Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia.
Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Parkville, Victoria, Australia.
J Nutr. 2021 Oct 1;151(10):2908-2916. doi: 10.1093/jn/nxab231.
Nutritional epidemiology research using self-reported dietary intake is prone to measurement error. Objective methods are being explored to overcome this limitation.
We aimed to examine 1) the association between plasma markers related to inflammation and derive marker scores for dietary patterns [Mediterranean dietary score (MDS), energy-adjusted Dietary Inflammatory Index (E-DIITM), Alternative Healthy Eating Index 2010 (AHEI)] and 2) the associations of these marker scores with mortality.
Weighted marker scores were derived from the cross-sectional association between 30 plasma markers and each dietary score (assessed using food-frequency questionnaires) using linear regression for 770 participants in the Melbourne Collaborative Cohort Study (aged 50-82 y). Prospective associations between marker scores and mortality (n = 249 deaths) were assessed using Cox regression (median follow-up: 14.4 y).
The MDS, E-DII, and AHEI were associated (P < 0.05) with 9, 14, and 11 plasma markers, respectively. Healthier diets (higher MDS and AHEI, and lower anti-inflammatory, E-DII) were associated with lower concentrations of kynurenines, neopterin, IFN-γ, cytokines, and C-reactive protein. Five of 6 markers common to the 3 dietary scores were components of the kynurenine pathway. The 3 dietary-based marker scores were highly correlated (Spearman ρ: -0.74, -0.82, and 0.93). Inverse associations (for 1-SD increment) were observed with all-cause mortality for the MDS marker score (HR: 0.84; 95% CI: 0.72-0.98) and the AHEI marker score (HR: 0.76; 95% CI: 0.66-0.89), whereas a positive association was observed with the E-DII marker score (HR: 1.18; 95% CI: 1.01-1.39). The same magnitude of effect was not observed for the respective dietary patterns.
Markers involved in inflammation-related processes are associated with dietary quality, including a substantial overlap between markers associated with the MDS, the E-DII, and the AHEI, especially kynurenines. Unfavorable marker scores, reflecting poorer-quality diets, were associated with increased mortality.
使用自我报告的饮食摄入进行营养流行病学研究容易出现测量误差。目前正在探索客观方法来克服这一局限性。
我们旨在检验 1)与炎症相关的血浆标志物之间的关联,并为饮食模式(地中海饮食评分(MDS)、能量调整的饮食炎症指数(E-DIITM)、2010 年替代健康饮食指数(AHEI))得出标志物评分,以及 2)这些标志物评分与死亡率之间的关联。
使用线性回归从墨尔本合作队列研究中 770 名参与者(年龄 50-82 岁)的 30 种血浆标志物与每种饮食评分(使用食物频率问卷评估)的横断面关联中得出加权标志物评分。使用 Cox 回归评估标志物评分与死亡率(n=249 例死亡)之间的前瞻性关联(中位随访时间:14.4 年)。
MDS、E-DII 和 AHEI 分别与 9、14 和 11 种血浆标志物相关(P<0.05)。更健康的饮食(更高的 MDS 和 AHEI,以及更低的抗炎、E-DII)与犬尿氨酸、新蝶呤、IFN-γ、细胞因子和 C 反应蛋白的浓度降低相关。3 种饮食评分共有的 6 种标志物中的 5 种是犬尿氨酸途径的组成部分。3 种基于饮食的标志物评分高度相关(Spearman ρ:-0.74、-0.82 和 0.93)。对于 MDS 标志物评分(HR:0.84;95%CI:0.72-0.98)和 AHEI 标志物评分(HR:0.76;95%CI:0.66-0.89),所有原因死亡率的逆相关(1-SD 增量)观察到,而 E-DII 标志物评分呈正相关(HR:1.18;95%CI:1.01-1.39)。对于各自的饮食模式,并没有观察到相同程度的效果。
参与炎症相关过程的标志物与饮食质量相关,包括与 MDS、E-DII 和 AHEI 相关的标志物之间存在大量重叠,尤其是犬尿氨酸。反映较差饮食质量的不利标志物评分与死亡率增加相关。