Joshua N. Sampson

Association of Leisure-Time Physical Activity With Risk of 26 Types of Cancer in 1.44 Million Adults

IMPORTANCE Leisure-time physical activity has been associated with lower risk of heart-disease and all-cause mortality, but its association with risk of cancer is not well understood. OBJECTIVE To determine the association of leisure-time physical activity with incidence of common types of cancer and whether associations vary by body size and/or smoking. DESIGN, SETTING, AND PARTICIPANTS We pooled data from 12 prospective US and European cohorts with self-reported physical activity (baseline, 1987-2004). We used multivariable Cox regression to estimate hazard ratios (HRs) and 95% confidence intervals for associations of leisure-time physical activity with incidence of 26 types of cancer. Leisure-time physical activity levels were modeled as cohort-specific percentiles on a continuous basis and cohort-specific results were synthesized by random-effects meta-analysis. Hazard ratios for high vs low levels of activity are based on a comparison of risk at the 90th vs 10th percentiles of activity. The data analysis was performed from January 1, 2014, to June 1, 2015. EXPOSURES Leisure-time physical activity of a moderate to vigorous intensity. MAIN OUTCOMES AND MEASURES Incident cancer during follow-up. RESULTS A total of 1.44 million participants (median [range] age, 59 [19-98] years; 57% female) and 186 932 cancers were included. High vs low levels of leisure-time physical activity were associated with lower risks of 13 cancers: esophageal adenocarcinoma (HR, 0.58; 95% CI, 0.37-0.89), liver (HR, 0.73; 95% CI, 0.55-0.98), lung (HR, 0.74; 95% CI, 0.71-0.77), kidney (HR, 0.77; 95% CI, 0.70-0.85), gastric cardia (HR, 0.78; 95% CI, 0.64-0.95), endometrial (HR, 0.79; 95% CI, 0.68-0.92), myeloid leukemia (HR, 0.80; 95% CI, 0.70-0.92), myeloma (HR, 0.83; 95% CI, 0.72-0.95), colon (HR, 0.84; 95% CI, 0.77-0.91), head and neck (HR, 0.85; 95% CI, 0.78-0.93), rectal (HR, 0.87; 95% CI, 0.80-0.95), bladder (HR, 0.87; 95% CI, 0.82-0.92), and breast (HR, 0.90; 95% CI, 0.87-0.93). Body mass index adjustment modestly attenuated associations for several cancers, but 10 of 13 inverse associations remained statistically significant after this adjustment. Leisure-time physical activity was associated with higher risks of malignant melanoma (HR, 1.27; 95% CI, 1.16-1.40) and prostate cancer (HR, 1.05; 95% CI, 1.03-1.08). Associations were generally similar between overweight/obese and normal-weight individuals. Smoking status modified the association for lung cancer but not other smoking-related cancers. CONCLUSIONS AND RELEVANCE Leisure-time physical activity was associated with lower risks of many cancer types. Health care professionals counseling inactive adults should emphasize that most of these associations were evident regardless of body size or smoking history, supporting broad generalizability of findings.

Projecting the performance of risk prediction based on polygenic analyses of genome-wide association studies

We report a new method to estimate the predictive performance of polygenic models for risk prediction and assess predictive performance for ten complex traits or common diseases. Using estimates of effect-size distribution and heritability derived from current studies, we project that although 45% of the variance of height has been attributed to SNPs, a model trained on one million people may only explain 33.4% of variance of the trait. Models based on current studies allow for identification of 3.0%, 1.1% and 7.0% of the populations at twofold or higher than average risk for type 2 diabetes, coronary artery disease and prostate cancer, respectively. Tripling of sample sizes could elevate these percentages to 18.8%, 6.1% and 12.2%, respectively. The utility of polygenic models for risk prediction will depend on achievable sample sizes for the training data set, the underlying genetic architecture and the inclusion of information on other risk factors, including family history.

Rare missense variants in POT1 predispose to familial cutaneous malignant melanoma

Although CDKN2A is the most frequent high-risk melanoma susceptibility gene, the underlying genetic factors for most melanoma-prone families remain unknown. Using whole-exome sequencing, we identified a rare variant that arose as a founder mutation in the telomere shelterin gene POT1 (chromosome 7, g.124493086C>T; p.Ser270Asn) in five unrelated melanoma-prone families from Romagna, Italy. Carriers of this variant had increased telomere lengths and numbers of fragile telomeres, suggesting that this variant perturbs telomere maintenance. Two additional rare POT1 variants were identified in all cases sequenced in two separate Italian families, one variant per family, yielding a frequency for POT1 variants comparable to that for CDKN2A mutations in this population. These variants were not found in public databases or in 2,038 genotyped Italian controls. We also identified two rare recurrent POT1 variants in US and French familial melanoma cases. Our findings suggest that POT1 is a major susceptibility gene for familial melanoma in several populations.

