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BACKGROUND: Sarcopenia is known as a decline in skeletal muscle quality and function that is associated with age. Sarcopenia is linked to diverse health problems, including endocrine-related diseases. Environmental chemicals (ECs), a broad class of chemicals released from industry, may influence muscle quality decline. OBJECTIVES: In this work, we aimed to simultaneously elucidate the associations between muscle quality decline and diverse EC exposures based on the data from the 2011-2012 and 2013-2014 survey cycles in the National Health and Nutrition Examination Survey (NHANES) project using machine learning models. METHODS: Six machine learning models were trained based on the EC and non-EC exposures from NHANES to distinguish low from normal muscle quality index status. Different machine learning metrics were evaluated for these models. The Shapley additive explanations (SHAP) approach was used to provide explainability for machine learning models. RESULTS: Random forest (RF) performed best on the independent testing data set. Based on the testing data set, ECs can independently predict the binary muscle quality status with good performance by RF (area under the receiver operating characteristic curve = 0.793; area under the precision-recall curve = 0.808). The SHAP ranked the importance of ECs for the RF model. As a result, several metals and chemicals in urine, including 3-phenoxybenzoic acid and cobalt, were more associated with the muscle quality decline. CONCLUSIONS: Altogether, our analyses suggest that ECs can independently predict muscle quality decline with a good performance by RF, and the SHAP-identified ECs can be closely related to muscle quality decline and sarcopenia. Our analyses may provide valuable insights into ECs that may be the important basis of sarcopenia and endocrine-related diseases in United States populations.

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A poorly studied issue in women with breast cancer is the role of incretins (GIP (glucose-dependent insulinotropic polypeptide) and GLP-1 (glucagon-like peptide-1)) in the quantity and quality of muscle mass in lean and obese individuals. The current report aims to analyze the patterns of association and the role of incretin in muscle functionality and body composition in women with cancer compared with healthy women (mammography BI-RADS I or II) to elucidate whether GIP and GLP-1 can be used to estimate the risk, in conjunction with overweight or obesity, for breast cancer. We designed a case-control study in women with a breast cancer diagnosis confirmed by biopsy in different clinical stages (CS; n = 87) and healthy women with a mastography BI-RADS I or II within the last year (n = 69). The women were grouped according to body mass index (BMI): lean (<25 kg/m2BS), overweight (≥25-<30 kg/m2BS), and obese (≥30 kg/m2BS). We found that GLP-1 and GIP levels over 18 pg/mL were associated with a risk of breast cancer (GIP OR = 36.5 and GLP-1 OR = 4.16, for the entire sample), particularly in obese women (GIP OR = 8.8 and GLP-1 OR = 6.5), and coincidentally with low muscle quality indexes, showed an association between obesity, cancer, incretin defects, and loss of muscle functionality.

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BACKGROUND AND OBJECTIVE: Cardiovascular disease (CVD) is the leading cause of morbidity and mortality in the United States. However, current evidence on the association between muscle quality and CVD is limited. This study investigates the potential association between the muscle quality index (MQI) and the prevalence of CVD and CVD-related mortality. METHODS: Participants were selected from the National Health and Nutrition Examination Survey (NHANES) 2011-2014. Data on mortality and causes of death were obtained from the National Death Index (NDI) records through December 31, 2019. Statistical analysis used in this study, including weighted multivariable linear and logistic regression, cox regression and Kaplan-Meier (K-M) analysis, to estimate the association between MQI and all-cause mortality as well as CVD mortality. In addition, subgroup analysis was used to estimate the association between MQI and CVD subtypes, such as heart attack, coronary heart disease, angina, congestive heart failure, and stroke. RESULTS: A total of 5,053 participants were included in the final analysis. Weighted multivariable linear regression models revealed that a lower MQI.total level was independently associated with an increased risk of CVD development in model 3, with t value =-3.48, 95%CI: (-0.24, -0.06), P = 0.002. During 5,053 person-years of 6.92 years of follow-up, there were 29 deaths from CVD. Still, the association between MQI.total and CVD mortality, as well as all-cause mortality did not reach statistical significance in the fully adjusted model (HR = 0.58, 95% CI: 0.21-1.62, P = 0.30; HR = 0.91, 95% CI:0.65,1.28, P = 0.59, respectively). Subgroup analysis confirmed that MQI.total was negatively associated with congestive heart failure (OR = 0.35, 95% CI = 0.18,0.68, P = 0.01). CONCLUSION: This study highlights the potential of MQI as a measure of muscle quality, its negative correlation with congestive heart failure (CHF). However, MQI was not very useful for predicting the health outcomes such as CVD and mortality. Therefore, more attention should be paid to the early recognition of muscle weakness progression in CHF. Further studies are needed to explore more effective indicator to evaluate the association between muscle quality and health outcomes.

