Pedersen S D, Astrup A V, Skovgaard I M
Department of Human Nutrition, Faculty of Life Sciences, University of Copenhagen, Copenhagen, DenmarkDepartment of Basic Sciences and Environment, Faculty of Life Sciences, University of Copenhagen, Copenhagen, Denmark.
Clin Obes. 2011 Apr;1(2-3):69-76. doi: 10.1111/j.1758-8111.2011.00016.x. Epub 2011 Jul 5.
• Body mass index (BMI) is not accurate in the classification of excess body fat, failing to identify as many as half of individuals with excess per cent fat mass. • Normal-weight obesity, which goes undiagnosed when BMI is the only measure of adiposity utilized, has been shown to be associated with an increased risk of cardiovascular comorbidities and mortality. • Dual-energy X-ray absorptiometry (DXA) is an accurate and relatively inexpensive method for indirect assessment of body composition.
• The formulae developed allow the clinician to utilize information from one baseline DXA scan to calculate a patient's per cent fat mass with a future change in weight, thus allowing the clinician to more accurately determine whether and when an individual patient should be classified as obese and thus be managed appropriately. • The formulae developed enable the clinician to calculate a patient-specific BMI treatment goal, below which the patient would no longer meet the per cent fat mass criteria for obesity.
Recognition is increasing for the errors of body mass index (BMI) in classification of excess body fat. Dual-energy X-ray absorptiometry (DXA) is accurate to assess body fat mass per cent (%FM), but is underutilized clinically. We examined the prevalence of obesity misclassification by BMI in comparison to body %FM by DXA scanning, and whether there is a time-stable individual relation between the %FM and the BMI in patients scanned several times. We aimed to develop a formula where, based on a single DXA scan, %FM could be predicted following a change in weight, and a patient-specific BMI threshold could be calculated (BMIT ), above which the patient would be obese by %FM criteria. Data were collected from individuals who had a DXA scan as part of a nutritional research study at the University of Copenhagen. BMI incorrectly classified 48/329 (14.6%) of men and 52/589 (8.8%) of women. The majority of men with BMI 25-27 kg m(-2) and women with BMI 24-26 kg m(-2) were misclassified. Using multiple scan data (189 men, 311 women) and calculating the patient-specific constant C = (1 - %FM/100)(3/2) × BMI from baseline BMI and %FM, misclassification rates were halved for both genders when a personal threshold, BMIT , was used ([BMIT = C/(0.75)(3/2) ] for men and [BMIT = C/(0.65)(3/2) ] for women). We conclude that simple formulae allow evaluation of fatness of individual patients more accurately than BMI, with the use of one baseline DXA scan, and enable the establishment of patient-specific obesity treatment targets in clinical practice.
体重指数（BMI）在对身体脂肪过多进行分类时并不准确，多达一半的体脂百分比过高的个体无法通过BMI识别出来。
正常体重肥胖症在仅使用BMI作为肥胖程度唯一衡量指标时会被漏诊，已证实其与心血管合并症及死亡率风险增加相关。
双能X线吸收法（DXA）是一种准确且相对廉价的间接评估身体成分的方法。
所开发的公式使临床医生能够利用一次基线DXA扫描的信息来计算患者体重发生未来变化时的体脂百分比，从而使临床医生能够更准确地确定个体患者是否以及何时应被归类为肥胖，进而进行适当管理。
所开发的公式使临床医生能够计算特定患者的BMI治疗目标，低于该目标患者将不再符合肥胖的体脂百分比标准。
人们越来越认识到体重指数（BMI）在对身体脂肪过多进行分类时存在误差。双能X线吸收法（DXA）在评估体脂百分比（%FM）方面很准确，但在临床中未得到充分利用。我们通过DXA扫描比较了BMI对肥胖分类错误的患病率与身体%FM，并研究了多次扫描患者中%FM与BMI之间是否存在随时间稳定的个体关系。我们旨在开发一个公式，基于单次DXA扫描，能够预测体重变化后的%FM，并计算特定患者的BMI阈值（BMIT），高于该阈值患者按%FM标准将被归类为肥胖。数据收集自哥本哈根大学一项营养研究中接受DXA扫描的个体。BMI将48/329名男性（14.6%）和52/589名女性（8.8%）错误分类。BMI在25 - 27 kg m⁻²的大多数男性和BMI在24 - 26 kg m⁻²的大多数女性被错误分类。使用多次扫描数据（189名男性，311名女性）并根据基线BMI和%FM计算特定患者常数C = (1 - %FM/100)(³/₂)×BMI，当使用个人阈值BMIT时，男女错误分类率均减半（男性为[BMIT = C/(0.75)(³/₂)]，女性为[BMIT = C/(0.65)(³/₂)]）。我们得出结论，简单公式通过使用一次基线DXA扫描比BMI能更准确地评估个体患者的肥胖程度，并能在临床实践中确立特定患者的肥胖治疗目标。
