School of Nursing Sciences, Faculty of Medicine & Health Sciences, Edith Cavell Building, University of East Anglia, Norwich, Norfolk NR4 7TJ, UK.
Med Hypotheses. 2012 Nov;79(5):656-62. doi: 10.1016/j.mehy.2012.08.003. Epub 2012 Aug 30.
The problem of obesity over the last 10 years has consistently been referred to as a 'global epidemic'. The Body Mass Index (BMI) is the currently accepted measure for classifying weight-related risk, but is a crude measure that has not changed in 150 years. It is recognised as having significant limitations, largely due to its lack of distinction between fat and muscle tissue. As the health risks of obesity are linked to the fat content of the body, a more accurate method of classifying would be Percentage Body Fatness (PBF). Although skinfold thickness analysis is recognised as a valid and accurate estimate of PBF in field studies, this method is not routinely used in clinical practice. Using data collected from young adults in the United Kingdom, we compared classifications (underweight, normal weight, overweight and obese) using BMI, with classifications using estimated PBF (from skinfold thickness analysis). We identified disparity between these two methods in approximately 1/3 of participants. BMI correctly classified 66.5% of females and 62.7% of males, with different gender profiles of incorrect classification. Regression analysis was conducted using estimated PBF (by skinfold thickness analysis) as the dependent variable, with explanatory variables of age, height, weight, systolic blood pressure, frequency of vigorous exercise and grip strength. The resulting gender-specific formulae derived from this regression analysis provides a regression R(2) of around 65%, and improved correct classifications to 74% for females and 76% for males. This represents an average improvement of roughly ten percentage points over BMI (male: 7.2% points; female: 13.4% points). We hypothesise that the presented formulae provide gender-specific calculations of PBF, which result in a more accurate indicator of weight-related health risk, compared with BMI in this population. This provides a new approach to an increasingly important clinical issue. These formulae use data that can be easily, quickly and cost-effectively measured in a practice setting. If shown to be repeatable with larger and more diverse populations, the PBF formulae could provide an alternative to the BMI as the major indicator of body-composition related health risk. This would ensure resources are targeted more appropriately and efficiently.
在过去的 10 年中，肥胖问题一直被称为“全球流行病”。身体质量指数（BMI）是目前用于分类体重相关风险的公认指标，但它是一种 150 年来没有变化的粗略指标。它被认为存在重大局限性，主要是因为它无法区分脂肪和肌肉组织。由于肥胖的健康风险与身体脂肪含量有关，因此更准确的分类方法将是体脂肪百分比（PBF）。虽然皮褶厚度分析被认为是现场研究中 PBF 的有效和准确估计，但这种方法在临床实践中并不常用。使用从英国年轻人那里收集的数据，我们比较了使用 BMI 的分类（体重不足、正常体重、超重和肥胖）与使用估计的 PBF（来自皮褶厚度分析）的分类。我们发现这两种方法在大约 1/3 的参与者中存在差异。BMI 正确分类了 66.5%的女性和 62.7%的男性，而不同性别的分类错误模式不同。使用皮褶厚度分析估计的 PBF 作为因变量，使用年龄、身高、体重、收缩压、剧烈运动频率和握力作为解释变量，进行回归分析。从回归分析中得出的性别特异性公式提供了约 65%的回归 R(2)，并将女性的正确分类提高到 74%，男性提高到 76%。这代表了与 BMI 相比，平均提高了大约十个百分点（男性：7.2%；女性：13.4%）。我们假设，与该人群中的 BMI 相比，这些公式提供了 PBF 的性别特异性计算，这导致了一种更准确的体重相关健康风险指标。这为一个日益重要的临床问题提供了一种新方法。这些公式使用的数据可以在实践环境中轻松、快速且具有成本效益地测量。如果在更大和更多样化的人群中显示可重复，那么 PBF 公式可以替代 BMI 作为与身体成分相关健康风险的主要指标。这将确保资源更有针对性和更有效地利用。
