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Open AccessOpen Access JUSTC Life Sciences 17 January 2024

Machine-learning diet quality score and risk of cardiovascular disease

  • 1.

    Department of Health Data Science, Anhui Medical University, Hefei 230032, China

  • 2.

    Clinical Pharmacology and Quantitative Science, Genmab Inc., Princeton, NJ 08540, USA

  • 3.

    MOE Key Laboratory of Population Health Across Life Cycle, Hefei 230032, China

Cite this:
https://doi.org/10.52396/JUSTC-2023-0067
More Information
  • Author Bio:

    Can Yang is a graduate student at the School of Health Management, Anhui Medical University. Her research mainly focuses on big data analysis in public health

    Min Yuan is a Professor at the School of Health Management, Anhui Medical University. She received her Ph.D. degree from the University of Science and Technology of China in 2009. Her research mainly focuses on genome-wide association studies for Alzheimer’s disease, longitudinal data analysis, and statistical models and applications in public health and biomedicine

  • Corresponding author: E-mail: myuan@ustc.edu.cn
  • Received Date: 20 April 2023
  • Accepted Date: 17 July 2023
    • Available Online: 17 January 2024
    Abstract

  • Abstract

    Objectives: Various diet scores have been established to measure overall diet quality, especially for the prevention of cardiovascular disease (CVD). Diet scores constructed by utilizing modern machine learning techniques may contain independent information and can provide better dietary recommendations in combination with the existing diet scores. Methods: We proposed a novel machine-learning diet quality score (DQS) and examined the performance of DQS in combination with the Healthy Eating Index-2015 (HEI2015), Mediterranean Diet Score (MED), Alternative Healthy Eating Index-2010 (AHEI) and Dietary Approaches to Stop Hypertension score (DASH score). The data used in this study were from the 2011–2012 to 2017–2018 cycles of the US National Health and Nutrition Examination Survey (NHANES). Participants aged above 20 self-reported their food intake and information on relevant covariates. We used an elastic-net penalty regression model to select important food features and used a generalized linear regression model to estimate odds ratios (ORs) and 95% CIs after controlling for age, sex, and other relevant covariates. Results: A total of 16756 participants were included in the analysis. DQS was significantly associated with coronary artery disease (CAD) risk after adjusting for one of the other common diet scores. The ORs for DQS combined with the HEI2015, MED, AHEI, and DASH scores were all approximately 0.900, with p values smaller than 0.05. The OR for DQS in the full score model including all other scores was 0.905 (95% CI, 0.828–0.989, p=0.028). Only marginal associations were found between DQS and other CVDs after adjusting for other diet scores. Conclusions: Based on data from four continuous cycles of the NHANES, higher DQS was found to be consistently associated with a lower risk of CAD. The DQS captured unique predictive information independent of the existing diet scores and thus can be used as a complementary scoring system to further improve dietary recommendations for CAD patients.

    Graphical abstract

    A novel machine learning method was introduced to assess diet quality. The newly proposed diet quality score effectively captured distinctive predictive information, independent of existing diet scores, and consistently demonstrated an association with a reduced risk of cardiovascular disease.

    Abstract

    Objectives: Various diet scores have been established to measure overall diet quality, especially for the prevention of cardiovascular disease (CVD). Diet scores constructed by utilizing modern machine learning techniques may contain independent information and can provide better dietary recommendations in combination with the existing diet scores. Methods: We proposed a novel machine-learning diet quality score (DQS) and examined the performance of DQS in combination with the Healthy Eating Index-2015 (HEI2015), Mediterranean Diet Score (MED), Alternative Healthy Eating Index-2010 (AHEI) and Dietary Approaches to Stop Hypertension score (DASH score). The data used in this study were from the 2011–2012 to 2017–2018 cycles of the US National Health and Nutrition Examination Survey (NHANES). Participants aged above 20 self-reported their food intake and information on relevant covariates. We used an elastic-net penalty regression model to select important food features and used a generalized linear regression model to estimate odds ratios (ORs) and 95% CIs after controlling for age, sex, and other relevant covariates. Results: A total of 16756 participants were included in the analysis. DQS was significantly associated with coronary artery disease (CAD) risk after adjusting for one of the other common diet scores. The ORs for DQS combined with the HEI2015, MED, AHEI, and DASH scores were all approximately 0.900, with p values smaller than 0.05. The OR for DQS in the full score model including all other scores was 0.905 (95% CI, 0.828–0.989, p=0.028). Only marginal associations were found between DQS and other CVDs after adjusting for other diet scores. Conclusions: Based on data from four continuous cycles of the NHANES, higher DQS was found to be consistently associated with a lower risk of CAD. The DQS captured unique predictive information independent of the existing diet scores and thus can be used as a complementary scoring system to further improve dietary recommendations for CAD patients.

    • Public Summary

    • A modern machine learning-based diet quality score (DQS), developed using modern machine learning techniques, offers unique and independent insights beyond conventional diet scores, leading to improved dietary recommendations for CAD prevention.
    • Higher DQS consistently correlated with a reduced risk of CAD in various NHANES cycles, indicating its potential as a valuable and reliable tool for evaluating and managing CAD risk.
    • DQS serves as a powerful complement to existing diet scores, such as the HEI2015, MED, AHEI, and DASH scores, working together to provide more accurate and comprehensive dietary recommendations for CAD patients.

