Predicting muscle mass from anthropometry using magnetic resonance imaging as reference: a systematic review

Skeletal muscle estimation: A review of techniques and their applications

Quantifying skeletal muscle size is necessary to identify those at risk for conditions that increase frailty, morbidity, and mortality, as well as decrease quality of life. Although muscle strength, muscle quality, and physical performance have been suggested as important assessments in the screening, prevention, and management of sarcopenic and cachexic individuals, skeletal muscle size is still a critical objective marker. Several techniques exist for estimating skeletal muscle size; however, each technique presents with unique characteristics regarding simplicity/complexity, cost, radiation dose, accessibility, and portability that are important factors for assessors to consider before applying these modalities in practice. This narrative review presents a discussion centred on the theory and applications of current non-invasive techniques for estimating skeletal muscle size in diverse populations. Common instruments for skeletal muscle assessment include imaging techniques such as computed tomography, magnetic resonance imaging, peripheral quantitative computed tomography, dual-energy X-ray absorptiometry, and Brightness-mode ultrasound, and non-imaging techniques like bioelectrical impedance analysis and anthropometry. Skeletal muscle size can be acquired from these methods using whole-body and/or regional assessments, as well as prediction equations. Notable concerns when conducting assessments include the absence of standardised image acquisition/processing protocols and the variation in cut-off thresholds used to define low skeletal muscle size by clinicians and researchers, which could affect the accuracy and prevalence of diagnoses. Given the importance of evaluating skeletal muscle size, it is imperative practitioners are informed of each technique and their respective strengths and weaknesses.

Efficacy of Split Versus Full-Body Resistance Training on Strength and Muscle Growth: A Systematic Review With Meta-Analysis

ABSTRACT

Ramos-Campo, DJ, Benito-Peinado, PJ, Caravaca, LA, Rojo-Tirado, MA, and Rubio-Arias, JÁ. Efficacy of split versus full-body resistance training on strength and muscle growth: a systematic review with meta-analysis. J Strength Cond Res 38(7): 1330-1340, 2024-No previous study has systematically compared the effect of 2 resistance training routines commonly used to increase muscle mass and strength (i.e., split [Sp] and full-body [FB] routines). Our objective was to conduct a systematic review and meta-analysis following PRISMA guidelines to compare the effects on strength gains and muscle growth in healthy adults. 14 studies (392 subjects) that compared Sp and FB routines in terms of strength adaptations and muscle growth were included. Regarding the effects of the Sp or FB routine on both bench press and lower limbs strength, the magnitude of the change produced by both routines was similar (bench press: mean difference [MD] = 1.19; [-1.28, 3.65]; p = 0.34; k = 14; lower limb: MD = 2.47; [-2.11, 7.05]; p = 0.29; k = 14). Concerning the effect of the Sp vs. FB routine on muscle growth, similar effects were observed after both routines in the cross-sectional area of the elbow extensors (MD = 0.30; [-2.65, 3.24]; p = 0.84; k = 4), elbow flexors (MD = 0.17; [-2.54, 2.88]; p = 0.91; k = 5), vastus lateralis (MD = -0.08; [-1.82, 1.66]; p = 0.93; k = 5), or lean body mass (MD = -0.07; [-1.59, 1.44]; p = 0.92; k = 6). In conclusion, the present systematic review and meta-analysis provides solid evidence that the use of Sp or FB routines within a resistance training program does not significantly impact either strength gains or muscle hypertrophy when volume is equated. Consequently, individuals are free to confidently select a resistance training routine based on their personal preferences.

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.

