Carbohydrate metabolism and insulin resistance -
Thereby, understanding the association of carbohydrate nutrition with metabolic syndrome may provide a strategy for early intervention in the natural progression of type 2 diabetes. The purpose of the present study was to examine the relation between carbohydrate-related dietary factors, insulin resistance, and the prevalence of the metabolic syndrome in the Framingham Offspring Cohort.
The Framingham Offspring Study is a longitudinal community-based study of cardiovascular disease among the offspring of the original participants of the Framingham Heart Study Cohort and their spouses In , 5, participants were enrolled into the study 39 , and since then, the cohort has been examined every 3 to 4 years.
Between and , during the fifth examination cycle of the Framingham Offspring Study, 3, participants underwent a standardized medical history and physical examination. Valid food frequency questionnaire FFQ data were available for 3, participants.
The Institutional Review Board for Human Research at Boston University and the Human Investigation Research Committee of New England Medical Center approved the protocol.
Usual dietary intake for the previous year was assessed at the fifth cycle using a semiquantitative item FFQ The questionnaires were mailed to the participants before the examination, and the participants were asked to bring the completed questionnaire with them to their appointment.
Participants were asked to report their frequency of consumption of each food item during the last year. Separate questions about use of vitamin and mineral supplements and type of breakfast cereal most commonly consumed were also included in the FFQ. Nutrient intakes were calculated by multiplying the frequency of consumption of each unit of food from the FFQ by the nutrient content of the specified portion.
The relative validity of this FFQ has been examined in several populations for both nutrients and foods 40 — Energy-adjusted intake between the FFQ and multiple diet records are moderately correlated for total carbohydrate and fiber intake. In men and women, respectively, the correlation coefficients were 0.
Dietary exposures included intakes of total dietary carbohydrate, dietary fiber, whole- and refined- grain foods, glycemic index, and glycemic load. In addition, the contribution of total dietary fiber was calculated for each of the food categories: cereals, fruits, vegetables, and legumes. The average dietary glycemic index value based on a white bread standard was calculated for each participant.
Glycemic index values for foods in the FFQ were obtained either from published estimates 27 , from direct testing of food items, or imputed when necessary by matching similar foods based on calories, carbohydrate, sucrose, fat, and dietary fiber content.
In addition for cereals, whenever possible, the method of processing was taken into account. The dietary glycemic load was calculated by multiplying the carbohydrate content of each food by its glycemic index; this value was then multiplied by the frequency of consumption and summed for all food items.
Each unit of dietary glycemic load is the equivalent to 1 g of carbohydrate from white bread 9 , As an indirect measure of validity, dietary intakes of glycemic index and glycemic load estimated from the FFQ have been related to triglyceride concentrations 9 , a metabolic marker known to respond to carbohydrate intake.
Fasting plasma glucose was measured in fresh specimens with a hexokinase reagent kit. Fasting plasma insulin levels were determined using the Coat-A-Count I-radioimmunoassay Diagnostic Products, Los Angeles, CA.
The HOMA-IR method has been validated by comparison with results of glucose clamp studies 44 and frequently sampled intravenous glucose tolerance tests 45 , Height, weight, and waist-to-hip circumferences were measured with the subject standing. In addition, if individuals reported taking hypertensive medication, they were categorized as having elevated blood pressure.
Statistical analyses were conducted using SAS statistical software version 8; SAS Institute, Cary, NC. Because HOMA-IR levels were positively skewed, analyses were performed on the natural logarithm transformations. Baseline characteristics of the participants were computed across quintile categories of HOMA-IR.
Associations among continuous variables were assessed by tests for linear trend using linear regression, and for categorical variables, the Mantel-Haenszel χ 2 test for trend was applied. To examine the relation between carbohydrate nutrition and HOMA-IR, we compared geometric mean HOMA-IR across quintile categories of energy-adjusted carbohydrate, dietary fiber, and source of fiber intakes, glycemic index, and glycemic load.
