Abstract
The aging population contributes to increasing economic and social burden worldwide. Sarcopenia, an age-related degenerative disease and progressive disorder, is characterized by a reduction in skeletal muscle mass and function. This study aims to assess the association between dietary factors and sarcopenia in the Korean elderly using nationwide data. A total of 801 subjects aged 70–84 years were included in this analysis. Subjects were divided into two groups: sarcopenic and nonsarcopenic groups according to the sarcopenia criteria established by the Asian Working Group for Sarcopenia. Nutrient and food intakes were assessed using a 24-h recall method. Logistic regression analysis was used to assess the association between sarcopenia and food group and nutrient intakes. In the multivariable models, the meat/fish/egg/legume food group, vegetable group, and total food intake were inversely associated with the prevalence of sarcopenia. The high intakes of energy, carbohydrate, protein, fiber, zinc, carotene, and vitamin B6 were associated with the lower prevalence of sarcopenia. Therefore, consuming sufficient nutrients through various protein source foods and vegetables will help prevent sarcopenia in the Korean elderly.
Keywords: sarcopenia, elderly, Korean, food group, nutrients
1. Introduction
Aging is a multifactorial and complex process, and the population is aging worldwide. According to a World Health Organization report, there should be at least 2.1 billion of the elderly population in 2050 [1]. The aging population phenomenon is increasing the social and economic burden worldwide. In particular, the anticipated degree of old age dependency ratio in 2050 in South Korea is considerably high (0.77) compared with that of the United States (0.37) and that of the United Kingdom (0.47) [2]. Sarcopenia is a degenerative skeletal muscle disorder characterized by loss of skeletal muscle mass and function, resulting in falls and loss of mobility [3,4]. Sarcopenia has been implicated in the pathogenesis of neuromuscular disorders, and condition of chronic diseases. [5,6]. After the age of 60, the muscle mass reduction accelerates to about 3% per year compared to the age of 20 [7]. The major risk factor for sarcopenia is the aging process, which is related to an altered hormonal metabolism, inflammation, decreased α-motor neurons, and redox imbalance, etc. [8]. In addition, the pathogenesis of sarcopenia is also known to be related to other aggravating factors such as gender, lifestyle, and other pathological conditions [4]. Accumulating evidence suggests that a sedentary lifestyle, bed rest periods, an inadequate dietary intake of energy and protein, malabsorption of nutrients, cachexia, gastrointestinal disorders, and chronic diseases such as diabetes and obesity may promote loss of skeletal muscle mass [9].
The Korean elderly tend to have insufficient protein and calcium intake [10]. Inadequate intake of nutrients such as protein, vitamin D, calcium, and vitamin C was associated with muscle loss in elderly Korean men [11]. Recently, fruits and vegetables have been attracting attention as having a positive effect on sarcopenia [12]. However, as far as we know, very few studies have been conducted to analyze sarcopenia and dietary factors in the Korean elderly using nationwide data [11,13]. Therefore, it is necessary to propose a dietary guideline for the prevention of sarcopenia by identifying which foods or nutrient intakes are related to sarcopenia in the Korean elderly.
This study aims to assess the associations of food group intake and nutritional status with the prevalence of sarcopenia in the Korean elderly using nationwide data and to provide fundamental data to establish dietary guidelines for the prevention of sarcopenia.
2. Materials and Methods
2.1. Participants
The Korean Frailty and Aging Cohort Study (KFACS) is a nationwide multicenter longitudinal cohort study that started in 2016. The detailed study design and process for the KFACS have been previously published [14]. Participants aged 70–84 years were recruited from sex- and age-stratified communities in urban and rural areas across South Korea. Among the 1559 participants enrolled in the KFACS in 2016, those who did not participate in the nutritional survey (n = 557) and those who did not have muscle mass data measured by dual-energy x-ray absorptiometry (DEXA) (n = 201) were excluded from the study. In total, 801 participants were included in the final analysis. The protocol of the KFACS was approved by the institutional review board (IRB) of the Clinical Research Ethics Committee of the Kyung Hee University Medical Center (IRB number: 2015-12-103), and written informed consent was obtained from all participants before they participated in the study.
2.2. Definition of Sarcopenia
The Asian Working Group for Sarcopenia (AWGS) established the diagnostic criteria for sarcopenia in the Asian population using muscle mass, muscle strength, and physical performance as the criteria [15]. Skeletal mass was assessed using DEXA (GE Healthcare Lunar, Madison, WI, USA; Hologic DXA Systems, Hologic, Inc., Bedford, MA, USA), appendicular lean mass was calculated as the sum of the lean mass measurements from both the arms and legs (kg), and appendicular skeletal muscle mass (ASM) was calculated by dividing the appendicular lean mass by the height squared (kg/m2). The cut-offs of <7.0 kg/m² for men and <5.4 kg/m² for women were used as the criteria for a low skeletal muscle mass when skeletal mass was measured using DEXA. Muscle strength was determined by the handgrip strengths of both hands, each repeated twice, using a digital grip strength dynamometer (TTK-5401; Takei Ltd., Tokyo, Japan). The mean of the four grip strength measurements was used. Cut-offs <26 kg for men and <18 kg for women were used to define a low muscle strength. Gait speed based on over 4 m with acceleration and deceleration phases of 1.5 m each was measured using an automatic timer (Gaitspeedmeter, Dynamicphysiology, Daejeon, Korea). Low physical performance was defined as a gait speed ≤0.8 m/s [16]. In this study, sarcopenia was diagnosed satisfying both a decreased muscle mass (<7.0 kg/m² for men and <5.4 kg/m² for women) and low muscle strength (<26 kg for men and <18 kg for women) according to the AWGS 2014 criteria [15]. Additionally, patients with low physical performance (gait speed ≤ 0.8 m/s) were diagnosed with severe sarcopenia. However, only few subjects (n = 10) were diagnosed with severe sarcopenia in this study, therefore the data are not shown.
2.3. Dietary Assessment
We used the released individual food and nutrient intake data in our study. The nutritional survey and calculation methods of nutrient intakes have been reported in detail [14,17]. To explain briefly, the dietary assessment was conducted using the 24-h recall method by trained interviewers. Food intake was estimated using measuring cups and spoons, ruler, and real-size-picture of bowls and plates developed by the National Institutes of Health (NIH) and the Korea Disease Control and Prevention Agency (KDCA) and using the 24-h recall dietary assessment system of the NIH and KDCA.
