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Cancer Risk of Patients With Overweight and Obesity: A Predictive Model
Obesity is the most common chronic disease in the U.S. Patients with obesity have many risk factors for cancer, often modifiable. It is important to identify patients with obesity at high risk of cancer to be able to appropriately direct treatment and resources. Given that the risk pool is large, it...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8089530/ http://dx.doi.org/10.1210/jendso/bvab048.011 |
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author | Turchin, Alexander Morrison, Fritha Shubina, Maria Shinde, Shraddha Ahmad, Nadia Kan, Hongjun |
author_facet | Turchin, Alexander Morrison, Fritha Shubina, Maria Shinde, Shraddha Ahmad, Nadia Kan, Hongjun |
author_sort | Turchin, Alexander |
collection | PubMed |
description | Obesity is the most common chronic disease in the U.S. Patients with obesity have many risk factors for cancer, often modifiable. It is important to identify patients with obesity at high risk of cancer to be able to appropriately direct treatment and resources. Given that the risk pool is large, it is imperative to identify a clinically meaningful metric for risk stratification to help guide interventions. We conducted an observational study of electronic medical records data for 394,161 adults aged between 18 and 80, with BMI ≥ 25 kg/m(2) and without baseline history of cancer between 2000 and 2019. We first identified a literature-based pool of risk factors for cancer onset and conducted variable selection by applying least absolute shrinkage and selection operator (LASSO) penalized Cox regression with ten-fold cross-validation on an 80% training dataset. Effects of the selected variables on risk of cancer (excluding non-melanoma skin cancer) onset were assessed using Cox regression on the 80% training dataset. The resulting model accuracy was evaluated using Cox regression on a withheld 20% validation dataset. Participants had a mean age of 46.7 (SD: 15.5) years and mean body mass index (BMI) of 30.5 (SD: 5.4) kg/m(2); 51.9% were women. Over a mean of 7.5 years of follow-up, 34,679 (8.8%) of study patients developed cancer. The predictive model achieved a Harrell’s C-statistic of 0.73. The greatest risk of cancer incidence was associated with HIV infection (HR 2.22; 95% CI 1.88–2.63; 0.27% of patients), older age (HR 2.05 per 1 SD = 15.5 years; 95% CI 2.01- 2.09), hepatitis C infection (HR 1.48; 95% CI 1.34–1.63; 0.96% of patients), and family history of cancer (HR 1.44; 95% CI 1.41–1.48; 42.5% of patients). Additional patient characteristics found in >5% of patients that also carried risk included proteinuria (5.8% of patients; HR 1.23; 95% CI 1.18–1.29) and history of smoking (40.7% of patients; HR 1.20; 95% CI 1.17–1.23). Each standard deviation increase in BMI (5.4 kg/m(2)) was associated with a hazard ratio of 1.06 (95% CI 1.05–1.07) for incident cancer. It is feasible to use predictive modeling to identify patients with overweight and obesity at high cancer risk. This approach could be utilized to guide population management and clinical treatment decisions. |
format | Online Article Text |
id | pubmed-8089530 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-80895302021-05-06 Cancer Risk of Patients With Overweight and Obesity: A Predictive Model Turchin, Alexander Morrison, Fritha Shubina, Maria Shinde, Shraddha Ahmad, Nadia Kan, Hongjun J Endocr Soc Adipose Tissue, Appetite, and Obesity Obesity is the most common chronic disease in the U.S. Patients with obesity have many risk factors for cancer, often modifiable. It is important to identify patients with obesity at high risk of cancer to be able to appropriately direct treatment and resources. Given that the risk pool is large, it is imperative to identify a clinically meaningful metric for risk stratification to help guide interventions. We conducted an observational study of electronic medical records data for 394,161 adults aged between 18 and 80, with BMI ≥ 25 kg/m(2) and without baseline history of cancer between 2000 and 2019. We first identified a literature-based pool of risk factors for cancer onset and conducted variable selection by applying least absolute shrinkage and selection operator (LASSO) penalized Cox regression with ten-fold cross-validation on an 80% training dataset. Effects of the selected variables on risk of cancer (excluding non-melanoma skin cancer) onset were assessed using Cox regression on the 80% training dataset. The resulting model accuracy was evaluated using Cox regression on a withheld 20% validation dataset. Participants had a mean age of 46.7 (SD: 15.5) years and mean body mass index (BMI) of 30.5 (SD: 5.4) kg/m(2); 51.9% were women. Over a mean of 7.5 years of follow-up, 34,679 (8.8%) of study patients developed cancer. The predictive model achieved a Harrell’s C-statistic of 0.73. The greatest risk of cancer incidence was associated with HIV infection (HR 2.22; 95% CI 1.88–2.63; 0.27% of patients), older age (HR 2.05 per 1 SD = 15.5 years; 95% CI 2.01- 2.09), hepatitis C infection (HR 1.48; 95% CI 1.34–1.63; 0.96% of patients), and family history of cancer (HR 1.44; 95% CI 1.41–1.48; 42.5% of patients). Additional patient characteristics found in >5% of patients that also carried risk included proteinuria (5.8% of patients; HR 1.23; 95% CI 1.18–1.29) and history of smoking (40.7% of patients; HR 1.20; 95% CI 1.17–1.23). Each standard deviation increase in BMI (5.4 kg/m(2)) was associated with a hazard ratio of 1.06 (95% CI 1.05–1.07) for incident cancer. It is feasible to use predictive modeling to identify patients with overweight and obesity at high cancer risk. This approach could be utilized to guide population management and clinical treatment decisions. Oxford University Press 2021-05-03 /pmc/articles/PMC8089530/ http://dx.doi.org/10.1210/jendso/bvab048.011 Text en © The Author(s) 2021. Published by Oxford University Press on behalf of the Endocrine Society. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs licence (http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) ), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Adipose Tissue, Appetite, and Obesity Turchin, Alexander Morrison, Fritha Shubina, Maria Shinde, Shraddha Ahmad, Nadia Kan, Hongjun Cancer Risk of Patients With Overweight and Obesity: A Predictive Model |
title | Cancer Risk of Patients With Overweight and Obesity: A Predictive Model |
title_full | Cancer Risk of Patients With Overweight and Obesity: A Predictive Model |
title_fullStr | Cancer Risk of Patients With Overweight and Obesity: A Predictive Model |
title_full_unstemmed | Cancer Risk of Patients With Overweight and Obesity: A Predictive Model |
title_short | Cancer Risk of Patients With Overweight and Obesity: A Predictive Model |
title_sort | cancer risk of patients with overweight and obesity: a predictive model |
topic | Adipose Tissue, Appetite, and Obesity |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8089530/ http://dx.doi.org/10.1210/jendso/bvab048.011 |
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