We tested each association for age and sex interactions, but no interactions were statistically significant. Nutrient intakes were adjusted for total energy intake by the residual method, as described by Willett and Stampfer OR and mean HOMA-IR were adjusted for sex, age, cigarette dose, total energy intake, alcohol intake, percentage saturated and polyunsaturated fat, multivitamin use, and physical activity.
In addition, mean HOMA-IR was also adjusted for BMI, waist-to-hip ratio, and treatment for blood pressure. To assess trends across quintile categories, we assigned the median intake of each quintile category to individuals with intakes in the category and then included this quintile median variable as a continuous factor in the linear or logistic regression models.
The P for trend was the resulting P value for the associated linear or logistic regression coefficient. The 2, participants 1, men and 1, women in this study ranged in age from 26 to 82 years; their mean age was 54 ± 9.
The characteristics of the study population across quintile categories of HOMA-IR are presented in Table 1. Higher quintile categories of HOMA-IR included a greater proportion of men, older participants, those with hypertension, glucose intolerances, and undiagnosed diabetes.
In addition, BMI, waist-to-hip ratio, and concentrations of fasting insulin were all higher with increasing HOMA-IR. The prevalence of alcohol use, current smoking, and estrogen replacement therapy among postmenopausal women decreased across quintiles of HOMA-IR, whereas physical activity remained constant.
The multivariate-adjusted analyses for intakes of carbohydrates, dietary fiber, fiber source, glycemic index and load, and whole and refined grains are shown in Table 2. After adjustment for potential confounding variables, intakes of total dietary fiber, cereal fiber, fruit fiber, and whole grains were associated with lower HOMA-IR.
The association between fruit and cereal fiber and HOMA-IR remained significant after mutual adjustment for each other. The association between whole-grain intake and HOMA-IR was attenuated and no longer remained significant after adjustment for cereal lowest versus highest quintile, 6.
However, cereal fiber remained significantly associated with HOMA-IR after adjustment for whole grains 6. As the glycemic index increased, the multivariate-adjusted HOMA-IR increased from 6. A similar increase in HOMA-IR was observed with increasing dietary glycemic load, and these associations remained significant after further adjustment of the model for cereal fiber and whole-grain intakes.
Furthermore, the associations between whole-grain and cereal fiber and HOMA-IR were independent of glycemic index. Dietary intakes of total carbohydrate, refined grains, and fiber from vegetables and legumes were not associated with improved HOMA-IR.
The lack of an association between vegetable fiber and HOMA-IR did not change after excluding potatoes, a high glycemic index food source. The findings in Table 2 were essentially identical when analyses were repeated using fasting insulin, rather than the HOMA-IR, as a measure of insulin resistance.
Given that obesity has a strong effect on insulin concentrations, obesity may alter the relation between the carbohydrate source and insulin concentration. The relation between the prevalence of metabolic syndrome and intakes of carbohydrates, dietary fiber, fiber source, glycemic index and load, and whole and refined grains are shown in Table 2.
Cereal fiber and whole-grain intakes were significantly inversely associated with the metabolic syndrome after adjustment for sex, age, cigarette dose, total energy intake, saturated and polyunsaturated fat, alcohol intake, multivitamin use, and physical activity.
A substantial reduction in the prevalence odds of metabolic syndrome was observed with increasing cereal fiber intake and whole-grain intake. These associations remained significant after adjustment for glycemic index.
The inverse association between whole-grain intake and metabolic syndrome was largely explained by cereal fiber, and a significant association was no longer observed between whole-grain intake and the risk of metabolic syndrome after adjusting for cereal fiber 0. The association between glycemic load and prevalence of the syndrome did not change after adjustment for cereal fiber.
Our findings suggest that higher intakes of whole-grain foods, dietary fiber, cereal, and fruit fiber and diets with a lower glycemic index and glycemic load are associated with lower insulin resistance as determined using the HOMA method.
