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Validity of the Lumen ® hand-held metabolic device to measure fuel utilization in healthy young adults

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Abstract and Figures

Background Metabolic carts measure the carbon dioxide produced and oxygen consumed from the breath in order to assess metabolic fuel usage (carbohydrates vs. fats). However, these systems are expensive, time-consuming, and only available in the clinic. A small hand-held device capable of measuring metabolic fuel via CO 2 from exhaled air has been developed Objective To evaluate the validity of a novel hand-held device (Lumen ® ) for measuring metabolic fuel utilization in healthy young adults Methods Metabolic fuel usage was assessed in healthy participants (n = 33; age: 23.1 ± 3.9 y) via respiratory exchange ratio (RER) values from the “gold-standard” metabolic cart as well as %CO 2 from the Lumen device. Measurements were performed at rest in two conditions, fasting, and after consuming 150 grams of glucose in order to determine changes in metabolic fuel. Reduced major axis regression was performed as well as Bland-Altman plots and linear regressions to test for agreement between RER and Lumen %CO 2 . Results Both RER and Lumen %CO 2 significantly increased after glucose intake compared with fasting conditions ( P < .0001). Regression analyses and Bland-Altman plots revealed an agreement between the two measurements with a systematic bias resulting from the nature of the different units. Conclusions This study shows the validity of Lumen ® to estimate metabolic fuel utilization in a comparable manner with the “gold-standard” metabolic cart, providing the ability for real-time metabolic information for users under any circumstances.
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Validity of the Lumen® hand-held metabolic device to measure fuel utilization
in healthy young adults
Kent A. Lorenz1*, Shlomo Yeshurun2, Richard Aziz1, Julissa Ortiz-Delatorre1, James R. Bagley1, Merav
Mor2*, and Marialice Kern1
1Exercise Physiology Laboratory, San Francisco State University, Department of Kinesiology, San
Francisco, CA USA.
2Metaflow Ltd., Tel Aviv, Israel
* Correspondence:
Kent A. Lorenz
Merav Mor
Keywords: Resting Metabolic Rate, Lumen®, ParvoMedics TrueOne® 2400, Validation, Respiratory
Exchange Ratio, Metabolism, Fuel Utilization, Indirect Calorimetry
Background: Metabolic carts measure the carbon dioxide produced and oxygen consumed from the
breath in order to assess metabolic fuel usage (carbohydrates vs. fats). However, these systems are
expensive, time-consuming, and only available in the clinic. A small hand-held device capable of measuring
metabolic fuel via CO2 from exhaled air has been developed. Objective: To evaluate the validity of a novel
hand-held device (Lumen®) for measuring metabolic fuel utilization in healthy young adults. Methods:
Metabolic fuel usage was assessed in healthy participants (n = 33; age: 23.1 ± 3.9 y) via respiratory
exchange ratio (RER) values from the “gold-standard” metabolic cart as well as %CO2 from the Lumen
device. Measurements were performed at rest in two conditions, fasting, and after consuming 150 grams
of glucose in order to determine changes in metabolic fuel. Reduced major axis regression was performed
as well as Bland-Altman plots and linear regressions to test for agreement between RER and Lumen %CO2.
Results: Both RER and Lumen %CO2 significantly increased after glucose intake compared with fasting
conditions (P < .0001). Regression analyses and Bland-Altman plots revealed an agreement between the
two measurements with a systematic bias resulting from the nature of the different units. Conclusions:
This study shows the validity of Lumen® to estimate metabolic fuel utilization in a comparable manner
with the “gold-standard” metabolic cart, providing the ability for real-time metabolic information for users
under any circumstances.
Respiratory quotient (RQ) is the ratio of carbon dioxide produced to oxygen consumed (VCO2/VO2)
measured directly at the cellular level to estimate metabolic fuel utilization. However, this method
requires the insertion of a catheter into the vein and artery for a blood sample or taking a tissue sample,
which makes the RQ measurement invasive and infeasible outside of laboratory setting [1]. With the use
of a metabolic cart, an indirect measure of RQ can be made by indirect calorimetry through respiratory
gas exchange of O2 and CO2. This is the respiratory exchange ratio (RER), which is currently the preferred
method for determining metabolic fuel utilization. Unlike RQ, RER measures the carbon dioxide produced
(VCO2) and oxygen consumed (VO2) from exhaled air [1,2]. Both RQ and RER indicate the relative
contribution of carbohydrate and lipid to energy expenditure [3]. Though RER measurement is not
invasive, this method is time-consuming (up to 40 minutes) and is only available in test laboratory setting
and it requires technical and physiological expertise for handling the metabolic cart and interpretation of
the metabolic data obtained.
