Validity Of Predictive Equations Research Articles

Association of changes in predicted body composition with subsequent risk of dementia.

The effect of body composition change on the risk of dementia is not clear. This study analyzed the associations of changes in predicted lean body mass index (pLBMI), predicted appendicular skeletal muscle mass index (pASMI), and predicted body fat mass index (pBFMI) with the risk of dementia. In this nationwide cohort study, data were obtained from the Korean National Health Insurance Service database. The exposure was defined as changes in pLBMI, pASMI, and pBFMI derived from validated prediction equations. The outcome was dementia, defined based on the dementia diagnosis with prescription of anti-dementia medication. Cox proportional hazards regression analyses were performed to obtain the hazard ratio with a 95% confidence interval for risk of dementia according to changes in predicted body composition. A total of 13,215,208 individuals with no prior record of dementia who underwent health screenings twice between 2009-2010 and 2011-2012 were included. A 1-kg/m2 increase in pLBMI and pASMI had an association with reduced risk of dementia (aHR: 0.85, 95% CI 0.84-0.87; aHR: 0.70, 95% CI 0.69-0.72, respectively for men, and aHR: 0.69, 95% CI 0.67-0.71; aHR: 0.59, 95% CI 0.57-0.61, respectively for women). A 1-kg/m2 increase in pBFMI had an association with a raised risk of dementia (aHR: 1.19, 95% CI 1.17-1.21 for men and aHR: 1.53, 95% CI 1.48-1.57 for women). These results remained consistent regardless of sex or weight change. Increase in pLBMI or pASMI, or reduction in pBFMI was linked to lower risk of dementia.

Adaptation of risk prediction equations for cardiovascular outcomes among patients with type 2 diabetes in real-world settings: a cross-institutional study using common data model approach

ObjectiveTo adapt risk prediction equations for myocardial infarction (MI), stroke, and heart failure (HF) among patients with type 2 diabetes in real-world settings using cross-institutional electronic health records (EHRs) in Taiwan.MethodsThe EHRs from two medical centers, National Cheng Kung University Hospital (NCKUH; 11,740 patients) and National Taiwan University Hospital (NTUH; 20,313 patients), were analyzed using the common data model approach. Risk equations for MI, stroke, and HF from UKPDS-OM2, RECODe, and CHIME models were adapted for external validation and recalibration. External validation was assessed by (1) discrimination, evaluated by the area under the receiver operating characteristic curve (AUROC) and (2) calibration, evaluated by calibration slopes and intercepts and the Greenwood–Nam–D’Agostino (GND) test. Recalibration was conducted for unsatisfactory calibration (p-value of GND test < 0.05) by adjusting the baseline hazards of original equations to address variations in patients’ cardiovascular risks across institutions.ResultsThe CHIME risk equations had acceptable discrimination (AUROC: 0.71–0.79) and better calibration than that for UKPDS-OM2 and RECODe, although the calibration remained unsatisfactory. After recalibration, the calibration slopes/intercepts of the CHIME-MI, CHIME-stroke, and CHIME-HF risk equations were 0.9848/− 0.0008, 1.1003/− 0.0046, and 0.9436/0.0063 in the NCKUH population and 1.1060/− 0.0011, 0.8714/0.0030, and 1.0476/− 0.0016 in the NTUH population, respectively. All the recalibrated risk equations showed satisfactory calibration (p-values of GND tests ≥ 0.05).ConclusionsWe provide valid risk prediction equations for MI, stroke, and HF outcomes in Taiwanese type 2 diabetes populations. A framework for adapting risk equations across institutions is also proposed.

