DOI: https://doi.org/10.21203/rs.3.rs-2037581/v1
Dietary protein sources and protein adequacy are crucial modulators of muscle quality and body composition. We investigated the association between dietary protein sources (and their adequacy) and prevalence of sarcopenic obesity (SO) in South Korean populations according to weight status.
The participants (n = 1,967) were classified into SO, obese, sarcopenia, and normal groups. A cross-sectional survey was conducted using the KS-15 questionnaire, short-form Food Frequency Questionnaire, and anthropometric measurements.
Percentage of body fat (male: 28.43 ± 0.61%; female: 39.95 ± 0.36%) was significantly high, while appendicular skeletal muscle (ASM; male: 40.30 ± 0.36%, female: 32.47 ± 0.20%) was low in the SO and OB groups. Beef and pork consumption was negatively associated with ASM (%) but positively associated with body fat (kg and %) in the normal group. Among the people with excessive protein intake, the lowest quintile (Q1: 5.7 g/day) of beans and tofu consumption showed a 2.4-fold increase in the risk of developing SO (adjusted odds ratio: 2.41, confidence interval: 1.07–7.80), when compared with the highest quintile (Q5: 60.1 g/day). Similarly, with beans and tofu consumption, there was a 2.5-fold higher risk of developing sarcopenia in participants who had < 5.7 g/day intake in the excessive protein intake individuals.
Daily poultry and egg intake was positively linked with muscle function in the participants with sarcopenia, while red meat showed a negative effect on imbalanced body composition with increased fat mass (kg and %) and decreased ASM (%) in participants with normal weight. Furthermore, lower intake of healthy protein foods, such as beans and tofu or poultry and eggs, was strongly associated with SO prevalence in people who consumed excessive daily dietary protein.
Sarcopenic obesity (SO) is a double-burden condition characterized by a decline in muscular quality and excess body fat mass (1). SO is caused not only by aging but also by non-communicable diseases, such as cardiovascular diseases (CVDs), metabolic syndrome (MetS), and obesity (2). Obesity is a risk factor for chronic diseases, including type 2 diabetes mellitus (T2DM), CVDs, and all-cause mortality (3–5). The coexistence of excess adiposity and loss of muscle mass negatively affects the prognosis of obese individuals (1).
As a multifactorial disease, SO is driven by metabolic health status and lifestyle factors, such as diet and physical activity (PA) (6). Unhealthy lifestyles, including smoking, alcohol consumption, lack of PA, and adherence to an unhealthy diet, showed strong associations with the development of cardiometabolic diseases in a multinational prospective cohort study (7). Korean medicine (KM) type (8) has a genetic predisposition for abdominal obesity (9), insulin resistance (10), metabolic syndrome (11, 12), and inflammatory status (13) in various South Korean population-based studies.
Body composition is a reliable predictor of good health status and is evaluated by fat-free mass, fat mass, and bone mineral density (14). Body composition disorder is the development of a systemic proinflammatory state related to insulin resistance and progression of osteosarcopenic obesity (15). The adipose tissue secretes proinflammatory cytokines associated with protein degradation, which will eventually lead to the development of obesity. Therefore, the evaluation of muscle health is suggested in individuals of all age groups.
Dietary protein sources and adequacy are known modifiable risk factors for SO prevalence (16, 17). It has been proposed that plant-based protein food sources, such as fresh fruits, vegetables, dried nuts, flax seeds, whole grains, and soybeans, have the beneficial effects of improving body composition (18), lipid metabolism (19), and cardiovascular health (20). In contrast, red meat-based diets are reported to have detrimental cardiometabolic effects on adults (21). It is necessary to prevent these negative effects on muscle health caused by CVDs and comorbidities associated with incongruities between body fat and muscle mass. Therefore, we investigated the association between dietary protein sources (and their adequacy) and the prevalence of SO in South Korean populations, according to weight status.