A Review of Anthropometric Measurements for Saudi Adults and Elderly, Directions for Future Work and Recommendations to Establish Saudi Guidelines in Line with the Saudi 2030 Vision

Body weight is a significant risk factor for the disease burden of noncommunicable diseases (NCDs). Anthropometric measurements are the first step in determining NCDs risk, and clinicians must have access to valid cutoffs. This study aims to review the literature of Saudi national guidelines and studies previously conducted in Saudi Arabia (SA) and to provide insights and recommendations to establish national guidelines in anthropometric measurements for Saudi adults/elderly in line with the Saudi 2030 Vision. In total, 163 studies were included, and 12 of them contributed to the development of specific anthropometric cutoffs. Cutoffs for metabolic syndrome, waist circumference, and body mass index were established in Saudi adults. However, limited studies were conducted in the elderly. This review warrants establishing standard cutoffs of Saudi adult anthropometrics to avoid over/underreporting of malnutrition and adiposity. This review will help policymakers and the Ministry of Health to establish national guidelines and standard cutoffs to be used in SA for anthropometric measurements that may assist in detecting malnutrition and NCDs.

Machine and deep learning-based clinical characteristics and laboratory markers for the prediction of sarcopenia

BACKGROUND

Sarcopenia is an age-related progressive skeletal muscle disorder involving the loss of muscle mass or strength and physiological function. Efficient and precise AI algorithms may play a significant role in the diagnosis of sarcopenia. In this study, we aimed to develop a machine learning model for sarcopenia diagnosis using clinical characteristics and laboratory indicators of aging cohorts.

METHODS

We developed models of sarcopenia using the baseline data from the West China Health and Aging Trend (WCHAT) study. For external validation, we used the Xiamen Aging Trend (XMAT) cohort. We compared the support vector machine (SVM), random forest (RF), eXtreme Gradient Boosting (XGB), and Wide and Deep (W&D) models. The area under the receiver operating curve (AUC) and accuracy (ACC) were used to evaluate the diagnostic efficiency of the models.

RESULTS

The WCHAT cohort, which included a total of 4057 participants for the training and testing datasets, and the XMAT cohort, which consisted of 553 participants for the external validation dataset, were enrolled in this study. Among the four models, W&D had the best performance (AUC = 0.916 ± 0.006, ACC = 0.882 ± 0.006), followed by SVM (AUC =0.907 ± 0.004, ACC = 0.877 ± 0.006), XGB (AUC = 0.877 ± 0.005, ACC = 0.868 ± 0.005), and RF (AUC = 0.843 ± 0.031, ACC = 0.836 ± 0.024) in the training dataset. Meanwhile, in the testing dataset, the diagnostic efficiency of the models from large to small was W&D (AUC = 0.881, ACC = 0.862), XGB (AUC = 0.858, ACC = 0.861), RF (AUC = 0.843, ACC = 0.836), and SVM (AUC = 0.829, ACC = 0.857). In the external validation dataset, the performance of W&D (AUC = 0.970, ACC = 0.911) was the best among the four models, followed by RF (AUC = 0.830, ACC = 0.769), SVM (AUC = 0.766, ACC = 0.738), and XGB (AUC = 0.722, ACC = 0.749).

CONCLUSIONS

The W&D model not only had excellent diagnostic performance for sarcopenia but also showed good economic efficiency and timeliness. It could be widely used in primary health care institutions or developing areas with an aging population.

TRIAL REGISTRATION

Chictr.org, ChiCTR 1800018895.

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.

Low calf circumference adjusted for body mass index is associated with prolonged hospital stay

BACKGROUND

Calf circumference (CC) is of emerging importance because of its practicality, high correlation with skeletal muscle, and potential predictive value for adverse outcomes. However, the accuracy of CC is influenced by adiposity. CC adjusted for BMI (BMI-adjusted CC) has been proposed to counteract this problem. However, its accuracy to predict outcomes is unknown.

OBJECTIVES

To evaluate the predictive validity of BMI-adjusted CC in hospital settings.

METHODS

A secondary analysis of a prospective cohort study in hospitalized adult patients was conducted. The CC was adjusted for BMI by reducing 3, 7, or 12 cm for BMI (in kg/m²) of 25-29.9, 30-39.9, and ≥40, respectively. Low CC was defined as ≤34 cm for males and ≤33 cm for females. Primary outcomes included length of hospital stay (LOS) and in-hospital death, and secondary outcomes were hospital readmissions and mortality within 6 mo after discharge.

