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Background/objectives: The effect of a low glycemic load (GL) diet on insulin-like growth factor-1 (IGF-1) concentration is still unknown but may contribute to lower chronic disease risk. We aimed to assess the impact of GL on concentrations of IGF-1 and IGF-binding protein-3 (IGFBP-3)., Subjects/methods: We conducted a randomized, controlled crossover feeding trial in 84 overweight obese and normal weight healthy individuals using two 28-day weight-maintaining high- and low-GL diets. Measures were fasting and post-prandial concentrations of insulin, glucose, IGF-1 and IGFBP-3. In all 80 participants completed the study and 20 participants completed post-prandial testing by consuming a test breakfast at the end of each feeding period. We used paired t-tests for diet component and linear mixed models for biomarker analyses., Results: The 28-day low-GL diet led to 4% lower fasting concentrations of IGF-1 (10.6 ng/ml, P=0.04) and a 4% lower ratio of IGF-1/IGFBP-3 (0.24, P=0.01) compared with the high-GL diet. The low-GL test breakfast led to 43% and 27% lower mean post-prandial glucose and insulin responses, respectively; mean incremental areas under the curve for glucose and insulin, respectively, were 64.3±21.8 (mmol/l/240 min; P<0.01) and 2253±539 (μU/ml/240 min; P<0.01) lower following the low- compared with the high-GL test meal. There was no effect of GL on mean homeostasis model assessment for insulin resistance or on mean integrated post-prandial concentrations of glucose-adjusted insulin, IGF-1 or IGFBP-3. We did not observe modification of the dietary effect by adiposity., Conclusions: Low-GL diets resulted in 43% and 27% lower post-prandial responses of glucose and insulin, respectively, and modestly lower fasting IGF-1 concentrations. Further intervention studies are needed to weigh the impact of dietary GL on risk for chronic disease.

Background/objectives: We examined the associations between carbohydrate substitutions (total; low-, medium-, high-glycemic index (GI) carbohydrates) for fat or protein and risk of type 2 diabetes., Subjects/methods: The cohort comprised 25,943 male smokers among whom 1098 diabetes cases were identified from a national register during a 12-year follow-up. Diet was assessed by a validated food frequency questionnaire. The relative risks (RR) and confidence intervals (CI) for diabetes were analyzed using Cox proportional hazard modeling, and multivariate nutrient density models were applied to examine the associations between substitutions of macronutrients and diabetes risk., Results: The risk of diabetes was lower when fat or protein was replaced with an isoenergetic amount (2% of energy intake) of carbohydrates, the multivariate RRs were 0.96 (95% CI: 0.94, 0.99) and 0.85 (95% CI: 0.80, 0.90), respectively. The lower risks were due to replacing saturated plus trans fatty acids, and meat, milk or plant protein with carbohydrates, respectively. Low-, medium- or high-GI carbohydrates did not associate with lower diabetes risk when replacing fat or fatty acids, except when total fat was replaced with medium-GI carbohydrates. Low-, medium- and high-GI carbohydrates had similar inverse associations with diabetes risk when they replaced total, meat or milk protein., Conclusion: Higher carbohydrate intake at the expense of fat, attributable to trans and saturated fatty acids, or protein was associated with decreased diabetes risk. Replacing fat or protein with lower-GI carbohydrates was not more beneficial than replacing it with higher-GI carbohydrates.

Background/objectives: The glycaemic index (GI) is used to describe the blood glucose-raising potential of carbohydrate-containing foods. Only a few descriptions of the addition of GI values to national food composition databases (FCDBs) exist. We tested whether the value documentation framework established within the European Food Information Resource (EuroFIR) Network could be used for GI values when adding them to the Finnish FCDB., Methods: The list of foods requiring GI values was based on the National FINDIET 2007 Survey data and extended with foods encoded in a food-frequency questionnaire used in other nationally representative studies. The minimum quality of GI measurements was verified when gathering values from various sources, using earlier defined criteria. If a measured GI value for a food was directly available, or could be imputed or estimated, the value was added to the Finnish FCDB and documented using core standard vocabularies of EuroFIR. The GI values of composite foods were calculated using recipe calculation software., Results: A total of 2210 foods required a GI value. GI values for 1322 foods were available and added to the FCDB. The remaining 888 foods were composite foods and received a GI value through recipe calculation. The standard vocabularies describing the origin of the GI values, the methods used in their derivation and their qualitative characteristics were suitable for GI values., Conclusions: GI values can be added to FCDBs and documented using terms similar to those used for traditional food composition data. Standardised value documentation may provide transparency for GI database compilation processes.