Reliable Noninvasive Methods for Assessing Nutritional Status and Basal energy Expenditure in MHD: Focus on Basal Energy Expenditure

Reliable Noninvasive Methods for Assessing Nutritional Status and Basal energy Expenditure in MHD: Focus on Basal Energy Expenditure | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Biological Sciences - Article Reliable Noninvasive Methods for Assessing Nutritional Status and Basal energy Expenditure in MHD: Focus on Basal Energy Expenditure Jenn- Yeu Wang, MingChun CHiang, Shu- Chin Chen, Hsiao-Yun Hu Hsiao-Yun Hu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6036874/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Our aim was to (1) evaluate the agreement between basal energy Expenditure (BEE) derived from bioelectrical impedance analysis (BIA) immediately after hemodialysis (HD) and BEE determined using the Harris–Benedict equation and (2) identify the determinants of BEE in patients receiving HD. This cross-sectional study included 35 patients treated with HD two to three times a week from 2003 to 2004 at Taipei Municipal Zhongxiao Hospital. BEE was measured using BIA immediately after HD. Next, Altman–Bland analysis of BEE was performed to evaluate the agreement between the BIA method and the Harris–Benedict method. Correlation and multiple linear regression analyses were performed to examine the association of BEE with demographic data, anthropometric data, and body composition. BEE derived from BIA immediately after HD exhibited poor agreement with BEE determined using the Harris–Benedict method and was nonsignificantly larger. BEE was also positively correlated with lean body mass. In the multiple linear regression analysis, lean body mass was the most powerful independent determinant of BIA-derived BEE. Gender-based differences, height and lean body mass were noted in the prediction of BIA-derived BEE. This study revealed that understanding of the changes of scheduled basal energy Expenditure is paramount and related to changes of lean body mass condition of maintenance hemodialysis patients. Biological sciences/Biological techniques/Biophysical methods/Electrophysiology Biological sciences/Biophysics/Bioenergetics Basal energy expenditure Lean body mass Hemodialysis Bioelectrical impedance analysis Figures Figure 1 INTRODUCTION Basal energy Expenditure (BEE) represents the energy consumption of an individual at complete rest. Rising basal energy Expenditure in maintenance hemodialysis (MHD) patients necessitates increased energy replenishment, esp. in anorexic patients in order to increase serum amino acid concentration and reduce high incidence of wasting syndrome and malnutrition(Slomowitz, Monteon, Grosvenor, Laidlaw, & Kopple, 1989 ).Therefore, the determination of BEE is critical to establishing energy recommendations and maintaining nutritional balance in patients receiving hemodialysis (HD) (Basile et al., 2010 ). Using blood temperature monitor in HD, low temperature dialysis (dialysate temperature < 37C) and isothermal HD can maintain hemodynamic stability, which requires active cooling. The amount of this negative energy transfer is directly proportional to the amount of blood volume loss induced by ultrafiltration(Horácek et al., 2007 ). In addition to signaling human appetite control from adipose tissue(Wang, Lu, Lin, & Hu, 2003 ) with leptin and gut hormone, BEE, in proportion to basal energy requirements also provide a feedback signal of driving habitual food intake and human appetite control. BEE is also useful for individualizing dialytic prescriptions for these patients (Selby & McIntyre, 2006 ). Consequences of advanced chronic kidney disease are principal contributors to elevated BEE in patients on HD. It seems reasonable to speculate that the increase in BEE is attributable to protein catabolism caused by the inflammation and uremia. Uremic conditions cause proteolysis and lean body mass depletion owing to the inactivation of alanine aminotransferase and glycogenolysis because of decreased phosphofructokinase and pyruvate kinase activities in patients on HD (Conjard et al., 1995 ). Besides ,renal failure may be related to increased energy metabolism due to metabolic acidosis, secondary hyperparathyroidism, insulin resistance (Avesani, Draibe, Kamimura, Colugnati, & Cuppari, 2004 ), and microinflammation, with elevated C-reactive protein and cytokine levels (Deger et al., 2017 ), despite the role of the kidney as an essential metabolically active organ (Gallagher et al., 1998 ).HD patients with moderate to severe hyperparathyroidism also exhibit lean body mass depletion and increased BEE (Baczynski et al., 1985 ; Cuppari et al., 2004 ).Hyperparathyroidism causes skeletal myopathy. PTH not only causes abnormalities of skeletal muscle of bioenergetics, but also cause muscle proteolysis. Animal study showed the mechanism of increased BEE in hyperparathyroidism that PTH facilitate entering of excess calcium into the cells, precipitation as calcium phosphate compounds, and reducing the concentration of inorganic phosphorus, and then reducing high energy compound, such as ATP(Baczynski et al., 1985 ; Cuppari et al., 2004 ). Common comorbidities, such as diabetes, heart failure, and chronic inflammation, in advanced chronic kidney disease also contribute to elevated BEE in patients on HD. The development of type 2 diabetes, occurring early in the transition from normal glucose tolerance to impaired glucose tolerance is accompanied not only by an increase in BEE but also a decrease in insulin induced thermogenesis, which associated with progressive metabolic abnormalities independent of body size and body composition(Nawata, Sohmiya, Kawaguchi, Nishiki, & Kato, 2004 ). Besides well known risk factors for diabetic retinopathy, such as duration of diabetes, long standing hyperglycemia, subclinical inflammation, increased oxidative stress and hypercholesterolemia, a community-based cross-sectional study involving 1,184 participants with Type 2 diabetic retinopathy, a most specific microvascular complication of diabetes found that visceral fat, subcutaneous fat, body fat and increased basal energy expenditures were also the novel risk factors for diabetic retinopathy. The mechanism involves excessive gluconeogenesis and catabolism, leading to increased rates of energy expenditure. Sustained increased basal energy expenditure can manifest as sustained unintended weight loss(Sasongko et al., 2018 ).Skeletal muscle catabolism has been observed in HD patients with poorly controlled diabetes with retinopathy and nephropathy owing to a combined effect of uremic factors and insulin resistance (Pupim et al., 2005 ) and lean body mass depletion and elevated BEE (Nawata et al., 2004 ; Sasongko et al., 2018 ). The frequent occurrence of systemic microinflammation and various catabolic conditions in patients with chronic kidney disease may directly affect BEE. If CKD patients are in a state of subclinical inflammation, they are at risk for malnutrition. Not only increased BEE but also other related responses to inflammation contribute to the linkage of inflammation and malnutrition, which include increased oxygen consumption, increased systemic total protein turnover and peripheral amino acid mobilization, enhanced lipolysis and fat utilization, and increased concentrations of catecholamines, glucagon, and cortisol(Avesani et al., 2004 ). In addition, suppression of appetite may also be implicated. In some ESRD patients, peripheral blood mononuclear cells (PBMCs) overproduce pro-inflammatory IL-6 cytokines which induces protein catabolism, lipolysis, insulin resistance, and suppression of appetite and pathophysiologic ally link inflammation and malnutrition(Kamimura et al., 2007 ).Following weight loss in obese patient in weight control center, the cellularity model predicts decline of BEE more effectively than the body fat model(Bernstein et al., 1983 ) An overhydrated status in patients undergoing HD may increase BEE because of increased myocardial demand, increased activation of the sympathetic system, increased serum levels of tumor necrotic factors, and increased metabolism of the respiratory muscles (Poehlman, Scheffers, Gottlieb, Fisher, & Vaitekevicius, 1994 ). One study identified several persistent pathophysiological alterations related to dialytic procedures, some even lasting up to 2 hours after HD, such as hypovolemia (van der Sande et al., 2005 ), dialytic loss of amino acids and protein catabolism (Gutierrez, Bergström, & Alvestrand, 1994 ; Ikizler et al., 2002 ), activation of complement system due to bio-incompatibility of dialysis membranes (Gutierrez, Alvestrand, Wahren, & Bergström, 1990 ; Hakim, 1993 ), and changes in hormonal levels, with significant increase in BEE after HD (Ikizler et al., 1996 ). To date, few studies have incorporated body composition, anthropometric data, diabetes mellitus (DM) status, and demographic data in predicting bioelectrical impedance analysis (BIA)–determined BEE in patients receiving HD. Therefore, in this study, we compared BIA-derived BEEs and BEEs calculated using the Harris–Benedict equation in patients undergoing HD, and we identified the determinants of BEE. METHODS This study was reviewed and approved by the Department of Teaching and Research of Taipei City Hospital (case no. : 10939) and the Institutional Review Board of Taipei City Hospital (case no.: TCHIRB-10907008-E). Patients Thirty-five patients receiving HD were recruited from the nephrological division of the Medical Department of Taipei Municipal Zhongxiao Hospital from 2003 to 2004. Their physical conditions were stable. All patients were dialyzed two to three times