Metaflow Ltd. developed Lumen®, a novel metabolic fuel utilization breathalyzer which is a personalized
hand-held device that provides an individuals’ metabolic state in real-time, by measuring CO2 from
exhaled breath (Figure 1). The device indirectly measures metabolic fuel usage via a CO2 sensor and a flow
sensor, to determine the rate of CO2 production from a single breath maneuver. The CO2 concentration in
the exhaled volume of air is determined from a specific breathing maneuver with a breath hold of 10
seconds. This concept is based on the fact that oxygen consumption is stable under resting conditions [4];
thus, a change in the metabolic fuel use will generally be represented by changes in CO2 production. For
carbohydrate oxidation more carbon dioxide is produced, relative to the consumption of oxygen. For fat
oxidation, less carbon dioxide is produced. Thus, from the resulting RER, for a resting state ranging
between 0.7 and 1, the contribution of lipid or carbohydrate usage can be determined. The breath
maneuver enables quantifying the changes in CO2 production, allowing the user to estimate their
metabolic state [5]. The use of a smartphone application enables the user to track metabolic status
outside of physiologic test laboratories.
Internal studies (unpublished data) showed the potential of Lumen to accurately measure the metabolic
fuel usage in response to diet and exercise, comparable to the “gold standard” metabolic cart. In this
study, we aim to evaluate agreement between Lumen measurement and metabolic cart in healthy
participants before and after glucose ingestion under stable resting conditions.
Figure 1. A schematic representation of the Lumen® device and application.
Fifty-four healthy volunteers reported to the Exercise Physiology Laboratory from the Department of
Kinesiology at San Francisco State University to participate in this study. To be included for the study,
participants must have been between the ages of 18-45 years old; with a BMI less than 30 kg/m2;and not
doing any high intensity aerobic exercise training over 3 times per week and without any known
cardiovascular-, pulmonary-, and/or metabolic diseases. The study was approved by the University’s
Institutional Review Board for Human Subjects, and written informed consent was obtained from each
participant before testing.
Study Design
Participants were recruited and their height and weight measured using a stadiometer and Seca scale
(Seca, Hamburg, Germany). If they met the BMI criteria, they were provided their own Lumen device
which was labeled with their unique identification number. The Lumen device was paired and
synchronized to the participant smartphone together with the Lumen application. Participants practiced
the Lumen breathing technique while supervised and took the device home for further familiarization
period in order to show proficiency with the device and application. They were instructed to perform
Lumen metabolic measurements for at least 30 sessions, with each session consisting of 3 breath
maneuvers, and to take 3 sessions at different time points each day. After the minimum amount of home
breath sessions were collected, participants were scheduled for the study laboratory measurement day.
All participants came to the test laboratory between 07:00 a.m. and 11:00 a.m. after a 12-h fast and had
abstained from any form of physical activity (other than walking).
On the laboratory testing day, blood glucose samples were taken by sterile finger prick blood sample and
measured by a glucometer (OneTouch, LifeScan Inc. Milpitas, CA). For the indirect calorimetry
measurement, the participant had to lay down in supine position on a padded examination table, where
a rigid clear plastic canopy with a comfortable, flexible seal was placed over the head and upper part of
the torso. Once the metabolic cart measurement was ended, the participant was seated in a comfortable
chair. After five minutes of rest, they were asked to perform two Lumen breath sessions (5-minute break
between each session). A valid Lumen session measurement for the evaluation with a difference of less
than 0.2 %CO2 between the breaths in a session. The first Lumen session immediately after the metabolic
cart measurement was used for data analysis. In case with an invalid first session, the second session was
Once finished, participants were asked to drink 150 grams of a glucose solution (3 servings of 50 grams
with 20 minutes intervals between each serving). Forty-five minutes after the intake of the first drink
(corresponding to 5 minutes after finishing the last serving), their glucose levels were reassessed, and the
same assessment procedures as during the fasted state before the glucose intake were repeated.
Participants were removed from the analysis if they were unable to finish all glucose drinks.
