0 [8.7–11.5] | |
| Current smoker (%) | 19.3 [18.3–20.3] | 14.3 [12.7–15.9] | <0.001 |
| Self-reported regular walking (≥5 days/week, ≥30 min) (%) | 43.3 [41.8–44.8] | 44.9 [42.3–47.7] | 0.294 |
| Self-reported resistance exercise (≥3 days/week) (%) | 19.3 [18.2–20.5] | 26.3 [24.3–28.4] | <0.001 |
| Nutrition intake/day (N=11,604) | |||
| Total energy (kcal) | 1,817 (1,790–1,843) | 1,806 (1,769–1,843) | 0.651 |
| Carbohydrates (g) | 316 (311–321) | 266 (261–272) | <0.001 |
| Protein (g) | 62 (61–63) | 68 (66–70) | <0.001 |
| Fat (g) | 17 (15–19) | 28 (27–29) | <0.001 |
| Estimated energy requirement (kcal) | 1,784 (1,776–1,792) | 1,844 (1,828–1,859) | <0.001 |
| Low energy intake (%)a) | 20.1 [19.0–21.2] | 29.0 [26.7–31.4] | <0.001 |
| Diabetes mellitus (%) | 15.4 [14.6–16.3] | 21.0 [19.3–22.8] | <0.001 |
| Hypertension (%) | 51.1 [49.7–52.4] | 54.8 [52.2–57.4] | 0.014 |
| Chronic kidney disease (%) | 1.2 [1.0–1.5] | 3.6 [2.9–4.5] | <0.001 |
| DXA whole body composition | |||
| ALM (kg) | 17.2 (17.1–17.3) | 17.0 (16.8–17.2) | 0.069 |
| ALM index (kg/m2) | 6.6 (6.6–6.7) | 6.3 (6.3–6.4) | <0.001 |
| ALM/BMI | 0.72 (0.71–0.73) | 0.70 (0.70–0.71) | <0.001 |
| Body fat (%) | 33.4 (33.1–33.6) | 36.3 (35.8–36.7) | <0.001 |
| Low muscle mass (ALM index) (%) | 27.2 [25.8–28.6] | 47.5 [44.9–49.9] | <0.001 |
| Low muscle mass (ALM/BMI) (%) | 20.6 [19.3–21.9] | 27.7 [25.5–30.0] | <0.001 |
| Handgrip strength (kg)b) | NA | 31.1 (30.7–31.5) | |
| Low muscle strength (%) | NA | 10.1 [8.6–11.8] | |
| Sarcopenia (ALM index) (%) | NA | 6.7 [5.6–8.1] | |
| Sarcopenia (ALM/BMI) (%) | NA | 4.8 [4.0–5.9] |
All analyses incorporated the complex sampling design and sampling weights of Korea National Health and Nutrition Examination Survey (KNHANES), allowing estimates to be generalized to the Korean population aged ≥50 years (about 32 million). All values are survey-weighted estimates expressed as mean (95% confidence interval) or % [95% confidence interval]. Unweighted sample counts (N) are shown in column headers. P-values from surveyadjusted Wald tests.
Low energy intake was defined as daily energy intake <75% of estimated energy requirement.
Handgrip strength was not measured in KNHANES 2008–2011.
BMI, body mass index; DXA, dual energy X-ray absorptiometry; ALM, appendicular lean mass; NA, not applicable.
2. Prevalence of low muscle mass between KNHANES 2008-2011 and 2024
Although DXA ALM was numerically lower in KNHANES 2024 than in KNHANES 2008–2011 (17.0 vs. 17.2 kg), the difference was not statistically significant (Table 1). In contrast, both the DXA ALM index (6.3 vs. 6.6 kg/m2) and DXA ALM-to-BMI ratio (ALM/BMI, 0.70 vs. 0.72) were significantly lower in KNHANES 2024 (Table 1, Supplementary Fig. 1). These differences were accompanied by an increase in the age-standardized, sampling-weighted prevalence of low muscle mass using both definitions, observed in the overall population (27.2%-47.1%) as well as in women (22.8%-50.0%) and men (33.3%-44.2%) separately (Fig. 2). In KNHANES 2024, the prevalence of low muscle strength was 10.1%. The age-standardized prevalence of sarcopenia was 6.7% overall, with prevalences of 6.0% in women and 7.5% in men (Fig. 2).
