Development of New Predictive Equations for the Resting Metabolic Rate (RMR) of Women with Lipedema

This study aimed to develop a novel predictive equation for calculating resting metabolic rate (RMR) in women with lipedema. We recruited 119 women diagnosed with lipedema from the Angiology Outpatient Clinic at Wroclaw Medical University, Poland. RMR was assessed using indirect calorimetry, while body composition and anthropometric measurements were conducted using standardized protocols. Due to multicollinearity among predictors, classical multiple regression was deemed inadequate for developing the new equation. Therefore, we employed machine learning techniques, utilizing principal component analysis (PCA) for dimensionality reduction and predictor selection. Regression models, including support vector regression (SVR), random forest regression (RFR), and k-nearest neighbor (kNN) were evaluated in Python's scikit-learn framework, with hyperparameter tuning via GridSearchCV. Model performance was assessed through mean absolute percentage error (MAPE) and cross-validation, complemented by Bland-Altman plots for method comparison. A novel equation incorporating body composition parameters was developed, addressing a gap in accurate RMR prediction methods. By incorporating measurements of body circumference and body composition parameters alongside traditional predictors, the model's accuracy was improved. The segmented regression model outperformed others, achieving an MAPE of 10.78%. The proposed predictive equation for RMR offers a practical tool for personalized treatment planning in patients with lipedema.

Development and validation of novel equation for prediction of resting energy expenditure in active Saudi athletes

Being the most stable component of energy expenditure, resting metabolic rate (RMR) is usually used in the calculation of energy requirements for athletes. An adequate energy prescription is essential in supporting athlete development. This work aims to develop and validate an equation for calculating energy requirements for Arabic Saudi athletes. This cross-sectional study included 171 active athletes aged 18 to 45 years. The sample was divided into a development group (n = 127) and a validation group (n = 44). Anthropometry, indirect calorimetry, and body composition analysis via bioelectric impedance analysis were performed on all participants. The novel predictive equations were created by using stepwise linear regression analyses. The accuracy of the novel equations was compared with 10 equations, and Bland and Altman plots were used to estimate the limits of agreement between measured RMR and novel equations. The first novel equation used a set of basic measures, including weight, gender, and age, was [RMR = 1137.094 + (Wt × 14.560)-(Age × 18.162) + (G × 174.917)] (R = 0.753, and R2 = 0.567, wt = weight, G = gender; for male use 1 and female 0). The second equation used fat-free mass, age, and weight [RMR = 952.828 + (fat-free mass × 10.970)-(Age × 18.648) + (Wt × 10.297)] (R = 0.760 and R2 = 0.577). Validation of the second novel equation increased the prediction of measured RMR to 72.7% and reduced the amount of bias to 138.82 ± 133.18 Kcal. Finally, the new set of equations was designed to fit available resources in clubs and showed up to 72.73% accurate prediction and good agreement with measured RMR by Bland and Altman plots.

Predictive Equation for Basal Metabolic Rate in Normal-Weight Chinese Adults

This study aimed to develop a predictive equation for basal metabolic rate (BMR) in normal-weight Chinese adults and provide a reference for establishing the national recommended dietary energy intake. A new equation for BMR was derived from a sample of 516 normal-weight Chinese adults (men = 253, women = 263), and this sample was collected from two previous studies. Furthermore, the accuracy of this new equation and eight other previous predictive equations was reviewed. The agreement and reliability were compared in terms of bias, accuracy, the intraclass correlation coefficient, and Bland-Altman plots between predictive equations. In addition, the newly developed equation was further verified using a small independent sample, which contained 41 healthy Chinese adults (men = 21, women = 20). The measured BMR (mBMR) of all participants, measured using indirect calorimetry, was 1346.2 ± 358.0 kcal/d. Thirty participants were excluded based on Cook's distance criteria (Cook's distance of ≥0.008). Previous equations developed by Henry, Schofield, Harris-Benedict (H-B), Yang, and Hong overestimated the BMR of healthy Chinese adults. The present equation displayed the smallest average bias (0.2 kcal/d) between the mBMR and predicted basal metabolic rate (pBMR). The limits of agreement of the present equation from Bland-Altman plots were -514.3 kcal/d and 513.9 kcal/d, which is the most narrow and balanced limit of agreement. Moreover, in the verification of the testing database, the pBMR of the new equation was not significantly different from the mBMR, and the accuracy was 75.6%. Compared with pre-existing equations, the present equation is more applicable to the prediction of BMR in healthy Chinese adults. However, further studies are required to verify the accuracy of this new equation.

Machine learning models using non-linear techniques improve the prediction of resting energy expenditure in individuals receiving hemodialysis

PURPOSE

Approximately 700,000 people in the USA have chronic kidney disease requiring dialysis. Protein-energy wasting (PEW), a condition of advanced catabolism, contributes to three-year survival rates of 50%. PEW occurs at all levels of Body Mass Index (BMI) but is devastating for those people at the extremes. Treatment for PEW depends on an accurate understanding of energy expenditure. Previous research established that current methods of identifying PEW and assessing adequate treatments are imprecise. This includes disease-specific equations for estimated resting energy expenditure (eREE). In this study, we applied machine learning (ML) modelling techniques to a clinical database of dialysis patients. We assessed the precision of the ML algorithms relative to the best-performing traditional equation, the MHDE.

METHODS

This was a secondary analysis of the Rutgers Nutrition and Kidney Database. To build the ML models we divided the population into test and validation sets. Eleven ML models were run and optimized, with the best three selected by the lowest root mean squared error (RMSE) from measured REE. Values for eREE were generated for each ML model and for the MHDE. We compared precision using Bland-Altman plots.

