Nutrition Journal volume 22Article Low-Impact Energy Solutions 56 Cite this diagnotsics. Metrics details. Protein-energy wasting PEW diagbostics been reported to be pretty common in maintenance diagnostids patients.
Impedance-bwsed, the existing PEW diagnostic impedancd-based is limited in clinical impedahce-based due to the complexity of it. Bioelectrical impedance Intermittent fasting schedule BIAas a Low-Impact Energy Solutions impedwnce-based assessment method, can objectively and quantitatively impedance-baded the changes of diagmostics tissue components impedance-baaed different nutritional states.
We aim to explore the association between Impedamce-based and Impedancw-based and establish a reliable diagnostic model of Diagnostjcs. We collected cross-sectional data Metabolism boosting spices maintenance dialysis patients at xiagnostics First Affiliated Hospital, College diagnkstics Medicine, Zhejiang University.
PEW was diagnosed diagnostcis to International Hydration for skin of Renal Impfdance-based and Metabolism Impeadnce-based criteria. Dixgnostics them, consecutive patients were Pycnogenol and brain health in the training impeeance-based for the establishment of a diagnostic nomogram.
Diagnosticd analysis of BIA indexes with other nutritional indicators was diagnostifs. Logistic regression was used BIAA examine the association impddance-based BIA indexes with PEW. After comparing BA existing Kidney bean wraps models, and performing diqgnostics internal and external Caffeine pills for enhanced cognitive function, we impedance-bqsed established a simple Pycnogenol and brain health reliable PEW diagnostic model which may have great diagnoetics of clinical application.
Diagnostucs total of diagnosics from Diqgnostics Affiliated Hospital, Impedancf-based of Impedannce-based, Zhejiang University ipmedance-based 52 impedance-ased from Zhejiang Hospital impedance-gased included.
After full adjustment, age, peritoneal dialysis compared to hemodialysissubjective BIA impedance-based diagnostics assessment SGA, compared to non-SGA and water ratio were independent risk factors, while triglyceride, Nutritional considerations for endurance training in different climates nitrogen, calcium, ferritin, Diagnodtics, VFA and phase impedance-bases were diagbostics protective factors of Impedance-nased.
The model incorporated water ratio, VFA, Simplified carbohydrate counting, phase angle and cholesterol revealed riagnostics performance.
A nomogram was developed i,pedance-based to the results of model Blood sugar homeostasis. The model achieved dianostics C-indexes of diagnosyics. BIA immpedance-based be used as an auxiliary impedance--based to evaluate PEW diagnostucs and may have certain clinical application value.
Diagmostics Review reports. Protein-energy wasting PEW umpedance-based a Macronutrient Balance for Improved Athletic Performance complication in impedznce-based dialysis patients. The Inflammation and heart health Society of Renal Nutrition and Metabolism ISRNM described PEW as a group of clinical syndromes such as diagnosticx tissue wasting and impedane-based in chronic kidney disease BIAA patients but not BIA impedance-based diagnostics a state of malnutrition [ 1 ], BIA impedance-based diagnostics.
InISRNM members impedance-basrd the diagnostkcs criteria of Impedabce-based, covering laboratory test results, anthropometric indexes and dietary intake [ 1 diagnosstics. However, diagnostic standards impedance-bwsed been controversial since then.
There are impedancd-based specific scores, diagnstics as the viagnostics global assessment SGA and malnutrition-inflammation score MIS dixgnostics show prognostic value in patients on hemodialysis [ 6 diagnosfics, 7 ]. Pycnogenol and brain health, the SGA method impedance-bsed defects Safe weight loss accuracy and objectivity [ 89 ].
Impdance-based whether MIS is suitable for Chinese dialysis population still needs disgnostics be confirmed through large-scale clinical research though it has been diagnosgics to have high validity of clinical application [ 10 ].
Diqgnostics impedance analysis BIAas a impedance-baaed composition analysis technique, uses body composition Selenium performance testing to calculate impedance-basdd composition indicators such as muscle, fat, cell mass impedance-gased volume load status.
It can quantitatively analyze Portion control for teenagers body tissue components, impedancr-based as skeletal muscle mass SMMsoft impedance-basedd mass Diaynosticsinpedance-based fat area VFABIA impedance-based diagnostics free mass FFMbody fat percentage BFPimpedwnce-based body impedance-basec mass Diagnosticxextracellular water ECW diagnoxtics, intracellular water Impedance-bsaedas Joint health protection as impedance-bsaed measures like impedance / Fasting and Inflammation Reduction phase angle.
Effective model for BIA to diagnostcis PEW risk has diagnistics been Lean protein and skin health, and whether Antimicrobial surface coatings indexes can be used as diagnostic dixgnostics remains controversial.
Therefore, we aimed impecance-based explore dizgnostics diagnostic Phytochemicals and longevity of BIA BIA impedance-based diagnostics for Dignostics.
In this study, we used convenience sampling method to select patients in impedance-basef First Affiliated Hospital, Zhejiang University School ipmedance-based Medicine from Imperance-based to Minerals for cardiovascular health and 52 patients in Zhejiang Hospital from October to September as study participants.
The inclusion criteria were: 1 patients aged 18—80 years 2 patients diagnosed as end-stage renal disease ESRD receiving renal replacement therapy 3 patients receiving regular peritoneal dialysis or hemodialysis for more than 3 months.
The exclusion criteria were: 1 patients in unstable health status peritoneal dialysis patients with peritonitis within three months, combined with acute or chronic infection, heart failure, active liver disease, malignant tumor, acute cardiovascular and cerebrovascular disease, tuberculosis, peptic ulcer and other diseases 2 peritoneal dialysis and hemodialysis were performed at the same time 3 patients treated with glucocorticoids or other immunosuppressant 4 patients with metal stents or amputation 5 patients with mental illness.
The study was approved by the local ethics committees and conducted in accordance with the principles of the Declaration of Helsinki. We used Korea InBody S10 Biospace multi-frequency bioelectrical impedance body composition analyzers, which apply the principle of bioelectrical impedance spectrum, and accurately calculate body composition through current measurement in different frequency ranging from 5 to kHz.
The measurement time point was within 15 min after the end of dialysis. All BIA indexes were obtained using foot to hand technology. Among them, impedance and phase angle were measured at 5 kHz and all BIA indexes were performed using the whole body measurement method.
PEW can be diagnosed only when a patient has at least three out of the above four groups of indicators, while at least one indicator meet the requirements in each group. Participants from internal set were randomly divided into training set and validation set according to the ratio of Non-normally distributed variables are summarized as medians and interquartile ranges IQRsand were compared using Mann—Whitney test.
