Diet and Cancer Risk in Northern Italy: An Overview from Various Case-Control Studies

1990 ◽  
Vol 76 (4) ◽  
pp. 306-310 ◽  
Author(s):  
Carlo La Vecchia ◽  
Eva Negri ◽  
Fabio Parazzini ◽  
Ettore Marubini ◽  
Dimetrios Trichopolous
Oncology ◽  
1988 ◽  
Vol 45 (5) ◽  
pp. 364-370 ◽  
Author(s):  
Carlo La Vecchia ◽  
Adriono Decarli ◽  
Eva Negri ◽  
Fabio Parazzini

1990 ◽  
Vol 45 (2) ◽  
pp. 275-279 ◽  
Author(s):  
Carlo La Vecchia ◽  
Eva Negri ◽  
Fabio Parazzini ◽  
Peter Boyle ◽  
Barbara D'Avanzo ◽  
...  

1999 ◽  
Vol 58 (2) ◽  
pp. 261-264 ◽  
Author(s):  
Michael J. Hill

In early epidemiological studies of diet and cancer the stress was on the search for causal factors. Population (ecological) studies tended to show a strong correlation between meat intake, particularly red meat, and the risk of colo-rectal cancer. They also tended to show meat to be strongly inversely correlated with cancers of the stomach and oesophagus and liver. Early case- control studies tended to support the postulated role for red meat in colo-rectal carcinogenesis, although more recent case-control studies, particularly those from Europe, have tended to show no relationship. The cohort studies in general failed to detect any relationship between meat intake and colo-rectal cancer risk. The available evidence points to the intake of protective factors such as vegetables and whole-grain cereals being the main determinants of colo-rectal cancer risk, with meat intake only coincidentally related.


BMC Medicine ◽  
2021 ◽  
Vol 19 (1) ◽  
Author(s):  
R. D. McDowell ◽  
C. Hughes ◽  
P. Murchie ◽  
C. Cardwell

Abstract Background Studies systematically screening medications have successfully identified prescription medicines associated with cancer risk. However, adjustment for confounding factors in these studies has been limited. We therefore investigated the association between frequently prescribed medicines and the risk of common cancers adjusting for a range of confounders. Methods A series of nested case-control studies were undertaken using the Primary Care Clinical Informatics Unit Research (PCCIUR) database containing general practice (GP) records from Scotland. Cancer cases at 22 cancer sites, diagnosed between 1999 and 2011, were identified from GP records and matched with up to five controls (based on age, gender, GP practice and date of registration). Odds ratios (OR) and 95% confidence intervals (CI) comparing any versus no prescriptions for each of the most commonly prescribed medicines, identified from prescription records, were calculated using conditional logistic regression, adjusting for comorbidities. Additional analyses adjusted for smoking use. An association was considered a signal based upon the magnitude of its adjusted OR, p-value and evidence of an exposure-response relationship. Supplementary analyses were undertaken comparing 6 or more prescriptions versus less than 6 for each medicine. Results Overall, 62,109 cases and 276,580 controls were included in the analyses and a total of 5622 medication-cancer associations were studied across the 22 cancer sites. After adjusting for comorbidities 2060 medicine-cancer associations for any prescription had adjusted ORs greater than 1.25 (or less than 0.8), 214 had a corresponding p-value less than or equal to 0.01 and 118 had evidence of an exposure-dose relationship hence meeting the criteria for a signal. Seventy-seven signals were identified after additionally adjusting for smoking. Based upon an exposure of 6 or more prescriptions, there were 118 signals after adjusting for comorbidities and 82 after additionally adjusting for smoking. Conclusions In this study a number of novel associations between medicine and cancer were identified which require further clinical and epidemiological investigation. The majority of medicines were not associated with an altered cancer risk and many identified signals reflected known associations between medicine and cancer.


2014 ◽  
Vol 136 (2) ◽  
pp. 360-371 ◽  
Author(s):  
Dario Consonni ◽  
Sara De Matteis ◽  
Angela C. Pesatori ◽  
Pier Alberto Bertazzi ◽  
Ann C. Olsson ◽  
...  

2018 ◽  
Vol 120 (5) ◽  
pp. 7199-7210 ◽  
Author(s):  
Mohammad Hashemi ◽  
Abdolkarim Moazeni‐Roodi ◽  
Saeid Ghavami

BMC Cancer ◽  
2012 ◽  
Vol 12 (1) ◽  
Author(s):  
Peng Zou ◽  
Aihua Gu ◽  
Guixiang Ji ◽  
Lin Zhao ◽  
Peng Zhao ◽  
...  

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