RESULTS

Individuals were 41.4% female and 82.0% African American. The mean age was 56.4 ± 11.1 years, and the median BMI was 28.8 (IQR = 24.8 - 34.0) kg/m². The best ML models were SVR, Linear Regression and Elastic net with RMSE of 103.6 kcal, 119.0 kcal and 121.1 kcal respectively. The SVR demonstrated the greatest precision, with 91.2% of values falling within acceptable limits. This compared to 47.1% for the MHDE. The models using non-linear techniques were precise across extremes of BMI.

CONCLUSION

ML improves precision in calculating eREE for dialysis patients, including those most vulnerable for PEW. Further development for clinical use is a priority.

Current methods for developing predictive energy equations in maintenance dialysis are imprecise

PURPOSE

For individuals receiving maintenance dialysis, estimating accurate resting energy expenditure (REE) is essential for achieving energy balance, and preventing protein-energy wasting. Dialysis-specific, predictive energy equations (PEEs) offer a practical way to calculate REE. Three PEEs have been formulated via similar methods in different demographic samples; the Maintenance Haemodialysis Equation (MHDE REE), Vilar et al. Equation (Vilar REE) and the Fernandes et al. Equation (Cuppari REE). We compared them in a US cohort and assessed precision relative to measured REE (mREE) from indirect calorimetry. Because of expected imprecision at the extremes of the weight distribution, we also assessed the PEEs stratified by body mass index (BMI) subgroups.

METHODS

This analysis comprised of 113 individuals from the Rutgers Nutrition and Kidney Database. Estimated REE (eREE) was calculated for each PEE, and agreement with mREE was set at > 50% of values within the limits of ±10%. Reliability and accuracy were determined using intraclass correlation (ICC) and a Bland Altman plot, which analysed the percentage difference of eREE form mREE.

RESULTS

Participants were 58.4% male and 81.4% African American. Mean age was 55.8 ± 12.2 years, and the median BMI was 28.9 (IQR = 25.3 - 34.4) kg/m². The MHDE REE achieved 58.4% of values within ±10% from mREE; Cuppari REE achieved 47.8% and Vilar REE achieved 46.0% agreement. Reliability was good for the MHDE REE (ICC = 0.826) and Cuppari REE (ICC = 0.801), and moderate for the Vilar REE (ICC = 0.642) (p < .001 for all). The equations performed poorly at the lowest and highest BMI categories.

CONCLUSION

Dialysis-specific energy equations showed variable accuracy. When categorized by BMI, the equations performed poorly at the extremes, where individuals are most vulnerable. Innovation is needed to understand these variances and correct the imprecision in PEEs for clinical practice.KEY MESSAGESPotentially impacting over millions of patients worldwide, our long-term goal is to understand energy expenditure (EE) across the spectrum of CKD (stages 1-5) in adults and children being treated with dialysis or transplantation, with the intent of providing tools for the health professional that will improve the delivery of quality care.Our research has identified and focussed on disease-specific factors which account for 60% of the variance in predicting EE in patients on MHD, but significant gaps remain.Thus, our central hypotheses are that (1) there are unique disease-specific determinants of EE and (2) prediction of EE for individuals diagnosed with CKD can be vastly improved with a model that combines these factors with more sophisticated approaches.

Prediction of fresh and ripened cheese yield using detailed milk composition and udder health indicators from individual Brown Swiss cows

The composition of raw milk is of major importance for dairy products, especially fat, protein, and casein (CN) contents, which are used worldwide in breeding programs for dairy species because of their role in human nutrition and in determining cheese yield (%CY). The aim of the study was to develop formulas based on detailed milk composition to disentangle the role of each milk component on %CY traits. To this end, 1,271 individual milk samples (1.5 L/cow) from Brown Swiss cows were processed according to a laboratory model cheese-making procedure. Fresh %CY (%CY_CURD), total solids and water retained in the fresh cheese (%CY_SOLIDS and %CY_WATER), and 60-days ripened cheese (%CY_RIPENED) were the reference traits and were used as response variables. Training-testing linear regression modeling was performed: 80% of observations were randomly assigned to the training set, 20% to the validation set, and the procedure was repeated 10 times. Four groups of predictive equations were identified, in which different combinations of predictors were tested separately to predict %CY traits: (i) basic composition, i.e., fat, protein, and CN, tested individually and in combination; (ii) udder health indicators (UHI), i.e., fat + protein or CN + lactose and/or somatic cell score (SCS); (iii) detailed protein profile, i.e., fat + protein fractions [CN fractions, whey proteins, and nonprotein nitrogen (NPN) compounds]; (iv) detailed protein profile + UHI, i.e., fat + protein fractions + NPN compounds and/or UHI. Aside from the positive effect of fat, protein, and total casein on %CY, our results allowed us to disentangle the role of each casein fraction and whey protein, confirming the central role of β-CN and κ-CN, but also showing α-lactalbumin (α-LA) to have a favorable effect, and β-lactoglobulin (β-LG) a negative effect. Replacing protein or casein with individual milk protein and NPN fractions in the statistical models appreciably increased the validation accuracy of the equations. The cheese industry would benefit from an improvement, through genetic selection, of traits related to cheese yield and this study offers new insights into the quantification of the influence of milk components in composite selection indices with the aim of directly enhancing cheese production.

Baseline intracardiac echocardiography predicts haemodynamic changes and Doppler velocity patterns during follow-up after percutaneous pulmonary valve implantation

BACKGROUND

Intracardiac echocardiography Doppler-derived gradients have previously been shown to correlate with post-procedure echocardiographic evaluations when compared with invasive gradients measured during percutaneous pulmonary valve implantation, suggesting that intracardiac echocardiography could offer an accurate and predictable starting point to estimate valve function after percutaneous pulmonary valve implantation.

