https://orcid.org/0000-0001-7206-1724
Abstract
Objective
Low muscle mass and muscle function are associated with adverse health outcomes in older adults. This study examined nutrient intake as a potential contributing factor for low muscle mass, muscle strength, and muscle power in geriatric outpatients.
Method
This cross-sectional study included geriatric outpatients (n = 58, 38 female) with a mean age of 77.2 ± 9.0 years referred to the Falls and Balance outpatient clinic between December 2017 and January 2019. Nutrient intake (macro- and micronutrients) was examined using a 3-day food diary. Energy-adjusted nutrient intake was calculated using the residual method. Sex-standardized muscle measures included muscle mass assessed using bioelectrical impedance analysis (skeletal muscle mass [SMM in kilograms], SMM index [SMM/height2 in kg/m2], and SMM/body mass index), handgrip strength (muscle strength) assessed using a dynamometer, and chair-stand test (muscle power). Univariate linear regression analyses were used to examine the associations of nutrient intake with muscle measures adjusted for age and body weight. A Bonferroni correction was applied to account for multiple testing (p < 0.001).
Results
Higher energy, iodine, and folate intake were associated with higher muscle mass, and higher folate intake was associated with higher muscle strength (p < 0.05). After Bonferroni correction, none of the nutrient intakes remained statistically significant. None of the other nutrients was associated with muscle measures.
Conclusions
Only a few nutrients were associated with muscle measures. Nutrient intake appears to be more related to muscle mass than muscle strength and muscle power in geriatric outpatients.
Introduction
Muscle mass and muscle function decline with advancing age (Citation1, Citation2). Sarcopenia, low muscle mass and muscle function, is associated with falls, fractures, physical dependence, hospitalization, and mortality in older adults (Citation3–7). Nutrition has the potential role of being a key modifiable factor to prevent the decline in muscle measures such as muscle mass and muscle function (Citation8, Citation9), via a number of mechanisms including anti-inflammatory, anti-oxidative, and anabolic-promoting functions (Citation10, Citation11). About 20% of community-dwelling older adults reported a loss of appetite or a decrease in food intake (Citation12). A decrease in food intake may result in decreased intake of energy, protein, and other nutrients (Citation13).
Cross-sectional studies in community-dwelling older adults showed that higher protein and calcium intake were associated with higher muscle mass (Citation14, Citation15); higher protein, vitamin C, carotene, selenium, fiber, and polyunsaturated fatty acid intake with higher muscle strength (Citation16–19); and higher protein, β-carotene, and vitamin C intakes with higher muscle power (Citation20, Citation21). A longitudinal study of community-dwelling older adults showed that higher protein, iron, magnesium, phosphorus, and zinc intakes at baseline were associated with increased muscle mass at 2.6 years’ follow-up, while no associations were found between nutrient intake and muscle strength (Citation22). Data on the association between nutrient intake and muscle measures in geriatric outpatients are not available. These older adults are referred to a geriatric outpatient clinic with multiple problems in physical, social, functional, or psychological domains (Citation23). Low nutrient intake and hence its role as a potential contributing factor for low muscle measures may be even more pronounced in geriatric outpatients (Citation24).
This study aimed to examine the associations between nutrient intake and muscle mass, muscle strength, and muscle power in geriatric outpatients. We hypothesized that higher nutrient intake is associated with higher muscle mass, muscle strength, and muscle power in geriatric outpatients.
Methods
Study design
Data were derived from the SHAPE cohort, including community-dwelling older adults referred to the Falls and Balance outpatient clinic (December 2017 to January 2019). Outpatients were assessed by a multidisciplinary team using the Comprehensive Geriatric Assessment (CGA) as part of usual care. The CGA included questionnaires and assessments of nutritional status, physical function, cognition, and psychological well-being. No exclusion criteria were applied; inclusion was based on referral and written informed consent was obtained for the use of the medical information and an additional nutritional assessment, including the completion of a food diary. Among 59 outpatients referred to the clinic and completing a food diary, data on at least one of the muscle measures (i.e., muscle mass, muscle strength, and muscle power) were available in 58 outpatients. Therefore, data on 58 outpatients were used in the present analysis. The study was approved by the local Institutional Review Board. National and international ethical guidelines were followed in accordance with the Declaration of Helsinki (Citation25).
