Linkage of in situ ruminal degradation of crude protein with ruminal degradation of amino acids and phytate from different soybean meals in dairy cows

The objectives of this study were to determine the range in ruminal degradability of crude protein (CP) and intestinal digestibility of rumen undegradable protein in commercial soybean meal (SBM) and to investigate the range in in situ ruminal AA and phytate (InsP6) degradation and their relationship to CP degradation. An in situ study was conducted using 3 lactating Jersey cows with permanent rumen cannulas. Seventeen SBM variants from Europe, Brazil, Argentina, North America, and India were tested for ruminal CP and AA degradation, and in vitro intestinal digestibility of rumen undegradable protein. Nine variants were used to investigate the ruminal degradation of InsP6. The estimated rapidly degradable fraction (a) of CP showed an average value of 4.5% (range: 0.0%-9.0%), the slowly degradable fraction (b) averaged 95% (91%-100%), and the potential degradation was complete for all 17 SBM variants. The degradation of fraction b started after a mean lag phase of 1.7 h (1.1-2.0 h) at an average rate (c) of 10% per hour, but with a high range from 4.5% to 14% per hour. Differences in the degradation parameters induced a considerable range in CP effective degradation at a rumen passage rate of 6% per hour (CPED6) from 38% to 67%; hence, the concentration of rumen undegradable protein varied widely from 33% to 62%. The range in AA degradation between the SBM variants was high, with Ser showing the widest range, from 28% to 96%, and similar for the other AA. The regression equations showed close relationships between CP and AA degradation after 16 h of in situ incubation. However, the slopes of the linear regressions were significantly different between AA, suggesting that degradation among individual AA differs upon a change in CP degradation. The concentrations of InsP6 and myo-inositol pentakisphosphate in bag residues in the in situ study decreased constantly with longer ruminal incubation times. The ruminal degradation parameters of InsP6 ranged from 11% to 37% for fraction a, 63% to 89% for fraction b, and from 7.7% to 21% per hour for degradation rate c, with average values of 21%, 79%, and 16% per hour, respectively. The calculated InsP6 effective degradation at a rumen passage rate of 6% per hour (InsP6ED6) varied from 61% to 84% among the SBM variants. Significant correlations were detected between InsP6ED6 and CPED6 and between InsP6ED6 and chemical protein fractions A, B1, B2, B3, and C. Linear regression equations were developed to predict ruminal InsP6 degradation using CPED6 and chemical protein fractions B3 and C chosen by a stepwise selection procedure. We concluded that a high range in CP, AA, and InsP6 degradation exists among commercial SBM, suggesting that general degradability values may not be precise enough for diet formulation for dairy cows. Degradation of CP in SBM may be used to predict rumen degradation of AA and InsP6 using linear regression equations. Degradation of CP and InsP6 could also be predicted from the chemical protein fractions.

Predicting feed intake in confined beef cows

Six existing equations (three for nonlactating and three for lactating; NRC, 1987, Predicting feed intake of food-producing animals. Washington, DC: The National Academies Press, National Academy of Science; doi: 10.17226/950; NRC, 1996, Nutrient requirements of beef cattle, 7th Revised Edition: Update 1996. Washington, DC: The National Academies Press; doi: 10.17226/9791; Hibberd and Thrift, 1992. Supplementation of forage-based diets. J. Anim. Sci. 70:181. [Abstr]) were evaluated for predicting feed intake in beef cows. Each of the previously published equations are sensitive to cow-shrunk BW and feed energy concentration. Adjustments in feed intake prediction are provided for level of milk yield in NRC (1987. Predicting feed intake of food-producing animals. Washington, DC: The National Academies Press, National Academy of Science; doi: 10.17226/950) and NRC (1996 Nutrient requirements of beef cattle, 7th Revised Edition: Update 1996. Washington, DC: The National Academies Press; doi: 10.17226/9791) equations. The equation published in 1996 used data generated between 1979 and 1993. Our objectives were to validate the accuracy of the published equations using more recent data and to propose alternative prediction models. Criteria for inclusion in the evaluation dataset included projects conducted or published since 2002, direct measurement of feed intake, adequate protein supply, and pen feeding (no metabolism crate data). After removing outliers, the dataset included 53 treatment means for nonlactating cows and 32 treatment means for lactating cows. Means for the nonlactating dataset were dry matter intake (DMI) = 13.2 ± 2.9 kg/d, shrunk body weight (SBW) = 578 ± 83.9 kg, body condition score = 5.7 ± 0.73, and Mcal net energy for maintenance (NEm)/kg of feed = 1.27 ± 0.15 Mcal/kg. Means for the lactating dataset were DMI = 14.6 ± 2.24 kg/d, SBW = 503 ± 73.4 kg, body condition score = 4.7 ± 0.58, and Mcal NEm/kg feed = 1.22 ± 0.16. Simple linear regression was used to determine slope, intercept, and bias when observed DMI (y) was regressed against predicted DMI (x). The NRC (1996. Nutrient requirements of beef cattle, 7th Revised Edition: Update 1996. Washington, DC: The National Academies Press; doi: 10.17226/9791) nonlactating equation underestimated feed intake in diets moderate to high in energy density with intercept differing from 0 and slope differing from one (P ≤ 0.01). Average deviation from observed values was 2.4 kg/d. Similarly, when the NRC (1996. Nutrient requirements of beef cattle, 7th Revised Edition: Update 1996. Washington, DC: The National Academies Press; doi: 10.17226/9791) equation was used to predict DMI in lactating cows, the slope differed from one (P < 0.01) with average deviation from observed values of 3.0 kg/d. New models were developed by pooling the two datasets and including a categorical variable for stage of production (0 = nonlactating and 1 = lactating). Continuous variables included study-average SBW0.75 and diet NEm, Mcal/kg. The best-fit empirical model accounted for 68% of the variation in daily feed intake with standard error of the estimate Sy root mean squared error = 1.31. The proposed equation needs to be validated with independent data.

Accuracy of equations for estimating resting energy expenditure in children and adolescents living with phenylketonuria

BACKGROUND

Measuring resting energy expenditure (REE) in individuals living with phenylketonuria (PKU) using indirect calorimetry (IC) is unusual in healthcare facilities because it requires specific protocols and expensive equipment. Considering that determining REE is crucial for devising nutritional strategies for the management of PKU, the aim of this study was to identify the predictive equations that provide the best estimates of REE in children and adolescents living with PKU and to propose a predictive equation for determining REE in this population.

METHODS

An REE concordance study was conducted with children and adolescents living with PKU. Anthropometric and body composition assessments using bioimpedance and REE assessment using IC were performed. The results were compared to 29 predictive equations.

RESULTS

Fifty-four children and adolescents were evaluated. The REE obtained using IC differed from all estimated REE, except Henry's equation for male children (p = 0.058). Only this equation showed good agreement (0.900) with IC. Eight variables were associated with the REE obtained using IC with emphasis on fat-free mass (kg) (r = 0.786), weight (r = 0.775), height (r = 0.759) and blood phenylalanine (r = 0.503). With these variables, three REE equations were suggested, with R2  = 0.660, 0.635 and 0.618, respectively, and the third equation, which involves weight and height, showed adequate sample size for a statistical power of 0.942.

CONCLUSION

Most equations, not specific for individuals living with PKU, overestimate the REE of this population. We propose a predictive equation for assessing REE for children and adolescents living with PKU to be used in settings where IC is not available.

Differences in calculated body fat percentage estimated from published equations based on bioelectric impedance analysis in healthy young South African adults

Background

Adult overweight and obesity, in addition to the intake of saturated fat and total serum cholesterol must be monitored as biological risk factors for non-communicable diseases (NCDs). Bioelectric impedance analysis (BIA) provides data on body fat for use in epidemiological settings. However, optimized equations should be used to calculate percentage body fat (%BF). The purpose of this study was to assess the differences between %BF calculated using different published BIA equations and %BF measured by BIA in young South African adults.

Design and methods

In this observational study, differences in calculated %BF were assessed, with different BIA equations retrieved from the literature used in 1128 healthy young adults aged 20-30 years. The %BF (measured by BIA) was compared between equations, between Black and White men and women, respectively.

Results

The results showed statistically significant differences in the %BF calculated from published BIA equations when used in young South African adults (χ² = 946, χ² = 2528, χ² = 2088, respectively, p < 0.0001). In Black and White men and women, respectively, %BF levels were significantly higher when calculated by equations, than when measured by BIA (p < 0.0001).

Conclusion

There seem to be large discrepancies in estimating %BF by BIA equations and these values cannot be used interchangeably for young South African adults. A South African age, ethnicity and sex-specific BIA equation needs to be developed to accurately estimate %BF in young South African adults.

Morphological Description and Live Weight Prediction from Body Measurements of Socorro Island Merino Lambs

The aim of this study was to describe the morphology and estimate live weight from body measurements of Socorro Island Merino lambs. A group of Socorro Island Merino lambs was recorded from birth to year for live weight, rump width, rump length, withers height, body length, cannon bone perimeter, and chest girth, width, and depth. The effect of the lamb type on body measurements and live weight was analyzed using ANOVA, Pearson's correlation analysis was performed to estimate the relationship between body measurements and live weight, multiple linear regressions were fitted to obtain prediction equations of live weight from the body measurements and finally, chest girth was used to generate prediction equations using linear and exponential models. At birth and at year, differences were observed in body measurements, especially those related to the thoracic region, with crossbred males showing the highest values. Live weight was correlated with almost all the body measurements, with the highest coefficients observed with chest girth, chest width, and chest depth. Live weight can be accurately predicted from multiple regression equations using several body measurements, but using only chest girth (CG) as a predictor, the exponential equation W_0-365 = 0.9142 exp^{(0.0462 CG)} showed the best accuracy.

