Quantification of the level descriptors for the standard EQ-5D three-level system and a five-level version according to two methods

Objectives Our aim was to compare the quantitative position of the level descriptors of the standard EQ-5D three-level system (3L) and a newly developed, experimental five-level version (5L) using a direct and a vignette-based indirect method.
Introduction
The EQ-5D is a widely used instrument to describe and value generic health (status) in terms of five dimensions: mobility, self-care, usual activities, pain/discomfort, and anxiety/depression. Each dimension comprises three levels, indicating no problems, some or moderate problems, and extreme problems, resulting in a total of 243 (35) unique health states [1].
The condensed format of the EQ-5D has undoubtedly contributed to its global dissemination, as it is easy to include in existing surveys by questionnaire designers, easy to fill out by respondents, and easy to report by analysts. However, compared with other generic preference based instruments such as the Health Utilities Index Mark 2 and Mark 3 (HUI2 and HUI3) and the Short Form 6D (SF-6D), which define respectively 24,000, 972,000, and 18,000 unique health states, the EQ-5D is lacking descriptive richness [2–5]. Although the EQ-5D descriptive system has demonstrated strong psychometric properties in general, its restricted ability to discriminate (clinically relevant) small to moderate differences in health status between individuals or within individuals over time is recognized [6–9]. Moreover, several studies have reported on the ceiling effect of the EQ-5D in the general population as well as in patient populations [10–15].
A straightforward way of improving the discriminatory potential of the EQ-5D descriptive system is to increase the number of response options. In most health-status classification systems, the response options are ordered in terms of severity along a hypothetical measurement continuum. Since the exact position of the response options defines the discriminatory abilities of the descriptive system [16, 17], it is important to know where on the measurement continuum the level descriptors are quantitatively positioned.
Previous research in which a five-level (5L) version of EQ-5D was compared with the standard three-level (3L) EQ-5D demonstrated increased discriminatory power, increased reliability, and satisfactory validity [18, 19]. This paper presents a head-to-head comparison of the quantitative positioning of the level descriptors of the standard 3L EQ-5D descriptive system versus a newly developed, experimental 5L system, which covers 3,125 unique health states (55). Two independent methods were used. The first method directly compared the nonextreme level descriptors (for 3L: the level two midcategory; for 5L: the level two, level three, and level four categories) for each dimension separately on a visual analogue scale (VAS). The second, indirect, method required respondents to score complete health scenarios (vignettes) on dimension-specific VAS scales and subsequently to classify the same vignettes on the two EQ-5D instruments (3L and 5L).
Methods
Instruments
Three instruments were used in this study: the standard EQ-5D3L version, an adapted Dutch 5L version developed in 1993 [20], and a set of five dimension-specific VAS scales. The version of the 5L EQ-5D used in this study was an experimental version, since at the time of this study, no official five-level version had been advocated by the EuroQol Group. We chose to test a five-level EQ-5D system, even though we also could have chosen four or six levels. An increase in the number of levels is always an increase of discriminatory potential at the cost of a more complex descriptive system (which might compromise the robustness of the value function). Five levels appears to be an optimal number of response options concerning reliability [21, 22]. Furthermore, Preston et al. (2000) investigated feasibility for 11 different rating formats (ranging from 2 to 11 and a 101 point scale) and found that feasibility peaked at five levels [23]. We chose to add two in-between levels to the existing 3L descriptive system (between levels 1 and 2 and levels 2 and 3) because we considered this the most obvious option in regard to the objective of refining the EQ-5D instrument. In any preference-based instrument, level descriptors are practically required for valuation research in which generic profiles are to be valued. A small focus group was assigned to determine the wording of the level descriptors. The level descriptors presented here were translated from Dutch. The one-, three-, and five-level descriptors in 5L were the same as the one-, two-, and three--level descriptors in the standard EQ-5D3L. The grading terms that were used for the intermediate levels two and four in the 5L-system were “a little” for level 2 (5L-2) in Anxiety/Depression and “mild problems” for the remaining dimensions; and “severe” for level 4 (5L-4) in Pain/Discomfort, “very” for Anxiety/Depression, and “many problems” for the remaining dimensions. One further alteration was made to both the 3L and 5L systems: the most severe response category in Mobility was changed from “confined to bed” to “unable to walk about”, so it would be analogous to the extreme response categories of the other dimensions. Table 1 displays the exact wording of the descriptors in the 3L and 5L systems, respectively.
Table 1Direct quantification of three- and five- level (3L, 5L) descriptorsNumberMeanMedian95% CI3LMobility    No problems in walking abouta––––    Some problems in walking about7426.702222.82−30.59    Unable to walk abouta––––Self-care    No problems with self-carea––––    Some problems washing or dressing self7430.182826.10−34.25    Unable to wash or dress selfa––––Usual activities    No problems with performing usual activitiesa––––    Some problems with performing usual activities7729.742525.95−33.53    Unable to perform usual activitiesa––––Pain/Discomfort    No pain or discomforta––––    Moderate pain or discomfort6632.333128.56−36.10    Extreme pain or discomfort6686.368983.75−88.98    Worst imaginable pain or discomforta––––Anxiety/Depression    Not anxious or depresseda––––    Moderately anxious or depressed6733.943429.89−37.99    Extremely anxious or depresseda6788.829086.88−90.77    Worst imaginable anxiety or depressiona––––5LMobility    No problems in walking abouta––––    Mild problems in walking about7511.31119.73–12.88    Some problems in walking about7538.394035.39–41.39    Many problems in walking about7579.808276.81–82.79    Unable to walk abouta––––Self-care    No problems with self-carea––––    Mild problems washing or dressing self7611.24109.72–12.76    Some problems washing or dressing self7637.143834.14–40.15    Many problems washing or dressing self7680.618177.81–83.40    Unable to wash or dress selfa––––Usual activities    No problems with performing usual activitiesa––––    Mild problems with performing usual activities7711.08109.29–12.87    Some problems with performing usual activities7739.014036.12–41.90    Many problems with performing usual activities7780.818377.70–83.91    Unable to perform usual activitiesa––––Pain/Discomfort    No pain or discomforta––––    Mild pain or discomfort538.8587.43–10.26    Moderate pain or discomfort5332.323129.58–35.06    Severe pain or discomfort5367.946864.98–70.90    Extreme pain or discomfort5391.269488.96–93.57    Worst imaginable pain or discomforta––––Anxiety/Depression    Not anxious or depresseda––––    A little anxious or depressed599.4687.97–10.94    Moderately anxious or depressed5932.563330.01–35.11    Very anxious or depressed5967.376664.55–70.20    Extremely anxious or depressed5991.349289.42–93.25    Worst imaginable anxiety or depressiona––––CI confidence intervalaLevel descriptor used as anchor in visual analog scale
To obtain quantitative values for each level descriptor of 3L and 5L, the VAS was used. We used five VAS scales, one for each EQ-5D dimension. Each VAS consisted of a horizontal hashmarked line without corresponding numbers, with the extreme-level descriptors belonging to that dimension as anchors. Respondents were asked to indicate their score on the VAS by marking the line. For the most severe category of Pain/Discomfort and Anxiety/Depression, the original descriptor was labeled “extreme”. Because the study was part of a larger process of choosing the definite level descriptors for the official five-level version of the EQ-5D, we decided to use the entire continuum of disability (extreme included), and used “worst imaginable” as upper VAS anchor for these two dimensions. This is analogous to the other three dimensions, which ranged from “no problems” to “unable to”.
Study design
Data collection took place in the form of one of two panel sessions and a follow-up postal survey 2 weeks later. A convenience sample of 82 laypeople from an existing general population panel (N = 560) participated. All participants were familiar with the vignette presentation form used in the indirect method.
All participants completed both the direct and the indirect quantification task. For the direct method, all 3L answers were obtained during the panel sessions and all 5L answers as part of the postal survey to avoid memory effects. For the indirect method, participants scored ten health states in the panel sessions (acute pharyngitis, exacerbation of eczema, hip fracture, cerebrovascular accident/stroke with moderate impairments, moderate gastritis, low spinal cord lesion, mild depression, back and neck pain, severe dementia, and acute multiple injury) and the remaining five in the survey (otitis externa, severe stable brain injury, irritable bowel syndrome, acute large burn, and posttraumatic stress disorder), because we expected that more than ten health states within one session could lead to concentration problems. The two sets of health states were balanced according to severity and duration. Following this design, the indirect method provided 225 responses for each respondent: 15 diseases × 5 dimensions × 3 response scales.
Direct quantification of level descriptors
In the direct method, respondents were asked to project the 3L and the 5L descriptors on the VAS scales for each dimension separately. As the extreme levels were used as anchors of the VAS, for 3L only, the midcategory (3L-2) level descriptor needed to be scored, except for Pain/Discomfort and Anxiety/Depression, which needed additional scoring of 3L-3 (extreme). Similarly, the midcategories 5L-2, 5L-3, and 5L-4 descriptors were scored for each dimension, except for Pain/Discomfort and Anxiety/Depression, which included the scoring of 5L-5.
Indirect quantification of level descriptors
As an alternative to the direct method, we developed an indirect method that we believe lies closer to the actual use of the EQ-5D instrument, as it uses a (hypothetical) health state as a calibrator or medium to derive a VAS score. In contrast to the direct method, the object of measurement in the indirect method is not a 3L or 5L descriptor but a complete health scenario (vignette). Each vignette was scored with the 3L and 5L descriptors and on a VAS, one for each separate dimension, independently. Consequently, an indirect head-to-head comparison of 3L and 5L scores could be made, calibrated via the common VAS score.
Figure 1 shows one of the vignettes. Each vignette was designed to present a disease as close to clinical reality as possible, therefore also including information on disease duration. All 15 diseases were presented on a standardized sheet (vignette) that contained (1) a disease label with a naturalistic description of the disease; (2) the course of the disease over a 1-year period using a calendar (the grey scales represent the duration of the disease); (3) the location of the disease with, if relevant, a visual representation; and (4) the EQ-5D dimensions, of which the levels were left unspecified, as the respondents were invited to select the appropriate EQ-5D level (according to his or her own view) for each dimension. Respondents were asked to read each vignette carefully and to select the level of each dimension of the EQ-5D descriptive system that best described the presented health state in their view using three response scales: the standard 3L response scale, the new 5L scale, and the VAS scale (similar to the VAS used in the direct method).
