External validation of a prediction model for estimating fat mass in Arab children and adolescents

IF 5.7 2区医学 Q1 ENDOCRINOLOGY & METABOLISM Diabetes, Obesity & Metabolism Pub Date : 2025-02-25 DOI:10.1111/dom.16281

Tareq Al-Ati PhD, Khalid El Kari PhD, Lara Nasreddine PhD, Hessa Al-Kandari MRCPCH, Richard D. Riley PhD, Peter H. Whincup FRCP, Christopher G. Owen PhD, Mohammed T. Hudda PhD

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Abstract

Background/Aims

Current childhood fat mass (FM) assessment techniques are not suitable for clinical and population-level adiposity assessment. A prediction model, which accurately estimates childhood FM using predictor variables of weight, height, age, sex and ethnicity, requires validation in Arab populations. We evaluate the model's performance in Kuwaiti, Lebanese and Moroccan children/adolescents.

Methods

Data from three cross-sectional studies on 471 individuals, aged 6–15 years, were obtained with complete information on predictors and the outcome of log transformed fat-free mass assessed by reference standard deuterium dilution (lnFFM). Country-specific predictive performance statistics of R², calibration slope and calibration-in-the-large (measures the calibration/agreement between observed and predicted lnFFM with ideal values of 1 and 0, respectively) and root mean square error (RMSE) were quantified and pooled across countries via random-effects meta-analysis. FM estimates from bioimpedance were also available for Lebanese children and were compared to the reference standard.

Results

The model showed strong predictive ability in all populations. Pooled R² calibration slope and calibration-in-the-large values on the original lnFFM scale were 87.73% (95% CI: 77.20, 98.26%), 0.95 (95% CI: 0.83, 1.08) and −0.03 (95% CI: −0.16, 0.11), respectively. Model intercepts were recalibrated in each country to improve accuracy; updated country-specific equations are provided. After recalibration, RMSEs on the FM scale were 1.3, 1.6 and 2.8 kg in Kuwait, Lebanon and Morocco, respectively. The RMSE from the model was lower than bioimpedance (2.4 kg) amongst Lebanese children.

Interpretation

The model explained a large proportion of the variance in FM, produced well-calibrated predictions and relatively low RMSEs in Arab settings. It predicted FM more accurately than bioimpedance, indicating its potential for implementation in clinical- and population-level settings, particularly in low- and middle-income countries.

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阿拉伯儿童和青少年脂肪量预测模型的外部验证。

背景/目的：目前的儿童脂肪量（FM）评估技术不适合临床和人群水平的肥胖评估。使用体重、身高、年龄、性别和种族等预测变量准确估计儿童FM的预测模型需要在阿拉伯人口中进行验证。我们评估了该模型在科威特、黎巴嫩和摩洛哥儿童/青少年中的表现。方法：对471名年龄在6-15岁的个体进行了三项横断面研究，获得了预测因素的完整信息，并通过参考标准氘稀释（lnFFM）评估了对数转化无脂质量的结果。通过随机效应荟萃分析，对R2、校准斜率和校准-in- large（测量观测到的lnFFM与预测lnFFM之间的校准/一致性，理想值分别为1和0）和均方根误差（RMSE）的国别预测性能统计进行了量化，并在各国进行了汇总。黎巴嫩儿童也可获得生物阻抗的FM估计值，并与参考标准进行比较。结果：该模型对所有人群均具有较强的预测能力。原始lnFFM量表的合并R2校准斜率和校准大值分别为87.73% （95% CI: 77.20, 98.26%）、0.95 （95% CI: 0.83, 1.08）和-0.03 （95% CI: -0.16, 0.11）。在每个国家重新校准了模型拦截，以提高准确性；提供了最新的国别方程式。重新校正后，科威特、黎巴嫩和摩洛哥FM尺度上的均方根误差分别为1.3、1.6和2.8 kg。在黎巴嫩儿童中，该模型的RMSE低于生物阻抗（2.4 kg）。解释：该模型解释了FM的很大一部分差异，产生了校准良好的预测，并且在阿拉伯环境中rmse相对较低。它比生物阻抗更准确地预测FM，表明其在临床和人群水平环境中，特别是在低收入和中等收入国家实施的潜力。

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来源期刊

Diabetes, Obesity & Metabolism 医学-内分泌学与代谢

CiteScore

10.90

自引率

6.90%

发文量

319

审稿时长

3-8 weeks

期刊介绍： Diabetes, Obesity and Metabolism is primarily a journal of clinical and experimental pharmacology and therapeutics covering the interrelated areas of diabetes, obesity and metabolism. The journal prioritises high-quality original research that reports on the effects of new or existing therapies, including dietary, exercise and lifestyle (non-pharmacological) interventions, in any aspect of metabolic and endocrine disease, either in humans or animal and cellular systems. ‘Metabolism’ may relate to lipids, bone and drug metabolism, or broader aspects of endocrine dysfunction. Preclinical pharmacology, pharmacokinetic studies, meta-analyses and those addressing drug safety and tolerability are also highly suitable for publication in this journal. Original research may be published as a main paper or as a research letter.