American Journal of Epidemiology

Information on dietary supplements, medications, and other xenobiotics in epidemiologic surveys is usually obtained from questionnaires and is subject to recall and reporting biases. The authors used metabolite data obtained from hydrogen-1 (or proton) nuclear magnetic resonance (1H NMR) analysis of human urine specimens from the International Study of Macro-/Micro-Nutrients and Blood Pressure (INTERMAP Study) to validate self-reported analgesic use. Metabolic profiling of two 24-hour urine specimens per individual was carried out for 4,630 participants aged 40–59 years from 17 population samples in Japan, China, the United Kingdom, and the United States (data collection, 1996–1999). 1H NMR-detected acetaminophen and ibuprofen use was low (~4%) among East Asian population samples and higher (>16%) in Western population samples. In a comparison of self-reported acetaminophen and ibuprofen use with 1H NMR-detected acetaminophen and ibuprofen metabolites among 496 participants from Chicago, Illinois, and Belfast, Northern Ireland, the overall rate of concordance was 81%–84%; the rate of underreporting was 15%–17%; and the rate of underdetection was approximately 1%. Comparison of self-reported unspecified analgesic use with 1H NMR-detected acetaminophen and ibuprofen metabolites among 2,660 Western INTERMAP participants revealed similar levels of concordance and underreporting. Screening for urinary metabolites of acetaminophen and ibuprofen improved the accuracy of exposure information. This approach has the potential to reduce recall bias and other biases in epidemiologic studies for a range of substances, including pharmaceuticals, dietary supplements, and foods.

With the advent of Internet-based 24-hour recall (24HR) instruments, it is now possible to envision their use in cohort studies investigating the relation between nutrition and disease. Understanding that all dietary assessment instruments are subject to measurement errors and correcting for them under the assumption that the 24HR is unbiased for usual intake, here the authors simultaneously address precision, power, and sample size under the following 3 conditions: 1) 1–12 24HRs; 2) a single calibrated food frequency questionnaire (FFQ); and 3) a combination of 24HR and FFQ data. Using data from the Eating at America’s Table Study (1997–1998), the authors found that 4–6 administrations of the 24HR is optimal for most nutrients and food groups and that combined use of multiple 24HR and FFQ data sometimes provides data superior to use of either method alone, especially for foods that are not regularly consumed. For all food groups but the most rarely consumed, use of 2–4 recalls alone, with or without additional FFQ data, was superior to use of FFQ data alone. Thus, if self-administered automated 24HRs are to be used in cohort studies, 4–6 administrations of the 24HR should be considered along with administration of an FFQ.

As with other instrumental variable (IV) analyses, Mendelian randomization (MR) studies rest on strong assumptions. These assumptions are not routinely systematically evaluated in MR applications, although such evaluation could add to the credibility of MR analyses. In this article, the authors present several methods that are useful for evaluating the validity of an MR study. They apply these methods to a recent MR study that used fat mass and obesity-associated (FTO) genotype as an IV to estimate the effect of obesity on mental disorder. These approaches to evaluating assumptions for valid IV analyses are not fail-safe, in that there are situations where the approaches might either fail to identify a biased IV or inappropriately suggest that a valid IV is biased. Therefore, the authors describe the assumptions upon which the IV assessments rely. The methods they describe are relevant to any IV analysis, regardless of whether it is based on a genetic IV or other possible sources of exogenous variation. Methods that assess the IV assumptions are generally not conclusive, but routinely applying such methods is nonetheless likely to improve the scientific contributions of MR studies.