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BACKGROUND: The SARS-CoV2 pandemic impacted many critically ill patients, causing sequelae, affecting lung function, and involving the musculoskeletal system. We evaluated the association between lung function and muscle quality index in severely ill post-COVID-19 patients. METHODS: A cross-sectional study was conducted on a post-COVID-19 cohort at a third-level center. The study included patients who had experienced severe-to-critical COVID-19. Anthropometric measurements, such as body mass index (BMI) and handgrip strength, were obtained to calculate the muscle quality index (MQI). Additionally, spirometry, measurements of expiratory and inspiratory pressure, and an assessment of DLCO in the lungs were performed. The MQI was categorized into two groups: low-MQI (below the 50th percentile) and high-MQI (above the 50th percentile), based on sex. Group differences were analyzed, and a multivariate linear regression analysis was performed to assess the association between respiratory function and MQI. RESULTS: Among the 748 patients analyzed, 61.96% required mechanical ventilation, and the median hospital stay was 17 days. In patients with a low MQI, it was observed that both mechanical respiratory function and DLCO were lower. The multivariate analysis revealed significantly lower findings in mechanical respiratory function among patients with a low MQI. CONCLUSION: The Low-MQI is an independent predictor associated with pulmonary function parameters in subjects with Post-COVID-19 syndrome.

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The aim of this study was to compare the effects of sit-to-stand (STS) training programs with 5 vs. 10 repetitions on muscle architecture and muscle function in sedentary adults. Sixty participants were randomly assigned into three groups: five-repetition STS (5STS), 10-repetition STS (10STS), or a control group (CG). Participants performed three sets of five or 10 repetitions of the STS exercise three times per week for 8 weeks. Before and after 8 weeks, all groups performed ultrasound measures to evaluate muscle thickness (MT), pennation angle (PA), and fascicle length (FL), and the five-repetition STS test to estimate the relative STS power and muscle quality index (MQI). After 8 weeks, both experimental groups improved MQI (40-45%), relative STS power (29-38%), and MT (8-9%) (all p < 0.001; no differences between the 5STS vs. 10STS groups). These improvements in both groups resulted in differences regarding the CG, which did not present any change. In addition, only the 5STS group improved PA (15%; p = 0.008) without differences to the 10STS and CG.This suggests that STS training is time-effective and low-cost for improving muscle function and generating adaptations in muscle architecture.

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BACKGROUND: Muscle quality index (MQI) is an emerging health indicator obtained by dividing handgrip strength by body mass index (BMI) that needs to be studied in morbidly obese patients (defined by BMI ≥ 35 kg/m2). OBJECTIVE: To determine the association between MQI, metabolic syndrome (MetS) markers, and cardiorespiratory fitness (CRF), and as a second objective to determine the potential mediation role of MQI in the relationship between abdominal obesity and systolic blood pressure (SBP) in this sample. METHODS: This cross-sectional study included 86 severely/morbidly obese patients (age = 41.1 ± 11.9 y, nine men). MQI, metabolic syndrome markers, CRF, and anthropometric parameters were measured. Two groups were developed according to MQI; High-MQI (n = 41) and Low-MQI (n = 45). RESULTS: The Low-MQI group reported higher abdominal obesity (High-MQI: 0.7 ± 0.1 vs. Low-MQI: 0.8 ± 0.1 WC/height; p = 0.011), SBP (High-MQI: 133.0 ± 17.5 vs. Low-MQI: 140.1 ± 15.1 mmHg; p = 0.048), and lower CRF (High-MQI; 26.3 ± 5.9 vs. Low-MQI; 22.4 ± 6.1 mL/kg/min, p = 0.003) than the High-MQI group. Waist-to-height ratio (ß: -0.07, p = 0.011), SBP (ß: -18.47, p = 0.001), and CRF (ß: 5.21, p = 0.011) were linked to MQI. In a mediation model, the indirect effect confirms that MQI is a partial mediator of the association between abdominal obesity with SBP. CONCLUSIONS: MQI in morbidly obesity patients reported an inverse association with MetS markers and a positive association with CRF (VO2max). It mediates the relationship between abdominal obesity and SBP.