a week. No patient used corticosteroids or immunosuppressive drugs. Patients with malignant tumors were excluded from the study. Anthropometry Patients were weighed while wearing light clothes without shoes. Height was measured on the platform scale. Body mass index was calculated by dividing dry body weight by the square of height. Body composition determined from bioelectrical impedance analysis Although the human body is not a uniform cylinder, the current passing through the human body provides impedance to the current in two types, namely reactance and resistance. Reactance comes from cell membrane, whereas resistance comes from extracellular fluid and intracellular fluid. The circuit representing the tissue in the human body includes the first arm of the extracellular fluid resistance circuit and the parallel second arm of the cell membrane and intracellular fluid circuit at low frequencies, the current does not penetrate the cell membrane instead of the extracellular fluid, which is the so-called resistance. At high frequencies in multiple frequency BIA, the current penetrates the cell membranes. The 50 kHz current passes through both intracellular and extracellular fluids, although the ratio varies by tissue(Kyle, Bosaeus, De Lorenzo, Deurenberg, Elia, Gómez, et al., 2004). The inductive reactance, captative reactance and resistance reflects the different electrical properties of tissues, which are affected in various ways by ascites (Wang, Hwang, Lin, & Chen, 2012 ), nutritional status, and hydration status. Bioelectrical impedance analysis has been validated as a tool for measuring body composition in patients on HD (Lukaski, Johnson, Bolonchuk, & Lykken, 1985 ). In this study, a single-frequency tetrapolar bioelectrical impedance analyzer (BA-200, Mesmed System Co., Ltd) was used to measure body composition immediately after HD. With the patient in a sitting posture (legs separated and arms abducted from the trunk), electrodes were placed in standard positions on the opposite side of vascular access. Each subject wore clothes, but without shoes or socks, and after cleaning all skin contact areas with alcohol to minimize skin-electrode contact impedance, aluminum foil point electrodes were placed on the surface of the dorsum of wrist joint and forearm proximal to the wrist joint, and on the surface of the lateral ankle and leg proximal to the ankle. A single-frequency low current of 800 µA at 50 kHz was applied to the source electrodes and a voltage drop was detected at the proximal electrodes of the forearm and leg. Lean body mass was estimated according to the equation provided by the manufacturer (Kyle, Bosaeus, De Lorenzo, Deurenberg, Elia, Manuel Gómez, et al., 2004). Resting energy expenditure In this cross-sectional study, the BIA method was used to determine the BEE of patients receiving HD. BEE, derived using the manufacturer-provided equation, was acquired immediately after dialysis (15–30 min). The Harris–Benedict method was based on the following equations (Rainey-Macdonald, Holliday, & Wells, 1982 ): BEE (kcal/day) for men = 66.4730 + 13.7516 × body weight (kg) + 5.0033 × body height (cm) − 6.7550 × age (years) BEE (kcal/day) for women = 655.0955 + 9.5634 × body weight (kg) + 1.8496 × body height (cm) − 4.6756 × age (years) Statistical analysis The cutoff point of lean body mass, as determined using a receiver operating characteristic analysis, was 44.8 kg for low and high BEEs. Data of low and high lean body mass groups are expressed as mean ± standard deviation or as ratios. An independent t test and chi-square test were used to evaluate the data for low and high lean body mass groups. Pearson biserial correlation and multiple linear regression were used to analyze demographic data, DM status, anthropometric data, and body composition. Collinearity diagnostic is evaluated using Variation inflection factor (VIF). The variation inflection factors of independent variables in the multiple linear regressions were all smaller than 10. Altman–Bland analysis (Bland & Altman, 1986 ) was performed to assess the agreement between BEE derived from BIA immediately after HD and that derived from the Harris–Benedict equation. RESULTS Table 1 outlines the characteristics of the study patients. BEE was positively correlated with lean body mass, dry body weight, body height, and gender. Conversely, BEE was negatively correlated with percent fat mass (Table 2 ). In the multiple linear regression analysis, lean body mass was the independent variable for predicting BIA-determined BEE (Table 3 ). In the second multiple linear regression analysis, gender and height were the independent variables for predicting BIA-determined BEE (Table 4 ). The intra-class correlation coefficient of test–retest reliability of total body water was 0.997 (95% confidence interval [CI] 0.996 to 0.999). Table 1 Descriptive Characteristics of the Study Patients Variable Low LBM High LBM Total P N 19 16 35 Gender (M: F) 1:18 16:0 35 < 0.001 Age(years) 62.63 ± 13.23 62.63 ± 13.67 62.63 ± 13.23 NS Body height(cm) 155.83 ± 5.07 167.88 ± 4.86 161.34 ± 7.81 < 0.001 Dry body weight(kg) 52.54 ± 7.29 62.43 ± 8.30 57.06 ± 9.14 0.001 Basal energy Expenditure by HB equation (kcal/24hour) 1150.58 ± 83.07 1341.81 ± 172.78 1238.00 ± 161.76 0.001 Percent fat mass 25.47 ± 8.23 15.64 ± 7.54 20.98 ± 9.25 0.001 Body mass index(kg/m 2 ) 21.62 ± 2.94 22.09 ± 2.61 21.83 ± 2.77 NS Fat mass(kg) 13.81 ± 6.00 11.20 ± 6.13 12.62 ± 6.11 NS Basal energy Expenditure by BIA (kcal/24hour) 1421.92 ± 93.63 1738.64 ± 128.62 1566.71 ± 193.82 < 0.001 DM status (positive: negative) 12:7 8:8 35 NS Abbreviations: HB, Harris–Benedict equation; DM, diabetes mellitus; BIA, bioelectrical impedance analysis; LBM, lean body mass; M, male; F, female Table 2 Pearson Biserial Correlation Coefficients of Predictive Variables of BIA-determined Basal energy Expenditure with Levels of Significance ranking variables Biserial Pearson r P 1 LBM 0.996 < 0.001 2 Body height 0.849 < 0.001 3 gender 0.769 < 0.001 4 DBW 0.753 < 0.001 5 %FM -0.492 0.003 6 BMI 0.292 0.089 7 DM status -0.222 0.200 8 Age -0.102 0.560 Abbreviations: BIA, bioelectrical impedance analysis; LBM, lean body mass; DBW, dry body weight; %FM, percent fat mass; BMI, body mass index; DM, diabetes mellitus Table 3 Multiple Linear Regression for Predicting BIA-determined Basal energy Expenditure Based on Gender, Body Height, and Lean Body Mass predictor Beta coefficient Standard error of estimate Standardized Beta coefficient t P value Collinearity statistics gender -12.224 9.073 -0.032 -1.347 0.188 2.711 Body height -0.599 0.716 -0.024 -0.837 0.409 4.006 Lean body mass 24.562 0.742 1.042 33.083 < 0.001 4.777 Total R square = 0.994 Significance of model P < 0.001 Table 4 Multiple Linear Regression for Predicting BIA-determined Basal energy Expenditure Based on Gender, Body Height, and Percent Fat Mass predictor Beta coefficient Standard error of estimate Standardized Beta coefficient t P value Collinearity statistics gender 113.838 50.89 0.298 2.237 0.033 2.363 Body height 15.111 3.23 0.609 4.678 < 0.001 2.260 % fat mass -0.896 2.154 -0.043 -0.416 0.680 1.410 Total R square = 0.767 Significance of model P < 0.001 The p values of simple linear regression with the differences between BEE derived from BIA and BEE determined using the Harris–Benedict method as the dependent variable and the average of REE derived from BIA and REE determined using the Harris–Benedict method as independent variable.is significant, we accepted the null hypothesis that the two methods is inconsistent. BIA-determined BEE exhibited poor agreement with BEE derived from the Harris–Benedict equation was nonsignificantly larger. The mean and standard deviation of the absolute difference between BIA-derived BEE and BEE calculated using the Harris–Benedict equation was 328.707 ± 118.783 kcal/24 hour. The 95% CI (mean ± 1.96 × standard deviation) of the absolute difference between BIA-derived BEE and BEE determined using the Harris–Benedict equation ranged between 95.892 and 561.522 kcal/24 hour. In other words, the upper limit of agreement and lower limit of agreement were 561.522 kcal/24 hour (mean + 1.96 SD) and 95.892 kcal/24 hour (mean – 1.96 SD), respectively. The distribution of data points also displayed poor agreement between REE derived from BIA and REE determined using the Harris–Benedict method (Fig. 1 ). DISCUSSION The main findings of this study were as follows: BEE derived from BIA immediately after HD exhibited poor agreement with BEE determined using the Harris–Benedict equation and was nonsignificantly larger. Our correlation analysis revealed that BIA-derived BEE was significantly correlated with lean body mass, gender, body height, and percent fat mass. Multiple regression analysis showed that lean body mass was the most potent determinant of BIA-derived BEE. A previous study showed that gender-based differences in BIA-derived BEE were independent of lean body mass, level of fitness, age, and menopause (Arciero, Goran, & Poehlman, 1993 ). The main mechanism of gender-based differences in BEE (a higher BEE in men than in women) are as follows: In addition to regulation of food intake and distribution of fat, estrogens (hormone signal) can also control subcutaneous brown adipose tissue thermogenesis through action on estrogen receptor-alpha of ventromedial nucleus(VMN) of hypothalamus resulting in activation of AMP kinase(AMPK) and then activation of beta- adrenergic sympathetic nervous system (SNS) activation, and final activation of thermogenesis from brown adipose tissue(BAT)(Xue & Kahn, 2006 ). The above mechanism is the so called VMN-AMPK-SNS-BAT axis. Recent