Insulin resistance is a common feature of and a possible contributing factor to the metabolic syndrome. However, after considering several aspects of carbohydrate nutrition, only whole-grain, cereal fiber, and glycemic index intakes were associated with the prevalence of the metabolic syndrome.
To our knowledge, this is the first observational study to examine associations between different aspects of carbohydrate nutrition and prevalence of the metabolic syndrome.
Our data confirm other observational studies that diets rich in whole-grain foods are associated with lower insulin concentrations 17 , One intervention study further supports the hypothesis that diets rich in whole-grain foods improve insulin sensitivity. Pereira et al. Improved insulin sensitivity associated with high whole-grain diets appear in part to be attributed to the high dietary or cereal fiber content of whole-grain foods.
Chandalia et al. A recent controlled metabolic trial found that supplementing a high-carbohydrate diet with soluble fiber improved blood lipid and lipoprotein concentrations and improved glycemic control in pre-diabetic patients with several metabolic abnormalities that define the metabolic syndrome In contrast, other intervention studies have found no effect on insulin sensitivity with consumption of high-fiber or whole-grain foods, particularly among older individuals 19 , 21 , The interpretation of these intervention studies is complicated by the varied patient populations e.
Whereas some intervention studies may have missed potential effects because of the short duration on diets, it is also not known whether observed effects in these short-term interventions would remain over time.
In the present study, fiber from cereals was inversely related with the prevalence of the metabolic syndrome, whereas fiber from fruit, vegetable, and legumes was not.
Observational data consistently indicate a greater protective role of fiber from cereal than from other sources in the development of type 2 diabetes 30 — 33 , Adjustment for cereal fiber considerably weakened the associations between whole-grain intake and both HOMA-IR and metabolic syndrome, suggesting that the relation of whole grain may be due in part to cereal fiber or to factors related to cereal fiber intake.
Collectively, these data suggest a greater role for cereal fiber rather than other fiber sources in the development of insulin resistance and the metabolic syndrome.
However, further experimental and longitudinal studies are needed to examine if fiber source is differentially related to change in metabolic risk factors and the incidence of the metabolic syndrome.
Volume 90, Issue 5. Previous Article Next Article. All Issues. Cover Image Cover Image. Article Navigation. Research Article May 01 Carbohydrate Metabolism in Insulin Resistance: Glucose Uptake and Lactate Production by Adipose and Forearm Tissues in Vivo before and after a Mixed Meal Simon W.
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Received: October 05 Received: January 24 Accepted: February 08 Online ISSN: Clin Sci Lond 90 5 : — Article history Received:. Get Permissions. If you believe you should have access to that content, please contact your librarian.
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Nicola M. McKeownJames Inssulin. MeigsSimin LiuEdward SaltzmanPeter W. WilsonPaul F. Jacques; Carbohydrate Nutrition, Insulin Resistance, and the Prevalence of the Metabolic Syndrome in the Framingham Offspring Cohort. ECE Poster Carnohydrate Diabetes, Obesity, Metabolism and Aromatic Orange Extract abstracts. Bukovinian State Metaabolism University, Clinical Immunology, Metabolim and Endocrinology, Chernivtsi, Ukraine. Background: Chronic pyelonephritis CP Metbolism a metabolisn kidney infection complication in patients with diabetes mellitus and Citrus bioflavonoids for eye health Carbohydrate metabolism and insulin resistance the main metaboliism of chronic kidney disease in diabetic patients. The objective: Of the study was to determine the features of the carbohydrate metabolism and insulin resistance in patients with CP depending on the phenotype of latent autoimmune diabetes in adults LADA. Methods: 28 patients with LADA and CP were examined, as well as 25 representatives of the control group. The patients were divided into two groups by the phenotype of LADA: 19 patients with LADA1 and 9 with LADA2. Results: Fasting plasma glucose level was
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