Assessment of Energy Consumption Patterns and Validation of Predictive Equations among ICU Patients at Muhimbili National Hospital in Dar es Salaam, Tanzania

Background: Nutritional support in critically-ill patients is one of the most important parameters guarding the prognosis and influencing morbidity and mortality in these patients, owing to that fact, accurate measurement of the resting energy expenditure using Indirect calorimetry is recommended by guidelines as a gold standard. But due to lack of resources and other technicalities predictive equations are conveniently used as surrogates. Aims: This study was intended to examine the extent of malnutrition, poor nutrition support practices and to validate the common used predictive equations in critically-ill patients in our setting. Methodology: A hospital-based descriptive cross-sectional study was conducted on consecutively sampled 110 mechanically ventilated ICU patients at Muhimbili National Hospital Mloganzila. Anthropometric measurements, duration of stay in ICU, Temperature and Minute Volumes were recorded so as to estimate resting energy expenditure, REE from different predictive equations. Using Indirect Calorimetry Module patient’s REE was measured and recorded then a statistical data analyzed using SPSS software version 23. Results: The prevalence of poor nutritional support was 69%; underfeeding and overfeeding were observed in 41.8% and 27.3% of all participants respectively. Prevalence of malnutrition was 51.8%; underweight and overweight were found to be in 29.1% and 22.7% of all participants respectively. The accuracy of predictive equation in ±10% difference was 30%, 45.5%, 46.4% and 68.2% in HB, MSJ, ESPEN and PENN respectively. Conclusion and Recommendation: Malnutrition and Poor Nutritional support are common problems in ICU. Predictive equations have poor accuracy and validity in comparison to indirect calorimetry. Penn State Equation was the most accurate and with the highest agreement with IC.

Is the Ventilatory Efficiency in Endurance Athletes Different?-Findings from the NOODLE Study.

Background: Ventilatory efficiency (VE/VCO2) is a strong predictor of cardiovascular diseases and defines individuals' responses to exercise. Its characteristics among endurance athletes (EA) remain understudied. In a cohort of EA, we aimed to (1) investigate the relationship between different methods of calculation of VE/VCO2 and (2) externally validate prediction equations for VE/VCO2. Methods: In total, 140 EA (55% males; age = 22.7 ± 4.6 yrs; BMI = 22.6 ± 1.7 kg·m-2; peak oxygen uptake = 3.86 ± 0.82 L·min-1) underwent an effort-limited cycling cardiopulmonary exercise test. VE/VCO2 was first calculated to ventilatory threshold (VE/VCO2-slope), as the lowest 30-s average (VE/VCO2-Nadir) and from whole exercises (VE/VCO2-Total). Twelve prediction equations for VE/VCO2-slope were externally validated. Results: VE/VCO2-slope was higher in females than males (27.7 ± 2.6 vs. 26.1 ± 2.0, p < 0.001). Measuring methods for VE/VCO2 differed significantly in males and females. VE/VCO2 increased in EA with age independently from its type or sex (β = 0.066-0.127). Eleven equations underestimated VE/VCO2-slope (from -0.5 to -3.6). One equation overestimated VE/VCO2-slope (+0.2). Predicted and observed measurements differed significantly in nine models. Models explained a low amount of variance in the VE/VCO2-slope (R2 = 0.003-0.031). Conclusions: VE/VCO2-slope, VE/VCO2-Nadir, and VE/VCO2-Total were significantly different in EA. Prediction equations for the VE/VCO2-slope were inaccurate in EA. Physicians should be acknowledged to properly assess cardiorespiratory fitness in EA.

Development and cross-validation of prediction equations for body composition in adult cancer survivors from the Korean National Health and Nutrition Examination Survey (KNHANES).

