Diagnostic Accuracy of Transient Elastography in Hepatosteatosis in Youth With Obesity.

Context: Steatotic liver disease is common but overlooked in childhood obesity; diagnostic methods are invasive or expensive. Objective: We sought to determine the diagnostic accuracy of vibration-controlled transient elastography (VCTE) compared with magnetic resonance imaging (MRI) in adolescents with obesity and high risk for hepatosteatosis. Methods: Baseline data in 3 clinical trials enrolling adolescents with obesity were included (NCT03919929, NCT03717935, NCT04342390). Liver fat was assessed using MRI fat fraction and VCTE-based controlled attenuation parameter (CAP). Hepatosteatosis was defined as MRI fat fraction ≥5.0%. The area under the receiver-operating characteristic curves (AUROCs) for CAP against MRI was calculated, and optimal CAP using the Youden index for hepatosteatosis diagnosis was determined. Results: Data from 82 adolescents (age 15.6 ± 1.4 years, body mass index 36.5 ± 5.9 kg/m2, 81% female) were included. Fifty youth had hepatosteatosis by MRI (fat fraction 9.3% ; 95% CI 6.7, 14.0), and 32 participants did not have hepatosteatosis (fat fraction 3.1%; 95% CI 2.2, 3.9; P < .001). The hepatosteatosis group had higher mean CAP compared with no hepatosteatosis (293 dB/m; 95% CI 267, 325 vs 267 dB/m; 95% CI 248, 282; P = .0120). A CAP of 281 dB/m had the highest sensitivity (60%) and specificity (74%) with AUROC of 0.649 (95% CI 0.51-0.79; P = .04) in the entire cohort. In a subset of participants with polycystic ovary syndrome (PCOS), a CAP of 306 dB/m had the highest sensitivity (78%) and specificity (52%) and AUROC of 0.678 (95% CI 0.45-0.90; P = .108). Conclusion: CAP of 281 dB/m has modest diagnostic performance for hepatosteatosis compared with MRI in youth with significant obesity. A higher CAP in youth with PCOS suggests that comorbidities might affect optimal CAP in hepatosteatosis diagnosis.

Prediction of resting energy expenditure for adolescents with severe obesity: A multi-centre analysis.

BACKGROUND AND OBJECTIVES: Resting energy expenditure (REE) assessments can help inform clinical treatment decisions in adolescents with elevated body mass index (BMI), but current equations are suboptimal for severe obesity. We developed a predictive REE equation for youth with severe obesity and obesity-related comorbidities and compared results to previously published predictive equations. METHODS: Data from indirect calorimetry, clinical measures, and body composition per Dual x-ray absorptiometry (DXA) were collected from five sites. Data were randomly divided into development (N = 438) and validation (N = 118) cohorts. A predictive equation was developed using Elastic Net regression, using sex, race, ethnicity, weight, height, BMI percent of the 95th%ile (BMIp95), waist circumference, hip circumference, waist/hip ratio, age, Tanner stage, fat and fat-free mass. This equation was verified in the validation cohort and compared with 11 prior equations. RESULTS: Data from the total cohort (n = 556, age 15 ± 1.7 years, 77% female, BMIp95 3.3 ± 0.94) were utilized. The best fit equation was REE = -2048 + 18.17 × (Height in cm) - 2.57 × (Weight in kg) + 7.88 × (BMIp95) + 189 × (1 = male, 0 = female), R2 = 0.466, and mean bias of 23 kcal/day. CONCLUSION: This new equation provides an updated REE prediction that accounts for severe obesity and metabolic complications frequently observed in contemporary youth.

Metabolic effects of an essential amino acid supplement in adolescents with PCOS and obesity.

OBJECTIVE: Polycystic ovary syndrome (PCOS) is characterized by hyperandrogenism, insulin resistance, and hepatic steatosis (HS). Because dietary essential amino acid (EAA) supplementation has been shown to decrease HS in various populations, this study's objective was to determine whether supplementation would decrease HS in PCOS. METHODS: A randomized, double-blind, crossover, placebo-controlled trial was conducted in 21 adolescents with PCOS (BMI 37.3 ± 6.5 kg/m2, age 15.6 ± 1.3 years). Liver fat, very low-density lipoprotein (VLDL) lipogenesis, and triacylglycerol (TG) metabolism were measured following each 28-day phase of placebo or EAA. RESULTS: Compared to placebo, EAA was associated with no difference in body weight (p = 0.673). Two markers of liver health improved: HS was lower (-0.8% absolute, -7.5% relative reduction, p = 0.013), as was plasma aspartate aminotransferase (AST) (-8%, p = 0.004). Plasma TG (-9%, p = 0.015) and VLDL-TG (-21%, p = 0.031) were reduced as well. VLDL-TG palmitate derived from lipogenesis was not different between the phases, nor was insulin sensitivity (p > 0.400 for both). Surprisingly, during the EAA phase, participants reported consuming fewer carbohydrates (p = 0.038) and total sugars (p = 0.046). CONCLUSIONS: Similar to studies in older adults, short-term EAA supplementation in adolescents resulted in significantly lower liver fat, AST, and plasma lipids and thus may prove to be an effective treatment in this population. Additional research is needed to elucidate the mechanisms for these effects.

