Introduction
Sarcopenia is a progressive and generalized skeletal muscle disorder with age-related loss of skeletal muscle mass, strength, and function [
1,
2]. Therefore, this condition is related to an increased likelihood of adverse outcomes, including falls, fractures, and more mortality and morbidity in older people [
1]. Also, sarcopenia can lead to decreased mobility, physical inactivity, decreased walking speed, and reduced endurance [
3]. The estimated prevalence of sarcopenia in Western societies and Asian countries has been reported as 1–29% and 2–46%, respectively [
4]. The findings of a study estimated the prevalence of sarcopenia in Iran between 16.5% and 32.5% [
5].
Nutrition and physical activity are essential in managing and preventing sarcopenia [
5]. There is considerable evidence that nutrition plays an essential role in the strength, performance, and muscle mass of the elderly [
6]. A healthy diet, such as a lower intake of added sugar and a higher intake of whole fruit, is related to muscle strength [
7]. It has also been reported that sugar-sweetened beverages (SSBs) are associated with the loss of muscle mass [
8]. Beverage patterns are related to health and dietary patterns [
9]. The healthy beverage index (HBI) can be applied to evaluate the total quality of beverages and reveal whether changing beverage patterns are associated with further health [
10]. This index can be used to evaluate the quality of people’s nutrition and drink consumption to help individuals choose healthy drinks [
11]. Also, the HBI can be used to examine the synergistic effects of different beverages rather than the effect of a single beverage on health-related outcomes [
12]. This index includes fluid intake, total beverage energy, and eight beverage categories [
13].
Studies have shown SSBs could harm muscle mass [
8,
14]. In contrast, the positive effect of caffeine on handgrip strength (HGS) and improved muscle function and physical performance has been shown [
15,
16]. It has also been demonstrated that beverages like milk, green tea, and coffee can positively increase lean body mass [
17‐
19]. Moreover, it has been shown that consumption of normal/high-fat products is associated with greater muscle mass and lean body mass in Japanese women aged 40–60 years [
20]. In addition, a study found a positive relationship between coffee consumption and muscle mass in older and middle-aged Japanese people [
21]. However, another study found no relationship between coffee consumption and muscle mass [
22].
Considering that the number of elderly people is increasing in the new century, it is crucial to find approaches to prevent sarcopenia to avoid the epidemic of disability in the future [
23]. To our knowledge, no study has investigated the association between HBI and sarcopenia in older adults. Therefore, the present study aimed to investigate the association between HBI and sarcopenia in Iranian older adults. The results of the present study can help us understand the relationship between the consumed beverages and the odds of sarcopenia.
Results
Baseline characteristics of the study population are shown in Table
1. According to the table, the median age significantly differed between both groups (
P = 0.001). Also, energy, fiber, water and tea consumption, weight, height, BMI, muscle strength, SMI, and gait speed were different between the case and control groups (P<0.001 for all except gait speed).
Table 1
The basic characteristics of the study participants
Age (year) 1 | 70.0 (8.0) | 68.0 (6.0) | 0.001 |
Total HBI score 1 | 80.0 (4.0) | 80.0 (4.0) | 0.104 |
Energy (kcal/day) 2 | 1329.3 ± 472.9 | 1861.9 ± 450.4 | <0.001 |
Fiber (gr/day) 2 | 26.1 ± 12.5 | 36.1 ± 11.2 | <0.001 |