RESULTS

We included 554 patients (55.2 ± 14.9 y, 52.9% men). Among them, 25.3% presented with low CC, whereas 60.6% had BMI-adjusted low CC. In-hospital death occurred in 13 patients (2.3%), and median LOS was 10.0 (5.0-18.0) d. Within 6 mo from discharge, 43 patients (8.2%) died, and 178 (34.0%) were readmitted to the hospital. BMI-adjusted low CC was an independent predictor of LOS ≥ 10 d (odds ratio = 1.70; 95% confidence interval: 1.18, 2.43], but it was not associated with the other outcomes.

CONCLUSIONS

BMI-adjusted low CC was identified in more than 60% of hospitalized patients and was an independent predictor of longer LOS.

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.

Vergleichende Untersuchung der posturalen Kontrolle bei 20–40-Jährigen und Karate-Kaderathleten mittels eines neuromuskulären Trainingsgerätes

Hintergrund Die posturale Kontrolle dient dem Erhalt der aufrechten Körperhaltung und trägt zur Gleichgewichtsregulation bei. Die im höheren Alter zunehmenden neuromuskulären Defizite führen zu einer Verschlechterung der posturalen Kontrolle, die durch neuromuskuläres Training verbessert werden kann. In der Annahme, dass neuromuskuläre Defizite bereits früh auftreten, wurden die posturalen Fähigkeiten von 20–40-Jährigen untersucht. Zudem wurde der Einfluss von Leistungssport auf die posturale Kontrolle durch die Untersuchung von Karate-Kaderathleten geprüft.

Methodik Für einen Altersvergleich wurden 123 altershomogene Gesunde zwischen 20 und 40 Jahren untersucht. 24 Karate-Kaderathleten wurden mit den 20–25-jährigen Gesunden der „Normalbevölkerung“ verglichen. Zur Datenerhebung wurde ein neuromuskuläres Trainingsgerät, der Human Body Equilibrium 360 (Huber 360), verwendet.

Ergebnisse Im Altersvergleich zeigten sich für alle 7 Messungen keine signifikanten Unterschiede. Die Probanden waren zu jung, um Defizite zu erkennen. Jedoch zeigten sich einige allgemeine Erkenntnisse. Im beidbeinigen Stand war bei allen Gruppen eine signifikante Zunahme der Körperschwerpunkt (KSP) -Schwankungen nach Augenschluss zu sehen. Der Einfluss der Füßigkeit bzw. Händigkeit zeigte sich im Einbeinstand und Krafttest. Geschlechtsspezifische Unterschiede waren zu verzeichnen. Bei der Untersuchung der Karate-Kaderathleten zeigten sich verschiedene trainingsspezifische Anpassungen. So konnten Karatesportler den Wegfall der visuellen Information besser kompensieren. Karatesportler wiesen außerdem ein signifikant breiteres Gangbild und ein größeres Stabilitätsareal auf. Sportler konnten ihren KSP weiter verlagern, ohne aus dem Gleichgewicht zu geraten.

Diskussion Der Huber 360 ist als Rehabilitations- und Trainingsgerät aufgrund der geringen Trennschärfe der Tests in seinem gesamten Anwendungsspektrum nur bedingt geeignet, da signifikante Unterschiede zwischen den Gruppen schwer zu detektieren sind. Einzelne trainingsspezifische Anpassungen durch leistungsorientiertes Karatetraining sind erkennbar und unterstreichen den Einfluss des Trainings auf die posturale Kontrolle. Zusammenfassend lässt sich sagen, dass die Anwendung des Huber 360 zur Beurteilung von posturalen Fähigkeiten im Altersvergleich und bei Unterscheidung trainingsspezifischer Anpassungen einen differenzierten und selektiveren Einsatz der einzelnen Tests bedarf. Weitere Untersuchungen mit älteren Patienten mit unterschiedlichen Erkrankungen sind geplant.