Epidemiological studies frequently use indices of adiposity related to mortality. However, no studies have validated prediction equations for body composition in adult cancer survivors. We aimed to develop and cross-validate prediction equations for body fat mass (BFM), lean body mass (LBM), trunk fat mass (TFM), and appendicular lean mass (ALM) in adult cancer survivors using sociodemographic, anthropometric, and laboratory test data. This study included adult cancer survivors from the Korean National Health and Nutrition Examination Survey 2008-2011 with complete data on Dual-energy X-ray absorptiometry (DXA) measurements. A total of 310 participants were randomly divided into development and cross-validation groups (5:5 ratio). Age, height, weight, waist circumference, serum creatinine levels, and lifestyle factors were included as independent variables The predictive equations were developed using a multiple linear regression and their predictive performances were primarily evaluated with R2 and Concordance Correlation Coefficient (CCC). The initial equations, which included age, height, weight, and waist circumference, showed different predictive abilities based on sex for BFM (total: R2 = 0.810, standard error of estimate [SEE] = 3.072 kg, CCC = 0.897; men: R2 = 0.848, SEE = 2.217 kg CCC = 0.855; women: R2 = 0.791, SEE = 2.194 kg, CCC = 0.840), LBM (total: R2 = 0.736, SEE = 3.321 kg, CCC = 0.838; men: R2 = 0.703, SEE = 2.450 kg, CCC = 0.774; women: R2 = 0.854, SEE = 2.234 kg, CCC = 0.902), TFM (total: R2 = 0.758, SEE = 1.932 kg, CCC = 0.844; men: R2 = 0.650, SEE = 1.745 kg, CCC = 0.794; women: R2 = 0.852, SEE = 1.504 kg, CCC = 0.890), and ALM (total: R2 = 0.775, SEE = 1.726 kg, CCC = 0.876; men: R2 = 0.805, SEE = 1.320 kg, CCC = 0.817; women: R2 = 0.726, SEE = 1.198 kg, CCC = 0.802). When additional factors, such as creatinine, smoking, alcohol consumption, and physically inactive were included in the initial equations the predictive performance of the equations were generally improved. The prediction equations for body composition derived from this study suggest a potential application in epidemiological investigations on adult cancer survivors.

Assessing the carbon footprint in dairy cattle farms in the northern temperate region of Spain

The availability of models constitutes a key factor when selecting a decision support tool aimed at improving the production and environmental aspects of farms. There is a need for robust models that are user-friendly, facilitating the estimation of farm emissions and the analysis of their temporal fluctuations. The objectives of this study were i) to calculate both the partial (PCF) and total carbon (TCF) footprints of 212 dairy farms, distinguishing those with and without maize cultivation; ii) to identify critical variables related to feed, nutrition, productivity and environmental efficiency; and iii) to formulate and validate prediction equations based on available data from dairy farms. The database encompasses information from 212 dairy cattle farms situated in the temperate-humid zone of northern Spain, spanning the period from 2014 to 2018. Farm classification was based on the presence (CcMz) or absence (ScCMz) of maize cultivation for silage production, resulting in 96 farms in the CcMz category and 116 farms in the ScCMz category.Among the variables considered, the variable herd N-use efficiency (NUECR) for (PCF) showed the lowest root mean square error of prediction at 0.39% and the correspondingly lowest root men. The root mean squared percentage error (RMSPE): standard deviation ratio (RSR) at 0.52. In the case of total carbon footprint (TCF), herd N-use efficiency (NUECR) again showed the lowest root mean square error of prediction at 0.52%. Regarding TCF, herd feed efficiency (EACR) was the variable with the lowest both RMSPE and RSR, with 0.65 and 0.64, respectively. Consequently, the estimation of the PCF and TCF of 1 ​kg of milk from the temperate-humid zone of northern Spain at the farm gate can be feasibly accomplished utilizing NUECR and EACR, respectively.

Estimation of kidney function for medication dosing in adult patients with chronic kidney disease: a practice update

AbstractChronic kidney disease (CKD) is a major health concern with a growing disease burden and inequalities in access to treatments. Glomerular filtration rate (GFR) is accepted as the best overall measure of kidney function and is considered the most important measure for medications cleared by the kidneys. In clinical practice, equations that estimate GFR using validated prediction equations are routinely used. This practice update was developed by a Working Group comprising clinical pharmacists representing the Society of Hospital Pharmacists of Australia (SHPA) Specialty Practice streams of Nephrology, Oncology and haematology, Critical care and Infectious diseases. It is intended to provide practical recommendations for clinical pharmacists who use equations to estimate GFR for medication dosing decisions. The limitations of the various equations in use — such as the Chronic Kidney Disease Epidemiology Collaboration (CKD‐EPI), the Cockcroft–Gault equation and Modification of Diet in Renal Disease — are summarised and compared to direct measures of kidney function using exogenous markers. The CKD‐EPI equation is recommended to be used routinely as a primary measure of kidney function. Dose adjustments should also consider medication‐specific, patient‐related, and disease‐related characteristics. Kidney function and the response to therapy should be continuously assessed by monitoring the signs, symptoms and disease outcomes, the emergence of adverse reactions or medication‐induced disorders and use therapeutic drug monitoring (if available) to adjust doses accordingly.