Fig. 1Disease vignette with empty EQ-5D descriptive system
The 5L and 3L response scales were presented on the left and the right side of one page (per dimension), respectively. The respondents were first invited to score the 5L descriptors for all dimensions and all vignettes while covering the right side of the page that showed the 3L descriptors. Next, they were instructed to return to the first vignette, asked to cover the left side with the 5L scores, and provide the 3L response for all vignettes. Pilot testing revealed that when respondents scored 3L first, there was a tendency to avoid the in-between levels 2 and 4 of 5L, and for this reason, all respondents were asked to score 5L first. Adequate instruction was critical, stressing that 3L and 5L were two independent ways of scoring (in the postal survey, these instructions were repeated in writing). Subsequently, VAS scores were obtained on a separate form without respondents having access to the 3L and 5L scores. The demanding task of first providing 5L classifications on all five dimensions of all 15 vignettes minimized possible memory effects when the participants were instructed to return to the first vignette to score the 3L classifications while covering the 5L responses.
Analysis
Results of the direct and indirect methods are presented with conventional descriptive statistics. Results of the indirect method were derived by grouping 3L-VAS pairs and 5L-VAS pairs for each respondent per vignette and subsequently by calculating level means over all vignettes and all respondents combined. For each respondent, scorings were removed for the combined 3L, 5L, and VAS scores if at least one of the 3L, 5L, or VAS scores was missing, equalizing the number of VAS observations between 3L and 5L.
Characteristics
For both the direct and indirect methods, the 3L–5L extension of EQ-5D was investigated in terms of three characteristics. First, equidistance addresses the degree to which 3L and 5L level descriptors are distributed evenly over the VAS continuum, either without or with transformation. Equidistance is determined for each dimension and each instrument (3L and 5L) separately. Untransformed equidistance implies that level descriptors are distributed according to VAS ratings of 0–50–100 for 3L and 0–25–50–75–100 for 5L. There is evidence that the precision of the VAS might be illusory, as respondents mentally divide the VAS continuum in a smaller number of segments, which is nine or ten at maximum [23, 24]. Therefore, we defined a deviation of 5 VAS points as the maximum acceptable deviation (which makes a segment of 10 VAS points, as the deviation can be either way). Furthermore, a deviation of 5 VAS points has been used before [16]. If untransformed equidistance is rejected, equidistance using power [y  = (ax)b] transformation is considered. A power relation of, e.g., y = (5.38*x)1.5 for 5L would result in a VAS rating distribution of 0–12–35–65–100. Note that transformation is only possible for 5L, as there is only one 3L observation apart from the anchors.
Part of the evaluation of equidistance is analysis of the position of the extreme levels according to the indirect method: are the VAS ratings for the extreme level descriptors close to the supposed anchor values for the indirect method? Ideally, 3L-1 and 5L-1 scores would equal 0 and 3L-3 and 5L-5 scores would equal 100, except for Pain/Discomfort and Anxiety/Depression in which the 3L and 5L extreme level descriptors were not identical to the VAS anchors.
Second, isoformity is the degree to which the positions of 3L-2 and 5L-3 level descriptors (and also 3L-3 versus 5L-5 for Pain/Discomfort and Anxiety/Depression) are similar. Isoformity directly compares the 3L and 5L descriptive systems for each separate dimension between instruments. For the indirect method, all 3L level means, including 3L-1 and 3L-3, can be compared with 5L. Analysis of isoformity is based on paired 3L–5L response means for each dimension separately. For the direct method, isoformity was tested with a paired t test between the 3L and 5L scorings. For the indirect method, a deviation of 5 VAS points was defined as the maximum acceptable deviation.
Finally, consistency between dimensions is the degree to which the positions of the same level descriptors differ across dimensions. Consistency, between dimensions was tested for each instrument (3L, 5L) separately. The first three dimensions (Mobility, Self-Care, and Usual Activities) were distinguished from the last two (Pain/Discomfort and Anxiety/Depression), as these—in Dutch—share identical level descriptors, e.g., some problems for Mobility, Self-Care, and Usual Activities. For the direct method, analysis of variance (ANOVA) was used for each identical level descriptor for the first three dimensions combined (one comparison for 3L and three for 5L) and Pain/Discomfort and Anxiety/Depression combined (two comparisons for 3L and four for 5L), resulting in a total of ten comparisons . For the indirect method, consistency is tested with a generalizability study (G-study). In a G-study, one is able to separate multiple sources of error variance [25]. Generalizability coefficients (G-coefficients) can be constructed as functions of the estimated variance components, expressing consistency on a 0–1 scale, with 1 expressing perfect consistency [26, 27]. We used a variance components analysis based on the restricted maximum likelihood method and identified four possible sources of variance: label, vignette, dimension, and respondent. Four separate G-studies were conducted, one on the first three dimensions and one on the remaining two dimensions, for each instrument (3L, 5L) separately. A G-coefficient expressing consistency between dimensions was calculated on the basis of these variance components (“Appendix A”).
We regarded transformed or untransformed equidistance to be a desirable characteristic for the new 5L system as opposed to no systematic relation between the quantitative position of the level descriptors at all. Consistency between identical-level descriptors across dimensions was also regarded as a desirable property because this expresses that respondents have a consistent conceptualization of the grading terms used over different dimensions of health. When consistency is achieved, this does not imply that utility values would also be expected to be consistent over dimensions, because utility values are an expression of an entire EQ-5D profile, whereas we investigated VAS scores within each dimension separately. Furthermore, a choice-based method presumably leads to different results than the dimension-specific VAS scales we used. We investigated isoformity to see whether the new 5L system was a refinement or a new system, and whether isoformity was achieved or not does not tell us anything about the 5L system in itself.
Results
The mean age of the participants was 53.6 years, with 42.7% being men. Of the 82 respondents who attended in the panel sessions, 81 returned the survey. Three respondents (4%) were of Turkish nationality, two (2%) were of Moroccan nationality, and the remaining 75 (94%) were of Dutch origin. In the Pain/Discomfort and Anxiety/Depression dimensions, respondents often failed to score the extreme-level descriptor when using the direct method (8 and 9 for 3L, respectively, and 22 and 16 for 5L, respectively). For these respondents, the remaining scorings were deleted for that dimension because of possible context effects (i.e., spreading out the VAS scores of the remaining 3L descriptors over the VAS scale). For the direct method, missing responses for 3L ranged from 6.1% (Usual Activities) to 19.5% (Pain/Discomfort) and for 5L from 4.9% (Usual Activities) to 34.6% (Pain/Discomfort). For the indirect method, missing responses ranged from 1.1% (Usual Activities) to 2.5% (Pain/Discomfort) for the three response scales (3L, 5L, and VAS) combined.
Characteristics: direct method
Results for the direct method are shown in Table 1 and Fig. 2. Untransformed equidistance was rejected for all level descriptors except 5L-4 in Mobility (80), although Self-Care and Usual Activities were only 1 VAS point away for Mobility. Regardless of dimension, level descriptors were positioned systematically lower than the expected value for equidistance for 3L-2 (16–23 VAS points lower), 5L-2 (14–16 points lower), and 5L-3 level (11–18 points lower), whereas 5L-4 was sometimes higher (4–5 points) and sometimes lower (7–8 points). Transformed equidistance (power function) provided an excellent fit for all dimensions of 5L (R2 ≥ 0.99).
Fig. 2Direct quantification of the three- and five-level (3L, 5L) descriptors. Visual analog scale (VAS) means by dimension
Isoformity could not be established except for the middle-level descriptors (3L-2 vs. 5L-3) for Pain/Discomfort and Anxiety/Depression (Table 2). Relatively large gaps appeared between 3L-2 and 5L-3 for Mobility (11), Self-Care (8), and Usual Activities (9), with 5L-3 showing systematically higher values. Although there was a statistically significant difference between the extreme level descriptors (3L-3 vs. 5L-5) for Anxiety/Depression, the absolute difference was 3 VAS points.
Table 2Isoformity of identical three-and five-level (3L, 5L) descriptors for the direct quantification methodDimensionComparisonMean differenceP valueMobility3L-25L-3−11.4<0.001Self-care3L-25L-3−8.00.002Usual activities3L-25L-3−9.4<0.001Pain/Discomfort3L-25L-3−1.40.501Pain/Discomfort3L-35L-5−4.90.012Anxiety/Depression3L-25L-32.80.276Anxiety/Depression3L-35L-5−3.00.025
Consistency between dimensions gives supportive results for both 3L and 5L, as none of the ten comparisons (ANOVA) showed significant differences (see Fig. 2). Generally, VAS means are similar among the first three dimensions as well as among Pain/Discomfort and Anxiety/Depression.
Characteristics: indirect method
Results of the indirect method are shown in Table 3 and Fig. 3. Untransformed equidistance of 3L-2 was rejected for all dimensions (systematically 7–14 VAS points too low) as well as for 5L-2 (systematically 8–13 points lower) and 5L-3 (systematically 8–17 points lower). Untransformed equidistance was achieved only for the 5L-4 level for all dimensions (systematically 1–5 points lower), with VAS scores ranging from 70 (Mobility and Usual Activities) to 74 (Anxiety/Depression). Transformed equidistance (power function) provided an excellent fit for all dimensions of 5L (R2 ≥ 0.99).
