OBJECTIVE: To accurately measure human energy metabolism with high temporal resolution, a respiratory gas analysis system was designed using a breath-by-breath approach. METHODS: Firstly, indirect calorimetry was employed in respiratory gas analysis to measure the respiratory flow and concentration signals in real-time. Secondly, oxygen consumption QO2 and carbon dioxide production QCO2 were calculated through respiratory characteristic alignment and respiratory signal segmentation. Finally, metabolic indexes were calculated according to the Weir formula. Furthermore, a controlled trial was formulated for validation comparisons with the MGC ULTIMA SYSTEM PFX CARDIO2. RESULTS: The results indicate that the data points of metabolic indexes measured by this system all fall within the confidence interval when compared with those of the MGC. CONCLUSION The system has high consistency with the MGC measurement results. Thus, the system can accurately measure metabolism in real-time and provide data support for clinical nutrition assessment and therapy.
Humans ; Energy Metabolism ; Breath Tests/instrumentation* ; Calorimetry, Indirect/instrumentation* ; Equipment Design

Nutritional support therapy is one of the extremely important treatment methods for patients in the intensive care unit. Timely and effective nutritional support regimens can improve patients' immune function, reduce complications, and optimize clinical outcomes. Energy expenditure is influenced by multiple factors, including patients' baseline characteristics (such as physical condition, gender, age) and dynamic changes in indicators (such as body temperature, nutritional support regimens, and therapeutic interventions). The currently recognized "gold standard" for accurately assessing energy metabolism in clinical practice is the indirect calorimetry system, also known as the metabolic cart. This device monitors carbon dioxide production and oxygen consumption in real time and uses specific algorithms to estimate the metabolic proportions of the three major nutrients (carbohydrates, fats, and proteins) in energy expenditure. An appropriate nutrient ratio helps maintain the balance between supply and demand in the body's nutritional metabolism. In the management of critically ill patients, the application of the metabolic cart enables personalized nutritional therapy, avoiding over- or under-supply of energy and optimizing the use of medical resources. Furthermore, with real-time, quantitative data support from the energy metabolism monitoring system, clinicians can develop more precise nutritional intervention strategies, thereby improving patient prognosis. This article provides a systematic review of the technical features of the metabolic cart and its application value in various critical care scenarios, aiming to offer a reference for indirect calorimetry in clinical practice.
Humans ; Critical Illness/therapy* ; Nutritional Support ; Energy Metabolism ; Calorimetry, Indirect

Objective: To compare the differences to determine resting energy expenditure (REE) measured with indirect calorimetry and REE predicted by formula method and body composition analyzer in patients with decompensated hepatitis B cirrhosis, so as to provide theoretical guidance for the implementation of precision nutrition intervention. Methods: Patients with decompensated hepatitis B cirrhosis who were admitted to Henan Provincial People's Hospital from April 2020 to December 2020 were collected. REE was determined by the body composition analyzer and the H-B formula method. Results: were analyzed and compared to REE measured by the metabolic cart. Results A total of 57 cases with liver cirrhosis were included in this study. Among them, 42 were male, aged (47.93 ± 8.62) years, and 15 were female aged (57.20 ± 11.34) years. REE measured value in males was (1 808.14 ± 201.47) kcal/d, compared with the results calculated by the H-B formula method and the measured result of body composition, and the difference was statistically significant (P = 0.002 and 0.003, respectively). REE measured value in females was (1 496.60 ± 131.28) kcal/d, compared with the results calculated by the H-B formula method and the measured result of body composition, and the difference was statistically significant (P = 0.016 and 0.004, respectively). REE measured with the metabolic cart had correlation with age and area of visceral fat in men (P = 0.021) and women (P = 0.037). Conclusion: Metabolic cart use will be more accurate to obtain resting energy expenditure in patients with decompensated hepatitis B cirrhosis. Body composition analyzer and formula method may underestimate REE predictions. Simultaneously, it is suggested that the effect of age on REE in H-B formula should be fully considered for male patients, while the area of visceral fat may have a certain impact on the interpretation of REE in female patients.
Humans ; Male ; Female ; Energy Metabolism ; Liver Cirrhosis/metabolism* ; Calorimetry, Indirect/methods* ; Hospitalization