Effect of changes in sleeping behavior on skeletal muscle and fat mass: a retrospective cohort study

BackgroundAn association between sleep behaviors and muscle-fat mass is continuously interesting topic.MethodsBased on the survey on sleep behaviors (quality and duration), the poor quality of sleep was evaluated when the subject did not feel satisfied after sleep, while the good quality was evaluated as they feel refreshed. A total of 19,770 participants were divided into the four groups according to changes in sleep quality: Good-to-Good (those who continuously maintained good quality), Good-to-Poor (those who reported initial good quality but subsequently reported a poor quality), Poor-to-Poor (those who continuously maintained poor quality), and Poor-to-Good (those who reported improved quality of sleep). As changes in skeletal muscle and fat mass index [kg/m2] were estimated by a validated prediction equation, multiple linear regression was used to calculate adjusted mean (adMean) of muscle and fat mass according to changes in sleep behavior.ResultsWhen sleep duration decreased and quality of sleep deteriorated (from good to poor), fat mass index significantly increased (adMean: 0.087 for the Good-to-Good group and 0.210 for the Good-to-Poor group; p-value = 0.006). On the other hand, as the quality of sleep deteriorated, skeletal muscle mass more decreased despite the maintained sleep duration (adMean: -0.024 for the Good-to-Good group and − 0.049 for the Good-to-Poor group; p-value = 0.009).ConclusionOur results showed that changes in sleep quality and duration affect changes in muscle and fat mass. Thus, we suggest maintaining a good quality of sleep, even if sleep duration is reduced, to preserve muscle mass and inhibit the accumulation of fat.

Development and validation of a prediction equation for body fat percentage from measured BMI: a supervised machine learning approach

Body mass index is a widely used but poor predictor of adiposity in populations with excessive fat-free mass. Rigorous predictive models validated specifically in a nationally representative sample of the US population and that could be used for calibration purposes are needed. The objective of this study was to develop and validate prediction equations of body fat percentage obtained from Dual Energy X-ray Absorptiometry using body mass index (BMI) and socio-demographics. We used the National Health and Nutrition Examination Survey (NHANES) data from 5931 and 2340 adults aged 20 to 69 in 1999–2002 and 2003–2006, respectively. A supervised machine learning using ordinary least squares and a validation set approach were used to develop and select best models based on R2 and root mean square error. We compared our findings with other published models and utilized our best models to assess the amount of bias in the association between predicted body fat and elevated low-density lipoprotein (LDL). Three models included BMI, BMI2, age, gender, education, income, and interaction terms and produced R-squared values of 0.87 and yielded the smallest standard errors of estimation. The amount of bias in the association between predicted BF% and elevated LDL from our best model was −0.005. Our models provided strong predictive abilities and low bias compared to most published models. Its strengths rely on its simplicity and its ease of use in low-resource settings.

Changes in body composition and subsequent cardiovascular disease risk among 5-year breast cancer survivors.

Cardiovascular disease (CVD) remains a leading cause of death in breast cancer survivors, a growing population. The aim of this study was to determine whether changes in body composition, commonly observed in breast cancer survivors, is associated with subsequent CVD risk. This cohort study used the Korean National Health Insurance Service database. The study population included 73,271 5-year breast cancer survivors aged 40 years or above. To assess changes in body composition and its effect on the risk of CVD, validated prediction equations and multivariate Cox proportional hazards regression were used. Changes in metabolic markers (blood pressure, total cholesterol, and fasting serum glucose) according to changes in body composition were calculated by multiple linear regression. Having persistently high predicted lean body and appendicular skeletal muscle mass percentages (LBMP and ASMP, respectively) among breast cancer survivors was associated with 32% and 40% lower CVD risks than a persistently low predicted LBMP or ASMP, respectively. Conversely, persistently high predicted body fat mass percentage (BFMP) was associated with a higher CVD risk than persistently low predicted BFMP. Additionally, those with a low to high change in predicted BFMP had a higher risk of CVD than those with persistently low predicted BFMP. Changes in body composition were accompanied by changes in metabolic markers. Maintaining high percentages of lean body and appendicular skeletal muscle mass and preventing an increase in fat mass may be beneficial in preventing CVD in breast cancer survivors.

