Development and validation of a prediction model for fat mass in children and adolescents: meta-analysis using individual participant data

Isotemporal substitution of accelerometer-derived sedentary behavior and physical activity on physical fitness in young children

This study investigates the effects of different types of physical activity (PA) on the physical fitness (PF) of young children in Japan, with a particular focus on how substituting sedentary behavior (SB) with active behaviors influences PF. We conducted a cross-sectional analysis of 1843 participants aged 3-6 years from northeastern Japan. Using triaxial accelerometers, we quantified PA, and PF was assessed via standardized tests. The innovative application of isotemporal substitution modeling (ISM) allowed us to analyze the impact of reallocating time from SB to more active states, specifically moderate-to-vigorous physical activity (MVPA) and light physical activity (LPA). Our findings reveal a robust association between increased MVPA and enhanced PF outcomes, underscoring the health benefits of reducing SB. Notably, replacing SB with LPA also showed beneficial effects on certain PF metrics, indicating LPA's potential role in early childhood fitness. These results highlight the critical importance of promoting MVPA and minimizing sedentary periods to bolster PF in young children. The study offers vital insights for shaping public health policies and emphasizes the need to cultivate an active lifestyle from an early age to secure long-term health advantages.

From Beats to Metabolism: the Heart at the Core of Interorgan Metabolic Cross Talk

The heart, once considered a mere blood pump, is now recognized as a multifunctional metabolic and endocrine organ. Its function is tightly regulated by various metabolic processes, at the same time it serves as an endocrine organ, secreting bioactive molecules that impact systemic metabolism. In recent years, research has shed light on the intricate interplay between the heart and other metabolic organs, such as adipose tissue, liver, and skeletal muscle. The metabolic flexibility of the heart and its ability to switch between different energy substrates play a crucial role in maintaining cardiac function and overall metabolic homeostasis. Gaining a comprehensive understanding of how metabolic disorders disrupt cardiac metabolism is crucial, as it plays a pivotal role in the development and progression of cardiac diseases. The emerging understanding of the heart as a metabolic and endocrine organ highlights its essential contribution to whole body metabolic regulation and offers new insights into the pathogenesis of metabolic diseases, such as obesity, diabetes, and cardiovascular disorders. In this review, we provide an in-depth exploration of the heart's metabolic and endocrine functions, emphasizing its role in systemic metabolism and the interplay between the heart and other metabolic organs. Furthermore, emerging evidence suggests a correlation between heart disease and other conditions such as aging and cancer, indicating that the metabolic dysfunction observed in these conditions may share common underlying mechanisms. By unraveling the complex mechanisms underlying cardiac metabolism, we aim to contribute to the development of novel therapeutic strategies for metabolic diseases and improve overall cardiovascular health.

Risk of bias assessments in individual participant data meta-analyses of test accuracy and prediction models: a review shows improvements are needed

OBJECTIVES

Risk of bias assessments are important in meta-analyses of both aggregate and individual participant data (IPD). There is limited evidence on whether and how risk of bias of included studies or datasets in IPD meta-analyses (IPDMAs) is assessed. We review how risk of bias is currently assessed, reported, and incorporated in IPDMAs of test accuracy and clinical prediction model studies and provide recommendations for improvement.

STUDY DESIGN AND SETTING

We searched PubMed (January 2018-May 2020) to identify IPDMAs of test accuracy and prediction models, then elicited whether each IPDMA assessed risk of bias of included studies and, if so, how assessments were reported and subsequently incorporated into the IPDMAs.

RESULTS

Forty-nine IPDMAs were included. Nineteen of 27 (70%) test accuracy IPDMAs assessed risk of bias, compared to 5 of 22 (23%) prediction model IPDMAs. Seventeen of 19 (89%) test accuracy IPDMAs used Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2), but no tool was used consistently among prediction model IPDMAs. Of IPDMAs assessing risk of bias, 7 (37%) test accuracy IPDMAs and 1 (20%) prediction model IPDMA provided details on the information sources (e.g., the original manuscript, IPD, primary investigators) used to inform judgments, and 4 (21%) test accuracy IPDMAs and 1 (20%) prediction model IPDMA provided information or whether assessments were done before or after obtaining the IPD of the included studies or datasets. Of all included IPDMAs, only seven test accuracy IPDMAs (26%) and one prediction model IPDMA (5%) incorporated risk of bias assessments into their meta-analyses. For future IPDMA projects, we provide guidance on how to adapt tools such as Prediction model Risk Of Bias ASsessment Tool (for prediction models) and QUADAS-2 (for test accuracy) to assess risk of bias of included primary studies and their IPD.

CONCLUSION

Risk of bias assessments and their reporting need to be improved in IPDMAs of test accuracy and, especially, prediction model studies. Using recommended tools, both before and after IPD are obtained, will address this.

BMI Z-Score (SDS) versus Calculated Body Fat Percentage: Association with Cardiometabolic Risk Factors in Obese Children and Adolescents

INTRODUCTION

BMI or BMI-standardized deviation score (SDS) in children and adolescents is still the standard for weight classification. [BMJ. 2019;366:4293] developed a formula to calculate body fat percentage (%BF) based on age, sex, height, weight, and ethnicity. Using data from the German/Austrian APV registry, we investigated whether the calculated %BF is superior to BMI-SDS in predicting arterial hypertension, dyslipidaemia, and impaired glucose metabolism.

METHODS

94,586 children and adolescents were included (12.5 years, 48.3% male). Parental birth country (BC) was used to depict ethnicity (15.8% migration background); 95.67% were assigned to the ethnicity "white." %BF was calculated based on the Hudda formula. The relationship between BMI-SDS or %BF quartiles and outcome variables was investigated by logistic regression models, adjusted for age, sex, and migration background. Vuong test was applied to analyse predictive power.

RESULTS

58.4% had arterial hypertension, 33.5% had dyslipidaemia, and 11.6% had impaired glucose metabolism. Boys were significantly more often affected, although girls had higher calculated %BF (each p < 0.05). After adjustment, both models revealed significant differences between the quartiles (all p < 0.001). The predictive power of BMI-SDS was superior to %BF for all three comorbidities (all p < 0.05).

DISCUSSION

The prediction of cardiometabolic comorbidities by calculated %BF was not superior to BMI-SDS. This formula developed in a British population may not be suitable for a central European population, which is applicable to this possibly less heterogeneous collective. Additional parameters, especially puberty status, should be taken into account. However, objective determinations such as bioimpedance analysis may possibly be superior to assess fat mass and cardiometabolic risk than calculated %BF.

Multivariable Model to Predict an ACTH Stimulation Test to Diagnose Adrenal Insufficiency Using Previous Test Results

Context

The adrenocorticotropin hormone stimulation test (AST) is used to diagnose adrenal insufficiency, and is often repeated in patients when monitoring recovery of the hypothalamo-pituitary-adrenal axis.

