“…Additionally, inadequate intake of nutrients was inversely associated with ALM/Weight. A recently published cross-sectional study reported that lower intake of total energy and protein was risk factors of low ALM/ BMI in older men; however, this study did not examine micronutrient intake (35) . Interestingly, the FNIH cut-off for ALM/BMI appears to identify a higher number of older men with low muscle mass compared with the other two criteria, resulting in lower statistical power to identify a significant association.…”
Objective:
To assess the associations between nutrient intake and dietary patterns with different sarcopenia definitions in older men.
Design:
Cross-sectional study.
Setting:
Sarcopenia was defined using the Foundation for the National Institutes of Health (FNIH), the European Working Group on Sarcopenia in Older People (EWGSOP) and the European Working Group on Sarcopenia in Older People 2 (EWGSOP2). Dietary adequacy of fourteen nutrients was assessed by comparing participants’ intakes with the Nutrient Reference Values (NRV). Attainment of NRV for nutrients was incorporated into a variable ‘poor’ (meeting ≤ 9) v. ‘good’ (meeting ≥ 10) using the cut-point method. Also, two different dietary patterns, monounsaturated:saturated fat and n-6:n-3 fatty acids ratio and individual nutrients were used as predictor variables.
Participants:
A total of 794 men aged ≥75 years participated in this study.
Results:
The prevalence of sarcopenia by the FNIH, EWGSOP and EWGSOP2 definitions was 12·9 %, 12·9 % and 19·6 %, respectively. With the adjustment, poor nutrient intake was significantly associated with FNIH-defined sarcopenia (OR: 2·07 (95 % CI 1·16, 3·67)), but not with EWGSOP and EWGSPOP2 definitions. The lowest and second-lowest quartiles of protein, Mg and Ca and the lowest quartiles of n-6 PUFA and n-3 PUFA intakes were significantly associated with FNIH-defined sarcopenia. Each unit decrease in n-6:n-3 ratio was significantly associated with a 9 % increased risk of FNIH-defined sarcopenia (OR: 1·09 (95 % CI 1·04, 1·16)).
Conclusions:
Inadequate intakes of nutrients are associated with FNIH-defined sarcopenia in older men, but not with the other two sarcopenia definitions. Further studies are required to understand these relationships.
“…Additionally, inadequate intake of nutrients was inversely associated with ALM/Weight. A recently published cross-sectional study reported that lower intake of total energy and protein was risk factors of low ALM/ BMI in older men; however, this study did not examine micronutrient intake (35) . Interestingly, the FNIH cut-off for ALM/BMI appears to identify a higher number of older men with low muscle mass compared with the other two criteria, resulting in lower statistical power to identify a significant association.…”
Objective:
To assess the associations between nutrient intake and dietary patterns with different sarcopenia definitions in older men.
Design:
Cross-sectional study.
Setting:
Sarcopenia was defined using the Foundation for the National Institutes of Health (FNIH), the European Working Group on Sarcopenia in Older People (EWGSOP) and the European Working Group on Sarcopenia in Older People 2 (EWGSOP2). Dietary adequacy of fourteen nutrients was assessed by comparing participants’ intakes with the Nutrient Reference Values (NRV). Attainment of NRV for nutrients was incorporated into a variable ‘poor’ (meeting ≤ 9) v. ‘good’ (meeting ≥ 10) using the cut-point method. Also, two different dietary patterns, monounsaturated:saturated fat and n-6:n-3 fatty acids ratio and individual nutrients were used as predictor variables.
Participants:
A total of 794 men aged ≥75 years participated in this study.
Results:
The prevalence of sarcopenia by the FNIH, EWGSOP and EWGSOP2 definitions was 12·9 %, 12·9 % and 19·6 %, respectively. With the adjustment, poor nutrient intake was significantly associated with FNIH-defined sarcopenia (OR: 2·07 (95 % CI 1·16, 3·67)), but not with EWGSOP and EWGSPOP2 definitions. The lowest and second-lowest quartiles of protein, Mg and Ca and the lowest quartiles of n-6 PUFA and n-3 PUFA intakes were significantly associated with FNIH-defined sarcopenia. Each unit decrease in n-6:n-3 ratio was significantly associated with a 9 % increased risk of FNIH-defined sarcopenia (OR: 1·09 (95 % CI 1·04, 1·16)).
Conclusions:
Inadequate intakes of nutrients are associated with FNIH-defined sarcopenia in older men, but not with the other two sarcopenia definitions. Further studies are required to understand these relationships.