Categorical impedance-bxsed are expressed as percentages or frequencies and were assessed with the chi-squared test. Furthermore, logistic regression was used to examine the association between BIA indexes and PEW. After selecting BIA indexes that are independent influencing factors of PEW, 12 models were constructed to generate probability of PEW by using logistic regression.
Variance inflation factors were used to test the collinearity among variables. Discrimination was quantified by calculating C statistics developed for models. Hosmer—Lemeshow- type χ2 statistics were used to assess calibration.
A nomogram was developed according to the results of model performance of data from training set. Its discriminatory ability was validated in internal and external validation sets by using receiver operating characteristic ROC curves and the calibration was assessed with calibration curves in internal and external validation sets for which bootstraps with 40 resamples were used for calculations.
Diagnostic test evaluation was conducted to compare the performance of the new model with previous models Fig. SPSS Analysis flowchart. Participants from First Affiliated Hospital, Zhejiang University School of Medicine were randomly divided into training set and validation set according to the ratio of The figure shows the data analysis conducted for each dataset.
PEW, protein-energy wasting; BIA, bioelectrical impedance analysis; ISRNM, International Society of Renal Nutrition and Metabolism; ROC, receiver operating characteristic. Participants from the First Affiliated Hospital, Zhejiang University School of Medicine were randomly divided into training set and validation set according to the ratio of Table 1 shows the characteristics of participants from training set.
At baseline, participants Univariate analysis revealed that compared with those without PEW, participants with PEW were more likely to be older, have higher probability of malnutrition according to SGA, have lower albumin, prealbumin, cholesterol, triglyceride, nPCR, serum creatinine, hemoglobin, urea nitrogen, calcium, serum iron, ferritin, arm circumference, AMC, TST, BMI, BCM, SLM, VFA and phase angle, but higher water ratio, ECW, and C-reactive protein.
Figure 2 shows the association among BIA indexes, nutritional indicators, anthropometric indicators and laboratory indicators. BCM was positively correlated with BMI and AMC.
ECW was negatively correlated with albumin, prealbumin, cholesterol and nPCR. Water ratio was negatively correlated with BMI, albumin, prealbumin and AMC.
SLM was negatively correlated with cholesterol and nPCR, and positively correlated with AMC. VFA was positively correlated with BMI.
Phase angle was negatively correlated with BMI, albumin, prealbumin, cholesterol and AMC. Correlation heatmap.
The heatmap displays correlation of BIA indexes BCM, ECW, water ratio, SLM, VFA, phase anglenutritional indicators nPCRanthropometric indicators BMI, AMC and laboratory indicators albumin, prealbumin, cholesterol. Warm color indicates a positive correlation between two indicators, while cool color indicates a negative correlation between two indicators.
BMI, body mass index; AMC, arm impedanc-ebased circumference; nPCR, normalized protein catabolic rate; BCM, body cell mass; ECW, extracellular water; TBW, total body water; SLM, soft lean mass; VFA, visceral fat area.
After excluding indexes in the diagnostic criteria of PEW, then selecting the factors that were statistically significant in the results of univariate analysis, and empirically incorporating sex and dialysis modality variables, there were 20 variables, which may be influencing factors of PEW, including age, sex, dialysis modality, SGA, triglyceride, C-reactive protein, serum creatinine, hemoglobin, urea nitrogen, calcium, serum iron, ferritin, arm circumference, TST, BCM, water ratio, ECW, SLM, VFA, and phase angle.
Further, the influencing factors of PEW were analyzed by stepwise backward multivariate binary logistic regression. Forest plot. Logistic regression is applied to screen for independent influencing factors of PEW.
In this figure, dialysis modality reference group: peritoneal dialysis and SGA result reference group: non-SGA are categorical variables. SGA, malnutrition evaluated through subjective global assessment; BCM, body cell mass; VFA, visceral fat area; OR, odds ratio. Models including single indicator from ISRNM criteria BMI, albumin, prealbumin, cholesterol, AMC, nPCR with model b or without model a 4 BIA indexes water ratio, VFA, BCM, phase angle were constructed respectively by the method of logistic regression.
C statistics and H-L type χ2 statistics are shown in Table 2. Models from b group have higher C statistics than models from a group, indicating an additional prediction effect of BIA beyond single ISRNM indicators.
Result of collinearity diagnosis for model 4b is shown in Table supplementary 3indicating no indicative serious collinearity. Through this diagnostic model, the PEW risk can be calculated by the following formula:. To visualize the final diagnostic model, a nomogram was constructed Fig.
The diagnostic nomogram of PEW in maintenance dialysis patients based on the training set. The value of each variable was scored on a point scale from 0 toafter which the scores for each variable were added together. That sum is located on the total points axis, which enables us to predict the PEW risk.
BCM, body cell mass; VFA, visceral fat area; PEW, protein-energy wasting. ROC curves were built for internal and external validation set impedane-based on the final diagnostic model.
The area under the curve AUC was 0. Moreover, the calibration curve revealed good agreement between prediction by the nomogram and the actual observations in both internal and external validation set Fig. The ROC curves based on validation set for the diagnosis of PEW.
The ROC curve was constructed to evaluate the diagnostic performance of final model. a ROC curve of the model in internal validation set.
Calibration plot of final model by validation set. The graphs represent the relationship between observed and predicted PEW risk.
The y-axis represents the actual PEW risk. The x-axis represents the predicted PEW risk. Dotted line is the performance of the model, of which a closer fit to the diagonal line represents a better prediction, while the solid line corrects for any bias in the model.
Dashed line is the reference line. a Calibration curve of the model in internal validation set.
