Bone turnover markers in response to short-term high energy availability high carbohydrate or ketogenic, or low energy availability diets in elite athletes

This study has been submitted for publication. Ethics approval was obtained from the ethics committees of the Australian Institute of Sport (ref: 20181203) and the Australian Catholic University (ref: 2020-238HC). This study took place over 2 training camps, each comprised of two, 6 day phases. Prior to study commencement, baseline characteristics were measured (bone mineral density, body composition, VO2max). During phase 1 (Baseline), all athletes adhered to a high carbohydrate /high EA (&gt; 40kcal.kg^-1 FFM.d^-1) control (CON) diet. For phase 2 (Adaptation), athletes were assigned to one of three diets: high carbohydrate/high energy availability (EA) (CON, n=10), low carbohydrate/high fat/high EA (LCHF, n=8), or low EA (&lt; 15kcal.kg^-1 FFM.d^-1; LEA, n=10). Due to the inability to blind participants to the diet, allocations to dietary interventions were based on athlete preference whilst matching for individual characteristics (age, 20 km personal best time, training status). During both phases, a structured training plan was followed to ensure similar training volume and intensity among groups. On the final day of each phase, a 25 km racewalking protocol was performed, where venous blood samples were taken to measure bone turnover markers. Blood samples were taken at rest (fasted), pre-exercise, and immediately, 1 h, and 3 h post-exercise. Samples were collected into BD Vacutainer SST II tubes (East Rutherford, NJ, USA), which were left to clot at room temperature for 30 min prior to being centrifuged at 1500g at 4°C for 10 min. Serum was aliquoted into 1 ml Eppendorf tubes and frozen at -80°C for batch analysis. Concentrations of carboxy-terminal telopeptide (CTX), procollagen-1 N-terminal peptide (P1NP), carboxylated osteocalcin (gla-OC), and undercarboxylated osteocalcin (glu-OC) were measured from each sample. <br><br>Body composition and bone mineral density were measured with the Lunar iDXA machine (v16, GE Healthcare). CTX and P1NP concentrations were assessed by electrochemiluminescence immunoassay (Cobas e411, Roche Diagnostics, Basel, Switzerland). Carboxylated and undercarboxylated osteocalcin measurements were performed using enzyme immunoassay (EIA) kits (Takara Bio inc., Shiga, Japan) analyzed on a FLUROstar OPTIMA microplate reader (BMG Labtech, Ortenberg, Germany). Diets were constructed and analysed with Foodworks Professional Edition 9 (Xyris Software). Area under the curve concentrations (pre- to 3h post-exercise) were calculated with PKSolver add-in in Microsoft Excel (v16.48). Statistical analysis was conducted with R Studio (v1.4.1106) using linear mixed models through the 'lme4' package.<br>

本研究已提交待发表。本研究已获得澳大利亚体育学院伦理委员会（批准号：20181203）与澳大利亚天主教大学伦理委员会（批准号：2020-238HC）的伦理审批。<br><br>本研究在2次训练营中开展，每次训练营包含两个为期6天的阶段。研究启动前，已对受试者的基线特征进行测定，包括骨密度、身体成分以及最大摄氧量（VO2max）。阶段1（基线期）中，所有运动员均遵循高碳水化合物/高能量供应（energy availability, EA）饮食方案，即对照组（CON），能量摄入＞40kcal·kg⁻¹ 去脂体重（free fat mass, FFM）·d⁻¹。阶段2（适应期）中，运动员被分配至3组饮食干预方案：①高碳水/高EA对照组（CON，n=10）；②低碳水/高脂肪/高EA组（LCHF，n=8）；③低EA组（LEA，能量摄入＜15kcal·kg⁻¹ 去脂体重（FFM）·d⁻¹，n=10）。由于无法对受试者实施饮食盲法，分组依据运动员的个人偏好，并匹配个体特征（年龄、20公里个人最佳成绩、训练状态）进行分配。<br><br>两个阶段均采用标准化训练计划，确保各组间的训练量与训练强度保持一致。每个阶段的最后一天，受试者完成25公里竞走测试方案，期间采集静脉血样以检测骨转换标志物。采血时点包括：静息（空腹）状态、运动前、运动后即刻、运动后1小时及3小时。血样采集于BD Vacutainer SST II采血管（美国新泽西州东卢瑟福），室温静置30分钟待凝血后，于4℃下以1500g离心10分钟。血清分装至1ml Eppendorf管中，置于-80℃冰箱冻存以待批量检测。<br><br>所有样本均检测了羧基末端肽（carboxy-terminal telopeptide, CTX）、I型前胶原N端肽（procollagen-1 N-terminal peptide, P1NP）、羧化骨钙素（carboxylated osteocalcin, gla-OC）以及未羧化骨钙素（undercarboxylated osteocalcin, glu-OC）的浓度。身体成分与骨密度采用Lunar iDXA仪器（版本16，通用电气医疗集团（GE Healthcare））进行测定。CTX与P1NP浓度采用电化学发光免疫分析法（electrochemiluminescence immunoassay）检测（Cobas e411，罗氏诊断（Roche Diagnostics），瑞士巴塞尔）。羧化与未羧化骨钙素采用酶联免疫吸附试验（enzyme immunoassay, EIA）试剂盒（Takara Bio株式会社，日本滋贺县）检测，检测仪器为FLUROstar OPTIMA微孔板阅读器（BMG Labtech，德国奥滕伯格）。饮食方案的制定与营养成分分析采用Foodworks Professional Edition 9软件（Xyris Software）。运动前至运动后3小时的浓度曲线下面积采用Microsoft Excel（版本16.48）的PKSolver插件计算。统计分析采用R Studio（版本1.4.1106），通过'lme4'包构建线性混合模型进行分析。