Eligible participants were enrolled in the Korean Medicine Daejeon Citizen Cohort (KDCC) study between 2017 and 2019. KDCC is the first prospective ongoing cohort study that is based on traditional KM to examine established associations between chronic disease and lifestyle risk factors (8). We performed a cross-sectional survey in 1,967 of these study participants using the Korean Sasang constitutional diagnostic questionnaire (KS-15) to identify the KM type, and a general health-related questionnaire (supplied by KM). All data were handled by the web-based Korean Medicine Data Center (KDC) electronic data capture system of the Korean Institute of Oriental Medicine (KIOM). Ethical approval was obtained after the participants provided written informed consent. This study was approved by the Ethics Committee of the Korean Institute of Oriental Medicine (IRB No. I-1703/002–002, DJDSKH-17-BM-12). Height, weight, and body composition (skeletal muscle mass [SMM] and body fat mass (22)) were measured by bioelectrical impedance analysis using the measuring stations BSM 370 and InBody 770 (InBody, Seoul, South Korea). Body mass index (BMI) was calculated as weight in kilograms divided by height in meters squared (kg/m2). Handgrip strength (kg, on either or both hands) was assessed using a grip strength dynamometer (Takei 5401; Takei Scientific Instruments Co., Ltd, Niigata, Japan); the maximum reading of two trials was used.
SO is defined as the coexistence of excess adiposity and low muscle mass/function (23). The diagnosis of SO was performed in two steps: (1) The participants were screened to detect an elevated body mass index (BMI). Obesity was defined as a BMI ≥ 25 kg/m2 using BIA for both sexes (24). (2) Altered skeletal muscle functional parameters and body composition (reduced muscle mass [kg and %] and increased %BF) were used to establish a firm diagnosis of SO.
A handgrip strength value < 28.0 kg for males and < 18.0 kg for females or the lowest quintile of muscle strength among the study participants indicated low muscle strength (25).
The skeletal muscle mass index (SMI) was calculated as the sum of the appendicular skeletal muscle (ASM) divided by height squared (kg/m2) (25). ASM was calculated as a percentage of body weight using Janssen’s formula (ASM/weight (kg), %) and as one standard deviation below the sex-specific mean for the young reference group (male: 243, female: 438, aged 20–40 years) (26). The upper two quintiles of the total body fat percentage (%BF) using BIA for each sex were used. For females, %BF quintiles were Q1: 25.9, Q2: 29.3–32.7, Q3: 32.8–35.5, Q4: 35.6–38.8, and Q5: 38.9–51.5; for males, these values were Q1: 16.9, Q2: 20.3–23.6, Q3: 23.7–26.3, Q4: 26.4–30.1, and Q5: 30.2–41.3.
Then, we classified participants into the SO, obese (OB), sarcopenic (S), and normal groups.
The dietary intake of the participants was assessed using a validated short-form food frequency questionnaire (8), which contains 34 food items with serving sizes. Daily food amounts in grams and frequency were calculated from the energy (kcal/day) and macronutrients (carbohydrate, fat, and protein) (g/day) intakes using a computer-aided nutritional analysis program (CAN Pro, Version 5.0, The Korean Nutrition Society, 2015). This program is based on the recommended nutritional intake from the Dietary Reference Intake for Koreans (Korean Nutrients Society, 2020). To evaluate the protein adequacy of the individual diet among the participants, dietary protein sources (g/day) were classified into beans and tofu, fish, beef and pork, and poultry and eggs based on the Korean Nutrient Database.
The participants’ age, sex, lifestyle choices (i.e., smoking status, alcohol consumption, and physical activity level), KM type, and energy intake (kcal/day) were used as covariates in the statistical analysis. Various South Korean population-based studies have reported KM type to have a genetic predisposition for abdominal obesity, insulin resistance, metabolic syndrome and/or inflammatory status. Individual characteristics of the KM type, such as personality, physiological functions, and symptoms of the participants, were assessed using the KS-15 (27). The KS-15 is a well-validated, shortened version, and cost-effective screening instrument for assessing the KM type that adapts the BMI and age- and sex-specific weighted values for higher coincidence with clinical relevance (Cronbach a = 0.630) (27).As previously reported, cardiometabolic outcomes (12) and inflammatory status (13) were assessed according to the two different KM types (Taeeum vs. Non-Taeeum [Soeum or Soyang]) in South Korean adults. Age, and energy intake were classified as continuous variables, while sex (male vs. female), smoking (no vs. yes), drinking (no vs. yes), physical activity (insufficient vs. sufficient), and KM type (Taeeum vs. Non-Taeeum) were classified as categorical variables.