researches implicate that AMPK is also a mediator of the actions of adipocyte-derived and intestine -derived hormones on fatty acid oxidation and glucose uptake in peripheral tissues(Xue & Kahn, 2006 ). Testosterone can increase the sensitivity of insulin receptors of visceral white adipose tissue (WAT) which facilitate activation of lipoprotein lipase resulting in lipolytic mobilization of free fatty acid from WAT for energetic demands. The composition and bioenergetics of skeletal muscle fibers, such as greater skeletal muscle AMPKα phosphorylation could contribute to the main mechanism of gender-based differences in BEE (a higher BEE in men than in women)(Ferraro et al., 1992 ; Guadalupe-Grau et al., 2016 ). Muscles from females have a greater proportion of type I myofibril, smaller cross-sectional area of myofibrils, lower glycolytic potential of metabolic properties(Simoneau & Bouchard, 1989 ). Other mechanisms of gender-based differences in BEE, including, gluconeogenesis in the liver(Tran et al., 2010 ), Na+-K + ATPase activity(Scarrone et al., 2007 ), and regulation of body core temperature(Anderson et al., 2022 ) merit further study. Body height is easily measured and frequently used as a proxy of body size and stature, which are related to body mass (Heymsfield et al., 2012 ; Heymsfield, Thomas, Bosy-Westphal, & Müller, 2019 ). Metabolically active organs include the heart, liver, kidney, brain, skeletal muscle, and other tissues. Skeletal muscle represents the largest metabolically active organ in humans, which explains why lean body mass is the most robust predictor of BEE derived from BIA (Gallagher et al., 1998 ).Although total skeletal muscle mass is the main depot of total body protein, splanchnic area plays an important regulatory role in the total body protein turnover(Tessari et al., 1996 ). According a previous study, BEEs calculated through indirect calorimetry at three time points (pre-dialysis, beginning of dialysis, and 30 days after dialysis) were lower than BEEs derived from the Harris–Benedict equation. The authors reported that despite biological day-to-day variations, BEE predicted using the Harris–Benedict equation frequently exceeded BEE measured using calorimetry (de Oliveira, Bufarah, Ponce, & Balbi, 2018 ),(Haugen, Melanson, Tran, Kearney, & Hill, 2003 ). By contrast, our study showed that BEE calculated using BIA immediately after HD was nonsignificantly larger than BEE derived from the Harris–Benedict equation. These findings suggest an increase in BEE immediately after HD. Several mechanisms contribute to an increase in BEE immediately after HD, including 1.volume theory (van der Sande et al., 2005 ); 2. elevated NEFA (non-esterified fatty acids) levels; 3. microinflammatory theory; 4. hormone theory, and 5. protein catabolism due to loss of amino acids during dialysis (Ikizler et al., 1996 ).. The increase in BEE after HD has been linked with increased cardiac output, activation of the sympathetic system, and vasoconstriction of the skin as compensation for hypovolemia (Rosales, Schneditz, Morris, Rahmati, & Levin, 2000 ; van der Sande et al., 2005 ), the so-called ‘volume theory’ (Schneditz, Rosales, Kaufman, Kaysen, & Levin, 2002 ). Within the first hour of HD, NEFA (non-esterified fatty acids) levels were found to increase, then decrease but not return to baseline values. The initial rise in NEFA was attributed to heparin being an activator of lipoprotein lipase, which may further rise due to loss of glucose into the dialysate. Oxidation of NEFA reduces RQ and temporarily increases oxygen consumption and BEE. RQ (respiratory quotient) equals to carbon production divided by oxygen consumption(Horácek et al., 2007 ). HD itself may trigger an inflammatory reaction with elevated interleukin-6. Studies have shown that muscle proteolysis increases significantly during HD (Gutierrez et al., 1994 ). One pathway of blood membrane interaction in maintenance hemodialysis patients results in catabolic processes of protein and malnutrition. The effect of bio-incompatibility on the rate of subsequent proteolysis of muscle can be evaluated by measuring the net release of specific amino acids(Tessari et al., 1996 ) , (Hakim, 1993 ). Blood contact with a bioincompatible dialysis membrane induces the activation of complement and elevates prostaglandin, resulting in protein catabolism (Gutierrez et al., 1990 ). Furthermore, endotoxins in the dialysate stimulate increased production of interleukin-1, resulting in protein catabolism and increased BEE (Gutierrez et al., 1990 ). During the dialysis period and the following 2 hours, protein catabolism persists and causes increased BEE, the so-called microinflammatory theory. Loss of amino acids into the dialysate triggers protein catabolism, which cannot be prevented by the addition of glucose to the dialysate but can be waived by intravenous amino acid solution (Gutierrez et al., 1994 ). Elevation of several stress hormones, such as cortisol, glucagon, and catecholamine, is observed after HD. Cortisol, in elevated amounts in the physiological value, is the major hormone responsible for protein catabolism, albeit a minor role compared with other mechanisms (Simmons, Miles, Gerich, & Haymond, 1984 ).In conclusion, our study suggests that the timing of BEE measurement in relation to HD is crucial in serial follow-up for stable hemodialysis patients prescribed with constantly adequate dialysis dosage. Summary The field of application of basal energy Expenditure measurements provides unprecedented opportunities for improving assessment and management of low temperature dialysis, energy, and nutritional recommendations in maintenance hemodialysis patients. The current evidence from previous literatures affecting basal energy Expenditure in chronic renal failure focused largely on comorbidities, consequences of chronic kidney disease, systemic microinflammation, and the consequences of dialysis procedures. Our study was a small study, limited by the acquisition of informed consent, and focused on identifying determinants of resting energy expenditure. Three variables, lean body mass, height, and gender explained 99.4% of the variation in resting energy expenditure. In patients undergoing maintenance hemodialysis, basal energy Expenditure predicted by the Harris-Benedict equation was not significantly smaller than basal energy Expenditure predicted by bioelectrical impedance analysis in Bland and Altman analysis. Indirect inference from a previous research paper demonstrated that basal energy Expenditure increases after hemodialysis. Our study provides a novel conceptual framework for building predictive models from lean body mass subgroup analysis and correlation analysis of variables. We proposed to explain the theoretical rationale for low temperature dialysis prescription using indirect reasoning from the Bland and Altman analysis. This conceptual framework needs to be validated by future large-scale studies using gold standard methods of measuring basal energy Expenditure and more evidence is needed to build upon it. We believed that the proposed measurement of basal energy Expenditure by bioelectrical impedance analysis is an important step towards improving clinical practice in maintenance hemodialysis patients and will ultimately enable clinicians to improve outcomes in maintenance hemodialysis patients. Limitations Our study included small number of patients due to the difficulty in acquirement of informed consent. Besides, the gold standard of basal energy Expenditure was not used. Declarations Conflicts of Interest: The authors declare no potential conflict of interest. Author Contributions: Jenn-Yeu Wang conceived and designed the experiments; Jenn-Yeu Wang and Hsiao-Yun Hu analyzed the data; Jenn-Yeu Wang and Shu-Chin Chen performed the experiments; Jenn-Yeu Wang, Ming-Chun Chiang, Yuh Feng Lin and Betau Hwang contributed to the writing of the manuscript. Acknowledgments: The authors thank the Department of Teaching and Research of Taipei City Hospital and the Institutional Review Board of Taipei City Hospital for supporting this project. References Anderson, C. A. J., Stewart, I. B., Stewart, K. L., Linnane, D. M., Patterson, M. J., & Hunt, A. P. (2022). Sex-based differences in body core temperature response across repeat work bouts in the heat. Appl Ergon, 98 , 103586. doi: 10.1016/j.apergo.2021.103586 Arciero, P. J., Goran, M. I., & Poehlman, E. T. (1993). Resting metabolic rate is lower in women than in men. 