Chen Yi-Lin, You Jia, Guo Yu, Zhang Yi, Yao Bing-Ran, Wang Ji-Jing, Chen Shi-Dong, Ge Yi-Jun, Yang Liu, Wu Xin-Rui, Wu Bang-Sheng, Zhang Ya-Ru, Dong Qiang, Feng Jian-Feng, Tian Mei, Cheng Wei, Yu Jin-Tai
Department of Neurology and National Center for Neurological Disorders, Huashan Hospital, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China.
Institute of Science and Technology for Brain-inspired Intelligence (ISTBI), Fudan University, Shanghai, China.
Metabolism. 2025 Mar;164:156126. doi: 10.1016/j.metabol.2024.156126. Epub 2024 Dec 29.
Multimorbidity, the coexistence of multiple chronic diseases, is a rapidly expanding global health challenge, carrying profound implications for patients, caregivers, healthcare systems, and society. Investigating the determinants and drivers underlying multiple chronic diseases is a priority for disease management and prevention.
This prospective cohort study analyzed data from the 53,026 participants in the UK Biobank from baseline (2006 to 2010) across 13.3 years of follow-up. Using Cox proportional hazards regression model, we characterized shared and unique associations across 38 incident outcomes (31 chronic diseases, 6 system mortality and all-cause mortality). Furthermore, ordinal regression models were used to assess the association between protein levels and multimorbidity (0-1, 2, 3-4, or ≥ 5 chronic diseases). Functional and tissue enrichment analysis were employed for multimorbidity-associated proteins. The upstream regulators of above proteins were identified.
We demonstrated 972 (33.3 %) proteins were shared across at least two incident chronic diseases after Bonferroni correction (P < 3.42 × 10, 93.3 % of those had consistent effects directions), while 345 (11.8 %) proteins were uniquely linked to a single chronic disease. Remarkably, GDF15, PLAUR, WFDC2 and AREG were positively associated with 20-24 incident chronic diseases (hazards ratios: 1.21-3.77) and showed strong associations with multimorbidity (odds ratios: 1.33-1.89). We further identified that protein levels are explained by common risk factors, especially renal function, liver function, inflammation, and obesity, providing potential intervention targets. Pathway analysis has underscored the pivotal role of the immune response, with the top three transcription factors associated with proteomics being NFKB1, JUN and RELA.
Our results enhance the understanding of the biological basis underlying multimorbidity, offering biomarkers for disease identification and novel targets for therapeutic intervention.
多重疾病，即多种慢性疾病并存，是一个在全球范围内迅速扩大的健康挑战，对患者、护理人员、医疗保健系统和社会都有着深远影响。研究多种慢性疾病背后的决定因素和驱动因素是疾病管理和预防的首要任务。
这项前瞻性队列研究分析了英国生物银行中53026名参与者从基线期（2006年至2010年）开始，长达13.3年随访的数据。使用Cox比例风险回归模型，我们对38种发病结局（31种慢性疾病、6种系统死亡率和全因死亡率）的共同和独特关联进行了特征描述。此外，使用有序回归模型评估蛋白质水平与多重疾病（0 - 1、2、3 - 4或≥5种慢性疾病）之间的关联。对与多重疾病相关的蛋白质进行了功能和组织富集分析。确定了上述蛋白质的上游调节因子。
在Bonferroni校正后，我们发现972种（33.3%）蛋白质在至少两种发病慢性疾病中存在共享（P < 3.42×10，其中93.3%具有一致的效应方向），而345种（11.8%）蛋白质与单一慢性疾病有独特关联。值得注意的是，生长分化因子15（GDF15）、尿激酶型纤溶酶原激活物受体（PLAUR）、乳清酸蛋白2（WFDC2）和双调蛋白（AREG）与20 - 24种发病慢性疾病呈正相关（风险比：1.21 - 3.77），并与多重疾病有很强的关联（优势比：1.33 - 1.89）。我们进一步确定蛋白质水平由常见风险因素解释，特别是肾功能、肝功能、炎症和肥胖，这提供了潜在的干预靶点。通路分析强调了免疫反应的关键作用，与蛋白质组学相关的前三个转录因子是核因子κB1（NFKB1）、原癌基因蛋白c-Jun（JUN）和原癌基因蛋白RelA（RELA）。
我们的结果加深了对多重疾病生物学基础的理解，为疾病识别提供了生物标志物，并为治疗干预提供了新的靶点。