A high-precision human metabolic measurement system is designed. The system uses STM32F103 as the main control chip to acquire oxygen, carbon dioxide and flow signals to calculate four quantitative indicators: oxygen consumption(V_O2), carbon dioxide production(V_CO2), respiratory entropy(RQ) and resting energy metabolism(REE), and finally uses an upper computer to display the calculation results.In this paper, the signal acquisition circuit design was carried out for the oxygen sensor, carbon dioxide sensor and flow sensor, and the validity of the device was verified with the American machine MGCDiagnositcs using Bland-Altman analysis method, and the results showed that the four parameters of V_O2,V_CO2, RQ and REE of both devices fell in the agreement interval of more than 95%. The device thus provides accurate metabolic measurements and offers an effective tool for the field of general health and clinical nutrition support in China.
Calorimetry, Indirect ; Carbon Dioxide/metabolism* ; Energy Metabolism ; Humans ; Oxygen ; Oxygen Consumption

Objective: To investigate the effect of sedation on resting energy expenditure (REE) in patients with extremely severe burns and the choice of REE estimation formula during the treatment. Methods: A retrospective non-randomized controlled clinical study was conducted. From April 2020 to April 2022, 21 patients with extremely severe burns who met the inclusion criteria and underwent mechanical ventilation treatment were admitted to the Department of Burn and Wound Repair of Second Affiliated Hospital of Zhejiang University School of Medicine, including 16 males and 5 females, aged 60 (50, 69) years. Early anti-shock therapy, debridement, skin transplantation, nutritional support, and other conventional treatments were applied to all patients. Patients were sedated when they had obvious agitation or a tendency to extubate, which might lead to aggravation of the disease. REE measurement was performed on patients using indirect calorimetry on post-injury day 3, 5, 7, 9, 11, 14 and every 7 days thereafter until the patient died or being successfully weaned from ventilator. Totally 99 times of measurements were carried out, of which 58 times were measured in the sedated state of patients, and 41 times were measured in the non-sedated state of patients. The age, weight, body surface area, residual wound area, post-injury days of patients were recorded on the day when REE was measured (hereinafter briefly referred to as the measurement day). The REE on the measurement day was calculated with intensive care unit conventional REE estimation formula Thumb formula and special REE estimation formulas for burns including the Third Military Medical University formula, the Peng Xi team's linear formula, Hangang formula. The differences between the sedated state and the non-sedated state in the clinical materials, measured and formula calculated values of REE of patients on the measurement day were compared by Mann-Whitney U test and independent sample t test. The differences between the REE formula calculated values and the REE measured value (reflecting the overall consistency) in the sedated state were compared by Wilcoxon signed rank-sum test. The Bland-Altman method was used to assess the individual consistency between the REE formula calculated value and the REE measured value in the sedated state, and to calculate the proportion of the REE formula calculated value within the range of ±10% of the REE measured value (hereinafter referred to as the accuracy rate). Root mean square error (RMSE) was used to evaluate the accuracy of the REE formula calculated value relative to the REE measured value. Results: Compared with those in the non-sedated state, there was no statistically significant change in patient's age or post-injury days on the measurement day in the sedated state (P>0.05), but the weight was heavier (Z=-3.58, P<0.01), and both the body surface area and the residual wound area were larger (with Z values of -2.99 and -4.52, respectively, P<0.01). Between the sedated state and the non-sedated state, the REE measured values of patients were similar (P>0.05). Compared with those in the non-sedated state, the REE values of patients calculated by Thumb formula, the Third Military Medical University formula, the Peng Xi team's linear formula, and Hangang formula on the measurement day in the sedated state were significantly increased (with Z values of -3.58 and -5.70, t values of -3.58 and -2.74, respectively, P<0.01). In the sedated state, compared with the REE measured value, there were statistically significant changes in REE values of patients calculated by Thumb formula, the Third Military Medical University formula, and Hangang formula on the measurement day (with Z values of -2.13, -5.67, and -3.09, respectively, P<0.05 or P<0.01), while the REE value of patients calculated by the Peng Xi team's linear formula on the measurement day did not change significantly(P>0.05). The analysis of the Bland-Altman method showed that in the sedated state, compared with the REE measured value, the individual consistency of the calculated value of each formula was good; Thumb formula and Hangang formula significantly underestimated the patients' REE value (with the average value of the difference between the formula calculated value and the measured value of -1 463 and -1 717 kJ/d, the 95% confidence interval of -2 491 to -434 and -2 744 to -687 kJ/d, respectively), but the individual differences were small; the Third Military Medical University formula significantly overestimated the patients' REE value (with the average value of the difference between the formula calculated value and the measured value of 3 530 kJ/d, the 95% confidence interval of 2 521 to 4 539 kJ/d), but the individual difference was small; the Peng Xi team's linear formula did not significantly overestimate the patients' REE value (with the average value of the difference between the formula calculated value and the measured value of 294 kJ/d, the 95% confidence interval of -907 to 1 496 kJ/d), while the difference standard deviation was 4 568 kJ/d, which showed a large individual difference. In the sedated state, relative to the REE measured value, the accuracy rates of REE values calculated by Thumb formula, the Third Military Medical University formula, the Peng Xi team's linear formula, and Hangang formula were 25.9% (15/58), 15.5% (9/58), 10.3% (6/58), and 15.5% (9/58), respectively, and RMSE values were 4 143.6, 5 189.1, 4 538.6, and 4 239.8 kJ/d, respectively. Conclusions: Sedative therapy leads to a significant decrease in REE in patients with extremely severe burns undergoing mechanical ventilation treatment. When REE cannot be regularly monitored by indirect calorimetry to determine nutritional support regimens, patients with extremely severe burns undergoing sedation may be prioritized to estimate REE using Thumb formula.
Burns/therapy* ; Calorimetry, Indirect ; Energy Metabolism ; Female ; Humans ; Male ; Nutritional Support ; Retrospective Studies

