Clinical and Genetic Factors Associated with Neuroendocrine Neoplasms - a UK Biobank Study

生命银行 医学 内科学 肿瘤科 人口 肺癌 体质指数 生物信息学 生物 环境卫生
作者
Harry Green,Marie Line El-Asmar,Brian Rous,Gareth Hawkes,María Trinidad Moreno-Montilla,Christina Thirlwell,John Ramage
出处
期刊:Neuroendocrinology [Karger Publishers]
卷期号:: 1-10
标识
DOI:10.1159/000545114
摘要

Background Incidence of neuroendocrine neoplasms (NEN) is rising globally, yet clinical and genetic factors remain poorly understood. Evidence for the role of obesity is conflicted and studies on prospectively collected data is sparse. We aimed to identify clinical and germline genetic risk factors associated with NEN in the UK Biobank. Methods Cases of NEN were identified in the UK Biobank’s cancer registry data (N~500,000). Using a combination of ICD-O3 codes for cancer site and histology, NEN cases were stratified into neuroendocrine tumour (NET), neuroendocrine carcinoma (NEC) and small / large cell lung cancer (SLCLC). A Cox-proportional hazards model was used to test for an association between clinical phenotypes and increased NEN risk, and a gene burden test in Regenie was used to test for causal variants in the exome sequencing data. Results We identified 704 NET, 340 NEC, and 550 SLCLC cases. Obesity (body mass index or waist-hip-ratio) and lower cholesterol (LDL, HDL or total) had a significantly significant association with NEN risk, however the effect size was marginal. Smoking and HbA1c associated only with SLCLC. Air pollution was not significantly associated when adjustment was made for socio-economic status. We replicated a known germline association between loss of function variants in MEN-1 and NEC, but did not detect any novel association in exome variants. Discussion This is the first large prospective population-based study to identify potential clinical and genetic risk factors for NEN and defined a novel phenotype in the UK Biobank. More research is needed to establish whether these relationships are causal. The exome study was underpowered, and future work in this area should focus on meta-analysing multiple large datasets.

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