GLI-12 Reference Values versus Fixed 0.7 Ratio for the Detection of Airflow Obstruction in the Presence of Lung Hyperinflation

Introduction

Airflow obstruction (AO) is evidenced by reduced forced expiratory volume in 1 s/forced vital capacity (FEV1/FVC) with the threshold for diagnosis often being set at <0.7. However, currently the ATS/ERS standards for interpretation of lung function tests recommend the lower limit of normal (LLN), calculated by reference equations of the Global Lung Initiative from 2012 (GLI-12), as a threshold for AO diagnosis. The present study aims to investigate phenotypes, with focus on hyperinflation, which influence AO prevalence defined by FEV1/FVC < LLN when compared to the fixed 0.7 threshold.

Methods

Data from 3,875 lung function tests (56.4% men, aged 18-95) including 3,824 body plethysmography recordings performed from July 2021 to June 2022 were analysed. The difference between both classifiers was quantified, before and after stratification by sex, age, and hyperinflation.

Results

AO diagnosis was significantly less frequent with the LLN threshold (18.2%) compared to the fixed threshold (28.0%) (p < 0.001) with discordance rate of 10.5%. In the presence of mild or moderate hyperinflation, there was substantial agreement (Cohen's kappa: 0.616, 0.718) between the classifiers compared to near perfect agreement in the presence of severe hyperinflation (Cohen's kappa: 0.896). In addition, subgroup analysis after stratification for sex, age, and hyperinflation showed significant differences between both classifiers.

Conclusion

The importance of using the LLN threshold instead of the fixed 0.7 threshold for the diagnosis of AO is highlighted. When using the fixed threshold AO, misdiagnosis was more common in the presence of mild to moderate hyperinflation.

New prediction equations for knee isokinetic strength in young and middle-aged non-athletes

BACKGROUND

This study aimed to develop alternative prediction equations to predict isokinetic muscle strength at 60°/s based on anthropometric characteristics, including body mass, height, age, and sex for young and middle-aged non-athlete populations.

METHODS

Three hundred and thirty-two healthy non-athletic participants (174 females, 158 males) between 20 and 59 years underwent a 60°/s isokinetic knee joint concentric contraction test. Forty people were randomly selected for retesting to assess the reliability of the isokinetic instrument. Multivariate linear regression was used to establish extension peak torque (EPT) and flexion peak torque (FPT) prediction equations. Sixty extra participants were used individually to validate the prediction equations, and Bland Altman plots were constructed to assess the agreement of predicted values with actual measurements.

RESULTS

The result demonstrated that the instrument we used has excellent reliability. The multivariable linear regression model showed that body mass, age, and sex were significant predictors of PT (EPT: Adjusted R2 = 0.804, p < 0.001; FPT: Adjusted R2 = 0.705, p < 0.001). Furthermore, the equations we established had higher prediction accuracy than those of Gross et al. and Harbo et al. CONCLUSION: The equations developed in this study provided relatively low bias, thus providing a more suitable reference value for the knee isokinetic strength of young and middle-aged non-athletes.

Adults with early diagnosis of phenylketonuria have higher resting energy expenditure than adults with late diagnosis

INTRODUCTION

To date, there is a gap regarding resting energy expenditure (REE) in adults with phenylketonuria (PKU), whether PKU type and time of diagnosis interfere with REE, and whether the available predictive equations are valid in this population.

OBJECTIVE

To compare the REE of adult subjects with PKU with healthy subjects and secondarily, examine the REE of adults with PKU according to type and time of diagnosis, and check the agreement of commonly used predictive equations of REE.

METHODS

Concordance study with adults with PKU and a comparison group (CG) with healthy adults. Anthropometric and body composition assessments and REE assessment by indirect calorimetry (IC) were performed. The results obtained by IC were compared with predictive equations.

RESULTS

Sixty-nine adults were evaluated (PKU: 36; CG: 33). The REE of adults with mild and classic PKU is similar (p>.05) and similar to CG (p>.05). The REE of individuals with early diagnosis is higher (p < .05) than the REE of individuals with late diagnosis. The REE obtained by IC differed (p < .05) from all estimated REE.

CONCLUSION

Late diagnosis of PKU showed lower REE compared to individuals with early diagnosis. The REE of adults with PKU does not differ in relation to the type of PKU, nor does it differ from the CG. Predictive equations overestimate REE.

Practical Utilization of Prediction Equations in Chronic Kidney Disease

Chronic kidney disease (CKD) is common and can lead to kidney failure, cardiovascular complications, and early mortality. While nephrologists can provide valuable insights for patients at all stages of CKD, these scarce resources should be targeted at patients with the highest risk of progression and adverse outcomes. Prediction models are tools that can help providers risk stratify patients if they are effectively implemented into the clinical workflow. We believe these equations should demonstrate (1) clinical utility: where they can provide useful information to the physician and patients; and (2) clinical usability: where they are able to be easily integrated into clinical workflow and do not result in unnecessary costs or visits. CKD often remains unrecognized until later stages when a large window of opportunity to delay progression has already passed. Models to determine progression of CKD using thresholds such as a 40% decline in eGFR can provide clinical utility in risk stratifying patients at all stages of CKD, an endpoint that has been recommended by the FDA for the evaluation of drug approvals for disease-modifying therapies. For patients at more advanced stages of CKD with a greater risk of kidney failure, tools such as the kidney failure risk equation can be implemented to help guide most costly decisions, such as referral to multidisciplinary care, commencing dialysis modality education, or planning for vascular access placement surgery. In addition, models focused on determining outcomes following dialysis initiation can help inform shared decision-making between patient and provider to better inform decisions around conservative care. To ensure widespread adoption of these tools, it is important to ensure that they are broadly generalizable to many health settings and easily implemented into existing clinic workflows with minimum disruption.