Regression calibration has been described as a means of correcting effects of measurement error for normally distributed dietary variables. When foods are the items of interest, true distributions of intake are often positively skewed, may contain many zeroes, and are usually not described by well-known statistical distributions. The authors considered the validity of regression calibration assumptions where data are non-Gaussian. Such data (including many zeroes) were simulated, and use of the regression calibration algorithm was evaluated. An example used data from Adventist Health Study 2 (2002–2008). In this special situation, a linear calibration model does (as usual) at least approximately correct the parameter that captures the exposure-disease association in the "disease" model. Poor fit in the calibration model does not produce biased calibrated estimates when the "disease" model is linear, and it produces little bias in a nonlinear "disease" model if the model is approximately linear. Poor fit will adversely affect statistical power, but more complex linear calibration models can help here. The authors conclude that non-Gaussian data with many zeroes do not invalidate regression calibration. Irrespective of fit, linear regression calibration in this situation at least approximately corrects bias. More complex linear calibration equations that improve fit may increase power over that of uncalibrated regressions.

Although low- and middle-income countries still bear the burden of major infectious diseases, chronic noncommunicable diseases are becoming increasingly common due to rapid demographic, epidemiologic, and nutritional transitions. However, information is generally scant in these countries regarding chronic disease incidence, social determinants, and risk factors. The Brazilian Longitudinal Study of Adult Health (ELSA-Brasil) aims to contribute relevant information with respect to the development and progression of clinical and subclinical chronic diseases, particularly cardiovascular diseases and diabetes. In this report, the authors delineate the study’s objectives, principal methodological features, and timeline. At baseline, ELSA-Brasil enrolled 15,105 civil servants from 5 universities and 1 research institute. The baseline examination (2008–2010) included detailed interviews, clinical and anthropometric examinations, an oral glucose tolerance test, overnight urine collection, a 12-lead resting electrocardiogram, measurement of carotid intima-media thickness, echocardiography, measurement of pulse wave velocity, hepatic ultrasonography, retinal fundus photography, and an analysis of heart rate variability. Long-term biologic sample storage will allow investigation of biomarkers that may predict cardiovascular diseases and diabetes. Annual telephone surveillance, initiated in 2009, will continue for the duration of the study. A follow-up examination is scheduled for 2012–2013.

The authors conducted a nationwide cohort study to evaluate the association between postmenopausal hormone therapy and meningioma incidence in Finland. All women who had used hormone therapy at least for 6 months at the age of 50 years or older during 1994&ndash;2009 were included. Women who had used postmenopausal hormone therapy were identified from the medical reimbursement register of the Social Insurance Institution (131,480 estradiol users and 131,248 estradiol-progestin users), and meningioma cases were identified from the Finnish Cancer Registry. During the average 9 years of follow-up, 289 estradiol users and 196 estradiol-progestin users were diagnosed with meningioma. Ever use of estradiol-only therapy was associated with an increased risk of meningioma (standardized incidence ratio = 1.29, 95% confidence interval: 1.15, 1.44). Among women who had been using estradiol-only therapy for at least 3 years, the incidence of meningioma was 1.40-fold higher (95% confidence interval: 1.18, 1.64; P < 0.001) than in the background population. In contrast, this risk was not increased in users of combination therapy (standardized incidence ratio = 0.93, 95% confidence interval: 0.80, 1.06). There was no difference in risk between continuous and sequential use of hormone therapy. Estradiol-only therapy was accompanied with a slightly increased risk of meningioma.

This study examined associations between mortality and demographic and risk characteristics among young injection drug users in San Francisco, California, and compared the mortality rate with that of the population. A total of 644 young (<30 years) injection drug users completed a baseline interview and were enrolled in a prospective cohort study, known as the UFO ("U Find Out") Study, from November 1997 to December 2007. Using the National Death Index, the authors identified 38 deaths over 4,167 person-years of follow-up, yielding a mortality rate of 9.1 (95% confidence interval: 6.6, 12.5) per 1,000 person-years. This mortality rate was 10 times that of the general population. The leading causes of death were overdose (57.9%), self-inflicted injury (13.2%), trauma/accidents (10.5%), and injection drug user-related medical conditions (13.1%). Mortality incidence was significantly higher among those who reported injecting heroin most days in the past month (adjusted hazard ratio = 5.8, 95% confidence interval: 1.4, 24.3). The leading cause of death in this group was overdose, and primary use of heroin was the only significant risk factor for death observed in the study. These findings highlight the continued need for public health interventions that address the risk of overdose in this population in order to reduce premature deaths.

