Using food network analysis to understand meal patterns in pregnant women with high and low diet quality

Carolina Schwedhelm, Leah M Lipsky, Grace E Shearrer, Grace M Betts, Aiyi Liu, Khalid Iqbal, Myles S Faith, Tonja R Nansel, Carolina Schwedhelm, Leah M Lipsky, Grace E Shearrer, Grace M Betts, Aiyi Liu, Khalid Iqbal, Myles S Faith, Tonja R Nansel

Abstract

Background: Little is known about how meal-specific food intake contributes to overall diet quality during pregnancy, which is related to numerous maternal and child health outcomes. Food networks are probabilistic graphs using partial correlations to identify relationships among food groups in dietary intake data, and can be analyzed at the meal level. This study investigated food networks across meals in pregnant women and explored differences by overall diet quality classification.

Methods: Women were asked to complete three 24-h dietary recalls throughout pregnancy (n = 365) within a prospective cohort study in the US. Pregnancy diet quality was evaluated using the Healthy Eating Index-2015 (HEI, range 0-100), calculated across pregnancy. Networks from 40 food groups were derived for women in the highest and lowest HEI tertiles at each participant-labeled meal (i.e., breakfast, lunch, dinner, snacks) using Gaussian graphical models. Network composition was qualitatively compared across meals and between HEI tertiles.

Results: In both HEI tertiles, breakfast food combinations comprised ready-to-eat cereals with milk, quick breads with sweets (e.g., pancakes with syrup), and bread with cheese and meat. Vegetables were consumed at breakfast among women in the high HEI tertile only. Combinations at lunch and dinner were more varied, including vegetables with oils (e.g., salads) in the high tertile and sugary foods with nuts, fruits, and milk in the low tertile at lunch; and cooked grains with fats (e.g., pasta with oil) in the high tertile and potatoes with vegetables and meat in the low tertile at dinner. Fried potatoes, sugar-sweetened beverages, and sandwiches were consumed together at all main meals in the low tertile only. Foods were consumed individually at snacks in both tertiles; the most commonly consumed food were fruits in the high HEI tertile and cakes & cookies in the low tertile.

Conclusions: In this cohort of pregnant women, food network analysis indicated that food combinations differed by meal and between HEI tertiles. Meal-specific patterns that differed between diet quality tertiles suggest potential targets to improve food choices at meals; the impact of meal-based dietary modifications on intake of correlated foods and on overall diet quality should be investigated in simulations and intervention studies.

Trial registration: PEAS was registered with number NCT02217462 in Clinicaltrials.gov on August 13, 2014.

Keywords: Breakfast; Diet quality; Dinner; Gaussian graphical models; Healthy eating index; Lunch; Meals; Network analysis; Pregnancy; Snacks.

Conflict of interest statement

The authors have no competing interests to declare.

© 2021. The Author(s).

Figures

Fig. 1
Fig. 1
Flow diagram of PEAS participants for analysis in the present study
Fig. 2
Fig. 2
Heatmap of inter-meal and inter-individual variation in food intake (n = 243)1. HEI: Healthy Eating Index-2015; ICC: Intra-class correlation; RTE: ready-to-eat; SSB: sugar-sweetened beverages. ICCs of > 0.30 are marked in bold. 1 Intra-class correlation is presented for food groups consumed in at least 5% of the modelled recalls/meals by HEI tertile. 2 Variance explained by type of meal, considering inter-individual variation. 3 Variance explained by inter-individual variation for each meal type separately
Fig. 3
Fig. 3
Meal food networks among PEAS participants in the low and the high HEI tertiles. HEI: Healthy Eating Index-2015. The thickness of the edges is proportional to the strength of the correlation. Blue, dashed edges indicate negative correlations and red, continuous edges indicate positive correlations. The size of the nodes is proportional to the percentage of meals in which the food was consumed and node role is indicated by colors yellow (provincial hub), purple (connector hub), and orange (non-hub connector). Communities are shown in different background colors around the nodes and are labeled C1-C6. An asterisk (*) next to food group labels indicates significant difference in node size by HEI tertile (p < 0.05), determined by Chi-square test

