Abstract
The Food Compass is a nutrient profiling system (NPS) to characterize the healthfulness of diverse foods, beverages and meals. In a nationally representative cohort of 47,999 U.S. adults, we validated a person’s individual Food Compass Score (i.FCS), ranging from 1 (least healthful) to 100 (most healthful) based on cumulative scores of items consumed, against: (a) the Healthy Eating Index (HEI) 2015; (b) clinical risk factors and health conditions; and (c) all-cause mortality. Nationally, the mean (SD) of i.FCS was 35.5 (10.9). i.FCS correlated highly with HEI-2015 (R = 0.81). After multivariable-adjustment, each one SD (10.9 point) higher i.FCS associated with more favorable BMI (−0.60 kg/m² [−0.70,–0.51]), systolic blood pressure (−0.69 mmHg [−0.91,-0.48]), diastolic blood pressure (−0.49 mmHg [-0.66,-0.32]), LDL-C (−2.01 mg/dl [-2.63,-1.40]), HDL-C (1.65 mg/d [1.44,1.85]), HbAlc (-0.02% [−0.03,−0.01]), and fasting plasma glucose (-0.44 mg/dL [−0.74,−0.15]); lower prevalence of metabolic syndrome (OR = 0.85 [0.82,0.88]), CVD (0.92 [0.88,0.96]), cancer (0.95 [0.91,0.99]), and lung disease (0.92 [0.88,0.96]); and higher prevalence of optimal cardiometabolic health (1.24 [1.16,1.32]). i.FCS also associated with lower all-cause mortality (HR = 0.93 [0.89,0.96]). Findings were similar by age, sex, race/ethnicity, education, income, and BMI. These findings support validity of Food Compass as a tool to guide public health and private sector strategies to identify and encourage healthier eating.
Generated Summary
This research presents a study validating the Food Compass, a nutrient profiling system (NPS) designed to characterize the healthfulness of diverse foods, beverages, and meals. The study utilized a nationally representative cohort of 47,999 U.S. adults (1999-2018) to examine the relationship between individual Food Compass Scores (i.FCS), which range from 1 (least healthful) to 100 (most healthful), and various health outcomes. The methodology involved correlating i.FCS with the Healthy Eating Index (HEI) 2015, clinical risk factors and health conditions, and all-cause mortality. The study’s approach included survey-weighted linear regression models to assess associations, adjusting for key sociodemographic and lifestyle factors. Additionally, the study explored how each of the 9 scoring domains of the Food Compass separately related to the health outcomes. This comprehensive approach aimed to validate the Food Compass as a tool for identifying and promoting healthier eating habits.
Key Findings & Statistics
- The mean (SD) of i.FCS was 35.5 (10.9).
- i.FCS correlated highly with HEI-2015 (R = 0.81).
- Each one SD (10.9 point) higher i.FCS associated with more favorable BMI (-0.60 kg/m² [-0.70,-0.51]), systolic blood pressure (-0.69 mmHg [-0.91,-0.48]), diastolic blood pressure (-0.49 mmHg [-0.66,-0.32]), LDL-C (-2.01 mg/dL [-2.63,-1.40]), HDL-C (1.65 mg/dL [1.44,1.85]), HbAlc (-0.02% [-0.03,-0.01]), and fasting plasma glucose (-0.44 mg/dL [-0.74,-0.15]).
- Lower prevalence of metabolic syndrome (OR = 0.85 [0.82,0.88]), CVD (0.92 [0.88,0.96]), cancer (0.95 [0.91,0.99]), and lung disease (0.92 [0.88,0.96]).
- Higher prevalence of optimal cardiometabolic health (1.24 [1.16,1.32]).
- i.FCS also associated with lower all-cause mortality (HR = 0.93 [0.89,0.96]).
- Among U.S. adults, the mean (SD) i.FCS was 35.5 (10.9), with 5th and 95th values of 19.5 and 55.3.
- Nearly all (99.5%) U.S. adults had an i.FCS below 70.
- 32.7% of U.S. adults had an i.FCS of 30 or below.
- Individuals with higher i.FCS (≥70) consumed a greater number of and percentage total energy contribution from products with FCS ≥ 70 (median [IQR] count: 13 [8, 20]; percentage energy: 65.6% [57.9, 71.4%])) compared to products with FCS 31-69 (6 [3, 9]; 14.2% [7.3, 23.2%]) or FCS ≤ 30 (2 [1, 4]; 2.3% [0.2, 6.1%]).
- The mean (SD) age was 47.2 y (17.1), 52.2% were female, and 27.8% had a college degree or more (Table 1).
