Blood biomarkers of various dietary patterns correlated with metabolic indicators in Taiwanese type 2 diabetes

  • Meng-Chuan Huang Graduate Institute of Medicine and Department of Public Health and Environmental Medicine, School of Medicine, Kaohsiung Medical University
  • Chaio-I Chang Graduate Institute of Medicine and Department of Public Health and Environmental Medicine, School of Medicine, Kaohsiung Medical University
  • Wen-Tsan Chang Department of Surgery, Kaohsiung Medical University Hospital, Kaohsiung Medical University
  • Yen-Ling Liao Graduate Institute of Medicine and Department of Public Health and Environmental Medicine, School of Medicine, Kaohsiung Medical University.
  • Hsin-Fang Chung School of Public Health, University of Queensland
  • Chih-Cheng Hsu Institute of Population Health Sciences, National Health Research Institutes
  • Shyi-Jang Shin Division of Endocrinology and Metabolism, Department of Internal Medicine, Kaohsiung Medical University Hospital, Kaohsiung Medical University.
  • Kun-Der Lin Department of Internal Medicine, Kaohsiung Municipal Ta-Tung Hospital, Kaohsiung Medical University.
DOI: https://doi.org/10.29219/fnr.v63.3592
Keywords: dietary pattern, ferritin, n-3 PUFA, Taiwan, type 2 diabetes

Abstract

Background: Metabolic alterations correlate with adverse outcomes in type 2 diabetes. Dietary modification serves as an integral part in its treatment.

Objective: We examined the relationships among dietary patterns, dietary biomarkers, and metabolic indicators in type 2 diabetes (n = 871).

Design: Diabetic patients (n = 871) who provided complete clinical and dietary data in both 2008 and 2009 were selected from a cohort participating in a diabetic control study in Taiwan. Dietary data were obtained using a short, semiquantitative food frequency questionnaires, and dietary pattern identified by factor analysis. Multiple linear regressions were used to analyze the association between dietary biomarkers (ferritin, folate, and erythrocyte n-3 polyunsaturated fatty acids [n-3 PUFAs]) and metabolic control upon adjusting for confounders.

Results: Three dietary patterns (high-fat meat, traditional Chinese food–snack, and fish–vegetable) were identified. Ferritin correlated positively with high-fat meat factor scores (P for trend <0.001). Erythrocyte n-3 PUFAs (eicosapentaenoic acid [EPA] + docosahexaenoic acid [DHA], n-3/n-6 PUFA ratio) correlated positively with fish–vegetable factor scores (all P for trends <0.001). Multiple linear regressions revealed a positive relationship between ferritin concentrations and fasting plasma glucose (FPG), hemoglobin A1c (HbA1c), and triglycerides, but a negative relationship with high-density lipoprotein cholesterol (HDL-C). Erythrocyte n-3 PUFA, EPA+DHA, and n-3/n-6 PUFA ratio were negatively linked to FPG, HbA1c, and triglycerides (all P < 0.05) and positively with HDL-C (though n-3/n-6 ratio marginally correlated).

Conclusions: Ferritin and n-3 PUFA can serve as valid biomarkers for high-fat meat and fish–vegetable dietary patterns. Unlike ferritin, erythrocyte n-3 PUFA status was related to better glycemic and blood lipid profiles. Our results suggest that habitual consumption of diet pattern rich in fish and vegetables may contribute in part to a healthier metabolic profile in type 2 diabetes.

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Author Biography

Meng-Chuan Huang, Graduate Institute of Medicine and Department of Public Health and Environmental Medicine, School of Medicine, Kaohsiung Medical University

 

 

 

References


  1. Yang G, Kong L, Zhao W, Wan X, Zhai Y, Chen LC, et al. Emergence of chronic non-communicable diseases in China. Lancet 2008; 372(9650): 1697–705. doi: 10.1016/S0140-6736(08)61366-5.

  2. Whiting DR, Guariguata L, Weil C, Shaw J. IDF diabetes atlas: global estimates of the prevalence of diabetes for 2011 and 2030. Diabetes Res Clin Pract 2011; 94(3): 311–21. doi: 10.1016/j.diabres.2011.10.029.

  3. Jiang YD, Chang CH, Tai TY, Chen JF, Chuang LM. Incidence and prevalence rates of DM in Taiwan: analysis of the 2000–2009 Nationwide Health Insurance database. J Formos Med Assoc 2012; 111(11): 599–604. doi: 10.1016/j.jfma.2012.09.014.

