Development and validation of a simple anthropometric equation to predict appendicular skeletal muscle mass

GLIM criteria-based identification of severe malnutrition and its relationship with the risk of mortality among older Chinese adults with arthritis or rheumatism

BACKGROUND

Malnutrition is a well-known risk factor for mortality among older adults. Arthritis and rheumatism are characterized by chronic inflammation and are also related to malnutrition as diagnosed using the Global Leadership Initiative on Malnutrition (GLIM) criteria. This study was thus developed to examine the associations linking malnutrition and all-cause death among older adults in China, employing the GLIM criteria to assess malnutrition.

METHODS

Two waves of the China Health and Retirement Longitudinal Study from 2013 and 2018 were used to conduct this study. Moderate malnutrition was defined as low BMI (< 18.5 and < 20 for individuals < 70 and 70 + years of age, respectively), an unintended 10-20% decrease in weight, or low muscle mass based on the sex-specific lowest 20% of the height-adjusted muscle mass as < 5.039 kg/m2 in women and < 6.866 kg/m2 in men. Severe malnutrition was defined as a > 20% unintended decrease in weight only or the combination of both low muscle mass and an unintended reduction of over 10% in weight. Associations between malnutrition and the risk of all-cause death were assessed through Cox regression analyses.

RESULTS

Overall, this study enrolled 1766 subjects 60 + years of age, of whom 57.36% (1033/1766) were female. Malnutrition was estimated to affect 418 (23.67%) of these individuals at baseline, with 21.06% and 2.60% affected by moderate and severe malnutrition, respectively. Over the 5-year follow-up, 189 of these individuals died. Covariate-adjusted Cox regression analyses confirmed a significant association between severe malnutrition and the risk of death in this cohort (HR = 2.196, 95%CI 1.125-4.286, P = 0.021).

CONCLUSIONS

Severe malnutrition, identified through screening based on the GLIM criteria, was associated with an increased risk of all-cause death among older Chinese adults with arthritis or rheumatism.

Prevalence of low calf circumference in hospitalized patients classified by raw or body mass index-adjusted values

BACKGROUND

Adiposity can influence the estimation of muscle mass using calf circumference (CC) and underestimate the frequency of low CC. An adjustment for CC using body mass index (BMI) was proposed to reduce this effect. We aimed to compare the low CC frequency in hospitalized patients when considering raw and BMI-adjusted values and explore data by sex, age, and race (white and non-white).

METHODS

Secondary analysis from two cohort studies conducted with adult hospitalized patients using BMI and CC data collected in the first 72 h after hospital admission. We classified low CC by two approaches: (1) raw CC; (2) BMI-adjusted CC for patients with BMI ≥ 25. Cutoff values for low CC were ≤34 cm (men) and ≤33 cm (women).

RESULTS

Among 1272 patients (54.1 ± 15.3 years old; 51.7% women; 82.1% White race), low CC frequency was 30.6% and low BMI-adjusted CC was 68.9%. For all elevated BMI categories, the low CC frequency was higher when considering BMI-adjusted values (P < 0.001). Low CC was more frequent (P < 0.001) in older adults (38.7% by raw; 79.1% by BMI-adjusted value) than in younger adults (27.6% by raw; 65.2% by BMI-adjusted value) and it was not associated with race. Low CC by raw values was more frequent in men than in women (35.0% versus 26.4%; P = 0.001), but did not differ between sexes when classified by BMI-adjusted values (70.7% versus 67.1%; P = 0.184).

CONCLUSION

Low CC BMI adjusted was 2.2 times more frequent in comparison with raw CC values, and it was identified in >60% of patients with BMI ≥ 25.

Association between nutrition intake and muscle mass in adult inpatients receiving nutrition support: A prospective cohort study

OBJECTIVE

The present study aimed to evaluate the association between muscle mass variation, estimated by different equations, during hospitalization with the energy and protein intake and clinical and nutrition outcomes of patients using nutrition support.

METHODS

A prospective observational study with patients older than 18 years in use of enteral and/or parenteral nutrition therapy and monitored by the Nutritional Therapy Committee between December 14, 2021, and December 14, 2022. Data were collected from the electronic records and were applied in 11 equations to estimate the four different portions of muscle mass of patients receiving nutrition support at the beginning and the end of hospitalization.

