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Whole Grain and Cereal Fibre Intake in the Australian Health Survey: Associations to CVD Risk Factors

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Date 2020 Mar 24
PMID 32200767
Citations 12
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Abstract

Objective: To explore associations of whole grain and cereal fibre intake to CVD risk factors in Australian adults.

Design: Cross-sectional analysis. Intakes of whole grain and cereal fibre were examined in association to BMI, waist circumference (WC), blood pressure (BP), serum lipid concentrations, C-reactive protein, systolic BP, fasting glucose and HbA1c.

Setting: Australian Health Survey 2011-2013.

Participants: A population-representative sample of 7665 participants over 18 years old.

Results: Highest whole grain consumers (T3) had lower BMI (T0 26·8 kg/m2, T3 26·0 kg/m2, P < 0·0001) and WC (T0 92·2 cm, T3 90·0 cm, P = 0·0005) compared with non-consumers (T0), although only WC remained significant after adjusting for dietary and lifestyle factors, including cereal fibre intake (P = 0·03). Whole grain intake was marginally inversely associated with fasting glucose (P = 0·048) and HbA1c (P = 0·03) after adjusting for dietary and lifestyle factors, including cereal fibre intake. Cereal fibre intake was inversely associated with BMI (P < 0·0001) and WC (P < 0·0008) and tended to be inversely associated with total cholesterol, LDL-cholesterol and apo-B concentrations, although associations were attenuated after further adjusting for BMI and lipid-lowering medication use.

Conclusions: The extent to which cereal fibre is responsible for the CVD-protective associations of whole grains may vary depending on the mediators involved. Longer-term intervention studies directly comparing whole grain and non-whole grain diets of similar cereal fibre contents (such as through the use of bran or added-fibre refined grain products) are needed to confirm independent effects.

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References
1.
Montonen J, Knekt P, Jarvinen R, Reunanen A . Dietary antioxidant intake and risk of type 2 diabetes. Diabetes Care. 2004; 27(2):362-6. DOI: 10.2337/diacare.27.2.362. View

2.
Berry J, Dyer A, Cai X, Garside D, Ning H, Thomas A . Lifetime risks of cardiovascular disease. N Engl J Med. 2012; 366(4):321-9. PMC: 3336876. DOI: 10.1056/NEJMoa1012848. View

3.
Vega-Lopez S, Venn B, Slavin J . Relevance of the Glycemic Index and Glycemic Load for Body Weight, Diabetes, and Cardiovascular Disease. Nutrients. 2018; 10(10). PMC: 6213615. DOI: 10.3390/nu10101361. View

4.
Thies F, Masson L, Boffetta P, Kris-Etherton P . Oats and CVD risk markers: a systematic literature review. Br J Nutr. 2014; 112 Suppl 2:S19-30. DOI: 10.1017/S0007114514002281. View

5.
Moshfegh A, Rhodes D, Baer D, Murayi T, Clemens J, Rumpler W . The US Department of Agriculture Automated Multiple-Pass Method reduces bias in the collection of energy intakes. Am J Clin Nutr. 2008; 88(2):324-32. DOI: 10.1093/ajcn/88.2.324. View