Ultra-Processed Food Intake and Smoking Interact in Relation with Colorectal Adenomas
Abstract
:1. Introduction
2. Materials and Methods
2.1. Definition of Cases and Controls
2.2. Data Collection
2.3. Evaluation of UPF Intake
2.4. Statistical Analysis
3. Results
3.1. Characteristics of the Study Population and Comparison between Cases and Controls
3.2. The Association between UPF Intake and Colorectal Adenomas
3.3. Association between UPF Intake and Colorectal Adenomas and Its Interaction with Smoking
3.4. The Association between UPF Intake and Colonic Adenomas as Compared with Other Major Risk Factors
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Conflicts of Interest
References
- Dekker, E.; Tanis, P.J.; A Vleugels, J.L.; Kasi, P.M.; Wallace, M.B. Colorectal cancer. Lancet 2019, 394, 1467–1480. [Google Scholar] [CrossRef]
- Thanikachalam, K.; Khan, G. Colorectal Cancer and Nutrition. Nutrients 2019, 11, 164. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Feakins, R.M. Obesity and metabolic syndrome: Pathological effects on the gastrointestinal tract. Histopathology 2016, 68, 630–640. [Google Scholar] [CrossRef] [PubMed]
- Davenport, J.R.; Su, T.; Zhao, Z.; Coleman, H.G.; Smalley, W.E.; Ness, R.M.; Zheng, W.; Shrubsole, M.J. Modifiable lifestyle factors associated with risk of sessile serrated polyps, conventional adenomas and hyperplastic polyps. Gut 2016, 67, 456–465. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Fliss-Isakov, N.; Kariv, R.; Webb, M.; Ivancovsky, D.; Margalit, D.; Zelber-Sagi, S. Mediterranean dietary components are inversely associated with advanced colorectal polyps: A case-control study. World J. Gastroenterol. 2018, 24, 2617–2627. [Google Scholar] [CrossRef] [PubMed]
- De Jáuregui, D.R.-F.; Evans, C.E.L.; Jones, P.; Greenwood, D.C.; Hancock, N.; Cade, J.E. Common dietary patterns and risk of cancers of the colon and rectum: Analysis from the United Kingdom Women’s Cohort Study (UKWCS). Int. J. Cancer 2018, 143, 773–781. [Google Scholar] [CrossRef] [PubMed]
- Fliss-Isakov, N.; Zelber-Sagi, S.; Webb, M.; Halpern, Z.; Kariv, R. Smoking Habits are Strongly Associated with Colorectal Polyps in a Population-based Case-control Study. J. Clin. Gastroenterol. 2018, 52, 805–811. [Google Scholar] [CrossRef] [PubMed]
- Fliss-Isakov, N.; Grosso, G.; Salomone, F.; Godos, J.; Gavalno, F.; Ivancovsky-Wajcman, D.; Shibolet, O.; Kariv, R.; Zelber-Sagi, S. High Intake of Phenolic Acids Is Associated With Reduced Risk of Colorectal Adenomas Among Smokers. Clin. Gastroenterol. Hepatol. 2020, 18, 1893–1895.e3. [Google Scholar] [CrossRef]
- Le Marchand, L.; Hankin, J.H.; Wilkens, L.R.; Pierce, L.M.; Franke, A.; Kolonel, L.N.; Seifried, A.; Custer, L.J.; Chang, W.; Lum-Jones, A.; et al. Combined effects of well-done red meat, smoking, and rapid N-acetyltransferase 2 and CYP1A2 phenotypes in increasing colorectal cancer risk. Cancer Epidemiol. Biomark. Prev. 2001, 10, 1259–1266. [Google Scholar]