RESULTS

A total of 261 patients were evaluated, with a median age of 61.0 (49.0-69.75) years, and 106 were women (40.6%). According to the nutrition diagnosis, several participants had severe malnutrition (39.5%). The most muscle mass estimation equations indicated a reduction of muscle mass during hospitalization. All patients presented negative energy and protein balances during hospitalization, but greater protein intake increased the lean soft tissue. Also, the greater the number of infections, metabolic complications, and scheduled diet interruption, the greater was the chance of losing muscle mass.

CONCLUSION

There can be an association between the variation in muscle mass and energy and protein intake during hospitalization of patients using nutrition support. In addition, variation in muscle mass was associated with complications from nutrition support. The results emphasize the importance of anthropometric measurements to estimate muscle mass when other methods are not available.

Development and Validation of Estimation Equations for Appendicular Skeletal Muscle Mass in Chinese Community-Dwelling Older Adults

Purpose

This study aimed to establish equations for estimating muscle mass through anthropometric parameters or together with physical function parameters in the community-dwelling older adults, providing a simple way of muscle mass assessment.

Methods

In this cross-sectional descriptive study, a total of 1537 older adults were recruited from the community and accepted the measurements of height, weight, upper arm and calf circumferences, grip strength, and walking speed. Body composition including appendicular skeletal muscle mass (ASM) was measured using bioelectrical impedance analysis (BIA). Participants were randomly divided into the development or validation group. Stepwise multiple linear regression was applied to develop equations in the development group. Thereafter, Pearson correlation coefficients, Bland-Altman plots, paired t-test, intraclass correlation coefficient (ICC) and paired-samples t-tests were used to assess the validity of the equations.

Results

All parameters were significantly correlated with ASM (r = 0.195~0.795, P < 0.001) except for the age in the validation group (P = 0.746). The most optimal anthropometric equation was: [adjusted R2 = 0.911, standard error of the estimate (SEE) = 1.311, P < 0.001]. Comparatively speaking, this equation showed high correlation coefficient (r = 0.951, P < 0.001) and ICC (ICC = 0.950, P < 0.001). No significant differences were found between BIA-measured ASM and the estimated ASM. The Bland-Altman plot showed that the mean difference between the estimated ASM and BIA-measured ASM was 0 kg and the limits of agreement of ASM was -2.70~2.60 kg. Furthermore, inclusion of physical function did not significantly improve the adjusted R2 and SEE.

Conclusion

The anthropometric equation offers a practical alternative simple and dependable method for estimating ASM in community-dwelling older adults.

Establishment and validation of novel nomograms to predict muscle quality in colorectal cancer patients

OBJECTIVES

The skeletal muscle mass index and skeletal muscle radiodensity have promise as specific diagnostic indicators for muscle quality. However, the difficulties in measuring low skeletal muscle mass index and low skeletal muscle radiodensity limit their use in routine clinical practice, impeding early screening and diagnosis. The objective of this study is to develop a nomogram that incorporates preoperative factors for predicting low skeletal muscle mass index and low skeletal muscle radiodensity.

METHODS

A total of 1692 colorectal cancer patients between 2015 and 2021 were included. The patients were randomly divided into a training cohort (n = 1353) and a validation cohort (n = 339). Nomogram models were calibrated using the area under the curve, calibration curves, and the Hosmer-Lemeshow test to assess their predictive ability. Finally, a decision curve was applied to assess the clinical usefulness.

RESULTS

In a prediction model for low skeletal muscle mass index, age, body mass index, and grip strength were incorporated as variables. For low skeletal muscle radiodensity, age, sex, body mass index, serum hemoglobin level, and grip strength were included as predictors. In the training cohort, the area under the curve value for low skeletal muscle mass index was 0.750 (95% CI, 0.726-0.773), whereas for low skeletal muscle radiodensity, it was 0.763 (95% CI, 0.739-0.785). The Hosmer-Lemeshow test confirmed that both models fit well in both cohorts. Decision curve analysis was applied to assess the clinical usefulness of the model.

CONCLUSIONS

The incorporation of preoperative factors into the nomogram-based prediction model represents a significant advancement in the muscle quality assessment. Its implementation has the potential to early screen patients at risk of low skeletal muscle mass index and low skeletal muscle radiodensity.