Objective

To develop and validate a prediction model that uses previous AST results with new baseline cortisol to predict the result of a new AST.

Methods

This was a retrospective, longitudinal cohort study in patients who had undergone at least 2 ASTs, using polynomial regression with backwards variable selection, at a Tertiary UK adult endocrinology center. Model was developed from 258 paired ASTs over 5 years in 175 adults (mean age 52.4 years, SD 16.4), then validated on data from 111 patients over 1 year (51.8, 17.5) from the same center, data collected after model development. Candidate prediction variables included previous test baseline adrenocorticotropin hormone (ACTH), previous test baseline and 30-minute cortisol, days between tests, and new baseline ACTH and cortisol used with calculated cortisol/ACTH ratios to assess 8 candidate predictors. The main outcome measure was a new test cortisol measured 30 minutes after Synacthen administration.

Results

Using 258 sequential ASTs from 175 patients for model development and 111 patient tests for model validation, previous baseline cortisol, previous 30-minute cortisol and new baseline cortisol were superior at predicting new 30-minute cortisol (R2 = 0.71 [0.49-0.93], area under the curve [AUC] = 0.97 [0.94-1.0]) than new baseline cortisol alone (R2 = 0.53 [0.22-0.84], AUC = 0.88 [0.81-0.95]).

Conclusion

Results of a previous AST can be objectively combined with new early-morning cortisol to predict the results of a new AST better than new early-morning cortisol alone. An online calculator is available at https://endocrinology.shinyapps.io/sheffield_sst_calculator/ for external validation.

LSS rs2254524 Increases the Risk of Hypertension in Children and Adolescents with Obesity

Childhood obesity and its related comorbidities have become major health issues over the last century. Among these comorbidities, cardiovascular diseases, especially hypertension, are the most significant. Recently, a polymorphism affecting the activity of lanosterol synthase has been associated with an increased risk of hypertension in adolescents. In this study, we aimed to investigate the effect of LSS rs2254524 polymorphism on blood pressure in children and adolescents with obesity. We enrolled 828 obese children aged 6-17 years. Subjects carrying the A allele showed higher rates of systolic and diastolic stage I hypertension and stage II hypertension. Carriers of the A allele showed a 2.4-fold (95% C.I. 1.5-4.7, p = 0.01) higher risk for stage II hypertension and a 1.9-fold higher risk for stage I hypertension (95% C.I. 1.4-2.6, p < 0.0001). The risk was independent of confounding factors. In conclusion, LSS rs2254524 worsens the cardiovascular health of children and adolescents with obesity, increasing their blood pressure.

Prediction of fat mass from anthropometry at ages 7 to 9 years in Samoans: a cross-sectional study in the Ola Tuputupua'e cohort

BACKGROUND/OBJECTIVE

With increasing obesity prevalence in children globally, accurate and practical methods for quantifying body fat are critical for effective monitoring and prevention, particularly in high-risk settings. No population is at higher risk of obesity than Pacific Islanders, including children living in the independent nation of Samoa. We developed and validated sex-specific prediction models for fat mass in Samoan children.

SUBJECTS/METHODS

Dual X-ray absorptiometry (DXA) assessments of fat mass and weight, height, circumferences, and skinfolds were obtained from 356 children aged 7-9 years old in the Ola Tuputupua'e "Growing Up" study. Sex-specific models were developed from a randomly selected model development sample (n = 118 females, n = 120 males) using generalized linear regressions. In a validation sample (n = 59 females; n = 59 males), Lin's concordance and Bland-Altman limits-of-agreement (LoA) of DXA-derived and predicted fat mass from this study and other published models were examined to assess precision and accuracy.

RESULTS

Models to predict fat mass in kilograms were: e^[(-0.0034355 * Age8 - 0.0059041 * Age9 + 1.660441 * ln (Weight (kg))-0.0087281 * Height (cm) + 0.1393258 * ln[Suprailiac (mm)] - 2.661793)] for females and e^[-0.0409724 * Age8 - 0.0549923 * Age9 + 336.8575 * [Weight (kg)]-2 - 22.34261 * ln (Weight (kg)) [Weight (kg)]-1 + 0.0108696 * Abdominal (cm) + 6.811015 * Subscapular (mm)-2 - 8.642559 * ln (Subscapular (mm)) Subscapular (mm)-2 - 1.663095 * Tricep (mm)-1 + 3.849035]for males, where Age8 = Age9 = 0 for children at age 7 years, Age8 = 1 and Age9 = 0 at 8 years, Age8 = 0 and Age9 = 1 at 9 years. Models showed high predictive ability, with substantial concordance (ρC > 0.96), and agreement between DXA-derived and model-predicted fat mass (LoA female = -0.235, 95% CI:-2.924-2.453; male = -0.202, 95% CI:-1.977-1.572). Only one of four existing models, developed in a non-Samoan sample, accurately predicted fat mass among Samoan children.

CONCLUSIONS

We developed models that predicted fat mass in Samoans aged 7-9 years old with greater precision and accuracy than the majority of existing models that were tested. Monitoring adiposity in children with these models may inform future obesity prevention and interventions.

The Association of Body Mass Index and Fat Mass with Health-Related Physical Fitness among Chinese Schoolchildren: A Study Using a Predictive Model

Body fat mass (FM) has advantages over body mass index (BMI) in terms of accuracy of fitness assessment and health monitoring. However, the relationship between FM and fitness in Chinese children has not yet been well studied. This study aimed to investigate the relationship between health-related physical fitness, BMI, and FM, which was estimated using a predictive model among elementary schoolchildren in China. This cross-sectional study included 2677 participants (boys, 53.6%; girls, 46.4%) who underwent anthropometric measurements (height, weight, BMI, and FM) and five health-related fitness tests: 50-m sprint (speed), sit and reach (flexibility), timed rope-skipping (coordination), timed sit-ups (muscular endurance), and 50-m × 8 shuttle run (endurance). In boys, BMI showed a positive correlation with speed (p < 0.001) and endurance (p < 0.006) tests and a negative correlation with flexibility (p < 0.004) and coordination (p < 0.001) tests. In girls, a positive correlation between speed (p < 0.001) and endurance (p < 0.036) tests was observed. Both BMI and FM (estimated using the predictive model) were strongly associated with the health-related physical fitness of elementary schoolchildren. Our findings indicate that health-related physical fitness was similarly affected by FM and BMI. As FM can be quantified, it could therefore be used to develop strategies and intervention programs for the prevention and management of obesity in children.