Frequencies and percentages were used as categorical variables in the descriptive analyses. The chi-square (χ2) test was used to compare the general and health-related characteristics (sex, smoking, alcohol consumption, physical activity, and KM type) in the descriptive analysis (Table 1). All data on continuous variables related to body composition, sarcopenia (ASM, kg, kg/m2, and %), body fat mass (kg, %) and grip strength (kg) (Table 1), macronutrients (carbohydrates, fat, and protein) intake, and dietary protein sources (beans and tofu, fish, beef and pork, and poultry and eggs) are presented as means ± standard errors (Table 2). Multiple linear regression models were used to examine the association between dietary protein sources, body fat mass (kg, %), SMI (kg/m2), ASM (%) and grip strength (kg) (Table 3). To deal with missing data, multiple imputation was employed to provide unbiased valid estimates of associations based on information from the available data. Multivariate logistic regression was used to evaluate the association between different protein sources (g/day) and the prevalence of sarcopenia or SO, according to the sarcopenia and obesity statuses. Adjusted odds ratios (AORs) and 95% confidence intervals (CIs) were also estimated after adjusting for age, sex, energy intake (kcal), smoking, alcohol consumption, physical activity, and KM type (Table 4). The relationship between dietary protein sources (A. beans and tofu; B. poultry and eggs) and total sarcopenia prevalence was also estimated through multivariate logistic regression based on the protein intake adequacy of the participants (Fig. 1). All analyses were performed using SAS version 9.4 (SAS Institute, Inc., Cary, NC, USA). All statistical tests were two-tailed, and p-values < 0.05 indicated statistical significance.
A total of 1,967 participants with ages ranging from 30 to 55 years (average age 43.7 ± 0.2 years) were included, and 69.5% (n = 1,387) of the participants were female individuals. Higher prevalence of SO or sarcopenia was observed in female participants (SO; [N = 133, 66.8%], S; [N = 281, 83.1%]). Regarding the KM type, a higher ratio of Taeeum was observed in the SO (N = 173, 86.9%) and OB (N = 566, 91.7%) groups (p < 0.0001). Regarding health-related behaviors, more individuals who smoked and consumed alcohol were observed in the OB group (p < 0.0001). Inadequate physical activity was observed in the SO and S groups (p < 0.0001). Body composition statistics showed that body fat mass (male: 28.43 ± 0.61%; female: 39.95 ± 0.36%) was significantly higher in the SO and OB groups than in the other groups (p < 0.001). However, weight adjusted ASM (%) among individuals of both sexes was the lowest in the SO (male: 40.30 ± 0.36%; female: 32.47 ± 0.20%) and OB (male: 40.74 ± 0.17%; female: 32.90 ± 0.13%) groups than in the other groups (p < 0.0001) (Table 1).
Table 2 shows the daily energy (kcal/day), macronutrients, and dietary protein sources (g/day) consumed by participants according to their sarcopenia and obesity statuses. Compared with those in the other groups, lower energy intake was observed in male individuals in the SO group (p = 0.025). No significant differences were observed in the daily macronutrient intake of the participants (both sexes) among the groups. However, protein per kilogram of body weight (g/kg) was the lowest in the SO and OB groups (SO: 1.05 ± 0.02 and OB: 1.00 ± 0.01) than in the other groups (S: 1.28 ± 0.01 and normal: 1.22 ± 0.01) (p < 0.0001) (Table 2).
The association between dietary protein sources and body composition of the participants according to their sarcopenia and obesity statuses is presented in Table 3. There was no association between beans and tofu, poultry and eggs (g/day), and fish intakes and body composition (SMI, ASM, BFM, and PBF) among the participants. The consumption of beef and pork was positively associated with BFM (kg: beta = 0.003, p < 0.01), PBF (%: beta = 0.003, p < 0.01), and weight-adjusted ASM (%: beta=-0.002, p < 0.05) in the normal group (Table 3).
The number of participants with SO (n = 199) and sarcopenia (n = 338) is presented in Table 4. Multivariate logistic regression was performed by adjusting for covariates, including age, sex, energy intake (kcal), smoking, alcohol consumption, PA, and KM type (Table 4). No significant association was observed between dietary protein sources and SO or sarcopenia prevalence in the participants (Table 4).
Regarding the consumption of beans and tofu (g/day) and adequacy of protein intake, the lowest (Q1: 5.7 g/ day) quintiles showed a 2.5-fold increase in the risk of SO prevalence (AORs: 2.51, CI: 1.07–8.80), compared with the highest quintile (Q5: 60.1 g/day) in the excessive protein intake groups. Similarly, the lowest (Q1: 18.5 g/ day) quintiles of poultry and eggs intake showed a great increase in SO prevalence (AORs: 3.40, CI: 1.08–10.73) in the excessive protein intake groups, compared with the highest quintile (Q5: 137.5 g/day) (Fig. 1).