Geriatric outpatient characteristics
Questionnaires included information about demographics including marital status, living arrangement, education, smoking status, and physical activity. The number of diseases and medications were extracted from medical records. Multimorbidity was defined as the presence of two or more diseases. Polypharmacy was defined as taking five or more medications (Citation26). The Mini-Mental State Examination was used to assess cognitive function (Citation27). Nutritional status was screened using the full Mini-Nutritional Assessment (MNA) (Citation28), with 24 to 30 points defined as normal nutritional status, 17.0 to 23.5 points as at risk of malnutrition and < 17.0 points as malnutrition. Body weight and standing height were measured to the nearest 0.1 kg and 0.1 cm, respectively, and were used to calculate body mass index (BMI) in kg/m2. BMI was also categorized to define underweight (< 22 kg/m2), normal weight (22–27 kg/m2), and overweight (> 27 kg/m2) (Citation29). It is still under debate which cutoff of BMI should be applied in older adults. The BMI categorization in our study was based on the literature that showed that older adults with BMI < 22 kg/m2 was associated with worse lower limb strength/endurance compared with older adults with BMI 22–27 kg/m2 (Citation29).
Nutrient intake
Outpatients were instructed to complete a food diary over 3 days (2 weekdays and 1 weekend day) to assess nutrient intake. Outpatients were provided with verbal and written instructions by trained researchers on how to record their food and beverage consumption. Outpatients were instructed to use a kitchen scale if possible or common household measures to quantify the food consumed. Food diaries were completed with caregiver assistance if needed. Trained researchers checked food diaries and any missing information was clarified with the outpatients at the clinic. Food diaries were entered into the nutrient analysis software FoodWorks using AusBrands 2017, AusFoods 2017, and NUTTAB 2010 databases (Xyris Software, Highgate Hill, Queensland, Australia, Version 9, 2017). The nutrient analysis provided the intake for energy (kcal), macronutrients (carbohydrate [g], protein [g], fat [g], saturated fat [g], fibers [g]), and micronutrients (sodium [mg], calcium [mg], iron [mg], zinc [mg], potassium [mg], magnesium [mg], selenium [µg], iodine [µg], vitamin A [µg], vitamin E [mg], riboflavin [mg], folate [µg], thiamin [mg], niacin [mg], vitamin B6 [mg], vitamin B12 [µg], and vitamin C [mg]). The percentage of energy from macronutrients was calculated for carbohydrate, protein, fat, and saturated fat. Protein intake was additionally expressed as protein intake relative to body weight (g/kg body weight).
Muscle measures
Muscle mass
Body composition was measured using direct segmental multi-frequency bioelectrical impedance analysis (DSM-BIA, In-Body 770; Biospace Co., Ltd, Seoul, Korea). The DSM-BIA has been shown to be valid compared to dual-energy X-ray absorptiometry (DXA) in community-dwelling individuals (Citation30). Excellent agreements were observed between DSM-BIA and DXA in whole-body lean mass and segmental lean mass quantification. Muscle mass was expressed as total skeletal muscle mass (SMM) in kilograms, SMM index (SMI) by SMM divided by height squared in kg/m2, and SMM divided by BMI (SMM/BMI).
Muscle strength
Handgrip strength (HGS) was measured to estimate muscle strength using handheld dynamometry (JAMAR, Sammons Preston, Inc., Bolingbrook, IL, USA). HGS measurements were performed three times alternately for each hand and the best performance was used for analysis and expressed in kg (Citation31).