Prediction Equations for In Vitro Ileal Disappearance of Dry Matter and Crude Protein Based on Chemical Composition in Dog Diets

The aims of this study were to determine in vitro ileal disappearance (IVID) of dry matter (DM) and crude protein (CP) in commercial dog diets and to develop equations for predicting the IVID of DM and CP in dog diets based on chemical composition. Eighteen commercial dog diets were analyzed for IVID of DM and CP using a two-step in vitro procedure for dogs. The diet samples in flasks with digestive enzymes were incubated for 2 h and 4 h to simulate digestion in the stomach and the small intestine, respectively. The contents of CP, ether extract, neutral detergent fiber (NDF), and ash in the diets ranged from 14.4 to 42.5%, 3.5 to 23.5%, 6.4 to 34.6%, and 4.9 to 10.0%, respectively, on an as-is basis. The NDF contents were negatively correlated with the IVID of DM and CP (r = -0.73 and r = -0.62, respectively; p < 0.05). The most suitable prediction equations for the IVID of DM and CP in the dog diets were: IVID of DM (%) = 81.33 + 0.46 × CP - 0.77 × NDF, R² = 0.78; IVID of CP (%) = 81.25 + 0.33 × CP - 0.49 × NDF, R² = 0.64, where all nutrients were in % on an as-is basis. In conclusion, dry matter and protein utilization of dog diets based on in vitro digestibility assays can be estimated fairly well using protein and fiber concentrations as independent variables.

Predicting microbial crude protein synthesis in cattle from intakes of dietary energy and crude protein

Accurate predictions of microbial crude protein (MCP) synthesis are needed to predict metabolizable protein supply in ruminants. Since 1996, the National Academies of Sciences, Engineering, and Medicine series on beef cattle nutrient requirements has used the intake of total digestible nutrients (TDN) to predict ruminal MCP synthesis. Because various tabular energy values for feeds are highly correlated, our objective was to determine whether intakes of digestible energy (DE), metabolizable energy (ME), and net energy for maintenance (NEm) could be used as predictors of MCP synthesis in beef cattle. A published database of 285 treatment means from experiments that evaluated MCP synthesis was updated with 50 additional treatment mean observations. When intakes of TDN, fat-free TDN, DE, ME, NEm, dry matter, organic matter, crude protein (CP), ether extract, neutral detergent fiber, and starch were used in a stepwise regression analysis to predict MCP, only intakes of DE and CP met the P < 0.10 criterion for entry into the model. Mixed-model regression analyses were used to adjust for random intercept and slope effects of citations to evaluate intake of DE alone or in combination with CP intake as predictors of MCP synthesis, and the intakes of TDN, ME, and NEm as alternatives to DE intake. Similar precisions in predicting MCP synthesis were obtained with all measures of energy intake (CV = root mean square error [RMSE] as a percentage of the overall mean MCP varied from 9% to 9.67%), and adding CP intake to statistical models increased precision (CV ranged from 8.43% to 9.39%). Resampling analyses were used to evaluate observed vs. predicted values for the various energy intake models with or without CP intake, as well as the TDN-based equation used in the current beef cattle nutrient requirements calculations. The coefficient of determination, concordance correlation coefficient, and RMSE of prediction as a percentage of the mean averaged 0.595%, 0.730%, and 28.6% for the four measures of energy intake, with average values of 0.630%, 0.757%, and 27.4%, respectively, for equations that included CP intake. The TDN equation adopted by the 2016 beef cattle nutrient requirements system yielded similar results to newly developed equations but had a slightly greater mean bias. We concluded that any of the measures of energy intake we evaluated can be used to predict MCP synthesis by beef cattle and that adding CP intake improves model precision.

Application of Bioelectrical Impedance Analysis (BIA) to Assess Carcass Composition and Nutrient Retention in Rabbits from 25 to 77 Days of Age

The aim of this study was to assess and validate, using independent data, the prediction equations obtained to estimate in vivo carcass composition using bioelectrical impedance analysis (BIA) to determine the nutrient retention and overall energy and nitrogen retention efficiencies of growing rabbits. Seventy-five rabbits grouped into five different ages (25, 35, 49, 63 and 77 days) were used in the study. A four-terminal body-composition analyzer was applied to obtain resistance (Rs, Ω) and reactance (Xc, Ω) values. All the animals were stunned and bled at each selected age, and the chilled carcasses were analyzed to determine water, fat, crude protein (CP), ash and gross energy (GE). Multiple linear regression analysis was conducted to determine the equations, using body weight, length and impedance data as independent variables. The coefficients of determination (R2) to estimate the content of water, protein, fat and ash in grams, and energy in Mega Jules(MJ), were: 0.99, 0.99, 0.95, 0.96 and 0.98, respectively, and the relative mean prediction errors (RMPE) were: 4.20, 5.48, 21.9, 9.10 and 6.77%, respectively. Carcass yield (%) estimation had values of 0.50 and 10.0 for R2 and RMPE, respectively. When water content was expressed as a percentage, the R2 and RMPE were 0.79 and 1.62%, respectively. When the protein, fat and ash were expressed as a percentage of dry matter (%DM) and the energy content as kJ/100 g DM, the R2 values were 0.68, 0.76, 0.66 and 0.82, respectively, and the RMPEs were 3.22, 10.5, 5.82 and 2.54%, respectively. Energy Retention Efficiency was 20.4 ± 7.29%, 21.0 ± 4.18% and 20.8 ± 2.79% from 35 to 49, from 49 to 63 and from 35 to 63 d, respectively. Nitrogen Retention Efficiency was 46.9 ± 11.7%, 34.5 ± 7.32% and 39.1 ± 3.23% for the same periods. Energy was retained in body tissues for growth with an efficiency of approximately 52.5%, and the energy efficiency for protein and fat retention was 33.3 and 69.9%, respectively. This work shows that BIA is a non-invasive and good method to estimate in vivo carcass composition and to determine the nutrient retention of growing rabbits from 25 to 77 days of age.

External Validation with Accuracy Confounders of VCO2-Derived Predicted Energy Expenditure Compared to Resting Energy Expenditure Measured by Indirect Calorimetry in Mechanically Ventilated Children

Optimal energy provision, guided by measured resting energy expenditure (REE) and determined by indirect calorimetry (IC), is fundamental in Intensive Care Units (ICU). Because IC availability is limited, methods to predict REE based on carbon dioxide production (VCO2) measurements (REEVCO2) alone have been proposed as a surrogate for REE measured by IC (REEIC). The study aimed at externally and internally validating the accuracy of the REEVCO2 as an alternative to REEIC in mechanically ventilated children. A ventilator’s integrated gas exchange module (E-COVX) was used to prospectively measure REEIC and predict REEVCO2 on 107 mechanically ventilated children during the first 24 h of admission. The accuracy of the REEVCO2 compared to REEIC was assessed through the calculation of bias and precision, paired median differences, linear regression, and ROC analysis. Accuracy within ±10% of the REEIC was deemed acceptable for the REEVCO2 equation. The calculated REEVCO2 based on respiratory quotient (RQ) 0.89 resulted in a mean bias of −72.7 kcal/day (95% limits of agreement −321.7 to 176.3 kcal/day) and a high coefficient of variation (174.7%), while 51.4% of the calculations fell outside the ±10% accuracy rate. REEVCO2 derived from RQ 0.80 or 0.85 did not improve accuracy. Only measured RQ (Beta 0.73, p < 0.001) and no-recorded neuromuscular blocking agents (Beta −0.13, p = 0.044) were independently associated with the REEVCO2−REEIC difference. Among the recorded anthropometric, metabolic, nutrition, or clinical variables, only measured RQ was a strong predictor of REEVCO2 inaccuracy (p < 0.001). Cutoffs of RQ = 0.80 predicted 89% of underestimated REEIC (sensitivity 0.99; specificity 0.89) and RQ = 0.82 predicted 56% of overestimated REEIC (sensitivity of 0.99; specificity 0.56). REEVCO2 cannot be recommended as an alternative to REEIC in mechanically ventilated children, regardless of the metabolic, anthropometric, or clinical status at the time of the evaluation.