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    • References(17)
  • [1]
    Roth G A, Mensah G A, Johnson C O, et al. Global burden of cardiovascular diseases and risk factors, 1990–2019: update from the GBD 2019 study. Journal of the American College of Cardiology, 2020, 76 (25): 2982–3021. doi: 10.1016/j.jacc.2020.11.010
    [2]
    Afshin A, Sur P J, Fay K A, et al. Health effects of dietary risks in 195 countries, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. The Lancet, 2019, 393 (10184): 1958–1972. doi: 10.1016/S0140-6736(19)30041-8
    [3]
    Miller V, Micha R, Choi E, et al. Evaluation of the quality of evidence of the association of foods and nutrients with cardiovascular disease and diabetes: A systematic review. JAMA Network Open, 2022, 5 (2): e2146705. doi: 10.1001/jamanetworkopen.2021.46705
    [4]
    Krebs-Smith S M, Pannucci T E, Subar A F, et al. Update of the healthy eating index: HEI-2015. Journal of the Academy of Nutrition and Dietetics, 2018, 118 (9): 1591–1602. doi: 10.1016/j.jand.2018.05.021
    [5]
    Fung T T, Chiuve S E, McCullough M L, et al. Adherence to a DASH-style diet and risk of coronary heart disease and stroke in women. Archives of Internal Medicine, 2008, 168 (7): 713–720. doi: 10.1001/archinte.168.7.713
    [6]
    Sacks F M, Svetkey L P, Vollmer W M, et al. Effects on blood pressure of reduced dietary sodium and the Dietary Approaches to Stop Hypertension (DASH) diet. New England Journal of Medicine, 2001, 344 (1): 3–10. doi: 10.1056/NEJM200101043440101
    [7]
    McCullough M L, Willett W C. Evaluating adherence to recommended diets in adults: the Alternate Healthy Eating Index. Public Health Nutrition, 2006, 9 (1a): 152–157. doi: 10.1079/PHN2005938
    [8]
    Chiuve S E, Fung T T, Rimm E B, et al. Alternative dietary indices both strongly predict risk of chronic disease. The Journal of Nutrition, 2012, 142 (6): 1009–1018. doi: 10.3945/jn.111.157222
    [9]
    Willett W C, Sacks F, Trichopoulou A, et al. Mediterranean diet pyramid: a cultural model for healthy eating. The American Journal of Clinical Nutrition, 1995, 61 (6): 1402S–1406S. doi: 10.1093/ajcn/61.6.1402S
    [10]
    Trichopoulou A, Costacou T, Bamia C, et al. Adherence to a Mediterranean diet and survival in a Greek population. The New England Journal of Medicine, 2003, 348 (26): 2599–2608. doi: 10.1056/NEJMoa025039
    [11]
    Shan Z, Li Y, Baden M Y, et al. Association between healthy eating patterns and risk of cardiovascular disease. JAMA Internal Medicine, 2020, 180 (8): 1090–1100. doi: 10.1001/jamainternmed.2020.2176
    [12]
    Hu E A, Steffen L M, Coresh J, et al. Adherence to the healthy eating index-2015 and other dietary patterns may reduce risk of cardiovascular disease, cardiovascular mortality, and all-cause mortality. The Journal of Nutrition, 2020, 150 (2): 312–321. doi: 10.1093/jn/nxz218
    [13]
    Schwingshackl L, Hoffmann G. Diet quality as assessed by the Healthy Eating Index, the Alternate Healthy Eating Index, the Dietary Approaches to Stop Hypertension score, and health outcomes: a systematic review and meta-analysis of cohort studies. Journal of the Academy of Nutrition and Dietetics, 2015, 115 (5): 780–800.e5. doi: 10.1016/j.jand.2014.12.009
    [14]
    Patel Y R, Robbins J M, Gaziano J M, et al. Mediterranean, DASH, and Alternate Healthy Eating Index dietary patterns and risk of death in the physicians’ health study. Nutrients, 2021, 13 (6): 1893. doi: 10.3390/nu13061893
    [15]
    World Health Organization. International classification of diseases—Ninth revision (ICD-9). Weekly Epidemiological Record , 1988, 63 (45): 343–344.
    [16]
    Danese E, Montagnana M. An historical approach to the diagnostic biomarkers of acute coronary syndrome. Annals of Translational Medicine, 2016, 4 (10): 194. doi: 10.21037/atm.2016.05.19
    [17]
    Zou H, Hastie T. Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2005, 67 (2): 301–320. doi: 10.1111/j.1467-9868.2005.00503.x
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    Figure  1.  Schematic diagram of data preprocessing, construction of DQS and analytic methods. CVD: cardiovascular disease; CAD: coronary artery disease; CHF: congestive heart failure; MI: heart attack or myocardial infarction; STROKE: stroke; ENET: elastic net regression.

