Table_1_Balanced Hybrid Nutrient Density Score Compared to Nutri-Score and Health Star Rating Using Receiver Operating Characteristic Curve Analyses.docx

BackgroundNutrient profiling (NP) models that are used to assess the nutrient density of foods can be based on a combination of key nutrients and desirable food groups.
ObjectiveTo compare the diagnostic accuracy of a new balanced hybrid nutrient density score (bHNDS) to Nutri-Score and Health Star Rating (HSR) front-of-pack systems using receiver operating characteristic (ROC) curve analyses. The diet-level bHNDS was first validated against Healthy Eating Index (HEI-2015) using data from the 2017–18 National Health and Nutrition Examination Survey (2017–18 NHANES). Food-level bHNDS values were then compared to both the Nutri-Score and HSR using ROC curve analyses.
ResultsThe bHNDS was based on 6 nutrients to encourage (protein, fiber, calcium, iron, potassium, and vitamin D); 5 food groups to encourage (whole grains, nuts and seeds, dairy, vegetables, and fruit), and 3 nutrients (saturated fat, added sugar, and sodium) to limit. The algorithm balanced components to encourage against those to limit. Diet-level bHNDS values correlated well with HEI-2015 (r = 0.67; p < 0.001). Food-level correlations with both Nutri-Score (r = 0.60) and with HSR (r = 0.58) were significant (both p < 0.001). ROC estimates of the Area Under the Curve (AUC) showed high agreement between bHNDS values and optimal Nutri-Score and HSR ratings (>0.90 in most cases). ROC analysis identified those bHNDS cut-off points that were predictive of A-grade Nutri-Score or 5-star HSR. Those cut-off points were highly category-specific.
ConclusionThe new bHNDS model showed high agreement with two front-of-pack labeling systems. Cross-model comparisons based on ROC curve analyses are the first step toward harmonization of proliferating NP methods that aim to “diagnose” high nutrient-density foods.

背景 用于评估食品营养密度的营养成分分析（Nutrient profiling, NP）模型，可基于关键营养素与推荐食物组别的组合构建。
目的 采用受试者工作特征（Receiver Operating Characteristic, ROC）曲线分析法，对比新型均衡混合营养密度评分（balanced hybrid nutrient density score, bHNDS）与Nutri-Score及健康星级评分（Health Star Rating, HSR）这两款包装正面标识系统的诊断准确性。本研究首先依托2017–2018年美国国家健康与营养检查调查（2017–18 National Health and Nutrition Examination Survey, 2017–18 NHANES）的数据，针对膳食层面的bHNDS开展验证，以健康饮食指数（Healthy Eating Index, HEI-2015）作为参照标准；随后再次采用ROC曲线分析法，将食品层面的bHNDS数值与Nutri-Score及HSR进行对比。
结果 bHNDS纳入6种推荐摄入的营养素（蛋白质、膳食纤维、钙、铁、钾与维生素D）、5类推荐摄入的食物组别（全谷物、坚果与籽类、乳制品、蔬菜及水果），以及3种需限制摄入的营养素（饱和脂肪、添加糖与钠）。该算法对推荐摄入与需限制的组分进行均衡权衡。膳食层面的bHNDS数值与HEI-2015呈现显著相关性（r=0.67；p < 0.001）。食品层面的bHNDS数值与Nutri-Score（r=0.60）及HSR（r=0.58）均存在显著相关性（两者p均<0.001）。受试者工作特征曲线下面积（Area Under the Curve, AUC）的ROC估计结果显示，bHNDS数值与最优Nutri-Score及HSR评级之间具有高度一致性（多数场景下AUC值>0.90）。ROC分析确定了可预测A级Nutri-Score或5星HSR的bHNDS临界值，该类临界值具有较强的类别特异性。
结论 新型bHNDS模型与两款包装正面标识系统展现出高度一致性。基于ROC曲线分析法开展的跨模型对比，为统一当前日益增多的、旨在“诊断”高营养密度食品的NP方法迈出了第一步。