Metabolic cart
RER was analyzed using a calibrated TrueOne® 2400 metabolic cart (ParvoMedics, Murray, UT, USA). This
system uses a paramagnetic oxygen analyzer and infrared carbon dioxide analyzer with a Hans Rudolph
heated pneumotach. The ParvoMedics system was warmed up for at least 60 minutes each day before
testing, in order to ensure accurate and stable readings. The gas analyzers and flow sensor were calibrated
as per manufacturer’s recommendations. Calibration of the analyzers performed using a high precision
gas mixture (O2, CO2, remainder N2) and calibrated and accepted with a less than 0.1% error with the
calibration gas. Flow and volume were calibrated using a calibrated 3 L syringe (Hans Rudolph, model
5530) to 1% error. The ambient temperature was kept between 22 and 26°C in the test laboratory.
Relative humidity was maintained stable at roughly 60%. Once calibration was acceptable and complete,
a ventilated hood with subject cover was placed over the participant’s head and positioned around the
upper torso area to ensure no escaping air from the hood. The participants were required to stay awake
during the measurement procedure. The hood ventilation (VE) was measured during the recording, and
CO2 and O2 concentrations were measured from it. VCO2, and VO2 parameters were calculated and taken
as 30-s averages. For this study we defined the subject steady-state metabolic measurement based on
observed variations in the VO2 and VCO2 of less than ≤ 5% CV for period of at least five consecutive
minutes. Inability to meet these criteria resulted in removal of the data from the analysis.
During the measurement day, participants took 2 sessions of 3 Lumen breaths after the metabolic cart.
The Lumen breathing maneuver consists of three phases, starting from the end of a normal expiration
(functional residual capacity). The participant takes a deep breath in through the Lumen device, followed
by a 10 second breath hold. Afterwards, the subject exhales through the Lumen device, with a steady
exhalation flow to at least the starting level of the maneuver. The Lumen smart phone application guides
the participant through each phase of the lumen maneuver. Each Lumen session was repeated after a 5-
minute pause interval. Validity of breath maneuvers was systematically evaluated by the Lumen
application. Inability to perform valid Lumen breath measures resulted in removal of the data from the
Statistical Analyses
All variables were tested and visualized for normal distribution before the tests.
To evaluate the changes after glucose intake, two-tailed paired parametric t-tests were performed for
blood glucose levels, RER levels, and Lumen %CO2 before and after glucose intake.
For agreement validations, major axis regression (‘Deming’s method’) was performed in order to compare
RER of the metabolic cart and %CO2 from the Lumen device [6]. As RER and %CO2 are in different units,
the analysis is identical to ordinary least product regression (also known as reduced major axis regression),
which is the most suitable analysis for comparison between two methods of measurement [7]. Moreover,
Bland-Altman plot was created to demonstrate limits of agreement, together with a linear regression to
test the distribution of error to identify if any systematic bias existed between Lumen and RER measures.
In addition, a simple linear regression (ordinary least squares) was performed to determine the ability to
predict Lumen values from the gold-standard value of RER.
Statistical analyses were performed using GraphPad Prism 8 (GraphPad Software Inc., LA Jolla, CA). The
threshold for significance was set at P < .05.
From the original fifty-four participants recruited, twelve were excluded prior to laboratory testing and
nine had to be excluded during the testing day for failing to meet the inclusion criteria as detailed in the
methods section: one participant was unable to consume all glucose drinks due to nausea, three
participants did not achieve 5 mins of stable metabolic cart measurement (CV < 5% in VO2 and VCO2), and
five participants were unable to perform a valid Lumen measurement (Figure 2). Characteristics of the
final thirty-three participants presented in Table 1.
Figure 2. Consolidated Standards of Reporting Trials (CONSORT) flow diagram.
Table 1. Descriptive statistics of study’s participants.
Age (years)
Weight (kg)
BMI (kg/m
24.0 ± 3.0
73.7 ± 10.2
171.7 ± 7.8
24.9 ± 2.5
22.3 ± 4.5
59.1 ± 6.4
160.9 ± 5.5
22.9 ± 2.6
23.1 ± 3.9
66.2 ± 11.1
166.1 ± 8.6
23.9 ± 2.7
Data are presented as mean ± SD.