- Lilla, C.; Verla-Tebit, E.; Risch, A.; Jäger, B.; Hoffmeister, M.; Brenner, H.; Chang-Claude, J. Effect of NAT1 and NAT2 Genetic Polymorphisms on Colorectal Cancer Risk Associated with Exposure to Tobacco Smoke and Meat Consumption. Cancer Epidemiol. Biomark. Prev. 2006, 15, 99–107. [Google Scholar] [CrossRef] [Green Version]
- Steele, E.M.; Popkin, B.M.; Swinburn, B.; A Monteiro, C. The share of ultra-processed foods and the overall nutritional quality of diets in the US: Evidence from a nationally representative cross-sectional study. Popul. Health Metr. 2017, 15, 1–11. [Google Scholar]
- A Monteiro, C.; Cannon, G.; Moubarac, J.-C.; Levy, R.B.; Louzada, M.L.C.; Jaime, P.C. The UN Decade of Nutrition, the NOVA food classification and the trouble with ultra-processing. Public Health Nutr. 2018, 21, 5–17. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Hall, K.D.; Ayuketah, A.; Brychta, R.; Cai, H.; Cassimatis, T.; Chen, K.Y.; Chung, S.T.; Costa, E.; Courville, A.; Darcey, V.; et al. Ultra-Processed Diets Cause Excess Calorie Intake and Weight Gain: An Inpatient Randomized Controlled Trial of Ad Libitum Food Intake. Cell Metab. 2019, 30, 67–77.e3. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Steele, E.M.; Juul, F.; Neri, D.; Rauber, F.; Monteiro, C.A. Dietary share of ultra-processed foods and metabolic syndrome in the US adult population. Prev. Med. 2019, 125, 40–48. [Google Scholar] [CrossRef]
- Srour, B.; Fezeu, L.K.; Kesse-Guyot, E.; Allès, B.; Méjean, C.; Andrianasolo, R.M.; Chazelas, E.; Deschasaux, M.; Hercberg, S.; Galan, P.; et al. Ultra-processed food intake and risk of cardiovascular disease: Prospective cohort study (NutriNet-Santé). BMJ 2019, 365, l1451. [Google Scholar] [CrossRef] [Green Version]
- Fiolet, T.; Srour, B.; Sellem, L.; Kesse-Guyot, E.; Allès, B.; Méjean, C.; Deschasaux, M.; Fassier, P.; Latino-Martel, P.; Beslay, M.; et al. Consumption of ultra-processed foods and cancer risk: Results from NutriNet-Santé prospective cohort. BMJ 2018, 360, k322. [Google Scholar] [CrossRef] [Green Version]
- Trudeau, K.; Rousseau, M.; Parent, M. Élise Extent of Food Processing and Risk of Prostate Cancer: The PROtEuS Study in Montreal, Canada. Nutrients 2020, 12, 637. [Google Scholar] [CrossRef] [Green Version]
- Lieberman, D.A.; Rex, D.K.; Winawer, S.J.; Giardiello, F.M.; Johnson, D.A.; Levin, T.R. Guidelines for Colonoscopy Surveillance After Screening and Polypectomy: A Consensus Update by the US Multi-Society Task Force on Colorectal Cancer. Gastroenterology 2012, 143, 844–857. [Google Scholar] [CrossRef] [Green Version]
- Levin, B.; Lieberman, D.A.; McFarland, B.; Smith, R.A.; Brooks, D.; Andrews, M.K.S.; Dash, C.; Giardiello, F.M.; Glick, S.; Levin, T.R.; et al. Screening and Surveillance for the Early Detection of Colorectal Cancer and Adenomatous Polyps, 2008: A Joint Guideline from the American Cancer Society, the US Multi-Society Task Force on Colorectal Cancer, and the American College of Radiology. CA: A Cancer J. Clin. 2008, 58, 130–160. [Google Scholar] [CrossRef] [Green Version]
- ICD-10-CM Chapters List. Available online: https://icd.codes/icd10cm (accessed on 11 April 2018).