References

    1. Bodnar LM, Simhan HN, Parker CB, Meier H, Mercer BM, Grobman WA, et al. Racial or ethnic and socioeconomic inequalities in adherence to national dietary guidance in a large cohort of US pregnant women. J Acad Nutr Diet. 2017;117(6):867–77.e3. doi: 10.1016/j.jand.2017.01.016.
    1. AMA-Ohoo H, Nunnery DL, Ammerman A, Dharod JM. Racial/Ethnic differences in diet quality and eating habits among WIC pregnant women: implications for policy and practice. 2019. pp. 2168–6602.
    1. Bodnar LM, Siega-Riz AM. A diet quality index for pregnancy detects variation in diet and differences by sociodemographic factors. Public Health Nutr. 2002;5(6):801–809. doi: 10.1079/PHN2002348.
    1. Rifas-Shiman SL, Rich-Edwards JW, Kleinman KP, Oken E, Gillman MW. Dietary quality during pregnancy varies by maternal characteristics in project viva: a US cohort. J Am Diet Assoc. 2009;109(6):1004–1011. doi: 10.1016/j.jada.2009.03.001.
    1. Shapiro ALB, Kaar JL, Crume TL, Starling AP, Siega-Riz AM, Ringham BM, et al. Maternal diet quality in pregnancy and neonatal adiposity: the healthy start study. Int J Obes. 2016;40(7):1056–1062. doi: 10.1038/ijo.2016.79.
    1. Brantsæter AL, Haugen M, Samuelsen SO, Torjusen H, Trogstad L, Alexander J, et al. A dietary pattern characterized by high intake of vegetables, fruits, and vegetable oils is associated with reduced risk of preeclampsia in nulliparous pregnant Norwegian women. J Nutr. 2009;139(6):1162–1168. doi: 10.3945/jn.109.104968.
    1. Gernand AD, Schulze KJ, Stewart CP, West KP, Christian P. Micronutrient deficiencies in pregnancy worldwide: health effects and prevention. Nat Rev Endocrinol. 2016;12(5):274–289. doi: 10.1038/nrendo.2016.37.
    1. Chia A-R, Chen L-W, Lai JS, Wong CH, Neelakantan N, van Dam RM, et al. Maternal dietary patterns and birth outcomes: a systematic review and meta-analysis. Adv Nutr. 2019;10(4):685–695. doi: 10.1093/advances/nmy123.
    1. Institute of Medicine . Weight gain during pregnancy: reexamining the guideline. Washington: The National Academic Press; 2009.
    1. National Academies of Sciences E, Medicine . In: Nutrition during pregnancy and lactation: exploring new evidence: proceedings of a workshop. Meghan H, editor. Washington: The National Academies Press; 2020.
    1. Krebs-Smith SM, Smiciklas-Wright H, Guthrie HA, Krebs-Smith J. The effects of variety in food choices on dietary quality. J Am Diet Assoc. 1987;87(7):897–903.
    1. Leech RM, Worsley A, Timperio A, McNaughton SA. Understanding meal patterns: definitions, methodology and impact on nutrient intake and diet quality. Nutr Res Rev. 2015;28(1):1–21. doi: 10.1017/S0954422414000262.
    1. Murakami K, Livingstone MBE, Shinozaki N, Sugimoto M, Fujiwara A, Masayasu S, et al. Food Combinations in relation to the quality of overall diet and individual meals in Japanese adults: a nationwide study. Nutrients. 2020;12(2):327. doi: 10.3390/nu12020327.
    1. Rebuli MA, Williams G, James-Martin G, Hendrie GA. Food group intake at self-reported eating occasions across the day: secondary analysis of the Australian National Nutrition Survey 2011–2012. Public Health Nutr. 2020;23(17):3067–80.
    1. Marshall D, Pettinger C. 28 - Revisiting British meals. In: Meiselman HL, editor. Meals in Science and Practice: Woodhead Publishing. 2009. pp. 638–663.
    1. Marshall D, Bell R. Meal construction: exploring the relationship between eating occasion and location. Food Qual Prefer. 2003;14(1):53–64. doi: 10.1016/S0950-3293(02)00015-0.
    1. Afeiche MC, Taillie LS, Hopkins S, Eldridge AL, Popkin BM. Breakfast dietary patterns among Mexican children are related to Total-day diet quality. J Nutr. 2017;147(3):404–412.
    1. Deshmukh-Taskar PR, Radcliffe JD, Liu Y, Nicklas TA. Do breakfast skipping and breakfast type affect energy intake, nutrient intake, nutrient adequacy, and diet quality in young adults? NHANES 1999–2002. J Am Coll Nutr. 2010;29(4):407–418. doi: 10.1080/07315724.2010.10719858.
    1. Shiraishi M, Haruna M, Matsuzaki M. Effects of skipping breakfast on dietary intake and circulating and urinary nutrients during pregnancy. Asia Pac J Clin Nutr. 2019;28(1):99.