Population pharmacokinetic modeling of daridorexant, a novel dual orexin receptor antagonist

The analysis aimed at identifying subject-specific characteristics (covariates) influencing exposure to daridorexant and quantification of covariate effects to determine clinical relevance. Data from 13 phase I, two phase II, and two phase III studies were pooled to develop a population pharmacokinetic model describing daridorexant concentration over time. Covariate effects were quantified based on model predictions. A two-compartment model with dose-dependent bioavailability, absorption lag time, linear absorption, and nonlinear elimination described the data best. Statistically significant covariates were food status on absorption (lag time and rate constant), time of drug administration (morning, bedtime) on absorption rate constant, lean body weight on central volume of distribution and elimination, fat mass on peripheral volume of distribution and intercompartmental drug transfer, and age and alkaline phosphatase on elimination. Age, lean body weight, fat mass, and alkaline phosphatase influence exposure (area under the curve, time of maximum concentration after dose administration, maximum plasma concentration, and next-morning concentration) to a limited extent, that is, less than 20% difference from a typical subject. Morning administration is not relevant for daridorexant use by insomnia patients. The food effect with simultaneous intake of a high-fat, high-calorie food is an extreme-case scenario unlikely to occur in clinical practice. Body composition, alkaline phosphatase, and age showed clinically negligible effects on exposure to daridorexant. Lean body weight and fat mass described the pharmacokinetics of daridorexant better than other body size descriptors (body weight, height, body mass index), suggesting a convenient physiological alternative to reduce the number of covariates in population pharmacokinetic models. The results indicate that differences between subjects do not require dose adjustments.

Equations based on anthropometric measurements for adipose tissue, body fat, or body density prediction in children and adolescents: a scoping review

PURPOSE

Assessing the body composition of children and adolescents is important to monitor their health status. Anthropometric measurements are feasible and less-expensive than other techniques for body composition assessment. This study aimed to systematically map anthropometric equations to predict adipose tissue, body fat, or density in children and adolescents, and to analyze methodological aspects of the development of anthropometric equations using skinfolds.

METHODS

A scoping review was carried out following the PRISMA-ScR criteria. The search was carried out in eight databases. The methodological structure protocol of this scoping review was retrospectively registered in the Open Science Framework ( https://osf.io/35uhc/ ).

RESULTS

We included 78 reports and 593 anthropometric equations. The samples consisted of healthy individuals, people with different diseases or disabilities, and athletes from different sports. Dual-energy X-ray absorptiometry (DXA) was the reference method most commonly used in developing equations. Triceps and subscapular skinfolds were the anthropometric measurements most frequently used as predictors in the equations. Age, stage of sexual maturation, and peak height velocity were used as complementary variables in the equations.

CONCLUSION

Our scoping review identified equations proposed for children and adolescents with a great diversity of characteristics. In many of the reports, important methodological aspects were not addressed, a factor that may be associated with equation bias.

LEVEL IV

Evidence obtained from multiple time series analysis such as case studies. (NB: dramatic results in uncontrolled trials might also be regarded as this type of evidence).

External validation of a prediction model for estimating fat mass in children and adolescents in 19 countries: individual participant data meta-analysis

OBJECTIVE

To evaluate the performance of a UK based prediction model for estimating fat-free mass (and indirectly fat mass) in children and adolescents in non-UK settings.

DESIGN

Individual participant data meta-analysis.

SETTING

19 countries.

PARTICIPANTS

5693 children and adolescents (49.7% boys) aged 4 to 15 years with complete data on the predictors included in the UK based model (weight, height, age, sex, and ethnicity) and on the independently assessed outcome measure (fat-free mass determined by deuterium dilution assessment).

MAIN OUTCOME MEASURES

The outcome of the UK based prediction model was natural log transformed fat-free mass (lnFFM). Predictive performance statistics of R², calibration slope, calibration-in-the-large, and root mean square error were assessed in each of the 19 countries and then pooled through random effects meta-analysis. Calibration plots were also derived for each country, including flexible calibration curves.

RESULTS

The model showed good predictive ability in non-UK populations of children and adolescents, providing R² values of >75% in all countries and >90% in 11 of the 19 countries, and with good calibration (ie, agreement) of observed and predicted values. Root mean square error values (on fat-free mass scale) were <4 kg in 17 of the 19 settings. Pooled values (95% confidence intervals) of R², calibration slope, and calibration-in-the-large were 88.7% (85.9% to 91.4%), 0.98 (0.97 to 1.00), and 0.01 (-0.02 to 0.04), respectively. Heterogeneity was evident in the R² and calibration-in-the-large values across settings, but not in the calibration slope. Model performance did not vary markedly between boys and girls, age, ethnicity, and national income groups. To further improve the accuracy of the predictions, the model equation was recalibrated for the intercept in each setting so that country specific equations are available for future use.

CONCLUSION

The UK based prediction model, which is based on readily available measures, provides predictions of childhood fat-free mass, and hence fat mass, in a range of non-UK settings that explain a large proportion of the variability in observed fat-free mass, and exhibit good calibration performance, especially after recalibration of the intercept for each population. The model demonstrates good generalisability in both low-middle income and high income populations of healthy children and adolescents aged 4-15 years.

Obesity Prediction with EHR Data: A deep learning approach with interpretable elements

Childhood obesity is a major public health challenge. Early prediction and identification of the children at an elevated risk of developing childhood obesity may help in engaging earlier and more effective interventions to prevent and manage obesity. Most existing predictive tools for childhood obesity primarily rely on traditional regression-type methods using only a few hand-picked features and without exploiting longitudinal patterns of children's data. Deep learning methods allow the use of high-dimensional longitudinal datasets. In this paper, we present a deep learning model designed for predicting future obesity patterns from generally available items on children's medical history. To do this, we use a large unaugmented electronic health records dataset from a large pediatric health system in the US. We adopt a general LSTM network architecture and train our proposed model using both static and dynamic EHR data. To add interpretability, we have additionally included an attention layer to calculate the attention scores for the timestamps and rank features of each timestamp. Our model is used to predict obesity for ages between 3-20 years using the data from 1-3 years in advance. We compare the performance of our LSTM model with a series of existing studies in the literature and show it outperforms their performance in most age ranges.

A height-weight formula to measure body fat in childhood obesity

Background

The assessment of body composition is central in diagnosis and treatment of paediatric obesity, but a criterion method is not feasible in clinical practice. Even the use of bioelectrical impedance analysis (BIA) is limited in children. Body mass index (BMI) Z-score is frequently used as a proxy index of body composition, but it does not discriminate between fat mass and fat-free mass. We aimed to assess the extent to which fat mass and percentage of body fat estimated by a height-weight equation agreed with a BIA equation in youths with obesity from South Italy. Furthermore, we investigated the correlation between BMI Z-score and fat mass or percentage of body mass estimated by these two models.