The association between passive cigarette smoke exposure and breast cancer risk is inconclusive and may be modified by genotype. The authors investigated lifetime passive cigarette smoke exposures, 36 variants in 12 carcinogen-metabolizing genes, and breast cancer risk among Ontario, Canada, women who had never smoked (2003&ndash;2004). DNA (saliva) was available for 920 breast cancer cases and 960 controls. Detailed information about passive smoke exposure was collected for multiple age periods (childhood, teenage years, and adulthood) and environments (home, work, and social). Adjusted odds ratios and 95% confidence intervals were estimated by multivariable logistic regression, and statistical interactions were assessed using the likelihood ratio test. Among postmenopausal women, most associations between passive smoke and breast cancer risk were null, whereas among premenopausal women, nonsignificant positive associations were observed. Significant interactions were observed between certain types of passive smoke exposure and genetic variants in CYP2E1, NAT2, and UGT1A7. While these interactions were statistically significant, the magnitudes of the effect estimates were not consistent or easily interpretable, suggesting that they were perhaps due to chance. Although the results of this study were largely null, it is possible that premenopausal women exposed to passive smoke or carrying certain genetic variants may be at higher risk of breast cancer.

Children with asthma can experience chronic morbidity that may interfere with education and career progression. The authors investigated retrospectively whether a history of childhood asthma is associated with educational level and longest-held occupation, by gender. Cross-sectional analysis included a nationally representative sample of 10,452 adults aged &ge;20 years who participated in the US National Health and Nutrition Examination Survey (2001&ndash;2004). Logistic regression was used to assess associations between a childhood-asthma history and educational level, employment, and longest-held occupation. An estimated 6.9% of men and 5.8% of women had a childhood-asthma history. Persons with a childhood-asthma history tended to have a higher educational level than those with no asthma history. Among those who ever worked, and after adjustment for age and race/ethnicity, men with a childhood-asthma history were more likely to work in health-diagnosing occupations, other professional occupations, and as cooks; women with a childhood-asthma history were more likely to work in management-related, entertainment-related, and health service occupations. Compared with those with no asthma history, persons with a childhood-asthma history tended to achieve a higher educational level and, if they worked, were more likely to work in particular occupations.

Studies of the relation between exposure to drinking water disinfection by-products and pregnancy outcomes have been limited by the complexity of the exposure itself (consisting of hundreds of different chemicals), the diverse pathways contributing to exposure, and the difficulty in assessing behavioral determinants of exposure. Therefore, exposure biomarkers offer great promise of enhancing exposure assessment, the limiting factor in the quality and conclusiveness of epidemiologic studies. However, there are significant conceptual and logistical challenges in developing biomarkers for the various constituents of concern that are sensitive to typical variation in exposure, reflective of the time periods of interest, not susceptible to interference from exposures other than water, not subject to reverse causality by correlates of adverse pregnancy outcomes, reflective of the chemicals of interest, and feasible for large-scale epidemiologic studies. Urinary trichloroacetic acid has been the leading candidate exposure biomarker for over a decade, and the first attempt to incorporate it into an epidemiologic study (Am J Epidemiol. 2012;175(4):263&ndash;275) is notable&mdash;the considerable limitations notwithstanding. In future efforts, investigators need to combine biomarker development with substantive epidemiologic studies to improve on this initial effort and prepare for more definitive research.

Although prenatal exposure to water disinfection by-products does not appear to affect the duration of gestation, its impact on fetal growth remains an open question. The authors studied the associations between prenatal exposure to disinfection by-products and fetal growth restriction (FGR) and preterm birth in the PELAGIE cohort, a French birth cohort comprising 3,421 pregnant women recruited between 2002 and 2006. Exposure was assessed by estimating levels of trihalomethanes (THMs) in tap water during pregnancy and maternal water use and by measuring maternal urinary levels of trichloroacetic acid (TCAA) during early pregnancy in a nested case-control design that compared 174 FGR cases, 114 preterm births, and 399 controls. Higher uptake of THMs (especially brominated THMs) was associated with a higher risk of FGR. Women with TCAA detected in their urine (>0.01 mg/L) had a higher risk of FGR than those with TCAA levels below the detection limit (adjusted odds ratio = 1.8, 95% confidence interval: 0.9, 3.7) and had an odds ratio for preterm birth below 1 (adjusted odds ratio = 0.8, 95% confidence interval: 0.3, 2.6). Results from this prospective study, the first to use a biomarker of disinfection by-product exposure, suggest that prenatal exposure affects fetal growth, but the causal agent or agents remain to be identified.