Table 3Indirect quantification of three- and five-level (3L, 5L) descriptorsNumberMeanMedianCI3L Mobility    No problems in walking about5991.6901.31–2.07    Some problems in walking about40342.944040.24–45.64    Unable to walk about18091.709989.28–94.12Self-care    No problems with self-care4823.2402.40–4.08    Some problems washing or dressing self43539.183436.58–41.78    Unable to wash or dress self27385.479582.77–88.16Usual activities    No problems with performing usual activities2354.5023.49–5.51    Some problems with performing usual activities58236.553034.40–38.71    Unable to perform usual activities37888.549586.87–90.22Pain/Discomfort    No pain or discomfort24612.6449.94–15.34    Moderate pain or discomfort64335.763133.92–37.60    Extreme pain or discomfort27583.218980.82–85.61Anxiety/Depression    Not anxious or depressed4336.2915.01–7.57    Moderately anxious or depressed47842.454040.26–44.63    Extremely anxious or depressed27084.809082.73–86.865LMobility    No problems in walking about5471.3000.92–1.69    Mild problems in walking about14715.331112.64–18.02    Some problems in walking about15936.483133.00–39.97    Many problems in walking about21769.827666.72–72.92    Unable to walk about11297.3610095.24–99.48Self-care    No problems with self-care3982.4501.43–3.48    Mild problems washing or dressing self20412.70910.76–14.64    Some problems washing or dressing self18436.093333.00–39.17    Many problems washing or dressing self25771.207868.33–74.06    Unable to wash or dress self14791.379987.80–94.94Usual activities    No problems with performing usual activities1363.2201.49–4.95    Mild problems with performing usual activities26812.39910.68–14.10    Some problems with performing usual activities22832.533029.97–35.09    Many problems with performing usual activities35169.547567.18–71.90    Unable to perform usual activities21295.3510093.74–96.96Pain/Discomfort    No pain or discomfort1458.3405.32–11.37    Mild pain or discomfort27417.271215.13–19.41    Moderate pain or discomfort36736.833534.91–38.76    Severe pain or discomfort26371.727969.05–74.39    Extreme pain or discomfort11592.769889.86–95.65Anxiety/Depression    Not anxious or depressed3054.7503.07–6.43    A little anxious or depressed24116.481014.33–18.63    Moderately anxious or depressed27141.984139.72–44.25    Very anxious or depressed24874.198071.69–76.70    Extremely anxious or depressed11692.339789.61–95.04CI confidence intervalFig. 3Indirect quantification of the three- and five-level (3L, 5L) descriptors. Visual analog scale (VAS) means by dimension
VAS results for the extreme-level descriptors show that the lower extreme is close to 0, except for Pain/Discomfort (3L-1 = 13; 5L-1 = 8). VAS results for the upper extreme values are systematically higher for 5L than for 3L (range of difference: 6–10). Noticeable are large deviations in Self-Care (3L-3 = 85; 5L-5 = 91) and Usual Activities (3L-3 = 89). Isoformity was accepted for 3L-1 versus 5L-1 for all dimensions and for 3L-2 vs. 5L-3 for all dimensions except Mobility (showing a gap of 7 points). Isoformity was rejected for the upper extreme comparison 3L-3 versus 5L-5 for all dimensions. Consistency between dimensions gave supportive results for both 3L and 5L. Table 4 shows the G-study results. Most variance is attributed to the label component, whereas less than 2% of variance is attributed to the components including dimension, which is reflected in high G-coefficients for all comparisons. Consistency for 5L is somewhat higher (0.87; 0.86) than for 3L (0.86; 0.81).
Table 4Consistency between dimensions for the indirect quantification method. Variance components estimates (percentages) and generalizability coefficients (G-coefficients) for comparable dimensions of three- and five-level (3L, 5L) instruments3L5LMobility/Self-care/Usual activitiesLabel66.12Label71.52Vignette8.05Vignette6.35Dimension0.26Dimension0.04Respondent0.33Respondent0.79Label × vignette5.60Label × vignette2.91Label × dimension0.22Label × dimension0.12Label × respondent2.20Label × respondent2.59Vignette × dimension0.60Vignette × dimension0.17Vignette × respondent3.77Vignette × respondent2.57Dimension × respondent0.76Dimension × respondent0.60Residual12.09Residual12.34G-coefficient0.86G-coefficient0.87Pain/Discomfort; Anxiety/DepressionLabel65.25Label73.58Vignette4.95Vignette2.73Dimension0.00Dimension0.00Respondent0.65Respondent0.77Label × vignette1.91Label × vignette1.02Label × dimension0.04Label × dimension0.00Label × respondent2.96Label × respondent3.36Vignette × dimension1.06Vignette × dimension0.17Vignette × respondent5.52Vignette × respondent4.50Dimension × respondent0.88Dimension × respondent0.45Residual16.78Residual13.42G-coefficient0.81G-coefficient0.86
Discussion
In this study, we compared the quantitative position of the level descriptors of the standard EQ-5D3L and a new five-level version using two independent methods. The study showed that the extension of the EQ-5D3L to a five-level version by inserting two extra levels, leaving the existing descriptors unaltered, is not a simple refinement but a redesign. The inserted levels pushed the extreme levels closer to the anchors, which indicates that 5L makes better use of the measurement continuum, contributing to superior descriptive power of the 5L version. In both the 3L and 5L versions, the position of the 3L or 5L descriptors, reassuringly, was independent of dimension.
Equidistance was not achieved for both systems, in most cases showing values lower than the equidistant values. Both methods revealed a large gap between the 5L-3 and 5L-4 levels, regardless of dimension. This could be caused by the wording of 5L-3 [some and moderate(ly)] being interpreted as fairly mild.
In Pain/Discomfort, respondents tended to avoid the lower anchor of the scale, indicating some pain or discomfort on VAS while scoring no problems on 3L and 5L. This indicates that respondents preferred a more refined response scale for scoring pain or discomfort, maybe a scale with even more than five response options (as is the case of, e.g., the HUI3 or SF-36). Also noticeable were the gaps observed for the upper extreme in Self-Care, for which we cannot provide an explanation.
Isoformity between 3L and 5L showed mixed results. The 3L-1 vs. 5L-1 descriptors showed isoformity (indirect method only), as expected, as these both indicated the upper ceiling (no problems). Isoformity was also established for the middle level descriptors of Pain/Discomfort and Anxiety/Depression for both methods. This could be due to the wording of the middle level descriptors, as the descriptor some problems represented a wider range and hence more potential variation, than moderate(ly), as used in Pain/Discomfort and Anxiety/Depression. Assuming that the descriptor some problems was a well-considered choice in the development of the original EQ-5D3L system in order to cover the entire range between the two extremes, it is questionable whether that descriptor is still suitable in a 5L version.
Direct quantification is a well-known method of estimating the magnitude of level descriptors or response labels [16, 17, 28, 29]. This approach, however, ignores the fact that the VAS values expressed for the level descriptors did not necessarily reflect the self-report use of such descriptors (and the use in subsequent valuation studies) in a similar way, because the valuation of an abstract level descriptor might lead to different results than self-reported health. The indirect method is novel: to our knowledge, this is the first time a quantification of level descriptors is estimated with this method. The indirect method has several advantages. First, we believe it is a better representation of the hypothesized measurement continuum of EQ-5D, as the medium of the vignette (disease) was used to calibrate 3L and 5L descriptors on a VAS scale. Second, it is closer to the general use of the EQ-5D instrument as a self-report health status assessment measure and is therefore likely to be more valid. Classifying a vignette can be regarded similarly to a health status classification by proxy assessment. Other advantages of the indirect method are analytical: values can be calculated for all level descriptors, including the anchors, and it is possible to investigate explained variance for various components (G-study). Furthermore, the indirect method proved to be much more feasible than the direct method, considering the lower number of missing responses. Disadvantages are that no direct comparison (e.g., paired t test) between 3L and 5L is possible, as there is only one VAS value for each 3L–5L response pair, and that the indirect method is more time consuming.
A potential weakness of the study procedure is that 3L and 5L were presented on one sheet, and panelists were asked to score 5L dimensions first while covering 3L and vice versa. We cannot be sure that respondents actually complied to the blinding procedure in the follow-up measurement. Also, there might have been an order effect, as 5L always preceded 3L.
The 5L instrument presented here obviously improves the discriminatory potential of the EQ-5D descriptive system, as the level descriptors generally capture a larger part of the measurement continuum and broaden the measurement space. Furthermore, 5L showed slightly better consistency between levels. In a previous study, we demonstrated increased discriminatory power of the same 5L version of EQ-5D, as well as superior reliability (interobserver and test–retest) and face validity when compared with the standard EQ-5D3L [18]. Awaiting a valuation study for an official version of 5L, a set of preference weights was developed for this 5L version of EQ-5D using item response theory (IRT) methodology [30]. An officially sanctioned five-level descriptive system will become available within a short period [31] and is expected to be in use alongside the standard three-level EQ-5D.
The experimental five-level EQ-5D version presented here is likely to demonstrate a less severe ceiling effect. Assuming that milder states are more common in the general population, we expect increased benefit in the detection of mild problems and in measuring and monitoring general population health, although the extra 5L-4 level is expected to also lead to better differentiation and detection of more severe health states. The methodology presented here can be of use in the development of generic or disease-specific health status measures.

eq-5d
psychometrics
health status
methodology
health-related quality of life


Virtual reality training for endoscopic surgery: voluntary or obligatory?

Introduction Virtual reality (VR) simulators have been developed to train basic endoscopic surgical skills outside of the operating room. An important issue is how to create optimal conditions for integration of these types of simulators into the surgical training curriculum. The willingness of surgical residents to train these skills on a voluntary basis was surveyed.
Endoscopic surgery requires dedicated skills such as three-dimensional orientation in a two-dimensional representation of the operating field and complex instrument handling [8, 7, 5]. Training of these skills in the operating room (OR) is under pressure due to planning issues and ethical considerations. Virtual reality (VR) simulators have been developed to train basic endoscopic surgical skills outside of the OR. Several simulators have been validated and found adequate for the transfer of skills from the simulator to the OR [2, 6, 9, 13, 14]. However, discussion arises on how to integrate these simulation-based training modalities in the surgical training curriculum.
A questionnaire was distributed to 245 Dutch surgical residents to explore the perspective of the trainee on this issue. Approximately 75% of residents felt that endoscopic skills training outside the OR is useful [12]. In another study, sixty Dutch gynaecology residents responded positively (3.9 on a five-point Likert scale) with regard to training in laparoscopic skills before real surgery [10]. Fifty-five percent of these 60 residents did not have the opportunity to train laparoscopic skills. However, those that did appeared to train only once or twice a year and 33% did not use available skills trainers voluntarily at all. We hypothesized that insufficient simulator access might be the reason for this contradiction. Therefore, we investigated the willingness of surgical residents to train in endoscopic skills on a voluntary basis when VR simulators were indeed readily available. We also evaluated the effect of competitive incentives on the frequency and duration of simulator training.