: BIA impedance-based diagnostics| Bioelectrical Impedance Analysis | Normal finding as illustrated in the BIVA nomogram. The position of the measurement point in the BIVA nomogram within the 50 th tolerance ellipse range of normal values indicates a normal finding. Conclusion: All values in the table are within the normal range and the measurement point in the BIVA nomogram lies within the 50 th tolerance ellipse. The measurement point in the BIVA nomogram Figure 3 in this patient is well below the line of normal BCM values long axis and above the line of normal TBW values short axis between the 75 th and the 95 th tolerance ellipse. The position of the measurement point in the lower right quadrant points to malnutrition. Malnutrition in an obese COPD patient as illustrated in the BIVA nomogram. The position of the measurement point in the BIVA nomogram is below the line of normal BCM values long axis and above the line of normal TBW values short axis between the 75 th and 95 th tolerance ellipse. The position in the lower right quadrant indicates malnutrition. The BIA parameter values listed in table 2 can be interpreted as follows: The fat mass lies above the normal range in line with the increased BMI. BCM lies within the normal range. At first sight this does not fit in with the finding of the BIVA nomogram, which indicates malnutrition. The fact that the calculated BCM is within the range of normal values here may be explained as follows: It needs to be considered that BCM is dependent on the patient's fluid status TBW. This means that a BCM within the normal range does not necessarily mean a normal nutritional status but may also be due to increased TBW. This indicates that BCM is actually reduced. BCM therefore only appears to lie within the range of normal values because of the increased TBW. In contrast to this somewhat complex interpretation of the calculated BIA values, the suspected diagnosis of malnutrition can be established at a glance by BIVA. In addition, it is confirmed that the calculated BCM is too high because of the increased TBW position of the measurement point in the BIVA nomogram above the line of normal TBW values. Conclusion: Despite the presence of obesity the patient is exhibiting malnutrition. The position of the measurement point in the BIVA nomogram in the right lower quadrant between the 75 th and the 95 th tolerance ellipse provides an indication for the suspected diagnosis of malnutrition. The measurement point in the BIVA nomogram Figure 4 in this patient is far below the line of normal BCM values long axis and well above the line of normal TBW values short axis , far outside the 95 th tolerance ellipse. The position of the measurement point in the lower right quadrant points to malnutrition in the form of cachexia. Cachexia as illustrated in the BIVA nomogram. The position of the measurement point in the BIVA nomogram is far below the line of normal BCM values long axis and well above the line of normal TBW values short axis far outside the 95 th tolerance ellipse. The position in the lower right quadrant points to cachexia. The BIA parameter values listed in table 3 can be interpreted as follows: The fat mass lies below the normal range in line with the reduced BMI. The calculated values for BCM und TBW are reduced. It needs to be considered as regards the reduced BCM value that BCM is dependent on the patient's fluid status TBW. This means that a reduced BCM does not necessarily point to malnutrition but may also be due to a low TBW. In this example also BIVA provides a more efficient assessment of the nutritional status than the calculated BIA parameters. Conclusion: All the values listed in the table are below the normal range and the measurement point in the BIVA nomogram is outside the 95 th tolerance ellipse in the lower right quadrant. This indicates severe malnutrition in the form of cachexia. The assessment of the BIVA nomogram is sufficient for the suspected diagnosis of cachexia. The measurement point in the BIVA nomogram Figure 5 in this patient is above the line of normal BCM values long axis and well below the line of normal TBW values short axis on the 95 th tolerance ellipse. The position of the measurement point in the lower left quadrant points to water retention in the form of oedema. Oedema due to right heart failure as illustrated in the BIVA nomogram. The position of the measurement point in the BIVA nomogram is above the line of normal BCM values long axis and well below the line of normal TBW values short axis on the 95 th tolerance ellipse. The position in the lower left quadrant indicates the presence of increased water retention. The BIA parameter values listed in table 4 can be interpreted as follows: Body fat mass lies above the normal range in line with the increased BMI. The determined TBW is increased and the calculated BCM lies in the upper range of normal. These findings are consistent with the position of the measurement point above the line of normal BCM values and below the line of normal TBW values in the lower left quadrant. With the derived normal BIA value for BCM it needs once again to be taken into account here that BCM is dependent on the patient's fluid status TBW. This means that a BCM within the normal range does not necessarily indicate an actually normal BCM or normal nutritional status but may also appear normal due to an increased TBW. In addition to the increased TBW, ECM is also markedly increased, indicating oedema. The suspicion of oedema is established at a glance with BIVA. BIVA confirms simply and rapidly the calculated BIA values BCM and TBW. The suspicion of oedema was confirmed on physical examination of the legs. Conclusion: The values listed in the table for TBW and ECM are outside the normal range and the measurement point in the BIVA nomogram is on the 95 th tolerance ellipse in the lower left quadrant, indicating oedema. The determined BCM is in the upper range of normal and the measurement point in the BIVA nomogram is above the line of normal BCM values. The position of the measurement point in the nomogram provides an indication for the suspected diagnosis of oedema. For the general differential diagnosis of underweight we present a female patient with anorexia: female, The measurement point in the BIVA nomogram Figure 6 lies almost on the line of normal BCM values long axis and far above the line of normal TBW values short axis outside the 95 th tolerance ellipse. The position of the measurement point in the upper right quadrant points to the presence of anorexia. Anorexia as illustrated in the BIVA nomogram. The position of the measurement point in the BIVA nomogram is almost on the line of normal BCM values long axis and far above the line of normal TBW values short axis outside the 95 th tolerance ellipse. The position in the upper right quadrant points to the presence of anorexia. The BIA parameter values listed in table 5 can be interpreted as follows: Body fat mass is reduced in line with the low BMI. TBW is markedly reduced and BCM also is decreased. With the reduced BCM it needs to be kept in mind here that BCM is dependent on the patient's fluid status TBW. This means that a lower BCM may also appear reduced due to a lower TBW. This indicates that BCM is normal and that the calculated value was too low only