Randomized clinical trials (RCTs) are usually the preferred strategy with which to generate evidence of comparative effectiveness, but conducting an RCT is not always feasible. Though observational studies and RCTs often provide comparable estimates, the questioning of observational analyses has recently intensified because of randomized-observational discrepancies regarding the effect of postmenopausal hormone replacement therapy on coronary heart disease. Reanalyses of observational data that excluded prevalent users of hormone replacement therapy led to attenuated discrepancies, which begs the question of whether exclusion of prevalent users should be generally recommended. In the current study, the authors evaluated the effect of excluding prevalent users of statins in a meta-analysis of observational studies of persons with cardiovascular disease. The pooled, multivariate-adjusted mortality hazard ratio for statin use was 0.77 (95% confidence interval (CI): 0.65, 0.91) in 4 studies that compared incident users with nonusers, 0.70 (95% CI: 0.64, 0.78) in 13 studies that compared a combination of prevalent and incident users with nonusers, and 0.54 (95% CI: 0.45, 0.66) in 13 studies that compared prevalent users with nonusers. The corresponding hazard ratio from 18 RCTs was 0.84 (95% CI: 0.77, 0.91). It appears that the greater the proportion of prevalent statin users in observational studies, the larger the discrepancy between observational and randomized estimates.

The objective of the present commentary is to suggest that epidemiologists explore the use of anti-M&uuml;llerian hormone (AMH) as a new measurement tool in fecundability studies. The authors briefly summarize the advantages and limitations of the 3 current approaches to studies of fecundability. All 3 approaches involve the collection of time-to-pregnancy or attempt-time data, and most are limited to participants who plan their pregnancies. AMH is produced by ovarian follicles during their early growth stages and is measured clinically to assess ovarian reserve (the number of remaining oocytes). Unlike time to pregnancy, serum AMH level can be assessed regardless of pregnancy-attempt status. Measurements are not significantly affected by phase of the menstrual cycle, oral contraceptive use, or early pregnancy. The authors suggest that AMH measurement can be a valuable addition to traditionally designed fecundability studies. In addition, this hormone should be investigated as an independent measure of fecundability in studies that focus on exposures hypothesized to target the ovary.

The anticarcinogenic potential of vitamin D might be mediated by not only calcium metabolism but also other mechanisms initiated by vitamin D receptor (VDR). The authors measured plasma 25-hydroxyvitamin D in healthy volunteer examinees who underwent total colonoscopy in Tokyo, Japan, 2004&ndash;2005, and evaluated its influence on colorectal adenoma, both alone and in interaction with VDR polymorphisms, which correspond to the FokI and TaqI restriction sites. The main analysis of plasma 25-hydroxyvitamin D included 737 cases and 703 controls. Compared with the lowest quintile of plasma 25-hydroxyvitamin D, only the highest was related to a significantly decreased odds ratio of colorectal adenoma (odds ratio = 0.64, 95% confidence interval: 0.45, 0.92). In contrast, all but the lowest quintile of dietary calcium intake presented similarly reduced odds ratios (odds ratio for the highest = 0.67, 95% confidence interval: 0.47, 0.95). Of note, the association between plasma 25-hydroxyvitamin D levels and colorectal adenoma was modified by the TaqI polymorphism of the VDR gene (Pinteraction = 0.03) but not by dietary calcium intake (Pinteraction = 0.93). These observations highlight the importance of vitamin D in colorectal tumorigenesis. Vitamin D might protect against colorectal neoplasia, mainly through mechanisms other than the indirect mechanism via calcium metabolism.