External Validation of Equations to Estimate Resting Energy Expenditure in Critically Ill Children and Adolescents with and without Malnutrition: A Cross-Sectional Study

We evaluated the validity of sixteen predictive energy expenditure equations for resting energy expenditure estimation (eREE) against measured resting energy expenditure using indirect calorimetry (REEIC) in 153 critically ill children. Predictive equations were included based on weight, height, sex, and age. The agreement between eREE and REEIC was analyzed using the Bland−Altman method. Precision was defined by the 95% limits of the agreement; differences > ±10% from REEIC were considered clinically unacceptable. The reliability was assessed by the intraclass correlation coefficient (Cronbach’s alpha). The influence of anthropometric, nutritional, and clinical variables on REEIC was also assessed. Thirty (19.6%) of the 153 enrolled patients were malnourished (19.6%), and fifty-four were overweight (10.5%) or obese (24.8%). All patients received sedation and analgesia. Mortality was 3.9%. The calculated eREE either underestimated (median 606, IQR 512; 784 kcal/day) or overestimated (1126.6, 929; 1340 kcal/day) REEIC compared with indirect calorimetry (928.3, 651; 1239 kcal/day). These differences resulted in significant biases of −342 to 592 kcal (95% limits of agreement (precision)−1107 to 1380 kcal/day) and high coefficients of variation (up to 1242%). Although predicted equations exhibited moderate reliability, the clinically acceptable ±10% accuracy rate ranged from only 6.5% to a maximum of 24.2%, with the inaccuracy varying from −31% to +71.5% of the measured patient’s energy needs. REEIC (p = 0.017) and eREE (p < 0.001) were higher in the underweight compared to overweight and obese patients. Apart from a younger age, malnutrition, clinical characteristics, temperature, vasoactive drugs, neuromuscular blockade, and energy intake did not affect REEIC and thereby predictive equations’ accuracy. Commonly used predictive equations for calculating energy needs are inaccurate for individual patients, either underestimating or overestimating REEIC compared with indirect calorimetry. Altogether these findings underscore the urgency for measuring REEIC in clinical situations where accurate knowledge of energy needs is vital.

Predicted estimates of resting energy expenditure have limited clinical utility in patients with cirrhosis

BACKGROUND & AIMS

Malnutrition is associated with adverse clinical outcomes in patients with cirrhosis. Accurate assessment of energy requirements is needed to optimize dietary intake. Resting energy expenditure (REE), the major component of total energy expenditure, can be measured using indirect calorimetry (mREE) or estimated using prediction equations (pREE). This study assessed the usefulness of predicted estimates of REE in this patient population.

METHODS

Individual mREE data were available for 900 patients with cirrhosis (mean [±1 SD] age 55.7±11.6 years-old; 70% men; 52% south-east Asian) and 282 healthy controls (mean age 36.0±12.8 years-old; 52% men; 18% south-east Asian). Metabolic status was classified using thresholds based on the mean ± 1 SD of the mREE in the healthy controls. Comparisons were made between mREE and pREE estimates obtained using the Harris-Benedict, Mifflin, Schofield and Henry equations. Stepwise regression was used to build 3 new prediction models which included sex, ethnicity, body composition measures, and model for end-stage liver disease scores.

RESULTS

The mean mREE was significantly higher in patients than controls when referenced to dry body weight (22.4±3.8 cf. 20.8±2.6 kcal/kg/24 hr; p <0.001); there were no significant sex differences. The mean mREE was significantly higher in Caucasian than Asian patients (23.1±4.4 cf. 21.7±2.9 kcal/kg/24 hr; p <0.001). Overall, 37.1% of Caucasian and 25.3% of Asian patients were classified as hypermetabolic. The differences between mREE and pREE were both statistically and clinically relevant; in the total patient population, pREE estimates ranged from 501 kcal/24 hr less to 548 kcal/24 hr more than the mREE. Newly derived prediction equations provided better estimates of mREE but still had limited clinical utility.

CONCLUSIONS

Prediction equations do not provide useful estimates of REE in patients with cirrhosis. REE should be directly measured.

LAY SUMMARY

People with cirrhosis are often malnourished and this has a detrimental effect on outcome. Provision of an adequate diet is very important and is best achieved by measuring daily energy requirements and adjusting dietary intake accordingly. Prediction equations, which use information on age, sex, weight, and height can be used to estimate energy requirements; however, the results they provide are not accurate enough for clinical use, particularly as they vary according to sex and ethnicity.

Measures Matter-Determining the True Nutri-Physiological Value of Feed Ingredients for Swine

Many types of feed ingredients are used to provide energy and nutrients to meet the nutritional requirements of swine. However, the analytical methods and measures used to determine the true nutritional and physiological ("nutri-physiological") value of feed ingredients affect the accuracy of predicting and achieving desired animal responses. Some chemical characteristics of feed ingredients are detrimental to pig health and performance, while functional components in other ingredients provide beneficial health effects beyond their nutritional value when included in complete swine diets. Traditional analytical procedures and measures are useful for determining energy and nutrient digestibility of feed ingredients, but do not adequately assess their true physiological or biological value. Prediction equations, along with ex vivo and in vitro methods, provide some benefits for assessing the nutri-physiological value of feed ingredients compared with in vivo determinations, but they also have some limitations. Determining the digestion kinetics of the different chemical components of feed ingredients, understanding how circadian rhythms affect feeding behavior and the gastrointestinal microbiome of pigs, and accounting for the functional properties of many feed ingredients in diet formulation are the emerging innovations that will facilitate improvements in precision swine nutrition and environmental sustainability in global pork-production systems.

The perils of using predicted values in place of observed covariates: an example of predicted values of body composition and mortality risk

BACKGROUND

Several studies have assessed the relation of body composition to health outcomes by using values of fat and lean mass that were not measured but instead were predicted from anthropometric variables such as weight and height. Little research has been done on how substituting predicted values for measured covariates might affect analytic results.

OBJECTIVES

We aimed to explore statistical issues causing bias in analytical studies that use predicted rather than measured values of body composition.

METHODS

We used data from 8014 adults ≥40 y old included in the 1999-2006 US NHANES. We evaluated the relations of predicted total body fat (TF) and predicted total body lean mass (TLM) with all-cause mortality. We then repeated the evaluation using measured body composition variables from DXA. Quintiles and restricted cubic splines allowed flexible modeling of the HRs in unadjusted and multivariable-adjusted Cox regression models.

RESULTS

The patterns of associations between body composition and all-cause mortality depended on whether body composition was defined using predicted values or DXA measurements. The largest differences were observed in multivariable-adjusted models which mutually adjusted for both TF and TLM. For instance, compared with analyses based on DXA measurements, analyses using predicted values for males overestimated the HRs for TF in splines and in quintiles [HRs (95% CIs) for fourth and fifth quintiles compared with first quintile, DXA: 1.22 (0.88, 1.70) and 1.46 (0.99, 2.14); predicted: 1.86 (1.29, 2.67) and 3.24 (2.02, 5.21)].

CONCLUSIONS

It is important for researchers to be aware of the potential pitfalls and limitations inherent in the substitution of predicted values for measured covariates in order to draw proper conclusions from such studies.

Models to predict dry feed intake in Holstein calves to 4 months of age

Voluntary daily dry feed intake (DFI) in Holstein calves was predicted using 60,761 individual daily observations collected from 1,235 Holstein calves in 30 experiments from 4 research stations in the United States and Europe. Consumption of dry feed (calf starter and hay, kg/d or percent of body weight) was measured from 3 to 114 d of age. Linear models and 2- and 3-parameter nonlinear models were evaluated to predict DFI using age of calf, intake of milk replacer, ambient temperature, percent forage, and neutral detergent fiber concentration in ration dry matter (DM) as independent variables. The initial data set was randomly divided within study location into development (80% of all observations) and validation data sets, and initial screening was conducted using the development data set. Five nonlinear models and 3 linear models (candidate models) were identified and used in further model evaluation. Cross-validation studies (n = 20) with the validation data set were conducted by linear regression of DFI with predicted DFI as independent variable. Candidate models were subsequently evaluated with data from 12 published studies in 2 analyses. The exponential model that best predicted daily DFI in Holstein calves in original and external data sets was DFI (kg/d) = 1.3207 × e^{[(-5.3892 + 0.6376 × MEgap) × EXP(-0.0392 × Age)]} - 0.0013 × Temp + 0.0032 × NDFDM + 0.0026 × Age × MEgap - 0.3646 × PctForage [coefficient of determination (R²) = 0.92, concordance correlation coefficient (CCC) = 0.96, and mean square error of prediction (MSEP) = 0.10 kg]; where MEgap (Mcal/d) = difference of daily metabolizable energy (ME) requirement and ME intake from milk replacer; Age = age of calf (d) from 3 to 114, Temp = mean daily ambient temperature (°C), NDFDM = ration neutral detergent fiber (% DM); PctForage = percent forage in ration DM. The linear model that best predicted DFI was DFI (kg/d = -0.1349 + 0.0106 × Age + 0.1808 × MEgap + 0.0013 × Age × MEgap + 0.0001 × Temp + 0.00002 × Age × Temp (R² = 0.93, CCC = 0.96, and MSEP = 0.10 kg). When Temp and ration characteristics were not included, optimal models were 1.4362 × e^{[(-4.6646 + 0.5234 × MEgap) × EXP(-0.0361 × Age)]} + 0.0025 × Age × MEgap (R² = 0.92, CCC = 0.96, and MSEP = 0.11 kg) and -0.1344 + 0.0102 × Age + 0.1810 × MEgap + 0.0013 × Age × MEgap [R² = 0.93, CCC = 0.96, and MSEP = 0.10 kg]. Models of daily DFI may improve prediction of nutrient supply to young Holstein calves to approximately 4 mo of age, thereby increasing prediction of growth performance.