We examined the relationship between dietary protein sources and protein adequacy, body composition, and prevalence of SO according to weight status in South Korean populations. It was found that lower consumption of plant-based protein foods, such as beans and tofu, or poultry and egg consumption among people who consumed excess daily dietary protein was strongly associated with SO prevalence. A positive association was observed between poultry and egg consumption and muscle strength in the sarcopenia group. Furthermore, red meat consumption showed a negative effect on imbalanced body composition by increased fat mass (kg and %) and decreased ASM (%) in participants with normal weight.
Our results are consistent with those of other studies on sarcopenia in the South Korean population (28, 29), using the weight-adjusted ASM to identify the prevalence of SO. Obesity is a low chronic inflammation factor that may lead to the development of SO by the infiltration of lipid deposition in the muscle tissue from extensive fat accumulation (30). In line with this, the combination of sarcopenia and obesity more significantly accelerates deteriorating muscle health when compared with the entities associated with chronic disease states in South Korean adults (31). Similarly, our results showed higher risks of body composition changes and prevalence of SO in the OB group.
From the perspective of muscle physiology, sarcopenia is independently associated with physical dysfunction and disability (32). We observed that higher physical inactivity reduced muscle function and mass, whereas it increased fat accumulation in the SO and sarcopenia groups. Currently, the coronavirus disease 2019 (COVID-19) pandemic has served as a potential risk factor for the onset of sarcopenia by interrupting physical activity, dietary habits, and sleep patterns in thousands of people who have been isolated, with excess time spent at home and owing to social distancing (33). Sarcopenia can lead to acute body composition changes, mitochondrial dysfunction, and insulin resistance, and may also increase the risk of COVID-19 infection and severity of symptoms.
A previous study reported decreased lean body mass and increased fat mass in the overweight/obese group, compared with healthy lean controls (34). There were positive associations with intermuscular fat mass and high sensitivity C-reactive protein (hs-CRP) and cortisol concentrations, as well as a negative association with SMM (34). Similarly, the SO and OB groups included more Taeeum-type individuals with predisposing metabolic risk factors (12, 13), compared with the BMI < 25 kg/m2 group. Our cross-sectional results from a previous cohort study showed associations with a higher prevalence of pre-MetS and higher hs-CRP levels in Taeeum-type South Korean populations (12, 13). This reflects obesity-related physiological characteristics associated with the Taeeum-type, which can be positively associated with SO prevalence in people who are overweight and obese. Contributing lifestyle factors include the consumption of uncontrolled or western-type diet and physical inactivity or sedentary lifestyle, which lead to imbalanced inflammatory mediators stimulating muscle tissues that aggravate low-grade inflammation (35).
Dietary protein ingestion is a key modulator of muscle mass and function (36). We found that the consumption of poultry and eggs positively affected grip strength (kg) in the sarcopenia group, while fat mass percentage increased with meat intake (g/d), whereas ASM (%) decreased in the normal group. It could be argued that the energy balance and protein adequacy might be problematic dietary risk factors in the participants who already had excess intake of energy (S [male: 2303.51 ± 46.86, female: 2072.89 ± 27.95] and normal [male: 2303.51 ± 46.86, female: 2072.89 ± 27.95], kcal/day) and protein (S: 1.28 ± 0.01 and normal: 1.22 ± 0.01, g/kg) compared with their dietary reference intake.
In a previous study, we found an approximately 2-fold higher meat intake increase in people with SO (PR: 1.93, 95% CI: 1.07–3.50), compared with that of the lowest tertile in older South Koreans with cardio-metabolic diseases (37). Consistent with these findings, our results showed that, participants who consumed < 18.5 g/day of poultry and eggs in the excessive protein intake individuals (> 1.2 g/kg/day) showed 3.4 times higher risk of developing SO compared with those in the highest intake group (Q5: 137.5 g/day). Similarly, with beans and tofu consumption, there was a 2.5-fold higher risk of developing sarcopenia in participants who had < 5.7 g/day intake in the excessive protein intake individuals.