Nutrient intakes (energy, carbohydrate, protein, fat, fiber, calcium, phosphorus, iron, sodium, potassium, vitamin A, retinol, carotenes, vitamin B1, vitamin B2, niacin, vitamin C, zinc, vitamin B6, vitamin B12, folate, vitamin D, vitamin E, vitamin K, and cholesterol) were calculated using food composition database based on the National Rural Living Science Institute [18]. The intakes of the six food groups (grains, meat/fish/egg/legume, vegetables, fruits, milk/dairy products, and oils/fats/sugars) and total food were assessed.
2.4. Covariates
Age, sex, family type (living alone, with spouse, with offspring, with spouse/offspring, or other), marital status (married, bereaved, or other), education (illiterate, able to read and write, elementary school, middle school, high school, college, or university and over), income level (5,000,000 won and over, 3,000,000–5,000,000 won, 2,000,000–3,000,000 won, 1,000,000–2,000,000 won, under 1,000,000 won, or unknown), smoking status (nonsmoker, ex-smoker, or current smoker), drinking status (none, 1–4 times/month, or 2–4 times/week) were investigated as general sociodemographic characteristics, and anthropometric data, including height, weight, and body mass index (BMI, kg/m2) were also assessed.
2.5. Statistical Analysis
Differences in the general and socioeconomic data between the nonsarcopenic and sarcopenic groups were compared using Student’s t-test for continuous variables and the chi-squared test for categorical variables. Comparisons of the least squares mean (LSmeans) of food and nutrient intakes between the two groups were performed using a general linear model (GLM) adjusting for age, sex, and weight. The differences of nutrient intake percentages according to the 2020 Dietary Reference Intakes for Koreans (KDRIs) [19] between the two groups were compared using Student’s t-test. Among the KDRIs, estimated average requirement (EAR) for energy, adequate intake (AI) for fiber and sodium, and recommended nutrient intake (RNI) for other nutrients were used. Logistic regression analysis was applied to determine the risk of sarcopenia according to each food group intake and nutrient intake by evenly dividing the participants into quartiles. Multivariable analysis was performed using two models: model 1 adjusted for age and sex; and model 2 adjusted for age, sex, BMI, family type, marital status, education level, income level, smoking status, and drinking status. All statistical analyses were performed using SAS (version 9.4, SAS Institute, Cary, NC, USA), and the significance level for the analyses was set at p < 0.05.
3. Results
The characteristics of the nonsarcopenic and sarcopenic groups are presented in Table 1. The prevalence of sarcopenia in all subjects was 13.9% (n = 111), and there was no significant difference by gender. The mean age was significantly higher in the sarcopenic group than in the nonsarcopenic group (p < 0.0001), and the prevalence of sarcopenia increased with age. Body weight, BMI, muscle mass, and handgrip strength were significantly lower in the sarcopenic group than in the nonsarcopenic group.
Table 1.
The characteristics of the participants.
| Variables | Nonsarcopenic | Sarcopenic | p-Value | ||||
|---|---|---|---|---|---|---|---|
| (n = 690) | (n = 111) | ||||||
| Sex, n (%) | 0.2477 | ||||||
| Men | 326 | (47.2) | 59 | (53.2) | |||
| Women | 364 | (52.8) | 52 | (46.8) | |||
| Age (year) (1) | 76.0 | ± | 0.2 | 78.2 | ± | 0.4 | <0.0001 |
| Age group, n (%) | <0.0001 | ||||||
| 70–74 years | 282 | (40.9) | 20 | (18.0) | |||
| 75–79 years | 247 | (35.8) | 40 | (36.0) | |||
| 80–84 years | 161 | (23.3) | 51 | (46.0) | |||
| Education level, n (%) | 0.151 | ||||||
| Illiterate | 27 | (3.9) | 7 | (6.3) | |||
| Able to read and write | 125 | (18.1) | 11 | (9.9) | |||
| Elementary | 159 | (23.0) | 33 | (29.7) | |||
| Middle school | 116 | (16.8) | 14 | (12.6) | |||
| High school | 124 | (18.0) | 25 | (22.5) | |||
| College | 31 | (4.5) | 6 | (5.4) | |||
| University and above | 108 | (15.7) | 15 | (13.5) | |||
| Family type, n (%) | 0.5803 | ||||||
| Alone | 172 | (24.9) | 30 | (27.0) | |||
| with spouse | 357 | (51.7) | 61 | (55.0) | |||
| with offspring | 60 | (8.7) | 8 | (7.2) | |||
| with spouse and offspring | 89 | (12.9) | 12 | (10.8) | |||
| other | 12 | (1.7) | 0 | (0.0) | |||
| Marital status, n (%) | 0.8966 | ||||||
| Married | 463 | (67.1) | 72 | (64.9) | |||
| bereaved | 209 | (30.3) | 36 | (32.4) | |||
| Others | 18 | (2.6) | 3 | (2.7) | |||
| Smoking status, n (%) | 0.0156 | ||||||
| None | 436 | (63.2) | 60 | (54.1) | |||
| Ex-smoker | 226 | (32.8) | 40 | (36.0) | |||
| Current smoker | 28 | (4.1) | 11 | (9.9) | |||
| Drinking status, n (%) | 0.372 | ||||||
| None | 438 | (63.5) | 78 | (70.3) | |||
| ≥1 times/month | 124 | (18.0) | 17 | (15.3) | |||
| 2–4 times/week | 128 | (18.6) | 16 | (14.4) | |||
| Income, n (%) | 0.7023 | ||||||
| 5,000,000 won and over | 39 | (5.7) | 6 | (5.4) | |||
| 3,000,000–5,000,000 won | 97 | (14.1) | 12 | (10.8) | |||
| 2,000,000–3,000,000 won | 80 | (11.6) | 11 | (9.9) | |||
| 1,000,000–2,000,000 won | 154 | (22.3) | 21 | (18.9) | |||
| under 1,000,000 won | 266 | (38.6) | 51 | (46.0) | |||
| unknown | 54 | (7.8) | 10 | (9.0) | |||
| Height (cm) | 158.1 | ± | 0.3 | 156.4 | ± | 0.8 | 0.0681 |
| Weight (kg) | 61.6 | ± | 0.4 | 56.0 | ± | 0.8 | <0.0001 |
| BMI (kg/m2) | 24.6 | ± | 0.1 | 22.9 | ± | 0.3 | <0.0001 |
| Muscle mass (kg/m2) | 6.56 | ± | 0.04 | 5.65 | ± | 0.07 | <0.0001 |
| Handgrip strength (kg) | 24.9 | ± | 0.3 | 19.0 | ± | 0.4 | <0.0001 |
| Gait speed (m/s) | 0.94 | ± | 0.01 | 1.06 | ± | 0.03 | 0.0003 |
(1) mean ± standard error. The continuous data are presented as the mean ± standard error.