Precise Prediction of Total Body Lean and Fat Mass From Anthropometric and Demographic Data: Development and Validation of Neural Network Models

BACKGROUND

Estimating body composition is relevant in diabetes disease management, such as drug administration and risk assessment of morbidity/mortality. It is unclear how machine learning algorithms could improve easily obtainable body muscle and fat estimates. The objective was to develop and validate machine learning algorithms (neural networks) for precise prediction of body composition based on anthropometric and demographic data.

METHODS

Cross-sectional cohort study of 18 430 adults and children from the US population. Participants were examined with whole-body dual X-ray absorptiometry (DXA) scans, anthropometric assessment, and answered a demographic questionnaire. The primary outcomes were predicted total lean body mass (_predLBM), total body fat mass (_predFM), and trunk fat mass (_predTFM) compared with reference values from DXA scans.

RESULTS

Participants were randomly partitioned into 70% training (12 901) data and 30% validation (5529) data. The prediction model for _predLBM compared with lean body mass measured by DXA (_DXALBM) had a Pearson's correlation coefficient of R = 0.99 with a standard error of estimate (SEE) = 1.88 kg (P < .001). The prediction model for _predFM compared with fat mass measured by DXA (_DXAFM) had a Pearson's coefficient of R = 0.98 with a SEE = 1.91 kg (P < .001). The prediction model for _predTFM compared with DXA measured trunk fat mass (_DXAFM) had a Pearson's coefficient of R = 0.98 with a SEE = 1.13 kg (P < .001).

CONCLUSIONS

In this study, neural network models based on anthropometric and demographic data could precisely predict body muscle and fat composition. Precise body estimations are relevant in a broad range of clinical diabetes applications, prevention, and epidemiological research.

Derivation and validation of a simple anthropometric equation to predict fat-free mass in patients with chronic hepatitis C

BACKGROUND

Loss of skeletal muscle mass is very common in chronic liver diseases and affects 30.0-70.0% of the patients with cirrhosis. Given the relevance of muscle wasting in hepatic diseases, a practical screening tool for earlier detection of skeletal muscle mass loss is of utmost significance.

AIMS

To develop and validate a simple anthropometric prediction equation for fat-free mass estimation by using Bioelectrical Impedance Analysis (BIA) as a reference method in patients with chronic hepatitis C (CHC).

METHODS

We prospectively, included 209 CHC patients, randomly allocated into two groups, 158 patients in a development model (derivation sample) and 51 patients in a validation group (validation sample). Predictive equations were developed using backward stepwise multiple regression and the most adequate and simplest derived predictive equation was further explored for agreement and bias in the validation sample. The accuracy of the predictive equation was evaluated using the coefficient of determination (R²).

RESULTS

The predictive equation with an optimal R² was Fat-Free Mass (Kg) = Sex × 0.17 + Height (m) × 16.83 + Weight (Kg) × 0.62 + Waist Circumference (cm) × (-0.15) + Weight (Kg) × Sex × (-0.30) + Sex × Waist Circumference (cm) × 0.14-6.23; where sex = 1 for female and 0 for male. R² = 0.93, standard error of the estimate = 2.6 Kg and coefficient of variation = 20.0%, p < 0.001.

CONCLUSIONS

Our developed and cross-validated anthropometric prediction equation for fat-free mass estimation by using BIA attained a high coefficient of determination, a low standard error of the estimate, and lowermost coefficient of variation. This study indicates that predictive equations may be reliable and useful alternative methods for clinical evaluation of fat-free mass in patients with CHC.

