ESPE2022 Poster Category 1 Fat, Metabolism and Obesity (73 abstracts)
1Elias University Hospital, Bucharest, Romania; 2Carol Davila University of Medicine and Pharmacy, Bucharest, Romania; 3Trestioreanu Oncologic Institute, Bucharest, Romania
Background: Endocrine disturbances are the most prevalent complications in childhood cancer survivors (CSS), especially in those treated with cranial and cervical radiation for brain tumours, such as medulloblastoma. Recent data have shown frequent delays in the diagnosis and treatment of these complications that may lead to potential side-effects on general health. Apart from the well-known hypothalamic–pituitary and growth disorders observed in CSS, these patients are predisposed to the development of obesity and metabolic syndrome (MS) which increase the risk of cardiovascular disease (CVD) and type 2 diabetes mellitus. In this study we aimed to determine adiposity by dual-energy x-ray absorptiometry (DXA) and compare it with conventional anthropometry and identify their association with markers of cardio-metabolic risk in a cohort of childhood medulloblastoma survivors.
Material and method: Anthropometric measurements and metabolic/hormonal profile were obtained from 16 survivors of childhood meduloblastoma that were evaluated in our clinic for long-term endocrine complications. Total body fat mass and android/gynoid (A/G) ratio were measured by whole-body DXA in 15 subjects.
Results: Our study showed a statistically significant correlation between FMI z-score and anthropometric parameters, such as BMI z-score (r=0.882, P<0.001), waist/height ratio (r=0.786, P=0.004) and waist/hip ratio (r=0.786, P=036) and HOMA index (r=0.773, P=0.002), respectively. Furthermore, we found a strong positive correlation between A/G ratio and HOMA index (r=0.744, P=0.006) in our cohort.
Conclusion: Our study demonstrated a strong relationship between conventional anthropometric measurements and adipose indices measured by DXA and HOMA index. Therefore, we suggest that these parameters can be used in this population to predict the metabolic risk.