Predictive Performance Models in Long-Distance Runners: A Narrative Review

Physiological variables such as maximal oxygen uptake (VO₂max), velocity at maximal oxygen uptake (vVO₂max), running economy (RE) and changes in lactate levels are considered the main factors determining performance in long-distance races. The aim of this review was to present the mathematical models available in the literature to estimate performance in the 5000 m, 10,000 m, half-marathon and marathon events. Eighty-eight articles were identified, selections were made based on the inclusion criteria and the full text of the articles were obtained. The articles were reviewed and categorized according to demographic, anthropometric, exercise physiology and field test variables were also included by athletic specialty. A total of 58 studies were included, from 1983 to the present, distributed in the following categories: 12 in the 5000 m, 13 in the 10,000 m, 12 in the half-marathon and 21 in the marathon. A total of 136 independent variables associated with performance in long-distance races were considered, 43.4% of which pertained to variables derived from the evaluation of aerobic metabolism, 26.5% to variables associated with training load and 20.6% to anthropometric variables, body composition and somatotype components. The most closely associated variables in the prediction models for the half and full marathon specialties were the variables obtained from the laboratory tests (VO₂max, vVO₂max), training variables (training pace, training load) and anthropometric variables (fat mass, skinfolds). A large gap exists in predicting time in long-distance races, based on field tests. Physiological effort assessments are almost exclusive to shorter specialties (5000 m and 10,000 m). The predictor variables of the half-marathon are mainly anthropometric, but with moderate coefficients of determination. The variables of note in the marathon category are fundamentally those associated with training and those derived from physiological evaluation and anthropometric parameters.

Predicting milk yield in Pelibuey ewes from the udder volume measurement with a simple method

In this research communication we describe the creation of an equation for the prediction of milk yield (MY) from udder volume (UV). A total of 280 measurements were collected between 5 and 15 d postpartum (pp) from 36 multiparous Pelibuey ewes. Study variables were measured between 2 and 9 weeks pp and MY was measured by manual milking, UV prior to and following milking was measured using the technique of making moulds from aluminium foil. The MY ranged from 0.09 to 0.83 kg/d, meanwhile UV prior and following milking ranged from 155 to 1940 and 90 to 1520 cm3, respectively. Measurements of UV had a moderate to high (P < 0.01; 0.58 ≤ r ≤ 0.78) correlation with MY. The UV prior to milking was the best prediction model for MY, which explained 62% of the variation in MY. This equation presented moderate precision (r2 = 0.61) and high accuracy (bias correction factor = 0.94), confirming a good reproducibility index (concordance correlation coefficient = 0.73). Modelling efficiency (MEF = 0.59) showed moderate concordance between observed and predicted values. In conclusion, MY in lactating Pelibuey ewes could be predicted in a moderate way using the predictor variable UV measured with the technique of moulds made with aluminium foil.

External Validation of Equations to Estimate Resting Energy Expenditure in 2037 Children and Adolescents with and 389 without Obesity: A Cross-Sectional Study

We performed an external cross-validation study of 10 equations to estimate resting energy expenditure (REE) in 2037 children with and 389 without obesity. Inclusion criteria were Caucasian ethnicity, age ≤ 18 years, and availability of REE. REE was measured using indirect calorimetry. The correct classification fraction (CCF) of an equation was defined as the fraction of subjects whose estimated REE was within 10% of measured REE. The Molnár equation was the most accurate REE prediction equation with CCFs of 0.70 (95% CI 0.65 to 0.76) in girls without obesity, 0.64 (95% CI 0.61 to 0.66) in girls with obesity, 0.76 (95% CI 0.67 to 0.83) in boys without obesity, and 0.66 (95% CI 0.63 to 0.69) in boys with obesity. The Mifflin equation was the second most accurate equation with CCFs of 0.67 (95% CI 0.61 to 0.73) in girls without obesity, 0.61 (95% CI 0.58 to 0.64) in girls with obesity, 0.75 (95% CI 0.66 to 0.82) in boys without obesity, and 0.66 (95% CI 0.63 to 0.69) in boys with obesity.

Prediction of Metabolizable Energy Concentrations of Herbage in the Qinghai-Tibetan Plateau Using Tibetan Sheep Digestibility Data

Simple Summary

Robust prediction of herbage nutritive value is critical to improve grazing efficiency and to maintain a sustainable environment in the Qinghai–Tibet Plateau. A range of prediction equations were developed in the present study using sheep digestibility data which can produce an accurate estimation of herbage nutritive value. The adaptation of the present equations is expected to benefit local farmers with higher economical return and to improve the fragile ecological systems the Qinghai–Tibet Plateau.

Abstract

Due to its extremely harsh environment, including high altitude, hypoxia, long cold season, and strong ultraviolet radiation in the Qinghai–Tibet Plateau (QTP), herbage species and nutritional value of the pasture may differ considerably from elsewhere across the world. The aim of the present study was to develop biologically relevant equations for estimating the metabolizable energy (ME) value of fresh native herbages in the QTP using digestibility variables and chemical concentrations in the herbage offered to Tibetan sheep at the maintenance level. A total of 11 digestibility trials (6 sheep/trial) were performed in different grazing seasons from 2011 to 2016. The herbage was harvested daily in the morning and offered to sheep at the maintenance feeding level. Thirty-seven equations were developed for the prediction of herbage digestible energy (DE) and ME energy values. The mean prediction error for ME was the lowest when using herbage gross energy digestibility as a sole predictor. When using other digestibility variables (e.g., dry matter and organic matter) as primary predictors, addition of herbage nutrient concentration reduced the difference between predicted and actual values. When DE was used as the primary explanatory variable, mean prediction error was reduced with the addition of ash, nitrogen (N), diethyl ether extract (EE), neutral detergent fiber (NDF), and acid detergent fiber (ADF) concentrations. The internal validation of the present equations showed lower prediction errors when compared with those of existing equations for prediction of DE and ME concentrations in the herbage. Equations developed in the current study may thus allow for an improved and accurate prediction of metabolizable energy concentrations of herbage in practice, which is critical for the development of sustainable grazing systems in the QTP.

Validity of Prediction Equations of Maximal Heart Rate in Physically Active Female Adolescents and the Role of Maturation

Background and objectives: Maximal heart rate (HR_max) is an important training and testing tool, especially in the context of evaluating intensity in exercise prescription; however, few studies have examined the validity of prediction equations of HR_max in physically active female adolescents and the role of maturation level. Therefore, the aim of the present study was to examine the differences between measured and predicted HR_max in a sample of physically active female adolescents. Materials and Methods: Seventy-one selected volleyball players (age 13.3 ± 0.7 years, body mass 62.0 ± 7.2 kg, height 1.72 ± 0.06 m) performed a 20 m shuttle run endurance test, and the actual HR_max was compared with Tanaka HR_max (‘208 − 0.7 × age’) and Fox HR_max (‘220 − age’). Results: A large main effect of assessment method on HR_max was found (p < 0.001, η² = 0.486) with Fox overestimating actual HR_max by 6.8 bpm (95% confidence intervals, CI; 4.2, 9.3) and Tanaka underestimating actual HR_max by −2.6 bpm (95% CI; −5.1, −0.1). The more matured participants had similar actual HR_max (mean difference −2.4 bpm; 95% CI; −6.5, 1.7; p = 0.242, d = −0.28), difference Fox − actual HR_max (1.5 bpm; 95% CI; −2.6, 5.6, p = 0.466, d = 0.17), and difference Tanaka − actual HR_max (1.7 bpm; 95% CI; −2.4, 5.8; p = 0.414, d = 0.19) to the less matured participants. Conclusions: These findings suggest that age-based prediction equations of HR_max developed in adult populations should be applied with caution in physically active female adolescents, and Tanaka should be preferred instead of the Fox equation.

Cooper Test Provides Better Half-Marathon Performance Prediction in Recreational Runners Than Laboratory Tests

This study compared the ability to predict performance in half-marathon races through physiological variables obtained in a laboratory test and performance variables obtained in the Cooper field test. Twenty-three participants (age: 41.6 ± 7.6 years, weight: 70.4 ± 8.1 kg, and height: 172.5 ± 6.3 cm) underwent body composition assessment and performed a maximum incremental graded exercise laboratory test to evaluate maximum aerobic power and associated cardiorespiratory and metabolic variables. Cooper's original protocol was performed on an athletic track and the variables recorded were covered distance, rating of perceived exertion, and maximum heart rate. The week following the Cooper test, all participants completed a half-marathon race at the maximum possible speed. The associations between the laboratory and field tests and the final time of the test were used to select the predictive variables included in a stepwise multiple regression analysis, which used the race time in the half marathon as the dependent variable and the laboratory variables or field tests as independent variables. Subsequently, a concordance analysis was carried out between the estimated and actual times through the Bland-Altman procedure. Significant correlations were found between the time in the half marathon and the distance in the Cooper test (r = -0.93; p < 0.001), body weight (r = 0.40; p < 0.04), velocity at ventilatory threshold 1, (r = -0.72; p < 0.0001), speed reached at maximum oxygen consumption (vVO₂max), (r = -0.84; p < 0.0001), oxygen consumption at ventilatory threshold 2 (VO₂VT2) (r = -0.79; p < 0.0001), and VO₂max (r = -0.64; p < 0.05). The distance covered in the Cooper test was the best predictor of time in the half-marathon, and might predicted by the equation: Race time (min) = 201.26 - 0.03433 (Cooper test in m) (R ² = 0.873, SEE: 3.78 min). In the laboratory model, vVO2max, and body weight presented an R ² = 0.77, SEE 5.28 min. predicted by equation: Race time (min) = 156.7177 - 4.7194 (vVO₂max) - 0.3435 (Weight). Concordance analysis showed no differences between the times predicted in the models the and actual times. The data indicated a high predictive power of half marathon race time both from the distance in the Cooper test and vVO2max in the laboratory. However, the variable associated with the Cooper test had better predictive ability than the treadmill test variables. Finally, it is important to note that these data may only be extrapolated to recreational male runners.