Methods

One-hundred-seventy-four youths with obesity (52.3% males, mean age 10.8 ± 1.9) were enrolled in this cross-sectional study. Fat mass and percentage of body fat were calculated according to a height-weight based prediction model and to a BIA prediction model.

Results

According to Bland–Altman statistics, mean differences were relatively small for both fat mass (+ 0.65 kg) and percentage of body fat (+ 1.27%) with an overestimation at lower mean values; the majority of values fell within the limits of agreement. BMI Z-score was significantly associated with both fat mass and percentage of body fat, regardless of the method, but the strength of correlation was higher when the height-weight equation was considered (r = 0.82; p < 0.001).

Conclusions

This formula may serve as surrogate for body fat estimation when instrumental tools are not available. Dealing with changes of body fat instead of BMI Z-score may help children and parents to focus on diet for health.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13052-022-01285-8.

[Development of anthropometric equations for predicting total body fat percentage in Chilean children and adolescents]

INTRODUCTION

studying the percentage of body fat (%BF) in children and adolescents is very relevant, since a high level of body fat in childhood and adolescence represents overweight and obesity.

OBJECTIVE

to identify the anthropometric indicators related to %BF and to validate regression equations to predict %BF in children and adolescents using dual-energy X-ray absorptiometry (DXA) as a reference method.

METHODS

a descriptive study (cross-sectional) was designed in 1126 schoolchildren (588 males and 538 females) from the Maule region (Chile). The age range ranged from 6.0 to 17.9 years. Weight, height, two skinfolds (tricipital and subscapular and waist circumference (WC) were evaluated. Body mass index (BMI), triponderal mass index (TMI), waist height index (WHtR) were calculated. Body fat percentage (%BF) was assessed by DXA scanning.

RESULTS

the relationships between Σ (Tricipital + Subscapular), TMI and WHtR with %BF (DXA) ranged from R2 = 52 % to 54 % in men, and from R2 = 41 % to 49 % in women. The equations generated for men were: %BF = 9.775 + [(0.415 * (Tr + SE)] + (35.084 * WHtR) - (0.828 * age), R2 = 70 %, and %BF = 20.720 + [(0.492 * (Tr + SE)] + (0.354 * TMI) - (0.923 * age), R2 = 68 %], and for women: %BF = 8.608 + [(0.291 * (Tr + SE)] + (38.893 * WHtR) - (0.176 * age), R2 = 60 %, and %BF = 16.087 + [(0.306 * (Tr + SE)] + (0.818 * TMI) - (0.300 * age), R2 = 59 %.

CONCLUSION

this study showed that the sum of tricipital and subscapular skinfolds, IP and WHtR are adequate predictors of %BF. These indicators allowed the development of two regression equations acceptable in terms of precision and accuracy to predict %BF in children and adolescents of both sexes.

Machine Learning Does Not Improve Humeral Torsion Prediction Compared to Regression in Baseball Pitchers

Background

Humeral torsion is an important osseous adaptation in throwing athletes that can contribute to arm injuries. Currently there are no cheap and easy to use clinical tools to measure humeral torsion, inhibiting clinical assessment. Models with low error and "good" calibration slope may be helpful for prediction.

Hypothesis/Purpose

To develop prediction models using a range of machine learning methods to predict humeral torsion in professional baseball pitchers and compare these models to a previously developed regression-based prediction model.

Study Design

Prospective cohort.

Methods

An eleven-year professional baseball cohort was recruited from 2009-2019. Age, arm dominance, injury history, and continent of origin were collected as well as preseason shoulder external and internal rotation, horizontal adduction passive range of motion, and humeral torsion were collected each season. Regression and machine learning models were developed to predict humeral torsion followed by internal validation with 10-fold cross validation. Root mean square error (RMSE), which is reported in degrees (°) and calibration slope (agreement of predicted and actual outcome; best = 1.00) were assessed.

Results

Four hundred and seven pitchers (Age: 23.2 +/-2.4 years, body mass index: 25.1 +/-2.3 km/m2, Left-Handed: 17%) participated. Regression model RMSE was 12° and calibration was 1.00 (95% CI: 0.94, 1.06). Random Forest RMSE was 9° and calibration was 1.33 (95% CI: 1.29, 1.37). Gradient boosting machine RMSE was 9° and calibration was 1.09 (95% CI: 1.04, 1.14). Support vector machine RMSE was 10° and calibration was 1.13 (95% CI: 1.08, 1.18). Artificial neural network RMSE was 15° and calibration was 1.03 (95% CI: 0.97, 1.09).

Conclusion

This is the first study to show that machine learning models do not improve baseball humeral torsion prediction compared to a traditional regression model. While machine learning models demonstrated improved RMSE compared to the regression, the machine learning models displayed poorer calibration compared to regression. Based on these results it is recommended to use a simple equation from a statistical model which can be quickly and efficiently integrated within a clinical setting.

Levels of Evidence

2.

Positive circumferential resection margin in locally advanced esophageal cancer: an updated systematic review and meta-analysis

BACKGROUND

The impact of positive circumferential resection margin on prognosis in esophageal cancer is under controversy. Previous systematic reviews and meta-analyses had limitations. This updated systematic review and meta-analysis aimed to assess the prognostic impact of positive circumferential resection margin in esophageal cancer.PubMed and Web of Science were searched for studies investigating the association between circumferential resection margin status and prognosis in esophageal cancer. Study population were focused on T3 and/or T4a patients. Study selection was based on availability of survival information (Kaplan-Meier curves and adjusted analysis). Random-effects models were used to summarize hazard ratios for overall survival and disease-free survival.According to College of American Pathologists criteria, circumferential resection margin-positive patients had shorter median overall survival (P < 0.0001) and shorter median disease-free survival (P < 0.0001) compared with circumferential resection margin-negative patients. The pooled hazard ratios for overall survival and disease-free survival were 2.06 (95% confidence interval, 1.68-2.53; P < 0.0001) and 2.00 (95% confidence interval, 1.41-2.84; P < 0.0001), respectively. According to the Royal College of Pathologists criteria, circumferential resection margin-positive patients had shorter median overall survival (P < 0.0001) and shorter median disease-free survival (P < 0.0001) compared with circumferential resection margin-negative patients. The pooled hazard ratios for overall survival and disease-free survival were 1.31 (95% confidence interval, 1.16-1.48; P < 0.0001) and 1.31 (95% confidence interval, 1.09-1.57; P < 0.0001), respectively.ompared with negative circumferential resection margin, positive circumferential resection margin is associated with worse survival outcomes in esophageal cancer.