The comparison of food group intakes between the nonsarcopenic and sarcopenic groups are presented in Table 2. The intakes of vegetables and total food were significantly lower in the sarcopenic group than in the nonsarcopenic group (p < 0.05). However, the intakes of grains, meats/fish/eggs/legumes, fruits, milk/dairy products, and oils/fats/sugars did not show any difference between the sarcopenic and nonsarcopenic groups.
Table 2.
Comparison of food group intakes.
| Food Group (g) | Nonsarcopenic (n = 690) |
Sarcopenic (n = 111) |
p-Value * |
|---|---|---|---|
| LSmeans ± SE | LSmeans ± SE | ||
| Grains | 862.0 ± 12.8 | 814.8 ± 33.1 | 0.1884 |
| Meat/fish/eggs/legumes | 278.9 ± 8.0 | 247.2 ± 20.5 | 0.1539 |
| Vegetables | 72.5 ± 1.6 | 62.6 ± 4.2 | 0.0304 |
| Fruits | 90.3 ± 3.9 | 74.3 ± 10.1 | 0.1432 |
| Milk/dairy products | 35.3 ± 2.4 | 31.4 ± 6.2 | 0.5629 |
| Oils/fats/sugars | 73.1 ± 2.6 | 70.2 ± 6.7 | 0.6905 |
| Total food intake | 1037.8 ± 14.1 | 953.7 ± 36.3 | 0.0329 |
* Comparison of values adjusted for age, sex, and weight between the two groups; LSmeans: the least squares mean; SE: standard error.
The comparison of nutrient intakes between the two groups after being adjusted for age, sex, and weight are shown in Table 3. Energy intake was significantly lower in the sarcopenic group than in the nonsarcopenic group (p = 0.0152), and carbohydrates intake showed significant differences between the two groups (p < 0.05).
Table 3.
Comparison of nutrient intakes.
| Nutrient | Nonsarcopenic (n = 690) |
Sarcopenic (n = 111) |
p-Value * |
|---|---|---|---|
| LSmeans ± SE | LSmeans ± SE | ||
| Energy (kcal) | 1512.1 ± 18.3 | 1387.9 ± 47.1 | 0.0152 |
| Carbohydrate (g) | 256.1 ± 3.3 | 237.0 ± 8.4 | 0.0360 |
| Protein (g) | 56.7 ± 0.9 | 52.9 ± 2.2 | 0.1226 |
| Fat (g) | 28.4 ± 0.7 | 25.8 ± 1.8 | 0.1806 |
| Fiber (g) | 6.07 ± 0.11 | 5.55 ± 0.29 | 0.0946 |
| Cholesterol (mg) | 194.7 ± 6.5 | 196.5 ± 16.7 | 0.9232 |
| Calcium (mg) | 446.8 ± 9.6 | 458.1 ± 24.6 | 0.6737 |
| Phosphate (mg) | 958.8 ± 12.7 | 915.8 ± 32.6 | 0.2228 |
| Iron (mg) | 13.0 ± 0.3 | 12.7 ± 0.7 | 0.6576 |
| Sodium (mg) | 3937.9 ± 75.5 | 3566.6 ± 194.3 | 0.0782 |
| Potassium (mg) | 2728.7 ± 40.0 | 2575.0 ± 103.0 | 0.1687 |
| Zinc (mg) | 7.22 ± 0.14 | 6.47 ± 0.37 | 0.0583 |
| Vitamin A (R.E.) | 676.2 ± 20.3 | 576.9 ± 52.3 | 0.0803 |
| Retinol (µg) | 110.6 ± 22.5 | 84.6 ± 57.8 | 0.6782 |
| Carotenes (µg) | 3609.4 ± 117.7 | 3107.6 ± 303.0 | 0.1269 |
| Vitamin D (µg) | 5.51 ± 0.30 | 6.06 ± 0.78 | 0.5181 |
| Vitamin E (mg) | 7.77 ± 0.15 | 7.37 ± 0.40 | 0.3564 |
| Vitamin K (µg) | 95.5 ± 4.9 | 75.9 ± 12.7 | 0.1570 |
| Vitamin B1 (mg) | 1.55 ± 0.34 | 1.04 ± 0.88 | 0.5951 |
| Vitamin B2 (mg) | 1.00 ± 0.02 | 0.91 ± 0.06 | 0.1843 |
| Niacin (mg) | 14.8 ± 0.2 | 14.2 ± 0.6 | 0.3311 |
| Vitamin C (mg) | 99.3 ± 2.7 | 105.5 ± 6.9 | 0.4023 |
| Vitamin B6 (mg) | 1.43 ± 0.03 | 1.37 ± 0.07 | 0.4341 |
| Vitamin B12 (µg) | 5.62 ± 0.26 | 5.48 ± 0.67 | 0.8522 |
| Folate (µg) | 296.9 ± 6.1 | 273.5 ± 15.7 | 0.1701 |
* Comparison of values adjusted for age, sex, and weight between the two groups; LSmeans: the least squares mean; SE: standard error.
The intake percentages of each nutrient according to the KDRIs between the two groups are shown in Table 4. The intake percentages of energy and protein compared to KDRIs levels were significantly lower in the sarcopenic group than in the nonsarcopenic group (79.1% vs. 87.5%, p = 0.0031 for energy; 93.0% vs. 103.5%, p = 0.0038 for protein). The intake percentages of carbohydrate, fiber, zinc, vitamin A, vitamin B2, and folate of the sarcopenic group were significantly lower than the nonsarcopenic group (p < 0.05).