Interest in self-rated health (SRH) as a tool for use in disease and mortality risk screening is increasing. The authors assessed the discriminatory ability of baseline SRH to predict 10-year mortality rates compared with objectively measured health status. Principal component analysis was used to create a health score that included systolic blood pressure, presence of diabetes mellitus, body mass index, electrocardiographic parameters, B-type natriuretic peptide, and other biochemical and hematologic measures. From 1997 to 2007, a total of 474 of the 1,388 baseline participants died and 81 were lost to follow-up, yielding 11,833 person-years of observation. The adjusted hazard ratio for death was 1.74 (95% confidence interval (CI): 1.32, 2.29) for persons reporting poor health versus those reporting good health. When combined with age and sex, SRH had a C statistic to predict death equal to 0.69 (95% CI: 0.67, 0.71), which was comparable to that of the inclusive health score (C = 0.69, 95% CI: 0.67, 0.72). The addition of other parameters, such as lifestyle, physical functioning, mental symptoms, and physical symptoms, had little effect on these 2 predictive models (C = 0.71 (95% CI: 0.69, 0.73) and C = 0.71 (95% CI: 0.69, 0.74), respectively). The abilities of the SRH and the health score models to predict death decreased in parallel fashion over time. These results suggest that older adults who report poor health warrant particular attention as persons who have accumulated biologic markers of disease.

From March to July of 2011, the authors investigated the prospective association between illicit drug use and cognitive functioning during the midadult years. A total of 8,992 participants who were surveyed at 42 years of age in the National Child Development Study (1999&ndash;2000) were included. The authors analyzed data on 3 cognitive functioning measures (memory index, executive functioning index, and overall cognitive index) when the participants were 50 years of age (2008&ndash;2009). Illicit drug use at 42 years of age was based on self-reported current or past use of any of 12 illicit drugs. Multivariable regression analyses were performed to estimate the association between different illicit drug use measures at 42 years of age and cognitive functioning at 50 years of age. A positive association was observed between ever (past or current) illicit drug use and cognitive functioning (&beta; = 0.62, P < 0.001), although the effect size was small. Even though there was no clear evidence against the null hypothesis, drug dependence (&beta; = &ndash;0.27, P = 0.58) and long-term illicit drug use (&beta; = &ndash;0.04, P = 0.87) tended to be negatively associated with cognitive functioning. At the population level, it does not appear that current illicit drug use is associated with impaired cognitive functioning in early middle age. However, the authors cannot exclude the possibility that some individuals and groups, such as those with heavier or more prolonged use, could be harmed.

Although missing outcome data are an important problem in randomized trials and observational studies, methods to address this issue can be difficult to apply. Using simulated data, the authors compared 3 methods to handle missing outcome data: 1) complete case analysis; 2) single imputation; and 3) multiple imputation (all 3 with and without covariate adjustment). Simulated scenarios focused on continuous or dichotomous missing outcome data from randomized trials or observational studies. When outcomes were missing at random, single and multiple imputations yielded unbiased estimates after covariate adjustment. Estimates obtained by complete case analysis with covariate adjustment were unbiased as well, with coverage close to 95%. When outcome data were missing not at random, all methods gave biased estimates, but handling missing outcome data by means of 1 of the 3 methods reduced bias compared with a complete case analysis without covariate adjustment. Complete case analysis with covariate adjustment and multiple imputation yield similar estimates in the event of missing outcome data, as long as the same predictors of missingness are included. Hence, complete case analysis with covariate adjustment can and should be used as the analysis of choice more often. Multiple imputation, in addition, can accommodate the missing-not-at-random scenario more flexibly, making it especially suited for sensitivity analyses.

One goal in the post-genome-wide association study era is characterizing gene-environment interactions, including scanning for interactions with all available polymorphisms, not just those showing significant main effects. In recent years, several approaches to such "gene-environment-wide interaction studies" have been proposed. Two contributions in this issue of the American Journal of Epidemiology provide systematic comparisons of the performance of these various approaches, one based on simulation and one based on application to 2 real genome-wide association study scans for type 2 diabetes. The authors discuss some of the broader issues raised by these contributions, including the plausibility of the gene-environment independence assumption that some of these approaches rely upon, the need for replication, and various generalizations of these approaches.