Etiologic Heterogeneity Among Non-Hodgkin Lymphoma Subtypes: The InterLymph Non-Hodgkin Lymphoma Subtypes Project

BACKGROUND Non-Hodgkin lymphoma (NHL) comprises biologically and clinically heterogeneous subtypes. Previously, study size has limited the ability to compare and contrast the risk factor profiles among these heterogeneous subtypes. METHODS We pooled individual-level data from 17 471 NHL cases and 23 096 controls in 20 case-control studies from the International Lymphoma Epidemiology Consortium (InterLymph). We estimated the associations, measured as odds ratios, between each of 11 NHL subtypes and self-reported medical history, family history of hematologic malignancy, lifestyle factors, and occupation. We then assessed the heterogeneity of associations by evaluating the variability (Q value) of the estimated odds ratios for a given exposure among subtypes. Finally, we organized the subtypes into a hierarchical tree to identify groups that had similar risk factor profiles. Statistical significance of tree partitions was estimated by permutation-based P values (P NODE). RESULTS Risks differed statistically significantly among NHL subtypes for medical history factors (autoimmune diseases, hepatitis C virus seropositivity, eczema, and blood transfusion), family history of leukemia and multiple myeloma, alcohol consumption, cigarette smoking, and certain occupations, whereas generally homogeneous risks among subtypes were observed for family history of NHL, recreational sun exposure, hay fever, allergy, and socioeconomic status. Overall, the greatest difference in risk factors occurred between T-cell and B-cell lymphomas (P NODE < 1.0×10(-4)), with increased risks generally restricted to T-cell lymphomas for eczema, T-cell-activating autoimmune diseases, family history of multiple myeloma, and occupation as a painter. We further observed substantial heterogeneity among B-cell lymphomas (P NODE < 1.0×10(-4)). Increased risks for B-cell-activating autoimmune disease and hepatitis C virus seropositivity and decreased risks for alcohol consumption and occupation as a teacher generally were restricted to marginal zone lymphoma, Burkitt/Burkitt-like lymphoma/leukemia, diffuse large B-cell lymphoma, and/or lymphoplasmacytic lymphoma/Waldenström macroglobulinemia. CONCLUSIONS Using a novel approach to investigate etiologic heterogeneity among NHL subtypes, we identified risk factors that were common among subtypes as well as risk factors that appeared to be distinct among individual or a few subtypes, suggesting both subtype-specific and shared underlying mechanisms. Further research is needed to test putative mechanisms, investigate other risk factors (eg, other infections, environmental exposures, and diet), and evaluate potential joint effects with genetic susceptibility.

Central Obesity, Incident Microalbuminuria, and Change in Creatinine Clearance in the Epidemiology of Diabetes Interventions and Complications Study

Weight gain and central obesity are associated with insulin resistance, hypertension, and dyslipidemia in type 1 diabetes. These metabolic abnormalities are risk factors for kidney disease in the general population, but data addressing the relationship of central obesity with kidney disease in type 1 diabetes are limited. Whether waist circumference is associated with incident microalbuminuria and change in creatinine clearance was examined among 1279 participants who had type 1 diabetes and were enrolled in the Epidemiology of Diabetes Interventions and Complications Study, the observational extension of the Diabetes Control and Complications Trial (DCCT). Ninety-three of 1105 participants with normal albumin excretion rate (AER) at DCCT closeout developed incident microalbuminuria over 5.8 yr of follow-up. The hazard ratio for incident microalbuminuria that was associated with each 10-cm greater waist circumference at DCCT closeout was 1.34 (95% confidence interval 1.07 to 1.68), after adjustment for DCCT closeout age, gender, duration of diabetes, treatment group, smoking status, glycosylated hemoglobin, and AER. This increased risk was modestly attenuated when additional adjustment was made for levels of BP and serum lipids. Creatinine clearance declined by an average of 0.34 ml/min per 1.73 m2 each yr over 8 yr of follow-up. Greater rate of decline in creatinine clearance was associated with greater age, conventional insulin therapy during the DCCT, smoking, and greater glycosylated hemoglobin and AER at DCCT closeout but not with waist circumference. In conclusion, waist circumference predicts the subsequent development of microalbuminuria in type 1 diabetes. In contrast, no association of waist circumference with decline in creatinine clearance was observed.