Table 4.
Comparison of nutrient intake percentages of the 2020 Dietary Reference Intakes for Koreans (KDRIs) (1).
| Nutrient (%) | Nonsarcopenic (n = 690) |
Sarcopenic (n = 111) |
p-Value |
|---|---|---|---|
| Mean ± SE | Mean ± SE | ||
| Energy | 87.5 ± 1.1 | 79.1 ± 2.6 | 0.0031 |
| Protein | 103.5 ± 1.7 | 93.0 ± 3.2 | 0.0038 |
| Carbohydrate | 197.3 ± 2.6 | 180.8 ± 6.7 | 0.0176 |
| Fiber | 27.3 ± 0.5 | 24.1 ± 1.2 | 0.0179 |
| Calcium | 60.2 ± 1.3 | 60.8 ± 3.5 | 0.8570 |
| Phosphate | 137.4 ± 1.9 | 127.9 ± 4.2 | 0.0640 |
| Sodium | 335.6 ± 6.8 | 313.8 ± 14.6 | 0.2239 |
| Iron | 153.9 ± 3.1 | 145.4 ± 9.4 | 0.3894 |
| Zinc | 91.1 ± 1.9 | 80.0 ± 3.2 | 0.0030 |
| Vitamin A | 105.1 ± 3.2 | 86.2 ± 7.1 | 0.0274 |
| Vitamin B1 | 153.6 ± 33.3 | 94.8 ± 4.1 | 0.0804 |
| Vitamin B2 | 84.6 ± 1.9 | 75.4 ± 3.6 | 0.0244 |
| Niacin | 114.8 ± 2.0 | 108.5 ± 4.4 | 0.2288 |
| Vitamin C | 99.8 ± 2.6 | 102.5 ± 7.9 | 0.7425 |
| Vitamin B6 | 99.1 ± 1.8 | 91.0 ± 4.5 | 0.1008 |
| Vitamin B12 | 234.9 ± 11.1 | 223.3 ± 22.0 | 0.6379 |
| Folate | 74.7 ± 1.6 | 65.6 ± 3.2 | 0.0111 |
(1) Energy, estimated average requirement (EAR); fiber and sodium, adequate intake (AI); other nutrients, recommended nutrient intake (RNI) used as a dietary Reference Intakes for Koreans; SE: standard error; p-value: calculated using Student’s t-test.
Table 5 shows adjusted odds ratio (OR) and 95% confidence interval (CI) for the associations between sarcopenia risk and food group quartile intake. The risk of sarcopenia was lower in the highest quartile for the intake of meat/fish/egg/legume compared to the lowest quartile of that food group in the multivariable adjusted model (OR = 0.50, 95% CI: 0.26–0.97, p for trend = 0.0475). When compared with the lowest quartile of vegetable and total food intakes, there was an inverse association with sarcopenia in the highest quartile group (OR = 0.28, 95% CI: 0.13–0.59, p for trend = 0.0006 for vegetables; OR = 0.31, 95% CI: 0.15–0.65, p for trend = 0.0038 for total food intake).
Table 5.
Odds ratios (OR) and 95% confidence intervals (CI) of sarcopenia by quartiles (Q) of food group intake and total food intake.
| Food Group | Median (g) |
Total (n) |
Sarcopenia (n) |
Age, Sex-Adjusted OR (95% CI) |
Multi-Adjusted OR (95% CI) |
|||
|---|---|---|---|---|---|---|---|---|
| Grains | Q1 | 489 | 200 | 35 | 1.00 | 1.00 | ||
| Q2 | 719 | 200 | 26 | 0.73 | (0.42–1.29) | 0.66 | (0.36–1.22) | |
| Q3 | 921 | 201 | 23 | 0.65 | (0.36–1.17) | 0.56 | (0.29–1.08) | |
| Q4 | 1224 | 200 | 27 | 0.74 | (0.42–1.32) | 0.57 | (0.31–1.07) | |
| p for trend | 0.3128 | 0.089 | ||||||
| Meat/fish /eggs/legumes |
Q1 | 68 | 200 | 36 | 1.00 | 1.00 | ||
| Q2 | 171 | 200 | 29 | 0.74 | (0.43–1.28) | 0.62 | (0.34–1.13) | |
| Q3 | 293 | 201 | 24 | 0.61 | (0.34–1.09) | 0.47 | (0.25–0.89) | |
| Q4 | 505 | 200 | 22 | 0.56 | (0.30–1.02) | 0.50 | (0.26–0.97) | |
| p for trend | 0.0579 | 0.0475 | ||||||
| Vegetables | Q1 | 27 | 200 | 37 | 1.00 | 1.00 | ||
| Q2 | 53 | 200 | 32 | 0.85 | (0.50–1.46) | 0.85 | (0.48–1.51) | |
| Q3 | 78 | 200 | 28 | 0.76 | (0.44–1.33) | 0.66 | (0.36–1.22) | |
| Q4 | 118 | 201 | 14 | 0.34 | (0.17–0.66) | 0.28 | (0.13–0.59) | |
| p for trend | 0.0014 | 0.0006 | ||||||
| Fruits | Q1 | 0 | 213 | 35 | 1.00 | 1.00 | ||
| Q2 | 42 | 188 | 30 | 0.96 | (0.56–1.65) | 0.98 | (0.54–1.77) | |
| Q3 | 88 | 200 | 24 | 0.68 | (0.39–1.21) | 0.66 | (0.35–1.26) | |
| Q4 | 192 | 200 | 22 | 0.68 | (0.38–1.22) | 0.72 | (0.38–1.35) | |
| p for trend | 0.1408 | 0.2253 | ||||||
| Milk/dairy products | Q1 | 0 | 525 | 80 | 1.00 | 1.00 | ||
| Q2 | 20 | 76 | 9 | 0.72 | (0.34–1.53) | 0.81 | (0.36–1.80) | |
| Q3 | 120 | 200 | 22 | 0.71 | (0.43–1.18) | 0.70 | (0.40–1.22) | |
| p for trend | 0.2003 | 0.2189 | ||||||
| Oils/fats/ sugars |
Q1 | 5 | 200 | 33 | 1.00 | 1.00 | ||
| Q2 | 38 | 200 | 30 | 0.86 | (0.50–1.49) | 0.73 | (0.40–1.33) | |
| Q3 | 75 | 201 | 26 | 0.78 | (0.44–1.37) | 0.78 | (0.42–1.46) | |
| Q4 | 148 | 200 | 22 | 0.69 | (0.38–1.24) | 0.57 | (0.29–1.10) | |
| p for trend | 0.2071 | 0.1284 | ||||||
| Total food intake |
Q1 | 617 | 200 | 35 | 1.00 | 1.00 | ||
| Q2 | 887 | 200 | 31 | 0.90 | (0.52–1.55) | 0.82 | (0.45–1.50) | |
| Q3 | 1101 | 201 | 31 | 0.90 | (0.52–1.57) | 0.92 | (0.49–1.71) | |
| Q4 | 1437 | 200 | 14 | 0.36 | (0.18–0.72) | 0.31 | (0.15–0.65) | |
| p for trend | 0.0059 | 0.0038 | ||||||
Multi-adjusted: adjusted for age, sex, BMI, family type, marital status, education level, income level, smoking status, and drinking status.
