Novel Anthropometry-Based Calculation of the Body Heat Capacity in the Korean Population.

Heat capacity (HC) has an important role in the temperature regulation process, particularly in dealing with the heat load. The actual measurement of the body HC is complicated and is generally estimated by body-composition-specific data. This study compared the previously known HC estimating equations and sought how to define HC using simple anthropometric indices such as weight and body surface area (BSA) in the Korean population. Six hundred participants were randomly selected from a pool of 902 healthy volunteers aged 20 to 70 years for the training set. The remaining 302 participants were used for the test set. Body composition analysis using multi-frequency bioelectrical impedance analysis was used to access body components including body fat, water, protein, and mineral mass. Four different HCs were calculated and compared using a weight-based HC (HC_Eq1), two HCs estimated from fat and fat-free mass (HC_Eq2 and HC_Eq3), and an HC calculated from fat, protein, water, and mineral mass (HC_Eq4). HC_Eq1 generally produced a larger HC than the other HC equations and had a poorer correlation with the other HC equations. HC equations using body composition data were well-correlated to each other. If HC estimated with HC_Eq4 was regarded as a standard, interestingly, the BSA and weight independently contributed to the variation of HC. The model composed of weight, BSA, and gender was able to predict more than a 99% variation of HC_Eq4. Validation analysis on the test set showed a very high satisfactory level of the predictive model. In conclusion, our results suggest that gender, BSA, and weight are the independent factors for calculating HC. For the first time, a predictive equation based on anthropometry data was developed and this equation could be useful for estimating HC in the general Korean population without body-composition measurement.

Thermoregulatory Responses to Graded Exercise Differ among Sasang Types.

We compared sweat rate and variables such as workload (W e ), metabolic heat production (H prod), and temperature increment load (T inc) across Sasang types. 304 apparently healthy participants aged 20-49 years with their Sasang type determined were enrolled. Local sweat rates on the chest (LSRchest) and back (LSRback) were measured using a perspiration meter during a maximum treadmill exercise test. Oxygen uptake was measured continuously using a breath-by-breath mode indirect calorimeter. The TaeEum (TE) type had a larger body size, a higher percent body fat, and a lower body area surface area (BSA) to body mass compared with the other Sasang types, particularly the SoEum (SE) type. The TE type tended to have a shorter exercise time to exhaustion and lower maximal oxygen uptake (mL·kg(-1)·min(-1)) than the other types. LSRchest in TE types was greater than that of the SE and SoYang (SY) types in men, whereas LSRback was higher in the TE type than that of the other types in women. After normalizing LSR for W e , H prod, T inc, and BSA, this tendency still remained. Our findings suggest that the thermoregulatory response to graded exercise may differ across Sasang types such that the TE type was the most susceptible to heat stress.

Personalized metabolic profile estimations using oral glucose tolerance tests.

BACKGROUND & OBJECTIVES: Oral glucose tolerance tests (OGTTs) are used commonly to diagnose diabetes mellitus (DM). However, blood glucose data and the changes in insulin induced by OGTTs contain information regarding intestinal absorption, hepatic control of glucose and insulin, pancreatic insulin secretion and peripheral tissue glucose and insulin control. Therefore, an appropriate dynamic model could reveal the above information from OGTT data. METHODS: We developed an OGTT model containing five compartments for insulin dynamics and two compartments for glucose dynamics based on previous reports. Anthropometric data of individuals were used to assume the cardiac output. Simplex and Levenberg-Marquardt algorithms were then used to fit the data obtained from 42 normal subjects (24 males and 20 females) and eight subjects with DM. RESULTS: We found clear gender differences in the intestinal glucose absorption kinetics, glucose sensitivity in the pancreas, maximal insulin production capacity and endogenous glucose production. There were also differences between normal and DM subjects. For example, pancreatic and liver dysfunctions were evident in DM cases. The differences between normal and DM subjects in glucose and insulin dynamics in the pancreas, liver and peripheral tissues, such as insulin resistance, insulin secretion and the relative roles of glucose disposal in each organ, were demonstrated clearly and quantitatively in a time-dependent manner. CONCLUSION: This study revealed the quantitative dynamic interaction between glucose and insulin using OGTT data and revealed organ function during the OGTT. Using this approach, we identified the dysfunctional organs for glucose and insulin regulation. Data produced using this model will allow a personalized and targeted approach for health issues related to glucose and insulin.