Materials and methods
Equipment, tasks and scoring system
This study is performed with the LapSim virtual reality simulator, which uses the Virtual Laparoscopic Interface (VLI) hardware, (Immersion Inc., San Jose, CA, USA) The VLI has an interface with a 2600 MHz hyperthreading processor Pentium IV computer running Windows XP and is equipped with 256 random-access memory (RAM), a GeForce graphics card and an 18-inch thin-film transistor (TFT) monitor. The systems feature LapSim Basic Skills 3.0 software (Surgical Science Ltd, Göteburg, Sweden), from the LapSim Basic Skills package, consisting of nine tasks.
A training program was designed that included all nine tasks: camera navigation, instrument navigation, coordination, grasping, lifting and grasping, cutting, clipping and cutting, suturing and fine dissection [4].
The computer stores and displays between seven and eleven parameters of performance per task. These parameters are related to time, errors or efficiency of handling.
Tasks can be adjusted to different levels of difficulty. The training program for this study was set at an advanced level with thresholds that are based on the performance of 30 experienced endoscopic surgeons (more than 100 endoscopic procedures).
The scoring system is two-tiered. First, for any given parameter the system determines whether or not the participant passes or fails the test. Secondly, if a participant passes, a score of between 0% and 100% is attached to his or her performance on that particular parameter. The overall score per task is determined by the sum score of the parameters, divided by their number. Hence, an overall score of 100% can only be obtained by scoring 100% on each of the individual parameters measured during performance of the particular task. An outcome score of 100 points is given to those participants who score a 100% on the task performed. Logically, a score of 85% thus translates into 85 points. A maximum overall score of 900 could be obtained (i.e., 100 points on each of the nine tasks measured).
Participants
Twenty-one surgical residents, ranging from postgraduate year (PGY) -one level to PGY 6 level, with different endoscopic surgical experience, were given unlimited access to the simulator. Seven residents were at the beginning of their surgical educational program (PGY 1 and PGY 2) and therefore inexperienced in endoscopic surgery. Seven residents were in the middle (PGY 3 and PGY 4) and eight residents were at the end (PGY 5 and PGY 6) of their surgical educational program.
Setting and incentives
In the period May 2005 to January 2006 a simulator was placed in the general room for surgical residents at the surgical ward of the University Medical Centre in Utrecht. Before the study, residents were instructed on how to operate the simulator, and allocated a personal login number for the simulator. By placing the simulator in the general residents’ room, it was readily and easily accessible 24 hours a day. The room is secured by a code-locked door and accessible by residents only.
During the first four months, there were no additional incentives other than the permanent (24-hour) accessibility to the residents for training on the simulator. After these four months, a competitive element was introduced in which the frequency of training was also rewarded (bi-weekly). The overall end-score was calculated every other week by adding this frequency bonus to the highest scores for each task. These overall end-scores for each resident were publicly announced to the complete department of surgery and the winner (the resident with the highest score) was awarded a prize.
Questionnaire
After eight months all residents were requested to fill out a questionnaire.
Ten questions were presented on a five-point Likert scale, concerning their perception of their own experience level in endoscopic procedures, their opinion of the possibility to develop and train endoscopic skills within the current surgical curriculum, and their opinion about the application of virtual reality as a means to training endoscopic skills. Value 1 was assigned to “totally agree”, value 5 to “totally disagree”. In addition, the residents were asked about their frequency of usage of the simulator. If a participant indicated little usage, he or she was questioned why, and what could motivate increased usage.
Results
In the first four months only two of the 21 residents (10%) trained on the simulator, for a total of 163 minutes. One resident was a PGY 2, the other one a PGY 5. In the second period of four months the number of trainees increased to seven residents (33%, two PGY 2, two PGY 3, one PGY 5 and two PGY 6). The duration of training increased to 738 minutes, thereby constituting an average increase of 23.9 minutes per subject. Fifty-eight percent of training was performed during night shifts.
All 22 residents (100%) replied to the questionnaire. The total training time, as an accumulation of estimation on individual training time, was 4140 minutes. The actual cumulated training time for all residents was 901 minutes (22%). Thirteen out of 15 residents who did not train at all (86%) stated that this was due to a lack of time during the day. One resident (7%) stated he had been not interested enough to train and indicated that he had alternative priorities. Another resident (7%) stated that she was fully occupied due to an intensive-care traineeship and maternity leave.
Residents suggested that the use of the VR trainers could be enhanced by incorporating a mandatory VR training into the surgical curriculum (9x = people agreeing), to oblige certain skills level on VR simulator before starting endoscopic surgery in the OR (3x), to implement competitive training with coaching (2x), to diminish working pressure (2x), to have more-advanced exercises available on the simulator (3x), to place the VR simulator in a location other than in the residents’ room (1x). Only two stated that more initiative of residents was required to improve outcome.
Figure 1 refers to the perception of residents’ own experience level in endoscopic procedures, their opinion of the possibility to develop and train endoscopic skills within the current surgical curriculum and their opinion about the application of virtual reality as a means to training endoscopic skills.
Fig. 1.Results of questionnaire
In general, the opinion of the residents on their own experience level, on the possibility to develop and train endoscopic skills during their training and on the role of virtual reality varies considerably [standard deviation (SD) 0.44 to 1.39]. Their opinion on obligation of VR training to improve endoscopic skills and having VR training as a mandatory part of the basic skills training is most uniform. Residents do not have a marked positive, nor a marked negative opinion on the presented statements on receiving enough training for acquiring basic skills (mean 2.63, SD 1.19), on receiving sufficient training time in the OR to train in endoscopic skills (mean 3.47 SD 1.17) and on acquiring a satisfactory level of basic psychomotor skills (mean 2.42, SD 1.16). The same applies to their opinion on the representation of their training results on the simulator (mean 2.98, SD 0.83); as well as on the statement that thresholds should be reached before training in the OR is allowed (mean 2.47, SD 1.39). There is one statement they do not agree with; I will not train unless it is obligatory (mean 4.26, SD 1.10).
Discussion
Virtual reality training has the potential to improve and professionalize the training in endoscopic basic psychomotor skills [2, 6, 9, 13, 14]. Training results can be shown instantly to demonstrate objective performance and progress of performance assessment. However, one of the main concerns in acquisition of expensive equipment for educational purposes is its effectiveness. While initial enthusiasm about new innovative equipment is usually high among the surgical community, actual usage tends to be disappointing [3]. This study was undertaken to evaluate the aptitude for training on a voluntary basis when a VR simulator was readily available. Free unlimited access to a VR simulator without obligation or assessment in our setting did not seem to motivate surgical residents to use the simulator for improvement of their psychomotor endoscopic skills level. The addition of a competitive element and a desirable prize had only a marginal effect on the frequency and duration of training. We believe that the effort required to provide this incentive is disproportionate to its marginal effect.
The majority of residents (86%) stated that ‘lack of time due to high working pressure’ is the most important reason for not using the simulator. Following a recent European guideline, as set by the European Commission, a working week for a resident in training is being reduced from 70 to 48 hours [1, 11]. This may have led to an increase in pressure during working hours, with little time available for voluntary training. However, spare time has increased vastly compared to the former curriculum. Residents did not use personal free time for VR simulator training to improve their skills.
The perception of their own experience level in endoscopic procedures and the possibility of developing and training in endoscopic skills within the current surgical curriculum was in general neutral. Therefore no conclusions can be drawn from this. There is favorable, uniform opinion on the desirability of integration of skills training into the curriculum. In addition, residents believe skills training ought to be mandatory for marked improvement of their psychomotor skills. Interestingly, the disagreement on the statement of not training unless it is obligatory (mean 4.26, SD 1.10) appears to have no bearing in reality, because our study shows very limited use of the simulator. This incongruence might be caused by political correctness or by a discrepancy between intentions and actions.
It must be said that our result reflects the quantity of training on a voluntary basis of 22 residents in a single institute only, and might therefore not represent the attitude of national or international surgical residents.
In conclusion, the acquisition of expensive devices to train basic psychomotor skills for endoscopic surgery is probably only effective when it is a mandatory part of the curriculum.

endoscopic surgery
surgical training
virtual reality simulation
curriculum design
resident participation


Carbohydrate supplementation during prolonged cycling exercise spares muscle glycogen but does not affect intramyocellular lipid use

Using contemporary stable-isotope methodology and fluorescence microscopy, we assessed the impact of carbohydrate supplementation on whole-body and fiber-type-specific intramyocellular triacylglycerol (IMTG) and glycogen use during prolonged endurance exercise. Ten endurance-trained male subjects were studied twice during 3 h of cycling at 63 ± 4% of maximal O2 uptake with either glucose ingestion (CHO trial; 0.7 g CHO kg−1 h−1) or without (CON placebo trial; water only). Continuous infusions with [U-13C] palmitate and [6,6-2H2] glucose were applied to quantify plasma free fatty acids (FFA) and glucose oxidation rates and to estimate intramyocellular lipid and glycogen use. Before and after exercise, muscle biopsy samples were taken to quantify fiber-type-specific IMTG and glycogen content. Plasma glucose rate of appearance (Ra) and carbohydrate oxidation rates were substantially greater in the CHO vs CON trial. Carbohydrate supplementation resulted in a lower muscle glycogen use during the first hour of exercise in the CHO vs CON trial, resulting in a 38 ± 19 and 57 ± 22% decreased utilization in type I and II muscle-fiber glycogen content, respectively. In the CHO trial, both plasma FFA Ra and subsequent plasma FFA concentrations were lower, resulting in a 34 ± 12% reduction in plasma FFA oxidation rates during exercise (P < 0.05). Carbohydrate intake did not augment IMTG utilization, as fluorescence microscopy revealed a 76 ± 21 and 78 ± 22% reduction in type I muscle-fiber lipid content in the CHO and CON trial, respectively. We conclude that carbohydrate supplementation during prolonged cycling exercise does not modulate IMTG use but spares muscle glycogen use during the initial stages of exercise in endurance-trained men.
