Zoekresultaten

Bio-electrical impedance analysis

Background: The diagnosis of sarcopenia is essential for early treatment of sarcopenia in older adults, for which assessment of appendicular lean mass (ALM) is needed. Multi-frequency bio-electrical impedance analysis (MF-BIA) may be a valid assessment tool to assess ALM in older adults, but the evidences are limited. Therefore, we validated the BIA to diagnose low ALM in older adults.Methods: ALM was assessed by a standing-posture 8 electrode MF-BIA (Tanita MC-780) in 202 community-dwelling older adults (age ≥ 55 years), and compared with dual-energy X-ray absorptiometry (DXA) (Hologic Inc., Marlborough, MA, United States; DXA). The validity for assessing the absolute values of ALM was evaluated by: (1) bias (mean difference), (2) percentage of accurate predictions (within 5% of DXA values), (3) the mean absolute error (MAE), and (4) limits of agreement (Bland-Altman analysis). The lowest quintile of ALM by DXA was used as proxy for low ALM (< 22.8 kg for men, < 16.1 kg for women). Sensitivity and specificity of diagnosing low ALM by BIA were assessed.Results: The mean age of the subjects was 72.1 ± 6.4 years, with a BMI of 25.4 ± 3.6 kg/m2, and 71% were women. BIA slightly underestimated ALM compared to DXA with a mean bias of -0.6 ± 1.2 kg. The percentage of accurate predictions was 54% with a MAE of 1.1 kg, and limits of agreement were -3.0 to + 1.8 kg. The sensitivity for ALM was 80%, indicating that 80% of subjects who were diagnosed as low ALM according to DXA were also diagnosed low ALM by BIA. The specificity was 90%, indicating that 90% of subjects who were diagnosed as normal ALM by DXA were also diagnosed as normal ALM by the BIA.Conclusion: This comparison showed a poor validity of MF-BIA to assess the absolute values of ALM, but a reasonable sensitivity and specificity to recognize the community-dwelling older adults with the lowest muscle mass.

Accuracy of bioelectrical impedance analysis and skinfold thickness in the assessment of body composition in people with chronic spinal cord injury

STUDY DESIGN: Cross-sectional study.OBJECTIVES: This study: (1) investigated the accuracy of bioelectrical impedance analysis (BIA) and skinfold thickness relative to dual-energy X-ray absorptiometry (DXA) in the assessment of body composition in people with spinal cord injury (SCI), and whether sex and lesion characteristics affect the accuracy, (2) developed new prediction equations to estimate fat free mass (FFM) and percentage fat mass (FM%) in a general SCI population using BIA and skinfolds outcomes.SETTING: University, the Netherlands.METHODS: Fifty participants with SCI (19 females; median time since injury: 15 years) were tested by DXA, single-frequency BIA (SF-BIA), segmental multi-frequency BIA (segmental MF-BIA), and anthropometry (height, body mass, calf circumference, and skinfold thickness) during a visit. Personal and lesion characteristics were registered.RESULTS: Compared to DXA, SF-BIA showed the smallest mean difference in estimating FM%, but with large limits of agreement (mean difference = -2.2%; limits of agreement: -12.8 to 8.3%). BIA and skinfold thickness tended to show a better estimation of FM% in females, participants with tetraplegia, or with motor incomplete injury. New equations for predicting FFM and FM% were developed with good explained variances (FFM: R2 = 0.94; FM%: R2 = 0.66).CONCLUSIONS: None of the measurement techniques accurately estimated FM% because of the wide individual variation and, therefore, should be used with caution. The accuracy of the techniques differed in different subgroups. The newly developed equations for predicting FFM and FM% should be cross-validated in future studies.

