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. 2022 Nov 22;13(1):7066.
doi: 10.1038/s41467-022-34195-8.

Validation of Food Compass with a healthy diet, cardiometabolic health, and mortality among U.S. adults, 1999-2018

Meghan O'Hearn  1 , Joshua Erndt-Marino  2 , Suzannah Gerber  3 , Brianna N Lauren  3 , Christina Economos  3 , John B Wong  4   5 , Jeffrey B Blumberg  3 , Dariush Mozaffarian  3   4
Affiliations

Validation of Food Compass with a healthy diet, cardiometabolic health, and mortality among U.S. adults, 1999-2018

Meghan O'Hearn et al. Nat Commun. .

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]), HbA1c (-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.

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Conflict of interest statement

Ms. O’Hearn reports research funding from the National Institutes of Health and Vail Innovative Global Research. Dr. Erndt-Marino reports income from Bespoke Analytics, LLC, which received research funding from the Florida Department of Citrus, outside the submitted work. Ms. Gerber reports research funding from the National Institute of Food and Agriculture (USDA) and the National Institutes of Health, and additional income from Inova Medical Systems in the Beatty Liver and Obesity Research Program as well as the USDA-Tufts Human Nutrition Research Center on Aging, all outside the submitted work. Ms. Lauren has received personal fees from Abt Associates and the Centers for Disease Control and Prevention, both outside the submitted work. Dr. Economos reports research funding from the United States Department of Agriculture National Institutes of Health, JPB Foundation, and Newman’s Own Foundation. She also reports her position as Vice Chair to National Academies of Science Roundtable on Obesity Solutions (unpaid) and her Advisory Board position at Care/of Scientific. None of the above relate to the manuscript. Dr. Wong reports research funding from the National Institutes of Health and membership in the US Preventive Services Task Force (unpaid) and National Academies of Sciences, Engineering and Medicine Committee on Evaluating the Process to Develop the Dietary Guidelines for Americans, 2020-2025 (unpaid), outside the submitted work. Dr. Blumberg reports being on the scientific advisory board for Bragg Live Food Products, LLC, California Prune Board, California Walnut Commission, Cranberry Institute, Good Pharma, LLC, Guiding Stars Licensing Co., Inside Tracker/Segeterra Inc., and January.ai, also all outside the submitted work. Dr. Mozaffarian reports research funding from the National Institutes of Health, Gates Foundation, Rockefeller Foundation, and Vail Innovative Global Research; and scientific advisory board for Beren Therapeutics, Brightseed, Calibrate, DayTwo (ended 6/2021), Elysium Health, Filtricine, Foodome, HumanCo, January Inc., Perfect Day, Season and Tiny Organics, all outside the submitted work.

Figures

Fig. 1
Fig. 1. Population energy-weighted, individual Food Compass Score (i.FCS) and individual component Domain Score densities among U.S. adults, 1999–2018.
Density plots reflect the population distribution of i.FCS among 47999 U.S. adults from 1999 to 2018. i.FCS was calculated as the energy-weighted mean of the Food Compass Score of all foods consumed, as reported in all available 24 hr recalls, ranging from 1 to 100. The same methods were used for calculating the component domain scores at the individual level. i.FCS individual Food Compass Score.
Fig. 2
Fig. 2. Examples of individuals having the mean U.S. i.FCS of 35.5, demonstrating variation in the consumption of products with FCS ≤ 30, 31–69, and ≥70 that achieves a similar overall score.
The distribution (count and contribution to total energy intake [%]) of consumed food and beverage products, based on previously defined product healthfulness thresholds (i.e., FCS ≤ 30 as products to minimize; FCS 31-69 as products to be consumed in moderation; and FCS ≥ 70 as products to be encouraged) was assessed for all NHANES participants. Examples are shown for 10 participants with i.FCS at the U.S. mean (35.5 ±1) and consuming at least 10 different products over 2 days of 24 hr recalls. The numbers within each stacked bar graph indicate the count of food and beverage products consumed from that category of FCS across two days of reported intake; and the color bars represent the percentage energy contribution of food and beverage products from that category of FCS across two days of reported intake. FCS Food Compass Score, grad graduate, HS high school, i.FCS individual Food Compass Score.
Fig. 3
Fig. 3. Relationship between the Healthy Eating Index (HEI) 2015 and energy-weighted, individual Food Compass Score (i.FCS) among U.S. adults, 1999–2018.
i.FCS was calculated by taking the energy-weighted mean of the respective score for all foods consumed by that individual reported in up to two 24 hr recalls. Black dots represent each NHANES respondent; solid line, the line of best fit between all NHANES respondents; and R value, the correlation and p-value (2-sided) for the Spearman correlation. i.FCS individual Food Compass Score, NHANES National Health and Nutrition Examination Survey.
Fig. 4
Fig. 4. Prospective associations of the individual Food Compass Score (i.FCS), sociodemographic factors, and other factors with all-cause mortality among U.S. adults, 1999–2018.
The i.FCS for each person was calculated as the energy-weighted mean of the FCS of all unique foods and beverages consumed, based on up to two 24 hr recalls per person, with a potential range from 1 to 100. Boxes represent the multivariable-adjusted hazard ratio; and error bars, the 95% CI; based on survey-weighted Cox proportional hazard models incorporating NHANES dietary recall sample weights to account for the complex survey design and response rates and provide nationally representative estimates for the non-institutionalized U.S. population. All variables in the Figure were included together in the multivariable model. AA Associates Degree, grad graduate, HS high school, i.FCS individual Food Compass Score, MET metabolic equivalent of task, %E percent energy, PIR ratio of family income to poverty threshold, SD standard deviation.
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References

    1. El-Abbadi NH, Taylor SF, Micha R, Blumberg JB. Nutrient profiling systems, front of pack labeling, and consumer behavior. Curr. Atherosclerosis Rep. 2020;22:36. doi: 10.1007/s11883-020-00857-5. - DOI - PubMed
    1. Labonté M, et al. Nutrient profile models with applications in government-led nutrition policies aimed at health promotion and noncommunicable disease prevention: a systematic review. Adv. Nutr. 2018;9:741–788. doi: 10.1093/advances/nmy045. - DOI - PMC - PubMed
    1. McColl K, Lobstein T, Brinsden H, World Health Organization. Regional Office for, E. Nutrient profiling could be used to transform food systems and support health-promoting food policies. Public health Panor. 2017;3:586–597.
    1. O’Hearn, M., Gerber, S., Cruz, S. M., & Mozaffarian, D. The time is ripe for ESG + Nutrition: evidence-based nutrition metrics for Environmental, Social, and Governance (ESG) investing. Eur. J. Clin. Nutr. 76, 1047–1052 (2022). - PMC - PubMed
    1. Southey, F. 7 European countries team up to propel Nutri-Score rollout. Food Navigator Retrieved from https://www.foodnavigator.com/Article/2021/02/12/7-Euopean-countries-tea... (Accessed April 2021).

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