Introduction
During exercise, there is a complex interaction between carbohydrate and fat metabolism to provide the required adenosine triphosphate (ATP) to sustain the necessary power output. The relative utilization of endogenous fat and carbohydrate stores during exercise can vary enormously and strongly depends on exercise intensity [28, 38], duration [28, 41, 45], and training status [19, 36]. During moderate intensity exercise, the oxidation of plasma-derived free fatty acids (FFA) in conjunction with other fat sources (muscle- and/or lipoprotein-derived triacylglycerol) contributes approximately half of the total energy required for contracting skeletal muscle. As the exercise duration lengthens, there is an increasing contribution of fat oxidation to total energy expenditure, which is primarily attributed to an increase in the release, uptake, and oxidation of plasma FFA [28, 41, 45]. Studies utilizing more prolonged (≥2 h) moderate-intensity exercise protocols have consistently shown that the progressive increase in plasma-FFA delivery to working skeletal muscle late in the exercise coincides with an inhibition of intramyocellular lipid (intramyocellular triacylglycerol [IMTG]) mobilization and/or oxidation [28, 41, 45]. In accordance, pharmacological inhibition of adipose tissue lipolysis has been shown to stimulate the use of intramyocellular lipids during the latter stages of prolonged exercise [42, 44, 47]. Carbohydrate supplementation can also serve to decrease FFA delivery and lower whole-body fat-oxidation rates during exercise [9, 10]. Therefore, it could be hypothesized that the reduced FFA availability via carbohydrate supplementation stimulates IMTG use during the latter stages of prolonged exercise. In contrast, other studies have reported an inhibitory effect of glucose ingestion on hormone-sensitive lipase activity [48], which would actually reduce IMTG hydrolysis [11]. It therefore remains to be established how carbohydrate supplementation during exercise modulates IMTG utilization.
Carbohydrate supplementation during exercise has been reported to improve endurance performance by maintaining euglycemia during the late stages of prolonged exercise and/or by sparing the muscle glycogen stores [9]. However, the latter remains equivocal, as most studies have failed to report muscle glycogen sparing after carbohydrate supplementation during prolonged cycling exercise [5, 9, 13, 17, 24, 25]. The inconsistent findings of the effects of carbohydrate supplementation on subsequent muscle glycogen sparing may likely be attributed to differences in the applied research design, with specific reference to the mode of exercise (running vs cycling) and/or specific muscle group and/or fiber type recruitment [33].
The present study applies continuous infusions of [U-13C] palmitate and [6,6-2H2] glucose with muscle biopsy sampling before and after exercise to quantify both whole-body and muscle-fiber-type-specific IMTG and glycogen use. The purpose of this study was to assess the impact of carbohydrate supplementation on skeletal muscle glycogen and intramyocellular lipid use during prolonged cycling exercise in endurance-trained men and, as such, to determine whether carbohydrate supplementation reduces muscle glycogen use and/or augments the use of the IMTG pool as a substrate source.
Materials and methods
Subjects
Ten endurance-trained male cyclists were selected to participate in this study. Subjects’ characteristics are provided in Table 1. Subjects were informed about the nature and risks of the experimental procedures before their written informed consent were obtained. This study was approved by the local Medical Ethical Committee of the Academic Hospital Maastricht.
Table 1Subjects’ characteristics (n = 10)Characteristics Age (years)23 ± 3Height (m)1.83 ± 0.04Body mass (kg)72.5 ± 9.6BMI (kg m−2)21.7 ± 1.9Body fat percentage (%)9.8 ± 2.4Fat free mass (kg)65.3 ± 7.9Basal plasma glucose (mmol l−1)5.1 ± 0.3Plasma glucose120 min (mmol l−1)3.9 ± 1.1Basal plasma insulin (mU l−1)9.0 ± 2.6HbA1c (%)5.2 ± 0.2VO2max (ml kg−1 min−1)59.8 ± 5.6Wmax (W)398 ± 41Values are expressed as means ± SD. Body mass index (BMI) is calculated by dividing body mass by the square of the height.Plasma glucose120 min Plasma glucose concentration 120 min after ingesting 75 glucose in the oral glucose tolerance test
Pretesting
Subjects initially performed a continuous incremental cycling test on a cycle ergometer (LODE Instrument, Groningen, The Netherlands) to exhaustion to determine peak pulmonary oxygen uptake (VO2max; Oxycon-β, Mijnhart, The Netherlands) and maximal workload capacity (Wmax). After an overnight fast, body composition was assessed using the hydrostatic weighing method, with corrections made for residual lung volume via the helium dilution technique (Volugraph 2000, Mijnhart, Bunnik, Netherlands). Body-fat percentage was calculated using Siri’s [31] equation. To assess whole-body insulin sensitivity an oral glucose tolerance test was performed in each subject according to the World Health Organization criteria [2]. In addition, insulin resistance was estimated using the homeostasis model assessment for insulin resistance or HOMA-IR index [23].
Diet and activity before testing
All subjects were instructed to maintain their usual dietary and physical activity patterns throughout the entire experimental period. In addition, they filled out a food intake diary for 2 days before the first exercise trial to keep their dietary intake as identical as possible before the other trials. Subjects refrained from heavy physical labor and exercise training for 3 days before each trial. Dietary analyses showed no differences in energy intake during the days before each of the trials with an average energy intake of 12.7 ± 2.4 MJ, with 57 ± 7, 28 ± 5, and 15 ± 3% of the energy intake (energy%) derived from carbohydrate, fat, and protein, respectively. The evening before each trial, subjects received the same standardized meal (41.2 kJ kg bw−1; containing 72, 11, and 17 energy% carbohydrate, fat, and protein, respectively).
Experimental trials
Each subject performed three trials, separated by at least 1 week: two experimental trials (CON: control vs CHO: carbohydrate) and an acetate correction trial (Fig. 1). Each trial consisted of 60 min of resting measurements, followed by 180 min of cycling at 50%Wmax. In the main trials, an [U-13C] palmitate and [6,6-2H2] glucose tracer were infused continuously at rest and during exercise with breath and blood samples collected at regular intervals. Muscle biopsy samples were collected before and immediately after exercise. In the carbohydrate supplementation trial (CHO), plasma FFA availability was reduced, and plasma glucose availability was augmented through carbohydrate ingestion. To allow for a continuous supply of glucose from the gut, subjects ingested an 8% CHO solution every 20 min during exercise at the rate of 0.7 g CHO kg−1 h−1, as recommended by the American College of Sports Medicine [1]. In the control trial, a placebo water drink was provided (CON). In a third trial, [1,2-13C] acetate was infused continuously at rest and during exercise, and only breath samples were collected. Both the CON and acetate trials were performed in an overnight fasted state. The acetate recovery factor was used to accurately correct [U-13C] palmitate oxidation rates for carbon label retention in the bicarbonate pool(s) and by way of isotopic exchange reactions in the TCA-cycle for each subject [40].
Fig. 1Schematic of study protocol. After an initial resting blood, breath, and muscle biopsy sample, a continuous infusion with [U-13C] palmitate and 6,6-2H2 glucose was started for 240 min. At rest and during 3 h of cycling at 50% of Wmax substrate use was assessed with (CHO trial) and without (CON trial) carbohydrate supplementation
Protocol
After an overnight fast, subjects arrived at the laboratory at 8:00 a.m. by car or public transportation. After 30 min of supine rest, a percutaneous muscle biopsy was taken from the vastus lateralis muscle [3]. A Teflon catheter (Baxter, Utrecht, The Netherlands) was inserted into an antecubital vein of one arm for blood sampling, another catheter was inserted in the antecubital vein of the contralateral arm for isotope infusion. Thereafter, a resting blood sample was taken and expired breath samples were collected into vacutainer tubes. Subsequently, subjects were administered a single intravenous dose of NaH13CO3 (0.06375 mg kg−1), to prime the bicarbonate pool(s), followed by a [6,6-2H2] glucose prime (13.5 μmol kg−1). Thereafter, a continuous infusion of [6,6-2H2] glucose (0.3 μmol kg−1 min−1) and [U-13C] palmitate (0.01 μmol kg−1 min−1; or [1,2-13C] acetate in the acetate recovery trial) was started (t = 0 min) via a calibrated IVAC pump (IVAC 560, San Diego, CA) and continued for 4 h. At t = 60 min, an initial drink bolus (7 ml kg−1) was given, and then nine more drink boluses (2.5 ml kg−1) were given at t = 80, 100, 120, 140, 160, 180, 200, 220, and 240 min of either an 8% carbohydrate solution or a placebo. At t = 60 min, subjects started to exercise on a cycle ergometer at a workload of 50%Wmax for 3 h. Expired VO2 and VCO2 were measured (Oxycon-β) at rest and throughout exercise for 5 min every 20 min before sampling of blood and expired breath collection. Immediately after cessation of exercise, a second muscle biopsy was taken (t = 240 min). Breath and blood samples were collected at t = 0, 30, and 60 min (during rest) and at t = 80, 100, 120, 140, 160, 180, 200, 220, and 240 min (during exercise).
Beverages
In the CHO trial, subjects received a beverage volume of 2.5 ml kg−1 every 20 min to ensure a given dose of 0.7 g CHO kg−1 (50% as glucose and 50% as maltodextrin) every hour. Repeated boluses were administered to enable a continuous supply of glucose in the circulation, preventing perturbations in [6,6-2H2] glucose enrichment. In the CON trial, a sweetened placebo drink, which contained no carbohydrate, was provided. Glucose and maltodextrin were obtained from AVEBE (Veendam, The Netherlands). To make the taste comparable in all experiments, beverages were uniformly flavored by adding 0.2 g sodium–saccharine solution (25% w/w), 1.8 g citric acid solution (50% w/w), and 5 g of cream vanilla flavor (Numico Research, Wageningen, The Netherlands) for each liter of beverage. Experiments were performed in a randomized order, with test drinks provided in a double-blind fashion.