Associations between cardiorespiratory fitness and lifestyle-related factors with DNA methylation-based ageing clocks in older men: WASEDA'S Health Study

DNA methylation-based age estimators (DNAm ageing clocks) are currently one of the most promising biomarkers for predicting biological age. However, the relationships between cardiorespiratory fitness (CRF), measured directly by expiratory gas analysis, and DNAm ageing clocks are largely unknown. We investigated the relationships between CRF and the age-adjusted value from the residuals of the regression of DNAm ageing clock to chronological age (DNAmAgeAcceleration: DNAmAgeAccel) and attempted to determine the relative contribution of CRF to DNAmAgeAccel in the presence of other lifestyle factors. DNA samples from 144 Japanese men aged 65-72 years were used to appraise first- (i.e., DNAmHorvath and DNAmHannum) and second- (i.e., DNAmPhenoAge, DNAmGrimAge, and DNAmFitAge) generation DNAm ageing clocks. Various surveys and measurements were conducted, including physical fitness, body composition, blood biochemical parameters, nutrient intake, smoking, alcohol consumption, disease status, sleep status, and chronotype. Both oxygen uptake at ventilatory threshold (VO2 /kg at VT) and peak oxygen uptake (VO2 /kg at Peak) showed a significant negative correlation with GrimAgeAccel, even after adjustments for chronological age and smoking and drinking status. Notably, VO2 /kg at VT and VO2 /kg at Peak above the reference value were also associated with delayed GrimAgeAccel. Multiple regression analysis showed that calf circumference, serum triglyceride, carbohydrate intake, and smoking status, rather than CRF, contributed more to GrimAgeAccel and FitAgeAccel. In conclusion, although the contribution of CRF to GrimAgeAccel and FitAgeAccel is relatively low compared to lifestyle-related factors such as smoking, the results suggest that the maintenance of CRF is associated with delayed biological ageing in older men.

Development and validation of equations for predicting appendicular skeletal muscle mass in male patients with head and neck cancer and normal hydration status

OBJECTIVE

Muscle mass is typically assessed by abdominal computed tomography, magnetic resonance imaging, and dual-energy x-ray absorptiometry. However, these tests are not routinely performed in patients with head and neck cancer (HNC), making sarcopenia assessment difficult. The aim of this study was to develop and validate equations for predicting appendicular skeletal muscle mass (ASM) from data obtained in daily medical practice, with bioelectrical impedance analysis (BIA)-measured appendicular skeletal muscle mass (BIA-ASM) as a reference.

METHODS

This cross-sectional study included 103 men with HNC who were randomly placed into development and validation groups. The prediction equations for BIA-ASM were developed by multiple regression analysis and validated by Bland-Altman analyses. The estimated skeletal muscle mass index (eSMI) was also statistically evaluated to discriminate the cutoff value for BIA-measured SMI according to the Asian Working Groups for Sarcopenia.

RESULTS

Two practical equations, which included 24-h urinary creatinine excretion volume (24hUCrV), handgrip strength (HGS), body weight (BW), and body height (BHt), were developed: ASM (kg) = -39.46 + (3.557 × 24hUCrV [g]) + (0.08872 × HGS [kg]) + (0.1263 × BW [kg]) + (0.2661 × BHt [cm]) if available for 24hUCrV (adjusted R2 = 0.8905), and ASM (kg) = -42.60 + (0.1643 × HGS [kg]) + (0.1589 × BW [kg]) + (0.2807 × BHt [cm]) if not (adjusted R2 = 0.8589). ASM estimated by these two equations showed a significantly strong correlation with BIA-ASM (R > 0.900). Bland-Altman analyses showed a good agreement, and eSMI accuracy was high (>80%) in both equations.

CONCLUSIONS

These two equations are a valid option for estimating ASM and diagnosing sarcopenia in patients with HNC in all facilities without special equipment.

Prediction equations to estimate muscle mass using anthropometric data: a systematic review

CONTEXT

Reduced muscle mass is linked to poor outcomes in both inpatients and outpatients, highlighting the importance of muscle mass assessment in clinical practice. However, laboratory methods to assess muscle mass are not yet feasible for routine use in clinical practice because of limited availability and high costs.

OBJECTIVE

This work aims to review the literature on muscle mass prediction by anthropometric equations in adults or older people.

DATA SOURCES

The following databases were searched for observational studies published until June 2022: MEDLINE, Embase, Scopus, SPORTDiscus, and Web of Science.