Fig. 2.
Age-standardized, sampling-weighted prevalence of low muscle mass and sarcopenia among participants aged 50 or older in Korea National Health and Nutrition Examination Survey (KNHANES) 2008–2011 and KNHANES 2024. Sarcopenia was not evaluated in KNHANES 2008–2011 due to the absence of handgrip strength measurements. (A) Overall participants. (B) Women. (C) Men.
3. Factors associated with low muscle mass prevalence
Compared with KNHANES 2008–2011, the prevalence of low muscle mass defined by the ALM index was 1.74-fold higher in KNHANES 2024 (prevalence ratio [PR], 1.74; 95% confidence interval [CI], 1.62-1.87; P<0.001; Table 2). In multivariable analyses, male sex, greater height, lower body weight, lower total energy intake, a higher proportion of energy derived from fat, and the presence of diabetes mellitus were independently associated with a higher prevalence of low muscle mass. Adjustment for these factors only partially explained the temporal difference, and the higher prevalence of low muscle mass in KNHANES 2024 remained statistically significant (adjusted PR, 1.76; 95% CI, 1.61-1.91; P<0.001). These findings were consistent in sensitivity analyses using an alternative definition of low muscle mass based on ALM/BMI (adjusted PR, 1.74; 95% CI, 1.56-1.94; P<0.001; Supplementary Table 1).
Table 2.
Sampling-weighted prevalence ratio of low muscle mass (defined by appendicular lean mass index) between KNHANES 2008–2011 and KNHANES 2024
| Variables | Univariate |
Multivariable model |
||
|---|---|---|---|---|
| PR (95% CI) | P-value | aPR (95% CI) | P-value | |
| KNHANES period (2024 vs. 2008–2011) | 1.74 (1.62–1.87) | <0.001 | 1.76 (1.61–1.91) | <0.001 |
| Age | 1.12 (1.08–1.16) | <0.001 | 1.01 (0.97–1.04) | 0.454 |
| Women | 0.96 (0.89–1.03) | 0.265 | 0.68 (0.62–0.76) | <0.001 |
| Height | 1.02 (0.98–1.06) | 0.260 | 1.67 (1.58–1.75) | <0.001 |
| Weight | 0.57 (0.55–0.59) | <0.001 | 0.36 (0.35–0.38) | <0.001 |
| Current smoker | 1.16 (1.06–1.27) | <0.001 | 1.03 (0.93–1.13) | 0.560 |
| Alcohol intake (Ref: at least monthly) | ||||
| Less than monthly | 1.09 (1.01–1.17) | 0.029 | 1.02 (0.94–1.09) | 0.685 |
| Heavy | 0.96 (0.83–1.10) | 0.550 | 0.95 (0.84–1.06) | 0.368 |
| Self-reported regular walking | 0.98 (0.91–1.04) | 0.548 | 0.96 (0.91–1.02) | 0.174 |
| Self-reported resistance exercise | 1.01 (0.93–1.09) | 0.897 | 0.96 (0.88–1.03) | 0.274 |
| Total energy intake | 0.88 (0.85–0.91) | <0.001 | 0.91 (0.88–0.95) | <0.001 |
| Fat/total energy (%) | 1.10 (1.07–1.14) | <0.001 | 1.06 (1.02–1.10) | 0.002 |
| Diabetes mellitus | 0.95 (0.87–1.04) | 0.248 | 1.10 (1.02–1.20) | 0.020 |
| Hypertension | 0.80 (0.73–0.86) | <0.001 | 1.06 (0.99–1.13) | 0.088 |
| Chronic kidney disease | 0.97 (0.76–1.25) | 0.861 | 0.96 (0.78–1.18) | 0.702 |
Prevalence ratios (PRs) for continuous variables were presented per one standard deviation increment of each variable.
