| 1. |
|
|
| 2. |
|
|
| 3. |
- Duell, EJ, et al.
(författare)
-
Alcohol consumption and gastric cancer risk in the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort
- 2011
-
Ingår i: AMERICAN JOURNAL OF CLINICAL NUTRITION. - 0002-9165. ; 94:5, s. 1266-1275
-
Tidskriftsartikel (refereegranskat)abstract
- Abstract: Background: Gastric cancer (GC) is the second leading cause of cancer death worldwide. The association between alcohol consumption and GC has been investigated in numerous epidemiologic studies with inconsistent results. Objective: We evaluated the association between alcohol consumption and GC risk. Design: We conducted a prospective analysis in the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort, which included 444 cases of first primary gastric adenocarcinoma. HRs and 95% CIs for GC were estimated by using multivariable Cox proportional hazards regression for consumption of pure ethanol in grams per day, with stratification by smoking status, anatomic subsite (cardia, noncardia), and histologic subtype (diffuse, intestinal). In a subset of participants, results were further adjusted for baseline Helicobacter pylori serostatus. Results: Heavy (compared with very light) alcohol consumption (>= 60 compared with 0.1-4.9 g/d) at baseline was positively associated with GC risk (HR: 1.65; 95% CI: 1.06, 2.58), whereas lower consumption amounts (<60 g/d) were not. When we analyzed GC risk by type of alcoholic beverage, there was a positive association for beer (>= 30 g/d; HR: 1.75; 95% CI: 1.13, 2.73) but not for wine or liquor. Associations were primarily observed at the highest amounts of drinking in men and limited to noncardia subsite and intestinal histology; no statistically significant linear dose-response trends with GC risk were observed. Conclusion: Heavy (but not light or moderate) consumption of alcohol at baseline (mainly from beer) is associated with intestinal-type noncardia GC risk in men from the EPIC cohort. Am J Clin Nutr 2011;94:1266-75.
|
|
| 4. |
|
|
| 5. |
|
|
| 6. |
- Jay, R, et al.
(författare)
-
Alcohol consumption and the risk of renal cancers in the European Prospective Investigation into Cancer and Nutrition (EPIC). Wozniak MB, Brennan P, Brenner DR, Overvad K, Olsen A, Tjønneland A, Boutron-Ruault MC, Clavel-Chapelon F, Fagherazzi G, Katzke V, Kühn T, Boeing H, Bergmann MM, Steffen A, Naska A, Trichopoulou A, Trichopoulos D, Saieva C, Grioni S, Panico S, Tumino R, Vineis P, Bueno-de-Mesquita HB, Peeters PH, Hjartåker A, Weiderpass E, Arriola L, Molina-Montes E, Duell EJ, Santiuste C, Alonso de la Torre R, Barricarte Gurrea A, Stocks T, Johansson M, Ljungberg B, Wareham N, Khaw KT, Travis RC, Cross AJ, Murphy N, Riboli E, Scelo G.Int J Cancer. 2015 Oct 15;137(8):1953-66. [Epub 2015 Apr 28]. doi: 10.1002/ijc.29559
- 2017
-
Ingår i: Urologic oncology. - : Elsevier BV. - 1873-2496. ; 35:3, s. 117-
-
Tidskriftsartikel (refereegranskat)
|
|
| 7. |
|
|
| 8. |
|
|
| 9. |
- Obon-Santacana, M, et al.
(författare)
-
Meta-Analysis and Validation of a Colorectal Cancer Risk Prediction Model Using Deep Sequenced Fecal Metagenomes
- 2022
-
Ingår i: Cancers. - : MDPI AG. - 2072-6694. ; 14:17
-
Tidskriftsartikel (refereegranskat)abstract
- The gut microbiome is a potential modifiable risk factor for colorectal cancer (CRC). We re-analyzed all eight previously published stool sequencing data and conducted an MWAS meta-analysis. We used cross-validated LASSO predictive models to identify a microbiome signature for predicting the risk of CRC and precancerous lesions. These models were validated in a new study, Colorectal Cancer Screening (COLSCREEN), including 156 participants that were recruited in a CRC screening context. The MWAS meta-analysis identified 95 bacterial species that were statistically significantly associated with CRC (FDR < 0.05). The LASSO CRC predictive model obtained an area under the receiver operating characteristic curve (aROC) of 0.81 (95%CI: 0.78–0.83) and the validation in the COLSCREEN dataset was 0.75 (95%CI: 0.66–0.84). This model selected a total of 32 species. The aROC of this CRC-trained model to predict precancerous lesions was 0.52 (95%CI: 0.41–0.63). We have identified a signature of 32 bacterial species that have a good predictive accuracy to identify CRC but not precancerous lesions, suggesting that the identified microbes that were enriched or depleted in CRC are merely a consequence of the tumor. Further studies should focus on CRC as well as precancerous lesions with the intent to implement a microbiome signature in CRC screening programs.
|
|
| 10. |
|
|