DATA EXTRACTION

Of 6437 articles initially identified, 63 met the inclusion criteria for this review. Four independent reviewers, working in pairs, selected and extracted data from those articles.

DATA ANALYSIS

Two studies reported new equations for prediction of skeletal muscle mass: 10 equations for free-fat mass and lean soft tissue, 22 for appendicular lean mass, 7 for upper-body muscle mass, and 7 for lower-body muscle mass. Twenty-one studies validated previously proposed equations. This systematic review shows there are numerous equations in the literature for muscle mass prediction, and most are validated for healthy adults. However, many equations were not always accurate and validated in all groups, especially people with obesity, undernourished people, and older people. Moreover, in some studies, it was unclear if fat-free mass or lean soft tissue had been assessed because of an imprecise description of muscle mass terminology.

CONCLUSION

This systematic review identified several feasible, practical, and low-cost equations for muscle mass prediction, some of which have excellent accuracy in healthy adults, older people, women, and athletes. Malnourished individuals and people with obesity were understudied in the literature, as were older people, for whom there are only equations for appendicular lean mass.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO registration number CRD42021257200.

[Body composition models based on anthropometry, systematic literature review]

Introduction

The body composition determination is carried out through measurement methods that require the use of specialized equipment that is difficult to acquire and manipulate. Therefore, different authors have developed mathematical models for its calculation. The target of this review was to analyze the work carried out on mathematical models of body composition variables from different anthropometric measurements, answering the following questions: what is the corporal variable that the model predicts?, what are the input variables for model development?, how is the patients typification in each model?, what data analysis method has been used?, and how has been the model evaluated? The search was limited to journals found in repositories in the areas of Medicine, Nursing, Biochemistry, Biology, Health, Pharmacology, Immunology, Engineering, and Mathematics. Four hundred and twenty-four articles were found, which were reduced to 30 by applying the systematic literature review process. The analyzed studies are oriented to the prediction of variables related to body fat mass. The evaluation results found for fat-free mass, fat mass and metabolic rate differ according to the comparison technique and the body segments analyzed. The evaluation is mainly based on the intraclass correlation, the Pearson correlation and the coefficient of determination (r2), and they present a good correlation for the population under study.

Sarcopenia prevalence in patients with cancer and association with adverse prognosis: A nationwide survey on common cancers

OBJECTIVE

Although previous studies have implicated the negative outcomes of sarcopenia, evidence is limited to one or a few types of cancer. The aim of this study was to evaluate the distribution and influencing factors of sarcopenia, and explore the relationship between sarcopenia and cancer prognosis in a large oncological population.

METHODS

This observational cohort study included patients diagnosed with malignant cancer between May 2011 and January 2019. Hematologic and anthropometric parameters were collected prospectively. Low skeletal muscle mass and radiodensity were diagnosed using clinical indicators, according to the two prediction models. The importance of potential risk factors for sarcopenia was estimated by subtracting the predicted degrees of freedom from the partial χ2 statistic. Hazard rates of death were calculated using the hazard function and Cox regression analyses.

RESULTS

We included 13 761 patients with cancer; the prevalence of sarcopenia was 33%. The median age was 58 y and 7135 patients (52%) were men. Patients with sarcopenia had a worse nutritional status and quality of life than those without sarcopenia. Age was the most important risk factor for sarcopenia compared with body mass index or TNM stage. Additionally, patients with sarcopenia had a significantly higher and earlier peak risk for mortality. After adjusting for baseline characteristics, sarcopenia was independently associated with mortality in the research population (hazard ratio, 1.429; P < 0.001) and most cancer types.

CONCLUSION

Age is the most important risk factor for sarcopenia even in patients with cancer. Sarcopenia is strongly associated with a poor quality of life and reduced overall survival.

Plant-based dietary pattern and low muscle mass: a nation-wide cohort analysis of Chinese older adults

BACKGROUND

It remains unclear whether plant-based or animal-based dietary patterns are more beneficial for older adults more in maintaining muscle mass. Using a prospective cohort with nationwide sample of China older adults in this study, we aimed to examine the relationship between adhering to plant-based diet patterns or animal-based diet patterns and muscle loss.