KNHANES, Korea National Health and Nutrition Examination Survey; aPR, adjusted PR; CI, confidence interval; Ref, reference.
4. Age-stratified prevalence of low muscle mass and sarcopenia in KNHANES 2024
In analyses restricted to KNHANES 2024, sampling-weighted prevalences of low muscle mass and sarcopenia were examined across age groups (Fig. 3). An age-related increase in prevalence, especially in oldest group, was observed. The prevalence of low muscle mass increased from 44.6% among the youngest age group (50-59 years) to 57.4% in the oldest (80 years or older), while sarcopenia prevalence rose from 5.6% to 21.5% across the same age groups (P for trend <0.001 for both). When sarcopenia was alternatively defined using ALM/BMI in combination with HGS (Supplementary Fig. 2), a comparable age-related increase in prevalence was observed.
Fig. 3.
Sampling-weighted prevalence of low muscle mass (by dual energy X-ray absorptiometry appendicular lean mass [ALM] index) and sarcopenia (presence of low muscle mass and low muscle strength) in Korea National Health and Nutrition Examination Survey 2024. (A) Overall participants. (B) Women. (C) Men.
5. Association of sarcopenia with comorbidities in KNHANES 2024
Among participants in KNHANES 2024, the associations between sarcopenia and comorbidities were examined (Table 3). Sarcopenia was associated with higher odds of diabetes mellitus (age- and sex-adjusted odds ratio [aOR], 1.50), anemia (aOR, 2.40), malnutrition (aOR, 1.44), poor self-rated health (aOR, 1.86), and having been bedridden during the preceding month (aOR, 2.10; P<0.05 for all). These associations remained largely robust when sarcopenia was alternatively defined using ALM/BMI. Although the associations with anemia (aOR, 1.58; P=0.059) and malnutrition (aOR, 1.38; P=0.097) marginally failed to reach statistical significance, the magnitude of the effect estimates was comparable to those observed when sarcopenia was defined using the ALM index.
Table 3.
Association of sarcopenia with comorbidities in KNHANES 2024
| Outcomes | Age- and sex-adjusted odds ratio (95% CI) by presence of sarcopenia | P-value |
|---|---|---|
| Sarcopenia (DXA ALM index + HGS) | ||
| Diabetes mellitus | 1.50 (1.05–2.13) | 0.025 |
| Anemia | 2.40 (1.67–3.43) | <0.001 |
| Malnutritiona) | 1.44 (1.02–2.03) | 0.035 |
| Poor self-rated health | 1.86 (1.25–2.78) | 0.002 |
| Bedridden during the past month | 2.10 (1.03–4.28) | 0.040 |
| Sarcopenia (DXA ALM/BMI + HGS) | ||
| Diabetes mellitus | 2.02 (1.31–3.09) | 0.001 |
| Anemia | 1.58 (0.98–2.56) | 0.059 |
| Malnutritiona) | 1.38 (0.94–2.03) | 0.097 |
| Poor self-rated health | 1.89 (1.17–3.06) | 0.009 |
| Bedridden during the past month | 2.29 (1.07–4.89) | 0.032 |
Malnutrition was defined as daily energy intake less than 75% of estimated energy requirement or body mass index (BMI) lower than 18.5 kg/m2.
KNHANES, Korea National Health and Nutrition Examination Survey; DXA, dual energy X-ray absorptiometry; ALM, appendicular lean mass; HGS, handgrip strength; CI, confidence interval.
DISCUSSION
In this study, the age-standardized, sampling-weighted prevalence of DXA-derived low muscle mass among adults aged ≥50 years increased from 27.2% in KNHANES 2008–2011 to 47.1% in KNHANES 2024, with similar patterns observed in both men and women. Despite increases in height and body weight over time, total energy intake remained largely unchanged between the two survey periods, resulting in a higher proportion of individuals with low energy intake in KNHANES 2024, particularly among older adults. The prevalence of diabetes mellitus, hypertension, and chronic kidney disease was also higher in KNHANES 2024 than in KNHANES 2008–2011. However, the between-period difference in low muscle mass prevalence persisted after adjustment for age, body size, physical activity, energy intake, and comorbidities, including diabetes mellitus, suggesting that these factors did not fully account for the observed increase.
