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Muscle density measurement

Muscle density measurement

Home Blog Balance Muscle densiyt vs Fat: how Muscle density measurement affects your measursment. These Brain boosters for workout focus dejsity to a certain value, and different values and heights represent various body fat percentages. It can be used if patients do not fit in the DXA field-of-view [ 5152 ]. You can also search for this author in PubMed Google Scholar. Gibson DJ, Burden ST, Strauss BJ, et al.


The KEY to Muscle Density and STRENGTH Measuremnet too long ago I wrote a blog on muscle dehsity vs. muscle quality. I received a Brain boosters for workout focus of questions about Energy metabolism and antioxidants density and whether it is the same Muscle density measurement muscle quality. Measuring Muzcle density is needed to determine muscle quality. The quality of muscle is important for individuals who want to increase their strength without increasing the area of muscle. Think of a cyclist who wants to be stronger, but does not necessarily want to increase the overall size of a muscle. An increase in muscle area would create more surface area and ultimately increase the potential for air resistance.

Muscle density measurement -

The main goal of the DXA is to provide you with an in-depth analysis of the main components of your body; fat, muscle and bone. After the scan, you will be given a multi-paged print out where you will see percentages, mass, and images accounting for the various data obtained.

The great thing about the DXA scan is that it requires very minimal preparation. For more accurate results you should make sure you are well hydrated and not have any food in your stomach at least 3 hours since your last meal. It is also important to not take calcium supplements 24 hours prior to your test to ensure accurate bone density readings.

Upon arriving at our medical office you will be greeted and taken back to meet with the licensed technologist who will perform your scan for you. After measuring your height and weight, you will be asked to lie down and get comfortable and the scan will begin.

The scan takes 6 minutes. Once the scan is over you will be able to sit down with the exercise specialist to go over your results. Your results will be explained to you and suggestions will be given according to goals that you have i.

You will be able to keep your packet of results as a reference in the case that a follow up is desired in the future. Note: it is beneficial to do this scan every months for body composition and every year if you are looking to modify something specific such as bone density.

Because this test gives so much detailed information regarding various components in your body, it is a scan that can be used for anyone. Athletes can get this scan done if they are curious to track their muscle mass as well as overall fat percentage. Due to its broad uses, the average person who is simply curious about their health could get this scan in order to gain insight regarding their body composition.

This will change based on the amount of fat there is as well as the amount of lean mass there is. Fat Mass Index FMI : The total amount of fat you have in kilograms relative to your height in meters 2. It is a measure of how much total fat you have, relative to your size and independent of lean mass.

Visceral Adipose Tissue VAT : VAT is a hormonally active component of total body fat. The measurement reflects the amount of internal abdominal fat around the organs.

The intra- and inter-operator reproducibility of CT-measured PMA and PMD were evaluated in a randomly selected sample of 30 patients; the measurements were blindly performed by two investigators T. and M. The intra-observer intraclass correlation coefficients ICCs for the PMA measurements were 0.

A, whereas those for the PMD measurements were 0. and 0. The inter-observer ICCs for the PMA and PMD measurements were 0. In the univariate regression analysis, both PMI and PMD were significantly and negatively associated with age and log C-reactive protein CRP , and were significantly and positively associated with geriatric nutritional risk index GNRI and SCI.

In addition, PMI was also significantly and negatively associated with male sex. In the multivariate regression analysis, PMI was independently associated with male sex and GNRI, whereas PMD was independently associated with age and log CRP Table 2.

During the median follow-up of 3. In the univariate Cox proportional hazards analysis, PMI and PMD were identified as significant predictors for all-cause mortality and were significantly associated with all-cause mortality in each sex group [women: Hazard ratio HR 0.

To maximize the predictive value of PMI and PMD for all-cause mortality in each sex group, an ROC analysis was performed, which revealed cut-off values of 2. The 7-year survival rates were In the multivariate Cox proportional hazards analysis adjusted by sex and age, history of CVD, GNRI, SCI, and log CRP, which were significant factors on univariate analysis, lower PMI and lower PMD were independently associated with an increased risk of all-cause mortality, respectively [adjusted HR aHR 2.

In the multivariate Cox proportional hazards analysis, the aHRs for all-cause mortality were as follows: 1. G1, 3. G1, and 5. G1, respectively Table 3. Kaplan—Meier survival curves for all-cause mortality. All-cause mortality for lower PMI vs.

higher PMI a , lower PMD vs. higher PMD b , and in the four groups G1 to G4 with PMI and PMD c. G1 higher PMI and higher PMD, G2 lower PMI and higher PMD, G3 higher PMI and lower PMD, G4 lower PMI and lower PMD, PMI psoas muscle index, PMD psoas muscle density.

The C-index of all-cause mortality significantly improved in the ascending order of adding PMI alone 0. We demonstrated that PMI and PMD were independently associated with GNRI and log CRP, respectively.