BACKGROUND: Various methods and equations are available to predict the basal metabolic rate (BMR). A published study comparing the Harris-Benedict Equation, Bioelectrical Impedance Analysis, and Indirect Calorimetry (IC), was done among Filipinos, and was able to obtain a novel formula for BMR. The purpose of this study is to validate this novel formula. METHODS: This is a multi-center, cross-sectional, validation study of the novel BMR equation, done among adult overweight and obese Filipinos, who were seen at St. Luke’s Medical Center and Providence Hospital in Quezon City, Outpatient Clinics from August 2019 to March 2020. Purposive sampling was done, and upon giving consent, subjects had undergone interview, anthropometrics measurement, and IC. RESULTS: 174 samples were enrolled. Mean age is 43 years old, majority are females. 27% have no co-morbidities; of those with co-morbidities, half have diabetes mellitus (DM). Mean weight is 74.30 kg; mean BMI is 29.78 kg/m2 . The mean computed BMR is 1174.70 kcal/day, which is 145.83 significantly lower than the BMR derived with calorimetry: 1320.53 kcal/day (P-value 0.000). However, the scatterplot reveals the linearity of positive direction for both values. 31% of the computed BMR fell within the +/-10% estimate of the actual BMR. Stratification of the results between those with DM and without, lowered the difference between the calculated and actual BMR to 46 kcal/day (from 145.83) among the DM subgroup, and increased the estimated accuracy to 38% falling within the +/- 10% estimate of the actual values. CONCLUSION: The novel BMR formula is linearly reflective of the basal metabolism of adult overweight and obese Filipinos, but the numerical values are lower compared to actual calorimetry results, yielding more accuracy when applied among patients with diabetes.
Calorimetry, Indirect ; Basal Metabolism ; Obesity Management

BACKGROUND/OBJECTIVES: There are various factors that affect metabolic abnormalities related to obesity. The purpose of this study is to analyze the differences in dietary intakes and body compositions of obese women according to metabolic risks and to classify them as metabolically healthy obese (MHO) or metabolically abnormal obese (MAO). SUBJECTS/METHODS: This study was conducted on 59 obese Korean women aged 19 to 60 years. NCEP-ATPIII criteria were applied and the women classified as MHO (n = 45) or MAO (n = 14). Body composition of each subject was measured by using dual-energy x-ray absorptiometry (DEXA). Three-day food records were used to analyze dietary intake. Eating habits and health-related behaviors were determined through questionnaires. Indirect calorimetry was used to measure resting metabolic rate and respiratory rate. RESULTS: The average age of the subjects was 43.7 years. The analysis of body composition according to phenotype revealed significantly higher body fat mass (P < 0.05), arm fat mass (P < 0.05), and android fat mass (P < 0.05), as measured by DEXA, in the MAO group than in the MHO group. There was no significant difference in the dietary intake of the two groups. However, eating behaviors differed. Compared to the MHO group, the MAO women had a shorter meal time (less than 10 minutes), a preference of oily foods, and a tendency to eat until full. Therefore, the eating habits of MHO women were more positive than those of MAO women. CONCLUSIONS: The results suggest that fat distribution in each body region affects various metabolic abnormalities. A high level of arm fat mass in obese Korean women may increase metabolic risk. In addition, eating habits of obese Korean women are considered to be environmental factors affecting the metabolic phenotype of obese Korean women.
Absorptiometry, Photon ; Adipose Tissue ; Arm ; Basal Metabolism ; Body Composition ; Body Regions ; Calorimetry, Indirect ; Diet ; Eating ; Feeding Behavior ; Female ; Humans ; Meals ; Methyltestosterone ; Monoamine Oxidase ; Obesity ; Phenotype ; Respiratory Rate