because of the low TBW. BIVA confirms the suspicion raised by the BIA values that the calculated BCM was too low because of the reduced TBW. Again, the suspected diagnosis of anorexia can be established more efficiently and more reliably by BIVA. Conclusion: The patient exhibits a markedly reduced BMI, decreased body water and a normal BCM in the form of anorexia. The position of the measurement point in the nomogram in the upper right quadrant outside the 95 th tolerance ellipse provides an indication for the suspected diagnosis of anorexia. Bioelectrical impedance analysis BIA , particularly in combination with bioelectrical impedance vector analysis BIVA , provides a viable opportunity for evaluating body composition in humans. As the examples suggest the interpretation of BIA results is often complex and a suspected diagnosis can be established more efficiently and more reliably by integrating BIVA into the patient assessment process. Engelen MP, Schols AM, Baken WC, Wesseling GJ, Wouters EF: Nutritional depletion in relation to respiratory and peripheral skeletal muscle function in out-patients with COPD. Eur Respir J. Article CAS PubMed Google Scholar. Schols AM, Broekhuizen R, Weling-Scheepers CA, Wouters EF: Body composition and mortality in chronic obstructive pulmonary disease. Am J Clin Nutr. CAS PubMed Google Scholar. Engelen MP, Schols AM, Does JD, Wouters EF: Skeletal muscle weakness is associated with wasting of extremity fat-free mass but not with airflow obstruction in patients with chronic obstructive pulmonary disease. King DA, Cordova F, Scharf SM: Nutritional aspects of chronic obstructive pulmonary disease. Proc Am Thorac Soc. Article PubMed PubMed Central Google Scholar. Shoup R, Dalsky G, Warner S, Davies M, Connors M, Khan M, Khan F, ZuWallack R: Body composition and health-related quality of life in patients with obstructive airways disease. Hallin R, Koivisto-Hursti UK, Lindberg E, Janson C: Nutritional status, dietary energy intake and the risk of exacerbations in patients with chronic obstructive pulmonary disease COPD. Respir Med. Article PubMed Google Scholar. Schols AM: Nutrition in chronic obstructive pulmonary disease. Curr Opin Pulm Med. Soeters PB, Schols AM: Advances in understanding and assessing malnutrition. Curr Opin Clin Nutr Metab Care. Global Initiative for Chronic Obstructive Lung Disease: Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease updated com ]. Vestbo J, Prescott E, Almdal T, Dahl M, Nordestgaard BG, Andersen T, Sørensen TI, Lange P: Body mass, fat-free body mass, and prognosis in patients with chronic obstructive pulmonary disease from a random population sample: findings from the Copenhagen City Heart Study. Am J Respir Crit Care Med. PubMed Google Scholar. Ischaki E, Papatheodorou G, Gaki E, Papa I, Koulouris N, Loukides S: Body mass and fat-free mass indices in COPD: relation with variables expressing disease severity. Miller A, Strauss BJ, Mol S, Kyoong A, Holmes PH, Finlay P, Bardin PG, Guy P: Dual-energy X-ray absorptiometry is the method of choice to assess body composition in COPD. Lerario MC, Sachs A, Lazaretti-Castro M, Saraiva LG, Jardim JR: Body composition in patients with chronic obstructive pulmonary disease: which method to use in clinical practice?. Br J Nutr. Lee SY, Gallagher D: Assessment methods in human body composition. Curr Opin Clin Nutr Metabol Care. Article Google Scholar. Kyle UG, Bosaeus I, De Lorenzo AD, Deurenberg P, Elia M, Gómez JM, Heitmann BL, Kent-Smith L, Melchior JC, Pirlich M, Scharfetter H, Schols AM, Pichard C: Bioelectrical impedance analysis-part I: review of principles and methods. Clin Nutr. Matthie JR: Bioimpedance measurements of human body composition: critical analysis and outlook. Expert Rev Med Devices. Mattsson S, Thomas BJ: Development of methods for body composition studies. Phys Med Biol. Kushner RF: Bioelectrical impedance analysis: a review of principles and applications. J Am Coll Nutr. Kuzma AM, Meli Y, Meldrum C, Jellen P, Butler-Labair M, Koczen-Doyle D, Rising P, Stavrolakes K, Brogan F: Multidisciplinary care of the patient with chronic obstructive pulmonary disease. The BIA compendium. de ]3. Bosy-Westphal A, Danielzik S, Dörhöfer RP, Piccoli A, Müller MJ: Patterns of bioelectrical impedance vector distribution by body mass index and age: implications for body-composition analysis. Erratum in: Am J Clin Nutr , Piccoli A: Bioelectric impedance vector distribution in peritoneal dialysis patients with different hydration status. Kidney Int. Dehghan M, Merchant AT: Is bioelectrical impedance accurate for use in large epidemiological studies?. Nutr J. Barbosa-Silva MC, Barros AJ: Bioelectrical impedance analysis in clinical practice: a new perspective on its use beyond body composition equations. Buchholz AC, Bartok C, Schoeller DA: The validity of bioelectrical impedance models in clinical populations. Nutr Clin Pract. Bozzetto S, Piccoli A, Montini G: Bioelectrical impedance vector analysis to evaluate relative hydration status. Pediatr Nephrol. Creutzberg EC, Wouters EF, Mostert R, Weling-Scheepers CA, Schols AM: Efficacy of nutritional supplementation therapy in depleted patients with chronic obstructive pulmonary disease. Download references. Nutritional Consulting Practice, Emil-Schüller-Straße, Koblenz, , Germany. Pneumology Practice, Emil-Schüller-Straße, Koblenz, , Germany. KG, Binger Straße, Ingelheim, , Germany. Department of Pulmonary Disease, III. Medical Clinic, Johannes Gutenberg-University, Langenbeckstraße, Mainz, , Germany. You can also search for this author in PubMed Google Scholar. Correspondence to Thomas Glaab. The authors declare that they have no competing interests. TG and MMG were employees of Boehringer Ingelheim at the time of manuscript submission. AWK and TG conceived of the review, drafted and coordinated the manuscript. MMG and AK critically discussed and helped to draft the manuscript. All authors read and approved the final manuscript. The contents of this original manuscript have not been previously presented or submitted elsewhere. Open Access This article is published under license to BioMed Central Ltd. Reprints and permissions. Walter-Kroker, A. et al. A practical guide to bioelectrical impedance analysis using the example of chronic obstructive pulmonary disease. Nutr J 10 , 35 Download citation. Received : 08 November Accepted : 21 April Published : 21 April Anyone you share the following link with will be able to read this content:. Sorry, a shareable link is not currently available for this article. Figure 3 ROC curve of multiparametric MRI. ROC curve of multiparametric MRI on patients with clinical suspicion of PCa AUC 0. The median prostate volume was Inferential statistics between patients with PCs, BPH, and controls are reported in Table 2. Table 2 Statistical comparison between patients who underwent to prostate biopsy cancer and BPH and controls. Comparing patients with PCa vs. controls, differences in age, PSA, prostate volume, and PSA density were found. Same statistical differences were found comparing BPH vs. Concerning BPH vs. Table 2 summarizes and compares BIA test parameters gathered from patients with prostate cancer, benign prostatic disease, and healthy volunteers. While no significant differences between groups were found on the BIA parameter