Tracer infusion
Infusion rates of [U-13C] palmitate and [6,6-2H2] glucose averaged 7.3 ± 0.8 and 817.5 ± 30.4 nmol kg−1 min−1, respectively, in the CON and CHO trial. At the onset of exercise, [U-13C] palmitate infusion rates were doubled (14.6 ± 1.6 nmol kg−1 min−1). In the acetate recovery trial, a corresponding amount of 13C was infused, resulting in an average [1,2-13C] acetate infusion rate of 61.6 ± 2.4 and 123 ± 5 nmol kg−1 min−1 at rest and during exercise, respectively. Palmitate, glucose, and acetate tracer concentrations in the infusates averaged 1.03 ± 0.07, 64.8 ± 7.2, and 4.74 ± 0.18 mmol l−1, respectively.
Blood- and breath-sample analysis
Blood samples (7 ml) were collected in ethylenediamine tetraacetic acid (EDTA)-containing tubes and centrifuged at 1,000×g for 10 min at 4°C. Aliquots of plasma were frozen immediately in liquid nitrogen and stored at −80°C. Plasma glucose (Uni Kit III, Roche, Basel, Switzerland), lactate [16], FFA (NEFA-C, Wako Chemicals, Neuss, Germany), free glycerol (148270, Roche Diagnostics, Indianapolis, IN), and triglyceride (TG; GPO-trinder 337B, Sigma Diagnostics, St. Louis, MO) concentrations were analyzed with a COBAS semi-automatic analyzer (Roche).
Expired breath samples were analyzed for 13C/12C ratio by gas chromatography continuous flow isotope ratio mass spectrometry (GC-IRMS; Finnigan MAT 252, Bremen, Germany). For determination of plasma palmitate and FFA kinetics, FFA were extracted from plasma, isolated by thin-layer chromatography, and derivatized to their methyl esters. Palmitate concentration was determined on an analytical gas chromatograph with flame ionization detection using heptadecanoic acid as an internal standard and on average comprised 26 ± 2 and 24 ± 3% of total FFA for the CON and CHO trials, respectively. Isotope tracer/tracee ratio of [U-13C] palmitate was determined using GC-combustion IRMS (Finnigan MAT 252). Plasma glucose was first extracted with chloroform–methanol–water and derivatization was performed with butylboronic acid and acetic anhydride as described previously [27]. After derivatization, plasma [6,6-2H2] glucose enrichment was determined by electron ionization GC-MS (Finnigan INCOS-XL). Glucose (Uni Kit III, Roche) and acetate (Kit 148261, Boehringer) concentrations in the infusates were determined with the COBAS FARA.
Calculations
From respiratory measurements, total fat and carbohydrate oxidation rates were calculated using the nonprotein respiratory quotient [26].
with VO2 and VCO2 in liters per minute (l min−1) and oxidation rates in grams per minute (g min−1). Breath and plasma enrichments are expressed as tracer/tracee ratios (TTR);
in which sa indicates sample and bk indicates background value. Rate of palmitate and glucose appearance (Ra) and disappearance (Rd) were calculated using the single-pool nonsteady-state Steele [32] equations adapted for stable isotope methodology as described elsewhere [49]. As such, plasma palmitate and glucose Rd were calculated by correcting the Ra for the time-dependent changes in plasma metabolite concentration.
where F is the infusion rate (μmol kg−1 min−1); V is the distribution volume for palmitate or glucose (40 and 160 ml kg−1, respectively); C1 and C2 are the palmitate or glucose concentrations (mmol l−1) at time 1(t1) and 2(t2), respectively, and E2 and E1 are the plasma palmitate or glucose enrichments (TTR) at time 1 and 2, respectively. 13CO2 production (Pr13CO2; mol min−1) from the infused palmitate tracer was calculated as:
where TTRCO2 is the breath 13C/12C ratio at a given time point, VCO2 is the carbon dioxide production (l min−1), k is the volume of 1 mol of CO2 (22.4 l mol−1), and Ar is the fractional 13C label recovery in breath CO2 observed after the infusion of labeled acetate [30] and calculated as:
where F is the infusion rate of [1,2-13C] acetate (mol min−1). Plasma palmitate oxidation (Rox; mol min−1) can subsequently be calculated as:
where Rd palmitate is the rate of palmitate disappearance (mol min−1); F is the palmitate infusion rate (mol min−1), and 16 is the number of carbon atoms in palmitate. Total plasma FFA oxidation was calculated by dividing palmitate oxidation rates by the fractional contribution of plasma palmitate to total plasma FFA concentration. Muscle derived TG use was estimated by subtracting plasma FFA oxidation from total fat oxidation. However, it should be noted that the indirect stable isotope methodology does not differentiate between muscle- and lipoprotein-derived TG use. However, the contribution of lipoprotein-derived TG oxidation to total energy expenditure is assumed to be of relative minor quantitative importance, especially in an overnight-fasted state [37].
In a previous study, where we applied both an [U-13C] and [6,6-2H2] glucose tracer [18] during moderate intensity exercise, it was shown that the percentage of plasma glucose Rd that was oxidized varied between 96–100%. Therefore, plasma glucose oxidation rate during exercise was calculated as:
Therefore, muscle glycogen oxidation was calculated by subtracting plasma glucose oxidation from total carbohydrate oxidation.
Muscle-sample analyses
Muscle tissue samples were freed from any visible nonmuscle material and rapidly frozen in liquid nitrogen. About 15 mg of each muscle sample was frozen in liquid nitrogen-cooled isopentane and embedded in Tissue-Tek (Sakura Finetek, Zoeterwoude, The Netherlands). Multiple serial sections (5 μm) from all biopsy samples were thaw mounted together per subject on uncoated, precleaned glass slides and stained with either acid-Schiff (PAS) [29] or immunolabelled oil red O [21] to measure fiber-type-specific glycogen or intramyocellular lipid content, respectively. To determine the muscle-fiber typing, (type I vs type II), we performed a myosin adenosine 5′-triphosphatase stain [22]. As previously described, the applied fluorescence and bright field microscopy techniques represent semiquantitative methods that can be used to compare fiber-type-specific IMTG and glycogen content [39, 41, 43, 44]. Fiber-type-specific IMTG content was expressed as the fraction of the measured area that was stained with oil red O. For IMTG quantification, average lipid-droplet size was calculated by dividing the total number of lipid aggregates by the total area measured. Mixed muscle-lipid content, lipid-droplet size, and lipid-droplet density were determined by calculating the average value in the type I and type II muscle fibers, with a correction for the relative area occupied by each fiber type within each field of view of each muscle cross-section, within each individual subject. The oil red O epifluorescence signal was recorded for each muscle fiber resulting, on average, in a total of 71 ± 19 muscle fibers analyzed for each muscle cross-section (45 ± 14 type I, 26 ± 10 type II) per subject. The PAS bright-field images were converted post hoc into eight-bit grayscale values to quantify glycogen. The mean optical density of the PAS-stained muscle fibers were determined by averaging the optical density measured in every pixel in the cell, corrected for the mean optical density of the background stain, containing no muscle fibers. For PAS analysis, on average per subject, a total of 151 ± 61 muscle fibers were analyzed for each muscle cross-section (92 ± 32 type I, 59 ± 26 type II).
Statistics
All data are expressed as mean ± SD. Before statistical analysis, data normality was confirmed at P > 0.05 using the Kolmogorov–Smirnov test on log-transformed raw data. To compare tracer kinetics, substrate utilization rates, IMTG contents, and/or plasma metabolite concentrations over time between trials, a two-way repeated measures analysis of variance was applied. When a significant F ratio was obtained, post hoc analyses were completed using a Student–Newman–Keuls test. For nontime-dependent variables, a Student’s t test for paired observations was used. Statistical significance was accepted at P < 0.05.
Results
Plasma metabolite and hormone concentrations
Plasma FFA, triacylglycerol (TG), glycerol, glucose, lactate, and insulin concentrations over time during exercise are shown in Fig. 2. There were no differences in plasma FFA and glycerol between trials at rest or during the first 40 min of exercise (Fig. 2a,c). However, at all time points after 60 min, plasma FFA and glycerol concentrations increased over time in both trials, but levels remained significantly lower in the CHO vs CON trial (P < 0.01). Plasma TG levels declined throughout exercise in both trials but were significantly higher in the CHO vs the CON trial (P < 0.05; Fig. 2b). Plasma glucose concentrations increased in the CHO trial at the initiation of carbohydrate ingestion and were significantly higher in the CHO vs CON trial (P < 0.05; Fig. 2d). Over the last hour of exercise, plasma glucose steadily declined in both trials but remained significantly higher in the CHO vs CON trial. Plasma insulin levels in the CHO trial significantly increased during the initial 90 min of exercise as compared to CON (P < 0.05; Fig. 2f). Thereafter, plasma insulin steadily declined in both trials. Plasma lactate concentrations increased above baseline levels, with plasma lactate concentrations being greater in the CHO vs CON trial (Fig. 2e). In contrast, during the latter stages of exercise, significantly higher plasma lactate levels were observed in the CON vs CHO trial. Plasma norepinephrine concentrations increased threefold within the first 20 min of exercise and continued to rise throughout the exercise period in both trials, with no significant differences between trials (Fig. 3). Plasma epinephrine levels increased gradually in both trials during the first 2 h of exercise, after which, levels increased substantially (P < 0.001; Fig. 3). However, in the CHO trial, there was an attenuated increase in epinephrine, resulting in significantly (P < 0.05) lower concentrations vs CON.
Fig. 2Plasma metabolite concentrations during 3 h of cycling at 50% Wmax in CON and CHO trials. Values are means ± SD, n = 10. Crosses, significantly different than CON trial (P < 0.05)Fig. 3Plasma catecholamine concentrations during 3 h of cycling at 50% Wmax in CON and CHO trials. Values are means ± SD, n = 10. Crosses, significantly different from the CON trial (P < 0.05)
Plasma tracer kinetics
As plasma FFA and glucose concentrations varied over time in both trials (Fig. 2a,d), nonsteady-state Steele [32] equations were applied to calculate tracer kinetics. At rest, tracer kinetics did not differ between trials as carbohydrate supplementation did not start until the onset of exercise. At rest, plasma glucose Ra and Rd averaged 14.9 ± 1.6 and 15.0 ± 1.8 μmol kg−1 min−1, respectively. Resting palmitate Ra, Rd, and Rox averaged 1.92 ± 0.43, 1.89 ± 0.55, and 0.74 ± 0.22 μmol kg−1 min−1, respectively, with 38 ± 4% of palmitate Ra being oxidized. The onset of exercise significantly elevated plasma glucose and palmitate Ra, Rd, and/or Rox (Table 2). Plasma glucose Ra and Rd, and palmitate Ra, Rd, and Rox increased continuously during the exercise in both trials (Table 2). During the entire exercise period, average glucose Ra and Rd was 49 ± 9% greater in the CHO compared with the CON trial (P < 0.05; Table 2). In contrast, plasma palmitate Ra, Rd, and Rox was 41 ± 12% lower during exercise in the CHO vs CON trial (P < 0.05; Table 2). Acetate label recovery maintained a constant value during exercise and averaged 86 ± 4% of the acetate infusion rate. In a pilot experiment, we confirmed that carbohydrate supplementation does not affect acetate label recovery during exercise, which was in accordance to previous findings [44].