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B., Thomas, D., Bosy-Westphal, A., Shen, W., Peterson, C. M., & Müller, M. J. (2012). Evolving concepts on adjusting human resting energy expenditure measurements for body size. Obes Rev, 13 (11), 1001–1014. doi: 10.1111/j.1467-789X.2012.01019.x Heymsfield, S. B., Thomas, D. M., Bosy-Westphal, A., & Müller, M. J. (2019). The anatomy of resting energy expenditure: body composition mechanisms. Eur J Clin Nutr, 73 (2), 166–171. doi: 10.1038/s41430-018-0319-3 Horácek, J., Sulková, S. D., Fortová, M., Lopot, F., Kalousová, M., Sobotka, L.,.. . Zima, T. (2007). Resting energy expenditure and thermal balance during isothermic and thermoneutral haemodialysis–heat production does not explain increased body temperature during haemodialysis. Nephrol Dial Transplant, 22 (12), 3553–3560. doi: 10.1093/ndt/gfm436 Ikizler, T. A., Pupim, L. B., Brouillette, J. R., Levenhagen, D. K., Farmer, K., Hakim, R. M., & Flakoll, P. J. (2002). Hemodialysis stimulates muscle and whole body protein loss and alters substrate oxidation. Am J Physiol Endocrinol Metab, 282 (1), E107-116. doi: 10.1152/ajpendo.2002.282.1.E107 Ikizler, T. A., Wingard, R. L., Sun, M., Harvell, J., Parker, R. A., & Hakim, R. M. (1996). Increased energy expenditure in hemodialysis patients. J Am Soc Nephrol, 7 (12), 2646–2653. Kamimura, M. A., Draibe, S. A., Dalboni, M. A., Cendoroglo, M., Avesani, C. M., Manfredi, S. R.,.. . Cuppari, L. (2007). Serum and cellular interleukin-6 in haemodialysis patients: relationship with energy expenditure. Nephrol Dial Transplant, 22 (3), 839–844. doi: 10.1093/ndt/gfl705 Kyle, U. G., Bosaeus, I., De Lorenzo, A. D., Deurenberg, P., Elia, M., Gómez, J. M.,.. . Pichard, C. (2004). Bioelectrical impedance analysis–part I: review of principles and methods. Clin Nutr, 23 (5), 1226–1243. doi: 10.1016/j.clnu.2004.06.004 Kyle, U. G., Bosaeus, I., De Lorenzo, A. D., Deurenberg, P., Elia, M., Manuel Gómez, J.,.. . Pichard, C. (2004). Bioelectrical impedance analysis-part II: utilization in clinical practice. Clin Nutr, 23 (6), 1430–1453. doi: 10.1016/j.clnu.2004.09.012 Lukaski, H. C., Johnson, P. E., Bolonchuk, W. W., & Lykken, G. I. (1985). Assessment of fat-free mass using bioelectrical impedance measurements of the human body. Am J Clin Nutr, 41 (4), 810–817. doi: 10.1093/ajcn/41.4.810 Nawata, K., Sohmiya, M., Kawaguchi, M., Nishiki, M., & Kato, Y. (2004). Increased resting metabolic rate in patients with type 2 diabetes mellitus accompanied by advanced diabetic nephropathy. Metabolism, 53 (11), 1395–1398. doi: 10.1016/j.metabol.2004.06.004 Poehlman, E. T., Scheffers, J., Gottlieb, S. S., Fisher, M. L., & Vaitekevicius, P. (1994). Increased resting metabolic rate in patients with congestive heart failure. Ann Intern Med, 121 (11), 860–862. doi: 10.7326/0003-4819-121-11-199412010-00006 Pupim, L. B., Flakoll, P. J., Majchrzak, K. M., Aftab Guy, D. L., Stenvinkel, P., & Ikizler, T. A. (2005). Increased muscle protein breakdown in chronic hemodialysis patients with type 2 diabetes mellitus. Kidney Int, 68 (4), 1857–1865. doi: 10.1111/j.1523-1755.2005.00605.x Rainey-Macdonald, C. G., Holliday, R. L., & Wells, G. A. (1982). Nomograms for predicting resting energy expenditure of hospitalized patients. JPEN J Parenter Enteral Nutr, 6 (1), 5–60. doi: 10.1177/014860718200600105 Rosales, L. M., Schneditz, D., Morris, A. T., Rahmati, S., & Levin, N. W. (2000). Isothermic hemodialysis and ultrafiltration. Am J Kidney Dis, 36 (2), 353–361. doi: 10.1053/ajkd.2000.8986 Sasongko, M. B., Widyaputri, F., Sulistyoningrum, D. C., Wardhana, F. S., Widayanti, T. W., Supanji, S.,.. . Agni, A. N. (2018). Estimated Resting Metabolic Rate and Body Composition Measures Are Strongly Associated With Diabetic Retinopathy in Indonesian Adults With Type 2 Diabetes. Diabetes Care, 41 (11), 2377–2384. doi: 10.2337/dc18-1074 Scarrone, S., Balestrino, M., Frassoni, F., Pozzi, S., Gandolfo, C., Podestà, M., & Cupello, A. (2007). Sex differences in human lymphocyte Na,K-ATPase as studied by labeled ouabain binding. Int J Neurosci, 117 (2), 275–285. doi: 10.1080/00207450500534050 Schneditz, D., Rosales, L., Kaufman, A. M., Kaysen, G., & Levin, N. W. (2002). Heat accumulation with relative blood volume decrease. Am J Kidney Dis, 40 (4), 777–782. doi: 10.1053/ajkd.2002.35689 Selby, N. M., & McIntyre, C. W. (2006). A systematic review of the clinical effects of reducing dialysate fluid temperature. Nephrol Dial Transplant, 21 (7), 1883–1898. doi: 10.1093/ndt/gfl126 Simmons, P. S., Miles, J. M., Gerich, J. E., & Haymond, M. W. (1984). Increased proteolysis. An effect of increases in plasma cortisol within the physiologic range. J Clin Invest, 73 (2), 412–420. doi: 10.1172/jci111227 Simoneau, J. A., & Bouchard, C. (1989). Human variation in skeletal muscle fiber-type proportion and enzyme activities. Am J Physiol, 257 (4 Pt 1), E567-572. doi: 10.1152/ajpendo.1989.257.4.E567 Slomowitz, L. A., Monteon, F. J., Grosvenor, M., Laidlaw, S. A., & Kopple, J. D. (1989). Effect of energy intake on nutritional status in maintenance hemodialysis patients. Kidney Int, 35 (2), 704–711. doi: 10.1038/ki.1989.42 Tessari, P., Garibotto, G., Inchiostro, S., Robaudo, C., Saffioti, S., Vettore, M.,.. . Deferrari, G. (1996). Kidney, splanchnic, and leg protein turnover in humans. Insight from leucine and phenylalanine kinetics. J Clin Invest, 98 (6), 1481–1492. doi: 10.1172/jci118937 Tran, C., Jacot-Descombes, D., Lecoultre, V., Fielding, B. A., Carrel, G., Lê, K. A.,.. . Tappy, L. (2010). Sex differences in lipid and glucose kinetics after ingestion of an acute oral fructose load. Br J Nutr, 104 (8), 1139–1147. doi: 10.1017/s000711451000190x van der Sande, F. M., Rosales, L. M., Brener, Z., Kooman, J. P., Kuhlmann, M., Handelman, G.,.. . Levin, N. W. (2005). Effect of ultrafiltration on thermal variables, skin temperature, skin blood flow, and energy expenditure during ultrapure hemodialysis. J Am Soc Nephrol, 16 (6), 1824–1831. doi: 10.1681/asn.2004080655 Wang, J. Y., Hwang, B., Lin, Y. F., & Chen, J. D. (2012). Development of refractory ascites during amiodarone therapy in a hemodialysis patient with nephrogenic and cardiogenic ascites. Ren Fail, 34 (8), 1033–1036. doi: 10.3109/0886022x.2012.705206 Wang, J. Y., Lu, K. C., Lin, Y. F., & Hu, W. M. (2003). Correlation of serum leptin concentrations with body composition and gender in Taiwanese hemodialysis patients without diabetes. Ren Fail, 25 (6), 953–966. doi: 10.1081/jdi-120026030 Xue, B., & Kahn, B. B. (2006). AMPK integrates nutrient and hormonal signals to regulate food intake and energy balance through effects in the hypothalamus and peripheral tissues. J Physiol, 574 (Pt 1), 73–83. doi: 10.1113/jphysiol.2006.113217 Additional Declarations There is NO Competing Interest. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6036874","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Biological Sciences - Article","associatedPublications":[],"authors":[{"id":436751995,"identity":"cac44779-e0e6-4a51-a1c9-c7bd159a01a0","order_by":0,"name":"Jenn- Yeu Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9klEQVRIiWNgGAWjYHACNiC2kGNgYGx8wNggQbQWCWMGBuZmA7AWEJ8hgbCWxAYG9jYJxgYGwlrM2XuPPa6okEhf238QqGWHRR6/fAPbg48/cGux7DmXbnjmjETuthuJzRaMZySKJdsY2A1n4LHF4EaOmWRjG0gLY+MNxjaJxA3HGNikefBpuf8GrCXd7PzBBgmQlv0gLX/w2sID1pJgdiCxCaxlAxtQCz7vG5zJS5NsOCNhCPKLQSJQy4xjiW2SPWl4tBw/e0yyocJG3uz88YcPPrbVJfY3Hz4m8cMGtxYGBh4kNsQ54NghVssoGAWjYBSMAmwAAMnAUFmug+GIAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-7610-9374","institution":"Taipei City Hospital, Zhongxiao campus","correspondingAuthor":true,"prefix":"","firstName":"Jenn-","middleName":"Yeu","lastName":"Wang","suffix":""},{"id":436751996,"identity":"6ac2d5b8-7b46-4be1-a1cb-cf9fb771dd87","order_by":1,"name":"MingChun CHiang","email":"","orcid":"","institution":"Taipei Medical University","correspondingAuthor":false,"prefix":"","firstName":"MingChun","middleName":"","lastName":"CHiang","suffix":""},{"id":436751997,"identity":"b2e10944-4e4a-41ed-a0ef-180fd00fcf53","order_by":2,"name":"Shu- Chin Chen","email":"","orcid":"","institution":"Chinese Cultural University","correspondingAuthor":false,"prefix":"","firstName":"Shu-","middleName":"Chin","lastName":"Chen","suffix":""},{"id":436751998,"identity":"96bb10d1-3a6b-4547-a1d1-0fd6ccca7fb2","order_by":3,"name":"Hsiao-Yun Hu Hsiao-Yun Hu","email":"","orcid":"","institution":"National Yang Ming University","correspondingAuthor":false,"prefix":"","firstName":"Hsiao-Yun","middleName":"Hu Hsiao-Yun","lastName":"Hu","suffix":""},{"id":436751999,"identity":"6acf3a86-49bd-45c5-b248-c42338f25dc1","order_by":4,"name":"Betau Hwang","email":"","orcid":"","institution":"National Yang Ming University","correspondingAuthor":false,"prefix":"","firstName":"Betau","middleName":"","lastName":"Hwang","suffix":""},{"id":436752000,"identity":"1781ff4d-e1a0-4039-9c08-88d30a1bfa0f","order_by":5,"name":"Yuh-Feng Lin","email":"","orcid":"","institution":"Taipei Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yuh-Feng","middleName":"","lastName":"Lin","suffix":""}],"badges":[],"createdAt":"2025-02-15 13:40:30","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6036874/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6036874/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":95808598,"identity":"998bdf63-8769-41c9-b630-0b00009256ab","added_by":"auto","created_at":"2025-11-13 08:49:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":114566,"visible":true,"origin":"","legend":"\u003cp\u003eBland and Altman analysis for basal energy Expenditure predicted by the Harris–Benedict (HB) equation against basal energy Expenditure predicted by bioelectrical impedance analysis (BIA) in patients receiving hemodialysis\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6036874/v1/d3be8457dcd425575ffe3a70.png"},{"id":95810628,"identity":"c0aa78e2-466a-462e-9752-a8e19d5b8b16","added_by":"auto","created_at":"2025-11-13 08:53:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":786925,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6036874/v1/219f3a6a-a0c9-4e29-a722-8efdd2d46671.