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Background <p>Blood lactate profiling is widely used to monitor metabolic stress in rowing, but lactate alone may not describe how circulating glucose behaves during and after severe exercise. This exploratory cross-sectional study examined whether paired capillary glucose and lactate kinetics describe distinct response patterns in elite male rowers.</p> Methods <p>Twenty-six national-level male open-weight rowers completed a discontinuous incremental rowing-ergometer test in a thermostat-controlled laboratory. The first five stages were 4-min submaximal bouts separated by 1-min passive recovery for blood sampling, followed by a free-paced all-out final stage. Capillary glucose and lactate were sampled at rest, during each inter-stage recovery interval, and at 3 and 5&#xa0;min of recovery. Glucose slope during stages 4–6 (Kglu) and post-exercise glycaemic rebound (recovery 3-min glucose minus end-exercise glucose) were used for z-standardised k-means clustering. Cluster stability was examined using leave-one-out refitting and bootstrap resampling. Linear mixed-effects models tested group-by-stage interactions for glucose and lactate. Lactate-derived intensity proxies were analysed to assess whether the glucose patterns overlapped with traditional lactate markers.</p> Results <p>Two clusters were obtained (silhouette = 0.42): a Coupled response pattern (<i>n</i> = 17) and a Rebound-dominant response pattern (<i>n</i> = 9). The Rebound-dominant cluster was characterised descriptively by lower Kglu and larger early recovery rebound; these two variables were clustering inputs and are therefore not presented as confirmatory between-group tests in the abstract. Leave-one-out refitting reproduced the original assignment for all 26 rowers, and bootstrap resampling showed median label agreement of 96.2% (mean 91.9%) with the original solution. Because the glucose slope defined the clusters, the mixed-effects model showed the expected group-by-stage glucose interaction (-0.335 mmol/L/stage, <i>P</i> &lt; 0.001), indicating a temporal rather than a baseline difference; the comparisons independent of the clustering — lactate slope, peak lactate, and lactate-derived proxies — did not differ between groups. The Rebound-dominant group had a higher body fat percentage than the Coupled group (14.8 +/- 2.7% vs. 11.8 +/- 3.0%, <i>P</i> = 0.021).</p> Conclusions <p>High-intensity glucose slope and early recovery glycaemic rebound described exploratory glucose-response patterns that were not captured by lactate kinetics alone. The practical significance of these patterns remains unknown because individual workload-normalised outcomes, stage-6 duration, heart-rate time courses, gas-exchange data, and direct rowing-performance outcomes were not available in the metabolite-analysis dataset. Mechanistic and applied interpretations should therefore remain hypothesis-generating.</p>

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Post-exercise glycaemic rebound characterises exploratory glucose-lactate response patterns during incremental rowing in elite male rowers: a cross-sectional study

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  • 发表日期：

    2026年7月17日

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  • Shuwei Chen,
  • Yingping Zhang,
  • Xiaohua Li,
  • Yang Liu,
  • Sen Yang,
  • Xiubing Zhang,
  • Weibao Liang

摘要

Background

Blood lactate profiling is widely used to monitor metabolic stress in rowing, but lactate alone may not describe how circulating glucose behaves during and after severe exercise. This exploratory cross-sectional study examined whether paired capillary glucose and lactate kinetics describe distinct response patterns in elite male rowers.

Methods

Twenty-six national-level male open-weight rowers completed a discontinuous incremental rowing-ergometer test in a thermostat-controlled laboratory. The first five stages were 4-min submaximal bouts separated by 1-min passive recovery for blood sampling, followed by a free-paced all-out final stage. Capillary glucose and lactate were sampled at rest, during each inter-stage recovery interval, and at 3 and 5 min of recovery. Glucose slope during stages 4–6 (Kglu) and post-exercise glycaemic rebound (recovery 3-min glucose minus end-exercise glucose) were used for z-standardised k-means clustering. Cluster stability was examined using leave-one-out refitting and bootstrap resampling. Linear mixed-effects models tested group-by-stage interactions for glucose and lactate. Lactate-derived intensity proxies were analysed to assess whether the glucose patterns overlapped with traditional lactate markers.

Results

Two clusters were obtained (silhouette = 0.42): a Coupled response pattern (n = 17) and a Rebound-dominant response pattern (n = 9). The Rebound-dominant cluster was characterised descriptively by lower Kglu and larger early recovery rebound; these two variables were clustering inputs and are therefore not presented as confirmatory between-group tests in the abstract. Leave-one-out refitting reproduced the original assignment for all 26 rowers, and bootstrap resampling showed median label agreement of 96.2% (mean 91.9%) with the original solution. Because the glucose slope defined the clusters, the mixed-effects model showed the expected group-by-stage glucose interaction (-0.335 mmol/L/stage, P < 0.001), indicating a temporal rather than a baseline difference; the comparisons independent of the clustering — lactate slope, peak lactate, and lactate-derived proxies — did not differ between groups. The Rebound-dominant group had a higher body fat percentage than the Coupled group (14.8 +/- 2.7% vs. 11.8 +/- 3.0%, P = 0.021).

Conclusions

High-intensity glucose slope and early recovery glycaemic rebound described exploratory glucose-response patterns that were not captured by lactate kinetics alone. The practical significance of these patterns remains unknown because individual workload-normalised outcomes, stage-6 duration, heart-rate time courses, gas-exchange data, and direct rowing-performance outcomes were not available in the metabolite-analysis dataset. Mechanistic and applied interpretations should therefore remain hypothesis-generating.