OBJECTIVES: The purpose of this study was to compare predictions and measurements of the resting energy expenditure (REE) of overweight and obese adult women in Korea. METHODS: The subjects included 65 overweight or obese adult women ranging in age from 20~60 with a recorded body mass index (BMI) of 23 or higher. Their height, weight, waist-hip ratio, and blood pressure were measured. The investigator also measured their body fat, body fat percentage, and body composition of total weight without fat using Dual energy X-ray absorptiometry (DXA) and measured resting energy expenditure by indirect calorimetry. Measured resting energy expenditures were compared with predictions from six methods: Harris-Benedict, Mifflin, Owen, WHO-WH, Henry-WH, and KDRI. RESULTS: Harris-Benedict predictions showed the smallest differences from measured resting energy expenditure at an accurate prediction rate of 70%. The study analyzed regression between measured resting energy expenditure and body measurements including height, weight and age. The formula proposed by this research is as follows: Proposed REE equation for overweight and obese Korean women = 721 − (1.5 × age) + (0.4 × height) + (9.9 × weight). CONCLUSIONS: These findings suggest that age is a significant variable when predicting resting energy expenditure in overweight and obese women. Therefore, prediction of resting energy expenditure should consider age when determining energy requirements in overweight and obese women.
Absorptiometry, Photon ; Adipose Tissue ; Adult ; Blood Pressure ; Body Composition ; Body Mass Index ; Calorimetry, Indirect ; Energy Metabolism ; Female ; Humans ; Korea ; Overweight ; Research Personnel ; Waist-Hip Ratio

During the management of critical illness, optimal nutritional support is an important key for achieving positive clinical outcomes. Compared to healthy people, critically ill patients have higher energy expenditure, thereby their energy requirements and risk of malnutrition being increased. Assessing individual nutritional requirement is essential for a successful nutritional support, including the adequate energy supply. Methods to assess energy requirements include indirect calorimetry (IC) which is considered as a reference method, and the predictive equations which are commonly used due to the difficulty of using IC in certain conditions. In this study, a literature review was conducted on the energy metabolic changes in critically ill patients, and the implications for the estimation of energy requirements in this population. In addition, the issue of optimal caloric goal during nutrition support is discussed, as well as the accuracy of selected resting energy expenditure predictive equations, commonly used in critically ill patients.
Calorimetry, Indirect ; Critical Illness* ; Energy Metabolism ; Humans ; Malnutrition ; Methods ; Nutritional Requirements ; Nutritional Support

A precision instrument is required to assess the nutritional status. This study was conducted on comparison of 3 nutritional questionnaires to determine energy intake (EI) accuracy in adults in Ravansar Non-Communicable Chronic Disease (RaNCD) cohort study. This cross-sectional study was conducted on 118 of participant's RaNCD. EI was evaluated with 3 questionnaires including food frequency questionnaire (FFQ), 24-hours recall (24HR), and food habits questionnaire (FHQ). Resting metabolic rate (RMR) was measured using indirect calorimetry. We used EI/RMR cut off to evaluate EI reporting status. The mean ± standard deviation of age in men and women were 44.1 ± 6.5 and 43.7 ± 5.25 respectively and 50.8% of participants were men. Among 3 EI estimating questionnaires, FFQ was more accurate than 2 other questionnaires (67.8%). We observed that implausible reporters of 24HR were likely overweight (p < 0.005) but we did not observe a significant difference between EI reporting of FFQ and FHQ with participants' body composition. Our finding showed that EI underreporting of 24HR and FHQ were high. Under reporters were seemed to be overweight. Therefore, these results suggested that among 3 nutritional questionnaires the FFQ was an appropriate approach to determine EI in this population due to plausible EI reporting was higher than 2 other nutritional questionnaires (24HR and FHQ).
Adult* ; Body Composition ; Calorimetry, Indirect ; Chronic Disease ; Cohort Studies ; Cross-Sectional Studies ; Energy Intake* ; Female ; Food Habits ; Humans ; Male ; Nutritional Status ; Overweight