In muscle protein synthesis, leucine is the major contributor to postprandial muscle anabolic synthesis (38). Compared with animal proteins, plant-based proteins have low leucine content, which may affect the anabolic properties (39). Nevertheless, plant-based proteins have various health-related benefits, including decreased BMI, reduced body fat, decreased caloric intake, increased energy expenditure, reduced insulin resistance, and prevention of obesity, T2DM, and CVDs (40). Cardiometabolic diseases often coexist with chronic inflammation and obesity, which are linked to poor diet quality and excess body weight. Therefore, an adequate amount of high-quality protein and supplementation with more plant-derived foods, such as legumes and beans, and low-saturated fat diet, could reduce the risk of underlying health conditions.
Plant-based diets have been recommended to prevent chronic diseases by the Joint Food and Agriculture Organization of the United Nations and World Health Organization expert consultations since 2004 (41). In the EPIC-Oxford cohort study, individuals who were vegetarian or vegan and had a higher intake of fiber and polyunsaturated fatty acid and a lower intake of saturated fatty acid (SFA) had a decreased risk of ischemic heart disease, compared with meat-eaters (42).
A balanced diet is recommended, with anti-inflammatory effects derived from the consumption of plentiful vegetables and fruits, as well as the moderating effects of whole grains, low-fat dairy products, fish, legumes, and poultry and eggs consumption while avoiding SFAs found in sugars and snacks. Finally, individuals who have metabolic high-risk factors, such as obesity, insulin resistance, and metabolic syndrome, should have a healthy diet to improve their health. Moreover, having a healthy and active lifestyle is necessary for achieving weight control and lowering the glucose levels and lipid level parameters in people with obesity.
To the best of our knowledge, this is the first study to evaluate the associations between SO prevalence and dietary protein sources by considering the predominant personality type with complex and different lifestyles, including diet, using the KM type. We also used valid and reliable nationally-representative, multilayer sampling data from the KDCC study. Finally, a wide range of covariates were considered in the adjustment of the multivariate analyses, including age, sex, health-related behaviors (smoking status, alcohol consumption, and physical activity level), KM type, and energy intake (kcal/day). These methods yielded descriptive results which greatly enhanced our understanding of SO prevalence and risk factors.
This study has some limitations. First, this cross-sectional study cannot provide causal inferences due to the limited evidence. Second, we did not consider the residual confounding factors, such as genetic or medical history, as covariates. Lastly, reverse causality might exist because the individuals with SO tended to consume lesser protein sources than their counterparts. In particular, lower energy intake was described in male participants with SO than in male participants of in the normal group. However, there was no variation in dietary intake between groups according to the status of sarcopenia and obesity in the participants; however, protein (g/kg) consumption of the participants varied between groups. The reason could be that people with higher BMI values did not consume adequate and/or high-quality protein relative to their body weight. It is also predicted that they consumed higher percentages of fat- or carbohydrate-rich foods in their diet. Therefore, careful interpretation is required to ensure the generalizability of our findings.
In conclusion, dietary protein sources and adequacy are crucial modulators of muscle quality in South Korean populations. Therefore, optimal dietary choices and adequate nutritional support are essential to maintain muscle health in people who follow an obesogenic lifestyle with an unhealthy diet.
appendicular skeletal muscle
body fat mass
skeletal muscle mass index
sarcopenic obesity
percent body fat
skeletal muscle mass
Ethics approval and consent to participate
The study was approved by the Ethics Committee of the Korean Institute of Oriental Medicine (IRB No. I-1703/002-002, DJDSKH-17-BM-12). Written informed consent was obtained from all participants prior to data collection.
Consent for publication
Not applicable
Data andmaterials
The datasets are not available due to confidentiality and ethical concerns. Further inquiries can be directed to the corresponding author.
Competing Interests
The authors have no competing interests to declare.
Funding
This work was supported by the Korea Institute of Oriental Medicine (Grant number KSN2023120).
