Comparison of multiple and novel measures of dietary glycemic carbohydrate with insulin resistant status in older women
1 Institute of Health and Biomedical Innovation, Queensland University of Technology, Kelvin Grove, QLD, Australia
2 School of Population Health, University of Western Australia, Perth, WA, Australia
3 The Betty Byrne Henderson Research Centre, Royal Brisbane and Women's Hospital, Herston, QLD, Australia
4 School of Health Sciences, University of Wollongong, Wollongong, NSW, Australia
Nutrition & Metabolism 2010, 7:25 doi:10.1186/1743-7075-7-25Published: 7 April 2010
Previous epidemiological investigations of associations between dietary glycemic intake and insulin resistance have used average daily measures of glycemic index (GI) and glycemic load (GL). We explored multiple and novel measures of dietary glycemic intake to determine which was most predictive of an association with insulin resistance.
Usual dietary intakes were assessed by diet history interview in women aged 42-81 years participating in the Longitudinal Assessment of Ageing in Women. Daily measures of dietary glycemic intake (n = 329) were carbohydrate, GI, GL, and GL per megacalorie (GL/Mcal), while meal based measures (n = 200) were breakfast, lunch and dinner GL; and a new measure, GL peak score, to represent meal peaks. Insulin resistant status was defined as a homeostasis model assessment (HOMA) value of >3.99; HOMA as a continuous variable was also investigated.
GL, GL/Mcal, carbohydrate (all P < 0.01), GL peak score (P = 0.04) and lunch GL (P = 0.04) were positively and independently associated with insulin resistant status. Daily measures were more predictive than meal-based measures, with minimal difference between GL/Mcal, GL and carbohydrate. No significant associations were observed with HOMA as a continuous variable.
A dietary pattern with high peaks of GL above the individual's average intake was a significant independent predictor of insulin resistance in this population, however the contribution was less than daily GL and carbohydrate variables. Accounting for energy intake slightly increased the predictive ability of GL, which is potentially important when examining disease risk in more diverse populations with wider variations in energy requirements.