Association between empirically driven dietary patterns and cardiometabolic disease risk factors: a cross-sectional analysis in disease-free adults.

IF 3.9 2区医学 Q2 NUTRITION & DIETETICS

Nutrition & Metabolism Pub Date : 2025-07-09 DOI:10.1186/s12986-025-00965-6

Arife Yilmaz, Michelle Weech, Vasiliki Bountziouka, Kim G Jackson, Julie A Lovegrove

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引用次数: 0

Abstract

Background: Although links between dietary patterns (DPs) and cardiometabolic disease (CMD) risk markers have been identified in UK populations, these studies often rely on less quantitative measures of dietary assessment and include only a limited number of risk markers.

Objective: This cross-sectional analysis aimed to identify DP in self-reported disease-free adults using weighed diet diaries and explore relationships with a broad range of CMD risk factors and diet quality.

Methods: Data were collated from five studies conducted in adults living in the UK (2009-2019) and DPs were a posteriori extracted from habitual dietary intake data using principal component analysis. Associations between quartiles (Q) of adherence to the DPs with CMD risk markers, nutrient intakes and the Alternative Healthy Eating Index (AHEI-2010) were evaluated using ANCOVA.

Results: In our cohort [n = 646, 58.4% female, mean (SD) age 44 (14) years, and body mass index (BMI) 25.2 (4.0) kg/m²] two DPs explained 12% of the variance in habitual food intake. Highest adherence to DP1 (Q4), characterised by diets rich in fermented dairy, fruits, vegetables, wholegrains, nuts/seeds, unsaturated fats/oils and milk and lower in red meat dishes and processed meat, was associated with a lower BMI, waist circumference, diastolic blood pressure, fasting triacylglycerol, non-high-density lipoprotein-cholesterol, remnant-like particle-cholesterol, and total cholesterol:HDL-C ratio and a higher HDL-C and AHEI-2010 score versus Q1 (all P ≤ 0.006). In contrast, Q4 vs Q1 of DP2, high in refined carbohydrates, milk and unsaturated fats/oils and low in cruciferous vegetables/spinach, and nuts/seeds, was only associated with a lower HDL-C (P = 0.006) and AHEI-2010 score (P < 0.001).

Conclusions: In disease-free adults, greater adherence to DP1, which broadly aligned with UK food-based dietary guidelines, was favourably associated with diet quality and CMD risk markers. Our findings could contribute to the evidence base for future food-based dietary recommendations, particularly highlighting the importance of fermented dairy foods.

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经验驱动的饮食模式与心脏代谢疾病危险因素之间的关联：无病成人的横断面分析

背景：虽然饮食模式（DPs）和心血管代谢疾病（CMD）风险标志物之间的联系已经在英国人群中被确定，但这些研究往往依赖于较少的定量饮食评估措施，并且只包括有限数量的风险标志物。目的：本横断面分析旨在通过称重饮食日记确定自我报告无疾病的成年人的DP，并探讨各种CMD危险因素和饮食质量之间的关系。方法：整理了2009-2019年在英国生活的成年人中进行的五项研究的数据，并使用主成分分析从习惯性饮食摄入数据中提取了DPs。使用ANCOVA评估四分位数(Q)遵守DPs与CMD风险标志物、营养摄入量和替代健康饮食指数（AHEI-2010）之间的关系。结果：在我们的队列中[n = 646, 58.4%为女性，平均（SD）年龄44（14）岁，体重指数（BMI） 25.2 (4.0) kg/m2]，两个DPs解释了12%的习惯性食物摄入差异。最高坚持DP1 (Q4)，其特征是饮食中富含发酵乳制品、水果、蔬菜、全谷物、坚果/种子、不饱和脂肪/油和牛奶，红肉菜肴和加工肉类较少，与较低的BMI、腰围、舒张压、空腹甘油三酯、非高密度脂蛋白胆固醇、残留物样颗粒胆固醇和总胆固醇相关：HDL-C比率和较高的HDL-C和AHEI-2010评分（均P≤0.006）相比Q1（均P≤0.006）。相比之下，DP2的Q4 vs Q1，精制碳水化合物、牛奶和不饱和脂肪/油含量高，十字花科蔬菜/菠菜和坚果/种子含量低，只与较低的HDL-C （P = 0.006）和AHEI-2010评分相关(P结论：在无疾病的成年人中，更坚持DP1，这与英国基于食物的饮食指南基本一致，与饮食质量和CMD风险标志物有利。我们的发现可以为未来基于食物的饮食建议提供证据基础，特别是强调发酵乳制品的重要性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Nutrition & Metabolism 医学-营养学

CiteScore

8.40

自引率

0.00%

发文量

审稿时长

4-8 weeks

期刊介绍： Nutrition & Metabolism publishes studies with a clear focus on nutrition and metabolism with applications ranging from nutrition needs, exercise physiology, clinical and population studies, as well as the underlying mechanisms in these aspects. The areas of interest for Nutrition & Metabolism encompass studies in molecular nutrition in the context of obesity, diabetes, lipedemias, metabolic syndrome and exercise physiology. Manuscripts related to molecular, cellular and human metabolism, nutrient sensing and nutrient–gene interactions are also in interest, as are submissions that have employed new and innovative strategies like metabolomics/lipidomics or other omic-based biomarkers to predict nutritional status and metabolic diseases. Key areas we wish to encourage submissions from include: -how diet and specific nutrients interact with genes, proteins or metabolites to influence metabolic phenotypes and disease outcomes; -the role of epigenetic factors and the microbiome in the pathogenesis of metabolic diseases and their influence on metabolic responses to diet and food components; -how diet and other environmental factors affect epigenetics and microbiota; the extent to which genetic and nongenetic factors modify personal metabolic responses to diet and food compositions and the mechanisms involved; -how specific biologic networks and nutrient sensing mechanisms attribute to metabolic variability.