“…31 Additionally, the dataset did not specify ethnicity or nutritional factors, including protein, energy intake and vitamin D, which may also influence sarcopenia and muscle function. [32][33][34] These limitations may result in an underestimation of the prevalence of probable sarcopenia. Similar to previous studies, probable sarcopenia was defined by hand grip strength; however, future work could include other measures of muscle strength such as chair rise test and application of the Sarc-F screening tool.…”
Introduction
Maintaining skeletal muscle function throughout life is a crucial component of successful aging. Disadvantaged socioeconomic position (SEP) is associated with adverse health outcomes, but has not been extensively studied for the muscle disease sarcopenia. We aimed to determine the prevalence of probable sarcopenia, a precursor to sarcopenia diagnosis, based on the European Working Group on Sarcopenia in Older People 2 (EWGSOP2) guidelines overall, and according to SEP.
Methods
This cross-sectional study comprised 3342 community-dwelling older adults [mean age (SD) 68.9 ± 6.3 years] from Wave 1 of the Irish Longitudinal Study on Ageing. Probable sarcopenia was identified using gender-specific cut-off values for handgrip strength as recommended by EWGSOP2. SEP was defined by educational attainment. Multivariate regression analysis was employed to determine associations between probable sarcopenia and pre-defined risk factors.
Results
Overall, 23.4% of the population had probable sarcopenia and was significantly higher in the subset with low compared with high SEP (28.9% vs 18.1%, p<0.001). Consistent with this, multivariate logistic regression analysis showed that disadvantaged SEP was a significant determinant of probable sarcopenia [OR, CI 1.48 (1.17, 1.87) p<0.001]. Other known risk factors, namely, increased age, low physical activity, comorbidity, and osteoarthritis were significantly associated with an increased likelihood of probable sarcopenia, while overweight/obesity appeared to be protective.
Conclusion
Disadvantaged SEP was an independent determinant of probable sarcopenia in community-dwelling older adults. These findings highlight that SEP and health inequality should be considered in prevention and treatment policy for sarcopenia in the community.
“…In addition, smoking and drinking were associated with low ASMI. Previous research reported that lifestyle-related risks for low muscle mass include physical inactivity 20 , smoking 21 , and drinking 14 . Low physical activity leads to muscle decline due to disuse atrophy 57 , 58 .…”
Section: Discussionmentioning
confidence: 99%
“…In older adults, anorexia of aging is associated with low muscle mass and strength 19 . Lifestyle-related risks such as physical inactivity 20 , smoking 21 , and drinking 14 are also associated with poor muscle health. Figure 1 Factors contributing to poor muscle health and its impact in older adults.…”
The population is rapidly aging worldwide, and there is an age-related decline in muscle mass. Therefore, it is important to examine the prevalence and associated factors of low appendicular skeletal muscle mass index (ASMI) in older adults. The objectives of this cross-sectional study were (i) to determine the prevalence of low ASMI (ASM/height2) and (ii) to identify factors associated with low ASMI. This study included 1211 community-dwelling adults aged ≥ 65 years. Low ASMI was defined as < 7.0 kg/m2 in males and < 5.7 kg/m2 in females (bioelectrical impedance analysis). Gender-specific cut-off values of calf circumference for low ASMI were determined. The prevalence of low ASMI in the overall cohort was 59.9%, i.e., 57.0% among males and 61.8% among females, with no significant difference between genders (P = 0.1068). The prevalence of low ASMI was 81.3% in individuals at risk of malnutrition compared to 20.6% in their counterparts with normal nutritional status (P < 0.0001). Participants with low ASMI were older, had lower physical activity scores, and greater likelihood of hospitalization in prior 6 months compared with normal ASMI (all P < 0.0001). Low ASMI was associated with risk of malnutrition (odds ratio: 3.58 for medium risk, odds ratio: 12.50 for high risk), older age, smoking, drinking, smaller calf circumference, and lower bone mass (all P ≤ 0.0328). Cut-off values of calf circumference for low ASMI for males was 33.4 cm and for females was 32.2 cm. In conclusion, we found that low ASMI was highly prevalent among community-dwelling older adults at risk of malnutrition. Other significant factors associated with low ASMI were age, smoking, drinking, calf circumference, and bone mass. Screening community-dwelling older adults for risk of malnutrition can prevent or delay onset of low ASMI.
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