Prediction of enteric methane production and yield in dairy cattle using a Latin America and Caribbean database

Successful mitigation efforts entail accurate estimation of on-farm emission and prediction models can be an alternative to current laborious and costly in vivo CH₄ measurement techniques. This study aimed to: (1) collate a database of individual dairy cattle CH₄ emission data from studies conducted in the Latin America and Caribbean (LAC) region; (2) identify key variables for predicting CH₄ production (g d^-1) and yield [g kg^-1 of dry matter intake (DMI)]; (3) develop and cross-validate these newly-developed models; and (4) compare models' predictive ability with equations currently used to support national greenhouse gas (GHG) inventories. A total of 42 studies including 1327 individual dairy cattle records were collated. After removing outliers, the final database retained 34 studies and 610 animal records. Production and yield of CH₄ were predicted by fitting mixed-effects models with a random effect of study. Evaluation of developed models and fourteen extant equations was assessed on all-data, confined, and grazing cows subsets. Feed intake was the most important predictor of CH₄ production. Our best-developed CH₄ production models outperformed Tier 2 equations from the Intergovernmental Panel on Climate Change (IPCC) in the all-data and grazing subsets, whereas they had similar performance for confined animals. Developed CH₄ production models that include milk yield can be accurate and useful when feed intake is missing. Some extant equations had similar predictive performance to our best-developed models and can be an option for predicting CH₄ production from LAC dairy cows. Extant equations were not accurate in predicting CH₄ yield. The use of the newly-developed models rather than extant equations based on energy conversion factors, as applied by the IPCC, can substantially improve the accuracy of GHG inventories in LAC countries.

The need for race-specific reference equations for pulmonary diffusing capacity for nitric oxide

BACKGROUND

Few reference equations exist for healthy adults of various races for pulmonary diffusing capacity for nitric oxide (DLNO). The purpose of this study was to collect pilot data to demonstrate that race-specific reference equations are needed for DLNO.

METHODS

African Americans (blacks) were chosen as the comparative racial group. In 2016, a total of 59 healthy black subjects (27 males and 32 females) were recruited to perform a full battery of pulmonary function tests. In the development of DLNO reference equations, a white reference sample (randomly drawn from a population) matched to the black sample for sex, age, and height was used. Multiple linear regression equations for DLNO, alveolar volume (VA), and pulmonary diffusing capacity for carbon monoxide (DLCO) using a 5-6 s breath-hold were developed.

RESULTS

Our models demonstrated that sex, age2, race, and height explained 71% of the variance in DLNO and DLCO, with race accounting for approximately 5-10% of the total variance. After normalizing for sex, age2, and height, blacks had a 12.4 and 3.9 mL/min/mmHg lower DLNO and DLCO, respectively, compared to whites. The lower diffusing capacity values in blacks are due, in part, to their 0.6 L lower VA (controlling for sex and height).

CONCLUSION

The results of this pilot data reveal small but important and statistically significant racial differences in DLNO and DLCO in adults. Future reference equations should account for racial differences. If these differences are not accounted for, then the risk of falsely diagnosing lung disease increase in blacks when using reference equations for whites.

Validity of predictive equations to estimate RMR in females with varying BMI

Estimation of RMR using prediction equations is the basis for calculating energy requirements. In the present study, RMR was predicted by Harris–Benedict, Schofield, Henry, Mifflin–St Jeor and Owen equations and measured by indirect calorimetry in 125 healthy adult women of varying BMI (17–44 kg/m²). Agreement between methods was assessed by Bland–Altman analyses and each equation was assessed for accuracy by calculating the percentage of individuals predicted within ± 10 % of measured RMR. Slopes and intercepts of bias as a function of average RMR (mean of predicted and measured RMR) were calculated by regression analyses. Predictors of equation bias were investigated using univariate and multivariate linear regression. At group level, bias (the difference between predicted and measured RMR) was not different from zero only for Mifflin–St Jeor (0 (sd 153) kcal/d (0 (sd 640) kJ/d)) and Henry (8 (sd 163) kcal/d (33 (sd 682) kJ/d)) equations. Mifflin–St Jeor and Henry equations were most accurate at the individual level and predicted RMR within 10 % of measured RMR in 71 and 66 % of participants, respectively. For all equations, limits of agreement were wide, slopes of bias were negative, and intercepts of bias were positive and significantly (P < 0⋅05) different from zero. Increasing age, height and BMI were associated with underestimation of RMR, but collectively these variables explained only 15 % of the variance in estimation bias. Overall accuracy of equations for prediction of RMR is low at the individual level, particularly in women with low and high RMR. The Mifflin–St Jeor equation was the most accurate for this dataset, but prediction errors were still observed in about one-third of participants.