Blood glucose levels increased from 90.6 ± 9.2 mg/dL to 145.2 ± 25.3 mg/dL as a result of glucose intake
(t(32) = 11.04, P < .0001; Figure 3A). RER levels increased from 0.787 ± 0.043 to 0.876 ± 0.053 in response
to glucose intake (t(32) = 10.84, P < .0001; Figure 3B). Moreover, Lumen %CO2 concentrations significantly
rose from 4.20 ± 0.4 to 4.48 ± 0.34 (t(32) = 5.978, P < .0001; Figure 3C). These analyses have confirmed the
ability of both the metabolic cart and Lumen to detect changes in metabolic fuel utilization.
Figure 3. Changes of blood glucose (A), RER (B), Lumen %CO2 (C), after glucose intake. Data are presented as mean
± SD. ****p < 0.0001. n = 33 for each state.
In order to look for agreement between RER units from the metabolic cart and %CO2 from Lumen, reduced
major axis regression was performed [8]. It revealed a significant relationship between RER and Lumen
%CO2 (F(1,63) = 18.54, P < .0001, y = 6.111x - 0.7445, x-intercept = 0.1218; Figure 4). This analysis validated
the agreement between Lumen %CO2 and metabolic cart RER, with a systemic bias resulting from the
nature of the different units.
Figure 4. Reduced major axis regression of RER from the metabolic cart and Lumen’s %CO2 measurements for
metabolic activity. n = 33 for each state.
A Bland-Altman plot was made to discuss limit of association between RER and Lumen %CO2 [9]. Bland-
Altman plots are used to calculate the level of agreements between two measures by studying the mean
difference between measurements and constructing limits of agreement [10]. Bland-Altman analysis
revealed a mean difference between RER units and Lumen %CO2 of 3.505 with 95% limits of agreement
between 2.784 to 4.226 (Figure 5). Since the Lumen device always provides a measurement that is
numerically much higher than RER, Lumen measurements with smaller values should produce smaller
differences and larger Lumen values will produce larger differences. To test the bias in measurements, a
regression of difference (Lumen %CO2 RER) scores on average ((Lumen %CO2 + RER)/2) values was
performed. Simple linear regression showed that a significant model effect of the bias (F(1,63) = 721, P <
.0001, R2 = 0.9196). As the vertical distribution of scores are tight to the line, it indicates that the errors
are consistent across a range of values.
Figure 5. Bland-Altman analysis with simple linear regression. The solid line represents the mean bias between the
Lumen %CO2 and RER. The upper and lower dashed lines represent the 95% confidence intervals (±2 SD) from the
mean bias. Linear regression line: y = 1.646*x - 0.7472. n = 33 for each state.
To determine the ability of RER to predict Lumen %CO2, ordinary least squared regression was performed
to estimate Lumen values from RER measures, with the assumption that RER is an accurate measure. With
RER as the independent variable, we used linear regression to predict Lumen %CO2, and a significant
model effect was present (F(1,63) = 18.54, P < .0001, R2 = 0.2274; Figure 6). The RER parameter estimate
indicated that for every one-unit increase in RER, a 2.914-unit increase (SE = 0.6767) in Lumen %CO2 is
expected. However, since a full unit increase in RER is not a plausible outcome, this parameter estimate
can be interpreted similarly by saying a 0.1-unit increase in RER (e.g., 0.7 to 0.8) will produce a 0.2914-
unit increase in Lumen %CO2.
Figure 6. Ordinary least squared regression of RER and Lumen %CO2. n = 33 for each state.
This study evaluated the capability of the Lumen device to assess changes in the body’s metabolic fuel
utilization, compared to gold standard indirect calorimetry metabolic cart measurement in healthy young
adults. Our results show that Lumen %CO2 levels are in agreement with RER values from the metabolic
cart, which correspond to relative changes in metabolic fuel utilization.
Both Lumen %CO2 and metabolic cart RER showed significant increases in metabolic levels, as a result of
glucose intake in healthy individuals in resting conditions (Figure 3). These results can be expected as cells
using more carbohydrates as fuel, produce more CO2 relative to the O2 consumption compared to cells
metabolizing fat. The ratio between the CO2 production and the O2 consumption in this process is known
as respiratory quotient (RQ) or RER. RQ and RER vary depending on the energy source of the cell
(carbohydrate vs. fat), and the acronyms are commonly used interchangeably [1–3]. In resting conditions,
oxygen consumption is fairly stable [11,12], meaning that participants’ changes in the RQ are due to
changes in CO2 production. This is the concept underlying the Lumen device, enabling it to track changes
in metabolic fuel utilization. For that reason, it was important to ensure that participants in this study
were at rest before and during their measurements.
