Conditioning with slowly digestible starch diets in mice reduces jejunal α-glucosidase activity and glucogenesis from a digestible starch feeding.

OBJECTIVES: Maltase-glucoamylase (Mgam) and sucrase-isomaltase (Si) are mucosal α-glucosidases required for the digestion of starch to glucose. We hypothesized that a dietary approach to reduce Mgam and Si activities can reduce glucose generation and absorption, and improve glucose control. METHODS: Rice starch was entrapped in alginate microspheres to moderate in vitro digestion properties. Three groups of 8-wk old mice (n = 8) were conditioned for 7 d with low 13C-starch-based materials differing in digestion rates (fast, slow, and slower), and then given a digestible 13C-labeled cornstarch test feeding to determine its digestion to glucose. RESULTS: Conditioning of the small intestine with the slowly digestible starches for 7 d reduced jejunal α-glucosidase and sucrase activities, as well as glucose absorption for the slowly digestible starch slower group (P < 0.01). A correlative relationship was found between glucose absorption from a cornstarch test feeding given at d 7 and jejunal α-glucosidase and sucrase activities (R2 = 0.64; 0.67). However, total prandial glucose levels during the 2-h feeding period did not differ. CONCLUSIONS: Decreased glucogenesis from a digestible starch feeding was found in mice conditioned on slowly digestible starch diets, suggesting that a dietary approach incorporating slowly digestible starches may change α-glucosidase activities to moderate glucose absorption rate.

Energetic adaptations persist after bariatric surgery in severely obese adolescents.

OBJECTIVE: Energetic adaptations induced by bariatric surgery have not been studied in adolescents or for extended periods postsurgery. Energetic, metabolic, and neuroendocrine responses to Roux-en-Y gastric bypass (RYGB) surgery were investigated in extremely obese adolescents. METHODS: At baseline and at 1.5, 6, and 12 months post-baseline, 24-h room calorimetry, body composition, and fasting blood biochemistries were measured in 11 obese adolescents relative to five matched controls. RESULTS: In the RYGB group, mean weight loss was 44 ± 19 kg at 12 months. Total energy expenditure (TEE), activity EE, basal metabolic rate (BMR), sleep EE, and walking EE significantly declined by 1.5 months (P = 0.001) and remained suppressed at 6 and 12 months. Adjusted for age, sex, fat-free mass, and fat mass, EE was still lower than baseline (P = 0.001). Decreases in serum insulin, leptin, and triiodothyronine (T3), gut hormones, and urinary norepinephrine (NE) paralleled the decline in EE. Adjusted changes in TEE, BMR, and/or sleep EE were associated with decreases in insulin, homeostatic model assessment, leptin, thyroid stimulating hormone, total T3, peptide YY3-36, glucagon-like peptide-2, and urinary NE and epinephrine (P = 0.001-0.05). CONCLUSIONS: Energetic adaptations in response to RYGB-induced weight loss are associated with changes in insulin, adipokines, thyroid hormones, gut hormones, and sympathetic nervous system activity and persists 12 months postsurgery.

Support vector machines classifiers of physical activities in preschoolers.

The goal of this study is to develop, test, and compare multinomial logistic regression (MLR) and support vector machines (SVM) in classifying preschool-aged children physical activity data acquired from an accelerometer. In this study, 69 children aged 3-5 years old were asked to participate in a supervised protocol of physical activities while wearing a triaxial accelerometer. Accelerometer counts, steps, and position were obtained from the device. We applied K-means clustering to determine the number of natural groupings presented by the data. We used MLR and SVM to classify the six activity types. Using direct observation as the criterion method, the 10-fold cross-validation (CV) error rate was used to compare MLR and SVM classifiers, with and without sleep. Altogether, 58 classification models based on combinations of the accelerometer output variables were developed. In general, the SVM classifiers have a smaller 10-fold CV error rate than their MLR counterparts. Including sleep, a SVM classifier provided the best performance with a 10-fold CV error rate of 24.70%. Without sleep, a SVM classifier-based triaxial accelerometer counts, vector magnitude, steps, position, and 1- and 2-min lag and lead values achieved a 10-fold CV error rate of 20.16% and an overall classification error rate of 15.56%. SVM supersedes the classical classifier MLR in categorizing physical activities in preschool-aged children. Using accelerometer data, SVM can be used to correctly classify physical activities typical of preschool-aged children with an acceptable classification error rate.

Modeling energy expenditure in children and adolescents using quantile regression.