- Kassi, E.; Pervanidou, P.; Kaltsas, G.; Chrousos, G.P. Metabolic syndrome: Definitions and controversies. BMC Med. 2011, 9, 48. [Google Scholar] [CrossRef] [Green Version]
- Khera, A.V.; Emdin, C.A.; Drake, I.; Natarajan, P.; Bick, A.G.; Cook, N.R.; Chasman, D.I.; Baber, U.; Mehran, R.; Rader, D.J.; et al. Genetic Risk, Adherence to a Healthy Lifestyle, and Coronary Disease. N. Engl. J. Med. 2016, 375, 2349–2358. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Lloyd-Jones, D.M.; Hong, Y.; Labarthe, D.; Mozaffarian, D.; Appel, L.J.; Van Horn, L.; Greenlund, K.; Daniels, S.; Nichol, G.; Tomaselli, G.F.; et al. Defining and Setting National Goals for Cardiovascular Health Promotion and Disease Reduction. Circulation 2010, 121, 586–613. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Kaluski, D.N.; Goldsmith, R.; Arie, O.M.; Mayer, C.; Green, M. The first Israeli national health and nutrition survey (MABAT) as a policy maker. Public Health Rev. 2000, 28, 23–26. [Google Scholar] [PubMed]
- Gibney, M.J. Ultra-Processed Foods: Definitions and Policy Issues. Curr. Dev. Nutr. 2019, 3, nzy077. [Google Scholar] [CrossRef] [Green Version]
- Cediel, G.; J, M.R.; Corvalán, C.; Levy, R.B.; Uauy, R.; A Monteiro, C. Ultra-processed foods drive to unhealthy diets: Evidence from Chile. Public Health Nutr. 2020, 1–10. [Google Scholar] [CrossRef]
- Bleiweiss-Sande, R.; Sacheck, J.M.; Chui, K.; Goldberg, J.P.; Bailey, C.; Evans, E.W. Processed food consumption is associated with diet quality, but not weight status, in a sample of low-income and ethnically diverse elementary school children. Appetite 2020, 151, 104696. [Google Scholar] [CrossRef]
- Karnopp, E.V.N.; Vaz, J.D.S.; Schäfer, A.A.; Muniz, L.C.; Souza, R.D.L.V.D.; Dos Santos, I.; Gigante, D.P.; Assunção, M.C.F. Food consumption of children younger than 6 years according to the degree of food processing. J. Pediatr. 2017, 93, 70–78. [Google Scholar] [CrossRef] [Green Version]
- Frampton, M.; Houlston, R.S. Modeling the prevention of colorectal cancer from the combined impact of host and behavioral risk factors. Genet. Med. 2016, 19, 314–321. [Google Scholar] [CrossRef] [Green Version]
- Espejo-Herrera, N.; Gràcia-Lavedan, E.; Boldo, E.; Aragonés, N.; Pérez-Gómez, B.; Pollán, M.; Molina, A.J.; Fernández, T.; Martín, V.; La Vecchia, C.; et al. Colorectal cancer risk and nitrate exposure through drinking water and diet. Int. J. Cancer 2016, 139, 334–346. [Google Scholar] [CrossRef]
