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Genetic Risk Score Helps Predict Severe Obesity Later in Life in Childhood Cancer Survivors

NEW YORK – A team led by researchers at St. Jude Children's Research Hospital has demonstrated that polygenic risk scores implicated in severe obesity can help predict which survivors of childhood cancers – such as acute lymphoblastic leukemia, Hodgkin lymphoma, or central nervous system tumors – appear particularly likely to become severely obese later in life, which increases their risk for other chronic health conditions.

"Although clinical, treatment, and lifestyle risk factors contribute to the risk of severe obesity in survivors, inherited genetic factors contribute appreciably to the risk," senior and co-corresponding author Yutaka Yasui, a researcher at St. Jude, and his colleagues wrote in Nature Medicine on Monday.

Past studies have pointed to higher-than-usual rates of obesity in childhood cancer survivors compared to their cancer-free counterparts. For their study, the researchers considered ties between obesity-related genetic risk scores (GRS) and severe obesity in more than 2,500 long-term childhood cancer survivors of European ancestry from the St. Jude Lifetime cohort, using available clinical profiles, body mass index (BMI) measurements, and whole-genome sequencing- or array-based genotyping data.

"The purpose of this study was to characterize severe obesity among long-term survivors of childhood cancer using the St. Jude Lifetime (SJLIFE) cohort, and to develop and validate clinically applicable prediction models that identify survivors at high risk for severe obesity based on patient characteristics, cancer treatment, and inherited genetic variation," the authors wrote, noting that "early identification of survivors at risk of preventable risk factors [of chronic health conditions] is important and provides opportunity for targeted interventions."

Individuals in the top GRS tier were roughly 53 times as likely to be classified as severely obese as cancer survivors from the lowest GRS group, the researchers reported. In contrast, they noted that obesity risk was about 25 times different in the top and bottom tier GRS groups in the general, cancer-free population.

Likewise, the team found that they could significantly boost the performance of treatment- and lifestyle-based obesity risk prediction models by including genetic insights from GRSs.

In particular, the team noted, it appeared possible to track down more than four times as many childhood cancer survivors at higher-than-usual risk of severe obesity in adulthood with prediction models that incorporated polygenic risk profiles — results they validated with data for nearly 6,100 more participants in the Childhood Cancer Survivor Study.

"Genetic predictors improve identification of patients who could benefit from heightened surveillance and interventions to mitigate the risk of severe obesity and associated cardio-metabolic complications," the authors reported. They cautioned, however, that "prediction models that we developed in SJLIFE survivors of European ancestry did not perform well among SJLIFE survivors of African ancestry."

Even so, they suggested that "[g]enetic testing, along with assessment of clinical risk factors, could be used to identify survivors at high risk for severe obesity who may benefit from aggressive primary interventions as early as the time of cancer diagnosis as well as secondary interventions."