Glomerular filtration rate correlation and agreement between common predictive equations and standard 24-hour urinary creatinine clearance in medical critically ill patients.

BackgroundDetermining kidney function in critically ill patients is paramount for the dose adjustment of several medications. When assessing kidney function, the glomerular filtration rate (GFR) is generally estimated either by calculating urine creatinine clearance (UCrCl) or using a predictive equation. Unfortunately, all predictive equations have been derived for medical outpatients. Therefore, the validity of predictive equations is of concern when compared with that of the UCrCl method, particularly in medical critically ill patients. Therefore, we conducted this study to assess the agreement of the estimated GFR (eGFR) using common predictive equations and UCrCl in medical critical care setting.MethodsThis was the secondary analysis of a nutrition therapy study. Urine was collected from participating patients over 24 h for urine creatinine, urine nitrogen, urine volume, and serum creatinine measurements on days 1, 3, 5, and 14 of the study. Subsequently, we calculated UCrCl and eGFR using four predictive equations, the Cockcroft–Gault (CG) formula, the four and six-variable Modification of Diet in Renal Disease Study (MDRD-4 and MDRD-6) equations, and the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation. The correlation and agreement between eGFR and UCrCl were determined using the Spearman rank correlation coefficient and Bland–Altman plot with multiple measurements per subject, respectively. The performance of each predictive equation for estimating GFR was reported as bias, precision, and absolute percentage error (APE).ResultsA total of 49 patients with 170 urine samples were included in the final analysis. Of 49 patients, the median age was 74 (21–92) years-old and 49% was male. All patients were hemodynamically stable with mean arterial blood pressure of 82 (65–108) mmHg. Baseline serum creatinine was 0.93 (0.3–4.84) mg/dL and baseline UCrCl was 46.69 (3.40–165.53) mL/min. The eGFR from all the predictive equations showed modest correlation with UCrCl (r: 0.692 to 0.759). However, the performance of all the predictive equations in estimating GFR compared to that of UCrCl was poor, demonstrating bias ranged from −8.36 to −31.95 mL/min, precision ranged from 92.02 to 166.43 mL/min, and an unacceptable APE (23.01% to 47.18%). Nevertheless, the CG formula showed the best performance in estimating GFR, with a small bias (−2.30 (−9.46 to 4.86) mL/min) and an acceptable APE (14.72% (10.87% to 23.80%)), especially in patients with normal UCrCl.ConclusionFrom our finding, CG formula was the best eGFR formula in the medical critically ill patients, which demonstrated the least bias and acceptable APE, especially in normal UCrCl patients. However, the predictive equation commonly used to estimate GFR in critically ill patients must be cautiously applied due to its large bias, wide precision, and unacceptable error, particularly in renal function impairment.

Validation and predicting total body water in people with spinal cord injury using bioelectrical impedance analysis

The purpose of this study was validation of predictive equations for total body water (TBW) in people with spinal cord injury (SCI) compared to healthy subjects (HS) using bioelectrical impedance analysis (BIA). The method used was sample size of 104 (52 each group) with 41 males in SCI, 39 in HS, 11 females in SCI, and 13 in HS. A T-test was applied to statistical analysis between groups. The results showed significant differences between groups of gender, with p-value < P 0.0013, the difference (4.6115), with a 95%CI (1.8480 to 7.3750), and SE (1.3932). The SCI group noted a difference within BIA and formula calculations. In conclusion, we found the current equations inaccurate in SCI society and recommended BIA as non-invasive, speedy, portable, accessible, and cost-effective.