- Urrutia-Ortega, I.M.; Garduño-Balderas, L.G.; Delgado-Buenrostro, N.L.; Freyre-Fonseca, V.; Flores-Flores, J.O.; González-Robles, A.; Pedraza-Chaverri, J.; Hernández-Pando, R.; Rodriguez-Sosa, M.; León-Cabrera, S.; et al. Food-grade titanium dioxide exposure exacerbates tumor formation in colitis associated cancer model. Food Chem. Toxicol. 2016, 93, 20–31. [Google Scholar] [CrossRef]
- Goncalves, M.D.; Lu, C.; Tutnauer, J.; Hartman, T.E.; Hwang, S.-K.; Murphy, C.J.; Pauli, C.; Morris, R.; Taylor, S.; Bosch, K.; et al. High-fructose corn syrup enhances intestinal tumor growth in mice. Science 2019, 363, 1345–1349. [Google Scholar] [CrossRef] [PubMed]
- Oplatowska-Stachowiak, M.; Elliott, C.T. Food colors: Existing and emerging food safety concerns. Crit. Rev. Food Sci. Nutr. 2017, 57, 524–548. [Google Scholar] [CrossRef] [PubMed]
- Barrubés, L.; Babio, N.; Becerra-Tomás, N.; Toledo, E.; Ramírez-Sabio, J.B.; Estruch, R.; Ros, E.; Fitó, M.; Arós, F.; Fiol, M.; et al. Dairy product consumption and risk of colorectal cancer in an older mediterranean population at high cardiovascular risk. Int. J. Cancer 2018, 143, 1356–1366. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Barrubés, L.; Babio, N.; Becerra-Tomás, N.; Rosique-Esteban, N.; Salas-Salvadó, J. Association Between Dairy Product Consumption and Colorectal Cancer Risk in Adults: A Systematic Review and Meta-Analysis of Epidemiologic Studies. Adv. Nutr. 2019, 10, S190–S211. [Google Scholar] [CrossRef] [PubMed]
- Zeng, L.; Ruan, M.; Liu, J.; Wilde, P.; Naumova, E.N.; Mozaffarian, D.; Zhang, F.F. Trends in Processed Meat, Unprocessed Red Meat, Poultry, and Fish Consumption in the United States, 1999-2016. J. Acad. Nutr. Diet. 2019, 119, 1085–1098.e12. [Google Scholar] [CrossRef]
- Demeyer, D.; Mertens, B.; De Smet, S.; Ulens, M. Mechanisms Linking Colorectal Cancer to the Consumption of (Processed) Red Meat: A Review. Crit. Rev. Food Sci. Nutr. 2016, 56, 2747–2766. [Google Scholar] [CrossRef] [Green Version]
- Nöthlings, U.; Yamamoto, J.F.; Wilkens, L.R.; Murphy, S.P.; Park, S.-Y.; Henderson, B.E.; Kolonel, L.N.; Le Marchand, L. Meat and heterocyclic amine intake, smoking, NAT1 and NAT2 polymorphisms, and colorectal cancer risk in the multiethnic cohort study. Cancer Epidemiol. Biomark. Prev. 2009, 18, 2098–2106. [Google Scholar] [CrossRef] [Green Version]
- Lin, S.; Wang, X.; Huang, C.; Liu, X.; Zhao, J.; Yu, I.T.; Christiani, D.C. Consumption of salted meat and its interactions with alcohol drinking and tobacco smoking on esophageal squamous-cell carcinoma. Int. J. Cancer 2015, 137, 582–589. [Google Scholar] [CrossRef]
