Nutrition Intervention in Pediatric Acute Lymphoblastic Leukemia Patients with Down Syndrome




Hamby, Tyler
Bricker, Madeleine
Hill, Rachel


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Abstract: Purpose: Children and adolescents with Down Syndrome (DS) are more likely to become overweight or obese than those without DS. Additionally, children with DS develop acute lymphoblastic leukemia (ALL) at higher rates than the general population, and pediatric ALL treatment is associated with excessive weight gain. Despite DS-ALL patients’ increased risk for obesity and its complications, there remains a lack of research on preventing weight gain in this specific population. Our objective was to determine if a three-visit nutritional intervention in maintenance therapy was effective at reducing weight gain in DS-ALL patients. Methods: In a retrospective analysis, medical records of the intervention group were compared to historical controls on the same ALL treatment protocol. Anthropometrics were collected throughout intensive therapy and at every monthly visit during the 12 months of maintenance therapy. Results: Nine patients met the inclusion criteria: 5 males, 7 Caucasian and 2 Hispanic, and 5 on high risk protocols. The median age was 4.07 years (range, 1.60-14.26). Three and five patients had unhealthy BMIs at diagnosis and month 12 of maintenance, respectively. When comparing patients who had healthy BMIs at diagnosis, the intervention group had smaller increases in BMI than the control group. However, patients who had unhealthy BMIs at diagnosis had unhealthy BMIs at month 12 of maintenance therapy, regardless of intervention. Conclusions: These results provide evidence that DS patients do tend to gain weight during treatment for ALL, but the data were insufficient to determine whether the nutrition intervention was successful for this population. To our knowledge, this is the first study to investigate obesity prevention in DS-ALL patients. One approach for future studies is an inter-institutional collaboration to obtain a sample size large enough to draw conclusions using inferential statistics.