Reduced major axis regression revealed an agreement between RER and Lumen %CO2 (Figure 4). This
analysis enables to test for agreement between methods with different units and verified the validity of
the Lumen device with the gold standard metabolic cart. The Bland-Altman plot showed a mean bias of
3.505, and when used in conjunction with a significant linear model of difference scores compared to
mean values [13], support the agreement between the two methods despite measuring in different units
(Figure 5). These results demonstrate the ability of Lumen to provide comparable results to the metabolic
cart in assessing the metabolic fuel utilization.
Furthermore, the results from the simple linear regression predicting Lumen %CO2 using RER values,
suggest that while there is measurement agreement between the Lumen %CO2 and RER, the proportion
of explained variance remains low (Figure 6). Thus, Lumen can be seen to be an effective instrument for
monitoring relative, individual changes in metabolic responses (within-subject consistency), rather than a
substitute for the metabolic cart (between-subject precision).
Evidence suggests that the assessment of RER can be a benefit for multiple conditions, such as nourishing,
diabetes prevention, weight management, physical activity, and healthy lifestyle [14,15]. It has previously
been shown that RER could be a prognostic marker of weight loss and a predictor of weight gain [16,17].
Moreover, minute-by-minute RER corresponded directly to intensity of exercise, and slopes of RER were
different in response to different dietary treatments [18]. However, although RER is currently the
preferred method for determining metabolic fuel, it is a costly, time consuming, uncomfortable and an
impractical tool for assessing metabolic activity over time and for real time day to day usage. In contrast,
the Lumen device is small, relatively cheap, mobile, user-specific, delivers the outcome immediately to
the user and enables taking real time decisions.
This study is the first to show agreement between Lumen %CO2 and RER. However, it is important to note
that participants in this study were young (mean age: 22.4 y) and healthy individuals. With increasing age,
metabolism changes as can be seen in metabolic cart studies [19–21]. Future studies will need to examine
whether RER metabolic cart levels correspond to Lumen CO2% levels in older subjects and those with
metabolic conditions.
Another limitation is that unlike the metabolic cart, the Lumen device does not measure oxygen
consumption. Accordingly, the Lumen measurement should be performed under resting conditions with
subsequent stable VO2, allowing the correct interpretation of changes of %CO2 as changes in metabolic
In addition, results from this study show high peak of blood glucose levels 45 minutes after glucose intake
(5 minutes after the 3rd drink), whereas both RER and Lumen %CO2 showed a more moderate increase in
levels. Therefore, it is possible that the metabolic cart and Lumen measurements were performed too
early, as it may be that in some of our participants the peak glucose occurred later than at 45 minutes,
thus not yet fully metabolized [22].
In summary, Lumen® can provide valid information regarding an individual's resting metabolic state, and
in agreement with results from metabolic cart. Unlike the metabolic cart, Lumen measurement can be
performed anywhere, without the need for a specialized lab, equipment, and technical support. The
capability of taking metabolic measurements continuously can provide numerous insights about the
metabolic state of an individual, for further scientific knowledge and understanding about metabolism
and nutrition.
Conflict of Interest
SY and MM are employees of Metaflow Ltd., and contributed to the design and analysis of the study as
well as the preparation of the manuscript. The other authors declare no conflicts of interest.
Ethics Statement
This study was approved by the University’s Institutional Review Board for Human Subjects, and written
informed consent was obtained from each participant before testing.
Author Contributions
KAL analyzed the data and prepared the manuscript
SY analyzed the data and prepared the manuscript
RA coordinated the project and collected the data
JO coordinated the project and collected the data
JRB reviewed and edited the manuscript
MM conceived, designed, and supervised the study as well as reviewed and edited the manuscript
MK conceived, designed, and supervised the study as well as reviewed and edited the manuscript
All authors approved the manuscript before submission
This work was supported by Metaflow Ltd.
We would like to thank the participants for their time in taking part in this study, and the Lumen team for
their support. We would like to acknowledge Casey Curl for his work in setting up the protocols and
procedures during preliminary testing.
1. Brooks GA, Fahey TD, Baldwin KM. Exercise physiology: human bioenergetics and its applications.
Exerc Physiol Hum Bioenerg its Appl. Independently Published; 2019.
2. Benedict FG, Cathcart EP. Muscular Work, a Metabolic Study with Special Reference to the
Efficiency of the Human Body as a Machine. Carnegie Inst Washingt. Carnegie institution of
Washington; 1913.