- Hansen, R.D.; Albieri, V.; Tjønneland, A.; Overvad, K.; Andersen, K.K.; Raaschou–Nielsen, O. Effects of Smoking and Antioxidant Micronutrients on Risk of Colorectal Cancer. Clin. Gastroenterol. Hepatol. 2013, 11, 406–415.e3. [Google Scholar] [CrossRef]
- Kato, I.; Boleij, A.; Kortman, G.A.M.; Roelofs, R.; Djuric, Z.; Severson, R.K.; Tjalsma, H. Partial Associations of Dietary Iron, Smoking and Intestinal Bacteria with Colorectal Cancer Risk. Nutr. Cancer 2013, 65, 169–177. [Google Scholar] [CrossRef] [Green Version]
- Rastogi, Y.R.; Saini, A.K.; Thakur, V.K.; Saini, A.K. New Insights into Molecular Links Between Microbiota and Gastrointestinal Cancers: A Literature Review. Int. J. Mol. Sci. 2020, 21, 3212. [Google Scholar] [CrossRef] [PubMed]
- Chen, J.; Pitmon, E.; Wang, K. Microbiome, inflammation and colorectal cancer. Semin. Immunol. 2017, 32, 43–53. [Google Scholar] [CrossRef] [PubMed]
- Watson, K.M.; Gaulke, C.A.; Tsikitis, V.L. Understanding the microbiome: A primer on the role of the microbiome in colorectal neoplasia. Ann. Gastroenterol. 2020, 33, 223–236. [Google Scholar] [CrossRef] [PubMed]
Controls (n = 358) | Cases with Adenoma (n = 294) | p | Cases with Non-Advanced Adenoma (n = 147) | p | Cases with Advanced Adenoma (n = 147) | p | Cases with Proximal Adenoma (n = 143) | p | Cases with Distal Adenoma (n = 151) | p | |
---|---|---|---|---|---|---|---|---|---|---|---|
Age (years) | 57.9 ± 6.8 | 59.4 ± 9.6 | 0.004 | 59.3 ± 6.5 | 0.037 | 59.7 ± 6.2 | 0.006 | 60.5 ± 5.8 | <0.001 | 58.5 ± 6.7 | 0.337 |
Gender (% male) | 46.2 | 56.3 | 0.012 | 54.5 | 0.100 | 57.6 | 0.023 | 49.3 | 0.558 | 62.4 | 0.001 |
Low socio-economic status a (%) | 5.6 | 8.1 | 0.220 | 8.5 | 0.245 | 7.7 | 0.384 | 8.0 | 0.332 | 8.1 | 0.306 |
Never smoked (%) | 54.2 | 41.2 | 0.002 | 42.2 | 0.077 | 40.4 | 0.002 | 44.8 | 0.052 | 38.2 | 0.003 |
Past smoker (%) | 33.2 | 37.8 | 40.1 | 35.6 | 35.0 | 40.1 | |||||
Current smoker (%) | 12.6 | 21.1 | 17.7 | 24.0 | 20.3 | 20.8 | |||||
BMI (kg/m2) | 27.3 ± 4.8 | 29.0 ± 5.8 | <0.001 | 29.0 ± 5.0 | 0.001 | 28.9 ± 6.5 | 0.010 | 28.3 ± 4.9 | 0.043 | 29.5 ± 6.5 | <0.001 |
Aspirin use (%) | 23.5 | 32.6 | 0.011 | 35.0 | 0.010 | 31.3 | 0.079 | 31.4 | 0.075 | 33.6 | 0.022 |
Physical inactivity b (%) | 40.5 | 47.2 | 0.094 | 46.1 | 0.245 | 47.9 | 0.127 | 51.1 | 0.031 | 43.8 | 0.474 |