Table 6 shows that higher intakes of energy, carbohydrates, proteins, fiber, zinc, carotene, and vitamin B6 are associated with a lower risk of sarcopena. The highest quartile of energy had a 0.44-fold lower prevalence of sarcopenia compared to the lowest quartile in the multivariate adjusted model (95% CI: 0.22–0.90, p for trend = 0.0089). In the cases of carbohydrate, protein, and fiber intakes, the highest quartile intake was associated with a lower prevalence of sarcopenia compared to the lowest quartile (OR = 0.40, 95% CI: 0.21–0.77, p for trend = 0.0048 for carbohydrate; OR = 0.44, 95% CI: 0.20–0.93, p for trend = 0.0222 for protein; and OR = 0.48, 95% CI: 0.25–0.93, p for trend = 0.0421 for fiber).
Table 6.
Odds ratios (OR) and 95% confidence intervals (CI) of sarcopenia by quartiles (Q) of nutrient intake.
| Nutrient | Median | Total | Sarcopenia | Age, Sex-Adjusted | Multi-Adjusted | |||
|---|---|---|---|---|---|---|---|---|
| (n) | (n) | OR (95% CI) | OR (95% CI) | |||||
| Energy | Q1 | 928.1 | 200 | 32 | 1.00 | 1.00 | ||
| Q2 | 1301.3 | 200 | 36 | 1.24 | (0.72–2.13) | 1.13 | (0.63–2.02) | |
| Q3 | 1590.5 | 201 | 25 | 0.79 | (0.43–1.43) | 0.61 | (0.32–1.17) | |
| Q4 | 2062.3 | 200 | 18 | 0.55 | (0.28–1.06) | 0.44 | (0.22–0.90) | |
| p for trend | 0.0379 | 0.0089 | ||||||
| Carbohydrate | Q1 | 160.3 | 200 | 40 | 1.00 | 1.00 | ||
| Q2 | 220.4 | 200 | 26 | 0.63 | (0.36–1.11) | 0.63 | (0.34–1.15) | |
| Q3 | 273.4 | 201 | 24 | 0.57 | (0.32–1.00) | 0.51 | (0.27–0.94) | |
| Q4 | 349.9 | 200 | 21 | 0.49 | (0.27–0.89) | 0.40 | (0.21–0.77) | |
| p for trend | 0.0175 | 0.0048 | ||||||
| Protein | Q1 | 30.1 | 199 | 29 | 1.00 | 1.00 | ||
| Q2 | 47.2 | 201 | 35 | 1.25 | (0.72–2.19) | 1.11 | (0.61–2.01) | |
| Q3 | 59.8 | 201 | 31 | 1.10 | (0.62–1.95) | 0.87 | (0.46–1.65) | |
| Q4 | 81.8 | 200 | 16 | 0.50 | (0.25–1.00) | 0.44 | (0.20–0.93) | |
| p for trend | 0.0429 | 0.0222 | ||||||
| Fat | Q1 | 9.6 | 200 | 29 | 1.00 | 1.00 | ||
| Q2 | 19.1 | 199 | 34 | 1.23 | (0.71–2.14) | 1.18 | (0.65–2.15) | |
| Q3 | 29.3 | 201 | 26 | 0.94 | (0.52–1.70) | 0.77 | (0.41–1.46) | |
| Q4 | 48.2 | 201 | 22 | 0.76 | (0.41–1.41) | 0.69 | (0.35–1.36) | |
| p for trend | 0.2394 | 0.1489 | ||||||
| Fiber | Q1 | 2.9 | 200 | 36 | 1.00 | 1.00 | ||
| Q2 | 4.8 | 200 | 26 | 0.67 | (0.38–1.17) | 0.65 | (0.35–1.18) | |
| Q3 | 6.4 | 201 | 28 | 0.79 | (0.45–1.38) | 0.69 | (0.37–1.27) | |
| Q4 | 9.3 | 200 | 21 | 0.53 | (0.29–0.96) | 0.48 | (0.25–0.93) | |
| p for trend | 0.0603 | 0.0421 | ||||||
| Cholesterol | Q1 | 32.6 | 200 | 30 | 1.00 | 1.00 | ||
| Q2 | 102.0 | 200 | 33 | 1.09 | (0.63–1.90) | 1.04 | (0.57–1.88) | |
| Q3 | 199.5 | 201 | 23 | 0.71 | (0.39–1.28) | 0.7 | (0.37–1.34) | |
| Q4 | 407.5 | 200 | 25 | 0.82 | (0.46–1.48) | 0.78 | (0.40–1.54) | |
| p for trend | 0.3441 | 0.3526 | ||||||
| Calcium | Q1 | 208.9 | 200 | 30 | 1.00 | 1.00 | ||
| Q2 | 341.6 | 200 | 23 | 0.76 | (0.42–1.37) | 0.70 | (0.37–1.31) | |
| Q3 | 468.0 | 201 | 31 | 1.02 | (0.58–1.79) | 0.88 | (0.48–1.64) | |
| Q4 | 706.6 | 200 | 27 | 0.93 | (0.52–1.66) | 0.89 | (0.47–1.69) | |
| p for trend | 0.9623 | 0.9513 | ||||||
| Phosphate | Q1 | 574.8 | 200 | 31 | 1.00 | 1.00 | ||
| Q2 | 833.2 | 200 | 32 | 1.07 | (0.62–1.87) | 0.88 | (0.48–1.61) | |
| Q3 | 1013.6 | 201 | 26 | 0.89 | (0.50–1.61) | 0.69 | (0.36–1.32) | |
| Q4 | 1323.0 | 200 | 22 | 0.69 | (0.37–1.29) | 0.57 | (0.28–1.14) | |
| p for trend | 0.2033 | 0.0885 | ||||||
| Iron | Q1 | 6.4 | 200 | 29 | 1.00 | 1.00 | ||
| Q2 | 10.0 | 201 | 35 | 1.29 | (0.74–2.24) | 1.47 | (0.80–2.71) | |
| Q3 | 13.4 | 199 | 25 | 0.88 | (0.48–1.60) | 0.75 | (0.39–1.47) | |