3. McClave SA, Lowen CC, Kleber MJ, McConnell JW, Jung LY, Goldsmith LJ. Clinical use of the
respiratory quotient obtained from indirect calorimetry. J Parenter Enter Nutr 2003;27(1):21–26.
4. Moon JK, Butte NF. Combined heart rate and activity improve estimates of oxygen consumption
and carbon dioxide production rates. J Appl Physiol 1996;81(4):1754–1761. [doi:
5. Tsoukias NM, Tannous Z, Wilson AF, George SC, Nikolaos M, Tannous Z, Wil- AF, George SC.
Single-exhalation profiles of NO and CO 2 in humans : effect of dynamically changing flow rate. J
Appl Physiol 1998;85(2):642–652.
6. Brace RA. Fitting straight lines to experimental data. Am J Physiol 1977;233(3).
7. Ludbrook J. Linear regression analysis for comparing two measurers or methods of
measurement: But which regression? Clin Exp Pharmacol Physiol 2010;37(7):692–699. [doi:
8. Ludbrook J. Comparing measurment methods. Clin Exp Pharmacol Physiol 1997;24(July
9. Giavarina D. Understanding Bland Altman Analysis. Biochem Medica 2015;25(2):141–51.
10. Bland MJ, Altman DG. Statistical Methods for Assessing Agreement Between Two Methods of
Clinical Measurement. Lancet [Internet] 1986;327(8476):307–310. PMID:2868172
11. Spurr GB, Prentice AM, Murgatroyd PR, Goldberg GR, Reina JC, Christman NT. Energy expenditure
from minute-by-minute heart-rate recording: Comparison with indirect calorimetry. Am J Clin
Nutr 1988;48(3):552–559. PMID:3414570
12. Green JA. The heart rate method for estimating metabolic rate: Review and recommendations.
Comp Biochem Physiol - A Mol Integr Physiol Elsevier Inc.; 2011 Mar 1;158(3):287–304. [doi:
13. Woo P, Murthy G, Wong C, Hursh B, Chanoine JP, Elango R. Assessing resting energy expenditure
in overweight and obese adolescents in a clinical setting: Validity of a handheld indirect
calorimeter. Pediatr Res 2017;81(1):51–56. [doi: 10.1038/pr.2016.182]
14. Ferrannini E. The theoretical bases of indirect calorimetry: A review. Metabolism. Elsevier; 1988.
p. 287–301. [doi: 10.1016/0026-0495(88)90110-2]
15. Ramos-Jiménez A, Hernández-Torres RP, Torres-Durán P V., Romero-Gonzalez J, Mascher D,
Posadas-Romero C, Juárez-Oropeza MA. The Respiratory Exchange Ratio is Associated with
Fitness Indicators Both in Trained and Untrained Men: A Possible Application for People with
Reduced Exercise Tolerance. Clin Med Circ Respirat Pulm Med 2008;2:CCRPM.S449.
16. Valtueña S, Salas-Salvadó J, Lorda PG. The respiratory quotient as a prognostic factor in weight-
loss rebound. Int J Obes 1997;21(9):811–817. [doi: 10.1038/sj.ijo.0800480]
17. Zurlo F, Lillioja S, Puente AED, Nyomba BL, Raz I, Saad MF, Swinburn BA, Knowler WC, Bogardus
C, Ravussin E. Low ratio of fat to carbohydrate oxidation as predictor of weight gain: Study of 24-
h RQ. Am J Physiol - Endocrinol Metab 1990;259(5 22-5). PMID:2240203
18. Gribok A, Leger JL, Stevens M, Hoyt R, Buller M, Rumpler W. Measuring the short-term substrate
utilization response to high-carbohydrate and high-fat meals in the whole-body indirect
calorimeter. Physiol Rep 2016;4(12). [doi: 10.14814/phy2.12835]
19. Riera CE, Dillin A. Tipping the metabolic scales towards increased longevity in mammals. Nat Cell
Biol [Internet] Nature Publishing Group; 2015 Mar 27 [cited 2020 May 4];17(3):196–203. [doi:
20. Short KR, Vittone JL, Bigelow ML, Proctor DN, Nair KS. Age and aerobic exercise training effects
on whole body and muscle protein metabolism. Am J Physiol - Endocrinol Metab 2004;286(1 49-
1):92–101. [doi: 10.1152/ajpendo.00366.2003]
21. Gupta RD, Ramachandran R, Venkatesan P, Anoop S, Joseph M TN. Indirect Calorimetry: From
Bench to Bedside. Indian J Endocrinol Metab | 2017;21(4):594–599. [doi:
22. Eyth E, Basit H, Smith CJ. Glucose Tolerance Test. StatPearls Publishing; 2019. PMID:30422510
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The paper demonstrates that minute-to-minute metabolic response to meals with different macronutrient content can be measured and discerned in the whole-body indirect calorimeter. The ability to discriminate between high-carbohydrate and high-fat meals is achieved by applying a modified regularization technique with additional constraints imposed on oxygen consumption rate. These additional constraints reduce the differences in accuracy between the oxygen and carbon dioxide analyzers. The modified technique was applied to 63 calorimeter sessions that were each 24 h long. The data were collected from 16 healthy volunteers (eight males, eight females, aged 22-35 