Table 2Tracer kinetics and respiratory measures during exercise First hourSecond hourThird hourExercise averageCONCHOCONCHOCONCHOCONCHOGlucose Ra (μmol kg−1 min−1)21.1 ± 2.746.8 ± 9.6a28.8 ± 4.855.8 ± 6.8a38.1 ± 7.572.9 ± 8.2a30.4 ± 4.7 59.9 ± 7.2a Rd (μmol kg−1 min−1)21.9 ± 3.148.3 ± 12.3a30.3 ± 7.960.0 ± 11.2a39.5 ± 7.272.9 ± 8.5a31.7 ± 4.7 61.9 ± 7.3aPalmitate Ra (μmol kg−1 min−1)2.49 ± 0.802.03 ± 0.40a3.55 ± 0.781.97 ± 0.57a6.43 ± 0.893.60 ± 1.92a4.36 ± 0.712.59 ± 0.70a Rd (μmol kg−1 min−1)2.48 ± 0.792.06 ± 0.40a3.50 ± 0.791.96 ± 0.56a6.36 ± 0.883.57 ± 1.34a4.32 ± 0.702.58 ± 0.69a Rox (μmol kg−1 min−1)2.28 ± 0.731.84 ± 0.42a3.49 ± 0.721.91 ± 0.68a6.35 ± 0.733.62 ± 1.38a4.26 ± 0.592.53 ± 0.77a %Ra ox91.5 ± 5.990.9 ± 8.698.7 ± 5.8395.6 ± 8.899.4 ± 7.899.8 ± 7.097.2 ± 6.296.0 ± 6.8 Ac recovery81.7 ± 5.7–86.5 ± 4.7–86.2 ± 4.1–85.8 ± 3.9–Respiratory measures VO2 (ml)2,666 ± 912,690 ± 902,713 ± 882,709 ± 982,773 ± 932,772 ± 1092,717 ± 892,724 ± 97 VCO2 (ml)2,361 ± 812,412 ± 762,336 ± 762,424 ± 84a2,327 ± 882,453 ± 94a2,342 ± 792,430 ± 83a RER0.89 ± 0.010.90 ± 0.010.86 ± 0.010.90 ± 0.01a0.84 ± 0.010.89 ± 0.01a0.86 ± 0.010.89 ± 0.01aValues are expressed as means ± SD, n = 10. Tracer kinetics during each hour of exercise and averaged during 3 h of cycling at 50% Wmax in the CON and CHO trial.Ra Rate of appearance; Rd rate of disappearance; Rox rate of oxidation; %Raox %Ra palmitate oxidized; Ac recovery fraction 1,2-13C acetate label recovery in expired CO2aSignificantly different compared with the CON trial (P < 0.05)
Substrate source utilization at rest
There were no differences in total resting energy expenditure (5.37 ± 0.52 vs 5.32 ± 0.53 kJ min−1 in the CON and CHO trial, respectively) or substrate source utilization rates between trials. Total fat oxidation averaged 0.08 ± 0.01 g min−1, contributing 58 ± 6% to total energy expenditure. Plasma FFA oxidation rates averaged 0.061 ± 0.011 g min−1, contributing 53 ± 7 to total energy expenditure at rest. The use of muscle- and/or lipoprotein-derived TG sources averaged 0.016 ± 0.008 g min−1 and contributed 7 ± 20% to total energy expenditure. Total carbohydrate oxidation rates averaged 0.13 ± 0.03 g min−1, contributing 42 ± 6% to total energy expenditure.
Substrate source utilization during exercise
The applied 50% Wmax workload resulted in subjects cycling at 199 ± 21 W, corresponding with a relative workload intensity of 63 ± 1% VO2max From rest to exercise, energy expenditure increased tenfold. No differences in energy expenditure were observed over time or between trials (57.3 ± 5.9 and 57.8 ± 6.4 kJ min−1 in the CON and CHO trial, respectively; Fig. 5). Total carbohydrate oxidation rates were significantly higher in the CHO vs the CON trial (2.35 ± 0.37 vs 1.97 ± 0.33 g min−1, respectively; P < 0.001). Concomitantly, total fat oxidation rates were higher in the CON vs CHO trial (0.62 ± 0.12 vs 0.48 ± 0.16 g min−1, respectively; P < 0.001). In the CHO trial, carbohydrate and fat oxidation contributed 66 ± 6 and 34 ± 5% to total energy, respectively. In the CON trial, carbohydrate and fat oxidation contributed 56 ± 5 and 44 ± 7% to total energy expenditure during exercise.
The use of specific substrate sources over time during exercise is illustrated in Fig. 4. The calculations for substrate source utilization in Figs. 4 and 5 commences at 40 min because of the nonsteady-state transition from rest to exercise. Plasma FFA and glucose oxidation rates increased over time in both trials (P < 0.01; Fig. 4a,c). In contrast, the respective use of muscle- (and lipoprotein) derived TG and glycogen declined over time (P < 0.01; Fig. 4b,d). Plasma glucose oxidation was significantly greater in the CHO vs CON trial and averaged 13.2 ± 2.1 vs 6.7 ± 1.1 kJ min−1, respectively (P < 0.001: Fig. 4c). Plasma FFA oxidation rates were substantially greater in the CON vs CHO trial and averaged 13.8 ± 2.9 vs 9.1 ± 2.8 kJ min−1, respectively (P < 0.001; Fig. 4a). No significant differences were observed in the use of muscle (and lipoprotein-derived) TG and glycogen over time between trials.
Fig. 4a Plasma FFA, b other fat sources, c plasma glucose, and d muscle glycogen rate of oxidation (kJ min−1) during 3 h of cycling at 50% Wmax in CON and CHO trials. Values are means ± SD, n = 10. Crosses, significantly different from CON trial (P < 0.05)Fig. 5Whole-body substrate source utilization (kJ min−1) during the first, second, and third hour of cycling and averaged over the entire 3 h, at 50% Wmax in the CON and CHO trial. Crosses, plasma FFA, other fat sources, total carbohydrate, and/or plasma glucose and/or glycogen use significantly different from the CON trial (P < 0.05)
Figure 5 illustrates average substrate source utilization rates calculated over each consecutive hour of exercise. During the first hour of exercise, there were no differences in endogenous fat source utilization between trials. In contrast, plasma glucose oxidation rates were higher in the CHO trial, resulting in a 10 ± 6% lower glycogen use compared with the CON trial (P < 0.05; Fig. 5). During the latter stages of exercise, plasma glucose oxidation rates remained higher in the CHO vs CON trial, with no differences in muscle glycogen use between trials (Fig. 5). In the first hour, fat contributed nearly the same amount of energy in the CON and CHO trial, 36 ± 6 and 33 ± 7%, respectively. In contrast, during the second and third hour of exercise, total fat oxidation rates increased substantially, with greater fat use in the CON vs CHO trial. By the third hour, fat oxidation contributed 51 ± 8% to total energy expenditure in the CON trial, whereas fat oxidation contributed only 36 ± 9% in the CHO trial (P < 0.01; Fig. 5). In the CHO trial, the attenuated increase in fat oxidation rate during exercise was offset by a significant increase in total carbohydrate oxidation rate. Total carbohydrate oxidation rates during exercise averaged 1.97 ± 0.33 and 2.35 ± 0.37 g min−1 in the CON and CHO trial, respectively (P < 0.01). As there were no differences in the average rate of glycogen utilization between treatments during the second and third hour of exercise (Figs. 4d and 5), the increased carbohydrate use in the CHO trial was fully attributed to increases in plasma glucose oxidation over the last 2 h of cycling (Figs. 4c and 5). Muscle (and lipoprotein-derived) TG and glycogen use declined over time but did not differ between trials during the latter stages of exercise (Fig. 5).
Skeletal muscle lipid content
Preexercise fiber-type-specific muscle IMTG was not different between conditions and averaged 0.067 ± 0.025 and 0.018 ± 0.010 arbitrary units (AU) in type I and II muscle fibers, respectively. There was a significantly greater net decline in intramyocellular lipid content in the type I vs type II muscle fibers after exercise (Fig. 6a). No differences in the net decline in myocellular lipid content were observed between trials. Exercise resulted in a 76 ± 21 and 78 ± 22% reduction in type I muscle-fiber lipid content in the CHO and CON trial, respectively (P < 0.01). Type II muscle-fiber lipid content was not significantly reduced after exercise and showed a net decline of 0.003 ± 0.014 and 0.008 ± 0.0012 AU in the CHO and CON trials, respectively (P = NS). The observed decrease in type I muscle intramyocellular lipid content was attributed to significant decreases in both lipid-droplet size (preexercise, 0.560 ± 0.150; vs postexercise, 0.359 ± 0.152 μm2) and lipid-droplet density (preexercise, 0.119 ± 0.031; vs postexercise, 0.041 ± 0.022 droplets μm−2).