Authors’ contributions and Acknowledgments
Statement of authors’ contributions to manuscript:Conceptualization: J.K.; Data curation& resources: K.J. and S.L.a; Analysis and interpretation of data: J.K. and Y.B.; Original manuscript: J.K.; Review and editing: Y.B. and S.L.b; Funding acquisition and project administration: S.L. b
| General characteristics | SO (n = 199) | OB (n = 617) | S (n = 338) | Normal (n = 813) | p-value | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Age (years) (mean ± SE) | < 0.001 | ||||||||||||||
| 30–40 years (n, %) | 47 | (23.6) | 222 | (36.0) | 103 | (30.5) | 304 | (37.4) | |||||||
| 41–55 years | 152 | (76.4) | 395 | (64.0) | 235 | (69.5) | 509 | (62.6) | |||||||
| Sex (%) | < 0.0001 | ||||||||||||||
| Male | 66 | (33.2) | 285 | (46.2) | 57 | (16.9) | 195 | (24.0) | |||||||
| Female | 133 | (66.8) | 332 | (53.8) | 281 | (83.1) | 618 | (76.0) | |||||||
| KM type1 (%) | < 0.0001 | ||||||||||||||
| Taeeum | 173 | (86.9) | 566 | (91.7) | 75 | (22.2) | 186 | (22.9) | |||||||
| Soeum | 2 | (1.0) | 4 | (0.7) | 130 | (38.5) | 253 | (31.1) | |||||||
| Soyang | 24 | (12.4) | 47 | (7.6) | 133 | (39.4) | 374 | (46.0) | |||||||
| Smoking (%) | < 0.0001 | ||||||||||||||
| No | 149 | (74.9) | 445 | (72.1) | 300 | (88.8) | 670 | (82.4) | |||||||
| Yes | 50 | (25.1) | 172 | (27.9) | 38 | (11.2) | 143 | (17.6) | |||||||
| Drinking (%) | < 0.001 | ||||||||||||||
| No | 87 | (43.7) | 206 | (33.4) | 155 | (45.9) | 338 | (41.6) | |||||||
| Yes | 112 | (56.3) | 411 | (66.6) | 183 | (54.1) | 475 | (58.4) | |||||||
| Physical activity2 (%) | < 0.05 | ||||||||||||||
| Insufficient | 144 | (72.3) | 404 | (65.5) | 245 | (72.5) | 536 | (65.9) | |||||||
| Sufficient | 55 | (27.6) | 213 | (34.5) | 93 | (27.5) | 277 | (34.1) | |||||||
| Body composition | |||||||||||||||
| SMI, kg/m2 | |||||||||||||||
| Male | 10.90 | ± 0.09b | 11.31 | ± 0.05a | 9.96 | ± 0.10c | 10.17 | ± 0.06c | < 0.0001 | ||||||
| Female | 8.73 | ± 0.06b | 9.07 | ± 0.04a | 7.87 | ± 0.04d | 8.10 | ± 0.03c | < 0.0001 | ||||||
| ASM, % | |||||||||||||||
| Male | 40.30 | ± 0.36d | 40.74 | ± 0.17c | 43.78 | ± 0.38b | 44.47 | ± 0.21a | < 0.0001 | ||||||
| Female | 32.47 | ± 0.20c | 32.90 | ± 0.13c | 36.36 | ± 0.14b | 37.22 | ± 0.09a | < 0.0001 | ||||||
| Body fat mass, kg | |||||||||||||||
| Male | 22.39 | ± 0.67a | 23.35 | ± 0.31a | 14.55 | ± 0.70b | 14.39 | ± 0.38b | < 0.0001 | ||||||
| Female | 26.53 | ± 0.37b | 27.73 | ± 0.24a | 17.31 | ± 0.26c | 17.20 | ± 0.17c | < 0.0001 | ||||||
| Body fat mass, % | |||||||||||||||
| Male | 28.43 | ± 0.61a | 27.96 | ± 0.29a | 21.82 | ± 0.65b | 20.87 | ± 0.35b | < 0.0001 | ||||||
| Female | 39.95 | ± 0.36a | 39.53 | ± 0.23a | 31.96 | ± 0.25b | 30.83 | ± 0.17c | < 0.0001 | ||||||
| Body mass index, kg/m2§ | 27.14 | ± 0.16b | 27.81 | ± 0.09a | 22.11 | ± 0.13c | 22.25 | ± 0.08d | < 0.0001 | ||||||
| Grip strength, kg | |||||||||||||||
| Male | 30.97 | ± 0.61b | 41.69 | ± 0.29a | 29.40 | ± 0.65b | 40.50 | ± 0.35a | < 0.0001 | ||||||