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Reliable Noninvasive Methods for Assessing Nutritional Status and Basal energy Expenditure in MHD: Focus on Basal Energy Expenditure","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eBasal energy Expenditure (BEE) represents the energy consumption of an individual at complete rest. Rising basal energy Expenditure in maintenance hemodialysis (MHD) patients necessitates increased energy replenishment, esp. in anorexic patients in order to increase serum amino acid concentration and reduce high incidence of wasting syndrome and malnutrition(Slomowitz, Monteon, Grosvenor, Laidlaw, \u0026amp; Kopple, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1989\u003c/span\u003e).Therefore, the determination of BEE is critical to establishing energy recommendations and maintaining nutritional balance in patients receiving hemodialysis (HD) (Basile et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Using blood temperature monitor in HD, low temperature dialysis (dialysate temperature\u0026thinsp;\u0026lt;\u0026thinsp;37C) and isothermal HD can maintain hemodynamic stability, which requires active cooling. The amount of this negative energy transfer is directly proportional to the amount of blood volume loss induced by ultrafiltration(Hor\u0026aacute;cek et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn addition to signaling human appetite control from adipose tissue(Wang, Lu, Lin, \u0026amp; Hu, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) with leptin and gut hormone, BEE, in proportion to basal energy requirements also provide a feedback signal of driving habitual food intake and human appetite control. BEE is also useful for individualizing dialytic prescriptions for these patients (Selby \u0026amp; McIntyre, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eConsequences of advanced chronic kidney disease are principal contributors to elevated BEE in patients on HD. It seems reasonable to speculate that the increase in BEE is attributable to protein catabolism caused by the inflammation and uremia. Uremic conditions cause proteolysis and lean body mass depletion owing to the inactivation of alanine aminotransferase and glycogenolysis because of decreased phosphofructokinase and pyruvate kinase activities in patients on HD (Conjard et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). Besides ,renal failure may be related to increased energy metabolism due to metabolic acidosis, secondary hyperparathyroidism, insulin resistance (Avesani, Draibe, Kamimura, Colugnati, \u0026amp; Cuppari, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2004\u003c/span\u003e), and microinflammation, with elevated C-reactive protein and cytokine levels (Deger et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), despite the role of the kidney as an essential metabolically active organ (Gallagher et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1998\u003c/span\u003e).HD patients with moderate to severe hyperparathyroidism also exhibit lean body mass depletion and increased BEE (Baczynski et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1985\u003c/span\u003e; Cuppari et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2004\u003c/span\u003e).Hyperparathyroidism causes skeletal myopathy. PTH not only causes abnormalities of skeletal muscle of bioenergetics, but also cause muscle proteolysis. Animal study showed the mechanism of increased BEE in hyperparathyroidism that PTH facilitate entering of excess calcium into the cells, precipitation as calcium phosphate compounds, and reducing the concentration of inorganic phosphorus, and then reducing high energy compound, such as ATP(Baczynski et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1985\u003c/span\u003e; Cuppari et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2004\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCommon comorbidities, such as diabetes, heart failure, and chronic inflammation, in advanced chronic kidney disease also contribute to elevated BEE in patients on HD. The development of type 2 diabetes, occurring early in the transition from normal glucose tolerance to impaired glucose tolerance is accompanied not only by an increase in BEE but also a decrease in insulin induced thermogenesis, which associated with progressive metabolic abnormalities independent of body size and body composition(Nawata, Sohmiya, Kawaguchi, Nishiki, \u0026amp; Kato, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Besides well known risk factors for diabetic retinopathy, such as duration of diabetes, long standing hyperglycemia, subclinical inflammation, increased oxidative stress and hypercholesterolemia, a community-based cross-sectional study involving 1,184 participants with Type 2 diabetic retinopathy, a most specific microvascular complication of diabetes found that visceral fat, subcutaneous fat, body fat and increased basal energy expenditures were also the novel risk factors for diabetic retinopathy. The mechanism involves excessive gluconeogenesis and catabolism, leading to increased rates of energy expenditure. Sustained increased basal energy expenditure can manifest as sustained unintended weight loss(Sasongko et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).Skeletal muscle catabolism has been observed in HD patients with poorly controlled diabetes with retinopathy and nephropathy owing to a combined effect of uremic factors and insulin resistance (Pupim et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) and lean body mass depletion and elevated BEE (Nawata et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Sasongko et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe frequent occurrence of systemic microinflammation and various catabolic conditions in patients with chronic kidney disease may directly affect BEE. If CKD patients are in a state of subclinical inflammation, they are at risk for malnutrition. Not only increased BEE but also other related responses to inflammation contribute to the linkage of inflammation and malnutrition, which include increased oxygen consumption, increased systemic total protein turnover and peripheral amino acid mobilization, enhanced lipolysis and fat utilization, and increased concentrations of catecholamines, glucagon, and cortisol(Avesani et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). In addition, suppression of appetite may also be implicated. In some ESRD patients, peripheral blood mononuclear cells (PBMCs) overproduce pro-inflammatory IL-6 cytokines which induces protein catabolism, lipolysis, insulin resistance, and suppression of appetite and pathophysiologic ally link inflammation and malnutrition(Kamimura et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).Following weight loss in obese patient in weight control center, the cellularity model predicts decline of BEE more effectively than the body fat model(Bernstein et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1983\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eAn overhydrated status in patients undergoing HD may increase BEE because of increased myocardial demand, increased activation of the sympathetic system, increased serum levels of tumor necrotic factors, and increased metabolism of the respiratory muscles (Poehlman, Scheffers, Gottlieb, Fisher, \u0026amp; Vaitekevicius, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1994\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOne study identified several persistent pathophysiological alterations related to dialytic procedures, some even lasting up to 2 hours after HD, such as hypovolemia (van der Sande et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), dialytic loss of amino acids and protein catabolism (Gutierrez, Bergstr\u0026ouml;m, \u0026amp; Alvestrand, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Ikizler et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), activation of complement system due to bio-incompatibility of dialysis membranes (Gutierrez, Alvestrand, Wahren, \u0026amp; Bergstr\u0026ouml;m, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1990\u003c/span\u003e; Hakim, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1993\u003c/span\u003e), and changes in hormonal levels, with significant increase in BEE after HD (Ikizler et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). To date, few studies have incorporated body composition, anthropometric data, diabetes mellitus (DM) status, and demographic data in predicting bioelectrical impedance analysis (BIA)\u0026ndash;determined BEE in patients receiving HD. Therefore, in this study, we compared BIA-derived BEEs and BEEs calculated using the Harris\u0026ndash;Benedict equation in patients undergoing HD, and we identified the determinants of BEE.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003eThis study was reviewed and approved by the Department of Teaching and Research of Taipei City Hospital (case no. : 10939) and the Institutional Review Board of Taipei City Hospital (case no.: TCHIRB-10907008-E).\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003ePatients\u003c/h2\u003e \u003cp\u003eThirty-five patients receiving HD were recruited from the nephrological division of the Medical Department of Taipei Municipal Zhongxiao Hospital from 2003 to 2004. Their physical conditions were stable. All patients were dialyzed two to three times a week. No patient used corticosteroids or immunosuppressive drugs. Patients with malignant tumors were excluded from the study.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAnthropometry\u003c/h3\u003e\n\u003cp\u003ePatients were weighed while wearing light clothes without shoes. Height was measured on the platform scale. Body mass index was calculated by dividing dry body weight by the square of height.