METHODS

We performed a retrospective chart review of 51 patients who underwent percutaneous pulmonary valve implantation between September 2018 and December 2019 in whom intracardiac echocardiography was performed immediately after valve implantation. We evaluated the correlation between intracardiac echocardiography gradients and post-procedural Doppler-derived gradients. Among the parameters assessed, those which demonstrated the strongest correlation were used to create a predictive model of expected echo-derived gradients after percutaneous pulmonary valve implantation. The equation was validated on the same sample data along with a subsequent cohort of 25 consecutive patients collected between January 2020 and July 2020.

RESULTS

All the assessed correlation models between intracardiac echocardiography evaluation and post-procedure transthoracic echocardiographic assessments were statistically significant, presenting moderate to strong correlations. The strongest relationship was found between intracardiac echocardiography mean gradients and post-procedural transthoracic echocardiographic mean gradients. Therefore, an equation was created based on the intracardiac echocardiography-derived mean gradient, to allow prediction of the post-procedural and follow-up transthoracic echocardiographic-derived mean gradients within a range of ±5 mmHg from the observed value in more than 80% of cases.

CONCLUSIONS

There is a strong correlation between intracardiac echocardiography and post-procedure transthoracic echocardiographic. This allowed us to derive a predictive equation that defines the expected transthoracic echocardiographic Doppler-derived gradient following the procedure and at out-patient follow-up after percutaneous pulmonary valve implantation.

Predictive Equation for Angle Opening Distance at 750 μm After Laser Peripheral Iridotomy in Primary Angle Closure Suspects

Aim: The aim of this study was to investigate the changes in anterior segment parameters as assessed by ultrasound biomicroscopy (UBM) after laser peripheral iridotomy (LPI) and to propose a prediction equation for the width of the angle after LPI. Design: This was a prospective study. Participants: The participants included 100 subjects with primary angle closure suspect (PACS). Methods: Anterior segment UBM parameters were measured, whereas AOD750 was chosen to indicate the width of the angle associated with gonioscopic angle closure, as found in a prior study. Main Outcome Measures: Angle parameters, iris parameters, anterior chamber parameters and ciliary body parameters. Results: All angle parameters increased after LPI, including the mean angle opening distance at 750 μm (AOD750), mean angle opening distance at 500 μm from the scleral spur (AOD500), mean angle opening distance at 750 μm from the scleral spur (AOD750), and mean angle recess area at 750 μm from the scleral spur (ARA750). Among iris parameters and ciliary body parameters, the iris thickness at 2,000 μm (IT2000), iris curvature (IC), and trabecular-ciliary process distance (ICPD) were reduced after LPI. The final equation consisted of four parameters: anterior chamber depth (ACD), iris thickness at 750 μm from the scleral spur (IT750), AOD750, and lens vault (LV). This equation explained 42.7% of the variability in the angle opening indicator AOD750 after LPI, whereas in the plateau iris configuration subgroup, the accuracy of the prediction equation reached the highest a maximum of 68.6%. Conclusions: There was an increase in angle opening and iris flattening after LPI. An equation involving four angle parameters was constructed, this equation which could explained 42.7% of the variability in the angle opening indicator AOD750 after LPI whereas in the plateau iris configuration subgroup, the accuracy of the prediction equation reached a maximum of 68.6%.

Resting Energy Expenditure of Master Athletes: Accuracy of Predictive Equations and Primary Determinants

Resting energy expenditure (REE) is determined mainly by fat-free mass (FFM). FFM depends also on daily physical activity. REE normally decreases with increased age due to decreases in FFM and physical activity. Measuring REE is essential for estimating total energy expenditure. As such, there are a number of different equations in use to predict REE. In recent years, an increasing number of older adults continue to participate in competitive sports creating the surge of master athletes. It is currently unclear if these equations developed primarily for the general population are also valid for highly active, older master athletes. Therefore, we tested the validity of six commonly-used equations for predicting REE in master athletes. In conjunction with the World Masters Athletic Championship in Malaga, Spain, we measured REE in 113 master athletes by indirect calorimetry. The most commonly used equations to predict REE [Harris & Benedict (H&B), World Health Organization (WHO), Müller (MÜL), Müller-FFM (MÜL-FFM), Cunningham (CUN), and De Lorenzo (LOR)] were tested for their accuracies. The influences of age, sex, height, body weight, FFM, training hours per week, phase angle, ambient temperature, and athletic specialization on REE were determined. All estimated REEs for the general population differed significantly from the measured ones (H&B, WHO, MÜL, MÜL-FFM, CUN, all p < 0.005). The equation put forward by De Lorenzo provided the most accurate prediction of REE for master athletes, closely followed by FFM-based Cunningham’s equation. The accuracy of the remaining commonly-used prediction equations to estimate REE in master athletes are less accurate. Body weight (p < 0.001), FFM (p < 0.001), FM (p = 0.007), sex (p = 0.045) and interestingly temperature (p = 0.004) are the significant predictors of REE. We conclude that REE in master athletes is primarily determined by body composition and ambient temperature. Our study provides a first estimate of energy requirements for master athletes in order to cover adequately athletes’ energy and nutrient requirements to maintain their health status and physical performance.

Resting Energy Expenditure in the Elderly: Systematic Review and Comparison of Equations in an Experimental Population

Elderly patients are at risk of malnutrition and need an appropriate assessment of energy requirements. Predictive equations are widely used to estimate resting energy expenditure (REE). In the study, we conducted a systematic review of REE predictive equations in the elderly population and compared them in an experimental population. Studies involving subjects older than 65 years of age that evaluated the performance of a predictive equation vs. a gold standard were included. The retrieved equations were then tested on a sample of 88 elderly subjects enrolled in an Italian nursing home to evaluate the agreement among the estimated REEs. The agreement was assessed using the intraclass correlation coefficient (ICC). A web application, equationer, was developed to calculate all the estimated REEs according to the available variables. The review identified 68 studies (210 different equations). The agreement among the equations in our sample was higher for equations with fewer parameters, especially those that included body weight, ICC = 0.75 (95% CI = 0.69-0.81). There is great heterogeneity among REE estimates. Such differences should be considered and evaluated when estimates are applied to particularly fragile populations since the results have the potential to impact the patient's overall clinical outcome.

