| Title: | Ultra-processed food intake and incident venous thromboembolism risk: Prospective cohort study |
| Journal: | Clinical Nutrition |
| Published: | 15 Jun 2023 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/37348154/ |
| DOI: | https://doi.org/10.1016/j.clnu.2023.06.016 |
| URL: | http://www.clinicalnutritionjournal.com/article/S0261561423002017/pdf |
Publication 10502
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Abstract
BACKGROUND & AIMS: Ultra-processed food (UPF) intake has been associated with multiple health outcomes, but data on the association between UPF intake and venous thromboembolism (VTE) risk are lacking. We conducted this study to examine the association between UPF intake and the risk of incident VTE.</p>
METHODS: This prospective cohort study was based on 186,323 participants free of baseline VTE from the UK Biobank. UPF intake was assessed by 24-h recall questionnaires. Data on incident VTE came from the nationwide inpatient and primary care datasets and the death registry. Cox proportional hazards regression was used to estimate the association between UPF intake and incident VTE risk. Multiplicative interactions and stratified analyses by age, sex, and body mass index were performed.</p>
RESULTS: During a 10.5-year (median) follow-up, 4235 incident VTE cases were diagnosed. After adjusting for covariates, the hazard ratio of VTE among individuals with the highest quintile of UPF intake was 1.05 (95% confidence interval [CI] 0.94, 1.17) for UPF in servings, 1.12 (95% CI 1.01, 1.24) in grams, 1.10 (95% CI 1.00, 1.22) in grams %, 1.21 (95% CI 1.10, 1.33) in energy, and 1.15 (95% CI 1.05, 1.27) in energy % compared to those in the lowest quintile. Age, sex, and body mass index did not modify the associations (Pinteraction > 0.05).</p>
CONCLUSIONS: Higher UPF intake was associated with a moderately increased risk of VTE.</p>
7 Authors
- Shuai Yuan
- Jie Chen
- Tian Fu
- Xue Li
- Maria Bruzelius
- Agneta Åkesson
- Susanna C Larsson
1 Application
| Application ID | Title |
|---|---|
| 66354 | Investigating the genetic, environmental and clinical determinants and their interactions on cancer risk, and developing risk prediction and prognosis models for common cancer types |
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