\u003c/p\u003e\n\u003ch3\u003eBody composition determined from bioelectrical impedance analysis\u003c/h3\u003e\n\u003cp\u003eAlthough the human body is not a uniform cylinder, the current passing through the human body provides impedance to the current in two types, namely reactance and resistance. Reactance comes from cell membrane, whereas resistance comes from extracellular fluid and intracellular fluid. The circuit representing the tissue in the human body includes the first arm of the extracellular fluid resistance circuit and the parallel second arm of the cell membrane and intracellular fluid circuit at low frequencies, the current does not penetrate the cell membrane instead of the extracellular fluid, which is the so-called resistance. At high frequencies in multiple frequency BIA, the current penetrates the cell membranes. The 50 kHz current passes through both intracellular and extracellular fluids, although the ratio varies by tissue(Kyle, Bosaeus, De Lorenzo, Deurenberg, Elia, G\u0026oacute;mez, et al., 2004). The inductive reactance, captative reactance and resistance reflects the different electrical properties of tissues, which are affected in various ways by ascites (Wang, Hwang, Lin, \u0026amp; Chen, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), nutritional status, and hydration status.\u003c/p\u003e \u003cp\u003eBioelectrical impedance analysis has been validated as a tool for measuring body composition in patients on HD (Lukaski, Johnson, Bolonchuk, \u0026amp; Lykken, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e1985\u003c/span\u003e). In this study, a single-frequency tetrapolar bioelectrical impedance analyzer (BA-200, Mesmed System Co., Ltd) was used to measure body composition immediately after HD. With the patient in a sitting posture (legs separated and arms abducted from the trunk), electrodes were placed in standard positions on the opposite side of vascular access. Each subject wore clothes, but without shoes or socks, and after cleaning all skin contact areas with alcohol to minimize skin-electrode contact impedance, aluminum foil point electrodes were placed on the surface of the dorsum of wrist joint and forearm proximal to the wrist joint, and on the surface of the lateral ankle and leg proximal to the ankle. A single-frequency low current of 800 \u0026micro;A at 50 kHz was applied to the source electrodes and a voltage drop was detected at the proximal electrodes of the forearm and leg. Lean body mass was estimated according to the equation provided by the manufacturer (Kyle, Bosaeus, De Lorenzo, Deurenberg, Elia, Manuel G\u0026oacute;mez, et al., 2004).\u003c/p\u003e\n\u003ch3\u003eResting energy expenditure\u003c/h3\u003e\n\u003cp\u003eIn this cross-sectional study, the BIA method was used to determine the BEE of patients receiving HD. BEE, derived using the manufacturer-provided equation, was acquired immediately after dialysis (15\u0026ndash;30 min). The Harris\u0026ndash;Benedict method was based on the following equations (Rainey-Macdonald, Holliday, \u0026amp; Wells, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1982\u003c/span\u003e):\u003c/p\u003e \u003cp\u003eBEE (kcal/day) for men\u0026thinsp;=\u0026thinsp;66.4730\u0026thinsp;+\u0026thinsp;13.7516 \u0026times; body weight (kg)\u0026thinsp;+\u0026thinsp;5.0033 \u0026times; body height (cm)\u0026thinsp;\u0026minus;\u0026thinsp;6.7550 \u0026times; age (years)\u003c/p\u003e \u003cp\u003eBEE (kcal/day) for women\u0026thinsp;=\u0026thinsp;655.0955\u0026thinsp;+\u0026thinsp;9.5634 \u0026times; body weight (kg)\u0026thinsp;+\u0026thinsp;1.8496 \u0026times; body height (cm)\u0026thinsp;\u0026minus;\u0026thinsp;4.6756 \u0026times; age (years)\u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe cutoff point of lean body mass, as determined using a receiver operating characteristic analysis, was 44.8 kg for low and high BEEs. Data of low and high lean body mass groups are expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation or as ratios. An independent \u003cem\u003et\u003c/em\u003e test and chi-square test were used to evaluate the data for low and high lean body mass groups. Pearson biserial correlation and multiple linear regression were used to analyze demographic data, DM status, anthropometric data, and body composition. Collinearity diagnostic is evaluated using Variation inflection factor (VIF). The variation inflection factors of independent variables in the multiple linear regressions were all smaller than 10. Altman\u0026ndash;Bland analysis (Bland \u0026amp; Altman, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1986\u003c/span\u003e) was performed to assess the agreement between BEE derived from BIA immediately after HD and that derived from the Harris\u0026ndash;Benedict equation.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e outlines the characteristics of the study patients. BEE was positively correlated with lean body mass, dry body weight, body height, and gender. Conversely, BEE was negatively correlated with percent fat mass (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In the multiple linear regression analysis, lean body mass was the independent variable for predicting BIA-determined BEE (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In the second multiple linear regression analysis, gender and height were the independent variables for predicting BIA-determined BEE (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The intra-class correlation coefficient of test\u0026ndash;retest reliability of total body water was 0.997 (95% confidence interval [CI] 0.996 to 0.999).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive Characteristics of the Study Patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow LBM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigh LBM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (M: F)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1:18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16:0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge(years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e62.63\u0026thinsp;\u0026plusmn;\u0026thinsp;13.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.63\u0026thinsp;\u0026plusmn;\u0026thinsp;13.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e62.63\u0026thinsp;\u0026plusmn;\u0026thinsp;13.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody height(cm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e155.83\u0026thinsp;\u0026plusmn;\u0026thinsp;5.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e167.88\u0026thinsp;\u0026plusmn;\u0026thinsp;4.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e161.34\u0026thinsp;\u0026plusmn;\u0026thinsp;7.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDry body weight(kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52.54\u0026thinsp;\u0026plusmn;\u0026thinsp;7.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e62.43\u0026thinsp;\u0026plusmn;\u0026thinsp;8.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e57.06\u0026thinsp;\u0026plusmn;\u0026thinsp;9.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasal energy Expenditure by HB equation (kcal/24hour)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1150.58\u0026thinsp;\u0026plusmn;\u0026thinsp;83.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1341.81\u0026thinsp;\u0026plusmn;\u0026thinsp;172.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1238.00\u0026thinsp;\u0026plusmn;\u0026thinsp;161.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePercent fat mass\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.47\u0026thinsp;\u0026plusmn;\u0026thinsp;8.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.64\u0026thinsp;\u0026plusmn;\u0026thinsp;7.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20.98\u0026thinsp;\u0026plusmn;\u0026thinsp;9.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody mass index(kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.62\u0026thinsp;\u0026plusmn;\u0026thinsp;2.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22.09\u0026thinsp;\u0026plusmn;\u0026thinsp;2.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21.83\u0026thinsp;\u0026plusmn;\u0026thinsp;2.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFat mass(kg)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13.81\u0026thinsp;\u0026plusmn;\u0026thinsp;6.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.20\u0026thinsp;\u0026plusmn;\u0026thinsp;6.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.62\u0026thinsp;\u0026plusmn;\u0026thinsp;6.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasal energy Expenditure by BIA (kcal/24hour)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1421.92\u0026thinsp;\u0026plusmn;\u0026thinsp;93.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1738.64\u0026thinsp;\u0026plusmn;\u0026thinsp;128.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1566.71\u0026thinsp;\u0026plusmn;\u0026thinsp;193.