METHODS

We included 2771 older adults (≥ 65 years) from the Chinese Longitudinal Health Longevity Survey (CLHLS) with normal muscle mass at baseline (2011 and 2014 waves), which followed up into 2018. Plant-based dietary pattern scores and preference subgroups were constructed using 16 common animal-based and plant-based food frequencies. We used the corrected appendicular skeletal muscle mass (ASM) prediction formula to assess muscle mass. We applied the Cox proportional hazard risk regression to explore associations between dietary patterns and low muscle mass (LMM).

RESULTS

During a mean of 4.1 years follow-up, 234 (8.4%) participants with normal muscle mass at baseline showed LMM. The plant-based dietary pattern reduced the risk of LMM by 5% (Hazard Ratios [HR]: 0.95, 95% confidence intervals [95%CI]: 0.92-0.97). In addition, a high plant-based food company with a high animal-based food intake pattern reduced the risk of LMM by 60% (HR: 0.40, 95% CI: 0.240-0.661) and 73% (HR: 0.27, 95% CI: 0.11-0.61) in the BADL disability and IADL disability population compared with a low plant-based food and high animal-based food intake, whereas a high plant-based food and low animal-based food intake was more beneficial in reducing the risk of LMM in the normal BADL functioning (HR: 0.57, 95% CI: 0.35-0.90) and IADL functioning (HR: 0.51, 95% CI: 0.28-0.91) population.

CONCLUSIONS

When it comes to maintaining muscle mass in older Chinese people with functional independence, a plant-based diet pattern is more beneficial and effective than the animal-based one. People with functional dependence may profit from a combination of plant-based and animal-based diets to minimize muscle loss.

Sex-specific equations to estimate body composition: Derivation and validation of diagnostic prediction models using UK Biobank

BACKGROUND & AIMS

Body mass index and waist circumference are simple measures of obesity. However, they do not distinguish between visceral and subcutaneous fat, or muscle, potentially leading to biased relationships between individual body composition parameters and adverse health outcomes. The purpose of this study was to develop and validate prediction models for volumetric adipose and muscle.

METHODS

Based on cross-sectional data of 18,457, 18,260, and 17,052 White adults from the UK Biobank, we developed sex-specific equations to estimate visceral adipose tissue (VAT), abdominal subcutaneous adipose tissue (ASAT), and total thigh fat-free muscle (FFM) volumes, respectively. Volumetric magnetic resonance imaging served as the reference. We used the least absolute shrinkage and selection operator and the extreme gradient boosting methods separately to fit three sequential models, the inputs of which included demographics and anthropometrics and, in some, bioelectrical impedance analysis parameters. We applied comprehensive metrics to assess model performance in the temporal validation set.

RESULTS

The equations that included more predictors generally performed better. Accuracy of the equations was moderate for VAT (percentage of estimates that differed <30% from the measured values, 70 to 78 in males, 64 to 69 in females) and good for ASAT (85 to 91 in males, 90 to 95 in females) and FFM (99 to 100 in both sexes). All the equations appeared precise (interquartile range of the difference, 0.89 to 1.76 L for VAT, 1.16 to 1.61 L for ASAT, 0.81 to 1.39 L for FFM). Bias of all the equations was negligible (-0.17 to 0.05 L for VAT, -0.10 to 0.12 L for ASAT, -0.07 to 0.09 L for FFM). The equations achieved superior cardiometabolic correlations compared with body mass index and waist circumference.

CONCLUSIONS

The developed equations to estimate VAT, ASAT, and FFM volumes achieved moderate to good performance. They may be cost-effective tools to revisit the implications of diverse body components.

Anthropometric equations to estimate appendicular muscle mass from dual-energy X-ray absorptiometry (DXA): A scoping review

BACKGROUND

Appendicular skeletal muscle mass (ASM) obtained from dual-energy x-ray absorptiometry (DXA) is recommended to quantify sarcopenia, but has limited availability in disadvantaged-income countries, moreover in an epidemiological context. Predictive equations are easier and less costly to apply, but a review of all available models is still lacking in the scientific literature. The objective of this work is to map, with a scoping review, the different proposed anthropometric equations to predict ASM measured by DXA.

METHODS

Six databases were searched without restriction on publication date, idiom, and study type. A total of 2,958 studies were found, of which 39 were included. Eligibility criteria involved ASM measured by DXA, and equations proposed to predict ASM.