Predicting Temporal Gait Kinematics: Anthropometric Characteristics and Global Running Pattern Matter

Equations predicting stride frequency (SF) and duty factor (DF) solely based on running speed have been proposed. However, for a given speed, kinematics vary depending on the global running pattern (GRP), i.e., the overall individual movement while running, which depends on the vertical oscillation of the head, antero-posterior motion of the elbows, vertical pelvis position at ground contact, antero-posterior foot position at ground contact, and strike pattern. Hence, we first verified the validity of the aforementioned equations while accounting for GRP. Kinematics during three 50-m runs on a track (n = 20) were used with curve fitting and linear mixed effects models. The percentage of explained variance was increased by ≥133% for DF when taking into account GRP. GRP was negatively related to DF (p = 0.004) but not to SF (p = 0.08), invalidating DF equation. Second, we assessed which parameters among anthropometric characteristics, sex, training volume, and GRP could relate to SF and DF in addition to speed, using kinematic data during five 30-s runs on a treadmill (n = 54). SF and DF linearly increased and quadratically decreased with speed (p < 0.001), respectively. However, on an individual level, SF was best described using a second-order polynomial equation. SF and DF showed a non-negligible percentage of variance explained by random effects (≥28%). Age and height were positively and negatively related to SF (p ≤ 0.05), respectively, while GRP was negatively related to DF (p < 0.001), making them key parameters to estimate SF and DF, respectively, in addition to speed.

Resting Energy Expenditure Prediction Equations in the Pediatric Population: A Systematic Review

Background and Aims: The determination of energy requirements is necessary to promote adequate growth and nutritional status in pediatric populations. Currently, several predictive equations have been designed and modified to estimate energy expenditure at rest. Our objectives were (1) to identify the equations designed for energy expenditure prediction and (2) to identify the anthropometric and demographic variables used in the design of the equations for pediatric patients who are healthy and have illness. Methods: A systematic search in the Medline/PubMed, EMBASE and LILACS databases for observational studies published up to January 2021 that reported the design of predictive equations to estimate basal or resting energy expenditure in pediatric populations was carried out. Studies were excluded if the study population included athletes, adult patients, or any patients taking medications that altered energy expenditure. Risk of bias was assessed using the Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies. Results: Of the 769 studies identified in the search, 39 met the inclusion criteria and were analyzed. Predictive equations were established for three pediatric populations: those who were healthy (n = 8), those who had overweight or obesity (n = 17), and those with a specific clinical situation (n = 14). In the healthy pediatric population, the FAO/WHO and Schofield equations had the highest R ² values, while in the population with obesity, the Molnár and Dietz equations had the highest R ² values for both boys and girls. Conclusions: Many different predictive equations for energy expenditure in pediatric patients have been published. This review is a compendium of most of these equations; this information will enable clinicians to critically evaluate their use in clinical practice. Systematic Review Registration: https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=226270, PROSPERO [CRD42021226270].

Anthropometric Prediction of DXA-Measured Percentage of Fat Mass in Athletes With Unilateral Lower Limb Amputation

To date there is no anthropometric equation specific to athletes with unilateral lower limb amputation to estimate the percentage of fat mass (%FM). This study investigated the accuracy of a set of anthropometric equations validated on able-bodied populations to predict the %FM assessed by-means of dual-energy x-ray absorptiometry (DXA) in athletes with unilateral lower limb amputation. Furthermore, a predictive anthropometric equation specific to athletes with unilateral lower limb amputation was developed from skinfold thickness measurements using DXA as the reference method for the estimation of the %FM. Twenty-nine white male athletes with unilateral lower limb amputation underwent a DXA scan and an anthropometric assessment on the same day. The %FM, calculated through several existing anthropometric equations validated upon able-bodied populations, was compared with the DXA-measured %FM (%FM_DXA). Accuracy and agreement between the two methods was computed with two-tailed paired-sample t-test, concordance correlation coefficient, reduced major axis regression and Bland-Altman analysis. A stepwise multiple regression analysis with the %FM_DXA as the dependent variable and age and nine skinfold thicknesses as potential predictors was carried out and validated using a repeated 10-fold cross-validation. A linear regression analysis with the sum of nine skinfolds as the independent variable was also carried out and validated using a repeated 10-fold cross-validation. The results showed that the anthropometric equations validated on able-bodied populations are inaccurate in the estimation of %FM_DXA with an average bias ranging from 0.51 to -13.70%. Proportional bias was also found revealing that most of the anthropometric equations considered, tended to underestimate/overestimate the %FM_DXA as body fat increased. Regression analysis produced two statistically significant models (P < 0.001 for both) which were able to predict more than 93% of total variance of %FM_DXA from the values of four skinfold measurements (i.e., thigh, abdominal, subscapular and axillary skinfold measurements) or from the sum of 9 skinfolds. Repeated cross-validation analysis highlighted a good predictive performance of the proposed equations. The predictive equations proposed in this study represent a useful tool for clinicians, nutritionists, and physical conditioners to evaluate the physical and nutritional status of athletes with unilateral lower limb amputation directly in the field.

