Tobacco and alcohol use are leading causes of mortality that influence risk for many complex diseases and disorders 1 . They are heritable 2 , 3 and etiologically related 4 , 5 behaviors that have been resistant to gene discovery efforts 6 – 11 . In sample sizes up to 1.2 million individuals, we discovered 566 genetic variants in 406 loci associated with multiple stages of tobacco use (initiation, cessation, and heaviness) as well as alcohol use, with 150 loci evidencing pleiotropic association. Smoking phenotypes were positively genetically correlated with many health conditions, whereas alcohol use was negatively correlated with these conditions, such that increased genetic risk for alcohol use is associated with lower disease risk. We report evidence for the involvement of many systems in tobacco and alcohol use, including genes involved in nicotinic, dopaminergic, and glutamatergic neurotransmission. The results provide a solid starting point to evaluate the effects of these loci in model organisms and more precise substance use measures.

BACKGROUND Specific dietary and other lifestyle behaviors may affect the success of the straightforward-sounding strategy “eat less and exercise more” for preventing long-term weight gain. METHODS We performed prospective investigations involving three separate cohorts that included 120,877 U.S. women and men who were free of chronic diseases and not obese at baseline, with follow-up periods from 1986 to 2006, 1991 to 2003, and 1986 to 2006. The relationships between changes in lifestyle factors and weight change were evaluated at 4-year intervals, with multivariable adjustments made for age, baseline body-mass index for each period, and all lifestyle factors simultaneously. Cohort-specific and sex-specific results were similar and were pooled with the use of an inverse-variance–weighted meta-analysis. RESULTS Within each 4-year period, participants gained an average of 3.35 lb (5th to 95th percentile, −4.1 to 12.4). On the basis of increased daily servings of individual dietary components, 4-year weight change was most strongly associated with the intake of potato chips (1.69 lb), potatoes (1.28 lb), sugar-sweetened beverages (1.00 lb), unprocessed red meats (0.95 lb), and processed meats (0.93 lb) and was inversely associated with the intake of vegetables (−0.22 lb), whole grains (−0.37 lb), fruits (−0.49 lb), nuts (−0.57 lb), and yogurt (−0.82 lb) (P≤0.005 for each comparison). Aggregate dietary changes were associated with substantial differences in weight change (3.93 lb across quintiles of dietary change). Other lifestyle factors were also independently associated with weight change (P<0.001), including physical activity (−1.76 lb across quintiles); alcohol use (0.41 lb per drink per day), smoking (new quitters, 5.17 lb; former smokers, 0.14 lb), sleep (more weight gain with <6 or >8 hours of sleep), and television watching (0.31 lb per hour per day). CONCLUSIONS Specific dietary and lifestyle factors are independently associated with long-term weight gain, with a substantial aggregate effect and implications for strategies to prevent obesity. (Funded by the National Institutes of Health and others.)

The authors assessed the reproducibility and validity of an expanded 131-item semiquantitative food frequency questionnaire used in a prospective study among 51,529 men. The form was administered by mail twice to a sample of 127 participants at a one-year interval. During this interval, men completed two one-week diet records spaced approximately 6 months apart. Mean values for intake of most nutrients assessed by the two methods were similar. Intraclass correlation coefficients for nutrient intakes assessed by questionnaires one year apart ranged from 0.47 for vitamin E without supplements to 0.80 for vitamin C with supplements. Correlation coefficients between the energy-adjusted nutrient intakes measured by diet records and the second questionnaire (which asked about diet during the year encompassing the diet records) ranged from 0.28 for iron without supplements to 0.86 for vitamin C with supplements (mean r = 0.59). These correlations were higher after adjusting for week-to-week variation in diet record intakes (mean r = 0.65). These data indicate that the expanded semiquantitative food frequency questionnaire is reproducible and provides a useful measure of intake for many nutrients over a one-year period.

The Healthy Eating Index-2005 (HEI-2005) measures adherence to the 2005 Dietary Guidelines for Americans, but the association between the HEI-2005 and risk of chronic disease is not known. The Alternative Healthy Eating Index (AHEI), which is based on foods and nutrients predictive of chronic disease risk, was associated inversely with chronic disease risk previously. We updated the AHEI, including additional dietary factors involved in the development of chronic disease, and assessed the associations between the AHEI-2010 and the HEI-2005 and risk of major chronic disease prospectively among 71,495 women from the Nurses' Health Study and 41,029 men from the Health Professionals Follow-Up Study who were free of chronic disease at baseline. During ≥24 y of follow-up, we documented 26,759 and 15,558 incident chronic diseases (cardiovascular disease, diabetes, cancer, or nontrauma death) among women and men, respectively. The RR (95% CI) of chronic disease comparing the highest with the lowest quintile was 0.84 (0.81, 0.87) for the HEI-2005 and 0.81 (0.77, 0.85) for the AHEI-2010. The AHEI-2010 and HEI-2005 were most strongly associated with coronary heart disease (CHD) and diabetes, and for both outcomes the AHEI-2010 was more strongly associated with risk than the HEI-2005 (P-difference = 0.002 and <0.001, respectively). The 2 indices were similarly associated with risk of stroke and cancer. These findings suggest that closer adherence to the 2005 Dietary Guidelines may lower risk of major chronic disease. However, the AHEI-2010, which included additional dietary information, was more strongly associated with chronic disease risk, particularly CHD and diabetes.