Metabolomics in nutritional epidemiology: identifying metabolites associated with diet and quantifying their potential to uncover diet-disease relations in populations.

BACKGROUND Metabolomics is an emerging field with the potential to advance nutritional epidemiology; however, it has not yet been applied to large cohort studies. OBJECTIVES Our first aim was to identify metabolites that are biomarkers of usual dietary intake. Second, among serum metabolites correlated with diet, we evaluated metabolite reproducibility and required sample sizes to determine the potential for metabolomics in epidemiologic studies. DESIGN Baseline serum from 502 participants in the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial was analyzed by using ultra-high-performance liquid-phase chromatography with tandem mass spectrometry and gas chromatography-mass spectrometry. Usual intakes of 36 dietary groups were estimated by using a food-frequency questionnaire. Dietary biomarkers were identified by using partial Pearsons correlations with Bonferroni correction for multiple comparisons. Intraclass correlation coefficients (ICCs) between samples collected 1 y apart in a subset of 30 individuals were calculated to evaluate intraindividual metabolite variability. RESULTS We detected 412 known metabolites. Citrus, green vegetables, red meat, shellfish, fish, peanuts, rice, butter, coffee, beer, liquor, total alcohol, and multivitamins were each correlated with at least one metabolite (P < 1.093 × 10(-6); r = -0.312 to 0.398); in total, 39 dietary biomarkers were identified. Some correlations (citrus intake with stachydrine) replicated previous studies; others, such as peanuts and tryptophan betaine, were novel findings. Other strong associations included coffee (with trigonelline-N-methylnicotinate and quinate) and alcohol (with ethyl glucuronide). Intraindividual variability in metabolite levels (1-y ICCs) ranged from 0.27 to 0.89. Large, but attainable, sample sizes are required to detect associations between metabolites and disease in epidemiologic studies, further emphasizing the usefulness of metabolomics in nutritional epidemiology. CONCLUSIONS We identified dietary biomarkers by using metabolomics in an epidemiologic data set. Given the strength of the associations observed, we expect that some of these metabolites will be validated in future studies and later used as biomarkers in large cohorts to study diet-disease associations. The PLCO trial was registered at clinicaltrials.gov as NCT00002540.

Improving Self-Reports of Active and Sedentary Behaviors in Large Epidemiologic Studies

Questionnaires that assess active and sedentary behaviors in large-scale epidemiologic studies are known to contain substantial errors. We present three options for improving measures of physical activity behaviors in large-scale epidemiologic studies, discuss the problems and prospects for each of these options, and highlight a new direction for measuring these behaviors in such studies.

Body Mass Index and Risk of Second Obesity-Associated Cancers After Colorectal Cancer: A Pooled Analysis of Prospective Cohort Studies

PURPOSE To determine whether prediagnostic body mass index (BMI) is associated with risk of second obesity-associated cancers in colorectal cancer (CRC) survivors, and whether CRC survivors have increased susceptibility to obesity-associated cancer compared with cancer-free individuals. PATIENTS AND METHODS Incident first primary CRC cases (N = 11,598) were identified from five prospective cohort studies. We used Cox proportional hazards regression models to examine associations between baseline (prediagnostic) BMI and risk of second obesity-associated cancers (postmenopausal breast, kidney, pancreas, esophageal adenocarcinoma, endometrium) in CRC survivors, and compared associations to those for first obesity-associated cancers in the full cohort. RESULTS Compared with survivors with normal prediagnostic BMI (18.5-24.9 kg/m(2)), those who were overweight (25-29.9 kg/m(2)) or obese (30+ kg/m(2)) had greater risk of a second obesity-associated cancer (n = 224; overweight hazard ratio [HR], 1.39; 95% CI, 1.01 to 1.92; obese HR, 1.47; 95% CI, 1.02 to 2.12; per 5-unit change in BMI HR, 1.12; 95% CI, 0.98 to 1.29). The magnitude of risk for developing a first primary obesity-associated cancer was similar (overweight HR, 1.18; 95% CI, 1.14 to 1.21; obese HR, 1.61; 95% CI, 1.56 to 1.66; per 5-unit change in BMI HR, 1.23; 95% CI, 1.21 to 1.24). Before diagnosis CRC patients were somewhat more likely than the overall cohort to be overweight (44% v 41%) or obese (25% v 21%). CONCLUSION CRC survivors who were overweight or obese before diagnosis had increased risk of second obesity-associated cancers compared with survivors with normal weight. The risks were similar in magnitude to those observed for first cancers in this population, suggesting increased prevalence of overweight or obesity, rather than increased susceptibility, may contribute to elevated second cancer risks in colorectal cancer survivors compared with the general population. These results support emphasis of existing weight guidelines for this high-risk group.