Prediction of body fat in Sri Lankan adults: Development and validation of a skinfold thickness equation

OBJECTIVE

The study aimed to develop and validate a percentage body fat (%BF) prediction equation using SFT for Sri Lankan adults.

METHODS

Healthy adults (≥18 years) were randomly selected and SFT was measured in five areas (triceps, biceps, calf, suprailliac and subscapular). Body composition analysis was evaluated by Deuterium oxide (D₂O) dilution. Prediction equation for %BF was derived by linear-regression-analysis.

RESULTS

Study population included 170 adults (Males: 32.9%; age: 43.2 ± 12.6 years). Final equation for %BF (r = 0.715, p < 0.001) correlated significantly D₂O dilution derived %BF.

CONCLUSIONS

The equation is suitable for estimation of %BF in Sri Lankan adults and is possibly appropriate for other South-Asian populations.

Bias due to Berkson error: issues when using predicted values in place of observed covariates

Studies often want to test for the association between an unmeasured covariate and an outcome. In the absence of a measurement, the study may substitute values generated from a prediction model. Justification for such methods can be found by noting that, with standard assumptions, this is equivalent to fitting a regression model for an outcome variable when at least one covariate is measured with Berkson error. Under this setting, it is known that consistent or nearly consistent inference can be obtained under many linear and nonlinear outcome models. In this article, we focus on the linear regression outcome model and show that this consistency property does not hold when there is unmeasured confounding in the outcome model, in which case the marginal inference based on a covariate measured with Berkson error differs from the same inference based on observed covariates. Since unmeasured confounding is ubiquitous in applications, this severely limits the practical use of such measurements, and, in particular, the substitution of predicted values for observed covariates. These issues are illustrated using data from the National Health and Nutrition Examination Survey to study the joint association of total percent body fat and body mass index with HbA1c. It is shown that using predicted total percent body fat in place of observed percent body fat yields inferences which often differ significantly, in some cases suggesting opposite relationships among covariates.

Spirometry: A Need for Periodic Updates of National Reference Values

The aim of the study was to assess the need for changes in spirometry reference values in the Polish population with time lapse, as the aftereffect of a radical socioeconomic overturn of the 1990. We retrospectively analyzed data files on forced expiratory volume in 1 s (FEV1), vital capacity (VC), and forced VC (FVC) in healthy, never-smoking Caucasians (731 females and 327 males) obtained in in 1993-1998. We assessed a discrepancy between the then measured values of these variables, on the one side, and the corresponding European Community for Steel and Coal (ECSC) predicted values or the current updated predicted values for the Polish population, on the other side. We found that those old measured values approximately corresponded to the ECSC reference, but they were appreciably lower than the current Polish reference values; the younger the subjects the greater the difference. The current Polish reference values of FVC were much closer to the old measured VC than to the old measured FVC values, which introduces a substantial discrepancy between the past and present FVCs. We conclude that the spirometry reference values may change with time lapse. Thus, accuracy of prediction equations should be periodically updated, which seems to particularly concern the equations elaborated for the nations that undergo rapid economic developments connected with changes in living standards.

Reference equations for spirometry in healthy Asian children aged 5 to 18 years in Taiwan

Background and objective

This study aimed to establish reference equations for spirometry in healthy Taiwanese children and assess the applicability of the Global Lung Function Initiative (GLI)-2012 equations to Taiwanese children.

Methods

Spirometric data collected from 757 healthy Taiwanese children aged 5 to 18 years in a population-based cohort study. Prediction equations derived using linear regression and the generalized additive models for location, scale and shape (GAMLSS) method, respectively.

Results

The GLI-2012 South East Asian equations did not provide a close fit with mean ± standard error z-scores of −0.679 ± 0.030 (FVC), −0.186 ± 0.044 (FEV₁), −0.875 ± 0.049 (FEV₁/FVC ratio) and −2.189 ± 0.063 (FEF_25-75) for girls; and 0.238 ± 0.059, −0.061 ± 0.053, −0.513 ± 0.059 and −1.896 ± 0.077 for boys. The proposed GAMLSS models took age, height, and weight into account. GAMLSS models for boys and girls captured the characteristics of spirometric data in the study population closely in contrast to the linear regression models and the GLI-2012 equations.

Conclusion

This study provides up-to-date reference values for spirometry using GAMLSS modeling in healthy Taiwanese children aged 5 to 18 years. Our study provides evidence that the GLI-2012 reference equations are not properly matched to spirometric data in a contemporary Taiwanese child population, indicating the urgent need for an update of GLI reference values by inclusion of more data of non-Caucasian decent.

Evaluating human basal metabolism: the erroneous and misleading use of so-called "prediction equations"

Prediction (regression) equations are widely used, but their reliability as predictive tools is questionable as they provide contradicting results. The key point is that values calculated by regression equations are not precisely defined numbers but lie within a range of possible values in the standard deviation interval, none of which can be considered as the most probable. Ignoring this point leads to illicit/improper calculations, generating wrong results, which may have adverse consequences for human health. To demonstrate this, we applied the equations of Harris and Benedict in a reverse method, i.e. calculating (predicting) the daily energy expenditure in the same subjects used to obtain the equations and comparing values with the original measured data. We used the Bland-Altman and frequency distribution analyses. We found large differences in both individual data and population characteristics, showing that prediction equations are not predictive tools.

