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Research Article
Fen Liu
Capital Medical University
Corresponding Author
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Background: Dietary patterns and symptoms research among Chinese with esophageal squamous cell carcinoma (ESCC) and its precursor lesions is limited, especially as it relates to multiple food consumption and multiple co-occurring symptoms. The aim of our study was to identify the dietary patterns and severity of symptom classes with the risk of esophageal squamous cell carcinoma and its histological precursor lesions, and develop a risk prediction model for different stages of esophageal disease.
Methods: We analyzed data from a multicenter cross-sectional study carried out in ESCC high incidence areas between 2017 and 2018, which included 34,707 individuals aged 40-69 years. Dietary patterns and severity of symptom classes were derived by applying a latent class analysis (LCA). A multiple logistic regression model was used to derive the odds ratio (ORs) and corresponding 95% confidence intervals (CIs) for ESCC and the different stages of esophageal disease according to the dietary patterns and severity of symptom classes identified. We built the risk prediction model by using a nomogram.
Results: We identified five dietary patterns and three severity of symptom classes. The dietary patterns were classified as follows: “Healthy”, “Western”, “Lower consumers-combination”, “Medium consumers-combination” and “Higher consumers-combination” patterns based on the intake of foods such as red meat, vegetables and fruits. The severity of symptoms was categorized into “Asymptomatic”, “Mild symptoms” and “Overt symptoms” classes based on health-related symptoms reported by the participants. Compared to the “Healthy” pattern, the other four patterns were all associated with an increased risk of esophageal disease. Similarly, the other two symptom classes present different degrees of increased risk of esophageal disease compared to the “Asymptomatic”. The nomograms reflect the good predictive ability of the model.
Conclusion: Among individuals aged 40-69 years in high incidence regions of upper gastrointestinal cancer, the results supplied important epidemiological evidence and given further insights into dietary patterns and symptoms research.
Keywords
Esophageal squamous cell carcinoma, Precancerous lesions, Latent class analysis, Dietary patterns, Symptom
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No competing interests reported.
This is a list of supplementary files associated with this preprint. Click to download.
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CURRENT STATUS: Under Revision
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On 12 Nov, 2021
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Cancer Biology
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This preprint is under consideration at BMC Cancer. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research Article
Fen Liu
Capital Medical University
Corresponding Author
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Revision
Version 1
Posted 10 Nov, 2021
No community comments so far
Editorial decision: Major revision
On 03 Dec, 2021
Reviews received
Received 15 Nov, 2021
Reviewers agreed
On 13 Nov, 2021
Reviewers agreed
On 12 Nov, 2021
Reviewers invited
Invitations sent on 12 Nov, 2021
Editor assigned
On 12 Nov, 2021
Editor invited
On 07 Nov, 2021
Submission checks complete
On 07 Nov, 2021
First submitted
On 01 Nov, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Subject Areas
Cancer Biology
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background: Dietary patterns and symptoms research among Chinese with esophageal squamous cell carcinoma (ESCC) and its precursor lesions is limited, especially as it relates to multiple food consumption and multiple co-occurring symptoms. The aim of our study was to identify the dietary patterns and severity of symptom classes with the risk of esophageal squamous cell carcinoma and its histological precursor lesions, and develop a risk prediction model for different stages of esophageal disease.
Methods: We analyzed data from a multicenter cross-sectional study carried out in ESCC high incidence areas between 2017 and 2018, which included 34,707 individuals aged 40-69 years. Dietary patterns and severity of symptom classes were derived by applying a latent class analysis (LCA). A multiple logistic regression model was used to derive the odds ratio (ORs) and corresponding 95% confidence intervals (CIs) for ESCC and the different stages of esophageal disease according to the dietary patterns and severity of symptom classes identified. We built the risk prediction model by using a nomogram.
Results: We identified five dietary patterns and three severity of symptom classes. The dietary patterns were classified as follows: “Healthy”, “Western”, “Lower consumers-combination”, “Medium consumers-combination” and “Higher consumers-combination” patterns based on the intake of foods such as red meat, vegetables and fruits. The severity of symptoms was categorized into “Asymptomatic”, “Mild symptoms” and “Overt symptoms” classes based on health-related symptoms reported by the participants. Compared to the “Healthy” pattern, the other four patterns were all associated with an increased risk of esophageal disease. Similarly, the other two symptom classes present different degrees of increased risk of esophageal disease compared to the “Asymptomatic”. The nomograms reflect the good predictive ability of the model.
Conclusion: Among individuals aged 40-69 years in high incidence regions of upper gastrointestinal cancer, the results supplied important epidemiological evidence and given further insights into dietary patterns and symptoms research.
Figure 1
Figure 2
Figure 3
No competing interests reported.
This is a list of supplementary files associated with this preprint. Click to download.
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