Yang Shaopeng, Xin Zhuoyao, Cheng Weijing, Zhong Pingting, Liu Riqian, Zhu Ziyu, Zhu Lisa Zhuoting, Shang Xianwen, Chen Shida, Huang Wenyong, Zhang Lei, Wang Wei
State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Study Center for Ocular Diseases, Guangzhou, China.
Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
Nat Commun. 2025 Jan 15;16(1):697. doi: 10.1038/s41467-024-55035-x.
Photoreceptors are specialized neurons at the core of the retina's functionality, with optical accessibility and exceptional sensitivity to systemic metabolic stresses. Here we show the ability of risk-free, in vivo photoreceptor assessment as a window into systemic health and identify shared metabolic underpinnings of photoreceptor degeneration and multisystem health outcomes. A thinner photoreceptor layer thickness is significantly associated with an increased risk of future mortality and 13 multisystem diseases, while systematic analyses of circulating metabolomics enable the identification of 109 photoreceptor-related metabolites, which in turn elevate or reduce the risk of these health outcomes. To translate these insights into a practical tool, we developed an artificial intelligence (AI)-driven photoreceptor metabolic window framework and an accompanying interpreter that comprehensively captures the metabolic landscape of photoreceptor-systemic health linkages and simultaneously predicts 16 multisystem health outcomes beyond established approaches while retaining interpretability. Our work, replicated across cohorts of diverse ethnicities, reveals the potential of photoreceptors to inform systemic health and advance a multisystem perspective on human health by revealing eye-body connections and shared metabolic influences.
光感受器是视网膜功能核心的特殊神经元，具有光学可及性以及对全身代谢应激的极高敏感性。在此，我们展示了无风险的体内光感受器评估作为洞察全身健康的窗口的能力，并确定了光感受器退化和多系统健康结果的共同代谢基础。较薄的光感受器层厚度与未来死亡风险增加以及13种多系统疾病显著相关，而对循环代谢组学的系统分析能够识别出109种与光感受器相关的代谢物，这些代谢物进而会升高或降低这些健康结果的风险。为了将这些见解转化为实用工具，我们开发了一个由人工智能（AI）驱动的光感受器代谢窗口框架以及一个配套的解释器，该框架和解释器全面捕捉了光感受器 - 全身健康联系的代谢格局，同时能够预测16种既定方法之外的多系统健康结果，且保留了解释性。我们的研究在不同种族的队列中得到了重复验证，揭示了光感受器通过揭示眼 - 身联系和共同代谢影响来为全身健康提供信息并推进对人类健康的多系统观点的潜力。