Hypoglycemia in children and young adults with cystic fibrosis during oral glucose tolerance testing vs. continuous glucose monitoring.

BACKGROUND: Hypoglycemia is common in people with cystic fibrosis (pwCF) during oral glucose tolerance tests (OGTTs) and in the free-living setting, yet its pathophysiology remains unclear. OBJECTIVE: To evaluate hypoglycemia in children and young adults with CF by OGTT and continuous glucose monitoring (CGM). METHODS: A 3-h OGTT was performed in children and young adults with CF and healthy controls (HC). Individuals were classified as experiencing hypoglycemia on OGTT (glucose <70 mg/dL) or not. Insulin, C-peptide, glucose, glucagon, and incretins were measured. CGM was performed for 7 days in the free-living setting. Measures of insulin sensitivity, beta cell function accounting for insulin sensitivity, and insulin clearance were calculated. RESULTS: A total of 57 participants (40 CF and 17 HC) underwent assessment. Rates of hypoglycemia by OGTT were similar in pwCF (53%, 21/40) compared to HC (35%, 6/17), p = 0.23. PwCF compared to HC had higher A1c; on OGTT higher and later glucose peaks, later insulin peaks; and on CGM more glucose variability. CF Hypo+ versus CF Hypo- had higher lung function, higher insulin sensitivity, higher beta cell function accounting for insulin sensitivity, and decreased CGM variability. When comparing CF Hypo+ to HC Hypo+, although rates of hypoglycemia are similar, pwCF had blunted glucagon responses to hypoglycemia. OGTT hypoglycemia was not associated with CGM hypoglycemia in any group. CONCLUSION: Youth with CF have increased insulin sensitivity and impaired glucagon response to hypoglycemia on OGTT. Hypoglycemia on OGTT did not associate with free-living hypoglycemia.

11-Oxygenated Androgen Metabolite Concentrations Are Affected by Pubertal Progression and Obesity.

INTRODUCTION: 11-oxygenated C19 steroids (11-oxyandrogens) have been shown to rise during adrenarche and remain higher throughout adulthood than in early childhood. The patterns of circulating 11-oxyandrogens throughout normal puberty have not yet been described. METHODS: We conducted a secondary analysis of healthy youth participants, both males and females, enrolled in six prior endocrine studies (N = 249). Participants were classified according to Tanner stage and body mass index (BMI). Concentrations of three adrenal-specific 11-oxygenated androgens, 11ß-hydroxyandrostenedione (11OHA4), 11ß-hydroxytestosterone (11OHT), and 11-ketotestosterone (11KT), were measured in fasting serum samples. RESULTS: 11OHA4 and 11OHT increased modestly between early and late puberty in youth with normal weight (p < 0.05), whereas increases in 11KT did not reach statistical significance (p < 0.06). 11KT levels differed between sexes throughout puberty (p < 0.01), and changes in 11-oxyandrogens were small compared to the marked increases for estradiol in girls or testosterone in boys. The trajectories of 11KT and 11OHA4 changes throughout puberty differed by BMI category (p < 0.05). CONCLUSION: Beyond adrenarche, 11-oxyandrogens continue to rise during pubertal development. The differences in 11KT trajectories in males and females are small compared to changes in testosterone for males and estradiol for females during puberty. Obesity appears to influence the trajectories of 11-oxyandrogens during puberty.

Survival prediction models: an introduction to discrete-time modeling.

BACKGROUND: Prediction models for time-to-event outcomes are commonly used in biomedical research to obtain subject-specific probabilities that aid in making important clinical care decisions. There are several regression and machine learning methods for building these models that have been designed or modified to account for the censoring that occurs in time-to-event data. Discrete-time survival models, which have often been overlooked in the literature, provide an alternative approach for predictive modeling in the presence of censoring with limited loss in predictive accuracy. These models can take advantage of the range of nonparametric machine learning classification algorithms and their available software to predict survival outcomes. METHODS: Discrete-time survival models are applied to a person-period data set to predict the hazard of experiencing the failure event in pre-specified time intervals. This framework allows for any binary classification method to be applied to predict these conditional survival probabilities. Using time-dependent performance metrics that account for censoring, we compare the predictions from parametric and machine learning classification approaches applied within the discrete time-to-event framework to those from continuous-time survival prediction models. We outline the process for training and validating discrete-time prediction models, and demonstrate its application using the open-source R statistical programming environment. RESULTS: Using publicly available data sets, we show that some discrete-time prediction models achieve better prediction performance than the continuous-time Cox proportional hazards model. Random survival forests, a machine learning algorithm adapted to survival data, also had improved performance compared to the Cox model, but was sometimes outperformed by the discrete-time approaches. In comparing the binary classification methods in the discrete time-to-event framework, the relative performance of the different methods varied depending on the data set. CONCLUSIONS: We present a guide for developing survival prediction models using discrete-time methods and assessing their predictive performance with the aim of encouraging their use in medical research settings. These methods can be applied to data sets that have continuous time-to-event outcomes and multiple clinical predictors. They can also be extended to accommodate new binary classification algorithms as they become available. We provide R code for fitting discrete-time survival prediction models in a github repository.