Lower PMI and lower PMD were independently associated with an increased risk of all-cause mortality. Patients with both low PMI and low PMD had the worst all-cause survival rate.

In addition, the predictability of all-cause mortality improved the most when PMI and PMD were added to the established risk model. Therefore, both PMI and PMD may be surrogate markers of PEW and may be clinically useful to stratify the risk of all-cause mortality and improve the accuracy of mortality prediction in patients undergoing hemodialysis.

Thus, our findings suggest the merit of simultaneously measuring these indicators in this population. Recently, both muscle quantity and quality have attracted attention as important indicators of sarcopenia 11 , 12 , 13 ; decreased muscle quality or myosteatosis may lead to lower muscle strength and physical function in patients with chronic kidney disease and those undergoing hemodialysis 20 , Muscle quantity is commonly evaluated by measuring the PMA at the L3 level, whereas muscle quality is evaluated by measuring the average CT attenuation value of psoas muscle In this study, PMI and PMD were calculated at the L3 level using a cross-sectional CT image.

PMI was positively and independently associated with male sex and GNRI. Previous studies have reported that muscle mass volume differed with sex 6 , 14 , 17 , 18 ; in this study, the PMI was significantly higher in men than in women.

In patients undergoing hemodialysis, PEW is highly prevalent and is associated with increased risks of morbidity and mortality 3 , 4 , 5 , 6.

GNRI, which is easily calculable based on the serum albumin level and BMI, is a useful nutritional indicator to stratify the risk of PEW 22 , 23 , 24 , 25 , 26 ; therefore, our results suggest that PMI may be an indicator of PEW.

Conversely, PMD was independently negatively associated with age and log CRP, an inflammatory marker, in this study. Some previous reports showed that muscle quality may deteriorate with age 27 , Furthermore, Raj et al.

reported that expression of the inflammatory cytokine IL-6 in the muscle intensified muscle protein catabolism and amino acid release, resulting in acute-phase protein synthesis in patients undergoing hemodialysis The development of chronic inflammation in chronic kidney disease has been described as the consequence of a multifactorial etiology of interactions that emerge in the uremic circumstances.

Chronic inflammation is recognized as a component of the uremic phenotype closely linked to PEW 30 ; thus, PMD may be also a surrogate maker of PEW. Interestingly, we noted that PMD was significantly correlated with PMI; therefore, our results demonstrate that muscle quantity and quality might have deteriorated to the same extent in our study cohort.

However, although PMI differed with sex, PMD did not. This is an observation that we cannot explain adequately. Although no study has examined PMD in hemodialysis patients, several previous studies reported similar findings 16 , This might be because muscle quantity was maintained in these male patients, but their muscle quality deteriorated.

In this study, a lower PMI was independently associated with an increased risk of all-cause mortality in patients undergoing hemodialysis.

Several studies have evaluated the associations between PMI and mortality in Japanese patients undergoing hemodialysis. Kurumisawa et al. reported that PMI measured before cardiovascular surgery was a predictor of survival after surgery Takata et al.

recently reported that PMI was correlated with bioelectrical impedance analysis-measured skeletal muscle mass index, and that lower PMI was associated with increased risks of mortality Our results were similar to those reported previously.

In this study, a lower PMD was independently associated with the risk of all-cause mortality in hemodialysis patients. Several studies have demonstrated that PMD can predict mortality in patients with various types of cancer, type 2 diabetes, and trauma 16 , 17 , 18 , As mentioned above, PMD may reflect inflammation, which may lead to PEW; therefore, the measurement of PMD may be clinically useful as a predictor of mortality in this population.

To the best of our knowledge, this study is the first to investigate the relationship of PMI, PMD, and the two combined with mortality in patients undergoing hemodialysis. Patients with a lower PMI and PMD had the worst all-cause mortality rate. Interestingly, the predictive accuracy of all-cause mortality improved the most when both PMI and PMD were added as factors to the baseline risk model.

This may be because PMI and PMD are used to evaluate different aspects of PEW and sarcopenia, as mentioned above; therefore, the combination of these indicators may increase the accuracy of mortality prediction.

Thus, when CT is used to evaluate muscle wasting, the simultaneous assessment of PMI and PMD may be recommended to stratify the risk of all-cause mortality and predict mortality in patients undergoing hemodialysis. Indeed, the clinical use of CT for measuring body composition may be limited due to concerns of radiation exposure and its high cost.

However, CT can be applied to patients who are not able to perform bioimpedance analysis: i. Moreover, when abdominal CT is performed for other purposes, including cancer screening as in the present study, it might also be useful for detecting muscle wasting or for predicting mortality.

Therefore, studies that will examine the clinical utility of CT compared with clinically available bioimpedance analysis may be required in the future.