Muscle mass assessment in renal disease: the role of imaging techniques

Muscle wasting is a frequent finding in patients with chronic kidney disease (CKD), especially in those with end-stage kidney disease (ESKD) on chronic dialysis. Muscle wasting in CKD is a main feature of malnutrition, and results principally from a vast array of metabolic derangements typical of the syndrome, that converge in determining reduced protein synthesis and accelerated protein catabolism. In this clinical setting, muscle wasting is also frequently associated with disability, frailty, infections, depression, worsened quality of life and increased mortality. On these grounds, the evaluation of nutritional status is crucial for an adequate management of renal patients, and consists of a comprehensive assessment allowing for the identification of malnourished patients and patients at nutritional risk. It is based essentially on the assessment of the extent and trend of body weight loss, as well as of spontaneous dietary intake. Another key component of this evaluation is the determination of body composition, which, depending on the selected method among several ones available, can identify accurately patients with decreased muscle mass. The choice will depend on the availability and ease of application of a specific technique in clinical practice based on local experience, staff resources and good repeatability over time. Surrogate methods, such as anthropometry and bioimpedance analysis (BIA), represent the most readily available techniques. Other methods based on imaging modalities [dual-energy X-ray absorptiometry (DXA), magnetic resonance imaging (MRI), and whole body computed tomography (CT)] are considered to be the "gold standard" reference methods for muscle mass evaluation, but their use is mainly confined to research purposes. New imaging modalities, such as segmental CT scan and muscle ultrasound have been proposed in recent years. Particularly, ultrasound is a promising technique in this field, as it is commonly available for bedside evaluation of renal patients in nephrology wards. However, more data are needed before a routine use of ultrasound for muscle mass evaluation can be recommended in clinical practice.

Pre-Disease and Pre-Surgery BMI, Weight Loss and Sarcopenia Impact Survival of Resected Lung Cancer Independently of Tumor Stage

Lower pre-surgery Body Mass Index (BMI) and low muscle mass impact negatively long-term survival of non-small cell lung cancer (NSCLC). We investigated their influence on survival after major lung resection for NSCLC.

METHODS

A retrospective analysis of a prospectively collected database was made on 304 consecutive patients.

RESULTS

Underweight, normal, overweight and obese patients represented 7.6%, 51.6%, 28.6%, and 12.6% of the pre-disease population. Weight loss and gain were recorded in 5% and 44.4% of patients, respectively. Low muscle mass was more frequently associated with BMI < 25 kg/m2 (p < 0.000001). Overall survival was positively affected by pre-disease (p = 0.036) and pre-surgery (p = 0.017) BMI > 25 kg/m2, and, even more, in case of BMI > 25 kg/m2 and increasing weight (p = 0.012). Long-term outcome was negatively influenced by low muscle mass (p = 0.042) and weight loss (p = 0.0052) as well as age (p = 0.017), ASA categories (p = 0.025), extent of resection (p = 0.0001), pleural invasion (p = 0.0012) and higher pathologic stage (p < 0.0001). Three stepwise multivariable models confirmed the independent favorable prognostic value of higher pre-disease (RR 0.66[0.49-0.89], p = 0.006) and pre-surgery BMI (RR 0.72[0.54-0.98], p = 0.034), and the absence of low muscle mass (RR 0.56[0.37-0.87], p = 0.0091).

CONCLUSIONS

Body reserves assessed by simple clinical markers impact survival of surgically treated NSCLC. Strategies improving body fat and muscular mass before surgery should be considered.

Posttransplant muscle mass measured by urinary creatinine excretion rate predicts long-term outcomes after liver transplantation

Long-term survival in orthotopic liver transplant (OLT) recipients remains impaired because of many contributing factors, including a low pretransplant muscle mass (or sarcopenia). However, influence of posttransplant muscle mass on survival is currently unknown. We hypothesized that posttransplant urinary creatinine excretion rate (CER), an established noninvasive marker of total body muscle mass, is associated with long-term survival after OLT. In a single-center cohort study of 382 adult OLT recipients, mean ± standard deviation CER at 1 year posttransplantation was 13.3 ± 3.7 mmol/24 h in men and 9.4 ± 2.6 mmol/24 h in women. During median follow-up for 9.8 y (interquartile range 6.4-15.0 y), 104 (27.2%) OLT recipients died and 44 (11.5%) developed graft failure. In Cox regression analyses, as continuous variable, low CER was associated with increased risk for mortality (HR = 0.43, 95% CI: 0.26-0.71, P = .001) and graft failure (HR = 0.42, 95% CI: 0.20-0.90, P = .03), independent of age, sex, and body surface area. Similarly, OLT recipients in the lowest tertile had an increased risk for mortality (HR = 2.69; 95% CI: 1.47-4.91, P = .001) and graft failure (HR = 2.77, 95% CI: 1.04-7.39, P = .04), compared to OLT recipients in the highest tertile. We conclude that 1 year posttransplant low total body muscle mass is associated with long-term risk of mortality and graft failure in OLT recipients.

