The sympathetic nervous system is recognized to play a role in the etiology of animal and possibly human obesity through its impact on energy expenditure and/or food intake. We, therefore, measured fasting muscle sympathetic nerve activity (MSNA) in the peroneal nerve and its relationship with energy expenditure and body composition in 25 relatively lean Pima Indian males (means±SD; 26±6 yr, 82±19 kg, 28±10% body fat) and 19 Caucasian males (29±5 yr, 81±13 kg, 24±9% body fat). 24-h energy expenditure, sleeping metabolic rate, and resting metabolic rate were measured in a respiratory chamber, whereas body composition was estimated by hydrodensitometry.Pima Indians had lower MSNA than Caucasians (23±6 vs 33±10 bursts/min, P = 0.0007). MSNA was significantly related to percent body fat in Caucasians (r = 0.55, P = 0.01) but not in Pimas. MSNA also correlated with energy expenditure adjusted for fat-free mass, fat mass, and age in Caucasians (r = 0.51, P = 0.03; r = 0.54, P = 0.02; and r = 0.53, P = 0.02 for adjusted 24-h energy expenditure, sleeping metabolic rate, and resting metabolic rate, respectively) but not in Pima Indians.In conclusion, the activity of the sympathetic nervous system is a determinant of energy expenditure in Caucasians. Individuals with low resting MSNA may be at risk for body weight gain resulting from a lower metabolic rate. A low resting MSNA and the lack of impact of MSNA on metabolic rate might play a role in the etiology of obesity in Pima Indians. (J.
The aim of the present study was to determine whether any benefit might occur from lowering the glycaemic index of diet in the medium term in diabetic patients. Eighteen well-controlled diabetic patients (12 Type 1 and 6 Type 2 non-insulin-treated), were assigned to either a high mean glycaemic index or low mean glycaemic index diet for 5 weeks each in a random order using a cross-over design. The two diets were equivalent in terms of nutrient content and total and soluble fibre content. The glycaemic indices were 64 +/- 2 (mean +/- SD) % and 38 +/- 5% for the two diets. The high glycaemic index diet was enriched in bread and potato and the low glycaemic index diet in pasta, rice, and legumes. At the end of the study periods, the following variables were improved on the low compared to the high glycaemic index diet: fructosamine (3.9 +/- 0.9 vs 3.4 +/- 0.4 mmol l-1, p less than 0.05); fasting blood glucose (10.8 +/- 2.8 vs 9.6 +/- 2.7 mmol l-1, p less than 0.02); 2-h postprandial blood glucose (11.6 +/- 2.9 vs 10.3 +/- 2.5 mmol l-1, p less than 0.02); mean daily blood glucose (12.0 +/- 2.5 vs 10.4 +/- 2.7 mmol l-1, p less than 0.02); serum triglycerides (1.5 +/- 0.9 vs 1.2 +/- 0.6 mmol l-1, p less than 0.05). No significant differences were found in body weight, HbA1C, insulin binding to erythrocytes, insulin and drug requirements, and other circulating lipids (cholesterol, HDL-cholesterol, phospholipids, Apolipoprotein A1, Apolipoprotein B). Thus the inclusion of low glycaemic index foods in the diet of diabetic patients may be an additional measure which slightly but favourably influences carbohydrate and lipid metabolism, requires only small changes in nutritional habits and has no known deleterious effects.
Since females have a greater prevalence of obesity compared with males, the question arises whether females have lower metabolic rate than males after adjusting for differences in body weight and composition. 24-h energy expenditure (24EE), basal metabolic rate (BMR), and sleeping metabolic rate (SMR) were measured in a respiratory chamber in 235 healthy, nondiabetic Caucasian subjects (114 males, 121 females). Body composition was determined by hydrodensitometry. 24EE was 124 +/-38 kcal/d (P less than 0.002) higher in males than females after adjusting for differences in fat-free mass, fat mass, and age. Spontaneous physical activity was not significantly different between males and females. Since adjusted 24EE was 106 +/-39 kcal/d (P less than 0.01) higher in females during the luteal phase of the menstrual cycle compared with females during the follicular phase, energy expenditure was analyzed in a subset (greater than 50 yr) to minimize the confounding effect of menstrual status. 24EE (160 +/-66 kcal/d; P less than 0.03), BMR (116 +/-45; P less than 0.02), and SMR (208 +/-68 kcal/d; P less than 0.005) were higher in males compared with females of the older subset after adjusting for differences in body composition, age, and activity. In summary, sedentary 24EE is approximately 5-10% lower in females compared with males after adjusting for differences in body composition, age, and activity.
The glycemic index concept neglects the insulin secretion factor and has not been systematically studied during mixed meals. Six starch-rich foods were tested alone and in an isoglucido-lipido-protidic meal in 18 NIDDs and compared with a glucose challenge. These test meals were randomly assigned using a three factor experiment design. All three tests contained 50 g carbohydrate; mixed meals were adjusted to bring the same amount of fat (20 g), protein (24 g), water (300 mL), and calories (475 kcal) but not the same amount of fiber. Whatever the tested meals, foods elicited a growing glycemic index hierarchy from beans to lentils, rice, spaghetti, potato, and bread (mean range: 0.21 +/- 0.12-92 +/- 0.12, p less than 0.001). Mixing the meals significantly increased the insulinemic indexes (p less than 0.05) and introduced a positive correlation between glycemic and insulinemic indexes (n = 6, r = 0.903; p less than 0.05). The glycemic index concept remains discriminating, even in the context of an iso-glucido-lipido-protidic meal. Insulinemic indexes do not improve discrimination between foods taken alone in type 2 diabetics: they only discriminate between foods during mixed meals, similarly to glycemic indexes.
The agreement between the two modes of administration of the questionnaire suggests that the self-administered version of the MAQ is a valuable tool to assess past-year physical activity and inactivity in self-administered conditions. This instrument could be used in large-scale population studies investigating the relationships between physical activity and health outcomes.
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