Advanced mathematical models have the potential to capture the complex metabolic and physiological processes that result in energy expenditure (EE). Study objective is to apply quantile regression (QR) to predict EE and determine quantile-dependent variation in covariate effects in nonobese and obese children. First, QR models will be developed to predict minute-by-minute awake EE at different quantile levels based on heart rate (HR) and physical activity (PA) accelerometry counts, and child characteristics of age, sex, weight, and height. Second, the QR models will be used to evaluate the covariate effects of weight, PA, and HR across the conditional EE distribution. QR and ordinary least squares (OLS) regressions are estimated in 109 children, aged 5-18 yr. QR modeling of EE outperformed OLS regression for both nonobese and obese populations. Average prediction errors for QR compared with OLS were not only smaller at the median τ = 0.5 (18.6 vs. 21.4%), but also substantially smaller at the tails of the distribution (10.2 vs. 39.2% at τ = 0.1 and 8.7 vs. 19.8% at τ = 0.9). Covariate effects of weight, PA, and HR on EE for the nonobese and obese children differed across quantiles (P < 0.05). The associations (linear and quadratic) between PA and HR with EE were stronger for the obese than nonobese population (P < 0.05). In conclusion, QR provided more accurate predictions of EE compared with conventional OLS regression, especially at the tails of the distribution, and revealed substantially different covariate effects of weight, PA, and HR on EE in nonobese and obese children.

Cross-sectional time series and multivariate adaptive regression splines models using accelerometry and heart rate predict energy expenditure of preschoolers.

Prediction equations of energy expenditure (EE) using accelerometers and miniaturized heart rate (HR) monitors have been developed in older children and adults but not in preschool-aged children. Because the relationships between accelerometer counts (ACs), HR, and EE are confounded by growth and maturation, age-specific EE prediction equations are required. We used advanced technology (fast-response room calorimetry, Actiheart and Actigraph accelerometers, and miniaturized HR monitors) and sophisticated mathematical modeling [cross-sectional time series (CSTS) and multivariate adaptive regression splines (MARS)] to develop models for the prediction of minute-by-minute EE in 69 preschool-aged children. CSTS and MARS models were developed by using participant characteristics (gender, age, weight, height), Actiheart (HR+AC_x) or ActiGraph parameters (AC_x, AC_y, AC_z, steps, posture) [x, y, and z represent the directional axes of the accelerometers], and their significant 1- and 2-min lag and lead values, and significant interactions. Relative to EE measured by calorimetry, mean percentage errors predicting awake EE (-1.1 ± 8.7%, 0.3 ± 6.9%, and -0.2 ± 6.9%) with CSTS models were slightly higher than with MARS models (-0.7 ± 6.0%, 0.3 ± 4.8%, and -0.6 ± 4.6%) for Actiheart, ActiGraph, and ActiGraph+HR devices, respectively. Predicted awake EE values were within ±10% for 81-87% of individuals for CSTS models and for 91-98% of individuals for MARS models. Concordance correlation coefficients were 0.936, 0.931, and 0.943 for CSTS EE models and 0.946, 0.948, and 0.940 for MARS EE models for Actiheart, ActiGraph, and ActiGraph+HR devices, respectively. CSTS and MARS models should prove useful in capturing the complex dynamics of EE and movement that are characteristic of preschool-aged children.

Validation of uniaxial and triaxial accelerometers for the assessment of physical activity in preschool children.

PURPOSE: Given the unique physical activity (PA) patterns of preschoolers, wearable electronic devices for quantitative assessment of physical activity require validation in this population. Study objective was to validate uniaxial and triaxial accelerometers in preschoolers. METHODS: Room calorimetry was performed over 3 hours in 64 preschoolers, wearing Actical, Actiheart, and RT3 accelerometers during play, slow, moderate, and fast translocation. Based on activity energy expenditure (AEE) and accelerometer counts, optimal thresholds for PA levels were determined by piecewise linear regression and discrimination boundary analysis. RESULTS: Established HR cutoffs in preschoolers for sedentary/light, light/moderate and moderate/vigorous levels were used to define AEE (0.015, 0.054, 0.076 kcal·kg-1·min-1) and PA ratio (PAR; 1.6, 2.9, 3.6) thresholds, and accelerometer thresholds. True positive predictive rates were 77%, 75%, and 76% for sedentary; 63%, 61%, and 65% for light; 34%, 52%, and 49% for moderate; 46%, 46%, and 49% for vigorous levels. Due to low positive predictive rates, we combined moderate and vigorous PA. Classification accuracy was improved overall and for the combined moderate-to-vigorous PA level (69%, 82%, 79%) for Actical, Actiheart, and RT3, respectively. CONCLUSION: Uniaxial and triaxial accelerometers are acceptable devices with similar classification accuracy for sedentary, light, and moderate-to-vigorous levels of PA in preschoolers.