Muscle power
The chair-stand test (CST) was used as a proxy for muscle power (Citation32, Citation33). The CST was performed as part of the Short Physical Performance Battery (SPPB) (Citation34) and expressed in seconds. Outpatients were asked to perform a timed five rises from a chair to an upright position as fast as possible without the use of arms. The total amount of time to complete five rises was used in the analysis. Outpatients who could not finish the test or used their hands to get up were given a time score of 100 seconds to be able to include them in the analysis (Citation35).
Statistical analysis
Continuous variables were presented as mean and standard deviation (SD) when normally distributed or as the median and interquartile range (IQR) when skewed distributed. Categorical variables were presented as number (n) and percentage (%).
Nutrients were adjusted for total energy intake using the residual method to remove variation due to the individual differences in total energy intake (Citation36). Briefly, the residuals for each individual from the linear regression model with absolute nutrient intake as the dependent variable and total energy intake as the independent variable were added to a constant, which is the expected nutrient intake for the mean energy intake of the study population, to calculate energy-adjusted nutrient intakes. Muscle measures were standardized into sex-specific z scores. Linear regression analyses were conducted to examine the associations of single energy-adjusted nutrient intake (independent variables) with standardized muscle mass, muscle strength, and muscle power (dependent variables). In model 1, an adjustment was performed for age. In model 2, further adjustments were performed for body weight as body weight is closely related to overall dietary intake and individuals with a lower body weight may have a lower muscle mass and a lower HGS and may require less time to complete the CST (Citation37, Citation38). Since SMM/BMI already took body weight into consideration, the association between nutrient intake and SMM/BMI was adjusted for model 1 only.
All statistical analyses were performed using Statistical Package for the Social Science (IBM SPSS Statistics for Windows, Version 25.0, IBM Corp., Armonk, NY, USA). Data were presented as effect estimates (β) and standard error (SE). As multiple nutrients (n = 28) were analyzed as well as three different muscle measures, a Bonferroni correction was applied for 84 tests resulting in a corrected significance level of p < 0.001 (α 0.05/84 variables) considered as statistically significant.
Results
shows the characteristics of the geriatric outpatients. The mean age was 77.2 years (SD = 9.0), and 38 were female. Multimorbidity and polypharmacy were present in 58 and 44 outpatients, respectively. More than half (n = 39) of the outpatients had a normal nutritional status based on the MNA score. The mean BMI was 27.9 kg/m2 (SD = 5.1). shows the daily nutrient intake. The mean energy intake was 1591 kcal (SD = 504). The mean protein intake was 1.0 g/kg body weight (SD = 0.2), and 46 outpatients did not meet the protein requirement of 1.2 g/kg body weight.
Table 1. Characteristics of geriatric outpatients (n = 58).
Table 2. Daily energy-adjusted nutrient intake of geriatric outpatients.
shows the associations between energy-adjusted nutrient intake and muscle mass in geriatric outpatients. Before Bonferroni correction, higher energy, iodine, and folate intake was associated with higher SMM and SMI (except energy) after adjusting for age. These associations did not change after further adjustment for body weight. Lower relative protein intake (g/kg body weight) and higher sodium and thiamin intake were associated with higher SMM and SMI after adjusting for age, but these associations disappeared after further adjusting for body weight. Higher energy and sodium intake was associated with higher SMM/BMI after adjusting for age. After Bonferroni correction, none of the nutrients remained statistically significant, except for the inverse association between relative protein intake (g/kg body weight) and SMI after adjusting for age (p < 0.001); after adjusting for body weight, this significance disappeared. Further exploration of the data revealed no significant differences in absolute protein intake among BMI groups, but a significantly higher consumption of protein intake relative to body weight (g/kg body weight) in underweight outpatients compared to normal weight and overweight outpatients (Supplemental Online Material 1).