Improved prediction equations for estimating height in adults from ethnically diverse backgrounds

BACKGROUND & AIMS

When body height cannot be measured, it can be predicted from ulna length (UL). However, commonly used published prediction equations may not provide useful estimates in adults from all ethnicities. This study aimed to evaluate the relationship between UL and height in adults from diverse ethnic groups and to consider whether this can be used to provide useful prediction equations for height in practice.

METHODS

Standing height and UL were measured in 542 adults at seven UK locations. Ethnicity was self-defined using UK Census 2011 categories. Data were modelled to give two groups of height prediction equations based on UL, sex and ethnicity and these were tested against an independent dataset (n = 180).

RESULTS

UL and height were significantly associated overall and in all groups except one with few participants (P = 0.059). The new equations yielded predicted height (H_p) that was closer to measured height in the Asian and Black subgroups of the independent population than the Malnutrition Universal Screening Tool (MUST) equations. For Asian men, (H_p (cm) = 3.26 UL (cm) + 83.58), mean difference from measured (95% confidence intervals) was -0.6 (-2.4, +1.2); Asian women, (H_p = 3.26 UL + 77.62), mean difference +0.5 (-1.4, 2.4) cm. For Black men, H_p = 3.14 UL + 85.80, -0.4 (-2.4, 1.7); Black women, H_p = 3.14 UL + 79.55, -0.8 (-2.8, 1.2). These differences were not statistically significant while predictions from MUST equations were significantly different from measured height.

CONCLUSIONS

The new prediction equations provide an alternative for estimating height in adults from Asian and Black groups and give mean predicted values that are closer to measured height than MUST equations.

Development of an Easy-to-Use Visual Aid for the Prediction of Body Fat Based on Waist Circumference and Height in Asian Chinese Adults

BACKGROUND

Adiposity is a major risk factor for metabolic and cardiovascular diseases. Initial prediction equations to estimate adiposity are complex, requiring skinfold measurements that cannot be obtained conveniently by the general population.

OBJECTIVE

To develop simplified prediction equations to estimate body fat percentage (%BF) in Asian Chinese adults, evaluate the validity of the simplified %BF prediction equations, compare the simplified %BF prediction equations with an existing equation, and create visual charts to enable easy assessment of adiposity by the general public.

DESIGN

Simplified prediction equations were developed and evaluated for validity using anthropometric measurements obtained from a cross-sectional study.

PARTICIPANTS AND SETTING

Healthy participants with no major diseases and not taking long-term medications were recruited in a cross-sectional study conducted at Clinical Nutrition Research Centre, Singapore, between June 2014 and October 2017. A total of 439 participants were used for model building (269 women and 170 men) and another 107 participants were used for evaluating validity (62 women and 45 men).

MAIN OUTCOME MEASURES

Simplified but acceptable prediction models and generation of user-friendly charts.

STATISTICAL ANALYSES PERFORMED

Simplified sex-specific %BF prediction equations were developed using stepwise regression and the model-building dataset. The best models were selected using the Akaike information criterion. The models were further simplified and their performance was compared using the validation dataset before choosing the final prediction equations.

RESULTS

The final selected models for women and men included waist circumference and height with nonsignificant prediction bias in %BF of 0.84%±3.94% (P=0.098, Cohen's d_z=0.21) and -0.98%±3.65% (P=0.079, Cohen's d_z=0.27), respectively. The final equations were split into three height categories from which the sex-specific prediction charts were generated.

CONCLUSIONS

The sex-specific prediction charts provide a good visual guide for estimating %BF using height and waist circumference values that are easy to obtain by the general public.

An anthropometry-based equation of fat mass percentage as a valid discriminator of obesity

OBJECTIVE

To develop a new predictive equation for fat mass percentage (%FM) based on anthropometric measurements and to assess its ability to discriminate between obese and non-obese individuals.

DESIGN

Cross-sectional study.

SETTING

Mexican adults.ParticipantsAdults (n 275; 181 women) aged 20-63 years with BMI between 17·4 and 42·4 kg/m2.

RESULTS

Thirty-seven per cent of our sample was obese using %FM measured by air-displacement plethysmography (BOD POD®; Life Measurement Instruments). The fat mass was computed from the difference between weight and fat-free mass (FFM). FFM was estimated using an equation obtained previously in the study from weight, height and sex of the individuals. The %FM estimated from the obtained FFM showed a sensitivity of 90·3 (95 % CI 86·8, 93·8) % and a specificity of 58·0 (95 % CI 52·1, 63·8) % in the diagnosis of obesity. Ninety-three per cent of participants with obesity and 65 % of participants without obesity were correctly classified.

CONCLUSIONS

The anthropometry-based equation obtained in the present study could be used as a screening tool in clinical and epidemiological studies not only to estimate the %FM, but also to discriminate the obese condition in populations with similar characteristics to the participant sample.

Development of regression equations for estimating height and weight using body segments in Argentine children

OBJECTIVES

Body weight and height measurements are essential in children for assessing growth and nutrition, for the calculation of medication doses, and for the effectiveness of medical interventions. When direct measurements cannot be made, segmental measures can be used to estimate weight and height. The equations available to estimate height and weight, however, are limited. The aim of this study was to use segmental measures to develop equations for use in pediatric clinical practice.