- The mean (SD) BMI was overweight (28.8 kg/m² [6.8]); blood glucose levels, HbAlc: 5.6% (0.9); fasting plasma glucose: 105.8 mg/dL (30.8).
- About 42.0% of U.S. adults had metabolic syndrome; 12.9%, diabetes; 7.7%, clinical CVD; 18.9%, lung disease; and 9.8%, cancer.
- Only 7.4% had optimal cardiometabolic health.
- Each SD increase in i.FCS (~10.9 points out of 100) was associated with lower BMI (-0.60 kg/m² [-0.70, -0.51]), systolic blood pressure (-0.69 mmHg [-0.91, -0.48]), LDL-C (-2.01 mg/dL [-2.63, -1.40]), HbAlc (-0.02% [-0.03, -0.01]); and fasting plasma glucose (-0.44 mg/dL [-0.74, -0.15]); and higher HDL-C (1.65 mg/d: [1.44, 1.85]).
- For diabetes, i.Nutrient Ratios had the strongest inverse association (0.94 [0.89, 0.99]); for CVD, i.Minerals, i.Food Ingredients, i.Additives, i.Processing, i.Fiber and Protein, and i.Specific Lipids were each inversely associated (OR 0.91 to 0.95 each).
- For cancer, i.Nutrient Ratios, i.Additives, i.Processing and i.Fiber and Protein were each inversely associated (OR 0.93 to 0.95 each); and for lung disease, all domains except i.Food Ingredients were inversely associated.
- Each SD increase in i.FCS (~10.9 points) was associated with 15% lower prevalence of metabolic syndrome (OR = 0.85 [95%CI: 0.82, 0.88]), 8% lower prevalence of CVD (0.92 [0.88, 0.96]), 5% lower prevalence of cancer (0.95 [0.91, 0.99]),and 8% lower prevalence of lung disease (0.92 [0.88, 0.96]).
- Each SD (10.9) increase in i.FCS was prospectively associated with a 7% lower risk of all-cause mortality (HR = 0.93 [0.89, 0.96]).
Other Important Findings
- The study found that individuals with higher i.FCS (≥70) consumed a greater number of products with high FCS values.
- The 9 scoring domains of the Food Compass showed varying degrees of correlation with health outcomes, with the i.FCS showing the strongest associations.
- Higher i.FCS was associated with a 24% higher prevalence of optimal cardiometabolic health.
- The relationship between i.FCS and all-cause mortality appeared potentially nonlinear, with a stronger protective association until an i.FCS of ~40 (approximately the 75th percentile score), with a less strong inverse relationship thereafter, but this potential nonlinearity was not statistically significant (p-nonlinearity = 0.12).
- Significant inverse associations with all-cause mortality were seen for i.Nutrient Ratios (0.92 [0.89, 0.96]), i.Food Ingredients (0.95 [0.91, 0.99]), i.Additives (0.95 [0.92, 0.98]), i.Processing (0.93 [0.89, 0.96]), i.Fiber and Protein (0.93 [0.90, 0.96]), and i.Specific Lipids (0.96 [0.92, 1.00]) (Table S8).
Limitations Noted in the Document
- The study’s cross-sectional design limits the assessment of temporality, though findings were consistent in prospective analyses of all-cause mortality.
- The use of energy-weighting to calculate i.FCS may provide lower weighting to foods with fewer calories per servings, such as fruits and vegetables.
- Some simplifications were required in the scoring process, such as using the RDA for 19-50 year-old men as target thresholds for several nutrients.
- Certain nutrients such as Vitamin D, choline, and flavonoids were only available in certain NHANES cycles, requiring imputation in other cycles.
- Misreporting and omission of food items by dietary recall participants was also possible.
- The study’s findings are observational, and residual confounding cannot be excluded.
Conclusion
The study’s findings support the validity of the Food Compass as a tool for scoring the healthfulness of individual food and beverage products, with associations to a healthy diet pattern, clinical risk factors, prevalent conditions, and reduced mortality. The low average i.FCS across the U.S. adult population indicates that the leading caloric contributors to American diets are, on average, mostly foods and beverages that should be minimized or avoided. The study emphasizes the need for interventions to improve diet quality, including policy, business, and other systems interventions. The Food Compass, with its ability to score mixed meals and complex foods, has broader applications beyond front-of-pack labeling, including the potential for nutrient profiling of restaurant menus, expanding economic incentives, and guiding investments toward healthier food choices. The study’s findings support the use of the Food Compass to guide public and private strategies, fostering a shift towards healthier food and beverage options.