| Female | 17.78 | ± 0.27b | 24.26 | ± 0.17a | 17.54 | ± 0.18b | 23.98 | ± 0.12a | < 0.0001 | ||||||
| † The status of sarcopenia and obesity was defined by muscle strength (kg), body composition (reduced muscle mass (kg and %) and increased %BF) after screening for elevated body mass index (BMI) of the participants. | |||||||||||||||
| a The different letters indicate statistically significant differences (p < 0.05), analyzed using ANCOVA followed by Bonferroni’s multiple comparison test. | |||||||||||||||
| § The least-square is presented as means ± SEs adjusted for age and sex (age only adjusted for body composition). | |||||||||||||||
| 1 KM (Korean medicine) type was categorized into three groups: Taeeum, Soeum, and Soyang | |||||||||||||||
| 2 Physical activity (total MET minutes per week): insufficient (< 600 MET-min/week) and sufficient (≥ 600 MET-min/week). | |||||||||||||||
| SMI: skeletal muscle index, ASM: appendicular skeletal muscle | |||||||||||||||
| Dietary intake and protein sources | SO (n = 199) | OB (n = 617) | S (n = 338) | Normal (n = 813) | p-value | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Energy intake (kcal/day)1 | |||||||||||||||
| Male | 2071.70 | ± 81.35b | 2238.34 | ± 38.89a | 2406.19 | ± 87.02a | 2303.51 | ± 46.86a | 0.025 | ||||||
| Female | 2112.53 | ± 60.41 | 2099.36 | ± 38.09 | 2010.15 | ± 41.38 | 2072.89 | ± 27.95 | 0.364 | ||||||
| Macronutrients (g/day) | |||||||||||||||
| Carbohydrates (g) | 320.00 | ± 3.00 | 317.13 | ± 1.73 | 321.35 | ± 2.31 | 319.48 | ± 1.49 | 0.504 | ||||||
| Fat (g) | 52.17 | ± 0.99 | 53.78 | ± 0.57 | 53.15 | ± 0.76 | 52.59 | ± 0.49 | 0.351 | ||||||
| Protein (g) | 71.37 | ± 0.73 | 72.20 | ± 0.42 | 72.11 | 0.56 | 71.26 | ± 0.36 | 0.312 | ||||||
| Protein (g/kg) | 1.05 | ± 0.02c | 1.00 | ± 0.01c | 1.28 | ± 0.01a | 1.22 | ± 0.01b | < 0.0001 | ||||||
| C : F: P (%) | 59.6: 22.0: 13.4 | 59.8: 22.7: 13.5 | 60.9: 22.4: 13.5 | 60.2: 22.2: 13.4 | N/S | ||||||||||
| Dietary protein sources (g/day)2 | |||||||||||||||
| Beans and tofu | 26.07 | ± 2.09 | 29.29 | ± 1.20 | 27.64 | ± 1.62 | 28.32 | ± 1.03 | 0.578 | ||||||
| Poultry and eggs | 73.35 | ± 3.92 | 71.69 | ± 2.26 | 70.10 | ± 3.02 | 68.28 | ± 1.63 | 0.569 | ||||||
| Beef and pork | 102.07 | ± 7.23 | 107.89 | ± 4.16 | 113.30 | ± 5.59 | 109.43 | ± 3.58 | 0.663 | ||||||
| Fish | 13.63 | ± 1.28 | 13.72 | ± 0.74 | 15.20 | ± 0.99 | 14.26 | ± 0.63 | 0.655 | ||||||
| 1 Adjusted for age alone | |||||||||||||||
| 2 The least-square is presented as means ± SEs adjusted for age, sex, and energy intake (kcal). | |||||||||||||||
| a The different letters indicate statistically significant differences (p < 0.05), analyzed using ANCOVA followed by Bonferroni’s multiple comparison test. | |||||||||||||||
| SO (n = 199) | OB (n = 617) | S (n = 338) | Normal (n = 813) | ||||||
|---|---|---|---|---|---|---|---|---|---|
| Protein sources (g/day) | beta | p | beta | p | beta | p | beta | p | |