METHODS

A cross-sectional study design was used to collect data from 861 healthy children (484 females and 377 males) ages 2 to 18 y to develop equations for estimating weight and height from midarm circumference (MAC) and knee-heel height (KH), respectively. A multi-linear regression model was used to develop the equations.

RESULTS

The high correlation between MAC and the actual weight and KH and height indicates strong agreement. Four equations were developed to estimate weight and height using segmental measures. 1. To estimate weight from MAC for females: W = 2.37 × MAC + 1.64 × age (y) - 28.28. 2. To estimate weight for males: W = 2.54 × MAC + 1.82 × age (y) - 32.73. 3. To estimate height from KH for females: H = 2.88 × KH + 0.15. 4. To estimate height from KH for males: H = 2.73 × KH + 0.21.

CONCLUSIONS

MAC and KH can be used for estimation equations for weight and height with a very good predictive power. Sex and age were significant covariates in estimating weight. To predict height, only sex was needed to fit the model.

Methodologies on estimating the energy requirements for maintenance and determining the net energy contents of feed ingredients in swine: a review of recent work

In the past two decades, a considerable amount of research has focused on the determination of the digestible (DE) and metabolizable energy (ME) contents of feed ingredients fed to swine. Compared with the DE and ME systems, the net energy (NE) system is assumed to be the most accurate estimate of the energy actually available to the animal. However, published data pertaining to the measured NE content of ingredients fed to growing pigs are limited. Therefore, the Feed Data Group at the Ministry of Agricultural Feed Industry Centre (MAFIC) located at China Agricultural University has evaluated the NE content of many ingredients using indirect calorimetry. The present review summarizes the NE research works conducted at MAFIC and compares these results with those from other research groups on methodological aspect. These research projects mainly focus on estimating the energy requirements for maintenance and its impact on the determination, prediction, and validation of the NE content of several ingredients fed to swine. The estimation of maintenance energy is affected by methodology, growth stage, and previous feeding level. The fasting heat production method and the curvilinear regression method were used in MAFIC to estimate the NE requirement for maintenance. The NE contents of different feedstuffs were determined using indirect calorimetry through standard experimental procedure in MAFIC. Previously generated NE equations can also be used to predict NE in situations where calorimeters are not available. Although popular, the caloric efficiency is not a generally accepted method to validate the energy content of individual feedstuffs. In the future, more accurate and dynamic NE prediction equations aiming at specific ingredients should be established, and more practical validation approaches need to be developed.

New Equations to Predict Body Fat in Asian-Chinese Adults Using Age, Height, Skinfold Thickness, and Waist Circumference

BACKGROUND

Adiposity is an independent predictor of metabolic disease. However, highly accurate body fat assessment is not routinely done due to limited access to expensive and labor-intensive methods.

OBJECTIVE

The aim of the study was to develop body fat prediction equations for Asian-Chinese adults using easily attainable anthropometric measurements.

DESIGN

Prediction equations of body fat were developed using anthropometric and skinfold thickness measurements obtained from a cross-sectional study. These new equations were then validated using baseline data from an independent randomized controlled study.

PARTICIPANTS/SETTING

Healthy participants with no major diseases and not taking long-term medications were recruited in an ongoing cross-sectional study that began in June 2014 (n=439, 170 males, 269 females), as well as a randomized controlled trial (n=108, 58 males, 50 females) conducted from January 2013 to October 2014. Both the studies were conducted at Clinical Nutrition Research Center located in Singapore.

MAIN OUTCOME MEASURES

Data used to develop and validate equations were from two original studies that assessed body fat by dual-energy x-ray absorptiometry, age, waist circumference, height, and biceps and triceps skinfolds.

STATISTICAL ANALYSIS PERFORMED

Sex-specific percent body fat prediction equations were developed using stepwise regression with Akaike Information Criterion on the cross-sectional data. The equations were then validated using data from the randomized controlled study and also compared against Asian-specific Davidson equations.

RESULTS

The best body fat prediction model (R²=0.722, standard error of estimation=2.97 for females; R²=0.815, standard error of estimation=2.49 for males) for both sexes included biceps and triceps skinfolds, waist circumference, age, and height. The new equations developed resulted in modest discrepancies in body fat of 1.8%±2.7% in males (P<0.001) and 0.7%±3.1% in females (P=0.125; not significant) compared with the Asian-specific Davidson equations (-7.4%±3.2% [P<0.001] and -7.4%±2.7% [P<0.001], respectively).

CONCLUSIONS

Sex-specific equations to predict the percent body fat of Asian-Chinese adults with a higher degree of accuracy were developed. Ease of use in both field and clinical settings will be a major advantage.

Resting energy expenditure in male athletes with a spinal cord injury

OBJECTIVE

To investigate whether there are differences in the resting energy expenditure (REE) and body composition of athletes with a spinal cord injury (SCI) compared to active able-bodied controls.

DESIGN

In this cross sectional study, male athletes with a SCI were compared to active able-bodied controls matched for age, stretch stature and body mass. In addition, the accuracy of standard REE prediction equations in estimating REE was assessed.

PARTICIPANTS

Seven male wheelchair athletes with a SCI and six matched active able-bodied controls volunteered to participate.