Table 3. Associations between energy-adjusted nutrient intake and muscle mass in geriatric outpatients (n = 49).
shows the associations between energy-adjusted nutrient intake with muscle strength and muscle power in geriatric outpatients. Before Bonferroni correction, higher folate intake was associated with higher HGS after adjusting for age. The association did not change after further adjustment for body weight. After Bonferroni correction, none of the nutrients showed a statistically significant association with muscle strength and muscle power.
Table 4. Associations between energy-adjusted nutrient intake and muscle strength (n = 45) and muscle power (n = 57) in geriatric outpatients.
Discussion
In a clinically relevant population of geriatric outpatients, higher energy, iodine, and folate intake was associated with higher muscle mass, and higher folate intake was associated with higher muscle strength. After Bonferroni correction, none of the nutrient intakes remained statistically significant.
Higher energy intake was associated with higher muscle mass, which is in line with a study among community-dwelling middle-aged and older adults (Citation39). This association was expected as energy intake has a close relationship with body weight. A change in body weight (loss or gain) could change the body composition and therefore change the ratio between muscle and fat mass (Citation40). A potential mechanism could be the fact that muscle is responsible for a substantial portion of the body’s total energy expenditure; consuming sufficient energy may contribute to the conservation of skeletal muscle mass (Citation39). Vitamin Bs are involved in multiple aspects of energy and protein metabolism and neural integrity and function. Symptoms associated with inadequate vitamin Bs include motor weakness, loss of motor function, and gait ataxia (Citation41). Our finding of a higher folate intake being associated with higher muscle mass and muscle strength is in line with cross-sectional studies in community-dwelling older adults (Citation22, Citation42). The potential mechanism could be that adequate folate intake can help return hyper-homocysteine to a normal level (Citation43). Hyper-homocysteine has been shown to mediate skeletal muscle dysfunction via oxidative stress, inflammation, and matrix degradation (Citation44). Other vitamin Bs are also involved in homocysteine metabolism. For example, vitamin B12 is required as a cofactor of the enzyme methionine synthase for the remethylation of homocysteine to methionine (Citation45). However, we showed that other vitamin Bs were not associated with muscle measures in our population. In the present study, higher iodine intake was associated with higher muscle mass. To the best of our knowledge, this association has not been studied before. The potential mechanism could be the need for iodine for thyroid hormone production (Citation46). Thyroid hormones have been suggested to induce the transition from a slower fiber type into a faster one and could be involved in the sarcopenic process (Citation47, Citation48).
Protein is an essential component of muscle metabolism. The current body of evidence showed inconsistent results of the association between protein intake and muscle measures in community-dwelling older adults. In cross-sectional studies with large sample sizes of community-dwelling older adults (n = 554 to 3213), higher protein intake was associated with higher muscle mass (Citation15, Citation22), muscle strength (Citation21), and muscle power (Citation21), while other studies did not find this association (Citation22, Citation49). In longitudinal studies among community-dwelling older adults, higher protein intake was associated with higher muscle mass (Citation22, Citation50, Citation51), muscle strength (Citation21), and muscle power (Citation21), while one study found an insignificant association between protein intake and muscle strength (Citation22). This inconsistency may be related to different study designs, study populations, assessment methods of muscle mass and muscle strength, and the use of absolute versus relative protein intake. In the present study, there was an absence of the association between absolute protein intake and muscle measures. Before Bonferroni correction, we did find that lower relative protein intake (g/kg body weight) was associated with higher muscle mass when adjusting for age. However, this association disappeared after further adjustment for body weight. The association between lower relative protein intake and higher muscle mass may be explained by higher consumption of protein intake relative to body weight (g/kg body weight) in underweight compared to overweight individuals (Citation49), which was indeed found in our study. The fact that relative protein intake (g/kg body weight) was higher in underweight outpatients compared to the normal and overweight outpatients may be attributed to underreporting of the actual intake among overweight individuals or an absolute protein intake divided by a lower body weight in underweight individuals, resulting in a higher relative protein intake value (Citation52).