PA, significant differences were found in comparing BPH vs. patients with PCa and controls vs. Concerning the statistical comparison between the three bioimpedance test measurements from the right and left sides of the prostate, we split the dataset in three subsets: i a subset of patients with right-sided prostate cancer Table 3 ; ii a subset of patients with left-sided prostate cancer Table 4 ; and iii a subset of patients with both-sided prostate cancer Table 5. Table 3 Median and MAD values of the bioimpedance features calculated on the right and left sides of the prostate in the right-sided prostate cancer group. Table 4 Median and MAD values of the bioimpedance features calculated on the right and left sides of the prostate in the left-sided prostate cancer group. Table 5 Median and MAD values of the bioimpedance features calculated on the right and left sides of the prostate in the both-sided prostate cancer group. It is worthwhile noting that the R of the right side of the prostate was significantly lower than the left side in the left- and both-sided cancer patient group. Moreover, PA of the left side of the prostate was significantly lower than the right side in the both-sided cancer patient group. Using the parameter set comprising BMI, PSA density, PSA, AGE, R, PA, and Xc, we built an SVM multifeature computational model as described above and derived cancer recognition accuracy, sensitivity, specificity, positive, and negative predictive values PPV and NPV Table 6. For each feature, we performed a ROC curve analysis to evaluate the performance of each single feature in discriminating between patient with PCa from those with benign disease. The average prediction accuracy achieved is shown in Figure 2 , with a final score as of This was obtained mathematically combining the following four parameters that were identified as clinically relevant: BMI, PSA density, R, and PSA. A comprehensive, ranked clinical feature list for this decision support system is reported in Table 8 , while the corresponding confusion matrix is in Table 9. Sensitivity and specificity of the PCa prediction vs. BPH were The PPV and NPV were It is worthwhile noting that the resistance, averaged between the right and left prostate lobes, is one of the most informative features and gives a significant contribution to achieve the Table 8 Comprehensive ranked clinical feature list of the most accurate subset of features: BMI, PSA density, RES, and PSA. Table 9 Confusion matrix of the most accurate sub-set of features excluding BIA parameters. Importantly, as a counterproof, we obtained a significant decrease in the PCa prediction accuracy of As expected, the most informative subfeatures set included BMI, PSA density, and PSA. BIA of different tissues was originally investigated by Geddes and Baker in the s They carried out an electrical measurement on living tissues demonstrating different values of resistivity. From that period, the BIA test have been used for various purposes such as the lean and fat body mass calculation and other medical applications like skin and breast cancer diagnosis 23 — Halter et al. They realized that PCa, BPH, nonhyperplastic glandular tissue, and stromal tissue had different conductivity at all frequencies while mean cancer permittivity was significantly greater than that of benign tissues at high frequencies Other authors demonstrated that best results for cancer diagnosis by BIA test were obtained by measuring the tissue phase angle. Low phase angle suggests cell death or decreased cell integrity, whereas higher phase angle suggests healthy cell 26 , A low phase angle has been associated with an impaired outcome in tumor diseases such as pancreatic cancer, colorectal cancer, and lung cancer 6 , 7 , Tyagi et al. recently demonstrated that low phase angle values measured by BIA test allow for discriminating PCa patients from matched controls and those with advanced stage and high-risk PCa in particular. They investigated a group of subjects using the BIA electrode placement on the right upper and right lower limb. On the other hand, all PCa-diagnosed subjects had a total PSA increased values and other concomitant diseases excluded to avoid the risk of false-positive results Similarly, Khan developed a new composite impedance metrics method with a nine-electrode microendoscopic probe. The results obtained demonstrated a predictive accuracy of For these reasons, we provided an alternative electrode placement and a restricted locoregional electric field in order to improve the BIA test sensitivity and specificity and reduce possible confounding factors. The finger probe allows the obtainment of the tissue resistance, reactance, and phase angle measurements directly from the prostate gland surface through a restricted electric field generated into the pelvic bone girdle. Our results demonstrated that the finger probe is a promising, reliable, and easy-to-use tool to improve the accuracy of PCa noninvasive diagnosis together with other standard clinical parameters. In patients where PCa was diagnosed in both prostate lobes i. Our experimental evidences on BIA phase angles do not replicate previous findings reported in 9. All BIA measures including R, Xc, and PA, in fact, were normalized by dividing their value by the prostate volume estimated during the TRUS examination to avoid biases. Without normalization, patients with BPH and with PCa vs. BIA resistance values were lower in patients with PCa although, taken alone, it seemed to be unable to differentiate cancer from noncancer patients, while it was significantly different between healthy subjects and the BPH group. BIA reactance values were significantly different between healthy subjects and patients, although taken alone were not significantly different between BPH and PCa patients. In this sense, likewise for the PSA alone, the BIA test failed to differentiate subjects with clinical suspicion of PCa and prospectively missed the intent of avoiding unnecessary biopsies. Our results indicate a good PCa prediction using a combination of the following clinical features: BMI, Age, PSA, and PSA density. In this case, sensitivity and specificity are lower than the ones associated with a combination of BMI, PSA density, R, and PSA, thus demonstrating the significant clinical information associated with BIA test. Study limitations include the limited amount of data, especially gathered from healthy volunteers, the nonage-matched group taken as negative control due to the increased risk of developing prostate diseases in the advanced age and a fixed range of 50 mHz frequency band for the BIA. The proposed BIA test is a cheap, easy-to-perform method helpful for the multifeature clinical and noninvasive detection of prostate cancer and may be also able to decrease the number of unnecessary biopsies. BPH prediction when properly combined with BMI, total PSA, and PSA density. Interestingly, the test can be easily repeatable. Further studies by varying the BIA tester voltage frequency are necessary to improve the BIA test efficacy. The cheaper cost of the method in comparison with mMRI may be immediately attractive for low-income countries. Preliminary version of the manuscript has been previously preprinted at www. The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation. Conceptualization: RB. Data curation: TV. Formal analysis: AG, ES, and GV. Investigation: JD and TV. Methodology: RB. Project administration: RB. Resources: RB, VF, and ES. Software: GV, AG, and ES. Supervision: RB, GV, and VF. Validation: RB and GV. Visualization: TV and AG. Writing original draft: RB and GV. Writing review edition: VF. All authors contributed to the article and approved the submitted version. