定义低牛奶供应:一个数据驱动的诊断框架和哺乳妇女的风险因素分析
Xuehua Jin1,2,3, Ching Tat Lai1,2,3, Sharon L Perrella1,2,3
1School of Molecular Sciences, The University of Western Australia, Crawley, WA 6009, Australia.
Nutrients
|November 27, 2025
概括
这项研究开发了基于数据的低牛奶供应 (LMS) 定义,确定了牛奶生产和婴儿成长的新值. 关键的LMS风险因素包括怀孕期间乳房的微小生长和母亲的晚年.
科学领域:
- 哺乳期和婴儿营养
- 生殖健康 生殖健康
- 在医疗保健中的数据科学.
背景情况:
- 目前对低牛奶供应 (LMS) 的定义依赖于主观认知或任意值,导致潜在的错误分类.
- 对LMS的客观评估对于准确的诊断和及时的干预至关重要.
研究的目的:
- 用数据驱动的方法重新评估LMS的定义.
- 识别和调查与LMS亚组相关的孕产妇风险因素.
主要方法:
- 使用了隐形形状分析和接收器运行特征 (ROC) 曲线分析.
- 数据包括460名产后母亲的24小时牛奶产量 (MP),婴儿摄入量,婴儿生长 (体重对年龄z-score) 和母亲/婴儿特征.
- 用多项逻辑回归来评估风险因素.
主要成果:
- 确定了四个不同的牛奶供应类别,其中38.3%被归类为LMS.
- 为24小时MP (708毫升),婴儿母乳摄入量 (694毫升),婴儿体重增加 (26克/天),配方奶粉摄入量 (122毫升/天) 和配方奶粉与生长比率 (4毫升/克) 建立了新的,高度准确的诊断值.
- 显著的LMS风险因素包括怀孕期间乳腺的最小生长,晚期母亲年龄和妊娠期糖尿病,对于组合因素观察到的协同效应.
结论:
- 基于数据的LMS分类可以提高诊断精度.
- 鉴定特定的风险因素,可以进行有针对性的干预,以支持患有LMS的哺乳期母亲.
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