Sarcopenia, defined as the coexistence of low muscle mass and low muscle strength, could be assessed only in KNHANES 2024 because HGS was not measured in earlier survey cycles. The age-standardized prevalence of sarcopenia in KNHANES 2024 was 6.7% and increased markedly with age, from 5.6% among those aged 50–59 years to 21.5% among those aged ≥80 years. Sarcopenia was associated with 1.5- to 2.4-fold higher odds of diabetes mellitus, anemia, malnutrition, poor self-rated health, and recent bedridden status, independent of age and sex. These associations remained robust when sarcopenia was alternatively defined using DXA-derived ALM/BMI.
Although overall body size increased over time, absolute ALM remained relatively stable, suggesting that the marked rise in low muscle mass prevalence was driven primarily by unfavorable changes in body composition, particularly a relative increase in body fat proportion.[11] This interpretation is further supported by the observation that total energy intake did not increase in proportion to gains in height and body weight, resulting in a higher prevalence of low energy intake, especially among older adults. Inadequate energy intake relative to physiological requirements may accelerate age-related muscle loss by limiting substrate availability for muscle protein synthesis and promoting negative energy balance, a mechanism that has been consistently highlighted in prior studies.[12,13] Although self-reported engagement in regular exercise increased in KNHANES 2024, such measures may insufficiently capture the intensity, frequency, or resistance-based components of physical activity necessary for the preservation of muscle mass.[14] Together, these findings suggest that age-related shifts in body composition reflect not only aging itself but also a growing mismatch between energy intake, metabolic demand, and effective muscle-loading activity, underscoring the need for targeted nutritional adequacy and appropriately prescribed resistance exercise, particularly in older adults.
The increasing prevalence of diabetes mellitus may have contributed to the burden of low muscle mass, as diabetes mellitus is a well-established and clinically important risk factor for sarcopenia.[15] Prior studies have consistently reported a significant association between diabetes mellitus and reduced muscle mass through mechanisms involving insulin resistance, chronic inflammation, and impaired muscle protein synthesis.[16] In line with these observations, our study demonstrated that diabetes mellitus was a significant factor associated with an increased prevalence of low lean mass, and sarcopenia was significantly associated with higher odds of diabetes mellitus. Given the rising prevalence of diabetes mellitus in Korea, together with increasingly sedentary lifestyles, sarcopenia is increasingly recognized as a key metabolic complication of diabetes. [15,17] These findings highlight the potential need for tailored strategies for muscle health assessment and management in individuals with diabetes mellitus.
In the KNHANES 2024, the overall age-standardized, sampling-weighted prevalence of sarcopenia was 6.7%. This estimate falls within the lower-to-mid range of sarcopenia prevalence reported in prior Korean and other Asian studies.[3,18-20] These variations in prevalence across studies are likely attributable, at least in part, to variations in sarcopenia definitions, as well as differences in age structure and muscle mass assessment methods. Of note, consistent with prior literature, sarcopenia prevalence increased markedly with advancing age, reaching approximately 20% among individuals aged 80 years or older.[18] Given that the population aged ≥80 years in Korea was approximately 2.2 million in 2023 according to national population statistics, this corresponds to an estimated 450,000 individuals with sarcopenia in this age group alone, underscoring a substantial absolute burden in a rapidly aging society.
In KNHANES 2024, sarcopenia was significantly associated with multiple comorbidities, including diabetes mellitus, anemia, malnutrition, and recent bed-ridden status. This highlights the close interrelationship between muscle health, metabolic status, and nutritional vulnerability in older adults. These findings reinforce sarcopenia as a systemic, age-related condition with important clinical implications rather than a simple consequence of aging. In this context, conditions such as diabetes mellitus, anemia, and malnutrition may serve as potential case-finding indicators for sarcopenia, which could be incorporated into clinical evaluation pathways for sarcopenia assessment. Such integration may facilitate earlier identification and more targeted management strategies to mitigate the progression of sarcopenia in clinical practice.