Metabolic syndrome (%) | 42.9 | 66.1 | <0.001 | 66.9 | <0.001 | 64.3 | <0.001 | 65.4 | <0.001 | 66.2 | <0.001 |
Indication for colonoscopy | |||||||||||
Screening (%) | 60.5 | 39.9 | <0.001 | 39.9 | <0.001 | 39.6 | <0.001 | 40.7 | <0.001 | 38.9 | <0.001 |
Alarming symptoms (%) | 34.0 | 35.4 | 30.8 | 40.3 | 30.7 | 39.6 | |||||
Surveillance (%) | 5.5 | 24.7 | 29.4 | 20.1 | 28.6 | 21.5 |
Controls (n = 358) | Cases with Adenoma (n = 294) | p | Cases with Non-Advanced Adenoma (n = 147) | p | Cases with Advanced Adenoma (n = 147) | p | Cases with Proximal Adenoma (n = 143) | p | Cases with Distal Adenoma (n = 151) | p | |
---|---|---|---|---|---|---|---|---|---|---|---|
Dietary intake | |||||||||||
Caloric intake (Kcal/day) | 2031 ± 687 | 2027 ± 705 | 0.944 | 2058 ± 683 | 0.687 | 1994 ± 723 | 0.596 | 2020 ± 651 | 0.874 | 2036 ± 753 | 0.932 |
Protein (% of total kcal) | 18.2 ± 4.4 | 18.3 ± 4.6 | 0.753 | 18.5 ± 4.5 | 0.527 | 18.1 ± 4.8 | 0.838 | 17.3 ± 4.2 | 0.023 | 19.4 ± 4.8 | 0.012 |
Fat (% of total kcal) | 36.4 ± 6.6 | 35.8 ± 6.4 | 0.215 | 35.8 ± 6.6 | 0.399 | 36.0 ± 6.3 | 0.498 | 36.0 ± 6.2 | 0.563 | 35.7 ± 6.7 | 0.274 |
SFA (% of total kcal) | 12.3 ± 3.7 | 12.2 ± 3.7 | 0.795 | 12.0 ± 3.4 | 0.381 | 12.5 ± 4.1 | 0.473 | 12.2 ± 3.4 | 0.754 | 12.3 ± 4.0 | 0.868 |
MUFA/SFA ratio | 1.06 ± 0.45 | 1.00 ± 0.33 | 0.041 | 1.03 ± 0.36 | 0.322 | 0.99 ± 0.32 | 0.037 | 1.02 ± 0.34 | 0.233 | 0.99 ± 0.33 | 0.041 |
Carbohydrates (% of total kcal) | 41.6 ± 8.7 | 42.2 ± 8.6 | 0.354 | 42.0 ± 8.1 | 0.702 | 42.3 ± 9.0 | 0.432 | 43.4 ± 8.2 | 0.046 | 41.1 ± 8.9 | 0.536 |
Fiber (gr/day) | 23.6 ± 11.5 | 27.4 ± 12.8 | 0.547 | 24.5 ± 10.7 | 0.417 | 21.5 ± 12.8 | 0.076 | 23.1 ± 12.0 | 0.672 | 23.0 ± 12.0 | 0.611 |
Sodium (mg/day) | 2774 ± 1048 | 2773 ± 1037 | 0.975 | 2876 ± 1056 | 0.321 | 2669 ± 1011 | 0.299 | 2665 ± 1014 | 0.284 | 2876 ± 1048 | 0.313 |
Total caloric intake from food groups | |||||||||||
Bread, pastries and starch (kcal) | 403.1 ± 230.4 | 420.2 ± 239.6 | 0.360 | 450.8 ± 251.7 | 0.044 | 391.9 ± 223.9 | 0.621 | 414.7 ± 237.3 | 0.622 | 425.1 ± 251.74 | 0.340 |
Snacks (kcal) | 208.5 ± 218.7 | 207.8 ± 202.3 | 0.966 | 182.4 ± 175.9 | 0.206 | 233.8 ± 224.4 | 0.249 | 233.1 ± 201.5 | 0.252 | 184.2 ± 200.3 | 0.246 |
Beverages (kcal) | 150.2 ± 175.1 | 174.0 ± 206.8 | 0.118 | 161.3 ± 211.0 | 0.567 | 181.3 ± 195.8 | 0.089 | 180.3 ± 217.1 | 0.117 | 166.9 ± 196.9 | 0.363 |
Oils and spreads (kcal) | 188.1 ± 143.9 | 184.5 ± 176.8 | 0.779 | 203.3 ± 192.5 | 0.329 | 167.9 ± 160.3 | 0.179 | 194.5 ± 205.2 | 0.686 | 175.7 ± 144.7 | 0.392 |