  4. Quispe R, Martin SS, Jones SR. Triglycerides to high-density lipoprotein- cholesterol ratio, glycemic control and cardiovascular risk in obese patients with type 2 diabetes. Curr Opin Endocrinol Diabetes Obes 2016; 23(2): 150–6. doi: 10.1097/MED.0000000000000241.

  5. Erber E, Hopping BN, Grandinetti A, Park SY, Kolonel LN, Maskarinec G. Dietary patterns and risk for diabetes: the multiethnic cohort. Diabetes Care 2010; 33(3): 532–8. doi: 10.2337/dc09-1621.

  6. Malik VS, Fung TT, van Dam RM, Rimm EB, Rosner B, Hu FB. Dietary patterns during adolescence and risk of type 2 diabetes in middle-aged women. Diabetes Care 2012; 35(1): 12–18. doi: 10.2337/dc11-0386.

  7. Nettleton JA, Steffen LM, Ni H, Liu K, Jacobs DR Jr. Dietary patterns and risk of incident type 2 diabetes in the Multi-Ethnic Study of Atherosclerosis (MESA). Diabetes Care 2008; 31(9): 1777–82. doi: 10.2337/dc08-0760.

  8. Reeds J, Mansuri S, Mamakeesick M, Harris SB, Zinman B, Gittelsohn J, et al. Dietary Patterns and Type 2 Diabetes Mellitus in a First Nations Community. Can J Diabetes 2016; 40(4): 304–10. doi: 10.1016/j.jcjd.2016.05.001.

  9. Newby PK, Tucker KL. Empirically derived eating patterns using factor or cluster analysis: a review. Nutr Rev 2004; 62(5): 177–203. doi: 10.1301/nr.2004.may.177-203.

  10. Odegaard AO, Koh WP, Butler LM, Duval S, Gross MD, Yu MC, et al. Dietary patternsand incident type 2 diabetes in chinese men and women: the Singapore Chinese health study. Diabetes Care 2011; 34(4): 880–5. doi: 10.2337/dc10-2350.

  11. Chung HF, Hsu CC, Mamun AA, Long KZ, Huang YF, Shin SJ, et al. Dietary patterns, dietary biomarkers, and kidney disease in patients with type 2 diabetes: a repeated-measure study in Taiwan. Asia Pac J Clin Nutr. 2018; 27(2): 366–74. doi: 10.6133/apjcn.042017.15.

  12. Hsu CC, Jhang HR, Chang WT, Lin CH, Shin SJ, Hwang SJ, et al. Associations between dietary patterns and kidney function indicators in type 2 diabetes. Clin Nutr 2014; 33(1): 98–105. doi: 10.1016/j.clnu.2013.04.010.

  13. Fung TT, Rimm EB, Spiegelman D, Rifai N, Tofler GH, Willett WC, et al. Association between dietary patterns and plasma biomarkers of obesity and cardiovascular disease risk. Am J Clin Nutr 2001; 73(1): 61–7. doi: 10.1093/ajcn/73.1.61.

  14. Couillard C, Lemieux S, Vohl MC, Couture P, Lamarche B. Carotenoids as biomarkers of fruit and vegetable intake in men and women. Br J Nutr 2016; 116(7): 1206–15. doi: 10.1017/S0007114516003056.

  15. Lee JE, Chan AT. Fruit, vegetables, and folate: cultivating the evidence for cancer prevention. Gastroenterology 2011; 141(1): 16–20. doi: 10.1053/j.gastro.2011.05.020.

  16. Kondo K, Morino K, Nishio Y, Kondo M, Nakao K, Nakagawa F, et al. A fish- based diet intervention improves endothelial function in postmenopausal women with type 2 diabetes mellitus: a randomized crossover trial. Metabolism 2014; 63(7): 930–40. doi: 10.1016/j.metabol.2014.04.005.

  17. Chung HF, Long KZ, Hsu CC, Al Mamun A, Jhang HR, Shin SJ, et al. Association of n-3 polyunsaturated fatty acids and inflammatory indicators with renal function decline in type 2 diabetes. Clin Nutr 2015; 34(2): 229–34. doi: 10.1016/j.clnu.2014.02.009

  18. Huang MC, Lin KD, Chen HJ, Wu YJ, Chang CI, Shin SJ, et al. Validity of a short food frequency questionnaire assessing macronutrient and fiber intakes in patients of Han Chinese descent with type 2 diabetes. Int J Environ Res Public Health 2018; 15(6): 1142–55. doi: 10.3390/ijerph15061142.

  19. Bligh EG, Dyer WJ. A rapid method of total lipid extraction and purification. Can J Biochem Physiol 1959; 37(8): 911–17. doi: 10.1139/o59-099.

