从可穿戴设备和常规血液生物标志物的胰岛素抵抗预测
Ahmed A Metwally1, A Ali Heydari2, Daniel McDuff2
1Google Research, Mountain View, CA, USA. aametwally@google.com.
Nature
|March 17, 2026
概括
我们开发了一种使用可穿戴设备和人工智能的新方法来检测胰岛素抵抗 (IR),这是2型糖尿病的前体. 这种可访问的框架使早期检测和个性化的生活方式干预成为可能.
科学领域:
- 生物医学工程 生物医学工程
- 代谢健康 代谢健康
- 医疗保健中的人工智能
背景情况:
- 胰岛素抵抗 (IR) 是2型糖尿病的关键前体,但目前的诊断方法昂贵且难以获得,限制了早期干预.
- 早期发现IR对于及时改变生活方式至关重要,以防止糖尿病的进展.
研究的目的:
- 利用可穿戴设备数据和人工智能开发可扩展和可访问的框架,用于早期检测胰岛素抵抗.
- 评估综合可穿戴传感器数据,人口统计信息和常规血液生物标志物用于IR预测的多式联络深度学习模型的性能.
主要方法:
- 在WEAR-ME研究 (n=1,165) 中,使用可穿戴设备和常规血液生物标志物的时间序列数据来训练针对HOMA-IR (IR的恒常模型评估) 的深度神经网络.
- 一个可穿戴基础模型 (WFM) 在广泛的传感器数据上进行了微调,以加强对时间序列可穿戴数据的分析.
- 一个包含WFM衍生的表示,人口统计数据和常规血液生物标志物的多式模式模型被评估了IR预测准确性.
主要成果:
- 多式模式模型在检测IR (AUROC=0.80,灵敏度=76%,特异性=84%) 中使用可穿戴数据,人口统计和常规生物标志物实现了强大的性能.
- 与没有可穿戴数据的模型相比,将WFM衍生的表示与人口和临床数据集成显著改善了IR预测准确性 (AUROC=0.88对比0.76).
- 使用WFM表示和人口统计数据的模型在独立验证队列中超过了仅人口统计数据的基线 (AUROC=0.75对0.66).
结论:
- 这项研究提供了一个可扩展和可访问的框架,用于早期检测代谢风险,特别是胰岛素抵抗.
- 可穿戴技术和人工智能的整合为个性化风险评估和及时干预提供了一个有希望的途径,以预防2型糖尿病.
- 开发的框架通过广泛的语言模型集成来促进个性化建议,促进积极的健康管理.
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