Dairy (kcal) | 245.0 ± 200.4 | 242.0 ± 203.2 | 0.894 | 237.9 ± 177.5 | 0.760 | 250.8 ± 228.8 | 0.736 | 231.0 ± 184.0 | 0.514 | 256.6 ± 221.5 | 0.528 |
Meat, poultry and fish (kcal) | 288.8 ± 191.9 | 304.0 ± 231.0 | 0.368 | 314.7 ± 257.3 | 0.224 | 292.1 ± 203.2 | 0.869 | 258.3 ± 169.3 | 0.102 | 346.8 ± 269.6 | 0.007 |
The proportional caloric intake of UPFs by food group | |||||||||||
Total UPF kcal/total kcal (%) | 36.9 ± 16.4 | 39.2 ± 16.4 | 0.043 | 38.2 ± 15.6 | 0.251 | 40.3 ± 16.9 | 0.019 | 40.4 ± 16.2 | 0.016 | 38.0 ± 16.4 | 0.422 |
Bread, pastries and starch UPF kcal/group kcal (%) | 19.2 ± 24.3 | 17.4 ± 22.2 | 0.327 | 19.0 ± 22.8 | 0.936 | 15.7 ± 22.2 | 0.129 | 18.3 ± 22.0 | 0.686 | 16.4 ± 22.4 | 0.229 |
Snacks UPF kcal/group kcal (%) | 71.1 ± 31.2 | 77.3 ± 27.5 | 0.010 | 76.2 ± 28.5 | 0.102 | 78.3 ± 26.4 | 0.020 | 76.5 ± 27.8 | 0.084 | 77.9 ± 7.4 | 0.030 |
Beverages UPF kcal/group kcal (%) | 59.8 ± 37.9 | 66.8 ± 37.9 | 0.026 | 64.7 ± 39.0 | 0.210 | 69.1 ± 36.9 | 0.016 | 68.6 ± 36.2 | 0.022 | 64.9 ± 37.7 | 0.187 |
Oils and spreads UPF kcal/group kcal (%) | 56.2 ± 33.3 | 66.1 ± 32.5 | <0.001 | 66.9 ± 33.4 | 0.001 | 64.9 ± 32.2 | 0.009 | 64.1 ± 32.9 | 0.018 | 67.8 ± 32.2 | <0.001 |
Dairy UPF kcal/group kcal (%) | 35.5 ± 31.2 | 43.5 ± 32.6 | 0.002 | 42.5 ± 31.1 | 0.025 | 44.9 ± 34.1 | 0.005 | 43.5 ± 30.9 | 0.011 | 43.5 ± 34.1 | 0.021 |
Meat, poultry and fish UPF kcal/group kcal (%) | 8.8 ± 14.2 | 8.8 ± 13.0 | 0.327 | 9.7 ± 12.9 | 0.283 | 7.7 ± 12.9 | 0.399 | 18.3 ± 22.0 | 0.883 | 9.5 ± 14.2 | 0.370 |
Cases with Adenoma OR (95%CI) p | Cases with Non-Advanced Adenoma OR (95%CI) p | Cases with Advanced Adenoma OR (95%CI) p | Cases with Proximal Adenoma OR (95%CI) p | Cases with Distal Adenoma OR (95%CI) p | ||
---|---|---|---|---|---|---|
Total study population (n = 652) Cases/controls 294/358 | 1st tertile of UPF intake | |||||
Cases/controls | 83/131 | 44/131 | 39/131 | 36/131 | 47/131 | |
Ref. | Ref. | Ref. | Ref. | Ref. | ||
2nd tertile of UPF intake b | ||||||
Cases/controls | 103/122 | 58/122 | 45/122 | 52/122 | 51/122 | |
1.58 (1.04–2.40) 0.030 | 1.67 (1.00–2.78) 0.048 | 1.39 (0.81–2.38) 0.219 | 1.99 (1.15–3.44) 0.013 | 1.32 (0.79–2.19) 0.278 | ||
3rd tertile of UPF intake b | ||||||
Cases/controls | 108/105 | 45/105 | 63/105 | 55/105 | 53/105 | |
1.75 (1.14–2.68) 0.009 | 1.31 (0.76–2.25) 0.325 | 2.17 (1.29–3.65) 0.003 | 2.38 (1.37–4.11) 0.002 | 1.39 (0.82–2.34) 0.212 | ||