C-Reactive Protein as Predictive Biomarker for Response to Chemoradiotherapy in Patients with Locally Advanced Rectal Cancer: A Retrospective Study

Simple Summary

Most patients with locally advanced rectal cancer present resistance or a moderate response to neoadjuvant chemoradiotherapy (nCRT), which is considered the standard of care. To select patients who could benefit from nCRT, while avoiding unnecessary treatment-induced toxicity and surgery-associated morbidity, it is urgent to find biomarkers of response to chemoradiotherapy. Therefore, the aim of our retrospective study was to assess the potential of classical blood analytes collected before chemoradiotherapy as biomarkers of response to treatment and prognostics in locally advanced rectal cancer. Our results identified C-reactive protein ≤3.5 as a strong independent predictor of response to treatment and an independent predictor of disease-free survival (DFS) and overall survival (OS). Additionally, platelets were found to be independent predictors of DFS and OS and hemoglobin of DFS. These data might contribute to the personalization of rectal cancer treatment by guiding clinicians in decision-making regarding the best treatment strategy for each patient.

Abstract

The standard of care for the treatment of locally advanced rectal cancer is neoadjuvant chemoradiotherapy (nCRT) followed by surgery, but complete response rates are reduced. To find predictive biomarkers of response to therapy, we conducted a retrospective study evaluating blood biomarkers before nCRT. Hemoglobin (Hg), C-reactive protein (CRP), platelets, carcinoembryonic antigen, carbohydrate antigen 19.9 levels, and neutrophil/lymphocyte ratio were obtained from 171 rectal cancer patients before nCRT. Patients were classified as responders (Ryan 0–1; ycT0N0), 59.6% (n = 102), or nonresponders (Ryan 2–3), 40.3% (n = 69), in accordance with the Ryan classification. A logistic regression using prognostic pretreatment factors identified CRP ≤ 3.5 (OR = 0.05; 95%CI: 0.01–0.21) as a strong independent predictor of response to treatment. Multivariate analysis showed that CRP was an independent predictor of disease-free survival (DFS) (HR = 5.48; 95%CI: 1.54–19.48) and overall survival (HR = 6.10; 95%CI 1.27–29.33) in patients treated with nCRT. Platelets were an independent predictor of DFS (HR = 3.068; 95%CI: 1.29–7.30) and OS (HR= 4.65; 95%CI: 1.66–13.05) and Hg was revealed to be an independent predictor of DFS (HR = 0.37; 95%CI: 0.15–0.90) in rectal cancer patients treated with nCRT. The lower expression of CRP is independently associated with an improved response to nCRT, DFS, and OS.

Biomarkers as predictors of recurrence of atrial fibrillation post ablation: an updated and expanded systematic review and meta-analysis

Background

A high proportion of patients undergoing catheter ablation (CA) for atrial fibrillation (AF) experience recurrence of arrhythmia. This meta-analysis aims to identify pre-ablation serum biomarker(s) associated with arrhythmia recurrence to improve patient selection before CA.

Methods

A systematic approach following PRISMA reporting guidelines was utilised in libraries (Pubmed/Medline, Embase, Web of Science, Scopus) and supplemented by scanning through bibliographies of articles. Biomarker levels were compared using a random-effects model and presented as odds ratio (OR). Heterogeneity was examined by meta-regression and subgroup analysis.

Results

In total, 73 studies were identified after inclusion and exclusion criteria were applied. Nine out of 22 biomarkers showed association with recurrence of AF after CA. High levels of N-Terminal-pro-B-type-Natriuretic Peptide [OR (95% CI), 3.11 (1.80–5.36)], B-type Natriuretic Peptide [BNP, 2.91 (1.74–4.88)], high-sensitivity C-Reactive Protein [2.04 (1.28–3.23)], Carboxy-terminal telopeptide of collagen type I [1.89 (1.16–3.08)] and Interleukin-6 [1.83 (1.18–2.84)] were strongly associated with identifying patients with AF recurrence. Meta-regression highlighted that AF type had a significant impact on BNP levels (heterogeneity R² = 55%). Subgroup analysis showed that high BNP levels were more strongly associated with AF recurrence in paroxysmal AF (PAF) cohorts compared to the addition of non-PAF patients. Egger’s test ruled out the presence of publication bias from small-study effects.

Conclusion

Ranking biomarkers based on the strength of association with outcome provides each biomarker relative capacity to predict AF recurrence. This will provide randomised controlled trials, a guide to choosing a priori tool for identifying patients likely to revert to AF, which are required to substantiate these findings.

Graphical abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s00392-021-01978-w.

Machine Learning and Statistical Prediction of Pitching Arm Kinetics

BACKGROUND

Over the past decade, research has attempted to elucidate the cause of throwing-related injuries in the baseball athlete. However, when considering the entire kinetic chain, full body mechanics, and pitching cycle sequencing, there are hundreds of variables that could influence throwing arm health, and there is a lack of quality investigations evaluating the relationship and influence of multiple variables on arm stress.

PURPOSE

To identify which variables have the most influence on elbow valgus torque and shoulder distraction force using a statistical model and a machine learning approach.

STUDY DESIGN

Cross-sectional study; Level of evidence, 3.

METHODS

A retrospective review was performed on baseball pitchers who underwent biomechanical evaluation at the university biomechanics laboratory. Regression models and 4 machine learning models were created for both elbow valgus torque and shoulder distraction force. All models utilized the same predictor variables, which included pitch velocity and 17 pitching mechanics.

RESULTS

The analysis included a total of 168 high school and collegiate pitchers with a mean age of 16.7 years (SD, 3.2 years) and BMI of 24.4 (SD, 1.2). For both elbow valgus torque and shoulder distraction force, the gradient boosting machine models demonstrated the smallest root mean square errors and the most precise calibrations compared with all other models. The gradient boosting model for elbow valgus torque reported the highest influence for pitch velocity (relative influence, 28.4), with 5 mechanical variables also having significant influence. The gradient boosting model for shoulder distraction force reported the highest influence for pitch velocity (relative influence, 20.4), with 6 mechanical variables also having significant influence.

CONCLUSION

The gradient boosting machine learning model demonstrated the best overall predictive performance for both elbow valgus torque and shoulder distraction force. Pitch velocity was the most influential variable in both models. However, both models also revealed that pitching mechanics, including maximum humeral rotation velocity, shoulder abduction at foot strike, and maximum shoulder external rotation, significantly influenced both elbow and shoulder stress.

CLINICAL RELEVANCE

The results of this study can be used to inform players, coaches, and clinicians on specific mechanical variables that may be optimized to mitigate elbow or shoulder stress that could lead to throwing-related injury.

Development and internal validation of a humeral torsion prediction model in professional baseball pitchers

BACKGROUND

Humeral torsion (HT) has been linked to pitching arm injury risk after controlling for shoulder range of motion. Currently measuring HT uses expensive equipment, which inhibits clinical assessment. Developing an HT predictive model can aid clinical baseball arm injury risk examination. Therefore, the purpose of this study was to develop and internally validate an HT prediction model using standard clinical tests and measures in professional baseball pitchers.