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Background: The muscle quality index (MQI), as an important component of sarcopenia, is defined as the ratio of muscle strength to muscle mass. Lung function, is a clinical indicator to assess ventilation and air exchange function. This study investigated the relationship between lung function indices and MQI in the NHANES database from 2011 to 2012. Methods: This study included 1,558 adults from the National Health and Nutrition Examination Survey from 2011 to 2012. Muscle mass and muscle strength were assessed using DXA and handgrip strength, and all participants underwent pulmonary function measurements. Multiple linear regression and multivariable logistic regression were used to assess the correlation between the MQI and lung function indices. Results: In the adjusted model, MQI was significantly correlated with FVC% and PEF%. And, after quartiles of MQI in Q3, where FEV1%, FVC%, and PEF% were all associated with MQI, in Q4, a lower relative risk of a restrictive spirometry pattern was linked to increased MQI. Compared to the lower age group, the relationship between the MQI and lung function indices was more significant in the higher age group. Conclusion: There was an association between the MQI and lung function indices. Furthermore, in the middle-aged and older adult populations, lung function indicators and restrictive ventilation impairment were significantly associated with MQI. This implies that improving lung function through muscle training may be beneficial to this group.

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BACKGROUND: Trouble sleeping is one of the major health issues nowadays. Current evidence on the correlation between muscle quality and trouble sleeping is limited. METHODS: A cross-sectional study design was applied and participants aged from 18 to 60 years in the National Health and Nutrition Examination Survey (NHANES) 2011-2014 was used for analysis. Muscle quality index (MQI) was quantitatively calculated as handgrip strength (HGS, kg) sum/ arm and appendicular skeletal muscle mass (ASM, kg) by using the sum of the non-dominant hand and dominant hand. Sleeping data was obtained by interviews and self-reported by individuals. The main analyses utilized weighted multivariable logistic regression models according to the complex multi-stage sampling design of NHANES. Restricted cubic spline model was applied to explore the non-linear relationship between MQI and trouble sleeping. Moreover, subgroup analyses concerning sociodemographic and lifestyle factors were conducted in this study. RESULTS: 5143 participants were finally included in. In the fully adjusted model, an increased level of MQI was significantly associated with a lower odds ratio of trouble sleeping, with OR = 0.765, 95% CI: (0.652,0.896), p = 0.011. Restricted cubic spline showed a non-linear association between MQI and trouble sleeping. However, it seemed that the prevalence of trouble sleeping decreased with increasing MQI until it reached 2.362, after which the odds ratio of trouble sleeping reached a plateau. Subgroup analyses further confirmed that the negative association between the MQI and trouble sleeping was consistent and robust across groups. CONCLUSION: Overall, this study revealed that MQI can be used as a reliable predictor in odds ratio of trouble sleeping. Maintaining a certain level of muscle mass would be beneficial to sleep health. However, this was a cross-sectional study, and causal inference between MQI and trouble sleeping was worthy of further exploration.

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Screen time (ST) and abdominal obesity have a negative effect on health-related quality of life (HRQoL). However, there is little information regarding the mediating role of the muscle quality index (MQI) in these relationships. The aim of the present study was to investigate the association between HRQoL, physical status (i.e., anthropometrics and fitness), lifestyle (i.e., ST and physical activity), and the MQI, and then to determine the potential mediating role of the MQI in the relation of ST and abdominal obesity with HRQoL in Chilean schoolchildren. The cross-sectional study included 750 schoolchildren (girls, n = 332 and boys, n = 418) aged between 10 and 14 years (11.73 ± 1.08 y). MQI, lifestyle, fitness parameters, waist-to-height ratio (WtHR) and HRQoL were measured. HRQoL presented a significant correlation with WtHR (r: -0.19), VO2max (r: 0.20), physical activity after school (r: 0.26), ST (r: -0.26) and MQI (r: 0.15). According to MQI, the high-MQI group reported higher HRQoL than the low-MQI group (low MQI: 36.10 ± 3.63 vs. high MQI: 37.43 ± 4.00, p < 0.001). In the mediation model, ST and abdominal obesity were negatively linked to HRQoL; the indirect effect confirmed that MQI is a partial mediator in the relation between ST and HRQoL (indirect effect = -0.04; SE = 0.02; 95% CI: -0.09, -0.01) and in the relation between abdominal obesity and HRQoL (indirect effect = -1.81; SE = 0.83; 95% CI: -3.41, -0.40). In conclusion, MQI is related to better HRQoL in schoolchildren, and the negative relation of ST and abdominal obesity with HRQoL is mediated by MQI.

