ABSTRACT
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.
Subject(s)
Body Composition , Body Mass Index , Energy Metabolism , Obesity, Morbid , Pediatric Obesity , Humans , Female , Male , Adolescent , Pediatric Obesity/metabolism , Pediatric Obesity/epidemiology , Obesity, Morbid/metabolism , Obesity, Morbid/physiopathology , Energy Metabolism/physiology , Absorptiometry, Photon , Calorimetry, Indirect , Basal Metabolism , Predictive Value of TestsABSTRACT
BACKGROUND: A standardized approach for identifying and treating hypothalamic obesity (HO) in children with hypothalamic tumours is lacking. OBJECTIVES: To describe children with hypothalamic tumours at risk for obesity, assess outcomes of a novel HO clinical algorithm, and identify factors associated with weight gain. METHODS: Retrospective analysis of youth with hypothalamic and suprasellar tumours, seen at a paediatric tertiary care centre from 2010 to 2020. RESULTS: The study cohort (n = 130, 50% female, median age at diagnosis 5 [range 0-17]y) had a median duration of follow up of 5 (0.03-17)y. At last recorded body mass index (BMI) measurement, 34% had obesity, including 17% with severe obesity. Median onset of overweight and obesity after diagnosis was 6.2 (0.3-134) and 8.9 (0.7-65) months, respectively. After algorithm implementation (n = 13), the proportion that had an early dietitian visit (within 6 months) increased from 36% to 54%, (p = 0.498) and weight management referrals increased from 51% to 83% (p = 0.286). Higher BMI z-score at diagnosis was associated with overweight and obesity development (p < 0.001). CONCLUSION: Patients with hypothalamic tumours commonly develop obesity. Use of a clinical algorithm may expedite recognition of HO. Further research is needed to identify predictors of weight gain and to develop effective treatment.
Subject(s)
Brain Neoplasms , Hypothalamic Diseases , Hypothalamic Neoplasms , Adolescent , Algorithms , Body Mass Index , Brain Neoplasms/complications , Child , Female , Humans , Hypothalamic Diseases/complications , Hypothalamic Diseases/drug therapy , Hypothalamic Neoplasms/complications , Hypothalamic Neoplasms/diagnosis , Hypothalamic Neoplasms/epidemiology , Male , Obesity/complications , Obesity/diagnosis , Obesity/epidemiology , Overweight/complications , Overweight/epidemiology , Retrospective Studies , Risk Factors , Weight GainABSTRACT
OBJECTIVES: Low-carbohydrate and ketogenic diets are becoming increasingly popular choices for people with type 1 diabetes (T1D) aiming to achieve optimal glycemic control. A carbohydrate-restricted diet in children has been associated with negative health effects including poor linear growth and inadequate bone mineralization. Guidelines for monitoring children and adolescents choosing to follow a carbohydrate-restricted diet do not exist. We aimed to create a clinical protocol outlining how to clinically and biochemically follow patients choosing a carbohydrate-restricted diet with the goal of medical safety. METHODS: An interdisciplinary committee was formed and reviewed current consensus guidelines for pediatric patients on carbohydrate-restricted diets for epilepsy and metabolic disorders. A literature search was done to determine management strategies for children with T1D on a low-carbohydrate or ketogenic diet. Key health parameters that require monitoring were identified: growth, glycemic control, bone health, cardiometabolic health, and nutritional status. These health outcomes were used to develop a protocol for monitoring children on carbohydrate-restricted diets. RESULTS: A one-page protocol for medical providers and educational materials for families interested in following a low-carbohydrate or ketogenic diet were developed and successfully implemented into clinical care. CONCLUSION: Implementing a protocol for children on carbohydrate-restricted diets in clinic allows medical providers to ensure medical safety while being open to discussing a family's dietary preferences. Following children in the protocol over time will lead to informed clinical guidelines for patients with T1D who choose to follow a carbohydrate-restricted diet.