The Incremental Shuttle Walk Test

The Incremental Shuttle Walk Test

Resting Energy Expenditure Early after Cardiac Surgery and Validity of Predictive Equations: A Prospective Observational Study

Objective: Several predictive equations have been used to estimate patients’ energy expenditure. The study aimed to describe the characteristics of resting energy expenditure (REE) in patients undergoing mechanical ventilation during early postoperative stage after cardiac surgery and evaluate the validity of 9 REE predictive equations. Methods: This was a prospective observational study. Patients aged 18–80 years old, undergone open-heart surgery, were enrolled between January 2017 and 2018. The measured REE (mREE) was evaluated via indirect calorimetry (IC). The predictive resting energy expenditure (pREE) was suggested by 9 predictive equations, including Harris-Benedict (HB), HB coefficient method, Ireton-Jones, Owen, Mifflin, Liu, 25 × body weight (BW), 30 × BW, and 35 × BW. The association between mREE and pREE was assessed by Pearson’s correlation, paired t test, Bland-Altman method, and the limits of agreement (LOA). Results: mREE was related to gender, BMI, age, and body temperature. mREE was significantly correlated with pREE, as calculated by 9 equations (all p < 0.05). There was no significant difference between pREE and mREE, as calculated by 30 × BW kcal/kg/day (t = 0.782, p = 0.435), while significant differences were noted between mREE and pREE calculated by other equations (all p < 0.05). Taking the 30 × BW equation as a suitable candidate, most of the data points were within LOA, and the percentage was 95.6% (129/135). Considering the rationality of clinical use, accurate predictions (%) were calculated, and only 40.74% was acceptable. Conclusions: The 30 × BW equation is relatively acceptable for estimating REE in 9 predictive equations in the early stage after heart surgery. However, the IC method should be the first choice if it is feasible.

Is it possible to predict milk processing into butter using infrared spectroscopy?

Description of the subject. Given the current low price of milk, a lot of producers have decided to process their milk into products with a higher added-value, including butter. However, all milks are not suitable to be transformed into butter. It would thus be useful to be able to predict milk processing properties. Objectives. The aim of this paper was to study the ability of milk to be processed into butter using infrared spectrophotometry. Method. A normalized protocol for the production of butter was developed. Milk samples (n = 110) collected between 2013 and 2016 were analyzed by near and medium infrared spectrometry (315 spectra). Butter samples were also analyzed by visible-near infrared spectrometry (220 spectra). Composition of the products was subsequently assessed using validated prediction equations. Principal components analyses were performed to discriminate samples. Results. Butter properties seemed to be influenced by seasons and feedings. Water content and color parameters could be predicted on the basis of butter infrared spectra. Conclusions. It was possible to correlate butter characteristics with milk properties. However, it was not possible to predict butter characteristics on the basis of milk near infrared spectra. It could be interesting to try predictions from milk medium infrared spectra.

Resting Energy Expenditure of Physically Active Boys in Southeastern Poland-The Accuracy and Validity of Predictive Equations.

Optimization of energy intake in the diet of young athletes is of primary importance. In addition to the energy expenditure associated with their body development, the demand resulting from intensive physical activity also increases. The aim of this study was to compare the accuracy of formulas commonly used for resting energy expenditure (REE) calculations with values obtained from measurements using indirect calorimetry among male children and adolescents practicing football. The study was conducted among 184 boys aged 9 to 17 using a calorimeter and a device for assessing body composition by means of electrical bioimpedance using a segment analyzer. The mean error ranged from −477 kcal/d by the Maffeis formula to −182 kcal/d for the Institute of Medicine of the National Academies (IMNA) formula. A statistically significant difference was found for all formulas in the calculated value in relation to the measured REE value (p < 0.0001). Most “ready-to-use” formulas underestimate REE, which can be a risk in determining the total energy demand in a group that requires more calories, especially when due to intensive growth and development and the expenditure associated with regular training and increased physical activity.

