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A mediation analysis on the relationship between dietary glycemic load, obesity and cardiovascular risk factors in children

International Journal of Obesity volume 46, pages 774–781 (2022)Cite this article

Subjects

Abstract

Background/Objective

Adiposity may mediate the effect of dietary glycemic load (GL) on lipid profiles in children, as studies have shown an association between dietary GL and adiposity and between adiposity and lipid profiles. Our objective was to evaluate the role of adiposity as a mediator in the association between dietary GL and lipid profiles after 2 years.

Subjects/Methods

The Quebec Adipose and Lifestyle InvesTigation in Youth study included 630 children, 8–10 years old at recruitment with at least one parent with overweight or obesity with 2-year follow-up. Three baseline 24-h dietary recalls were administered by a dietitian at baseline. Child and parent characteristics were obtained through direct measurement (blood lipids, anthropometrics) or questionnaires (socio-economic characteristics). Indicators of adiposity, including body mass index (BMI) z-score and percent body fat, were the mediators of interest. A conventional approach using the Baron and Kenny method was used. A causal approach using marginal structural models (MSM) was used to estimate the controlled direct effect.

Results

Mean age at baseline was 9.6 years and 33% were overweight or obese. Both methods revealed that the effect of GL on blood lipids was mediated by adiposity. The weighted MSM did not show evidence of a direct effect (TG: β =;0.01, 95% CI = −0.01,0.02; HDL: β = 0.005, 95%CI = −0.002,0.01), whereas the conventional method did for TG but not HDL (TG:β = 0.04, 95%CI = 0.01,0.07; HDL: β = −0.01, 95%CI = −0.03,0.01).

Conclusion

Adiposity contributes substantially to the association between GL and blood lipids. The choice of approach for mediation analysis should be based on the fulfilment of conditions of each method.

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Fig. 1: Mediation models of BMI z-score as a mediatory in the association between glycemic load and (A) TG and (B) HDL cholesterol.
Fig. 2: Graph illustrating the predicted (A) TG and (B) HDL cholesterol on glycemic load for the total effect, Baron & Kenny mediation and Causal mediation by category of obesity (defined as normal weight (BMI z-score ≤1), overweight (BMI z-score >1) and obese (BMI z-score >2)).

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References

  1. Sahoo K, Sahoo B, Choudhury AK, Sofi NY, Kumar R, Bhadoria AS. Childhood obesity: causes and consequences. J Family Med Prim Care. 2015;4:187–92.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Friedemann C, Heneghan C, Mahtani K, Thompson M, Perera R, Ward AM. Cardiovascular disease risk in healthy children and its association with body mass index: systematic review and meta-analysis. BMJ. 2012;345:e4759.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Juhola J, Magnussen CG, Viikari JS, Kahonen M, Hutri-Kahonen N, Jula A, et al. Tracking of serum lipid levels, blood pressure, and body mass index from childhood to adulthood: the Cardiovascular Risk in Young Finns Study. J Pediatr. 2011;159:584–90.

    Article  CAS  PubMed  Google Scholar 

  4. Baker JL, Olsen LW, Sorensen TI. Childhood body-mass index and the risk of coronary heart disease in adulthood. N Engl J Med. 2007;357:2329–37.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Morrison JA, Glueck CJ, Horn PS, Yeramaneni S, Wang P. Pediatric triglycerides predict cardiovascular disease events in the fourth to fifth decade of life. Metabolism. 2009;58:1277–84.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Shikany JM, Tinker LF, Neuhouser ML, Ma Y, Patterson RE, Phillips LS, et al. Association of glycemic load with cardiovascular disease risk factors: the Women’s Health Initiative Observational Study. Nutrition. 2010;26:641–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. McKeown NM, Meigs JB, Liu S, Saltzman E, Wilson PW, Jacques PF. Carbohydrate nutrition, insulin resistance, and the prevalence of the metabolic syndrome in the Framingham Offspring Cohort. Diabetes Care. 2004;27:538–46.

    Article  PubMed  Google Scholar 

  8. Song S, Lee JE, Song WO, Paik HY, Song Y. Carbohydrate intake and refined-grain consumption are associated with metabolic syndrome in the Korean adult population. J Acad Nutr Diet. 2014;114:54–62.

    Article  PubMed  Google Scholar 

  9. Ford ES, Liu S. Glycemic index and serum high-density lipoprotein cholesterol concentration among us adults. Arch Internal Med. 2001;161:572–6.

