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Dietary factors associated with metabolic risk score in Finnish children aged 6–8 years: the PANIC study

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Abstract

Purpose

Previous evidence for the associations of eating frequency and food consumption with clustering of metabolic risk factors among children is limited. We therefore investigated association of the daily number of main meals and snacks and food consumption with a metabolic risk score and individual metabolic risk factors in primary school children.

Methods

The subjects were a population sample of Finnish girls and boys 6–8 years of age. Dietary factors were measured by a four-day food record. Metabolic risk score was calculated summing up the Z-scores of waist circumference, systolic and diastolic blood pressure, and concentrations of fasting serum insulin and fasting plasma glucose, triglycerides and high-density lipoprotein cholesterol, the latest multiplying by −1.

Results

Skipping main meals (standardized regression coefficient β = −0.18, P < 0.001), a higher consumption of non-root vegetables (β = 0.18, P < 0.01), low-fat vegetable-oil-based margarine (β = 0.13, P < 0.01) and sugar-sweetened beverages (β = 0.11, P < 0.05) and a lower consumption of vegetable oils (β = −0.10, P < 0.05) were associated with a higher metabolic risk score after adjustment for age, sex, total physical activity, electronic media time, energy intake and other dietary factors. The consumption of red meat was directly related to the metabolic risk score, but the association was not statistically significant after adjustment for energy intake.

Conclusions

Eating main meals regularly, decreasing the consumption of sugar-sweetened beverages and low-fat margarine and increasing the consumption of vegetable oils should be emphasized to reduce metabolic risk among children.

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References

  1. Mattsson N, Ronnemaa T, Juonala M, Viikari JS, Raitakari OT (2008) Childhood predictors of the metabolic syndrome in adulthood. The cardiovascular risk in young Finns study. Ann Med 40:542–552. doi:10.1080/07853890802307709

    Article  CAS  Google Scholar 

  2. Franks PW, Hanson RL, Knowler WC, Sievers ML, Bennett PH, Looker HC (2010) Childhood obesity, other cardiovascular risk factors, and premature death. N Engl J Med 362:485–493. doi:10.1056/NEJMoa0904130

    Article  CAS  Google Scholar 

  3. Nguyen QM, Srinivasan SR, Xu JH, Chen W, Kieltyka L, Berenson GS (2010) Utility of childhood glucose homeostasis variables in predicting adult diabetes and related cardiometabolic risk factors: the Bogalusa Heart Study. Diabetes Care 33:670–675. doi:10.2337/dc09-1635

    Article  Google Scholar 

  4. Eisenmann JC (2008) On the use of a continuous metabolic syndrome score in pediatric research. Cardiovasc Diabetol 7:17. doi:10.1186/1475-2840-7-17

    Article  Google Scholar 

  5. Kelly AS, Steinberger J, Jacobs DR, Hong CP, Moran A, Sinaiko AR (2011) Predicting cardiovascular risk in young adulthood from the metabolic syndrome, its component risk factors, and a cluster score in childhood. Int J Pediatr Obes 6:e283–e289. doi:10.3109/17477166.2010.528765

    Google Scholar 

  6. Bel-Serrat S, Mouratidou T, Bornhorst C, Peplies J, De Henauw S, Marild S, Molnar D, Siani A, Tornaritis M, Veidebaum T, Krogh V, Moreno LA (2013) Food consumption and cardiovascular risk factors in European children: the IDEFICS study. Pediatr Obes 8:225–236. doi:10.1111/j.2047-6310.2012.00107.x

    Article  CAS  Google Scholar 

  7. Kelishadi R, Gouya MM, Adeli K, Ardalan G, Gheiratmand R, Majdzadeh R, Mahmoud-Arabi MS, Delavari A, Riazi MM, Barekati H, Motaghian M, Shariatinejad K, Heshmat R, CASPIAN Study Group (2008) Factors associated with the metabolic syndrome in a national sample of youths: CASPIAN study. Nutr Metab Cardiovasc Dis 18:461–470. doi:10.1016/j.numecd.2007.02.014

    Article  Google Scholar 

  8. Eloranta AM, Lindi V, Schwab U, Tompuri T, Kiiskinen S, Lakka HM, Laitinen T, Lakka TA (2012) Dietary factors associated with overweight and body adiposity in Finnish children aged 6–8 years: the PANIC study. Int J Obes (Lond) 36:950–955. doi:10.1038/ijo.2012.89

    Article  CAS  Google Scholar 

  9. Jääskeläinen A, Schwab U, Kolehmainen M, Pirkola J, Järvelin MR, Laitinen J (2012) Associations of meal frequency and breakfast with obesity and metabolic syndrome traits in adolescents of Northern Finland Birth Cohort 1986. Nutr Metab Cardiovasc Dis; epub ahead of print. doi:10.1016/j.numecd.2012.07.006

  10. Saari A, Sankilampi U, Hannila ML, Kiviniemi V, Kesseli K, Dunkel L (2011) New Finnish growth references for children and adolescents aged 0 to 20 years: length/height-for-age, weight-for-length/height, and body mass index-for-age. Ann Med 43:235–248. doi:10.3109/07853890.2010.515603