Simple cardiovascular risk stratification using anthropometric measures instead of serum cholesterol. The MORGAM Prospective Cohort Project

Abstract Background Body composition predicts cardiovascular outcomes, but it is uncertain whether anthropometric measures can replace the more expensive serum total cholesterol for cardiovascular risk stratification in low resource settings. Purpose The purpose of the study was to compare the additive prognostic ability of serum total cholesterol with that of body mass index (BMI), waist/hip ratio (WHR), and estimated fat mass (EFM, calculated using a validated prediction equation), individually and combined. Methods We used data from the MORGAM (MONICA, Risk, Genetics, Archiving, and Monograph) Prospective Cohort Project, an international pooling of cardiovascular cohorts, to determine the relationship between anthropometric measures, serum cholesterol, and cardiovascular events, using multivariable Cox proportional-hazards regression analysis. We further investigated the ability of these measures to enhance prognostication beyond a simpler prediction model, consisting of age, sex, smoking status, systolic blood pressures, and country, using comparison of area under the receiver operating characteristics curve (AUCROC) derived from binary logistic regression models. The primary endpoint was major adverse cardiovascular events (MACE), defined as a composite of death from coronary heart disease, myocardial infarction, or stroke. Results The study population consisted of 52,188 apparently healthy subjects (56.3% men) aged 47±12 years ranging from 20 to 84, derived from 37 European cohorts, with baseline between 1982–2002 all followed for 10 years during which MACE occurred in 2465 (4.7%) subjects. All anthropometric measures (BMI: hazard ratio (HR) 1.04 [95% confidence interval (CI): 1.03–1.05] per kg/m2; WHR: HR 7.5 [4.0–14.0] per unit; EFM: HR 1.02 [1.01–1.02] per kg) as well as serum total cholesterol (HR 1.20 [1.16–1.24] per mmol/l) were significantly associated with MACE (P&amp;lt;0.001 for all), independently of age, sex, smoking status, systolic blood pressures, and country. The addition of serum cholesterol significantly improved the predictive ability of the simple model (AUCROC 0.818 vs. 0.814, P&amp;lt;0.001), as did the combination of WHR, BMI, and EFM (AUCROC 0.817 vs. 0.814, P=0.004). When assessed individually, BMI (AUCROC 0.816 vs. 0.814, P=0.004) and WHR (AUCROC 0.815 vs. 0.814, P=0.02) improved model performance, while EFM narrowly missed significance (AUCROC 0.815 vs. 0.814, P=0.06). There was no significant difference in the predictive ability of a model including serum cholesterol versus that including all three anthropometric measures (AUCROC 0.818 vs. 0.817, P=0.13). The figure shows the pertinent areas under the ROC curve in predicting MACE. Conclusion In this large population-based cohort study, the addition of a combination of anthropometric measures, i.e. BMI, WHR, and EFM, raised the predictive ability of a simple prognostic model comparable to that obtained by the addition of serum total cholesterol. Figure 1 Funding Acknowledgement Type of funding source: None

Utility of Insulin Resistance in Estimating Cardiovascular Risk in Subjects with Type 1 Diabetes According to the Scores of the Steno Type 1 Risk Engine.

Background: We sought to assess the potential of insulin resistance (IR) for estimating cardiovascular disease (CVD) risk in adults with type 1 diabetes (T1DM) according to the scores of the Steno Type 1 Risk Engine (ST1RE). Methods: A total of 179 adults with T1DM (50.8% men, age 41.2 ± 13.1 years, duration of T1DM 16 (12–23) years) without established CVD were evaluated. IR was assessed by the estimation of insulin sensitivity (eIS) using two validated prediction equations: the estimated insulin sensitivity developed from the Pittsburgh Epidemiology of Diabetes Complications Study (eIS-EDC) and the estimated insulin sensitivity developed from Coronary Artery Calcification in T1DM Study (eIS-CACTI) ST1RE was used to estimate 10-year CVD risk and to classify subjects into three groups according to their risk: low (<10%; n = 105), moderate (10–20%; n = 53), and high (≥20%; n = 21). Results: Both eIS-EDC and eIS-CACTI correlated negatively with ST1RE scores (eIS-EDC: r = −0.636, p < 0.001; eIS-CACTI: r = −0.291, p < 0.001). The C-statistic for predicting moderate/high risk and high risk was 0.816 (95% confidence interval (CI): 0.754–0.878) and 0.843 (95% CI: 0.772–0.913), respectively, for the eIS-EDC equation, and was 0.686 (95% CI: 0.609–0.763) and 0.646 (95% CI: 0.513–0.778), respectively, for the eIS-CACTI equation. The eIS-EDC equation had a significantly higher C-statistic both for moderate-/high-risk (p = 0.001) and high-risk (p = 0.007) subjects. Two cut-off points of eIS-EDC were identified for detecting moderate/high risk (8.52 mg·kg−1·min−1; sensitivity 74% and specificity 76%) and high risk (8.08 mg·kg−1·min−1; sensitivity 65% and specificity 95%) with potential applicability in clinical practice. Conclusions: eIS negatively correlates with the score of CVD risk in the ST1RE. Two cut-off points of eIS are reported with potential utility in clinical practice for detecting adults with T1DM with the highest CVD risk.