Water (cc/day) 1 | 720.0 (700.0) | 1000.0 (915.0) | <0.001 |
Tea (cc/day) 1 | 500.0 (250.0) | 1000.0 (500.0) | <0.001 |
Low-fat milk (cc/day) 2 | 54.8 ± 10.2 | 69.9 ± 11.3 | 0.324 |
High-fat milk (cc/day) 2 | 5.7 ± 4.0 | 1.2 ± 1.2 | 0.288 |
Fruit juice (cc/day) 2 | 2.2 ± 1.6 | 0.0 ± 0.0 | 0.174 |
Sugar-sweetened beverages (cc/day) 2 | 24.7 ± 9.5 | 5.7 ± 1.6 | 0.052 |
Weight (kg) 2 | 59.6 ± 9.2 | 78.1 ± 9.1 | <0.001 |
Height (cm) 2 | 157.2 ± 9.8 | 163.6 ± 8.9 | <0.001 |
BMI (kg/m2) 2 | 24.5 ± 4.1 | 29.2 ± 3.8 | <0.001 |
Physical activity (MET-h/week) 1 | 429.0 (1020.7) | 462.0 (1386.0) | 0.650 |
Sex, % 3 | | | 1.000 |
Male | 55.0 | 55.0 | |
Female | 45.0 | 44.0 | |
Education, % 3 | | | 0.053 |
Illiterate | 31.3 | 13.8 | |
Primary education | 33.7 | 47.5 | |
Secondary education | 22.5 | 22.5 | |
Higher education | 12.5 | 16.3 | |
Income in month, % 3 | | | 0.127 |
Less than 30 million IRR | 38.8 | 36.3 | |
30–60 million IRR | 47.5 | 37.5 | |
More than 60 million IRR | 13.7 | 26.2 | |
Smoking, % 3 | | | 0.718 |
Yes | 23.7 | 27.5 | |
No | 76.3 | 72.5 | |
Muscle strength (kg) 1 | 16.0 (10.7) | 50.8 (24.2) | <0.001 |
SMI (kg/m2) 1 | 6.1 (1.4) | 7.9 (0.9) | <0.001 |
Gait speed (m/second) 2 | 0.70 ± 0.10 | 1.00 ± 0.95 | 0.007 |
The nutrient intakes of the study population in various tertiles of HBI are shown in Table
2. Participants in the last tertile of the HBI score had significantly higher intakes of polyunsaturated fatty acids (PUFAs) and monounsaturated fatty acids (MUFAs) than those in thefirst tertile (
P = 0.031 and
P = 0.049, respectively), but carbohydrate intake was significantly lower in the last tertile (
P = 0.032).
Table 2
The nutrient intakes based on HBI tertile
Carbohydrate (% energy) | 65.57 (9.60) | 61.83 (11.72) | 63.50 (9.95) | 0.032 |
Protein (% energy) | 13.47 (2.82) | 13.51 (3.13) | 14.15 (2.25) | 0.334 |
SFA (% energy) | 7.45 (3.44) | 7.96 (4.12) | 7.65 (3.10) | 0.552 |
MUFA (% energy) | 8.06 (2.27) | 9.29 (3.50) | 8.46 (3.65) | 0.049 |
PUFA (% energy) | 5.23 (2.03) | 5.90 (1.79) | 5.30 (2.65) | 0.031 |
Crude and multivariable-adjusted odds ratio (OR) and 95% confidence intervals (CIs) for HBI score with the odds of sarcopenia are presented in Table
3. In the crude model, no significant relationship was observed between HBI and the odds of sarcopenia. Still, after adjusting for age, BMI, smoking history, education level, income, physical activity, energy, protein and saturated fatty acid (SFA) intake (energy%), the odds of developing sarcopenia decreased significantly in the second and last tertiles (T) (T
2– OR = 0.04, 95% CI: 0.01–0.25 and T
3– OR = 0.10, 95% CI: 0.01–0.60).
Table 3
Association between healthy beverage index and sarcopenia
Healthy beverage index |
T1 (≤ 77) | 33/27 | 1.00 | Ref. | 1.00 | Ref. | 1.00 | Ref. |
T2 (78–80) | 31/31 | 0.81 | 0.40–1.66 | 0.77 | 0.32–1.87 | 0.04 | 0.01–0.25 |
T3 (≥ 81) | 16/22 | 0.59 | 0.26–1.35 | 0.72 | 0.26–1.99 | 0.10 | 0.01–0.60 |
Ptrend | | 0.219 | 0.588 | 0.020 |
Discussion
The current study’s findings indicated a significant negative association between HBI and sarcopenia in older adults. The chance of sarcopenia was decreased by 90% with a higher HBI score.
In most elderly patients, the onset of sarcopenia is multifactorial [
32], including weight loss, an increase of pro-inflammatory cytokines, loss of anabolic hormones, age-related mitochondrial dysfunction, reduction of physical activity, and loss of motor neuron end plates [
33]. Sarcopenia caused by these factors can negatively affect the general health of the elderly. Sarcopenia is related to functional decline and poor physical performance, which can cause an increase in hospitalization, an increase in co-morbidities, and disability [
34].
