Background: Obesity during treatment of childhood acute lymphoblastic leukemia (ALL) is associated with treatment-related toxicity (Orgel, JCO, 2014) and an increased hazard of relapse (Wadhwa, Cancer, 2023). In the general population, poverty is associated with childhood obesity (Yusuf, IJMCH & AIDS, 2020). Children with ALL are at risk of obesity due to factors such as corticosteroids and cranial irradiation (Withycombe, PBC, 2015). Whether household poverty is associated with an increased risk of obesity independent of therapeutic exposures remains unknown.
Methods: We conducted a secondary analysis of COG-AALL03N1 (PI: S Bhatia); the primary aim was to examine mercaptopurine adherence during maintenance. We aimed to examine the association between household poverty and risk of obesity during maintenance treatment for ALL. Parents (or patients if ≥18y) self-reported sex, race/ethnicity, parental education, annual household income and household structure (including number of household members) at study enrollment during maintenance. Participating sites reported National Cancer Institute (NCI) risk group, ALL immunophenotype, blast cytogenetics, therapeutic protocol as well as height and weight of participants at study enrollment. Data from the US Census Bureau were used to categorize patients as living in household poverty vs not, based on year-specific federal poverty thresholds using household income and number of household members. BMI was calculated as= weight (in kg) / [height (in m)]2; obesity was defined as BMI percentile ≥95 (among <19yo) or absolute BMI ≥30 kg/m2 (among >19yo) at study enrollment per Center for Disease Control and Prevention guidelines. The association between household poverty and obesity was examined using logistic regression. Model 1 adjusted for age at study enrollment, sex, NCI risk group (standard vs high), ALL immunophenotype (B- vs T-cell), blast cytogenetics (favorable, unfavorable, neutral), therapeutic protocol (CCG1991, AALL0232, CCG1961, AALL0331, others) and household structure (single parent household vs other). Model 2 added parental education (≤high school [HS] vs >HS) to Model 1 and Model 3 added race/ethnicity to Model 2. All analyses were performed on SPSS and SAS; 2-sided P<0.05 were considered statistically significant.
Results: Of the 742 participants enrolled on AALL03N1, 134 (18.1%) were excluded due to missing poverty (n=110) or BMI (n=34) data; 608 patients were included in this secondary analysis. Median age at study enrollment was 6y; 68.6% were male; 31.1% were non-Hispanic white (NHW), 36.5% Hispanic, 13.5% Asian, 18.9% African-American (AA); those excluded had similar characteristics. At study enrollment, 30.3% (n=184) were obese. Household poverty was associated with a 1.7-fold higher odds of obesity (95%CI=1.2-2.6, P=0.007) (Model 1). Household poverty remained associated with higher risk of obesity after addition of parental education (OR=1.6, 95%CI=1.0-2.5, P=0.03 [Model 2]). Addition of race/ethnicity attenuated the association between household poverty and obesity (OR=1.5, 95%CI=1.0-2.4, P=0.07 [Model 3]), likely due to collinearity between poverty, parental education and race/ethnicity.
Conclusions: Household poverty was associated with obesity in children with ALL undergoing maintenance therapy. Understanding the underlying causes of this association will inform intervention strategies to mitigate the risk of obesity and its downstream consequences.
No relevant conflicts of interest to declare.
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