Fig. 6Fiber-type-specific net intramyocellular lipid and glycogen utilization (expressed as mean arbitrary units ± SD, n = 10) during 3 h of cycling at 50% Wmax in CON and CHO trials. Asterisk, type II significantly different than type I (P < 0.05); Crosses, significantly different from the CON trial (P < 0.05)
Skeletal muscle glycogen content
At rest, glycogen contents did not differ between trials (0.087 ± 0.018 vs 0.103 ± 0.021 AU in the CHO vs CON trial, respectively; P > 0.05) or between fiber types (0.095 ± 0.019 vs 0.094 ± 0.021 AU in the type I vs type II muscle fibers, respectively; P > 0.05). Exercise resulted in a significantly greater net reduction in muscle glycogen content in the type I vs the type II muscle fibers (P < 0.05; Fig. 6b). The net decline in muscle glycogen content after exercise was significantly greater in the CON vs the CHO trial (38 ± 19 and 57 ± 22% greater in the type I and II muscle fibers, respectively; P < 0.01)
Discussion
The aim of this study was to examine substrate source utilization with (CHO) or without (CON) carbohydrate supplementation on subsequent glycogen and IMTG use during prolonged cycling exercise in endurance-trained male cyclists. Carbohydrate ingestion increased total carbohydrate oxidation, which was entirely attributed to a greater plasma glucose appearance (Ra) and disappearance (Rd) rate. The greater glucose availability in the CHO trial lowered muscle glycogen use during the first hour of exercise, resulting in significant muscle glycogen sparing in both type I and II muscle fibers after 3 h of exercise. Carbohydrate ingestion inhibited adipose tissue lipolysis, resulting in lower plasma FFA Ra, Rd, and Rox. The lower total fat oxidation rate was entirely attributed to reduced plasma FFA oxidation, as muscle IMTG use was not affected by carbohydrate supplementation.
IMTG use during prolonged exercise
The importance of the IMTG pool as a substrate source during exercise has been disputed. Many of the inconsistencies in the literature regarding IMTG use seem to be explained by the methodological limitations associated with the biochemical mixed-muscle TG extraction analyses [46]. More recent studies using either stable isotope tracers, 1H-magnetic-resonance spectroscopy, electron microscopy, and/or immunofluorescence microscopy on ORO stained muscle cross-sections have now established that IMTG is indeed a viable substrate source during exercise in healthy humans [37]. In accordance, substantial net reductions in type I muscle-fiber lipid content as assessed by immunohistochemical oil red O staining of muscle cross-sections have been observed after both endurance and resistance type exercise [11, 20, 41, 44]. Furthermore, our laboratory has found a significant correlation between IMTG contents assessed by biochemical TG extraction analyses and immunofluorescence microscopy on ORO stained muscle cross-sections (unpublished observations). In the present study, we observed a significant 20 ± 3 and 19 ± 2% contribution from muscle- and/or lipoprotein-derived TG sources use during exercise in the CHO and CON trial, respectively (Fig. 5). Concomitantly, muscle biopsy analyses revealed a 76 ± 21 and 78 ± 22% net decline in type I muscle-fiber IMTG content in the CHO and CON trial, respectively (Fig. 6a). This translated in a 67 ± 19 and 70 ± 20% decline in mixed-muscle intramycellular lipid content in the CHO and CON trial, respectively.
IMTG utilization during prolonged exercise with carbohydrate supplementation
Earlier estimations [28] and more recent findings [41, 45] show that the progressive increase in plasma FFA delivery during the latter stages of prolonged exercise suppresses IMTG mobilization and/or oxidation. In agreement, pharmacological inhibition of adipose tissue lipolysis has been shown to reduce plasma FFA availability and augments IMTG use during exercise [42, 44, 47]. This possibly suggests that during situations of decreased FFA availability there is a compensatory increase in IMTG utilization in vivo in humans. Carbohydrate supplementation during exercise also inhibits adipose tissue lipolysis and reduces plasma FFA availability [9, 10]. Therefore, it could be speculated that carbohydrate supplementation could augment IMTG use during the latter stages of prolonged exercise. In the present study, carbohydrate supplementation resulted in a 23 ± 17% reduction in whole-body fat oxidation because of the concerted reductions in adipose tissue lipolysis with a concomitant 41 ± 12% lower plasma palmitate Ra, Rd, and Rox when compared with the CON trial. Despite the attenuated increase in plasma FFA availability in the CHO trial, no differences in estimated IMTG use were observed between trials (Figs. 4b and 5). In agreement, after exercise, type I muscle-fiber lipid content was similarly reduced by 76 ± 21 and 78 ± 22% in the CHO and CON trial, respectively (Fig. 6a).
In the present study, we did not observe any differences in IMTG use after carbohydrate ingestion. This might be because of the metabolic effects of increased glucose availability in the CHO trial, as we observed a 49 ± 9% greater glucose Ra (Table 2) and a subsequent 48 ± 9% higher plasma glucose oxidation rate throughout exercise (Fig. 4c). Furthermore, carbohydrate ingestion elevated plasma insulin concentrations by nearly 60% in the first hour of exercise vs CON (Fig. 2f). Elevated plasma insulin is a potent inhibitor of adipose tissue lipolysis [6], resulting in a substantially lower FFA availability in the CHO vs CON trial (Table 2). Although reduced plasma FFA availability could stimulate IMTG hydrolysis by reducing the allosteric inhibition of hormone-sensitive lipase (HSL) [44], we did not observe greater IMTG use in the CHO trial (Figs. 4b and 6a). However, this elevated plasma insulin could also serve to inhibit HSL activity, as a recent study showed blunted HSL activity during exercise when glucose is ingested [48]. Watt et al. [48] suggested that the decreased HSLa was most likely mediated via increased insulin and decreased epinephrine concentrations, and that even in situations when local intramuscular mediators of HSL are altered (i.e., muscle contraction, substrates/products), the concurrent hormonal milieu may play a greater role in the activation of HSL. This previous study also found no effect of glucose ingestion on estimated IMTG oxidation, despite a decrease in plasma FFA availability and HSL activity [48].
Our present findings are not in line with de Bock et al.[11], who reported attenuated IMTG use after carbohydrate supplementation during a more intense exercise session in less-well-trained subjects. As such, we conclude that the proposed stimulating effects of reduced plasma FFA availability on IMTG mobilization and/or oxidation are offset by the inhibitory effects of carbohydrate supplementation on IMTG hydrolysis. The latter likely includes the greater glycolytic flux (Table 2), concomitantly higher circulating insulin (Fig. 2f), and decreased epinephrine levels (Fig. 3), all of which have been associated with reduced HSL activity in muscle tissue [48].
Effects of carbohydrate supplementation on muscle glycogen use
It has been established that prolonged moderate-intensity exercise results in greater glycogen depletion in type I vs type II muscle fibers [8, 14]. In accordance, we observed a 73 ± 25 and 42 ± 18% greater net utilization of type I vs type II muscle-fiber glycogen content in the CHO and CON trial, respectively (Fig. 6b). This is the first study to show glycogen sparing during prolonged cycling exercise in both type I and II muscle fibers (Fig. 6b). This finding seems to be in contrast to most previous studies that failed to observed muscle glycogen sparing after carbohydrate supplementation during cycling exercise [5, 9, 13, 17, 24, 25]. In line with our findings, some studies have also reported muscle glycogen sparing after carbohydrate supplementation during running exercise [34, 35]. However, these studies reported muscle glycogen sparing to be restricted to the type I muscle fibers only [34, 35]. The apparent discrepancy with our findings is likely explained by the differences in the type (running vs cycling) and duration (1–2 vs 3 h) of the exercise that was implemented, as with increasing exercise duration, muscle-fiber-type recruitment shifts to include more type II fiber activation as the type I fibers become glycogen depleted [15].
Our findings of muscle glycogen sparing with carbohydrate supplementation during prolonged constant intensity cycling are in line with some [4, 12], but definitely not with the majority of studies [5, 9, 13, 17, 24, 25]. The apparent discrepancy between the current study and prior work that failed to observe muscle glycogen sparing with carbohydrate supplementation during cycling might be explained by differences in training status of the subjects, the intensity and duration of the exercise, the amount and timing of the ingested carbohydrate, the absence of the time-dependent assessment of muscle glycogen use, and the lack of muscle-fiber-type-specific glycogen analyses.
Glycogen sparing occurs early in exercise with carbohydrate supplementation
In agreement with our findings of skeletal muscle glycogen sparing, whole-body muscle glycogen use was lower during the initial hour of exercise in the CHO vs the CON trial (Fig. 5). In accordance to the hypothesis set forth by Coggan and Coyle [7], we observed a greater dependence on muscle glycogen as a substrate source during the early stages of exercise in the CON trial (Fig. 5). As exercise duration lengthened, the contribution of muscle glycogen decreased over time as muscle glycogen stores became depleted, while blood glucose uptake and oxidation rates increased progressively (Fig. 4). In line with this hypothesis, we observed lower muscle glycogen use during the initial hour of exercise in the CHO trial. At the onset of exercise, carbohydrate administration caused a 49 ± 9% greater plasma glucose Ra and Rd in the CHO compared with the CON trial (Table 2), which paralleled a 60% increase in plasma insulin concentrations as compared to CON during the first hour of exercise (Fig. 2). Concomitantly, these changes because of the augmented glucose availability at exercise onset decreased the reliance of muscle glycogen as a fuel source during the first hour of exercise. The latter is in accordance with a previous work reporting significant glycogen sparing after carbohydrate supplementation after 60 min of running exercise [34]. We conclude that the majority of glycogen sparing because of carbohydrate supplementation occurs during the initial stages (<1 h) of prolonged exercise, resulting in lowered glycogen utilization in type I and II muscle fibers. As our findings show that muscle glycogen sparing after carbohydrate supplementation occurs early in exercise, endurance athletes should be recommended to initiate sports-drink consumption immediately at the onset of exercise to maximize the potential for glycogen sparing, along with maintaining euglycemia and high blood glucose oxidation rates late in exercise.
Conclusions
This study provides compelling evidence that carbohydrate supplementation spares muscle glycogen utilization in a time-dependent manner during prolonged exercise in endurance-trained cyclists. Carbohydrate supplementation augments plasma glucose uptake and oxidation rate, while reducing muscle glycogen use during the early stages of exercise, thereby sparing type I and II muscle fiber glycogen stores. Carbohydrate supplementation during exercise lowers plasma FFA rate of appearance, uptake, and oxidation, but does not modulate the use of IMTG as a substrate source during cycling exercise.

exercise
glycogen
isotopes
intramyocellular triacylglycerol
imtg
fat metabolism
sports drinks