Developing an Impedance Based Equation for Fat-Free Mass of Black Preadolescent South African Children

Bioelectrical impedance analysis (BIA) is a practical alternative to dual-energy X-ray absorptiometry (DXA) for determining body composition in children. Currently, there are no population specific equations available for predicting fat-free mass (FFM) in South African populations. We determined agreement between fat-free mass measured by DXA (FFM_DXA) and FFM calculated from published multi-frequency bioelectrical impedance prediction equations (FFM_BIA); and developed a new equation for predicting FFM for preadolescent black South African children. Cross-sectional data on a convenience sample of 84 children (mean age 8.5 ± 1.4 years; 44 {52%} girls) included body composition assessed using Dual X-ray Absorptiometry (FFM_DXA) and impedance values obtained from the Seca mBCA 514 Medical Body Composition analyzer used to calculate FFM using 17 published prediction equations (FFM_BIA). Only two equations yielded FFM estimates that were similar to the DXA readings (p > 0.05). According to the Bland–Altman analysis, the mean differences in FFM (kg) were 0.15 (LOA: −2.68; 2.37) and 0.01 (LOA: −2.68; 2.66). Our new prediction equation, FFM=105.20+0.807×Sex+0.174×Weight+0.01×Reactance+15.71×log(RI), yielded an adjusted R² = 0.9544. No statistical shrinkage was observed during cross-validation. A new equation enables the BIA-based prediction of FFM in the assessment of preadolescent black South African children.

Models for predicting protein requirements for meat quail

Three experiments were carried out to determine the crude protein requirements for maintenance (CPm) and weight gain (CPg) of meat quail and to develop protein-requirement prediction models. Experiment 1 was conducted to determine CPm by the nitrogen-balance technique. The regression of nitrogen balance on nitrogen intake revealed a CPm requirement of 2.94 g/kg^0.75 /day. Experiment 2 was aimed at determining CPm by the comparative-slaughter technique. Retained nitrogen (RN) and nitrogen intake (NI) were quantified considering the metabolic weight of the birds. The linear regression of RN on NI provided a CPm estimate of 6.63 g/kg^0.75 /day. Experiment 3 was conducted to determine CPg. The regression of body nitrogen from the carcasses on fasted body weight revealed CPg estimates of 407.68 (0-7 days), 501.76 (8-14 days), 470.40 (0-14 days), 517.44 (15-21 days), 627.20 (22-28 days), 423.36 (29-35 days), and 517.44 mg/g (15-35 days). The protein-requirement prediction models developed for meat quail aged 0-7, 8-14, 0-14, 15-21, 22-28, 29-35, and 15-35 days were CP = 2.94.W^0.75  + 0.408.G; CP = 2.94.W^0.75  + 0.502.G; CP = 2.94.W^0.75  + 0.470.G; CP = 2.94.W^0.75  + 0,517.G; CP = 2.94.W^0.75  + 0.627.G; CP = 2.94.W^0.75  + 0.423.G; CP = 2.94.W^0.75  + 0.517.G, respectively, where: W^0.75  =  metabolic weight (kg), and G =  daily weight gain (g).

Handheld Indirect Calorimetry as a Clinical Tool for Measuring Resting Energy Expenditure in Children with and without Obesity

Background: Resting energy expenditure (REE) is a valuable measure in clinical management of obesity and other chronic illnesses. Gold standard methods for measuring REE (e.g., Douglas bags and metabolic cart) are too expensive and cumbersome for an outpatient clinical setting. The purpose of this study was to determine the accuracy of a handheld indirect calorimeter (HHIC) and prediction equations (PEs) for measurement of REE in youth with and without obesity. Methods: Fifty-three children and adolescents (12.8 ± 4.3 years, 50.9% female) had REE measured first with a MedGem™ HHIC for 10 minutes, followed by a reference indirect calorimeter system (ParvoMedics TrueOne 2400™) with hood canopy and dilution pump for 30 minutes. REE was also estimated using nine PEs as follows: Henry-1, Henry-2, Schofield, World Health Organization, Molnar, Muller, Herrmann, Schmelzle, and Harris-Benedict. Concordance correlation coefficients and Bland-Altman analyses were used for comparisons among PEs, MedGem HHIC, and metabolic cart. Results: The observed correlation between the HHIC and the reference system was rc = 0.89 with a mean bias of 2.27 ± 3.41 kcal/(kg·d) (9.1% ± 14.7%). Regarding PE, Molnar had the highest agreement with the reference system [rc = 0.93, bias of 2.17 ± 2.04 kcal/(kg·d); 9.8% ± 8.1%], followed by Harris-Benedict (rc = 0.89; 13.8% ± 8.9%), Henry-2 (rc = 0.89; 15% ± 7.6%), and Henry-1 (rc = 0.86; 16.7% ± 7.3%). All PEs were less accurate for children with overweight/obesity. Conclusions: Compared to PE, the HHIC provided more accurate REE estimates for children across the age and BMI spectrum, although positive bias was present throughout. Difference in positive bias between the HHIC and the Molnar equation may be clinically significant for youth with overweight/obesity.

Determination and prediction of the apparent and standardized ileal amino acid digestibility in cottonseed meals fed to growing pigs

This experiment was conducted to evaluate the chemical composition and apparent ileal digestibility (AID) and standardized ileal digestibility (SID) of amino acids (AA) in cottonseed meals (CSM) and to develop prediction equations for estimating AA digestibility for growing pigs based on the chemical composition and AA content of CSM. 10 CSM were collected from 10 provinces in China. 11 barrows (initial body weight: 34.4 ± 1.9 kg), fitted with an ileal T-cannula, were allotted to 6 × 11 Youden square with 6 periods and 11 diets. The diets included one N-free diet and 10 CSM test diets containing 40% CSM as the sole source of AA. The nitrogen-free diet was used to measure the basal endogenous losses of CP and AA. The AID and SID for crude protein (CP), Lys, Met, Trp, and Thr among the 10 CSM differed (p < 0.05). With R² values exceeding 0.30, significant linear regression equations for CP, Lys, and Thr were obtained. The best fit equations for predicting the AID and SID of Lys were: AID of Lys = 82.94-0.65 neutral detergent fiber, with R²  = 0.44, residual standard deviation (RSD) = 4.03 and p = 0.04; and SID of Lys = 135.34-1.01 neutral detergent fiber + 34.27 Thr - 58.06 Trp - 84.93 Met, with R²  = 0.90, RSD = 1.42 and p = 0.01. In conclusion, the results of AID, SID and prediction equations could be used to evaluate the digestibility of CSM in growing pigs.

Assessment of fat-free mass from bioelectrical impedance analysis in men and women with Prader-Willi syndrome: cross-sectional study

We have recently shown that population-specific formulae are required to estimate fat-free mass (FFM) from bioelectrical impedance analysis (BIA) in obese women with Prader-Willi syndrome (PWS) matched by age and percent fat mass (FM) to non-PWS women. The present cross-sectional study was aimed at developing generalised BIA equations that could be used in PWS subjects independently of sex and FM. We used dual-energy X-ray absorptiometry to measure FFM and BIA to measure whole-body impedance at 50 kHz (Z₅₀) in 34 women and 21 men with PWS. The impedance index, that is, height (cm)²/Z₅₀ (Ω), explained 77% (BCa-bootstrapped 95% CI 65 to 85%) of the variance of FFM with a root mean squared error of the estimate of 3.7 kg (BCa-bootstrapped 95% CI 3.2 to 4.5 kg). BIA can be used to estimate FFM in obese and non-obese PWS men and women by means of population-specific equations.

Iodine Status Assessment in South African Adults According to Spot Urinary Iodine Concentrations, Prediction Equations, and Measured 24-h Iodine Excretion

The iodine status of populations is conventionally assessed using spot urinary samples to obtain a median urinary iodine concentration (UIC) value, which is assessed against standard reference cut-offs. The assumption that spot UIC reflects daily iodine intake may be flawed because of high day-to-day variability and variable urinary volume outputs. This study aimed to compare iodine status in a sample of South African adults when determined by different approaches using a spot urine sample (median UIC (MUIC), predicted 24 h urinary iodine excretion (PrUIE) using different prediction equations) against measured 24 h urinary iodine excretion (mUIE). Both 24 h and spot urine samples were collected in a subsample of participants (n = 457; median age 55 year; range 18⁻90 year) in the World Health Organization Study on global AGEing and adult health (SAGE) Wave 2 in South Africa, in 2015. Kawasaki, Tanaka, and Mage equations were applied to assess PrUIE from predicted urinary creatinine (PrCr) and spot UIC values. Adequacy of iodine intake was assessed by comparing PrUIE and mUIE to the Estimated Average Requirement of 95 µg/day, while the MUIC cut-off was.