Prediction of Somatotype from Bioimpedance Analysis in Elite Youth Soccer Players

The accurate body composition assessment comprises several variables, causing it to be a time consuming evaluation as well as requiring different and sometimes costly measurement instruments. The aim of this study was to develop new equations for the somatotype prediction, reducing the number of normal measurements required by the Heath and Carter approach. A group of 173 male soccer players (age, 13.6 ± 2.2 years, mean ± standard deviation; body mass index, BMI, 19.9 ± 2.5 kg/m²), members of the academy of a professional Italian soccer team participating in the first division (Serie A), participated in this study. Bioelectrical impedance analysis (BIA) was performed using the single frequency of 50 kHz and fat-free mass (FFM) was calculated using a BIA specific, impedance based equation. Somatotype components were estimated according to the Heath-Carter method. The participants were randomly split into development (n = 117) and validation groups (n = 56). New anthropometric and BIA based models were developed (endomorphy = −1.953 − 0.011 × stature²/resistance + 0.135 × BMI + 0.232 × triceps skinfold, R² = 0.86, SEE = 0.28; mesomorphy = 6.848 + 0.138 × phase angle + 0.232 × contracted arm circumference + 0.166 × calf circumference − 0.093 × stature, R² = 0.87, SEE = 0.40; ectomorphy = −5.592 − 38.237 × FFM/stature + 0.123 × stature, R² = 0.86, SEE = 0.37). Cross validation revealed R² of 0.84, 0.80, and 0.87 for endomorphy, mesomorphy, and ectomorphy, respectively. The new proposed equations allow for the integration of the somatotype assessment into BIA, reducing the number of collected measurements, the instruments used, and the time normally required to obtain a complete body composition analysis.

A New Strategy to Integrate Heath-Carter Somatotype Assessment with Bioelectrical Impedance Analysis in Elite Soccer Player

Easy-to-apply and quick methods for evaluate body composition are often preferred when assessing soccer teams. This study aimed to develop new equations for the somatotype quantification that would reduce the anthropometric measurements required by the Heath and Carter method, integrating the somatotype assessment to the bioelectrical impedance analysis (BIA). One hundred and seventy-six male elite soccer players (age 26.9 ± 4.5 years), registered in the Italian first division (Serie A), underwent anthropometric measurements and BIA. Endomorphy, mesomorphy, and ectomorphy were obtained according to the Heath and Carter method, while fat mass (FM) and fat free mass (FFM) estimated using a BIA-derived equation specific for athletes. The participants were randomly split into development (n = 117) and validation groups (n = 59, 1/3 of sample). The developed models including resistance2/stature, FM%, FFM, contracted arm and calf circumference, triceps, and supraspinal skinfolds had high predictive ability for endomorphy (R2 = 0.83, Standard Error of Estimate (SEE) = 0.16) mesomorphy (R2 = 0.80, SEE = 0.36), and ectomorphy (endomorphy (R2 = 0.87, SEE = 0.22). Cross validation revealed R2 of 0.80, 0.84, 0.87 for endomorphy, mesomorphy, and ectomorphy, respectively. The proposed strategy allows the integration of somatotype assessment to BIA in soccer players, reducing the number of instruments and measurements required by the Heath and Carter approach.

Development of an Easy-to-Use Prediction Equation for Body Fat Percentage Based on BMI in Overweight and Obese Lebanese Adults

An accurate estimation of body fat percentage (BF%) in patients who are overweight or obese is of clinical importance. In this study, we aimed to develop an easy-to-use BF% predictive equation based on body mass index (BMI) suitable for individuals in this population. A simplified prediction equation was developed and evaluated for validity using anthropometric measurements from 375 adults of both genders who were overweight or obese. Measurements were taken in the outpatient clinic of the Department of Nutrition and Dietetics at Beirut Arab University (Lebanon). A total of 238 participants were used for model building (training sample) and another 137 participants were used for evaluating validity (validation sample). The final predicted model included BMI and sex, with non-significant prediction bias in BF% of −0.017 ± 3.86% (p = 0.946, Cohen’s d = 0.004). Moreover, a Pearson’s correlation between measured and predicted BF% was strongly significant (r = 0.84, p < 0.05). We are presenting a model that accurately predicted BF% in 61% of the validation sample with an absolute percent error less than 10% and non-significant prediction bias (−0.028 ± 4.67%). We suggest the following equations: BF% _females = 0.624 × BMI + 21.835 and BF% _males = 1.050 × BMI − 4.001 for accurate BF% estimation in patients who are overweight or obese in a clinical setting in Lebanon.

Resting energy expenditure in Parkinson's disease patients under dopaminergic treatment

Background: Weight homeostasis is complex in Parkinson's disease (PD) and body weight changes substantially throughout the course of the disease. We designed a case-control study to (i) investigate whether PD is associated with changes in resting energy expenditure (REE), (ii) to assess how accurately REE could be predicted for individuals with PD utilizing the equations constructed for healthy individuals, and (iii) to eventually construct a new equation.Materials & Methods: Measured REE (mREE) was compared between 122 PD patients and 122 gender and body mass index (BMI)-matched controls. The accuracy of estimated REE by 5 common equations (Harris/Benedict-1919, Roza/Shizgal-1984, Mifflin St. Jeor, WHO/FAO and aggregate formula) was investigated in PD using Bland-Altman analysis and reported as the frequency of accurate predictions (±10%). Concordance correlation coefficients (CCC) were also calculated. Then, we regressed a new REE equation - using gender, age, weight, height and Hoehn-Yahr stage - and validated it in an independent sample (N = 100).Results: No significant difference in mREE was recorded between the whole PD sample and healthy controls. However, mREE was increased in patients with BMI ≥ 30 kg/m² and Hoehn-Yahr stage ≥ 3. Limited accuracy was present in the available REE equations (accurate prediction [±10%] frequency, <60% for all). For the new equation, the proportion of accurate prediction was 67.0% (overestimation, 24.0%) and CCC was 0.77.Conclusion: PD patients are not commonly characterized by an increase in REE. This is limited to patients suffering from obesity and more severe disease. Common REE equations appear to be inaccurate. The new predictive equation proposed in this study provided better REE estimates.