    1. Schwedhelm C, Iqbal K, Schwingshackl L, Agogo GO, Boeing H, Knüppel S. Meal analysis for understanding eating behavior: meal- and participant-specific predictors for the variance in energy and macronutrient intake. Nutr J. 2019;18(1):15. doi: 10.1186/s12937-019-0440-8.
    1. Trinca V, Morrison J, Slaughter S, Keller H. Making the Most of mealtimes (M3): effect of eating occasions and other covariates on energy and protein intake among Canadian older adult residents in long-term care. J Hum Nutr Diet. 2020;33(1):3–11. doi: 10.1111/jhn.12686.
    1. Iqbal K, Buijsse B, Wirth J, Schulze MB, Floegel A, Boeing H. Gaussian graphical models identify networks of dietary intake in a German adult population. J Nutr. 2016;146(3):646–652. doi: 10.3945/jn.115.221135.
    1. Schwedhelm C, Knüppel S, Schwingshackl L, Boeing H, Iqbal K. Meal and habitual dietary networks identified through semiparametric Gaussian copula graphical models in a German adult population. PLoS One. 2018;13(8):e0202936. doi: 10.1371/journal.pone.0202936.
    1. Murakami K, Livingstone MBE, Sasaki S. Establishment of a meal coding system for the characterization of meal-based dietary patterns in Japan. J Nutr. 2017;147(11):2093–2101.
    1. Woolhead C, Gibney MJ, Walsh MC, Brennan L, Gibney ER. A generic coding approach for the examination of meal patterns. Am J Clin Nutr. 2015;102(2):316–323. doi: 10.3945/ajcn.114.106112.
    1. Nansel TR, Lipsky LM, Siega-Riz AM, Burger K, Faith M, Liu A. Pregnancy eating attributes study (PEAS): a cohort study examining behavioral and environmental influences on diet and weight change in pregnancy and postpartum. BMC Nutr. 2016;2(1):45. doi: 10.1186/s40795-016-0083-5.
    1. Subar AF, Kirkpatrick SI, Mittl B, Zimmerman TP, Thompson FE, Bingley C, et al. The automated self-administered 24-hour dietary recall (ASA24): a resource for researchers, clinicians, and educators from the National Cancer Institute. J Acad Nutr Diet. 2012;112(8):1134–1137. doi: 10.1016/j.jand.2012.04.016.
    1. Food Surveys Research Group. The USDA Food and Nutrient Database for Dietary Studies [Internet]. [cited 2021 Jul 19]. Available from: .
    1. U. S. Department of Health and Human Services and U.S. Department of Agriculture. 2015–2020 Dietary Guidelines for Americans [Internet]. [cited 2021 Jul 19]. Available from: .
    1. National Cancer Institute Division of Cancer Control and Population Sciences. Developing the Healthy Eating Index [Internet]. [cited 2019 Oct 30]. Available from:
    1. Krebs-Smith SM, Pannucci TE, Subar AF, Kirkpatrick SI, Lerman JL, Tooze JA, et al. Update of the healthy eating index: HEI-2015. J Acad Nutr Diet. 2018;118(9):1591–1602. doi: 10.1016/j.jand.2018.05.021.
    1. Rifas-Shiman SL, Rich-Edwards JW, Willett WC, Kleinman KP, Oken E, Gillman MW. Changes in dietary intake from the first to the second trimester of pregnancy. Paediatr Perinat Epidemiol. 2006;20(1):35–42. doi: 10.1111/j.1365-3016.2006.00691.x.
    1. Savard C, Lemieux S, Carbonneau É, Provencher V, Gagnon C, Robitaille J, et al. Trimester-specific assessment of diet quality in a sample of Canadian pregnant women. Int J Environ Res Public Health. 2019;16(3):311. doi: 10.3390/ijerph16030311.
    1. Wright B, Lipsky L, Siega-Riz AM, Liu A, Nansel T. Changes in maternal diet quality across pregnancy and postpartum and associations with sociodemographic characteristics [conference abstract] Curr Dev Nutr. 2020;4(Supplement_2):1105. doi: 10.1093/cdn/nzaa054_177.
    1. National Cancer Institute Division of Cancer Control & Population Sciences Epidemiology and Genomics Research Program. Healthy Eating Index SAS Code [Internet]. [cited 2020 Jul 14]. Available from: .
    1. Rhee JJ, Sampson L, Cho E, Hughes MD, Hu FB, Willett WC. Comparison of methods to account for implausible reporting of energy intake in epidemiologic studies. Am J Epidemiol. 2015;181(4):225–233. doi: 10.1093/aje/kwu308.
    1. Most J, Dervis S, Haman F, Adamo KB, Redman LM. Energy intake requirements in pregnancy. Nutrients. 2019;11(8):1812. doi: 10.3390/nu11081812.
    1. Rappoport L, Peters GR, Downey R, McCann T, Huff-Corzine L. Gender and age differences in food cognition. Appetite. 1993;20(1):33–52. doi: 10.1006/appe.1993.1004.