Never smokers (n = 315) Cases/controls 121/194 | 1st tertile of UPF intake | |||||
Cases/controls | 45/67 | 22/67 | 23/67 | 22/67 | 23/67 | |
Ref. | Ref. | Ref. | Ref. | Ref. | ||
2nd tertile of UPF intake b | ||||||
Cases/controls | 43/67 | 28/67 | 15/67 | 25/67 | 18/67 | |
1.10 (0.62–1.97) 0.730 | 1.44 (0.71–2.95) 0.309 | 0.67 (0.31–1.47) 0.326 | 1.32 (0.63–2.74) 0.453 | 0.88 (0.41–1.87) 0.748 | ||
3rd tertile of UPF intake b | ||||||
Cases/controls | 33/60 | 13/60 | 20/60 | 17/60 | 16/60 | |
0.84 (0.45–1.55) 0.589 | 0.56 (0.24–1.29) 0.176 | 1.02 (0.48–2.15) 0.947 | 0.90 (0.41–1.98) 0.808 | 0.72 (0.33–1.60) 0.432 | ||
Smokers a (n = 337) Cases/controls 173/164 | 1st tertile of UPF intake | |||||
Cases/controls | 38/64 | 22/64 | 16/64 | 14/64 | 24/64 | |
Ref. | Ref. | Ref. | Ref. | Ref. | ||
2nd tertile of UPF intake b | ||||||
Cases/controls | 60/55 | 30/55 | 30/55 | 27/55 | 33/55 | |
2.43 (1.31–4.52) 0.005 | 2.06 (0.96–4.39) 0.060 | 2.86 (1.30–6.26) 0.009 | 3.40 (1.43–8.05) 0.005 | 1.97 (0.96–4.02) 0.061 | ||
3rd tertile of UPF intake b | ||||||
Cases/controls | 75/45 | 32/45 | 43/45 | 38/45 | 37/45 | |
3.54 (1.90–6.61) < 0.001 | 2.60 (1.20–5.63) 0.015 | 4.76 (2.20–10.30) < 0.001 | 6.23 (2.67–14.52) < 0.001 | 2.49 (1.21–5.13) 0.013 | ||
P for interactionbetween UPF intake and smoking status c | 2nd tertile of UPF intake | 0.100 | 0.533 | 0.017 | 0.137 | 0.159 |
3rd tertile of UPF intake | 0.004 | 0.019 | 0.007 | 0.026 | 0.004 |
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Fliss-Isakov, N.; Zelber-Sagi, S.; Ivancovsky-Wajcman, D.; Shibolet, O.; Kariv, R. Ultra-Processed Food Intake and Smoking Interact in Relation with Colorectal Adenomas. Nutrients 2020, 12, 3507. https://doi.org/10.3390/nu12113507
Fliss-Isakov N, Zelber-Sagi S, Ivancovsky-Wajcman D, Shibolet O, Kariv R. Ultra-Processed Food Intake and Smoking Interact in Relation with Colorectal Adenomas. Nutrients. 2020; 12(11):3507. https://doi.org/10.3390/nu12113507
Chicago/Turabian StyleFliss-Isakov, Naomi, Shira Zelber-Sagi, Dana Ivancovsky-Wajcman, Oren Shibolet, and Revital Kariv. 2020. "Ultra-Processed Food Intake and Smoking Interact in Relation with Colorectal Adenomas" Nutrients 12, no. 11: 3507. https://doi.org/10.3390/nu12113507
APA StyleFliss-Isakov, N., Zelber-Sagi, S., Ivancovsky-Wajcman, D., Shibolet, O., & Kariv, R. (2020). Ultra-Processed Food Intake and Smoking Interact in Relation with Colorectal Adenomas. Nutrients, 12(11), 3507. https://doi.org/10.3390/nu12113507