OUTCOME MEASURES

REE was measured using indirect calorimetry and estimated using population-specific prediction equations. Body composition (lean tissue mass, fat mass and bone mineral content) was measured by dual energy X-ray absorptiometry (DXA).

RESULTS

While absolute and adjusted REE in the athletes with SCI was lower than controls, this difference was not significant (P = 0.259). When adjusted for lean tissue mass (LTM), REE was significantly higher (P = 0.038) in the athletes with SCI compared to the controls (146 ± 29kJ/kg LTM vs. 125 ± 8kJ/kg LTM). LTM was significantly lower in the athletes with SCI (44.35 ± 6.98 kg) compared to the able-bodied controls (56.02 ± 4.93 kg; P < 0.01). The differences between predicted and measured REE in the athletes with SCI were not statistically significant (except for the Owen equation), however there was no significant correlation between the measures.

CONCLUSION

This suggests that existing prediction equations used to estimate energy requirements may require modification for athletes with SCI.

Evaluation of the convergent validity of an estimated cardiorespiratory fitness algorithm

PURPOSE

Examine the convergent validity of a cardiorespiratory fitness (CRF) algorithm when compared to treadmill-assessed CRF.

METHODS

Data from the 1999-2004 NHANES were used (N = 3259 adults 20-49 years). Cardiorespiratory fitness was estimated from an algorithm. Participants completed a submaximal treadmill-based protocol. We (1) evaluated the pairwise association (and ICC) between estimated and measured cardiorespiratory fitness, (2) employed a paired samples t test to examine potential mean differences between estimated and measured cardiorespiratory fitness, (3) constructed a Bland-Altman plot and 95% limits of agreement (LoA) to explore systematic differences and random error between estimated and measured cardiorespiratory fitness, and (4) examined the association (via linear regression) of estimated and measured cardiorespiratory fitness with chronic disease prevalence and C-reactive protein (CRP).

RESULTS

Mean estimated CRF (10.68 METs) was lower than the mean measured CRF of 11.37 METs (p < 0.0001). The calculated pairwise correlation was of a moderate strength, r = 0.43 (p < 0.0001), with an ICC of 0.40 (p < 0.001). Calculated LoA indicated that estimated CRF may differ from measured CRF by 40% below to 48% above. Regression analyses yielded statistically significant inverse associations of estimated (unstandardized coefficient = - 0.026; p < 0.001) and measured (unstandardized coefficient = - 0.007; p = 0.002) CRF with chronic disease and estimated (unstandardized coefficient = - 0.08; p < 0.001) and measured (unstandardized coefficient = - 0.03; p < 0.001) CRF with CRP.

CONCLUSION

Measured and estimated CRF were moderately correlated. However, estimated and measured CRF were statistically significant different from one another with noteworthy scatter around the average difference. As such, when feasible, objective measurements of CRF should be taken.

Development of equations and proposed reference values to estimate body fat mass among Chilean children and adolescents

INTRODUCTION

The assessment of body composition is relevant to establish nutritional status and identify potential health risks.

OBJETIVE

a) To develop regression equations to predict fat mass (FM) using a dual-energy X-ray absorptiometry as reference method; b) to propose reference FM values based on chronological and biological age for Chilean children and adolescents.

METHODOLOGY

Cross-sectional study in children and adolescents aged 5.0 to 18.9 years from the Maule Region (Chile). The sample was made up of 3593 subjects in a probabilistic fashion (stratified). Subjects' weight, standing height, sitting height, and waist circumference were assessed. Body mass index and age at peak development velocity (APGV) were estimated. Body composition (FM, fat-free mass, bone mass, and fat percentage) were established based on a dual-energy X-ray absorptiometry scan.

RESULTS

APGV (biological age) was 14.9 ± 0.9 years among boys and 11.5 ± 0.7 among girls. Equations were developed to estimate FM among boys and girls using chronological age, APGV, and waist circumference as predictors. Percentiles were estimated to assess FM by dual-energy X-ray absorptiometry and regression equations.

CONCLUSION

Equations were acceptable to establish FM; in addition, reference values were proposed to assess FM based on chronological and biological age.

Measurement of lean body mass using bioelectrical impedance analysis: a consideration of the pros and cons

The assessment of body composition has important applications in the evaluation of nutritional status and estimating potential health risks. Bioelectrical impedance analysis (BIA) is a valid method for the assessment of body composition. BIA is an alternative to more invasive and expensive methods like dual-energy X-ray absorptiometry, computerized tomography, and magnetic resonance imaging. Bioelectrical impedance analysis is an easy-to-use and low-cost method for the estimation of fat-free mass (FFM) in physiological and pathological conditions. The reliability of BIA measurements is influenced by various factors related to the instrument itself, including electrodes, operator, subject, and environment. BIA assumptions beyond its use for body composition are the human body is empirically composed of cylinders, FFM contains virtually all the water and conducting electrolytes in the body, and its hydration is constant. FFM can be predicted by BIA through equations developed using reference methods. Several BIA prediction equations exist for the estimation of FFM, skeletal muscle mass (SMM), or appendicular SMM. The BIA prediction models differ according to the characteristics of the sample in which they have been derived and validated in addition to the parameters included in the multiple regression analysis. In choosing BIA equations, it is important to consider the characteristics of the sample in which it has been developed and validated, since, for example, age- and ethnicity-related differences could sensitively affect BIA estimates.