Differences in DXA machines and measurement conditions between the 2008-2011 and 2024 survey periods may have contributed to the observed differences in low muscle mass prevalence. Although recalibration was applied to align lean mass values across survey periods, residual variability between DXA systems, even from the same manufacturer, may remain, as hardware and analytic differences may not be fully captured by a single linear adjustment. However, the observed increase is unlikely to be explained solely by measurement artifacts, given that absolute ALM values were similar between periods, whereas body size-adjusted indices declined consistently, accompanied by increases in adiposity and metabolic comorbidities. The between-period differences remained robust after multivariable adjustment.
This study has several limitations. Cross-sectional, observational study design did not allow causal inference between low muscle mass, sarcopenia, and related factors. KNHANES 2024 represents part of the ongoing 9th survey cycle, and further validation will be possible once DXA assessments are completed for the entire survey period. Physical activity was assessed using self-reported questionnaires, which may not adequately capture the intensity, frequency, or resistance-based components of exercise most relevant to muscle mass preservation. Several factors known to influence muscle mass, such as inflammatory markers, hormonal status, and neuromuscular conditions, were not available. In addition, functional outcomes reflecting muscle performance, including chair rise test and gait speed, were not available in this dataset.
In summary, using nationally representative DXA data from KNHANES, we observed a substantial increase in the prevalence of low muscle mass among Korean adults aged ≥50 years between 2008–2011 and 2024. Sarcopenia, assessed for the first time using DXA in KNHANES 2024, was common in older adults and was strongly associated with multiple metabolic and health-related conditions. These findings highlight sarcopenia as an important and growing public health concern in Korea and underscore the need for early identification and targeted interventions in aging populations.
Footnotes
Acknowledgments
We sincerely thank the investigators and staff of the KNHANES conducted by the Korea Disease Control and Prevention Agency (KDCA), as well as all study participants, for their invaluable contributions to this study.
Ethics approval and consent to participate
All protocols of the Korea National Health and Nutrition Examination Survey (KNHANES) were approved by the Institutional Review Board of the Korea Disease Control and Prevention Agency (KDCA) (2007–2014, 2018).
Conflict of interest
Young-Kyun Lee has been the Editor-in-chief of the Journal of Bone Metabolism since January 17, 2020, but has no role in the decision to publish this article. Except for that, no potential conflict of interest relevant to this article was reported.
Supplementary Information
REFERENCES
- 1.Kirk B, Cawthon PM, Arai H, et al. The conceptual definition of sarcopenia: Delphi consensus from the Global Leadership Initiative in Sarcopenia (GLIS) Age Ageing. 2024;53:afae052. doi: 10.1093/ageing/afae052. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Nguyen TT, Dao T, Nguyen HT, et al. Sarcopenia and muscle aging: Updated insights into molecular mechanisms and translational therapeutics. Endocrinol Metab (Seoul) 2026;41:57–85. doi: 10.3803/EnM.2025.2656. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Chen LK, Hsiao FY, Akishita M, et al. A focus shift from sarcopenia to muscle health in the Asian Working Group for Sarcopenia 2025 consensus update. Nat Aging. 2025;5:2164–75. doi: 10.1038/s43587-025-01004-y. [DOI] [PubMed] [Google Scholar]