| Q4 | 20.0 | 201 | 22 | 0.79 | (0.42–1.46) | 0.76 | (0.38–1.51) | |
| p for trend | 0.246 | 0.1631 | ||||||
| Sodium | Q1 | 1987.2 | 200 | 34 | 1.00 | 1.00 | ||
| Q2 | 3071.4 | 200 | 36 | 1.10 | (0.64–1.87) | 1.11 | (0.62–1.96) | |
| Q3 | 4031.5 | 201 | 19 | 0.48 | (0.26–0.90) | 0.42 | (0.21–0.83) | |
| Q4 | 6035.7 | 200 | 22 | 0.55 | (0.30–1.01) | 0.49 | (0.25–0.96) | |
| p for trend | 0.0157 | 0.0107 | ||||||
| Potassium | Q1 | 1494.7 | 200 | 35 | 1.00 | 1.00 | ||
| Q2 | 2264.1 | 200 | 30 | 0.91 | (0.52–1.57) | 0.85 | (0.47–1.54) | |
| Q3 | 3000.0 | 201 | 22 | 0.58 | (0.32–1.06) | 0.55 | (0.28–1.05) | |
| Q4 | 3945.2 | 200 | 24 | 0.69 | (0.38–1.27) | 0.63 | (0.32–1.24) | |
| p for trend | 0.1264 | 0.1083 | ||||||
| Zinc | Q1 | 3.9 | 199 | 33 | 1.00 | 1.00 | ||
| Q2 | 5.7 | 201 | 30 | 0.93 | (0.53–1.61) | 0.92 | (0.51–1.67) | |
| Q3 | 7.3 | 201 | 29 | 0.85 | (0.48–1.50) | 0.78 | (0.42–1.44) | |
| Q4 | 10.2 | 200 | 19 | 0.53 | (0.28–1.00) | 0.39 | (0.19–0.80) | |
| p for trend | 0.0451 | 0.0074 | ||||||
| Vitamin A | Q1 | 174.9 | 200 | 34 | 1.00 | 1.00 | ||
| Q2 | 398.0 | 201 | 36 | 1.14 | (0.67–1.92) | 0.92 | (0.52–1.64) | |
| Q3 | 677.0 | 200 | 19 | 0.57 | (0.31–1.05) | 0.44 | (0.23–0.86) | |
| Q4 | 1239.2 | 200 | 22 | 0.69 | (0.38–1.24) | 0.54 | (0.28–1.03) | |
| p for trend | 0.0835 | 0.0298 | ||||||
| Retinol | Q1 | 4.0 | 199 | 36 | 1.00 | 1.00 | ||
| Q2 | 19.6 | 201 | 28 | 0.71 | (0.41–1.23) | 0.71 | (0.39–1.30) | |
| Q3 | 58.0 | 201 | 19 | 0.45 | (0.25–0.83) | 0.40 | (0.21–0.77) | |
| Q4 | 146.0 | 200 | 28 | 0.72 | (0.41–1.25) | 0.67 | (0.36–1.26) | |
| p for trend | 0.4057 | 0.3352 | ||||||
| Carotene | Q1 | 844.3 | 200 | 38 | 1.00 | 1.00 | ||
| Q2 | 1973.3 | 200 | 29 | 0.74 | (0.43–1.27) | 0.64 | (0.36–1.15) | |
| Q3 | 3539.6 | 201 | 22 | 0.58 | (0.33–1.04) | 0.49 | (0.26–0.91) | |
| Q4 | 6856.5 | 200 | 22 | 0.59 | (0.33–1.06) | 0.49 | (0.26–0.93) | |
| p for trend | 0.0874 | 0.0397 | ||||||
| Vitamin D | Q1 | 0 | 202 | 31 | 1.00 | 1.00 | ||
| Q2 | 1.6 | 199 | 22 | 0.70 | (0.38–1.26) | 0.56 | (0.29–1.06) | |
| Q3 | 4.9 | 203 | 27 | 0.92 | (0.52–1.63) | 0.82 | (0.44–1.53) | |
| Q4 | 12.3 | 197 | 31 | 1.01 | (0.58–1.76) | 0.92 | (0.51–1.68) | |
| p for trend | 0.5653 | 0.6279 | ||||||
| Vitamin E | Q1 | 3.4 | 207 | 33 | 1.00 | 1.00 | ||
| Q2 | 5.9 | 192 | 33 | 1.08 | (0.63–1.86) | 1.02 | (0.56–1.85) | |
| Q3 | 8.3 | 202 | 26 | 0.84 | (0.47–1.49) | 0.71 | (0.38–1.33) | |
| Q4 | 12.2 | 200 | 19 | 0.61 | (0.33–1.15) | 0.53 | (0.26–1.06) | |
| p for trend | 0.0873 | 0.0438 | ||||||
| Vitamin K | Q1 | 9.0 | 200 | 36 | 1.00 | 1.00 | ||
| Q2 | 37.5 | 200 | 26 | 0.75 | (0.43–1.32) | 0.70 | (0.38–1.26) | |
| Q3 | 76.0 | 201 | 29 | 0.83 | (0.48–1.44) | 0.75 | (0.41–1.38) | |
| Q4 | 185.0 | 200 | 20 | 0.55 | (0.30–1.00) | 0.51 | (0.27–0.98) | |
| p for trend | 0.0678 | 0.0707 | ||||||
| Vitamin B1 | Q1 | 0.5 | 201 | 27 | 1.00 | 1.00 | ||
| Q2 | 0.8 | 199 | 37 | 1.54 | (0.88–2.69) | 1.52 | (0.84–2.76) | |
| Q3 | 1.0 | 201 | 19 | 0.76 | (0.40–1.45) | 0.62 | (0.31–1.25) | |
| Q4 | 1.5 | 200 | 28 | 1.06 | (0.58–1.94) | 1.12 | (0.58–2.15) | |
| p for trend | 0.6475 | 0.7303 | ||||||
| Vitamin B2 | Q1 | 0.5 | 195 | 32 | 1.00 | 1.00 | ||
| Q2 | 0.8 | 202 | 34 | 1.03 | (0.60–1.77) | 1.05 | (0.58–1.89) | |
| Q3 | 1.0 | 204 | 21 | 0.64 | (0.35–1.17) | 0.55 | (0.28–1.07) | |
| Q4 | 1.5 | 200 | 24 | 0.71 | (0.39–1.30) | 0.67 | (0.34–1.32) | |
| p for trend | 0.1398 | 0.1103 | ||||||
| Niacin | Q1 | 7.4 | 198 | 30 | 1.00 | 1.00 | ||
| Q2 | 11.5 | 200 | 29 | 0.99 | (0.56–1.76) | 0.93 | (0.50–1.73) | |
| Q3 | 15.9 | 202 | 29 | 0.94 | (0.53–1.68) | 0.77 | (0.41–1.45) | |