  20. Abril-Ulloa V, Flores-Mateo G, Sola-Alberich R, Manuel-y-Keenoy B, Arija V. Ferritin levels and risk of metabolic syndrome: meta-analysis of observational studies. BMC Public Health 2014; 14: 483. doi: 10.1186/1471-2458-14-483.

  21. Orban E, Schwab S, Thorand B, Huth C. Association of iron indices and type 2 diabetes: a meta-analysis of observational studies. Diabetes Metab Res Rev 2014; 30(5): 372–94. doi: 10.1002/dmrr.2506.

  22. Ren Y, Tian H, Li X, Liang J, Zhao G. Elevated serum ferritin concentrations in a glucose-impaired population and in normal glucose tolerant first-degree relatives in familial type 2 diabetic pedigrees. Diabetes Care 2004; 27(2): 622–3. doi: 10.2337/diacare.27.2.622.

  23. Jehn M, Clark JM, Guallar E. Serum ferritin and risk of the metabolic syndrome in U.S. adults. Diabetes Care 2004; 27(10): 2422–8. doi: 10.2337/diacare.27.10.2422.

  24. Jiang L, Wang A, Molyneaux LM, Constantino MI, Yue DK. The long-term impact of ferritin level on treatment and complications of type 2 diabetes. Diabetes Obes Metab 2008; 10(6): 519–22. doi: 10.1111/j.1463-1326.2008.00855.x.

  25. Fernandez-Real JM, Penarroja G, Castro A, Garcia-Bragado F, Hernandez-Aguado I, Ricart W. Blood letting in high-ferritin type 2 diabetes: effects on insulin sensitivity and beta-cell function. Diabetes 2002; 51(4): 1000–4. doi: 10.2337/diabetes.51.4.1000.

  26. Qian M, Liu M, Eaton JW. Transition metals bind to glycated proteins forming redox active “glycochelates”: implications for the pathogenesis of certain diabetic complications. Biochem Biophys Res Commun 1998; 250(2): 385–9. doi: 10.1006/bbrc.1998.9326.

  27. Wirfalt E, Hedblad B, Gullberg B, Mattisson I, Andren C, Rosander U, et al. Food patterns and components of the metabolic syndrome in men and women: a cross-sectional study within theMalmö Diet and Cancer cohort. Am J Epidemiol 2001; 154(12): 1150–9. doi: 10.1093/aje/154.12.1150.

  28. Wittenbecher C, Muhlenbruch K, Kroger J, Jacobs S, Kuxhaus O, Floegel A, et al. Amino acids, lipid metabolites, and ferritin as potential mediators linking red meat consumption to type 2 diabetes. Am J Clin Nutr 2015; 101(6): 1241–50. doi: 10.3945/ajcn.114.099150.

  29. Cooksey RC, Jouihan HA, Ajioka RS, Hazel MW, Jones DL, Kushner JP, et al. Oxidative stress, beta-cell apoptosis, and decreased insulin secretory capacity in mouse models of hemochromatosis. Endocrinology 2004; 145(11): 5305–12. doi: 10.1210/en.2004-0392

  30. Niederau C, Berger M, Stremmel W, Starke A, Strohmeyer G, Ebert R, et al. Hyperinsulinaemia in non-cirrhotic haemochromatosis: impaired hepatic insulin degradation? Diabetologia 1984; 26(6): 441–4. doi: 10.1007/BF00262217

  31. Ferrannini E. Insulin resistance, iron, and the liver. Lancet 2000; 24; 355(9222): 2181–2. doi: 10.1016/S0140-6736(00)02397-7.

  32. Yao D, Shi W, Gou Y, Zhou X, Yee Aw T, Zhou Y, et al. Fatty acid-mediated intracellular iron translocation: a synergistic mechanism of oxidative injury. Free Radic Biol Med 2005; 39(10): 1385–98. doi: 10.1016/j.freeradbiomed.2005.07.015.

  33. Savolainen O, Lind MV, Bergstrom G, Fagerberg B, Sandberg AS, Ross A. Biomarkers of food intake and nutrient status are associated with glucose tolerance status and development of type 2 diabetes in older Swedish women. Am J Clin Nutr 2017; 106(5): 1302–10. doi: 10.3945/ajcn.117.152850.

  34. Li K, Wu K, Zhao Y, Huang T, Lou D, Yu X, et al. Interaction between marine- derived n-3 long chain polyunsaturated fatty acids and uric acid on glucose metabolism and risk of type 2 diabetes mellitus: a case-control study. Mar Drugs 2015; 13(9): 5564–78. doi: 10.3390/md13095564.