    Article  Google Scholar 

  11. Cole TJ, Bellizzi MC, Flegal KM, Dietz WH (2000) Establishing a standard definition for child overweight and obesity worldwide: international survey. BMJ 320:1240–1243. doi:10.1136/bmj.320.7244.1240

    Article  CAS  Google Scholar 

  12. Farshchi HR, Taylor MA, Macdonald IA (2004) Regular meal frequency creates more appropriate insulin sensitivity and lipid profiles compared with irregular meal frequency in healthy lean women. Eur J Clin Nutr 58:1071–1077. doi:10.1038/sj.ejcn.1601935

    Article  CAS  Google Scholar 

  13. Eloranta AM, Lindi V, Schwab U, Kiiskinen S, Kalinkin M, Lakka HM, Lakka TA (2011) Dietary factors and their associations with socioeconomic background in Finnish girls and boys 6–8 years of age: the PANIC study. Eur J Clin Nutr 65:1211–1218. doi:10.1038/ejcn.2011.113

    Article  CAS  Google Scholar 

  14. Pan Y, Pratt CA (2008) Metabolic syndrome and its association with diet and physical activity in US adolescents. J Am Diet Assoc 108:276–286. doi:10.1016/j.jada.2007.10.049

    Article  Google Scholar 

  15. Yoo S, Nicklas T, Baranowski T, Zakeri IF, Yang SJ, Srinivasan SR, Berenson GS (2004) Comparison of dietary intakes associated with metabolic syndrome risk factors in young adults: the Bogalusa Heart Study. Am J Clin Nutr 80:841–848

    CAS  Google Scholar 

  16. Riserus U, Willett WC, Hu FB (2009) Dietary fats and prevention of type 2 diabetes. Prog Lipid Res 48:44–51. doi:10.1016/j.plipres.2008.10.002

    Article  CAS  Google Scholar 

  17. Lichtenstein AH, Schwab US (2000) Relationship of dietary fat to glucose metabolism. Atherosclerosis 150:227–243. doi:10.1016/S0021-9150(99)00504-3

    Article  CAS  Google Scholar 

  18. Sharma S, Roberts LS, Lustig RH, Fleming SE (2010) Carbohydrate intake and cardiometabolic risk factors in high BMI African American children. Nutr Metab (Lond) 7:10. doi:10.1186/1743-7075-7-10

    Article  Google Scholar 

  19. Davis JN, Ventura EE, Weigensberg MJ, Ball GD, Cruz ML, Shaibi GQ, Goran MI (2005) The relation of sugar intake to beta cell function in overweight Latino children. Am J Clin Nutr 82:1004–1010

    CAS  Google Scholar 

  20. Ludwig DS (2002) The glycemic index: physiological mechanisms relating to obesity, diabetes, and cardiovascular disease. JAMA 287:2414–2423. doi:10.1001/jama.287.18.2414

    Article  CAS  Google Scholar 

  21. Ambrosini GL, Huang RC, Mori TA, Hands BP, O’Sullivan TA, de Klerk NH, Beilin LJ, Oddy WH (2010) Dietary patterns and markers for the metabolic syndrome in Australian adolescents. Nutr Metab Cardiovasc Dis 20:274–283. doi:10.1016/j.numecd.2009.03.024

    Article  CAS  Google Scholar 

  22. Klesges RC, Hanson CL, Eck LH, Durff AC (1988) Accuracy of self-reports of food intake in obese and normal-weight individuals: effects of parental obesity on reports of children’s dietary intake. Am J Clin Nutr 48:1252–1256

    CAS  Google Scholar 

  23. Crawford PB, Obarzanek E, Morrison J, Sabry ZI (1994) Comparative advantage of 3-day food records over 24-hour recall and 5-day food frequency validated by observation of 9- and 10-year-old girls. J Am Diet Assoc 94:626–630. doi:10.1016/0002-8223(94)90158-9

    Article  CAS  Google Scholar 

  24. Viitasalo A, Laaksonen DE, Lindi V, Eloranta AM, Jääskeläinen J, Tompuri T, Väisänen S, Lakka HM, Lakka TA (2012) Clustering of metabolic risk factors is associated with high-normal levels of liver enzymes among 6- to 8-year-old children: the PANIC study. Metab Syndr Relat Disord 10:337–343. doi:10.1089/met.2012.0015

    Article  CAS  Google Scholar 

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Acknowledgments

We thank all voluntary subjects and their families and the research team. This work was financially supported by grants from Ministry of Social Affairs and Health of Finland, Ministry of Education and Culture of Finland, Finnish Innovation Fund Sitra, Social Insurance Institution of Finland, Finnish Cultural Foundation, Juho Vainio Foundation, Foundation for Paediatric Research, Paavo Nurmi Foundation, Diabetes Research Foundation, Paulo Foundation and Kuopio University Hospital.

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The authors declare that they have no conflict of interest.

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Correspondence to A. M. Eloranta.

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Eloranta, A.M., Lindi, V., Schwab, U. et al. Dietary factors associated with metabolic risk score in Finnish children aged 6–8 years: the PANIC study. Eur J Nutr 53, 1431–1439 (2014). https://doi.org/10.1007/s00394-013-0646-z

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  • DOI: https://doi.org/10.1007/s00394-013-0646-z

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