| Q4 | 22.3 | 201 | 23 | 0.76 | (0.40–1.45) | 0.66 | (0.33–1.33) | |
| p for trend | 0.384 | 0.202 | ||||||
| Vitamin C | Q1 | 34.8 | 199 | 26 | 1.00 | 1.00 | ||
| Q2 | 67.4 | 201 | 35 | 1.43 | (0.82–2.51) | 1.57 | (0.84–2.92) | |
| Q3 | 106.1 | 201 | 23 | 0.90 | (0.49–1.65) | 0.86 | (0.44–1.66) | |
| Q4 | 173.9 | 200 | 27 | 1.07 | (0.59–1.94) | 1.07 | (0.56–2.07) | |
| p for trend | 0.768 | 0.6419 | ||||||
| Vitamin B6 | Q1 | 0.7 | 202 | 34 | 1.00 | 1.00 | ||
| Q2 | 1.1 | 196 | 30 | 0.91 | (0.52–1.58) | 0.90 | (0.49–1.66) | |
| Q3 | 1.5 | 203 | 29 | 0.87 | (0.49–1.53) | 0.85 | (0.45–1.60) | |
| Q4 | 2.1 | 200 | 18 | 0.51 | (0.27–0.97) | 0.45 | (0.22–0.91) | |
| p for trend | 0.0431 | 0.0247 | ||||||
| Vitamin B12 | Q1 | 0.6 | 199 | 25 | 1.00 | 1.00 | ||
| Q2 | 2.3 | 201 | 29 | 1.23 | (0.69–2.21) | 1.02 | (0.55–1.91) | |
| Q3 | 4.9 | 201 | 28 | 1.22 | (0.68–2.21) | 1.00 | (0.53–1.89) | |
| Q4 | 11.7 | 200 | 29 | 1.24 | (0.69–2.24) | 1.17 | (0.62–2.21) | |
| p for trend | 0.6002 | 0.5874 | ||||||
| Folate | Q1 | 144.1 | 199 | 33 | 1.00 | 1.00 | ||
| Q2 | 223.8 | 201 | 23 | 0.70 | (0.39–1.26) | 0.55 | (0.29–1.06) | |
| Q3 | 304.4 | 200 | 36 | 1.13 | (0.66–1.96) | 1.01 | (0.56–1.84) | |
| Q4 | 451.6 | 201 | 19 | 0.56 | (0.30–1.04) | 0.51 | (0.26–1.01) | |
| p for trend | 0.1518 | 0.1789 | ||||||
Multi-adjusted: adjusted for age, sex, BMI, family type, marital status, education level, income level, smoking status, and drinking status.
Participants in the highest quartile of zinc intake had a 0.39-fold lower risk of sarcopenia than participants in the lowest quartile of zinc intake (95% CI: 0.19–0.80, p for trend = 0.0074). The highest quartile of carotene and vitamin B6 intake was 0.49-fold and 0.45-fold lower for the risk of sarcopenia compared to the lowest quartile, respectively (95% CI: 0.26–0.93, p for trend = 0.0397 for carotene, 95% CI: 0.22–0.91, p for trend = 0.0247 for vitamin B6).
4. Discussion
In this study, we found that a high intake of the meat/fish/eggs/legumes and vegetable food groups, total food intake, and several nutrients (energy, carbohydrate, protein, fiber, zinc, carotene, and vitamin B6) was associated with the lower prevalence of sarcopenia in the Korean elderly.
Among the food groups, meat/fish/eggs/legumes, vegetables, and fruits are very important for body composition, especially for muscle mass and metabolism [20]. As for the consumption of meat, a significant difference between the Korean population and Western populations should be considered. The traditional Korean dietary pattern is characterized by a low meat and high rice consumption unlike Western dietary pattern [21]. Therefore, it is recommended that the elderly in Korea consume meat that provides high-quality protein to maintain muscle mass and prevent chronic diseases. A previous cross-sectional study using the Korea National Health and Nutrition Examination Survey data showed an adverse associations between vegetable and fruit intake and sarcopenia [22]. A prospective cohort study in community-dwelling older people in Hong Kong also reported results that are in accordance with those of the present study, presenting inverse associations between vegetables and fruits and the prevalence of sarcopenia [23]. Furthermore, the meat, fish, and vegetable dietary pattern was positively associated with pre-frailty and exhaustion in the Korean elderly population [17]. Consumption of fruits and vegetables has a positive effect on sarcopenia by preventing inflammation and acidosis [24], and the intake of phytochemicals rich in fruits and vegetables increased grip strength and physical performance [12].