Validity of anthropometric equations to estimate infant fat mass at birth and in early infancy

BACKGROUND

In newborns and children, body fat estimation equations are often used at different ages than the age used to develop the equations. Limited validation studies exist for newborn body fat estimation equations at birth or later in infancy. The study purpose was to validate 4 newborn fat mass (FM) estimation equations in comparison to FM measured by air displacement plethysmography (ADP; the Pea Pod) at birth and 3 months.

METHODS

Ninety-five newborns (1-3 days) had their body composition measured by ADP and anthropometrics assessed by skinfolds. Sixty-three infants had repeat measures taken (3 months). FM measured by ADP was compared to FM from the skinfold estimation equations (Deierlein, Catalano, Lingwood, and Aris). Paired t-tests assessed mean differences, linear regression assessed accuracy, precision was assessed by R² and standard error of the estimate (SEE), and bias was assessed by Bland-Altman plots.

RESULTS

At birth, FM measured by ADP differed from FM estimated by Deierlein, Lingwood and Aris equations, but did not differ from the Catalano equation. At 3 months, FM measured by ADP was different from all equations. At both time points, poor precision and accuracy was detected. Bias was detected in most all equations.

CONCLUSIONS

Poor agreement, precision, and accuracy were found between prediction equations and the criterion at birth and 3 months.

Prediction equations for spirometry in four- to six-year-old children

OBJECTIVE

To generate prediction equations for spirometry in 4- to 6-year-old children.

METHODS

Forced vital capacity, forced expiratory volume in 0.5s, forced expiratory volume in one second, peak expiratory flow, and forced expiratory flow at 25-75% of the forced vital capacity were assessed in 195 healthy children residing in the town of Sete Lagoas, state of Minas Gerais, Southeastern Brazil. The least mean squares method was used to derive the prediction equations. The level of significance was established as p<0.05.

RESULTS

Overall, 85% of the children succeeded in performing the spirometric maneuvers. In the prediction equation, height was the single predictor of the spirometric variables as follows: forced vital capacity=exponential [(-2.255)+(0.022×height)], forced expiratory volume in 0.5s=exponential [(-2.288)+(0.019×height)], forced expiratory volume in one second=exponential [(-2.767)+(0.026×height)], peak expiratory flow=exponential [(-2.908)+(0.019×height)], and forced expiratory flow at 25-75% of the forced vital capacity=exponential [(-1.404)+(0.016×height)]. Neither age nor weight influenced the regression equations. No significant differences in the predicted values for boys and girls were observed.

CONCLUSION

The predicted values obtained in the present study are comparable to those reported for preschoolers from both Brazil and other countries.

Cross-Validation of Resting Metabolic Rate Prediction Equations

BACKGROUND

Resting metabolic rate (RMR) measurement is time consuming and requires specialized equipment. Prediction equations provide an easy method to estimate RMR; however, their accuracy likely varies across individuals. Understanding the factors that influence the accuracy of RMR predictions will help to revise existing, or develop new and improved, equations.

OBJECTIVE

Our aim was to test the validity of RMR predicted in healthy adults by the Harris-Benedict, World Health Organization, Mifflin-St Jeor, Nelson, Wang equations, and three meta-equations of Sabounchi.

DESIGN

Predicted RMR was tested for agreement with indirect calorimetry.

PARTICIPANTS/SETTING

Men and women (n=30) age 18 to 65 years from Grand Forks, ND, were recruited and included for analysis during spring/summer 2014. Participants were nonobese or obese (body mass index range=19 to 39) and primarly white.

MAIN OUTCOME MEASURE

Agreement between measured (indirect calorimetry) and predicted RMR was measured.

STATISTICAL ANALYSIS

The methods of Bland and Altman were employed to determine mean bias (predicted minus measured RMR, kcal/day) and limits of agreement between predicted and measured RMR. Repeated-measures analysis of variance was used to test for bias in RMR predicted from each equation vs the measured RMR.

RESULTS

Bias (mean±2 standard deviations) was lowest for the Harris-Benedict (-14±378 kcal/24 h) and World Health Organization (-25±394 kcal/24 h) equations. These equations also predicted RMR that were not different from measured. Mean RMR predictions from all other equations significantly differed from indirect calorimetry. The 2 standard deviation limits of agreement were moderate or large for all equations tested, ranging from 314 to 445 kcal/24 h. Prediction bias was inversely associated with the magnitude of RMR and with fat-free mass.

CONCLUSIONS

At the group level, the traditional Harris-Benedict and World Health Organization equations were the most accurate. However, these equations did not perform well at the individual level. As fat-free mass increased, the prediction equations further underestimated RMR.