Oxidative stress has been suggested to play an important role in the etiology of sarcopenia and poor muscle function (Citation53, Citation54). Therefore, antioxidant nutrients (such as carotenoids, selenium, vitamin C, and vitamin E) may prevent the loss of muscle mass and muscle function by inactivating the reactive oxygen species and decreasing oxidation in muscle fibers (Citation55). We did not find an association between antioxidant nutrients and muscle measures. The current body of evidence shows inconsistent associations between antioxidant nutrients and muscle measures among community-dwelling older adults (Citation16, Citation18, Citation20, Citation22, Citation56). Moreover, the association between antioxidant nutrients and muscle measures has been shown to be dependent on sex (Citation20). It is possible that the antioxidant defenses in our study population with multimorbidity are more compromised, and there may be a threshold associated with the protective effect of antioxidant nutrients (Citation57).
Another explanation for the inconsistent findings between the present and previous studies may be the use of different dietary assessment methods to assess dietary intake. Previous studies mostly used food frequency questionnaires (FFQs) (Citation15, Citation18, Citation20, Citation22, Citation49–51, Citation56), while only a few studies used 3-day food diaries (Citation21) and 24-hour dietary recalls (Citation14, Citation16). Different FFQs have been developed and validated for different study purposes and may be tailored to specific nutrients of interest (Citation58). However, similar to 24-hour dietary recalls, FFQs are prone to recall bias (Citation59). The 3-day food diary only captures the dietary intake of the individuals over a short time, and individuals may intentionally alter their dietary behaviors to avoid burden on responses (Citation60). Nonetheless, trained researchers in our study have instructed outpatients to record their actual food and beverage consumption to be as detailed as possible and not to alter their dietary behaviors.
It should be noted that nutrients are not consumed in isolation and interaction of nutrients from other foods may affect the nutrient bioavailability (Citation61). The study of specific nutrient intake could be a first step to guide future studies when using food groups or dietary patterns and examining their associations with muscle measures. Furthermore, our study showed that nutrient intake appears to be more related to muscle mass compared to muscle strength and muscle power in geriatric outpatients. This indicates that other factors, such as physical activity, may be more important for maintaining muscle function, and diet alone could not slow down the rate of age-related decline in muscle strength and muscle power (Citation22).
Strengths and limitations
The strength of our study is the measurements of different muscle measures in a clinically relevant population. The cross-sectional design hinders the determination of causality of the associations. A relatively small number of outpatients may have limited our study. HGS was used as a measure of muscle strength; however, HGS should not be used alone to represent overall muscle strength (Citation62). The association of nutrient intake with lower body strength may be different. Last, although instruments to measure muscle power such as computer-interfaced pneumatic resistance machine and linear position transducer are most often used (Citation63), these instruments are complex and expensive and are not practical in clinical settings because of time constraints and a lack of standardized protocols (Citation64). Therefore, CST, which also requires balance and coordination, was used as an indirect measure of muscle power (Citation65).
Conclusion
In a cohort of geriatric outpatients, only a few nutrients were associated with muscle measures. Nutrient intake appears to be more related to muscle mass than muscle strength and muscle power. However, none of the nutrient intakes remained statistically significant after Bonferroni correction. Future studies with larger sample sizes and longitudinal study designs should investigate the associations between food groups and dietary patterns with muscle measures.
Supplemental Material
Download MS Word (481 KB)Acknowledgements
We would like to thank the clinical team of the Falls and Balance outpatient clinic at the Royal Melbourne Hospital for their contribution in the study: Katrina Hopkins, Laura Iacobaccio, Aileen Kalogeropoulos, Dayalini Kumarasamy, Anne McGann, Eric Seal, and Cathy Wilson.
Disclosure statement
On behalf of all authors, the corresponding author states that there is no conflict of interest.
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References
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