Among the nutrients, energy, carbohydrate, protein, fiber, zinc, carotene, and vitamin B6 showed adverse associations with the prevalence of sarcopenia. High energy is known to have a beneficial effect on the risk of sarcopenia [25]. This may be because energy deficiency compromises mitochondrial energy metabolism, which results in muscle fatigue, muscle weakness, and muscle atrophy [26] Another study found that higher energy intake and higher physical activity were independently associated with a reduced risk of sarcopenia in the elderly than in the younger group [13]. A recent systematic review reported that older adults with sarcopenia had significantly lower energy and carbohydrate consumptions than those without sarcopenia [27]. An intervention study suggested a preventive effect of carbohydrate and protein supplementation on muscle protein loss in bedridden patients [28]. In general, preventive guidelines for sarcopenia recommend an increased need for dietary protein because protein intake can stimulate muscle synthesis. In particular, the elderly needs more protein to prevent sarcopenia because their metabolic efficiency is low [29]. A positive association between dietary protein intake and muscle mass has been consistently reported, regardless of study designs and populations [30]. However, despite the emphasis on dietary protein intake for the prevention of sarcopenia, the status of dietary protein consumption in the Korean elderly has not met the recommended daily allowance (RDA). According to Park (2018), 47.9% of men and 60.1% of women had an insufficient protein intake based on the RDA [31]. Therefore, various methods to increase the protein intake in the Korean elderly should be explored. Fiber is abundant in fruits and vegetables, and the risk of sarcopenia has been reduced in older Chinese adults with high ”vegetable-fruit” dietary pattern scores [23]. An inverse association between the consumption of fruits and sarcopenia has also been observed in elderly people living in low-and middle-income countries [32]. The Women’s Health and Aging Study reported that carotenoid and α-tocopherol levels were associated with muscle strength in older women [33]. These results suggest that oxidative stress is a major mechanism of sarcopenia, therefore an intake of antioxidants such as carotenoids and vitamin C may prevent skeletal muscle damage [34]. Our study also demonstrated an adverse association between zinc intake and sarcopenia. Zinc deficiency contributes to the pathogenesis of anorexia nervosa [35]. Zinc has been found to stimulate appetite and may play a role in the prevention against degenerative diseases, such as sarcopenia and cachexia [35]. A narrative review elucidated the therapeutic effect of the oral zinc administration on taste disorders. Carbonic anhydrase IV, a zinc metalloenzyme, has been reported to play a role in ion transport, saliva production and secretion, and saliva pH regulation [36]. Taken together, the deficiency in dietary zinc from an inadequate diet can lead to the loss of appetite, leading to a vicious cycle of malnutrition among the elderly. The Maastricht Sarcopenia Study reported that individuals with sarcopenia had lower vitamin B6 intake and higher homocysteine levels than those without sarcopenia [37]. Vitamin B6, vitamin B12, and folate are known cofactors in homocysteine metabolism [37], and a recent review article elucidated that folate deficiency can contribute to the development of homocysteinemia [38]. A previous study using the Longitudinal Aging Study Amsterdam data showed an association between increased homocysteine levels and reduction in grip strength in men [39]. In addition, it has been hypothesized that higher homocysteine levels may aggravate muscle protein degradation and physical functioning in the elderly [40].
The strengths of the present study are as follows: (1) it uses nationwide data of the community-dwelling Korean elderly, and (2) it uses accurate muscle mass measurements using DEXA. However, since DEXA is exposed to radiation, an alternative method to measure muscle mass is needed. If bioelectrical impedance analysis (BIA) is used, more subjects can be involved. However, there are currently no national data linking BIA body composition with 24-h recall or dietary record. Additionally, this study has several limitations: (1) the cross-sectional study design leads to the uncertainty of a causal relationship; therefore, future prospective studies are needed to confirm the causal relationship between sarcopenia and dietary factors in the Korean elderly population, (2) it is difficult to assess exact energy intake using the 24-h recall method, and (3) the nutritional survey was a subcohort of KFACS, a random sampling cohort, and 48% of the total subjects were excluded in our analysis. It may not be representative of the Korean elderly. However, characteristics of the included subjects (52%) and excluded subjects (48%) are very similar in gender, age, height, weight, and other social factors.
According to the results of this study, sufficient consumption of nutrients through various protein source foods and vegetables will help prevent sarcopenia in the Korean elderly. This study can provide basic data for establishing dietary guidelines for the prevention of sarcopenia in the Korean elderly.
5. Conclusions
The present study was conducted to assess of the effects of dietary factors on sarcopenia among the Korean elderly using nationwide data. A high intake of protein-source food, vegetables, total food intake, energy, carbohydrates, proteins, fiber, zinc, carotene, and vitamin B6 were associated with lower prevalence of sarcopenia in the Korean elderly.
Author Contributions
Conceptualization, H.-J.L. and C.W.W.; formal analysis, S.-J.P.; methodology, S.-J.P. and H.-J.L.; writing—original draft preparation, J.P.; writing—review and editing, S.-J.P. and H.-J.L.; project administration, C.W.W. All authors have read and agreed to the published version of the manuscript.
Funding
This work was supported by the Korea Institute of Planning and Evaluation for Technology in Food, Agriculture and Forestry (IPET) through the High Value-Added Food Technology Development Program, funded by the Ministry of Agriculture, Food and Rural Affairs (MAFRA) (grant number 321024-04-1-HD020) and partly supported by the “Cooperative Research Program of the Center for Companion Animal Research (Project No. PJ01398402)” of the Rural Development Administration, Republic of Korea.
Institutional Review Board Statement
The study was conducted in accordance with the guidelines of the Declaration of Helsinki and approved by the institutional review board of the Clinical Research Ethics Committee of the Kyung Hee University Medical Center (IRB number: 2015-12-103, approval date 30 December 2015).
Informed Consent Statement
Informed consent was obtained from all subjects involved in this study.
Data Availability Statement
All datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.
Conflicts of Interest
The authors declare no conflict of interest.
Footnotes
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
All datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.