Standard Operating Procedure - Single Frequency Bio-Impedantie Analyse

Identifying critically ill patients with low muscle mass

Background & aims: Low muscle mass and -quality on ICU admission, as assessed by muscle area and -density on CT-scanning at lumbar level 3 (L3), are associated with increased mortality. However, CT-scan analysis is not feasible for standard care. Bioelectrical impedance analysis (BIA) assesses body composition by incorporating the raw measurements resistance, reactance, and phase angle in equations. Our purpose was to compare BIA- and CT-derived muscle mass, to determine whether BIA identified the patients with low skeletal muscle area on CT-scan, and to determine the relation between raw BIA and raw CT measurements. Methods: This prospective observational study included adult intensive care patients with an abdominal CT-scan. CT-scans were analysed at L3 level for skeletal muscle area (cm2) and skeletal muscle density (Hounsfield Units). Muscle area was converted to muscle mass (kg) using the Shen equation (MMCT). BIA was performed within 72 h of the CT-scan. BIA-derived muscle mass was calculated by three equations: Talluri (MMTalluri), Janssen (MMJanssen), and Kyle (MMKyle). To compare BIA- and CT-derived muscle mass correlations, bias, and limits of agreement were calculated. To test whether BIA identifies low skeletal muscle area on CT-scan, ROC-curves were constructed. Furthermore, raw BIA and CT measurements, were correlated and raw CT-measurements were compared between groups with normal and low phase angle. Results: 110 patients were included. Mean age 59 ± 17 years, mean APACHE II score 17 (11–25); 68% male. MMTalluri and MMJanssen were significantly higher (36.0 ± 9.9 kg and 31.5 ± 7.8 kg, respectively) and MMKyle significantly lower (25.2 ± 5.6 kg) than MMCT (29.2 ± 6.7 kg). For all BIA-derived muscle mass equations, a proportional bias was apparent with increasing disagreement at higher muscle mass. MMTalluri correlated strongest with CT-derived muscle mass (r = 0.834, p < 0.001) and had good discriminative capacity to identify patients with low skeletal muscle area on CT-scan (AUC: 0.919 for males; 0.912 for females). Of the raw measurements, phase angle and skeletal muscle density correlated best (r = 0.701, p < 0.001). CT-derived skeletal muscle area and -density were significantly lower in patients with low compared to normal phase angle. Conclusions: Although correlated, absolute values of BIA- and CT-derived muscle mass disagree, especially in the high muscle mass range. However, BIA and CT identified the same critically ill population with low skeletal muscle area on CT-scan. Furthermore, low phase angle corresponded to low skeletal muscle area and -density. Trial registration: ClinicalTrials.gov (NCT02555670).

Dietary protein intake is not associated with 5-y change in mid-thigh muscle cross-sectional area by computed tomography in older adults: the Health, Aging, and Body Composition (Health ABC) Study

Background: A higher protein intake is suggested to preserve muscle mass during aging and may therefore reduce the risk of sarcopenia.Objectives: We explored whether the amount and type (animal or vegetable) of protein intake were associated with 5-y change in mid-thigh muscle cross-sectional area (CSA) in older adults (n = 1561).Methods: Protein intake was assessed at year 2 by a Block foodfrequency questionnaire in participants (aged 70–79 y) of the Health, Aging, and Body Composition (Health ABC) Study, a prospective cohort study. At year 1 and year 6 mid-thigh muscle CSA in square centimeters was measured by computed tomography. Multiple linearregression analysis was used to examine the association between energy-adjusted protein residuals in grams per day (total, animal, and vegetable protein) and muscle CSA at year 6, adjusted for muscle CSA at year 1 and potential confounders including prevalent health conditions, physical activity, and 5-y change in fat mass.Results: Mean (95% CI) protein intake was 0.90 (0.88, 0.92) g ·kg–1 · d–1 and mean (95% CI) 5-y change in muscle CSA was −9.8 (−10.6, −8.9) cm2. No association was observed between energyadjusted total (β = −0.00; 95% CI: −0.06, 0.06 cm2; P = 0.982), animal (β = −0.00; 95% CI: −0.06, 0.05 cm2; P = 0.923), or plant(β = +0.07; 95% CI: −0.06, 0.21 cm2; P = 0.276) protein intake and muscle CSA at year 6, adjusted for baseline mid-thigh muscle CSA and potential confounders.Conclusions: This study suggests that a higher total, animal, or vegetable protein intake is not associated with 5-y change in midthigh muscle CSA in older adults. This conclusion contradicts some, but not all, previous research. This trial was registered at www.trialregister.nl as NTR6930.

Computed tomography reference values for visceral obesity and increased metabolic risk in a Caucasian cohort

BACKGROUND: Visceral obesity is associated with the metabolic syndrome. The metabolic risk differs per ethnicity, but reference values for visceral obesity for body composition analyses using Computed Tomography (CT) scans in the Caucasian population are lacking. Therefore, the aim of this study was to define gender specific reference values for visceral obesity in a Caucasian cohort based upon the association between the amount of visceral adipose tissue (VAT) and markers of increased metabolic risk.METHODS: Visceral Adipose Tissue Area Index (VATI cm 2/m 2) at the level of vertebra L3 was analyzed using CT scans of 416 healthy living kidney donor candidates. The use of antihypertensive drugs and/or statins was used as an indicator for increased metabolic risk. Gender specific cut-off values for VATI with a sensitivity ≥80% were calculated using receiver operating characteristic (ROC) curves. RESULTS: In both men and women who used antihypertensive drugs, statins or both, VATI was higher than in those who did not use these drugs (p ≤ 0.013). In males and females respectively, a value of VATI of ≥38.7 cm 2/m 2 and ≥24.9 cm 2/m 2 was associated with increased metabolic risk with a sensitivity of 80%. ROC analysis showed that VATI was a better predictor of increased metabolic risk than BMI (area under ROC curve (AUC) = 0.702 vs AUC = 0.556 in males and AUC = 0.757 vs AUC = 0.630 in females). CONCLUSION: Gender and ethnicity specific cut-off values for visceral obesity are important in body composition research, although further validation is needed. This study also showed that quantification of VATI is a better predictor for metabolic risk than BMI.