Zoometric measures and their utilization in prediction of live weight of local goats in southern México

Objectives of this study were: (a) to compare live weight (LW) and zoometric measures (ZM) of local goats in two locations, (b) to fit the best regression equation for goat LW prediction using ZM. LW, body length (BL), trunk length (TL), withers height (WH), hearth girth (HG), rump width (RW), rump length (RL), head length (HL), head width (HW), and ear length (EL) were measured in 318 Local does in Amatepec and Tejupilco, State of Mexico. Statistical methods included student’s “t” tests for comparison of means, and correlation, principal components (PC), and multiple linear regression analyses. To evaluate the goodness of fit for LW prediction models the R² value was used as a criterion. Differences (P ≤ 0.05) were found between does of Amatepec and Tejupilco in LW, BL, TL, HG, RL, HL, HW, and EL. In Amatepec, LW was correlated with HG, BL, and HW (P ≤ 0.01), whereas in Tejupilco LW was correlated with HG, BL, TL, and HW (P ≤ 0.01). From the Amatepec measures 5 PC were extracted, and which in a multiple regression analysis explained 83.3 % of the total variance, whereas from Tejupilco 4 PC were extracted, and which in a multiple regression analysis explained 82.4 % of the total variance. The best regression model to predict doe LW in Amatepec included TL, HG, RW, and HW, whereas for Tejupilco the best model included BL, HG, HW, and EL. It is concluded that: (1) Amatepec does surpass those of Tejupilco in LW and most ZM, (2) there are reliable ZM for predicting LW of local does in both locations, HG, and HW being common measures for both populations.

Derivation and validation of simple anthropometric equations to predict adipose tissue mass and total fat mass with MRI as the reference method

The reference organ-level body composition measurement method is MRI. Practical estimations of total adipose tissue mass (TATM), total adipose tissue fat mass (TATFM) and total body fat are valuable for epidemiology, but validated prediction equations based on MRI are not currently available. We aimed to derive and validate new anthropometric equations to estimate MRI-measured TATM/TATFM/total body fat and compare them with existing prediction equations using older methods. The derivation sample included 416 participants (222 women), aged between 18 and 88 years with BMI between 15·9 and 40·8 (kg/m2). The validation sample included 204 participants (110 women), aged between 18 and 86 years with BMI between 15·7 and 36·4 (kg/m2). Both samples included mixed ethnic/racial groups. All the participants underwent whole-body MRI to quantify TATM (dependent variable) and anthropometry (independent variables). Prediction equations developed using stepwise multiple regression were further investigated for agreement and bias before validation in separate data sets. Simplest equations with optimal R (2) and Bland-Altman plots demonstrated good agreement without bias in the validation analyses: men: TATM (kg)=0·198 weight (kg)+0·478 waist (cm)-0·147 height (cm)-12·8 (validation: R 2 0·79, CV=20 %, standard error of the estimate (SEE)=3·8 kg) and women: TATM (kg)=0·789 weight (kg)+0·0786 age (years)-0·342 height (cm)+24·5 (validation: R (2) 0·84, CV=13 %, SEE=3·0 kg). Published anthropometric prediction equations, based on MRI and computed tomographic scans, correlated strongly with MRI-measured TATM: (R (2) 0·70-0·82). Estimated TATFM correlated well with published prediction equations for total body fat based on underwater weighing (R (2) 0·70-0·80), with mean bias of 2·5-4·9 kg, correctable with log-transformation in most equations. In conclusion, new equations, using simple anthropometric measurements, estimated MRI-measured TATM with correlations and agreements suitable for use in groups and populations across a wide range of fatness.

Prediction and validation of total and regional skeletal muscle volume using B-mode ultrasonography in Japanese prepubertal children

Very few effective field methods are available for accurate, non-invasive estimation of skeletal muscle volume (SMV) and mass in children. We aimed to develop regression-based prediction equations for SMV, using ultrasonography, in Japanese prepubertal children, and to assess the validity of these equations. In total, 145 healthy Japanese prepubertal children aged 6-12 years were randomly divided into two groups: the model development group (sixty boys, thirty-seven girls) and the validation group (twenty-nine boys, nineteen girls). Reference data in the form of contiguous MRI with 1-cm slice thickness were obtained from the first cervical vertebra to the ankle joints. The SMV was calculated by the summation of digitised cross-sectional areas. Muscle thickness was measured using B-mode ultrasonography at nine sites in different regions. In the model development group, strong, statistically significant correlations were observed between the site-matched SMV (total, arms, trunk, thigh and lower legs) measured by MRI and the muscle thickness×height measures obtained by ultrasonography, for both boys and girls. When these SMV prediction equations were applied to the validation groups, the measured total and regional SMV were also very similar to the values predicted for boys and girls, respectively. With the exception of the trunk region in girls, the Bland-Altman analysis for the validation group did not indicate any bias for either boys or girls. These results suggest that ultrasonography-derived prediction equations for boys and girls are useful for the estimation of total and regional SMV.

Metabolic rate of carrying added mass: a function of walking speed, carried mass and mass location

The effort of carrying additional mass at different body locations is important in ergonomics and in designing wearable robotics. We investigate the metabolic rate of carrying a load as a function of its mass, its location on the body and the subject's walking speed. Novel metabolic rate prediction equations for walking while carrying loads at the ankle, knees and back were developed based on experiments where subjects walked on a treadmill at 4, 5 or 6km/h bearing different amounts of added mass (up to 2kg per leg and 22kg for back). Compared to previously reported equations, ours are 7-69% more accurate. Results also show that relative cost for carrying a mass at a distal versus a proximal location changes with speed and mass. Contrary to mass carried on the back, mass attached to the leg cannot be modeled as an increase in body mass.