Thumb adductor muscle thickness used in the nutritional assessment of chronic kidney disease patients under conservative treatment

INTRODUCTION

Evaluate the association between the thumb adductor muscle thickness and the patient's nutritional status, and propose cutoff points for muscle mass depletion in elderly patients with chronic kidney disease (CKD) under conservative treatment. Epidemiological and cross-sectional study, including patients with CKD stages 3 to 5, older than 60 years. Socioeconomic, clinical, physical activity and anthropometric data was obtained. TAMT was described and compared according to CKD stage, socioeconomic data, physical activity, nutritional status and correlated with age, glomerular filtration rate and anthropometric variables. Receiver Operating Characteristic (ROC) curves were produced, considering the lean tissue index classification as reference. The cut-off point was defined by the Youden index.

RESULTS

We evaluated 137 individuals. The TAMT was lower in malnourished and/or depleted muscle mass individuals; among males it was higher among those who practiced physical activities (p <0.05). This measure was moderately correlated with BMI, calf and brachial circumferences, lean body tissue, lean tissue index and body cell mass (r <0.7); negatively with age (r = -0.34). The ROC curve analysis determined cut points of 15.33 mm for females and 20.33 mm for males, with 72.22% and 62.50% accuracy, respectively.

CONCLUSION

TAMT is used to estimate muscle mass and we suggest the cutoff point is useful to rule out the likelihood of muscle mass depletion. It is recommended that it be used in a complementary way in nutritional assessment.

Adductor Pollicis Muscle Thickness for nutritional assessment: a systematic review

OBJECTIVE

The aim of this study is to systematically review the scientific findings about the efficacy of the measure of the Adductor Pollicis Muscle Thickness for nutritional assessment of individuals in various clinical conditions.

METHOD

Systematic review study performed according to the methodology Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA).

RESULTS

13 original articles published between 2004 and 2016 were included. The measure was associated/correlated to parameters of nutritional status (such as weight, body mass index and Global Subjective Assessment) and muscle mass markers (such as circumference brachial muscle circumference, brachial muscle area, calf circumference, and muscle mass). All these correlations were weak or moderate.

CONCLUSION

The measurement can be used in different populations, being able to estimate nutritional status and muscle mass. However, it is suggested that it be used in a complementary way to the nutritional evaluation, not constituting a single diagnostic/monitoring parameter.

Development and validation of anthropometric prediction equations for lean body mass, fat mass and percent fat in adults using the National Health and Nutrition Examination Survey (NHANES) 1999-2006

Quantification of lean body mass and fat mass can provide important insight into epidemiological research. However, there is no consensus on generalisable anthropometric prediction equations to validly estimate body composition. We aimed to develop and validate practical anthropometric prediction equations for lean body mass, fat mass and percent fat in adults (men, n 7531; women, n 6534) from the National Health and Nutrition Examination Survey 1999-2006. Using a prediction sample, we predicted each of dual-energy X-ray absorptiometry (DXA)-measured lean body mass, fat mass and percent fat based on different combinations of anthropometric measures. The proposed equations were validated using a validation sample and obesity-related biomarkers. The practical equation including age, race, height, weight and waist circumference had high predictive ability for lean body mass (men: R 2=0·91, standard error of estimate (SEE)=2·6 kg; women: R 2=0·85, SEE=2·4 kg) and fat mass (men: R 2=0·90, SEE=2·6 kg; women: R 2=0·93, SEE=2·4 kg). Waist circumference was a strong predictor in men only. Addition of other circumference and skinfold measures slightly improved the prediction model. For percent fat, R 2 were generally lower but the trend in variation explained was similar. Our validation tests showed robust and consistent results with no evidence of substantial bias. Additional validation using biomarkers demonstrated comparable abilities to predict obesity-related biomarkers between direct DXA measurements and predicted scores. Moreover, predicted fat mass and percent fat had significantly stronger associations with obesity-related biomarkers than BMI did. Our findings suggest the potential application of the proposed equations in various epidemiological settings.