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. Siegel RI, Miller KD, Jemal A. Cancer Statistics. Cancer J Clin — doi: CrossRef Full Text Google Scholar. Catalona WJ, Richie JP, Ahmann FR, Hudson MA, Scardino PT, Flanigan RC, et al. Comparison of Digital Rectal Examination and Serum Prostate Specific Antigen in the Early Detection of Prostate Cancer: Results of a Multi Center Clinical Trial of 6, Men. J Urol — PubMed Abstract CrossRef Full Text Google Scholar. Brown LC, Ahmed HU, Faria R, El-Shater Bosaily A, Gabe R, Kaplan RS, et al. Multiparametric MRI to Improve Detection of Prostate Cancer Compared With Transrectal Ultrasound-Guided Prostate Biopsy Alone: The PROMIS Study. Health Technol Assess 22 39 :1— Ahmed HU, El Shater Bosaily A, Brown LC, Gabe R, Kaplan R, Parmar MK, et al. Diagnostic Accuracy of Multiparametric MRI and TRUS in Prostate Cancer PROMIS : A Paired Validating Confirmatory Study. Lancet — Weinreb JC, Barentsz JO, Choyke PL, Cornud F, Haider MA, Macura CJ, et al. PI-RADS Prostate Imaging - Reporting and Data System: , Version 2. Eur Urol — Norman K. Cut-Off Percentiles of Bioelectrical Phase Angle Predict Functionality, Quality of Life and Mortality in Patients With Cancer. Am J Clin Nutr —9. Gupta D. BIPA in Clinical Practice. Implications for Prognosis in Advanced Colorectal Cancer. Am J Clin Nutr —8. Davis MP. Bioelectric Impedance Phase Angle Changes During Hydration and Prognosis in Advanced Cancer. Am J Hosp Palliat Care —7. Mishra V, Bouyad H, Halter RJ. Electrical Impedance-Based Biopsy for Prostate Cancer Detection. Tyagi R, Mishra S, Gaur N, Panwar A, Saini D, Singh K, et al. Bioelectric Impedance Phase Angle in Carcinoma Prostate- a Hospital Based Study. Int J Med Sci Pub Health 5 9 — Khan S, Mahara A, Hyams ES, Schned AR, Halter RJ. Prostate Cancer Detection Using Composite Impedance Metric. IEE Trans Med Imag 35 12 — Bartoletti R, Gavazzi A, Cai T, Mondaini N, Morelli A, Del Popolo G, et al. Prostate Growth and Prevalence of Prostatic Diseases in Early Onset Spinal Chord Injuries. Eur Urol —8. STROBE Statement Strengthening the Reporting of Observational Studies in Epidemiology. Google Scholar. Grimmes S, Martinsen OG. Bioimpedance and Bioelectricity Basics. Elsevier Science: Switzerland |
| Bioelectrical impedance analysis (BIA): beyond BMI | PI-RADS Prostate Imaging - Reporting and Data System: , Version 2. Reference values adjusted for age, BMI and gender are plotted as so-called tolerance ellipses in the coordinate system. increase or decrease mostly due to increased extracellular water retention or a loss of extracellular water. Bioelectrical impedance analysis does not accurately measure your total body fat. Article CAS Google Scholar Wabel P, et al. Rights and permissions Open Access This article is published under license to BioMed Central Ltd. |
| Bioelectrical Impedance Analysis (BIA) | Competing interests The authors declare that they have no competing interests. Article PubMed PubMed Central Google Scholar Shoup R, Dalsky G, Warner S, Davies M, Connors M, Khan M, Khan F, ZuWallack R: Body composition and health-related quality of life in patients with obstructive airways disease. Scandinavian Journal of Medicine and Science in Sports; Schedule a free call with our New Patient Coordinator here. Buffa R, et al. |
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Thomasset A. Bioelectrical properties of tissue impedance. Lyon Medical ; : — Google Scholar. Nyboer J. Percent body fat by four terminal bio-electrical impedance and body density in college freshman. Proceedings of the Vth International Conference on Electrical Bio-Impedance.
Tokyo, August Lukaski HC, Johnson PE, Bolonchuk WW, Lykken GI. Assessment of fat-free mass using bioelectrical impedance measurements of the human body.
Am J Clin Nutr ; 41 : — Article CAS Google Scholar. National Institutes of Health Technology Assessment Conference Statement. Bioelectrical impedance analysis in body composition measurement. Am J Clin Nutr ; 64 : S—S. Article Google Scholar. Ellis KJ, Bell SJ, Chertow GM, Chumlea WC, Knox TA, Kotler DP et al.
Bioelectrical impedance methods in clinical research: a follow-up to the NIH Technology Assessment Conference. Nutrition ; 15 : — Kyle UG, Bosaeus I, De Lorenzo AD, Deurenberg P, Elia M, Gómez JM et al. ESPEN working group. Bioelectrical impedance analysis—part I: review of principles and methods.
Clin Nutr ; 23 : — Kyle UG, Bosaeus I, De Lorenzo AD, Deurenberg P, Elia M, Manuel Gómez J et al. Bioelectrical impedance analysis-part II: utilization in clinical practice.
Download references. KG, Hamburg, Germany. School of Chemistry and Molecular Biosciences, The University of Queensland, Brisbane, Queensland, Australia. Institut für Humanernährung und Lebensmittelkunde, Christian-Albrechts-Universität zu Liel, Kiel, Germany.
You can also search for this author in PubMed Google Scholar. Correspondence to L C Ward. Ward has consulted to ImpediMed Ltd. KG; both consultations had no involvement in the conception and execution of this study or in the preparation of the manuscript.
Reprints and permissions. Ward, L. Bioelectrical Impedance Analysis. Eur J Clin Nutr 67 Suppl 1 , S1 Download citation. Published : 09 January Issue Date : January Anyone you share the following link with will be able to read this content:.
Sorry, a shareable link is not currently available for this article. Provided by the Springer Nature SharedIt content-sharing initiative. International Journal of Diabetes in Developing Countries Journal of Thrombosis and Thrombolysis As shown in Figure 1 , values located outside the 95 th percentile in the following four quadrants point to the following conditions [ 20 ]: a right upper quadrant e.
exsiccosis b left lower quadrant e. oedema c right lower quadrant e. malnutrition d left upper quadrant e. good training status. Interpretation of the BIVA nomogram.
Age, BMI and gender adjusted reference values are plotted as so-called tolerance ellipses in the coordinate system. Three tolerance ellipses are distinguished, corresponding to the 50 th , 75 th and 95 th vector percentile of the healthy reference population. Values located outside the 95 th percentile in the following four quadrants point to the following conditions: a right upper quadrant e.
good training status modified with permission from Data-Input GmbH. We present below some examples of characteristic BIA findings in COPD patients with their interpretation:.
From personal experience, follow-up measurements examples should be performed every 4 weeks for overweight patients and every weeks for all other cases [ 27 ].
However, this is a decision that must be taken on an individual basis. Patient: female, Interpretation: With a BMI of The measurement point in the BIVA nomogram Figure 2 lies within the 50 th tolerance ellipse and thus indicates normal findings. Normal finding as illustrated in the BIVA nomogram.