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDM status (positive: negative)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12:7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8:8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAbbreviations: HB, Harris\u0026ndash;Benedict equation; DM, diabetes mellitus; BIA, bioelectrical impedance analysis; LBM, lean body mass; M, male; F, female\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePearson Biserial Correlation Coefficients of Predictive Variables of BIA-determined Basal energy Expenditure with Levels of Significance\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eranking\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003evariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBiserial Pearson r\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLBM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBody height\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003egender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.769\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDBW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.753\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e%FM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBMI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDM status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.200\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.560\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eAbbreviations: BIA, bioelectrical impedance analysis; LBM, lean body mass; DBW, dry body weight; %FM, percent fat mass; BMI, body mass index; DM, diabetes mellitus\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultiple Linear Regression for Predicting BIA-determined Basal energy Expenditure Based on Gender, Body Height, and Lean Body Mass\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003epredictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBeta coefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandard error of estimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStandardized\u003c/p\u003e \u003cp\u003eBeta coefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCollinearity statistics\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-12.224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.711\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody height\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.599\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.716\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.837\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.409\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLean body mass\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.562\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.742\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4.777\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eTotal R square\u0026thinsp;=\u0026thinsp;0.994 Significance of model P\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultiple Linear Regression for Predicting BIA-determined Basal energy Expenditure Based on Gender, Body Height, and Percent Fat Mass\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003epredictor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBeta coefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandard error of estimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStandardized\u003c/p\u003e \u003cp\u003eBeta coefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCollinearity statistics\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e113.838\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.363\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody height\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.609\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.678\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.260\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e% fat mass\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.416\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.680\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.410\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003eTotal R square\u0026thinsp;=\u0026thinsp;0.767 Significance of model P\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe p values of simple linear regression with the differences between BEE derived from BIA and BEE determined using the Harris\u0026ndash;Benedict method as the dependent variable and the average of REE derived from BIA and REE determined using the Harris\u0026ndash;Benedict method as independent variable.is significant, we accepted the null hypothesis that the two methods is inconsistent. BIA-determined BEE exhibited poor agreement with BEE derived from the Harris\u0026ndash;Benedict equation was nonsignificantly larger.\u003c/p\u003e \u003cp\u003eThe mean and standard deviation of the absolute difference between BIA-derived BEE and BEE calculated using the Harris\u0026ndash;Benedict equation was 328.707\u0026thinsp;\u0026plusmn;\u0026thinsp;118.783 kcal/24 hour. The 95% CI (mean\u0026thinsp;\u0026plusmn;\u0026thinsp;1.96 \u0026times; standard deviation) of the absolute difference between BIA-derived BEE and BEE determined using the Harris\u0026ndash;Benedict equation ranged between 95.892 and 561.522 kcal/24 hour. In other words, the upper limit of agreement and lower limit of agreement were 561.522 kcal/24 hour (mean\u0026thinsp;+\u0026thinsp;1.96 SD) and 95.892 kcal/24 hour (mean \u0026ndash; 1.96 SD), respectively. The distribution of data points also displayed poor agreement between REE derived from BIA and REE determined using the Harris\u0026ndash;Benedict method (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThe main findings of this study were as follows: BEE derived from BIA immediately after HD exhibited poor agreement with BEE determined using the Harris\u0026ndash;Benedict equation and was nonsignificantly larger. Our correlation analysis revealed that BIA-derived BEE was significantly correlated with lean body mass, gender, body height, and percent fat mass. Multiple regression analysis showed that lean body mass was the most potent determinant of BIA-derived BEE.\u003c/p\u003e \u003cp\u003eA previous study showed that gender-based differences in BIA-derived BEE were independent of lean body mass, level of fitness, age, and menopause (Arciero, Goran, \u0026amp; Poehlman, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1993\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe main mechanism of gender-based differences in BEE (a higher BEE in men than in women) are as follows: In addition to regulation of food intake and distribution of fat, estrogens (hormone signal) can also control subcutaneous brown adipose tissue thermogenesis through action on estrogen receptor-alpha of ventromedial nucleus(VMN) of hypothalamus resulting in activation of AMP kinase(AMPK) and then activation of beta- adrenergic sympathetic nervous system (SNS) activation, and final activation of thermogenesis from brown adipose tissue(BAT)(Xue \u0026amp; Kahn, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). The above mechanism is the so called VMN-AMPK-SNS-BAT axis. Recent researches implicate that AMPK is also a mediator of the actions of adipocyte-derived and intestine -derived hormones on fatty acid oxidation and glucose uptake in peripheral tissues(Xue \u0026amp; Kahn, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Testosterone can increase the sensitivity of insulin receptors of visceral white adipose tissue (WAT) which facilitate activation of lipoprotein lipase resulting in lipolytic mobilization of free fatty acid from WAT for energetic demands. The composition and bioenergetics of skeletal muscle fibers, such as greater skeletal muscle AMPKα phosphorylation could contribute to the main mechanism of gender-based differences in BEE (a higher BEE in men than in women)(Ferraro et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; Guadalupe-Grau et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Muscles from females have a greater proportion of type I myofibril, smaller cross-sectional area of myofibrils, lower glycolytic potential of metabolic properties(Simoneau \u0026amp; Bouchard, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e1989\u003c/span\u003e). Other mechanisms of gender-based differences in BEE, including, gluconeogenesis in the liver(Tran et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), Na+-K\u0026thinsp;+\u0026thinsp;ATPase activity(Scarrone et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), and regulation of body core temperature(Anderson et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) merit further study. Body height is easily measured and frequently used as a proxy of body size and stature, which are related to body mass (Heymsfield et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Heymsfield, Thomas, Bosy-Westphal, \u0026amp; M\u0026uuml;ller, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMetabolically active organs include the heart, liver, kidney, brain, skeletal muscle, and other tissues. Skeletal muscle represents the largest metabolically active organ in humans, which explains why lean body mass is the most robust predictor of BEE derived from BIA (Gallagher et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1998\u003c/span\u003e).Although total skeletal muscle mass is the main depot of total body protein, splanchnic area plays an important regulatory role in the total body protein turnover(Tessari et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1996\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAccording a previous study, BEEs calculated through indirect calorimetry at three time points (pre-dialysis, beginning of dialysis, and 30 days after dialysis) were lower than BEEs derived from the Harris\u0026ndash;Benedict equation. The authors reported that despite biological day-to-day variations, BEE predicted using the Harris\u0026ndash;Benedict equation frequently exceeded BEE measured using calorimetry (de Oliveira, Bufarah, Ponce, \u0026amp; Balbi, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2018\u003c/span\u003e),(Haugen, Melanson, Tran, Kearney, \u0026amp; Hill, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). By contrast, our study showed that BEE calculated using BIA immediately after HD was nonsignificantly larger than BEE derived from the Harris\u0026ndash;Benedict equation. These findings suggest an increase in BEE immediately after HD.