Development of Predictive Equations for Nocturnal Hypertension and Nondipping Systolic Blood Pressure

Background Nocturnal hypertension, defined by a mean asleep systolic blood pressure (SBP)/diastolic blood pressure (BP) ≥120/70 mm Hg, and nondipping SBP, defined by an awake-to-asleep decline in SBP <10%, are each associated with increased risk for cardiovascular disease. Methods and Results We developed predictive equations to identify adults with a high probability of having nocturnal hypertension or nondipping SBP using data from the CARDIA (Coronary Artery Risk Development in Young Adults) study (n=787), JHS (Jackson Heart Study) (n=1063), IDH (Improving the Detection of Hypertension) study (n=395), and MHT (Masked Hypertension) study (n=772) who underwent 24-hour ambulatory BP monitoring. Participants were randomized to derivation (n=2511) or validation (n=506) data sets. The prevalence rates of nocturnal hypertension and nondipping SBP were 39.7% and 44.9% in the derivation data set, respectively, and 36.6% and 44.5% in the validation data set, respectively. The predictive equation for nocturnal hypertension included age, race/ethnicity, smoking status, neck circumference, height, high-density lipoprotein cholesterol, albumin/creatinine ratio, and clinic SBP and diastolic BP. The predictive equation for nondipping SBP included age, sex, race/ethnicity, waist circumference, height, alcohol use, high-density lipoprotein cholesterol, and albumin/creatinine ratio. Concordance statistics (95% CI) for nocturnal hypertension and nondipping SBP predictive equations in the validation data set were 0.84 (0.80-0.87) and 0.73 (0.69-0.78), respectively. Compared with reference models including antihypertensive medication use and clinic SBP and diastolic BP as predictors, the continuous net reclassification improvement (95% CI) values for the nocturnal hypertension and nondipping SBP predictive equations were 0.52 (0.35-0.69) and 0.51 (0.34-0.69), respectively. Conclusions These predictive equations can direct ambulatory BP monitoring toward adults with high probability of having nocturnal hypertension and nondipping SBP.

Estimation of Resting Energy Expenditure Using Predictive Equations in Critically Ill Children: Results of a Systematic Review

Provision of adequate energy intake to critically ill children is associated with improved prognosis, but resting energy expenditure (REE) is rarely determined by indirect calorimetry (IC) due to practical constraints. Some studies have tested the validity of various predictive equations that are routinely used for this purpose, but no systematic evaluation has been made. Therefore, we performed a systematic review of the literature to assess predictive equations of REE in critically ill children. We systematically searched the literature for eligible studies, and then we extracted data and assigned a quality grade to each article according to guidelines of the Academy of Nutrition and Dietetics. Accuracy was defined as the percentage of predicted REE values to fall within ±10% or ±15% of the measured energy expenditure (MEE) values, computed based on individual participant data. Of the 993 identified studies, 22 studies testing 21 equations using 2326 IC measurements in 1102 children were included in this review. Only 6 equations were evaluated by at least 3 studies in critically ill children. No equation predicted REE within ±10% of MEE in >50% of observations. The Harris-Benedict equation overestimated REE in two-thirds of patients, whereas the Schofield equations and Talbot tables predicted REE within ±15% of MEE in approximately 50% of observations. In summary, the Schofield equations and Talbot tables were the least inaccurate of the predictive equations. We conclude that a new validated indirect calorimeter is urgently needed in the critically ill pediatric population.).

Reliability of CKD-EPI predictive equation in estimating chronic kidney disease prevalence in the Croatian endemic nephropathy area

Introduction

Chronic kidney disease (CKD) is a significant public health problem and it is not possible to precisely predict its progression to terminal renal failure. According to current guidelines, CKD stages are classified based on the estimated glomerular filtration rate (eGFR) and albuminuria. Aims of this study were to determine the reliability of predictive equation in estimation of CKD prevalence in Croatian areas with endemic nephropathy (EN), compare the results with non-endemic areas, and to determine if the prevalence of CKD stages 3-5 was increased in subjects with EN.

Materials and methods

A total of 1573 inhabitants of the Croatian Posavina rural area from 6 endemic and 3 non-endemic villages were enrolled. Participants were classified according to the modified criteria of the World Health Organization for EN. Estimated GFR was calculated using Chronic Kidney Disease Epidemiology Collaboration equation (CKD-EPI).

Results

The results showed a very high CKD prevalence in the Croatian rural area (19%). CKD prevalence was significantly higher in EN then in non EN villages with the lowest eGFR value in diseased subgroup.

Conclusions

eGFR correlated significantly with the diagnosis of EN. Kidney function assessment using CKD-EPI predictive equation proved to be a good marker in differentiating the study subgroups, remained as one of the diagnostic criteria for EN.

The Pandolf load carriage equation is a poor predictor of metabolic rate while wearing explosive ordnance disposal protective clothing

This investigation aimed to quantify metabolic rate when wearing an explosive ordnance disposal (EOD) ensemble (~33kg) during standing and locomotion; and determine whether the Pandolf load carriage equation accurately predicts metabolic rate when wearing an EOD ensemble during standing and locomotion. Ten males completed 8 trials with metabolic rate measured through indirect calorimetry. Walking in EOD at 2.5, 4.0 and 5.5km·h^-1 was significantly (p < 0.05) greater than matched trials without the EOD ensemble by 49% (127W), 65% (213W) and 78% (345W), respectively. Mean bias (95% limits of agreement) between predicted and measured metabolism during standing, 2.5, 4 and 5.5km·h^-1 were 47W (19 to 75W); -111W (-172 to -49W); -122W (-189 to -54W) and -158W (-245 to -72W), respectively. The Pandolf equation significantly underestimated measured metabolic rate during locomotion. These findings have practical implications for EOD technicians during training and operation and should be considered when developing maximum workload duration models and work-rest schedules. Practitioner Summary: Using a rigorous methodological design we quantified metabolic rate of wearing EOD clothing during locomotion. For the first time we demonstrated that metabolic rate when wearing this ensemble is greater than that predicted by the Pandolf equation. These original findings have significant implications for EOD training and operation.