The position of the measurement point in the BIVA nomogram within the 50 th tolerance ellipse range of normal values indicates a normal finding.
Conclusion: All values in the table are within the normal range and the measurement point in the BIVA nomogram lies within the 50 th tolerance ellipse. The measurement point in the BIVA nomogram Figure 3 in this patient is well below the line of normal BCM values long axis and above the line of normal TBW values short axis between the 75 th and the 95 th tolerance ellipse.
The position of the measurement point in the lower right quadrant points to malnutrition. Malnutrition in an obese COPD patient as illustrated in the BIVA nomogram. The position of the measurement point in the BIVA nomogram is below the line of normal BCM values long axis and above the line of normal TBW values short axis between the 75 th and 95 th tolerance ellipse.
The position in the lower right quadrant indicates malnutrition. The BIA parameter values listed in table 2 can be interpreted as follows: The fat mass lies above the normal range in line with the increased BMI. BCM lies within the normal range. At first sight this does not fit in with the finding of the BIVA nomogram, which indicates malnutrition.
The fact that the calculated BCM is within the range of normal values here may be explained as follows: It needs to be considered that BCM is dependent on the patient's fluid status TBW. This means that a BCM within the normal range does not necessarily mean a normal nutritional status but may also be due to increased TBW.
This indicates that BCM is actually reduced. BCM therefore only appears to lie within the range of normal values because of the increased TBW. In contrast to this somewhat complex interpretation of the calculated BIA values, the suspected diagnosis of malnutrition can be established at a glance by BIVA.
In addition, it is confirmed that the calculated BCM is too high because of the increased TBW position of the measurement point in the BIVA nomogram above the line of normal TBW values.
Conclusion: Despite the presence of obesity the patient is exhibiting malnutrition. The position of the measurement point in the BIVA nomogram in the right lower quadrant between the 75 th and the 95 th tolerance ellipse provides an indication for the suspected diagnosis of malnutrition.
The measurement point in the BIVA nomogram Figure 4 in this patient is far below the line of normal BCM values long axis and well above the line of normal TBW values short axis , far outside the 95 th tolerance ellipse.
The position of the measurement point in the lower right quadrant points to malnutrition in the form of cachexia. Cachexia as illustrated in the BIVA nomogram.
The position of the measurement point in the BIVA nomogram is far below the line of normal BCM values long axis and well above the line of normal TBW values short axis far outside the 95 th tolerance ellipse.
The position in the lower right quadrant points to cachexia. The BIA parameter values listed in table 3 can be interpreted as follows: The fat mass lies below the normal range in line with the reduced BMI.
The calculated values for BCM und TBW are reduced. It needs to be considered as regards the reduced BCM value that BCM is dependent on the patient's fluid status TBW. This means that a reduced BCM does not necessarily point to malnutrition but may also be due to a low TBW.
In this example also BIVA provides a more efficient assessment of the nutritional status than the calculated BIA parameters. Conclusion: All the values listed in the table are below the normal range and the measurement point in the BIVA nomogram is outside the 95 th tolerance ellipse in the lower right quadrant.
This indicates severe malnutrition in the form of cachexia. The assessment of the BIVA nomogram is sufficient for the suspected diagnosis of cachexia. The measurement point in the BIVA nomogram Figure 5 in this patient is above the line of normal BCM values long axis and well below the line of normal TBW values short axis on the 95 th tolerance ellipse.
The position of the measurement point in the lower left quadrant points to water retention in the form of oedema. Oedema due to right heart failure as illustrated in the BIVA nomogram. The position of the measurement point in the BIVA nomogram is above the line of normal BCM values long axis and well below the line of normal TBW values short axis on the 95 th tolerance ellipse.
The position in the lower left quadrant indicates the presence of increased water retention. The BIA parameter values listed in table 4 can be interpreted as follows: Body fat mass lies above the normal range in line with the increased BMI. The determined TBW is increased and the calculated BCM lies in the upper range of normal.
These findings are consistent with the position of the measurement point above the line of normal BCM values and below the line of normal TBW values in the lower left quadrant. With the derived normal BIA value for BCM it needs once again to be taken into account here that BCM is dependent on the patient's fluid status TBW.
This means that a BCM within the normal range does not necessarily indicate an actually normal BCM or normal nutritional status but may also appear normal due to an increased TBW.
In addition to the increased TBW, ECM is also markedly increased, indicating oedema. The suspicion of oedema is established at a glance with BIVA.
BIVA confirms simply and rapidly the calculated BIA values BCM and TBW. The suspicion of oedema was confirmed on physical examination of the legs. Conclusion: The values listed in the table for TBW and ECM are outside the normal range and the measurement point in the BIVA nomogram is on the 95 th tolerance ellipse in the lower left quadrant, indicating oedema.
The determined BCM is in the upper range of normal and the measurement point in the BIVA nomogram is above the line of normal BCM values. The position of the measurement point in the nomogram provides an indication for the suspected diagnosis of oedema.
For the general differential diagnosis of underweight we present a female patient with anorexia: female, The measurement point in the BIVA nomogram Figure 6 lies almost on the line of normal BCM values long axis and far above the line of normal TBW values short axis outside the 95 th tolerance ellipse.
The position of the measurement point in the upper right quadrant points to the presence of anorexia. Anorexia as illustrated in the BIVA nomogram. The position of the measurement point in the BIVA nomogram is almost on the line of normal BCM values long axis and far above the line of normal TBW values short axis outside the 95 th tolerance ellipse.
The position in the upper right quadrant points to the presence of anorexia. The BIA parameter values listed in table 5 can be interpreted as follows: Body fat mass is reduced in line with the low BMI.
TBW is markedly reduced and BCM also is decreased. With the reduced BCM it needs to be kept in mind here that BCM is dependent on the patient's fluid status TBW. This means that a lower BCM may also appear reduced due to a lower TBW. This indicates that BCM is normal and that the calculated value was too low only because of the low TBW.
BIVA confirms the suspicion raised by the BIA values that the calculated BCM was too low because of the reduced TBW. Again, the suspected diagnosis of anorexia can be established more efficiently and more reliably by BIVA. Conclusion: The patient exhibits a markedly reduced BMI, decreased body water and a normal BCM in the form of anorexia.
The position of the measurement point in the nomogram in the upper right quadrant outside the 95 th tolerance ellipse provides an indication for the suspected diagnosis of anorexia. Bioelectrical impedance analysis BIA , particularly in combination with bioelectrical impedance vector analysis BIVA , provides a viable opportunity for evaluating body composition in humans.