Based on the current study’s findings, the odds of sarcopenia decreased with increasing HBI. A study by Guo et al. showed that caffeinated coffee consumption in elderly mice prevents the decline of muscle strength and muscle weight, can reduce pro-inflammatory mediators, and has beneficial effects on reducing the risk of age-related sarcopenia [
35]. Also, a study by Kim et al. indicated a 31% reduction in the risk of sarcopenia with one cup of coffee per day in men [
36]. Coffee contains chemical compounds with antioxidant and anti-inflammatory properties that can cause autophagy and have beneficial effects on reducing the risk of sarcopenia [
36]. Catechin and polyphenols in green tea cause its antioxidant properties, which may be associated with a reduced risk of sarcopenia [
37,
38].
Studies have also been conducted on the effect of milk consumption on sarcopenia. A cross-sectional study in Korea has shown that milk consumption significantly reduces performance disability in men [
39]. Also, another cross-sectional study demonstrated that milk consumption was associated with increased skeletal muscle mass, fat-free mass, and HGS in elderly women [
40]. Also, a systematic review study demonstrated that the consumption of low-fat milk can be useful in reducing the risk of sarcopenia in the elderly by improving skeletal muscle mass due to its protein and nutrients [
41]. Milk contains many bioactive components and nutrients that may benefit muscles; for example, it contains proteins useful for muscle synthesis and has muscle-protective properties [
42]. Also, milk contains some antioxidant elements, such as β-lactoglobulin, which positively impact sarcopenia [
42].
Fruit juice consumption is another component of the HBI, which has been shown to reduce sarcopenia [
43,
44]. Oxidative stress is an important factor that causes metabolic disorders and changes in muscle function [
45]. Oxidative stress can play a role in the pathophysiology of sarcopenia and can increase its risk [
46]. The beneficial effects of fruits in reducing oxidative stress have been demonstrated in previous studies [
47]. Fruits owe their antioxidant properties to vitamins C and E, phenolic compounds, and carotenoids, which destroy free radicals and prevent damage to deoxyribonucleic acid (DNA) and cellular structures [
48‐
50]. In addition, fruit juices act like a buffer with their alkaline properties, reduce catabolism and proteolysis of amino acids, and increase muscle mass [
51]. We can also mention vitamin C’s role in synthesizing collagen and carnitine in skeletal muscle, which can be useful in reducing the risk of sarcopenia [
52‐
54].
Regarding the consumption of SSBs, studies illustrate that limiting their consumption is related to the increase in the expression of mitophagy-related proteins in the quadriceps muscles [
55]. A study by Bragança et al. also indicated that daily consumption of SSBs was associated with a decrease in muscle mass index [
14]. Moreover, a study by Hao et al. showed that SSB consumption is related to a decline in muscle mass by 0.12 kg/m
2 in adolescents [
8]. Studies reveal that muscle fat increases with increased SSB consumption [
56,
57]. An increase in fat in muscle cells could simultaneously increase lipolysis and autophagy in muscle cells [
58]. Impairment of autophagy reduces myogenesis and causes a decrease in muscle mass [
59]. Also, the role of a diet with higher sugar has been shown to reduce the function of mitochondria and muscle cells [
60]. Also, the consumption of SSBs is associated with the loss of muscle mass and, thus, the risk of sarcopenia through their effects on impaired glucose, lipid metabolism, reduced protein synthesis, and decreased efficient muscle contraction [
61‐
64].
In general, HBI components seem to reduce the risk of sarcopenia by decreasing oxidative stress, anti-inflammatory effects, having bioactive nutrients, and increasing protein synthesis in muscles.
This study had some limitations and strengths. Since the nature of the study is case-control, we cannot directly determine causation, but the association between exposure (HBI) and outcome (sarcopenia) was assessed. Also, there may be other confounding variables that were not considered in the present study, such as polypharmacy, which is common in older adults. In addition, completing the FFQ relies on people’s memory, and there may be errors in dietary assessment. In terms of the strength of the current study, it can be mentioned that this study is the first case-control study that examined the association between HBI and the odds of sarcopenia in older adults.
In this way, in this type of study, although causation cannot be determined directly; instead, researchers can identify associations and calculate measures such as odds ratios to estimate the strength of the association between exposure and outcome.
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