The present study indicates that the imminent change of tar yields in the European Union to comply with an upper limit of 12 mg/cigarette will not increase (and may somewhat decrease) the incidence of myocardial infarction, unless they indirectly help perpetuate tobacco use. Even low tar cigarettes still greatly increase rates of myocardial infarction, however, especially among people in their 30s, 40s, and 50s, and far more risk is avoided by not smoking than by changing from one type of cigarette to another.
OBJECTIVE: To examine the associations of diet and other lifestyle factors with body mass index (BMI) using data from the Oxford Vegetarian Study. SUBJECTS: 1914 male and 3378 female non-smokers aged 20±89 y at recruitment to the study. MEASUREMENTS: All subjects completed a dietalifestyle questionnaire at recruitment giving details of their usual diet and other characteristics including height and weight, smoking and drinking habits, amount of exercise, occupation and reproductive history. Answers to the food frequency questionnaire were used to classify subjects as either meat eaters or non-meat eaters, and to estimate intakes of animal fat and dietary ®bre. Subjects were further classi®ed according to their alcohol consumption, exercise level, social class, past smoking habits and parity. RESULTS: Mean BMI was lower in non-meat eaters than in meat eaters in all age groups for both men and women. Overall age-adjusted mean BMIs in kgam 2 were 23.18 and 22.05 for male meat eaters and non-meat eaters respectively (P`0.0001) and 22.32 and 21.32 for female meat eaters and non-meat eaters respectively (P`0.0001). In addition to meat consumption, dietary ®bre intake, animal fat intake, social class and past smoking were all independently associated with BMI in both men and women; alcohol consumption was independently associated with BMI in men, and parity was independently associated with BMI in women. After adjusting for these factors, the differences in mean BMI between meat eaters and non-meat eaters were reduced by 36% in men and 31% in women. CONCLUSIONS: Non-meat eaters are thinner than meat eaters. This may be partly due to a higher intake of dietary ®bre, a lower intake of animal fat, and only in men a lower intake of alcohol.
Background/ObjectivesThe influence of dietary factors remains controversial for screen-detected prostate cancer and inconclusive for clinically-detected disease. We aimed to examine these associations using prospectively collected food diaries.Methods1,717 prostate cancer cases in middle-aged and older UK men were pooled from four prospective cohorts with clinically-detected disease (n = 663) with routine data follow-up (means 6.6-13.3 years) and a case-control study with screen-detected disease (n = 1,054) nested in a randomised trial of prostate cancer treatments (ISCTRN 20141297). Multiple-day food diaries (records) completed by men prior to diagnosis were used to estimate intakes of 37 selected nutrients, food groups and items including carbohydrate, fat, protein, dairy products, fish, meat, fruit and vegetables, energy, fibre, alcohol, lycopene and selenium. Cases were matched on age and diary date to at least one control within study (n = 3,528). Prostate cancer risk was calculated using conditional logistic regression (adjusted for baseline covariates) and expressed as odds ratios in each quintile of intake (± 95% confidence intervals). Prostate cancer risk was also investigated by localised or advanced stage and by cancer detection method.ResultsThere were no strong associations between prostate cancer risk and 37 dietary factors.ConclusionsProstate cancer risk, including by disease stage, was not strongly associated with dietary factors measured by food diaries in middle-aged and older UK men.
We previously reported a validation study in 26 000 British residents, showing that postcodes were good indicators of a wide range of household characteristics related to socioeconomic status, including occupation, education, home ownership and access to various consumer goods. 1 That report, however, was too brief to include details of the correlations between postcode estimates and reported household income in potentially relevant subgroups, and therefore it did not describe the data by the sex or age of the head of household, by the geographical region of residence, or by diVerent income levels. As such details might be relevant to epidemiologists and statisticians who use postcode estimates in population based studies, this report provides supplementary data. Methods During 1995-6, members of 26 445 (70%) of 37 712 private households surveyed in England, Scotland and Wales reported their income (taken as the sum of all sources of reported pretaxation income, apart from the Housing Benefit) to the Family Resources Survey. 2 During 1985 to 1993 members of 11 million households, or about half of all households in Britain, provided information to a marketing company about annual income and gave their complete (that is, 6 or 7 character) postcode address. This information was used to produce commercial software that estimates household incomes from postcodes after interpolating or smoothing the data in various ways.
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