- 4.The Lancet Regional Health-Western P South Korea's population shift: Challenges and opportunities. Lancet Reg Health West Pac. 2023;36:100865. doi: 10.1016/j.lanwpc.2023.100865. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Kweon S, Kim Y, Jang MJ, et al. Data resource profile: The Korea National Health and Nutrition Examination Survey (KNHANES) Int J Epidemiol. 2014;43:69–77. doi: 10.1093/ije/dyt228. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Oh K, Kim Y, Kweon S, et al. Korea National Health and Nutrition Examination Survey, 20th anniversary: Accomplishments and future directions. Epidemiol Health. 2021;43:e2021025. doi: 10.4178/epih.e2021025. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Schoeller DA, Tylavsky FA, Baer DJ, et al. QDR 4500A dualenergy X-ray absorptiometer underestimates fat mass in comparison with criterion methods in adults. Am J Clin Nutr. 2005;81:1018–25. doi: 10.1093/ajcn/81.5.1018. [DOI] [PubMed] [Google Scholar]
- 8.Petak S, Barbu CG, Yu EW, et al. The official positions of the international society for clinical densitometry: Body composition analysis reporting. J Clin Densitom. 2013;16:508–19. doi: 10.1016/j.jocd.2013.08.018. [DOI] [PubMed] [Google Scholar]
- 9.National Academies of Sciences, Engineering, and Medicine . Dietary reference intakes for energy. Washington, DC: National Academies Press; 2023. [PubMed] [Google Scholar]
- 10.Inker LA, Eneanya ND, Coresh J, et al. New creatinine- and cystatin c-based equations to estimate GFR without race. N Engl J Med. 2021;385:1737–49. doi: 10.1056/NEJMoa2102953. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Tyrovolas S, Koyanagi A, Olaya B, et al. Factors associated with skeletal muscle mass, sarcopenia, and sarcopenic obesity in older adults: A multi-continent study. J Cachexia Sarcopenia Muscle. 2016;7:312–21. doi: 10.1002/jcsm.12076. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Bartali B, Frongillo EA, Bandinelli S, et al. Low nutrient intake is an essential component of frailty in older persons. J Gerontol A Biol Sci Med Sci. 2006;61:589–93. doi: 10.1093/gerona/61.6.589. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Cho YJ, Lim YH, Yun JM, et al. Sex- and age-specific effects of energy intake and physical activity on sarcopenia. Sci Rep. 2020;10:9822. doi: 10.1038/s41598-020-66249-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Prince SA, Adamo KB, Hamel ME, et al. A comparison of direct versus self-report measures for assessing physical activity in adults: A systematic review. Int J Behav Nutr Phys Act. 2008;5:56. doi: 10.1186/1479-5868-5-56. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Mesinovic J, Fyfe JJ, Talevski J, et al. Type 2 diabetes mellitus and sarcopenia as comorbid chronic diseases in older adults: Established and emerging treatments and therapies. Diabetes Metab J. 2023;47:719–42. doi: 10.4093/dmj.2023.0112. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Cleasby ME, Jamieson PM, Atherton PJ. Insulin resistance and sarcopenia: Mechanistic links between common comorbidities. J Endocrinol. 2016;229:R67–81. doi: 10.1530/joe-15-0533. [DOI] [PubMed] [Google Scholar]
- 17.Ha KH, Kim DJ. Changes in the epidemiological landscape of diabetes in South Korea: Trends in prevalence, incidence, and healthcare expenditures. Endocrinol Metab (Seoul) 2024;39:669–77. doi: 10.3803/EnM.2024.2073. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Kim S, Ha YC, Kim DY, et al. Recent update on the prevalence of sarcopenia in Koreans: Findings from the Korea National Health and Nutrition Examination Survey. J Bone Metab. 2024;31:150–61. doi: 10.11005/jbm.2024.31.2.150. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Chen LK, Woo J, Assantachai P, et al. Asian Working Group for Sarcopenia: 2019 consensus update on sarcopenia diagnosis and treatment. J Am Med Dir Assoc. 2020;21:300–7.e2. doi: 10.1016/j.jamda.2019.12.012. [DOI] [PubMed] [Google Scholar]
- 20.Weng SE, Huang YW, Tseng YC, et al. The evolving landscape of sarcopenia in Asia: A systematic review and meta-analysis following the 2019 Asian Working Group for Sarcopenia (AWGS) diagnostic criteria. Arch Gerontol Geriatr. 2025;128:105596. doi: 10.1016/j.archger.2024.105596. [DOI] [PubMed] [Google Scholar]
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