METHODS

An 11-year (2009-2019) prospective professional baseball cohort was used for this study. Participants were included if they were able to participate in all practices and competitions and were under a Minor League Baseball contract. Preseason shoulder range of motion (external rotation [ER], internal rotation [IR], horizontal adduction [HA]) and HT were collected each season. Player age, arm dominance, arm injury history, and continent of origin were also collected. Examiners were blinded to arm dominance. An a priori power analysis determined that 244 players were needed for accurate prediction models. Missing data was low (<3%); thus, a complete case analysis was performed. Model development followed the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) recommendations. Regression models with restricted cubic splines were performed. Following primary model development, bootstrapping with 2000 iterations were performed to reduce overfitting and assess optimism shrinkage. Prediction model performance was assessed through root mean square error (RMSE), R², and calibration slope with 95% confidence intervals (CIs). Sensitivity analyses included dominant and nondominant HT.

RESULTS

A total of 407 professional pitchers (age: 23.2 [standard deviation 2.4] years, left-handed: 17%; arm history prevalence: 21%) participated. Predictors with the highest influence within the model include IR (0.4, 95% CI 0.3, 0.5; P < .001), ER (-0.3, 95% CI -0.4, -0.2; P < .001), HA (0.3, 95% CI 0.2, 0.4; P < .001), and arm dominance (right-handed: -1.9, 95% CI -3.6, -0.1; P = .034). Final model RMSE was 12, R² was 0.41, and calibration was 1.00 (95% CI 0.94, 1.06). Sensitivity analyses demonstrated similar model performance.

CONCLUSIONS

Every 3° of IR explained 1° of HT. Every 3° of ER explained 1° less of HT, and every 7° of HA explained 1° of HT. Right-handers had 2° less HT. Models demonstrated good predictive performance. This predictive model can be used by clinicians to infer HT using standard clinical test and measures. These data can be used to enhance professional baseball arm injury examination.

Predictive Ability of the Estimate of Fat Mass to Detect Early-Onset Metabolic Syndrome in Prepubertal Children with Obesity

Body mass index (BMI), usually used as a body fatness marker, does not accurately discriminate between amounts of lean and fat mass, crucial factors in determining metabolic syndrome (MS) risk. We assessed the predictive ability of the estimate of FM (eFM) calculated using the following formula: FM = weight - exp(0.3073 × height2 - 10.0155 ×d-growth-standards/standards/body-mass-index-for-age-bmi-for-age weight- 1 + 0.004571 × weight - 0.9180 × ln(age) + 0.6488 × age0.5 + 0.04723×male + 2.8055) (exp = exponential function, score 1 if child was of black (BA), south Asian (SA), other Asian (AO), or other (other) ethnic origin and score 0 if not, ln = natural logarithmic transformation, male = 1, female = 0), to detect MS in 185 prepubertal obese children compared to other adiposity parameters. The eFM, BMI, waist circumference (WC), body shape index (ABSI), tri-ponderal mass index, and conicity index (C-Index) were calculated. Patients were classified as having MS if they met ≥ 3/5 of the following criteria: WC ≥ 95th percentile; triglycerides ≥ 95th percentile; HDL-cholesterol ≤ 5th percentile; blood pressure ≥ 95th percentile; fasting blood glucose ≥ 100 mg/dL; and/or HOMA-IR ≥ 97.5th percentile. MS occurred in 18.9% of obese subjects (p < 0.001), with a higher prevalence in females vs. males (p = 0.005). The eFM was correlated with BMI, WC, ABSI, and Con-I (p < 0.001). Higher eFM values were present in the MS vs. non-MS group (p < 0.001); the eFM was higher in patients with hypertension and insulin resistance (p < 0.01). The eFM shows a good predictive ability for MS. Additional to BMI, the identification of new parameters determinable with simple anthropometric measures and with a good ability for the early detection of MS, such as the eFM, may be useful in clinical practice, particularly when instrumentation to estimate the body composition is not available.

"Fitness and Fatness" in Children and Adolescents: An Italian Cross-Sectional Study

Children with obesity tend to have lower level of physical activity compared to non-obese peers. In fact, sedentary behaviors are prevalent in obese children causing difficulties to perform motor tasks and engaging in sport activities. This, in turn, has direct repercussions on adiposity and related comorbidities. The aim of the study was to investigate several components of fitness and their relationship with the degree of fatness in children. We considered 485 Italian schoolchildren (9.5 ± 1.12 years). BMI and prediction modelling outputs of fat mass were employed as markers of body fatness. Physical fitness (PF) was assessed by the 9-item test battery (explosive power, leg muscle power, arm muscle power, upper body power, coordination, agility, speed and endurance). Differences between groups in the PF tests (p < 0.05) were noted. A similar pattern was reflected in both genders. The relationship between anthropometrics' characteristics and PF tests showed that weight and fat mass had a high level of correlation with different PF tests. Our findings highlight the importance of investigating the degree of fatness in relation with different components of fitness, in children and adolescents. This combination of proxies may cover an unexpectedly helpful screening of the youth population, for both health and performance.

Interaction between Autonomic Regulation, Adiposity Indexes and Metabolic Profile in Children and Adolescents with Overweight and Obesity

Early obesity predicts initial modifications in cardiac and vascular autonomic regulation. The aim of this study was to assess the possible interaction between non-invasive measures of autonomic cardiovascular control and peripheral endothelium regulation in children with overweight and obesity. We involved 114 young subjects (77M/37F, 12.7 ± 2.2 years) with normal weight (NW, n = 46) to overweight or obesity (OB, n = 68). Multivariate statistical techniques utilizing a collection of modern indices of autonomic regulation, adiposity indexes and metabolic profile were employed. Resting values show substantial equivalence of data. Conversely, blood pressure variance is greater in NW/OB groups. The correlation matrix between major autonomic and metabolic/hemodynamic variables shows a clustered significant correlation between homogeneous indices. A significant correlation between metabolic indices and endothelial and autonomic control, mostly in its vascular end, was recorded. Particularly, the alpha index is significantly correlated with triglycerides (r = −0.261) and endothelial indices (RHI, r = 0.276). Children with obesity show a link between indices of autonomic and endothelial function, fat distribution and metabolic profile. The optimization of autonomic control, for instance by exercise/nutrition interventions, could potentially prevent/delay the occurrence of structural vascular damage leading to reduced cardiovascular health.