Is adductor pollicis muscle thickness a good predictor of lean mass in adults?

BACKGROUND & AIMS

Lean mass (LM) is an important parameter in clinical outcomes, which highlights the necessity of reliable tools for its estimation. The adductor pollicis muscle thickness (APMT) is easily accessible and suffers minimal interference from the adjacent subcutaneous fat tissue.

OBJECTIVE

To assess the relationship between the APMT and LM in a sample of Southern Brazilian adults.

METHODS

Participants were adults from the 1982 Pelotas (Brazil) Birth Cohort. LM was measured by dual energy X-ray absorptiometry (DXA). LM and lean mass index (LMI - LM divided by the square of height - kg/m(2)) were the outcomes. APMT was measured using a skinfold caliper. The mean of three measurements in the non-dominant hand was used in the analyses. APMT was described according to socio-demographic characteristics and nutritional status. The relationship between APMT and both LM and LMI was evaluated by correlation coefficient and linear regression using APMT as a single anthropometric parameter and also in addition to BMI.

RESULTS

APMT was assessed in 3485 participants. APMT was higher in males, non-whites, less-schooled and obese individuals. APMT was moderately correlated to LM and LMI (ranged from 0.44 to 0.57). Correlation coefficients were higher for LMI as outcome and in females (LM: 0.51 and LMI: 0.57). APMT explained 19% and 26% of the variance in LM in males and females, respectively, whereas it explained 26% and 33% of the variance in LMI. APMT increased the prediction for LM in 3 and 4 percentage points in males and females, in comparison to explained by BMI. BMI explained 48% and 59% of the variance of LMI in males and females whereas APMT increased it to 51% and 62% for both sexes, respectively.

CONCLUSIONS

Results were not good enough to promote the APMT as a single predictor of LM or LMI in epidemiological studies. APMT has a little predictive capacity in estimating LM or LMI when BMI is also considered.

Evaluation of the site specificity of acute disuse muscle atrophy developed during a relatively short period in critically ill patients according to the activities of daily living level: A prospective observational study

INTRODUCTION

In critically ill patients, excessive bed rest and immobilisation have been shown to cause disuse muscle atrophy, which contributes to prolonged hospitalisation and decreased activity of daily living (ADL) levels. However, the degree and site specificity of acute disuse muscle atrophy in critically ill patients during a relatively short intensive care unit (ICU) stay have not been fully elucidated.

METHODS

Critically ill patients, who required bed rest on ICU admission, were eligible for this study. The degree of skeletal muscle atrophy was evaluated on the day of, and 72 and 144h after ICU admission by measuring the limb circumference in ADL-dependent or -independent patients separately at five different sites: the midpoint of the upper limb between the acromion and the olecranon, the maximum diameter of the triceps surae in the lower leg, and three different sites in the thigh at 5, 10, and 15cm above the superior pole of the patella. Value of the limb circumference was presented as a percentage relative to the baseline (median).

RESULTS

In ADL-dependent patients, limb circumferences at all five sites were decreased significantly at 144h compared with the baseline (98.9-100% in the upper limbs, 97.1-97.2% in the lower legs, and 96.5-99.1% in the thighs), but not at 72h. In contrast, the limb circumferences at almost all sites decreased significantly at both 72 and 144h (100% in the upper limbs, 94.5-94.7% in the lower legs, and 89.7-94.7% in the thighs), compared with the baseline in ADL-independent patients. Muscle atrophy was greater at the four different lower-limb sites compared to the upper limb during 144h in the ICU in the ADL-independent, but not in the ADL-dependent patients.

CONCLUSIONS

Compared to ADL-dependent patients, ADL-independent patients are prone to develop muscle weakness, especially in the lower limbs.

