New model may help predict weight changes in children on CF treatment
Study: Method tracks grip strength, body composition in patients on Trikafta
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A new model based on body composition and hand grip strength may help clinicians identify children with cystic fibrosis (CF) at risk of rapid weight gain or weight loss after starting Trikafta (elexacaftor/tezacaftor/ivacaftor), a new study suggests.
In 27 children treated with Trikafta, researchers found that weight responses varied widely, with some children experiencing excessive weight gain, others rapid weight loss, and nearly half experiencing both. Overall, the model improved the prediction of weight changes compared with routine clinical information alone.
“Assessing both body composition and physical function during weight monitoring could enable clinicians to seek more targeted interventions for healthier levels of fat vs. fat-free mass (e.g., early counseling on healthy diet and exercise when children are at high risk),” researchers wrote.
The study, “Real-time prediction of rapid weight change in children with cystic fibrosis who have initiated modulator therapy,” was published in Clinical Nutrition ESPEN.
Children with CF may experience poor growth, low body weight
CF is caused by genetic mutations that lead to the absence or dysfunction of the CFTR protein, resulting in the accumulation of thick, sticky mucus in various organs that causes most CF symptoms.
Children with CF may experience poor growth and low body weight due to factors such as increased energy needs, reduced food intake, and pancreatic insufficiency, a common complication of CF in which the pancreas cannot release enough digestive enzymes to absorb nutrients properly.
Trikafta is an approved CFTR modulator designed to improve the function of defective CFTR in patients with certain CF-causing mutations. Studies suggest that children with CF tend to gain weight after starting the Vertex Pharmaceuticals‘ treatment, but weight changes vary widely between individuals.
Weight changes varied considerably among children on Trikafta
In this study, researchers aimed to develop a tool to evaluate weight changes in real time by combining routine clinical information with body composition and physical function measures. The goal is to help clinicians monitor and personalize nutritional care in children with CF receiving Trikafta.
For that, they analyzed 27 children with CF, ages 6 to 11 years, treated at a care center in the U.S.. The children participated in a yearlong observational study examining how body composition and muscle strength were associated with growth and lung function after starting Trikafta.
At treatment initiation (baseline), most children had a healthy weight (74.07%), while 18.52% were overweight and 7.41% obese, based on their body mass index (BMI), a measure that compares a child’s BMI with that of other children of the same age and sex. BMI measures body fat based on height and weight.
Weight changes varied considerably among the children after starting Trikafta. About 22% experienced clinically important rapid weight loss, while another 22% showed excessive weight gain. Nearly half (48.2%) went through periods of both rapid weight gain and weight loss, a pattern known as weight cycling, whereas 7.4% maintained a relatively stable weight.
The researchers found that a model using body composition and hand grip strength measures performed better than one using routine clinical data alone. In the final model, higher grip strength corresponded to slight weight gain over time.
Our prototyping approach provides an implementation framework for enhancing point-of-care around shared decision-making and maintaining healthy weight during childhood development.
An analysis then looked at the correlations between age at treatment start, BMI change, and fat (or fat-free) mass. It showed that the relationship between age and BMI (relative to age norms) depended on fat-free mass, whereas fat mass was consistently associated with BMI variations.
“Our findings with the final model suggest that [Trikfata] use elicits highly variable yet predictable changes in weight, emphasizing the need to monitor real-time and forecasted change to identify clinically meaningful weight gain or loss,” the researchers wrote.
The team then tested whether their model could predict future weight changes in individual children, illustrated by two examples. A 9-year-old boy had a mild decrease in his age-adjusted BMI that later stabilized, while an 11-year-old girl experienced steady and then accelerated weight gain. The model accurately predicted a low risk of rapid weight loss in the boy and a higher risk of rapid weight gain in the girl.
Researchers suggest that, after further validation, the model could support more personalized monitoring of children with CF receiving Trikafta.
“Our prototyping approach provides an implementation framework for enhancing point-of-care around shared decision-making and maintaining healthy weight during childhood development,” the researchers wrote.
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