Nutrition and Bioprogramming Coordination, Instituto Nacional de Perinatología Isidro Espinosa de los Reyes, CP 11000, Ciudad de México, México.
Bioinformatics and Statistical Analysis Department, Instituto Nacional de Perinatología Isidro Espinosa de los Reyes, CP 11000, Ciudad de México, México.
Eur J Clin Nutr. 2023 Jul;77(7):748-756. doi: 10.1038/s41430-023-01285-9. Epub 2023 Apr 13.
BACKGROUND/OBJECTIVES: Fat-mass (FM) assessment since birth using valid methodologies is crucial since excessive adiposity represents a risk factor for adverse metabolic outcomes.
To develop infant FM prediction equations using anthropometry and validate them against air-displacement plethysmography (ADP).
SUBJECTS/METHODS: Clinical, anthropometric (weight, length, body-mass index -BMI-, circumferences, and skinfolds), and FM (ADP) data were collected from healthy-term infants at 1 (n = 133), 3 (n = 105), and 6 (n = 101) months enrolled in the OBESO perinatal cohort (Mexico City). FM prediction models were developed in 3 steps: 1) Variable Selection (LASSO regression), 2) Model behavior evaluation (12-fold cross-validation, using Theil-Sen regressions), and 3) Final model evaluation (Bland-Altman plots, Deming regression).
Relevant variables in the FM prediction models included BMI, circumferences (waist, thigh, and calf), and skinfolds (waist, triceps, subscapular, thigh, and calf). The R of each model was 1 M: 0.54, 3 M: 0.69, 6 M: 0.63. Predicted FM showed high correlation values (r ≥ 0.73, p < 0.001) with FM measured with ADP. There were no significant differences between predicted vs measured FM (1 M: 0.62 vs 0.6; 3 M: 1.2 vs 1.35; 6 M: 1.65 vs 1.76 kg; p > 0.05). Bias were: 1 M -0.021 (95%CI: -0.050 to 0.008), 3 M: 0.014 (95%CI: 0.090-0.195), 6 M: 0.108 (95%CI: 0.046-0.169).
Anthropometry-based prediction equations are inexpensive and represent a more accessible method to estimate body composition. The proposed equations are useful for evaluating FM in Mexican infants.
背景/目的：使用有效的方法从出生开始评估脂肪量（FM）至关重要，因为过多的肥胖是不良代谢结果的危险因素。
使用人体测量法开发婴儿 FM 预测方程，并通过空气置换体描记法（ADP）对其进行验证。
受试者/方法：健康足月婴儿在 1（n=133）、3（n=105）和 6（n=101）个月龄时参加了 OBESO 围产期队列（墨西哥城），收集了临床、人体测量学（体重、身长、体重指数-BMI-、周长和皮褶厚度）和 FM（ADP）数据。FM 预测模型分 3 步开发：1）变量选择（LASSO 回归），2）模型行为评估（使用 Theil-Sen 回归进行 12 折交叉验证），3）最终模型评估（Bland-Altman 图，Deming 回归）。
FM 预测模型中的相关变量包括 BMI、周长（腰围、大腿和小腿）和皮褶厚度（腰围、三头肌、肩胛下、大腿和小腿）。每个模型的 R 值分别为 1 个月时为 0.54，3 个月时为 0.69，6 个月时为 0.63。预测的 FM 与 ADP 测量的 FM 高度相关（r≥0.73，p<0.001）。预测的 FM 与实测 FM 之间无显著差异（1 个月时：0.62 与 0.6；3 个月时：1.2 与 1.35；6 个月时：1.65 与 1.76kg；p>0.05）。偏倚为：1 个月时-0.021（95%CI：-0.050 至 0.008），3 个月时：0.014（95%CI：0.090-0.195），6 个月时：0.108（95%CI：0.046-0.169）。
基于人体测量法的预测方程价格低廉，是一种更易于获得的评估身体成分的方法。所提出的方程可用于评估墨西哥婴儿的 FM。
