Characteristics of diet patterns in metabolically obese, normal weight adults (Korean National Health and Nutrition Examination Survey III, 2005)

BACKGROUND AND AIMS

Metabolically obese normal weight (MONW) subjects are a subgroup of individuals who have a normal weight and body mass index (BMI), but exhibit obesity-related abnormalities. The objective of this study was to analyze the prevalence of metabolic syndrome (MetS) and characteristics of diet patterns in MONW Koreans.

METHODS AND RESULTS

We analyzed the data of 3050 adults > 20 years of age with a normal BMI (18.5~24.9 kg/m(2)) obtained from the Korea National Health and Nutrition Examination Survey III. Anthropometric measurements and information on health behaviors were obtained. The diagnostic criteria for MetS were defined by the International Diabetes Federation consensus. Dietary intake was assessed by the 24-h recall method. The weighted prevalence of MONW was 14.3%. The risk of MONW correlated inversely with the frequency of snacking and positively with the type of snack, particularly those with high carbohydrates. A high carbohydrate diet (≥73.9% of energy intake) compared to a low carbohydrate diet (<59.9% of energy intake) was positively associated with the risk of MONW (OR = 2.54; 95% CI: 1.41, 4.56), whereas a high protein diet (≥17.1% of energy intake) compared to a low protein diet (<12.2% of energy intake) reduced the risk of MONW (OR = 0.60; 95% CI: 0.39, 0.92) in females, but not in males.

CONCLUSIONS

This study suggests that a reduced intake of carbohydrates and carbohydrate snacks were associated with a lower prevalence of MONW in females.

A walsh-hadamard based distributed storage device for the associative search of information

A computer model for a distributed associative memory has been developed based on Walsh-Hadamard functions. In this memory device, the information storage is distributed over the entire memory medium and thereby lends itself to parallel comparison of the input with stored data. These inherent economic storage and parallel processing capabilities may be found effective especially in real-time processing of large amount of information. However, overlaying different pieces of data in the same memory medium creates the problem of interference or crosstalk between stored data and may lead to recognition errors. In this paper, a crosstalk reduction technique utilizing the gradient descent procedure is developed first. This minimizes the memory processing error and enhances memory saving. Second, for an efficient implementation of the memory structure, these associative memories are configured in a hierarchical structure which not only expands storage capacity but also utilizes the speed of tree search. Finally, a self-correcting technique is developed which achieves error-free recognition of near neighbors for any training pattern even among the presence of crosstalk.