This study had some limitations. First, this retrospective, single-center study included a small number of patients undergoing hemodialysis. Second, only Japanese patients undergoing hemodialysis, who reportedly have a better prognosis than those in the United States of America and Europe 31 , were enrolled, thereby limiting the generalizability of our results to patients undergoing hemodialysis in other countries.

Third, the PMI and PMD values used for the data analyses were measured only at enrollment, and any changes in these values during the follow-up period were not evaluated. More considerations are required to decide the optimal cutoff value of PMI and PMD in patients undergoing hemodialysis.

Further prospective, large-scale, and multicenter studies are required to validate our results. In conclusion, CT-measured PMI and PMD were significant predictors for all-cause mortality and may be surrogate markers of PEW in patients undergoing hemodialysis.

The combination of PMI and PMD was effective for stratifying the risk of all-cause mortality and improving the accuracy of mortality prediction in this population.

Therefore, the simultaneous assessment of these indicators may be recommended to predict mortality in patients undergoing hemodialysis. The requirement for informed consent was waived by Matsunami Generel Hospital Medical Ethics Committee owing to the nature of retrospective study design and the analysis of anonymised patient data.

Patients with a history of diabetes mellitus or using glucose-lowering medications were considered to have diabetes mellitus. Blood samples were obtained with the patients in supine position before the initiation of a hemodialysis session on a Monday or Tuesday, and the CT and laboratory data collected from the same month were used for data analysis.

The PMI and PMD were assessed using abdominal CT performed after a hemodialysis session. Using a wide-bore 16 slice multi-detector CT scanner LightSpeed RT16; GE Healthcare, Waukesha, WI, USA ; 5 mm-thick slices were acquired.

A cross-sectional CT image at the level of L3 was selected. The picture archiving and communication system, Xtrek View J-mac System, Inc. The Polygon tool was used to trace the periphery of the bilateral psoas muscles without CT value thresholding.

First, the PMA value was obtained by summing the area of right and left psoas muscles. The PMA was normalized by the height squared to obtain the PMI value. The PMD was then measured based on the average CT value of the bilateral psoas muscles, determined by summing the product of the mean right CT value and right PMA and the product of mean left CT value and left PMA, and dividing this value by the PMA.

The study endpoint was all-cause mortality. Using a receiver operating characteristic analysis, the cut-off values of PMI and PMD, which maximally predicted all-cause mortality, were obtained for each sex. Subsequently, patients were divided by each sex-specific cut-off value lower PMI vs.

higher PMI groups and lower PMD vs. higher PMD groups. Thereafter, they were divided into four groups according to these cut-off values G1: higher PMI and higher PMD; G2: lower PMI and higher PMD; G3: higher PMI and lower PMD; G4: lower PMI and lower PMD. Patients were followed up until December To compare the differences among the four groups, a one-way analysis of variance or the Kruskal—Wallis test for continuous variables and the chi-squared test for categorical variables were used.

A univariate regression analysis was performed to assess the baseline factors associated with the PMI and PMD, respectively. The Kaplan—Meier method was used to estimate survival, which was analyzed using the log-rank test.

The proportional hazards assumption was assessed with log-minus-log plot and Schoenfeld residuals, and no apparent violation of the assumption was detected. The NRI is a relative indicator of the number of patients for whom the predicted probabilities for mortality improve, whereas the IDI represents the average improvement in predicted probabilities for mortality after the addition of variables to the baseline model Statistical analyses were performed using SPSS version 24 IBM Corp.

The dataset analyzed in the present study is available in this published article as a supplementary information files. Giglio, J. et al. Association of sarcopenia with nutritional parameters, quality of life, hospitalization, and mortality rates of elderly patients on hemodialysis.

Article PubMed Google Scholar. Sabatino, A. Sarcopenia in chronic kidney disease: What have we learned so far?. Fouque, D. A proposed nomenclature and diagnostic criteria for protein—energy wasting in acute and chronic kidney disease. Kidney Int. Currently, there are no widely accepted cut-off points to categorize patients as sarcopenic low skeletal muscle mass or myosteatotic based on computed tomography CT measurements.

Moreover, little is known about skeletal muscle mass in healthy subjects, particularly in a Western-European population. Differences between sex, body mass index BMI , age groups, and American Society of Anesthesiologists ASA classification were assessed.

Of the included patients, Male gender, increased age, and increased BMI were significantly associated with both skeletal muscle mass and density. Skeletal muscle density and mass were significantly associated with sex, age, and BMI in a large cohort of healthy Western-European subjects.

The newly developed nomograms may be used to calculate the estimated healthy skeletal muscle mass for individuals in patient populations.

This is a preview of subscription content, access via your institution. Heymsfield SB, Wang Z, Baumgartner RN, Ross R. Human body composition: advances in models and methods. Annu Rev Nutr. Article CAS PubMed Google Scholar.

