Skeletal muscle mass obtained by anthropometric equation and presence of sarcopenia in postmenopausal women.

Objective: To analyze the amount of muscle and the presence of sarcopenia in postmenopausal women using different methods, verifying the agreement between them as to skeletal muscle mass (SMM). Methods: This cross-sectional observational study was conducted with postmenopausal women aged ≥ 50 years. SMM was obtained from a predictive equation, Bioelectrical Impedance (BIA), and Dual Energy X-Ray Absorptiometry (DXA). The skeletal muscle mass index (SMI) and the appendicular skeletal muscle mass index (ASMI) were calculated. The cut-off point of SMI was determined for the population itself. The agreement between the SMI obtained using the different methods was verified. Sarcopenia was diagnosed according to the criteria proposed by the European Working Group on Sarcopenia in Older People 2 (EWGSOP2). The significance level adopted for all tests was 5.0%. Results: A total of 112 women were evaluated, with an average age of 66.1 ± 5.65 years. Among them, 51.8% were sufficiently active and 43.8% were overweight and obese. The SMI cut-offs were 6.46 kg/m2 for the predictive equation and 7.66 kg/m2 for BIA, with high sensitivity and specificity. There was an excellent agreement in the identification of SMM by the predictive equation (0.89 [0.824-0.917], p < 0.001) and BIA (0.92 [0.883-0.945], p < 0.001), in reference to DXA. The prevalence of sarcopenia was 0.9%, 1.8%, and 2.7% according to BIA, DXA, and the predictive equation, respectively. Conclusion: The predictive equation showed the expected agreement in estimating skeletal muscle mass in postmenopausal women, offering a viable and accurate alternative.

Skeletal muscle mass obtained by anthropometric equation and presence of sarcopenia in postmenopausal women

Abstract Objective: To analyze the amount of muscle and the presence of sarcopenia in postmenopausal women using different methods, verifying the agreement between them as to skeletal muscle mass (SMM). Methods: This cross-sectional observational study was conducted with postmenopausal women aged ≥ 50 years. SMM was obtained from a predictive equation, Bioelectrical Impedance (BIA), and Dual Energy X-Ray Absorptiometry (DXA). The skeletal muscle mass index (SMI) and the appendicular skeletal muscle mass index (ASMI) were calculated. The cut-off point of SMI was determined for the population itself. The agreement between the SMI obtained using the different methods was verified. Sarcopenia was diagnosed according to the criteria proposed by the European Working Group on Sarcopenia in Older People 2 (EWGSOP2). The significance level adopted for all tests was 5.0%. Results: A total of 112 women were evaluated, with an average age of 66.1 ± 5.65 years. Among them, 51.8% were sufficiently active and 43.8% were overweight and obese. The SMI cut-offs were 6.46 kg/m2 for the predictive equation and 7.66 kg/m2 for BIA, with high sensitivity and specificity. There was an excellent agreement in the identification of SMM by the predictive equation (0.89 [0.824-0.917], p < 0.001) and BIA (0.92 [0.883-0.945], p < 0.001), in reference to DXA. The prevalence of sarcopenia was 0.9%, 1.8%, and 2.7% according to BIA, DXA, and the predictive equation, respectively. Conclusion: The predictive equation showed the expected agreement in estimating skeletal muscle mass in postmenopausal women, offering a viable and accurate alternative.

Nutrient Patterns and Risk of Osteopenia in Postmenopausal Women.

Nutrient patterns (NPs) and the synergistic effect between nutrients have been shown to be associated with changes in bone mineral density (BMD). This study aimed to identify NPs and to associate them with BMD categories in postmenopausal women. This cross-sectional, observational, analytical study was carried out with women in menopause for at least 12 months, aged ≥50 years. Sociodemographic, lifestyle, and clinical variables were investigated. BMD was assessed using dual energy X-ray absorptiometry. A dietary assessment was conducted using a food frequency questionnaire, and three nutrient patterns (NP1, NP2, and NP3) were extracted from the principal component analysis. Multivariate logistic regression was applied to investigate the association between BMD classifications and NP consumption. A total of 124 women, aged on average, 66.8 ± 6.1 years, were evaluated. Of these, 41.9% had osteopenia and 36.3% had osteoporosis. The NP1 (OR: 6.64, [CI95%: 1.56-28.16]; p = 0.010), characterized by vitamin B12, pantothenic acid, phosphorus, riboflavin, protein (total and animal), vitamin B6, potassium, vitamin D, vitamin E, calcium, cholesterol, ß-carotene, omega 3, magnesium, zinc, niacin, and selenium; and the NP2 (OR: 5.03, [CI95%: 1.25-20.32]; p = 0.023), characterized by iron, vegetable protein, thiamine, folate, fibers (soluble and insoluble), PUFA, vitamin A, vitamin K, alpha-tocopherol, copper, sodium, and retinol, was inversely associated with osteopenia. The lower consumption of NP1 and NP2 by postmenopausal women was associated with a higher risk of osteopenia, but not osteoporosis.

Dietary consumption of selenium inversely associated with osteoporosis in postmenopausal women.