SummaryBackgroundHigh plasma HDL cholesterol is associated with reduced risk of myocardial infarction, but whether this association is causal is unclear. Exploiting the fact that genotypes are randomly assigned at meiosis, are independent of non-genetic confounding, and are unmodified by disease processes, mendelian randomisation can be used to test the hypothesis that the association of a plasma biomarker with disease is causal.MethodsWe performed two mendelian randomisation analyses. First, we used as an instrument a single nucleotide polymorphism (SNP) in the endothelial lipase gene (LIPG Asn396Ser) and tested this SNP in 20 studies (20 913 myocardial infarction cases, 95 407 controls). Second, we used as an instrument a genetic score consisting of 14 common SNPs that exclusively associate with HDL cholesterol and tested this score in up to 12 482 cases of myocardial infarction and 41 331 controls. As a positive control, we also tested a genetic score of 13 common SNPs exclusively associated with LDL cholesterol.FindingsCarriers of the LIPG 396Ser allele (2·6% frequency) had higher HDL cholesterol (0·14 mmol/L higher, p=8×10−13) but similar levels of other lipid and non-lipid risk factors for myocardial infarction compared with non-carriers. This difference in HDL cholesterol is expected to decrease risk of myocardial infarction by 13% (odds ratio [OR] 0·87, 95% CI 0·84–0·91). However, we noted that the 396Ser allele was not associated with risk of myocardial infarction (OR 0·99, 95% CI 0·88–1·11, p=0·85). From observational epidemiology, an increase of 1 SD in HDL cholesterol was associated with reduced risk of myocardial infarction (OR 0·62, 95% CI 0·58–0·66). However, a 1 SD increase in HDL cholesterol due to genetic score was not associated with risk of myocardial infarction (OR 0·93, 95% CI 0·68–1·26, p=0·63). For LDL cholesterol, the estimate from observational epidemiology (a 1 SD increase in LDL cholesterol associated with OR 1·54, 95% CI 1·45–1·63) was concordant with that from genetic score (OR 2·13, 95% CI 1·69–2·69, p=2×10−10).InterpretationSome genetic mechanisms that raise plasma HDL cholesterol do not seem to lower risk of myocardial infarction. These data challenge the concept that raising of plasma HDL cholesterol will uniformly translate into reductions in risk of myocardial infarction.FundingUS National Institutes of Health, The Wellcome Trust, European Union, British Heart Foundation, and the German Federal Ministry of Education and Research.

These data suggest that waist circumference may be a better indicator than WHR of the relationship between abdominal adiposity and risk of diabetes. Although early obesity, absolute weight gain throughout adulthood, and waist circumference were good predictors of diabetes, attained BMI was the dominant risk factor for NIDDM; even men of average relative weight had significantly elevated RRs.

Background:Recently, the analysis of dietary patterns has emerged as a possible approach to examining diet-disease relations. Objective: We examined the reproducibility and validity of dietary patterns defined by factor analysis using dietary data collected with a food-frequency questionnaire (FFQ). Design: We enrolled a subsample of men (n = 127) from the Health Professionals Follow-up Study in a diet-validation study in 1986. A 131-item FFQ was administered twice, 1 y apart, and two 1-wk diet records and blood samples were collected during this 1-y interval. Results: Using factor analysis, we identified 2 major eating patterns, which were qualitatively similar across the 2 FFQs and the diet records. The first factor, the prudent dietary pattern, was characterized by a high intake of vegetables, fruit, legumes, whole grains, and fish and other seafood, whereas the second factor, the Western pattern, was characterized by a high intake of processed meat, red meat, butter, high-fat dairy products, eggs, and refined grains. The reliability correlations for the factor scores between the 2 FFQs were 0.70 for the prudent pattern and 0.67 for the Western pattern. The correlations (corrected for week-to-week variation in diet records) between the 2 FFQs and diet records ranged from 0.45 to 0.74 for the 2 patterns. In addition, the correlations between the factor scores and nutrient intakes and plasma concentrations of biomarkers were in the expected direction. Conclusions: These data indicate reasonable reproducibility and validity of the major dietary patterns defined by factor analysis with data from an FFQ.Am J Clin Nutr 1999;69:243-9. KEY WORDSDiet, dietary pattern, factor analysis, biomarker, reproducibility, validity, men, Health Professionals Follow-up Study, food-frequency questionnaire INTRODUCTIONTraditional analyses in nutritional epidemiology typically examine diseases in relation to a single or a few nutrients or foods. However, people do not eat isolated nutrients. Instead, they eat meals consisting of a variety of foods with complex combinations of nutrients. The single-nutrient approach may be inadequate for taking into account complicated interactions among nutrients in studies of free-living people (eg, enhanced iron absorption in the presence of vitamin C) (1). Also, the high level of intercorrelation among some nutrients (such as potassium and magnesium) makes it difficult to examine their effects separately (2). Moreover, because nutrient intakes are commonly associated with certain dietary patterns (3, 4), single-nutrient analysis may be confounded by the effect of dietary patterns (5).To overcome these limitations, several authors recently proposed to study overall dietary patterns by considering how foods and nutrients are consumed in combination (4, 6-13). In a dietary pattern analysis, the collinearity of nutrients and foods can be used to advantage because patterns are characterized on the basis of habitual food consumption. Examination of dietary patterns would more closely parallel ...

Among women, adherence to lifestyle guidelines involving diet, exercise, and abstinence from smoking is associated with a very low risk of coronary heart disease.