  35. Lapolla A, Sartore G, Della Rovere GR, Romanato G, Zambon S, Marin R, et al. Plasma fatty acids and lipoproteins in type 2 diabetic patients. Diabetes Metab Res Rev 2006; 22(3): 226–31. doi: 10.1002/dmrr.607.

  36. Sun Q, Ma J, Campos H, Hankinson SE, Hu FB. Comparison between plasma and erythrocyte fatty acid content as biomarkers of fatty acid intake in US women. Am J Clin Nutr. 2007; 86(1): 74–81. doi: 10.1093/ajcn/86.1.74.

  37. Jo S, An WS, Park Y. Erythrocyte n-3 polyunsaturated fatty acids and the risk of type 2 diabetes in Koreans: a case-control study. Ann Nutr Metab 2013; 63(4): 283–90. doi: 10.1159/000357018.

  38. Lee C, Liese A, Wagenknecht L, Lorenzo C, Haffner S, Hanley A. Fish consumption, insulin sensitivity and beta-cell function in the Insulin Resistance Atherosclerosis Study (IRAS). Nutr Metab Cardiovasc Dis 2013; 23(9): 829–35. doi: 10.1016/j.numecd.2012.06.001.

  39. Flachs P, Rossmeisl M, Kopecky J. The effect of n-3 fatty acids on glucose homeostasisand insulin sensitivity. Physiol Res 2014; 63(Suppl 1): S93–118. doi: 10.1186/s12944-017-0528-0.

  40. Alhassan A, Young J, Lean MEJ, Lara J. Consumption of fish and vascular risk factors: a systematic review and meta-analysis of intervention studies. Atherosclerosis 2017; 266: 87–94. doi: 10.1016/j.atherosclerosis.2017.09.028.

  41. Larsson SC, Orsini N. Fish consumption and the risk of stroke: a dose-response meta-analysis. Stroke 2011; 42(12): 3621–3. doi: 10.1161/STROKEAHA.111.630319.

  42. Djousse L, Akinkuolie AO, Wu JH, Ding EL, Gaziano JM. Fish consumption, omega-3 fatty acids and risk of heart failure: a meta-analysis. Clin Nutr 2012; 31(6): 846–53. doi: 10.1016/j.clnu.2012.05.010.

  43. Zheng J, Huang T, Yu Y, Hu X, Yang B, Li D. Fish consumption and CHD mortality: an updated meta-analysis of seventeen cohort studies. Public Health Nutr 2012; 15(4): 725–37. doi: 10.1017/S1368980011002254.

  44. Wheeler ML, Dunbar SA, Jaacks LM, Karmally W, Mayer-Davis EJ, Wylie- Rosett J, et al. Macronutrients, food groups, and eating patterns in the management of diabetes: a systematic review of the literature, 2010. Diabetes Care 2012; 35(2): 434–45. doi: 10.2337/dc11-2216.

  45. Belalcazar LM, Reboussin DM, Haffner SM, Reeves RS, Schwenke DC, Hoogeveen RC, et al. Marine omega-3 fatty acid intake: associations with cardiometabolic risk and response to weight loss intervention in the Look AHEAD (Action for Health in Diabetes) study. Diabetes Care 2010; 33(1): 197–9. doi: 10.2337/dc09-1235.

  46. Evert AB, Boucher JL, Cypress M, Dunbar SA, Franz MJ, Mayer-Davis EJ, et al. Nutrition therapy recommendations for the management of adults with diabetes. Diabetes Care. 2014; 37(Suppl 1): S120–43. doi: 10.2337/dc14-S120.

  47. Akbari M, Tabrizi R, Lankarani KB, Heydari ST, Karamali M, Kashanian M, et al. The Effects of folate supplementation on diabetes biomarkers among patients with metabolic diseases: a systematic review and meta-analysis of randomized controlled trials. Horm Metab Res. 2018;50(2):93–105. doi: 10.1055/s-0043-125148.

Published
2019-11-20
How to Cite
Huang M.-C., Chang C.-I., Chang W.-T., Liao Y.-L., Chung H.-F., Hsu C.-C., Shin S.-J., & Lin K.-D. (2019). Blood biomarkers of various dietary patterns correlated with metabolic indicators in Taiwanese type 2 diabetes. Food & Nutrition Research, 63. https://doi.org/10.29219/fnr.v63.3592
Issue
Vol 63 (2019)
Section
Original Articles

Authors retain copyright of their work, with first publication rights granted to SNF Swedish Nutrition Foundation. Read the full Copyright- and Licensing Statement.

 

 

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