Comparison of multiple and novel measures of dietary glycemic carbohydrate with insulin resistant status in older women

Breakfast and dinner insulin index and insulin load in relation to overweight in children and adolescents

PURPOSE

Hyperinsulinemia is related to the development of several chronic diseases, particularly obesity. Therefore, this study aimed to examine the association between the insulinemic potential of both total diet and meals, measured by the glycemic index (GI), glycemic load (GL), insulin index (II), and insulin load (IL), and overweight risk among children and adolescents.

METHODS

This cross-sectional study was conducted on 205 overweight and 146 normal-weight participants aged 6-18 years. Overweight was defined as body mass index ≥ 85th percentile of Turkish growth-reference data. Through the use of standard methodology, dietary and meal GI, GL, II, and IL were derived from dietary data collected via a 3-day dietary record. Associations were investigated using multivariable-adjusted regression analysis.

RESULTS

When controlling for potential covariates, a greater dietary II (OR 2.69, 95% CI 1.28, 5.68) and IL (OR 5.22, 95% CI 2.39, 11.38), as well as GL (OR 3.89, 95% CI 1.77, 8.56), was strongly associated with higher odds of overweight (both P_{for trend} < 0.001). Furthermore, breakfast GL (OR 4.87, 95% CI 2.15, 11.01), II (OR 3.88, 95% CI 1.79, 8.39), IL (OR 4.93, 95% CI 2.20, 11.05) and dinner GL (OR 2.39, 95% CI 1.10, 5.20), II (OR 3.81, 95% CI 1.73, 8.41), IL (OR 3.63, 95% CI 1.67, 7.91) were found to be a significant independent predictor of overweight (all P_{for trend} < 0.001) in pediatric population.

CONCLUSION

Our results suggest that dietary insulin demand, particularly for breakfast and dinner, was independently associated with overweight in children and adolescents. These findings may shed light on the relevance of considering meal insulin demand while developing dietary strategies in this population.

Higher breakfast glycaemic load is associated with increased metabolic syndrome risk, including lower HDL-cholesterol concentrations and increased TAG concentrations, in adolescent girls

Almost all previous studies examining the associations between glycaemic load (GL) and metabolic syndrome risk have used a daily GL value. The daily value does not distinguish between peaks of GL intake over the day, which may be more closely associated with the risk of the metabolic syndrome. The aim of the present study was to investigate the cross-sectional associations between daily and mealtime measures of GL and metabolic syndrome risk, including metabolic syndrome components, in adolescents. Adolescents participating in the 14-year follow-up of the Western Australian Pregnancy Cohort (Raine) Study completed 3 d food records and metabolic assessments. Breakfast GL, lunch GL, dinner GL and a score representing meal GL peaks over the day were determined in 516 adolescents. Logistic regression models were used to investigate whether GL variables were independent predictors of the metabolic syndrome in this population-based cohort (3·5 % prevalence of the metabolic syndrome). Breakfast GL was found to be predictive of the metabolic syndrome in girls (OR 1·15, 95 % CI 1·04, 1·27; P <0·01), but not in boys. Other meal GL values and daily GL were found to be not significant predictors of the metabolic syndrome. When breakfast GL was examined in relation to each of the components of the metabolic syndrome in girls, it was found to be negatively associated with fasting HDL-cholesterol concentrations (P= 0·037; β = − 0·004; 95 % CI − 0·008, − 0·002) and positively associated with fasting TAG concentrations (P= 0·008; exp(β) = 1·002; 95 % CI 1·001, 1·004). The results of the present study suggest that there may be an association between breakfast composition and metabolic syndrome components in adolescent girls. These findings support further investigation into including lower-GL foods as part of a healthy breakfast in adolescence, particularly for girls.

Dietary treatment options for depression among diabetic patient, focusing on macronutrients

There is a bidirectional adverse association between diabetes and depression. The odds for experiencing depressive symptoms in diabetic patients are two times more than nondiabetic persons, and depression is an independent predictor for the onset of diabetes. However, depression has been approximately unrecognized and untreated in two-thirds of diabetic patients, which may lead to worsened diabetes complications. A cornerstone strategy for managing depression among diabetic patients is the use of diet to improve both health problems. Because of similar pathophysiology for chronic diseases and depression, it seems that similar dietary recommendations could be useful. However, few studies have been conducted among diabetic patients. Regarding the complications of diabetes such as renal diseases and coronary heart diseases, the proper range of various macronutrients should be clarified in depressed diabetic patients as well as the proper type of each macronutrient. In this paper, we reviewed the available data on the treatment of depression in diabetic patients.

Nutrigenomics and personalized diets: What will they mean for food?

The modern food system feeds six billion people with remarkable diversity, safety, and nutrition. Yet, the current rise in diet-related diseases is compromising health and devaluing many aspects of modern agriculture. Steps to increase the nutritional quality of individual foods will assist in personalizing health and in guiding individuals to achieve superior health. Nutrigenomics is the scientific field of the genetic basis for varying susceptibilities to disease and the diverse responses to foods. Although some of these genetic determinants will be simple and amenable to personal genotyping as the means to predict health, in practice most will not. As a result, genotyping will not be the secret to personalizing diet and health. Human assessment technologies from imaging to proteomics and metabolomics are providing tools to both understand and accurately assess the nutritional phenotype of individuals. The business models are also emerging to bring these assessment capabilities to industrial practice, in which consumers will know more about their personal health and seek personal solutions.