RESULTS

predictive equations (n = 122) were gathered for 18 countries. The development phase involves sample size, coefficient of determination (r²), and a standard error of estimative (SEE) varying between 15 and 15,239 persons, 0.39 and 0.98, 0.07 and 3.38 kg, respectively. The validation phase involves a sample size, accuracy, and a SEE between 15 and 3,003 persons, 0.61 and 0.98, 0.09 and 3.65 kg, respectively.

CONCLUSIONS

The different proposed predictive anthropometric equations of ASM DXA were mapped, including validated pre-existing equations, offering an easy-to-use referential article for clinical and research applications. It is necessary to propose more equations for other continents (Africa and Antarctica) and specific health-related conditions (e.g., diseases), once the equations can only have sufficient validity and accuracy to predict ASM generally when applied to the same population.

Narrative Review of Sex Differences in Muscle Strength, Endurance, Activation, Size, Fiber Type, and Strength Training Participation Rates, Preferences, Motivations, Injuries, and Neuromuscular Adaptations

ABSTRACT

Nuzzo, JL. Narrative review of sex differences in muscle strength, endurance, activation, size, fiber type, and strength training participation rates, preferences, motivations, injuries, and neuromuscular adaptations. J Strength Cond Res 37(2): 494-536, 2023-Biological sex and its relation with exercise participation and sports performance continue to be discussed. Here, the purpose was to inform such discussions by summarizing the literature on sex differences in numerous strength training-related variables and outcomes-muscle strength and endurance, muscle mass and size, muscle fiber type, muscle twitch forces, and voluntary activation; strength training participation rates, motivations, preferences, and practices; and injuries and changes in muscle size and strength with strength training. Male subjects become notably stronger than female subjects around age 15 years. In adults, sex differences in strength are more pronounced in upper-body than lower-body muscles and in concentric than eccentric contractions. Greater male than female strength is not because of higher voluntary activation but to greater muscle mass and type II fiber areas. Men participate in strength training more frequently than women. Men are motivated more by challenge, competition, social recognition, and a desire to increase muscle size and strength. Men also have greater preference for competitive, high-intensity, and upper-body exercise. Women are motivated more by improved attractiveness, muscle "toning," and body mass management. Women have greater preference for supervised and lower-body exercise. Intrasexual competition, mate selection, and the drive for muscularity are likely fundamental causes of exercise behaviors in men and women. Men and women increase muscle size and strength after weeks of strength training, but women experience greater relative strength improvements depending on age and muscle group. Men exhibit higher strength training injury rates. No sex difference exists in strength loss and muscle soreness after muscle-damaging exercise.

Epidemiological, mechanistic, and practical bases for assessment of cardiorespiratory fitness and muscle status in adults in healthcare settings

Given their importance in predicting clinical outcomes, cardiorespiratory fitness (CRF) and muscle status can be considered new vital signs. However, they are not routinely evaluated in healthcare settings. Here, we present a comprehensive review of the epidemiological, mechanistic, and practical bases of the evaluation of CRF and muscle status in adults in primary healthcare settings. We highlight the importance of CRF and muscle status as predictors of morbidity and mortality, focusing on their association with cardiovascular and metabolic outcomes. Notably, adults in the best quartile of CRF and muscle status have as low as one-fourth the risk of developing some of the most common chronic metabolic and cardiovascular diseases than those in the poorest quartile. The physiological mechanisms that underlie these epidemiological associations are addressed. These mechanisms include the fact that both CRF and muscle status reflect an integrative response to the body function. Indeed, muscle plays an active role in the development of many diseases by regulating the body's metabolic rate and releasing myokines, which modulate metabolic and cardiovascular functions. We also go over the most relevant techniques for assessing peak oxygen uptake as a surrogate of CRF and muscle strength, mass, and quality as surrogates of muscle status in adults. Finally, a clinical case of a middle-aged adult is discussed to integrate and summarize the practical aspects of the information presented throughout. Their clinical importance, the ease with which we can assess CRF and muscle status using affordable techniques, and the availability of reference values, justify their routine evaluation in adults across primary healthcare settings.

Alternative skeletal muscle index for sarcopenia diagnosis in elderly patients with type 2 diabetes mellitus: A pilot study

PURPOSE

To determine an alternative skeletal muscle index (a-SMI), easy diagnosis of sarcopenia in elderly patients with type 2 diabetes mellitus (T2DM).