Clinical Outcomes Related to Protein Delivery in a Critically Ill Population: A Multicenter, Multinational Observation Study

OBJECTIVE

Optimal intake of energy and protein is associated with improved outcomes, although outcomes relative to protein intake are very limited. Our purpose was to evaluate the impact of prescribed protein delivery on mortality and time to discharge alive (TDA) using data from the International Nutrition Survey 2013. We hypothesized that greater protein delivery would be associated with lower mortality and shorter TDA.

METHODS

The sample included patients in the intensive care unit (ICU) ≥ 4 days (n = 2828) and a subsample in the ICU ≥ 12 days (n = 1584). Models were adjusted for evaluable nutrition days, age, body mass index, sex, admission type, acuity scores, and geographic region. Percentages of prescribed protein and energy intake were compared with mortality outcomes using logistic regression and with Cox proportional hazards for TDA.

RESULTS

Mean intake for the 4-day sample was protein 51 g (60.5% of prescribed) and 1100 kcal (64.1% of prescribed); for the 12-day sample, mean intake was protein 57 g (66.7% of prescribed) and 1200 kcal (70.7% of prescribed). Achieving ≥ 80% of prescribed protein intake was associated with reduced mortality (4-day sample: odds ratio [OR], 0.68; 95% confidence interval [CI], 0.50-0.91; 12-day sample: OR, 0.60; 95% CI, 0.39-0.93), but ≥ 80% of prescribed energy intake was not. TDA was shorter with ≥ 80% prescribed protein (hazard ratio [HR], 1.25; 95% CI, 1.04-1.49) in the 12-day sample but longer with ≥ 80% prescribed energy in the 4-day sample (HR, 0.82; 95% CI, 0.69-0.96).

CONCLUSION

Achieving at least 80% of prescribed protein intake may be important to survival and shorter TDA in ICU patients. Efforts to achieve prescribed protein intake should be maximized.

Validation of predictive equations for resting energy expenditure in Japanese pediatric Crohn's disease patients: preliminary study

BACKGROUND

Predictive equations are often used to estimate resting energy expenditure (REE). Determining the appropriate equation for different patient types, however, remains inconclusive, as in the case of Japanese children with Crohn's disease (CD). The aim of this study was to identify an appropriate predictive equation for measuring REE in Japanese children with CD.

METHODS

Twelve Japanese children with CD managed at the National Center for Child Health and Development in Tokyo, Japan, were studied. REE (kcal/day) was measured using indirect calorimetry. The predictive equations used were the Japanese Dietary Reference Intakes (2010), the Schofield equation, the Food and Agriculture Organization/World Health Organization/United Nations University (FAO/WHO/UNU) equation and the Cunningham equation. Difference between predicted and measured REE was analyzed on Bland-Altman plot.

RESULTS

Japanese Dietary Reference Intakes (2010) had the smallest difference between predicted and measured REE. Weight was the primary predictor of REE on multiple regression analysis. As well, Japanese Dietary Reference Intakes (2010) had the highest ratio of weight to predicted REE (98.5%).

CONCLUSIONS

Of the four equations, Japanese Dietary Reference Intakes (2010) appeared to be the most practical and accurate predictive equation for REE in Japanese children with CD.

Validity of predictive equations for resting energy expenditure according to the body mass index in a population of 1726 patients followed in a Nutrition Unit

BACKGROUND & AIMS

The resting energy expenditure (REE) predictive formulas are often used in clinical practice to adapt the nutritional intake of patients or to compare to REE measured by indirect calorimetry. We aimed to evaluate which predictive equations was the best alternative to REE measurements according to the BMI.

METHODS

28 REE prediction equations were studied in a population of 1726 patients without acute or chronic high-grade inflammatory diseases followed in a Nutrition Unit for malnutrition, eating disorders or obesity. REE was measured by indirect calorimetry for 30 min after a fasting period of 12 h. Some formulas requiring fat mass and free-fat mass, body composition was measured by bioelectrical impedance analysis. The percentage of accurate prediction (±10%/REE measured) and Pearson r correlations were calculated.

RESULTS

Original Harris & Benedict equation provided 73.0% of accurate predictions in normal BMI group but only 39.3% and 62.4% in patients with BMI < 16 kg m(-2) and BMI ≥ 40 kg m(-2), respectively. In particularly, this equation overestimated the REE in 51.74% of patients with BMI < 16 kg m(-2). Huang equation involving body composition provided the highest percent of accurate prediction, 42.7% and 66.0% in patients with BMI < 16 and >40 kg m(-2), respectively.

CONCLUSION

Usual predictive equations of REE are not suitable for predicting REE in patients with extreme BMI, in particularly in patients with BMI <16 kg m(-2). Indirect Calorimetry may still be recommended for an accurate assessment of REE in this population until the development of an adapted predictive equation.

Clinical Outcomes in Critically Ill Patients Associated With the Use of Complex vs Weight-Only Predictive Energy Equations

BACKGROUND

The energy intake goal is important to achieving energy intake in critically ill patients, yet clinical outcomes associated with energy goals have not been reported.

METHODS

This secondary analysis used the Improving Nutrition Practices in the Critically III International Nutrition Surveys database from 2007-2009 to evaluate whether mortality or time to discharge alive is related to use of complex energy prediction equations vs weight only. The sample size was 5672 patients in the intensive care unit (ICU) ≥ 4 days and a subset of 3356 in the ICU ≥ 12 days. Mortality and time to discharge alive were compared between groups by regression, controlling for age, sex, admission type, Acute Physiology and Chronic Health Evaluation II score, ICU geographic region, actual energy intake, and obesity.