Background: Osteoporosis is a skeletal disease characterized by reduced bone mineral density (BMD), which increases the risk of falls and fractures and reduces mobility. Some nutrients have a well-established role in maintaining bone health and preventing osteoporosis, while selenium (Se) has aroused interest in bone health possibly because of its anti-inflammatory and antioxidant capacity. The aim of this study was to evaluate the association between dietary Se consumption and BMD in postmenopausal women. Materials and methods: Cross-sectional, observational, analytical study carried out with women in menopause for at least 12 months, aged ≥ 50 years. Sociodemographic, lifestyle, and clinical data variables were studied. BMD was assessed using Dual Energy X-ray Absorptiometry (DXA) and the participants classified as having normal BMD, osteopenia, or osteoporosis. Dietary consumption of Se was assessed by the food frequency questionnaire (FFQ) and classified into quartiles of consumption. Multivariate logistic regression with three fit models was applied to investigate the association of BMD with Se consumption quartiles. The significance level adopted for all tests was 5.0%. Results: The final sample consisted of 124 women aged in average 66.8 ± 6.1 years and with a time since menopause of 19.6 ± 8.8 years. According to the BMD, 41.9% of the women had osteopenia and 36.3% osteoporosis. The mean consumption of Se was 154.4 ± 88.7 µg/day. The highest consumption of Se was observed among women with normal BMD (51.9%), whereas lower consumption levels were found in 57.7% of women with osteopenia and in 60.0% of women with osteoporosis (p = 0.003). In the multivariate analysis, after adjusting for possible confounding variables, Se remained associated with the group of women with osteoporosis. Postmenopausal women in the highest quartile (≥94.0 µg/day) of Se consumption had an OR of 0.02 (95%CI: 0.001-0.41; p = 0.012) of having osteoporosis when compared with women in the lowest quartile. Conclusion: Se consumption was associated with BMD and postmenopausal women with higher Se consumption were less likely to have osteoporosis.

Low appendicular skeletal muscle mass index is associated with the anthropometric variables of post-menopausal women.

BACKGROUND: Skeletal muscle mass is a central component of body composition and its decline is enhanced during aging. We verified the association between the appendicular skeletal muscle mass index (ASMI) with the anthropometric variables, biochemical variables, and lifestyle of postmenopausal women. METHODS: Cross-sectional observational study conducted with postmenopausal women. Sociodemographic, clinical, lifestyle, physical activity level, biochemical, and anthropometric markers were collected. Body composition was assessed by dual-energy densitometry. Multivariate logistic regression analysis was applied. RESULTS: One hundred fourteen women aged in average 66.0 ± 5.8 years were evaluated. There was a significant association between ASMI and age (p = 0.004), body mass (p < 0.001), body mass index (BMI) (p < 0.001), adductor pollicis muscle thickness (APMT) (p < 0.001), plasma calcium levels (p = 0.003), calf circumference (CC), and waist circumference (WC) (p < 0.001 for both). Adjusted regression analyses revealed the influence of BMI, CC, and APMT in the 1st tertile of ASMI (p < 0.05), BMI and CC in the 2rd tertile of ASMI. CONCLUSIONS: ASMI was associated with BMI and muscle mass reserve indicators such as CC and DAPMT. In clinical practice, this indicates that simple, low-cost measures with good applicability can be used to predict and track the risk of depletion of skeletal muscle mass and consequent sarcopenia.

Influence of the appendicular skeletal muscle mass index on the bone mineral density of postmenopausal women.

BACKGROUND: The appendicular skeletal muscle mass index (ASMI) is an important risk indicator for osteoporosis because of the anatomical proximity and metabolic connection between muscle and bone mass. The present study investigated the relationship between ASMI and the bone mineral density (BMD) categories of postmenopausal women. METHODS: In this cross-sectional study with a probabilistic sample, sociodemographic, lifestyle, menopause time, anthropometric, and physical activity variables were collected. ASMI and BMD were assessed by dual-energy X-ray absorptiometry (DXA). Participants were grouped according to BMD values into normal density, osteopenia, and osteoporosis. Multivariate logistic regression models were applied to verify the influence of ASMI on BMD. Data were analyzed using the SPSS statistical software, version 22. The significance level for all tests was set at 5%. RESULTS: Of the 114 women analyzed, most were between 60 and 69.9 years of age (62.3%), on menopause for ≤19.0 (51.8%), self-declared brown race/color (49.1%), had < 4 years of education (41.2%), never smoked (69.0%) or drank alcohol (62.8%). Of these, 52.6% were classified as sufficiently active and 52.2% had regular sun exposure. Women with osteoporosis were older (p = 0.035), on menopause for a longer time (p = 0.011), underweight (p = 0.004), had adequate waist circumference (p = 0.017), and low ASMI values (p = 0.002). There was an association between the 1st tertile of ASMI and osteoporosis. However, after adjustments for age, race/color, and body mass index, the strength of association between BMD and ASMI was not maintained. CONCLUSIONS: ASMI was not associated with the BMD of the postmenopausal women evaluated. Total body and muscle mass, in addition to bone mass, should be monitored during menopause treatment. Longitudinal studies must be conducted to elucidate the mechanisms and gaps in this relationship.