    1. Mason MA, Fanelli Kuczmarski M, Allegro D, Zonderman AB, Evans MK. The impact of conventional dietary intake data coding methods on foods typically consumed by low-income African-American and white urban populations. Public Health Nutr. 2015;18(11):1922–1931. doi: 10.1017/S1368980014002687.
    1. Edwards D. Introduction to graphical modelling: Springer Science & Business Media. 2012.
    1. Friedman J, Hastie T, Tibshirani R. Sparse inverse covariance estimation with the graphical lasso. Biostatistics. 2007;9(3):432–441. doi: 10.1093/biostatistics/kxm045.
    1. Liu H, Lafferty J, Wasserman L. The nonparanormal: semiparametric estimation of high dimensional undirected graphs. J Mach Learn Res. 2009;10(Oct):2295–2328.
    1. Liu H, Han F, Yuan M, Lafferty J, Wasserman L. High-dimensional semiparametric Gaussian copula graphical models. Ann Stat. 2012;40(4):2293–2326. doi: 10.1214/12-AOS1037.
    1. Meo PD, Ferrara E, Fiumara G, Provetti A. Generalized Louvain method for community detection in large networks. 2011 11th International Conference on Intelligent Systems Design and Applications; 2011 22-24 Nov. 2011.
    1. Blondel VD, Guillaume J-L, Lambiotte R, Lefebvre E. Fast unfolding of communities in large networks. J Stat Mech. 2008;2008(10):P10008. doi: 10.1088/1742-5468/2008/10/P10008.
    1. Guimerà R, Nunes Amaral LA. Functional cartography of complex metabolic networks. Nature. 2005;433(7028):895–900. doi: 10.1038/nature03288.
    1. Cohen JR, D'Esposito M. The segregation and integration of distinct brain networks and their relationship to cognition. J Neurosci. 2016;36(48):12083–12094. doi: 10.1523/JNEUROSCI.2965-15.2016.
    1. Bell BA, Ene M, Smiley W, Schoeneberger JA. A multilevel model primer using SAS PROC MIXED. SAS Global Forum. 2013.
    1. Zhao T, Liu H, Roeder K, Lafferty J, Wasserman L. The huge package for high-dimensional undirected graph estimation in R. J Mach Learn Res. 2012;13(Apr):1059–1062.
    1. Staedler N, Dondelinger F. Package 'nethet': A bioconductor package for high-dimensional exploration of biological network heterogeneity. Bioconductor Repository. 2019;Version 1.17.0:1–46.
    1. Friedman J, Hastie T, Tibshirani R. Package ‘glasso’: Graphical Lasso: Estimation of Gaussian Graphical Models. CRAN Repository. 2019;Version 1.11:1–6.
    1. Christensen A. Package 'NetworkToolbox': Methods and Measures for Brain, Cognitive, and Psychometric Network Analysis. CRAN Repository. 2019;Version 1.3.2:1–69.
    1. Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome research. 2003;13(11):2498-504.
    1. Gofton L, Ness M. Twin trends: health and convenience in food change or who killed the lazy housewife? Br Food J. 1991;93(7):17–23. doi: 10.1108/EUM0000000002352.
    1. Doyle I-M, Borrmann B, Grosser A, Razum O, Spallek J. Determinants of dietary patterns and diet quality during pregnancy: a systematic review with narrative synthesis. Public Health Nutr. 2017;20(6):1009–1028. doi: 10.1017/S1368980016002937.
    1. Siega-Riz AM, Bodnar LM, Savitz DA. What are pregnant women eating? Nutrient and food group differences by race. Am J Obstet Gynecol. 2002;186(3):480–486. doi: 10.1067/mob.2002.121078.
    1. Fowles ER, Timmerman GM, Bryant M, Kim S. Eating at fast-food restaurants and dietary quality in low-income pregnant women. West J Nurs Res. 2011;33(5):630–651. doi: 10.1177/0193945910389083.
    1. Ziauddeen N, Almiron-Roig E, Penney TL, Nicholson S, Kirk SFL, Page P. Eating at food outlets and "On the Go" is associated with less healthy food choices in adults: cross-sectional data from the UK national diet and nutrition survey rolling programme (2008-2014) Nutrients. 2017;9(12):1315. doi: 10.3390/nu9121315.
    1. Stroebele N, De Castro JM. Effect of ambience on food intake and food choice. Nutrition. 2004;20(9):821–838. doi: 10.1016/j.nut.2004.05.012.
    1. Thompson FE, Subar AF. Chapter 1 - Dietary Assessment Methodology. In: Coulston AM, Boushey CJ, Ferruzzi MG, Delahanty LM, editors. Nutrition in the Prevention and Treatment of Disease (Fourth Edition): Academic Press. 2017. pp. 5–48.

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