静息代谢率测量预测运动相关月经紊乱的敏感性和特异性

Nicole C. A. Strock, K. Koltun, Emily A Southmayd, N. Williams, Mary Jane De Souza
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引用次数: 0

摘要

运动女性能量不足会导致月经紊乱(MD)。没有准确估计能量缺乏的黄金标准。测量值与预测值的静息代谢率(RMR)之比已被用作将女性分类为能量缺乏的代理。目的:评估测量到的预测RMR比值是否可以预测闭经或其他MD。方法:我们对223名排卵期(OV)、闭经(AMEN)或亚临床MD (sMD)(包括月经少、无排卵和黄体期缺陷)的运动女性(≥2小时/周,年龄18-35岁,BMI 16-30 kg/m)进行了横断面比较。月经状况是通过尿液中的生殖激素和月经日历来确定的。用DXA测量体成分,用SensorMedics Vmax测量RMR。采用Harris-Benedict、Cunningham和DXA方程计算预测HBRMR、CRMR和DXARMR,并计算实测RMR与预测RMR的比值。方差分析和Kruskal-Wallis检验确定组间差异,逻辑回归确定AMEN或任何MD的预测因子。敏感性、特异性和阳性预测值(PPV)的计算评估预测的准确性。结果:各组在瘦质量和无脂质量上没有差异。与OV(1227±20 kcal/d)和sMD(1233.68±17 kcal/d)相比,AMEN的体质量(p<0.01)低于sMD, BMI、体脂百分比、脂肪质量(p<0.001)和RMR(1172±21 kcal/d)低于OV(1227±20 kcal/d)和sMD (p<0.05)。AMEN组HBRMR(1402±8 kcal/d)低于sMD组(1434±9 kcal/d) (p<0.05)。AMEN的CRMR比值(0.84±0.01)低于OV(0.88±0.01)(p<0.05),而DXARMR比值(0.90±0.01)低于OV(0.96±0.01)和sMD(0.95±0.01)(p<0.01)。各比值预测AMEN (HBRMR: χ =4.822, p<0.05;CRMR: χ =8.708, p<0.01;DXARMR: χ =14.068, p<0.001),但仅DXARMR比值预测MD (χ =6.795, p<0.01)。DXARMR比值正确识别了大多数患有AMEN的女性(ppv=0.5;敏感性= 0.49,特异性= 0.74)和任何MD (AMEN+sMD: ppv=0.75;敏感性= 0.39,特异性= 0.75)。结论:各比值均可用于预测AMEN,但无论严重程度如何,只有DXARMR能显著预测MD。同样,DXARMR比值正确识别了大多数受试者。DXARMR比值可用于正确鉴别因能量缺乏而继发的AMEN或MD。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Sensitivity And Specificity Of Resting Metabolic Rate Measures To Predict Exercise Associated Menstrual Disturbances
Energy deficiency in exercising women can lead to menstrual disturbances (MD). There is no gold standard to accurately estimate energy deficiency. Ratios of measured to predicted resting metabolic rate (RMR) have been used as a proxy to categorize women as energy deficient. PURPOSE: To evaluate whether measured to predicted RMR ratios are predictive of amenorrhea or other MD. METHODS: We performed a cross-sectional comparison of 223 exercising women (≥2 hrs/wk, age 18-35 years, BMI 16-30 kg/m) who were ovulatory (OV), amenorrheic (AMEN), or subclinical MD (sMD) (including oligomenorrhea, anovulation, and luteal phase defects). Menstrual status was determined using urinary measures of reproductive hormones and menstrual calendars. Body composition was measured with DXA and RMR with the SensorMedics Vmax. Harris-Benedict, Cunningham, and DXA equations were used to calculate predicted HBRMR, CRMR, and DXARMR and to calculate the measured to predicted RMR ratio. ANOVA and Kruskal-Wallis tests determined group differences and logistic regression determined predictors of AMEN or any MD. Calculations of sensitivity, specificity and positive predictive value (PPV) assessed accuracy of predictions. RESULTS: Groups did not differ in lean or fat free mass. AMEN had lower body mass (p<0.01) than sMD, and lower BMI, percent body fat, fat mass (p<0.001) and measured RMR (1172 ± 21 kcal/d) (p<0.05) than OV (1227 ± 20 kcal/d) and sMD (1233.68 ± 17 kcal/d). HBRMR was lower in AMEN (1402± 8 kcal/d) vs sMD (1434 ± 9 kcal/d) (p<0.05). CRMR ratio (0.84 ± 0.01) was lower in AMEN vs OV (0.88 ± 0.01) (p<0.05), but DXARMR ratio (0.90 ± 0.01) was lower in AMEN vs both OV (0.96 ± 0.01) and sMD (0.95 ± 0.01) (p<0.01). Each ratio predicted AMEN (HBRMR: χ =4.822, p<0.05; CRMR: χ =8.708, p<0.01; DXARMR: χ =14.068, p<0.001), but only DXARMR ratio predicted any MD (χ =6.795, p<0.01). DXARMR ratio correctly identified the most women with AMEN (ppv=0.5; sensitivity= 0.49, specificity= 0.74) and with any MD (AMEN+sMD: ppv=0.75; sensitivity= 0.39, specificity= 0.75). CONCLUSIONS: Each ratio may be used to predict AMEN, but only DXARMR significantly predicts MD, regardless of severity. Similarly, DXARMR ratio correctly identified the most subjects. DXARMR ratio can be utilized to correctly identify women with AMEN or MD secondary to energy deficiency.
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