A Pipeline for the Implementation and Visualization of Explainable Machine Learning for Medical Imaging Using Radiomics Features.

Machine learning (ML) models have been shown to predict the presence of clinical factors from medical imaging with remarkable accuracy. However, these complex models can be difficult to interpret and are often criticized as "black boxes". Prediction models that provide no insight into how their predictions are obtained are difficult to trust for making important clinical decisions, such as medical diagnoses or treatment. Explainable machine learning (XML) methods, such as Shapley values, have made it possible to explain the behavior of ML algorithms and to identify which predictors contribute most to a prediction. Incorporating XML methods into medical software tools has the potential to increase trust in ML-powered predictions and aid physicians in making medical decisions. Specifically, in the field of medical imaging analysis the most used methods for explaining deep learning-based model predictions are saliency maps that highlight important areas of an image. However, they do not provide a straightforward interpretation of which qualities of an image area are important. Here, we describe a novel pipeline for XML imaging that uses radiomics data and Shapley values as tools to explain outcome predictions from complex prediction models built with medical imaging with well-defined predictors. We present a visualization of XML imaging results in a clinician-focused dashboard that can be generalized to various settings. We demonstrate the use of this workflow for developing and explaining a prediction model using MRI data from glioma patients to predict a genetic mutation.

11-Oxyandrogens in Adolescents With Polycystic Ovary Syndrome.

Context: Polycystic ovary syndrome (PCOS) is common and diagnosis requires an elevated testosterone. The clinical importance of adrenal 11-oxyandrogens in PCOS is unclear. Objective: We sought to determine if 11-oxyandrogens 1) better identify PCOS diagnosis compared to testosterone, 2) predict clinical comorbidities of PCOS, and 3) are altered with an combined oral contraceptive pill (COCP) or metformin therapy. Methods: Data from 200 adolescent female participants aged 12 to 21 years, most with obesity, enrolled across 6 studies in pediatric endocrinology were included: 70 non-PCOS controls, 115 untreated PCOS, 9 PCOS + obesity treated with COCP, and 6 PCOS + obesity treated with metformin. 11-Hydroxyandrostenedione (11-OHA4), 11-hydroxytestosterone (1-OHT), 11-ketotestosterone (11-KT), and testosterone were measured with liquid chromatography-tandem mass spectrometry. Data between 1) untreated PCOS and controls and 2) untreated PCOS and the 2 treatment groups were compared. Results: Untreated girls with PCOS had higher 11-OHA4 (P = .003) and 11-OHT (P = .005) compared to controls, but not 11-KT (P = .745). Elevated 11-OHA4 remained statistically significant after controlling for obesity. Testosterone better predicted PCOS status compared to 11-oxyandrogens (receiver operating characteristic curve analysis: 11-OHA4 area under the curve [AUC] = 0.620, 11-OHT AUC = 0.638; testosterone AUC = 0.840). Among untreated PCOS patients, all 3 11-oxyandrogens correlated with hirsutism severity. 11-KT (P = .039) and testosterone (P < .006) were lower in those on COCP treatment compared to untreated PCOS. Metformin treatment had no effect on 11-oxyandrogens, although testosterone was lower (P = .01). Conclusion: Although 11-oxyandrogens do not aid in the diagnosis of PCOS, they relate to excess hair growth. COCP treatment may related to 11-KT; however, further work is needed to determine causality, relationship with metabolic outcomes, and the clinical utility of measuring these androgens in PCOS.

Aminoaciduria and metabolic dysregulation during diabetic ketoacidosis: Results from the diabetic kidney alarm (DKA) study.

OBJECTIVE: We examined changes in the excretion of various amino acids and in glycolysis and ketogenesis-related metabolites, during and after diabetic ketoacidosis (DKA) diagnosis, in youth with known or new onset type 1 diabetes (T1D). METHODS: Urine samples were collected from 40 youth with DKA (52% boys, mean age 11 ± 4 years, venous pH 7.2 ± 0.1, blood glucose 451 ± 163 mg/dL) at 3 time points: 0-8 h and 12-24 h after starting an insulin infusion, and 3 months after hospital discharge. Mixed-effects models evaluated the changes in amino acids and other metabolites in the urine. RESULTS: Concentrations of urine histidine, threonine, tryptophan, and leucine per creatinine were highest at 0-8 h (148.8 ± 23.5, 59.5 ± 12.3, 15.4 ± 1.4, and 24.5 ± 2.4% of urine creatinine, respectively), and significantly decreased over 3 months (p = 0.028, p = 0.027, p = 0.019, and p < 0.0001, respectively). Urine histidine, threonine, tryptophan, and leucine per urine creatinine decreased by 10.6 ± 19.2, 0.7 ± 0.9, 1.3 ± 0.9, and 0.5 ± 0.3-fold, respectively, between 0 and 8 h and 3 months. CONCLUSIONS: In our study, DKA was associated with profound aminoaciduria, suggestive of proximal tubular dysfunction analogous to Fanconi syndrome.