目的：基于实验室的指标通常用于年轻自行车手的表现评估。然而，支持使用这些指标的证据大多来自横断面研究，它们作为未来潜在表现预测指标的有效性仍不清楚。我们的目的是评估实验室变量在预测年轻自行车手从 U23（23 岁以下）向专业类别过渡时的作用。方法：对 65 名 U23 男性公路自行车运动员（19.6 [1.5] 岁）进行了研究。确定了耐力（最大分级测试和模拟 8 分钟计时试验 [TT]）、肌肉力量/爆发力（深蹲、弓步和髋部推力）和身体成分（用双能 X 射线吸收测定法评估）指标。随后对参与者进行跟踪和分类，关注他们在研究期间是否已过渡（“专业”）或未（“非专业”）到专业类别。结果：中位随访期为 3 年。职业自行车手 (n = 16) 在通气阈值、峰值功率输出、峰值摄氧量和 TT 表现方面表现出明显高于非职业骑手 (n = 49) 的值（所有 P < .05，效应大小 > 0.69）且 更低脂肪量和骨矿物质含量/密度水平（ P < .05，效应大小 > 0.63）。然而，肌肉力量/功率指标没有发现显着差异（ P >0.05，效应大小<49）。最准确的个体预测因子是 TT 表现（对于 5.6 W·kg -1的截止值，总体预测值 = 76%）。然而，一些在单变量分析中未达到统计显着性的变量对多变量模型有显着贡献（ R 2 = .79，总体预测值 = 94%）。结论：虽然不同的“经典”实验室耐力指标可以预测 U23 自行车运动员达到专业级别的潜力，但 8 分钟 TT 表现等实用指标显示出最高的预测准确性。
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What Does It Take to Become a Professional Cyclist? A Laboratory-Based Longitudinal Analysis in Competitive Young Riders
Purpose: Laboratory-based indicators are commonly used for performance assessment in young cyclists. However, evidence supporting the use of these indicators mostly comes from cross-sectional research, and their validity as predictors of potential future performance remains unclear. We aimed to assess the role of laboratory variables for predicting transition from U23 (under 23 y) to professional category in young cyclists. Methods: Sixty-five U23 male road cyclists (19.6 [1.5] y) were studied. Endurance (maximal graded test and simulated 8-min time trial [TT]), muscle strength/power (squat, lunge, and hip thrust), and body composition (assessed with dual-energy X-ray absorptiometry) indicators were determined. Participants were subsequently followed and categorized attending to whether they had transitioned (“Pro”) or not (“Non-Pro”) to the professional category during the study period. Results: The median follow-up period was 3 years. Pro cyclists (n = 16) showed significantly higher values than Non-Pro riders (n = 49) for ventilatory thresholds, peak power output, peak oxygen uptake, and TT performance (all P < .05, effect size > 0.69) and lower levels of fat mass and bone mineral content/density (P < .05, effect size > 0.63). However, no significant differences were found for muscle strength/power indicators (P > .05, effect size < 49). The most accurate individual predictor was TT performance (overall predictive value = 76% for a cutoff value of 5.6 W·kg−1). However, some variables that did not reach statistical significance in univariate analyses contributed significantly to a multivariate model (R2 = .79, overall predictive value = 94%). Conclusions: Although different “classic” laboratory-based endurance indicators can predict the potential of reaching the professional category in U23 cyclists, a practical indicator such as 8-minute TT performance showed the highest prediction accuracy.