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BACKGROUND & AIMS: Muscle quality index (MQI) is used to measure the quality of the muscles. It is defined as the ratio of muscle strength per unit of muscle mass, but since this might vary by rage and ethnicity, we aimed to develop sex, and population-specific normative data and cutoff values for MQI (extremally low and low) using the arm or appendicular skeletal muscle mass (ASM) obtained from a population-representative sample. METHODS: This cross-sectional analysis included data from 4849 volunteers (aged 20-59 years) from the National Health and Nutrition Examination Survey (NHANES) 2011-2014. Measures of handgrip strength (HGS) were performed using a hand dynamometer. ASM was assessed by dual-energy X-ray absorptiometry (DXA). Arm ASM mass was used to calculate MQIArm [dominant HGS/dominant arm ASM (kg/kg)]; ASM was used to calculate MQIApp [dominant HGS/ASM (kg/kg)]; and the sum of the non-dominant hand and dominant hand were used to calculate MQItotal [HGS sum/ASM (kg/kg)]. Cutoff values were derived from a young reference subgroup (n = 1625 aged, 20-39 years), with low and extremely low MQI defined as 1 and 2 sex-specific standard deviations below the mean, respectively. RESULTS: MQIArm, MQIApp, and MQITotal differed by sex and population studied. Overall, using the proposed cutoffs, men showed lower values of MQIArm than women, and higher MQIApp, and MQITotal values. Compared to non-Hispanic Whites, non-Hispanic Asians had higher values of MQI while non-Hispanic Black people had lower values. CONCLUSION: MQIs cutoffs were established for both sexes and different populations studied. MQIArm, MQIApp, and MQITotal values were lower after the fifth-decade in men, but not in women.

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INTRODUCTION: Chronic kidney disease (CKD) is associated with a reduction in bone mineral density (BMD), but less is understood regarding the relation between BMD and muscle mass, especially in non-dialysis dependent-CKD (NDD-CKD). The aim of this study was to explore the prevalence and association of low BMD (osteopenia and osteoporosis) with markers of muscle mass and function in patients with NDD-CKD. METHODOLOGY: This cross-sectional observational study included patients with NDD-CKD. Routine biochemical parameters including those related to mineral and bone metabolism were evaluated. Body composition was assessed by dual energy x-ray absorptiometry (DXA) for BMD (g/cm2), total and trunk body fat (%), total lean soft tissue (LST; kg), and appendicular skeletal muscle mass (ASM; kg) as the sum of the LST from the limbs. The latter two variables were used as markers of muscle mass, together with its height indexed values: ASM/height2 as ASM index (ASMI; kg/m2), and LST/height2 as LST index (LSTI, kg/m2). Muscle quality index (MQI) was calculated as handgrip strength (HGS)/mean ASMarms (kg/kg). Osteosarcopenia was defined according to referenced cut-points for patients presenting with low ASMI, HGS and BMD. RESULTS: Patients (n = 257, 57.6% males) had a mean age = 64.8 ± 12.9 years, estimated glomerular filtration rate (eGFR) = 30.1 ± 12.9 ml/min and body mass index (BMI) = 26.8 ± 4.8 kg/m2. Patients with low BMD (39.4%) presented with lower BMI, LST, LSTI, ASM and ASMI for both sexes. BMD was positively and significantly correlated with LST, LSTI, ASM, ASMI and HGS. Low ASM was associated with low BMD (odds-ratio-OR; 95% confidence interval-CI: males OR = 4.54, 2.02-10.21; females OR = 4.45, 1.66-11.93). Linear multiple regression analysis (adjusted for sex and eGFR) showed significant associations between T-score with HGS (R2 = 0.288, R2 adjusted = 0.272, standardized coefficient ß = 0.536, p < 0.0001) and also with MQI (R2 = 0.095, R2 adjusted = 0.075, standardized coefficient ß = 0.309, p = 0.024). Osteosarcopenia was present in about 7% of participants and similarly distributed between sexes. CONCLUSION: Low BMD was prevalent, and associated with low markers of muscle mass and quality, in NDD-CKD patients of both sexes. In view of the known significance of these conditions, targeted interventions are needed to optimize body composition and functional status of these patients.

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