Validation of cross-sectional time series and multivariate adaptive regression splines models for the prediction of energy expenditure in children and adolescents using doubly labeled water.

Accurate, nonintrusive, and inexpensive techniques are needed to measure energy expenditure (EE) in free-living populations. Our primary aim in this study was to validate cross-sectional time series (CSTS) and multivariate adaptive regression splines (MARS) models based on observable participant characteristics, heart rate (HR), and accelerometer counts (AC) for prediction of minute-by-minute EE, and hence 24-h total EE (TEE), against a 7-d doubly labeled water (DLW) method in children and adolescents. Our secondary aim was to demonstrate the utility of CSTS and MARS to predict awake EE, sleep EE, and activity EE (AEE) from 7-d HR and AC records, because these shorter periods are not verifiable by DLW, which provides an estimate of the individual's mean TEE over a 7-d interval. CSTS and MARS models were validated in 60 normal-weight and overweight participants (ages 5-18 y). The Actiheart monitor was used to simultaneously measure HR and AC. For prediction of TEE, mean absolute errors were 10.7 +/- 307 kcal/d and 18.7 +/- 252 kcal/d for CSTS and MARS models, respectively, relative to DLW. Corresponding root mean square error values were 305 and 251 kcal/d for CSTS and MARS models, respectively. Bland-Altman plots indicated that the predicted values were in good agreement with the DLW-derived TEE values. Validation of CSTS and MARS models based on participant characteristics, HR monitoring, and accelerometry for the prediction of minute-by-minute EE, and hence 24-h TEE, against the DLW method indicated no systematic bias and acceptable limits of agreement for pediatric groups and individuals under free-living conditions.

Multivariate adaptive regression splines models for the prediction of energy expenditure in children and adolescents.

Advanced mathematical models have the potential to capture the complex metabolic and physiological processes that result in heat production or energy expenditure (EE). Multivariate adaptive regression splines (MARS) is a nonparametric method that estimates complex nonlinear relationships by a series of spline functions of the independent predictors. The specific aim of this study is to construct MARS models based on heart rate (HR) and accelerometer counts (AC) to accurately predict EE, and hence 24-h total EE (TEE), in children and adolescents. Secondarily, MARS models will be developed to predict awake EE, sleep EE, and activity EE also from HR and AC. MARS models were developed in 109 and validated in 61 normal-weight and overweight children (ages 5-18 yr) against the criterion method of 24-h room respiration calorimetry. Actiheart monitor was used to measure HR and AC. MARS models were based on linear combinations of 23-28 basis functions that use subject characteristics (age, sex, weight, height, minimal HR, and sitting HR), HR and AC, 1- and 2-min lag and lead values of HR and AC, and appropriate interaction terms. For the 24-h, awake, sleep, and activity EE models, mean percent errors were -2.5 +/- 7.5, -2.6 +/- 7.8, -0.3 +/- 8.9, and -11.9 +/- 17.9%, and root mean square error values were 168, 138, 40, and 122 kcal, respectively, in the validation cohort. Bland-Altman plots indicated that the predicted values were in good agreement with the observed TEE, and that there was no bias with increasing TEE. Prediction errors for 24-h TEE were not statistically associated with age, sex, weight, height, or body mass index. MARS models developed for the prediction of EE from HR monitoring and accelerometry were demonstrated to be valid in an independent cohort of children and adolescents, but require further validation in independent, free-living populations.

Application of cross-sectional time series modeling for the prediction of energy expenditure from heart rate and accelerometry.

Accurate estimation of energy expenditure (EE) in children and adolescents is required for a better understanding of physiological, behavioral, and environmental factors affecting energy balance. Cross-sectional time series (CSTS) models, which account for correlation structure of repeated observations on the same individual, may be advantageous for prediction of EE. CSTS models for prediction of minute-by-minute EE and, hence, total EE (TEE) from heart rate (HR), physical activity (PA) measured by accelerometry, and observable subject variables were developed in 109 children and adolescents by use of Actiheart and 24-h room respiration calorimetry. CSTS models based on HR, PA, time-invariant covariates, and interactions were developed. These dynamic models involve lagged and lead values of HR and lagged values of PA for better description of the series of minute-by-minute EE. CSTS models with random intercepts and random slopes were investigated. For comparison, likelihood ratio tests were used. Log likelihood increased substantially when random slopes for HR and PA were added. The population-specific model uses HR and 1- and 2-min lagged and lead values of HR, HR(2), and PA and 1- and 2-min lagged values of PA, PA(2), age, age(2), sex, weight, height, minimum HR, sitting HR, HR x height, HR x weight, HR x age, PA x weight, and PA x sex interactions (P < 0.001). Prediction error for TEE was 0.9 +/- 10.3% (mean +/- SD). Errors were not correlated with age, weight, height, or body mass index. CSTS modeling provides a useful predictive model for EE and, hence, TEE in children and adolescents on the basis of HR and PA and other observable explanatory subject characteristics of age, sex, weight, and height.