PATIENTS AND METHODS

This cross-sectional study included 223 inpatients with T2DM (100 males, age range 60-89; 123 females, age range 60-87). Screening for grip strength and gait speed, measuring SMI by dual-energy X-ray absorptiometry (d-SMI) for sarcopenia diagnosis, according to the Asian Working Group for Sarcopenia (AWGS) 2019 consensus. The a-SMI was established by binary logistic regression analysis with positive screening population. To assess the conformance of the new diagnostic approach with the AWGS 2019.

RESULTS

Sarcopenia was present in 36.3% of the study population. 59 had normal d-SMI and 81 had low d-SMI in screening patients with probable sarcopenia. In univariate analyses for all positive screening population, body mass index (BMI), 25-hydroxyvitamin D (25 - (OH) VitD), high density lipoprotein cholesterol (HDL-C), hypertension (HTN), and gender were correlates of d-SMI. Binary logistic regression analysis revealed that male (B = 2.463, 95%CI: 3.640 ~ 37.883, p = 0.000), HTN (B = 1.404, 95%CI: 1.599 ~ 10.371, p = 0.003), BMI (B = -0.344, 95%CI: 0.598 ~ 0.839, p = 0.000), 25-(OH) VitD (B = -0.058, 95%CI: 0.907 ~ 0.982, p = 0.004) were independent factors for d-SMI detection. Based on the extracted four correlates, the a-SMI was determined. The area under receiver operating characteristic (ROC) curve was 0.842, sensitivity and specificity for the new diagnostic approach were 84.0% and 84.5%. In a statistical measure of agreement between the AWGS 2019 and the new diagnostic approach, the kappa coefficient was 0.669 (p < 0.001).

CONCLUSION

The a-SMI - based on gender, obesity status, 25-(OH) VitD, and HTN history - can be used in the absence of the d-SMI to supplement the algorithm for sarcopenia diagnosis in elderly patients with T2DM.

Fat-Free Mass Index as a Surrogate Marker of Appendicular Skeletal Muscle Mass Index for Low Muscle Mass Screening in Sarcopenia

OBJECTIVES

We aimed to examine the relationship between the fat-free mass index (FFMI; FFM/height²) and appendicular skeletal muscle mass index (ASMI; ASM/height²), measured using both bioelectrical impedance analysis (BIA) and dual-energy X-ray absorptiometry (DXA), and investigate the effects of age and obesity. We also evaluated the suitability of BIA-measured FFMI as a simple surrogate marker of the ASMI and calculated the optimal FFMI cutoff value for low muscle mass screening to diagnose sarcopenia.

DESIGN

Cross-sectional study.

SETTING AND PARTICIPANTS

This study included 1313 adults (women, 33.6%) aged 40-87 years (mean age, 55 ± 10 years) from the WASEDA'S Health Study.

METHODS

Body composition was measured using multifrequency BIA and DXA. Low muscle mass was defined according to the criteria of the Asian Working Group for Sarcopenia 2019.

RESULTS

BIA-measured FFMI showed strong positive correlations with both BIA- (r = 0.96) and DXA-measured (r = 0.95) ASMIs. Similarly, in the subgroup analysis according to age and obesity, the FFMI was correlated with the ASMI. The areas under the receiver operating characteristic curve for screening low muscle mass defined by DXA-measured ASMI using BIA-measured FFMI values were 0.95 (95% CI 0.93-0.97) for men and 0.91 (95% CI 0.87-0.94) for women. The optimal BIA-measured FFMI cutoff values for screening low muscle mass defined by DXA-measured ASMI were 17.5 kg/m² (sensitivity 89%, specificity 88%) for men and 14.6 kg/m² (sensitivity 80%, specificity 86%) for women.

CONCLUSIONS AND IMPLICATIONS

The FFMI showed a strong positive correlation with BIA- and DXA-measured ASMIs, regardless of age and obesity. The FFMI could be a useful simple surrogate marker of the ASMI for low muscle mass screening in sarcopenia in community settings. The suggested FFMI cutoff values for predicting low muscle mass are <18 kg/m² in men and <15 kg/m² in women.

