Center for Psychosomatic Medicine, Schoen Klinik Roseneck, Am Roseneck 6, 83209 Prien am Chiemsee, Germany.
Forschungsprogramm für Psychotherapieevaluation im Komplexen Therapiesetting, Paracelsus Medizinische Universität, 5020 Salzburg, Austria.
Nutrients. 2023 Jul 24;15(14):3262. doi: 10.3390/nu15143262.
Anorexia nervosa is associated with a significant risk of morbidity and mortality. In clinical practice, health risk is assessed and estimated using routinely collected laboratory data. This study will develop a risk score using clinically relevant laboratory parameters. The related question is how to estimate the health risk associated with underweight using body weight, height and age.
We used routinely collected laboratory parameters from a total of 4087 patients. The risk score was calculated on the basis of electrolytes, blood count, transaminases and LDH. The nine parameters used were summed as zlog-transformed values. Where appropriate, the scales were inverted so that high values represented higher risk. For statistical prediction of the risk score, weight/height and age reference values from the WHO, the CDC (Center of Disease Control) and representative studies of German children and adults (KIGGS and NNS) were used.
The score calculated from nine laboratory parameters already shows a convincing relationship with BMI. Among the weight measures used for height and age, the z-score from the CDC reference population emerged as the best estimate, explaining 34% of the variance in health risk measured by the laboratory score. The percentile rank for each age-specific median weight from the KIGGS/NNS still explained more than 31% of the variance. In contrast, percentiles explained less variance than BMI without age correction.
The score we used from routine laboratory parameters appears to be an appropriate measure for assessing the health risk associated with underweight, as measured by the quality of the association with BMI. For estimating health risk based on weight, height and age alone, z-scores and percentages of age-specific median weight, as opposed to percentiles, are appropriate parameters. However, the study also shows that existing age-specific BMI reference values do not represent risk optimally. Improved statistical estimation methods would be desirable.
神经性厌食症与发病率和死亡率显著升高相关。在临床实践中,健康风险通过常规收集的实验室数据进行评估和估计。本研究将使用临床相关的实验室参数开发风险评分。相关问题是如何使用体重、身高和年龄估计与消瘦相关的健康风险。
我们使用了总共 4087 例患者的常规收集的实验室参数。风险评分是基于电解质、血液计数、转氨酶和 LDH 计算的。使用的九个参数作为 zlog 转换值进行求和。在适当的情况下,对量表进行了反转,以便高值代表更高的风险。为了对风险评分进行统计预测,我们使用了来自世卫组织、疾病控制与预防中心(CDC)以及德国儿童和成人代表性研究(KIGGS 和 NNS)的体重/身高和年龄参考值。
由九个实验室参数计算得出的评分已经与 BMI 显示出令人信服的关系。在用于身高和年龄的体重测量中,CDC 参考人群的 z 评分成为最佳估计值,解释了实验室评分所测健康风险的 34%。KIGGS/NNS 中每个年龄特异性中位数体重的百分位数排名仍解释了超过 31%的方差。相比之下,未经年龄校正的百分位数解释的方差少于 BMI。
我们使用的常规实验室参数评分似乎是评估消瘦相关健康风险的合适指标,因为其与 BMI 的相关性较好。对于仅根据体重、身高和年龄来估计健康风险,z 评分和年龄特异性中位数体重的百分比,而不是百分位数,是合适的参数。然而,该研究还表明,现有的年龄特异性 BMI 参考值并不能最佳地代表风险。需要改进的统计估计方法。