Body size, body composition, and metabolic profile explain higher energy expenditure in overweight children.

Lower relative rates of energy expenditure (EE), increased energetic efficiency, and altered fuel utilization purportedly associated with obesity have not been demonstrated indisputably in overweight children. We hypothesized that differences in energy metabolism between nonoverweight and overweight children are attributable to differences in body size and composition, circulating thyroid hormones, sympathetic nervous system, and adrenomedullary activity. A total of 836 Hispanic children, 5-19 y old, participated in 24-h calorimetry, anthropometric, and dual-energy X-ray absorptiometry measurements. Biochemistries were determined by standard techniques. Absolute total EE (TEE) and its components (sleep EE, basal EE, sedentary EE, cycling EE, walking EE, activity EE, nonexercising activity thermogenesis) were higher in overweight children (P = 0.001). Net mechanical energetic efficiency of cycling was lower in overweight children (P = 0.001). Adjusting for body size and composition accounted for differences in TEE, its components, and energetic efficiency. Net carbohydrate and fat utilization did not differ between groups. TEE was independently influenced by sex, Tanner stage, fat free mass, fat mass (FM), fasting serum nonesterified fatty acids (NEFA), leptin, free thyroxine, triiodothyronine, and 24-h urinary norepinephrine and epinephrine. Fat utilization was independently associated with age2, sex, FM, fasting serum NEFA, triacylglycerol, adiponectin, leptin, total thyroxine, and free triiodothyronine. Higher EE in overweight children was largely explained by differences in body size and composition, with minor contributions of thyroid and sympathoadrenal systems. Alterations in EE, energetic efficiency, and substrate utilization were not evident in the overweight children.

Physical activity in nonoverweight and overweight Hispanic children and adolescents.

BACKGROUND: Despite the high prevalence of childhood obesity among U.S. Hispanic children and adolescents, quantitative, objective data on their patterns and levels of physical activity are scarce. OBJECTIVES: 1) To describe qualitatively the types of physical activities in which nonoverweight and overweight Hispanic children and adolescents participate; 2) to use accelerometry to quantitatively describe the duration, intensity, and frequency of physical activity; 3) to examine the influence of age, gender, and BMI status on physical activity levels; and 4) to determine the relationships between physical activity and adiposity, fitness, and risk for the metabolic syndrome. METHODS: Cross-sectional assessment of physical activity using accelerometers was made for three continuous days in 897 nonoverweight and overweight Hispanic children, ages 4-19 yr. Ancillary measurements included blood pressure, anthropometry, body composition by dual-energy x-ray absorptiometry, fitness by VO2peak test, and metabolic risk factors, using standard clinical and biochemical methods. RESULTS: Types and levels of physical activity were influenced by age, gender, and body mass index (BMI) status. Total physical activity counts declined markedly with increasing age (P = 0.001) and were consistently higher in boys than in girls (P = 0.001). Total activity counts were lower (P = 0.002) and sedentary activity counts were higher in overweight than in nonoverweight children (P = 0.001). Sleep duration (min.d(-1)) was slightly lower in overweight compared with nonoverweight children, ages 4-8 yr (P = 0.03). Physical activity levels were significantly associated with percent FM, VO2peak, fasting serum insulin, and waist circumference, although the strength of the associations were generally low. CONCLUSION: Efforts should be made to shift the time in sedentary activity to light activity, and to increase the time spent in moderate to vigorous activity in U.S. Hispanic children and adolescents, with special attention given to the overweight, girls, and adolescents.

Normalization of energy expenditure data for differences in body mass or composition in children and adolescents.

