PDF(Pubmed)
Abstract：
OBJECTIVE: Plant-based diets are associated with a lower risk of chronic diseases. Large-scale proteomics can identify objective biomarkers of plant-based diets, and improve our understanding of the pathways that link plant-based diets to health outcomes. This study investigated the plasma proteome of four different plant-based diets [overall plant-based diet (PDI), provegetarian diet, healthful plant-based diet (hPDI), and unhealthful plant-based diet (uPDI)] in the Atherosclerosis Risk in Communities (ARIC) Study and replicated the findings in the Framingham Heart Study (FHS) Offspring cohort.
METHODS: ARIC Study participants at visit 3 (1993-1995) with completed food frequency questionnaire (FFQ) data and proteomics data were divided into internal discovery (n = 7690) and replication (n = 2543) data sets. Multivariable linear regression was used to examine associations between plant-based diet indices (PDIs) and 4955 individual proteins in the discovery sample. Then, proteins that were internally replicated in the ARIC Study were tested for external replication in FHS (n = 1358). Pathway overrepresentation analysis was conducted for diet-related proteins. C-statistics were used to predict if the proteins improved prediction of plant-based diet indices beyond participant characteristics.
RESULTS: In ARIC discovery, a total of 837 diet-protein associations (PDI = 233; provegetarian = 182; hPDI = 406; uPDI = 16) were observed at false discovery rate (FDR) < 0.05. Of these, 453 diet-protein associations (PDI = 132; provegetarian = 104; hPDI = 208; uPDI = 9) were internally replicated. In FHS, 167/453 diet-protein associations were available for external replication, of which 8 proteins (PDI = 1; provegetarian = 0; hPDI = 8; uPDI = 0) replicated. Complement and coagulation cascades, cell adhesion molecules, and retinol metabolism were over-represented. C-C motif chemokine 25 for PDI and 8 proteins for hPDI modestly but significantly improved the prediction of these indices individually and collectively (P value for difference in C-statistics<0.05 for all tests).
CONCLUSIONS: Using large-scale proteomics, we identified potential candidate biomarkers of plant-based diets, and pathways that may partially explain the associations between plant-based diets and chronic conditions.
METHODS: ARIC Study participants at visit 3 (1993-1995) with completed food frequency questionnaire (FFQ) data and proteomics data were divided into internal discovery (n = 7690) and replication (n = 2543) data sets. Multivariable linear regression was used to examine associations between plant-based diet indices (PDIs) and 4955 individual proteins in the discovery sample. Then, proteins that were internally replicated in the ARIC Study were tested for external replication in FHS (n = 1358). Pathway overrepresentation analysis was conducted for diet-related proteins. C-statistics were used to predict if the proteins improved prediction of plant-based diet indices beyond participant characteristics.
RESULTS: In ARIC discovery, a total of 837 diet-protein associations (PDI = 233; provegetarian = 182; hPDI = 406; uPDI = 16) were observed at false discovery rate (FDR) < 0.05. Of these, 453 diet-protein associations (PDI = 132; provegetarian = 104; hPDI = 208; uPDI = 9) were internally replicated. In FHS, 167/453 diet-protein associations were available for external replication, of which 8 proteins (PDI = 1; provegetarian = 0; hPDI = 8; uPDI = 0) replicated. Complement and coagulation cascades, cell adhesion molecules, and retinol metabolism were over-represented. C-C motif chemokine 25 for PDI and 8 proteins for hPDI modestly but significantly improved the prediction of these indices individually and collectively (P value for difference in C-statistics<0.05 for all tests).
CONCLUSIONS: Using large-scale proteomics, we identified potential candidate biomarkers of plant-based diets, and pathways that may partially explain the associations between plant-based diets and chronic conditions.
摘要：
目的：植物性饮食与较低的慢性病风险相关。大规模蛋白质组学可以识别植物饮食的客观生物标志物，并提高我们对将植物性饮食与健康结果联系起来的途径的理解。本研究调查了四种不同植物性饮食的血浆蛋白质组[整体植物性饮食（PDI），原素食饮食，健康的植物性饮食（hPDI），和不健康的植物性饮食（uPDI）]在社区动脉粥样硬化风险（ARIC）研究中，并在弗雷明汉心脏研究（FHS）后代队列中复制了该发现。
方法：ARIC研究参与者在第3次（1993-1995年）时使用完整的食物频率问卷（FFQ）数据和蛋白质组学数据分为内部发现（n=7690）和复制（n=2543）数据集。使用多变量线性回归来检查基于植物的饮食指数（PDIs）与发现样品中的4955种单个蛋白质之间的关联。然后，在ARIC研究中内部复制的蛋白质在FHS中进行了外部复制测试（n=1358）。对饮食相关蛋白进行通路过度表达分析。C统计量用于预测蛋白质是否改善了对植物性饮食指数的预测，超出了参与者的特征。
结果：在ARIC发现中，在错误发现率(FDR)<0.05的情况下,共观察到837例饮食-蛋白质关联(PDI=233;原素食者=182;hPDI=406;uPDI=16).其中,453饮食-蛋白质关联（PDI=132；原素食=104；hPDI=208；uPDI=9）在内部复制。在FHS,167/453饮食-蛋白质关联可用于外部复制，其中8种蛋白质(PDI=1;前素食=0;hPDI=8;uPDI=0)复制。补体和凝血级联,细胞粘附分子，和视黄醇代谢过度。用于PDI的C-C基序趋化因子25和用于hPDI的8种蛋白质适度但显著地改善了这些指数的单独和共同的预测(对于所有测试,C统计量差异的P值<0.05)。
结论：使用大规模蛋白质组学，我们确定了植物性饮食的潜在候选生物标志物，以及可能部分解释植物性饮食与慢性病之间关联的途径。
方法：ARIC研究参与者在第3次（1993-1995年）时使用完整的食物频率问卷（FFQ）数据和蛋白质组学数据分为内部发现（n=7690）和复制（n=2543）数据集。使用多变量线性回归来检查基于植物的饮食指数（PDIs）与发现样品中的4955种单个蛋白质之间的关联。然后，在ARIC研究中内部复制的蛋白质在FHS中进行了外部复制测试（n=1358）。对饮食相关蛋白进行通路过度表达分析。C统计量用于预测蛋白质是否改善了对植物性饮食指数的预测，超出了参与者的特征。
结果：在ARIC发现中，在错误发现率(FDR)<0.05的情况下,共观察到837例饮食-蛋白质关联(PDI=233;原素食者=182;hPDI=406;uPDI=16).其中,453饮食-蛋白质关联（PDI=132；原素食=104；hPDI=208；uPDI=9）在内部复制。在FHS,167/453饮食-蛋白质关联可用于外部复制，其中8种蛋白质(PDI=1;前素食=0;hPDI=8;uPDI=0)复制。补体和凝血级联,细胞粘附分子，和视黄醇代谢过度。用于PDI的C-C基序趋化因子25和用于hPDI的8种蛋白质适度但显著地改善了这些指数的单独和共同的预测(对于所有测试,C统计量差异的P值<0.05)。
结论：使用大规模蛋白质组学，我们确定了植物性饮食的潜在候选生物标志物，以及可能部分解释植物性饮食与慢性病之间关联的途径。