    Article  CAS  Google Scholar 

  10. Zhang X, Zhu Y, Cai L, Ma L, Jing J, Guo L, et al. Dietary glycemic index and glycemic load and their relationship to cardiovascular risk factors in Chinese children. Appl Physiol Nutr Metab. 2016;41:391–6.

    Article  CAS  PubMed  Google Scholar 

  11. Slyper A, Jurva J, Pleuss J, Hoffmann R, Gutterman D. Influence of glycemic load on HDL cholesterol in youth. Am J Clin Nutr. 2005;81:376–9.

    Article  CAS  PubMed  Google Scholar 

  12. Baron RM, Kenny DA. The moderator-mediator variable distinction in social psychological research: conceptual, strategic, and statistical considerations. J Pers Soc Psychol. 1986;51:1173–82.

    Article  CAS  PubMed  Google Scholar 

  13. Cole SR, Platt RW, Schisterman EF, Chu H, Westreich D, Richardson D, et al. Illustrating bias due to conditioning on a collider. Int J Epidemiol. 2009;39:417–20.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Cole SR, Hernan MA. Fallibility in estimating direct effects. Int J Epidemiol. 2002;31:163–5.

    Article  PubMed  Google Scholar 

  15. Kaufman JS, Maclehose RF, Kaufman S. A further critique of the analytic strategy of adjusting for covariates to identify biologic mediation. Epidemiol Perspect Innov. 2004;1:4.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Nandi A, Glymour MM, Kawachi I, VanderWeele TJ. Using marginal structural models to estimate the direct effect of adverse childhood social conditions on onset of heart disease, diabetes, and stroke. Epidemiology. 2012;23:223–32.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Van der Wal WM, Geskus RB. An R package for inverse probability weighting. J Stat Softw. 2011;43:1–23.

    Article  Google Scholar 

  18. Lambert M, Van Hulst A, O’Loughlin J, Tremblay A, Barnett TA, Charron H, et al. Cohort profile: the Quebec adipose and lifestyle investigation in youth cohort. Int J Epidemiol. 2012;41:1533–44.

    Article  PubMed  Google Scholar 

  19. Suissa K, Benedetti A, Henderson M, Gray-Donald K, Paradis G. Effects of dietary glycemic index and load on children’s cardiovascular risk factors. Ann Epidemiol. 2019;40:e3.

    Article  Google Scholar 

  20. Centers for Disease Control and Prevention, National Center for Health Statistics. CDC growth charts: United States. http://www.cdc.gov/growthcharts/. In, May 30, 2000.

  21. World Health Organization (WHO). BMI-for-age (5-19 years). Accessed on September 15, 2020 from https://www.who.int/growthref/who2007_bmi_for_age/en/. In.

  22. Friedewald WT, Levy RI, Fredrickson DS. Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clin Chem. 1972;18:499–502.

    Article  CAS  PubMed  Google Scholar 

  23. Colley R, Connor Gorber S, Tremblay MS. Quality control and data reduction procedures for accelerometry-derived measures of physical activity. Health Reports. 2010;21:63–9.

    PubMed  Google Scholar 

  24. Henderson M, Gray-Donald K, Mathieu ME, Barnett TA, Hanley JA, O’Loughlin J, et al. How are physical activity, fitness, and sedentary behavior associated with insulin sensitivity in children? Diabetes Care. 2012;35:1272–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Marshall WA, Tanner JM. Variations in pattern of pubertal changes in girls. Arch Dis Child. 1969;44:291–303.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Marshall WA, Tanner JM. Variations in the pattern of pubertal changes in boys. Arch Dis Child. 1970;45:13–23.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Shim JS, Oh K, Kim HC. Dietary assessment methods in epidemiologic studies. Epidemiol Health. 2014;36:e2014009.

    Article  PubMed  PubMed Central  Google Scholar 

  28. Johnson RK, Driscoll P, Goran MI. Comparison of multiple-pass 24-hour recall estimates of energy intake with total energy expenditure determined by the doubly labeled water method in young children. J Am Diet Assoc. 1996;96:1140–4.

    Article  CAS  PubMed  Google Scholar 

  29. Louie JC, Flood V, Turner N, Everingham C, Gwynn J. Methodology for adding glycemic index values to 24-hour recalls. Nutrition. 2011;27:59–64.