Self-Reported Physical Fitness in Children and Adolescents with Obesity: A Cross-Sectional Analysis on the Level of Alignment with Multiple Adiposity Indexes

Obesity has been associated with several alterations that could limit physical activity (PA) practice. In pediatrics, some studies have highlighted the importance of enjoyment as a motivation to begin and maintain adherence in PA. Since self-reported physical (SRPF) fitness was related to motivation, the aim of this study was to investigate the existence of differences between SRPF in children with obesity (OB) compared to normal weight (NW). The International Fitness Enjoyment Scale (IFIS) questionnaire was administered to 200 OB and 200 NW children. In all the subjects, height, weight, and BMI and in OB children adiposity indexes including waist circumference (WC), body shape index (ABSI), triponderal mass index (TMI), and fat mass were measured. NW group showed higher IFIS item scores than the OB group (p < 0.01), except in muscular strength. In OB, the anthropometric outcomes were inversely correlated to SRPF outcome except for muscular strength. OB children reported a lower perception of fitness that could limit participation in PA/exercise programs. The evaluation of anthropometric patterns may be useful to prescribe a tailored exercise program considering individual better self-perception outcomes to obtain an optimal PA adherence.

A nomogram to predict the cancer-specific survival of stage II-IV Epithelial ovarian cancer after bulking surgery and chemotherapy

Objective

In order to predict the survival rate of ovarian cancer patients, multiple independent risk factors are integrated to establish a prognostic nomogram.

Methods

Cox analysis was used to construct the nomogram. All of the mainly independent factors, which can be used to predict 3‐year and 5‐year survival rates for cancer in the training cohort, were incorporated to establish nomograms. The C‐index, operating characteristic, ROC curves, and calibration plots can show evaluation results of performance.

Results

Model derivation was based on 3277 patients who belong to different races. The best threshold for age was 51, 59, and 67 year old and the older the people, the worse their survival. Meanwhile, many lymph node examinations indicated a favorable survival and the survival of the positive set was worse than of that. In addition, the optional threshold was 64 mm for tumor size and the set larger than 64 mm had a better survival than that less than 64 mm. Univariate Cox proportional hazards regression model showed that the similar worse outcomes were showed in black race, advanced grade, stage T3, stage M1, lymph nodes positive, and CA125 positive compared with the first group. We found that the number of lymph nodes examined and tumor size had an inverse relationship with its corresponding score of CSS in training cases with bulking surgery and chemotherapy.

Conclusions

We developed a model which relatively accurately predicted the prognosis of ovarian cancer by multiple univariate analysis, at the same time, the proposed nomograms exhibit superior prognostic discrimination and survival prediction.

Obesity cardiomyopathy: evidence, mechanisms, and therapeutic implications

The prevalence of heart failure is on the rise and imposes a major health threat, in part, due to the rapidly increased prevalence of overweight and obesity. To this point, epidemiological, clinical, and experimental evidence supports the existence of a unique disease entity termed “obesity cardiomyopathy,” which develops independent of hypertension, coronary heart disease, and other heart diseases. Our contemporary review evaluates the evidence for this pathological condition, examines putative responsible mechanisms, and discusses therapeutic options for this disorder. Clinical findings have consolidated the presence of left ventricular dysfunction in obesity. Experimental investigations have uncovered pathophysiological changes in myocardial structure and function in genetically predisposed and diet-induced obesity. Indeed, contemporary evidence consolidates a wide array of cellular and molecular mechanisms underlying the etiology of obesity cardiomyopathy including adipose tissue dysfunction, systemic inflammation, metabolic disturbances (insulin resistance, abnormal glucose transport, spillover of free fatty acids, lipotoxicity, and amino acid derangement), altered intracellular especially mitochondrial Ca²⁺ homeostasis, oxidative stress, autophagy/mitophagy defect, myocardial fibrosis, dampened coronary flow reserve, coronary microvascular disease (microangiopathy), and endothelial impairment. Given the important role of obesity in the increased risk of heart failure, especially that with preserved systolic function and the recent rises in COVID-19-associated cardiovascular mortality, this review should provide compelling evidence for the presence of obesity cardiomyopathy, independent of various comorbid conditions, underlying mechanisms, and offer new insights into potential therapeutic approaches (pharmacological and lifestyle modification) for the clinical management of obesity cardiomyopathy.

Fat mass and vascular health in overweight/obese children

BACKGROUND AND AIM

Childhood obesity is one of the most serious public health challenges of the 21st century. Body mass index (BMI), the most widely used marker of body fatness, has serious limitations, particularly in children, since it does not accurately discriminate between lean and fat mass. Aim of our study was to investigate if the estimate of fat mass, as derived by a new prediction model, was associated with carotid intima media thickness (IMT) and the cross-sectional area of the intima media complex (CSA-IMC) in overweight or obese children.

METHODS AND RESULTS

As many as 375 overweight/obese Italian children, 54.7% males, aged 5-15 years, admitted to a tertiary care hospital, were consecutively enrolled in a study on cardiovascular markers of atherosclerosis. All children underwent an ultrasound carotid examination. Mean weight was 62.2 ± 20.8 Kg and fat-mass was 26.2 ± 10.7 Kg. Multiple regression analyses showed a significant association of fat mass with carotid IMT (β 0.156, p 0.01) and CSA-IMC (β 0.216, p < 0.001); these associations remained significant after controlling for the main cardiovascular risk factors (age, sex, blood pressure, HOMA-index, triglycerides, LDL-cholesterol, HDL-cholesterol, birth weight and high-sensitivity C-reactive protein).

CONCLUSION

Fat mass calculated with the new formula is independently associated with subclinical atherosclerosis in overweight/obese children.

Association of Childhood Fat Mass and Weight With Adult-Onset Type 2 Diabetes in Denmark

IMPORTANCE

Childhood obesity, defined by cutoffs based on the weight-based marker of body mass index, is associated with adult type 2 diabetes (T2D) risk. Whether childhood fat mass (FM) is the driver of these associations is currently unknown.

OBJECTIVE

To quantify and compare height-independent associations between childhood FM and weight with adult T2D risk in a historic Danish cohort.

DESIGN, SETTING, AND PARTICIPANTS

This population-based retrospective cohort study included schoolchildren from The Copenhagen School Health Records Register born between January 1930 and December 1985 with follow-up to adulthood through December 31, 2015. Analyses were based on 269 913 schoolchildren aged 10 years with 21 896 established adult T2D cases and 261 192 children aged 13 years with 21 530 established adult T2D cases for whom childhood height and weight measurements, as well as predicted FM, were available. Statistical analyses were performed between April 2019 to August 2020.

EXPOSURES

Childhood FM and weight at ages 10 and 13 years.

MAIN OUTCOMES AND MEASURES

Diagnoses of T2D were established by linkage to national disease registers for adults aged at least 30 years. Sex-specific Cox regression quantified associations, adjusted for childhood height, which were evaluated within 5 birth-cohort groups. Group-specific results were pooled using random-effects meta-analyses accounting for heterogeneity across group-specific associations.