Kvist H, Sjostrom L, Tylen U. Adipose tissue volume determinations in women by computed tomography: technical considerations. Int J Obes. CAS PubMed Google Scholar. Prado CM, Birdsell LA, Baracos VE. The emerging role of computerized tomography in assessing cancer cachexia.

Curr Opin Support Palliat Care. Article Google Scholar. Shen W, Punyanitya M, Wang Z, Gallagher D, St-Onge MP, Albu J. et al. Total body skeletal muscle and adipose tissue volumes: estimation from a single abdominal cross-sectional image. J Appl Physiol Schweitzer L, Geisler C, Pourhassan M, Braun W, Gluer CC, Bosy-Westphal A, et al.

What is the best reference site for a single MRI slice to assess whole-body skeletal muscle and adipose tissue volumes in healthy adults? Am J Clin Nutr. Article CAS Google Scholar. Prado CM, Lieffers JR, McCargar LJ, Reiman T, Sawyer MB, Martin L.

Prevalence and clinical implications of sarcopenic obesity in patients with solid tumours of the respiratory and gastrointestinal tracts: a population-based study.

Lancet Oncol. Levolger S, van Vugt JL, de Bruin RW, IJzermans JN. Systematic review of sarcopenia in patients operated on for gastrointestinal and hepatopancreatobiliary malignancies. Br J Surg. van Vugt JL, Levolger S, de Bruin RW, van Rosmalen J, Metselaar HJ, IJzermans JN.

Systematic review and meta-analysis of the impact of computed tomography assessed skeletal muscle mass on outcome in patients awaiting or undergoing liver transplantation.

Am J Transplant. Mourtzakis M, Prado CM, Lieffers JR, Reiman T, McCargar LJ, Baracos VE. A practical and precise approach to quantification of body composition in cancer patients using computed tomography images acquired during routine care. Appl Physiol Nutr Metab. van Vledder MG, Levolger S, Ayez N, Verhoef C, Tran TC, IJzermans JN.

Body composition and outcome in patients undergoing resection of colorectal liver metastases. Article PubMed Google Scholar. Coelen RJ, Wiggers JK, Nio CY, Besselink MG, Busch OR, Gouma DJ, et al.

Preoperative computed tomography assessment of skeletal muscle mass is valuable in predicting outcomes following hepatectomy for perihilar cholangiocarcinoma. HPB Oxford. Martin L, Birdsell L, Macdonald N, Reiman T, Clandinin MT, McCargar LJ. Cancer cachexia in the age of obesity: skeletal muscle depletion is a powerful prognostic factor, independent of body mass index.

J Clin Oncol. Masanes F, Rojano ILX, Salva A, Serra-Rexach JA, Artaza I, Formiga F, et al. Cut-off points for muscle mass—not grip strength or gait speed—determine variations in sarcopenia prevalence.

J Nutr Health Aging. van Vugt JL, Levolger S, de Bruin RW, IJzermans JN. A comparative study of software programs for cross-sectional skeletal muscle area measurements on abdominal computed tomography scans. J Cachexia Sarcopenia Muscle. van Vugt JLA, Coebergh van den Braak RRJ, Schippers HJW, Veen KM, Levolger S, de Bruin RWF, et al.

Contrast-enhancement influences skeletal muscle density, but not skeletal muscle mass, measurements on computed tomography. Clin Nutr. pii: S; van der Werf A, Langius JAE, de van der Schueren MAE, Nurmohamed SA, van der Pant K, Blauwhoff-Buskermolen S. Percentiles for skeletal muscle index, area and radiation attenuation based on computed tomography imaging in a healthy Caucasian population.

Eur J Clin Nutr. Carey EJ, Lai JC, Wang CW, Dasarathy S, Lobach I, Montano-Loza AJ, et al. A multicenter study to define sarcopenia in patients with end-stage liver disease. Liver Transpl. Golse N, Bucur PO, Ciacio O, Pittau G, Sa Cunha A, Adam R, et al. A new definition of sarcopenia in patients with cirrhosis undergoing liver transplantation.

Yoshizumi T, Shirabe K, Nakagawara H, Ikegami T, Harimoto N, Toshima T, et al. Skeletal muscle area correlates with body surface area in healthy adults. Hepatol Res. Collaboration NCDRF.

Lower muscle mass in populations Muscle density measurement obesity is Muscle density measurement obesity-related denity like measuremeent and type 2 diabetes measuremen. Bariatric surgery leads to sustained weight loss. During the weight reduction, loss of muscle should be minimized. Thus reliable quantification of muscle mass is much needed and therefore the also the need for validated methods. Imaging methods, magnetic resonance imaging and computed tomography scan, have been the gold standard for many years. Muscle density measurement

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