| Beans and tofu | |||||||||
| SMI, kg/m2 | -0.001 | 0.52 | 0.000 | 0.81 | 0.001 | 0.29 | 0.000 | 0.80 | |
| ASM, % | -0.003 | 0.68 | 0.000 | 0.41 | -0.005 | 0.36 | -0.003 | 0.24 | |
| BFM, kg | 0.003 | 0.77 | -0.007 | 0.39 | 0.014 | 0.08 | 0.005 | 0.23 | |
| PBF, % | 0.004 | 0.74 | -0.005 | 0.42 | 0.011 | 0.23 | 0.005 | 0.27 | |
| HGS, kg | 0.009 | 0.32 | -0.004 | 0.48 | 0.008 | 0.28 | 0.000 | 0.99 | |
| Poultry and eggs | |||||||||
| SMI, kg/m2 | 0.002 | 0.08 | 0.001 | 0.37 | 0.000 | 0.91 | 0.000 | 0.50 | |
| ASM, % | 0.005 | 0.09 | 0.003 | 0.10 | -0.004 | 0.11 | -0.002 | 0.19 | |
| BFM, kg | -0.003 | 0.53 | -0.004 | 0.44 | 0.004 | 0.27 | 0.002 | 0.43 | |
| PBF, % | -0.007 | 0.15 | -0.006 | 0.11 | 0.005 | 0.18 | 0.003 | 0.23 | |
| HGS, kg | 0.003 | 0.40 | 0.001 | 0.70 | 0.007 | 0.04 | -0.002 | 0.39 | |
| Beef and pork | |||||||||
| SMI, kg/m2 | 0.001 | 0.12 | -0.001 | 0.76 | 0.000 | 0.61 | -0.001 | 0.89 | |
| ASM, % | 0.003 | 0.09 | 0.000 | 0.82 | -0.002 | 0.14 | -0.002 | 0.02 | |
| BFM, kg | -0.002 | 0.48 | 0.002 | 0.47 | 0.001 | 0.56 | 0.003 | 0.01 | |
| PBF, % | -0.004 | 0.13 | 0.001 | 0.74 | 0.006 | 0.20 | 0.003 | 0.01 | |
| HGS, kg | -0.001 | 0.57 | 0.000 | 0.89 | -0.001 | 0.74 | -0.001 | 0.62 | |
| Fish | |||||||||
| SMI, kg/m2 | 0.001 | 0.99 | -0.004 | 0.05 | 0.000 | 0.42 | 0.000 | 0.99 | |
| ASM, % | -0.006 | 0.68 | 0.000 | 0.93 | -0.001 | 0.11 | -0.002 | 0.13 | |
| BFM, kg | -0.004 | 0.51 | 0.004 | 0.21 | 0.005 | 0.54 | 0.001 | 0.30 | |
| PBF, % | 0.010 | 0.67 | -0.003 | 0.80 | 0.021 | 0.14 | 0.011 | 0.13 | |
| HGS, kg | -0.022 | 0.25 | -0.008 | 0.49 | -0.003 | 0.78 | -0.003 | 0.58 | |
| Adjusted for age, sex, energy intake (kcal), smoking, alcohol consumption, physical activity, and Korean Medicine type | |||||||||
| Statistical significance was accepted at *P < 0.05. | |||||||||
| BMI: body mass index, SMI: skeletal muscle index, ASM: appendicular skeletal muscle, BFM: body fat mass, PBF: percent body fat | |||||||||
| Dietary protein sources | Sarcopenic obesity (n = 199) | Sarcopenia (n = 338) | |
|---|---|---|---|
| Beans and tofu | |||
| (ref, highest: 60.1 g/day) | ORs | ||
| intermediate: 17.0 g/day | 1.60 (0.86–3.00) | 1.04 (0.64–1.69) | |
| lowest: 5.7 g/day | 1.84 (0.96–3.54) | 0.80 (0.78–1.34) | |
| Poultry and eggs | |||
| (ref, highest: 137.5 g/day) | ORs | ||
| intermediate: 51.5 g/day | 0.66 (0.40–1.11) | 1.16 (0.78–1.74) | |
| lowest: 18.5 g/day | 1.11 (0.62–1.98) | 0.67 (0.43–1.06) | |
| Beef and pork | |||
| (ref, highest: 169.6 g/day) | ORs | ||
| intermediate: 44.6 g/day | 1.31 (0.80–2.52) | 0.84 (0.57–1.23) | |
| lowest: 10.4 g/day | 1.36 (0.74–2.52) | 0.77 (0.47–1.27) | |
| Fish | |||
| (ref, highest: 121.8 g/day) | ORs | ||
| intermediate: 17.0 g/day | 0.93 (0.55–1.56) | 0.86 (0.57–1.32) | |
| lowest: 1.1 g/day | 0.89 (0.53–1.51) | 0.83 (0.54–1.23) | |
| Age, sex, energy intake (kcal), smoking, drinking, physical activity, and Korean Medicine type were adjusted. | |||