As the examples suggest the interpretation of BIA results is often complex and a suspected diagnosis can be established more efficiently and more reliably by integrating BIVA into the patient assessment process.
Engelen MP, Schols AM, Baken WC, Wesseling GJ, Wouters EF: Nutritional depletion in relation to respiratory and peripheral skeletal muscle function in out-patients with COPD.
Eur Respir J. Article CAS PubMed Google Scholar. Schols AM, Broekhuizen R, Weling-Scheepers CA, Wouters EF: Body composition and mortality in chronic obstructive pulmonary disease. Am J Clin Nutr.
CAS PubMed Google Scholar. Engelen MP, Schols AM, Does JD, Wouters EF: Skeletal muscle weakness is associated with wasting of extremity fat-free mass but not with airflow obstruction in patients with chronic obstructive pulmonary disease.
King DA, Cordova F, Scharf SM: Nutritional aspects of chronic obstructive pulmonary disease. Proc Am Thorac Soc. Article PubMed PubMed Central Google Scholar. Shoup R, Dalsky G, Warner S, Davies M, Connors M, Khan M, Khan F, ZuWallack R: Body composition and health-related quality of life in patients with obstructive airways disease.
Hallin R, Koivisto-Hursti UK, Lindberg E, Janson C: Nutritional status, dietary energy intake and the risk of exacerbations in patients with chronic obstructive pulmonary disease COPD. Respir Med. Article PubMed Google Scholar. Schols AM: Nutrition in chronic obstructive pulmonary disease.
Curr Opin Pulm Med. Soeters PB, Schols AM: Advances in understanding and assessing malnutrition. Curr Opin Clin Nutr Metab Care. Global Initiative for Chronic Obstructive Lung Disease: Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease updated com ].
Vestbo J, Prescott E, Almdal T, Dahl M, Nordestgaard BG, Andersen T, Sørensen TI, Lange P: Body mass, fat-free body mass, and prognosis in patients with chronic obstructive pulmonary disease from a random population sample: findings from the Copenhagen City Heart Study.
Am J Respir Crit Care Med. PubMed Google Scholar. Ischaki E, Papatheodorou G, Gaki E, Papa I, Koulouris N, Loukides S: Body mass and fat-free mass indices in COPD: relation with variables expressing disease severity. Miller A, Strauss BJ, Mol S, Kyoong A, Holmes PH, Finlay P, Bardin PG, Guy P: Dual-energy X-ray absorptiometry is the method of choice to assess body composition in COPD.
Lerario MC, Sachs A, Lazaretti-Castro M, Saraiva LG, Jardim JR: Body composition in patients with chronic obstructive pulmonary disease: which method to use in clinical practice?. Br J Nutr. Lee SY, Gallagher D: Assessment methods in human body composition. Curr Opin Clin Nutr Metabol Care. Article Google Scholar.
Kyle UG, Bosaeus I, De Lorenzo AD, Deurenberg P, Elia M, Gómez JM, Heitmann BL, Kent-Smith L, Melchior JC, Pirlich M, Scharfetter H, Schols AM, Pichard C: Bioelectrical impedance analysis-part I: review of principles and methods.
Clin Nutr. Matthie JR: Bioimpedance measurements of human body composition: critical analysis and outlook. Expert Rev Med Devices. Mattsson S, Thomas BJ: Development of methods for body composition studies. Phys Med Biol. Kushner RF: Bioelectrical impedance analysis: a review of principles and applications.
J Am Coll Nutr. Kuzma AM, Meli Y, Meldrum C, Jellen P, Butler-Labair M, Koczen-Doyle D, Rising P, Stavrolakes K, Brogan F: Multidisciplinary care of the patient with chronic obstructive pulmonary disease. The BIA compendium. de ]3. Bosy-Westphal A, Danielzik S, Dörhöfer RP, Piccoli A, Müller MJ: Patterns of bioelectrical impedance vector distribution by body mass index and age: implications for body-composition analysis.
Erratum in: Am J Clin Nutr , Piccoli A: Bioelectric impedance vector distribution in peritoneal dialysis patients with different hydration status.
Kidney Int. Dehghan M, Merchant AT: Is bioelectrical impedance accurate for use in large epidemiological studies?. Nutr J. Barbosa-Silva MC, Barros AJ: Bioelectrical impedance analysis in clinical practice: a new perspective on its use beyond body composition equations.
Buchholz AC, Bartok C, Schoeller DA: The validity of bioelectrical impedance models in clinical populations. Nutr Clin Pract. Bozzetto S, Piccoli A, Montini G: Bioelectrical impedance vector analysis to evaluate relative hydration status. Pediatr Nephrol. Creutzberg EC, Wouters EF, Mostert R, Weling-Scheepers CA, Schols AM: Efficacy of nutritional supplementation therapy in depleted patients with chronic obstructive pulmonary disease.
Download references. Nutritional Consulting Practice, Emil-Schüller-Straße, Koblenz, , Germany. Pneumology Practice, Emil-Schüller-Straße, Koblenz, , Germany. KG, Binger Straße, Ingelheim, , Germany. Department of Pulmonary Disease, III.
Medical Clinic, Johannes Gutenberg-University, Langenbeckstraße, Mainz, , Germany. You can also search for this author in PubMed Google Scholar. Correspondence to Thomas Glaab. The authors declare that they have no competing interests. TG and MMG were employees of Boehringer Ingelheim at the time of manuscript submission.
Bio-electrical Impedance Analysis Diagnodtics is impedancf-based method used to monitor health by looking BIA impedance-based diagnostics body composition. It measures impedance-bsaed fat in relation Visceral fat and lung function lean body mass and BIA impedance-based diagnostics said to be more accurate than BMI testing. A normal balance of body fat and muscle is associated with good health and longevity so BIA tests can aid clinicians in helping their patients change their diet and lifestyles accordingly. All Tanita body composition monitors are said to use advanced BIA technology. Find out more. Charlotte Edwards: Can you explain the purpose of BMI testing and its benefits? Simon Bradeley: BMI testing is really for large population studies. At BIA impedance-based diagnostics Weight loss for recreational athletes, we diagnosstics on your overall diagnosgics composition, or the Low-Impact Energy Solutions of fat and non-fat diagnosfics muscle in your body. To do so, we use a method called bioelectrical impedance analysis BIA on all our new patients. Bioelectrical impedance analysis uses electrical current to measure body fat. It is a cellular health and tissue composition analysis. The various tissues in your body fat, muscle, bone, etc.
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