Derivation and validation of simple anthropometric equations to predict adipose tissue mass and total fat mass with MRI as the reference method

The reference organ-level body composition measurement method is MRI. Practical estimations of total adipose tissue mass (TATM), total adipose tissue fat mass (TATFM) and total body fat are valuable for epidemiology, but validated prediction equations based on MRI are not currently available. We aimed to derive and validate new anthropometric equations to estimate MRI-measured TATM/TATFM/total body fat and compare them with existing prediction equations using older methods. The derivation sample included 416 participants (222 women), aged between 18 and 88 years with BMI between 15·9 and 40·8 (kg/m2). The validation sample included 204 participants (110 women), aged between 18 and 86 years with BMI between 15·7 and 36·4 (kg/m2). Both samples included mixed ethnic/racial groups. All the participants underwent whole-body MRI to quantify TATM (dependent variable) and anthropometry (independent variables). Prediction equations developed using stepwise multiple regression were further investigated for agreement and bias before validation in separate data sets. Simplest equations with optimal R (2) and Bland-Altman plots demonstrated good agreement without bias in the validation analyses: men: TATM (kg)=0·198 weight (kg)+0·478 waist (cm)-0·147 height (cm)-12·8 (validation: R 2 0·79, CV=20 %, standard error of the estimate (SEE)=3·8 kg) and women: TATM (kg)=0·789 weight (kg)+0·0786 age (years)-0·342 height (cm)+24·5 (validation: R (2) 0·84, CV=13 %, SEE=3·0 kg). Published anthropometric prediction equations, based on MRI and computed tomographic scans, correlated strongly with MRI-measured TATM: (R (2) 0·70-0·82). Estimated TATFM correlated well with published prediction equations for total body fat based on underwater weighing (R (2) 0·70-0·80), with mean bias of 2·5-4·9 kg, correctable with log-transformation in most equations. In conclusion, new equations, using simple anthropometric measurements, estimated MRI-measured TATM with correlations and agreements suitable for use in groups and populations across a wide range of fatness.

Derivation and validation of simple equations to predict total muscle mass from simple anthropometric and demographic data

BACKGROUND

Muscle mass reflects and influences health status. Its reliable estimation would be of value for epidemiology.

OBJECTIVE

The aim of the study was to derive and validate anthropometric prediction equations to quantify whole-body skeletal muscle mass (SM) in adults.

DESIGN

The derivation sample included 423 subjects (227 women) aged 18-81 y with a body mass index (BMI; in kg/m(2)) of 15.9-40.8. The validation sample included 197 subjects (105 women) aged 19-83 y with a BMI of 15.7-36.4. Both samples were of mixed ethnic/racial groups. All underwent whole-body magnetic resonance imaging to quantify SM (dependent variable for multiple regressions) and anthropometric variables (independent variables).

RESULTS

Two prediction equations with high practicality and optimal derivation correlations with SM were further investigated to assess agreement and bias by using Bland-Altman plots and validated in separate data sets. Including race as a variable increased R(2) by only 0.1% in men and by 8% in women. For men: SM (kg) = 39.5 + 0.665 body weight (BW; kg) - 0.185 waist circumference (cm) - 0.418 hip circumference (cm) - 0.08 age (y) (derivation: R(2) = 0.76, SEE = 2.7 kg; validation: R(2) = 0.79, SEE = 2.7 kg). Bland-Altman plots showed moderate agreement in both derivation and validation analyses. For women: SM (kg) = 2.89 + 0.255 BW (kg) - 0.175 hip circumference (cm) - 0.038 age (y) + 0.118 height (cm) (derivation: R(2) = 0.58, SEE = 2.2 kg; validation: R(2) = 0.59, SEE = 2.1 kg). Bland-Altman plots had a negative slope, indicating a tendency to overestimate SM among women with smaller muscle mass and to underestimate SM among those with larger muscle mass.

CONCLUSIONS

Anthropometry predicts SM better in men than in women. Equations that include hip circumference showed agreement between methods, with predictive power similar to that of BMI to predict fat mass, with the potential for applications in groups, as well as epidemiology and survey settings.