A Preliminary Study of DBH (Encoding Dopamine Beta-Hydroxylase) Genetic Variation and Neural Correlates of Emotional and Motivational Processing in Individuals With and Without Pathological Gambling

Background and aims Corticostriatal-limbic neurocircuitry, emotional and motivational processing, dopaminergic and noradrenergic systems and genetic factors have all been implicated in pathological gambling (PG). However, allelic variants of genes influencing dopaminergic and noradrenergic neurotransmitters have not been investigated with respect to the neural correlates of emotional and motivational states in PG. Dopamine beta-hydroxylase (DBH) converts dopamine to norepinephrine; the T allele of a functional single-nucleotide polymorphism rs1611115 (C-1021T) in the DBH gene is associated with less DBH activity and has been linked to emotional processes and addiction. Here, we investigate the influence of rs1611115 on the neural correlates of emotional and motivational processing in PG and healthy comparison (HC) participants. Methods While undergoing functional magnetic resonance imaging, 18 PG and 25 HC participants, all European Americans, viewed gambling-, sad-, and cocaine-related videotapes. Analyses focused on brain activation differences related to DBH genotype (CC/T-carrier [i.e., CT and TT]) and condition (sad/gambling/cocaine). Results CC participants demonstrated greater recruitment of corticostriatal-limbic regions, relative to T-carriers. DBH variants were also associated with altered corticostriatal-limbic activations across the different videotape conditions, and this association appeared to be driven by greater activation in CC participants relative to T-carriers during the sad condition. CC relative to T-carrier subjects also reported greater subjective sadness to the sad videotapes. Conclusions Individual differences in genetic composition linked to aminergic function contribute significantly to emotional regulation across diagnostic groups and warrant further investigation in PG

Food insulin index: physiologic basis for predicting insulin demand evoked by composite meals

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Validation of the food insulin index in lean, young, healthy individuals, and type 2 diabetes in the context of mixed meals : an acute randomized crossover trial

Background: The Food Insulin Index (FII) is a novel classification of single foods based on insulin responses in healthy subjects relative to an isoenergetic reference food. Objective: Our aim was to compare day-long responses to 2 nutrient-matched diets predicted to have either high or low insulin demand in healthy controls and individuals with type 2 diabetes (T2DM). Design: Twenty adults (10 healthy adults and 10 adults with T2DM) were recruited. On separate mornings, subjects consumed either a high- or low-FII diet in random order. Diets consisted of 3 consecutive meals (breakfast, morning tea, and lunch), matched for macronutrients, fiber, and glycemic index (GI), but with 2-fold difference in insulin demand as predicted by the FII of the component foods. Postprandial glycemia and insulinemia were measured in capillary plasma at regular intervals over 8 h. Results: As predicted by their GI, there were no differences in glycemic responses between the 2 diets in either group (mean ± SEM; healthy: 6.2 ± 0.2 compared with 6.1 ± 0.1 mmol/L · min, P = 0.429; T2DM: 9.9 ± 1.3 compared with 10.3 ± 1.6 mmol/L · min, P = 0.485). Compared with the high-FII diet, mean postprandial insulin response over 8 h was 53% lower with the low-FII diet in healthy subjects (mean ± SEM; incremental AUCinsulin 31,900 ± 4100 pmol/L · min compared with 68,100 ± 11,400 pmol/L · min, P = 0.003) and 41% lower in subjects with T2DM (mean ± SEM; incremental AUCinsulin 11,000 ± 1800 pmol/L · min compared with 18,700 ± 3100 pmol/L · min, P = 0.018). Incremental AUCinsulin was statistically significantly different between diets when groups were combined (P = 0.001). Conclusions: The FII algorithm may be a useful tool for reducing postprandial hyperinsulinemia in T2DM, thereby potentially improving insulin resistance and β-cell function. This trial was registered at the Australian New Zealand Clinical Trials Registry as ACTRN12611000654954.6 page(s

Prediction of postprandial glycemia and insulinemia in lean, young, healthy adults : glycemic load compared with carbohydrate content alone

Background: Dietary glycemic load (GL; defined as the mathematical product of the glycemic index and carbohydrate content) is increasingly used in nutritional epidemiology. Its ability to predict postprandial glycemia and insulinemia for a wide range of foods or mixed meals is unclear. Objective: Our objective was to assess the degree of association between calculated GL and observed glucose and insulin responses in healthy subjects consuming isoenergetic portions of single foods and mixed meals. Design: In study 1, groups of healthy subjects consumed 1000-kJ portions of 121 single foods in 10 food categories. In study 2, healthy subjects consumed 2000-kJ servings of 13 mixed meals. Foods and meals varied widely in macronutrient content, fiber, and GL. Glycemia and insulinemia were quantified as area under the curve relative to a reference food (= 100). Results: Among the single foods, GL was a more powerful predictor of postprandial glycemia and insulinemia than was the available carbohydrate content, explaining 85% and 59% of the observed variation, respectively (P < 0.001). Similarly, for mixed meals, GL was also the strongest predictor of postprandial glucose and insulin responses, explaining 58% (P = 0.003) and 46% (P = 0.01) of the variation, respectively. Carbohydrate content alone predicted the glucose and insulin responses to single foods (P < 0.001) but not to mixed meals. Conclusion: These findings provide the first large-scale, systematic evidence of the physiologic validity and superiority of dietary GL over carbohydrate content alone to estimate postprandial glycemia and insulin demand in healthy individuals.13 page(s

Improving the estimation of mealtime insulin dose in adults with type I diabetes : the Normal Insulin Demand for Dose Adjustment (NIDDA) study

OBJECTIVE Although carbohydrate counting is routine practice in type 1 diabetes, hyperglycemic episodes are common. A food insulin index (FII) has been developed and validated for predicting the normal insulin demand generated by mixed meals in healthy adults. We sought to compare a novel algorithm on the basis of the FII for estimating mealtime insulin dose with carbohydrate counting in adults with type 1 diabetes. RESEARCH DESIGN AND METHODS A total of 28 patients using insulin pump therapy consumed two different breakfast meals of equal energy, glycemic index, fiber, and calculated insulin demand (both FII = 60) but approximately twofold difference in carbohydrate content, in random order on three consecutive mornings. On one occasion, a carbohydrate-counting algorithm was applied to meal A (75 g carbohydrate) for determining bolus insulin dose. On the other two occasions, carbohydrate counting (about half the insulin dose as meal A) and the FII algorithm (same dose as meal A) were applied to meal B (41 g carbohydrate). A real-time continuous glucose monitor was used to assess 3-h postprandial glycemia. RESULTS Compared with carbohydrate counting, the FII algorithm significantly decreased glucose incremental area under the curve over 3 h (–52%, P = 0.013) and peak glucose excursion (–41%, P = 0.01) and improved the percentage of time within the normal blood glucose range (4–10 mmol/L) (31%, P = 0.001). There was no significant difference in the occurrence of hypoglycemia. CONCLUSIONS An insulin algorithm based on physiological insulin demand evoked by foods in healthy subjects may be a useful tool for estimating mealtime insulin dose in patients with type 1 diabetes.6 page(s