years). Each volunteer performed four 24-h long calorimeter sessions. At each session, they received one of four treatment combinations involving exercise (high or low intensity) and diet (a high-fat or high-carbohydrate shake for lunch). One volunteer did not complete all four assignments, which brought the total number of sessions to 63 instead of 64. During the 24-h stay in the calorimeter, subjects wore a continuous glucose monitoring system, which was used as a benchmark for subject's postprandial glycemic response. The minute-by-minute respiratory exchange ratio (RER) data showed excellent agreement with concurrent subcutaneous glucose concentrations in postprandial state. The averaged minute-to-minute RER response to the high-carbohydrate shake was significantly different from the response to high-fat shake. Also, postprandial RER slopes were significantly different for two dietary treatments. The results show that whole-body respiration calorimeters can be utilized as tools to study short-term kinetics of substrate oxidation in humans.
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In a contemporary clinical laboratory it is very common to have to assess the agreement between two quantitative methods of measurement. The correct statistical approach to assess this degree of agreement is not obvious. Correlation and regression studies are frequently proposed. However, correlation studies the relationship between one variable and another, not the differences, and it is not recommended as a method for assessing the comparability between methods. In 1983 Altman and Bland (B&A) proposed an alternative analysis, based on the quantification of the agreement between two quantitative measurements by studying the mean difference and constructing limits of agreement. The B&A plot analysis is a simple way to evaluate a bias between the mean differences, and to estimate an agreement interval, within which 95% of the differences of the second method, compared to the first one, fall. Data can be analyzed both as unit differences plot and as percentage differences plot. The B&A plot method only defines the intervals of agreements, it does not say whether those limits are acceptable or not. Acceptable limits must be defined a priori, based on clinical necessity, biological considerations or other goals. The aim of this article is to provide guidance on the use and interpretation of Bland Altman analysis in method comparison studies.
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A hallmark of ageing is dysfunction in nutrient signalling pathways that regulate glucose homeostasis, negatively affecting whole-body energy metabolism and ultimately increasing the organism's susceptibility to disease. Maintenance of insulin sensitivity depends on functional mitochondrial networks, but is compromised by alterations in mitochondrial energy metabolism during ageing. Here we discuss metabolic paradigms that influence mammalian longevity, and highlight recent advances in identifying fundamental signalling pathways that influence metabolic health and ageing through mitochondrial perturbations.
The ability to switch fuels for oxidation in response to changes in macronutrient composition of diet (metabolic flexibility) may be informative of the individual susceptibility to weight gain. Seventy-nine healthy, weight-stable participants underwent 24-h assessments of energy expenditure and respiratory quotient (RQ) in a whole-room calorimeter during energy balance (EBL; 50% carbohydrate, 30% fat) and then during 24-h fasting and three 200% overfeeding diets in a crossover design. Metabolic flexibility was defined as the change in 24-h RQ from EBL during fasting and standard (STOF: 50% carbohydrate, 30% fat), high-fat (HFOF: 60% fat, 20% carbohydrate), and high-carbohydrate (HCOF: 75% carbohydrate, 5% fat) overfeeding diets. Free-living weight change was assessed after 6 and 12 months. Compared to EBL, RQ decreased on average by 9% during fasting and by 4% during HFOF, while increasing by 4% during STOF and by 8% during HCOF. Smaller decrease in RQ, reflecting smaller increase in lipid oxidation rate, during HFOF but not during other diets, predicted greater weight gain at both 6 and 12 months. An impaired metabolic flexibility to acute, high-fat overfeeding can identify individuals prone to gain weight, indicating that the individual capacity to oxidize dietary fat is a metabolic determinant of weight change.
