Association between Carbohydrate Intake and Serum Lipids

Hafner

et al. 2006

Nutrition

Self Cite

Objective-This study examined baseline dietary intake, body weight, and physiologic status in patients enrolled in a dietary intervention for type 2 diabetes mellitus (T2DM).Methods-Dietary, physiologic, and demographic information were collected at baseline from 40 adult patients with poorly controlled T2DM (glycosylated hemoglobin >7%) who participated in a clinical trial at an academic medical center in Worcester, Massachusetts, USA.Results-The average age at enrollment was 53.5 y (SD 8.4), average body mass index was 35.48 kg/m 2 (SD 7.0), and glycosylated hemoglobin was 8.3% (SD 1.2). Participants were predominantly white, married, and employed full time. Forty-eight percent were men. Seventy-eight percent had hyperlipidemia, and 68% had hypertension. Reported baseline daily average energy intake was 1778 kcal (SD 814), daily carbohydrate was 159 g (SD 71.5), and dietary fiber was 11.4 g (SD 5.2). The dietary composition was 35% carbohydrate, 45% fat (15% saturated fat), and 20% protein. The American Diabetes Association (ADA) guidelines recommends 45-65% of energy from carbohydrate, 20-35% from fat (<7% saturated), and 20% from protein.Conclusion-These patients reported a low-carbohydrate, low-fiber, high-fat (especially saturated) diet, although they stated they are not following any of the popular low-carbohydrate diets. Patients with T2DM may find the current trend toward reducing weight through low-carbohydrate diets attractive for control of blood glucose, despite ADA recommendations. This dietary pattern may represent a popular trend that extends beyond our particular study and, if so, has serious cardiovascular implications in this vulnerable population of T2DM patients.

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Low-carbohydrate and high-fat intake among adult patients with poorly controlled type 2 diabetes mellitus

Hafner

et al. 2006

Nutrition

Self Cite

“…The objectives of the present study are to describe the methods used to establish the GI and GL from an extensive set of 24HRs and to calculate overall dietary GI and GL. The database of foods comes from the 24HRs collected from a healthy population in central Massachusetts, as part of the Seasonal Variation of Blood Cholesterol Levels (SEASONS) study, in which we also examined variations of GI and GL and then associations with body weight and blood lipids using 24HRs [20][21][22][23]. This documentation will help to continue the effort to establish a consistent methodology to add GI values to dietary assessment tools and may result in greater use of the GI in research and clinical care.…”

Section: Introductionmentioning

confidence: 99%

Methodology for adding glycemic index and glycemic load values to 24-hour dietary recall database

Culver³

et al. 2006

Nutrition

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Objectives-We describe a method of adding the glycemic index (GI) and glycemic load (GL) values to the nutrient database of the 24-hour dietary recall interview (24HR), a widely used dietary assessment. We also calculated daily GI and GL values from the 24HR.Methods-Subjects were 641 healthy adults from central Massachusetts who completed 9067 24HRs. The 24HR-derived food data were matched to the International Table of Glycemic Index and Glycemic Load Values. The GI values for specific foods not in the table were estimated against similar foods according to physical and chemical factors that determine GI. Mixed foods were disaggregated into individual ingredients.Results-Of 1261 carbohydrate-containing foods in the database, GI values of 602 foods were obtained from a direct match (47.7%), accounting for 22.36% of dietary carbohydrate. GI values from 656 foods (52.1%) were estimated, contributing to 77.64% of dietary carbohydrate. The GI values from three unknown foods (0.2%) could not be assigned. The average daily GI was 84 (SD 5.1, white bread as referent) and the average GL was 196 (SD 63).Conclusion-Using this methodology for adding GI and GL values to nutrient databases, it is possible to assess associations between GI and/or GL and body weight and chronic disease outcomes (diabetes, cancer, heart disease). This method can be used in clinical and survey research settings where 24HRs are a practical means for assessing diet. The implications for using this methodology compel a broader evaluation of diet with disease outcomes.

“…The Glycemic Index Meal Planner software was provided by the Glycemic Diet Software, LLC (http://www.glycemicdietsw.com). Study investigators provided the software company with nutrient and GI values for food equivalent to those from the International Table of GI (Foster-Powell et al, 2002), as well as those on 400 additional foods that are commonly consumed by Americans (Ma et al, 2005(Ma et al, , 2006. The Meal Planner can be accessed easily from the PDA home screen.…”

Section: Study Design and Intervention Formatmentioning

confidence: 99%

“…The GI score was determined from the intake data collected through the 7DDR using published tables (Foster-Powell et al, 2002;Brand-Miller et al, 2003b). GI and GL (GI of a food times the amount of carbohydrate in grams eaten divided by 100) were also calculated, as in previous reports (Ma et al, 2005(Ma et al, , 2006.…”

Section: Assessment Of Study Outcomesmentioning

confidence: 99%

PDA-assisted low glycemic index dietary intervention for type II diabetes: a pilot study

Chiriboga

et al. 2006

Eur J Clin Nutr

Background: Epidemiological and dietary intervention studies suggest that a low-glycemic index (GI) diet is beneficial for blood glucose control; however, long-term clinical utility of the low GI diet has not been fully investigated. Objectives: To evaluate the feasibility and efficacy of a nutritionist-delivered low-GI dietary intervention, with the support of a personal digital assistant (PDA), for adult patients with poorly controlled type II diabetes. Method: The low-GI intervention consisted of six counseling sessions and the use of a PDA-based food database with GI scores for 6 months. Study outcomes included feasibility measures, glycosylated hemoglobin levels (HbA1c), GI and glycemic load (GL) score of self-reported dietary intake, body weight, depression and quality of life (QOL). Measures were obtained at baseline, 3 and 6 months. Results: Of 31 adult patients approached, 15 met study eligibility criteria and were enrolled in the study. Thirteen patients (87%) completed all study assessments. Findings included decreases in average HbA1c (À0.5% P ¼ 0.02), body weight, hip circumference, blood pressure, dietary GI and daily caloric intake. Diabetes impact scores also decreased. All but one participant completed all components of the intervention. There were mixed reports regarding the usefulness of the PDAs; however, participants offered helpful suggestions for further development. Conclusions: Results of this pilot study support the feasibility of implementing a nutritionist-delivered, PDA-assisted low-GI dietary intervention for patients with poorly controlled type II diabetes. Encouraging initial efficacy data require further testing in the context of a randomized clinical trial.