    Article  PubMed  Google Scholar 

  30. Atkinson FS, Foster-Powell K, Brand-Miller JC. International tables of glycemic index and glycemic load values: 2008. Diabetes Care. 2008;31:2281–3.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Olendzki BC, Ma Y, Culver AL, Ockene IS, Griffith JA, Hafner AR, et al. Methodology for adding glycemic index and glycemic load values to 24-hour dietary recall database. Nutrition. 2006;22:1087–95.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Willett WC, Howe GR, Kushi LH. Adjustment for total energy intake in epidemiologic studies. Am J Clin Nutr. 1997;65:1220S–1228S. 4 Suppldiscussion 1229S-1231S

    Article  CAS  PubMed  Google Scholar 

  33. Suissa K, Benedetti A, Henderson M, Gray-Donald K, Paradis G. The cardiometabolic risk profile of underreporters of energy intake differs from that of adequate reporters among children at risk of obesity. J Nutr. 2019;149:123–30.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Nandi A, VanderWeele TJ. Mediation analysis in social epidemiology. Methods in social epidemiology. Hoboken: John Wiley & Sons; 2006. p. 398–430.

    Google Scholar 

  35. VanderWeele TJ. Marginal structural models for the estimation of direct and indirect effects. Epidemiology. 2009;20:18–26.

    Article  PubMed  Google Scholar 

  36. Valeri L, Lin X, VanderWeele TJ. Mediation analysis when a continuous mediator is measured with error and the outcome follows a generalized linear model. Stat Med. 2014;33:4875–90.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Robins JM, Hernan MA, Brumback B. Marginal structural models and causal inference in epidemiology. Epidemiology. 2000;11:550–60.

    Article  CAS  PubMed  Google Scholar 

  38. Ludwig DS. The glycemic index: physiological mechanisms relating to obesity, diabetes, and cardiovascular disease. JAMA. 2002;287:2414–23.

    Article  CAS  PubMed  Google Scholar 

  39. Te Morenga LA, Howatson AJ, Jones RM, Mann J. Dietary sugars and cardiometabolic risk: systematic review and meta-analyses of randomized controlled trials of the effects on blood pressure and lipids. Am J Clin Nutr. 2014;100:65–79.

    Article  Google Scholar 

  40. Song S, Paik HY, Song WO, Park M, Song Y. Three distinct clustering patterns in metabolic syndrome abnormalities are differentially associated with dietary factors in Korean adults. Nutr Res. 2014;34:383–90.

    Article  CAS  PubMed  Google Scholar 

  41. Becker GF, Passos EP, Moulin CC. Short-term effects of a hypocaloric diet with low glycemic index and low glycemic load on body adiposity, metabolic variables, ghrelin, leptin, and pregnancy rate in overweight and obese infertile women: a randomized controlled trial. Am J Clin Nutr. 2015;102:1365–72.

    Article  CAS  PubMed  Google Scholar 

  42. Murakami K, McCaffrey TA, Livingstone MB. Dietary glycaemic index and glycaemic load in relation to food and nutrient intake and indices of body fatness in British children and adolescents. Br J Nutr. 2013;110:1512–23.

    Article  CAS  PubMed  Google Scholar 

  43. Barba G, Sieri S, Russo MD, Donatiello E, Formisano A, Lauria F, et al. Glycaemic index and body fat distribution in children: the results of the ARCA project. Nutr Metab Cardiovascular Dis. 2012;22:28–34.

    Article  CAS  Google Scholar 

  44. Gopinath B, Flood VM, Rochtchina E, Baur LA, Louie JC, Smith W, et al. Carbohydrate nutrition and development of adiposity during adolescence. Obesity. 2013;21:1884–90.

    Article  CAS  PubMed  Google Scholar 

  45. Nielsen BM, Bjornsbo KS, Tetens I, Heitmann BL. Dietary glycaemic index and glycaemic load in Danish children in relation to body fatness. Br J Nutr. 2005;94:992–7.

    Article  CAS  PubMed  Google Scholar 

  46. Schwingshackl L, Hobl LP, Hoffmann G. Effects of low glycaemic index/low glycaemic load vs. high glycaemic index/ high glycaemic load diets on overweight/obesity and associated risk factors in children and adolescents: a systematic review and meta-analysis. Nutr J. 2015;14:87.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Thomas DE, Elliott EJ, Baur L Low glycaemic index or low glycaemic load diets for overweight and obesity. Cochrane Database Syst Rev. 2007: Cd005105. https://doi.org/10.1002/14651858.CD005105.pub2.

  48. Luma GB, Spiotta RT. Hypertension in children and adolescents. Am Fam Phys. 2006;73:1558–68.

    Google Scholar 

  49. Plourde G. Impact of obesity on glucose and lipid profiles in adolescents at different age groups in relation to adulthood. BMC Family Pract. 2002;3:18.

