Background: Adiposity has been associated with an increased risk of head and neck cancer (HNC). Although body mass index (BMI) has been inversely associated with HNC risk among smokers, this is likely due to confounding. Previous Mendelian randomization (MR) studies could not fully discount causality between adiposity and HNC due to limited statistical power. Hence, we aimed to revisit this using the largest genome-wide association study (GWAS) of HNC available, which has more granular data on HNC subsites.
Methods: We assessed the genetically predicted effects of BMI (N=806,834), waist-to-hip ratio (WHR; N=697,734) and waist circumference (N=462,166) on the risk of HNC (N=12,264 cases and 19,259 controls) and its subsites (oral, laryngeal, hypopharyngeal and oropharyngeal cancers) using a two-sample MR framework. We used the inverse variance weighted (IVW) MR approach and multiple sensitivity analyses including the weighted median, weighted mode, MR-Egger, MR-PRESSO, and CAUSE approaches. We also used multivariable MR (MVMR) to explore the direct effects of the adiposity measures on HNC, while accounting for smoking behaviour, a well-known HNC risk factor.
Results: In univariable MR, higher genetically predicted BMI increased the risk of overall HNC (IVW OR=1.17 per 1 standard deviation [1-SD] higher BMI, 95% CI 1.02-1.34, p=0.03), with no heterogeneity across subsites (Q p=0.78). However, the effect was not consistent in sensitivity analyses. The IVW effect was attenuated when smoking was included in the MVMR model (OR accounting for comprehensive smoking index=0.96 per 1-SD higher BMI, 95% CI 0.80-1.15, p=0.64) and CAUSE indicated the IVW results could be biased by correlated pleiotropy. Furthermore, we did not find a link between genetically predicted WHR (IVW OR=1.05 per 1-SD higher WHR, 95% CI 0.89-1.24, p=0.53) or waist circumference and HNC risk (IVW OR=1.01 per 1-SD higher waist circumference, 95% CI 0.85-1.21, p=0.87).
Conclusions: Our findings suggest that adiposity does not play a role in HNC risk.
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http://dx.doi.org/10.1101/2024.11.21.24317707 | DOI Listing |
Int J Mol Sci
March 2025
Department of Health Informatics, Faculty of Health Sciences, University of Debrecen, 4028 Debrecen, Hungary.
Head and neck cancers (HNCs) are often diagnosed late, leading to poor prognosis. Chronic inflammation, particularly periodontitis, has been linked to carcinogenesis, but systemic inflammatory markers remain underexplored. This study was the first to examine whether elevated C-reactive protein (CRP) can serve as a cost-effective adjunct in HNC risk assessment, alongside oral health indicators.
View Article and Find Full Text PDFCancers (Basel)
February 2025
College of Medicine and Dentistry, James Cook University, Smithfield, QLD 4878, Australia.
: The Bayesian mapping approach has not been used to identify head and neck cancer hotspots in Australia previously. This study aims to identify rural communities at risk of head and neck cancer (HNC) for targeted prevention programs. : This study included data from 23,853 cases recorded in the Queensland Cancer Register between 1982 and 2018.
View Article and Find Full Text PDFBMC Cancer
March 2025
Dept. Radiation Medicine, Roswell Park Comprehensive Cancer Center, Buffalo, USA.
Background: Patients with head and neck cancer (HNC) receiving radiation therapy (RT) are at increased risk for symptoms of oral mucositis (OM), opioid use, and declines in physical function, outcomes that contribute to increased morbidity and mortality. The study objective was to determine the effects of respiratory muscle training (RMT) on OM and opioid use, as well as functional performance in patients with HNC receiving RT with or without concurrent chemotherapy (CCRT).
Methods: Patients aged ≥ 18 years of age with stage I to IV HNC being treated with RT or CCRT receiving a home-based respiratory muscle training (RMT) (n = 20) were compared to a 5:1 matched historical group (n = 100) who did not receive RMT.
Phys Med Biol
March 2025
Departamento de Bioingeniería, Universidad Carlos III de Madrid, Avenida de la Universidad, 30, Madrid, 28911, SPAIN.
Cone beam computed tomography (CBCT) has become an essential tool in head and neck cancer (HNC) radiotherapy (RT) treatment delivery. Automatic segmentation of the organs at risk (OARs) on CBCT can trigger and accelerate treatment replanning but is still a challenge due to the poor soft tissue contrast, artifacts, and limited field-of-view of these images, alongside the lack of large, annotated datasets to train deep learning models. This study aims to develop a comprehensive framework to segment 25 HN OARs on CBCT to facilitate treatment replanning.
View Article and Find Full Text PDFHead Neck
March 2025
Department of Radiation Oncology, Cancer Center Clínica Universidad de Navarra (CCUN), Pamplona, Spain.
Background And Purpose: Full-dose postoperative external beam radiation therapy (EBRT)/chemoradiation is the standard of care in patients with resected, unirradiated head and neck cancer (HNC). This study aims to determine the long-term results of adjuvant high-dose rate (HDR) brachytherapy ± intermediate-dose postoperative external beam radiation therapy (EBRT)/chemoradiation in this HNC patient population.
Materials And Methods: From 2000 to 2018, a total of 152 patients diagnosed with HNC were treated with surgery and adjuvant HDR brachytherapy alone (n = 32) or combined with EBRT ± chemotherapy (n = 120).
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