The question of which statistical approach is the most effective for investigating gene-environment (G-E) interactions in the context of genome-wide association studies (GWAS) remains unresolved. By using 2 case-control GWAS (the Nurses&rsquo; Health Study, 1976&ndash;2006, and the Health Professionals Follow-up Study, 1986&ndash;2006) of type 2 diabetes, the authors compared 5 tests for interactions: standard logistic regression-based case-control; case-only; semiparametric maximum-likelihood estimation of an empirical-Bayes shrinkage estimator; and 2-stage tests. The authors also compared 2 joint tests of genetic main effects and G-E interaction. Elevated body mass index was the exposure of interest and was modeled as a binary trait to avoid an inflated type I error rate that the authors observed when the main effect of continuous body mass index was misspecified. Although both the case-only and the semiparametric maximum-likelihood estimation approaches assume that the tested markers are independent of exposure in the general population, the authors did not observe any evidence of inflated type I error for these tests in their studies with 2,199 cases and 3,044 controls. Both joint tests detected markers with known marginal effects. Loci with the most significant G-E interactions using the standard, empirical-Bayes, and 2-stage tests were strongly correlated with the exposure among controls. Study findings suggest that methods exploiting G-E independence can be efficient and valid options for investigating G-E interactions in GWAS.

Several methods for screening gene-environment interaction have recently been proposed that address the issue of using gene-environment independence in a data-adaptive way. In this report, the authors present a comparative simulation study of power and type I error properties of 3 classes of procedures: 1) the standard 1-step case-control method; 2) the case-only method that requires an assumption of gene-environment independence for the underlying population; and 3) a variety of hybrid methods, including empirical-Bayes, 2-step, and model averaging, that aim at gaining power by exploiting the assumption of gene-environment independence and yet can protect against false positives when the independence assumption is violated. These studies suggest that, although the case-only method generally has maximum power, it has the potential to create substantial false positives in large-scale studies even when a small fraction of markers are associated with the exposure under study in the underlying population. All the hybrid methods perform well in protecting against such false positives and yet can retain substantial power advantages over standard case-control tests. The authors conclude that, for future genome-wide scans for gene-environment interactions, major power gain is possible by using alternatives to standard case-control analysis. Whether a case-only type scan or one of the hybrid methods should be used depends on the strength and direction of gene-environment interaction and association, the level of tolerance for false positives, and the nature of replication strategies.

Despite the growing burden of chronic disease globally, few studies have examined the socioeconomic patterning of risk across countries. The authors examined differences in the social patterning of body mass index (BMI) and current smoking by urbanicity among 70 countries from the 2002&ndash;2003 World Health Surveys. Age-adjusted, gender-stratified ordinary least squares and logistic regression analyses were conducted in each country to assess the relation between education and BMI or smoking. Meta-analytic techniques were used to assess heterogeneity between countries in the education-risk factor relations. Meta-regression was used to determine whether the heterogeneity could be explained by country-level urbanicity. In the least urban countries, persons with higher education had a higher BMI, while the opposite pattern was seen in the most urban countries, with this pattern being especially pronounced among women. In contrast, smoking was consistently concentrated among persons of lower education among all men and among women in the least urban countries. For women in the most urban countries, higher education was associated with higher odds of smoking, although there was substantial variability in this relation. These results highlight a global trend toward an increasing burden of chronic disease risk among persons of lower socioeconomic position as countries become more urban.