\u003c/p\u003e \u003cp\u003eSeveral mechanisms contribute to an increase in BEE immediately after HD, including 1.volume theory (van der Sande et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2005\u003c/span\u003e); 2. elevated NEFA (non-esterified fatty acids) levels; 3. microinflammatory theory; 4. hormone theory, and 5. protein catabolism due to loss of amino acids during dialysis (Ikizler et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1996\u003c/span\u003e).. The increase in BEE after HD has been linked with increased cardiac output, activation of the sympathetic system, and vasoconstriction of the skin as compensation for hypovolemia (Rosales, Schneditz, Morris, Rahmati, \u0026amp; Levin, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2000\u003c/span\u003e; van der Sande et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2005\u003c/span\u003e), the so-called \u0026lsquo;volume theory\u0026rsquo; (Schneditz, Rosales, Kaufman, Kaysen, \u0026amp; Levin, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2002\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWithin the first hour of HD, NEFA (non-esterified fatty acids) levels were found to increase, then decrease but not return to baseline values. The initial rise in NEFA was attributed to heparin being an activator of lipoprotein lipase, which may further rise due to loss of glucose into the dialysate. Oxidation of NEFA reduces RQ and temporarily increases oxygen consumption and BEE. RQ (respiratory quotient) equals to carbon production divided by oxygen consumption(Hor\u0026aacute;cek et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHD itself may trigger an inflammatory reaction with elevated interleukin-6. Studies have shown that muscle proteolysis increases significantly during HD (Gutierrez et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). One pathway of blood membrane interaction in maintenance hemodialysis patients results in catabolic processes of protein and malnutrition. The effect of bio-incompatibility on the rate of subsequent proteolysis of muscle can be evaluated by measuring the net release of specific amino acids(Tessari et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1996\u003c/span\u003e)\u003csup\u003e,\u003c/sup\u003e (Hakim, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1993\u003c/span\u003e). Blood contact with a bioincompatible dialysis membrane induces the activation of complement and elevates prostaglandin, resulting in protein catabolism (Gutierrez et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1990\u003c/span\u003e). Furthermore, endotoxins in the dialysate stimulate increased production of interleukin-1, resulting in protein catabolism and increased BEE (Gutierrez et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1990\u003c/span\u003e). During the dialysis period and the following 2 hours, protein catabolism persists and causes increased BEE, the so-called microinflammatory theory.\u003c/p\u003e \u003cp\u003eLoss of amino acids into the dialysate triggers protein catabolism, which cannot be prevented by the addition of glucose to the dialysate but can be waived by intravenous amino acid solution (Gutierrez et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1994\u003c/span\u003e). Elevation of several stress hormones, such as cortisol, glucagon, and catecholamine, is observed after HD. Cortisol, in elevated amounts in the physiological value, is the major hormone responsible for protein catabolism, albeit a minor role compared with other mechanisms (Simmons, Miles, Gerich, \u0026amp; Haymond, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e1984\u003c/span\u003e).In conclusion, our study suggests that the timing of BEE measurement in relation to HD is crucial in serial follow-up for stable hemodialysis patients prescribed with constantly adequate dialysis dosage.\u003c/p\u003e"},{"header":"Summary","content":"\u003cp\u003eThe field of application of basal energy Expenditure measurements provides unprecedented opportunities for improving assessment and management of low temperature dialysis, energy, and nutritional recommendations in maintenance hemodialysis patients. The current evidence from previous literatures affecting basal energy Expenditure in chronic renal failure focused largely on comorbidities, consequences of chronic kidney disease, systemic microinflammation, and the consequences of dialysis procedures. Our study was a small study, limited by the acquisition of informed consent, and focused on identifying determinants of resting energy expenditure. Three variables, lean body mass, height, and gender explained 99.4% of the variation in resting energy expenditure. In patients undergoing maintenance hemodialysis, basal energy Expenditure predicted by the Harris-Benedict equation was not significantly smaller than basal energy Expenditure predicted by bioelectrical impedance analysis in Bland and Altman analysis. Indirect inference from a previous research paper demonstrated that basal energy Expenditure increases after hemodialysis. Our study provides a novel conceptual framework for building predictive models from lean body mass subgroup analysis and correlation analysis of variables. We proposed to explain the theoretical rationale for low temperature dialysis prescription using indirect reasoning from the Bland and Altman analysis. This conceptual framework needs to be validated by future large-scale studies using gold standard methods of measuring basal energy Expenditure and more evidence is needed to build upon it. We believed that the proposed measurement of basal energy Expenditure by bioelectrical impedance analysis is an important step towards improving clinical practice in maintenance hemodialysis patients and will ultimately enable clinicians to improve outcomes in maintenance hemodialysis patients.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eLimitations\u003c/strong\u003e \u003cp\u003eOur study included small number of patients due to the difficulty in acquirement of informed consent. Besides, the gold standard of basal energy Expenditure was not used.\u003c/p\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eConflicts of Interest:\u003c/h2\u003e \u003cp\u003eThe authors declare no potential conflict of interest.\u003c/p\u003e\u003ch2\u003eAuthor Contributions:\u003c/h2\u003e \u003cp\u003eJenn-Yeu Wang conceived and designed the experiments; Jenn-Yeu Wang and Hsiao-Yun Hu analyzed the data; Jenn-Yeu Wang and Shu-Chin Chen performed the experiments; Jenn-Yeu Wang, Ming-Chun Chiang, Yuh Feng Lin and Betau Hwang contributed to the writing of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgments:\u003c/h2\u003e \u003cp\u003e The authors thank the Department of Teaching and Research of Taipei City Hospital and the Institutional Review Board of Taipei City Hospital for supporting this project.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAnderson, C. A. J., Stewart, I. B., Stewart, K. 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AMPK integrates nutrient and hormonal signals to regulate food intake and energy balance through effects in the hypothalamus and peripheral tissues. J Physiol, \u003cem\u003e574\u003c/em\u003e(Pt 1), 73\u0026ndash;83. doi:\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1113/jphysiol.2006.113217\u003c/span\u003e\u003cspan address=\"10.1113/jphysiol.2006.113217\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Basal energy expenditure, Lean body mass, Hemodialysis, Bioelectrical impedance analysis","lastPublishedDoi":"10.21203/rs.3.rs-6036874/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6036874/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eOur aim was to (1) evaluate the agreement between basal energy Expenditure (BEE) derived from bioelectrical impedance analysis (BIA) immediately after hemodialysis (HD) and BEE determined using the Harris\u0026ndash;Benedict equation and (2) identify the determinants of BEE in patients receiving HD. This cross-sectional study included 35 patients treated with HD two to three times a week from 2003 to 2004 at Taipei Municipal Zhongxiao Hospital. BEE was measured using BIA immediately after HD. Next, Altman\u0026ndash;Bland analysis of BEE was performed to evaluate the agreement between the BIA method and the Harris\u0026ndash;Benedict method. Correlation and multiple linear regression analyses were performed to examine the association of BEE with demographic data, anthropometric data, and body composition. BEE derived from BIA immediately after HD exhibited poor agreement with BEE determined using the Harris\u0026ndash;Benedict method and was nonsignificantly larger. BEE was also positively correlated with lean body mass. In the multiple linear regression analysis, lean body mass was the most powerful independent determinant of BIA-derived BEE. Gender-based differences, height and lean body mass were noted in the prediction of BIA-derived BEE. This study revealed that understanding of the changes of scheduled basal energy Expenditure is paramount and related to changes of lean body mass condition of maintenance hemodialysis patients.\u003c/p\u003e","manuscriptTitle":"Reliable Noninvasive Methods for Assessing Nutritional Status and Basal energy Expenditure in MHD: Focus on Basal Energy Expenditure","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-13 08:20:51","doi":"10.21203/rs.3.rs-6036874/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bd74f2da-dc80-4eed-82fa-ddf95414073b","owner":[],"postedDate":"November 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":46503701,"name":"Biological sciences/Biological techniques/Biophysical methods/Electrophysiology"},{"id":46503702,"name":"Biological sciences/Biophysics/Bioenergetics"}],"tags":[],"updatedAt":"2025-11-13T08:20:51+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-13 08:20:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6036874","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6036874","identity":"rs-6036874","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}