The most appropriate model for normalization of energy expenditure (EE) data for body mass or composition in growing children and adolescents has not been studied extensively. In this study, we investigated allometric modeling for the normalization of EE data for body mass or composition in a large cohort of children (n = 833), ages 5-19 y for a wide range of physical activities. Anthropometry was performed by standard techniques, and total body fat-free mass (FFM) and fat mass (FM) were determined by dual-energy X-ray absorptiometry (DXA). Weight status was defined as nonoverweight or overweight based on the 95th percentile for BMI. Total energy expenditure (TEE), basal energy expenditure (BEE), sleeping energy expenditure (SEE), and cycling EE were measured during 24-h room respiration calorimetry. Walking and maximal EE (MaxEE) were measured according to a treadmill protocol. Allometric or power function models were used to identify appropriate scaling parameters for EE. For BEE and lower levels of EE, weight scaled to 0.5. For cycling and treadmill walking/running, the weight exponent approached 0.7. Scaling EE for FFM resulted in exponents of 0.6 for lower rates of EE and 0.8-1.0 for higher rates of EE. Appropriate scaling of EE for body weight and composition of children and adolescents varied primarily as a function of the level of EE. In some instances, the exponents for scaling EE by body weight or composition were influenced by gender and weight status, but not by age.

Prediction of activity energy expenditure using accelerometers in children.

PURPOSE: To validate two accelerometer-based activity monitors as measures of children's physical activity using energy expenditure as the criterion measure. METHODS: Actiwatch (AW) and Actical (AC) activity monitors were validated against continuous 4-h measurements of energy expenditure (EE) in a respiratory room calorimeter and 1-h measurements in an exercise laboratory using a portable calorimeter and treadmill in 32 children, ages 7-18 yr. The children performed structured activities including basal metabolic rate (BMR), playing Nintendo, using a computer, cleaning, aerobic exercise, ball toss, treadmill walking, and running. Equations were developed to predict activity energy expenditure (AEE = EE - BMR), and physical activity ratio (PAR = EE/BMR) from a power function of AW or AC, and age, sex, weight, and height. Thresholds were determined to categorize sedentary, light, moderate, and vigorous levels of physical activity. RESULTS: Activity counts accounted for the majority of the variability in AEE and PAR, with small contributions of age, sex, weight, and height. Overall, AW equations accounted for 76-79% and AC equations accounted for 81% of the variability in AEE and PAR. Relatively wide 95% prediction intervals suggest the accelerometers are best applied to groups rather than individuals. Sensitivities were higher for the vigorous threshold (97%) than the other thresholds (86-92%). Specificities were on the order of 66-73%. The positive predictive values for sedentary, light, moderate, and vigorous categories were 80, 66, 69, and 74% for AW, respectively, and 81, 68, 72, 74% for AC, respectively. CONCLUSION: Both accelerometer-based activity monitors provided valid measures of children's AEE and PAR, and can be used to discriminate sedentary, light, moderate, and vigorous levels of physical activity but require further development to accurately predict AEE and PAR of individuals.

Validation and calibration of physical activity monitors in children.

OBJECTIVE: This study was designed to validate accelerometer-based activity monitors against energy expenditure (EE) in children; to compare monitor placement sites; to field-test the monitors; and to establish sedentary, light, moderate, and vigorous threshold counts. RESEARCH METHODS AND PROCEDURES: Computer Science and Applications Actigraph (CSA) and Mini-Mitter Actiwatch (MM) monitors, on the hip or lower leg, were validated and calibrated against 6-hour EE measurements by room respiration calorimetry, activity by microwave detector, and heart rate by telemetry in 26 children, 6 to 16 years old. During the 6 hours, the children performed structured activities, including resting metabolic rate (RMR), Nintendo, arts and crafts, aerobic warm-up, Tae Bo, treadmill walking and running, and games. Activity energy expenditure (AEE) computed as EE - RMR was regressed against counts to derive threshold counts. RESULTS: The mean correlations between EE or AEE and counts were slightly higher for MM-hip (r = 0.78 +/- 0.06) and MM-leg (r = 0.80 +/- 0.05) than CSA-hip (r = 0.66 +/- 0.08) and CSA-leg (r = 0.73 +/- 0.07). CSA and MM performed similarly on the hip (inter-instrument r = 0.88) and on the lower leg (inter-instrument r = 0.89). Threshold counts for the CSA-hip were <800, <3200, <8200, and > or = 8200 for sedentary, light, moderate, and vigorous categories, respectively. For the MM-hip, the threshold counts were <100, <900, <2200, and > or = 2200, respectively. DISCUSSION: The validation of the CSA and MM monitors against AEE and their calibration for sedentary, light, moderate, and vigorous thresholds certify these monitors as valid, useful devices for the assessment of physical activity in children.