RESULTS

This cohort study analyzed data from 269 913 children aged 10 years (135 940 boys [50.4%]) with 21 896 established adult T2D cases and 261 192 children aged 13 years (131 025 boys [50.2%]) with 21 530 established adult T2D cases. After adjusting for childhood height, increases in FM and weight (per kilogram) among boys aged 10 years were associated with elevated T2D risks at age 50 years of 12% (hazard ratio [HR], 1.12; 95% CI, 1.10-1.14) and 7% (HR, 1.07; 95% CI, 1.05-1.09), respectively, and among girls aged 10 years of 15% (HR, 1.15; 95% CI, 1.13-1.17) and 10% (HR, 1.10; 95% CI, 1.08-1.11), respectively. Among children aged 13 years, increases in FM and weight (per kilogram) were associated with increased T2D risks at age 50 years of 10% (HR, 1.10; 95% CI, 1.09-1.10) and 6% (HR, 1.06; 95% CI, 1.05-1.07) for boys, respectively, and of 10% (HR, 1.10; 95% CI, 1.10-1.11) and 7% (HR, 1.07; 95% CI, 1.06-1.08), respectively, for girls.

CONCLUSIONS AND RELEVANCE

This cohort study found that a 1-kg increase in childhood FM was more strongly associated with increased adult T2D risk than a 1-kg increase in weight, independent of childhood height. Information on FM, rather than weight-based measures, focuses on a modifiable component of weight that may be associated with adult T2D risk. These findings support the assessment of childhood FM in adiposity surveillance initiatives in an effort to reduce long-term T2D risk.

Prediction of future wrinkles for middle-aged women: A 7-year longitudinal study on the progression of wrinkles in Japanese women

BACKGROUND

It is commonly believed that there is serious heterogeneity in the rate of wrinkle progression among individuals. Although several skin characteristics have been shown to influence wrinkle progression, the ability to predict which individuals with skin characteristics are likely to develop wrinkles is still limited.

OBJECTIVES

The purpose of this study is to develop and validate an effective prediction model for longitudinal changes in wrinkles.

METHODS

We collected annual wrinkle scores and multiple skin physiological characteristics in 48 Japanese women over a period of 7 years. We developed a multivariable prediction model for predicting future wrinkle status based on the various skin physiological characteristics using a linear mixed-effects model.

RESULTS

After variable selection by backwards, the final wrinkle prediction model included age, sebum volume, redness of skin color, lightness of skin color, and an interaction term between sebum volume and redness of skin color. The developed prediction model showed favorable prediction accuracy (R²  = 87.92%, 95% confidence interval 84.27%-90.68%).

CONCLUSIONS

The developed model accurately predicted levels of wrinkles in Japanese women aged 22-60 years. The prediction model is based on age and three practical skin characteristics, which might implicate an essential insight to prevent wrinkle progression in individuals.

Adult obesity and its complications: a pediatric disease?

PURPOSE OF REVIEW

Approximately 2.6 million people die each year secondary to obesity related diseases. The risk of developing serious comorbidities depends on the age of onset as well as duration of obesity. In this review, we discuss trends in BMI trajectories from early childhood to adulthood with latest evidence on comorbidities in adulthood stemming from pediatric obesity and benefits of early intervention and treatment in childhood obesity.

RECENT FINDINGS

Childhood obesity poses high risk of metabolic and cardiovascular disorders like type 2 diabetes, hypertension, atherosclerosis, coronary artery disease, and some types of cancer in adulthood. Early life obesity also increases risks of developing menstrual irregularities, infertility, and pregnancy complications. Several grave concerns including malignancies, autoimmune disorders, higher asthma morbidity, and psychiatric implications are found to be associated with childhood obesity. Disease outcomes can be transgenerational, causing suboptimal health in children of mothers with obesity. Encouragingly, many risks associated with childhood obesity can be reduced, delayed, or even reversed by early resolution of obesity necessitating close BMI monitoring and treatment early.

SUMMARY

Early identification and aggressive management of childhood obesity is critical in prevention of debilitating comorbidities in adult life.

VIDEO ABSTRACT

http://links.lww.com/COE/A19.

Minimum sample size for external validation of a clinical prediction model with a continuous outcome

Clinical prediction models provide individualized outcome predictions to inform patient counseling and clinical decision making. External validation is the process of examining a prediction model's performance in data independent to that used for model development. Current external validation studies often suffer from small sample sizes, and subsequently imprecise estimates of a model's predictive performance. To address this, we propose how to determine the minimum sample size needed for external validation of a clinical prediction model with a continuous outcome. Four criteria are proposed, that target precise estimates of (i) R2 (the proportion of variance explained), (ii) calibration-in-the-large (agreement between predicted and observed outcome values on average), (iii) calibration slope (agreement between predicted and observed values across the range of predicted values), and (iv) the variance of observed outcome values. Closed-form sample size solutions are derived for each criterion, which require the user to specify anticipated values of the model's performance (in particular R2 ) and the outcome variance in the external validation dataset. A sensible starting point is to base values on those for the model development study, as obtained from the publication or study authors. The largest sample size required to meet all four criteria is the recommended minimum sample size needed in the external validation dataset. The calculations can also be applied to estimate expected precision when an existing dataset with a fixed sample size is available, to help gauge if it is adequate. We illustrate the proposed methods on a case-study predicting fat-free mass in children.

Quantifying childhood fat mass: comparison of a novel height-and-weight-based prediction approach with DXA and bioelectrical impedance

Accurate assessment of childhood adiposity is important both for individuals and populations. We compared fat mass (FM) predictions from a novel prediction model based on height, weight and demographic factors (height–weight equation) with FM from bioelectrical impedance (BIA) and dual-energy X-ray absorptiometry (DXA), using the deuterium dilution method as a reference standard. FM data from all four methods were available for 174 ALSPAC Study participants, seen 2002–2003, aged 11–12-years. FM predictions from the three approaches were compared to the reference standard using; R², calibration (slope and intercept) and root mean square error (RMSE). R² values were high from ‘height–weight equation’ (90%) but lower than from DXA (95%) and BIA (91%). Whilst calibration intercepts from all three approaches were close to the ideal of 0, the calibration slope from the ‘height–weight equation’ (slope = 1.02) was closer to the ideal of 1 than DXA (slope = 0.88) and BIA (slope = 0.87) assessments. The ‘height–weight equation’ provided more accurate individual predictions with a smaller RMSE value (2.6 kg) than BIA (3.1 kg) or DXA (3.4 kg). Predictions from the ‘height–weight equation’ were at least as accurate as DXA and BIA and were based on simpler measurements and open-source equation, emphasising its potential for both individual and population-level FM assessments.