Prediction of digestible and metabolizable energy of corn distillers dried grains with solubles for growing pigs using in vitro digestible nutrients

The objective of this study was to develop prediction equations (EQS) that estimate DE and ME content of corn distillers dried grains with solubles (DDGS) using digestible (DIG) nutrients from in vitro or in vivo assays. Chemical composition data from 12 sources of corn DDGS were obtained using laboratory chemical analysis (LCA) and near infrared spectroscopy (NIRS). In vitro DM disappearance (IVDMD) from gastric and small intestine hydrolysis (IVDMDh), large intestine fermentation (IVDMDf), and total tract digestion (IVDMDt) were also determined along with in vivo apparent total tract digestibility (ATTD) of DM, CP, ether extract (EE), NDF, and ADF, and energy values (GE, DE, and ME). Correlation analysis was used to compare chemical composition from LCA with NIRS, and a stepwise selection of variables was performed using linear regression to establish DE and ME prediction EQS. Composition determined by NIRS did not correlate with values from LCA. Consequently, significance and R2 were poorer when using NIRS data as inputs to predict DE (P = 0.11; R2 = 0.23) and ME (P = 0.11; R2 = 0.24). However, when using LCA data, DE (P = 0.04; R2 = 0.35) and ME (P = 0.04; R2 = 0.52) estimates of corn DDGS obtained from prediction EQS had significant P values, but low R2. Better prediction of DE (P < 0.01; R2 = 0.83) and ME (P < 0.01; R2 = 0.76) was observed when using in vivo DIG nutrients as inputs (DM basis) for DE, kcal/kg = 854.5 + (3.6 × DIG DM, g/kg) + (3.7 × DIG EE, g/kg) + (2.0 × DIG NDF, g/kg) and ME, kcal/kg = 704.5 + (3.3 × DIG DM, g/kg) + (4.8 × DIG EE, g/kg) + (2.6 × DIG NDF, g/kg). In vitro DIG DM, but not in vitro DIG NDF, was selected for use in EQS (7) DE, kcal/kg = 6,383.6 - (42.5 × in vitro DIG DM, g/kg) + (35.4 × DIG EE, g/kg) and (8) ME, kcal/kg = 6,635.1 - (49.8 × in vitro DIG DM, g/kg) + (41.3 × DIG EE, g/kg), but the significance and accuracy for both DE (P = 0.07; R2 = 0.45) and ME (P = 0.05; R2 = 0.49) predictions was less using in vitro DIG nutrient values than using in vivo DIG nutrient values. If chemical composition was used to replace in vivo EE, along with in vitro DIG NDF and DM, the prediction EQS only used the concentration of ADF and EE as predictors instead of in vitro DIG NDF and DM. In conclusion, in vivo DIG NDF, DM, and EE are the best predictors for DE and ME content of corn DDGS for swine. Using NIRS to determine chemical composition, in vitro DIG NDF, and in vitro DIG DM did not result in accurate predictions of DE and ME.

Are Predictive Equations for Estimating Resting Energy Expenditure Accurate in Asian Indian Male Weightlifters?

BACKGROUND

The accuracy of existing predictive equations to determine the resting energy expenditure (REE) of professional weightlifters remains scarcely studied. Our study aimed at assessing the REE of male Asian Indian weightlifters with indirect calorimetry and to compare the measured REE (mREE) with published equations. A new equation using potential anthropometric variables to predict REE was also evaluated.

MATERIALS AND METHODS

REE was measured on 30 male professional weightlifters aged between 17 and 28 years using indirect calorimetry and compared with the eight formulas predicted by Harris-Benedicts, Mifflin-St. Jeor, FAO/WHO/UNU, ICMR, Cunninghams, Owen, Katch-McArdle, and Nelson. Pearson correlation coefficient, intraclass correlation coefficient, and multiple linear regression analysis were carried out to study the agreement between the different methods, association with anthropometric variables, and to formulate a new prediction equation for this population.

RESULTS

Pearson correlation coefficients between mREE and the anthropometric variables showed positive significance with suprailiac skinfold thickness, lean body mass (LBM), waist circumference, hip circumference, bone mineral mass, and body mass. All eight predictive equations underestimated the REE of the weightlifters when compared with the mREE. The highest mean difference was 636 kcal/day (Owen, 1986) and the lowest difference was 375 kcal/day (Cunninghams, 1980). Multiple linear regression done stepwise showed that LBM was the only significant determinant of REE in this group of sportspersons. A new equation using LBM as the independent variable for calculating REE was computed. REE for weightlifters = -164.065 + 0.039 (LBM) (confidence interval -1122.984, 794.854]. This new equation reduced the mean difference with mREE by 2.36 + 369.15 kcal/day (standard error = 67.40).

CONCLUSION

The significant finding of this study was that all the prediction equations underestimated the REE. The LBM was the sole determinant of REE in this population. In the absence of indirect calorimetry, the REE equation developed by us using LBM is a better predictor for calculating REE of professional male weightlifters of this region.

Meta-analysis of the energy and protein requirements of hair sheep raised in the tropical region of Brazil

The objective of this study was to estimate, through mathematical models, energy and protein requirements for maintenance and gain of hair sheep raised in the tropical region of Brazil. To determine the equation parameters, a meta-analysis of seven independent experiments of nutrient requirements was performed, comprising a total of 243 experimental units (animals), which were conducted under tropical conditions, using hair sheep in growing and finishing phases and endowed of the following quantitative data for each animal: body weight (BW), empty body weight (EBW), average daily gain (ADG), empty body gain (EBG), heat production (HP), metabolizable energy intake (MEI), retained energy (RE), metabolizable protein intake (MPI) and body protein content. The regression equations generated were as follows: for Net Energy for maintenance, (NE_m ): LogHP(MJEBW-0.75day-1)=-0.6090(±0.07470)+0.5149(±0.07216)×MEI(MJEBW-0.75day-1); for Net Energy for gain, (NE_g ): LogRE(MJEBW-0.75day-1)=0.03084(±0.05334)+0.8455(±0.04355)×LogEBG(kg/day); for Metabolizable Protein for maintenance,(MP_m ): MPI_(g/day)  = 24.8470 (±7.3646) + 560.28 (±99.6582) × EBG_(kg/day) ; for Net Protein for gain, (NP_g ): NPg(kg/day)=0.1941×EBW(kg)-0.1058. The NE_m requirement was 0.246 MJ EBW^-0.75  day^-1 . The metabolizable energy for maintenance requirement was 0.391 MJ EBW^-0.75  day^-1 . Considering an ADG of 100 g, the NE_g requirement ranged from 0.496 to 1.701 MJ/day for animals with BW ranging from 10 to 40 kg respectively. The efficiencies of use of the metabolizable energy for maintenance and gain were 0.63 and 0.36 respectively. The MP_m requirement was 3.097 g EBW^-0.75  day^-1 . Considering an ADG of 100 g, the NP_g requirement ranged from 12.4 to 10.5 g/day for animals with BW ranging from 10 to 40 kg respectively. The total metabolizable energy and protein requirements were lower than those reported by the NRC and AFRC systems. Thus, our results support the hypothesis that nutrient requirements of hair sheep raised in tropical regions differ from wool sheep raised in temperate regions. Therefore, the use of the equations designed in this study is recommended.

Prediction equations for diffusing capacity (transfer factor) of lung for North Indians

Background:

Prediction equations for diffusing capacity of lung for carbon monoxide (DLCO), alveolar volume (VA), and DLCO/VA using the current standardization guidelines are not available for Indian population. The present study was carried out to develop equations for these parameters for North Indian adults and examine the ethnic diversity in predictions.

Materials and Methods:

DLCO was measured by single-breath technique and VA by single-breath helium dilution using standardized methodology in 357 (258 males, 99 females) normal nonsmoker adult North Indians and DLCO/VA was computed. The subjects were randomized into training and test datasets for development of prediction equations by multiple linear regressions and for validation, respectively.

Results:

For males, the following equations were developed: DLCO, −7.813 + 0.318 × ht −0.624 × age + 0.00552 × age²; VA, −8.152 + 0.087 × ht −0.019 × wt; DLCO/VA, 7.315 − 0.037 × age. For females, the equations were: DLCO, −44.15 + 0.449 × ht −0.099 × age; VA, −6.893 + 0.068 × ht. A statistically acceptable prediction equation was not obtained for DLCO/VA in females. It was therefore computed from predicted DLCO and predicted VA. All equations were internally valid. Predictions of DLCO by Indian equations were lower than most Caucasian predictions in both males and females and greater than the Chinese predictions for males.

Conclusion:

This study has developed validated prediction equations for DLCO, VA, and DLCO/VA in North Indians. Substantial ethnic diversity exists in predictions for DLCO and VA with Caucasian equations generally yielding higher values than the Indian or Chinese equations. However, DLCO/VA predicted by the Indian equations is slightly higher than that by other equations.

Anthropometric predictors of body fat in a large population of 9-year-old school-aged children

Objective

To develop and cross‐validate predictive models for percentage body fat (%BF) from anthropometric measurements [including BMI z‐score (zBMI) and calf circumference (CC)] excluding skinfold thickness.