Estimation of Appendicular Skeletal Muscle Mass for Women Aged 60-70 Years Using a Machine Learning Approach

OBJECTIVES

This article aimed to develop and validate an anthropometric equation based on the least absolute shrinkage and selection operator (LASSO) regression, a machine learning approach, to predict appendicular skeletal muscle mass (ASM) in 60-70-year-old women.

DESIGN

A cross-sectional study.

SETTING AND PARTICIPANTS

Community-dwelling women aged 60-70 years.

METHODS

A total of 1296 community-dwelling women aged 60-70 years were randomly divided into the development or the validation group (1:1 ratio). ASM was evaluated by bioelectrical impedance analysis (BIA) as the reference. Variables including weight, height, body mass index (BMI), sitting height, waist-to-hip ratio (WHR), calf circumference (CC), and 5 summary measures of limb length were incorporated as candidate predictors. LASSO regression was used to select predictors with 10-fold cross-validation, and multiple linear regression was applied to develop the BIA-measured ASM prediction equation. Paired t test and Bland-Altman analysis were used to validate agreement.

RESULTS

Weight, WHR, CC, and sitting height were selected by LASSO regression as independent variables and the equation is ASM = 0.2308 × weight (kg) - 27.5652 × WHR + 8.0179 × CC (m) + 2.3772 × Sitting height (m) + 22.2405 (adjusted R² = 0.848, standard error of the estimate = 0.661 kg, P < .001). Bland-Altman analysis showed a high agreement between BIA-measured ASM and predicted ASM that the mean difference between the 2 methods was -0.041 kg, with the 95% limits of agreement of -1.441 to 1.359 kg.

CONCLUSIONS AND IMPLICATIONS

The equation for 60-70-year-old women could provide an available measurement of ASM for communities that cannot equip with BIA, which promotes the early screening of sarcopenia at the community level. Additionally, sitting height could predict ASM effectively, suggesting that maybe it can be used in further studies of muscle mass.

Association between skeletal muscle mass or percent body fat and metabolic syndrome development in Japanese women: A 7-year prospective study

Previous cross-sectional studies have indicated that low relative appendicular lean mass (ALM) against body weight (divided by body weight, ALM/Wt, or divided by body mass index, ALM/BMI) was negatively associated with metabolic syndrome (MetS). Conversely, previous cross-sectional studies have indicated that the absolute ALM or ALM divided by squared height (ALM/Ht2) were positively associated with MetS. The aim of this longitudinal study was to investigate the association between low absolute or relative skeletal muscle mass, leg muscle power, or percent body fat and the development of MetS in Japanese women in a 7-y prospective study. The study participants included 346 Japanese women aged 26 to 85 years. The participants were divided into low and high groups based on the median values of ALM/Wt, ALM/BMI, ALM/Ht2, absolute ALM, or leg power. The longitudinal relationship between ALM indices or leg power and MetS development was examined using Kaplan-Meier curves and Cox regression models (average follow-up duration 7 years, range 1 to 10 years). During follow-up, 24 participants developed MetS. MetS incidence was higher in the low ALM/Wt group than the high ALM/Wt group even after controlling for age, obesity, waist circumference, family history of diabetes, smoking, and physical activity [adjusted hazard ratio = 5.60 (95% CI; 1.04-30.0)]. In contrast, MetS incidence was lower in the low ALM/Ht2 group than the high ALM/Ht2 group [adjusted hazard ratio = 10.6 (95%CI; 1.27-89.1)]. MetS incidence was not significantly different between the low and high ALM/BMI, absolute ALM, and leg power groups. Both ALM/Ht2 and ALM/Wt were not significant predictive variables for MetS development when fat mass or percent body fat was taken into account in the Cox model. At the very least, the results of this study underscore the importance of body composition measurements in that percent body fat, but not ALM, is associated with MetS development.

Linear Regression vs. Deep Learning: A Simple Yet Effective Baseline for Human Body Measurement

We propose a linear regression model for the estimation of human body measurements. The input to the model only consists of the information that a person can self-estimate, such as height and weight. We evaluate our model against the state-of-the-art approaches for body measurement from point clouds and images, demonstrate the comparable performance with the best methods, and even outperform several deep learning models on public datasets. The simplicity of the proposed regression model makes it perfectly suitable as a baseline in addition to the convenience for applications such as the virtual try-on. To improve the repeatability of the results of our baseline and the competing methods, we provide guidelines toward standardized body measurement estimation.