Legg-Calv&eacute;-Perthes&rsquo; disease (Perthes&rsquo; disease) is a childhood osteonecrosis of the hip for which the disease determinants are poorly understood. In this review, the authors identify studies of Perthes&rsquo; disease incidence published up to December 2010 and make denominator populations comparable in order to allow meaningful between-study evaluation. Incidence rates and confidence intervals were determined, and, where appropriate, denominator populations were obtained from national statistical offices. Poisson regression was used to determine the influence of race and geography. The review included 21 studies that described 27 populations in 16 countries, with 124 million person-years of observation. The annual incidence among children under age 15 years ranged from 0.2 per 100,000 to 19.1 per 100,000. Race was a key determinant, with East Asians being least affected and whites most affected, though data were insufficient to consider incidence among blacks (for South Asians vs. East Asians, incidence rate ratio = 2.9, 95% confidence interval (CI): 2.4, 3.5; for whites vs. East Asians, incidence rate ratio = 8.8, 95% CI: 8.2, 9.6). Latitude was a strong predictor of disease, even after adjustment for race. Each 10&deg; increase in latitude was associated with an incidence increase of 1.44 (95% CI: 1.30, 1.58) times. While much of the international variation appears to be a function of race, latitude demonstrates a strong association. This observation may offer new epidemiologic insights into the determinants of Perthes&rsquo; disease.

Few prospective studies have assessed the incidence and determinants of asymptomatic hyperuricemia in free-living populations. The authors&rsquo; goals in this study were to estimate the incidence of hyperuricemia and quantify the dose-response relations of specific risk factors with hyperuricemia in middle-aged South Korean male workers. The authors followed a cohort of 10,802 hyperuricemia-free men aged 30&ndash;59 years, examining them annually or biennially at a university hospital in Seoul, South Korea, from 2002 to 2009. A parametric Cox model and a pooled logistic regression model were used to estimate adjusted hazard ratios for incident hyperuricemia (defined as serum uric acid level &ge;7.0 mg/dL) according to prespecified risk factors. During 51,210.6 person-years of follow-up, 2,496 men developed hyperuricemia (incidence rate = 48.7 per 1,000 person-years, 95% confidence interval: 46.8, 50.7). The incidence of hyperuricema increased across baseline categories of age, body mass index, alcohol intake, blood pressure, metabolic syndrome, high-sensitivity C-reactive protein, triglycerides, gamma-glutamyltransferase, and fatty liver, whereas fasting glucose, estimated glomerular filtration rate, and high density lipoprotein cholesterol levels were inversely associated with incident hyperuricemia. Development of hyperuricemia, a very common outcome among apparently healthy South Korean men, was predicted by a variety of cardiovascular and metabolic risk factors, suggesting that lifestyle modification may help reduce the incidence of hyperuricemia.

The authors investigated whether early-life residency in certain areas of Iceland marked by distinct differences in milk intake was associated with risk of prostate cancer in a population-based cohort of 8,894 men born between 1907 and 1935. Through linkage to cancer and mortality registers, the men were followed for prostate cancer diagnosis and mortality from study entry (in waves from 1967 to 1987) through 2009. In 2002&ndash;2006, a subgroup of 2,268 participants reported their milk intake in early, mid-, and current life. During a mean follow-up period of 24.3 years, 1,123 men were diagnosed with prostate cancer, including 371 with advanced disease (stage 3 or higher or prostate cancer death). Compared with early-life residency in the capital area, rural residency in the first 20 years of life was marginally associated with increased risk of advanced prostate cancer (hazard ratio = 1.29, 95% confidence interval (CI): 0.97, 1.73), particularly among men born before 1920 (hazard ratio = 1.64, 95% CI: 1.06, 2.56). Daily milk consumption in adolescence (vs. less than daily), but not in midlife or currently, was associated with a 3.2-fold risk of advanced prostate cancer (95% CI: 1.25, 8.28). These data suggest that frequent milk intake in adolescence increases risk of advanced prostate cancer.

Recently, there has been interest in determining whether diet is associated with the risk of venous thromboembolism. The article by Varraso et al. (Am J Epidemiol. 2012;175(2):114&ndash;126) published in this issue of the Journal is an important contribution to this literature. In this commentary, the author discusses the findings of Varraso et al. within the context of the existing literature and posits epidemiologic explanations for why investigators might have failed to identify strong associations between diet and venous thromboembolism.