Methods

A descriptive study was carried out in 3,084 pre‐pubertal children. Regression models and neural network were developed with %BF measured by Bioelectrical Impedance Analysis (BIA) as the dependent variables and age, sex and anthropometric measurements as independent predictors.

Results

All %BF grade predictive models presented a good global accuracy (≥91.3%) for obesity discrimination. Both overfat/obese and obese prediction models presented respectively good sensitivity (78.6% and 71.0%), specificity (98.0% and 99.2%) and reliability for positive or negative test results (≥82% and ≥96%). For boys, the order of parameters, by relative weight in the predictive model, was zBMI, height, waist‐circumference‐to‐height‐ratio (WHtR) squared variable (_Q), age, weight, CC_Q and hip circumference (HC)_Q (adjusted r² = 0.847 and RMSE = 2.852); for girls it was zBMI, WHtR_Q, height, age, HC_Q and CC_Q (adjusted r² = 0.872 and RMSE = 2.171).

Conclusion

%BF can be graded and predicted with relative accuracy from anthropometric measurements excluding skinfold thickness. Fitness and cross‐validation results showed that our multivariable regression model performed better in this population than did some previously published models.

Current Capabilities and Limitations of Stable Isotope Techniques and Applied Mathematical Equations in Determining Whole-Body Vitamin A Status

BACKGROUND

Retinol isotope dilution (RID) methodology provides a quantitative estimate of total body vitamin A (VA) stores and is the best method currently available for assessing VA status in adults and children. The methodology has also been used to test the efficacy of VA interventions in a number of low-income countries. Infections, micronutrient deficiencies (eg, iron and zinc), liver disease, physiological age, pregnancy, and lactation are known or hypothesized to influence the accuracy of estimating total body VA stores using the isotope dilution technique.

OBJECTIVE

Our objectives were to review the strengths and limitations of RID methods, to discuss what is known about the impact of various factors on results, and to summarize contributions of model-based compartmental analysis to assessing VA status.

METHODS

Relevant published literature is reviewed and discussed.

RESULTS

Various equations and compartmental modeling have been used to estimate the total body VA stores using stable isotopes, including a newer 3-day equation that provides an estimate of total body VA stores in healthy adults. At present, there is insufficient information on absorption of the isotope tracer, and there is a need to further investigate how various factors impact the application of RID techniques in field studies.

CONCLUSIONS

Isotope dilution methodology can provide useful estimates of total body VA stores in apparently healthy populations under controlled study conditions. However, more research is needed to determine whether the method is suitable for use in settings where there is a high prevalence of infection, iron deficiency, and/or liver disease.

Determination of the energy value of corn distillers dried grains with solubles containing different oil levels when fed to growing pigs

This experiment used indirect calorimetry to determine the net energy (NE) content of five corn distillers dried grains with solubles (corn DDGS) containing different oil levels and to compare the NE obtained using indirect calorimetry with that calculated using previously published prediction equations. There were two samples of high-oil DDGS, one sample of medium-oil DDGS and two samples of low-oil DDGS. Twelve barrows (initial BW of 32.8 ± 2.0 kg) were used in a repeated 3 × 6 Youden square design with three periods and six diets. The diets were comprised of a corn-soybean meal basal diet and five diets containing 29.25% of one of the corn DDGS added at the expense of corn and soybean meal. During each period, the pigs were individually housed in metabolism crates for 16 days which included 7 days for adaption to feed and environmental conditions. On day 8, the pigs were transferred to respiration chambers and fed one of the six diets at 2300 kJ ME/kg BW^0.6 /day. Faeces and urine were collected from day 9 to 13 and heat production (HP) was also measured. From day 14 to 15, the pigs were fed 893 kJ ME/kg BW^0.6 /day to allow them to adapt from the fed to the fasted state. On the last day of each period (day 16), the pigs were fasted and fasting HP was measured. The digestible energy value was 16.0, 17.1 and 15.3 MJ/kg DM, the metabolizable energy value was 14.6, 15.5 and 13.7 MJ/kg DM and the NE value was 10.7, 11.0 and 9.4 MJ/kg DM, for the high-oil, medium-oil and low-oil corn DDGS, respectively. The NE obtained with indirect calorimetry in the present study did not differ from values calculated using previously published prediction equations.

Prediction of Android and Gynoid Body Adiposity via a Three-dimensional Stereovision Body Imaging System and Dual-Energy X-ray Absorptiometry

OBJECTIVE

Current methods for measuring regional body fat are expensive and inconvenient compared to the relative cost-effectiveness and ease of use of a stereovision body imaging (SBI) system. The primary goal of this research is to develop prediction models for android and gynoid fat by body measurements assessed via SBI and dual-energy x-ray absorptiometry (DXA). Subsequently, mathematical equations for prediction of total and regional (trunk, leg) body adiposity were established via parameters measured by SBI and DXA.

METHODS

A total of 121 participants were randomly assigned into primary and cross-validation groups. Body measurements were obtained via traditional anthropometrics, SBI, and DXA. Multiple regression analysis was conducted to develop mathematical equations by demographics and SBI assessed body measurements as independent variables and body adiposity (fat mass and percentage fat) as dependent variables. The validity of the prediction models was evaluated by a split sample method and Bland-Altman analysis.

RESULTS

The R(2) of the prediction equations for fat mass and percentage body fat were 93.2% and 76.4% for android and 91.4% and 66.5% for gynoid, respectively. The limits of agreement for the fat mass and percentage fat were -0.06 ± 0.87 kg and -0.11% ± 1.97% for android and -0.04 ± 1.58 kg and -0.19% ± 4.27% for gynoid. Prediction values for fat mass and percentage fat were 94.6% and 88.9% for total body, 93.9% and 71.0% for trunk, and 92.4% and 64.1% for leg, respectively.

CONCLUSIONS

The three-dimensional (3D) SBI produces reliable parameters that can predict android and gynoid as well as total and regional (trunk, leg) fat mass.

Cystatin C- and creatinine-based equations in the assessment of renal function in HIV-positive patients prior to commencing Highly Active Antiretroviral Therapy

BACKGROUND

We evaluated the accuracy and precision of creatinine- and cystatin C-based prediction equations for estimating glomerular filtration rate compared to measured glomerular filtration rate in an antiretroviral-naive human immunodeficiency virus population.

METHODS

The study population consisted of 100 treatment-naive HIV patients. Glomerular filtration rate was estimated using the Cockcroft-Gault, Modification of Diet in Renal Disease (MDRD) and Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equations, as well as cystatin C-based equations (CKD-EPIcystatin C, cystatin Cvan Deventer and CKD-EPIcombined)) compared to (51)Cr-EDTA plasma clearance-measured glomerular filtration rate. We calculated percentage bias, standard deviation of the differences, accuracy within 15 and 30% of measured glomerular filtration rate and sensitivity and specificity for predicting measured glomerular filtration rate <60 mL/min/1.73 m(2).

RESULTS

Bias for all estimating glomerular filtration rate equations ranged from -9.4% to 38.4%. The CKD-EPIcombined without ethnicity correction factor equation had the least bias, 2.9% (-2.9 to 8.8). Bias was higher for the Modification of Diet in Renal Disease and CKD-EPI equation with the African-American ethnicity factor (38.4 and 33.7%) than without (14.2 and 15.3%). Standard deviation of the differences ranged from 29.2% (CKD-EPIcombined without ethnicity factor) to 54.0% (Modification of Diet in Renal Disease with ethnicity factor). Accuracy within 30% of measured glomerular filtration rate ranged from 78% for CKD-EPIcombined without ethnicity factor to 56.7% for the Cockcroft-Gault equation. Sensitivity for creatinine-based equations was less than 50% and for the CKD-EPIcystatin C equation was 75%.

CONCLUSION

Sensitivity of creatinine-based equations for predicting glomerular filtration rate was poor in this group of patients. The CKD-EPIcombined equation performed better than creatinine-based equations.

Assessment of fat-free mass from bioelectrical impedance analysis in obese women with Prader-Willi syndrome

BACKGROUND

Fat-free mass (FFM) is lower in obese subjects with Prader-Willi syndrome (PWS) than in obese subjects without PWS. FFM prediction equations developed in non-PWS subjects may, thus, not work in PWS subjects.

AIM

To test whether the estimation of FFM from bioelectrical impedance analysis (BIA) in PWS subjects requires population-specific equations.

METHODS

Using dual-energy X-ray absorptiometry, this study measured FFM in 27 PWS and 56 non-PWS obese women and evaluated its association with the impedance index at 50 kHz (ZI50), i.e. the ratio between squared height and whole-body impedance at 50 kHz.

RESULTS

At the same level of ZI50, PWS women had a lower FFM than non-PWS women. However, when PWS-specific equations were used, FFM was accurately estimated at the population level. An equation employing a dummy variable coding for PWS status was able to explain 85% of the variance of FFM with a root mean squared error of 3.3 kg in the pooled sample (n = 83).

CONCLUSION

Population-specific equations are needed to estimate FFM from BIA in obese PWS women.

Age-predicted vs. measured maximal heart rate in young team sport athletes

BACKGROUND

Although maximal heart rate (HR)max is used widely to assess exercise intensity in sport training and particularly in various team sports, there are limited data with regards to the use of age-based prediction equations of HRmax in sport populations. The aim of this study was to compare the measured-HRmax with three prediction equations (Fox-HRmax = 220-age and Tanaka-HRmax = 208-0.7×age and Nikolaidis-HRmax = 223-1.44×age) in young team sport athletes.

