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Towards a genetic obesity risk score in a single-center study of children and adolescents with obesity

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Abstract

The objective of this study was to identify clinical and/or metabolic predictive factors of genetic obesity. Subjects aged ≤ 18 years with obesity (BMI ≥ 97 th percentile) followed-up at the Paediatric Endocrinology Clinic of Maggiore Hospital in Novara, Italy were screened for genes associated with obesity by next-generation sequencing. Anamnestic, anthropometric, and biochemical data were collected, and parents completed two questionnaires, to screen for hyperphagia and daytime sleepiness, respectively. The study included 50 patients. Six genetic variants (6/50 patients,12%) were classified as pathogenic/likely pathogenic, three (3/50 patients,6%) as polygenic, and 16 (13/50 patients,26%) as variants of uncertain clinical significance (VUS). Eight patients carried > 1 variant. All pathogenic mutations were in genes implicated in the hypothalamic melanocortin pathway or responsible for syndromic obesity. All subjects with definitive genetic diagnosis developed obesity before five years of age. There were no statistically significant differences in auxological nor metabolic parameters between the three genetic patterns of absent genetic mutations, VUS, and pathogenic/likely pathogenic mutations. Finally, a Genetic Obesity Risk Score was developed using logistic regression analysis, selecting Hyperphagia Questionnaire score, age of onset of obesity, and family history as variables. Genetic screening of our cohort of children and adolescents with severe obesity revealed pathogenic/polygenic variants in 18% of cases, with PCSK1 the most frequently mutated gene and with a definitive genetic diagnosis in 3 patients. Identifying clinical, behavioral, and metabolic features predictive of genetic obesity would facilitate early diagnosis and tailored management.

Original languageEnglish
Article number14180
JournalScientific Reports
Volume15
Issue number1
DOIs
Publication statusPublished - Dec 2025

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • Adolescent
  • Child
  • Genetics
  • Obesity
  • Risk score

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