Medical history, lifestyle, family history, and occupational risk factors for peripheral T-cell lymphomas: the InterLymph Non-Hodgkin Lymphoma Subtypes Project.

BACKGROUND Accounting for 10%-15% of all non-Hodgkin lymphomas in Western populations, peripheral T-cell lymphomas (PTCL) are the most common T-cell lymphoma but little is known about their etiology. Our aim was to identify etiologic risk factors for PTCL overall, and for specific PTCL subtypes, by analyzing data from 15 epidemiologic studies participating in the InterLymph Consortium. METHODS A pooled analysis of individual-level data for 584 histologically confirmed PTCL cases and 15912 controls from 15 case-control studies conducted in Europe, North America, and Australia was undertaken. Data collected from questionnaires were harmonized to permit evaluation of a broad range of potential risk factors. Odds ratios (OR) and 95% confidence intervals (CI) were calculated using logistic regression. RESULTS Risk factors associated with increased overall PTCL risk with a P value less than .05 included: a family history of hematologic malignancies (OR = 1.92, 95% CI = 1.30 to 2.84); celiac disease (OR = 17.8, 95% CI = 8.61 to 36.79); eczema (OR = 1.41, 95% CI = 1.07 to 1.85); psoriasis (OR = 1.97, 95% CI = 1.17 to 3.32); smoking 40 or more years (OR = 1.92, 95% CI = 1.41 to 2.62); and employment as a textile worker (ever) (OR = 1.58, 95% CI = 1.05 to 2.38) and electrical fitter (ever) (OR = 2.89, 95% CI = 1.41 to 5.95). Exposures associated with reduced overall PTCL risk included a personal history of allergies (OR = 0.69, 95% CI = 0.54 to 0.87), alcohol consumption (ever) (OR = 0.64, 95% CI = 0.49 to 0.82), and having ever lived or worked on a farm (OR = 0.72, 95% CI = 0.55% to 0.95%). We also observed the well-established risk elevation for enteropathy-type PTCL among those with celiac disease in our data. Conclusions Our pooled analyses identified a number of new potential risk factors for PTCL and require further validation in independent series.

Metabolomics in Epidemiology: Sources of Variability in Metabolite Measurements and Implications

Background: Metabolite levels within an individual vary over time. This within-individual variability, coupled with technical variability, reduces the power for epidemiologic studies to detect associations with disease. Here, the authors assess the variability of a large subset of metabolites and evaluate the implications for epidemiologic studies. Methods: Using liquid chromatography/mass spectrometry (LC/MS) and gas chromatography-mass spectroscopy (GC/MS) platforms, 385 metabolites were measured in 60 women at baseline and year-one of the Shanghai Physical Activity Study, and observed patterns were confirmed in the Prostate, Lung, Colorectal, and Ovarian Cancer Screening study. Results: Although the authors found high technical reliability (median intraclass correlation = 0.8), reliability over time within an individual was low. Taken together, variability in the assay and variability within the individual accounted for the majority of variability for 64% of metabolites. Given this, a metabolite would need, on average, a relative risk of 3 (comparing upper and lower quartiles of “usual” levels) or 2 (comparing quartiles of observed levels) to be detected in 38%, 74%, and 97% of studies including 500, 1,000, and 5,000 individuals. Age, gender, and fasting status factors, which are often of less interest in epidemiologic studies, were associated with 30%, 67%, and 34% of metabolites, respectively, but the associations were weak and explained only a small proportion of the total metabolite variability. Conclusion: Metabolomics will require large, but feasible, sample sizes to detect the moderate effect sizes typical for epidemiologic studies. Impact: We offer guidelines for determining the sample sizes needed to conduct metabolomic studies in epidemiology. Cancer Epidemiol Biomarkers Prev; 22(4); 631–40. ©2013 AACR.