Validation of predictive equations against DXA for estimation of body fat composition in Vietnamese children

Childhood overweight and obesity are becoming more pronounced in Vietnam, so an assessment tool of high efficiency in the community is warranted. This study sought to validate skinfold thickness (SFT) equations for estimation of body fatness by Goran and Slaughter against DXA to aid in assessing obesity. A cross-sectional study was conducted on 144 healthy children (ages 6-17) who were conveniently sampled from schools within an urban district. Their anthropometric measurements (height, weight, and SFT) and DXA whole-body results were taken to record body fat percentage (BF%). Bland-Altman analysis and correlation between bias and body fat were employed to understand the agreement between results from each equation and DXA whole body. BF% was 32.2 ± 7.6% (mean ± SD). 52.8% of the children were overweight or obese. Bland-Altman plots showed that all four SFT equations had wide limits of agreement (LOAs) and largely underestimated the reference BF% by up to 8.90%. Goran equation predicted better when BF% decreased, whereas Slaughter equations produced less bias when there was more body fat. The prevalence rate of overweight and obesity has become alarming. Besides, Goran and Slaughter equations cannot be used as alternatives for DXA scanning to measure body fat due to their underestimation.

Development and validation of prediction equations for the assessment of muscle or fat mass using anthropometric measurements, serum creatinine level, and lifestyle factors among Korean adults.

BACKGROUND/OBJECTIVESThe measurement of body composition, including muscle and fat mass, remains challenging in large epidemiological studies due to time constraint and cost when using accurate modalities. Therefore, this study aimed to develop and validate prediction equations according to sex to measure lean body mass (LBM), appendicular skeletal muscle mass (ASM), and body fat mass (BFM) using anthropometric measurement, serum creatinine level, and lifestyle factors as independent variables and dual-energy X-ray absorptiometry as the reference method.SUBJECTS/METHODSA sample of the Korean general adult population (men: 7,599; women: 10,009) from the Korean National Health and Nutrition Examination Survey 2008–2011 was included in this study. The participants were divided into the derivation and validation groups via a random number generator (with a ratio of 70:30). The prediction equations were developed using a series of multivariable linear regressions and validated using the Bland–Altman plot and intraclass correlation coefficient (ICC).RESULTSThe initial and practical equations that included age, height, weight, and waist circumference had a different predictive ability for LBM (men: R2 = 0.85, standard error of estimate [SEE] = 2.7 kg; women: R2 = 0.78, SEE = 2.2 kg), ASM (men: R2 = 0.81, SEE = 1.6 kg; women: R2 = 0.71, SEE = 1.2 kg), and BFM (men: R2 = 0.74, SEE = 2.7 kg; women: R2 = 0.83, SEE = 2.2 kg) according to sex. Compared with the first prediction equation, the addition of other factors, including serum creatinine level, physical activity, smoking status, and alcohol use, resulted in an R2 that is higher by 0.01 and SEE that is lower by 0.1.CONCLUSIONSAll equations had low bias, moderate agreement based on the Bland–Altman plot, and high ICC, and this result showed that these equations can be further applied to other epidemiologic studies.