RESULTS

There was no difference in mortality between the use of complex and weight-only equations (odds ratio [OR], 0.90; 95% confidence interval [CI], 0.86-1.15), but obesity (OR, 0.83; 95% CI, 0.71-0.96) and higher energy intake (OR, 0.65; 95% CI, 0.56-0.76) had lower odds of mortality. Time to discharge alive was shorter in patients fed using weight-only equations (hazard ratio [HR], 1.11; 95% CI, 1.01-1.23) in patients staying ≥ 4 days and with greater energy intake (HR, 1.19; 95% CI, 1.06-1.34) in patients in the ICU ≥ 12 days.

CONCLUSION

These data suggest that higher energy intake is important to survival and time to discharge alive. However, the analysis was limited by actual energy intake <70% of goal. Delivery of full goal intake will be needed to determine the relationship between the method of determining energy goal and clinical outcomes.

Renal insufficiency in Ghanaian HIV infected patients: need for dose adjustment

BACKGROUND

Antiretrovirals (ARVs) could lead to clinically significant nephrotoxicity and as such will require dose adjustments in the presence of renal insufficiency.

OBJECTIVE

To explore renal function estimating equations as alternatives for glomerular filtration rate (GFR) measurement in a stable cohort of HIV-infected patients.

METHOD

In estimating renal insufficiency in Ghanaian HIV-infected patients, GFR for 276 HAART-naïve patients and 166 patients on HAART was estimated with the Cockcroft-Gault, 4v-MDRD and CKD-EPI estimating equations.

RESULTS

Females outnumbered males by 3 to 1 in the HAART-naïve group and 4 to 1 in subjects on HAART. The prevalence of renal insufficiency calculated with the Cockcroft-Gault, 4v-MDRD and CKD-EPI equations was 8.7%, 9.1% and 8.7% in HAART-naïve patients; 14.5%, 12.6% and 12.6% in patients on HAART; 7.7%, 11.5% and 11.5% in HAART-naïve males; 10.8%, 8.1% and 8.1% in males on HAART; 9.1%, 8.0% and 7.5% in HAART-naïve females and 15.5%, 14.0% and 14.0% in females on HAART. The CKD-EPI equation yielded lower bias when compared to the Cockcroft-Gault and 4v-MDRD equations.

CONCLUSION

Renal insufficiency is not uncommon among HIV infected Ghanaian patients. A significant proportion (10 to 11%) will require ARV dose adjustment at the time of initiating therapy or sometime during on-going therapy.

Accuracy of predictive equations for resting energy expenditure (REE) in non-obese and obese Korean children and adolescents

Weight-controlling can be supported by a proper prescription of energy intake. The individual energy requirement is usually determined through resting energy expenditure (REE) and physical activity. Because REE contributes to 60-70% of daily energy expenditure, the assessment of REE is very important. REE is often predicted using various equations, which are usually based on the body weight, height, age, gender, and so on. The aim of this study is to validate the published predictive equations for resting energy expenditure in 76 normal weight and 52 obese Korean children and adolescents in the 7-18 years old age group. The open-circuit indirect calorimetry using a ventilated hood system was used to measure REE. Sixteen REE predictive equations were included, which were based on weight and/or height of children and adolescents, or which were commonly used in clinical settings despite its use based on adults. The accuracy of the equations was evaluated on bias, RMSPE, and percentage of accurate prediction. The means of age and height were not significantly different among the groups. Weight and BMI were significantly higher in obese group (64.0 kg, 25.9 kg/m(2)) than in the non-obese group (44.8 kg, 19.0 kg/m(2)). For the obese group, the Molnar, Mifflin, Liu, and Harris-Benedict equations provided the accurate predictions of > 70% (87%, 79% 77%, and 73%, respectively). On the other hand, for non-obese group, only the Molnar equation had a high level of accuracy (bias of 0.6%, RMSPE of 90.4 kcal/d, and accurate prediction of 72%). The accurate prediction of the Schofield (W/WH), WHO (W/WH), and Henry (W/WH) equations was less than 60% for all groups. Our results showed that the Molnar equation appears to be the most accurate and precise for both the non-obese and the obese groups. This equation might be useful for clinical professionals when calculating energy needs in Korean children and adolescents.

Framingham risk prediction equations for incidence of cardiovascular disease using detailed measures for smoking

Current prediction models for risk of cardiovascular disease (CVD) incidence incorporate smoking as a dichotomous yes/no measure. However, the risk of CVD associated with smoking also varies with the intensity and duration of smoking and there is a strong association between time since quitting and the risk of disease onset. This study aims to develop improved risk prediction equations for CVD incidence incorporating intensity and duration of smoking and time since quitting.

The risk of developing a first CVD event was evaluated using a Cox’s model for participants in the Framingham offspring cohort who attended the fourth examination (1988–92) between the ages of 30 and 74 years and were free of CVD (n=3751). The full models based on the smoking variables and other risk factors, and reduced models based on the smoking variables and non-laboratory risk factors demonstrated good discrimination, calibration and global fit. The incorporation of both time since quitting among past smokers and pack-years among current smokers resulted in better predictive performance as compared to a dichotomous current/non-smoker measure and a current/quitter/never smoker measure. Compared to never smokers, the risk of CVD incidence increased with pack-years. Risk among those quitting more than five years prior to the baseline exam and within five years prior to the baseline exam were similar and twice as high as that of never smokers. A CVD risk equation incorporating the effects of pack-years and time since quitting provides an improved tool to quantify risk and guide preventive care.