The current study was undertaken to investigate fatty acid metabolism by skeletal muscle to examine potential mechanisms that could lead to increased muscle triglyceride in obesity. Sixteen lean and 40 obese research volunteers had leg balance measurement of glucose and free fatty acid (FFA) uptake (fractional extraction of [9,10 ³ H]oleate) and indirect calorimetry across the leg to determine substrate oxidation during fasting and insulin-stimulated conditions. Muscle obtained by percutaneous biopsy had lower carnitine palmitoyl transferase (CPT) activity and oxidative enzyme activity in obesity ( P < 0.05). During fasting conditions, obese subjects had an elevated leg respiratory quotient (RQ, 0.83 ± 0.02 vs. 0.90 ± 0.01; P < 0.01) and reduced fat oxidation but similar FFA uptake across the leg. During insulin infusions, fat oxidation by leg tissues was suppressed in lean but not obese subjects; rates of FFA uptake were similar. Fasting values for leg RQ correlated with insulin sensitivity ( r = −0.57, P < 0.001). Thirty-two of the obese subjects were restudied after weight loss (WL, −14.0 ± 0.9 kg); insulin sensitivity and insulin suppression of fat oxidation improved ( P < 0.01), but fasting leg RQ (0.90 ± 0.02 vs. 0.90 ± 0.02, pre-WL vs. post-WL) and muscle CPT activity did not change. The findings suggest that triglyceride accumulation in skeletal muscle in obesity derives from reduced capacity for fat oxidation and that inflexibility in regulating fat oxidation, more than fatty acid uptake, is related to insulin resistance.
Metabolic flexibility is the ability to respond or adapt to conditional changes in metabolic demand. This broad concept has been propagated to explain insulin resistance and mechanisms governing fuel selection between glucose and fatty acids, highlighting the metabolic inflexibility of obesity and type 2 diabetes. In parallel, contemporary exercise physiology research has helped to identify potential mechanisms underlying altered fuel metabolism in obesity and diabetes. Advances in “omics” technologies have further stimulated additional basic and clinical-translational research to further interrogate mechanisms for improved metabolic flexibility in skeletal muscle and adipose tissue with the goal of preventing and treating metabolic disease.
Background: Accurately determining energy requirements is key for nutritional management of pediatric obesity. Recently, a portable handheld indirect calorimeter, MedGem (MG) has become available to measure resting energy expenditure (REE). Our work aims to determine the clinical validity and usefulness of MG to measure REE in overweight and obese adolescents. Methods: Thirty-nine overweight and obese adolescents (16 male (M): 23 female (F), 15.2 ± 1.9 y, BMI percentile: 98.6 ± 2.2%) and 15 normal weight adolescents (7M: 8F, age 15.2 ± 2.0 y, BMI percentile: 39.2 ± 20.9%) participated. REE was measured with both MG and standard indirect calorimeter (VMax) in random order. Results: MG REE (1,600 ± 372 kcal/d) was lower than VMax REE (1,727 ± 327 kcal/) in the overweight and obese adolescents. Bland Altman analysis (MG -VMax) showed a mean bias of -127 kcal/d (95% CI = -72 to -182 kcal/d, P < 0.001), and a proportional bias existed such that lower measured REE by VMax was underestimated by MG, and higher measured REE by VMax were overestimated by MG. Conclusion: MG systematically underestimates REE in the overweight and adolescent population, thus the MG portable indirect calorimeter is not recommended for routine use. Considering that it is a systematic underestimation of REE, MG may be clinically acceptable, only if used with caution.
Normal energy metabolism is characterized by periodic shifts in glucose and fat oxidation, as the mitochondrial machinery responsible for carbon combustion switches freely between alternative fuels according to physiological and nutritional circumstances. These transitions in fuel choice are orchestrated by an intricate network of metabolic and cell signaling events that enable exquisite crosstalk and cooperation between competing substrates to maintain energy and glucose homeostasis. By contrast, obesity-related cardiometabolic diseases are increasingly recognized as disorders of metabolic inflexibility, in which nutrient overload and heightened substrate competition result in mitochondrial indecision, impaired fuel switching, and energy dysregulation. This Perspective offers a speculative view on the molecular origins and pathophysiological consequences of metabolic inflexibility. Copyright © 2014 Elsevier Inc. All rights reserved.