    Figure  2.  Odds ratio trends for the population with a 50%, 37.5%, 25%, and 12.5% reduction and a 12.5%, 25%, 37.5% and 50% increase in median diet score DQS relative to the reference population in stratified high and low common diet score groups. Reference diet scores were defined as the median value of DQS without incident CVDs.

    Figure  3.  Stratified analysis for potential risk modifiers including gender, education, marital status, race, smoking status, PIR and BMI. OR and 95% confidence intervals for the univariate diet score model and multiple diet score model for CAD were reported. The columns with “s_” and “m_” refer to the univariate and multiple score regression models, respectively. The “m_HEI2015+DQS” columns refer to the results for DQS in the combined HEI2015 model. Similar explanations for the other columns.

    [1]
    Roth G A, Mensah G A, Johnson C O, et al. Global burden of cardiovascular diseases and risk factors, 1990–2019: update from the GBD 2019 study. Journal of the American College of Cardiology, 2020, 76 (25): 2982–3021. doi: 10.1016/j.jacc.2020.11.010
    [2]
    Afshin A, Sur P J, Fay K A, et al. Health effects of dietary risks in 195 countries, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. The Lancet, 2019, 393 (10184): 1958–1972. doi: 10.1016/S0140-6736(19)30041-8
    [3]
    Miller V, Micha R, Choi E, et al. Evaluation of the quality of evidence of the association of foods and nutrients with cardiovascular disease and diabetes: A systematic review. JAMA Network Open, 2022, 5 (2): e2146705. doi: 10.1001/jamanetworkopen.2021.46705
    [4]
    Krebs-Smith S M, Pannucci T E, Subar A F, et al. Update of the healthy eating index: HEI-2015. Journal of the Academy of Nutrition and Dietetics, 2018, 118 (9): 1591–1602. doi: 10.1016/j.jand.2018.05.021
    [5]
    Fung T T, Chiuve S E, McCullough M L, et al. Adherence to a DASH-style diet and risk of coronary heart disease and stroke in women. Archives of Internal Medicine, 2008, 168 (7): 713–720. doi: 10.1001/archinte.168.7.713
    [6]
    Sacks F M, Svetkey L P, Vollmer W M, et al. Effects on blood pressure of reduced dietary sodium and the Dietary Approaches to Stop Hypertension (DASH) diet. New England Journal of Medicine, 2001, 344 (1): 3–10. doi: 10.1056/NEJM200101043440101
    [7]
    McCullough M L, Willett W C. Evaluating adherence to recommended diets in adults: the Alternate Healthy Eating Index. Public Health Nutrition, 2006, 9 (1a): 152–157. doi: 10.1079/PHN2005938
    [8]
    Chiuve S E, Fung T T, Rimm E B, et al. Alternative dietary indices both strongly predict risk of chronic disease. The Journal of Nutrition, 2012, 142 (6): 1009–1018. doi: 10.3945/jn.111.157222
    [9]
    Willett W C, Sacks F, Trichopoulou A, et al. Mediterranean diet pyramid: a cultural model for healthy eating. The American Journal of Clinical Nutrition, 1995, 61 (6): 1402S–1406S. doi: 10.1093/ajcn/61.6.1402S
    [10]
    Trichopoulou A, Costacou T, Bamia C, et al. Adherence to a Mediterranean diet and survival in a Greek population. The New England Journal of Medicine, 2003, 348 (26): 2599–2608. doi: 10.1056/NEJMoa025039
    [11]
    Shan Z, Li Y, Baden M Y, et al. Association between healthy eating patterns and risk of cardiovascular disease. JAMA Internal Medicine, 2020, 180 (8): 1090–1100. doi: 10.1001/jamainternmed.2020.2176
    [12]
    Hu E A, Steffen L M, Coresh J, et al. Adherence to the healthy eating index-2015 and other dietary patterns may reduce risk of cardiovascular disease, cardiovascular mortality, and all-cause mortality. The Journal of Nutrition, 2020, 150 (2): 312–321. doi: 10.1093/jn/nxz218
    [13]
    Schwingshackl L, Hoffmann G. Diet quality as assessed by the Healthy Eating Index, the Alternate Healthy Eating Index, the Dietary Approaches to Stop Hypertension score, and health outcomes: a systematic review and meta-analysis of cohort studies. Journal of the Academy of Nutrition and Dietetics, 2015, 115 (5): 780–800.e5. doi: 10.1016/j.jand.2014.12.009
    [14]
    Patel Y R, Robbins J M, Gaziano J M, et al. Mediterranean, DASH, and Alternate Healthy Eating Index dietary patterns and risk of death in the physicians’ health study. Nutrients, 2021, 13 (6): 1893. doi: 10.3390/nu13061893
    [15]
    World Health Organization. International classification of diseases—Ninth revision (ICD-9). Weekly Epidemiological Record , 1988, 63 (45): 343–344.
    [16]
    Danese E, Montagnana M. An historical approach to the diagnostic biomarkers of acute coronary syndrome. Annals of Translational Medicine, 2016, 4 (10): 194. doi: 10.21037/atm.2016.05.19
    [17]
    Zou H, Hastie T. Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2005, 67 (2): 301–320. doi: 10.1111/j.1467-9868.2005.00503.x
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