MATERIALS AND METHODS

Athletes of soccer, futsal, basketball and water polo, classified into three age groups (u-12, 9-12 years, n = 50; u-15, 12-15 years, n = 40; u-18, 15-18 years, n = 57), all members of competitive clubs, voluntarily performed a graded exercise field test (20 m shuttle run endurance test) to assess HRmax.

RESULTS

Fox-HRmax and Nikolaidis-HRmax overestimated measured-HRmax, while Tanaka-HRmax underestimated it (P < 0.001). However, this trend was not consistent when examining each group separately; measured-HRmax was similar with Tanaka-HRmax in u-12 and u-15, while it was similar with Nikolaidis-HRmax in u-18.

CONCLUSION

The results of this study failed to validate two widely used and one recently developed prediction equations in a large sample of young athletes, indicating the need for specific equation in different age groups. Therefore, coaches and fitness trainers should prefer Tanaka-HRmax when desiring to avoid overtraining, while Fox-HRmax and Nikolaidis-HRmax should be their choice in order to ensure adequate exercise intensity.

Mixed dentition analysis in Libyan schoolchildren

OBJECTIVES

The primary aim of the present study was to assess the applicability of Tanaka and Johnston and Moyers' methods of prediction in Libyan subjects and secondary aim was to develop a new prediction method for the examined population if required.

MATERIALS AND METHODS

The study sample comprised 343 Libyan schoolchildren with age ranged from 12 to 17 years; 169 males age matched with 174 females, all with no craniofacial abnormalities and orthodontically untreated. The mesiodistal (MD) tooth widths were measured and compared with the estimated values derived from Tanaka and Johnston equations and from Moyers' probability tables at 35%, 50% and 75% respectively using Paired t-tests. The constants a and b in the linear regression equation (y = a + bx), the correlation coefficient, the coefficient of determination and the standard errors of estimate were computed.

RESULTS

Significant sexual dimorphism in lower incisors mesiodistal width was observed. There were significant discrepancies between the current measurements and those estimated from Tanaka and Johnston's equation and Moyers' tables. New linear regression equations were derived for both sexes to allow precise prediction for Libyan subjects. The correlation coefficients between the total MD width of the mandibular permanent incisors and that of the maxillary and mandibular canines and premolars were found to be 0.66 and 0.68 for males and 0.57 and 0.58 in females, respectively.

CONCLUSIONS

It appears that there is a limitation in the application of Tanaka and Johnston's equation and Moyers' methods to Libyan subjects. The developed prediction equation is more accurate for predicting the MD widths of unerupted canine and premolars of Libyan population.

Accuracy and preference of measuring resting energy expenditure using a handheld calorimeter in healthy adults

BACKGROUND

Accurate estimates of energy expenditure are required in clinical nutrition in order to determine the requirements of individuals and to inform feeding regimes. Calorimetry can provide accurate measurements but is often impractical in clinical or community settings; prediction equations are widely used to estimate resting energy expenditure (REE) but have limited accuracy. A portable, self-calibrating, handheld calorimeter (HHC) may offer an alternative way of determining REE. The aim of the study was to evaluate whether estimates of REE derived using an HHC are closer to accurate measurements than values calculated using selected prediction equations.

METHODS

REE was measured in 36 healthy adults aged 21-58 years using a flow-through indirect calorimeter (FIC) and HHC. Estimated REE was calculated using three predictive equations (Harris & Benedict; Schofield; Henry). Differences in REE between the 'gold standard' values derived using the FIC and those derived using the HHC and equations were examined using paired t-tests and Bland Altman plots.

RESULTS

Mean REEHHC was significantly lower than mean REEFIC [4556 ± 1042 kJ (1089 ± 249 kcal) versus 6230 ± 895 kJ (1489 ± 214 kcal), P = 0.000] and also significantly lower than mean values calculated using all three equations. The mean difference between REEHHC and REEFIC [1674 ± 908 kJ (400 ± 217 kcal)] was significantly greater (P = 0.000) than the mean differences between the values calculated using the three prediction equations [272 ± 490 kJ (65 ± 117 kcal) (Harris-Benedict), 264 ± 510 kJ (63 ± 122 kcal) (Schofield), 84 ± 502 kJ (20 ± 120 kcal) (Henry)].

CONCLUSIONS

The HHC provides estimates of REE in healthy people that are less accurate than those calculated using the prediction equations and so does not provide a useful alternative.

Common Prediction Equations Overestimate Measured Resting Metabolic Rate in Young Hispanic Women

The accuracy of 6 resting metabolic rate (RMR) prediction equations to indirect calorimetry was compared in 38 Hispanic women (age = 30 ± 7 years; body mass index = 28.9 ± 7.2 kg/m2; body fat = 42% ± 8%). Paired t tests examined differences between predicted and measured RMR; significance defined as P < 0.05. Bias and agreement were displayed using Bland-Altman plots. Accuracy was defined when the predicted RMR was ± 10% of the measured RMR. Data were analyzed with SPSS (version 19). Only the equation of Owen et al was not significantly different from the measured RMR (1336 ± 142 and 1322 ± 203 kcal/d, respectively). The equation of Owen et al was accurate for 84.2% of women; RMR prediction equations had limited applicability for young Hispanic women.

Assessment of renal function in clinical practice at the bedside of burn patients

WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT

* In burn patients it has been shown ([2]), that there is a correlation between the creatinine clearance (CL(CR)) and the clearance of inulin. * The CL(CR) has never been studied in burn patients who have normal serum creatinine. * The Robert, Kirkpatrick and sMDRD formulae have never been evaluated in burn patients.

WHAT THIS STUDY ADDS

* Despite burn patients having normal serum creatinine concentrations, the study showed that there are large variations in CL(CR) which cannot be detected by single serum creatinine measurements, and which have important implications for drug therapy. * It showed that the formulae currently used to calculate creatinine clearance on the basis of serum creatinine are inadequate for use in burn patients, and they should be abandoned in favour of direct measurement from a 24 h urine collection.

AIMS

The aim of this study was to evaluate whether the renal function of burn patients could be correctly assessed using a single serum creatinine measurement, within normal limits, and three prediction equations of glomerular filtration taking into account, serum creatinine, age, weight and sex.

METHODS

This was a prospective study comprising 36 adult burn patients with a serum creatinine <120 micromol l(-1), within the second or third week following the burn injury. Renal function was assessed using serum creatinine, 24 h urinary CL(CR), and the Cockcroft-Gault, Robert, Kirkpatrick and simplified MDRD equations.

RESULTS

Despite normal serum creatinine concentrations in all patients, a significant number had a decreased CL(CR). The urinary CL(CR) was <80 ml(-1) min(-1) 1.73 m(-2) in nine patients (25%), and <60 ml(-1) min(-1) 1.73 m(-2) in five patients (14%). Between the groups having a CL(CR) lower or greater than 80 ml(-1) min(-1) 1.73 m(-2) there were no differences in gender, burn indices, percentage of mechanically ventilated patients or length of hospital stay, but a difference in age. The highest CL(CR) (>140 ml(-1) min(-1) 1.73 m(-2)) was found in 13 patients younger than 40 years. Regression analysis, residual and Bland-Altman plots revealed that neither the Cockcroft-Gault, Robert, Kirkpatrick nor sMDRD equations were specific enough for the assessment of renal function.

CONCLUSIONS

In burn patients with normal serum creatinine during the hypermetabolic phase, serum creatinine and creatine based predictive equations are imprecise in assessing renal function.

Comparison of the validity of anthropometric and bioelectric impedance equations to assess body composition in adolescent girls

OBJECTIVE: The purpose of this study was to examine the validity of two anthropometric and four bioelectric impedance (BIA) equations to estimate body composition from dual-energy x-ray absorptiometry (DXA) in adolescent girls of various ethnicities. The rationale for this study was to develop a prediction equation for percent body fat in a multi-ethnic, representative sample of sixth to eighth grade girls. DESIGN: One-hundred and sixty-six girls (51 African-American, 45 non-Black Hispanic, 55 non-Hispanic Caucasian, 15 multi-ethnic) participated. Estimates of percent fat and fat-free mass (FFM) from six published BIA and anthropometric equations and the equation developed from this study were compared to body composition determined from DXA. An RJL Systems analyzer was used to measure BIA. Anthropometry included body weight, height, and triceps and calf skinfolds. RESULTS: Average (± SD) age, size and body composition was as follows: age, 12.1±1.2 yrs, body mass 52.7±15.9 kg, height, 154.6±8.1 cm; DXA percent fat, 27.9±10.4; fat mass (FM), 15.6±10.2 kg; and fat free mass (FFM) 35.7±6.8 kg. No ethnic differences were found in the relationships between estimated and DXA measured body composition, with the exception of the skinfold equation. The six equations explained on average 82% of the variance in percent fat, 94% of the variance in fat mass, and 88% in fat free mass. Bland-Altman analysis indicated that none of the equations performed satisfactorily in our sample. CONCLUSIONS: The BIA and anthropometric equations were significantly related to DXA body composition parameters, however none met the criteria for cross-validation.