    Article  Google Scholar 

  50. Berenson GS, Wattigney WA, Bao W, Srinivasan SR, Radhakrishnamurthy B. Rationale to study the early natural history of heart disease: the Bogalusa Heart Study. Am J Med Sci. 1995;310:S22–8. Suppl 1

    Article  PubMed  Google Scholar 

  51. Tracy RE, Newman WP,3rd, Wattigney WA, Berenson GS. Risk factors and atherosclerosis in youth autopsy findings of the Bogalusa Heart Study. Am J Med Sci. 1995;310 Suppl 1:S37–41.

    Article  PubMed  Google Scholar 

  52. Stoner L, Weatherall M, Skidmore P, Castro N, Lark S, Faulkner J, et al. Cardiometabolic risk variables in preadolescent children: a factor analysis. J Am Heart Assoc. 2017;6:10.

    Article  Google Scholar 

  53. Pires A, Martins P, Pereira AM, Marques M, Castela E, Sena C, et al. Childhood adiposity: being male is a potential cardiovascular risk factor. Eur J Pediatr. 2016;175:63–9.

    Article  PubMed  Google Scholar 

  54. Telford RD, Cunningham RB, Waring P, Telford RM, Potter JM, Hickman PE, et al. Sensitivity of blood lipids to changes in adiposity, exercise, and diet in children. Med Sci Sports Exerc. 2015;47:974–82.

    Article  CAS  PubMed  Google Scholar 

  55. Dong JY, Zhang YH, Wang P, Qin LQ. Meta-analysis of dietary glycemic load and glycemic index in relation to risk of coronary heart disease. Am J Cardiol. 2012;109:1608–13.

    Article  CAS  PubMed  Google Scholar 

  56. Valeri L, Vanderweele TJ. Mediation analysis allowing for exposure-mediator interactions and causal interpretation: theoretical assumptions and implementation with SAS and SPSS macros. Psychological Methods. 2013;18:137–50.

    Article  PubMed  PubMed Central  Google Scholar 

  57. Cole SR, Hernan MA. Constructing inverse probability weights for marginal structural models. Am J Epidemiol. 2008;168:656–64.

    Article  PubMed  PubMed Central  Google Scholar 

  58. Williamson T, Ravani P. Marginal structural models in clinical research: when and how to use them?. Nephrol Dial Transpl. 2017;32 Suppl 2:ii84–ii90.

    Article  Google Scholar 

  59. Hernan MA. A definition of causal effect for epidemiological research. J Epidemiol Commun Health. 2004;58:265–71.

    Article  CAS  Google Scholar 

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Acknowledgements

The QUALITY study is funded by the Canadian Institutes of Health Research (CIHR), the Heart and Stroke Foundation of Canada (HFSC), as well as the Fonds de la recherche du Québec en santé (FRQS). The authors wish to thank the QUALITY research team and especially Louise Johnson-Down for her help with the dietary data. Dr. Marie Lambert passed away on 20 February 2012, her leadership and devotion to the QUALITY study will always be remembered and appreciated.

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Authors and Affiliations

  1. Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, QC, Canada

    Karine Suissa, Andrea Benedetti & Gilles Paradis

  2. Department of Medicine, McGill University, Montreal, QC, Canada

    Andrea Benedetti

  3. Respiratory Epidemiology and Clinical Research Unit, McGill University Health Centre, Montreal, QC, Canada

    Andrea Benedetti

  4. Research Center of Centre Hospitalier Universitaire Sainte-Justine, Montreal, QC, Canada

    Mélanie Henderson

  5. Department of Pediatrics, Faculty of Medicine, Université de Montréal, Montreal, QC, Canada

    Mélanie Henderson

  6. School of Dietetics and Human Nutrition, McGill University, Montreal, QC, Canada

    Katherine Gray-Donald

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  1. Karine Suissa
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Contributions

KS designed the research question for this project, conducted the analysis, interpreted results and wrote the manuscript. AB, MH, KGD and GP participated in the research question design (defining outcomes, identifying confounders, determining appropriate analysis methods), reviewed and edited the manuscript.

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Correspondence to Gilles Paradis.

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Suissa, K., Benedetti, A., Henderson, M. et al. A mediation analysis on the relationship between dietary glycemic load, obesity and cardiovascular risk factors in children. Int J Obes 46, 774–781 (2022). https://doi.org/10.1038/s41366-021-00958-4

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